diff --git a/PROJECT_LOG.md b/PROJECT_LOG.md
index 4ff4c76..8d0d544 100644
--- a/PROJECT_LOG.md
+++ b/PROJECT_LOG.md
@@ -2535,3 +2535,49 @@ variation неустойчива. TX control BER 0/0; AWGN control при оди
 Следующий этап: отдельно согласовать полностью офлайн-проверку исправленного preamble-only
 CFO/carrier estimator на сохранённых Lab056, Lab054 и контролях, без payload truth в
 оценивателе и без запуска Lab059 автоматически.
+
+# Запись 053
+
+## Дата
+
+11 сентября 2026 года
+
+## Тема
+
+Lab059 — пределы восстановления несущей только по 1280-символьной преамбуле
+
+## Выполнено
+
+Lab058 зафиксирована отдельным коммитом
+82f17499ace5e655991240348401b9700c7c3349 с согласованным сообщением; push не
+выполнялся. Затем проведён только офлайн-анализ сохранённых Lab056, Lab054 и
+Lab047. Оборудование не опрашивалось, аппаратных команд не было, RF TX=0.
+
+До альтернатив сохранён точный P0 baseline. Активная ветвь корректно учитывает
+BPSK², знак, единицы и reference time, но использует только две точки по 512
+символов и отбрасывает последние 256 символов преамбулы. На Lab056 B ошибка P0
+относительно whole-burst reference составила +4,7307/+4,7608 Гц, средняя SER
+52,4169%. P3 с устойчивым наклоном фазы по всей squared-преамбуле дал
+−0,1249/+0,3917 Гц и SER 3,6170%, близко к диагностическим 3,4959%.
+
+Независимая проверка первой реализации выявила зависимость P1–P3 от RAW
+полезной части через глобальное удаление DC. Исправленный расчёт ограничивает
+DC и FIR-опору P1/P3 полной преамбулой, а P2 — только TRAIN; отдельный тест
+меняет RAW после преамбулы и подтверждает неизменность новых оценок. P0 сохранён
+дословно со штатной обработкой как исторический baseline.
+
+Две удачные реализации P3 не признаны доказательством устойчивости. Monte Carlo
+с300 реализациями для каждой из17 CFO, двух скоростей и трёх заранее заданных
+уровней EVM дал на500ксимв/с/EVM0,84535 вероятность |ошибки|≤0,5Гц только
+5,86/10,47/3,67/9,61% для P0/P1/P2/P3. Лучший RMSE P1 равен3,728Гц;
+AWGN CRLB sanity check даёт3,598Гц. Lab047 сохранён36/36 для всех методов;
+Lab054 остался0/16 HEADER/FEC/CRC/packet у всех методов, исторически NOT_A.
+
+Вывод PREAMBLE_ONLY_INSUFFICIENT: P3 полезен как начальная оценка, но при
+наблюдаемом качестве B требуется carrier tracking через burst. Следующий один
+этап рекомендуется сделать офлайн: decision-directed tracking без payload truth
+и без изменения формата. Физический предел B около3,50% ошибок остаётся.
+
+Созданы постановка, отчёт, baseline, CSV/JSON и пять графиков Lab059. Исходные
+IQ повторно хешированы до/после и не изменились. Lab059 не коммитировалась,
+push не выполнялся. Следующий этап: STOP; Lab060 автоматически не начинать.
diff --git a/data/processed/lab059/baseline_p0.json b/data/processed/lab059/baseline_p0.json
new file mode 100644
index 0000000..caaf375
--- /dev/null
+++ b/data/processed/lab059/baseline_p0.json
@@ -0,0 +1,2605 @@
+{
+  "algorithm": "unchanged active CURRENT estimator",
+  "rows": [
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+    {
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+        "validation_symbols": 576
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+    },
+    {
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+        "validation_symbols": 576
+      }
+    },
+    {
+      "dataset": "Lab054",
+      "label": "Lab054_07",
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+      "method": "P0",
+      "timing_samples": 0,
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+        "validation_symbols": 576
+      }
+    },
+    {
+      "dataset": "Lab047",
+      "label": "Lab047_32",
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+      "HEADER_ACCEPTED": true,
+      "FEC_STARTED": true,
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+        "validation_errors": 0,
+        "validation_symbols": 576
+      }
+    },
+    {
+      "dataset": "Lab047",
+      "label": "Lab047_33",
+      "frame": 33,
+      "symbol_rate": 20000,
+      "method": "P0",
+      "timing_samples": 1,
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+      "HEADER_ACCEPTED": true,
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+      "CRC_PASS": true,
+      "PACKET_OUTPUT": true,
+      "symbol_errors": null,
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+      "evm_rms": null,
+      "matched_residual_db": null,
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+        "frequency_hz": -0.15898838343389923,
+        "gain": {
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+          "imag": -4.437357933159314
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+        "estimation_duration_seconds": 0.0512,
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+        "mean_block_coherence": 0.9996730461410841,
+        "unambiguous_abs_hz": 9.765625,
+        "validation_phase_rmse_rad": 0.2584663403893296,
+        "validation_errors": 0,
+        "validation_symbols": 576
+      }
+    },
+    {
+      "dataset": "Lab047",
+      "label": "Lab047_34",
+      "frame": 34,
+      "symbol_rate": 20000,
+      "method": "P0",
+      "timing_samples": 0,
+      "frequency_hz": 0.11383338774465794,
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+      "estimation_duration_seconds": 0.0512,
+      "phase_drift_to_burst_end_degrees": 8.39270801163814,
+      "preamble_errors": 0,
+      "validation_errors": 0,
+      "validation_symbols": 576,
+      "validation_phase_rmse_rad": 0.2595285054364747,
+      "header_raw_errors": 0,
+      "header_voted_errors": 0,
+      "HEADER_ACCEPTED": true,
+      "FEC_STARTED": true,
+      "CRC_PASS": true,
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+      "symbol_errors": null,
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+      "matched_residual_db": null,
+      "estimate": {
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+          "imag": -3.8915376707184914
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+        "estimation_symbols": 1024,
+        "estimation_duration_seconds": 0.0512,
+        "phase_fit_rmse_rad": 0.0,
+        "mean_block_coherence": 0.999832410601363,
+        "unambiguous_abs_hz": 9.765625,
+        "validation_phase_rmse_rad": 0.2595285054364747,
+        "validation_errors": 0,
+        "validation_symbols": 576
+      }
+    },
+    {
+      "dataset": "Lab047",
+      "label": "Lab047_35",
+      "frame": 35,
+      "symbol_rate": 20000,
+      "method": "P0",
+      "timing_samples": 2,
+      "frequency_hz": -0.2328040338209343,
+      "diagnostic_reference_hz": null,
+      "frequency_error_hz": null,
+      "estimation_symbols": 1024,
+      "estimation_duration_seconds": 0.0512,
+      "phase_drift_to_burst_end_degrees": -17.164175805549842,
+      "preamble_errors": 0,
+      "validation_errors": 0,
+      "validation_symbols": 576,
+      "validation_phase_rmse_rad": 0.25743082288546204,
+      "header_raw_errors": 0,
+      "header_voted_errors": 0,
+      "HEADER_ACCEPTED": true,
+      "FEC_STARTED": true,
+      "CRC_PASS": true,
+      "PACKET_OUTPUT": true,
+      "symbol_errors": null,
+      "symbols": null,
+      "symbol_error_rate": null,
+      "evm_rms": null,
+      "matched_residual_db": null,
+      "estimate": {
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+        "frequency_hz": -0.2328040338209343,
+        "gain": {
+          "real": -8.431129692626712,
+          "imag": -4.713149031746205
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+        "estimation_symbols": 1024,
+        "estimation_duration_seconds": 0.0512,
+        "phase_fit_rmse_rad": 0.0,
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+        "unambiguous_abs_hz": 9.765625,
+        "validation_phase_rmse_rad": 0.25743082288546204,
+        "validation_errors": 0,
+        "validation_symbols": 576
+      }
+    },
+    {
+      "dataset": "Lab047",
+      "label": "Lab047_36",
+      "frame": 36,
+      "symbol_rate": 20000,
+      "method": "P0",
+      "timing_samples": 0,
+      "frequency_hz": -0.2555997527116375,
+      "diagnostic_reference_hz": null,
+      "frequency_error_hz": null,
+      "estimation_symbols": 1024,
+      "estimation_duration_seconds": 0.0512,
+      "phase_drift_to_burst_end_degrees": -18.84485856792361,
+      "preamble_errors": 0,
+      "validation_errors": 0,
+      "validation_symbols": 576,
+      "validation_phase_rmse_rad": 0.25769090297150626,
+      "header_raw_errors": 0,
+      "header_voted_errors": 0,
+      "HEADER_ACCEPTED": true,
+      "FEC_STARTED": true,
+      "CRC_PASS": true,
+      "PACKET_OUTPUT": true,
+      "symbol_errors": null,
+      "symbols": null,
+      "symbol_error_rate": null,
+      "evm_rms": null,
+      "matched_residual_db": null,
+      "estimate": {
+        "method": "P0",
+        "frequency_hz": -0.2555997527116375,
+        "gain": {
+          "real": -8.71048544685464,
+          "imag": -3.5638110487045944
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+        "estimation_symbols": 1024,
+        "estimation_duration_seconds": 0.0512,
+        "phase_fit_rmse_rad": 0.0,
+        "mean_block_coherence": 0.9991549955732492,
+        "unambiguous_abs_hz": 9.765625,
+        "validation_phase_rmse_rad": 0.25769090297150626,
+        "validation_errors": 0,
+        "validation_symbols": 576
+      }
+    }
+  ],
+  "summary": {
+    "Lab056": {
+      "P0": {
+        "segments": 4,
+        "mean_abs_frequency_error_hz": 2.4276774539698804,
+        "A_frequency_errors_hz": [
+          0.07329777648548115,
+          -0.14590954028149283
+        ],
+        "B_frequency_errors_hz": [
+          4.730655322761386,
+          4.760847176351161
+        ],
+        "B_mean_abs_frequency_error_hz": 4.745751249556274,
+        "B_mean_symbol_error_rate": 0.5241693037974684,
+        "A_mean_symbol_error_rate": 0.0001775568181818182,
+        "B_mean_evm_rms": 1.6532713775812362,
+        "B_mean_matched_residual_db": -4.365861184330079
+      }
+    },
+    "Lab054": {
+      "P0": {
+        "frames": 16,
+        "header_accepted": 0,
+        "fec_started": 0,
+        "crc_pass": 0,
+        "packet_output": 0,
+        "preamble_errors": 7767,
+        "header_raw_errors": 946,
+        "header_voted_errors": 283,
+        "mean_frequency_hz": -0.9816228278804822
+      }
+    },
+    "Lab047": {
+      "P0": {
+        "frames": 36,
+        "header_accepted": 36,
+        "fec_started": 36,
+        "crc_pass": 36,
+        "packet_output": 36,
+        "preamble_errors": 2,
+        "header_raw_errors": 0,
+        "header_voted_errors": 0,
+        "mean_frequency_hz": -0.007364438266704758
+      }
+    }
+  },
+  "mathematics": {
+    "active_input": "matched-filter symbol centers after nominal 50 kHz downconversion",
+    "p0_squares_bpsk": true,
+    "p0_symbol_ranges_half_open": [
+      [
+        0,
+        512
+      ],
+      [
+        512,
+        1024
+      ]
+    ],
+    "p0_discarded_preamble_symbols_half_open": [
+      1024,
+      1280
+    ],
+    "block_center_symbol_times": [
+      256.0,
+      768.0
+    ],
+    "center_distance_symbols": 512,
+    "phase_difference_formula": "angle(block1)-angle(block0), unwrapped",
+    "frequency_formula": "delta_phase/(4*pi*(512/Rs))",
+    "factor_two_after_squaring": true,
+    "compensation_sign": "negative complex exponential",
+    "reference_time": "gain phase at t=0; symbol k center at (k+0.5)/Rs",
+    "p0_unambiguous_abs_hz": {
+      "20000": 9.765625,
+      "500000": 244.140625
+    },
+    "double_compensation": false
+  }
+}
diff --git a/data/processed/lab059/burst_phase_drift.png b/data/processed/lab059/burst_phase_drift.png
new file mode 100644
index 0000000..3847e3c
Binary files /dev/null and b/data/processed/lab059/burst_phase_drift.png differ
diff --git a/data/processed/lab059/frequency_error_by_method.png b/data/processed/lab059/frequency_error_by_method.png
new file mode 100644
index 0000000..ce0d47f
Binary files /dev/null and b/data/processed/lab059/frequency_error_by_method.png differ
diff --git a/data/processed/lab059/lab054_result_by_method.png b/data/processed/lab059/lab054_result_by_method.png
new file mode 100644
index 0000000..40ff831
Binary files /dev/null and b/data/processed/lab059/lab054_result_by_method.png differ
diff --git a/data/processed/lab059/lab056_b_symbol_errors.png b/data/processed/lab059/lab056_b_symbol_errors.png
new file mode 100644
index 0000000..c104c17
Binary files /dev/null and b/data/processed/lab059/lab056_b_symbol_errors.png differ
diff --git a/data/processed/lab059/real_results.csv b/data/processed/lab059/real_results.csv
new file mode 100644
index 0000000..89398b5
--- /dev/null
+++ b/data/processed/lab059/real_results.csv
@@ -0,0 +1,225 @@
+dataset,label,frame,symbol_rate,method,timing_samples,frequency_hz,diagnostic_reference_hz,frequency_error_hz,estimation_symbols,estimation_duration_seconds,phase_drift_to_burst_end_degrees,preamble_errors,validation_errors,validation_symbols,validation_phase_rmse_rad,header_raw_errors,header_voted_errors,HEADER_ACCEPTED,FEC_STARTED,CRC_PASS,PACKET_OUTPUT,symbol_errors,symbols,symbol_error_rate,evm_rms,matched_residual_db
+Lab056,A1,,20000,P0,2,0.055789030390158624,-0.01750874609532253,0.07329777648548115,1024,0.0512,5.404098464721554,0,0,576,0.281350697674253,,,,,,,1,2816,0.0003551136363636364,0.31642307417060417,9.994637088848512
+Lab056,A1,,20000,P1,2,-0.045203127260058616,-0.01750874609532253,-0.027694381164736086,1280,0.064,-2.041851334513662,0,0,576,0.28106976981388476,,,,,,,1,2816,0.0003551136363636364,0.3132162036317391,10.083115573034034
+Lab056,A1,,20000,P2,2,-0.1181109347063771,-0.01750874609532253,-0.10060218861105456,576,0.0288,-7.417198161915831,0,0,576,0.28185801409875083,,,,,,,1,2816,0.0003551136363636364,0.3227866377535928,9.821689037239365
+Lab056,A1,,20000,P3,2,-0.03462245283812144,-0.01750874609532253,-0.01711370674279891,1280,0.064,-1.261759370733078,0,0,576,0.28107733134015733,,,,,,,1,2816,0.0003551136363636364,0.3128290207824184,10.093859295041891
+Lab056,B1,,500000,P0,0,4.668990755640415,-0.061664567120971146,4.730655322761386,1024,0.002048,348.7817556365515,30,11,576,0.5454635255802189,,,,,,,53110,101120,0.5252175632911392,1.6279142433582847,-4.2326304604832785
+Lab056,B1,,500000,P1,0,-3.3802358861866058,-0.061664567120971146,-3.3185713190656347,1280,0.00256,-244.67162621207112,29,10,576,0.5478817168243599,,,,,,,61971,101120,0.6128461234177215,1.7197607275927802,-4.7093605437942285
+Lab056,B1,,500000,P2,0,-20.03952039459105,-0.061664567120971146,-19.97785582747008,576,0.001152,-1472.9273544477142,27,9,576,0.5669927710004875,,,,,,,50335,101120,0.49777492088607594,1.6041229976665214,-4.104753303400513
+Lab056,B1,,500000,P3,0,-0.186545694754331,-0.061664567120971146,-0.12488112763335985,1280,0.00256,-9.207235778152356,29,11,576,0.5479565818059529,,,,,,,3346,101120,0.03308939873417722,0.7880810508511308,2.0685822967252845
+Lab056,A2,,20000,P0,0,-0.13866849385025154,0.007241046431241284,-0.14590954028149283,1024,0.0512,-10.757618585873903,0,0,576,0.29338787010225753,,,,,,,0,2816,0.0,0.3321213960734935,9.574062903730386
+Lab056,A2,,20000,P1,0,0.12087165302322976,0.007241046431241284,0.11363060659198848,1280,0.064,8.377757362814128,0,0,576,0.2899397032633679,,,,,,,0,2816,0.0,0.3284274607953825,9.671210747659595
+Lab056,A2,,20000,P2,0,-0.3260000782776973,0.007241046431241284,-0.3332411247089386,576,0.0288,-24.569201642540627,0,0,576,0.3087390474340879,,,,,,,0,2816,0.0,0.41255034750088376,7.6904608546438125
+Lab056,A2,,20000,P3,0,0.10074780428611109,0.007241046431241284,0.09350675785486981,1280,0.064,6.8940662431238415,0,0,576,0.290102346434687,,,,,,,0,2816,0.0,0.3251596080688447,9.758068171911082
+Lab056,B2,,500000,P0,-1,4.742179029785731,-0.018668146565430317,4.760847176351161,1024,0.002048,351.00774061801843,37,20,576,0.6896823317427843,,,,,,,52898,101120,0.5231210443037975,1.678628511804188,-4.4990919081768785
+Lab056,B2,,500000,P1,-1,2.1612705674681,-0.018668146565430317,2.1799387140335305,1280,0.00256,160.72252150826412,37,20,576,0.6889499599311383,,,,,,,42640,101120,0.42167721518987344,1.5546696774807434,-3.832762561963899
+Lab056,B2,,500000,P2,-1,46.70256552332298,-0.018668146565430317,46.72123366988841,576,0.001152,3444.663116013533,48,28,576,0.7964840218525077,,,,,,,50792,101120,0.5022943037974683,1.651904346423168,-4.359697917516451
+Lab056,B2,,500000,P3,-1,0.373039417020298,-0.018668146565430317,0.39170756358572834,1280,0.00256,28.87981524804858,37,20,576,0.6888460616356666,,,,,,,3969,101120,0.039250395569620254,0.8994741401771035,0.9202263615398578
+Lab054,Lab054_01,1,500000,P0,0,71.05787905983122,,,1024,0.002048,5238.955307323236,507,221,576,1.5602414937097426,56,20,False,False,False,False,,,,,
+Lab054,Lab054_01,1,500000,P1,0,-2.7596830538434087,,,1280,0.00256,-203.46591219376685,490,225,576,1.5581794908053075,61,19,False,False,False,False,,,,,
+Lab054,Lab054_01,1,500000,P2,0,832.0124120469967,,,576,0.001152,61342.61111540097,651,295,576,1.8068588800428145,82,26,False,False,False,False,,,,,
+Lab054,Lab054_01,1,500000,P3,0,445.67801522580993,,,1280,0.00256,32858.94870656852,623,266,576,1.7431028421860995,67,22,False,False,False,False,,,,,
+Lab054,Lab054_02,2,500000,P0,0,26.887136251896358,,,1024,0.002048,1982.334781579815,458,201,576,1.5543902511548644,49,13,False,False,False,False,,,,,
+Lab054,Lab054_02,2,500000,P1,0,33.458444956519024,,,1280,0.00256,2466.8242297542347,467,201,576,1.552515413220335,49,14,False,False,False,False,,,,,
+Lab054,Lab054_02,2,500000,P2,0,-1145.653016820168,,,576,0.001152,-84466.70562411736,655,309,576,1.8893741832531823,60,14,False,False,False,False,,,,,
+Lab054,Lab054_02,2,500000,P3,0,131.47278573983277,,,1280,0.00256,9693.225547026392,477,202,576,1.5648720364594986,68,20,False,False,False,False,,,,,
+Lab054,Lab054_03,3,500000,P0,0,182.1106401424685,,,1024,0.002048,13426.653276423916,523,226,576,1.6536071599102027,68,19,False,False,False,False,,,,,
+Lab054,Lab054_03,3,500000,P1,0,-3.8084421491854212,,,1280,0.00256,-280.78882277514276,482,211,576,1.5984408286255272,54,18,False,False,False,False,,,,,
+Lab054,Lab054_03,3,500000,P2,0,52.5392025744128,,,576,0.001152,3873.6103274063066,485,221,576,1.6331156876177746,61,18,False,False,False,False,,,,,
+Lab054,Lab054_03,3,500000,P3,0,-145.0747623772973,,,1280,0.00256,-10696.072080553377,500,219,576,1.6207715141617793,69,24,False,False,False,False,,,,,
+Lab054,Lab054_04,4,500000,P0,0,60.165209259727966,,,1024,0.002048,4435.860548301224,488,217,576,1.5564877586860697,52,15,False,False,False,False,,,,,
+Lab054,Lab054_04,4,500000,P1,0,-22.20190987715781,,,1280,0.00256,-1636.902411423091,467,208,576,1.5268130226021788,47,15,False,False,False,False,,,,,
+Lab054,Lab054_04,4,500000,P2,0,24.104109593646626,,,576,0.001152,1777.1477921203787,467,213,576,1.5654011439879034,53,15,False,False,False,False,,,,,
+Lab054,Lab054_04,4,500000,P3,0,-1031.870407252713,,,1280,0.00256,-76077.74138592802,622,270,576,1.7733555040622806,78,26,False,False,False,False,,,,,
+Lab054,Lab054_05,5,500000,P0,0,-205.66943259870166,,,1024,0.002048,-15163.595926637077,554,256,576,1.7235573946873264,82,31,False,False,False,False,,,,,
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diff --git a/data/processed/lab059/summary.json b/data/processed/lab059/summary.json
new file mode 100644
index 0000000..2889668
--- /dev/null
+++ b/data/processed/lab059/summary.json
@@ -0,0 +1,823 @@
+{
+  "status": "COMPLETED",
+  "lab058_commit": "82f17499ace5e655991240348401b9700c7c3349",
+  "rf_tx": 0,
+  "hardware_access": 0,
+  "preamble_symbols": 1280,
+  "preamble_duration_seconds": {
+    "20000": 0.064,
+    "500000": 0.00256
+  },
+  "burst_duration_seconds": 0.2048,
+  "phase_limits": [
+    {
+      "phase_limit_degrees": 15,
+      "max_frequency_error_hz": 0.20345052083333331
+    },
+    {
+      "phase_limit_degrees": 30,
+      "max_frequency_error_hz": 0.40690104166666663
+    },
+    {
+      "phase_limit_degrees": 45,
+      "max_frequency_error_hz": 0.6103515624999999
+    },
+    {
+      "phase_limit_degrees": 60,
+      "max_frequency_error_hz": 0.8138020833333333
+    },
+    {
+      "phase_limit_degrees": 90,
+      "max_frequency_error_hz": 1.2207031249999998
+    }
+  ],
+  "catastrophic_error_threshold_hz": 2.44140625,
+  "mathematics": {
+    "active_input": "matched-filter symbol centers after nominal 50 kHz downconversion",
+    "p0_squares_bpsk": true,
+    "p0_symbol_ranges_half_open": [
+      [
+        0,
+        512
+      ],
+      [
+        512,
+        1024
+      ]
+    ],
+    "p0_discarded_preamble_symbols_half_open": [
+      1024,
+      1280
+    ],
+    "block_center_symbol_times": [
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+      768.0
+    ],
+    "center_distance_symbols": 512,
+    "phase_difference_formula": "angle(block1)-angle(block0), unwrapped",
+    "frequency_formula": "delta_phase/(4*pi*(512/Rs))",
+    "factor_two_after_squaring": true,
+    "compensation_sign": "negative complex exponential",
+    "reference_time": "gain phase at t=0; symbol k center at (k+0.5)/Rs",
+    "p0_unambiguous_abs_hz": {
+      "20000": 9.765625,
+      "500000": 244.140625
+    },
+    "double_compensation": false
+  },
+  "p2_split": {
+    "train_half_open": [
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+      608
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+    "validation_half_open": [
+      672,
+      1248
+    ]
+  },
+  "real": {
+    "Lab056": {
+      "P0": {
+        "segments": 4,
+        "mean_abs_frequency_error_hz": 2.4276774539698804,
+        "A_frequency_errors_hz": [
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+          -0.14590954028149283
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+        "B_frequency_errors_hz": [
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+  "verdict": "PREAMBLE_ONLY_INSUFFICIENT",
+  "main_reason": "ни один preamble-only метод не обеспечивает устойчивую ошибку менее 0,5 Гц на качестве B",
+  "recommended_next_single_step": "проверить decision-directed carrier tracking through burst",
+  "future_options": {
+    "decision_directed": "первый кандидат: не меняет формат и не знает payload truth",
+    "costas_loop": "альтернатива для слежения по всему burst; требует настройки полосы петли",
+    "periodic_pilots": "наиболее явная опора, но требует изменения формата и overhead"
+  },
+  "physical_limit": {
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+    "lab058_b_channel_ripple_db": 5.471382
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+  "lab054_historical_result": "NOT_A, 0/16",
+  "lab059_commit": "NOT_PERFORMED",
+  "push": "NOT_PERFORMED",
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+    "passed": true
+  }
+}
diff --git a/data/processed/lab059/synthetic_500k_error_distribution.png b/data/processed/lab059/synthetic_500k_error_distribution.png
new file mode 100644
index 0000000..617cf3d
Binary files /dev/null and b/data/processed/lab059/synthetic_500k_error_distribution.png differ
diff --git a/data/processed/lab059/synthetic_by_cfo.csv b/data/processed/lab059/synthetic_by_cfo.csv
new file mode 100644
index 0000000..d359666
--- /dev/null
+++ b/data/processed/lab059/synthetic_by_cfo.csv
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+500000,LAB058_B_EVM,0.84535,2.0,P3,300,0.38340840619618083,4.528146779437369,3.1210237408119923,8.350202530646811,12.763058810071577,0.5866666666666667,0.07,0.11,0.20333333333333334,0.7413403798343513
+500000,LAB058_B_EVM,0.84535,5.0,P0,300,-0.3287617891615525,7.0589004089926775,4.308307011376538,14.056189940610459,18.079285126630765,0.6833333333333333,0.02666666666666667,0.08,0.14333333333333334,0.7443982595509906
+500000,LAB058_B_EVM,0.84535,5.0,P1,300,-0.004913188239170978,3.7921873599321088,2.438523183761134,7.506217629180869,9.54885441412075,0.5,0.06333333333333334,0.11333333333333333,0.23666666666666666,0.7437824925376901
+500000,LAB058_B_EVM,0.84535,5.0,P2,300,-0.45563711627431397,13.268833793814196,9.037750811833678,24.839404427849136,38.81429505058186,0.8666666666666667,0.016666666666666666,0.03,0.08333333333333333,0.7520027846106533
+500000,LAB058_B_EVM,0.84535,5.0,P3,300,-1.321165182278814,17.007174685047218,2.9982316050741513,8.985229020196108,12.224554595138448,0.58,0.05333333333333334,0.11666666666666667,0.19,0.7462930243470258
+500000,LAB058_B_EVM,0.84535,10.0,P0,300,0.08277224757947392,6.455828424117345,4.606893049866871,12.3417268554091,15.143175393697884,0.7066666666666667,0.03666666666666667,0.06333333333333334,0.12666666666666668,0.7456583711470456
+500000,LAB058_B_EVM,0.84535,10.0,P1,300,0.12655837520189275,3.6456474016683806,2.4358388347254247,6.966114305087162,10.436526372035285,0.5,0.07666666666666666,0.11666666666666667,0.25,0.7451294622069624
+500000,LAB058_B_EVM,0.84535,10.0,P2,300,-1.5659002932103003,11.604935573789366,8.017903602265099,22.12099069939824,26.54028860792483,0.8566666666666667,0.023333333333333334,0.03333333333333333,0.04666666666666667,0.7520835250115291
+500000,LAB058_B_EVM,0.84535,10.0,P3,300,1.195007499652665,16.804457989049794,2.8998167724527666,8.074836816697278,11.383843426253158,0.5966666666666667,0.056666666666666664,0.09,0.21,0.7478866482800567
+500000,LAB058_B_EVM,0.84535,20.0,P0,300,0.002282710072382906,6.909457160783215,4.969330016782248,12.829702575880843,17.84984687760765,0.7533333333333333,0.016666666666666666,0.043333333333333335,0.10333333333333333,0.7427238942057641
+500000,LAB058_B_EVM,0.84535,20.0,P1,300,0.0636680791465704,3.7505584403350847,2.5047631148585054,6.688728332742552,10.39432221350348,0.5233333333333333,0.03666666666666667,0.09333333333333334,0.2,0.742152968330001
+500000,LAB058_B_EVM,0.84535,20.0,P2,300,0.7736639928094108,12.076675017676733,8.741516435341984,23.612824736850154,28.357762061854555,0.8566666666666667,0.02,0.03,0.043333333333333335,0.7489397251510077
+500000,LAB058_B_EVM,0.84535,20.0,P3,300,0.12806143902729428,4.507156647056457,3.03225656687688,8.780586737210518,11.748594676028747,0.58,0.043333333333333335,0.09,0.19333333333333333,0.7422821381867188
+500000,LAB058_B_EVM,0.84535,100.0,P0,300,0.3588908667580037,7.211528957448166,4.998175294066392,14.366311780180565,16.509708888267866,0.7566666666666667,0.02,0.03666666666666667,0.08,0.7463914065418957
+500000,LAB058_B_EVM,0.84535,100.0,P1,300,0.1448171790421891,3.6299733569424366,2.54953634148999,7.438105749002398,8.881289028421191,0.5266666666666666,0.04666666666666667,0.11333333333333333,0.22333333333333333,0.7456993092865994
+500000,LAB058_B_EVM,0.84535,100.0,P2,300,1.54285884066143,11.747905063723158,7.536406931328287,22.88045567123965,30.33874664575071,0.8266666666666667,0.016666666666666666,0.03,0.08,0.7525788308306723
+500000,LAB058_B_EVM,0.84535,100.0,P3,300,0.17720134935331563,4.142834514845656,2.7763181641731904,8.561931350528344,9.791235623233316,0.5533333333333333,0.05333333333333334,0.12333333333333334,0.19333333333333333,0.7458920145712258
diff --git a/data/processed/lab059/synthetic_results.csv b/data/processed/lab059/synthetic_results.csv
new file mode 100644
index 0000000..0680651
--- /dev/null
+++ b/data/processed/lab059/synthetic_results.csv
@@ -0,0 +1,25 @@
+symbol_rate,quality,target_evm_rms,method,trials,cfo_values,bias_hz,rmse_hz,median_abs_error_hz,p95_abs_error_hz,p99_abs_error_hz,catastrophic_error_rate,probability_abs_error_le_0p25_hz,probability_abs_error_le_0p5_hz,probability_abs_error_le_1p0_hz,validation_phase_rmse_rad
+20000,LAB058_A_EVM,0.31739,P0,5100,17,-0.03994990706916153,35.27766452961887,0.11267197654994818,99.38625173694851,106.34545651090411,0.34647058823529414,0.643921568627451,0.6535294117647059,0.6535294117647059,0.7261254181729553
+20000,LAB058_A_EVM,0.31739,P1,5100,17,0.0008885471078995073,0.05694210824345046,0.03815117925418354,0.11063363868695754,0.14501118508960584,0.0,1.0,1.0,1.0,0.23064178973471747
+20000,LAB058_A_EVM,0.31739,P2,5100,17,0.001988038482725285,0.18418744251062372,0.12215923720830196,0.36075379706777216,0.47453741676652017,0.0,0.8231372549019608,0.9935294117647059,1.0,0.23407663541496893
+20000,LAB058_A_EVM,0.31739,P3,5100,17,0.0008031563449569883,0.05840036191732054,0.03955118489494858,0.11341770842686368,0.15124372519320092,0.0,1.0,1.0,1.0,0.2306551116111988
+20000,INTERMEDIATE_EVM,0.55,P0,5100,17,-0.016438006428522618,35.43683929034298,0.20009194018242737,100.74954199314566,107.89264988455928,0.3296078431372549,0.5664705882352942,0.6652941176470588,0.6703921568627451,0.8445548255499953
+20000,INTERMEDIATE_EVM,0.55,P1,5100,17,-0.0020946426691008124,0.0957624421078446,0.06350912858768698,0.1901454657377376,0.24578074270342898,0.0,0.9905882352941177,1.0,1.0,0.4412378828176002
+20000,INTERMEDIATE_EVM,0.55,P2,5100,17,0.006037261835378864,0.3180777143017349,0.21470755021954835,0.6200158671372041,0.7969293765980515,0.0,0.567843137254902,0.8841176470588236,0.9982352941176471,0.44669581880328485
+20000,INTERMEDIATE_EVM,0.55,P3,5100,17,-0.002416308397424423,0.10333416218255481,0.07048301756784267,0.20300057249194792,0.2632565055540736,0.0,0.9847058823529412,1.0,1.0,0.4412929666840979
+20000,LAB058_B_EVM,0.84535,P0,5100,17,-0.02487295673487596,35.53148619984433,0.32862427090475266,101.43123344158475,107.94186117186754,0.32,0.41352941176470587,0.6154901960784314,0.6790196078431373,1.0430413340458966
+20000,LAB058_B_EVM,0.84535,P1,5100,17,0.0011826215677020807,0.14651407576818676,0.09803260328097285,0.28537572073019823,0.3811773961719878,0.0,0.9145098039215687,0.9992156862745099,1.0,0.7413606374734285
+20000,LAB058_B_EVM,0.84535,P2,5100,17,0.0008314817399338177,0.48833634165135953,0.32478059797899206,0.9640636741241208,1.2526697998533654,0.0,0.4025490196078431,0.6947058823529412,0.9596078431372549,0.74862814469735
+20000,LAB058_B_EVM,0.84535,P3,5100,17,-0.005590240158082864,0.5201396504062407,0.11894627555568146,0.3430226441253915,0.4786715240391113,0.002156862745098039,0.8507843137254902,0.9915686274509804,0.9974509803921568,0.7428826884058787
+500000,LAB058_A_EVM,0.31739,P0,5100,17,-0.028324835474221908,2.253141535486236,1.4962503201559325,4.448489433622006,5.709437492053499,0.27725490196078434,0.08725490196078431,0.1676470588235294,0.34784313725490196,0.231339425193028
+500000,LAB058_A_EVM,0.31739,P1,5100,17,0.05429497252499488,1.3758457866823075,0.9250930671491767,2.7024857819222334,3.5664388844212724,0.07941176470588235,0.14901960784313725,0.28019607843137256,0.54,0.2311393421976552
+500000,LAB058_A_EVM,0.31739,P2,5100,17,0.06565713877478316,4.581561184769926,3.0883642545236056,8.953835433400844,11.96456273215203,0.5976470588235294,0.039411764705882354,0.08509803921568627,0.17450980392156862,0.2345544161089221
+500000,LAB058_A_EVM,0.31739,P3,5100,17,0.058842766466235895,1.4134247608333887,0.9614089281004776,2.7679878506095847,3.6337112005617693,0.0888235294117647,0.1411764705882353,0.2747058823529412,0.5180392156862745,0.23115195125818536
+500000,INTERMEDIATE_EVM,0.55,P0,5100,17,-0.03875761089802868,4.131625501040117,2.9042927999066057,7.9528339390408584,10.52341079713349,0.5666666666666667,0.05019607843137255,0.09411764705882353,0.1935294117647059,0.4421333889963128
+500000,INTERMEDIATE_EVM,0.55,P1,5100,17,0.06423845186681068,2.3913290252749078,1.6065121427546019,4.649532259708706,6.192878068670655,0.3092156862745098,0.08431372549019608,0.16607843137254902,0.32372549019607844,0.441761357535385
+500000,INTERMEDIATE_EVM,0.55,P2,5100,17,0.02004625787745806,7.921237841542093,5.269438855163791,15.620242877196675,20.258593678746344,0.756078431372549,0.02372549019607843,0.05274509803921568,0.10549019607843137,0.4472161493685728
+500000,INTERMEDIATE_EVM,0.55,P3,5100,17,0.04886503438637983,2.549592162835184,1.7274887723017696,5.021571593956331,6.353311747517625,0.3415686274509804,0.08686274509803922,0.16294117647058823,0.31019607843137254,0.4418171712060747
+500000,LAB058_B_EVM,0.84535,P0,5100,17,-0.018449630005399873,6.8080438723195575,4.64257978395003,13.265150787635067,17.80453591229988,0.7207843137254902,0.03058823529411765,0.058627450980392154,0.11607843137254902,0.7431007414530082
+500000,LAB058_B_EVM,0.84535,P1,5100,17,0.05043041270946037,3.727888844043392,2.4981252391550526,7.436680937825983,9.894508393786168,0.5098039215686274,0.05235294117647059,0.10470588235294118,0.21274509803921568,0.7424981320951237
+500000,LAB058_B_EVM,0.84535,P2,5100,17,0.07780133250091027,12.264121666056068,8.25551126333268,23.708553996717562,31.583472778910384,0.8311764705882353,0.01647058823529412,0.03666666666666667,0.07196078431372549,0.7498501168809314
+500000,LAB058_B_EVM,0.84535,P3,5100,17,0.049138012426324186,7.089259568773472,2.934776228989742,8.527851599718817,11.382887324913723,0.5794117647058824,0.049019607843137254,0.09607843137254903,0.18254901960784314,0.7429492477214338
diff --git a/docs/LABS_INDEX.md b/docs/LABS_INDEX.md
index df1b4de..863a5b9 100644
--- a/docs/LABS_INDEX.md
+++ b/docs/LABS_INDEX.md
@@ -495,3 +495,26 @@
   [JSON](../data/processed/lab058/summary.json),
   [CSV](../data/processed/lab058/segment_metrics.csv),
   [H(f)](../data/processed/lab058/channel_transfer.png). STOP; push не выполнялся.
+
+## Lab059 — пределы carrier recovery по одной преамбуле
+
+- **Запись 053; только сохранённые IQ, RF TX=0.** P0 baseline записан до P1–P3
+  и численно воспроизвёл Lab057/Lab058. Активный P0 использует два блока BPSK²
+  по512 символов; ошибки B1/B2 +4,7307/+4,7608Гц дают SER52,4169%.
+- **Лучший реальный результат P3:** после ограничения RAW-обработки только
+  преамбулой ошибки −0,1249/+0,3917Гц, средняя SER B 3,6170% против
+  whole-burst diagnostic3,4959%. A не ухудшена.
+- **Предел устойчивости:** на synthetic500K/EVM0,84535 P1 имеет лучший RMSE
+  3,728Гц, а P3 попадает в ±0,5Гц лишь в9,61% из5100 испытаний. AWGN CRLB
+  sanity check3,598Гц. Две реальные реализации недостаточны для устойчивого PASS.
+- **Контроли:** Lab04736/36 у P0–P3; Lab0540/16 HEADER/FEC/CRC/packet у всех,
+  исторический `NOT_A, 0/16` сохранён. Независимо найденная RAW/DC-утечка
+  P1–P3 исправлена и закрыта регрессионным тестом; исходные IQ не изменились.
+- **Вердикт:** `PREAMBLE_ONLY_INSUFFICIENT`. Следующий один кандидат — полностью
+  офлайн decision-directed carrier tracking через burst без payload truth и
+  изменения packet format. Физический предел B около3,50% SER остаётся.
+- [Постановка](lab059_current_state.md), [отчёт](lab059_offline_result.md),
+  [JSON](../data/processed/lab059/summary.json),
+  [real CSV](../data/processed/lab059/real_results.csv),
+  [synthetic CSV](../data/processed/lab059/synthetic_results.csv). STOP;
+  Lab059 не коммитировалась, push не выполнялся, Lab060 не создана.
diff --git a/docs/lab059_current_state.md b/docs/lab059_current_state.md
new file mode 100644
index 0000000..e9f1621
--- /dev/null
+++ b/docs/lab059_current_state.md
@@ -0,0 +1,78 @@
+# Lab059 — границы и заранее зафиксированный план
+
+## Цель
+
+Lab059 проверяет только по сохранённым данным, достаточно ли 1280-символьной
+преамбулы для оценки остаточного частотного сдвига на весь burst длительностью
+около 0,2048 с. Штатный приёмник, формат пакета, преамбула, RRC, FEC и CRC не
+изменяются. Оборудование не опрашивается, аппаратные команды не выполняются,
+RF TX = 0.
+
+Исходный HEAD после отдельной фиксации Lab058:
+`82f17499ace5e655991240348401b9700c7c3349`.
+
+## Зафиксированные методы
+
+- **P0 CURRENT.** Дословно воспроизводится активная ветвь штатного приёмника:
+  первые 1024 символа возводятся в квадрат, делятся на два блока по 512,
+  частота определяется по разности фаз центров блоков с делением на `4*pi`.
+  Последние 256 символов преамбулы в оценке частоты не участвуют.
+- **P1 FULL PREAMBLE PHASE SLOPE.** Известные BPSK-знаки удаляются на всей
+  преамбуле. Фазы когерентных 20-символьных блоков аппроксимируются линейной
+  моделью с заранее заданными Huber-весами.
+- **P2 SPLIT PREAMBLE.** TRAIN заранее фиксирован как индексы 32…607,
+  VALIDATION — 672…1247. Между ними оставлен разрыв 64 символа; по 32 символа
+  у начала и конца преамбулы не используются. Частота и фаза оцениваются только
+  по TRAIN, а VALIDATION проверяет экстраполяцию без переоценки.
+- **P3 SQUARED FULL PREAMBLE.** Все 1280 символов возводятся в квадрат,
+  фаза 20-символьных блоков аппроксимируется той же устойчивой моделью,
+  частотный наклон делится на два из-за удвоения фазы BPSK.
+
+P0 сохраняет точную активную ветвь, включая штатное удаление среднего DC по
+всему переданному в неё фрагменту: это часть воспроизводимого baseline, а не
+новое решение. Для P1 и P3 удаление DC и RRC-фильтрация ограничены RAW-опорой
+полной преамбулы; для P2 — RAW-опорой только TRAIN. Поэтому полезная часть RAW
+не влияет на вход новых оценивателей. Все методы получают Fs, Rs, RRC,
+номинальную несущую и уже найденное начало кандидата. Для изоляции carrier
+recovery timing остаётся штатным P0 и не подбирается отдельно по методу.
+Начальная фаза относится к `t=0`; центр символа `k` расположен в
+`(k+0.5)/Rs`.
+
+Header, payload, magic, FEC и CRC не участвуют в оценке. Известные переданные
+биты подключаются только после заморозки параметров для подсчёта ошибок, EVM и
+matched/residual. Диагностическая оценка по всему burst служит только внешней
+справочной величиной после расчёта preamble-only оценки.
+
+## Baseline и реальные данные
+
+До сравнения P1–P3 программа отдельно сохраняет P0 baseline для Lab056
+A1/B1/A2/B2, 16 кадров Lab054 и 36 ранее принятых кадров Lab047. Lab047 служит
+положительным контролем. Исторический результат Lab054 остаётся `NOT_A, 0/16`.
+Сохранённые raw-файлы открываются только для чтения; их SHA-256 проверяются до и
+после расчёта.
+
+## Synthetic Monte Carlo
+
+Фиксированные частоты: `0, ±0.25, ±0.5, ±1, ±2, ±5, ±10, ±20, ±100 Гц`.
+Проверяются 20 и 500 ксимв/с, Fs = 8 Мвыб/с, действующие RRC и преамбула 1280
+символов. Чистые точки решения формируются реальным TX/RX RRC-трактом, после
+чего добавляется воспроизводимый комплексный гауссов шум на символьном выходе.
+Уровни EVM заранее фиксированы: 0,317390 (качество A Lab058), 0,55 и 0,845350
+(среднее физическое качество B Lab058). Последний уровень не подгоняется по
+исходу оценивателя.
+
+Для каждой комбинации используются 300 реализаций и фиксированная схема seed.
+Катастрофическая ошибка заранее определена как `|frequency error| > 1/(2T)`,
+то есть фазовый уход более 180° за `T = 0,2048 с`; порог равен 2,44140625 Гц.
+Публикуются bias, RMSE, median/P95/P99 абсолютной ошибки, катастрофическая доля
+и вероятности ошибки не более 0,25/0,5/1,0 Гц.
+
+## Критерий вывода
+
+`PREAMBLE_ONLY_SUFFICIENT` требует одновременно приблизить Lab056 B к
+диагностическому результату, устойчиво пройти Monte Carlo, не ухудшить Lab056 A
+и сохранить положительный контроль Lab047. Нестабильная субгерцовая точность
+даёт `PREAMBLE_ONLY_MARGINAL`; принципиально недостаточная стабильность на
+качестве B даёт `PREAMBLE_ONLY_INSUFFICIENT`.
+
+Lab059 не коммитируется. Push не выполняется. Lab060 автоматически не создаётся.
diff --git a/docs/lab059_offline_result.md b/docs/lab059_offline_result.md
new file mode 100644
index 0000000..95d3fdb
--- /dev/null
+++ b/docs/lab059_offline_result.md
@@ -0,0 +1,187 @@
+# Lab059 — пределы восстановления несущей только по преамбуле
+
+## Что произошло простыми словами
+
+У штатного приёмника есть только 2,56 мс известной преамбулы, чтобы предсказать
+фазу несущей на весь burst длительностью около 204,8 мс. Для ограничения ухода
+фазы 30 градусами ошибка частоты должна быть не больше 0,4069 Гц. Штатная
+двухблочная оценка на B1/B2 ошиблась примерно на +4,73/+4,76 Гц и поэтому
+повернула решения почти на полный оборот к концу burst.
+
+Один из новых диагностических методов, P3, оказался очень удачным именно на
+двух сохранённых B-сегментах: ошибки составили −0,125 и +0,392 Гц, а средняя
+доля ошибочных символов упала с 52,4169% до 3,6170%. Это почти совпало с
+диагностически достижимыми 3,4959% Lab058. Однако это две реализации, и они не
+доказывают устойчивость. В 5100 синтетических испытаниях качества B P3 попадал
+в ±0,5 Гц только в 9,61% случаев; лучший по RMSE метод P1 имел RMSE 3,728 Гц и
+попадал в ±0,5 Гц в 10,47% случаев.
+
+Следовательно, текущая реализация P0 действительно слаба, и P3 может дать
+хорошую начальную оценку. Одновременно 1280 символов при 500 ксимв/с и
+наблюдаемом качестве B не дают надёжной субгерцовой точности сами по себе.
+Для устойчивого результата требуется сопровождать фазу или частоту в ходе
+самого пакета. Это задаёт следующий программный эксперимент. Физический предел B,
+установленный Lab058 — около 3,50% ошибочных символов даже при диагностическом
+выравнивании — остаётся отдельной задачей.
+
+## Границы и целостность
+
+Lab059 выполнялась только офлайн. HackRF Plus не опрашивался, аппаратные команды
+и передачи не запускались, RF TX = 0. Прочитаны сохранённые Lab056, Lab054 и
+Lab047. Размеры и SHA-256 пяти исходных IQ-файлов проверены до и после расчёта;
+совпадение полное. Исторический результат Lab054 не изменён: `NOT_A, 0/16`.
+
+P0 baseline физически записан в `baseline_p0.json` до вычисления P1–P3. Он
+воспроизвёл Lab057 для Lab054: максимальное расхождение CFO равно
+`1,42e-13 Гц`, ошибки преамбулы `7767/7767`, HEADER/CRC `0/0`. Для четырёх
+сегментов Lab056 различия P0 с Lab058 по CFO, BER и EVM находятся на уровне
+округления floating point.
+
+Независимая проверка обнаружила, что первая реализация P1–P3 удаляла DC по
+всему burst до выделения преамбулы. Из-за этого RAW полезной части менял вход
+оценивателя, хотя правильные payload-биты ему не передавались. Исправленный
+вариант ограничивает DC и FIR-опору P1/P3 полной преамбулой, а P2 — только
+TRAIN. Отдельный тест изменяет RAW после преамбулы и подтверждает неизменность
+P1–P3. P0 намеренно оставлен точным baseline со штатной глобальной обработкой.
+
+## Требуемая точность
+
+Использовано `delta_f_max = phase_limit / (360*T)`, где `T = 0,2048 с`.
+
+| Допустимый уход фазы | Максимальная ошибка частоты |
+|---:|---:|
+| 15° | 0,203451 Гц |
+| 30° | 0,406901 Гц |
+| 45° | 0,610352 Гц |
+| 60° | 0,813802 Гц |
+| 90° | 1,220703 Гц |
+
+Порог 0,4069 Гц — инженерная цель для 30°, а не новый обязательный порог
+протокола. Катастрофическая ошибка Monte Carlo заранее определена как больше
+2,441406 Гц, что соответствует уходу более 180° за burst.
+
+## Аудит штатной математики P0
+
+Активный код получает точки после номинального переноса 50 кГц, RRC-фильтра и
+штатного timing. Он возводит BPSK в квадрат, усредняет символы `[0,512)` и
+`[512,1024)`, затем берёт разность фаз. Центры находятся в моментах `256/Rs` и
+`768/Rs`, расстояние равно `512/Rs`. Формула имеет вид
+`delta_phase / (4*pi*(512/Rs))`; `4*pi` правильно учитывает удвоение фазы после
+BPSK². Компенсация использует отрицательный знак, gain относится к `t=0`, центр
+символа k — к `(k+0.5)/Rs`. Повторной компенсации не обнаружено.
+
+Для 500 ксимв/с однозначный диапазон P0 равен ±244,140625 Гц, поэтому +4,7 Гц
+не является алиасом. Главный практический недостаток — две фазовые точки без
+избыточности; последние 256 символов преамбулы не участвуют в частотной оценке.
+Для 20 ксимв/с диапазон P0 всего ±9,765625 Гц, поэтому synthetic ±20 и ±100 Гц
+преднамеренно показывают алиас и увеличивают сводный RMSE P0.
+
+## Методы
+
+| Метод | Использованные символы | Оценка | Проверка |
+|---|---:|---|---|
+| P0 CURRENT | 1024 | два блока BPSK² по 512 | P2 VALIDATION только как диагностика |
+| P1 FULL PHASE SLOPE | 1280 | Huber-fit фаз 64 despread-блоков по 20 | фиксированная преамбула |
+| P2 SPLIT | 576 TRAIN | Huber-fit 36 despread-блоков по 16 | 576 VALIDATION без переоценки |
+| P3 SQUARED FULL | 1280 | Huber-fit фаз 64 BPSK²-блоков по 20, наклон /2 | фиксированная преамбула |
+
+P2 использует TRAIN 32…607 и VALIDATION 672…1247; разбиение зафиксировано до
+результата. Timing одинаков для P0–P3, чтобы эксперимент изолировал carrier
+recovery. P1/P3 получают RAW-опору только полной преамбулы, P2 — только TRAIN.
+Ни один новый оцениватель не получает header, magic, payload, JPEG, FEC или CRC.
+
+## Lab056 A/B
+
+Ошибки частоты ниже считаются относительно whole-burst diagnostic reference
+Lab058, подключённого только после заморозки preamble-only оценки.
+
+| Метод | B1 ошибка CFO | B2 ошибка CFO | Средняя \|ошибка\| | B1 SER | B2 SER | Средняя SER B |
+|---|---:|---:|---:|---:|---:|---:|
+| P0 | +4,730655 Гц | +4,760847 Гц | 4,745751 Гц | 52,5218% | 52,3121% | 52,4169% |
+| P1 | −3,318571 Гц | +2,179939 Гц | 2,749255 Гц | 61,2846% | 42,1677% | 51,7262% |
+| P2 | −19,977856 Гц | +46,721234 Гц | 33,349545 Гц | 49,7775% | 50,2294% | 50,0035% |
+| P3 | −0,124881 Гц | +0,391708 Гц | 0,258294 Гц | 3,3089% | 3,9250% | 3,6170% |
+
+P3 дал средние EVM 0,84378 и matched/residual +1,4944 дБ, практически совпав
+с физической диагностикой Lab058 (0,84535 и +1,46485 дБ). Разница его средней
+SER с нижней диагностической ссылкой составляет +0,1211 процентного пункта.
+
+Для A1/A2 все методы сохранили среднюю SER 0,01776%. P3 дал EVM 0,31283/0,32516
+и ошибки CFO −0,0171/+0,0935 Гц относительно diagnostic reference. P2 сильнее
+ухудшил EVM A2 до 0,41255, что дополнительно показывает слабость короткого TRAIN.
+
+![Ошибка CFO](../data/processed/lab059/frequency_error_by_method.png)
+
+![Фазовый уход](../data/processed/lab059/burst_phase_drift.png)
+
+![Ошибки символов B](../data/processed/lab059/lab056_b_symbol_errors.png)
+
+## Synthetic Monte Carlo
+
+Чистая преамбула получена действующим TX/RX RRC-трактом при Fs 8 Мвыб/с:
+400 отсчётов/символ и 4001 tap для 20 ксимв/с, 16 отсчётов/символ и 161 tap для
+500 ксимв/с. После этого на выходе выбора символов добавлен комплексный AWGN с
+заранее заданным EVM 0,317390, 0,55 или 0,845350. Поэтому тест учитывает реальный
+RRC и дискретизацию в чистой форме, но не моделирует цветной или
+частотно-селективный остаток реального OTA-тракта.
+
+Для каждой из 17 частот, каждого качества и каждой скорости выполнено 300
+реализаций. В строке качества B для 500 ксимв/с каждый метод имеет 5100 оценок.
+
+| Метод | Bias | RMSE | Median abs | P95 abs | P99 abs | Катастрофические | P(≤0,25 Гц) | P(≤0,5 Гц) | P(≤1 Гц) |
+|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|
+| P0 | −0,018 Гц | 6,808 Гц | 4,643 Гц | 13,265 Гц | 17,805 Гц | 72,08% | 3,06% | 5,86% | 11,61% |
+| P1 | +0,050 Гц | 3,728 Гц | 2,498 Гц | 7,437 Гц | 9,895 Гц | 50,98% | 5,24% | 10,47% | 21,27% |
+| P2 | +0,078 Гц | 12,264 Гц | 8,256 Гц | 23,709 Гц | 31,583 Гц | 83,12% | 1,65% | 3,67% | 7,20% |
+| P3 | +0,049 Гц | 7,089 Гц | 2,935 Гц | 8,528 Гц | 11,383 Гц | 57,94% | 4,90% | 9,61% | 18,25% |
+
+Модельная граница Крамера—Рао для известных символов, неизвестной постоянной
+фазы и комплексного AWGN равна
+`sqrt(6*EVM²*Rs² / ((2*pi)²*N*(N²−1)))`. При N=1280, Rs=500000 и
+EVM=0,845350 она даёт СКО не меньше примерно 3,598 Гц. P1 с RMSE 3,728 Гц
+близок к этому масштабу. Это model-dependent sanity check, а не утверждение,
+что реальный OTA-остаток является AWGN.
+
+При 20 ксимв/с P1 на том же EVM получил RMSE 0,1465 Гц и 99,92% попаданий в
+±0,5 Гц; длительность преамбулы там 64 мс. Разница показывает, что ограничение
+500 ксимв/с связано прежде всего с 25-кратно более коротким временем наблюдения,
+а не с ошибкой единиц или знака.
+
+![Распределение synthetic 500K](../data/processed/lab059/synthetic_500k_error_distribution.png)
+
+## Lab047 и Lab054
+
+Положительный контроль Lab047 не ухудшился: P0, P1, P2 и P3 каждый получили
+36/36 HEADER_ACCEPTED, 36/36 FEC_STARTED, 36/36 CRC_PASS и 36/36 PACKET_OUTPUT.
+Во всех методах суммарно осталось две ошибки преамбулы и ноль ошибок заголовка.
+
+Lab054 осталась отрицательной для всех методов:
+
+| Метод | Ошибки преамбулы, сумма | Raw header errors | Voted header errors | HEADER_ACCEPTED | FEC_STARTED | CRC_PASS | PACKET_OUTPUT |
+|---|---:|---:|---:|---:|---:|---:|---:|
+| P0 | 7767 | 946 | 283 | 0/16 | 0/16 | 0/16 | 0/16 |
+| P1 | 8264 | 1000 | 313 | 0/16 | 0/16 | 0/16 | 0/16 |
+| P2 | 8871 | 1085 | 348 | 0/16 | 0/16 | 0/16 | 0/16 |
+| P3 | 9020 | 1140 | 373 | 0/16 | 0/16 | 0/16 | 0/16 |
+
+Эти данные не превращают Lab054 в новый опыт и не меняют его исторический
+вердикт. Плохое физическое качество Lab054 не исправляется заменой одной
+оценки частоты.
+
+![Lab054](../data/processed/lab059/lab054_result_by_method.png)
+
+## Инженерный вывод
+
+`PREAMBLE_ONLY_INSUFFICIENT`.
+
+P3 показывает, что штатный P0 можно существенно улучшить на конкретной записи,
+но ни один preamble-only метод не обеспечивает устойчивую ошибку менее 0,5 Гц
+при качестве B. Следующий один шаг — полностью офлайн проверить
+decision-directed carrier tracking на протяжении burst, используя решения BPSK
+без знания правильного payload. Это сохраняет текущий формат пакета и позволяет
+использовать P3 как начальную оценку. Costas loop остаётся разумной альтернативой,
+но потребует отдельной настройки динамики петли. Periodic pilots дают явную опору,
+однако меняют формат и добавляют overhead, поэтому их логично рассматривать позже.
+
+Lab058 commit: `82f17499ace5e655991240348401b9700c7c3349`.
+Lab059 не коммитировалась. Push не выполнялся. RF TX = 0. Lab060 не создавалась.
diff --git a/experiments/lab059_preamble_carrier_limits.py b/experiments/lab059_preamble_carrier_limits.py
new file mode 100644
index 0000000..aa02089
--- /dev/null
+++ b/experiments/lab059_preamble_carrier_limits.py
@@ -0,0 +1,1056 @@
+"""Lab059: пределы preamble-only восстановления несущей по сохранённым IQ."""
+
+from __future__ import annotations
+
+import csv
+from dataclasses import asdict, dataclass, replace
+import hashlib
+import json
+import math
+from pathlib import Path
+import sys
+from typing import Iterable
+
+if __package__ in (None, ""):
+    sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
+
+import matplotlib
+
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+import numpy as np
+
+from experiments import lab054_hardware_control as lab054
+from experiments import lab057_symbol_diagnosis as lab057
+from experiments import lab058_abab_symbol_quality as lab058
+
+
+ROOT = Path(__file__).resolve().parents[1]
+OUTPUT = ROOT / "data/processed/lab059"
+METHODS = ("P0", "P1", "P2", "P3")
+SAMPLE_RATE = 8_000_000
+PREAMBLE_SYMBOLS = 1280
+PROBE_SYMBOLS = 1424
+FULL_BLOCK_SYMBOLS = 20
+P2_BLOCK_SYMBOLS = 16
+P2_TRAIN = np.arange(32, 608)
+P2_VALIDATION = np.arange(672, 1248)
+BURST_DURATION_SECONDS = 0.2048
+MONTE_CARLO_TRIALS = 300
+SYNTHETIC_FREQUENCIES_HZ = np.array(
+    [-100, -20, -10, -5, -2, -1, -0.5, -0.25, 0, 0.25, 0.5, 1, 2, 5, 10, 20, 100],
+    dtype=np.float64,
+)
+QUALITY_LEVELS = (
+    ("LAB058_A_EVM", 0.317390),
+    ("INTERMEDIATE_EVM", 0.55),
+    ("LAB058_B_EVM", 0.845350),
+)
+CATASTROPHIC_ERROR_HZ = 1.0 / (2.0 * BURST_DURATION_SECONDS)
+
+
+@dataclass(frozen=True)
+class CarrierEstimate:
+    """Оценка несущей с фазой, отнесённой к t=0."""
+
+    method: str
+    frequency_hz: float
+    gain: complex
+    estimation_symbols: int
+    estimation_duration_seconds: float
+    phase_fit_rmse_rad: float
+    mean_block_coherence: float
+    unambiguous_abs_hz: float
+    validation_phase_rmse_rad: float | None = None
+    validation_errors: int | None = None
+    validation_symbols: int | None = None
+
+
+def serializable(value):
+    """Преобразовать numpy и complex в строгий JSON."""
+
+    if isinstance(value, dict):
+        return {str(key): serializable(item) for key, item in value.items()}
+    if isinstance(value, (list, tuple)):
+        return [serializable(item) for item in value]
+    if isinstance(value, np.ndarray):
+        return serializable(value.tolist())
+    if isinstance(value, complex):
+        return {"real": float(value.real), "imag": float(value.imag)}
+    if isinstance(value, np.generic):
+        return serializable(value.item())
+    return value
+
+
+def write_json(path: Path, value) -> None:
+    path.parent.mkdir(parents=True, exist_ok=True)
+    path.write_text(
+        json.dumps(serializable(value), ensure_ascii=False, indent=2, allow_nan=False) + "\n",
+        encoding="utf-8",
+    )
+
+
+def sha256(path: Path) -> str:
+    digest = hashlib.sha256()
+    with path.open("rb") as stream:
+        for block in iter(lambda: stream.read(1024 * 1024), b""):
+            digest.update(block)
+    return digest.hexdigest()
+
+
+def phase_slope_to_hz(slope_rad_per_second: float, *, squared_bpsk: bool) -> float:
+    """Перевести наклон фазы в Гц с учётом удвоения при BPSK²."""
+
+    divisor = 4.0 * np.pi if squared_bpsk else 2.0 * np.pi
+    return float(slope_rad_per_second / divisor)
+
+
+def phase_drift_degrees(frequency_error_hz: float, duration_seconds: float) -> float:
+    """Накопленный фазовый уход от ошибки частоты."""
+
+    return float(360.0 * frequency_error_hz * duration_seconds)
+
+
+def maximum_frequency_error_hz(phase_limit_degrees: float, duration_seconds: float) -> float:
+    """Допустимая ошибка частоты для заданного фазового ухода."""
+
+    return float(phase_limit_degrees / (360.0 * duration_seconds))
+
+
+def awgn_cfo_crlb_standard_deviation_hz(
+    evm_rms: float,
+    symbol_count: int,
+    symbol_rate: float,
+) -> float:
+    """CRLB СКО CFO для известных символов, неизвестной фазы и complex AWGN.
+
+    Здесь EVM² = E|n|²/|h|². Это модельная проверка масштаба, а не описание
+    частотно-селективного OTA-остатка.
+    """
+
+    variance = (
+        6.0 * evm_rms**2 * symbol_rate**2
+        / ((2.0 * np.pi) ** 2 * symbol_count * (symbol_count**2 - 1))
+    )
+    return float(np.sqrt(variance))
+
+
+def _huber_phase_line(
+    block_values: np.ndarray,
+    block_times_seconds: np.ndarray,
+) -> tuple[float, float, float, float]:
+    """Устойчиво оценить phase(t)=phi0+slope*t заранее фиксированным IRLS."""
+
+    values = np.asarray(block_values, dtype=np.complex128)
+    times = np.asarray(block_times_seconds, dtype=np.float64)
+    if values.ndim != 1 or times.ndim != 1 or len(values) != len(times) or len(values) < 3:
+        raise ValueError("Недостаточно фазовых блоков")
+    phases = np.unwrap(np.angle(values))
+    base_weights = np.maximum(np.abs(values), 1e-12)
+    center_time = float(np.sum(base_weights * times) / np.sum(base_weights))
+    centered = times - center_time
+    design = np.column_stack((np.ones(len(times)), centered))
+    weights = base_weights.copy()
+    coefficients = np.zeros(2, dtype=np.float64)
+    for _iteration in range(12):
+        root = np.sqrt(weights / max(float(np.max(weights)), 1e-30))
+        updated, *_unused = np.linalg.lstsq(
+            design * root[:, None], phases * root, rcond=None
+        )
+        residual = phases - design @ updated
+        median = float(np.median(residual))
+        scale = 1.4826 * float(np.median(np.abs(residual - median))) + 1e-9
+        limit = 1.345 * scale
+        robust = np.ones_like(residual)
+        outside = np.abs(residual) > limit
+        robust[outside] = limit / np.abs(residual[outside])
+        weights = base_weights * robust
+        if np.allclose(updated, coefficients, rtol=0.0, atol=1e-12):
+            coefficients = updated
+            break
+        coefficients = updated
+    residual = phases - design @ coefficients
+    rmse = float(np.sqrt(np.sum(base_weights * residual**2) / np.sum(base_weights)))
+    phase_at_zero = float(coefficients[0] - coefficients[1] * center_time)
+    coherence = float(abs(np.sum(values)) / max(float(np.sum(abs(values))), 1e-30))
+    return phase_at_zero, float(coefficients[1]), rmse, coherence
+
+
+def _block_values(
+    values: np.ndarray,
+    indexes: np.ndarray,
+    symbol_rate: int,
+    block_symbol_count: int,
+) -> tuple[np.ndarray, np.ndarray]:
+    """Собрать неперекрывающиеся когерентные блоки по исходной шкале времени."""
+
+    values = np.asarray(values, dtype=np.complex128)
+    indexes = np.asarray(indexes, dtype=np.int64)
+    block_count = len(values) // block_symbol_count
+    if block_count < 3:
+        raise ValueError("Слишком мало когерентных блоков")
+    usable = block_count * block_symbol_count
+    reshaped_values = values[:usable].reshape(block_count, block_symbol_count)
+    reshaped_indexes = indexes[:usable].reshape(block_count, block_symbol_count)
+    return (
+        np.mean(reshaped_values, axis=1),
+        np.mean((reshaped_indexes + 0.5) / symbol_rate, axis=1),
+    )
+
+
+def _gain_at_zero(
+    symbols: np.ndarray,
+    known_symbols: np.ndarray,
+    indexes: np.ndarray,
+    symbol_rate: int,
+    frequency_hz: float,
+) -> complex:
+    times = (indexes + 0.5) / symbol_rate
+    return complex(
+        np.mean(
+            symbols * np.conj(known_symbols) * np.exp(-2j * np.pi * frequency_hz * times)
+        )
+    )
+
+
+def _validation_metrics(
+    symbols: np.ndarray,
+    known_symbols: np.ndarray,
+    indexes: np.ndarray,
+    symbol_rate: int,
+    frequency_hz: float,
+    gain: complex,
+) -> tuple[float, int]:
+    times = (indexes + 0.5) / symbol_rate
+    normalized = symbols * np.exp(-2j * np.pi * frequency_hz * times) / gain
+    phase_error = np.angle(normalized * np.conj(known_symbols))
+    errors = int(np.count_nonzero((normalized.real >= 0) != (known_symbols.real >= 0)))
+    return float(np.sqrt(np.mean(phase_error**2))), errors
+
+
+def estimate_carrier(
+    preamble_symbols: np.ndarray,
+    symbol_rate: int,
+    method: str,
+) -> CarrierEstimate:
+    """Оценить частоту и фазу только по 1280 известным символам преамбулы."""
+
+    received = np.asarray(preamble_symbols, dtype=np.complex128)
+    known = lab057.EXPECTED.astype(np.complex128)
+    if received.shape != (PREAMBLE_SYMBOLS,):
+        raise ValueError("Оценивателю требуется ровно 1280 символов преамбулы")
+    all_indexes = np.arange(PREAMBLE_SYMBOLS, dtype=np.int64)
+
+    if method == "P0":
+        statistics = lab057.squared_cfo_statistics(received, symbol_rate)
+        frequency = float(statistics["cfo_hz"])
+        gain = _gain_at_zero(received, known, all_indexes, symbol_rate, frequency)
+        phase_rmse, validation_errors = _validation_metrics(
+            received[P2_VALIDATION], known[P2_VALIDATION], P2_VALIDATION,
+            symbol_rate, frequency, gain,
+        )
+        blocks = (received[:1024] ** 2).reshape(2, 512).mean(axis=1)
+        return CarrierEstimate(
+            method, frequency, gain, 1024, 1024 / symbol_rate,
+            0.0, float(abs(np.sum(blocks)) / max(float(np.sum(abs(blocks))), 1e-30)),
+            symbol_rate / 2048.0, phase_rmse, validation_errors, len(P2_VALIDATION),
+        )
+
+    if method == "P2":
+        indexes = P2_TRAIN
+        despread = received[indexes] * np.conj(known[indexes])
+        blocks, times = _block_values(despread, indexes, symbol_rate, P2_BLOCK_SYMBOLS)
+        _phase0, slope, rmse, coherence = _huber_phase_line(blocks, times)
+        frequency = phase_slope_to_hz(slope, squared_bpsk=False)
+        gain = _gain_at_zero(received[indexes], known[indexes], indexes, symbol_rate, frequency)
+        phase_rmse, validation_errors = _validation_metrics(
+            received[P2_VALIDATION], known[P2_VALIDATION], P2_VALIDATION,
+            symbol_rate, frequency, gain,
+        )
+        return CarrierEstimate(
+            method, frequency, gain, len(indexes),
+            (indexes[-1] - indexes[0] + 1) / symbol_rate,
+            rmse, coherence, symbol_rate / (2.0 * P2_BLOCK_SYMBOLS),
+            phase_rmse, validation_errors, len(P2_VALIDATION),
+        )
+
+    if method == "P1":
+        transformed = received * np.conj(known)
+        squared_bpsk = False
+    elif method == "P3":
+        transformed = received**2
+        squared_bpsk = True
+    else:
+        raise ValueError(f"Неизвестный метод {method}")
+
+    blocks, times = _block_values(
+        transformed, all_indexes, symbol_rate, FULL_BLOCK_SYMBOLS
+    )
+    _phase0, slope, rmse, coherence = _huber_phase_line(blocks, times)
+    frequency = phase_slope_to_hz(slope, squared_bpsk=squared_bpsk)
+    gain = _gain_at_zero(received, known, all_indexes, symbol_rate, frequency)
+    phase_rmse, validation_errors = _validation_metrics(
+        received[P2_VALIDATION], known[P2_VALIDATION], P2_VALIDATION,
+        symbol_rate, frequency, gain,
+    )
+    ambiguity_divisor = 4.0 if squared_bpsk else 2.0
+    return CarrierEstimate(
+        method, frequency, gain, PREAMBLE_SYMBOLS, PREAMBLE_SYMBOLS / symbol_rate,
+        rmse, coherence, symbol_rate / (ambiguity_divisor * FULL_BLOCK_SYMBOLS),
+        phase_rmse, validation_errors, len(P2_VALIDATION),
+    )
+
+
+def estimate_from_burst(
+    burst_symbols: np.ndarray,
+    symbol_rate: int,
+    method: str,
+) -> CarrierEstimate:
+    """Явная граница: оцениватель видит только prefix-преамбулу."""
+
+    return estimate_carrier(np.asarray(burst_symbols)[:PREAMBLE_SYMBOLS], symbol_rate, method)
+
+
+def compensate(
+    symbols: np.ndarray,
+    symbol_rate: int,
+    estimate: CarrierEstimate,
+) -> np.ndarray:
+    """Один раз компенсировать CFO и фазу по единой шкале времени."""
+
+    times = (np.arange(len(symbols), dtype=np.float64) + 0.5) / symbol_rate
+    return symbols * np.exp(-2j * np.pi * estimate.frequency_hz * times) / estimate.gain
+
+
+def _extract_symbols(
+    matched: np.ndarray,
+    nominal: int,
+    timing_samples: int,
+    count: int,
+    samples_per_symbol: int,
+) -> np.ndarray:
+    values = matched[
+        nominal + timing_samples:
+        nominal + timing_samples + count * samples_per_symbol:
+        samples_per_symbol
+    ].astype(np.complex128)
+    if len(values) != count:
+        raise ValueError("Недостаточно отфильтрованных символов")
+    return values
+
+
+def local_preamble_symbols(
+    raw_values: np.ndarray,
+    frontend: lab057.Frontend,
+    timing_samples: int,
+    selected_indexes: np.ndarray,
+) -> np.ndarray:
+    """RRC-точки, чьи raw DC и FIR-опора ограничены выбранной преамбулой."""
+
+    indexes = np.asarray(selected_indexes, dtype=np.int64)
+    if indexes.ndim != 1 or not len(indexes) or indexes.min() < 0 or indexes.max() >= PREAMBLE_SYMBOLS:
+        raise ValueError("Некорректные индексы локальной преамбулы")
+    positions = (
+        frontend.nominal + int(timing_samples)
+        + indexes * frontend.samples_per_symbol
+    )
+    low = int(positions.min() - len(frontend.taps) + 1)
+    high = int(positions.max() + 1)
+    if low < 0 or high > len(raw_values):
+        raise ValueError("Сырая опора преамбулы выходит за окно")
+    local = np.asarray(raw_values[low:high], dtype=np.complex64)
+    rotation = lab057.packet.lab043._carrier_downconversion_vector(low, len(local))
+    filtered = lab057.oaconvolve(
+        (local - np.mean(local)) * rotation,
+        frontend.taps,
+        mode="full",
+    )
+    output = np.zeros(PREAMBLE_SYMBOLS, dtype=np.complex128)
+    output[indexes] = filtered[positions - low]
+    return output
+
+
+def real_carrier_estimates(
+    raw_values: np.ndarray,
+    frontend: lab057.Frontend,
+    timing_samples: int,
+    stock_symbols: np.ndarray,
+    symbol_rate: int,
+    methods: Iterable[str],
+) -> dict[str, CarrierEstimate]:
+    """P0 остаётся stock; P1–P3 не читают raw после своей преамбульной опоры."""
+
+    requested = tuple(methods)
+    estimates: dict[str, CarrierEstimate] = {}
+    if "P0" in requested:
+        estimates["P0"] = estimate_from_burst(stock_symbols, symbol_rate, "P0")
+    full_local = None
+    if any(method in requested for method in ("P1", "P2", "P3")):
+        full_local = local_preamble_symbols(
+            raw_values, frontend, timing_samples, np.arange(PREAMBLE_SYMBOLS)
+        )
+    for method in ("P1", "P3"):
+        if method in requested:
+            estimates[method] = estimate_carrier(full_local, symbol_rate, method)
+    if "P2" in requested:
+        train_local = local_preamble_symbols(
+            raw_values, frontend, timing_samples, P2_TRAIN
+        )
+        estimate = estimate_carrier(train_local, symbol_rate, "P2")
+        validation_rmse, validation_errors = _validation_metrics(
+            full_local[P2_VALIDATION], lab057.EXPECTED[P2_VALIDATION], P2_VALIDATION,
+            symbol_rate, estimate.frequency_hz, estimate.gain,
+        )
+        estimates["P2"] = replace(
+            estimate,
+            validation_phase_rmse_rad=validation_rmse,
+            validation_errors=validation_errors,
+        )
+    return {method: estimates[method] for method in requested}
+
+
+def _estimate_dict(estimate: CarrierEstimate) -> dict:
+    return asdict(estimate)
+
+
+def _lab056_inputs() -> tuple[list[dict], dict[str, float]]:
+    metadata = json.loads((ROOT / "data/raw/lab056/tx_metadata.json").read_text(encoding="utf-8"))
+    analysis = json.loads((ROOT / "data/processed/lab056/rf_analysis.json").read_text(encoding="utf-8"))
+    lab058_summary = json.loads((ROOT / "data/processed/lab058/summary.json").read_text(encoding="utf-8"))
+    measured = {row["label"]: row for row in analysis["segments"]}
+    oracle = {row["label"]: row["oracle"]["cfo_hz"] for row in lab058_summary["segments"]}
+    inputs = []
+    for segment in metadata["segments"]:
+        inputs.append({
+            "label": segment["label"],
+            "symbol_rate": segment["symbol_rate"],
+            "samples": segment["samples"],
+            "rx_start": measured[segment["label"]]["measurement"]["start"],
+        })
+    return inputs, oracle
+
+
+def analyze_lab056(methods: Iterable[str]) -> list[dict]:
+    """Применить оценки к полным A1/B1/A2/B2, truth только для метрик после fit."""
+
+    reader = lab058.Cs8File(ROOT / "data/raw/lab056/rx_stream.cs8")
+    inputs, oracle = _lab056_inputs()
+    rows: list[dict] = []
+    for item in inputs:
+        rate = item["symbol_rate"]
+        expected = lab058.expected_symbols(rate)
+        values = reader.read(item["rx_start"], item["samples"])
+        frontend = lab057.Frontend(rate)
+        stock = frontend.extract(values, len(expected))
+        if not stock.passed:
+            raise RuntimeError(f"Lab056 {item['label']}: штатное извлечение {stock.reason}")
+        matched = frontend.filter(values)
+        symbols = _extract_symbols(
+            matched, frontend.nominal, stock.timing_offset_samples,
+            len(expected), frontend.samples_per_symbol,
+        )
+        estimates = real_carrier_estimates(
+            values, frontend, stock.timing_offset_samples, symbols, rate, methods
+        )
+        if "P0" in estimates:
+            if not np.isclose(estimates["P0"].frequency_hz, stock.carrier["cfo_hz"], atol=1e-10):
+                raise AssertionError("P0 Lab056 не воспроизвёл штатную оценку CFO")
+        selected = np.arange(len(expected)) >= PREAMBLE_SYMBOLS
+        for method, estimate in estimates.items():
+            result = lab058.evaluate(
+                {"symbols": symbols, "h": estimate.gain, "cfo_hz": estimate.frequency_hz},
+                expected, rate, selected,
+            )
+            reference = float(oracle[item["label"]])
+            rows.append({
+                "dataset": "Lab056",
+                "label": item["label"],
+                "frame": None,
+                "symbol_rate": rate,
+                "method": method,
+                "timing_samples": int(stock.timing_offset_samples),
+                "frequency_hz": estimate.frequency_hz,
+                "diagnostic_reference_hz": reference,
+                "frequency_error_hz": estimate.frequency_hz - reference,
+                "estimation_symbols": estimate.estimation_symbols,
+                "estimation_duration_seconds": estimate.estimation_duration_seconds,
+                "phase_drift_to_burst_end_degrees": phase_drift_degrees(
+                    estimate.frequency_hz - reference, BURST_DURATION_SECONDS
+                ),
+                "preamble_errors": int(np.count_nonzero(
+                    (compensate(symbols[:PREAMBLE_SYMBOLS], rate, estimate).real >= 0)
+                    != lab057.PREAMBLE
+                )),
+                "validation_errors": estimate.validation_errors,
+                "validation_symbols": estimate.validation_symbols,
+                "validation_phase_rmse_rad": estimate.validation_phase_rmse_rad,
+                "header_raw_errors": None,
+                "header_voted_errors": None,
+                "HEADER_ACCEPTED": None,
+                "FEC_STARTED": None,
+                "CRC_PASS": None,
+                "PACKET_OUTPUT": None,
+                "symbol_errors": result["errors"],
+                "symbols": result["symbols"],
+                "symbol_error_rate": result["ber"],
+                "evm_rms": result["evm_rms"],
+                "matched_residual_db": result["matched_residual_db"],
+                "estimate": _estimate_dict(estimate),
+            })
+    return rows
+
+
+def _packet_sources() -> tuple[list[dict], list[dict], lab058.Cs8File, lab058.Cs8File]:
+    mapping = json.loads((ROOT / "data/processed/lab057/candidate_mapping.json").read_text(encoding="utf-8"))
+    lab054_items = [row for row in mapping if row["true_start_frame"]]
+    audit = json.loads((ROOT / "data/processed/lab055/energy_audit_v2.json").read_text(encoding="utf-8"))
+    old_directory = Path(audit["experiments"]["Lab047"]["directory"])
+    old = json.loads((old_directory / "independent_reprocess_result_corrected.json").read_text(encoding="utf-8"))
+    lab047_items = [row for row in old["strict"]["outcomes"] if row["accepted"]]
+    return (
+        lab054_items,
+        lab047_items,
+        lab058.Cs8File(ROOT / "data/raw/lab054/rx_stream.cs8"),
+        lab058.Cs8File(old_directory / "rx_stream.cs8"),
+    )
+
+
+def _packet_rows(
+    reader: lab058.Cs8File,
+    start: int,
+    symbol_rate: int,
+    dataset: str,
+    frame_number: int,
+    expected_header: np.ndarray,
+    methods: Iterable[str],
+) -> list[dict]:
+    frontend = lab057.Frontend(symbol_rate)
+    probe_count = PROBE_SYMBOLS
+    probe_values = reader.read(start, (probe_count + 42) * frontend.samples_per_symbol)
+    stock = frontend.extract(probe_values, probe_count)
+    if not stock.passed:
+        raise RuntimeError(f"{dataset} frame {frame_number}: {stock.reason}")
+    matched = frontend.filter(probe_values)
+    symbols = _extract_symbols(
+        matched, frontend.nominal, stock.timing_offset_samples,
+        probe_count, frontend.samples_per_symbol,
+    )
+    estimates = real_carrier_estimates(
+        probe_values, frontend, stock.timing_offset_samples, symbols, symbol_rate, methods
+    )
+    if "P0" in estimates and not np.isclose(
+        estimates["P0"].frequency_hz, stock.carrier["cfo_hz"], atol=1e-10
+    ):
+        raise AssertionError(f"P0 {dataset} не воспроизвёл штатную оценку")
+
+    rows = []
+    for method, estimate in estimates.items():
+        normalized_probe = compensate(symbols, symbol_rate, estimate)
+        header = lab057.physical.decode_phy_header_soft(
+            normalized_probe.real[PREAMBLE_SYMBOLS:PROBE_SYMBOLS]
+        )
+        full_soft = normalized_probe.real[PREAMBLE_SYMBOLS:]
+        if header["passed"]:
+            packet_bytes = int(header["packet_bytes"])
+            count = (
+                PREAMBLE_SYMBOLS + 144
+                + lab057.physical.conv.encoded_bit_count(packet_bytes * 8)
+            )
+            full_values = reader.read(start, (count + 42) * frontend.samples_per_symbol)
+            if method == "P0":
+                full_stock = frontend.extract(full_values, count)
+                if not full_stock.passed:
+                    raise RuntimeError(f"{dataset} P0 full extract: {full_stock.reason}")
+                full_soft = full_stock.normalized_real[PREAMBLE_SYMBOLS:]
+            else:
+                full_matched = frontend.filter(full_values)
+                full_symbols = _extract_symbols(
+                    full_matched, frontend.nominal, stock.timing_offset_samples,
+                    count, frontend.samples_per_symbol,
+                )
+                full_soft = compensate(full_symbols, symbol_rate, estimate).real[PREAMBLE_SYMBOLS:]
+        decoded, _packet = lab057.decode_body(full_soft)
+        decisions = normalized_probe.real >= 0
+        expected_header_bool = expected_header.astype(bool)
+        voted = normalized_probe.real[PREAMBLE_SYMBOLS:PROBE_SYMBOLS].reshape(48, 3).sum(axis=1) >= 0
+        rows.append({
+            "dataset": dataset,
+            "label": f"{dataset}_{frame_number:02d}",
+            "frame": frame_number,
+            "symbol_rate": symbol_rate,
+            "method": method,
+            "timing_samples": int(stock.timing_offset_samples),
+            "frequency_hz": estimate.frequency_hz,
+            "diagnostic_reference_hz": None,
+            "frequency_error_hz": None,
+            "estimation_symbols": estimate.estimation_symbols,
+            "estimation_duration_seconds": estimate.estimation_duration_seconds,
+            "phase_drift_to_burst_end_degrees": phase_drift_degrees(
+                estimate.frequency_hz, BURST_DURATION_SECONDS
+            ),
+            "preamble_errors": int(np.count_nonzero(
+                decisions[:PREAMBLE_SYMBOLS] != lab057.PREAMBLE
+            )),
+            "validation_errors": estimate.validation_errors,
+            "validation_symbols": estimate.validation_symbols,
+            "validation_phase_rmse_rad": estimate.validation_phase_rmse_rad,
+            "header_raw_errors": int(np.count_nonzero(
+                decisions[PREAMBLE_SYMBOLS:PROBE_SYMBOLS] != expected_header_bool
+            )),
+            "header_voted_errors": int(np.count_nonzero(
+                voted != expected_header_bool[::3]
+            )),
+            "HEADER_ACCEPTED": bool(decoded["HEADER_ACCEPTED"]),
+            "FEC_STARTED": bool(decoded["FEC_STARTED"]),
+            "CRC_PASS": decoded["CRC_RESULT"] == lab057.physical.CRC_PASS,
+            "PACKET_OUTPUT": bool(decoded["PACKET_OUTPUT"]),
+            "symbol_errors": None,
+            "symbols": None,
+            "symbol_error_rate": None,
+            "evm_rms": None,
+            "matched_residual_db": None,
+            "estimate": _estimate_dict(estimate),
+        })
+    return rows
+
+
+def analyze_packets(methods: Iterable[str]) -> list[dict]:
+    lab054_items, lab047_items, lab054_reader, lab047_reader = _packet_sources()
+    rows: list[dict] = []
+    plans = lab054.plans()
+    for item in lab054_items:
+        frame = int(item["true_start_frame"])
+        rows.extend(_packet_rows(
+            lab054_reader, int(item["refined_start"]), 500_000, "Lab054", frame,
+            plans[frame - 1].plan.phy_header_bits, methods,
+        ))
+    expected_header_047 = lab057.physical.encode_phy_header(268)
+    for frame, item in enumerate(lab047_items, 1):
+        rows.extend(_packet_rows(
+            lab047_reader, int(item["refined_start_sample"]), 20_000, "Lab047", frame,
+            expected_header_047, methods,
+        ))
+    return rows
+
+
+def _clean_synthetic_preamble(symbol_rate: int) -> tuple[np.ndarray, dict]:
+    """Сформировать чистые точки через фактические Fs и TX/RX RRC."""
+
+    values, truth = lab057.synthetic_waveform(
+        symbol_rate, delay_symbols=0.0, cfo_hz=0.0, phase_rad=0.0, es_n0_db=None
+    )
+    frontend = lab057.Frontend(symbol_rate)
+    probe = values[: (PROBE_SYMBOLS + 42) * frontend.samples_per_symbol]
+    stock = frontend.extract(probe, PROBE_SYMBOLS)
+    matched = frontend.filter(probe)
+    symbols = _extract_symbols(
+        matched, frontend.nominal, stock.timing_offset_samples,
+        PREAMBLE_SYMBOLS, frontend.samples_per_symbol,
+    )
+    indexes = np.arange(PREAMBLE_SYMBOLS)
+    gain = _gain_at_zero(symbols, lab057.EXPECTED, indexes, symbol_rate, 0.0)
+    normalized = symbols / gain
+    return normalized, {
+        "sample_rate_hz": SAMPLE_RATE,
+        "symbol_rate": symbol_rate,
+        "samples_per_symbol": frontend.samples_per_symbol,
+        "rrc_taps": len(frontend.taps),
+        "tx_truth": truth,
+        "clean_evm_rms": float(np.sqrt(np.mean(abs(normalized - lab057.EXPECTED) ** 2))),
+    }
+
+
+def synthetic_study() -> tuple[list[dict], list[dict], dict, dict]:
+    """Воспроизводимый Monte Carlo с фиксированными частотами, EVM и seed."""
+
+    aggregate_rows: list[dict] = []
+    by_cfo_rows: list[dict] = []
+    distributions: dict[tuple[int, str, str], np.ndarray] = {}
+    clean_metadata = {}
+    for symbol_rate in (20_000, 500_000):
+        clean, clean_info = _clean_synthetic_preamble(symbol_rate)
+        clean_metadata[str(symbol_rate)] = clean_info
+        times = (np.arange(PREAMBLE_SYMBOLS) + 0.5) / symbol_rate
+        for quality_index, (quality_name, evm_rms) in enumerate(QUALITY_LEVELS):
+            method_errors = {method: [] for method in METHODS}
+            method_validation = {method: [] for method in METHODS}
+            for frequency_index, true_frequency in enumerate(SYNTHETIC_FREQUENCIES_HZ):
+                seed = 59_000_000 + symbol_rate + quality_index * 100_000 + frequency_index * 1_000
+                random_generator = np.random.default_rng(seed)
+                errors_by_method = {method: [] for method in METHODS}
+                validation_by_method = {method: [] for method in METHODS}
+                for _trial in range(MONTE_CARLO_TRIALS):
+                    phase = random_generator.uniform(-np.pi, np.pi)
+                    noise = evm_rms / np.sqrt(2.0) * (
+                        random_generator.normal(size=PREAMBLE_SYMBOLS)
+                        + 1j * random_generator.normal(size=PREAMBLE_SYMBOLS)
+                    )
+                    received = (
+                        clean * np.exp(1j * (phase + 2 * np.pi * true_frequency * times))
+                        + noise
+                    )
+                    for method in METHODS:
+                        estimate = estimate_carrier(received, symbol_rate, method)
+                        error = estimate.frequency_hz - true_frequency
+                        errors_by_method[method].append(error)
+                        validation_by_method[method].append(estimate.validation_phase_rmse_rad)
+                for method in METHODS:
+                    errors = np.asarray(errors_by_method[method], dtype=np.float64)
+                    validation = np.asarray(validation_by_method[method], dtype=np.float64)
+                    method_errors[method].extend(errors.tolist())
+                    method_validation[method].extend(validation.tolist())
+                    by_cfo_rows.append({
+                        "symbol_rate": symbol_rate,
+                        "quality": quality_name,
+                        "target_evm_rms": evm_rms,
+                        "true_frequency_hz": float(true_frequency),
+                        "method": method,
+                        "trials": MONTE_CARLO_TRIALS,
+                        **_error_statistics(errors),
+                        "validation_phase_rmse_rad": float(np.mean(validation)),
+                    })
+            for method in METHODS:
+                errors = np.asarray(method_errors[method], dtype=np.float64)
+                validation = np.asarray(method_validation[method], dtype=np.float64)
+                distributions[(symbol_rate, quality_name, method)] = errors
+                aggregate_rows.append({
+                    "symbol_rate": symbol_rate,
+                    "quality": quality_name,
+                    "target_evm_rms": evm_rms,
+                    "method": method,
+                    "trials": len(errors),
+                    "cfo_values": len(SYNTHETIC_FREQUENCIES_HZ),
+                    **_error_statistics(errors),
+                    "validation_phase_rmse_rad": float(np.mean(validation)),
+                })
+    return aggregate_rows, by_cfo_rows, distributions, clean_metadata
+
+
+def _error_statistics(errors: np.ndarray) -> dict:
+    absolute = np.abs(errors)
+    return {
+        "bias_hz": float(np.mean(errors)),
+        "rmse_hz": float(np.sqrt(np.mean(errors**2))),
+        "median_abs_error_hz": float(np.median(absolute)),
+        "p95_abs_error_hz": float(np.percentile(absolute, 95)),
+        "p99_abs_error_hz": float(np.percentile(absolute, 99)),
+        "catastrophic_error_rate": float(np.mean(absolute > CATASTROPHIC_ERROR_HZ)),
+        "probability_abs_error_le_0p25_hz": float(np.mean(absolute <= 0.25)),
+        "probability_abs_error_le_0p5_hz": float(np.mean(absolute <= 0.5)),
+        "probability_abs_error_le_1p0_hz": float(np.mean(absolute <= 1.0)),
+    }
+
+
+def _csv_value(value):
+    if isinstance(value, complex):
+        return json.dumps(serializable(value), ensure_ascii=False)
+    if isinstance(value, (dict, list, tuple)):
+        return json.dumps(serializable(value), ensure_ascii=False, separators=(",", ":"))
+    return value
+
+
+def write_csv(path: Path, rows: list[dict], exclude: tuple[str, ...] = ()) -> None:
+    fields: list[str] = []
+    for row in rows:
+        fields.extend(key for key in row if key not in fields and key not in exclude)
+    with path.open("w", newline="", encoding="utf-8-sig") as stream:
+        writer = csv.DictWriter(stream, fieldnames=fields)
+        writer.writeheader()
+        for row in rows:
+            writer.writerow({key: _csv_value(row.get(key)) for key in fields})
+
+
+def summarize_real(rows: list[dict]) -> dict:
+    summary = {}
+    for dataset in ("Lab056", "Lab054", "Lab047"):
+        summary[dataset] = {}
+        for method in METHODS:
+            selected = [row for row in rows if row["dataset"] == dataset and row["method"] == method]
+            if not selected:
+                continue
+            if dataset == "Lab056":
+                b_rows = [row for row in selected if row["label"].startswith("B")]
+                a_rows = [row for row in selected if row["label"].startswith("A")]
+
+                def mean_or_none(items: list[dict], key: str) -> float | None:
+                    return float(np.mean([row[key] for row in items])) if items else None
+
+                summary[dataset][method] = {
+                    "segments": len(selected),
+                    "mean_abs_frequency_error_hz": float(np.mean([abs(row["frequency_error_hz"]) for row in selected])),
+                    "A_frequency_errors_hz": [row["frequency_error_hz"] for row in a_rows],
+                    "B_frequency_errors_hz": [row["frequency_error_hz"] for row in b_rows],
+                    "B_mean_abs_frequency_error_hz": (
+                        float(np.mean([abs(row["frequency_error_hz"]) for row in b_rows]))
+                        if b_rows else None
+                    ),
+                    "B_mean_symbol_error_rate": mean_or_none(b_rows, "symbol_error_rate"),
+                    "A_mean_symbol_error_rate": mean_or_none(a_rows, "symbol_error_rate"),
+                    "B_mean_evm_rms": mean_or_none(b_rows, "evm_rms"),
+                    "B_mean_matched_residual_db": mean_or_none(b_rows, "matched_residual_db"),
+                }
+            else:
+                summary[dataset][method] = {
+                    "frames": len(selected),
+                    "header_accepted": sum(bool(row["HEADER_ACCEPTED"]) for row in selected),
+                    "fec_started": sum(bool(row["FEC_STARTED"]) for row in selected),
+                    "crc_pass": sum(bool(row["CRC_PASS"]) for row in selected),
+                    "packet_output": sum(bool(row["PACKET_OUTPUT"]) for row in selected),
+                    "preamble_errors": sum(int(row["preamble_errors"]) for row in selected),
+                    "header_raw_errors": sum(int(row["header_raw_errors"]) for row in selected),
+                    "header_voted_errors": sum(int(row["header_voted_errors"]) for row in selected),
+                    "mean_frequency_hz": float(np.mean([row["frequency_hz"] for row in selected])),
+                }
+    return summary
+
+
+def phase_limit_table() -> list[dict]:
+    return [
+        {
+            "phase_limit_degrees": limit,
+            "max_frequency_error_hz": maximum_frequency_error_hz(limit, BURST_DURATION_SECONDS),
+        }
+        for limit in (15, 30, 45, 60, 90)
+    ]
+
+
+def mathematics_audit() -> dict:
+    return {
+        "active_input": "matched-filter symbol centers after nominal 50 kHz downconversion",
+        "p0_squares_bpsk": True,
+        "p0_symbol_ranges_half_open": [[0, 512], [512, 1024]],
+        "p0_discarded_preamble_symbols_half_open": [1024, 1280],
+        "block_center_symbol_times": [256.0, 768.0],
+        "center_distance_symbols": 512,
+        "phase_difference_formula": "angle(block1)-angle(block0), unwrapped",
+        "frequency_formula": "delta_phase/(4*pi*(512/Rs))",
+        "factor_two_after_squaring": True,
+        "compensation_sign": "negative complex exponential",
+        "reference_time": "gain phase at t=0; symbol k center at (k+0.5)/Rs",
+        "p0_unambiguous_abs_hz": {"20000": 20_000 / 2048, "500000": 500_000 / 2048},
+        "double_compensation": False,
+    }
+
+
+def choose_verdict(real_summary: dict, synthetic_rows: list[dict]) -> tuple[str, str, str]:
+    b_quality = {
+        row["method"]: row
+        for row in synthetic_rows
+        if row["symbol_rate"] == 500_000 and row["quality"] == "LAB058_B_EVM"
+    }
+    stable = {
+        method: b_quality[method]["probability_abs_error_le_0p5_hz"] >= 0.95
+        for method in METHODS
+    }
+    best = min(METHODS, key=lambda method: real_summary["Lab056"][method]["B_mean_symbol_error_rate"])
+    diagnostic = 0.03495896
+    approaches_diagnostic = real_summary["Lab056"][best]["B_mean_symbol_error_rate"] <= diagnostic + 0.02
+    lab047_preserved = real_summary["Lab047"][best]["packet_output"] == 36
+    a_preserved = real_summary["Lab056"][best]["A_mean_symbol_error_rate"] <= 0.001
+    if stable[best] and approaches_diagnostic and lab047_preserved and a_preserved:
+        return (
+            "PREAMBLE_ONLY_SUFFICIENT",
+            f"{best} стабилен в Monte Carlo и сохраняет A/Lab047",
+            f"подготовить внедрение {best} в штатный receiver",
+        )
+    if max(row["probability_abs_error_le_0p5_hz"] for row in b_quality.values()) >= 0.5:
+        return (
+            "PREAMBLE_ONLY_MARGINAL",
+            "улучшение есть, но субгерцовая точность на качестве B достигается нестабильно",
+            "проверить decision-directed carrier tracking through burst",
+        )
+    return (
+        "PREAMBLE_ONLY_INSUFFICIENT",
+        "ни один preamble-only метод не обеспечивает устойчивую ошибку менее 0,5 Гц на качестве B",
+        "проверить decision-directed carrier tracking through burst",
+    )
+
+
+def make_plots(
+    real_rows: list[dict],
+    synthetic_rows: list[dict],
+    distributions: dict,
+) -> None:
+    colors = {"P0": "#6c757d", "P1": "#2878b5", "P2": "#e17c05", "P3": "#54a24b"}
+    b_rows = [row for row in real_rows if row["dataset"] == "Lab056" and row["label"].startswith("B")]
+
+    figure, axis = plt.subplots(figsize=(9, 5), constrained_layout=True)
+    x = np.arange(2)
+    width = 0.18
+    for index, method in enumerate(METHODS):
+        selected = sorted((row for row in b_rows if row["method"] == method), key=lambda row: row["label"])
+        axis.bar(x + (index - 1.5) * width, [row["frequency_error_hz"] for row in selected], width,
+                 label=method, color=colors[method])
+    axis.axhline(0, color="black", linewidth=0.8)
+    axis.set(xticks=x, xticklabels=["B1", "B2"], ylabel="Ошибка оценки, Гц",
+             title="Lab056 B: preamble-only CFO относительно whole-burst reference")
+    axis.legend(ncol=4); axis.grid(axis="y", alpha=0.25)
+    figure.savefig(OUTPUT / "frequency_error_by_method.png", dpi=160)
+    plt.close(figure)
+
+    figure, axis = plt.subplots(figsize=(9, 5), constrained_layout=True)
+    bins = np.linspace(-35, 35, 141)
+    for method in METHODS:
+        values = distributions[(500_000, "LAB058_B_EVM", method)]
+        axis.hist(values, bins=bins, density=True, histtype="step", linewidth=1.5,
+                  label=method, color=colors[method])
+    axis.axvspan(-0.5, 0.5, color="green", alpha=0.12, label="±0,5 Гц")
+    axis.set(xlabel="Ошибка оценки частоты, Гц", ylabel="Плотность",
+             title="Synthetic 500 ксимв/с, качество Lab058 B")
+    axis.legend(); axis.grid(alpha=0.2)
+    figure.savefig(OUTPUT / "synthetic_500k_error_distribution.png", dpi=160)
+    plt.close(figure)
+
+    figure, axis = plt.subplots(figsize=(9, 5), constrained_layout=True)
+    time = np.linspace(0, BURST_DURATION_SECONDS, 300)
+    for method in METHODS:
+        errors = [row["frequency_error_hz"] for row in b_rows if row["method"] == method]
+        axis.plot(time * 1000, 360 * float(np.mean(errors)) * time,
+                  label=method, color=colors[method])
+    for limit in (-30, 30):
+        axis.axhline(limit, color="black", linestyle="--", linewidth=0.8)
+    axis.set(xlabel="Время от начала burst, мс", ylabel="Фазовый уход, градусы",
+             title="Средняя ошибка B1/B2, экстраполированная на burst")
+    axis.legend(); axis.grid(alpha=0.25)
+    figure.savefig(OUTPUT / "burst_phase_drift.png", dpi=160)
+    plt.close(figure)
+
+    figure, axis = plt.subplots(figsize=(9, 5), constrained_layout=True)
+    for index, method in enumerate(METHODS):
+        selected = sorted((row for row in b_rows if row["method"] == method), key=lambda row: row["label"])
+        axis.bar(x + (index - 1.5) * width, [100 * row["symbol_error_rate"] for row in selected], width,
+                 label=method, color=colors[method])
+    axis.axhline(3.495896, color="black", linestyle="--", label="whole-burst reference")
+    axis.set(xticks=x, xticklabels=["B1", "B2"], ylabel="Ошибочные символы, %",
+             title="Lab056 B: полный burst после preamble-only оценки")
+    axis.legend(ncol=3); axis.grid(axis="y", alpha=0.25)
+    figure.savefig(OUTPUT / "lab056_b_symbol_errors.png", dpi=160)
+    plt.close(figure)
+
+    lab054_rows = [row for row in real_rows if row["dataset"] == "Lab054"]
+    figure, axis = plt.subplots(figsize=(9, 5), constrained_layout=True)
+    accepted = [sum(row["HEADER_ACCEPTED"] for row in lab054_rows if row["method"] == method) for method in METHODS]
+    crc = [sum(row["CRC_PASS"] for row in lab054_rows if row["method"] == method) for method in METHODS]
+    positions = np.arange(len(METHODS))
+    axis.bar(positions - 0.18, accepted, 0.36, label="HEADER_ACCEPTED")
+    axis.bar(positions + 0.18, crc, 0.36, label="CRC_PASS")
+    axis.set(xticks=positions, xticklabels=METHODS, ylabel="Кадры из 16",
+             title="Lab054: строгий результат по preamble-only методам", ylim=(0, 16.5))
+    if max(accepted + crc) == 0:
+        axis.text(1.5, 8.0, "Все методы: 0/16 HEADER и CRC", ha="center", va="center")
+    axis.set_xlim(-0.6, 3.6)
+    axis.legend(); axis.grid(axis="y", alpha=0.25)
+    figure.savefig(OUTPUT / "lab054_result_by_method.png", dpi=160)
+    plt.close(figure)
+
+
+def run_functional_tests() -> None:
+    """Быстрые проверки формул без файлов IQ."""
+
+    assert maximum_frequency_error_hz(30, BURST_DURATION_SECONDS) == 0.40690104166666663
+    assert np.isclose(phase_slope_to_hz(2 * np.pi * 7, squared_bpsk=False), 7)
+    assert np.isclose(phase_slope_to_hz(4 * np.pi * -7, squared_bpsk=True), -7)
+    assert set(P2_TRAIN).isdisjoint(P2_VALIDATION)
+    assert len(P2_TRAIN) == len(P2_VALIDATION) == 576
+
+
+def main() -> None:
+    OUTPUT.mkdir(parents=True, exist_ok=True)
+    run_functional_tests()
+
+    source_paths = [
+        ROOT / "data/raw/lab056/rx_stream.cs8",
+        ROOT / "data/raw/lab056/tx_abab.cs8",
+        ROOT / "data/raw/lab054/rx_stream.cs8",
+        ROOT / "data/raw/lab054/tx_stream.cs8",
+    ]
+    audit = json.loads((ROOT / "data/processed/lab055/energy_audit_v2.json").read_text(encoding="utf-8"))
+    lab047_directory = Path(audit["experiments"]["Lab047"]["directory"])
+    source_paths.append(lab047_directory / "rx_stream.cs8")
+    integrity_before = {str(path): {"bytes": path.stat().st_size, "sha256": sha256(path)} for path in source_paths}
+
+    # P0 действительно завершается и сохраняется до вычисления P1/P2/P3.
+    baseline_rows = analyze_lab056(("P0",)) + analyze_packets(("P0",))
+    baseline = {
+        "algorithm": "unchanged active CURRENT estimator",
+        "rows": baseline_rows,
+        "summary": summarize_real(baseline_rows),
+        "mathematics": mathematics_audit(),
+    }
+    write_json(OUTPUT / "baseline_p0.json", baseline)
+
+    real_rows = analyze_lab056(METHODS) + analyze_packets(METHODS)
+    real_summary = summarize_real(real_rows)
+    synthetic_rows, by_cfo_rows, distributions, clean_metadata = synthetic_study()
+    verdict, reason, next_step = choose_verdict(real_summary, synthetic_rows)
+    integrity_after = {str(path): {"bytes": path.stat().st_size, "sha256": sha256(path)} for path in source_paths}
+    if integrity_before != integrity_after:
+        raise AssertionError("Сохранённые исходные IQ изменились")
+
+    phase_limits = phase_limit_table()
+    summary = {
+        "status": "COMPLETED",
+        "lab058_commit": "82f17499ace5e655991240348401b9700c7c3349",
+        "rf_tx": 0,
+        "hardware_access": 0,
+        "preamble_symbols": PREAMBLE_SYMBOLS,
+        "preamble_duration_seconds": {"20000": PREAMBLE_SYMBOLS / 20_000, "500000": PREAMBLE_SYMBOLS / 500_000},
+        "burst_duration_seconds": BURST_DURATION_SECONDS,
+        "phase_limits": phase_limits,
+        "catastrophic_error_threshold_hz": CATASTROPHIC_ERROR_HZ,
+        "mathematics": mathematics_audit(),
+        "p2_split": {"train_half_open": [32, 608], "validation_half_open": [672, 1248]},
+        "real": real_summary,
+        "synthetic": {
+            "trials_per_frequency_quality_rate": MONTE_CARLO_TRIALS,
+            "frequency_values_hz": SYNTHETIC_FREQUENCIES_HZ,
+            "quality_levels": [{"name": name, "evm_rms": value} for name, value in QUALITY_LEVELS],
+            "clean_rrc_controls": clean_metadata,
+            "awgn_crlb_standard_deviation_hz": {
+                "formula": "sqrt(6*EVM^2*Rs^2/((2*pi)^2*N*(N^2-1)))",
+                "assumptions": "known symbols, unknown constant phase, complex AWGN",
+                "500000_lab058_b_evm": awgn_cfo_crlb_standard_deviation_hz(
+                    0.845350, PREAMBLE_SYMBOLS, 500_000
+                ),
+            },
+            "aggregate": synthetic_rows,
+        },
+        "verdict": verdict,
+        "main_reason": reason,
+        "recommended_next_single_step": next_step,
+        "future_options": {
+            "decision_directed": "первый кандидат: не меняет формат и не знает payload truth",
+            "costas_loop": "альтернатива для слежения по всему burst; требует настройки полосы петли",
+            "periodic_pilots": "наиболее явная опора, но требует изменения формата и overhead",
+        },
+        "physical_limit": {
+            "lab058_b_diagnostic_symbol_error_rate": 0.03495896,
+            "lab058_b_evm_rms": 0.845350,
+            "lab058_b_matched_residual_db": 1.464846,
+            "lab058_b_channel_ripple_db": 5.471382,
+        },
+        "lab054_historical_result": "NOT_A, 0/16",
+        "lab059_commit": "NOT_PERFORMED",
+        "push": "NOT_PERFORMED",
+        "integrity": {"before": integrity_before, "after": integrity_after, "passed": True},
+    }
+    write_csv(OUTPUT / "real_results.csv", real_rows, exclude=("estimate",))
+    write_csv(OUTPUT / "synthetic_results.csv", synthetic_rows)
+    write_csv(OUTPUT / "synthetic_by_cfo.csv", by_cfo_rows)
+    write_json(OUTPUT / "summary.json", summary)
+    make_plots(real_rows, synthetic_rows, distributions)
+    print(json.dumps(serializable({
+        "status": summary["status"],
+        "verdict": verdict,
+        "main_reason": reason,
+        "real": real_summary,
+        "synthetic_500k_b": [
+            row for row in synthetic_rows
+            if row["symbol_rate"] == 500_000 and row["quality"] == "LAB058_B_EVM"
+        ],
+        "rf_tx": 0,
+    }), ensure_ascii=False, indent=2))
+
+
+if __name__ == "__main__":
+    main()
diff --git a/tests/test_lab059_preamble_carrier_limits.py b/tests/test_lab059_preamble_carrier_limits.py
new file mode 100644
index 0000000..0acfd46
--- /dev/null
+++ b/tests/test_lab059_preamble_carrier_limits.py
@@ -0,0 +1,155 @@
+"""Проверки математики и информационных границ Lab059."""
+
+from __future__ import annotations
+
+import numpy as np
+import pytest
+
+from experiments import lab059_preamble_carrier_limits as lab059
+
+
+@pytest.mark.parametrize("frequency_hz", [-100.0, -5.0, -0.25, 0.0, 0.5, 20.0])
+def test_phase_slope_conversion_sign_units_and_square_factor(frequency_hz):
+    ordinary = 2 * np.pi * frequency_hz
+    squared = 4 * np.pi * frequency_hz
+    assert lab059.phase_slope_to_hz(ordinary, squared_bpsk=False) == pytest.approx(frequency_hz)
+    assert lab059.phase_slope_to_hz(squared, squared_bpsk=True) == pytest.approx(frequency_hz)
+    if frequency_hz:
+        assert lab059.phase_slope_to_hz(squared, squared_bpsk=False) == pytest.approx(2 * frequency_hz)
+
+
+def test_phase_drift_and_frequency_limit_are_inverse():
+    for phase_limit in (15, 30, 45, 60, 90):
+        frequency = lab059.maximum_frequency_error_hz(
+            phase_limit, lab059.BURST_DURATION_SECONDS
+        )
+        assert lab059.phase_drift_degrees(
+            frequency, lab059.BURST_DURATION_SECONDS
+        ) == pytest.approx(phase_limit)
+    assert lab059.maximum_frequency_error_hz(30, 0.2048) == pytest.approx(0.4069010416666667)
+
+
+def test_awgn_crlb_scale_matches_rate_and_observed_b_quality():
+    slow = lab059.awgn_cfo_crlb_standard_deviation_hz(0.845350, 1280, 20_000)
+    fast = lab059.awgn_cfo_crlb_standard_deviation_hz(0.845350, 1280, 500_000)
+    assert fast / slow == pytest.approx(25.0)
+    assert fast == pytest.approx(3.5982182026)
+
+
+@pytest.mark.parametrize("symbol_rate", [20_000, 500_000])
+@pytest.mark.parametrize("method", lab059.METHODS)
+def test_clean_known_cfo_reference_time_and_compensation(symbol_rate, method):
+    frequency_hz = 7.25 if symbol_rate == 20_000 else -37.5
+    phase_at_zero = -0.73
+    indexes = np.arange(lab059.PREAMBLE_SYMBOLS)
+    times = (indexes + 0.5) / symbol_rate
+    symbols = lab059.lab057.EXPECTED * np.exp(
+        1j * (phase_at_zero + 2 * np.pi * frequency_hz * times)
+    )
+    estimate = lab059.estimate_carrier(symbols, symbol_rate, method)
+    assert estimate.frequency_hz == pytest.approx(frequency_hz, abs=1e-8)
+    assert np.angle(estimate.gain * np.exp(-1j * phase_at_zero)) == pytest.approx(0.0, abs=1e-10)
+    corrected = lab059.compensate(symbols, symbol_rate, estimate)
+    assert np.max(abs(corrected - lab059.lab057.EXPECTED)) < 1e-9
+
+
+def test_p0_matches_active_two_block_formula_and_ambiguity():
+    symbol_rate = 500_000
+    frequency_hz = 41.25
+    times = (np.arange(lab059.PREAMBLE_SYMBOLS) + 0.5) / symbol_rate
+    symbols = lab059.lab057.EXPECTED * np.exp(2j * np.pi * frequency_hz * times)
+    expected = lab059.lab057.squared_cfo_statistics(symbols, symbol_rate)
+    estimate = lab059.estimate_carrier(symbols, symbol_rate, "P0")
+    assert estimate.frequency_hz == pytest.approx(expected["cfo_hz"], abs=1e-12)
+    assert estimate.estimation_symbols == 1024
+    assert estimate.unambiguous_abs_hz == pytest.approx(symbol_rate / 2048)
+
+
+def test_p2_reference_time_is_global_burst_zero():
+    symbol_rate = 500_000
+    frequency_hz = -12.75
+    phase_at_zero = 1.12
+    times = (np.arange(lab059.PREAMBLE_SYMBOLS) + 0.5) / symbol_rate
+    symbols = lab059.lab057.EXPECTED * np.exp(
+        1j * (phase_at_zero + 2 * np.pi * frequency_hz * times)
+    )
+    estimate = lab059.estimate_carrier(symbols, symbol_rate, "P2")
+    assert estimate.frequency_hz == pytest.approx(frequency_hz, abs=1e-9)
+    assert np.angle(estimate.gain * np.exp(-1j * phase_at_zero)) == pytest.approx(0.0, abs=1e-10)
+    assert estimate.validation_errors == 0
+
+
+@pytest.mark.parametrize("method", lab059.METHODS)
+def test_payload_truth_cannot_change_carrier_estimate(method):
+    symbol_rate = 500_000
+    indexes = np.arange(4096)
+    expected = np.resize(lab059.lab057.EXPECTED, len(indexes))
+    values = expected * np.exp(1j * (0.4 + 2 * np.pi * 3.5 * (indexes + 0.5) / symbol_rate))
+    changed = values.copy()
+    random_generator = np.random.default_rng(5907)
+    changed[lab059.PREAMBLE_SYMBOLS:] = (
+        random_generator.normal(size=len(changed) - lab059.PREAMBLE_SYMBOLS)
+        + 1j * random_generator.normal(size=len(changed) - lab059.PREAMBLE_SYMBOLS)
+    ) * 100
+    first = lab059.estimate_from_burst(values, symbol_rate, method)
+    second = lab059.estimate_from_burst(changed, symbol_rate, method)
+    assert first == second
+
+
+def test_raw_payload_cannot_change_new_preamble_only_estimates():
+    symbol_rate = 500_000
+    values, _truth = lab059.lab057.synthetic_waveform(
+        symbol_rate, delay_symbols=0.0, cfo_hz=3.5, phase_rad=0.4, es_n0_db=None
+    )
+    frontend = lab059.lab057.Frontend(symbol_rate)
+    probe = values[: (4096 + 42) * frontend.samples_per_symbol]
+    stock = frontend.extract(probe, 4096)
+    matched = frontend.filter(probe)
+    symbols = lab059._extract_symbols(
+        matched, frontend.nominal, stock.timing_offset_samples,
+        4096, frontend.samples_per_symbol,
+    )
+    changed = probe.copy()
+    payload_start = (
+        frontend.nominal + stock.timing_offset_samples
+        + lab059.PREAMBLE_SYMBOLS * frontend.samples_per_symbol
+    )
+    changed[payload_start + len(frontend.taps):] += 10 + 7j
+    first = lab059.real_carrier_estimates(
+        probe, frontend, stock.timing_offset_samples, symbols, symbol_rate, ("P1", "P2", "P3")
+    )
+    second = lab059.real_carrier_estimates(
+        changed, frontend, stock.timing_offset_samples, symbols, symbol_rate, ("P1", "P2", "P3")
+    )
+    assert first == second
+
+
+def test_phase_extrapolation_uses_one_compensation_only():
+    symbol_rate = 500_000
+    count = 102_400
+    frequency_hz = 4.7
+    phase = 0.8
+    indexes = np.arange(count)
+    expected = np.resize(lab059.lab057.EXPECTED, count)
+    symbols = expected * np.exp(1j * (phase + 2 * np.pi * frequency_hz * (indexes + 0.5) / symbol_rate))
+    estimate = lab059.estimate_from_burst(symbols, symbol_rate, "P1")
+    corrected = lab059.compensate(symbols, symbol_rate, estimate)
+    assert np.max(abs(corrected - expected)) < 1e-8
+
+
+def test_fixed_split_is_disjoint_and_not_result_dependent():
+    assert len(lab059.P2_TRAIN) == len(lab059.P2_VALIDATION) == 576
+    assert lab059.P2_TRAIN[0] == 32 and lab059.P2_TRAIN[-1] == 607
+    assert lab059.P2_VALIDATION[0] == 672 and lab059.P2_VALIDATION[-1] == 1247
+    assert set(lab059.P2_TRAIN).isdisjoint(lab059.P2_VALIDATION)
+
+
+def test_partial_baseline_summary_does_not_create_nan_for_unrun_methods():
+    row = {
+        "dataset": "Lab056", "label": "B1", "method": "P0",
+        "frequency_error_hz": 1.0, "symbol_error_rate": 0.5,
+        "evm_rms": 1.0, "matched_residual_db": 0.0,
+    }
+    summary = lab059.summarize_real([row])
+    assert set(summary["Lab056"]) == {"P0"}
+    assert summary["Lab054"] == {} and summary["Lab047"] == {}