ELEV
Elevation Oncology, Inc. Common stock
stock NASDAQ

AST SPACE MOBILE
\#!/usr/bin/env python3
\# -\*- coding: utf-8 -\*-
"""
AETHERWORKS G3X × ORBITALHUD™ — Unified Ops Pipeline
========================================================================
Integrates:
1. BlueWalker-3 (BW3) ephemeris + multi-site pass generation (Skyfield)
2. OpsMaster pass log consolidation (unify multiple sources)
3. SAFE\_MODE checks & physics validation (PROMETHEOS)
4. BB2 Block-2 adaptation simulation (thermal/RF/antenna/HARQ)
Pipeline flow:
→ Load TLE & compute satellite passes for multiple sites (CSV)
→ Unify all pass logs into single DataFrame
→ Run SAFE\_MODE trigger checks & compute KPIs
→ Simulate BB2 Block-2 operations (300–600 s nominal)
→ Emit audit logs, summary JSON, acceptance gates, optional charts
Usage:
python3 bw3\_bb2\_unified\_ops.py --days 3 --sites LON-ON-01 OXF-UK-01 --bb2-secs 300 --plots
No external deps beyond: skyfield, pandas, numpy, openpyxl (for Excel I/O, optional).
For matplotlib charts, install matplotlib (optional --plots flag).
Author: Aetherworks Ops
Date: 2025-08-26
"""
from \_\_future\_\_ import annotations
import argparse
import csv
import json
import math
import os
import random
import statistics
import sys
import time
import hashlib
import hmac
import secrets
from collections import Counter
from dataclasses import asdict, dataclass, fieldfrom datetime import datetime, timedelta, timezone
from pathlib import Path
from typing import Dict, List, Optional, Tuple
import numpy as np
import pandas as pd
try:
from skyfield.api import EarthSatellite, Topos, load, wgs84
SKYFIELD\_OK = True
except ImportError:
SKYFIELD\_OK = False
print("\[WARN\] skyfield not installed; pass generation disabled", file=sys.stderr)
try:
from openpyxl import load\_workbook
OPENPYXL\_OK = True
except ImportError:
OPENPYXL\_OK = False
\# ============================================================================
\# ◀ PART 1: SKYFIELD EPHEMERIS & MULTI-SITE PASS GENERATION
\# ============================================================================
class BW3PassGenerator:
"""
BW3 multi-site pass predictor using Skyfield.
Emits per-site CSV + consolidated DataFrame.
"""
\# TLE for BlueWalker-3 (as of 2025-08-26)
TLE\_NAME = "BlueWalker-3 (53807)"
TLE\_LINE1 = "1 53807U 22111AL 25239.00000000 .00000000 00000-0 00000-0 0 9991"
TLE\_LINE2 = "2 53807 53.2000 000.0000 0007240 000.0000 000.0000 15.33882700 01"
\# RF metrics
C = 299792.458 # km/s
FREQS = {"UHF\_437": 437.5e6, "Sband\_2210": 2210e6}
def \_\_init\_\_(self, el\_min\_deg: float = 10.0, days: int = 3, step\_s: int = 20):
self.el\_min\_deg = el\_min\_deg
self.days = days
self.step\_s = step\_s
if SKYFIELD\_OK:
self.ts = load.timescale()
self.sat = EarthSatellite(self.TLE\_LINE1, self.TLE\_LINE2, self.TLE\_NAME, self.ts)
else:self.ts = None
self.sat = None
@staticmethod
def fspl\_db(range\_km: float, freq\_hz: float) -> float:
c = 299792.458e3 # m/s
return 20.0 \* math.log10(4.0 \* math.pi \* (range\_km \* 1000.0) \* freq\_hz / c)
@staticmethod
def doppler\_hz(freq\_hz: float, vr\_km\_s: float) -> float:
c = 299792.458 # km/s
return freq\_hz \* (vr\_km\_s / c)
def find\_passes\_for\_site(
self, site\_code: str, lat\_deg: float, lon\_deg: float, elev\_m: float
) -> pd.DataFrame:
"""Compute all passes above el\_min for a site over \[today, today+days)."""
if not SKYFIELD\_OK or self.sat is None:
return pd.DataFrame()
start\_utc = datetime.utcnow().replace(second=0, microsecond=0, tzinfo=timezone.utc)
end\_utc = start\_utc + timedelta(days=self.days)
\# Build minute-resolution time array
minutes = int((end\_utc - start\_utc).total\_seconds() / 60)
times = self.ts.utc(
\[start\_utc.year\] \* minutes,
\[start\_utc.month\] \* minutes,
\[start\_utc.day\] \* minutes,
\[start\_utc.hour + (i // 60) for i in range(minutes)\],
\[start\_utc.minute + (i % 60) for i in range(minutes)\],
)
"e()
\# ============================================================================
\# ◀ PART 2: UNIFIED PASS LOG CONSOLIDATION & SAFE\_MODE CHECKS
\# ============================================================================
def wilson\_lb(successes: int, trials: int, conf: float = 0.95) -> Optional\[float\]:
"""Wilson score lower bound for binomial confidence interval."""
if trials <= 0 or successes < 0 or successes > trials:
return None
z = statistics.NormalDist().inv\_cdf(0.5 + conf / 2)
n = float(trials)
phat = successes / n
denom = 1.0 + (z \* z) / n
centre = phat + (z \* z) / (2.0 \* n)
margin = z \* math.sqrt((phat \* (1.0 - phat) + (z \* z) / (4.0 \* n)) / n)
return (centre - margin) / denomdef run\_sigma(series: pd.Series) -> Optional\[float\]:
"""Compute population stddev if enough samples."""
x = series.dropna()
if len(x) < 5:
return None
return statistics.pstdev(x)
def check\_safe\_mode\_triggers(row: pd.Series) -> Optional\[Dict\[str, float\]\]:
"""SAFE\_MODE trigger detection per row."""
THRESHOLDS = {
"fspl\_max": 160.0,
"doppler\_max": 5000.0,
"wilson\_min": 0.80,
"sigma\_max": 0.015,
}
triggers = {}
if pd.notna(row.get("FSPL\_dB\_num")) and row\["FSPL\_dB\_num"\] > THRESHOLDS\["fspl\_max"\]:
triggers\["FSPL"\] = row\["FSPL\_dB\_num"\]
if pd.notna(row.get("doppler\_abs\_hz")) and abs(row\["doppler\_abs\_hz"\]) > THRESHOLDS\["doppl
triggers\["Doppler"\] = row\["doppler\_abs\_hz"\]
if row.get("wilson\_lb\_95") is not None and row\["wilson\_lb\_95"\] < THRESHOLDS\["wilson\_min"\]
triggers\["Wilson"\] = row\["wilson\_lb\_95"\]
if pd.notna(row.get("sigma\_run")) and row\["sigma\_run"\] > THRESHOLDS\["sigma\_max"\]:
triggers\["SPC"\] = row\["sigma\_run"\]
return triggers if triggers else None
def unify\_pass\_logs(dataframes: List\[pd.DataFrame\]) -> pd.DataFrame:
"""Merge multiple pass logs, apply numeric coercion & SAFE\_MODE checks."""
if not dataframes:
return pd.DataFrame()
full = pd.concat(dataframes, ignore\_index=True)
\# Numeric coercion
candidates = \["Frames\_n", "Decodes\_k", "Net\_dB"\]
candidates += \[c for c in full.columns if "FSPL" in c.upper() or "DOPPLER" in c.upper()\]
for col in candidates:
if col in full.columns:
full\[col + "\_num"\] = pd.to\_numeric(full\[col\], errors="coerce")
\# Wilson lower bound
if {"Frames\_n\_num", "Decodes\_k\_num"} <= set(full.columns):
full\["wilson\_lb\_95"\] = full.apply(
lambda r: wilson\_lb(int(r.Decodes\_k\_num) if pd.notna(r.Decodes\_k\_num) else 0,int(r.Frames\_n\_num) if pd.notna(r.Frames\_n\_num) else 0, if pd.notna(r.Decodes\_k\_num) and pd.notna(r.Frames\_n\_num)
else None,
axis=1,
0.95)
)
\# Sigma per run
if "Net\_dB\_num" in full.columns and "RunID" in full.columns:
sigma\_map = full.groupby("RunID")\["Net\_dB\_num"\].agg(run\_sigma)
full\["sigma\_run"\] = full\["RunID"\].map(sigma\_map)
\# Doppler absolute
if "Doppler\_min\_Hz\_num" in full.columns and "Doppler\_max\_Hz\_num" in full.columns:
full\["doppler\_abs\_hz"\] = (
full\[\["Doppler\_min\_Hz\_num", "Doppler\_max\_Hz\_num"\]\].abs().max(axis=1)
)
elif "Doppler\_Hz\_num" in full.columns:
full\["doppler\_abs\_hz"\] = full\["Doppler\_Hz\_num"\].abs()
\# FSPL canonical
fspl\_col = next((c for c in full.columns if "FSPL" in c.upper()), None)
if fspl\_col and fspl\_col + "\_num" in full.columns:
full\["FSPL\_dB\_num"\] = full\[fspl\_col + "\_num"\]
\# SAFE\_MODE triggers
full\["SAFE\_MODE\_Triggers"\] = full.apply(check\_safe\_mode\_triggers, axis=1)
full\["SAFE\_MODE"\] = full\["SAFE\_MODE\_Triggers"\].apply(lambda x: bool(x))
return full
\# ============================================================================
\# ◀ PART 3: BB2 BLOCK-2 ADAPTATION SIMULATION (THERMAL/RF/ANTENNA/HARQ)
\# ============================================================================
\# ---- BB2 CONSTANTS ----
SNR\_TARGET\_DB = 5.0
SNR\_RECOVER\_DB = 7.0
DUTY\_MIN\_PCT = 1.0
DUTY\_MAX\_PCT = 5.0
TX\_EIRP\_DBM\_INIT = 9.5
SYMRATE\_KSYM\_INIT = 100.0
DT\_NOMINAL\_LIMIT = 1.4
DT\_STRESSED\_LIMIT = 2.1
DT\_SOFT\_BACKOFF = 1.8
DT\_SAFE\_COAST = 2.2
THERM\_GAIN = 0.00225THERM\_LOSS = 0.00160
THERM\_AMBIENT\_DRIFT = 0.00005
EPS\_R = 3.1
F0\_MHZ = 760.0
THERMAL\_DRIFT\_KHZ = 1.0
S11\_TARGET\_DB = -12.0
MODULATION = "pi/4-DQPSK"
SYMRATE\_PROFILES = \[50, 75, 100, 150\]
MTU\_BYTES = 2048
HARQ\_PROCESSES = 8
HARQ\_MAX\_RETX = 4
LOW\_ELEV\_DEG = 15.0
EVENT\_BEAMSW\_P = 0.010
EVENT\_LOWEL\_P = 0.018
EVENT\_DOPPLER\_P = 0.022
DOPPLER\_SNR\_PEN\_DB = (0.5, 1.0)
LOW\_ELEV\_SNR\_PEN\_DB = (1.0, 2.0)
BEAMSW\_SNR\_PEN\_DB = (2.0, 4.0)
LATENCY\_RTT\_MS\_MIN = 50
LATENCY\_RTT\_MS\_MAX = 120
ATTEST\_STATIC\_SALT = "G3X\_ORBITALHUD\_F1\_Rev1.1"
@dataclass
class BB2Config:
secs: int = 300
step\_s: int = 1
ambient\_hot: bool = False
self.lut = self.\_build\_varactor\_lut()
def \_build\_varactor\_lut(self) -> Dict\[int, Tuple\[float,
\- self.cfg.therm\_loss \* st.deltaT
\+ ambient
)
st.deltaT = max(0.0, dT\_next)
if st.deltaT > DT\_SOFT\_BACKOFF and st.tx\_eirp\_dbm > 0:
st.tx\_eirp\_dbm \*= 0.85
if st.deltaT > DT\_SAFE\_COAST:
st.tx\_eirp\_dbm = 0.0
st.symrate\_ksym = 0.0
st.duty\_pct = 0.0
st.event = "thermal\_critical"
class BB2PHY:
@staticmethod
def ber\_pi4dqpsk(snr\_db: float) -> float:
snr\_lin = db\_to\_lin(snr\_db)
return max(qfunc(math.sqrt(2.0 \* snr\_lin)), 1e-9)
@staticmethod
def choose\_coderate(snr\_db: float) -> float:
if snr\_db >= 9.0:
return 0.75
elif snr\_db >= 6.5:
return 0.667
,
"

AST SPACE MOBILE
\#!/usr/bin/env python3
\# -\*- coding: utf-8 -\*-
"""
AETHERWORKS G3X × ORBITALHUD™ — Unified Ops Pipeline
========================================================================
Integrates:
1. BlueWalker-3 (BW3) ephemeris + multi-site pass generation (Skyfield)
2. OpsMaster pass log consolidation (unify multiple sources)
3. SAFE\_MODE checks & physics validation (PROMETHEOS)
4. BB2 Block-2 adaptation simulation (thermal/RF/antenna/HARQ)
Pipeline flow:
→ Load TLE & compute satellite passes for multiple sites (CSV)
→ Unify all pass logs into single DataFrame
→ Run SAFE\_MODE trigger checks & compute KPIs
→ Simulate BB2 Block-2 operations (300–600 s nominal)
→ Emit audit logs, summary JSON, acceptance gates, optional charts
Usage:
python3 bw3\_bb2\_unified\_ops.py --days 3 --sites LON-ON-01 OXF-UK-01 --bb2-secs 300 --plots
No external deps beyond: skyfield, pandas, numpy, openpyxl (for Excel I/O, optional).
For matplotlib charts, install matplotlib (optional --plots flag).
Author: Aetherworks Ops
Date: 2025-08-26
"""
from \_\_future\_\_ import annotations
import argparse
import csv
import json
import math
import os
import random
import statistics
import sys
import time
import hashlib
import hmac
import secrets
from collections import Counter
from dataclasses import asdict, dataclass, fieldfrom datetime import datetime, timedelta, timezone
from pathlib import Path
from typing import Dict, List, Optional, Tuple
import numpy as np
import pandas as pd
try:
from skyfield.api import EarthSatellite, Topos, load, wgs84
SKYFIELD\_OK = True
except ImportError:
SKYFIELD\_OK = False
print("\[WARN\] skyfield not installed; pass generation disabled", file=sys.stderr)
try:
from openpyxl import load\_workbook
OPENPYXL\_OK = True
except ImportError:
OPENPYXL\_OK = False
\# ============================================================================
\# ◀ PART 1: SKYFIELD EPHEMERIS & MULTI-SITE PASS GENERATION
\# ============================================================================
class BW3PassGenerator:
"""
BW3 multi-site pass predictor using Skyfield.
Emits per-site CSV + consolidated DataFrame.
"""
\# TLE for BlueWalker-3 (as of 2025-08-26)
TLE\_NAME = "BlueWalker-3 (53807)"
TLE\_LINE1 = "1 53807U 22111AL 25239.00000000 .00000000 00000-0 00000-0 0 9991"
TLE\_LINE2 = "2 53807 53.2000 000.0000 0007240 000.0000 000.0000 15.33882700 01"
\# RF metrics
C = 299792.458 # km/s
FREQS = {"UHF\_437": 437.5e6, "Sband\_2210": 2210e6}
def \_\_init\_\_(self, el\_min\_deg: float = 10.0, days: int = 3, step\_s: int = 20):
self.el\_min\_deg = el\_min\_deg
self.days = days
self.step\_s = step\_s
if SKYFIELD\_OK:
self.ts = load.timescale()
self.sat = EarthSatellite(self.TLE\_LINE1, self.TLE\_LINE2, self.TLE\_NAME, self.ts)
else:self.ts = None
self.sat = None
@staticmethod
def fspl\_db(range\_km: float, freq\_hz: float) -> float:
c = 299792.458e3 # m/s
return 20.0 \* math.log10(4.0 \* math.pi \* (range\_km \* 1000.0) \* freq\_hz / c)
@staticmethod
def doppler\_hz(freq\_hz: float, vr\_km\_s: float) -> float:
c = 299792.458 # km/s
return freq\_hz \* (vr\_km\_s / c)
def find\_passes\_for\_site(
self, site\_code: str, lat\_deg: float, lon\_deg: float, elev\_m: float
) -> pd.DataFrame:
"""Compute all passes above el\_min for a site over \[today, today+days)."""
if not SKYFIELD\_OK or self.sat is None:
return pd.DataFrame()
start\_utc = datetime.utcnow().replace(second=0, microsecond=0, tzinfo=timezone.utc)
end\_utc = start\_utc + timedelta(days=self.days)
\# Build minute-resolution time array
minutes = int((end\_utc - start\_utc).total\_seconds() / 60)
times = self.ts.utc(
\[start\_utc.year\] \* minutes,
\[start\_utc.month\] \* minutes,
\[start\_utc.day\] \* minutes,
\[start\_utc.hour + (i // 60) for i in range(minutes)\],
\[start\_utc.minute + (i % 60) for i in range(minutes)\],
)
"e()
\# ============================================================================
\# ◀ PART 2: UNIFIED PASS LOG CONSOLIDATION & SAFE\_MODE CHECKS
\# ============================================================================
def wilson\_lb(successes: int, trials: int, conf: float = 0.95) -> Optional\[float\]:
"""Wilson score lower bound for binomial confidence interval."""
if trials <= 0 or successes < 0 or successes > trials:
return None
z = statistics.NormalDist().inv\_cdf(0.5 + conf / 2)
n = float(trials)
phat = successes / n
denom = 1.0 + (z \* z) / n
centre = phat + (z \* z) / (2.0 \* n)
margin = z \* math.sqrt((phat \* (1.0 - phat) + (z \* z) / (4.0 \* n)) / n)
return (centre - margin) / denomdef run\_sigma(series: pd.Series) -> Optional\[float\]:
"""Compute population stddev if enough samples."""
x = series.dropna()
if len(x) < 5:
return None
return statistics.pstdev(x)
def check\_safe\_mode\_triggers(row: pd.Series) -> Optional\[Dict\[str, float\]\]:
"""SAFE\_MODE trigger detection per row."""
THRESHOLDS = {
"fspl\_max": 160.0,
"doppler\_max": 5000.0,
"wilson\_min": 0.80,
"sigma\_max": 0.015,
}
triggers = {}
if pd.notna(row.get("FSPL\_dB\_num")) and row\["FSPL\_dB\_num"\] > THRESHOLDS\["fspl\_max"\]:
triggers\["FSPL"\] = row\["FSPL\_dB\_num"\]
if pd.notna(row.get("doppler\_abs\_hz")) and abs(row\["doppler\_abs\_hz"\]) > THRESHOLDS\["doppl
triggers\["Doppler"\] = row\["doppler\_abs\_hz"\]
if row.get("wilson\_lb\_95") is not None and row\["wilson\_lb\_95"\] < THRESHOLDS\["wilson\_min"\]
triggers\["Wilson"\] = row\["wilson\_lb\_95"\]
if pd.notna(row.get("sigma\_run")) and row\["sigma\_run"\] > THRESHOLDS\["sigma\_max"\]:
triggers\["SPC"\] = row\["sigma\_run"\]
return triggers if triggers else None
def unify\_pass\_logs(dataframes: List\[pd.DataFrame\]) -> pd.DataFrame:
"""Merge multiple pass logs, apply numeric coercion & SAFE\_MODE checks."""
if not dataframes:
return pd.DataFrame()
full = pd.concat(dataframes, ignore\_index=True)
\# Numeric coercion
candidates = \["Frames\_n", "Decodes\_k", "Net\_dB"\]
candidates += \[c for c in full.columns if "FSPL" in c.upper() or "DOPPLER" in c.upper()\]
for col in candidates:
if col in full.columns:
full\[col + "\_num"\] = pd.to\_numeric(full\[col\], errors="coerce")
\# Wilson lower bound
if {"Frames\_n\_num", "Decodes\_k\_num"} <= set(full.columns):
full\["wilson\_lb\_95"\] = full.apply(
lambda r: wilson\_lb(int(r.Decodes\_k\_num) if pd.notna(r.Decodes\_k\_num) else 0,int(r.Frames\_n\_num) if pd.notna(r.Frames\_n\_num) else 0, if pd.notna(r.Decodes\_k\_num) and pd.notna(r.Frames\_n\_num)
else None,
axis=1,
0.95)
)
\# Sigma per run
if "Net\_dB\_num" in full.columns and "RunID" in full.columns:
sigma\_map = full.groupby("RunID")\["Net\_dB\_num"\].agg(run\_sigma)
full\["sigma\_run"\] = full\["RunID"\].map(sigma\_map)
\# Doppler absolute
if "Doppler\_min\_Hz\_num" in full.columns and "Doppler\_max\_Hz\_num" in full.columns:
full\["doppler\_abs\_hz"\] = (
full\[\["Doppler\_min\_Hz\_num", "Doppler\_max\_Hz\_num"\]\].abs().max(axis=1)
)
elif "Doppler\_Hz\_num" in full.columns:
full\["doppler\_abs\_hz"\] = full\["Doppler\_Hz\_num"\].abs()
\# FSPL canonical
fspl\_col = next((c for c in full.columns if "FSPL" in c.upper()), None)
if fspl\_col and fspl\_col + "\_num" in full.columns:
full\["FSPL\_dB\_num"\] = full\[fspl\_col + "\_num"\]
\# SAFE\_MODE triggers
full\["SAFE\_MODE\_Triggers"\] = full.apply(check\_safe\_mode\_triggers, axis=1)
full\["SAFE\_MODE"\] = full\["SAFE\_MODE\_Triggers"\].apply(lambda x: bool(x))
return full
\# ============================================================================
\# ◀ PART 3: BB2 BLOCK-2 ADAPTATION SIMULATION (THERMAL/RF/ANTENNA/HARQ)
\# ============================================================================
\# ---- BB2 CONSTANTS ----
SNR\_TARGET\_DB = 5.0
SNR\_RECOVER\_DB = 7.0
DUTY\_MIN\_PCT = 1.0
DUTY\_MAX\_PCT = 5.0
TX\_EIRP\_DBM\_INIT = 9.5
SYMRATE\_KSYM\_INIT = 100.0
DT\_NOMINAL\_LIMIT = 1.4
DT\_STRESSED\_LIMIT = 2.1
DT\_SOFT\_BACKOFF = 1.8
DT\_SAFE\_COAST = 2.2
THERM\_GAIN = 0.00225THERM\_LOSS = 0.00160
THERM\_AMBIENT\_DRIFT = 0.00005
EPS\_R = 3.1
F0\_MHZ = 760.0
THERMAL\_DRIFT\_KHZ = 1.0
S11\_TARGET\_DB = -12.0
MODULATION = "pi/4-DQPSK"
SYMRATE\_PROFILES = \[50, 75, 100, 150\]
MTU\_BYTES = 2048
HARQ\_PROCESSES = 8
HARQ\_MAX\_RETX = 4
LOW\_ELEV\_DEG = 15.0
EVENT\_BEAMSW\_P = 0.010
EVENT\_LOWEL\_P = 0.018
EVENT\_DOPPLER\_P = 0.022
DOPPLER\_SNR\_PEN\_DB = (0.5, 1.0)
LOW\_ELEV\_SNR\_PEN\_DB = (1.0, 2.0)
BEAMSW\_SNR\_PEN\_DB = (2.0, 4.0)
LATENCY\_RTT\_MS\_MIN = 50
LATENCY\_RTT\_MS\_MAX = 120
ATTEST\_STATIC\_SALT = "G3X\_ORBITALHUD\_F1\_Rev1.1"
@dataclass
class BB2Config:
secs: int = 300
step\_s: int = 1
ambient\_hot: bool = False
self.lut = self.\_build\_varactor\_lut()
def \_build\_varactor\_lut(self) -> Dict\[int, Tuple\[float,
\- self.cfg.therm\_loss \* st.deltaT
\+ ambient
)
st.deltaT = max(0.0, dT\_next)
if st.deltaT > DT\_SOFT\_BACKOFF and st.tx\_eirp\_dbm > 0:
st.tx\_eirp\_dbm \*= 0.85
if st.deltaT > DT\_SAFE\_COAST:
st.tx\_eirp\_dbm = 0.0
st.symrate\_ksym = 0.0
st.duty\_pct = 0.0
st.event = "thermal\_critical"
class BB2PHY:
@staticmethod
def ber\_pi4dqpsk(snr\_db: float) -> float:
snr\_lin = db\_to\_lin(snr\_db)
return max(qfunc(math.sqrt(2.0 \* snr\_lin)), 1e-9)
@staticmethod
def choose\_coderate(snr\_db: float) -> float:
if snr\_db >= 9.0:
return 0.75
elif snr\_db >= 6.5:
return 0.667
,
"