For years, the MH370 satellite handshakes were
often shown as large arcs across the Indian Ocean.
But before we can understand the path of the
aircraft, we first need to understand what a
handshake signal actually is. A handshake is
not a GPS position. It's a timing measurement
between the aircraft and the satellite. Every
time the aircraft and satellite communicated,
The system measured how long the signal took
to travel there and back. That timing created
a distance relationship. Distance rings expanding
outward from a signal source. The arcs are not
flight paths. To understand MH370 satellite data,
we first need to understand what these arcs actually
represent. These are called BTO arcs, burst timing
offset arcs. Each ring represents a measured
signal distance between the aircraft and satellite.
The signal traveled from the aircraft to the
satellite and back again. By measuring how long
that took, the system could estimate distance.
The farther the signal traveled, the larger the
arc became. Each handshake created another distance
boundary. The aircraft was not flying on the
ring itself. The ring only represents where the
aircraft could exist at that signal distance.
The challenge was never simply drawing arcs.
The challenge was determining how the aircraft
continuously moved through them over time. The
satellite system itself was stable. Once the
satellite handshakes established distance through
the BTO arcs, Investigators then had to examine
how stable those measurements actually were.
This graph shows the measured BTO error behavior
over time. Each vertical spike represents variation
between the measured signal timing and the expected
timing calculated from the satellite geometry.
This first clip is the raw coexistence phase.
The red waveform represents the dominant stability
layer of the signal environment. The blue waveform
represents the embedded continuity behavior hidden
inside that larger structure. The beginning of
separation between dominant stability and embedded
continuity. Now the blue signal starts becoming
smoother, more coherent, and more directional.
At the same time, the red structure still exists,
but the viewer begins recognizing the continuity
path underneath the noise. This is essentially
the filtering phase or continuity extraction
phase, BTO error environment evolving over time.
The red waveform shows the dominant stability
envelope of the satellite timing system, the
larger signal environment surrounding the aircraft
communication path. Each fluctuation represents
variation between the measured signal timing
and the calculated timing expected from the satellite
geometry. At first glance, the signal appears
noisy and unstable with spikes and rapid changes
occurring continuously throughout the timeline.
When we compare the original BTO error graph
to the reconstructed signal environment, an important
pattern begins to emerge. At first, the original
measurements appeared chaotic. a dense field
of spikes, timing variation and overlapping signal
behavior. But once the signal is reconstructed
as a continuous waveform environment, we begin
recognizing that the same structure was already
present inside the original data. The reconstructed
red stability envelope now closely mirrors the
overall shape and density of the original BTO
error field. What initially appeared to be random
noise begins revealing organized timing behavior
surrounding a stable continuity carrier. The
blue core signal remains centered through the
middle of the environment, showing that beneath
the larger fluctuations, a continuous underlying
structure was always present inside the handshake
system. This is the critical transition point
in the analysis. The goal is no longer simply
plotting individual handshake measurements, but
understanding how the dominant stability environment
and the embedded continuity signal interact together
over time. As the two views begin resembling
one another more closely, the apparent confusion
inside the original graph starts resolving into
a recognizable signal structure. transforming
isolated spikes into an evolving waveform relationship.
The red signal field still contains individual
fluctuations and isolated events, but the white
curve exposes the dominant trend running through
the center of the data. Instead of disconnected
measurements, the signal now behaves like a continuous
waveform system moving around a stable equilibrium.
The majority of the BTO behavior naturally concentrates
toward the center of the structure, while larger
deviations become increasingly rare farther from
the core. This is where the analysis begins transitioning
from raw observation into signal interpretation.
The chaotic appearance of the original handshake
environment starts collapsing into a measurable
continuity pattern. revealing that beneath the
spikes and short -term variation, the system
was continuously operating inside a structured
timing relationship. As the signal structure
becomes clearer, a second behavior now begins
separating itself from the surrounding noise.
The blue continuity waveform starts emerging
independently from inside the larger red signal
environment. Even though the dominant stability
field still contains spikes, variation, and short
-term disturbances, the blue line continues maintaining
its own smooth directional behavior through the
center of the data. This is an important transition
in the analysis because the continuity signal
is no longer completely hidden inside the chaos.
Instead, it begins revealing itself as an independent
structure operating within the larger timing
environment. The red field still represents the
overall signal variability and dominant envelope
behavior, but the blue continuity carrier now
demonstrates that a stable relationship persists
through the middle of the system despite the
surrounding fluctuations. What initially appeared
to be random handshake noise now starts behaving
more like layered signal interaction. The outer
red environment reflects the dominant timing
variability, while the blue waveform reveals
an embedded continuity structure moving through
it independently. Rather than isolated spikes,
the signal begins behaving like multiple interacting
layers evolving together over time. At this point,
the continuity signal is no longer simply existing
inside the waveform. it is now demonstrating
directional continuation through the signal environment
itself. The yellow directional marker highlights
the transition point where the embedded continuity
carrier continues progressing forward while the
dominant red stability field begins collapsing
toward equilibrium. This becomes a major visual
shift in the analysis. Earlier, the signal appeared
dominated by spikes and instability. but now
the blue continuity waveform maintains a smooth
directional descent through the center of the
system, even while large red disturbances continue
occurring around it. The yellow arrow emphasizes
that the continuity relationship persists independently
from the surrounding noise and continues evolving
through time. What makes this important is that
the signal now begins behaving less like a random
statistical distribution and more like a guided
continuity path moving through a structured timing
field. The dominant red environment still reflects
the larger BTO variability, but the embedded
blue carrier demonstrates stable progression
through the system, revealing continuity, direction,
and persistence hidden inside the original handshake
behavior. The next stage of the analysis begins
introducing progressive filtering directly onto
the original BTO environment. Instead of forcing
the signal into a final shape immediately, the
waveform is processed step by step through multiple
continuity -preserving stages. Each layer reduces
instability, while retaining the underlying timing
behavior hidden inside the original handshake
structure. The raw signal begins with dense spikes,
rapid fluctuation, and short -term timing variation.
As outlier removal and smoothing are introduced,
the larger instability begins collapsing inward
while the dominant continuity relationship remains
preserved. Rather than destroying the signal,
the filtering stages begin exposing the stable
timing behavior already operating underneath
the noise. Each color layer represents a different
phase of stabilization, beginning with raw, noisy
measurements and progressively moving toward
structured continuity recognition. The lower
processing stages show how the signal evolves
from raw data through outlier removal, smoothing,
trend extraction, continuity lock, and finally
into stabilized structure. Rather than destroying
the original signal, each stage preserves the
underlying continuity while reducing short -term
instability and random variation. The multiple
colored waveforms begin converging toward one
another, revealing that beneath the noise, the
dominant signal behavior remains remarkably consistent
across each processing phase. The vertical yellow
continuity tracers now behave like an etch -a
-sketch continuity recorder, tracking the signal
one step at a time as it progresses forward through
the waveform environment. This becomes important
because the signal is no longer behaving like
disconnected handshake events. Instead it behaves
like a continuously evolving motion structure
where continuity persists across every filtering
stage. By the final structure phase, the underlying
waveform relationship becomes stabilized, measurable,
and directionally coherent through time.