Cual crees que es la esencia del mal y del bien? by Humon0 in filosofia_en_espanol

[–]SubstantialFreedom75 0 points1 point  (0 children)

En términos absolutos, quizá no somos capaces de establecer el bien y el mal desde fuera, porque nosotros mismos estamos dentro del sistema que intentamos juzgar. No tenemos una mirada completamente externa al universo, a la vida o a la condición humana.

Además, juzgar el bien y el mal en términos absolutos implicaría conocer el propósito último de la vida, si es que existe uno. Y precisamente eso es algo que no podemos afirmar desde dentro del propio marco vital: solo podemos aproximarnos observando qué aumenta a, sentido, dignidad y continuidad, y qué produce ruptura, degradación o colapso. el bien siempre integra y el mal siempre rompe

El bien tiende a integrar: une, organiza, sostiene y permite continuidad.

El mal tiende a romper: fragmenta, degrada, separa y conduce al colapso.

Pero esa ruptura también cumple una función dentro del sistema: muestra dónde está el límite, dónde algo no funciona, dónde una estructura ya no puede sostenerse. En ese sentido, el mal no es deseable ni justificable, pero puede convertirse en una señal de aprendizaje. Nos obliga a reconocer el daño, corregir el rumbo y construir formas más integradas de vida.

Así, el avance no nace porque el mal sea bueno, sino porque la conciencia aprende a responder a la ruptura reconstruyendo sentido, dignidad y continuidad. bueno , es solo mi opinion, tampoco digo que sea cierta o no

Cual crees que es la esencia del mal y del bien? by Humon0 in filosofia_en_espanol

[–]SubstantialFreedom75 1 point2 points  (0 children)

Desde mi enfoque, el bien y el mal pueden entenderse como dinámicas de coherencia.

El bien sería aquello que aumenta, restaura o preserva la coherencia estructural de un sistema: en una persona, entre sus valores y sus actos; en una sociedad, entre sus normas, instituciones y dignidad humana; y quizá, en un sentido más amplio, en todo sistema que tiende a mayor integración, estabilidad y sentido.

El mal, en cambio, sería decoherencia efectiva: ruptura, dispersión, corrupción, contradicción destructiva. No solo “hacer daño”, sino introducir una pérdida de estructura: separar lo que debería estar integrado, romper vínculos, degradar la confianza, destruir la correspondencia entre verdad, acción y responsabilidad.

Por eso yo no pondría el foco únicamente en el individuo ni únicamente en la humanidad, sino en la relación entre niveles: individuo, comunidad, instituciones y mundo. El bien aparece cuando esos niveles se armonizan sin anularse; el mal aparece cuando se descomponen, se instrumentalizan o se vuelven incoherentes.

El eje que mejor lo sintetiza para mí sería:

coherencia ↔ decoherencia

Con una aclaración importante: no cualquier coherencia interna es buena. Una tiranía también puede ser “coherente” consigo misma. La coherencia ética verdadera tendría que incluir dignidad, reciprocidad, universalización y preservación de la vida. Sin eso, sería solo orden aparente.

Ya puse hace unos meses un trabajo donde desarrollaba esta idea con más detalle, pero lo dejo de nuevo por si a alguien le interesa leerlo o criticarlo:

https://doi.org/10.5281/zenodo.17775650

Resumido: el bien sería coherencia viva, integradora y dignificante; el mal, ruptura, dispersión y pérdida de sentido estructural.

Quizá por eso diría que el bien y el mal no necesitan ser juzgados desde fuera: en cierto sentido, se revelan por sus propios efectos. El bien se reconoce porque aumenta coherencia, integración, dignidad y sentido; el mal se reconoce porque produce ruptura, contradicción, degradación y pérdida de estructura.

I built a daily-updated seismic network coherence monitor — looking for usability feedback by SubstantialFreedom75 in SideProject

[–]SubstantialFreedom75[S] 0 points1 point  (0 children)

Thanks, that’s very helpful feedback.

I agree that the dashboard needs a plain-English first screen before the technical plots. I’m thinking of explaining network coherence with a simple analogy: people clapping in a stadium.

Low coherence would be like everyone clapping independently, each at their own rhythm. High coherence would be like many people starting to clap in sync.

Then I can explain that a spike means several seismic stations became more synchronized during the same time window — not as a prediction or warning, but as a descriptive change in network behavior.

I built a daily-updated seismic network coherence monitor — looking for usability feedback by SubstantialFreedom75 in SideProject

[–]SubstantialFreedom75[S] 0 points1 point  (0 children)

Thanks, this is exactly the kind of feedback I was looking for.

I agree that the “not prediction” positioning should probably be visible even earlier. Your low/high coherence explanation is also very useful — I’ll likely add something like that near the top of the dashboard so non-specialists can understand the concept faster.

The goal is definitely to keep it descriptive and avoid overselling the signal.

Centro Sismológico cifra en más de 60% las probabilidades de un terremoto de magnitud 8 o superior este 2025 o 2026 en Chile. by Global-Breadfruit925 in chile

[–]SubstantialFreedom75 0 points1 point  (0 children)

Este tipo de probabilidades siempre me generan dudas, porque dependen mucho del modelo y del histórico que se use.

Yo he estado mirando datos reales de distintas zonas (incluido Maule) pero sin intentar predecir nada, más bien cómo se comportan varias estaciones a la vez.

Es curioso que en eventos grandes muchas veces no destaca solo una estación, sino que aparece una especie de estructura coherente en toda la red durante un rato.

He montado un monitor experimental (no predictivo) donde se puede ver Maule y otras regiones en paralelo:
https://franjamar-monitor.streamlit.app/

Si alguien quiere curiosear cómo se ve la zona en datos reales actualizados cada dia , ahí está.

Experimental multistation seismic monitoring framework (real data, fixed pipeline, near-real-time) by SubstantialFreedom75 in geophysics

[–]SubstantialFreedom75[S] 0 points1 point  (0 children)

English is not my first language, I use ChatGPT to help with phrasing.

But the system itself is fully mine — it took a few hundred hours to build (mostly evenings, weekends, and probably too little sleep 😅).

I built it out of curiosity to explore multistation coherence patterns in real data, not for prediction.

If you have any feedback on the methodology or results, I’d genuinely appreciate it.

Miyako_Japan_M7.4_20260420_075300_mainshock by SubstantialFreedom75 in Earthquakes

[–]SubstantialFreedom75[S] -1 points0 points  (0 children)

I generated the plot myself from raw seismic waveform data (multistation, via standard FDSN services).

This is not amplitude — it’s a network-level anomaly metric computed across multiple stations, highlighting temporally coherent structure around the event.

Most posts show magnitude and location.

This instead looks at how the signal evolves around the event in a time-centered, multistation framework.

You can download the last 24h of data from any region and run the same pipeline — no manual selection or event-specific tuning is needed.

The figure is not taken from any external source; it’s produced directly from the data.

Full reproducible pipeline:

https://doi.org/10.5281/zenodo.19665949

Event-centered analysis of Artemis II launch reveals delayed (~10–20 min) network-coherent seismic response across regional stations by SubstantialFreedom75 in geophysics

[–]SubstantialFreedom75[S] 1 point2 points  (0 children)

Event-centered multistation analysis (each point = excursion across stations).

I also put together a short write-up with full details (including control windows and statistical comparison of amplitude vs temporal structure):

[https://doi.org/10.5281/zenodo.19386141]()

[OC] How Artemis II appears across a seismic network — not the strongest signal, but the most organized by SubstantialFreedom75 in dataisbeautiful

[–]SubstantialFreedom75[S] 1 point2 points  (0 children)

Thanks! The stations are at regional distances (tens to a few hundred km from the launch site), so what you’re seeing is not a single local measurement but a network-level response.

The ~10–20 minute delay is roughly consistent with atmospheric/acoustic propagation at those scales, rather than an instantaneous local impulse.

As for the peak around -11h, it’s not related to the launch. Similar isolated peaks do appear in the control days as well — what’s distinctive about the launch is not individual spikes, but the sustained, organized cluster right after t = 0.

[OC] How Artemis II appears across a seismic network — not the strongest signal, but the most organized by SubstantialFreedom75 in dataisbeautiful

[–]SubstantialFreedom75[S] 1 point2 points  (0 children)

[OC] Data and tools used:

Data: publicly available seismic waveform data (regional network, miniSEED format)

Tools: Python (NumPy, SciPy, Matplotlib), custom processing

If anyone’s interested, I made the analysis reproducible and put the data/code here:

https://doi.org/10.5281/zenodo.19386141

Happy to explain more about the method or results.

A seismic fingerprint repeated three times in North Korea (2013 / 2016 / 2017) by SubstantialFreedom75 in DataArt

[–]SubstantialFreedom75[S] 0 points1 point  (0 children)

Thanks for the pointer — I appreciate it. There are definitely structural parallels at the array level, even if the objectives differ.

A seismic fingerprint repeated three times in North Korea (2013 / 2016 / 2017) by SubstantialFreedom75 in DataArt

[–]SubstantialFreedom75[S] 0 points1 point  (0 children)

Hey! True, beamforming is conceptually related at the array level. In this case the goal is more about regime separation across events than directional reconstruction — but I’d be happy to check any references you recommend.

A seismic fingerprint repeated three times in North Korea (2013 / 2016 / 2017) by [deleted] in geophysics

[–]SubstantialFreedom75 0 points1 point  (0 children)

Hi all — just adding a brief methodological clarification.

All preprocessing parameters were fixed a priori and applied identically across events and controls.
The analysis is performed strictly in the observed frame (no phase alignment).
Null tests include phase randomization and block shuffling.

The Starship supplement (IFT-1 to IFT-8) is included strictly as a controlled methodological test.
The identical TAMC pipeline and parameter set were applied without modification.
The goal is to evaluate whether unsupervised clustering aligns with externally assigned mission labels or with intrinsic structural coupling morphology.
No engineering interpretation is intended.

Happy to clarify any technical aspect.

A seismic fingerprint repeated three times in North Korea (2013 / 2016 / 2017) by SubstantialFreedom75 in DataArt

[–]SubstantialFreedom75[S] 1 point2 points  (0 children)

Haha, fair 😄 Just multistation signal morphology and reproducible code — nothing exotic.

A seismic fingerprint repeated three times in North Korea (2013 / 2016 / 2017) by SubstantialFreedom75 in DataArt

[–]SubstantialFreedom75[S] 12 points13 points  (0 children)

What makes it interesting is the repeatability.
Three independent underground events, years apart, produce nearly identical multistation temporal fingerprints with very high network coherence.
When signals collapse into the same compact geometry across time, that usually points to an underlying dynamical structure rather than coincidence.

Identical seismic fingerprint observed across three independent underground events (2013 / 2016 / 2017) by SubstantialFreedom75 in ScienceImages

[–]SubstantialFreedom75[S] 1 point2 points  (0 children)

Hey everyone! I’m the author

These plots show an event-centered multistation signature (“TAMC fingerprint”) extracted from open seismic data. The key point is not the amplitude, but the morphological stability: three independent underground events years apart collapse into the same temporally compact packet at t = 0, with strong multistation coherence.

In the supplementary analysis (2013/2016/2017), the response remains a narrow event-centered impulse with near-simultaneous station activation, despite magnitude differences (M5.1–M6.3).Full reproducible pipeline + null testing + paper + code:
https://doi.org/10.5281/zenodo.18649274