Particle Detection System Analysis in the Stratosphere Using High-Altitude Platforms Based on a MMPP-2 Model

Many space missions using High-Altitude Platforms (HAPs) are designed to measure contaminant particles in the stratosphere. However, there is no previous performance analysis of the sensors installed in the HAP in terms of the energy required by the detection system and the efficiency of the experiment. In this regard, it is not possible to assess the number of measurements that may be taken by the mission and the energy that it will consume in advance. Considering that energy resources are extremely limited in these space missions, especially in HAPs attached to hot-air balloons that effectively provide High-Altitude Platforms (HAPs), where the weight of the payload is of major importance to the success of the mission, a previous analysis is required to account for the feasibility and pertinence of the contaminant detection system. Building on this, we propose a mathematical analysis to determine the energy consumption of the measurement system based on the potential trajectories and the particle density. Also, the analysis provides an estimation of the number of particles that can be detected by the experiment in order to determine the performance of the sensor system. The model is based on an MMPP-2 (Markov Modulated Poisson Process with 2 states) model under exponential distribution assumptions, which provides a basic model that can be easily extended to other distributions in future works.

Authors

Institutions

Publication Details

Journal
Sensors
Published
2025-12-02
DOI
https://doi.org/10.3390/s25237340
Primary Topic
Atmospheric Ozone and Climate
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Particle Detection System Analysis in the Stratosphere Using High-Altitude Platforms Based on a MMPP-2 Model

Mario Alberto Mendoza-Bárcenas, Izlian Y. Orea-Flores, Iclia Villordo-Jiménez, Mario E. Rivero-Ángeles et al.
Sensors
Atmospheric Ozone and Climate
article

Particle Detection System Analysis in the Stratosphere Using High-Altitude Platforms Based on a MMPP-2 Model

Mario Alberto Mendoza-Bárcenas, Izlian Y. Orea-Flores, Iclia Villordo-Jiménez, Mario E. Rivero-Ángeles, Edgar Hernan Rosas Espinosa
article en

Abstract

Many space missions using High-Altitude Platforms (HAPs) are designed to measure contaminant particles in the stratosphere. However, there is no previous performance analysis of the sensors installed in the HAP in terms of the energy required by the detection system and the efficiency of the experiment. In this regard, it is not possible to assess the number of measurements that may be taken by the mission and the energy that it will consume in advance. Considering that energy resources are extremely limited in these space missions, especially in HAPs attached to hot-air balloons that effectively provide High-Altitude Platforms (HAPs), where the weight of the payload is of major importance to the success of the mission, a previous analysis is required to account for the feasibility and pertinence of the contaminant detection system. Building on this, we propose a mathematical analysis to determine the energy consumption of the measurement system based on the potential trajectories and the particle density. Also, the analysis provides an estimation of the number of particles that can be detected by the experiment in order to determine the performance of the sensor system. The model is based on an MMPP-2 (Markov Modulated Poisson Process with 2 states) model under exponential distribution assumptions, which provides a basic model that can be easily extended to other distributions in future works.

SensorsVol. 25(23)
Instituto Politécnico Nacional (MX)
Openalex Percentile: Top 57%
Atmospheric Ozone and Climate
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.