esa-sceva/satcom-chunk-collection huggingface dataset:
# Outside Dynamic Evacuation Routes to Escape a Wildfire: A Prototype App for Forest Firefighters
## 2 State of the Art
### Evacuation Routes in Emergencies  
Per the Ecuadorian Institute of Standardization INEN-56 ([[https://www.normalizacion.gob.ec/buzon/normas/GPE-60-1.pdf](https://www.normalizacion.gob.ec/buzon/normas/GPE-60-1.pdf)]([https://www.normalizacion.gob.ec/buzon/normas/GPE-60-1.pdf](https://www.normalizacion.gob.ec/buzon/normas/GPE-60-1.pdf)), accessed on 15 October 2020), any infrastructure that houses more than 50 people or has a public area greater than 1000 m\({}^{2}\) must have evacuation routes and emergency exits in a mandatory way to safeguard people's lives against harm. Different models and computational tools have been developed to streamline this process that offer efficient response plans to the agents in charge of reacting to dangerous events. [PERSON] et al. [23] described a particle swarm optimization (PSO) technique to minimize hazardous conditions in the evacuation process during an indoor fire. The route's orientation depends on the type of infrastructure, the smoke and light conditions, and the distance to the building's safe exit points. Likewise, in [24], several technologies related to smart cities and big data ecosystems are described to develop and present an architecture proposal for fast data services, useful for managing and improving response times in an emergency. The architecture's effectiveness [25] and ability to handle and process streaming data to generate early alerts for each unforeseen event were validated. Finally, a fire simulation to affect the studied scenario and analyze its response times was implemented.  
The evacuation of natural persons and the rescue team is essential in wildfires, thus, e.g., a large-scale evacuation model called buildingEXODUS was developed by [PERSON] et al. [26], which includes the entire road network, open spaces, and buildings in a specific area. They used a wildfire spread model to assess various evacuation procedures pedestrians had to follow based on the route taken, providing key statistics such as time to meet, distance, congestion experienced by officers, and margins associated with the use of each evacuation route. [PERSON] et al. [27] described a forest fire simulator to model people's behavior through dynamic factors to identify populations at risk and carry out precise emergency planning. They calculated detailed projections of how the scenarios would play out using behavioral models, microscopic traffic simulators, warning generators, and other existing metrics. The experiments provided information on the role of dynamic factors in modeling bowel movements. They provided a Software as a Service (SaaS) system. This approach allows users to easily explore and understand the risk of possible scenarios, controlling the location of the ignition of the fire, the speed of the wind, the capacity of the shelter, and its position.
# DRL-Based Dynamic SFC Orchestration Algorithm for LEO Satellite Networks
## 1 Introduction
Physical resource constraints pose significant limitations, as underlying network nodes possess finite capacities with upper bounds on computational resources such as CPU, memory, and storage. From a user perspective, network services must satisfy strict end-to-end latency requirements, as links exceeding these thresholds cannot deliver functional network services to users. The complexity of deployment and redeployment decisions is further compounded by the vast solution space created by the numerous possible combinations of node placement locations and virtual link mappings. Indeed, the SFC deployment problem has been proven to be NP-hard [11], reflecting the computational challenges in finding optimal solutions within this expansive decision space. From the perspective of network service providers, operators are more concerned with balancing the profits from providing services to users and the costs associated with the orchestration of network functions, in order to maximize overall benefits. While research on terrestrial network SFC has reached maturity, the unique characteristics of LEO satellite networks present distinct challenges. The orbital motion of LEO satellites results in continuous variations in inter-satellite and satellite-ground station geometries, leading to dynamic link stability and the temporal evolution of the network topology [3]. This inherent topological dynamism poses significant challenges for onboard SFC orchestration. However, the deterministic nature of satellite orbits enables precise position prediction through ephemeris calculations, creating an environment that is both predictable and dynamic. This dual characteristic offers opportunities to optimize SFCorchestration by leveraging topological predictability while addressing dynamic requirements, thereby enhancing service quality and operational efficiency. This paper makes the following contributions:  
1. It introduces and analyzes LEO satellite network SFC deployment scenarios, establishing the imperative for dynamic orchestration;
2. It develops a comprehensive mathematical framework modeling dynamic SFC orchestration as a Markov decision process, incorporating future topology states;
3. It proposes a novel DRL-based dynamic SFC orchestration algorithm optimized for LEO satellite networks, designed to maximize service provider benefits. The paper is structured as follows: Section 2 examines the existing literature on satellite network SFC orchestration; Section 3 presents the mathematical formulation of satellite network SFC orchestration and establishes it as a constrained optimization problem; Section 4 formulates the problem as a Markov decision process and details the proposed DRL-based algorithm; Section 5 presents simulation results and analysis; and Section 6 concludes with findings and implications.
# Carbon Fiber Papers Prepared by Wet-Laid Technique Using PVB/PF Composite Fibers as the Binders
###### Abstract  
Carbon fiber paper (CFP) is one of the most important units of gas diffusion layer (GDL) in proton exchange membrane fuel cells (PEMFCs). The binder used in the wet-laid technique has a significant effect on the properties of CFP. In this work, the polyvinyl butyl/phenol-formaldehyde resin (PVB/PF) composite fibers firstly prepared by a dry spinning method were applied for CFP fabrication to replace traditional binders during the papermaking process and remove the PF impregnation process. In the composite fibers with a mass ratio of 5:5, PF phase with a size of about 2-3 \(\upmu\)m evenly distributed in PVB matrix. PVB and PF were miscible to some degree, which was beneficial for their binding effect during hot-press. These composite fibers can successfully bind carbon fibers (CFs) during the papermaking process, and their residual carbon efficiently welded the CFs after heat treatment. The content and length of composite fibers in the mat affected the binding structure among CFs, which influenced the properties of CFP, increased the composite fibers' content and reduced their length, significantly improving the strength of CFP. Therefore, the application of this solid fiber binder could enhance the comprehensive properties of CFP by adjusting the fibers' parameters in the mat and also make the fabrication of CFP more environmentally friendly and low-cost.  
carbon fiber paper; polyvinyl butyl; phenol-formaldehyde; composite fibers; binder Article
## 1 Introduction
This approach ensured a more representative distribution of cases across both common and rare symptoms, which, in turn, improved the model's ability to detect high-risk patients with underrepresented symptoms. This study analyzed various methods for addressing data imbalance and classification to optimize symptom-based COVID-19 diagnosis using the EPIVIGILA dataset. It focuses on different ML techniques and resampling approaches to improve model performance with imbalanced data, employing metrics related to classification and medical diagnosis. This investigation is vital for enhancing diagnostic management during the pandemic, potentially leading to more effective diagnostic tools and improved patient outcomes. The primary aim is to assess the impact of imbalanced data on ML performance with and without sampling methods, ensemble, and cost-sensitive techniques. This study compares multiple techniques using evaluation metrics such as accuracy, precision, recall, F1 score, sensitivity, specificity, positive likelihood ratio (LR+), negative likelihood ratio (LR-), diagnostic odds ratio (DOR), and the area under the receiver operating character istic curve (AUC-ROC). We apply different methods to modify the dataset distribution and achieve a more balanced distribution between the positive (confirmed) and negative (discarded) classes. While machine learning is increasingly utilized in medical diagnostics, challenges such as data imbalance in large-scale, real-world healthcare datasets like EPIVIGILA remain underexplored. Current studies often focus on algorithmic accuracy without fully addressing the need for effective resampling techniques tailored to imbalanced datasets in healthcare applications, particularly for COVID-19 diagnostics. Few studies examine how these techniques can be optimized for large, diverse datasets or discuss their broader diagnostic implications. This study seeks to bridge this gap by systematically evaluating the effectiveness of resampling methods in enhancing model performance for COVID-19 diagnostics and exploring the potential of these techniques for pandemic management applications. The structure of this paper is organized as follows: Section 2 provides an extensive review of the related literature and prior work. Section 3 presents the study's methodology, detailing each component in subsections: Section 3.2 introduces the research methodology; Section 3.3 describes the dataset and demographics; Section 3.4 focuses on data preprocessing steps; Section 3.5 outlines the machine learning techniques; Section 3.6 discusses ensemble learning techniques; Section 3.7 covers cost-sensitive learning; Section 3.8 details sampling techniques for data imbalance; and Section 3.9 defines the evaluation metrics. Section 4 presents the experimental findings and performance of various approaches. Section 5 discusses the results' implications and significance in clinical applications. Finally, Section 6 summarizes the main findings, acknowledges study limitations, and suggests directions for future research.
# Evaluating the Reliability of Powder Bed Fusion for Biomedical Materials: An Experimental Approach
## 2 Materials and Methods
### Evaluation of the PBF Part Quality  
The reliability of the PBF process for producing high-quality parts from specific materials, along with the criteria end users should consider evaluating whether PBF parts meet the required specifications, is thoroughly addressed in this paragraph. It aims to provide comprehensive answers and practical guidelines for effective PBF applications.  
Figure 1: (**a**) Geometrical dimensions of standard samples, according to ISO 6892 [43]. (**b**) Support type: block (e.g., 1 \(\times\) 1 mm) and conical (e.g., \(\O\) 1.2 mm).  
From a theoretical and scientific perspective, various researchers have successfully improved individual features of PBF specimens. However, from a practical standpoint, the main objective is to achieve an acceptable overall quality for industrial parts. In this experimental research, the PBF processing of each specific material was closely examined. Achieving this practical objective was made possible by thoroughly monitoring the manufacturing process and compiling detailed scientific reports for each set of PBF parameters. These comprehensive reports addressed all technological defects, as well as the interventions and distortions that occurred after removing the specimens from the platform.  
To evaluate the manufacturing outcomes, a four-level ranking scale was developed for each bio-metal analyzed, as detailed below:  
* Rank 1--High risk of failure: Characterizes the inability to complete the manufacturing process due to high levels of residual stresses that damage either the part or its supports. In such cases, the PBF process must be halted at 40-50% completion (see Figure 2b,c).
* Rank 2--Moderate risk of failure: Describes an unstable manufacturing process (see Figure 2a--left and middle sections) that may require temporary interruptions to remove some parts from the platform or cancel their production if multiple parts are involved. This rank may include successfully completed parts.
* Rank 3--Moderate chance of success: Represents a stable process that requires continuous monitoring until the last layer is deposited. The manufacturing could be impacted by minor separations at the corners of the parts or micro-explosions that displace the powder (see Figure 2a--right side). In this case, despite manufacturing issues, such as the breaking of some supports due to internal stresses, the process can continue until completion. These phenomena can negatively affect the macro- and micro-structure of the parts, potentially leading to cracks or geometric deviations.
* Rank 4--High chance of success: Reflects a successful and stable PBF process that does not require continuous observation. The macro- and micro-structures of the produced PBF parts meet the specified requirements.  
Figure 2: Samples made from CoCr with manufacturing issues. (**a**) Supports detached from the parts and SLM platform. (**b**) Parts with cracks caused by residual stresses. (**c**) Supports with cracks caused by residual stresses.
## 2 Implementation of Active Localization: Signal Model, Processing, and Feasibility Test
### Signal Model and Definition of Sound Field Variation
\[\Delta\mathrm{H_{m}}=\mathrm{H_{m}^{event}}-\mathrm{H_{m}^{ref}}=\frac{\mathrm{ G_{m}}}{\mathrm{X}}-\frac{\mathrm{Y_{m}}}{\mathrm{X}}=\frac{(\mathrm{G_{m}}- \mathrm{Y_{m}})}{\mathrm{X}}=\frac{\mathrm{R_{m}^{effect}}}{\mathrm{X}} \tag{4}\]  
where \(\mathrm{H_{m}^{ref}}\) is the transfer function of the control area under the reference scenario shown in Figure 2a, \(\mathrm{H_{m}^{event}}\) is the transfer function under the event scenario shown in Figure 2b, \(\mathrm{X}\) is the input signal, \(\mathrm{G_{m}}\) represents the signals measured by the microphone array after an intrusion, \(\mathrm{Y_{m}}\) represents the reference signals before an intrusion, \(\mathrm{R_{m}^{effect}}\) represents the changed spatial effects, and \(\mathrm{m}\) is a microphone index. The spatial effects \(\mathrm{R_{m}^{effect}}\) are assumed to include the sound signals emitted as reflections by the silent intruder. In other words, \(\mathrm{R_{m}^{effect}}\) can be assumed to also consider the new sound source. This is because the intruder changes the sound field formed from the sound source of a loudspeaker, and then the intruder's position is estimated using the measured \(\mathrm{R_{m}^{effect}}\) at a microphone array. This is the same concept in which the incident, reflected, and transmitted phenomena of pressure distribution on the flat surface of a discontinuity are considered to be the sum of the blocked pressure and the radiation pressure in [31]. If the blocked pressure is the signal of the reference scenario in the control space and the radiation pressure is the signal of the event scenario, we can consider it as a new sound source because only the radiation signal remains when the reference scenario signal is removed from the measured signal. From this concept, the loudspeaker is the sound source that generates the sound field in a control area, whereas in the proposed approach, the sound wave formed by the intruder is a new source and the location of the silent intruder can be detected.
## 3 Results and Discussion
### I-TASSER Studies  
To determine secondary structural elements of the 23-amino-acid sequence from the Cluster 2 domain of LRP1 as well as each of the fourteen designed peptides, we conducted I-TASSER studies. The results obtained are shown in Table 3. In general, the C-Score values are predicted in a range of \(-5\) to \(+2\). Based on the C-Scores obtained, the highest C-Score was seen for TauRP (1-10) and the lowest was seen for TauRP (1-12). The results were obtained from calculations from threading template proteins from the PDB in combination with the sequence profiles derived from sequence databases. The top ten threading templates of highest significance were selected for determining the secondary structures, as shown in Supplementary Materials Table S2. Overall, the results obtained predict mostly coiled structures for most of the peptides, with the exception of TauRP (1-2) and TauRP (1-1), which also showed the presence of \(\beta\)-strands, and TauRP (1-14), which included helices along with coils. The 23-amino-acid sequence showed the presence of coils, helices and beta-strands. In addition, the TM (template modeling) scores, which are usually in the range of 0 to 1, provided a quantitative comparison of the similarities between the predicted and template structures. The results obtained showed a TM score between 0.6 and 1.0.  
\begin{table}
\begin{tabular}{c c} \hline
**Sequence Abbreviation** & **Sequence Designed** \\ \hline TauRP1-9 & DNDCEDNSDE \\ TauRP1-10 & NDCEDNSDEE \\ TauRP1-11 & DCEDNSDEEN \\ TauRP1-12 & CEDNSDEEN \\ TauRP1-13 & EDNSDEENCE \\ TauRP1-14 & DNSDEENCES \\ \hline \end{tabular}
\end{table}
Table 2: _Cont._  
\begin{table}
\begin{tabular}{l l c c} \hline
**Peptide** & **Secondary Structure** & **C-Score** & **Estimated TM-Score** \\ \hline
23-AA sequence from Cluster 2 Domain & CCCSSSSCCCCCCCCCCCHHHCCCC & 0.35 & \(0.76\pm 0.10\) \\ SKAWVCDGDNDCEDN-SDEENCES & CCCSSCCCCCC & \(-0.59\) & \(0.64\pm 0.13\) \\ \hline TauRP1-2 & CCSSCCCCCC & \(-0.33\) & \(0.67\pm 0.13\) \\ \hline TauRP1-3 & CCCCCCCCCCC & \(-0.15\) & \(0.69\pm 0.12\) \\ \hline TauRP1-4 & CCCCCCCCCCC & \(-0.16\) & \(0.69\pm 0.12\) \\ \hline TauRP1-5 & CCCCCCCCCCC & \(-0.76\) & \(0.62\pm 0.14\) \\ \hline TauRP1-6 & CCCCCCCCCCC & \(-0.05\) & \(0.71\pm 0.12\) \\ \hline TauRP1-7 & CCCCCCCCCCC & \(-0.30\) & \(0.67\pm 0.12\) \\ \hline TauRP1-8 & CCCCCCCCCCC & 0.05 & \(0.72\pm 0.11\) \\ \hline TauRP1-9 & CCCCCCCCCCCCC & \(-0.60\) & \(0.64\pm 0.13\) \\ \hline TauRP1-10 & CCCCCCCCCCC & 0.48 & \(0.78\pm 0.10\) \\ \hline TauRP1-11 & CCCCCCCCCCCCC & 0.07 & \(0.72\pm 0.11\) \\ \hline TauRP1-12 & CCCCCCCCCCCCC & \(-0.79\) & \(0.61\pm 0.14\) \\ \hline TauRP1-13 & CCCCCCCCCCCCC & \(-0.38\) & \(0.66\pm 0.13\) \\ \hline TauRP1-14 & CCCCHHCCCC & \(-0.30\) & \(0.67\pm 0.12\) \\ \hline \end{tabular}
\end{table}
Table 3: Peptide secondary structures based on I-TASSER results.
## 1 Introduction
The path loss model for maritime communication during shadowing is considered, and the model is close to the real channel model. The optimal number of samples are found and used to calculate the entropy of the sensed signal as the measure of information of the received PU signal for the test statistic. We proposed a cooperative spectrum sensing scheme for MCRN, which considers the conditions of the sea environment and uses the entropy-based detector with the optimal number of samples as a local detector. The throughput optimization of the \(m\)-out-of-\(n\) rule is also considered with respect to the sea conditions. The optimal \(m\) and \(n\) are derived for the throughput optimization setup. The rest of the paper is organized as follows. Related work is discussed in Section 2, while Section 3 gives a brief overview of maritime cognitive radio network and channel modeling. A system model, the optimization problem of a number of samples in the entropy-based detection scheme and centralized cooperative spectrum sensing are discussed in Section 4 Section 5 demonstrates the simulation results, and finally, conclusions are drawn in Section 6.
# Joint Image Compression and Encryption Using IWT with SPIHT, Kd-Tree and Chaotic Maps
## 6 Conclusions  
In this paper, a joint image compression and encryption algorithm that is based on IWT with SPIHT, Kd-tree and multiple chaotic maps was proposed. First, compression is performed via lifting-based IWT and SPIHT. The diffusion of the major coefficients of IWT is carried out through nonlinear inverse operations. Permutations of the lower-frequency-band coefficients are performed via Kd-tree. Then, this SPIHT bit stream is subjected to Kd-tree for further compression, permutations and diffusions. In Kd-tree, permutations are performed with pseudo-random sequences. The diffusion of leaf nodes is achieved through matrix vocabulary. The Kd-tree output is further compressed via a modified wavelet tree and Huffman coding. Using this method, we achieved a high compression ratio and maximum confusion and diffusion operations. The main advantage of this scheme is that, with a robust security level, the compression performance is not degraded, as is the case with most joint compression and encryptions schemes. Finally, this bit stream is encrypted via varying parameter logistic maps and modified quadratic maps. Multi-level security is achieved that can withstand cryptanalysis and all types of known attacks. Moreover, the key space is very large, which makes the scheme resistant to brute-force attacks. The test results demonstrate that the system is sufficiently secure with satisfactory lossless compression performance.  
Conceptualization and experiments, N. and J.S.; Data curation, J.S. and M.A.A.; Data analysis, N. and B.C.; Writing-Original Draft Preparation, N. and J.S.; Writing-Edit and Review, H.X., J.S. and H.H.  
This work was supported by National Key R &D Program of China (No. 2017 YFB0802400) and Chongqing Research Program of Basic Science and Frontier Technology (No. cstc2017)cyjB0305).  
The authors declare no conflict of interest.
## 1 Introduction
### Modeling of Demagnetization Fault Identified Signal
#### 2.2.1 Modeling of the Main Identification Signal  
The main identification signal \(SI_{\text{m-PMk}}\) generated by the \(k\)th PM (PMk, \(k\) is the PM number, which is the natural number ranging from 1 to 2p) is expressed as in (1).  
\[\left\{\begin{aligned} SI_{\text{m-PMk}}&=(-1)^{k-1}E_ {1}\sin(\alpha-(k-1)\pi)\\ (k-1)\pi&\leq\alpha\leq(k+1)\pi\end{aligned}\right., \tag{1}\]  
where \(E_{1}\) is the amplitude of the fundamental component of \(SI_{\text{m-PMk}}\). \(\alpha\) represents the electrical angle between a point on the inner circle of the stator and the origin of the coordinates. The coordinate origin is defined as the intersection point of the coil axes and the inner circle of the stator. The coil axes refer to the axes of TYC1 and TYC2. When the axes of PM1 and PM2 align with the coil axes of TYC1 and TYC2 for the first time, the search coil begins to collect signals.  
The \(i\)th electrical period signal of \(SI_{\text{m}}\) (\(SI_{\text{m-}i}\)) can be derived as follows:  
\[SI_{\text{m-FM(2p)}}+SI_{\text{m-PM1}}+SI_{\text{m-PM2}}\quad(i=1)\] \[\sum_{k=2i-2}^{2i}SI_{\text{m-PMk}}\quad(i=2,3,4\dots\dots\,p) \quad\quad, \tag{2}\]  
Figure 1: Placement scheme of toroidal-yoke-search coil.  
where \(i\) represents the number of pole pairs, as well as representing the number of electric cycles of the identification signal, and \(i\) has a value from \(1\) to \(p\). Taking \(i=1\) as an example, \(1\) represents the first pair of magnets and the first electrical cycle signal of \(SI_{\text{m}}\).