esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background: Existing Space-Based Monitoring Systems
### Spectroradiometry-Based Systems (IR, UV)  
The development of thermal remote sensing volcanic activity has seen a long evolution since the 1960s, with a significant expansion and development of autonomous monitoring systems after the beginning of the new millennium [14].  
MODVOLC, developed in 2002 by the Hawai'i Institute of Geophysics and Planetology [40; 41; 42], is a widely used volcano thermal monitoring system which can be considered as the archetype of open-access satellite monitoring services. It is based on the autonomous analysis of MODIS (Moderate Resolution Imaging Spectroradiometer) IR data, which has 1 km spatial resolution. The system provides the location of high-temperature thermal anomalies, spectral radiance data, and heat flux estimates in NRT over all volcanoes around the globe, through an open interactive Web-GIS interface ([[http://modis.hisp.hawaii.edu/](http://modis.hisp.hawaii.edu/)]([http://modis.hisp.hawaii.edu/](http://modis.hisp.hawaii.edu/))).  
MIROVA (Middle InfraRed Observation of Volcanic Activity, www.mirovaweb.it) is an enhanced NRT volcanic hot-spot detection system, based on the analysis of the MODIS Middle InfraRed (MIR) bands, developed by the University of Torino and the University of Firenze [43]. It currently monitors 215 volcanoes, and displays time series of various measurements, including the Volcanic Radiative Power (VRP) which measures the heat radiated by the volcanic activity, and higher level post-processed products such as lava effusion rates [44; 45]. MOUNTS is strongly inspired by MIROVA, whose website template was used.  
HOTVOLC is a monitoring system designed to achieve NRT monitoring of volcanic activity using geostationary satellite data (e.g., MSG-0 SEVIRI). It uses both IR and UV spectroradiometry analysis to monitor volcanic thermal activity, lava effusion rate [46], SO\({}_{2}\) gas plume [47], and volcanic ash plume [48]. It is developed at the Observatoire de Physique du Globe de Clermont-Ferrand (France), and monitors \(\sim\)50 volcanoes worldwide. HOTVOLC provides an interactive Web-GIS interface on which geocoded raster images and time series of the above mentioned parameters are visualized ([[http://hotvolc.opgc.fr](http://hotvolc.opgc.fr)]([http://hotvolc.opgc.fr](http://hotvolc.opgc.fr))).  
The Global Sulfur Dioxide Monitoring Home Page, developed and maintained by NASA's Atmospheric Chemistry and Dynamics Laboratory (www. [[https://so2.gsfc.nasa.gov/](https://so2.gsfc.nasa.gov/)]([https://so2.gsfc.nasa.gov/](https://so2.gsfc.nasa.gov/))), is a reference portal that brings together volcanic and anthropogenic SO\({}_{2}\) emission data from various monitoring systems. The results are based on the analysis of IR and UV data from different sensors that allow the development of the first inventory of volcanic SO\({}_{2}\) emission at global scale [49]. The website provides an archive of daily SO\({}_{2}\) emissions detected over different regions of volcanic interest that can be freely downloaded. NRT maps are also available through the various systems linked to the Global Sulfur Dioxide Monitoring Home Page.
## 3 Robustness Analysis Problem Formulation in CPS
The \(i\)-th element \(\delta_{i}\) of the perturbation vector \(\delta\) is a non-negative integer that represents the number of tokens lost in the place \(p_{i}\), \(p_{i}\in P\), due to resource failures, where \(i\in\{1,2,\ldots,|P|\}\). In this study, a discrete time horizon of \(\Pi\) periods is considered. To describe the time intervals of resource failures, we define a discrete time failure interval. Note that if \(\delta_{i}>1\), it means that there are \(\delta_{i}\) failures of multiple resources in place \(p_{i}\). As the time interval of each resource failure may not be the same, we define a failure interval as follows. For each resource failure \(l\in\{1,2,\ldots,\delta_{i}\}\), the corresponding discrete time failure interval that describes the starting time and the end time of the resource failure is denoted by \(\omega_{il}=[\alpha_{il}\ \beta_{il}]\), where \(i\in\{1,2,\ldots,|P|\}\). Note that if \(\delta_{i}=0\), \(\omega_{il}=[L\ \infty)\), where \(L\) is a number larger than \(\Pi\). According to Definition 6, a failure taking place outside the time horizon \(\Pi\) can be represented by \(\delta_{i}=0\) with \(\omega_{il}=[L\ \infty)\), where \(L\) is a number larger than \(\Pi\). We use \(\omega\) to refer to all failure intervals \(\omega_{il}\), \(i\in\{1,2,\ldots,|P|\}\), \(l\in\{1,2,\ldots,\delta_{i}\}\), associated with \(\delta\). Given a nominal cyber-physical system model \(G=(P,T,F,m_{0},\mu)\), an uncertainty model is defined based on a marking \(m\) reachable from \(m_{0}\). As the dimension of a marking is \(|P|\), the uncertainty model is defined as follows:  
A \(|P|\) dimensional uncertainty model \(\Delta(m)\) for a reachable marking \(m\) is described by a three-tuple \(\Delta(m)=(m,\,\delta,\,\omega)\), where the \(|P|\) dimensional perturbation vector \(\delta\) represents perturbation of nominal marking and the \(|P|\) dimensional vector \(\omega\) describes all failure intervals associated with the perturbation \(\delta\). In this paper, we will study whether deadline \(\theta\) can be met in the presence of uncertainty \(\Delta(m)=(m,\,\delta,\,\omega)\) based on the following problem formulation. Robustness Analysis Problem (RAP)  
Given a DTPN model \(G=(P,T,F,m_{0},\mu)\) under a reachable marking \(m\), a target marking \(m_{d}\) defined by \(m_{d}(p)=\left\{ \begin{array}{c}0\ p\in P\backslash\left(P^{F}\cup\{r_{s}|s \in R\,\}\right)\\ Q_{j}\ p=p^{j}_{2N_{j}+1}\in P^{F},j\in\{1,2,3,\ldots,J\}\end{array} \right.\) and \(\theta\), suppose there exists a firing sequence (a nominal solution) that brings \(G\) from \(m\) to \(m_{d}\) by the deadline \(\theta\). Determine whether there exists a firing sequence that brings \(G\) from \(m\) to \(m_{d}\) by the deadline \(\theta\) under the uncertainty \(\Delta(m)=(m,\delta,\omega)\).
## 3 Results
### Temporal Distribution of GPP
#### 3.3.2 GPP Variation Trends  
Through the Theil-Sen median slope estimation, the GPP slope of the study area was obtained and visualized using ArcGIS (Version 10.8), as shown in Figure 6a. Firstly, it can be seen that the areas with higher slope values are predominantly distributed at the northern and southern ends, which are regions with greater forest cover density, while the areas with lower slope values are mostly located at the eastern and western ends, corresponding to urban areas directly associated with urban land use and development activities. Overall, there is no significant difference in the area between regions exhibiting an increasing trend and those showing a decreasing trend. However, the area of significant increase surpasses that of significant decrease. Referring to Figure 3d, the regions of significant increase also correspond to areas of higher mean GPP values. Thus, it can be preliminarily concluded that the GPP in the study area exhibits an overall upward trend.  
The GPP slope was overlaid with the Mann-Kendall trend test for analysis and classified according to the conditions specified in Table 3, yielding the trend classification of the GPP changes in the study area. To more intuitively analyze the distribution of the different trend types, the results were visualized along the elevation distribution, as shown in Figure 6c. Firstly, overall, apart from some unchanged areas in low-elevation regions due to water bodies, the trends of increase and decrease are balanced. However, these trends exhibit opposing patterns with elevation. At elevations of 2000 m and below, a decreasing trend predominates, with significant decreases accounting for approximately 15% and non-significant decreases for about 60%. Above 2000 m, an increasing trend prevails, with significant increases comprising roughly 15% and non-significant increases about 60%. At an elevation of 1000 m, the proportion of significant decreasing trends peaks, indicating substantial impacts from climate change and urban development in this area. At 2500 m, the proportion of significant increasing trends peaks, suggesting notable success in the ecological restoration efforts at this elevation.  
Figure 5: Temporal distribution of GPP (\(y\)-axis: mean GPP value; \(x\)-axis: time series at a 16-day resolution).  
Figure 6: GPP trend analysis from 2001 to 2020. (**a**) Theil–Sen median slope of GPP. (**b**) Z-value of the trend. (**c**) GPP variation trends (SP: significant positive trend; NSP: non-significant positive trend; NC: no change; NSN: non-significant negative trend; SN: significant negative trend).
# Selection of Features Based on Electric Power Quantities for Non-Intrusive Load Monitoring
## 7 Test and Results  
To obtain the results, data where split in three parts. For each appliance, the 80% of events is used for training, the 10% for validation and the other 10% for test. On this training set, cross-validation was applied in order to avoid overfitting. From the cross-validation results, optimal configuration settings for the methods were obtained and the evaluation on the test set gave us the final performance. The composition of the three datasets is reported in Table 3.
###### Abstract  
The IoE forms the foundation of the modern digital ecosystem by enabling seamless connectivity and data exchange among smart devices, sensors, and systems. However, the inherent nature of this structure, characterized by high heterogeneity, distribution, and resource constraints, renders traditional security approaches insufficient in areas such as data privacy, authentication, access control, and scalable protection. Moreover, centralized security systems face increasing fragility due to single points of failure, various AI-based attacks, including adversarial learning, model poisoning, and deepfakes, and the rising threat of quantum computers to encryption protocols. This study systematically examines the individual and integrated solution potentials of technologies such as Blockchain, Edge Computing, Artificial Intelligence, and Quantum-Resilient Cryptography within the scope of IoE security. Comparative analyses are provided based on metrics such as energy consumption, latency, computational load, and security level, while centralized and decentralized models are evaluated through a multi-layered security lens. In addition to the proposed multi-layered architecture, the study also structures solution methods and technology integrations specific to IoE environments. Classifications, architectural proposals, and the balance between performance and security are addressed from both theoretical and practical perspectives. Furthermore, a future vision is presented regarding federated learning-based privacy-preserving AI solutions, post-quantum digital signatures, and lightweight consensus algorithms. In this context, the study reveals existing vulnerabilities through an interdisciplinary approach and proposes a holistic framework for sustainable, scalable, and quantum-compatible IoE security.  
Internet of Everything; blockchain-based security; edge computing; quantum resilient cryptography; post-quantum cryptography; federated learning; access control mechanisms; data privacy; AI-driven threat detection +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Antioxidants in TPW
#### 2.7.3 Chromatographic Determination of Carotenoids  
Carotenoids from TPW were extracted according to the method described by [PERSON] and co-authors [44], with slight modifications. Briefly, 400 mg of the sample was weighed into the extraction vials and extracted with 10 mL of methanol. After the solvent was added, the samples were vortexed briefly, sonicated for 35 s at 40% power (no pulse mode) using a stepped titanium micro tip (OmniRuptor Ultrasonic Homogenizer, Kennesaw, GA, USA) and centrifuged at 2000\(\times\)\(g\) for 5 min (Heraeus SEPATECH Megafuge 1.0, Heraeus, Thermo Scientific, Waltham, MA, USA). The supernatant was collected, and the extraction was repeated with 10 mL of acetone-hexane mixture (1:1, \(v/v\)) containing 0.01% of butylhydroxyanisole (BHA), until the supernatant was colorless. Subsequently, 10 mL of water was added to facilitate the phase separation. The organic layer was collected and filtered through a 0.45 \(\upmu\)m PTFE syringe filter into glass vials for quantification of \(\beta\)-carotene and lycopene. Extracts were analyzed in duplicate by reversed-phase high-performance liquid chromatography (RP-HPLC) using an Agilent 1260 Infinity II system (Agilent Technologies, Santa Clara, CA, USA), according to the method described by [PERSON] and co-authors [45], with minor modifications. Separation was performed on a C30 column (Phenomenex Develosil, 5 \(\upmu\)m, 4.6 \(\times\) 250 mm, Torrance, CA, USA) maintained at 30 \({}^{\circ}\)C. The mobile phases were 20 mM ammonium acetate in methanol-water (98:2, \(v/v\); phase A) and methyl tert-butyl ether (MTBE; phase B). The flow rate was set at 1.0 mL/min, and the injection volume at 30 \(\upmu\)L. The gradient was used as follows: from 97% A to 92% A over 1 min, from 92% A to 85% A over 7 min, and from 85% A to 0% A over the next 17 min. The column was held at 0% A for 0.5 min and switched to 97% A over 9.5 min for column reconditioning. Spectral data were acquired from 360 to 550 nm. Carotenoids were detected at 450 nm and quantified using external calibration curves.
###### Abstract  
We numerically investigate the effects of an incoherent front cover glass on the current-voltage (J-V) characteristics of a Cu(In,Ga)Se\({}_{2}\) (CIGS) solar cell using an integrated optoelectronic model. A 3-mm cover glass--the thickness of which was larger than the coherence length of sunlight--was incoherently modeled based on the equispaced thickness averaging method, where coherent simulation results of the wave equation were averaged over a set of equispaced phase thicknesses. The changes in optical power dissipation, absorptivity and electron-hole pair generation rate were calculated depending on the variation of the equispaced phase thickness. The calculation results of the J-V curves were obtained through numerical solutions of the coupled Poisson and continuity equations. By comparing the J-V curves calculated between coherently and incoherently modeled cover glass, we obtained a maximum \(\pm\)0.54% deviation of the short-circuit current density. This demonstrates that the front cover glass should be modeled as optically incoherent to improve the calculation accuracy of the electrical J-V curves as well as the optical absorption characteristics in the optoelectronic modeling of CIGS solar cells.  
Keywords:photovoltaics; optoelectronic modeling; optical incoherence; spectral effect +
Footnote †: journal: Applied Sciences
# Battery-Free Pork Freshness Estimation Based on Colorimetric Sensors and Machine Learning
## 2 System Configuration  
In this study, a color sensor that detects the value of RGB and a gas sensor that detects TVOC and eCO\({}_{2}\) are used to measure the freshness of packaged red meat, and the system's entire architecture is shown in Figure 1.  
A training dataset was generated using the values measured from pork in the smart sensor tag system equipped with RF energy harvesting capability. Four types of machine learning models are built, and their performances are compared:  
* A Support Vector Machine (SVM) model;
* A Multilayer Perceptron (MLP) model;
* A 1D-CNN model with the characteristics of a CNN applied;
* A ConvLSTM model, which applies a CNN to the long short-term memory (LSTM). This model is specialized for time-series training characteristics.
# Assessing the Potential of PlanetScope Imagery for Iron Oxide Detection in Antimony Exploration
## 3 Materials and Methods  
The step-by-step process illustrated in Figure 2 outlines the methodology used in this study. It can be divided into two main stages. The first stage, spectral data acquisition, involves collecting spectral data from rock samples, organizing it into a spectral library, and analyzing it to identify target minerals. The second stage, satellite image processing, focuses on calculating specific BRs to map target minerals, identify potential areas of interest, and compare the results obtained from the PS sensor with other satellites. This comparison helps assess the degree of similarity between datasets and evaluate the PS sensor's ability to detect iron oxides.
## 2 State of the Art
### VR Applications on Railways  
At present, the application of virtual reality technology in the fields of aerospace and education has achieved some results. [PERSON] et al. confirmed the benefits of using virtual reality in training conductors and determined the most effective training strategy [16]. Based on the previous research and practical developments, VR technology can also be used to re-design a railway locomotive [17]. In general, a regular railway operation training system is an expensive and complex procedure that comprises a data layer, a functional layer, a data interface layer and an application layer. For this reason, [PERSON] et al. proposed a railway operation training system based on VR to reduce the costs of actual facility occupation, loss and waste due to mis-operation of training objects [18]. According to [PERSON] et al.'s studies, a synthetic virtual environment can also integrate with supervision, examination, and tracking units to improve the performance of training and examining of the locomotive crew [19]. So far, no researcher has applied VR technology to locomotive crew driving and emergency skills training. Based on VR technology, in this study, we designed and developed a locomotive crew driving and emergency skills training system that mainly solves the problems of invisible structure, difficulties in the understanding of working principles, and high training costs in the process of locomotive crew training, so that students can receive training in virtual scenes and meet qualification requirements.
## 3 Experimental Results and Analyses
### Experiment in Waters with Flat Seabed Topography
#### 3.1.1 Experiment in Constant-Altitude and Fixed-Point Modes  
The objective of the experiment is to assess the applicability of constant-altitude and height-varying fixed-point modes in flat terrain and to validate the directional principles of survey line planning. To achieve this, specific requirements must be established for the experimental conditions:  
1. It is essential to ensure that the equipment maintains a low height above the ground in order to acquire high-resolution image data at both high- and low-frequency rates;
2. It is crucial to ensure optimal usage of the effective working range to meet the overlap coverage requirements according to the survey line planning principles;
3. The survey line coverage range must ensure complete coverage of the target area;
4. The survey line direction must adhere to the established survey line planning principles;
5. The flight speed setting must ensure data clarity and quality.  
In this experiment, the AUV consistently maintained an altitude of 20 m while using low-frequency side-scan sonar with a 150 m measuring range. Four survey lines were employed, each measuring 500 m long and spaced 75 m apart. The AUV was configured to operate at a cruising speed of 2 kt, and the survey direction alternated between \(\pm 45^{\circ}\) from the horizontal plane for two sets of repeated experiments. Moreover, an experiment was conducted using a mid-sized AUV, while another set of repeated experiments was carried out with a small-sized AUV equipped with a 75-m range capability and cruising at a speed of 3 kt. The specific parameters are shown in Table 3. Figure 13 displays the AUV's depth, course, and image data.  
Figure 12: Flat seabed topography.  
Several sets of experiments conducted under constant-altitude mode have validated the applicability of the constant-altitude and fixed-point mode in flat waters, in addition to the planning principles for survey line direction, spacing, and overlap.
## 5 Obstacles and Challenges in Evolving V2X Communication
### Security Risks of 5G Ran
#### 5.2.2 Pilot Contamination (PC)  
The estimation of the propagation channel properties in telecommunication radio systems is performed by pilot transmissions. The channel state information (CSI) must be estimated frequently for MIMO systems because of the large number of antennas and the relatively short channel coherence time. Thus, this system is sensitive to incorrect CSI estimation, which degrades the communication link properties. Hence, there are already existing threats called pilot contamination (PC) attacks, where the malicious participant is broadcasting equivalent pilot signals, making [PERSON] use a false CSI estimation. As the system is expecting an altered channel rather than reality, the transmission will perform poorly. [PERSON] will use a non-optimal beam-forming and power levels, which would result in a possibility for increased vulnerability to passive attacks [104]. The detection of such threats is already studied [101], but the secrecy aspects are not yet available. Recent publications propose possible counter-measures, e.g., keeping the pilot assignments hidden and also using a pilot set that scales with the number of antennas in the system [104]. Furthermore, a possible solution can be the use of a smart algorithm (machine learning) that can detect PC attacks and alter the pilot assignment and the CSI estimation system [105].
## 3 Experiments and Results
### Evaluating Indicator  
This study adopted the commonly used evaluation metrics for classification tasks, namely accuracy (_Acc_), precision (_P_), recall (_R_), and the harmonic mean _F1_ of precision and recall. Their specific formulas are given in Equations (3)-(6).  
\[Acc=\frac{TP+TN}{TP+TN+FN+FP} \tag{3}\]  
\[P=\frac{TP}{TP+FP} \tag{4}\]  
\[R=\frac{TP}{TP+FN} \tag{5}\]  
\[F1=2\frac{P*R}{P+R} \tag{6}\]  
In the above formulas, _TP_ denotes true positives, _TN_ denotes true negatives, _FP_ denotes false positives, and _FN_ denotes false negatives. For multi-class classification tasks, such as the three-class classification situation in this model, the macro-average method was adopted. First, the metrics (such as precision and recall) are calculated for each class independently, and then, the arithmetic mean of these metrics was computed. The macro-average calculation formulas for precision and recall are shown in Equations (7) and (8).  
\[macro-P=\frac{1}{C}\mbox{$\sum_{i=1}^{C}$}\frac{TP}{TP+FP} \tag{7}\]  
\[macro-R=\frac{1}{C}\mbox{$\sum_{i=1}^{C}$}\frac{TP}{TP+FN} \tag{8}\]