esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Characterization  
Powder X-ray diffraction (XRD) patterns of the samples were recorded on a Bruker D8 (Bruker AXS, Karlsruhe, Germany) advanced diffractometer using a Cu Kalpha radiation (\(\lambda\) = 0.15405 nm). The morphologies of the samples were investigated by scanning electron microscope (SEM, GeminiSEM 500, Carl Zeiss AG, Oberkochen, Germany) and transmission electron microscopy (TEM, HitachiHT-7700 at 100 kV, Hitachi Limited, Tokyo, Japan). Fourier transform infrared spectroscopy (FT-IR) was acquired using a Nicolet iS10 FT-IR spectrometer (Thermo Fisher Scientific, Waltham, MA, USA). Raman spectroscopies were studied by a single monochromator with a microscope (Renishaw inVia, Renishaw plc, London, UK) equipped with CCD array detector (1024 \(\times\) 256 pixels) and an edge filter and 633 nm. X-ray photoelectron spectroscopy (XPS) was measured with a Thermo Fisher Scientific ESCALAB Xi\({}^{+}\) spectrometer (Thermo Fisher Scientific, Waltham, MA, USA) equipped with a monochromatic Al K\(\alpha\) radiation. The binding energies were calibrated using the C 1s peak at 284.4 eV. The relative complex EM parameters in the 2-18 GHz frequency were measured by a microwave vector network analyzer (N5227A, Agilent technologies, Santa Clara, CA, USA).
# Cold Boot Attacks on the Supersingular Isogeny Key Encapsulation (SIKE) Mechanism
## 2 Background
### Previous Work
#### 2.3.2 Discrete Logarithm Setting  
The first paper that explored this attack in the discrete logarithm setting was that of [PERSON] et al. [31]. Their key-recovery algorithm is limited due to the cold boot attack model they assumed. In particular, their work focused on recovering the secret key \(x\), given the public key \(g^{x}\), with \(g\) being a field element and \(x\) being an integer. Their model, in addition to assuming the attacker has knowledge of the public key \(g^{x}\) and of the noisy version of the private key \(x\), supposes the adversary knows an upper bound on the number of errors found in the noisy version of the secret key. Since knowing such an upper bound may not be practical and small redundancy in the secret key was exploited, their key-recovery algorithm is not expected to recover keys if these are subjected to a high level of noise, or if a bit-flipping model is assumed. A follow-up work by [PERSON] and [PERSON] [32] also studied this attack in the discrete logarithm setting, more concretely in the elliptic curve cryptography setting. Their work was more practical, since they had a deep review of two implementations for elliptic curve cryptography. With such review, they found redundant in-memory formats and focused on two common secret key in-memory formats from two popular ECC implementations from TLS libraries. The first one is the windowed non-adjacent form (wNAF) representation, and the second is the comb-based representation. For each format, they developed dedicated key-recovery algorithms and tested them both in the bit-flipping model.
## Appendix A  
Unlike ordinary least squares fitting (OLSF), iterative weighted least squares fitting (IWLSF) uses a weight function to determine the weight coefficients of each data point; data points with large residuals are given less weight, and the weight coefficients are optimized by iterative iterations. The objective function of IWLSF is:  
\[\sum_{i=1}^{n}w_{i}(y_{i}-\varphi(x_{i}))^{2}=\text{min} \tag{1}\]  
where \(y_{i}\) is the measured data, \(n\) is the total number of data points, \(\varphi(x)\) is the fitted model, and \(w_{i}\) is the weight function. The weight function defined by [PERSON] [35] is used, and its expression is as follows:  
\[w_{i}=\begin{cases}1,&|u_{i}|\leq c_{h}\\ \frac{c_{h}}{|u_{i}|},&|u_{i}|>c_{h}\end{cases} \tag{2}\]  
In the above formula, \(c_{h}\) is a constant, which is taken as 1.345, \(u_{i}\) is the residual of the \(i\)th data point after normalization, which can be expressed as \(u_{i}=e_{i}/s\), where \(e_{i}\) is the residual of the \(i\)th data point, and \(s\) is the residual scale factor, which can be written as the following formula:  
\[s=\frac{\text{MAD}(e_{1},e_{2},\dots,e_{i})}{0.6745} \tag{3}\]\[\text{MAD}(e_{1},e_{2},\ldots,e_{i})=\text{Median}|e_{i}-\text{Median}(e_{1},e_{2}, \ldots,e_{i})| \tag{10}\]  
where MAD is the median absolute deviation of the residuals.  
For the curve fitting of waveform-type data, the fitted model \(\varphi(x)\) uses the trigonometric polynomial:  
\[\varphi(x)=a_{0}+\sum_{i=1}^{n}a_{i}\cos(ix)+b_{i}\sin(ix) \tag{11}\]  
where \(a_{0}\), \(a_{i}\), and \(b_{i}\) are the coefficients to be estimated and the trigonometric polynomial is very suitable for fitting waveform-type curves. After solving for the coefficients to be estimated using IWLSF, the corresponding amplitudes can be calculated by substituting the horizontal coordinates of the extended region into \(\varphi(x)\).
## 2 System Design and Its Components
### System Configuration and Component Deployment
ODC Core is used in various geospatial data analysis and visualization fields, such as disaster response, environmental monitoring, agriculture, and geographic information systems. ODC Explorer is a visualization and analysis tool for the ODC database, an open-source data cube framework for storing and analyzing large-scale geospatial data. The ODC Explorer provides a web interface for users to visualize and manipulate various layers of the ODC database. Users can render ODC data on a map, adjust time series using a slider bar, filter the range of data, and query data using various analysis tools. It enables users to utilize ODC data in various fields, such as terrain analysis, crop detection, water resource management, and environmental monitoring. Additionally, the ODC Explorer can be easily expanded through a plugin system, allowing users to add new features and tools available through the web interface. The ODC Explorer is written in Python and JavaScript.
## 3 Results
### Analysis of Regional Drought Characteristics of Converted Land Uses in the Shendong Mining Area
#### 3.4.2 Change Region Ecological Drought Characteristics  
Unlike meteorological drought, ecological drought exhibits a lower frequency and intensity. As shown in Figure 14, overall, the mutual transformation among the three types of land use has distinct characteristics. Specifically, when grassland and cultivated land were converted into each other, there was no significant changes in the frequency, duration, or intensity of ecological drought, indicating high consistency. This suggests that although the vegetation cover type changes, minor alterations in the coverage degree do not affect the fundamental characteristics of ecological drought. Compared to bare land, ecological drought had a higher frequency and intensity but a shorter duration. This implies that a reduction in grassland cover can intensify ecological drought due to decreased soil and water conservation ability, nutrient loss, and altered evapotranspiration patterns. When cultivated land was transformed into bare land, the frequency, duration, and intensity of ecological drought decreased, with a greater fluctuation in duration. Cultivated crops exhibit seasonality; even after harvest, surface residues can mitigate ecological drought occurrence. However, during crop replanting, artificial management is required, leading to weaker resistance against ecological drought, thus making it more likely to occur.  
Figure 14: Regional ecological drought characteristics of land use type changes in the Shendong mining area.
# Fires Drive Long-Term Environmental Degradation in the Amazon Basin
## 2 Materials and Methods
### Fire Foci and Emissions  
Fire foci data were calculated using the MODIS sensor product MCD14 DL (TERRA/AQUA). Near real-time (NRT) MODIS Thermal Anomalies/Fire locations--Collection 6 were processed by NASA's Land, Atmosphere Near real-time Capability for EOS Fire Information for Resource Management System (FIRMS), using swath products (MOD14/MYD14) instead of the tiled MOD14A1 and MYD14A1 products. The thermal anomalies/active fires represent the center of a 1 km pixel that was flagged by the MODIS MOD14/MYD14 Fire and Thermal Anomalies algorithm [70] as containing at least one fire. This is the most basic fire product, in which active fires and other thermal anomalies, such as volcanoes, are identified. Data were directly downloaded from a FIRMS [50] shapefile format. The FIRMS Fire Map allows the interactive browsing of the full archive of global active fire detections obtained from MODIS and Visible Infrared Imaging Radiometer Suite (VIIRS). NRT fire data are available within approximately 3 h of satellite overpass, and imagery is available within 4-5 h.  
C emission data were verified and extracted using the fourth version of the Global Fire Emissions Database (GFED4s), as described in [PERSON] et al. [75]. The GFED4s provides C data via the contribution of different types of fire in order to calculate gas traces using emission factors [76]. All the contributing datasets are based on burned areas driven by small fires. The spatial resolution of global files is 0.25\({}^{\circ}\), and the different fields have 720 rows and 1440 columns. Emission data have monthly temporal resolution, but it are also provided as a day cycle based on [PERSON] et al. [77]. The emissions group consists of a 12-month dataset (01, 02\(\ldots\), 12), of calculated C with units of g C m\({}^{-2}\) month\({}^{-1}\). Biosphere flows contain monthly net primary production, heterotrophic respiration, and fire emissions, all of which have the unit g C m\({}^{-2}\) month \({}^{-1}\).
# Cyber-Secure IoT and Machine Learning Framework for Optimal Emergency Ambulance Allocation
## 3 Methodology
### Variable Design and Data Preprocessing
In addition, potential multicollinearity between variables with high correlation, such as alcohol consumption rates and the number of food establishments, was addressed using correlation-based filtering and Lasso regression for variable selection. Through this process, 6 key predictive features were extracted from over 20 candidate variables, and these served as the core inputs for both model training and simulation. As a result of these data preprocessing efforts, the proportion of missing values was maintained below 5 percent, the rate of outliers was kept under 2 percent, and no unresolved merge or mapping errors remained in the final dataset. This indicates that the data used in this study met rigorous statistical quality standards and were well-suited for both practical applications and policy-oriented analyses. Furthermore, this preprocessing approach contributed not only to improving the generalizability of the prediction model but also to enhancing the interpretability of its outputs. It ultimately supports evidence-based decision-making for future ambulance deployment planning. As illustrated in Figure 5, a dedicated visualization dataset was developed to enhance the interpretability of model outputs and support spatially informed policy decisions. The dataset was structured based on EMS service jurisdictions and incorporated key spatial features, such as the three nearest emergency medical institutions within a 10 km radius, regional population distributions, and the density of critical public facilities. To ensure consistency and accurate spatial matching, address information was converted into geographic coordinates via geocoding and reverse geocoding, then standardized using administrative district codes. Hospital-level data were collected from the Ministry of Health and Welfare's national emergency facility registry and regional health information systems. Information on capacity indicators--such as bed availability, operating hours, and emergency care capabilities--was refined into structured variables. Accessibility between EMS centers and hospitals was assessed through both distance-based metrics and transportation infrastructure indicators, including average driving speed and congestion levels on major roads. The processed dataset enabled the generation of visual tools such as weighted resource allocation maps, hospital-to-center connectivity networks, and district-level response time heat maps. Variables identified as highly important by the prediction model were prioritized for visualization, serving as foundational elements for developing a policy dashboard. This system offers decision-makers an intuitive understanding of spatial disparities in EMS coverage and supports evidence-based adjustments in ambulance distribution and infrastructure planning.
## 2 Methods
### LSTM Neural Network  
The LSTM network, introduced by [PERSON] and [PERSON] in 1997 [21], is an optimization of the traditional RNN. Its primary innovation lies in the introduction of gate mechanisms to control long-term states, effectively addressing the long-term dependency issue found in RNNs. This improvement has led to the widespread application of LSTM in various domains, such as speech recognition and image processing, as confirmed by the studies of [PERSON] et al. [22] and [PERSON] et al. [23]. The architecture of the LSTM network predominantly features an input layer, hidden layer, and output layer illustrated in Figure 1.  
In Figure 1, A, B, and C represent three nonlinear gate functions termed the forget gate, the input gate, and the output gate, respectively. Unlike traditional RNNs that directly memorize all historical information, LSTM selectively updates and discards historical data through three gates. The algorithm for LSTM is shown in Equations (4)-(9) [24]:  
\[f_{t}=\sigma\left(W_{f}\cdot[h_{t-1},x_{t}]+b_{f}\right) \tag{4}\]  
\[i_{t}=\sigma(W_{t}\cdot[h_{t-1},x_{t}]+b_{i}) \tag{5}\]  
\[\widetilde{C}_{t}=\tanh(W_{c}\cdot[h_{t-1},x_{t}]+b_{c}) \tag{6}\]  
\[C_{t}=f_{t}*C_{t-1}+\ i_{t}*\widetilde{C}_{t} \tag{7}\]  
\[o_{t}=\sigma(W_{o}\cdot[h_{t-1},x_{t}]+b_{o}) \tag{8}\]  
\[h_{t}=o_{t}*\tanh(C_{t}) \tag{9}\]  
In the equations, \(W_{f}\), \(W_{i}\), \(W_{c}\), and \(W_{o}\) are the weight matrices for the input vector \(x_{t}\). \(b_{f}\), \(b_{i}\), \(b_{c}\), and \(b_{o}\) represent the bias vectors at time \(t\). \(\widetilde{C}_{t}\) represents the candidate neuron state at the current time step, encompassing the hidden layer state \(h_{t-1}\) from the \(t-1\) time step and the neuron state \(C_{t-1}\) from the \(t-1\) time step. \(C_{t}\) is the current neuron state; \(h_{t}\) represents the hidden layer state at the current time step; and \(\sigma\) and \(\tanh\) are nonlinear activation functions.
# Fuzzy-PID-Based Atmosphere Packaging Gas Distribution System for Fresh Food
## 2 Materials and Methods
### Fuzzy PID Algorithm
#### 2.4.1 Principle of Fuzzy PID  
As is well known, conventional PID is mainly composed of three links: proportional, integral, and differential. Additionally, the regulation of the system is all based around the error between the feedback value and the set value of the system. The formula is as follows:  
\[u(t)=Kp\cdot e(t)+\frac{K_{P}}{T_{i}}\int e(t)dt+K_{P}\cdot T_{d}\frac{de(t)}{ dt} \tag{7}\]  
Figure 3: Data transmission and architecture diagram of the gas distribution system.  
where \(K_{p}\) is the coefficient of the proportional link; \(K_{i}=\frac{K_{p}}{T_{i}}\) is the coefficient of the integration link; and \(K_{d}=K_{p}\cdot T_{d}\) is the coefficient of the differential link. The effect of \(K_{p}\) is that \(K_{p}\) causes the error to change in the direction of reduction when the system is in error. When \(K_{p}\) is too small, the system approaches the step value more slowly and takes longer to stabilize. Excessive \(K_{p}\), although faster to regulate, can result in excessive overshoot, making the system less stable. Only by choosing the right \(K_{p}\) can the overshoot and the steady-state error be reduced and the speed of regulation be improved.  
The function of the integral link coefficient \(K_{i}\) is to eliminate the steady-state error of the system by adjusting the coefficient. When \(K_{i}\) is too large, the output of the control system is unstable and produces oscillations; too small a \(K_{i}\), although stable, makes it take longer to eliminate the error, resulting in an increase in the regulation time of the system.  
The function of the differential link coefficient \(K_{d}\) is to increase the speed of regulation, reduce the amount of overshoot, and improve the stability of the system.  
Compared to conventional PID, the fuzzy PID control algorithm is characterized by its higher efficiency and accuracy in the control of closed-loop systems with negative feedback. It obtains the system deviation \(e\) and deviation rate of change \(ec\) by comparing the real-time detection value with the preset value through the feedback loop. The deviation \(e\) and deviation rate of change \(ec\) are used as inputs to the fuzzy PID control algorithm, and then the proportional link coefficient \(K_{p}\), the integral link coefficient \(K_{i}\), and the differential link coefficient \(K_{i}\) are updated in real time through fuzzy logic control methods (fuzzification, fuzzy inference, and defuzzification) to obtain the optimal parameters. The online self-tuning of the PID control parameters is achieved. The initial values of the fuzzy PID controller is also obtained according to the stable PID control parameters. Therefore, the fuzzy PID control algorithm not only shows a significant improvement in system control accuracy and efficiency compared to conventional PID, but it also shows a significant improvement in robustness [37; 38; 39; 40]. The schematic diagram of the fuzzy PID controller structure is shown in Figure 4.
## 4 Conclusions  
A study on the influence of the main technological parameters on the formation of defects and roughness zones during abrasive water jet machining of MMC material was carried out. The following conclusions were drawn from the study carried out in relation to T formation:  
* In the evaluation of the taper angle, values between 1\({}^{\circ}\) and 2\({}^{\circ}\) were obtained. The lowest values of T were obtained using high cutting pressures and low feed rates because these are the significant parameters in defect formation.
* In those tests where the jet lacked sufficient kinetic energy to penetrate the material, it was possible to detect the formation of the striated zone and jet deflection at the exit of the cut through the use of a high-speed camera. The following conclusions were drawn from the study carried out in Zone 1, Zone 2, and Zone 3:
* The most favorable surface quality values, with roughness of around 5 \(\upmu\)m, were obtained using low feed speeds, pressures in the low to medium range, and high abrasive rates.
* No major differences were observed between the roughness values measured in Zone 1 and Zone 2. This highlighted the difficulty in establishing differences between IDR and SCR. However, through SEM analysis, it was possible to detect material-specific wear mechanisms that made it possible to distinguish between the two areas.
* The increase in the measuring distance to the thickness of the specimen made it possible to detect striation marks due to jet delay in Zone 3 or RCR. In certain cases, values exceeding 50 \(\upmu\)m were reached under unfavorable cutting conditions.
* Depending on the parameter combination used, specimens with two or three regions were characterized: IDR + SCR, IDR + RCR, and IDR + SCR + RCR.  
Finally, in order to reach a compromise and minimize taper angle and roughness defects during composite machining, the authors recommend the use of low feed rates, medium-high pressure levels, and intermediate abrasive rates.  
Conceptualization, P.F.M.A.; Methodology, P.F.M.A., L.R.-P. and A.G.-P.; Software, L.R.-P. and A.G.-P.; Validation, M.B.P.; Formal Analysis, P.F.M.A.; Investigation, P.F.M.A. and M.B.P.; Resources, L.R.-P. and M.B.P.; Data Curation, P.F.M.A. and M.B.P.; Writing--Original Draft Preparation, P.F.M.A. and A.G.-P.; Writing--Review and Editing, P.F.M.A. and A.G.-P.; Supervision, M.B.P.; Project Administration, P.F.M.A. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the programme for the Promotion and Impulse of Research and Transfer of the University of Cadiz. Project: PR PR2017-086, Machining of composite materials of strategic use in the aeronautical industry using AWJM.  
The authors declare no conflict of interest.
# TACRP: Traffic-Aware Clustering-Based Routing Protocol for Vehicular Ad-Hoc Networks
## 4 Traffic-Aware Clustering-Based Routing Protocol (TACRP)
### Road Side Unit (RSU)  
The RSU is located to ensure high-quality communication among the vehicles in highly congested areas of the network. RSUs are interconnected to vehicles by wireless links and equipped with sensors for communication. The RSU collects the location information and traffic network pattern of the vehicles and shares the details with the TMU. Here, the vehicles are grouped into clusters that are controlled by a CH, that performs the process of data aggregation, intra-cluster communication with the CMs, and inter-cluster communication with other CHs or RSUs [20].
# Real-Time Computing Strategies for Automatic Detection of EEG Seizures in ICU
## 2 Materials and Methods  
### Description of Database  
We recruited 460 video-electroencephalography recordings from 71 monitored patients for at least 24 h, admitted to the Intensive Care Unit (ICU) in La Princesa University Hospital in Madrid (Spain), approved by its research ethics committee, with local reference number 3665 in April 2019. We performed long-term EEG monitoring to monitor sedation or detect seizures at suspicion. We selected 5 to 10 recordings from each patient, each lasting 3 to 5 h. These recordings were chosen based on the presence or absence of seizures, ensuring that the entire duration of each seizure was included in the segments. Additionally,electroencephalographic findings indicative of stationary activity and high suspicion of status were considered in the selection process. These recordings came from patients with different acute illnesses, which caused the order of this test: brain haemorrhage, CET, CRA, infection, known epilepsy, and brain ischaemia. We excluded patients from the infection and brain ischaemia subgroups for the posterior EEG analysis because of their small sample size. The sample selection lasted from July 2015 to July 2019 (Table 1).  
Moreover, the recordings were subclassified into two categories: normal (without epileptic seizures) and containing seizure recordings. The seizure recordings were further classified as focal or generalised, and as convulsive or non-convulsive episodes. This classification was conducted by two highly experienced neurophysiologists specialising in electroencephalography, with clinical support from the intensivist in charge. To optimise the efficiency of the machine learning algorithm, each extract from the recordings containing entire seizures was ensured to include distinct patterns of either focal or generalised, and convulsive or non-convulsive, without any intermixed patterns. The distribution of different seizures is shown in the diagram of Figure 1.  
The electroencephalographic recordings were conducted using the standard 10-20 system montage on the scalp. This montage includes the following channels: Fp1/Fp2, F3/F4, F7/F8, Fz, C3/C4, Cz, T3/T4, T5/T6, P3/P4, and O1/O2.