esa-sceva/satcom-chunk-collection huggingface dataset:
# A Multi-Source Separation Approach Based on DOA Cue and DNN
###### Abstract  
Multiple sound source separation in a reverberant environment has become popular in recent years. To improve the quality of the separated signal in a reverberant environment, a separation method based on a DOA cue and a deep neural network (DNN) is proposed in this paper. Firstly, a pre-processing model based on non-negative matrix factorization (NMF) is utilized for recorded signal dereverberation, which makes source separation more efficient. Then, we propose a multi-source separation algorithm combining sparse and non-sparse component points recovery to obtain each sound source signal from the dereverberated signal. For sparse component points, the dominant sound source for each sparse component point is determined by a DOA cue. For non-sparse component points, a DNN is used to recover each sound source signal. Finally, the signals separated from the sparse and non-sparse component points are well matched by temporal correlation to obtain each sound source signal. Both objective and subjective evaluation results indicate that compared with the existing method, the proposed separation approach shows a better performance in the case of a high-reverberation environment.  
multi-source separation; dereverberation; deep neural network; direction of arrival 2022
## 5 Experimental Results and Discussion
### Deep Learning Experimental Results
When comparing the results of tests performed on the Active MQ data set, the RNNBDL data was slightly higher than those acquired in both machine learning and deep learning algorithms. The RNNBDL algorithm, on the other hand, is thought to provide better outcomes on selected data sets with large sample sizes since it is a deep learning-based algorithm, and thus it is capable of making high-level interpretations with DL and being a semantically-enriched feature extraction.
# Low-Order Electrochemical State Estimation for Li-Ion Batteries
## 2 Methodology and Models
### The Single Particle Model Revisited  
The porous electrode theory for DUAL configuration, based on the work of [PERSON] and [PERSON], was chosen because it is general enough to be adapted to other batteries. To simplify the electron transport, the single particle model is used to provide an average interaction between the surface of the active material spheres and the electrolyte. Additionally, the simplified model still provides a deep understanding of the electrochemical interactions that occur on each electrode, in the electrolyte and the solid electrolyte interphase (SEI). The theory divides the Li-ion battery into: _Solid phase_ (concerning the electrodes), _electrolyte phase_, and _reaction rate_ (concerning the interaction of the input current, the current of the phases, temperature and the potential at the surface of the electrodes according to the Buttler-Vollmer kinetics [55]). Figure 1 shows a scheme of the DUAL configuration of the model.  
The model equation for the different components are briefly summarized in the following (cp. [54]).  
**Solid phase**: The lithium concentration in the electrode is governed by  
\[\frac{\partial C_{s}(r,x,t)}{\partial t}=\frac{1}{r^{2}}\frac{ \partial}{\partial r}\bigg{(}D_{s}r^{2}\frac{\partial C_{s}(r,x,t)}{\partial r }\bigg{)},\qquad t>0,\quad r\in(0,R_{s}),\quad x\in(0,L)\] (1a) with initial condition \[C_{s}(\cdot,\cdot,0)=C_{s}^{0}\] and boundary conditions \[\frac{\partial C_{s}(r,x,t)}{\partial r}=0, t>0, r=0, x\in(0,L) \tag{1b}\] \[D_{s}\frac{\partial C_{s}(r,x,t)}{\partial r}=-j_{n}(x,t), t>0, r=R_{s}, x\in(0,L), \tag{1c}\]  
where \(C_{s}(r,x,t)\) is the lithium concentration in either of the electrodes, \(r\) is the radius in spherical coordinates of the electrode-sphere particle with maximum value \(R_{s}\), \(D_{s}\) is the diffusion coefficient, and \(j_{n}(x,t)\) is the pore-wall flux. Note that (1a) corresponds to a spherical diffusion equation under the assumption of angular homogeneity regarding the azimuthal and longitudinal directions.  
The potential of the electrode is governed by  
\[\frac{\partial\Phi_{s}(x,t)}{\partial x}=-\frac{I(t)-i_{e}(x,t)}{ \sigma}, t>0,\quad x\in(0,L) \tag{2a}\]  
Figure 1: DUAL cell sandwich battery, cross-sectional view. The current collectors are located at the extremes of the cell, inspired by [54].  
with boundary conditions  
\[\frac{\partial\Phi_{\mathrm{s}}(x,t)}{\partial x} =0, t>0, x\in\{\delta_{-},\delta_{-}+\delta_{\mathrm{s}}\} \tag{2b}\] \[\frac{\partial\Phi_{\mathrm{s}}(x,t)}{\partial x} =-\frac{I(t)}{\sigma}, t>0, x\in\{0,L\}, \tag{2c}\]  
where \(\Phi_{\mathrm{s}}(x,t)\) is the electrode's potential, \(I(t)\) is the input current density, \(i_{e}(x,t)\) is the electrolyte current and \(\sigma\) is the conductivity of the electrode.
## 3 Proposed Methodology
### Experimental Procedure  
To evaluate the practical benefits of the proposed framework, we compare it against the semi-supervised FS method introduced by [PERSON] et al. [19], using the same xAPI dataset and identical experimental conditions. Their method leverages both labeled and unlabeled data to rank feature relevance and improve classification accuracy. By adopting identical evaluation settings, we ensure a fair comparison with our proposed AutoML-enhanced DL framework. Although semi-supervised FS methods offer interpretability through dimensionality reduction, they may overlook complex feature interactions; however, DL models can autonomously learn hierarchical representations from raw data. This comparison highlights the trade-offs between automation, interpretability, and predictive performance in real-world EDM scenarios.  
Further details about the dataset, its structure, and feature distribution are presented in Section 4.  
Figure 2: The ensemble process for DNNs. The ensemble integrates multiple DNN models trained on diverse subsets or configurations into a cohesive framework via stacking. Predictions are aggregated through weighted or soft voting to improve robustness and generalization.
# Low-Cost Sensor Based on SDR Platforms for TETRA Signals Monitoring
## 2 Material and Methods
### Sensor Testing Methodology in the Real Environment
A study of the influence of certain different dynamics situations on data accuracy of the TETRA channels measurements was also performed.
## 1 Introduction  
In what follows, we distinguish between stationary waves (pulses) and wave fronts. A traveling wave \(Q(z)\) (\(z=x-ct\)) is a front if it travels at speed \(c\
eq 0\) and  
\[\lim_{z\to\pm\in}Q(z)=Q_{\pm}.\]On the other hand, for a stationary pulse (with \(c=0\)) and  
\[\lim_{z\to\pm\in}Q(z)=Q_{0}.\]  
In order to determine the stability of the solutions of a given partial differential equation, we linearize it about the wave solution. Then, with a view to recognizing the stability, it suffices to determine that the spectrum in the left-half plane (\(\Re(\alpha)<0\)) corresponds to stable places and that the spectrum in the right-half plane (\(\Re(\alpha)>0\)) corresponds to unstable places. There are many applications of the Legendre polynomials as they arise in mathematical models of the heat conduction and fluid flow problems in spherical coordinates. The novelty of the proposed method via the finding of the eigenvalues exactly is shown in Section 4. This is the advantage of our proposed method over a previous method (see, for details [1]).  
This paper is constructed as follows. It is devoted to a systematic study of the stability of traveling waves through the use of the exponential dichotomies and the Evans function. In Section 2, we will discuss the stability of pulses. In Section 3, we illustrate the results by considering two examples. In Section 4, we will introduce the direct approach for analyzing the stability of traveling wave solutions and compute the eigenvalues for the associated Legendre equation arising from the stability analysis of traveling waves after making a convenient transformation. Finally, conclusions will be presented in Section 4.  
We first introduce the stability analysis of these waves by using exponential dichotomies.
# Cloud-Edge Collaborative Model Adaptation Based on Deep Q-Network and Transfer Feature Extraction
## 2 Preliminaries
### Problem Statement  
In collaborative decision making for cloud-edge object detection, edge-side detection results are expected to closely approximate cloud-side fusion outcomes under limited computational resources. Given the high cost and time-consuming nature of adjusting the entire model on the edge side, frequent full-model updates are impractical. Therefore, this approach introduces reinforcement-learning-based decision and policy mechanisms along with a localized classification head transfer strategy. It dynamically determines whether to execute transfer based on the state of each category and selectively replaces corresponding classification weights, thereby enhancing edge model performance at a low cost.
## 3 Wood-Based Composite Boards
### Wood-Based Binderless Boards
#### 3.2.1 Self-Adhesion Mechanisms for Wood-Based Binderless Boards  
Binderless panels can be made from lignocellulosic materials, since these have intrinsic adhesive properties that can be activated when certain components are subjected to elevated temperature or pressure. Several reactions such as hydrolysis, dehydration and thermal oxidation have been observed [55; 58]. Hemicelluloses have lower temperature stability unlike cellulose and lignin, so hemicelluloses are the first components of the lignocellulosic material modified during the panel-forming process, releasing compounds such as organic acids and acetone [61]. These organic acids, such as acetic acid, will then catalyze degradation reactions of cellulose and hemicelluloses [62], releasing furan compounds from pentoses or hexoses in the hemicellulose. These furan compounds include furfural or hydroxymethylfurfural, which can later recondense or establish new bonds with other compounds during cooling [63].  
The compounds released by cellulose and hemicelluloses will then take part in other reactions, notably with lignin. Lignin undergoes plasticization under the effect of heat; its glass transition temperature is exceeded, and it begins to soften [64; 65; 66]. The inter-monomer bonds will thus break, making the lignin more condensed on the one hand and, on the other hand, allowing for the creation of bonds with other compounds, such as cellulose and hemicelluloses [63; 67; 68]. An increase in the number of \(\beta\)-O-4 bonds in lignin was observed by [PERSON] et al. [69].  
Thus, as a result of the reactions undergone by the components of lignocellulosic material with an increase in temperature, acidic compounds are released from hemicelluloses, catalyzing the degradation of cellulose and hemicelluloses. The compounds then recondense with each other, forming a natural resin, or form bonds with lignin. Lignin, having undergone plasticization, surrounds the cellulose and hemicelluloses before hardening when the temperature is lowered at the end of the process. This plays an important role in the mechanical properties of the final material. Lignocellulosic materials, thanks to their various components and properties, are therefore perfectly suited to the production of binderless panels.  
The parameters of the production process of these binderless boards have an impact on panel properties. It has been demonstrated that increasing the temperature and/or the pressure applied during the process can lead to binderless boards with better mechanical characteristics and water resistance [70; 71]. The time of process, the size, shape, quantity, and moisture content of the raw material also influence the properties of the binderless boards produced depending on the raw material [72; 73; 74; 75].
# A Lookahead Behavior Model for Multi-Agent Hybrid Simulation
###### Abstract  
In the military field, multi-agent simulation (MAS) plays an important role in studying wars statistically. For a military simulation system, which involves large-scale entities and generates a very large number of interactions during the runtime, the issue of how to improve the running efficiency is of great concern for researchers. Current solutions mainly use hybrid simulation to gain fewer updates and synchronizations, where some important continuous models are maintained implicitly to keep the system dynamics, and partial resynchronization (PR) is chosen as the preferable state update mechanism. However, problems, such as resynchronization interval selection and cyclic dependency, remain unsolved in PR, which easily lead to low update efficiency and infinite looping of the state update process. To address these problems, this paper proposes a lookahead behavior model (LBM) to implement a PR-based hybrid simulation. In LBM, a minimal safe time window is used to predict the interactions between implicit models, upon which the resynchronization interval can be efficiently determined. Moreover, the LBM gives an estimated state value in the lookahead process so as to break the state-dependent cycle. The simulation results show that, compared with traditional mechanisms, LBM requires fewer updates and synchronizations.  
Keywords:discrete event simulation; agent-based modeling; time advance mechanism; state update mechanism; time window +
Footnote †: journal: Applied Sciences
## 2 Materials, Equipment and Context
### GFP Imager  
Telemetric data collection of GFP required the development and construction of prototype hardware that is compatible with the GFP gene expression system, with the biosensor plants, and with the power and communications environment of spaceflight-related system. The TAGES Imaging System-II (TIS-II) represents the second generation of hardware designed to collect GFP expression data in real-time aboard a spaceflight experiment (Figure 1). Both TAGES GFP imagers use a system of blue-light light emitting diodes (LEDs) (Lumileds, a division of Philips semiconductor) for excitation of GFP and a specially designed narrow band pass filter with a spectrum spread of \(\lambda\) 470 nm to 488 nm, with a spectral mid point \(\lambda\) of 475 nm. A longpass filter (500 nm +) was designed for the optical lens cell for the imager sensor. The primary differences between TIS-I and TIS-II reside in the configuration of the camera and sophistication of the optics [13].  
Imaging is accomplished with a 5 mega pixel CMOS imager that can capture the square Petri growth plate in a 1280 x 1280 pixel image and transfer that image into on-board 3 mega pixel video FIFO Ram chips before processing and storage on a local MMC/SD card. An on-board Microchip PIC embedded processor is used to control all the imager and lighting functions, with communications through a local area network (LAN). The imager camera settings can be modified by uploading commands through the LAN. In addition commands can be issued to perform all the necessary functions of taking and downloading images from the TIS-II Imager to the local controlling computer.  
The Lumiled LED emitters are used to provide illumination because these devices consume less power and produce more light than earlier models such as the T1-3/4 emitters. The LEDs are packaged with a slug extruding from the rear to remove heat and are surface mounted on a custom designed circuit board using 48 of the Lumileds. An equivalent board using older LEDs would have to be populated with over of the 500 T1-3/4 devices. For image capture, the LEDs are flashed on for a period of two video frames while the image is being taken. Also during the 'on' period the LEDs are controlled with a PWM (pulse width modulation) allowing the intensity to be changed to meet imaging requirements.
# Neural Network System for Predicting Anomalous Data in Applied Sensor Systems
## 2 Materials and Methods  
### Development of the Proposed Neural Network System for Predicting Anomalous Data in Sensor Systems  
The block diagram of the proposed neural network system for predicting anomalous data in sensor systems (hereinafter referred to as the \"system\") is shown in Figure 1. The system is created for each sensor of the sensor system under research and includes a preprocessor, predictor, reconstructor, and anomaly detector.  
The preprocessor prepares sensor data for subsequent processing. The predictor uses a neural network to predict the next sensor value based on historical data. The reconstructor checks if the current value is an outlier and replaces it with the predicted value from the predictor if necessary. The anomaly detector identifies anomalous sequences in the data to alert the operator. The working cycle of the sensor data cleaning module is as follows. The preprocessor operates at a frequency set by the operator, extracting raw data from storage and forming a training dataset for the predictor's neural network. The predictor runs according to the data acquisition frequency, predicting the current value. If the sensor returns an empty value, it is replaced with the predicted one. Otherwise, the reconstructor classifies the value as either an outlier or normal. If an anomalous value is recognized, it is replaced with the predicted one. The system then transmits this value for storage. At the end of the cycle, the anomaly detector determines if an anomalous sequence is ending and notifi  
Figure 1: Block diagram of the proposed neural network system for predicting anomalous data in sensor systems.  
When the predicting system detects anomalous data, it performs a series of actions to ensure accurate reporting and system integrity. Upon identifying an anomaly, the system initially replaces the anomalous data with the predicted value from the neural network. Following this replacement, the anomaly detector assesses whether the anomalous sequence is concluding. If the sequence is deemed significant, the system generates a notification for the operator, providing a detailed analysis of the detected anomalies. This analysis includes information on the anomalies and the nature of the potential impacts on the sensor system, enabling the operator to make informed decisions regarding further actions or adjustments. Thus, the system not only corrects the data but also actively informs the operator about potential issues, enhancing overall system reliability and response capability.
# Energy Consumption Framework and Analysis of Post-Quantum Key-Generation on Embedded Devices
## 6 Conclusions
### Use of Elliptic Curve Cryptography  
From the perspective of Elliptic Curve Algorithms at NIST Level 3, the post-quantum ML-KEM methods remained close to the energy used by the classical ECC method (see Figure 4 and Table 2). However, ML-KEM had a discernible advantage at NIST security Levels 4 and above in the tested environment. When comparing with RSA, ECC should be preferred from a purely energy-based perspective where it is still necessary to use a classical technique, and where this option is available. Use of such algorithms is likely to be prevalent for some time, especially as the NIST post-quantum standards [33] have only recently been published (2024-2025) and are not yet commonly deployed or available in mainstream hardware and software libraries [10].
# Predicting Rogue Content and Arabic Spammers on Twitter
## 11 Conclusions and Future Work  
In this paper, we address the issue of using text classification to detect rogue and spam accounts with Arabic tweets. We offer a specific contribution to the characterization and modeling of Arab spammers on Twitter, a topic that represents an urgent financial, political, and computational issue [2]. We discovered that the populations of English and Arabic spammers vary considerably, making any spam detection model unsuitable for one population. Moreover, the evasion methods of spammers evolve intrinsically. They modify their behavior continually, drifting away from their old behavioral models and rendering old detection systems ineffective. In this work, we collected a large-scale tweet data set. We describe the spammers' evasion methods and how detection features can be tailored to take into account the consequences of evasion. Using these features for our approach, we achieved high performance by implementing one of the well-known classification algorithms, random forest. We analyzed the 47 features generated and selected the 16 most significant ones. We also maximized the accuracy, sensitivity, and specificity rates by tuning the proposed classifier.  
In our future work, we plan to refine our results by expanding the data set. We also intend to further explore this topic, particularly in terms of detecting and characterizing spam content, methods, and campaigns (as opposed to spamming accounts) [38]. In addition, we will try to distinguish translated tweets created by an Arab writer [39]. We may attempt to construct a framework with more features to identify rogue and spam accounts in other languages.  
The authors contributed equally to this work.  
This work was supported by Deanship of Scientific Research in University of Tabuk. The fund number is S-0261-1439.  
The authors declare no conflict of interest.  
Figure 11: Performance results for different numbers of users.
# DeepGOMIMO: Deep Learning-Aided Generalized Optical MIMO with CSI-Free Detection
## 4 Simulation Results
### BER Performance
It can be further observed from Figures 4 and 5 that the optimal training SNRs for the ZF-DNN detector and the CSI-free DNN detector at the same receiver location are generally the same in GOMIMO systems.
# A New Approach to Estimating the Path Loss in Underground Wireless Sensor Networks
## 3 TDR and Field Trials
### Trial Setup
In addition, it was also important that the excavation process was carried out in multiple stages to avoid mixing different layers of soil during the backfilling stage. Different equipment can have different performance characteristics; i.e., the antennas and spectrum analysers can have different gains and noise floors. Therefore, to avoid variances in the performance characteristics of the equipment negatively affecting the measurements, the same equipment was used in all of the tests. Measurements of signal intensity were repeated 5 times to investigate repeatability and reliability of the measurements. Figure 3: Schematic of the RF trials.
# A Novel CDMA-Based Secret Message Multiplexing Scheme by Image Data Hiding +
## 3 The Proposed Secret Message Multiplexing Scheme
### Embedding Capacity  
In the proposed secret multiplexing scheme, according to the total number of users \(N\), the value of modification for each pixel will lie within the range of \([-N,+N]\). Therefore, a \((2N+1)\)-ary CDMA codeword digit can be hidden in a single pixel. This strategy is similar to the well-known exploiting modification direction (EMD) method [39]. In the EMD method, to carry a \((2N+1)\)-ary secret digit, a group of \(N\) pixels must be used and among those \(N\) pixels, at most only one pixel value is increased or decreased by 1. Owing  
Figure 3: The flowchart of secret extraction procedure.  
to the larger pixel value modification, our strategy will lead to greater distortion in the marked images. Nevertheless, the benefit is the increase of the embedding capacity.  
On the basis of assumptions that \(N\) is power-of-2 and the image size \(H\times W\) is divisible by \(N\), the embedding capacity can reach the maximum value by setting the length of secret message bits for all users an identical value \(L\) equal to \(H\times W/N\). In that case, by summing up all users' capacities, the total embedding capacity is \(H\times W\) and the total embedding rate (ER) measured in bits per pixel (bpp) is 1 bpp. Accordingly, for any individual participant, the embedding rate is \(1/N\) bpp. That is to say, the total embedding capacity is fixed and shared by all the participants. In general, when \(N\) is power-of-2 but the image size \(H\times W\) is not divisible by \(N\), there will exist some pixels not used for data embedding. Hence, the total embedding rate will be slightly less than 1 bpp.