esa-sceva/satcom-chunk-collection huggingface dataset:
# Towards a Real-Time Description of the Ionosphere:
## 2 IRI and IRTAM Models: A Brief Recall
### Real-Time IRI and the IRTAM Method
As a first step, for each assimilated station, NECTAR considers the 24 h values recorded by the ionosonde prior to the assimilation time and calculates the differences between observed and modeled (by IRI) values; the procedure is the same for both _fo_F2 and _hm_F2. The 24 h time series of detrended values is used to describe the corresponding diurnal trend trough a 6 th order Fourier analysis (the same as that of IRI) plus a linear term, for a total of 14 time coefficients. The second step concerns the spatial interpolation of the detrended diurnal coefficients at locations different from assimilating sites. This task is accomplished by NECTAR through a recurrent Hopfield neural networkoptimizer [54], which is used as a spatial interpolator to smoothly spread the information from the assimilating sites to the entire global grid. Once the global grid of detrended diurnal coefficients is obtained, the third step consists in retrieving the 76 spatial coefficients to be used as correction terms in the Jones and Gallet spatial harmonic expansion. The output of the NECTAR method is then 14 (diurnal) \(\times\) 76 (spatial) correction coefficients to be added to the original ones. The original coefficients are intended to represent the climate behavior of the ionosphere; as a consequence, the correction coefficients calculated by IRTAM describe the departures from the climatological behavior of the ionosphere.
# Electric Field and Temperature Effects on the Ab Initio Spectroscopy of Liquid Methanol
## 1 Introduction
Finally, although it has been claimed that the \"electrofreezing\" phenomenon occurs in liquid water by pioneering simulation studies conducted with classical force-field water models [46], more recent and sophisticated DFT-based molecular dynamics investigations revealed that, upon the electric field exposure, the liquid water structure becomes more \"ice like\" [47]. Such evidence has recently been strongly supported by prolonged force-field molecular dynamics simulations [48]. Such a fascinating process, evidencing some analogies between the application of an external electric field and the decrease in the temperature of the sample, has never been extensively investigated for H-bonded systems other than water. In the current study, by means of state-of-the-art ab initio molecular dynamics (AIMD) simulations, we report on the IR spectrum of liquid methanol under the action of static and homogeneous electric field intensities up to 0.30 V/A, a value slightly beneath the field-induced molecular dissociation threshold [49; 50]. Moreover, with the aim of comparing electric-field-induced and temperature-induced effects, IR spectra of liquid methanol at different temperatures (from 240 K up to 350 K) are also determined.
# A Comparative Evaluation of Transformers and Deep Learning Models for Arabic Meter Classification
###### Abstract  
Arabic poetry follows intricate rhythmic patterns known as 'arud' (prosody), which makes its automated categorization particularly challenging. While earlier studies primarily relied on conventional machine learning and recurrent neural networks, this work evaluates the effectiveness of transformer-based models--an area not extensively explored for this task. We investigate several pretrained transformer models, including Arabic Bidirectional Encoder Representations from Transformers (Arabic-BERT), BERT base Arabic (AraBERT), Arabic Efficiently Learning an Encoder that Classifies Token Replacements Accurately (AraELECTRA), Computational Approaches to Modeling Arabic BERT (CAMeLBERT), Multi-dialect Arabic BERT (MARBERT), and Modern Arabic BERT (ARBERT), alongside deep learning models such as Bidirectional Long Short-Term Memory (BiLSTM) and Bidirectional Gated Recurrent Units (BiGRU). This study uses half-verse data across 14 m. The CAMeLBERT model achieved the highest performance, with an accuracy of 90.62% and an F1-score of 0.91, outperforming other models. We further analyze feature significance and model behavior using the Local Interpretable Model-Agnostic Explanations (LIME) interpretability technique. The LIME-based analysis highlights key linguistic features that most influence model predictions. These findings demonstrate the strengths and limitations of each method and pave the way for further advancements in Arabic poetry analysis using deep learning.  
A Article  
A. Editors: [PERSON] and [PERSON]  
Received: 20 March 2025  
Revised: 23 April 2025  
Accepted: 28 April 2025  
Published: 29 April 2025  
**Citation**: [PERSON]; [PERSON] A Comparative Evaluation of Transformers and Deep Learning Models for Arabic Meter Classification. _Appl. Sci._**2025**, _15_, 4941. [[https://doi.org/10.3390/app15094941](https://doi.org/10.3390/app15094941)]([https://doi.org/10.3390/app15094941](https://doi.org/10.3390/app15094941))  
Copyright: 0 2025 by the authors.  
License MDPI, Basel, Switzerland.  
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# Efficient LDPC Encoder Design for IoT-Type Devices
## 6 Numerical Results
The RS encoder we use for comparison is a byte-size (8 bit) LSFR (linear-feedback shift register) implementation, following the source codes provided in [50], while the BCH encoder we base on the description provided in [51]. The encoding process in each algorithm is based on three steps: data preparation, encoding algorithm execution and storing the results. The encoding in all cases is based mostly on operations: XOR, OR and register shift. The basic LDPC, RS and BCH algorithms need to execute a similar order of the number of operations. Meanwhile, the inherent advantage of QC-LDPC is due to the following reasons:  
* The encoding combines operation for a blocks of \(P\) bits because multiplication by a whole submatrix can be done in a single shift operation;
* The QC-LDPC parity-check matrix, which can be used directly for encoding, is by definition sparse; therefore, the number of multiplications by submatrices is also relatively small. This explains the reduction in time processing, which is shown in the following results.
# Effect of Waste Engine Oil and Waste Cooking Oil on Performance Improvement of Aged Asphalt
## 4 Results and Discussion
### Rheological Properties  
#### 4.6.1 Phase Angle (5)  
The temperature dependence of the phase angle of rejuvenated asphalts is illustrated in Figures 11 and 12. It revealed that viscous property performance increased with increasing temperature. Also, the phase angle increased with increasing waste oil content. Compared with the 5 h aging time, it had much better improvement than that of 15 h aging time at the same temperature and waste oil content.  
Meanwhile, different elastic recovery performance occurred with WEO and WCO. At the same temperature and oil content, rejuvenated asphalt with WCO had a slightly larger phase angle than that of WEO. Asphalt with a smaller phase angle has better elastic recovery, and WEO had better impact on elastic recovery performance than WCO. With the temperature increasing, all the phase angles increased, but the rejuvenated asphalt with WCO had a much more stable change. Thus maintaining stable elastic recovery performance and reducing the occurrence of damages due to the viscous response  
Figure 10: FTIR spectra results of VA, aged asphalt, and rejuvenated asphalt.  
change. The phase angles increased obviously when the temperature increased from 58 \({}^{\circ}\)C to 70 \({}^{\circ}\)C. There was no significant difference when the oil content was 2%, 3%, and 4% with the temperature rising. This indicated there would be a suitable dosage for better elastic recovery performance of aged asphalt which depended on the aged asphalt types and field construction requirements.
## 1 Introduction
### Objective of This Study  
This study is conducted according to the following procedures:  
1. In order to verify the accuracy of the nonlinear analysis using the STERA_3D (Structural Earthquake Response Analysis 3D) [26] software, four sets of RC frame-wall specimens tested by a shaking table [28] were analyzed, and the nonlinear responses were compared with the test results.
2. Two 5-bays two dimensional (2D) RC frames with 10 stories and 20 stories using three different coverage percentages of RC shear walls were analyzed to examine the damage concentration at the upper part of the structure without walls.
3. A new structural system for RC frames with curtailed walls by installing BRBs in the upper stories was proposed, and the optimum location of BRBs was obtained by employing the Genetic Algorithm. Three parameters were adopted to assess the genetic algorithm's fitness function, namely the number of BRBs, the inter-story drift response, and the damage indices of beam elements.
## 4 Use of the Controller-Driven OppNet Architecture to Manage Congestion
### Directive Generation for Congestion Management  
Based on its congestion prediction (\(c_{t+n}\)), the controller calculates the maximum number of copies of a message allowed to be in the network (\(l\)). For that matter, this proposal considers a proportional controller (P-controller) [29] using an additive increase and multiplicative decrease (AIMD) factor to adjust the manipulated variable (\(l\)). The considered  
Figure 5: Closed-loop control system for congestion control.  
AIMD factor uses \(k_{1}\) as a multiplicative decrease factor and \(k_{2}\) as an additive increase factor. Out of the predicted network congestion, the P-controller calculates the new replication limit \(l^{\prime}\).  
\[l^{\prime}(c_{t+n})=\begin{cases}l&\text{if }c_{t+n}=\text{optimal}\\ l\cdot k_{1}&\text{if }c_{t+n}=\text{CONGESTED}\\ l+k_{2}&\text{if }c_{t+n}=\text{UNDER_USED}\end{cases} \tag{7}\]  
With this control function, if the predicted network's congestion state is _OPTIMAL_, it is not necessary neither to increase nor decrease the replication limit of a message. When the predicted network's congestion state is _CONGESTED_ or _UNDER_USED_, the controller decreases or increases the message's replication limit, respectively.  
The controller encapsulates the calculated \(l^{\prime}\) in a directive: \(\delta_{l}=(H_{l},l^{\prime})\), where \(H_{l}\) is the identifier of the message replication limit setting and \(l^{\prime}\) is the calculated replication limit. Following the flowchart described in Figure 4, when the controller contacts a node, it forwards the former directive along with the calculation of its buffer occupancy.
## 2 Materials and Methods
### Research on Numerical Simulation of Direct Shear Test
#### 2.2.3 Calibration of Meso-Parameters  
In order to determine reasonable stiffness parameters, the numerical simulation results are closest to the laboratory test results. Initial normal stiffness and shear stiffness of 0.001-, 0.01-, and 0.1-times are, respectively, set to simulate the shear behavior of interface changing with stiffness parameters under four normal stresses (0.5 MPa, 1.0 MPa, 1.5 MPa, 2.0 MPa).  
The shear stress-shear strain curves during interfacial shear are similar to those of direct shear tests in laboratory. They can be divided into two stages, strain softening and strain hardening, without changing the stiffness parameters and normal stresses. Under the same normal stresses, the peak shear stress and peak shear strain at the interface show a non-linear upward trend with a decrease in stiffness parameters; at the same stiffness parameter, the peak shear stress and peak shear strain at the interface also show a non-linear upward trend with an increase in normal stress applied to the model. It is shown that the interfacial micro-stiffness parameter will affect the overall strength of the interfacial in the model but will not change the development trend of the interfacial shear stress-shear strain curve. The fitting curves of peak shear stress and normal stress under different stiffness parameters are shown in Figure 5. After adjusting, [PERSON]'s correlation coefficient R\({}^{2}\) = 1.  
Figure 5: Peak stress fitting curves with different stiffness parameters.  
In order to comprehensively compare and analyze the numerical simulation test results under different stiffness parameters with the laboratory test results, the peak shear stress, cohesion, and internal friction angle of the test results are extracted and plotted as a pie-column diagram for comparison, as shown in Figures 6 and 7.  
Combining the above numerical simulation results for analysis, the initial normal stiffness, \(\mathrm{k_{n0}}\), and initial tangential stiffness, \(\mathrm{k_{s0}}\), of the decomposition surface are chosen to be 2.14 \(\times\) 10\({}^{7}\) Pa/m, for which the interfacial cohesion is 0.105 MPa and the internal friction angle is 20.49\({}^{\circ}\). The results of numerical simulation direct shear tests are closest to those of laboratory tests, which can effectively simulate the mechanical characteristics of filling weak surfaces.  
Figure 6: Comparison of peak shear stress: (**a**) Normal stress is 0.5 MPa; (**b**) Normal stress is 1.0 MPa; (**c**) Normal stress is 1.5 MPa; (**d**) Normal stress is 2.0 MPa.  
Figure 7: Cohesion and friction angle: (**a**) Comparison of Cohesion; (**b**) Comparison of internal friction angle.
# Performance and Challenges of Service-Oriented Architecture for Wireless Sensor Networks
## 7 Service-Oriented Architectures Approaches for WSNs
### Open Geospatial Consortium with Sensor Web Enablement (OGC SWE)
An Open Geospatial Consortium (OGC) with SWE that provides a direction for semantic standardization of sensor networks is presented in [72]. The components of SWE are SensorML (Sensor Model Language) and an SOS (Sensor Observation Service) [72]; it can be interoperable for processing data online [72]. The SensorML is XML and used to represent different features of a sensors' system. It provides performance characteristics such as accuracy and the capability to describe the sensor system, process models, and connect sensor networks over the internet. The OGC SWE through SOA was implemented by using two distributed sensing systems.
## 5 Evaluation
### Experimental Methodology
#### 5.2.2 Comparative Methods  
To provide a comprehensive comparison and benchmark our proposed approach, we selected two prominent agent projects within the CTF domain: NYUCTF Agents [28; 39] and HackSynth [27]. These projects represent influential efforts in automated CTF participation. Consider:  
**NYUCTF Agents:** We evaluated NYUCTF Agents by specifically utilizing their _D-CIPHER_ Multiagent System. To ensure compatibility, our datasets were converted into the JSON format acceptable by their system. This allowed for a direct evaluation against a sophisticated multi-agent architecture designed for CTF challenges.  
**HackSynth:** HackSynth implements benchmarks derived from PicoCTF and OverTheWire, and we adapted our datasets to a similar format. It is worth noting that HackSynth's support for file operations was not fully robust in its original implementation. Therefore, we applied minor modifications to its codebase to enable the normal execution of our tests, and these modifications did not alter HackSynth's core logic or performance characteristics.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Source** & & **All** & **Type** & **Count** \\ \hline \multirow{3}{*}{GTW} & EN & 38 & Crypto & 38 \\  & ZH & 76 (55 plaintext, 21 multimodal) & Crypto & 44 \\  & & Others & & \\  & & & (Misc, Reverse, & 32 \\  & & & Pwn, etc.) & \\ \multirow{3}{*}{THUCTF} & \multirow{3}{*}{70} & Crypto & 30 \\  & & & Misc & 40 \\ \hline \hline \end{tabular}
\end{table}
Table 2: An overview of the dataset used for evaluation. GITW refers to puzzles from English and Chinese books of Ghost In The Wires, where the puzzles in the Chinese book include both plain text and multimodal problems. THUCTF is a real CTF competition dataset, of which we chose Crypto and Misc problems as experiments.  
For a fair and consistent comparison across all methods, including our own, we employed GPT-4o as the underlying base model for all agents. Furthermore, all comparative experiments were conducted and scored under the exact same stringent standards and evaluation metrics as our proposed work, ensuring an unbiased assessment of relative performance.
# Using the \(\beta\)/\(\alpha\) Ratio to Enhance Odor-Induced EEG Emotion Recognition
## 3 Results
### EEG Spectral Analysis
#### 3.3.1 Analysis of Topological Maps  
Compared to the RS, inhalation of either SEO or BEO leads to a varying increase in the intersubject average PSD across the \(\alpha\)1 and \(\alpha\)2 frequency bands in all ROIs. Notably, inhalation of SEO reduces the intersubject average PSD of the \(\beta/\alpha\) ratio across all ROIs, whereas inhalation of BEO increases it (Figure 3A-G).  
For the SEO group (Figure 3D,F), compared to the RS group, the PSD of the \(\alpha\)1 rhythm significantly increased, primarily in the F-ROI, the right T-ROI (\"T8\"), the right and central C-ROI, and several electrodes in the P-ROI (\"P7\", \"Pz\", \"P8\"), as well as in the left O-ROI (\"O1\"). The PSD of the \(\alpha\)2 rhythm significantly increased mainly in the F-ROI, a single electrode in the central C-ROI (\"Cz\"), the right and central P-ROI (\"P8\", \"Poz\"), and the O-ROI. The PSD of the \(\beta/\alpha\) ratio decreased significantly across all ROIs, except for \"P7\" (specific \(p\)-values and \(t\)-values are provided in Supplementary Table S2).  
For the BEO group (Figure 3E,G), compared to the RS group, the PSD of the \(\alpha\)1 rhythm significantly increased, primarily in several electrodes in the anterior F-ROI (\"Fp1\", \"Fpz\", \"F7\", \"F3\", \"FC1\"), the left T-ROI (\"T7\"), a few electrodes in the central C-ROI (\"CP1\", \"CP2\"), and a few electrodes in the central P-ROI (\"Pz\"). The PSD of the \(\alpha\)2 rhythm significantly increased mainly in the F-ROI, the right and central P-ROI (\"Pz\", \"P4\", \"P8\"), and the left O-ROI (\"O1\"). The PSD of the \(\beta/\alpha\) ratio decreased significantly only at the \"T8\" electrode. Specific \(p\)-values and \(t\)-values are provided in Supplementary Table S3. The exact electrode locations are shown in the \"Channel Location\" module of Figure 1B.
# Robust Evolutionary-Game-Based Routing for Wireless Multimedia Sensor Networks
## 4 EGR Protocol
### CH Election Process Using EG Theory
Equations (12) and (13) present the utility of nodes in _NL_ to become CHs by choosing \(T_{CH}\) strategy and the utility of nodes in _NL_ set to become CMs by choosing the \(T_{NCH}\) strategy, respectively:  
\[U_{NL(CH)}=\rho\beta+(1-\rho)(\beta+\delta)=\beta+\delta-\rho\delta. \tag{12}\]  
\[U_{NL(NCH)}=\rho(\beta+2\delta)+(1-\rho)(-\delta)=\beta\rho+3\rho\delta-\delta. \tag{13}\]  
Therefore, the total revenue for nodes in set _NL_ is expressed as:  
\[\overline{U_{NL}}=\eta(\beta+\delta-\rho\delta)+(1-\eta)(\beta\rho+3\rho\delta -\delta). \tag{14}\]  
The replicator dynamic equation for nodes in _NL_ set is formulated as:  
\[S=\{T_{CH},T_{NCH}\}, \tag{6}\]0  
Figure 9 shows the utility matrix for the formulated game. It shows the amount of reward a node in _NH_ and _NL_ receives by selecting either \(T_{CH}\) or \(T_{NCH}\) strategy. When the nodes get divided into clusters, it is necessary to divide them into _NH_ and _NL_. All the nodes in different clusters send an initial message to the sink node. This message contains two information. One is the node ID, and another is the residual energy level of the node, \(N_{RE}\). The sink node segregates the nodes based on the \(N_{RE}\) of the node. A median value is selected as \(E_{T}\), which represents the energy threshold. The sink node broadcasts this value to the nodes in different clusters. All the nodes in the clusters divide themselves into either _NL_ or _NH_ based on the threshold value. After that, utilities are calculated using Equations (10) and (14) presented earlier. The nodes either decide to be CH or CM by choosing the \(T_{CH}\) or \(T_{NCH}\) strategies based on the calculated utilities.
## 3 Proposed C-HARQ Protocol Design
### Network Scenario  
As this work primarily focuses on the ARQ aspect, not on medium access control (MAC) issues, we have assumed that all the media access issues have been resolved so that the underwater sensor network under consideration is only a single-hop case between a source and a destination with some neighbour nodes in the surroundings. All the nodes remain static at their respective positions. The network scenario is depicted in Figure 1.  
Figure 1: Network Configuration.  
The overlapped area between two circles having a same radius of the source-to-destination distance, each centered at source and destination is called the cooperative region. Any neighbour node within this region can overhear any transmission from the source and destination and, hence, has the potential to provide cooperation; thus these nodes are hereafter referred to as cooperative nodes (_CNodes_).
## 4 Experiments and Numerical Results
### Modeling Capability for Complex Relation  
To better demonstrate the model's ability to handle complex relations, we conducted visual experiments to separately evaluate the modeling speed and effectiveness of the ablation model for four types of complex relations. Based on Figure 10, the four graphs in each column correspond to the modeling process of the ablation model for four distinct complex relation types on the same datasets. Meanwhile, the four graphs in each row provide a comparison of modeling for the same complex relation type across the four datasets.  
From the results, it is evident that there is no significant difference in the modeling speed and effectiveness of each ablation model for the one-to-one and m-to-n relations, indicating that each ablation model does not have a specialized ability to handle these two complex relations. However, there are significant differences in the modeling processes of one-to-n and m-to-one relations among different ablation models. Specifically, \"RiQ-KGC\" and \"RiQ-KGC w/o-Q\" usually perform better, whereas \"RiQ-KGC w/o-234N\" and \"RiQ-KGC w/o-R\" usually perform worse, and \"RiQ-KGC w/o-all-N\" is the worst in all scenarios. This demonstrates the targeted contribution ability of \"RiQ-KGC-w/o-234N\" and \"RiQ-KGC-w/o-R\" to complex relationships, which is consistent with the theory in Section 3.2.  
Additionally, the one-hop neighbor was found to have a strong overall contribution ability to the model, consistent with the results of the ablation experiment.  
Figure 9: Ablation study on each component.