esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions and Future Work
Involving real users, particularly those with disabilities, in the development and testing process is crucial for validating the accessibility features of HSALA. Engaging users through usability studies and focus groups will provide valuable qualitative feedback on usability and user experience. This user-centered approach ensures that the application meets the diverse needs of its intended audience and adheres to the principles of inclusive design. We also plan to upgrade and broaden the incorporation of semantic web technique [65], such as the Web Ontology Language (OWL) and the Resource Description Framework (RDF), as well as integrate artificial intelligence (AI)-driven personalization and ensure the quality of experience (QoE) [66]. The combination of semantic web technologies, AI, and a focus on QoE can create web interfaces that are not only secure and accessible but also provide a smooth, intelligent, and enjoyable user experience. Features such as fast loading times, intuitive and responsive interfaces, and custom interactions can significantly enhance user satisfaction. Incorporating AI-driven personalization enables interfaces to adapt dynamically to user preferences, thereby enhancing both accessibility and overall user experience. Developing machine learning models capable of predicting user requirements, enhancing security by detecting anomalous behavior, and adjusting content presentation can further strengthen the model's capabilities. In conclusion, while HSALA represents a significant advancement in integrating accessibility and cybersecurity within web application development, practical implementation and empirical validation are necessary to fully realize its potential. By addressing the identified limitations through the proposed future work, HSALA can evolve into a robust framework that meets contemporary demands for inclusive and secure digital platforms. The ongoing refinement of the model, engagement with real users, and embracing technological innovations will contribute to creating web applications that are resilient, user-friendly, and equitable for all users. Future research and collaboration will not only validate HSALA's effectiveness but also drive progress in developing digital ecosystems that prioritize both security and accessibility. This holistic approach is essential for fostering inclusivity and trust in an increasingly digital society. Conceptualization, G.A.S. and L.S. ; methodology, L.S. and N.E.-D.; resources, L.S. ; writing--original draft preparation, G.A.S., L.S. and N.E.-D.; writing--review and editing, G.A.S., L.S. and N.E.-D.; supervision, G.A.S. and L.S. ; funding acquisition, L.S. and N.E.-D. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by the Transilvania University of Brasov. **Data Availability Statement:** No new data were created or analyzed in this study. Data sharing is not applicable to this article. **Conflicts of Interest:** The authors declare no conflicts of interest.
## 5 Engineering Case Analysis  
### Microsiastic-Signal-Waveform Analysis  
The microseismic-monitoring waveform collected from a coal mine was used for experimental analysis to verify the effectiveness of the method proposed in this article. The mine installed the Canadian ESG microseismic-monitoring system with a sampling rate of 5000 Hz. There are two kinds of microseismic signals that are easy to be confused on site: effective coal-and-rock-mass-fracture events and artificial blasting-vibration events such as construction blasting.  
Figure 4: EMD decomposition result of the synthesized signal and its frequency spectrum. (**a**) EMD decomposition-component waveform; (**b**) EMD decomposition-component frequency spectrum.  
Figure 5: LMD decomposition result of the synthesized signal and its frequency spectrum. (**a**) LMD decomposition-component waveform; (**b**) LMD decomposition-component frequency spectrum.  
As shown in Figure 6, the system collected two typical coal-and-rock-mass-rupture signals and blasting-vibration signals and their frequency spectra. The obvious difference between the two waveforms is found by comparison. First, the dominant frequency of the coal-rock-fracture-signal waveform is low (below 100 Hz), accompanied by a long duration and slow attenuation rate. Second, the dominant frequency of the blasting-vibration-signal waveform is high (above 100 Hz), accompanied by large amplitude, rapid attenuation rate and short coda. Although these differences can be manually distinguished, it is difficult to automatically identify signals based on the differences. Therefore, the differences between the two signals must be quantified by feature extraction, so as to achieve the purpose of effective automatic recognition.
# Bearing Fault Diagnosis Using a Vector-Based Convolutional Fuzzy Neural Network
## 4 Experimental Results  
### Data Acquisition  
The bearing data provided by Case Western Reserve University were used in experiments [27]. According to the specifications of the bearing data, the faults were intentionally introduced to the motor bearings via an electrical discharge machining procedure. The faults were seeded at the inner raceway, outer raceway, or rolling element (i.e., ball). The actual test conditions of the motor and the bearing fault status were documented for each experiment. The apparatus is depicted in Figure 4, and the 12-kHz vibration signals of normal bearing, fault in the inner race, fault in the ball, and fault in the outer race are illustrated in Figure 5a-d, respectively. The various signal conditions were applied as input data in different deep learning models (ANN, FNN, CNN, CFNN, and vector-CFNN) and the output was the fault classification results. Finally, the accuracy and the parameters used for each model were compared with the vector-CFNN model.  
Figure 4: Apparatus used in Case Western Reserve University.  
Figure 3: Schematic of (**a**) conventional convolution and (**b**) vector-based convolution.
# Analyzing the Long-Term Phenological Trends of Salt Marsh Ecosystem across Coastal LOUISIANA
After smoothing the raw phenology, most of the start and end of the season are defined using different methods, such as derivative analysis of the smoothed time series, matching phenology dates derived from in situ observations such as PhenoCam, or using known start of season/end of season (SOS/EOS) thresholds on the smoothed time series (particularly for smoothed Normalized Difference Vegetation Index (NDVI) phenology) [32; 55]. Phenology has been studied in detail in a variety of different terrestrial environments, such as tropical rainforests, deciduous habitats, and grasslands; it has also been studied in aquatic ecosystems, such as that of phytoplankton [56; 57; 58; 59]. However, very few studies have emphasized the need to analyze the long-term phenology of salt marsh habitats [12; 60; 61; 62]. In this paper, we derived the long-term trends in the phenology and seasonality parameters of the salt marshes in Louisiana (LA) using existing MODIS-derived biophysical parameters, and analyzed the broader implications of the observed trends. We first describe and test methods for deriving noise-free phenology and seasonality parameters for salt marshes in LA. After that, we validate our methods by analyzing the annual variations. Finally, we evaluate the long-term change trends in the seasonality parameters using statistical trend analysis techniques.
# Design and Analysis of Internal-External Composite Meshing Hy-Vo Chain
## 3 Internal Meshing Profile
### Continuous Meshing Condition  
If we rigidify the external meshing Hy-Vo chain into a rigid body, the chain can be regarded as a rack, and the sprocket can be regarded as a short addendum gear. At this time, the meshing of the rigid Hy-Vo chain and the involute sprocket will be the same as the gear meshing, and the meshing is continuous, as Figure 7 shows. The distance between the pitch line of the rigid Hy-Vo chain and the rotation center of the sprocket is \(s_{\rm j}\). According to the gear meshing theory, if the sprocket rotates angle \(\beta\) anticlockwise from its initial position, the rigid Hy-Vo chain will horizontally move \(\beta r_{\rm j}\) to the left. It should be noted that \(r_{\rm j}\) is the radius of the sprocket pitch circle when the Hy-Vo chain can be regarded as a rack. In this case, based on Equation (3), the value of \(r_{\rm j}\) is as follows:  
\[r_{\rm j}=\frac{pz}{2\pi}, \tag{12}\]  
Figure 7: Meshing of the rigid Hy-Vo chain and the involute sprocket.  
Similarly, we suppose that the right-side external meshing profile of chain plate 2 is T\({}_{1}\)-T\({}_{1}\), the relative sprocket tooth involute profile is \(\Gamma_{1}\), and the relative rotation angle of involute \(\Gamma_{1}\) on the base circle of the sprocket from the \(y\)-axis to its practical position is \(\theta_{1}\). According to the relations in Figure 7, the function of line T\({}_{1}\)-T\({}_{1}\) in \(S(xOy)\) is as follows:  
\[y=x\cot\alpha+\beta r_{\rm j}\cot\alpha-f_{1}\csc\alpha-\frac{p}{2}\cot\alpha+ s_{\rm j}, \tag{13}\]  
The tangent line of involute \(\Gamma_{1}\) is as follows:  
\[y=x\cot(\Phi+\theta_{1}-\beta)+\frac{r_{\rm b}\Phi}{\sin(\Phi+\theta_{1}-\beta )}, \tag{14}\]  
Because the rigid Hy-Vo chain can properly mesh with the sprocket, involute \(\Gamma_{1}\) coincides with line T\({}_{1}\)-T\({}_{1}\):  
\[\left\{\begin{aligned} \cot\alpha=\cot(\Phi+\theta_{1}-\beta) \\ \beta r_{\rm j}\cot\alpha-f_{1}\csc\alpha-\frac{p}{2}\cot\alpha+s_{\rm j}= \frac{r_{\rm b}\Phi}{\sin(\Phi+\theta_{1}-\beta)}\end{aligned}\right., \tag{15}\]  
If \(p_{1}=p\) and \(\alpha_{1}=\alpha\), combined with Equation (12), the solution of Equation (15) is as follows:  
\[\theta_{1}=\alpha+\frac{f_{1}}{r_{\rm b}}+\frac{\pi}{z}-\frac{s_{\rm j}\sin \alpha}{r_{\rm b}}, \tag{16}\]  
Based on Equations (11) and (16), when \(\theta=\theta_{1}\), we obtain the following:  
\[s_{\rm j}=s_{\rm jmin}, \tag{17}\]  
Equation (17) means that if the distance between the pitch line of the rigid chain and the sprocket rotation center \(s_{\rm j}\) is made to always equal to \(s_{\rm jmin}\), the rigid chain will mesh with the sprocket continuously.
# Recent Web Platforms for Multi-Omics Integration Unlocking Biological Complexity
## 2 Methods
### Platforms for Disease-Specific Research and Drug Discovery
#### 2.1.2 PALMO: A Tool for Longitudinal Multi-Omics Analysis Across Diverse Diseases  
In contrast to AlzGPS's Alzheimer's-focused network analysis, PALMO offers a more flexible approach by concentrating on longitudinal studies across multiple diseases. PALMO integrates transcriptomics and proteomics data, focusing on longitudinal studies. Unlike AlzGPS, it does not provide a web-based interface, but is available as an open-source software package [15]. PALMO enables longitudinal tracking of gene expression and protein abundance over time, helping researchers understand dynamic changes in diseases such as lymphoma and diabetes. Its Stability Pattern Evaluation (SPECT) and Outlier Detection Analysis (ODA) help detect abnormal molecular trends in disease progression. However, the absence of a user-friendly interface may limit its accessibility to non-experts, and its reliance on high-quality multi-time-point datasets poses challenges for smaller labs.  
The platform tracks changes in gene expression and protein levels over multiple time points, making it useful for studies on diseases such as COVID-19, diabetes, and lymphoma. It uses variance decomposition and time-course analysis to analyze longitudinal datasets, identifying stable patterns or outliers in gene expression and protein abundance over time. PALMO's ability to track longitudinal changes makes it ideal for understanding dynamic biological processes and disease progression. Its open-source nature allows for flexibility and customization in a research setting. The absence of a web interface means that users must be comfortable with running the platform locally using R or Python, which may limit its accessibility to non-expert users. Longitudinal studies require multiple time-point data, which can be challenging to collect and analyze, especially for smaller labs.
## 4 Performance Test of SOADS  
Two experiments were conducted to evaluate the performance of the SOADS proposed in this study. The first is an experiment to determine the pattern derivation speed of the Frequent Subsequence Miner. The pattern derivation speed was determined with and without using the _RELATIONAL DEPENDENCY TABLE_ for each event log size. The second is a stress experiment of the Anomaly Evaluator. The anomaly scoring speed was measured by adjusting the number of sequences in the event stream. The event log used in the experiment is simulation data for a battery manufacturing process with a high degree of freedom of process logistics.
## 2 Materials and Experimental Procedure
### Materials
#### 2.1.4 Alkali Activators  
To start the polymerization process, a chemical activator is needed. Geopolymers are often aluminosilicate binding materials that are activated by silicates or alkaline hydroxides in settings with high pH [48].  
1. The polymerization procedure in this research used sodium hydroxide (NaOH) and sodium silicate (Na\({}_{2}\)SiO\({}_{3}\)) solutions as alkali activators. Local sources were contacted in order to obtain these substances. Purified water was used to dissolve sodium hydroxide (NaOH) until the molarity reached 10.
2. Sodium Hydroxide (NaOH)
3. A part of the heat is usually lost as the NaOH solution is being prepared, and another part is used up when the solution evaporates. Since NaOH is the most accessible and inexpensive alkali hydroxide, it is the preferred option for use as a hydroxide activator in geopolymer synthesis.
4. Sodium Silicate (Na\({}_{2}\)SiO\({}_{2}\))  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Oxides** & **SiO\({}_{2}\)** & **Al2O\({}_{3}\)** & **Fe2O\({}_{3}\)** & **CaO** & **SiO\({}_{2}\)/Al2O\({}_{3}\)** & **MgO** & **K2O** & **SO\({}_{3}\)** & **MnO** & **TiO\({}_{2}\)** \\ \hline FA & 28.05 & 13.93 & 6.55 & 45.09 & 2.01 & - & 2.12 & 3.45 & - & 0.78 \\ BFS & 35.00 & 16.00 & 1.40 & 37.50 & 2.18 & 5.25 & - & - & 1.75 & - \\ LP & 4.08 & 1.62 & 0.58 & 43.00 & 2.51 & 9.34 & 0.19 & - & - & 0.11 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The nominal chemical composition of BFS, FA, and LP.  
Among the alkalis utilized in geopolymer formation, the density of Na\({}_{2}\)SiO\({}_{3}\) ranges from 1410 to 1435 g/cm\({}^{3}\). The Na\({}_{2}\)SiO\({}_{3}\) solution, one of the alkali activators employed in this study, is an alkali with a solid content of 40%, containing Na\({}_{2}\)O and SiO\({}_{2}\). The alkali solutions, formed by mixing Na\({}_{2}\)SiO\({}_{3}\) and NaOH solutions, were prepared several hours in advance before being used in the study.
## 4 Results and Discussion
### Three-Phase Evaluation  
In this section, we present a comprehensive evaluation of the EduDCM framework through three distinct experimental phases. These phases are designed to assess the effectiveness of EduDCM-generated datasets in various contexts, including individual dataset performance, the combination of EduTalk and TalkMoves datasets, and a comparison with other data generation methods. Each phase aims to provide valuable insights into the scalability and generalizability of the EduDCM framework.
# Direct Solar Thermal Water-Splitting Using Iron and Iron Oxides at High Temperatures: A Review
## 2 Theoretical Background
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Chemical Species**} & **Gibbs Free Energy** & **Enthalpy** & **Entropy** & **Temp. K** & **Refs.** \\  & **\(\Delta\)G (kJ/mol)** & **AH (kJ/mol)** & **S (kJ/mol)** & **(1 atm)** & **Refs.** \\ \hline H\({}_{2}\)O\({}_{\text{(J)}}\) & \(-237.1\) & \(-285.8\) & \(70.0\) & \(298\) & [31; 32] \\ \hline \multirow{2}{*}{H\({}_{2}\)O\({}_{\text{(g)}}\)} & \(-228.6\) & \(-241.8\) & \(188.8\) & \(298\) & [31; 32] \\ \cline{2-6}  & \(-214.1\) & \(-244.7\) & \(213.8\) & \(600\) & \\ \cline{2-6}  & \(-198.2\) & \(-247.1\) & \(228.6\) & \(900\) & [32] \\ \hline \multirow{2}{*}{H\({}_{2}\)(g)} & \(0\) & \(0\) & \(130.6\) & \(298\) & [31; 32] \\ \cline{2-6}  & \(0\) & \(0\) & \(151.0\) & \(600\) & \\ \cline{2-6}  & \(0\) & \(0\) & \(163.1\) & \(900\) & \\ \hline OH\({}^{-}\) (aqueous ion) & \(-157.3\) & \(-230.0\) & \(-10.7\) & \(298\) & [31] \\ \hline \multirow{2}{*}{OH\({}^{-}\)(g)} & \(34.7\) & \(38.9\) & \(183.7\) & \(298\) & \\ \cline{2-6}  & \(29.5\) & \(38.9\) & \(204.4\) & \(600\) & [32] \\ \cline{2-6}  & \(24.9\) & \(38.4\) & \(216.5\) & \(900\) & \\ \hline Fe\({}^{0}\) & \(0\) & \(0\) & \(27.09\) & \(298\) & [31] \\ \hline Fe\({}^{2+}\) & \(-90.0\) & \(-91.1\) & \(-107.1\) & \(298\) & [31] \\ \hline Fe\({}^{3+}\) & \(-16.7\) & \(-49.9\) & \(-280.0\) & \(298\) & [31] \\ \hline \multirow{2}{*}{Fe\({}_{0.947}\)O} & \(-244.9\) & \(-266.3\) & \(56.6\) & \(298\) & [31] \\ \cline{2-6}  & \(-245.1\) & \(-266.3\) & \(57.5\) & \(298\) & \\ \cline{2-6}  & \(-224.8\) & \(-263.7\) & \(93.0\) & \(600\) & [32] \\ \cline{2-6}  & \(-205.8\) & \(-262.7\) & \(115.1\) & \(900\) & \\ \hline \multirow{2}{*}{FeO} & \(-251.4\) & \(-272.0\) & \(60.6\) & \(298\) & [31; 32] \\ \cline{2-6}  & \(-231.7\) & \(-269.4\) & \(97.3\) & \(600\) & \\ \cline{2-6}  & \(-213.1\) & \(-268.4\) & \(120.2\) & \(900\) & [32] \\ \hline \multirow{2}{*}{Fe\({}_{2}\)O\({}_{3}\)} & \(-774.4\) & \(-826.2\) & \(87.4\) & \(298\) & [31; 32] \\ \cline{2-6}  & \(-742.3\) & \(-824.2\) & \(87.4\) & \(298\) & \\ \cline{2-6}  & \(-661.4\) & \(-817.6\) & \(173.3\) & \(600\) & [32] \\ \cline{2-6}  & \(-585.3\) & \(-808.4\) & \(235.4\) & \(900\) & \\ \hline \multirow{2}{*}{Fe\({}_{3}\)O\({}_{4}\)} & \(-731.4\) & \(-811.6\) & \(93.0\) & \(298\) & [33] \\ \cline{2-6}  & \(-1012.7\) & \(-1115.7\) & \(146.1\) & \(298\) & [31] \\ \cline{2-6}  & \(-1015.2\) & \(-1118.4\) & \(146.1\) & \(298\) & \\ \cline{2-6}  & \(-914.4\) & \(-1107.4\) & \(272.1\) & \(600\) & [32] \\ \cline{2-6}  & \(-821.8\) & \(-1088.6\) & \(368.3\) & \(900\) & \\ \hline FeO(OH) & \(-491.8\) & \(-562.6\) & \(60.4\) & \(298\) & [31] \\ \hline \multirow{2}{*}{Fe(OH)\({}_{2}\)} & \(-486.9\) & \(-568.9\) & \(87.9\) & \(298\) & \\ \cline{2-6}  & \(-405.7\) & \(-564.1\) & \(160.4\) & \(600\) & [32] \\ \cline{2-6}  & \(-327.6\) & \(-558.9\) & \(207.7\) & \(900\) & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Thermodynamic properties of water, oxides, and hydroxides.
## 5 Simulation-Based Performance Evaluation
### Emergency Braking Scenario Analysis  
In order to further assess the controller's robustness in safety-critical situations, a sudden emergency braking scenario was introduced, simulating a lead vehicle deceleration  
Figure 9: Control performance under representative traffic scenarios: (**a**) cut-in; (**b**) stop-and-go; (**c**) lead vehicle departure; (**d**) cruise following.  
to a complete stop at 20 s. This test isolates a high-risk event to examine not only tracking accuracy but also responsiveness and safety margins under time-sensitive conditions.  
As depicted in Figure 9(a), both controllers manage to follow the deceleration profile; however, DWMPC exhibits a noticeably faster reaction time, initiating braking earlier and reducing velocity lag. This enhanced responsiveness is reflected in the spacing trajectory shown in Figure 9(b), where fixed-MPC briefly violates the safe following distance with a minimum headway of \(-0.34\) m, whereas DWMPC consistently maintains a positive gap, effectively avoiding collision risk.  
The underlying mechanism is revealed in Figure 9(c), where the dynamic weight coefficient \(C_{1}\) spikes sharply upon detecting the threat, reaching values above 600. This surge temporarily increases the weight on velocity tracking, thereby intensifying deceleration behavior. As the critical phase passes, \(C_{1}\) gradually returns to nominal levels, preventing aggressive control oscillations.  
Figure 9(d) further illustrates the control mode transition, where DWMPC swiftly shifts from economic mode (Mode 2) to more urgent states (Modes 3 and 4), thereby relaxing acceleration constraints and allowing decisive braking. After the threat is mitigated, the controller seamlessly reverts to less aggressive operation, preserving comfort and energy efficiency.  
Overall, this scenario highlights a key advantage of the proposed strategy: DWMPC not only ensures rapid and stable emergency response through its adaptive weighting and mode-switching mechanisms but also preserves post-event drivability, demonstrating an intelligent and risk-aware control architecture suitable for real-world safety challenges.  
Figure 10: Emergency braking response under critical conditions: (**a**) vehicle speed tracking; (**b**) relative inter-vehicle distance; (**c**) velocity tracking weight coefficient \(C_{1}\); (**d**) mode transition signal.
# Quantum PSO-Based Optimization of Secured IRS-Assisted Wireless-Powered IoT Networks
## 2 System Model and Problem Formulation
### Energy Transmission  
During WET, an arbitrary energy signal, denoted \(s_{0}\), is transmitted by the H-AP to the users and the jammer. The energy carried by \(s_{0}\) is an independent and identically distributed random signal with zero mean and unit variance. Consequently, the received signal at the \(k_{th}\) user and the jammer, respectively, using channel links (shown in Table 3) can be expressed as follows:  
\[y_{k}=\sqrt{P_{t}}(\mathbf{h}_{iu_{k}}^{H}\mathbf{\Phi_{0}g_{ai}})s_{0}+z_{k}, \quad\forall k \tag{1}\]  
\[y_{j}=\sqrt{P_{t}}(\mathbf{h}_{ij}^{H}\mathbf{\Phi_{0}g_{ai}})s_{0}+z_{j} \tag{2}\]  
where \(E[|s_{0}|^{2}]=1\) indicates the expected power and \(\mathbf{\Phi_{0}}=\text{diag}(\alpha_{1}e^{i\theta_{1}},\alpha_{2}e^{i\theta_ {2}},\dots,\alpha_{N}e^{i\theta_{N}})\) represents the diagonal matrix for the downlink phase where the diagonal elements correspond to the phase-shift matrix of the IRS. Here, \(\theta_{n}\in[0,2\pi]\) and \(\alpha_{n}\in[0,1]\) denote the phase-shift and amplitude reflection coefficients, respectively, for every element of the IRS. In order to enhance reflection of the signal by the IRS, we set the amplitude reflection  
Figure 1: The secure IRS-assisted WPIN providing WET on downlink to all users and WIT on uplink while an EH jammer interferes with an eavesdropper.  
Figure 2: Time frame divisions in the IRS-aided secured WPIN.  
coefficient \(\alpha_{n}=1\forall n\), and \(z_{k}\) represents the noise at the \(k_{th}\) user. However, in practical scenarios, for energy receivers, typically the receiver's noise is negligible. Therefore, \(z_{k}\) is ignored in subsequent problem formulations.  
The energy harvested by the \(k_{th}\) user and the jammer on downlink can be expressed with \(E_{k}\) and \(E_{j}\), respectively, as follows [30]:  
\[E_{k}=\xi_{k}\Big{|}(\mathbf{h}_{iu_{k}}^{H}\mathbf{\Phi}_{0}\mathbf{g}_{ai})\Big{|} ^{2}t_{0}P_{t}\quad\forall k \tag{3}\]  
\[E_{j}=\xi_{j}\Big{|}(\mathbf{h}_{ij}^{H}\mathbf{\Phi}_{0}\mathbf{g}_{ai})\Big{|}^{ 2}t_{0}P_{t} \tag{4}\]  
where \(0<\xi_{k},\xi_{j}<1,k=1,2,3,\dots,K\) are the energy harvesting efficiencies at each user device and the jammer, respectively. The transmit power available at the H-AP is denoted by \(P_{t}\).