esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Thermal Analysis  
The thermal analysis (TGA-DTA) of synthesized geopolymer specimens, with or without PVA fiber, is given in Figure 11. It can be seen that both specimens showed analogous characteristics up to 1000 \({}^{\circ}\)C. A substantial decrease in mass is observed from ambient temperature to 400 \({}^{\circ}\)C in both specimens. In the DTA curves, endothermic peaks at 57.53 \({}^{\circ}\)C, 178.15 \({}^{\circ}\)C, 59.6 \({}^{\circ}\)C, and 178.46 \({}^{\circ}\)C were found for the GP0 and GP5 specimens, respectively. This indicated the elimination of free and interstitial water molecules from both geopolymer specimens and a break up of the side chain of the PVA polymer, particularly in GP5. Between 400 \({}^{\circ}\)C and 600 \({}^{\circ}\)C, an extra weight loss was observed, which is attributed to the release of water by polymerization of Si-OH and Al-OH groups and a decomposition of the main chain of the PVA polymer [47]. When the temperature was further increased from 600 \({}^{\circ}\)C to 1000 \({}^{\circ}\)C, the specimens GP5 and GP0 exhibited a loss in weight by 1.287% and 1.299%, respectively, which corresponds to the disintegration of carbonate species in geopolymers and the burning of leftovers of coal in fly ash (Table 1) [48]. The highest weight loss was found at 1000 \({}^{\circ}\)C for GP0, as compared with GP5, which illustrated that 5% fiber-reinforced geopolymers possessed more thermal stability than the reference sample.  
Figure 11: Thermal analysis (TGA-DTA): (**a**) Without PVA fiber; (**b**) with PVA fiber.
## 5 Conclusions  
Through the introduction of the engineering background, a description of the processes of 2D and 3D numerical analyses, the verification of calculations, and a comparison with monitoring data, the following conclusions can be drawn:  
1. This study adopted a 3D finite element analysis program developed by a laboratory, and its contents included a pre-processing program, central calculation program, and post-processing program.
2. The results of a comparison of the 3D finite element analysis and monitoring values showed that the method of constructing retaining piles in this study was able to overcome the traffic safety impact of the adjacent railway tracks caused by the excavation of the gravel layer, and it is, indeed, a construction method with a high level of safety.
3. The results of the comparison between the 3D finite element analysis and monitoring values, such as the values from an inclinometer (SID) and railway track subsidence points (SRs), were similar and far from the warning value.
4. The results of this study show the feasibility and convenience of 3D numerical analysis. It is suggested that in the design stage, the use of a 3D simulation analysis is more in line with the actual construction conditions.  
According to the results of this study, after pumping water, the gravel layer had the characteristics of good self-support and high shear strength. In general, on construction sites in the Taichung area, ground-retaining facilities that are commonly used in deep  
Figure 15: Comparison of the results of the 3D FEM analysis with the inclinometer monitoring data (SID-002) in the retaining piles for the total displacement of the excavation in stages 1 and 2: (**a**) along the CD direction; (**b**) along the EF direction.  
excavations undergo a construction method that uses the mechanical excavation of retaining piles, which should be suitable for this gravel layer. In addition, this construction method for retaining piles has the advantages of low noise, low dust pollution, less vibration during construction, etc., and it is currently the most important construction method for retaining structures for excavations in urban areas. The key to the success of this project lies in the use of mechanical excavation and the use of vertical walls, retaining piles, and tie-beams as the main supporting members when excavating the gravel layer adjacent to the railway track. The results of this study will be provided as a future reference for related projects.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Photocatalytic Activity Assessment
erosion is almost negligible, the degradation is related to the supported photocatalyst, not the TiO\({}_{2}\) erosion, as with the 10% \(w/w\) load. Still, in Figure 4, comparing the degradation curves between the photocatalysts (3.6%TiO\({}_{2}\)/Leca and 5%TiO\({}_{2}\)/Leca), it is noted that 71 to 85% of SMX were degraded over 6 h, and the 5%TiO\({}_{2}\)/Leca presents better efficiency during the degradation process. It was observed that the amount of supported TiO\({}_{2}\) load and irradiation time influence the performance of the photocatalytic activity and the amount of SMX potentially degraded. Therefore, according to [PERSON] et al. [42], TiO\({}_{2}\) nanoparticles coated on fixed substrates (glass and iron beads) lose many active sites from the surface binding site between the catalyst and the substrate, thus contributing to the reduction of the degradation rate. Similar conclusions were obtained by [PERSON] et al. [25] with removal rates of 85% ammonia for 5 h in a fluidized system, with 30 g of Leca coated with TiO\({}_{2}\) at pH 11. Other authors, [PERSON] et al. [43], have found that for effective ammonia removal, the dosage of the hybrid catalyst (TiO\({}_{2}\)-ZnO/Leca) was varied from 15 to 35 g/L with pH 11 solution and 180 min of UV irradiation. Furthermore, the low efficiency of immobilized ceramic material compared to the TiO\({}_{2}\) powder can be a suitable alternative to powder since it can be reused without significant TiO\({}_{2}\) loss from the Leca surface. Therefore, it can be concluded that the photocatalyst prepared by the impregnation method represents a good alternative to solve the limitations of using powder catalysts. However, there is a need to optimize photocatalytic processes to improve their suitability for wastewater treatment and reuse them without the need for added chemicals.
Some previous studies in the literature deal with the characterization of apple, orange, pear, peach, apricot, blueberry, cranberry, plum, lemon, and cherry juices by means of volatile compounds, polyphenols, spectra profiling, mineral and trace element analysis, and sensory metrics determination, using solid phase micro-extraction coupled to gas chromatography mass spectrometry (SPME-GC/MS), high performance liquid chromatography (HPLC), Fourier transform infrared spectroscopy (FTIR), nuclear magnetic resonance (1H-NMR), inductively coupled plasma atomic (or optical) emission spectrometry (ICP-AES or ICP-OES), inductively coupled plasma mass spectrometry (ICP-MS), instrumental neutronic activation analysis (INAA) and the electronic tongue [8; 9; 10; 11; 12; 13; 14]. As a matter of fact, application of innovative statistical approaches on the aforementioned chemical or sensory markers, namely principal component analysis (PCA), linear discriminant analysis (LDA), soft independent modeling of class analogies (SIMCA), hierarchical cluster analysis (HCA), k-nearest neighbor analysis (KNN), and partial least square-discriminant analysis (PLS-DA), has led to the development of accurate models for fruit juice authentication [7]. However, data involving the mineral content and volatile profile of prickly pear juice prepared from Greek wild-grown prickly pear fruits has not been previously reported. In addition, correlation of the mineral content of soil samples with those of fruit juice samples has been scarcely reported. Considering the above, the objectives of the present study were to: (i) investigate the mineral content of prickly pear juice and correlate it with the mineral content of soil, where fruits of the naturally grown wild cultivars were collected, (ii) determine the volatile compounds that are responsible for prickly pear juice aroma, and (iii) check whether the differences in mineral or volatile compound content may be used for prickly pear juice differentiation according to geographical origin using a supervised chemometric tool, such as linear discriminant analysis. Practical applications of the present study may be focused on two basic axes: (i) exploitation of prickly pear juice and (ii) creation of a data base of mineral and volatile compound content of prickly pear juice prepared from wild fruit cultivars, as these are the intermediates for the cultivation of hybrid cultivars often termed as \"less wild ones\".
## 3 Theoretical Background of the Predictive Torque Control
### Predictive Torque Control Based on PWM
To keep a constant superposition torque, it is necessary to reduce the outgoing phase torque gradually and build up the incoming phase torque simultaneously. However, discrete commutation usually leads to the problem of torque ripple during commutation, which makes the reasonable allocation of phase torque a challenge.
###### Abstract  
Over the past 20 years, the use of the global positioning system (GPS) in football has become widespread. This technology has facilitated the tracking of external load both during training sessions and matches. Creating an external load profile for each playing position within specific formations can assist the coaching staff in shaping the training load of the microcycle according to the demands of each position. The purpose of this study was to create the running performance profile for high-level young football players based on their playing position in the 1-4-2-3-1 formation and to investigate potential differences between positions. Additionally, the study aimed to compare the players' running performance across the two halves of the match. The study involved 22 Under-19 players from the academy of a professional football team. Only matches where the team used the 1-4-2-3-1 formation were analyzed (10 matches). The playing positions were categorized as: Central Defensive Fielders (CDFs), Central Midifielders (CMFs), Forwards (FWDs), Wide Defensive Fielders (WDFs), and Wide Midfielders (WMFs). Player movement was tracked using GPS devices and categorized into four velocity zones: (Zone 1: 3.6-10.8 km/h, Zone 2: 10.9-18.0 km/h, Zone 3: 18.1-25.2 km/h, Zone 4: \(>\)25.2 km/h). Depending on whether normality was present in our data, either a one-way analysis of variance (ANOVA) or a Kruskal-Wallis test was conducted. A subsequent analysis was performed to compare the performance between the first and second halves of the match, using either the independent samples t-test or the Mann-Whitney U test. The results showed that CMF players covered the greatest total distance and had the highest movement velocity (distance/min) compared to all other positions (\(p\) = 0.001). In high-speed running (\(>\)18 km/h), CMF again covered the greatest distance, followed by WDF. In sprinting, CDF covered the shortest distance and reached the lowest maximum speed (\(p\) = 0.001). CMF performed the most accelerations and decelerations across all positions (\(p\) = 0.001). As for the entire team, total distance, movement rate, and the number of accelerations and decelerations decreased in the second half. All players except the WDF also showed a decrease in total distance, while midfielders experienced a notable drop in sprint distance. In conclusion, this study underscores the positional specificity of physical demands in elite football and the systematic decline in physical output as matches progress. While all positions demonstrated some level of second-half performance deterioration, midfielders experienced the most significant decreases in both volume and intensity-related metrics. These insights offer valuable implications for position-specific physical condition, recovery planning, and substitution strategies,helping to optimize performance and manage player load in elite football environments. It should be reiterated that the results of the present study apply exclusively to the 1-4-2-3-1 formation.  
tactical formation; GPS tracking; running profile; game position; acceleration; deceleration
## 5 Discussion  
This study planned to improve on [PERSON] et al.'s [23] research by adding more features, such as demographic and learning program outcomes, that would help in identifying student patterns. By using K-means clustering, the student behavioral pattern was identified. There are mainly three types of students, which are low-, average-, and high-performance based on the learning program outcome feature. These students show different behavior performed by those groups throughout the weeks. Early in the semester, it shows that the student's behavior does not have a great impact on the student's performance by the end of the semester. However, in the last week, those with high self-efficacy and learning styles had high performance. Meanwhile, the student with low performance had average to low self-efficacy and learning style. A student's behavior on week 9 is the most important as it affects their performance the most.  
In addition to that, research conducted by [PERSON] et al. [23] did not focus on the accuracy of the prediction model; therefore, the significance of the features used cannot be evaluated properly, unlike in research by [PERSON] et al. [4]. This study has demonstrated that building  
Figure 6: Accuracy before and after clustering.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Week**} \\ \hline
**Metric** & **6** & **7** & **8** & **9** \\ \hline Accuracy & 0.83 & 0.90 & 0.76 & 0.86 \\ \hline Precision & 0.81 & 0.91 & 0.78 & 0.85 \\ \hline Recall & 0.83 & 0.90 & 0.76 & 0.86 \\ \hline F-Measure & 0.80 & 0.89 & 0.77 & 0.85 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Evaluation metrics for the prediction model before clustering.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Week**} \\ \hline
**Metric** & **6** & **7** & **8** & **9** \\ \hline Accuracy & 0.88 & 0.90 & 0.90 & 0.93 \\ \hline Precision & 0.89 & 0.91 & 0.92 & 0.94 \\ \hline Recall & 0.88 & 0.90 & 0.90 & 0.93 \\ \hline F-Measure & 0.88 & 0.90 & 0.91 & 0.93 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Evaluation metrics for the prediction model after clustering.  
a new model based on clustering enhances the ability to predict student learning outcomes. The accuracy of the prediction model was higher than before the clustering, as shown in Figure 6. Week 8 had the biggest improvement in terms of accuracy, with 0.76 increasing to 0.90 after clustering. Therefore, using this combination of unsupervised and supervised learning is proven to give better results in terms of the accuracy of the prediction model.
## 1 Introduction
[PERSON] [15] established and validated a numerical calculation and analysis of three-dimensional transient flow inside a high-speed vortex refrigeration compressor, addressing the issue of insufficient radial gap grid density. [PERSON] [16] established a mathematical model of a vortex disk with a base circle involute-higher-order curve combined profile, conducting finite element analysis on the geometric structure and dynamic characteristics of the vortex disk. Literature [17] measured the back pressure level of an asymmetric algebraic vortex compressor experimentally, studied other factors affecting back pressure, developed a mathematical model of the vortex compressor to determine the impact of leakage through profiles and radial gaps, and calculated the pressure distribution at the tip using interpolation. Literature [18] proposed a principle of vortex profile mechanism based on the center of natural time involute and the meshing area in the contour design theory of vortex compressors, and the performance characteristics of various involutes were studied accordingly. In summary, the aforementioned scholars have made certain research achievements in optimizing vortex compressor profiles and simulating the flow and leakage characteristics of working fluid in compressor chambers. However, at the current stage, uniform wall thickness vortex profiles have a small compression ratio and serious leakage, while traditional combined profiles have complex and error-prone theoretical calculations for joint point coordinates, leading to difficulties in connection and manufacturing [19; 20]. This paper adopts a new model of a variable diameter dual circular arc combined profile vortex compressor. By analyzing the baseline drawing process and general theoretical equations of the new combined profile, a mathematical model for geometric performance indicators such as stroke volume and leakage line length is established. The flow field characteristics of the new variable diameter dual circular arc combined profile working chamber are analyzed, controlling relevant parameters, introducing uniform wall thickness vortex compressors, and using CFD software (21.1.0.) to explore the transient distribution of pressure, temperature, and velocity of the working fluid in the working chamber. The dynamic characteristics of the working chamber are summarized according to the variation in the main shaft angle, and a comparative analysis of the dynamic performance parameters and leakage performance indicators of the vortex compressor is conducted. The research process and results provide a theoretical basis for the structural design and performance improvement of vortex compressors. To resolve the problems of small compression ratios and severe leakage in uniform thickness compressors, a novel approach is provided.
# 5D Gauss Map Perspective to Image Encryption with Transfer Learning Validation
## 3 Proposed Method  
### 5D Gauss Map Equation and Plot (Iteration)  
A chaotic map generates pseudo random values, and is then utilized in the encryption operation. The input parameters influence the pseudo-randomness of the outcomes produced by chaotic maps. In this paper we developed the idea of the five dimensions Gauss map by using (1), which is as follows:  
\[x_{i+1}=e^{(-x_{i}^{2})}+d+by_{i}^{2}x_{i}+az_{i}^{3} \tag{2}\]  
Figure 4: Transfer Learning using AlexNet.  
\[y_{i+1} =e^{(-\sigma_{i}^{2})}+d+bz_{i}^{2}y_{i}+ax_{i}^{3} \tag{3}\] \[z_{i+1} =e^{(-\alpha_{i}^{2})}+d+bx_{i}^{2}z_{i}+ay_{i}^{2}\] (4) \[w_{i+1} =e^{(-\alpha_{i}^{2})}+d+bs_{i}^{2}w_{i}+ax_{i}^{2}\] (5) \[s_{i+1} =e^{(-\alpha_{i}^{2})}+d+bx_{i}^{2}s_{i}+aw_{i}^{2} \tag{6}\]  
Here, \(x\), \(y\), \(z\), \(w\), and \(s\) are intervals, and \(a\), \(b\), \(c\), and \(d\) are real parameters. 5D Gauss map is more complicated and secure because of quadric, cubic, quadratic coupling, and four constant terms. Plotting of \(x\), \(y\), \(z\), \(w\), and \(s\) components are shown in Figure 5. These plots describe the value range of \(x\), \(y\), \(z\), \(w\), and \(s\). Plots horizontal axis shows a number of iterations, and the vertical axis shows the components range.
## 3 Results
### Applications Across Industrial Domains
#### 3.3.2 Industrial Equipment  
Industrial IoT and multi-sensor approaches enhance rotating equipment maintenance. A low-cost system used vibration, temperature, and sound signals for bearing health monitoring, with DTs outperforming other models [40]. Utility theory was integrated with GB and RF to optimize fault detection in pump systems [41]. LSTM networks outperformed traditional models for RUL prediction in compressors [42], while SVMs achieved top results for motor fault diagnosis [43]. RFs also proved effective in oil analysis for gearbox monitoring [44] and IoT driven motor diagnostics [45]. XGBoost offered the best accuracy-speed tradeoff in bearing systems [46], and both regression and ANN models predicted tool wear under varying lubrications [47]. Applications in mining, thermal systems, and cement plant fans further illustrate the adoption of ML models across industries [48; 49; 50].  
General production systems combine DL and optimization. A CNN-bidirectional LSTM model, as demonstrated in [51], detected actuator and turbine faults with 96% recall using the MIMI dataset. In [52], a predictive framework combined ANNs, SVMs, and the extended great deluge (EGD) algorithm to forecast degradation and optimize multi-level maintenance scheduling.  
Robotics and automation systems benefit from both historical and real-time data. An ANN model trained on enterprise management records and average time-to-failure statistics predicted packaging robot failures with 91% accuracy without an IoT infrastructure [53]. Correspondingly, a discrete Bayes filter (DBF) outperformed naive Bayes (NB) in forecasting degradation in robot power systems [54], while, in [55], a digital twin framework using RF accurately detected conveyor belt anomalies like chain slack.
# A New LoRa-like Transceiver Suited for LEO Satellite Communications +
###### Abstract
This work can be seen as an improvement of [21], which did not deal with the Doppler time variation. This improvement has led us to propose a new receiver adapted to such conditions. Hence, the main contributions of our work are to:  
* Modify the CSS modulation in order to enhance its robustness to time and frequency synchronization errors, especially when the latter are time varying. Subsequently, our approach would allow to deal with Doppler shifts with much faster variations in time than the existent LoRa-like receivers. * Release the constraint of a maximum allowed CFO of \(\frac{B}{4}\) caused by the classical synchronization algorithms in LoRa [14; 19; 20; 26; 27]. To address this, the time synchronization is implemented regardless for the CFO. Currently, the frequency mismatch of local oscillators (LOs) between the transmitter and the receiver in LoRa-based communications do not reach this value maximum allowed CFO. However, this mismatch of LOs, combined with significant Doppler shifts, in the context of LEO communications, could lead to a CFO that exceeds the quarter of the bandwidth. Hence, with our approach, we can propose reducing the bandwidth of the chirped signals, without worrying about the occurrence of a CFO that exceeds the latter constraint, which would provide a gain in sensitivity (the actual choices of bandwidth for LoRa-like signal are based on the local oscillator precision to satisfy the \(\frac{B}{4}\) constraint) and increase the capacity of LoRa-based networks. To achieve all of these features, we propose a receiver based on the DCSS technique, which consists of transmitting symbols obtained by an integration processing (i.e., in each symbol time the cumulative sum of the current symbol and the previous ones is transmitted). At the receiver side, a differentiation is performed to recover the original symbols. As proved in [21], the latter differential processing allows the DCSS receiver not to perform a complete CFO estimation and tolerates more important time synchronization errors than existing algorithms in the literature. In addition, thanks to the differential process associated with an interpolation of the peaks in the Fourier transform, our proposal can tolerate important Doppler time variation. However, an estimation of this variation is needed for some configurations. Finally, the performance of our receiver has been validated with simulations in LEO satellite conditions. The remainder of the paper is organized as follows. In Section 2.1, we provide a brief overview of LoRa PHY layer. The impact of synchronization errors on the symbol detection is detailed in Section 2.2. Building upon these models, we present, in Section 3, the DCSS technique and the proposed synchronization algorithm in six main steps. Before concluding our work, simulation results are proposed and interpreted in Section 4.