esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Results
### Validation Experiments
#### 4.2.4 Validation of the Hyperparameters  
To obtain better hyperparameters, we generated an independent validation set for parameter optimization. This paper mainly discusses three parameters, namely, the number of graph convolution layers \(L\), the max order \(K\) of subgraph whose information is aggregated in the graph convolution layer, and the probability \(P_{u}\) of expert policy replacement. Figure 5 shows the evaluation results with different hyperparameters, and the actual values of different evaluation metrics are mapped to the same interval for better visualization.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Template** & **VR** & **DR** & **GIE** & **TS** & **SR** \\ \hline With the anti-overfitting mechanism & \(-\)**209.553** & \(-\)**169.048** & **1.863** & **15.925** & **0.85** \\ Without the anti-overfitting mechanism & \(-\)321.548 & \(-\)209.437 & 1.851 & 19.183 & 0.79 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Evaluation results under the control of the strategies learned from the templates with and without the anti-overfitting mechanism. \(P_{u}\) is set to be 0.9. The bold data represent the optimal values of different strategies on the same evaluation metric.  
Figure 5: Evaluation results with different hyperparameters. (**a**) Results with different \(L\). (**b**) Results with different \(K\). (**c**) Results with different \(P_{u}\).  
Considering the performance under different parameters and the complexity of the networks, the final values of the three parameters are chosen to be \(L=2\), \(K=3\), and \(P_{u}=0.8\). Optimal values of some other hyperparameters are also obtained by validation experiments. In summary, we build a fully connected neural network with hidden layers \(L=2\) and 64 neurons of each layer. The network is implemented in PyTorch, using the Adam optimizer with learning rate \(5\times 10^{-5}\) and forgetting factors \(\beta_{1}=0.9\) and \(\beta_{2}=0.999\). The size of the training set is 600 episodes, with 3000 steps per episode. The validation and test set consist of 200 episodes, respectively, with 5000 steps per episode.
## 4 Results  
### Technology Usage Inventory  
To assess the level of technology acceptance (intention to use) and its specific aspects for the two exergame versions, the Technology Usage Inventory was used. No significant differences in mean scores were found in any of the subscales (Table 1). A Wilcoxon signed-rank test indicated that the median TUI post-test ranks of the VR-SET in the subscale usability, Mdn = 17.00, were not significantly different from the median TUI post-test ranks of the VR-ET in the same subscale, Mdn = 18.00, \(Z=-0.445\), \(p\) = 0.656. On average, the intention to use was higher for the VR-SET exergame version, Mdn = 147.00, than in the VR-ET, Mdn = 132.00. However, no significant results could be achieved, \(Z=-0.261\), \(p\) = 0.794. Thus, the acceptance did not differ between the guided instruction VR-SET and the gamified VR-ET exergame.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**TUI** & **Training** & **Mean (SD)** & **Median (95\% CI)** & **Z-Score** & \(p\)**-Value** \\ \hline \multirow{2}{*}{Interest} & SET & 16.50 (5.48) & 17.00 (14.07–18.93) & \(-\)0.035 & 0.972 \\  & ET & 16.59 (5.78) & 19.00 (14.03–19.15) & & \\ \hline \multirow{2}{*}{Usability} & SET & 16.55 (3.49) & 17.00 (15.00–18.09) & \(-\)0.445 & 0.656 \\  & ET & 16.29 (3.81) & 18.00 (14.55–18.02) & & \\ \hline \multirow{2}{*}{Immersion} & SET & 16.68 (5.41) & 17.00 (14.28–19.08) & \(-\)0.141 & 0.888 \\  & ET & 16.86 (5.06) & 16.00 (14.62–19.10) & & \\ \hline \multirow{2}{*}{Usefulness} & SET & 13.55 (6.11) & 13.00 (10.83–16.26) & \(-\)0.358 & 0.720 \\  & ET & 13.82 (4.49) & 14.00 (11.83–15.81) & & \\ \hline \multirow{2}{*}{Skepticism} & SET & 10.18 (5.23) & 10.00 (7.87–12.50) & \(-\)1.196 & 0.232 \\  & ET & 11.67 (4.95) & 13.00 (9.41–13.92) & & \\ \hline \multirow{2}{*}{Accessibility} & SET & 10.18 (4.03) & 10.00 (8.39–11.97) & \(-\)1.734 & 0.083 \\  & ET & 11.77(3.61) & 12.00 (10.17–13.37) & & \\ \hline \multirow{2}{*}{Intention to Use} & SET & 134.00 (78.30) & 147.00 (99.29–168.71) & \(-\)0.261 & 0.794 \\  & ET & 140.68 (86.99) & 132.00 (102.11–179.25) & & \\ \hline \hline \end{tabular}  
* Results shown as arithmetic mean (standard deviation) and median (95% confidence interval). Scale minimum and maximum: Interest (min. 4, max: 28), Usability (min. 3, max: 21), Imperson (min.: 4, max: 28), Usefulness (min.: 4, max: 28), Skepticism (min. 4, max: 28), Accessibility (min. 3, max: 21), Intention to Use (min.: 3, max: 300). Inter-group differences were calculated by Wilcoxon signed-rank test.  
\end{table}
Table 1: Results of the Technology Usage Inventory (TUI) for the strength endurance training (SET) and endurance training (ET).
## 8 Conclusions  
The present study provides a comprehensive understanding of freely available MSA corpora. A key contribution of this work is the introduction of AraFast, an Arabic corpus designed to facilitate language modeling and NLP tasks. This readily available, preprocessed corpus can expedite the development of language models and enhance the generation of contextually relevant Arabic text. The potential applications of AraFast span various domains, including chatbots, QA systems, summarization tools, and Arabic text generation. Furthermore, the impact of segmentation and data quality on the AraFast corpus was evaluated. The study findings also highlight the challenges posed by web-scraped data, which often introduce noise and inconsistencies, affecting the model's efficiency. These evaluations not only validate the robustness of AraFast, but can also guide future corpus development for Arabic NLP tasks.  
For future research, it is suggested to further develop the AraFast corpus by collecting and preprocessing data on Arabic dialects (AD), including them in the corpus. When the AraFast corpus contains both MSA and AD, the Arabic PLMs trained on the developed corpus will be able to handle other NLP tasks for AD as well. Moreover, the proposed AraFastQA-base model will be compared with a greater number of existing Arabic PLMs and evaluated on additional Arabic QA datasets.  
The authors confirm contribution to the paper as follows: study conception and design: A.A., F.A. and M.S.; data collection: A.A.; analysis and interpretation of results: A.A., F.A. and M.S.; draft manuscript preparation: A.A., F.A. and M.S. All authors reviewed the results. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.
# An Algorithm of Acoustic Emission Location for Complex Composite Structure
## 5 Discussion
### Limitations of the Algorithm  
Some limitations of our algorithm were found and listed as follows when it was applied to calculate the ray path and travel time.  
1. Only the first arrival wave can be used to obtain the path and travel time by using our algorithm. The first arrival wave may be P wave, head wave, or diffraction wave.
2. Our algorithm is only suitable for models with step-like change in wave velocity, such as layered model or block model, but not for models with gradual change in wave velocity.
3. Our algorithm stipulates that two nodes in the same block are adjacent nodes if the spacing in all directions of \(x\), \(y\) and \(z\) between the two nodes is less than or equal to the adjacency radius, and that the waves can directly propagate between the adjacent nodes. As a result, an interlayer of the third material between two nodes, which is thinner than the adjacency radius, will be omitted during the calculation so as to guarantee calculation speed. To solve this problem, the material, which contains the thin interlayer, should be cut into two blocks along the two boundaries of the thin interlayer during 3D model meshing.
# Effect of DEM Used for Terrain Correction on Forest Windthrow Detection Using COSMO SkyMed Data
## 2 Materials and Methods
### Materials
#### 2.1.2 SAR Data  
COSMO SkyMed, managed by the Italian Space Agency (ASI), is a SAR satellite constellation with four medium-sized satellites. It offers global coverage and can revisit areas of interest twice daily, regardless of weather conditions. The SAR data, available in X-band and single- or dual-polarisation modes, have varying spatial resolutions based on acquisition modes, and they are acquired on demand. For this study, two pre-event and two post-event images acquired in Stripmap HIMAGE mode with HH polarisation were used (ascending and descending, Table 2). ASI provided the images in processing level 1B--Detected Ground Multi-look (DGM), with a range and azimuth resolution of 5 meters, pixel spacing of 2.5 m, and swath width and scene length of 40 km. The equivalent number of looks is slightly greater than 3 for all images.
## 3 A Machine Learning Approach for Predicting Dementia
### Data Pre-Processing
#### 3.2.2 Data Normalization  
Data normalization is an important process that impacts the overall performance of the model. Normalizing data ensures stable and efficient optimization and better generalization in ML and DL models. The ADNIMERGE dataset has a diverse range of values in different features. Thus, this dataset was normalized between 0 and 1 using the minimax technique. Equation (1) shows the mathematical expression used for dataset normalization.  
\[z_{i}=\frac{x_{i}-\min(x)}{\max(x)-\min(x)} \tag{1}\]  
where \(x\) denotes the input data and \(z_{i}\) denotes the \(i_{th}\) normalized data; \(\min(x)\) and \(\max(x)\) are the minimum and maximum values, respectively.
# Enhancing Embedded Space with Low-Level Features for Speech Emotion Recognition
###### Abstract  
This work proposes an approach that uses a feature space by combining the representation obtained in the unsupervised learning process and manually selected features defining the prosody of the utterances. In the experiments, we used two time-frequency representations (Mel and CQT spectrograms) and EmoDB and RAVDESS databases. As the results show, the proposed system improved the classification accuracy of both representations: 1.29% for CQT and 3.75% for Mel spectrogram compared to the typical CNN architecture for the EmoDB dataset and 3.02% for CQT and 0.63% for Mel spectrogram in the case of RAVDESS. Additionally, the results present a significant increase of around 14% in classification performance in the case of happiness and disgust emotions using Mel spectrograms and around 20% in happiness and disgust emotions for CQT in the case of best models trained on EmoDB. On the other hand, in the case of models that achieved the highest result for the RAVDESS database, the most significant improvement was observed in the classification of a neutral state, around 16%, using the Mel spectrogram. For CQT representation, the most significant improvement occurred for fear and surprise, around 9%. Additionally, the average results for all prepared models showed the positive impact of the method used on the quality of classification of most emotional states. For the EmoDB database, the highest average improvement was observed for happiness--14.6%. For other emotions, it ranged from 1.2% to 8.7%. The only exception was the emotion of sadness, for which the classification quality was average decreased by 1% when using the Mel spectrogram. In turn, for the RAVDESS database, the most significant improvement also occurred for happiness--7.5%, while for other emotions ranged from 0.2% to 7.1%, except disgust and calm, the classification of which deteriorated for the Mel spectrogram and the CQT representation, respectively.  
speech emotion recognition; deep learning; audio features +
Footnote †: journal: Applied sciences  
0000-0001-5000-5000][PERSON] 1  
1 Faculty of Computer Science and Information Technology, West Pomeranian University of Technology, Szczeeni, Zolnierska 52, 71-210 Szczeeni, Poland; [EMAIL_ADDRESS]
# The Impact of Contact Force on Signal Quality Indices in Photoplethysmography Measurements
## 4 Results
### Skewness (\(S_{SQI}\)) Analysis  
As displayed in Table 1, the asymmetry in the lowest CF range (0-0.2) is close to zero, indicating a nearly symmetrical distribution of the signal values. The outliers in this range point to sporadic variations, likely resulting from inconsistent contact between the sensor and the skin. As the CF between the probe and the skin increases from the lowest range to the intermediate ranges (0.2-0.4 and 0.4-0.6), the asymmetry of the PPG AC signal tends to become positive, indicating better signal capture with heavier tails towards high values. The mean skewness of 0.35 in the 0.4-0.6 CF range is particularly meaningful because it indicates a general tendency towards higher values without extreme deviations. This balance suggests that most of the signals within this range are of good quality but include enough variability to capture important physiological details without being overshadowed by noise or extreme values. In contrast, in the highest CF ranges (0.6-0.8 and 0.8-1.0), the asymmetry becomes negative and variable, reflecting the distortions caused by excessive pressure that compresses the tissue and alters the signal, with the outliers indicating sporadic variations due to inconsistent contact between the sensor and the skin.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**CF Range** & **Mean** & **Std** & **Min** & **Max** & **Median** \\ \hline
0–0.2 & \(-\)0.08 & 1.04 & \(-\)4.42 & 0.48 & 0.22 \\
0.2–0.4 & 0.33 & 0.23 & \(-\)0.15 & 0.71 & 0.35 \\
**0.4–0.6** & **0.35** & **0.29** & \(-\)**0.16** & **1.05** & **0.37** \\
0.6–0.8 & \(-\)0.19 & 0.90 & \(-\)3.68 & 0.84 & \(-\)0.02 \\
0.8–1.0 & \(-\)0.73 & 2.77 & \(-\)5.33 & 7.18 & \(-\)0.80 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Descriptive statistics of \(S_{SQI}\) by CF range, highlighting in bold the identified optimal range.  
Figure 5: Boxplots of signal quality metrics at different CF levels for the 940 nm wavelength. Five normalized CF ranges segment each analysis.
## 4 SnZn-Based Solders
### Mechanical Properties  
The addition of 1.0 wt.% Sb nanoparticles greatly increased the tensile strength and decreased the elongation of the composite solder, as shown in Figure 13[81]. The formation of \(\varepsilon\)-Sb\({}_{3}\)Zn\({}_{4}\) not only reduced the amount of Zn atoms to form Zn-rich phase, but also acted as nucleation sites, making the distribution of Zn-rich phase more uniform. Thus, the tensile properties of the SnZn-nano Sb\({}_{3}\)Zn\({}_{4}\) composite solder were improved. However, the microstructure of the solder matrix deteriorated when excessive Sb was added, resulting in the decrease of the tensile strength. It was reported that Al\({}_{2}\)O\({}_{3}\) nanoparticles have a similar effect on the mechanical properties of SnZn solders [78].  
[PERSON] et al. [82] found Ag nanoparticles could react with Sn atoms to form spherical-shaped Ag\({}_{3}\)Sn IMC phase, which refined the solder matrix and improved the mechanical properties. SnZn solder joints showed a typical brittle fracture, while SnZn-Ag solder joints presented dimple failure morphologies because of the refining effect of Ag nanoparticles. Due to the pinning effect and dispersion strengthening of ZnO, the sliding of grain boundaries was retarded and the tensile creep properties of the composite were improved [80].  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Solder** & \begin{tabular}{c} **Maximal Size** \\ **(\(\upmu\)m)** \\ \end{tabular} & \begin{tabular}{c} **Minimal Size** \\ **(\(\upmu\)m)** \\ \end{tabular} & \
\begin{tabular}{c} **Average** \\ **Size (\(\upmu\)m)** \\ \end{tabular} &
\begin{tabular}{c} **Average Spacing** \\ **(\(\upmu\)m)** \\ \end{tabular} \\ \hline SnZnBi & 10.5 & 1.2 & 4.6 \(\pm\) 0.09 & 2.2 \(\pm\) 0.1 \\ SnZnBi–0.25 ZrO\({}_{2}\) & 2.5 & 0.5 & 1.5 \(\pm\) 0.1 & 1.3 \(\pm\) 0.04 \\ SnZnBi–0.5 ZrO\({}_{2}\) & 6.6 & 0.4 & 2.8 \(\pm\) 0.3 & 1.1 \(\pm\) 0.06 \\ SnZnBi–1 ZrO\({}_{2}\) & 8.3 & 0.6 & 3.4 \(\pm\) 0.5 & 2.1 \(\pm\) 0.08 \\ \hline \end{tabular}
\end{table}
Table 1: Grain size and spacing of Zn-rich phase in the SnZnBi–\(\chi\)ZrO\({}_{2}\) solder.  
Figure 12: Microstructure of: (**a**) SnZnBi solders after being reflowed for 5 min; (**b**) SnZnBi–0.5 Ni composite solders after being reflowed for 30 min; (**c**) SnZnBi soldiers after being reflowed for 30 min; (**d**) SnZnBi–0.5 Ni composite solders after being reflowed for 30 min [77].  
In summary, Table 2 lists the mechanical properties of some lead-free solders bearing nanoparticles. As seen from the table, SnBi-based solders present higher strength and carbon nanomaterials (CNTs, GNSs) have great influence of the composite solders. In general, the addition of nanoparticles improves the tensile strength of the solders, but simultaneously reduces the elongation due to their strong restriction effect on the movements of dislocation. Therefore, the optimum content should be further determined by comprehensive comparison of mechanical properties and other performance such as wettability and reliability.
# Dynamic Multiple Junction Selection Based Routing Protocol for VANETs in City Environment
## 3 Dynamic Multiple Junction Based Source Routing Protocol
### DMJSR Protocol Overview
#### 3.2.3 Illustrative Example for DMJSR Working  
Consider the scenario of Figure 2 where the source vehicle S is at current junction J\({}_{1}\) and is dispatching packet towards the destination D using DMJSR. In this case, there are 4 candidate two-hop neighbor junctions of current junction J\({}_{1}\) available through which packet can be routed toward the destination. These include J\({}_{4}\), J\({}_{5}\), J\({}_{6}\) and J\({}_{8}\).  
Figure 2: Working of Dynamic Multiple Junction Selection based Routing Protocol (DMJSR).  
Our proposed algorithm will assign weights to each of two-hop neighbor junctions of J1 according to the traffic density and shortest curve metric distance to the destination. City streets that are connecting J1 to J4 have higher traffic density than J1-J5, J1-J8 and J1-J6. Consequently, J1 and J4 have higher connectivity than J1-J6, J1-J8 and J1-J5.Therefore, DMJSR will assign more weight to J4 as compared to J5, J6 and J8 and it will be chosen as next destination junction. In this way, our routing approach will remove the limitations of GyTAR, E-GyTAR, and TFOR. In the case of such routing protocols, J3 would have been selected instead of J4, which will not be considered because all these protocols based on the selection of one-hop junction, which results in a sub-optimal choice in this case. The packet would have been stuck in local maximum because after selecting J3, junctions J5 and J6 have no traffic density as these are along dead streets (those streets that contain no vehicle for carrying packet towards the destination). Our routing protocol route the packet from current junction J1 to J4 through J2 and in this way it will relay the packet towards the destination node. Therefore, in DMJSR, a packet will travel successively closer towards the destination node along the urban streets where there are adequate vehicular nodes to provide connectivity. It is worth mentioning that Figure 2 is just an illustration of one of the possible cases where the considered protocols would suffer in terms of performance and the proposed protocol will work well. The proposed protocol works well for all the considered simulation scenarios as discussed in Section 4.
## 3 Results and Discussion
### Micro- and Macro-Elements Concentration in Lentil Wholemal Samples  
The concentrations of micro- and macro-elements in lentil samples are presented in Table 7. Almost all concentrations of macro-elements were significantly higher in L samples, in comparison with LR ones, except magnesium. Moreover, of all essential micro-elements, only phosphorus, manganese, and selenium concentrations in the L sample group were significantly higher than in the LR group. Moreover, iron and nickel concentrations in the LR samples were significantly higher than in the L samples.  
Lentils are defined as a leguminous crop, containing significant amounts of macro-elements and essential micro-elements, such as phosphorus, potassium, iron, and zinc [72; 73]. Furthermore, the concentration of essential microelements, such as iron, is high enough for the addition of lentils in a diet to prevent iron deficiency anaemia [5]. The other minerals that are plentiful in lentils are crucial for important biological functions in humans [74]. These functions include, but are not limited to, the following: muscle and nerve functioning (Mg and Ca); cell growth, function, and energy production (P); electrolyte balance and muscle contraction (Na); the metabolism of fat and carbohydrates (Cr); the structural components of many enzymes (Mn, Zn, and Cu); etc. [74; 75].  
According to various sources in the literature, the concentrations of various essential microelements can vary within the ranges of 2000-5300 mg/kg, 0.12-0.60 mg/kg, 10.5-29.0 mg/kg, 49-81.4 mg/kg, 1.1-1.8 mg/kg, 9.4-14.3 mg/kg, 36.7-64.2 mg/kg, and 0.18-1.6 mg/kg for phosphorus, chromium, manganese, iron, nickel, copper, zinc, and selenium, respectively [76; 77; 78; 1; 72]. The mineral composition of lentils can be affected by differences in growing conditions (e.g., phosphorus and other mineral fertilization, water availability, and environmental conditions), plant origin (e.g., genotype and cultivar), or other parameters (e.g., harvesting time) [79; 80; 81; 82; 83].  
However, the aforementioned micronutrients have their bioavailability restrained due to the existing antimutrient factors (phytates, oxalates, and phenolic compounds) [72; 84]. Mineral bioavailability can be enhanced by employing certain strategies, such as genetic manipulation, dehulling, soaking, enzymatic treatment, or fermentation [5; 72; 84; 85]. The bioavailability enhancement of minerals via fermentation can be explained by the disruption of plant cell wall structures, production of phytase enzymes, or even the activation of endogenous phytase enzymes, due to the existing low pH environment [86; 87; 26].