esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Deacidification Treatment on the Flavor Quality of Zaosu Pear-Kiwifruit Wine
## 3 Results and Discussion
### Sensory Characteristics of Pear-Kiwifruit Wines  
Chemical treatment or MLF deacidification had little effect on the color or clarity (Figure 5A,B, \(p>0.05\)) and mean taste scores of the wines, while the scores of the samples after both treatments were significantly higher than those of the wines from the initial alcoholic fermentation. The MLF wine scored highest for aroma, taste, and overall acceptability. Chemical deacidification improved the taste and overall acceptability scores of wines, but reduced the aroma scores (Figure 5B). Overall, MLF wine had a superior sensory profile.  
Principal component analysis (PCA) was performed to further characterize the check-all-that-apply (CATA) sensory descriptors of pear-kiwifruit wine (Supplementary Table S2). The first and second principal components (PC1 and PC2) explained 49.9% and 35.6% of the variation, respectively (85.5% combined) (Figure 5C); the selected attributes provided a clear distinction among untreated wine, MLF wine, and chemically deacidified wine. Before deacidification, the wine was characterized as \"fruity\", \"acidic\", \"astringent\", and \"sharp\", whereas MLF wine, located at the positive end of PC1, was described as \"pear-like\", \"fruity\", \"acidic/fresh\" (balance between sweetness and acidity), \"diverse\", and \"floral\" (Supplementary Table S6). This result was consistent with the large reduction in l-malic and quintic acids (Figure 3A), as well as the increase in aromatic compounds (Supplementary Table S5), after MLF with \(O\). _oeni_ GF-2. The chemically deacidified wine was characterized as \"acidic/fresh\", \"buttery\", and \"bitter\" (Supplementary Table S6); the marked decrease in acidity appears to have rendered the bitterness of the wine more noticeable.  
Figure 5: Sensory characteristics of pear–kiwifruit wine with and without deacidification treatment: (**A**) fruit wine samples; (**B**) sensory profiles obtained for AF wine (—), MLF wine (—), and CD wine (—); ns = not significant, \"\" \(p<0.01\), values with different letters are significantly different among the samples; (**C**) principal component analysis for check-all-that-apply attributes; \(n=36\).
###### Abstract  
Research on students' immersive experiences with fully immersive virtual reality (VR) technologies is extensively documented across diverse educational settings; however, in maritime higher education, it remains relatively underrepresented. Therefore, by using segmentation analysis, this study aims to profile maritime engineering students at the Faculty of Maritime Studies, University of Rijeka, by perceived immersion (PIMM) within a Head-Mounted Display (HMD) VR engine room simulator and to explore differences in their perceived learning benefits (PLBs), future behavioural intentions (FBI), and satisfaction (SAT) with the HMD-VR experience. The sample comprised 84 participants who engaged in preliminary HMD-VR engine room simulator trials. A non-hierarchical (K-mean) cluster analysis, combined with the Elbow method, identified two distinct and homogeneous groups: _Immersionists_ and _Contromists_. The results of an independent sample \(t\)-test indicated that _Immersionists_ exhibited significantly higher scores regarding perceived learning benefits, future behavioural intentions, and overall satisfaction than _Contromists_. The study results underscore the significance of understanding students' subjective perception of immersion in the implementation and further development of fully immersive VR technologies within maritime education and training (MET) curricula. However, as the study is based on a specific case within a particular educational context, the result may not directly apply to the broader student population.  
virtual reality; perceived immersion; perceived learning benefits; future behavioural intentions; engine room simulator; simulation-based learning; segmentation; maritime education; serious games Article
# A Gamified AI-Driven System for Depression Monitoring and Management
## 2 Background and Related Works
### AI for Personalized Depression Support  
Artificial intelligence has become essential for identifying depression-related signals from diverse data sources. Techniques such as natural language processing (NLP), supervised learning, and deep neural networks have been applied to detect depressive symptoms from text entries, speech patterns, facial expressions, and mobile usage data [17].  
Many approaches rely on passive data collection and generalized models, which may not fully capture individual variations in emotional expression or symptom progression [18]. A growing number of research points toward the benefits of integrating self-report data, behavioral patterns, and environmental signals to create more comprehensive, responsive systems [19]. In particular, in depression, combining multiple data streams and incorporating adaptive learning techniques, such as reinforcement learning, can significantly improve early detection and personalized feedback. Some studies also stress the importance of explainable AI (XAI) to increase user trust and interpretability in clinical contexts [20].  
Recent large-scale advancements further support this direction. [PERSON] et al. [21] introduced a conversational system using large language models (LLMs) that achieved 89% precision in depression detection, outperforming standard clinical tools like the PHQ-9. Similarly, [PERSON] et al. [22] developed EmoAgent, a multi-agent AI framework designed to ensure emotional safety and stability during human-AI interaction. These systems demonstrate the growing feasibility of real-time, AI-guided mental health monitoring and validate our system's integration of adaptive feedback and explainability.
## 6 Case Study
### Rolling Compaction Calibration Test  
To establish the quality-level classification standard for the evaluation indices, referring to Reference [26], a rolling compaction calibration test is conducted prior to helping establish the rating matrix. The target values of physical and mechanical indices are produced from these calibration strips as references for compaction quality control during the earth-rock dam construction.  
To execute the test, one working face in the core wall was selected. According to the specifications [3], the compaction parameters (i.e., \(N_{s}\), \(N_{p}\), \(N_{v}\) and \(N_{t}\)) and the material parameters (i.e., \(M_{g}\) and \(M_{m}\)) should be prudently guaranteed to meet the construction requirements.  
Through the calibration test, the relationship of compaction passes with the physical and mechanical indices can be obtained as shown in Figure 7. In Figure 7a, the mean \(K\) increases from 89.48 to 99.90 (%), and the \(std\) decreases from 2.01 to 0.85 (%); the mean \(\rho_{d}\) increases from 2.10 to 2.29 (\(\times 10^{3}\)kg/m\({}^{3}\)), and the \(std\) decreases from 0.066 to 0.033 (\(\times 10^{3}\)kg/m\({}^{3}\)). In Figure 7b, the mean \(E_{vd}\) increases from 7.62 to 9.74 (MPa), and the \(std\) decreases from 0.759 to 0.265 (MPa); the mean \(E_{0}\) increases from 13.42 to 16.87 (MPa), and the \(std\) decreases from 2.32 to 0.68 (MPa); the mean \(K_{30}\) increases from 16.77 to 30.20 (MPa/m), and the \(std\) decreases from 4.78 to 1.37 (MPa/m). When compaction passes reaches ten, the physical and mechanical indices almost reach maximum and tend to be stable, thus they are selected as the basis for formulating the scoring criterion.  
Figure 7: Relationship of compaction passes with the indices and the mean errors of test points: (**a**) physical indices; (**b**) mechanical indices.  
Referring to Reference [22], the compaction quality in earth-rock dam construction can be divided into five classifications: good, fine, ordinary, poor and bad. The quality-level classification standard in the core wall can be obtained with an expert grading method as shown in Table 3, and the compaction quality rating of the calibration area is good, which lays a foundation for the following comprehensive evaluation of compaction quality.
# LRP-DS: Lightweight RepPoints with Decoupled Sampling Point Set
## 4 The Lightweight RepPoints with Decoupled Sampling Point Set
### Reduce the Mismatch between Classification Confidence and Regression Localization  
Non maximum suppression (NMS) is an important part of the current CNN object detection algorithm to remove duplicate bounding boxes. The core of NMS is to select the prediction bounding box with the highest classification confidence in the same category, and eliminate the same category bounding box with IoUs greater than a certain value. Then, NMS performs iteratively in the above manner. This makes the classification confidence naturally assigned two responsibilities of describing the category probability and localization accuracy. However, in the training process, no matter whether the IoU value between the predicted bounding box and the corresponding truth box is high or  
Figure 5: The examples of perception error for the classification branch of LRP-DS-V1. (**a**) a perception error between the girl and the horse; (**b**) a perception error between the cat and the chair.  
low, the classification confidence of positive samples is expected to be one. Therefore, the correlation between the classification confidence and localization accuracy is low, which seriously affects the localization accuracy of the model.  
Although the proposed method of decoupling classification and localization tasks can be beneficial to the feature sampling of classification and location, it makes the mismatch between the classification confidence and localization accuracy more serious. Therefore, this paper hopes to introduce a localization score to describe the localization accuracy, so that the classification score only needs to bear the responsibility of category probability. In this paper, for the realization of localization score, the current popular IoU score is selected. The IoU score is obtained by the output of the IoU branch parallel to the location branch, as shown in the location score map in Figure 4. Specifically, the IoU score is obtained by a \(3\times 3\) convolution, and the sigmoid activation function is used to normalize the IoU score to [0, 1]. In training, this paper applies supervisory signals to IoU scores by only positive samples, but does not use the more robust training methods like IoU-Net. In the process of training, the multi-task loss calculation formula of LRP-DS proposed in this paper is as follows:  
\[L=\lambda_{1}*L_{\text{cls}}+\lambda_{2}*L_{\text{init\_cls}}+\lambda_{3}*L_{ \text{refine\_loc}}+\lambda_{4}*L_{\text{iou}}, \tag{5}\]  
where, \(L_{\text{cls}}\) is the classification loss, \(L_{\text{init\_loc}}\) is the initial localization loss, \(L_{\text{refine\_loc}}\) is the fine-tuned localization loss, and \(L_{\text{iou}}\) is the IoU score loss.  
The classification confidence \(p\) describes the category probability of the bounding box, and the IoU score describes the localization accuracy of the bounding box. The object score in the non-maximum suppression NMS can be recalculated by formula (6).  
\[Score=p*IoU, \tag{6}\]
# Utilization of Quasi-Zenith Satellite System for Navigation of a Robot Combine Harvester
## 2 Materials and Methods
### Positioning Augmentation Systems
Figure 3: Diagram of the Multi-Global Navigation Satellite System (GNSS) Advanced Demonstration tool for Orbit and Clock Analysis (MADOCA) positioning augmentation system. State Space Modeling and the wide-area dynamic error model form the GPS and the QZS data monitored by the GEONET to estimate distance-dependent GNSS errors [16]. Via the LEX signal, the correction information in the state space representation (SSR) form which can be applied to precise point positioning (PPP-RTK) was also generated by the CMAS [15; 17]. The correction of the positioning error includes satellite orbit corrections, satellite clock corrections, satellite phase biases, and ionosphere and troposphere delays. However, the CMAS generates the correction of the ionosphere and troposphere delays for an area of the QZSS's coverage similar to the VRS-RTK system. Therefore, there is a limitation for using the CMAS all over the country simultaneously [4]. According to the CMAS success, the CLAS divides the service arena of GEONET into twelve parts covering the territory of Japan and nearby ocean areas with an addition of artificial grid points covering each area with an interval of 60 km. Those twelve parts of GEONET are also called the continuously operation reference stations (CORS), represented by the flags in Figure 4. The artificial grid points are used for the ionospheric and tropospheric delay correction. The user corrects its own observations and gives centimeter-accuracy solutions within 1 min using the PPP-RTK positioning method [18]. The CLAS receiver obtains the L6D signal which contains the satellite orbit and the satellite clock corrections, ionospheric and troposphere delays from the QZS to the user. Please note that the LEX band was changed to L6-band which has 2 channels; L6D for the CLAS and L6E for the MADOCA. To make a comparison or to use the positioning results of VRS-RTK and PPP-RTK together requires the coordinate transformations for compensation because the CLAS neglects the crustal movement of the earth [4].
# Uses of Nanoclays and Adsorbents for Dye Recovery: A Textile Industry Review
## 2 Sewage Treatment
### With Nanotechnologies
#### 2.2.5 Saponite  
Saponite (Sap) is a trioctahedral clayey mineral that belongs to the 2:1-type smectites group, with silicate layers that interperse with another gibbsite layer in the following form: silicate-gibbsite-silicate. It possesses negatively charged layers that are, in turn, neutralized by other ions with positive Na\({}^{+}\) Mg\({}^{2+}\) charges. Saponite's cation exchange capacity (CEC) has been quantified as 100 cmol\(\cdot\)kg\({}^{-1}\)[194; 195; 196; 197; 198]. XRD and SEM/EDX characterizations shows the clay mineral has the structural formula \({}^{\text{INT}}\)Na\({}^{\text{TET}}\)[Si\({}_{7}\)Al]\({}^{\text{OCT}}\)[Mg\({}_{6}\)]O\({}_{20}\)(OH)\({}_{4}\)[199].  
Its specific surface area (SSA) is large-sized and acidic. The specific surface area (SSA) was determined using ethylene glycol monomethyl ether, 100-115 m\({}^{2}\cdot\)g\({}^{-1}\)[200]. Compared to other nanoclay minerals such as MMT, it can be stated that Sap offers greater  
Figure 5: Halloysite SEM (**left**) and TEM (**right**) images.  
thermal stability [201], a smaller particle size (approx. 50 nm) [194; 195; 196; 197; 198], and is simpler to delaminate and exfoliate in nanoplate units, or as individual nanolayers in aqueous solution [202]. However, the downside of natural Spa is that it is not abundantly available. When Sap is exploited, a large quantity of impurities appear in the deposits, which entails time-consuming purification that considerably increases the cost of obtaining it [203]. The chemical composition of Sap varies significantly depending on the geological process that has formed it [204]. These defects can limit its use during adsorption processes [205].  
The above-described problems have led researchers to opt to synthetically obtain Sap. This process consists of synthesizing similar solids to natural Sap with a controlled chemical composition. The most conventional method followed to synthesize Sap is normally hydrothermal synthesis [206; 207; 208; 209; 210; 211; 212]. This method can be modified to make this synthesis microwave-assisted [213; 214].  
Despite the characteristics described for both natural and synthetic Sap, Sap is not normally employed in its fundamental state without previously submitting it to modification, such as intercalation or hybridization. Researchers have put saponite swelling to best use [215; 216; 217; 218] to introduce functional groups into the structure. This enables the modification of acidity and porosity, and different physico-chemical properties. Distinct hybrids have been successfully achieved by following these guidelines [219; 220; 221; 222]. An increasing number of studies have demonstrated that Sap can be employed as an efficient adsorbent [222; 223]. In the work by [PERSON] et al., dye Acid Orange 7 was degraded by Sap by employing H\({}_{2}\)O\({}_{2}\) at 70 \({}^{\circ}\)C as a catalyst, which achieved over 90% degradation when clay was impregnated with Fe (II) [224].
## 7 Conclusions  
Wadi Ibib-Wadi Shani (WI-WS) ultramafics represent relics of dismembered ophiolitic rocks in the southwestern extension of the largest Gerf ophiolitic nappe, South Eastern Desert, Egypt. Based on their petrographic description, the ultramafic rocks can be divided into serpentinites and serpentinized peridotites; the former consist mainly of serpentine minerals, whereas the latter comprise serpentine minerals with relics of primary minerals such as pyroxene, Cr-spinel, and olivine. These primary minerals are completely replaced by serpentine, magnetite, and carbonate minerals in serpentinites. The abundance of mesh and bastite textures reflects dunite and harzburge protooliths. The composition of primary mantle minerals such as orthopyroxene (Mg# (Mg/Mg+Fe\({}^{2+}\)), 89.65 to 93.18), clinopyroxene (depletion of Al\({}_{2}\)O\({}_{3}\), Na\({}_{2}\)O, and Cr\({}_{2}\)O\({}_{3}\)), high Fo content of olivine, and high Cr# of Cr-spinel reflect a forearc setting. The calculated temperatures of the WI-WS serpentinized peridotites using olivine-spinel (av. 781 \({}^{\circ}\)C) and Al in orthopyroxene (av. 859 \({}^{\circ}\)C) thermometer calibrations are consistent with those of Egyptian forearc mantle rocks.  
The following are available online at [[https://www.mdpi.com/article/10.3390/app112210524/s1](https://www.mdpi.com/article/10.3390/app112210524/s1)]([https://www.mdpi.com/article/10.3390/app112210524/s1](https://www.mdpi.com/article/10.3390/app112210524/s1)): Table S1: Representative microprobe analysis of orthopyroxene in serpentinized peridotites; Table S2: Representative microprobe analysis of clinopyroxene in serpentinized peridotites; Table S3: Representative microprobe analysis of Cr-spinel in serpentinized peridotites; Table S4: Representative microprobe analysis of Al-spinel in serpentinized peridotites; Table S5: Representative microprobe analysis of serpentine; Table S6: Representative microprobe analysis of chlorite in serpentinized peridotites; Table S7: Representative microprobe analysis of tremolite in serpentinized peridotites; Table S8: Representative microprobe analysis of magnetite; Table S9: Representative microprobe analysis of talc in serpentinized peridotites; Table S10: Average temperatures of serpentinized peridotites calculated using different calibrations.  
Conceptualization, E.S.R.L., G.M.S. and F.M.K.; methodology, E.S.R.L. and M.A.; software, E.S.R.L., G.M.S. and F.M.K.; validation, E.S.R.L., G.M.S. and F.M.K.; formal analysis, G.M.S., F.M.K. and M.A.; investigation, E.S.R.L., G.M.S. and F.M.K.; data curation, E.S.R.L., G.M.S. and F.M.K.; writing--original draft preparation, E.S.R.L., G.M.S., M.A. and F.M.K.; writing--review and editing, G.M.S., F.M.K. and E.S.R.L.; funding acquisition, M.A. All authors have read and agreed to the published version of the manuscript.  
Tail University: Researchers Supporting Project grant number (TURSP-2020/196).  
Figure 11: Schematic illustration showing the tectonic setting of the examined ultramafic rocks, modified from [79].
## 3 Numerical Simulation  
### Computational Fluid Dynamics Method  
Numerous applications have been performed to validate the high effectiveness of the computational fluid dynamics (CFD) method in exploring flow mechanisms. In the three-dimensional coordinate system, the compressible Navier-Stokes equations were solved by the CFL3D code [42]  
\[\frac{\partial\hat{Q}}{\partial t}+\frac{\partial\left(\hat{E}-\hat{E}_{V} \right)}{\partial\xi}+\frac{\partial\left(\hat{F}-\hat{F}_{V}\right)}{\partial \eta}+\frac{\partial\left(\hat{G}-\hat{G}_{V}\right)}{\partial\zeta}\ =\ 0 \tag{1}\]  
where \(Q\) represents the conservation variable, \(E/F/G\) represent inviscid vector flux, \(E_{0}/F_{v}/G_{v}\) represent viscous vector flux, and the symbol \(t\) represents time. CFL3D is a Reynolds-averaged thin-layer Navier-Stokes flow solver for structured grids, which became open source from August 2017. More detail about the governing equation can be found in references [42; 43; 44; 45].  
Discrete solution of Equation (1) was performed by the finite volume method. The Shear Stress Transport (SST) two-equation turbulence model was applied during the solution. The lower-upper Symmetric Gauss-Seidel (LU-SGS) implicit method was applied for time advancement, the Roe format was applied for inviscid term space marching, and the central difference format was applied for viscous term space marching. The gas viscosity coefficient was calculated by the Sutherland formula. The boundary conditions include symmetry, wall, and far field. The wall of the wind tunnel and connecting device of support were not simulated, so that the rear end of the V-shaped support was just floating in mid-air.  
The calculation objects were the \"BWB model with V-shaped support\" and \"BWB model without V-shaped support\". The influence of the V-shaped support on the aerodynamic characteristics was obtained by comparing the results with and without the support. The simulation of parameter influence was realized by changing the geometric parameters (such as D, L, H, and P) of the V-shaped support.  
Most BWB aircraft cruise at high subsonic speed [46; 47], for instance, the cruise Mach number of NPU-150 is 0.78. Thus, the simulating Mach number was designed as 0.75 in the current research. The angle of attack was \(-1^{\circ}\)\(\sim\)\(4^{\circ}\), the static temperature was 288K, and the Reynolds number based on average aerodynamic chord length of the wing was \(5\times 10^{6}\).
## 4 Experimental Results and Discussion
### Transmission Results
Figure 6: Electrical spectrum of the received QPSK IF signal: (**a**) 24 GBaud signal with 24.24 GHz BW; (**b**) 28 GBaud signal with 28.28 GHz BW; (**c**) 32 GBaud signal with 32.32 GHz BW; (**d**) 36 GBaud signal with 36.36 GHz BW.
## 6 Use Case: Privacy Identity Management Enrollment
### Machine Learning
#### 6.5.1 Evaluation Metrics  
Evaluating anomaly detection algorithms is a challenging task due to the absence of ground-truth-labeled data. Nevertheless, there are some commonly accepted classification-based metrics to estimate the model's predictive performance. In this project, we use three ranking-based metrics to assess our model's ability to separate between negative and positive deficiency diagnoses, along with two metrics that do not require labeled data.  
* Area Under the Receiver Operating Characteristics Curve (AUC-ROC): This metric takes into account the entire outlier ranking and is used to measure how well the anomaly detection system is capable of separating the classes of normal and anomalous instances. A high ROC AUC score indicates that, in the overall ranking, outliers are more likely to be ranked ahead of inliers, but it does not necessarily mean that the top rankings are dominated by outliers.
* Average Precision (AP): Average precision is a way to summarize the precision-recall curve within a single value representing the average precision across the recall levels. In the context of anomaly detection, higher AP scores represent higher-quality predictions.
* Similarity-based Internal Relative Evaluation of Outlier Solutions (SIREOS): This metric is a recent advancement in the domain of evaluating unsupervised algorithms in the absence of ground truth labels. SIREOS uses a similarity measure to discover how distinct the detected outliers (in the project's score, positive deficiency diagnoses) are, relative to the rest of the data points. This metric is proposed to overcome the computational demands of IREOS while showing comparable or even superior quality to similar unsupervised metrics. In the context of anomaly detection, lower SIREOS values indicate high-quality model predictive performance.  
Figure 4: TSNE visualization per client.