esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Changes in the Tumor Weight  
Significant reductions (\(p<0.01\)) in the tumor absolute and relative weights were confirmed in all the drug administration groups in comparison with the tumor transplantation control group. In particular, significant reductions in the tumor relative and absolute weights were confirmed in all the sasam-Kyeongokgo and gefitinib combination groups (\(p<0.01\) or \(p<0.05\)), even compared with the gefitinib-alone group (Tables 3 and 4).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Groups** & **Tumor Volume (mm\({}^{3}\))** & **Changes (mm\({}^{3}\))** \\ \cline{2-4}  & **First Administration (A)** & **Sacrifice (B)** & **(B\(-\)A)** \\ \hline Control & & & \\ TB & 469.02 \(\pm\) 102.47 & 6487.36 \(\pm\) 862.66 & 6018.34 \(\pm\) 774.83 \\ Single formula treated & & & \\ Gefitinib & 462.10 \(\pm\) 105.87 & 2858.51 \(\pm\) 377.01 \({}^{\rm a}\) & 2396.41 \(\pm\) 329.84 \({}^{\rm a}\) \\ SKOG & 472.89 \(\pm\) 141.15 & 3972.27 \(\pm\) 655.53 \({}^{\rm ab}\) & 3499.37 \(\pm\) 557.32 \({}^{\rm ab}\) \\ Gefitinib and SKOG & & & \\
400 mg/kg & 473.39 \(\pm\) 137.02 & 1209.50 \(\pm\) 335.04 \({}^{\rm ab}\) & 736.11 \(\pm\) 235.75 \({}^{\rm ab}\) \\
200 mg/kg & 477.76 \(\pm\) 161.99 & 1670.83 \(\pm\) 471.94 \({}^{\rm ab}\) & 1193.07 \(\pm\) 375.71 \({}^{\rm ab}\) \\
100 mg/kg & 475.73 \(\pm\) 122.90 & 2125.75 \(\pm\) 442.71 \({}^{\rm ab}\) & 1650.02 \(\pm\) 431.10 \({}^{\rm ab}\) \\ \hline \hline \end{tabular} TB = tumor-bearing; AR = Adenophorae Radix; KOG = _Kyeongokgo_; SKOG = _sasam-Kyeongokgo_. Before the first administration and sacrifice, all the rodents were fasted overnight. \({}^{\rm a}\)\(p<0.01\) versus TB control, assessed by the Mann–Whitney U test; \({}^{\rm b}\)\(p<0.01\) versus gefitinib-single-formula-treated rodents, assessed by the Mann–Whitney U test.  
\end{table}
Table 2: Changes in the tumor volume in NCI-H520 tumor cell xenograft mice.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Groups** & **Tumor Mass** & **Spleen** & **Submandibular** & **Periovarian Fat Pad** \\ \hline Controls & & & \\ \multicolumn{1}{c}{Intact} & 0.110 \(\pm\) 0.011 & 0.013 \(\pm\) 0.004 & 0.050 \(\pm\) 0.013 \\ TB & 2.808 \(\pm\) 0.282 & 0.054 \(\pm\) 0.005 \({}^{\rm a}\) & 0.004 \(\pm\) 0.001 \({}^{\rm d}\) & 0.008 \(\pm\) 0.002 \({}^{\rm d}\) \\ Single-formula-treated & & & & \\ \multicolumn{1}{c}{Gefitinib} & 1.664 \(\pm\) 0.292 \({}^{\rm f}\) & 0.051 \(\pm\) 0.007 \({}^{\rm a}\) & 0.004 \(\pm\) 0.001 \({}^{\rm d}\) & 0.007 \(\pm\) 0.002 \({}^{\rm d}\) \\ SKOG & 2.036 \(\pm\) 0.249 \({}^{\rm f}\) & 0.080 \(\pm\) 0.008 \({}^{\rm abc}\) & 0.008 \(\pm\) 0.002 \({}^{\rm dfg}\) & 0.024 \(\pm\) 0.004 \({}^{\rm dfg}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Changes in the absolute tumor mass and organ weights in the NCI-H520 tumor cell xenograft mice.
# Unlocking the Detoxification of Phenanthrene from Water Using Alkali-Activated Slag Mortar
## 3 Results and Discussions
### Characterization of Ground Granulated Blast Furnace Slag (GGBFS) and Alkali-Activated Slag (AAS)
#### 3.1.4 Nitrogen Sorption-Desorption Isotherm  
The N\({}_{2}\) sorption-desorption curves of GGBFS were found to fit type II, which resulted from the nonporous structure, whereas type I was found to be appropriate for describing the sorption-desorption plots of AAS, revealing its microporous and mesoporous characteristics (Figure 3a) [29]. After alkali activation, the specific surface area (SSA) of AAS increased from 0.53 to 61.59 m\({}^{2}\)/g (Table 2). The increase in SSA of AAS was attributed to the formation of calcium silicate hydrate minerals [30]. Additionally, the pore diameters of AAS exhibited a pore size distribution mainly ranging from 1 to 10 nm (Figure 3b), clearly indicating mesoporous characteristics.
# Research on Localization Algorithms Based on Acoustic Communication for Underwater Sensor Networks
###### Abstract  
The water source, as a significant body of the earth, with a high value, serves as a hot topic to study Underwater Sensor Networks (UWSNs). Various applications can be realized based on UWSNs. Our paper mainly concentrates on the localization algorithms based on the acoustic communication for UWSNs. An in-depth survey of localization algorithms is provided for UWSNs. We first introduce the acoustic communication, network architecture, and routing technique in UWSNs. The localization algorithms are classified into five aspects, namely, computation algorithm, spatial coverage, range measurement, the state of the nodes and communication between nodes that are different from all other survey papers. Moreover, we collect a lot of pioneering papers, and a comprehensive comparison is made. In addition, some challenges and open issues are raised in our paper.  
acoustic communication localization; underwater sensor network; survey; spatial coverage; range measurement +
Footnote †: journal: Sensors
## 6 Discussion
### Explainability Approaches and Alternatives  
In this part, we discuss the necessity of explainability in the healthcare domain and different approaches: the white (i.e., transparent) and black (i.e., opaque) boxes. In AI, there is an inherent conflict between performance and explainability: the most complex models(e.g., ensemble trees and neural networks) usually perform best but are less interpretable. Many ML studies predicting hospital admissions have used decision trees or ensemble models, which offer good performance and some level of interpretability. ANN models have been less commonly used--probably due to the challenges of interpretation and the higher computational costs involved in their design and training [15; 16; 17]. Nevertheless, deep learning is a useful method when the data are unstructured (such as the text in clinical notes) or when the number of variables is high [15; 16; 17]. The accuracy-explainability trade-off is therefore domain-specific; in the field of healthcare, the end users (i.e., clinicians and, in some cases, patients) and data scientists must agree on the trade-off point.  
Several XAI studies of the prediction of hospital admission have focused on the importance of the input features. For example, [PERSON] et al. applied SHAP to a prediction model of psychiatric ward readmissions in which multivariate sources of information (demographics, pharmacology, psychology, and vital signs) were combined [35]. Recent studies have applied deep learning and XAI techniques to predict COVID-19-related hospital admissions, resource needs, and clinical deterioration [63; 64; 65], given the importance of diagnosing and understanding subtle disease patterns. Hence, an interpretable deep learning model was developed in order to predict the ICU admission and death of patients with COVID-19 [78]. Furthermore, [PERSON] et al. leveraged iterative feature selection to improve the interpretability of their ML framework for predicting the patient-level risk of hospital admission, ICU admission, the need for mechanical ventilation, and death [79]. In addition, the timely decision-making capabilities that AI models offer in ICUs have been applied to children with pneumonia; SHAP was used to assess random-forest-based prediction models (AUROC: 98.7%) and thus obtain a feature importance list that was subsequently validated by physicians [80]. XAI-based feature relevance techniques are used to support AI models for predicting hospital admission in other fields of medicine, including cardiology [81], organ transplantation [82], spine surgery [83], acute gastrointestinal bleeding [84], and general readmissions [85].
# Data Naming Mechanism of LEO Satellite Mega-Constellations for the Internet of Things
## 2 LEO Mega Constellation for the IoT
### Interstellar Link of LEO Mega Constellation  
For an LEO satellite mega constellation oriented to the IoT, intersatellite communication is required. The global coverage of the satellite constellation and the interconnection of everything in ground-side communications cannot be achieved without ISL. According to the topological characteristics of the LEO mega constellation, intersatellite communication links include links between adjacent satellites on the same orbital plane and satellite links between adjacent orbital planes.
## 3 Results and Discussion
### Stuck Fermentation Altered the Profile of Non-Volatile Metabolites in Summer Black Tea
#### 3.2.6 Saccharides  
Studies show that the sweetness of tea depends on the presence of saccharides. As shown in Figure 3F, the content of D-sorbitol (CAS:50-70-4), ribitol (CAS:488-81-3), dulcitol (CAS:608-66-2), xylitol (CAS:87-99-0), L-arabitol (CAS:7643-75-6) and D-arabitol (CAS:488-82-4) increased after stuck fermentation. These sugar alcohols are widely used as sweeterers in the food industry due to their high sweetness and low glycemic index. In addition, we also detected a significant increase in sucrose (CAS:50-99-7) levels in FHYH. On the other hand, D-glucose (CAS:50-99-7) and D-fructose (CAS:57-48-7) levels decreased after fermentation, which may be due to the microbial synthesis of sucrose, or utilization of glucose and fructose by _Aspergillus_ as a source of energy [29]. [PERSON] et al. also reported a decrease in D-glucose and D-fructose in microbially fermented tea [30]. The concentration of sweet compounds in the tea soup, however, is below the threshold concentration for taste perception, according to earlier research, and they thus do not considerably add to the sweetness of the soup [31]. Nevertheless, we have evidence that stuck fermentation can increase the content of saccharides in summer black tea, ultimately enhancing the sweetness of the tea soup.
# Antennas for Licensed Shared Access in 5G Communications with LTE Mid- and High-Band Coverage
## 3 Dual-Band Antenna for LSA Bands and LTE Mid and High Bands
### Radiation Performance  
The predicted antenna efficiency and gain as a function of frequency are shown in Figure 10. The maximum antenna efficiencies in the mid and high bands are 90% and 88%, respectively, and the minimum antenna efficiencies are 62% and 66%, respectively. The realized gain varies between 1.4 and 3.4 dBi in the mid band, and between 4.2 and 6.1 dBi in the high band.  
The normalized \(E_{\phi}\) and \(E_{\phi}\) radiation patterns in the three principal planes at 1.8, 2.6, and 3.6 GHz are shown in Figure 11. The radiation patterns are normalized with respect to the maximum of the corresponding principal plane. The \(E_{\phi}\) pattern in the x-z plane at 3.6 GHz is more uniform (nearly omnidirectional) than the \(E_{\phi}\) pattern in the same plane at 1.8 and 2.6 GHz. A uniform \(E_{\phi}\) pattern can be observed in the y-z plane at 1.8 GHz. Strong \(E_{\phi}\) radiation can be observed along the y-axis at 3.6 GHz compared to the z-axis. In addition, the \(E_{\phi}\) pattern is more uniform in the x-y plane at 1.8 GHz than the \(E_{\phi}\) pattern at 3.6 GHz. The \(E_{\phi}\) pattern in the x-y plane has two nulls at 3.6 GHz and one null at 2.6 GHz. Overall, the \(E_{\phi}\) component is always stronger than \(E_{\phi}\) in the y-z and x-y planes at 1.8 and 2.6 GHz and in the x-z and x-y planes at 3.6 GHz. In addition, the \(E_{\phi}\) and \(E_{\theta}\) levels are comparable in the x-z plane at 1.8 and 2.6 GHz and in the y-z plane at 3.6 GHz, an additional benefit of practical applications with complex propagation environments [18; 24; 26].  
Figure 10: Predicted antenna efficiency and realized gain of the dual-band antenna (Ant#2).
# A Novel Technique for Semantic Segmentation of Hyperspectral Images Using Multi-View Features
###### Abstract
A variety of features like Local Binary Pattern (LBP), Gabor Filters, Gray Level-Gradient Co-occurrence matrix (GLCM) were fused to provide important features to SVM to detect weed and corn in a field [27]. Kernelized SVM helped to fuse different selected features for multi-class classification with recursive feature elimination [28]. Multi-view features were embedded with SVM to detect clouds in remote sensing images [29]. Multiple feature views extracted through GLCM and LBP fused with SVM also improved classification of mitotic and non-mitotic cells in breast cancer images [30]. Hence, Multi-view feature learning forms a strong research area for application on HSI. HSI by their very nature, can be treated as a multi-view dataset by partitioning them on the basis of each wavelength at which the scene has been captured. Each spectral band provides us a distinct view of the same scene. To extract useful patterns from multiple views and extract features simultaneously, Canonical Correlation Analysis (CCA) has been proposed to be a useful technique.
# Fault-Resilient Manufacturing Scheduling with Deep Learning and Constraint Solvers
## 7 Case Study and Results
### Performance Evaluation  
This section evaluates the computational efficiency of the proposed framework integrating deep learning-based fault prediction with SMT-based scheduling optimization. The evaluation focuses on processing times for both components, demonstrating the framework's capability for real-time operation in edge computing environments. Table 8 summarizes the computational performance metrics.  
The results demonstrate the framework's efficiency in both prediction and optimization phases:  
* Fault Prediction Performance: The deep learning model achieves an average inference time of 0.01875 s with high consistency (standard deviation = 0.00171 s), enabling rapid fault probability assessment for each machine.
* Scheduling Optimization Performance: The SMT-based scheduler generates optimized schedules in 0.02038 s on average (standard deviation = 0.00548 s), efficiently handling complex constraints including predicted fault probabilities and task dependencies.  
The total processing time of approximately 0.04 s (prediction + optimization) enables the framework to respond to dynamic changes in near real-time. In combination with the system's consistent performance as indicated by the low standard deviations, this rapid response capability makes it particularly suitable for practical implementation in dynamic manufacturing environments where quick scheduling decisions are crucial.
## 4 Discussion
### Role of the Plio-Quaternary Succession Load  
Possible relationships between subsidence and spatial variation of the Plio-Quaternary succession thickness in the Cracti Delta area (Figure 7a) as reconstructed from multichannel seismic profiles and well data [75] was investigated. The thickness observed in the seismic profile has been converted from Two Way Travel time to meter using an average velocity of 2000 m/s for the Pliocene and Pleistocene deposits based on the sonic logs, available for Thurio and Ogliastrello wells, analyzed by [44].  
The Envisat Vertical component superimposed to the Plio-Quaternary succession isopach map (Figure 7b) shows a prevalent distribution of vertical displacement rates toward the coastline, where the deposits are thicker.  
We compared ground velocity profiles, for the Envisat Vertical component, with the corresponding profiles of the Plio-Quaternary deposits thickness. The comparison (Figure 7c) shows a good correlation between the general subsidence trend and the Plio-Quaternary succession thickness. We observed higher subsidence values in the E and NE sectors of Cracti Delta, where the Plio-Quaternary succession is thicker. In the SW sector, the reduced thickness corresponds with lower subsidence values. Lower values are also detected in the S-SE sector (Corigliano harbor area), where the presence of shallow igneous-metamorphic bedrock drives the Plio-Quaternary succession thickness.  
Furthermore, we considered the ascending and descending ground velocity values in correspondence of the seismic profiles and wells locations, correlating the ground velocity with the thickness of Plio-Quaternary deposits (Figure 8). Both ascending and descending velocities increase with the growth of the succession thickness.  
Based on the above analysis results, we found a direct correlation between subsidence spatial trends and distribution of the thickness of Plio-Quaternary deposits, which amplify the subsidence phenomenon as well documented during the evolution of the sedimentary basins [76; 77; 78; 79; 80; 81].  
Figure 6: Envisat and COSMO-SkyMed both ascending and descending time series (error = \(\pm\)1 mm) for the three selected sectors delimited by ITHACA [46] faults (location in Figure 5). Black dotted line represents the earthquakes (M = 4.5) occurred on 27 June 2006.
###### Abstract  
Fabrication of a clinically successful dental prosthesis requires clear and effective communication between dentists and dental technicians. Adequate completion of work authorization by dentists provides a means for increased professional quality assurance and satisfaction in dental prosthesis. The aim of this study was to evaluate the quality of communication between dentists and dental technicians via work authorization for fixed and removable dental prosthesis in Pakistan. This cross-sectional study was conducted for a period of 6 months to assess the quality of communication between dentists and dental technicians. A well-constructed questionnaire regarding work authorization of removable and fixed prostheses was used as a tool to collect data from 453 dentists. A linear regression analysis was performed to evaluate the relationship of the independent and dependent variables. A p value of \(\leq\)0.05 was considered statistically significant. It was observed that 92.1% of the dentists leave removable partial denture design to the dental technicians. About 56.5% of the dentists indicated information about base retention of partial dentures in work authorization. The majority of the dentists (56.5%) mentioned the material to be used for the construction of casting partial dentures. Nearly 66% of the dentists indicated the design of margins, and 44.6% of the dentists did not select the shade for fixed prostheses. Nearly 43% of the dentists did not draw the design of restoration, 21.9% chose to draw the design on paper, and 32.2% of the dentists drew the design on the cast. Meanwhile, 76.8% of the participants chose to directly communicate with their dental technicians personally. Nearly 41% of the dentists were satisfied with the restoration design, and 13.5% of the participants were not satisfied with the designs. Poor communication between dentists and technicians was observed, as the majority of the design decisions were left to the dental technicians. Therefore, communication between dentists and dental technicians should be improved by conducting combined workshops for the successful construction of dental prostheses.  
communication; dentist; dental technician; removable partial denture; fixed prosthesis +
Footnote †: journal: Applied Sciences
# Privacy-Preserving Authentication Based on PUF for VANETs
## 3 Preliminaries
### Design Goals  
**Authentication and Integrity:** Upon receiving a message, both vehicles and RSUs must possess the capability to ascertain its validity. Should the message be falsified or altered during transmission by unauthorized parties, the receiver should be equipped to detect such tampering.  
**Physical protection:** In order to ensure the security of the vehicles, the secret keys of the vehicles must not be physically stolen.  
**Anonymity and Traceability:** There is nobody else but CA who can obtain the vehicles' real identity through the messages from the given vehicles.
# Remote Sensing Image Target Detection Method Based on Refined Feature Extraction
## 3 Experimental Results and Analysis
### Introduction to the Dataset  
To evaluate the effectiveness and generalization of the proposed method, comprehensive experiments were performed on two widely used remote sensing image target detection datasets: DOTAv1.0 and DIOR. For the DOTAv1.0 dataset, preprocessing was conducted by segmenting the original images into sub-images of size 1024 \(\times\) 1024 pixels with an overlap degree of 200. From the segmented images, a total of 10,000 samples were randomly selected. These samples were then divided into a training set, a validation set, and a test set, following a split ratio of 7:1:2, respectively. This preprocessing step ensures that the DOTAv1.0 dataset is appropriately prepared for the training, validation, and testing of the proposed method. It enables the algorithm to learn and generalize from diverse sub-images, enhancing its ability to detect targets in remote sensing imagery accurately.
# Evaluation of a Newly Developed Device for Resonance Disorders: A Clinical Pilot Study
## 3 Results
### Similarity Evaluation
#### 3.2.2 Evaluation of Test-Retest Reliability of Both Devices in the Hypernasality Group  
In the hypernasality group, the test-retest reliability of Smart NasoTM for nasal sound was very high (0.90 or higher) in the sentence patterns of /a/, /i/, /papi papi/, /p\({}^{\text{h}}\)ap\({}^{\text{h}}\)p\({}^{\text{h}}\)ap\({}^{\text{h}}\)i p\({}^{\text{h}}\)ap\({}^{\text{h}}\)i/, /mami mamiami/, /p\({}^{\text{h}}\)aie p\({}^{\text{h}}\)arul parusio/, /suk\({}^{\text{h}}\)itu turesurul posip's'io/, and /ammanum remontuusuul mandurajo/ and high (0.75 or higher) in the sentence patterns of /p'ap'i p'ap'i p'ap'i/ of /i/, /nani nani/, and /anip aipn aipn/.  
The test-retest reliability of the Nasometer II 6450 in the same group was very high (\(\geq\)0.90) for the sentence patterns /a/, /i/, /p\({}^{\text{h}}\)ap\({}^{\text{h}}\)i p\({}^{\text{h}}\)ap\({}^{\text{h}}\)i/, /p'ap'i p'ap'i'p'ap'i/, and /p\({}^{\text{h}}\)aie p\({}^{\text{h}}\)arul parusio/, while demonstrating high reliability (\(\geq\)0.75) for the sentence patterns /papi papi/, /mami mamiami/, /nani nani/, /anip aipn aipn/,  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Sentence Pattern**} & \multicolumn{6}{c}{**Test-Retest Reliability in the Normal Group**} \\  & **Reliability of Smart NasoTM Results** & \multicolumn{6}{c}{**Reliability of Nasometer II 6450 Results**} \\  & **Session 1** & **Session 2** & **ICC** & **Session 1** & **Session 2** & **ICC** \\  & & & **(2,1)** & & **(2,1)** \\ \hline /a/ & 39.67 & 37.44 & 0.859 & 31.67 & 23.78 & 0.861 \\  & \(\pm\)13.73 & \(\pm\)10.45 & \(\pm\)24.78 & \(\pm\)16.87 & 0.861 \\  & 47.89 & 38.22 & 0.810 & 37.22 & 31.00 & \\ /i/ & \(\pm\)23.01 & \(\pm\)19.89 & & \(\pm\)21.62 & \(\pm\)20.96 & 0.896 \\  & 35.11 & 28.44 & 0.780 & 21.00 & 23.11 & 0.751 \\  & \(\pm\)14.53 & \(\pm\)8.56 & & \(\pm\)11.73 & \(\pm\)16.97 & 0.751 \\  & 25.11 & 22.67 & & 18.44 & 15.67 & \\  & \(\pm\)8.05 & \(\pm\)11.40 & 0.858 & \(\pm\)11.70 & \(\pm\)12.43 & 0.019 \\  & 33.22 & 27.33 & & 18.89 & 12.56 & \\  & \(\pm\)10.41 & \(\pm\)8.50 & & \(\pm\)12.12 & \(\pm\)4.77 & \\  & 72.56 & 66.89 & & 66.00 & 60.56 & \\  & \(\pm\)9.46 & \(\pm\)8.45 & & \(\pm\)12.70 & \(\pm\)10.42 & 0.910 \\  & 71.23 & 68.22 & 0.594 & 67.33 & 64.44 & \\  & \(\pm\)10.66 & \(\pm\)9.09 & & \(\pm\)9.95 & \(\pm\)8.76 & 0.751 \\  & 79.89 & 76.89 & & 76.33 & 75.00 & 0.858 \\  & \(\pm\)11.02 & \(\pm\)10.53 & & \(\pm\)9.17 & \(\pm\)8.51 & \\  & 25.78 & 23.89 & & 17.56 & 12.22 & \\  & \(\pm\)6.82 & \(\pm\)8.75 & & \(\pm\)9.55 & \(\pm\)7.03 & 0.689 \\  & 19.67 & 17.44 & 0.863 & 16.67 & 12.00 & \\  & \(\pm\)6.96 & \(\pm\)4.30 & & \(\pm\)10.46 & \(\pm\)5.20 & 0.760 \\  & 54.22 & 50.11 & 0.920 & 49.56 & 43.22 & \\  & \(\pm\)7.12 & \(\pm\)7.85 & & \(\pm\)10.99 & \(\pm\)7.03 & 0.802 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Evaluation of the Smart NasoTM test-retest reliability in the normal group.  
and /sukhiu turesurul posip'sio/, and low reliability (<0.50) for the sentence pattern /ammanum remontcusuul mandur.ajo/ (Table 6).