esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Experimental Platform
### Instrumentation of Fuel Pin and Validation
Second, we compared the Bragg wavelengths \(\lambda_{\mathrm{B}}\) with those determined when the fibres were unmounted and thus not strained. Third, we determined two characteristics of each reflection spectrum to indicate the level of distortion of the spectrum. These two characteristics were the full width at half maximum (FWHM) and the so-called unbalance (UNB) defined as  
\[FWHM= \lambda_{+}-\lambda_{-}\] \[\mathit{UNB}= |(\lambda_{+}-\lambda_{B})-(\lambda_{B}-\lambda_{-})|\]  
where \(\lambda_{+}\) and \(\lambda_{-}\) are the wavelengths for which the peak power is halved. Thus, the FWHM provides information about the width of the reflection peak, whilst the UNB measures the asymmetry. Note that we did not evaluate the reflectivity of the FBGs with each mounting step as this can vary significantly due to, for example, output power variations of the light source or dust on the fibre connector. The experimental results of the validation are summarized in Table 1. No FBGs were lost during the mounting on the fuel pin and installing in the test section and test facility, even when filling with LBE at elevated temperatures. We observed that the mean wavelength shifts after mounting on the fuel pin corresponded to a small compression of the FBG due to the shrinkage during curing of the adhesive. Moreover, the mean wavelength shift after installation in the heated test facility was on the order of 4.7 nm which is close to 0.4% strain, which confirms the need to allow for sufficient wavelength separation between the successive DTGs. Note that the spread of the wavelength shift is about 4 times the average value after mounting, which was the result of the wire spacer wrapped around the fuel pin exerting forces on the fuel pin that are non-homogeneous throughout the pin's length. The high density of FBGs and the 4 fibres on the same fuel pin even allowed for the determination of the pitch of the wire spacer (equal to 265 mm) based on the variation of the Bragg wavelength shifts. The same is true for the spread of the wavelength shifts as determined after installation in the heated and filled test facility, which also illustrates the need for sufficient wavelength spacing between the FBGs. Finally, we observed that the FWHM and the UNB were only slightly increased by the mounting and installation. These results indicate the validity of the mounting procedure in the fuel pin and the installation in the test facility. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Status** & **Unmounted FBGs** & **Mounted on Fuel Pin** & **Installed in Heated Test** \\  & **(At Room Temperature)** & **(At Room Temperature)** & **Section Filled With LBE** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Validation of the mounting procedure at 3 different phases.
# DTI Histogram and Texture Features as Early Predictors of Post-Radiotherapy Cognitive Decline
## 4 Discussion
Second, this study included patients receiving different radiotherapy regimens,i.e., WBRT and STI. Because our primary objective was to identify the DTI correlates of radiotherapy-induced cognitive decline, and partly due to the small sample size within each regimen, we did not evaluate treatment-specific microstructural WM changes or cognitive outcomes. Third, data collection at only two time points prevented tracking the longitudinal evolution of microstructural changes. Fourth, our whole-brain WM analysis lacked regional specificity, which may have masked important localized effects in critical cognitive networks. Fifth, the follow-up period was limited to four months post-radiotherapy, although cognitive changes may continue to develop over longer periods. Sixth, although regression models showed significant associations, some correlations were weak, particularly the associations of \(\Delta\)AD\({}_{\text{skewness}}\) (r = 0.073) and \(\Delta\)RD\({}_{\text{kurtosis}}\) (r = \(-\)0.172) to semantic fluency. This is thought to suggest that these individual features are yet to be clinically actionable on their own. Finally, the relatively large slice thickness of our DTI data may have reduced sensitivity to subtle changes and introduced partial volume effects. To mitigate some of these challenges, we limited feature extraction to only 10 features. While the extraction of hundreds of features is technically possible, our approach prioritized statistical validity and clinical interpretability over sheer quantity. In small sample studies, focusing on a concise feature set reduces the risk of spurious associations and alleviates the penalties associated with multiple comparisons--an established concern in high-dimensional, low-sample-size research. We also employed the non-parametric Wilcoxon signed-rank test--well-suited for small samples and non-normal data--and applied FDR correction to control for multiple comparisons, further enhancing the robustness of our findings. While not limitations per se, these methodological choices strengthen the validity of our results. Moreover, although we did not conduct treatment-specific analyses, the cohort's heterogeneity reflects real-world clinical diversity and underscores the need for future research to disentangle regimen-specific effects on WM and cognition.
# Hybrid ANN-Based and Text Similarity Method for Automatic Short-Answer Grading in Polish
## 4 Artificial Neural Network-Based Methods  
### ANN-Based Method  
The ANN-based method introduced a neural network model for scoring short answers. A dedicated multi-layer perceptron ANN was trained for each question using pre-processed student responses. The training process included the following steps:  
1. Data preparation: Text preprocessing was performed to filter punctuation, split sentences, and assign numerical representations to words using a dictionary [48]. Levenshtein distance was used for unmatched words, as shown in Listing 3.  
**Listing 3.** Reading dictionary for phrases.  
\begin{tabular}{|l l|} \hline function & Read\_Dict; \\ Input: \\ \(f\) - text file with words \\ Output: \\ \(DI\) - associative array (key: string, value: float) \\ \(N\) - number of lines (and integer index of last word in a basic form) \\ \(lines\) \(\leftarrow\) split\_to\_lines(f); \\ \(N\) \(\leftarrow\) length(\(lines\)) \\ for \(j\) from 0 to \(N\)-1 do \\ \(words\) \(\leftarrow\) split\_to\_words(\(lines\)[\(j\)]); \\ \(M\) \(\leftarrow\) length(\(words\)); \\ \(nums\) \(\leftarrow\) [\(j\), \(j\) + 0.5/\(M\),..., \(j\) + 0.5] \\ for \(k\) from 0 to \(M\)-1 do \\ \(DI[words[k]]\) \(\leftarrow\)\(nums[k]\); \\ end for; \\ end for; \\ end. \\ \hline \end{tabular}
2. Input normalisation: To accelerate training, Min-max normalisation scaled the input data between 0 and 1. \[V^{\prime}=\frac{V-Min_{A}}{Max_{A}-Min_{A}}\left(New_{Max_{A}}-New_{Min_{A}} \right)+New_{Min_{A}}\] (2)
3. Model structure: A compact (5-3-1) MLP architecture was used, where five input nodes represented the processed text, three hidden neurons captured patterns, and a single output node provided the score.  
Training and validation: The dataset was split into 80% for training and 20% for testing, and the models were trained for up to 15,000 epochs until achieving an optimal mean squared error (MSE).  
MSE Equation:  
\[MSE=\frac{1}{n}{\sum_{k=0}^{n}}(Y-X)^{2} \tag{3}\]  
where \(n\) is the number of symbols in the input dataset and \((Y-X)\) is the difference between the actual output and the desired output from the ANN training process.
## 4 Discussion
### Comparison with the Existing Literature  
Despite the broad interest in XAI method development, it remains challenging to compare different evaluations due to the absence of standardized evaluation metrics. This need for a common framework of metrics that can be applied universally is repeatedly concluded in the literature [12; 16; 55]. Similar to our findings, [PERSON] et al. (2024) mention a focus on visual and post hoc explanations, pointing out the unclear impact these explanations have on clinical outcomes. Additionally, they note the risk of overreliance on the model when providing users with insubstantial explanations, potentially even reducing decision performance. They emphasize the importance of improving AI's operational transparency in future XAI developments [12]. Similarly, [PERSON] et al. (2021) observed that XAI could boost clinicians' confidence and generate causal hypotheses in clinical decision support systems, increasing the systems' trustworthiness and acceptability. However, they mention a distinct shortage in XAI evaluations, particularly in user studies that address clinician needs [16]. In support, [PERSON] et al. (2020) argue that neglecting explainability in clinical decision support systems poses a threat to individual and public health [55]. The variability and scarcity of objective evaluations complicate effective comparisons of XAI methods' impacts. Developing standardized, clinically relevant evaluation metrics for XAI is needed to assess both the technical performance and explainability of AI models.
# Performance Analysis of a Keyword-Based Trust Management System for Fog Computing
## 3 Methodology
### Trusted Management System
Figure 2 presents a flowchart outlining the proposed system's steps. It visually represents how the trust management system operates, from initial data requests to establishing trust through keyword matching. Figure 1 presents the proposed trust model in the fog computing environment, illustrating domains, server nodes within each domain, and the mobile fog client nodes connected to them. It aims to depict how fog client nodes can move and connect with different server nodes within the same domain or neighboring domains. In the context of a fog node requesting data and the denial process, a more detailed explanation is as follows. Fog Node Data Request: When a fog node intends to request data from a server node, it initiates a connection request. The request includes the fog node's identity and potentially other relevant information, depending on the specific system design. Server Node Verification: Upon receiving the connection request, the server node verifies the fog node's trustworthiness using the trust management system. This verification involves checking the fog node's keywords against its own trust keywords table. If the fog node's keywords match the ones in the server node's table, the verification process proceeds. Figure 1: The proposed trust model. Trust Evaluation: The verification process is not solely based on keyword matching, but can involve other trust evaluation mechanisms, as previously discussed. These mechanisms assess various factors, such as past interactions, behavior analysis, or reputation systems, to determine the fog node's trustworthiness beyond the keywords. Decision and Denial: Based on the trust evaluation, if the fog node is deemed untrustworthy or if its keywords do not match the server node's trust keywords table, the server node can deny the connection request. This denial can take the form of rejecting the connection outright or redirecting the fog node to a different node within the fog computing environment. Because the scheme works on a request basis, any node becomes a server node that has the requested keyword. As such, it will save time if the keyword is not found, following which the fog node requests a new keyword. The trust is built through keywords. If the server node has information saved in its table of request keywords, then a connection and link are created for information sharing. The node table is created in the application layer. The proposed scheme works when a node requests data, then it becomes a fog node and the request moves among all nodes in the network. The node with information becomes a server node, and then the trust process starts by checking keyword information. If the keyword of that node is available in the server node, then the connection is built; otherwise, the node will request a new keyword. When all keywords are checked and none is found, then the request is rejected. The proposed approach builds trust using keywords, avoids bad nodes, and always gives an authentic node for connection.
# Fatique Crack Growth Studies under Mixed-Mode Loading in AISI 316 Stainless Steel
## 4 Conclusions  
This study investigated the mixed-mode fatigue crack growth in CTS specimens using finite element analyses with ANSYS Mechanical APDL. Upon conducting finite element analyses, the crack growth results were compared with experimental data for different loading angles. The results showed that the predicted stress-intensity factors obtained from the analytical solutions were in good agreement with the findings of this study. The mixed-mode fatigue life was predicted and compared with experimental data for loading angles of 30\({}^{\circ}\), 45\({}^{\circ}\), and 60\({}^{\circ}\). This study validated the accuracy of its simulation by comparing the predicted fatigue crack propagation paths, stress-intensity factors, and fatigue life cycles with analytical and experimental results from other researchers.  
Several significant findings were obtained as a result of the investigation presented in this study.  
* Investigating the in-plane mixed-mode I/II fatigue crack growth behavior of the AISI 316 austenitic stainless steel alloy is crucial for ensuring the safety and reliability of marine structures and various structural components subjected to mixed-mode loading.
* The 45-degree loading angle in a compact tension shear specimen corresponds to the maximum shear stress and maximum von Mises stress components, indicating a pure shear loading condition where the tensile and compressive stress components are balanced and cancel each other out.
* Advanced numerical simulation techniques such as the SMART crack growth feature in ANSYS APDL 19.2 have enabled more efficient and accurate modeling of mixed-mode fatigue crack growth in CTS specimens. This feature automates the process of crack creation and growth and uses advanced algorithms to predict the crack growth path.
* The findings may be useful for improving the design and optimization of materials for marine applications and structural components subjected to mixed-mode loading.  
Conceptualization, A.M.A.; Methodology, A.M.A.; Software, A.M.A.; Validation, A.M.A. and A.H.B.; Formal analysis, A.M.A.; Investigation, A.M.A.; Resources, A.M.A. and A.H.B.; Data curation, A.M.A.; Writing--original draft, A.M.A.; Writing--review & editing, A.M.A. and A.H.B.; Visualization, A.M.A. and A.H.B.; Supervision, A.M.A. and A.H.B.; Project administration, A.M.A. and A.H.B.; Funding acquisition, A.M.A. and A.H.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
All relevant data are contained in the present manuscript.  
The author declares no conflict of interest.
## 3 Problem Formulation
### Content Placement Algorithm Based on the Cross-Entropy Method (CPCE)  
Before joining in a network, a specific UE will measure Channel State Information (CSI) and choose the candidate BSs with the biggest reference signal received power (RSRP). To investigate the content placement strategies of BSs, we assume the conventional \(N\)-Best scheme for user association in the first place. BSs in the network are modeled as decision-making entities in the CE method, and feasible content placement candidates are strategies of each entity. The CPCE algorithm can be depicted as Algorithm 1.  
The optimized content placement strategy \(\mathbf{Y}\) can be given by Algorithm 1, under the assumption of the \(N\)-best user association scheme. User association results can be further optimized with the obtained \(\mathbf{Y}\).  
```
1:User association under \(N\)-Best strategy.
2:Generate content placement request samples of UEs \(\boldsymbol{q}_{m}\) based on Zipf distribution. Map BSs \(\longleftrightarrow\) Decision-making entities in CE method.
3:Map the content placement strategy set \(\longleftrightarrow\) Strategies in CE method.
4:Map the sum backhaul load \(\longleftrightarrow\) Fitness value in CE method.
5:Execute CPCE.
6:Map the obtained solution into the best content placement strategies of BSs and output \(\mathbf{Y}\).
```  
**Algorithm 1** Content Placement based on CE method (CPCE)
###### Abstract  
Energy-efficient driving systems can effectively reduce energy consumption during vehicle operation. Most of the existing studies focus on the driving strategies in a fixed signal timing environment, whereas the standardized Signal Phase and Timing (SPaT) data can help the vehicle make the optimal decisions. However, with the development of artificial intelligence and communication techniques, the conventional fixed timing methods are gradually replaced by adaptive traffic signal control (ATSC) approaches. The previous studies utilized SPaT information that cannot be applied directly in the environment with ATSC. Thus, a framework is proposed to implement energy-efficient driving in the ATSC environment, while the ATSC is realized by the value-based reinforcement learning algorithm. After giving the optimal control model, the framework draws upon the Markov Decision Process (MDP) to make an approximation to the optimal control problem. The state sharing mechanism allows the vehicle to obtain the state information of the traffic signal agents. The reward function in MDP considers energy consumption, traffic mobility, and driving comfort. With the support of traffic simulation software SUMO, the vehicle agent is trained by Proximal Policy Optimization (PPO) algorithm, which enables the vehicle to select actions from continuous action space. The simulation results show that the energy consumption of the controlled vehicle can be reduced by 31.73%-45.90% with a different extent of mobility sacrifice compared with the manual driving model. Besides, we developed a module based on SHapley Additive exPlanations (SHAP) to explain the decision process in each timestep of the vehicle. That can make the strategy more reliable and credible.  
energy efficient driving; electric vehicles; reinforcement learning; signalized intersection +
Footnote †: journal: Applied Sciences
## 3 Underwater Routing Protocols
### Lasr  
The authors in [27] modify the dynamic source routing (DSR) to location-aware source routing (LASR) and include two more features. The first feature signifies that nodes are aware of their positions as the network topology changes with water currents. The second feature includes link quality to route packets along the paths with minimal noise and interference. However, the route information may not be updated in time due to high propagation delay in underwater communications. As a result, a forwarder is selected on the basis of old route information. Consequently, the best forwarder nodes may not be selected. For instance, a forwarder may change its position with water currents or its route information may change due to the unpredictable and dynamic nature of the underwater medium. At the same time, other nodes may not become aware of these changes due to high propagation delay in underwater communications.
# Effect of Endodontic Irrigation on the Bond Strength of Composite Restoration Repair
## 4 Discussion
Composite-composite bond strength varies depending on the filler particle size and distribution of the material. Microhybrid composites interact more effectively with silane and binder systems due to their larger filler particles and generally offer higher bond strength during repair. On the other hand, nanocomposites may be more sensitive to surface treatments due to their smaller particle sizes. However, treatments performed to increase surface roughness (such as sandblasting and silica coating) have been observed to increase bond strength in both microhybrid and nanocomposites, while microhybrid composites maintain their superiority [42,43]. Filtek Supreme Z250 with microhybrid content was preferred in our study. In composite repair studies, the use of total-etch and self-etch techniques is among the most important factors affecting bond strength. The total-etch technique completely removes the oxygen inhibitor layer and surface contaminants on the existing composite surface and prepares a suitable ground for micromechanical and chemical bonding. This technique, especially when combined with silane application and binder systems, offers higher values in repair bond strength. Although the literature indicates that total-etch techniques generally provide superior bond strength, self-etch techniques can also produce acceptable results, especially in combination with surface treatment [15,21,44]. As one of the limitations of the study, the SEM images presented were not part of the primary analysis method but were obtained from a limited number of specimens to provide a visual impression of possible surface changes on the composite material following different irrigation protocols. Since there is no standardized SEM evaluation scale for composite surfaces and the number of specimens was low, detailed morphological analysis was not performed; the images were presented solely for supportive purposes. In this regard, the SEM images are not considered a main outcome of the study but rather supplementary data that may serve as a basis for future, more comprehensive investigations. Although no clear difference was detected through qualitative analysis, it should be noted that all images were obtained at a standard accelerating voltage of 20 kV, as determined by the imaging center. Lower kV settings might have provided enhanced surface detail with reduced charging artifacts, and this should be considered in future studies involving resin-based materials.
## 3 Results
### Surface Properties  
Figures 2 and 3 show the FTIR analysis for all samples to investigate the surface properties and the stability of synthesized samples in liquid media. Starting by measuring the ZP values of all samples, it was found that the ZP of AC-OH is less than 20 mV, suggesting the good stability of the materials. Although, ZP results confirmed the successful addition of hydroxyl groups at the AC surface, as supported by noticeable decreases in ZP. The surface charge of the particles has a potential effect on catalytic activity, as it  
Figure 1: SEM images of AC (**a–c**), AC-OH (**d–f**), and AC-TiO\({}_{2}\) (**g–i**).  
could involve such interaction with pharmaceutical pollutants. The results on the AC-TiO\({}_{2}\) surface displayed a marked decrease in the zeta potential from 8 mV for AC to \(-\)14 mV for AC-TiO\({}_{2}\). The negative surface charge could play a key role in the adsorption of pollutants from the water.  
According to the Fourier transform infrared spectrum shown in Figure 2, the peaks of the AC surface before and after hydroxylation treatment are different from one sample to another. For AC, the peak located at 1579 cm\({}^{-1}\) is assigned to the stretching vibration of C=C, whereas the band observed at 1259 cm\({}^{-1}\) is attributed to the bending vibration of C-H in the methylene group. At the same time, the spectrum of AC-OH displayed a visible change due to the hydroxylation steps. The new band that appeared at 3415 cm\({}^{-1}\) is associated with hydroxyl groups' stretching vibration (-OH). Accordingly, the chemical structure of the synthesized samples, the bands centered at around 1720 cm\({}^{-1}\) and 1105 cm\({}^{-1}\), are attributed to the presence of the C=O and C-O groups, respectively.  
Figure 3: FTIR spectra of AC and AC-TiO\({}_{2}\).  
Figure 2: FTIR spectra of AC and AC-OH.  
A comparison between the FTIR spectra of AC and AC-TiO\({}_{2}\) is shown in Figure 3. The stretching vibration of the hydroxyl group registered at 3400 cm\({}^{-1}\) is assigned to the physisorbed surface water [34]. A slight shift to a lower wavenumber is observed and explained by the presence of TiO\({}_{2}\) on the AC surface. According to [PERSON] et al., the peak that appeared at 768 cm\({}^{-1}\) is assigned to the stretching vibration of Ti-O [34].