esa-sceva/satcom-chunk-collection huggingface dataset:
# Understanding Balance Control in the Context of Riding a Personal Mobility Device
## 3 Results
### Balance Task
#### 3.2.2 Subjective Measures  
The ANOVA results showed that the target distance (PP: F(2, 40) = 8.59, \(p\) \(<\) 0.001, \(\eta\)p2 = 0.30; ET: F(2, 40) = 28.91, \(p\) \(<\) 0.001, \(\eta\)p2 = 0.59; PS: F(1.31, 26.23) = 102.06, \(p\) \(<\) 0.001, \(\eta\)p2 = 0.84; PC: F(1.67, 33.34) = 37.89, \(p\) \(<\) 0.001, \(\eta\)p2 = 0.65), target direction (PS: F(3.03, 60.60) = 10.41, \(p\) \(<\) 0.001, \(\eta\)p2 = 0.34; PC: F(4, 80) = 5.55, \(p\) \(=\) 0.001, \(\eta\)p2 = 0.22), and interaction of the target distance and direction (ET: F(8, 160) = 5.53, \(p\) \(<\) 0.001, \(\eta\)p2 = 0.22; PS: F(5.79, 115.84) = 0.62, \(p\) \(<\) 0.001, \(\eta\)p2 = 0.23; PC: F(8, 160) = 3.72, \(p\) \(=\) 0.001, \(\eta\)p2 = 0.16) had significant effects on the subjective evaluation of the balance control task. In the post-hoc tests, all the subjective ratings significantly decreased as the target distance increased (Table 5). The PP and ET were only affected by the target distance, but there was a significant interaction effect between the target distance and direction on the ET. Both the PS and PC were also significantly affected by the target direction and there were significant interaction effects of the target distance and direction on them. In a comparison of the values in the directions at each distance, the ET, PS, and PC showed significant differences only at distance 3. The post-hoc tests on the simple effects revealed that the ET, PS, and PC decreased from the left and right directions (W and E) to the forward directions (NW, NE, and N).
# A Printed Wearable Dual-Band Antenna for Wireless Power Transfer
## 3 Experimental and Simulation Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Air**} & \multicolumn{2}{c}{**Air**} & \multicolumn{2}{c}{**Phantom**} & \multicolumn{2}{c}{**Phantom**} \\  & **(First Resonance)** & **(Second Resonance)** & **(First Resonance)** & **(Second Resonance)** \\ \hline
**Parameter** & **M** & **S** & **M** & **S** & **M** & **S** & **M** & **S** \\ \hline S\({}_{11}\) & 2.50 & 2.32 & 4.53 & 4.39 & 2.50 & 2.31 & 4.65 & 4.44 \\ S\({}_{22}\) & 2.28 & 2.05 & 4.43 & 4.28 & 2.30 & 2.03 & 4.43 & 4.26 \\ S\({}_{21}\) & 2.48 & 2.32 & 4.45 & 4.28 & 2.51 & 2.31 & 4.68 & 4.26 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Resonance frequencies (GHz) (M: Measurement, S: Simulation). Figure 5: Total gain of the TX antenna (dBi) (**a**) at 2.5 GHz, (**b**) at 4.5 GHz. The maximum permissible exposure (MPE) in uncontrolled environments for electromagnetic field strengths is evaluated by the value of specific absorption rate (SAR). Based on IEEE Std. C95.1, SAR should be below 0.08 \(\frac{W}{kg}\) as averaged over the whole body and the maximum SAR value should be below 1.6 \(\frac{W}{kg}\) as averaged over any 1 g of the tissue. However, the maximum SAR value should be below 4 \(\frac{W}{kg}\) as averaged over any 10 g of the tissue for the hands, feet, ankles, and wrists [38]. As it is indicated in Figure 8, the maximum absorption rate is 0.21 \(\frac{W}{kg}\) at 2.5 GHz and 0.57 \(\frac{W}{kg}\) at 4.5 GHz. The proposed antenna is within the safety standards. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**First Resonance**} & \multicolumn{4}{c}{**Second Resonance**} \\ \hline \multirow{2}{*}{**Parameter**} & **E-Plane** & **E-Plane** & **H-Plane** & **H-Plane** & **E-Plane** & **E-Plane** & **H-Plane** & **H-Plane** \\  & **M** & **S** & **M** & **S** & **M** & **S** & **M** & **S** \\ \hline Directivity (dBi) & 4.57 & 3.98 & 2.94 & 3.59 & 4.45 & 5.21 & 3.61 & 4.19 \\ Gain (dBi) & \(-\)6.45 & \(-\)5.34 & \(-\)6.16 & \(-\)5.34 & \(-\)5.04 & \(-\)4.49 & \(-\)4.76 & \(-\)4.49 \\ Beam Width (\({}^{\circ}\)) & 34 & 35 & 24 & 20 & 48 & 55 & 38 & 30 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Antenna specifications (M: Measurement, S: Simulation). Figure 6: Radiation patterns of the TX antenna (dB) (**a**) E-plan at 2.5 GHz, (**b**) H-plan at 2.5 GHz, (**c**) E-plan at 4.5 GHz, (**d**) H-plan at 4.5 GHz.
# Calibration and Validation of a Measurements-Independent Model for Road Traffic Noise Assessment
## 3 Results and Discussions
### Simulation and Calibration Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Q (veh/h)** & **V\({}_{\text{L}}\) (km/h)** & **V\({}_{\text{M}}\) (km/h)** & **V\({}_{\text{H}}\) (km/h)** & **P (\%)** & **d (m)** & **Computed** \\  & & & & & & \(L_{\alpha\alpha,h}\) (dBA)** \\ \hline  
\hline \hline \end{tabular}
\end{table} 
Table 1: Preview of the free flow traffic dataset showing some of the occurrences. \begin{table}
\begin{tabular}{c c} \hline \hline Model: & OLS \\ Df Model: & 6 \\ R-squared: & 0.986 \\ Log-Likelihood: & \(-2664.5\) \\ AIC: & 5343 \\ BIC: & 5382 \\ Intercept & 27.362713 \\ Coeff. log(Q) & 10.022377 \\ Coeff. log(VL) & 19.902456 \\ Coeff. log(VM) & 1.382846 \\ Coeff. log(VH) & 1.921894 \\ Coeff. log(P) & 2.825697 \\ Coeff. log(d) & \(-25.699334\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Multilinear regression for the calibration of the model. The results of the multilinear regression indicate how distance, \(d\), is the independent variable that most affects the \(L_{eq,h}\) values, contributing at the highest level to the variance in the model, followed by the velocities of light vehicles, \(V_{L}\) and \(Q\). The distance dependence from \(L_{eq,h}\) is also the only one that is inverse with a negative coefficient, as expected (the higher the distance, the lower the \(L_{eq,h}\) value). The calibration step goes on with a second linear regression, univariate, which was implemented by correlating \(L_{eq,h}\) as a function of all the independent values together, according to the \(X\) variable defined in Equation (6). The results of the regression are reported in Figure 2a. Figure 2b shows a distribution of the residuals that presents a mean value that is basically null (5.4 \(\times\) 10\({}^{-15}\) dBA) and a standard deviation of 0.92 dBA. The kurtosis index is 1.07, making the distribution slightly \"leptokurtic\", and the skewness is 0.69, meaning that its right tail is slightly more pronounced than the left one. Figure 3 shows the distributions of \(L_{eq,h}\) values simulated by the presented model and the \(L_{eq,h}\) measured and reported in the original database, as well as the scatterplot between the two sets of noise levels.
## 6 Results and Discussion
### Ensemble Method  
This is the main module that consists of three main algorithms as discussed previously. They are trained with the Twitter Glove word embedding [37] dataset for all dimensions 25, 50, 100, 200. The results of each dimension are compared to our four datasets for each model as shown in Figures 9-11. We found that the results for the 200 dimensions are better in the three models, CNN, LSTM, and CNN with SVM.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Method** & **Precision** & **Recall** & **F-Measure** \\ \hline \multirow{3}{*}{**First module**} & MaxEntropy & 0.96 & 0.95 & 0.95 \\ \cline{2-4}  & RandomForest & 0.96 & 0.95 & 0.95 \\ \cline{2-4}  & SVM & 0.96 & 0.96 & 0.96 \\ \hline
**LSTM** & 0.98 & 0.93 & 0.95 \\ \hline
**CNN** & 0.95 & 0.89 & 0.93 \\ \hline
**CNN + SVM** & 0.97 & 0.89 & 0.93 \\ \hline
**Random Forest (user-based feature)** & 0.60 & 0.70 & 0.65 \\ \hline
**[PERSON] et al.**[41] & 0.58 & 0.67 & 0.62 \\ \hline
**[PERSON] et al.**[40] & 0.79 & 0.76 & 0.77 \\ \hline
**[PERSON] et al.**[42] & 0.92 & 0.94 & 0.93 \\ \hline
**Proposed method** & 0.97 & 0.95 & 0.96 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Evaluation Results for Dataset 4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Method** & **Precision** & **Recall** & **F-Measure** \\ \hline \multirow{3}{*}{**First module**} & MaxEntropy & 0.96 & 0.95 & 0.95 \\ \cline{2-4}  & RandomForest & 0.96 & 0.95 & 0.95 \\ \cline{2-4}  & SVM & 0.97 & 0.96 & 0.96 \\ \hline
**LSTM** & 0.95 & 0.96 & 0.95 \\ \hline
**CNN** & 0.92 & 0.95 & 0.93 \\ \hline
**CNN + SVM** & 0.95 & 0.95 & 0.95 \\ \hline
**Random Forest (user-based feature)** & 0.96 & 0.90 & 0.93 \\ \hline
**SVM (user-based feature)** & 0.94 & 0.84 & 0.89 \\ \hline
**[PERSON] et al.**[41] & 0.85 & 0.64 & 0.73 \\ \hline
**[PERSON] et al.**[40] & 0.94 & 0.80 & 0.86 \\ \hline
**[PERSON] et al.**[42] & 0.94 & 0.95 & 0.94 \\ \hline
**Proposed method** & 0.96 & 0.96 & 0.96 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Evaluation Results for Dataset 1.  
Figure 10: ROC curve for LSTM model results for each dataset as a third component in our ensembl method.  
Figure 9: ROC curve for CNN model results for each dataset as a first component in our ensemble method.  
The CNN model is very good at finding the patterns. Each convolution will fire when a learned pattern is detected, but it suffers from long patterns or long tweets, which make the results of precision and F1-measure less. So, we embedded that LSTM model that is built using RNN, which is the strongest one with long sequences compared to CNN. Some studies [43] conducted an alternative for the last softmax function by SVM model. It aims to decide the optimal hyperplane for isolating the two classes in the dataset, and a multinomial case is apparently disregarded. With the utilization of SVM in a multinomial classification, the case turns into a one-versus-all, in which the positive class has the highest score, while the rest has the negative class.
# A U-Shaped Architecture Based on Hybrid CNN and Mamba for Medical Image Segmentation
## 3 Method
### Skip Connection Dual Attention  
To improve segmentation accuracy, particularly in preserving fine-grained anatomical structures, the integration of features between the encoder and decoder stages is strengthened. However, existing attention mechanisms, which operate solely on the spatial or channel dimension, often fail to capture comprehensive contextual dependencies. When only the importance or positional information of features is considered, the model's capacity to learn fully discriminative representations becomes constrained. Motivated by global attention mechanisms [36], we propose a Skip Connection Dual Attention (SCDA) module, developed to prevent spatial information loss in the encoder's down-sampling process, while also strengthening the interaction of features between the encoder and decoder. SCDA incorporates both channel and spatial attention mechanisms, enabling the model to capture richer contextual information than employing either mechanism in isolation. As illustrated in Figure 3, adaptive reweighting is applied along the channel dimension to emphasize task-relevant features and suppress redundant information, facilitated by the channel attention submodule. In parallel, the spatial attention submodule assigns importance to specific spatial locations, guiding the model to focus on relevant regions while reducing background interference. By embedding both attention mechanisms within a unified skip connection framework, SCDA promotes enhanced multi-scale feature fusion and facilitates the aggregation of both global and local contextual information. This design not only preserves structural details but also improves overall segmentation performance.  
Figure 3: The architecture of SCDA, which combines features from the encoder and decoder. These features are refined through channel and spatial attention modules to enhance model representation.  
Figure 2: Structure of the HCM block: The upper branch captures local features via convolution, complementing the lower branch’s global context modeling achieved through Mamba-based processing. PWConv modifies and combines channel-level information, maintaining the spatial resolution throughout the process. BN and LN refer to batch normalization and layer normalization, respectively.
# A Novel Integrated Fault Diagnosis Method Based on Digital Twins
## 4 Experiments and Results
### Comparative Experiments  
To validate the superiority of the proposed DCBA fault diagnosis model, we conducted comparative experiments with traditional machine learning methods (e.g., SVM) and classical deep learning methods (e.g., CNN, LSTM, and ANN). We evaluated the models based on metrics such as prediction accuracy(PA), Noise Suppression (NS), and Recall Rate (RC) to determine their effectiveness.
## 4 Discussion  
In this section, we discuss more details about the result of our method. As shown in Table 4, our replacement of the tags extracting module with YOLO9000 showed great improvement. Compared to our previous method [17], we achieved better results on all evaluation metrics. On BLEU metric, we obtained about 0.03 higher score on average. On METEOR and ROUGE-L metric, we obtained about 0.02 higher scores. Especially on CIDEr metric, we achieved a significant improvement with 0.11 higher value, an increase from 0.835 to 0.942. This shows that, with more detected tag names, the model receives more information, therefore can generate more accurate captions. We compared the results with other methods. We achieved higher results on all metrics compared to some previous method such as NeuralTalk [12], Nearest Neighbor [32], and Montreal/Toronto [14], and slightly higher compared to Google-NIC [11]. However, our work was still lower than some current state-of-the-art methods such as Watson Multimodal [30]. Despite not achieving high scores in the evaluation metrics, our model could generate captions with clear and rich information that is helpful for retrieval. As shown in Figure 5, the generated captions were relevant to the main content of the image and describe enough detail such as the name of the fruits or the location in the image. These information can be used to easily retrieve relevant content given a query. Our model can also focus on certain regions to generate the corresponding words. This let us keep track of the generating process in case of wrong caption is generated.  
We developed our system for generating personal lifelog caption using our proposed image captioning method. From photos take from smart glasses, our system can generate description relevant to the content. When a user is staying in the smart environment where connection to the server is available, such as a smart home or smart office, photos can be transmitted periodically (1 photo/minute) to the server, and then our model will process the image and add the description of that photo to the database within 2 s. This allows real time capturing of important events for the user. When the user moves out of the smart environment and loses connection, photos can be stored in a SD-card of the smart glasses and will be uploaded to our server later. The design and implementation of smart glasses are also optimized for more effective transmission and energy consumption.  
Figure 6: Some generate captions of our model from images taken by ourselves.  
Figure 7: Visualization of our model’s ability to focus on certain regions while generating the caption.  
For future research, we aim to extend the proposed method with more domain knowledge to further improve the quality of image description. Besides, security features should be considered. Some security methods should be integrated into the system of personal lifelog generation to protect communications between devices and the server. We also intend to apply document embeddings into our system for better querying. Users can query the events of interest based on semantic similarity of the needs and the captions.
## 4 Foundations of HWSNs  
A HWSN is a conceptual network that supports processes being performed in a physical fashion; for instance the search-and-rescue process performed by firefighters during large fires or after a building collapse. This network represents a bridge between the physical and the digital worlds. The nodes participating in a HWSN can consume, deliver, store and process information, and also interact with the real world, e.g., delivering visual or sound messages.  
Each network node (e.g., a smartphone or a surveillance camera) has at least a communication interface that allows it to interact with other nodes. The interaction between nodes is based on message passing. According to the definition presented in Equation (1), each message must count on at least the following attributes:  
\[\text{message}::=<\text{sender, destination, timestamp, timeout, information}> \tag{1}\]  
where the timestamp indicates the message delivery time and the timeout is the message expiration time. The information held in the message is relevant only for the destination node and such content is processed at the application level; therefore the information piece must have at least the following attributes:  
\[\text{information}::=<\text{source, timestamp, type, weight, data}> \tag{2}\]  
where source indicates the user (and/or the role) that is sharing the piece of information (typically it is the owner) and the timestamp indicates when this particular sharing process was triggered. This information is typically used to determine the age or validity of the information piece. The information type represents meta-information that can be used by the destination node to decide how to process such data. For instance, this attribute can indicate if the data is a video, text, image or a resource with a particular data structure (e.g., a personal record). The information weight is also meta-information that is used by some nodes to decide how to perform the role assigned to them. For instance, processing or transferring heavy-weight resources could take long time; therefore the node could prioritize to serve other low-demand requests. Next we describe the roles that can be played by the network nodes.
# Risk Assessment in Supplier Selection for Intelligent Manufacturing Systems Based on PLS-SEM
## 4 Empirical Analysis
### Effect Test of IMSS Suppliers Risk Evaluation Model
#### 4.2.5 Visualization of Inspection Results  
The visual index inspection with power BI software (as shown in Figure 5) is as follows:  
Figure 5: Visualization of index test results.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Path Coefficient** & **2.5\%** & **97.5\%** & **T Statistic** & \(p\) \\ \hline SS-\(>\)EV-IMSS suppliers & 0.158 & 0.142 & 0.176 & 17.528 & 0.000 \\ SSC-\(>\)EV-IMSS suppliers & 0.258 & 0.224 & 0.289 & 14.546 & 0.000 \\ SCT-\(>\)EV-IMSS suppliers & 0.164 & 0.145 & 0.187 & 15.664 & 0.000 \\ SISS-\(>\)EV-IMSS & 0.145 & 0.131 & 0.160 & 18.061 & 0.000 \\ suppliers & 0.230 & 0.200 & 0.257 & 15.718 & 0.000 \\ SP-\(>\)EV-IMSS suppliers & 0.181 & 0.164 & 0.202 & 18.862 & 0.000 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Bootstrapping test of IMSS suppliers risk evaluation model.  
CHECK 1: There are two dashed lines in the figure, representing the values of 0.5 and 0.7, respectively. It can be seen from Figure 5 that the CR and AVE indexes of the model are above the dotted line of 0.5, Cronbach's alpha and rho_A Index a is above the dotted line of 0.7, with good fitting effect, strong interpretation effect of internal relations, acceptable estimation effect and good reliability of the questionnaire.  
CHECK 2: The light blue area indicates that the prediction ability value is greater than 0.25, the dark blue area indicates that the prediction ability value is greater than 0.5, and the gray horizontal line represents the Q\({}^{2}\) value. The Q\({}^{2}\) bullet diagram is in the blue area, indicating that the overall prediction ability of the model belongs to the upper-middle level.  
CHECK 3: As shown in the thermodynamic diagram, the diagonal color is darker than the color block of the correlation coefficient (the values are greater than all the values of the correlation coefficient), indicating that the indicators at all levels have different connotations in theory, and they have good differential validity.  
CHECK 4: In the bootstrapping test, the yellow area is T > 1.96, the gray green area is T > 2.58, and the blue area is T > 3.29. The gray horizontal line represents the size of the T value, which is in the blue area, and the \(p\) value is at the level of 0.001, indicating that the index is highly significant, meaning that the stability of the model structure is very good.  
Using the visual inspection results of power BI software can more intuitively display the data, correlation and trend between data, so as to help enterprise decision makers quickly understand the information, master the dynamic risk of IMS suppliers, and provide decision-making reference for manufacturing enterprises to select high-quality IMSS suppliers.
# Ultra-High-Performance Concrete (UHPC) Piles and Splicing Options
## 5 Discussion  
This paper examines various forms of driven piles utilized in the construction of pile foundations. The UHPC pile is a viable alternative to traditional piles (such as timber, steel, concrete, and composite piles) due to its superior durability, performance, and driveability. UHPC piles in different forms have been effectively devised and employed in numerous projects, with other projects exploring UHPC as a possible alternative for conventional steel piles or PPCPs.  
After reviewing the existing choices, many pile-splicing methods suitable for UHPC piles have been identified. These pile-splicing methods may be regarded as a viable choice for UHPC piles if they satisfy the standard requirements for the strength of pile splices in both preplanned and unplanned situations. Welded, bolted, post-tensioned, epoxy-bonded dowel, FRP sheet or jacket, NSM FRP, and mechanical coupler pile splice methods have been studied, and a comparison of these options is presented in Table 3 based on the following criteria:  
* Unplanned situation: the possibility to perform the splice system in an unforeseen situation.
* Capacity: the system's ability to resist the required level of tension, compression, and flexure.
* Preparations: the time and effort for preparing before installation of the system.
* Installation: the time and effort for installation of the system.
* Durability: system functionality in extreme conditions, such as a corrosive environment.
* Cost: the system's overall cost in terms of manufacturing and installation.  
Numerous variables influence the pile-splicing technique selected for a given project, including materials, cost, construction feasibility, and method of construction schedule. Diverse pile splice methods may be integrated to accomplish the design objective.  
Figure 19: Mechanical coupler pile-splicing method [60].