esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experiment One: Localisation Thresholds for Natural Sound Sources
### Results
#### 2.2.2 The Effect of ICTD  
As it was identified that the effect of stimulus presentation on the localisation threshold was not significant, the results for stereophonic and quadraphonic presentation were combined. Figure 5 shows the effect of ICTD on the localisation thresholds obtained, with combined results for stimulus presentation. As before, the median localisation thresholds have been plotted with notch edges. Consideration of the notch edges suggests that the effect of ICTD on the localisation threshold was significant for at least some of the sound sources. The median threshold for the guitar, for example, looks significantly lower for 0 ms (\(-\)10 dB) than for 1 and 10 ms (\(-\)7.5 dB). Equally, the quartet at 0 ms (\(-\)9.5 dB) looks significantly lower than for 1 ms (\(-\)6 dB). Friedman tests (critical \(p\) value = 0.05) showed that the effect of ICTD was significant for the guitar (\(p\) = 0.002), speech (\(p\) = 0.021) and quartet (\(p\) = 0.005). The effect was not significant for the oboe (\(p\) = 0.418) and conga (\(p\) = 0.788). A Wilcoxon test was subsequently conducted for the guitar, speech and quartet sources, with the Bonferroni correction being applied to reduce type-I errors ([PERSON], 2013). The results showed the following: For the guitar, significant  
Figure 4: Medians and associated notch edges for each experimental condition. ICTD: interchannel time difference.  
differences were identified between the 0-ms ICTD and both the 1-ms (\(p\) = 0.015) and 10-ms (\(p\) = 0.027) ICTDs. For speech, there were significant differences between the 10 ms ICTD and both the 0 ms (\(p\) = 0.012) and 1 ms (\(p\) = 0.024) ICTDs. For the quartet, the 0-ms and 10-ms (\(p\) = 0.015) ICTDs were significantly different from one another. These results generally agree with the notch edges shown in Figure 5, although there are some small differences. It can therefore be concluded that the effect of ICTD on localisation threshold was significant.
# A Framework for the Detection of Search and Rescue Patterns Using Shapelet Classification
## 2 Related Work
### Shapelets  
According to the pioneering work of [PERSON] and [PERSON] [21] shapelets are time series subsequences that are identified in any time-series or 1-D series generated from image outlines. The shapelet identification process can be disconnected from the classification algorithm by performing a shapelet transformation. The result of this separation is that the k best shapelets extracted in a single pass can be used as features in the time-series classification tasks as representatives of class membership. The transformation into the new feature space can improve classification accuracy whilst still retaining the explanatory power provided by shapelets [18].  
The concept of shapelets has recently been introduced in the domain of trajectory classification, by the Movelets method [22], which discovers relevant sub-trajectories for trajectory classification, without a pre-defined criteria for trajectory partition. In that work, authors generalise the basic concept of Shapelet Analysis, which is defined for only one variable over time, to the multi-dimensional (actually 2-D position over time) Movelet Analysis. The Movelet method is based on the computation of distances between trajectory points, which is then generalised to the distance between points and trajectories, or sub-trajectories and trajectories.  
In our work, we build on the two step process of the original Shapelet Analysis for 1-D series. We apply different shapelet transformations to the various features of a vessel trajectory (e.g., course over ground, bearing, speed, etc.) and concatenate the resulting feature vectors in an ensemble vector that contains all the respective shapelet features. Then we train a classifier to distinguish between SAR maneuvers and random movements.
## 3 Results
### Feature Importance Analysis  
Figure 3 illustrates the feature importance from a model trained with all available samples (\(n=63\)). The displayed importances depict the relative average improvement in prediction accuracy across all of the 100 decision trees in the model where a certain feature is utilized. The importance of each feature should be considered as relative to the others. As is presented in Figure 3, the two most important features for predicting the ORR were itching and fatigue. Figure 3 reveals that roughly half of the features contributed to the predictions. The features which do not contribute to the predictions could be removed from the prediction model using feature selection, but it does not impact the model performance, due to the tree structure of the used gradient boosting algorithm.  
\begin{table}
\begin{tabular}{c c} \hline \hline  & \(n\) (\%) \\ \hline Age (median) & 61.7 \\ \hline Gender & \\ \hline Male & 21 (67.7) \\ Female & 10 (32.3) \\ \hline Tumor type & \\ \hline Melanoma & 8 (25.8) \\ Lung cancer & 12 (38.7) \\ GU cancer & 7 (22.6) \\ H\&N & 4 (12.9) \\ \hline Stage at diagnosis & \\ \hline Stage III & 4 (12.9) \\ Stage IV & 27 (87.1) \\ \hline ECOG & \\ \hline
0 & 20 (64.5) \\
1 & 11 (35.5) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The demographics of the study population.  
Figure 3: Feature importance of ORR prediction model trained with all available samples. The displayed importances depict the relative average improvement in prediction accuracy across all 100 trees in the model where a certain feature is utilized. The importance of each feature should be considered as relative to the others.  
Figure 2: Confusion matrix for predicted ORR. Upper-left corner shows correctly classified negative, lower-right corner correctly classified positive, upper-right corner false positive, and lower-left corner false negative samples. Negative samples consist of SD and PD responses and positive samples CR and PR responses.
# A Design of a Lightweight In-Vehicle Edge Gateway for the Self-Diagnosis of an Autonomous Vehicle
## 2 Related Works
### Overview  
This paper proposes a Lightweight In-Vehicle Edge Gateway (LI-VEG) for the self-diagnosis of an autonomous vehicle, which supports the communication among FlexRay, CAN, Ethernet, and MOST protocols.  
The LI-VEG enables mutual communication by translating the messages of the FlexRay, MOST, and CAN inside a vehicle into the message type of a destination protocol and translates the structure of an in-vehicle communication protocol into an Ethernet structure to support the mutual communication with the RSU outside a vehicle, and the connection with an Ethernet backbone. Because the paper on the self-diagnosis module has already been published [38], this paper only covers LI-VEG, not the self-diagnosis. Figure 2 shows the structure of OBD-II including the LI-VEG which is a part for the self-diagnosis of an autonomous vehicle.  
The LI-VEG working in OBD consists of three layers: InV-SRL, InV-ML and InV-DTL. First, the InV-SRL receives the messages from FlexRay, CAN, MOST, and Ethernet and then transfers the received messages into the Gateway. Second, the InV-ML manages the message transmission and reception of FlexRay, CAN, MOST, and Ethernet and the Address Mapping Table. Third, the InV-DTL decomposes the messages of FlaxRay, CAN, MOST, and Ethernet and recomposes them to the frame suitable for a destination.
## 3 Empirical Results  
### Patient Selection  
After exclude younger than 60 years old samples from the database population (n = 990,801), there were 166,274 persons older than 60 years old as our study samples during the period of this study  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Method** & **Parameters** & **Value Setting** \\ \hline C4.5 & Confidence factor & 0.25 \\  & Minimum number of instances per & 20 \\  & leaf & 20 \\ Random Forest & Number of trees & 10 \\  & Number of attributes to be used in & 4 \\  & random selection & \\ Support Vector Machines & Kernel & PolyKernel \\  & Number of hidden nodes & 7 \\  & Learning rate & 0.3 \\  & Momentum factor & 0.2 \\  & Maximum number of epochs & 500 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameter setting in Weka.  
where 85,593 (51.4%) persons were male and 80,681 (48.6%) persons were female. In, the mean age was 71.5 years old and it had no significant difference between both genders in this study (as Table 2).  
The incidence of new hip fractures was 4.0% from 2001 to 2010, where female incidence was significantly higher than male is plotted in Figure 2 (5.0% vs. 3.1%, \(p<0.001\)). The situation could also be observed in both the male and female subgroups. In the male subgroup, the incidence was 435/100,000 in 2001 and increased to 486/100,000 in 2010. In the female subgroup, the incidence was increased from 683/100,000 to 685/100,000 from 2001 to 2010. Among them, Figure 3 illustrated the first-year mortality rate in elderly hip fracture patients was 21.46% (1166/6679), on the contrary, the first-year mortality rate in male (29.32%) was significantly higher than female (15.91%).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & **No.** & **\%** & & \\ \hline Gender & male & 509081 & 51.4 & & & \\  & female & 481720 & 48.6 & & & \\  & total & 990801 & 100 & & & \\ \hline  & & mean & std. err & 95\% C.I. & \(p\) value \\ \hline Age & male & 34.20 & 0.03 & 34.14 & 34.25 & \\  & female & 34.17 & 0.03 & 34.11 & 34.23 & \\  & total & 34.18 & 0.02 & 34.14 & 34.22 i0 ii & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Demographics data of random sample from National Health Insurance Research Database.  
Figure 2: Incidence (per 100,000 persons per year) of hip fracture in age \(\geq\)  
As shown in Table 3, the first-year mortality rate of the overall study group was 21.5% in 2001 and 15.0% in 2010. There was a significant decreasing tendency from 2001 to 2010 (\(p\) for trend \(<\)0.001). In the male subgroup, the first-year mortality rate was 29.3% in 2001 and decreased to 17.3% in 2010. There was also a significant decreasing tendency from 2001 to 2010 (\(p\) for trend \(<\)0.001). In the female group, the first-year mortality rate was from 15.9% in 2001 and decreased to 13.5% in 2010. The tendency towards a decrease from 2001 to 2010 was significant (\(p\) for trend =0.01). The long term trend of first-year mortality in elderly hip fracture declined by 15.1% for women and by 41.0% for men, respectively.
## 1 Introduction
### Plastic as an Additive in Cement-Based Materials  
The advantageous properties (low density, high durability, high strength-to-weight ratio, low cost, and ease of manufacture) of plastics have led to their use as valuable construction materials utilized as additives (virgin and recycled waste plastic) in conventional concrete over the past few decades. Firstly, they are used as resin to produce a polymer mortar and concrete by adding water-soluble or dispersed polymers in parallel during mixing. Secondly, they are used as plastic powders in the replacement of binder materials, as plastic fibers used in fiber-reinforced concrete, and as plastic aggregates in the replacement of natural aggregates. The latter method will be the focus of this paper. The physical and mechanical properties of cementitious composites fabricated according to different plastic types, shapes, and substitution ratios related to the use (virgin and waste) of plastics in cementitious composites (mortar and concrete) have been investigated by numerous researchers [3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19]. Table 1 provides information on the types, replacement ratios, and investigated properties of plastics used in cement-based composite materials investigated by existing researchers.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Plastic Type** & **Shape** & **Replacement Types and Ratio** & **Studied Properties** \\ \hline
[3] & PE & Powder & Cement: 15, 25, \\
[4] & PO + PP + PE & Pellet & 1 FA: 10 and 25 vol.\% \\
[5] & HDPE & Pellet & 2 CA: 25, 50, and 100 vol.\% \\
[6] & PO + other types & Irregular & 9.6, 12.5, and 15 vol.\% \\
[7] & PP, HDPE & & CA/FA: 25, 50, \\  & and PVC & Irregular & and 75 vol.\% \\
[8] & Tire rubber & Irregular & FA: 10, 20, and 30 vol.\% \\
[9] & PET & Pellet & FA: 5, 10, and 15 vol.\% \\
[10] & PC & Pellet & FA: 5, 10, and 20 vol.\% \\
[11] & PET & Flake & CA/FA: 5, 10, 15, \\  & & and 20 vol.\% \\
[12] & PE & /Flake & CA: 25, 50, 75, \\  & & /Fiber & and 100 vol.\% \\
[13] & PP & Pellet & CA: 10, 20, 30, \\  & & and 40 vol.\% & tensile and flexural strength \\ \hline \hline \end{tabular}
\end{table}
Table 1: Literature studies related to properties of cementitious composites that contain plastic materials.  
From the viewpoint of reducing the weight of concrete, most of the studies reported in the literature suggest that the unit weight of cementitious composites decreases with an increase in the replacement rate of plastic aggregates. Conversely, the compressive strength, splitting tensile strength, and flexural strength decrease with an increase in the replacement rate of plastic aggregates. In addition, it was confirmed that in the manufacture of cementitious composites containing plastics, plastics of a single material were mostly used as aggregates, centering on PET of relatively excellent quality. The replacement rate compared to natural aggregates was investigated in various ways and ranged from 5 to 100%, but 25 and 50% were commonly used in most studies.
## 2 Materials and Methods
It is clear that the spatial resolution varies from 83 \(\upmu\)m to 382 \(\upmu\)m, with a tuning range around of 300 \(\upmu\)m. The tuning range can be further enlarged using the Wollaston prism with a larger separation angle or longer-focus convex lens. According to Ref. [29], transfer function \(H\) is an important index for FLDI system performance. It can be described as  
\[H=\frac{2}{k\Delta x}\sin\left(\frac{k\Delta x}{2}\right)\exp\left(-\frac{w_{0 }^{2}k^{2}}{8}\right)\]  
here, \(k\) is the wavenumber of the density disturbance field and not the wavenumber of the laser. \(w_{0}\) is the beam waist radius. The transfer function \(H\) determines the cut-off frequency of the FLDI system, which characterizes the ability of this system to recognize different density fluctuation scales. It is clear that the transfer function is not only affected by the Gaussian characteristics of the beam, but also by the separation distance \(\Delta x\), that is, the spatial resolution of the FLDI system. Although the latter has a weaker impact than the former, its influence on the system performance cannot be ignored. Figure 1: (**a**) Schematic diagram and (**b**) image of FLDI system. P1, P2, polarizer; H, half-wave plate; PH, pinhole; W1, W2, Wollaston prism; L1, L2, convex lens; D, fast photodetector; MS, moving stage. The cutoff frequency of the FLDI system decreases as the separation distance \(\Delta x\) increases, and then the small density fluctuation scale cannot be recognized effectively. Therefore, we should choose a small separation distance \(\Delta x\), but also make sure that the two focal points are completely separated to avoid crosstalk. Here the separation distance \(\Delta x\) was selected as 182 \(\upmu\)m in the experiment because the two convergence points are already distinguished. The result is smaller than those of other systems previously reported, which indicates this FLDI system has a high spatial resolution. The distance between the two convergence points is measured by a CCD camera, shown in Figure 2b.
# A Novel Deep Learning Method for Recognition and Classification of Brain Tumors from MRI Images
## 2 Related Work
They used the technique of data augmentation with dice-based loss function to improve segmentation accuracy. In [49], a watershed segmentation algorithm was used to segment a brain tumor. However, they employed low dimensional hand-crafted feature vectors as training to the KNN classifier for classification. They achieved an average accuracy of 86%. In [50], an encoder-decoder-based architecture is presented, which performed the pixel-wise segmentation of tumor tissues from normal brain cells. This model used SegNet architecture with an encoder of depth four and VGG16 for generating feature maps, and performed non-linear up-sampling. This approach does not need any post-processing phase and reported an average dice score of 0.931. In FR-MRINet [51], a 33-layer deep model and an encoder with a fully connected decoder instead of the deconvolutional decoder were proposed. However, the approach produces anomaly areas, i.e., small non-tumor areas are predicted to contain tumors and cleaned by using the neighborhood cleaning rule. This work reported a segmentation accuracy of 91.4%. In this work, we adopt a kind of DL method, Mask-RCNN [37] architecture, that uses end-to-end training for the localization and classification of brain tumors. Instead of applying a threshold-based or boundary-based model for accurate segmentation, this method uses region-based segmentation and generates a mask that achieves improved tumor boundary segmentation accuracy.
## 3 Results and Discussion
### Analysis of Model Optimization Results
#### 3.3.1 Optimized Drainage Network Layout and LID Measures
Using permeable paving materials, including permeable pricks and concrete, facilitates the rapid penetration of rainwater into the subterranean environment, thereby reducing surface runoff. The permeable pavement in each area is approximately 5000 square meters. By optimizing the pavement design, the safe passage of pedestrians and vehicles is ensured while simultaneously enhancing the infiltration and storage capacity of rainwater. Permeable pavement mitigates the risk of internal flooding, improves groundwater recharge, and enhances the overall quality of the ecological environment. The maintenance of permeable pavements involves regular vacuum sweeping and power washing to remove debris and prevent clogging, with maintenance scheduled quarterly. Long-term benefits of permeable pavements include extended pavement lifespan, reduced stormwater runoff, and enhanced groundwater recharge. Permeable pavements also reduce the urban heat island effect due to their lighter-colored surfaces, which reflect more sunlight. However, the effectiveness of permeable pavements can decrease over time if not maintained, particularly in high-traffic areas where clogging may be more frequent. Proper maintenance ensures functionality for up to 25 years, supporting the area's long-term ecological and hydrological stability. The parameters for the various drainage components, including permeable pavement, rain gardens, and stormwater storage ponds, are provided in Table 5.
## 3 Method
### Associated Entity Detection
\(X=\{x_{1},x_{2},\ldots,x_{N}\}\), \(C=\{c_{1},c_{2},\ldots,c_{K}\}\), \(r_{ik}\) denotes the difference between \(x_{i}\) and \(c_{k}\). The learned attentional weights are then used to weight the residual vectors to obtain a fixed-length coded representation, i.e.,  
\[e_{k}=\sum_{i=1}^{N}e_{ik}=\sum_{i=1}^{N}\alpha_{ik}r_{ik} \tag{13}\]  
The output of the encoding layer \(E=\{e_{1},e_{2},\ldots,e_{K}\}\) is fed into a convolutional and mean layer with SiLU activation to fuse all the coded representations, i.e.,  
\[e=\sum_{i=1}^{K}BSM(e_{k}) \tag{14}\]  
Figure 8: LREM architecture. Figure 7: Detailed architecture of the DMLP network. The resulting \(e\) is fed into the fully connected layer and multiplied channel-by-channel with the original input features. The entire process is expressed in Equation (15):  
\[F_{2}=X+X\times FC(e) \tag{15}\]  
where \(FC\) denotes a fully connected layer. The output of IFAM is directly concatenated from the output feature map \(F_{1}\) of DMLP with the output feature map \(F_{2}\) of LREM along the channel dimensions, i.e.,  
\[F=conv(concat(F_{1},F_{2})) \tag{16}\]  
The fused multi-scale features are fed into the decoupled head for confidence and regression frame predictions, respectively. The final MER-associated entity detection result is obtained by concatenating the target box category score, coordinate information, and an indication of whether it is foreground or background.
# Enhancing Propaganda Detection in Arabic News Context Through Multi-Task Learning
## 4 Experiments
### Evaluation Measurements  
The macro- and micro-averaged F1 scores were computed to evaluate the models' performance for propaganda, sentiment, and emotion tasks. These measures were particularly suitable for unbalanced datasets and were widely utilized in previous studies to report the effectiveness of models for these tasks [23]. Moreover, accuracy evaluation measures were used. A more detailed explanation of the accuracy of the calculation of the F1-macro and F1-micro measures is provided in this section. For this purpose, a confusion matrix was used to analyze the performance of a binary class. In this matrix, each row contained information about the actual class, whereas each column contained information about the predicted class. Accordingly, the confusion matrix aimed to analyze how well a classification could recognize the instances of different classes. Table 7 presents the confusion matrix [58].  
In the context of the propaganda classification problem, true positives (TPs) referred to instances where text news containing propaganda was correctly classified as propaganda. True negatives (TNs) referred to instances where text news not containing propaganda was correctly classified as nonpropaganda (i.e., these were the correct decisions, represented along the diagonal of the confusion matrix). False positives (FPs) occurred when text news  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameter** & **Value** \\ \hline Max. sequence length & 256 \\ \hline Batch size & 8 \\ \hline Number of epochs & 5 \\ \hline Early stop patience & 2 \\ \hline Optimizer & AdamW \\ \hline Learning rate & \(2\times 10^{-5}\) \\ \hline Weight decay & 0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Hyperparameter values.  
that did not contain propaganda was incorrectly classified. Meanwhile, false negatives (FNs) arose when text news containing propaganda was incorrectly classified as nonpropaganda.
# Large Language Models' Trustworthiness in the Light of the EU AI Act--A Systematic Mapping Study
## 5 Results and Discussion
### Results of RQ1(a-b)
#### 5.1.1 RQ1a: Which LLMs Are Most Studied for Trustworthiness?  
**Objective:** Identify specific LLMs that have been researched with a focus on trustworthiness.  
**Results:** Figure 4 presents a bar chart summarizing the frequency of studies on LLMs in relation to trustworthiness. The analysis reveals that GPT models are the most extensively explored, with 1099 studies (35.1%), followed by BERT with 906 studies (28.9%) and general references to LLMs with 691 studies (22.1%). Other notable models include T5 (61 studies, 1.9%), LLaMa (54 studies, 1.7%), and BARD (54 studies, 1.7%).  
Relatively fewer studies focus on emerging or specialized models such as XLNet (39 studies, 1.2%), Claude (26 studies, 0.8%), and BingAI (18 studies, 0.6%). The least explored models include Mistral (6 studies, 0.2%), FLAN (5 studies, 0.2%), and ChatGLM (4 studies, 0.1%). Notably, single studies were identified for models such as CoPilot, ELECTRA, CodeGen, and Zephyr, each contributing 0.0% to the total.  
These results suggest that research on trustworthiness primarily focuses on widely adopted and versatile LLMs like GPT and BERT, while newer or less commonly used models remain underexplored. This highlights potential opportunities for further research on the trustworthiness of emerging and niche LLMs.