esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results  
In this section, we evaluate the performance of our proposed method compared with two well-known routing protocols: (i) QELAR, a machine learning-based protocol designed for minimizing and balancing node energy consumption [7]; (ii) DBR, a data forwarding method for UWSNs based on the depth of the sender [15]. It is worth mentioning that we use the total residual energy of sensor nodes, Value of Information and the ratio of packet delivery to sink nodes as the main metrics of performance evaluation.
# Assessing Drivers' Physiological Responses Using Consumer Grade Devices
## 3 Results  
The results of HRV measurements are presented in four groups: Mean, standard deviation, shorter time-frame variables and successive differences, followed by skin temperature and GSR measurements. A summary of the results is provided in Table 1.  
It is important to mention that E4's built-in algorithm for calculating the HRV did often not provide any output. This resulted in missing HRV samples from E4 at easy driving (58%) and demanding driving (59%). It is important to notice that the missing values may have influenced the results of HRV successive differences, therefore they should be interpreted with caution. On the other hand, Faroos provided all HRV samples during every measurement phase.  
\begin{table}
\begin{tabular}{c c c} \hline  & **E4** & **Faroos 360** \\ \hline Mean and median HRV & X & X \\ \hline HRV SDNN & baseline \(<\) easy driving & easy driving \(<\) demanding driving \\  & baseline \(<\) demanding driving & baseline \(<\) demanding driving \\ \hline HRV SDANN & baseline \(<\) easy driving & easy driving \(<\) demanding driving \\  & baseline \(<\) demanding driving & baseline \(<\) demanding driving \\ \hline HRV SDNN Index & X & X \\ \hline HRV RMSSD & baseline \(<\) easy driving & X \\  & baseline \(<\) demanding driving & X \\ \hline HRV pNN50 & baseline \(<\) easy driving & \multirow{2}{*}{X} \\  & baseline \(<\) demanding driving & \\ \hline Mean skin temperature & baseline \(<\) easy driving & \multirow{2}{*}{\begin{tabular}{c} baseline \(<\) easy driving \\ baseline \(<\) demanding driving \\ easy driving \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} baseline \(<\) easy driving \\ baseline \(<\) demanding driving \\ easy driving \\ \end{tabular} } \\  & baseline \(<\) easy driving & \multirow{2}{*}{\
\begin{tabular}{c} baseline \(<\) easy driving \\ baseline \(<\) demanding driving \\ \end{tabular} } \\ \hline Standard deviation of skin & baseline \(<\) easy driving & baseline \(<\) easy driving \\  & baseline \(<\) demanding driving & baseline \(<\) demanding driving \\ \hline Mean and standard deviation of & \multirow{2}{*}{
\begin{tabular}{c} baseline \(<\) demanding driving \\ \end{tabular} } & \multirow{2}{*}{N/A} \\  & GSR & \\ \hline \end{tabular}
\end{table}
Table 1: Results of each device (columns) and each dependent variable (rows). ‘\(<\)’ indicates a statistically significant increase in the corresponding dependent variable between the two phases. X indicates that no significant differences among three phases of driving were found. N/A indicates the measurements were not applicable for the corresponding device.
## 3 Results and Discussion
### Seismic Analysis on the Retrofitted RC Building  
The numerical analysis of the reinforced building aimed to evaluate the effectiveness of the designed retrofitting system in enhancing seismic safety and reducing structural vulnerabilities according to Eurocodes 2, 3 and 8 [32; 33; 34]. After installing the Resisto 5.9 Tube, the retrofitted RC building was subjected to pushover analysis to derive the capacity curves. Figure 10 illustrates these curves under the most severe loading conditions for each direction. In these curves, the red square indicates the point where the first damage occurred in the structure reinforced with the cold-formed exoskeleton.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Level** & **H 1 [m]** & **d\({}_{0}\) 1 [cm]** & **F\({}_{\text{i}}\) 1 [kN]** & **K\({}_{\text{si}}\) 1 [kN/cm]** & **r\({}_{\text{k}}\) 1** & **K\({}_{\text{Ri}}\) 1 [kN/cm]** \\ \hline
4 & 12 & 2.13 & 5074 & 2382.2 & & 12,349.6 \\
3 & 9 & 1.84 & 4428 & 2406.5 & & 12,475.9 \\
2 & 6 & 1.30 & 2952 & 2270.8 & & 11,772.1 \\
1 & 3 & 0.59 & 1478 & 2505.1 & & 12,986.8 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Design stiffness of the Resisto 5.9 Tube system.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Level** & **n\({}_{\text{Ri}}\)** & **K\({}_{\text{Ri,s}}\) [N/mm]** & **L\({}_{\text{i}}\) [mm]** & **A\({}_{\text{i}}\) [mm\({}^{2}\)]** & **b\({}_{\text{i}}\) [mm]** & **t\({}_{\text{i}}\) [mm]** \\ \hline
4 & 93 & 6639.6 & 1291.4 & 50 & 50 & 0.99 \\
3 & 93 & 6707.5 & 1291.4 & 50 & 50 & 1.00 \\
2 & 93 & 6329.1 & 1291.4 & 47 & 50 & 0.94 \\
1 & 53 & 12,251.7 & 1317.9 & 97 & 50 & 1.93 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Design thickness of diagonal members.  
The main parameters of the analysis are summarized in Table 9, which highlights that the overall behavior of the retrofitted structure remained similar to that of the existing one, with increased ductility in the transverse direction (Y) and enhanced stiffness and strength in the longitudinal direction (X). Even after installing the exoskeleton, the first point of damage in the structure still occurred in the longitudinal direction.
## 2 Related Work
### Approaches Based on Image Gradient  
[PERSON] [15] shares a sub-pixel localization technique based on the observation that a vector from a corner to any part of its adjacent area is perpendicular to the image gradient of the corner. Points from the adjacent area are used to apply some mathematical treatments for solving a location iteratively; this location is taken as the new center of the adjacent area, until the center stays within a set threshold. [PERSON] [16] adopts a sub-pixel finder algorithm based on Harris detector. From the given initial corner locations, the algorithm iteratively updates the individual corner locations to the largest gradient values using patch-based structure tensor calculation. The algorithm calculates the structure tensor by directly interpolating the gradients, instead of first interpolating the image and second computing the gradients for reducing computational costs. The first mentioned category has been implemented by a toolbox [17] and a library function [18] and, therefore, frequently employed in many application scenarios. These approaches can achieve high efficiency, but they are sensitive to image noise, and often lead to unstable results for on-site use.
# Effect of DEM Used for Terrain Correction on Forest Windthrow Detection Using COSMO SkyMed Data
## 2 Materials and Methods
### Materials
#### 2.1.3 Digital Elevation Models  
We considered five different DEM models for this study (Table 3): (i) DTM: a local DTM extracted from a LiDAR flight released in 2020 [22]; (ii) DSM: a local DSM extracted from a LiDAR flight released in 2020 [23]; (iii) ALOS: the ALOS Global Digital Surface Model released in 2021 (version 3.2) [11]; (iv) COP: the Copernicus global DEM released in 2019 [12]; and (v) SRTM: the Shuttle Radar Topography Mission (SRTM) released in 2003. The pixel spacing of each DEM model is detailed in Table 3. It is worth noting that for the global DEM, the native pixel spacing was in degrees (1 arcsecond), and they were all reprojected in the local reference system in meters (i.e., EPSG 25832). Thus, the pixel spacing in meters reported in Table 3 are the ones after reprojection to the common system.  
The three global DEMs (i.e., ALOS, COP, and SRTM) are generated from three different sources. ALOS is generated starting from the Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM), which was a panchromatic radiometer with 2.5 m spatial resolution at nadir. The objective of PRISM was to obtain high-resolution stereo data (pixel size of 2.5 m) for cartographic applications (extraction of highly accurate digital elevation models, etc.). Thus, ALOS DEMs are based on optical stereo data at high resolution. COP and SRTM DEMs are based on SAR data. COP is generated from the TanDEM-X SAR images acquired in band X, while SRTM is generated from C-band SAR data. The five DEMs were upsampled/downsampled using bilinear interpolation to a pixel spacing of 2 m and 30 m, and the upsampled/downsampled DEMs were used in the elaborations.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Direction** & **Time** & **Date** & **Incidence Angle** \\ \hline \multirow{3}{*}{Ascending} & Pre-event & 16 August 2018 & 26.5\({}^{\circ}\)–27.1\({}^{\circ}\) \\ \cline{2-4}  & Post-event & 3 August 2019 & 26.5\({}^{\circ}\)–27.1\({}^{\circ}\) \\ \hline \multirow{3}{*}{Descending} & Pre-event & 9 August 2018 & 31.4\({}^{\circ}\)–31.9\({}^{\circ}\) \\ \cline{2-4}  & Post-event & 23 August 2019 & 31.4\({}^{\circ}\)–31.9\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: COSMO SkyMed images used in this study.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**DEM** & **Origin** &
\begin{tabular}{c} **Data Acquisition** \\ **Year(s)** \\ \end{tabular} & **Pixel Spacing (m)** \\ \hline DTM & Airborne LiDAR data & 2014–2018 & 0.5 \\ \hline DSM & Airborne LiDAR data & 2014–2018 & 0.5 \\ \hline ALOS & Stereo optical data & 2006–2011 & \(\sim\)26.5 \\ \hline COP & Band-X SAR data & 2011–2015 & \(\sim\)23.7 \\ \hline SRTM & Band-C SAR data & 2000 & \(\sim\)26.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: DEM characteristics.
# Effects of Distracted Pedestrian Behavior on Transportation Safety: Causes and Contributing Factors
## 1 Introduction
### Understanding Pedestrian Behavior  
Pedestrian distraction involves various behaviors that divert attention away from a person's surroundings while walking. Common distractions include using handheld electronic devices and smartphones for texting, browsing, or listening to music [4; 5; 6; 7]. Other wearable devices such as smartwatches, headphones, and VR/AR headsets also contribute to distracted walking [8]. For example, a study on the impact of distractions on pedestrian safety at the mid-block crosswalk at the University of North Carolina at Charlotte found that 16% of pedestrians were talking on the phone and 7% were texting while crossing a road [7]. Another study conducted at Western Washington University concluded that phone users often miss important visual cues, like a unicyclist, because their focus is on conversation [9].  
Distractions are typically grouped into three types: visual (e.g., texting or reading), auditory (e.g., listening to music), and cognitive (e.g., multitasking) [10]. Simulation-based studies have demonstrated that distracted pedestrians often have a delayed crossing initiation and a slower walking pace [11; 12; 13; 14]. The authors of one study suggested that pedestrians could still cross the road while distracted if they felt confident [15]. This sense of confidence can sometimes lead to pedestrians ignoring signals, even in dangerous situations like crossing on a red light, while they are not inclined to believe that they are at higher risk compared to other pedestrians [16].  
Distraction can also be influenced by factors like gender, age, and environment [17; 18; 19]. Some studies suggest that men tend to experience higher levels of distraction, while others show that women across different age groups are more susceptible to distraction due to multitasking and safety concerns. Teenagers and younger people are generally more likely to be distracted using their cell phones compared to older people [20; 21].  
Environmental factors also play a role in influencing pedestrian behavior. Visual distractions, as compared to audio distractions, tend to have a greater impact. Distraction can be more common in areas like college campuses, but compared to busier downtown areas with higher traffic volumes [22]. Pedestrian confidence during crossing can also depend on the type of crossing, with marked crossings providing more assurance for distracted pedestrians [15].
## 3 Validation
### Probabilistic Numerical Model
#### 3.2.1 Distribution of Random Material Properties  
For the tensile strength, the material behavior was represented using the Weibull distribution. The probability density function, \(f_{w}(x,b,c)\), for a random variable \(x\geq 0\) is presented in Equation (9).  
\[f_{w}(x,b,c)=\frac{b}{c}\Big{(}\frac{x}{c}\Big{)}^{b-1}e^{-\big{(}\frac{x}{c} \big{)}^{b}} \tag{9}\]The terms \(b>0\) and \(c>0\) are the shape and scale parameters of the distribution, related to the dispersion and mean value of \(x\), respectively. The mean \(\mu_{w}\) and standard deviation \(\sigma_{w}\) of the distribution are evaluated, respectively, according to Equations (10) and (14).  
\[\mu_{w}=c\Gamma\left(1+\frac{1}{b}\right) \tag{10}\]  
\[\sigma_{w}=\sqrt{c^{2}\Gamma\left(1+\frac{2}{b}\right)-\mu^{2}} \tag{11}\]  
For the critical fracture energy, the lognormal distribution was chosen to describe the material behavior. Its probability density function, \(f_{L}(x,\mu_{L},\sigma_{L})\), is presented in Equation (12), where \(\mu_{L}\) is the mean and \(\sigma_{L}\) is the standard deviation of the variable's natural logarithm. The expected mean value \(E_{L}(X)\) and variance \(Var_{L}(X)\) of the distribution are presented in Equation (13) and Equation (14), respectively.  
\[f_{L}(x,\mu_{L},\sigma_{L})=\frac{1}{\mu_{L}\sigma_{L}\sqrt{2\pi}}e^{-\frac{( \ln(x)-\mu_{L})^{2}}{2\sigma_{L}^{2}}} \tag{12}\]  
\[E_{L}(X)=e^{\mu_{L}+\frac{\sigma^{2}}{2}} \tag{13}\]  
\[Var(x)=\left(e^{\sigma_{L}^{2}}-1\right)e^{2\mu_{L}+\sigma_{L}^{2}} \tag{14}\]
## 3 Results and Discussion
### Corrosion Inhibition Mechanism
#### 3.5.3 The Synergistic Effect
Moreover, it was intervened not only in interatomic but also in intermolecular interactions, with the final layer to have significantly increased resistance to corrosive factors, encountering both mass transfer and electrochemical attacking phenomena. The rust layer's outer surface is considered to have a chemical composition consisting of green rusts, FeO(OH), and Fe\({}_{\text{x}}\)O compounds, binding with hydrogen bonds and oxygen bridges; therefore, the point of zero charge of them fluctuates, especially for the \(\alpha\)-crystals, between iron oxides and oxyhydroxides, from 7.5 to 9.38 at 22 \({}^{\circ}\)C. That confers to the layer to have a dynamic response to the increase of the pH locally, for a specific quantity i.e., of a basic compound and to function as an ion trap for the metallic surface [111; 124; 127].
# User Comment-Guided Cross-Modal Attention for Interpretable Multimodal Fake News Detection
## 3 Methodology
### Fusion of User Comments and News Content
#### 3.5.3 Hierarchical Comment Importance Scoring
```
0: Comment set \(F_{\text{C}}=\{c_{1},c_{2},\ldots,c_{n}\}\), text features \(H_{\text{T}}\), hyperparameters \(\alpha,\beta,\gamma,\lambda\)
0: Importance weight set \(S_{importance}=\{s_{1},s_{2},\ldots,s_{n}\}\)
1:for\(i=1\) ndo
2:\(S_{rel}(c_{i})\leftarrow\text{cosine\_similarity}(c_{i},H_{\text{T}})\)\(\triangleright\) Relevance score
3:\(S_{crd}(c_{i})\leftarrow\text{CredibilityScorer}(c_{i})\)\(\triangleright\) Credibility score
4:\(S_{temp}(c_{i})\leftarrow\exp(-\lambda\cdot(t_{current}-t_{ comment}(c_{i})))\)\(\triangleright\) Temporal decay
5:\(s_{i}\leftarrow\alpha\cdot S_{rel}(c_{i})+\beta\cdot S_{crd}(c_{i})+\gamma\cdot S_{temp}(c_{i})\)\(\triangleright\) Weighted fusion
6:endfor
7:Normalize \(S_{importance}\) such that \(\sum_{i=1}^{n}s_{i}=1\)
8:return\(S_{importance}\)
```  
**Algorithm 2** User comment importance scoring algorithm. The final importance score is obtained through weighted linear combination and normalized to ensure that the sum of all comment weights equals 1. This design enables the model to automatically identify and focus on the most informative comments.
## 4 Experimental Result  
### Experimental Setup  
This section describes how we trained and fine-tuned various BERT-based models for clinical NLP tasks, focusing on MIMIC-III discharge summaries and the BLUE benchmark. As a foundational baseline, BERT-Base was used with its standard 12-layer Transformer, 768 hidden units, and 12 attention heads, pre-trained on Wikipedia and BookCorpus. By contrast, Discharge Summary BERT shares the same architecture but specializes in the MIMIC-III Discharge Summary dataset (59,652 notes), improving its handling of domain-specific terms and contexts.  
All experiments were run on a TPU with eight Google Cloud Platform cores. We set the sequence length to 128 and a batch size of 128 for pre-training, with a learning rate of \(5\times 10^{-5}\). During fine-tuning, the batch size was reduced to 32 but retained the same learning rate, and each model was trained for 30 epochs. Every experiment was repeated five times with different random seeds, and we used the median accuracy for consistency. While BERT-Base served solely as a fine-tuning baseline, Discharge Summary CRPT, and  
Figure 5: Comparison of Random Masking (RM) vs. Whole-Word Masking (WWM). RM independently masks subword tokens, leading to fragmented context, while WWM masks entire words, preserving semantic meaning and context.  
Bio+Discharge Summary CRPT were pre-trained under a contrastive objective combined with Whole-word masking. To ensure a fair comparison, all models were trained and fine-tuned under identical hyperparameters--200K pre-training steps, 30 fine-tuning epochs, max seq-len 128, learning rate \(5\times 10^{-5}\), batch size \(128/32\). Full settings are summarized in Table 2.  
Table 2 outlines the configuration of all BERT-based models, including initial checkpoints, datasets, and training steps. This setup allowed us to systematically compare how each initialization, ranging from general-domain BERT-Base to specialized CRPT variations, affects performance on multiple clinical downstream tasks. By maintaining consistent hyperparameters across models, we isolate the impact of domain-specific pre-training and contrastive learning strategies on clinical NLP outcomes.
###### Abstract  
Spatiotemporal big data contain information in multiple dimensions such as space and time. Spatiotemporal data have the characteristics of large volume, intricate spatiotemporal relationship, and uneven spatiotemporal distribution. Index structure is one of the most important technologies used to improve system data analysis and workload. However, it is difficult to dynamically adjust with data density, resulting in increased maintenance costs and retrieval complexity. At the same time, maintaining the proximity of spatiotemporal data in spatial or temporal dimensions is crucial for efficient spatiotemporal analysis. To address these challenges, this paper proposes a learned index method, GLMI (Geohash and piecewise linear model-based index for spatiotemporal data). GLMI uses dynamic space partitioning based on the Hilbert curve to reduce the impact of data skew on index performance. In the time dimension, a piecewise linear model was constructed using the ShrinkingCone algorithm, and a buffer was designed to support the fast writing of spatiotemporal data. Compared with the current mainstream traditional high-dimensional indexes and the ZM index, GLMI has a smaller space consumption and shorter construction time compared to high-dimensional learned indexes on real traffic itinerary and trajectory record datasets. Meanwhile, GLMI also has an advantage in query efficiency.  
Geohash; learned index; piecewise linear model; spatiotemporal index +
Footnote †: journal:  
Article
# Mobile Autonomous Sensing Unit (MASU): A Framework That Supports Distributed Pervasive Data Sensing
## 4 MASU Evaluation Using Simulations
### Simulation Results  
Next we present the simulations results that show the behaviour of the Control Tier of the MASU framework for the different network infrastructures and mobility patterns. It also considers the costs of the actions performed by such a tier, in terms of network utilisation. In order to verify that the Control Tier works as expected, we performed various simulations of the Dynamic Centralized and the Distributed role architecture approaches considered by the MASU framework and compared them with a Fixed Centralized method (used as baseline), where there is no dynamic selection and activation of roles in the Control Tier. For both approaches we implemented real algorithms in order to confirm that the MASU framework behaves as expected. In case of the Dynamic Centralized approach, we implemented a distributed election algorithm for the selection of a Manager every time it fails. Similarly, in the Distributed approach we implemented a distributed consensus algorithm for the selection of a Producer when necessary. Next we present the evaluation of the role architectures of the Control Tier considering different (i) network infrastructures and (ii) mobility patterns.
## 2 Numerical Methodology
### Mesh Generation and Model Validation  
Unstructured mesh was used, and the mesh of the fluid boundary region and chip region were refined. The heat sink with a jet height (\(H\)) of 2 mm and a square-edged jet diameter (\(d\)) of 2 mm was selected for the mesh independence tests. Models were simulated with different numbers of meshes including 26,718, 68,546, 127,091, 323,075, and 998,199. The maximum temperature of the heat sink bottom (\(T_{\rm max}\)) and the pressure drop (\(\Delta P\)) were compared to evaluate the convergence with increasing mesh number. The criterion \(\big{|}\big{(}X^{j}-X^{j+1}\big{)}/X^{j}\big{|}<1\times 10^{-3}\) was chosen for the mesh independence test, where \(X^{j}\) is the computed value of the variable for the current mesh and \(X^{j+1}\) is the value of the same variable after mesh refinement. The results are shown in Table 2. To ensure calculation accuracy and minimize computing costs, the mesh density of mesh 4 was selected for the meshing scheme of other structures.  
\begin{table}
\begin{tabular}{c c c} \hline
**Therrophysical Properties** & **Solid** & **Liquid** \\ \hline \(\rho\) (kg/m\({}^{3}\)) & 9060 & 8200 \\ \(C_{p}\) (J\(\cdot\)kg\({}^{-1}\cdot\)K\({}^{-1}\)) & 323 & 721 \\ \(k\) (W\(\cdot\)m\({}^{-1}\cdot\)K\({}^{-1}\)) & 33.2 & 10.6 \\ \(\Delta\)T/2 (K) & 1.5 & \\ \(L\) (J/kg) & 28,980 & \\ \hline \end{tabular}
\end{table}
Table 1: The thermophysical properties of the 49 Bi/21 In/18 Pb/12 Sn alloy [14; 40].  
[PERSON] et al. [4; 6; 41] carried out confined jet impingement experiments with water as the cooling medium. The parameters in the experiments were as follows: the jet diameter (\(d\)) was 1.59\(\sim\)12.7 mm, the Reynold number (\(Re\)) was 4000\(\sim\)23,000, and the ratio of jet height to jet diameter (\(H\)/\(d\)) was 1-5. Based on the experimental data, the heat transfer correlation was proposed for the Nusselt number of jet stagnation point (\(Nu_{0}\)). Under the corresponding experimental conditions, a two-dimensional axisymmetric model for the confined jet impingement was constructed, and the relevant settings of the model were the same as above. The simulation conditions were all within the applicable range of the correlation. The results of \(Nu_{0}\) determined by the numerical simulation and the experimental correlation are shown in Figure 2. The calculation values of \(Nu_{0}\) were in good agreement with the values predicted by experimental correlation, and the deviations were within \(\pm\)10%.