esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing Street-Crossing Safety for Visually Impaired Pedestrians with Haptic and Visual Feedback
## 2 Related Works
### Challenges in Mobility for Visually Impaired Individuals
#### 2.1.1 Street-Crossing Barriers for Visually Impaired Pedestrians  
As a fundamental daily activity, crossing streets, a basic activity for sighted individuals, constitutes a significant challenge for visually impaired pedestrians [4; 5]. They not only need to accurately identify and interpret the remaining time displayed on pedestrian traffic signals [5; 19; 20] but also must assess the availability of sufficiently large traffic gaps or rely on drivers to yield in order to cross safely [21]. This safety assessment primarily depends on two critical factors [6]: the time required for the pedestrian to cross the street and the available time gap before the next vehicle arrives. Studies have shown that, even when using a white cane, the rate of drivers yielding to blind pedestrians is only 37% [7], forcing them to rely more heavily on their judgment of traffic gaps. However, complex intersection designs and environmental noise on streets severely impact this judgment process [8]. For instance, in roundabouts and other segments without fixed crossing times, visually impaired pedestrians find it challenging to reliably assess drivers' intentions to yield. In some cases, they even require intervention from others to avoid collisions [35]. These factors collectively result in longer decision-making times for visually impaired pedestrians, significantly affecting both their travel efficiency and safety.  
To address the challenges faced by visually impaired individuals during mobility, researchers have developed a variety of assistive technological solutions. These solutions include smartphone-based navigation applications [9; 10; 11; 12], smart canes [13; 14; 15], and intelligent guide robots [16; 17; 18]. These innovations provide essential functions such as obstacle detection, the interpretation of traffic patterns, and directional guidance. Despite their significant advancements in daily navigation, these technologies still encounter challenges in crossing scenarios, particularly in accurately conveying critical information such as the remaining time on pedestrian traffic signals [5; 19; 20] and safe traffic gaps [21].
# Power Distribution IoT Tasks Online Scheduling Algorithm Based on Cloud-Edge Dependent Microservice
## 3 Task Scheduling Model for Distribution Stations under Cloud-Edge Collaboration
### Cloud-Edge Scheduling Mechanism for Power Tasks under Microservice Architecture  
The system consists of several edge devices and a central cloud, which can be denoted as \(\mathrm{E=}\{\mathrm{e_{1}},\mathrm{e_{2}},\ldots,\mathrm{e_{N}}\}\), where \(\mathrm{N}\) is the total number of edge devices and the central cloud, \(\mathrm{e_{N}}\) is the central cloud, and the rest are edge devices. Each edge device can be responsible for the processing of tasks within one or several distribution stations, and the central cloud manages all devices. After a power task request is generated, it is submitted to the edge device in the region to wait for processing. Until the task arrives, the task information is unknown, such as the microservice invoked, the amount of input data, the deadline, etc.  
\(\mathrm{v_{i}^{m}}\) denotes the \(i\)-th microservice invoked by task \(\mathrm{s_{m}}\). The processing time of \(\mathrm{v_{i}^{m}}\) at device \(\mathrm{e_{n}}\) is \(\mathrm{r_{i,m}^{m}}\), which can be estimated based on historical information [17]. Due to the independence between microservices, the edge devices offload some or all of the microservices to the cloud or other edge devices according to the resources of the devices and the needs of the tasks, and collaborate with each other to perform the tasks through the replacement of communication and computational resources. In the data transfer process, the data transfer rate between devices \(\mathrm{e_{i}}\) and \(\mathrm{e_{j}}\) is \(\mathrm{u_{ij}}\), and \(\mathrm{u_{ij}}\)= \(\mathrm{u_{ji}}\). When \(\mathrm{i}=\mathrm{j}\), then \(\mathrm{u_{ij}}\)= \(+\infty\), ignoring the transmission time of data within the same device. The completion of the virtual exit microservice indicates the completion of the whole task. The cloud-edge scheduling mechanism for power tasks under the microservice architecture is shown in Figure 1.
## 3 Results
### Results of Combined Features by Wavelengths and Texture Information
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Coal Quality** **Indices**} & \multirow{2}{*}{**Feature** **Data**} & \multirow{2}{*}{**Number of** **Features**} & \multicolumn{4}{c}{**Calibration Set**} & \multicolumn{4}{c}{**Prediction Set**} \\ \cline{3-10}  & & & \(R_{c}^{2}\) & \(RMSE_{c}\) & \(R_{cv}^{2}\) & \(RMSE_{cv}\) & \(R_{p}^{2}\) & \(RMSE_{p}\) & \(RPD\) \\ \hline \multirow{4}{*}{Ash (\%)} & HS & 50 & 0.986 & 1.331 & 0.852 & 4.326 & 0.783 & 3.901 & 2.223 \\  & GLEM & 44 & 0.980 & 1.661 & 0.580 & 7.591 & 0.644 & 3.569 & 1.736 \\  & Gabor & 37 & 0.992 & 1.027 & 0.947 & 2.601 & 0.811 & 3.452 & 2.379 \\  & Combined & 71 & 0.998 & 0.476 & 0.986 & 1.341 & 0.993 & 0.659 & 12.217 \\  & HS & 41 & 0.980 & 0.880 & 0.822 & 2.623 & 0.846 & 2.371 & 2.634 \\  & GLEM & 44 & 0.991 & 0.616 & 0.856 & 2.414 & 0.892 & 1.999 & 3.149 \\  & Gabor & 49 & 0.994 & 0.509 & 0.923 & 1.878 & 0.927 & 1.135 & 3.842 \\  & Combined & 43 & 0.999 & 0.230 & 0.994 & 0.497 & 0.989 & 0.583 & 9.904 \\  & HS & 37 & 0.963 & 0.152 & 0.730 & 0.412 & 0.583 & 0.486 & 1.603 \\  & GLEM & 18 & 0.856 & 0.299 & 0.638 & 0.476 & 0.502 & 0.563 & 1.467 \\  & Gabor & 33 & 0.991 & 0.075 & 0.923 & 0.218 & 0.957 & 0.174 & 4.991 \\  & Combined & 56 & 0.995 & 0.055 & 0.961 & 0.154 & 0.979 & 0.112 & 7.159 \\  & HS & 41 & 0.865 & 1.960 & 0.472 & 3.875 & 0.514 & 2.259 & 1.485 \\  & GLEM & 62 & 0.967 & 0.975 & 0.357 & 4.315 & \(-\)0.932 & 2.847 & 0.745 \\  & Gabor & 22 & 0.942 & 1.311 & 0.736 & 2.794 & \(-\)0.904 & 2.261 & 0.750 \\  & Combined & 78 & 0.989 & 0.553 & 0.923 & 1.438 & 0.948 & 0.838 & 4.544 \\  & HS & 37 & 0.979 & 0.567 & 0.874 & 1.391 & 0.565 & 1.481 & 1.570 \\  & GLEM & 49 & 0.970 & 0.665 & 0.595 & 2.452 & 0.646 & 1.771 & 1.741 \\  & Gabor & 56 & 0.992 & 0.365 & 0.864 & 1.487 & 0.619 & 0.994 & 1.676 \\  & Combined & 45 & 0.998 & 0.157 & 0.989 & 0.409 & 0.994 & 0.217 & 13.849 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Coal quality indices prediction model results based on texture features and combined spectra-texture features. Combination = combination of spectra and texture features. Generally, this paper realizes the accurate prediction of coal quality indices with combined spectral-spatial texture information.
# Explainable AI Frameworks: Navigating the Present Challenges and Unveiling Innovative Applications
## 4 Discussion
### XAI Frameworks  
#### 4.1.1 LIMEs  
Local interpretable model-agnostic explanations (LIMEs) introduce a novel approach to delivering explanations at an individual level for predictions. They operate in an extrinsic manner, meaning they provide explanations post hoc without delving into the inner workings of the model. This framework is particularly notable for its model-agnostic nature, capable of explaining predictions across diverse models without needing access to their internal mechanisms. To unravel the contributions of interpretable input to predictions, LIMEs employ a perturbation technique within the input's neighborhood and observe the resulting behavior of the model's predictions. This process involves creating a new dataset comprising perturbed samples and their corresponding predictions from the black-box model. Subsequently, LIMEs utilize this new dataset to train an interpretable model, which is weighted based on the proximity of the sampled instances to the instance under examination [28; 56].
# Influence of Girder Flaring on Load Effect in Girders of Composite Steel Bridges
###### Abstract  
A flared or splayed girder bridge is a structure made up of a concrete slab on girders with linearly varying spacing along the length. For such an irregular bridge, the girder distribution factors in the AASHTO LRFD Bridge Design Specifications are not applicable. In lieu of using a refined method of analysis, the study at hand proposes a simple approach for computing the dead and live load effect in the girders. To do so, fifteen composite steel girder bridges are analyzed by the finite element method to determine the influence of the girder flaring angle, girder spacing, number of girders, deck slab thickness, span length, girder stiffness, and presence of cross-bracing on the load distribution within the bridge. This study showed that the tributary width concept is a reliable approach for determining the dead load effect on the splayed girders, especially for the case of shored construction. The girder distribution factors for flexure in the AASHTO specifications can be reasonably utilized for such irregular bridges if the girder spacing at the location of each truck axle is considered, leading to a maximum of 14% difference on the conservative side between the AASHTO approach and the finite element analysis. On the other hand, the lever rule can provide a good estimate of the live load distribution among the splayed girders when subjected to shear, as the maximum safe deviation from the finite element outcome in this situation is less than 10%.  
finite element method; flared girders; gravity loads; highway bridges; composite steel girders; girder distribution factor; splayed girders; structural analysis Article
## 3 Results and Discussion
### Experimental Analysis of Indents in a Matrix
The influence of surface treatment on mechanical properties is not the scope of the current article, and we leave this discussion for another work. Figure 8: Average percentage difference of hardness and modulus values for three indents within a group for various maximum indentation depth and separation. (**a**) orientation 0\({}^{\circ}\), \(h=1\) μm; (**b**) orientation 90\({}^{\circ}\), \(h=1\) μm; (**c**) orientation 0\({}^{\circ}\), \(h=5\) μm; (**d**) orientation 90\({}^{\circ}\), \(h=5\) μm. What is also important in matrix type measurements is the number of neighboring indents. It does not matter how large the matrix is, the maximum number of 4 neighbors can be achieved (Figures 1 and 6b). The results on hardness and reduced modulus for two different depths as a function of the number of neighbors and spacing is shown in Figure 9 for \(h=1\)m and 5m, Figure S3.2 for \(h=10\)m.  
In general, the influence of nearby indents reduces with increasing separation, implying that spacing affects the values more rather than the number of neighbors. When comparing the properties behavior between different depths for PS, one can notice that with increasing depth, the constant \(H\) and \(Er\) values between neighboring indents can be achieved at small separations. Similarly as reported in [22], hardness and modulus for each neighboring indent become constant with \(d\)/\(h=5\)-\(6\) for a maximum depth of 5m (Figure 9c,d) and 10m (Support Information S3, Figure S3.2). The average absolute mechanical property values are more or less constant at \(d\)/\(h=10\) and above for \(h=5\)m (Figure 9c,d). For a depth of 10m, hardness between each indent and overall values behave in a similar way, but modulus results approach consistency at \(d\)/\(h>15\) (Figure S3.2). This might be because when indents were done at a depth of 10m, more piling up was observed and the deformed area surrounding the indent increased. When one compares results at maximum depth of 1m, the larger data scatter is observed. Figure 9a,b shows that for depth of 1m, constant results between neighboring indents as well as average values are achieved at \(d\)/\(h>15\) for PS when two dimensional arrays are set. The need for larger spacing for PS at lower depths might be because of the higher influence of the strain-hardening effect [34] that results in the overlap of plastic zones of neighboring indents at  
Figure 9: (**a,c**) hardness and (**b,d**) reduced modulus as a function of the number of neighboring indents and spacing for maximum depth of (**a**) and (**b**) 1m; (**c**) and (**d**) 5m. those depths, even though optical images showed that each indent was well separated at depths of _d/h_ = 10 (Figure 6b). Although, as already mentioned above, a depth of 1 \(\upmu\)m is deep enough and, yet for PS, the values of \(H\) and \(Er\) are still changing with the indentation depth. Nevertheless, overall minimum separation found through experimental studies is lower than the advised 30 times maximum indentation depth.
# A Case Study of Floating Offshore Super-Long Steel Pipeline Combing with Field Monitoring
## 5 Discussion  
1. During the floating transportation of pipelines 1 and 2, the strain of the upstream and the downstream was obviously greater than that of the top, which was because that the upstream and the downstream were directly affected by water flow and waves, while the top part was less affected. In addition, the strain at the bent point of the pipeline was also larger than that of other parts. The bend point was not only affected by the vertical action of water flow and waves, but also affected by the component force transmitted by the bend section.
2. Floating posture and wind speed will affect the amplitude of the pipeline, but not the vibration frequency of the pipeline. However, increasing the number of tugboats will increase the vibration frequency of the pipeline and decrease the amplitude. For the same vibration system, the amplitude may change, but the vibration frequency is unchanged. Floating posture and wind speed do not change the weight and stiffness of the pipeline structure (i.e., vibration system), but increasing or decreasing the number of tugboats will change the weight and stiffness of the whole structure composed of tugboats and steel pipelines.
3. The strain at the bending point of the pipeline must be paid special attention to during the dynamic construction of the floating pipeline. The construction institution shall reasonably arrange the number of tugboats according to the real-time monitoring data, pay attention to controlling the speed of tugboats and the size of the towing force, and prevent the damage of anticorrosive coating and the vibration damage of pipeline due to excessive bending strain.
4. Through the reasonable arrangement of tugboats and buoyancy tanks for the super-long pipeline, the dynamic construction of the long-distance floating process can be realized with on-site monitoring. Ensure that the deformation and vibration of the  
Figure 13: Frequency spectrum of pipeline segment 2 floating transport.  
steel pipeline during floating transport are within a safe range. The obtained data and conclusions can provide a useful reference for similar pipeline floating construction.
5. The limitation of the discrete sensor is that it can only measure a finite section of the structure and cannot cover the whole length. If conditions permit, construction institutions or researchers can use distributed optical fiber sensors to realize non-destructive monitoring of a full kilometer distance, with more accurate event identification and positioning capabilities and more accurate and timely early warning.
# Influence of Porosity on Drag Coefficient of Perforated Road Signs
## 3 Results and Discussion
### Drag Coefficient of Perforated Road Signs According to the Wind Speed  
To calculate the drag coefficient of perforated road signs according to the wind speed, CFD analysis was conducted at different wind speeds of from 10 to 60 m/s. The wind pressure and wind speed distribution of perforated road signs according to the wind speed is shown in the following figures. Among them, Figures 10 and 11 show the results of a case with an aspect ratio of 2.0, a clearance ratio of 0.5 and a porosity of 10%.  
Figure 8: Correlation between porosity and drag coefficient.  
Figure 9: Drag coefficient reduction ratio of perforated road sign by porosity.  
Figure 11: Wind speed distribution of perforated road sign by wind speed.  
Figure 10: Wind pressure distribution of perforated road sign by wind speed.  
In Figure 10, the difference in wind pressure distribution occurring at the front and rear of the perforated road sign did not show a significant change according to the wind speed. In Figure 11, the wind speed distribution, the length and the area of the separation region at the rear of the perforated road sign did not show a significant effect on the wind speed.  
The Royal Netherlands Meteorological Institute [28] analyzed the relationship between wind speed and drag coefficient. It was found that wind speed and drag coefficient had a parabolic relationship. According to the study, it was analyzed that the drag coefficient increases as the wind speed increases, and shows the maximum value under wind speeds of 30-35 m/s, and decreases when the wind speed exceeds 35 m/s.  
However, the drag coefficient of the perforated road sign does not show a clear trend with the wind speed. Figure 12 shows the drag coefficient of perforated road signs according to the wind speed.
# Knowledge Sharing in Security-Sensitive Communities
## 1 Introduction  
Knowledge sharing in a security-sensitive community presents unique challenges. This community includes organizations involved in intelligence gathering and research, such as government bodies, military intelligence, and civil defense departments. The intelligence community encourages joint learning, thinking, and originality [1]. However, its work involves a paradox: while intelligence forces handle data specification, collection, evaluation, and distribution, sharing is restricted by secrecy regulations and compartmentalization [2]. To evaluate this paradox's impact on knowledge sharing, we compare communication patterns between homogeneous security-sensitive communities (with uniform information sensitivity) and non-sensitive communities, where sensitivity varies. All communities discuss the same subject matter.  
Past studies have examined the relationship between heterogeneous groups, trust, and knowledge sharing [3], comparing influencing factors in homogeneous versus heterogeneous groups. Cultural diversity also impacts knowledge-sharing [4]. [PERSON] and [PERSON] [5] found that reducing diversity dimensions by increasing trust aids knowledge sharing. [PERSON] [6] used Social Network Analysis (SNA) to show that homophily (similarity between people) improves knowledge sharing, with social capital mediating homophily's effect in building network ties and spreading knowledge across diverse backgrounds.  
This empirical study examines differences in knowledge-sharing in security-sensitive versus non-sensitive communities. Communication among members is represented by network graphs, with metrics reflecting trust's influence on knowledge-sharing: clustering coefficient, transitivity, diameter, radius, and density. These metrics are divided into measures of communication levels and knowledge-sharing efficiency, calculated for both types of communities.  
The paper starts by explaining the unique case of security-sensitive organizations that are the focus of the current research. We proceed to explain metrics from Social Network Analysis, which are useful for understanding the spread of knowledge and the level of communication. Section 4 describes our unique comparative data. After a report of the findings and their implications, the paper concludes with recommendations for internal and external knowledge-sharing procedures, collective intelligence system design, online sharing regulations to improve knowledge-sharing, and educational outlines for senior management in these organizations.
# Optimization of Organic Content Removal from Aqueous Solutions by Fenton-Ozonation
## 3 Results
### Fenton Process  
Figure 2 shows the evolution of the mineralization efficiency of 2,4-D (\(\gamma_{\rm TOC}\)) over time for the 17 experiments. Overall, a two-phase kinetic behavior can be observed. The first phase corresponds to a relatively rapid increase in mineralization during the first 30 min, suggesting a fast initial reaction in which the pollutant is easily degradable. The second phase reveals a more gradual rise in the mineralization rate, with a near plateau from 120 to 180 min. This indicates slower degradation of the pollutant, potentially explained by the formation of 2,4-D residues or degradation by-products that are more recalcitrant and harder to break down. Similar findings have been reported in the literature for 2,4-D oxidation (16; 26). Furthermore, the presence of this near plateau suggests that beyond 180 min, any additional improvement in mineralization capacity is strongly limited.  
Figure 2: TOC removal efficiency by the Fenton process. Experimental data shown in circles (\(\Circle\)), and trend line shown in a dotted line (- -). (**a**) runs 1–4; (**b**) runs 5–8; (**c**) runs 9–12; (**d**) runs 13–17. According to the experimental design detailed in Table 3.  
Run 2 (Figure 2a) exhibits the best performance after 180 min, with a final mineralization rate of 78%, followed by runs 1, 4 (Figure 2a), and 13 (Figure 2)), which reach 59% mineralization after 180 min. In contrast, runs 7 (Figure 2b) and 9 (Figure 2c) show much lower efficiency (21% and 24%, respectively). This difference in performance among the tests is mainly explained by the operating conditions, namely the L27-Fe dose, pH, and H\({}_{2}\)O\({}_{2}\) dose (Table 2).  
The degradation kinetics of 2,4-D by the Fenton process, monitored by HPLC (Figure 3), reveal higher degradation rates than those indicated by TOC analyses. This confirms the presence of degradation by-products not detected by HPLC but still measurable by the TOC meter. The works of [PERSON] [16] and [PERSON] [26] have confirmed this trend.
## 2 HY-2B/2C/2D Spacecraft  
To provide a more comprehensive understanding of the orbit performance and accuracy of the satellite-borne GPS receiver, \"HY2_Receiver,\" developed in China, we shall commence with a concise introduction of the HY-2B/2C/2D satellites and their associated attitude characteristics.