esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
However, results found no significant deformation in the time series before the collapse. Time series in [8] might include vertical and horizontal motion, which resulted in clear deformation before the collapse. While the result of deformation surrounds the two buildings in [10], it was not clear, which supports the need for further spatial analysis like what has been done in our study. The existence of subsidence and instability of buildings on clay deposit soils is well known in similar areas [11]. However, our results revealed no correlation between extracted spatiotemporal patterns and lithology. The interaction of lithology and spatiotemporal pattern result needs more investigation in future studies for better preparation of maps showing building collapse hazard. Although leveling measurement is costly and has limited time and space measurements, those measurements are still the main method to monitor building deformation [7]. The applicability of InSAR measurements to monitor building stability was confirmed in many previous studies. Here, with the help of spatiotemporal analysis, our study shows two common vertical deformations based on spatiotemporal patterns that could be used as inputs for modeling building collapse hazard. To confirm the applicability of those two patterns to initially predict collapse incidents, we applied a buffer of 90 m for new cold/hot spot points, presented in Figure 7, to check how this input layer would look. Figure 8 shows the temporal vulnerability maps of the four validation buildings that could be used as input layers for a temporal building collapse hazard model. Figure 8: Potential input layer for a building collapse hazard model. Maps are presented in a stretch Sentinel-2 image acquired on 24 October 2018: (**a**) building collapse vulnerability map for 18 February 2019 incidents, (**b**) building collapse vulnerability map for the 22 March 2019 incident, (**c**) illustrates building collapse vulnerability map for the 1 April 2019 incident, and (**d**) shows the one square kilometer grid of Alexandria with the validation area highlighted. The four collapsed buildings were located within the temporal vulnerability map of the area, as shown in Figure 8. However, the three vulnerability map results cover more than 50% of the study area in all cases, which shows a need to further improve the prediction accuracy. Hence, more investigations and correlation tests are needed to reduce the area of vulnerability maps to their minimum distribution. Additionally, land classification would be useful to eliminate unimportant land use and land cover types from our interest, such as the large cemetery area in the mid-south of the AOI of this test. Generally, we believe this study is a step towards a comprehensive building collapse risk assessment model based on PS-InSAR deformation measurement.
## 3 Simulation Results and Discussions
### Artificial Neural Network Setup
The next step was the definition of the number of hidden layers as well as the number of neurons. Then the trainings were performed. It should be mentioned that for each specific number of hidden layers, several neuron number tests were performed in order to find the optimal number of neurons in each hidden layer. When the training and test error goal was achieved, the training was stopped and the neural network solution and results were stored; otherwise, a different number of hidden layers and neurons were tested. Figure 3: Neural network architecture for four hidden layers [580 280 180 80][6].
## 2 Calculation Model and Method
### Thermal-Hydraulics Model
#### 2.3.1 Heat Conduction in Fuel Elements  
In order to obtain the spatial distribution of temperatures of fuel and cladding, the fuel element is divided into K nodes in the axial direction and L nodes in the radial direction, as shown in Figure 5. Since the thickness and thermal resistance of cladding are much smaller than those of the fuel pellet, the cladding is only divided into three nodes in the radial direction to obtain its inner surface and outer surface temperatures. Axial heat conduction is ignored because of prevailing radius to length ratios and axial symmetry [31]. Therefore, the heat conduction equation for the fuel pellet in the axial node \(i\) could be written as:  
\[\rho_{f}^{i}C_{f}^{i}\frac{\partial T_{f}^{i}(r,t)}{\partial t}=\frac{1}{r} \frac{\partial}{\partial r}\left(rk_{f}^{i}\frac{\partial T_{f}^{i}(r,t)}{ \partial r}\right)+q_{v}^{i} \tag{7}\]  
where \(\rho_{f}^{i}\) and \(C_{f}^{i}\) denote the density and specific heat of the fuel pellet in the axial node \(i\), respectively, \(T_{f}^{i}\) and \(k_{f}^{i}\) denote the temperature and heat conductivity of the fuel pellet in the axial node \(i\), respectively, \(q_{v}^{i}\) is the volumetric heat rate.  
Ignoring the heat generated in the cladding, we obtain the heat conduction equation for the cladding in the axial node \(i\) as:  
\[\rho_{cl}^{i}C_{cl}^{i}\frac{\partial T_{cl}^{i}(r,t)}{\partial t}=\frac{1}{ r}\frac{\partial}{\partial r}\left(rk_{cl}^{i}\frac{\partial T_{cl}^{i}(r,t)}{ \partial r}\right) \tag{8}\]  
where \(\rho_{cl}^{i}\) and \(C_{cl}^{i}\) denote the density and specific heat of the cladding in the axial node \(i\), respectively, \(T_{cl}^{i}\) and \(k_{cl}^{i}\) denote the temperature and heat conductivity of the cladding.
## 5 Experiments and Results
### Ablation Studies
#### 5.2.1 Feature Sampling and Aggregation Module  
We study the effect of feature sampling and feature aggregation modules in the entire PoseTrack framework. In the person detection stage, we use Cascade R-CNN detector with ResNet-101 backbone. In the pose estimation stage, the backbone is ResNet-50, and the input size of the image is 256 \(\times\) 192 pixels. In the tracking stage, we use greedy matching approach to compute the similarity using bounding boxes overlap cost criterion in the adjacent frames across the time.  
* Method (a) represents the single-frame baseline, which achieves a 73.1 mAP for multi-person pose estimation and a 58.3 MOTA for multi-person pose tracking. This baseline already demonstrates competitiveness compared to the state-of-the-art results on the PoseTrack2017 validation dataset.
* Method (b) is a degraded version that excludes the feature sampling module and sets all adaptive weights to 1/2S (as described in Equation (2)). This results in a decrease in mAP to 71.4 and MOTA to 57.3, indicating the importance of motion information on the feature level for video object detection.
* Method (c) enhances (b) by incorporating adaptive weights \(\omega_{\text{t,t+k}}\) calculated using Equation (2). This leads to an increase in mAP to 73.0 and MOTA to 58.1, surpassing the performance of (b). This highlights the criticality of designing an adaptive-weighted sub-network in the feature aggregation module.
* Method (d) is our proposed feature sampling and feature aggregation method, which adds the feature sampling module to (c). This further improves the mAP to 73.5 and MOTA to 62.8. These results demonstrate the effectiveness of our feature sampling and aggregation modules in leveraging motion information from adjacent frames to address challenges like motion blur and occlusion.
## 1 Introduction
[PERSON] et al. [21] carried out tests and numerical analyses to investigate the effect of apex plate length and thickness on the CFS roof trusses. The apex plate and the C-shaped sections were connected using bolts. [PERSON] et al. [22] carried out experimental and numerical analyses on seven CFS floor trusses to investigate bearing capacity and structural behavior. This study aims to propose and study a new joint for connecting thin-walled (thickness 0.6-2 mm) square hollow section (SHS) members to form a light-steel warren truss. Figure 1 illustrates the proposed joint in which two braces are connected to the chord using a patented U-shape connector and self-drilling screws. The U-shape connector is fabricated by stamping thin-walled hot galvanizing steel sheet. Screw holes are fabricated in the steel sheet before stamping it. The proposed joint has several superior advantages. Firstly, it is very easy to deliver all steel members to the construction site and assemble them together to form a warren truss using the proposed joint. No in-site welding work is required so that welding flaws can be avoided, and a high construction speed can be achieved. In addition, the pre-drilled screw holes of the U-shape connector can prevent sliding of the screw during screw drilling and expedite assembling speed; secondly, impairment to the SHS members is small as no cutting slot is required; finally, the U-shape connector can accommodate the flexibility of the brace diagonal angle from 30\({}^{\circ}\) to 90\({}^{\circ}\) and at least three self-drilling screws are guaranteed at each connecting surface of the brace. When designing a truss using the U-shape connector, pinned connection is assumed at the joint. After the axial forces of the braces are determined, the number of self-drilling screws can be calculated using the assumption that the screw swarm is under shear and takes all the force transferred from the brace. As the proposed joint has never been studied, it is necessary to investigate its bearing capacity and failure mode before it is used in reality.
# A Novel Adaptive Cluster Based Routing Protocol for Energy-Harvesting Wireless Sensor Networks
## 2 Adaptive Hierarchical-Clustering-Based Routing Protocol for EH-WSNs
### Environment-Adaptive Hierarchical Clustering
Then, the distance between the \(i\)th node and the \(j\)th node is calculated and recorded as \(d_{ij}\), and the proportion of \(d_{ij}<D_{c}\) could be calculated and recorded as \(\sigma\). Thus, the deduced proportion \(\sigma\) could participate in the calculation of the threshold \(T\) according to (3). The clustering termination is synergistically decided by the maximum distance and reliable data-transmission distance between the deployed nodes, which can guarantee a difference between clusters and reduce the difference inside the formed cluster. Therefore, the fusion of the confidence distance and the node topology could accomplish a more reasonable node clustering. To transfer a certain amount of data to the base station, the energy consumption of each cluster would increase with the increase in the threshold distance. However, the number of clusters would decrease accordingly, and reduce the transmission amount of the data package. The optimization of the threshold distance could be meaningful when improving the energy efficiency. Therefore, the proposed environment-adaptive hierarchical clustering could increase the MLC of WSNs by optimizing the node topology.
# Leveraging Machine-Learning for D2D Communications in 5G/Beyond 5G Networks
## 5 Challenges and Future Research Directions
### MmWave Environment  
Recent research directions that use ML algorithms for tackling small and separated problems can not efficiently address the highly dynamic and ultra-sense features in B5G and 6G networks, especially in mmWave based environment. The existing ML algorithms are applied without considering the adversarial mmWave environment, such as path blocking and spatial transmissions coming from BF. Moreover, DL-based solutions utilize continuous optimization that increases the system overhead. In addition, these solutions require an incredible offline-learning phase, making it unsuitable for future mmWave B5G/6G applications. However, online/discrete adaptive optimization techniques willbe more suitable to cope with the mmWave nature, especially for multi-hop transmission scenarios.
# The Optimal Deployment Strategy of Mega-Constellation Based on Markov Decision Process
###### Abstract  
LEO satellite mega-constellation projects have been proposed by many countries or commercial organizations in recent years. With more than 2000 satellites launched by SpaceX to configure the Starlink system, the orbital resources are more constrained given the existence of spacecrafts and countless orbital debris. Due to this, the operating environment is full of uncertainty and information symmetry is absent for designers and stakeholders during the process of project deployment. The flux model of space debris on orbit has been built for assessing the LEO operation environment. Based on the orbital debris flux model, the collision probability can be calculated, which is an important variable of the state space. Given the condition that tge number of satellites decreases due to collision between satellites and debris, the [PERSON] decision model has been built for optimal deployment strategy and decision-making. In order to assure that the mega-constellation system could provide services when satellites have failed, additional satellites need to be launched. The optimal deployment is the decision to launch a moderate number of satellites to maximize the benefit and minimize the cost. Assuming that at least 30 satellites need to be operated, 4 deployment scenarios are considered and the optimal deployment strategies can be obtained.  
optimal deployment strategy; collision probability; Markov decision process Article  
The Optimal Deployment Strategy of Mega-Constellation Based on Markov Decision Process  
2023  
152024[[https://doi.org/10.3390/sym15051024](https://doi.org/10.3390/sym15051024)]([https://doi.org/10.3390/sym15051024](https://doi.org/10.3390/sym15051024))
# Research on Decision-Making Methods for Autonomous Navigation in Inland Tribitary Waterways
## 2 Analysis of the Autonomous Navigation Process
### Autonomous Navigation Decision-Making Framework for Tributary Waterways  
The objective of ship autonomous navigation is to safely and efficiently guide the vessel to its destination based on real-time static and dynamic environmental data. However, autonomous navigation systems are typically susceptible to three categories of errors: environmental perception inaccuracies, ship motion modeling deviations, and control algorithm limitations. To mitigate these challenges, a time-rolling optimization algorithm has been developed. This algorithm iteratively corrects navigational errors through periodic status updates, as depicted in Figure 5.  
To achieve dynamic adaptive autonomous navigation, the process is structured into three sequential phases: Initially, the physical environment is digitized into a computational model compatible with autonomous navigation decision-making requirements. Subsequently, the potential collision risks are evaluated by synthesizing inland waterway navigation rules, good seamanship, and the vessel's maneuvering dynamics, coupled with real-time encounter scenario classification. In the decision-making phase, autonomous navigation strategies are formulated by synthesizing the predicted trajectories of TSs, the derived collision avoidance trajectories for the OS, and the application of the OS's ship domain model. Subsequently, autonomous navigation decisions are executed to control the ship for the desired steering or telegraphing maneuvers. Finally, the time-rolling model dynamically adjusts decisions to compensate for environmental perturbations and system uncertainties, ensuring continuous strategy refinement. This model ensures real-time adaptation of the navigation strategy in response to evolving environments and potential disturbances. This framework enhances navigational resilience through closed-loop adap  
Figure 4: Severity of conflict and classification of principles of action of OS.  
Figure 5: The time-rolling autonomous navigation decision-making framework.  
tation, where each iteration improves decision accuracy based on updated sensor inputs and error feedback.
## 6 General Discussion
### Limitations and Future Directions  
While this study offers valuable insights, some limitations must be acknowledged. Although the sample size of 355 respondents is substantial and consistent, it exclusively comprises recruiters from Portugal. This limits the generalizability of the findings, as the sample is not sufficiently representative of broader populations. Future research should aim to include larger and more diverse samples to enhance representativeness and allow for a more comprehensive analysis of AI adoption across different cultural and organizational contexts.  
Another limitation stems from the study's exclusion of external variables, such as organizational factors, trust, or social impact, which could significantly influence the adoption of AI tools in recruitment. The absence of these variables prevents a more holistic understanding of the determinants driving AI adoption. Future studies should explore these factors to provide a more nuanced perspective on the dynamics influencing recruiters' intentions to use AI technology.  
Moreover, it would be particularly interesting to investigate the motivation behind recruiters' use of AI tools. Are they adopting these technologies willingly, recognizing their potential benefits, or are they compelled to do so by organizational mandates or protocols? Understanding these motivations could offer critical insights into the interplay between individual agencies and organizational influence in the adoption of AI tools.  
Additionally, the responses from recruiters who have previously used AI tools and those who have never used them may differ significantly, introducing variability that complicates the interpretation of findings. A more homogeneous sample could have allowed for clearer conclusions.  
To address these limitations, future research could adopt a longitudinal approach, focusing on a specific group of recruiters. For example, assessing their perceptions and intentions before and after engaging in a project where they actively use AI tools over a period such as one month could provide richer insights. This approach would help isolate the effects of hands-on experience with AI tools, offering a more detailed understanding of how exposure impacts recruiters' attitudes and intentions.
# Evaluation and Comparison of Spatial Clustering for Solar Irradiance Time Series
###### Abstract  
This work exposes an innovative clustering method of solar radiation stations, using static and dynamic parameters, based on multi-criteria analysis for future objectives to make the forecasting of the solar resource easier. The innovation relies on a characterization of solar irradiation from both a quantitative point of view and a qualitative one (variability of the intermittent sources). Each of the 76 Spanish stations studied is firstly characterized by static parameters of solar radiation distributions (mean, standard deviation, skewness, and kurtosis) and then by dynamic ones (Hurst exponent and forecastability coefficient, which is a new concept to characterize the \"difficulty\" to predict the solar radiation intermittence) that are rarely used, or even never used previously, in such a study. A redundancy analysis shows that, among all the explanatory variables used, three are essential and sufficient to characterize the solar irradiation behavior of each site; thus, in accordance with the principle of parsimony, only the mean and the two dynamic parameters are used. Four clustering methods were applied to identify geographical areas with similar solar irradiation characteristics at a half-an-hour time step: hierarchical, k-means, k-medoids, and spectral cluster. The achieved clusters are compared with each other and with an updated Koppen-Geiger climate classification. The relationship between clusters is analyzed according to the Rand and Jaccard Indexes. For both cases (five and three classes), the hierarchical clustering algorithm is the closest to the Koppen classification. An evaluation of the clustering algorithms' performance shows no interest in implementing k-means and spectral clustering simultaneously since the results are similar by more than 90% for three and five classes. The recommendations for operating a solar radiation clustering are to use k-means or hierarchical clustering based on mean, Hurst exponent, and forecastability parameters.  
solar irradiation; data mining; time-series clustering; artificial intelligence; statistics methods +
Footnote †: journal: Applied  
0000-0002-23 August 2022
# Numerical Investigation on Novel Shear Connectors in Prefabricated Composite Beams
## 2 Push-Out Test for Shear Performance Analysis
### Failure Characteristics  
The failure characteristics of the two specimens are observed after the test and the components are separated, as shown in Figure 5.  
The inner damage of HS14 is shown in Figure 5a. It is obvious that the local crushing zone appears below the roots of the studs, but the concrete crack is not obvious. All the studs display shank failure, there are concrete scratches around the studs on the steel plate.  
Figure 5b shows the inner damage of VS4. The local crushing zone appears below the roots of the studs, which is more obvious than for HS14, the concrete cracks are serious and the crack widths are large, even extending into each other. There are also concrete scratches around the studs on the steel plate.  
There are several reasons that lead to the different damage situations of the two types of test specimens. Firstly, compared with the VS4, the shear studs of HS14 are arranged in both sides of the concrete, which reduces the stress interaction zone of the concrete. Secondly, the studs are weakly constrained from the edge of the concrete in VS4. Furthermore, the main reason is that the vertical steel plate in HS14 constrains the core concrete from outside, improving the concrete compression performance, so the concrete cracks cannot extend.  
It is found that the ductility and bearing capacity of shear studs will improve when the concrete around the studs has existing lateral pressure [23]. The concrete in HS14 is enclosed  
Figure 5: The damage diagram of push-off test specimens. (a) 1/2 HS14 specimen. (b) 1/2 VS4 specimen.  
by the steel plates, so the passive lateral pressure is produced under the longitudinal shear force, which is a similar principle. The test results of HS14 and VS4 are the best proof.  
The above test results show that the shear performance of prefabricated shear studs is better than conventional shear studs when the design geometry size and the number of studs are the same.  
Usually, the factors that influence the shear performance of conventional shear studs are stud diameter, concrete strength, stirrup diameter, etc., but for prefabricated shear studs, the factors are still unknown.  
The economic cost and time cost of continuing to research the shear performance effects of prefabricated shear studs by push-out testing are significant. Therefore, using the FEA software ABAQUS to continue the research is the best choice.  
Therefore, three design parameters are considered for the prefabricated shear studs finite element simulation study, including concrete strength \(f_{c}\), stud diameter \(D\), and lateral spacing of the vertical steel plates \(L\). The research results are expected to guide the design optimization of prefabricated shear studs.