esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on Seismic Performance of Steel Fiber Reinforced Concrete Pier Under Bending-Torsion Coupling
## 3 Numerical Simulations  
### Model Parameters  
In order to better simulate the mechanical properties of SFRC, the Umat subroutine is used to randomly generate steel fibers in the concrete, and the relevant parameters are listed in Table 4. In terms of model elements, concrete is simulated using the eight-node hexahedral reduced integration solid element (C3D8R), while steel fibers and rebars are simulated using the truss element (T3D2). In terms of material properties, the ideal elastoplastic model is used to simulate the constitutive behavior of steel fibers. The concrete damage plasticity model is used to simulate the constitutive behavior of concrete, and the damage factors in this model have a good simulation effect on the mechanical properties of concrete under cyclic loading [21]. The relevant parameters of the concrete are listed in Table 5. Considering the damage of concrete, the stress-strain curves are as shown in Figure 6, and the specific parameters are determined based on a material performance test and relevant specifications [22].  
In Figure 6, \(\sigma_{c}\) and \(\varepsilon_{c}\) represent compressive stress and compressive strain; \(\varepsilon_{c}^{in}\), \(\varepsilon_{0c}^{d}\), \(\varepsilon_{c}^{pl}\), and \(\varepsilon_{c}^{el}\) represent the inelastic strain, elastic strain (undamaged), plastic strain, and elastic strain (damaged) under compression; \(\sigma_{c0}\) and \(\sigma_{c  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Material** & \begin{tabular}{c} **Elastic** \\ **Modulus** \\ **Type** \\ \end{tabular} & \begin{tabular}{c} **Tensile** \\ **Modulus** \\ **/MPa** \\ \end{tabular} & \begin{tabular}{c} **Cross-Sectional** \\ **Area/mm2** \\ \end{tabular} & \
\begin{tabular}{c} **Density** \\ **(g.cm\({}^{-3}\))** \\ \end{tabular} &
\begin{tabular}{c} **Poisson’s** \\ **Ratio** \\ \end{tabular} \\ \hline Steel fiber & 200 & 2000 & 0.23 & 7.85 & 0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Relevant parameters of scet fiber.  
Figure 6: Stress–strain curves of concrete. (**a**) Compression behavior; (**b**) tensile behavior.  
ultimate stress under compression; \(E_{0}\) represents the initial elastic modulus; \(d_{c}\) represents the compressive damage factor; \(\sigma_{t}\) and \(\varepsilon_{t}\) represent tensile stress and tensile strain; \(\varepsilon_{t}^{ck}\), \(\varepsilon_{0t}^{cl}\), \(\varepsilon_{t}^{pl}\), and \(\varepsilon_{t}^{cl}\) represent the cracking strain, elastic strain (undamaged), plastic strain, and elastic strain (damaged) under tension; \(\sigma_{t0}\) represents the ultimate stress under tension; and \(d_{t}\) represents the tensile damage factor.  
To simulate the bond-slip phenomenon between the concrete and rebar under cyclic loading, the rebar material model developed by [PERSON] [23] is used in ABAQUS (2018 version). The numerical model of the specimen is shown in Figure 7.
# Deep Learning Models for Road Passability Detection during Flood Events Using Social Media Data
## 2 Related Work
State-of-the-art CNNs such as AlexNet [35], DenseNet201 [36], InceptionV3[37], InceptionResNetV2 [38], ResNet [39], VGG [40] or YOLOv3 [41] were taken after they had been pre-trained on popular and wide datasets such as ImageNet [42], Places365 [43] or VOC [44], and then fine-tuned on the dataset of this work. Leveraging on pre-trained networks is a common practice in deep learning research: training a single model from scratch requires prohibitive computational performances, nearly inaccessible to most research centres or universities. Within the context of this paper, using CNNs that were pretrained on datasets containing a variety of places, environments, and buildings, enables them to represent and recognize objects and shapes in their internal layers. Fine-tuning such networks on a smaller dataset for specific tasks, such as the ones used in this work, allows them to reuse the pre-trained knowledge to achieve the goal more effectively. Most of the works proposed during the MediaEval competition used the aforementioned CNNs to extract visual features from the last layers of the networks. Moreover, besides extracting _global_ features (pertinent to the whole image), [PERSON] et al. [45] and [PERSON] et al. [46] also combined information related to single entities (i.e., cars, boats, persons), named _local_ features. Then, extracted features were used for classification in several manners. One option [20; 22; 47] was to feed them as input for a neural network having few fully connected layers and using softmax for classification. Other approaches used other state-of-the-art machine learning algorithms, such as Support Vector Machine (SVM) [21; 23; 45; 46; 48], Multinomial Naive-Bayes, Random Forest and SRKDA [21]. The approaches which performed best exploited ensemble models, where their final output was determined by majority voting or averaging each model's prediction. Ensemble models were also used for feature extraction, combining features extracted from the same picture by several CNNs [20]. Finally, two strategies were used to merge metadata and visual information: _early_ and _late fusion_. The _early fusion_ combines the features before being computed by the classifier(s), while _late fusion_ averages the prediction of the approaches separately developed for the two domains. In our work, we introduce a novel lightweight network architecture, able to achieve comparable results of the winner-approach, namely an ensemble model of 45 CNNs per task. The proposed approach leverages on a custom loss function, and accomplishes both the tasks at once, reducing the number of needed parameters.
# Landslide Susceptibility Assessment Using the Geographical-Optimal-Similarity Model
## 3 Research Methodology
### Geographical-Optimal-Similarity Model Prediction  
The GOS model is grounded in the principle of geographic similarity and aims to predict landslide occurrence likelihood by quantifying the similarity in geographic configuration attributes between unknown and known locations. The core concept of the model states that landslide occurrence likelihood is closely linked to the region's geographic configuration similarity. The model infers the landslide susceptibility of unknown locations by learning from the geographic configuration of known landslide locations. Meanwhile, the GOS model optimises parameter selection using methods such as cross-validation to minimise prediction errors and ensure the model's stability and accuracy across different regions and datasets. Moreover, the GOS model generates landslide susceptibility predictions and evaluates the uncertainty of prediction results, offering more reliable support for decision-making.  
The primary process (Figure 4) of the GOS model includes geographical configuration representation using explanatory variables, similarity assessment, optimal percentage threshold estimation, optimal similarity sample selection, landslide susceptibility prediction, and uncertainty assessment [24].  
Figure 4: GOS model schematic diagram.
# Designing Automated Logistics Warehouse Stackable Bidirectional Infinite-Loop Modules
## 3 Materials and Methods  
### Stackable Bidirectional Infinite-Loop Module Concept  
The typical configuration of an existing logistics warehouse is depicted in Figure 1(a), where racks are arranged with aisles designed between them for inventory transportation. A variety of transportation devices, including forklifts, stacker cranes, and lifts, operate within these aisles. The selection of these devices depends on factors such as warehouse type, size, degree of automation, and inventory characteristics.  
This study aims to improve upon various warehouse storage methods, particularly the configuration depicted in Figure 1(a). In Figure 1(b), we propose the removal of aisles and the addition of racks to accommodate more inventory items to maximize spatial utilization. Figure 1(c) illustrates the benefits of implementing the improvements suggested in Figure 1(b) through a floor plan.  
This innovative method has the potential for efficient application not only in automated logistics warehouses but also in smart farms, particularly vertical farms, which demand optimized space usage within confined spaces (Figure 1) [47]. Vertical farming, which is gaining attention as a future urban agricultural model, entails growing crops vertically in urban settings, offering advantages such as minimized water and land usage, pest control in controlled environments, consistent year-round production, and resource-efficient crop growth [14]. Additionally, utilizing the proposed stackable bidirectional infinite-loop module approach will serve as an effective solution to current issues such as land scarcity and labor shortages owing to an aging workforce [48].
# Potential and Limitations of the New European Ground Motion Service in Landslides at a Local Scale
## 4 Discussion  
In this section, results are analyzed and discussed in order to establish the potential and limitations of the EGMS to detect and monitor landslides at a local scale by comparing it with (1) spatial and qualitative data (geomorphological evidence and building damage inventory) and (2) quantitative temporal rainfall data.
# Numerical Modeling of Single Pile Behaviors Due to Groundwater Level Rising
## 2 Study Area
### Bangkok Subsoil and Pile Foundation  
From several reports, the subsoil profiles of the Bangkok area have been established [24; 25; 26; 27]. Generally, it consists of layers from the ground surface in order:  
1. Weathered crust of backfill material, 0 m to 2 m thick. The high soil density layer was usually affected by human activities.
2. Very soft clay, Soft Bangkok Clay, low strength and highly compressible clay, from 2 m to 14 m. The color varies from medium gray to dark gray. The natural water content of the soil layer varies from 60 to 105%.
3. Medium clay, 14 m to 20 m thick. The color is dark gray to brownish gray. The natural water content ranges from 31 to 62%.
4. Stiff to very stiff clay (referred to as Stiff Bangkok Clay), usually 5 m to 10 m, but extending up to the depth of 20 m to 30 m. The water content is relatively low, 15 to 32%.
5. First sand layer, 8 m to 16 m thick, generally found at 20 m to 30 m. It can be fine to medium sand and occasionally contains traces of clay or silt. The color is yellowish to grayish brown. The water content varies from 12 to 25%.
6. Very stiff to hard clay layer, found below the medium to dense sandy layer, color varying from light gray to grayish brown, about 10 m to 15 m thick, extending to a depth of 30 m to 40 m. The water content ranges from 15 to 22%. It should be noted that the hard clay layer at some location appears below the second sand layer, a depth greater than 65 m.
7. Second sand layer (very dense sand layer) is found below the very stiff to hard clay layer, all the way down to the end of the borehole, at 50 to 65 m. The sand is silty, yellowish brown to brownish gray in color. Most of the large bore piles  
Figure 1: Location and Geology of Bangkok area: (**a**) Map showing study area in Bangkok and sampling points; (**b**) stratigraphy of Lower Central Plain basin.  
for the high-rise buildings require pile tips to be based in this layer to yield their maximum capacities.  
The piled foundations are deep foundations, that are a main part of a building structure to transferring its load to layers of soil below. The piled foundations are important for the construction of many building in the Bangkok area, since it is located on compressible soil at a shallow depth. Foundations in Bangkok are generally focused on deep piled foundations, which, with significant groundwater level rises, affect behavior between 0 m to 80 m below the ground. Usually, the pile foundations have a length of \(\sim\)15-60 m, with lengths of 15-28 m and 0.20-0.60 m diameters for medium, and lengths of 30-60 m and 0.60-2.00 m diameters for high rise buildings.
## 3 Proposed Method  
### Overall Framework  
As shown in Figure 3, our approach consists of a semantic segmentation inpainting network (SSIN) and discriminator network. The inpainting network SSIN is placed to replace the dynamic labels of the segmentation map with the static label, and the discriminator attempts to differentiate the predicted segmentation map from the ground-truth map. Before feeding the raw input of the segmentation map to the discriminator, preprocessing is applied to both maps, which matches the probability distribution of the ground-truth label and inpainted map.  
Proposed SSIN \(\mathbf{G(x,M)}\) takes \(\mathbf{x}\) (the dynamic segmentation map) and \(\mathbf{M}\) (the mask) as input to replace the dynamic label with a static label. The dynamic segmentation map \(\mathbf{x}\) includes both dynamic and static objects with a probability of \(\mathbf{x}\in\{0,1\}\) and its shape is H\(\times\)W\(\times\)C, where \(W\) is the width of the map, \(H\) is the height, and \(C\) is the number of segmentation classes. The binary mask image \(\mathbf{M}\) separates the dynamic and static objects (1 for a static object and 0 for a dynamic object) in the shape of H\(\times\)W\(\times\)1. The output of the SSIN \(\widetilde{\mathbf{x}}\) has the same shape as the input, where dynamic objects are replaced with nearby static objects.  
The discriminator network \(\mathbf{D(x)}\) takes the segmentation map and discriminates whether the segmentation map is generated or real with the output size of 1. In detail, the input of the discriminator is either the preprocessed SSIN output map \(\mathbf{\overline{x}}\) or the preprocessed static map \(\mathbf{\overline{y}}\). While the discriminator focuses on discerning the domain of the segmentation map with the preprocessed maps, the generator aims to deceive the discriminator and learns how to remove and change the dynamic class to the static class similar to the given ground-truth data.
# A Game-Theoretic Rent-Seeking Framework for Improving Multipath TCP Performance +
## 5 Evaluation
### Experimental Setup  
We evaluated our _cooperative MPTCP_ algorithm based on the rent-seeking framework (RSF) by comparing it with three other well-known MPTCP algorithms: LIA [4], OLIA [9], and BALIA [23]. The latter three algorithms are all available in an experimental Linux implementation ([PERSON] et al., \"Multipath TCP in the Linux Kernel\", available from [[https://www.multipath-tcp.org](https://www.multipath-tcp.org)]([https://www.multipath-tcp.org](https://www.multipath-tcp.org)) (released: 2 November 2017)) of MPTCP v0.93 by [PERSON] et al. We used this Linux implementation as the basis for our experiments and added implementations for our RSF MPTCP, including both delay-balanced (RSF-DB) and loss-balanced (RSF-LB) versions.  
The test environment in our lab consisted of three laptops, all running Ubuntu Linux 16.04. As shown in Figure 2, one of these was set up as an HTTP server, while the other two were clients. The laptops were connected to an Ethernet switch, each via two 1 Gbps interfaces, thus creating two separate paths for our experiments. In particular, each of our MPTCP connections had two subflows in our experiments (for simplicity [23]). We used the NetEm (NetEm: [[https://wiki.linuxfoundation.org/networking/netem](https://wiki.linuxfoundation.org/networking/netem)]([https://wiki.linuxfoundation.org/networking/netem](https://wiki.linuxfoundation.org/networking/netem)), accessed on 13 May 2022) network emulator to control the bandwidth, delay, and packet loss on the Gigabit Ethernet links in order to emulate the characteristics of wireless paths. We used \(d=100\) ms and \(\mu=4\) packets/s in all of our experiments.  
The performance evaluation of the _MPTCP_ algorithms was carried out with the following settings. We used five MPTCP connections (each with two subflows) between the server and the two clients. The server ran a single MPTCP implementation at a time for each experiment. The packet traffic was generated by a large file transfer from the HTTP server, where the file size ranged from 2 to 10 MB. Packet traces were capturedusing _tcpdump_ ([[https://www.tcpdump.org](https://www.tcpdump.org)]([https://www.tcpdump.org](https://www.tcpdump.org)), accessed on 13 May 2022) and analyzed with _wireshark_ ([[https://www.wireshark.org](https://www.wireshark.org)]([https://www.wireshark.org](https://www.wireshark.org)), accessed on 13 May 2022).  
Table 2 provides a concise summary of our results, which are presented and explained in the following subsections. We check for the target loss (\(\mu\)) and delay (\(d\)) in all of our experiments.
# A Cloud-Based Ambulance Detection System Using YOLOv8 for Minimizing Ambulance Response Time
###### Abstract  
Ambulance vehicles face a challenging issue in minimizing the response time for an emergency call due to the high volume of traffic and traffic signal delays. Several research works have proposed ambulance vehicle detection approaches and techniques to prioritize ambulance vehicles by turning the traffic light to green for saving patients' lives. However, the detection of ambulance vehicles is a challenging issue due to the similarities between ambulance vehicles and other commercial trucks. In this paper, we chose a machine learning (ML) technique, namely, YOLOv8 (You Only Look Once), for ambulance vehicle detection by synchronizing it with the traffic camera and sending an open signal to the traffic system for clearing the way on the road. This will reduce the amount of time it takes the ambulance to arrive at the traffic light. In particular, we managed to gather our own dataset from 10 different countries. Each country has 300 images of its own ambulance vehicles (i.e., 3000 images in total). Then, we trained our YOLOv8 model on these datasets with various techniques, including pre-trained vs. non-pre-trained, and compared them. Moreover, we introduced a layered system consisting of a data acquisition layer, an ambulance detection layer, a monitoring layer, and a cloud layer to support our cloud-based ambulance detection system. Last but not least, we conducted several experiments to validate our proposed system. Furthermore, we compared the performance of our YOLOv8 model with other models presented in the literature including YOLOv5 and YOLOv7. The results of the experiments are quite promising where the universal model of YOLOv8 scored an average of 0.982, 0.976, 0.958, and 0.967 for the accuracy, precision, recall, and F1-score, respectively.  
ambulance detection; machine learning; YOLOv8; cloud computing
# Simulated Altitude Training and Sport Performance: Protocols and Physiological Effects
## 3 Discussion
### Effects of Hypoxia Exposure Training on Power Performance  
Regarding anaerobic fitness performance, studies have also shown that the power peak in exercise performance [20; 31] and the power average increased significantly after hypoxia exposure training [1; 19]. The possible reason for this result is a shift in ATP production toward an increased contribution of glycolysis [38]. Additionally, there is a transition towards increased carbohydrate utilization and reduced fat usage in oxidative phosphorylation [17; 45]. Furthermore, exercise leads to improved efficiency in excitation-contraction processes [46], or a shift in mitochondrial regulation towards a more oxidative profile [47]. [PERSON] et al. [19] reported that significant molecular adaptations were observed, demonstrating increased variability in blood perfusion within active muscles. Through interval training during repetitive sprinting, the main action muscle groups may make better use of fast-twitch fibers, potentially delaying fatigue by using less anaerobic energy. [PERSON] et al. [21] examined the effects of intermittent hypoxia training on a 20 km time trial and 30 s anaerobic performance, and showed that intermittent hypoxia training improved anaerobic performance but had little effect on 20 km time trial performance. [PERSON] et al. [16] collected intravenous blood to determine the hemoglobin concentration, hematocrit, ferritin, erythrocyte sedimentation rate, and white cell count. Their findings indicated a 6.8% enhancement in peak power, an 8.3% improvement in mean repeat sprint power, and a 3.6% increase in hemoglobin concentration.  
Hypoxia exposure training and HHIT have shown significant increases in power peak and power average. Intermittent hypoxia intervals lead to improved anaerobic performance and a shift in ATP production. Additionally, adaptations in blood perfusion and muscle fiber utilization contribute to enhanced exercise efficiency. However, the impact on endurance performance appears to be limited. Overall, these findings emphasize the importance of power training and the potential benefits of incorporating specific training protocols to optimize sports performance. Additional research is required to gain a deeper understanding of these mechanisms and to further refine training protocols.
# Non-Uniform-Illumination Image Enhancement Algorithm Based on Retinex Theory
###### Abstract  
To address the issues of fuzzy scene details, reduced definition, and poor visibility in images captured under non-uniform lighting conditions, this paper presents an algorithm for effectively enhancing such images. Firstly, an adaptive color balance method is employed to address the color differences in low-light images, ensuring a more uniform color distribution and yielding a low-light image with improved color consistency. Subsequently, the image obtained is transformed from the RGB space to the HSV space, wherein the multi-scale Gaussian function is utilized in conjunction with the Retinex theory to accurately extract the lighting components and reflection components. To further enhance the image quality, the lighting components are categorized into high-light areas and low-light areas based on their pixel mean values. The low-light areas undergo improvement through an enhanced adaptive gamma correction algorithm, while the high-light areas are enhanced using the Weber-Fechner law for optimal results. Then, each block area of the image is weighted and fused, leading to its conversion back to the RGB space. And a multi-scale detail enhancement algorithm is utilized to further enhance image details. Through comprehensive experiments comparing various methods based on subjective visual perception and objective quality metrics, the algorithm proposed in this paper convincingly demonstrates its ability to effectively enhance the brightness of non-uniformly illuminated areas. Moreover, the algorithm successfully retains details in high-light regions while minimizing the impact of non-uniform illumination on the overall image quality.  
low-light images; light component; gamma correction; weighted fusion +
Footnote †: journal: Applied
# Forced Convection Heat Transfer for Stratospheric Airship Involved Flight State
## 2 Numerical Method
### Control Equations  
The phenomena of flow and heat transfer were controlled by the following equations [28]:  
Continuity equation  
\[\frac{\partial V_{x}}{\partial x}+\frac{\partial V_{y}}{\partial y}+\frac{ \partial V_{z}}{\partial z}=0 \tag{2}\]  
Momentum equation  
\[\rho\bigg{(}V_{x}\frac{\partial V_{i}}{\partial x}+V_{y}\frac{\partial V_{i}}{ \partial y}+V_{z}\frac{\partial V_{i}}{\partial z}\bigg{)}=-\frac{\partial P} {\partial i}+\rho g_{i}+\mu\bigg{(}\frac{\partial^{2}V_{i}}{\partial x^{2}}+ \frac{\partial^{2}V_{i}}{\partial y^{2}}+\frac{\partial^{2}V_{i}}{\partial z^{ 2}}\bigg{)} \tag{3}\]  
where \(i\) represents x-component, y-component, and z-component, \(V\) is the velocity vector, \(\mu\) is the viscosity of the fluid, \(P\) is the pressure of the fluid, \(\rho\) is the density of the fluid, \(g\) is the gravitational acceleration.  
Energy equation  
\[\rho c_{p}(V_{x}\frac{\partial T}{\partial x}+V_{y}\frac{\partial T}{\partial y }+V_{z}\frac{\partial T}{\partial z})=k(\frac{\partial^{2}T}{\partial x^{2}} +\frac{\partial^{2}T}{\partial y^{2}}+\frac{\partial^{2}T}{\partial z^{2}}) \tag{4}\]  
where \(c_{p}\) is the specific heat of the fluid, k is the thermal conductivity and T is the temperature of the fluid.