esa-sceva/satcom-chunk-collection huggingface dataset:
# Erythermal Solar Irradiance, UVER, and UV Index from Ground-Based Data in Central Spain
## 1 Introduction
Several researchers now propose smartphone applications for UVI forecasts, given that occupational or recreational sun exposure is crucial to public health. Recent publications have developed algorithms for evaluating erytnal UV doses on sloped surfaces and their application to the human body [20]. In addition, methods for daily vitamin D synthesis in outdoor and indoor conditions are being studied, with a small outdoor UVI sensor sending information to the smartphone, evaluating the user's daily dose, estimating the amount of vitamin synthesis, and gauging the UVB LED lighting required in indoor conditions [21; 22]. In this context, solar measurement station data play a key role and allow UVER irradiance climatology, UVER exposure distribution, UV Index, and sun protection at low UVI levels to be studied to reduce UVER effects on people. The station also advises photo-protection action required for workers, sportsmen and women, walkers, cyclists, children, and so on. The current manuscript provides an analysis of UVB measurements recorded during the period 2013-2019 in Valladolid, Spain, at an altitude of 702 m a.s.l. for estimating UVER, UVI, cumulative doses, and sun protection impact when UVI is below three. Although the authors previously performed a study of radiative and meteorological variables in the same region in a rural area [23; 24], the new characteristics and novelties of this work are that the present UVB measurement station is located in the city of Valladolid itself. Data were recorded every minute, with this being the first time that such research has been performed in this area using one-minute data as a measurement timescale. In addition, information about the UVER station in central Spain has been included as one of the 160 solar stations in 25 European countries, showing the station's activity via web and publishing certain information, as can be seen in reference [25]. The rest of the article is set out as follows: Section 2 offers a description of the location, the different data control tests, and the methodology. The results and discussion are shown in Sections 3 and 4. Finally, a summary is provided, together with the most relevant conclusions.
# Memory and Cache Contention Denial-of-Service Attack in Mobile Edge Devices
## 5 Evaluation
### Performance
Although the worst-case \(\text{R}_{\text{perf\_recovery}}\) is still higher than 74%, in some workloads, they show relatively lower \(\text{R}_{\text{perf\_recovery}}\)s. For example, in the case of _namd_, \(\text{{R}}_{\text{perf\_recovery}}\)s when running malicious code type-1 and type-4 are relatively lower than the cases when running type-2 and type-3. Because of the conservative \(\text{{Thres}}_{\text{upper}}\) and \(\text{{Thres}}_{\text{lower}}\), our detection mechanism may falsely regard the malicious program as normal one, which, in turn, leads to a little lower \(\text{{R}}_{\text{perf\_recovery}}\). However, it is very rare case, as relatively low \(\text{{R}}_{\text{perf\_recovery}}\)s are only shown in several workloads. Although we could increase the range of \(\text{{Thres}}_{\text{upper}}\) and \(\text{{Thres}}_{\text{lower}}\), it might also increase the false alarm rates. Therefore, we use relatively conservative \(\text{{Thres}}_{\text{upper}}\) and \(\text{{Thres}}_{\text{lower}}\), since it still shows over 90% \(\text{{R}}_{\text{perf\_recovery}}\)s on average. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & _spec+mal\_t1\_ft_ & _spec+mal\_t2\_ft_ & _spec+mal\_t3\_ft_ & _spec+mal\_t4\_ft_ \\ \hline gemsFDTD & 95.1\% & 95.1\% & 95.2\% & 95.1\% \\ \hline bwaves & 95.0\% & 95.0\% & 95.0\% & 95.0\% \\ \hline bzip2 & 95.7\% & 95.7\% & 94.1\% & 93.7\% \\ \hline gcc & 91.1\% & 91.3\% & 91.0\% & 91.5\% \\ \hline h264 ref & 90.5\% & 88.5\% & 81.9\% & 88.7\% \\ \hline lbm & 94.8\% & 94.7\% & 94.8\% & 90.6\% \\ \hline leslie3d & 85.2\% & 83.9\% & 94.5\% & 92.5\% \\ \hline mcf & 94.6\% & 94.6\% & 94.6\% & 94.6\% \\ \hline milc & 93.9\% & 93.8\% & 93.9\% & 93.9\% \\ \hline namd & 75.6\% & 97.3\% & 97.4\% & 74.3\% \\ \hline perlbench & 94.0\% & 93.9\% & 93.9\% & 93.9\% \\ \hline xalancbmk & 94.1\% & 93.4\% & 94.1\% & 93.3\% \\ \hline sjeng & 94.3\% & 94.3\% & 94.3\% & 94.3\% \\ Geomean & 91.7\% & 93.1\% & 93.4\% & 91.5\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: \(\text{R}_{\text{perf\_recovery}}\) for each workload in the cases of _spec_+_mal_tX_ft_.
## 3 Methods  
The following section provides a detailed explanation of the proposed MFCF network utilizing 3D-CNNs. It begins by outlining the overall network architecture and subsequently delves into the unique design of the Multi-level Feature Cross-Fusion module in Section 3.1. The CAM is discussed in Section 3.2, followed by the introduction of the decoder block in Section 3.3. The mixed loss function is then elucidated in Section 3.4.  
To improve boundary detection accuracy and reduce false positives, this study presents a Multi-level Feature Cross-Fusion Network using 3D convolution. The network comprises four key components: a 3D Feature Encoder based on an enhanced 3D convolutional ResNet-18 [28], a CAM module for model interpretability and performance enhancement, a MFCF module for cross-fusion of multi-level features, and a 3D decoder for fine feature fusion. This approach results in the generation of an accurate change map.  
Figure 1 shows the proposed network architecture of MFCF 3D-Net.  
Our network takes two 256 \(\times\) 256 dual-temporal images as input. These images are initially concatenated in the depth dimension using the TC (Temporal Concatenation) module. The TC module first combines two dual-phase images along the depth dimension to create a three-dimensional tensor, allowing the model to process each pixel position in both space and time. This representation enables the model to learn directly from sequential changes between two temporal instances of each spatial location. The main advantage of the TC module is its ability to enhance the model's capacity to model timing dependencies by integrating temporal information directly into the network architecture. This integration helps capture pixel-level changes over time, crucial for tasks like change detection in remote sensing. This deep concatenation through the TC module allows the model to capture more intricate temporal features, enhancing its ability to model timing dependencies. By incorporating previous information through deep concatenation, the model can better recognize timing patterns. The tensor output from the TC module is subsequently input into the 3D Feature Encoder module for feature extraction. This module, which is built on 3D ResNet, is designed to extract image features. Specifically, it is employed to capture the  
Figure 1: MFCF 3D-Net network structure.  
feature information of the two-phase image post-time series. The study's model conducts a 4-layer feature extraction process to obtain 4-scale features. Attention is applied to enhance the quality of feature extraction. The resulting 4-scale feature map undergoes MFCF, combining feature information from various layers to generate four cross-fused feature maps. The fused features integrate spatial and semantic information from high- and low-level feature maps. These fused features are then refined by the cross-fused decoder module to gradually reconstruct the feature map size and enhance the overall detection accuracy.
# Design Procedures for Sustainable Structural Concretes Using Wastes and Industrial By-Products
## 4 Discussion
### Physical Properties
#### 4.3.3 Permeability to Air and Water  
As shown in Tables 16 and 17, the coefficients of variation of air and water permeability were 6.7% and 40.1%, respectively. Some authors suggest that for concretes with \(w/c\) ratio below 0.53, the coefficient of variation (CoV) is small, and six samples are sufficient, while for concretes with a \(w/c\) ratio above 0.65, the CoV is large, and it is necessary to increase the number of samples to consider the increase in concrete heterogeneity translated by a higher CoV. They also noted that a CoV of up to 20% is considered acceptable for air and water permeability coefficients, \(K_{G}\) and \(K_{W}\)[61, 68]. In this study, the CoV of air permeability coefficient was within the order of magnitude of the CoV expected for that type of test, while in the case of water permeability coefficient the CoV result was higher. Probably the higher CoV obtained for water permeability coefficient can be justified by the use of EAFS aggregates, which made the concrete matrix more heterogeneous due to its irregularity and porosity, and therefore it is recommended to increase the number of test samples to reduce the CoV.  
Figure 20 shows the air and water permeability coefficients obtained. The average air and water permeability coefficients were, respectively, 0.558 \(\times\) 10\({}^{-16}\) and 4.20 \(\times\) 10\({}^{-18}\) m\({}^{2}\). For conventional concretes and concretes with higher amounts of bottom or fly ash, of a strength class C25/C30, other studies found \(K_{G}\) values between 0.39-0.88 \(\times\) 10\({}^{-16}\) m\({}^{2}\), which was in accordance with the \(K_{G}\) value obtained in the present study [64, 65, 69]. By comparing the \(K_{W}\) value obtained with other studies, it was found that for conventional and higher amount of FA concretes of a strength class C25/C30 values of 6.4 and 2.16 \(\times\) 10\({}^{-18}\) m\({}^{2}\) were obtained [61, 64]. Hence, the result of \(K_{W}\) determined in this research was in accordance with the literature. It is possible to classify qualitatively the concrete based on the coefficient of water permeability, using the classification proposed by [PERSON]. [67] according to the Table 21. In this way, the EAFS8D1 mixture can be classified as a concrete of normal quality in terms of water permeability.
# A Review of Posture Detection Methods for Pigs Using Deep Learning
## 2 Data Acquisition and Sub-Tasks for Pig Posture Detection
### Sub-Tasks for Image-Based Pig Posture Detection  
Standard object detection refers to the discovery and location of target objects in a specific image and the concurrent identification of the category of every object [36]. As an extension of object detection, generic pig posture detection generally consists of pig localization and posture classification. Furthermore, in demanding application and research scenarios, identification of pig identity and timely tracking of pigs are included in this task for better practicability [28], and pig segmentation is included for better performance [37]. To more comprehensively introduce computer vision tasks for pig posture detection, this section decomposes pig posture detection into four technical submodules: pig localization, posture classification, pig segmentation, as well as pig identification and tracking, which will be explained in the following sections. Figure 2 shows the pipeline of utilizing the obtained raw data to apply to these four tasks. Particularly, the two fundamental issues of pig localization and posture classification, will be introduced in more detail for universality. Notably, pig localization and posture classification are executed in two separate stages in most traditional image processes and some deep learning methods, which are known as two-stage methods. However, they can be simultaneously completed in one stage in many state-of-the-art deep learning detectors.
## 4 Results and Discussion
### Islanding Mode
Moreover, SMC exhibits a huge amount of chattering throughout the operation of the microgrid, which is not suitable from the DC bus regulation point of view. However, BFASMC performs better during the islanded mode of microgrid as compared to other controllers.
# Urban Seismic Networks: A Worldwide Review
## 2 USN Catalogue
### Objectives  
The implementation of USNs enables several tasks, particularly those related to the management of the seismic risk and applications related to civil engineering problems. However, other applications in the broad field of monitoringare gaining interest as well (c.f. [145]). Each USN is established to accomplish a clear objective. This main objective can sometimes be followed by secondary objectives. Therefore, logistic and technological choices of each USN are properly conceived to cope with these objectives. We identified six recurring objectives among the collected USNs:  
* _Earthquake early warning_. USNs are usually designed to provide a warning in the very first moments after the occurrence of a strong earthquake. Such systems are based on the early processing of the seismic signals recorded at the network nodes by means of opportune algorithms. The two possible approaches to this task are the \"network- or regional-based\" and the \"on site (or single station)\" approaches. The on-site approach is the one most commonly adopted by USNs devoted to earthquake early warning. It does not require an extended network, and the alert time is reduced compared to the regional approach, which need not necessarily include the accurate location of the event. For this reason, unlike the regional approach, low-cost sensors can be employed and transmission can even be neglected [146]. In particular, on-site early warning systems are based on the detection of P-wave arrivals and estimation of the intensity of the incoming ground shaking in a few fractions of second, which is done by taking advantage of the difference in velocity between the P and S waves [147; 148; 149]. * _Earthquake monitoring_. USNs are frequently established to monitor earthquakes in urban areas. they are sometimes implemented as permanent networks, while other times they are expressly temporary and established only after the main earthquake of a sequence with the main objective of monitoring any following aftershocks. * _Post-event damage assessment_. This group of networks includes those specially planned for the implementation of systems that function to estimate earthquake damage, casualties, or losses in short time after an earthquake [150; 151]. They are often referred to as \"rapid response systems\" because they account for their task immediately after an earthquake. They can quickly support the creation of basic seismic intensity maps.
# Efficient and Effective Unsupervised Entity Alignment in Large Knowledge Graphs
## 2 Related Work  
### EA Encoders  
EA encoders are specifically designed to learn the graph structures of KGs. They can be categorized into two main types: TransE-based encoders and GNN-based encoders.  
The former employs the translational model, TransE, for learning entity embedding, while the latter utilizes advanced GNNs, such as Graph Convolutional Networks (GCNs) [16] and Graph Attention Networks (GATs) [17]. Currently, GNN-based methods demonstrate exceptional performance, due to their robust modeling capabilities in capturing graph structures through anisotropic attention mechanisms. However, conventional GCN and GAT architectures often fall short in adequately modeling the intricate graph structures of KGs and extracting sufficient information for EA tasks. Consequently, extensive research is being conducted in this area. For example, GMNN [18], CAECGAT [19], NMN [20], Dual-AMN [21], and PEEA [9] effectively integrate both intra-graph and inter-graph information. Moreover, MuGNN [22], NAEA [23], MRAA [24], RREA [25], RPR-RHGT [7] and RANM [8] propose or implement various heterogeneous GNNs to enhance KGs modeling. Building upon traditional GNN frameworks, these heterogeneous models incorporate relation and attribute edges within the KG's graph structure. Nevertheless, optimizing these methods frequently results in increased model complexity.
## 4 Results and Discussion
### Facades with One Wall and a Roof  
In case of rooms featuring a roof portion, the simulations were performed considering a room with a minimum internal height of 2 m, 40% roof slope and a flat facade without balconies. The center of the facade was considered at the center of the vertical wall and the loudspeaker was placed on the ground. In such conditions the walls are the only elements directly hit by the sound wave. The centers of the facades were considered at a height of 1, 4, 7 and 10 m. The overall average results for the room are summarized in Table 3.  
It can be concluded that the width of the room does not substantially affect the attenuation values. Indeed, throughout the simulated cases the maximum variation in attenuation due to room size change has been found to be just 1.7 dB (F0, 3 x 3-6 x 3). The minimum attenuations relative to the roof for floors F0, F1, F2 and F3 are 10.5 dB, 16.7 dB, 16.3 dB and 15.4 dB, respectively.
# Optimization Design of Space Camera Enclosure Based on Bionics
## 2 Biomimetic Structural Design
### Analysis of Leaf Structure of Victoria lindl  
The diameter of _Victoria lindl_'s leaves is relatively large, disk-shaped, and has a certain regularity in its veins, which gives it great buoyancy and strength. Generally, mature _Victoria lindl_'s leaves can support an adult and are called the _Victoria lindl_. This characteristic is related to its unique blade structure [18].  
From a radial perspective, the leaves of _Victoria lindl_ generally have 8 to 10 primary veins, radiating outwards from the center. And the tendons and veins near the center are thick and robust, while the tips gradually become thinner. The secondary tendons grow at the golden ratio point of the primary tendons. The angle between the first- and second-level tendons is about 36\({}^{\circ}\). If 90\({}^{\circ}\) is used as the basis, the growth angle of the second-level tendons also conforms to the golden ratio. Through comparative research, it was found that the distribution of tendons and veins in the leaves of _Victoria lindl_ conforms to \"Ludwig's Law\" in plant morphology principles [19; 20].  
In the circumferential direction, there are many horizontal tertiary veins distributed in the leaves of _Victoria lindl_, forming a grid-like distribution, which is beneficial for improving the overall stiffness of the leaves and plays an important role in maintaining the overall consistency of the leaves, as shown in Figure 2.  
Therefore, this article selects the _Victoria lindl_ tendon structure as the biomimetic prototype of the spatial camera box with a reinforced plate structure to achieve high stiffness, high fundamental frequency, and high lightweight design of the reinforced plate.
## 1 Introduction
We hypothesize that the structural characteristics of transportation networks vary depending on the adopted network scenarios (i.e., degree centrality focused, betweenness centrality focused, or vulnerability focused) used for vertiport location allocation. To verify this, we employ a network analysis-based approach to examine how different strategies influence key network indicators. Based on the empirical findings, this study offers strategic insights into vertiport placement planning during the early, pre-commercial phase of urban air mobility (UAM). To enhance the synergy between UAM and existing urban transportation networks, it is necessary to develop a methodological framework for network-based vertiport allocation. Since UAM requires vertiports for takeoff and landing, the placement of vertiports within the transportation network will be a key determinant of UAM's role within the broader system [33, 34]. UAM operates as a high-speed, small-scale urban transport mode that requires dedicated stations while utilizing designated airspace and flight corridors, making it distinct from traditional terrestrial modes. Furthermore, UAM is expected to overcome the limitations of helicopter transport while achieving a higher passenger capacity. Consequently, how vertiports are positioned within transportation networks will play a crucial role in shaping the future development of UAM. This study proposes a network science-based methodology for allocating optimal vertiport locations and applies it to the urban railway network of the Seoul Metropolitan Area, South Korea. Section 2 reviews network methodologies for analyzing transportation structures, prior research on airport and infrastructure siting, and existing studies on vertiport placement. Section 3 details the proposed methodology, datasets, and case study procedures. Section 4 presents a comparative analysis of changes in network characteristics under different vertiport placement scenarios. Finally, Section 5 discusses the findings, academic contributions, and concluding remarks.
# Pose Measurement Method Based on Machine Vision and Novel Directional Target
## 3 Methods
### Algorithms Related to Target Image Processing
#### 3.2.3 Topology-Based Fitting for Intersection  
In perspective projection, the coordinates of an object in the camera's coordinate system \(X_{w}\) and the coordinates of the object in the image plane of the object \(X_{i}\) is a linear transformation relationship, i.e., \(X_{i}=\alpha X_{w}\) where \(\alpha\) represents the coefficients of the linear transformation. It can be seen that the image formed by the perspective projection of a straight line in space is still a straight line. The straight line \(f=ax+by+c\). The point of the line in the area is \(f^{\prime}=a^{\prime}x+b^{\prime}y+c\), both of which satisfy the relationship shown in Equation (15):  
\[\forall(x,y)\in f\rightarrow(ax,ay)\in f^{\prime},a\
eq 0\] \[f=ax+by+c\to f=\frac{a}{\alpha}(ax)+\frac{b}{\alpha}( ax)+c \tag{15}\]  
The coordinates of the feature points in a row or column of the target, which are arranged in a straight line, remain unchanged in the row and column arrangement after the target undergoes pose transformation. Based on this, the obtained sub-pixel coordinates are arranged in rows and columns with 21 rows and 21 columns. Each row and column contains 21 sub-pixel coordinates and 441 sub-pixel coordinates, i.e., the intersection points of all rows and columns. However, since the 21 coordinates of each row and each column are not exactly on a straight line, the optimal straight line formula can be obtained by least squares fitting, and the intersection coordinates of the 21 row and 21 column coordinates can be obtained again to obtain more accurate sub-pixel coordinates of the 441 feature points.