esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulation
### Hop Count Evaluation  
We also evaluate the average number of hops of the end-to-end route generated by the approach. We fix the number of compromised nodes to 10 and evaluate the average hop count for each algorithm with the same number of data collection tasks. Figure 6(d) depicts a plot of the average hop count for the data collection with different algorithms. It can be seen that the overall performance of SPDC is better than DRP and NRRP. It takes fewer hops by using SPDC to perform data collection.  
As long as a secure path is constructed, subsequent data packets will be sent through the path. It is no need to perform random routing any more. The SPDC algorithm reduces additional random data packet forwarding in random dispersive algorithms like DRP and NRRP. Therefore, the hop count of data collection with SPDC is much fewer than that with DRP or NRRP.
## 3 Hierarchical Graph Learning for Next POI Recommendation  
Current POI recommendation methods typically rely on single-layer graphs that fail to capture the hierarchical nature of human mobility patterns. These approaches suffer from limited local information, missing hierarchical relationships, and restricted information propagation across different spatial scales.  
We propose a novel Hierarchical Graph Learning (HGL) framework for next POI recommendation that effectively captures multi-scale mobility patterns. As illustrated in Figure 2, our approach consists of two main components: (1) a hierarchical graph construction module that builds a three-level graph structure representing POI relationships at different scales, (2) a cross-layer information propagation mechanism that enables bidirectional message passing between hierarchical levels, and, finally, (3) a next POI prediction module that leverages the learned multi-scale representations. This architecture addresses the limitations of existing single-layer graph methods by modeling both fine-grained POI transitions and coarse-grained regional movement patterns simultaneously.
# A Study on the Effects of Vertical Mass Irregularity on Seismic Behavior of BRBFs and CBFs
## 4 Seismic Performance
### Acceleration  
This study examined the floor acceleration as a fundamental parameter in designing non-structural components, as shown in Figure 9. The maximum acceleration was recorded in the top floors of both the 10-and 15-story frames under the effect of #EQ8 (Gilroy Array #3 California/USA Loma Prieta). Comparing the mean values of the maximum acceleration of the 10-story frames showed negligible differences among these values. Irrespective of the number of floors, the highest and lowest values of the mean acceleration were respectively associated with the frames with mass irregularity BRBs and regular CBFs. Reductions in the floor acceleration caused by CBFs were higher than those caused by BRBFs.  
Figure 8: Average base shear comparison of (**a**) 10-story frames and (**b**) 15-story frames.  
Figure 9: Average acceleration comparison of (**a**) 10-story frames and (**b**) 15-story frames.  
With the highest PGA (0.55) among the 12 selected records, the Gilroy Array #3 record imposed the highest acceleration on the top floors of all the eight frames. Irrespective of the number of floors, the maximum acceleration was reported in the MIR-BRB. Amounts of maximum acceleration are illustrated in Table 5.
## 2 Materials and Methods
### Experimental Methodology  
Powder mixing: 5 g of W powder was mixed with 1% (relative to the W powder mass) Ni powder, Cu powder, or Ni+Cu powder (with mass ratios of Ni:Cu = 1:9 and Ni:Cu = 1:1) or a small amount of Ag powder. The mixture was ground in a grinding bowl for 30 min to form a composite powder.  
Compaction: If the porosity of the W skeleton is too high, the molten Ag cannot fill the pore volume adequately, resulting in excessive residual porosity and a reduced electrical conductivity. Conversely, if the porosity is too low, the molten Ag fills the pores completely, and the excess Ag adheres to the outer surface of the product, leading to an insufficient Ag content within the product. To achieve the desired porosity, the composite powder was compacted into cylindrical preforms using a compaction machine at a pressure of 5-9 MPa.  
Sintering: The compacted samples were heated in an H\({}_{2}\) atmosphere tube furnace at a rate of 7.5 \({}^{\circ}\)C/min. Pre-sintering was conducted at temperatures of 900, 950, 1000, and 1050 \({}^{\circ}\)C for 90, 120, and 150 min, respectively, to produce the W skeleton. To remove oxides from the mixed powder, the W powder and additives were uniformly blended and compacted into preforms, which were then subjected to sintering in a tube furnace under an H\({}_{2}\) atmosphere. The W skeletons were sintered using the tube furnace illustrated in Figure 3.  
Figure 1: Morphology of pure W powder: (**a**) 20 \(\upmu\)m; (**b**) 5 \(\upmu\)m.  
Figure 2: Morphologies of (**a**) Cu and (**b**) Ni powders.  
A total of 45 samples were co-sintered with different sintering temperatures, times, and additives.  
Infiltration: The W skeletons obtained under the optimal conditions were placed in an H\({}_{2}\) atmosphere tube furnace. A certain mass of Ag powder was pressed into a Ag disc with the same diameter as the W skeletons and positioned on top of them. As the temperature reached the melting point of Ag, the liquid Ag infiltrated the W skeleton. This process led to the fabrication of contact materials; specifically, the corresponding Ag amounts of AgW (70 wt%), AgW (75 wt%), AgW (80 wt%), and AgW (85 wt%) were 2.15 g, 1.67 g, 1.25 g, and 0.89 g, respectively. The infiltration apparatus is depicted in Figure 4.
## 3 Results
### Evaluation of the Accuracy of GNSS Control Points  
In many cases, the GNSS RTK method is much faster than the total station measurements. However, the chapter \"Test field specifications\" shows that their accuracy is less than that of TS points.  
Table 9 shows the standard deviations calculated from the coordinate differences of the GNSS control points and TS control points. From these values, it can be predicted what effect the use of GNSS control points may have on the calculated point cloud.
# Coverage Path Planning with Semantic Segmentation for UAV in PV Plants
## 2 Materials and Methods
### Deep Learning (DL) Server for Segmentation  
A deep learning (DL) server for segmentation was necessary to extract the RoIs from the Google Maps images. Additionally, the server was used to achieve this task automatically with the process called semantic segmentation, wherein each pixel is labeled with the class of its enclosing object [65; 66]. In previous work, a convolutional neural network was proposed, wherein a public database was used; this data was prepared, resized for training, and assessed with two network structures. The U-net network had the best performance, in terms of metrics, in the semantic segmentation task [65]. Then a DL server was employed to perform image segmentation and obtain the RoI [81].
# Research on Penetration Loss of D-Band Millimeter Wave for Typical Materials
## 5 Conclusions  
This paper discusses the penetration loss of D-band millimeter waves when shielded by various materials, such as vegetation, board, glass, and slate, as well as blocking measurement experiments in an indoor environment. The experimental results show that, under the given experimental conditions, the average transmission attenuation of D-band millimeter waves caused by a pot of vegetation is about 12 dB, implying that the receiving antenna receives only about 6.5 percent of the transmit power. As the number of vegetation increases, the attenuation of the D-band millimeter-wave signal increases sharply. In our experiment, when the amount of vegetation is increased to three pots, the receiving end can hardly receive the D-band millimeter-wave signal. For the measurement of the wooden board, the transmittance decreases with the increase in the thickness of the wooden board. Millimeter waves can penetrate thin boards, but when the thickness of the boards exceeds 1 cm, D-band millimeter waves can hardly penetrate obstacles. The average transmission attenuation coefficient of the thin glass shield to the D-band millimeter wave is about 4.4 dB, that is, only about 35% of the transmitted power is received by the receiving antenna. The loss of the D-band signal that passes through 5 cm-thick slate is about 20 dB, indicating that it can barely penetrate 5 cm-thick slate. The experimental measurement values are consistent with the theoretical value in general. The experimental measurement results of various materials show that the influence of occlusions on D-band millimeter-wave transmission cannot be ignored, which has a potential application prospect for estimating the channel attenuation characteristics of 5G or 6G systems with obstructions. In addition, we will explore the transmission loss of more frequency points and more materials in future work.  
Conceptualization, X.W. and M.W.; methodology, W.L. and M.W.; validation, Y.W. and W.Z.; formal analysis, X.W.; data curation, W.L. and C.B.; writing--original draft preparation, X.W.; writing--review and editing, W.Z.; visualization, C.B.; project administration, W.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China (2018 YFB1801004) and NNSF of China, grant numbers 6212780022, 61935005, 91938202, 61720106015, 61835002 and 61805043.
# Transmission Removal from a Single OSN-Shared Glass Mixture Image
## 2 Related Works
### Transmission Layer Removal  
Many efforts have successfully separated this component of the reflection layer from the glass mixture image. More specifically, the extracted reflection layer has been applied to the actual scene; for example, [PERSON] et al. [4] proposed and solved a face-image reflection removal problem. They recovered the important facial structures by incorporating inpainting concepts into a guided reflection removal framework, which took two images as the input and considers various face-specific priors. [PERSON] et al. [5] developed a network structure based on a deep encoder-decoder repetition-reduction network (RRNet). The authors found that the redundant information increased the difficulty of predicting images in the network; thus, they used mixed reflection image cascaded edges as input to the network. [PERSON] et al. [6] proposed joint reflection removal and depth estimation from a single image by concatenating intermediate features. [PERSON] et al. [7] proposed an iterative boost convolutional long short-term memory (LSTM) network that enabled cascaded prediction for reflection removal. The estimates of the transmission and reflection layers could be iteratively refined by boosting each other's prediction quality, and information across the cascade steps was transferred using an LSTM.  
The mixture image reflection extraction process is an extension of the study of glass image de-reflection and is mainly divided into statistical feature algorithms and deep learning algorithms, both of which distinguish the difference in features between the two image layers in the mixture image. [PERSON] and [PERSON] [8] analyzed the statistical feature distribution of the two image layers to determine the boundary and thus achieve separation. For deep learning algorithms, it is difficult to utilize the statistical information of the images, which requires some additional a priori knowledge as an aid; for example, the gradient information of the two layers can be used as the essential feature, to distinguish the two due to their different priorities in the mixture image. [PERSON] et al. [9] used gradient information and proposed a gradient loss function as a way to separate the two image layers. Similarly, [PERSON] et al. [10; 11] and [PERSON] et al. [6] constructed a predicted gradient network and then used this predicted gradient as a prior to assist in image layer extraction. In addition, the final experimental results were refined by continuous iterations of deep learning. [PERSON] et al. [7] proposed a cascade network that allowed two image layers to be used as a prior for each other, thus iteratively highlighting the information in the predicted image. [PERSON] et al. [12] recovered reflection layer images that were as close as possible to the ground truth images by analyzing the texture dimension as well as style dimension.
# Developing a GIS Model for Identifying Bear Corridors with Open-Source Data: The Romanian Context
## 3 Results  
### Determining the Favourability of Hunting Grounds  
The importance of the factors in relation to the area they occupy in a non-financial fund was quantified based on the qualitative assessment according to the provisions of Order 393/2002 [33], resulting in a spatial database with integer values for each factor considered. Thus, the abiotic factors receive 200 points, biotic factors 210 points, and negative anthropogenic factors 80 points [33].  
It is thus noted that most of the hunting grounds in the mountainous area of the Romanian Carpathians are hunting grounds in the very high favorability class (25%). The hunting grounds located in the forested areas of the Fagaras (southern part), Bucegi, Ciucas, Retezat, Parang, Hasmas, Calimani, and Ceahlau massifs are the most favorable for _Ursus arctos_ due to the provision of good living conditions and development of the species, taking into account the fact that they are located in areas with rich food resources and favorable morphological conditions for the safety of the species.  
Most of the hunting grounds located in the study area (38%) are still in the high favorability class, followed by 14% in the medium favorability class and 23% in the low favorability class (Figure 3).  
The hunting grounds located in the low favorability class are mainly located in the low, plain area (Western Plain, the Subcarpathian area of the Curvature Subcarpathians, and the Getic Subcarpathians, as well as the Junti Plateau, and in the area of the Transylvanian Depression, in the area of the Mures corridor between Alba Iulia and Turda, and in the area of the Tarnavelor Plateau) (Figure 9). The reason why these areas have low levels of favorability is mainly due to the lack of sufficiently extensive forest areas to provide optimal conditions for survival and reproduction. Unfavorable hunting grounds in the Transylvanian Depression are occasionally used by bears only to move to other favorable habitats.
## 5 Graph Simulations and Results
### Setup  
The performance of the three different types of jitter (\"standard\" jitter, window jitter and adaptive jitter) is evaluated in the shortest-delay mode and shortest-path mode of RREQ flooding (see Section 2.1.1) for different network graph scenarios. The different network scenarios are defined by triplets \((N,\rho,\text{metric})\), where:  
* \(N\) is the network population (number of routers in the graph);
* \(\rho\) is the network router density (number of routers per km\({}^{2}\)); and
* \"metric\" identifies the link metric model: uniform (hop count, in which all available links have cost one) or random (links have a random integer cost from one to 10).  
Values for each network profile are averaged over 20 samples, each sample corresponding to a random (static) distribution of routers over the network grid, in which RREQs are sent from a fixed random source to a fixed random destination. Each value related to a distribution corresponds to the average (\(\mu\)) of 10 RREQ flooding procedures simulated between the source and destination; standard deviation (\(\sigma\)) intervals around the mean value \((\mu-\sigma,\mu+\sigma)\) are also displayed.  
The following metrics are used for evaluating the performance of different jitter mechanisms:  
* **Number of (auto-)collisions** Consistently with the definition provided in Section 4.2.1, an auto-collision (denoted as a collision in this section) is counted when a router receives two retransmissions (copies) of the same RREQ, simultaneously.
* **Optimality index** Given a source \(s\) and a destination \(d\), the optimality index for a path between \(s\) and \(d\) is the cost of the shortest path divided by the cost of the discovered path. The path with the index closer to one indicates a better path.
* **Routing overhead** Measured in the number of RREQ or RREP retransmissions.
* **Route discovery delay** In shortest-delay mode, this is the time required in order to discover the first path. In shortest-path mode, it is the time required in order to discover the best path.
# Real-Time Pipeline Leak Detection: A Hybrid Deep Learning Approach Using Acoustic Emission Signals
## 2 Proposed Methodology
### CNN and LSTM Hybrid Structure
#### 2.3.1 Convolutional Neural Network
Sigmoid:  
\[f(x)=\frac{1}{(1+e^{-x})} \tag{5}\]  
Tanh:  
\[f(x)=\frac{(e^{x})-(e^{-x})}{(e^{x})+(e^{-x})}=\left(\frac{2}{(1+e^{-2x})} \right)-1 \tag{6}\]  
The CNN architecture was designed with a focus on computational efficiency. Convolutional layers with small kernel sizes (3 \(\times\) 3) were used to effectively extract spatial features while minimizing computational costs. Max-pooling layers (2 \(\times\) 2) were employed to reduce feature map dimensions progressively, significantly lowering computational overhead. The number of filters (32, 64, and 128) were chosen based on empirical experiments to balance feature extraction capability and computational complexity. Activation functions, ReLU for CNN, were selected for their effectiveness in capturing non-linear relationships and temporal dependencies, respectively. Training was conducted with mini-batch processing, utilizing GPU acceleration and early stopping to optimize training speed.
## Materials and Methods  
### Materials  
Rokopol RF-551, a sorbitol oxyalkylation product (PCC Rokita S.A., Brzeg Dolny, Poland), was used as a reference polyol (hydroxyl number 420 mg KOH/g, molecular weight = 650 g/mol, functionality 4,5), to prepare RPU/PIR foams. Catalytic system in the RPU/PIR formulation were 33% solution of anhydrous potassium acetate (Chempur, Piekary Slaskie, Poland) in diethylene glycol (Chempur, Piekary Slaskie, Poland), as a trimerization catalyst and 33% solution of DABCO--1,4-diazabicyclo[2.2.2]octane (Alfa Aesar, Haverhill, MA, USA) in diethylene glycol--as a polyurethane bond catalyst. The stabilizer of the foam structure was poly (oxyalkilene siloxane) surfactant Tegostab 8460 (Evonik, Essen, Germany). Carbon dioxide produced in situ in the reaction between water and isocyanate groups was a blowing agent. Furthermore, a commercial flame retardant, Antiblaze TCMP--tris-(2-chloropropyl) phosphate (Albemarle, Charlotte, NC, USA)--was added into some of the foams. The isocyanate raw material was a technical polymeric diisocyanate Purocyn B (supplied by Purinova, Bydgoszcz, Poland), whose main component was 4,4'-diphenyl-methane-diisocyanate (MDI). The density of Purocyn B at a temperature of 25 \({}^{\circ}\)C was 1.23 g/cm\({}^{3}\), viscosity was 200 mPas, and content of -NCO groups was 31.0%. Polyether and diisocyanate were characterized in accordance with appropriate standards like ASTM D 2849-69 and ASTM D 1638-70. Cinnamon extract--CE (Agrema Sp. z o.o., Wroclaw, Poland)--was used as a physical filler. According to the manufacturer information, this extract contains 5% of polyphenols. The main polyphenols in the cinnamon extract were, respectively, chlorogenic acid, flavonoids, and phenolic acids. In this biofiller are also metals in trace amounts (Table 1). The rest of the extract was an inert carrier of the active substances (polyphenols) [28]. Cinnamon was analyzed by FTIR technique (Figure 1). Cellulose in cinnamon comes from the bark of a cinnamon tree.  
FTIR analysis of cinnamon (Figure 1) showed the presence of groups -OH (3346.25 cm\({}^{-1}\)) and -C-H (2928.26 cm\({}^{-1}\), 1416.64 cm\({}^{-1}\), 1367.87 cm\({}^{-1}\), 848.63 cm\({}^{-1}\)) derived from -CH\({}_{2}\)OH or from the ring in cellulose; the aromatic ring (1631.68 cm\({}^{-1}\), 763.47 cm\({}^{-1}\), and 707.31 cm\({}^{-1}\)) derived from cellulose, flavonoids, and phenol acids; and 1152.23 cm\({}^{-1}\) represents the aromatic ether from flavonoids. -OH groups can be largely derived from cellulose.  
\begin{table}
\begin{tabular}{c c} \hline
**Compound** & **Content** \\ \hline (a) polyphenols & \\ Chlorogenic acid & together \\  & 5\% \\ Flavonoids & \\ Phenol acids & \\ heavy metals & \\ lead & 0.49 ppm \\  & 0.15 ppm \\  & arsenic & 0.81 ppm \\  & mercury & 0.09 ppm \\  & chromium & 1.06 ppm \\  & chloride & 0.51 ppm \\  & sulphate & 3.00 ppm \\ (c) carrier of the active substance (cellulose) & about 94.9\% \\ \hline \end{tabular}
\end{table}
Table 1: Cinnamon extract composition.
## 2 Materials and Methods
### In Situ Spectroscopic Analysis  
In situ, FTIR spectra were collected utilizing the same spectrometer and detector as in ex situ studies, equipped with Pike GladiATR Attenuated Total Reflection (ATR) device with a diamond crystal and an in-build, heated, stainless-steel plate with temperature regulation up to 250 \({}^{\circ}\)C. Here predefined and constant pressure between the sample andthe ATR window was kept during 12 h of aging and measuring. FTIR spectra were collected, averaging 200 scans about every 80 s of 720 min, repeated thrice, and averaged.
## 4 Simulation Results and Analysis
### Comparison of Solidification Process and Melting Process  
Through the above analysis, we found that, for the same type of phase change heat exchanger, the melting rate is much larger than the solidification rate. For example, for a cylindrical phase change heat exchanger with an inner diameter of 0.22 m, an outer diameter of 0.26 m, and a height of 0.95 m, it takes only 520 s to complete the melting process, but it takes 4800 s to complete the solidification process when the seawater temperature is 10 \({}^{\circ}\)C. The main reason is that the influence of natural convection in the liquid phase during the melting process makes the effective thermal conductivity much higher than the thermal conductivity of the phase change material during solidification. Therefore, the solidification process of the phase change material is the main process that affects and restricts the phase change process.
# Intelligent Space Object Detection Driven by Data from Space Objects
###### Abstract  
With the rapid development of space programs in various countries, the number of satellites in space is rising continuously, which makes the space environment increasingly complex. In this context, it is essential to improve space object identification technology. Herein, it is proposed to perform intelligent detection of space objects by means of deep learning. To be specific, 49 authentic 3D satellite models with 16 scenarios involved are applied to generate a dataset comprising 17,942 images, including over 500 actual satellite Palatino images. Then, the five components are labeled for each satellite. Additionally, a substantial amount of annotated data is collected through semi-automatic labeling, which reduces the labor cost significantly. Finally, a total of 39,000 labels are obtained. On this dataset, RepPoint is employed to replace the 3 \(\times\) 3 convolution of the EIAN backbone in YOLOv7, which leads to YOLOv7-R. According to the experimental results, the accuracy reaches 0.983 at a maximum. Compared to other algorithms, the precision of the proposed method is at least 1.9% higher. This provides an effective solution to intelligent recognition for spatial target components.  
space object identification; deep learning; YOLO; RepPoint +
Footnote †: journal: Applied sciences  
Article