esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Paremeter Setting  
This section provides a detailed overview of the parameter settings used in the GBNSS model. These parameters were selected through empirical tuning to ensure optimal performance in predicting biological network similarity. The GBNSS architecture incorporates multiple layers, each designed to capture distinct aspects of the network structure and interactions.  
The model begins with three graph convolutional network (GCN) layers, each progressively capturing deeper neighborhood information. These layers enable the model to learn both local and global structural features from the biological networks. The output dimensions for the three GCN layers are 128, 64, and 32, respectively.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Species**} & \multicolumn{1}{c}{**Bos Taurus**} & \multicolumn{1}{c}{**Homo Sapiens**} & \multicolumn{1}{c}{**Mus Musculus**} & \multicolumn{1}{c}{**Saccharomyces c.**} \\ \hline \multirow{3}{*}{Database} & Networks & 40 & 270 & 260 & 70 \\  & Nodes & 4–28 & 4–470 & 3–431 & 3–128 \\  & Edges & 3–145 & 3–4480 & 3–3390 & 3–373 \\ \hline \multirow{3}{*}{Query Set} & Networks & 5 & 44 & 41 & 11 \\  & Nodes & 6–51 & 6–422 & 6–478 & 6–62 \\ \cline{1-1}  & Edges & 5–259 & 4–1984 & 4–2057 & 4–110 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data statistics for four species of KEGG PATHWAY datasets.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Graphs** & **Pairs** & **Training Set** & **Testing Set** \\ \hline \hline Bos Taurus & 40 & 800 & 560 & 240 \\ Homo Sapiens & 270 & 36,450 & 25,515 & 10,935 \\ Mus Musculus & 260 & 33,800 & 23,660 & 10,140 \\ Saccharomyces c. & 70 & 2450 & 1715 & 735 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistics of model datasets.  
Following the GCN layers, we incorporate a neural tensor network (NTN) to compute similarity scores between pairs of biological networks. This layer enhances the model's ability to identify meaningful relationships between networks. The NTN layer has an output dimension of 16.  
To refine the representations and generate the final similarity score, the model includes two fully connected (FC) layers. These layers convert the high-dimensional outputs from previous layers into a single scalar similarity score. The output dimensions for the first and second FC layers are 16 and 1, respectively.  
To prevent overfitting, we applied dropout with a rate of 0.5 between layers. This technique deactivates 50% of the neurons at random during training, which helps ensure the model does not rely too heavily on any particular neurons. As a result, the model is better able to generalize to new data. In addition to dropout, we implemented five-fold cross-validation to further safeguard against overfitting. This method splits the data into five parts, using four for training and reserving one for validation in each iteration. By repeating this process five times, we ensure that every subset is used for validation once. This approach offers a more comprehensive evaluation of the model's performance and reduces the risk of overfitting to a single subset.
# Noise Reduction Based on a CEEMD-WPT Crack Acoustic Emission Dataset
## 6 Conclusions  
1. In order to solve the problem of the poor noise reduction effect and the stability of the traditional CEEMD noise reduction algorithm, this paper designed the CEEMD-WPT noise reduction method, and verified the performance of the algorithm by using analog signals and an AE open dataset \"Acoustic Event Dataset\". The results show that, compared with traditional CEEMD and CEEMDAN, CEEMD-WPT has the best noise reduction effect and the least noise residue. Moreover, the statistical variance in noise reduction of CEEMD-WPT is one order of magnitude smaller than that of traditional CEEMD, and it has greater stability.
2. In order to solve the problem of the noise reduction effect of real signals being difficult to quantify, the PIC index is designed according to the law of noise distribution. In the simulation signal test, it is proved that the PIC index has the same quantization reliability as the SNR and RMSE, and the quantization effect of the dnSNR is poor.  
Conceptualization, Y.Z.; methodology, Y.Z.; software, Y.Z.; validation, Y.M.; investigation, J.D.; data curation, C.W.; writing--original draft preparation, D.X.; writing--review and editing, W.X.; supervision, Z.Z.; project administration, R.Z.; funding acquisition, J.C. All authors have read and agreed to the published version of the manuscript.  
State Grid Henan Electric Power Company project (SGHADK00 DYJS2100546), Henan Province major science and technology project (221100220100).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
# Development and Application of Digital Twin-BIM Technology for Bridge Management
## 3 Digital Twin and BIM Strategy  
### Digital Twin  
A digital twin (DT) is a highly promising technology as it enables to realize smart manufacturing and Industry 4.0. It is characterized by the seamless integration between cyber and physical spaces [1,2]. Moreover, digital twin technology provides a dynamic and up-to-date representation of a physical object or system. By consolidating all data in one centralized location, the digital twin evolves in real time with inputs from sensors and other sources. It offers several benefits, including: (a) An optimized performance with actionable insights, simulations, and a holistic view of both online and offline data enabling swift and informed decision-making to optimize performance and efficiency. (b) A comprehensive data visibility: by eliminating data silos and integrating historical and real-time data, the digital twin unlocks value throughout the life cycle of a project. (c) Intelligent predictions: real-time sensor data and predictive recommendations through machine learning and artificial intelligence greatly enhance maintenance and operational capabilities. (d) Faster results: creating digital twins of complex assets, factories, and processes exponentially enhances operational value, reduces development efforts, and accelerates time to market. Therefore, in this study, we explored the use of the digital twin-BIM technology for landscape visualization, traffic flow simulation, finite element analysis, and structural maintenance.  
Figure 3: Location map.  
Figure 2: Geometrical model of Comoro Bridge.
## 2 Design and Fabrication
### Design and Circuit Analysis
#### 2.1.4 Substrate-Related Parasitic Effects  
The parasitic effects of the substrate and the passivation layer on the capacitance and inductance should be considered if the design goals are to be achieved. Therefore, three types of the components are introduced in the model, namely, the capacitance associated with the passivation layer (\(C_{SiNx}\) and \(C_{SiNx\_AB}\)), the conductance related to the substrate (\(G_{Si\underline{U}B}\) and \(G_{Si\underline{U}B\_AB}\)), and the capacitance related to the substrate (\(C_{SUB}\) and \(C_{SUB\_AB}\)). The losses caused by eddy currents are negligible since the resistivity of the GaAs substrate is greater than 10 \(\Omega\)-cm [22]. The above six parameters can be calculated as given in Equations (12)-(17):  
\[C_{SiNx}=\frac{\varepsilon_{0}\varepsilon_{SiNx}(\mathit{air}\ -\ \mathit{bridge}\ \mathit{metal}\ \mathit{area})}{t_{SiNx}}, \tag{12}\]  
\[C_{SUB\_AB}=\frac{\varepsilon_{0}\varepsilon_{GaAs}(\mathit{air}\ -\ \mathit{bridge}\ \mathit{metal}\ \mathit{area})}{t_{GaAs}}, \tag{13}\]  
\[G_{Si\underline{U}B\_AB}=\frac{\mathit{air}\ -\ \mathit{bridge}\ \mathit{metal}\ \mathit{area}}{t_{GaAs}\times\rho_{GaAs}}, \tag{14}\]  
\[C_{SiNx}(f)=\frac{\varepsilon_{0}\varepsilon_{eff}(f)}{2F\Big{(}W_{eff},\ t_{SiNx} \Big{)}}\times l, \tag{15}\]  
\[C_{SUB}(f)=\frac{\varepsilon_{0}\varepsilon_{eff}(f)}{2F\Big{(}W_{eff},\ t_{Si \underline{U}B}\Big{)}}\times l, \tag{16}\]  
\[C_{SUB}(f)=\frac{\sigma_{SUB}\Big{(}1+\sqrt{1+10t_{SUB}/W_{eff}}\Big{)}}{2F \
## 2 Experimental Procedures  
Tubular AAO films with an average pore size of about 60 nm and the film thickness of 40 \(\upmu\)m were fabricated from 6061 aluminum alloy (Mg: 0.8-1.2%, Si: 0.4-0.8%, Cu: 0.15-0.40%,Al: base) tubes with a diameter of 20 mm by anodization processes. The experimental chemicals that were used in this experiment were of reagent grade without further purification, including perchloric acid (HCIO\({}_{4}\)), ethylene glycol butyl ether (CH\({}_{3}\)(CH\({}_{2}\))\({}_{3}\)OCH\({}_{2}\)CH\({}_{2}\)OH), ethanol (C\({}_{2}\)H\({}_{6}\)O), oxalic acid (C\({}_{2}\)H\({}_{2}\)O\({}_{4}\)), chromium trioxide (CrO\({}_{3}\)), phosphoric acid (H\({}_{3}\)PO\({}_{4}\)), cupric chloride (CuCl\({}_{2}\)), and hydrogen chloride (HCI). The experimental steps are as below: (a) stress relief (6061 Al, 300 \({}^{\circ}\)C, 1 h); (b) mechanical grinding on the outer surface of the Al tube (#2000 SiC paper); (c) electrolytic polishing (15 vol. % HClO\({}_{4}\) + 15 vol. % (CH\({}_{3}\)(CH\({}_{2}\))\({}_{3}\)OCH\({}_{2}\)CH\({}_{2}\)OH + 70 vol. % C\({}_{2}\)H\({}_{6}\)O, 15 \({}^{\circ}\)C, 42 V, 10 min); (d) 1 st anodization (3 wt. % C\({}_{2}\)H\({}_{2}\)O\({}_{4}\), 10 \({}^{\circ}\)C, 40 V, 1 h); (e) removal of 1 st anodization film (1.8 wt. % CrO\({}_{3}\) + 6 vol. % H\({}_{3}\)PO\({}_{4}\) + 92 vol. % H\({}_{2}\)O, 70 \({}^{\circ}\)C, 50 min); (f) 2 nd anodization (3 wt. % C\({}_{2}\)H\({}_{2}\)O\({}_{4}\), 10 \({}^{\circ}\)C, 40 V, 5 h); (g) removal of Al substrate (12 wt. % CuCl\({}_{2}\) + 8 vol. % HCl + 80 vol. % H\({}_{2}\)O, 25 \({}^{\circ}\)C, 14 min); (h) removal of barrier layer (5 vol. % H\({}_{3}\)PO\({}_{4}\), 25 \({}^{\circ}\)C, 1 h); and, (i) pore widening (5 vol. % H\({}_{3}\)PO\({}_{4}\), 25 \({}^{\circ}\)C, 45 min).  
The AAO thickness between 30 to 100 \(\upmu\)m were controlled by the anodization time from 3 to 24 h. The transmembrane pressure test on the tubular AAO films was carried out by purging Ar gas, and the exudation characteristic of the tubular AAO was tested with a red ink solution.  
All of the experimental sample images were acquired using a digital camera (Nikon, COOLPIX A900, Japan). The microstructures of the fabricated samples were observed by a scanning electron microscope (SEM, JEOL 6500).
# A Survey on Energy Drainage Attacks and Countermeasures in Wireless Sensor Networks
## 2 Security Issues in WSN
### Literature Review For Overall Attacks
Furthermore, secure multipath routing and hierarchical routing were presented. In 2017, [PERSON] et al. [6] presented a novel survey paper focusing on systematic studies by studying popular protocols in WSN deployments. They included IPv6 over low-power wireless personal area networks, collection tree protocols, and constrained application protocols. In response to these requirements, three additional requirements were defined: data, access, and network-level requirements. First, anonymity, which hides the source of the data, was defined for information protection and confidentiality. Furthermore, robustness/resiliency implies a lack of significant impact for networks without considering the number of nodes that increased. The most important contribution of this study is to address possible attacks and their countermeasures for protocols in each layer. This is in contrast to previous work. Specifically, some relevant works were also analyzed and compared to address the shortage of IPv6 routing protocols for low-power and lossy networks (RPL) designed to support confidentiality, integrity, availability, and non-repudiation. Authors in [7] mapped various security factors and established the relationships between them through a newly defined systematic analysis framework. As a primary technical contribution, they illustrated the relationship among constraints in the security aspect, security requirements, and countermeasures. Figure 2 shows an example of security requirements for energy and low specification. In this study, two more security requirements were introduced, namely forward and backward secrecy and quality of service. The forward and backward secrecy implies that encrypted data should not be decrypted at the intermediate node in WSNs, whereas the quality of service is focused on the quality of service by congestion at the base station. Some side effects, such as buffer overflow and channel contention caused by congestion, should be mitigated. Unlike the above-mentioned papers, attacks and countermeasures were mapped based on the characteristics of the security aspect. For example, secure infrastructure to ensure easy access can be implemented using various techniques, such as encryption, digital signature, and maintenance of the timing previously related to time synchronization.
## 3 Methodology
### Decision Tree
Along with configuring the Gini and Entropy indexes with Random and Best splitters, multiple training and testing ratios were used in the DT classification process. The optimization took into account the training-to-testing ratios of 40:60, 50:50, 60:40, 70:30, 80:20, and 90:10. Additionally, the tree depth was taken into account between 4 and 7. To optimize the model development, accuracies of the tree generation was considered for 100 runs per combination to assess the consistency of the generated trees.
# A Computer-Vision Based Application for Student Behavior Monitoring in Classroom
## 3 Experiment
### Experiment  
We used the pre-trained model for face detection, facial landmark detection, facial representation, and gaze estimation tasks. For the face detection task, we used SSH [31] face detector, since the facial data from our dataset might be equal or more difficult compared to Hard-Set of the WIDER FACE [49] dataset due to the students not looking into the camera most of the time, and some other difficulties, such as blurry images, partially visible faces, etc. For the facial landmark detection task, we used O-Net and L-Net (which are parts of MTCNN [25]) For facial representation, Arcface [40] was chosen. The inputs for Arcface are cropped to \(112\times 112\) px, face-centered images, and outputs are 512-dimension vectors. For the gaze estimation, we used Hopenet [46] to estimate pose of the head, which is also the estimation of the gaze. For the face classification, we used weighted K Nearest Neighbors (w-KNN) for the classification task with a custom metric (custom distance formula) which is defined below:  
\[distance(\overrightarrow{x},\overrightarrow{y})=\sqrt{1-\left( \overrightarrow{x}\cdot\overrightarrow{y}\right)\|\overrightarrow{x}- \overrightarrow{y}\|}.\]  
For the gaze classification task, several simple models (SVM [50], decision tree [51], gradient boosting [52], and random forest [53]) were chosen for the experiments. The models were trained through a partial section of the dataset. The input was the estimated coordination and head-pose while the output was the classification (1, 2, or 3) of gaze (or the point where the student was looking at). We also tested the synthetic minority oversampling technique (SMOTE) [54] for these experiments, since the dataset for gaze classification was imbalanced. All experiments were run on the system of an CPU Intel(r) Coretm i5-9400F, with a NVIDIA GEFORCE GTX 1070 and 16 GB of RAM.
# Keyframe Insertion: Enabling Low-Latency Random Access and Packet Loss Repair
## 4 Results  
This section presents and evaluates the experimental results concerning the proposed method based on keyframe insertion for H.265/HEVC. First, Section 4.1 describes the setup that was used to perform the experiments. Then, Sections 4.2 and 4.3 analyze the impact on the frame size and quality, respectively.
# A Survey on Adaptive Data Rate Optimization in LoRaWAN: Recent Solutions and Major Challenges
## 5 Research Gaps and Future Direction
### Perfect Orthogonality  
LoRa uses orthogonal SFs. These allow the network to preserve the battery life of the end nodes linked to the network by adaptive optimization of each end node's power setting and data rate. Many ADR algorithms assume perfect orthogonality and do not consider inter-SF interference which is vital for huge network loads. Inter-SF interference decreases network performance considerably, especially for high SFs where packets have a higher ToA. The models can be improved to not only improve the packet loss ratio in the network, but in addition, the PLR distribution and to take into account the non-orthogonality of SFs in the model.
# Efficient Node Deployment of Large-Scale Heterogeneous Wireless Sensor Networks
## 6 Performance Evaluation
### Results and Discussion
The comparison again is made against the deployment algorithms in [6,13]. A different number of sensor nodes is utilized. Throughout this experiment, the number of sensor nodes is varied from 90 to 130 nodes. The percentage of the mobile nodes is set at 30%. Figure 12 examines the coverage ratio with a different number of sensor nodes. As shown in this figure, we can easily observe that the proposed algorithm improves the coverage ratio similar to the deployment algorithm in [13]. At the same time, it outperforms the deployment algorithm in [6] with almost 4%. The third experiment of this set is performed to study the variation of the coverage ratio using mobile nodes' initial deployment strategies listed in Table 5. This simulation experiment was conducted by fixing the mobile nodes' percentage and the number of sensor nodes to 30% and 90 nodes. Figure 11: Influence of increasing percentage of mobile nodes on coverage ratio. Figure 12: Influence of increasing number of sensor nodes on the coverage ratio. Figure 13 shows the variation of the coverage ratio with the initial deployment strategies of mobile nodes. It is obvious from this figure that the initial deployment at the network corners gives the best coverage ratio. The reason is the highest priority zones are covered at that initial deployment strategy. However, the proposed algorithm again slightly achieves the same performance as the deployment algorithm in [13], but it outperforms the deployment algorithm presented in [6] with almost 3%.
## 3 Multi-Source Data in the Construction of Smart City
### Data Sources
Citizen feedback data needs to be cleaned, preprocessed, and analyzed in combination with other data sources to provide decision support, evaluate urban management effectiveness, and promote public service innovation. However, problems such as information overload and difficulty in distinguishing authenticity in citizen feedback data need to be further resolved [13]. GIS (Geographic Information System) data includes spatial data and non-spatial data, involving information such as the geographic location, shape, size, etc., of surface objects, as well as attribute information related to geographic location, such as population statistics, traffic flow, and land use [14]. These data can improve the scientific nature and decision support of urban planning and real estate management through accurate spatial information collection, processing, analysis, and display, but GIS data acquisition and processing face problems such as data quality and sharing and require the support of advanced data fusion and analysis algorithms [15].
# Real-Time Water Level Monitoring Based on GNSS Dual-Antenna Attitude Measurement
## 2 Methodology
### Altimetry Principle  
In the GNSS dual antenna attitude measurement-based water level monitoring method, the right-hand circularly polarized (RHCP) antenna is installed skyward to receive direct GNSS satellite signals. The left-hand circularly polarized (LHCP) antenna is installed towards the water to receive GNSS signals reflected from the water surface. The phase centers of both antennas are aligned on the same plumb line. Figure 2 illustrates a schematic diagram depicting the geometric relationship of the GNSS dual-antenna attitude measurement-based water level monitoring method. As shown in Figure 2, the positions of the RHCP and LHCP antennas remain unchanged. Compared to the direct signal received by the RHCP antenna, the reflected signal received by the LHCP antenna has an additional propagation path. As such, the LHCP antenna can be considered a virtual antenna located beneath the water surface. The distance between the virtual antenna and the water surface is equal to the distance between the LHCP antenna and the water surface. When the water surface height changes, the additional propagation path of the reflected signal also changes. Consequently, the position of the virtual antenna changes accordingly. As a result, the GNSS dual-antenna attitude measurement technique can be used to calculate the baseline length between the RHCP and LHCP antennas in real-time.  
Figure 1: Flow chart of real-time water level monitoring method based on GNSS dual antenna attitude measurement.  
Figure 2: Geometry of the GNSS-R experiment.  
As shown in Figure 2, the water surface height is related to the baseline length between the two antennas. This relationship can be derived from their geometric relationship:  
\[\mathrm{H}=\mathrm{Z}-\left(\frac{\mathrm{M}-\mathrm{S}}{2}+\mathrm{S}\right) \tag{1}\]  
where, H is the water surface height; Z is the measured distance from the antenna phase center to the water reference point; M is the baseline length; and S is the measured distance between the sky observation antenna and the water observation antenna.
## 3 Results
### Properties of Torrefied Biomass
#### 3.2.2 Energy Densification Results  
The energy density of wheat straw increases as the isothermal torrefaction temperature increases. The O/C and H/C atomic ratios are low as the temperature increases. In addition, it has a low moisture content. Figure 6 shows the energy density ratios at torrefaction temperatures of 250, 275, 300, and 325 \({}^{\circ}\)C. The black represents the data obtained experimentally, with 1.034-1.259%; the blue curves were obtained from the analysis of the global parameters, with 1.034-1.226%; and the red curves were obtained from the individual parameters, with 1.009-1.198%. There is a close relationship between the torrefaction temperature and energy density. From the curves, the analysis used individual parameter deviations from the experimental data to a greater degree, with a mean relative error of  
Figure 4: Experimental data: (**a**) temperature curves at a heating rate of 20 \({}^{\circ}\)C/min, with isothermal temperatures of 250, 275, 300, and 325 \({}^{\circ}\)C; (**b**) solid yield at different temperatures and torrefaction times.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Temperature (\({}^{\circ}\)C)**} & \multicolumn{3}{c}{**Ultimate Analysis (\%wt), Dry-Ash-Free**} & \multicolumn{2}{c}{**HHV**} & \multirow{2}{*}{**O/C**} & \multirow{2}{*}{**H/C**} \\ \cline{2-2} \cline{5-6}  & **C** & **H** & **N** & **O \({}^{\mathrm{a}}\)** & & **(MJ\(\cdot\)kg\({}^{-1}\))** & \\ \hline raw & 46.205 & 6.275 & 3.612 & 43.908 & 18.535 \(\pm\) 0.436 & 0.713 & 1.623 \\
250 & 49.573 & 5.715 & 3.721 & 40.991 & 19.689 \(\pm\) 0.759 & 0.620 & 1.383 \\
275 & 57.089 & 4.893 & 3.806 & 34.212 & 21.373 \(\pm\) 0.044 & 0.450 & 1.028 \\
300 & 60.127 & 4.413 & 4.761 & 30.699 & 22.239 \(\pm\) 0.087 & 0.383 & 0.881 \\
325 & 68.596 & 4.007 & 7.922 & 22.475 & 23.431 \(\pm\) 0.156 & 0.246 & 0.701 \\ \hline \end{tabular} \({}^{\mathrm{a}}\) by difference.  
\end{table}
Table 3: Elemental analysis of wheat straw torrefaction.  
7.788, while when using global parameters, the mean relative error is 2.945. This is mainly due to the dependence of energy density on HHVs. At 275 \({}^{\circ}\)C, the error is significant due to errors in the prediction based on the analysis of the elemental components.