esa-sceva/satcom-chunk-collection huggingface dataset:
# Effective Bus Travel Time Prediction System of Multiple Routes: Introducing PMLNet Based on MDARNN
###### Abstract  
Accurate bus travel time prediction is crucial for improving travel experience, especially in transfer journeys. This study introduces a novel multi-route bus travel time prediction system-based PMLNet, a partition and combination prediction framework, addressing the gap in accurate prediction models by incorporating macro and local impact factors. The system employs a pre-processing algorithm for constructing travel chains, partitions travel time into four components, utilizes LSTM along with the newly proposed MDARNN model for predicting each component, and applies four real-time traffic impact factors to calibrate the predictions of each component. Experimental validation on four bus routes demonstrates PMLNet's superior performance, achieving mean absolute percentage errors (MAPE) as low as 2.91% and mean absolute errors (MAE) below 1.45 min, outperforming traditional models and various partitioned combination frameworks. These findings underscore PMLNet's potential to significantly improve public transportation services by providing more accurate travel time predictions, ultimately enhancing the user experience in intelligent transportation systems.  
intelligent transportation; bus travel time prediction system; LSTM; DA-RNN Article
# Research on Lateral Deformation Control Criteria of Metro Shield Tunnels with Excessive Ellipticity
## 2 Excessive Transverse Deformation Cases in Ghuangzhou Metro
### Summary of Engineering Cases  
The deformation limit, grounded in ellipticity, varies among cities due to stratigraphic differences and distinct design parameters of shield tunnels. Typically, this limit ranges between 20% and 25% based on empirical data. Determining whether a tunnel's lateral deformation exceeds this threshold and consequently indicates a decrease in bearing capacity or the presence of structural issues requires careful assessment. In the case discussed in this study, the shield tunnel's maximum ellipticity reached about 30% before reinforcement, showing clear structural problems. However, other tunnels with elliptic deformation exceeding 20-25% displayed no detectable structural damage. In contrast, tunnels with minor lateral deformation can still present issues such as cracks or leaks. Therefore, the steel-concrete composite structure remains stable after reinforcing tunnels showing signs of deterioration or excessive deformation. This demonstrates the effectiveness of the steel plate reinforcement method for damaged shield tunnels. However, the indiscriminate application of steel plate reinforcement is not recommended. Beyond its significant cost, the construction process can disrupt the tunnel structure's stability. Therefore, establishing appropriate deformation limits for shield tunnels, considering varied external conditions, is essential. To assist in developing this standard, this study employs finite-element analysis based on the discussed engineering case.
# Research on a Mixed Gas Classification Algorithm Based on Extreme Random Tree
## 1 Introduction
While the traditional feature extraction method, the principal component analysis (PCA), is a dimensionality reduction operation, when the algorithm's dimensionality is not high enough, it is necessary to construct its features. In addition, among the classification algorithms, there are few algorithms that have strong anti-overfitting ability as well as a high training speed, training time efficiency, and classification accuracy [31; 32]. To improve the anti-overfitting ability and accuracy for machine olfactory detection of mixed gases, this paper proposes a data screening method based on a dynamic time warping algorithm (DTW) to improve the gas classification accuracy. To address the problem of too few features, the PCA feature construction and extraction methods are proposed. The proposed new extreme random tree  
Figure 1: Diagram of the machine olfactory system. SVM: support vector machine KNN: K-nearest neighbour. algorithm is used to further improve the detection and analysis performance of the machine olfactory system [33; 34]. Taking carbon monoxide, methane and ethylene as examples, this paper studies a mixed gas detection algorithm based on extreme random trees to improve the classification accuracy rate. It can provide mixed gas detection and concentration ratio technical support for industrial production, chemical material production, and the light industry [35; 36; 37]. This study provides a theoretical reference for the simulation of olfactory algorithms. The main points raised in this paper are as follows:  
(1) To improve the classification accuracy and time efficiency of mixed gas detection, the random forest algorithm is improved. This paper proposes a new classification algorithm called DTW-Extra tree. The similarity processing of the data is mainly carried out by the DTW algorithm so that the discrimination among the data is improved. Feature extraction is carried out by a feature construction method. Finally, the extra-tree algorithm is used for gas classification to improve the classification accuracy and time efficiency. (2) The mixed gas datasets of ethylene and carbon monoxide; and ethylene and methane were obtained experimentally, and the proposed algorithm was verified experimentally. The analysis shows that the algorithm has higher accuracy for gas classification and improves the time efficiency of the random forest algorithm. The content of this paper is organised as follows: Section 2 introduces the gas detection algorithm research, including the dynamic time warping algorithm (DTW), feature construction, feature extraction, principal component analysis and the extreme random tree algorithm. Section 3 carries out experimental verification and analyses the results. Section 4 summarizes the conclusions and results.
## 6 Security and Performance Analysis
### Implementation Result  
Through the characteristic comparison presented in Table 1, our scheme SP-MAACS has been shown to be better than other schemes in terms of features attained. We implemented our scheme and the fully secure decentralized CP-ABE scheme [26] and assessed the performance. The authors of fully secure decentralized CP-ABE presented two constructions [26] and demonstrated them to be secure under the standard model. Their first construction used a composite-order bilinear group and was confirmed to be fully secure by taking static assumptions. The scheme applies to any monotone access structure.
# Device-to-Device (D2D) Multi-Criteria Learning Algorithm Using Secured Sensors
## 4 Simulation Setup
### Results and Discussion
The reward value significantly trains the source node to fetch the previous information of the selected neighbors from its node-level table and optimize the performance for the constraint network. Moreover, it also decreases the extra energy consumption in sending the data from agricultural land using mobile sensors, which near uniformly balances the load on nodes. Additionally, only those nodes that fall into the coverage range exchange their information to proceed with the data routing. In case no node is found, then the next inline gateway device is assigned the responsibility of achieving the routing process. In all these processes, the proposed algorithm decreases the load on the mobile nodes and explicitly optimizes the consumption of energy resources. Figure 9(a),b demonstrate the experimental results of the proposed algorithm compared to the existing solution for computational complexity. The results determined the number of processing overheads it takes to execute the proposed algorithm. It was seen  
Figure 9: The performance evaluation of the proposed algorithm compared to QL-MAC and CTEER for average energy consumption. that the proposed algorithm minimized the computational complexity by 36% and 39% for varying numbers of nodes and rounds. In computing the computational time, the proposed algorithm measures the number of route requests and route response packets, especially in the presence of malicious nodes. Moreover, it also considers the number of retransmissions in computing the computational time of the proposed algorithm. Based on the security function, the proposed algorithm efficiently identified the false requester, which significantly decreases the ratio for a computational time as compared to other solutions. Furthermore, using the reinforcement learning technique, balanced the resources' consumption among the nodes and decreased the communication complexity by minimizing the least distance towards the sink node. The gateways perform the role of local supervision and reduced the cost of D2D communication by utilizing the method of coverage limit. Unlike QL-MAC and CTEER, the proposed algorithm supports the authentication and verification phase for mobile sensors and avoids the chance of malicious nodes generating excessive false traffic. Additionally, such security methods of the proposed algorithm impose the least communication complexity on constraint devices in the presence of malicious nodes, with affordable data retransmissions. Moreover, the link cost function identifies the more appropriate trusted links by utilizing the information of packets' reception and data error.
## 5 Discussions  
LEO-LEO occultation is an advanced and supplementary remote sensing technique for GNSS RO, but there is currently no space-borne LEO-LEO occultation constellation. Nowadays, with the development of small-satellite and instrument miniaturization technologies as well as the demonstration of the LEO-LEO occultation technique by numerical and ground-based experiments, there are more opportunities for the demonstration of large, space-borne, and operational LEO-LEO constellations. Therefore, it is the right time to conduct a detailed LEO-LEO occultation orbit analysis and a constellation assessment to provide references for future LEO-LEO occultation constellation designs.
# Cooperative Friendly Jamming Techniques for Drone-Based Mobile Secure Zone
## 5 Experiment
### Field Experiment  
#### 5.2.1 Effect of Friendly Jamming  
In this section, we conducted experiments using Raspberry Pi. The friendly jamming drones and the eavesdroppers were also implemented using Raspberry Pi. In the communication between Raspberry Pi devices, packets were sent using the D2D communication method. The source node became the AP using the host mode, and the destination node was connected to the AP of the source node. The maximum transmission power of each node was 24 dBm. Jamming signals were generated using the Ping of Death method.  
For the data, a string \"1\" consisting of a total of 256 bits was used, and as the preamble bits for synchronization, a string \"a\" consisting of 128 bits was used. Figure 6 shows how the preamble bits were processed. If \"a\" with less than 64 bits is received, it corresponds to a case in which the ILR is less than 0.5. Therefore, the data received through the corresponding packet are all processed as an unanalyzable state. If \"a\" with 64 or more bits is received, the number of error bits is obtained after removing the preamble data. The number of error bits is calculated by adding the number of lost bits and the number of unmatched bits. The number of lost bits is calculated by subtracting the number of receiving bits from the number of sending bits, and the number of unmatched bits is calculated by counting 1 after the sequential Xor. The BER of the destination node and the eavesdroppers is obtained using the error bits.  
First, the friendly jamming model of Figure 1a was implemented to verify the effectiveness of the jamming signals. One jamming drone node was deployed based on the locations of the source and destination nodes.  
The transmitted data of the source node, and the BER of both the destination node and the eavesdroppers, were measured. Table 5 shows the average BER obtained by repeatedly applying the model of Figure 1a 1000 times. The average BER of the destination node is zero. In other words, the destination node can communicate normally with the source node because it is unaffected by the jamming signals. The average BER of the eavesdroppers located around the drone nodes (eve3, eve4, and eve5) is 0.975, 0.992, and 0.938, respectively, showing a result close to 1. This experimental result shows that the legitimate destination node can safely deliver information while reducing the amount of information leaked to the surrounding eavesdroppers by friendly jamming. Furthermore, the average BER of the eavesdroppers that are not located around the drones (eve1, eve2, and eve6) is 0.389, 0.012, and 0.562, respectively, demonstrating relatively low values. Therefore, if the friendly jamming technique is used, the eavesdropping probability of the eavesdroppers decreases, improving the communication security.
# Intelligent Reception of Frequency Hopping Signals Based on CVDP
## 2 CVDP Network Design
### Network Structure  
Figure 2 depicts the architecture of the CVDP network, which comprises two distinct modules, namely CNN, and VIT, where the VIT module consists of Embedding, Transformer Encoder, and MLP(Multi-Layer Perception) Head. The CNN module plays a vital role in reducing the dimensions of the input time-frequency spectrograms and extracting additional inductive bias information. The Embedding module is responsible for converting the CNN output feature map into a one-dimensional vector through linear mapping and introducing a learnable class vector to represent the global features of the encoded image. Additionally, the Embedding module preserves the spatial information between the feature maps through positional encoding. The Transformer Encoder module employs a self-attention mechanism to calculate the self-attention of key points and extract the time-frequency features of frequency hopping signals. Finally, the MLP Head module transforms the output dimensions into classification quantities, thereby enabling efficient classification.
## 2 Ising Model  
When solving a combinatorial optimization problem using a quantum annealer, it is necessary to transform it into an equation referred to as the Ising model. The Ising model is a statistical mechanics model that represents the properties of a magnetic material, and the state of the magnetic material is evaluated by calculating the upward or downward state of the spin. Spin has two forces: a local field that acts only on that spin, and an interaction that acts on the connected spins. The state of the spin is updated by these forces so that the total energy is stabilized.  
When the entire system is in the most stable state, the Hamiltonian \(\mathcal{H}\), which represents the total energy of the Ising model, is the minimum (ground state). Equation (1) expresses this Hamiltonian. Here, it should be noted that \(s_{i}\) and \(s_{j}\) are the directions (\(\pm 1\)) of the spins \(i\) and \(j\), respectively, \(J_{ij}\) represents the interaction, \(h_{i}\) is the local field of spin \(i\), \(E\) is the number of edges, and \(V\) is the number of spins:  
\[\mathcal{H}=\sum_{(i,j)\in E}J_{ij}s_{i}s_{j}-\sum_{i\in V}h_{i}s_{i} \tag{1}\]
## 4 Results and Discussion
### Optimal Weight Coefficients
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & \begin{tabular}{c} **Total** \\ **Transportation** \\ **Cost (CNY)** \\ \end{tabular} & \begin{tabular}{c} **Total Waiting** \\ **Time (hours)** \\ \end{tabular} & \begin{tabular}{c} **Total** \\ **Transportation** \\ **Distance (km)** \\ \end{tabular} & \begin{tabular}{c} **Number of** \\ **Battery Swaps** \\ \end{tabular} \\ \hline \hline \begin{tabular}{c} Schem 1 \\ \end{tabular} & 12,805.64 & 34.17 & 2278.8 & 61 \\ \begin{tabular}{c} Schem 2 \\ \end{tabular} & 13,062.37 & 29.96 & 2356.9 & 66 \\ \begin{tabular}{c} Schem 3 \\ \end{tabular} & 13,713.89 & 22.96 & 2496.3 & 73 \\ \
\begin{tabular}{c} Schem 4 \\ \end{tabular} & 14,903.59 & 15.31 & 2640.2 & 80 \\
\begin{tabular}{c} Schem 5 \\ \end{tabular} & 15,565.07 & 12.08 & 2678.3 & 85 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Experimental results for the five weight distribution schemes. Figure 3: Sensitivity analysis of different weight schemes. (**a**) Progressive increase in total transportation cost, (**b**) Decline in waiting time from Scheme 1 to Scheme 5, (**c**) Incremental increase in total distance traveled, indicating higher utilization, (**d**) Gradual increase in number of battery changes required.
# Vehicle Distance Measurement Method of Two-Way Two-Lane Roads Based on Monocular Vision
## 3 Experiments and Discussions  
### Training and Effect of Vehicle Detection Model of Two-Way Two-Lane Roads  
The model training and verification experiment were carried out on the computer. The operating system was Windows 10, 64-bit, using the PyTorch framework. The processor was Intel Gold 6240R 2.40 GHz; GPU was NVIDIA GeForce GTX 1660 Ti. The program was written in Python. The software platform was Windows 10 + Python 1.10.0 + CUDA10.2 + PyCharm, and the software environment was supported by LabelImg, OpenCV, etc. Before starting, the data set and tag file were divided into a training set, verification set, and test set according to an 8:1:1 ratio and then imported into the YOLOv5s network to train the vehicle detection model of two-way two-lane roads. In the training phase, the number of training epochs was set to 600; batch_size was set to 16; Adam was used as the optimizer; the initial learning rate was set to 0.001; and the learning rate adopted the cosine annealing strategy. The detection performance of the trained vehicle detection model of two-way two-lane roads for forward vehicles and opposite vehicles is shown in Table 2. It can be seen from the table that the average precision (_AP_) of each vehicle category and mean average precision (_mAP_) are greater than 0.9. The model has good detection performance and good classification performance for vehicles on two-way two-lane roads, which meets the detection requirements for vehicles in this scene in this paper.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Vehicle Category** & _AP_ & _mAP_ \\ \hline back & 0.978 & 0.964 \\ front & 0.951 & 0.964 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Detection performance of model.
### Advantages of Antenna Systems in Underground Mining  
The underground mine's safety and productivity are mostly dependent on monitoring and tracking various activities, such as ensuring miner and machinery location, health, and safety. Continuous advancement in mining, on the other hand, necessitates a reliable, resilient, low-cost, easy-to-deploy wireless monitoring system capable of transmitting data from the underground working area to the control room. For a reliable wireless communication system, the antenna system needs to radiate to provide maximum coverage and enhance signal strength and data rate. Apparently, the unsuitable environment and non-line-of-sight (NLOS) in the underground mine make electromagnetic signal propagation difficult due to multipath signals. To overcome the difficulty of multipath signals, antenna systems must be designed to give higher coverage, reduced interference, and fewer multipath signals [14].  
As shown in Figure 1, the omnidirectional antenna radiates electromagnetic energy in all directions, increasing signal interference due to the reflection and diffraction of signals transmitted towards the tunnel walls. The majority of the antennas now used in underground mines have an omnidirectional radiation pattern, radiating energy in all directions, including those where radiation is not required, such as towards the tunnel wall [15], whereas directional antennas radiate electromagnetic energy exclusively in the required directions, increasing the coverage area of the wireless communication system [16; 17].  
Another significant challenge of wireless communication in underground mines is signal strength loss. Most antennas used in mining are linearly polarized, resulting in polarization mismatch of signals between the sending and receiving antennas [18]. As a result of the polarization mismatch, the receiving antenna receives only partial signals. As a result, building a circularly polarized (CP) antenna is an appropriate technology for reducing polarization mismatches. Furthermore, when compared to single-element antennas, adopting a directional array antenna increased the gain and signal strength of the communication established in underground mines [19; 20].  
With the proliferation of applications and wire-free communication devices in underground mines, the requirement for high data speeds has skyrocketed. Additionally, high-data-rate communication in underground mines is possible by boosting coverage area, signal intensity, and, eventually, multipath fading [21]. As a result, planning and considering the antenna system is critical for improving the performance of underground communication networks.
## 4 Experiments
### Evolution of Explicit and Implicit Bias  
**Results**: In this group of experiments, we compare the evolution of explicit and implicit bias over time. We use English and Chinese social media corpora (i.e., Twitter and Weibo), with a timestamp for each statement. To track the evolution of the biases, we broke each corpus into subsets of months, spreading over 36 months. Then, we measured explicit and implicit bias month by month using the proposed sentiment-based bias measurement. In Figures 5 and 6, we illustrate the detailed evolution tracks of each concept pair on each corpus. In Figure 7, we compare the stability of explicit and implicit bias in evolution. The stability is calculated with the standard deviation of the bias over time.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Variable** & **Twitter** & **E-Wiki** & **Weibo** & **C-Wiki** \\ \hline Words & 97.8\% & 99.8\% & 96.4\% & 98.3\% \\ Analyzers & 96.7\% & 90.4\% & 92.8\% & 91.2\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: The percentage that experimental observations are maintained across procedural variables.  
**Discussion**: In Figures 5-7, we have two main observations:  
1. **Explicit and implicit bias is different in stability:** beyond existing psychological studies, a new observation of evolution experiments is that implicit biases are more stable than explicit biases in most cases. In Figures 5-7, with the exception of the concept pair \"China vs. America\", implicit biases in other concept pairs are more stable than explicit biases. This may indicate that although people's explicit public bias may vary due to the changing social context (e.g., the scenario or the talking partner), their internal implicit bias tends to be more consistent over time. This possibly explains the finding that implicit bias is a better predictor of certain kinds of behavior than explicit bias [6; 7].
2. **Factors influencing the stability:** in this experiment, the sociality of concepts also influences the evolution of biases. In Figures 5 and 6, implicit biases of unsocial concept pairs (i.e., _\"Insect_ vs. _Flower_\" and \"_Weapon_ vs. _Instrument_\") are more stable than that of social concept pairs (i.e., _\"Afri-American_ vs. _Euro-American_\" and \"_China_ vs. _America_\").  
In terms of its relationship with previous work, the dynamic analysis presented in this paper builds upon the existing concepts of psychological bias measurement. However, its novelty lies in the fact that existing psychological measurements primarily focus on static analysis, whereas our study delves into the dynamic evolution and comparison of explicit and implicit biases. While our work has successfully revealed the differences in the evolution of these two types of biases, its limitation lies in the fact that we have not yet empirically and deeply analyzed the impact of these differences on behavior. This aspect remains an area for further exploration in future research.
# Adaptive DCS-SOMP for Localization Parameter Estimation in 5G Networks
## 4 Proposed Method  
For ease of understanding, we divide our proposal into two stages: (1) sensing matrix construction and (2) 3D parameter estimation using the DCS-SOMP algorithm.