esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy Transmission and Equilibrium Scheme in Data Communication Opportunistic Networks
## 3 Simulation  
The simulation adopts the simulator ONE (opportunistic networking environment) [10] and extends the model in the opportunistic network to compare energy consumption in these algorithms. Parameters can be settled based on the random model in opportunistic networks. Parameters adopted in the experiment are set as follows. The simulation time is 12 h; the simulation area is 4500 m \(\times\) 3400 m in the map of Helsinki; the involved nodes are 100 to 400; the transmission pattern is broadcast; the maximum transmission area of each node is 10 m\({}^{2}\); the sending frequency of a data packet is 25-35 s; the type of data packet is a random array; a node consumes one unit of energy when it sends a data packet; each node carries 10 data packets and 100 units of energy; the energy lower limit for each node is 30 units; the transmission pattern of the nodes is a social model; the transmission speed of the node is 0.5-1.5 m/s; and the cache of each node is 5 MB.  
In the simulation, each one of the 600 nodes is recorded to show the energy exchange in the opportunistic network. The simulation time and energy consumption are recorded and compared with the epidemic algorithm, direct delivery algorithm, spray and wait algorithm, and PSDRA. The simulation is as follows.  
Figure 2 shows the node and delivery ratio. The nodes are from 100 to 600 in with all the algorithms. The epidemic algorithm and direct delivery algorithm are shown as the low delivery ratio. Since energy has no supply and consumption, PSDRA is better than others as the nodes can acquire energy supply. Moreover, the nodes in the communication can send messages.  
Figure 3 shows the cache time in the communication. The nodes are from 100 to 600 in with all the algorithms. The direct delivery algorithm needs a lot of cache time to solve the data transmission.  
Figure 1: Data packets transmission and energy equilibrium in the epidemic algorithm.  
Figure 2: The delivery ratio with algorithm.  
PSDRA is also better than the others algorithms, since the energy supply improves the activity of the nodes. Moreover, additional nodes can be added to deliver data.  
Figure 4 shows the node energy expended in the communication. The direct delivery algorithm does not consume energy due to its direct transmission. PSDRA is better than the epidemic and spray and wait algorithms. According to the energy supply in the communication, the node can expend less energy and deliver more data between nodes.  
In data communication, extending life-times is important to keep the active nodes transmitting data packets. The simulation result shows that PSDRA can guarantee nodes to acquire energy to transmit data packets. It avoids node death when the nodes join in communication.  
Figure 4: The expended node energy.  
Figure 3: The cache time with algorithm.
# 47.5 GHz Membrane-III-V-on-Si Directly Modulated Laser for Sub-pJ/bit 100-Gbps Transmission
## 4 Discussion  
The 50-GHz-class DML presented in this work can be compared to previous InP-based DML designs that used a similar DBR-based PPR-generation mechanism [11]. Compared to [11], the 3-dB bandwidth presented here was slightly lower (47.5 GHz compared to 55 GHz), but the achieved BER and energy/bit performance for the 60 GBaud PAM-4 signals denotes a higher temperature operation as well as a ~3.9x bias current reduction at RT. In addition, there is no fundamental limitation for our design to achieve 3-dB bandwidths even higher than 55 GHz by further optimizing the longitudinal design. We plan to explore such optimized designs in the future.  
Finally, for practical transceiver implementations, our DML design should be expanded to a multichannel array that can support both WDM and SDM multiplexing. For WDM, we have previously demonstrated SiN AWGs integrated with membrane DMLs on Si in [7]. SiN has the advantage of low-loss and small footprint. Furthermore, we have previously demonstrated low-loss and low-crosstalk coupling to multicore fibers based on fiber-based multiplexers [8]. We plan to work on combining these technologies with our new PPR-based DML designs in the future. Moreover, since the detuning mechanism for the PPR for higher temperatures depends only on a bias detuning of a few mA, traditional control electronics can be used for a stable PPR operation over a large temperature range.  
Figure 5: 60 GBaud PAM-4 signal evaluation: (**a**) Experimental setup; (**b**) Bit-error rate (BER) versus received optical power at back-to-back (BTB) and after 2-km SSMF transmissions, and (**c,d**) Eye diagrams after equalization. Measurements were performed at 25 \({}^{\circ}\)C (\(l_{b}=9.1\) mA) and 50 \({}^{\circ}\)C (\(l_{b}=15.1\) mA).
## 7 Future Perspectives  
To obtain the highest extraction efficiency under cavitation-based methods, optimum values of solution temperature, ultrasound intensity, sonication frequency, and HC pressure should be decided. Future investigations should also focus on the implementation of negative pressure cavitation extraction for a wide range of products. Intensification in the generation of bubbles during cavitation-based methods via innovative reactor design and uniform distribution of cavitational energy throughout the extraction solution should also be explored. Implementation of the cavitation-based extraction method could lead to a promising novel greener extraction technique for the recovery of useful natural products. Most of the reported data indicated the use of a simple reactor configuration, which may not be feasible for large-scale applications. With innovative design, CE can be made efficient and sustainable for industrial applications. Following essential factors are to be considered for large-scale implementation of CE.  
1. Use of multi-frequency and multiple transducer sonoreactor.
2. Developing a continuous flow cell reactor, while introducing ultrasonic transducers in periodic spaces.
3. Eliminating erosion of transducer material via suitable building materials.
# Enhanced Classification of Video-Evoked Stress Response Using Power Spectral Density Features
## 3 Materials and Methods  
In this section, the proposed methodology for stress classification and assessment is presented in response to videos, as shown in Figure 2. We have selected the videos of the bench-marked DEAP dataset [38] for stimulating stress and relaxation conditions[PERSON] A brief description of the DEAP dataset is presented in Table 2. These videos are available on the YouTube platform[PERSON] The two-dimensional valence arousal model is used for labeling, and participants rate the videos based on their valence and arousal. Valence and arousal are used to label the participants in stressed and relaxed conditions. The participants' EEG data was decomposed into four frequency bands, delta, theta, alpha, and beta, the frequency ranges of which are given in Table 3[PERSON]  
After participants are marked for labeling, PSD features for delta, theta, alpha, and beta frequency bands are extracted using the MATLAB 2020b code. Machine learning classifiers are trained to classify participants' stressful and relaxed conditions, and their accuracy is determined. A statistical \(t\)-test is conducted on each PSD feature to check the statistical significance of each feature in distinguishing stressed and relaxed conditions.
# 3 GPP 5G V2X Error Correction Coding for Various Propagation Environments: A QoS Approach
## 2 The 5G V2X System Model  
The V2X system model that we use in our study is a stochastic V2X communication model, based on 3 GPP technical reports for 4G-LTE and 5G-NR V2X [8; 9] communications. The large-scale and small-scale fading characteristics of the model, as well as the method of implementation, are fully described and analyzed in [37].  
This system model simulates the data transmission between two moving vehicles in two possible traffic models. In the first model, the vehicles are moving in urban fabric, and in the second model, the vehicles are moving on highways. Apart from the traffic model, the V2X simulation system model can have three different states [8; 9]:  
* Line of sight (LOS): The vehicles are on the same street and the path has no blocking obstacles.
* Non-line of sight with vehicle blockage (NLOSv): The vehicles are on the same street and the path is partially blocked by other vehicles.
* Non-line of sight (NLOS): The vehicles are on different streets and the path is blocked by permanent blockages. The NLOS state is absent from the highway scenario since the vehicles are moving on the same street.
## 3 Research Model Design
### Variable Selection and Data Sources
#### 3.3.2 Data Source  
Based on the operability of the data, we selected panel data from 30 provinces (cities, districts) in China (excluding Tibet, Taiwan, Hong Kong and Macau) from 2006 to 2019 as the empirical research sample, with an observation value of 420. Except for agricultural patent data, basic data were obtained from the China Statistical Yearbook, China Fiscal Yearbook, China Rural Statistical Yearbook and the statistical yearbooks of various provinces and cities. The agricultural patent data were obtained from the patent database of the State Intellectual Property Office of China. The classification numbers representing agricultural patents were selected from the International Patent Classification System (IPC) to obtain relevant statistics on the number of agricultural invention patents and utility models granted. In order to eliminate problems such as heteroscedasticity in the regression process, logarithmic processing was performed on the relevant variables. The specific descriptive statistical results are shown in Table 4.
# MVCG-SPS: A Multi-View Contrastive Graph Neural Network for Smart Ponzi Scheme Detection
## 5 Experiments
### Results and Analysis
#### 5.4.5 Complexity and Scalability  
We analyzed the computational complexity and scalability of our approach. Let \(N\), \(E\), \(M\), and \(d\) denote nodes, edges, views, and feature dimension, respectively. The time complexity of our method is governed by multiple components. The interaction enhancement phase, which involves meta-path-guided random walks, operates in \(O(N\cdot L)\), where \(L\) is the average walk length. The feature encoding process, executed by type-specific MLPs for multi-view aggregation, incurs a cost of \(O(N\cdot d^{2})\). Additionally, the computation of contrastive loss, thanks to the use of negative sampling, scales as \(O(M\cdot N)\). In terms of space complexity, storing the sparse adjacency matrix requires \(O(N+E)\) space, and maintaining multi-view features demands \(O(M\cdot N\cdot d)\) space.  
* _Time:_\(O(N\cdot L)\) (interaction enhancement) + \(O(N\cdot d^{2})\) (encoding) + \(O(M\cdot N)\) (contrastive loss)
* _Space:_\(O(N+E)\) (sparse adjacency) + \(O(M\cdot N\cdot d)\) (multi-view features)  
Scalability experiments conducted on synthetic graphs further demonstrated the efficiency of our approach. As shown in Table 7, the training time per epoch increased linearly with the number of nodes, achieving an \(R^{2}\) of 0.98, which indicated an excellent parallelization performance. Furthermore, the GPU memory usage increased sublinearly, with a graph of 1 million nodes requiring only 0.5 GB of memory. Importantly, the F1 score remained robust across various scales, confirming that our model maintained a competitive detection performance even as the dataset size grew.
# Trajectory Prediction of Marine Moving Target Using Deep Neural Networks with Trajectory Data
## 7 Limitation of the Study  
In this paper, a method is presented for sparse trajectory data with long non-uniform time series intervals. Experiments show that better prediction results can be obtained for the requirement of long and non-equidistant prediction duration on the hourly scale. The limitation of this method is that this method does not necessarily show better prediction performance for minute-scale dense time series trajectory data. In addition, since the method is based on deep learning method, the distribution of test samples should be consistent with the training samples. Otherwise, the accuracy of prediction may be affected. Despite some research progress, effective and reliable trajectory prediction remains challenging, as marine navigation is a complex system of stochastic time variations.
###### Abstract  
At present, the remote meter reading problem for a large number of existing domestic diaphragm gas meters has not been reliably solved. The diaphragm gas meter uses a physical structure to measure gas consumption, so the main problem of remote meter reading is the digitization of gas consumption. Although it is possible to digitize the consumption data by modifying the internal structure of the gas meter or adding sensors, thereby realizing the function of remote meter reading, the modification process is complicated, the modification cost is high, and manual meter reading can only be used for the existing diaphragm gas meters. For this reason, this paper proposes an indirect measurement method of gas consumption based on gas pressure signal detection. The method can digitalize the metering results of the mechanical diaphragm gas meter and develop a state detection function by analyzing the pressure signal of the outlet of the meter without modifying the internal structure of a meter, thus addressing the key difficulties in the construction of a gas meter remote meter reading system. The experimental results show that this method has small calculation error and high reliability.  
re mode meter reading; diaphragm gas meter; gas pressure signal detection; digitization Article
# Anomaly Detection of CAN Bus Messages Using a Deep Neural Network for Autonomous Vehicles
## 3 Proposed Method
### The Overall Framework  
Figure 2 presents the whole structure of CAN bus messages anomaly detection. We define the annotated CAN bus messages as an anchor, which is pre-processed off-line. The negative and positive represent two collected real-time CAN bus messages. The first three CAN bus messages make up a batch. As time goes by, we will collect a lot of CAN data sequences, and the two sequences collected in each adjacent time will form a batch with the annotated sequence, so that there will be a lot of batch data sequences. In short, the aim of this paper is to find out these abnormal sequences from lots of batches. Similar to a convolution neural network (CNN), the function of a DNN is to extract data features. Three data sequences from the same batch are imported into three networks, which are structurally consistent and share weights with each other. After that, we represent the extracted three sets of data features as three independent feature vectors. Finally, referring to the similarity calculation of a handwritten image [21; 22] and face recognition [23; 24], the triplet loss function is used to calculate the similarity between random data sequences and the anchor, which aims to close the distance between the normal data sequence and the annotated data sequence, and at the same time push the distance between the abnormal data sequence and the annotated data sequence.
## 3 Results
### Graph Modeling and Analysis with GNN  
In this study, the brain networks of the trauma-exposed group and the control group were modeled as graphs, with 116 nodes representing predefined Regions of Interest (ROIs). Edges were generated based on the correlations between ROIs, with edge weights reflecting the correlation coefficients. These correlation networks were used as input for graph neural network (GNN) analysis.  
The structural analysis of the graphs revealed that the trauma-exposed group had fewer edges and lower mean edge weights compared to the control group, indicating reduced connectivity strength. Additionally, the trauma-exposed group exhibited higher variability in edge weights, suggesting irregular connection patterns.  
When training the GNN model on these graph data, the trauma-exposed group showed slower learning progress and less effective graph reconstruction compared to the  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Control Group**} & \multicolumn{2}{c}{**Trauma Group**} & \multicolumn{2}{c}{**t-Statistic**} & \multicolumn{1}{c}{_p-Value_} \\
**ROI** & **Mean** & **SD** & **Mean** & **SD** & **t-Statistic** & _p-Value_ \\ \hline Middle Temporal Gyrus & 1742.616 & 737.373 & 1101.460 & 609.891 & 2.688 & 0.012 \\ Left Inferior Temporal Gyrus & 2966.947 & 345.417 & 2665.265 & 354.601 & 2.468 & 0.019 \\ Right Thalamu & 3587.764 & 449.178 & 3236.774 & 351.921 & 2.462 & 0.020 \\ Left Fusiform Gyrus & 3073.378 & 424.227 & 2764.026 & 295.638 & 2.383 & 0.025 \\ Right Amygdala & 3057.784 & 525.829 & 2632.298 & 597.391 & 2.175 & 0.037 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Top five brain regions showing significant group differences in signal intensity.  
Figure 1: Mean signal intensities for 116 brain regions across trauma-exposed and control groups.  
Figure 2: Boxplots of signal intensity for regions with significant group differences.  
control group. The reconstruction loss was consistently higher for the trauma-exposed group, suggesting that their brain network patterns were more challenging for the GNN to model effectively. These observations point to notable differences in brain network structure and connectivity between the two groups. Figure 3 illustrates the consistently higher reconstruction loss for the trauma-exposed group across training epochs, reflecting the greater difficulty in modeling their brain network patterns.  
Further analysis will explore specific ROIs contributing to these group differences, providing a more detailed understanding of the regional characteristics of the brain networks.
# Seismogram Rearrangement as a Perspective Basis for Defining New Attributes
## 3 Results and Analysis
### Attributes from Rearranged and Ordered Fluctuations  
The results of simple manipulations with the EFs/SFs are not only constrained to seismogram waveform but can even reach into the spectral domain.  
As can be seen in Figure 2, it is a simple matter to measure durations of SFs. It was performed for SFs within two intervals: for the 1 st segment (Figure 12a) and for the 2 nd + 3 rd segments (Figure 12b).  
Figure 11: (**a**) The EFs from the first three segments from Figure 2 are overlapped with coinciding inflection points. (**b**) The peaks of SFs within EFs depicted in (**a**), are approximated by triangles, can be simply considered as points of certain scatter diagram, where each of them has its coordinates (\(x,y\)). Then a regression line can be calculated to show trend existing in the cloud of points. (**c**) The structural axes of EFs (see Figure 5) make the scatter plot from (**b**) more transparent.  
In the first case (the 1 st segment), the durations of SFs, separately measured for SF\({}^{+}\)s and SF\({}^{-}\)s, are vertically justified and ordered by the length. Almost linear dependence suggests uniform distribution (of probability), indicated by dash line rectangles in Figure 12a, both for durations of positive and negative SFs. The mean (expectation) of positive SFs (red color) gives \(T_{1}{}^{+}\) = 0.15 s. For the negative SFs (blue color), it is \(T_{1}{}^{-}\) = 0.2 s. Taking into account that the natural form that better corresponds to the sinusoid shape is the EF rather than the SF (see Figure 13), one can figure out that the mean period of fluctuations within the 1 st segment is \(T_{\rm mean}=0.15+0.2=0.35\) s.  
When Figure 11b is concerned, there are more interesting relations. The 1 st segment is apparently uniform in regard to fluctuation patterns. In the other interval, which comprises the 2 nd and 3 rd segments, the SFs are much more versatile, both in amplitude and duration, not even to speak about SF shapes. Otherwise, the other interval is more representative, statistically, than the first one.
# Magnetic Moment Estimation Algorithm Based on Convolutional Neural Network
## 3 Model and Experiment  
### Neural Network Model  
CNN comprise deep learning models particularly well-suited for processing data with grid-like structures, such as images, videos, audio, and time series data [28]. In this paper,  
Figure 5: Schematic diagram of magnetic anomaly detection.  
a 2D CNN architecture is introduced to estimate the target magnetic moment, relying on the three-component geomagnetic field signal and the scalar magnetic anomaly signal obtained from the measured target. Compared to fully connected neural networks, the CNN is better at capturing local information features. By convolving the target magnetic anomaly signal with the geomagnetic field, spatial local features within the signal are extracted. Furthermore, due to the use of convolutional kernels, memory consumption and computational costs are reduced.  
The structure of the CNN model proposed in this paper is illustrated in Figure 6 composed of three convolutional layers, two pooling layers, and one fully connected layer, with the detailed settings provided in Table 1. The \(3\times 3\) convolution kernel, compared to larger kernels like \(5\times 5\), incurs lower computational cost while achieving a larger receptive field through the stacking of multiple \(3\times 3\) kernels. The design of the three convolution layers aims to progressively extract features from low order to high order, striking a balance between feature extraction and computational efficiency. As the number of convolutional layers increases, the receptive field expands, enhancing the network's ability to capture local features [29]. The network also includes two pooling layers, which downsample the convolutional data to reduce computational complexity and improve the model's generalization ability. The network achieves a lightweight design while maintaining strong predictive performance.  
The model described in this article was trained on a laptop computer. The training time was approximately 14 min, with 50 training epochs, a learning rate of \(10^{-3}\), and a batch size of 5. The specifications of the computer used for training are as follows:  
Processor: 13 th Gen Intel(R) Core(TM) i5-13500 HY 2.50 GHz;  
GPU: NVIDIA GeForce RTX 4060 Laptop GPU 8 GB;  
Memory: 16 GB.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Layer** & **Parameter Settings** \\ \hline Conv1 & Conv2D: Kernel number: 16; kernel size: 3 \(\times\) 3; strides: 1; padding: “same”; ReLU \\ Conv2 & Conv2D: Kernel number: 32; kernel size: 3 \(\times\) 3; strides: 1; padding: “same”; ReLU \\ Conv3 & Conv2D: Kernel number: 64; kernel size: 3 \(\times\) 3; strides: 1; padding: “same”; ReLU \\ Pool1 & MaxPooling2D: pool size: 2 \(\times\) 2; strides: 2; padding: “same” \\ Pool2 & MaxPooling2D: pool size: 2 \(\times\) 2; strides: 2; padding: “same” \\ Flatten & Flatten the input to 1D vector; ReLU \\ \hline \hline \end{tabular}
\end{table}
Table 1: Detailed settings of the neural network.  
Figure 6: Neural network architecture diagram.