esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comprehensive Survey of Recent Routing Protocols for Underwater Acoustic Sensor Networks
## 2 Characteristics of the Underwater Acoustic Channel
### Attenuation  
Pathloss of acoustic signals in the underwater acoustic channel is given by Equation (2). The path loss is a function of frequency \(f\) and distance \(d\) in kilometers:  
\[A(d,f)=d^{S}.\alpha(f)^{d} \tag{2}\]where \(S\), \(d\), \(d_{ref}\) and \(\alpha(f)\) are the spreading factor, distance and absorption coefficient, respectively. The path loss is given in dB using Equation (3):  
\[10 logA(d,f)=S.10\ \log\ d+d.10\ log\ \alpha(f) \tag{3}\]  
The geometry of a transmitted acoustic signal is described by its spreading factor (\(S\)). Typical values of \(S\) are 2 for spherical spreading, 1 for cylindrical spreading and 1.5 for practical spreading.  
The absorption coefficient or \(\alpha(f)\) can be computed using the formula give in Equation (4) which expresses absorption coefficient in dB/km for \(f\) in kilohertz as [4]:  
\[10\log\ \alpha(f)=0.11\frac{f^{2}}{1+f^{2}}+44\frac{f^{2}}{4100+f^{2}}+2.75.10^{ -4}f^{2}+0.003 \tag{4}\]  
The formula in Equation (4) is generally effective when the frequency \(f\) is higher than a few hundred Hz. Equation (5) can be used for lower frequencies:  
\[10\log\ \alpha(f)=0.002+0.11\frac{f^{2}}{1+f^{2}}+0.011f^{2} \tag{5}\]  
Figure 2[11] shows absorption coefficient as a function of frequency. \(\alpha(f)\) increases rapidly with the increase in frequency, thus resulting in a bound on the maximum usable frequency for a link between nodes with a given distance \(d\) between them.
# Research on Agricultural Product Price Prediction Based on Improved PSO-GA
## Appendix A
### Variational Modal Decomposition (VMD)  
Variational Modal Decomposition is a signal processing technique that decomposes a complex signal into a set of intrinsic modal functions (IMFs).  
The VMD is based on the construction and solution of a variational problem that aims to find a set of IMFs with localized center frequencies whose sum is equal to the original signal.  
1. Constructing a variational problem: Construct a variational problem that seeks a set of IMFs with finite bandwidths that are localized around their center frequencies and whose sum is equal to the original signal. The objective is to minimize a cost functionconsisting of the sum of the squares of the differences between the signal and the estimated IMFs and the sum of the bandwidths of the IMFs.  
2. Solving the variational problem: The variational problem is solved using the alternating direction multiplier method (ADMM). In each iteration, the IMFs and their center frequencies are updated to minimize the cost function.  
3. Determine the IMFs and center frequencies: Optimize the IMFs and their center frequencies through an iterative process until the convergence criteria are met. The final IMFs obtained are the decomposition of the original signal.  
4. Signal Reconstruction: The original signal can be reconstructed by summing all IMFs.  
The variational problem in VMD can be expressed as follows:  
\[\min_{\text{u}_{\text{k}},\text{w}_{\text{k}}}\sum_{\text{k}}||\partial_{ \text{t}}\Big{[}(\text{u}_{\text{k}}-\text{f}(\text{t}))\text{e}^{-\text{i} \text{w}_{\text{k}}\text{t}}\Big{]}||_{2}^{2}+\lambda||\text{f}(\text{t})-\sum_ {\text{k}}\text{u}_{\text{k}}||_{2}^{2}\]  
In the above equation, \(\text{u}_{\text{k}}\) is the kth IMF, \(\text{w}_{\text{k}}\) is the corresponding center frequency, f(t) is the original signal, \(\lambda\) is the equilibrium parameter, and \(\partial_{\text{t}}\) is the derivative with respect to time.
## 3 C/N Penalty of RoF Link
### C/N Penalty of RoF Link with Irregular \(\theta\)
#### 3.3.2 Irregular \(\theta\) Values for Scheme B1, B2, B3 and B4  
An irregular \(\theta\) value for schemes B1, B2, B3 and B4 is attained by repeating the steps in finding irregular \(\theta\) values for schemes A1, A2, A3 and A4 but using \(\gamma\) = 1/2. The curve of irregular \(\theta\) searching results for schemes B1, B2, B3 and B4, respectively, can be seen in Figure 7a-d.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Bias Point**} & \multicolumn{4}{c}{**Power Ratio of P\({}_{\text{DD-MZM}}\)/P\({}_{\text{IN}}\)**} \\ \cline{2-5}  & **4:4** & **3:4** & **2:4** & **1:4** \\ \hline \({}^{1}\)/4 & Scheme A1 & A2 & A3 & A4 \\ \({}^{1}\)/2 & Scheme B1 & B2 & B3 & B4 \\ \({}^{3}\)/4 & Scheme C1 & C2 & C3 & C4 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Irregular \(\theta\) scheme based on LD power distribution and variation in DD-MZM bias point.  
Figure 7 reveals that for each \(m\) used, there are two values of irregular \(\theta\): irregular \(\theta\) I (\(\theta_{\text{I}}\)) and irregular \(\theta\) II (\(\theta_{\text{II}}\)). The different \(m\) value and different schemes produce different irregular \(\theta\). The minimum DF for irregular \(\theta\) I (\(\theta_{\text{I}}\)) and irregular \(\theta\) II (\(\theta_{\text{II}}\)) is shown in Table 4.  
The DF values for each irregular \(\theta\) at \(0.1\leq m\leq 4\) of schemes B1, B2, B3 and B4 are illustrated in Figure 8. The DF values for the RoF link with an irregular \(\theta\) at \(m=1\) for schemes B1, B2, B3 and B4 are  
Figure 5: Deviation factor as the \(\theta\) function for **(a)** scheme A1; **(b)** A2, **(c)** A3; and **(d)** A4.  
Figure 6: DF value for each irregular \(\theta\) at \(0.1\leq m\leq 4\) of scheme A1, A2, A3 and A4.  
0.093, 0.524, 0.348, and 0.131, respectively, which shows that at \(m=1\), irregular \(\theta\) with scheme B1 could overcome DPF most effectively on the RoF link that uses DD-MZM with CA that is biased at \(\gamma=\
icefrac{{1}}{{2}}\).
# ZigBeeNet: Decrypted Zigbee IoT Network Traffic Dataset in Smart Home Environment
## 7 Zigbee Network Traffic Dataset and Its Characteristics
### Data Postprocessing  
The collected dataset consists of multiple pcap files capturing Zigbee network traffic. During initial inspection, some files were found to be corrupted, displaying the following message: \"The capture file appears to have been cut short in the middle of a packet\" when opened in Wireshark (Figure 4). This issue likely resulted from incomplete packet captures, which could hinder subsequent analysis.  
The issue arises from an incorrectly decoded data packet signaling the end of transmission, which causes the recording data to stop and the pcap file to close suddenly without the proper file termination. To address this, a postprocessing step was necessary. First, a custom script was developed to re-save each pcap file using the editcap tool, which helped repair any malformed packets and ensured the integrity of the packet captures. Once the individual files were corrected, they were merged into a single comprehensive pcap file using the mergecap tool. This allowed for more efficient analysis of the entire dataset as a unified whole.  
Figure 3: Plan of the smart-home installation.  
For the purposes of temporal analysis and to simplify further processing, the merged pcap file was then divided into equal one-hour segments using mergecap. This segmentation facilitated time-based exploration of the data while maintaining manageable file sizes for analysis.  
This postprocessing pipeline was essential to ensure the consistency and usability of the dataset, enabling more reliable and structured analysis of Zigbee network traffic in subsequent stages of the research. Figure 5 shows how all the packets look in Wireshark when we open the pcap file. The file is 1.64 GB in size, and Wireshark requires 10 GB RAM to open the file. As seen in the figure, Wireshark, by default, displays the following seven columns: no., time, source, destination, protocol, length, and info.
# Context-Aware Complex Human Activity Recognition Using Hybrid Deep Learning Models
## 5 Result and Discussion
### Recognition Accuracy for Simple and Complex Activities
Nevertheless, the _contextual_ model did not perform less in any activity class under the simple and complex activity categories (Figure 12). Instead, it continues to prove that a model trained with rich contextual data from ambient sensors can consistently outperform the same model trained with only traditional inertia signals. To answer the research question regarding the improvement observed, the _contextual_ model, going by Table 5, demonstrated about 0.91% and 4.38% improvement in simple and complex activities, respectively, compared to the _noncontextual_ model. To test the hypothesis that combining inertial and environmental sensing signals would produce better performance for the hybrid model, the significance of the performance differences was computed using a pairwise t-test at a 5% significance level. To have adequate samples to carry out the hypothesis tests, two models were trained and evaluated repeatedly 20 times. Each time, we noted the macro-average F-score for both models. Nevertheless, we obtained an extremely small \(p\)-value, which prompted us to reject the null hypothesis that the _noncontextual_ model is at par with the _contextual_ average performance. In other words, with a 95% confidence interval, the _noncontextual_ model's mean performance F-score was significantly lower than that of the _contextual_ model. As a result, we conclude that the average generalization ability of the _contextual_ model is superior to that of the _noncontextual_ model. Given that both models share the same architecture, matching parameters, and training environment, we can only attribute this improvement to the main difference between the two models, which is the additional signal input. The contextual data proved to be the distinguishing factor in this study. The _contextual_ model, despite the additional sensor modalities, was able to achieve convergence well before the _noncontextual_ model. The _noncontextual_ model had a significant 2.77% improvement in generalization accuracy compared to the work by [71] using traditional machine learning models. Their model architecture did not combine LSTM with CNN as we did; it only managed to achieve about 93.6% accuracy. Another similar work in [55] used a hybrid model but without context-aware models and achieved an overall performance of 96.71% compared to the 98.72% accuracy of our context-aware models. In addition, the work in [56] achieved up to 99.74% accuracy; however, their conclusion is that even though their model is effective for both complex and simple activities, but it especially more suitable for complex activities, whereas ours is not only effective but also more suitable for both complex and simple activities. The inclusion of LSTM as well as context awareness in the network models clearly demonstrates a significant improvement in the performance of the models.
# Sensor-Based Hand Gesture Detection and Recognition by Key Intervals
## 3 Proposed Algorithm
### Proposed Neural Network Model  
The architecture of the proposed neural network model for gesture detection and classification is shown in Figure 2. Similar to the study in [28], the proposed architecture contains a backbone, which is shared by three branches. As shown in Figure 2, the lower two branches are used for producing PKI and SKI scores for gesture detection, respectively. The topmost branch is adopted for delivering the classification results. Therefore, the proposed model is a multitask network offering the simultaneous learning of gesture detection and classification.  
It can be observed from Figure 2 that a convolution layer containing a convolution (CONV) operation, a Relu-based activation, and a group normalization (GN) [31] form the basic building block for the backbone and the branches. The kernel size, stride size, and number of output channels associated with the CONV operation of each basic building block are also shown in the figure. There are 4 convolution layers (denoted by C1, C2, C3, and C4) in the backbone. In addition to the basic building blocks, the backbone consists of two shortcuts [32] for enhancing the effectiveness of the feature extraction. The convolution layers (denoted by C5, C6, C7, and C8), dense layers (denoted by F1, F2, and F3), average pooling (AP), and softmax are employed in the branches for summarizing the features produced by the backbone for the PKI and SKI detection as well as the gesture classification.  
Figure 1: An example of the operations of the proposed SBR algorithm. The top graph of this example shows the sensory data from a 3-axis accelerometer. The second and third graphs reveal the PKI and SKI scores produced by the proposed network model, respectively. In the example, a matched detection for class 2 gesture is first identified. A number of mismatched detections are then followed. Finally, a matched detection for class 1 gesture is found.
# Parallel-Structure Deep Learning for Prediction of Remaining Time of Process Instances
### Objectives  
As already stated, PBPM is a branch of process mining that aims to provide predictions of measures of interest. Of the several measures of interest that PBPM can provide, in this study, we focused on the prediction of the remaining time needed by a running case to complete. Our study's expected contributions to the field of PBPM research are as follows:  
1. Development of an NN architecture that incorporates two models, a convolutional neural network (CNN) and a multi-layer perceptron with data-aware entity embedding (MLP + DAEE), and the proposal of a new method of encoding as applied to event-log-type data;
2. Improvement of the accuracy of a deep-learning-based method for the prediction of the remaining time of an ongoing case problem, as confirmed by comparing our method with another deep-learning method used as a baseline method;
3. Experimental demonstration that the utilization of chart image representation, specifically the inter-case dependency feature contained therein, can increase the accuracy over the remaining time of the ongoing process prediction problem;
4. Provision, to PBPM, of a functional solution in the form of an introduced new deep-learning-based method that does not use a recurrent NN. (To date, all deep-learning-based methods use a recurrent NN.)  
Our research was inspired by various other studies, which we will explain in the literature review section (see Section 2 below).
# Compression of GNSS Data with the Aim of Speeding up Communication to Autonomous Vehicles
###### Abstract  
Autonomous vehicles contain many sensors, enabling them to drive by themselves. Autonomous vehicles need to communicate with other vehicles (V2V) wirelessly and with infrastructures (V2I) like satellites with diverse connections as well, to implement safety, reliability, and efficiency. Information transfer from remote communication appliances is a critical task and should be accomplished quickly, in real time and with maximum reliability. A message that arrives late, arrives with errors, or does not arrive at all can create an unsafe situation. This study aims at employing data compression to efficiently transmit GNSS information to an autonomous vehicle or other infrastructure such as a satellite with maximum accuracy and efficiency. We developed a method for compressing NMEA data. Furthermore, our results were better than other ones in current studies, while supporting error tolerance and data omission.  
GNSS; data compression; autonomous vehicles 2023
# Sample-Wise Aiding in GPS/INS Ultra-Tight Integration for High-Dynamic, High-Precision Tracking
## 4 Test Results and Discussion
### Aiding Effects on Carrier Phase Errors with no Thermal Noise in the Signal
The errors in Figure 10 are also contributed by the alignment errors between the simulator and the receiver (related to the signal dynamics) and the quantization errors.
# A Multi-Constrained Optimization Method for THz Backward Wave Oscillators
## 4 Case Studies of THz BWOs
### First Case  
For the first case, the operation frequency range is set to be 900-1100 GHz for comparison with the results in Ref. [22]. The conductivity of the SWS is 5.96 \(\times\) 10\({}^{7}\) S/m, which is the same as that used in Ref. [22]. The optimized geometrical parameters are \(L\) = 52 \(\upmu\)m, \(s\) = 22 \(\upmu\)m, \(h\) = 61 \(\upmu\)m, \(w\) = 215 \(\upmu\)m, \(b\) = 124 \(\upmu\)m, and \(N\) = 217. The current density is 520 A/cm\({}^{2}\), the beam voltage ranges from 8 to 16 kV, and the guiding magnetic field is 1 T. The dispersion curves of the optimized SWS are shown in Figure 8. The 16-kV beam  
Figure 7: Flow chart of the algorithm searching global optimum.  
voltage line has no intersection point with the dispersion curves of higher-order modes as shown in Figure 8. The optimized geometrical parameters are the same as the optimized parameters in a previous paper [7] because (i) two methods converge to the same solution and (ii) the parameters are rounded to be integer numbers in the units of \(\mu\)m.  
The starting currents of the lowest and second lowest oscillation modes are shown in Figure 9a. The beam current can be calculated as \(0.4(b-h)\times 0.33w\times 520\) A/cm\({}^{2}\) = 9 mA. It can be observed that the starting currents of the second lowest mode are always larger than 9 mA in the operation frequency range. The output powers of the optimized BWO are compared with the results in Ref. [22] in Figure 9b. The output powers are simulated using CST Particle Studio, and the time signal and its spectrum are shown in Figure 10. The optimized BWO has acceptable output powers that are close to those published in Ref. [22]. Under the same beam voltage range and beam current, the optimized BWO satisfies all constraints, delivers a stable output signal at correct frequencies and provides comparable output powers with the BWO in [22]. Thus, the case under discussion demonstrates the validity of the proposed method.  
Figure 8: Dispersion curves of the SWS of the optimized 900–1100 GHz BWO [7].  
Figure 9: Starting currents vs frequency (**a**) and Output powers vs beam voltage (**b**) obtained by theoretical calculation and numerical simulation of the optimized 900–1100 GHz BWO. The star-marked curve is obtained from Ref. [22].  
It should be mentioned that the optimization results in Figures 8-10 are close but different from those in Ref. [22]. Note that the second constraint requires the beam line not to intersect with the dispersion curve of TE\({}_{x20}\) mode in the \(-1\)-harmonic region; however, in Ref. [22], the beam line is permitted to do this.
# Petri Net Modeling for Ising Model Formulation in Quantum Annealing
## 4 Binary Quadratic Net Construction from Problem Domain Petri Nets
### Firing-Based Construction
#### 4.3.1 Resource Conflict  
If transitions \(T_{i}\) and \(T_{j}\) have a common input place and conflict with a single token, \(X_{k}(T_{i})=X_{k}(T_{j})=1\) cannot be allowed. Therefore, we need the following energy function:  
\[H_{conflict}(\mathbf{M})=\sum_{k=0}^{K}\sum_{(T_{i},T_{j})\in C}M(\hat{p}_{i}^ {k})M(\hat{p}_{j}^{k}), \tag{40}\]  
where \(C\) is a set of conflict transitions. In addition, \(C\) can be extracted from the problem-domain Petri net.  
In timed Petri net models, we need to consider the firing duration. Let \(N=(P,T,F,TS,FD)\) with \(P=\{P_{1},P_{2},\ldots,P_{n}\}\) and \(T=\{T_{1},T_{2},\ldots,T_{m}\}\) be a timed Petri net, where \(FD:T\rightarrow\mathbb{N}\) is a function that returns the firing duration. We then extend the resource conflict in the stepwise firing into the timed firing.  
\[H_{conflict}(\mathbf{M}) =\sum_{(T_{i},T_{j},k,h)\in C^{timed}}M(\hat{p}_{i}^{k})M(\hat{p}_ {j}^{k}), \tag{41}\] \[C^{timed} =\{(T_{i},T_{j},k,h)|\forall(T_{i},T_{j})\in C,h\leq k+FD(T_{i}) \text{ or }k\leq h+FD(T_{j})\}, \tag{42}\]  
where \(C^{timed}\) is the timed conflict set such that conflict transitions cannot fire until the firing duration is complete. We can obtain \(C^{timed}\) from the given timed Petri net.
## 4 Implementation of MIMO-VLC Using OptiSystem
### Without Ambient Light  
Figures 3 and 4 illustrate the implementation of SISO-VLC and MIMO-VLC systems, respectively, without considering the effects of ambient light noise. The implementation is done using three different types of line coding techniques: NRZ, RZ and PSK.
# Toward High Bit Rate LoRa Transmission via Joint Frequency-Amplitude Modulation
## 5 Evaluations
### Evaluation Results
#### 5.2.1 Bit Rate Performance  
* BR vs. SF, when distance = 100 m  
First, we evaluate the BR performance with different SFs. In order to reveal the advantages of the QR\(-\)LoRa, we chose an open outdoor field(LOS scenario). The distance between the transceivers is 100 m. According to different values of SF, the BR curve is presented in Figure 13. With the increase in the spreading factor, the BR curves of the conventional LoRa, ICS-LoRa and QR\(-\)LoRa show a downward trend. QR\(-\)LoRa always outperforms the others, especially twice as much as the conventional LoRa. We conclude that the bit rate of LoRa can be effectively improved by adding amplitude modulation.
* BR vs. Distance, when SF = 7  
Figure 14 depicts the BR curves with different propagation distances. We know from the analysis that the performance of QR\(-\)LoRa in the amplitude dimension is limited by the SNR. When the SNR is lower than 0 dB, the amplitude demodulation will completely fail. Therefore, we set the maximum distance of the transceiver to 150 m, and the test was conducted in a line-of-sight (LOS) scenario. As observed from the experimental results shown in Figure 14, the BR is almost stationary when the propagation distance changes from 10 m to 150 m. Because there is nearly no packet loss in the 150 m distance, QR\(-\)LoRa has above 30 Kbps BR, significantly higher than the conventional LoRa and ICS\(-\)LoRa.  
Figure 12: Experiment settings and plans of the LOS propagation and NLOS propagation.  
SR vs. LOS and NLOS, when distance = 100 m, SF = 7  
In order to further evaluate the anti-multipath ability of QR\(-\)LoRa, we verify the impact of LOS and NLOS propagation on the BR performance in an outdoor environment. The experimental results are shown in Figure 15. It can be found that the BR of LoRa, ICS-LoRa, and QR\(-\)LoRa in the NLOS propagation are 12.5 Kbps, 16.2 Kbps, and 27.5 Kbps, respectively, and all of them reduce to different degrees compared with the LOS propagation. The reason is around 20 dB descending in the received signal strength caused by the blockage of the building in the NLOS propagation.  
Figure 14: BR vs. distance measured results.  
Figure 13: BR vs. SF measured results.  
Figure 17 presents the relationship between BER and the communication distance. We compare the BER performance between QR\(-\)LoRa and the conventional LoRa with different distances (10 m, 25 m, 50 m, 75 m, 100 m, 125 m and 150 m). According to the experimental results, the frequency demodulation of QR\(-\)LoRa has a similar performance to the conventional LoRa in the communication range of 150 m, mainly in the line\(-\)of\(-\)sight (LOS) scenario. Both of their BERs are below the \(10^{-4}\) level. The amplitude modulation of QR\(-\)LoRa can ensure the accuracy of demodulation within 100 m. Obviously, further improving the communication range of amplitude modulation should be a focus of our future work.