esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix A Metadata Collection for Routing Framework  
The cost per byte of cellular links is usually 20 times that of DSRC links [87], so, if those links are always utilized, even for metadata collection for routing, the average communication cost will be high. Thus, for metadata collection for the proposed routing framework in SDVN architecture, we used the optimization framework that was proposed by us in our previous work for SDVNs [65], which optimally collects data by reducing total communication cost, delay, and overhead. The objective function of that optimization framework for status data collection is given in Equation (A1):  
\[minimize\ C_{CN}\sum_{i=1}^{\mathcal{N}}\mathcal{N}_{i}z_{i}+C_{CA}\sum_{i=1} ^{\mathcal{N}}\mathcal{B}_{i}x_{i}-C_{O}\sum_{i=1}^{\mathcal{N}}(x_{i}\sum_{ m\in\mathcal{S}_{i}}z_{m})\] (A1)  
Note that, in Equation (A1), \(C_{CN},C_{CA}\), and \(C_{O}\) are optimization coefficients, whereas \(x\) and \(z\) are the decision variables. For the \(i\)th node, if \(x_{i}=1\), it is a unicasting agent node; otherwise, if \(z_{i}=1\), it is a broadcasting node. \(\mathcal{N}\) represents the total number of nodes, while \(\mathcal{N}_{i}\) represents the total number of nodes in the neighborhood of the \(i\)th node. Furthermore, \(\mathcal{B}_{i}\) is the combined communication cost of the \(i\)th agent node. The objective given in Equation (A1) should be achieved, subject to the constraints given in Equations (A2) and (A3):  
\[x_{i}+\sum_{m\in\mathcal{S}_{i}}x_{m}\ >=\ 1;\ \forall\ i\in\mathcal{S}\] (A2)  
In Equation (A2), \(\mathcal{S}\) is the set of one-hop neighbors in the whole vehicular network. The constraint given in Equation (A2) specifies that there should be at least one agent in the neighborhood of a given node, including the node itself.  
\[z_{i}\ +\ x_{i}\ =1;\ \forall\ i\in\mathcal{S}\] (A3)  
The constraint in Equation (A3) specifies that broadcasting nodes and agent nodes should be mutually exclusive. Thus, using the optimization framework given in Equations (A1)-(A3), we optimally collect metadata for computing parameters at the controller required for the proposed routing algorithm in the SDVN paradigm. Note that, in order to have a fair comparison with the Dijkstra shortest path routing algorithm used for routing in SDVN, we use the same optimization framework for metadata collection.
## 4 Results
### Assessment-Item Analysis (IA)  
In order to assess the workflow of the Item Analysis component, an implementation in the form of a web application (Figure 11) was established. The implementation was made using the PHP web programming language, and the interface was created using the Bootstrap library, which is based on HTML, CSS and JavaScript languages. Then, the model was tested based on a focus group context. The initial context was considered to be a group of 20 students who attended an ICT course over a period of one semester (14 weeks) and a total of five tests were taken during this period. Each test was generated to contain five questions with specific topics related to the use of various applications (Word, Excel) or notions related to the Internet, programming and operating systems. The type of all questions was multiple choice.  
For the items described in the initial data analysis, the responses were analyzed by determining the values of the model parameters considered. For this specific example, the score was equal to the number of correct answers due to the fact that each question was scored with 1 point. The results are presented in Table 10. The columns presented in Table 10 show the degree of difficulty (dd_q), standard deviation (sd_q), item discrimination (d_q), point-biserial (pbs_q), mean score (m_q), and the number of correct answers (l_q).  
Following the responses, several items were determined to be more difficult than others and the list of reviewable topics that was obtained from the analysis of the results contains topics, such as operating systems, Windows OS, programming, Microsoft Word, formatting, algorithm, algorithm characteristics and practical applications related to programming. In this matter, the number of articles that were selected was about 27% of the total number of articles. Items were selected based on a threshold of statistical significance, such as the top-bottom number representing 27% of the total number of items, or, in the case of large sets of items, items that scored lower than 27% of the maximum score of the test. The items that generated these reviewable topics were Q2 of Test 1, Q4 and Q5 of Test 2, and Q2 and Q5 of Test 4, which obtained the lowest number of correct answers.
# GA-PSO Algorithm for Microseismic Source Location
## 3 Experiments and Results
### Validation Through Engineering Case Studies  
#### 3.2.1 Engineering Experiment for Microseismic Source Location  
To further verify the accuracy of the GA-PSO algorithm, this study utilizes data from an artificial blasting positioning experiment conducted by [PERSON] et al. [3] at the Shizhuyuan mine to compare and analyze the positioning effects of the five algorithms. The blasting coordinates are recorded as 8732.7 m, 6570.6 m, and 511.3 m. Among the 30 stations, a total of eight stations detected vibration signals, with their location information and P-wave arrival times shown in Table 7.
## 5 Performance Evaluation
### Quantum Approach (IBM's Quantum Simulator Results and Discussions)
The amplitudes of the N possible states associated with the newly decreased mean are inverted by a diffuser. The target state's negative phase is reversed by this inversion, which also contributes to differentiating the target from the other states. The last and final step is the measurement of the qubits. After conducting this measurement, the qubits are not in the superposition state, and they collapse to give the outcome of one of the possible states. The execution of the proposed quantum algorithm is performed on IBM's Qasm-Simulator, with the results being shown in Figure 7. The marked states are found within (\(t_{0}\)) = 2 iterations with 1024 shots, as shown in Figure 7a. If we increase the number of shots to 8192, then the probability of finding the states remains the same at the highest values of the two states. Figure 6: **(a)** Initialization of the quantum circuit; (b,c) represent Oracle marked states \(|1001\rangle\) and \(|1100\rangle\), (Note: the #1100 element is marked as (lsb to msb)); (d) the diffusion operator to invert the mean; (e) the complete quantum circuit ready to run on a simulator or quantum system to find the target element with high probability. The above results indicate that for the two states, \(|1001\rangle\) and \(|1100\rangle\), the probabilities are 40% and 37%, respectively, which means if we run the algorithm every time, the two states will have the highest probability. Within two iterations, we will reach our probable destination. There are negligible discrepancies between the theoretical values of the iterations and the simulation results because if we increase the iteration values in the simulator, then the probability of success will increase by around 10%, but we can still obtain the same state with the same results. Therefore, we can state that with the two single Grover's operator, the desired results can be found. The maximum number of iterations for this experiment is O (\(\frac{\pi}{4}\sqrt{N}\)), where \(N=16\) nodes. However, here, we can see that within two iterations, we can find our target state. If we increase the number of iterations (\(t_{0}\)) of Grover's operator, the algorithmic success probability (ASP) will be high. In the classical algorithm, in order to find the search state, we need to check each value (in the worst case) for the required time complexity O(\(N\)). For time complexity, the quantum algorithm surpasses the classical algorithm in terms of the CH selection procedure. In sum, this reduction in time will improve the overall performance of the network by decreasing the end-to-end delay and power consumption.
## 3 Synergy and Challenges of UAV-Assisted THz-VLC Systems
### Challenges in Integrating THz with UAV Platforms  
The challenges associated with integrating THz with UAV platforms are illustrated in Figure 1 and are described below.  
UAVs are prone to wobbling and unpredictable movements due to wind, turbulence, and rapid changes in direction. These factors can disrupt the THz signal transmission, affecting the reliability and stability of communication links [20].  
Figure 1: Challenges in integrating THz communication with UAV platforms. This diagram illustrates key challenges in implementing THz communication systems on UAV platforms, including high energy consumption of payload, design complexity of transceivers, uncertainty of medium, dense UAV deployment, obstructions, and instability and movement of UAVs.  
THz communication systems and their associated payloads are power-intensive, requiring significant energy to operate. This high energy consumption reduces the UAV's flight time and operational efficiency, necessitating advanced power management solutions [21].  
Developing transceivers capable of operating efficiently at THz frequencies involves intricate design and engineering challenges. These transceivers must handle high data rates, maintain signal integrity, and operate within the limited size and weight constraints of UAVs [22].  
THz signals are highly susceptible to blockages from physical objects such as buildings, trees, and even atmospheric particles. These obstructions can significantly degrade signal quality and limit the effective range and reliability of THz communication systems [22].  
Deploying a large number of UAVs in a confined area can lead to interference and signal congestion. Managing the spectrum and ensuring seamless communication among multiple UAVs require sophisticated coordination and interference mitigation strategies [20].  
The propagation characteristics of THz signals are influenced by environmental conditions such as humidity, temperature, and atmospheric composition. This variability introduces uncertainty in the communication medium, complicating the prediction and optimization of THz signal performance [21].
## 2 Materials and Methods
### Experimental Method  
#### 2.2.1 Fluorescence-Based Oil Swelling  
To implement an accurate quantification of time-dependent oil swelling by SDS and CO\({}_{2}\) gas, we prepared a microfluidic platform consisting of a PMMA channel (1.5 mm (W) \(\times\) 5 cm (L) \(\times\) 1 mm (H)), inlet and outlet ports connected to the injection solutions and a Petri dish, respectively. The thickness of the microchannel was chosen to be 1000 \(\upmu\)m to guarantee one-dimensional (1D) diffusion of CO\({}_{2}\) gas into oil. It should be noted that PMMA has excellent transmission of visible light (\(>\)92%), suitable for direct optical microscopy. PMMA sheets were cut using a laser cutter (Universal Laser Systems, Inc., VLS 2.30, Scottsdale, AZ, USA) with a unique AutoCAD design. As shown in Figure 1, the PMMA sheet containing the microchannel was stacked with two other PMMA sheets, top and bottom. The top sheet was cut to create two holes for the inlet and outlet ports, while the bottom sheet was kept plain. These three sheets were thermally bonded in a sandwich-style arrangement to create a trapped microchannel in the middle. For thermal bonding, a solution with a 7:3 ratio of isopropyl alcohol (IPA) and ethanol was used between the sheets, and then the PMMA assembly was placed in the oven for 30 min at 50 \({}^{\circ}\)C. The 50 \({}^{\circ}\)C was used to keep the channel's shape intact [25]. A special nano port fitting (IDEX Health & Science, N-333, Manhattan, NY, USA) was clamped with two additional PMMA sheets (3.175 mm thick) and four screws to ensure effective injection of CO\({}_{2}\) without leakage. Approximately 90 \(\upmu\)L of oil was injected into the microchannel using the syringe pump, and the inlet was carefully closed. A pressure controller was used to maintain the CO\({}_{2}\) gas at a constant pressure on the other end of the channel. The microchannel was placed under the UV light source (Figure 1). Crude oil naturally exhibits autofluorescence due to the presence of polycyclic aromatic hydrocarbon (PAH) compounds under UV light, which changes its emission intensity because of its swelling upon CO\({}_{2}\) dissolution [26; 27]. Figure 1b shows an example of experimental images of oil's emission change in the microchannel over time under constant CO\({}_{2}\) pressure.
## 2 Height Factor Model for GNSS Tomography
### Construction of the Height Factor Model
#### 2.3.1 Isotropic Component of SWD for Inside Signals
It should be noted that the different weather conditions during the 30 years are not classified, which may have an impact on obtaining the SWV of side signals and needs to be analyzed in future work. Table 1 shows the optimal coefficient for each month. Furthermore, the conclusion that the root mean square error (RMSE) of the 12 months of fitting results is less than 0.05 and the R-square representing the quality of the fitting results is greater than 0.98 can be inferred from Figure 4. It should be noted that the closer R-square gets to 1, the better the fitting results are [14]. Consequently, the isotropic component of SWD for inside signals could be accurately calculated using the HFM, as shown in Equation (9). \[SWD_{isotropic}=\lambda_{iso}(h)\cdot mf_{w}(\varepsilon)\cdot ZWD_{zenith} \tag{9}\] \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Month** & \(a_{1}\) & \(b_{1}\) & \(a_{2}\) & \(b_{2}\) \\ \hline January & 1.093 & \(-\)0.044 & \(-\)0.184 & \(-\)1.305 \\ February & 1.090 & \(-\)0.042 & \(-\)0.176 & \(-\)1.351 \\ March & 1.122 & \(-\)0.053 & \(-\)0.220 & \(-\)1.155 \\ April & 1.132 & \(-\)0.060 & \(-\)0.213 & \(-\)1.133 \\ May & 1.117 & \(-\)0.048 & \(-\)0.206 & \(-\)1.054 \\ June & 1.181 & \(-\)0.070 & \(-\)0.264 & \(-\)0.911 \\ July & 1.089 & \(-\)0.041 & \(-\)0.153 & \(-\)1.126 \\ August & 1.121 & \(-\)0.051 & \(-\)0.191 & \(-\)1.019 \\ September & 1.088 & \(-\)0.040 & \(-\)0.158 & \(-\)1.165 \\ October & 1.119 & \(-\)0.053 & \(-\)0.199 & \(-\)1.130 \\ November & 1.089 & \(-\)0.042 & \(-\)0.172 & \(-\)1.255 \\ December & 1.053 & \(-\)0.026 & \(-\)0.125 & \(-\)1.516 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The optimal coefficient of the HFM for each month. Figure 3: The isotropic height factor changes with height in different months. (**a**) January; (**b**) February; (**c**) March; (**d**) April; (**e**) May; (**f**) June; (**g**) July; (**h**) August; (**i**) September; (**j**) October; (**k**) November; and (**l**) December.
# Noncoherent Channel Combining for GNSS Signal Tracking with an Adaptive Antenna Array
## 4 Simulation Results
### Discriminator Output Analysis  
This subsection further analyzes the discriminator outputs obtained by different methods.  
Figure 5 shows the code and carrier discriminator outputs. This figure can further explain the tracking results obtained in the previous subsection. It can be seen from Figure 5a that the code discriminator output of the proposed method is the least noisy. This proves that it can significantly reduce the code discriminator noise by properly exploiting the signal power of different channels. This explains the reason for the improvement in code phase precision of the proposed method shown in Section 4.2. The MVDR-like and MMSE algorithms cannot produce a satisfactory noise reduction performance in this weak signal condition. This is because the increased tracking errors will degrade the beamforming performance of these methods, as stated in Section 3.4. From Figure 5b, it can be seen that the proposed method can slightly reduce the carrier discriminator noise compared to the PI algorithm, as expected. In addition, the random noise in the carrier discriminator output is significantly reduced by the MVDR-like and MMSE algorithms compared to the other two methods. However, there are obvious low-frequency errors caused by the carrier tracking errors introduced by array weighting. These low-frequency errors cannot be filtered out by filter loops and actually interfere with regular carrier phase tracking, as described in Section 3.4. This explains the reason for the degradation in carrier phase precision of these algorithms shown in Section 4.2.  
Figure 4: CDFs of code phase and carrier phase absolute errors. (**a**) Code phase. (**b**) Carrier phase.
# A Deep-Learning-Based Fault Diagnosis Method of Industrial Bearings Using Multi-Source Information
## 4 Performance Evaluation
### Experimental Settings and Performance Metrics
Precision \(P\) represents the proportion of samples predicted as faults that are correctly identified as faulty classes, which can be expressed as follows:  
\[P=\frac{TP}{TP+FP}. \tag{36}\]  
The recall rate \(R\) represents the proportion of all fault samples that are correctly identified as fault categories, which can be expressed as follows:  
\[R=\frac{TP}{TP+FN}. \tag{37}\]  
Figure 7: Time-domain waveform diagram of the signal under different operating conditions of the bearing. F1-Score \(F1\) is the harmonic mean of precision \(P\) and recall \(R\). F1-Score is positively correlated with the performance of the algorithm. The larger the value, the better the performance of the algorithm. The value range of F1 is between 0 and 1, which can be expressed as follows:  
\[FI=\frac{2\times P\times R}{P+R}. \tag{38}\]  
Usually, the F1-Score indicator is mostly used in binary classification problems. To measure the comprehensive performance of the MLMF-CR algorithm in dealing with multi-classification problems such as fault diagnosis, the traditional F1-Score indicator is expanded in this paper. Each time a bearing data sample category (bearing operating status) is selected as the positive class, the rest of the bearing data sample categories (bearing operating status) are used as the negative class. Then the precision and recall rate of different types of data samples are calculated. Hence, the F1-Score index of each data sample category can be obtained by Equation (38). In summary, it can be found that the fault diagnosis accuracy index is the ratio of the number of correctly identified data samples to the total number of data samples, which can reflect the effective recognition degree of the fault diagnosis algorithm. The F1-Score index not only fully shows the comprehensive performance of the fault diagnosis algorithm, but also reflects the performance of the algorithm under different operating conditions of the bearing. Therefore, the fault diagnosis and recognition accuracy and F1-Score index are used as the performance metrics in this paper.
An Off-Body Narrowband and Ultra-Wide Band Channel Model for Body Area Networks in a Ferryboat Environment  
[PERSON]  
1 Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, 80-233 Gdansk, Poland; [EMAIL_ADDRESS] (S.J.A.); [EMAIL_ADDRESS] (P.R.)1 Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, 80-233 Gdansk, Poland; slawomir.  
(reaching 10 Gbps), network reliability, and energy efficiency of devices, and reduced transmission delays (less than 1 ms), but also the introduction of new frequency bands, multi-antenna techniques, and the development of a completely new architecture. The emergence of the Internet of Things (IoT) concept, i.e., gathering and data exchange between smart devices, has resulted in a rapid increase in radio communications between such devices installed in homes, in cars, on the streets, or even on and inside the human body. In particular, devices working within the human body, e.g., monitoring life parameters, performing localization services, or providing media services, have become more important.  
According to the data from analytical companies [4], in 2016 the number of wearable devices increased much faster than the number of smartphones, and in the next years the disproportion will become significant due to the exponential growth in the number of the former. This means that the device used as a mobile phone is no longer the main telemetry device, and it will actually become a controller of peripheral devices located around it. This type of a radio network, consisting of peripheral devices operating within the human body, is called a wireless body area network (WBANs), defined also as a body area network (BANs).  
The problems associated with this kind of network are known from previous-generation systems, or even wireless sensor networks (WSNs), and include, among others, energy consumption, quality of service (QoS), transmission techniques, dynamic frequency band management, and radio channel modelling in various propagation environments. However, one should remember that, in WBANs, the body (human or animal) itself has a crucial impact on the radio link, as well as its motion, and the placement of wearable or implantable nodes [2; 5; 6; 7; 8; 9; 10].  
The rest of the paper is structured as follows: In Section 2, the current state of the art is briefly presented. Section 3 describes the used measurement stand. In Section 4, measurement scenarios are characterized. Section 5 contains an analysis of the model components, while the final model statistical analysis is provided in Section 6. The article is concluded in Section 7.
## 5 Experiments with Models and Frameworks
The inference time is only about 1/4 that of ResNet, and its accuracy is significantly higher than that of the other models. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{4}{c}{**Accuracy for Different Scenes**} & \multicolumn{2}{c}{**Average**} & \multicolumn{1}{c}{**Recognition Speed**} \\ \cline{2-7}  & **Bedroom** & **Kitchen** & **Living Room** & **Office** & **Store** & **Accuracy** & **(ms/Sheet)** \\ \hline Work [4] & 65.00\% & 60.00\% & 70.00\% & 65.00\% & 95.00\% & 71.00\% & 360 \\ Work [38] & 95.00\% & 75.00\% & 98.33\% & 73.33\% & 93.33\% & 87.00\% & 510 \\ CLM+SVM & 95.00\% & 80.00\% & 95.00\% & 86.67\% & 98.33\% & 91.04\% & 360 \\ ResNet & 82.29\% & 59.89\% & 76.64\% & 86.98\% & 80.46\% & 77.25\% & 221.75 \\ MobileNetV3 & 72.55\% & 91.01\% & 88.30\% & 71.69\% & 62.05\% & 77.12\% & 50.23 \\
**Pre-trained** & & & & & & \\
**MobileNetV3** & **94.26\%** & **76.54\%** & **93.83\%** & **87.00\%** & **89.14\%** & **88.15\%** & **63.11** \\ (**CIFAR-100**) & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison of different methods. Figure 13: Loss values and accuracy. Note that further expanding the dataset including more diverse and challenging indoor environments would be beneficial to the model, helping it to remain adaptable to a wider range of real-world situations and have a high scalability in various home settings.