esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 HYDE Algorithm Description
In this step, the reference system still centered on \(P_{i}\), and the algorithm estimates, in all angular directions, the number (\(L\)) of hypocenters located in the \(V_{j}\) volumes, applying the two rotation matrices around the x and y axes. Note that in doing so, the algorithm works on two dimensions instead of on three dimensions, greatly speeding up the calculations. For each \(P_{i}\) and for each rotation (\(\theta\)) around the x and (\(\varphi\)) y axes, the number of hypocenters \(L\) intercepted by the \(V_{j}\) volume is stored. This step consists of the maximization of the hypocenter number \(L\) in the \(V_{j}\) volumes as a function of the \(\theta\) and \(\varphi\) angles and \(P_{i}\):  
\[max\{L\big{[}(V_{j}(P_{i},\theta_{r},\varphi_{q})\big{]}\}=>\pi_{0}(P_{0}, \theta_{0},\varphi_{0})(i=1,2,\ldots,K;r=1,2\ldots R;q=1,2,\ldots Q) \tag{1}\]  
where \(\theta\in[0^{\circ},180^{\circ}]\), \(\varphi\in[0^{\circ},180^{\circ}]\) and \(R\) and \(Q\) represent the number of angular discretizations. In this way, we individuate the plane \(\pi_{0}\) that represents a fault plane found in the whole explored volume \(V=U(V_{j})\). Step 4. The following step consists of setting different fault dimensions (x, y) and the thickness (\(t\)) by increasing its initial value up to a few times \(\sigma\). This method explores the parameters space (\(P\), \(\theta\), \(\varphi\),), even considering the thickness values. In particular, the study of the earthquake number belonging to the fault, as a function of the thickness \(t\), gives information on the best \(t\) and on the goodness of the fault approximation. Figure 1: Blue and red points are the whole earthquakes dataset. Hypocenter pivots (red points) represent the barycenters of the exploration volume (\(V\)). Figure 2: A scheme of a pivot (the cross-point of the two rotation axes) as the barycenter of a parallelepiped containing hypocenters. At the end of the procedure, the solution is represented by a parallelepiped containing the maximum number of earthquake hypocenters. But how constrained is the solution? If we rotate the parallelepiped, how much does the earthquake count change? Figure 3a illustrates how the solution evolves as the reference system rotates around the two axes. A peaked solution indicates stability, whereas, for example, in a scenario without any faults (Figure 3a), the solution is completely unstable, showing multiple peaks and a lower number of counts. Furthermore, when a fault is present, it is expected that moving orthogonally away from the fault will result in a rapid decrease in the number of events. Specifically, if we translate the pivot that defines the solution parallelepiped orthogonally to the fault and construct a new parallelepiped (parallel to the solution), the number of events within the new parallelepiped will progressively decreases we move further away from the solution. The algorithm was developed using Matlab R2024b adopting parallel computing.
## 4 Experiment and Verification
### Model Verification
The systematic error removed measurement data that was compared with the model predicted data corresponding to the same time by SPSS software. The analysis results based on [PERSON]'s W consistency test showed that the value of the asymptotic significance is less than 0.05, and the null hypothesis was accepted. The model prediction data are consistent with the experimental data, and the consistency is as high as 0.897. Even if a few points with obvious deviations are included, the model predicts experimental values that are highly consistent in numerical value and distribution, as shown in Table 3.  
These data were repeatedly measured and checked during the test, and the results were almost identical. The research value of the deviation points could not be determined during the experiment, so the originality of the measured data has been retained to the greatest extent. Thus far, the correctness of the model was verified by experiments. Through the error of the verification process, the above model achieves a parameter analysis of the surface roughness and tool life. For the tool performance design, the model provides quantitative and qualitative simulation analyses. Thus, the influence of the tool design parameters and cutting parameters on the tool performance can be directly derived. The processing method in this study has been determined as dry cutting. However, there are still many physical or chemical factors in the actual machining process that significantly influence \(R_{a}\), such as the milling force, milling heat, vibration of the system, milling path, differences in the surface integrity caused by material property change during machining [37; 38]. The effects of the milling forces, milling heat, and part of the material properties are expressed in analytical form in the wear study phase in Equation (12), but the modeling process cannot fully involve all the factors. Although other factors, such as vibration and material property change, are not explicitly expressed in analytical form, the model can achieve sufficient accuracy because the factors not expressed in the model are still implicitly included in the coefficients, such as \(K\) and \(C\) in Equation (9), which were corrected from real experimental data. Therefore, they contain all the physical or chemical factors that the actual processing should. This is why the general modeling method can maintain an acceptable accuracy under ideal conditions.
# A High-Thrust Screw-Type Piezoelectric Ultrasonic Motor with Three-Wavelength Exciting Mode
## 4 Fabrication and Measurement
### Vibration CharacteristicsMeasurement  
A scanning laser Doppler vibrometer (PSV-400-M2, Polytec, Waldbronn, Germany) was used to measure the vibration performance of the motor. The outside surfaces and the end surfaces were chosen as the testing surfaces. The vibration mode shapes and vibration velocity average response spectrums under the excitations of the exciting group A is shown in Figure 6. The results indicate that the stator was excited to generate a combined circumferential-axial vibration mode. The combined vibration mode includes the first bending vibration mode along the axial direction seen in Figure 6a and the third bending vibration mode along the circumferential direction seen in Figure 6c, respectively. The tested vibration shapes of the motor agree well with the working modal shape of the stator, as shown in Figure 3. Figure 6 also states that the resonant frequencies of the axial and the circumferential vibration modes by means of the exciting group A are 28.95 kHz and 29.00 kHz, as shown in Figure 6b,d.  
Figure 7 shows the vibration mode shapes and the vibration velocity average response spectrums by means of the exciting group B. The stator was excited to generate a combined circumferential-axial mode. The first and third bending vibration mode along the axial and circumferential direction is excited shown by Figure 7a,c. The resonant frequencies of the axial and the circumferential vibration modes by the exciting group B are 28.98 kHz and 28.96 kHz asseen in Figure 7b,d. From the Figure 4, it can be seen that the resonant frequency by harmonic response analysis is 30.05 kHz. Thus, the tested results have a good consistency with harmonic response analysis results. There is a slight deviation between the simulation and the experiment results. The reasons for deviation may be concluded as follows: (1) parameter discrepancies between the real material properties and ideal models; (2) diminishment of finite element model results with the increased resonant frequency that may be caused by the neglect of the glue layer; (3) the fabrication and assembly errors.  
Figure 6: Vibration testing result of the motor under the exciting group A: (**a**) axial vibration mode shape; (**b**) axial vibration velocity average response spectrum; (**c**) circumferential vibration mode shape; (**d**) circumferential vibration velocity average response spectrum.
# Efficient High-Dimensional Kernel k-Means++ with Random Projection
## 3 Our Method
### Efficient Initialization
#### 3.2.3 k-Means++ with Buffered Random Projection (kmBRP)  
Buffering a fixed number of random matrices is also considered for the initial center selection. The aim is to combine the advantages of the previous two variants with a limited number of random projections for efficiency. Although multiple random projections could help to reduce the bias in approximation, a new random matrix at each iteration may not be necessary. At the initialization stage, we have a buffer \(\mathbf{B}\) with \(b\) random matrices that, \(\mathbf{B}:=\{\mathbf{R}_{1},\mathbf{R}_{2},\ldots,\mathbf{R}_{b}\}\). At each iteration, a random index \(i\) is generated for \(\mathbf{R}_{i}\in\mathbf{B}\).  
k-means++ with fixed random projection (kmFRP) is the simplest and the most computationally efficient variant of our method, but it may lead to a larger degradation of performance, as discussed previously. k-means++ with iterative random projection (kmIRP) theoretically provides the least biased clustering at different iterations with more computation for random projections. k-means++ with buffered random projection (kmBRP) is something in the middle between kmFRP and kmIRP that strikes a balance for good optimization performance while maintaining reasonable computational efficiency. With the free parameter \(b\), which is the buffer size, the user can control the trade-off between optimization performance and computational efficiency.  
```
Input: type is the type of the random matrix, which can be kmFRP, kmIRP or kmBRP Input: b is required for the buffer size if type is kmBRP Output: k initial centers selected
1 Choose initial center \(\mathbf{c}_{1}\) uniformly at random from \(\mathbf{X}\) \(\mathbf{R}_{i}=\overrightarrow{N}(0,1)\cdot\frac{1}{\sqrt{d}}\) for\(i\in 2,\ldots,k\)do \(\mathbf{X}^{proj}=\mathbf{X}\cdot\mathbf{R}\)  Choose \(\mathbf{c}_{i}=\mathbf{x}\in\mathbf{X}^{proj}\) with probability \(\frac{Dist(\mathbf{x})^{2}}{\sum_{\mathbf{x}\in\mathbf{X}^{proj}}Dist(\mathbf{ x})^{2}}\) iftype is either kmIRP or kmBRPthen
2 R = call Algorithm 2 with type, b
3 end
```  
**Algorithm 3**Proposed k-means++ approximation with different types of random projection.
# Development and Research of a Medium Vertical Dipole Electromagnetic Pulse Simulator
## 4 Experiments and Characteristics Research
### Influence of the Cable Number of the Wire-Mesh Antenna  
Figure 7 depicts the radiation field waveform variation at different points along the radius extension line in the positive north direction, as well as with different cable numbers of the wire-mesh antenna. It is observed that the amplitude of the radiation field at any given point decreases as the number of antenna cables reduces. Specifically, when the number of cables is less than 12, the decline in the peak value of the radiation field becomes significant. The convergence of wire-mesh antenna cables to the metal cone is shown in Figure 1, with the radiation field waveforms near the cone vertex remaining nearly constant when the number of antenna cables decreases, as illustrated in Figure 7a. However, at points away from the cone vertex, the radiation field waveforms are affected by the change in the number of wire-mesh antenna cables, as shown in Figure 7b. A distortion in the radiation field waveform is evident at the coordinates (5, 0, 0.2) when the number of antenna cables is less than 12. The severity of distortion increases as the number of cables decreases, indicating that the number of antenna lines should not be less than 12. In the subsequent research, the tests were conducted using either 24 or 48 antenna lines.
# Real-Time Communication Support for Underwater Acoustic Sensor Networks +
## 7 Simulations  
In this section we present several simulations that show the overall performance of the network under diverse initial conditions. We used the example of Section 5 to measure end-to-end delay, the load in the different nodes, and we introduced random faults to check the network reliability. In what follows we will explain with more details the experimental framework and the tests we performed to validate the model.
## 3 DenseNet-Hybrid Control Plane Architecture
### Hybrid Node Architecture
#### 3.2.4 Canonical-SDN Operation  
Let us briefly recall how Canonical-SDN operates in our setup and what kind of messages are exchanged between nodes and the SDN controller.  
In the centralized SDN setup, when a new packet comes to the ingress port of a node (e.g., Open vSwitch), the node looks up in its flow tables for a flow entry match for the packet. If no match is found in the flow tables, the node sends the packet as a _PacketIn_ message to the SDN controller using the _flow-miss_ entry rule. Then, the SDN controller calculates the shortest path for the packet, and, using a _PacketOut_ message, the SDN controller instructs the forwarding node to install flow rules into the flow table of the nodes. The forwarding nodes keep the rules for an infinite period unless the nodes get further instruction from the SDN controller to modify or delete the rules. With the subsequent flow of packets, the forwarding nodes simply forward the packet according to the rules installed previously with the intervention of the SDN controller.  
In the case of a change of network topology, for instance, the link between two nodes goes down, the nodes that are affected instruct the SDN controller immediately about the impairment by sending _PortStatus_ messages. In our case, when the SDN controller receives _PortStatus_ messages, the SDN controller instructs, by means of _FlowMod_ messages, the affected nodes as well as all the nodes in the affected path to delete all the previous rules from their flow tables except the _flow-miss_ entry rule.  
The _flow-miss_ entry rule is not deleted because, using this rule, the forwarding node sends new packets to the SDN controller when a matching rule can not be found in the forwarding table of the node. When the forwarding nodes in the affected path receives instructions from the SDN controller to delete rules, the nodes perform deletion of rules according to the instructions of the SDN controller.  
As the rules have been deleted from the flow table of the nodes, when a packet comes to the ingress port of the nodes, the nodes again send the packet as a _PacketIn_ message to the SDN controller using the _flow-miss_ entry rule. The SDN controller then sets a new policy for the incoming packets and instructs all the nodes in the new path to install new rules in their flow table. The nodes then forward subsequent packets according to the new rules.  
All these control messages are fundamental for the operation of the network. Therefore, any loss affecting them may have noticeable consequences on network performance. The impacts of such losses as well as the operation of our proposed solution are evaluated in Section 4.
## 2 Materials and Methods
### Antimicrobial Activity of AgNPs  
The antimicrobial activities of the produced AgNPs were tested against some targeted pathogenic microorganisms obtained from the American type culture collection (ATCC; Rockville, MD, USA) and the culture collection of the National Research Centre (NRC, Cairo, Egypt). The tested organisms were _Staphylococcus aureus_ ATCC- 47077, _Bacillus cereus_ ATCC-12228, _Escherichia coli_ ATCC-25922, _Pseudomonas aeruginosa_, _Candida albicans_ ATCC-10231, and _Saccharomyces cerevisiae_ATCC-9763. The stock cultures of microorganisms used in this study were maintained on nutrient agar slants at 4 \({}^{\circ}\)C. The Agar well diffusion method was employed to study the antimicrobial activities of the synthesized AgNPs according to the method described by [PERSON] et al. [43] and [PERSON] et al. [44].
## 3 The FAURAS Framework
### Bandwidth Allocation
#### 3.1.1 Determining the Fair Share
Consequently, as all clients are assigned with the same bandwidth \(C_{t}^{f}\) by the Bandwidth Allocation module, they are likely to select similar video bitrates, thus achieving fairness. It should be noted that we assume that every client shares the same characteristics (e.g., screen size, device type, subscription plan, etc.) in this paper.
# Challenges of Malware Detection in the IoT and a Review of Artificial Immune System Approaches
## 4 AIS to Secure the IoT: Literature Review and Analysis
### AIS in Malware Detection in the IoT
#### 4.1.2 Negative and Neural Networks  
The authors in [54] proposed using a negative selection algorithm combined with neural networks (NSSN) for intrusion detection in the IoT. The research goal is to develop an algorithm that meets IoT requirements, is lightweight enough to apply to a wide range of IoT use cases, is capable of detecting previously unknown intrusion vectors, and provides an acceptable detection rate. The dataset used in this experiment is KDD NSL [55]. The authors use only the basic traffic features, which provide most of the needed information. The different types of intrusions are divided into 23 different sets (22 types of attacks and one normal). Then, the attack types are divided into three attack sets: denial of service (DOS), PROBE, and All Attack Types (AAT). They tested the algorithm against different percentages of normal and attacks of each type (10%, 20%...90% attack and subsequently 10%, 20%...90% of normal). Each one of the 27 sets iterated 100 times with different test data sets every time. The trained NSSN algorithm was tested against the dataset, and thefollowing coefficients were calculated: positive predictive value, negative predictive value, sensitivity, specificity, accuracy, Matthews correlation coefficient (MCC), and F1-Score (the harmonic mean of the precision and recall). This research succeeded in achieving an F1-Score of 0.77 in the DOS simulation, 0.72 in the PROBE simulation, and 0.73 in all AAT simulation results. The researchers in [54] claimed that their work is limited to the creation of the negative selection and neural network algorithm only. Currently, they make no claims about the best way to implement an online learning mechanism for it. Furthermore, they noted that the test set used in the experiment is dated, and the results should be used only for comparison purposes and not to demonstrate the actual performance of the algorithm. In addition to the presented shortcomings, we find the F1-Score of this algorithm to be unreliable in securing the IoT systems.
## 1 Introduction
Both of them are reliable precipitation sources for daily and sub-semiditurnal hydrological simulations in the Yellow River source region (YRSR) [14]. [PERSON] et al. evaluated the performance of IMERG product-driven hydrological forecasts in different regions of China and found that the IMERG FR and ER products provided comparable performances to measurement-based precipitation while the TRMM 3B42 product performed relatively poorly in hydrological simulations as indicated by positive skill score (SS) values and larger Kling-Gupta Efficiency (KGE) values. In addition, the three products generally performed better in wet areas than in dry areas [1]. The general results indicate that GPM satellite precipitation products developed based on TRMM satellites are more accurate, have more potential for hydrological applications [14], and have value in hydrological applications for some countries and regions, especially in areas where information is scarce. Since it is difficult to set up dense rain gauge networks in mountainous basins due to the complicated topography, it is necessary to evaluate the hydrological application effect of satellite-based precipitation products as supplementary data sources. The Xinfengjiang River basin is an important mountainous river in the humid climate region of South China and an important source of drinking water for the residents of Shenzhen, Dongguan, Huizhou, and other cities in the lower reaches of the East River as well as Hong Kong, and it provides 6 billion cubic meters of clean water to the East River every year. Nevertheless, hydrological evaluations of IMERG series products in the Xinfengjiang River reservoir basin (XRRB) have not yet been reported until the present moment. Moreover, previous results are generally unacceptable due to the higher error limitations of early satellite products [24; 25; 26; 27]. Thus, the hydrological utility of the new generation of IMERG products must be explored in the mountainous areas of southern China. In this paper, the accuracy of IMERG products on daily and monthly time scales is evaluated in the XRRB. Then, their hydrological utility in streamflow simulations is assessed by using the soil and water assessment tool (SWAT) model. The article is organized as follows: the second part provides an overview of the study area and data sets; the third part provides an introduction to the methodology; the fourth part evaluates the accuracy of the satellite products in the XRRB, constructs the hydrological model of the basin, and assesses the utility of the hydrological applications of the IMERG series products; the fifth part provides a discussion; and the sixth part presents the conclusions of this study.
## 4 Results and Discussion
### Prediction of Human Glucose Concentration
#### 4.2.3 Clarke EGA  
Clarke EGA, the \"gold standard\" for blood glucose sensing, shows regions A and B with clinically acceptable errors of less than 20% and 40%, while regions C, D, and E present significant clinical risks that must be avoided in blood glucose sensing [44]. Figure 6a shows the Clarke EGA image (left) and error distribution fan diagram (right) of predicted  
Figure 5: (a–c) Bland–Altman analysis of predicted results obtained by HMML-BP network using SNE, diffusion map, and factor analysis algorithms, respectively.  
results made by the HMML-BP network using the SNE algorithm, showing that 90% of the predicted results fall in region A, while 10% fall in region B. In addition, the Clarke EGA image of the predicted results obtained by the HMML-BP network using the diffusion map algorithm, as shown in Figure 6b, exhibits improved predicted accuracy, with regions A and B at 95% and 5%, respectively. Furthermore, Figure 6c presents the EGA image of predicted results made by the HMML-BP network using the factor analysis algorithm; here, all tested results are located in region A, showing the highest predicted accuracy compared to those produced with SNE and diffusion maps. All these improvements obtained using the factor analysis algorithm show higher accuracy in human glucose sensing, offering a more effective way of monitoring human daily glucose.  
The predicted results of our method fall exclusively in region A, outperforming commercial continuous glucose-monitoring (CGM) systems (e.g., Dexcom and Abbott Libre), whose results are distributed across both regions A and B. Our system achieves a MARD of 9.98%, which is comparable to commercial CGMs (typically \(\sim\)9%) [45]. In addition, for daily blood glucose sensing in humans, long-term cost is one of the most critical considerations. Current CGM systems, which involve inserting electrodes under  
Figure 6: (a-c) Clarke EGA images (left) and [PERSON]’s error distribution fan diagram (right) of predicted results made by HMML-BP networks using SNE, diffusion maps, and factor analysis algorithms, respectively.  
the skin for 7-14 days [46], can cause physical discomfort and impose financial burden on users. In contrast, our system eliminates the recurring expenses associated with sensor replacements, offering a more affordable and patient-friendly solution.