esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Vibration Measurement and Analysis
### Operational Deflection Shape (ODS)
ODS analysis revealed the mode shape for each natural frequency band, representing the dynamic behavior of the sunroof while the vehicle was in motion, as shown in Figure 15. ODS analysis revealed that within the first resonance frequency band of 15.6 Hz, the deformation shape of the front portion was marginal, as determined by the sunroof front support. On the other hand, the rear sunroof glass exhibited a bending mode shape characterized by up and down bending. The sunroof exhibited a torsional mode shape at the second resonance frequencies of 22.9 Hz and 28.9 Hz, with the left and right sides bowed along the X-axis line that connected the front and rear of the sunroof. The degree of torsion in the mode shape underwent a rapid change at 22.9 Hz, as evidenced by the measurement outcomes of the G:25 accelerometer and the main vibration frequency (40 km/h), because the amplitude surpassed the resonance frequency of 28.9 Hz. The EMA test and ODS analysis were conducted by comparing the natural frequencies of the sunroof glass, which were confirmed by EMA at 15.742 Hz, 22.245 Hz, and 28.501 Hz, with the primary resonance frequencies identified via ODS analysis at 15.6 Hz, 22.9 Hz, 27.0 Hz, and 28.9 Hz. Table 5 lists the error and error rate of the natural frequency. The error and error rate comparison table for natural and resonance frequency data was obtained via ODS analysis and the EMA test. The table presents the error rate, which varied according to frequency mode and reached a maximum of 2.944%. The frequency deviation was capped at 0.655 Hz, with a margin of error of less than 1 Hz. The findings from both the EMA test and ODS test analysis were corroborated. Nevertheless, it differed slightly from the theoretical natural frequency obtained using the theoretical approach because it accounted for the properties of the plate and not the curved surface of the vehicle sunroof, which is its actual shape. The distinction between natural frequency and resonance frequency is hypothesized to have resulted from the omission of conditions pertaining to  
Figure 14: G:25 measurement results and main vibration frequency (50 km/h): (**a**) waterfall contour map, (**b**) frequency spectra in all directions. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Mode**} & \multicolumn{4}{c}{**Natural Frequency [Hz]**} \\ \cline{2-5}  & **EMA** & **ODS** & **Difference** & **Percentage (\%)** \\ \hline \#1 & 15.742 & 15.600 & 0.142 & 0.902 \\ \#2 & 22.245 & 22.900 & 0.655 & 2.944 \\ \#3 & 28.501 & 28.900 & 0.399 & 1.400 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Natural frequency comparison of the EMA and ODS results. the actual vehicle sunroof system, including load conditions and the state of the bracket supporting the glass.
# Monitoring Perishable Commodities Using Cellular IoT:
## 2 Materials and Methods
### Comparative Test with Other Dataloggers  
In order to verify the correct operation of the designed system when measuring, a comparative test was carried out to compare the measurements of the device with two commercial sensors, which were the dataloggers PKDL A1 from Parkside (Stiffsbergstrasse, 1, Neckarsulm, Germany) [68] and the Tinytag Ultra 2 model TGU-4500 from Gemini Data Loggers (Terminus Road, Chichester, UK) [69], both used for temperature and relative humidity, which are the most critical measurements when monitoring the condition of perishable products. Their precisions were \(\pm\)0.5 \({}^{\circ}\)C/ \(\pm\) 2.5% RH and \(\pm\)0.5 \({}^{\circ}\)C/ \(\pm\) 3% RH, respectively. The test was performed inside a refrigerator set to 1 \({}^{\circ}\)C.  
This comparison was made with the objective of verifying that the trends of all the dataloggers were the same and that, therefore, the designed system, as a whole, performed the measurements properly. With this, the accuracy of the device was determined by the accuracy of the sensors included in it, which were detailed in Section 2.2.1.  
This test was static and designed specifically to assess temperature and humidity readings, so vibrations and luminosity were not relevant as there was no movement or incoming light. Similarly, CO\({}_{2}\) concentration was not monitored in this test because, in a closed refrigeration environment with no product, CO\({}_{2}\) levels remain relatively stable and do not affect product quality over short periods, making it unnecessary for this comparative analysis. The CO\({}_{2}\) sensor performance was later verified in a separate test to confirm that it operates within normal environmental ranges, as well as the luminosity and vibration sensors, in a moving test with products described in the following section.  
The similarity between trends was quantified by obtaining the maximum, minimum and mean absolute and relative errors, as well as their medians and standard deviations, and the root mean square error (RMSE) was also selected as an indicator. This can be visualized in the Results section in Section 3.4.
# Towards the Detection of GPS Spoofing Attacks against Drones by Analyzing Camera's Video Stream
## 8 Summary  
GPS spoofing attacks can cause dangerous navigation problems. Currently, product manufacturers only offer chip-level solutions, which do not provide a satisfactory means of detecting or preventing such attacks. In this article, we focused on devices that contain video cameras and GPS sensors and proposed a real-time method for the detection of GPS spoofing attacks on drones, which can be used by consumers. The proposed method makes use of the video stream captured by a drone's camera and the measurements obtained by the GPS sensor. We demonstrated the proposed method on delivery drones, since their role in everyday life is expanding; given the ease with which GPS spoofing attacks can be performed, amateurs may try to attack delivery drones in order to steal the goods they transport. Our evaluation results demonstrate our method's ability to protect delivery drones from GPS spoofing attacks. Our findings show that it can provide a high level of security to a drone flying at altitudes of 50-100 m over an urban area at an average speed of 4 km/h in conditions of low ambient light; in this scenario, the proposed method can provide a level of security that detects any GPS spoofing attack in which the spoofed location is a distance of 1-4 m (an average of 2.5 m) from the real location. Our method's advantages include the fact that it does not require any extra hardware or prior knowledge on the flight area.  
**Author Contributions:** Investigation, B.D.; Methodology, B.D.; Project administration, B.D.; Writing--original draft, B.D.; Supervision, B.N. and Y.E.; Validation, B.N. and Y.E.; Writing--review & editing, B.N. and Y.E. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Structural Optimization Method of a FinRay Finger for the Best Wrapping of Object
###### Abstract  
Soft gripping, in which the gripper adapts to differently shaped objects, is in great demand for use in unknown or dynamically changing environments and is one of the main research subjects in soft robotics. Several systems have already been created, one of which is a passive shape-adaptable finger based on the FinRay effect. The geometric shape of this finger ensures that the finger wraps around the object it grips. FinRay fingers have been studied in several studies, which have changed the internal structure and examined how gripping force's dependence on finger deformation changes. So far, however, no specific way has been determined to evaluate the proposed finger regarding its ability to wrap around the object. This work comes up with a new and simple method to evaluate the finger's wrapping around the object mathematically. Based on this evaluation method, several different patterns of the internal structure of FinRay fingers were tested. The fingers were first tested in a simulation program, which simulated a steel roller indentation with a diameter of 20 mm in the middle of the finger's contact surface. Based on the simulation results, selected types of structure were made by the Fused Filament Fabrication method from a flexible filament and tested on a real test rig to verify the results of the simulation and compare it with the real behaviour. According to the methodology used, the results show that the most suitable structure of the selected tested fingers from the point of view of wrapping the finger around the object is a structure without internal filling. Designers can simply use the new evaluation method to compare their designed finger variants and select the most suitable one according to the ability to wrap around the gripped object. They can also use graphs from this work's results and determine the finger's dimensions without internal filling according to the required forces and deflection.  
FiniteRay finger; soft robotics; soft gripper; wrapping of object +
Footnote †: journal: Applied Sciences
## 5 Challenges, Open Research Problems, and Future of WBANs
### Ease of Deployment and Scalability  
Similar to user-friendliness, developing easily deployable pervasive systems is also an important and nontrivial problem for WBANs. When the number of patients and caregivers increase, the scalable and easily deployable applications that can also support multiple receivers will attain much importance.  
WBANs and pervasive health monitoring systems typically require the simultaneous use of several sensors, communication devices and software. With these diverse components, ease of deployment becomes a problem to be considered for research.  
For this purpose, a software as a service approach can be used for both scalability and ease of deployment, together with small and easily configurable sensors. The system must support the addition of new components at runtime, in order to adapt the system to changing disabilities over time. The software platforms and distributed services will be required for seamless integration of the hardware and the application levels and interoperability among these will be essential as in [79, 101].
## 3 Numerical Simulation Analysis
### Establishment and Verification of a Finite Element Model for Cylindrical Shells  
#### 3.1.1 Model Establishment  
Following an underwater experimental study on the vibration and acoustic radiation of double-layer shells by [PERSON] [24], a corresponding finite element simulation model is established. The initial pre-processing, as shown in Figure 5, involves the construction of a geometric model based on the dimensions of the experimental components reported in the literature. The geometric parameters of the two-layer cylindrical shell are as follows: the length of the cylindrical shell is \(l\) = 0.8 m, the radius of the inner shell is \(R_{1}\) = 0.25 m, the radius of the outer shell is  
Subsequently, mesh partitioning is carried out. The finite element software includes an internal \"Mesh\" module for fast and convenient mesh partitioning. This module allows the acoustic mesh sizes to be automatically adjusted during the calculation process according to the corresponding calculation frequencies, significantly reducing the calculation time while maintaining accuracy requirements. In this module, the maximum frequency is set to 1500 Hz and the number of mesh elements per wavelength is set to five to meet the accuracy requirements of the simulation.  
Calculations are performed within the frequency range of 250-1500 Hz, as indicated in the literature. The analysis of the vibration and acoustic radiation performance of the double-layer cylindrical shell is completed with the default solver. Subsequently, sound pressure levels are obtained for the purpose of data comparison.
## 2 Detection System and Data  
### Wideband Electromagnetic Pulse Detector  
[PERSON] et al. [10] have provided an in-depth introduction to the wideband electromagnetic pulse detector (WEMPD) system, outlining its fundamental components and operational parameters. The WEMPD is composed of a high-performance receiving antenna and a sophisticated receiver. The antenna, with a 3 dB beamwidth of approximately 130\({}^{\circ}\), is designed to capture signals with specific frequency bands and polarization modes while maintaining a parallel orientation to the ground during satellite orbit. The receiver is adept at amplifying, detecting, classifying, and storing signals within a 25 to 75 MHz frequency range, facilitated by a sampling rate of 160 MS/s.  
Detecting wideband EMPs from space is primarily challenging due to the prevalence of numerous interference events [2; 16; 17] (17 submitted to Advances in Space Research). Therefore, the receiver is equipped with a fast detection mode that employs an advanced short-time Fourier algorithm to identify EMPs with a distinct \"L\"-shaped time-frequency distribution. This approach demonstrates a marked improvement over the sub-band triggering scheme based on analog circuits used in the payload on the FORTE satellite [18], offering a simpler circuit design, robust anti-interference capabilities, and enhanced channel consistency. In fast-detection mode, the system initiates a sampling length of 256 \(\upmu\)s and incorporates a GPS timestamp at 102.4 \(\upmu\)s, which corresponds to 40% into each frame. Furthermore, the receiver is equipped with a data-acquisition system that contains ample random-access memory, enabling it to continuously acquire data for up to 1.5 s. This robust system is designed to meet the demands of high-fidelity EMP detection.
## 4 Experimental Results and Analysis
### Road Data Extraction  
The extraction of road scene information from the meticulously gathered images assumes a pivotal role in the overarching endeavor of dataset creation. This component is crucial because it gives us a complete picture of the current road conditions and traffic density. The core of the process involved the application of three sophisticated You Only Look Once (YOLO) models, strategically chosen to cover different aspects of the street scene. Collectively, these models represent a fusion of advanced computer vision techniques and machine learning algorithms working in tandem to extract meaningful information from the captured visual data.  
The first model's task is to quantify the density of vehicle traffic in a street scene. It can accurately measure the number of vehicles present so that the traffic situation can be quantitatively assessed at different time intervals.  
The second model, with a targeted focus, zeroes in on the identification of traffic signals and pedestrian crossings, as well as zebra crossings. This feature-rich identification process is crucial for understanding the interaction points between pedestrians and vehicles and for identifying traffic control mechanisms.  
In turn, the resulting model plays a crucial role in recognizing and decoding traffic signs in street scenes. This includes a wide range of regulatory, warning, and information signs and adds a layer of semantic understanding to the dataset.
# Coalition Formation Game for Cost-Efficient Multiparty Payment Channel in Payment Channel Networks
## 4 Multiparty Payment Channel
### MPC State Synchronization
#### 4.3.2 Partial Synchronization
```
1:Initialization:
2:Set \(K_{p}^{i},i\in N\)
3:Set last MPC state \(S(t)=\{A_{i}(t)|i\in N\}\)
4:Receive blocks \(\{B_{t}(i,j)|t\in[n,n+\lfloor(N/2)\rfloor]\},i,j\in N\}\), where each block \(B_{t_{m}}(i,j)\) has \(\{A_{i}(t_{m})\), \(A_{j}(t_{m})\), \(\mathcal{H},Sign(\mathcal{H},K_{s}^{i})\), \(Sign(\mathcal{H},K_{s}^{i}),t_{m}\}\)
5:Instruction:
6:for\(t_{m}\in[n,n+\lfloor(N/2)\rfloor]\)do
7:if\(t_{m}==\gamma_{v_{ij}}\)and Verification \(Sign(\mathcal{H},K_{s}^{i}),Sign(\mathcal{H},K_{s}^{i})\) by \(K_{p}^{i},K_{p}^{i}\)and \(A_{i}(t_{m})+A_{j}(t_{m})==A_{i}(t)+A_{j}(t)\)then
8: Update MPC state \(\mathcal{M}(t)\leftarrow\mathcal{M}(t_{m})\) with new \(A_{i}(t_{m})\), \(A_{j}(t_{m})\)
9:else
10: Invoke dispute procedure
11:endif
12:endfor
13:return \(\mathcal{M}(t)\)
```  
**Algorithm 1** MPC state update. We discuss the possible security threats in the MPC and their resolutions through the dispute algorithm. * Double spending: if a malicious user attempts to pay with the wrong BP information, it is detected by a counterparty based on previous blocks, which denies the proposed transaction. Although the counterparty accidentally signs the transaction of the double spending at \(T_{n}\), the smart contract can later detect the transaction that contradicts a previous \(T_{n-1}\) MPC state. * Sybil attack: if user B, C, or D of Figure 4 colludes and increases their balance after \(T_{n-k}\) of user A's SPC, the smart contract will choose blocks from \(T_{n-k}\) to \(T_{n-k-V}\) uploaded by user A because the chains from the other users have invalid information between \(T_{n-k}\) and \(T_{n-k+1}\) despite the highest round count. Each user stores blocks of the last SPC cycle, \([n-V,n-1]\), at the current \(t=n\) to prove their balance; a user does not need to maintain older transactions than the \(n-V\)since it already confirms the state before \(n-V\), and we can save local storage by removing obsolete transactions.
## 3 GAs for Multi-Objective Routing Optimization
### Fundamentals of GAs  
GAs, inspired by natural selection and genetics [4], offer a powerful optimization technique for addressing complex problems with multiple objectives, including message routing optimization in EnFVs. The core principle of GAs involves iteratively improving a population of potential solutions to discover optimal or near-optimal outcomes.  
The key components of a GA encompass solution representation and encoding, fitness evaluation, selection, crossover, and mutation operators, as illustrated in Figure 1[24]. These components collectively drive the search process towards the optimal solution space.  
Solution representation and encoding are vital in GAs, especially in message routing optimization for EnFVs. Routes need to be represented in formats that capture essential problem characteristics [25]. Fitness evaluation plays a crucial role in GAs, as it assesses the quality of potential solutions based on defined objectives. In message routing optimization, the fitness function evaluates routes in terms of multiple objectives, such as minimizing  
Figure 1: GA Process.  
travel time, reducing energy consumption, and adhering to EnFV-specific constraints [19]. The function assigns a numerical value to each solution, indicating its suitability or fitness for the problem. Selection involves choosing the fittest individuals from the current population for reproduction, akin to the principle of survival of the fittest in natural selection [26]. Various selection strategies, such as tournament selection, roulette wheel selection, or rank-based selection, ensure diversity and balance exploration and the exploitation of the solution space. Crossover, inspired by genetic recombination in natural reproduction, combines genetic information from two parent solutions to generate offspring. In message routing optimization, crossover exchanges genetic material between the routes of two parents, creating new potential routes for the next generation. Mutation introduces random changes in the genetic material of solutions, exploring new regions of the solution space. It is crucial in maintaining diversity and preventing premature convergence to suboptimal solutions [14]. In message routing optimization, mutation involves altering the specific nodes or components of a route, enabling the exploration of alternative routes.  
The next generation of solutions is determined through selection, crossover, and mutation operators, creating a new population of potential solutions. This iterative process continues until a termination criterion is met, such as reaching a maximum number of generations or achieving a satisfactory level of fitness.
# Improving Passive Time Reversal Underwater Acoustic Communications Using Subarray Processing
## 3 Receiver Algorithms  
### Mc-Dfe  
The MC-DFE is very effective for removing ISI caused by multipath propagation. When the channel is unknown, the equalizer tap weights are determined by minimizing the mean square error (MSE) between the received data symbols and the recovered data symbols. Channel tracking is accomplished by combining adaptive recursive least squares (RLS) and a second-order DPLL [5]. The standard MC-DFE block diagram is displayed in Figure 1 and involves a bank of feedforward filters \(a_{i}[n]\) followed by a single feedback filter \(b[n]\).  
Figure 1: Multichannel decision feedback equalization (MC-DFE) block diagram.  
The soft output is  
\[\begin{array}{rcl}\tilde{d}(n)&=&p(n)-q(n)\\ &=&\sum\mathbf{a}_{k}^{{}^{\prime}}\mathbf{y}_{k}(n)e^{-j\hat{\theta}_{k}(n)}- \mathbf{b}^{{}^{\prime}}\hat{\mathbf{d}}(n).\end{array} \tag{4}\]  
The estimated error is  
\[e(n)=d(n)-\tilde{d}(n), \tag{5}\]  
where the known training sequence is \(d(n)\) when the system is in training mode, but is replaced by \(\hat{d}_{n}\), which is the decision result of \(\tilde{d}_{n}\) when the system is in decision feedback mode. The coefficients of feedforward and feedback filters and DPLL are updated by adaptive algorithms to minimize the output \(\text{MSE}=E\{|e(n)|^{2}\}\). The detail of MC-DFE is described in [1]. One major issue for MC-DFE is its complexity. The complexity is very high, especially for an array with a large number of elements [7].
# Interactive Visualization of Geographic Vector Big Data Based on Viewport Generalization Model
## 3 Materials and Methods
### Viewport Generalization Visualization Model
From Equation (4), \(R(p_{i})\) can be regarded as a spatial topology intersection operation. Then, tree indexing can be used, and the computational complexity of \(R(p_{i})\) in Equations (4) and (5) is generally \(O(\log N)\). The overall complexity of VGVM is shown as Equation (6). It can be seen that the cost is related to \(C\) and \(N\). \(C\) is the total number of pixels within the viewport, which can be regarded as a constant, and its value is related to the size of the viewport. In currently used visualization devices, \(C\) is generally no more than a million in magnitude. \(N\) is the total number of vector features. \[O(C\times\log N) \tag{6}\]  
Compared with Equation (2), the VGVM has performance advantages. First, when \(N\) is large, the total calculation time of VGVM, as shown in Equation (6), is less than that of the data-driven model. Second, as \(N\) increases, the complexity of Equation (2) increases linearly, while the complexity of Equation (6) increases by \(O(1/N)\). When \(N\) is very large, the growth tends to zero. Furthermore, if we design a reasonable index structure to further improve the solving speed of \(S(p_{i})\cap D\) in Equation (4), it will have better performance for BGVD visualization. In this process, in order to further improve the solving speed in Equation (4), a specific spatial index structure can be designed to rapidly generate pixel display values. The basic flow of the visualization strategy supporting VGVM is shown in Figure 1.