esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Results
### Effect of Vibration on 1C Discharge Capacity
#### 3.1.3 Kolmogorov-Smirnov (K-S) Test for 1C Capacity Distribution  
In the case where the population distribution function is completely unknown or only its form known but its parameters unknown, the hypothesis test is generally performed in order to infer some of the properties of the population. In this study, the K-S test is adopted to test whether the capacity of the samples was in accordance with the normal distribution. Based on the cumulative distribution function \(F_{n}(c)\) and the specified distribution function \(G(c)\), the tested statistic is constructed as follows:  
\[D_{n}=\max(|F_{n}(c)-G(c)|)\leq D_{n,\alpha}, \tag{8}\]  
The null hypothesis is expressed as follows:  
\[H_{0}:F_{n}(c)=G(c), \tag{9}\]  
where \(D_{n}\) represents the critical value, \(n\) represents the number of the total samples, and \(\alpha\) represents the significant level. For a given value of \(n\) and \(\alpha\), by looking up the quantile table of Kolmogorov distribution the critical value \(D_{n,\alpha}\) can be obtained to satisfy \(P\{D_{n}\leq D_{n,\alpha}\}=1-\ \alpha\); then, if \(D_{n}\) is less than \(D_{n,\alpha}\), the null hypothesis is accepted, otherwise it is rejected.  
For the capacity distribution of samples before vibration, by taking advantage of MATLAB R2012a, \(D_{n}\) is obtained as 0.1711, and for the given significant level \(\alpha=0.05\), \(D_{n,\alpha}\) is also obtained as 0.2379 by looking at the table. Obviously, it can be seen that \(D_{n}\) is less than \(D_{n,\alpha}\), so the null hypothesis cannot  
Figure 8: The distribution function and theoretical normal distribution function of the capacity samples after vibration.  
Figure 7: The distribution function and theoretical normal distribution function of the capacity samples before vibration.  
be rejected. In other words, it is believed that the capacity distribution of samples before vibration obeys the normal distribution \(N\) (2988.9, 24.17942).  
Similarly, for the capacity distribution of the samples after vibration, \(D_{n}\) is obtained as 0.2065, and for the given significant level \(\alpha=0.05\)\(D_{n,\alpha}\), is obtained as 0.2379. Obviously, it can be also seen that \(D_{n}\) is less than \(D_{n,\alpha}\), so the null hypothesis cannot be rejected; in other words, it is also believed that the capacity distribution of the samples after vibration obeys the normal distribution \(N\) (2920.1, 29.32572).
## 3 Methodology
### Accuracy Metrics
The computation time of the prediction model for the environment to be checked in real-time should be considered, along with the accuracy of the prediction. The ANN terminates the operation when the indicator specifying the error between the measured value and the predicted value reaches a certain level. Table 17 lists the computation time analysis results of the initial coefficient of the performance prediction model developed using ANNs and SVMs. The ANN model has a shorter computation time than the SVM. A real-time prediction is possible because both models were calculated within one minute. The performance of the initial predictive model was improved by optimizing the structure and parameters of the predictive model.
# Cybersecurity and Medical Imaging: A Simulation-Based Approach to DICOM Communication
## 3 Methodology
The SOP Classes play a categorical role in delineating various types of medical studies or objects, each tailored to a specific imaging modality such as CT scanners or MRI machines. They detail the required attributes and structural elements crucial for accurate representation of data. Intimately connected to this are the concepts around Transfer Syn  
Figure 1: Dependency graph for DICOM/PACS communication. taxes, which encompass the encoding principles governing the compression and formatting of DICOM data. These principles ensure seamless interoperability across a diverse array of devices. During communication, Presentation Contexts come into play by establishing agreements between devices regarding the supported SOP Classes and Transfer Syntaxes for the duration of the communication session. These fundamental concepts, intricate in their complexity and significant in their role, are poised for a more profound exploration later in this section. As presented in Figure 1, to establish the association between the AE and the PACS, in this research we employed _Pynetdicom_[45]. _Pynetdicom_ is a Python implementation of the DICOM networking protocol that enables communication between different DICOM entities. It provides the necessary functionalities to establish association, transfer DICOM data, and manage network connections. In terms of data security during transmission, both Orthanc and _Pynetdicom_ support encryption. Encryption is a crucial aspect of securing sensitive medical data during transmission to ensure privacy and prevent unauthorized access. The use of encryption adds an extra layer of protection to the data being transferred between the AE and the PACS, reducing the risk of potential security breaches.
## 3 Application
### Timed CP-Net Models of the RBS
#### 3.1.1 RBS Timed Physical CPN Model
The arcs linking the failure transitions and the repair places contain as well the variable **r**, the various components 'failure times, **Ped_MFT**,**MC_MFT**,**Res_MFT**,**EM_MFT**, **Disc_MFT**,**Cal_MFT**,**BP_MFT**,**Type_MFT**,**SS_EFT**,**DC_EFT**,**Bat_EFT**,**ECU_EFT**,**ADA_SFT**,**OS_SFT**,**IO_SF**,**wait**, **waitrep**, and a Boolean equal to **false**. A transfer function is defined at failure transitions to calculate the repair time needed for each component using Erlang distribution. If a repair operation occurs in **component name repair** place of type **RBS_Repair** (MC repair place in the case of the master cylinder), and then a repair transition is enabled, after the repair time **waitrep**, the output arc of the repair transition communicates the information to the place **RBS status**, mentioning that the component is repaired and returning a **true** Boolean value. The output of the repair places contains the variable **r**, the different failure times, **wait**, **waitrep**, and the repair time **waitrep**. The restart of the RBS is ensured via the restart transitions which are executed instantaneously; we assume that the system restarts instantly after a repair. Figure 4: Master cylinder: timed CPN physical model. The restart transitions are identified as **SComponentNameR**, and, in the case of the master cylinder, is **SMCR**.
# Optimization of DLTS Hinges for the Assembly of the Solar Arrays of a Communication CubeSat
## 3 Computational Modeling  
### Generation of Finite Element Model  
The FEA was utilized for the simulation of the DLTS hinge model. Before starting the static simulation, the model was meshed by employing a quadrilateral shell element, and RF\({}_{\text{s}}\) (reference nodes) were determined and positioned at the four edges of the DLTS. Pure bending was accomplished by establishing two RF\({}_{\text{s}}\) which were connected to the edges of the DLTS end cross-section, as visualized in Figure 5. This connection occurred by employing multi-point constraints, where definite equal and opposite angular displacements were applied [17]. One of the RFs was enabled to rotate around the bending axis, while the other  
Figure 4: (**a**) Positive folding/equal sense bending of DLTS; (**b**) reverse folding/opposite sense bending of DLTS [11].  
Figure 3: Geometric configuration of the DLTS hinge [24].  
was set to freely rotate around the axis and translate across the entire length of the DLTS. During the folding process, if RF\({}_{1}\) and RF\({}_{2}\) are positioned at \(90^{\circ}\) and \(-90^{\circ}\) displacement, respectively, the folding is determined to be positive. On the other hand, if RF\({}_{1}\) and RF\({}_{2}\) are set at \(-90^{\circ}\) and \(90^{\circ}\) displacement, then a reverse folding occurs. A positive folding was selected for the achievement of the optimum folding and deploying process of the DLTS hinge. The deploying process was accomplished by reversing the directions of the rotational displacement of the RF\({}_{1}\) and RF\({}_{2}\) to \(-90^{\circ}\) and \(90^{\circ}\). Thus, the DLTS hinge was rotated back to its initial straight configuration.  
A mesh sensitivity study (Figure 6) was conducted to retrieve the suitable quadrilateral shell element. The range of the element size was examined from 0.075 mm to 0.0175 mm, where the convergence was noticed between the 0.1 mm and 0.075 mm value. These values showcased similar maximum Von Misses stress (\(\sigma_{\rm v}\)) with a percent difference of 0.74%. Thus, the slope did not fluctuate in this range, indicating a consistent convergence. The appropriate element size was determined to be 0.1, with a maximum \(\sigma_{\rm v}\) of \(2.32\times 10^{3}\) N/mm\({}^{2}\) as the slope began to moderately decrease and the mesh was generated effectively after 0.075 mm.
## 4 Conclusions  
This is the first study to investigate the potential use of a solar dryer under Moroccan environmental conditions, to dry a very perishable fruit (_Arbutus unedo_ L.) as an eco-friendly, low energy-intensive drying technique to be adopted by small farmers in remote rural areas. The study fully described the drying kinetics of the indirect forced convection solar dryer (effective diffusivity, activation energy, total energy usage). Also, the effect of the drying process on the phenolic content was investigated for the first time using the liquid chromatographic separation technique. The main highlights of the present study are as follows: Based on these results, we can conclude that the selection of higher temperature resulted in faster drying rates, which is translated to a faster dehydration process, with important economic benefits for farmers. At lower temperature (60 \({}^{\circ}\)C), the drying period was longer and didn't benefit extensively from the solar radiation. Moreover, the temperature was limited at 80 \({}^{\circ}\)C to avoid any further degradation of phenolic compounds and, therefore, preserve the original color, original flavor and nutritional quality of the dried strawberry tree fruits. In the temperature range of 60-80 \({}^{\circ}\)C, the effective moisture diffusivity varied between 1.5147 \(\times\) 10\({}^{-9}\) and 2.7046 \(\times\) 10\({}^{-9}\) m\({}^{2}\)/s. It increased proportionally as the temperature increased. The activation energy recorded a value of 2203.62 kJ/kg.  
The electrical heating consumption was less than 5%. This emphasis on this solar dryer is viable, eco-friendly, efficient, and can be considered an affordable alternative to conventional open sun drying.  
The Midilli-Kucuk drying model described well the solar drying kinetics of _Arbutus unedo_ L. fruits.  
Higher temperature resulted in a decrease from 15.54% (60 \({}^{\circ}\)C) to 62.78% (80 \({}^{\circ}\)C) in total phenolic compounds content.  
Consequently, for optimal preservation of bioactive compounds and maintaining the quality of the dried fruits, it is recommended to adopt the investigated drying conditions that have been defined for the convective solar dryer. Overall, the solar dryer can be applied for industrial purposes especially by farmer cooperatives in rural areas for drying harvested strawberry tree fruits as it consumes least time and energy while extending the shelf life of a very sensitive and perishable seasonal fruit.  
Conceptualization, A.B., S.E. and R.L.; methodology, A.B., S.E., O.M., R.O. and R.L.; software, A.B., A.S., R.O., N.E., S.C. and A.I. validation, A.B., S.E., O.M., H.H. and R.L.; formal analysis, A.B., R.O. and S.E.; investigation, A.B.,N.E. and R.O.; resources, O.M., R.L. and S.E.; data curation, A.B., N.E. and R.O.; writing--original draft preparation, A.B. and R.O.; writing--review and editing, S.E., A.S., R.L. and O.M.; supervision, S.E. and O.M.; project administration, S.E.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.
## 2 Materials and Methods
### Wavelet Transform
#### 2.7.8 Algorithms Compared in This Study  
In view of the above considerations, we selected nine algorithms of PAC computation for the benchmark LFP data, and a related but distinct set of nine algorithms for the 3-back EEG data.  
Algorithms for Benchmark LFP Data  
All coupling calculations for the benchmark data are within-channel.  
All the algorithms from Table 1 were used to compute comodulograms for the benchmark data over the frequency ranges of 4-20 Hz with a 0.5 Hz step for phase and 20-200 Hz with a 5 Hz step for amplitude (slightly modified from the scripts deposited by [PERSON] et al.). The maximum PAC was then selected in the windows of 4-10 Hz for phase and 30-200 Hz for amplitude.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Letter in Figures** & **Short Algorithm Name** & **Extraction of Oscillations** & **Width Parameters** & **Epoch Selection** & **PAC Measure** & **Normalization** \\ \hline A & basic & two-way FIR filter & & 4, 20 Hz & no & KL-MI & no \\  & & & two-way & & & & \\ B & Butterworth & Butterworth filter & 4, 20 Hz & no & KL-MI & no \\  & & (butter, filtfit) & & & & & \\ C & Butterworth & two-way & 4 Hz, & & & & \\  & adaptive & Butterworth filter & 2 f\({}_{\text{p}}\) + 4 Hz & no & KL-MI & no \\  & & delay-corrected & & & & & \\ D & eegfiltnew & one-way FIR filter & 4, 20 Hz & no & KL-MI & no \\  & & (pop\_eegfiltnew) & & & & & \\ E & wavelet & & Morlet wavelet & 6, 4 cycles & no & KL-MI & no \\  & & transform & & & & & \\ F & wavelet adaptive & Morlet wavelet & adaptive cycles as in & & & & \\  & transform & & PACTools & no & KL-MI & no \\ G & normalized & two-way FIR filter & 4, 20 Hz & no & KL-MI & 200 ‘split-swap’ \\ H & MVL normalized & two-way FIR filter & 4, 20 Hz & no & MVL-MI & 200 ‘split-swap’ \\ I & simulated epochs & two-way FIR filter & 4, 20 Hz & 200-2250 ms & KL-MI & no \\ \hline \end{tabular}
\end{table}
Table 1: Parameters of the algorithms used to compute PAC in the benchmark LFP data.  
Algorithms for the EEG Data with the 3-Back Task  
In the following descriptions, 'Fz-Pz' means that theta phase was extracted from channel Fz, and gamma amplitude, from channel Pz.  
All algorithms from Table 2 were used to compute the comodulograms for the n-back EEG data over the frequency ranges of 3-9 Hz with a 0.5 Hz step for phase and 20-70 Hz with a 5 Hz step for amplitude, with maximum PAC selected in the windows 4-8 and 30-60 Hz, respectively.
# Malicious Traffic Detection on Tofino Using Graph Attention Model
###### Abstract  
With the surge of malicious traffic in networks, existing detection methods struggle to balance real-time performance and efficiency. Data plane programmability, as an emerging technology, offers rapid control loops that can more effectively detect and mitigate network attacks. This paper presents Maltof, a malicious traffic detection method based on P4 programmable switches, aimed at enhancing the accuracy and efficiency of real-time detection. First, Maltof leverages the P4 programming language to embed a rule-based random forest model into the Tofino data plane of the switch, enabling quick feature matching to efficiently filter out potential malicious traffic. Subsequently, Maltof runs a lightweight Edge-based Graph Attention Network model on the CPU data plane of the switch, performing in-depth analysis on suspicious packets identified in the initial screening, learning and capturing complex relational features in network traffic to further determine whether malicious behavior is present. Through this staged detection mechanism, Maltof significantly improves performance while maintaining high detection accuracy, enabling efficient online detection of malicious traffic and ensuring timely and effective identification and mitigation of network threats.  
malicious traffic detection; data plane programmability; P4 programmable switch; Edge-based Graph Attention Network 2020 ac academic Editor: [PERSON] Received: 9 May 2025  
Revised: 22 June 2025  
Accepted: 23 June 2025  
Published: 26 June 2025  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Malicious Traffic Detection on Tofino Using Graph Attention Model. _Appl. Sci._**2025**, 15, 7179, [[https://doi.org/10.3390/appl51537179](https://doi.org/10.3390/appl51537179)]([https://doi.org/10.3390/appl51537179](https://doi.org/10.3390/appl51537179))  
**Copyright:** 62025 by the authors.  
Liconsec MDPL, Basel, Switzerland.  
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license  
([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 2 System Architecture and Hardware Layout
### Hardware Architecture of the System  
The hardware architecture of the system is divided into three core components, as shown in Figure 4:  
Figure 3: Camera layout of this system. A, B, a, b, c, and d are the locations of the cameras.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Baseline** & **Location** \\ \hline I & & A, B \\ II & 2 m & a1, a2, b1, b2, c1, c2, d1, d2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Camera layout.  
Figure 2: Schematic diagram of binocular camera monitoring range. L and R are the locations of the cameras; \(\theta\) is the horizontal field of view angle; d is the baseline distance; x is the length of the monitoring blind zone.  
1. Management side: The YOLO target detection model, which is specially designed to identify and locate objects in the image, is trained on the PC. After the training is completed, the model will be deployed to the edge computing device.
2. Data acquisition: IP cameras installed in the transport vehicle body are responsible for capturing the environmental image of the vehicle in real time. Through real-time transport protocol (RTSP) and power over Ethernet (PoE), these image data are efficiently transmitted to network switches and then routed to edge computing devices.
3. Data processing and display: Edge computing devices quickly and accurately calculate and process the transmitted data. The processed information is converted into an intuitive visual format and displayed on a dedicated monitoring platform for real-time viewing and analysis by drivers or managers.
# Credible Navigation Algorithm for GNSS Attack Detection Using Auxiliary Sensor System
## 5 Experimental Analysis
### Results of Data Set \(\widetilde{\Psi}_{4}\)  
Figure 17 shows the position error of ACNA, EKF and RIO after GNSS jump and slow-change attacks (data set \(\widetilde{\Psi}_{4}\)). After GNSS attacks on data set \(\widetilde{\Psi}_{4}\), the 1\(\sigma\) positioning accuracy of the GNSS increases to 11.80 m. Figure 18 shows the corresponding movement trajectory of the three algorithms under GNSS attacks. Their east, north and relative positioning accuracy (\(\sigma_{\text{east}}\), \(\sigma_{\text{north}}\) and  
It can be seen from the results that EKF is unable to detect GNSS jump attacks or slow-change attacks and its \(1\sigma\) positioning accuracy increases to \(10.65\) m, with an improvement of \(10\%\) compared with the GNSS. The RIO algorithm can detect jump attacks but not slow-change attacks in time. Its \(1\sigma\) positioning accuracy is \(6.75\) m, which is improved by \(43\%\) compared with GNSS. On the other hand, the ACNA algorithm can detect both GNSS jump attacks and slow-change attacks in time. Its positioning accuracy is \(0.81\) m, with an improvement of \(93\%\) compared with the GNSS.  
In contrast, the navigation performance of \(1\sigma\) positioning accuracy for the ACNA algorithm is better than the other two algorithms, no matter whether GNSS jump attacks occur or GNSS slow-change attacks occur.
# Evaluating the Vulnerability of Several Geodetic GNSS Receivers under Chirp Signal L1/E1 Jamming
## 2 GNSS Use in Geodesy and Jamming
### Receiver Position and Dispersion of Coordinates While Jamming
This reduces the first term to \((1-r)\sigma_{N}^{2}\). Meanwhile, \(\sigma_{N}\) (and consequently \(s\)) is less than \(\sigma_{I}\) for at least a factor of 100. This means that the exponential function in the second term can be approximated to 1 within the interval of integration with the error of order \(x^{2}/\sigma_{N}^{2}\), which can easily be neglected. A similar reasoning can be applied to the calculation of \(u\). With these simplifications, the above formula can be reduced to:  
\[s^{2}=\frac{(1-r)\sigma_{N}^{2}+2\frac{r}{\sqrt{2\pi}\ \sigma_{I}}\frac{(3s)^{3} }{3}}{1-r+\frac{r}{\sqrt{2\pi}\ \sigma_{I}}6s}. \tag{13}\]  
Although the above formula could be solved numerically for a particular \(r\) and the ratio \(\sigma_{N}/\sigma_{I}\), it is useful to get an analytical approximation for the solution. Since \(s\approx\sigma_{N}\), a new variable \(h\) will be introduced such that \(s=(1+h)\sigma_{N}\). Assuming both \(h\) and \(\sigma_{N}/\sigma_{I}\) are small and linearising the above expression in both terms (that includes neglecting all the terms \(ho_{N}\)), an expression for the approximate solution of the above equation can be found:  
\[h=\frac{6}{\sqrt{2\pi}}\frac{r}{1-r}\frac{\sigma_{N}}{\sigma_{I}}. \tag{14}\]  
Since \(\sigma_{I}\) is at least 100 times greater than \(\sigma_{N}\), the resulting \(s\) varies from \(\sigma_{N}\) by an order of 1%, which can again be neglected. The above reasoning gives a good justification for using such a method for the mean value and standard deviation calculation. Once the mean values were obtained, the position discrepancies were defined as:  
\[\delta H_{n}=\sqrt{\left(n_{n}-\overline{n}\right)^{2}+\left(e_{n}-\overline{ e}\right)^{2}} \tag{15}\]  
and:  
\[\delta V_{n}=\left|v_{n}-\overline{v}\right| \tag{16}\]  
for the horizontal and vertical error respectively.