esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### 'Plate-Sole' Stiffness  
Figure 4 illustrates the composite stiffness of the midsole with embedded plates of varying thicknesses and curvature radii. For the 1 mm thick plate, increasing the curvature radius from 200 mm to 250 mm resulted in a 10.38% increase in composite stiffness, while a further increase to 300 mm raised the stiffness by 5.06%. For the 1.5 mm thick plate, the stiffness increased by 9.64% when the curvature radius was changed from 200 mm to 250 mm, and by 9.99% when the radius increased from 250 mm to 300 mm. For the 2 mm thick plate, the stiffness increased by 11.79% from 200 mm to 250 mm, and by 11.85% from 250 mm to 300 mm.  
Similarly, when varying the thickness of the plates, at a curvature radius of 200 mm, increasing the thickness from 1 mm to 1.5 mm resulted in a 46.03% increase in composite stiffness, while further increasing the thickness from 1.5 mm to 2 mm raised the stiffness by 25.42%. At a curvature radius of 250 mm, the stiffness increased by 45.05% when the thickness was changed from 1 mm to 1.5 mm, and by 27.88% when the thickness was increased from 1.5 mm to 2 mm. For a curvature radius of 300 mm, the increase in thickness from 1 mm to 1.5 mm raised the stiffness by 51.86%, while the increase from 1.5 mm to 2 mm led to a 30.05% rise in stiffness.  
Figure 4: Three-dimensional line graph and heatmaps of stiffness of plates and ‘plate-sole’ system. In three-dimensional line graph, hollow circles represent curvature radius of 200 mm, hollow triangles represent curvature radius of 250 mm, and hollow squares represent curvature radius of 300 mm. Red represents thickness of 1 mm, orange represents 1.5 mm, and yellow represents 2 mm. In heatmaps, deeper colors indicate greater stiffness.
## 3 Greedy Scanning Data Collection Strategy
### Data Collection
#### 3.3.1 Data Routing  
The purpose of data routing is to deliver sensory data packets to the mobile sink from source nodes. When source nodes sense data, they encapsulate the sensory data and \(RCN\) of the source node together into a data packet, as shown in Figure 7. These data packets are routed to the sink hop by hop among head nodes.  
When a node generates a data packet, it transmits the latter to its head node in the same grid cell. That means each head node collects data packets from source nodes which are firstly located in  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Criterion/Condition** & **Category** \\ \hline \(M_{r}^{\prime}=M_{r}+1\&M_{c}^{\prime}=M_{c}\) & \(UG\) \\ \(M_{r}^{\prime}=M_{r}-1\&M_{c}^{\prime}=M_{c}\) & \(DG\) \\ \(M_{r}^{\prime}=M_{r}\&M_{c}^{\prime}=M_{c}-1\) & \(LG\) \\ \(M_{r}^{\prime}=M_{r}\&M_{c}^{\prime}=M_{c}+1\) & \(RG\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: The criterion of neighbor table establishment.  
\begin{table}
\begin{tabular}{l l} \hline \hline \(UG\) & coordinate of the upside neighbor head node \\ \(DG\) & coordinate of the downside neighbor head node \\ \(LG\) & coordinate of the left side neighbor head node \\ \(RG\) & coordinate of the right side neighbor head node \\ \hline \hline \end{tabular}
\end{table}
Table 3: Neighbor table.  
Figure 7: The data packet structure of sensory data.  
the same grid. Then, these head nodes transmit the data packets to a neighbor head node. After the neighbor head node gets the data packets, it relays the data packets to its neighbor head node until the \(DN\) of the current head node is 0. Finally, the head node transmits the data packet to the sink, and the transmission of source data is completed. By checking the \(DN\), one head node can figure out the direction to transmission. Hence, based on the direction to transmission, the head node can locate the next hop neighbor head node from the neighbor table, as shown in Table 5.  
For example, if the \(DN\) of a head node \(A\) is 1, then node \(A\) checks \(LG\) column of its neighbor table (this column records the coordinate of the left neighbor head node). We assume the node recorded in \(LG\) column is node \(B\). Node \(B\) is designated as the next hop node, and the data packet is delivered to node \(B\). When node \(B\) receives the data packet, it also checks its \(DN\) and executives the same procedure. Finally, the data packet will reach to node \(X\) which \(DN\) is 0. This condition indicates that node \(X\) and sink are in the same grid. Hence, node \(X\) uploads the data packet directly to the sink. The routing process can be described by the flow chart in Figure 8, and we assume the head node which holds the data packet is \(NDP\) for short.
# Multi-Objective PSO with Variable Number of Dimensions for Space Robot Path Optimization
## 4 Results and Discussion  
The novel VNDMOPSO algorithm is validated here to prove its ability to solve as many different VND MO problems as possible. First, it solves various benchmark problems and the results are compared with two state-of-the-art VND MO algorithms, namely VLMOPSO and VNDGDE3. Then, VNDMOPSO is applied to five pathfinding problem instances. The results are compared with results obtained for VLMOPSO, VNDGDE3, and SCMOPSO algorithms. All the algorithms except for SCMOPSO are implemented using a standalone MATLAB-based toolbox called Fast Optimization ProcedureS (FOPS) [38] that is available online. FOPS is an in-house code developed and maintained at Brno University of Technology. All the comparative tests are set so that the compared algorithms use the same number of agents, \(N_{\text{A}}\), and iterations \(N_{\text{t}}\) to keep the comparisons fair. If not otherwise stated, the controlling parameters of VNDMOPSO are set as summarized in Table 2. The other algorithms use their default settings as described in [38] and in [30] (for the SCMOPSO). Next, the influence of the VNDMOPSO control parameters is evaluated using the benchmark problems.
## 3 Positioning Method
### Calculation of Image Area of Image Sensor and Location Change with Camera FOV  
The image area on the image sensor depends on the size of the LED luminaires, the distance between the LED light and the camera image sensor that we previously described in Section 3.1, the intensity of the LED light signal during transmission, and the number of LED lights in the camera FOV. The number of LED lights within the camera FOV changes with respect to the movement of the camera location in the indoor environment. Our proposed system enables estimation of the possible position by capturing image data from more than three LEDs within the camera FOV, which can change the location from that of single LEDs up to four LEDs for measuring the distance. Figure 8b shows the possible location estimation process done by changing the pixel value with respect to the camera FOV within the positioning environment, where the camera image sensor moves from location 1 to location 5. We assumed that the LED luminaires were distributed in a square on the ceiling; furthermore, the number of lights within the camera FOV depended on the transmission height, inter-LED distances, and FOV angle. The camera FOV was assigned with a circular shape, as shown in Figure 8b, where locations 1, 2, 3, and 5 of the camera image sensor were within two LED lights of the image on the images sensor. Meanwhile, locations 2 and 4 of the camera image sensor were within four LED lights of the image on  
Figure 8: (**a**) Determination of area of the LED light; (**b**) location estimate and change of LEDs within FOV of camera from one location to another.  
the image sensor and so on. However, if the number of LED lights is increased within the camera FOV, then the positioning accuracy will be increased.  
Basically, the LED light spreads from the ceiling to the ground in a full circular form. Consequently, at the specific distance from the LED light, the light intensities are equal inside each LED luminaire. Therefore, if the image sensor is located inside those locations and can detect the image area on the image sensor with a single LED luminaire, then an estimate of the degree to which an incorrect location causes more location errors can be determined. Meanwhile, attempting to eliminate the location error, another LED added as a reference transmitter with a single LED also could not provide the appropriate location information of the image sensor. Hence, a third LED luminaire is needed to deliver accurate positioning information of the image sensor. However, the location estimation problem and location error occurred because the three LED transmitters either did not lie lay in a straight line or lay in a straight line when the image sensor moved through the lighting environment. To compensate for those location errors in our proposed system, we added more than three LED transmitters on the ceiling. Additionally, we measured the coordinate distance from four LED luminaires simultaneously by using the image sensor and located the accurate position of the image sensor that enhanced the positioning accuracy.
## 6 Application to a Real Dataset
### Hyperparameter Optimization  
For the MCDO model, a systematic grid search was conducted to optimize key hyperparameters, including the number of layers, neurons, dropout rates, epochs, and batch sizes. The best-performing configuration consisted of a neural network with layers [30,60,120,180], using ReLU activation, dropout rates of [0.5, 0.4, 0.3, 0.2], trained for 50 epochs, with a batch size of 16. This setup achieved a robust performance with an RMSE of 18.54 and an \(R^{2}\) of 0.9379, as summarized in Figure 16.  
In comparison, the SARIMA model was optimized using a stepwise search strategy, selecting the configuration that minimized the Akaike Information Criterion (AIC). The optimal SARIMA configuration was ARIMA(3,0,0)(0,1,0)\({}_{12}\), which resulted in an RMSE of 18.57 and an \(R^{2}\) of 0.9377. Both models demonstrated strong predictive capabilities, with the MCDO model slightly outperforming SARIMA in accuracy metrics. \begin{table}
\begin{tabular}{l c c} \hline \hline \multirow{2}{*}{**Model**} & **RMSE** & **R\({}^{2}\)** \\  & **(Original Scale)** & **(Original Scale)** \\ \hline Model 1: constant amplitude & 25.27 & 0.943 \\ Model 2: varying amplitude & 24.49 & 0.947 \\ Model 3: mixed frequency & 17.59 & 0.973 \\ Model 4: phase shift & 25.27 & 0.943 \\ Model 5: nonlinear modulation & 25.22 & 0.944 \\ Model 6: noise variability & 24.73 & 0.946 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Forecasting performance of MCDO neural networks for each sinusoidal model using the AirPassengers dataset (original scale). Figure 15: Comparison of predictions for the 6 models on the training set.
# Intelligent Detection Method for Satellite TT&C Signals under Restricted Conditions Based on TATR
## 6 Experiment and Results
### Experimental Results and Analysis  
To validate the strength of our proposed method, we used the Sat_SD2023 dataset to perform performance validation on the proposed TATR model to demonstrate the effectiveness and superiority of the algorithm proposed in this paper. By evaluating the loss value of the model, the convergence and accuracy of the model during the training process can be determined. We first validate the effectiveness of the anchor-free framework.
# Interference Mitigation for GNSS Receivers Using FFT Excision Filtering Implemented on an FPGA
## 5 Results
### Addition of Interferences  
Before applying the interference mitigation algorithm, the interference signal should be added to the original raw data. \((J/No)\) is the value of the ratio of jamming to signal and it ranges from 0 to 130 dB-Hz. The jammer frequency is calculated by \((f_{0}+df_{i})\), which is within the interval of [5.5-7.5] MHz. The interference type is 0 for the CW jammer or 1 for the sweeping jammer. The setting contains different parameters such as the frequency sampling, which is 26 MHz; the skipping number of bytes; the number of channel; the time of processing (ms), etc. Figure 15 shows the original signal in blue with the added interference signal in red, with \((J/No)\) equal to 80 dB-Hz. It is obvious that the interference covers the whole original signal. The same type of jammer in Figure 15, which is CW shown in Figure 16, but with 4 CW jammers was added with different jammer frequencies of 5.5, 6.5, 6.5033, and 7 MHz, with \((J/No)\) of 40 dB-Hz.  
Figure 15: Frequency power spectrum of the raw sampled RF front-end data with added CW interference at \(J/No=80\) dB-Hz and freqj = 6.5033 MHz.  
The original signal with the added jammers is shown in Figure 16. It is noticeable that when we increase the (\(J/No\)) value, the interference shape becomes sharper, which means the distortion of the original signal increases as well. Figure 17 shows the original signal in blue with the added sweeping jammer signal in red. Markedly, the sweeping interference is stronger than the continuous wave (CW) because it uses a range of frequencies to attenuate the signal while CW uses a fixed frequency.  
Other different scenarios were tested by changing the value of the jammer frequency to ensure it is within [5.5 to 7.5 MHz]. For each scenario, a different value of jamming-to-noise (\(J/No\)) was tested to validate the value of the carrier-to-noise (\(C/No\)) in dB-Hz. Figure 18 displays the \((C/No)\) degradation with different types of jammers. The blue line represents a single CW jammer, and the red line represents five CW jammers. The green line represents a sweeping jammer. It shows that the sweeping jammer results have the worst \((C/No)\) compared to the CW single jammer and the five CW jammers. After applying the mitigation filter in Figure 19, the performance of \((C/No)\) increased. With a (\(J/No\)) value of more than 70 dB-Hz until 90 dB-Hz, the single jammer and multi-CW jammer lines dropped rapidly to reach \((C/No)\) of below 25 dB-Hz. The sweeping jammer signal reached less than 20 dB-Hz when the (\(J/No\)) value was 90 dB-Hz.  
Figure 16: Frequency spectrum of the raw sampled RF front-end data with added 4 CW interference at \(J/No=40\) dB-Hz and freqj = 5.5, 6.5, 6.5033, and 7 MHz.  
Figure 17: Frequency spectrum of the raw sampled RF front-end data with added sweeping interference at \(J/No=80\) dB-Hz and freqj = 5.5 to 7.5 MHz.
# A Construction Method for a Dynamic Weighted Protein Network Using Multi-Level Embedding
## 3 Experiments and Results
### Evaluation Metrics  
To comprehensively evaluate the superiority of the DWPNMLE method, the following metrics are used to evaluate the performance of different methods:  
(1) Overlapping score (OS). The OS evaluates the degree of individual match between the protein complexes recognized by the algorithm and the standard complexes. The recognized protein complexes are represented by set \(I\), while the standard complexes are represented by set \(S\); the OS is defined as follows:  
\[OS(I_{m},S_{n})=\frac{|I_{m}\cap S_{n}|^{2}}{|I_{m}|\cdot|S_{n}|}, \tag{22}\]  
where \(I_{m}\) is a recognized protein complex and \(I_{m}\in I\); \(S_{n}\) is a standard protein complex and \(S_{n}\in S\). The \(|I_{m}\cap S_{n}|\) indicates the number of intersections of the recognized protein complex \(I_{m}\) with the standard protein complex \(S_{n}\). According to most previous research, two protein complexes are matched if the calculation of \(OS(I_{m},S_{n})\) exceeds the threshold of 0.2, so we also set the threshold to 0.2.  
(2) Precision, recall, and F1-measure [36]. Precision represents the ratio of correctly recognized protein complexes to the total number of recognized protein complexes. Recall reflects the coverage of the algorithm in recognizing accurate protein complexes; the higher the recall value, the more real protein complexes the algorithm can recognize. The harmonic mean F1-measure of precision and recall is used to evaluate the overall performance of a protein complex prediction algorithm. These metrics are defined below:  
\[Precision=\frac{|N_{ci}|}{|I|}, \tag{23}\]  
\[N_{ci}=\mid\{i\mid i\in I,\exists s\in S,OS(i,s)\geq 0.2\}\mid, \tag{24}\]  
\[Recall=\frac{|N_{cs}|}{|S|}, \tag{25}\]  
\[N_{cs}=\mid\{s\mid s\in S,\exists i\in I,OS(s,i)\geq 0.2\}\mid, \tag{26}\]  
\[F1\text{-}measure=\frac{2\times Precision\times Recall}{Precision+Recall}, \tag{27}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Datasets** & **Proteins** & **Interactions** \\ \hline DIP & 4957 & 20,836 \\ BioGRID & 5628 & 56,328 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The Statistics of PPI Datasets.  
where \(|N_{c|i}|\) indicates the number of recognized complexes with at least one match to a known complex, and \(|N_{cs}|\) is the number of known complexes with at least one match to a recognized complex.
# Effect of Ti-B Grain Refiners on Wear and Corrosion of the A332 Alloy with Sr Modification
## 3 Results
### Corrosion Analysis  
Figure 14 shows the open circuit potential. All samples have shown a similar behavior regarding the open circuit potential. The average E\({}_{\text{ocp}}\) calculated for the last 5 min of the measurement is shown in Table 5. A steady measurement means a steady state corrosion process, which is desired prior to any further analysis. It can be seen a slight tendency to reduce the values of open circuit potential (V) in the Al5 Ti1B through time, an almost stable condition with small variations in the sample with Al5 Ti2B refiner and a more significant variation in the as-cast sample.  
Figure 15, shows Tafel plots for all the analyzed samples. The as-cast sample, which consists of A332 without refiner has shown, using ASTM G102 [24], a better corrosion resistance when compared to sample Al5 Ti2B. Table 5 shows that the refiner used decreases corrosion resistance for Al5 Ti2B. However, the open circuit potential and corrosion potential were slightly improved in samples refined with Al5 Ti2B. Nevertheless, the current density increased, making it less resistant. Despite of the increase in the current density, pitting formation was observed at higher corrosion potential for both grain refiners. The refiner Al5 Ti1B has preserved most of the corrosion resistance of the aluminum alloy while increasing pitting corrosion resistance.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Sample** & **Rp (\(\Omega\))** & **Ba** & **Bc** & \
\begin{tabular}{c} **Icorr** \\ **(\(\mu\)A/cm\({}^{2}\))** \\ \end{tabular} & **Ecor (V)** & **Eocp (V)** &
\begin{tabular}{c} \(\eta\) \\ **(V)** \\ \end{tabular} & **Corrosion Rate** \\ \hline As Cast & 14,315 & 0.06434 & 1.2661 & 3.29 & \(-\)0.7039 & \(-\)0.6610 & 0.0429 & 0.034 \\ Al5 Ti2B & 7305.6 & 0.05561 & 1.8894 & 12.1 & \(-\)0.6015 & \(-\)0.6408 & \(-\)0.0393 & 0.126 \\ Al5 Ti1B & 10,653 & 0.05926 & 0.8459 & 1.85 & \(-\)0.6484 & \(-\)0.6237 & 0.0247 & 0.019 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Corrosion results for different conditions.  
Figure 14: Open circuit potential from as-cast condition without grain refiner; grain refiner Al5 Ti2B; grain refiner Al5 Ti1B.  
In Figure 16, it is possible to see how the pitting grows across the materials. The Al5 FeSi phase works as an anodic site in corrosion reaction. For all the tested samples, it is possible to observe that the pitting either has to grow along the needles or to dodge it. For Al5 Ti2B, it is possible to observe that pits are closer, which causes the anodic region created by them to overlap, which could be the reason of improvement in circuit potential and corrosion potential. Visually, Al5 Ti1B has not drastically changed when compared to Al5 Ti2B; however, an improvement is shown in the electrochemical corrosion. In the case of the as-cast, the observed pits were more superficial, even the pitting formation starting at lower values of corrosion potential.
# A Systematic Review of Cybersecurity Risks in Higher Education
## 9 Analysis and Discussion of Cyber Risks and Countermeasures in HE
### Data Loss  
Irrecoverable data loss is a dreaded risk. Ransomware encrypts data and demands a ransom to decrypt it. Ransomware can enter the network via different channels, for example, phishing emails, software vulnerabilities, BYOD, and other hacking attacks. Ransomware attacks are particularly severe when networks are poorly segmented, as the attacker can, manually or automatically, traverse significant parts of the network unhindered and encrypt data. Furthermore, if the information is not backed up, or the backups are poorly protected, ransomware can lead to irrevocable data loss. Multiple universities have already ended up paying the ransom in the face of a data loss caused by ransomware. In 2020 alone, known cases include Maastricht University paying 250 thousand Euro in January ([[https://nltimes.nl/2020/01/24/maastricht-univ-paid-eu250k-ransomware-hackers-report](https://nltimes.nl/2020/01/24/maastricht-univ-paid-eu250k-ransomware-hackers-report)]([https://nltimes.nl/2020/01/24/maastricht-univ-paid-eu250k-ransomware-hackers-report](https://nltimes.nl/2020/01/24/maastricht-univ-paid-eu250k-ransomware-hackers-report)) (Visited 1 November 2020)), University of California paying 1.14 million dollars in June ([[https://www.bbc.com/news/technology-53214783](https://www.bbc.com/news/technology-53214783)]([https://www.bbc.com/news/technology-53214783](https://www.bbc.com/news/technology-53214783)) (Visited 1 November 2020)), and the University of Utah paying 457 thousand dollars in August ([[https://attheu.utah](https://attheu.utah)]([https://attheu.utah](https://attheu.utah)).  
edu/facultystaff/university-of-utah-update-on-data-security-incident/ (Visited 1 November 2020)). Consider the asset lists in Tables 4 and 5, how much did it cost to produce these assets and how much should be spent on adequate protection? There is also no guarantee that the attackers will restore data upon received payment.  
_Countermeasures:_ Adapting a sufficient information security framework to minimize the information security risk at an academic institution is essential. [PERSON] and [PERSON] [4] highlights in their paper that university that had adapted the ISO/IEC 27001 were more successful in counteracting data loss and information security risks. Data loss can also occur through human or system error combined with inadequate security routines and backup. Taking backup is a well-known security measure that is easily overlooked. For example, all of the Turkish Universities had backup, but only one of six had tested restoration from backup [4].