esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Analysis
### Method Validation
The 2-Norm error expression is:  
\[L_{2}=\frac{\left\|\hat{g}_{k}(t)-g_{k}(t)\right\|_{2}}{\left\|g_{k}(t)\right\| _{2}} \tag{42}\]  
where \(\left\|\cdot\right\|\) is the 2-Norm of the function, and \(\hat{g}_{k}(t)\) is the \(k-th\) estimated component signal. Figure 3: Analysis results. (**a**) The estimated modes by VSME (blue: estimated modes; red: estimation errors). (**b**) The estimated modes by VMD. Figure 6 shows the calculated values of RMSE and 2-Norm of the estimated component signal under different SNR conditions. When the SNR is 12 dB, the values of RMSE is less than 0.02, and 2-Norm does not exceed 0.2; when the SNR is more than 20 dB, the values of RMSE and 2-Norm tend to decline steadily and are less than 0.05. In this section, we simplify the problem by extracting the interested component of a simulated signal, and verify the reliability of VSME algorithm. It uses the same mode definition as VMD, especially using Wiener filter to detect the signal mode. The difference is that the number of modal  
Figure 4: Analysis results with SNR = 12 dB. **(a)** The estimated modes by VSME. **(b)** Frequency of estimated component signal extracted by VSME. **(c)** Absolute error of estimated component signal frequency extracted by VSME. \[f_{ig1}(t)=\cos\!\left(2\pi\!\left(0.3t^{2}+12t+0.6\right)\right) \tag{37}\]0 Analysis results with SNR = 18 dB. **(a)** The estimated modes by VSME. **(b)** Frequency of estimated component signal extracted by VSME. **(c)** Absolute error of estimated component signal frequency extracted by VSME. components is determined before VMD calculation, and VSME only extracts the mode of interest through its center frequency. VMD uses iterative method to determine all modal components, and the calculation can be stopped until all modes reach the optimal response. Therefore, this property of VMD reduces the convergence rate of calculation. In addition, the performance of VMD is greatly impacted by the number of modal components, which is given at the beginning of calculation. However, VSME only searches for the component of interest, thus its computational efficiency is not affected by modal number. Without setting the property of specific mode in advance, VSME can extract the interested mode component from a mixed signal, such as the defect signal detection of buried pipeline. The results showed that VSME can achieve better performance and higher calculation efficiency.
# Dynamic Analysis of a Delta Parallel Robot with Flexible Links and Joint Clearances
## 3 Results and Discussions
### Scenario 4  
In this subsection, we analyze the cumulative influence of friction in the spherical joints and elasticity of the rod elements. We compare and analyze the cumulative resulting deviations with the sum of the deviations identified in Scenarios 1 and 2 to identify the coupling effect between these two factors.  
Figure 14 shows the kinematic behavior of the Delta robot with the elastic elements and the joint friction, highlighting both the deviations of the effector motion from the ideal case (red, solid line) and the differences from the case of summing the separate effects of the two factors (blue, dashed line). For the characteristic point displacement (Figure 14a), it can be observed that the cumulative effect of the factors leads to a deviation (in absolute value) of up to a maximum of 4.23\(\cdot\)10\({}^{-6}\) m (compared to a maximum of \(\sim\)4\(\cdot\)10\({}^{-6}\) m for the case of summing the effects), up to 4.2\(\cdot\)10\({}^{-4}\) m/s for velocity (Figure 14b) compared to the maximum of 3.5\(\cdot\)10\({}^{-4}\) m/s for the additive case, and up to \(\sim\)6 m/s\({}^{2}\) for acceleration (Figure 14c) compared to the maximum \(\sim\)6 m/s\({}^{2}\) for the case of summing the effects. These  
Figure 12: Kinematical deviation in Scenario 3 for (**a**) displacement; (**b**) velocity and (**c**) acceleration.  
Figure 13: Driving torque deviations in Scenario 3 for (**a**) arm A; (**b**) arm B and (**c**) arm C.  
results highlight that the kinematic effects of the two factors (friction in the spherical joints) and the elasticity of the elements) are not additive.  
Similar to the motion case, the cumulative effect of the two factors results in a decrease in the maximum values of the driving torque deviations compared to the additive case (Figure 15). Therefore, it can be concluded that these factors have no significant coupling effect. Friction (with less significance) does not affect the shape of the deviation curve, but rather contributes to better curve shapes for the torque deviation values.
# Elastic Hop Count Trickle Timer Algorithm in Internet of Things
## 2 The Trickle Timer Algorithm  
Three configuration parameters make up the conventional trickling algorithm: the minimum interval size (Imin), the maximum interval size (Imax), and the redundancy constant (k). Moreover, it keeps track of three variables: the size of the current interval (I), a counter (c), and the current interval time (t) [16; 17].  
An interval belongs to each vertex. Imin is the beginning of the interval, while Imax is the end. There are sub-intervals within each main interval. Istart = Imin is at the start of the sub-interval, and Iend = Istart \(\times\) 2 is at the end. When the initial subinterval's time is up, a new subinterval begins until the main interval Imax [18] is reached. As illustrated in Figure 1, the vertex checks each time if it has reached the maximum of the interval. This demonstrates the processes involved in the conventional trickle algorithm's operation.  
Controlling the flow of control messages across the network is the primary goal of the traditional trickle algorithm. It maintains track of the expected time for a control message to traverse among vertices.  
Figure 1: The conventional trickle algorithm for each vertex.  
On the other hand, the conventional trickle timer method has several flaws, such as an extended listen-only period. In a traditional trickle timer, the listen-only period is equal to half of the interval. A vertex cannot transmit any messages during this time, but it can listen for and receive messages from its neighbors. Counter c is raised by one each time a fresh message is received. After the first half of the period has gone, a random number t is selected, enabling a node to communicate. Moreover, a vertex cannot transmit directly. Vertex does not broadcast until counter c, which contains the number of received messages, equals or exceeds a certain value (k). Otherwise, when counter c is smaller than k, it sends DODAG information object (DIO) messages. The interval is then doubled. Imax continues until the main interval. More precisely, Figure 2 illustrates the rules of each interval of the trickle timer algorithm.
## 5 Evaluation
### BER Analysis  
We conducted two types of experiments to evaluate the bit error rate performance of our system: (a) BER under different types of interference, and (b) BER under selective mapping of signal and shutter pixels. We calculated the BER as the ratio of total number of bit decoding errors to the total number of transmitted bits per trial. The BER experiments involved, in general, transmitting a random stream of 30,000 bits and logging the decoded bits at the receiver. Each experiment trial was repeated 5 times, BER was computed per trial and the average BER is reported. Unless otherwise specified, the BER values reported in this paper refer to the average BER over 5 experiment trials. We chose LED 1 as the desired transmitter and LED 2 as interference. The LEDs were modulated using the baseband signal from the USRP, where a pulse waveform at a specific (generation) transmit frequency was input to the LEDs which mapped a 1 to pulse HIGH and 0 to pulse LOW. Since the operation was in baseband, the transmit frequency is essentially equal to the transmitter data rate. The data rates were thus chosen as per the experiment goals:  
* Goal A: The 100 bits/sec is chosen arbitrarily, as the primarily goal of this experiment is to validate the additive nature of optical signal at the photodiode receiver.
* Goal B: We chose operation at (500 k, 1 M, 2 M) bits/sec rates and measured the BER and PER at different shutter configurations at those rates for our proposed pixelated LCD architecture. The 2 Mbps (2 MHz) limit for evaluation was due to the limited operable range of the LFTX/LFRX USRP daughterboard. In our future work we intend to evaluate our system for Gbps range using SLD Laser type transmitters and using FPGA (Field-programmable Gate Array) computing nodes.
# Enhancing Route Guidance with Integrated V2X Communication and Transportation Systems: A Review
## 2 Research Methodology  
A systematic review of the literature was carried out in several databases related to the research topic. The main aim of the review is to identify different RGS requirements and objectives, as well as suitable V2X technologies that could be used in RGS architectures. The research in this paper was carried out using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology [15]. The phases of the review are detailed in what follows.
## 2 Design Issues of Drone Routing for Drone-Based Delivery Systems
### Weather Conditions  
Weather consideration is crucial to ensure drone-based delivery during an emergency. Bad weather conditions, such as high wind speed and rainfall, can significantly affect drone flight. Therefore, the weather data are considered major pre-requisites to ensure reliable drone performance. For example, when the wind speed is significantly higher or rainfall increases, it is better to discontinue the flight, otherwise, the drone would incur significant damage, which would increase the overall cost. Thus, depending on the periodical weather data, such as wind and rain, the system can decide whether it should continue the mission or recall the drone to the depot [37].
## 2 Results and Discussion  
### Results  
#### 2.1.1 Animal Population  
A total of 70 patients with lameness in the legs were assessed for eligibility. Only 53 of them presented a hip problem, from which 14 were not included, as they did not meet the inclusion criteria. Finally, 39 animals were screened, and randomized into their corresponding group. Four were lost to follow up, 1 in the aMSCs group and 3 in the PRGF group (Figure 1).  
Animals were recruited from January 2002 to January 2003. Animals included in the study, attended clinic visits at the time of randomization (baseline) and at 1, 3 and 6 months after receiving the treatment.  
The mean age and weight of the patients was 53 \(\pm\) 43 months (age range: 8-135 months), and 34.9 \(\pm\) 12.8 kg (weight range: 18.3-66.2 kg) in the aMSCs group and 93 \(\pm\) 35.5 (age range: 18-66) and 36.5 \(\pm\) 10.6 kg (weight range 20-62.8 kg), in the PRGF group. Twenty-four of the patients were male, 10 in aMSCs and 14 in PRGF, and 11 were female, 8 in the aMSCs group and 3 in the PRGF group (Table 1).
# A Novel Encryption Scheme in Ship Remote Control against Differential Fault Attack
###### Abstract
In the selection of stream cipher and block cipher, a stream cipher such as RC4 is bit-to-bit encryption, and the change of a single bit can be easily seen in the ciphertext. Therefore, it is not suitable for hard disk encryption, which means that it is not suitable for encryption of a ship's historical state database. If the stream cipher is used for remote control commands and the block cipher is used for other parts, this will increase the complexity of the ship CPS and may increase other unforeseen risks and vulnerabilities. Therefore, the whole ship CPS uses the same encryption algorithm, that is, SM4 block cipher.This paper applies the SM4 algorithm to encrypt data in the border security gateway to meet the stability and data security requirements of intelligent ship CPS. With the aim of tackling the problem of DFA in the symmetric encryption algorithms, the application scheme and optimization scheme are designed. The rest of this paper is organized as follows:  
1. Preliminaries: We introduce SM4 encryption algorithm and specific model of DFA;
2. Prevention of DFA: It is proposed to use the WGAN-GP to generate S-boxes dynamically with good cryptographic indicators to replace the original S-boxes, to confuse the differential distribution table. An algorithm optimization scheme is proposed. The combined transform is further applied after the round encryption, so that the attacker cannot recover the correct encryption key through the output ciphertext obtained by DFA;
3. Results and discussion: We analyze, using the CPS of an intelligent ship as an example, the data security threats in ship-shore communication and evaluate the S-boxes. Before and after applying the optimization scheme, we compare the plaintext of the control instruction data frame with the ciphertext to verify the scheme's effectiveness in preventing DFA. In addition, some safety suggestions on hardware and management are put forward;
4. Conclusions.
## 2 Materials and Methods
### Seasonality Metrics Extraction  
In order to explore the behavior that may be masked by analysis of means, especially temporal patterns, we undertook an analysis of seasonality for the vegetation classes in the biomes. We generated time series of vegetation-specific \(\mathrm{SIF_{d}}\) for each biome to inspect the interannual variability of \(\mathrm{SIF_{d}}\) measurements (Figure S11). Based on this inspection, we determined that the Pampa DBF data had too many missing consecutive months for the study period to be reliable. We then applied the TIMESAT algorithms to each of the other vegetation class' \(\mathrm{SIF_{d}}\) time series for each biome to calculate seasonality metrics [47; 138]. TIMESAT was designed to analyze time series data from satellite sensors and generate seasonality metrics such as start of season, end of season, peak of season, season length, base value, maximum value, amplitude value, rates of increase and decrease, large (total) and small (pulse) integrals [138; 139] (Figure 2). The time series of \(\mathrm{SIF_{d}}\) for each vegetation class within each biome were smoothed using the median of a moving window by more than one standard deviation. After the time series were smoothed, we used the TIMESAT double logistic functions fitting method setting. The average of the seasonality metrics for seasons detected over the three-year study period (n = 2) in each time series was calculated to represent annual metrics for \(\mathrm{SIF_{d}}\) seasonality of each vegetation class in each biome. A sample diagram of raw and fit data is shown in Figure 2. The algorithm determines the base as the average of the left and right minimum values, and the amplitude as the difference between the base value and the maximum (labels d and g, respectively, Figure 2). The time for begin and end of season are determined using 0.5 of the amplitude from the left and right, respectively, following methods for determining seasonality parameters in the literature [47; 56; 140].  
From TIMESAT results, the per-season base integrated \(\mathrm{SIF_{d}}\) and the pulse integrated \(\mathrm{SIF_{d}}\) were analyzed to quantify annual \(\mathrm{SIF_{d}}\) productivity for each vegetation class in each biome. The length of season (LOS) in days was calculated for each vegetation class in each biome. The mean LOS for a biome was calculated as the average of the \(\mathrm{SIF_{d}}\) LOS of DBF, EBF, GRA, SAV and WSAV \(\mathrm{SIF_{d}}\) within that biome. We examined differences in means of LOS among biomes utilizing the Kruskal Wallace test and Pairwise Wilcoxon Tests. Additionally, we used a generalized linear model (GLM) to determine if length of \(\mathrm{SIF_{d}}\) season would explain the variation found in the total integrated \(\mathrm{SIF_{d}}\).
## 1 Introduction
Hence, in order to differentiate them, we replace \(k\) with \(h\) to represent the number of misdiagnosed nodes in the t/k diagnosis. For any integers \(n\geq 3\), \(k\geq 2\), we use \(BC_{n,k}\) to denote a k-dimensional BCCC with n-port switches. In this paper, we first explore the g-extra connectivity and g-extra conditional diagnosability of \(BC_{n,k}\). Next, based on the conclusion of the largest connected component in g-extra connectivity, we study the t/k-diagnosability of \(BC_{n,k}\) and propose a t/k diagnosis algorithm under the MM* model for \(1\leq k\leq n-1\). The major contributions are as follows:  
1. The g-extra connectivity of \(BC_{n,k}\) is \((g+1)(k-1)+n\), where \(n\geq 3\), \(k\geq 2\) and \(0\leq g\leq n-1\). 2. The g-extra conditional diagnosability of \(BC_{n,k}\) is \((g+1)k+n\) under the MM* model, where \(n\geq 3\), \(k\geq 2\) and \(1\leq g\leq n-1\). 3. \(BC_{n,k}\) is \([(h+1)(k-1)+n]/h\)-diagnosable under the MM* model, where \(n\geq 3\), \(k\geq 2\) and \(0\leq h\leq n-1\). 4. We give an \(O(N(n+k-1))\) t/h diagnosis algorithm, where N is the number of vertices in \(BC_{n,k}\). Provided the number of faulty nodes \(|F|\leq(h+1)(k-1)+n\), the algorithm can correctly identify all nodes except at most k nodes undiagnosed. The remainder of this paper is organized as follows. Section 2 introduces the BCCC structure and related knowledge of graph theory. Section 3 evaluates the g-extra connectivity of \(BC_{n,k}\); Section 4 establishes the g-extra conditional diagnosability of \(BC_{n,k}\) under the MM* model; Section 5 determines the t/k-diagnosability of \(BC_{n,k}\) under the MM* model; Section 6 presents the t/k diagnosis algorithm, analyzes its time complexity and proves its correctness and validity; Section 7 draws a conclusion.
## 4 Data Simulation  
To test the performance of the algorithm presented in Section 3, we resorted to simulation. Realistic Medium Earth Orbits (MEOs) were simulated via the software Earth Parameter and Orbit System--Orbit Computation [9], developed and provided by the German Research Center for Geosciences. The software applies up-to-date, complex dynamical models to generate realistic trajectories of Galileo satellites. The orbits were simulated with a 30 s time step for a period of 10 days. A scheduler was implemented to generate links between satellites and ground stations using available orbital data. The ISR and GSR observations are generated every 30 s. The scheduling used to define which links occur at a given epoch is planned such that satellite-to-satellite links have a persistence of at least 1 min, whereas the ground-to-satellite links have a persistence of at least 15 min. This implies that approximately every minute, the links between satellites change, and every 15 min, a ground station links to a different satellite. Further details about the link scheduling are provided in [7, 8]. The true range measurements were generated by computing the Euclidean distance (the norm term of (1)) between two satellites and between a satellite and a ground station. Gaussian noise and biases were then added to the true ranges to create observations according to stochastic Model (1): \(\sigma_{\mathrm{ISR}}=1\) mm and \(\sigma_{\mathrm{CSR}}=50\) mm. To emulate systematic error components, constant biases, randomly distributed in the interval 1-10 cm, were added to both ISR and GSR observations for the entire duration of the links.
## 4 Definition of System Output and Optimization Goal  
Referring again to Figure 1, the output vector \(\mathbf{z}\) defines the optimization objective and depends linearly on the state vector \(\mathbf{x}\), which collects all the generalized displacements resulting from the finite element discretization. This relationship is expressed as:  
\[\mathbf{z}=\mathbf{Cx}. \tag{31}\]  
A notable advantage of employing a norm minimization strategy is its ability to address multiple objectives--represented by the components of the output vector \(\mathbf{z}\)--simultaneously through a standard scalar minimization procedure. This approach effectively transforms a multi-objective optimization problem into a single-objective one. Such scalarization techniques are widely recognized for their efficacy in simplifying complex optimization tasks while preserving the integrity of the original multi-objective problem [25].  
Regarding the components of the output vector \(\mathbf{z}\), a few feasible options are outlined below.
## 4 ML-Based Models for GDS Generation and Matching
### Training and Testing Processes  
After the training and testing process of the models on our geofences datasets, the suitable ML model parameters were discovered and are briefly discussed in this section. First are the classical machine learning models. In the SVM model, we have C equals 1.0, kernel: rbf, and gamma: scale. The kNN with n_neighbors is equal to 5. Then there is a random forest model with n_estimators equal to 100, max_depth: none, and max_features: auto.  
Second are the deep learning models. The ANN model consists of three layers: input, hidden and output classifier dense layer with (10-8-1) neurons with 10 input features. Each layer has a ReLU activation function except the output with a sigmoid one recommended for binary classification. The optimization algorithm and the loss function are the Adam optimizer and the binary cross-entropy, respectively. The training epochs equal 150 with early stopping that is applied to prevent overfitting of the training process. In the CNN model, the difference in the preprocessing step changes from the StandardScaler to the MinMaxScaler [35]. For each value in a feature, the MinMaxScaler subtracts the minimum value in the feature and then divides it by the range. The range is the difference between the original maximum and the original minimum. MinMaxScaler preserves the shape of the original distribution. The required range for the feature returned by MinMaxScaler is 0 to 1. To make the data ready as an input to the CNN layers, we reshaped them from an array of 10 by 1 to an array of 5 \(\times\) 2 \(\times\) 1 in the case of Wi-Fi metrics, 9 \(\times\) 2 \(\times\) 1 in case of magnetics data and 14 \(\times\) 2 \(\times\) 1 in case of using all the features. The model has three layers of convolutional 2D layers with (64-128-256) filters, a kernel of 2 \(\times\) 2 then 1 \(\times\) 1 and the input size has dimensions 5 \(\times\) 2 \(\times\) 1 in the case of Wi-Fi metrics. We used Conv 2D instead of Conv 1D due to reshaping the data as a 2D image in the CNN models after using the TSMF approach on the time series data collected. This adjustment increased the accuracy by 2% to 3% using our dataset. The activation functions are ReLU. Due to the small input size, this CNN model does not have max-pooling layers. Finally, we will flatten the output of the CNN layers, feed it into two fully-connected dense layers with (100-50) neurons and then to a sigmoid layer for binary classification with training epochs also equal to 150 with early stopping applied. Both ANN and CNN had batch sizes equal to 32. Some other parameters used were the validation split with a value of 0.1 and model checkpoints.  
During the implementation and testing, we used TensorFlow and Keras library [36] for deep neural networks and Scikit-learn [37] for the classical machine learning models implementation and the typical machine learning operations.