esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simple Approach for Optimizing the Offline Motion Preparation
### Workspace Evaluation
#### 4.2.3 Error Estimation  
Besides the linearization itself, several simplifications have been made so far. This concerns on the one hand the calculation of the Jacobian matrix. Here one-sided differences are used instead of central differences to save 25% of the transformations. This error is estimated to be negligibly small.  
It is well known that with a smaller step size the Jacobian matrix becomes more accurate when differentiating, but if the steps are too small then numerical issues can cause problems. To estimate the magnitude of this, the ratio between \(\lambda_{2}\) calculated with large \(\varepsilon\) (\(\varepsilon=2\) mm) and small \(\varepsilon\) (\(\varepsilon=0.1\) um) is shown in Figure 12. From the result a maximum error of about 0.5% in the realistic workspace is estimated.  
Figure 11: Distortion \(\lambda_{2}/\lambda_{1}\) in the workspace of the manipulator.  
Figure 12: Error when using large \(\varepsilon\) instead of small \(\varepsilon\) for calculating the Jacobian in the manipulator’s workspace.  
Further errors result from the calculation of \(\lambda_{2}\) in the workspace, Figure 10. If too few points (e.g., only the four corners of the workspace) are calculated, the resolution is too low, a minimum cannot be found with sufficient certainty. Depending on the selected grid, the global minimum may be located between the grid points. In order to stay safely below, its value is to be estimated. Therefore, the points (Figure 13, light red and red) surrounding the minimum (black) are used. Their maximum (red) should be selected and extrapolated beyond the minimum point (black). The value of the resulting estimated point (blue) is to be used for calculating an estimated minimum error. The finer the grid, the more accurately the minimum can be calculated; but this is associated with increased additional calculation effort. In our example, a 2.5 mm grid in \(x\) and \(y\) direction was used (about 30 points in \(x\) direction). The error results in being 0.23%.  
Splitting \(r_{\text{gt}}\)over the two axes in Equation (8) does not cause errors. Using this approach, only a square inside the inscribed circle in the ellipse is considered. Due to the smaller area tested, it is expected that this will result in at most 64% of all suitable set points being declared as good. In this case, the number of coefficients for a given motion accuracy will increase. In addition, the spline will be closer to the optimized target set point, the accuracy of the spline will be higher than required.  
Overall, the simplification results in a total estimated error of about 1%. The following calculations are therefore made with \(\lambda_{\text{2.corr}}=0.99\cdot\lambda_{\text{2.min}}=6.95\) rad/m. At axis level, \(\lambda_{\text{2.corr.ax}}=1/\sqrt{2}\cdot\lambda_{\text{2.corr}}=4.91\) rad/m will be used. With a known TCP accuracy, an axis accuracy can be calculated by Equation (7).
## 4 Examples
### Three-Bay Two-Story Framework
#### 4.1.2 Parameter Control for the Three-Bay Two-Story Framework  
As parameters \(t_{c}\) and \(\alpha_{0}\) are closely related, and both define a strategy for moving from the exploration phase to the exploitation phase that is unique from start to finish, they were considered constant in the PCPFAM procedure proposed in this work. The value of \(t_{c}=0.1\) was adopted to guarantee a sufficient margin of passage from the exploration phase to the exploitation phase. The value of \(\alpha_{0}=1.0\) was also chosen so the subpopulations could initially explore the entire design space in the exploration phase regardless of the values of the other parameters. Furthermore, the value of 2 was given to parameter \(m\), as it was found to have little effect on the results. However, parameter \(nt\) depends on the number of variables considered in each problem, affecting the entire process as \(t_{c}\) and \(\alpha_{0}\) do, so the value found in the previous sensitivity analysis was used. Finally, the proposed PCPFAM procedure was applied to the \(\gamma\) parameter, which greatly affected the efficiency and robustness results.  
Considering both semi-rigid connections EEP2 and rigid connections, and constant values \(\beta_{0}=1\), \(k_{max}=100\), \(t_{c}=0.1\), \(\alpha_{0}=1.0\), and \(m=2\), the PCPFAM procedure was applied to parameter \(\gamma\). The range of values for \(\gamma\) was [1; 50]. The evolution of parameter \(\gamma\) in each subpopulation for a run is shown in Figure 26. The accuracy and standard deviation results are shown in Table 28, and there were 50 independent runs.
# FA-VTON: A Feature Alignment-Based Model for Virtual Try-On
## 5 Experiments  
### Experimental Settings  
We conduct our experiments on the VITON [5] and VITON-HD [45] datasets. VITON is the most popular dataset used in image-based virtual try-on tasks. The VITON dataset consists of a training set containing 14,221 pairs of images and a test set containing 2032 pairs of images. Each image pair includes a target garment image and a person image, with both having a resolution of 256 \(\times\) 192. VITON-HD is the same as VITON, except that the image resolution is 512 \(\times\) 384.  
We train our model on the VITON dataset using the Adam optimizer. We first train the teacher network using the clothes image and the image of the person wearing the clothes. The parsing results are also utilized in this phase. Then, we train the student network. Each model is trained for 100 epochs with an initial learning rate of 5 \(\times\) 10\({}^{-4}\) and is linearly decayed after 50 epochs. The hyperparameters are set as follows: \(\lambda_{per}\) = 1.0, \(\lambda_{1}\) = 1.0, \(\lambda_{dis}\) = 0.2, \(\lambda_{sec}\) = 6.0, and \(\lambda_{cud}\) = 0.8. During testing, the reference person image and the target clothes image are provided as inputs to the student network to generate the output image. Unlike the training phase, additional inputs, such as human parsing results, are not used.  
We compare FA-VTON with several methods, including ACGPN [10], PF-AFN [6], SDAFN [30], FS-VTON [7], and DM-VTON [28]. All models are trained on the same VITONdataset by using the official codes provided by the authors to ensure the fairness of the experiments.  
We evaluate the similarity between the generated images and ground truth using three widely used metrics: the Structural Similarity Index (SSIM) [46], Learned Perceptual Image Patch Similarity (LPIPS) [47], and Frechet Inception Distance (FID) [48]. The SSIM and LPIPS are used for paired images, while FID is used for unpaired images.
# Steady-Speed Traffic Capacity Analysis for Autonomous and Human-Driven Vehicles
## 3 Methodology
### Calculation of Traffic Flow Parameters  
After obtaining the distances between two consecutive vehicles, hereafter referred to as \"Clearances\", the analysis of data began by comparing ACC-equipped and human-driven vehicles through illustrative figures. The relationship between clearances and the corresponding driving speeds was also depicted to better investigate the differences. Subsequently, other traffic flow parameters such as space headway, time headway, density, and flow, as defined in ref. [32] and shown in Figure 4, were calculated using the relevant equations as follows:  
\[\text{Avg.Space headway}[\text{m}/\text{veh}]=\frac{\text{Avg.Clearance }[\text{mm}]+\text{Vehicle length }[\text{mm}]}{1000}, \tag{1}\]  
\[\text{Avg.Time headway}[\text{s}/\text{veh}]=\frac{\text{Avg.Space headway }[\text{m}/\text{veh}]}{\text{Speed }[\text{m}/\text{s}]}, \tag{2}\]  
\[\text{Avg.Density}[\text{veh}/\text{km}]=\frac{1000}{\text{Avg.Space headway }[\text{m}/\text{veh}]}, \tag{3}\]  
\[\text{Avg.Flow }[\text{veh}/\text{h}]=\text{Avg.Density }[\text{veh}/\text{km}]\times\text{Speed }[\text{km}/\text{h}], \tag{4}\]  
Traffic flow and traffic capacity are distinct concepts in transportation engineering, where traffic flow refers to the actual movement of vehicles, and traffic capacity represents the maximum sustainable flow rate that a roadway or transportation facility can handle. It is important to note that, in our specific analysis, we have employed a unique perspective in which, as mentioned earlier, the shortest clearances using ACC setting 1 were considered in data collection with a multiple of controlled constant speed. Consequently, a maximum traffic flow was calculated under these specific conditions, and for the purpose of our study, this calculated maximum flow has been assumed to be synonymous with the term \"traffic capacity\".
## 2 Background
### Reliability Block Diagram
Consider a **series structure** (as the one depicted in Figure 4a) composed of \(n\)**independent** components, where \(p_{i}(t)=P\{x_{l}(t)=1\}\) are the functioning probabilities of blocks \(b_{i}\). These probabilities could be reliabilities or availabilities, for instance. The probability for the system to be operational is:  
\[P\{\phi(\textbf{x}(t))=1\}=P\{\phi(x_{1}(t),x_{2}(t),...,x_{i}(t),...,x_{n}(t) )=1\}=\prod_{i=1}^{n}P\{x_{i}(t)=1\}=\prod_{i=1}^{n}p_{i}(t)=1 \tag{2}\]  
Therefore, the system reliability is:  
\[R_{S}(t)=P\{\phi(\textbf{x}(t))=1\}=\prod_{i=1}^{n}P\{x_{i}(t)=1\}=\prod_{i=1 }^{n}R_{i}(t) \tag{3}\]  
where \(R_{i}(t)\) is the reliability of block \(b_{i}\). Therefore, a system with \(n\) components in series (as shown in Figure 4a) has reliability (at time \(t\)) equal to the product of the reliability of the blocks that compose it, as shown in Equation (4):  
Figure 4: RBD organized in (**a**) series; (**b**) parallel and (**c**) combined. \[R(t)=\ \prod_{i=1}^{n}R_{i}(t) \tag{4}\]  
For example, an RBD with three blocks (each having a reliability of 0.9) has reliability equal to 0.729. On the other hand, if a system, composed by \(n\) components, works if at least one component properly works, the respective system RBD is arranged in parallel. Hence, a system with \(n\) components (as shown in Figure 4b) has reliability equal to the complement of products of unreliabilities of all blocks at time \(t\) (as shown in Equation (5)):  
\[R(t)=\ 1-\prod_{i=1}^{n}(1-R_{i}(t)) \tag{5}\]  
For example, the reliability of a system with three blocks (each having its reliability set to 0.9) is equal to 0.999. Additionally, it is important to note how the organization of the blocks influences the reliability of the whole system. In addition to these two organizations, it is possible to combine blocks, creating other formats: series-parallel, parallel-series, bridge and \(k\)-_out-of-n_. These formats use Equations (4) and (5) to calculate the system reliability. For example, Figure 4c represents a RBD series-parallel where blocks \(B1\) and \(B2\) are in parallel, and \(B3\) is in series. First, it is necessary to solve the reliability of the parallel blocks and then the series one. Thus, if each block has reliability equal to 0.9, the reliability of the system is 0.891. In some cases, simplifying the structure function may not be an easy task. Therefore, a logic function may be adopted to simplify system's functions through Boolean algebra. Using the Boolean notation, a Boolean variable \(s_{i}\) is equivalent to \(x_{i}\), its complement \(\overline{s_{i}}\) is represented by \(1-x_{i}\), the value \(\phi(\mathbf{x})=1\) is represented by a \"true\" predicated \(\varphi(\mathbf{bs})\), and \(\phi(\mathbf{x})=0\) value by the respective complement \(\overline{\varphi(\mathbf{bs})}\). Besides, \(\Lambda\) represents \(\times\), and \(\
u\) is the respective counterpart of \(+\).
# Developing Quantum Trusted Platform Module (QTPM) to Advance IoT Security
## 2 Preliminaries
### Randomness Assessment  
A range of studies have explored the effectiveness of various tests in evaluating the randomness of random number generators. The numbers generated by the VIC-20 computer were found by [23] to be sufficiently random for practical use, using eight standard tests. Statistical distribution theory was applied by [24] to test the typical random number generator GGNRF, checking for independence and distributional properties. The need for resources to analyze the randomness of cryptographic RNGs was addressed by [25], introducing new metrics developed by NIST. An empirical test on an additive random number generator identified non-randomness when n is less than 16 [26]. To assess the quality of random number generators, statistical tests were discussed in [24; 27], including those for independence, distributional properties, and bit pattern recurrence. The empirical analysis involves evaluating RNG through practical experimentation, which includes measuring the statistical properties of the generated sequences, such as the mean, variance, and autocorrelation, and comparing them to expected values for true random sequences [25; 28]. The empirical spectral test is developed to assess the spatial uniformity of random number sequences [29]. In [26], empirical tests evaluated based on an additive random number generator found that uniformly distributed numbers can be produced with no significant serial correlation but exhibited non-randomness for specific parameters.
# Performance Enhancement of Consumer-Grade MEMS Sensors through Geometrical Redundancy
## 4 Realized Prototype of Redundant IMU
### Software Architecture  
With regard to the software architecture, firmware for SensorTiles, GNSS module and concentrator has been implemented through Mbed RTOS [36]; in particular, MbedCLI integrated development environment has been adopted to allow debugging facilities during the implementation stage.  
To make the prototype compatible with aeronautical applications and constraints, the inertial sensor of each tile must be sampled at rate not lower than 100 Hz. On the other side, iNemo can acquire acceleration and angular rate with a lowest sampling period equal to 150 us (as an example, for electronic image stabilization in mobile phone or camera applications). A proper tradeoff must be found between navigation and computational burden associated with the execution of the algorithm implementing the proposed method; a sampling period equal to 8 ms has thus been chosen.  
As regards the measurement operations, the concentrator exploits the channel selection lines of the SPI protocol to simultaneously provide SensorTile with a trigger event each for the measurement of triaxial acceleration and triaxial angular rate. It is worth noting that an ad hoc SPI firmware has been implemented in the Tiles to reduce as low as possible the communication time usually taken by the available drivers. Moreover, configuration registers of the iNemo are set in such a way as to update inertial measures with a sample rate of 416 Hz, thus assuring new values of accelerations and angular rates for each measurement request. Finally, full-scale values of \(\pm\)2 g and \(\pm\)125 dps have been chosen to gain the best sensitivity.  
The concentrator then polls each SensorTileTM individually to obtain the measured values, already rotated to be aligned with a reference frame. To reduce the transmission interval, the output code, expressed through two-bytes signed integer representation and provided by the 16-bit analog-to-digital converter integrated into the iNemo is transmitted for each quantity of interest [33]; this way, the whole set of six inertial measurands (three accelerations and three angular rates) requires only 12 bytes to be transmitted, with a nominal time interval equal to 10 ms for each SensorTileTM. Received data are then forwarded to the Micro SD-card for storage and to the personal computer for navigation processing; the whole needed time is equal to about 5 ms; the remaining 3 ms are exploited by a MATLABTM application running to carry out a prediction/correction cycle of the Kalman-based navigation algorithm. As stated above, the inputs of the navigation algorithm are the averaged inertial quantities, for each sensing axes. Finally, GNSS measures are also sent to the algorithm for the correction step.
# Recurrence Quantification Analysis as a Form of Postural Control Assessment: A Systematic Review
## 1 Introduction
### Embedding Parameters for RQA Analysis
#### 1.2.1 Time Lag Determination Methods  
Average Mutual Information Function  
[PERSON] and [PERSON] [36] developed a method for finding optimal time delays based on time-delayed coordinates that are as independent as possible of each other. They defined this relationship as the mutual information AMIF(x(t), x(t + tau)) between the original time series x(t) and the tau-shifted time series x(t + tau). Since mutual information is calculated for a time series and a time-shifted version of the same time series, it is called mutual auto-information or average mutual information (AMIF). To measure AMIF, a data histogram is created using bins, where pi is the probability that the signal has a value inside the i-th bin, p(tau) is the probability that x(t) is in the i-th bin and x(t + tau) is in bin j.  
\[\text{AMIF}(tau)=\sum_{i,j}p_{ij}(tau)log\left(\frac{p_{ij}(tau)}{p_{i}p_{j}}\right)\]  
[PERSON] and [PERSON] [36] proposed using the position of the first minimum of AMIF(x(t), x(t + _tau_)) as the optimal value of tau to obtain the most independent coordinates for time-delayed phase space embedding. However, AMIF may not have a local minimum. Therefore, other criteria were developed, such as the smallest value of tau for which the AMIF function falls below 1/e [35].  
Time Series Autocorrelation Method  
Autocorrelation (ACF) is the correlation of a signal with a delayed copy of itself as a function of delay. The lag is chosen as the first zero-crossing of the autocorrelation function for the data or, if there is no zero-crossing, at which the first local minimum of the ACF occurs [15].  
The AVD, like the autocorrelation method, involves comparing a time series to its lagged version at different time lags to determine the optimal time lag for analysis. It can be calculated by taking the absolute value of the difference between the corresponding values in the two series and then taking the average of those differences [38]. The time lag corresponding to the minimum average displacement can be considered the optimal time lag for analysis. Otherwise, the tau value at which the slope of the AVD curve decreases by 40% of the initial slope is selected, as recommended by [PERSON] et al. [38]. In summary, this method quantifies the extension of the reconstructed trajectory from the line of identity as a function of tau and seeks to achieve a balance between the error associated with redundancy and irrelevance.
# Advancements in Machine Learning-Based Intrusion Detection in IoT: Research Trends and Challenges
## 4 Deployment of the IDS
### Software-Defined Networks (SDNs)
[37] specifically highlighted the performance and scaling capabilities of their research, suggesting that high-throughput research centers could be beneficiaries. The paper compares the performance of their IDS system in a centralized vs. distributed context of IoT networks, and found that the IDS system tested uses fewer resources and is quite suitable for low-power devices, such as IoT devices. [PERSON]. [38] establishes the GADAD (Genetic Algorithm DDoS Attack Detection) system, which focuses on edge-based technologies in stateful SDN-based networks. The GADAD system employs tree-based learning techniques and is designed to be deployed on edge devices in IoT networks to detect both high- and low-volume DDoS attacks. There are three main phases: network traffic preprocessing, feature engineering, and learning. In the first phase, it captures network traffic data exchanged between sensors and the edge server using Wireshark, and flow features are extracted using Zeek. The flow features are effective in both high- and low-volume attacks compared to packet-based features. The system introduces feature and depth tuning, a dual method that reduces memory usage without compromising the system's detection capabilities. These trained models are then employed to detect and classify incoming network traffic data on the edge server. However, SDN networks introduce trade-offs, such as a centralized controller being a single point of failure. Additionally, as put in [10], the low-resource IoT devices are still a bottleneck despite improving SDN networks; hence, scaling issues can appear when low-latency response times require optimized and high-performance algorithms. Ref. [35] states that the standard SDN structure suffers from single point of failure (SPOF) limitations, a single controller, which also hinders scalability and performance.
###### Abstract  
Ecological restoration and climate change in the Loess Plateau region have become research hotspots. Climate change and anthropogenic activities have led to spatial-temporal pattern variations in vegetation and extreme climatic indices and meteorological factors. Therefore, obtaining a better understanding is necessary of the internal relations between vegetation and meteorological factors. In this paper, the interplay between vegetation index and various factors, including extreme climatic indices and meteorological factors, during a long-term time series is investigated using Mann-Kendall trend analysis, and Pearson correlation coefficient analysis. The mechanisms of interaction between vegetation growth and various factors in the Loess Plateau are then analyzed. Results reveal that (i) the rapid growth of vegetation during 2000-2015 has made a major contribution to the growth trend of the Loess Plateau in the past 33 years (1982-2015). During 2000-2015, the increase of vegetation may inhibit the increase of extreme warm index and the decrease of extreme cold index; (ii) a warm and dry climate developed with decreasing relative humidity and increasing temperature; (iii) the normalized vegetation index (NDVI) is strongly correlated with extreme climatic indices and meteorological factors, especially precipitable water vapor (PWV), with a correlation coefficient of 0.94; and (iv) the daily temperature range, diurnal temperature range and sunshine duration (SSD) exerted different time-delay effects on vegetation growth in the Loess Plateau. The above findings provide an essential theoretical basis for ecological policy formulation in the Loess Plateau.  
Loess Plateau; extreme climatic indices; meteorological factors; normalized vegetation index; time delay effect +
Footnote †: journal: Applied Sciences