esa-sceva/satcom-chunk-collection huggingface dataset:
# Secure Partitioning of Cloud Applications, with Cost Look-Ahead
## 5 Determining the Minimal Safe Partitioning
### Declarative Strategy for the Minimal Safe Partitioning
We indicate with \(\lambda\) the function that gives the labelling of a software component and \(\phi\) the function that gives the labelling of the domain needed by a software component. The function \(partitioning\) has the following invariant:  
\[\forall(d,safe,sw)\in P:\quad\forall s\in sw\quad\lambda(s)=(d,c)\quad c\geq d \quad\phi(s)=(d,safe) \tag{6}\]  
\[\forall(d,c,sw)\in P\wedge c\
eq safe:\quad\forall s\in sw\quad\lambda(s)=( d,c)\quad c\leq d\quad\phi(s)=(d,c) \tag{7}\]  
\[\forall(d,c,sw),(d^{\prime},c^{\prime},sw^{\prime})\in P:\quad d=d^{\prime} \wedge c=c^{\prime}\implies sw=sw^{\prime} \tag{8}\]  
\[\forall(d,c,sw)\in P:sw\
eq\emptyset \tag{9}\]  
Equations (6) and (7) indicate the labelling of software components and domains to satisfy the data-consistent and reliable-domain properties. Equation (8) indicates that no domains with the same labelling are admitted. Equation (9) indicates that empty domains do not exist. We can prove by induction that the function \(partitioning\) creates the minimal partitioning. **Base Case**  
\[partitioning([],P)=P\]  
an empty list of software components does not modify the partitioning. **Induction Step**  
\[P_{i}=partitioning([sw_{i}:sws],P_{i-1})\]  
\[P_{i}=\left\{ \begin{aligned} P_{i-1}\setminus\{(d,c,sw)\}\cup\{(d, c,\{sw\cup sw_{i}\})\}&\quad\text{if}\phi(sw_{i})=(d,c)\in P_{i-1}\\ &\\ P_{i-1}\cup\{(d,c,\{sw_{i}\})\}&\quad\text{otherwise} \end{aligned} \right.\]  
We assume, by the inductive hypothesis, that the invariant holds up to the \(i\)-th step, i.e., for the domain of components \([sw_{1},\dots,sw_{i}]\). It is easy to see that at step \((i+1)\) the current \(sw_{i+1}\) is suitably placed into an existing domain (first clause of partitioning/3) or inserted into a new one (second clause of partitioning/3) by following the rule above. Assume that after this step, the invariant does not hold, i.e., that the resulting domain is non-minimal. Then, as \(sw_{i+1}\) was correctly sorted, it means that some of the software in \([sw_{1},\dots,sw_{i}]\) were not. This implies that the invariant did not hold in some of the previous steps, which contradicts the inductive hypothesis. Note that the minimal partitioning is unique. At any step \(i\), the software \(sw_{i}\) must go in a domain having \(d\) and \(c\) equal to those of \(\phi(sw_{i})\). Given that we do not admit domains with the same labelling (Equation (8)), there is only one domain having the labels required by \(sw_{i}\). Thus, there is a unique way to determine the partitioning.
## 3 Target Tracking
### KCF Tracker
The kernel function \(k\) is used to compute the algorithm in terms of dot-products, as shown below:  
\[\varphi^{T}(x)\varphi(x^{\prime})=k(x,x^{\prime}) \tag{41}\]  
where all the dot-products between samples are stored in a \(n\times n\) kernel matrix \(K\), with elements:  
\[K_{ij}=k(x_{i},x_{j}) \tag{42}\]  
and the regression function \(f(z)\) can be expressed as:  
\[f(z)=w^{T}z=\sum_{i=1}^{n}\alpha_{i}k(z,x_{i}) \tag{43}\]  
The solution of this regression function can be given as:  
\[\alpha=(K+\lambda I)^{-1}y \tag{44}\]  
where \(K\) is the kernel matrix and \(\alpha\) is the vector of coefficients \(\alpha_{i}\), which express the solution in the dual space. By making \(K\) circulant, Equation (44) can be diagonalized as in the linear case, obtaining:  
\[\hat{\alpha}=\frac{\hat{y}}{\hat{k}^{xx}+\lambda} \tag{45}\]  
where \(\hat{k}^{xx}\) is the correlation kernel of \(x\) with itself in Fourier domain and \(\hat{\alpha}\), \(\hat{y}\) are the DFT of vector \(\alpha\), \(y\). For the kernel matrix \(K^{z}\) between all training samples (cyclic shifts of \(x\)) and candidate image patches (cyclic shifts of base patch \(z\)), each element of \(K^{z}\) is given by \(k(P^{i-1}z,P^{j-1}x)\). From Equation (43), the regression function can be computed for all candidate patches with:  
\[E=\underset{w}{\min}\sum_{i}\left(f(x_{i})-y_{i}\right)^{2}+\lambda\|w\|^{2} \tag{37}\]0  
where \(f(z)\) is a vector, containing the output of all cyclic shift of \(z\), which is the full detection response. The position where the output response takes the maximum value is the position of the target in a new frame. To compute Equation (46) efficiently, it is diagonalized as shown below:  
\[E=\underset{w}{\min}\sum_{i}\left(f(x_{i})-y_{i}\right)^{2}+\lambda\|w\|^{2} \tag{37}\]1  
where a hat \(\wedge\) denotes the DFT of a vector. In this paper, given the nonlinear Gaussian kernel \(k(x,x^{\prime})=\exp(-\frac{1}{\sigma^{2}}\|x-x\|^{2})\), we get:  
\[E=\underset{w}{\min}\sum_{i}\left(f(x_{i})-y_{i}\right)^{2}+\lambda\|w\|^{2} \tag{37}\]2  
where the kernel correlation can be computed by using a few DFT/IDFT and element-wise operations in \(O(n\log n)\) time. [PERSON] et al. [42] proved that the conversion from inverse operation of matrix in spatial domain to matrix multiplication in Fourier domain would greatly reduce the computational complexity and shorten the computation time. In the tracking process, considering the target variations, such as illumination, scale, occlusion, and deformation factors, the target apparent model and coefficient vector are updated after each frame [58], as shown below:  
\[E=\underset{w}{\min}\sum_{i}\left(f(x_{i})-y_{i}\right)^{2}+\lambda\|w\|^{2} \tag{37}\]3  
\[E=\underset{w}{\min}\sum_{i}\left(f(x_{i})-y_{i}\right)^{2}+\lambda\|w\|^{2} \tag{37}\]4  
where \(x_{t-1}\), \(x_{t}\) are the target model updated after the \(t-1\) and the \(t\) frame. \(\alpha_{t-1}\), \(\alpha_{t}\) are the coefficient vector updated after the \(t-1\) and the \(t\) frame. \(\eta_{t}\) is the learning rate.
## 3 Results
### Effect of Operating Parameters on Specific Energy of R\({}_{\rm VA}\)
#### 3.1.2 Effect of Cone index  
CI had a significant effect on SPE (F\({}_{2,80}\) = 183.19, \(p\) \(<\) 0.0001). The interaction of DO and CI affected SPE requirements (F\({}_{4,80}\) = 5.76, \(p\) = 0.0006). Figure 7 shows SPE to increase with the increase in CI. As CI was increased from 300-500 kPa, SPE increased from 0.19 to 0.33 (SE: \(\pm\)0.06) kWh ha\({}^{-1}\), 0.12 to 0.30 (SE: \(\pm\)0.05) kWh ha\({}^{-1}\), and 0.08 to 0.26 (SE: \(\pm\)0.03) kWh ha\({}^{-1}\) at \(u/v\) ratios of 1.7, 2.2, and 3, respectively, at the lowest DO of 20 mm. When CI was increased from 300 to 500 kPa, SPE increased from 0.44 to 0.73 (SE: \(\pm\)0.11) kWh ha\({}^{-1}\) for the \(u/v\) ratio of 1.7, from 0.41 to 0.70 (SE: \(\pm\)0.03) kWh ha\({}^{-1}\) for the \(u/v\) ratio of 2.2, and from 0.37 to 0.66 (SE: \(\pm\)0.07) kWh ha\({}^{-1}\) for the \(u/v\) ratio of 3 for the highest DO of 60 mm.
## 4 Finite Element Simulation Results  
### Modal Analysis  
The elastic modulus of steel is temperature-dependent, and under the combined effects of fire and wind load, the dynamic characteristics of the transmission tower-line system change with increasing temperature. Using the subspace iteration method in ABAQUS, modal analysis was conducted on a three-tower, four-line model at temperatures of ambient temperature, 400 \({}^{\circ}\)C, and 600 \({}^{\circ}\)C. The calculation results are presented in Table 2 and Figure 6. The results indicate that as the temperature increases, the modal frequencies of the transmission tower-line system gradually decrease. Under the condition of ambient temperature, the first modal frequency of the transmission tower-line system is 0.56 Hz, with a period of 1.79 s. When the temperature rises to 600 \({}^{\circ}\)C, the first modal frequency decreases to 0.42 Hz, the period increases to 2.38 s, and the structural natural frequency decreases by 23.81%. The second and third modal frequencies of the transmission tower-line system also exhibit a similar trend. This is attributed to the reduction in the elastic modulus of steel with increasing temperature, which in turn decreases the overall stiffness of the transmission tower-line system. This trend reflects that under fire conditions, the overall stiffness of the transmission tower-line system diminishes, leading to an increase in the fundamental period.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Modal Order**} & \multicolumn{2}{c}{**Ambient Temperature**} & \multicolumn{2}{c}{**400 \({}^{\circ}\)C**} & \multicolumn{2}{c}{**600 \({}^{\circ}\)C**} \\ \cline{2-7}  & **Frequency/Hz** & **Period/s** & **Frequency/Hz** & **Period/s** & **Frequency/Hz** & **Period/s** \\ \hline First Order & 0.56 & 1.79 & 0.51 & 1.96 & 0.42 & 2.38 \\ Second Order & 0.67 & 1.49 & 0.62 & 1.61 & 0.53 & 1.89 \\ Third Order & 0.84 & 1.19 & 0.79 & 1.27 & 0.71 & 1.41 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Natural vibration characteristics of transmission tower-line systems.  
Figure 5: Time-history curve of wind load.  
Figure 6: Natural vibration characteristics of transmission tower-line systems.
# A New Sensor System for Accurate 3D Surface Measurements and Modeling of Underwater Objects
## 1 Introduction
This is due to the relatively long exposure time necessary for acquisition. A typical temporal sequence length necessary for realization of point correspondence is ten images. Hence, without motion correction, the distance covered in the time needed to record ten images leads to a change or smearing of the projected pattern observed in a given pixel. [PERSON] et al. [35] introduced a novel motion compensation method for structured light-based scanning systems, where the projection unit actively takes part in 3D point calculation. Other techniques exploit the motion blur of the projected pattern for 3D shape estimation [36]. Recently, low-budget 3D reconstruction and merging was applied to underwater 3D seabed measurement in nearshore shallow waters using video streams of an action sport cam and structure from motion (SfM) technology [37]. Another interesting application of merged 3D point clouds was introduced by [PERSON] et al. [38] for boat shape measurement in air using a 3D hand scanner and photo-modeling technique. These new methodologies for 3D model generation with a moving camera or scanner, respectively, were introduced at a workshop on metrology for the sea [39]. Our aim was to investigate the application of a new stereo scanning systems supported by structured illumination for precise, continuous 3D capturing and modeling of underwater structures at medium-range measurement distances (up to 3 m) and fields of view of about 1 m\({}^{2}\). In the remainder of this paper, the development of an appropriate 3D sensor system is described; first, results are presented, as well as examples of measurements and the construction of 3D models of the captured objects.
## 3 Inversion Experiments and Results  
In this section, we present the results derived from inverting synthetic data and two forms of survey data to test the deep learning inversion method proposed in this paper. The training of the CNN was performed on an existing package, Tensorflow, using a TeslaP100 GPU card. Forward model generation, forward modelling, and inversion testing were all carried out on an office PC (Intel(r) CoreTM i7-8565U CPU @ 1.80 GHz, 8.0 GB RAM).
## 5 Data and Results
### Final Results
Due to the contrast and lighting conditions, both confidence values are set as \(HIGH\), and both sensors usually detect humans in the scene. The thermal infrared spectrum only shows problems when the human is close to the building's wall, while the human detection in the visible spectrum shows false negatives when humans are close to each other (as shown in Figure 7c) and when a human is covered in shadows far away from the camera. In the sequence _15\({}^{\circ}\)Cloudy_, temperatures of the humans are similar to the thermal readings of the elements in the scene and only motion information prevents the results of human detection in the thermal infrared spectrum from being lower. For example, Figure 7d shows how the human could not be detected in thermal infrared, since the thermal readings of his/her clothes are similar to the temperature of the concrete platform where he/she is sitting. Because of this fact, the contrast value \(\upsilon_{IR}\) of the frames composing this sequence is usually intermediate, making the confidence \(C_{IR}\) on the infrared spectrum to be set as \(MEDIUM\). However, the performance of the human detection in the visible spectrum is excellent, due to the illumination conditions of the scene, as shown in Figure 7d. These conditions confirm the confidence value on the visible spectrum to be assigned as \(HIGH\). Finally, sequence _28\({}^{\circ}\)Sunny_ shows a poor performance of the human detection in the thermal infrared spectrum, since the thermal readings of the human are very similar to the temperature of the remaining elements in the scene. An example of this situation is shown in Figure 7e. A hint to this problem is shown by the high value of contrast \(\upsilon_{IR}\) in the frames composing this sequence, which results in the confidence value \(C_{IR}\) of the thermal infrared spectrum being set to \(LOW\). This confirms the difficulty for distinguishing humans with the thermal infrared camera under these conditions. On the other hand, a lot of groups appear in this video. This circumstance causes the performance of the human detection in the visible spectrum to worsen. However, when individuals walk alone, they are detected without any problem, as shown in Figure 7e. The lighting conditions of the scene confirm these results, and the confidence value \(C_{V}\) is set to \(HIGH\).
# Enhanced Flow Control for Low Latency in QUIC
## 2 Related Works
Compared to prior works, the proposed scheme aims to reduce the transmission delay caused by flow control. The improved static allowance uses the allocated receive window fully. However, if the maximum receive window is smaller than the transmission rate, the transmission is delayed until the MAX_STREAM_DATA frame is received, because the improved static allowance also utilizes a fixed memory size. The autotuning allowance increases the receive window size to prevent transmission interruption. However, to determine an adequate maximum receive window, the autotuning allowance requires several RTTs, increasing the transmission latency. Moreover, for small data sizes, an adequate receive window will not be identified until transmission completion. The proposed fast autotuning allowance reduces the search time for finding an adequate receive window. The receiver increases the receive window exponentially if free buffer space is sufficient; otherwise, the receive window is not enlarged to avoid buffer overflow. The computational overhead is minimal because of the low complexity of the proposed algorithm.
# Research on Advancing Radio Wave Source Localization Technology Through UAV Path Optimization
## 3 Materials and Methods
### Estimation Method for Source Coordinates Using RSSI  
Based on the previously described method in Section 3.3, a normal distribution \(N(\mu,\sigma)\) is created using the \(\mu\) and \(\sigma\) values obtained from regression curves, which are optimized for ray-tracing simulations under LOS and NLOS conditions. The distribution of the training (tng) data used for estimation with RSSI is shown in Figure 7. Additionally, Figure 8 presents a comparison where the target (tgt) data are overlaid on the training data, confirming that there is minimal difference between the target data and the training data.  
Figure 6: In this study, a circular orbit with a radius of 100 m is primarily assumed for the UAV, as shown in Figure 6. This choice follows previous work by [PERSON] [8], demonstrating improved accuracy over random trajectories. Although this radius is fixed in the baseline setup, we later investigate the impact of varying the orbit radius on localization accuracy in Section 4.2.  
Figure 7: Distribution of RSSI values used in the training phase. These data form the basis of the location fingerprint database for RSSI-based localization.
## 2 Materials and Methods
### Data Analysis Method  
The statistical analysis of the data generated during the treatment process of ADHD participants was performed using SPSS for Windows 23.0, and a significance level of \(p<0.05\) was applied. This study calculated the minimum sample size through a prior power analysis. It was calculated based on an effect size of d = 0.8, a significance level of \(\alpha\) = 0.05, and a power of 0.8, which indicated that approximately 26 participants were required. However, 30 participants were recruited, ensuring that a sufficient sample size was obtained to detect clinically meaningful differences between the two groups. The means and standard deviations of the experimental results were analyzed using a two-sample \(t\)-test to verify the homogeneity of the baseline between the two groups and a paired \(t\)-test to compare the differences measured using the same participants. This method was selected because it is suitable for comparing differences in data measured using the same subjects [28]. Additionally, the test results for the FAIR, K-ARS, and K-CBCL scales in the experimental and control groups were analyzed using analysis of covariance to adjust for baseline differences and account for potential confounding variables, comparing the scores at 2 and 4 weeks between the groups [29]. Continuous variables were defined using their means and standard deviations and compared between groups using the two-sample \(t\)-test. When the normality assumption was not met, the Mann-Whitney U test was used to compare the median values between groups, and the [PERSON] rank-sum test was applied to compare the differences in group means [30]. For categorical variables, frequencies and proportions were defined, and [PERSON]'s chi-square test was used to compare groups [31]. A repeated measures analysis of variance (ANOVA) was applied to compare the means of three dependent variables (baseline, 2 weeks, and 4 weeks) for the same or matched participants, based on the study design [32]. Before performing the statistical analyses, the assumption of normality was tested using the Shapiro-Wilk test, and the assumption of homogeneity of variances was verified using [PERSON]'s test for equality of variances [33; 34]. After confirming that these assumptions were met, the appropriate statistical tests were applied, leading to valid and reliable results based on the study design and data characteristics. The validity of the study was evaluated using the modified intent-to-treat analysis, with per-protocol analysis conducted as a secondary method. For the validity evaluation, in cases of participant dropout or missing data before the completion of the clinical trial, the last observation carried forward method was used to replace the missing data with the most recent available measurement from the participant [35].
# Grain Sieve Loss Fuzzy Control System in Rice Combine Harvesters
## Material and Methods
### Working Principle of the Multi-Duct Cleaning System  
A cleaning test bench is shown in Figure 1. Some working parameters of the test bench, such as sieve opening, vibration frequency, and fan revolution speed can be adjusted separately. In the working parameters' adjustment process, a push rod is used to drive the relevant mechanism to fulfill the task of sieve opening and guide adjusting the plate angle. The fan speed and sieve vibration frequency can be controlled by adjusting the corresponding motors' shaft revolution. In addition, the cleaning throughput can be adjusted within 0.5-4.0 kg.s\({}^{-1}\) by controlling the vibration frequency of the electromagnetic vibration feeder. The test bench is also equipped with sensors to extract the airflow velocity, fan speed, and grain sieve loss. The working process of the multi-duct cleaning device is as follows: a return plate was added under the longitudinal axial flow threshing rotor to concentrate its threshed material and the tailings' return to the start of the cleaning shoe, and evenly distribute them. The grain pan and sieve have the same vibration frequency and amplitude. However, they have an opposite vibration direction angle to reduce the overall vibration. Thus, all of the threshed outputs can enter into the sieve surface in a uniform way. Then, at the joint action of vibration and airflow from different fan outlets, the grain will penetrate the sieve promptly. The designed working parameter adjusting mechanism is shown in Figures 2 and 3. The principle of the working parameter adjusting mechanism can be found in our patent (PCT/CN2015/074348). The adjustment range of the working parameters is shown in Table 1.  
Figure 1: Schematic diagram of the multi-duct cleaning device. 1. Human–Machine Interface (HMI), 2. Threshed outputs container, 3. Electromagnetic vibration feeder, 4. Grain pan, 5. Vertical grain auger, 6. Return plate, 7. Vertical tailings auger, 8. Framework, 9. Louver sieve, 10. Tailing sieve, 11. Sieve-opening adjusting mechanism, 12. Horizontal tailing auger, 13. Sieve-driven motor, 14. Return plate-driven motor, 15. Grain/tailing auger-driven motor, 16. Horizontal grain auger, 17. Woven screen, 18. Fan-driven motor, 19. Airflow deflector i adjustment mechanism, 20. Airflow deflector ii adjustment mechanism, and 21. Centrifugal fan.  
Figure 2: Working principle of the guide plate-angle adjusting system. 1. Direction converter, 2. Displacement sensor 1, 3. Push rod 1, 4. Airflow inlet adjust plate, 5. Displacement sensor 2, 6. Push rod 2, 7. Push rod 3, 8. Displacement sensor 3, 9. Joint bearing.