esa-sceva/satcom-chunk-collection huggingface dataset:
# Visuo-Haptic Simulations to Understand the Dependence of Electric Forces on Distance
## 4 Data Analysis and Results
### Student \(t\)-Tests and Significance  
To determine if the learning gains presented in Tables 5 and 6 were statistically significant, Student \(t\)-tests were performed for the complete questionnaire and for each question in the tests. A comparison of paired sample means was performed for the complete five-question questionnaire, and also for the three-question questionnaire that resulted from removing questions \(Q1\) and \(Q2\), which already had zero learning gains (see Table 6). For both questionnaires, the grading scale was re-scaled to 0-100 points.  
In Table 7, the two-tail \(p\)-values corresponding to the comparison between \(<Post>\) and \(<Pre>\) values are included. The null hypothesis was:  
\[H_{0}:<Post>=<Pre>\]  
The pre-test and post-test averages and standard deviations as well as the average absolute and relative learning gains, \(<G>\) and \(<G_{rel}>\), are also included in Table 7.  
Figure 10: Comparison of average pre-test and average post-test grades for the five studied sections (EM1, EM2,...), and for individual questions \(Q3\), \(Q4\), and \(Q5\), in a scale 0–100.  
Figure 9: Comparison of average pre-test and average post-test grades for the five studied sections, considering only questions \(Q3\), \(Q4\), and \(Q5\), in a scale 0–100.  
It can be seen from Table 7 that the learning gain between the average post-test and average pre-test is statistically significant for both questionnaires (\(p\)-value \(<\) 0.05), thus the null hypothesis \(H_{0}\) is rejected. Comparing the three-questions questionnaire with the five-questions questionnaire, it can be seen that for the three-question questionnaire the average pre-test and post-test values are lower, but the absolute learning gain is larger (17.4 points). The relative learning gains are similar for both questionnaires (0.472), and the \(p\)-values are zero up to four significant figures also for both questionnaires.  
From Table 6, it can be observed that the learning loss for question \(Q3\), noticed previously, is not statistically significant (\(p>0.05\)). This suggests that overall, the use of a visuo-haptic simulator had no impact on improving or worsening students' understanding of the dependence on the distance of the electric force between point charges.  
On the other hand, the learning gains for questions \(Q4\) and \(Q5\) are very good, in particular for the latter. The use of the visuo-haptic simulators had a positive impact on improving the understanding of the distance dependence of electric forces between a point charge and a line distribution (\(Q4\)) or a plane charge distribution (\(Q5\)). The statistical significance provided by the low \(p\)-values (\(p<0.05\)) confirms this result. The effect on question \(Q5\) is particularly remarkable: most students obtained the correct answer for the plane charge distribution in the post-test.
## 2 Sample Delivery Methods  
### Actual Standard Sample Delivery Methods  
A major concern when performing SFX, especially TR-SFX as a higher number of datasets are required, is the large sample number that is needed. Based on our experience, for a static dataset, the average number can vary between a few hundreds of microliters to milliliters of crystal suspension with around 25% (\(v/v\)) crystals, based on hit rate, sample quality and injection method. This is especially relevant for megahertz repetition rate X-ray facilities operating with bunch patterns, such as the EuXFEL, and is an important challenge for sample production laboratories, which limits the scope of sample availability. At the moment, the AGIPD 1M is one of the few detectors able to operate at a megahertz rate with the unique pulse pattern of the EuXFEL [11; 12]. Sample injection in a vacuum at such repetition rates is a difficult task and is currently limited to a few methods and devices that are able to prevent freezing and replenish the sample fast enough. The most commonly used injection method at the SPB/SFX instrument that is compatible with the abovementioned constraints is liquid jets, based either on a gas dynamic virtual nozzle (GDVN) (Figure 1a) [37] or a double flow focusing nozzle (DFFN) [38] (Figure 1b). Both of these types of nozzles contain an internal and an external capillary, which allow the flow of the sample and helium gas to accelerate and focus the sample stream, respectively. DFFNs contain an additional capillary to deliver a focusing solution, usually ethanol [38]. GDVNs are well-characterized devices and have been used since 2008 [37; 39; 40; 41]. They were first made of glass capillaries and had a manual method, meaning that each nozzle was different, their preparation time consuming and they required specific expertise. Recently, the increasing availability of 3D printing methods and 2-photon polymerization techniques has improved the production of GDVNs with high quality and high reproducibility [16; 42; 43]. However, even with such a novel nozzle production method, the standard liquid jets still have limitations regarding: their sensitivity to clogging issues, owing to the sample viscosity or crystal preparation; their high sample consumption, as the sample needs to be continuously flowing even when no X-rays are present; and high flow rates are needed for stable jetting. In standard liquid jet injection, 300 times more sample is injected than is actually probed by the X-rays. DFFNs, with the use of a focusing liquid, address these limitations, as they allow the use of higher viscosity samples and reduce the minimal sample flow rate for stable injection by a factor of 4 [16; 42]. Notwithstanding these improvements, they still do not solve the problem of wasted samples (amounting to more than 99%) and generate additional background due to the sheathing liquid. Very recently, [PERSON] et al. presented extensive details about all current sample delivery methods available at the EuXFEL, including those used at the SPB/SFX instrument [16]. In the next section, we highlight the current developments that are underway to cope with such limitations.
## 4 Discussion
### Lateral Bending Test  
The final test conducted on the model was lateral bending. The left (negative direction) and right (positive direction) bending of the FSU resulted from the use of two moments of 1 Nm each. As seen in Figure 6c, the behavior of the model is fairly similar to the experimental one, although the simulated ROM is stiffer and shows values lower than the standard deviation. There are several possible reasons for this situation, including ligament positioning, the original orientation of the spine, linear elastic properties of the ligaments, and misalignment between the applied moment and the vertebrae.  
Nevertheless, the uppermost verteba bends either right or left based on the applied moment, as expected. As in the previous cases, when the verteba moves, the vertebral body rotates and compresses a region of the intervertebral disc, while the other region undergoes traction. As the model leans sideways, the disc shows a concentration of stress on the corresponding side.  
Similar to axial rotation, a single facet joint is activated during this movement. Unlike axial rotation, however, the active joint is on the same side of the applied load direction. When the spine moves to one side, the ligaments are stretched on the opposite side. In this case, the ligaments are split almost symmetrically in the middle through the sagittal plane. As a result, half of a ligament is active and the other half is inactive during the same movement. Figure 9 does not show this peculiarity, as it only shows the maximum stress recorded on the ligaments; however it shows that they respond symmetrically to lateral bending. The ISL is the sole exception; it is not active during these movements, as it is aligned with the sagittal plane. However, a small stress was found on the model during right bending. This is either due to the original orientation of the spine, ligament positioning, or a misalignment between the applied moment and the verteba.  
Figure 9: Maximum stress registered by each ligament during lateral bending.  
It is important to point out that the same stiffness in the C6-C7 segment is present in other FEMs, including those that use the experiments of [PERSON] et al. [34] to validate their work. Additionally, comparison of the FEM with previously validated models has been performed before. In [PERSON] and [PERSON] [13], the model was validated against data obtained from several in vitro tests, then compared with the results obtained from the FEM of [PERSON] et al. [33]. The model created by [PERSON] et al. [14] was set against the studies from [PERSON] et al. [37] and [PERSON] et al. [33]. Later, [PERSON] et al. [12] compared their model with [PERSON] et al. [37] as well as those created by [PERSON] and [PERSON] [38] and [PERSON] [39]. As such, a comparison of the simulated FSU with other FEMs [14; 32; 33; 40] was preformed, with the results shown in Figure 10. The results of the comparative analysis reveal that they all produce similar outcomes, with the largest deviation being 28.5% and the smallest being 2.0%.
# GNSS Signal Compression Acquisition Algorithm Based on Sensing Matrix Optimization
## 3 Sensing Matrix Optimization of Compression Acquisition
### Objective Function  
As we know, when \(\mathbf{\Theta}\mathbf{r}_{1}\)=\(\mathbf{\Theta}\mathbf{r}_{2}\) is satisfied, it cannot distinguish the difference between the two sparse signals \(\mathbf{r}_{1}\) and \(\mathbf{r}_{2}\). One sufficient condition to recover \(K\)-sparse [28] signals exactly is  
\[2K<1+\frac{1}{\mu(\mathbf{\Theta})} \tag{10}\]  
where mutual coherence \(\mu(\mathbf{\Theta})\) is defined as the maximum absolute value and normalized inner product between all columns in \(\mathbf{\Theta}\) that can be described as [29]:  
\[\mu(\mathbf{\Theta})=\max_{1\leq i\
eq j\leq L}\frac{\left|\theta_{i}^{\intercal }\theta_{j}\right|}{\left\|\theta_{i}\right\|_{2}\times\left\|\theta_{j} \right\|_{2}} \tag{11}\]  
where \(\left\{\theta_{i}\right\}_{i=1}^{L}\) denotes the column vectors of \(\mathbf{\Theta}\).  
Since \(\mu(\mathbf{\Theta})\) is defined via the entries of the Gram matrix, we propose an iterative scheme that designs the Gram matrix \(\mathbf{G}=\mathbf{\Theta}^{\intercal}\mathbf{\Theta}\) as close to the target matrix \(\mathbf{G}_{\text{t}}\) as possible. \(\mathbf{G}\) denotes  
\[\mathbf{G}=\left(\begin{array}{ccc}\theta_{1}^{\intercal}\theta_{1}&\dots& \theta_{1}^{\intercal}\theta_{L}\\ \vdots&\ddots&\vdots\\ \theta_{L}^{\intercal}\theta_{1}&\dots&\theta_{L}^{\intercal}\theta_{L}\\ \end{array}\right) \tag{12}\]  
From Equation (11), the mutual coherence \(\mu(\mathbf{\Theta})\) can be defined as the maximum absolute value of nondiagonal elements in matrix \(\mathbf{G}\). Then, it can be formulated as  
\[\mu=\max_{i\
eq j}\left|\mathbf{g}_{ij}\right| \tag{13}\]where \(\mathrm{g}_{ij}\) is the element of row \(i\) and column \(j\) in matrix \(\mathbf{G}\). This equation indicates that a well-designed sensing matrix should make to the matrix \(\mathbf{G}\) with element as small as possible. In other words, the Gram matrix has its absolute off-diagonal elements close to zero and diagonal elements equal to one. Thus, the objective function is given as  
\[\rho(\mathbf{\Phi},\mathbf{G}_{\mathrm{t}})=\left\|\mathbf{G}_{\mathrm{t}}- \overline{\mathbf{G}}\right\|_{\mathrm{F}}^{2} \tag{14}\]  
where \(\left\|\cdot\right\|_{\mathrm{F}}\) denotes the Frobenius norm. \(\overline{\mathbf{G}}=\overline{\mathbf{\Theta}}^{\mathrm{T}}\overline{ \mathbf{\Theta}}=\mathbf{\Theta}_{x}^{\mathrm{T}}\mathbf{\Theta}^{\mathrm{T}} \mathbf{\Theta}\mathbf{\Theta}_{x}\) is the column normalization of the Gram matrix, \(\mathbf{\Theta}_{x}=\mathrm{diag}(\ \cdots\ \ \ \left\|\mathbf{\theta}_{k}\right\|_{2}^{-1}\ \ \cdots\ \ )\) is a diagonal matrix. The target matrix \(\mathbf{G}_{\mathrm{t}}\) belongs to a set \(\delta_{\mathrm{e}}\) as [30]  
\[\delta_{\mathrm{e}}=\left\{\begin{array}{cc}\mathbf{G}_{\mathrm{t}}\in R^{ L\times L}:\mathbf{G}_{\mathrm{t}}=\mathbf{G}_{\mathrm{t}}^{\mathrm{T}},& \mathbf{G}_{\mathrm{t}}(k,k)=1\\ \max\limits_{i\
eq j}\left|\mathbf{G}_{\mathrm{t}}(i,j)\right|\leq\xi,&\xi>0 \end{array}\right\} \tag{15}\]
# First-Principles Study for Gas Sensing of Defective SnSe\({}_{2}\) Monolayers
## 3 Results  
### SnSe\({}_{2}\) Monolayers  
The pristine 1T phase SnSe\({}_{2}\) monolayer is a hexagonal crystal structure as shown in Figure 1a. A Sn atom is sandwiched between two Se atoms which form a Se-Sn-Se arrangement with ABC stacking as shown in Figure 1e. The defective SnSe\({}_{2}\) monolayer with Se vacancy in the center of supercell is represented in Figure 1b,f. Figure 1c,g are the defective SnSe\({}_{2}\) monolayer in which a Seatom is substituted by O atom. Furthermore, Figure 1d,h are the defective SnSe\({}_{2}\) monolayer in which a Se atom is substituted by a N atom. The O and N dopants are sucked into the vacancy site as shown in Figure 1g,h.  
Figure 2 is the DOS with or without defects on the SnSe\({}_{2}\) monolayer. The pristine SnSe\({}_{2}\) monolayer has an indirect band gap of 0.78 eV as shown in Figure 2a, which is consistent with the previous reported value [8; 20]. In the Se-vacancy SnSe\({}_{2}\) monolayer, there are occupied states very near E\({}_{\text{F}}\) as shown in Figure 2b. However, the DOS are very different between the O-doped and the N-doped SnSe\({}_{2}\) monolayer. The DOS of the O-doped SnSe\({}_{2}\) monolayer is similar to the pristine SnSe\({}_{2}\) monolayer with the energy gap 0.84 eV because the states contributed from impurities are far from E\({}_{\text{F}}\), as shown in Figure 2c. The resultant impurity state from the doped O atom is near the band edge, above 0.8 eV and below \(-0.1\) eV denoted by the orange line in Figure 2c. For the N-doped SnSe\({}_{2}\) monolayer, the partial density of state (PDOS) induced by the N dopant is near VBM denoted by the blue line in Figure 2d. The DOS of the SnSe\({}_{2}\) monolayer with N and O dopant also is consistent with the result of [PERSON] et al. [9].  
Figure 1: The structures of different SnSe\({}_{2}\) monolayer systems which including the top view of (**a**) pristine, (**b**) Se vacancy, (**c**) O-doped and (**d**) N-doped SnSe\({}_{2}\) monolayer and the side view of (**e**) pristine, (**f**) Se vacancy, (**g**) O-doped and (**h**) N-doped SnSe\({}_{2}\) monolayer, respectively. The Sn, Se, O, and N atom are purple, green, red and grey, respectively.  
Figure 2: _Cont._
The results of this study indicated that the LSTM model outperformed BPNN and SVM in estimating crop yield and is robust to climate and site fluctuations. Time series data as deep learning samples have been extensively studied at this stage [24; 26]. Therefore, the samples used for training deep learning models mainly include remote sensing data and meteorological data based on multiple fertility periods and long time series, which provide a basis for further improving the accuracy of yield estimation. The network structure of deep learning is complex with many hyperparameters, and many scholars use deep learning algorithms to estimate crop yield by using empirical methods to determine the values of these hyperparameters. The combination of hyperparameters is very important to the prediction accuracy effect of neural network models, and a good combination of hyperparameters can improve the training accuracy of the model and the generalization ability of the test set. In order to speed up the training of neural networks, save training cost, and improve network performance, the optimization of LSTM neural network model hyperparameters using the Bayesian optimization algorithm is proposed. Commonly used hyperparameter optimization methods include grid search [27], random search, and particle swarm optimization algorithms [28]. The idea of the grid search algorithm is exhaustive search; the search time of this method increases exponentially with the number of parameters. For the case of more hyperparameters, this method faces performance problems. The results obtained by random search vary widely each time, and there is the problem of poor accuracy. The particle swarm optimization calculation is easy to fall into local optimum, resulting in low convergence accuracy. Compared to other optimization algorithms, Bayesian optimization algorithms are able to find better hyperparameters in a shorter time, compared to other optimization algorithms, and are a very effective global optimization algorithm [29; 30; 31]. Based on the current status of the recent studies presented above and the limitations, we take winter wheat in Hengshui City as an example. A BO-LSTM neural network model is proposed to predict winter wheat yield using satellite meteorological data on the basis of crop phenology. This study's aims are the following three objectives: (i) speed up the training of neural networks, save training costs, and improve network performance. (ii) Determine which combinations of input variables are best for winter wheat yield estimation based on the BO-LSTM model. (iii) Compare the predictive performance of machine learning (SVM), deep learning (BO-LSTM), and linear regression (Lasso). Finally, explore what kind of environment our proposed model BO-LSTM is suitable for in order to obtain the most accurate prediction.
## 1 Introduction
They conducted simulated experiments using structural deflection data as measurement points on concrete beams with single and multiple damages, and the results showed that the algorithm provided a simple and effective method for locating defects in beam-like structures. Beams are one of the most fundamental structural elements. The commonly employed theories for modeling the behavior of beams consist of the Euler-Bernoulli beam theory and the Timoshenko beam theory. The Euler-Bernoulli beam theory is often used in the analysis of long and slender beam structures, while the Timoshenko beam theory considers the effects of transverse rotatory inertia and shear deformation, so it is commonly used in the analysis of short and thick beam structures [24]. The classical Euler-Bernoulli beam theory is simplified in its analysis, while the Timoshenko beam theory provides a more accurate structural response [25, 26, 27]. In most cases, the impact of shear deformation on the structure is significantly smaller than that of the flexural one. Therefore, some studies tend to ignore the effect of shear deformation on the structure to simplify the calculation. However, in certain structures, such as deep beams, shear deformation may play a critical role [28, 29]. In such structures, ignoring the impact of shear deformation on the structure may lead to some unreasonable rough results of measured data or model data in the structural analysis. Several studies revealed that even small variations in the model parameters or measurement data can lead to large errors in the identified damage locations and severities [30, 31, 32]. For this purpose, the NDE method based on the static response for beam-like structures was proposed in this paper, which considered the influence of shear deformation. Firstly, the NDE method and a principle based on static displacement response were introduced, and the influence of shear deformation during the process was also discussed. Then, an analysis was conducted on the effect of shear deformation on the nodal displacement response of a simply supported beam structure with different cross-sectional shapes and different damage scenarios. The proposed method was used for identifying corrosion in the beam elements of the structure, demonstrating its effectiveness and accuracy. Finally, the method was applied to the more complex beam-like structure for damage identification, and conclusions were drawn based on the results.
# Development of A Loss Minimization Based Operation Strategy for Embedded BTB VSC HVDC
## 3 Determination of Embedded HVDC Operation Point
### Security-Constrained Loss Minimization (SCLM) Control  
When a disturbance occurs in the system, line and transformer overloads occur in the system. In order to eliminate the overloads, it is necessary to control the operating point of the HVDC, which is called a \"security-constrained loss minimization (SCLM) control.\" The action is as follows.  
1. BTB VSC HVDC normally operates with the operating point calculated in Section 3.1.
2. In the event of a contingency, it detects the overloads of the other line and calculates the operation range where the overload of the line can be eliminated.
3. Within the previously determined operating area, a new operating point is calculated that minimizes losses and maintains system reliability.
4. At this time, it is necessary to check whether the other lines are overloaded at the determined operating point. If there are overloads of other lines, an optimum operating point should be found in the operating area where the overloads of the line are eliminated.  
Figure 5 shows an algorithm of the SCLM control in the event of a contingency.
## 3 Experimental Activities
### Evaluation of the Ballistic Response of Satellite Systems  
The ballistic response of structures and systems is usually investigated through hypervelocity impact tests and often through simulations to explore impact ranges not achievable in the laboratory. These studies aim to identify the projectile critical diameter (usually, in respect of the impact velocity and/or other impact parameters) which makes a structure or a system fail in the case of a collision. The ballistic response can be expressed through Ballistic Limit Equations (BLEs). In this context, the investigations performed by the Space Debris group of the University of Padova focused on the BLEs for tape tethers.
## 2 Materials and Methods
### Reconstruction Parameters  
The projection data using the simulation program was reconstructed with various filters, including Ram-Lak, Shepp-Logan, Cosine, Hamming, and Hann filters [17]. These filters were formulated based on the window required to adjust an ideal ramp filter in the frequency domain:  
\[H(\varphi)=|\varphi|W(\varphi) \tag{2}\]  
where \(\varphi\) is the spatial frequency, and \(W(\varphi)\) is defined separately for each filter:  
\[\text{Ram}-\text{Lak filter}:\;W(\varphi)=\text{rect}(\varphi/2) \tag{3}\]  
\[\text{Shepp}-\text{Logan filter}:\;W(\varphi)=\text{rect}(\varphi/2)\text{ sinc}(\varphi/2) \tag{4}\]  
\[\text{Cosine filter}:\;W(\varphi)=\text{rect}(\varphi/2)\cos(\varphi/2) \tag{5}\]  
\[\text{Hamming filter}:\;W(\varphi)=\text{rect}(\varphi/2)(0.54+0.46\text{cos}( \varphi)) \tag{6}\]  
\[\text{Hann filter}:\;W(\varphi)=\text{rect}(\varphi/2)(0.5+0.5\text{cos}( \varphi)) \tag{7}\]  
Various numbers of projections were used for a 360\({}^{\circ}\) circular scan to reconstruct the images, to evaluate the effects of the number of projections and different filters on image quality. For a circular scan of 360\({}^{\circ}\), projection data was acquired at angular increments of 0.4\({}^{\circ}\), ranging from 0.4\({}^{\circ}\) to 6.0\({}^{\circ}\). The size of the reconstructed images was constant because the reconstruction was performed with a constant FOV.