esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods  
### Dataset Preparation  
The data preparation process consisted of data collection, data labeling, dataset splitting, and data augmentation stages. The data used in this study are images of blood pressure monitor measurement results that were collected through a combination of public datasets and image-scraping algorithms. Details about the sources of these datasets can be found in Table 1. Data sorting was performed to eliminate irrelevant or poor-quality data. Once the data was filtered, the labeling process was conducted using Roboflow [11] by applying bounding boxes to the input data (images), covering a total of 11 classes that included 10 classes for digits (0 to 9) and 1 class for indicating systolic pressure, diastolic pressure, or pulse rate. Figure 1 displays the class distribution of the whole dataset. In the dataset, Class 10 has the highest occurrence, as each image typically contains three objects from Class 10, representing the three blood pressure metrics: systolic, diastolic, and pulse rate. Following Class 10, Class 1 has the second-highest occurrence because systolic pressure values generally fall in the range of hundreds. The relatively high frequencies of Classes 7 and 8 can be attributed to diastolic pressure and pulse-rate values, which commonly fall within the 70 s to 80 s range.  
The labeled data was then divided into three sets: training, validation, and testing, with percentages of 70%, 20%, and 10%, respectively. To ensure the robustness of the trained model, the validation and test sets were carefully curated to be representative of the overall dataset in terms of image quality and real-world variability. Images exhibiting variations in blur levels, lighting conditions, and other artifacts commonly found in real-world settings were deliberately included in all sets. The final stage was data augmentation, which aimed to increase the volume and variety of training data twofold. This was intended to improve the model's accuracy and flexibility in real-world conditions. The data augmentation process included rotations of \(\pm 15^{\circ}\), shearing of \(\pm 15^{\circ}\), brightness adjustments of \(\pm 15\%\), blurring of up to 3.5 pixels, and adding noise up to 5% of the total pixels. The dataset splits before and after the augmentation process can be seen in Table 2.  
To further assess the model's robustness in real-world scenarios, we also performed cross-validation using 10 additional images captured directly from a physical digital blood pressure monitor. These images were collected independently from the original dataset and were not part of the training, validation, or test sets. They reflect authentic variations in image quality, including lighting conditions, viewing angles, and device display differences that may occur during practical usage.
# Prediction of Structural Damage Trends Based on the Integration of LSTM and SVR
## 4 Structural Damage Prediction of Engineering Vibration Signals
### Analysis of Prediction Results  
As can be observed from Figures 11-13, the model based on the integration of LSTM and SVR has high accuracy for data prediction under the three conditions. From Tables 2-4, it can be found that  
1. The RMSE values of the single LSTM model in both the undamaged and two damaged states are 0.6549, 0.8156, and 0.8278, respectively, and based on the LSTM-SVR integration method, the RMSE values are 0.4740, 0.5346, and 0.4973, respectively. The integration method had a smaller prediction error for the engineering data, higher accuracy, and better prediction performance than the LSTM model.
2. The MAPE values of the single model under the three operating conditions are 0.59, 1.91, and 1.88. The MAPE values of the integrated method are 0.36, 0.25, 0.95. It indicated that the prediction error of the integrated method based on LSTM and SVR is smaller than that of the LSTM model.
3. The SDAPE values of the single model are 0.8635, 0.8321, and 0.8345, and those of the integrated method are 0.7641, 0.7452, and 0.7424. The SDAPE values of the three operating conditions under the integrated method are smaller than those of the LSTM model, which shows that the integrated method based on LSTM and SVR can improve the stability of prediction to a certain extent.
4. For the \(R^{2}\), the correlation between the actual prediction effect of the integrated algorithm and the actual value is also better, and the numerical value is closer to 1.
5. The RWI values of the three operating conditions under the integrated method are higher than those of the LSTM model, and the prediction accuracies of the integrated method have been improved 6.56%, 2.56%, and 3.7%, respectively.
## 2 Materials and Methods
### Stability Analysis  
Based on the results presented previously, the stability for the finite-time convergence condition is proved by the Lyapunov stability theorem.  
**Theorem 1** ([16]).: _An equilibrium point is set as \(x=0\) for the fractional-order system and_  
\[D^{\gamma}x(t)=f(x,t), \tag{47}\]  
_where \(f(x,t)\) satisfies the Lipschitz condition with a constant \(0<\gamma<1\). It is assumed that a Lyapunov function \(V(t,x(t))\), \(\gamma_{1},\ \gamma_{2},\) and \(\ \gamma_{3}\) satisfy the condition_  
\[\begin{array}{c}\gamma_{1}(\left\|x\right\|)\leq V(t,x(t))\leq\gamma_{2}( \left\|x\right\|),\\ D^{\alpha}V(t,x(t))\leq-\gamma_{3}(\left\|x\right\|),\end{array} \tag{48}\]  
_where \(0<\alpha<1\). The equilibrium point of the system (47) is then asymptotically stable._  
**Theorem 2**.: _If the tracking error, the command filter, and the fractional-order sliding mode surface are selected as (19), (20), and (24), respectively, the system (9) is controlled by the virtual controls (31), (37), and the final control input (43), then, the closed-loop system of (1) is asymptotically stable and the system trajectory will converge to zero within the finite time \[t_{fi}\leq\left(\frac{V_{i,n_{i},1}^{\overline{\gamma}_{i}i,n_{i}-1}(t_{ri})}{ \Gamma(\overline{\gamma}_{i,n_{i},1})\sum\limits_{k=1}^{n}{(k_{i,k,2}-\beta_{ si,k})\Lambda_{i,k}}}\right)^{1/(1-\overline{\gamma}_{i,n_{i},1})}, \tag{49}\]  
_where \(k_{i,k,2}\) is selected appropriately such that \(k_{i,k,2}>\beta_{si,k}\) is satisfied._  
**Proof.** Considering Theorem 1, if \(k_{i,k,2}>\beta_{si,k}\) from (46) is satisfied, then, the asymptotic stability condition is satisfied. Next, taking the fractional-order integral of (46) from the reaching time \(t_{ri}\) to settling time \(t_{fi}\), we obtain  
\[\begin{split}& V_{i,n_{i}}(t_{fi})-V_{i,n_{i},1}^{\overline{\gamma}_ {i}i,n_{i}-1}(t_{ri})\frac{t_{i}^{\overline{\gamma}_{i}i,n_{i}-1}}{\Gamma( \overline{\gamma}_{i,n_{i},1})}\\ \leq&-\sum\limits_{k=1}^{n_{i}}{(k_{i,k,2}-\beta_{ si,k})D^{-\overline{\gamma}_{i}i,n_{i}}|s_{i,k}|}^{3/2}.\end{split} \tag{50}\]  
Based on [17], there is a positive constant \(\Lambda_{i,k}\) such that \(D^{-\overline{\gamma}_{i}i,n_{i}}|s_{i,k}|^{3/2}\geq\Lambda_{i,k}\). (50) can be expressed as  
\[-V_{i,n_{i}}^{\overline{\gamma}_{i}i,n_{i}-1}(t_{ri})\frac{t_{fi}^{\overline{ \gamma}_{i}i,n_{i}-1}}{\Gamma(\overline{\gamma}_{i,n_{i},1})}\leq-\sum\limits _{k=1}^{n_{i}}{(k_{i,k,2}-\beta_{si,k})\Lambda_{i,k}}. \tag{51}\]  
Because \(V_{i,n_{i}}(t_{f})=0\) owing to \(s_{i,n_{i}}=0\) at \(t_{f}\) in (51), then, we obtain  
\[t_{fi}\leq\left(\frac{V_{i,n_{i}}^{\overline{\gamma}_{i}i,n_{i}-1}(t_{ri})}{ \Gamma(\overline{\gamma}_{i,n_{i},1})\sum\limits_{k=1}^{n_{i}}{(k_{i,k,2}- \beta_{si,k})\Lambda_{i,k}}}\right)^{1/(1-\overline{\gamma}_{i,n_{i},1})}. \tag{52}\]  
This completes the proof. \(\square\)
## 2 Materials and Methods
### Ship Linearized Model Identification
In maritime autonomous surface ship (MASS) predictive control there is a need to predict future output instead of the force acting on the ship's hull during simulation. Thus, it was decided to find empirically the state-space model order. As a first guess, the third order, based on [37], was analyzed. In the next step, the prediction based on the higher-order models was checked. A significant improvement in prediction was noticed after the model order increased to four, but no significant prediction improvement was noticed after further model order increase. Thus, it was decided to use the fourth-order  
Figure 2: Linear model structure schematic. state-space predictive model for further regularization. Individual matrices are described by Equations (6)-(9). \[\mathbf{A}=\left[ \begin{array}{cccc}0&1&0&0\\ 0&0&1&0\\ 0&0&0&1\\ a_{1}&a_{2}&a_{3}&a_{4}\end{array} \right] \tag{6}\]  
\[\mathbf{B}=\left[ \begin{array}{c}b_{1}\\ b_{2}\\ b_{3}\\ b_{4}\end{array} \right] \tag{7}\]  
\[\mathbf{C}=\left[ \begin{array}{cccc}1&0&0&0\end{array} \right] \tag{8}\]  
\[\mathbf{K}=\left[ \begin{array}{c}k_{1}\\ k_{2}\\ k_{3}\\ k_{4}\end{array} \right] \tag{9}\]  
Matrices \(\mathbf{A},\mathbf{B},\mathbf{C}\), and \(\mathbf{K}\) describe model \(M(\theta)\) with particular estimated parameters creating vector \(\hat{\theta}\) given by (10). \[\hat{\theta}=\left[\hat{a}_{1},\hat{a}_{2},\hat{a}_{3},\hat{a}_{4},\hat{b}_{1 },\hat{b}_{2},\hat{b}_{3},\hat{b}_{4},\hat{k}_{1},\hat{k}_{2},\hat{k}_{3}, \hat{k}_{4}\right] \tag{10}\]  
where: \(\hat{\cdot}\)--estimated value of the particular parameter. \[\epsilon(k,\theta)=y(k)-\hat{y}(k|\hat{\theta}) \tag{11}\]  
where: \(\hat{y}\)--parameterized model output prediction. \[V_{N}(\theta)=\frac{1}{N}\sum_{n=1}^{N}\epsilon^{2}(n,\theta) \tag{12}\]  
where:  
\(N\)--number of samples,  
\(\theta\)--estimated model parameters vector. According to [PERSON] [38], when taking into account prediction as the goal of a model, prediction error is determined by (11). In PEM, the quadratic norm of (11) acts as a cost function during the optimization procedure. In this approach, the variances of the parameter estimates may be very large because they are not included the cost function and optimization problem constraints. Cost function (12) is defined as the normalized sum of squared prediction errors. Vectors of the estimated parameter values are calculated during the optimization procedure that consist of minimizing the cost function. A linear state-space predictive model was identified using the PEM method, based on computer simulation results. Pseudorandom pod angle of rotation sequences (\(\delta_{z}(t)\)) were used as the source of input signals and were delivered to the inputs of the simulation model. They were used then as input data for identification. The ship's rotational speed (\(r(t)\)), computed based on the nonlinear mathematical model, was registered and used as output data during the identification process.
# Construction of Compact Finite Difference Schemes by Classic Differential Quadrature
## 3 Construction of Compact Difference Schemes by DQM
### The Extended Trapezoidal Rules
#### 3.4.2 Extended Trapezoidal Rules of the Second Kind  
Let \(s\) be an odd number, and the expression of \(v\) is also the same with Equation (31). According to matrix Equation (12), it can be drawn that  
\[\widetilde{\mathbf{G}}\left[\begin{array}{c}y_{n+1}\\ \vdots\\ y_{n+v-1}\\ y_{n+v}\\ \vdots\\ y_{n+s}\end{array}\right]+\mathbf{G}_{0}y_{n}=h\left[\begin{array}{c}f_{n+1 }\\ \vdots\\ f_{n+v-1}\\ f_{n+v}\\ \vdots\\ f_{n+s}\end{array}\right] \tag{38}\]  
In the above matrix equation, add \((v-1)\)-th equation to the \(v\)-th equation, we can get  
\[\sum_{z=0}^{s}\overline{\pi}_{z}y_{n+z}=h(f_{n+v-1}+f_{n+v}) \tag{39}\]  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(s\) & \(v\) & \(\rho_{0}\) & \(\rho_{1}\) & \(\rho_{2}\) & \(\rho_{3}\) & \(\rho_{4}\) \\ \hline
1 & 1 & \(\frac{1}{2}\) & & & & \\
3 & 2 & \(-\frac{1}{2}\) & \(\frac{13}{74}\) & & & & \\
5 & 3 & \(\frac{11}{7230}\) & \(-\frac{31}{2600}\) & \(\frac{401}{600}\) & & & \\
7 & 4 & \(-\frac{191}{846720}\) & \(\frac{1879}{846720}\) & \(-\frac{333}{31360}\) & \(\frac{68,323}{846720}\) & \\
9 & 5 & \(\frac{2497}{65318400}\) & \(-\frac{28939}{65318400}\) & \(\frac{581}{233280}\) & \(-\frac{40,111}{4,082,400}\) & \(\frac{2,067,169}{2,00}\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Coefficients of extended trapezoidal rules of the first kind (ETR\({}_{1}\)) using uniform grids.  
where,  
\[\overline{\alpha}_{z}=g_{v-1,z}+g_{v,z},\ z\in(0,\ s) \tag{40}\]  
Considering that \(s=2v-1\), as well as the weighting coefficient \(g_{pq}\) is centro-antisymmetrical and \(\overline{\alpha}_{z}\) is centro-antisymmetrical, the Equation (39) could lastly be written as  
\[\sum_{z=0}^{v-1}\omega_{z}(\widetilde{x}_{n+z}-\widetilde{x}_{n+s-z})=\frac{h} {2}(f_{n+v-1}+f_{n+v}) \tag{41}\]  
where,  
\[\omega_{z}=\overline{\alpha}_{z}/2=(g_{v-1,z}+g_{v,z})/2,\ z\in(0,\ v-1) \tag{42}\]  
When selecting the uniform grids, the coefficient values (\(\omega_{z}\)) in Equation (41) are shown in the following Table 7.  
Obviously, when \(s=1\), the aforementioned method is just the implicit trapezoidal method; thus, this numerical method can be named the extended trapezoidal rules of the second kind (ETR\({}_{2}\)).  
According to Table 7, it can be verified that the method (41) is consistent with ETR\({}_{2}\) methods that proposed by [PERSON] et al. [19]. The stability regions of ETR\({}_{2}\) methods are the same with that of classic implicit trapezoidal integration rule, and they are all A-stable methods.
## 1 Introduction
For example, the Boundary Layer Approach (BLA) [22; 41; 42], High-Order Sliding Mode Control (HOSMC) [43; 44; 45; 46], Full-Order Sliding Mode Control (FOSMC) [47; 48; 49; 50], and Fuzzy-Sliding Mode Control (F-SMC) [51; 52; 53; 54]. Among these controllers, FOSMC not only effectively removes chattering, but also inherits the advantages of NFTSMC, such as non-singularity, fast convergence, robustness against uncertain terms, and high accuracy. Consequently, the main goal of our study is to propose a new S-FOTSMC for a 3-DOF planar parallel robotic manipulator with uncertain dynamics which has the following benefits: (1) inherits the advantages of the synchronization control and FOTSMC in terms of robustness, fast convergence of position and synchronization errors, singularity removal, and high tracking accuracy; (2) the accuracy in the trajectory tracking control was further improved; (3) all joints of the parallel robot are synchronously driven; (4) the control input system is smooth with minimum chattering. The rest of this study is arranged as follows. The problem formulations are depicted in Section 2. Section 3 describes the proposed method synthesis. Following Section 3, the proposed control controller is utilized to allow the trajectory tracking control simulation for a 3-DOF parallel robot in Section 4. After its trajectory tracking performance is compared with those of the SMC, the S-SMC and NFTSMC, Section 5 summarizes some highlighted conclusions.
# Impact of One Additional Substitution on Player Load and Coaching Tactics in Elite Football
## 1 Introduction
#### Study 1  
Evaluation of TOS: An objective data evaluation of the time of substitution (TOS), i.e., the minute of the substitution, was carried out to prove whether an additional substitution in regular or extra time would theoretically be beneficial for the reduction of player load for the team as well as the tactical influence of the coaches on the match. Calculations were made to assess the potential benefit over the period of one match and one season.  
Coaches survey: A qualitative coaches survey was conducted in order to examine whether an additional substitution in regular or extra time would be practically beneficial to the reduction of player load or not. Furthermore, the tactical influence of coaches on the match was subsequently determined. Responses were analysed to assess coaches' evaluation of the practical impact of additional substitution in terms of potential benefits over the period of one match and one season.
###### Abstract  
In the medical care of partial and full-thickness wounds, autologous skin grafting is still the gold standard of dermal replacement. In contrast to spontaneous repepithelializing of superficial wounds, deep dermal wounds often lead to disturbing scarring, with cosmetically or functionally unsatisfactory results. However, modern wound dressings offer promising approaches to surface reconstruction. Against the background of our future aim to develop an innovative skin substitute, we investigated the behavior of two established dermal substitutes, a crosslinked and a non-crosslinked collagen biomatrix. The products were applied topically on a total of 18 full-thickness skin defects paravertebrally on the back of female Gottingen Minipigs--six control wounds remained untreated. The evaluation was carried out planimetrically (wound closure time) and histologically (neoepidermal cell number and epidermis thickness). Both treatment groups demonstrated significantly faster repithelialization than the controls. The histologic examination verified the highest epidermal thickness in the crosslinked biomatrix-treated wounds, whereas the non-crosslinked biomatrix-treated wounds showed a higher cell density. Our data presented a positive influence on epidermal regeneration with the chosen dermis substitutes even without additional skin transplantation and, thus, without additional donor site morbidity. Therefore, it can be stated that the single biomatrix application might be used in a clinical routine with small wounds, which needs to be investigated further in a clinical setting to determine the size and depths of a suitable wound bed. Nevertheless, currently available products cannot solely achieve wound healing that is equal to or superior to autologous tissue. Thus, the overarching aim still is the development of an innovative skin substitute to manage surface reconstruction without additional skin grafting.  
Keywords:animal model; dermal regeneration; Integra; Matriderm; skin substitute; wound healing +
Footnote †: The work was performed at the University of Tubingen, Germany.
## 3 ML Application in O-RAN  
One key advantage of the Open RAN architecture is its ability to separate intelligent controls from the core network. This architecture gives flexibility for RAN to incorporate intelligence toward network control. With this architecture, ML can be easily integrated into Open RAN, not only to automate and optimize network operations, but also to improve network efficiency, introducing new use cases and services that are traditionally challenging to implement in the RAN.  
While being developed, the concept of Open RAN has already sparked many research works in investigating the potential of Open RAN and its performance benefits. Notably, substantial research works focusing on applying ML algorithms in Open RAN have appeared in the literature. Improvements, such as optimizing radio resource management and network slicing, automating component deployment for efficient use of computing and communication resources, or improving energy efficiency, are some research activities receiving attention. The structure of this section is summarized in Table 2, and the content in each subsection is summarized in Tables 3-12.
## 4 Empirical Analysis
### Results of the Unit Root Test  
Before conducting the VARM and causality analyses, the stationarity of the time series was tested using the Augmented Dickey-Fuller (ADF) test, a standard method for unit root testing. The null hypothesis of the ADF test states that \"the time series contains a unit root,\" while the alternative hypothesis is that \"the time series is stationary.\" The unit root test results for the original data indicated that both the 15 global logistics companies and the GSCPI failed to reject the null hypothesis, implying that a unit root exists and the time series is non-stationary.  
To ensure the stationarity of the data, each time series was differenced once, and the ADF test was re-conducted. After the first differencing, all the time series rejected the null hypothesis, confirming that the data became stationary. Therefore, the first-differenced values of each time series were utilized in the VARM analysis. The results of the ADF test are presented in Table 3.
## 3 Methodology
### Landslide Control Parameters
#### 3.1.2 Parameters Associated with the Slope Instability Location  
Some control parameters are related to the distances of the landslides to the rivers or the nearest roads [35; 36]. These parameters provide information about the position of the instability with respect to the environment in which it is located. The distance to the nearest active fault, as well as the distance to the epicenter, are variables that are usually included in landslide susceptibility studies [37; 38]. They provide information on the proximity of the slope area to the earthquake location. These parameters can also influence the size and extent of landslides: the closer the area is to the epicenter and the faults, the more likely it is that larger and more extensive landslides can occur. Another parameter with information about the location widely used is the elevation above sea level [35; 36; 39]. All of these factors were obtained using ArcGIS, based on cartographic databases from the Spanish Geographic Institute [34].
# High Performance Computing in Satellite SAR Interferometry: A Critical Perspective
## 5 Selected HPC Approaches in InSAR Fundamental Functional Stages
### SAR Image Coregistration  
Image co-registration is a fundamental procedure in many fields, and more specifically, SAR image coregistration operation at the subpixel level has high computational complexity (see Section 2.3).  
A first parallel approach to SAR image coregistration for SAR interferometry applications was proposed in [190]. The relevant implementation uses MPI; unfortunately, in this case, performance metrics have not been reported.  
In [191], a toolchain for the accurate co-registration of multiband optical and SAR images was presented. Relevant implementation exploits commercial hardware-based on GPU parallel architecture. The conducted performance analysis shows that the execution time is on the order of the minute.  
As far as the exploitation of the parallelism offered by shared-memory architectures is concerned, an efficient and scalable processor for SAR image geometric coregistration was recently proposed in [49]. Specifically, the inherent computationally intensive problem is efficiently solved by designing a parallel algorithm incorporating thread-level parallelism. The developed scheme implements parallelism by using OpenMP, and it is specifically targeted at (shared-memory) general-purpose multicore processors. The scalable performance analysis was conducted with high-resolution SAR COSMO-SkyMed data and on different platforms in terms of parallel speedup and efficiency, thus demonstrating that the sub-pixel co-registration is practicable in a timely fashion (indicatively under one minute) on a typical 16-core machine.  
Finally, we mention the work in [192], in which a swarm-intelligent GPU parallel pixel-level registration is proposed, which considers not only the acceleration of the correlation operation but also the reduction in searching times. Nonetheless, a suitable version of this method that can be applied to the fine registration process is unavailable.