esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Cascaded Weighted Sum and Its Field of Application
### Number of Objectives  
As already discussed in Section 3.3 objectives can conflict more or less. We consider here only objectives, the decision maker regards as conflicting in the sense that they shall be part of Pareto optimality. The amount of these objectives plays an important role for the practical applicability of the Pareto method. The Pareto front of up to three objectives can be visualized easily. For up to four or five objectives, decision maps, polyhedral approximation, or other visualization techniques can be used, see [20]. Interactive visualization techniques may support perception for more than three objectives, \"but this requires more cognitive effort if the number of objectives increases\", as [PERSON] and [PERSON] summarize their chapter on visualization of the Pareto frontier in [20]. Thus, we can conclude that from five and in particular from more criteria, the perception and the comprehension of the Pareto front become increasingly difficult and turn into a business for experienced experts.  
Another question is the effort to determine the Pareto front. For an acceptable visualization of the Pareto front, approximations like the one described in [21] may be used. Depending on the desired quality of approximation, a number of 5 to 7, in general \(s\), Pareto-optimal solutions may be sufficient for two objectives. Assuming that the same quality of interpolation and granularity of support points shall be maintained when further objectives are added, \(s^{(k-1)}\) support points are required for the interpolation of the hyperplane of the Pareto front, provided that all areas shall be examined. With interactive approaches, this can be reduced to some extent, but at the risk of missing promising regions. Figure 9 illustrates this growth of required Pareto optimal solutions. For 5 objectives, for example, 625 solutions are required to interpolate the entire hyperplane with 5 data points per axis. For a better interpolation quality obtained from 7 data points per axis 2401 solutions are needed. It should be noted that every data point requires several evaluations of solutions according to the optimization  
Figure 9: The number of required data points (Pareto-optimal solutions) of an approximation of a Pareto front increases exponentially with a growing number of conflicting objectives. The green line is based on a resolution of 7 data points per additional objective (axis), while the blue one uses 5 only.  
or approximation procedure used. Depending on the application, evaluations may be based on time-consuming simulations and last several seconds or minutes each. This clearly limits the practical applicability of the Pareto method for growing numbers of conflicting objectives. One solution is to reduce the number of objectives by an aggregation of less conflicting objectives to one.
## 6 Results and Discussion
### Micro-Hydropower Plant Efficiency  
Figure 12 shows the efficiency of the Barreda micro-hydropower plant using the three control systems. According to Figure 12a, the efficiency of the micro hydroelectric power plant decreases with the decreasing available flow. The maximum power point tracking and optimal water level control systems obtain similar results at low flow rates, while the decrease in efficiency is remarkable with the water level control system. Figure 12b also shows how the efficiency of the micro-hydropower plant increases using the three control systems when there is an increase in the available flow. Better efficiencies are obtained with the maximum power point tracking and optimal water level control systems, although the difference with the water level control system is much smaller.  
Figure 13 shows the average efficiency of the Barreda micro-hydropower plant using the three control systems. The maximum power point tracking system and the optimal water level control system obtain similar average efficiency results for both high and low  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Day**} & \multirow{2}{*}{**Flow Conditions**} & **Water Level Control** & **Optimal Water Level** \\  & & **System** & **Control System** \\ \hline
25 May 2023 & Low flow day & \(-10.94\%\) & \(0.5\%\) \\
26 June 2023 & High flow day & \(-2.88\%\) & \(0.31\%\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Energy gain.  
Figure 12: Efficiency of the Barreda micro-hydropower plant using the three control systems.  
flow rates. In contrast, the water level control system obtains worse results for the flow rates studied. This is particularly evident in the low flow study. These results can be seen in the determination of the average efficiency gain.  
Table 5 shows the average efficiency gain. Maximum power point tracking and optimal water level control systems achieve similar average efficiency gains. In contrast, the water level control system performs worse, especially at low flow rates.
## 4 Additional CT-Measurable Parameters for Cardiovascular Risk Stratification
### Liver Fat  
Cardiac CT scans used for CAC scoring can also identify both epicardial and liver fat (LF) without any modifications to the scan protocol; this latter is identified as a decreased liver attenuation [140]. The attenuation measurement obtained with CT is more reliable than that obtained with echocardiography or cardiac magnetic resonance (CMR), especially in a homogeneous and non-hypervascular parenchymal organ [141]. Cardiac CT generally does not include the whole liver in the scans. However, it has been demonstrated that the attenuation measurement of two or three sites of the liver reflects that of the whole liver [142].  
LF is considered a marker of cardio-metabolic health because it is closely associated with non-alcoholic fatty liver disease (NAFLD), which is correlated to metabolic syndrome and CV disease [143; 144]. Indeed, it has been shown that patients with NAFLD have an increased risk of developing atherosclerotic disease [144]. In this regard, NAFLD has been independently associated with increased carotid intima-media thickness (CIMT) and CAC, both measures of atherosclerosis severity [145]. In addition, the ROMICAT II trial demonstrated that high-risk plaque features evaluated by CCTA (positive remodeling, napkin-ring sign, spotty calcification, low attenuation) were more frequent in patients with NAFLD compared with patients without NAFLD [146]. Furthermore, [PERSON] et al. investigated the relationship between LF, traditional cardiovascular risk factors and subclinical coronary artery disease [147]. The authors found that LF was associated with all traditional cardiovascular risk factors, specifically to waist circumference, diastolic blood pressure, HDL cholesterol and diabetes mellitus. Additionally, a greater volume of LF was associated with a higher burden of subclinical CAD, regardless of the presence of other cardiovascular risk factors [147].  
Several mechanisms have been proposed to motivate the relationship between LF and CAD. Firstly, the fatty liver excretes high levels of substances that damage the cardiovascular system, such as coagulation factors, very low-density lipoproteins, and C-reactive protein. Additionally, LF is closely linked to insulin resistance and pro-inflammatory status [148; 149]. All this evidence suggests that LF, measured with CT, can be valid for a comprehensive cardiovascular risk assessment together with other CT parameters.
## 4 Results
### Influence of User's Characteristics to the BCI Performance  
BCI systems can also be affected by anthropogenic factors; thus, it is hard to draw solid conclusions based only upon the limitations of the machine level.  
Thus far, we have examined the influence of different types of classifiers, feature-extraction methods, and the impact of providing different feedback modalities. Nonetheless, since our target is the applicability of BCIs in neurorehabilitation, we also accounted for the user type, and, specifically, compared the performance between healthy and patients. Previous research has revealed that the EEG of patients is considerably different from signals recorded in healthy subjects [90]. In fact, it is not yet clear if post-stroke patients have features distinguishable from the imagination of upper-limb movements. Hence, our survey included studies examining the classification accuracy in both patients and healthy participants. Contrary to our expectations, patients and healthy subjects performed with a similar accuracy (Figure 2a, \((Z=-0.0890,p=0.9291)\).  
In terms of the user's characteristics, we attempted to determine the effect of the subject's age on the BCI performance. Interestingly, the correlation of the user's age and BCI performance was almost zero (Figure 2b, \(r=-0.03,p=0.91\)). These results are in-line with the prior work of [PERSON] et al., 2022, where no statistically significant correlation was found between age and classifier performance [91]. However, we cannot neglect the negative trend, which declines among the classification performance and the age of the subjects in each experiment.
# Physical and Augmented Dynamics of a Cultural Event
## 4 Discussion
Scales and dynamics were interconnected, in which festivals were scaled up to multiple land uses, scaled down from remote sensing to 3D scan, and further transferred to digital form suitable for situated applications. This technology advantage, which is available to scale down from festival to personal experience, facilitates the dynamics between cloud-accessed installations and new fabrics of various scales. The interaction presented a new interpretation of the cultural event and further promoted it in an evolved scale and manner. Table 2 presents the differentiated characteristics between the physical and augmented dynamics. The evolving trend includes the practice of a new augmented identity in an enriched cultural landscape, or a new enriched cultural identity in an augmented landscape anywhere. The physical-to-augmented dynamics were inspired by a city-wide site distribution. The simulation can be applied in a wider area by mobile AR. The augmented-to-physical dynamics were expected to enrich the content yearly and to assist the cultural preservation for future activation. More diversified allocation of AR should enable local-identity enhanced-augmented tourism. \begin{table}
\begin{tabular}{c l l} \hline  & **Physical Dynamics** & **Augmented Dynamics** \\ \hline Land use & Host-city dependent & Situation dependent \\ \hline Installations & Temporary & Cloud access \\ \hline Order & Land use goes before installation & Equally involved \\ \hline Festivalscape & From macro- to micro-representation & From micro- to macro-representation \\ \hline Involved database and devices & GIS@5, 3D scanner & Augment® AR platform and cloud \\ \hline Measures & Areas, land-use types, no. of revisions, distribution (over mobility, broad allocation of lantern zones) & No. of AR models, size of database, applied scenes and locations, geographic distribution of applications \\ \hline Occurrence & By cities annually & Free of rotation \\ \hline Scales of dynamics & 2 km \(\times\) 2 km & Island-wide or worldwide \\ \hline Involvers & Group collaboration and exchange & Individuals \\ \hline Timing of execution & Two years ahead of TLF + after & Unrestricted \\ \hline Duration of preparation & Two years ahead of TLF & Days for 3D scan and model conversion \\ \hline Development Pace & Short-term, by rotations & Long-term \\ \hline Geographic distribution & Host-city centered & Island wide \\ \hline \end{tabular}
\end{table} 
Table 2: Differentiated characteristics between physical and augmented dynamics.
# Near-Space Wide-Area and High-Resolution Imaging System Design and Implementation
## 4 Conclusions  
This paper adopts a 64 M pixels CMOS sensor to obtain high-resolution images and generates a large-field-of-view image through the swing scanning of the photoelectric sphere. The flight results indicate that the system can achieve seamless image stitching at a resolution of 0.2 m@20 km and coverage of 33.3\({}^{\circ}\).  
In addition, the high-resolution camera adopts zoom lenses (focal length range of 320 mm-106.7 mm) to achieve flexible applications. For application scenarios with ultra-wide (field of view exceeding 100\({}^{\circ}\)) high-resolution imaging, ground resolution sharply decreases with an increase in the scanning angle, and the resolution of the edge area is about half of the center area, seriously affecting the image stitching effect. The zoom swing scanning wide-area and high-resolution imaging system in this article can adjust the camera focal length in real-time during the swing scanning process, ensuring that the imaging resolution remains unchanged. It provides a new solution for ultra-wide and  
Figure 16: Result of the flight experiment image resolution.  
Figure 17: Result of the flight experiment image stitching.  
high-resolution imaging. This system will be used in other near-space flight experiments to verify its application.  
Software, W.Z.; Resources, Y.S.; Data curation, Y.Z.; Writing--original draft, Z.W.; Writing--review & editing, Z.Z. and G.W.; Visualization, X.L.; Supervision, M.H.; Project administration, L.Q.; Funding acquisition, L.Q. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the strategic priority research program of the Chinese Academy of Science (Grant NO: XDA17020600) and Youth Innovation Promotion Association CAS.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Reconstruction of Rainfall Field by OELs Network  
### Reconstruction Algorithm  
In this paper, we reconstruct the rainfall field by establishing an OELs network. Firstly, each OEL measures the rain intensity at the corresponding position \((x_{i},y_{i})\) based on the method in Section 2. Secondly, the rainfall measurements by all OELs are used to reconstruct the rainfall field. Here, the ordinary kriging interpolation algorithm (OK) is adopted. It is assumed that the rain intensity in observed region are related to each other, and the closer locations have much stronger correlations [41]. As for the target position \((x_{t},y_{t})\) where the OELs are not deployed, its rain intensity \(R(x_{t},y_{t})\) can be estimated by:  
\[\left\{\begin{array}{l}R(x_{t},y_{t})=\sum\limits_{i=1}^{n}\lambda_{i}R(x_{i },y_{i})\\ \sum\limits_{i=1}^{n}\lambda_{i}=1\end{array}\right. \tag{14}\]  
where the \(R(x_{i},y_{i})\) is the measured rain intensity by OELs at \((x_{i},y_{i})\), \(n\) denotes the number of OEL measurements, \(\lambda_{i}\) is the weight that represents the contribution of \(R(x_{i},y_{i})\) to \(R(x_{t},y_{t})\). To obtain the weights, we need to solve a system of linear equations given by [42]:  
\[\left[\begin{array}{ccccc}\gamma_{11}&\gamma_{12}&\cdots&\gamma_{1n}&1\\ \gamma_{21}&\gamma_{22}&\cdots&\gamma_{2n}&1\\ \cdots&\cdots&\cdots&\cdots&\cdots\\ \gamma_{n1}&\gamma_{n2}&\cdots&\gamma_{nn}&1\\ 1&1&1&1&0\end{array}\right]\left[\begin{array}{c}\lambda_{1}\\ \lambda_{2}\\ \cdots\\ \lambda_{n}\\ -\varphi\end{array}\right]=\left[\begin{array}{c}\gamma_{1t}\\ \gamma_{2t}\\ \cdots\\ \gamma_{nt}\\ 1\end{array}\right] \tag{15}\]  
where \(\varphi\) is the Lagrange's multiplier, and \(v_{\bar{y}}\) is the semi-variance between \(R_{i}\) and \(R_{j}\).  
\[\gamma_{ij}=\frac{1}{2}E\left[\left(R(x_{i},y_{i})-R(x_{j},y_{j})\right)^{2}\right] \tag{16}\]  
In the application of OK interpolation, it considers that semi-variance is a function of the distance and direction between observation- and target position. Its usual formula is:  
\[\hat{\gamma}(\mathbf{h})=\frac{1}{2M(\mathbf{h})}\sum\limits_{i=1}^{M(\mathbf{ h})}\left[R(\mathbf{x}_{i})-R(\mathbf{x}_{i}+\mathbf{h})\right]^{2} \tag{17}\]  
where \(R(\mathbf{x})\) is the rain intensity at position \(\mathbf{x}\), \(R(\mathbf{x+h})\) is the value at \((\mathbf{x+h})\), \(\mathbf{h}\) is the separation in both distance and direction and not the actual position, and the M(\(\mathbf{h})\) is the number of pairs of observations separated by the lag \(\mathbf{h}\). Here, the function that \(\gamma\) relates to \(\mathbf{h}\) is the variogram used to describe the variation within limited neighborhoods. Because the variation of rainfall field is generally considered isotropic, \(\mathbf{h}\) only represents the lag distance of the experimental variogram. In the computer process, we choose a variogram model, like gaussian, spherical or stable, and fit it based on the OEL observations. The selection of variogram model will be discussed in Section 5 and this paper uses the stable model to reconstruct rainfall field [42].
# Revolutionizing Soccer Injury Management: Predicting Muscle Injury Recovery Time Using ML
###### Abstract  
Predicting the optimal recovery time following a soccer player's injury is a complex task with heavy implications on team performance. While most current decision-based models rely on the physician's perspective, this study proposes a machine learning (ML)-based approach to predict recovery duration using three modeling techniques: linear regression, decision tree, and extreme gradient boosting (XGB). Performance is compared between the models, against the expert, and together with the expert. The results demonstrate that integrating the expert's predictions as a feature improves the performance of all models, with XGB performing best with a mean \(R^{2}\) score of 0.72, outperforming the expert's predictions with an \(R^{2}\) score of 0.62. This approach has significant implications for sports medicine, as it could help teams make better decisions on the return-to-play of their players, leading to improved performance and reduced risk of re-injury.  
return-to-play; machine learning; recovery estimation; soccer injuries +
Footnote †: journal:  
0000-0001-9993-3000-0000-0000-0000-0000-0000-00000-0000-0000-0000-0000-00000-0000-000  
sports medicine, ML algorithms have been increasingly utilized. Although most of the available research focuses on assessing the risk of injury [10; 11; 12; 13], there have also been incentives to include ML into the process of determining RTP time [14]. Because deep-learning-based computer-aided diagnosis (CAD) systems have been proven to increase radiologists' diagnosis accuracy [15], we hypothesize that physicians' RTP predictions could also be improved using ML. However, given the nature of medical records and their tabular representation, simpler ML approaches, such as decision trees, may be utilized [16].  
Muscle injuries are among the most common in professional soccer [3], with varying recovery time length [17]. [PERSON] et al. [14] attempted to predict RTP time following a hamstring muscle injury by applying ML techniques on data derived from magnetic resonance (MR) images. Their system achieved an \(R^{2}\) score of 0.48, and exhibited a mean absolute error of 9.8 days. The data which were input to their model consisted of multiple attributes describing the location and severity of injuries, including injury chronology (index injury, re-injury). We hypothesize that the accuracy of such models, similar to CAD systems [15], could be further improved by coupling these types of attributes with physicians' predictions. The aim of this study is to assess the recovery time after muscle injury by integrating ML techniques and expert judgment. To the best of our knowledge, no similar study has attempted to integrate these approaches for this particular topic.
## 3 Results and Discussion
### Corrosion Current Density (\(i_{\text{corr}}\))
#### 3.2.2 \(i_{\mathrm{corr}}\)
Likewise, the specimen that presented the best performance is the BSC2-MS-GS specimen, which at the end of the monitoring presented an increased trend, but without reaching values greater than 0.10 \(\mu\)A/cm\({}^{2}\), presenting a negligible level of corrosion throughout the exposure time. The benefit of the use of galvanized steel observed in the present investigation is greater, due to the exposure medium, but it has been shown that its anticorrosive efficiency decreases in the presence of chlorides, recommending its use in harsh environments that have up to a concentration of 1% of NaCl as reported in the literature [103]. Other studies evaluated the effect of carbonation on the corrosion of galvanized steel [104], where a protection period was also reported, the same was observed in an investigation where they used corrosion inhibitors applied to galvanized steel exposed in pore solution [105]. All of the above indicates that although galvanized steel protects against corrosion when used as reinforcing steel in concrete structures. The multiple possibilities of means of contact and life in service allow us to continue researching on the subject to increase its resistance to corrosion due to aggressive agents such as sulfates and chlorides, which are the most aggressive and main cause of corrosion in reinforced concrete structures, the most widely used construction system in the world. According to the results obtained in the present investigation, the use of agro-industrial and industrial residues (SCBA and SF), as partial substitutes for cement in the preparation of binary sustainable concretes, are an excellent option to increase resistance to corrosion by sulfates in addition to contribute to the decrease in CO\({}_{2}\) emissions due to the manufacture of OPC.
# Application of Sensitivity Analysis to Progressive Collapse Resistance of Planar Truss Structures
## 2 Sensitivity Analysis Method  
Sensitivity analysis is initially applied in economic investment to quantitatively study the impact of related factors changes on an index in the system. The general assumption of the analysis model is \(y=f(x_{1},x_{2},\ldots\ x_{i},\ldots,x_{n})\), where \(y\) is an index and \(x_{i}\) is the \(i\)th factor.  
The significance of element importance is the influence of element damage or failure on structural performance.  
Therefore, applying the sensitivity analysis method to the element is an important evaluation. Any index \(y\) can be the specific performance of the overall structure, such as stiffness, bearing capacity, and strain energy. The relevant factors in the model are defined as the damage or failure of elements. The following calculation formula can be derived from element importance evaluation based on sensitivity analysis.  
\(\beta\) is defined as the damage parameter of the structure, \(S_{ij}\) is the sensitive index of element \(i\) corresponding to the damage parameter \(\beta_{j}\), and \(\beta_{j}\) is the removal of the failed element \(j\). The expression of \(S_{ij}\) is:  
\[S_{ij}=\left(\gamma-\gamma^{\prime}\right)/\gamma \tag{1}\]where \(\gamma\) and \(\gamma\)' are the response of element \(i\) under normal conditions and after structural damage, respectively, and the response can be the displacement, bearing capacity, stress, and strain of the structure under a conventional load.  
The importance coefficient \(\alpha_{j}\) of element \(j\) can be deduced from the sensitive index \(S_{ji}\). When the response of a single element is the research object of the sensitivity analysis, we take the mean value of the sensitive indices of the statistical objects as the importance coefficient of the failure element \(j\), i.e.,  
\[\left\{\begin{array}{ll}\alpha_{j}=\sum_{i=1,\ i\
eq j}^{n}|s_{ij}|/(n-1)&j \in n\\ \alpha_{j}=\sum_{i=1,i\
eq j}^{n}|s_{ij}|/n&j\
otin n\end{array}\right. \tag{2}\]  
where \(n\) is the total number of statistical objects.  
Sensitivity analysis can evaluate the importance of each element in the structure before progressive collapse design, which can be used as the judgment basis for initial failure elements in the AP method. It can be used for the progressive collapse analysis after initial failure and to discuss the internal force redistribution of residual structure. It is a practical method for analyzing the progressive collapse of non-frame structures.