esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Data Fusion and Data Preprocessing  
Data preprocessing and data fusion of the different datasets were performed with MATLAB(r). For the pressure plate, preprocessing of the data included the following steps. First, the raw data comprising the pressure measurements of each cell of the plate over time are preprocessed to get the vertical ground reaction force (vGRF) curve for each foot. The segmentation of the pressure measurements into the left foot and right foot is provided by the FDM-T software. Therefore, the vGRF can be calculated by simply summing up the corresponding pressure measurements of the left and right foot over time.  
From this vGRF measurement, it is possible to extract gait-related events. As shown in Figure 4, any of the standardized events of the stance phase of the human gait cycle can be detected from the preprocessed pressure plate data, although in the present pilot study, only initial contact (IC) and toe-off (TO) are used as markers.  
For the analysis of gERPs, the IC as a prominent gait event was considered as marker. In total, 3750 (for each person, about 1256) events related to the left IC and the right IC could be identified across the seven experimental conditions, resulting in a total of 7500 gait markers for EEG data analysis. The EEGlab toolbox and the Brain Vision Analyzer software were used for EEG data preprocessing and gait-related EEG analysis. The EEG data were filtered with an Infinite Impulse Response (IIR) filter (order 2) from 0.1-100 Hz and line noise was discarded with a notch filter at 50 Hz. The 0.1 Hz filter setting was chosen to attenuate slow frequency drifts. The upper cut-off of 100 Hz (instead of more  
Figure 4: Vertical ground reaction force (vGRF) for left and right foot calculated from a pressure plate for gait event extraction. Green vertical lines show the extracted initial contact (IC) events, in black, toe-off events (TO).  
conservative cut-offs of, e.g., 30 Hz) was chosen to include ERP characteristics with high frequency proportions (for a discussion of EEG filter settings see [25]). The filters were applied to the continuous raw EEG data. The filtered continuous EEG data were visually inspected for artifacts and artifacts were removed before the data were segmented into epochs. In addition to visual and manual artifact removal, independent component analysis (ICA) was also used for EEG decomposition to separate and correct EEG signals from signals elicited by e.g., eye movements, movement artifacts, and other artifacts. Artifact-corrected data were then segmented into epochs, baseline corrected, and averaged to inspect gERPs elicited by the left IC or the right IC, respectively.
## 2 Problem Description and Model Analysis
### Bounded Analysis
Under the constraints \(M^{\mathrm{T}}(t)M(t)\leq I\), Lyapunov function is considered in combination with the above two lemmas:  
\[V(x)=x^{\mathrm{T}}(t)Px(t)\]Then:  
\[\dot{V}(x)=x^{\rm T}(t)(A^{\rm T}P+PA)x(t)\] \[=x^{\rm T}(t)([A+BK+EM(F_{a}+F_{b}K)]^{\rm T}P+P[A+BK+EM(F_{a}+F_{b}K )])x(t)\] \[\leq x^{\rm T}\{symm(P[A+BK]+[A+BK]^{\rm T}P)+EM(F_{a}+F_{b}K)\}x+ \epsilon_{1}x^{\rm T}PDD^{\rm T}Px+\] \[\epsilon_{1}{}^{-1}x^{\rm T}(E_{1}-E_{2}K)^{\rm T}(E_{1}-E_{2}K)x+ \epsilon_{2}x^{\rm T}P[I_{n}\otimes(x^{\rm T}D)][I_{n}\otimes(D^{\rm T}x)]Px\] \[\leq x^{\rm T}\{P[A+BK]+[A+BK]^{\rm T}P+EM(F_{a}+F_{b}K)\}x\] \[<-x^{\rm T}(t)(Q+K^{\rm T}RK)x(t)\]  
It can be seen that the Lyapunov derivative is negative definite, then \((Q+K^{\rm T}RK)=-I\). As \(Q\) and \(R\) are positive definite real symmetric matrices, there is a solution \(P\) of positive definite real symmetric matrix, so that the uncertain system (12) tends to be asymptotically stable. It has been proved that the system is asymptotically stable. Then, when \(x(\infty)=0\), there are:  
\[J=\int_{0}^{\infty}\Big{(}x^{\rm T}(t)Qx(t)+u^{\rm T}(t)Ru(t)\Big{)}dt\leq V( x(0))-V(x(\infty))=V(x(0))=x_{0}{}^{\rm T}Px_{0} \tag{18}\]  
From the above conditions, \(\Delta\hat{\Theta}(t)\), the lower and upper bounds can be displayed here as follows:  
\[J\leq x_{0}^{\mathrm{T}}Px_{0} \tag{17}\]0  
\(\Box\)  
**Remark 1**. : _(11) and constraint condition \(M^{\rm T}(t)M(t)\leq I\) are admissible conditions, which are widely used to represent the parameter uncertainty in the control system. When other disturbances are not considered, the derivative of the quadratic Lyapunov function along the system trajectory is in the form of \(V(x)=x^{\rm T}(t)Px(t)\) to prove and infer Lemma 2._  
**Remark 2**. : _In uncertain structure matrices \(E\), \(F_{a}\), \(F_{b}\) and \(M\), according to the restricted condition of Lebesque measurable matrix value, the function conforms to the above operation within a certain bounded range._
## 3 Results and Discussion of Biosensor Formation and Its Validation
### Limit of detection (LOD) and Limit of Quantification (LOQ)  
The LOD and LOQ were determined by measuring 10 replicates of the lowest concentration detectable by the device detector (1 pg/mL) for both proteins. Their standard deviation (SD) values were determined. LOD was taken as 3.SD and LOQ as 3-LOD. The values of the limits determined were as follows: for VEGF-A, LOD = 1.07 pg/mL, LOQ = 3.21 pg/mL; for FGF-2, LOD = 1.64 pg/mL, LOQ = 4.92 pg/mL.
## 6 Results and Discussion: Forecasting Performance
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Subset FS** & **Spain** & \multicolumn{2}{c}{**Madrid**} & \multicolumn{2}{c}{**Alicante**} & \multicolumn{2}{c}{**Segovia**} \\ \hline  & **Standard** & **(RMSE 6-Steps)** & **Standard** & **(RMSE 6-Steps)** & **Standard** & **(RMSE 6-Steps)** & **Standard** & **(RMSE 6-Steps)** & **Standard** \\  & **Deviation** & **Deviation** & **Deviation** & **Deviation** & **Deviation** & **Deviation** \\ \hline \multicolumn{6}{c}{_Forwardcasting technique. LR_} \\ \hline No FS. & 0.7624 & 0.3922 & 1.1144 & 0.5175 & 1.1849 & 0.2454 & 1.7304 & 0.5124 \\ MOES-LR & 0.2942 & 0.0413 & 0.3430 & 0.1167 & 0.4724 & 0.1636 & 0.5129 & 0.1208 \\ MOES-RF & 0.1870 & 0.0312 & 0.2709 & 0.0066 & 0.3974 & 0.0704 & 0.3104 & 0.1108 \\ MOES-IR & 0.3202 & 0.1638 & 0.5108 & 0.2423 & 0.9960 & 0.3369 & 0.8974 & 0.1692 \\ Rank-LR & 0.2940 & 0.1203 & 0.3519 & 0.1406 & 0.8825 & 0.0843 & 0.3370 & 0.0107 \\ Rank-RF & 0.2615 & 0.0861 & 0.3153 & 0.0493 & 0.4081 & 0.0047 & 0.2756 & 0.1074 \\ Rank-RF & 0.4188 & 0.0217 & 0.5127 & 0.1821 & 1.1618 & 0.3647 & 1.2120 & 0.1902 \\ Rank-PCA & 0.3565 & 0.0729 & 0.6136 & 0.0748 & 1.0383 & 0.3725 & 1.15588 & 0.7191 \\ \hline
**RMSE** & **0.3618** & **0.5041** & **0.5139** & **0.5043** & **0.5043** & **0.5043** & \\ \hline \multicolumn{6}{c}{_Forwardcasting technique. RF_} \\ \hline No FS. & 0.2522 & 0.0253 & 0.3198 & 0.0208 & 0.4245 & 0.0384 & 0.7339 & 0.0178 \\ MOES-LR & 0.1910 & 0.0160 & 0.3047 & 0.0224 & 0.3922 & 0.0360 & 0.6163 & 0.0118 \\ MOES-RF & 0.1686 & 0.0143 & 0.2928 & 0.0258 & 0.3776 & 0.0297 & 0.5756 & 0.0127 \\ MOES-IBk & 0.2058 & 0.0176 & 0.3051 & 0.0245 & 0.3978 & 0.0350 & 0.6592 & 0.0144 \\ Rank-LR & 0.2031 & 0.0157 & 0.2990 & 0.0171 & 0.3804 & 0.0418 & 0.6195 & 0.0160 \\ Rank-RF & 0.1894 & 0.0183 & 0.2921 & 0.0151 & 0.3704 & 0.00303 & 0.5798 & 0.0154 \\ Rank-RIf & 0.2172 & 0.0179 & 0.3158 & 0.0155 & 0.4026 & 0.0348 & 0.7262 & 0.0288 \\ Rank-PCA & 0.2095 & 0.0193 & 0.3113 & 0.0218 & 0.4047 & 0.0324 & 0.6665 & 0.0231 \\ \hline
**RMSE** & **0.2046** & **0.3051** & **0.3938** & **0.6471** & **0.3948** & **0.6471** & **0.36**72** \\ \hline \multicolumn{6}{c}{_Forwardcasting technique. SVM_} \\ \hline No FS. & 0.4885 & 0.0618 & 0.8038 & 0.2934 & 1.4879 & 0.4664 & 1.7207 & 0.7698 \\ MOES-LR & 0.2352 & 0.0365 & 0.3276 & 0.1057 & 0.4940 & 0.0888 & 0.4714 & 0.0987 \\ MOES-RF & 0.1780 & 0.0434 & 0.2508 & 0.0329 & 0.3407 & 0.0620 & 0.3418 & 0.1222 \\ MOES-IBk & 0.3222 & 0.0837 & 0.4588 & 0.1980 & 0.5229 & 0.1560 & 0.5738 & 0.1021 \\ Rank-LR & 0.1819 & 0.0618 & 0.4583 & 0.1478 & 0.4866 & 0.1523 & 0.5341 & 0.4749 \\ Rank-RF & 0.1789 & 0.0264 & 0.2620 & 0.00866 & 0.2975 & 0.0681 & 0.4103 & 0.2492 \\ Rank-RIf & 0.3674 & 0.1605 & 0.5824 & 0.0747 & 0.8858 & 0.3994 & 1.0350 & 0.1614 \\ Rank-PCA & 0.4185 & 0.2032 & 0.4866 & 0.1357 & 0.7613 & 0.0778 & 0.6652 & 0.3641 \\ \hline
**RMSE** & **0.2963** & **0.4538** & **0.6596** & **0.7190** & **0.7190** & **0.74**19** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Average RMSE to 6-step GBV complaints forecasting in different territories.
## 1 Introduction
Another way to optimise spray applications is to adapt the nozzle spacing and boom height to the bed width. This strategy allows to better limit the spray liquid to the targeted crop and to minimise losses between the beds. Spray patterns for different bed widths and the ability to apply different dose rates depending on the crop canopy density were studied [17; 18; 19] However, these studies did not consider the possibility of adjusting nozzle spacing and boom height to the canopy width that increases during the growing season. The benefit of spray configurations with adjustable nozzle spacing and height is still to be demonstrated in terms of canopy deposition, ground losses, and spray drift, possibly further reducing PPP use and environmental contamination, as was the goal in this study. To select the most optimal sprayer configurations and their most appropriate nozzle spacing and height for different target zone widths, which depends on the growth stage of the bed-grown carrots, preliminary spray distribution measurements were carried out and compared to a reference broadcast application [20]. Four bed spray configurations were identified for spraying different target zone widths (ranging from 1.2 to 2.2 m) with high uniformity (CV < 12%) and minimal losses out of the target zone (<17%), when applied at the most appropriate nozzle spacing and height (varying from 0.35 to 0.65 m). The objective of the present study is to validate these four bed spray configurations under semi-field conditions for (i) spray deposition on early and full-grown carrots and (ii) spray drift potential using a drift test bench according to ISO 22401 (2015), by comparing them with a broadcast application.
## 4 Discussion
### Limitations  
This study has several limitations that should be considered when interpreting the findings. First, the study focused solely on the knock-out phase of the UEFA Youth League during a single season, limiting the generalizability of the findings to the entire competition. Additionally, only 18 of the 23 matches played during the knockout phase were analyzed due to data availability constraints on the Wyscout platform. Moreover, the study relied on observational data, which may have been subject to observer bias and limitations in data collection. The study did not account for individual player characteristics, such as age, playing position, and technical abilities, which may significantly influence individual and team performance. Another potential limitation of this study is the assumption of independence in the analysis of goal-scoring attempts. Since certain teams appeared in multiple matches, repeated observations could introduce dependencies within the dataset. While each match was treated as an independent case, and the unit of analysis focused on individual goal-scoring attempts rather than team performance, we acknowledge that a multi-level or random-effects approach could have accounted for potential within-team correlations. Future studies should consider implementing hierarchical models to address the nested structure of the data better and further validate the findings. Finally, this study did not explicitly control for potential confounding variables, such as team quality, coaching styles, and opponent strength, which may have influenced the observed patterns in attacking play.
## 4 Discussion  
This section includes an analysis of the results in Section 4.1, a discussion of the advantages of the system in Section 4.2, and a discussion of its limitations in Section 4.3.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Exam** \\ **No.** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Experience** \\ **>3 Years** \\ \end{tabular} } & \multicolumn{2}{c}{\begin{tabular}{c} **With System** \\ **Punctures** \\ **No.** \\ \end{tabular} } & \multicolumn{2}{c}{\
\begin{tabular}{c} **Without System** \\ **Total Time** \\ \end{tabular} } & \multicolumn{2}{c}{
\begin{tabular}{c} **Time** \\ **Difference** \\ \end{tabular} } \\ \hline
1 & NO & 1 & 03:08 & 2 & 09:13 & 66\% \\
2 & NO & 1 & 00:09 & 1 & 00:17 & 47\% \\
3 & NO & 2 & 00:42 & 4 & 01:27 & 52\% \\
4 & YES & 1 & 00:17 & 3 & 01:08 & 75\% \\
5 & YES & 1 & 00:14 & 4 & 01:37 & 86\% \\
6 & YES & 1 & 00:07 & 1 & 00:13 & 46\% \\
7 & YES & 1 & 00:40 & 2 & 01:21 & 51\% \\
8 & YES & 3 & 02:31 & 3 & 02:14 & \(-\)13\% \\
9 & NO & 1 & 00:06 & 2 & 00:23 & 74\% \\
10 & YES & 2 & 00:57 & 3 & 01:42 & 44\% \\ \hline Average & & 1.4 & 00:53 & 2.5 & 01:57 & 53\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results of the injection time test.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Lesion Diameter [cm]** \\ \end{tabular} } & \multicolumn{2}{c}{\begin{tabular}{c} **Distance to Lesion** \\ **Range [cm]** \\ \end{tabular} } & \multicolumn{2}{c}{\
\begin{tabular}{c} **Correct Punctures** \\ **No.** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Accuracy** \\ \end{tabular} } \\ \hline
5 & 4–10 & 24 & 100\% \\
2 & 3.6–8 & 23 & 95.83\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of material collection efficiency test.
## 4 Results and Discussion
### Developed Research Methodology  
Based on the research results shown in Figures 6-8 a method of introducing CACW-stabilized soil comprises the steps:  
1. Introducing the CACW additive with the optimum percentage of CACW, i.e., 10%, that shows the highest values of UPV, CBR, and UCS in the dry silty sand soil.
2. Adding the corresponding optimum water content (9.2%) and operating the mixer such that a CACW-soil mix forms.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **\% CACW** & **2\% C + CACW** \\ \hline Mean & 736.8 & 1063 \\ Variance & 32,829.2 & 64,595 \\ Observations & 5 & 5 \\ Pearson Correlation & 0.959028 & \\ Hypothesized Mean & 0 & \\ Difference & 4 & \\ tf & \(-\)7.64721 & \\ P (T \(<\) t) one-tail & 0.000786 & \\ t Critical one-tail & 2.131847 & \\ P (T \(<\) t) two-tail & 0.001571 & \\ t Critical two-tail & 2.776445 & \\ \hline \hline \end{tabular}
\end{table}
Table 2: \(t\)-test: Paired two samples for means for UCS results.  
Figure 8: Effect of CACW addition on UCS.  
3. Introducing the Portland cement (2%) into the mixer and operating the mixer such that the cement-CACW stabilized soil forms.
4. Constructing specimens for durability tests and microscopic investigation
5. Comparing the results with the rigid and flexible pavement layer structure requirements and making a decision.
## 2 Converter Transformer Fire Calculation Model and Operating Condition Settings
### Model Configuration  
The basic parameters and overall view of the converter transformer in the fire scene selected in this paper are shown in Figure 1, and the model component materials are mainly steel materials for the purpose of accurately simulating the heat conduction situation.  
To address model simplification needs, the fire source fuel material for the insulating oil, generally the esters formed by C6-14 straight-chain or branched-chain saturated or unsaturated fatty acids and glycerol, uses heptane approximation to simplify the settings for the combustion model of the calculation model, simulating the pool fire on the converter transformer; due to the FDS grid using an orthogonal mesh, the liquid pool is set up as a square liquid pool with an equivalent circular area. The combustion model and boundary conditions are set up as shown in Figure 1 in addition to the lower layer for the inert boundary for simulation. The remaining five boundary conditions are set as an open boundary to ensure material exchange.  
In the selected fire scenario of this article, the basic parameters and overall view of the transformer are shown in Figure 1. The material of the model components is primarily steel for the purpose of accurately simulating heat conduction.  
In this study, a fixed heat release rate (HRR) was not chosen as the input condition for the fire source. Instead, the liquid evaporation model in FDS was used to calculate the mass loss rate of the liquid pool through the heat of evaporation, from which the HRR was  
Figure 1: Introduction of converter transformer model and simulation conditions.  
derived. This approach not only allows for obtaining the cooling effect of the fine water mist spray but also its extinguishing effect on the pool fire. Two protection measures were applied in the simulation; the parameters of the fireproof coating are shown in Table 2, and the parameters of the water spray system are based on the default settings in FDS.
# System of Systems Lifecycle Management--A New Concept Based on Process Engineering Methodologies
## 7 Introducing the System of Systems Lifecycle Management Model
### An Implemented System Integration for Arrowhead Using the Newly Developed SoSLM Methodology
#### 7.2.1 Design  
To present a comprehensive management model, the design main cycle must be subdivided into other subcycles. Our approach suggests extending this using the described PLM processes in Section 5.3.2 to cover the precisely identified gaps.  
Figure 10: A practical example of system of systems lifecycle management—the main cycles, subcycles, processes, and tools used within the proposed dynamic, multi-level workflow management concept [87].  
* Research: for IoT application development, the first step is to identify the requirements and, based on them, find the optimum solution for the sphere of activities. The earlier presented case [99] was a bit complex because our workflow management approach addresses two kinds of workflow levels (enterprise and production); therefore, in our case, the research subcycle is about the selection of the appropriate workflow modeling languages. This separation subdivides the research subcycle into two further processes on the third ring, and accordingly, the selected languages can be seen at the fourth ring.
* Development: after the requirement specification, development can start. As it can be seen in Figure 10, the inputs of the third and fourth rings of the development subcycle are the outputs of the similar rings of the research subcycle. Additionally, appropriate development environments are chosen. In the current case, the development phase is split into three other processes: one environment for development and two further tools for the modeling languages.
## 3 Results and Analysis
### Evolutionary Characteristics of Dynamic Strain
#### 3.2.2 Strain Curve at Different Static Forces  
Figure 12 shows the dynamic strain evolution curves of the tunnel lining under four static load levels. The following remarks can be observed from Figure 12.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Condition**} & \multicolumn{2}{c}{**Dynamic Tensile Stress**} & \\  & \multicolumn{2}{c}{**Increment (MPa)**} & **Location** \\ \hline \multirow{4}{*}{Cross-angle} & 0\({}^{\circ}\) & 0.378 & Invert of upper tunnel \\  & 30\({}^{\circ}\) & 0.385 & Invert of upper tunnel \\  & 60\({}^{\circ}\) & 0.398 & Invert of upper tunnel \\  & 90\({}^{\circ}\) & 0.513 & Invert of upper tunnel \\ \hline \multirow{4}{*}{Rock pillar thickness between two tunnels} & 1 m & 0.513 & Invert of upper tunnel \\  & 3 m & 0.461 & Invert of upper tunnel \\ \cline{1-1}  & 5 m & 0.432 & Invert of upper tunnel \\ \cline{1-1}  & 10 m & 0.348 & Invert of upper tunnel \\ \hline \hline \end{tabular}
\end{table}
Table 7: \(Cont.\)The strain cumulative evolution curve presents the typical three-stage characteristics: a disproportionate increase in deformation, a linear increase in deformations, and a destruction stage due to the unstable increase in crack; the test results agree with the theoretical curve overall. The evident increase occurs in the first and third stage, due to the deformation of microscopic cracks for the former and the rapid increase of macroscopic fracture until failure for the latter. The dynamic strain of the tunnel lining increases with the cyclic ratio (\(N/N_{f}\)) and shows the characteristics of nonlinear enlargement in the second stage.  
The fatigue failure of the lining structure is closely related to the stress level. When the static load stress level is less than 0.6, no failure stage or material damage occur in the structural strain process (Figure 12a). When the static load stress level is greater than 0.6, the entire strain growth process of the structure can be divided into three different stages. The change in structural strain is relatively stable in the middle stage, the growth of structural strain is relatively fast in the initial stage, while the structural strain increases evidently and structural damage occurs during the failure stage (Figure 12b-d).  
In the first stage of specimen failure, no visible cracks or evident deformations can be observed on the surface of the structure, indicating that the evolution of structural damage is the development process of the defects, such as micropores and microcracks inside the material, which conform to the irreversible cumulative damage and failure characteristics of the structure. The appearance of these phenomena indicates that the specimen firstly develops the initial crack in the lower tensile region. After a long period of damage accumulation, the microcrack runs through and rapidly expands into a macroscopic crack, which eventually forms a bottom-up failure crack (Figure 13).  
Figure 12: Dynamic strain curve: (**a**) static load = 25 kN, (**b**) static load = 27 kN, (**c**) static load = 29 kN, and (**d**) static load = 31 kN.
# Research on Optimized Energy Scheduling of Rural Microgrid
## 4 Short-Term Rural Electricity Load Forecast
### Establish a Database of Crop Growth Environment Indicators  
The growth environment required for greenhouse crops is related to their growth stage and species. Different crop types and growth stages require different growing environments and consume different amounts of electricity. Therefore, in order to accurately predict the electricity consumption of agricultural loads, it is necessary to establish a database of crop growth environment indicators based on the actual situation in rural areas.  
In the process of formulating environment indicators for greenhouse crops, the indicators selected in this paper are mainly related to the following four aspects: light intensity (I), ambient temperature (T), soil moisture (\(\theta\)) and carbon dioxide concentration (C). By analyzing the degree of correlation between these environmental indicators and electricity consumption, the indicators with high correlation coefficient are selected as the input feature vector, which is used to train the prediction model.  
Figure 3: Greenhouse system model.