esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Video and music as a dissociative attention stimulus during exercise is known to distract from the discomfort of physical exertion and improve exercise adherence; however, the influence of video-based feedback and engagement during pedalling on the performance and motivation of pedalling in stroke patients is poorly understood. The aim of this study was to employ a novel video-based engagement paradigm for pedalling in stroke patients and evaluate its capacity to influence the cadence control, physiological output, and perceived motivation and enjoyment. Thirteen stroke patients were recruited with low-to-moderate lower-limb disability (mean age: 64.0 yrs.). A reference group of 18 healthy young adult subjects (mean age: 27.7 yrs.) was also recruited to assess the broad applicability of the techniques to a contrasting non-pathological cohort. The participants pedalled at a slow (60 RPM) and fast (100 RPM) target speed with constant resistance in 15 min pedalling bouts that included (i) baseline pedalling with real-time visual feedback of cadence deviation from the target provided only in the first 20 s (ii) real-time visual feedback of cadence data over the entire pedalling session, and (iii) real-time engagement to maintain the playback rate of a prerecorded video by pedalling at the target speed. During low speed pedalling, stroke patients demonstrated significantly smaller absolute cadence deviation during pedalling with feedback (mean difference: 1.8 RPM, \(p=0.014\)) and video-based engagement (mean difference: 2.4 RPM, \(p=0.006\)) compared to the baseline pedalling. For the healthy adults, feedback and video-based engagement reduced cadence deviation significantly at all speeds (\(p<0.05\)). All but one stroke patient either enjoyed or really enjoyed the video engagement during pedalling and felt motivated to undertake this form of exercise in therapy in the future. This proof-of-concept study showed that feedback and video-based engagement may improve the targeted pedalling performance in stroke patients, and by helping dissociate subjects from physical cues associated with fatigue, may ultimately improve exercise motivation and compliance.  
rehabilitation; therapy; exercise performance; cadence control Article
# Investigation of the Effects of Different Working Fluids on Compressor Cascade Performance
## 3 Computational Techniques
### Mesh Sensitivity Study  
To avoid the effect of outlet and inlet boundary on flow field in cascade, the inlet boundary is fixed at 1.5 of chord length away from leading edge. The outlet boundary is situated at twice the chord length away from trailing edge. In order to save the calculation cost, half of the blade channel is selected, and symmetric boundary conditions are fixed at 50% blade height. The simulation of a blade channel is conducted with periodic interface in horizontal direction. The computational grid is generated using ANSYS ICEM CFD. Cell-centers next to the surfaces are set at y+ \(<\) 1. Figure 1 shows representative computational grids of this model.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Unit** \\ \hline Chord length (C) & 60 & [mm] \\ Inlet geometry angle (\(\alpha\)1) & 48 & [\({}^{\circ}\)] \\ Aspect ratio & 1.67 & [/] \\ Axial chord length (Cx) & 55 & [mm] \\ Outlet geometry angle (\(\alpha\)2) & 90 & [\({}^{\circ}\)] \\ Blade pitch (t) & 33 & [mm] \\ \hline \hline \end{tabular}
\end{table}
Table 1: NACA65-K48-Basic parameters [20].  
Figure 1: The NACA65-K48 grid structure.  
To avoid the influence of mesh size on the computation, the study of mesh independence is conducted on the numerical model. Four models are numerically simulated using the same outlet pressure with variation in grid points. As shown in Table 2, the change of mass flow and static pressure ratio with a grid number is not obvious within the selected grid number range. Total pressure loss coefficient distribution contours at various grid points, as presented in Figure 2. The variation in total pressure loss coefficient distribution is negligible with a rise in grid points above 1.78 million. As a result, rotor grid points are fixed at approximately 1.78 million.
## 2 Explicit Element Geometric Transformation
### Basic Geometric Transformation
With a user-specified scaling factor \(\lambda\), the new node positions of the transformed element can be calculated by:  
\[p_{k}^{{}^{\prime}}=p_{k}+\lambda n_{k}. \tag{3}\]  
The above formula shows that the moving distance of node \(p_{k}\) is proportional to the magnitude of its stretching vector \(n_{k}\). Therefore, the proposed stretching operation is adaptive to element size. The size of a transformed element after the stretching operation is usually larger than that of the initial element. This can be avoided by scaling the element after stretching operation with respect to the original centroid. Such an operation ensures that the transformed element with more regular shape is close to the initial element and has a small change in element size. In general, there are two schemes for element scaling, the mean edge length or mean area preserving scaling. The mean edge length preserving scaling scheme is used in this paper for the sake of simplicity. With this scheme, the scaled transformed element with new nodes \(p_{k}^{s}\) is given by:  
\[p_{k}^{s}=c+k(p_{k}{}^{\prime}-c^{\prime}), \tag{4}\]  
where \(c\) and \(c^{\prime}\) represent the initial and transformed element centroid, respectively. The scaling factor \(k\) is defined as the mean edge length of the initial element divided by that of the enlarged transformed element. The stretching and shrinking operation together make up a complete element geometric transformation process. The final node positions of the scaled transformed element can be obtained easily and explicitly with Equations (1)-(4). The geometric transformation process of the same inverted hexagonal element as in Figure 2 using the modified transformation operation is shown in Figure 3. It can be seen that the element becomes a nearly regular hexagonal element after applying the modified transformation for one hundred times, which intuitively illustrates the regularizing capacity of the modified transformation operation. In fact, the modified element geometric transformation operation can successfully transform an arbitrary polygonal element into its regular counterpart after enough iterations, which will be proved in the following subsection. Figure 3: (**a**) Initial inverted hexagonal element. (**b**) Hexagonal element after applying the modified transformation for one hundred times.
## 3 Model Evaluation
### Estimation Results by Penetration Rate of Probe Data  
To evaluate the performance of the novel estimation method during peak periods (6:30-13:00), this study compared average absolute estimation errors by different penetration rate of probes from random sampling in Figure 2. Among all the results, the estimation error has a pattern in which it decreases as the penetration rates of probes increases. Although the estimation error gradually decreases as the number of probes increases, moving average methods show constant errors since the moving average is not intensely related to the penetration rate of probe data. The simple mean method produces similar patterns of estimation error with the Bayesian mixture estimation, but the average absolute estimation error is higher than the Bayesian mixture estimation. The Bayesian mixture estimation shows higher accuracy than other models for a 3-13% penetration rate of probe data. However, the Bayesian mixture estimation has limitations in that it cannot be used to perform estimations at under 1-2% penetration rate of probes due to constraints of the model at low sample numbers.
## Materials and Methods
### Time Series Decomposition and Trend Estimation  
The TWSA series for different GRACE products was obtained by regional averaging. The seasonality of the GRACE-derived TWSA series was removed based on local regression (STL) [68]. STL uses the local regression smoothing (LOESS) operator to decompose the TWSA time series into three components as Equation (7), where Ttotal represents the GRACE-derived TWSA. The TWSA trend refers to the linear trends estimated from Tlong-term, Tseasonal, and Tresidual that represent the long-term trend, the seasonal signal, and the sub-seasonal signal or noise, respectively. All units in Equation (7) are in cm.  
\[\text{T}_{\text{total}}=\text{T}_{\text{long-term}}+\text{T}_{\text{seasonal}}+\text{T}_{\text{residual}} \tag{7}\]  
Additionally, the Mann-Kendall trend test (M-K test) was used to calculate the trend from the de-seasonalized TWSA series and other variables. The M-K test is a nonparametric and robust test commonly used to detect trends in hydrologic data [69]. Further details about the M-K test can be found in [70; 71].
# Train Wheel Condition Monitoring via Cepstral Analysis of Axle Box Accelerations
## 4 Experimental Data
### Cepstrum Analysis of Experimental Data  
The cepstrum analysis is performed for the data represented in Figure 5. First, the cepstrum was calculated for the whole length of the data (11,100 m). The cepstrum shows a distinct peak at a navewumber of 3 m that corresponds to the wheel circumference (Figure 6).  
Figure 4: GNSS train position and speed.  
Figure 5: Axle-box accelerations (ABA) data (solid blue line) and speed data (dashed red line) in the time domain.  
Figure 6: Cepstrum of ABA data calculated from the whole journey with a length of 11,100 m.  
In order to investigate the changes of the cepstrum along the train journey, the data was divided into segments of a certain length using a Hann window with 50% overlap. Then the cepstrum was calculated for each window and the peak position and amplitude determined between navewumbers of 2 m and 4 m. Different window sizes were tested. The results are shown in Table 2. For each test the median peak position across all windows was computed. Then, the percentage of windows in which the peak occurred in a range of 0.01 m around the median peak position and the mean amplitude of those peaks were determined. In Table 2, we refer to these peaks close to the median position as \"correctly\" depicted peaks. It can be seen that the median peak position is constant for all tests. The percentage of windows in which the peak position was close to the median position is very similar for windows larger than 20 m. It can be assumed that smaller windows are more affected by track singularities, which might mask the cepstral response of the wheel. The mean amplitude of the peaks of the shortest windows might be affected for the same reason.  
The position of the cepstrum peaks and their amplitudes for segments of 40 m length are shown in Figure 7. The position of the cepstrum peak could be precisely recovered apart from a few locations. Especially at the beginning and the end of the journey, where the train speed is low, the algorithm struggles to find the right peak location. Therefore, we assume that a minimum speed of 15 m/s is necessary to provide reliable results.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Window** & **Number of** & **Median Peak** & **Percentage of “Correctly”** & **Mean Amplitude of** \\
**Length in m** & **Windows** & **Position in m** & **Depicted Peaks** & **“Correctly” Depicted Peaks** \\ \hline
10 & 2222 & 2.99 & 56.57 & 0.28 \\
20 & 1112 & 2.99 & 79.77 & 0.52 \\
40 & 557 & 2.99 & 86.71 & 0.53 \\
80 & 279 & 2.99 & 86.02 & 0.52 \\
160 & 140 & 2.99 & 89.29 & 0.51 \\
320 & 71 & 2.99 & 84.51 & 0.56 \\
640 & 36 & 2.99 & 88.89 & 0.52 \\
1280 & 19 & 2.99 & 84.21 & 0.54 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of segmented cepstrum analysis with different window sizes.  
Figure 7: Cepstrum analysis of 40-m-long track segments; (**a**) position of cepstrum peaks at navewumbers between two and four meters and (**b**) corresponding peak amplitude.
# A Switching Observer for State-of-Charge Estimation of Reconfigurable Supercapacitors
## 3 Observer Design for Switching Systems
### System Properties
: To assess the observability of the system, let us examine the controllability and observability matrices derived from Equations (10) and (11):  
\[\text{C}_{1}=0,\text{C}_{2}=\frac{1}{\text{C}_{eq}v_{rated}} \tag{16}\]  
\[\text{O}_{1}=v_{rated},\text{O}_{2}=v_{rated} \tag{17}\]  
These matrices reveal the following ranks: \(rank(\text{C}_{1})=0\), \(rank(\text{C}_{2})=1\), \(rank(\text{O}_{1})=1\), and \(rank(\text{O}_{2})=1\). When the capacitor is short-circuited (\(s=0\)), the system becomes uncontrollable, as indicated by \(rank(\text{C}_{1})=0\). However, crucially, the system remains observable in both switch states, evidenced by the full rank of the observability matrices (\(rank(\text{O}_{1})=rank(\text{O}_{2})=1\)). Therefore, even though controllability is lost when the capacitor is short-circuited, the system's observability persists, allowing us to estimate the state (SOC) based on the available measurements.
# Adaptive Tolerance Dehazing Algorithm Based on Dark Channel Prior
## 4 Comparison and Analysis of Experiential Results
### Quantitative Comparison
#### 4.3.2 Structural Similarity (SSIM) Image Quality Assessment  
The structural similarity (SSIM) image quality assessment index [35] is introduced to evaluate the ability to preserve the structural information of the algorithms. This indicator was first proposed by the Image and Video Engineering Laboratory at the University of Texas at Austin. It is often used in image processing, especially in image denoising, which comprehensively surpasses SNR and PSNR in image similarity evaluation. The high SSIM indicates that the haze-free image is highly similar to the ground truth image, while the low SSIM indicates the opposite.  
To directly compare the structural similarity between the restored image and the real image, we compare the dehazing results of the synthesized depth image in Figure 7. Table 2 shows the SSIM of the four restored images in Figure 7. The SSIM results of Tarel are all less than 0.8, indicating that a large amount of structural information is lost in the restored image. It is clear that [PERSON]'s SSIMs are higher than those of the other four algorithms. [PERSON]'s algorithm is a deep learning algorithm that continuously trains to obtain the best-restored image by learning the relevant features of the synthesized depth image. The algorithm can save structural information well. Our results reach the highest SSIM results except for Cai. It is proved that the ability of the algorithm to save structural information is superior to the general algorithm.  
All the simulations for our proposed algorithm are carried out in MATLAB R2016a environment running on a personal computer with an Intel Core (i3-8100) central processing unit running at 3.6 GHz with 4 GB of RAM. The average computing time for dehazing processing of our algorithm is less than 0.5 seconds. We think it is possible to apply it to a video including several frames in the future.
## 3 Experimental Design and Case Study
### Case Study: NA-150 Road  
We selected a section of the NA-150 road in Navarra, Spain, as our case study (Figure 4). This is a two-lane interurban road with generally low traffic density. Crucially, a spare dark fiber-optic cable exists, buried approximately 40 cm deep in the shoulder along the westbound side.  
Figure 3: Comparison of a real DAS signal corresponding to a light vehicle (Mercedes Class B) and the synthetic strain-rate signal simulating the DAS measurement for the same car model, created by combining the point load effects of each wheel using the Flamant–Boussinesq approximation.  
The chosen 1 km section includes features that challenge DAS measurements (Figure 5):  
* **Strip A (Abandoned Service Station):** The cable deviates significantly from the road shoulder, following the path of an old access lane. This increases the distance (\(y\)) to vehicles on the main road, weakening the signal.
* **Strip B (Urbi River Bridge):** The cable runs through a side conduit on the bridge, rather than being buried beneath the pavement. This alters the vibrational coupling compared to the buried sections.  
These features provide opportunities to test the methodology's ability to model and potentially compensate for non-ideal layouts. Real DAS data collected from this fiber using a differential-phase coherent optical time-domain reflectometry (COTDR) interrogator [18] were used for comparison and validation of the models trained on synthetic data.  
The VISSIM model for this section was created as described in Section 2.1, incorporating the 3D road geometry, the detailed fiber path (including depth and deviations at A and B), and enriched vehicle types (cars, trucks, motorbikes, bicycles) with appropriate weights and dimensions. Simulations were run for 10 h each at low (50 veh./h/lane), intermediate (250 veh./h/lane), and high (500 veh./h/lane) traffic densities, generating trajectories for 998, 6162, and 10,070 vehicles, respectively.
# Bridge Assessment under Earthquake and Flood-Induced Scour
## 3 Case Study
### FE Modeling of Elements  
The structure of the bridge is analyzed using CSIBridge v25.0.0 software. The effectiveness of the analytical model for the bridge relies on its elements' functionality and interconnectivity. The FE model of the bridge shown in Figure 4 includes some assumptions and idealization, as described below. Characteristic values of materials are used as given in the project details. Concrete class C30/37 is used for reinforced concrete components and C45/55 for precast girders. The increment of existing concrete strength due to the  
Figure 3: Accelerograms and elastic spectra obtained from the earthquake that took place on November 26 (M = 6.4), 2019, as observed by the accelerometric station located in Tirana.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Flood Probability of Exceedance** & **1\%** & **2\%** & **5\%** & **10\%** & **20\%** \\ \hline
**Q [m3/s]** & & **661** & **595** & **528** & **463** & **397** \\ \hline  & Bent No. 1 & 3.48 & 2.94 & 2.59 & 2.32 & 2.12 \\  & Bent No. 2 & 3.40 & 2.86 & 2.51 & 2.24 & 2.03 \\ Scour depth (\(y_{\text{s}}\)), & Bent No. 3 & 3.54 & 2.99 & 2.64 & 2.37 & 2.18 \\
[m] & Bent No. 4 & 3.62 & 3.07 & 2.72 & 2.45 & 2.27 \\  & Bent No. 5 & 3.53 & 2.99 & 2.42 & 2.12 & 1.91 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Local scour depths at bent foundations.  
aging of concrete is not considered. The steel class of B500C is used for reinforcing rebars. In the seismic analysis, the permanent masses of self-weight of structural elements, wearing surfaces, sidewalks, and barriers are considered, and the moving loads are excluded (EN1998-2) [22]. Also, referring to the research works carried out by [PERSON] et al. (2020, 2022) [9; 10], the bridge is visually inspected, and it is concluded that the reinforced concrete elements and precast girders have adequate concrete cover and no exposed steel rebars are observed. So, the influence of corrosion induced by the carbonation phenomenon is not considered in the analysis.
## 4 Experiments
### Next Spot Prediction Task  
In the next spot prediction experiment, the last four continuous spots were considered the input and the next spot was considered the desired output, i.e., \(\{u(t-3),u(t-2),u(t-1),u(t)\}\) was used to predict \(u(t+1)\). The number of the train samples and the test samples was 1800 and 2000, 200 samples before the first training sample was used to wash out the initial transient (see Figure 6). The reservoir diversity was quantized by the ASE (see: Section 2.3) of the combined training and testing neuron states activated by training and testing samples. The predictive error performance was quantized by the normalized root mean square error:  
\[\textit{NRMSE}=\sqrt{\sum_{t=1}^{T}\frac{\mid\textbf{y}(t)-\hat{\textbf{y}}(t )\mid^{2}}{T\mid\textbf{y}(t)-\bar{\textbf{y}}(t)\mid^{2}}}, \tag{25}\]  
where \(T\) is sample number, \(\hat{\textbf{y}}(t)\) is the desired output, \(\textbf{y}(t)\) is the readout output, and \(\bar{\textbf{y}}(t)\) is the average of \(\textbf{y}(t)\).  
Two different initial reservoir size conditions of DeepESN were performed on both datasets. The first is four stacked reservoirs with 100 neurons in each reservoir (abbreviated to: \(4\times 100\)), and the second is eight stacked reservoirs with 50 neurons in each reservoir (abbreviated to: \(8\times 50\)). All experiments were carried out under two different initial conditions on two datasets, and model hyperparameters tuned by PSO were recorded in Table A1.  
Figure 6: \(MG\) (**a**) and \(LZ\) (**b**) datasets.
# Design of a Phase Calibrator to Evaluate Non-Matched Microphones for Intensity Probes
## 2 Materials and Methods  
The main concept behind the design of the phase calibrator is exposing the two microphones to the same acoustic field to extract the intrinsic phase difference between them. Following a definition of the numerical model to understand the behavior of the acoustic volume of the calibrator, a prototyping phase started; both microphones and speakers were chosen given their frequency response, geometric characteristics and cost. The last step was the testing of the system to provide for some refinements that led to the final product. The milestones of the project were the following:  
* Definition of the numerical model to understand the behavior of the acoustic model with respect to the internal geometry of the calibrator;
* Microphones and speaker choice for frequency range and geometric references;
* Calibrator geometry design, prototyping and software implementations;
* Phase difference extraction and configuration refinements;
* Final configuration definition.
# Weather Files for the Calibration of Building Energy Models
## 2 Methodology
### Calibration Process with Or-Site and Third-Party Weather Data
The results are presented in Section 3. \[MAE=\frac{1}{n}\sum\limits_{i=1}^{n}|y_{i}-\hat{y}_{i}|\] (5)
* The correlation coefficient of Spearman's range (\(\rho\), Equation (6)) estimates the level of correspondence of the form. This uncertainty index measures the degree of correspondence that exists between the ranges that are assigned to the values of the analyzed variables. \[\rho=\frac{\sum\limits_{i=1}^{n}\left(rg(y_{i})-\overline{rg(y)}\right)\left( rg(\hat{y}_{i})-\overline{rg(y)}\right)}{\sqrt{\left(\sum\limits_{i=1}^{n} \left(rg(y_{i})-\overline{rg(y)}\right)^{2}\right)\left(\sum\limits_{i=1}^{n} \left(rg(y_{i})-\overline{rg(y)}\right)^{2}\right)}}\] (6)  
The non-dominated sorting genetic algorithm (NSGA-II) [46] was chosen to guide these functions towards the optimal result, as it is one of the most widely used algorithms in building optimization strategies [47; 48; 49]. The proposed combinations for the algorithm to work and, thus, obtain quality results, were based on the parameters of capacitance, thermal mass, and infiltration of each thermal zone (see Figure 3). Once the chosen period has been calibrated according to this methodology, the model that best suits it will be chosen. The calibration process will be repeated with the two proposed weather files (on-site and third-party). In order to validate the quality of the models, the limits established by Annex 58 were used as a reference, where the BEM was valid if a mean absolute error (MAE) equal to or less than 1 \({}^{\circ}\)C and a Spearman's rank correlation coefficient (_rho_) equal to or greater than 90% were obtained. This is because other standards, such as IPMVP or ASHRAE [26; 50], do not give any reference for the evaluation of temperature, but only mention the energy behavior of the BEM. The results are presented in Section 3.2. Once all of the models were developed, their degrees of fit (with respect to the real data) were analyzed to see whether it was possible to calibrate a BEM using a third-party weather. Figure 3: Calibration and evaluation environments.