esa-sceva/satcom-chunk-collection huggingface dataset:
# Towards Improving the Durability and Overall Performance of PV-ETICS by Application of a PCM Layer
## 1 Introduction
One of the ideas of how to stabilize the temperature of a PV panel is to increase the thermal inertia of the layer between it and the thermal insulation [21]. A high latent heat capacity layer consisting of phase change material (PCM) is a promising solution if it is properly sized [22]. The construction of the external PCM layer is a further development of the composite investigated experimentally in the structure of a ventilated facade [23]. Comparison of a cooled and uncooled PV system resulted in the improvement of electrical efficiency obtained experimentally by active cooling up to 12-14% [24]. Therefore, the reason for using PCM integrated with PV is increasing the efficiency of energy conversion [25]. In the case studied here, the PCM layer is devoted to protecting against overheating above extreme temperatures, above 80 \({}^{\circ}\)C, which can cause permanent damage to the PV panel. The main challenge was to determine the layer capacity for a daily cycle of energy storage from solar radiation and then check the effectiveness of the proposed solution during the whole year. In order to consider all physical processes, the proposed model used in the simulation study takes into account the combined heat and power flow on a building element scale. The simulation model of En-ActivETICS was developed using ESP-r software, applying a special material method [26]. The approach to power flow modelling was based on [27], using the WATSUN-PV model to convert solar radiation into electricity [28]. The analysis was performed for 20 designated locations in Europe (five northern, ten central and five southern). Such analysis under different weather conditions allowed for the investigation of the impact of overheating on the overall system performance and underlined the need for protection of the system in extreme weather conditions. The analysis was carried out for a test room with an outer wall partially covered with En-ActivETICS and partially with typical ETICS.
# Effect of Compressive Strain Rate on Auxetic Foam
## 3 Results
### Poisson's Ratio  
Auxetic foams exhibited NPR up to \(\sim\)2% compression for all applied strain rates (Figures 4 and 5, Supplementary Figure S1). Lateral strain mapping (\(x\)-axis) at 2% compression parallel to the \(y\)-axis shows the anisotropic open cell foam in predominantly lateral contraction (Figure 4a,b) at the lowest and highest applied strain rates. Axial strain was higher (\(\sim\)50%) within \(\sim\)2 mm of the compression plates than in the central region of the sample, where it was close to the applied value of 2% (Figure 4c,d); attributed to contact effects [58]. Measured maximum strain rates in the central region of the samples were, then, lower (80 s\({}^{-1}\)) than imposed values (200 s\({}^{-1}\), Supplementary Figure S1). Maximum measured strains were also lower (10 to 60%, Figure 4e,f and Supplementary Figure S1) than imposed values (40 to 80%), partly due to lower stiffness in the contact regions, but also due to a safety feature in the test device limiting the applied strain at higher strain rates.  
Figure 3: Microscopic images of 1 to 2 mm thick slices of (**a**) unconverted open cell foam, (**b**) anisotropic open cell foam, (**c**) triaxial open cell foam. Inserts in (**a**) to (**c**) show single cells with underlying unconnected ribs removed. (**d,e**) Renderings from micro-computed tomographic of \(\sim\)4 \(\times\) 5 \(\times\) 20 mm samples of (**d**) unconverted closed cell foam and (**e**) triaxial closed cell foam.  
The magnitude of [PERSON]'s ratio (Figure 5) increased with applied strain rates (of 0.005 to 200 s\({}^{-1}\)) for unconverted open cell foam (\(\
u_{\rm{xx}}\) = 0.27 \(\pm\) 0.04 to 0.44) and decreased for all other foams (open cell anisotropic, \(\
u_{\rm{yx}}\) = \(-\)2.02 \(\pm\) 0.38 to \(-\)1.24; open cell triaxial, \(\
u_{\rm{zx}}\) = \(-\)0.35 \(\pm\) 0.03 to \(-\)0.30; closed cell unconverted, \(\
u_{\rm{zx}}\) = 0.32 \(\pm\) 0.03 to 0.18 and closed cell triaxial, \(\
u_{\rm{zx}}\) = \(-\)0.12 \(\pm\) 0.01 to \(-\)0.06). These differences in [PERSON]'s ratios with applied strain rates were similar across the lateral vs. axial strain range (Figure 4e,f), so the differences in [PERSON]'s ratio reported in Figure 5 are a good representation of the effect of strain rate. Standard deviations indicate differences between samples at the lowest strain rate were also relatively small (maximum standard error = 20% of mean [PERSON]'s ratio, Figure 5).  
Figure 4: (**a,b**) DIC contour plots of anisotropic open cell foam showing lateral strain (\(x\)-axis) used for [PERSON]’s ratio measurement at 2% compression (\(y\)-axis) and an applied strain rate of (**a**) 0.005 s\({}^{-1}\), (**b**) 200 s\({}^{-1}\); (**c,d**) DIC contour plots of anisotropic open cell foam showing axial strain (\(y\)-axis) at 2% compression and an applied strain rate of (**c**) 0.005 s\({}^{-1}\), (**d**) 200 s\({}^{-1}\); (**e,f**) Lateral vs. axial strain relationships for (**e**) open cell foams; (**f**) closed cell foams at applied strain rates of 0.005 s\({}^{-1}\) and 200 s\({}^{-1}\). Legend (RHS) is the same for (**a**) to (**d**).
# Multi-Identity Recognition of Darknet Vendors Based on Metric Learning
## 4 Evaluation  
We constructed a model as shown in Figure 3 and designed the following experiment to validate the effectiveness of the model.  
1. We employed a tenfold cross-validation to more comprehensively evaluate the model's performance and reduce errors caused by sample set partitioning. In other words, the sample set was divided into ten parts, and ten models were trained with a training-to-test ratio of 9:1 to obtain ten evaluation results.
2. The parameter \(margin\) in Equation (1) determined how far the negative sample pairs were pulled apart from each other, and different \(margin\) were chosen to verify their effect on the model's effectiveness.
3. [PERSON] et al. [17] proposed the eDarkFind model for identifying multiple identities of darknet vendors. The specific method involved using cosine similarity to determine whether two vendor accounts were controlled by the same real vendor. Our experiment compared the performance of our method with the eDarkFind model on the dataset collected in this study.
# Image Deblurring Based on an Improved CNN-Transformer Combination Network
## 2 Related Work
### Vision Transformer  
The transformer model was first used in natural language tasks [21,22]. Due to its strong capability to learn long-range dependencies between pixels or channels, the transformer has also been adopted for low-level computer vision tasks, including image enhancement [20], image recognition [23,24], and object detection [25,26]. The vision transformer [24] uses the image like a language sequence by introducing the concept of a patch. That is, the input image is divided into patches one by one, and then uses the transformer structure to obtain their relationships. However, ignoring convolution completely is not a good idea since the transformer only relies on global-level attention but does not capture local fine-grained details. As far as we know, local features and global information are both important for obtaining high-quality image reconstruction. Therefore, how to effectively combine these features is the core goal of our work.
# Point Cloud Repair Method via Convex Set Theory
## 2 Methods
### Generate Fill Points  
After twice screening, subunit \(s^{*}\) with the highest probability in space \(S\) around \(x_{i}\) and \(x_{j}\) was calculated; \(s^{*}\) and central subunit \(s_{1}\) together formed a specific subunit \(s\) (\(s\) includes the boundary area and the hole area). Then, we needed to add new points to the subunit \(s\). Let the direction from \(x_{i}\) to \(x_{j}\) be \(n_{i,j}\) according to the definition of the weakened convex set when all the new filling points are in the closed area contained by \(s\), all the new points are in the direction domain of line segment \(C_{i,j}\) connected by \(x_{i}\) and \(x_{j}\), \(s\) is a convex set. The filling direction of the new point is:  
\[v=\frac{\widehat{\dot{q}}\times n_{i,j}}{||\widehat{\dot{m}}\times n_{i,j}||_ {2}^{2}}, \tag{11}\]  
where \(v\) is a normalized vector pointing in the direction of hole shrinkage. By combining the Markov random field and convex set theory, we determined the distribution area of new points (along the direction of hole shrinkage). In combination with density \(\rho\) of the neighborhood point set, enough points were uniformly sampled on segment \(C_{i,j}\) and new points were added along the direction of \(v\):  
\[x_{new}=q+\zeta v, \tag{12}\]  
where \(q\) is a point on \(C_{i,j}\), and \(\zeta\) is a controllable parameter to ensure that \(x_{new}\) is in the subunit. After generating enough 3D points in specific subunit \(s\), \(x_{i}\) was moved out of the \(X\) set and continued to iterate \(x_{i+1}\) and \(x_{j+1}\) in the same way. When there is only one element left in \(X\), and the hole boundary is closed, the last element should pair with the first element in \(X\) to form a unit \(S\). When \(X\) was an empty set, we added the boundary formed by the new 3D points to \(X\) to generate new fill points again. During the whole repair process, the specific subunit gradually moved to the center of the hole region until the hole had been completely repaired.
## 4 Analysis
### Revenue Allocation in the Medical Alliance
The reason is that, with the medical alliance, the revenue from Region-1, i.e., \((V_{1}-c_{1}/(\mu_{1}-\Lambda_{1}+\lambda_{12}^{*}))(\Lambda_{1}-\lambda_{12 }^{*})\), is less sensitive to patients' unit waiting cost when compared to the case without demand sharing, i.e., \(\pi_{1}^{0}\). The difference between the two revenues, that is \(\pi_{1}^{0}-(V_{1}-c_{1}/(\mu_{1}-\Lambda_{1}+\lambda_{12}^{*}))(\Lambda_{1}- \lambda_{12}^{*})\), decreases as patients become more delay sensitive. Finally, the total revenue gain, which is a combination of the trend of revenue from commission and revenue loss in Region-1, first increases and subsequently decreases in the delay sensitivity of patients in the Region-1. The total revenue of the alliance, which is equal to \(\pi_{1}^{*}(\alpha+\beta)/\alpha\), has the same trend. Subsequently, we consider the case wherein the service quality of the two hospitals are different, that is, \(V_{1}<V_{2}\) or \(V_{1}>V_{2}\). Equation (8) is the equilibrium condition, which is the first order condition. It must be noted that closed-form solutions cannot be obtained since they are roots of a quadratic equation. We conduct a numerical study for the case with a unique equilibrium, as shown in Figure 5. We try to investigate the impact of the service quality on the commission fee, the rate of sharing patient flow, the revenue loss in Region-1, and the total revenue gain. Figure 5 shows that the total revenue gain and the demand sharing rate \(\lambda_{12}\) are all decreasing in the HD's service quality \(V_{1}\). This observation implies that the HD with a lower service quality has more incentives to share its demand with the HS, which means the HS can replace the HD to a greater extent. However, the commission fee is not necessarily monotonous with respect to \(V_{1}\). In fact, the commission fee is increasing in \(V_{1}\) when \(V_{1}<V_{2}\), and decreasing in \(V_{1}\) when \(V_{1}>V_{2}\) (reaching the maximum at \(V_{1}=V_{2}\)).
# Novel Technologies Used in the Assessment of Patellofemoral Pain: A Scoping Review
## 4 Discussion
### Recommendations for Future Studies
#### 4.4.4 Acoustic Emission  
Acoustic emission is a technique used to detect the waveform features, which has been shown to provide potential biomarkers collected from the knee joint. [PERSON] et al. conducted a study on osteoarthritis patients to demonstrate the differences in the knee from degeneration and age in the flexion-extension-related tasks [86]. The results showed that there was higher movement variation and increased asymmetry in the patient group compared to healthy individuals. Acoustic emission is shown as a measurement reflecting a composite of structural changes and joint loading factors and is highlighted as a potential candidate for relevant future studies [87]. However, even collecting data has some challenges such as application difficulties, optimal placement, accessibility, and being a novel method that still needs improvement. It can also be considered as a promising technology with the advantages of being non-invasive and non-radioactive and providing information about internal joint contact forces [88; 89].
# Adaptive Global Fast Sliding Mode Control for Steer-by-Wire System Road Vehicles
## 6 Simulation Results
\begin{split} u=-\text{sat}(s)\Big{[}\hat{a}_{1}\bigg{(}\frac{ \overline{\hat{\delta}}_{d}}{\hat{\delta}_{d}}\bigg{)}+\hat{b}_{1}\Big{|}\dot {\hat{\delta}}_{fw}\Big{|}+\hat{c}_{0}+\hat{c}_{1}\Big{|}\dot{\delta}_{fw} \Big{|}+\hat{c}_{2}\Big{|}\dot{\hat{\delta}}_{fw}\Big{|}+\lambda\hat{a}\hat{a} \hat{\rho}(e)^{\left(\frac{q}{p}-1\right)}|\dot{e}|\bigg{]}-\frac{\phi}{2}s\\ -k_{1}\text{sign}(s)-k_{2}s\\ \dot{\hat{\delta}}_{0}=\eta_{1}|s|(1-\sigma\hat{c}_{0})\\ \dot{\hat{c}}_{1}=\eta_{2}|s|\Big{|}\dot{\delta}_{fw}\Big{(}1- \sigma\hat{c}_{1})\\ \dot{\hat{c}}_{2}=\eta_{3}|s|\Big{|}\dot{\hat{\delta}}_{fw}\Big{(} 1-\sigma\hat{c}_{2})\\ \dot{\hat{a}}_{1}=\bigg{(}\eta_{4}|s|\bigg{(}\frac{\overline{\hat {\delta}}_{d}}{\hat{\delta}_{d}}\bigg{)}+\eta_{4}|s|\lambda\frac{q}{p}(e)^{\left( \frac{q}{p}-1\right)}|\dot{e}|\bigg{)}\big{(}1-\sigma\hat{a}_{1}\big{)}\\ \dot{\hat{b}}_{1}=\eta_{5}|s|\Big{|}\dot{\hat{\delta}}_{fw}\Big{(} 1-\sigma\hat{b}_{1}\big{)},\dot{\hat{\rho}}=\eta_{6}\frac{s^{2}}{2}(1-\sigma \hat{\rho}),\end{split}  \tag{71}\]  
where \(\lambda,p,q,\text{sat}(s)\), and \(\phi\) have the same values as those defined in AGFSMC. Moreover, the control parameters and adjustable parameters for adaptive laws are selected according to [27] as follows: \(\eta_{1}=4,\eta_{2}=\eta_{3}=\eta_{4}=\eta_{5}=\eta_{6}=2,k_{1}=0.001,k_{2}=4, \widetilde{\hat{\delta}}_{d}=2\) and \(\rho=0.001\), respectively. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value (s)** \\ \hline m (kg) & 1270 \\ \(I_{z}\) (kg\(\cdot\)m\({}^{2}\)) & 1537 \\ \(I_{f}\), \(I_{r}\) (m) & 1.015, 1.895 \\ \(I_{\text{\&}k}\) & 0.28 \\ \(B_{\text{\&}k}\) & 0.88 \\ \(k\) & 18 \\ \(t_{m}\), \(t_{p}\) (m) & 0.023, 0.016 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Vehicle and SbW system parameters.
# Dynamic Parameter Identification of a Lower Extremity Exoskeleton Using RLS-PSO
## 1 Introduction
In conclusion, the current parameter identification algorithms for exoskeletons are either complicated, have low accuracy, or lack of quantitative accuracy verification. The identification algorithms are generally based on the LS method. The performance of other parameter identification algorithms in exoskeleton dynamic parameter identification has not been studied. However, [PERSON] [32] has demonstrated that Particle Swarm Optimization (PSO) has higher identification accuracy than LS on an RX-60 manipulator. Therefore, the introduction of the PSO is expected to improve the accuracy of identification of an exoskeleton's dynamic parameters. PSO identifies the parameters through the intelligence of particle swarm and iterative optimization processes [33]. Each dimension represents a parameter to be identified, and the ranges of parameters to be identified are often unknown. In order to avoid losing the real value of parameters, PSO optimization is often carried out in the infinite domain [34]. If the number of iterations is insufficient or the factors are not set properly, the probability that PSO converges to the local minima increases. Taking the local minima as the identification values, the error of the exoskeleton dynamic model will increase. Therefore, it is necessary to reduce the search space of PSO from the infinite domain to the finite domain in advance, to reduce the number of potential local minima within the search space as much as possible. The fluctuation of exoskeleton parameters provides a possibility to reduce the search space. The approximate range of each parameter can be identified by the recursive identification algorithm before accurate identification of PSO. Recursive least squares (RLS) can identify the fluctuation of parameters. Therefore, the search space of PSO can be defined by RLS. The accuracy of PSO is expected to be further improved with the finite search space. The existing identification algorithms of exoskeleton dynamic parameters are generally based on LS with low accuracy. In this paper, a PSO with search space defined by RLS is designed, named RLS-PSO. The RLS-PSO was verified on a hydraulically driven exoskeleton of the lower extremity. The accuracy of RLS-PSO was compared quantitatively with LS and PSO.
## 3 Methodology
### Dataset  
Satellite images of the Dubai dataset [39] provide their volume with segmentation labels in six classes. The classes are building, land, road, vegetation, water, and unlabeled. The provided images and segmentation labels are cropped to a size of 320 \(\times\) 256. The total number of images is 1404. Even though the dataset provides its own color scheme in segmentation labels, this work used more intuitive colors for each region type (buildings: blue, vegetation: green, water: light blue, roads: yellow, unlabeled: gray). Samples from the Dubai dataset are shown in Figure 2.
## 3 Results and Analysis
### Consistency of Latitude Changes in Vegetation Phenology
#### 3.3.2 Consistency of Phenological Trends of Different Vegetation Types by Latitude
“0” represents the trend has not changed. ENF, DBF, MF, OS, WS, S, G and C on the abscissa represent different vegetation types, and are Evergreen Needleleaf Forests (ENF), Deciduous Broadleaf Forests (DBF), Mixed Forests (MF), Open Shrublands (OS), Woody Savannas (WS), Savannas (S), Grasslands (G), Croplands (C), respectively, different colours represent the trend change. All the trend values indicate an increase or decrease at a significant level of 95% (\(p<0.05\)).
# Special Issue on Transportation Big Data and Its Applications
## 3 Conclusions
Data Eng._**2021**, _34_, 5415-5428. [CrossRef]
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* [PERSON] et al. (2022) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Reinforcement learning-empowered mobile edge computing for 6G edge intelligence. _IEEE Access_**2022**, _10_, 65156-65192. [CrossRef]
* [PERSON] et al. (2023) [PERSON]; [PERSON]; [PERSON]; [PERSON] Guest Editorial Introduction to the Special Issue on Graph-Based Machine Learning for Intelligent Transportation Systems. _IEEE Trans. Intell. Transp. Syst._**2023**, _24_, 8393-8398. [CrossRef]
* [PERSON] et al. (2022) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Forecasting Transportation Network Speed Using Deep Capsule Networks with Nested LSTM Models. _IEEE Trans. Intell. Transp. Syst._**2022**, _22_, 4813-4824. [CrossRef]
* [PERSON] et al. (2022) [PERSON]; [PERSON]; [PERSON] Learning Dynamic and Hierarchical Traffic Spatiotemporal Features with Transformer. _IEEE Trans. Intell. Transp. Syst._**2022**, _23_, 2236-22399. [CrossRef]
* [PERSON] et al. (2021) [PERSON]; [PERSON]; [PERSON] Spatial-temporal convolutional model for urban crowd density prediction based on mobile-phone signaling data. _IEEE Trans. Intell. Transp. Syst._**2021**, _23_, 14661-14673. [CrossRef]
* [PERSON] et al. (2021) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Urban mobility analytics: A deep spatial-temporal product neural network for traveler attributes inference. _Transp. Res. Part C Emerg. Technol._**2021**, _124_, 102921. [CrossRef]
* [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Understanding intra-urban human mobility through an exploratory spatiotemporal analysis of bike-sharing trajectories. _Int. J. Geogr. Inf. Sci._**2020**, _34_, 2451-2474. [CrossRef]
* [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Identifying and segmenting commuting behavior patterns based on smart card data and travel survey data. _Sustainability_**2020**, _12_, 5010. [CrossRef]
* [PERSON] et al. (2021) [PERSON]; [PERSON]; [PERSON] Optimizing electric bus charging infrastructure considering power matching and seasonality. _Transp. Res. Part D Transp. Environ._**2021**, _100_, 103057. [CrossRef]
* [PERSON] et al. (2023) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] A solar-powered bus charging infrastructure location problem under charging service degradation. _Transp. Res. Part D Transp. Environ._**2023**, _119_, 103770. [CrossRef]  
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No studies have been conducted so far to evaluate the GPM-IMERG product for flood modeling, neither in Morocco nor in Maghreb countries with similar climatic conditions. To fill this gap, the purpose of this study is to evaluate (i) the accuracy and performance of the GPM-IMERG product to reproduce observed rainfall and (ii) the capability to model flood events with a hydrological model based on Soil Conservation Service-Curve Number (SCS-CN). This type of model is chosen since it is widely used in the Mediterranean region for floods [3]. This model could serve as a tool to assist decision makers (water basin agencies, water departments) in flood prediction and management, as traditional warning systems do not provide forecasts in time for effective intervention [41].