esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
The selection of a PCM to be implemented in thermal processes working under partial load operating conditions in a temperature range between 120 and 200 \({}^{\circ}\)C was successfully performed. Sixteen PCMs were preselected for this study. The selection procedure followed a methodology which studied the health hazard, cycling stability and thermal stability of the PCMs in the mentioned range. In this study, the cycling stability tests were performed at a higher sample mass (150 \(\pm\) 20 g) than in previous studies and under real atmospheric conditions which can hinder the PCM performance in some cases due to oxygen presence [11].  
Considering the results obtained, adipic acid and high-density polyethylene (HDPE) are suitable options. They do not present loses of more than 12% of their thermal storage capacity after 100 cycles, do not show significant changes in the chemical structure and presents low hazardous values, and their degradation starts at temperatures higher than 200 \({}^{\circ}\)C.  
Commodity polymers such as HDPE are cheap plastics to produce. In an industrial application, price is proved to be one of the most important aspects in the material selection process [10]. Based on price and health hazard, HDPE is the material selected for application between 120 and 200 \({}^{\circ}\)C. Further research on different partial loads profiles at a pilot plant scale implementing HDPE will be performed.  
The work was partially funded by the Spanish government (ENE2015-64117-C5-1-R (MINECO/FEDER)). The authors would like to thank the Catalan Government for the quality accreditation given to their research group GREA and DIOPMA (2014 SGR 123, 2014 SGR 1543). GREA and DIOPMA are certified agents TECNIO in the category of technology developers from the Government of Catalonia. This project has received funding from the European Commission Seventh Framework Programme (FP/2007-2013) under Grant agreement No. PIRSES-GA-2013-610692 (INNOSTORAGE) and from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 657466 (INPATH-TES). [PERSON] would like to thank the Departament d'Universitats, Recerca i Societaat de la Informacio de la Generalitat de Catalunya for his research fellowship (2017 FI_B1 00092). [PERSON] and [PERSON] would like to thank the Ministerio de Economia y Competitividad de Espana for [PERSON] FJCI-2014-22886 and FJCI-2015-25741, respectively.  
[PERSON], [PERSON] and [PERSON] conceived and designed the study; [PERSON] and [PERSON] performed the experiments in the laboratory; all the coauthors collaborated with respect to the interpretation of the results and in the preparation of the manuscript.  
The authors declare no conflict of interest.
# Dynamic Response of Slender Vertical Structures Subjected to Thunderstorm Outflows
## 4 Comparison between Thunderstorm and Synoptic Dynamic Response
### Reference Velocity Ratio \(R_{v}\)  
The wind speed is defined following the spirit of design codes and standards. In this example, the Italian Guide for the Assessment of Wind Actions and Effects on Structures [40] is taken as a reference. The mean wind speed for thunderstorm outflow and for synoptic events with a mean return period of 50 years are derived from statistical analyses carried out by [PERSON] et al. [10] on full-scale anemometer data. In particular, the wind velocity collected from an anemometer in the port of Livorno in the High Tyrrhenian Sea is considered, located at 20 m above the ground. Table 2 summarizes the main properties of the wind velocity data of both the thunderstorm outflow (T) and synoptic event (S) that are employed in this study. The mean wind speeds with a return period of 50 years are obtained from the peak wind velocities \(\bar{v}_{\max}(h)\) and \(\bar{v}_{S}(h)\) as \(\overline{v}_{\max}(h)=\bar{v}_{\max}(h)/G_{v,T}\) and \(\overline{v}_{S}(h)=\bar{v}_{S}(h)/G_{v,S}\), respectively, where \(G_{v,T}\) and \(G_{v,S}\) are the gust factor of the thunderstorm and synoptic wind velocities, respectively [10].  
It can be noticed that the mean wind velocity in Table 2 associated with synoptic winds \(\overline{v}_{S}(h)\) is significantly lower than the reference value of 28 m/s furnished in [40], which is associated with a reference height \(h=10\) m, a return period of 50 years, and flat terrain with \(z_{0}=0.05\) m. This is due to the fact that the values reported in codes are based on statistical analyses that do not distinguish between wind events of a different nature, and hence they provide intermediate values between velocities associated with thunderstorms and synoptic winds [10; 50]. In this framework, the data in Table 2 furnish a more authentic value for synoptic winds being the result of an accurate separation procedure [51]. Moreover, it is important to observe that the maximum mean wind speed due to thunderstorms is greater than the one related to synoptic events by 46%, which leads to a value of the reference velocity ratio \(R_{v}\) of greater than two. It is worth pointing out that since the reference velocity depends on the reference height according to the vertical profile of the mean wind speed, the reference velocity ratio \(R_{v}\) also depends on the reference height. For low reference heights, where the thunderstorm profile is generally larger than the synoptic one, \(R_{v}>1\) is expected. Finally, it is worth noticing that the analyses are based on the data collected in a thunderstorm-prone area; therefore, different statistics might be expected in other areas less affected by such extreme events.
# Smart Logistics Warehouse Moving-Object Tracking Based on YOLOv5 and DeepSORT
## 2 Related Work
### YOLO (You Only Look Once)  
The YOLO algorithm proposed in 2016 was designed as a new network for object detection from end to end. Its unique design approach increases the speed of the entire network operation by casting the object detection entirely into a regression problem, spanning from input-image segmentation to the classification of targets in each grid [10].  
As shown in Figure 2, the process of YOLO is as follows:  
* The input image is divided into several grids, and each grid takes responsibility for detecting potential targets within its confines.
* The image of each grid is convolved to obtain the positions of the bounding boxes and the corresponding confidences for multiple targets.
* The final prediction box for each grid's multiple targets forecast is obtained using non-maximum suppression (NMS), which is the result of object detection.  
Figure 1: Faster R-CNN algorithm process.
## 2 Literature Review
### Digital Data Superimposition Applications  
Superimposition literally means the placement of one image on top of an existing image to enhance the overall image quality. In the construction industry, the Scan to BIM process and digital data superimposition have been jointly applied to develop augmented reality (AR) and mixed reality (MR) systems as a project progress-monitoring scheme, which visualizes the discrepancy between the as-built and as-planned performance on a project [23]. AR superimposes digital information on real-world components, retaining the real world foundation but enhancing it with digital applications. MR is the merging of real and virtual worlds to produce new environments and visualizations, where physical and digital objects coexist and interact in real time. [PERSON] et al. [24] proposed a BIM-based AR. The application combined a location-based management system and BIM to visualize progress and performance of construction works by use of superimposition. [PERSON] et al. presented a MR application based on BIM data and drone videos, allowing off-site construction supervision [25]. It permitted to make annotations about differences between as-planned in BIM and as-built, using superimposition of the two sources. [PERSON] and [PERSON] developed \"AR navi-X,\" the digital contents of construction management based on AR to support decision making in free-form building projects [26].
## 3 Results
### Results from Design Phase and Validation Phase
#### 3.1.2 Calculated Stored Elastic Energy  
For the calculation of the elastic energy stored by the elastic elements during the midstance, the energy to be released during the push-off, only the static tests of dorsiflexion were considered. The results of the static tests in dorsiflexion are shown in Figure 8, where at the top they are presented as reaction force-platform displacement, while at the bottom as reaction force-foot rotation. As suggested by the AOPA guideline, for the energy calculation, the reaction force-platform displacement curves were considered.  
Figure 6: Comparisons among the static plantarflexion and dorsiflexion 2D FEAs and 3D FEAs and the mechanical tests on the physical prototype.  
Figure 7: The interference between the middle blade and the foot shell (not designed for MyFlex-\(\delta\)).  
The elastic energy for each foot prosthesis was calculated as the area below the curve through the trapezoid method up to the platform displacement which corresponds to a reaction force equivalent to the 108% of the weight category. The reaction force value for each of the weight categories is shown in Table 3, as well as the displacement of the platform, the corresponding rotation of the foot, and the elastic energy accumulated in absolute and normalized with respect to the weight category.  
The elastic energy accumulated by the MyFlex-\(\delta\) prostheses, normalized for the weight categories, can be compared with the positive work of the human ankle during the push-off. The work of the ankle during the push-off is between 0.16 J/kg and 0.26 J/kg according to the results reported by [PERSON] and [PERSON] [14] and 0.21 J/kg and 0.37 J/kg according to the study conducted by [PERSON] and [PERSON] [114]. According to these two studies, the positive work by the human ankle increases as the walking speed increases.
# Demand Prediction Using a Soft-Computing Approach: A Case Study of Automotive Industry
## 3 Proposed Model
### Nonlinear Autoregressive Neural Network with External Input  
The application of artificial neural networks can generate future data reflecting the historical data trend. The projection of the demand allows generating future data and it is an essential activity in the organizational planning process. It is an activity that is usually done using mathematical and statistical techniques. It also generates hybrid models that integrate different machine learning techniques with classical methodologies.  
Make forecasts are made by some mathematical technologies, among them the perceptron multilayer, feed forward or nonlinear autoregressive neural network or in its variant with inputs in parallel. One of the main features is the recursive process in which past values are taken in the future prediction. There are two variations; normal projection one-step without performing the recursion or openloop and multi-steps in which a feedback loop or closeloop is established [62; 63; 64]. Another feature in the type of network is recurrence, while the feedback is adjusted and trained with the output values.  
The NARX application is characterized to incorporate two or more vectors, two types of sigmoid and linear functions. This network type is broken down from the ARX [65]. The application of the NARX type network is characterized to incorporate two or more vectors, two types of sigmoid and linear functions that are trained by the output values. The application of the proposed methodology is then performed.
## 2 Overview of SDN Architecture  
SDN consists of three planes: application plane (AP), control plane (CP), and data plane (DP), as depicted in Figure 1. Application planes consist of network applications such as network virtualization, firewalls, intrusion detection system (IDS), and mobility management that leverage the exposed northbound (NB) application interface to interact with the control plane. The NB interface provides a stable, consistent way for network administrators and application developers to efficiently use SDN services to implement important network management at the control plane. The CP is the most essential part of the SDN structure, it provides fine-grained control over the networking element at the DP and offers many network services, which include routing computation, monitoring, load balancing. The CP translates these services from application-level into a clear set of instructions and requests in form of flow entries and installs them in the corresponding switch devices. Single CP can be configured for the entire network, but for scalability reasons, it can be extended to distributed or multiple controllers. As for the DP, they are used as a simpler forwarding element with no software capable of making an instant decision. Therefore, network intelligence is withdrawn and shifted to a centrally logical controller. For any control decision, the switch must consult the controller for further action. This paper focus on the control plane and data plane.
## 2 Materials and Methods
### Methods
#### 2.2.2 Calculation of NDVI  
The MODIS NDVI was calculated by [35]:  
\[NDVI=\frac{\rho_{nir}-\rho_{rad}}{\rho_{nir}+\rho_{rad}}, \tag{1}\]  
in Equation (1), the \(NDVI\) denotes the normalized vegetation index, and \(\rho_{nir}\) and \(\rho_{red}\) denote surface reflectance in the near infrared band and the red band, respectively.  
Figure 1: Location of the study area.  
The GF-4 NDVI was calculated as for the MODIS NDVI, shown in Equation (1), but the apparent reflectance of the near infrared (the red band) was derived by [36]:  
\[\rho_{\lambda}=\frac{\pi L_{\lambda}d^{2}}{\cos\theta_{s}ESUN_{\lambda}} \tag{2}\]  
in Equation (2), where \(\rho_{\lambda}\) is the at-satellite reflectance, \(ESUN_{\lambda}\) is the mean solar exoatmospheric irradiance in W-(m\({}^{2}\)-ster-um)\({}^{-1}\), \(\theta_{s}\) is the solar zenith angle in degrees, and \(d\) is the earth-sun distance in astronomical units. \(L_{\lambda}\) is the at-satellite spectral radiance in W-(m\({}^{2}\)-ster-um)\({}^{-1}\). \(\theta_{s}\) can be obtained from the head file. The \(ESUN\) value for each GF-4 band has not been published yet, but can be calculated using the spectral response function of the solar spectrum curve and the sensor, as shown in Equation (3) [37].  
\[ESUN=\frac{\int_{\lambda_{1}}^{\lambda_{2}}E(\lambda)S(\lambda)d_{\lambda}}{ \int_{\lambda_{1}}^{\lambda_{2}}S(\lambda)d_{\lambda}}, \tag{3}\]  
In Equation (3), \(ESUN\) denotes the band average solar radiation outside of the atmosphere; \(\lambda_{1}\) and \(\lambda_{2}\) denote the upper and the lower integration limit of wavelength of band range; \(E(\lambda)\) denotes the solar spectrum radiation of the remote sensor out of the atmosphere at band \(\lambda\), where different solar spectrum curves have different \(E(\lambda)\) values. Current studies show that the World Radiation Center (WRC) solar spectrum curve is the most favorable for the calculation of the \(ESUN\) using this sensor at a medium resolution in China [38]. According to the WRC solar spectrum curve, the \(E(\lambda)\) value could be in the range of \(\lambda_{1}\) and \(\lambda_{2}\); and \(S(\lambda)\) denotes the spectral response function of the remote sensor at band \(\lambda\). The \(ESUN\) values for all GF-4 bands calculated using Equation (3) are shown in Table 3.  
The equation converting the \(DN\) value of satellite images to radiance images using the absolute calibration coefficient is [39]:  
\[L_{\lambda}=A\times DN, \tag{4}\]  
where GF-4 provides gain values (\(A\)) under 5 statuses, for example, status 2-6-4-6-6 denotes an integration time for full color, blue, green, red, and near infrared of 2, 6, 4, 6, and 6 ms, respectively, the other statuses please see Table 4. Users need to determine the status of image bands according to the appropriate XML file parameters of GF-4 images, and then select the corresponding calibration coefficient.
# Mapping Surface Flow Velocity of Glaciers at Regional Scale Using a Multiple Sensors Approach
## Data and Methods
### Description of the Workflow
#### 2.2.1 Database Initialization and Image Search  
First, a shapefile with the contour of the study area is generated from the Randolph Glacier Inventory v6.0 [6; 19]. This contour includes not only glaciers but a whole mountain range in order to preserve sufficiently stable ground surface outside the glaciers to be able to calibration while still limiting the size of the area to be treated, hence reducing the computation time. For each region, an SQL database is initialized in which is stored a list of all existing images and associated metadata for the studied region. Existing Sentinel-2 and Landsat 7/8 images are searched using the API from ESA  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Sensor** & **Alps** & **Andes** & **NZ** & **Band** & **Repeat Period** & \(\lambda\) **(nm)** & **Res (m)** & **Ortho** & **Agency** \\ \hline Sentinel-2 & Yes & Yes & Yes & B8 & 5 d & 780–886 & 10 & Yes & ESA \\ Landsat 8 & Yes & Yes & Yes & B8 & 16 d & 500–680 & 15 & Yes & USGS/NASA \\ Landsat 7 & Yes & Yes & Yes & B8 & 16 d & 520–900 & 15 & Yes & USGS/NASA \\ Venus & No & No & Yes & B6 & 5 d & 600–640 & 5 & Yes & CNES/ISA \\ Pleiades & Yes & No & No & Panch. & Upon request & 430–830 & 0.5 & No & Airbus DS \\ Planet & Yes & No & No & B4 & Upon avail. & 455–860 & 3 & Yes & Planet Labs Inc. \\ GPS & Yes & No & No & / & Field camp. & / & Point & / & IGE et al. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of satellite and ground observations used for the Andes, Alps and New-Zealand (NZ) along with technical information. Repeat period defines the satellite acquisition frequency, \(\lambda\) is the wavelength in nanometers, Res is the pixel resolution in meters, Ortho indicates if orthorectified images are provided.  
and USGS, respectively. When the images are then paired to map the displacement, another database is initialized. This second SQL database is then fully integrated into the rest of the workflow and used to monitor and update the status of the processing step for each individual image pair. The use of an SQL database to update the processing allows different image pairs to be easily processed in parallel, taking advantage of the full capacity of the HPC infrastructure.
## 3 Data and Methods
### Numerical Simulation of 3D In Situ Stress  
Shale oil reservoirs exhibit strong heterogeneity and anisotropy, compounded by the presence of natural fractures that complicate geomechanical parameters. Consequently, accurately constructing geomechanical models presents challenges. Unlike previous 3D stress field numerical simulations, this study has established a precise 3D model of rock  
Figure 6: Comprehensive interpretation of the in situ stress and mechanical parameters based on FMI and conventional logging of Ma54.  
mechanics parameters through logging, seismic data, and mechanical experiments (Table 2). Consideration was given to the heterogeneity and anisotropy of the formation. Furthermore, the construction process of fault models was optimized, enhancing the accuracy and reliability of the models. Numerical simulations of in situ stress in shale reservoirs were conducted using the finite element method (Figure 7). The simulations have two prerequisites: (1) The stress field deformation is linear elastic without artificial intervention [41; 42]. (2) In situ stress results from gravity, whereas paleostress is influenced by plate movements and tectonic deformation [43].
## 5 Pollutant Source Localization
### Integrated Algorithm  
Figure 7 shows the complete proposed algorithm, which integrates the heuristic, calculated on the base of the LMLS, to the metaheuristic algorithm. Processes in red are performed at each time \(t_{q}\) and those filled with blue run at time \(t_{k}\), with \(t_{q}<<t_{k}\).  
In the algorithm, the first step is to get the initial heuristic (\(h_{0}\)) of the location of the source in the environment. This is solved by using the LMLS calculated with an initial deterministic navigation of the UAV. The heuristic \(h(t_{k})\) of the source location is the position with the highest probability in the LMLS at time \(t_{k}\). The next steps guide the algorithm to use \(h\) obtained from LMLS, which is  
Figure 6: Updating methodology of the LMLS.  
continuously updated according to new binary sensor information and the Bayesian methodology previously presented.  
The metaheuristic process loop calculates the positions to move the UAV among the waypoints \(p_{k}\). To verify increasing concentration of pollutant, between the positions \(p_{k-1}\) and \(p_{k}\), the historical information captured in \(\psi_{k}=\{\psi(t):p(t)\;\epsilon\;(p_{k-1},p_{k})\}\) is used.  
The path planning algorithm will help in the navigation of the UAV to reach each waypoint \(p_{k}\) calculated in the metaheuristic loop. This algorithm will grant the finding of the shortest path among the waypoints but avoiding any possible obstacles in dynamic unknown terrains. In addition, the path planning algorithm will depend on partial information of the environment and the obstacle detection sensors in at least one direction, however this approach goes beyond the scope of this work, where the experiments were limited to obstacle-free environment, and the path planning calculates direct trajectories among the positions \(p_{k}\).  
To estimate the source location, at each time \(t_{q}\), the historical information of concentration measurements is used. The centroid of the three positions with the highest concentration of pollutant represents the source location. If insignificant concentrations are found in the whole searching process or the procedure stops when the initial deterministic navigation has not finished, the source position is defined using the LMLS, as the cell with the higher probability.  
The algorithm conditions to stop include--low battery of the UAV, very high concentration of pollutant found, maximum number of waypoints set by the user and emergency stopping. Regardless of the stopping condition and time, the proposed method will always return the most probable source location.
# Combination of Transfer Learning Methods for Kidney Glomeruli Image Classification
## 1 Introduction
As a result, the combination of these approaches is used as a benchmark for comparing the proposed study with the EfficientNet method [9]. In the previous two years, this technique has been enhanced by altering the model's structure, combining the models and employing the iterative model process. The ImageNet dataset, which comprises random pixel data (non-medical picture), has recently been used in some studies to show that this method's accuracy rate approaches 99.70 percent [10]. This result is utilized as a proof-of-concept that the EfficientNet technique can provide high accuracy when utilizing the medical picture dataset. Therefore, the goal of this research is to demonstrate how this approach may be used in medical imaging collections. The majority of deep learning in the categorization of glomeruli comprises of normal, sclerosed and non-glomerular classes, according to the prior studies. As a result, this study proposes three possible classifications: normal, sclerosed and focal-segmental sclerosed. Since focal-segmental sclerosed glomeruli [11] are a type of scleross that cause anomalies in the glomerular tissue and affect a large number of people, it is critical to correctly detect the focal-segmental anatomy of sclerosed glomeruli in kidney disease diagnosis. The normal glomerulus, sclerosed glomerulus and focal-segmental glomerulus were all used to create this approach for transfer learning. Sections 2 and 3 provide detailed explanations of the database, materials and research techniques, whereas Sections 4 and 5 contain the results of the tests and conclusions. Figure 1: Anatomies of normal glomeruli, sclerosed glomeruli and focal-segmental glomeruli. (**Left**) Normal glomeruli; (**Center**) sclerosed glomeruli and (**Right**) focal-segmental glomeruli.