esa-sceva/satcom-chunk-collection huggingface dataset:
# Impact of HPC and Automated CFD Simulation Processes on Virtual Product Development--A Case Study
## 1 Introduction  
High-performance computing (HPC) accelerates product development by decreasing the computing time of numerical-intensive simulations, such as CFD analyses, and allows for an increase in design iterations. Automating the simulation-driven optimization processes can further increase the efficiency of product development. However, utilizing HPC systems in this way requires specialist knowledge, dedicated hardware and comprehensive software licenses for proprietary simulation software. The monetary expenditure is easy to forecast but it is hard to determine the benefit regarding the time reduction of development cycles or the implications for the product's quality. For that reason, HPC-driven product development is still a considerable cost factor for small and medium-sized enterprises (SMEs) with uncertain profitability.  
In recent years, there has been a great deal of interest in outlining the advantages of HPC and automation for simulation and optimization processes. Thus far, comparatively few attempts have been made to measure their technical and economic effects on product development. Studying technical and non-technical effects of new technologies on existing systems and organizations is an important aspect of technology adaption and has already been addressed for similar fields, e.g., in [1] for cloud computing.  
The aim of this paper is to quantify the effects of HPC on product development, to make a case for future profitability calculations and to increase HPC's acceptance among SMEs. Therefore, we used a case study approach and accompanied a formula student racing team over several seasons. We developed and implemented an HPC-driven optimizationapproach based on ANSYS Fluent and studied the team's productivity before and after deploying HPC-based development procedures. We focused on the following questions:  
1. How many major design iterations can be conducted in a predetermined period with the help of HPC and automation compared to the conventional approach of not utilizing HPC (quantitative effect)?
2. How does the quantitative effect affect the product's key performance indicators (qualitative effect)?  
This article is structured as follows: First, we give a brief insight into product development to underline the relevance and analyze the challenges and requirements when setting up an automated and HPC-based development process. In Section 2, we describe brief theoretical foundations of parallel processing, present the investigated case and the details of the implemented and HPC-based design optimization approach. Section 3 then moves on to present the results of the HPC adoption for the investigated case. In Section 4, we discuss those results and assess the implications for product development aided by HPC. Finally, we summarize our work and evaluate the case study critically.
## 2 Materials and Methods
### Building Description and Simulation  
This study examined a multi-story building, as depicted in Figure 1, showcasing the geometric model. The building model under investigation was proposed by the National Agency for the Development of Renewable Energy and Energy Efficiency (ADEREE). It was segmented into four levels, with two apartments on each floor. Occupancy patterns during weekdays involve each flat being occupied by 5 people from 05:00 p.m. to 07:30 a.m. and by 2 people during the remaining time. On weekends, each home is occupied by 5 people. The overall window-to-wall ratio is 21%, and the net floor space covers 588 m\({}^{2}\). The windows are single-glazed with a heat-transfer coefficient (U-value) of 5.74 W/m\({}^{2}\).K and a solar heat gain coefficient (SHGC) of 0.87. External shading devices (50%) are utilized during the summer from 07:30 a.m. to 05:00 p.m. Additional information about the building, architectural plan, floor areas, and external surfaces can be found in [13]. The building was simulated in TRNSYS 18 software for 8760 h (one year) with a 1 h time step. The assumptions employed to calculate the heating and cooling loads are shown in [5; 14]. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Location** & **Climatic** & **Climate** & **Minimum** & **Mean** & **Maximum** & **CDD Base** & **HDD Base** \\
**(Morocco)** & **Zone** & **Type** & **(°C)** & (°C) & (°C) & **(°C)** & **24 \({}^{\circ}\)C** & **18 \({}^{\circ}\)C** \\ \hline \hline \multirow{2}{*}{
\begin{tabular}{c} Meknes \\ Ifrane \\ Marrakech \\ \end{tabular} } & Z3 & Mediterranean & 0.12 & 17.66 & 43.95 & 223.45 & 1007.32 \\  & Z4 & Cold & \(-\)4.0 & 15.09 & 34.1 & 156.65 & 1711.17 \\  & Z5 & Semi-arid & 2.4 & 20.3 & 43.7 & 459.12 & 565.85 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Locations and climate characteristics. The case study was based on a single prototypical residential LEB, selected to ensure a consistent basis for model comparison across different climatic zones. This reference building aligns with widely accepted design practices and performance standards in energy-efficient construction. The rationale for this selection lies in the need to isolate and analyze the effects of climate and predictor variables without introducing confounding variability from differing building geometries or operational profiles. Despite this focused approach, the framework developed is inherently flexible and can be applied to various building types, provided appropriate input data are available. Future studies will aim to validate the generalizability of the method across multiple building archetypes and use cases. Table 2 outlines the characteristics of the envelope materials, with the thermophysical properties sourced from the TRNSYS library. The set-point temperature is maintained at 20 \({}^{\circ}\)C in winter and 26 \({}^{\circ}\)C in summer, following the Moroccan standard (NM ISO 7730) [5; 14].
## 2 Laser-Driven Very-High-Energy Electron/Photon Beam Radiation Therapy System
### An Embodiment of Laser-Driven VHE Electron- and Photon-Beam Sources  
Figure 6 schematically illustrates the gantry head, the most crucial part of the radiation therapy system, where are installed a final focus optics, electron laser plasma accelerator, and beam focusing system for VHE electron beam therapy or Bremsstrahlung conversion target for VHE photon beam therapy. The ultrashort intense laser pulse is focused by a spherical mirror or off-axis parabolic mirror on the entrance of a two-stage gas cell, of which the first cell referred to an injector is filled with a mixed gas, e.g., helium gas mixed with nitrogen, and the second cell referred to as an accelerator is filled with a pure gas, e.g., hydrogen or helium. The gases are fed through a gas flow control system to the two-stage gas cell separately at the different pressures. According to the abovementioned mechanism, in the injector of the gas cell, the laser pulse excites large-amplitude plasma wakefields, of which an accelerating electric field can trap plasma electrons exclusively out of the inner shell electrons and accelerate them owing to ionization-induced injection. A pre-accelerated electron beam from the injector is further accelerated to the relativistic energy in the accelerator gas cell, where the laser pulse generates plasma wakefields.  
A collimated electron beam from the laser plasma accelerator is provided to the target in the patient by the beam focusing system comprising permanent quadrupole magnets, as shown in Figure 6. The field gradient of the two dimensional Halbach-type permanent quadrupole magnet (PMQ) is given by \(B^{\prime}=2B_{r}\left(r_{i}^{r-1}-r_{o}^{r-1}\right)\), where \(B_{r}\) is the tip field strength, \(r_{i}\) is the inner radius, and \(r_{0}\) is the outer radius of PMQ [35]. With \(B_{r}=1.45\) T for Nd-Fe-B type rare earth magnet (Nd\(\mathrm{zFe}  
(FDF) installed into a housing chamber. For the photon beam therapy, VHE photons can be produced from a photon beam target system comprising a Bremsstrahlung conversion target and collimator, where the beam focusing system is retracted and electron beams are directly irradiated, as shown in Figure 6.
## 5 Conclusions  
A novel MIMO ViSAR system and signal processing method were presented. This paper described theoretical aspects of ViSAR in terms of two important parameters: frame rate and image size. A MIMO signal model was selected for the proposed system, assuming broadside antenna beam orientation, and a collocated antenna configuration. The signal processing procedures for generating SAR video were then proposed.  
The proposed system was able to overcome frame rate and image size limitations caused by azimuth ambiguities, using two notable advanced techniques: the MCRA, which is a multi-channel azimuth processing technique, and MIMO technology. The MCRA is used to increase the Doppler bandwidth by suppressing the azimuth ambiguities in multiple azimuth channels. MIMO technology was used to increase the equivalent number of receiving channels, by forming a virtual array using BFD FMCW as the orthogonal waveform.  
The signal model and the signal processing method took into consideration Doppler compensation, required for mitigating effects caused by continuous antenna motion in FMCW-based SAR systems. Since the model used is based on a MIMO signal, it is easy to understand the relationship between beamsteering techniques for Tx and Rx, and multi-channel azimuth processing techniques.  
Simulation results showed that the proposed MIMO ViSAR system and signal processing method improved performance, as measured by the frame rate and image size of the SAR video. Further research can be easily extended to multi-mode operation of ViSAR systems with MIMO technology, for example, InSAR and PolSAR.  
**Acknowledgments: This research was supported by the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the \"ICT Consilience Creative Program\" (IITP-2017-2017-0-01015) supervised by the IITP (Institute for Information & Communications Technology Promotion), and the Civil Military Technology Cooperation Program. This work was supported by ICT R&D program of MSIP/IITP [2017-0-00286].**  
**Author Contributions:** [PERSON] is responsible for the theoretical work, simulation and the writing of the manuscript. [PERSON] and [PERSON] assisted the theoretical study. [PERSON] assisted to write the manuscript. [PERSON] supervised the research.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Electromagnetic Field Distribution and Data Characteristics of SUTEM of Multilayer Aquifers
## 4 Electromagnetic Field Diffusion Characteristics Study of SUTEM
### Analysis of the Influence Degree of Different Parameters of Shallow Aquifers  
Therefore, we know that the existence of shallow aquifers exhibits a certain influence on the detection of deep geological bodies by TEM. The main factors affecting the distribution and diffusion of the secondary field of TEM are the resistivity, thickness, and depth of the aquifer. Here, we refer to these as the \"three elements\" of the shallow aquifer influencing electromagnetic exploration. Then, we study the influence of the three elements of shallow aquifers. First, we fix two of the three elements, change one of the elements for numerical simulation, and use data fitting to analyze the influence of these three elements  
Figure 16: Time-varying curves and change rate curves of the \(z\) components of the EMF. Here, (**a**) is the \(\partial B_{z}/\partial t\) time-varying curve at different depths below the receiving point (0,0,0); (**b**) is the \(\partial B_{z}/\partial t\) change rate curves.  
of shallow aquifers on the detection of deep aquifers by TEM. Finally, we simultaneously change the three elements of the shallow aquifer and draw a three-dimensional map of their influence on the receiving points at different depths. In this procedure, the fixed thickness of the deep aquifer is 100 m, the buried depth of the top interface is 300 m, the resistivity is 10 \(\Omega\cdot\)m, and the size of the transmitting loop is 500 m \(\times\) 500 m. According to the distribution of the transient electromagnetic field in double aquifers and the characteristics of SUTEM data, the influence characteristics of deep aquifers on receiving points at different depths are divided into three categories: the surface measuring points and those above the aquifer, the measuring points at the top interface of the aquifer, and the measuring points at the bottom interface of the aquifer and below. Therefore, the locations of the receiving points in this part of the simulation are set at the surface (0,0,0), the top interface of the deep aquifer (0,0, \(-\)300), and below the deep aquifer (0,0, \(-\)500).
# Vitamin D and Rehabilitation after Stroke: Status of Art
## 5 Vitamin D and Post Stroke Recovery: Focus on Rehabilitation  
Is there a link between Vitamin D status and rehabilitation outcome in post-stroke patients? May patients undergoing rehabilitation benefit from sufficient vitamin D status considering its involvement in neurological, psychological, cognitive and musculoskeletal function?  
Post-stroke patients often exhibit vitamin D deficiency that could be due to scarce mobility, absent or decreased sunlight exposure, inadequate diet or difficulty swallowing (dysphagia) [68]. The prevalence of dysphagia after stroke is 41% in the neurorehabilitation unit [93].  
Several biochemical and neurophysiological aspects regarding neuronal cell activities, neurochemical and neuroimmunological processes, beyond the musculoskeletal system, have a relationship with the regulation and modulation of vitamin D levels. All these aspects are the main vitamin-D-dependent contributors to outcomes in patients with stroke undergoing rehabilitation treatment (Figure 2).
# Robust IoT Activity Recognition via Stochastic and Deep Learning
## 6 Experimental Results and Analysis
### Computational Efficiency Analysis  
A critical contribution of our study is the comprehensive evaluation of computational efficiency--particularly relevant for resource-constrained IoT applications. Figure 11 presents a detailed comparison of the inference time and parameter count across all models.  
Traditional machine learning models demonstrated superior efficiency, with Decision Tree achieving exceptional performance (inference time: 0.00033 s, parameters: 189, as shown in Figure 12) while maintaining strong accuracy. Despite its superior classification performance, Random Forest required moderately higher computational resources (inference time: 0.00574 s, parameters: 18,486). In stark contrast, all deep learning models  
Figure 11: Comparative analysis of model inference times across traditional and deep learning models, highlighting the efficiency advantages of traditional approaches for resource-constrained IoT applications.  
Figure 10: Training and validation accuracy curves for deep learning models across epochs, highlighting the superior convergence properties of TransUNet compared to other architectures.  
exhibited substantially greater computational demands, with Swin Transformer having the longest inference time (0.06217 s) and CNN the highest parameter count (282,059).  
This efficiency analysis reveals a crucial tradeoff in IoT applications: while deep learning models theoretically offer superior representation capacity, their practical deployment incurs substantial computational overhead. These findings are particularly relevant for edge computing scenarios where processing power and energy consumption are constrained.
# Recent Progress on Vegetation Remote Sensing Using Spaceborne GNSS-Reflectometry
## 4 Model Simulations and Discussion
### Polarization Difference  
When the incident signal polarization is RHCP and the received signal is circular polarization and linear polarization, the coherent (\(\theta_{\rm i}=\theta_{\rm s},\varphi_{\rm s}=0^{\circ}\)) and incoherent (\(\theta_{\rm i}=30^{\circ}\), \(\varphi_{\rm s}=120^{\circ}\)) scattering properties are shown in Figure 1. Here \(\theta_{\rm i}\) is the incidence angle, \(\theta_{\rm s}\) is the scattering zenith angle, while \(\varphi_{\rm s}\) is the scattering azimuth angle. As most of the frequency bands of the present GNSS constellations cover the lower L-band to upper L-band, and the scattering properties at these bands are almost the same, we select GPS L1 band for the following simulations. For the same polarization, the energy value of coherent scattering (Figure 3a) is significantly greater than that of incoherent scattering (Figure 3b). For the same kind of ground objects under the same observation geometry, when the polarization characteristics change, the corresponding microwave scattering characteristics also have obvious differences (Figure 1). The difference in the scattering characteristics of different polarizations is related to the observation geometry. When the angle is different, the scattering properties of the various polarizations are different. It is worth noting that the VR polarization has a scattering groove in the simulated angle range (Figure 3a), which is mainly caused by the Brewster angle. Figure 3b has a scattering peak at thetas = 30\({}^{\circ}\), which is mainly caused by the strong scattering of the tree trunk.  
It can be seen from the simulation in Figure 3 that the scattering characteristics of vegetation at different polarizations are significantly different, so how to effectively use polarization information in vegetation monitoring is an important content of subsequent development.
# Automatic Boundary Extraction for Photovoltaic Plants Using the Deep Learning U-Net Model
## 2 Materials and Methods
### Traditional Image Processing (TIP)  
The process to obtain the boundary pixels of a target can be achieved by means of traditional image processing techniques with functions that extract, increase, filter, and detect the features of an image and obtain its segmentation [27; 32]. The main stages were used to remove the borders of PV plants out of an image, as shown in Figure 1[10]. In the first stage, the original image was filtered using \"filter2D\" function from OpenCV, that is a convolution filter with 5 \(\times\) 5 averaging filter kernel, as shown at Algorithm 1. This filter is compound with various Low-Pass Filters (LPF) and High-Pass Filters (HPF). LPF helps in removing noise, blurring images. HPF filters help in finding edges in images. In the second stage, the filtered image is transformed into the HSV (hue, saturation, and value) representation. The transformation lessens reflection caused by environmental light during aerial image collection. Furthermore, this transformation helps in the color-based segmentation required in the next stages. In the third stage, each channel was processed separately to extract the area of the PV plants. This was achieved by applying thresholding operations on the HSV image. To extract the PV blue color out of the image, the HSV range limits for thresholding where determined: from \((50,0,0)\) to \((110,255,255)\). Thresholding was implementing using the inRange function of OpenCV. At the fourth stage, two morphological operators were applied: the \"erode\" and \"dilate\" functions. Together these operations helps to reduce noise and to better define the boundaries of the PV devices, the application of erosion followed by dilation is also known as opening operation. Erosion and dilation requires an structuring element (also known as kernel) to be applied to the images. In this case, a rectangular kernel of 2 \(\times\) 2 pixels (MORPH_RECT,\((2,2)\)) was used for both operations. Lines 13, 14 and 15 from Algorithm 1 show the creation of the structuring element and the successive use of the erode and dilate functions. Then, the \"findCountours\" function was used to help in extracting the contours from the image. The contour can be defined as a curve joining all the continuous points in the boundary of the PV installation. The input parameters for this function are: the image (dilated image from previous stage), the type of contour to be extracted (in this case only the external contours, RETR_EXTERNAL) and the contour approximation method (in this case not approximation, CHAIN_APPROX_NONE). Finally the area was recognized using  
Figure 1: Steps of boundary extraction by image analysis with two techniques. a multi-stage algorithm to detect a wide range of edges in images, known as the Canny edge detection \"Canny\" [44]. The pseudo-code of the Traditional Image Processing is shown in Algorithm 1, and was implemented in Python 3 using OpenCV library.
# Airborne and Dermal Collection Methods of Gunshot Residue for Toxicity Studies
## 3 Results/Discussion
### Oxidative Potential (All Samples)
#### 3.5.3 De-Leading Wipes/Hand Swabs  
Oxidative potential was detected for all hand swab collected samples. The average DTT consumed and SEM for after single shot (137.8 \(\pm\) 46.2 pmol), after using de-leading wipe one (174.2 \(\pm\) 63.0 pmol), after triple shots (102.8 \(\pm\) 45.8 pmol), and after using de-leading wipe two (199.4 \(\pm\) 78.8 pmol) is shown in Figure 6C. The de-leading wipe DTT consumption did not provide reproducible results which was likely due to the composition  
Figure 6: Oxidative Potential. DTT consumption and SEM (pmol) are shown for the filters (**A**), DST (**B**), and hand swab samples (**C**). No significant differences were detected during statistical analysis using a [PERSON]’s \(t\)-test (filters and DST) and one-way ANOVA (hand swabs) with \(p\)-values of \(\leq\)0.05 considered significant.  
of the de-leading wipes themselves because the blank, non-used de-leading wipes had high oxidative potential values, preventing blank correction of samples. The hand swabs directly following shooting scenarios had lower DTT consumption than the samples directly following the use of a de-leading wipe (Figure 6C). The higher values of the hand swab samples after de-leading wipe could be explained by the de-leading wipe residue left on the shooters hands potentially reacting with the DTT. The de-leading wipes also contain EDTA, a compound that suppresses the DTT response from metals, which likely impacted the oxidative potential results from the de-leading wipe samples [64]. Even though EDTA itself suppresses DTT response, other components of the de-leading wipes could directly react with reagents in the assay. Overall, the DTT consumption values for the hand swab samples were higher than both the filter and DST samples, which may be a result of the higher collection of GSR elemental concentrations (Table 1). While the DTT assay was effective for extracted hand swabs following GSR collection, the analysis method is not optimal for de-leading wipe analysis.