esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Results of Numerical Simulations and Model Validation  
Analyzing the stress distributions and deformations of the systems shown in Figures 7-12, it can be observed that in the case of the 3 m beam, the loss of load-bearing capacity is a result of the material yielding in the central zone of the beam (red color) as well as at the connector-beam junction and the connector itself. In the case of the 6 m beams, a different pattern of load-bearing capacity loss is observed. The beams do not yield in the central or support zones but undergo buckling. In the case of both the 3 m and 6 m beams, the connector yields each time. Similar effects were also observed during experimental tests as shown in Figures 10 and 11.  
Figure 6: Material sample with an extensometer during testing.  
Figure 8: Reduced Mises stress distribution for a 3 m beam—cross-section.  
Figure 7: Reduced Mises stress distribution for a 3 m beam.  
Figure 9: Reduced Mises stress distribution for a 6 m beam.  
Validation in numerical simulations is essential as it ensures the credibility of the results, helps understand the limitations of the model, and contributes to its refinement, thereby increasing the accuracy and utility of the simulations. In the present study, model validation was performed through both qualitative and quantitative comparisons. The qualitative comparison involved checking whether the same behavior of the analyzed structural system was observed in both the experiment and numerical simulations. The results of these comparisons are presented in Figures 11 and 12. These clearly show different forms of destruction through buckling of the beam or material yielding.  
Figure 11: Loss of stability (buckling) of the central 6 m beam.  
Figure 10: Reduced Mises stress distribution for a 6 m beam—cross-section.  
The final confirmation of the correctness of the numerical model was the comparison of the force-displacement relationships obtained from experimental tests and computer simulations. These are shown in Figure 17.  
The discrepancy between the laboratory test results and the numerical model for the 3 m-long beams at higher displacement values is due to the material models adopted in the numerical simulations. These are linear elastic-plastic models without damage. Such models in numerical simulation do not reproduce the actual force-displacement curve at higher displacement values. However, these models are sufficient to capture the onset of material yielding.  
Analyzing the presented results, it can be concluded that the adopted model simulates the real system with satisfactory accuracy, and further numerical simulations can be conducted based on it. In all cases, the discrepancies between the numerical model and experimental tests did not exceed 10%.
# Structured Data REST Protocol for End to End Data Mashup
## 4 Our Contributions
### Contribution#1  
Involvement of all the stakeholders and the development of hybrid data mashup are two most important achievements of the proposed work. The data mashup can be classified as direct data mashup, intermediate data mashup and hybrid data mashup, based on mashup communication among stakeholders for performing data mashup. In the direct data mashup, end user directly communicates with DSPs, which are the actual data sources and highly recommended for private data mashup. In intermediate data mashup, MSPs are placed in between end user and the DSPs and is used for public data mashup but may cause of security breach if it is used for private data mashup. We have developed user friendly approach to develop hybrid data mashup in such a way that an ordinary user can perform mashup to get the private data directly from the original data source without any intervention of MSP and can perform public data mashup through MSP/DSP. The proposed work recommends the standard REST protocol for communication between various stakeholders for both the public and private data mashup. Hybrid data mashup shown in Figure 1 is most suitable for EU because it not only involves MSPs for public data mashup but also allows mashing up the private data directly from DSPs. In Figure 1, EU#01 is connected with MSP#01 and DSP#03 directly. MSP#01 is communicating with DSP#01 and DSP#02 for providing mashed up data to EU#01.
# Methodology for CubeSat Debris Collision Avoidance Based on Its Active ADCS System
## 3 Materials and Methods  
### LUIME CubeSat  
In 2007, the University of Vigo inaugurated Spain's inaugural CubeSat project, known as Xatcobeo. This endeavor brought together various interdisciplinary research groups to spearhead the development of the satellite. Since that milestone achievement, these collaborative efforts have persisted and flourished in numerous other projects centered around small satellite technology. Notably, the University of Vigo has been instrumental in conceiving, launching, and managing five satellites. The latest addition to their satellite portfolio is Lume-1, a pivotal component of the Fire-RS program. This satellite was specifically designed to establish an autonomous network encompassing satellites and Unmanned Aerial Vehicles (UAVs) for the purpose of wildfire detection and monitoring. On 27 December 2018, Lume-1 was successfully launched from the Vostochny Cosmodrome in Russia, sharing the journey with 27 other satellites aboard a Soyuz rocket.  
Figure 5 shows the Lume-1 before its launch. It is an active, 2.1 kg, 2U-CubeSat, with a Sun Synchronous Orbit (SSO) with an inclination of 97.3\({}^{\circ}\) and a semi-major axis of 6878 km in the orbit insertion [22]. The reaction Wheel used is a GOMSpace NanoTorque GSW-600, with a mass of 180 g, a size of 44.0 \(\times\) 44.0 \(\times\) 27.0 (mm), and a flywheel inertia of 300 gm\({}^{2}\).
## 5 Discussion
### Comparison of EUS in Diagnosis of Pancreas Divisum with Other Diagnostic Approaches  
An accurate diagnosis is crucial in order to manage and treat symptomatic patients with a suspected pancreas divisum appropriately. While MRCP is non-invasive and commonly used, its sensitivity is slightly lower compared to EUS [39]. MRCP may miss subtle ductal anomalies that EUS can detect due to its superior spatial resolution and the ability to perform real-time assessment, although advances in imaging techniques (e.g., MRCP with secretin stimulation) may allow for more accurate identification of pancreas divisum. In addition to MRI and CT, EUS has become a crucial diagnostic tool for evaluating pancreatic disorders, including all their anatomical variants. EUS has shown high sensitivity and specificity in diagnosing pancreas divisum. Studies indicate that EUS can achieve sensitivity rates ranging from 80% to 100% and specificity rates around 97% to 100% for detecting pancreas divisum. Based on these figures, EUS is the most reliable imaging system in terms of diagnostic capability compared with other imaging systems [40].
# GAN-Based Map Generation Technique of Aerial Image Using Residual Blocks and Canny Edge Detector
## 2 Related Works
### Edge Detection  
Edge detection is a fundamental task in computer vision, focused on identifying boundaries or transitions between regions in an image to extract structural information. However, accurately preserving edge details while detecting them remains a significant challenge. Consequently, extensive research has been conducted to develop advanced methodologies that address this issue.  
[PERSON] et al. [24] presented a refined edge detection approach using a cascaded and high-resolution network to tackle challenges in detecting precise edges in complex scenes. The method emphasizes maintaining edge resolution throughout the network stages and refines edges using batch normalization layers as erosion operations. The method proposed in this paper focuses on preserving and enhancing edge information using the Canny edge detector, whereas the research by [PERSON] et al. [24] differentiates itself by employing a deep learning model that leverages advanced computational techniques in the field of image processing for edge detection.  
[PERSON] et al. [25] proposed a network based on edge detection to enhance the semantic segmentation performance of remote sensing images. This method utilizes an edge detection guide module to effectively combine detailed and semantic information between high-level and low-level features, enabling data fusion centered on boundary information. However, unlike the method proposed in this paper, their research emphasizes the design of a deep learning network specifically for edge detection as its primary objective, with semantic segmentation as a supporting task. To achieve this, the network is trained using paired data with edge ground truth, enhancing the accuracy of edge detection and maximizing semantic segmentation performance.  
[PERSON] et al. [26] proposed a Multi-scale Patch Generative Adversarial Network (MPGE) based on edge detection to enhance image inpainting performance. The MPGE integrates an edge detector into the generator to refine edge contours in reconstructed images and combines L2 loss with a patch-GAN to maintain high resolution and stylistic consistency. Additionally, a multi-head attention mechanism is employed to improve global consistency in the inpainting process. The similarity between this study and MPGE lies in utilizing edge information as a loss function. However, while this paper employs the Canny edge detector to preserve edge details, MPGE leverages a custom-designed network to effectively incorporate edge information, highlighting the methodological distinction.
# Algorithmic Coverage Quantification and Visualization in Range-Free Sensor Networks
## 3 Results  
This section presents four distinct example scenarios that aim to illustrate the capabilities of the proposed tool and its adaptability to diverse conditions. For these scenarios, a flat circular AoI of 11,300 km\({}^{2}\) was randomly chosen. Since the locations of the SRs and the TRs were also chosen randomly, the calculated triangulated areas (in square meters) serve only as an indication of the differences between the scenarios, reflecting the geometry of each setup.  
The first two scenarios aim to illustrate the creation of blind zones (gaps in the field of view of an SR) caused by nearby TRs and how these blind zones can automatically adapt and change when the SR is relocated, remaining close to the TRs. The last two scenarios illustrate how triangulated areas are mapped, after being identified as common detection areas of three or more SRs, and how the calculation of these areas can automatically adapt following a change in the setup, such as a node failure or a change in the position of the SRs or TRs.
# A Dual-Polarized and Broadband Multiple-Antenna System for 5G Cellular Communications
## 1 Introduction
To address these challenges, we introduce a new 5G MIMO antenna featuring four-element/eight-port radiators with a CPW feeding technique, ensuring a quite broad impedance bandwidth and facilitating polarization/pattern diversity functionality. CPW-fed antennas and microwave components have gained significant popularity in wireless applications due to their appealing characteristics, including compact size, conformal shape, lightweight composition, ease of fabrication, and seamless integration. In comparison to probe/microstrip-fed antennas, CPW-fed antennas offer the advantage of achieving wideband impedance matching effortlessly [36; 37]. Moreover, the suggested design comprises four pairs of compact antennas positioned at the four edges of the smartphone's board featuring dual-polarization characteristics. Realized on an inexpensive FR-4 substrate, each element incorporates a trapezoid slot antenna complemented by an L-shaped microstrip stub. To enhance the isolation between adjacent slot antennas, the arrow-shaped strip between antennas serves as a decoupling structure. The compact antenna elements efficiently operate within the frequency range of 3.2-6 GHz, exhibiting dual-polarization functionality and providing full radiation coverage, ensuring comprehensive connectivity. The novelty of the proposed MIMO smartphone antenna lies in its development as a compact, wideband, and dual-polarized antenna array designed to overcome key limitations of existing designs. Unlike most of the reported antennas with limited bandwidth, this array offers an extended frequency range of over 2800 MHz, effectively covering a broad spectrum of sub-6 GHz frequencies. In addition, its planar and compact design allows for seamless integration into a single-layer PCB, reducing system complexity. Furthermore, the array features dual-polarized radiation, enhancing coverage and performance across various device orientations. These innovations provide superior functionality, distinguishing the proposed antenna from other sub-6 GHz MIMO smartphone antenna designs. In addition to the sub-6 GHz spectrum, future smartphones are expected to support high frequencies like mmWave (the 24-100 GHz range) [38]. Compact antennas arranged as arrays on the PCB can form linear phased arrays with high gain and directional radiation beams [39]. A new, compact phased array mmWave antenna with broad bandwidth and end-fire radiation is introduced, and its fundamental characteristics are investigated. It consists of eight loop-dipole antennas arranged linearly, enabling easy integration into smartphone systems. The properties and performance of the element, as well as the entire MIMO smartphone design and the mmWave array, are meticulously evaluated using CST software 2022 [40]. Through rigorous investigations, the capabilities and efficacy of the proposed antenna system are thoroughly examined, establishing a foundation for an advanced and efficient wireless communication solution for future smartphones.
## 1 Introduction
### Calculation of Optimum Insulation Thickness and Annual Energy Cost  
Insulating the buildings' external walls significantly reduces the heat gain and loss through the walls' surface. However, a cost analysis should be conducted to obtain the optimum insulation thickness. The annual cost of consumed energy \(C_{A,C}\) is calculated by [35], as follows:  
\[C_{A,C}=\frac{86,400\times CDD\times\text{C}_{e}}{\left(\text{R}_{w,t}+\text{ R}_{ins.}\right)\times COP} \tag{12}\]  
where \(C_{e}\) is the present electricity cost, which is 0.18 SAR/kWh for the case study presented in this study.  
To determine the optimal insulation thickness, various financial techniques are employed. One of the most commonly used methods is the Simple Payback Period (_SPP_), which evaluates the time required for energy cost savings to offset the initial insulation investment. However, SPP does not consider the time value of money, which is a critical financial parameter [36]. To overcome this limitation, Life Cycle Cost (_LCC_) analysis is widely adopted [35]. _LCC_ provides a comprehensive evaluation of the total cost of insulation over its lifespan. In this study, LCC was implemented to ensure a cost-optimal insulation strategy that balances initial investment with long-term energy efficiency benefits.  
The optimum insulation method in this study is obtained using the life cycle cost analysis technique. The annual energy cost is calculated based on the Present Value Factor (_PVF_) and the lifetime (_N_). The _PVF_ is determined based on the interest (_i_) and inflation (_f_) rates, as follows:  
If i \(>f\)  
\[\text{r}=\frac{i-f}{1+f} \tag{13}\]  
If \(i<f\)  
\[\text{r}=\frac{f-i}{1+i} \tag{14}\]  
\[\text{PVF}=\frac{\left(1+r\right)^{N}-1}{r\left(1+r\right)^{N}} \tag{15}\]  
If \(i=f\)  
\[\text{PVF}=\frac{N}{1+i} \tag{16}\]  
where \(r\) is the actual interest rate. Then, the cost of insulation is determined by  
\[\text{C}_{ins.}=\text{C}_{u}\times x \tag{17}\]  
where \(\text{C}_{u}\) is the cost of the insulation per unit volume. According to the life cycle cost analysis, the total cooling cost of an insulated building is obtained by  
\[\text{C}_{t,C}=\text{C}_{A,C}\times PWF+\text{C}_{u}\times x \tag{18}\]  
The derivative of the total cost equation \(C_{t,c}\) is taken and set equal to zero, which gives the optimum insulation thickness (\(X_{opt,c}\)), as follows:  
\[\text{X}_{opt,c}=293.94\times\left(\frac{\text{C}_{e}\times PWF\times CDD \times K}{\text{C}_{u}\times COP}\right)^{1/2}-k\times\text{R}_{w,t} \tag{19}\]
## 3 Results and Discussion
### Cell Viability  
Cell lines of non-malignant murine (L929) fibroblasts and malignant murine melanoma (B16F10) cells were employed in this study. We examined whether the transferred CAP could kill B16F10 cancer cells in an in vitro model as well as explore the potential mechanisms that allow for the specific ablation of cancer cells without affecting non-malignant (L929) cells. We cultured cells in cell medium as shown in Figure 5a. Non-malignant (L929) cells and cancerous B16F10 cells were next collected by trypsinization and centrifugation (at 1200 rpm for 5 min). Collected cells were sub-cultured until used further. For plasma treatment, we removed the culture medium and applied the transfer-CAP system to cells. As a result, after transfer-CAP treatment, using an MTT assay and visible microscopy, we investigated cell viability and morphology as shown in Figure 5b,c, respectively. Cell viability results showed that the number of viable non-malignant (L929) cells was ca. 2-3-fold higher than the number of cancer B16F10 cells after the transferred CAP treatment. Therapeutic effect is one of the most imperative considerations of cancer treatment. Spec  
Figure 3: Transferred cold atmospheric plasma (CAP) emission captured by an ICCD camera with an exposure time of 0.5 s. (**a**) Transferred CAP emission without an optical filter. (**b**) Hydroxyl radical emission using an optical filter with 50% transmission. (**c**) Atomic oxygen radical emission using an optical filter with 50% transmission. (**d**) A typical spectrum obtained with a spectrometer during transferred CAP treatment including hydroxyl and (**e**) atomic oxygen radicals.  
troscopic (Figure 3d,e) and MTT (Figure 5b) data suggest that the generation of RONS by transferred CAP preferentially killed malignant melanoma (B16F10) cells without exhibiting significant cytotoxicity toward non-malignant (L929) cells. Similar published findings also supported our experimental data [23; 24]. In addition, the optical morphological data showed that the transferred CAP resulted in the separation of cancer cells compared to the before transfer-CAP treatment (Figure 5c). As shown in Figure 5c, optical image data suggests that the transferred CAP also could cause cell shrinkage and damage cancer cells. Transfer-CAP treatment thus led to morphological changes of melanoma cancer cells.
# Green Communication for Wireless Body Area Networks: Energy Aware Link Efficient Routing Approach
## 3 Green Computing for WBANs
### Path Cost Estimation
#### 3.2.3 Path Loss Model  
The propagation of wireless signals in WBANs experience shadowing and fading effects of the human body. Several more complex path-loss prediction models are available in the literature such as [40; 41; 42; 43]. These models have been for different environment specific variations and have their own pros and cons. However, we exploit a Friis formula-based path loss model as used in our benchmark protocols and by other recent studies in WBANs [18; 44; 45]. The usage of more complex path-loss prediction models requires more computation in signal characterization leading to higher energy consumption. Considering our energy centric communication model development for wireless body area networking, we employ the simplistic path loss models. This model defines PL as a linear function of the distance \(d\) between the nodes. The path loss \(PL_{ij}\) in decibel (dB) between node \(i\) and node \(j\) can be formulated in Equation (6).  
\[PL_{ij}(d)=PL_{0}+10(n)\log_{10}\frac{d_{ij}}{d_{0}}+X_{\sigma} \tag{7}\]  
where \(PL_{0}\) is the path loss at a reference distance \(d_{0}\) which is considered 10 cm in our simulation similar to [26], \(n\) is the path loss coefficient which is considered 2 as it is implemented in free space,\(X\) represents Gaussian random variable [45], and \(\sigma\) is the standard deviation [46]. The \(PL_{0}\) can be further derived in Equation (7).  
\[PL_{0}=10\log_{10}\frac{\left(4\pi d_{0}\right)^{2}}{s\lambda^{2}} \tag{8}\]  
where \(s\) denotes the speed of light and \(\lambda\) represents the wavelength.
# A Real-Time Safety Helmet Wearing Detection Approach Based on CSYOLOv3
## 2 Original YOLOv3 Model
### Loss Function  
The loss function of YOLOv3 is comprised of the prediction frame(x,y), the prediction frame size(w,h), the prediction class(C), and the prediction confidence(conf), as shown in Equation (2).  
\[\text{Loss}=\frac{1}{n}\sum_{i=1}^{n}\text{loss}_{xy}+\frac{1}{n}\sum_{i=1}^{n} \text{loss}_{wh}+\frac{1}{n}\sum_{i=1}^{n}\text{loss}_{C}+\frac{1}{n}\sum_{i= 1}^{n}\text{loss}_{confi} \tag{2}\]  
where \(n\) is the total number of trained targets, and the components of loss function are described in detail as follows:  
\[\text{loss}_{xy}=slw*(2-w*h)*bst(\text{true}xy,\text{pred}xy) \tag{3}\]  
\[\text{loss}_{wh}=slw*(2-w*h)*0.5*S(\text{true}wh,\text{pred}wh) \tag{4}\]  
\[\text{loss}_{\text{C}}=shw*bst(trueC,predC) \tag{5}\]  
\[\text{loss}_{confi}=shw*bst(slw,\text{pred}shw)+(1-slw)*bst(slw,\text{pred}shw )*ignoreshw \tag{6}\]  
where \(slw\) indicates the point of the detected targets, and \((w,h)\) represents the size of length and breadth about the forecast frame, \(bst\) denotes the binary cross entropy function, \(S\) means the function of variance. \(truexy\) is the concrete target coordinate position on each image while \(predxy\) indicates the forecast position; \(truewh\) is the actual groundtruth box size, \(predwh\) means the prediction box size; \(trueC\) refers to the realistic target class, \(predC\) represents the prediction class; \(predshw\) is the forecast object point, \(ignoreshw\) is associated with the value of IOU, if the value of IOU is less than the threshold, \(ignoreshw\) is 0.
## 3 Literature Review
### Behavioral Biometrics
#### 3.1.4 Gait Dynamics
In [73], researchers proposed a hybrid deep learning model (HDLN) for gait mobile authentication. Their hybrid architecture consisted of two LSTM layers and three one-dimensional CNN layers for feature extraction, followed by a convergence layer and a SoftMax layer for interpretation. A specialized segmentation algorithm was used to divide gait cycles to use as input for the HDLN. Accelerometer and gyroscope data from 40 individuals performing gait activity were used to train their model. A visualization of the gyroscope signals of three different subjects is presented in Figure 7. In comparison to other systems, HDLN performed the best with an accuracy of 95.79% and a run-time of 2.92 s. Using the IDNet dataset [63], this model was able to produce a 99.65% accuracy in a more realistic scenario. \begin{table}
\begin{tabular}{c c c} \hline
**Study** & **Algorithm** & **Accuracy (\%)** \\ \hline
[68] & LSTM + SGD & 97.00 \\
[70] & CNN + LSTM & 97.70 \\
[73] & CNN + LSTM & 95.79 \\ \hline \end{tabular}
\end{table} 
Table 3: Comparison of studies on IDNet dataset [68,70,73].