esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This study calibrated and compared the capabilities of hourly global horizontal irradiance (GHI) clear sky models for six Moroccan locations, using the McClear clear sky model as a reference. Complex clear sky models, namely Bird, Simplified Solis, Ineichen and Perez, and simple clear sky models, namely Adnot-Bourges-Campana-Gicquel (ABCG), Berger-Duffie, and Haurwitz were tested. The SOLCAST satellite-based dataset estimates were validated against the McClear clear sky model. pvlib python was used to configure the models, and ERA5 hourly fractional cloud cover was used to identify clear-sky days. The study period was from 2014 to 2021, and the study sites were in different climatic regions in Morocco. Bar graphs, tables, and quantitative statistical metrics, namely relative mean bias error (rMBE), relative root mean square error (rRMSE), relative mean absolute error (rMAE), and the coefficient of determination (R\({}^{2}\)), were used to quantify the skill of the clear sky model at different sites. The overall rMBE was negative in 5/6 sites, indicating consistent overestimation of GHI, and positive in Tantan (14.4%), indicating frequent underestimation of GHI. The overall rRMSE varied from 6 to 22%, suggesting strong agreement between clear sky models and the McClear clear sky model. The overall correlation was greater than 0.96, indicating a very strong relationship. Overall, the [PERSON] clear sky model proved to be the most feasible. Complex clear sky models outperformed simple clear sky models. The SOLCAST satellite-based dataset and ERA5 cloud fraction information could well be used with quantifiable certainty as an accurate clear sky model in the study region and in other areas where complex clear sky models' inputs are not available.  
clear sky model; global horizontal irradiance; calibration; validation; solar energy applications; pvlib python; SOLCAST; fifth-generation European Centre for Medium-Range Weather Forecasts atmospheric reanalysis (ERA5); statistical metrics
# The Helical Resonator: A Scheme for Radio Frequency Plasma Generation
## 4 The Helical Resonator as a Transmission Line
It is instructive to plot the resistance at the point of purely resistive impedance near resonance and the voltage amplification at the same point as a function of the tap position \(\delta\) for different attenuation factor values. This is depicted in \[V_{0}^{+}+V_{0}^{-}=0 \tag{34}\]2 On the same figures, the resistance (real part of the impedance) and the amplification factor computed precisely at resonance are shown as dashed lines. It is possible to observe that the resistance curves at resonance display a steep rise for low values of \(\delta\), a maximum, and then a slower fall. This behavior suggests that if one wants to work at resonance and a specific value of this resistance is sought (typically 50 \(\Omega\) for good matching), then one may wish to stay on the right of the peak, where the slow variation is forgiving in regards to imprecision in the construction. This also gives slightly higher voltage amplification than at very low \(\delta\) values. Additionally, one should observe that if the dissipation is too low, then there exists the possibility that matching is not achievable because the input resistance is lower than that required for all possible \(\delta\) values. However, if instead of working precisely at resonance, the nearby point of purely resistive input impedance is chosen, it can be seen that a monotonously decreasing  
\[V_{0}^{+}+V_{0}^{-}=0 \tag{34}\]3 Behavior of the helical resonator at the peak resistance and at the resonance, plotted as a function of the normalized attenuation factor \(\alpha H\), for three different values of the relative tap position \(\delta\). Top left: input resistance at peak, normalized to the characteristic impedance \(Z_{c}\). Top right: input resistance at resonance, normalized to the characteristic impedance \(Z_{c}\). Bottom left: ratio of the previous two quantities. Bottom right: voltage amplification factor \(|V_{out}/V_{in}|\). resistance is found. In fact, for \(\delta\) values that are sufficiently high, the curves superpose to those obtained at resonance, indicating a very small difference between the two points. A larger discrepancy appears at low \(\delta\), where the resistance at resonance decreases, while the other value increases steeply. In this same region, the amplification factor at the point of purely resistive impedance decreases with respect to the one at resonance. Generally speaking, it seems advisable to avoid the low \(\delta\) values where a strong discrepancy of the two conditions appears, and where small errors in determining \(\delta\) may translate to large changes in input resistance. For these values of the attenuation factor, this suggests a \(\delta\) value of 0.1 or larger.
## 3 Enabling Technologies and Challenges for Futuristic Vehicular Networks
### Machine Learning  
The traditional approaches used for solving VNs problems include convex optimization, game-theoretic approaches, and several other metaheuristics. These techniques mainly suffer from the heavy computational burden with exponentially increasing search space in large-scale scenarios; this is even more enforced when the considered VN system employs multiple EC nodes, and network softwarization solutions, enabling more flexible implementations. Heuristic approaches used for solving the VN problems with their NP-hardness are not able to adapt to the increasing complexity of the new applications.  
Figure 1: Vehicular edge computing framework.  
With the addition of IoT techniques, enabling what is usually referred to as IoV [5, 50, 52, 53], vehicles become an excellent element for training data that can be utilized for solving vehicular problems (i.e., ML-based solutions). Several complex vehicular problems such as dynamic resource allocation, traffic predictions, cooperative congestion control, content caching, computation offloading, intrusion detection, anomaly detection can be solved by using popular ML methods such as supervised learning, unsupervised learning, reinforcement learning, etc. [24]. Among other ML techniques, Deep Reinforcement Learning (DRL) is a potential solution for many complex vehicular scenarios, which allows exploiting Deep Neural Networks (DNN) for analyzing VNs data without requiring any prior knowledge of the VN environment, which is hard to capture [54], e.g., correct state transmission matrix over VN states for Markov Decision Processes (MDP) based solutions. ML solutions outperform the heuristic and one-shot-based optimization techniques with better long-term performance.  
Different approaches are available for the integration of ML-based solutions into vehicular environments. In each of these approaches, various communication, and computation strategies can be involved during the training process of an ML model. For example, a centralized ML model training approach requires each VN to send its data towards a centralized, more powerful server. In another case, a centralized ML server can further split the training data and corresponding operations with nearby servers to reduce the required computation time. A new collaborative learning-based approach such as federated learning (FL) can allow participating VUs to train ML models locally. The powerful central server can be employed for collecting and averaging the local training ML models parameters to combine the VUs learning experience. These technologies can have certain advantages, disadvantages and can face several new challenges in vehicular environments. In the following, we describe each of these approaches in detail.
# Using Fast Frequency Hopping Technique to Improve Reliability of Underwater Communication System
## 1 Introduction
This article is a development of research carried out by the author over the past few years on reliable underwater communication systems for work in shallow coastal waters [14; 15]. Its main subject is to specify the parameters for the FFH technique and to present the results of experimental tests on the lake. The compromised choice of parameters consisted of the selection between the narrow band of the hydroacoustic transducer and the maximum number of carrier frequency hops, which results from the need to take into account the effects of the Doppler effect. In turn, the number of hops and the symbol duration were selected adequately for the occurrence of multipath propagations of an acoustic wave.
## 6 Results
### Energy Reduction
#### 6.3.2 Radio Conditions and Message Size  
We now examine the impact of the message size and radio conditions on the energy consumption of a device in the case of NIDD without SCHC. The average values of the measurements are presented in Figure 12, which shows that energy consumption is very  
Figure 11: Energy consumption (\(mWh\)) during the DTLS handshake.  
low when no repetitions are used (radio conditions 0 and 1) and becomes more important with the degradation of radio conditions when several repetitions are triggered. Similar to the impact on the latency, the figure shows that the message size is less important for good radio conditions (0 and 1) and becomes significant for worse radio conditions. In particular, the message size becomes critical in radio condition 4, where the energy consumption can be very high. As a reference, a single uplink and downlink of 200 bytes requires more than 3.8 mWh, which is more than 50 times the same exchange in radio condition 0. Note that the case of the IP follows the same trend.
# Dual Attention Equivariant Network for Weakly Supervised Semantic Segmentation
## 3 Model  
### Motivation  
Current models using CAM on weakly supervised semantic segmentation rely mainly on the regional discriminant capability provided by convolutional classification networks and treat each channel feature map independently, which may overlook some important regions due to the lack of accurate pixel-level labeling. Generally, different feature maps have rich correlations in channel and spatial dimension. Specifically, different channel feature maps can complement and influence each other, with certain channels playing pivotal roles in recognizing and localizing specific targets, while others capture detailed information from different regions. Moreover, information exchange between different channels can also occur through spatial dimensions. For instance, in target localization tasks, different channel feature maps can correspond to different regions of the target in the spatial domain, and their spatial correlations may be crucial in achieving precise localization accuracy.  
Based on the above insights, we design a channel-spatial attention module (CSM) to fully leverage the correlation among feature maps in different channels provided by the classification network. The CSM module can adaptively adjust the weights and spatial distributions of channel feature maps to enable the network to focus more on features of target objects. Then, by integrating the CSM module with equivariant regularization and pixel-level correlation modules, we propose the DAEN model, as shown in Figure 1, to achieve more accurate and effective pixel-level semantic segmentation. In the following, we shall introduce the implementation of DAEN in detail, including the CSM module, equivariant regularization, the pixel-correlation module, and a loss function.
# A Multi-Step Image Pre-Enhancement Strategy for a Fish Feeding Behavior Analysis Using Efficientnet
## 2 Methodology
### Multi-Step Image Pre-Enhancement Strategy
#### 2.3.2 Multi-Metric Driven CLAHE and Unsharp Masking
Conversion from BGR to LAB Color Space: The LAB color space separates luminance and color information, allowing for operations to be carried out on luminance alone, thus avoiding impacts on color to maintain color fidelity. 2. Independent Processing of the L Channel: This involves separating the L channel from the LAB image, performing subsequent processing only on the L channel, and  
Figure 5: Flowchart of Multi-Metric-Driven CLAHE and Unsharp Masking. applying the CLAHE algorithm to the L channel. This approach focuses on enhancing the luminance details of the image, which is crucial for improving image quality. 3. Initialization of clipLimit and tileGridSize: Setting these parameters serves as the basis for beginning CLAHE processing, which can be adjusted based on outcomes to achieve desired effects. 4. CLAHE Application: Contrast Limited Adaptive Histogram Equalization (CLAHE) is applied to the L channel using the initial parameters set in the previous step. This stage is the core of the entire process; by limiting contrast enhancement, noise can be reduced, and over-enhancement can be prevented. 5. Image Quality Assessment: The quality of the enhanced image is evaluated using PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity Index) as metrics for image quality. PSNR measures the similarity between the reconstructed image and the original image, while SSIM assesses the structural information, luminance, and contrast of the image. Through the evaluation with PSNR and SSIM, the quality of the image can be quantified to ensure that the visual improvements of the enhanced image are effective. Setting these conditions as quality thresholds ensures that further processing is only undertaken when the image reaches a certain quality standard, thereby avoiding unnecessary computations and potential quality degradation. By setting the initial clipLimit and tileGridSize parameters, the algorithm controls the intensity of local contrast enhancement while enhancing the image contrast, preventing over-processing and noise generation. The independent processing of the luminance channel maintains color fidelity, avoiding color distortion. The quality of the enhanced image is evaluated using metrics such as PSNR and SSIM, ensuring that improvements in image quality are made without sacrificing detail. Overall, this processing workflow aims to enhance the visibility and detail of images, especially under uneven lighting conditions, while maintaining the naturalness of color and reducing noise. This approach provides more accurate and authentic visual information for various applications.
###### Abstract  
Due the fact that the required therapy to treat Ventricular Fibrillation (\(VF\)) is aggressive (electric shock), the lack of a proper detection and recovering therapy could cause serious injuries to the patient or trigger a ventricular fibrillation, or even death. This work describes the development of an automatic diagnostic system for the detection of the occurrence of \(VF\) in real time by means of the time-frequency representation (\(TFR\)) image of the ECG. The main novelties are the use of the \(TFR\) image as input for a classification process, as well as the use of combined classifiers. The feature extraction stage is eliminated and, together with the use of specialized binary classifiers, this method improves the results of the classification. To verify the validity of the method, four different classifiers in different combinations are used: Regression Logistic with L2 Regularization (\(L2 RLR\)), adaptive neural network (\(ANNC\)), Bagging (\(BAGG\)), and K-nearest neighbor (\(KNN\)). The Hierarchical Method (HM) and Voting Majority Method (VMM) combinations are used. ECG signals used for evaluation were obtained from the standard MIT-BIH and AHA databases. When the classifiers were combined, it was observed that the combination of \(BAGG\), \(KNN\), and \(ANNC\) using the Hierarchical Method (HM) gave the best results, with a sensitivity of 95.58 \(\pm\) 0.41%, a 99.31 \(\pm\) 0.08% specificity, a 98.6 \(\pm\) 0.04% of overall accuracy, and a precision of 98.25 \(\pm\) 0.29% for \(VF\). Whereas a sensitivity of 94.02 \(\pm\) 0.58%, a specificity of 99.31 \(\pm\) 0.08%, an overall accuracy of 99.14 \(\pm\) 0.43%, and a precision of 98.59 \(\pm\) 0.09% was obtained for \(VT\) with a run time between 0.07 s and 0.12 s. Results show that the use of \(TFR\) image data to feed the combined classifiers yields a reduction in execution time with performance values above to those obtained by individual classifiers. This is of special utility for \(VF\) detection in real time.  
biomedical systems; ECG electrocardiogram signals; time-frequency representation; non-stationary signals; image analysis; combined classification algorithms; hierarchical classifiers; voting majority method classifiers +
Footnote †: journal: Applied Sciences
# Experimental and Numerical Study of Orifice Coefficient of Cargo Tank Design of LNG Vessels
###### Abstract  
Liquid cargo storage tanks of liquefied natural gas (LNG) carriers are designed by strict standards to maintain the cryogenic state (\(-163\)\({}^{\circ}\)C). For most LNG cargo storage tanks, it is mandatory to install a system that can safely store leaked fluid for 15 days in the case of leakage of liquid cargo due to crack of the insulation system. To ensure safety, it is necessary to predict the amount of LNG spilling from the cracks in the insulation panels. Although international regulations are provided, they rely on a conservative and consistent coefficient. In this study, experimental and numerical methods were applied to examine the design factor used to predict the flow rate in the tank design process. To check the amount of leakage that occurs under pressure conditions of LNG tanks, an experiment was conducted using crack specimens and pressure containers filled with water. In order to simulate the leakage of LNG, the amount of leakage was predicted using the Computational Fluid Dynamics (CFD) method. The distribution of leakage quantity was investigated according to the shape of the crack through the pressure vessel experiment and the analysis. Through CFD analysis, the leakage rate of LNG was calculated for each operating pressure condition through the crack. Finally, the results of this study examined the need to identify and reconsider the coefficients due to international guidelines and other factors in calculating orifice coefficients applied to the design of LNG tanks.  
orifice coefficient; LNG leakage; LNG cargo containment system; leakage test; computational fluid dynamics +
Footnote †: journal: Applied Sciences
## 4 Dataset and Evaluation Protocol
### Dataset Constructed with Poisson Blending and Relief Simulation
This operation enhances the diversity of visual styles, mimicking stone materials such as bluish-gray slate, reddish sandstone, and weathered limestone. As shown in Figure 6, the synthesized result in (c) demonstrates improved visual integration in terms of lighting consistency, surface realism, and perceptual depth. In contrast, the baseline results in (a) and (b) provide only basic compositing without capturing the physical characteristics of stone inscriptions. The proposed method effectively models the visual properties of carved text by combining directional filtering, transparency-based blending, and color perturbation. It produces high-quality samples that are useful for training robust models. Figure 6: Comparison of stone-style text synthesis results using (a) alpha blending with transparency \(\alpha=0.5\), (b) Poisson image blending, and (c) the proposed method based on relief simulation. The Chinese text is used as visual content to evaluate synthesis quality and does not require translation.
## 2 Materials and Methods
### MODIS Data Products  
MODIS collection 5.1 land products are available from the Land Processes DAAC [45]. Using the flux tower position (30\({}^{\circ}\)28\({}^{\prime}\)08.50\({}^{\prime\prime}\)N, 91\({}^{\circ}\)03\({}^{\prime}\)44.50\({}^{\prime\prime}\)E) as the center pixel, we extracted the MODIS LAI/FPAR (LAI\({}_{\text{M}}\)/FPAR\({}_{\text{M}}\)) from MOD15A2 and GPP (GPP_MOD) products from MOD17A2 at 1 km spatial resolution and 8-day time step from 1 January 2009 to 31 December 2013 [45]. Here we didn't use the MODIS collection 6 land products because we have no access, so far, to these data at our site. Thus, we used the MOD17A2 Collection 5.1 that is more complete and accessible to our study area. The GPP_MOD (Kg C day\({}^{-1}\)) were calculated from the MOD17A2 algorithm [11] based on a light energy use efficiency (LUE) model (Equation (1)) [7; 11; 46].  
\[GPP=\varepsilon\times APAR \tag{1}\]  
where \(\varepsilon\) is the PAR conversion efficiency (Kg C MJ\({}^{-1}\)) [47]. The two parameters for Tmin and the two parameters for VPD are used to calculate the scalars that attenuate \(\varepsilon_{\text{max}}\) to produce the final \(\varepsilon\) (Equation (2)) [11].  
\[\varepsilon=\varepsilon_{\text{max}}\times T\text{min\_scalar}\times VPD\_ scalar \tag{2}\]  
where the value of \(\varepsilon_{\text{max}}\) is obtained from the Biome Properties Look-Up Table (BPLUT) [7; 11]. The attenuation scalars (Tmin_scalar and VPD_scalar) are simple linear ramp functions of the daily minimum air temperature (Tmin) and daytime average VPD (Equations (3) and (4)), which range from 0 to 1 [23; 48].  
\[T\text{min\_scalar}=\left\{\begin{array}{cc}0,&T\text{min}\in(-\infty,T \text{min\_min}]\\ \frac{T\text{min\_}-T\text{min\ min}}{T\text{min\_max}-T\text{min\_min}},&T \text{min}\in(T\text{min\_min},T\text{min\_max})\\ 1,&T\text{min}\in[T\text{min\_max},+\infty)\end{array}\right\} \tag{3}\]  
\[VPD\_scalar=\left\{\begin{array}{cc}0,&VPD\in[VPD\text{max},+\infty)\\ \frac{VPD\text{max}-VPD}{VPD\text{max}-VPD\text{min}},&VPD\in(VPD\text{min}, VPD\text{max})\\ 1,&VPD\in(-\infty,VPD\text{min}]\end{array}\right\} \tag{4}\]  
where Tmin and VPD are obtained from the NASA Data Assimilation Office (DAO)dataset [49].  
APAR is the absorbed PAR (MJ m\({}^{-2}\) day\({}^{-1}\)) by vegetation canopy, which can be calculated as Equation (5).  
\[APAR=FPAR\times PAR \tag{5}\]  
where FPAR is the fraction of absorbed PAR by vegetation canopy, and derived from the MOD15A2 product (FPAR\({}_{\text{M}}\)). PAR is PAR incident on the vegetative surface, which estimated from incident shortwave radiation (SWRad, provided in the DAO dataset) as Equation (6).  
\[PAR=0.45\times\text{SWRad} \tag{6}\]
## 2 Structural Description and Numerical Modeling  
### Frame Construction  
The fan uses a DC brushless motor, and the centrifugal impeller has five blades. The solute-less centrifugal fan frame is used to install the whole machine, as shown in Figure 1. The connecting tube, connection block, and the motor plate are welded to form a cantilever structure for fixing the installation position of the motor and centrifugal impeller. The mounting plate connects the cantilever structure and fixes the whole. The current collector is connected to the installation base plate to improve airflow loss. The structural design was created using SolidWorks software 2020, and the finite element model was built and structurally analyzed using Ansys Workbench 2022R1.  
The volute-less centrifugal fan typically operates within a rotational speed range of 1800-2600 rpm. According to Equation (1), it can be inferred that the excitation shaft frequency range is 30-43.3 Hz, where \(n\) is the rotation speed. The calculation method for blade passing frequency is to multiply the number of blades by the rotational speed shaft frequency, and the harmonic order is obtained by dividing the vibration frequency of the fan by the shaft frequency.  
\[f=\frac{n}{60} \tag{1}\]  
In practical application, the centrifugal impeller and the motor comprise all the excitation sources on the fan frame. The unbalanced force affects the low-frequency range, and the electromagnetic force affects the high-frequency range. When the fan runs, the excitation force is transmitted to the whole frame through the motor plate. The frame structure consists of components made from different materials: the connecting tubes are made of Q195 steel, while the motor plate, connection block, current collector, and mounting plates are made from SS400 steel. The properties of these materials are detailed in Table 1.