esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Modelling and Analysis
### Nonlinear Plastic Hinges and Chord Rotation Capacity Checks
#### 3.3.1 Shear Failure Check  
In structural analysis, ensuring the safety and reliability of a design is of utmost importance. A critical failure mode to address in this context is shear failure. One of the pivotal metrics used to gauge the structure's resilience against this mode of failure is the performance ratio. A performance ratio exceeding 1 suggests that the demand on a structural element surpasses its limit, flagging it for potential concern.  
Our analysis commenced at step 1, but it was not until output number 7 (with a load factor of 0.897), Figure 12 that we observed the first signs of shear failure. In this instance, the shear wall (W21_1-Sec(a)) was the first to exhibit this vulnerability. As the analysis progressed and the load factor increased, more elements began to show susceptibility to shear failure. By output number 51, with a load factor of 1.27607, an array of elements illustrated potential shear failure, as denoted by their performance ratios. For Output Number 7 with a load factor of 0.897, shear failure initiates, especially with the shear wall being the first to exhibit such concerns, as shown below.  
Figure 10: ATC method presented in a simplified manner. (**a**) Pushover Capacity Curve; (**b**) Capacity Spectrum; (**c**) illustration of performance point according to capacity spectrum method; (**d**) Traditional Spectrum (S\({}_{\text{a}}\) vs. T); (**e**) ADRS Spectrum (S\({}_{\text{a}}\) vs. S\({}_{\text{d}}\)).  
As the analysis progresses, by Output Number 20 with a load factor of 1.2354, Figure 13 the onset of shear failure becomes more widespread, affecting various structural elements as depicted in the following representation:  
Figure 11: Plastic Hinges Constitutive Law.  
Figure 12: Performance ratio for output 7.  
Figure 13: Performance ratio for Output Number 20.  
The situation becomes even more pronounced by Output Number 51 with a load factor of 1.27607, Figure 14. At this stage, a multitude of structural elements indicate signs of nearing or reaching shear failure. This progression is captured below.  
The progression from Output Numbers 7 to 51 showcases a significant increase in the number of elements approaching or achieving their shear failure limits. Such insights are invaluable for further design considerations and potential retrofitting measures to enhance structural resilience.
###### Abstract  
The performance of a solar hot water system is assessed for heat pump and domestic heating applications. Thermodynamic analysis on a serpentine-type thermosyphon flat-plate solar heater is conducted using the Second Law of thermodynamics. Exergetic optimization is first performed to determine the parameters for the maximum exergy efficiency using MATLAB optimization toolbox. Geometric parameters (collector surface area, dimensions, and pipe diameter), optical parameters (transmittance absorptance product), ambient temperature, solar irradiation and operating parameters (mass flow rate, fluid temperature, and overall heat transfer (loss) coefficient) are accounted for in the optimization scheme. The exergy efficiency at optimum condition is found to be 3.72%. The results are validated using experimental data and found to be in good agreement. The analysis is further extended to the influence of various operating parameters on the exergetic efficiency. It is observed that optical and thermal exergy losses contribute almost 20%, whereas approximately 77% exergy destruction is contributed by the thermal energy conversion. Exergy destruction due to pressure drop is found negligible. The result of this analysis can be used for designing and optimization of domestic heat pump system and hot water application.  
**Keywords:** exergy; optimization; serpentine; thermosyphon; solar water heater; exergy destruction **2018** 8, 391; doi:10.3390/app8030391
## 4 Experiments
### End-to-End Latency  
It is the time taken for broadcasting a packet from source to destination and is measured in Seconds. Figure 1 depicts the comparative analysis of the End-End latency of the proposed method with other methods.
## 1 Introduction
Nonetheless, even after the removal of the dependencies of radiomic features with size, it is possible that below a certain VOI size, not enough information is available for the proper calculation of the radiomic feature, making it inaccurate and hence unable to distinguish two different textures, potentially reflecting the different clinical outcomes radiomics aims to predict. [PERSON] and [PERSON] [6; 7] analyzed the behavior of texture quantifiers as a function of the tumor volume on \({}^{18}\)F-fluorodeoxyglucose positron emission tomography (\({}^{18}\)F-FDG PET) images. They concluded that small tumors (volume below 700 voxels) should not be included in studies of intratumoral heterogeneity of tracer uptake since they do not include a high enough number of voxels to adequately sample the underlying tracer distribution and properly calculate some radiomic features. Similar research has not yet been performed in magnetic resonance imaging (MRI). We hypothesize that, also in MRI, some radiomic features may lose their texture-discriminative power when obtained using volumes below a certain threshold to be identified. This study aimed to investigate how the ability of MRI-based radiomic features to discriminate different textures varies as a function of the volume of the region considered for the feature extraction. For this purpose, an MRI phantom specifically designed for radiomic applications in the pelvic region, embedding inserts with different textures, was imaged on three scanners with a T\({}_{2}\)-weighted sequence included in the clinical protocol for the diagnosis of pelvic tumors.
# Spread Spectrum Image Watermarking Through Latent Diffusion Model
## 2 Method
### Diffusion and Inversion  
The diffusion process in DDPM [18] adds Gaussian noise to the original data in \(T\)-steps through a Markov chain to obtain a noisy image \(x_{T}\) as  
\[q(x_{t}|x_{t-1})=\mathcal{N}(x_{t};\sqrt{1-\beta_{t}}x_{t-1},\beta_{t}\mathbf{ I}), \tag{1}\]  
where \(\beta_{t}\in(0,1)\) is the variance at the step of \(t\) and \(t\in(0,1,\ldots,T-1)\). In the reverse process, the generation requires a noise predictor with which the noise is removed to restore the images. However, this diffusion process is stochastic and is therefore detrimental to the reconstruction of watermarked images. In contrast, DDIM inversion [19] implicitly adds predicted noise at each diffusion time step and outputs the final distribution as structured noise. This process can be defined as  
\[\hat{x}_{0}^{t}=\frac{x_{t}-\sqrt{1-\bar{\alpha}_{t}}\epsilon_{\theta}(x_{t})}{ \sqrt{\bar{\alpha}_{t}}}, \tag{2}\]  
\[x_{t+1}=\sqrt{\bar{\alpha}_{t+1}}\hat{x}_{0}^{t}+\sqrt{1-\bar{\alpha}_{t+1}} \epsilon_{\theta}(x_{t}), \tag{3}\]  
where \(\bar{\alpha}_{t}=\prod_{i=0}^{t}(1-\beta_{t})\), \(t\in(0,1,\ldots,T-1)\) and \(\epsilon_{\theta}(\cdot)\) is a noise predictor based on a time-conditional UNet [6]. For efficiency, DDIM is employed in this work to generate the watermarked images.  
To enhance the robustness and reduce computational complexity, we employ the variational autoencoder to transform images into the latent distribution and apply sampling  
Figure 2: The proposed framework.  
in latent space [20]. As the latent space is much smaller compared to the original image space, the denoising and its reverse process run much faster.
# Design and Control of a Battery Charger/Discharger Based on the Flyback Topology
## 3 Sliding-Mode Controller Design
### Bus Capacitance and Voltage Perturbation  
The term switching function of the SMC is designed to compensate for the perturbation at the bus current; however, an instantaneous change on the bus current will produce a voltage perturbation on the DC bus. This phenomenon is unavoidable since the capacitor current is the result of the instantaneous change on the bus current and the leakage current, which has a non-instantaneous change defined by (8). Therefore, after a perturbation in the bus current, the capacitor current is not zero, which produces a bus voltage perturbation. This phenomenon is illustrated in Figure 4, where a step-like change in the bus current \(i_{dc}\) is considered. In this example, the bus current changes from the maximum discharge value \(i_{dc(0)}>0\) to the maximum charge value \(i_{dc(f)}<0\) with almost an almost infinite derivative \(\frac{d\dot{i}_{dc}}{dt}\rightarrow-\infty\), which produces the following behavior:  
* A stable bus voltage requires to fulfill the charge balance principle; thus, the average capacitor current must be zero, i.e., \(i_{Cdc}=i_{k}-i_{dc}=0\). * The leakage current \(i_{k}\) was originally defined to have an average value equal to the DC current previous to the perturbation \(i_{dc(0)}\). * The step-change on the DC current to the new value \(i_{dc(f)}\) produces an instantaneous change on the capacitor current equal to \(i_{dc(0)}-i_{dc(f)}\). * The SMC must reduce \(i_{k}\) to have an average value equal to the new DC current \(i_{dc(f)}\), thus the control signal must be set to \(u=0\). * The leakage current changes with a limited derivative defined by (5) and (8), thus \(i_{k}\) takes a time \(T_{d}\) to reach \(i_{Cdc}=i_{k}-i_{dc}=0\). * During \(T_{d}\), the \(i_{Cdc}\) current is positive because \(i_{k}>i_{dc}\); hence, a positive charge is introduced into the capacitor, which increases the bus voltage. * This process produces an undesired deviation \(\Delta v_{dc}\) into the bus voltage, which must be limited to avoid damages on both the loads and sources connected to the bus. Therefore, this paper proposes to develop a design equation for \(C_{dc}\) based on the maximum \(\Delta v_{dc}\) value acceptable for a safe operation of the devices connected to the bus. The first step is to calculate the maximum change on the capacitor current, which occurs when the dc current changes at the peak of the leakage current, i.e., \(\max(i_{k})\). From Equation (8), it is noted that \(i_{k}=\frac{in}{n}\) for \(u=0\).
# Enhanced Multicast Repair Fast Reroute Mechanism for Smart Sensors IoT and Network Infrastructure
## 6 Evaluation of the EM-REP Proposal
### Time of Repair: Algorithm Speed
This subsequently creates interfaces of the first arrival (PIM-DM RPF ports) and a chain of routers of an alternative path. Simulations show the speed of network recovery achieved by the EM-REP mechanism is comparable, respectively the same, as the network recovery speed achieved through the proactive FRR mechanisms. The main difference is that the EM-REP mechanism does not require preparatory calculations or additional router resources. On the other hand, the network load is initially higher, as is the case with the proactive FRR mechanisms. This is the result of the initial EM-MREP flooding process. However, the EM-REP was not designed to protect all flows affected by a failure. EM-REP protects only specific but important customer data flows that require special treatment or lossless delivery through an ISP. These flows are only a subset of all flows affected by the error. In addition, we expect that the EM-REP mechanism, like other IPFRRs, will only work for a short time, not longer than a few tens of milliseconds or a few seconds. It ends when the network convergence process is complete, the multicast tree is no longer used, and packets of protected flows are routed again as unicast packets.
# An Intelligent DOA Estimation Error Calibration Method Based on Transfer Learning
## 4 Simulation and Result Analysis
### Experimental Results  
We use the training dataset \(T_{1}\) to train the intelligent DOA estimation model to convergence, and test it on the test dataset \(D\). Then, set a smaller learning rate, use the training dataset \(T_{2}\) to fine-tune the model and update the parameters of all convolutional layers and the fully connected layer. When the model has trained to convergence again, test it on the test set \(D\). In Section 4.2.1, we show the test performance on the four evaluation metrics of the model before and after transfer learning. In Section 4.2.2, we show how close the predicted angles are to the actual angles before and after transfer learning. In Section 4.2.3, we show the test angle error distribution before and after transfer learning.
## 5 Summary and Conclusions  
In summary, a theoretical model of high-speed penetration of finite-diameter metal targets by a long-rod is established using a quasi-static finite cylindrical cavity expansion model. The calculated results are in good agreement with the experimental results. Studies have also been conducted on the factors influencing the long-rod penetration into metal targets with finite diameters. The conclusions are as follows:  
* The proposed model requires only the material and size parameters of the target and the long-rod as inputs, and predicts the penetration depth well across a range of \(r_{0}/R_{\rm P}\), varying target and long-rod materials, and diverse \(v_{0}\);
* The penetration depth increases by less than 10% between semi-infinite targets and \(r_{\rm t0}/R_{\rm P}=25\). However, an increase of more than 50% in the penetration depth occurs between \(r_{\rm t0}/R_{\rm P}=25\) and \(r_{\rm t0}/R_{\rm P}=5\). Therefore, the lateral boundary effect should be taken into account when \(r_{\rm t0}/R_{\rm P}<25\);
* The penetration depth increases dramatically with increasing \(v_{0}\) before the fluid dynamic limit. On the other hand, \(R_{\rm t}\) decreases significantly with \(v_{0}\) due to the increase in the channel radius caused by \(v_{0}\). Therefore, the lateral boundary effect is also influenced by \(v_{0}\);  
Figure 11: Comparison of penetration depth and normalized target resistance of different yield criteria: (**a**) penetration depth; (**b**) normalized target resistance.  
\begin{table}
\begin{tabular}{c c c} \hline \(r_{0}/R_{\rm P}\) & **Range of Penetration Depths (mm)** & **Range of \(R_{\rm t}/R_{\rm t}\)*** \\ \hline
3 & 114.74–117.44 & 0.368–0.386 \\
5 & 95.20–97.74 & 0.519–0.543 \\
7 & 85.37–87.77 & 0.622–0.649 \\
9 & 79.23–81.48 & 0.697–0.726 \\
11 & 75.04–77.71 & 0.754–0.783 \\
13 & 72.05–73.91 & 0.799–0.827 \\
15 & 69.89–71.52 & 0.835–0.860 \\
17 & 68.24–69.71 & 0.863–0.886 \\
19 & 67.09–68.27 & 0.886–0.905 \\
21 & 66.21–67.23 & 0.903–0.920 \\
30 & 64.05–64.62 & 0.947–0.957 \\
80 & 62.04–62.12 & 0.991–0.993 \\ \hline \end{tabular}
\end{table}
Table 5: Summary of penetration depth and normalized target resistance ranges.  
* The penetration depth decreases very little as \(b\) increases, which indicates that the yield criterion has little effect on the lateral boundary effect and \(R_{\text{t}}\).  
Conceptualization, Y.L. and X.T.; methodology, Y.L.; software, Y.L.; validation, Y.L. and J.S.; formal analysis, Y.L.; investigation, Y.L. and B.Z.; resources, X.T.; data curation, Y.L., X.X. and J.S.; writing--original draft preparation, Y.L.; writing--review and editing, X.T., Y.C. and B.Z.; visualization, Y.L., X.X. and J.W.; supervision, X.T.; funding acquisition, X.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
[PERSON] would like to extend his sincere gratitude to [PERSON] for her invaluable support.  
The authors declare no conflicts of interest.
# Retrieval of Nearshore Bathymetry around Ganquan Island from LiDAR Waveform and QuickBird Image
## 4 Results
### Comparison of Different Waveform Processing Means  
The LiDAR waveform data were fitted to extract the water depth using the multi-Gaussian function. The bathymmetric map of the whole water area (Figure 8) was obtained after tidal correction and data-level conversion.  
The water depth profile of northwest Ganquan Island is shown in Figure 9. The horizontal coordinate represents the offshore distance, and the vertical coordinate represents the water depth value. In the figure, the yellow points represent the sea level, and the green and blue points represent water depth at different ranges. The slope gradient was gentle, and the depth changed slightly from 3 to 17 m.  
Validation points were selected from the profile at intervals of 10 m to quantify the relationship between the depth inversion result of the multi-Gaussian function and the officially published value by the LiDAR processing software. The scatter plot of the two depth values is shown in Figure 10. The \(R^{2}\) of the result of the multi-Gaussian function could reach 0.9 with an M  
Figure 8: Bathymetric map of the whole sea area.  
Figure 9: Depth map of profile.  
depth ranges. However, the MRE was relatively large in extremely shallow waters, and the time difference of the returning echo signals from the water surface and the bottom part was somewhat small and difficult to distinguish. The MAE of the whole water depth was 58.2 cm. When the water depth exceeded 15 m, the MAE became larger. This phenomenon may be due to the weak echo signal from the bottom part of the deep water that is difficult to identify.
# Demonstration of Wastewater Recycling in a Slaughterhouse
## 3 Results and Discussion
### Reverse Osmosis
The manufacturer specifies a retention rate of 85-99% for COD, approximately 90% for nitrate, and 70-80% for ammonium, depending on the wastewater characteristics [41]. These values were confirmed here. It is apparent that in this system it is advantageous to operate with more nitrate than ammonium in the effluent of the MBR. In comparison with previous works, a comparable retention of 90% for COD could also be observed by [PERSON] et al. [8], whereas in the case of [PERSON] and [PERSON] [42] the retention was lower, at 85%. In the work of [PERSON] et al. [40], a retention of TN of over 99% was achieved, but the nitrogen species was not determined in more detail in this work. The RO permeate complies with the limit values for drinking water, which means that it can be used as process water. At the demonstration site, the water was used for cleaning tasks without product contact. Ultimately, wastewater recycling in the food industry is not only a technical challenge but also a legal one and a question of social acceptance of such an approach. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Mineral** & **Chemical Formula** & **Saturation Index** \\ \hline Hydroxyapatite & Ca\({}_{5}\)(PO\({}_{4}\))\({}_{3}\)OH & 5.51 \\ Calcite & CaCO\({}_{3}\) & \(-0.45\) \\ Aragonite & CaCO\({}_{3}\) & \(-0.60\) \\ Gypsum & CaSO\({}_{4}\cdot 2\)H\({}_{2}\)O & \(-0.77\) \\ Anhydrite & CaSO\({}_{4}\) & \(-1.08\) \\ Dolomite & CaMg(CO\({}_{3}\))\({}_{2}\) & \(-1.32\) \\ Sylvite & KCl & \(-6.17\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Saturation index of minerals in the reverse osmosis concentrate. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & **Unit** & **UF—P permeate** & **RO—P permeate** & **RO—Concentrate** & **Removal [\%]** & **Reuse Limit [26]** \\ \hline pH & - & 7 & 7 & 7 & - & 6.5–9.5 \\ Turbidity & NTU & 0.83 & 0.12 & \(1.1\) & - & 1 \\ Conductivity & \(\mu\)S\(\cdot\)cm\({}^{-1}\) & 2000 & 80 & 3800 & 95 & 2500 \\ COD & mg\(\cdot\)L\({}^{-1}\) & 35 & 4 & 80 & 89 & 5 \\ TOC & mg\(\cdot\)L\({}^{-1}\) & \(<\)5 & 1 & 4 & - & n.a.c.\({}^{*}\) \\ TN & mg\(\cdot\)L\({}^{-1}\) & 10 & 2 & 45 & 80 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Reverse osmosis performance and comparison with the limit values for process water recycling of the European Union.
###### Abstract  
Accurate segmentation of cardiovascular structures plays an important role in many clinical applications. Recently, fully convolutional networks (FCNs), led by the UNet architecture, have significantly improved the accuracy and speed of semantic segmentation tasks, greatly improving medical segmentation and analysis tasks. The UNet architecture makes heavy use of contextual information. However, useful channel features are not fully exploited. In this work, we present an improved UNet architecture that exploits residual learning, squeeze and excitation operations, Atrous Spatial Pyramid Pooling (ASPP), and the attention mechanism for accurate and effective segmentation of complex cardiovascular structures and name it AB-ResUNet+. The channel attention block is inserted into the skip connection to optimize the coding ability of each layer. The ASPP block is located at the bottom of the network and acts as a bridge between the encoder and decoder. This increases the field of view of the filters and allows them to include a wider context. The proposed AB-ResUNet+ is evaluated on eleven datasets of different cardiovascular structures, including coronary sinus (CS), descending aorta (DA), inferior vena cava (IVC), left atrial appendage (LAA), left atrial wall (LAW), papillary muscle (PM), posterior mitral leaflet (PML), proximal ascending aorta (PAA), pulmonary aorta (PA), right ventricular wall (RVW), and superior vena cava (SVC). Our experimental evaluations show that the proposed AB-ResUNet+ significantly outperforms the UNet, ResUNet, and ResUNet++ architecture by achieving higher values in terms of Dice coefficient and mIoU.  
AB-ResUNet+; ASPP; attention mechanism; artificial intelligence; CTA; cardiovascular segmentation; deep learning; residual learning Article
# Influence of Game Indicators on the Ranking of Teams in the Spanish Soccer League
###### Abstract  
Soccer is dominated by game indicators that can influence the performance of teams and their players. Therefore, this study analyzed the influence of game indicators on the partial and final ranking of Spanish LaLiga teams, independently for the pre-COVID-19, COVID-19 and post-COVID-19 periods. In addition, the statistical differences between the pandemic periods were identified. A total of 2660 Spanish LaLiga matches played over seven seasons (from the 2014-2015 to the 2020-2021 season) were analyzed. The game indicators analyzed were the yellow cards, red cards, ball possession, total shots, shots on goal, shots off goal, free kicks, corners, offsides, goalkeeper saves, fouks committed, attacks, dangerous attacks, total passes, and tackles. Data were collected from the official Spanish LaLiga website, and recorded on a post hoc observation sheet. The intra-observer concordance was almost perfect ([PERSON]'s kappa values > 0.83). In each pandemic period, the findings indicated that the statistically significant game indicators had a greater influence on the final ranking, with an intermediate and large effect (\(\eta^{2}\geq 0.060\)), than on the partial ranking (little or no effect). In this regard, the LaLiga teams ranked in a European competition position (final ranking) reported a higher ball possession (\(p<0.001\)) and total passes (\(p<0.001\)). A higher ball possession allowed them to take more shots (offensive actions), and therefore to have a better chance of winning. Similarly, these game indicators were higher post-COVID-19, compared to pre-COVID-19 and during COVID-19. This is interesting information for the preparation and management of matches.  
COVID-19 pandemic; elite soccer; LaLiga; match analysis; opponent's quality Article