esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Image Texture Analysis  
Texture parameters can be extracted from ESEM images (e.g., angular second moment (ASM), contrast, correlation, inverse difference moment (IDM) and entropy) using the GLCM and surface plot tools of ImageJ v.1.50d software (National Institutes of Health, Bethesda, MD, USA). These parameters are related to characteristics such as homogeneity, contrast and the presence of organized structures within the image.  
The GLCM is based on the idea that an image can be analyzed through the number of pixels or squares that form it in horizontal direction Nx and vertical direction Ny, in the matrix element P8 (i,j), with intensity values i and j at a particular displacement distance \(\delta\) from along a given direction \(\theta\)[38]. Furthermore, each of these pixels has a gray tone value that is quantified up to Ng levels (diagonally). Therefore, considering the above, an image can be represented as a function, which assigns different shades of gray for each pixel or set of pixels, and from this information the texture of an image can be characterized [39; 40; 31].  
The GLCM calculates up to 14 different descriptors. In this study, the following four were considered:  
1. Energy measures uniformity textural image and is an opposite parameter to entropy. The energy parameter is also known as uniformity, energy uniformity and angular second moment (ASM). \[ASM=\sum_{i=0}^{G-1}\sum_{j=0}^{G-1}\left\{P(i,j)\right\}^{2}\] (2)
2. Contrast is a measure of local variations in grayscale value pixels of an image. Contrast is also known as variance or inertia. \[CONTRAST=\sum_{n=0}^{G-1}\ n^{2}\left\{\sum_{i=1}^{G}\ \sum_{j=1}^{G}\ P(i,j)\right\},\ |i-j|=n\] (3)3. Inverse difference moment (IDM), also known as homogeneity parameter, which is a measure similar to energy; it also represents the homogeneity of the local image. \[IDM=\sum_{i=0}^{G-1}\ \sum_{j=0}^{G-1}\ \frac{1}{1+\left(i+j\right)^{2}}\ P(i,j)\] (4) (4) (4) (4) Entropy measures the disorder or randomness of images, can be used to characterize the texture of the image and is indicative of complexity within the image. Therefore, complex images will have high values of entropy. \[ENTROPY=-\sum_{i=0}^{G-1}\ \sum_{j=0}^{G-1}\ P(i,j)*log(P(i,j))\] (5)
## 3 Models and Design Goals
### Multicast Subsystem  
When an emergency occurs, impacted OLDs may request new movement paths rather than remaining stuck. Commonly, the conventional systems only replan new paths based on the present road conditions but do not take dynamically instantaneous changing into consideration. We design a multicast mechanism-based emergency system, which uses Stein Tree to compute a multicast tree between those nodes to inform affected OLDs in time.
# High Throughput Data Relay in UAV Wireless Networks
###### Abstract  
As a result of their high mobility and reduced cost, Unmanned Aerial Vehicles (UAVs) have been found to be a promising tool in wireless networks. A UAV can perform the role of a base station as well as a mobile relay, connecting distant ground terminals. In this paper, we dispatch a UAV to a disaster area to help relay information for victims. We involve a bandwidth efficient technique called the Dual-Sampling (DS) method when planning the UAV flight trajectory, trying to maximize the data transmission throughput. We propose an iterative algorithm for solving this problem. The victim bandwidth scheduling and the UAV trajectory are alternately optimized in each iteration, meanwhile a power balance mechanism is implemented in the algorithm to ensure the proper functioning of the DS method. We compare the results of the DS-enabled scheme with two non-DS schemes, namely a fair bandwidth allocation scheme and a bandwidth contention scheme. The DS scheme outperforms the other two non-DS schemes regarding max-min average data rate among all the ground victims. Furthermore, we derive the theoretical optimal performance of the DS scheme for a given scenario, and find that the proposed approach can be regarded as a general method to solve this optimization problem. We also observe that the optimal UAV trajectory for the DS scheme is quite different from that of the non-DS bandwidth contention scheme.  
U
## 5 Experiments
### Single-Antenna Frequency-Selective Channels  
Before considering multi-antenna frequency-selective channels, we first consider the impact of the level of frequency selectivity on the prediction of single-antenna frequency-selective channels. To this end, starting from 45 ns, we increase the delay spread by a multiplicative factor, and correspondingly also increase the number of taps by the same amount, which is referred to as delay spread ratio in Figure 6. The number of taps \(W\) is obtained as the smallest number of taps that contains more than 90% of the average channel power, following ITU-R report [65]. Figure 7 shows that the dependence on the delay spread of the channel is qualitatively similar to the dependence on the number of antennas in Figures 4 and 5, with the top of Figure 7 representing the performance under a fast-varying environment and the bottom figure depicting the NMSE for a slow-varying environment. Accordingly, as discussed in the previous subsection, meta-learning outperforms both transfer and conventional learning, as long as the parametrization is correctly selected: naive for slow-varying channels, and LSTD for fast-varying environments.  
Figure 6: Multi-antenna frequency-selective channel prediction performance as a function of the number of features \(K\), under 19-clustered, multi-taps \((W=4)\), multi-antenna \((N_{T}=8,N_{R}=8)\) 3 GPP SCM channel model for a fast-varying environment with number of training samples \(L^{\text{new}}=1\). Results are evaluated with number of previous frames \(F^{\text{tr}}=20\) for meta-training, \(F^{\text{val}}=20\) for meta-validation, and \(F^{\text{te}}=200\) for meta-test.  
Figure 7: Single-antenna frequency-selective channel prediction performance as a function of delay spread ratio, under 19-clustered, multi-taps, single-antenna \((N_{T}=1,N_{R}=1)\) 3 GPP SCM channel model for a fast-varying environment (**top**) and slow-varying environment (**bottom**) with number of training samples \(L^{\text{new}}=1\) (\(K=1\)).
# A Damage Model for Concrete under Fatigue Loading
## 2 Microcrack Mechanism in Concrete under Multi-Axial Loading
### Influence of the Mode-II Microcracks on the Irreversible Strains in Concrete  
It is revealed that the mode-II microcracks are attributed to crack blunting and the irreversible deformation (Figure 2) of material even in brittle material such as glass [21]. In this section, we briefly recall the methodology followed by [22] for the micro-mechanical description and further develop it with consideration of stochastic properties in a multi-axial tension case, as follows.  
For simplicity, we introduce a micro-cell damage model (Figure 3) considering the mode-II microcracks to describe the damage behaviors in concrete under biaxial tension. In Figure 3, the region near a certain defect is firstly highlighted and further discretized by amounts of micro-cells (micro-cell \(i\), micro-cell \(i+1\), etc.). The behavior of each micro-cell is modeled by two sets of springs (spring type A and B). The spring type A can be stretched vertically along the direction of the maximum principal loading, and spring type B is attached to the middle of spring type A. Unlike spring type A, spring type B cannot be stretched but it can slip between two parallel sets of micro-cells. In such a micro-cell damage model, the elastic behavior and elastic deformation damage are described by spring type A, and the irreversible deformation damage is modeled by spring type B. After unloading, there is a micro deformation \(b\) and an irreversible strain \(\varepsilon_{\text{I,f}}\) left in the material. The micro irreversible fractural opening is caused by mode-II microcracks illustrated in the micro-cell damage model.  
In summary, the elastic deformation damage in the micro-cell damage model corresponds to the stiffness degradation, and the irreversible deformation damage is responsible for a certain part of the irreversible strain. Specifically, the local shear stresses produce mode-II microcracks on the crack face and nearby areas, which generate the micro deformation \(b\) and an irreversible strain \(\varepsilon_{\text{I,f}}\) in the material (Figure 3).  
Figure 2: Sketch of microcracks under multi-axial tension, leading to both the stiffness degrading and the irreversible strain developing, where \(\sigma_{1}\) denotes the maximum principal tensile stress.
## 5 Methodology  
This section provides a detailed description of the methodology. It includes the deployment of the proposed architectures and the execution of tests for their subsequent analysis.  
In Section 4, we mentioned that the deployment of transparent architectures requires specific distributions of the 5G components across the different nodes of the testbed. For the envisaged architectures, Table 3 lists the 5G components assigned to each node of Figure 7 and indicates whether WireGuard or ZSTD compression is necessary or not. The VPN is necessary when _Machine (A)_ hosts the gNB or DU due to interface configuration requirements. Note that the 5G network is executed in the standalone mode and that RFSimulator operates in band 78 of Frequency Range 1 (FR1), specifically corresponding to the default gNB configuration of USRP 210 in band 78 and Physical Resource Block (PRB) 106 [57].  
The evaluation of the transparent architectures consists of conducting 2000 ping measurements with 1 s intervals. These measurements were sequentially divided into 20 batches to later calculate their mean RTT and their mean jitter. Note that both metrics serve as pivotal indicators of network performance, offering an insightful perspective on the constraints imposed by each architecture. Specifically, ping tests were conducted between the UE and the UPF, i.e., the 5 GC component that acts as an intermediary between the 5G network and the Data Network (DN), which is commonly known as the Internet. In essence, it is a high-performance forwarding engine responsible for the optimal management of user data traffic. It is noteworthy that traffic is routed through the Internet in these tests, introducing an additional degree of uncertainty in the results. The reason for this routing mechanism is that Starlink gateways always forward traffic via the Internet, a strategy that is reasonable in view of the primary aim of the project to achieve global Internet connectivity for end users.
###### Abstract  
The objective of this study was to quantify the relative bioavailability of microencapsulated vitamins A and E in nursery pigs and compare the effects of vitamin forms and vitamin levels on the plasma vitamin content and growth performance of weaned piglets. In experiment (Exp.) 1, 12 nursery pigs (fitted with jugular catheters) were supplied at 0 h with non-microencapsulated or microencapsulated vitamin A and E. Blood samples were collected at 1, 3, 6, 9, 12, 16, 18, 21, 24, 27, 30, 36, 48, and 72 h after feeding to compare the bioavailability of oral vitamins A and E. In Exp. 2, a total of 216 crossbred weaned piglets were assigned to six treatments. This experiment was a 2 x 3 factorial arrangement, with two factors for vitamin forms (non-microencapsulated and microencapsulated) and three factors for vitamin levels (the National Research Council level of vitamins, 75% commercial recommendations of vitamins (CRV) level, and a 100% CVR level). In Exp. 1, the relative bioavailability of microencapsulated vitamin E was significantly greater than that of non-microencapsulated vitamin E. In Exp. 2, the pigs fed diets containing 75% or 100% CRV levels of vitamins increased their growth performance and plasma vitamin concentrations compared to the control group. In conclusion, microencapsulation can improve the bioavailability of vitamins, and supplementation with high levels of vitamins was able to improve the growth performance of the piglets.  
bioavailability; growth performance; microencapsulation; plasma kinetics; vitamin 2020; 10, 4903; doi:10.3390/appj.10144903
## 3 Principals of Routing Protocols and Their Breakdowns
### Data-Based Routing Protocols
#### 3.4.8 UHRP [72]  
To decrease the routing overhead for UWSNs, an underwater hybrid routing protocol (UHRP) is suggested. The suggested protocol considers the hybrid characteristics of flooding-based routing methods and reactive ad-hoc routing protocols to achieve effective data packet transmission. Node location information is used in several flooding-based routing systems. However, current underwater localization systems are unable to give precise position information to all sensor nodes. The precise position of various sensor nodes is known by UHRP. The localized nodes use scoped flooding to send packets to the sink node, while the non-localized nodes use a reactive ad-hoc routing approach to construct a path from the non-localized node to the best-localized node (flooding forwarder node).
# An Automatic Approach for Individual HU-Based Characterization of Lungs in COVID-19 Patients
###### Abstract  
The ongoing COVID-19 pandemic currently involves millions of people worldwide. Radiology plays an important role in the diagnosis and management of patients, and chest computed tomography (CT) is the most widely used imaging modality. An automatic method to characterize the lungs of COVID-19 patients based on individually optimized Hounsfield unit (HU) thresholds was developed and implemented. Lungs were considered as composed of three components--aerated, intermediate, and consolidated. Three methods based on analytic fit (Gaussian) and maximum gradient search (using polynomial and original data fits) were implemented. The methods were applied to a population of 166 patients scanned during the first wave of the pandemic. Preliminarily, the impact of the inter-scanner variability of the HU-density calibration curve was investigated. Results showed that inter-scanner variability was negligible. The median values of individual thresholds th1 (between aerated and intermediate components) were \(-\)768, \(-\)780, and \(-\)798 HU for the three methods, respectively. A significantly lower median value for th2 (between intermediate and consolidated components) was found for the maximum gradient on the data (\(-\)34 HU) compared to the other two methods (\(-\)114 and \(-\)87 HU). The maximum gradient on the data method was applied to quantify the three components in our population--the aerated, intermediate, and consolidation components showed median values of 793 \(\pm\) 499 cc, 914 \(\pm\) 291 cc, and 126 \(\pm\) 111 cc, respectively, while the median value of the first peak was \(-\)853 \(\pm\) 56 HU.  
Covid-19; chest CT; lungs; HU density +
Footnote †: journal: Medical Physics Letters
The near-space platforms, as considered in this paper, involves stationary vehicles and maneuvering vehicles flying in near-space where the wind is mild and weather is virtually non-existent [7, 8]. More importantly, not constrained by orbital mechanics like satellites and high fuel consumption like airplanes, near-space platforms can stay at a specific site for a long time to provide a persistent region coverage. A literature search in the area of near-space reveals that most work concentrated on designing near-space platforms including stationary vehicles and maneuvering vehicles [9, 10]. Little work on the use of near-space sensors for communication and navigation applications has been reported [11, 12]. Even less effort has been devoted to new radar imaging techniques based on near-space platforms [13]. In fact, by placing radar transmitter/receiver inside near-space platforms, many functions that are currently performed with satellites or airplanes could be performed in a cheaper. Inspired by recent advances in near-space technology and radar techniques [14, 15], this paper describes several near-space radars for remote sensing applications. The system concepts, signal models and corresponding performance analysis are described, along with the potential applications, technical challenges and possible solutions. The remaining sections are organized as follows. The superiorities of near-space platform are outlined in Section 2. Next, several near-space vehicle-based microwave remote sensing techniques including passive bistatic radar imaging and high-resolution and wide-swath (HRWS) SAR imaging, along with their potentials and challenges, are investigated in Section 3 and Section 4, respectively. Strict position and altitude information are required to ensure success, but high-precision motion measurement assets widely used in current radar systems may not be reachable for near-space platforms because of its limited load capability. Hence, motion compensation is investigated in Section 5, and an active transponder-based motion compensation is described. This paper is concluded in Section 6.
## 5 Implementation Details
### Data Storage Infrastructure  
MongoDB is an open-source distributed and document-oriented database [44]. It ingests and stores data in near real time and in an operational capacity. MongoDB is a distributed database with high availability and horizontal scaling, and is easy to use. MongoDB is fully scalable compared to traditional relational databases like MySQL [45].  
In the prototype, MongoDB v3.4.5 was used, and a MongoDB cluster was deployed on e-Health-System-K8s-cluster nodepool-1 to facilitate access to data from subapplications. The deployed containers used images from the Docker Hub and were deployed using properly configured YAML. Ain't Markup Language (YAML) files.
## 3 Results and Discussion
### Optimization of Microalgal Harvesting Variables: Dosage, Settling Time, and Mixing Times
[33] used a concentration of 20 mg\(\cdot\)L\({}^{-1}\) and two mixing stages, but only reported 90% of flocculation efficiency. Chitosan is less charged, so the excessive fast mixing breaks the flocs. The SM stage ensures homogenization without causing floc breakage. Table 8 contains information about the market price of each flocculant and the price of flocculant required to harvest 100 m\({}^{3}\) of _C. vulgaris_ culture. Although chitosan is the most expensive flocculant per gram, the optimal dosage is quite low, making this flocculant the most economically viable, when comparing with aluminum and ferric sulfate. In addition, when employing chitosan as a flocculant, incorporating two mixing phases is unnecessary, potentially reducing time and energy expenses at an industrial scale, thereby facilitating the microalgae harvesting process through chemical flocculation. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Flocculant** & \multicolumn{2}{c}{**Regression Equation**} & **R2 (\%)** & **Predicted R2 (\%)** \\ \hline Aluminum sulfate & \(\eta\) (\%) = \(-8.77\) + 0.0520 \(\times\) A + 32.4 \(\times\) C + 5.86 \(\times\) D \(-\) & 96.56 & 95.99 \\  & \(1.99\)\(\times\) C \(\times\) D & & 97.00 & 97.00 \\ \hline Ferric sulfate & \(\eta\) (\%) = \(-0.459\) + 31.6 \(\times\) C + 6.61 \(\times\) D + 0.0246 \(\times\) A \(\times\) C \(-\) & 96.98 & 96.95 \\  & \(2.15\)\(\times\) C \(\times\) D & & 96.98 & 96.95 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Empirical models that relate the harvesting efficiency (\(\eta\)) with the flocculant dosage (A), settling time (B), rapid mixing time (C), and slow mixing time (D), and their main interactions. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Flocculant**} & \multicolumn{4}{c}{**Optimal Predicted Parameters**} & **Predicted** & **Experimental** & \multirow{2}{*}{**Error (\%)**} \\ \cline{2-3} \cline{5-8}  & **Dosage** & **Setting** & **RM Time** & **SM Time** & **Harvesting** & **Harvesting** \\  & **(mg\(\cdot\)L\({}^{-1}\))** & **Time (min)** & **(min)** & **(min)** & **Efficiency (\%)** & **Efficiency (\%)** & **Efficiency (\%)** \\ \hline Aluminum sulfate & 250 & \begin{tabular}{c} NSS \\ (5–15) \\ \end{tabular} & 3 & 15 & 99.8 & 99.1 \(\pm\) 0.1 & 0.7 \\ Ferric sulfate & \begin{tabular}{c} NSS \\ (150–200) \\ \end{tabular} & 30 & 3 & 15 & 99.8 & 98.4 \(\pm\) 0.1 & 1.4 \\ \hline Chitosan & \
\begin{tabular}{c} NSS \\ (10–50) \\ \end{tabular} &
\begin{tabular}{c} NSS \\ (5–15) \\ \end{tabular} & 0 & 15.5 & 100 & 99.5 \(\pm\) 0.1 & 0.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Experimental results of _C. vulgaris_ flocculation using aluminum sulfate, ferric sulfate, and chitosan with the optimal predicted parameters (the mean values of the not statistically significant (NSS) parameters were selected to determine the real harvesting efficiencies).
## 1 Introduction
It presents an intelligent scheme for high impedance fault detection using mathematical morphology and decision tree in [20]. However, the methods of [19; 20] are computationally complex and their reliability remains to be verified. There are also arc characteristic identification method [21], circuit analysis method [22; 23], artificial intelligence method [24], etc. In summary, HRFs are difficult to detect and may cause serious harm; few practical detection methods are available. In this paper, a fault detection method by combining singular value decomposition (SVD) with orthogonal wavelet fusion in the time-frequency domain was proposed and used to analyze the characteristics of HRFs in a DC microgrid. This paper presents the detection method, evaluates its utility, and provides our conclusions. The main innovations of this paper are as follows:  
1. This paper establishes a DC arc simulation model for high resistance faults in DC microgrids. 2. A detection method decomposes high frequency waveforms using orthogonal wavelets. The fault characteristics of the fault time domain are analyzed. 3. The amplitude of the wavelet coefficients is detected using the SVD. The Hankel-SVD can eliminate the specified switching frequency and the background noise, it can avoid the influence of the inverter noise and other electrical noise on the extraction of HRF characteristics. The rest of this paper is organized as follows: the second part is the HRF modeling of the DC microgrid; the third part analyzes high resistance fault characteristics; the fourth part is the HRF detection; the fifth part is the experimental verification; and the sixth part is the conclusion.
## 3 Results
### Analysis Results
From Figure 8d-f, although there is some discrimination of different classes in the two-dimensional space, there is still some overlap. This suggests that the features extracted by the FC1 layer have relatively weak discriminative power, requiring further feature extraction and processing. Compared to the FC1 layer, the FC2 layer shows improved clustering effects in the two-dimensional space, with more dispersed distribution and less overlap of different classes, indicating that the FC2 layer performs better in feature extraction and discrimination. The features of the FC3 layer exhibit more distinct clustering effects in the two-dimensional space, with clearer distribution of different classes. This suggests that as the network depth increases, the model's feature extraction ability gradually enhances, allowing for better differentiation of different classes. Overall, the feature extraction effects vary at different stages, with deeper models and the multi-head attention mechanism demonstrating superior performance in feature extraction and classification. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Model** & **F1-Score** & **MCC** & **Sensitivity** & **Specificity** & **Accuracy** & **Precision** \\ \hline
1 & ResNet1D & 0.85 & 0.8 & 0.87 & 0.83 & 0.84 & 0.86 \\
2 & ResNet2D & 0.91 & 0.88 & 0.92 & 0.89 & 0.9 & 0.91 \\
3 & CombinedModel & 0.93 & 0.9 & 0.94 & 0.91 & 0.92 & 0.93 \\
4 & CombinedModel + MultiHeadAttention\_4 Head & 0.95 & 0.92 & 0.96 & 0.94 & 0.95 & 0.95 \\
5 & CombinedModel + MultiHeadAttention\_8 Head & 0.98 & 0.96 & 0.98 & 0.97 & 0.97 & 0.97 \\
6 & CombinedModel + MultiHeadAttention\_12 Head & 1.0 & 1.0 & 1.0 & 1.0 & 1.0 & 1.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of different modules.