esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Parametric Analysis
### Reinforcement Ratio  
The reinforcement ratio was varied from 0.92% to 3.68% to study the effects of the reinforcement ratio on the performance of SFLC beams reinforced with GFRP bars. Figure 21 presents the ultimate moment of FE models, and Table 5 gives the details of the simulated results.  
As can be seen in Figure 21, the \(\rho_{f}\) value of the model with balanced failure was higher than that given by ACI 440.1R [33]. Due to the destructive feature of FRP tension failure, the designers should adopt a factor to ensure concrete crushing of the beam, as 1.4 in ACI 440.1R [33]. Furthermore, the capacity of the beam was not increased by the reinforcement ratio linearly, so that it was necessary to determine the critical ratio for concrete crushing in order to make relatively full use of the materials' strength.  
As shown in Table 5, the \(\Delta_{s}\) first increased and then decreased with the increasing \(\rho_{f}\). This result could be attributed to the fact that increasing the \(\rho_{f}\) had both positive and negative influences on the \(\Delta_{s}\), more specifically, improving the \(M_{s}\) as well as lowering the deflection simultaneously. For specimens with \(\rho_{f}\) values of 0.92% and 1.33%, their \(M_{s}\) were close to the cracking moment (\(M_{\sigma}\)), and thus led to very low \(\Delta_{s}\) values. Nevertheless, their failure modes, including FRP rupture and balanced failure, were brittle and catastrophic. When further increasing the \(\rho_{f}\) to 2.66%, the failure mode changed to concrete crushing, and the \(\Delta_{s}\) increased to 12.44 mm. It could be derived that the improvement in the stiffness failed to complement the increase in the \(M_{s}\). Comparing specimens that failed due to concrete crushing (with \(\rho_{f}\) of 2.66% and 3.68%) revealed that the increase in \(\rho_{f}\) had an obvious effect on the reduction of deflection. The reason behind this behavior was that increasing the \(\rho_{f}\) did not significantly increase the flexural capacity \(M_{s}\) for this failure mode.
## 6 Comparison of LW-CCA with ContikiMAC
### Average of Ticks in CPU, LPM, Rx, and Tx States in the Network  
Tick conversion simply refers to the ability to convert physical units to timer ticks [44]. On a system with a 2 MHz timer, for example, 1 ms is mapped to 2000 ticks. The calculation of the power consumption in different states for the nodes depends on how long the node stays in every state. The Energest considers the time spent in different states with the number of ticks in that state. Table 8 shows the average of ticks in CPU, LPM, Rx and Tx states in both low rate and high rate scenarios in LW-CCA and ContikiMAC nodes. Table 8 shows the average of ticks for CPU (numbers of ticks for CPU in active mode) in CPU_time, LPM(numbers of ticks in low power mode) in LPM_time, Rx (numbers of ticks in listening mode) in Rx_time and Tx(numbers of ticks in transition mode) in Tx_time. The LPM_time shows that LW-CCA by reducing RSSI status check time in false WakeUps and idle listenings, increase low power mode time in nodes. CPU_time simulation result shows that there is a small amount of computing overhead in LW-CCA. Rx_time also shows a significant decrease in listening time in LW-CCA, although there is little change in Tx_time. It should be noted that the parameters are averaged from all of the sender nodes.
## 3 Dataset Description
### HFAVD Dataset Structure
#### 3.3.3 Blood Pressure  
The estimation of blood pressure followed the approach proposed by [42]. The methodology consisted of the following steps: (a) Extraction of Regions of Interest: The left and right cheeks were identified as the Regions of Interest (ROIs) in each frame of every video. These sequential images were used as input for subsequent analysis. (b) Convolutional Neural Network (CNN) for spatial feature extraction: A CNN architecture was employed to capture spatial features from the ROIs. For systolic blood pressure estimation, EfficientNet_B3 was utilized for the left cheek, while EfficientNet_B5 was used for the right cheek. Conversely, for diastolic blood pressure estimation, an ensemble approach was adopted. This involved combining EfficientNet_B3 and ResNet50V2 for the left cheek and the right cheek, respectively. (c) Long Short-Term Memory (LSTM) for temporal feature extraction: The outputs obtained from the previous step were fed into an LSTM network. This enabled the extraction of temporal features within the sequence of images, considering the dynamic nature of blood pressure. (d) Fully connected layers for blood pressure estimation: Two fully connected layers were employed to derive the blood pressure values based on the extracted spatial and temporal features. Upon training and testing the proposed model, the mean absolute errors recorded were 11.8 and 10.7 mmHg for systolic and diastolic blood pressure, respectively. In parallel, the model's mean accuracies were found to be 89.5% for systolic blood pressure and 86.2% for diastolic blood pressure. The main challenge faced in training this model was the lack of diversity in the dataset, particularly in terms of the subjects' skin color. Most of the subjects had light-to-medium skin tones, which impacted the accuracy of the models in predicting blood pressure in individuals with dark skin tones. Another limitation was the dataset's imbalance, with a scarcity of unusual values for systolic and diastolic blood pressure.
## 2 Methodology
### Content Sampling  
The purpose of evaluating the content to be transmitted is to analyze the viability of the transmission. The concept of feasibility lies in the fact that we can guarantee a high QoE. In recent years, Ultra-High-Definition (UHD) content has proliferated [9; 10] due to the increase in devices with the ability to reproduce it. However, increasing the resolution does not always imply an improvement in image quality. Quality measurement is generally evaluated by bitrate [11]. It is common for certain occasions in content production, even in UHD, to not use high bitrates.  
The work is based on a preliminary study on remote production [8]. Although the work can be implemented in any video, in our case, the content is not generated in the research laboratory. Using uncontrolled content requires higher AI training, but is more useful in terms of demonstration purposes. Multimedia is produced by local German television [12] and is received through a radio antenna placed in the laboratory to receive multimedia content. It is received in raw format, but using image processing equipment, we encode the video using the H.264 codec. Its content schedule is varied and will serve as a demonstration of image quality using traditional types of video.  
In this demo, we are looking for high-quality streaming with playability on the widest range of devices possible. Instead of looking for UHD content and encoding it to lower the bitrate, we used high-bitrate Full-High-Definition (FHD) content. In this case, we are referring to an average bitrate of 10 Mbps in 16:9 format. Normally, the content at this resolution transmitted in the Spanish nation is transmitted at around 4 Mbps, so we can see that our transmission offers a higher quality of content.  
Traditional content is reproduced at 24 frames per second (fps) in Europe [13], which, with 10 Mbps streaming, makes it easier for the application components of the service to be deployed in any instance. This work proposal aims to guarantee an optimal QoE under every network condition, using standard dedicated server components. Increasing the bitrate and resolution further requires specific hardware components (graphics cards) to support lossless real-time encoding.
# Inhibitory Effect of Cold Atmospheric Plasma on Chronic Wound-Related Multispecies Biofilms
## 2 Materials and Methods
### Cytotoxicity Evaluation  
For evaluating the cytotoxicity of CAP, mouse fibroblast cell line (3T3) were grown in DMEM (Dulbecco's Modified Eagle's medium) supplemented with 10% of inactivated fetal bovine serum (FBS), 100 IU mL\({}^{-1}\) of penicillin, and 100 \(\upmu\)g mL\({}^{-1}\) of streptomycin. After, 200 \(\upmu\)L of a suspension containing 3.6 \(\times\) 10\({}^{4}\) cells/mL were transferred to the wells of 96-well plates. Plates were incubated for 24 h at 37 \({}^{\circ}\)C and in an atmosphere of 95% air and 5% CO\({}_{2}\). After, the culture medium was removed and 30 \(\upmu\)L of Hanks' Balanced Salt Solution with 10 mM 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid were added. The cells were exposed to CAP for 1, 3, 5 and 7 min using the same parameters adopted for microbiological evaluations. The cell viability was determined immediately, 24 and 48 h after plasma exposure, using the MTT colorimetric method at 570 nm. For the analyses of later effects (24 and 48 h), the culture medium was immediately added after plasma jet exposure. The experiments were performed in triplicate in two independent experiments. The results were expressed as a percentage of viable cells (%), using the number of cells grown in wells without CAP exposure as the negative control.  
The morphological grading of cytotoxicity, according to [52], was: 0 (not cytotoxic)--no reduction of cell growth; 1 (slight)--only slight growth inhibition observable; 2 (mid)--not more than 50% growth inhibition; 3 (moderate)-more than 50% growth inhibition observable; 4 (severe)--nearly complete or complete destruction of the cell layers.
# Multidisciplinary Reliability Design Optimization Modeling Based on SysML
## 3 Representation of Optimization Problems Using SysML
### Formal Expression of MDO Problems Using Extended SysML  
As previously mentioned, SysML can be extended according to the fundamental elements of a multidisciplinary optimization information model to express optimization objectives, optimization constraints, and optimization variables. However, it is also necessary to define a metamodel for multidisciplinary optimization problems, providing a method to represent the overall semantic information of the optimization problem organization through the extended framework.  
Figure 6: Extended definition of equation constraints and inequality constraints.  
Figure 7: Optimization objective extension model.  
Among the nine types of diagrams in SysML, parametric diagrams capture the internal structure of constraint blocks based on the parameters and the connections between them. A Block Definition Diagram (BDD) defines the constraint blocks and their relationships. Both contain the most features of the optimization information representation, and thus they can be used as the basis for defining the metamodel of the optimization problem using the parameter diagram and the module definition diagram. The MDO problem represents the system model situation that needs to be optimized in order to maximize or minimize a set of objective functions. As shown in Figure 8, the defined metamodel of the optimization problem is inherited from the parameter diagram. In this figure, the top-level \"MDO Problem\" block extends the original SysML construct type \"ConstraintBlock\". The \"MDO Problem\" block has a reference to \"SystemBlock\" and \"SubsystemBlock\", which can be used to represent and analyze the hierarchical relationship of the system. In addition, the \"MDO Problem\" contains elements such as optimization objectives, optimization constraints, and optimization variables.  
Since the MDO problem diagram is inherited from the parameter diagram, it is possible to bind each variable or parameter in the optimization constraint or optimization objective expression to a value present in the SysML model based on the inherited value binding property, and these constraints can be applied to a specific module. The value attributes that are bound can belong to the module itself or to component attributes or reference attributes of the module. In this way, it is possible to create an arbitrarily complex mathematical model and bind it to an arbitrarily complex institutional model component. In addition, a parameter is defined for each objective function to bind the output to the \"Opt Solution Block\", which allows the optimized results to be subsequently returned to the system design model.
# Settlement Prediction and Differential Settlement Criterion for Heightening and Thickening Leveee
## 5 Conclusions  
Aiming at the pavement cracking problem of an HTL, field monitoring and finite element analysis were employed to attain settlement prediction and a differential settlement control criterion. The main conclusions are as follow:  
1. The monitoring data showed that an HTL presents S-shaped and hyperbolic settlement rules, and the settlement law within 1 m of the top tends to be S-shaped, while the settlement law inside the HTL tends to hyperbolic. The settlement law of the S-shape is related to frozen soil. The rule of hyperbolic settlement is related to the decline of groundwater level.
2. A GSPM based on field monitoring data of an HTL was proposed. The effectiveness of the GSPM is validated from the point of view of engineering applications. The physical meanings of model parameters are analyzed. The GSPM proposed in this paper can be more widely used, more accurate, and more stable than the traditional logistic model and hyperbolic model.
3. The quantitative relationship between differential settlement and the horizontal stress of pavement is revealed using the finite element method. The maximum horizontal tensile stress appears at the junction of the backfill and the old levee, which is basically consistent with the crack location in the actual project. On this basis, the differential settlement control criterion for different pavements can be suggested. For example, when asphalt concrete pavement is used (whose ultimate tensile stress is 0.12 MPa), the corresponding differential settlement criterion is approximately 4.3 cm.  
In the end, a differential settlement control method for an HTL is also a question worth studying in the future.  
[PERSON] and [PERSON] designed the field monitoring. [PERSON] provided support for monitoring instruments. [PERSON] implemented the field monitoring; [PERSON] contributed the idea of settlement prediction; [PERSON] and [PERSON] analyzed the data. [PERSON] wrote the paper.  
This work is supported by the \"National Natural Science Foundation of China\" (Grant No. 51779084). We also gratefully acknowledge financial support from it.  
The authors would like to thank [PERSON] and [PERSON] for their help in the building and instrumentation of the apparatus. The authors wanted to sincerely thank two anonymous referees' constructive comments, which improved the quality of our paper significantly. In the end, author also sincerely thank the editor's patient and timely reply.  
The authors declare that there are no conflicts of interest regarding the publication of this paper.
# Distributed Satellite Relay Cooperative Communication with Optimized Signal Space Dimension
## 2 Materials and Methods
### Cooperative Transmission of Spatial Information Network
#### 2.2.1 Universal Multicast Transmission Strategy Based on Ring Planning
Multi-ring transmission: DOF tuples \(\mathbf{d}\) cannot be obtained by using the combination of one-way transmission and two-way transmission. As shown in Figure 2b, when the relay uses two-dimensional space to transmit the bidirectional signals of nodes 1 and 2, 2 and 3, the remaining DOF tuples become \(\mathbf{d}^{\prime}=(2,0,0,0,1,1,1,0,0,1,0,0)\). Align the signals \(W^{[1,2]}\) and \(W^{[2,3]}\) sent by node 1 and node 2 respectively to the one-dimensional signal space of the relay. Align the signals \(W^{[2,3]}\) and \(W^{[3,1]}\) sent by nodes 2 and 3 to another dimension space of the relay. The relay can obtain a linear combination of these signals \(L(W^{[1,2]},W^{[2,3]})\) and \(L(W^{[2,3]},W^{[3,1]})\). The signal \(W^{[2,3]}\) is sent twice and aligned to different directions. Each node decodes the received signal using its own transmitted signal in the MAC phase. The relay realizes the transmission of 3-rings (\(1\to 2\to 3\to 1\)) in two-dimensional space. As shown in Figure 2c. Using the multi-ring strategy, the relay only needs a \(d_{23}+d_{31}=2\) dimensional relay space to transmit one 3-ring signal. It saves one-dimensional resource than using one-way mode to transmit signals. Through the combination of bidirectional transmission and multi-ring transmission, the minimum space resource required by the transmission system is: (10). \[d_{\Sigma}=d_{21}+d_{23}+d_{24}+d_{31}+d_{41}\leq N \tag{10}\]  
It can be seen from formula (10) that after the initial tuple \(\mathbf{d}\) is processed by ring elimination, it is similar to a directed graph with no rings. It can be concluded that the key to studying the optimal communication scheme of a \(K\)-node multidirectional relay channel is the ring elimination technique in directed graph. For a certain DoF tuple \(\mathbf{d}\), one-way transmission, two-way transmission and multi-ring transmission should be used in combination. By optimizing the ring elimination strategy, the DoF upper bound of message transmission can be realized. For a 4-node multicast channel the DoF domain is represented by the following inequality:  
Figure 2: Directed graph of information interaction among nodes. (**a**) Initial message directed graph, (**b**) message directed graph after eliminating 2-ring, and (**c**) message directed graph after eliminating 3-ring (1,2,3). \[\sum\limits_{i=1}^{3}\sum\limits_{j=i+1}^{4}d_{p_{i}p_{j}}\leq N,\quad\forall \mathbf{p} \tag{11}\]  
where \(\mathbf{p}\) is the ordered permutation for \((1,2,3,4)\) and \(p_{i}\) is \(i\)-th element.
# Spatio-Temporal Variability of Water Temperature of Arctic Rivers in Russia over the Past 60 Years
## 3 Results
### Climatic Changes in the Russian Arctic  
The air temperature changes were estimated for the ERA5 reanalysis nodes closest to hydrological stations for the period 1961-2022. During the period 1991-2020, compared to the period 1961-1990, the average increase in air temperature in the Russian Arctic near hydrological stations was 1K (see Figure 5). The maximum increase in air temperature was observed in May and June in the Arctic regions of Western Siberia, the Arctic parts of the Yenisei and Khatanga basins, and in the lower reaches of rivers in the Laptev Sea region. In July and September, the air temperature near hydrological stations even decreased slightly in the river valleys of Siberia. It is worth noting the rather high spatial uniformity of changes in air temperature in the north of EPR. The maximum change in air temperature reached 3.6 K in June.  
The contribution of the past two decades to these changes has been significant. The differences in the values of air temperature calculated from data for the periods 1961-1990 and 1991-2020, and for the periods 1961-2000 and 2001-2022, vary by up to 0.2 K (Table 4).  
Figure 5: Changes in air temperature [K] (on nearest to hydrometric stations points of ERA5 reanalysis) in 1991–2020 in comparison with 1961–1990 during warmer months in hydrographic regions of the Russian Arctic.  
Long-term changes in water flow in Russia have been discussed in detail by [55]. There is a trend towards a decrease in the magnitude of spring floods, which is accompanied by an increase in winter and summer/autumn flow rates. At the same time, there has been a statistically insignificant increase in annual runoff for most rivers in the Russian Arctic since the 1980s.  
Between 1981 and 2010, in the entire Arctic region of Russia, the ice-free season increased by 10-14 days (in different regions) compared to the period between 1951 and 1980. This increase was also observed in the periods between 1961 and 1990, and between 1991 and 2016 [56]. Therefore, river break-up occurred earlier and river freeze-up occurred later during the past 30 years.
## 3 Description of the Launcher System Modules
### Launcher Coil  
This unit is the central part of this system. It is responsible for generating the magnetic field that exerts a magnetic force to accelerate the projectile. The launcher coil consists of several windings of a double-circuited coil wrapped around a hollow dielectric tube. This reduces the overall resistance and extends the coil's frequency range. To illustrate the idea of this work, a launcher with an iron yoke as shown in Figure 2 and an ordinary launcher without an iron yoke as shown in Figure 2 are manufactured. To demonstrate the effect of the iron yoke's cross-section area on the performance, two designs of the iron yoke as shown in Figure 3 are manufactured, one with a larger cross-section area than the other. The terminals of the coil are connected to the terminals of a capacitor bank through a power thyristor. The coil parameters are listed in Table 1. The efficiency peaks when the projectile and the launcher's coil have the same length as examined in [24].
## 2 Materials and Methods  
### In-Plane Speckle Photography  
When illuminating a rough surface, a so-called speckle pattern is formed in the image plane of an imaging camera system by constructive and destructive interference (see zoomed area \(W_{real}\) in Figure 1). Here, each surface point on the object surface, with the coordinates \(\xi\) and \(\psi\), is assigned to a point (\(x\),\(y\)) on the camera chip. If a point (\(\xi\),\(\psi\)) of the surface is shifted in-plane by \(\Delta\xi\) or \(\Delta\psi\) due to deformation, the corresponding point (\(x\),\(y\)) in the image plane is shifted by \(\Delta x\) or \(\Delta y\), respectively. This shift can be analyzed via the maximum of the cross-correlation function according to Figure 1. In order to measure a global deformation field of many object points in a region of interest \(W_{FOV}\), a small local evaluation window \(W_{real}\) is rasterized across the overall image \(W_{FOV}\).  
In order to determine the position of the maximum of the cross-correlation that is shown in Figure 1 with sub-pixel accuracy, interpolation methods are used based on curve approximation. The currently used sub-pixel interpolation algorithm, according to [PERSON] and [PERSON] [18], determines the shift of the maximum (\(\Delta x\),\(\Delta y\)) using an elliptic Gaussian function  
\[g(\hat{x},\hat{y})=a\cdot\exp\left\{b_{20}(\hat{x}-\Delta x)^{2}+b_{11}(\hat{x} -\Delta x)(\hat{y}-\Delta y)+b_{02}(\hat{y}-\Delta y)^{2}\right\}, \tag{1}\]  
with the variables \((\hat{x},\hat{y})\), which is fitted into to the cross-correlation function. It can be rewritten as:  
\[g(\hat{x},\hat{y})=\exp\left\{c_{00}+c_{10}\hat{x}+c_{20}\hat{x}^{2}+c_{01} \hat{y}+c_{11}\hat{x}\hat{y}+c_{02}\hat{y}^{2}\right\} \tag{2}\]  
with  
\[c_{00}=\ln(a)+b_{20}\Delta^{2}x+b_{11}\Delta x\Delta y+b_{02}\Delta^{2}y, \tag{3}\]  
\[c_{10}=-2b_{20}\Delta x-b_{11}\Delta y, \tag{4}\]  
\[c_{20}=b_{20}, \tag{5}\]  
\[c_{01}=-b_{11}\Delta x-2b_{02}\Delta y, \tag{6}\]  
\[c_{11}=b_{11}, \tag{7}\]  
\[c_{02}=b_{02}. \tag{8}\]  
The shift components \(\Delta x\), \(\Delta y\) of the maximum are then obtained from the Equations (3)-(8) as follows [18]:  
\[\Delta x=\frac{c_{11}c_{01}-2c_{10}c_{02}}{4c_{20}c_{02}-c_{11}^{2}}, \tag{9}\]  
\[\Delta y=\frac{c_{11}c_{10}-2c_{01}c_{20}}{4c_{20}c_{02}-c_{11}^{2}}. \tag{10}\]  
The relationship between the displacements \(\Delta x\), \(\Delta y\) in the image plane and the respective object displacements \(\Delta\hat{x}\), \(\Delta y\) within the measuring area are finally obtained according to the magnification \(A\) of the imaging system:  
\[\Delta\hat{x}=A\ \Delta x, \tag{11}\]  
\[\Delta\psi=A\ \Delta y. \tag{12}\]