esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Input Design
### Space Coordinate Field  
Simply using the wall distance field will result in the loss of some shape information. To illustrate this problem, the shape expressed in Figure 2 can be taken as an example. The red frame line is the receptive field of the convolutional neural network in a hidden layer. When it moves to the two positions shown in the figure, it will receive the same wall distance field information. However, these two positions are upstream and downstream of the flow field and have different effects on the pressure field and aerostatic performance. In order to solve this problem, we also need to highlight the flow field characteristics of the aerodynamic profile of the bluff body section.  
Figure 1: Wall distance field of an ellipse with AR = 4.  
Figure 2: Relationship between receptive field and shape.  
To this end, we propose to use the coordinate field describing the upstream-downstream relationship and the coordinate field describing the crosswind position as complementary information to the wall distance field as the other two inputs to the neural network.  
For the coordinate field \(I_{X}\), describing the upstream-downstream relationship, the assignment of the \(i\) spatial point is shown in Equation (2):  
\[I_{X,i}=\beta\times\frac{1}{1+e^{\frac{-X_{i}}{B}}} \tag{2}\]  
where \(X_{i}\) represents the downwind x coordinate of the \(i\) spatial point. The origin of the x-coordinate is at the center of the section. The settings of the exponential part can achieve both the size difference between the upstream and downstream data and the normalization of the input.  
Similarly, for the coordinate field \(I_{Y}\), describing the position of the crosswind direction, the assignment of the \(i\) spatial point is shown in Equation (3):  
\[I_{Y,i}=\beta\times e^{\frac{-|Y_{i}|}{B}} \tag{3}\]  
where \(Y_{i}\) represents the y coordinate of the crosswind direction of the \(i\) spatial point. The origin of the y-coordinate is located at the midpoint of the windward end: for circles and hexagons, it is the only windward end point, and for rectangles, it is the midpoint of the windward surface. The settings of the exponential part can achieve both the difference between the distance of the crosswind and the distance as well as the normalization of the input.  
Figures 3 and 4 show the values of \(I_{X}\) and \(I_{Y}\) in the spatial coordinate field for an example ellipse with an aspect ratio of 4.  
Figure 3: \(I_{X}\) field of an ellipse with AR = 4.
# Energy Saving in Wireless Sensor Networks via LEACH-Based, Energy-Efficient Routing Protocols
## 5 Comparative Performance Evaluation  
With the intention of appraising the energy efficiency of T-LEACH\({}_{\text{SAS}}\), its performance was comparatively evaluated against that of T-LEACH and LEACH. Actually, there are certain reasons that justify the selection of the specific protocols to compare their performance with that of T-LEACH\({}_{\text{SAS}}\). Specifically, LEACH is not only the ancestor of all protocols that perform hierarchical, energy-efficient routing in WSNs, but it also keeps on being the standard of comparison for all new protocols of this kind throughout time [34, 35, 36, 57].  
Additionally, T-LEACH, while it keeps on being referred to as achieving higher energy efficiency than LEACH [35; 57], is also the protocol that has the most similarities with T-LEACH\({}_{\text{SAS}}\) than any other hierarchical, energy-efficient protocol.
# Cloud Gaming Video Coding Optimization Based on Camera Motion-Guided Reference Frame Enhancement
## 2 Related Work
### Video Quality Enhancement  
The purpose of video quality enhancement is to reduce the distortion of compressed videos, and the methods based on deep learning have achieved remarkable results. The early ARCNN [26] and IFCNN [27] were inspired by the NN-based image super-resolution algorithm and improved based on the SRCNN [28] structure. VRCNN [29] introduces CNN in the loop filter of HEVC intra-coding and obtains improved coding performance. With the continuous development of deep learning, more complicated structures are applied to enhancement networks. For instance, RHCNN [30] introduces residual highway units to improve the quality of reconstructed frames. ACRN [31] uses asymmetric convolution and dense structure to better extract features. RRCNN [32] adopts the recursive residual module to achieve parameter sharing, obtaining excellent performance with fewer parameters, etc. Some other works apply coding information to the network, such as the partitioning map of the Coding Unit (CU) and Transform Unit (TU) [33; 34], coding residual [35], and Quantization Parameter (QP) [36] to help the network converge quickly and further improve the quality of reconstructed frames.  
However, the networks mentioned above only consider the frame to be enhanced, ignoring the temporal correlation between frames in the video sequence. To further take advantage of the inter-frame relationship, methods for multi-frame quality enhancement have emerged. QENet [37], LMVE [38], and MGANet [39] use the optical flow estimated by the optical flow network [40; 41] to align input frames to the current frame and feed them into the network for fusion and enhancement. MFQE [42] finds two peak quality frames to enhance the adjacent frame. MFQE 2.0 [43] further extends MFQE and significantly improves it. Moreover, STDF [44] and RFDA [45] learn a novel Spatio-Temporal Deformable Convolution (STDC) to aggregate temporal information while performing frame fusion. There are also works combining optical flow with deformable convolution for better alignment [46; 47]. Either using a single frame or multiple frames for video quality enhancement has shown excellent performance.
###### Abstract  
This paper proposes an online fault diagnosis system for bearings that detect emerging fault modes and then updates the diagnostic system knowledge (DSK) to incorporate information about the newly detected fault modes. New fault modes are detected using k-means clustering along with a new cluster evaluation method, i.e., multivariate probability density function's cluster distribution factor (MPDFCDF). In this proposed model, a heterogeneous pool of features is constructed from the signal. A hybrid feature selection model is adopted for selecting optimal feature for learning the model with existing fault mode. The proposed online fault diagnosis system detects new fault modes from unknown signals using \(k\)-means clustering with the help of proposed MPDFCDF cluster evaluation method. The DSK is updated whenever new fault modes are detected and updated DSK is used to classify faults using the \(k\)-nearest neighbor (\(k\)-NN) classifier. The proposed model is evaluated using acoustic emission signals acquired from low-speed rolling element bearings with different fault modes and severities under different rotational speeds. Experimental results present that the MPDFCDF cluster evaluation method can detect the optimal number of fault clusters, and the proposed online diagnosis model can detect newly emerged faults and update the DSK effectively, which improves the diagnosis performance in terms of the average classification performance.  
Keywords:online fault diagnosis; k-means clustering; hybrid feature selection; cluster evaluation; envelope analysis; and acoustic emission +
Footnote †: journal: Applied
## 6 Simulation and Discussion
### The Relationship between Interference-Optimality and Energy Efficiency  
In this section, we will evaluate the relationship between interference-optimality and energy-efficiency. According to the conclusions of Section 5.2, in heterogeneous networks, when the network is energy-efficient, the network is also interference-optimal with probability 1. The evaluation errors shown in Figure 20 are tiny. Therefore, in this part, we mainly investigate the relationship between the interference-optimality and the energy-efficiency in homogeneous networks, which has been analyzed in Section 5.1.  
According to Equations (43) and (44), the maximum and minimum probabilities that the network cannot meet interference-optimality and energy-efficiency simultaneously can be found in Figure 26. In Figure 26, the node density is 0.01.  
Figure 25: The probability under different transmission range in heterogeneous networks.  
From Figure 26, we can conclude that when the node density is 0.01, if the network is energy-efficient, the maximum and minimum probabilities that the network is not interference-optimal are only 0.028 and 0.0016, respectively. Therefore, the evaluation errors are small enough to be ignored. Based on the conclusions of Sections 5.1 and 5.2, we can conclude that when the network is energy-efficient, the network is also interference-optimal both in the homogeneous and heterogeneous WSNs.
# Developing and Field Testing Path Planning for Robotic Aquaculture Water Quality Monitoring
## 4 Results
### Flight Data  
Power data from the flights were recorded using the SplashDrone's flight log in order to validate the simulated power draw of each method. The SplashDrone flight logger is a proprietary SwellPro tool which saves data on the same device as the battery and was downloaded after the completion of all automated test flights. DO, temperature, and pressure data were not recorded as they were not the focus of this path planning experiment. Tables 8-10 shows similar results to the simulated data. GLOP is slightly more optimal than GAM, which are both significantly more efficient than HPP. In terms of power draw, GLOP and GAM were not significantly different. These results give validation to the assumption that using calculated horizontal distance is a good proxy for power consumption, and our simulations can give a good approximation of the range requirements for drones in HAUCS. This assumption may not hold for situations such as high winds or farms with significant changes in elevation.  
Wind conditions on the day of our testing were 0-5 mph in variable directions, so we did not have the opportunity to validate our simulation of steady wind conditions on path planning efficiency.
## 2 Materials and Methods
### Dynamometry Protocol  
A hand-held MicroFET 2 (Hoggan Scientific, LLC.) dynamometer was used to measure maximal isometric lower back extensor strength. The measurement range of the device is 0-300 lbs force/660 Newtons/135 kg. The test was performed in three positions, each following the methodology used in previous studies [17; 18; 19].  
First, the participant lay on the table in a prone position with legs fixed to the table by a non-elastic belt just above the ankles. The examiner stood next to them, resting their hands with the portable dynamometer on the participant's 7 th thoracic vertebra. The participant was asked to raise the upper body against the examiner's resistance, keeping the arms at the sides of the trunk (Figure 1a). The maximal isometric lower back extensor strength in the prone position was recorded.  
Next, the participant was seated on a purpose-designed chair that prevented the feet from reaching the floor. Each participant was asked to sit up straight, and the pelvis was bound by a non-elastic belt to the chair, 1 cm below the anterior superior iliac spine so that the pelvis was fixed and could not be moved throughout the test. The participant was then asked to cross their arms over the chest and bend forward so that the portable dynamometer could be placed at the level of the 7 th thoracic vertebra. After that, the participant was asked to push back as hard as possible (Figure 1b). The maximal isometric lower back extensor strength in the sitting position was recorded.  
The participant was then asked to stand back against a wall between two vertical anchor rails. Both feet were placed on the floor, heels together, touching the wall. The pelvis was fixed to the wall by a non-elastic belt looped through the anchor rails and tracing a line 1 cm below the anterior superior iliac spine so that the pelvis was fixed tight and could not be moved throughout the test. The participant was asked to cross their arms over the chest and bend forward so that the portable dynamometer could be placed at the level of the 7 th thoracic vertebra. After that, the participant was asked to push back as hard as possible (Figure 1c). The maximal isometric lower back extensor strength in a standing position was recorded.  
In each position, the participant had six attempts, with 20 s between them to rest. The result of every attempt was recorded to clarify the repeatability of the testing procedures in all 60 participants. To demonstrate the short-term and long-term test-retest reliability of the strength-testing procedures using HHD, participants who agreed to be retested (49 participants) were randomly divided into two groups by drawing a ticket. One group was asked to come back after an hour for retest of maximal isometric lower back extensor strength (24 participants) and the other group in two weeks (25 participants).
# Interactions between Phase-Separated Liquids and Membrane Surfaces
## 7 Active Processes and Nonequilibrium States in Cells Modify Biomolecular Condensate Properties
### Regulation of Cellular Processes Impacts Biomolecular Condensates  
Tuning of interaction strength is not only an artificial mechanism for regulating biomolecular condensates; it is also common biologically, in the form of post-translational modifications. A recently proposed model suggests two mechanisms of phosphorylation regulation leading to control of droplet size. In \"enrichment-inhibition,\" kinases that weaken interprotein interactions are enriched within biomolecular condensates, which leads to a \"stable radius\" for droplets where protein influx is counteracted by phosphorylation loss [103]. Those authors identified the synapsin condensate as matching these criteria [77]. In \"localization-induction,\" proteins are globally below the threshold for phase separation, but an immobilized kinase that enhances interprotein interactions locally increases phosphorylated protein concentrations above the threshold, generating a biomolecular condensate size-limited by the extent of kinase activity [103]. The authors identified the LAT signaling condensate as such a system [3].  
Though post-translational modifications are a major method of regulation for biomolecular condensates, the cell's control of protein synthesis and degradation also has consequences for biomolecular condensate properties, including at the membrane. A model that assumes a phase-separating molecule and a soluble counterpart that are interconverted by driven chemical reactions indicate that the formation and size of droplets can vary and Ostwald ripening even averted, depending on reaction kinetics [104]. This simple active-inactive interconversion model could be relevant to both reversible post-translational modifications and protein synthesis/degradation. A simulation of synthesis and degradation of a phase-separating particle indicates that rapid synthesis can slow the wetting of a surface like a membrane, and steady-state synthesis and degradation can also inhibit wetting [105].
## 3 Results
### Distribution of Electric Field  
#### 3.2.1 Electric Field Strength Distribution  
The distribution of the electric field around the arm is shown in Figure 4. The plane shown in Figure 4 is a vertical cross-section that passes through the feed point of the transmitter viewed from the right side of the human body model. Figure 4b shows the electric field distribution of the one-electrode transmitter. The electric field is distributed mainly above right arm in this case. In the case of the two-electrode transmitter, a strong electric field of \(-20\) to \(-10\) dB was distributed around the transmitter, inside the arm, and below the arm, as shown in Figure 4c.
# Study on Dynamic Mechanical Properties and Failure Pattern of Thin-Layered Schist
## 1 Introduction
[21] revealed anisotropy in wave velocity and peak strength for quartz mica schist, as well as the mechanism for macro-anisotropy. [PERSON] et al. [22] developed a nonlinear viscoelastic-plastic creep constitutive model, considering the rheological mechanical characteristics, to describe the entire creep process of schist. [PERSON] et al. [23] carried out triaxial impact tests on dry and saturated schist using improved triaxial split Hopkinson pressure bar system equipment to analyze the coupling effects of the strain rate, water content, and triaxial confining pressure on mechanical properties. [PERSON] et al. [24] studied the effect of schist anisotropy on elastic parameters such as dynamic Young's modulus and dynamic Poisson's ratio and discussed the negative effects of water on the dynamic mechanical properties of schist. As mentioned in previous reviews, there has been limited research on the dynamic properties and effects of schistity under impact loads for shists. Here, we study Wudang Group schist in the northwestern Hubei region of China, which is a typical thin-layered schist. The dynamic and static properties of thin-layered schist are greatly influenced by the schistity angle and schistity spacing. Therefore, this paper focuses on conducting dynamic impact tests using an SHPB test device on thin-layered shists with different angles of schistity (\(0^{\circ}\), \(30^{\circ}\), \(45^{\circ}\), \(60^{\circ}\), \(90^{\circ}\)) to analyze their dynamic mechanical characteristics. And numerical simulations are used to study the effect of angle and thickness on the dynamic mechanical characteristics and failure patterns.
# Automated Enemy Avoidance of Unmanned Aerial Vehicles Based on Reinforcement Learning
## 4 Problem Solving
### Learning Policy with the Sarsa Method
#### 4.2.2 CMAC Function Approximation  
In the proposed framework for the enemy avoidance problem, the state space is continuous. Therefore, the learning agent for the decision making UAV needs to use function approximation to generalize from limited experienced states. With function approximation, the action-value function \(Q(s,a)\) of the enemy avoidance problem is maintained in a parameterized functional form with parameter vector \(\overrightarrow{\theta}\). In this framework, the linear function form is used, as presented by Equation (7), where \(\overrightarrow{\theta}_{(s,a)}\) is the feature vector of the function approximation.  
\[Q(s,a)=\overrightarrow{\theta}^{T}\overrightarrow{\phi}_{(s,a)} \tag{7}\]  
The CMAC (cerebellar model arithmetic computer, [19]) is one of those linear function approximators, and thus is used to construct the feature vector \(\overrightarrow{\phi}_{(s,a)}\) in the proposed framework. To update the parameter vector \(\overrightarrow{\theta}\), the gradient-descent method is adopted in the proposed framework as well.  
The CMAC discretizes the continuous state space of the enemy avoidance problem by laying infinite axis-parallel tilings over all the eight state variables and then generalizes them via multiple overlapping tilings with some offset. Each element of a tiling is called a tile, which is a binary feature, as shown by Equation (8).  
\[\phi_{(s,a)}(i)=\begin{cases}1&\text{tile $i$ is activated.}\\ 0&\text{otherwise.}\end{cases} \tag{8}\]  
Therefore, the CMAC maintains \(Q(s,a)\) of the enemy avoidance problem in the following form:  
\[Q(s,a)=\sum_{i=1}^{n}\theta(i)\phi_{(s,a)}(i)=\sum_{i\in I(\overrightarrow{ \theta}_{(s,a)})}\theta(i) \tag{9}\]  
where \(I(\overrightarrow{\phi}_{(s,a)})\) is the set of tiles that are activated by the pair \((s,a)\) in the enemy avoidance problem, whose tile values are \(1\).  
The parameter vector \(\overrightarrow{\theta}\) is adjusted by the gradient-descent method, whose updating rule is as follows:  
\[\overrightarrow{\theta}_{t+1}=\overrightarrow{\theta}_{t}+\alpha\delta_{t} \tag{10}\]  
where \(\delta_{t}\) is the usual TD error,\[\delta_{t}=r_{t+1}+\gamma Q_{t}(s_{t+1},a_{t+1})-Q_{t}(s_{t},a_{t}) \tag{11}\]
## 5 Simulation Results  
This section provides the simulation results for evaluating DMAC and I-DMAC. Table 1 details the parameters for MAC layers. In simulations, the AP can communicate directly with all nodes and they can sense the carrier each other. We investigate the impact over system throughput, channel utilization and delivery delay, and compare them with the legacy 802.11 DCF mechanism and some existing analogous algorithm, such as hybrid MAC protocol with implicit ordering (IO-MAC) 19] and transmission time-based scheme (TTBS) [25].
# Enhancing Design for Additive Manufacturing Workflow: Optimization, Design and Simulation Tools
## 4 Optimization Strategy
### Generative Design  
Generative design (GD) describes a constrained design exploration process that allows finding multiple convergent solutions by using evolutionary approaches based on nature. This approach describes the artificial intelligence applied to structural optimization and works as an auxiliary tool for traditional TO algorithms, where an exploration strategy is added through a random disturbance to change search design direction [72].  
Exploration algorithm works as a black-box where the input information is: materials, manufacturing technology, physical constraints and design restrictions; while output information involves multiple solutions meeting the initial demand. Design solutions are categorized by a ranking performance to select an adequate candidate to manufacture.  
The main difference from TO traditional process is that GD does not require the definition of a design domain where the algorithm modifies the material distribution. In fact, domain restrictions associated to the assembly with other parts are defined. Then, the evolutionary algorithm creates a material path between fixed connections and domain limitations, providing more freedom of design.  
The main advantages of this design exploration strategy are part consolidation of complex assemblies [73], almost ready parametrized CAD design solutions requiring minimum modifications, multiple solutions obtained simultaneously for different materials and manufacturing technologies. However, as indicated in [74], the strategy implies a high consuming time task not recommended for simple designs where traditional TO is more effective. Available commercial software that includes GD approach includes: MSC Apex Generative Design (MSC Software Corporation, USA) [75], nTop (nTopology, USA) [76] and Autodesk Fusion360 (Autodesk, San Rafael, USA) [77]. In addition, it is possible to differentiate cloud-based GD from real-time GD. The first approach, used by Fusion360,takes advantage of cloud computing to efficiently solve different initial settings as materials, constraints and manufacturing technologies simultaneously; while real time GD describes an exploration analysis limited to fixed initial settings.