esa-sceva/satcom-chunk-collection huggingface dataset:
# Internet Video Delivery Improved by Super-Resolution with GAN
## 2 Background and Related Work
### Super-Resolution Using Convolutional Networks
However, the model's dense and deeper architecture shows a prohibitive computing cost for up-scaling HD and 4K videos in real applications. In recent years, several multi-frame super-resolution methods [34; 35; 36; 37; 38; 39] have manifested higher performance compared with SISRs. Multiframe-based methods explore temporal information from neighboring frames in addition to spatial information. In other words, exploring temporal information means that these methods include multiple frames as input to the neural network. To our knowledge, there are no practical solutions for videos with multiple scene changes in the multi-frame super-resolution model. The proposed multi-frame super-resolution methods have been tested with video datasets [40; 41; 42; 43] that present sequential frames recording movements in static background scenarios. In addition, such methods have been tested in low-resolution video datasets since they have a higher computational cost. This cost comes from the convolutional layer designed to receive multiple input images. Moreover, this convolution requires considerable memory for HD videos, which causes frequent GPU memory overflows. In this paper, we assume that videos are HD and FHD encoded with frequent changes in the background scenario. Therefore, our method is SISR-based and does not explicitly explore temporal information. This paper proposes a novel SR model assuming that a cloud-based video service manages all super-resolved videos. The built model, called VSRGAN+, has a low computational cost compared to the state-of-the-art approaches and is an improved version of our previous VSRGAN model [44]. The primary enhancement in VSRGAN+ is the introduction of RRDBs, a new perceptual loss function, and a RaD as shown in Section 4. This enhancement enables the generation of sharper images compared with the previous model VSRGAN. Table 1 shows the main characteristics of each SR-related model described in this section. We present the proposed model in Section 4.
## 4 Discussion
### Workload Measures and Match Category  
Match category exhibited significant associations with all three workload measures. Interestingly the post hoc analysis highlighted slight nuances between the measures, where pool play differed from cup quarterfinals and cup finals across average game work, sRPE, and the SDC model; however, pool play also differed from cup semifinal workload using sRPE, and cup semifinal and cup quarterfinal workloads differed using the SDC model. [PERSON] (2015) suggested that female soccer players experienced higher speeds and covered greater distances in playoff games compared to regular season games [21]. This suggests that games of critical importance, such as playoffs, which would be similar to the cup quarterfinal, cup semifinal, and cup final matches in this dataset, would potentially see players experiencing greater speeds, accelerations, and, subsequently, greater mechanical work measures. Research in men's basketball has shown increased sRPE in playoffs compared to regular (pool) play and finals compared to semifinals, suggesting that the athletes' perceptions of the value or importance of the match drives increases in RPE and, subsequently, sRPE [17,18]. Further, [PERSON] et al. (2019) conclude that the observed increases in workload may be explained by the psychological aspects associated with the competition  
Figure 4: (**a**) Average game work, (**b**) sRPE, (**c**) SDC model, workload measures by player experience, * denotes significant comparison (\(p<0.05\)).  
environment, which is to say that the athletes' identified value of playing in a medal final elicits the perception of increased effort [18]. Given the differences in the psychological perception of the importance of the match, or game category, it is possible to infer that the physical output changes with the category of the game [18]. For example, it is possible that in the medal knockout rounds, the quarterfinal, semifinal, and final, players may have a higher physical output as they recognize the psychological importance of the match [17; 18]. This is consistent with the observed results, where sRPE showed the most differences between match category. It is possible that any psychological perception of load is best expressed using this measure. Additionally, when considering both mechanical work and the SDC model, this psychological effect is not overtly accounted for and this shows the unique value of all workload measures, to either objectively evaluate the external measured work with (sRPE) or without (mechanical work, SDC model) psychological perception of effort.
# Current Trends on Green Wireless Sensor Networks
## 3 Energy Harvesting and Energy-Neutral Operation
### Energy Prediction  
One of the most popular energy predictors is the solar energy prediction model based on an Exponentially Weighted Moving-Average (EWMA) proposed by [PERSON] et al. [178], which takes into account energy harvested in the previous days. This model was improved by the prediction method Weather-Conditioned Moving Average (WCMA) proposed by [PERSON] et al. [182].  
Another popular energy prediction model is Pro-Energy (PROfile energy prediction model) by [PERSON] et al. [183], based on using past observations for both sun and wind. The main idea is to use profiles representing the energy available for different types of \"typical\" days (for example, sunny, cloudy, or rainy). Recently, [PERSON] and [PERSON] [184] propose a modification of Pro-Energy but using real-life solar traces for prediction.  
[PERSON] et al. [185] propose a new approach for energy predictions, based on weather forecasts, which is valid for sun and wind. [PERSON] and [PERSON] [186] present a solar energy predictor that also uses cloud cover and precipitation probability predictions from weather forecast. The errors of humidity and atmospheric pressure are used to feed the fuzzy logic filter.  
In [187], [PERSON] and [PERSON] also present a recent survey on solar energy forecasting techniques published before 2019 (time series models, neural networks and other advanced models based on machine learning, genetic algorithm or fuzzy logic). Moreover, a detailed study on power management techniques is also included (data aggregation, routing, adaptive duty cycle, among others).  
[PERSON] et al. [188] propose a Correlation Least Mean Square (C-LMS) prediction model for solar energy that introduces the correlation factor of weather changes.  
[PERSON] et al. [189] propose a novel energy prediction model that makes use of the altitude angle of the sun at different times of day to predict future solar energy availability. The great advantage of this interesting approach is that it does not need to maintain local energy-harvesting patterns of past days, or any particular tuning for each different scenario or location.
# Design and Implementation of a Polar Wind and Solar Hybrid Power Supply Controller
## 3 Controller Hardware Design
### Wind Power Generation Circuit  
Wind power circuit is a Boost structure, as shown in Figure 10.  
First, three-phase AC becomes DC with a rectifier bridge, then the obtained DC passes through a varistor and an input capacitor. After being boosted and filtered, the direct current flows into the DC bus through a current sensor. The function of the varistor is to clamp the voltage and absorb excess current to protect the sensitive device when the circuit withstands overvoltage.  
Figure 10: Wind power generation circuit.  
Figure 9: Auxiliary power circuit.  
The switch tube Q4 is used to open the unloading path, and the Q2 is used to open the output of the ventilation power generation circuit.  
The rated power of the wind turbine is 1200 W, the rated input voltage is 48 V, the DC bus voltage is 72 V, the inductor ripple factor is 10%, the voltage ripple factor is 1%, and the switching frequency of the main power tube is set to 48 kHz. After calculation, the main circuit inductance of this wind power generation is 220 \(\mu\)H (microhenrys), the output filter capacitor is 680 \(\mu\)F (microfanads), and 0.1 \(\mu\)F, and the input capacitance is 1000 \(\mu\)F and 0.1 \(\mu\)F. The N-channel MOS transistor of NCE0157A type is selected as the power transistor. The VDS voltage of the transistor is 100 V, the ID is 57 A, and the on-resistance RDS is less than 17 m. It has excellent performance and low power consumption. The continuous current diode V40100C is a MOS Schottky diode with ultra-low conduction voltage drop of 0.38 V. The forward current is 40 A, the voltage is 100 V, and the reverse voltage is 150 V.
# A Real-Time Cotton Boll Disease Detection Model Based on Enhanced YOLOv11n
## 5 Discussion and Conclusions
### Conclusions  
In this study, a novel model named YOLOv11n-ECS is proposed for rapid multi-target detection of cotton boll diseases in natural environments, based on the YOLOv11n architecture. The improved model achieves a precision of 92.2%, a recall of 84.3%, an [EMAIL_ADDRESS] of 85.6%, and an [EMAIL_ADDRESS]:0.95 of 62.7%. Compared to the original model, YOLOv11n-ECSachieves reductions of 3.2%, 4.0%, and 3.7% in model parameters, computational load, and weight file size, respectively. It thus effectively controls model complexity while improving detection performance.  
Compared to CenterNet, Faster R-CNN, YOLOv8-LSW, MSA-DETR, DMN-YOLO, and the original YOLOv11n, YOLOv11n-ECS demonstrates substantial improvements in both mAP metrics. Specifically, it achieves gains of 25.7, 21.2, 5.5, 4.0, 4.5, and 2.3 percentage points in [EMAIL_ADDRESS] and 25.6, 25.3, 8.3, 2.8, 1.8, and 1.9 percentage points in [EMAIL_ADDRESS]:0.95, respectively. Furthermore, YOLOv11n-ECS exhibits superior detection performance for diseased cotton bolls in natural scenes, significantly reducing both false negatives and false positives. When deployed on the Jetson TX2, it achieves a detection speed of 56 FPS with an [EMAIL_ADDRESS] of 84.2%, fulfilling the requirements for intelligent real-time recognition in field applications.  
Writing--original draft preparation, methodology, investigation, conceptualization, L.Y.; validation, data curation, W.C.; investigation, data curation, J.L.; data curation, formal analysis, G.H.; formal analysis, resources, Q.Z.; software, investigation, Y.L.; writing--review and editing, resources, funding acquisition, J.Z.; formal analysis, project administration, Y.Q. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Dataset available on request from the authors.  
The authors declare no conflicts of interest.
# Hand Gesture Recognition on Edge Devices:
## 5 Pre-Processing/Signal Processing
### Multiple-Input Multiple-Output  
Multiple-input multiple-output (MIMO) is based on the fact that a signal sent from different locations follows a different path even though the same receivers are used. Two senders and two receivers therefore produce four distinct signals that each contain different information and is a virtual array of four. When using one sender and three receivers we would only get the information from three receivers. Each transmitter doubles the output of the receivers. This technology is mainly used for improved angle resolution, calculated using the phase change between the signal coming from the senders.
## 2 Materials and Methods  
The S-AFM method typically rests on several approximations. The scanning tip is viewed as a rigid cone-shaped object with a probe sphere at its end; the molecular structure (sample) is represented by a static Van der Waals sphere atomic model placed on a solid surface (AFM substrate), and tip-sample interactions are considered to be non-elastic collisions. Within such approximations, S-AFM calculations are reduced to determine the single-point intersection of the 3D tip model with an atomic sphere, for which an explicit equation exists (from solving a second order polynomial equation, see [22] for details). However, computing the height topography of the entire sample for a given molecular orientation is still a time-consuming process. This is because for each cell along the scanning grid, generally, the contact points of the tip with all the atoms have to be evaluated in order to determine the largest possible height value relative to the AFM substrate, roughly corresponding to heights measured in an AFM experiment. In fact, a ubiquitous situation is that the tip collides first with atoms that are beyond those belonging to the scanned grid cell. On the other hand, it is obvious that the tip can only contact atoms which are exposed at the molecular surface. Therefore, the time required to compute a topographic image can in principle be reduced by introducing a method which, for a given molecular orientation, can filter surface atoms relevant for the S-AFM scanning process. The challenge is to construct a filtering algorithm that is fast enough to actually speed up the computation of the S-AFM image. For example, if filtering is more time consuming than the filter-free computation described above (for which just algebraic expressions must be evaluated), there is no efficiency gain.  
For the following explanation of the filtering method and simulated AFM computation, we consider the Van der Waals representation of a biomolecular structure in an arbitrary molecular orientation.
# Improving Route Selections in ZigBee Wireless Sensor Networks
## 3 Estimating Link and Route Costs
### Literature Review
The authors in [49] used the packet reception rates between nodes and their neighbors to select routes and propose a distributed learning automaton algorithm to choose routes that satisfy quality-of-service requirements. Although these algorithms are valuable and could improve the estimation of link costs and the selection of routes, such implementations would require nodes to implement machine learning techniques and require significant changes in the ZigBee specifications, protocol, and frame format.
# Improved Likelihood Probability in MIMO Systems Using One-Bit ADCs
## 3 Optimization Problem
### Problem Definition  
In this subsection, the optimization problem is defined to minimize the expectation of MSE between the true and estimated likelihood probabilities. The estimated likelihood probability \(\hat{p}_{r,k}\) is defined as a weighted linear combination of the pilot- and data-based likelihood probabilities in (8) and (12), respectively. This combination is employed because the data-based likelihood probability \(p_{r,k}^{da}\) is considered unreliable for a small \(n\), whereas the pilot-based likelihood probability is more reliable in this case. In addition, linear combinations have a simple structure and are mathematically tractable. The estimated likelihood probability is expressed as follows,  
\[\hat{p}_{r,k}(\alpha,\beta)=\alpha p_{r,k}^{pi}+\beta p_{r,k}^{da}, \tag{14}\]  
where \(\alpha,\beta\in[0,1]\). Using the estimated likelihood probability, and the optimization problem is defined as  
\[(\alpha,\beta)=\operatorname*{argmin}_{\alpha,\beta\in[0,1]}\mathbb{E}\{\|p_{ r,k}-\hat{p}_{r,k}(\alpha,\beta)\|^{2}\}, \tag{15}\]  
where \(\mathbb{E}(\cdot)\) denotes the expectation operation.  
Solving the optimization problem in (15) directly is difficult due to the non-linear function \(\Phi(\cdot)\). Moreover, the data-based likelihood probability cannot be readily expressed mathematically in a tractable form. Hence, a least-squares method is instead used to obtain the weights of \((\alpha,\beta)\).
# Task-Oriented Motion Planning of a Space Manipulator under Multiple Constraints
## HTN Fundamentals
### Planner Input  
A complete HTN planning requires four basic elements [14; 15]:  
1. An initial \"task network\" \(d\) that represents the problem to be solved. A \"task network\" is a collection of \"tasks\", representing what needs to be done. Each \"task\" has a name and a parameter list, which can be scalar or constant. Some tasks are \"original\" (for example, they can be executed directly), while others are \"non-original\" (for example, how to execute them needs to be solved in the planning period). The task network also contains the constraints of the task (which will limit the value range of some variables) and some rules in task execution.
2. An \"operator\" \(Op\) set that describes the execution effect of each original task (or action).
3. A collection of \"methods\" \(Me\) that describe how to perform various non-original tasks. Each mode is a parameter pair: \(m=(t,d)\), where \(t\) is a task, and \(d\) is a task network. It can be said that executing the task network described in \(d\) is a way to complete task \(t\) (assuming that there is an execution mode that meets all constraints).
4. Initial state (as an input, it exists objectively).
5. Among them, operators and methods are important components of the planner. They constitute the planning domain. The planner looks for the method to solve the problem, so that the general planning algorithm has the means to deal with the problems in specific fields.
# Thickness-Stretch Vibration of an Infinite Piezoelectric Plate with Flexoelectricity
## 1 Introduction
Exact solutions for pure thickness-stretch vibrations are obtained based on the one-dimensional model and two electric boundary conditions. The results reveal that the direct effect of flexoelectricity affects the thickness-stretch vibration by decreasing the fundamental frequency, and also has a significant size-dependent phenomenon. This model can aid in the understanding of the micro/nano mechanical behaviors of thickness-stretch vibrations, and can be used as guidance for the design of next-generation high-precision piezoelectric resonators.
# Context-Aware Gossip-Based Protocol for Internet of Things Applications
## 6 Effects on Application Requirements
### Comparative Assessment Results
#### 6.4.2 Rebroadcast Nodes  
The percentages of rebroadcast nodes for all three constructed protocols (Equations (10)-(12)), the traditional gossiping protocol, and FELGossiping are shown in Figure 7 for networks with \(50\) to \(250\) nodes. The traditional gossiping protocol results in \(100\%\) rebroadcast nodes as the number of nodes in the network increases. This means that all nodes in the network receive the message for rebroadcasting, making this protocol ill suited for bandwidth-intensive IoT applications. The percentages of rebroadcast nodes for the MFWFs designed for critical and bandwidth-intensive IoT applications (see Equations (10) and (12), respectively) are comparable, with values ranging from \(2\%\) to \(8\%\). These values clearly outperform those of the traditional gossiping protocol. Interestingly, FELGossiping proves to be well suited for routing messages in bandwidth-intensive IoT applications, with a performance comparable to those of the protocols for critical and bandwidth-intensive applications (see Equations (10) and (12), respectively). When only the residual energy (\(f1\)) and next-node distance (\(f2\)) are considered, as in the MFWF for energy-efficient IoT applications (see Equation (11)), the percentage of rebroadcast nodes slightly increases in comparison to the results for the other constructed protocols (Equations (10) and (12)) and FELGossiping. Nevertheless, the performance of this protocol remains superior to that of the traditional gossiping protocol. For all protocols, the percentage of rebroadcast nodes remains relatively stable as the number of nodes in the network increases, with only slight variations, ranging from none to \(4\%\).
# Deep Learning Resolves Representative Movement Patterns in a Marine Predator Species
## 3 Approach  
### Data Preprocessing  
Our approach considers two kinds of information obtained from animal trajectories. First, we use \(d_{t}\) to denote the distance travelled at time period \(t\). Second, we use \(\theta_{t}\) to denote the change in motion direction. The positions of an animal at the beginning of time period \(t-1\), \(t\), and \(t+1\) are \(L_{t-1}\), \(L_{t}\), and \(L_{t+1}\), respectively, as recorded by a telemetry device. Thus, \(d_{t}\) is the distance between \(L_{t}\) and \(L_{t+1}\), and \(\theta_{t}\) is the difference between the direction from \(L_{t-1}\) to \(L_{t}\) and that from \(L_{t}\) to \(L_{t+1}\).  
If we use \((Lo_{t},La_{t})\) to represent the longitude and latitude of an animal's location at time \(t\), we can calculate the distance between two consecutive locations (\((Lo_{t},La_{t})\) and \((Lo_{t+1},La_{t+1})\)) using the haversine formula, and use it as the traveling distance \(d_{t}\) at time \(t\). To determine the animal's turning angle, we first calculate the great circle angle relative to the North Pole for the trajectory from \((Lo_{t-1},La_{t-1})\) to \((Lo_{t},La_{t})\). We then calculate that angle for the trajectory from \((Lo_{t},La_{t})\) to \((Lo_{t+1},La_{t+1})\). By subtracting these two angles, we can obtain the turning angle \(\theta_{t}\) of that animal at time \(t\).  
We use a sliding window of size \(T\) to obtain segments of a continuous trajectory, and input these segments into our model. As defined above, one input segment contains two sets of variables.  
\[\mathbf{d} =(d_{1},d_{2},\cdots,d_{T}) \tag{1}\] \[\mathbf{\theta} =(\theta_{1},\theta_{2},\cdots,\theta_{T}) \tag{2}\]Data augmentation is also widely used for deep learning and other classification methods to reduce limitations of datasets [11; 25; 26]. For example, if a certain class has a much larger sample size comparing to other classes, it can mislead a classifier to make biased predictions towards that large class. In our work, we balance the class size before training by randomly oversampling minority classes [25; 26]. Namely, to create an oversampled class for class \(i\), each data item in class \(i\) was sampled \(\frac{N_{\max}}{N_{i}}\) times, where \(N_{\max}\) is the the largest class size and \(N_{i}\) is the class size of the \(i\)th class.