esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Setup
### Performance Metrics  
Two types of metrics are presented in this section: the metrics used to evaluate the performance of a swarm according to the coverage and the connectivity; the metrics used for comparing two sets of non-dominated solutions in multi-objective optimisation. As depicted in Figure 7, a heuristic \(H_{1}\) provides different solutions \(S_{1},S_{2},\cdots,S_{k}\). The swarm metrics evaluate these solutions according to coverage and connectivity. For a solution \(S_{i}\), a couple of values \((cov_{i},con_{i})\) is, therefore, returned and can be displayed in a two -dimensional coordinate system. Therefore, the obtained set of 2D points represents the heuristic \(H_{1}\), whose performance can finally be evaluated using the three MO metrics.
# Application of Predictive Maintenance Concepts Using Artificial Intelligence Tools
## 3 Application Example
### Class Imbalance in Maintenance Problem Applications  
Something to take into account in predictive maintenance is the fact that the occurrence of failures is rare during the life cycle of a given machine, when compared to normal operation. This leads to an imbalance between classes (Table 13), which usually leads to an illusory performance on the part of the algorithms, which tend to classify the most common example more often at the expense of the less common, since the total number of incorrect classifications are thus less. Therefore, the Recall and Precision values can be low, although the Accuracy value is high. A clear example of this phenomenon is, in the validation set (where most of the evaluation metrics will be calculated), where 98.11% (Table 12) of the data correspond to the Stable category (failure = none), that is, a model (without any use) that provides functioning stable values at all times would have an Accuracy of 98.11%. It is therefore essential to look at other evaluation metrics.  
For a considerable number of critical equipment applications, the model's inability to predict a failure can be exorbitantly expensive. In predictive maintenance, as a general rule, the most important is the number of real failures that the model is capable of predicting, that is, the model's Recall3. This parameter becomes even more important as the consequences of false negatives, that is, true failures that the model was unable to predict, exceed the consequences of false positives, that is, a false prediction of a failure. This phenomenon is known as \"incorrect classification cost\" and can be estimated by companies according to the cost of repair, parts and labor. Generally, it is preferable that the model errs as a precaution, since it will be more economical to carry out a maintenance check than a partial or total interruption of the operation. However, the wrong prediction of a failure, that is, a false positive, can also lead to a loss of time and resources. In this case, the model must be adjusted to a high Precision. However, as mentioned earlier, the Recall and Precision metrics are not independent: Increasing one implies decreasing the other.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Failure** & **\%** \\ \hline none & 285,684 & 98.06 \\ \hline comp2 & 1985 & 0.68 \\ \hline comp1 & 1464 & 0.50 \\ \hline comp4 & 1240 & 0.43 \\ \hline comp3 & 968 & 0.33 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Example of the imbalance between the different classes for the ‘failure’ feature in the total data set.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Quantity** & **/\%** & **Failure/\%** \\ \hline Training & 66.52 & 2.02 \\ \hline Validation & 16.57 & 1.89 \\ \hline Test & 16.91 & 1.92 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Amount of data attributed to each of the sets and the percentage corresponding to failures.
# Properties of a Lightweight Fly Ash-Slag Alkali-Activated Concrete with Three Strength Grades
## 1 Introduction
However, research on the lightweight alkali-activated concrete (LAAC) containing sintered fly ash aggregate as the structural materials is very limited. [PERSON] et al. [23] compared the uniaxial compressive behavior of a low-calcium fly ash-based AAC using two kinds of aggregates: normal-weight natural aggregates and non-pelletized lightweight fly ash aggregates. They found that the established relationship between the elastic modulus, strength and density for OPC concrete was not suitable for AAC and the stress-strain models for OPC concrete should be further modified to predict the behavior of the fly ash-based LAAC. [PERSON] et al. [24] found that the compressive strength of the slag-based LAAC slightly improved with the increase in water and binder content with a fixed water/solid (W/S) ratio. However, the mechanical and fracture properties of the structural LAAC with a different strength class has not been investigated yet. Therefore, the mechanical and fracture properties of LAAC are still inadequately defined, requiring further research. From the above literature review, it can be concluded that LAAC is a highly environmentally friendly concrete, which not only offer some valuable merits, such as recycling industrial by-products, reducing greenhouse gas emissions and natural resource exploitation and protecting the ecological environment, but also has similar merits than lightweight cement concrete. Therefore, this kind of concrete is worth popularizing in engineering applications. This study evaluated the compressive behavior, splitting tensile strength, modulus of elasticity, Poisson's ratio, stress-strain curve and fracture properties for normal-weight alkali-activated concrete (NAAC) with limestone aggregates and LAAC with sintered fly ash aggregates, and the results were compared with the typical behavior of the corresponding OPC concrete. The effects of strength class on the properties of the NAAC and LAAC were also investigated.
# Color Structured Light Stripe Edge Detection Method Based on Generative Adversarial Networks
## 4 Experimental Results and Discussion
### Pixel Location Accuracy
\(\Omega(\overline{\tau})\) is the standard error of \(\overline{\tau}\), and \(g_{\pi/2,(n-1)}\) indicates the \(100\%(1-\alpha)\) percentage point of a t-distributed with \(n-1\). Thus, \(g_{\pi/2,(n-1)}=g_{0.05A}=2.132\), which was obtained from the t-distribution table. With the data in Table 3, a 90% confidence interval for the MSE difference between the method in [16] and the proposed method is given by  
\[0.0082\pm 2.132*0.0012\text{ or } \tag{7}\]  
\[0.0082\pm 0.0026 \tag{8}\]  
So, the 90% confidence interval is (0.0056, 0.0108). Thus, we are 90% confident that the average MSE difference between the method in [16] and the proposed method is between 0.0056 and 0.0108. Similarly, a 90% confidence interval for the MSE difference between the method in [43] and the proposed method is given by  
\[0.0054\pm 2.132*0.0014\text{ or } \tag{9}\]  
\[0.0054\pm 0.0029 \tag{10}\]  
So, the 90% confidence interval is (0.0025, 0.0083). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Object** & **Method in [16] (C)** & **Method in [43] (S)** & **Proposed Method (P)** & **MSE Difference between C \& P** & **MSE Difference between S \& P** \\ \hline Colored textured plastic box & 0.0383 & 0.0350 & 0.0302 & 0.0081 & 0.0048 \\ Waved metal sheet & 0.0359 & 0.0332 & 0.0286 & 0.0073 & 0.0046 \\ Porcelain dish & 0.0331 & 0.0301 & 0.0275 & 0.0056 & 0.0026 \\ Colored textured carton box & 0.0437 & 0.0412 & 0.0306 & 0.0131 & 0.0106 \\ Open book & 0.0343 & 0.0315 & 0.0271 & 0.0072 & 0.0044 \\ \hline \hline \end{tabular}
\begin{tabular}{c c c c} \hline \hline  & Sample mean & & 0.0082 & 0.0054 \\ Sample variant & & & 0.0028 & 0.0031 \\ Standard error & & & 0.0012 & 0.0014 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of pixel location accuracy of color stripe edge detection methods (Unit: Pixels). Thus, we are 90% confident that the average MSE difference between the method in [43] and the proposed method is between 0.0025 and 0.0083. It can be seen in Table 3 that the MSEs achieved with the methods in [16; 43] were pretty large, especially for the objects with complex surface characteristics, such as the plastic box and the carton box, while deviations of the MSEs with different objects achieved by the proposed method were small. Furthermore, the 90% confidence intervals in Equations (8) and (10) were always above zero. This means that the proposed method achieved higher pixel location accuracy compared to its counterparts.
# Task Scheduling Mechanism Based on Reinforcement Learning in Cloud Computing
###### Abstract  
The explosive growth of users and applications in IoT environments has promoted the development of cloud computing. In the cloud computing environment, task scheduling plays a crucial role in optimizing resource utilization and improving overall performance. However, effective task scheduling remains a key challenge. Traditional task scheduling algorithms often rely on static heuristics or manual configuration, limiting their adaptability and efficiency. To overcome these limitations, there is increasing interest in applying reinforcement learning techniques for dynamic and intelligent task scheduling in cloud computing. How can reinforcement learning be applied to task scheduling in cloud computing? What are the benefits of using reinforcement learning-based methods compared to traditional scheduling mechanisms? How does reinforcement learning optimize resource allocation and improve overall efficiency? Addressing these questions, in this paper, we propose a Q-learning-based Multi-Task Scheduling Framework (QMTSF). This framework consists of two stages: First, tasks are dynamically allocated to suitable servers in the cloud environment based on the type of servers. Second, an improved Q-learning algorithm called UCB-based Q-Reinforcement Learning (UQRL) is used on each server to assign tasks to a Virtual Machine (VM). The agent makes intelligent decisions based on past experiences and interactions with the environment. In addition, the agent learns from rewards and punishments to formulate the optimal task allocation strategy and schedule tasks on different VMs. The goal is to minimize the total makespan and average processing time of tasks while ensuring task deadlines. We conducted simulation experiments to evaluate the performance of the proposed mechanism compared to traditional scheduling methods such as Particle Swarm Optimization (PSO), random, and Round-Robin (RR). The experimental results demonstrate that the proposed QMTSF scheduling framework outperforms other scheduling mechanisms in terms of the makespan and average task processing time.  
reinforcement learning; Q-learning; upper confidence bound; task scheduling  
1  
[PERSON], [PERSON], [PERSON], W. Task Scheduling Mechanism Based on Reinforcement Learning in Cloud Computing. _Mathematics_**2023**, 11, 3364. [[https://doi.org/10.3390/math115364](https://doi.org/10.3390/math115364)]([https://doi.org/10.3390/math115364](https://doi.org/10.3390/math115364))
# First Vegetation Optical Depth Mapping from Sentinel-1 C-band SAR Data over Crop Fields
## 2 Study Area and Database
### Database Description
#### 2.2.2 Sentinel-2 Images  
In this study, 24 cloud-free S2 images acquired between September 2017 and September 2019 were used (one image each month). The S2 images were obtained from the Theia website at the French Land Data Center ([[https://www.theia-land.fr/](https://www.theia-land.fr/)]([https://www.theia-land.fr/](https://www.theia-land.fr/)), last access September 2019). Theia provides S2 images corrected for atmospheric effects using the method developed by [PERSON] et al. (2012, 2019). The S2 data were used to generate NDVI images.
## 5 Results Analysis
### Healthy Conditions  
To illustrate the robustness of the proposed control (SMC with NESO) under normal operating conditions, we analyzed the effects of unmodeled dynamics and stator resistance uncertainty. The mismatched disturbances in (19) were modeled as follows: \(d_{1}(t)=-\frac{\Delta R_{s}}{L_{qp}}i_{qp}+\delta_{qp}\), \(d_{2}(t)=-\frac{\Delta R_{s}}{L_{qp}}i_{qp}+\delta_{qp}\), \(d_{3}(t)=-\frac{\Delta R_{s}}{L_{ab}}i_{ds}+\delta_{ds}\), and \(d_{4}(t)=-\frac{\Delta R_{s}}{L_{qp}}i_{qs}+\delta_{qs}\); where \(\Delta R_{s}=|R_{s}-R_{sn}|\); \(R_{sn}\) denotes the nominal value of the stator resistance. \(\delta_{qp}\), \(\delta_{qp}\), \(\delta_{ds}\), and \(\delta_{qs}\) represent the unmodeled dynamics, which are characterized by sinusoidal forms accounting for dead-time effects [30].  
Figures 8-11 show the FPMSM responses of the speed and torque under two scenarios involving step changes in load torque and reference speed. Figures 8 and 9 present the first scenario, where the speed reference was fixed at 300 rpm, and the step load torque changed from 20 Nm to 40 Nm at time 1 s. At time 2 s, unmodeled dynamics were introduced as \(\delta_{dp}=10\sin(6\theta)\) and \(\delta_{qp}=10\cos(6\theta)\), and the stator resistance was increased by \(+60\%R_{s}\), resulting in \(1.6R_{sn}\). It is evident that all three methods could track the speed reference in the face of the step-increased load torque and mismatched disturbances. However, the TSMC achieved a shorter settling time, with no overshoot compared to the PI strategy. Nonetheless, the PI and TSMC exhibited noticeable ripples and fluctuations in the torque and speed responses at time 2 s, reflecting poor robustness in the presence of disturbances. On the other hand, the proposed control (SMC with NESO) achieved the shortest settling time and significantly minimized torque ripples and speed fluctuations compared to the traditional controllers. In the second scenario, shown in Figures 10 and 11, the load torque was fixed at 40 Nm, and the step speed reference was changed from 100 rpm to 300 rpm  
Figure 6: Proposed control diagram under faulty conditions.  
Figure 7: Back-EMF of FPMSM under healthy conditions.  
and then back to 100 rpm at time 3 s. At time 2 s, the PMSM started operating under stator resistance uncertainty and unmodeled dynamics, as in the first scenario. It is clear that the SMC with NESO tracked the reference speed more accurately than the traditional controllers (PI and TSMC). Therefore, the effectiveness and robustness of the proposed control under healthy conditions were verified.  
Figure 8: Speed response to load changes in healthy conditions.  
Figure 10: Speed response to speed changes in healthy conditions.  
Figure 9: Torque response to load changes in healthy conditions.
# Dual Attention Fusion Enhancement Network for Lightweight Remote-Sensing Image Super-Resolution
## 4 Results
### Network Complexity and Inference Speed  
In addition to evaluating complexity, faster inference speed has also become a crucial metric for assessing whether these lightweight RSISR methods are suitable for real-time applications on edge devices with limited computational resources. To this end, we conduct timing tests on several representative RSISR methods using the same device equipped with an NVIDIA GeForce RTX 3090 GPU (manufactured by NVIDIA Corporation, Santa Clara, CA, USA) on the RS-T1 test set, as shown in Table 6. It can be found that DAFEN-S achieves  
Figure 8: Visualization results of our proposed networks (DAFEN and DAFEN-S) and other SR methods on real remote-sensing images for \(\times 4\) SR. (**a**) Residential areas and farmland. (**b**) Terraces and roads.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Params** & **Multi-Adds** & **Times** \\ \hline RCAN [14] & 15.67 M & 1.492 T & 0.12 s \\ SwinIR [36] & 11.55 M & 2.883 T & 0.23 s \\ HAT [18] & 20.53 M & 3.871 T & 0.32 s \\ DAFEN-S & 0.192M & 0.017T & 0.012s \\ DAFEN & 0.422M & 0.038T & 0.017s \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison results with non-lightweight state-of-the-art methods at a scaling factor of \(\times 3\). Due to the higher model complexity of non-lightweight methods, we present the data using larger magnitude units for ease of comparison. The best and second-best results are highlighted in red and blue, respectively. The Multi-Adds is calculated corresponding to a 1280 \(\times\) 720 HR image.  
optimal results in terms of parameter count, computational complexity, and inference time, while DAFEN achieves optimal performance and second-best inference speed. Notably, the parameter count of DAFEN-S is about half that of FeNet, yet its PSNR and SSIM values improved by approximately 0.04 dB and 0.0027, respectively. Additionally, the parameter count of DARN is about 1.5 times that of DAFEN, yet DAFEN still leads in PSNR and SSIM values by about 0.05 dB and 0.0057. Furthermore, our method, based on a CNN architecture, offers inference speeds over eight times faster than IFIN-S, which is based on a transformer architecture. This faster inference speed makes DAFEN more suitable for real-time applications on edge devices with limited computational resources. Overall, comparisons with existing state-of-the-art methods demonstrate the efficiency and lightness of the proposed method, as well as its high adaptability and greater flexibility in the use of remote-sensing devices.
# Feature Fusion Graph Consecutive-Attention Network for Skeleton-Based Tennis Action Recognition
## 7 Discussion
### Comparison with the State of the Art  
Spatial-temporal dependencies in motion capture data are crucial for action recognition performance. Sequence-vision-based data may suffer from the inconsistency in trajectories, which further results in misleading classification [41; 42]. Many deep learning models disregard the relationships between frames in dynamic movements, and thus long-term temporal dependencies are not sufficiently provided [43; 44]. In many studies, the motion capture data were split into an equal length set of frames, which may cause a loss of key information [45]. Usually, these kinds of data are registered utilizing systems calibrated for specific types of movements. In order to eliminate the above weaknesses, the Feature Fusion Graph Consecutive-Attention Network (FFGCAN) was proposed.  
It was compared with other models that were applied for three tennis motion capture datasets, THETIS, Tennis-Mocap, and 3 DTennisDS (Table 12). The SOTA results confirm that the proposed model was exceptionally effective in recognizing tennis strokes, regardless of the type of input data, gathered by markerless systems or passive optical ones. The FFGCAN was characterized by much higher performance, outperforming DL models such as LSTM, but also ST-GCNs. The obtained accuracy results are higher than in the case of input data fuzzification for classifiers based on graph neural networks. It is worth stressing that the proposed solution outperformed graph models with attention modules applied.
## 2 Theory and Methods
### Relationship between Floods and Multipaths  
The multipath effect is induced by a reflection signal, refraction signal, and scatter signal. In addition, the amplitude of a multipath is affected by the shape, size, and reflection coefficient of the multipath-inducing surface. The primary factor that affects the amplitude of the reflection or diffraction signal is the reflection coefficient of the multipath-inducing surface. Detailed treatment of reflection coefficients will not be provided here, but it must be noted that reflection coefficients are a function of the angle of incidence of the signal and typically approach unity for near parallel incidence (e.g., very small elevation angles in the case of ground reflection).  
The reflectivity and refractivity of different objects vary greatly. Table 1 summarizes the reflection coefficients and attenuation factors for common surfaces at normal incidences on a GPS L1 frequency [4]. It can be seen that the reflection coefficient of dry soil is only 0.268, which is much lower than that of fresh water. The reflection coefficient of water is 0.800, which is nearly three times that of dry soil. Therefore, for a static GPS observation station, the multipath error without surrounding by water is much lower than that with water. Thus, on the basis of the above analysis, it can be concluded that the multipath error of a static station will increase when a flood occurs.
# Classical Optimization of Cellulase and Xylanase Production by a Marine _Streptomyces_ Species
## 4 Discussion
### Time Course and Kinetics for Production of Cellulase and Xylanase  
The decline in cellulase production observed in this study at 72 and 84 h of incubation could be attributed to nutrient depletion, proteolysis, or both [55], while the further peak observed at 96 h could be due to some quantity of cellulase released from aging microbial cells undergoing autolysis [51]. Different optimal incubation times for cellulase and xylanase production by other bacterial strains have been documented [31; 41; 56]. Literature reports from previous studies reveal that the incubation time for optimal cellulase production by other _Streptomyces_ species were in the range of 72-196 h [28; 47; 56; 57]. Nevertheless, the findings of this study agree with that of other authors who reported 48 h as optimum for xylanase production [58; 59; 60]. However, most other studies recorded longer optimum incubation time for both cellulase and xylanase production using different substrates [27; 36; 45; 61].
## 3 System Design and Implementation
### Software Design
#### 3.3.1 UDP Communication  
The Modbus UDP transport layer operates on UDP. As shown in Figure 10, the UDP communication workflow begins with both the client and server creating sockets via the socket() system call. The server then binds to a specific port using bind() to enable persistent listening and initialize network parameters such as the local IP address and port. The client is dynamically assigned a temporary port by the system. After generating data at the application layer, the client sends packets using sendto(), which triggers the transport layer to append a UDP header, the network layer to encapsulate the destination IP, and the data link layer to construct Ethernet frames (including MAC addresses) before transmission via the physical layer. Upon receiving data, the server decapsulates each layer sequentially, validates the data, and uses the source port in the UDP header to identify the sender and the destination port to route packets correctly. The server then calls recvfrom() to receive the validated data. Finally, both parties close the sockets via close() to release allocated ports and kernel resources.