esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiment Results
### Results of HCNN as the Initial Solution
In the later iterations, the profit gap among three algorithms gradually decreases, indicating the eminent optimization ability of GRILS in the AEOSSP. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & & **50** & & **100** & & & **150** & & & **200** & \\ \cline{2-13}  & **P** & **std.** & **T(s)** & **P** & **std.** & **T(s)** & **P** & **std.** & **T(s)** & **P** & **std.** & **T(s)** \\ \hline WF & 128 & 0.00 & 0.2 & 162 & 0.00 & 0.5 & 178 & 0.00 & 0.8 & 163 & 0.00 & 1.2 \\ PF & 153 & 0.00 & 0.3 & 178 & 0.00 & 0.7 & 187 & 0.00 & 1.1 & 208 & 0.00 & 1.5 \\ HCNN & 166 & 0.00 & 0.8 & 202 & 0.00 & 1.7 & 228 & 0.00 & 2.4 & 247 & 0.00 & 3.2 \\ SA-W & 159.8 & 7.86 & 1819.11 & 188.6 & 12.18 & 3615.16 & 205 & 7.82 & 5423.85 & 188.3 & 12.42 & 5977.93 \\ SA-P & 163.6 & 5.12 & 1644.76 & 197.6 & 5.59 & 4413.37 & 214.1 & 8.23 & 5580.30 & 220.8 & 7.70 & 5530.17 \\ SA- & 178.4 & 2.20 & 1215.25 & 205.5 & 3.69 & 2665.23 & 237.7 & 6.48 & 3687.63 & 251.7 & 3.16 & 4646.30 \\ TCS-W & 175.9 & 4.18 & 2385.94 & 221.8 & 6.01 & 6065.45 & 236.1 & 7.41 & 6171.87 & 224.8 & 7.57 & 6264.22 \\ TS-P & 174.3 & 5.48 & 1940.55 & 215.1 & 5.45 & 6054.29 & 231.1 & 8.17 & 6206.29 & 240.9 & 6.74 & 6134.78 \\ TS- & 185.5 & 2.20 & 1711.24 & 225.6 & 1.81 & 6061.48 & 259.3 & 2.97 & 6162.82 & 262.4 & 3.80 & 6341.28 \\ HCNN & 195 & 3.77 & 563.68 & 235.1 & 5.03 & 2327.79 & 250.6 & 6.44 & 3109.30 & 256.9 & 5.56 & 4926.20 \\ G-P & 197.6 & 3.88 & 528.63 & 234.3 & 5.18 & 1899.61 & 251.9 & 7.03 & 2784.32 & 257.5 & 5.87 & 4346.50 \\ G- & 199.6 & 1.85 & 512.67 & 237.5 & 4.65 & 1583.61 & 253.7 & 3.11 & 2574.07 & 262.3 & 4.49 & 3946.41 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: The average total task profit results of the comparison algorithms under different task scales. Figure 11: The average profit of total tasks of the comparison algorithms under different task scales. In summary, the trained HCNN model as an initial solution of the iterative meta-heuristic algorithm is not only able to accomplish better profits than competitors but also costs a reduced number of iterations compared to others.
## 3 Low-Complexity Optimization of the Algorithm  
As the observation interval \(N\) increases, the performance advantages of the traditional MSD algorithm become more pronounced [18]. However, its complexity increases significantly, which hinders practical engineering implementation. This paper proposes a method to reduce complexity to address the issue of high computational load and complexity in traditional MSD algorithms. The proposed algorithm optimizes the traditional algorithm from two aspects: reducing matching branches and multiplexing filter.
# Federated Learning-Based Security Attack Detection for Multi-Controller Software-Defined Networks
## 3 The Proposed Methodology
### Model Training and Attack Classification
#### 3.4.2 Model Retraining and Attack Classification  
Each network-centric node starts to update its local model to the cloud server for aggregated global model generation. Consequently, the global server generates a global model by aggregating the n number of local models using the FedAvg algorithm. The FedSec exploits the following Equation (9) to aggregate the local models at the global server and generate the global model.  
\[\text{GM}=\sum_{i=1}^{n}\sum_{j=1}^{n}W_{i}*\text{LM}_{j} \tag{9}\]  
In Equation (9), the terms GM and LM represent the global model generated at the global server and the local model updated by a network-centric node. The term W is the weighting factor of a local model. The terms I and j are varied from 1 to n. Here, n denotes the total number of LMs the global server receives. The proposed model combines the local models of different edges using this equation. After generating the GM, the global server reversely updates it via the control plane to the network-centric nodes. After local model construction and update, the network-centric nodes retrain their learning models with globally shared parameters. By employing the globally shared knowledge,the proposed FedSec customizes the initial local learning parameters of GCNN-GRU and improves the attack detection efficiency. The global model consists of various novel attack patterns of various SDN domains. Thus, it effectively enhances the learning knowledge of GCNN-GRU without impacting the MC-SDN network performance. Moreover, the FedSec enhances the local learning accuracy of the hybrid learning model by incorporating the distributed federated learning strategy. Finally, the FedSec initiates the attack detection and classification process. During the attack detection phase, the network-centric nodes can determine various attacks and classify them under different classes like DoS, Probe, U2R, R2L, time jack, and peer flooding according to the local learning knowledge of GCNN-GRU during the testing phase. The following Algorithm 2 explains the model retraining and attack classification of FedSec.  
```
1:input: Local models of different network-centric nodes
2:Output: GM generation and attack classification
3:for each network-centric node do
4: Feed the output of GCNN-GRU as an LM input to the global server
5:Global server do:
6: Aggregate the LMs of high-context edges using Equation (9)
7: Generate the GM with global attack patterns
8: Feed the GMs to the network-centric node
9: Retrain the GCNN-GRU using the GM update
10: Improve the learning accuracy with wider attack knowledge
11: Initiate the attack detection and classification
12: Compare the testing data with learned data
13: Categorize the attacks under different categories
14:endfor
```  
**Algorithm 2** GM Generation and Attack Classification
## 3 Proposed Method
### Focal Loss as Objectiveness Score  
YOLOv3 computes four-loss functions namely: object confidence loss, classification loss, bounding box centroid, and width-height loss. Bounding box width-height loss is computed using a mean of square errors between predictions and labels. Other loss functions are computed using Binary Cross-Entropy (BCE) loss. In the proposed network, the loss function for confidence score loss is computed using the focal loss function [10]. Focal loss is considered an effective loss function for the detection of small-sized objects or when there is an imbalance of different class samples. Traffic signs appear smaller than the other objects in an image size of \(1360\times 800\). Hence, there exists a great deal of class imbalance among the samples of traffic signs and background objects. Therefore, we conclude that it is appropriate to use the Focal loss function for traffic signs.  
Focal loss function in Equation (5) is a modified version of binary cross-entropy loss as shown in Equation (4):  
\[BCE\ loss=-\log(p_{t}) \tag{4}\]  
\[FL=\alpha_{t}(1-p_{t})^{\gamma}\times(-\log(p_{t})) \tag{5}\]  
where \(\alpha_{t}\) is hyperparameter and \(\gamma\) is focusing or modulating parameter, the optimal values of which are required to be determined empirically. [PERSON] et al. [10] suggested optimal values for both parameters based on COCO dataset [26]. This dataset contains regular-sized objects with reference to the image, while traffic signs appear much smaller than those objects, the optimal values of 0.25 and 2 for alpha and gamma, respectively, fail. We adopted the experimentation process of [10] and found the optimal value of hyper-parameter alpha for the traffic signs by keeping gamma constant. Then by using each obtained value of alpha, gamma was varied, yielding an optimal gamma value. Hence, experiments prove that for traffic signs, these parameters have an optimal value of 0.75 and 1 for alpha and gamma, respectively, as shown in Figure 7.  
Figure 7: Effect of varying alpha and gamma on mAP (**a**) when gamma is set to 1 and alpha is varied (**b**) when gamma is varied keeping optimal value of alpha equal 0.75 constant.
# Advancing Road Infrastructure Safety with the Remotely Piloted Safety Cone
## 3 Results
### Use Case  
A case study that evaluates the performance and accuracy of the RPSC in three critical areas: positioning, navigation, and obstacle detection is presented. The tests were conducted at the As Lagoas Campus of the University of Vigo, located in Galicia, northwestern Spain--a controlled environment. Each test was designed to analyze specific aspects of the RPSC's behaviour, providing key insights to enhance its operational capabilities.  
Rosbag was used to collect data for the three case studies. It enabled recording data published on system topics, including telemetry, navigation, and vehicle status.
# SACRB-MAC: A High-Capacity MAC Protocol for Cognitive Radio Sensor Networks in Smart Grid
###### Abstract  
The Cognitive Radio Sensor Network (CRSN) is considered as a viable solution to enhance various aspects of the electric power grid and to realize a smart grid. However, several challenges for CRSNs are generated due to the harsh wireless environment in a smart grid. As a result, throughput and reliability become critical issues. On the other hand, the spectrum aggregation technique is expected to play an important role in CRSNs in a smart grid. By using spectrum aggregation, the throughput of CRSNs can be improved efficiently, so as to address the unique challenges of CRSNs in a smart grid. In this regard, we proposed Spectrum Aggregation Cognitive Receiver-Based MAC (SACRB-MAC), which employs the spectrum aggregation technique to improve the throughput performance of CRSNs in a smart grid. Moreover, SACRB-MAC is a receiver-based MAC protocol, which can provide a good reliability performance. Analytical and simulation results demonstrate that SACRB-MAC is a promising solution for CRSNs in a smart grid.  
s 201616464 doi:10.3390/s1604046410.3390/s16040464
# Physics-Informed Neural Networks for Cantilever Dynamics and Fluid-Induced Excitation
## 3 Experiments
### Flutter of a Slender Cantliever in Axial Flow
#### 3.2.2 Configuration  
The fluid-induced flutter of a cantilever arises above a certain critical velocity as the fluid dynamic pressure excites an oscillatory mode [43]. The characteristic timescale is then redefined by considering the balance of the fluid dynamic force and inertia as follows. The fluid force per unit length can be expressed as \(\frac{1}{2}\rho_{f}\mathcal{U}^{2}l^{2}\frac{\omega_{\text{Ref}}}{L}\), where \(\rho_{f}\) is the fluid density, \(U\) is the incoming fluid velocity, \(l\) is the width of the cantilever, and \(\frac{\omega_{\text{Ref}}}{L}\), is the scale of a tilted angle. When the inertia force \(\mu\frac{\omega_{\text{Ref}}}{\tau^{2}}\) is equated with the fluid force \(\frac{1}{2}\rho_{f}\mathcal{U}^{2}l^{2}\frac{\omega_{\text{Ref}}}{L}\), the characteristic timescale for fluid-induced flutter is obtained as \(\tau\sim(\frac{2\mu_{\text{I}}}{\rho_{f}\mathcal{U}^{2}})^{1/2}\). The losses in Equation (6) are then reconfigured with the modified timescale, which leads to scale variations in \(\widetilde{\mathcal{L}}_{IC,2}\) and \(\widetilde{\mathcal{L}}_{PDE}\). Furthermore, to improve the convergence, we employed the elaboration of relative tolerances in \(\varepsilon_{IC,1}=10^{-4}\), in addition to \(\varepsilon_{BC,1}=10^{-4}\). The rest of the relative tolerances were determined as \(\varepsilon_{IC}=\varepsilon_{BC}=10^{-3}\) and \(\varepsilon_{PDE}=10^{-2}\).
# Frequency Response Extension Method of MET Vector Hydrophone Based on Dynamic Feedback Network
## 2 Principles and Background
### Structure of Force Balance Feedback METH  
As shown in Figure 3, the structure of the force balance feedback METH includes a magnetic sound window composed of magnetic rubber and a coil. Subfigure (a) shows the structural details of the magnetic sound window, where the magnetic rubber is embedded with permanent magnetic material, which can drive the magnetic sound window to generate feedback force through electromagnetic induction when the coil is energized. The specific three-lobed design of the magnetic rubber maintains flexibility while evenly distributing mechanical stress, thereby enhancing the overall mechanical strength of the rubber membrane and enabling it to withstand high hydrostatic pressures in deep-sea environments. Although this relatively complex structure affects the intrinsic frequency characteristics, experimental results indicate that its impact on the overall frequency response of the METH is minimal and has been calibrated during the system calibration process. Subfigure (b) is a physical picture of the system after installation.  
The feedback circuit can use analog or digital technology. In this study, a digital signal processor (DSP) is used to facilitate real-time updating of feedback parameters. The electrochemical reaction chamber converts the vibration signal into an electrical signal and transmits it to the DSP for processing. After the feedback system, the DSP drives the feedback coil to apply feedback force on the magnetic sound window, forming force balance feedback. The frequency response of the force balance feedback closed-loop system is jointly determined by the inherent frequency response of the electrochemical transducer and the frequency response of the feedback system. By adjusting the feedback parameters, the frequency response characteristics of the closed-loop system can be flexibly changed. The adjustment of feedback parameters is systematically calculated based on real-time system identification and model-based optimization, as detailed in Sections 3.1 and 3.2.
# Analysis of the Effect of Pulse Length and Magnetic Field Strength on Nonlinear Thomson Scattering
## 3 Results
### Electron Motion and Corresponding Radiation Properties 3
And the reason why the electrons only show double peaks when the pulse length L is large is because when the pulse length is large, the gradient between the rising and falling edges is smaller, and the acceleration and deceleration are less pronounced. As a result, the radiated power changes more slowly, which is manifested in the trajectory of motion, which is more pronounced.
## 3 Results and Discussion
### Physicochemical Properties of JSP  
#### 3.3.1 Macromolecular Characteristics of JSP  
As illustrated in Table 3, JSP had a mass-average molecular mass (M\({}_{\rm w}\)) of \(8.0\times 10^{5}\) g/mol and a number-average molecular mass (M\({}_{\rm n}\)) of \(1\times 10^{5}\) g/mol.  
The SEC chromatograms of JSP showed various peaks (DRI and LS), suggesting the presence of various molecular masses and a significant degree of dispersion (data not shown). The PDI value showed JSP heterogeneity and could be due to the presence of floridean starch (\(\alpha\)-Glc) contamination and other larger molecular weight xylogalactans [49; 50]. This last statement is consistent with the literature concerning sulfated galactans from _Botryocladia occidentalis_[51] and _Gracilaria cornea_[52] red seaweeds. High M\({}_{\rm w}\) have been described for other sulfated polysaccharides isolated from red seaweeds having values larger than 1\(\times\) 10\({}^{5}\) g/mol [1; 53; 54]. The M\({}_{\rm w}\) value of JSP remained lower than those reported for sulfated xylogalactans extracted from calcareous red algae of the family Corallinaceae, such as _C. officinalis, B. orbigniana_ and _J. rubens_[3; 14]. The presence of short chains in the JSP structure could also decrease the M\({}_{\rm n}\) value and consequently increase the polydispersity [55; 56]. Note that further purification steps could be done in the future to eliminate the Glc-storage contamination and enhance the structural characterization of this xylogalactan.  
Polymer intrinsic viscosity ([\(\eta\)] mL/g) indicates the capacity of polymers to enhance the viscosity of fluids, dependent on their physicochemical properties, such as structural conformation, type and degree of branching units, M\({}_{\rm w}\), and solvent property [33; 49]. The [\(\eta\)] of 76 mL/g and the hydrodynamic radius R\({}_{\rm h}\) of 16.8 nm obtained for JSP suggest a flexible random coil conformation with a value of Mark-Houwink-Sakurada exponent \(\alpha\) ranging between 0.5 and 0.8. [33; 49; 56]. Finally, the theoretical critical concentration C* of JSP, which can be obtained by ks/[\(\eta\)] (with ks = 0,5 to 1 for coil polysaccharides in water) could range from 6.5 to 13.1 g/L.
## 2 Related Works
[9] presented an algorithm to avoid the \"energy hole\" problem with adjustable transmit power based on the distribution of network energy consumption, the lifetime of regional nodes, and the real-time data. Thus in order to prolong the network lifetime, the algorithm sends the corresponding proportion of data to the area with low energy consumption in a short communication radius. The energy hole can be alleviated by changing the transmission power to some extent, but it is difficult to determine the value of the adjustable transmission power, so it is difficult to apply it to the actual sensor networks.
## 1 Introduction
The aim of this study, therefore, is to illustrate the difference between the WMO climate normal and climatology for the same normal period using satellite-based climate data records (CDRs). Here, we focus on two key climate variables: cloud properties and incoming solar radiation at the surface. Cloud processes and feedbacks remain the largest source of uncertainty in climate models [29; 30; 31; 32; 33; 34; 35; 36; 37; 38], while the information on incoming solar radiation is key to tackling future energy demands while transitioning to renewable energy [39; 40; 41; 42; 43; 44] and for agricultural applications [45].
## 2 Materials and Methods
### Biomechanics Estimation  
For video analysis, YOLOv8 is used, which is a high-speed, high-precision DL-based model used in computer vision [8; 34]. It supports tasks such as detection, segmentation, pose estimation, tracking, and classification. These can be trained using custom databases to recognize specific objects, such as people, and to track key points. These are points of interest in an object that needs to be detected and, in the case of video, tracked. To develop the system, a set of 100 images extracted from videos of volunteers walking in the test area is used. The images are then loaded into CVAT ([[https://www.cvat.ai](https://www.cvat.ai)]([https://www.cvat.ai](https://www.cvat.ai)), accessed on 11 December 2023), which is an online tool to label images used in AI training [35]. There is a toolbox that allows the selection of key points, which, in this case, are placed to indicate the different segments of the left and right lower extremities. An example of this process is shown in Figure 3, where all the key points are visible. It is important to note that it was later decided not to use the reflective markers shown in the figure, as they were limited to monitoring only one limb. In addition to the high risk of not being able to detect all the markers, it requires very robust pre-processing and proper light conditioning to accurately segment the markers in each frame.  
Once all the images have been labeled, CVAT generates a database containing information about the boxes and points placed, such as their position and dimension. These points are then exported to Python, where they are used to train a DL model. For the development of the algorithm, the use of the open-source library Keras in Python was chosen, which is designed for the training and implementation of DL models [36; 37]. These models can be used both for the development of neural networks for the analysis of statistical databases and for the training of models for the analysis of images, which perform tasks of detection of objects, people, faces, and body orientation, to name a few. Therefore, using Keras and the database obtained from CVAT, the training of personalized models is performed, where the information and images are divided for training and validation. The resulting model is then used for the analysis of gait test videos, where each frame is taken to perform the identification of each lower limb section via key points. This results in an estimate of the subject's pose within the frame, as shown in Figure 4, which, when monitored over time, generates signals representing the biomechanics of walking.
###### Abstract  
The traditional signal of opportunity (SOP) positioning system is equipped with dedicated receivers for each type of signal to ensure continuous signal perception. However, it causes a low equipment resources utilization and energy waste. With increasing SOP types, problems become more serious. This paper proposes a new signal perception unit for SOP positioning systems. By extracting the perception function from the positioning system and operating independently, the system can flexibly schedule resources and reduce waste based on the perception results. Through time-frequency joint representation, time-frequency image can be obtained which provides more information for signal recognition, and is difficult for traditional single time/frequency-domain analysis. We also designed a convolutional neural network (CNN) for signal recognition and a negative learning method to correct the overfitting to noisy data. Finally, a prototype system was built using USRP and LabVIEW for a 2.4 GHz frequency band test. The results show that the system can effectively identify Wi-Fi, Bluetooth, and ZigBee signals at the same time, and verified the effectiveness of the proposed signal perception architecture. It can be further promoted to realize SOP perception in almost full frequency domain, and improve the integration and resource utilization efficiency of the SOP positioning system.  
title: 2021 2701. [[https://doi.org/10.3390/s21237871](https://doi.org/10.3390/s21237871)]([https://doi.org/10.3390/s21237871](https://doi.org/10.3390/s21237871))  
17 October 2021  
13 November 2021  
**Publisher's Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.**  
**Copyright:** 2021 by the authors. License MDP, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
###### Abstract  
Distal radius fractures (DRFs) constitute one of the most prevalent injuries in adults. This study compares the clinical and radiological outcomes of intra- and extra-articular DRFs treated with percutaneous Kirschner wires (PKW) or solar locking plates (VLP). **Materials and Methods:** A retrospective analysis was conducted on 42 patients (aged 18 to 85) treated between 2017 and 2019 with a minimum follow-up of five years. Outcomes were assessed using radiographic parameters and validated clinical scoring systems such as Disabilities of the Arm, Shoulder, and Hand (DASH), Patient-Rated Wrist Evaluation (PRWE), and Mayo Wrist Score (MAYO). **Results:** Clinical outcomes were better in the VLP group but not statistically significant (\(p>0.05\)). For extra-articular fractures, DASH were 1.5 (VLP) vs. 6.4 (PKW) (\(p=0.5007\)), PRWE were 1.3 (VLP) vs. 2.9 (PKW) (\(p=0.4049\)), and MAYO were 95 (VLP) vs. 86.1 (PKW) (\(p=0.2406\)). For intra-articular fractures, DASH were 6.6 (VLP) vs. 19.7 (PKW) (\(p=0.0981\)), PRWE 12.9 (VLP) vs. 21.1 (PKW) (\(p=0.3661\)), and MAYO 78.9 (VLP) vs. 72.2 (PKW) (\(p=0.4503\)). **Conclusions:** PKW and VLP showed satisfactory long-term outcomes. VLP fixation allowed better short-term recovery and anatomical restoration, but long-term functional outcomes were comparable.  
Keywords:distal radius fracture; percutaneous Kirschner wires; polar locking plates; DASH; PRWE; MAYO; fracture treatment; osteosynthesis +
Footnote †: journal: Applied sciences