esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Cuckoo Search Algorithm Adapted for Cognitive Radio ad Hoc Networks  
The CS scheme was adapted for CRAHNs to address the routing problem by utilizing the PDR and packet loss metrics. There is a fixed number of local area networks (LANs). A new solution node is established in every LAN, which serves as a gateway to other LANs. A LAN with a gateway node is considered the best, and it provides an extended lifespan of the network. The cluster head, the network gateway which is the cuckoo egg, minimizes routing overhead within a cluster and effectively communicates with other gateway nodes across different networks. Having investigated various routing metrics and protocols in CRAHNs, their challenges, efficiency, and point of interest, the study proposes a routing scheme known as the optimized CSAODV (OCS-AODV).  
The algorithm's objective is to combine the advantages of OCS and AODV to address the routing problem and evaluate the route stability of each link. To reduce the routing overheads within the network, the scheme considers various paths and calculates their fitness-function value to ensure that a selected path meets the desired conditions. Appropriate decisions are made before data transmission. The proposed OCS-AODV routing scheme is based on a WLAN whereby each node within the LAN is responsible for establishing information regarding its neighborhood. Hence the communication between the source and destinations is managed through gateway nodes of each LAN in communicating with other LANs. Figure 2 presents the network design for the proposed scheme which shows how the networks are connected through gateway nodes. The communication between different LANs is facilitated through the gateways nodes, which links different LANs. The gateway nodes are, ideally, the cuckoo nodes.  
The environment consist of two LANs, where each LAN has a network gateway node that helps other nodes within the LAN to communicate with nodes in another LAN through the two gateways. When node \(1\) in LAN \(1\) wants to communicate with node \(2\) in LAN \(2\), node \(1\) sends a route request and floods the network, which ultimately is received by the network gateway of LAN \(1\). Then, the gateway node in LAN \(1\) communicates with the gateway node in LAN \(2\), which floods its network with the route-request packet. When the destination node receives the route request, it first calculates the fitness functions of the routes and then selects an optimal path to send back the route-reply packet, enabling the source node to start transmission.
# Design of Exergaming Platform for Upper Limb Rehabilitation Using Surface Electromyography
## 2 Materials and Methods
### Exercise Protocol  
The defined hand exercises were considered to improve hand motor control and function, without increasing muscle tone or fatigue. Knowing that spasticity, which is most commonly noted in the flexor muscles of the upper extremities, is velocity-dependent, the muscle contractions should have duration, but they should not be phasic, and thus, the movements, especially the stretches, should not be rapid [36].  
Therefore, the exercises included in the exergame were selected with the purpose of opposing the pattern of spasticity, such as wrist extension and finger abduction exercises, improving hand muscle dexterity and functionality by including gripping actions like pinching and enhancing the muscle power and functionality of the whole hand with exercises such as wrist and finger flexion exercises.  
Research studying the effectiveness of electronic games in upper extremity rehabilitation suggests the use of different and varied protocols ranging from 15 to 18 h, spread over 3 to 6 weeks [37; 38; 39]. In line with these aforementioned studies, the exercise protocol for the present study was designed so that the exercises with the exergame lasted 4 weeks, 5 days per week, 1 h per day, totaling 16 h.  
According to personalized aims for each patient and depending on the motor function and dexterity of each patient, the duration of the contractions and relaxations could be changed. The purpose is that the contractions should last for at least 10 s so the muscles have time to be activated and react, and they should not be sudden so as not to increase spasticity, but they need to be long enough to improve muscle power and motor control [40]. Repetitions are necessary for motor rehabilitation, which operates through learning- and use-dependent mechanisms, according to European Stroke Organization guidelines [37]. The rest period following the contractions was set to protect the muscles from fatigue, and it was decided that its duration be at least the same as the contraction duration [41].
## 2 Preliminaries
To predict UOWC performance in such time-varying environments, we need to (i) estimate the downwelling sunlight; (ii) identify the vertical variation in attenuation parameters arising from ocean stratification; (iii) model the interactions between (i) and (ii) with depth; and (iv) apply UOWC propagative models in this environment. In this article, we propose and analyze the outcomes of an extended model for underwater ambient light distribution that performs tasks (i)-(iii), which in turn will build the stage for applying the propagative models for task (iv).
## 1 Introduction
Freezing and thawing cycles of permafrost roadbeds in cold regions also affect the stability of roadbeds [24; 25; 26; 27; 28]. [PERSON] et al. [29] selected seasonal permafrost in Northwest China as the research object and investigated the strength, stiffness, viscous properties, and damage evolution of specimens under freeze-thaw cycle conditions based on low-temperature triaxial test. [PERSON] et al. [30] conducted experiments on permafrost subjected to varying numbers of freeze-thaw cycles, along with impact loading tests performed at different strain rates, with the objective of investigating the mechanical behavior of permafrost under impact forces subsequent to freeze-thaw cycling. The serviceability of roadbed soil is significantly influenced by its mechanical properties, with the dynamic stability of the soil primarily governed by the CDS it can withstand [31]. [PERSON] et al. [32] proposed a critical stress ratio formula considering the effects of salt content and perimeter pressure based on triaxial tests on frozen salt-flour clay and introduced the critical stress ratio and boundary surface shape parameters to characterize the pressure melting and crushing properties of frozen soils under high perimeter pressure. [PERSON] et al. [33] conducted comprehensive triaxial dynamic experiments on frozen soil, meticulously examining the cumulative and elastic deformation characteristics exhibited under diverse cyclic loading conditions. Their analysis delved into the intricate influences of consolidation pressure, dynamic stress amplitude, dynamic loading waveform, initial stress path, and initial stress ratio on the cumulative and elastic deformation of the frozen soil specimens. While extensive domestic and international research has been undertaken to investigate the CDS and cumulative permanent deformation of non-permafrost materials [34; 35], the corresponding studies on permafrost remain relatively scarce. Notably, most of the existing permafrost-focused studies are confined to numerical simulation approaches [36; 37; 38], so there is a pressing need to embark on research endeavors specifically targeting the CDS of permafrost. In essence, the scientific community has undertaken a substantial body of research on the deformation behavior and mechanical properties of permafrost under mechanical testing. However, the majority of these studies have primarily centered their attention on the dynamic shear modulus and damping ratio of permafrost subjected to dynamic loading conditions. Comparatively, there is a scarcity of research examining the CDS and CPS of permafrost under dynamic loading in diverse environmental contexts. Drawing upon the frozen clay specimens sourced from the Qinghai-Tibet Railway's frozen soil subgrade, this paper investigates the influence of varying temperatures and water contents on the CPS and CDS of frozen soil under prolonged cyclic loading conditions, utilizing a low-temperature dynamic triaxial test. The findings of this study hold significant implications and could contribute valuable insights to the construction and experimental endeavors of subsequent projects in cold region environments.
## 2 Materials and Methods
### Acceleration Device and Placements  
To measure the LVP inputs and its transmission outputs, six wireless inertial measurement units (WIMU, Realtrack Systems, Almeria, Spain) with a 16 Hz processing capability were used. The WIMU consists of a triaxial accelerometer with a limit acceleration range of 100 g, a sensitivity of 0.000488 g, and an acquisition system recording at a sampling frequency of 1000 Hz. For LVP inputs, a WIMU was well-fixed to the centre of the surface by an adjustable fixing strap between the marks of the feet [35]. To calculate the human body outputs, a total of five WIMUs were attached to five body locations, the ankle (AN, medial malleolus), knee (KN, tibial tuberosity), hip (HI, great trochanter), lumbar (L3, third lumbar vertebra), and head (HE, forehead) using double-sided tape and elastic bands to minimise skin translation [21; 22; 24; 25].
# High-Precision Pointing and Tracking System Design for Near-Space Balloon-Based Optical Observation
## 3 Results
### Flight Result Analysis  
The azimuth curve of the primary platform, the offset angle relative to the sun of the sun sensor output, and the height change curve recorded by the spherical observation platform in this observation experiment are shown in Figure 12. The spherical coronagraph observation system conducted a total of five observation periods, as shown in the time periods 1 to 5, with different color rectangles.  
Figure 11: The landing status of the platform after the flight.  
Figure 12: Coronagraph observation platform data during this flight.  
Based on the observation of period 1, the azimuth angle of the primary platform and the deviation angle of the solar sensor during that period were analyzed, as shown in Figure 13. It can be seen that during this observation period, the orientation of the primary platform can reach or even exceed \(\pm 1^{\circ}\), reaching \(-0.8\)-\(0.6^{\circ}\), as shown in the purple box.  
The result of period 2 is shown in Figure 14. In this observation period, the primary platform pointing system can realize \(\pm 1^{\circ}\) pointing accuracy most of the time, while the whole accuracy is \(-1.1\) to \(0.9^{\circ}\), as shown in the purple box.  
The result of period 3 is shown in Figure 15. In this observation period, the primary platform pointing system can realize \(\pm 1^{\circ}\) pointing accuracy most of the time, while the whole accuracy is \(-1.8^{\circ}\) to \(2.4^{\circ}\), as shown in the purple box.  
Figure 14: Azimuth and sun offset-angle curve of period 2.  
Figure 13: Azimuth and sun offset-angle curve of period 1.  
The result of period 4 is shown in Figure 16. In this observation period, the primary platform pointing system can realize \(\pm 1^{\circ}\) pointing accuracy most of the time, while the whole accuracy is \(-2^{\circ}\) to \(2.5^{\circ}\), as shown in the purple box.  
The result of period 5 is shown in Figure 17. In this observation period, the primary platform pointing system can realize \(\pm 1^{\circ}\) pointing accuracy most of the time, while the whole accuracy is \(-3.5^{\circ}\) to \(2.5^{\circ}\) as shown in the purple box.  
The secondary precise tracking system also worked well during the experiment, and it ensured the coronagraph obtained more than 20,000 images. Another group in our team will analyze the obtained observation data as soon as possible and share it with our counterparts in the future.  
Figure 16: Azimuth and sun offset-angle curve of period 4.  
Figure 15: Azimuth and sun offset-angle curve of period 3.
## 2 Materials and Methods
### Data Processing  
The data are recorded and analyzed with Matlab. The datasets are synchronized by aligning the Arduino timing with the compact DAQ and the EMG shorts. Erroneous data caused by turning, pedaling backwards, no pedaling, or pedaling unsteadily are removed based on the encoder data. The following requirements are made to save the data: a minimum of five consecutive pedal cycles need to be counted, the deviation between the encoder peaks within the pedal cycle (deviation in cadence) is less than 5% and the cycling speed is 20 km/h with a maximal deviation of 2 km/h. No individual curves are analyzed. All cycles are averaged to an ensemble average curve (AVC), related to the pedal cycle, for which the original time scale is converted to a percent of a pedal cycle ranging from 0 to 100%.
## 1 Introduction
[25] simplified the dynamical model by neglecting the inverse calculation method of monorail road excitation and verified the model by comparing the experimental data with the simulation results. They found that the simulation results of acceleration under the effect of inverse calculation of the road unevenness were consistent with the experimental data, which confirms the validity of the model only at low frequencies. [PERSON] et al. [26] proposed a more accurate energy characterization method for the energy-based durability prediction of suspension coil springs under random loading conditions and found that the Morrow-based model provided the highest accuracy in fatigue life prediction. [PERSON] et al. [27] developed a time-domain coupled system by modeling a two-degree-of-freedom quarter-vehicle using [PERSON]'s method, which was solved using the Newmark-\(\beta\) direct numerical integration method based on the linear mean acceleration method by evaluating the response of the coupled system, effects of the vehicle speed, roughness of the road surface, stiffness of the soil, and effect of parameters such as suspension damping on the response. [PERSON] et al. [28] established a longitudinal-vertical coupled dynamics model for DCT vehicles considering powertrain mounts and compared the simulation results of the longitudinal-vertical coupled dynamic DCT vehicle model, considering road excitation, with the vehicle test results. They found that the model could accurately reflect the effect of road excitation on the dynamic performance of the DCT vehicle under the conditions of starting and gear shifting. For the lightweight and multi-objective optimal design of the fifth wheel, most scholars consider the mass, frequency, and material as the optimization objectives. Owing to the existence of a certain link between mass and fatigue performance, the smaller the mass, the lower the fatigue life, and it is not possible to ensure that the fatigue life meets the lightweight requirements simultaneously. In practical applications, the weight of the fifth wheel and its fatigue performance should be taken into account; therefore, this study establishes an optimization model with weight and fatigue life as the objectives so that the two can achieve synergistic optimization and validation.
# Learning Swimming Techniques by Means of Real-Time Monitoring with Embedded Devices
## 2 Materials and Methods
### Software Analysis
#### 2.2.5 Comparison Between DTW and PCC  
In the dryland environment proposed in this study, several considerations must be taken into account. The system is not waterproof; instead, it functions as a dryland simulator trainer. This implies that movements are generally faster and may exhibit larger amplitudes due to the absence of water resistance (drag). Additionally, movements in this environment tend to be more deliberate and repeatable, as they are not affected by external factors such as water, waves, and buoyancy. Consequently, execution variability is reduced in comparison to swimming in water.  
Dynamic Time Warping strengths refer to the following:  
* DTW effectively handles timing variability, allowing subjects to perform the same movement at different speeds while maintaining accurate sequence matching. This is particularly useful for swimmers whose execution times may fluctuate;
* The algorithm can analyze execution differences across a full stroke cycle rather than relying on momentary similarities;
* It can identify subtle inconsistencies in stroke mechanics more effectively than simpler comparison methods. Dynamic Time Warping is weak when the following occurs:
* In dryland training, where movements are often more consistent, minor timing differences could result in unnecessary penalization. However, in a water environment, this sensitivity would be advantageous;  
Figure 6: Correlation Coefficient example.  
DTW requires complex computations using a cost matrix proportional to input data. As the frequency of readings increases to improve accuracy, the cost matrix size expands, leading to greater computational demands. This necessitates close monitoring to prevent slow response times, particularly for real-time feedback applications.  
Pearson Correlation Coefficient strengths refer to the following:  
* PCC allows for rapid computation, making it well-suited to real-time applications where movements in a dryland environment are more predictable;
* When used for assessing general stroke correctness rather than precise motion alignment, PCC provides quick insights into an athlete's adherence to a prescribed dryland drill.
* Pearson Correlation Coefficient is weak when the following occurs:
* If a swimmer exhibits variations in force or range of motion between trials, the algorithm may yield a high correlation despite differences in effort or technique.
* PCC does not account for speed variations due to fatigue or training adaptations, potentially misclassifying strokes performed at different speeds as incorrect.
* When incorporating additional input values, such as gyroscope data from the X, Y, and Z axes (resulting in six total axes including accelerometer and gyroscope data), PCC may oversimplify the comparison, reducing the accuracy of the assessment.
## 4 Experimental Results and Discussion  
In this section, the proposed approach is verified with real sea trial data. As mentioned, the experiments were conducted with naval ships in the East Sea of Korea for several years. In order to acquire representative data from the sea area, we collected data over various sea areas, seasons, target moving patterns, water depths, sea states (from 0 to 2), and other environmental influences. We followed and checked all of the target routes for accurate analysis. Finally, we acquired data from 1123 pings spread across all experiments. Only a single target echo from each ping was used, and other echoes were regarded as clutter. We illustrate the characteristics of the proposed algorithm in scatter diagrams of several feature vectors and quantitatively summarize the performance in classification tables of all data.
# Intelligent Monitoring of Tunnel Fire Smoke Based on Improved YOLOX and Edge Computing
## 3 Improved Lightweight Real-Time Detection Algorithm Based on YOLOX
### Experimental Analysis of Lightweight Model  
To evaluate the lightweight performance of the YOLOX algorithm, improved through the strategies mentioned earlier, we conducted experiments within the following environment:  
-  Operating System: Ubuntu;  
-  CPU: Intel(R) Xeon(R) CPU E5-2630v4 @ 2.20 GHz;  
-  Memory: 128 GB;  
-  Graphics Card: Nvidia Tesla T4.  
The experiments were conducted using Python 3.9, CUDA 11.6, and PyTorch 1.12. The experimental framework utilized a set of frameworks provided by the OpenMMLab team [44]. For optimization, we employed AdamW, which combines the Adam optimizer with weight decay, with the weight decay coefficient set to 0.05. As shown in the learning rate scheduling curve in Figure 6, the initial learning rate was set to 0.002, following the best practices from prior studies on similar deep learning architectures. A warm-up strategy was applied during the initial 100 training epochs to prevent instability at the beginning of training, ensuring smoother convergence. The warm-up period was chosen based on empirical results demonstrating that a gradual increase in learning rate reduces the risk of divergence in the early training phase.  
Subsequently, a cosine annealing learning rate method was employed for adjustment during the latter half of the training process. This choice was motivated by its effectiveness in progressively reducing the learning rate, preventing premature convergence while allowing the model to fine-tune weights more effectively in later stages. The cosine decay schedule has been widely validated in the deep learning literature for its ability to improve generalization and stabilize optimization in long training schedules. Each hyperparameter was selected based on a combination of empirical validation, prior research insights, and hyperparameter tuning experiments, ensuring that the optimization strategy maximized both training stability and final model performance.  
Lastly, in Table 1, we present the model parameters (Parameters) and floating-point calculations (Flops), with model parameters totaling 52.316 million and calculations reaching 80.121 billion. In Table 2, we provide the experimental comparison results. These results demonstrate that in comparison to the standard YOLOX model, the enhanced YOLOX  
Figure 6: Learning rate scheduling curve.  
model achieves approximately a 1.8% improvement in accuracy. Furthermore, it surpasses the YOLOv5 model in terms of effectiveness.
## 5 Experiments and Analysis of Results
### Performance of DOA-BPNN Model
Although the advantage over other metaheuristic methods is relatively modest, the consistent performance of DOA-BP suggests that it is a reliable optimization approach for improving BPNN models, particularly in distinguishing normal communication behaviors from malicious activities. High performance in this binary classification task reduces the likelihood of false-positives, ensuring that normal T2G communication remains unaffected, which is crucial for avoiding unnecessary safety measures. This capability is essential in railway communication systems, where minimizing the impact on regular operations and ensuring timely, accurate responses to threats are critical. In T2G intrusion detection scenarios, both false-positive rates (FPR) and false-negative rates (FNR) are critical--a low FPR helps avoid unnecessary alerts, while a low FNR ensures that real threats are not overlooked. As illustrated in Figure 8, DOA-BP and BP  
Figure 6: Comparison of the DOA-BPNN with BPNN in multi-class detection. show similar performance in terms of FPR, with DOA-BP slightly outperforming BP in most classes, although it occasionally shows higher FPR in specific attack classes. Notably, DOA-BP demonstrates significantly lower FNR compared to BP across key attack classes such as DDoS, DoS, injection, password, ransomware, scanning, and XSS, reflecting its stronger ability to detect genuine attacks and reduce missed detections. For normal traffic, DOA-BP significantly reduces misclassification rates, dropping from 33.88% to 14.99%, indicating that fewer legitimate activities are flagged as threats. However, both models show relatively high misclassification rates in certain classes, with attack classes like password and ransomware exhibiting high FNR, and normal traffic showing considerable misclassification rates. This suggests substantial performance variation between different attack classes, an important observation that warrants further investigation. This balance between minimizing false-positives and reducing missed detections highlights the practical value of DOA-BP in enhancing T2G intrusion detection effectiveness.