esa-sceva/satcom-chunk-collection huggingface dataset:
# YOLO-SE: Improved YOLOv8 for Remote Sensing Object Detection and Recognition
## 3 Materials and Method
### Sef  
We replaced the standard convolutions in C2f with a more lightweight and efficient multi-scale convolution module called SEF. This module introduces multiple convolutions with different kernel sizes, enabling it to capture various spatial features at multiple scales. Additionally, SEF extends the receptive field using larger convolution kernels, enhancing its ability to model long-range dependencies.  
As shown in Figure 3, the SEConv2d operation partitions the input channels into four smaller channels. The first and third smaller channels remain unchanged, while the second and fourth channels undergo 3 \(\times\) 3 and 5 \(\times\) 5 convolution operations, respectively. Subsequently, a \(1\times 1\) convolution consolidates the features from these four smaller channels. By employing half of the features for convolution and then integrating them with the original features, the objective is to generate redundant features, decrease parameters and computational workload, and alleviate the influence of high-frequency noise. This  
Figure 2: The architecture of proposed YOLOv8.  
approach aims to reduce the number of parameters and computational expenses while preserving essential feature information. Each distinct convolutional mapping learns to focus on features of varying granularities adaptively. SEF excels in capturing local details, preserving the nuances and semantic information of target objects as the network deepens.  
The structure of SEF is shown in Figure 4. In summary, the SEF module reduces the network's parameter count, accelerates detection speed, and effectively captures multi-scale features and local details of the target.
###### Abstract  
Aerial images are an outstanding option for observing terrain with their high-resolution (HR) capability. The high operational cost of aerial images makes it difficult to acquire periodic observation of the region of interest. Satellite imagery is an alternative for the problem, but low-resolution is an obstacle. In this study, we proposed a context-based approach to simulate the 10 m resolution of Sentinel-2 imagery to produce 2.5 and 5.0 m prediction images using the aerial orthoimage acquired over the same period. The proposed model was compared with an enhanced deep super-resolution network (EDSR), which has excellent performance among the existing super-resolution (SR) deep learning algorithms, using the peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and root-mean-squared error (RMSE). Our context-based ResU-Net outperformed the EDSR in all three metrics. The inclusion of the 60 m resolution of Sentinel-2 imagery performs better through fine-tuning. When 60 m images were included, RMSE decreased, and PSNR and SSIM increased. The result also validated that the denser the neural network, the higher the quality. Moreover, the accuracy is much higher when both denser feature dimensions and the 60 m images were used.  
aerial orthoimage; Sentinel-2; super-resolution; image simulation; residual U-Net +
Footnote †: journal: Applied sciences
# Variation of Acoustic Transmission Spectrum during the Muscle Fatigue Process
## 4 Analyses and Discussion
### Limitations and Future Work  
Nine healthy male subjects were recruited in this study. Previous studies have shown that muscle fatigue between males and females, younger and older, have significant differences [32]. In different exercise modes, the mechanism of fatigue is also significantly different [33]. Therefore, future work needs to increase the experimental sample size, including different ages, genders, and different exercise modes and intensities. In addition, we only analyzed the spectrum changes in the RF echo signal during the fatigue process. It would be meaningful to explore the other parameters of the RF echo signal.  
Based on ultrasound images, muscle changes such as the thickness, pennate angle, and fascicle length can be analyzed with muscle fatigue [12; 13]. The ultrasonic image entropy can reflect the textural features and microstructure changes with muscle fatigue [14; 15]. The ultrasound echo signal can not only reflect the structure of the muscle, but can also carry a large amount of organizational information along its path. Therefore, ATS may be a sensitive modality to characterize the ultrastructural and interstitial fluid changes of muscle and could have great potential in evaluating muscle fatigue nondestructively.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Subject**} & \multirow{2}{*}{**MVC Force (N)**} & \multicolumn{4}{c}{**Variation Magnitude of the Coefficients**} \\ \cline{3-6}  & & \(\Delta\mathbf{\kappa}\) & \(\Delta\mathbf{\beta}\) & \(\Delta a\) & \(\Delta b\) \\ \hline
1 & 20.0 & 2118.85 & \(-1.15\) & \(-1.86\) & 13.89 \\ \hline
2 & 18.5 & 1418.44 & \(-1.14\) & \(-1.77\) & 13.32 \\ \hline
3 & 16.5 & 453.00 & \(-0.70\) & \(-0.53\) & 4.01 \\ \hline
4 & 16.0 & 498.60 & \(-0.94\) & \(-0.67\) & 5.14 \\ \hline
5 & 16.0 & 614.20 & \(-0.95\) & \(-0.61\) & 4.38 \\ \hline
6 & 16.0 & 558.76 & \(-1.03\) & \(-0.80\) & 5.75 \\ \hline
7 & 14.5 & 382.90 & \(-0.58\) & \(-0.14\) & 0.90 \\ \hline
8 & 14.0 & 280.20 & \(-0.65\) & \(-0.19\) & 1.46 \\ \hline
9 & 13.5 & 297.20 & \(-0.57\) & \(-0.12\) & 0.61 \\ \hline \end{tabular}
\end{table}
Table 3: Variation magnitudes of \(a\), \(b\), \(\alpha\), and \(\beta\) during the fatigue process.
## 2 Problem Description
### Uncertainty Parameters  
A major challenge in implementing the two-stage model in the previous section is the variation in demand and RESs power output. It can be seen that this scheduling model is solved several days in advance; at that time, information on demand and RESs power output is uncertain and unknown. Therefore, uncertainties of demand and RESs power  
Figure 2: The VPP scheduling strategy in BC, DA, and ID markets.  
output should be taken into account in this model to ensure that the results are meaningful in the actual operation.  
A common method to handle these uncertain parameters is to represent them as the sum of the predicted value and predictive errors as follows:  
\[\left\{\begin{array}{c}P_{RESmax}^{t}=P_{RESf}^{t}+P_{RES-error}^{t}P_{RESf}^{t} \\ P_{D}^{t}=P_{Df}^{t}+P_{D-error}^{t}P_{Df}^{t}\end{array}\right. \tag{1}\]  
where the errors \(P_{RES-error}^{t}\) and \(P_{D-error}^{t}\) have a normal distribution function with means of 0, and the standard deviations \(\sigma_{RES}^{t}\) and \(\sigma_{D}^{t}\), respectively. This means that the maximum difference between the actual value and the forecasted data would be approximately \(3\sigma_{RES}^{t}\) for RESs available power and \(3\sigma_{D}^{t}\) for demand.  
Another important uncertain parameter is the probability that the reserve is called and generated in each hour. Determining this parameter is difficult because it depends on many factors such as generator outages, line outages, market price, or bidding strategy. While [PERSON] [36] focuses on predicting the reserve probability based on an artificial neural network (ANN), other studies [26; 37; 38] use the loss of load probability (LOLP) or the expected energy not served (EENS) to determine the reserve requirements and build the reliability-constrained unit commitment model. A recent study [39] builds the probability functions of the reserve activation based on historical observations for the period 2015-2018, obtained from Elia, the Belgian transmission system operator. Then, this probability function is applied to determine the ESS scheduling in the day-ahead energy and reserve market.  
In this paper, the reserve activation probability in each hour is assumed to be an independent random variable that follows the uniform distribution function and can be predicted from historical outage data:  
\[p^{t}\sim U(0,p^{max}) \tag{2}\]  
where \(p^{max}\) is the maximum possible value of the reserve activation probabilities obtained from historical data.
## 4 Results  
A comprehensive analysis of Gaussian curvature (Figure 11) and radius (Figure 12) provided valuable insights into the thermoforming behavior, highlighting a direct correlation between surface curvature parameters and the occurrence of defects, such as folds and wrinkles, during manufacturing. These defects were particularly prevalent in areas of double curvature with radii smaller than 60 mm, underscoring the material's limitations in accommodating tight curvatures in complex geometries. While the manufacturer's specifications for Krion K-Life 1100 [25], such as a minimum interior radius of 20 mm for a 12 mm thick sheet, adequately address single-curvature surfaces, they do not fully account for the material's performance under double curvature, where simultaneous bending in two perpendicular directions imposes greater stress. Our findings further demonstrate that, while the material supports minimum radii of 20-40 mm for single-curvature surfaces without significant risk of defects, transitioning to double-curvature surfaces requires larger radii of 45-80 mm to maintain structural integrity and prevent material failure. These limitations arise from the additional stresses generated during multidirectional deformation, which exceed the material's capacity at smaller radii.  
This iterative process of analysis and modification highlighted the necessity of maintaining consistent cross-sections and carefully controlling curvature to preserve structural and load-bearing capabilities. It was determined that a minimum thickness of 10.8 mm was essential (Figure 12), ensuring a maximum material loss of only 10% in the most critical areas while maintaining strength and durability. Adhering to these parameters allowed the material to endure the stresses of thermoforming without developing thin spots or weaknesses that could compromise its performance under load [25].  
The results significantly enhance the understanding and application of Krion K-Life 1100, broadening the scope of possibilities achievable with this solid surface material. By rigorously examining its thermoformable properties, its feasibility for producing complex, double-curved surfaces that were once deemed challenging or impractical with conventional materials has been proved. The successful fabrication of these intricate geometries highlights its potential not only for specialized uses, but also for broader architectural and design contexts where complex forms are desired and needed.  
Figure 11: Comparative Gaussian analysis of module B showing the evolution of the surface between the first and last iteration.
# Securing Construction Workers' Data Security and Privacy with Blockchain Technology
## 1 Introduction
### Problem Statement  
The construction industry is characterized by its intricate dynamics, which involve a multitude of participants and a varied labor force. The industry encompasses a spectrum of projects, spanning from minor renovations to major infrastructure projects. Consequently, it becomes crucial to address the complexities and hurdles associated with information management within this dynamic setting. Traditional approaches to information management in construction often rely on manual processes, paper-based documentation, and centralized record-keeping systems. However, these methods are prone to inefficiencies, data inconsistencies, and security vulnerabilities. Such shortcomings pose a threat to the privacy and safety of workers' personal information, leaving them susceptible to risks like identity theft.  
Furthermore, due to the inherent risks associated with construction activities, workers are frequently exposed to non-fatal injuries, including work-related musculoskeletal disorders (WMSDs). WMSDs are functional disorders caused by external factors, such as repetitive movements, overexertion, awkward postures, and vibrations in the occupational setting [33]. These disorders can have a significant impact on workers' health and well-being, leading to increased sick leave and decreased productivity. For instance, a study by [PERSON] et al. [34] reported that the prevalence of WMSDs in the construction industry was found to be 31.2%. This highlights the urgent need for the construction industry to effectively manage information (i.e., risk assessment data) associated with such high-risk construction activities that threaten the safety of its workforce.  
In response to these challenges, this study seeks to improve information management practices--specifically, the privacy and safety of construction workers. This study leverages advanced technologies, particularly blockchain, to establish a secure, efficient, and decentralized system for managing workers' information. The integration of Hyperledger Fabric on the robust AWS cloud platform forms the backbone of this approach. This study uniquely contributes to the field by conducting a comprehensive case study on tiling activities within a renovation project to generate worker safety data, practical implementation of GDPR-compliant biodata, and relevant safety data assessment techniques. Through phases of experimental design, including blockchain infrastructure creation, chaincode development, and real-world deployment on AWS, this research establishes a fundamental change in information management practices, thereby emphasizing worker safety, privacy, and the overall success of construction endeavors.
## 4 Analytical Prediction of the Suppressive Impact of MRCFM on Sideband Constituents
### Analysis and Prediction of Sideband Acoustic Suppression Effects  
In order to investigate the sideband acoustic responses caused by different PWM strategies, simulation analysis is conducted using the multi-physics field simulation model mentioned above, and the obtained acoustic results are processed by using the A-weighting of the sound pressure level (SPL-A). The sideband acoustic response spectra of the prototype with different PWM strategies are shown in Figure 14. Combined with the vibration results, it can be seen that the sideband electromagnetic noise caused by PWM strategies has peak features corresponding to the vibration response. Figure 15 shows the sideband acoustic response spectra of each PWM strategy after the introduction of the MRCFM method. The peaks of the sideband acoustic responses of each strategy are significantly reduced; DPWMMIN/MAX, DPWMMIN/MAX, DPWM0/2 and DPWM1/3 are reduced by 15.3%, 12.9% and 12.7%, respectively, corresponding to optimization amplitudes of 7-9 dB.  
Figure 12: Simulation prediction of sideband vibration response under different PWM strategies: (**a**) DPWMMIN and DPWMMAX; (**b**) DPWM0 and DPWM2; (**c**) DPWM1 and DPWM3.  
Figure 13: Prediction of the suppression effect of Markov chain random carrier frequency modulation on sideband vibration response of different PWM strategies: (**a**) DPWMMIN and DPWMMAX; (**b**) DPWM0 and DPWM2; (**c**) DPWM1 and DPWM3.
## 5 Results and Discussion
### Turbine Map Generation
#### 5.3.2 Variable Nozzle Axial Turbine Map Generation  
Once the setup of the variable stator pivot angles to be tested were decided, the map generation was conducted using the same method that was used to generate the original axial turbine map (Section 5.3.1). The same speed lines and mass flow rates were used, but the process had to be repeated for each rack position; this led to a total of 343 CFD simulations to generate all rack positions. Once the results were obtained, they were again corrected to normalise them to ambient conditions (Figures 27 and 28). As before, it should be noted that these maps are based on the flow across the blade stage and are not representative of the full system. Overall, the result show that the variable geometry setup significantly affects the flow conditions of the turbine, providing higher efficiencies at lower pressure ratios than the original fixed geometry.  
Figure 26: Total to static efficiency vs. pressure ratio plots at constant shaft speeds for axial turbine.  
Figure 27: Corrected mass flow rate vs. pressure ratio plots at constant shaft speeds at different rack positions for VNT (Variable Nozzle Turbine)
## 2 Conventional Network and the Need for Smart Grids
#### AMI in Microgrids
Hence, the key is the integration of an adequate communications infrastructure that allows the aggregation of data and AMI to monitor and control the conventional electrical network. This allows the levels of voltage to be always known when introducing microgrids to run adequate processes of quality energy management. Therefore, in this paper, we propose a heuristic method capable of providing a roadmap for deployment of an advanced metering infrastructure. This method can be a solution to the sizing problem. In this way, it allows the planning of FiWi communication networks considering certain restrictions. The speed of transmission of data does not intervene in the model as a restriction, but it can be estimated knowing the packet rate, transmission rates and data length generated by the SMs. In this research, these values are taken from the literature. Another calculated parameter that depends directly on the distance is the FSPL. These parameters are referents to determine the importance of the topology and how it affects the network for the minimization of the end to end delay and the losses in the free space. The model minimizes the number of SMs that use cellular technology through the incorporation of WiFi technology. In summary, in this work, we intended to deploy a WiFi communication network optimizing resources through clustering techniques. These techniques are based on a variant of the Prim algorithm and the minimum expansion tree algorithm (Dijkstra). With these algorithms, the adjacency matrix (G) is constructed. This matrix is formed with the existing relations between the different elements of the communication network (SMs, UDAPs, BSs and central office). These elements will form the resulting route map for the optimal integration of DRES. The model includes the connectivity of BS and the central office using a fiber optic link. In this way, the communication resources are integrated into a FiWi network. Table 1 presents the model and parameters of simulation to be used in this paper.
## 5 Conclusions  
This work has successfully demonstrated the development of a neural network-based system for detecting and localizing faults in a ship's FiFi system. The use of pressure sensor data collected during both normal operation and pipe break events has shown that neural networks can effectively detect anomalies and pinpoint their location within the vessel.  
The results of the study highlight the importance of collecting data at higher sampling frequencies, particularly above 10 Hz, with a recommendation to use rates above 20 Hz for optimal performance. The neural network models have been demonstrated to be robust, effectively handling noisy data and varying sampling rates, and can reliably detect faults with minimal preprocessing.  
The methodology presented here holds great promise for real-world applications, especially when integrated into a ship's digital twin for autonomous fault detection. Future work will focus on expanding the system by continuously collecting onboard data, enabling the system to learn from new fault scenarios and further improve its fault localization capabilities.  
Furthermore, deploying this system on different ships may require retraining the neural network to accommodate varying ship configurations and sensor setups, ensuring the model's adaptability to different operational conditions.  
In conclusion, this study demonstrates the feasibility and potential of using neural networks for fault detection and localization in ship firefighting systems, contributing to improved safety and operational efficiency in maritime operations.  
Conceptualization, S.F.-G., V.D.-C. and M.M.-G.; methodology, S.F.-G., V.D.-C. and M.M.-G.; software, S.F.-G.; validation, S.F.-G.; formal analysis, S.F.-G., V.D.-C. and M.M.-G.; investigation, S.F.-G., V.D.-C. and M.M.-G.; resources, S.F.-G., V.D.-C., M.M.-G. and C.G.S.-G.; data curation, S.F.-G., V.D.-C. and M.M.-G.; writing--original draft preparation, S.F.-G.; writing--review and editing, S.F.-G. and V.D.-C.; visualization, S.F.-G., V.D.-[PERSON] and [PERSON]-[PERSON]; supervision, [PERSON], [PERSON]-[PERSON] and C.G.S.-G.; project administration, [PERSON]-C. and C.G.S.-G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Xunta de Galicia and Axencia Galega de Innovacion, grant numbers IN853C2022/01 and ED431C 2022/39.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The results presented in this paper are part of the work developed by the Joint Research Unit formed by Navantia and the University of A Coruna.  
Author [PERSON] was employed by the company Navantia SA. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 8 Integration of Blockchain Technology and Cloud Computing
### Open Research Challenges  
There is no denying the many advantages that cloud computing offers. The path ahead will not be devoid of obstacles in any way. The cloud stores a large variety of data types, some of which are quite sensitive. Following a breach of security in 2017, Amazon's Internet platforms were found to store the personal information of around 200 million voters in the United States. Due to a breach in the Alibaba cloud, the personally identifiable information of 1.1 billion customers who shop on the Taobao e-commerce site was made public [29].  
The Starwood division of Marriott Hotels was similarly compromised by an unauthorized individual, which led to a diminished reputation for the company's brand. A breach in the security of an organization's data might lead to monetary losses, the loss of customers, harm to the company's brand, and other impacts.  
Blockchain, which has its unique benefits in data security, comes to the rescue with other recent breakthroughs in cloud data security. The concept of cloud computing is predicated on utilizing centralized servers to store data and then to make that data accessible to consumers through software [37].  
It is common practice for businesses to have this form of centralized-based organization, which might undermine security, privacy, and authority. If you utilize the cloud unsafely, it will be easy for hackers and viruses to access your data and steal it for their purposes. As a direct result of this, confidential information may be made public.
# Design of a Tri-Band Wearable Antenna for Millimeter-Wave 5G Applications
## 2 Antenna Design Methodology
### Surface Current Distribution  
Figure 4 shows surface current distribution of the proposed antenna at various operating frequencies. Resonance frequencies (28 GHz and 38 GHz) are produced at the bottom side of the proposed antenna because of its longer effective electrical length, as shown in Figure 4a,b, respectively. As illustrated in Figure 4, the truncated side of the radiator has a major contribution in producing the 28 GHz frequency band. As shown in Figure 4b, the upper C-shaped slot helps to generate the 38 GHz frequency band, and the bottom U-shaped slot helps to operate at the 60 GHz frequency band.