esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This study had two purposes. The first purpose of the study was to compare the electromyographic(EMG) and dynamic characteristics in injured and non-injured legs during the one-legged heel-raise test after a unilateral Achilles repair. The second purpose was to determine the correlations between the EMG results and the dynamic characteristics and between the characteristics in the eccentric phase and jumping distance. Twenty-six participants who underwent an Achilles repair between 4 and 12 months prior to the measurement were recruited to perform the following bilateral tests: (1) one-legged heel-raise test with measurements of muscle activation, kinematics, and kinetics and (2) one-legged forward jumping. During the heel-raise exercise, there were increases of the EMG amplitudes in the soleus and tibialis anterior muscles, lower ankle joint angle and angular velocity, lower normalized ground reaction force, and mechanical work in the repaired legs in comparison to the non-injured legs. The EMG results of the medial gastrocnemius and soleus muscles correlated with the dynamic results (\(\mathrm{r_{s}}=0.467\) and \(-0.537\)). Furthermore, the dynamic data in the eccentric phase were correlated with the jumping performance (\(\mathrm{r_{s}}=0.575\) and \(-0.471\)). It is concluded the soleus muscle undergoes neuromechanical changes, including changes in EMG and dynamic characteristics, and changes affecting jumping performance.  
achemiles tendon; biomechanics; muscle activation; rehabilitation 2021
# A Geographically Weighted Random Forest Approach to Predict Corn Yield in the US Corn Belt
## 2 Materials and Methods
### Datasets
#### 2.2.4 Soil Data  
Soil and its associated properties are vital to crop growth and can affect crop yield [26]. To conduct this study, we focus on 11 soil properties including cation exchange capacity (CEC), soil pH, soil available water content (AWC), soil available water storage (AWS), soil bulk density, soil organic carbon (SOC), clay content, sand content, saturated hydraulic conductivity, wilting point, and field capacity. The soil properties selected from the Soil Survey Geographic (SURGO) database are based on their effects on crop yield and relevant literature [63; 64; 65]. These soil properties were derived using the 10 m gridded template compiled from the Gridded Soil Survey Geographic (gSSURGO) database.
# Application of Image Recognition Methods to Determine Land Use Classes
## 5 Discussion

Table 6: Post-processing thresholds after experiments (class code (CC), confidence value (CV)). yielded optimal balance in accuracy and computational efficiency. This finding aligns with previous research indicating that parameter tuning significantly impacts classifier performance [22; 25; 62]. Furthermore, the post-processing thresholds established in accordance with the confidence maps effectively filtered out low-confidence predictions, thereby enhancing the reliability of classification outputs. This methodological improvement aligns with insights from previous works, underscoring the value of confidence-based post-processing in satellite image classification [6; 59; 70]. These findings support the hypothesis that integrating cloud interpolation, spectral indices, and ML enhances the accuracy of land use classification under dynamic environmental conditions. The proposed hybrid method is robust, with implications that extend beyond the Lithuanian context. Its potential applications include regions with high cloud coverage or similar environmental variability [6; 7; 8; 69]. For future research, expanding training datasets temporally and spatially, incorporating data from diverse satellite missions, or exploring fusion with synthetic aperture radar (SAR) imagery, may further enhance classification accuracy and generalizability. Additionally, testing this approach in other geographical contexts, as suggested by [9; 71] would help validate the adaptability and effectiveness of the methodology more broadly. The study confirms that integrating pre-processing enhancements such as cloud removal and spectral indices improves classification accuracy. This indicates that the objective has been successfully met.
## 3 Results
### Collaborative Interface with ATC  
A virtual, controlled airspace with a designated ATC was simulated in the UTM network. The main focus was on voice-free communication between ATC and the UAS using the UTM network. The ATC's instructions (entry/exit clearance, transponder activation, change of transponder code, landing request) were carried out directly by the UAV fully  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**UE** & **SNR UL [dB]** & **DL Bit Rate [kbit/s]** & **UL Bit Rate [kbit/s]** \\ \hline uas1 & 16.1 & 23 & 49 \\ uas2 & 11.5 & 18 & 52 \\ User1 & 14.9 & 56 & 10 \\ User2 & 14.1 & 58 & 7 \\ UTM network server & 13.2 & 105 & 162 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Average SNR and bit rates of the UEs during V5 (without flight plan transmission).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Experiment** & **Min [ms]** & **Max [ms]** & **Mean [ms]** & **SD [ms]** \\ \hline V1 & 116.70 & 278.73 & 149.89 & 34.00 \\ V2 & 278.73 & 355.53 & 165.91 & 41.41 \\ V3 & 116.19 & 554.07 & 179.81 & 45.00 \\ V4 & 110.49 & 1030.77 & 205.74 & 85.95 \\ V5 & 110.40 & 571.79 & 159.29 & 45.84 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Trip times for telemetry message from uas1 to UTM server.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**UE** & **SNR UL [dB]** & **DL Bit Rate [kbit/s]** & **UL Bit Rate [kbit/s]** \\ \hline uas1 & 15.8 & 15 & 38 \\ User1 & 14.8 & 42 & 7 \\ User2 + uas2 (virtual) & 15.8 & 135 & 130 \\ UTM network server & 11.7 & 169 & 199 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average SNR and bit rates of the UEs during V4 (without flight plan transmission).  
automatically. The UAV also independently complied with the entry and exit rules and requested clearances from ATC at the appropriate positions. Figure 10 shows the UAV's holding points when flying through the controlled airspace.
## 2 System Model and Problem Formulation
### Downlink Wireless Energy Transfer  
In the downlink phase, the H-AP transmits an arbitrary energy signal, \(x_{A}\), where \(E[|x_{A}|^{2}]=P_{A}\), in which \(P_{A}\) is the transmit power available to the base station. The received signal by the \(k\)th user is \(y_{k}\) defined as  
\[y_{k}=h_{k}x_{A}+z_{k} \tag{2}\]  
where \(h_{k}\) is the channel between the H-AP and the \(k\)th user, and channel properties remain the same in a coherence interval. Here, \(z_{k}\) is the noise at \(U_{k}\). The amount of energy harvested by the \(k\)th user in a downlink can be expressed as \(E_{k}\):  
\[E_{k}\ =\ \zeta_{k}\left|h_{k}\right|^{2}P_{A}\tau_{0} \tag{3}\]  
where \(0{<\zeta_{k}<1,\ k=1,2,3,\ldots,K}\) is the energy harvesting efficiency at each receiver.
# A Systematic Review on Hybrid AI Models Integrating Machine Learning and Federated Learning
## 3 Background and Related Work
### Applications and Challenges of Machine Learning in Cybersecurity  
In recent years, the research area of ML-based cybersecurity solutions has gained increasing attention for performing tasks such as intrusion detection, anomaly detection, and malware analysis. The different supervised learning-based algorithms that show outstanding performance and accuracy for threat detection are random forest and SVM [10, 21, 22], as their successful performance relies heavily on labeled data through which any new instance would be rightly classified. For instance, studies such as [21, 22] have focused on the role of ML algorithms such as SVM and decision trees for robust classification performance, whereas [9] has explored the automation of threat detection using supervised methods. On the other hand, unsupervised approaches, including clustering algorithms, do a great job in finding new and unknown attack patterns by analyzing data distributions and deviations [23, 24]. As shown in the table below (Table 1), ref. [24] focuses on malware and anomaly detection, hence giving ample evidence that clustering techniques may work for adaptive threat responses. Despite these strengths, ML models tend to depend upon centralized data storage, which is significantly vulnerable to breachesand raises substantial concerns pertaining to privacy. In addition, such centralization of systems raises scalability issues, hence limiting the deployment of ML models in large-scale, real-time cybersecurity environments where distributed and adaptive solutions are increasingly required [16; 25; 26]. For instance, ref. [25] discusses challenges related to IoT security risks, while [16] proposes a thematic framework that addresses the scalability needs of modern cybersecurity systems.
## 5 Frequency Manipulation and Communication Feasibility
### Feasibility of Frequency Manipulation
#### 5.1.2 Procedure of the TCP/IP Algorithm  
The proposed TCP/IP reconfiguration method also operates in a client-server setup to manipulate SDR parameters. The server program uses a Python module block, and the client program is implemented with an external Python Integrated Development Environment (IDE) such as PyCharm or a Telnet client program [59]. For this program, a Python snippets module was implemented to give the client access to the server program and enable tuning of the SDR GNU Radio parameters. The snippet is executed as a thread that passes self-arguments to access variables from the main GNU Radio flowgraph [59]. Similar to the XMLRPC method, the reconfiguration with TCP/IP method is also feasible at the beginning, during, and at the end of mission execution. The flow graph in Figure 15 describes the TCP/IP architecture and operation procedure.  
Figure 15: Frequency manipulation flow diagrams: (**a**) XML-RPC algorithm architecture; (**b**) TCP/IP algorithm architecture.
## 2 Materials and Methods
### The Floodwater Depth Estimation Tool: FwDET  
The Floodwater Depth Estimation Tool (FwDET, [33]) is a tool that allows to evaluate water depths associated with flood areas retrieved by satellite. FwDET identifies the water depth of the flooded areas in each cell within a flooded domain based on its closest point located on the edge of the flooded area through the use of an appropriate digital terrain model (DTM). FwDET calculates water depth based solely on a flood polygon and a DTM, then eliminates the need for additional data while maintaining usability with complex and fragmented flood extension maps from any source and resolution (e.g., independent from the sensors used for the acquisition). Regarding the accuracy of FwDET in estimating flood depths, ref. [33] reports mean differences between FwDET and observations of 0.18 and 0.31 m for two case studies in the United States, based on a DEM resolution of 1 m and 10 m, respectively. In this case, the DTMs used is from Lidar and has a resolution of 2 m and is freely available from the Sicily Region ([[https://www.sitr.regione.sicilia.it/](https://www.sitr.regione.sicilia.it/)]([https://www.sitr.regione.sicilia.it/](https://www.sitr.regione.sicilia.it/))) (accessed on 11 December 2022). The version of FwDET used is an implementation in Google Earth Engine (GEE, [[https://earthegine.google.com](https://earthegine.google.com)]([https://earthegine.google.com](https://earthegine.google.com))) (accessed on 11 December 2022), a cloud-based open service that has the advantage of allowing large-scale and high-resolution calculation.
###### Abstract  
Climbing demands exceptional isometric finger flexor strength and neuromuscular efficiency. This study aimed to compare maximum isometric strength and muscle quality (MQ) between climbers and non-climbers and examine the influence of sex and specific grip types. Methods: 33 climbers (14 women) and 29 non-climbers (15 women) volunteered in this study. Maximum isometric strength was measured for handgrip, three-finger drag, and half-crimp grips, while forearm muscle mass was estimated using DXA. MQ was calculated as the ratio of peak isometric force to forearm muscle mass. Results: Climbers demonstrated significantly higher isometric strength in both the three-finger drag and half-crimp grips compared to non-climbers (\(p<0.01\)); however, non-significant differences were observed in handgrip strength. Despite similar forearm muscle mass, climbers exhibited greater MQ. Notably, female non-climbers showed higher MQ than their male counterparts (\(p<0.05\)), a sex difference that was not evident among climbers. All tests exhibited high repeatability (ICC \(>0.93\), CV \(<5.81\%\)) with low SEM and MDC95 values. Conclusions: The findings underscore the necessity of employing climbing-specific strength assessments to capture the unique neuromuscular adaptations induced by climbing training. Muscle quality emerges as a sex-neutral biomarker for strength performance evaluation, with potential applications in the optimization of training programs. Future research should further explore the predictive value of MQ and strive for standardized testing protocols.  
muscle quality; isometric strength; climbing; finger-grip; sex differences +
Footnote †: journal: Applied sciences
## Materials and Methods
### Room Segmentation
#### 3.3.1 Cell Decomposition
The line-fitting problem becomes the following IRLS problem after adding the M-estimator:  
\[min\sum\limits_{i=1}^{N}w(dis(P_{i},\;Seg))dis(P_{i},\;Seg)^{2} \tag{1}\]  
where \(w(dis(P_{i},\;Seg))\) is calculated by using the Welsch weighted function [57], which should be recalculated after each iteration and will be used in the next iteration. \[w(dis)=exp\bigg{(}-\frac{dis^{2}}{k_{Wu}^{\;2}}\bigg{)},\;k_{Wu}=2.985 \tag{2}\]  
The distance algorithm \(dis(P_{i},\;Seg)\) is calculated by  
\[dis(P_{i},\;Seg)=\|(x_{i}-\overline{x})\cdot n\|,\;\|n\|=1 \tag{3}\]  
where \(n\) is the normal of the line and \(\overline{x}\) is the mean of the point cloud. **Three-Line projection & fusion:** The extracted segments in each slice are then projected to a horizontal plane, as shown in Figure 3b. However, the projected segments contain considerable clutter because of the complex indoor environment. Furthermore, several segments are nearly coincident or collinear after the projection, as shown in Figure 3b. Line fusion is performed to reduce repeated wall lines and to obtain more accurate line segments. In this research, the wall segments are first projected to the x-y planes and sorted by length. Lines should be deleted when their length is less than the threshold. The longest projected segment is added into the final dataset. Then, each projected segment is compared to the segments in the final dataset. If a segment is neither a corridor nor parallel to the segments in the final dataset, this segment will be added in the final dataset. The working details are shown in Algorithm 1, and the fusion result is shown in Figure 3c. Two preconditions are imposed here: if the angular differences between two segments are smaller than the given threshold, then the segments can be considered to be parallel; and, if the distance between two parallel lines is smaller than the given thresholds, then the segments are considered to be collinear.
# Weld Defect Detection of a CMT Arc-Welded Aluminum Alloy Sheet Based on Arc Sound Signal Processing
## 3 Experiment and Result
### Defect Recognition
#### 3.3.2 Comparison of Multi-Channel Time-Frequency CNN  
The i-CTF dataset (i = 1, 2, 3, 4, and 5) was used to obtain the inputs of the customized CNN model, as shown in Figure 1, for training and testing the model. The training and testing model was used 10 times for each dataset. Each training and testing process had 40 epochs, and the learning rate was that shown in Equation (8).  
\[lr=lr_{init}\alpha^{\frac{epoch}{step}} \tag{8}\]  
where \(lr\) denotes the learning rate, \(lr_{init}\) is the initial learning rate, \(\alpha\) denotes the decay coefficient of the learning rate, and the range is [0, 1]. _step_ indicates the step of the learning rate. In this study, \(\alpha\) is 0.9, and _step_ is 10.  
Table 3 compares the average test accuracy of the different datasets. It is apparent from this table that the average accuracy of the multi-channel dataset is higher than that of the single-channel dataset, and that the average accuracy of the four-channel dataset is highest. This result is due to the multi-channel dataset containing both static and dynamic features of the time-frequency spectrum.
# Mobile Robot for Security Applications in Remotely Operated Advanced Reactors
## 9 Introduction to Mobile Dog Robots
### Sensory Capabilities
#### 9.2.1 Rad Sensors  
The SPOT robot uses RDS-32 m, Accurad PRD, and SPIR Explorer internal GMs (Mirion Technologies, Atlanta, GA, USA) for dosimetry. The Ghost Robotics and ANYmal Robotics robot do not have any equipment that carries out dosimetry. The SPOT robot has gas environment sensors, toxin-sensing chemical sensors, and Alpha/Beta CAM measurement sensors (Mirion Technologies), while the Ghost robot has CBRN sensors and the ANYmal robot does not have any environmental or toxin-measuring sensors. The SPOT robot has the equipment to carry out spectroscopy measurements, i.e., SPIRExplorer NAI/LaBr3, Osprey MCA, and MicroGe (Mirion Technologies), whereas both Ghost Robotics robots and ANYmal Robotics robots do not have any. Overall, The SPOT robot dominates the Rad sensors' capabilities.
## 2 Materials and Methods
### Microstructural Characterization  
The materials microstructure was analyzed using (i) Scanning Electron Microscopy (SEM) (Vega 3 XM Tescan apparatus, equipped with energy dispersive X-ray detector (EDX), accelerating voltage of 10-20 kV). For the experiments, the samples were covered by sputtering with a gold layer (Q150R ES Quorum(r) device), with a 5 nm min\({}^{-1}\) rate, for 5 min; (ii) Fourier Transform Infrared Spectroscopy (FTIR, Shimadzu Prestige 21 equipment, in the wavenumber range from 4000 to 600 cm\({}^{-1}\)), using KBr method (for the evaluation, pressed pellets formed from a homogeneous mixture of 10 mg of each macerated filament with 1 g of KBr crystals were used) and attenuated total reflectance (ATR) methodology (Shimadzu apparatus, 4000 to 500 cm\({}^{-1}\) range); and (iii) X-ray diffraction (XRD, Miniflex Rigaku equipment, Cu K\(\alpha\) radiation, \(\lambda\) = 1.54056 A, 40 kV voltage, current of 15 mA and scanning rate of 0.02\({}^{\circ}\)s\({}^{-1}\) in the 20 range of 5\({}^{\circ}\) to 50\({}^{\circ}\)). X-Pert HighScore software v. 5.2 was used to identify the phases of the materials.
# Efficient Dissemination of Safety Messages in Vehicle Ad Hoc Network Environments
###### Abstract  
The number of people owning vehicles has been steadily growing, resulting in increased numbers of vehicles on the roads, making roads more congested, and increasing the risk of accidents. In addition, heavy rain, snow, and fog have increased due to abnormal weather caused by global warming. These bad weather conditions can also affect the safety of vehicles and drivers. The need to disseminate safety messages on the social Internet of Vehicles due to these problems has been steadily increasing. In this paper, we propose an efficient safety message dissemination scheme that focuses on urban environments with high vehicle density and mobility to address these problems. The proposed scheme reduces packet loss by considering frequent cluster departures and subscriptions through an efficient cluster management technique. In a vehicle-to-vehicle environment, the dissemination of safety messages is divided into intracluster and intercluster emergencies, as well as a general safety message dissemination technique. In a vehicle-to-infrastructure environment, the proposed scheme reduces the number of processing requests and duplicate messages made to roadside units (RSUs) through a request operation process for each vehicle and an RSU scheduling technique. We conducted several performance evaluations of message packet loss and the number of RSU processing requests to demonstrate the superiority of the proposed scheme.  
social Internet of Things; social Internet of Vehicles; vehicle-to-vehicle; vehicle-to-infrastructure; vehicle ad hoc network; social networks Article
## 4 Results and Discussion
### Effect of Laaching Time on Product Composition and Micomorphology  
Figures 8 and 9 present SEM photos and the energy spectra of samples with different soaking times, illustrating the carbonization products. The original fly ash particles exhibit a relatively smooth surface with a regular spherical shape, along with a small number of attached tiny particles composed mostly of soluble substances and quartz. After 24 h of soaking, the particle surface gradually changes from smooth to rough, with the appearance of aggregated precipitates, indicating the dissolution of a significant number of alkaline substances. After 48 h of soaking, an increased presence of silicate (CSH) can be observed, potentially an intermediate product of fly ash hydration, with continuous formation and precipitation of disordered precipitates on the particle surface. The EDS analysis revealed that the main components of the precipitate were Si, Mg, Al, and Ca. After 72 h of soaking, the original fly ash particles become covered by a dense layer of hydration products, and the fly ash spheres are filled with sediment. As the hydration reaction products continue to generate and cover the particle surface, a layered hydration product layer forms, enclosing the products. The longer the soaking time, the denser the generated carbonate becomes, promoting the improvement of compressive strength. Additionally, more silicates (CSH)  
Figure 6: Effect of soaking time on the mineralization degree.  
Figure 7: Effect of soaking time on the mineralization degree (supercritical CO\({}_{2}\)).  
can be observed, which may indicate the intermediate products of fly ash hydration. During the subsequent curing process, the carbonate of the mineralized specimen gradually becomes enclosed and cemented by CSH, effectively filling the product's pores.