esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Fuzzy Logic and Load Balancing
To assess the proposed approach, the researchers utilized FI5 pro 3.5 and conducted evaluations on various scenarios involving different input values. The simulation results revealed that IoT nodes experienced increased trust levels when the inputs had higher parameter values, signifying better security attributes for the selected nodes. [PERSON] et al., 2018 [PERSON] et al., 2018 [PERSON] et al., 2018 [PERSON] et al., 2018 proposed a fuzzy logic-based energy-aware routing protocol (FLEARPL) to prolong the network lifetime and reduce the power consumption of IoT applications while using RPL. The algorithm assists in selecting the best route during transmission via RPL. It uses three inputs: the routing metrics load, residual energy (RER), and expected transmission count (ETX), to estimate the quality of routes for better selection. The Cooja simulator was used to evaluate the proposed algorithm. In comparison with RPL, MRHOF-RPL, and FL-RPL, the simulation results showed that the proposed algorithm outperforms them in terms of the packet delivery ratio and network lifetime. [PERSON]  
The primary goal has been to reduce power consumption and processing time by offloading tasks from one layer to another. However, the offloading process itself requires power, time, storage, and computing capacity. Therefore, it is crucial to minimize the offloading process in order to save energy, time, storage, and computing resources in the IoT system. However, this reduction must be achieved without negatively impacting the system, such as causing delays. The proposed approach aims to reduce the offloading process by distributing tasks among computing layers in the early stages and leveraging this reduction. By prioritizing tasks and allocating resources to them at an early stage, we can achieve our objective, as tasks will be sent directly to their designated resources unless exceptional cases arise, such as when the resources are full. To achieve this, we allocate resources for tasks processed at the Mist layer. The Mist layer, located close to the Edge layer (medical sensors), plays a crucial role in assisting patients with critical conditions and facilitating real-time monitoring for quick decision making. When allocating resources, we consider two main factors that help us make better decisions and minimize task offloading. The first factor relates to the health condition of the patients, while the second factor pertains to the capacity of the resources. In this context, we only consider the Mist nodes and the Fog nodes, and we do not take into account the capacity of the Cloud since it is centralized. By leveraging fuzzy logic systems, which are powerful tools for decision making and delivering accurate results, we can estimate and predict the optimal resource allocation for healthcare tasks. To study and analyse the proposed system, we will adopt healthcare systems as a model, focusing on the MFHS (Mist-based fuzzy healthcare system).
# USD-YOLO: An Enhanced YOLO Algorithm for Small Object Detection in Unmanned Systems Perception
## 2 Related Works  
### Object Enhancement Methods  
In visual perception tasks for unmanned systems, small objects often occupy only a minimal portion of the image [10]. Their feature responses are prone to information attenuation in deep networks.  
Feature enhancement technology can significantly improve the detection precision and localization of small objects. [PERSON] et al. [11] proposed the Feature Pyramid Network (FPN), which introduced an innovative top-down multi-scale feature fusion paradigm. By integrating high-resolution shallow features (rich in detail) with low-resolution deep features (rich in semantic information), FPN significantly enhanced the detection sensitivity for small objects. Subsequent researchers have proposed various improvements to FPN, such as PANet [12], NAS-FPN [13], and BiFPN [14]. These methods enhance multi-scale feature representation but often increase computational complexity. To address the limited feature representation capability for small objects, [PERSON] et al. [15] introduced the Dual Attention-guided Fusion mechanism, which refines feature maps through Channel and Spatial Attention Modules. While effective, these attention mechanisms can be computationally expensive. [PERSON] et al. [16] proposed the Non-local Neural Network, which constructs a global context modeling framework by computing long-range dependencies between spatial pixels. This approach improves localization precision but may struggle with real-time performance.  
In addition to featuring enhancement techniques, data augmentation and balancing techniques have also been explored to improve the performance of small object detection. For instance, ALADA, a lightweight automatic data augmentation framework, optimizes augmentation policies and detection networks simultaneously. It formulates a compact search space and a three-step bi-level optimization algorithm to address imbalanced sampling and redundant hyperparameter issues in search-based AutoDA methods [17]. Similarly, Balanced Weighted Extreme Learning Machine techniques have been proposed to handle imbalanced learning scenarios [18]. These techniques adjust the weights of minority and majority classes to ensure balanced learning, thereby enhancing the generalization performance of the detection model. While effective, they may require additional training time.
## 5 Core Structure of Ses Transmission
### Comparative Analysis of SES with Other Digitizing Schemes  
SES has some similarities with other digital encoding schemes. These similarities improve the reliability and validity of SES. Two main similarities exist in SES, with the digitizing scheme RZ shown in Figure 5 and AMI has shown in Figure 6.  
The SES level returns to zero after modulating a signal. The same modulation is performed in RZ. The difference is that RZ uses double bandwidth, and SES does not require this.  
AMI jumps from \(-\)A to \(+\)A and vice versa on a long stream of 1 bit. The SES signal variation is from \(+\)A to \(-\)A or vice versa when the string goes from 1 to 0 or 0 to 1.
# 5G-NR Physical Layer-Based Solutions to Support High Mobility in 6G Non-Terrestrial Networks
## 3 Overview of Non-Terrestrial Networks
### NTN Use Cases  
Non-terrestrial systems have been proposed to enable several applications, including weather forecasting, video surveillance, TV broadcast, remote sensing, and navigation, for several years. On the other hand, recent technological advances in the aerial/space sector have enabled the implementation of more sophisticated use cases, such as distributed computing and content broadcasting, service boosting for users in congested areas, eMBB in underserved areas, and multi-connectivity for service continuity via cellular networks [2; 4].
## 3 Results and Discussion
### Gamma and Particle Size Analysis of Marine Sediments  
Gamma analysis on sediment samples was carried out to determine the activity concentration of \({}^{137}\)Cs, a fission product both from the nuclear power industry and nuclear weapons, and of the widespread naturally occurring \({}^{40}\)K isotope. The scope was to compare their specific activity with the abundance of actinides in the spirit of other studies  
Figure 12: Map of the \({}^{236}\)U concentrations in marine sediments.  
Figure 13: Map of the \({}^{239}\)Pu concentrations in marine sediments.  
shown in [28; 29; 30], and to find confirmation of the previously seen behavior of actinides in marine sediments.  
Studies in the Garigliano area over the last decades on soil, river sediments, and vegetable and animal matrices found values of \({}^{137}\)Cs compatible with fallout from the Chernobyl accident [15; 31]. A recent analysis of the temporal behavior over 4 decades of the specific activity of \({}^{137}\)Cs, confirmed this hypothesis [32].  
Marine sediment samples from the three inner transects (T0, T1, and T5), where actinides have the highest values of specific activity and distinct trends, were chosen and analyzed for the \(\gamma\) study.  
The \({}^{137}\)Cs specific activity vs. the sample depth (Figure 14) shows the same behavior found for actinides: low values for shallow sediments, up to 20 m depth, then a neat increase at 40 m and a softer increment at 70/100 m, suggesting a possible plateau. Similar behavior can be seen for the naturally occurring \({}^{40}\)K specific activity, in the left axis, in red. The main difference consists of a relatively higher value for shallower samples.  
To highlight any effects due to particle size differences in the marine sediments of the study area, measurements were made on the sediment samples from transect T0 and all samples at 100 m bathymetry. The results show an increase in the percentage of finer particle size from the coast toward 100 m depth along T0 and a fairly uniform composition of samples from all transects at 100 m. The activity concentrations for the measured radionuclides agree with this result, since as the grain size decreases, the absorption surface area increases. The activity concentration of \({}^{40}\)K is quite uniform, and thus the Cs/K ratio follows the trend of \({}^{137}\)Cs, showing that the \({}^{137}\)Cs uptake was not particularly affected by possible large differences in potassium in the different sediment samples.
# Research Progress of Self-Powered Gait Monitoring Sensor Based on Triboelectric Nanogenerator
## 3 Applications of Self-Powered Triboelectric Nanogenerator in Gait Monitoring
### Rehabilitation Assessment and Auxiliary Training
In the study, the multidimensional motion sensor for monitoring movement was also placed at the ankle position, and the kicking motion was identified through 1D convolutional neural network (CNN) deep  
Figure 5: Applications of self-powered sensors based on TENG in the field of rehabilitation assessment and auxiliary training. (**a**) Self-powered gait analysis and long-term rehabilitation monitoring system. (**ai**) Schematic diagram of the gait monitoring device. (**aii**) Schematic diagram of the bionic sensor. (**aiii**) Signal response during the completion of the gait cycle. (**aiv**) Gait measurement of simulated patients. (**av**) Gait rehabilitation conditions for ACL tears and intervertebral disc protrusions. (**b**) Auxiliary training system based on triboelectric sensors. (**bi**) Structure of the insole integrated with two sensors. (**bii**) Schematic diagram of the process of the auxiliary training. (**biii**) The auxiliary training robot with the integration of triboelectric sensors. (**biv**) Schematic diagram of the specific process of gait training. (**c**) Structural design of the self-powered sensing network with multipoint distribution. (**d**) Process of the auxiliary training system. (**di**) Acquisition of patients’ gait signals. (**dii**) Gait recognition through machine learning. (**diii**) Doctors issue exercise prescriptions. (**div**) Carry out auxiliary rehabilitation training. (**e**) Human–machine interface of the personalized auxiliary rehabilitation training system. (**f**) Self-healing and high-temperature recoverable capabilities of the TENG. (**fi**) Schematic diagram of the self-healing ability. (**fii**) Schematic diagram of high-temperature recovery. (**fiii**) Corrective insoles are used for flatfoot support and signal collection. (**g**) Wearable multidimensional motion sensor for VR motion-assisted rehabilitation training. (**gi**) Schematic diagram of the wearable multidimensional motion sensor. (**gii**) VR control system and the complete operation process. (**giii**) Demonstration of the VR football shooting game. learning technology to achieve the control of VR games (Figure 5(giii)). The VR game controlled by this multidimensional motion sensor has the potential to provide a novel auxiliary training approach for the personalized training of patients with abnormal lower limb gaits.
# Analyzing the Characteristics of Soil Moisture Using GLDAS Data: A Case Study in Eastern China
## 2 Materials and Methods
The correlation coefficient formula is expressed as [23]:  
\[r=\frac{\sigma_{xy}^{2}}{\sigma_{x}\ \sigma_{y}}=\frac{\frac{1}{n}\sum(x- \overline{x})(y-\overline{y})}{\sqrt{\frac{1}{n}\sum(x-\overline{x})^{2}} \sqrt{\frac{1}{n}\sum(y-\overline{y})^{2}}} \tag{1}\]  
where \(n\) is the data number, \(\overline{x},\overline{y}\) are arithmetic average, \(\sigma_{x}\ \sigma_{y}\) are the standard deviation, and \(\sigma_{xy}^{2}\) is covariance. The Mann-Kendall test is a statistical test widely used for the analysis of trends in climatologic and hydrologic time series [24; 25]. According to this test, the null hypothesis H\({}_{0}\) assumes that there is no trend (the data is independent and randomly ordered) and this is tested against the alternative hypothesis H\({}_{1}\), which assumes that there is a trend. The formula is expressed as:  
\[Z_{c}=\left\{ \begin{array}{cc}\frac{S-1}{\sqrt{\text{var}(S)}},&S>0\\ 0,&S=0\\ \frac{S+1}{\sqrt{\text{var}(S)}},&S<0\end{array} \right. \tag{2}\]  
The test statistic \(Z_{c}\) is used as a measure of the significance of a trend. \[S=\sum_{i=1}^{n-1}\sum_{k=i+1}^{n}\text{sgn}(x_{k}-x_{i}) \tag{3}\]  
where, \(X_{k}\) and \(X_{i}\) are the annual values in years \(k\) and \(i\), and \(k>i\), respectively. \[\text{sgn}(\theta)=\left\{ \begin{array}{cc}1,&\theta>0\\ 0,&\theta=0\\ -1,&\theta<0\end{array} \right. \tag{4}\]  
\[\text{var}[S]=\left.\left[n(n-1)(2n+5)-\sum_{t}t(t-1)(2t+5)\right]/18\right. \tag{5}\]  
In which \(t_{i}\) denotes the number of ties to extent \(i\). The summation term in the numerator is used only if the data series contains tied values. \[\beta=\text{\emph{Median}}\ \left(\frac{x_{i}-x_{j}}{i-j}\right),\qquad\forall j<i \tag{6}\]\(1<j<i<n\), beta is represented by the slope; the positive value rises and negative value declines, thereby the value represents whether the trend is obvious.
# Machine Learning Reconstruction of Wyrtki Jet Seasonal Variability in the Equatorial Indian Ocean
## Appendix A
### Feature Importance Analysis of Marine Parameters in WJ Reconstruction  
In the reconstruction of the WJ in the equatorial Indian Ocean, marine environmental parameters differed in their contributions to model accuracy. To explore the importance weights of various input factors in model training and enhance the regression fitting process to approach the OSCAR-observed current values (U) for optimal inversion accuracy, this study used the XGBoost machine learning model to calculate the importance scores of 10 marine environmental parameters for U reconstruction, with 4 significant digits retained. The higher the importance score, the greater the parameter's contribution to the reconstruction results. Figure 14 shows the feature importance analysis results of the XGBoost model. The top five parameters and their scores were Ty (0.0255), LAT (0.0226), Tx (0.0171), SST (0.0147), and Curlz (0.0100). Ty reflects the north and south movement of the WJ axis, while Tx mainly influence the eastward current movement and related wind stress curl changes, which often lead to temperature increases in the eastern equatorial Indian Ocean.  
The other parameters, namely SSS, D20, LON, SLA, and MLD, had smaller contributions, with importance scores of 0.009, 0.008, 0.007, 0.006, and 0.005. Notably, the eastward WJ movement drove dynamical response in the eastern equatorial Indian Ocean, which manifested as increased salinity, a deepening of D20, elevated SLA, and a thickening of MLD. These results revealed the distinct roles of marine environmental parameters in U inversion, offering a basis for model optimization and key parameter selection in future studies.
## 5 Conclusions  
The shear-displacement-dependent and shearing-speed-dependent shear stress under different drainage conditions are examined using a ring shear apparatus. The main research findings follow:  
1. The shear stress of weathered soils encountered in landslides is generally dependent on the shear speed and drainage condition. For a given test condition, i.e., controlled drainage and shear speed, the soil behavior has almost the same strain-softening characteristics: there are distinct peak and residual values. At a shear speed of 0.1 mm/s (such as a moderate landslide rate), however, the peak shear stress has an extensive range, while the residual shear stress has a relatively small range. Both increase with an increase in shear speed for a given range 0.1 to 100 mm/s.
2. The shear zone in the ring shear box was observed after testing. The shear surface is identified by the naked eye when a relatively large displacement is applied (approximately 30 mm corresponding to a strain of 5% in shearing). The grain sizes are examined by sieve analysis for the soil samples taken from the upper, middle, and lower parts in the ring shear box. The minimum shear resistance, i.e., residual shear stress, can be determined, even though it is very difficult to identify the thickness of shear banding and particle crushing because of a limited shear displacement and speed. This shear resistance may indicate the critical value against deformation at the initiation of landslide movement.
3. The slope is distinctly stable under unsaturated soil conditions; in other words, it is stable if there is no rainfall event. However, the slope will be unstable when the slope is fully saturated after heavy rainfall; in this case, the cohesion goes to almost zero, and the internal friction angle goes to the angle of repose in the slope. Direct shear tests and ring shear tests are applicable to determine the shear resistance along the slip surface. In design practice, the determination of residual shear stress is crucial for estimating the reactivation of slope movement. The minimum residual shear stress is approximately 10 kPa for weathered soils at a shear speed of 0.1 mm/s, regardless of drainage conditions. The safety factor can be determined for the different slopes and soil thicknesses based on the test results. In conclusion, the use of residual shear stress obtained from this study can be applied at the beginning of landslide occurrence when we can take into account the reactivation of the slope.  
This work was supported by the Korea Institute of Geoscience and Mineral Resources (KIGAM) research project No. 21-3412-1; 22-3412-1.  
Not applicable.  
The author declares no conflict of interest.
## 2 Materials and Methods
### Characterization of the Applied Coatings
To evaluate the hydrophobic properties of the TP-Hy/N-TiA coating, static contact angle measurements were taken by the OCA25 OCA 25 device (DataPhysics Instruments, Filderstadt, Germany), using the sessile drop method, with 5 \(\upmu\)L drops applied via a needle to the stone surface before and after treatment, according to UNI 11207:2007 [84]. The artificial ageing test was performed on untreated and TP-Hy/N-TiA- and TP-Hy/P25-treated _Lecce stone_ (three samples for each case). Four cycles of exposure to rain and artificial solar radiation were made. Each 24 h cycle consists of 620 mm of water (i.e., the average annual rainfall in Salento) dropped on the samples, for 6 h, in the form of water mist, followed by 16 h of artificial solar irradiation under an Osram Ultra Vitalux lamp (300 W) (in UVA + B \(\sim\)5.5 mW/cm\({}^{2}\) and in the 300-800 nm range \(\sim\)56 mW/cm\({}^{2}\)[85]) and 2 h of dark at room conditions. The test was carried out in a closed container (45 \(\times\) 35 \(\times\) 30 cm\({}^{3}\)) equipped with a 45\({}^{\circ}\) inclined plane for housing the samples and with a lateral nebulization system with two nozzles. The hydrophobic properties of the coatings after ageing were tested by measuring the static contact angle at the end of each cycle. Resistance to salt crystallization was assessed by adapting the procedure described in the standard UNI-EN 12370-2001 [86] on three _Lecce stone_ samples (5 \(\times\) 5 \(\times\) 1 cm\({}^{3}\)) for each case. The samples, previously dried in an oven at 105 \({}^{\circ}\)C until a constant weight is reached, were weighed and then immersed in a 14% water solution of Na\({}_{2}\)SO\({}_{4}\cdot\)10H\({}_{2}\)O for 2 h at 20 \({}^{\circ}\)C. Then, the samples were dried in an oven at 105 \({}^{\circ}\)C for 16 h, cooled at room temperature in a desiccator for 2 h and weighed. The mass variation percentage with respect to the initial dry mass at the end of the cycles was calculated. The cycle was repeated five times.
## 4 The Importance of Variables
### Terminal-Lake  
In studies of lakes in arid regions of Iran and China, researchers have identified near-infrared spectral intervals, along with their derived indices and surface temperature, as key indicators for soil salinity inversion. In the Sirjan Playa area of southeastern Iran [130], despite considerable seasonal variability in soil electrical conductivity (ECe) values, indices derived from Sentinel-2 and Landsat OLI 8 images effectively captured these fluctuations. Notably, the Vegetative Soil Salinity Index (VSSI) emerged as a crucial predictor in explaining the winter-summer variability of electrical conductivity (ECe) when analyses were conducted using ANN and RF models. In the Lake Urmia region of Iran, [PERSON] et al. [85] found that surface temperature, land use, and vegetation indices were significant environmental variables influencing soil salinity inversion by analyzing data from 2005, 2010, 2015, and 2019. In the Ebinur Lake area of Xinjiang, China [89], it was observed that band 5 (705 nm) exhibited the most substantial correlation with soil ECe. The response of the red-edge band underscores the potential of Sentinel-2 in extracting soil salinity information. Additionally, vegetation environmental covariates play a vital role in soil salinity studies, with the triple band index (TBI) being the most critical among all environmental covariates. The findings from these three studies emphasize the importance of integrating remote sensing techniques with environmental covariates for effective soil salinity monitoring and inversion. [PERSON] et al. [131] utilized Landsat data to map the distribution of soil salinity on the exposed seabed of the Aral Sea from 1960 to the present. The results demonstrated a significantly strong correlation between the salinity of the surface soil (at a depth of 0-5 cm) and the soil salinity index 4 (SI4) among the seven indices analyzed.