esa-sceva/satcom-chunk-collection huggingface dataset:
## 9 Samples of the Results
### Collision Avoidance in Dense Crowds Using Hybrid Sensors and DRL  
[PERSON] et al. [44] proposed a novel algorithm called CrowdSteer, designed for real-time collision avoidance in densely crowded environments. The approach uses hybrid sensor data from a 2-D LiDAR and a depth camera, fused via a deep reinforcement learning (DRL) framework to enable robust navigation. The problem is modeled as a Partially Observable Markov Decision Process (POMDP) and solved using Proximal Policy Optimization (PPO). High-fidelity simulations, including complex pedestrian interactions, were used to train the policy, ensuring a smooth sim-to-real transfer. The CrowdSteer algorithm generates collision-free trajectories in scenarios with varying crowd densities, narrow passages, and occlusions. Evaluations were conducted on both Turtlebot and Jackal robots, demonstrating superior performance compared to state-of-the-art methods like Dynamic Window Approach (DWA) and [PERSON] et al.'s approach. A key visualization of the results is presented in Figure 22, which compares the trajectories generated by CrowdSteer and [PERSON] et al.'s method in static and occluded pedestrian scenarios. The figure highlights that CrowdSteer produces smoother trajectories and avoids oscillations, resulting in significantly improved success rates in dense environments. These results emphasize the effectiveness of combining LiDAR and depth camera data in achieving robust, real-world collision avoidance.  
Figure 21: Training results for 1D and 2D convolutional networks. The figure demonstrates the efficiency of 1D convolutions in achieving similar performance to 2D convolutions with reduced training time. Green circles represent the circulating robots, and red circles represent reduction of the simulation to one environmental map (Lab) (adapted from [38]).  
Figure 22: (**a**) Trajectories generated by [PERSON] et al.’s method in the Narrow-static (left) and Occluded-ped scenarios (right). (**b**) The trajectories generated by CrowdSteer for the same scenarios (Adapted from [44]).
# An Energy-Efficient Integration of a Digital Modulator and a Class-D Amplifier
## 3 Theoretical Background
If the trigonometric identities,  
\[\sin\alpha\pm\sin\beta=2\sin\frac{1}{2}(\alpha\pm\beta)\cos\frac{1}{2}(\alpha \mp\beta), \tag{4}\]  
\[\sin(\alpha+\beta)=\sin\alpha\cos\beta+\cos\alpha\sin\beta, \tag{5}\]  
are used, every term in the summations of (1)-(3) becomes  
\[\rm TE,\ k\ odd: \frac{4}{k\pi}\cos\!\left(\frac{k\pi x(t)}{2}\right)\cos\!\left( 2\pi f_{c}kt-\frac{k\pi x(t)}{2}+\frac{\pi}{2}\right), \tag{6}\] \[\rm TE,\ k\ even: \frac{4}{k\pi}\ \sin\!\left(\frac{k\pi x(t)}{2}\right)\cos\!\left(2\pi f _{c}kt-\frac{k\pi x(t)}{2}\right),\] (7) \[\rm LE,\ k\ odd: \frac{4}{k\pi}\ \cos\!\left(\frac{k\pi x(t)}{2}\right)\cos\!\left(2\pi f _{c}kt+\frac{k\pi x(t)}{2}-\frac{\pi}{2}\right),\] (8) \[\rm LE,\ k\ even: \frac{4}{k\pi}\ \sin\!\left(\frac{k\pi x(t)}{2}\right)\ \cos\!\left(2\pi f _{c}kt+\frac{k\pi x(t)}{2}\right),\] (9) Figure 1:0 (10) Figure 1:1  
When the six terms in (6)-(11) are observed, it is clear that all three PWM versions will include the frequency spectrum of \(x(t)\) in their base band, and, as a consequence, all of them can be used in class-D amplifiers. However, we want to now bring attention to the harmonic contents. Double-edge-modulated signals have harmonic components that are amplitude-modulated by \(x(t)\), while single-edge modulations have harmonic components modulated both in amplitude and phase. This remark leads to the idea of using a harmonic component to generate a simple modulation of \(x(t)\).
# The Response of NDVI to Climate Change in the Lowest and Hottest Basin in China
## 3 Results and Analysis
### Analysis of NDVI Spatial Variation Trend  
Figure 6 illustrates the spatial distribution of annual and seasonal NDVI trends in the Turpan-Hami Basin from 2001 to 2020. From an interannual perspective (Figure 5(a)), NDVI across the entire study area generally exhibited an upward trend, particularly in the southwestern and northeastern regions, where areas with significant increases were relatively extensive. However, in the western and central parts of the basin, there were some areas with notable NDVI declines, indicating potential severe vegetation degradation  
Figure 5: Spatiotemporal distribution of annual and seasonal NDVI in the Turpan-Hami Basin from 2001 to 2020: (**a**) annual average NDVI; (**b**) spring average NDVI; (**c**) summer average NDVI; (**d**) autumn average NDVI; (**e**) Winter average NDVI.  
issues in these regions. Compared to seasonal NDVI, annual NDVI showed improvement over a broader area and degradation over a narrower area. Among the seasons, NDVI improvements were dominant in all quarters, yet seasonal NDVI dynamics exhibited significant spatiotemporal heterogeneity (Figure 6). The areas with extremely significant and significant improvements in spring, summer, and autumn NDVI were similar, with the most pronounced improvement trends in the northeastern and southwestern parts of the basin, primarily concentrated in grassland areas (Figures 1 and 6). Additionally, the areas with extremely significant and significant degradation in NDVI during these three seasons were mainly located in the western and central parts of the basin (Figure 6b-d), suggesting that while vegetation recovery was widespread during these seasons, vegetation in some localized areas still faced challenges. Notably, winter NDVI changes (Figure 6e) differed from those in other seasons, with areas of extremely significant and significant improvement primarily distributed in the northeastern and central regions, while areas of significant degradation were predominantly located in the southwestern region.  
Figure 6: Spatial distribution of the changing trends of annual and seasonal NDVI in the Turpan-Hami Basin from 2001 to 2020.  
Figure 7 presents the areal statistics of different changing trends of NDVI in each season. Although the areas of NDVI improvement and degradation vary in different seasons, the total areas of improvement and degradation are similar. Specifically, the extremely significant improvement area ranges between 10% and 11.5%, the significant improvement area ranges between 17.9% and 22.7%, and the area with insignificant improvement constitutes the largest proportion, exceeding 58.6%. In contrast, the total degraded area is less than 12%. Notably, the improved area accounts for over 87% of the total area of the Turpan-Hami Basin, where vegetation improvement is substantially more extensive than vegetation degradation.
## 1 Introduction
Therefore, this paper proposes a novel high-spectral-efficiency maritime communication technique based on time-delay overlapping modulation (TDOM) and investigates its communication performance under maritime communication channels. Different from the traditional multi-carrier modulation technique, the core of TDOM lies in the fact that multiple carriers are delayed in turn and directly overlapped in the time domain. This will resolve the orthogonality constraint of traditional multicarrier modulation on carrier frequency and achieve non-orthogonal communications. Therefore, higher spectral efficiency and a higher data transmission rate can be achieved. This rest of this paper is organized as follows. Section 2 describes the transceiver structure of the proposed TDOM and its modulation and demodulation principles. Section 3 analyses the performance of the system, such as the spectral efficiency, complexity, and the origin of the bit error rate. Section 4 gives the simulation results and discussions, and Section 5 concludes the paper.
# Blockchain Based Delay and Energy Harvest Aware Healthcare Monitoring System in WBAN Environment
## 5 Experimental Results and Discussion
### Comparative Analysis  
In this section, the details of the simulation results are discussed in detail. The proposed B-DEAH is compared with three previous works, CF-EHARP, PCA and E-HARP. The main sets of parameters that are taken into account for comparison are network throughput, end-to-end delay, packet loss rate, authentication time, residual energy and success rate. Furthermore, the efficiency of the work is compared with the previous works with respect to fault data elimination check (%) and reliability (%) to the previous works.
# Multifeature Fusion Neural Network for Oceanic Phenomena Detection in SAR Images
## 3 Establishment of the Sample Dataset of Oceanic Phenomena
### Sample Dataset Annotation  
After generating the original oceanic phenomena dataset of SAR images, the oceanic phenomena of each SAR image in the dataset must be annotated by professional experts. We take into account the weak boundaries of oceanic phenomena and annotate only the core of oceanic phenomena to avoid introducing noise that affects the accuracy of detection when annotating. As shown in Figure 5, oceanic eddies were annotated in green, rain cells in blue, ship wakes in purple, oceanic fronts in red, and oil spills in yellow. Thereby, an annotated image for each slice image is obtained. The images of oceanic phenomena and the annotated results are collated to construct a complete sample dataset.
## 3 Dynamic Multi-Hop Routing (DMR) Technique
### Phases of the DMR Protocol
#### 3.2.2 The Announcement Phase  
In this phase, the DMR protocol assumes that all types of network nodes (CHs, INs, and Ns) periodically broadcast announcement messages. These messages declare the network nodes to each other. The DMR protocol utilizes two types of announcement messages. The first type is broadcast at the beginning of each round. Accordingly, Ns broadcast announcement messages to one another within each cluster. Typically, each announcement message includes the node's ID and its corresponding cluster ID, coordinates, level, and residual energy. All CHs and INs also exchange their own announcement messages, which include their IDs, geographical coordinates, levels, and residual energy. The second type is an updated message, which is usually broadcast during the round. The updated message broadcast by an N includes its ID, corresponding cluster ID, and residual energy, while the one broadcast by the CHs and INs contains their ID, level, and residual energy.
## 3 Methodology
### Data Pre-Processing
#### 3.3.2 Data Normalization  
Before inputting the Myogym dataset into the CNN-ResBiGRU model, we used min-max normalization to adjust the sensor input to a consistent range. Min-max normalization is a frequently employed approach that linearly converts the data to a predetermined range, usually from 0 to 1. The normalization step is conducted individually for each sensor channel using the following equation:  
\[x_{i}^{norm}=\frac{x_{i}-x_{i}^{min}}{x_{i}^{max}-x_{i}^{min}},i=1,2,3,\ldots \tag{1}\]  
where \(x_{i}^{norm}\) represents the normalized data, \(n\) represents the number of channels, and \(x_{i}^{max}\) and \(x_{i}^{min}\) are the maximum and minimum values of the \(i\)-th channel, respectively.  
Many reasons determined the selection of min-max normalization. First, it guarantees uniformity in scaling throughout several sensor modalities (IMU and EMG) by adjusting all channels to an identical range. It is crucial to include this factor when combining data from several sensors, as it avoids any one kind of sensor overpowering the others, owing to variations in magnitude. Furthermore, min-max normalization ensures the initial data distribution is maintained throughout the scaling process, preserving the corresponding connections across data points. This quality is essential for preserving the intrinsic structures and properties of the sensor signals.  
Furthermore, applying data normalization to a predetermined range is suitable for deep learning models and stabilizes the learning procedure. By normalizing the data to a range of 0 to 1, we ensure that the input to the CNN-ResBiGRU model is suitable for practical training and optimization. Min-max normalization is more resistant to outliers than other normalizing procedures, such as z-score normalization. Since the Myogym dataset consists of practical sensor data that can sometimes contain outliers, the use of min-max normalization helps to reduce the influence of these outliers on the general distribution of the data.  
Min-max normalization was applied to each sensor channel separately, such as the x-axis of the accelerometer, the y-axis of the gyroscope, and the first EMG channel. The normalizing approach was only applied to some channels collectively. This method guarantees that the scaling procedure is tailored to the distinct attributes of each specific channel, thus maintaining the proportional differences between various sensor modalities. By independently normalizing each channel, we preserve the unique characteristics and variations specific to each sensor signal while adjusting them to a standardized scale.
## 5 Feasibility and Evaluation
### Architectural Comparison
#### 5.5.11. Supportability  
Almost all traditional security systems are equipped with mature event frameworks and logging mechanisms that can be used to resolve errors, failure and trace user activity. Since EDAS utilizes the same utility, it can potentially provide the same level of support.
# Research on Residual Life Estimation Method for KMN Steel Based on Nonlinear Ultrasonic Testing
###### Abstract  
The testing of KMN steel bending fatigue with different cycles was carried out using a nonlinear ultrasonic detector to obtain its nonlinear coefficient. The experimental results show that the nonlinear coefficient first increases and then decreases with an increase in fatigue cycles. The relationship between the propagation of the micro-cracks inside the material and the nonlinear coefficient was researched by microscopic analysis in the dangerous position of the specimens. As the fatigue cycles increase, the microstructure of the specimen gradually deteriorates and cracks occur, which proves that nonlinear ultrasonic detection can be used to characterize the initiation of micro-cracks in the early fatigue stages of the material and that the nonlinear coefficient \(\beta\) of the material can be used to reflect the fatigue damage degree and fatigue life of the interior of the material. An analysis of the numerical statistics of the fatigue cracks inside the specimens was carried out, and the extreme value of fatigue cracks was calculated using the Gumbel distribution. An empirical formula for the nonlinear coefficient and crack growth size of KMN steel was established and then a method for estimating the fatigue life of KMN steel based on nonlinear ultrasonic testing was proposed.  
nonlinear ultrasonic; fatigue damage; crack characterization; residual life; KMN steel +
Footnote †: journal: Applied Sciences  
**Keywords:** nonlinear ultrasonic; fatigue damage; crack characterization; residual life; KMN steel +
Footnote †: journal: Applied Sciences
# Modeling and Profiling of Aggregated Industrial Network Traffic
## 1 Introduction
Consequently, the research works that chose the next best solution of developing methods using the lab or simulated data do not necessarily obtain the expected results when applied to the traffic collected from brownfield. Figure 1 illustrates examples of traffic collected from (a) a paper mill operational network [3], (b) an SCADA system testbed for wind turbine [11], and (c) a SCADA lab with industrial equipment [12]. It is evident that the traffic patterns and their complexity differ vastly even in small time intervals between the three sources of data. The reason for the complexity of the traffic collected from brownfield is twofold: overly tailored networks to fulfill the performance guarantees of a specific application or use-case, and long life span of the industrial systems where integration of new applications resulting in significant differences between the current states from the initial, and theory-supported, configuration. The evident consequences of this complexity are the high cost of network reconfiguration with flow-based measurement and modeling, and divergence from the theoretical class-based assumptions when modeling the aggregated traffic classes [10]. Figure 1: The complexity of the communication patterns from three different sources is visualized. Even in smaller time intervals, the aggregated traffic of the production line is more complex, while also showing the operational work-cycles.
## 1 Introduction  
Electrodeposition can be used to form nanostructures on electrode surfaces, offering a wide variety of morphologies such as supported isolated nanoparticles, regular structures formed on templates, isolated nanostructures with high degrees of ramification or variable shapes, and complex multicomponent composite structures. Each type of structure can present benefits for application as a supporting material for biosensor development resulting from properties that increase surface area, enhance electron transfer, support localized surface plasmons, or allow for improvements in the oriented immobilization of complex biomolecules. An especially interesting feature of nanostructured electrodes is their use as third-generation electrochemical biosensors, where a direct electron transfer between the electrode surface and the redox centers of the immobilized biomolecules takes place, as has recently been reviewed [1]. The formation of nanostructures by electrodeposition for use as supports for biosensor development is the focus of this review. The formation of nanostructures by alloy electrodeposition followed by dealloying to produce nanoporous gold and related materials for biomolecule immobilization is another large field; however, this has been the subject of other reviews [2; 3], and thus is not a focus here. This review also does not focus on other applications of electrodeposited nanostructures outside of biosensing, or on structures formed by electroless deposition. The types of electrodes that are considered here fall into broad categories of spherical or quasi-spherical nanoparticles that are directly electrodeposited onto a supporting electrode, nanoparticles electrodeposited through or onto some form of template, electrodeposition carried out under conditions that produced ramified or dendritic morphologies, and the electrodeposition of composite electrodes containing nanoparticles along with other components such as graphene or polymers. Some of the systems described here may fall into more than one category, and have been noted regarding their most dominant feature.
## 2 MWC System Model  
The MWC system framework is shown in Figure 1. The whole system can be divided into three parts, namely, signal sampling, support set recovery, and signal reconstruction. The dynamic sensing of spectrum resources by wireless devices can be based on the results of support set recovery, and the reconstructed signals will become communication information if the devices need to communicate.
## 3 Description of QLM-ChPs Matrix Approach
### The Essence of QLM  
The nonlinear model (11) can be further reformulated in a concise format as  
\[\frac{d^{2}}{dx^{2}}M(x)=\Omega\Big{(}x,M(x),\frac{d}{dx}M(x)\Big{)}, \tag{12}\]  
where  
\[\Omega\Big{(}x,M(x),\frac{d}{dx}M(x)\Big{)}:=-\frac{n}{x}\frac{d}{dx}M(x)+ \frac{2h^{2}\,M(x)}{\alpha+M(x)}.\]  
Note that the boundary conditions (2) are also given with (12). To begin with QLM, we are required to set an initial guess \(M_{0}(x)\) as an approximation for the true solution \(M(x)\) of (12). Hence, the QLM for the model Equation (12) is written generally as  
\[\frac{d^{2}}{dx^{2}}M_{e}(x) \approx\Omega\Big{(}x,M_{e-1}(x),\frac{d}{dx}M_{e-1}(x)\Big{)}\] \[+\Omega_{M}\Big{(}x,M_{e-1}(x),\frac{d}{dx}M_{e-1}(x)\Big{)} \Big{(}M_{e}(x)-M_{e-1}(x)\Big{)}\] \[+\Omega_{M^{\prime}}\Big{(}x,M_{e-1}(x),\frac{d}{dx}M_{e-1}(x) \Big{)}\Big{(}\frac{d}{dx}M_{e}(x)-\frac{d}{dx}M_{e-1}(x)\Big{)},\]  
for \(e=1,2,\ldots\). Here, the \(\Omega_{M}\) represents the derivative of \(\Omega\) with regard to \(M\). The same is valid for \(\Omega_{M^{\prime}}\). Making use of QLM for the model (12) gives us  
\[\frac{d^{2}}{dx^{2}}M_{e}(x)+\Big{(}\frac{n}{x}\Big{)}\frac{d}{dx}M_{e}(x)- \frac{2\alpha\,h^{2}}{\big{(}\alpha+M_{e-1}(x)\big{)}^{2}}\,M_{e}(x)=\frac{2\, h^{2}\,M_{e-1}^{2}(x)}{\big{(}\alpha+M_{e-1}(x)\big{)}^{2}},\,e=1,2,\ldots.\]In order to write the former equations in a more convenient format, we multiply both sides by \(x\cdot\left(\alpha+M_{e-1}(x)\right)^{2}\). By introducing the notations  
\[\kappa_{e-1,1}(x) :=x\left(\alpha+M_{e-1}(x)\right)^{2}, \kappa_{e-1,2}(x) :=n\left(\alpha+M_{e-1}(x)\right)^{2},\] \[\kappa_{e-1,3}(x) :=-2\alpha\,\hbar^{2}\,x, \pi_{e-1}(x) :=2\,h^{2}\,x\,M_{e-1}^{2}(x),\]  
we arrive at the next set of linearized equations  
\[\kappa_{e-1,1}(x)\,\frac{d^{2}}{dx^{2}}M_{e}(x)+\kappa_{e-1,2}(x)\,\frac{d}{dx }\,M_{e}(x)+\kappa_{e-1,3}(x)\,M_{e}(x)=\pi_{e-1}(x),\qquad e=1,2,\ldots. \tag{13}\]  
The boundary conditions (2) are now converted to the following forms  
\[\frac{d}{dx}M_{e}(0)=0,\quad\frac{d}{dx}M_{e}(1)+s_{h}\,M_{e}(1)=s_{h}. \tag{14}\]  
Now, the task is to solve the family of boundary value problems (13) and (14). This can now be accomplished by using the direct spectral collocation approach based on ChPs.
## 2 Experimental Procedures
### Sample Preparation by Electrospinning  
A CS solution with a concentration of 7 wt% was prepared by dissolving chitosan powder in a mixture of solvents of acetic acid and water (7:3 _w/w_). The mixture was mixed for two hours using a planetary centrifugal mixer (ARE-310, Thinky Co., Tokyo, Japan). The prepared solution was further mixed by a magnetic stirrer for two days at room temperature to obtain a homogeneous solution. At this point the solution was divided in two classes, one with BG (15 wt% of CS) and one without. Then the PEOpowder was added to the obtained solutions with the CS (PEO weight ratio of 2/8). The concentration of chitosan, biogas and PEO were decided following previous studies presented in literature [25; 26; 27]. The mixtures were further mixed for 1 h using a planetary centrifugal mixer. The viscosity of each solution was measured by a vibronic viscometer (SV-100A, A&D Co., Japan) at 25 \({}^{\circ}\)C; each solution was examined three times (Table 1). The PEO/CS/BG solutions presented a higher value (1.70 \(\pm\) 0.05 Pa) than the solution without BG (1.05 \(\pm\) 0.01 Pa).  
The fibrous coating layer was prepared using an electrospinning system of our own design (Figure 1).  
During the electrospinninging process, the feed rate of the solution was kept at 0.1 mL h\({}^{-1}\) by a syringe pump (KDS-100, KD Scientific Inc., Holliston, MA, USA). A voltage of 27 kV, generated from a high voltage power supply (HVU-30P100, MECC Co., Japan), was applied to the 30 G needle. The distance between the needle and the collector was fixed at 10 cm. A rotating drum covered by an aluminum foil was used as a collector with a constant rolling rate of 120 rpm. The relative humidity and temperature were monitored by a hygthermograph placed inside the electrospinninging chamber and maintained at the values of 55 \(\pm\) 5% and 24 \(\pm\) 2 \({}^{\circ}\)C, respectively. Circle-shaped Ti-alloy substrates with a diameter of 12.7 mm were fixed on the stainless steel drum covered by an aluminum foil as a collector. Before the coating procedure, the substrates were washed, sonicated in ethanol/acetone (1:1 _w/w_) for 30 min and left for drying at 50 \({}^{\circ}\)C in the oven. After the coating procedure, all the samples were dried under vacuum for 12 h at 50 \({}^{\circ}\)C to completely remove any retained solvents. The samples were placed in a vapor chamber containing 20 ml of GA and were allowed to crosslink the fibers for 24 h. After the crosslinking process, the samples were dried under vacuum for 12 h at 50 \({}^{\circ}\)C to remove any remained GA.