esa-sceva/satcom-chunk-collection huggingface dataset:
# Analyzing Data Reduction Techniques: An Experimental Perspective
## 7 Data Reduction Techniques
### Lossy Data Reduction Algorithms
By grouping related data points, clustering provides a condensed representation of the original dataset, highlighting commonalities and reducing the need to analyze each data point separately. K-Means algorithm assigns data points to clusters based on features or distances, providing manageable and interpretable representation of the data. This condensed form facilitates exploratory data analysis, pattern recognition, and insights into the inherent structure of the dataset, contributing to efficient data reduction and simplification. **Simple Random Sampling** is a data reduction technique that selects a subset of data points from a larger population to create a representative sample that preserves the essential characteristics of the entire dataset [40]. By working with a fraction of the original data, this algorithm allows for more efficient analysis, reduced computational requirements, and faster processing times. While sacrificing some granularity, a well-designed sample can still provide meaningful insights and conclusions about the population, making sampling a valuable data reduction strategy where analyzing the entire dataset is impractical or resource-intensive. The main use cases and applications for each algorithm are listed in Table 4.
# Evaluation of Green and Grey Flood Mitigation Measures in Rural Watersheds
## 2 Materials and Methods
### Asset Identification and Vulnerability Assessment
#### 2.4.1 Building Stock and Its Inventory  
In this paper, the micro-level flood damage assessment is conducted, meaning that evaluation of vulnerabilities is performed on a level of individual buildings for a series of hazard events. This approach is more detailed in comparison to the aggregated approach applied in the UNDP study [21]. A total of 5984 permanent structures were identified from aerial photogrammetric imagery within the river sectors' boundaries (the maximum potential extent of flooding). The number of structures and their occupational classes were verified in the field. Of these, 63.5% are residential buildings (primarily single family, low-density and farm houses), 27.8% are agricultural buildings, 6.4% are commercial, educational, and government structures, with the remaining 2.3% being light industrial buildings. The characteristics of the above types of structures (i.e., occupancy classes) are typical for this region and this part of the country [32].  
The damages to structures caused by floods are estimated using the depth-damage function (DDF). The damage as a function of water depth in the structure is typically expressed not in absolute monetary values, but as percentage of the \"maximum damage value\" [33], which is the value of replacing the completely destroyed structure with a new one.  
In this paper, the DDFs for residential, commercial, industrial, and transportation occupancy classes of buildings were adopted from the Joint Research Center's global database [33], with modifications applied for Serbia and for inflation. It was conservatively assumed that buildings have no basements. All the modifications applied to DDFs from [33] are made in accordance with field investigations.  
The shape of DDF for buildings was also adopted for the corresponding building content. The maximum damage value for the building content ranged between 50% and 150% of the maximum damage value for the corresponding building structure, as shown in Table 2. An example of the DDF for residential buildings used in the study is presented in Figure 5.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Building Type**} & \multicolumn{2}{c}{**Maximum Damage 1 **} & \multicolumn{1}{c}{**[\(\ell\)/m\({}^{2}\)]**} \\ \cline{2-3}  & **Structure** & **Content** \\ \hline Residential 2 & 203–271 & 101–136 \\ Commercial & 298 & 298 \\ Industrial & 207 & 310 \\ Transportation & 107 & n/a \\ Agricultural 3 & 149–298 & 75–298 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Maximum damage values used in the study.
## 4 Performance Analysis  
To assess the performance of the proposed FSACO protocol, we developed a study under a simulated environment on NS-2 (Version 2.35). We obtained the results in terms of different performances from simulating FSACO in comparison with EEABR [10], SensorAnt [21] and IACO [20] mentioned in Section 2.
# Upper-Bound Limit Analysis of Ultimate Pullout Capacity of Expanded Anchor Cable
## 5 Model Application
### Effect of the Buried Depth of the Anchor Cable  
Figure 19 presents the ultimate pullout capacity, lateral resistance, and end resistance variation curves according to the change in the buried depth of the anchor cable. The figure clearly shows that the lateral resistance, end resistance, and ultimate pullout capacity increased with the increase in the buried depth of the anchor cable. When the buried depth  
Figure 17: The ultimate force variation curves according to an increase in the anchorage segment length.  
Figure 18: The ultimate force variation curves according to an increase in the inclination angle of the anchor cable.  
increased from 6 m to 10 m, the lateral resistance increased from 561.2 kN to 756.8 kN by about 35%, the end resistance increased from 167.5 kN to 412.2 kN by about 146%, and the ultimate pullout capacity increased from 728.7 kN to 1169.0 kN by about 60%.
# Kinematic Analysis and Verification of a New 5-DOF Parallel Mechanism
## 4 Workspace Analysis  
### Influencing Factors of Workspace  
The main factors that affect the workspace of parallel robot are:  
(1) The limitation of the length of link. The link lengths of each limb are not random and must be limited to a certain range. When the length of the link is the minimum, the moving platform and the five limbs are in the same plane, and the mechanism has a singularity. When the length of the link is the maximum, the moving platform and the fixed platform coincide, the five sliders are located at the top of the lead screw, and the whole mechanism is locked.  
Figure 11: Acceleration curves of sliders.  
Figure 10: Velocity curves of sliders.  
(2) Limit of slider stroke. The influence of slider stroke range on workspace is mainly concentrated on \(Z\) dimension of workspace.  
(3) The limitation of the position and angle of the spherical hinge. Because the movement range of the spherical hinge is in a certain range, the change of the position of the spherical hinge arrangement will change the position where the spherical hinge reaches the limit.  
(4) Limit of link interference. In the actual prototype, each link has a certain size, so interference should be avoided.
## 5 Discussion
The results show that GBRT achieves the best performance, and GBRT was more robust for the training samples than the other models. Despite the promising results revealed by five ML regression methods, there are some limitations of our study, and further studies are needed to assess the generalization ability of the five ML regression methods. Firstly, we focus on only one crop type, and the performance of ML regression methods for other crop types has not been validated. It is necessary to evaluate ML algorithms with various crop types, provided there are enough field measurement LAI data of different crop types available. Secondly, some other crop biophysical parameters (e.g., Chlorophyll content and FVC) demand evaluation as well as ML regression methods, as different results may appear with different ML algorithms and different crop biophysical parameters. Thirdly, there are limitations with Sentinel-2 spectral bands sensitivity analysis, since some spectral bands are highly correlated, and redundancies remain among the spectral bands, so this process would be worth further investigations to explore a better spectral bands sensitivity analysis. Additionally, there are limitations to our quadrats in the field campaigns, as we chose only three sample points to represent a quadrant (20 m \(\times\) 20 m). Finally, the performance of five MLRAs has not yet been evaluated for large amounts of data (e.g., tens of thousands of data) due to the limited number of samples in our study, however, it remains to be seen whether a favorable performance can be obtained. In consideration of the ability of ML techniques, it is expected to obtain a better accuracy of the regression models provided there are enough samples, and a turning point should be obtained, as has been demonstrated in previous studies [19]. While before the turning point, the model accuracy increased rapidly with the increase in the training sample size, whereas, after the turning points, the model behaves more stable, in other words, the model accuracy remained unchanged with the increasing of training sample size. Future studies are required to investigate this problem. With the advance in ML technology, deep learning, a new subfield of ML, has been applied successfully to many remote sensing domains, especially with a large quantity of data [85]. It is not necessary to construct complex ML models (deep learning-based models) in our study, as traditional ML algorithms have the capability of establishing efficient, accurate and robust estimation models for cotton LAI retrieval. However, as there are greater amounts of data available, deep learning techniques may be promising alternatives for handling such large volume datasets and complex relationships among the input datasets, which deserve further study.
# Improved Traffic Sign Detection and Recognition Algorithm for Intelligent Vehicles
## 3 Improved LeNet-5 CNN Model
### Improved LeNet-5 Network Model  
#### 3.2.1 Image Preprocessing  
The ROI in the traffic sign training image is not 100% in the center of the image, and some edge background information is included around the traffic sign. With the change of illumination conditions, these useless interference areas will increase the influence on traffic sign recognition, thereby undoubtedly raising the computational complexity of the training network and the misrecognition rate of traffic signs. Therefore, image preprocessing is necessary.  
Image preprocessing mainly includes the following three stages:  
1. Edge clipping. Edge cropping is a particularly important step in the image preprocessing. Some background parts in the edge are not related to traffic signs, and these parts can account for approximately 10% of the entire image. The bounding box coordinates are used for proportional cropping to obtain the ROI. The removal of the interference region helps to reduce redundant information and speed up the network training.
2. Image enhancement. The recognition effects of the same type of traffic signs in the training network under different illumination conditions are significantly different. Therefore, reducing or removing the noise interference caused by the light change via image enhancement is necessary. Direct grey-scale conversion method is used to adjust the grey value of the original image using the transformation function, which presents clear details of the ROI and demonstrates a blurred interference area. Thus, this method effectively improves the image quality and reduces the computational load of the training network.
3. Size normalization. The same type of traffic signs may have different sizes. The different sizes of training images may have different feature dimensions during the CNN training process, which leads to difficulties in the subsequent classification and recognition. In this paper, the image is uniformly normalized in size, and the normalized image size is \(32\times 32\).
## 2 System Description
### Synchronization Signal
It is apparent from (3) that \(\epsilon\) and \(u_{i}\) affect the frequency domain signal \(Y_{l}(k)\) together, thus necessitating the use of joint detection in the traditional synchronization methods. The processing complexity of the IFO estimation can be significantly reduced when it is performed jointly with PSSS detection because there are fewer hypotheses in the PSSS matching process than in the SSSS. Similar to 4G networks, the 5G new radio (NR) synchronization signal is a physical layer specific signal and helps UEs to detect the cell ID. Unlike 4G LTE, the 5G NR PSSS consists of one of three 127-symbols m-sequences and is allocated on the first symbol of each synchronization signal block (SSB), while the SSSS consists of one of 336 127-symbols gold sequences and is allocated on the third symbol of each SSB [29]. Due to the absence of the conjugate property of the synchronization signal, the proposed scheme cannot be directly applied in the 5G-NR system. The 5G-NR encompasses both an evolution of 4G networks and the NR, the synchronization issue in the 4G network is still important. In this paper, we focus on the initial synchronization scheme in the 4G LTE-D2D system.
# A Novel Dynamic Transmission Power of Cluster Heads Based Clustering Scheme
## 4 Proposed Approach
### Link Expiration Time  
Using the movement prediction model, the CH can successfully predict the location of cluster members at the next instance. To adjust the transmitting power quickly and conveniently, the CH calculates the link connection time when the transmitting power needs to be changed by calculating the link connection time. This can avoid the resource waste caused by the frequent calculation of the CH.  
Figure 5 shows the movement trajectory of the CM\({}_{i}\) during a HELLO message switching period. At time \(t_{n}\), the CM\({}_{i}\) is in the light blue grid; after \(l\) time slots, the CM\({}_{i}\) moves to the dark blue grid. Then, the CM\({}_{i}\) moves to the light blue grid again until the next HELLO message exchange starts, i.e., the \(t_{n+1}\) moment. As shown in Table 3, the cluster head can predict the grid position of CM\({}_{i}\) at any time within the HELLO message exchange cycle according to the prediction model, so as to adjust its own transmitting power in time.  
The pseudocode of dynamic transmission power of cluster heads algorithm is listed in Algorithm 3.  
Figure 5: Prediction of cluster member movement.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Time** & \(t_{n}\) **to \(t_{n}+l\Delta t\)** & \(t_{n}+l\Delta t\)** to \(t_{n}+p\Delta t\)** & \(t_{n}+p\Delta t\)** to \(t_{n+1}\)** \\ \hline Grid & Light blue & Dark blue & Light blue \\ P/mW & \(P_{3}\) & \(P_{2}\) & \(P_{3}\) \\ \hline \end{tabular}
\end{table}
Table 3: Table of variation of cluster head transmitting power.  
```
0:Sender node: CH\({}_{i}\), receiver node: CM\({}_{j}\), data message
0:
1:CH\({}_{i}\) gets the velocity, direction angle and the position information of CM\({}_{j}\) by HELLO message
2:CH\({}_{i}\) calculates the position of CM\({}_{j}\) at time \(t\) by Equations (22) and (23)
3:After a simple calculation, CH\({}_{i}\) gets the grid which CM\({}_{j}\) is in at time \(t\)
4:Table 1 look-up process
5:return CH\({}_{i}\) will send the data message to CM\({}_{j}\) with the power of P\({}_{x}\) at time \(t\)
```  
**Algorithm 3** Dynamic Transmission Power of Cluster Heads Algorithm
# Systematic Review of Discrete Event Simulation in Healthcare and Statistics Distributions
## 4 Discussion
### Analysis of Statistical Distributions
#### 4.4.3 Service and Activity Times  
Service time modeling varies significantly by activity type. [PERSON] and [PERSON] [52] found the following:  
* Clinical consultations: Triangular distributions proved effective.
* Diagnostic procedures: Beta distributions showed better fit.
* Administrative tasks: Normal distributions adequately captured routine tasks.  
Their comprehensive analysis of 2500 service interactions demonstrated the importance of context-specific distribution selection.
# Extensible Hook System for Rendezvous and Docking of a CubeSat Swarm +
## 4 Simulation results  
The design of the EHS requires tvalidation through simulation. They provide valuable information about the required forces, torques, and energy to move the CubeSat and the EHS, allowing them to choose the best actuators, i.e., thrusters and motors. The parameters used for CubeSat and EHS are shown in Table 1.  
The actuators were controlled by adjusting a proportional, integrative, and derivative (PID) controller, and a trapezoidal trajectory generator was included in the model to perform the simulations.  
The simulations were performed using Matlab Simscape Multibody. This tool allows us to assemble all the pieces that were previously designed using SolidWorks v2023 and simulate their dynamics by solving the whole mechanic system equations. Moreover, this tool allows us to generate animations of the system, and it can be used to test different controllers by using the hardware and software in the loop concepts.  
During the simulation, guidance and navigation were omitted; due to their complexity, they are proposed as future work. Navigation was replaced by the absolute localization that provides the simulator, and control was implemented using PIDs and gain scheduling for the case of joint 3 of the EHS.  
To validate the proposed system, two simulations were carried out. The first one related to moving the CubeSat from an initial pose to a final one, and the second simulation was related to the extension of the EHS and the required forces and torques to perform the motion.
# A Coaxial Dielectric Barrier Discharge Reactor for Treatment of Winter Wheat Seeds
## 3 Results and Discussion
### Effects on Seed Germination of Wheat Treated with Argon and Helium Plasmas
According to the study performed by [PERSON] and coauthors [30], where different feed gases were used in a surface DBD reactor for seed treatment, nitrogen emission and excited species do not play an important role in the growth enhancement of radish, where the best results were obtained for the oxygen emission containing discharges, e.g., air, O\({}_{2}\) and NO (10%) + N\({}_{2}\). In a study using helium as the feed gas and an open DBD system, X-ray photoelectron spectroscopy (XPS) data indicated a significant input of oxygen to the wheat seed surface, while carbon concentration was decreased [24]. This was associated to the ionization of air molecules in the discharge that interact with the plasma activated surface, leading to oxidation of the pericarp [24]. With regard to the observed trends in WCA measurements, for treatment times below 10 min (Figure 6), argon and helium plasma treatments displayed quite similar wettability effects on the seed coat. In contrast, comparing the germination curves for 3 min (Figure 8a), 5 min (Figure 8b), and 10 min (Figure 8c) treatment times between seeds treated with argon and helium, it is evident that argon  
Figure 7: Germination acceleration of wheat seeds treated for different times (from 3 to 30 min) for both discharges (**a**) argon and (**b**) helium. Germination curves were fitted with the Hill function. plasma leads to faster germination than the helium discharge, irrespective of similar wettability values. Statistical significance was observed between argon and helium-treated seeds for observation times between 15 and 24 h for 3, 5, and 10 min of plasma exposure. Seed germination was significantly different from the control except for the 15 h observation time for helium-treated seeds (Figure 8a-c). Therefore, other factors besides an increase in surface wettability properties due to the attachment of polar functional groups to the seed surface might be taken into account mainly for the plasma treatment times shorter than 10 min. It is discussed for several plant species that interactions of plasmas reactive species with seeds not only alter seed coat characteristics but could have an impact on the physiology of germinating seeds as well [72]. Reactive species such as hydrogen peroxide and nitric oxide are known to have effects on many developmental processes in plants [73; 74]. Hydrogen peroxide has a central role in many signaling processes during the plant life cycle such as the release of dormancy during seed germination [75]. It is also known that external applied hydrogen peroxide stimulates seed germination [76; 77]. Thus, if ROS produced in the discharge can be converted by further reactions with water molecules [78], hydrogen peroxide can be formed, be taken up during the imbibition process, and influence metabolic processes in a positive way.
# PpyNT/NR/NBR Composites with Excellent Microwave Absorbing Performance in X-Band
###### Abstract
The synergistic effect of PPyNT itself, its unique distribution behavior in immiscible NR/NBR blend, and multiple interfaces structure in the rubber blend is made good use of, which leads to a strong reflection loss value of \(-\)56.67 dB and broad effective bandwidth of 3.7 GHz at 2.9 mm in X band when the PPyNT content is 6 phr, and the NR/NBR blending ratio is 90/10. This result surpasses most rubber-based absorbers reported in lots of literature over the same frequency range. In addition, a possible microwave absorbing mechanism for the optimal composite in this study is proposed. This work provides new insight into the design of rubber-based absorbers in the X band.
# A Unified Speech Enhancement System Based on Neural Beamforming With Parabolic Reflector
## 3 Experiment and Results
### Training  
Using IRM defined in Equation (4) as the learning target, DNN was designed, where the output could be considered as the probabilities of the existence of speech at each time-frequency bin. The magnitude, the second derivative of the magnitude, and the phase of the received signals for each STFT time frame were directly provided as the input to the system [12]. Amplitude Modulation Spectrogram (AMS), Relative Spectral Transform and Perceptual Linear Prediction (RST-PLP) [25] were also appended to the feature vector. In this paper, Restricted Boltzmann Machine (RBM) [26] based pre-training was used for DNN training. Supervised fine-tuning of the parameters throughout the whole network was performed using the Mean Square Error (MSE) criterion, as illustrated in Equation (14):  
\[\rho=\frac{1}{2N}\sum_{n}\sum_{k}\left(I_{IRM}(n,k)-\hat{I}(n,k)\right)^{2}, \tag{14}\]  
where \(\hat{I}(n,k)\) are the vectors of reference IRM, and \(N\) is the total number of frames for the input. The DNN architecture was 2075-1024-1024-1024-129, which denotes that the size was 2075 (\(415\times 4\) + 415, including two left and two right context frames, and one current frame in the input layer), 1024 units for each of the four hidden layers, and 129 for the output layer (DFT length was 256, where 256/2 + 1 is the total number of frequency bins). In this work, the dropout rate was 0.2, and the momentum rate was set to 0.5 for the first five epochs, and afterwards the rate was changed to 0.9 for the remaining 35 epochs. The mini-batch size was set to 512. The sigmoid activation functions were used for all hidden layers and the output layer. Other values were evaluated as well; however, this combination performed best empirically.  
In the following experimental evaluations, the method is called PR when processing noisy speech through the physical model. The DSB used for comparison utilized ideal parametric information [27]. Since the estimated IRM from DNN was directly applied to a reference microphone signal, the method was termed as IRM-F. Similarly, with the estimated CIRM, jointly estimating real and imaginary components of STFT [18], the corresponding method is referred to as CIRM-F. When it is used to estimate power spectral density matrices to be used within a MVDR beamformer, the method is referred to as IRM-BF. The method proposed in this work is named PR-IRM-BF.