esa-sceva/satcom-chunk-collection huggingface dataset:
# Mobile Network Operators' Assessment Based on Drive-Test Campaign in Urban Area for iPerf Scenario
## Appendix C
* (51)_ETSI TS 138 214 V16.5.0 (2021-04)_; 5G; NR; Physical Layer Procedures for Data (3 GPP TS 38.214 Version 16.5.0 Release 16). European Telecommunications Standards Institute (ETSI): Sophia Antipolis, France, 2021. * (52)_5G NR RSRQ Measurements and Its Mapping. Techplayon._ 2021. Available online: [[https://www.techplayon.com/5g-nr-rsrq-measurements-and-its-mapping/](https://www.techplayon.com/5g-nr-rsrq-measurements-and-its-mapping/)]([https://www.techplayon.com/5g-nr-rsrq-measurements-and-its-mapping/](https://www.techplayon.com/5g-nr-rsrq-measurements-and-its-mapping/)) (accessed on 3 January 2024). * (53) Synchronization Signal Reference Signal Received Power. Available online: [[http://comtech.vsb.cz/qual](http://comtech.vsb.cz/qual)]([http://comtech.vsb.cz/qual](http://comtech.vsb.cz/qual)) mob/rsrp_5g.html (accessed on 2 December 2023). * (54) [PERSON] _Statistics at Square One_, 12 th ed. ; Wiley-Blackwell: Hoboken, NJ, USA, 2021; ISBN 978-1-119-40130-8. * (55) Information about Spectrum Occupancy in Bands 420 MHz, 450 MHz, 800 MHz, 900 MHz, 1800 MHz, 2100 MHz, 2600 MHz, 3600 MHz (In Polish). Available online: [[https://bip.uke.gov.pl/dostepnosc-czestofluwosci/informacja-o-zajetosci-widdma-treesi/informacja-o-zajetosci-widdma-w-pasmach-420-mhz-450-mhz-800-mhz-900-mhz-1800-mhz-2100-mhz-2600-mhz-3600-mhz](https://bip.uke.gov.pl/dostepnosc-czestofluwosci/informacja-o-zajetosci-widdma-treesi/informacja-o-zajetosci-widdma-w-pasmach-420-mhz-450-mhz-800-mhz-900-mhz-1800-mhz-2100-mhz-2600-mhz-3600-mhz)]([https://bip.uke.gov.pl/dostepnosc-czestofluwosci/informacja-o-zajetosci-widdma-treesi/informacja-o-zajetosci-widdma-w-pasmach-420-mhz-450-mhz-800-mhz-900-mhz-1800-mhz-2100-mhz-2600-mhz-3600-mhz](https://bip.uke.gov.pl/dostepnosc-czestofluwosci/informacja-o-zajetosci-widdma-treesi/informacja-o-zajetosci-widdma-w-pasmach-420-mhz-450-mhz-800-mhz-900-mhz-1800-mhz-2100-mhz-2600-mhz-3600-mhz)), 13,0.html (accessed on 23 January 2024). * (56) Poland 5G--NR, 4G--LTE Frequency Spectrum Bands, 3G--WCDMA, 2G--GSM, FDD, TDD, FR1, FR2 mmWave Mobile Networks. Available online: [[https://www.spectrum-tracker.com/Poland](https://www.spectrum-tracker.com/Poland)]([https://www.spectrum-tracker.com/Poland](https://www.spectrum-tracker.com/Poland)) (accessed on 23 January 2024). **Disclaimer/Publisher's Note:** The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
# Correlation between Soil Moisture Change and Geological Disasters in E'bian Area (Sichuan, China)
## 2 Materials and Methods
### Research Method
The feature space of NDVI-TS took NDVI as the horizontal coordinate and LST as the vertical coordinate, and the scatter plot drawn presented a triangular form (Equation (4)). The negative correlation between the slope of LST and NDVI and soil water is an important statistical feature in the feature space (triangular space). The TVDI expression was as follows:  
\[\text{TVDI}=\frac{\text{T}_{\text{S}}-\text{T}_{\text{Smin}}}{\text{T}_{ \text{Smax}}-\text{T}_{\text{Smin}}} \tag{4}\]  
where T\({}_{\text{S}}\) is the surface temperature data (LST), T\({}_{\text{Smin}}\) is the lowest LST corresponding to NDVI pixel (soil moisture sufficiency), that is, the wet edge, and T\({}_{\text{Smax}}\) is the highest LST corresponding to NDVI pixel (soil drought), that is, the dry edge. a\({}_{1}\) and b\({}_{1}\) are the coefficients of the dry-side linear fitting equation, and a\({}_{2}\) and b\({}_{2}\) are the coefficients of the wet-side linear fitting equation. \[\text{T}_{\text{Smax}}=\text{a}_{1}+\text{b}_{1}\times\text{NDVI} \tag{5}\]  
\[\text{T}_{\text{Smin}}=\text{a}_{2}+\text{b}_{2}\times\text{NDVI} \tag{6}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Grade**} & **Lower** & **Low** & **Medium** & **High** \\  & **Coverage Area** & **Coverage Area** & **Coverage Area** & **Coverage Area** \\ \hline
2018 & 54.3 & 39.1 & 5.29 & 1.32 \\
2019 & 84.7 & 15.2 & 0.07 & 0.54 \\
2020-5 & 3.31 & 17.9 & 21.2 & 30.8 \\
2020-6 & 0.66 & 7.95 & 27.8 & 34.4 \\
2021 & 15.9 & 27.2 & 7.95 & 25.8 \\
2022 & 33.7 & 49.1 & 5.97 Based on 5 high-resolution Landsat remote sensing images and other data from 2018 to 2023, several sets of NDVI and LST remote sensing images were selected to fit the highest and lowest land surface values by the least square method. The slope corresponding to the dry side (Equation (5)) and the slope corresponding to the wet side (Equation (6)) of the NDVI\(-\)LST feature space were obtained. As can be seen from Figure 8, the variation amplitude of the wet edge was smaller than that of the dry edge and did not reach a parallel state with the X-axis. With the increasing NDVI value, the gap between T\({}_{\text{Smin}}\) and T\({}_{\text{Smax}}\) tended to minimize and finally presented a triangular feature space. The closer the TVDI value is to 1, the higher the degree of soil drought or the more obvious the trend is about to be dry; on the contrary, the smaller the TVDI value is, the higher the soil moisture or the more obvious the trend is about to be wet. The calculated dry edge correlation coefficients (R\({}^{2}\)) are 0.71, 0.72, 0.67, 0.69, 0.60, and 0.71. Compared with the range of correlation coefficients (R\({}^{2}\)) calculated from traditional data (0.47-0.55), TVDI generated by high-resolution Landsat remote sensing image data can more accurately reflect the characteristics of soil moisture change.
## 5 Experimental Validation
### Efficiency and Temperature  
In this section, the efficiency and case temperature of the simulation model is validated with actual experimental results at specific speed and torque. The thermocouple is mounted on the case of the inverter to monitor the case temperature. The ambient temperature during the test was 25 \({}^{\circ}\)C. It depends on case temperature; therefore, it is considered the same as the experimental data in the simulation model. Designed passive filters in Section 2 can be applied to reduce the ripples. However, in the Simrod inverter, there are no AC filters, and the inverter is directly connected to the IM. The loss of DC link capacitors is negligible because 44 capacitors are connected in parallel. Therefore, it has not been included in the electro-thermal modeling of an inverter. Three cases are considered for validation, as depicted in Table 4. Therefore, the simulation model gives more or less similar results to the real Simrod EV inverter.
## 3 Results  
### Study Selection  
Initially, 556 studies were identified by an electronic data search. After reviewing the titles and abstracts, 22 potentially relevant studies were subjected to a full text evaluation. Of these, 15 studies were excluded based on their study design, research question, or missing numerical data for all the variables to be assessed in this study (PD, BOP, clinical attachment level, and plaque index), hence only seven studies with a detailed listing of such data could be included in the quantitative comparison (Figure 1).
###### Abstract  
This study presents a novel approach for the detection and severity assessment of pest-induced damage in maize plants, focusing on the _Zyginidia pullula_ pest. A newly developed dataset is utilized, where maize plant images are initially classified into two primary categories: healthy and infected. Subsequently, infected samples are categorized into three distinct severity levels: low, medium, and high. Both traditional and deep learning-based feature extraction techniques are employed to achieve this. Specifically, hand-crafted feature extraction methods, including Gabor filters, Gray Level Co-occurrence Matrix, and Hue-Saturation-Value color space, are combined with CNN-based models such as ResNet-50, DenseNet-201, and EfficientNet-B2. The maize images undergo preprocessing and segmentation using Contrast Limited Adaptive Histogram Equalization and U2 Net, respectively. Extracted features are then fused and subjected to Principal Component Analysis for dimensionality reduction. The classification task is performed using Support Vector Machines, Random Forest, and Artificial Neural Networks, ensuring robust and accurate detection. The experimental results demonstrate that the proposed hybrid approach outperforms individual feature extraction methods, achieving a classification accuracy of up to 92.55%. Furthermore, integrating multiple feature representations significantly enhances the model's ability to differentiate between varying levels of pest damage. Unlike previous studies that primarily focus on plant disease detection, this research uniquely addresses the quantification of pest-induced damage, offering a valuable tool for precision agriculture. The findings of this study contribute to the development of automated, scalable, and efficient pest monitoring systems, which are crucial for minimizing yield losses and improving agricultural sustainability.  
machine learning; deep learning; feature extraction; pest damage detection; image segmentation; hybrid classification; precision agriculture; U2 Net; principal component analysis; convolutional neural networks +
Footnote †: journal: Applied sciences
## 4 Construction and Analysis of Sliding Mode Control Based on RBF Neural Network
### Sliding Mode Control Based on RBF Neural Network Approximation  
In this paper, the adaptive learning ability of neural network and the advantage of external anti-interference ability are used to replace the sliding mode control part, and the  
Figure 6: Iterative learning to control trajectory tracking error.  
Figure 7: Iterative learning to control trajectory tracking error based on RBF neural network.  
stochastic ability of RBF neural network adaptive sliding mode control is used to reduce the jitter problem in sliding mode control.  
Where \(q_{d}\) is the ideal motion joint angle command, the tracking error of the lower limb exoskeleton robot is:  
\[e(t)=q_{d}(t)-q(t) \tag{23}\]  
When the control objective is \(t\rightarrow\infty,e(t)\to 0,\dot{e}(t)\to 0\). Define the sliding mode error function as.  
\[s=\dot{e}+\lambda e \tag{24}\]  
where \(\lambda^{T}>0\), the reference state is defined:  
\[\dot{q}=s+\dot{q}_{d}-\lambda e \tag{25}\]  
\[\dot{D}\dot{s} =D\big{(}\ddot{q}_{d}-\ddot{q}+\lambda e\big{)}\] \[=D\big{(}\ddot{q}_{d}+\lambda e\big{)}-D\ddot{q}\] \[=D\big{(}\ddot{q}_{d}+\lambda e\big{)}+C\dot{q}+G+F+\tau_{r}-\tau\] \[=D\big{(}\ddot{q}_{d}+\lambda e\big{)}-Cr+C\big{(}\dot{q}_{d}+ \lambda e\big{)}+G+F+\tau_{r}-\tau\]  
In gait-walking rehabilitation training, there is an unknown uncertainty term in the exoskeleton model, which is expressed as:  
\[g(x)=D\big{(}\ddot{q}_{d}+\lambda e\big{)}+C\big{(}\dot{q}_{d}+\lambda e\big{)} +G+F \tag{26}\]  
However, in order to improve the stability of the exoskeleton system, the uncertainty \(g(x)\) needs to be approximated. The RBF network is used to approximate \(g(x)\). The ideal RBF network algorithm for the i-th motion joint is:  
\[\left\{\begin{array}{l}h_{ij}=\exp\left(\frac{\left\|x_{i}-c_{ij}\right\|^ {2}}{2b_{ij}^{2}}\right)\ \ j=1,2,3,\ldots,m\\ g_{i}=W_{i}^{T}h_{i}+\varepsilon_{i}\end{array}\right. \tag{27}\]  
where \(x_{i}=\big{[}e_{i},\dot{e}_{i},q_{di},\dot{q}_{di},\ddot{q}_{di}\big{]}\) is the i-th motion joint network input signal; \(h_{i}=[h_{i1},h_{i2},h_{i3},\ldots,h_{im}]\); \(\varepsilon_{i}\) is the i-th joint neural network approximation error; and \(W_{i}\) is the i-th motion joint ideal weights.  
According to the expression of \(g_{i}\), the network input is taken as \(x=\big{[}e,\dot{e},q_{d},\dot{q}_{d},\ddot{q}_{d}\big{]}\), then  
\[g=[g_{1},\ldots,g_{i},\ldots,g_{m}]^{T}=\left[\begin{array}{c}w_{1}^{T}h_{1 }+\varepsilon_{1}\\ \vdots\\ w_{i}^{T}h_{i}+\varepsilon_{i}\\ \vdots\\ w_{m}^{T}h_{m}+\varepsilon_{m}\end{array}\right] \tag{28}\]
# Development of the First Portuguese Radar Tracking Sensor for Space Debris
## 1 Introduction
On the French side, one of the countries in Europe that has more investments in the space segment, there have been several developments in radars, but the most relevant and interesting of them are in the military side. The French space surveillance network comprises a set of three tracking radars named SATAM, in three different locations (one of it with deployable capability). This configuration is not primarily dedicated to space surveillance, but is used for space events, detection of risk collision and atmospheric reentries, its speed (about 40\({}^{\circ}\)/s) being an incomparable added-value, notwithstanding the lower dimension of the dishes, compared with the previous ones. The European Space Surveillance and Tracking (EUSST) is a support framework setup by the European Commission to create an autonomous monitoring network supporting the detection and tracking of space debris and issue alerts when evasive actions may be necessary. EUSST aims to establish an SST capability at the European level with an appropriate level of European autonomy [22, 23]. Portugal is a member of the EUSST program and is developing capabilities both in optical and radar sensors. This paper describes the development of the first Portuguese tracking sensor for space debris and is organized as follows: Section 1 explains the necessity of monitoring the space environment and how nations worldwide have tackled this problem throughout the years. It also provides examples of already deployed tracking radars with similar functions to the current one in development. Section 2 introduces ATLAS (rAdio TeLescope pAmpillosa Serra) and provides a detailed technical description of its components: antenna, transmitter, receiver, clock generation, data acquisition, and controllers. Section 3 consists of simulation studies that use the technical characteristics of the system in order to estimate some performance metrics: minimum detectable target size, minimum elevation, number of expected observable objects and maximum simultaneous trackable targets. Section 4 explains the importance of waveform design on radar systems and provides some examplesof waveforms that can be used by ATLAS. Finally, Section 5 concludes the article and reveals the next steps towards the realization of this project.
## 2 Dense Feature Extraction and Matching Theory and Mismatch Cause Analysis
### Scale-Invariant Feature Extraction Algorithm  
SIFT [20; 21] is a milestone feature extraction algorithm in image processing due to its robustness to scale, illumination, noise, and rotation and is fundamental for image motion tracking and image stitching as it can extract a much larger number of feature points than other methods, which is advantageous in full-field structural displacement measurement. SIFT feature points are multiscale features [22], and images of different scale \(L(x,y,\sigma)\) are obtained by convolving the original images \(l(x,y)\) with a variable-scale Gaussian convolution kernel \(G(x,y,\sigma)\):  
\[L(x,y,\sigma)=G(x,y,\sigma)*I(x,y) \tag{1}\]  
where the Gaussian convolution kernel is calculated using the Gaussian convolution function:  
\[G(x,y,\sigma)=\frac{1}{2\pi\sigma^{2}}\exp\left(-\frac{\left(x-\frac{m}{2} \right)^{2}+\left(y-\frac{y}{2}\right)^{2}}{2\sigma^{2}}\right) \tag{2}\]  
where \((x,y)\) is the pixel position of the image, the values of \(m\) and \(n\) are generally determined by the scale parameter \(\sigma\), and \(*\) denotes convolution operation. After determining the size of the Gaussian convolution kernel as well as the scale factor \(\sigma\), the values at each position of the convolution kernel can be calculated with normalized Gaussian kernel. The image scale space is represented by the Gaussian pyramid as shown in Figure 1a, and the Gaussian difference is obtained by the Difference of Gaussians (DoG) shown in Figure 1b.  
In the scale space, each sample point is compared to its eight neighbors in the current image and nine neighbors in the scale above and below to det  
Figure 1: Gaussian difference in different scale space. (**a**) Image pyramid model. (**b**) Gaussian difference image.  
senting a scale-invariant feature point, and the gradient distribution characteristics within the neighborhood of a feature point are illustrated in Figure 2a. The statistical gradient information of the neighborhood is represented by a gradient direction histogram shown in Figure 2b, and the peak of the histogram serves as the main orientation of the feature point. Feature points extracted by SIFT algorithm are rotation-invariant and scale-invariant and are dense feature points.
## 4 Identification of a Tooth Defect Using the Proposed Technique
### Tooth Root Crack  
The collected original signal of the tooth root crack in the experiment is shown in Figure 13, where impulsive signatures cannot be found in the temporal waveform. It can be only seen in the spectrum that the calculated meshing frequency is about 864 Hz. The reason is that the original signal of the faulty gear is generally influenced by AM/FM components and the additive noise. It is thus difficult to distinguish the condition of the gears based on the above observation.  
The decomposed BLIMFs with VMD are shown in Figure 14a. The cyclical impulsive sub-band component (BLIMF2) can be clearly seen. Then, MID is used to further identify the modulation signatures in this BLIMF2. The result of the \(\text{MID}_{\Delta f}^{\text{SCoh}}\) of BLIMF2 is given in Figure 15a, where the modulation frequency (or cyclic frequency) and its multipliers can be clearly observed. The cyclic frequency \(\alpha\) is 29.13 Hz and is equal to the rotating frequency of the input shaft, which shows that those impulsive signatures were induced by a local defect in the gear. In addition, the carrier frequency is in nature dominated by the first-order gear meshing frequency (about 850 Hz). Therefore, the failure of the gear on the input shaft has also been demonstrated.  
Figure 12: (**a**) The parameter of the two-stage parallel gearbox and gears used in the experiment with (**b**) a chipped tooth, (**c**) a tooth root crack, (**d**) a broken tooth, and (**e**) tooth surface abrasion.  
Figure 13: The original signal of the gear with a tooth root crack: (**a**) temporal waveform (**b**) the FFT spectrum.  
The decomposed IMFs with EMD are shown in Figure 14b, where we can find that the cyclical impulsive component (IMF3) is detected. The MID\({}^{\text{SCoh}}_{\Delta f}\) of IMF3 is given in Figure 15b. It can be seen that the modulation frequency (the cyclic frequency) \(\alpha\) is also 29.13 Hz, which is equal to the rotating frequency of the input shaft. However, the carrier frequency represents the first-order gear meshing frequency (about 768 Hz). The carrier frequency is not identified in this case. It is obvious that a better result can be achieved using the proposed method than that of EMD combined with MID.
# Effective Adsorption of Phenoxyacetic Herbicides by Tomato Stem-Derived Activated Carbons
## 1 Introduction
The obtained TS-AC samples were subjected to physicochemical characterization and subsequently employed as adsorbents for the removal of organic contaminants--2,4-dichlorophenoxyoxyacetic acid (2,4-D) and 2-methyl-4-chlorophenoxyoxyacetic acid (MCPA)--from aqueous solutions. Furthermore, adsorption kinetics, equilibrium isotherms, and the effects of solution pH and ionic strength on adsorption efficiency were examined.
## 5 Case Studies
### Identification Performance Analysis  
Based on the previous accuracy analysis, the proposed model outperforms the traditional one-size-fits-all criterion. To further evaluate application to different stations and time periods, this section selects the number of transfer trips for MOE. Table 4 shows the dynamic elapsed time thresholds of four stations under six scenarios estimated by this paper. All recommended thresholds range from 18 min to 35 min. The corresponding transfer volumes for each scenario are calculated from the SC data of 21 March and 23 March as shown in Table 5. Similar to Table 3, the static model uses the traditional one-size-fits-all criterion of 30 min, and the dynamic one employs the specific-station threshold estimated by this paper.  
The total transfer volume recognized by this study is re less than that of the traditional static method during the morning and evening peak hours on weekdays, such as scenario S1, S2, S4, and S5. The main reason may be that commuters are highly concentrated due to work-related activities during peak hours, and thus they prefer taking transfer-time-saving routes. On the contrary, the fluctuation in passengers' transfer time distribution is very high and diverges during the  
Figure 9: Comparison of two metro-to-bus transfer identification methods at four stations at 18:00–20:00 on 23–24 March. (**a**) Guomao station. (**b**) Shenzhen university station. (**c**) Daxin station. (**d**) Airport east station.  
identified transfer time thresholds are greater than 30 min under scenarios S3 and S6, which means that the off-peak travelers consume more transfer time.
###### Abstract  
This paper introduces a novel hybrid control strategy for quadrotor UAVs inspired by neural dynamics. Our approach effectively addresses two common issues: the velocity jump problem in traditional backstepping control and the control signal chattering in conventional sliding mode control. The proposed system combines an outer-loop bioinspired backstepping controller with an inner-loop bioinspired sliding mode controller, ensuring smooth trajectory tracking even under external disturbances. We rigorously analyzed the system's stability using Lyapunov stability theory. To validate our algorithm's effectiveness, we conducted trajectory tracking experiments in both disturbance-free and step-disturbance conditions, comparing it with the traditional backstepping control, conventional sliding mode control, and saturated sliding mode control. The results demonstrate that our algorithm not only tracks trajectories more effectively but also significantly outperforms these methods in suppressing velocity jumps and signal chattering.  
bioinspired neural dynamics; trajectory tracking; sliding mode control; backstepping control; quadrotor control +
Footnote †: journal: Applied sciences
# Quality-of-Service-Linked Privileged Content-Caching Mechanism for Named Data Networks
## 5 Results and Discussion
### Hop Count and Content Store Size  
The hop count signifies the number of routers that an interest packet has to travel through to fetch the corresponding data packet. A reduction in hop count signifies a lower response time, lower traffic, and lower congestion possibilities. The hop count shows the availability of content near the consumer, which is one of the overall aims of NDN, and also of information-centric networks (ICN) in general. Section 5.5.1 evaluates the hop count corresponding to the QoS-node content store size for priority traffic among overall traffics. Section 5.5.2 evaluates the hop count corresponding to QoS-node content store size for overall traffic. By comparison of both of the evaluations, it can be inferred that QoS traffic has received better treatment through the QLPCC strategy.