esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Solution
### Feature Extraction  
Electric motors may suffer from many different types of failures, most of which, including those related to bearings, are manifested in the vibration signal of the motor. Vibration can be measured using displacement, velocity, and acceleration units. Since bearing failures tend to manifest themselves at high frequencies, acceleration is preferred, as higher frequency vibrations have less displacement and velocity for the same amount of power.  
Frequency-domain analysis techniques are more precise in identifying bearing failures. However, using the conventional FFT technique to extract frequency features from the raw data is inadequate, since the real vibration signals are non-stationary and the useful part of the bearing vibration signal can be hidden or covered by noise and disturbances. The Hilbert-Huang transform (HHT) ([PERSON], 1961) is widely used to analyze non-stationary signals arising out of defective rolling element bearings. HHT uses an empirical mode decomposition (EMD) process to decompose the raw signal into various intrinsic functions (IMFs) and a residue. Then, these IMFs are transformed using the Hilbert transform.  
IMFs contain frequencies in decreasing order, with the first IMF showing the highest frequencies contained in the denoised vibration signal [62]. Thus, the envelope of the first IMF of the decomposed vibration signal using HHT is transformed into the frequency domain using the FFT, so the amplitudes of the most interesting frequency bins are extracted.  
After empirically studying different sets of input features, the amplitudes of the bins corresponding to frequencies of 2X, 3X, and 4X \(BPFO\) provide the best results. \(BPFO\) is obtained with Equation (1), where \(N\) is the number of balls of the bearings, \(F\) is the shaft frequency (\(Hz\)), \(B\) is the ball diameter (mm), \(P\) is the pitch diameter (mm), and \(\theta\) is the contact angle.  
\[BPFO=\frac{N}{2}\times F\times\left(1-\frac{B}{P}\times\cos\theta\right) \tag{1}\]  
Errors in data gathering and the bearings not rotating smoothly can be issues when computing the FFT, causing the amplitudes of the bins to appear slightly off. To avoid this and to ensure the best performance, the amplitudes of the desired frequencies are obtained from Equation (2), where the amplitude of the bin (\(A_{n}\)) is obtained as the maximum of the raw amplitudes of bins \(a_{n-4}\) to \(a_{n+4}\).  
\[A_{n}=max\{a_{n-4},\cdots,a_{n+4}\} \tag{2}\]
# Combined Navigation and Tracking with Applications to Low Earth Orbit Satellites +
###### Abstract  
Low Earth Orbit (LEO) satellites complement classic GNSS by offering stronger signals, improved visibility, and system redundancy. Typical high speeds in LEO orbits generate rapid variations of the receiver-to-satellite geometry, which can improve the convergence of Precise Point Positioning (PPP) algorithms. However, high dynamics also induce strong Doppler rates at the receiver, which make the tracking procedures more difficult. In this paper, a loosely combined navigation and tracking architecture is applied to a Xona PULSAR(tm) Demonstration Signal in the L-Band such that the dynamic stress perceived by the receiver is mitigated. Other practical aspects of the Xona PULSAR(tm) receiver will be also discussed.  
GNSS; LEO; Xona; tracking; high dynamics +
Footnote †: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# Detection of Smoking in Indoor Environment Using Machine Learning
## 1 Introduction
### Sensing System for Indoor Air Quality Detection  
Recently, a system that monitors the environment in real time by combining ICT technology with sensor technology has been used in order to only complement the existing method. Many different technologies have been studied to identify powerful and practical ways to monitor air quality in daily life. Research was conducted mainly to minimize human intervention and to automatically monitor indoor air quality. The system included a physical circuit and an interactive platform, and the used main components in the physical circuit were the elements as Arduino Leonardo, a dust sensor, a temperature, humidity sensor, a Liquid Crystal Display (LCD). These are connected with sensor network or Ubidots (Boston, MA, USA) under specific conditions. And then air quality was analyzed [17]. Generally, pollutant concentrations of PM 2.5 and CO generated indoors were measured for pollutants using an IQ Air Visual Pro sensor (PM 2.5) and an electrochemical COA1 (CO, UEI, Gardena, CA, USA) detector adapter device [18]. Personal PM exposure of indoor and outdoor was evaluated using an inexpensive measuring device called DustDuino (Dominican Republic) consisting of a dust sensor (Plantower#3003, Plantpower, Beijing, China) that counts particles using infrared photodiodes [19] and changes in air conditions to check indoor air pollution. In addition, through a similar study, an IoT sensing system was developed that monitors and analyzes the indoor CO\({}_{2}\) concentration in real time. Additionally, it is intended to predict CO\({}_{2}\) accumulation. A hybrid prediction approach which combines ARIMA and SVR models is used, and this prediction performance was measured using Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE) and R Square (R\({}^{2}\)) [20]. Efficient reception is possible regardless of Internet connection, so it can be measured using IoT, which analyzes indoor air pollution. The used air quality sensor is a MQ135 (RJ TECH, Hong Kong, China), which can be used with a microcontroller to monitor indoor pollution [21].
###### Abstract  
Understanding vegetation's drought response helps predict ecosystem adaptations to climate change and offers scientific insights for managing extreme climate events. Using RS technology, this study systematically investigates the response mechanisms of vegetation to drought and their spatiotemporal variations in the ecologically sensitive semi-arid area and the national grain security zone--West Liao River Basin, China. The findings reveal that (1) from 2000 to 2018, NDVI exhibited a fluctuating upward trend, and drought trends remained pronounced in certain areas and seasons; (2) growing-season dropwiths impaired productivity, while winter droughts reduced soil moisture, with arid-zone vegetation being most vulnerable; (3) grasslands responded rapidly to drought, forests slowly via deep roots, and croplands suffered most during critical growth phases; and (4) drought-adapted western forests/shrubs recovered best, while eastern croplands required targeted measures like resilient crops and water management. The results of this study not only provide a scientific basis for ecological management in the West Liao River Basin but also offer valuable insights for vegetation and water resource management in other arid and semi-arid regions globally. This research holds significant importance for addressing climate change and achieving regional sustainable development.  
NDVI; SPEI; drought response; resistance; resilience Article
## 3 Different Extraction Methods of Polyphenols
### Ultrasound-Assisted Extraction (UIAE)
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Food** & **Solvent** & **Solid/Solvent** & **Power** & **Frequency (W) (Based on Amplitude)** & **Extraction** & **TPC \({}^{1}\) in Final Extract** & **Reference** \\ \hline Coffee & Methanol (80\%) & 1:50 & NR \({}^{3}\) & 20 & 10 & 8.94 \(\pm\) 0.01 & [52] \\ \hline Brazilian & Water & 0.5:25 & 247.5 & 20 & 29 & 80.51 \(\pm\) 1.52 & [57] \\ \hline Potato peels & Methanol (80\%) & 1:10 & 100 & 33 & 900 & 4.24 \(\pm\) 0.01 & [58] \\ \hline Tomato peel & Ethanol (70\%) & 1:50 & 380 & 30 & 15 & 36.43 \(\pm\) 0.1 & [59] \\ \hline Lemon wastes & Water & 1:100 & 250 & 43 \(\pm\) 2 & 45 & 18.10 \(\pm\) 0.24 & [60] \\ \hline Mango peels & Ethanol (50\%) & 1:30 & NR & NR & 10 & 35.5 & [61] \\ \hline Mango peels & Water & 1:6 & 160 & 50 & 15 & 9.72 & [35] \\ \hline Beet leaves & Water & 1:20 & 90 & 20 & 16 & 14.9 & [62] \\ \hline Pomegranate peels & Ethanol (70\%) & 1:70 & 140 & 40 & 30 & 69.89 \(\pm\) 0.45 & [63] \\ \hline Grape seeds & Ethanol (61.76\%) & 1:30 & 250 & 28 & 20 & 25.96 \(\pm\) 0.70 & [64] \\ \hline Olive leaves & Water & 0:25 & 112.5 & 20 & 25 & 79.77 & [65] \\ \hline \multirow{2}{*}{\begin{tabular}{c} Line peel \\ \end{tabular} } & Ethanol & \multirow{2}{*}{\begin{tabular}{c} Ethanol \\ (55\%) \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} 1.5:30 \\ \end{tabular} } & \multirow{2}{*}{NR} & NR & 4 & \multirow{2}{*}{
\begin{tabular}{c} 54 \(\pm\) 0.2 \\ \end{tabular} } & \multirow{2}{*}{[66]} \\ \hline \end{tabular}  
\({}^{1}\) Total phenolic content; \({}^{2}\) gallic acid equivalent; \({}^{3}\) not reported.  
\end{table} 
Table 1: Recovery of polyphenols from food by-products using UAE. intensity is inversely correlated with ultrasonic frequency. Enhanced extraction yield at a lower frequency may be associated with the production of larger but fewer cavitational bubbles collapsing with higher energy levels, which results in a more significant extent of cell disruption [58].
# Fine Recognition of MEO SAR Ship Targets Based on a Multi-Level Focusing-Classification Strategy
## 1 Introduction
**Coarse classification**: Each coarsely focused ship image is input into the coarse classification network to complete the classification of major target categories, including aircraft carriers, other military ships, and civilian ships. 3. **Fine refocusing**: The local region slice of the ship target image is extracted and fine-refocused by estimating and compensating for spatially varying higher-order motion errors. 4. **Fine classification**: The finely-focused image slice is input into the fine classification network to achieve fine recognition of specific target categories. The inputs of the proposed method are the complex data of potential ship targets in the range-compressed domain, which can be obtained by ship detection using range-compressed data [14; 15]. The proposed method can be seen as the subsequent processing of range-compressed domain detection. For open ocean scenarios, extracting potential targets in the range-compressed domain can significantly reduce the amount of data processingneeded. Therefore, using range-compressed domain data as input aligns with the future trend of efficient processing for maritime scenarios. The organization of this paper is as follows. In Section 2, the signal model for ship targets in MEO SAR is established. The defocusing characteristics of MEO SAR ship targets are analyzed in detail. Coarse-level focusing and classification and fine-level focusing and classification are described in Sections 3 and 4, respectively. Section 5 presents the dataset construction and experimental results. Section 6 gives the conclusion.
# A Survey on Satellite Communication System Security
## 5 Defense in Satellite Communication Systems
### Solutions for Integrity
#### 5.3.3 Cryptographic Methods
In this process, the spreading code is punctured by markers cryptographically generated using a key derived from the digitally signed navigation message. This integration of the navigation message and the spreading code into a single, inseparable signal makes it challenging for spoofers to replicate the correct signal.
# An Efficient Trajectory Planning Method for High-Speed Interception of Invasive Drones
## 4 Resistance Parameter Analysis
### Analysis of Rotor Resistance Parameters \(k_{rp}\)  
The rotor resistance parameter \(k_{rp}\) mainly represents the negative impact of factors related to quadcopter actuators (such as friction caused by rotor and blade rotation) on the acceleration performance of unmanned aerial vehicles. Figure 4 shows the optimal state trajectory \(s^{*}(t)\) corresponding to different rotor resistance parameters when the air resistance is approximately zero \(k_{rb}=0.01\). The resistance component \(f_{rp}\) of the propeller is inversely proportional to the thrust component \(f_{t}\) by a factor of \(k_{rp}\), and \(f_{t}\) changes with \(f_{rp}\) to offset its impact. Ultimately, only the stretching effect of \(f_{t}\) on the longitudinal axis is produced, without changing the moment when its extreme and zero points appear.
# Evaluation of Blue(r)m Mouthwash vs. Chlorhexidine
## 3 Results  
In this pilot study, a total of 50 participants were included (Figure 1); among them, 25 were male and the rest 25 were female. After 14 days, the plaque index, gingival index, and bleeding on probing (Figures 2-4) were lower among the chlorhexidine group than in the Blue(r)m mouthwash group. However, the effect of Blue(r)m mouthwash was more significant than chlorhexidine after the end of one month in all the aspects. At the baseline (day 0), the median plaque index and the gingival index recorded in the chlorhexidine group was 1.13 and 0.82, respectively, whereas these indices in the Blue(r)m group were 1.21 and 0.86, respectively. There were no significant differences between the groups for both plaque (\(p\) = 0.10) and the gingival index (\(p\) = 0.40) (Table 2). After 14 days, the median plaque index reduced to 0.76 (0.68 to 1.08) in the chlorhexidine group and 0.91 (0.76 to 1.08) in the Blue(r)m group. This reduction in plaque index score was not statistically significant (\(p\) = 0.22). At the end of the 30 th day, the median plaque index fell to 0.68 and \begin{table}
\begin{tabular}{c c c c} \hline \hline  & & **Chlorhexidine** & **Blue®m** \\ \hline \multirow{2}{*}{**Sex**} & Male & 12 (48\%) & 13 (52\%) \\  & Female & 13 (52\%) & 12 (48\%) \\  & Median age & 28 years & 24 years \\
**Age** & Inter quartile range & 21–34 years & 20–35 years \\  & Range & 19 to 54 years & 18 to 47 years \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Description of the study participants. Figure 1: Flow diagram showing recruitment and follow-up of the participants. 0.54 for chlorhexidine and the Blue(r)m group, respectively, and this time, the change in reduction in plaque index from the beginning of the study was statistically significant (\(p\) = 0.01). However, though the Blue(r)m mouthwash reduced the gingival index at the end of 30 days (Figure 3), the change in gingival index was no longer statistically significant in comparison to the chlorhexidine group after 2 weeks (\(p\) = 0.09) and after the 30 th day mark (\(p\) = 0.35). The bleeding on probing percentage reduced to 9.8% in the chlorhexidine group and 6.7% in the Blue(r)m group, respectively, after 1 month and was statistically significant for both the groups (\(p\) < 0.001). Figure 3: Comparison of the change in gingival index between the Chlorhexidine and Blue®m mouthwash group over one month. Figure 2: Comparison of the changes in the plaque index between the Chlorhexidine and Blue®m mouthwash group over one month. As for the patients' satisfaction, only 12% of patients reported an altered taste after using Blue(r)m mouthwash (Table 3), whereas those using chlorhexidine reported a burning sensation, altered taste, and teeth staining.
# Observing Material Properties in Composite Structures from Actual Rotations
Nevertheless, in some structures (such as deep beams) shear effects might play an important role. In these cases, shear effects should be introduced into the formulation in order to reduce the errors of SSI methods, and the inability of considering these effects should be considered a modeling error (error in the modeling of structures). The observability method (OM) is an SSI method based on the system of equations of the SMM. In this procedure, the mechanical properties (e.g., flexural stiffness, EI) can be quantified from the deformations measured in static tests. The OM has proved its efficiency in different structural typologies (such as trusses, beams, frame structures, and cable-stayed bridges) [41; 42]. The analysis of these structures is based on polynomial systems of equations, and it is not as simple as it may look due to the coupled equations. To solve this problem, a numerical optimization approach (constrained observability method (COM)) can be used to decouple the coupled linearized variables. Recently, [PERSON] et al. [43] included the effects of shear deformations into the COM. Unfortunately, this application (like other SSI methods based on SMM in the literature) is not able to take into account the actual rotations as the shear rotation effects are not included into the formulation. Because of the inability of this method to consider shear rotations in SMM, the COM is not able to observe the value of the mechanical properties correctly, when actual rotations are included into the measurement sets. For this reason, this procedure is not suitable for actual structures as wrong results are obtained even when noise-free measurements are considered. This paper aims to fill this gap by presenting a new method based on the COM to observe the structural properties from the actual rotations measured on-site for any kind of structure (even in those where shear rotations are not negligible). To do so, the use of an iterative process is proposed. In this process, estimated shear rotations are subsequently subtracted from the actual rotations on site. Then, the normal COM can be performed in terms of bending rotations and bending and shear vertical deflections. Also, throughout iterative steps, the structural properties are successively updated from the inverse observations. This article is organized as follows: In Section 2, a brief explanation of both the OM and the COM are presented and the importance of shear rotation is discussed. Also, a new method for analyzing the effects of the shear rotation in the COM is presented. In Section 3, different structures are analyzed to show the important role that shear rotations might play in some geometries. Moreover, the applicability and accuracy of the new method in different structures are presented. Finally, the conclusions obtained are drawn in Section 4.
# Analysis of Coastal Zone Data of Northern Yantai Collected by Remote Sensing from 1990 to 2018
## 2 Materials and Methods
### Calculation of Fractal Dimension of the Coastline  
In this study, the fractal dimension of the coastline is calculated by the grid method [29]. The basic principle of the grid method is to use square grids of different lengths to cover the measured coastline continuously and with no overlap. When the length of square grids \(\mathrm{r}\) takes different values, the number of grids \(\mathrm{N(r)}\) needed to cover the whole coastline will change accordingly [28]. The formula is as follows:  
\[\mathrm{N(r)}=\propto\mathrm{r}^{\mathrm{D}}. \tag{6}\]  
By taking the natural logarithm of both sides of equation (6), the following results are obtained:  
\[\mathrm{lnN(r)}=-\mathrm{D}\ln\mathrm{r}+\mathrm{C}, \tag{7}\]  
where \(\mathrm{D}\) is the fractal dimension of the measured coastline, and \(\mathrm{C}\) is a constant. The fractal dimension \(\mathrm{D}\) can be calculated by fitting and regression analysis of different \(\mathrm{R}\) values and corresponding \(\mathrm{N(r)}\) values. The larger the value of the fractal dimension, the more complicated and tortuous the coastline. On the contrary, the smaller the value of the fractal dimension, the smaller the tortuosity and complexity of the coastline [30]. Based on the ArcGIS platform and least squares method, the fractal dimension of the coastline is obtained by calculations followed by fitting as well as regression analysis.
## 5 DSS Techniques and Generalized Finite State Machine
### Dss2--Spectrum Sensing  
DSS2 is based on decentralized spectrum sensing and it requires spectrum monitoring capabilities at each receiver taking part in the satellite network. There exist technical solutions to achieve continuous run-time wide spectrum sensing without the need for a time-sharing approach between normal operation and spectrum sensing. Spectra can be monitored at the receiver of a communication link using the same antenna configuration of the receiver, capturing only relevant spectrum usage information in the receiver's immediate vicinity (spectrum usage that may affect directional satellite communication). By processing the raw I/Q samples, local energy maps can be created that can be employed for decision-making on spectrum allocation. In DSS2, the FSM from Figure 2a that includes spectrum sensing will be executed at the receiving entities.  
DSS2 does not require a CP or any exchange of state/status data between different communicating entities, it only requires a dedicated CC between the communicating entities for slot (re)allocation or release. Upon detection of an interference event, each receiving entity, based on locally acquired spectrum monitoring information (local energy map), decides which free slots are available for mitigating interference. Then, over the dedicated CC, the receiver will alert the transmitter and initiate a slot reallocation procedure to replace the interfered slot with the new slots. Whereas DSS1 can only detect known interference from CP advertisements, DSS2 can detect any interference source at the receiver. However, DSS2 requires changes to the radio modem to integrate spectrum monitoring at the receiver. If existing radio modems on already deployed satellites cannot be upgraded, the DSS2 technique can only be implemented in new satellite systems.
## 3 Numerical Results and Discussion
### Free Vibration Analysis
We consider the influence of the cone top angle \(\alpha\) of the conical cavity and the height \(L_{2}\) of the cylindrical cavity on the natural frequency of the coupled system between the composite laminated rotationally stiffened plate and the conical-cylindrical cavity, and the parametric analysis is carried out. Figure 13 illustrates the trend surface plots of the first four natural frequencies of the coupled system as \(\alpha\) and \(L_{2}\) vary. In this example, the number of stiffeners is \(n=2\), and the determined geometric parameters are as follows:  
Figure 11: Trends of the first four natural frequencies of the coupling system among the composite laminated rotationally stiffened plate and the cylindrical-cylindrical cavities as the height of the cylindrical cavities L1 and L2 vary. The material used is graphite epoxy resin with a layer angle of [90\({}^{\circ}\)/0\({}^{\circ}\)/90\({}^{\circ}\)], and the medium in the cavities is air. The boundary conditions are set as CCCC. From Figure 13, it can be observed that the natural frequencies of the coupled system at the same order decrease as the height \(L_{2}\) of the cylindrical cavity increases, and the frequencies increase with the increase of the cone top angle \(\alpha\) of the conical cavity. Compared to the rotationally acoustic cavity coupling system, the coupled system between the composite laminated rotationally stiffened plate and the acoustic cavity is also influenced by the essential parameters for the composite stiffened plate. Therefore, it needs to be parametrically studied. The coupling system's initial eight natural frequencies under different boundaries are shown in Table 10. In this example, the geometric parameters of the coupling system are the same as those in Tables 7-9, with a rotation angle of \(\vartheta\) = 180\({}^{\circ}\). The material used is isotropic steel. From Table 10, it can be observed that the natural frequencies of the three coupling systems rise as the stiffness values of the boundary springs increase, and the findings for elastic boundary 1 (E\({}^{1}\)) and elastic boundary 2 (E\({}^{2}\)) support this conclusion. Figure 12: Trends of the first four natural frequencies of the coupled system between the composite laminated rotationally stiffened plate and the spherical-cylindrical cavity as height L of the cylindrical cavity and the top angle \(\phi\) of the spherical cavity vary.