esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time Implementation of Multiband Spectrum Sensing Using SDR Technology
## 3 Implementation of MBSS Technique Using SDR Technology
### Power Spectrum Density Estimation  
From the received signal \(r_{i}(n)=x_{ii}(n)+jx_{Qi}(n)\), which is a complex signal that belongs to the time domain, the PSD is obtained on a discrete linear scale of the frequency \(R_{i}(k)\) through the Welch estimator [39] for each connected device every 100 ms (frame time).  
[PERSON]'s method [40] (also called the periodogram method) for estimating power spectra is carried out by dividing every signal frame of 100 ms into successive blocks or windows, estimating the periodogram (i.e., the squared magnitude of the fast Fourier transform (FFT)) for each block, and averaging over the total number of windows forming each frame. The \(m\)-th windowed, zero-padded block is considered from the signal \(r_{i}(n)\) denoted by  
\[r_{i,m}(n)\ =\ \omega(n)\,r_{i}(n+mh),\,n=0,1,\ldots,M-1,\,m=0,1,\ldots,K-1, \tag{1}\]  
where \(h\) is defined as the window hop size and \(K\) denotes the number of available windows. Then, the periodogram of the \(m\)-th window is given by  
\[P_{r_{i,m}}(k)=\frac{1}{M}\big{|}FFT(r_{i,m})\big{|}^{2}=\frac{1}{M}\Big{|} \sum_{n=0}^{M-1}r_{i,m}(n)e^{-j2\pi nk/M}\Big{|}^{2}\,k=0,1,\ldots,M-1, \tag{2}\]  
and the Welch estimate of the power spectral density is given by  
\[R_{i}(k)=\frac{1}{K}\sum_{m=0}^{K-1}P_{r_{i,m}}(k)\ \ k=0,1,\ldots,M-1. \tag{3}\]  
In other words, it is just an average of periodograms across time over each frame of \(P\) points in 100 ms. When \(\omega(n)\) is the rectangular window, the periodograms are formed from nonoverlapping successive blocks of data. For other window types, the analysis frames typically overlap. In this implementation, a 50% overlapped Hamming window is used and the number of \(M\) samples contained in \(R_{i}(k)\) is chosen according to the device: 512, 1024, 2048, or 4096 samples for the RTL-SDR. For the cases of HackRf One and the LimeSDR  
Figure 3: Carrier frequency assignment of each connected device to conform the observed multi-band spectrum.  
Mini, the number of samples can be 1024, 2048, 4096, or 8192. These essential data represent a compromise in the real-time implementation between having many samples to locate the \(PU\) with great precision or choosing few samples sacrificing the precision to locate the transmission of the \(PU\) to reduce the execution time and the assignment of computational resources. The structure and operation of this block are shown in Figure 4.
## 4 Using LoRaWAN in Linear Monitoring Systems: KSA Oil Pipeline Case Study
### Description of the Application Requirements and the Oil Pipeline Monitoring System in the KSA  
We follow the requirements for applying the oil pipeline monitoring system described in [9], where the pipeline is above ground and lies in the desert in the KSA. The length of the pipeline reaches 1200 km (266 km in the desert), and it is used to transfer oil and gas from the east of Saudi Arabia to the west. The desert part of the pipeline is deployed in a sparsely populated area where there is no power supply. There are four pump stations in this part, and we assume each station has a GW. As can be noticed, only four GWs support an area with a length of 266 km, providing a limited source of energy. Thus, the area is divided into three parts, each having a length of 88.67 km. The application requires that the distance between sensors does not exceed 200 m based on the result obtained in our previous paper.
## 3 Hierarchical Control System
### Optimization Process
#### 3.3.2 Optimization Methods  
The optimization task is nonlinear, nonconvex and hybrid (different forms of decision-making variables; two of them are real-type variables (\(x_{3}\), \(x_{4}\)), the third one is an integer (\(x_{2}\)), the last (\(x_{1}\)) is a binary variable. Initially, the previous optimization structure and algorithms were used [20]. During the research work, some other non-deterministic algorithms were found that could work better than the initially used method (evolutionary algorithm).  
The artificial bee colony (ABC) algorithm (see Figure 5) can achieve better results than the differential evolution algorithm [30; 31]. ABC algorithm is based on a simulation of a swarm of honey bees searching for sources feeds. This is a non-deterministic method and it uses both randomness and heuristic methods to find a global minimum of any function.  
The second optimization method is the direct search algorithm (DSA) (see Figure 5). This is a deterministic method which means that it does not have a stochastic factor in it. In order to find an optimal value, DSA needs information about a set of points around the starting one and then it calculates which one has the lowest value of the goal function [32].
## 2 Literature Review
### UUT Gas Leakage Detection and Computer Vision Application
At the same time, current detection methods are often limited by insufficient data. To address the above issues, this study aims to use DL and CV to address the limitations of sensor accuracy and insufficient case data in UUT gas leak detection. Unlike sensorsthat require actual destructive experiments and leak toxic gases to obtain experimental data, DL-based CV can identify and classify leak events only by the presence or absence of leaks and the shape of the leaked gas column, which provides a solution to the high requirements of gas-diffusion-based sensor detection on the concentration and gas type. In terms of scalability, the proposed method has advantages over traditional sensor detection. Traditional sensor-based methods [6; 7; 8; 9; 10; 11; 12] face scalability challenges due to the need to deploy a large number of physical sensors and perform calibration and maintenance in a large UUT network, and their costs continue to grow with the length and complexity of the UUT. In contrast, since the UUT needs to be equipped with real-time monitors to ensure daily safety and achieve full scene coverage, the CV-based method can utilize the existing monitor infrastructure in the UUT without the need for additional sensors or hardware. In addition, all camera data are aggregated to the server through the network for processing. The central server can exert centralized data processing capabilities and process input data from multiple cameras at the same time without excessive hardware requirements. On the other hand, in response to the existing CV detection method [12; 13; 14; 15; 16], this study built an experimental data acquisition platform. By extracting single-frame images from the video and combining data enhancement methods, a large amount of data can be provided, thereby overcoming the challenge of insufficient data. The experimental scenario has high scalability and can provide a simulation site for different gas leaks and different environmental conditions, ensuring the way to obtain data. At present, there are few studies on the application of DL and CV in UUT operation and maintenance. This study aims to develop an infrared-camera-based system using existing monitors, especially for low-light environmental conditions. The goal of this system is to use spatiotemporal sequence CRNN technology to build an infrared monitoring system for continuous monitoring inside the UUT, focusing on detecting high-risk events such as natural gas pipeline leaks.
## 2 Problem Definition and Use-Case Example
We are proposing a **four-step methodology** for the RFF-based pre-correlation spoofing detection and transmitter identification, as listed below. Each of these four steps is further detailed in Sections 3-6. Figure 2: The illustration of a spoofer attacking scenario. 1. **Identification of relevant features**--this step refers to first identifying the different RF 'features' created by the inherent hardware impairments in any transmitter. Several such features will be subsequently described in Section 3;
2. **Feature-extraction transform**--this steps refers to choosing a suitable feature-extraction transform to emphasize the selected features from the previous step. Several feature-extraction transforms are addressed in Section 5;
3. **Data pre-processing stage**--this step refers to choosing the most suitable format of saving the data at the output of the feature-extraction transform, namely as time-stamped vector data, in matrix form, as an image of certain size and number of pixels, etc. The data format selection will be influenced by the algorithms selected in the feature-classification step, as subsequently described in Section 6, as well as by the data type at the output of the feature-extraction step. For example, spectrogram-type data are also easily stored in image form, while transforms such as kurtosis or Teager-Kaiser are more suitable to be stored in a vector format;
4. **Feature classification**--this step refers to applying a selected classification methods, such as based on analytically-derived thresholds or on machine learning algorithms when training data are available, and classifying the received signal into 'genuine' versus 'non-genuine/spoofer' classes. Several feature classification approaches are discussed in Section 6. A qualitative discussion is then provided in Section 9. The workflow of an RFF algorithm based on the aforementioned four steps is illustrated in Figure 3.
## 2 Materials and Methods
### Fourier Analysis  
To analyze the recorded signal, a Fourier analysis technique using a hanning window was employed. First, a Hanning window was generated with an appropriate length corresponding to the duration of the recorded signal (3 s). Subsequently, this window was applied to the signal to mitigate edge effects and enhance frequency domain resolution. Once the frequency spectrum of the processed signals was obtained, the spectrum of the signal of interest was normalized to the reference signal. This was accomplished by computing the fast Fourier transform (FFT)--using MATLAB's FFT function--of each processed signal and subsequently dividing the frequency spectrum of the signal of interest by that of the reference (baseline) signal.
## 3. Results and Discussion
### Strain Sensing Information from Numerical Results
#### 3.2.1 Tension  
As shown in Figure 9b, in this loading case (tension along the X-axis), the maximum principal strain directions of FOS cores follow the tangents of their paths. In peak areas (horizontal portions), where the tangents are parallel to the X-axis (the loading direction), the longitudinal strain component (\(E_{11}\)) dominates, whose value is much higher than other strain components. For example, the \(E_{11}\) in peak areas is more than five times the \(E_{22}\) in mid-line crossing areas (vertical portions), arising from transverse contraction. This strain pattern remains consistent throughout the whole longitudinal span.  
Figure 9: Tension loading case: (**a**) plan view of the numerical model under tension; (**b**) the maximum principal strain contours of FOS cores; (**c**) \(E_{11}\) strain component of epoxy adhesive; (**d**) \(E_{22}\) strain component of epoxy adhesive.  
Compared to strains in host materials, the strains measured in FOS peak areas (Figure 9b) can represent the \(E_{11}\) component of the corresponding FOS embedment areas in epoxy adhesive (Figure 9c); similarly, the strains measured in mid-line crossing areas in Figure 9b correspond to those in Figure 9d, the \(E_{22}\) component. Negligible discrepancies (around 5%) between each pair are due to the fact that there is a buffer layer of FOS coating between the FOS core and epoxy adhesive, whose modulus is closer to but not the same as that of epoxy adhesive.  
The resulting strain contours from the tension case (Figure 9) are in accordance with expectations and confirm the strain-measuring advantages of the dual-antiphasic sinusoidal placement pattern by illustrating the capabilities of dual-sinusoidal FOSs to sense different strain components along their paths and to provide complementary strain-measuring areas. If structures are primarily under tension, where the main target should be to monitor longitudinal strains more accurately, it is desirable that the sinusoidal FOSs are stretched along the loading direction as much as possible to have larger period sizes.
## 5 Discussion
A major benefit of our reconstruction point is that only a small number of measurement points are required, making workload and resource allocation more acceptable. However, there are still some limitations for this work. First, in the longitudinal direction, the sine model reaches a good level of precision in the reconstruction work, but in the transverse direction, there is potential for improvement on the linear reconstruction model. In each transverse direction, if more measured points (more than three feature points) were adopted, the logistic model may be better for single and staggered-type road lighting, and the sine model may be better for double-type road lighting. Second, the reconstructed illuminances were based on a limited number of tests of lighting on real roads. More road lighting should be tested to verify the effectiveness of the reconstructed models. Third, for transformation from horizontal illuminance to luminance or corneal illuminance, rule-of-thumb formulas are applied. These require more refined formulae and precise coefficients in the future.
# Enhanced Dual Reversible Data Hiding Using Combined Approaches
## 2 Preliminaries
### Improvements over Existing Approaches  
In this study, we analyzed various data hiding techniques proposed in previous research and introduced a new method that improves upon them by combining Hamming coding, arithmetic coding, and an enhanced EMD technique. Traditional methods typically use LSB (least significant bit) embedding or conventional EMD techniques, which often suffer from limited embedding capacity and difficulties in maintaining high image quality.  
Our proposed method enhances data recovery accuracy through Hamming coding and improves compression efficiency using arithmetic coding, allowing for higher data embedding while achieving superior PSNR and SSIM values. While previous studies reported an average PSNR of 45-55 dB, our approach achieves 6-68 dB, minimizing visual quality degradation. Additionally, previous research often limited the embedding rate (ER) to 1.0 bpp or lower, whereas our method allows for up to 1.5 bpp, maximizing embedding efficiency.  
Furthermore, many existing methods do not consider error correction, whereas our approach incorporates Hamming coding to enhance data stability and robustness. Additionally, previous techniques often involve high computational complexity, making large-scale data processing inefficient, while our method optimizes encoding and decoding operations using arithmetic coding, thereby improving computational efficiency.  
In summary, the proposed method demonstrates significant improvements over previous studies in terms of higher embedding capacity, enhanced image quality (PSNR and SSIM), increased data stability (error correction), and improved computational efficiency (optimized coding operations).
_Article_  
Numerical Estimation of Bonding Force of EPDM  
Grommet Parts with Hollow Shaft Geometry  
[PERSON] 1,2, [PERSON] 2,3,* and [PERSON] 2  
Graduate School of Mechanical Engineering, Kongju National University, 1223-24, Cheonan-daero, Seobuk-gu, Cheonan-si, Chungcheongnam-do 31080, Korea; [EMAIL_ADDRESS] Industrial Technology Research Institute, Kongju National University, 1223-24, Cheonan-daero, Seobuk-gu, Cheonan-si, Chungcheongnam-do 31080, Korea; [EMAIL_ADDRESS] Department of Mechanical Engineering, Kongju National University, 1223-24, Cheonan-daero, Seobuk-gu, Cheonan-si, Chungcheongnam-do 31080, Korea Correspondence: [EMAIL_ADDRESS]; Tel.: +82-41-521-9284  
Received: 4 April 2020; Accepted: 29 April 2020; Published: 1 May 2020  
A comment is a representative component that fixes the position of a cable. It is made from hyper-elastic materials (rubber), such as ethylene propylene diene monomer (EPDM). The grommet and cable are conventionally fixed through bonding; however, this method has numerous disadvantages that can be improved through relevant research. To apply a fixing method using the elastic force of EPDM rubber, this paper presents an empirical equation for approximating the bonding force of EPDM grommet parts with a hollow shaft geometry. First, tensile tests and the inverse method were used to approximate the basic mechanical properties. The physical properties were derived through basic tests; furthermore, bonding force tests and the inverse method were used on a grommet with a hollow shaft structure. In addition, the Box-Behnken design of experiments was used to predict the amount of change in the bonding force according to the geometry variables. Finally, this study was validated by comparing the approximation results derived through the design of experiments with the analysis and bonding force test results.  
EPDM; grommet; inverse method; bonding force; approximation
# Fabrication of a Promising Hierarchical Porous Surface on Titanium for Promoting biocompatibility
## 2 Materials and Methods
### Surface Property Analysis  
The topography features of the investigated specimens were examined through a JEOL JSM6400 scanning electron microscope (SEM; Tokyo, Japan) under a chamber pressure of \(9.63\times 10^{-5}\) Pa and an operating voltage of 20 kV. The Perkin Elmer model PHI 1600 X-ray photoemission spectroscopy (XPS; Waltham, MA, USA) was employed to analyze the chemical bonding states of the investigated specimenswith a radiation source of monochromatic Mg K\(\alpha\) under 15 kV, 16.7 mA, and 250 W. The binding energy of the Ag 3d5/2 line on clean Ag was used to calibrate the XPS level. The incident angle of the X-ray beam to the specimen normal was 45\({}^{\circ}\). The X-ray beam with a diameter of \(\sim\)15 nm was used to detect the surface elements by high-resolution scans. Before analysis, the spectrum was curve-fitted with a Gaussian-Lorentzian peak. The phase compositions of the investigated specimens were identified via Rigaku 2200 thin film X-ray diffractometer (TF-XRD; Tokyo, Japan) with an incident angle (Cu K\(\alpha\) radiation) of 3\({}^{\circ}\) at 50 kV and 250 mA. Moreover, the JEOL-2100 high-resolution transmission electron microscope (TEM; Tokyo, Japan) was also employed to examine the microstructures of the investigated specimens. For the cross-sectional observation of TEM, electron-transparent samples were prepared using mechanical thinning and argon ion milling.
# A PUF- and Biometric-Based Lightweight Hardware Solution to Increase Security at Sensor Nodes
## 5 Lightweight Protocol to Authenticate Sensor Nodes by Dual-Factor
With them and \(\left\{HD_{K}^{D}\right\}\) the sensor node is able to recover the \(K_{old}^{D}\) (\(K_{0}\) in the first session) and the \(\left\{ID_{x}^{D}\right\}\). With them and \(\left\{SD_{X}^{D}\right\}\), the sensor node is able to recover the \(\left\{QFM_{x}^{X}\right\}\). Only the trusted node is able to recover the \(K_{old}^{D}\) and the biometric data of its user. The start-up values of a set of RND cells are also read to generate the \(\mathit{nonce}_{D}\). At step 4, sensor node D checks that the new key has been updated by the base station at step 3. Hence, it changes \(HD_{Ko}^{D}=\left\{ID_{Ko}^{D}\oplus\mathit{Kcoded}_{0}\right\}\) by \(HD_{\mathit{Know}}^{D}=\left\{ID_{Ko}^{D}\oplus\mathit{Kcoded}_{new}\right\}\), which will be used in the next session as the initial symmetric key to derive another session key. Sensor node D is able to decrypt the message sent by the base station and to verify the authenticity of base station because it knows, not only \(K_{old}^{D}\) but also \(h\big{(}\{QFM^{X}\},E\big{)}\), which is only stored at base station. Sensor node requires the user to place his/her fingers in the fingerprint sensor and the features \(\left\{\overline{QFM}^{X}\right\}\) are extracted and compared with \(\left\{QFM^{X}\right\}\) locally. If the user is legitimated as \(X\), the sensor node sends the requested data. At step 5, the base station checks the dual-factor authenticity of sensor node D and receives the data. Once the session finished, base station also stores \(K_{new}^{D}\) as \(K_{old}^{D}\) to use it in next sessions with sensor node D. Base station stores the \(N\) keys to communicate with the \(N\) nodes. Biometric data cannot be obtained from the interchanged messages because they are protected by a hash, which is a one-way function and, besides the result is encrypted together with a nonce, by a fresh key. The message indexes avoid replay attacks because they are used only once for each communication. The base station stores the \(M\)\(h\big{(}\{QFM^{X}\},E\big{)}\) to cope with the \(M\) individuals in the network, but even if all \(h\big{(}\{QFM^{X}\},E\big{)}\) are leaked, they don't reveal any sensitive information related to the individuals (they are like pseudonymous) and they are isolated to this network. This way if a network is successfully attacked it will not affect other networks and users. This protocol allows the highest level of security in electronic authentication of sensor nodes as defined by the digital identity guidelines of the NIST SP 800-63 [33], since it is based on the device authentication and also on the authentication of the individual which uses it.
# Stronger Cumulative than Lagged Effects of Drought on Vegetation in Central Asia
## 3 Results
### Comparison of Lagged and Cumulative Effects of Vegetation on Drought  
To determine the main way in which drought affects vegetation GPP, this study performed a comparison between the magnitude of Rmax_lag and Rmax_cum (\(\Delta\)Rmax = Rmax_lag-Rmax_cum), as shown in Figure 13. As shown in Figure 13a, the cumulative effect of drought on vegetation was higher than the lagged effect in 86.75% of the areas. The mean \(\Delta\)Rmax value for areas where the lagged effect was higher than the cumulative effect was 0.05, and the mean \(\Delta\)Rmax value for areas where the lagged effect was less than the cumulative effect was 0.13. As shown by the plot of the percent area of \(\Delta\)Rmax, the \(\Delta\)Rmax was mainly concentrated on the scale of \(-0.15\)--\(-0.05\) (39.45%), followed by \(-0.05\)-\(0\) (14.96%) and \(-0.20\)--\(-0.15\) (14.14%). The areas where the cumulative effect was dominant (\(\Delta\)Rmax \(<-0.20\)) were primarily located in southern and western Kazakhstan, eastern Uzbekistan, southwestern Tajikistan, and southern Turkmenistan. The regions where the lagged effect dominated were mainly located in southern Kyrgyzstan, central Tajikistan, south-central Uzbekistan, northern Turkmenistan, and the western region of Xinjiang, China.  
Figure 11: Change in cumulative effect correlation. (**a**) Percentage of area with different correlation coefficient classes at different cumulative months; (**b**) Mean correlation coefficients of different vegetation types at different cumulative months.  
Figure 12: Relationship of annual mean SPEI with (**a**) mean maximum correlation coefficient and (**b**) corresponding mean accumulated months.  
Figure 13: Comparing lagged and cumulative effects. (**a**) Spatial distribution and area percentage of \(\Delta\)Rmax. (**b**) Comparison of \(\Delta\)Rmax across vegetation types. \(\Delta\)Rmax is the difference between Rmax_lag and Rmax_cum. \(\Delta\)Rmax > 0, lagged effect dominates; \(\Delta\)Rmax < 0, cumulative effect dominates.  
In this study, the dominant effects of different vegetation types were further investigated. The results showed that the cumulative effect of drought was more powerful than the lagged effect for all vegetation types except forests, as shown in Figure 13b. Among the vegetation types with dominant cumulative effects, shrubland had the largest cumulative effect, with a mean \(\Delta\)Rmax of \(-0.22\), followed by grassland and farmland. This indicates that the cumulative effect of drought is more powerful than the lagged effect for large portions of the vegetation in Central Asia.