esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Simulation Results and Discussion
### Annular Array Transducer with Phase-Shifted Radiation
corresponds to the through-transmission mode of thickness \(z=2z_{0}\). It is noticed that the received fundamental wave amplitude does not depend on the boundary type. The received second harmonic amplitudes resulting from the stress-free boundary are lower than those from the rigid boundary in most phase shift cases. However, in the last three cases of phase shifts (\(18\pi/20\), \(19\pi/20\) and \(\pi\)) with the stress-free boundary, the received second harmonic amplitudes are larger than those of the rigid boundary, and the \(\pi\) phase shift provides the highest received amplitude. This amount of amplitude is about one order of magnitude larger than that of the single-element transducer with the stress-free boundary. When the phase shift near \(\pi\) is applied, the phase difference between the reflected and newly generated second harmonic waves reach the minimum for the stress-free boundary. Therefore, the pulse-echo harmonic generation in this condition corresponds to the optimal condition, which is consistent with our previous research [28]. In this case, the first and third elements have the same phase, and the second and fourth elements have the same phase. This indicates that only two amplifiers are required in the experiment, even if a four-element array transducer is used.
## 2 Background
### Super-Resolution Convolution Neural Network (SRCNN)  
The super-resolution convolution neural network (SRCNN) was the first model that applied DL in the field of super-resolution [12]. It used traditional bicubic interpolation was used for low-resolution images to increase their size to that of high-resolution images. The model involved passing through a convolution network thrice to enhance image quality. Traditional bicubic interpolation estimates the pixel value from the weighted average of the 16 closest neighboring pixels to expand the image pixels. The technique improved image quality while passing through a CNN during training [13].  
SRCNN is methodologically simple, yet it performs well and is the most basic DL model in the field of SR. As it is based on a CNN model, it requires supervised learning; that is, it requires high-resolution images for training. The model is trained using low-resolution images and ground-truth (GT) images. When trained to minimize the difference between the unscaled image and the GT image, it outputs a high-resolution image. In a recent study, SRCNN was redesigned as a faster SRCNN (FSRCNN) [14] with accelerated speed and superior restoration quality. The new structure is illustrated in Figure 1.
# Planar Array Diagnostic Tool for Millimeter-Wave Wireless Communication Systems
## 2 Antenna Array Diagnosis Problem Formulation  
Consider an antenna array in space (Figure 2a). The antenna radiated far-field is usually quantified by phase and amplitude. The AUT is depicted in Figure 2b. All the parameters associated with the AUT are marked with superscript \"\(u\)\". Specifically, \(E^{u}(x,y)\) is the tangential field situated on the antenna aperture, i.e.,  
\[E^{u}(x,y)=E^{u}_{x}(x,y)\hat{x}+E^{u}_{y}(x,y)\hat{y}, \tag{1}\]  
where \(E^{u}_{x}(x,y)\hat{x}\) and \(E^{u}_{y}(x,y)\hat{y}\) are the \(x\) and \(y\) planes of the aperture's electric field, respectively. Far-field \(F^{u}(r,\theta,\varnothing)\) is the measured field on part of the hemispherical surface \((0\leq\theta\leq\pi/2,0\leq\phi\leq 2\pi)\) at radius \(r\) from the phase center of the AUT, and \(r>2D^{2}/\lambda\), where \(D\) is the diameter. Also, the amplitude and phase of a reference array (RA; array without failures) shown in Figure 2b are assumed to be available. Associated quantities are marked with superscript \"\(\circ\)\". \(E^{\circ}(x,y)\) is the field on the aperture \(\Sigma\) of the reference array (RA) and \(\mathbf{F}^{\circ}(r,\theta,\varnothing)\) represents the far-field radiation. For the differential antenna (DA) shown in Figure 2c, the tangential distribution \(\mathbf{E}(x,y)\) on the aperture \(\Sigma\) is equal to the difference between the field distributions of the reference array and the antenna under test, and the corresponding far-field \(\mathbf{F}(r,\theta,\phi)\) is expressed as the difference between the fields of reference array (RA) and AUT as  
\[\mathbf{E}(x,y)=\mathbf{E}^{\circ}(x,y)-\mathbf{E}^{\circ}(x,y), \tag{2}\]  
\[\mathbf{F}(r,\theta,\phi)=\mathbf{F}^{\circ}(r,\theta,\phi)-\mathbf{F}^{\circ}(r,\theta, \phi). \tag{3}\]  
Figure 2: Antenna array: (**a**) reference antenna without failures; (**b**) antenna under test (AUT); (**c**) differential antenna (DA). The number of failures is 2 within the total element number \(N=21\).  
The differential antenna gives a resulting problem in which only the corresponding area to the field modification radiates as a result of failure. By visually monitoring the field distribution on the DA, the identification of faulty elements in the AUT can be observed.
# Nighttime Image Dehazing Based on Point Light Sources
## 4 Experiment  
### Experimental Settings  
In order to verify the reliability and validity of the method proposed in this paper. In this experiment, the algorithm programming is performed under Matlab2016b, and the hardware environment of the simulation experiment is i7-10750H, 2.60 GHz, 16 G RAM.  
During the experiment, the diameter of the low-pass filter window is 0.06 \(\times\) min(h, l), h and l are the numbers of pixels in the row and column in the image, respectively. \(A_{0}\) was set to 0.02\(\lambda\) was set to 0.1. In order to ensure the effectiveness of the algorithm at night, the dataset used in the experiment is NHRW, which is 150 real nighttime foggy images selected from 3000 images [25; 32], and the scenes include urban night scenes, roads, riversides, parks, and other scenes.
# Underlying Security Transmission Design for Orthogonal Time Frequency Space (OTFS) Modulation
## 4 Secret Key Generation Algorithm
### Characteristic Analysis for Secret Key
#### 4.2.2 Randomness  
In practical applications, for keys, good randomness is essential for secure transmission. Therefore, we use the randomness test suite developed by the National Institute of Standards and Technology (NIST) to evaluate the randomness of the generated keys, referred to as the NIST test. The NIST test uses 16 test methods for randomness testing. The significance level \(\alpha\) is used to characterize the randomness of the key, and it is set as 0.01. Furthermore, the \(p\)-value is the probability that a sequence has better randomness than a true random sequence. If the \(p\)-value \(\geq\alpha\), it means that the sequence has passed the randomness test. We use the software sts-2.1.2 to test the randomness of the generated sequence and compare it with the randomness of the sequence generated by the chaotic algorithm and the block cipher algorithm DES. It can be seen from Figure 7 that more than half of the sequences generated by the chaotic algorithm cannot exceed 0.01, so it did not pass the randomness test. Even the sequence generated by the GOST algorithm exceeds 0.05. Moreover, more than half of the \(p\)-value of the sequence generated by the GOST algorithm exceeds the \(p\)-value of the sequence generated by the DES algorithm. In summary, the key sequence generated by the proposed method has strong randomness.  
Figure 6: LZ complexity simulation results.  
Figure 7: NIST test.
## 5 Discussion
### Enhancing Risk Management Strategies  
The findings of this study have significant implications for risk management, particularly in areas prone to sinkhole formation. By utilizing SHAP-based machine learning models to assess sinkhole susceptibility, this research offers an effective framework for identifying and mitigating risks associated with sinkholes in karst regions. The model's ability to provide a clear and interpretable analysis of the factors contributing to sinkhole formation is invaluable for decision-makers and urban planners. One of the key implications is the ability to prioritize areas at high risk of sinkhole formation. This can guide land-use planning, zoning regulations, and infrastructure development, ensuring that vulnerable areas are either avoided or subjected to enhanced monitoring and precautionary measures. For example, regions identified as being at high risk due to factors such as proximity to fault lines, excessive groundwater extraction, or vulnerable geological formations can be subject to stricter building codes, groundwater management policies, and environmental monitoring programs. Additionally, understanding the dynamic nature of sinkhole susceptibility through SHAP analysis can help local authorities develop early warning systems. These systems could monitor changes in groundwater levels, precipitation patterns, and other contributing factors to predict potential sinkhole events before they occur. This proactive approach would significantly reduce the risks to human life and infrastructure, enhancing community resilience to sinkhole-related disasters. In conclusion, the application of machine learning models like the one developed in this study provides a powerful tool for risk management in karstic regions. By integrating these models into risk assessment frameworks and decision-making processes, authorities can better manage sinkhole risks and promote sustainable development in vulnerable areas.
# A Review of Regional and Global Gridded Forest Biomass Datasets
## 3 Current Gridded Forest Biomass Maps
### AGB Maps Derived from a Combination of Field Biomass, LiDAR Data and Optical and/or Radar Data  
#### 3.4.1 LiDAR-Based Biomass Estimates, Boreal Forest Biome, Eurasia, 2005-2006  
[PERSON] et al. [85] derived forest biomass for western Eurasia (roughly 50-70 N) from a series of models with a spatial resolution of 500 m. Ground-based measured biomass was initially related to Portable Airborne Laser System LiDAR metrics, then established biomass from this initial step was related to Geoscience Laser Altimeter System (GLAS) data, and finally, extrapolated GLAS biomass estimates were related to ancillary variables including land cover type data, ecoregion data, and topographic data. Since field biomass and optical data do not match well in spatial resolution, some forest biomass mapping studies use the above three-phase modelling method to combine field biomass, LiDAR data and optical and/or radar data.  
For eastern Eurasia forests, a two-phase sampling strategy was employed to derive biomass. Field measurements were directly related with GLAS data without the airborne data intermediary phase. Uncertainty of AGB datasets sourced from the sampling error and airborne-spaceborne model error were quantified by a model-based and two-phase estimator used in previous studies [86; 87].
## 3 Methodology
### Data Preprocessing
#### 3.2.2 Data Processing  
Since the original dataset is a component-level fault record, it is necessary to process the component-level fault data into system-level fault data according to the above indicators. The specific data processing flow is shown in Figure 2, and it includes cleaning invalid features, deduplication, sorting, conditional clustering and counting, and time interval calculation.  
The original dataset includes dimensions such as the occurrence time, site, fault device, fault content, fault type, work content, and response time. With reference to the determined indicators, redundant dimensions such as work content and response time are removed, and feature dimensions such as occurrence time and site are retained. After that, the fault time is deduplicated according to the time, and the first fault record is retained. Then, the data after deduplication are sorted in chronological order to obtain the preliminary processed data. The preliminary processed data are clustered by year and month, and then, the monthly faults are counted to obtain the number thereof. Then, according to the fault rate calculation formula, the monthly fault rate can be obtained by dividing the total number of faults. On the other hand, the fault interval time is calculated using seconds for the preliminarily processed data, and the monthly average is calculated. Finally, the monthly average fault interval days are converted, and the processed dataset is formed together with the monthly fault rate obtained through the previous processing.
# Image Encryption Algorithms: A Survey of Design and Evaluation Metrics
## 3 Evaluation Metrics
### Histogram Analysis  
Histogram analysis is used to ensure the security and robustness of image encryption algorithms by evaluating the uniformity of a cipher image's histogram. The histogram of an image is an important indicator that reflects the distribution of its pixel values. A cipher image with a close-to-uniformly distributed histogram prevents attackers' attempts to extract any useful information. Researchers in image encryption use a set of metrics to quantify histogram analysis, such as chi-square (\(\chi^{2}\)), maximum deviation, irregular deviation, and deviation from uniform histogram. Each of the aforementioned metrics is further discussed in the following subsections.
## 1 Introduction
### Aims and Objectives  
Currently, there are not many articles that analyze urban WWTPs as an integrated energy system, aiming for complete energy neutrality of WWTPs. In this article, we fill this gap by presenting an analysis of a system that includes various possible renewable energy production methods in current and future WWTPs and presents a balance of energy production and consumption in the WWTP.  
The innovativeness of this article is characterized by the fact that an analysis is presented in which the WWTP is viewed as an integrated energy system, which integrates the production of various types of energy and energy carriers, including electricity and thermal energy, biogas, biomethane and green hydrogen, hydropower, sludge gasification, wastewater heat recovery, and the use of by-products formed during green hydrogen production--oxygen and heat. An innovative approach for the selection of electrolyser power based on multiple technical criteria is also presented. The performed analysis of WWTP as an energy system will help to understand the requirements that must be met to achieve complete energy neutrality of WWTP.  
The main questions raised and analyzed in this article are as follows:  
1. What are the main presumptions for the successful integration of green hydrogen and biomethane technologies?
2. What is the relationship between the electrolyser capacity and the oxygen, hydrogen, and heat demand in a WWTP?
3. What is the relationship between the electrolyser capacity and the availability of green electricity?
4. What degree of energy neutrality can be achieved by integrating green hydrogen production into WWTP, and what other measures are needed to achieve complete energy neutrality of a WWTP?
5. In which external systems and in what way can the excess energy produced in WWTP be consumed?
## 4 Results and Discussion
### Mechanical Properties
#### 4.1.2 Static Bending Strength
In the tests under a temperature of \(80\)\({}^{\circ}\)C (Figure 13), the highest values of the bending modulus were reached by the materials filled with basalt fibre. However, the results dispersion was very high, which may have been caused by, as mentioned before, the difficulty to obtain reproducible fittings. Figure 13: [PERSON]’s E modulus vs. temperature for sample EP (REF) and composites EP reinforced with basalt powder (BP), basalt fibre (BF) and basalt textile (BT) (Table 11). As can be seen in the figure below, material EP/BT had definitely worse properties in comparison to its performance at room and negative temperatures. What is interesting, [PERSON]'s modulus was negligible for the reference materials. The bending modulus of the modified vinylester resin is presented in Figure 14. Similar to the epoxy resin, at a temperature of 25 \({}^{\circ}\)C, the highest value of 18 GPa was observed for the fittings with an addition of basalt textile. A negligible bending modulus was recorded for materials VE/BF. Moreover, a decrease in the modulus was seen for the basalt powder-modified resin. Filling the vinylester resin with basalt powder particles did not influence the maximum stress value as compared to the reference sample. Nevertheless, the moduli in the initial measurement stage, where the fitting was subjected to elastic deformation forces, were different by about 5000 MPa. However, reinforcing with basalt mats may not be an efficient solution in elements performing under high temperatures, which is due to a large volume of the vinylester resin between border layers (fabrics). Such a portion of vinylester resin chemically hardened at high temperatures may lose some of the unreacted components, which, while volatilizing, create microcracks/micropores in the composite structure, which then become sample deformation and damage hotbels in a fitting under a heavy load. The worst situation at high temperatures, though, was observed for the composites reinforced with the non-axially oriented fibres. An increase in the composite temperature leads to a decrease in its viscosity by more intense motions of some polymer chains. Such motions may cause small but quite significant shifts of the reinforcing fibres with respect to the acting load. Figure 14 presents also the bending modulus for the vinylester composites in the case of measurements taken at 80 \({}^{\circ}\)C. The modulus is the highest--16 GPa--for the fittings modified with basalt powder, which is a very high value taking into account the fact that for the best epoxy (EP/BF) sample the modulus was 750 MPa. Fibre orientation perpendicular to the force and fibre elasticity growth at an elevated temperature could have been among the main causes of the worst measurement results. Chopped fibres under a high temperature might have acted as not only a physical obstacle hindering the deformation of the material, but also as a heat conductor within the sample, leading to its partial degradation and micro-damage to the material structure.
# Iterative Receiver Design for the Estimation of Gaussian Samples in Impulsive Noise +
## 2 System Model
### Markov-Gaussian Noise  
The Bernoulli-Gaussian noise [3] is a simple two state model consisting of a background Gaussian noise sequence \(\{n_{k}^{G}\}\), with variance \(\sigma_{G}^{2}\), on the top of which another independent Gaussian noise sequence \(\{n_{k}^{I}\}\), with variance \(\sigma_{I}^{2}\), may appear, due to a source of extra noise, like an interferer. The occurrence of such an impulsive event clearly follows the Bernoulli distribution, where the probability of observing a sequence \(\{n_{k}^{B}\}=\{n_{k}^{G}+n_{k}^{I}\}\), i.e., to be in a bad channel condition (the superscripts \({}^{G}\), \({}^{I}\), and \({}^{B}\) stand for good, interferer, and bad, respectively) is usually much smaller than that of being in a good condition, with only background noise present. At the same time, the variance \(\sigma_{B}^{2}\) of the bad noise samples is usually much larger than that of the good ones, \(\sigma_{G}^{2}\), so that the power ratio \(R=\frac{\sigma_{B}^{2}}{\sigma_{G}^{2}}\) is typically much larger than one. According to the Bernoulli-Gaussian model, the probability density function (pdf) of each noise sample \(n_{k}\) is thus expressed as:  
\[p(n_{k})=\frac{P_{G}}{\sqrt{2\pi\sigma_{G}^{2}}}\text{exp}\left\{-\frac{n_{k}^ {2}}{2\sigma_{G}^{2}}\right\}+\frac{P_{B}}{\sqrt{2\pi\sigma_{B}^{2}}}\text{ exp}\left\{-\frac{n_{k}^{2}}{2\sigma_{B}^{2}}\right\} \tag{3}\]where \(P_{G}\) and \(P_{G}=1-P_{B}\) are the probabilities of the good or bad channel states, i.e., without or with an active source of interference. We call \(i_{k}\) the underlying Bernoulli variable, which acts as a switch to turn on/off the interferer's noise, so that the overall noise sample at time \(k\) is:  
\[n_{k}=n_{k}^{G}+i_{k}n_{k}^{I}\quad. \tag{4}\]  
In order to model the bursty nature of impulsive noise, a Markov-Gaussian model was proposed in [5]. The following transition probability matrix,  
\[\mathbf{P}=\left[\begin{array}{cc}P_{GG}&P_{GB}\\ P_{BG}&P_{BB}\end{array}\right]\quad, \tag{5}\]  
accounts for the correlation between successive noise states. As in any transition matrix, the elements are such that \(P_{GG}+P_{GB}=1\) and \(P_{BG}+P_{BB}=1\), so that in the state diagram representation of the Markov-Gaussian noise, in Figure 1, the outgoing transition probabilities sum to one. Moreover, by introducing a correlation parameter \(\gamma=(P_{GB}+P_{BG})^{-1}\), the steady-state probabilities of the two noise states are \(P_{G}=\gamma P_{GB}\) and \(P_{B}=\gamma P_{BG}\). The parameter \(\gamma\) determines the amount of correlation between successive noise states, so that a larger \(\gamma\) implies an increased burstiness of impulsive noise. More explicitly, the average duration of permanence in a given noise state is:  
\[T_{B}=\frac{\gamma}{P_{B}}\qquad\qquad\qquad T_{G}=\frac{\gamma}{P_{G}} \tag{6}\]  
whereas \(T_{B}=P_{B}^{-1}\) and \(T_{G}=P_{G}^{-1}\) would occur in the case of a memoryless Bernoulli-Gaussian process, to which the Markov-Gaussian model reduces in the case \(\gamma=0\).
# Computer Simulations of Dual-Antenna Microwave Ablation and Comparison to Experimental Measurements
## 3 Results
### FEM Model Verification by Ex Vivo Experiments
#### 3.1.1 Experimental Validation Based on Temperature Distribution  
The measured temperature changes at point P1 showed the same trend as the simulated results (Figure 4). The maximum temperatures measured at P1 for 20 mm spacing, 30 mm spacing, and 40 mm spacing were 106.88 \({}^{\circ}\)C, 82.16 \({}^{\circ}\)C, and 57.27 \({}^{\circ}\)C, respectively. The temperature rise curve shows that the smaller the values of the dual-antenna spacing, the higher the final temperature at P1 that can be reached. The average differences between the MWA simulation temperatures and the ex vivo measurements under the antenna spacings of 20 mm, 30 mm, and 40 mm were 1.08 \({}^{\circ}\)C, 0.95 \({}^{\circ}\)C, and 2.1 \({}^{\circ}\)C, respectively. When the spacing was 20 mm in Figure 4, the temperature rise rates of the two curves were very close in the first minute. During the ablation, there were some errors between the experimental data and the simulated temperature due to the thermal deformation of the tissue.  
The comparisons of characteristic lengths and temperature changes demonstrate that the FEM model can accurately simulate the actual effects of dual-antenna MWA. Therefore,  
Figure 4: Comparisons of the average temperature changes at P1 and the simulation results (three different dual-antenna spacings: 20 mm, 30 mm, and 40 mm, under the same heating parameters of 50 W matrix input power and 8 min treatment time).  
the FEM model can be used to conduct further studies for analyzing the effects of different heating parameters and antenna spacings in dual-antenna arrays for MWA.
# Smart Nanocomposites for Nanosecond Signal Control: The Nano4 waves Approach
## 2 Materials and Methods
### Strategy
#### 2.1.1 Selection and Combination of Nano-Inclusions  
Various types of nanocarbon supports (NCS) have been tested (Figure 1a): namely, multiwalled carbon nanotubes (MWCNTs), graphene nanoplatelets (GNP) and (reduced) graphene oxide (GO and rGO), which enable the tuning of various properties over a wide electromagnetic spectrum, in particular the dielectric constant \(\varepsilon\) of the resulting composites. We have systematically explored combinations of highly anisotropic NCS and spherical ferro- and ferri-magnetic nanoparticles (MNPs) (Figure 1b), in order to induce additional electromagnetic properties to the resulting composites. Indeed, MNPs show intrinsic resonance of their permeability \(\upmu\) in the range of 4-40 GHz, while a short distance between NCS induces huge coupling capacitances responsible for relaxation in the GHz-range [13; 14], and for enhanced permittivity \(\varepsilon_{\mathrm{r}}\). Controlling the composition and surface chemistry of the carbonaceous components and metal inclusions optimizes their combined nanostructuration (Figure 1c).