esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Hyperspectral Imagery and Preprocessing  
The HJ-1A, a Chinese satellite launched in September 2008, is part of the small satellite constellation A/B/C that conducts large-scale, all-day, all-weather ecological, environmental, and disaster monitoring. The HSI is a hyperspectral sensor onboard the HJ-1A satellite. The HSI image covers the swath width of 50 km with a spatial resolution of 100 m, containing 115 bands in a spectral wavelength ranging from 459 nm to 956 nm. The HSI bands have an unequal bandwidth from 2.08 nm to 8.92 nm at an average of 5 nm. As a result of the stripe noises in the first 20 bands, only those from 21 to 115 (band centers: 505.89 and 951.54 nm) were used in this study. The HSI images in the study area (Figure 1) were downloaded at the China Centre for Resources Satellite Data and Application [37]. Two HSI images acquired on 21 April 2014 were used to map the soil pH andEC. The HSI images are level 2 products that have been roughly radiometrically and geometrically corrected. Here, each image was further geo-corrected using about 30 ground control points [23] selected from the 1:50,000 topographic maps. Atmospheric correction was also conducted using the fast line-of-sight atmospheric analysis of spectral hypercube (FLAASH) module [38] in the ENVI 5.1 software, which converted digital numbers to surface reflectance. In this way, the resampled soil and image spectra are quantitatively comparable.  
Other land covers, such as water and dense vegetation, can block the presence of soil surface, thereby introducing noises to soil detection in this study. Here, the normalized difference vegetation index (NDVI) from red (band 68 with a center wavelength of 660.58 nm) and NIR. (band 101 with a center wavelength of 841.17 nm) was extracted to identify these non-soil surfaces. Water and paddy can be separated from bare soils with NDVI < 0.05. Pixels with NDVI > 0.3 were assumed vegetation [23; 39]. Residential pixels were identified through visual interpretation. All these non-soil land covers were masked out in this study.
## 1 Introduction
A review of the relevant literature has shown that although the existing literature has obtained some results and provided theoretical support to improve the dynamic scheduling strategy for carrier aircraft support operations, there are still some limitations. Most carrier aircraft scheduling methods are based on a static flight deck environment, and need to have a number of parameters defined before a scheduling plan is started. The impact of each activity's scheduling time on the available equipment and operational crews were not considered, including the operating time of all of the activities, the allocation of operation resources (renewable resources and non-renewable resources),and the number of aircraft that need to be supported. The time urgency may affect the selection of scheduling strategies, and the resource limitations can lead to dispatching conflict. The current research cannot meet the demand of real-time dynamic scheduling well for the carrier aircraft support operation. The proactive scheduling strategy can create a schedule that is robust with respect to dynamic disruptions, and make a plan regarding a temporal schedule and resource allocation, but it cannot generate rescheduling simultaneously when the dynamic disruption occurs. Some disruptions may cause a robust scheduling failure, which calls for a new schedule. The reactive strategy can repair a schedule that has been disrupted in terms of reducing disturbance from uncertainties, but it is difficult for these proposed algorithms to generate rescheduling in a short time, and they make little changes to the rescheduling sequence compared with the baseline scheduling. The relevant research studies have also shown that it is difficult to make static scheduling meet the needs of a dynamic deck scheduling environment, and these methods do not meet the requirements of real-life situations. In this paper, we consider actual dynamic deck environment factors, establish an integer programming mathematical model, and propose a dynamic rolling horizon (RH) scheduling strategy for carrier aircraft support operations under uncertain conditions. It is a very interesting work. The structure of the paper is as follows. In the next section, the dynamic scheduling problem of carrier aircraft support operations is described in detail. Some definitions are proposed; then, the new integer programming model for the carrier aircraft support is established. In Section 3, the periodic and event-driven RH scheduling strategies are proposed to deal with the dynamic deck environment factors and different disruptions, and the associative recovery strategies are discussed in the creation of new support schedules. In Section 4, the improved genetic algorithm for the dynamic deck environment is illustrated in detail. The computational results and analysis are provided in Section 5. Finally, we provide the overall conclusions in Section 6.
## 4 Dynamic Tests
### Calculation of FRFs with the Finite Element Method (FEM)  
Numerical FRFs were calculated with the finite element method (FEM) considering linear viscoelastic behaviour and the dynamic test boundary conditions. Figure 6 shows the FEM mesh and boundary conditions used for the FEM calculation of the FRFS.  
FRFs were calculated at the desired frequencies by resolving the following three-dimensional equation of motion in frequency domain:  
\[-\rho\omega^{2}\mathbf{u}-\
abla\cdot\sigma=0 \tag{10}\]  
where \(\rho\) is the bulk density of the material, \(\omega\) is the angular frequency, \(\mathbf{u}\) is the displacement vector, \(\
abla\) is the gradient tensor operator and \(\sigma\) is the Cauchy stress tensor. Free boundary conditions are assumed to solve Equation (10) except at the impact point where a cyclic load \(\mathrm{e}^{\mathrm{i}uct}\) is applied in the direction of the impact. Since the load amplitude is unity, the calculated FRFs correspond to the calculated acceleration in direction Z (direction of vibration of the accelerometer in physical tests). Back analysis was performed considering two LVE behaviour models successively, as explained below.
## 5 Numerical Simulations and Discussion  
To evaluate the proposed robust channel estimation algorithms, we compare these algorithms in terms of channel sparsity and non-Gaussian noise level. A typical broadband wireless communication system is considered in computer simulations [3]. The baseband bandwidth is assigned as 60 MHz and the carrier frequency is set as 2.1 GHz. Signal multipath transmission causes the 1.06 \(\upmu\)s delay spread. According to the Shannon sampling theory, the channel length is equivalent to \(n=128\). In addition, average mean square error (MSE) is adopted for evaluate the estimation error. The MSE is defined as  
\[\text{MSE}\left\{\mathbf{w}(n)\right\}\left(\text{dB}\right)\triangleq 10\text{log}_{10} \left(1/M\right)\sum_{m=1}^{M}\left||\mathbf{w}(n)-\mathbf{w}||_{2}^{2}/||\mathbf{w}||_{2}^ {2}\right. \tag{66}\]
## 4 Discussion
### Sensitivity Analysis on the Number of Clusters  
To investigate the impact of the number of representative scenarios on scheduling performance, a sensitivity analysis was conducted by varying the cluster count \(K\) used in the scenario reduction step. Specifically, the optimization model was solved for \(K=5\), \(K=10\), and \(K=15\), and the corresponding results are presented in Table 9.  
From Table 9, it can be observed that increasing the number of clusters from 5 to 15 leads to a general improvement in expected revenue, with the highest value (CNY 16,379.67) achieved when \(K=15\). However, this improvement is accompanied by a slight reduction in CVaR, decreasing from CNY 15,903.29 at \(K=10\) to CNY 15,770.80 at \(K=15\), as well as a decline in the minimum scenario revenue. This indicates that while a larger number of clusters provides a more comprehensive representation of wind power uncertainty, it may also introduce more extreme scenarios that negatively affect risk indicators.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Confidence Level** & **0.8** & **0.85** & **0.9** & **0.95** \\ \hline Initial \(SOC\) & 0.1 & 0.1 & 0.1 \\ Expected Revenue (CNY) & 16,447.05 & 16,453.56 & 16,233.78 & 15,772.14 \\ Minimum Scenario Revenue (CNY) & 15,163.38 & 15,136.98 & 15,532.48 & 15,736.71 \\ Maximum Scenario Revenue (CNY) & 16,500.54 & 16,508.42 & 16,263.00 & 15,773.62 \\ CVaR (CNY) & 16,076.46 & 16,166.63 & 15,903.29 & 15,676.59 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison of day-ahead economic benefits and risk indicators of the wind/storage system under different confidence levels.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number of Clusters** & **5** & **10** & **15** \\ \hline Initial \(SOC\) & 0.1 & 0.1 & 0.1 \\ Expected Revenue (CNY) & 16,180.98 & 16,233.78 & 16,379.67 \\ Minimum Scenario Revenue (CNY) & 15,476.75 & 15,532.48 & 15,269.88 \\ Maximum Scenario Revenue (CNY) & 16,233.98 & 16,263.00 & 16,439.82 \\ CVaR (CNY) & 15,636.42 & 15,903.29 & 15,770.80 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Sensitivity of scheduling performance to the number of clusters in scenario reduction.
## 4 Results and Discussion
### Phase 6--Development of Geographical Maps of Economic Benefits  
By recalling the aim of this research regarding the definition of the economic benefits associated with energy efficiency interventions, the outputs obtained from the application of the proposed evaluation model have been represented on geographical maps. Moreover, for each urban area of the selected provincial capitals, to quickly identify the most convenient areas for a potential private investor, convenience maps have been developed.
# Wideband Spectrum Sensing Based on Reconfigurable Filter Bank in Cognitive Radio
## 5 Simulation Results and Analysis  
### Data Preprocessing  
Real signals employed in our work are the Power Spectral Density (PSD) values measured by RWTH Aachen University in 2007 with the platform of Agilent E4440A spectrum analyzer at three different places as IN (indoor), NE (rooftop) and AB (third floor balcony), respectively, and the dataset covers the frequency range from 20 MHz to 6 GHz with the time of one week [22]. For the proposed method, which is based on RFB, is a fine sensing method used after optimal channels selection in rough estimation. The NE (rooftop) sub-band 1 300 MHz-700 MHz band with 24-hour measurements data of PSD values is applied in the next simulation. The spectrum occupancy is shown in Figure 7a, where the resolution is 200 kHz, plus the average sampling interval of 1.8 s and the power unit in dBm. As the measurement dataset is quite large, one sample value is taken every 60 measured data in time, and the energy threshold is set to -107 dBm/200 kHz based on the IEEE 802.22 standardization committee for the detection threshold of wireless microphones. Alternatively, when the sampling power value is greater than the energy threshold, the sub-band is occupied and represented by \"1\", otherwise the sub-band is idle and represented by \"0\".  
The sub-bands idle state proportion (ISP) is the ratio of the number of sub-bands of an idle state to the total number of sub-bands, and Figure 7b,c count the ISP from the frequency domain and the time domain, respectively. Figure 7b shows that some adjacent sub-bands have higher ISP, such as the 350 MHz and 550 MHz bands. In Figure 7c, observe that the sub-bands' ISP continues to be high for a fraction of the continuous time. Apparently, the spectrum utilization rate is low, so that the cognitive user is able to improve spectrum utilization through the combination of both the spectrum sensing technology and dynamic spectrum access technology.  
The prototype filter \(h(n)\) for FB is a FIR filter implemented in MATLAB with respect to the Remez algorithm simulation. The stop-band attenuation reaches -50 dB and satisfies the condition of the linear phase. The prototype filter coefficient is decimated and divided into \(M\) polyphase branches to form P-DFTFB. The real signals of PSD values are transformed to amplitude measurements and then filtered by the proposed P-DFTFB. The example of eight sub-bands divided by P-DFTFB is shown in Figure 8, and the X label is the normalized frequency of each sub-band, and the actual value of ISP is about 0.6.  
Figure 7: Twenty-four-hour PSD data preprocessing in 300 MHz–700 MHz band. (a) Sub-bands occupancy; (b) ISP in the frequency domain; (c) ISP in the time domain.  
As shown in Figure 8, the signals amplitudes of Sub-band 3, Sub-band 4 and Sub-band 7 are higher than the others obviously. Given a proper threshold, the sub-band state would be obtained by comparing the detection statistic with the threshold, and the above three sub-bands maybe determined as occupied, therefore, the measured ISP is 0.625 which is approximate to the actual value 0.6.
## 1 Introduction
Therefore, strategies of decreasing the cost of PACT are needed. Some researchers have attempted to reduce the cost of ultrasound detection units by using partial view detection with reflectors [31] or using sparse arrays along with a compressed sensing reconstruction algorithm [32]. In [33], a multispectral PACT system was implemented where 8 single element transducers were utilized in an arc fashion. The system took around 30 s to acquire an image. In [25], a PACT system was developed using only two unfocused transducers, and a complete rotation was performed in 3s. In [34], a single element focused transducer-based PACT system was developed for in-vivo rat brain imaging. The total acquisition time was 16 minutes with a scanning step size of 1.5\({}^{\circ}\). In [35], a spherical PACT system was developed to image zebra fish. The scanning step size was 0.75\({}^{\circ}\) with 480 scanning positions and 24 s acquisition time for each frame. In this article, we present the development and characterization of a low-cost, fast PACT system that has been optimized to achieve 1.5 s temporal resolution with a 240 \(\upmu\)m spatial resolution. The low number of elements (16) reduces the cost of the ultrasound unit as well as the need for an expensive DAQ. To reduce the cost of illumination, a Q-switched Nd:YAG laser was used. This comparatively lower-cost system provides fast, high-resolution images to potentially be used for small animal imaging. The performance of the system has been evaluated by imaging complex phantoms.
## 2 Selection of Pulse-like Ground Motions and the Engineering Background
### Engineering Background and Finite Element Modeling  
This study employs a practical five-span high-pier prestressed continuous rigid-frame bridge in China with a span arrangement of (\(70+3\times 127+70\)) m for this analysis, as shown in Figure 3. The main girder of this bridge is a single-box and single-chamber prestressed concrete box-girder, with a height of 7.3 m at the pier's top cross-section gradient, moving to 3.0 m at the mid-span cross-section according to a 1.8 times parabolic curve, constructed using C50 concrete. Piers numbered 1# to 4# are all variable cross-section hollow thin-wall piers made from reinforced C40 concrete; the longitudinal bar and stirrup all use HRB335 steel bars. Meanwhile, both abutments, 0# and 5#, are integral abutments with expanded foundations, and the abutment bearings use GPZ (II) 3.5 DX and GPZ (II) 3.5 SX pot-type rubber bearings. The detailed geometry of this multi-span high-pier continuous rigid-frame bridge can be seen in Figure 3.  
Figure 2: Response spectra of the selected pulse-like ground motions.  
To analyze the dynamic nonlinear response of this five-span high-pier continuous rigid-frame bridge under the influence of pulse-like ground motions, a 3D finite element model was established using OpenSees to conduct a numerical simulation. We used a displacement-based beam-column element combined with an elastic section to simulate the main girder, and the main piers (Piers 1# to 4#) were simulated with a nonlinear beam-column element combined with a fiber section, using a flexibility-based method. To simulate the pier-girder consolidation effect, rigid arms were employed to connect the main girder and the main piers in the analysis. The fiber section of the main pier was discretized into unconfined concrete fibers, confined concrete fibers, and longitudinal bar fibers, with unconfined and confined concrete being simulated using the Concrete02 model [28] and the longitudinal bars simulated using the Steel02 model. In addition, the force-deformation relationship between the Concrete02 model and Steel02 model can be seen in Figure 4. The secondary dead load and self-weight effects of the bridge were simulated using nodal mass points, while the consolidation constraints were used to simulate the boundary conditions at the bottom of the main piers. Meanwhile, the spring abutment modeling method was adopted in this study to establish an abutment model of abutments 0# and 5# to accurately simulate the interaction between the backfill behind the abutment, the abutment, and the main girder, as shown in Figure 5. Detailed descriptions of the modeling method for the spring abutment model of a five-span continuous rigid frame bridge can be found in the literature [9].  
Figure 4: Schematic diagram of the Concrete02 and Steel02 models.  
Figure 3: Diagram of the configuration and the finite element model of the bridge. Note: 1# represents No. 1, and so on.
# TKEO-Enhanced Machine Learning for Classification of Bearing Faults in Predictive Maintenance
## 2 Materials and Methods
### Features Used  
After reviewing the literature on Teager-Kaiser energy methods and applying programs on Kaggle such as SVC [24] and RFC [25], we identified the following 9 features used in these two models:  
* Max: xmax, the maximum value;
* Min: xmin, the minimum value;
* Mean: \(\mu=(\Sigma\mathrm{x})/\mathrm{N}\);
* SD (standard deviation): \(\sigma=\sqrt{[(\Sigma(\mathrm{x}-\,\mu)^{2})/\mathrm{N}]}\), the square root of the sum of squares of deviations from the mean, divided by the total number of values;
* RMS (root mean square): \(\sqrt{[(\Sigma\mathrm{x}^{2})/\mathrm{N}]}\);
* Skewness: \((\Sigma(\mathrm{x}-\,\mu)^{3})/[\mathrm{N}\cdot\sigma^{3}]\), the skewness of distribution relative to the mean indicates whether values lean more to the left or right;
* Kurtosis: \((\Sigma(\mathrm{x}-\,\mu)^{4})/[\mathrm{N}\cdot\sigma^{4}]-3\). Kurtosis measures the shape of the distribution. Positive kurtosis indicates values that are more concentrated around the mean (more peaks and troughs), while negative kurtosis indicates a flatter distribution compared with the normal pattern;
* Crest: xmax/xeff, the ratio of the highest peak of the wave to its effective value.
* Form: The shape of a signal that can describe its structure, frequency content, or other specific characteristics.  
The Figure 4 illustrates the importance of various features in the model. The last three features--form, crest, and skewness--each have an importance of less than 10%, as shown by the lower bars. This low importance is likely to be because these features provide limited additional information beyond what has already been captured by the six key features (e.g., RMS, Standard Deviation, and Kurtosis). For example, standard  
Figure 3: Flowchart of the Random Forest Classifier. Source: [PERSON], Medium [23].  
deviation is indirectly reflected in the calculations of skewness and crest, reducing their independent contribution. By removing these less important features, the model becomes simpler, more computationally efficient, and less prone to overfitting, while maintaining high predictive accuracy.
# Modification of Insulating Properties of Surfaces of Dielectric High-Voltage Devices Using Plasma
## 5 Methods of Adjusting the Electrical Strength of Dielectrics: Surface Strength
### Detailed Description of the Plasma Surface Treatment  
The plasma was generated through a setup including a power frequency generator and a plasma chamber producing a plasma slit beam (PS). The effective area of the plasma beam amounted to 150 mm. The plasma was generated in an argon atmosphere conducted into the plasma nozzle at a flow rate of \(w=4\) m\({}^{3}\)/h. The high-frequency power generator to produce the PSJ had an operating frequency of the first harmonic at\(f_{1}=13.56\) MHz, and the output was regulated to a power of up to \(P_{\text{out}}=600\) W. In the first series of experiments for the surface modification of polymer dielectric samples (six samples, two controls) the power was set to \(P_{\text{out}}=500\) W. The repeatability of the plasma impact on the sample was ensured by a belt conveyor having a set feed rate. The samples and their surfaces were exposed to the plasma discharge twice in order to ensure good homogeneity of the influence on the surface (the feed rate was set to \(v_{\text{d}}=22\) mm/s, and the height of the PSJ mouth above the sample surface equaled \(h=10\) mm). The selected sample size to demonstrate the effect was chosen to be \(a\times b=100\times 100\) mm; such a parameter facilitated easy handling and the capture of the homogeneity characteristics of the plasma exposure. All dielectric samples of the second series, both the thermoplastic and the thermoset types, were exposed in this way in a time sequence. In each of the two subsets, a total of \(N=8\) samples were exposed, while 2 samples were left unexposed to allow a comparison of the measured parameters. The distance to the plasma nozzle mouth from the sample surface was micrometrically set to \(d=10.0\) mm. For the measurement of the surface parameters (Figure 7) with the probe (Figure 7), and the required measurement purposes, the standard recommended \"suitable\" electrode and dielectric arrangement concept (IEC 61340-5-1- [47], ESD STM 11.11-2001- [48], ASTM Standard D 257-99- [49]) (Figure 7) was chosen. The test probe and specimens were chosen to accommodate a minimal deformation of the field lines due to the geometry of the surrounding environment and to satisfy the accuracy of the testing. The configuration could then be considered as a homogeneous and defined environment.
## 3 Method Proposal to Analyze AST n-Tools Combinations  
In this section we present a new methodology repeatable to combine, compare and rank the SAST, DAST and IAST tools combinations (see Figure 1).  
* Select the OWASP Benchmark project.
* Select the SAST, DAST and IAST tools. In this concrete instantiation of the methodology, we choice six commercial and open source tools according to the analysis of the related works in Section 2.3 and official lists of SAST, DAST and IAST tools.  
* Run the selected tools against the OWASP Benchmark project with the default configuration for each tool.
* Select appropriate metrics to analyze results according three different levels of web applications criticality.
* Metrics calculation.
* Analysis, discussion and ranking of the results.  
The following figure presents the proposed method in graphic form: