esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Content-Aware Multi-Resolution Disparity Estimation Using Semi-Global Block Matching
#### 2.2.3 Multi-Resolution Content Analysis  
Content-aware analysis is an essential process for evaluating the visual attributes in the EIs. Its purpose is to optimise the window size parameters used in disparity estimation based on the complexity and textures present at different scales. This analysis is particularly valuable for adjusting window size parameters at both single and multiple scales to enhance the precision and resilience of the disparity.  
EIs possess a high degree of sensitivity. Consequently, a simpler approach involving edge segmentation and texture analysis is employed. The Sobel filter is utilised for accomplishing edge detection. The filter's sensitivity is contingent upon the resolution of the images. Low-resolution images necessitate a higher threshold for detecting significant structures, while high-resolution images require a lower threshold to identify finer details as depicted in Figure 6.  
Figure 5: Multi-resolution pyramid of EIs.  
Textures are ideal to identify intensity patterns which is great for identifying regions for disparity estimation. Local Binary Patterns (LBPs) are used in this case to identify the textures in the EIs. Here, the focus is on larger patterns at lower resolutions and finer textural details at higher resolutions.  
\[LBP_{n}(p)=\sum_{k=0}^{P-1}2^{k}\cdot\mathbf{1}(I_{n}(p_{k})\geq I_{n}(p)) \tag{3}\]  
where \(LBP\) is computed for pixel \(p\) located at location \(n\) within the image used to classify the texture. \(P\) represents the total number of pixels neighbouring to \(p\), with the summation ranging from \(k=0\) to \(P-1\) and \(2^{k}\) represents the weighting factor assigned to each neighbouring element, which is determined by its location. The neighbouring pixel's (\(p_{k}\)) intensity is compared with the central pixel \(I_{n}(p)\). \(\mathbf{1}(I_{n}(p_{k})\geq I_{n}(p))\) returns \(1\) if it is true and \(0\) if false. Texture maps across different scales are shown in Figure 7.  
To enhance simplicity and preserve time, the edge map \(E\) and texture map \(T\) are combined to then form a dynamic adaptive window for the computation of disparities.  
\[F(x,y)=\alpha\cdot E(x,y)+(1-\alpha)\cdot T(x,y) \tag{4}\]  
The combined feature at pixel \((x,y)\) is denoted as \(F\), and it is influenced by a weighted factor, \(\alpha\), which ranges from \(0\) to \(1\). This factor determines the appropriate ratio between edge and texture data. The value of \(\alpha\) has been cautiously adjusted to achieve the ideal result for each image.  
Figure 6: Examples of extreme edge thresholds show that the sensitivity of the filter depends on the resolution of the image. High-resolution images need a lower threshold to identify finer structures, while low-resolution images need a higher threshold to identify significant features.  
Figure 7: LBP texture maps across different scales before filtering to show the effect.
## 5 Results
### Adversarial Example Attacks
#### 5.2.3 Adversarial Examples--Attack Objectives  
There is a distinction between the objectives of attacks: targeted or untargeted. An attack objective might be to cause a classifier to misclassify an input as _any_ other class (untargeted) or to misclassify an input as a _specific_ class (targeted). In the cyber-security domain, IDS often focus on binary classification: malicious or benign. For binary classification the effect of targeted and untargeted attacks is the same. More complex multi-class IDS can help network analysts triage or prioritise different types of intrusions. Network analysts would certainly treat a distributed denial of service (DDoS) attack differently than a BotNet or infiltration attempt. Adversaries could gain significant advantage through _targeted_ attacks, for example, by camouflaging an infiltration attack as a comparatively less serious network intrusion.  
Recent research goes beyond adversarial examples causing misclassification of a single input. [PERSON] et al. [107] further showed the existence of untargeted _universal_ adversarial perturbation (UAP) vectors for images, and ventured that this is problematic for classifiers deployed in real-world and hostile environments. In the cyber-security domain, [PERSON] et al. [99] demonstrated UAPs in the feature space of malware detection. They showed that UAPs have similar effectiveness to adversarial examples generated for specific inputs. [PERSON] et al. [56] looked at UAP in the constrained domain of intrusion detection. Adversaries need only calculate one UAP that could be applied to multiple inputs. Pre-calculation of a UAP could enable faster network attacks (DDoS) that would otherwise require too much calculation time. Table 5 shows that most research considers untargeted attacks. Targeted attacks are less represented in the literature. Furthermore, UAPs are a more recent avenue for research.
## 2 Materials and Methods
### Directional Reflectance Measurements  
A 410 Solar DHR reflectometer (Surface Optics Corporation, San Diego, CA, USA) was used to measure the total reflection from the skin surface in the range of 335 to 2500 nm. The 410 Solar reflectometer measures the integrated surface reflectance of a surface at 20 degrees incidence in seven wavebands in the visible and near infrared spectral regions. The integrating sphere captures the reflected light from the skin surface, integrating reflections in all directions. Detectors located in the integrating sphere measure the amount of reflected light and convert it into an analog electrical signal, which is processed to determine the reflectance of the test sample for an angle of incidence of 20 degrees and each of the wavelength bands.  
The directional reflectance is defined as the ratio of the total energy reflected of the light reflected from the skin to the measuring hemisphere, to the energy of the light incident on the surface [12].  
Directional hemispherical reflectance was measured in following wavelength bands (nm): 335-380, 400-540, 480-600, 590-720, 700-1100, 1000-1700 and 1700-2500 of both psoriatic skin and skin not covered with psoriatic scales. Before starting measurements, the reflectometer was calibrated with calibration coupons--specular and diffuse. The measurement regions were without hair on the surface. Measurements were made before the start of therapy, after 10 and after 20 irradiations. Each skin region was measured three times, and the results were averaged. Measurements were always made at the same locations (Figure 1).  
\begin{table}
\begin{tabular}{l l} \hline
**Inclusion Criteria** & **Exclusion Criteria** \\ \hline  & \(\bullet\) & Treatment within 4 weeks of the baseline visit with oral agents \\ \(\bullet\) & Patients with plaque-type psoriasis vulgaris & \(\bullet\) \\ \(\bullet\) & Psoriasis area and severity index (PASI) \(\geq\) 10 & \(\bullet\) \\ \(\bullet\) & Fitzpatrick skin type II & \(\bullet\) \\ \(\bullet\) & Pregnancy and lactation \\ \(\bullet\) & Fitzpatrick skin type I and \(>\)III \\ \hline \end{tabular}
\end{table}
Table 1: Inclusion and exclusion criteria.  
Figure 1: Diagram illustrating the concept of directional reflectance measurements of skin.
## 5 Case Study of Developing Liver Cancer Diagnosis
### Implementing the HCC Diagnosis System
#### 5.4.5 Execution Performance of the ML Models  
ML models, especially those based on deep neural network architectures such as CNNs and RNNs, run much slower than conventional algorithms like tree- or graph-based analysis. Hence, a direct comparison of execution times between ML models and traditional algorithms may not be feasible.  
Instead, we analyze the runtime performance of the ML models in the context of the liver cancer diagnosis case study. The liver cancer diagnosis system, utilizing ML models, processes a CECT scan as input and follows the ten steps outlined in Figure 6. Consequently, the time required for diagnosing liver cancer is heavily dependent on the size of the given CECT scan.  
Our dataset for training the ML models consists of 130 CECT scans, with an average of 447 slices per scan. The execution time for diagnosing liver cancer with a CT scan of this size was measured to be 18 min and 38 s. This execution time includes the time required to update the database contents and refresh the web-based user interface for each task involved in processing each CT slice, as shown in Figure 13. The actual time for making predictions with the ML models was measured to be approximately 14 min.
# Boundary-Aware Hashing for Hamming Space Retrieval
## 3 Boundary-Aware Hashing
### Problem Definition  
In supervised hashing, the supervised information is classified as pointwise labels [28; 29; 30], pairwise labels [13; 15; 24], and triplet labels [31; 32]. This paper focuses on a popular setting with pairwise labels in deep supervised hashing to validate our method. The techniques described in this paper can also be adopted with other supervised labels setting, as will be carried out in our future work.  
In supervised image retrieval systems, suppose that \(X=\{x_{i}\}_{i=1}^{n}\) indicates a training set of \(n\) points (images), where the supervised label constructs a pairwise similarity matrix \(S=\{s_{ij}\}\) with \(s_{ij}\in\{0,1\}\). \(s_{ij}=1\) if two data points \(x_{i}\) and \(x_{j}\) are semantically similar; otherwise, they are semantically dissimilar, i.e., \(s_{ij}=0\).  
The goal of BAH is to learn a nonlinear hash function \(h(x_{i})=b_{i}\in\{-1,1\}^{K}\), where \(K\) denotes the length of hash codes. The hash function encodes each data point into \(K\)-bit hash codes, which aims to learn a Hamming ball of radius \(H\leq 2\) to concentrate more similar data points inside the ball and distribute the dissimilar ones outside the ball.
# Estimating Double Cropping Plantations in the Brazilian Cerrado through PlanetScope Monthly Mosaics
## 3 Results  
### Field Data  
Table 2 summarizes the number of LULC classes visited in the two field campaigns. A total of 61 bare soils in October 2021, which correspond to the areas prepared for planting in this month, were revisited in April 2022. We found that only four sites were not used for second crop plantation. Maize and sorghum were the most common second crops in the municipality. Another six sites were planted with sugarcane.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Description** \\ \hline Spatial resolution & & 4.77 m \\ Processing level & & Atmospherically corrected, normalized, analytic mosaics \\ Spectral bands & Blue (0.455–0.515 μm), green (0.500–0.590 μm), red (0.590–0.670 μm), and \\  & & near-infrared (0.780–0.860 μm) \\ Vegetation indices & & NDVI, EVI, GRNDI \\ Textural features & Contrast, dissimilarity, homogeneity, angular second moment, energy, \\  & entropy, mean, variance, correlation, and maximum probability \\ Monthly mosaics & & October 2021 to March 2022 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of input parameters derived from six PlanetScope monthly mosaics for Random Forest classification, seeking the discrimination of the double cropping plantations and other land use and land cover classes in the 2021–2022 crop growing season in the municipality of Goiatuba, Goiás State.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**LULC Class** & \
\begin{tabular}{c} **Number of Sites Visited** \\ **in October 2021** \\ \end{tabular} & **LULC Class** &
\begin{tabular}{c} **Number of Sites** \\ **Visited in April 2022** \\ \end{tabular} \\ \hline Forestland & 36 & Forestland & – \\ \hline Shrubland & 10 & Shrubland & – \\ \hline Pastureland & 46 & Pastureland & – \\ \hline Sugarcane & 45 & Sugarcane & – \\ \hline Reforestation & 2 & Reforestation & – \\ \hline Cotton & 1 & Cotton & – \\ \hline \multirow{4}{*}{Bare soil} & & Maize & 35 \\ \cline{2-4}  & & Sorghum & 14 \\ \cline{1-1} \cline{2-4}  & & Sugarcane & 6 \\ \cline{1-1} \cline{2-4}  & & Crop residue & 4 \\ \cline{1-1} \cline{2-4}  & & Crotalaria & 2 \\ \hline TOTAL & 201 & & 61 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of number of sites per land use and land cover (LULC) class visited during the field campaigns conducted in October 2021 and April 2022.
## 4 Simulation Verification of the Method
### The Case of Random Error Signals
Analyzing the content of Equation (44), it can be noticed that the inaccuracy of the gain parameter estimation has a greater impact on the effectiveness of the proposed analysis method than the inaccuracy of the phase shift parameter estimation. Analyzing the measurement experiment described in work [16] it can be noticed, however, that in the case of the gain value, determining the dependence of this quantity on the pulsation of the signal processed by the measurement chain is much easier than in the case of determining this dependence for the phase shift.
## 3 Methods and Materials Processing
### Wavelet Transform
#### 3.2.1 Continuous Wavelet Transform  
The continuous wavelet transform (CWT) method extends a time series into an image of spectral power in the time-frequency domain, where the wavelet power is a function of time (or date) and the period (or frequency) of the time series [41]. The resulting wavelet power image can be considered as a time-frequency map showing the signal's power with time and frequency. Using this image, we can identify dates and corresponding periodicities that exhibit high wavelet power. These high-power regions in the time-frequency map indicate the presence of significant periodicities in the time series. Thus, the CWT method provides an intuitive way to identify significant periodicities in time series data.  
Assume a discrete time series \(X_{n}(n=0,1,\cdots,N-1)\), which is sampled at a fixed interval \(\Delta t\); its CWT can be defined as follows [26]:  
\[W_{i}^{X}(s)=\sqrt{\frac{\Delta t}{s}}\sum_{i=0}^{N-1}x_{i}\varphi_{0}^{*} \bigg{[}(i-n)\frac{\Delta t}{s}\bigg{]} \tag{1}\]  
where \(*\) denotes the complex conjugate; \(\varphi_{0}\) represents the mother wavelet, and in this paper, we have utilized the Morlet wavelet as the mother wavelet due to its excellent time-frequency localization capability [42]; and \(s\) is the wavelet scale. The continuous wavelet transform (CWT) can be thought of as a bandpass filter with different positions and widths [43; 44; 45].
## 3 Magnetorheological Dampers for Structural Buildings
### Application of MR Dampers in Building Structures
The factors taken into account in the study included architectural problems, programmatic issues, and construction capabilities. A simplified approach was used to achieve the same effective damping ratio and to characterize the mechanics of the case study. The approach was also used to mechanically characterize the floor displacement to make it consistent with optimal performance objectives. Based on a nonlinear dynamic analysis, the FVD was the most efficient of the three damping devices tested in this study. In terms of cost, the FVD was also the most cost-effective option for reducing economic losses arising from earthquakes [170]. In another study, [PERSON] describes a new MR damper-style tracking control scheme. Installation balancing was performed by steady-state primary nonlinear compensation combined with the modeling mapping approach of the MR damper device. To that end, [PERSON] reduced force tracing errors that were generated due to model imperfections. This was also due to parameter uncertainty, which was then reduced by the proportional feedback gain and the parallel integral, which is based on the absolute value of the actual device's damping force. In this case, [PERSON] performed experimental validation of the force tracking control scheme on rotational and long-stroke MR dampers, demonstrating their robustness and efficacy. The results show that a more accurate force is obtained using the combined feedback approach compared with the other two methods used in the study [171]. Several applications of MR dampers in building structures were briefly described above. However, when MR dampers are used in a controlled damping device with semi-active systems in building structures, the models and controls of the MR dampers warrant closer examination than their application. Unfortunately, this is an area of study that has been grossly overlooked. Therefore, we examine and discuss the various types of models and controls that are used in MR dampers.
# A Novel CDMA-Based Secret Message Multiplexing Scheme by Image Data Hiding +
###### Abstract  
In this paper, based on the concept of Code Division Multiple Access (CDMA), we propose an innovative secret message multiplexing scheme by digital image data hiding. Multiple secret messages distributed for several participants are encoded into a CDMA codeword through CDMA multiplexing and then embedded into a grayscale image. At the receiving side, each intended recipient will be assigned a distinct spreading code as the secret key for data extraction. In this way, even the authorized user can only extract his or her own secret message using an exclusive secret key and has no knowledge about other user's secret. This characteristic helps to ensure data security. Experimental results demonstrate that the scheme is also resistant to Gaussian noise attack, salt-and-pepper noise attack, and cropping attacks. Those merits sustain the feasibility of the proposed scheme.  
Code Division Multiple Access (CDMA); CDMA multiplexing; secret multiplexing; secret sharing; data hiding 202311704. [[https://doi.org/10.3390/math11030704](https://doi.org/10.3390/math11030704)]([https://doi.org/10.3390/math11030704](https://doi.org/10.3390/math11030704))
## 2 D2D Communications in LTE-A Standards  
### Proximity Service (ProSe) in LTE-A  
Proximity service (ProSe) is a proximity based D2D service defined in 3 GPP LTE-A Release 12 [16]. In ProSe, ProSe system architecture, discovery procedure, and D2D data services are defined. Unlike the existing network in which UEs connect to eNBs for data exchanging, UEs in ProSe can establish a direct D2D communication link without intervention of LTE core entities. In proximity services, UEs can select a D2D or cellular communication mode according to the channel quality. All UEs receive pilot signals broadcasted by eNBs on the downlink control channel. Then, UEs estimate the channel quality from eNBs in order to choose a communication mode between LTE and D2D. If the channel quality between UE and eNB is worse than it is between UEs, UE selects the D2D communication mode; otherwise, the cellular communication mode is used.
## 3 Results and Discussion  
All circuits as shown in Figure 11 are fabricated in a 0.18 \(\upmu\)m Complementary Metal Oxide Semiconductor (CMOS) technology package. Circuits are simulated by Cadence software.  
Figure 11: The basic architecture of the hybrid power supply modulator.  
Figure 10: Procedure and simplified circuit model of a hybrid power supply modulator.  
The simulation of the closed-loop performance of the linear amplifier is shown in Figure 12. Figure 12a shows the case of Miller compensation of the closed-loop performance. It shows a twice gain frequency of 32 MHz and a phase margin of 60\({}^{\circ}\); thus, the GBW is about 64 MHz. Figure 12b shows the case for compensation of the closed-loop performance using positive feedback to introduce extra zeros. This shows a twice gain frequency of 117 MHz and a phase margin of 42\({}^{\circ}\); thus, the GBW is about 234 MHz. At 100 MHz, the phase margin is about 60\({}^{\circ}\). Meanwhile, the 3-dB bandwidth of the linear amplifier simulated is about 180 MHz, as shown in Figure 13. Compared to the Miller compensation approach, this method is better.  
The instantaneous simulation of the hybrid power modulator is carried out, in which the input signal is a 5G-NR signal with a bandwidth of 100 MHz. The results show that the average power efficiency of the linear amplifier is 51%.  
Transient simulation of the hybrid power modulator is also carried out, in which the input signal is vip, the output current is \(\rm i_{out}\), the output voltage of switching converters is \(\rm V_{sw}\), and the output current through the inductor L is \(\rm i_{sw}\), \(\rm Rload=7\ \Omega\), as shown in Figure 14. The different VDD2 and different Rload on the output efficiency are shown in Figure 15.  
Figure 12: (a) Miller compensation, and (b) compensation by introducing extra zeros.  
Figure 13: The 3-dB bandwidth of the linear amplifier.  
A comparison of the performance of the hybrid power modulator proposed in this work and modulators reported in the literature is shown in Table 1. Hybrid power modulators consist principally of linear amplifiers, switching converters, and controllers. Optimizing the performance of each module reduces losses and improves bandwidth. To achieve this, changing the level number of the switching converter is a research trend [40]. The method by which the controller controls the switching converters by changing the way that voltage is detected [39; 41; 42; 43; 44] is also the focus of research to improve efficiency.  
Figure 14: The current of the hybrid power modulator.  
Figure 15: Influence of (**a**) different VDD2 and (**b**) different Rload on the output efficiency.