esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Background and Problem Statement
### Problem Statement  
Let us assume the relation \(R(A_{1},A_{2},\cdots,A_{m})\). Let \(A=\{A_{1},A_{2},\cdots,A_{m}\}\) be a set of attributes in \(R\). Let us further assume that \(Q\in A\) is a set of quasi-identifier attributes and \(S\in A\) is a set of sensitive attributes. In this paper, we then focus on a selection query on a single relation, which can be written as follows:  
Figure 3: Example domain generalization hierarchies for \(Age\) and \(Zipcode\), and the corresponding generalization lattice.  
Figure 2: (a) Original table and (b) \(k\)-anonymized table (\(k=3\)).  
SELECT_proj_list_ FROMRRWHERE_pred\({}_{1}\)AND_pred\({}_{2}\)AND_\(\cdots\)AND_pred\({}_{l}\).  
Here, we further assume the following:  
* _proj_list_ consists of attributes that are in either \(Q\) or \(S\),
* the condition in the WHERE clause consists of \(l\) conjunctive selection conditions, _pred\({}_{1}\)_, \(pred_{2},\cdots,pred_{l}\), and
* each predicate _pred\({}_{i}\)_ can be either an equality condition or a range condition on an attribute not in _proj_list_.  
In this paper, we consider a scenario in which the results of a query are anonymized using \(k\)-anonymity with generalization, which is the most popular type of scheme.  
We assume a distributed system running on a shared nothing architecture in which the data are horizontally split and stored in multiple nodes. In a shared nothing architecture, each node has its own private resources, such as memory or disk, and thus does not share resources with other nodes. In a distributed environment, given a selection query on a single relation, every slave node executes the query against its own data and sends the query result to the master node. The master node then aggregates all query results from all nodes and sends them to the user. In this distributed query processing scenario, none of the slave nodes can locally anonymize their own query results because the generalization level of each quasi-identifier attribute used to satisfy the \(k\)-anonymity requirement cannot be determined without combining all of the results from each of the slave nodes. Hence, the straightforward solution is to first aggregate the results from every slave node and then globally anonymize the aggregated results at the master node (Figure 4a). However, considering the large volume of data stored in the system, this global approach is highly inefficient because the data anonymization is performed solely by the master node. Furthermore, with a global approach, the resources of slave nodes are not utilized during the anonymization phase.  
A more promising solution is for each node to locally anonymize its own results and send the anonymized results to the master node, which combines the anonymized results from every slave node (Figure 4b). Thus, in this study, we develop a method that enables the query result at each slave node to be locally anonymized as much as possible, thereby fully utilizing the resources of the slave nodes during the anonymization phase.  
Figure 4: (**a**) Global anonymization vs. (**b**) local anonymization in a distributed environment.
## 3 Aspects Regarding the Nutritional Dynamics and Harvest Variability of Jerusalem  
**Artichoke Tubers and Aerial Parts (Steams, Branches, and Leaves)**  
In order to unlock the full nutritional value of novel and underutilized feed sources, and optimize their ideal formulation for diets, it is crucial to comprehensively assess and compare their chemical and physical properties, and nutritional composition to other varieties or raw materials commonly used, especially in monogastric rations. [PERSON] and [PERSON] [54] affirmed that the proximate analyses of J.A. tubers show a significant variation in data composition compared to other vegetables due to differences in cultivars, harvest period, production conditions, postharvest treatment, and preparation methods. According to [PERSON] [36], J.A. tubers are traditionally harvested in late autumn, especially during the period when carbohydrates are transferred from the plant's aerial parts to underground tubers. [PERSON] et al. [55] found that harvesting J.A. forage every 60 days optimally increased annual yields of dry matter, protein, and energy biomass in both tubers and forage, influenced by the maturity stage and harvesting frequency. According to [PERSON] [56], the proximate analysis values for the average metabolizable energy (ME) content of J.A. tubers was 15.0 MJ/kg, with a CP content of 69 g/kg in dry matter. The crude protein content of J.A. leaves is about four times higher compared to tubers [57] and three times higher compared to stems [58], and is especially rich in lysine and methionine [59]. It is well known that ensuring an optimum dietary level of methionine and lysine is crucial for promoting growth, maximizing feed efficiency, and maintaining overall health in monogastric animals. These amino acids are often supplemented in animal feeds to meet their specific requirements and optimize their performance. Table 1 presents the nutritional composition of Jerusalem artichoke's fresh aerial parts and tubers. The proximal composition between the two parts is highly significant when taking into consideration their utilization for different purposes or in farm animal feed, such as forage or raw/powder tuber consumption. Authors such as [PERSON] and [PERSON] [54] stated that information regarding the proximal composition of J.A. tubers is notably limited in comparison to other vegetables. They found that the CP content exhibits variability, ranging from 1.6 to 2.4 g per 100 g of fresh weight, but being constant during all developing stages [61]. Other authors, such as [PERSON] et al. [62], reported the following composition for J.A. tubers: 90.00%DM, 8.57% CP, 0.33% EE, 3.51% CF, 7.47% ash, 70.12% N-free extract, 55.76% fructans, and 13.79 MJ/kg of metabolizable energy for pigs. In their study on the chemical composition of J.A. tubers concerning the effects of variety and harvest date, [PERSON] et al. [63] recorded the following information regarding composition: 7.1-7.2 g protein/100 g dry matter, 21.5-25.6 g dry matter/100 g, 16.3-16.4 g dietary
## 2 Materials and Methods  
### Framework Overview  
Figure 1 shows the end-to-end DL reconstruction framework for PCCT with sparse-view projections. The network in the framework can update parameters of the CNNs in dual  
domains synchronously, which is indicated with the green dotted rectangle. This framework is referred to as DDPC-Net. The differential forward projection operator combined with the PCCT filtered back-projection (FBP) algorithm is required to transform the size of the sparse-view sinogram to be the same as the complete sinogram. In addition, a PCCT reconstruction layer allowing the gradients to backpropagate from the image domain to the projection sinogram domain is needed to achieve the mapping between dual domains and to output the reconstructed image while taking the projection sinogram as input. Therefore, the proposed framework has five components: (i) the FBP reconstruction for PCCT, (ii) the differential forward projection, (iii) the enhanced network in the projection sinogram domain, (iv) the PCCT reconstruction layer allowing for the backpropagation of gradients, and (v) the enhanced network in the image domain.  
Equations (1) and (2) present the fan-beam FBP algorithm for PCCT, where \(\delta(x,y)\) represents the reconstructed image, \(U\) represents the geometrical weight factor, \(\alpha_{\theta}(s)\) represents the sinogram, \(s\) represents the sinogram index, \(h\) represents the Hilbert filter, \(v\) represents the frequency, and \(\theta\) represents the rotation angle.  
\[\delta(x,y)=\frac{1}{2}\int_{0}^{2\pi}U\cdot\alpha_{\theta}(s)*h(v)d\theta \tag{1}\]  
\[h(v)=\frac{1}{2\pi}isgn(v) \tag{2}\]  
Equations (3) and (4) present the three-point differential forward projection operator in the proposed framework to generate PCCT sinograms, where \(P(s,\theta)\) represents the forward projection, and \(l\) represents the forward projection path.  
\[\alpha_{\theta}(s)\approx\frac{P(s+1,\theta)-P(s-1,\theta)}{2} \tag{3}\]  
\[P(s,\theta)=\int_{l}\delta(x,y)dl \tag{4}\]  
Figure 1: The architecture of the proposed DL framework for PCCT. It consists of the FBP algorithm for PCCT, the differential forward projection operation, and the neural network that allows the network parameters in dual domains to be synchronously updated.  
Equation (5) presents the end-to-end neural network. The information-missing sinogram is transmitted into the network, and then, the corresponding high quality reconstructed image \(\overline{r\overline{c}}\) is output.  
\[\overline{r\overline{c}}=DDPC(\alpha_{\theta}(s)) \tag{5}\]
## 5 Research Findings
### RQ1: What Techniques Are Utilized to Abuse the CPLS as C&C Communication Channels?
#### 5.2.2. Evolution and Trends  
The complexity of techniques used by attackers seems to be increasing over the years. While earlier years primarily witnessed encoding and the use of botmaster credentials or  
Figure 5: Number of abuses by year for each technique.  
hard-coded tokens, later years show a rise in more advanced techniques, such as encoding, cryptography, compromised accounts, and even AI-powered C&C.
## 4 Sensitivity Studies
### Edge Length Ratio  
In Figure 21, the optimization results are shown for different edge lengths of the FE mesh with a constant edge length of the level set mesh (change in ratio). It can be seen that the optimization runs in different local optima, but no systematic pattern can be identified. In addition, the local optima are very close to each other in terms of structure and material distribution.  
Further numerical experiments have shown that satisfactory results can always be achieved with approximately the same edge length of the two meshes.
# Study on Fluctuating Wind Characteristics and Non-Stationarity at U-Shaped Canyon Bridge Site
## 1 Introduction
[PERSON] et al. [12] analyzed wind sample non-stationarity using running tests and wavelet analysis. Their findings revealed that wind field characteristics in non-stationary wind speed models deviated from the recommended values in existing standards. [PERSON] et al. [19] investigated the non-stationary characteristics of plateau winds through experiments. They found that turbulence intensity and gust factors in plateau winds were significantly higher than those in plain areas. [PERSON] et al. [20] highlighted the importance of selecting appropriate models for wind field analysis in complex terrains by comparing stationary models with various non-stationary models. Currently, few studies focus on non-stationary winds in canyon areas, and the specific effects of such winds on fluctuating wind characteristics in mountainous regions remain unclear. Conducting in-depth analyses and discussions based on measured data is urgently needed to provide references for the wind resistance design of mountain structures. Using wind speed measurement data from a mountain bridge site, this study systematically analyzes the pulsating wind effects in canyon areas. This study aims to examine the non-stationary characteristics of wind fields in complex terrains and their effects on turbulence intensity, wind power spectra, and related parameters, thereby enriching measured data and analysis results for canyon wind fields. Section 2 outlines the equipment and layout used for wind field measurements. Section 3 analyzes the non-stationary characteristics of wind speed and the statistical properties of pulsatingwinds. It also compares pulsating wind characteristics across different wind speed models using turbulence intensity and gust factor results. Section 4 extracts the time-varying mean wind using discrete wavelet transform, analyzes fluctuating wind characteristics, proposes optimal decomposition layers, and verifies the effectiveness of wavelet decomposition.
# Exploring the Potential of Microextraction in the Survey of Food Fruits and Vegetable Safety
## 2 Extraction Approaches Used to Assess Food Safety
### Microextraction Approaches
#### 2.2.1 Sorbent-Based Microextraction Techniques
The technique of dispersive solid-phase extraction (dSPE) and its related formats involve combining the sample matrix with the sorbent to effectively capture the desired analytes in the sorbent particles. This can be accomplished by either column packing or using SPE tubes with vacuum or centrifugation. The analytes are then extracted using an appropriate elution solvent. Additionally, magnetic nanoparticles can be utilized to enhance the extraction process, allowing for easy isolation of the sorbent particles and analyte retention through decantation using a simple magnet [109]. The dSPE is also widely used as a clean-up procedure in the two steps QueChERS/dSPE methodology previously referred to.
# Design of Cislunar Navigation Constellation via Orbits with a Resonant Period
## 2 Fundamental Theory
### Earth-Moon System's Periodic Orbit Database Generation  
Libration points in the Earth-Moon system host a variety of periodic orbits with significant application potential. DROs (Distantly Retrograde Orbits) near the Moon represent a class of periodic orbits with substantial engineering value [22]. Algorithms for constructing these orbits under the CRTBP framework follow the methods presented in Refs. [23; 24; 25; 26; 27]. Leveraging these references, this study generates multiple orbit families to build a CRTBP periodic orbit library, forming the operational database for constellation design.  
North-south Halo orbit families near collinear points exhibit identical period distributions due to their XY-plane symmetry; thus, north Halos serve as baseline orbits for resonant constellation design. Similarly, L4 and L5 orbits are XZ-plane symmetric with matching periods, so L4 orbits are adopted as the baseline for periodic-resonant configurations.  
Key implementation details include the following:  
* DROs [22]: DROs are planar periodic orbits in the lunar vicinity, which are symmetric around the X-axis of the Earth-Moon rotating frame. These orbit families are generated through natural parameter continuation methods. They have an initial x-direction amplitude of 1000 km, which is continued along +X with step size \(1\times 10^{-4}\).
* Lyapunov and orbits: Lyapunov orbits constitute planar periodic trajectories near the collinear libration points that maintain X-axis symmetry in the Earth-Moon rotating frame. Their families are similarly constructed via natural parameter continuation. They have the same parameters as DROs but these continue along \(-\)X for L1/L3.
* Halo and Vertical orbits: Halo orbit families and vertical orbit families represent non-planar periodic solutions near collinear libration points, exhibiting symmetry about the XZ-plane. These are extended via Ref. [23]'s method, starting from z-direction amplitudes of 1000 km. The normalized continuation step size for x/z-directions at each collinear point is \(1\times 10^{-4}\).  
Orbits near triangular libration points are extended using the pseudo-arclength continuation method. Planar orbits have an initial amplitude of 10,000 km, while vertical orbits start with 1000 km. Likewise, the normalized continuation step size is \(1\times 10^{-4}\).  
To systematically categorize constellation types, the orbit families participating in resonant constellation generation are encoded, as shown in Table 1, which lists the minimum and maximum orbital periods for each type.  
Based on orbital period magnitudes, this study classifies orbits in resonant constellations into baseline orbits and resonant orbits. As indicated in the table, orbital types eligible as baseline orbits include DROs, all periodic orbits at L1, and Halo orbits at L2, while other types are restricted to resonant roles.
## 3 Multiscale Numerical Model
### Comparison between the Numerical and Monitoring Results  
Structural monitoring data were used to calibrate and validate the multiscale finite element model. First, long-term acceleration data were introduced and processed by frequency-domain analysis to obtain the first four dynamical modes and corresponding vibration frequencies. Then, modal analysis was conducted for the multiscale finite element model to calculate the first four dynamical modes, which is shown in Figure 4. The field calculations and numerical simulation results are shown in Table 3; the field calculation results were obtained by processing monitoring data of accelerated velocity [5]. As shown in the table, the first four dynamic modes of the numerical simulation fit well with the field calculations, where the maximum error in these four frequencies is within 1.0%. The above results indicate that the dynamic property of the numerical model is appropriate for further calculations.  
To further assess the veracity of the multiscale finite element model, computational and monitoring results on the bending moment of the shortest hanger, which has been proven to be highly susceptible to train loading, on the east side in the south-bound direction, has also been marked in Figure 1. As one of the shortest hangers in the side plane, this hanger was chosen not only because it sustains the greatest axial force but also because of its position near the busiest rails. Figure 5 shows the monitoring and numerical curves of the bending movement of this hanger under case 2. It is shown that the monitoring and numerical results fit well to a certain extent, indicating that the accuracy of the multilevel numerical model fulfills the fatigue requirements of the hanger-deck joining region.  
Figure 3: Two-level FE model of the high-speed railway bridge; (**a**) sub-modeling method; (**b**) refined model and connected boundary.
# Breast Cancer Detection in the Equivocal Mammograms by AMAN Method
## 3 Proposed Framework--AMAN
### Preprocessing  
The dataset was subjected to a number of pre-processing procedures. The goal of pre-processing is to remove noise from the dataset so that the models can perform at their best. As a start, the authors manually extracted and categorized the images into three classes: benign, normal, and malignant. Then, all records were reviewed to see whether there were any duplicates at this step. In addition, as Figure 7 shows, four breast implants have been removed in all the mammograms the authors selected, resulting in a total of 1798 mammograms. The breast implants were removed to prevent any confusion in the model.  
The mammography was then processed to remove as much noise as possible. Noise removal includes manual cropping of the mammogram's background and text. The manual cropping was crucial due to the high variety of breast shapes, sizes, and mammogram quality. Figure 8 will show a sample of this wide variety.  
Figure 6: Total of reports per each class.  
In terms of the number of mammograms, the patients' data covered most of the upper part of the breast, as shown in Figure 9a. In contrast, some breast views were in between an upper data part and a lower data part, as shown in Figure 9b. This was vital to remove because it significantly increased the computational time and reduced the mammogram quality in the upcoming phases, e.g., resizing.  
Figure 8: Breasts variety sample.  
Figure 7: (**a–d**) Four breast implants: left (CC), left (MLO), right (CC), and right (MLO), respectively.  
Figure 9: (**a,b**) Text over breast and breast view in between data, respectively. Removing noise from mammogram images–for confidentiality reasons, authors omitted the patient’s name and ID number from the image.  
After the manual cropping, only those images that fall within the categories of benign and malignant were augmented so that the total number of images in each category was equal to that of normal images. Figures 10 and 11 show the difference before and after applying the augmentation to the minority classes. Moreover, Figure 12: Augmentation sample clearly shows that the augmentation techniques such as rotation, flipping, zoom, filling, and brightness were only applied to the benign (B) and malignant (M) classes, while the normal (N) class remained intact.  
In addition, after the augmentation, to improve the classifiers' accuracy, the authors have performed some pre-processing techniques using OpenCV, such as Gaussian blur to reduce the noise, intensity normalization, and histogram equalization to enhance the edges and show the tumor clearly. Oddly, combining the three methods resulted in a decrease in performance for all the classifiers. The last step is, naturally, the resizing of this pre-processing, see Figure 13.  
Figure 11: Classes after augmentation.  
Figure 10: Classes before augmentation.
## 4 Ultrasonic Non-Destructive Testing of Headed Shear Studs
### Ultrasonic Non-Destructive Testing Equipment and Distance-Amplitude Curve  
In the crack detection of headed shear studs, the digital ultrasonic flaw detector (CTS-9006 PLUS) was used (Figure 9a). The detection range of the digital ultrasonic flaw detector (CTS-9006 PLUS) was 0 mm to 13,000 mm, and the working frequency range was 0.5 MHz to 10 MHz. The data acquisition module of the CTS-9006 PLUS was equipped with a 10-bit A/D resolution and 240 MHz maximum sampling frequency. The size of the data acquisition module was 152 mm \(\times\) 240 mm \(\times\) 52 mm. The vertical linearity error of the CTS-9006 PLUS was less than 3%, while the corresponding value for the horizontal linearity error was 0.5%.  
The longitudinal wave probe (straight probe) was employed to detect the crack properties when the studs were inspected by the ultrasonic non-destructive testing. The straight probe (5P14Z) is shown in Figure 9b. The chip diameter was 14 mm and the probe frequency could reach 5 MHz. The engine oil was adopted as the coupling agent because of the good lubrication and bonding effect between the probe and the workpiece surface. When the cyclic loading reached a certain number, the ultrasonic non-destructive crack testing was conducted to several target studs of the specimens following the detection scheme in Figure 9c. The straight probe was pasted on the inner side of the flange plate of the H-shaped steel column at the headed shear studs location by a coupling agent.  
With the increase in the detection depth, the echo amplitude decreased exponentially due to the influence of signal attenuation and sound beam diffusion. The distance-amplitude curve (DAC) was to connect the reflected echo amplitudes of the same defect at different depths [44]. In the DAC, the abscissa was the ultrasonic path, and the ordinate represented the echo signal amplitude. A smooth curve (called generatrix) was used to connect several wave peaks in the DAC, which represented the trend of the curve peak. Based on the generatrix, the DB values of waste line, quantitative line and evaluation line were determined by the corresponding flaw detection standards [45]. By recording the DAC of reflection echoes, the crack details of shear studs could be obtained.  
Figure 9: Ultrasonic non-destructive crack testing of headed shear studs: (**a**) digital ultrasonic flaw detector (CTS-9006 PLUS); (**b**) straight probe; (**c**) detection scheme.