esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The DCARC strategy is constructed based on a dynamical model of the system with prior knowledge of the bounds of the parametric uncertainties and the bounds of the unmodeled uncertainty and nonlinearity as well as disturbance. The parameters are adjusted on-line via certain parameter adaptation law to achieve an improved model compensation, while the model uncertainty and lumped disturbance are handled by certain robust law. Thus, the DCARC possesses the advantages of both control methods, which has a parametric adaptation ability and guaranteed performance to the model uncertainty and disturbance. In addition, compared with conventional adaptive robust control, the adaptive model compensation part of DCARC depends on the desired reference trajectory instead of actual state measurements, so the effect of measurement noise is minimized. However, the effects of unmodeled dynamics of the plant are suppressed by the robust control term in DCARC, so the final tracking performance is unavoidably conservative. In this paper, an optimization control method of DCARC, learning-based optimal desired compensation adaptive robust control (LODCARC) is proposed and implemented for a flexure-based parallel micro-motion stage. The stage is activated by piezoelectric actuators in \(X\)- and \(Y\)-axes. It has a decoupled symmetrical structure so the multi-input multi-output (MIMO) system can be simplified as two single-input single-output (SISO) sub-systems, with the proposed LODCARC being implemented in each sub-system. The DCARC term of the LODCARC is designed based on the dynamics of the micro-motion system under the consideration of parametric uncertainty and uncertain nonlinearity. Furthermore, the effect of measurement noise is minimized in DCARC by using the desired reference trajectory rather than actual state measurements. However, considering the complicated environment, the accurate dynamical model of flexure-based micro-motion system is difficult to obtain and for micro-scale motion, many motion tasks are repetitive in a finite time. Therefore, ILC was used to optimize the reference trajectory used in the DCARC term to compensate for the effects of unmodeled repetitive uncertainty and nonlinear disturbance. The whole control scheme with the stability analysis is presented and comparative experimental investigation of high-speed trajectory is carried out, with the experimental results showing that the tracking and contouring performances of the flexure-based micro-motion stage are greatly improved by adopting the proposed LODCARC.
## 2 Data  
**BNU-1 Imagery.** BNU-1 has obtained more than 6000 images covering Antarctica and Greenland since it was launched. It provides the observations in panchromatic, blue, green, red, and red-edge spectral bands. Twenty-eight images of Antarctica and fifteen images of Greenland in the panchromatic band were collected for geolocation and accuracy evaluation. As shown in Figure 1, the images of Antarctica are distributed over the Amery Ice Shelf, Victoria Land, Dronning Maud Land, and Pine Island Glacier. The images of Greenland cover the west and north of Greenland.  
**MODIS Imagery.** MODIS is a key instrument onboard the Terra and Aqua satellites, which were launched on 18 December 1999, and 4 May 2002, respectively, providing global coverage every one to two days. Since the MODIS sensor has high geolocation accuracy (50 m for one standard deviation) [4] and a daily revisit capability, we used MOD02 QKM and MYD02 QKM products as the reference images for the geometric correction of the BNU-1 images in this study. The geolocation error of MOD02 QKM (MYD02 QKM) is 50 m or better, which is finer than the pixel size of the MODIS image [13; 23; 24]. It is reasonable to use MODIS images as the reference data in this study since the spatial resolution of BNU-1 images is 80 m.  
**Coastline dataset.** We used the high-resolution vector polylines of the Antarctic coastline (7.4) [25] of 2021 from the British Antarctic Survey (BAS). We also used the  
Figure 1: Distributions of the sample images of BNU-1 for **(a)** Antarctica and **(b)** Greenland. Black and red rectangles refer to the footprints of 43 sample BNU-1 image scenes, in which red rectangles show the 6 Sample Images (A–F) used for the analysis in this study. Sample Images A–F were acquired for the Amery Ice Shelf on 8 October 2019, Victoria Land on 11 October 2019, Greenland on 7 July 2020, Greenland on 18 July 2020, Dronning Maud Land on 18 December 2019, and Pine Island Glacier on 28 December 2019, respectively.  
MEASUREs MODIS Mosaic of Greenland (MOG) 2005, 2010, and 2015 Image Maps, Version 2 [26] from the NASA National Snow and Ice Data Center (NSIDC) to obtain the Greenland coastline. We evaluated the geolocation error of the BNU-1 image visually by comparing the coastline dataset with the geolocation of the BNU-1 image.
###### Abstract  
Fifth-generation (5G) mobile networks have already marked their presence globally, revolutionizing entertainment, business, healthcare, and other domains. While this leap forward brings numerous advantages in speed and connectivity, it also poses new challenges for security protocols. Machine learning (ML) and deep learning (DL) have been employed to augment traditional security measures, promising to mitigate risks and vulnerabilities. This paper conducts an exhaustive study to assess ML and DL algorithms' role and effectiveness within the 5G security landscape. Also, it offers a profound dissection of the 5G network's security paradigm, particularly emphasizing the transformative role of ML and DL as enabling security tools. This study starts by examining the unique architecture of 5G and its inherent vulnerabilities, contrasting them with emerging threat vectors. Next, we conduct a detailed analysis of the network's underlying segments, such as network slicing, Massive Machine-Type Communications (mMTC), and edge computing, revealing their associated security challenges. By scrutinizing current security protocols and international regulatory impositions, this paper delineates the existing 5G security landscape. Finally, we outline the capabilities of ML and DL in redefining 5G security. We detail their application in enhancing anomaly detection, fortifying predictive security measures, and strengthening intrusion prevention strategies. This research sheds light on the present-day 5G security challenges and offers a visionary perspective, highlighting the intersection of advanced computational methods and future 5G security.  
5G networks; machine learning security; security in deep learning Article
## 3 The Proposed Imputation Method
### Practical Limitations of Our Imputation Model  
This imputation method has several limitations. First, it is designed exclusively for tabular data and does not handle raw spatial-temporal data, such as video data. Additionally, we have only focused on numeric data; thus, other data types, such as categorical or textual data, are not included in our approach. Second, in our initial experiments, we found that the model demands high computational power, which scales with the number of dimensions in the dataset. To address this, we employed Principal Component Analysis (PCA) for dimensionality reduction [69; 70]. PCA allows us to retain only the dimensionswith high variance, thereby reducing computational time and improving the applicability and reliability of our imputation method.
# A 12-Bit 1-GS/s Pipelined ADC with a Novel Timing Strategy in 40-nm CMOS Process
## 3 Circuit Implementation and Calibration
### Calibration of Mismatches and Errors  
As is shown in Table 1, this ADC adopts the following calibration strategies to reduce the sensitivity to mismatches and errors. The digital domain calibration technology of this paper adopts the following three methods: adjustable current source, Efuse combined with multi-target search, and pseudo-random noise injection. The inter-stage gain is affected not only by the finite open-loop gain of the operational amplifier but also by the capacitor mismatch error [31]. Since precise sub-DAC is the premise of correct background calibration, it is necessary to calibrate the DAC error caused by capacitor mismatch before background calibrations.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Mismatches and Errors** & **Calibration Method** & **On Chip** \\ \hline Threshold offset & Adjustable current source & Yes \\ Cap mismatches & Efuse combined with multi-target search & No \\ Finite inter-stage gain & Injection of dither in stage1–3 & No \\ Residual curve & Automatic calibration by plotting Vres & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 1: Calibration strategies of this ADC.  
Figure 9: (**a**) A fully differential 2.5-bit flip-around MDAC. The operational amplifier gain is equal to 4. The sampling switch is bootstrapped. (**b**) Operational amplifier schematic. Both the NMOS and PMOS sides are cascaded. AP1 and AP2 are auxiliary op-amps, added to improve gain.  
In this design, the capacitor mismatch should be corrected first through the foreground calibration, followed by the background calibration to rectify the inter-stage gain errors.
## 3 A Community Anomaly Detection Approach Based on Structural/Attribute Information
### Proposed Community Anomaly Detection Approach  
Our model proceeds in two phases, where the graph communities are obtained in the first phase, and the anomaly scores are associated with nodes in the second phase. The second phase combines structure-based scoring and attribute-based scoring. In this section, we present our method phases in detail with an illustrated example.
## 2 Materials and Methods
### Orbit Estimation with an Unscented Batch Filtering Method  
Building on the work of [PERSON] et al. (2018), the study introduces a novel orbit estimation approach for continuously maneuvering, low-thrust satellites by combining the UT with a batch processing technique. Compared to the original formulation, the proposed method adopts an alternative strategy for sigma points sampling that enhances computational efficiency while preserving the estimation accuracy of the UT framework. In addition, it utilizes a simplified dynamics model that explicitly accounts for thrust, thereby improving the robustness and precision of orbit determination under maneuvering conditions. The remainder of this section first presents the sigma points sampling strategy used in this study, followed by a detailed explanation of the integrated \"UT-Batch\" OD framework.  
Figure 1: Variations in orbit altitudes and inclinations over time for the chosen batch of Starlink satellites during the orbit-raising phase: (**a**) evolution of orbit altitudes; (**b**) evolution of inclinations.
# A Dense Neural Network Approach for Detecting Clone ID Attacks on the RPL Protocol of the IoT
## 3 Related Work  
Little is known about how perimeter defensive systems implement persuasive mitigation rules for a Clone ID attack, or which intelligent controls by ML algorithms can classify, group, or potentially evaluate node impersonation threats [38; 39] on the RPL protocol. Even so, in [40], a scheme to defeat the sybil, a closely related Clone ID attack, was proposed, which relied on radio resource testing and verification of key sets for random key pre-distribution, registration, and position verification. In [41], various sybil attacks over large IoT networks were addressed by means of Social Graph-based Sybil Detection (SGSD), Behavioural Classification-based sybil Detection (BCSD), and Mobile Sybil Defenses (MSD). It is important to mention that such proposals may rely on preventive controls, rather than detective controls, due to an absence of analysis and monitoring at the core of RPL authentication stages. In spite of the lack of countermeasures to detect Clone ID attacks, some authors have focused on other RPL-related attacks, specifically those affecting the DODAG node arrangement, using ML techniques. In [20], a DL-based security monitoring analysis was implemented for IoT network flows at upper layers, where a DNN algorithm was trained by means of data sets built over RPL messages with binary outcomes: Malicious and regular. To construct the predictive model, feature extraction techniques based on time windows were chosen, concluding that most ranking attacks can be substantially decreased, but covert attacks, such as the Clone ID, were on the edge to be explored in future works. Additionally, in [42], a Multi-level Perceptron (MLP) was trained with different Internet packet traces, in order to detect Distributed DoS or DoS (DDoS/DoS) attacks over RPL nodes. It is important to mention that the MLP algorithm only evaluates one type of DoS/DDoS at a time on UDP, a non-common protocol for WSNs that may not be feasible to track when counterfeited nodes propagate down the DODAG tree. Furthermore, in [43], a Deep Autoencoder Neural Network (DANN) was proposed for detecting DODAG intrusions; the algorithm was trained with remote-to-local (R2L), DoS, and user-to-root (U2R) samples from a well-known IoT data set known as NSL-KDD [44]. Even though Autoencoders can maximize the way that a DANN projects network features, the unbalanced nature of the NSL-KDD data set caused TCP/IP packets to compose the majority of samples, overshadowing the minority of RPL packets adjacent to a DODAG;
# Field and Numerical Investigation of Taihu Resort Cut Slope Failure in Suzhou, China
## 5 Simulation of Sequential Slopes under Excavation and Rainfall Scenarios  
Although the treated slopes that are exposed to intense weather conditions are generally able to withstand them, it is important to study the techniques that suit the nature of the slope in support of geotechnical engineers. In cases where slopes have complex shapes, an extensive geological examination is required before cutting the slope. In addition, geotechnical engineers should pay attention to the possibility of subsequent failure occurring in a large area once localized damage has occurred on the slope. Therefore, based on the geological profile of the Taihu slope, a two-dimensional numerical analysis of the Taihu slope under excavation and different rainfall scenarios is simulated effectively for slope stability assessment. The elastic-plastic constitutive model is adopted in this study and is based on the Mohr-Coulomb (MC) criterion. Midas GTS was used to simulate the slope Section (S6) as a representative landslide area. In order to study the results of the deformation and stability of the slope, this section presented an effective analysis of the slope failure subject. The slope excavation was carried out in stages and at different heights, starting at 3 m, 6.5 m, 2 m, and 4.5 m. The selected rainfall intensities are 170 mm/day (heavy rainfall) and 60 mm/day (moderate rainfall), and the surface flux was applied at the top of the slope.
###### Abstract  
The aim of this study is to use the response surface methodology (_RSM_) to mathematically model the response parameters and emission of greenhouse gases (\(GHG\)) and optimize the drying variables for a carrot dried with the microwave method using various pretreatments. To this end, the influence of the drying parameters (independent), such as microwave power and slice thickness dried by two pretreatments of ultrasonication at 30 \({}^{\circ}\)C for 10 min and blanching at 70 \({}^{\circ}\)C for 2 min, was explored on the dependent (response) parameters including the thermal properties (drying time, effective moisture diffusion coefficient (\(D_{\mathit{off}}\)), specific energy consumption, energy efficiency, quality features (color changes and shrinkage), and \(GHG\) emission (including CO\({}_{2}\) and NOx). It should be mentioned that the emission of \(GHG\) was determined based on the energy consumption of various types of power plants such as the gas turbine steam power turbine, and combined cycle turbines using various fuels such as natural gas, heavy oil, and gas oil. The results indicated that the ultrasonication and blanching pretreatments can decrement the drying time (linearly), energy consumption (linearly or quadratically), shrinkage(quadratically), and color changes(quadratically) and enhance the \(D_{\mathit{off}}\) (linearly) and energy efficiency (linearly or quadratically) in all samples with R\({}^{2}\) > 0.86. Moreover, the shortest drying time (42 min), lowest \(SEC\) (9.51 MJ/kg), and \(GHG\) emission ((4279.74 g CO\({}_{2}\) in the combined cycle turbines plant, and 18.16 g NOx in the gas turbine plant) with natural gas for both plants) were recorded for the samples pretreated with blanching while the lowest color changes (13.69) and shrinkage (21.29) were observed in the ultrasonicated samples. Based on the optimization results, a microwave power of 300 W and steam power turbine of 2 mm were the best variables with a desirability of about 80% which resulted in the highest-quality products at the lowest \(GHG\) emission.  
greenhouse gas emission; microwave power; drying time; specific energy consumption; RSM +
Footnote †: journal: Applied Sciences
## 5 Results and Discussions
### Successful of Finding a Solution
#### 5.2.2 Simulations for 15 Nodes  
In this simulation, we added five nodes, as follows. Nodes 11, 12, 13, 14, and 15 are located at (\(-\)150 m, \(-\)123 m), (170 m, 50 m), (100 m, 123 m), (\(-\)100 m, 60 m), and (0 m, \(-\)170 m), respectively. Moreover, four edges configurations in the new graph were established, called CONFIG 15-1, CONFIG 15-2, CONFIG 15-3, and CONFIG 15-4. The CONFIG 15-1 connectivity configuration is described in Figure 4. CONFIG 15-2 is constructed by adding an edge connecting Nodes 1 and 5 on the CONFIG 15-1. The CONFIG 15-3 configuration is performed by adding edges connecting Nodes 1 to 4, 2 to 12, and 7 to 10 to CONFIG 15-2. The CONFIG 15-4 configuration is performed by adding edges connecting nodes 4 to 7 and 3 to 6 to CONFIG 15-3. The degree of each edge in each connectivity configuration is described in Table 4.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Number of Degree of Nodes**} \\ \hline
**Node** & **CONFIG 15-1** & **CONFIG 15-2** & **CONFIG 15-3** & **CONFIG 15-4** \\ \hline
1 & 5 & 5 & 6 & 6 \\
2 & 5 & 5 & 6 & 6 \\
3 & 4 & 4 & 4 & 5 \\
5 & 5 & 6 & 6 & 6 \\
6 & 3 & 3 & 3 & 4 \\
7 & 3 & 3 & 4 & 5 \\
8 & 4 & 4 & 4 & 4 \\
9 & 4 & 4 & 4 & 4 \\
10 & 4 & 4 & 5 & 5 \\
11 & 3 & 3 & 3 & 3 \\
12 & 2 & 2 & 3 & 3 \\
13 & 3 & 3 & 3 & 3 \\
14 & 2 & 3 & 3 & 3 \\
15 & 3 & 3 & 3 & 3 \\ \hline Average & 3.60 & 3.73 & 4.13 & 4.40 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Simulation results for 15 nodes.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Number of Degree of Nodes**} \\ \hline
**Node** & **CONFIG 10-1** & **CONFIG 10-2** & **CONFIG 10-3** & **CONFIG 10-4** \\ \hline
1 & 3 & 3 & 3 & 4 \\
2 & 4 & 4 & 4 & 4 \\
3 & 3 & 3 & 3 & 4 \\
4 & 2 & 2 & 2 & 4 \\
5 & 5 & 4 & 3 & 5 \\
6 & 3 & 3 & 3 & 4 \\
7 & 3 & 3 & 3 & 5 \\
8 & 3 & 2 & 2 & 3 \\
9 & 4 & 4 & 4 & 3 & 4 \\
10 & 2 & 2 & 2 & 3 \\ \hline Average & 3.20 & 3.00 & 2.80 & 4.00 \\ \hline
**Number of** & **\(\boldsymbol{\varphi}\)** & **Success** & **\(\boldsymbol{\varphi}\)** & **Success** & **\(\boldsymbol{\varphi}\)** & **Success** \\
**Vehicles** & **Rate (\%)** & **\(\boldsymbol{\varphi}\)** & **Rate (\%)** & **Rate (\%)** & **Rate (\%)** & **Rate (\%)** \\ \hline
3 & 0.09 & 100.00 & 0.10 & 100.00 & 0.11 & 100.00 & 0.08 & 100.00 \\
4 & 0.13 & 90.00 & 0.13 & 100.00 & 0.14 & 100.00 & 0.10 & 100.00 \\
5 & 0.16 & 100.00 & 0.17 & 100.00 & 0.18 & 100.00 & 0.13 & 100.00 \\
6 & 0.19 & 90.00 & 0.20 & 90.00 & 0.21 & 0.00 & 0.15 & 100.00 \\
7 & 0.22 & 0.00 & 0.23 & 0.00 & 0.25 & 0.00 & 0.18 & 90.00 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation results for 10 nodes.  
From Table 4, it can be seen that the solution can be found for \(\phi\leq 0.08\), which means that, at the range of the \(\phi\), it can be concluded that, from the 10 trials, there always exists at least one successful trial.
## 3 Experimental Research
### Step 4: GR Calculations
#### 3.4.3 Resistance to Earth of the Connection of Three Rods in Line  
The total GR of the system for the configurations of three rods in a linear arrangement (see configuration 3 RP of Figure 1) is obtained by means of Equation (2). In this case, \(R_{1}\), \(R_{2}\) and \(R_{m}\) are established by Equations (8), (9) and (10), respectively.  
\[R_{1}=\frac{\rho}{2\pi d}\biggl{[}\ln\left(\frac{4d}{a^{\prime}}\right)+4k_{1} -k_{2}\biggr{]} \tag{8}\]  
\[R_{2}=\frac{\rho}{6\pi L_{r}}\biggl{[}\ln\left(\frac{4L_{r}}{b}\right)-1+\frac {4k_{1}\times L_{r}}{d}\Bigl{(}\sqrt{3}-1\Bigr{)}^{2}\biggr{]} \tag{9}\]  
\[R_{m}=R_{1}-\frac{\rho}{2\pi d}\biggl{[}\ln\left(\frac{L_{r}}{\sqrt{h\times 2a }}\right)-1\biggr{]} \tag{10}\]  
where:  
\(\rho\): ground resistivity, [\(\Omega\cdot\)m]  
\(L_{r}\): rod length, [m]  
\(b\): rod radius, [m]  
\(R_{1}\): resistance to earth of the conductor, [\(\Omega\)]  
\(R_{2}\): earth resistance of the rods, [\(\Omega\)]  
\(R_{m}\): mutual resistance between mesh and rods, [\(\Omega\)]  
\(d\): separation distance between two rods, \(d>L_{r}\), [m]  
\(h\): conductor burial depth, [m]  
\(a\): conductor radius, [m]  
\(a^{\prime}\): is \(a\) for the conductor on the surface of the earth, [m] or  
\(a^{\prime}\): is \(\sqrt{a\cdot 2h}\) for conductors buried at a depth \(h\), [m]  
\(k_{1}\) and \(k_{2}\): coefficients [see Figures 2 and 3, respectively]