esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
[PERSON] et al. [24] designed a reconfigurable control system for attitude takeover control of target spacecraft, which considers the changes of mass properties and reaction wheels' configuration. [PERSON] et al. [25] proposed a robust inertia free attitude takeover control scheme with guaranteed prescribed performance for post capture combined spacecraft with consideration of unmeasurable states, unknown inertial property, and external disturbance torque. [PERSON] et al. [26] presented a novel detumbling strategy for realizing detumbling of a non-cooperative, tumbling target by a space robot without prior knowledge of the target's inertial parameters (mass, inertia tensor, location of center of mass). However, the impact effect is not considered in the above control schemes. When the impact effect exceeds a certain value, the hybrid system will lose stability, which makes it difficult to realize stability control. In recent years, intelligent algorithms have been more and more applied to the control of robot systems [27; 28; 29]. Since the target satellite has a large initial velocity, the hybrid system after capture will be in a serious unstable state (e.g., involving overturning and rotation) and the traditional control method will make it difficult to complete its calm control. The actor-critic reinforcement learning (RL) control can optimize itself through trial-and-error and interaction with dynamic environment, and has strong environmental adaptability [30; 31; 32; 33; 34]. Therefore, an RL control scheme based on fuzzy wavelet network is proposed to realize stabilization of the hybrid system. The controller consists of a performance measurement unit (PMU), an associative search network (ASN) and an adaptive critic network (ACN). The controller obtains the primary reinforcement signal through the PMU, and then uses the ACN to collect the primary reinforcement signal for generating the enhancement signal, which is used to adjust the ASN dominate strategy function to realize the hybrid system stability control. The mechanism of the proposed reinforcement learning can be found in [35]. The food is the primary reinforcement signal, and the bell is the secondary reinforcement signal. In order to maintain a stronger effect, the food and the bell are matched to generate the enhancement signal. Compared to RL in our published previously work [3], this paper enhanced the primary reinforcement signal to become an enhancement signal containing more information (which ultimately makes the calm control of the hybrid system more stable). This paper is organized as follows. In Section 2, the structure of the SDD and the buffer compliance strategy are described. In Section 3, the hybrid system dynamic model is obtained, and the impact effect and force are analyzed. Furthermore, a RL control strategy based on fuzzy wavelet network is designed in Section 4. The results of the simulation are given in Section 5. Finally, the conclusion is summarized in Section 6.
# Partially versus Purely Data-Driven Approaches in SARS-CoV-2 Prediction
###### Abstract  
Prediction models of coronavirus disease utilizing machine learning algorithms range from forecasting future suspect cases, predicting mortality rates, to building a pattern for country-specific pandemic end date. To predict the future suspect infection and death cases, we categorized the approaches found in the literature into: first, a purely data-driven approach, whose goal is to build a mathematical model that relates the data variables including outputs with inputs to detect general patterns. The discovered patterns can then be used to predict the future infected cases without any expert input. The second approach is partially data-driven; it uses historical data, but allows expert input such as the SIR epidemic algorithm. This approach assumes that the epidemic will end according to medical reasoning. In this paper, we compare the purely data-driven and partially-data driven approaches by applying them to data from three countries having different past pattern behavior. The countries are the US, Jordan, and Italy. It is found that those two prediction approaches yield significantly different results. Purely data-driven approach depends totally on the past behavior and does not show any decline in the number of the infected cases if the country did not experience any decline in the number of cases. On the other hand, a partially data-driven approach guarantees a timely decline of the infected curve to reach zero. Using the two approaches highlights the importance of human intervention in pandemic prediction to guide the learning process as opposed to the purely data-driven approach that predicts future cases based on the pattern detected in the data.  
purely data-driven approach; partially data-driven approach; SIR model; linear regression; exponential smoothing model; corona virus detection; infected cases prediction Article
# Iterative Positioning Algorithm for Indoor Node Based on Distance Correction in WSNs
## 3 Iterative Positioning Algorithm for the Target Node Based on Distance Correction
### The Preprocessing of the Iterative Positioning Algorithm
#### 3.2.2 The Effect of Noise on RSSI Ranging  
The loss of wireless signals during propagation has a significant influence on the accuracy of the RSSI ranging algorithm and must be considered in practical applications. Next we will analyze the effect of signal propagation loss on RSSI ranging. In Equation (4), the measurement value _P_(_d_) is composed of the true signal strength value and the noise signal strength value, which are denoted _P_\({}_{T}\)(_d_) and _P_\({}_{N}\)(_d_), respectively. _P_(_d_\({}_{0}\)) is the RSSI value when the distance between the two nodes is _d_\({}_{0}\). To simplify the calculation, _d_\({}_{0}\) is usually taken as 1 and _P_(_d_\({}_{0}\)) is denoted as \(A\).  
Thus, the actual mathematical model of the log-distance distribution model is  
\[P_{T}(d)-P_{T}(d)=A-10n\lg(d), \tag{5}\]From Equation (5), the distance between two nodes can be calculated as  
\[d= 10^{\frac{A-[P_{T}(d)-P_{N}(d)]}{10n}}, \tag{6}\]  
Equation (6) can be calculated as follows by further mathematical transformation, i.e.,  
\[d= 10^{\frac{A-P_{T}(d)}{10n}}10^{\frac{P_{N}(d)}{10n}}, \tag{7}\]  
Assume that \(K_{1}=\frac{A-P_{T}(d)}{10n}\), \(K_{2}=\frac{P_{N}(d)}{10n}\).  
Equation (7) can be expressed as  
\[d= 10^{K_{1}}\big{(}1+10^{K_{2}}-1\big{)}, \tag{8}\]  
Equation (8) can be expanded further as  
\[d= 10^{K_{1}}+10^{K_{1}}\big{(}10^{K_{2}}-1\big{)}, \tag{9}\]  
Assuming that \(d_{T}=10^{K_{1}}\), \(F_{N}=10^{K_{2}}-1\), the calculation of distance \(d\) can be simplified to  
\[d= d_{T}+d_{T}F_{N}, \tag{10}\]  
where \(d_{T}=10^{K_{1}}\) is the real distance between the given anchor node and unknown target node and \(F_{N}=10^{K_{2}}-1\) is the impact factor of noise on distance measurement. \(F_{N}\) is designated the noise impact factor.
## 6 Numerical Study and Comparison
### Element Type and Material Properties  
The quadrilateral finite-membrane-strain shell element (Shell63) with both bending and membrane capabilities was used for modeling the three dimensional model PCCSW without flanges and stiffeners. This general purpose three dimensional reduced integration element with a 4-node elastic thin shell element is an appropriate element for most applications, and it is specified by its thickness. Each node of the element has six degrees of freedom, namely, translational displacements along the \(x\), \(y\) and \(z\) directions and rotational displacement around each axis. For complex buckling behavior, it provides accurate and reliable solutions. Material properties adopt the elastic stress-strain relationship of steel, as shown in Table 1, and at the same time, the material characteristics of the equivalent orthotropic shell are given.  
\begin{table}
\begin{tabular}{c c} \hline
**PCCSW** & **OOCCS** \\ \hline \(E_{s}=2.1\times 10^{5}\) MPa & \(E_{y}=2.33\times 10^{5}\) MPa, \(E_{x}=420\) MPa \\ \(\
u_{s}=0.3\) & \(\
u_{y}=0.3\), \(\
u_{x}=5.4\times 10^{-4}\) \\ \hline \end{tabular}
\end{table}
Table 1: Material constants needed in numerical simulation of Shinkai Bridge [31] as an example. PCCSW: plane curved corrugated steel web; OOCCS: orthotropic open circular cylindrical shell.
## 1 Introduction
Secondly, the algorithm has strong robustness when resisting against various common signal processing attacks, so it can be used to prove the owner of the audio media. Thirdly, the payload capacity of the algorithm reaches 172.27 bps which is higher than most related algorithms; thus, more watermarking information can be stored in the audio media. Finally, the algorithm does not require the participation of the original audio signal when extracting the watermark, which is very convenient for practical applications. The remaining parts of this paper are organized as follows: We review some related works about audio watermarking algorithm in Section 2. Section 3 describes the principle of the proposed watermarking algorithm in detail. This section is divided into three subjects, including the principle of watermark embedding, the principle of watermark extracting and the impact of the embedding depth on algorithm performance. The implementation of the proposed algorithm is described in Section 4, including the process of embedding and extracting. Section 5 evaluates the performance of this algorithm, including imperceptibility, capacity, and robustness, and then compares such experimental results with other algorithms in recent years. Finally, Section 6 draws up the conclusions and gives the possible future research task.
###### Abstract  
This paper studies synchronous Q-switch and mode-locking of an 2.7 \(\upmu\)m Er:Y\({}_{2}\)O\({}_{3}\) ceramic laser pumped with a high brightness 976 nm fiber laser and using a semiconductor saturable absorber mirror. The Er:Y\({}_{2}\)O\({}_{3}\) ceramic is home-developed with Er\({}^{3+}\) doping concentration of 7 at.%. The laser generated \(\sim\)92 mW of average output power with stable mode-locked pulses of 100% modulation depth and 130 MHz repetition rate embedded inside Q-switched envelopes of \(\sim\)1.2 \(\upmu\)s width. Repetition rates of the Q-switched pulse envelopes are tunable from 5.1 kHz to 29 kHz with the width varying from 2.7 \(\upmu\)s to 1.2 \(\upmu\)s.  
mid-infrared lasers; Q-switched mode-locking; ceramic lasers +
Footnote †: journal: Applied Sciences
## 4 The Principle of the Balanced Cross-Layer Fuzzy Logic Routing Algorithm  
In this section, we will introduce the principle of the Balanced Cross-layer Fuzzy Logic routing algorithm in detail. The principle of BCFL is shown in Figure 1. First, the source node collects the cross-layer parameters \(\beta_{n}\) from its neighbor nodes and calculates the dispersion \(D_{n}\) of each parameter. When the source node gets the dispersion \(D_{n}\), \(D_{n}\) will be inputted into the fuzzy logic inference system to decide the weights \(\alpha_{n}\). Finally, as shown in Equation (5), \(\alpha\) and \(\beta_{n}\) will be used to calculate the utility of each node, which is the reference to decide which neighbor node will be the best next hop relay node.
# Real-Time Geo-Localization for Land Vehicles Using LIV-SLAM and Referenced Satellite Imagery
## 4 Experiments  
To evaluate the performance of the proposed method, we conduct extensive experiments in public datasets, including the NCLT dataset [12] and R\({}^{3}\)LIVE dataset [3,4]. Both datasets contain LiDAR, IMU, and camera data. To better evaluate the position drift correction of the proposed method, we selected 14 long-distance sequences covering diverse environmental conditions. Tables 1 and 2 give the details of the selected sequences.  
Localization accuracy is evaluated using the Absolute Position Error (APE), which is calculated in terms of RMSE:  
\[\text{RMSE}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\lVert\hat{\mathbf{p}}_{i}-\mathbf{p}_{i} \rVert^{2}} \tag{16}\]  
where \(\hat{p}_{i}\) denotes the estimated position of the proposed method and \(p_{i}\) is the corresponding position of ground truth.  
Since matching-based positioning is infeasible indoors, some accuracy evaluations exclude all indoor results. Columns labeled as \"outdoor\" report statistics for outdoor localization only, while others include all results.  
In our implementation, the projection area is set to 150 m \(\times\) 150 m with an image resolution of 0.1 m/pixel, and the matching search radius is 20 m. The R\({}^{3}\)LIVE dataset  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sequence** & **Length** & **Duration** & **Ours** & **R\({}^{3}\)LIVE++** & **Fast-LIO2** \\ (**Date**) & **(km)** & **(s)** & **(Outdoor)** & **R\({}^{3}\)LIVE++** & **Fast-LIO2** \\ \hline
2012-02-02 1 & 6.2 & 5854.54 & 1.36 & 5.33 & 9.12 \\
2012-02-04 & 5.5 & 4659.86 & 1.85 & 5.58 & 7.19 \\
2012-02-05 & 6.5 & 5620.74 & 1.73 & 8.52 & 7.80 \\
2012-02-19 & 6.2 & 5319.48 & 1.79 & 8.52 & 5.98 \\
2012-03-17 & 5.8 & 4948.81 & 2.53 & 4.83 & 4.70 \\
2012-03-25 & 5.8 & 4840.98 & 4.63 & \(\backslash\) 2 & 19.30 \\
2012-04-29 & 3.1 & 2597.90 & 1.28 & 6.32 & 6.44 \\
2012-05-11 & 6.0 & 5073.34 & 2.54 & 3.70 & 4.13 \\
2012-06-15 & 4.1 & 3310.51 & 1.78 & 7.74 & 5.27 \\
2012-08-04 & 5.5 & 4814.31 & 1.91 & 3.85 & 6.96 \\
2012-10-28 & 5.6 & 5138.99 & 1.33 & 7.71 & 7.68 \\
2012-11-17 & 5.7 & 5315.78 & 1.68 & 8.68 & 5.83 \\ \hline \hline \end{tabular}  
* The dates represent the data acquisition dates of specific sequences from NCLT dataset. 2 The original R\({}^{3}\)LIVE++ paper does not report experimental results on the 2012-03-25 sequence.  
\end{table}
Table 1: Comparison of APE (m) on NCLT dataset.  
was collected in August 2021, and the corresponding referenced imagery was collected in April 2022. The NCLT dataset used in our experiments was recorded in 2012 from February to November, and the corresponding referenced imagery was captured in April 2015. All referenced imagery was acquired from Google Earth at zoom level 19, with an approximate ground resolution of 0.2986 m/pixel. During processing, the referenced imagery was resampled to 0.1 m/pixel to maintain consistency with the resolution of the projection map.
# New Generation of Electrochemical Sensors Based on Multi-Walled Carbon Nanotubes
## 3 MWCNT Electrochemical Properties
Vertically aligned structures also exhibit superior performance compared to those randomly immobilized [2; 16; 25; 26; 27; 28]. Therefore, MWCNT provide special characteristics in electrochemical sensors both in aqueous and non-aqueous electrolytes (electrocatalytic properties, high and fast electron-transfer, signal amplification and stability, among other particularities observed in different prototypes) when compared with the conventional carbonaceous electrodes (such as glassy carbon, highly-oriented pyrolytic graphite, carbon microfiber, and boron-doped diamond) [26; 27; 28]. However, the true reasons and fundamental electrochemical properties responsible for the reported performances (both when nanotubes are used alone or in (nano)composites) are still not fully elucidated and demand further research [2; 4; 6; 9; 17].
## 3 Methodology  
### Outline of Methodology  
The proposed methodology for malware classification has four major steps: (a) visualization of binary to grayscale image, (b) image augmentation, (c) feature extraction and fusion, and (d) feature selection and classification. The flow diagram of the proposed methodology is illustrated in Figure 1.  
The proposed method uses DCNN architecture with a combination of SFTA (scale feature texture analyzer) features [15] for malware classification. Firstly, we convert the binary dataset files into grayscale images for accurate malware classification. To overcome the challenge of the data imbalance problem between different malware classes, we use a data augmentation method [46]. In the feature extraction step, the SFTA features are extracted from grayscale images and the pre-trained DCNN models of AlexNet [47] and Inception-v3 [48] are used for deep feature extraction. Both SFTA features and DCNN features are combined into a single feature vector using a seri  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Case Study** & **Year** & **Data Analysis** & **Dataset** & \begin{tabular}{c} **Classification** \\ **Approach** \\ \end{tabular} & **Accuracy** \\ \hline \begin{tabular}{c} Malicious Code Localization \\ [41] \\ \end{tabular} & 2018 & Static & \begin{tabular}{c} Malware Dataset \\ Benign Dataset \\ Wild Dataset \\ \end{tabular} & \begin{tabular}{c} Graph Kernels \\ Support Vector \\ Machine (SVM) \\ \end{tabular} & 94\% \\ \hline \begin{tabular}{c} Malware Variants \\ Classified Behaviors [42] \\ \end{tabular} & 2018 & Dynamic & 1220 malware samples & \begin{tabular}{c} SVM, Decision \\ Tree, Naive Bayes \\ \end{tabular} & 88.3\% \\ \hline \begin{tabular}{c} Detection of \\ Android Native Code Malware \\ Variants [43] \\ \end{tabular} & 2018 & Static & \begin{tabular}{c} Benign Dataset \\ Malware Dataset \\ \end{tabular} & SVM & 93.22\% \\ \hline \begin{tabular}{c} Detecting and Classifying \\ Android Malware [44] \\ \end{tabular} & 2017 & Static & \begin{tabular}{c} Benign Dataset \\ Malware Dataset \\ \end{tabular} & SVM & 90\% \\ \hline \begin{tabular}{c} Android Malware Detection \\ System Based on Machine \\ Learning [45] \\ \end{tabular} & 2018 & Hybrid & \begin{tabular}{c} Benign Dataset \\ Malware Dataset \\ \end{tabular} & SVM & 95.2\% \\ \hline \begin{tabular}{c} Malicious Code Variants Based \\ on Deep Learning [12] \\ \end{tabular} & 2018 & Hybrid & Malware Dataset & SVM & 94.5\% \\ \hline \
\begin{tabular}{c} Image-Based malware \\ classification [33] \\ \end{tabular} & 2020 & Static & Malware Dataset &
\begin{tabular}{c} Ensemble of \\ convolutional \\ neural networks \\ \end{tabular} & 99.5\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of some recent malware detection approaches.  
Figure 1: Flow diagram of the proposed malware classification method.  
method, and the most informative and robust features are selected using a principal component analysis (PCA)-based feature selection method for accurate malware classification. The complete details of each step are listed in further subsections below.
# ARCFIRE: Experimentation with the Recursive InterNetwork Architecture
## 5 Discussion of Experimental Results
### Renumbering Experiments  
Shortcomings in the naming and addressing structure of the current Internet protocol suite make network renumbering a tedious, error-prone and expensive procedure. The lack of application names causes the network to bind an application flow to an IP address and a transport layer port number. If the IP address of the source or the destination of the flow changes, the flow identity is lost and the flow is no longer usable. In contrast, the comprehensive naming scheme of RINA makes renumbering problems in IP networks non-issues and enables dynamic network renumbering. Flows are associations between application names, only locally bound to IPC Processes via a port-id. Addresses are just location-dependent synonyms of IPC Process names. The identity of IPC Processes is represented by their location-independent application name: authentication and access control operations are performed in terms of AP names, not IPCP addresses. Hence renumbering does not interfere with such procedures.  
In the renumbering experiments we have analyzed how the complete naming and addressing architecture embodied by RINA allows RINA networks to be renumbered live, without significantly impacting the performance perceived by existing flows or impairing the ability to crate new ones. Renumbering a network with multiple layers today is a maintenance event: it has to be carefully planned, requires humans in the loop and it takes one or more days to complete. With RINA renumbering can be done life, dynamically, in a fully automated way and in a time period between tens or hundreds of milliseconds to a few seconds (depending on the network size).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**KPI** & **Part** & **Specialized KPI** & **Value** & **Description** \\ \hline  & DMS & Scenario Automation & High but no 100\% & Easy to get to 100\% \\ \cline{2-5}  & & Create Configuration & Very low & Automated \\  & & & & with scalingtime \\ \cline{2-5} Degree of Automation & Networks & Create Network & 100\% & Fully automated \\ \cline{2-5}  & & Create Network Graphs & 100\%, Medium & GraphML, \\  & & & & post-processing \\ \cline{2-5}  & Test runs & Complete a test run & Medium & Wait points (race conditions) \\ \hline Touch & Networks & Manual operations & 0–1, 0–4 & Mandatory, \\ \cline{2-5}  & Test runs & Manual operations & 122 in 139 networks & With better policies \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._This property can be applied to multiple use cases, such as: (i) _network address space consolidation_, merging two or more address spaces from different companies after an acquisition; (ii) _network address space optimisation_, changing the addressing policy of a DIF if it is no longer optimal due to changes in the DIF (e.g., the DIF has grown a lot since the addressing policy was designed, or its structure has changed); or (iii) _keeping addresses of mobile hosts aggregatable_ as they move through different subnets.
# Large Thermal Expansion LTCC System for Cofiring with Integrated Functional Ceramics Layers
## 2 Materials and Methods  
### Sample Preparation  
LTCC powder CT708 was provided by Heraeus Deutschland GmbH & Co. KG (Hanau, Germany). The tape casting slurry was prepared by mixing polyvinyl butyral PVB with methyl isobutyl ketone in a Nolan drum (Aufbereitungstechnologie NOLL GmbH, Bobingen, Germany) for 1 h at 20 rpm with ZrO\({}_{2}\) cylpeps (d = 10 mm and h = 10.5 mm). This was followed by the addition of plasticizer and methanol. After a mixing for 24 h at 20 rpm in the drum mill, CT708 powder was added and homogenized for further 24 h. Finally, the slurry was filtered and de-earated. Tape casting was carried out using a doctor blade casting machine with a drying channel length of 13 m and a casting width of 30 cm. The slurry was casted with a blade gap of 350 \(\upmu\)m and a speed of 0.4 m/min onto a polyester carrier tape, siliconized at one side, with a thickness of 75 \(\upmu\)m. 6\" sheets were cut from the green tape. Eight sheets of CT708 green tape were stacked on a setter plate and laminated using an isostatic laminator system IL-4008 PC (Pacific Trinetics Corp., Fremont, CA, USA) at 80 \({}^{\circ}\)C and a pressure of 24 MPa. The laminates were cut into samples of size 40 \(\times\) 60 mm\({}^{2}\) by laser cutting.  
Powders of a Sc-substituted hexagonal ferrite BaSc\({}_{0.5}\)Fe\({}_{11.5}\)O\({}_{19}\) (ScM) were prepared by mixing Fe\({}_{2}\)O\({}_{3}\), BaCO\({}_{3}\) and Sc\({}_{2}\)O\({}_{3}\) in stoichiometric proportions. After wet mixing of the starting materials, the powder mixture was calcined for 4 h at 1300 \({}^{\circ}\)C. The calcined powder was milled with deionized water and zirconia grinding media (diameter 1 mm) for 2 h in a planetary ball mill (Pulverisette 5, Fritsch, Idar-Oberstein, Germany). A mixture of 3 wt% of Bi-B-Si-Zn glass (BBSZ) powder and 2 wt% CuO was added as sintering aid during milling. A ferrite tape with 100 \(\upmu\)m thickness was prepared by tape casting as described above. For cofiring experiments, two ferrite tape layers were stacked between two CT708 LTCC layers on top and bottom and laminated.  
All laminates were slowly heated in the first step of the furnace temperature profile to 500 \({}^{\circ}\)C with a rate of 2 K/min for binder burnout. In the next ramp, the CT708 laminates were heated with different heating rates of 2 K/min, or 4 K/min, or 8 K/min to 900 \({}^{\circ}\)C and sintered for 2 h at 900 \({}^{\circ}\)C, followed by cooling with 10 K/min. The CT708/ ferrite co-laminates were heated with 2 K/min to 900 \({}^{\circ}\)C and sintered for 2 h at 900 \({}^{\circ}\)C as well.