esa-sceva/satcom-chunk-collection huggingface dataset:
# Double-Layer Compressive Sensing Based Efficient DOA Estimation in WSAN with Block Data Loss
## 3 Double-Layer CS Framework-Based DOA Estimation
### DOA Estimation by DCS-DOA
#### 3.2.2 DOA Estimation
Now, we construct two matrices: \(\mathbf{B}=\text{diag}\left\{\bar{\mathbf{\Lambda}}\left(f_{1},\bar{\mathbf{\theta}} \right),\bar{\mathbf{\Lambda}}\left(f_{2},\bar{\mathbf{\theta}}\right),...,\bar{\mathbf{ \Lambda}}\left(f_{K},\bar{\mathbf{\theta}}\right)\right\}\), \(\bar{\mathbf{\mathbf{S}}}=\left[\bar{\mathbf{\kappa}}\left[f_{1}\right],\bar{\mathbf{ \kappa}}\left[f_{2}\right],...,\bar{\mathbf{\kappa}}\left[f_{K}\right]\right]\). Combining all of the \(K\) nonzero frequency sub-bands, we have:  
\[\bar{\mathbf{\kappa}}=\mathbf{B}\bar{\mathbf{\kappa}}+\mathbf{n} \tag{13}\]  
where \(\bar{\mathbf{\kappa}}=\left[\bar{\mathbf{\kappa}}^{H}\left[f_{1}\right],...,\bar{\mathbf{ \kappa}}^{H}\left[f_{K}\right]\right]^{H}\) is the joint array output vector covering the \(K\) sub-bands, and \(\bar{\mathbf{\mathbf{S}}}=\text{vec}\left(\bar{\mathbf{\mathbf{S}}}\right)\) is a \(KQ\times 1\) joint sparse indicator vector by vectorizing \(\bar{\mathbf{\mathbf{S}}}\). Additionally, \(\mathbf{n}=\left[\mathbf{n}^{H}\left[f_{1}\right],...,\mathbf{n}^{H}\left[f_{ K}\right]\right]^{H}\) is the joint noise vector. We use the row vector \(\mathbf{s}_{q},q=1,...,Q\), to represent the \(q\)-th row of \(\mathbf{\tilde{S}}\). Based on the \(l_{2}\)-norm of \(\mathbf{s}_{q}\), we can formulate a new \(Q\times 1\) column vector \(\mathbf{\hat{s}}\) as \(\mathbf{\hat{s}}=\left[\left\|\mathbf{s}_{1}\right\|_{2},\left\|\mathbf{s}_{2 }\right\|_{2},...,\left\|\mathbf{s}_{Q}\right\|_{2}\right]^{T}\). Ultimately, the DOA estimation problem of wideband source signals based on group-sparsity is formulated as follows:  
\[ \begin{array}{ll}\min&\left\|\mathbf{\hat{s}}\right\|_{1}\\ \text{subject to }&\left\|\mathbf{\tilde{\alpha}}-\mathbf{B}\mathbf{\tilde{s}} \right\|_{2}\leq\varepsilon_{2}\end{array}  \tag{14}\]  
where \(\varepsilon_{2}\) is the user-specified error bound, and the problem is representable in an SOCP frame and solved using the SeDuMi toolbox [27]. The indexes of the nonzero elements in \(\mathbf{\hat{s}}\) indicate the wideband DOA estimation results.
# Distributed Consensus for Global Matrix Formation in the Principal Component Pursuit Scenario
## 2 Problem Formulation  
Suppose there are \(l\) agents represented by nodes in an undirected connected graph \(G=\{V,E\}\), where \(V=\{1,2,...,l\}\) is the set of vertices, and the edges \(E\subseteq V\times V\). There is an edge \((i,j)\in E\) if and only if agent \(i\) can communicate with \(j\) and vice versa. The neighborhood of agent \(i\) is defined as  
\[\mathcal{N}_{i}:=\{j\in V:(i,j)\in E\} \tag{1}\]  
This graph has an associated adjacency matrix \(A\in\mathcal{R}^{l\times l}\) and each agent \(i\) at iteration \(k\) has a state \(x_{i}^{k}\in\mathcal{R}^{n\times m}\).  
There is a global noise-corrupted matrix \(M\in\mathcal{R}^{n\times m}\) that is the sum of a low-rank matrix \(L\in\mathcal{R}^{n\times m}\) and a sparse matrix \(S\in\mathcal{R}^{n\times m}\), (2).  
\[M=L+S \tag{2}\]  
Unlike the small noise term \(S\) in classical PCA, the entries in \(S\) can have an arbitrarily large magnitude. Hence, it is called robust PCA. Each agent can only observe part of \(M\), and different agents can also have some coincident measurements. The distribution of what part of \(M\) every agent is measuring is irrelevant; they can have equal-row-distributed data or a sparse distribution. The restriction is that each agent knows which matrix values are being measured by itself, such that there are zeros in the unobserved entries.  
An algorithm is hypothesized such that every agent obtain the same estimation of \(L\) and \(S\) based only on its own measurements and the neighbor's information, solving:  
\[\min_{L,S}\quad rank(L)+\|S\|_{0}\qquad\text{s.t.}\qquad L+S=M \tag{3}\]  
where \(\|S\|_{0}\) counts the number of nonzero elements in \(S\), quantifying how sparse it is. However, (3) is NP-hard [28] and neither the \(rank(L)\) nor the \(\|S\|_{0}\) terms are convex.
## 3 Results and Analysis  
### Comparison of Mechanical Properties between Bedding Shale and Lamellar Shale  
The uniaxial compression test results of the samples of lamellar shale and bedding shale with different hydration times are shown in Table 2. The compressive strength (deviatoric stress) and elastic modulus of bedding shale are greater than those of lamellar shale under the same testing conditions. For example, for the samples with the same axial direction--lamina (bedding) angle (\(\beta\)), the dry bedding shale samples has 30.2-50.7% higher compressive strength and 34.1-50.2% higher elastic modulus than dry lamellar shale samples. The anisotropic magnitude of the dry lamellar shale sample under uniaxial conditions is 1.33 and that of the dry bedding shale sample is 1.15, which indicates that the anisotropy of the lamellar shale is stronger than that of the bedding shale.  
The influence of hydration on the strength and elastic modulus of lamellar shale is greater than that of bedding shale. For example, after 48-h hydration in distilled water, the strength of lamellar shale for \(\beta\) = 30\({}^{\circ}\) decreases by 19.5%, while the strength of bedding shale for \(\beta\) = 30\({}^{\circ}\) decreases by 13.40%. The elastic modulus of lamellar shale for \(\beta\) = 30\({}^{\circ}\) decreased by 15.66%, and the elastic modulus of bedding shale for \(\beta\) = 30\({}^{\circ}\) decreased by 14.50%.
# A Modified Bayesian Network Model to Predict Reorder Level of Printed Circuit Board
## Variables Specification and Data Preprocessing
### Principal Component Analysis  
There are significant differences in values among variables given in Table 1. Some variables may be redundant or not have a significant influence on the reorder level prediction. Furthermore, continuous-valued variables with a large amount of input types easily generate too many conditional probability tables (CPTs) with sparse samples for each value, which negatively affects the establishment of a robust model. It is, therefore, necessary to perform preprocessing before building a model.  
First, in order to eliminate the negative impact caused by the huge difference between each variable in terms of values, there is a need to normalize each variable ranging from 0 to 1. Second, the total data sample matrix 48,026 \(\times\) 23 (i.e., the number of the samples multiplied by the number of the input variables for each records) would be considerably complicated and time consuming to model and test for such a high-dimension data samples [22]. It is, therefore, essential for reducing the dimension of the data samples and extracting the typical features from the original data samples. Third, in order to reduce input type and get better performance for variables, it is important to discretize variables for BN model development [14].  
PCA is an effective statistical analysis method in multi-dimensional data compression and factors extraction. It can fuse relatively useful features and extract more sensitive factors through the evolution of the variance contribution rate and the cumulative variance contribution rate of each variable [22]. In this study, PCA was used for reducing variable redundancy for the proposed models. This could greatly reduce the modeling time and improve operational efficiency. The procedure of PCA is described below.  
PCA was conducted by Algorithm 1 based on training samples according to the 21 input variables given in Table 1 except for the layer number and recency with some initial experiment. Seven factors were extracted with 87.87% of the cumulative variance contribution rate, which means the extracted factors can represent 87.87% of information of the original 21 input variables. The variance contribution  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Samples**} & \multicolumn{3}{c}{**Number (Proportion of Different Reorder Levels \%)**} & \multirow{2}{*}{**Total Number**} \\ \cline{2-2} \cline{4-6}  & **1** & **2** & **3** & **4** \\ \hline All & 38,678 (80.54) & 6734 (14.02) & 1777 (3.70) & 837 (1.74) & 48,026 \\ Training & 26,963 (80.39) & 4735 (14.11) & 1225 (3.65) & 619 (1.85) & 33,542 \\ Test & 11,715 (80.88) & 1999 (13.80) & 552 (3.80) & 218 (1.52) & 14,484 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Samples and their proportion of different reorder levels.
###### Abstract  
With the implementation of the Internet of Things, the agricultural domain has become data-driven, allowing for well-timed and cost-effective farm management while remaining environmentally sustainable. Thus, the incorporation of Internet of Things in the agricultural domain is the need of the hour for developing countries whose gross domestic product primarily depends on the farming sector. It is worth highlighting that developing nations lack the infrastructure for precision agriculture; therefore, it has become necessary to come up with a methodological paradigm which can accommodate a complete model to connect ground sensors to the compute nodes in a cost-effective way by keeping the data processing limitations and constraints in consideration. In this regard, this review puts forward an overview of the state-of-the-art technologies deployed in precision agriculture for soil assessment and pollutant monitoring with respect to heavy metal in agricultural soil using various sensors. Secondly, this manuscript illustrates the processing of data generated from the sensors. In this regard, an optimized method of data processing derived from cloud computing has been shown, which is called edge computing. In addition to this, a new model of high-performance-based edge computing is also shown for efficient offloading of data with smooth workflow optimization. In a nutshell, this manuscript aims to open a new corridor for the farming sector in developing nations by tackling challenges and providing substantial consideration.  
food security; precision agriculture; Internet of Things; soil moisture; heavy metal; wireless sensors; edge computing +
Footnote †: journal: Agriculture
# Effective Seed Scheduling for Directed Fuzzing with Function Call Sequence Complexity Estimation
## 5 Evaluation
### Experimental Setup  
In this paper, we compare SEZZ with the following publicly available state-of-the-art fuzzers: AFLGo, Beacon, WindRanger, and SelectFuzz. We use the Magma [32] dataset, which contains various open-source software projects covering different application scenarios, such as file format parsers, protocol implementations, and image-processing tools. The dataset includes multiple vulnerabilities, such as buffer overflow, integer overflow, and null pointer dereference. In total, we include 35 unique Magma vulnerabilities in our dataset and list them in Table 1. Other vulnerabilities evaluated by recent DGFs are excluded from our evaluation mainly due to compilation issues, such as obsolete and missing dependencies and incompatible compilation environments. Since different initial seeds can lead to significant variations in fuzzing results, we use the initial seeds provided by Magma.  
To evaluate the fuzzers, we conducted experiments on a machine equipped with an Intel(R) Xeon(R) CPU E5-2680 v4 @ 2.40 GHz running Ubuntu 22.04 LTS with 8 cores. We measured the time to reach (TTR) the target site, the time to exposure (TTE) of the bugs, the number of times each bug was reached, and the number of times each bug was triggered. Each program was fuzzed five times, with each run lasting 24 h.
## 2 Problem Description  
Assuming that there are several suspicious areas on the battlefield, a heterogeneous UAV swarm with different reconnaissance and strike capabilities needs to be dispatched to perform the reconnaissance and strike mission, and the UAV nodes communicate with each other through a multi-hop ad hoc network. UAVs autonomously negotiate task-allocation schemes for reconnaissance and strike targets. Since there may be a synergistic relationship between enemy targets, the mission risk and mission completion time should be minimized during mission execution.  
The problem can be formalized as the problem of \(N_{U}\) UAVs \(U=\{u_{i}|i=1,2,\cdots,N_{U}\}\) completing \(N_{T}\) tasks \(T=\left\{t_{i}|j=1,2,\cdots,N_{T}\right\}\).  
The state of UAV \(u_{i}\) is denoted as \(u_{i}=\left\langle p_{i},v_{i},a_{i},T_{i}^{p},T_{i}^{b},\tilde{T}_{i}^{b},U_{i}\right\rangle\) where \(p_{i}\) is the current position; \(v_{i}\) is the maximum flight speed; \(\mathbf{a}_{i}\) is the load capacity matrix of \(u_{i}\), as shown in Table 1, the capacity between loads can be added but a single load cannot be split; \(T_{i}^{p}\) is the task set that is perceived but has not decided the assignment; \(T_{i}^{b}\) and \(T_{i}^{b}\) are the task queue that \(u_{i}\) will participate in and the task set that will not participate; \(U_{i}\) is the UAV swarm status perceived by the \(u_{i}\), which can be updated through communication with other UAVs.  
The state of the task can be expressed as \(t_{j}=\left\langle p_{j},s_{j},a_{j}\right\rangle\), where \(p_{j}\) is the position of the task, \(s_{j}\) is the area of the suspicious area where the task is located, and \(a_{j}\) is the strike capability required by the task. In the process of reconnaissance of suspicious areas by UAVs with reconnaissance capabilities, existing targets can be found and \(\mathbf{a}_{j}\) can be obtained; but when there is no target in the area, this conclusion can only be drawn after the UAV has scouted the entire area, in this case \(\mathbf{a}_{j}=\mathbf{0}\).  
The connection relationship between UAVs is expressed as an adjacency matrix \(\mathbf{L}=\left[l_{im}\right]_{i,m\in[1,N_{U}]}\), and there is \(l_{im}=1\) when distance \(d_{im}\leq d_{\delta}\), otherwise \(l_{im}=0\), and \(d_{\delta}\) is the maximum distance for single-hop communication. When the reconnaissance node  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Payload Type** & **Scout Speed** & **Penetration Ability** & **Damage Ability** & **Reusable** \\ \hline Scout payload & 50 & 0 & 0 & Y \\ Strike payload 1 & 0 & 40 & 60 & N \\ Strike payload 2 & 0 & 80 & 40 & N \\ \hline \hline \end{tabular}
\end{table}
Table 1: Example of payload capacity of \(u_{i}\).  
completes the reconnaissance task, it broadcasts the reconnaissance result (that is, whether there is a target in the area and the required strike capability) to the swarm by using the ad hoc network. Each UAV utilizes the perceived task status and the status of other UAVs to optimize the distribution of reconnaissance and strike tasks by negotiating with neighboring UAVs.
## 1 Introduction
### Theory
The relationship between the coefficients of the Zernike polynomials and wavelength can be expressed as follows [19]:  
\[a_{i}=A_{i}+\dfrac{B_{i}}{\lambda}+\dfrac{C_{i}}{\lambda^{3.5}} \tag{21}\]  
where \(a_{i}\) denotes the coefficient of the Zernike polynomial of order \(i\), and \(A_{i}\), \(B_{i}\), and \(C_{i}\) denote the coefficients of the Conrady-Zernike polynomial. When the wavelengths differ, the relationship between their corresponding coefficients can be expressed as follows:  
\[a_{i}(\lambda_{1})=\Bigg{[}\Bigg{(}A_{i}+\dfrac{B_{i}}{\lambda_{1}}+\dfrac{C _{i}}{\lambda_{1}^{3.5}}\Bigg{)}/\Bigg{(}A_{i}+\dfrac{B_{i}}{\lambda_{2}}+ \dfrac{C_{i}}{\lambda_{2}^{3.5}}\Bigg{)}\Bigg{]}a_{i}(\lambda_{2}) \tag{22}\]  
The proportionality correction factor between the \(i\)th order Zernike polynomial coefficients corresponding to the signal light and beacon light is as follows:  
\[Z_{i}=\Bigg{[}(A_{i}+\dfrac{B_{i}}{\lambda_{1}}+\dfrac{C_{i}}{\lambda_{1}^{3. 5}})/(A_{i}+\dfrac{B_{i}}{\lambda_{2}}+\dfrac{C_{i}}{\lambda_{2}^{3.5}})\Bigg{]} \tag{23}\]Considering the 15 th-order Zernike polynomial as an example, the Zernike polynomial coefficient vector corresponding to beacon light can be expressed as:  
\[\mathbf{A}_{15}(\lambda_{2})=[a_{1},a_{2},a_{3},\cdots,a_{15}]^{\text{T}} \tag{24}\]  
After multiplying the coefficient vector of the beacon light with the corresponding correction vector, we obtain:  
\[\mathbf{A}^{{}^{\prime}}_{15}(\lambda_{2}) =\left[ \begin{array}{l}z_{1},0,0,\ldots,0\\ 0,z_{2},0,\ldots,0\\ \vdots\\ 0,0,0,\ldots,z_{15}\end{array} \right][a_{1},a_{2},a_{3},\cdots,a_{15}]^{\text {T}} \tag{25}\] \[=[z_{1}a_{1},z_{2}a_{2},z_{3}a_{3},\cdots,z_{15}a_{15}]^{\text{T}}\]  
Furthermore, the mutual covariance matrix of the modified polynomial coefficients can be expressed as [20]:  
\[\mathbf{C}^{{}^{\prime}} =E\Big{[}A^{\prime}{}_{15}(\lambda_{2})\cdot A^{\prime}{}_{15}^{ \text{T}}(\lambda_{2})\Big{]} \tag{26}\] \[=\left[ \begin{array}{cccc}E(z_{1}a_{1},z_{1}a_{1})&E(z_{1}a_{1},z_{2}a_{2})&\cdots&E(z_{1}a_{1},z_{N}a_{N})\\ E(z_{2}a_{2},z_{1}a_{1})&E(z_{2}a_{2},z_{2}a_{2})&\cdots&E(z_{2}a_{2},z_{N}a_{N })\\ \vdots&\vdots&\ddots&\vdots\\ E(z_{N}a_{N},z_{1}a_{1})&E(z_{N}a_{N},z_{2}a_{2})&\cdots&E(z_{N}a_{N},z_{N}a_{N })\end{array} \right]\]  
The covariance matrix \(\mathbf{C}^{{}^{\prime}}\) is known as a Hermitian matrix [20]. Hence, there exists a unitary matrix \(\mathbf{X}\) such that it is a diagonal array. We perform singular value decomposition of the covariance matrix \(\mathbf{C}^{{}^{\prime}}\) as follows:  
\[\mathbf{C}^{{}^{\prime}}=\mathbf{X}\mathbf{S}\mathbf{X}^{\text{T}} \tag{27}\]  
It is assumed that the coefficient vector of another linear combination B of wavefront Zernike polynomials of the same dimension as A is \(\mathbf{B}=[b_{1},b_{2},\ldots,b_{N}]^{\text{T}}\). Then, the Zernike polynomial coefficients of the wavefront of the corresponding signal optical distortion are:  
\[\mathbf{A}^{{}^{\prime}}=\mathbf{X}\cdot\mathbf{B} \tag{28}\]
# Towards DC Energy Efficient Homes
## 3 Factors for DC Installations
### Batteries at Home  
An increasingly typical solution around the world is the application of HESS since it is an easy way to reduce electrical consumption and, as can be seen in Figure 3, prices have fallen in recent years by more than 50% [19]. In fact, HESS application in German homes has grown by around 50% in each of the last three years, according to data from the BSW-Solar association [52].  
Figure 3: Evolution of the mean price of the batteries in recent years. (Data collected from [53] on 26 May of 2021).  
Figure 2: EV connector types: (**a**) Type 2, (**b**) CCS combo 2, and (**c**) Type 4 (CHAdeMO).  
The different HESS solutions sold on the market allow it to be adapted to any house, depending on the specific characteristics, i.e., discharging and charging power, voltage levels, DoD, capacity, etc. The round trip efficiency is 95%. Additionally, due to the varied possibilities existing in the market, there is a wide range of prices, from $0.19 kWh/cycle to $1.07 kWh/cycle [54].  
Regarding the types of HESSs, there are mainly three different ones that, due to their characteristics, can be applied in houses, i.e., monoblock, stationary and lithium [55]. Nevertheless, the direction of manufacturers, such as LG Chem, Tesla, ABB, Huawei, or ByD, seem to be towards lithium [56]. Lithium offers a greater versatility, faster charge and discharge, longer life cycles, higher DoD, higher power density and higher round-trip efficiency than the other HESS types [57].  
The placement of a HESS allows to store energy during periods of lower demand and to power the home when necessary, by improving security and by controlling the supply. HESS makes it possible to improve household electricity consumption, alleviating the variations presented in the load demand and ensuring supply in the case of a grid outage [58; 59].  
The accumulation of energy in HESSs is carried out in DC, so connection to household devices requires a DC/AC conversion. On the other hand, as seen in Section 2, a large number of household devices operate in DC, even though they are AC powered. As such, it is necessary to convert the current twice to run most of home devices, and this results in an inefficient system. The layout of a fully DC scenario facilitates the ability to more easily and directly connect HESSs, improving the energy efficiency of a house. This, along with the flexibility of HESSs, enables to achieve emission reduction goals by creating a NZEH.
# New Gap-Filling Strategies for Long-Period Flux Data Gaps Using a Data-Driven Approach
## 1 Introduction
The primary purpose of long-period-gap-filling is to analyze variations with several-year cycles (e.g., ENSO, drought) using continuous time-series data and spectral analysis tools (e.g., Fourier transform, wavelet transform). The long gaps in 2007 and 2014 occurred periodically (i.e., every seven years), making such analyses difficult. There are some conditions necessary to produce time-series data using the machine-learning-based model, which could explain such interannual variability. Considering that the annual sums are accumulated from the daily values and the interannual variability is year-to-year fluctuations of the annual sums, it is a necessary condition that the model performs well at daily to seasonal timescales. If the relationships between the fluxes and their drivers (e.g., solar radiation, air temperature, surface greenness) change very little with time, we will have a greater chance of quantifying the interannual variability. Since errors on a daily timescale accumulate when we calculate the annual sums, and there is a greater possibility of changing the relationships between the fluxes and their drivers in the long term, it is typically more difficult to capture the interannual variability than the seasonal variability e.g., [8]. In this study, we evaluated the applicability of the data-driven approach to the filling of long gaps in flux data (i.e., gross primary production (GPP), ecosystem respiration (RE), net ecosystem exchange (NEE), and evapotranspiration (ET)) for the HFK site. The ultimate goal of this study was to establish a long-term flux database for the HFK site with no data gaps. Our specific objectives were to propose gap-filling strategies for long-period flux data using a data-driven approach to reduce the uncertainty related to gap-filling, and to verify that the long-period-gap-filled flux data can capture interannual variability.