esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of Heavy Metal Sources in Xutuan Mining Area Based on APCS-MLR and PMF Model
## 1 Introduction
In this study, the APCS-MLR and PMF models are applied to multi-source mixed pollution areas, and the model error is reduced by cross-validation. APCS-MLR evolved from PCA, and source contributions were obtained by performing regression between heavy metal content and the APCS [23]. PMF uses experimental uncertainty in the data matrix and decomposes the data matrix into factor contributions and factor distributions under non-negative constraints. At present, many scholars have applied APCS-MLR and PMF to analyze the source distribution of various pollutants in the atmosphere, soil, and dust [24; 25]. The present study aims to assess the concentrations and forms of six heavy metal elements (Cu, Pb, Zn, Cr, Cd, and As) in water bodies and sediments in the Xutuan mining area, taking into account the main findings revealed in previous related research [26; 27]. In addition, the ecological risks of the heavy metals in the mining area were further evaluated. The sources of the heavy metals in the sediments were also analyzed using the PMF model to provide a scientific basis for the comprehensive prevention and control of heavy metal pollution in the Xutuan mining area.
# Thermo-Viscoelastic Characterization of 3D Printing Polymers
## 3 Experiment
### Single Frequency Test  
The glass transition temperature (\(T_{g}\)) is the temperature at which polymers change from rigid glassy materials to soft materials. That is a direct measurement of molecular mobility because of a reduction in motion of large segments of molecular chains with decreasing temperature. Dynamic mechanical analysis is one analytical technique used to determine \(T_{g}\). The glass transition occurs over a range of temperatures and is not a point or single temperature. In general, the intercept from the glassy plateau of the storage modulus and the line after the sudden drop of the storage modulus in the transition region,  
\begin{table}
\begin{tabular}{l l c} \hline \hline
**Materials** & **Chemical Name** & **\%** \\ \hline \multirow{3}{*}{Polymer A} & Isobornyl acrylate & 30–40 \\  & 4-(1-Oxo-2-propenyl)-morpholine & 10–20 \\  & Diphenyl (2,4,6-trimethyl-benzoyl)phosphine oxide & 0.5–1.5 \\ \hline \multirow{6}{*}{Polymer B} & 2-Propenoic acid, 3-[2,2-dimethyl-1-oxo-3-\{[1(-oxo-2-propen-1-yl)\}\) & 15–25 \\  & oxy]propoxyl]-2,2-dimethylpropyl ester & 10–20 \\  & Tricyclodecane dimethanol diacrylate & 0.5–1.5 \\  & Diphenyl (2,4,6-trimethyl-benzoyl)phosphine oxide & 0.5–1.5 \\  & Isobornyl methacrylate & 15–25 \\  & 2-Propenoic acid, 2-[[(butylamino)carbonyl]oxy]ethyl ester & 25–35 \\ \hline \multirow{6}{*}{Polymer C} & 2-Propenoic acid, 1,1’-[(octahydro-4,7 methano-1 Hindene-5,7- & 50–90 \\  & diyl)bis(methylene)] ester & 2-Propenoic acid, 2-methyl-, (18,2R,4R)-1,7,7 trimethylbicyclo & 5–10 \\  & [2,2.1] hept2-yl ester, rel- & \\  & diphenyl (2,4,6- trimethylbenzoyl) phosphine oxide & 1–5 \\ \hline \multirow{6}{*}{Polymer D} & 2-Propen-1-one, 1-(4-morpholinyl) & 30–50 \\  & 2-Propenoic acid, 2-methyl-, (18,2R,4R)-1,7,7 trimethylbicyclo & 5–25 \\  & [2.2.1]hept2-yl ester, rel- & \\  & 3,3,5-Trimethylcyclohexyl acrylate & 5–10 \\  & Phosphine oxide, phenylbis(2,4,6-trimethylbenzoylyl) & 1–10 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical components of polymer materials tested.  
Figure 1: Specimens.  
the peak of loss modulus, or the peak of loss tangent are considered as \(T_{S}\)s. Frequency and temperature are considered the key variables in the study of the dynamic mechanical properties of polymers.  
Based on the single frequency test, the glass transition temperature was measured. In this study, dynamic measurements were carried out over a temperature range at constant frequency. A TA Instruments Q800 dynamical mechanical analyzer (DMA) was used to measure the glass transition temperature (\(T_{S}\)) of the specimens. A single cantilever was used at a frequency of 1 Hz, an amplitude of 25 microns/meter, and a ramp rate of 2 \({}^{\circ}\)C/min. Initially, it was equilibrated isothermally at \(-\)120 \({}^{\circ}\)C for 10 min. Then, the temperature was increased by 2 \({}^{\circ}\)C/min. The \(T_{S}\) measured was also used to determine the test window for the temperature-frequency sweep test.
## 2 Existing Technologies and the Position of the Present Research  
This section presents currently used underwater communication technologies, and describes advantages and drawbacks of technologies used to avoid contention in communication involving multi-access to the transmission medium. It also identifies the position of the present research.
# _photonics_
## 4 Simulation Results
### Practical Exploitation of Distilled Key Bits  
Beyond the QKD link performance evaluation, the use of quantum keys as part of the symmetric-key cryptography systems is discussed in this subsection. In our proposed scheme, the quantum keys distilled by the QKD satellite link are fed into an AES-256 encryption engine, providing ultra-secure protection for data flows in Gbps rates. When sufficient key bits can be distilled, the OGS is able to use them to securely communicate with a single satellite. In practice, this is not very useful, since every satellite is visible by the OGS only for a few minutes daily. To overcome this limitation, an alternative security scheme is proposed, where the satellites are considered as trusted nodes that share identical keys between two OGSs. The security levels of the AES-256 for the above security architectures are studied, focusing on the AES-256 key refresh time.
## 4 Location Estimation of Truck Cargo Box
### Pose Estimation Refinement  
The truck cargo box position was estimated with only five points of convergence results in the errors presented in Figure 14. This is caused by differences in the distance information estimated by the stereo camera and the generated 3D hexahedron model. The initial matching results show that more errors occurred at the longitudinal corner than at the rear of the cargo bed. Therefore, to minimize error, the points of convergence are sampled at regular intervals from the two corners along the longitudinal axis of the 3D hexahedron model, as shown in Figure 15. An iterative algorithm is used so that these points of convergence and the longitudinal axis of the cargo box obtained by the camera will match.  
A 3D transformation matrix, \(T_{M\to C}\), is used to project the sampled 3D point, \(M_{i}\), of the corner onto the camera's 2D image using Equation (9):  
\[m_{i}=k\ T_{M\to C}M_{i}. \tag{9}\]  
Here, \(k\) is the internal parameter of the stereo camera, and \(m_{i}\) is the point projected onto the 2D camera image, as shown in Figure 16. If the initial position transformation matrix, \(T_{M\to C}\), is accurate, the projected points are identical to the box corner. Otherwise, they do not match, as shown in Figure 16. Thus, the longitudinal axis of the cargo box is used to solve this issue. Figure 17 presents the method for detecting points of convergence. When they are projected onto the 2D image, the left corner of the cargo box is searched from the right side of the x-axis, and the right corner is searched from the left side. When a 3D value exists on the searched point, it is designated as the point of convergence. The initial transformation matrix, \(T_{M\to C}\), is refined after the ICP algorithm iteratively minimizes the errors of the points added to the corner and the plane [22,23]. The pseudocode of the iterative refinement is shown below:  
\(T_{M\to C}=InitializeMatrix\)  
\(While(\varepsilon>T_{\varepsilon})\)  
\(//\) Find the corresponding points \(m^{\prime}_{i}\) where the distance between  
\(m_{i}\) and \(m^{\prime}_{i}\) is minimum.  
\(m^{\prime}_{i}=ProjectionPoint(T^{\prime}_{M\to C},\ M_{i})\)  
\(//\) Find the T matrix that minimizes the error.  
\(\varepsilon=\sum_{n}||\ m^{\prime}_{i}-k\ T^{\prime}_{M\to C}M_{i}\ ||\)  
\(T^{\prime}_{M\to C}=T_{M\to C}T^{\prime}_{M\to C}\)  
\(//\) convert \(M_{i}\) to global coordinate system.  
\(M_{i}=T^{\prime}_{M\to C}M_{i}\)  
Figure 15: Example of 5 3D points sampled for each corner along the long axis of a cargo box.
## 4 Robust Stability Analysis  
The SISO system's gain is independent of the size of its input. The MIMO system, however, offers more degrees of freedom. Because of this, the gain is influenced by the direction of the disturbance d, as detailed in [52]. It is crucial to perform a robust stability study of MIMO systems due to model uncertainty and other parameter changes. The stability analysis is assessed using the singular-value uncertainty model. The schematic  
Figure 5: Schematics of the MFPOA-based FOPID control.  
of the input uncertainty taken into consideration throughout the analysis is shown in Figure 6a. The following conditions must be met for the closed-loop system to be stable.  
\[\|\Delta_{I}(j\omega)\|<\frac{1}{\overline{\phi}_{1}} \tag{38}\]  
where \(M(s)[1+\Delta_{I}(s)]\) is the input uncertainty, and \(\overline{\phi}_{1}\) is the maximum singular value (MSV) of the closed-loop system \(C(s)M(s)/(1+C(s)M(s))\). The MSV of \(N\) can be calculated using the maximum gain. Moreover, the matrix can be reduced to its single value as  
\[M=\sqsubseteq\varrho\updownarrow^{*} \tag{39}\]  
where \(\varrho\) is a matrix of the order \(x\times y\), and \(\updownarrow\) and \(\sqsubseteq\) are unitary matrices of the order \(y\times y\) and \(x\times x\), respectively. Similar to the discussion in [53], the closed-loop system for the multiplicative output uncertainty \(M(s)[1+\Delta_{o}(s)]\) will be stable if the following requirements are met:  
\[\|\Delta_{o}(j\omega)\|<\frac{1}{\overline{\phi}_{2}} \tag{40}\]  
where \(\overline{\phi}_{2}\) is the MSV of the closed-loop system \(C(s)M(s)/(1+C(s)M(s))\).  
Examining the frequency graphs of Equations (38) and (40) will reveal the stability bounds of the closed-loop system. The stability of the system can be determined by looking at the area under the curve. As a result, it is simple to examine the controllers' stability. The controller that covers the biggest area beneath the curve will be the most stable one.
## Abbreviations  
ACC Accuracy  
AI Artificial Intelligence  
API Application Programming Interface  
CNN Convolutional Neural Network  
CPU Central Processing Unit  
DB Database  
DNA Deoxyribonucleic Acid  
DNN Deep Neural Network  
EHR Electronic Health Record  
FN False Negative  
FP False Positive  
GAC Geometric Active Contour  
GC-SCNN GrabCut-Stacked Convolutional Neural Network  
GMM Gaussian Mixture Model  
Im image  
ILSVRC ImageNet Large-Scale Visual Recognition Challenge  
ISIC International Skin Imaging Collaboration  
JSON JavaScript Object Notation  
LSTM Long Short-Term Memory  
M Melanoma  
ML Machine Learning  
NN Neural Networks  
NM Non-Melanoma  
OCR Optical Character Recognition  
ROI Region Of Interest  
REST Representational State Transfer  
RF Random Forest  
RNN Recurrent Neural Network  
SVM Support Vector Machine  
TN True Negative  
TP True Positive  
UV Ultraviolet Radiation  
VGG Visual Geometry Group  
XML Extensible Markup Language  
YOLO You Only Look Once
# Characteristics of Aeolian Dune, Wind Regime and Sand Transport in Hobq Desert, China
## 2 Study Area  
The Hobq Desert is located in the south bank of the Yellow River and the northern part of the Ordos Plateau in Inner Mongolia, with an area of \(1.56\times 10^{4}\) km\({}^{2}\). The desert is narrow in the east and wide in the west, with a length of about 300 km from the east to the west. Ten tributaries (cross-desert ephemeral tributaries) flow through the east and middle of the Hobq Desert to the Yellow River on its right bank (Figure 1). Set the most western tributary of the ten tributaries as the boundary, the eastern desert is with a width of 8-28 km and that of the western desert is 40-90 km. Influenced by the east Asian summer monsoon, the average annual precipitation of the Hobq Desert decreased from 400 mm in the east to 150 mm in the west [23]. The rainfall appears mainly in the form of rainstorms and concentrates from July to September. Strong winds and sandstorms often happen in winter and spring with an annual average frequency of 34.5 and 19.2 days at Dongsheng meteorological station. At Baotou meteorological station, they are 46.8 and 21.6 days respectively [24]. The average day of sandstorms from 1981 to 2013 in Linhe weather station is 20 days [25].  
Resultant drift potential (RDP) represents the net DP or the vectorial sum of the DP values in each compass direction. It is calculated as follows [6]:  
\[RDP=\left(C^{2}+D^{2}\right)^{0.5}, \tag{2}\]  
\[C=\sum\left(VU\right)\sin(\theta), \tag{3}\]  
\[D=\sum\left(VU\right)\cos(\theta), \tag{4}\]  
where \(VU\) represents the \(DP\) in each wind direction (in this paper, we grouped winds into 16 directions), in vector units, and \(\theta\) is the midpoint of each wind orientation class measured clockwise from 0\({}^{\circ}\) (north) [3].  
The resultant drift direction (RDD) represents the direction of net trend of sand drift. It is calculated as follows [6]:  
\[RDD=\arctan(C/D). \tag{5}\]  
Directional variability (RDP/DP) is the ratio of the resultant drift potential (RDP) to the drift potential (DP). RDP/DP values close to 1 indicate a narrowly unidirectional wind regime, with a single dominant drift direction, whereas values close to 0 indicate a multidirectional wind regime with multiple significant drift directions [3,6]. Specifically, if RDP/DP is less than 0.3, the directional category is complex or obtuse bimodal. If RDP/DP is between 0.3 and 0.8, that is obtuse bimodal or acute bimodal. If RDP/DP is greater than 0.8, that is wide or narrow unimodal [26].
## 3 Kalman Filter Algorithms for the Case of Correlated Noise Terms  
The task of state estimation consists of estimating the series of state vectors, \(\mathbf{x}_{t}\), \(t=1,\ldots,N\), for given data, \(\mathbf{y}_{t}\), and given model parameter matrices, \(\mathsf{A}_{t}\), \(\mathsf{C}_{t}\), \(\mathsf{R}_{t}\), \(\mathsf{Q}_{t}\), and \(\mathsf{S}_{t}\); an estimate of the initial state should also be available, e.g., at time \(t=0\), denoted as \(\mathbf{x}_{0}\). The classical solution to this task is given by a twofold recursive algorithm consisting of a forward pass through the data by the Kalman filter, followed by a backward pass by a smoother, such as the Rauch-Tung-Striebel (RTS) smoother [3; 21]. The estimates are given by a sequence of mean vectors and covariance matrices, corresponding to multivariate Gaussian distributions.  
The Kalman filter consists of a recursion in the forward direction through time. At each time point, first the predicted states, \(\mathbf{x}_{t|t-1}\), and the corresponding covariance matrices, \(\mathsf{P}_{t|t-1}\), are computed. Then, second, the filtered states, \(\mathbf{x}_{t|t}\), and the corresponding covariance matrices, \(\mathsf{P}_{t|t}\), are computed. The notation \(x_{t_{1}|t_{2}}\) is defined as an \"estimate of \(x_{t_{1}}\) based on the information available at time \(t_{2}\)\".  
Instead of formulating the recursion for the states \(\mathbf{x}_{t|t-1}\) and \(\mathbf{x}_{t|t}\) and the corresponding covariance matrices, \(\mathsf{P}_{t|t-1}\) and \(\mathsf{P}_{t|t}\), it may also be formulated for the information states, \(\mathsf{P}_{t|t-1}^{-1}\mathbf{x}_{t|t-1}\) and \(\mathsf{P}_{t|t}^{-1}\mathbf{x}_{t|t}\), and the corresponding information matrices, \(\mathsf{P}_{t|t-1}^{-1}\) and \(\mathsf{P}_{t|t}^{-1}\), leading to information filters. Again, new symbols shall be introduced:  
\[\mathsf{X}_{t_{1}|t_{2}}:=\mathsf{P}_{t_{1}|t_{2}}^{-1}\mathsf{X}_{t_{1}|t_{ 2}}\;,\quad\mathsf{F}_{t_{1}|t_{2}}:=\mathsf{P}_{t_{1}|t_{2}}^{-1} \tag{11}\]  
Depending on whether covariance matrices or information matrices are employed, there are covariance Kalman filters or information Kalman filters (the latter usually simply called information filters).
## 6 Conclusions  
The performance of several topology-based routing protocols is examined by using simulation in this paper under various simulation environments in the UANET context. Furthermore, by tuning parameters, this paper enhances the performance of an OLSR protocol called E-OLSR, which outperforms standard ones defined in RFC 3626. In performance matrices such as throughput, delay, and data dropped, E-OLSR outperforms four other routing protocols (AODV, OLSR, DSR, and GRP). A brief qualitative analysis of the preceding routing protocols is provided based on significant parameters such as mobility, traffic density, routing overhead, and data rate. This research will assist network engineers in picking the optimal routing protocol for various UANET implementation scenarios.  
Conceptualization, E.A.T. and M.G.; methodology, E.A.T.; software, E.A.T.; validation, J.-M.L. and D.-S.K.; simulation, E.A.T.; data curation, J.-M.L.; writing--original draft preparation, E.A.T. and M.G.; writing--review and editing, E.A.T.; supervision, J.-M.L. and D.-S.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
Figure 6: Average data drop rate comparison of different routing protocols. (**a**) Scenario 1. (**b**) Scenario 2. (**c**) Scenario 3. (**d**) Scenario 4.  
**Acknowledgments:** This work was supported by Priority Research Centers Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (2018R1A6A1A03024003) and by National Research Foundation of Korea (NRF) grant funded by Korea Government (MSIT) (2019R1F1A1064055) and Grand Information Technology Research Center support program(IITP-2022-2020-0-01612) supervised by the IITP(Institute for Information communications Technology Planning Evaluation).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Tracking of Moving Targets Through Asynchronous Measures
## 4 Numerical Results
### Ranging Uncertainty  
In this section, we have simulated the scenario at different levels of ranging performance, changing the root mean square ranging error \(\sigma_{\rho}\), with the following assumptions:  
1. The altitude of the target is \(h=100\,\mathrm{m}\);
2. The velocity of the target is \(v=10\,\mathrm{m}/\mathrm{s}\);
3. The radius of the orbit is \(r=1000\,\mathrm{m}\);
4. For all the anchors the LoS probability is \(p=100\%\).  
We can observe in Figure 4 that the proposed algorithm consistently outperforms the Kalman filter when the range measurements are very precise, while the performance gap gets smaller and smaller as \(\sigma_{\rho}\) increases. This is due to the fact that the distance traveled by the target during the time required to collect all the anchors (which is constant), gets more and more negligible with respect to \(\sigma_{\rho}\), as \(\sigma_{\rho}\) increases.
# Secrecy Enhancing Scheme for Spatial Modulation Using Antenna Selection and Artificial Noise
## 3 Secrecy-Enhancing Spatial Modulation Scheme
### Perfect Cancellation of AN  
Consider the fact that perfect CSI is hard to obtain in practice due to the estimation errors. The channel gains, \(\mathbf{h}\) and \(\mathbf{g}\), can be expressed by:  
\[\mathbf{h}=\sqrt{1-\rho^{2}}\mathbf{\hat{h}}+\sqrt{\rho^{2}}\mathbf{\hat{h}}, \tag{6}\]  
and:  
\[\mathbf{g}=\sqrt{1-\rho^{2}}\mathbf{\hat{g}}+\sqrt{\rho^{2}}\mathbf{\hat{g}}. \tag{7}\]  
Here, \(\mathbf{\hat{h}}\in\mathbb{C}^{1\times N_{a}}\) and \(\mathbf{\hat{h}}\in\mathbb{C}^{1\times N_{a}}\) represent the estimation and the estimation error of the main channel, i.e., from [PERSON] to [PERSON], respectively. On the other hand, \(\mathbf{\hat{g}}\in\mathbb{C}^{1\times N_{a}}\) and \(\mathbf{\hat{g}}\in\mathbb{C}^{1\times N_{a}}\) represent the estimation and the estimation error of the eavesdropping channel, i.e., from [PERSON] to [PERSON], respectively. \(\rho^{2}\) is the variance of estimation error, and it reflects the accuracy of the CSI.  
Inserting (6) into (4) leads to:  
\[\begin{split} y_{b}=&\sqrt{1-\rho^{2}}\mathbf{\hat{h }}\mathbf{T}_{k}\mathbf{e}_{j}(x_{m}+\beta_{1}\
u)+\sqrt{\rho^{2}}\mathbf{\hat {h  
Therefore, Eve will suffer from the interference due to the AN of \(\sqrt{1-\rho^{2}}\hat{g}_{j}{}^{\prime}\beta_{1}\
u+\sqrt{1-\rho^{2}}\hat{g}_{i}{} ^{\prime}\beta_{2}\
u\).
## 5 Results and Discussion
### Box Model Test Results
#### 5.3.2 Effects of the Reinforcements on Bearing Capacity of NMC Clay (Comparing Cases 1, 3, and 4)  
The effects of reinforcement on NMC clay were analyzed using Figure 19. The graph shows the BCR relationships of reinforced NMC clay and unreinforced NMC clay, highlighting the contribution of crushed gravel and composite geotextiles to the improvement of NMC clay's bearing capacity. The BCR beyond 1.0 implies a higher bearing capacity, while a BCR of less than 1.0 implies a lower bearing capacity, with respect to its dividend. The graph clearly shows that Case 4 has approximately two times higher BCR than Case 3.  
With the addition of composite geotextiles, the loading applied on the NMC clay and crushed gravel layers was distributed properly throughout the area. Hence, the vertical force exerted by the loading towards the gravel (top, 4 th layer) and the normal force of the NMC clay (3 rd layer) were transferred to the composite geotextile. The composite geotextile's tensile strength resisted the vertical displacement of the soil material. At the same time, the drainage effects and the frictional forces of the crushed gravel increased the load-bearing capacity of the NMC clay [3,6].  
Based on the typical values of bearing capacity for clays (BS 8004:1986) [25], the bearing capacity obtained by Case 1 depicted that the NMC clay, with 35% water content, is a soft clay. Therefore, when NMC clay is layered with crushed gravel (Case 3), the bearing capacity increased by 144% at 0.17\(B\). Case 3 exhibited a bearing capacity of 92 kPa at 0.17\(B\), which depicted that the NMC clay was as strong as a firm clay, despite having a high water content. This may imply that the reinforcement of crushed gravel increased the bearing capacity of the NMC clay, and so much more when the composite geotextile is used.  
Figure 19: The comparison of BCR for the (**a**) effects of composite geotextiles and (**b**) effects of crushed gravel and composite geotextiles.  
geotextiles were installed between layers of NMC clay and crushed gravel (Case 4). In that case, the bearing capacity increased by 373% and 94%, compared with Case 1 and Case 3, respectively. As with Case 4, the bearing capacity, which was 178 kPa, depicted that the NMC clay was as strong as a stiff clay, despite having high water content. This may imply that adding composite geotextiles between the NMC clay and crushed gravel layers increased the bearing capacity.
# Optimal Design of a Hybrid Solar PV/BG-Powered Heterogeneous Network
## 5 Performance Analysis
### Optimal System Architecture  
Table 2 summarizes the optimal size of different components for diverse system bandwidths under average solar radiation. In the HOMER software, the simulation was performed considering the dynamical behavior of renewable sources and data traffic. Due to the growing quantity of energy consumption, a larger solar PV panel size is necessary for higher system bandwidths (for macro- and micro-BS). In addition, the optimal size of the other system components remains unchanged as system bandwidth changes. This means that the proposed system can satisfy the energy demand of the BS under various bandwidth situations without altering system components on a significant scale.