esa-sceva/satcom-chunk-collection huggingface dataset:
# AOA-Based Three-Dimensional Multi-Target Localization in Industrial WSNs for LOS Conditions
###### Abstract  
High-precision and fast relative positioning of a large number of mobile sensor nodes (MSNs) is crucial for smart industrial wireless sensor networks (SIWSNs). However, positioning multiple targets simultaneously in three-dimensional (3D) space has been less explored. In this paper, we propose a new approach, called Angle-of-Arrival (AOA) based Three-dimensional Multi-target Localization (ATML). The approach utilizes two anchor nodes (ANs) with antenna arrays to receive the spread spectrum signals broadcast by MSNs. We design a multi-target single-input-multiple-output (MT-SIMO) signal transmission scheme and a simple iterative maximum likelihood estimator (MLE) to estimate the 2D AOAs of multiple MSNs simultaneously. We further adopt the skew line theorem of 3D geometry to mitigate the AOA estimation errors in determining locations. We have conducted extensive simulations and also developed a testbed of the proposed ATML. The numerical and field experiment results have verified that the proposed ATML can locate multiple MSNs simultaneously with high accuracy and efficiency by exploiting the spread spectrum gain and antenna array gain. The ATML scheme does not require extra hardware or synchronization among nodes, and has good capability in mitigating interference and multipath effect in complicated industrial environments.  
Keywords:wireless sensor network; localization; smart industrial; angle of arrival; maximum likelihood estimator; antenna array +
Footnote †: journal: Sensors
## 3 The Security Mechanism
### Overall Detection
Hence, the probability that the witness nodes number less then \(t\) is:  
\[\text{P}(|n|<t)=\sum_{i=0}^{t-1}\frac{(\rho\pi R^{2})^{t}}{t!}e^{-\rho\pi R^{2}} \tag{1}\]  
and the probability that the number of witness nodes is greater than \(t\) is:\[\mathtt{P}(|n|\geq\mathtt{t})=1-\mathtt{P}(|N|<t) \tag{2}\]  
The factor \(\mathtt{p}\) denotes node density; here \(\mathtt{p}=\frac{|\mathtt{N}_{\mathtt{anl}}|}{\mathtt{A}}\) (\(|\mathtt{N}_{\mathtt{anl}}|\) denotes the total number of nodes and \(\mathtt{A}\) denotes the area of the deployment region). We assume that the maximum distance is \(\xi\) when node \(u\) has moved out of MSA but still does not update its location-based private key. The time interval that node \(u\) applies for updating key once more is \(\Delta\mathtt{t}\). Assume that node \(u\) has sent \(\mathtt{n}\) requests about updating the key when it receives a new key. This means the probability that \(\mathtt{u}\) still does not update its key when it is in a position where is \(\Delta_{\mathtt{u}}+\xi\) away from the MSA is:  
\[\mathtt{P}_{u}=\left(\mathtt{P}(|n|<t)\right)^{n-1}(\mathtt{P}(|n|\geq \mathtt{t})=\left(\sum_{i=0}^{t-1}\frac{(\rho\pi R^{2})^{i}}{i!}e^{-\rho\pi R^{ 2}}\right)^{\mathtt{n-1}}(1-\sum_{i=0}^{t-1}\frac{(\rho\pi R^{2})^{i}}{i!}e^{- \rho\pi R^{2}}) \tag{3}\]  
The value of the probability \(\mathtt{P}(|\mathtt{n}|<\mathtt{t})\) always approaches zero unless the nodes in networks are very sparse. That is to say, nodes would update their location-based private keys immediately when they move out of MSA, because the probability that there are enough witness nodes in nodes' communication range is actually substantial. In other words, \(\xi\to 0\).
# A Comprehensive Survey for Deep-Learning-Based
## 4 Abnormality Detection with Images over Long Range
### Grid Anomaly Detection Based on SAR Images  
In this section, we will introduce abnormality detection with SAR images in a smart grid environment. As a basis, [PERSON] et al. [16] studied how a power line tower was visible in time-frequency decomposition results. [PERSON] et al. [17] used a time series of SAR images and studied the backscattering behavior of a power line segment with six conductors. This also makes it possible to use SAR images to detect power grid abnormalities, such as disaster monitoring. Finally, we try to list the works using satellite SAR in power grid detection, although these works are not many. [PERSON] et al. [18] discussed the use of SAR data to monitor power transmission towers in natural disaster conditions, by extracting the height of towers from single images and exploiting this information to detect collapsed or distorted towers. [PERSON] et al. [13] detected the frozen scene in an ultra-high power tower with satellite SAR images. They studied the ultra-high voltage (UHV) power tower target interferometry in TerraSAR Spotlight (TSX-SL)-mode SAR images and testified that the variation of the radar scattering intensity on the tower cross could be used to quantitatively evaluate towers' icing state. Similarly, some work has studied the settlement of the power grid and the movement of power towers [14;15] with satellite SAR images. [PERSON] et al. [15] applied the persistent scatterer interferometry (PSI) method to monitor power towers' movement with the SAR data from two satellites. Based on the large-scale coverage of Sentinel-1 SLC images in IW TOPS mode, [PERSON] et al. [14] utilized persistent scatterer interferometry to measure the ground target movements along transmission lines with millimeter accuracy. Their experiments showcase that the time series of transmission tower displacement is related to the seasons, among which winter and spring are relatively quiet. These works also prove that advanced InSAR technology has broad application prospects in the realization of smart grids.  
Interested readers are also referred to the summaries in Table 4, in which we provide general platforms that one can choose for one's own works. In the following, we will provide methodologies related to abnormality detection in smart grids.
## 2 Overview of Artificial Intelligence and Machine Learning in Road Transport Systems
### Detection of Road Accidents Using Wireless Technology
#### 2.6.2 6G in Road Transport  
For 6G to achieve the aforementioned lofty goals, it will be necessary to integrate various cutting-edge technologies, such as more dependable and effective air interfaces, resource management, decision making, and processing. For example, UAVs and low-earth orbit satellites can provide V2X systems with considerably enhanced and seamless coverage, helping to improve the communication quality of the service, especially in some potential blind spots that could exist in conventional terrestrial communication systems [94]. A 6G-V2X network is predicted to feature a significant number of connected vehicles in a dense environment, a more diverse variety of applications, a complex signal analysis compared to 5G-based V2X systems, and a greater demand for dependability and computational power. Because of this, 6G-V2X networks require more processing power than is typically provided by the network [74].  
Mobility is the maximum speed at which a mobile station can move while being supported by a network and receiving an adequate quality of experience (QoE) [95; 96]. The deployment scenario for high-speed trains is supported by the maximum mobility allowed by 5G, which is 500 km/h. Commercial aircraft systems' top speed in 6G is 1000 km/h [97].  
In meeting the further stringent key performance indicators (KPIs), which only partially satisfied 5G for vehicle communication [98], and accomplishing the challenging objectives for IoV, 6G is essential [95; 99].  
After that, 6G technology allows these remote sensors to communicate with the cloud platform while maintaining a reliable data stream. Finally, it is possible to integrate the large-scale traffic-sensing data to produce a useful reference for traffic management [28], where 6G will serve as a powerful enabler and catalyst for CAV's key services. The primary use of 5G is for communications (either human-to-human or machine-to-machine). Beyond communication, 6G will do more. Radio frequency (RF) has been studied as a potential tool for positioning and object sensing. Our opinion is that 6G mobile systems, also known as CCPS for short, make it easier for missions to combine communications (C), computing (C), positioning (P), and sense (S) to meet service/application needs while being deployed at a wide and affordable scale [100].
# Research on Improving Satellite Positioning Precision Based on Multi-Frequency Navigation Signals
## 3 Experimental Data and Analysis Indicators
### Analysis Indicators
#### 3.3.3 Standard Deviation (STD)  
The standard deviation is used to reflect the degree of dispersion of the positioning results, that is, the difference between the calculated estimated value and the mean of the estimated value. The calculation formula is:  
\[\text{STD}=\sqrt{\frac{\sum_{i=1}^{n}\left(x_{i}-\overline{x}\right)^{2}}{n}} \tag{24}\]  
Figure 6: The situation of the visible satellites at the Fangshan station in Beijing on 8 September 2021.  
where i is the epoch time; n is the total epoch; x\({}_{\text{i}}\) is the estimated value calculated at the i-th epoch time; and \(\overline{\text{x}}\) is the average of the estimates computed at all epochs.
# Personal Identification Using Embedded Raspberry Pi-Based Face Recognition Systems
## 2 Materials and Methods  
### Characteristics Facial Features  
The face is the main feature of appearance that distinguishes us from each other. In most cases, this is what we remember when we meet new people, and we associate them mainly with this feature of their appearance. Each of us has probably had a situation when we met a person on the street whose face seemed familiar, but we could not remember his name and surname. It is this feature of appearance that we instinctively pay attention to first to identify a person. The human mind can recognize a face even from a distance when details are blurred. This means that the shape, size, or arrangement of organs on the face are sufficient to identify it. We can easily describe the characteristics of organs by locating characteristic points on the face image. Point selection is crucial for face recognition. We should choose those that represent the most important facial biometric features and are easy to \"extract\" from the photo.  
There are two groups of biometric features (Figure 1):  
(a) Anthropometric:  
- Distance between the nose and eyes;  
- Distance between the centers of the eyes;  
- Distance between the mouth line and the eye line;  
- Distance between the farthest points of the eyes;  
- Distance from the center of the mouth to the farthest point of the eye.  
(b) Geometric:  
- Lip shape;  
- Nose shape;  
- Chin shape;  
- Forehead shape;  
- Shape of the ears;  
- Face oval.  
Figure 1: Examples of characteristic points representing facial biometric features [56].  
By comparing biometric features in a group of people, we are able to determine the degree of similarity between them [1]. Several factors come into play when comparing the same face in two different photos. First, it is essential to select appropriate facial landmarks. Additionally, ensuring consistent environmental conditions--such as background and lighting--is crucial. The face's spatial orientation in the photo matters too. To achieve accurate comparisons, an algorithm performs normalization procedures. These include rotations around the X, Y, and Z axes, displacement in the XY plane (Figure 2), and rescaling of the examined image. However, remember that these additional operations increase computational demands, potentially impacting system efficiency [4].  
The critical issue is determining the appropriate tolerance within which the examined image may deviate from the norm, which involves establishing the limits within which anthropometric distances and facial contours may differ [36].
## 2 Methodology  
### Grouting Method and Slurry Material  
The schematic diagram of cyclic grouting is shown in Figure 1. Under medium-to-high pressure, the grout within the annular space is injected into the soil, and the portion of grout that is not absorbed by the soil will be returned to the grout storage tank. This method creates a circulation of slurry flow within the grouted borehole, preventing the clogging of slurry particles. In order to mitigate the size effect, the grouting area is chosen to be 20 m \(\times\) 20 m [2]. The diameter of the borehole is 96 mm. The currently commonly used grouting method is the first type, steady pressure. Recently, there has been an emergence of an intermittent pressure method to introduce pulsations. However, this method is difficult to control and has low flow rates. Therefore, this paper proposes the adoption of a high-frequency pulsating pressure grouting method.  
Cement slurry is a common particle grouting material with high strength, low cost, and wide applicability [12; 13]. Due to the long length of the grouting pipe during cyclic grouting, cement slurry typically employs a high water-to-cement ratio to ensure a certain level of fluidity. When the water-cement ratio is high, cement slurry can be considered as Newtonian fluid. Therefore, the rheological model of cement slurry in this paper adopts the Newtonian fluid model. And the soil splitting has been overlooked; the grouting mechanism was simplified as penetration grouting. The expression of the Newtonian fluid model is given in Equation (1) [1].  
\[\tau=\mu\cdot\gamma \tag{1}\]  
where \(\tau\) is the shear stress, \(\mu\) is the dynamic viscosity, \(\gamma\) is the shear rate.
## 2 Model Systems and Computational Methods
### The Exact Diagonalization Method  
The many-electron states in the QI are solutions to the stationary Schrodinger equation \(\mathcal{H}\Psi_{n}=E_{n}\Psi_{n}\), with the many-body Hamiltonian:  
\[\mathcal{H}=\sum_{a}\epsilon_{a}c_{a}^{\dagger}c_{a}+\frac{1}{2}\sum_{abcd}V_{ abcd}c_{a}^{\dagger}c_{b}^{\dagger}c_{a}c_{c}, \tag{2}\]  
where \(\epsilon_{a}\) are the energies of the single-particle states and the \(V_{abcd}\) coefficients are two-center integrals corresponding to the Coulomb interaction:  
\[V_{abcd}=\int d\mathbf{r}\int d\mathbf{r^{\prime}}\sum_{c_{s},\rho_{s}^{*}} \phi_{a,\sigma_{s}^{*}}^{*}(\mathbf{r})\phi_{b,\sigma_{s}^{*}}^{*}(\mathbf{r}^ {\prime})\frac{e^{2}}{4\pi\epsilon_{0}e_{r}}\frac{1}{|\mathbf{r}-\mathbf{r^{ \prime}}|}\phi_{c,\sigma_{s}}(\mathbf{r})\phi_{d,\sigma_{s}^{\prime}}(\mathbf{ r}^{\prime}). \tag{3}\]  
The one-particle states \(\{\phi_{a,\sigma_{s}}\}\) are determined as solutions to the one-particle Hamiltonian, which includes an external confinement potential. This orthonormal set is used to form the \(N\)-particle basis, i.e., the Slater determinants \(\{\psi_{k}\}\), used to solve the interacting Hamiltonian.  
The ground state of the interacting system can be written as a linear combination of Slater determinants:  
\[\Psi_{0}(\mathbf{r_{1}},s_{1},\ldots,\mathbf{r_{N}},s_{N})=\sum_{k}C_{0k}\psi _{k}(\mathbf{r_{1}},s_{1},\ldots,\mathbf{r_{N}},s_{N}), \tag{4}\]where \(C_{0k}\) coefficients are determined from the eigenvalue problem corresponding to the many-body Hamiltonian defined in Equation (2). The observable quantity is the ground state charge density, which can be obtained from:  
\[n_{0}(\mathbf{r}) = N\sum_{s_{1}}\cdots\sum_{s_{N}}\int d\mathbf{r}_{2}\cdots\int d \mathbf{r}_{N}|\Psi_{0}(\mathbf{r},s_{1},\mathbf{r}_{2},s_{2},\ldots,\mathbf{r }_{N},s_{N})|^{2} \tag{5}\] \[= \sum_{k}|C_{0k}|^{2}\sum_{p=1}^{N}\Big{[}|\phi_{i_{p}(k),\uparrow} (\mathbf{r})|^{2}+|\phi_{i_{p}(k),\downarrow}(\mathbf{r})|^{2}\Big{]},\]  
where \(\phi_{i_{p}(k),\uparrow}(\mathbf{r}),\phi_{i_{p}(k),\downarrow}(\mathbf{r})\) are the orbitals corresponding to the \(k\)-th Slater determinant.
## 2 Investigated Battery Module
### Experimental Setup
#### 2.1.4 Electronic Power Supply/Load  
The electronic load and power supply used in this study were the Electro-Automatik (EA) heavy duty laboratory Direct Current (DC) load and power supply. The versions used in conducting the experiments--the EA-EL 9200 electronic load capable of an output of 4000 W and the EA-PSI 9200--operated at efficiency of up to 95.5% (see Figure 12).  
To test the battery pack module for charge and discharge cycles during the experiments, 50 Amps-rated cables were used in connection between the battery pack, the electronic load and power supply.  
As testing for two current ratings on the battery pack module (0.5 C and 0.75 C) were to be investigated, the load and power supply were programmed to a constant current rating of 0.5 C,  
Figure 11: Battery vent configuration, inlet Fans I and II, outlet fans III and IV.  
Figure 12: EA electronic power supply and load.  
and the maximum and minimum voltages set to fit the specification of the battery pack maximum and minimum cut off voltages (see Table 2). The Graphic User Interface (GUI) of the load/supply provided real-time voltage and current readings of the battery pack for monitoring purposes during the charge/discharge cycles.
## 3 Results
### Site Heterogeneity Index Application  
Overall, we found the SHI for all DBF and ENF sites and for WSA sites (US-SRM) and the two OSH sites were zero, indicating site homogeneity. SHI at the other sites was \(>\)0, indicating site heterogeneity  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Site ID** & **PFT** & **Average*1000** & **Stdev** & **Max** & **Min** & **Amplitude** \\ \hline DE-Hai & DBF & \(-\)0.002 & 0.003 & 0.008 & \(-\)0.007 & 0.012 \\ US-Bar & DBF & \(-\)0.026 & 0.005 & 0.015 & \(-\)0.010 & 0.021 \\ US-MOz & DBF & \(-\)0.008 & 0.003 & 0.006 & \(-\)0.006 & 0.011 \\ BR-Sa3 & EBF & \(-\)0.001 & 0.004 & 0.004 & \(-\)0.005 & 0.017 \\ CA-NS5 & ENF & 0.008 & 0.002 & 0.004 & \(-\)0.006 & 0.010 \\ DE-Tha & ENF & 0.021 & 0.004 & 0.007 & \(-\)0.004 & 0.019 \\ DE-Wet & ENF & \(-\)0.006 & 0.003 & 0.006 & \(-\)0.007 & 0.010 \\ FI-Hyy & ENF & 0.005 & 0.003 & 0.005 & \(-\)0.006 & 0.010 \\ NL-Leo & ENF & 0.007 & 0.001 & 0.002 & \(-\)0.004 & 0.005 \\ DE-Meh & GRA & \(-\)0.001 & 0.003 & 0.007 & \(-\)0.005 & 0.010 \\ US-Fpe & GRA & 0.004 & 0.002 & 0.006 & \(-\)0.004 & 0.008 \\ US-Goo & GRA & 0.005 & 0.004 & 0.010 & \(-\)0.006 & 0.011 \\ CA-NS6 & OSH & 0.004 & 0.006 & 0.013 & \(-\)0.009 & 0.020 \\ CA-NS7 & OSH & 0.021 & 0.008 & 0.015 & \(-\)0.017 & 0.031 \\ US-SRM & WSA & 0.003 & 0.002 & 0.003 & \(-\)0.003 & 0.005 \\ \hline \hline \end{tabular}
\end{table}
Table 4: _Cont._  
Figure 4: Example output of in situ Fourier series model fitted to NDVI (NDVI\({}_{\text{is}}\)) time series data of: (**a**) a deciduous broadleaf forest (BDF); (**b**) a cropland (CRO); (**c**) an evergreen needle leaf forest (ENF); and (**d**) an evergreen broadleaf forest (EBF). Blue circles represent raw NDVI\({}_{\text{is}}\) data and red lines represent Fourier series fitted NDVI\({}_{\text{is}}\) data. Filled dots represent MODIS NDVI time series at different spatial resolutions (0.5 \(\times\) 0.5 km: Green; 1.5 \(\times\) 1.5 km: Orange; 2.5 \(\times\) 2.5 km: Dark green; and 3.5 \(\times\) 3.5 km: Black).  
(Table 5). SHI was particularly high at the US-Ne1 and US-Ne2 crop sites. This could be related to the fact that the SHI was developed over an area of \(7\times 7\) km, not considering the different crop parcels in this area.
# Weed Detection in Wheat Crops Using Image Analysis and Artificial Intelligence (AI)
## 3 Materials and Methods  
This research is carried out on the dataset collected from PMAS Arid Agriculture University research farm Koont Chakwal during 2020-21, with latitude N 33.7\({}^{\prime}\)11.604\({}^{\prime\prime}\) and longitude E 7\({}^{\prime}\)3.051.996\({}^{\prime\prime}\), to detect weeds in wheat crops through image analysis and artificial intelligence (AI).
## 2 Method and Data
### Models
#### 2.1.2 The MODIS Algorithm (MOD17)  
As the first regular, near-real-time data sets for repeated monitoring of vegetation primary production on vegetated land at 1-km resolution and an eight-day interval [25,49,50], the MODIS primary production products were applied for comparison with GIMMS NDVI3g based GPP. The MODIS algorithm (MOD17) also uses the concept of the light use efficiency. In the model, the actual light use efficiency (\(\varepsilon\)) calculation includes two parts: (1) the biome-level maximum light use efficiency estimate and (2) the environmental stress scalars:  
\[\varepsilon=\varepsilon_{\mathrm{max}}\times\mathit{TMIN}_{\mathit{scalar}} \times\mathit{VPD}_{\mathit{scalar}} \tag{6}\]  
The potential light use efficiency (\(\varepsilon_{\mathrm{max}}\)) is optimized in advance with a complex ecosystem model, BIOME-BGC, and is given in a Biome Parameter Look-up Table (BPLUT) for each land cover type in the PSN model. The \(\mathit{TMIN}_{\mathrm{scalar}}\) and \(\mathit{VPD}_{\mathrm{scalar}}\) are environmental stress factors and are parameterized according to Equations (7) and (8):  
\[\mathit{TMIN}_{\mathit{scalar}}=\begin{cases}1&\mathit{TMIN}<\mathit{TMIN}_{ \mathrm{max}}\\ \mathit{(TMIN-TMIN_{\mathrm{max}})}/\mathit{(TMIN_{\mathrm{max}}-TMIN_{ \mathrm{max}})}&\mathit{TMIN_{\mathrm{min}}<\mathit{TMIN}<\mathit{TMIN}_{ \mathrm{max}}\\ 0&\mathit{TMIN}>\mathit{TMIN}_{\mathrm{min}}\end{cases} \tag{7}\]  
\[\mathit{VPD}_{\mathit{scalar}}=\begin{cases}1&\mathit{VPD}<\mathit{VPD}_{ \mathrm{max}}\\ \mathit{(VPD-VPD_{\mathrm{min}})}/\mathit{(VPD_{\mathrm{max}}-VPD_{\mathrm{ min}})}&\mathit{VPD}_{\mathrm{min}}<\mathit{VPD}<\mathit{VPD}_{ \mathrm{max}}\\ 0&\mathit{VPD}>\mathit{VPD}_{\mathrm{min}}\end{cases} \tag{8}\]  
where \(\mathit{TMIN}\) and \(\mathit{VPD}\) are the daily minimum temperature ( \(\mathrm{\SIUnitSymbolCelsius}\)) and average vapor pressure deficit (Pa), \(\mathit{TMIN}_{\mathrm{max}}\) and \(\mathit{VPD}_{\mathrm{max}}\) are the daily minimum temperature and average vapor pressure deficit at which \(\varepsilon=\varepsilon_{\text{max}}\), and \(\text{{TMIN}}_{\text{min}}\) and \(\text{{VPD}}_{\text{min}}\) are the daily minimum temperature and average vapor pressure deficit at which \(\varepsilon=0\). These parameters were determined for each land cover type in the BPLUT.  
The main data inputs into the MOD17 model include: (1) FPAR and LAI from the MODIS MOD15 LAI/FPAR data products; (2) temperature, incoming solar radiation, and vapor pressure deficit from NCEP/DOE reanalysis II [51]; (3) land cover classification from the MODIS MCD12Q1 data product; and (4) a Biome Parameter Look-up Table (BPLUT) containing values of \(\varepsilon_{\text{max}}\) and other biome-specific physiological parameters for different vegetation types.