esa-sceva/satcom-chunk-collection huggingface dataset:
# UWB-Based Real-Time Indoor Positioning Systems: A Comprehensive Review
## 4 Commercial UWB Modules and Chips for IPSs
### Qorvo DW3000 UWB Family  
The DecaWave DW3000 transceiver module (Qorvo Inc., Greensboro, NC, USA) is the advanced version of the previous family DW1000 [32]. The module is an IEEE 802.15.4-2011 [33] and IEEE 802.15.4z [34] UWB module based on the Qorvo DW3000 chips, which include four different versions (3110, 3120, 3210, and 3220) depending on the package and the AoA/PDoA measurement capability. It is a fully integrated UWB transceiver with a ceramic antenna, clock circuit, power management, and all required RF circuits in one module. The module has high multipath fading immunity and can be used in two-way ranging (TWR) or time difference of arrival (TDoA) location algorithms to execute real-time tracking and positioning tasks. It supports channels 5 and 9 (6489.6 MHz and 7987.2 MHz) and data rates of 850 kbps and 6.8 Mbps. This module can be integrated with any host processor for IPS implementation and supports SPI interfaces of up to 38 MHz. It is interoperable with the Apple U1 and U2 chips and supports high tag densities in real-time location systems.
## 6 Data Transfer and the Creation of a 3D Model for VR and AR
### Results IV--VR/AR Workflows  
This study resulted in workflows for displaying Large Scale 3D models in VR and processing 3D models for use in AR. Over the course of the project, as it was a longer project, workflows were innovated and redesigned to be more efficient, helped by notable developments in the software used: Agisoft Metashape and Unreal Engine 5. The research showed the possibilities of modern visualization of 3D models in VR and AR and their potential to render a high-quality 3D model. Based on this research, it is possible to create a visualization of any monument that is correctly and well captured following the published procedure.
## 3 Results
### Effects of CDHEs on Forest Carbon Dynamics
#### 3.2.5 Forest Carbon Sequestration Change Caused by CDHEs  
The overall effect of CDHEs on the change in carbon sequestration in the two regions was manifested as net carbon loss. In Table 5, the CDHEs in Nanjing caused the most significant carbon loss (\(-\)0.0844 TgC) in August 2022 and the most minor (\(-\)0.0374 TgC) in June 2005. The CDHEs in Shaoguan caused the most significant carbon loss (\(-\)1.456 TgC) in July 2003 and the most minor (\(-\)0.705 TgC) in September 2021. Overall, CDHEs in Shaoguan caused  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Year/Month** & **Interaction Modes** & **Nanjing** & **Shaoguan** \\ \hline
2003/07 & D\(\cap\) H & & 0.225 \(\bigtriangledown\) \\
2005/06 & D\(\cap\) H & 0.298 \(\bigtriangledown\) & \\
2017/07 & D\(\cap\) H & 0.294 \(\bigtriangledown\) & \\
2021/09 & D\(\cap\) H & & 0.206 \(\diamond\) \\
2022/08 & D\(\cap\) H & 0.361 \(\bigtriangledown\) & 0.242 \(\bigtriangledown\) \\ Average & & 0.318 & 0.224 \\ \hline \hline \end{tabular} Note: \(\bigtriangledown\) represents a nonlinear-enhanced mode; \(\diamond\) represents a bi-factor-enhanced mode.  
\end{table}
Table 4: Interaction modes of the impacts of different drought and heat events on carbon dynamics.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Region** & **q** & **No Lag** & **1-Month Lag** & **2-Month Lag** & **3-Month Lag** \\ \hline Nanjing & q (drought) & 0.140 & 0.147 & 0.174 & 0.165 \\  & q (heat) & 0.112 & 0.140 & 0.169 & 0.149 \\ Shaoguan & q (drought) & 0.083 & 0.106 & 0.129 & 0.118 \\  & q (heat) & 0.089 & 0.098 & 0.149 & 0.111 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Lagged effects of drought and heat events in Nanjing and Shaoguan.  
a total carbon loss of \(-3.219\) TgC, much more significant than that in Nanjing (\(-0.186\) TgC) during 2000-2022. Based on Figure 9, Figure 10, and Figure S1, we found that the higher the intensity of CDHEs, the greater the forest carbon loss.
## 3 Results
### Response Mechanism of GPP to Extreme and Normal Climate Conditions  
#### 3.3.1 Temporal Lag Effects of GPP on Extreme and Normal Climate Indices  
The correlations and lag effects between GPP and normal climatic factors (T\({}_{\rm max}\), T\({}_{\rm min}\), PP) as well as 27 Extreme Climate Indices (ECI) from 2001 to 2023 are illustrated in Tables 2-4. In these tables, \"0\" denotes the correlation between the original GPP and each variable, whereas \"1-3\" indicates the correlations with a lag of 1 to 3 months, respectively. The results reveal that GPP exhibits varying degrees of lag in response to both normal and extreme climatic conditions. For normal climatic conditions, GPP demonstrates a 2-month lag in response to the normal climatic factor precipitation (PP) and a 1-month lag to maximum temperature (T\({}_{\rm max}\)), whereas its response to minimum temperature (T\({}_{\rm min}\)) is more immediate, as shown in Table 2.  
Regarding ECI, GPP exhibits immediate responses to most extreme temperature indices (Table 3), except for the Diurnal Temperature Range (DTR), which shows a 3-month lag, and the maximum daily maximum temperature (T\({}_{\rm XX}\)) and maximum daily minimum temperature (T\({}_{\rm N\rm X}\)), which display lags of 2 months and 1 month, respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Indices** & **Lag Time** & **0** & **1** & **2** & **3** \\ \hline PP & 0.88 *** & 0.916 *** & 0.79 *** & 0.921 *** \\ Tmax & 0.78 *** & 0.79 *** & 0.74 *** & 0.69 *** \\ Tmin & 0.81 *** & 0.80 *** & 0.74 *** & 0.71 *** \\ \hline \end{tabular} The results of the significance test are displayed as follows: ***: \(p\)\(<\) 0.001.  
\end{table}
Table 2: The lag coefficient of GPP with normal climate indices.  
In terms of extreme precipitation indices, GPP demonstrates varying lagged responses (Table 4): Consecutive Wet Days (CWD) and Consecutive Dry Days (CDD) exhibit the strongest response at 0 months, whereas moderate precipitation days (R\({}_{10}\)), extreme heavy precipitation (R\({}_{99P}\)), and the maximum 5-day precipitation (R\({}_{X5\text{day}}\)) show a 1-month lag. Additionally, heavy precipitation days (R\({}_{20}\)), very heavy precipitation days (R\({}_{25}\)), total precipitation (P\({}_{\text{RCPTOT}}\)), heavy precipitation (R\({}_{95P}\)), and the maximum 1-day precipitation (R\({}_{X1\text{day}}\)) display a 2-month lag. The longest lag time, 3 months, is observed for daily precipitation intensity (SDII).
# Simulated Annealing for Resource Allocation in Downlink NOMA Systems in 5G Networks
## 1 Introduction  
### Preliminaries  
In wireless communication, each generation from 1 st generation (1G), 2 nd generation (2G), 3 rd generation (3G), 4 th generation (4G), and 5 th generation (5G) is required to adopt different multiple access techniques to satisfy the system capacity and data traffic demand. The ever-increasing demand on data traffic for mobile users, while exacerbating the challenges of the spectrum and resource allocation problem in the 5G networks [1, 2, 3]. 5G networks require a technology that can meet this massive connectivity, high data rate, and high spectral efficiency demand. In previous network generations, the traditional orthogonal multiple access (OMA) can serve multiple users using the available orthogonal resources. These orthogonal resources are restricting the number of users employed in the system. Thus, OMA is an inadequate system to satisfy the requirements of the 5G network. Currently, non-orthogonal multiple access (NOMA) has been developed to overcome the inadequacy of OMA to meet 5G network requirements. NOMA is a promising technique, where multiple users can share the frequency, code, or time with different power levels in NOMA power domain multiplexing. Hence, NOMA technologies apply signal interference cancellation (SIC) at one of the receiving ends to mitigate the multiplexed users' interference. NOMA technologies have enormous potential advantages in providing low latency, supporting more devices, allowing massive connectivity, and providing better throughput fairness [4].  
The NOMA's full potential requires an optimal resource allocation such as user pairing, channel power allocation, and sufficient power for each multiplexed user on the samechannel. Furthermore, an overview of the literature related to the resource allocation schemes in NOMA is presented in the subsection.
## 8 Conclusions  
The study results led to the following conclusions:  
1. The downstream soils of the Tiga Dam in Nigeria, primarily composed of low-plasticity clays with high silt content (mean \(>\) 50%), exhibit good compaction characteristics (75% have MDD \(>\) 1.80 Mg/m\({}^{3}\)) but varying permeability (66.67% k \(<\) 10\({}^{-8}\) m/s) and shear strength (c: 1.4-17.9 kN/m\({}^{2}\), \(\varphi\): 4.6-24.7\({}^{\circ}\)), indicating a significant risk of internal erosion that could compromise the dam's safety.
2. The Tiga Dam in Kano State, Nigeria, shows concerning stability issues, particularly during drawdown scenarios. During rapid drawdown, the Factor of Safety (FOS) is just 1.006, barely above the failure threshold of 1.0. Similarly, during the slow drawdown, the FOS is an alarmingly low 1.002. These values are significantly below the recommended FOS of 1.5 by the US Army Corps of Engineers for steady-state seepage conditions, indicating high vulnerability during any drawdown event.
3. The dam's downstream soil exhibits varying levels of erodibility, with most samples falling into low to medium erodibility categories, with soil erodibility factor values ranging from 0.055 to 0.145, suggesting it is likely susceptible to erosion. This variability in soil erodibility across the downstream area highlights the need for targeted erosion control measures in high-risk zones.
4. The dam's downstream soil shows significant seasonal expansion patterns, with the highest expansion rates consistently occurring during summer (June, July, and August)--the expansion rates range from near 4 to about 19.45 mm/month. Additionally, there is a gradual increase in peak expansion rates over the years, possibly indicating long-term changes in soil properties or environmental factors like climate change.  
Methodology, H.L. and A.A.; laboratory experiments, I.M.S. and J.I.H.; validation, I.H.U. and H.L.; formal analysis, I.M.S.; writing--original draft, J.I.H., I.M.S. and I.H.U.; writing--review and editing, I.H.U., A.A. and H.L. All authors have read and agreed to the published version of the manuscript.  
This paper gets its funding from Projects (42277175) supported by the National Natural Science Foundation of China; Project (NRMSSHR-2022-Z08) supported by the Key Laboratory of Natural Resources Monitoring and Supervision in Southern Hilly Region, Ministry of Natural Resources; Project (2023 JJ30657) supported by the Hunan Provincial Natural Science Foundation of China; the Guizhou Provincial Major Scientific and Technological Program (2023-425); and the Hunan Provincial Department of natural resources geological exploration project (BSDZSB43202403). The authors wish to acknowledge these supports.  
Some or all data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors wish to acknowledge the support provided by the funding agencies mentioned in the Funding Statement.  
On behalf of all authors, the corresponding author states that there are no conflicts of interest.
## 2 Materials and Methods
### EMAPs with Spatial Information  
The morphological profiles (MPs) and attribute profiles (APs) were determined to merge the spatial and spectral information to assist classification. The attribute filters (AFs)for computing APs, using gray level images were defined in a mathematical morphological framework that operated by combining components at different threshold levels. The filtering operation applied on grayscale images with given threshold values checks whether the current component is going to merge lower gray levels or otherwise. Here, merging of components to lower gray level means thinning, and higher gray level means thickening. In this technique, the entire feature (spectral band) extracted from the original pixel was not considered, and several principal components are taken for this technique and the remaining components are ignored. This yields extended attribute profiles (EAPs). In this research work, the first three principal components are considered that hold most of the associated variance. The stacked EAPs, using different types of attributes, are called extended multi-attribute profiles (EMAP). These are used to enhance extractability related to spatial features from a landscape.
## 7 Underwater Localization Techniques
### Localization with AUV (AAL)  
An AAL-based strategy is recommended for a hybrid 3D UASN with stationary underwater sensor nodes and an AUV moving across the UASN sector. The AUV can use dead reckoning to find its location underwater. The expensive inertial piloting system allows for dead reckoning, and the position is regularly calibrated. To acquire GPS coordination from a satellite, the AUV occasionally makes its way to the water's surface. A wake-up message may be transmitted from a different place along the AUV's moving route at any time during its operating cycle. The AUV receives this signal as the underwater sensor node, which initiates the localization by sending a request signal. The underwater node may measureits location by using the alteration process after exchanging messages from three different non-coplanar AUV locations owing to the two-way technique. It was offered by the pair of requests and responses and the return packet's inclusion of the AUV coordinates. While AAL uses a two-way display to do away with the need for synchronization, a sensor node may consume more energy on a quiet algorithm than necessary, raising the protocol's overhead communication. The localization calibration frequency of the AUV has an impact on the AAL accuracy as well.
## 2 Materials and Methods
### Carbon Monoxide and Black Carbon Data  
The Sentinel-5 Precursor satellite launched on 13 October 2017, carrying the Tropospheric Monitoring Instrument (TROPOMI), provides daily global information on the concentrations of key atmospheric constituents, including carbon monoxide (CO) [73]. TROPOMI observes the global atmosphere daily with a fine resolution of 7 km \(\times\) 3.5 km [74] and enables the detection of even smaller CO plumes. In this study, we used TROPOMI Offline stream through the GEE code editor to assess the CO column density over the southern Cape forestry region where forest fires between late October and early November 2018 destroyed plantations near the town of George.  
Black carbon surface mass concentration data obtained from the Modern Retrospective Analysis for the Research Application (MERRA-2) model were used in this study. Additionally, this is because black carbon is released by incomplete combustion of biomass [75], and it was employed here to illustrate its presence over the fire-affected area.
## 2 Basic Theoretical Parameters of FE-SEA Method
### Modal Density
#### 2.1.1 Lateral Vibration Modal Density of One-Dimensional Beam  
Modeling of one-dimensional elements is often encountered in hybrid models. In the process of modeling, the transverse vibration of a one-dimensional beam is applied more often, and its free state vibration equation is [8; 9; 10; 11; 12]:  
\[\frac{\partial^{2}u(x,t)}{\partial t^{2}}+R^{2}C_{l}^{2}\frac{\partial^{4}u(x,t)}{\partial t^{4}}=0 \tag{1}\]  
where, \(u(x,t)\) is the displacement of point \(x\), \(R\) is the radius of rotation at point \(x\), \(C_{l}\) is the longitudinal wave velocity, and:  
\[C_{l}=\sqrt{\frac{E}{\rho}} \tag{2}\]  
In the above formula, E is the elastic modulus of the material and \(\rho\) is the material density. In Equation (1), \(u(x,t)=\varphi(x)e^{i\omega t}\), then the equation can be rewritten as [9; 10; 11; 12; 13]:  
\[\frac{\partial^{4}\varphi}{\partial x^{4}}-K_{B}^{4}\varphi=0 \tag{3}\]  
In the above formula, \(K_{B}\) is the number of bending waves, and:  
\[K_{B}=\sqrt{\frac{\omega}{RC_{l}}}=\frac{\omega}{\sqrt{\omega RC_{l}}}=\frac{ \omega}{C_{B}} \tag{4}\]  
In the above equation, \(C_{B}=\sqrt{\omega RC_{l}}\) is the bending wave velocity, then the modal density of the transverse vibrating beam is expressed as:  
\[n(\omega)=\frac{l}{2\pi C_{B}} \tag{5}\]  
\[n(f)=2\pi n(\omega)=\frac{l}{C_{B}} \tag{6}\]
# A Fast Parallel Random Forest Algorithm Based on Spark
## 2 Preliminary Knowledge  
### Random Forest Algorithm  
The random forest algorithm is an ensemble classification algorithm. First, it randomly extracts \(K\) different training subsets from the original data set to construct \(K\) decision trees. Next, all the decision trees are integrated into a random forest, and the final classification decision depends on the voting of decision trees.  
Given a data set \(S\) with \(M\) features, the construction steps of Random forest are as follows:  
Step 1, Data sampling. Randomly select \(K\) training subsets \(\{S_{1},S_{2},\ldots,S_{K}\}\) of the same size as the original data set from the training data set \(S\).  
Step 2, Constructing decision trees. These decision trees are constructed recursively by C4.5 or CART algorithms from the corresponding training subset. For any subset \(S_{i}\), \(m\) (\(m\ll M\)) feature variables are randomly selected first. In the next process of node split, all the Gini coefficients of a feature are calculated to find the optimal split point. This split process is repeated until a leaf node is generated. Finally, \(K\) decision trees are trained in the same way from \(K\) training subsets.  
Step 3, Voting decision. Combine \(K\) trained decision trees \(\{h_{1}(S_{1}),h_{2}(S_{2}),\ldots,h_{k}(S_{k})\}\) into a random forest model. The classification decision of RF model depends on voting among trees, and the most popular voting method is simple majority voting.
# Multiservice-Based Traffic Scheduling for 5G Access Traffic Steering, Switching and Splitting
## 2 System Model
### Network Model  
The channel gain between UE, \(i\), and the intended receiver, which depends on several factors, such as shadowing, path loss, and fading, is represented as \(h_{i,l}\) (\(h_{i,w}\)). We ignored the band allocation differences between cell-edge users and central users. The WLAN adopted the carrier sense multiple access/collision avoidance (CSMA/CA) mechanism, and the main purpose of converging the WLAN was to reduce the burden on the NR network. To better manage intra-cell interference, we assumed that the NR dedicated links may suffer intra-cell interference due to frequency reuse, but WLAN links operate on channels that are not on the same frequency as NR. Therefore, only limited noise is present. For UE, \(i\), with \(x_{i}^{(l)}\) and \(x_{i}^{(w)}\) as the unit-power complex-valued input symbols sent over the dedicated and adaptive links, respectively, the output symbols \(y_{i}^{(l)}\) and \(y_{i}^{(w)}\) can be expressed as follows [33]:  
\[\begin{split} y_{i}^{(l)}&=h_{iJ}\sqrt{P_{i}^{(l)}}x_{ i}^{(l)}+\sum\limits_{\forall j\
eq i}h_{j}\sqrt{p_{j}^{(l)}}x_{j}^{(l)}+v_{i}^{(l)},\\ y_{i}^{(w)}&=h_{i,w}\sqrt{P_{i}^{(w)}}x_{i}^{(w)}+v_ {i}^{(w)},\end{split} \tag{1}\]  
where \(v_{i}^{(l)}\) and \(v_{i}^{(w)}\) denote the zero-mean complex Gaussian noise on the dedicated (NR) and adaptive (WALN) links, respectively. Owing to the existing frequency characteristics, there was no interference between the dedicated and adaptive links. Thus, we assumed that interference exists only when users access the same bandwidth resource block, and the effective interference is then defined as follows:  
\[\begin{split} N_{i}^{(l)}&=\sum\limits_{j=1}^{m}h_{ jJ}P_{j}^{(l)}+n_{s},\\ N_{i}^{(w)}&=n_{w},\end{split} \tag{2}\]  
where \(n_{s}=Var[v_{i}^{(l)}]\) and \(n_{w}=Var[v_{i}^{(w)}]\) are the thermal noise powers. The corresponding signal to interference plus noise ratio can be expressed as follows:  
\[\begin{split}\gamma_{i}^{(l)}&=\frac{P_{i}^{(l)}}{ N_{i}^{(l)}}=\frac{\frac{P_{i}^{(l)}}{\sum\limits_{j=1}h_{jJ}P_{j}^{(l)}+n_{s}} },\\ \gamma_{i}^{(w)}&=\frac{p_{i}^{(w)}}{N_{i}^{(w)}}= \frac{P_{i}^{(w)}}{n_{w}}.\end{split} \tag{3}\]
## 4 Experimental Results: Short Beams
### UPV Test  
UPV was adopted to test the beams as well. The results are summarized in Table 9. The means of the modulus are much closer to each other than what was found with the HNSW method. This remarks upon the fact that the novel NDE method is capable of capturing surface conditions that may have been altered by the presence of water. Moreover, the RSD is smaller, suggesting more homogeneous conditions throughout the four samples. Finally, if we compare the UPV data from Tables 4 and 9 we notice that the UPV method estimates an amount of \(w/c\) ratio above 50% for all samples, without any ability at discriminating poor from good surface conditions.
# A Fluid-Structure Coupling Analysis of a Far-Field Flat Mirror for AliCPT-1 Telescope Calibration
## 3 Fluid Field Calculation
### Results Analysis  
After achieving computational convergence, the relevant parameters related to the reflector are obtained. A symmetrical plane perpendicular to the ground and passing through the mirror was established, and pressure contour plots and streamline plots were examined on this plane, as shown in Figure 10.  
When the mirror withstands the front wind, the pressure distribution map reveals that the front side of the reflector experiences the maximum pressure at 211 Pa. Conversely, the mirror's backside exhibits lower pressure, which is easily attributed to the formation of small vortices on the windward side. The pressure distribution on the reflector surface, as depicted in Figure 11, emphasizes the substantial pressure on the front side of the reflector and the windward face of the support structure during forward wind loading.  
In contrast, when the wind is loaded in the back direction, the distribution map reveals that the maximum pressure on the backside of the reflector reaches 263 Pa. Additionally, the low-pressure region during backloading is more significant compared to forward loading, which should be attributed to a deflection effect of the upward tile angle. Figure 11 illustrates that the pressure primarily concentrates on the reflector's back side and the support structure's windward face. Since the support structure has a smaller surface area, it is less conducive to vortex formation, resulting in the lowest pressure on the back side.  
Figure 10: Pressure distribution maps of the FFF mirror under wind loading: (**a**) Pressure contour plot under wind from the front. (**b**) Pressure contour plot for wind from the back. (**c**) Streamline plot for wind from the front. (**d**) Streamline plot for wind from the back.  
The FFF is fixed on a three-axis rotating mount. The mount will stand straight (without elevation tilt) when the telescope is calibrated. The elevation (EL) axis was locked up by two motors. The torque caused by wind pressure on the mirror should not be larger than the maximum load torque of the motors.  
With a maximum pressure of 263 Pa applied to a mirror with an area of 8.33 m\({}^{2}\), multiplied by the distance of 2.925 m between the center of gravity and the EL axis, and considering the FFF mirror inclined at an angle of 45 degrees, the maximum torque caused by wind pressure was calculated as 4531 N\(\cdot\)m. The EL motor reducer output torque is up to 18,945 N\(\cdot\)m; in addition, the motor is even more robust in a locked state than a rotating one, so the mount can support the FFF in the maximum wind speed loading condition.  
Overall, the results analysis provides a comprehensive understanding of the pressure distribution and torque effects on the FFF mirror, confirming the suitability of the mount to withstand the wind loading conditions.