esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
There has been a growing interconnection across the world owing to various multimedia applications and services. Fixed wireless access (FWA) is an attractive wireless solution for delivering multimedia services to different homes. With the fifth-generation (5G) and beyond mobile networks, the FWA performance can be enhanced significantly. However, their implementation will present different challenges on the transport network due to the incessant increase in the number of required cell-sites and the subsequent increase in the per-site requirements. This paper presents a comprehensive tutorial on the enabling technologies, design considerations, requirements, and prospects of broadband schemes. Furthermore, the related technical challenges of FWA are reviewed, and we profier potential solutions to address them. Besides, we review various transport network options that can be employed for FWA deployment. In this regard, we offer an in-depth discussion on their related requirements for different use cases. Moreover, we give an insight into the 3 GPP RAN functional split implementations and implications on the 5G FWA transport network solutions. The concepts of virtualized RANs for attending flexibly to the dynamic nature of different use cases are also presented.  
Keywords:5G; 6G; beamforming; enhanced mobile broadband; radio access network; fixed wireless access; fronthaul; functional split; massive MIMO; millimeter wave; passive optical network; path-loss; transport network +
Footnote †: journal: _Sci. 2021_, 11, 10427. [[https://doi.org/10.3390/app112110427](https://doi.org/10.3390/app112110427)]([https://doi.org/10.3390/app112110427](https://doi.org/10.3390/app112110427))
## Appendix A Calculation of Scatter Angle and Sub Volume Distances  
The elevation angles of the lowest transmit and receive beams with respect to straight line AB between transmitter and receiver are \(\alpha_{1}\) and \(\beta_{1}\), respectively, as shown in Figure 11.  
\(\alpha_{1}\) and \(\beta_{1}\) are given from [7] as follows:  
\[\begin{array}{l}\alpha_{1}=\theta_{1}+\frac{d}{2a}+\frac{h_{1}-h_{2}}{a}\\ \beta_{1}=\theta_{2}+\frac{d}{2a}-\frac{h_{1}-h_{2}}{a}\end{array} \tag{11}\]  
AC and BD represent the lower edges of the first transmit and first receive antenna beam, respectively, intersecting in the point \(1^{\prime}\). AC' and BD' are the horizontals at A and B, respectively. \(\theta_{1}\) and \(\theta_{2}\) are the elevation angles of the respective horizons from the transmitter and receiver with respect to the horizontals AC' and BD', respectively. \(\theta_{1}\) and \(\theta_{2}\) may be positive or negative, depending on whether the highest obstacles (horizons) in front of the transmitter and receiver are above or below the horizontals. \(d\) is the straight line distance between the transmitter and receiver and, \(a\) is the effective Earth's radius in meters. \(h_{1}\) and \(h_{2}\) are the heights of the transmitter and receiver above the Earth's surface in meters.  
The elevation angles of \(i\)th transmit and receive beams with respect to straight line AB are given as follows:  
\[\begin{array}{l}\alpha_{i}=\alpha_{1}+(i-1)\;\cdot\;\Delta\alpha\quad\text{ rad}\\ \beta_{i}=\beta_{1}+(i-1)\;\cdot\;\Delta\beta\quad\text{rad}\end{array} \tag{12}\]  
where \(\Delta\alpha\) is the difference between the elevation angles of successive transmit antenna beams, and \(\Delta\beta\) is the difference between the elevation angles of successive receive antenna beams. Both \(\Delta\alpha\) and \(\Delta\beta\) have been taken as equal to 3.1 mrad for the Ahmedabad link and 3.7 mrad for the Guwahati link, considering successive common volumes to be overlapping. \(\alpha_{(i,j)}\) and \(\beta_{(i,j)}\), elevation angles of the lowest rays of \((i,j)\)th transmit and receive sub-beams are given as follows:  
\[\begin{array}{l}\alpha_{(i,j)}=\alpha_{i}+(j-1)\frac{\omega}{M}\quad\text{ rad}\\ \beta_{(i,j)}=\beta_{i}+(j-1)\frac{\phi}{M}\quad\text{rad}\end{array} \tag{13}\]Scatter angle \(\psi_{(i,j)}\) for \((i,j)\)th sub-beam set can be given from [7] as follows:  
\[\psi_{(i,j)}=\alpha_{(i,j)}+\beta_{(i,j)}\ \ \ \text{rad} \tag{100}\]  
Distances of lowest point of \((i,j)\)th sub volume from transmitter and receiver are given from [7] as follows:  
\[\begin{array}{ll}R_{t(i,j)}=d\frac{\textit{sinsin}\ \hat{p}_{(i,j)}}{ \textit{sin}\ \psi_{(i,j)}}&\text{m}\\ R_{r(i,j)}=d\frac{\textit{sinsin}\ \alpha_{(i,j)}}{\textit{sin}\ \psi_{(i,j)}}&\text{m} \end{array} \tag{101}\]  
Distances of highest point of \((i,j)\)th sub volume from transmitter and receiver will be \(R_{t(i,j+1)}\) and \(R_{r(i,j+1)}\).
## 2 Materials and Methods
### Simulated Datasets
#### 2.1.2 DART Model Simulations  
The DART model [34] is a three-dimensional radiative transfer model that simulates the propagation of radiation across the whole optical domain, from the visible to the thermal infrared region, for natural landscapes such as forests, grasslands, and farmland, as well as for urban landscapes with topography and atmosphere. Recently, the FLUSPECT model was integrated into the DART model to simulate the radiative transfer of SIF within 3-D canopies [35]. In this study, the accuracy of the f\({}_{\text{esc}}\) was verified using DART v5.6.7, which simulated the SIF at both canopy and leaf levels, as well as the directional reflectance for fifty various viewing angles for maize canopies. The input variables for the DART model are listed in Table 2. To simulate sparse maize canopies, the value of LAI was set to 2. The soil spectrum from the DART model database was used (Figure S1). The specific simulated 3
## 3 The Location-aware Dynamic Session-key Generation for Grid-based WSNs Scheme
### Grid Formation  
The entire area of a wireless sensor network is partitioned into a 2D logical grid (a \(4\times 4\) grid, as illustrated in Figure 8). Each grid is a square of size \(d\times d\). Grids are identified (\(x\), \(y\)) using the conventional x y-coordinate system. So that it is aware of its location, each node is equipped with a positioning device, such as a GPS receiver, from which it can read its current location. For any given location, there is a predefined mapping of the location to its grid coordinate. Each grid ID, which is given by [_CX, CY_], is assigned as follows: in the first row, from left to right, the grid IDs are [1, 1], [2, 1], [3, 1], and [4, 1]. In the second row, the grid IDs are [1, 2], [2, 2], [3, 2], and [4, 2], and so on. Based on the coordinate (\(x\), \(y\)), each node computes its _CX_ and _CY_ as follows:  
\[CX=\left\lfloor\frac{x}{d}\right\rfloor\text{, }CY=\left\lfloor\frac{y}{d}\right\rfloor \tag{1}\]  
where \(d\) is the grid size, and \(CX\) and \(CY\) are the largest integers not greater than \(\frac{x}{d}\) and \(\frac{y}{d}\), respectively.  
Using Equation (1), each node determines which grid it belongs to. Each node will also maintain a neighbor table. The neighbor table is generated using the periodic HELLO protocol [28] at the beginning of a network life. The HELLO packet is small. In addition, the HELLO overhead from the periodic HELLO protocol is very small.  
Figure 7: The flooding region.  
Let \(r\) be the transmission distance of a radio signal. We use the maximum value \(d=r/2\sqrt{2}\). The maximum value \(d\) of a cluster node is located at a grid and is capable of talking to any of the cluster nodes of its 8 neighboring grids. However, a smaller \(d\) also means more cluster nodes in the network, which in turn implies a higher overhead for delivering a packet, as well as more broadcast storm. Thus, there exists some tradeoff in choosing a moderate \(d\) value.  
In each grid, one sensor node is selected to be the header of that grid. We call a node a \"header\" when it has more remaining energy than other nodes in that grid. Figure 9 shows a physical area partitioned into logical grids.  
Figure 8: Grid index.  
Figure 9: A physical area partitioned into logical grids
# Dispersion Management and Pulse Characterization of Graphene-Based Soliton Mode-Locked Fiber Lasers
## 5 Experimental Setup  
Figure 6 shows the experimental setup of graphene-based mode-locked EDFL. The laser cavity initially consists of 5 m long Lucent HP980 EDF, 1 m long Corning Hi-1060 fiber, and 10.4 m long Corning SMF-28. The EDF, which has a signal absorption coefficient of 3.5 dB/m at 1530 nm, is pumped by a 975 nm laser diode (LD) with a maximum operating power of approximately 100 mW via a 980/1550 nm wavelength division multiplexer (WDM). Two isolators, namely ISO-1 and ISO-2, are used to ensure unidirectional signal oscillation within the cavity in an anti-clockwise direction. The graphene/PMMA-SA fabricated in this work is placed in between ISO-1 and ISO-2. Since the fabricated SA is based on sandwiched-type thin film, there is a possibility of Fresnel reflection at the interface between fiber ferrule and the thin film itself. In this setup, ISO-1 is utilized to prevent the Fresnel reflection from entering the EDF. On the other hand, ISO-2 is employed to ensure that the backward light propagation from the EDF cannot reach the graphene/PMMA-SA, which leads to another Fresnel reflection phenomena. A polarization controller (PC) is used to control the polarization state of the signal, which is connected to the output of ISO-2. A 3-dB coupler is used to tap out 50% of the signal for analysis, while the other 50% of the signal is channeled back to the WDM to form the laser cavity. Analysis of the net cavity dispersion and the output performance commences with the additional SMF length of 4 m in the cavity. The additional SMF is trimmed by 0.5 m for each trial until the length is finally reduced to 0 m. The optical spectrum is measured by the OSA. The pulse train is monitored by a digital phosphor oscilloscope (Tektronix TDS 3012C) through a D-8 IR Picometrix photodetector. The radio frequency (RF) signal is monitored by an RF spectrum analyzer (GW INSTEK GSP-830). Measurement of the pulse duration is performed by using an Alnair HAC-200 autocorrelator.
## 1 Introduction
In this regard, as detailed in the Literature Review section, most of the existing research work is directed toward resource-rich languages. The Roman Urdu language has been recent adoption on social media, specifically in south Asian countries, and is highly resource deficient. Section 3 details the script, morphology, and challenges associated with the Roman Urdu language. This study puts novel efforts into addressing cyberbullyingdetection research problems in low-resource Roman Urdu language using natural language preprocessing, machine learning, and ensemble techniques. We have used explicitly designed preprocessing methods to handle unstructured micro text. The complications in analyzing the patterns and structure behind implicit and explicit cyberbullying behaviors, typically in newly embraced colloquial languages, and devising it as a comprehensive computational task is quite challenging. The key objectives of this research work are as follows:  
1. Analyzing low-resource colloquial Roman Urdu language text structure and performing exploratory data analysis to visualize data. 2. To devise a mechanism for performing advanced preprocessing techniques on Roman Urdu micro text and systematically applying the appropriate preprocessing phases to improve classification performance. This will also have broad implications over other natural language processing applications. 3. This research proposes an approach based on seven techniques combining ML and ensemble methods with a fusion of a vast number of features, GridsearchCV and experimentation to detect hateful and cyberbullying patterns in natural writing patterns of Roman Urdu text data. 4. To evaluate the performance and time efficiency of the proposed approach and visualize results. The rest of this paper is structured as follows:  
Multilingual research and related works are presented in Section 2. Section 3 states the structure of low resource Roman Urdu language and discusses the associated challenges. Section 4 elaborates the methodology used to accomplish this research work and highlights important phases and steps. Section 5 discusses Roman Urdu corpora. Section 6 details the systematic phases for advanced preprocessing and standardization on Roman Urdu micro text. Section 7 highlights exploratory data analysis to investigate data and understand important patterns and insights. Experimental setup and model hypermeters are specified in Section 8. Section 9 highlights and discusses study results. Section 10 elaborates the estimation of prediction probabilities for implicit and explicit instances. Finally, Section 11 concludes the research work and provides future directions for the research community.
# Automated Detection of Multi-Rotor UAVs Using a Machine-Learning Approach
## 6 Conclusions and Future Work  
A reliable detection mechanism of multi-rotor UAV was achieved by the usage of the TensorFlow library. For this purpose, the \"Common Objects in Context dataset\" was used and it was extended by 1000 samples of UAVs from the SafeShore dataset. A successful detection rate of 97.3% in optimal conditions was achieved.  
It can be concluded that the proposed detection principle was successful in described testing cases. The trained detection model was successful in almost every tested scenario. A problem occurred only when objects with similar features from another class were on the scene. Statistical evaluation of the detection model showed that the detection rate, in this case, was only 61.3%. Moreover, the bird should not be detected at all. Therefore, another detection model was used. This model was able to recognize two classes--drone and bird. From the experiments, it was found that if the detector was trained on several objects, the detection was much more successful. From this finding, we conclude that the biggest problem in drone detection is the occurrence of objects with similar properties. These objects comparable to the drone can include a bird, airplane, parachute, or paragliding wing. There are not so many of these objects, so it would be possible to train such a detection model with a sufficiently large data set. Such a detector can be even more successful than the one trained in two classes. On the other hand, the detection rate of 97.3% of the detector trained in two classes is sufficient for most of the basic security applications. Achieved success and system reliability can be compared with the common human observer. Testing was performed on all described data and conditions and our work presents a functional approach for multi-rotor drone detection under ideal flight conditions. Future work using our approach may be to focus on the cases described in the chapter Introduction, where the detector could represent not only the role of a common observer but also a sophisticated detection system usable in adverse conditions.  
[PERSON] was the main investigator. [PERSON] mainly proposed the methodology used in the article. [PERSON] supervised the article, [PERSON] provided the validation of the results and [PERSON] was responsible for the text reviewing and editing. All authors have read and agreed to the published version of the manuscript.  
This work was supported by projects APVV-17-0116, VEGA 1/0752/17, and DIH\({}^{2}\).  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 24: Testing the objects detection in the sky with an improved detector.
## 5 Application and Results
### Environmental Evaluation
#### 5.3.2. Noise Pollution  
Sound pressure level in the receiving position (\(\mathrm{L_{p}}\)) has been evaluated according to Lloyd's Register (2010), with \(\mathrm{L_{w}}\) based on regression line between DWT and sound power level for the different scenario's ships [20]. The results for distance between the source and receiver of 500 m (\(\mathrm{L_{P500}}\)) and 1000 m (\(\mathrm{L_{P1000}}\)) in comparison to conservative limits for noise pollution (\(\mathrm{L_{max}}\)(dB(A))) according to the European legislation [8] are reported in Table 3.  
Results show that in any of the scenarios conservative limits for emissions are touched, but not overcome; moreover, as shown in Figure 7, the urban centre of the city of Augusta is located at distances greater than 1000 m, and the impact of noise emissions would be  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Base Emission** & \multicolumn{2}{c}{**CD\({}_{\mathrm{CNTR,M}}\)**} & **CD\({}_{\mathrm{CNTR,B}}\)** & **CD\({}_{\mathrm{CNTR,L}}\)** & **CD\({}_{\mathrm{CNTR,T}}\)** & **CD\({}_{\mathrm{CNTR,R}}\)** & **CD\({}_{\mathrm{CNTR}}\)** \\ \cline{2-7}
**(kg/TEU)** & & **1.99** & **0.02324** & **1.39** & **0.04287** & **0.06873** & \\ \hline
**Scenario** & TEUs & & & & & & \\ \hline
1. Feeder & 3000 & 5970 & 69.72 & 8340 & 206.19 & 1159.35 & 15,745.26 \\
2. Panamax & 5000 & 9950 & 116.2 & 13,900 & 343.65 & 1932.25 & 26,242.1 \\
3. Hub & 14,000 & 27,860 & 325.36 & 116,760 & 962.22 & 10,820.6 & 156,728.18 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of total carbon emission for the terminal container of the Port of Augusta (kg).  
Figure 6: Optimization model result for the three scenarios.  
even lower. However, there are some small conurbations close to the port area which could be impacted by the operation; for this reason, it was nevertheless decided to include some measures to reduce noise emissions in the TDST.
## 2 Modeling and Analysis of Carrier-Phase Differential Positioning
### Analysis of Ill-Conditioned Problems for the Identification Matrix
Then, the corresponding covariance matrix of the estimated solution obtained by least squares of Equation (10) is:  
\[Q_{Y}=\left[ \begin{array}{cc}Q_{\hat{X}}&Q_{\hat{X}\hat{N}}\\ Q_{\hat{X}\hat{X}}&Q_{\hat{N}}\end{array} \right] \tag{18}\]  
Then, the integer ambiguity \(N\) can be searched under the following conditions:  
\[(\hat{N}-N)^{\rm T}Q_{\hat{N}}^{-1}(\hat{N}-N)=\min,\quad N\in Z^{n} \tag{19}\]  
The double-difference operation can lead to a certain correlation of the integer ambiguity, which will be further enhanced when the observation time is short. LAMBDA can convert the floating-point solution of the integer ambiguity and its variance-covariance matrix into a new space through integer Gaussian transformation, which reduces the correlation between the integer ambiguities and improves search efficiency. The conversion method is as follows:  
\[\left\{ \begin{array}{l}Q_{N}=z^{\rm T}Q_{\hat{N}}z\\ \bar{N}^{0}=z^{\rm T}\bar{N}\\ \bar{N}=z^{\rm T}N\end{array} \right. \tag{20}\]  
where \(N\), \(\bar{N}\), and \({}_{\hat{N}}\) are the integer solution, floating-point solution, and variance-covariance matrix of the integer ambiguity in the original search space, respectively. \(\bar{N}\), \(\bar{N}^{0}\) and \(Q_{\bar{N}}\) are the integer solution, floating-point solution, and variance-covariance matrix of the integer ambiguity in the new search space; \(z\) is an integer transformation matrix. The objective function can be written as:  
\[\left(\bar{N}^{0}-\bar{N}\right)^{T}Q_{N}^{-1}\left(\bar{N}^{0}-\bar{N}\right) \leq\chi^{2} \tag{21}\]The equation determines a multi-dimensional ellipsoid search space centered on position \(\hat{N}\). \(\chi^{2}\) determines the size of the ellipsoid. And the covariance matrix \(Q_{N}\) determines the shape of the ellipsoid. LAMBDA algorithm changes the shape of the original search space from an ellipsoid to an approximate sphere, reduces the combination of integer ambiguity, and transforms the integer ambiguity obtained in the new search space into the original space through the integer transformation matrix, which improves the search efficiency of the integer ambiguity.
# Analysis and Verification Results of Manual Inspection of Pavement Condition Index
## 3 Results
### Data Validation  
The primary goal of this study is to determine the validation criteria for manual Pavement Condition Index (PCI) inspections and standardize the validation data. This will serve as a unified reference standard for future studies according to FHWA quality guidelines [13; 14].  
This section focuses on the validation of three key aspects:  
1. **Inspector accuracy:** validating the individual accuracy of manual inspection results and the consistency of scores among different inspectors.
2. **Equipment validation:** ensuring the reliability and precision of the equipment used for inspecting pavement distresses.
3. **System validation:** verifying the PCI scores calculated by the system against actual field data. The following subsections provide detailed explanations for each validation item.
# Quantitative Analysis of Drag Reduction Methods for Blunt Shaped Automobiles
## 2 Literature Review  
In order to collect relevant amounts of information for this review, research and review papers were looked up in the database of Science Direct, Multidisciplinary Digital Publishing Institute (MDPI), and also in the Web of Science, between the beginning of 2000 and April 2020.  
The applied keyword combinations were drag, drag reduction, vortex generator, vehicle, ground vehicle. In addition to the above, reverse tracking was used as a reference for the pivotal papers in the area, as well as relevant references in the papers from the research. Only journal publications were included, except where important contributions were made from the available conference proceedings. Literature that examined the relationship between VGs and thermal reaction, flows above supersonic velocities, and underwater drag related problems were excluded from the analysis. Other fuel saving methods for ground vehicles that had no connection to aerodynamic drag were also eliminated. Few additional exceptions were made in regard of drag reduction, where its effect was examined in isolated subsonic scenarios. Figures from the research papers are not presented intentionally, yet, summarized illustrations are made based on their findings.  
The main objective of this study is to answer the following questions:  
* What typical flow structures are created around a vehicle?
* What methods were used to reduce drag in the past decade?
* From the available methods, can active systems, shape, or topology optimization be a viable solution to reduce drag?
* What are the drag reduction capability limits for different methods?
# Task-Based Motion Planning Using Optimal Redundancy for a Minimally Actuated Robotic Arm
## 2 MASR Mechanical Characteristics  
The solution to our motion planning problem depends, as is the case for many other problems, on boundary conditions, which in a manipulation task are composed of the geometric and mechanical constraints of the robotic arm. Thus, before elaborating on the motion plan itself, the key mechanical characteristics of the MASR are outlined. Figure 1 presents the design of the MASR robot fitted with five passive joints (the number of joints can be increased or decreased based on the task). For more a comprehensive description of its design and operation, as can be seen in [9; 10].  
In this example, the manipulator is composed of a 1 m-long planar arm comprised of two to five links (20, 40, 60, or 80 cm long, 4 cm wide, 5 cm high). The links are connected by passive revolute joints (2R-5R robot) whereas a single mobile actuator travels over the links to reach designated joints to rotate them (Figure 1). After being rotated by the mobile actuator, the joints remain locked, using a worm gear setup. A gripper is attached to the mobile actuator, thus enabling it to transport an object along the links to decrease the actuation of the joints and the work time. The relative angle \(\theta_{j}\) between two adjacent links (\(j-1\) and \(j\)) can be varied in the range of [\(-60^{\circ}\),\(60^{\circ}\)]. In the following sections, we describe the algorithm and present simulations based on the mechanical and geometric properties of the MASR. Note that although the algorithm solves the problem of a minimal actuated modular arm, it can also be used in simpler cases, such as a fully actuated manipulator (all-motorized joints), and at any level of redundancy, with or without modularity, as shown below.