esa-sceva/satcom-chunk-collection huggingface dataset:
# Monitoring System of Transmission Line in Mountainous Area Based on LPWAN
## 3 Model and Method
### Multi Objective Particle Swarm Optimization Algorithm  
Through the modeling of delay and energy consumption, it can be seen that the delay model is mainly related to the parameters of LoRa transmission equipment and the amount of data transmitted. The energy consumption model is mainly related to the receiving and sending power of the LoRa  
Figure 5: Long range radio (LoRa) communication energy consumption.  
module, the wireless transmission power of the cellular, the amount of data transmitted, and the transmission distance. Low delay and low energy consumption are contradictory goals. In order to meet the design requirements of the transmission line online monitoring system, particle swarm optimization (PSO) [40] can be used to optimize the delay \(T_{all}\) and energy consumption \(E_{all}\).  
The model of the multi-objective optimization algorithm with d-dimension decision variables and m-objective is defined as follows:  
\[\left\{\begin{array}{ccc}\min&y=f(x)=\left|f_{1}(X),f_{2}(X)\cdots,f_{m}(X) \right|\\ s.t.&x=(x_{1},x_{2}\cdots,x_{D})\in X\\ &y=(y_{1},y_{2}\cdots,y_{M})\in Y\end{array}\right. \tag{22}\]  
where \(x\) is the decision vector; \(X\) is the decision space; \(y\) is the target vector; and \(Y\) is the target space.  
In the PSO algorithm, each particle is a solution in the solution space. It adjusts its flight according to its own flight experience and the flight experience of its peers. The best position each particle has experienced in the flight process is the optimal solution found by the particle itself. The best position that the whole group has experienced is the optimal solution found by the whole group at present.  
Let \(H_{a}=(h_{a1},h_{a2},\cdots,h_{aD})\) be the dimensional position of particle \(a\)(\(a\) = 1,2,...,\(s\)), \(V_{a}=(v_{a1},v_{a2},\cdots,v_{ab},\cdots v_{aD})\) be the flight speed of particle \(a\), \(P_{a}=(p_{a1},p_{a2},\cdots,p_{ab},\cdots,p_{aD})\) be the individual optimal solution of particle \(a\), and \(P_{g}=\left(p_{g1},p_{g2},\cdots,p_{gb},\cdots,p_{gD}\right)\) be the global optimal solution of the whole population.  
In each iteration, the velocity and position of each particle are updated with Equations (23) and (24):  
\[v_{ab}(k+1)= \theta v_{ab}(k)+c_{1}r_{1}(p_{ab}-h_{ab}(k)) \tag{23}\] \[+c_{2}r_{2}(pg_{b}-h_{ab}(k))\] \[h_{ab}(k+1)=h_{ab}(k)+v_{ab}(k+1) \tag{24}\]  
where \(a\) = 1, 2,..., \(s\), \(s\) is the number of particles in the population, \(b\) = 1, 2,..., \(D\), \(h_{ab}\in[L_{b},U_{b}]\), \(L_{b}\) and \(U_{b}\) represent the lower bound and last term of the search space, \(i\in[v_{min},\)\(D,\)\(v_{max},\)\(D]\), \(v_{min}\) and \(v_{max}\) respectively represent the minimum and maximum speed of particle flight; \(c_{1}\) and \(c_{2}\) are learning factors; \(r_{1}\) and \(r_{2}\) are random numbers between 0 and 1; and \(\theta\) is inertia weight.
# Native Smartphone Single- and Dual-Frequency GNSS-PPP/IMU Solution in Real-World Driving Scenarios
## 4 Results Analysis
### Kinematic Performance Assessment with Different Processing Strategies
#### 4.2.3 Single- and Dual-Frequency PPP-IC/IMU Processing  
Single- and dual-frequency PPP (SFDF-PPP) processing is the main focus of this study, which utilizes not only GPS L1, GLONASS L1, Galileo E1, BeiDou B1 signals, but also GPS L5 and Galileo E5a signals if they are available, as well as ionospheric constraints from  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Elapsed Time (s)** & **917** & **918** & **919** & **920** & **921** & **922** & **923** & **924** & **925** \\ \hline
**DF-PPP Horizontal errors (m)** & 37.8 & 49.5 & 60.7 & 72.0 & 82.1 & 95.2 & 109.2 & 123.9 & 139.2 \\ \hline
**DF-PPP/IMU (MI8) horizontal errors (m)** & 1.0 & 1.4 & 1.9 & 2.5 & 3.2 & 4.1 & 5.3 & 6.7 & 8.2 \\ \hline
**DF-PPP/IMU (Xsens) horizontal errors (m)** & 0.9 & 1.1 & 1.1 & 1.2 & 1.3 & 1.6 & 2.0 & 2.5 & 3.1 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Horizontal errors growth with different sensor fusion combinations for dual-frequency PPP over Outage B.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Processing Strategy** & **Std Dev (m)** & **Rms (m)** & **95 th Percentile Error (m)** & **Maximum Error (m)** \\ \hline
**DF-PPP** & 7.8 & 8.3 & 6.7 & 139.2 \\ \hline
**DF-PPP/IMU (MI8)** & 1.6 & 2.4 & 3.3 & 23.3 \\ \hline
**DF-PPP/IMU (Xsens)** & 1.5 & 2.4 & 3.2 & 23.5 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Horizontal errors with different sensor fusion combinations for dual-frequency PPP processing.  
Figure 9: Horizontal error with respect to the reference trajectory for single-frequency PPP and dual-frequency PPP processing with same satellites.  
GIM. Figure 10 displays the time series of horizontal errors and precision for SFDF-PPP (GREC) processing strategies. In general, SFDF-PPP processing inherits the advantages of the aforementioned two processing strategies with more observations and good GNSS geometry, resulting in more stable positioning.  
Table 7 summarises the horizontal error statistics for SFDF-PPP processing with and without IMUs. On the one hand, accessible L1 and L5/E5a signals increases smartphone positioning overall performance, namely 5.0 m and 5.2 m of horizontal error standard deviation and rms can be observed, respectively, which are the best solutions among pure GNSS PPP strategies. On the other hand, the integration of IMUs further improves positioning accuracy and resilience, especially for all scenarios that suffer from GNSS outages (outliers, see Table 8). Compared to SFDF-PPP processing, 92% and 79% improvements are observed by integrating smartphone native IMU in terms of horizontal standard deviation and rms, respectively. In addition, use of the Xsens IMU enables improved horizontal solutions. Though slight improvement can be found for 95 th percentile error, the benefits of IMUs mainly contribute by mitigating the GNSS outliers and the maximum horizontal errors reduce from 90.3 m to under 10 m.
## 9 Routing in CRSNs
### CRSN Routing Modules
#### 9.1.2.2 Control Signaling
##### 9.1.2.2.3 Group-Based Control Channel  
The fact that group (_i.e._, cluster)-based algorithms are best suited for establishing sub-network-wide or complete network-wide coverage of the chosen control channel makes it the method of choice for CRSNs. This trend is due to a number of reasons. Ref [59] showed that due to spatial correlation availability, there is a high possibility of finding a common channel in certain restricted areas. Theissue of congestion of the CCC has been downplayed by the 15%-22% CCC usage, as illustrated in [44], and its potential of enhancing throughput [66].  
The most relevant work in this respect is [67] in which dedicated control channels are assigned by cluster heads to members based on the sensing results of the cluster members. As in traditional clustering algorithms, the manner in which the deafness issue is overcome is not mentioned. Thus, the probability of potential cluster members receiving the broadcast of neighbors cannot be ascertained. This scenario can best be described as a blind rendezvous scheme in which a rendezvous cannot be assured unless it is methodically planned, especially in the presence of multiple cluster members. Another major feature of the protocol is the decision scheme for selecting the control channel based on an approximated partially observable Markov decision process (A-POMDP) and the channel availability (CA) of both the PUs and SUs.  
Another significant work in this respect for traditional CRAHNs is [68], which proposes a design approach for an efficient recovery control channel (ERCC) that consists of three components: neighbor discovery, control channel (CCL) update and efficient PU activity recovery. Again, the traditional approach for cluster formation was followed. However, the issue of blind rendezvous was properly addressed. In the ERCC, the cluster formation is based on a rigorous neighborhood discovery scheme, while [20,65] make this process very simple by introducing the concept of virtual clustering, which maximizes the utilization of idle listening.
## 3 Results and Analysis
### Analysis of the Relationship between Compaction Deformation and Water Content  
Water content is an important factor affecting the compaction effect of crushed gangue. In order to compare and analyze the influence of the water content on the compaction characteristics, Figure 7 shows the relationship of the axial strain and water content of crushed gangue under different stresses.  
Figure 6: Strain–stress fitting curves under different water content conditions. (**a**) \(\omega=0\%\); (**b**) \(\omega=1.5\%\); (**c**) \(\omega=3.0\%\); (**d**) \(\omega=4.5\%\); (**e**) \(\omega=6.0\%\).  
An analysis based on Figure 7 shows that water content is an important factor affecting the compaction state of crushed gangue. When the maximum axial stress is 4 MPa and 8 MPa, the higher the water content of the crushed gangue, and the greater its axial strain. When the maximum axial stress is 12 MPa, the axial strain first increases continuously with the increase in water content, and then tends to remain unchanged. When the maximum axial stress is 16 MPa, 20 MPa, and 24 MPa, the axial strain first increases and then decreases with the increase in the water content, and the curve shows a convex distribution.  
When the maximum axial stress is above 16 MPa, the relationship between the strain and water content is very similar to the compaction test. That is, when the water content reaches a certain suitable value, the axial strain of the crushed gangue can reach the maximum value, and with the increase in the stress, the compactness that the crushed gangue can achieve is better.  
Following the data processing method of the compaction test, a 4 th-order polynomial fitting analysis was performed on the relationship curve between the axial strain and water content when the axial stress was above 16 MPa. The coordinate values of the peak point corresponding to the fitting curve are the optimal water content and maximum axial strain.  
Table 3 shows the fitting results of the relationship curve between the axial strain and water content. It can be seen that with the increase in axial stress, the maximum axial strain gradually increases, and the corresponding optimal water content gradually decreases.  
The relationship between the axial strain and water content of crushed gangue is closely related. The moisture in the crushed gangue will affect the frictional resistance and pore structure between the particles. As the water content increases, the lubricating effect of the moisture becomes more obvious, and the corresponding axial strain is relatively larger. At the same time, the moisture in the crushed gangue will fill the pores between the gangue particles, reduce the number and size of the pores, and hinder the further compaction of the gangue particles. Therefore, when the water content reaches a certain level, the axial strain will decrease instead.
## 2 Theoretical Framework
### Setting the Initial Value for SES  
Because SES requires the previous forecasted value at each stage, it must be apparent how to start the method. Different initialization and estimation methods for setting the initial value for SES have been discussed in the literature. Still, the state-of-the-art method is to estimate the optimum alpha and the initial value together by minimizing some loss function [31]. Typically, MSE is used, minimizing the squares of one step ahead in a sample forecast error. The first observation as the initial value for SES was suggested by [PERSON] and [PERSON] [1]. Additionally, many statistical packages, such as Minitab, have been used to set the initial value for SES with the average of the first six observations [23].  
The following are two methods that are most commonly used to set the initial value for SES and are used to compare with the proposed method.  
1. The first observation is used as the initial value of \(\alpha\) when using SES [22]. By simple algebra, of SES, can also be expressed as \(F_{1}=y_{1}\), \(F_{t+1}=\alpha y_{t}+(1-\alpha)F_{t}\) where \(F_{1}\) and \(y_{1}\) are the initial value and the actual value, respectively. The choice is preferable when the level changes rapidly (\(\alpha\) close to 1, or \(1-\alpha\) close to 0).
2. The average of the first six observations is used for the initial value [23]. By simple algebra of SES, can also be expressed as \(F_{1}=\left(\frac{1}{6}y_{1}+\frac{1}{6}y_{2}+\frac{1}{6}y_{3}+\frac{1}{6}y_{ 4}+\frac{1}{6}y_{5}+\frac{1}{6}y_{6}\right)\), \(F_{t+1}=\alpha y_{t}+(1-\alpha)F_{t}\) where \(F_{1}\) and \(y_{1},y_{2},\ldots,y_{6}\) are the initial value and the actual values, respectively.
## 4 Results and Discussion
### High-Quality Papers Details  
After quality assessment and overall distribution of the contribution of papers based on their quality, contributions of high quality are further analyzed. A detailed representation of high-quality contributions is shown in Figure 11, where a higher number of papers belong to the \"Medical domain\" and \"Computing discipline\" i.e., 15 contributions. Furthermore, the statistics show the number of contributions as 7, 10, and 8 for the application domains \"General problem solving\", \"Transportation\", and \"Business sector\", respectively. All these high-quality contributions are depicted in Table 9 along with their application domain, the year of their publication and reference no. ; a short description is provided in Table 8. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Citation** & **Short Description** & **Year** & **Application Domain** & **Quality** \\ \hline
[6] & The study proposed a DSS to measure job satisfaction with the help of the MUSA method and the continuous genetic algorithm. & 2017 & Business sector & High Quality \\ \hline
[28] & The study proposed systematic learning and an approach regarding the problem of price negotiation. & 2017 & Business sector & High Quality \\ \hline
[83] & The authors presented a decision support system in general along with some details regarding its use for business purposes that can be used by top management. & 2017 & Business sector & High Quality \\ \hline
[11] & The study proposed DSS help to reduce the cost of standardization as well as the implementation of a process that minimizes the degree of organizational changes required in standardization and ERP implementation project. & 2018 & Business sector & High Quality \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Details of high-quality contributions along with their description. Figure 10: Year-wise distribution of papers based on their quality.
## 4 Wind Field Characteristics
### Power Spectral Density and Gust Factor  
Power spectral density (PSD) describes the energy distribution of turbulent kinetic energy in different scales of vortices, and this is the basis for exploring wind-induced structural responses. Figure 13 shows the PSD curves at different heights caused by the thunderstorm wind. It can be observed that when it is expressed as a function of the turbulence integral length scales, the PSD curves at different heights can show basically uniform spikes. This is beneficial to the establishment of a PSD model of thunderstorm wind. The solid black line shows the mean of all heights, and the longitudinal result is slightly larger than the lateral.  
Figure 14 shows the PSD thunderstorm records of 16 m (a) and 64 m (b), and the comparisons are made among the records, the empirical PSD models of the Davenport spectrum (Chinese specification), Von Karman spectrum (Japanese specification), and Solari spectrum (European specification). All spectra satisfy the \(-5/3\) power law in the inertial subregion. The Davenport spectrum is significantly different from the thunderstorm spectrum. The Solari spectrum underestimates the spectral peak, and overestimat  
Figure 12: Spatial–temporal variation of longitudinal (red) and lateral (blue) turbulence integral length scale at some significant instants.  
Figure 13: Power spectral density of longitudinal (a) and lateral (b) reduced turbulent fluctuation.  
as high frequency parts. The Von Karman spectrum is relatively effective in fitting the thunderstorm wind spectrum, so it can be considered to model the thunderstorm wind.  
Gust factor is another important parameter in wind-induced response and wind-resistant design of structures. Because of this, the amplification effect of fluctuating wind load on structural response has to be taken into account. The gust factor based on the directional thunderstorm wind speed modeling method can be obtained from Equation (10). In view of the instantaneous characteristics of thunderstorm wind, the time interval of 1 s is generally taken for the longitudinal peak wind speed when calculating the gust factor of thunderstorm wind. The gust factor results at significant instants are shown in Table 3. The values during jumps in wind speed are higher, and they are larger at low altitude rather than high altitude. The results are smaller than the values obtained with those presented in [PERSON] et al. [21] because the different definition of the gust factor, while similar to the results based on a series of thunderstorms in Northern Tyrrhenian with the same definition. The along-height mean value of the ratio between the 1 s peak wind speed and 10 min mean wind speed, the definition of gust factor for synoptic winds, are 3.71 and 3.07 at 14.25 and 14.27, respectively. The results are much higher compared with the synoptic wind, and this is a serious threat to the safety of the structure. In addition, the gust factor at the height of 16 m is large, and the larger peak of gust wind speed is prone to occur at this height compared with others.
## 3 Materials and Methods
### Study Area and Collection of Baseline Data  
The Qassim region, one of the 13 administrative regions, located almost at the center of the country, accounts for about 3.2% of the total Kingdom's area. It is geographically located between 40\({}^{\circ}\)00\({}^{\prime}\) E, 45\({}^{\circ}\)00\({}^{\prime}\) E longitude and 23\({}^{\circ}\)30\({}^{\prime}\) N, 28\({}^{\circ}\)00\({}^{\prime}\) N latitude. The Qassim region consisting of ten sub-provinces and 155 local centers has been divided into 13 governorates, including Emara_Buraidah (the capital governorate), AlAsyah, Uyun Al-Jiwa, Al-Badaya'a,  
Figure 1: Traffic accident risk assessment framework.  
Al Bakiriyah, Daria, Al Mithnab, Al Nabhanya, Ar Rass, Riyadh Al-Khabra, Al Shammasiya, Unaizah and Oklat AlSkoor [46]. Figure 2 shows the location of the Qassim region and its governorates. With a population of 1,370,727 and an area of 58,046 km\({}^{2}\), the Qassim region is one of the Middle East's leading producers of luxury dates. Annual production of 205,000 tons of various varieties of dates (both the local consumption and international exports), enhances the economic value of the region. In addition to dates, Qassim has been known for other agricultural resources with an extensive production of fruits and vegetables. Such extensive agricultural activities add to the heavy traffic volumes on major highways.  
Being located in the center of the country, the Qassim region holds a particular geographical significance. It borders Hail Region to the north and north-west, the Northern border region to the northeast, Riyadh region to the south and east, and the Medina region to the West and Southwest (Figure 2). Its centrality makes it an important transit route between the various sections of the KSA, strengthened by the interconnected network of highways, railways, and air routes [21]. The capital city of Qassim is Buraydah, which accommodates around 49% of the region's total population. Qassim has a moderate level of traffic safety. Recently, [PERSON] et al. [47] identified over speeding, irregular bypasses, irregular rotations, lack of prioritizing of other vehicles, irregular stops, lack of road readiness, driver carelessness, use of a cell phone while driving, and disobedience with traffic laws as the 10 significant causes of road traffic accidents in addition to lack of use of seat belts. The 85 km long Buraydah Ring Road (BRR) connecting all major city's collectors and distributors is one of the most important highways of the region. Thus, we selected BRR as our study area and obtained the accident data from the Ministry of Transportation in Riyadh. Accident records for three years (2017 to 2019), along with the date and time, location, accident cause, and accident type were obtained and processed.  
Figure 2: Road network of Qassim Region. Qassim region is highlighted on small Saudi Arabia’s map given in the left side of the figure. Blue boundary marks the study area boundaries in Qassim Region.
# Practical 3-D Beam Pattern Based Channel Modeling for Multi-Polarized Massive MIMO Systems +
###### Abstract  
In this paper, a practical non-stationary three-dimensional (3-D) channel models for massive multiple-input multiple-output (MIMO) systems, considering beam patterns for different antenna elements, is proposed. The beam patterns using dipole antenna elements with different phase excitation toward the different direction of travels (DoTs) contributes various correlation weights for rays related towards/from the cluster, thus providing different elevation angle of arrivals (EAoAs) and elevation angle of departures (EAoDs) for each antenna element. These include the movements of the user that makes our channel to be a non-stationary model of clusters at the receiver (RX) on both the time and array axes. In addition, their impacts on 3-D massive MIMO channels are investigated via statistical properties including received spatial correlation. Additionally, the impact of elevation/azimuth angles of arrival on received spatial correlation is discussed. Furthermore, experimental validation of the proposed 3-D channel models on azimuth and elevation angles of the polarized antenna are specifically evaluated and compared through simulations. The proposed 3-D generic models are verified using relevant measurement data.  
3-D massive MIMO channel modeling; antenna elements space; elevation angle of departure; elevation angle of arrival; azimuth angle of departure; azimuth angle of arrival Article