esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey of Wi-Fi 6: Technologies, Advances, and Challenges
## 4 OFDMA in Wi-Fi 6
### Advances on the PHY Layer
#### 4.1.4 New Frame Format  
Four new frame formats and preamble fields are introduced to support the new features and functionalities of Wi-Fi 6. The general structure of the frame remains similar to legacy Wi-Fi for backward compatibility and coexistence. Figure 8 depicts these new frame formats. The preamble of the incoming packet includes specific fields to identify the used sub-channels and the frame format. The new preamble consists of two parts, legacy (non-HE) and HE fields. The legacy fields support IEEE 802.11n/ac and the HE fields are only readable by Wi-Fi 6.  
The four different types of HE Physical Layer Protocol Data Units ( PPDU) are distinguished by the preamble fields [5]:  
**HE Single-user (SU) PPDU:** this frame is used for single-user transmissions.  
**HE ER SU PPDU:** this frame is the same as the single-user transmission but designed for outdoor environments with an Extended transmission Range.  
**HE MU PPDU:** this frame is considered for one or multiple downlink transmission(s) by adding the HE-SIG-B field to the single-user transmission frame.  
**HE Trigger-Based (TB) PPDU:** this frame is used for multi-user uplink transmissions in response to the Trigger Frame (TF) issued by the access point.  
The new HE preamble fields that differentiate the frame formats are the following:  
**Repeated Legacy (non-HT) SIGNAL (RL-SIG):** This field detects the beginning of the HE frame.  
**HE-SIG-A:** This field is a common field in all four Wi-Fi 6 frame formats. It carries all the needed information for the types of transmissions and is two OFDM symbols long. The information in this field differs depending on the frame type and whether the transmission is single-user, multi-user, or TB. It contains information about the packet to determine the link type (uplink or downlink), BSS color, TXOP duration, bandwidth, number of spatial streams, and coding [30]. For an extended-range single-user transmission, this is repeated one more time to improve the robustness against interference and signal fading in outdoor scenarios [31].  
**HE-SIG-B:** This field is specific to downlink multi-user transmissions. It is divided into two parts. In the common part, it carries RU allocation information and it is decodable by all the stations in the same sub-channel. The user-specific part has a variable length and contains specific information for each user such as MCS, number of spatial streams, coding, and station ID [32].  
**HE-STF and HE-LTF:** The HE short training field (STF) and HE long training field (LTF) are specific to MIMO operations. The former synchronizes a receiver with the incoming frame in time and frequency. The latter is responsible for beamforming and spatial diversity. For a TB frame, the duration of the HE-STF is twice as long.
## 5 Conclusions  
Due to large dynamic and thermal effects, convective cells frequently occur on the TP in the summertime. Measurements of vertical air motion are useful for advancing our understanding of the dynamic and microphysical mechanisms of clouds, and they can be used to improve the parameterization of current numerical models at many scales. In this paper, a technique was presented for retrieving the high-resolution vertical air velocity in convective clouds over the TP by using vertically pointing Ka-band cloud radar. This technique further develops the small-particle-traced method and is based on the use of recorded Doppler spectra from radar and its necessary data processing, over three different modes. Spectral broadening corrections, uncertainty estimations, and results merging were used to ensure more accurate results.  
Figure 11: The time series of vertical air velocities derived by radar and disdrometer near the ground for case two, the radar retrievals are selected from the first detectable range gate at 150 m above the surface.  
Qualitative analysis of two typical convective cases showed that the retrievals of vertical air velocities were reasonable. Meanwhile, some observational features were also found. For small-scale shallow cumuli, air velocities alternately changed across the cumuli, updrafts appeared in the middle of cumulus cells, and downdrafts were located on both sides. For precipitating cumulonimbus clouds, updrafts mostly appeared in the upper half of the convective cells and gradually accelerated from cloud middle to cloud top, whereas the lower half of the cumulonimbus was dominated by downdrafts. Vertical air velocity could be intimately related to the development level of the cumulonimbus, with stronger updrafts corresponding to deeper cloud body. Quantitative retrieval of vertical air motion from a ground-based optical disdrometer was used to preliminarily validate the radar-derived results. The comparison illustrated that the trends of the two retrievals were similar, with the updraft or downdraft appearances coinciding. Cloud radar had much higher resolution retrieval results and could reveal the small-scale variation of vertical air motion. However, we noted that the different heights of the two retrievals and the uncertainty in the disdrometer-derived results, likely impacted this comparison.  
This study was funded by the National Sciences Foundations of China under Grant Nos. 41705008, 41675023, 91537214, 41375038, and 41605022, and Research Foundation of State Key Lab of Severe Weather under Grant No. 2016 LASW-B13.  
[PERSON] conceived, designed, processed, analyzed, and wrote the manuscript; [PERSON] and [PERSON] conducted the literature review; [PERSON] and [PERSON] processed the data used in this manuscript.  
The authors declare no conflict of interest.
# Satellite-Based Wireless Sensor Development and Deployment Studies for Surface Wave Testing
## 2 Hardware Architecture
### Computational Core  
Raspberry Pi 3 Model B+, a commercial portable single-board computer developed by Raspberry Pi Foundation in the United Kingdom was employed as the computational computer. Its computational core is Broadcom BCM2837B0 with 1.4 GHz 64-bit quad-core processor. The Raspberry Pi 3 Model B+ uses 1 GB synchronous dynamic random access memory (SDRAM) as memory, and the external SD slot makes larger storage possible. It has four USB 2.0 ports, 2.4 GHz and 5 GHz IEEE 802.11.b/g/n/ac wireless LAN, Bluetooth 4.2, 300 Mbps Gigabit Ethernet, and extended 40-pin GPIO header for multiple purpose communication. The operation system (OS) of Raspberry Pi is a Debian-based OS named Raspbian, which is highly optimized for the its tailored central processing unit (CPU). The Raspberry Pi is an ideal general-purpose computer for a wireless sensing node because of the low price (only 35 USD) and aforementioned high performance.
# End-of-Life Impact on the Cradle-to-Grave LCA of Light-Duty Commercial Vehicles in Europe
## 2 Materials and Methods
### LCA Methodology
#### 2.1.1 Goal of the Study and System Boundary  
The aim of this study is two-fold. First, this study contributes to addressing a well-known LCA methodological issue concerning the allocation method to be used if the EoL stage is included in the system boundary. Two EoL scenarios were considered for this purpose, each dealing with the allocation of burdens and benefits between connected product systems in a different way. Specifically, the environmental impacts calculated through the avoided burden (or 0:100) method were compared to the ones calculated with the circular footprint formula (CFF). To prevent double counting of burdens and benefits, the avoided burden method permits the inclusion of the recycling process in the system boundary and prohibits the inclusion of secondary material as input. The CFF method, instead, permits the inclusion in the system boundary of both the recycling and the secondary material (as input), preventing double counting through an economic allocation factor A. For a broader understanding of the outcomes, the following vehicle configurations characterized by different propulsion systems were considered:  
* [noitemsep,topsep=0 pt]
* Diesel oil internal combustion engine vehicle (DIE-ICEV);* Compressed natural gas internal combustion engine vehicle (CNG-ICEV);
* BEV.  
The second objective of this study is to evaluate the relevance of the EoL stage to the overall cradle-to-grave life cycle. For this purpose, this study also includes the cradle-to-grave LCA results of the three LDCVs considered. For each vehicle configuration, the following LCA stages (detailed in Appendix A) are constant as the EoL scenario assumed variations in  
* Manufacturing;
* Distribution;
* Use;
* Maintenance;
* Collection at EoL.  
In Figure 1, the system boundary is reported, wherein the EoL stage is highlighted in yellow. At the end of the manufacturing stage, the DIE-ICEV, CNG-ICEV, and BEV references of this study are characterized by a curb weight of 2700 kg, 2845 kg, and 2876 kg, respectively.  
Regarding the EoL stage, for each vehicle configuration, an LCI based on a detailed multilevel waste management scheme was developed in compliance with the 2000/53/CE directive and the ISO 22628 standard to chiefly investigate the environmental emissions of the EoL stage and their effect on the overall life cycle. For each EoL scenario, the depollution, dismantling, shredding, and postshredding stages were considered, along with the following fates of the materials: recycling, reuse, incineration with energy recovery, and landfilling. More detail regarding the other LCA stages can be found in the authors' previous works [40; 45].
# Research on Co-Channel Interference Cancellation for Underwater Acoustic MIMO Communications
## Appendix A Proof of (25)  
In this appendix, we simply provide the proof of (25).  
\[\begin{split}\mathbf{P}_{2}^{H}\mathbf{P}_{2}&=( \mathbf{I}-\mathbf{A}_{2}(\mathbf{A}_{2}^{H}\mathbf{A}_{2})^{-1}\mathbf{A}_{2}^ {H})^{H}(\mathbf{I}-\mathbf{A}_{2}(\mathbf{A}_{2}^{H}\mathbf{A}_{2})^{-1} \mathbf{A}_{2}^{H})\\ &=\mathbf{I}-\mathbf{A}_{2}(\mathbf{A}_{2}^{H}\mathbf{A}_{2})^{- 1}\mathbf{A}_{2}^{H}-(\mathbf{A}_{2}(\mathbf{A}_{2}^{H}\mathbf{A}_{2})^{-1} \mathbf{A}_{2}^{H})^{H}+\mathbf{A}_{2}(\mathbf{A}_{2}^{H}\mathbf{A}_{2})^{-1} \mathbf{A}_{2}^{H}\\ &=\mathbf{I}-\mathbf{A}_{2}(\mathbf{A}_{2}^{H}\mathbf{A}_{2})^{- 1}\mathbf{A}_{2}^{H}\\ &=\mathbf{P}_{2}\;.\end{split} \tag{10}\]  
In the similar way, it is easy to obtain \(\mathbf{P}_{2}\mathbf{P}_{2}^{H}=\mathbf{P}_{2}\).  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \(\mathbf{L}_{fb}\) = 18.7 ms & \(\mathbf{L}_{fb}\) = 62.5 ms \\ \hline CE\_IC+DFE\_IC & 80.41 s & 80.78 s \\ RLS\_DADFE & 45.85 s & 4408.8 s \\ IPNLMS\_DADFE & 4.05 s & 25.70 s \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average running time.  
It is assumed \(\mathbf{A}(\cdot;i)^{H}\mathbf{A}(\cdot;j)=b_{i,j}\), then \(b_{i,j}\) is the \((i,j)\)-th entry of matrix \(\mathbf{A}_{2}^{H}\mathbf{A}_{2}\). It is also assumed that, the energy of each transmitted symbol is \(\kappa\), the transmitted symbols are independent identically distribution, so when \(i=j\), \(b_{i,j}\approx P\kappa\), when \(i\
eq j\), \(b_{i,j}\approx 0\). Hence we can obtain \(b_{i,j}\ll P\kappa\). Finally, it can be obtained \(\mathbf{A}_{2}^{H}\mathbf{A}_{2}\approx(P\kappa)\mathbf{I}\). Similarly, it can be acheived \(\mathbf{A}_{2}\mathbf{A}_{2}^{H}\approx(L\kappa)\mathbf{I}\). As a result, we obtain  
\[\mathbf{A}_{2}(\mathbf{A}_{2}^{H}\mathbf{A}_{2})^{-1}\mathbf{A}_{2}\approx \frac{L}{P}\mathbf{I}\;, \tag{10}\]  
and  
\[\mathbf{P}_{2}\approx\frac{P-L}{P}\mathbf{I}\;. \tag{11}\]
# Efficient Feature Selection Using Weighted Superposition Attraction Optimization Algorithm
## 2 Methodology
### Metaheuristics
#### 2.3.2 Genetic Algorithm  
The GA is one the earliest algorithms, initially introduced by [PERSON] in 1958 [30]. It was inspired by [PERSON]'s theory of natural selection. The algorithm was later made famous by [PERSON] [12]. The GA, like any other usual algorithm, starts the task with an initialized population of candidate solutions (individuals). The chromosome would be the parents of the population. The performance of the individuals is then evaluated using the fitness function. Based on this fitness value, a selection scheme mechanism would be chosen to subsequently achieve the desired goal. Therefore, a new population is produced through crossover, mutation and reproduction. The pursuit of this algorithm reiterates until the optimal solution is reached. In this paper, a roulette-wheel-based selection method is considered for GA. In the roulette wheel selection method, the fitness of each individual chromosome is used to determine the probability that it would be selected for reproduction. The higher is the fitness of an individual, the greater is the probability that it would be chosen for reproduction. This probability is proportional to the fitness of the individual.  
Let \(f_{k}\) be the fitness value for each chromosome \(C_{k}\). The probability \(p_{k}\) is calculated using the following expression:  
\[p_{k}=\frac{f_{k}}{\sum_{i=1}^{n}f_{i}} \tag{2}\]  
where \(f_{i}\) is the fitness value of \(i\)th individual and n is the total number of individuals.  
The pseudocode for GA (roulette wheel) can be represented as shown in Algorithm 2.  
```
Objective function \(f(x)\), \(x=(x_{1},\ x_{2},\ldots,x_{d})\) Initialize population \(G=0\); Initialize all NP individuals  WHILE Iterations < Maximum Iterations  FOR i \(\gets\) 1 to NP  sum += fitness of this individual  FOR all members of population probability = sum of probabilities + (fitness/sum)  sum of probabilities += probability  END  number = random between 0 and 1  for all members of population  if number > probability but less than next probability  Iterations = Iterations + NP  END \(G=G+1\)  END
```  
**Algorithm 2**Genetic algorithm (roulette wheel)  
Similar to roulette wheel, tournament selection is another parent selection approach in the GA. Unlike the roulette wheel, tournament selection is usually restricted to two chromosomes. However, selection between more than two chromosomes can be carried out by adjusting the tournament size. The tournament selection approach can be represented using the pseudocode provided in Algorithm 3 [41].
## 2 Theoretical Background and Research Gaps
### BIM Implementation Barriers in Malaysian Context  
In Malaysia, BIM implementation is still in the developing stage. However, the construction industry in Malaysia has taken major steps to encourage and boost construction efficiency at the national level with the introduction of BIM [50; 51]. In addition, BIM is mandatory for public projects with a budget of RM 100 million or more from 2018 [52]. According to Datuk Seri Dr [PERSON] (PWD Director-General), a total of 18 construction projects have been initiated by BIM in various construction phases up to 2017 such as SMK Meru Raya Ipoh Perak, Health Clinic Maran Pahang, MACC Selangor Shah Alam, UTHM Batu Pahat Johor and Parit Buntar Hospital [53]. With the continuous implementation of BIM against sustainable building projects in Malaysia, the implementation of BIM still faces numerous barriers and BIM implementation in sustainable building projects has many concerns and challenges. Hence, there is a need to better understand barriers in order to make the construction smoother and more effective for boosting sustainability goals. Various researchers have also highlighted the barriers to BIM implementation. The most critical barrier which was identified by many researchers is high cost [54; 55; 56; 57]. Likewise, in Hong Kong and China, high cost is also considered as the BIM implementation barrier [47; 58]. Moreover, it was also described as the topmost critical barrier in the Middle East [59]. As a result, high costs are included as one of the barriers since they are commonly available in the literature. In the last year, another study was conducted to explore the level of adoption of BIM in Malaysia. This study found that only 13% of government and private participants use BIM in their organization, which is negative evidence that Malaysia remains a long way from the role it should play in implementing BIM [60]. Therefore, it is recommended to implement BIM by eliminating barriers and providing the strategies for sustainable building projects.
## 4 Proposed Network Model
### Network Model Assumptions  
Regarding the proposed protocol, some basic reasonable assumptions are adopted to define the scope of the proposed network model as follows:  
* The network has \(N\) sensors distributed in the Q \(\times\) Q square field;
* Each node has a unique ID;
* The sink node (SN) location is known by all the sensors, and vice versa;
* The network nodes are stationary;
* All the network nodes are homogeneous and energy-constrained;
* The SN of each cluster is located at the center of the cluster, so all nodes of the cluster can be connected with it using the single-hop model method;
* The SN has unlimited energy resource;
* The BSs are located at the center of each network. The BSs collect their data from rechargeable SNs;
* One BS is used for each 100 \(\times\) 100 m network size;
* Regular sensors can communicate directly with their cluster head (CH);
* The size of a cluster in a network concerning the number of regular sensors is equal;  
Figure 4: Illustration of the LEDCHE-WSN architecture.  
* In the beginning, all sensor nodes have the same energy level (maximum) and each of them can become the CH or a regular node;
* All the nodes have the capability with appropriate distance to send the information to the sink node.
## 5 The Simulation Validation and Results Analysis
### The Effect of Different Communication Topology Optimization Strategies
#### 5.2.2 Group B: Fixed Communication Topology Structure  
Figures 22-24 show the trajectories at 10 s, 20 s and 100 s, respectively. In Group B, identical to Reference [18], the fixed communication topology structure is \(\{A_{2}\to A_{1}\to B\}\). However, \(A_{1}\) is initially closer to the target and \(A_{2}\) is initially closer to the base station, so this fixed communication topology is not optimal obviously. To form and maintain this fixed topology, \(A_{1}\) moves toward the base station while \(A_{2}\) moves toward the target, as shown in Figure 22.  
Figure 21: The trace of the FIM for \(A_{1}\), \(A_{2}\) and base station (Group A in Scenario 2).  
Figure 23: Trajectories of UAVs at \(t=20\) s (Group B in Scenario 2).  
Figure 22: Trajectories of UAVs at \(t=10\) s (Group B in Scenario 2).  
The pre-set fixed topology is unreasonable, which causes \(A_{1}\) and \(A_{2}\) to perform unnecessary maneuvers. It can be seen from Figure 25 that, the transmission probabilities of \(A_{1}\) and \(A_{2}\) to base station are low, which reduce the global estimate performance. This can be confirmed in Figure 26. During the period of [0, 20] s, the trace of FIM in the base station is less than \(A_{1}\) and \(A_{2}\). This is because the ground station failed to receive the measurement packets from \(A_{1}\) and \(A_{2}\).  
Figure 24: Trajectories of UAVs at \(t=100\) s (Group B in Scenario 2).  
Figure 25: The probabilities of successful transmission of \(A_{1}\) and \(A_{2}\) to base station (Group B in Scenario 2).
## 5 Results and Discussion
### Comparative Analysis of Hybrid RF-PSC Energy Harvesting with Other Technologies  
The feasibility and advantages of the proposed hybrid RF-PSC energy harvesting system must be evaluated. Hence, a comparative assessment of the proposed hybrid RF-PSC system against alternative energy harvesting technologies has been incorporated. Thermoelectric energy harvesting (TEG) systems, which convert waste heat into electricity, offer a viable solution in industrial applications but are constrained by their lower power density than RF-PSC hybrids [61]. Vibrational energy harvesters, including piezoelectric and electromagnetic systems, have also been explored. While effective in converting mechanical vibrations into energy, these systems require frequent movement, limiting their suitability for static IoT applications. A comprehensive comparison table summarising these systems' power density, efficiency, scalability, and deployment costs is provided in Table 1.
# Calibration of Sliding Friction Coefficient in DEM between Different Particles by Experiment
## 1 Introduction
In order to study the influence of the friction coefficient between particles in a ball mill, [PERSON] [40] simulated the mechanical behavior of particles in a three-dimensional ball mill under different values of the sliding friction coefficients and found that the number of collisions decreased with an increase in the rolling friction coefficient. [PERSON] [41] established a three-dimensional DEM particle sandpile simulation under different gradations based on experiments and studied the influence of rolling friction tangential force on particle contact. As part of a study investing the influence of the rolling friction coefficient on particle motion, [PERSON] [42], through the numerical simulation of sandpile formation and rolling drum experiments, found that the angle of repose increases with an increase in the rolling friction coefficient and decreases with an increase in particle size. In addition, [PERSON] [43], [PERSON] [44], and [PERSON] [45] conducted a variety of simulations by using DEM, indicating the advancement of numerical simulation methods in the study of the friction coefficient in DEM. Although the parameter calibration of the friction coefficient in DEM has been studied extensively, most research focuses on the rolling friction coefficient. There are still many underrecognized areas in the study of the sliding friction coefficient between particles and the friction coefficient between boundaries and particles. The purpose of this paper is to study the influence of particles with different shapes and sizes on the sliding friction coefficient between particles and the sliding friction coefficient between boundaries and particles in DEM by experiment. In the following content, experiments designed to determine the sliding friction coefficient between particles and the sliding friction coefficient between particles and different boundaries are first introduced. Then, numerical simulations parallel to the experiments were carried out by using particles with different shapes and sizes. The simulated results are detailed and discussed with reference to the experimental results. Finally, the influence of the boundaries on the sliding friction coefficient of particles was also studied.