esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Scenarios and Setup  
### Measurement Scenario and Setup in mmWave Bands  
A mmWave channel measurement campaign at 28 GHz was performed with a wideband correlation sounder in a conference room (No. 510) of the Scientific Technology Building at the Beijing University of Posts and Telecommunications (BUPT), as shown in  
Figure 1: The comparison of frequency spectrum resources in mmWave and VLC bands. Generally, mmWave is located in 30–300 GHz. Moreover, some frequencies, e.g., 24 and 28 GHz, are also included in mmWave bands due to their similar characteristics to mmWave.  
Figure 2a. The map of this room and the 16 measured positions are marked in Figure 2b. The transmitter (TX) is placed at the corner of this conference room while the receiver (RX) is placed at the 16 measured positions successively. The measured distance is within the range of 3 to 10.51 m and all of these measurements are in line-of-sight (LOS) scenarios. In order to obtain the large-scale parameters and small-scale parameters of channels, we used the measurement platform to conduct two kinds of measurements in this conference room.  
* Setup one: Two omnidirectional biconical antennas (360\({}^{\circ}\) and 40\({}^{\circ}\) half power beam width (HPBW) in azimuth and elevation, respectively) were used at TX and RX sides to collect all multipath components (MPCs), and then 1000 channel impulse responses (CIRs) samples were measured in each position.
* Setup two: An omnidirectional biconical antenna was used at TX side while a directional horn antenna (10\({}^{\circ}\) and 11\({}^{\circ}\) HPBW in azimuth and elevation, respectively) was mounted in an electrical positioner at RX side. In these virtual measurements, the TX antenna was fixed and the RX antenna was rotated in steps of 5\({}^{\circ}\) in azimuth from 0\({}^{\circ}\) to 360\({}^{\circ}\), and there were three different elevations, \(-10^{\circ},0^{\circ}\), and 10\({}^{\circ}\). Channel characteristics in the spatial domain can be obtained through these virtual measurements. In each horn antenna pointing direction, we measured 1000 CIR samples.  
Based on these two measurements, we can obtain the path loss, RMS DS, K-factor, and spatial characteristics of mmWave channels. Furthermore, the parameters of the measurement setup are listed in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Central frequency & 28 GHz \\ RF bandwidth & 600 MHz \\ Chip sequence length & 511 \\ Chip rate & 400 MHz \\ Delay resolution & 2.5 ns \\ Pulse repetition interval & 1277.5 ns \\ Biconical antenna/Horn antenna gain & 2.93 dBi/25 dBi \\ Biconical antenna/Horn antenna polarization & Vertical/Vertical \\ Biconical antenna/Horn antenna azimuth HPBW & 360\({}^{\circ}\)/10\({}^{\circ}\) \\ Biconical antenna/Horn antenna elevation HPBW & 40\({}^{\circ}\)/11\({}^{\circ}\) \\ TX antenna/RX antenna height & 1.68 m/1.68 m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of measurement setup.  
Figure 2: Measurement scenario and map for this scenario, in which there are 16 measurement positions in total.
## 1 Introduction
### Contribution  
Although the importance of radio resource management (RRM) is apparent, RRM research for integrated networks are relatively rare. This paper is mainly focused on the RRM of the HAPS-IMT integration system. The main work and contributions of this paper are as follows:  
1. Decompose the optimization of resources allocation for integrated networks into two steps: long-cycle and sub-cycle optimization. The goal of the long-cycle optimization step is to determine a transmission protocol of the physical layer of the HAPS-IMT integrated system to maximize system throughput. The purpose of the sub-period optimization step is to maximize the utility of the integration system by allocating resource blocks (RB) and assigning transmission power reasonably and effectively.
2. The concept of \"utility function\" has been introduced in this paper, which quantifies users' QoS requirements. The utility function can facilitate the performance optimization of multi-layer integrated systems when allocating resources to multiple user traffic flows.
3. Based on the recent fairness research of the air-ground integrated network [16], this paper optimizes the fairness evaluation criteria of HAPS-IMT networks further to promote the development of a fairness evaluation scheme.  
The rest of this article is organized as follows. Section 2 describes the system model and establishes the HAPS-IMT system network model. In Section 3, for integrated cellular networks in TDD mode, configuring transmission protocol is designed to alleviate the interference from downlink to uplink. At the same time, the utility function is used to describe the characteristics of various services, and a resource allocation scheme for cross-link interference mitigation is designed. Section 4 evaluates the research result. Section 5 reviews the work in its entirety and concludes.
# Two Hop Adaptive Vector Based Quality Forwarding for Void Hole Avoidance in Underwater WSNs
## 5 Performance Evaluation  
This section contains the simulation analysis of the proposed schemes followed by the simulation setup and results. One hundred to 500 sensor nodes are deployed in a three-dimensional area of 10 km \(\times\) 10 km \(\times\) 10 km, and the sink is fixed on the surface. The maximum power for transmission and reception is 90 dB re \(\mu\) Pa and 10 dB re \(\mu\) Pa, respectively. The maximum transmission range is selected as 2000 m, because as in [9], an increase in transmission range does not affect network performance. To stop node movement outside the region, the random walk 2D mobility model is used. Each sensor moves at a speed of 1-3 m/s. Vertical movement is considered negligible, whereas horizontal movement in the X-Y plane is commonly considered in the underwater environment. The header size and data size of the data packet are 39 bytes and 72 bytes, respectively. Neighbor query and neighbor acknowledgment packets have 66 bits and 114 bits, respectively. In each round during the simulations, the simulation results were run over 50 times, and each time, the network topology was random. For simplicity, simulation parameters are tabulated in Table 3.  
Due to the detrimental characteristics of the aquatic environment including, but not only, ones having a limited bandwidth, high propagation and high attenuation, the MAC protocol is not very effective for identifying and avoiding collisions in the network [21]. In order to mitigate the aforementioned constraints, pure ALOHA is one of the widely-accepted protocols due to its insensitivity for long propagation delay [22]. Therefore, we have adopted pure ALOHA for our simulation results.  
Figure 7: Feasible region for throughput maximization.
# A Low-Complexity Ordered Statistics Decoding Algorithm for Short Polar Codes
## 8 Simulation Results
### Complexity Performance  
We have also compared the speed of OSD, the algorithm proposed in [10] and TH-OSD by counting the number of codewords tested during the decoding process. Figure 6 shows the proportion of the number of tested codewords compared to the number of all possible codewords during the decoding process of a (64, 32) polar code over a BIAWGN channel using OSD and TH-OSD decoding algorithms. We simulated each algorithm 1,000,000 times for a given SNR range. The figure shows that setting a threshold resulted in TH-OSD testing fewer codewords than OSD did, leading to less decoding time. This reduction was considerable in low SNR situations with acceptable WER performance loss. For example, by setting \(p_{th}\) to 0.8, TH-OSD tested 5% of the total possible codewords, on average, while OSD tested 55% with an SNR of 1 dB. Even setting \(p_{th}\) to 0.6, which resulted in a similar WER compared to OSD, TH-OSD still tested about 50% fewer codewords than OSD for a low SNR. Figure 6 also includes the complexity performance of the proposed algorithm in [10]. Although by setting threshold to \(3\sigma\) resulted in significant reduction of tested codewords, the WER performance was unsatisfactory, which can be seen in Figure 3. To achieve near MLD WER using the algorithm proposed in [10], we needed to set threshold to \(4\sigma\) or even higher, which led to fairly small reduction of the tested codewords compared to original OSD. Table 2 shows the numerical results of the reduction in tested codewords between the two methods. We also noticed that, in high SNR areas, OSD and TH-OSD were faster than SC in the simulations. This is partly because OSD and TH-OSD both terminate after testing only a few codewords. Additionally, the Gaussian elimination operations during RREF process are simple binary additions, while SC uses expensive hyperbolic function calculations. Although this comparison is not rigorous, it is consistent with our analysis.  
Figure 5: WER performance comparison between OSD-2, TH-OSD, TH-OSD list, CA-OSD, and CA-TH-OSDIn summary, with appropriate threshold settings, TH-OSD increases the decoding speed of OSD while maintaining similar WER performance. TH-OSD is faster than SC when the SNR is high. We also conclude that TH-OSD is faster than SCL with large list sizes because SCL with a list size of \(L\) runs \(1/L\) as fast as SC, which is already slower than TH-OSD.
## 4 Simulation Experiment
### Experiment Settings  
Five loudspeakers, labeled Id 1, Id 2, \(\ldots\), Id 5, are placed unevenly on the same ring, and their coordinates are shown in Table 1. The loudspeaker array has a radius of 2 m. The loudspeaker array surrounds the bright zone and dark zone, which are 0.4 m in diameter. The bright zone can accommodate a listener to listen to the sound. The distance from the center point of the bright zone to the center point of the dark zone is 0.6 m. See Table 1 for the coordinates of the center points of the bright and dark zones. The origin is point \(O\). The frequency of the original source signal changes from 100 to 1000 Hz. The original source's location is shown in Table 1. The sound speed expressed in c is 340 m per second, the wavenumber is \(k=2\pi f/c\), and \(f\) stands for signal frequency. The sampling interval in both the bright zone and the dark zone is 0.0364 m. The relative position of the unevenly placed five-channel system is shown by Figure 7.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name** & **Polar Radius (m)** & **Azimuthal Angle (Radian)** \\ \hline Id 1 & 2 & 0 \\ Id 2 & 2 & \(\pi/4\) \\ Id 3 & 2 & \(3\pi/4\) \\ Id 4 & 2 & \(5\pi/4\) \\ Id 5 & 2 & \(7\pi/4\) \\ Original sound source & 2.4 & \(5\pi/9\) \\ Original point \(O\) & 0 & 0 \\ Center of the dark zone & 0.3 & \(\pi\) \\ Center of the bright zone & 0.3 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Relevant coordinates in the system.  
Figure 7: Five-channel sound system structure diagram.
# A Systematic Deep Learning Based Overhead Tracking and Counting System Using RGB-D Remote Cameras
## 3 The Proposed System Methodology
### Feature Extraction
#### 3.3.1 Fused Joint HOG-LBP  
After the normalization of all the HOG features to obtain joint HOG features of different blocks (See Section 3.2), histograms for all the overlapping blocks are collected over the detection window [46]. Then, detection window values are fused with Local Binary Pattern (LBP) to improve the performance of feature extraction. Using a specific threshold, image pixels are labeled by comparing it with every neighboring pixel and then converting it into binary. Again, the window of an image is divided into cells of \(16\times 16\) pixels and each pixel is compared to its neighboring pixel, i.e., a central pixel's value is compared with the 8 neighboring pixels. If the value of the central pixel is greater than a neighboring pixel, it is replaced with the value 0, otherwise it is replaced with value 1. Figure 5 shows the results of fused joint HOG-LBP results over the PCDS dataset.
# Microbial Precipitation of Calcium Carbonate for Crack Healing and Stabilization of Sandy Soils
## 3 Results  
### Healing of Desiccation Cracks on the Soil Surface via MICP  
Figure 2 presents the changes in the desiccation crack patterns at the end of each cycle for the four treatments (water [W], CS, CS + B, and CS + BM). After the preliminary water treatment and drying, the soil was divided by cracks into separate clods (preliminary row, Figure 2). The soil samples exhibited different degrees of initial cracking (2.2-7.8%) due to differences in the shapes of the cracks (Figure 3A). For this reason, it was difficult to calculate and statistically compare the crack healing between the treatments, but the changes with each cycle could be tracked. With the water treatment (column W, Figure 3), few morphological changes were observed in the cracks, though the crack connectivity increased, with the crack ratio on the surface decreasing by up to 28.8%.  
The samples treated with the CS (CS, Figure 3) healed more cracks than those treated with water, resulting in a crack reduction of up to 48%. In the MICP treatment (CS + B, Figure 3), the crack ratio decreased by up to 70.9% after all of the treatment cycles, thus confirming its efficacy in reducing the desiccation cracks of soil. This was similar to the results of a previous study that employed MICP to heal shrinkage cracks in clayey soil [26]. On the other hand, the treatment with microbial metabolites but no live cells (CS + BM, Figure 3) healed up to 79.7% of the soil cracks through the precipitation of CaCO\({}_{3}\). Therefore, the chemical change in the medium due to bacterial growth and the incorporation of bacterial-secreted substances such as EPS led to rapid CaCO\({}_{3}\) precipitation that assisted in crack healing without the presence of live bacterial cells.
## 4 Experimental Study  
### Panel Manufacture and Geometry  
A 3 mm thick 6082-T6 aluminium plate was bonded to a 6082-T6 aluminium unequal angle stiffener to construct a stiffened panel with the dimensions shown in Figure 2. The dimensions of the panel were chosen to reduce the effects of edge reflections. The stiffener was bonded to the plate using commercially available Araldite(r) 420 (Huntsman, Woodlands, TX, USA) adhesive. The film thickness of the adhesive was regulated using 0.1 mm copper wire gauges to achieve the best shear strength [36].  
A geometrically similar second panel was also manufactured with an induced disbonded region of 25.4 mm length across the width of the stiffener. The disbonded region was induced by installing PTFE tape prior to applying the adhesive. This was then removed once the adhesive had cured.  
A commercially available PANCOM Pico-Z (200 kHz-500 kHz) transducer (PANCOM, Huntingdon, UK) was acoustically coupled to the panel using Loctite(r) (Loctite, Dusseldorf, Germany) Ethyl-2-Cyanoacrylate adhesive. This transducer was selected because of its flat broadband frequency response in the frequency range under investigation and its relatively small face (5 mm) as previous studies have shown that sources that are representative of a point source produce good results [37]. The transducer was located at the mid-point between the panel edge and the stiffener centreline to reduce the effects of edge reflections on the transmitted wave. This also was sufficient distance from the scan area to ensure that that the Lamb waves had fully formed.
## 4 Proposed Solution
### Route Formation  
For the route establishment phase, two new structures are introduced \"LINK_TABLE\" and \"LINK_INFO\". The \"LINK_TABLE\" is used by the nodes to store the multiple RREQ details they receive from neighboring nodes (Table 1). Information from this table is used by nodes to propagate RREP packets to source nodes. Once an intermediate node becomes part of a path, it generates and broadcasts a control packet, \"LINK_INFO\" (Figure 2), to update its and the next used node's availability in the \"LINK_TABLE\" of neighboring nodes. Upon receiving this control packet, all neighbor nodes mark the entry of the sending node, and its next used node as Invalid in their \"LINK_TABLE\". This feature ensures that the paths created are disjoint.
# Behind the Door: Practical Parameterization of Propagation Parameters for IEEE 802.11 ad Use Cases
## 4 Experimental Results
### Case 1: Angle Power Distribution in a Static Scenario  
In this case, where no movement exists, we measured the power distribution with the angle for each location, as detailed in the Experimental Methodology Section. Figure 2 presents a polar diagram of the STA antenna's power received (in dBm) for each measured location. In the figure, we observe that the geometric direction of the LOS does not always lead to the highest power level; consequently, we calculate the angle between the LOS direction and the direction of maximum received power for each case.  
Table 2 shows this estimate, where \(\theta_{TX}\) is defined as the angle of the shortest path to the AP (i.e., LOS). In turn, \(\theta_{MaxPR}\) corresponds to the angular direction for the maximum received power for each position. \(\Delta_{PR}\) represents the difference in dB between the powers received at \(\theta_{MaxPR}\) and \(\theta_{TX}\), and \(\Delta_{\theta}=\theta_{MaxPR}-\theta_{TX}\) represents the angular difference between them. In this way, if the maximum power is received from the LOS, \(\Delta_{PR}\) and \(\Delta_{\theta}\) will be close to \(0\). Both antennas (AP and STA) are adjusted to \(0^{\circ}\) in the elevation plane. The results in Table 2 quantify the benefits of using electronic antenna pointing schemes in the azimuth plane as the best transmission strategy. From the results, we infer that these eventual benefits are not negligible, particularly in NLOS links: \(\Delta_{PR}\) is up to \(13\) dB in Pos. 2 (without people), where the largest possible power can be obtained by pointing towards the office entrance. As expected, in positions where the link includes the LOS (Pos. 5 and 6), the dominant beam comes from a direction close to the LOS. Note that mm-wave channels are usually conceived as the LOS. Most of the published simulation results assume that the radiation pattern of highly directive antennas filters the multipath contribution, only considering the LOS. We observed (as evaluated in [17]) that this is not the case. Work in [17] shows that the mm-wave channel is clustered. Then, a single pedestrian moving along the path or near the antenna may block the contribution of significant clusters, turning \(\theta\) into a random variable.  
It is worth noting that, under conditions of total stability (no movement of people in the vicinity of the AP or STA), the link is stable, showing a peak-to-peak variation of no more than \(0.15\) dB.  
The received power can reduce the SNR to 0 dB, or be equal to the noise floor if there is no beamforming or adaptive pointing techniques applied to the receiving unit. On the other hand, the use of omnidirectional antennas does not take advantage of the different lobes appearing for each polar diagram depicted in Figure 3. Since there are at least two lobes with significant power in the positions under test, it is feasible to use diversity schemes or angular combiners to take advantage of a multipath increasing SNR.
# Adaptive Beamforming Applied to OFDM Systems
## 4 Simulations and Results
For this case, the minimal number of the GI samples, \(N_{GI}\), may be greater than 23 (\(N_{GI}\geq\) 23) to avoid the OFDM symbol interference (see Equations (16)-(18)). Similar to the Channel 1 simulations, \(GI=1/512\) (\(N_{GI}=4\)) and \(GI=1/2\) (\(N_{GI}=740\)) were used for three numbers of antennas, \(K=4\), \(K=6\) and \(K=8\). The results also show improved performance when compared to the OFDM system (\(K=1\)). Using the value of \(BER=10^{-4}\), the gain was about 12 dB, 17 dB and 18.5 dB for the \(K=4\), \(K=6\) and \(K=8\), respectively. For cases with \(N_{GI}<23\) (\(GI=1/512\)), the adaptive beamforming worked, and similar to Channel 1, there is a small gain, \(<\)2 dB, between \(GI=1/2\) and \(GI=1/512\) for all beamforming results (\(K=4\), \(K=6\) and \(K=8\)). Finally, Figure 8 shows the BER performance curve for Channel 3 (see Table 3). Channel 3 has \(L\approx 17\) OFDM samples (see Equations (10) and (11)), that is 2.1 \(\upmu\)s/\(t_{s}\approx\) 17. Based on Equations (16)-(18), the minimum number of GI samples, \(N_{GI}\), may be greater than 17 (\(N_{GI}\geq 17\)) to not allow the OFDM symbol  
Figure 6: BER performance curve for Channel 1 (see Table 3). Figure 7: BER performance curve for Channel 2 (see Table 3). interference. The results were obtained for two numbers of antennas \(K=8\) and \(K=10\), and the gains (using \(BER=10^{-4}\) as reference) were about \(6\,\mathrm{dB}\) and \(7.5\,\mathrm{dB}\) for \(K=8\) and \(K=10\), respectively. Such as other cases, the beamforming worked even for the cases where \(N_{GI}<17\) (\(GI=1/512\)). The works presented in [19; 22; 32; 33; 34; 35] had results with a guard interval great than channel length \(N_{GI}>L\), and this is mandatory for solutions where the array beamforming is updated in each OFDM symbol in the time domain. In this work, the array beamforming proposed was updated in each \(m\)-th sample in the frequency domain, and this allows one to work with a guard interval smaller than channel length \(N_{GI}<L\). The BER curves presented in the works [19; 22; 35] used the OFDM system with \(K=8\) antennas, \(C=64\) carriers (\(C_{D}=56\) and \(C_{P}=8\)) and \(GI=1/4\) (\(N_{GI}=16\)) in the three-path channel (\(L=3\)), that is \(N_{GI}>L\). However, in all simulations presented in this work (see Figures 6-8), the size of the guard interval (\(N_{GI}\)) had little influence on the BER performance (\(<\)1 dB), and this is a significant contribution when compared with other works presented in the literature. It is important to highlight that the reduction of the GI improves the channel capacity of the OFDM systems.
# Stability Analysis in Multi-VSC (Voltage Source Converter) Systems of Wind Turbines
## 5 Simulation Results
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Eigenvalue-Based Analysis** & **Time Domain Analysis** \\ \hline Current Controller & \(\text{K}_{\text{ECCIcr}}=104.2\) & \(\text{K}_{\text{ECCIcr}}=99.9\) \\  & \(\text{F}_{\text{cr}}=3.34\) kHz & \(\text{F}_{\text{cr}}=3.34\) kHz \\ \hline PLL & \(\text{K}_{\text{PPLLIcr}}=2.4759\) & \(\text{K}_{\text{PPLLIcr}}=2.55\) \\  & \(\text{F}_{\text{cr}}=187\) Hz & \(\text{F}_{\text{cr}}=182\) Hz \\ \hline AVC & \(\text{K}_{\text{Lalcr}}=857\) & \(\text{K}_{\text{Lalcr}}=750\) \\  & \(\text{F}_{\text{cr}}=45\) Hz & \(\text{F}_{\text{cr}}=44\) Hz \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Critical control gain and oscillation frequency in eigenvalue-based stability analysis and time domain analysis for a multi-VSC grid-connected system with \(n=2\) VSCs.