esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results and Discussion
### Joint Distribution Law of Droplet Size and Velocity  
The atomization process of the spray mist field is divided into five stages [13; 38; 39], as shown in Figure 10a. After conducting measurements and processing data from measuring points in the fog field, the influence of surface tension and viscosity of the dust suppression liquid on the overall distribution of the fog field was analyzed, and the results are presented in Figure 10. Figure 10a is a legend diagram, while Figure 10b-d are the development diagrams of pure water, 2% dodecyl alcohol, and 5% dodecyl alcohol fog fields, respectively. The color variation in the figure indicates the speed of the droplet, and the size of the colored sphere in the figure indicates the diameter of the droplet.  
Figure 9: Droplet velocity distribution on the radial direction.  
Figure 10b shows that the droplet size of pure water increases from 336 \(\upmu\)m to 450 \(\upmu\)m at \(Z\) = 11 cm, indicating an increase of 33.9%. The droplet velocity also decreased from 5.02 m/s to 3.22 m/s, a drop of 35.8%. The droplet size and velocity expansion phase occurred at \(Z\) = 11 cm. The particle size and velocity of pure water droplets were stable between \(Z\) = 15 cm and \(Z\) = 17 cm. The droplet size and velocity expansion phase of the 2% dodecyl solution was between \(Z\) = 9 cm and \(Z\) = 11 cm, and the droplet size and velocity stabilization phase lasted between \(Z\) = 11 cm and \(Z\) = 15 cm. Compared with pure water, the 2% lauryl alcohol solution had an earlier expansion period and a more continuous stable period. The droplet size and velocity expansion phase of the 5% dodecyl solution were the same as that of the 2% dodecyl solution, but the droplet size and velocity stabilization phase were more continuous, ranging from \(Z\) = 11 cm to \(Z\) = 17 cm. The comparative analysis shows that the droplet size and velocity expansion period are closer to the nozzle when the surface tension is 33.6 mN\(\cdot\)m\({}^{-1}\) (the smallest among the three solutions), and the stable region is more continuous when the viscosity is 1.12 \(\times\) 10\({}^{-3}\) Pa\(\cdot\)s (the largest among the three solutions).
## 3 Results and Discussion  
### Spatial and Temporal Distribution of Trend Breaks  
All but the monotonic trend types are characterized by a break at a certain point in time. By assessing the timing of the breakpoints, we found an increasing number of events occurring in more recent times (Figure 3(a), inset). With few exceptions, the increase appeared monotonic and almost linear, especially  
Figure 2: Examples of trend types. **Top**: monotonic change, either with or without abrupt change (shown: with), **Middle**: monotonic change in the gradual segments, but interrupted by an abrupt event in the opposite direction (burst or setback); **Bottom**: trend reversal between the gradual segments. The columns separate between recent greening (**left**) _versus_ recent browning (**right**). For visual convenience, the time series in these examples were trimmed to 10 years and centered around the date of the detected change point. The class labels match the color scheme of Figure 3(b).  
until the 2000s. This suggests that the relatively stable vegetation regimes of the early periods of the GIMMS time span have changed to higher turnover rates. This, in turn, supports the premise that monotonic trends do not suffice to capture the change in many parts of the world and that shifts in vegetation activity trends should therefore not be ignored.  
Early shifts (1980s) were concentrated in Botswana, Northeast Brazil and some parts of Europe (Figure 3(a), orange and light green hues). Later dates appeared geographically less confined, but showed distinct regional patterns in most cases. Several of these patterns are discussed in the next section. The better part of the latest shifts (after 2005) were concentrated in Australia and Argentina and coincided with a green-up reversal (see Section 3.2.3). In a framework of long-term structural shifts, careful interpretation of these early and late shifts is required. In such cases, one of the gradual-change segments is short, which puts (recovery from) short-term disturbances, rather than long-term shifts, forward as candidate driver.  
At global scale, the 1992 Mt Pinatubo eruption was found to have affected vegetation activity [39], but we did not find firm indications for a structural shift in our results. The years after the event show a slightly higher representation of breakpoints, but it would be premature to conclude on a causative relationship. Likely, the eruption lead to disturbances from which vegetation systems have recovered on a shorter time scale than addressed in this study.
## 3 Parallel Insertion and Indexing Method for Proposed Spatiotemporal Data
Then, a mapped Hilbert Curve value (cellID) is assigned to a tablet server, for example, cellIDs 0-3 and  
Figure 4: Data ingestion process and schemas for the index and data table. 16-19 are assigned to tablet server\({}_{1}\). The assignment depends on the number of servers and the size of the grid. Generally, locations of moving objects can be skewed to a specific area and the area may be changed with time. The indexing method described above cannot process efficiently the skewed location data. Therefore, we propose a dynamic grid technique that can be adapted to the skewed location data. Figure 6 shows the proposed dynamic grid indexing method. In our method, multilevel grid technique is used. Initially, multilevel grid starts with only level 1. Then, when the number of records contained in a grid cell exceeds a given threshold value, we create lower level grids for the cell. As shown in Figure 6, grid cell 7 in level 1 exceeds a threshold and then level 2 is created for the cell. CellID is calculated by Equation (1). In the figure, \(HCV_{i}\) means the Hilbert Curve value of \(i\)th level. \[\text{cellID}= HCV_{1}+\sum_{i=2}^{L}(HCV_{i}+1)\times 10^{i-1} \tag{1}\]  
In Figure 7, there is an example of the proposed indexing method. The threshold value for the number of data records for a cell is 3 in that example. O11, O31, and O41 are inserted sequentially into the area for the grid cell 7. According to the Equation (1), cellIDs of the newly created grid are 7.1, 7.2, 7.3, and 7.4. Then, level 2 grid for the grid cell is created, and after that, O12, O13, O42, and O32  
Figure 5: Proposed indexing method based on Hilbert Curve technique. Figure 6: Dynamic grid indexing for skewed data. are inserted. In this example, the grid cell 7.4 exceeds the threshold, so the level 3 grid is created for the cell. In the above example, cellIDs are assigned to the data records like Table 1. As shown in the table, data records inserted before a new level of grid is created have cellIDs assigned to it. Consequently, all levels of cellIDs must be considered to process a range query. In Figure 8, we show an example to process range queries. Figure 8a shows an example of range query processing on one level grid. Range queries Q1 and Q2 overlap the grid cell 7 so to process the queries we need to compare all data records in the cell. In Figure 8b, range queries are processed on dynamic multilevel grid indexing method. In this example, to process Q1 and Q2, retrieve 4 records and 6 records only, respectively. \begin{table}
\begin{tabular}{c c} \hline \hline
**CellID** & **Data Records** \\ \hline
7 & \(\text{O}_{11},\text{O}_{31},\text{O}_{41}\) \\ \hline
7.4 & \(\text{O}_{12},\text{O}_{13}\) \\ \hline
7.3 & \(\text{O}_{32}\) \\ \hline
7.2 & \(\text{O}_{42}\) \\ \hline
7.44 & \(\text{O}_{21}\) \\ \hline
7.42 & \(\text{O}_{23}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: CellIDs for data objects. Figure 8: Query processing of dynamic grid indexing method. Figure 7: Example of dynamic grid indexing.
# Smart Preliminary Channel Access to Support Real-Time Traffic in Wi-Fi Networks
###### Abstract  
Real-time applications (RTA) are an important use case for IEEE 802.11 be, a new amendment to the Wi-Fi standard. This amendment introduces new complicated mechanisms to provide low delay and high reliability for RTA, but many of them are not supported by legacy devices that may be present in future Wi-Fi networks. In contrast, the preliminary channel access (PCA) method is designed to satisfy strict RTA requirements even in the presence of legacy devices and does not require significant changes to the Wi-Fi protocol. However, it significantly reduces the capacity for non-RTA traffic. This paper introduces a Smart PCA method,  
support these methods, which may make them useless in heterogeneous networks with STAs of various generations. Moreover, some methods, such as restricted Target Wake Time (r-TWT), are optional, and the STAs, even the 802.11 be ones, that do not support them may violate the prioritization of RTA traffic. To support RTA in such networks, backward compatible and universal methods will be developed and investigated.  
In this paper, we investigate such methods based on changing the channel access parameters and preliminary channel access (PCA) [15]. The PCA method considers accessing the channel in advance before RTA data appearance to reserve the channel for RTA transmissions. However, in scenarios with many RTA STAs, the total duration of reservations becomes rather long, and it significantly degrades the channel usage _efficiency_ by non-RTA STAs, which we define as the portion of time when non-RTA STAs successfully transmit their payload. The main contribution of the paper is the enhanced method of PCA, called Smart PCA. In scenarios with many RTA STAs and given delay quantile limit for RTA STAs, Smart PCA allows increasing the efficiency of non-RTA STAs. In contrast with optional standard mechanisms for RTA prioritization, Smart PCA uses the basic NAV mechanism, mandatory for Wi-Fi devices since the very first generation. Those STAs that do not support Smart PCA cannot prevent RTA STAs from delivering their packets in time but obtain lower throughput. With simulation, we compare the reliability and delays of RTA frame transmission with various methods, as well as the efficiency of non-RTA STAs. As a result, we determine the parameters of RTA traffic and its QoS requirements for which it is advisable to use the proposed method.  
The rest of the paper is organized as follows. Section 2 contains a brief description of the basic channel access method in Wi-Fi networks. In Section 3, we describe RTA-aware solutions in Wi-Fi networks known in the literature. Section 4 introduces the investigated methods of RTA traffic service, including the Smart PCA method proposed in this paper. Section 5 discusses the obtained numerical results. Section 6 concludes the paper.
# New Satellite Selection Approach for GPS/BDS/GLONASS Kinematic Precise Point Positioning
## 2 PPP Mathematical Model and Satellite Selection Algorithms
### The Maximum Volume Algorithm  
With the certification that GDOP is inversely proportional to the volume of the tetrahedron formed by unit vectors pointing from user's position to four selected satellites, Kihara and Okada [14] introduced a heuristic algorithm, that is, the maximum volume algorithm. Compared with the Optimal DOP algorithm, the maximum volume algorithm has some advantages in computation and complexity. In this study, we made a few modifications to the maximum volume method. This algorithm can be described in the following four steps.  
Step 1: According to the elevation mask angle and Space Vehicle (SV) Health, select the healthy satellites.  
Step 2: Give all combinations when selecting four satellites from the healthy \(n\) visible satellites, \(C_{n}^{4}\). Sort by azimuth in descending order in each combination.  
Step 3: Compute the volume of the tetrahedron formed by the unit vectors pointing from the user's position to the four satellites of each combination. The volume equation of a tetrahedron is as follows.  
\[V=\frac{\left|\left(\overset{\rightarrow}{a}\times\overset{\rightarrow}{b} \right)\cdot\overset{\rightarrow}{c}\right|+\left|\left(\overset{ \rightarrow}{b}\times\overset{\rightarrow}{c}\right)\cdot\overset{ \rightarrow}{d}\right|}{6}, \tag{7}\]  
where \(V\) is the volume and \(\overset{\rightarrow}{a}\), \(\overset{\rightarrow}{b}\), \(\overset{\rightarrow}{c}\), and \(\overset{\rightarrow}{d}\) are the unit vectors of satellites S1, S2, S3, and S4, respectively.  
Step 4: Select the satellites of a combination that constitutes the largest tetrahedral volume.
## 4 Case Studies
### Validation of the Genetic Algorithm
#### 4.2.1 IEEE 30-Bus System  
As the first case study, this study optimally allocates RPG units in the IEEE 30-bus system in Figure 11. The proposed hybrid GA optimally allocates nine RPG units, with their total capacity of 183 MVA (47 MVA on bus 5, 32 MVA on bus 7, 56 MVA on bus 8, 7 MVA on bus 13, 11 MVA on bus 15, 5 MVA on bus 21, 5 MVA on bus 26, and 5 MVA on bus 30), at an objective function  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Operation** & **Method** & **Parameter** \\ \hline Reproduction [34, 47, 49] & Probability that produces the fittest offspring (scaled roulette wheel) & \(p=0.001\) \\ \hline Crossover [34, 47, 49] & Arithmetic crossover per offspring & \(P_{c}=1.0\) \\ \hline Mutation [34, 47, 49] & Uniform mutation & \(P_{m}=0.1\) \\ \hline \multirow{2}{*}{Experimental data [47]} & The number of populations & 5000 \\ \cline{2-3}  & The number of generations & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the genetic algorithm (GA).  
Figure 12: Voltage profile of the IEEE 57-bus system, in p.u.
# 3 GPP 5G V2X Error Correction Coding for Various Propagation Environments: A QoS Approach
## 5 Simulation Results
### Overall QoS Evaluation  
By studying the QoS parameters, namely, SNR, power, range, performance, end-to-end latency, number of vehicles and vehicle density, we can draw conclusions about the most effective coding scheme for each parameter separately. The most important thing, however, is to make a comprehensive evaluation of these parameters and study the influence these coding schemes have on them. For this purpose, we measured the average performance of each parameter for each coding scheme and then normalized these measurements in the range of 0-100 so that reliable comparisons can be made. The maximum of the rating scale corresponds to the optimal performance value for each parameter, meaning that for SNR, power and end-to-end latency, it corresponds to the minimum value, and for range, throughput, vehicle numbers and vehicle density, it corresponds to the maximum value. Figure 7 shows the overall performance of each encoding scheme based on the QoS parameters that we have examined. From this figure, we observe that maxlogBCJR and polar have the best overall performance, but they have different fields of optimal performance since maxlogBCJR is better in reliability-based parameters (SNR, power, range, throughput) and polar in latency-based parameters (density, vehicles, latency). SOVA and LDPC have inferior QoS performance in reliability-based parameters, but they reach optimality in latency-based parameters, whereas BCJR and logBCJR have the exact opposite QoS behavior.
## 4 Discussion
### Penetrometer Tests
#### 4.2.1 Effect of Biopolymer Type  
On both tested soil types, all the biopolymer treatments formed crusts, with penetration resistances ranging between 0.5 and 10.95 N, while the control group exhibited no measurable penetration resistance (e.g., Figures 8 and 9). Thereby, the biopolymer type has a significant effect (\(p<0.001\)) on the penetration resistance, with some biopolymers forming stronger crusts than others do (Tables 5 and 6). These trends are consistent with the existing literature, showing that biopolymer-treated soils form crusts with different strengths depending on the biopolymer type (e.g., [9; 10; 12; 41]).  
However, compared to results from the existing literature (Table 6), the penetration resistances measured in this study were mostly lower (Figures 8 and 9). For instance, for soil samples treated with Acacia Gum, [PERSON] et al. [12] reported penetration resistances between 10 and 145 N, between 7.5 and 25 N for Sodium alginate, and between 15 and 39 N for Pectin, respectively. While these penetration resistances are significantly higher than the results of the present study, [PERSON] et al. [12] tested higher application rates (1.3 and 3.5 L/m\({}^{2}\)) and concentrations (0.5 and 5.0 wt%), which explains the resulting discrepancies. In addition, as this study tested penetration resistance after performing the fifth wind tunnel cycle, the repeated handling, weighing, and wind tunnel exposure inevitably impaired the integrity of the crust, which is a further reason explaining the relatively low penetration resistances.  
For fine-grained silica sand, the test results showed that the protein treatments (WP and FBPC) did not achieve penetration resistances as high as those of the polysaccharides (XG, CMC, and CS), which were also applied to medium-grained sand samples treated with biopolymer concentrations <0.75 wt%. The WP treatments achieved the lowest penetration resistances on both the tested soil types (Figures 8 and 9). These trends are in agreement with a previous study by [PERSON] et al. [9], who also found that polysaccharide treatments tend to form stronger crusts than protein-treated samples do. In their study, WP treatments also produced relatively weak crusts. In addition, the results of this study show that at equal concentrations, treatments with XG form stronger crusts than CMC does. By contrast, [PERSON] and [PERSON] [10] reported that CM treatments produce higher penetration resistances than XG does. However, [PERSON] and [PERSON] [10] likely performed their tests with a less potent XG than that in the present study, as they tested XG applications up to 1.5 wt% concentration without reporting the difficulties regarding viscosity. By contrast, the XG used in this study already yields highly viscous solutions that cannot be sprayed at concentrations >0.75 wt%.
## 2 Experimental Section
### Instrumentation and Synthesis  
Gas chromatography and Mass Spectroscopy (GC/MS) spectra were obtained in either electron ionization (EI) or positive/negative electrospray (ESI) modes with the Varian Saturn 2200 GC/MS/MS (ion-trap) coupled to a Varian CP-3800 GC (FactorFour--Capillary Column; Stationary Phase: VF-5 ms; L(m) ID(mm) \(\times\) OD (mm): 30 \(\times\) 0.25 \(\times\) 0.39) or Micromass Platform II ESI/MS (240 V, 10 A). Resultant ions are expressed as m/z values.  
NMR spectroscopy was performed on Bruker Advance 300 MHz or Bruker Avance 300 III MHz spectrometer. Some FIDs from Bruker Advance 300 MHz were processed using Mestrec23. \({}^{1}\)H chemical shifts were recorded as \(\delta\) values in parts per million (ppm) downfield shifts, referenced internally to the residual CHCl\({}_{3}\) singlet at 7.26 ppm. Chemical shifts are presented in multiplicity, coupling constant(s) (\(J\) in Hz), integration and assignments. Abbreviations: s = singlet, d = doublet, t = triplet, q = quartet, p = pentet, dd = doublet of doublet, m = multiplet. \({}^{13}\)C chemical shifts were \({}^{1}\)H decoupled and recorded as \(\delta\) values in parts per million (ppm), referenced internally to the CHCl\({}_{3}\) triplet at 77.0 ppm. Assignments have typically been made using additional information from COSY and gCOSY spectra, DEPT, HMBC and HSQC experiments.  
Flash Column Chromatography was performed using silica gel 60 A, 0.04-0.06 mm (230-400 mesh ASTM).  
Melting Points were determined on a Gallenkamp melting point apparatus, and were uncorrected.
# Joint Radar and Communications Waveform Design Based on Complementary Sequence Sets
## 4 Results
### Performance of the JRC-CWG  
Let the number of WCIs \(M_{c}\) in each JRC-CWG be fixed to 1, and the sidelobe performance of the JRC-CWG with different numbers of waveforms \(M\) used in each JRC-CWG is shown in Figure 5. The sidelobe performances of the two other JRC waveforms, i.e., the LFM-BPSK waveform in [31] and the OPP waveform in [13], are also shown in Figure 5. In Figure 5, the OPP-BPSK denotes the OPP waveforms modulated by the BPSK waveforms. The length of each JRC waveform in Figure 5 is \(N=64\). To measure the sidelobe performance of the LFM-BPSK waveforms and the OPP-BPSK waveforms, the PSLR of SACF of \(M\) different LFM-BPSK waveforms and \(M\) different OPP-BPSK waveforms are calculated, respectively.  
In Figure 5a, the PSLRs of the JRC waveforms versus \(M\) are given. Moreover, the SACFs of the JRC waveforms with the number of waveforms \(M=\)2,4,6 are shown, respectively, in Figure 5b-d. In Figure 5a, when \(M\) increases, the PSLR of all JRC waveforms decreases, and the PSLRs of the designed JRC-CWG-BPSK and the JRC-CWG-16-PSK decrease faster than those of the other two JRC waveforms. Specifically, when \(M=2\), the PSLR of the JRC-CWG-BPSK is 4.8 dB lower than that of the OPP-BPSK waveform, and 14.1 dB lower than that of the LFM-BPSK waveform. When \(M=2\), the PSLR of the designed JRC-CWG-16-PSK is 3.9 dB lower than that of the OPP-BPSK waveform and 13.2 dB lower than that of the LFM-BPSK waveform. When \(M=4\), the PSLRs of the designed JRC-CWG-BPSK and JRC-CWG-16-PSK are at least 120 dB lower than those of the OPP-BPSK waveform and the LFM-BPSK waveform. As depicted in Section 2, \(M=M_{r}+M_{c}\), where \(M_{r}\) is the number of WTOs employed in each JRC-CWG. Thus, \(M_{r}\) will increase with the increasing \(M\) when \(M_{c}\) is fixed. With the increase in \(M_{r}\), more degrees of freedom will be employed to suppress the sidelobe of the JRC-CWG SACF according to (9), which will lead to a lower PSLR than other JRC waveforms. Due to the limitation of the computation accuracy of the used computer, the PSLR of the designed JRC-CWG is approximately \(-150\) dB and will not decrease anymore when \(M\geq 4\). Furthermore, it can be seen in Figure 5 that the sidelobe performance of the designed JRC-CWG-BPSK is close to that of the designed JRC-CWG-16-PSK with different \(M\). That is to say, the modulation approach has little influence on the sidelobe performance of the designed JRC-CWG.  
From Figure 5, we can see that with a sufficiently large \(M\), the sidelobe level of the designed JRC-CWG can be sufficiently suppressed. However, in practice, a large \(M\) is usually not recommended. With a large \(M\), it will take a longer time to transmit one JRC-CWG, and the coefficient \(\delta_{g}\) in (2) may be changed. In practice, the \(M\) should make sure that the coefficient \(\delta_{g}\) does not change during the CPI.  
Figure 5: (a) PSLRs of the JRC waveforms with different number of waveforms \(M\) in each JRC-CWG. (b) SACF with \(M=2\). (c) SACF with \(M=4\). (d) SACF with \(M=6\).
# Shear Strength Improvement of Clay Soil Stabilized by Coffee Husk Ash
## 3 Result and Discussion
### Compaction Test  
The MDD-water content plots for the CHA-treated clay are presented in Figure 7. In general, the MDD of soil samples enhanced with CHA was always above the dry unit weight of their original state. Increasing the CHA percentage to 25% improved the MDD of clay marginally by approximately 3%.  
Figure 5: PI-LL for untreated and treated samples using the plasticity charts of AASHTO (**a**) and USCS (**b**).  
The increase in MDD of clay can be possibly related to agglomeration and flocculation of the clay particles, which resulted in quick cation exchange in the CHA-clay mixture. Moreover, higher dry density results in increased particle interlocking. In addition, the increase in MDD of the clay is attributable to the improved gradation of clay as a result of the addition of CHA, as was previously found. On the other hand, the OMC of treated clay decreased marginally as the CHA concentration increased. The CHA addition appears to have decreased the water solubility of the soil, resulting in a decrease in the OMC [35]. Previous researchers have also reported an increase in MDD and a decrease in OMC when CHA is added [40; 60].