esa-sceva/satcom-chunk-collection huggingface dataset:
# Polar Codes and a M-ary Modulation-Based OFDM-PLC System
## 2 Polar Codes
### Encoding  
Polar codes, as a type of linear block codes, can be represented by encoding parameters \((N,K,A,u_{A^{c}})\). The encoder uses the channel polarization phenomenon to perform channel coding, making it possible to achieve symmetric channel capacity. Through estimating the reliability of polarized channels and performing Gaussian approximation to sort the sub-channels by confidence, reliable sub-channels with high reliability are selected to map \(K\) information bits \(u_{A}\), while low-reliability sub-channels are mapped frozen bits \(u_{A^{c}}\), which are often taken as an all-zero sequence. \(u_{A}\) and \(u_{A^{c}}\) form a complete sequence \(u_{1}^{N}:\,u_{1},u_{2},\ldots,u_{N}\), with a code length of \(N=2^{n}\). Multiplying this sequence by the generator matrix \(G_{N}\) of the polar code yields the encoded bit sequence \(x_{1}^{N}:x_{1},x_{2},\ldots,x_{N}\), which is represented as:  
\[x_{1}^{N}=u_{1}^{N}G_{N} \tag{1}\]  
where the generator matrix \(G_{N}\) is:  
\[G_{N}=B_{N}F^{\otimes n},\;n=\log_{2}N \tag{2}\]  
where \(B_{N}\) is the bit-flip permutation matrix, \(B_{N}=R_{N}(I_{2}\otimes B_{N/2})\), \(R_{N}\) is the parity interleaved, and \(F^{\otimes n}\) denotes the Kronecker product \(n\) times, and \(F=\begin{bmatrix}1&0\\ 1&1\end{bmatrix}\).
## 2 Description of the Study Area  
The study area is located in the northwestern part of Xinxiang City, Henan Province (Figure 1), with a temperate continental monsoon climate and a multi-year average temperature of 14 \({}^{\circ}\)C. The multi-year average precipitation is 586.32 mm, with more than 70% occurring in the rainy season spanning June to September. The multi-year average evaporation is 1772.62 mm, which is three to four times the precipitation with the strongest evaporation in June.  
Xinxiang City is located at the southeastern foot of Taihang Mountains and the northern edge of the alluvial plain in the middle and lower reaches of the Yellow River. The terrain generally inclines from northwest to southeast. The regional landform types are divided into three types of landforms: alluvial valley, alluvial sloping plains and alluvial plains. The geomorphological type of the study area is alluvial sloping plain. The surface of the study area reveals the Quaternary river-phase sedimentary layer with a thickness of 50-60 m, and the lithology is mainly clay and sand. The groundwater type is pore water of loose rock of the Quaternary, and the media of the aquifer are mainly medium sand and fine sand, with coarse sand locally. According to the profile, it can be seen that the lithology from 56 m to 49 m is silty clay and silt. The lithology from 59 m to 49 m is silty clay and silt. This is followed by a shallow aquifer with a thickness of 3 m. However, the aquifer thickness increased significantly at borehole S22. The lithology from 46 m to 36 m is mainly silty clay. The bottom lithology is mainly fine sand. The aquifer is mainly recharged by atmospheric precipitation, lateral runoff and irrigation recharge, and groundwater discharge is mainly exploitation, evaporation and lateral runoff.  
Chromium salt chemical plants existed in the study area during the historical period, and chromium slag from the production process brought serious contamination to the site  
Figure 1: Location of the study area and the sampling sites. DGW represents deep groundwater. SGW represents shallow groundwater. Dots represent shallow sampling points. Triangles represent deep sampling points. The boundary represents the boundary of the numerical model. Henan is located in the central part of China. Xinxiang City is located in the north of Henan Province.  
soil and groundwater (Figure 2). Test results showed that the average concentration of total chromium in the soil was 12,100 mg/kg, and work has been carried out to remediate the soil and groundwater.
# Optical Beamforming Networks for Millimeter-Wave Wireless Communications
## 5 Scalability and Application Potentials
### Scalability Comparison of Different OBFN Architectures  
Table 1 summarizes six performances related to the scalability of OBFN architectures which are elaborated above. The size and weight of OBFNs are intuitive performances corresponding to the number of free-space components and fiber components used in architectures [14]. The power consumptions of OBFNs are mainly induced by lasers, except for the electric and photonic amplifiers [65]. Here, the power consumptions of OBFNs are simply evaluated using the number of lasers applied in these architectures. OBFNs' loss originates from the insertion loss of components and propagation loss of waveguides. OBFNs' bandwidth is determined by the mechanisms of time delay and the bandwidth of optical devices. TTD architectures have a wider bandwidth than that of phase shifter array architectures, while MRR and MZI-TTD have a smaller bandwidth than other TTD architectures do since time delays of MRR and MZI-TTD remain constant within a limited frequency range [23; 45]. Multi-beam beamforming is a significant ability of OBFNs for massive PAAs which have a narrow beam and should meet the demand of multiusers [9]. The capability of the multi-beam beamforming is assessed via considering the feasibility of producing multiple beams with current architectures. It is shown in Table 1 that, architectures of MRR group delay, MZI delay, photonic crystal, time delay selection with integrated devices, and integrated phase shifter array are of a small size, are light weight and have relatively low power consumption, which are amendable for aerospace applications and pole-mounted base stations. The fiber dispersion delay architectures and time delay selection architectures of SLMs have the advantage of low loss and wide bandwidth. Meanwhile, matrix architectures have the inherently ability of multi-beam beamforming with a relatively small size, weight and power consumption. Therefore, the scalability of these architectures may be evaluated according to the application scenario, while there is not an architecture that can meet the requirements of all applications. For example, the time delay selection architectures of SLMs can easily establish a massive OBFN for massive PAAs in a scenario without considering its size and weight; however, integrated architectures are more appropriate for aerospace antennas and pole-mounted antennas.
# A Review of Geometry, Construction and Modelling for Carbon Nanotori
## 5 Physical Roles, Properties and Applications
### Magnetic Properties  
The magnetic properties of carbon nanotori are strongly dependent upon their size, curvature, symmetry and any defects [59; 60]. Typically, they have strong magnetic moments which are 130 times larger than those of the benzene molecule [61]. In the absence of the magnetic flux, the metallic nanotori exhibit paramagnetism, while the semiconducting tori show diamagnetism. The induced magnetic moments are independent of the minor radius of the nanotori, but inversely proportional to the major radius [62; 63]. Moreover, the magnitude of the moments decreases with increasing temperature as shown in Figure 15 [64].  
Persistent currents can be generated by introducing a magnetic flux through the carbon nanotori. Such currents originate from the electronic orbital motion and are periodical linear functions of the magnetic flux. The currents are strongly dependent upon the chiral angle of the chiral vector arising from the graphene sheet construction, and become stronger as the angle approaches \(\pm 15^{\circ}\)[65]. Further, the tube-ring interactions from the nanotori encapsulated with a carbon nanotube inside also induces a charge transfer between the interior cavity of the carbon nanotori [66]. However, the persistent currents in the carbon nanotori are diminished as the temperature increases due to the Fermi energy at the charge neutrality point [67].  
Carbon nanotori also possess high magnetic resistance which depends upon the strength and the number of the dephasing collisions of electrons inside. However, their magnetic resistance decreases with increasing magnetic field [68].
## 3 Methods and Data Sources
### Impact Factor Selection
#### 3.1.2 Remote Sensing Indicators  
This study utilizes NPP/VIIRS nighttime light data as remote sensing information, sourced from the NPP system established by NASA. This system effectively eliminates transient brightness caused by factors such as fires and explosions, retaining only relatively stable lighting. Furthermore, the data employed are all cloud free, allowing for a more accurate reflection of population distribution and economic development. For this research, we have collected and processed monthly and annual average NPP/VIIRS data collected since 2012. During this process, we first excluded certain special areas (such as the National Grand Theatre and major airports in Beijing) that consistently exhibited high brightness anomalies to minimize errors. The brightness values of these excluded regions were replaced with the average values from other pixels within those areas. Subsequently, we extracted and processed the illumination intensity for each pixel. Figure 1 shows the changes in lighting data for each district in Beijing since 2012. The meanNTL in the figure indicates the average lighting brightness value of the area, and the sumNTL indicates the total lighting brightness value. From the figure, it can be seen that several districts in Beijing, such as Chaoyang District, Dongcheng District, Xicheng District, and Haidian District, where development has just started, and Tongzhou District, Shijingshan District, etc., where nighttime lighting data has been on a sharp upward trend in the past two years, the light brightness value of each district is overall constantly increasing. However, under the influence of the COVID-19 pandemic in 2019, the rising trend of nighttime lighting data slowed down from 2019 to 2020, with some districts showing a decline. Considering the contribution of district area factors to municipal waste production, sumNTL was chosen as a key influencing factor.  
\begin{table}
\begin{tabular}{c c c} \hline
**Economic Indicators** & **Social Indicators** & **Demographic Indicators** \\ \hline Per capita consumption expenditure & Urban road area & Number of household population \\ \hline Disposable income per capita & Gross domestic product & Resident population density \\ \hline Total retail sales of social consumer goods & Street cleaning area & Number of resident populations \\ \hline Tertiary industry output & Public green space area & Number of inbound tourists \\ \hline - & Citywide centralized heating area & Number of vehicles operating in public transportation \\ \hline \end{tabular}
\end{table}
Table 1: Main factors affecting waste production.
# Modeling and Parameter Calibration of Morchella Seed Based on Discrete Element Method
## 3 Results and Discussion
### Parameter Optimization and Verification Test  
Using the repose angle of 37.63\({}^{\circ}\) measured from physical experiments as the target value, the regression model was optimized to obtain various parameter combinations, which were then individually validated through simulation. During the simulations, the remaining contact parameters were kept consistent with those used in the Plackett-Burman experiments. After multiple comparisons with the physical experiment results, the contact parameter combination that most closely matched the repose angle from the physical tests was determined as follows: the seed-to-seed rolling friction coefficient (\(C\)) was 0.092, the seed-to-seed surface energy (\(D\)) was 0.015, and the seed-to-steel rolling friction coefficient (\(G\)) was 0.086. By applying these optimized calibrated contact parameters to the discrete element model, and after conducting three repeated simulations, the average simulated repose angle was 37.69\({}^{\circ}\). This value had a relative error of only 0.16% compared to the physical experiment result of 37.6\({}^{\circ}\), indicating a high degree of consistency between the simulated and physical repose angles, as shown in Figure 7.  
\[\left\{\begin{array}{c}\text{min}|\theta(C,D,G)-37.63|\\ 0.05\ \leq\ C\ \leq\ 0.15\\ 0.01\ \leq\ D\ \leq\ 0.04\\ 0.05\ \leq\ G\ \leq\ 0.19\end{array}\right. \tag{8}\]  
Error: \((37.69\ -\ 37.63)/37.63\times 100\%=0.16\%\).
## 4 Conclusions  
From the results discussed above, we can conclude that, in general, physically activated biochars produced from both wheat straw and vine shoots are promising adsorbents for CO\({}_{2}\) capture under realistic postcombustion conditions. As expected, pore size distribution plays a key role in the CO\({}_{2}\) adsorption behavior. In this sense, biomass-derived activated carbons having a hierarchical structure (with a high volume of ultra-micropores but also a certain volume of mesopores) appear as the ideal candidates for an efficient and selective CO\({}_{2}\) adsorption from a dry flue gas under dynamic conditions. However, the presence of relatively high concentrations of water vapor in the feeding gas clearly interferes with the CO\({}_{2}\) adsorption mechanism, leading to significantly shorter breakthrough times (i.e., lower percentages of used bed). The differences observed here in the performance under humid conditions of two activated carbons (produced from different biochar precursors) suggest that surface chemistry, and especially the concentration and accessibility of hydrophilic OFGs, could exert considerable influence on the water adsorption rate. Therefore, future research should focus on synthetizing biomass-derived activated carbons with an appropriate hierarchical pore size distribution, relatively high bulk density, and low affinity to water molecules. For this purpose, the combined effects of the nature of the precursor, pyrolysis conditions, activation conditions, and degree of burn-off should properly be addressed.  
**Supplementary Materials:** The following are available online at [[http://www.mdpi.com/2076-3417/10/1/376/s1](http://www.mdpi.com/2076-3417/10/1/376/s1)]([http://www.mdpi.com/2076-3417/10/1/376/s1](http://www.mdpi.com/2076-3417/10/1/376/s1)). Figure S1: CO\({}_{2}\) breakthrough curves for five consecutive cycles (adsorption temperature = 25 \({}^{\circ}\)C) from a dry binary mixture of N\({}_{2}\) and CO\({}_{2}\) (with a CO\({}_{2}\) composition of 13.75-14.25 vol. %).  
**Author Contributions:** Conceptualization, [PERSON]; methodology, D.G.-M., B.G. and J.J.M.; software, D.G.-M. and J.J.M.; validation and formal analysis, D.G.-M., B.G. and J.J.M.; writing--original draft preparation, J.J.M.; project administration, B.G.; funding acquisition, J.J.M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received funding from the Spanish Ministry of Science, Innovation, and Universities (ERANET-MED Project MEDWASTE, ref. PCIN-2017-048).  
**Acknowledgments:** The authors also acknowledge the funding from the Aragon Government (Ref. T22_17R), co-funded by FEDER 2014-2020 \"Construyendo Europa desde Aragon\".  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Dilemma-Based Learning-to-Rank Approach for Generative Design in Urban Architectural Regeneration
## 2 Proposed Methodology
### Evaluation Module
#### 2.2.2 User-Based Ranking Mechanisms  
The AIGI technology enables the generation of a vast number of images with potential interventions, offering architects and urban planners inspirational ideas. However, the large volume of generated images poses a challenge for evaluation, as it is impractical for architects to manually assess every image. To tackle this, we introduce a dilemma-based learning approach, where the user selects the preferred image between pairs, helping to model their preferences effectively.  
This approach involves two key steps. First, all generated images are transformed into feature vectors based on predefined parameters such as green spaces and realism, as outlined in Section 2.2.1. Then, the user selects the best image from an indicative subsample of pairs. These selections provide data to train pairwise classification algorithms (learning-to-rank), which sort the entire dataset according to user preferences. Figure 4 outlines the workflow for this process.  
For the mathematical representation of the dilemma-based learning, let us define as \(I_{x}\) and \(I_{y}\) the two comparative images, and \(\bar{I}=[I_{x},I_{y}]\) the 2D-vector of images. In addition, the method for selecting the best of the two images is defined as \(BEST(I_{x},I_{y})=BEST(\bar{I})\). Thus, the dilemma-based learning problem can be expressed in the form of Equation (3). This formula is first introduced by [PERSON] et al. (2016) in 1997 as the problem of \"Learning to Order Things\" (i.e., rank ordering, sorting). The evolution in computation updated the initial definition by the introduction of several pairwise classification ([PERSON] et al., 2017) algorithms.  
\[\bar{I}\xrightarrow{BEST(\bar{I})}\begin{cases}0&\text{if $I_{x}$ is better than $I_{y}$}\\ 1&\text{if $I_{y}$ is better than $I_{x}$}\\ \frac{1}{2}&\text{otherwise}\end{cases} \tag{3}\]
# Stability Analysis of Soil Flow Protector and Design Method for Estimating Optimal Length
## 3 Results and Analysis
### Design Method  
Prior to developing the design method, we comprehensively analyzed the research results. The lower length of the SFP is a ground settlement reduction factor, and the upper length influences the SFP stability in terms of the ground settlement. Therefore, Equation (2) can be used to calculate the optimal length (\(L_{SFP}\)), while considering the function and role of the SFP that satisfies the safety factor (\(F_{s}\)) of 1.2 for the stability and has a significant settlement reduction effect. Here, \(l_{\alpha}\) is the lower length of the SFP and is a factor influencing the settlement reduction effect; and \(l_{\beta}\) is the upper length of the SFP and is obtained by applying a margin coefficient \(n\) for the predicted settlement (\(n\)-\(\delta\)). The value of the margin coefficient is used in the safety review, considering the ground conditions and the residual settlement; this value may be applied at the discretion of the technician. The maximum value of the upper length (\(l_{\beta}\)) does not exceed the height (\(l\)) of the box structure.  
\[L_{SFP}=l_{\alpha}+l_{\beta}\ (Fs>1.2) \tag{2}\]  
We found the sum of the 85.0 mm lower length (\(l_{\alpha}\)) (OLR of 0.34 and a surface settlement ratio of 0.45) and 52.1 mm upper length (\(l_{\beta}\)) with a margin factor of 1.5 to be 137.1 mm, using this equation. The method for calculating the total length of the SFP is depicted via a flowchart in Figure 14.  
Figure 13: Types of earth pressure acting on the SFP. \(P_{\text{p}}\): passive pressure; \(P_{\text{a}}\): active pressure [26].
# Model Modification of the Soil-Water Characteristic Curve of Unsaturated Weak Expansive Soil
## 3 Test Process  
### Mercury Intrusion Porosimetry Test  
Mercury intrusion porosimetry (MIP) was first proposed by [PERSON] and [PERSON] [20], while the mercury pressure test is based on mercury's non-moistness to solid surfaces. In this paper, it is assumed that the porous material is composed of cylindrical capillaries of various sizes. According to the lifting principle of liquid in the capillary, the microporous structure of the material is measured. Based on the Washburn Equation, the relationship between pressure P and capillary radius R for mercury is calculated as follows:  
\[P=-\frac{2\sigma cos\theta}{R} \tag{1}\]  
where P is the pressure of the intruded mercury (N\(\cdot\)m\({}^{2}\)), \(\sigma\) represents the surface tension of mercury (\(\sigma\) = 0.485 N\(\cdot\)m\({}^{-1}\)), \(\theta\) is the wetting angle between the measured porous material and mercury, and R denotes the pore radius (nm). (The symbols appearing in this manuscript are re-annotated in Appendix A).  
The test preparation had a dry density of 1.4-1.8 g/cm\({}^{3}\), and the expansion soil samples with equal intervals of 0.1 g/cm\({}^{3}\) were divided into five groups. Next, the specimens were cut into small pieces and put into a test tube filled with isopropyl. Each sample's internal moisture was converted into crystalline sublimation by vacuuming for 24 h. The samples were dried via lyophilization in liquid nitrogen to avoid changes in pore structure caused by water loss. Next, the dried samples were loaded into a dilatometer for low-pressure and high-pressure mercury intrusion tests, and then pore volume accumulation was generated.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Index Property** & **Value** \\ \hline Specific gravity & 2.74 \\ Montmorillonite content & 2.80\% \\ Illite content & 3.70\% \\ Free expansion rate & 57.00\% \\ Cation exchange capacity & 221.47 mmol\(\cdot\)kg\({}^{-1}\) \\ Liquid limit & 40.87\% \\ Plastic limit & 20.02\% \\ Plasticity index & 20.85\% \\ \hline \hline \end{tabular} Note: According to the test results, the expansion grade is defined by the “Railway Engineering Geotechnical Classification Standard” (TB10077-2019), while the result is weak expansion soil.  
\end{table}
Table 1: Basic physical and physical indicators.  
Figure 2: Comparison of expansive soil before and after encountering water.
## 5 Proposed Method
### Delay  
End-to-end delay is defined as the time required for sending packets from the source node to the destination node. The first scenario tests delay based on the packet sending rate. It is displayed in Figure 12. According to this figure, high PSR leads to high delay in all routing approaches because high PSR causes congestion in the network. As a result, data packets wait longer in the buffer queue, meaning that the queuing delay increases. Therefore, delay increases in the data transmission process. The second scenario evaluates delay based on the signal transmission radius (STR) of vehicles. This is shown in Figure 13. When STR is large, delay decreases in all methods because the number of hops is reduced in the routing path. According to Figures 12 and 13, QFHR has the least delay compared to other approaches. In Figure 12, the proposed scheme lowers delay by 16.19%, 22.82%, 34.15%, and 59.51%, compared to IRQ, IV2 XQ, QGrid, and GPSR, respectively. Moreover, in Figure 13, QFHR decreases delay by 19.86%, 34.26%, 44.09%, and 68.39% compared to IRQ, IV2 XQ, QGrid, and GPSR, respectively, because QFHR uses a distributed Q-learning-based routing process to choose the next intersection with regard to road congestion status and road connection quality. In addition, in the V2V routing process at each road section, a fuzzy logic-based route recovery process is considered to obtain the next-hop vehicle in accordance to the queue status and the connection quality of vehicles. These techniques can prevent congestion in the network and lower delay in the operation. On the other hand, IRQ, IV2 XQ, and QGrid are centralized routing methods because the central server performs the route discovery process and sends it to vehicles on the network. This boosts  
Figure 11: PDR in different approaches based on STR.  
Figure 10: PDR in different approaches based on PSR.  
the delay in the packet transmission operation. Moreover, IRQ must periodically send traffic messages to the central server to calculate routes based on these messages. This is another factor in increasing delay in IRQ. Note that IRQ and IV2 XQ are equipped with a congestion control mechanism. Thus, they have an acceptable delay. However, QGrid has not designed any congestion control mechanism on the network. This is the most important reason for the high delay in this method. GPSR has also experienced the worst delay in comparison with other methods because it involves the local optimum problem.