esa-sceva/satcom-chunk-collection huggingface dataset:
# Spatiotemporal Heterogeneous Effects of Built Environment and Taxi Demand on Ride-Hailing Ridership
## 3 Methodology
### Modeling Approach
In this study, the GWR can be expressed as follows:  
\[y_{i}=\beta_{i0}(u_{i},v_{i})+\sum_{j=1}^{l}\beta_{ij}(u_{i},v_{i})x_{ij}+ \epsilon_{i}, \tag{3}\]  
where \(y_{i}\) represents ride-hailing ridership in grid \(i\); \((u_{i},v_{i})\) represents the coordinates of the centroid of grid \(i\); \(\beta_{i0}\) represents the local intercept for grid \(i\); \(x_{ij}\) represents influencing factor \(j\) in grid \(i\); \(\beta_{ij}\) represents the corresponding effect of influencing factor \(j\) in grid \(i\). The parameters are commonly estimated based on the locally weighted least squares method in the GWR. The value of \(\beta_{ij}\) at \((u_{i},v_{i})\) can be estimated as follows:  
\[\hat{\beta}_{j}(u_{i},v_{i})=\left[X^{T}w(u_{i},v_{i})X\right]^{-1}X^{T}w(u_{ i},v_{i})Y, \tag{4}\]  
where \(w(u_{i},v_{i})\) represents the matrix of spatial weights. The adaptive bi-square kernel is used to obtain the matrix of spatial weights as follows:  
\[w_{ll}=\left\{ \begin{array}{l}\left(1-d_{il}^{2}/\theta_{i(l)}\right)^{2}, \;d_{iu}<\theta_{i(l)}\\ 0\;\;\;\;\;\;\;\;\;\;,d_{il}>\theta_{i(l)}\end{array} \right., \tag{5}\]  
where \(w_{il}\) represents the spatial weights between grids \(i\) and \(l\); \(d_{il}\) is the Euclidean distance between grids \(i\) and \(l\); \(\theta_{i(l)}\) is the nearest distance between grids \(i\) and \(l\). In the GWR model, grids near to \(i\) tend to generate greater effects than those farther from grid \(i\). In general, the GWR model has a fixed bandwidth, and it is determined by the Euclidean distance and number of nearest neighbors [35]. (3) MGWR  
MGWR is a multiscale extension of GWR. It relaxes the assumption of a fixed bandwidth and allows the link between the endogenous variable and exogenous variables to vary spatially and at different scales. These characteristics enable MGWR to provide more accurate and robust estimations of spatial links [26; 36; 37]. The MGWR of ride-hailing ridership and its influencing factors in this study are expressed as follows:  
\[y_{i}=\beta_{bw0}(u_{i},v_{i})+\sum_{j=1}^{l}\beta_{bwj}(u_{i},v_{i})x_{ij}+ \delta_{i}, \tag{6}\]  
where \(\beta_{bw0}(u_{i},v_{i})\) represents the intercept at grid \(i\); \(\beta_{bwj}(u_{i},v_{i})\) represents the local regression coefficient of factor \(j\); \(bwj\) represents the bandwidth of the coefficient of factor \(j\); \(\delta_{i}\) represents the random error term. Compared to GWR, MGWR has some advantages, such as accurately depicting spatial heterogeneity and reducing biased estimations [26; 38].
## 5 Comparative Study  
Table 3 shows a comparison of the proposed work with the most related works found in the literature. The comparison is carried out in terms of various parameters, such as unit cell size, geometry, supporting material, operating bandwidth, and angular stability, especially for the TM mode. The comparative study analysis reveals that most of the reported works have wide angular stability, provide good shielding performance, and have small dimensions. However, their shielding performance and the rejection bandwidth deteriorate with variations in the oblique angle, particularly for TM polarization mode. In contrast, the proposed FSS shield does not suffer from such shortcomings. It has a compact, fourfold symmetric structure, and angle variations do not affect its performance. Thus, it exhibits a highly stable, polarization-insensitive response for both the TE and TM wave modes up to 60\({}^{\circ}\), while maintaining a low profile. It can be used in two configurations as an additional feature.
# FOXS-GSC--Fast Offset Xpath Service with HexagonS Communication
## 3 Proposed Solution
### FoxS-GSC Route Service Operation  
The FOXS-GSC route service operation is divided into two stages: (i) data gathering and data processing, in which data transmission and road traffic classification occur; and (ii) service delivery, in which the proposed algorithms and techniques are used to compute new routes and deliver them to users.
## 4 Effect of the Subsidence Width in the Numerical Model
### Analysis of the Soil Displacement  
Figure 12 shows the soil displacement contours of the different subsidence widths. The comparison shows that the anchorage ratios of \(L=4.0B\), \(L=3.33B\), \(L=2.86B\), \(L=2.50B\), and \(L=2.22B\) (See Figure 12a-e), when the anchorage length is unchanged and the width of the subsidence area increases, the value of the vertical soil displacement at the bottom of the subsidence area increases accompanied by increasing the subsidence width. This indicates that the increase of the subsidence width promotes the increase of the soil displacement at the bottom of the subsidence area. The vertical soil displacement legend is shown at the right side of each cloud image. The maximun value of the soil displacement is at the bottom right of the subsidence area in each image, and the displacement occurs along the height. The soil displacement above the subgrade collapse shows an obvious \"semi-ellipse\" shape, which indicates that the geotextile in the subsidence area deforms downward after the soil moves downward, and the soil arch forms due to the small differential subgrade collapse which is helpful to reduce the soil displacement in the stable area.  
Figure 12: Vertical displacement cloud image of the soil at the final state of the subgrade collapse for the different subsidence widths: (**a**) \(L=4.0B\); (**b**) \(L=3.33B\); (**c**) \(L=2.86B\); (**d**) \(L=2.50B\); (**e**) \(L=2.22B\).  
Figure 13 is the vertical displacement of the surface distribution for the full section which is derived from the half-symmetric model. Each curve has a vertical settlement peak in the central position of the subsidence area and the settlements at other positions are symmetric around the central position.  
According to the BS 8006 [34], the ratio of the maximum subgrade collapse of the subgrade surface _ds_ to the deformation range of the pavement _Ds_ should be limited to 1%. The maximum subsidence with the anchorage ratios of \(L=4.0B\), \(L=3.33B\), \(L=2.86B\), \(L=2.50B\) and \(L=2.22B\) at the surface are shown in Table 5. The maximum subgrade collapse of all of the groups is smaller than the specified value of \(ds/Ds\) of 1% in BS 8006.  
The geotextile plays an important role in the treatment of the subsidence area. Overall, the subsidence width of the geotextile is a key factor. When geotextiles are used to treat a subgrade collapse of a subgrade collapse with the subsidence width of an anchorage ratio for \(L\leq 2.0B\), the geotextile will break or the soil structure will have a subgrade collapse, leading to the instability of the treated structure, which is consistent with the laboratory test results.
# First-Principles Study for Gas Sensing of Defective SnSe\({}_{2}\) Monolayers
## 4 Discussion
### Gas Molecules on the O-Doped SnSe\({}_{2}\) Monolayer  
When NO\({}_{2}\) adsorbed on the O-doped SnSe\({}_{2}\) monolayer, the values of \(\rm E_{ad}=-0.32\) eV and \(\rm\Delta Q_{b}=-0.145\)e are close to the molecule on the pristine SnSe\({}_{2}\) monolayer (\(\rm E_{ad}=-0.29\) eV, \(\rm\Delta Q_{b}=-0.164\)e [8]). The DOS of the O-doped SnSe\({}_{2}\) monolayer is similar to that of the pristine SnSe\({}_{2}\) monolayer, so that the similar appearance of DOS occurs in NO\({}_{2}\) adsorption on the pristine and O-doped SnSe\({}_{2}\) monolayer as shown in Figure 4a,c. The DOS of NO\({}_{2}\) on the O-doped SnSe\({}_{2}\) monolayer induces trap state crossover \(\rm E_{F}\) and decreases the carrier mobility. The electronic structure altered by NO\({}_{2}\) adsorbed on the O-doped SnSe\({}_{2}\) monolayer would contribute a change of electrical conductance, just like NO\({}_{2}\) adsorption on the pristine SnSe\({}_{2}\) monolayer [8].  
On the other hand, the value of \(\rm E_{ad}=-0.13\) eV and \(\rm\Delta Q_{b}=0.000\)e for NH\({}_{3}\) on the O-doped SnSe\({}_{2}\) monolayer are both the minimum among the NH\({}_{3}\) adsorption. The DOS of NH\({}_{3}\) on the O-doped SnSe\({}_{2}\) monolayer is below the \(\rm E_{F}\), which results in similar electrical properties with the O-doped SnSe\({}_{2}\) monolayer without gas adsorption. This means that there is no obvious change of electrical conductance.  
In conclusion, gas molecules on the O-doped SnSe\({}_{2}\) monolayer shows high sensitivity for NO\({}_{2}\) adsorption and even with a weaker detection for NH\({}_{3}\), compared to gases on the pristine SnSe\({}_{2}\). This result indicates that the O-doped SnSe\({}_{2}\) monolayer has better selectivity to these two gases in comparison with pristine SnSe\({}_{2}\).
## 5 Conclusions  
In conclusion, the effect of the DoP of the partially coherent Gaussian Schell-model beam on a heterodyne detection system between propagations along an uplink path and a downlink path in atmospheric turbulence has been studied. We found a new result according to which the DoP and heterodyne efficiency of an uplink path are higher than that of a downlink path when \(\sigma_{sy}>0.02\) m or \(\delta_{sy}\geq 0.03\) m, which indicates that \(\sigma_{sy}\) and \(\delta_{sy}\) had a more significant impact on heterodyne efficiency and DoP than the turbulence term on the uplink and downlink paths. We propose a method to obtain a higher heterodyne efficiency based on real propagation paths based on the above description, and demonstrate that this method is valid for any waist radius, higher and lower coherence cases, any atmospheric turbulence intensity, transmission distance and detector aperture.  
Methodology, Z.T.; formal analysis, Z.T.; investigation, S.L. and P.W.; resources, Y.K. and P.W.; data curation, J.W. and S.L.; writing--original draft preparation, Z.T., J.W. and Y.K.; writing--review and editing, J.W. and S.L.; supervision, Z.T. and J.W.; funding acquisition, Z.T. and J.W. All authors have read and agreed to the published version of the manuscript.  
This paper is supported by the National Natural Science Foundation of China (62001363, 62101313), Key R&D Program of Shaanxi Provincial Department of Science and Technology--General Projects in Industrial Field (2022 GY-100). Young Talent Support Project of Shaanxi Association for Science and Technology (20220142).  
Not applicable.  
The authors express their appreciation to the anonymous reviewers for their valuable suggestions.  
The authors declare no conflict of interest.  
Figure 8: Effect of detector aperture \(D\) on the heterodyne efficiency of an uplink path and a downlink path.
## 2 Materials and Methods
### Bacterial Isolates and Antimicrobial Susceptibility Determination  
Six non-replicate clinical MRSA isolates from specimens of blood, sputum, pus, and wound swaps were used for the investigation. They had been stored in the Microbank of the Microbiology division of the College of Medicine, King Faisal University at a temperature of \(-80\)\({}^{\circ}\)C. They were cultured on blood agar and incubated aerobically at 37 \({}^{\circ}\)C for 24 h. Isolates were identified with a Vitek compact 2 automated system (BioMereius, Marcy L'Etoile, France) using the Gram-positive ID cards according to the guidelines of the manufacturer. The antimicrobial susceptibility and minimum inhibitory concentrations were determined using the AST-GN cards of the Vitek 2 compact automated system (BioMereius, Marcy L'Etoile, France). They were against the following antibiotics: augmentin (AUG), benzyl penicillin (BENZ), oxacillin (OXA), cefuroxime (CEF), cefuroxime/axetil (CEF/AXE), clindamycin (CLD), amikacin (AK), impienem (IMP), ciprofloxacin (CIP), levofloxacin (LEVO), erythromycinycin (ERY), sulfamethoxazole/trimethoprim (SXT), tigecycline (TG), tetracycline (TET), rifampicin (RIF), and gentamicin (GEN). Confirmation of resistance against impienem (IMP), tigecycline (TG), and amikacin was by disc diffusion method (Oxoid Ltd., Basingstoke UK). All of the MRSA isolates had been confirmed by the presence of the _mecA_ gene.
# A Fuzzy-Logic-Based Load Balancing Scheme for a Satellite-Terrestrial Integrated Network
## 2 Materials and Methods  
This section firstly describes the structure and problems to be solved in STIN. Then the proposed solutions are described: The FEM is proposed to pre-evaluate the impact of users on overloading. In the proposed FL-LB, the access algorithm runs in a centralized control cell and determines initial access in order to prevent overloading before it occurs. The offloading algorithm runs in each cell and determines how to offload users in the overloaded cells. The proposed metric and algorithms are described in detail in the following subsections.
## 2 Literature Review  
### Ride-Hailing Services and Data Sources  
The sharing economy involves internet-based platforms that enable individuals to exchange capital, assets, and services, thereby making use of underutilized resources and shifting consumers from owning assets to sharing them [32]. Within this context, the transportation sector has been at the forefront of the sharing economy, introducing a range of technology-enabled mobility solutions such as car-sharing, bike-sharing, and scooters sharing [33; 34; 35]. Among these, ride-hailing, a commercial form of car-sharing, separates vehicle usage from vehicle ownership [36], allowing individuals without a personal car to access on-demand driver and vehicle services through smartphone applications [37; 38]. Studies indicate that the key factors attracting residents to ride-hailing services include lower costs, reasonable travel times, convenient payment, avoiding driving under the influence, and short waiting periods [36; 37; 38; 39; 40; 41]. These advantages not only streamline travel decisions but also gradually influence travel behaviors, shaping trip frequency and type, destination choices, travel times, and the range and nature of activities [42; 43; 44; 45]. Since ride-hailing services can pick up and drop off passengers at virtually any point in a city [46; 47; 48], they have a particularly strong connection to the built environment, offering new perspectives for examining interactions between urban spaces and travel behavior. However, many of these studies rely on data from specific regions or user surveys, which may limit the generalizability of their conclusions across different socio-spatial and cultural contexts.  
Recently, the data availability for analyzing ride-hailing demand has expanded significantly, with cities such as Chengdu [49], New York [50], and London [51] making ride-hailing data publicly accessible. This openness holds substantial potential for advancing the understanding of spatiotemporal travel demand. Traditional surveys or official statistics are often used to explore factors related to the built environment [52; 53], and ongoing developments in information technology have made data collection through web scraping more feasible [54]. POIs, serving as one representative form of open data, have gained popularity in land-use analysis and are considered a practical means of accurately identifying and interpreting the nature of specific areas [15; 55; 56]. Moreover, the broad use of social media platforms such as Twitter and Weibo offers an opportunity to examine the public sentiment and behavioral patterns related to ride-hailing services [57; 58; 59], supplementing or even replacing traditional surveys by providing real-time detailed insights.
## 2 Methods  
Poly(methylhydrosiloxane), PMHS (MW-1900, CAS: 63148-57-2) and divinylbenzene (DVB, technical grade, 80%, CAS: 1321-74-0) were purchased from Alfa Aesar (Alfa Aesar Ward Hilt, Haverhill, MA, USA). The solvents, cyclohexane and acetone, were bought from J. T. Baker (Fisher Scientific Italia, Rodano, Italy), tetrahydrofuran (THF) from Sigma-Aldrich (Sigma-Aldrich, Saint Louis, MO, USA) and n-hexane from Chem-Lab (Chem-Lab, West-Vlaanderen, Belgium). The catalyst (platinum divinylmethylsiloxane complex, \(\sim\)2% in xylene (CAS: 68478-92-2) was bought from Sigma Aldrich, Saint Louis, MO, USA. All the chemicals were used as purchased without any further purification steps.  
All samples were prepared with a 1:2 mass ratio of PMHS to DVB. In a standard reaction 0.5 g (0.47 cm \({}^{3}\)) PHMS and 1.0 g (0.93 cm\({}^{3}\)) DVB were mixed in 5.5 g of solvent. The mixture was homogenized with a magnetic stirrer for two minutes before 10 \(\upmu\)L of the Pt catalyst were added under stirring for another two minutes. Thereafter the mixture was transferred into a pressure reactor (Parr acid digestion vessel; model 4749 Parr, Moline, IL, USA) and placed into a furnace at 150 \({}^{\circ}\)C for 6 h. After crosslinking, the sample was taken off the furnace and allowed to cool to room temperature. The wet gel was extracted from the container and washed five times with solvent to eliminate unreacted reagents and catalyst. Thereafter, the wet gel was transferred into a home-made CO\({}_{2}\)-reactor equipped with two glass windows that allow visual control of the solvent exchange with CO\({}_{2}\) and supercritical drying process. Inside the reactor, the sample was washed with liquid CO\({}_{2}\) at 10 \({}^{\circ}\)C twice a day for a total of 10 solvent exchanges. As the final step of the drying process, the temperature in the dryer was slowly increased to 45 \({}^{\circ}\)C at a pressure of 100-110 bar. Supercritical drying was then performed overnight. The complete procedure was performed using THF, cyclohexane, n-hexane, and acetone as solvents.  
Porosity characterization was performed by N\({}_{2}\) physisorption at \(-\)196 \({}^{\circ}\)C with a Micromerities ASAP 2010 instrument (Micromerities, Norcross, GA, USA). Specific surface area (SSA) of the samples was determined in the relative pressure (p/p\({}_{0}\)) range between 0.05 and 0.30 using the Brunauer-Emmett-Teller approach (BET). The total pore volume (TPV) was calculated as TPV = V\(\upalpha\)D, where V\(\upalpha\) is the volume adsorbed at P/P\({}_{0}\) 0.99, and D equals the density conversion factor (0.0015468) for the nitrogen gas as adsorbate gas. The assumption that the pores are open-ended and cylindrical is used. The average pore size is calculated using the equation 4000 TPV/SSA with the average pore size given in nanometers.  
Microstructural characterization was performed by acquiring Field Emission Scanning Electron Microscopy, FE-SEM, images of the fracture surface of PDC aerogels with a Zeiss supra 60 equipment (Carl Zeiss NTS GmbH, Oberkochen, Germany) operating in high-vacuum mode at 2.00 kV and after sputtering the samples with a thin gold film.
# Influence of CeO\({}_{2}\) Addition to Ni-Cu/HZSM-5 Catalysts on Hydrodeoxygenation of Bio-Oil
## 2 Materials and Methods
### Preparation of Catalyst  
The CeO\({}_{2}\)-Ni-Cu/HZSM-5 catalyst was prepared by the co-impregnation method. First, the load of Ni-Cu (8:1) was 15%. \(X\) g of Ce(NO\({}_{3}\))\({}_{2}\)-6H\({}_{2}\)O (\(X\) = 0.5, 1.0, and 1.5), 2.643 g of Ni(NO\({}_{3}\))\({}_{2}\)\(\cdot\)6H\({}_{2}\)O, and 0.2534 g of Cu(NO\({}_{3}\))\({}_{2}\)\(\cdot\)3H\({}_{2}\)O were mixed and stirred with 60 mL of deionized water. Then, 4 g of HZSM-5 was added to the solution, which was then heated in a water bath at 60 \({}^{\circ}\)C and evaporated to a paste state. Subsequently, the specimen was dried at 110 \({}^{\circ}\)C for 12 h and then calcined at 400 \({}^{\circ}\)C for 2 h. Since the decomposition temperature of cerium nitrate is about 200 \({}^{\circ}\)C and that of nickel nitrate and cupric nitrate about 300 \({}^{\circ}\)C, to ensure a complete decomposition of these compounds into oxides, we used 400 \({}^{\circ}\)C for the calcination of our catalyst. This calcination temperature of 400 \({}^{\circ}\)C further ensured the stability of the catalyst at our reaction temperature (up to 330 \({}^{\circ}\)C). Finally, the catalyst was placed in a quartz tube, was mixed with hydrogen and argon (Ar:H\({}_{2}\) = 1) into a quartz tube, was increased to 460 \({}^{\circ}\)C at a rate of 5 \({}^{\circ}\)C/min, and then was maintained at 460 \({}^{\circ}\)C for 2 h. Thus, NiO in the catalyst was reduced to nickel in hydrogen atmosphere. The specimen used in the experiments was Ni-Cu/HZSM-5 with 0%, 5%, 15%, and 20% CeO\({}_{2}\) and labeled as Ni-Cu/HZSM-5, 5%CeO\({}_{2}\)-Ni-Cu/HZSM-5,15%CeO\({}_{2}\)-Ni-Cu/ HZSM-5, and 20%CeO\({}_{2}\)-Ni-Cu/HZSM-5, respectively.
## 6 Conclusions  
With the increasing use of sensor networks and the large number of important decisions being made from sensed data, the need for a suitable scheme for estimating the data security level is more and more evident. In this context, the sensor data security estimator proposed may provide a significant contribution regarding security quantification in WSNs for being a scheme that takes into account both prevention and detection mechanisms and for defining metrics based on mathematical equations that use configuration parameters of the network itself and its mechanisms. This eliminates the need for assigning subjective weights, which are used in other existing proposals for WSNs. Furthermore, SDSE is able to evaluate the security of different types of cryptography algorithms, key management schemes and intrusion detection systems, differently from some of the other works, which are only focused on a single specific security mechanism.  
Another aspect that makes SDSE stand out from other works is the fact that it has been validated through extensive simulations, presenting accuracies always between 90% and 100% (inclusive), for all of its estimates (security metrics, degree and level). Given the absence of validation for the existing security level estimation works proposed for WSNs, we have compared SDSE to security level estimation schemes proposed for traditional networks, such as the scheme proposed by [PERSON] _et al._[1], with accuracies between 78% and 83%, and the work proposed by [PERSON] _et al._[2], with accuracies between 55% and 100%. From that comparison, we can conclude that SDSE is a highly accurate scheme, reaching accuracies similar to (or even better than) those of traditional networks' schemes.  
Furthermore, as presented in Section 4, SDSE has been tested in a large amount of different scenarios, which allows us to conclude that SDSE is a viable scheme, can be used in both small and large networks and does not consume the energy of the sensors, being executed at the base station.  
Finally, we emphasize that another major contribution of this work is the proposed security metrics and their equations, which, in addition to being used in real time by SDSE to estimate the sensor data security level, can also be used separately, in offline mode, to compare security mechanisms and help network administrators decide which mechanisms of cryptography, key management and intrusion detection will be installed on a network. SDSE metrics can also be used to tune the configuration of a given security mechanism and see its impact on the security of the network, as shown in the metrics behavior analysis presented in Section 4.