esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Properties of GR
### Electronic Properties  
The electronic properties of the GR were calculated through the finite width of nanoribbons with the first-principles method (Figure 15), and it is noticed that the GR falls in the SC group with the band gap range of 0.59 to 1.25 eV [118]. The width of the calculated ribbons was 1 repeat unit to eight repeat units [118]. In the case of infinite sheets, the mechanical strain affects the band gap of GR [56]. It has been observed that the total value of the band gap increased 1 eV by increasing the tensile strain of 0.15 and decreased by 0.3 eV by reducing 1 compressive strain [56].  
Figure 14: **(a,b)** Elastic modulus calculations of graphyne sheets in armchair and zigzag directions using two modeling approaches: **(c)** virial stress (left) and energy minimization (right). Carbon atoms with sp and sp\({}^{2}\) hybridization are represented in grey and black balls, respectively. The rhombohedral unit cell is highlighted in red, while the rectangular unit cell is shown in blue. Reprinted with permission from reference [114] © 2016 Elsevier.  
In GR, the presence of double- and triple-bonded carbon atoms shows a potential for a Dirac cone. Because of this cone, a linear way is observed in the atomic structure. At the Fermi level, conduction and valence bands are overlapped and show a linear fashion. Because of this scheme, in case of no mass, electrons behave as energy, which is directly proportional to the momentum of electrons. GR has the same properties as G, and it shows independent electric properties against the direction of electrons. It is just because of the 6, 6, 12 symmetry in rectangular shape. The electrical grating is observed at the nanoscale due to the dependency of directional symmetry of 6, 6, 12. By controlling this directionality, GR is a considerable material for faster transistors and electronic components in a one-way current process.  
The previous study provides information on the changes in chemical properties due to transition metal doping on GR sheets [120]. GR changes its SC properties to spin-polarized metal properties when adsorbed with iron and chromium [120]. Furthermore, a narrow band gap or a spin-polarized half SC behavior was noticed while adsorbed with other transition metals. This property confirms that the transition metals are absorbed on the sheets of GR. Studies have been conducted by researchers to analyze the dielectric real and complex function of the materials that provide information about energy adsorbed in parallel and perpendicular directions [56]. Previously there were two cases studied and a 9-eV mark was used to find the difference among parallel and perpendicular electric fields [56], (i) If the energy is below 9 eV then the parallel electric field shows the greatest response against sensing and (ii) if above 9 eV then the perpendicular electric field shows the greatest response against sensing.
# Multiscenario Simulation and Prediction of Land Use in Huaibei City Based on CLUE-S and PLUS Models
## 2 Materials and Methods
### Data Source and Processing  
The sources and related descriptions of the study data are presented in Table 1, where natural environmental data and socioeconomic data were used as driving factors for the LU simulation: (1) LU data of Huaibei City for 1985, 1995, 2005, 2015, and 2020. The 2015 LU data were obtained from Landsat@OLI image interpretation of Geospatial Data Cloud ([[http://www.gscloud.cn](http://www.gscloud.cn)]([http://www.gscloud.cn](http://www.gscloud.cn)), accessed on 27 June 2022). Based on the LU classification of the Chinese Academy of Sciences, LU data were classified into five major classes: farmland, forestland, grassland, water, and construction land. (2) The 16 driving factors that can work on LUC were chosen in this study based on the selection principles [17]. These include the digital elevation model (DEM), aspect, slope, distance from the railroad (Railway_d), distance from the freeway (Freeway_d), distance from the roads (Road_d), distance from the rivers (River_d), distance from towns (Town_d), soil organic matter (Soil_SOM), total nitrogen (Soil_TN), total phosphorus (Soil_TP), total potassium (Soil_TK), gross domestic product (GDP), population density (POP), mean annual temperature (MAT), and mean annual precipitation (MAP). Figure 2 shows the driving factors after the normalization process. (3) Soil data were obtained from the soil sample datasets obtained from the 2010-2011 soil surveys, including soil organic matter, total nitrogen, total phosphorus, and total potassium data. The sample points are linked to the vector map according to the same soil type and transformed into a raster. (4) The slope and aspect data were extracted from DEM in ArcGIS. (5) Spatial distance driving factor maps from railroads, freeways, roads, rivers, and towns were gained from vector data acquired from Open Street Map by performing Euclidean distance analysis in ArcGIS.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & **Original Resolution** & **Data** & **Data Sources** \\ \hline \multirow{6}{*}{Land Use Data} & \multirow{6}{*}{30 m} & 1985, 1995, 2005 & \multicolumn{2}{c}{National Earth System Science Data Center ([[http://nnu.geodata.cn](http://nnu.geodata.cn)]([http://nnu.geodata.cn](http://nnu.geodata.cn)), accessed on 10 June 2022)} \\  & & & ([[http://www.gscloud.cn](http://www.gscloud.cn)]([http://www.gscloud.cn](http://www.gscloud.cn)), accessed on 27 June 2022) \\ \cline{1-1}  & & 2020 & Resource and Environment Sciences and Data Center ([[https://www.resdc.cn/Default.aspx](https://www.resdc.cn/Default.aspx)]([https://www.resdc.cn/Default.aspx](https://www.resdc.cn/Default.aspx)), accessed on 11 June 2022) \\ \cline{1-1}  & & & \\ \end{tabular}
\end{table}
Table 1: Data source information of the research.  
Figure 1: Geographical location of Huaibei City in China.
## 1 Introduction
### Motivations and Contributions of This Paper  
In the present work, a different and new method for RPP through state-feedback is proposed and validated, to solve the issues related to the features of vibrating systems, as previously discussed. The goal is to compute the control gains that exactly assign the set of dominant poles to the prescribed locations of the complex plane, while the remaining non-dominant poles are clustered into LMI regions to feature some dynamic properties. The method relies on two-stages. In the first stage, the receptance method is adopted to exactly place the dominant poles and to define the non-spillover condition on such poles. In the second stage, by using a first-order model on a reduced basis, the gains are modified to cluster the unassigned poles into the desired LMIs without spillover on the previously assigned dominant poles. The degrees of freedom in the assignment of the poles within the LMI is, indeed, useful to reduce the control effort. Hence, a norm minimization problem is formulated to solve the assignment problem in the second stage. The proposed method, therefore, is aimed at improving performances achievable by complete pole placement and by PPP too, by proposing a new mathematical framework that effectively solves a wide range of pole assignment tasks and exploits the presence of fewer specifications on the pole assignment. Four meaningful and challenging numerical test-cases, also borrowed from the recent literature, are exploited to validate the method that handles also asymmetric and unstable or marginally stable systems.
###### Abstract  
In this study, local measurement and computational fluid dynamics (CFD) were employed to evaluate the thermal comfort in a residential environment where desiccant cooling is performed in an outdoor air condition, which is the typical summer weather in Korea. The desiccant cooling system in the present study has been developed for multi-room control with a hybrid air distribution, whereby mixing and displacement ventilation occur simultaneously. Due to this distribution of air flow, the thermal comfort was changed, and the thermal comfort indicators conflicted. The evaluation indicators included the ASHRAE (American Society of Heating, Refrigerating and Air-Conditioning Engineers) comfort zone, predicted mean vote (PMV), and effective draft temperature (EDT). The dry-bulb temperature displayed a distribution of 26.2-26.8 \({}^{\circ}\)C in the cooling spaces, i.e., living room, kitchen, and dining room. When determined based on the standard ASHRAE comfort zone, the space where desiccant cooling takes place entered the comfort zone for summer. Due to the influence of solar radiation, the globe temperature was more than 2 \({}^{\circ}\)C higher than the dry-bulb temperature at the window. A difference of up to 6% in humidity was observed locally in the cooling space. In the dining room located along the outlet of the desiccant cooling device, the PMV entered the comfort zone, but was slightly above 1 in the rest of the space. Conversely, as for the EDT, its value was lower than \(-1.7\) in the dining room, but was included in the comfort zone in the rest of the space. By adjusting the discharge angle upward, the PMV and EDT were expected to be more uniform in the cooling space. In particular, the optimum discharge angle obtained was 40\({}^{\circ}\) upward from the discharge surface.  
Keywords:hybrid desiccant cooling system; PMV (predicted mean vote); EDT (effective draft temperature); ADPI (air diffusion performance index); CFD (computational fluid dynamics) +
Footnote †: journal: Applied Sciences
# BIM Policy Trends in Europe: Insights from a Multi-Stage Analysis
## 3 Results
### BIM Policy in Comparison with Statistical Indicators  
Figure 9 presents charts illustrating the relationship between BIM policy and various indicators such as GDP per capita, population, industry size, and land area for the given countries.  
Figure 8: A chart displaying BIM mandates in Europe. This study, based on [36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71; 72; 73; 74; 75; 76; 77; 78; 79; 80; 81; 82; 83; 84; 85; 86; 87; 88; 89; 90; 91].  
Among the displayed charts, a particularly noticeable relationship emerges between the introduction or planning of BIM, even if only partially implemented as a mandate, and the FTSE classification. The Financial Times Stock Exchange (FTSE) Russell Group publishes global benchmarks, analytics, and data solutions related to capital markets. The FTSE  
Figure 9: Charts depicting BIM policy for public investments in Europe relative to GDP per capita, population, industry size, land area, and FTSE classification. This study, based on (**a–d**) [122]; for Aland: [123]; population and land area for Channel Islands: [124]; for the Holy See: [125]; for Svalbard and Jan Mayen Islands.  
classification results used in Figure 9e are from a formal annual review within the FTSE global equity indices [127]. The majority (65%) of countries with or planning a BIM mandate (albeit partial) are classified by the FTSE as \"developed\". This correlation suggests a potential link between economic development levels, as indicated by the FTSE classification, and the adoption or intention to adopt BIM practices in the construction sector. Indeed, the implementation of BIM is closely tied to the necessity of significant financial investments, particularly when the market is not adequately prepared for such advancements. As shown in Figure 9a, most countries that do not plan to make BIM mandatory also have the lowest gross domestic product (GDP) per capita. These insights contribute to a deeper understanding of the intersection of the intersection between technological advancements in the construction industry, regulatory frameworks, and economic maturity, which would be a good topic for future research.  
Figure 9c reveals that BIM requirements are prevalent in countries where the added value (defined according to the International Industrial Classification's ISIC) from industry, including construction, accounts for a minimum of 15% of GDP. In Figure 9b shows that countries with the largest populations represent 23.5% of those with BIM requirements, compared with 6% without. According to Figure 9d, 91% of countries without BIM restrictions have smaller land areas, measuring less than 200,000 square kilometers.
# A Review of Sharp-Switching Band-Modulation Devices
## 6 Reconfigurable Sharp-Switching Modes
### Impact Ionization MOSFET (I-MOS)  
The Z\({}^{2}\)-FET in Figure 2a can also operate as a _P_-type I-MOS. The difference is twofold: (i) the I-MOS looks like a PIN diode, rather than a virtual thyristor, and (ii) it is biased in reverse mode. In Figure 23, the gate voltage is negative, the anode is grounded, and the cathode voltage is large and positive, so as to trigger impact ionization. The _N_-type version would have the gate located nearby to the cathode and the voltage polarities swapped (V\({}_{\text{FG}}\) > 0, V\({}_{\text{A}}\) < 0) [56].  
Figure 22: (**a**) Configuration of the Z\({}^{2}\)–FET operated as a biosensor for detection of negative and positive surface charges. (**b**) Output characteristics and (**c**) turn-on voltage vs. negative surface charge in devices with variable ungated region lengths.  
The principle is that in the off-state (V\({}_{\text{FG}}\) = 0) the entire body is fully depleted and the two terminals are too far apart for impact ionization, resulting in a negligible current. A negative gate voltage starts bringing holes to the gated region. Such electrostatic doping virtually expands the anode contact underneath the gate, shortening the depleted region of the body. Even with V\({}_{\text{K}}\) constant, the lateral electric field increases abruptly, initiating impact ionization in the ungated region. In other words, the I-MOS operates in the avalanche breakdown regime. The current turns on sharply, with an impressive subthreshold slope of 2 mV/decade [57].  
The transfer characteristics reproduced in Figure 23 exhibit a large lateral shift for small variations in V\({}_{\text{K}}\). The reason for this is the exponential dependence of the impact ionization rate on the electric field. An acceptable I\({}_{\text{ON}}\)/I\({}_{\text{OFF}}\) current ratio (\(\sim\)10\({}^{5}\)) is measured. Despite the very sharp switching and attractive threshold voltage (tunable via the cathode bias to less than 1 V), the I-MOS is ineligible for low-voltage applications. The cathode voltage is simply excessive, even when the gated and ungated regions are scaled down. The fundamental issue is that energy conservation during the impact ionization process imposes a minimum breakdown voltage of 3-4 E\({}_{\text{G}}\) \(\sim\) 3.5-4.5 V in silicon [58]. Semiconductors with a narrower bandgap \(E_{\text{G}}\) (Ge, SiGe with a large Ge content, InGaAs, etc.) can provide a lower operating voltage [59], but they are also subject to high BTBT currents, especially when the impact ionization region is scaled down. This leads to high and exponentially increasing I\({}_{\text{OFF}}\), dooming the I-MOS as being a low-power sharp-switching device.  
Furthermore, like most avalanche-based devices, the I-MOS suffers from long switching times. Another major inconvenience results from the high carrier energy: hot electrons and holes injected into the gate dielectric and BOX are responsible for premature device aging.
## 2 System Model
### Channel Model
Thus, the channel gain of \(n_{c}\)-th subcarrier between sensor \(i\) and sensor \(j\) is given by:  
\[h_{i,j}^{n_{c}}=\sqrt{G_{i}G_{r}}\sum_{\left(m,n\right)\text{$\succeq$}\text{ \emph{Nnode}}}C_{mn}(x_{0},y_{0})E_{m,n}^{\text{\tiny eign}}(x,y)e^{-\left(a_{m ^{+}},b_{m^{-}}\right)\left\{\text{$\succeq$}\text{$\succeq$}\text{$\succeq$} \text{$\succeq$}}\right]} \tag{4}\]  
where, \(C_{mn}\) is the mode intensity corresponding to mode \(\left(m,n\right)\) of the sensor, which is given by  
\[C_{mn}(x,y)=\frac{\pi}{ab\sqrt{1-(\frac{m\pi}{2 ak})^{2}-(\frac{n\pi}{2 bk})^{2 }}}E_{m,n}^{\text{\tiny eign}}(x,y)\text{ $;$ $G_{r}$ and $G_{r}$ are the antenna gains of transmitting sensor and receiving sensor respectively; \(N_{mode}\) is a collection of the 50 modes.  
It can be seen from Equation (4) that the channel gain of each subcarrier depends on its coordinates in the considered WSN when the \(G_{t}\) and \(G_{r}\) are invariant. Thus, the channel gain matrix \(\textbf{{h}}_{i,j}\) between transmitting sensor \(i(x_{0}\), \(y_{0}\), \(z_{0})\) and receiving sensor \(j(x,y,z)\) can be expressed as:  
\[\textbf{{h}}_{i,j}=diag(h_{i,j}(1),h_{i,j}(2),\cdots,h_{i,j}(N_{c}))\in(N_{c} \times N_{c}) \tag{5}\]
## 3 Results and Discussion
### Effects of Thermal Load Cycle on Satellite Health  
Figure 14 shows the effect of different heat load cycles on the dynamic temperature of the target damage component. The number of thermal load cycles directly affects the loading time of thermal load. Under the action of thermal load cycle, the cumulative temperature of damaged parts rises to about 220\({}^{\circ}\)C. The pulsed thermal load causes an increase in the temperature of the satellite components, and the longer the loading time results in a higher temperature rise of the damaged component. In Case IV, the temperature of damaged component dropped after 1884 s. This is because the satellite entered earth's shadow in 1884 s and became colder.  
Figure 15 shows the changes of satellite system health indicators when the cycle of thermal load is 40. The health degree of the satellite thermal control subsystem decreased exponentially to 0.87. In order to intuitively compare and analyze the impact of thermal load cycles on the health of satellite subsystem, this section shows the health impact of Case I and Case IV on the thermal control subsystem in Figure 16. As shown in Figure 16a, when the cycle of thermal load is 40, the health degree of the thermal control subsystem decreased to 0.87, while when the cycle of thermal load is 10, and the health degree of the thermal control subsystem decreased to 0.95. With the increase of thermal load cycle, the deterioration of thermal control subsystem becomes more serious.  
Figure 12: Health indicators of satellite subsystems (Case III). (a) Health degree. (b) Failure factor.  
Figure 13: Health indicators of TCS (Compare Case I and Case III). (a)Health degree. (b)Failure factor.  
These results show that the health of the components in the satellite subsystem is affected by time-varying and extreme thermal loads. The analysis of cases accords with the qualitative law of theory.
## 3 MALDI Non-Imaging MS Analysis of GSLs
### MALDI Non-Imaging MS Analysis of GSLs in the Healthy State
#### 3.4.3. GSLs Extracted from Human Blood Cells
Although the use of MALDI MS in the profiling of GSLs of leukocytes is poorly represented, the results of its utilization shed light on the interaction between GSLs and various (e.g., virus binding) pathogens, contributing to our understanding of host-pathogen interactions, with GSLs being highlighted as receptors in specific carbohydrate-protein interactions. GSLs have been identified as functional E-selectin receptors on human neutrophils. Low to moderate E-selectin tethering activity was noticed in fractions of monofucosylated and difucosylated GSLs with three to four LacNAc repeats, the most potent fractions being those with five to six LacNAc repeats and two to three Fuc residues. Interaction with E-selectin on endothelial cells mediates leukocyte extravasation when and where neededby their tethering and rolling, stopping, flattening, and squeezing into the surrounding tissue [184]. Nevertheless, signaling cascades downstream of E-selectin-GSL binding have yet to be clarified. Non-MS analyses showed that the GSL components of the different subclasses of leukocytes are mostly represented by Glcer, GalCer, LacCer, GM3, GM1, complex type (a-, b-, o- type), and in addition in lymphocytes by GD3, 7-O-GD3, and 9-O-GD3, along with Gb3 and Gb4. GSLs from the _lacto_- and _neolacto_-series are represented by Lc3 and nLc4 in lymphocytes and macrophages, and nLc6, S(3)nLc4, S(6)nLc4, and S(3)nLc6 appear in neutrophils and monocytes also. Eosinophilis contain only GM1, and in basophilis GSLs have not been reported [32]. GSL expression was shown to be modified during immune cell development and differentiation, maturation, and activation. Immune cell GSLs impact membrane organization and are essential for the enrolment of immune-related proteins to specific membrane microdomains, being involved in various molecular signaling, and transactions in cellular cross-talk, and thus, modulating the immune cell functions [32].
# Effect of Environmental Parameters on Structural Health Status Assessment Using OMA Techniques
## 4 Applied OMA Methods  
Three of the most used OMA methods are used in this paper. The results obtained from their application are compared to understand which method is the most effective in providing early warning of damage in the examined structure in a noisy environment. A parametric method in the time domain, Cov-SSI [1; 7; 11], and two methods in the frequency domain; a non-parametric method, peak-picking [1]; and a parametric method, poly-reference least-squares complex-frequency method (PolyMAX) [1; 12; 13], are examined.
# Machine Learning-Based Methods for Enhancement of UAV-NOMA and D2D Cooperative Networks
## 2 System Model
### Transmission and Reception Structure  
As previously stated, the UFBS forms \(K\) user pairs, where each pair \(k\) (\(1\leq k\leq K\)) consists of one strong GMT\({}_{i}\) and one weak GMT\({}_{j}\). Therefore, the wireless communication system under consideration comprises \(K\) strong GMTs and \(K\) weak GMTs (2K GMTs in total). Additionally, we assume that the UFBS transmits to the \(N\) GMTs without any delays. Such an assumption is acceptable for a broadcast system in which the UFBS transmits the information repeatedly, and the GMTs get this information immediately. Thus, the superimposed NOMA signal, transmitted to each pair \(k\) by the UFBS, is expressed as:  
\[x_{k}^{\mu}=\sqrt{G_{t}^{\mu}}\left(\sqrt{\alpha_{i}P_{u}}s_{i}+\sqrt{\alpha_{ j}P_{u}}s_{j}\right), \tag{4}\]  
where \(s_{i}\), \(s_{j}\in\mathbb{C}\) are the signals of GMT\({}_{i}\) and GMT\({}_{j}\), respectively. Also, \(\alpha_{i}\) and \(\alpha_{j}\) denote the fraction of the total UFBS transmit power \(P_{u}\) allocated to each GMT, with \(\alpha_{i}+\alpha_{j}=1\).  
The signals received by the strong GMT\({}_{i}\) and the weak GMT\({}_{j}\) for each \(k\) pair are obtained as follows:  
\[y_{i}^{\mu}=\sqrt{\frac{G_{t}^{\mu}}{\mathrm{PL}_{i}^{\mu}}}h_{i}^{\mu}x_{k}^ {\mu}+z^{\mu}, \tag{5}\]  
\[y_{j}^{\mu}=\sqrt{\frac{G_{t}^{\mu}}{\mathrm{PL}_{j}^{\mu}}}h_{j}^{\mu}x_{k}^ {\mu}+z^{\mu}, \tag{6}\]  
where \(z^{\mu}\sim\mathcal{N}\left(0,\sigma_{u}^{2}\right)\) represents the AWGN of the A2G link. Simultaneously, the received signal at the weak GMT\({}_{j}\) when the D2D cooperative transmission is activated, is given by the following expression:  
\[y_{j}^{d}=\sqrt{\frac{G_{r}^{d}}{\mathrm{PL}_{k}^{d}}}h_{k}^{d}x_{j}^{d}+z^{d}, \tag{7}\]  
where \(z^{d}\sim\mathcal{N}\left(0,\sigma_{d}^{2}\right)\) stands for the AWGN noise in the D2D link. Since we have considered the decode and forward (DF) operation regarding the D2D links, the strong user GMT\({}_{i}\) of each pair \(k\) immediately decodes the received UFBS NOMA signal \(x_{k}^{\mu}\) and then estimates the weak user's signal \(\hat{s_{j}}\). Subsequently, the strong user GMT\({}_{i}\) forwards \(\hat{s_{j}}\) to the weak user GMT\({}_{j}\) through transmitting the signal:  
\[x_{j}^{d}=\sqrt{G_{t}^{d}P_{d}}\hat{s_{j}}. \tag{8}\]