esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Human Health Risk Assessment
Generally, during the warm months, the average \(HQ_{1}\) and \(HQ_{2}\) values were more or less constant, while during the cold months they varied widely. The obtained data suggest that, under the best-case scenario, presently, the inhabitants of Cluj-Napoca are unlikely to develop adverse health effects after PM\({}_{10}\) exposure. However, in the worst-case scenario, the occurrence of adverse chronic health effects is still possible. The cumulative effects of PM\({}_{10}\) with other air pollutants could increase the probability of manifesting adverse chronic non-carcinogenic health effects, although the additivity or synergism of the air pollutants is not clearly elucidated, and is influenced by the meteorological conditions [52, 53, 54]. A possible mitigation measure could be the reduction in the hours spent in urban environments with high levels of PM\({}_{10}\) or other pollutants, and an increase in time spent in clean outdoor environments such as parks and green areas. The obtained results highlighted the contribution of local meteorology and winter sources to the PM\({}_{10}\) concentration. Although the PM\({}_{10}\) concentrations for Cluj-Napoca city generally did not  
Figure 5: Variation of hazard quotient (\(HQ\)) calculated based on EU limit values (**a**) and World Health Organization air quality guidelines (**b**) between the air quality monitoring stations in Cluj-Napoca city. exceed air quality standards, this study reveals that the human health risks resulting from exposure to air pollution are still possible. The health risk assessment method used in this study has a number of limitations, some of which are due to the fact that this approach focuses only on PM\({}_{10}\) without considering simultaneous exposure to other air pollutants, and thus underestimates the risk. The interactions between different pollutants were also neglected. The non-carcinogenic health risk was calculated based on several assumptions regarding the exposure time, exposure frequency and exposure duration that are not specific for the studied population, and neglects the different behaviors of individuals inside the studied population. Furthermore, we assumed that the average daily PM\({}_{10}\) concentrations measured by the for air quality monitoring stations are representative for the whole city. Despite these limitations, the findings may contribute to a better understanding of the potential health risks associated with human exposure to PM\({}_{10}\) in urban areas, providing valuable information for air quality managers and specialists. The next steps should include sources apportionment and the assessment of their contribution to PM\({}_{10}\) concentration, as well as assessing the carcinogenic human health risk of toxic compounds associated with PM\({}_{10}\).
# A Low-Cost CPW-Fed Multiband Frequency Reconfigurable Antenna for Wireless Applications
## 3 Results and Discussions
### Case 4: (Switches 2 and 3 ON, Switches 4 and 5 OFF)  
The measured and simulated S\({}_{11}\) in Case 4 are shown in Figure 15. Switches 2 & 3 are ON in this case, whereas switches 4 & 5 are OFF; thus, current passes through the right part of the antenna only. In this mode, the antenna resonates at three different operating bands with an impedance bandwidth of 2.2 GHz to 8.8 GHz. The radiation pattern of this case shows the same behavior, having an omnidirectional pattern in the H-plane and bidirectional pattern in the E-plane, as shown in Figure 16.  
The first band in this state ranges from 2.2 GHz to 3.3 GHz with an operating frequency of 2.7 GHz and an impedance bandwidth of 1100 MHz. This band is used in the aviation service, maritime service, and the radiolocation service. The second band ranges from 3.7 GHz to 4.5 GHz with an operating frequency of 4.2 GHz and an impedance bandwidth of 800 MHz. This band is used for long-distance radio telecommunications. The last band ranges from 8.1 GHz to 8.8 GHz with an operating frequency of 8.4 GHz and an impedance bandwidth of 700 MHz. This band covers a productive part of the X-band wireless application. The summarized results of this case are given in Table 6.  
Figure 16: Measured and simulated radiation pattern for switches 2 and 3 ON, switches 4 and 5 OFF.  
Figure 15: Measured and simulated S\({}_{11}\) for switches 2 and 3 ON, switches 4 and 5 OFF.
## 2 Finite Element Modeling  
Within this study, a numerical model is developed employing ABAQUS (2021) software to replicate the structural reactions of DS-CFCST columns when subjected to axial compression. The model effectively represents both the steel tube and the infill concrete, encompassing interface properties to capture their dynamic interactions. The fundamental objectives of the FE modeling approach encompass emulating the testing methodology, generating supplementary findings to explore composite behaviors, and conducting parametric analyses. This approach endeavors to provide valuable insights into the response characteristics of DS-CFCST columns under axial compression, thus advancing knowledge within this realm of research.
# Advanced Default Risk Prediction in Small and Medium-Sized Enterprises Using Large Language Models
## 4 Experiments and Results
### Quantitative Analysis
#### 4.1.4 Robustness Test  
Upon first establishing the baseline regression, this study adopted the number of defaults over the previous year as the pivotal dependent variable. The current model, however, has replaced this factor with the overall default rate, detailing every instance of default across the entirety of commercial bill acceptance operations for the given enterprises. Notwithstanding these adjustments, the regression model retains its original form. The robustness test results of the modified regression are outlined in Table 6.  
\begin{table}
\begin{tabular}{c c c} \hline
**Variable** & **Coefficient** & **T-Value** \\ \hline Age & 0.1267214 *** & 3.33 \\ Pla & 0.0376407 * & 2.12 \\ Nnr & 2.881501 * & 2.25 \\ Frn & 0.8146282 *** & 39.34 \\ Lcr & 0.088054 *** & 4.59 \\ Constant & \(-\)0.1043378 & \(-\)0.99 \\ \hline \multicolumn{3}{l}{_N_: 1972} \\ \multicolumn{3}{l}{_R-Square_: 0.4776} \\ \multicolumn{3}{l}{_Adj R-Square_: 0.4763} \\ \hline \end{tabular} Note: ***\(p<0.01\), *\(p<0.1\).  
\end{table}
Table 4: Baseline regression results.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Variable** & **Real Estate** & **Construction** & **Other Industries** \\ \hline Age & 0.012284 & 0.3644752 & 0.0881954 \\  & (0.17) & (1.71) & (1.56) \\ \hline Pla & \(-\)0.0225164 & 0.0137361 & 0.0929062 * \\  & (\(-\)0.95) & (0.19) & (3.21) \\ \hline Nnr & 3.005627 * & 8.428441 & 1.619403 \\  & (1.50) & (1.63) & (0.73) \\ \hline Frn & 1.04326 *** & 0.8155993 *** & 0.7177915 *** \\  & (28.30) & (13.08) & (24.36) \\ \hline Lcr & 0.111097 *** & 0.1653752 * & 0.0162129 \\  & (5.35) & (2.36) & (0.37) \\ \hline Constant & 0.1352571 & \(-\)0.922278 & 0.0761388 \\  & (0.82) & (\(-\)1.61) & (0.43) \\ \hline \multicolumn{3}{l}{_N_} \\ \multicolumn{3}{l}{_R-Square_} \\ \multicolumn{3}{l}{_Adj R-Square_} \\ \multicolumn{3}{l}{Note: ***\(p<0.01\), *\(p<0.1\)._} \\ \end{tabular} Note: ***\(p<0.01\), *\(p<0.1\).  
\end{table}
Table 5: Heteroscedasticity test results.  
The robustness analysis indicates a marginal lessening in the significance of firm age and defendant count, likely due to their comparatively trivial influence on the cumulative default rate. Nevertheless, a substantial positive correlation persists among variables such as negative news frequency, prior year's default count, and restrictions on excessive consumption, aligning with our antecedent hypotheses.  
The heatmap concisely displays the relatively low correlation between each dataset and default label. This elucidates the difficulty in predicting SME default rates, given the unavailability of significant financial disclosures often inherent to SMEs. Our objective is to discern indicators that influence bond defaults among SMEs drawn from publicly available data, thereby bringing relevance to our research. After data standardization, it was inputted into a variety of machine learning models for predictive analytics, and the optimal hyperparameters were estimated using a grid search.
## Materials and Methods
### Models  
#### 4.3.1 Optical Properties Predictions  
The experimentally measured absorbance, \(A_{\text{exp}}\), of a colloidal solution can be expressed in terms of predicted extinction cross section, \(\sigma_{\text{ext}}\) as:  
\[A_{\text{pred}}=N\frac{\sigma_{\text{ext}}}{2.303}d_{0} \tag{2}\]  
where \(N\) (m\({}^{-3}\)) is the number density of the NPs, \(d_{0}\) (cm) is the path length of the spectrometer. For spherical, homogeneous and isotropic NPs, the \"Mie scattering theory\" [45,50] can be used to compute the exact values of the \(Q_{\text{ext}}\), absorption (\(Q_{\text{abs}}\)) and scattering (\(Q_{\text{sca}}\)) efficiency as well as the anisotropy factor (\(g\)) as follows [46]:  
\[Q_{\text{ext}} =\frac{2}{k^{2}}\sum_{n=1}^{\infty}(2n+1)\text{Re}(a_{n}+b_{n}) \tag{3a}\] \[Q_{\text{sca}} =\frac{2}{k^{2}}\sum_{n=1}^{\infty}(2n+1)[(|a_{n}|^{2}+|b_{n}|^{2 })]\] (3b) \[Q_{\text{abs}} =Q_{\text{ext}}-Q_{\text{sca}}\] (3c) \[g =\frac{4}{k^{2}Q_{\text{sca}}}\sum_{n=1}^{\infty}\left[\frac{n(n+ 2)}{n+1}\text{ Re}(a_{n}a_{n+1}^{*}+b_{n}b_{n+1}^{*})+\frac{2n+1}{n(n+1)}\text{ Re }(a_{n}b_{n}^{*})\right] \tag{3d}\]where \(k\) is the NP size parameter (\(=2\pi a/\lambda\)). \(a_{n}\) and \(b_{n}\), the scattering coefficients in terms of the spherical Ricatti-Bessel functions, \(\psi_{n}\) and \(\eta_{n}\), respectively, are defined as:  
\[a_{n}=\frac{\psi_{n}^{\prime}(mx)\psi_{n}(x)-m\psi_{n}(mx)\psi_{n}^{\prime}(x)}{ \psi_{n}^{\prime}(mx)\eta_{n}(x)-m\psi_{n}(mx)\eta_{n}^{\prime}(x)} \tag{4a}\]  
\[b_{n}=\frac{m\psi_{n}^{\prime}(mx)\psi_{n}(x)-\psi_{n}(mx)\psi_{n}^{\prime}(x)} {m\psi_{n}^{\prime}(mx)\eta_{n}(x)-\psi_{n}(mx)\eta_{n}^{\prime}(x)} \tag{4b}\]  
where \(m\) is the ratio of complex refractive index (\(n_{S}=\sqrt{\epsilon_{S}}\)) of the sphere to that of the surrounding medium (\(n_{m}\)) asterix (\(*\)) and prime (\(\prime\)) indicate complex conjugate and derivative with respect to \(x\) and \(mx\), respectively. The numerical calculations were performed with a python code implementation of the original algorithm published by [PERSON] [51]. The wavelength dependent complex refractive index, \(n(\lambda)\), was obtained from Ref. [52].  
To account for polydispersity, the size range of the nanoparticle was discretized into a varying number of terms (\(n_{\text{t}}\)) and then number-averaged to obtain the ensemble optical properties,  
\[\overline{\sigma_{\text{k}}}=\frac{1}{n_{\text{t}}}\sum_{r=R_{l}}^{R_{u}} \sigma_{\text{k}}(r+i)\quad k=\text{ext},\text{ abs},\text{ sca},\quad n_{\text{t}}=2,3,4,\dots,N \tag{5}\]  
where \(R_{u}\) and \(R_{l}\) are the upper and lower limits of the NP size range, respectively. \(i\) is the step size which is calculated as: \(i=R_{u}-R_{l}/(n-1)\) and \(\overline{\sigma_{\text{k}}}\) is the mean \(k\) (i.e., extinction, absorption, scattering) cross sections of the NP.
# An Integrated Decision Support Model for Evaluating Public Transport Quality
## 3 Results and Discussion
### Comparison of the Proposed AHP-BWM Model with Traditional AHP and BWM Approaches  
Table 13 shows the basic differences between the proposed model and the pure AHP approach, taking in consideration the constructed hierarchy structure of public bus transport supply quality.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Level** & **Criteria** & **Weight Scores** & \begin{tabular}{c} **New Final** \\ **Weight Scores** \\ \end{tabular} & \
\begin{tabular}{c} **New Ranking** \\ **N** \\ \end{tabular} &
\begin{tabular}{c} **Old Ranking** \\ \end{tabular} \\ \hline \multirow{4}{*}{Level 1} & Transport Quality & 0.3559 & 0.36 & 1 & 1 \\  & Service Quality & 0.2076 & 0.21 & 3 & 3 \\  & Fare & 0.2371 & 0.23 & 2 & 2 \\  & Tractability & 0.1995 & 0.2 & 4 & 4 \\ \hline \multirow{4}{*}{Level 2} & Approachability & 0.0188 & 0.0190 & 13 & 13 \\  & Directness & 0.0226 & 0.0228 & 12 & 12 \\  & Time availability & 0.0980 & 0.0991 & 3 & 3 \\  & Speed & 0.0564 & 0.0571 & 8 & 8 \\  & Reliability & 0.0119 & 0.0120 & 14 & 14 \\  & Physical comfort & 0.0889 & 0.0900 & 5 & 6 \\  & Mental comfort & 0.0910 & 0.0920 & 4 & 4 \\  & Safety of travel & 0.1760 & 0.1780 & 1 & 1 \\  & Perspicity & 0.0586 & 0.0587 & 7 & 7 \\  & Info before travel & 0.0896 & 0.0898 & 6 & 5 \\  & Info during travel & 0.0513 & 0.0515 & 9 & 10 \\  & One-way tickets & 0.0473 & 0.0458 & 11 & 11 \\  & Weekly/monthly tickets & 0.0528 & 0.0512 & 10 & 9 \\  & Discounted tickets & 0.1370 & 0.1329 & 2 & 2 \\ \hline \multirow{4}{*}{Level 3} & Distance to stop & 0.0036 & 0.0036 & 10 & 10 \\  & Safety of stops & 0.0112 & 0.0113 & 7 & 9 \\  & Comfort of stops & 0.0040 & 0.0041 & 9 & 7 \\  & Need for transfer & 0.0068 & 0.0068 & 8 & 8 \\  & Fit connection & 0.0158 & 0.0160 & 5 & 5 \\  & Frequency of lines & 0.0644 & 0.0651 & 1 & 1 \\  & Limited time of use & 0.0336 & 0.0340 & 2 & 2 \\  & Journey time & 0.0123 & 0.0124 & 6 & 6 \\  & Awaiting time & 0.0231 & 0.0234 & 3 & 3 \\  & Time to reach stops & 0.0210 & 0.0212 & 4 & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Weight scores and ranking after the sensitivity check for passengers’ group.  
As Table 13 demonstrates, the proposed AHP-BWM model is between the AHP and BWM models from the aspect of pairwise comparisons, evaluation time, and level of consistency. We have to emphasize, however, that although BWM has significant practical advantage compared to AHP due to the less demanding evaluation phase, AHP covers all possible pairwise comparisons among the decision attributes. Thus, AHP provides more information on the relation of the attribute pairs than BWM. The AHP-BWM model created combines the advantages of the two original methods. It preserves the complete information of AHP in the case of smaller pairwise comparison branches in the decision structure, but in those cases, in which total pairwise comparisons would be demanding and probably inconsistent, it applies BWM.
# Role of Ion-Exchange Resins in Hydrogen Reactions
## 3 Ion-Exchange Resins Application to Hydrogenation Reactions
### Hydrogenation of Alkynes
The effect of the solvent nature on the observed activity was investigated, revealing that catalyst activity decreases in the order: alcohols > cyclohexane > water/ethanol mixture > octane \(\geq\) hexane \(\geq\) xylene > toluene > heptane, which apparently correlates to the solvent polarity. According to the authors, neither solvent-substrate interactions or hydrogen solubility in each solvent can explain the reported difference in catalytic activity and selectivity, but the strength of the solvent-cataly interactions can successfully account for the observed activity pattern [89]. As in the previous section, relevant alkynes hydrogenation reactions can be found in both Scheme 3 and Table 4, which also contain relevant information from the literature. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & **Brain** & & & & & & & & & \\ \hline
**Type** & **Polymer** & **Functional Group** & **Metal** & **Reduction Protocol** & **Tested Reaction** & **Raccione Conditions** & **Conversion** & **Solvability** & **Ref. ** \\ \hline Gal-type & \begin{tabular}{c} Global (turbospheric (GMA)) \\ ST, DECDMA \\ \end{tabular}  & \begin{tabular}{c} MRI-CH\({}_{3}\)CH\({}_{7}\)-NR\({}_{4}\) \\ \end{tabular}  & \begin{tabular}{c} MRI (0.125-0.50 wt\%) \\ \end{tabular}  & \ \begin{tabular}{c} TR/H-O solution of \\ N\({}_{4}\)H\({}_{5}\) \\ \end{tabular}  &
\begin{tabular}{c} TR/H-O solution of \\ N\({}_{4}\)H\(Only gel-type IERs and hyper-crosslinked unfunctionalized polymers have been used as support for alkynes hydrogations in the revised literature, with Pd being the most used metal. Interestingly, Ag, Ni, nor Cu showed any catalytic activity when used as monometallic catalysts supported on an AmberliteTM resin for the hydrogenation of phenylacetylene [87]. Again, different reduction protocols have been reported with no clear data elucidating the most favorable ones towards the hydrogenation of alkynes.
## 3 Theoretical Model
### Mie Theory
\begin{array}{c}\left[zj_{n}(z)\right]^{\prime}=zj_{n-1}(z)-nj_{n}(z),\\ \left[zh^{(1)}_{n}(z)\right]^{\prime}=h^{(1)}_{n-1}(z)-nh^{(1)}_{n}(z).\end{array} \right. \tag{19}\]  
For integrity, the relationship between the Bessel function and the spherical Bessel function is reported as:  
\[\left\{ \begin{array}{c}j_{n}(z)=\sqrt{\frac{\pi}{2z}}J_{n+0.5}(z),\\ y_{n}(z)=\sqrt{\frac{\pi}{2z}}Y_{n+0.5}(z),\end{array} \right. \tag{20}\]where \(J_{n}(z)\) and \(Y_{n}(z)\) are the Bessel functions of the first and second kinds, respectively. When \(n=0\) and \(1\), the spherical Bessel function can be given by:  
\[\left\{ \begin{array}{c}j_{0}(z)=\frac{\sin z}{z},\\ j_{1}(z)=\frac{\sin z}{z^{2}}-\frac{\cos z}{z},\\ y_{0}(z)=-\frac{\cos z}{z},\\ y_{1}(z)=-\frac{\cos z}{z^{2}}-\frac{\sin z}{z}.\end{array} \right. \tag{21}\]  
In addition, the recursive formula is defined as:  
\[f_{n-1}(z)+f_{n+1}(z)=\frac{2n+1}{z}f_{n}(z), \tag{22}\]  
where \(f_{n}(z)\) can be any one of the function \(j_{n}(z)\) and function \(y_{n}(z)\). As shown in Figure 2, the extinction efficiency factor oscillates with increasing size parameter, significantly oscillating within the range of \(0\) to \(20\). In the range of \(20\) to \(60\), the extinction efficiency factor tends to gradually stabized, and its value is stable at about \(2.1\). The figure shows a series of broader maxima and minima that manifest the interference between incident light and forward scattered light. Energy conservation requires that the light scattered in all directions and absorbed in particles is equivalent to the light eliminated by the interference from the incident beam. When the particle distribution spectrum is known, the particle attenuation of terahertz waves can be calculated by integrating the radii of all particles [25]:  
\[ \begin{split} A&=4.343\times 10^{3}\int\limits_{r_{ \min}}^{r_{\max}}Q_{e}(r,\lambda,m)\cdot\pi r^{2}\cdot n(r)dr\\ &=4.343\times 10^{3}\int\limits_{r_{\min}}^{r_{\max}}C_{e}(r, \lambda,m)\cdot n(r)dr(\mathrm{dB}/\mathrm{km}).\end{split}  \tag{23}\]  
Figure 2: Relationship between dimension parameter \(x\) and efficiency factor.
## 5 Simulation Results
### Case 3: BESS Included  
In this case, a BESS is added to the SH which can store the surplus power available in the SH. Figure 9 shows the optimal hourly allocation of the BESS proposed by the IHEMS. A detailed explanation of BESS charging and discharging and its energy routing dependence upon the efficiency has already been presented in [33] by the same authors. The operational cost of the SH is $2.337 in this case which is a reduction of 3% compared to case 2.
## 3 Network Slicing Concepts
### North Bound Interface API  
This interface is the basis of the slicing manager component as it exposes the entirety of its functionalities to the upper layer components. This component is the functional intelligence of the manager, as it covers various lifecycle operations, i.e., create, read, update, delete. The operation of the module serves as a middleman between the upper layer entities and the functional lower layers of the underlying 5G system. Additionally, it can modify existing slices, monitor the state of existing slices, and create dynamic interactions with other components.
###### Abstract  
Mobile virtual reality (VR) is considered a killer application for future mobile broadband networks. However, for cloud VR, the long content delivery path and time-varying transmission rate from the content provider's cloud VR server to the users make the quality-of-service (QoS) provisioning for VR users very challenging. To this end, in this paper, we design a 360\({}^{\circ}\) VR video service system that leverages proactive caching and mobile edge computing (MEC) technologies. Furthermore, we propose a multiuser access control algorithm tailored to the system, based on analytical results of the delay violation probability, which is derived considering the impact of both the multi-hop wired network from the cloud VR server to the MEC server and the wireless network from the MEC server-connected base station (BS) to the users. The proposed access control algorithm aims to maximize the number of served users by exploiting real-time and dynamic network resources, while ensuring that the end-to-end delay violation probability for each accessed user remains within an acceptable limit. Simulation results are presented to analyze the impact of diverse system parameters on both the user access probability and the delay violation probability of the accessed users, demonstrating the effectiveness of the proposed multiuser access control algorithm. It is observed in the simulation that increasing the computing capacity of the MEC server or the communication bandwidth of the BS is one of the most effective methods to accommodate more users for the system. In the tested scenarios, when the MEC server's computing capacity (the BS's bandwidth) increases from 0.8 Tbps (50 MHz) to 3.2 Tbps (150 MHz), the user access probability improves on average by 92.53% (85.49%).  
360\({}^{\circ}\) virtual reality; multiuser access control; proactive caching; mobile edge computing 2020 IEEE/RSJ International Conference on Intelligent Communications (ICRI), Vol., pp. 1-10, 2008-2010, 2010.
## 1 Introduction
Although very successful, this experiment exhibited two unexpected results: the output energy of 0.1 uJ was lower or similar to those measured with gas amplifiers over our different experiments. In addition to this, the pulse duration was still limited at about 1 ps. Indeed, solid amplifiers, having in general much higher electron density and temperature than gas amplifiers, are expected to present larger values of both spectral bandwidth, \(\Delta\lambda\), (\(\tau\propto\)1/\(\Delta\lambda\), \(\tau\) being the pulse duration) and saturation fluence, leading to shorter pulses. Considering the different unexplained issues described above and considering that plasmas emitting spontaneously millijoule of energy (ASE) are driven by low repetition-rate lasers (1 shot/20 mn at best), we considered it essential to precisely model the full seeding experiment prior to the shift from well-known gas amplifiers to solid ones. Using a Maxwell-Bloch time-dependant code, we have previously shown [12] that the key to extracting the energy from long pulse X-ray lasers is to stretch the seed, to accommodating the fast gain recovery time in higher density plasmas. Noticing that a high level of seed was necessary, we anticipated that plasma pre-amplifier, producing a uJ-level pulse with a bandwidth matched to the long pulse amplifier, should yield the highest final energy at the shortest pulse duration (\(>\)10 mJ, sub 200 fs at 21 nm). In this paper, we give the conditions for the pre-amplifier optimization through full hydrodynamic and radiation simulations, and show the working conditions for a Chirped Pulse Amplification (CPA) X-ray laser chains.