esa-sceva/satcom-chunk-collection huggingface dataset:
# How Does the Built Environment Affect Drunk-Driving Crashes? A Spatial Heterogeneity Analysis
## 2 Literature Review
### The Influence of Alcohol Outlets on Drunk Driving Crashes  
Naturally, there has been increasing research attention paid to the relationships between alcohol outlet density and drunk driving crashes. Alcohol outlets generally refer to the sources where alcohol is sold or consumed, such as homes, retail stores, liquor stores, bars, restaurants, entertainment venues, hotels, and drive-up liquor windows [14]. [PERSON] et al. concluded that alcohol availability is significantly related to rates of single vehicle night-time crashes (SVNs) [15]. They also determined the positive correlation between restaurant density and SVNs as well as the negative correlation between bar density and SVNs. [PERSON] et al. found that the rates of drunk driving are very likely to be related to the density of bars [16]. In addition, 74% of drunk drivers were arrested at an average distance of 3.4 miles from the drinking place in New Mexico, and 17.3% of respondents expressed that alcohol consumption happened during meals, according to a case study in Spanish [17]. Furthermore, some studies found that there was a negative correlation between off-premise outlets and crashes, while a positive correlation exists between on-premise outlets and crashes [18]. Meanwhile, other research based on different case studies argued that the densities of off-premise establishments and local bars were positively correlated with the local alcohol-related crash rates [5]. After the close relationship between traffic crashes and the built environment factors associated with alcohol availability eventually demonstrated by numerous studies, more attention has been paid to spatial analysis in recent years [19].
## 3 Special Cases of Weight Functions
### Local Convergence  
Table 1 lists test scalar functions to measure the convergence behavior of proposed scheme (1). Computed values of \(x_{n}\) is listed with up to 15 significant digits for proper readability. The error bound \(e=\frac{1}{2}\times 10^{-180}\) is assigned for scalar equations. According to Table 2, sixth-order convergence is clearly seen. The values of computational asymptotic error constant agree up to 10 significant digits with \(\eta\). It appears that the computational convergence order well approaches 6. In Table 3, we compare numerical errors \(|x_{n}-\alpha|\) of proposed Methods **LK1-LK10** with those of existing Methods **EM1-EM7**. The least errors within the prescribed error bound are highlighted in bold face. According to the comparison, Methods **LK1** and **LK8** display slightly better convergence for most test functions, while other remaining methods exhibit similar convergence. A close inspection of the asymptotic error constant \(\eta(\alpha,\theta_{i},T_{f},L_{f})\approx\frac{|x_{n+1}-\alpha|}{|x_{n}- \alpha|^{6}}\) addresses one's attention to the local convergence dependent on \(f(x)\), \(x_{0}\), \(\alpha\), \(T_{f}\) and \(L_{f}\). Accordingly, the convergence of one method is hardly expected to be always better than the others. \begin{table}
\begin{tabular}{c c c c} \hline \hline \(i\) & \(f_{i}(x)\) & \(\alpha\) & \(x_{0}\) \\ \hline
1 & \(\sin x-\log\left(1+x^{2}\right)\) & 0 & 0.01 \\
2 & \(3+\sin x-x^{2}\) & 1.97932014655621 & 2.0 \\
3 & \(2x-\pi+\cos x\cdot\log\left(x^{2}+1\right)\) & \(\frac{\pi}{2}\) & 1.53 \\
4 & \(2x^{3}+\epsilon^{-x^{2}}+\sin x-2\) & 0.719549366870672 & 0.73 \\
5 & \(x-\sqrt{3}x^{3}\cos\left(\frac{x}{\pi}\right)+\frac{1}{(x^{2}+1)}-\frac{11}{5} +4\sqrt{3}\) & 2 & 1.87 \\
6 & \(e^{\frac{\beta_{3}}{2+\gamma\cos\left(\frac{x^{2}+1}{4}\right)}}-1\) & \(\frac{1+i\sqrt{3}}{2}\) & \(0.52+0.85i\) \\
7 & \(x\log x-\sqrt{\pi}+x^{2}\) & 1 & 1.05 \\ \hline \multicolumn{3}{c}{Here \(\log z(z\in\mathbb{C})\) represents a principal analytic branch with \(-\pi\leq Im(\log z)<\pi\).} \\ \end{tabular}
\end{table} 
Table 1: Test functions \(f_{i}(x)\) with zeros \(\alpha\) and initial guesses \(x_{0}\).
## 1 Introduction
Therefore, to resolve the limitations of these traditional methods, machine learning (ML) approaches for estimating SSI have been in the spotlight [20; 21; 22; 23]. The National Meteorological Satellite Center (NMSC) in the Korea Meteorological Administration (KMA) has monitored the atmospheric phenomena which affect the Korean Peninsula using in situ measurements, ground-based radar observation, and satellite observation. Korea's first geostationary meteorological satellite, the Communication, Ocean, and Meteorological Satellite (COMS), was launched on 26 June 2010. COMS is equipped with a Meteorological Imager with five channels. The NMSC developed a model for the retrieval of SSI based on the lookup table method using a physical model, and COMS/MI obtained SSI measurements in real time every 15 min. In addition, the NMSC postprocessed nowcasted SSI measurements from COMS and reproduced them as Level 2 and 3 climatic data, as an essential climate variable. Recently, the NMSC launched the GEOstationary Korea Multi-Purpose SATellite 2A (GEO-KOMPSAT 2A, GK2A) on 4 December 2018. Thus, it was made possible for researchers to observe SSI with a higher spatiotemporal resolution with more channels than COMS/MI; this is expected to provide more accurate monitoring of SSI. Furthermore, the NMSC operationally produces SSI measurements based on the lookup table method using a physical model in real time with a specific temporal resolution depending on the spatial coverage. The Korean Peninsula has complex land cover, and its elevation dramatically varies across regions (Figure 1). Furthermore, because this is a monsoon region, it shows seasonal SSI variation. During summer, it generally shows high SSI, due to a long daylight time and low solar zenith angle (SZA), but frequently shows low SSI, due to extensive cloud cover. Because the Korean Peninsula shows high seasonal and regional variations in SSI, this area is suitable for the development and validation of SSI monitoring. Therefore, to monitor SSI with a high accuracy and resolution, we developed a model for the retrieval of SSI from the GK2A/AMI using an ML method centered on ground-based measurements in Korea. This study is organized as follows: Section 2 presents the data that were used, such as GK2A observations and in situ measurements; Section 3 describes the method, including data preprocessing and the ML method; Section 4 presents the assessment of the accuracy of SSI measurements from the GK2A/AMI; and Section 5 and 6 contain the discussion and the conclusion, respectively.
## 4 Proposed Hybrid Model of NLMPC and Data-Driven Approach
### Constraints  
The NMPC must respect several constraints, ensuring that the control actions are feasible and safe:  
* State Constraints: This includes limits on rotor angle, speed, and internal voltage. \[x_{min}\leq x_{i}(k)\leq x_{max}\] (25)
* Control Input Constraints: \[u_{min}\leq u_{i}(k)\leq u_{max}\] (26) This includes limits on excitation voltage and valve position.
* Output Constraints: \[y_{min}\leq y_{i}(k)\leq y_{max}\] (27) This includes constraints on power output, voltage, and frequency.
## 3 A Modified HOSM Controller Using a PID-Sliding Surface for an ABS Laboratory Setup
### Design of the Modified HOSM Control Using a PID-Sliding Surface  
In this subsection, the design of the modified HOSM control using a PID-sliding surface applied to the ABS laboratory configuration--and the proof of stability of the proposed controller based on a theoretical analysis of the Lyapunov function--are presented. **Theorem 1**: _For the system (5) and under Assumption 1, based on the sliding surface defined in (11), the following controller provides the solution that the slip-velocity error \(\dot{e}_{v}\) (6) will asymptotically converge to zero._  
\[ \begin{split} b(u)&=-\frac{1}{k_{d}k_{u}}\Big{(}k_{ p}\dot{e}_{v}+k_{i}\dot{e}_{v}+k_{d}f_{v}+\gamma_{1}\lfloor s_{v}\rceil^{1/2}+ \gamma_{2}s_{v}-\xi_{v}\Big{)}\\ \dot{\xi}_{v}&=-\gamma_{3}\lfloor s_{v}\rceil^{0} -\gamma_{4}s_{v}.\end{split}  \tag{14}\]  
_This controller will force the system toward the slip surface even in the presence of parametric uncertainty if the gains \(\gamma_{1},\gamma_{2},\gamma_{3}\), and \(\gamma_{4}\) are selected, such that_  
\[ \begin{split}\gamma_{1},\gamma_{2},\gamma_{3},\gamma_{4}& >0\\ 4\gamma_{3}\gamma_{4}&>\gamma_{2}^{2}\big{(}8\gamma_ {3}+9\gamma_{1}^{2}\big{)}\end{split}  \tag{15}\]  
_and \(k_{p},k_{i},k_{d}>0\), \(f_{v}\) as in (9)._  
To obtain the differential inclusion that represents the derivative of a sliding surface, the control input (14) needs to be used in (13), such that  
\[ \begin{split}\dot{s}_{v}&=-\gamma_{1}\lfloor s_{v} \rceil^{1/2}-\gamma_{2}s_{v}+\xi_{v}\\ \dot{\xi}_{v}&=-\gamma_{3}\lfloor s_{v}\rceil^{0} -\gamma_{4}s_{v}\end{split}  \tag{16}\]  
with \(\gamma_{1},\gamma_{2},\gamma_{3}\), and \(\gamma_{4}>0\), \(\lfloor s_{v}\rceil^{1/2}=|s_{v}|^{1/2}\text{sign}(s_{v})\), and \(\lfloor s_{v}\rceil^{0}=\text{sign}(s_{v})\). From [41], the following Lyapunov function is considered:  
\[\mathcal{V}=2\gamma_{3}\big{(}|s_{v}|\big{)}+\gamma_{4}s_{v}^{2}+\frac{1}{2} \xi_{v}+\frac{1}{2}\big{(}\gamma_{1}\lfloor s_{v}\rceil^{1/2}+\gamma_{2}s_{v} -\xi_{v}\big{)}^{2}\]  
in which \(\mathcal{V}\) is a continuous and differentiable Lyapunov function when \(s_{v}\
eq 0\). The Lyapunov function can be written as a quadratic form:  
\[\mathcal{V}=\frac{1}{2}\chi^{T}P\chi; \tag{17}\]  
where  
\[\chi=\left( \begin{array}{c}\lfloor s_{v}\rceil^{1/2}\\ s_{v}\\ \xi_{v}\end{array} \right);\quad P=\left( \begin{array}{ccc}\gamma_{1}^{2}+4 \gamma_{3}&\gamma_{1}\gamma_{2}&-\gamma_{1}\\ \gamma_{1}\gamma_{2}&2\gamma_{4}+\gamma_{2}^{2}&-\gamma_{2}\\ -\gamma_{1}&-\gamma_{2}&2\end{array} \right)>0\]  
since \(\gamma_{1},\gamma_{2},\gamma_{3}\), and \(\gamma_{4}>0\). Following [41], note that  
\[\lambda_{min}^{P}\||\chi\|_{2}^{2}\leq\mathcal{V}\leq\lambda_{max}^{P}\||\chi \|_{2}^{2} \tag{18}\]where \(\lambda_{min}^{P},\lambda_{max}^{P}\) are the minimum and maximum eigenvalues of \(P\), respectively. Let \(\dot{\chi}\) be the derivative with respect to the time of \(\chi\) as follows:  
\[\dot{\chi}=\left( \begin{array}{c}\frac{\mathrm{d}}{\mathrm{dt}}[s_{p}]^{1/2} \\ \dot{s}_{p}\\ \dot{\xi}_{p}\end{array}
# Rapid Construction of Aerocapture Attainability Sets Using Sequential Convex Programming
## 4 Solving with Sequential Convex Programming
### New Control Variable  
The bank angle \(\sigma\) is the only control variable which has lower and upper bounds on the magnitude  
\[\sigma_{\min}\leq\left|\sigma\right|\leq\sigma_{\max} \tag{12}\]where \(\sigma_{\min}\) is greater than 0 deg and \(\sigma_{\max}\) must be less than 180 deg, because flying at a bank angle of 0 deg or 180 deg will cause the vehicle to have no crossrange control ability [28]. The bank angle is in the form of sine in the dynamic equations, which impedes the convergence of the optimal control problem. To overcome this issue, \(u=\cos\sigma\) is defined as the new control variable. Then the constraint becomes  
\[u_{\min}\leq u\leq u_{\max} \tag{13}\]  
where \(u_{\min}=\cos\sigma_{\max}\) and \(u_{\max}=\cos\sigma_{\min}\). The constraint on \(u\) is naturally convex.
# Fault Detection of Bearing Systems through EEMD and Optimization Algorithm
## 4 Experimental Procedure  
This section illustrates the experimental validation of the aforementioned damage monitoring technique using a motor-driven shaft that is supported by roller-type bearings. Figure 10 shows that the vibration response of the system can be recorded through an accelerometer and pertinent data acquisition system. To suppress external disturbances, a mechanical coupling was positioned in the middle of the shaft. An optical encoder embedded in the brushless motor controlled the rotational speed to maintain 2400 rpm. An accelerometer (PCB 352C33) having 100 mV/g is mounted on top of the housing, to measure the vertical movement of the bearing system. Data acquisition hardware of PXI-1042Q (National Instruments) was used for data collection, with a sampling frequency of 10 kHz.  
The type of roller bearings used in the test was SKF NJ 202 ECP. Here, we chose an indent-style notch damage to imitate a partial spalling condition of the bearing surface. The indentation damage was easy to implement at a large quantity, and uniformly replicable for creating the identical damage severity of test bearings. For this indentation, we used HR-500, a diamond-tipped hardness test machine. To make three different levels of indentation defects, the HR-500 applied (15, 30, and 45) N forces to the surface of the inner-race of test bearings. Figure 10(a)-d shows the size of indentation defects on the inner-race created by (15, 30, and 45) N forces, respectively, applied by HR-500. Figure 10(a) shows the condition of healthy bearing with no defect. Hereinafter, the severity of defect was denoted as D15, D30, D45, and H for intact bearing. Figure 12 shows the time-series response of H, D15, D30 and D45. From the figure, both D30 and D45 show clear visual distinction caused by the presence of the bearing defect. Note that the case of D15 barely revealed defect-induced spike-type responses. Figure 10(a)-d  
Figure 10: Experimental setup for a roller bearing system.  
Figure 9: Isomap representation of testing characteristic vectors from simulated bearing model: conditions of intact (\(\blacksquare\)), inner-race defect (\(\blacktriangle\)), outer-race defect (\(\blacksquare\)), and rolling-element defect (\(\blacklozenge\)), (a) before, and (b) after PSO optimization.
## 2 Solid Waste Degradation of Fluorinated Compounds
### Biodegradation of Short-Chain PFAS  
In addition to thermal degradation of fluorinated compounds, environmental degradation occurs in landfills where most of the solid waste is disposed [93; 94; 51; 95]. Data from the EPA on the total generation and disposal of municipal solid waste (MSW) in the U.S. in 2017 found that 35% is composted and recycled, 12% is combusted for energy renewal, and slightly over 50% is discarded in landfills [96]. With over half of all U.S. waste being deposited in a landfill, the potential biodegradability of PFAS compound precursors is a relevant topic that has been investigated. In 2013, [PERSON] and [PERSON] completed a review on environmental biodegradability studies from the past decade carried out with microbial culture, activated sludge, soil, and sediment [51]. More recently in 2018, [PERSON] et al., completed a review focusing on the fate and transformation of PFAS in landfills specifically [95]. [PERSON] et al., (2015) modeled how PFAS enters leachate (water that travels through waste products and contains environmental pollutants) using anaerobic bioreactors filled with municipal solid waste [94]. As commercial products treated with PFAS are often disposed of in landfills, and thus, act as a secondary point source to surface water, the study showed the abundance of PFAS in landfill leachate from six U.S. locations to have a median molar composition in the range of 0.01% to 45%. Moreover, multiple factors that contribute to PFAS concentrations in landfill leachate including abiotic leaching, pH, substrate, precursor biodegradation, and sorption were modeled as sources of PFAS in leachate by using four lab-scale anaerobic bioreactors filled with MSW operated over 273 days. This included duplicate reactors monitored under biotic and abiotic conditions to evaluate influences attributable to biological activity by using reactors to simulate methanogenic conditions that developed in landfills producing roughly 140 mL CH4/dry g refuse. Their research connected leachate PFAS composition and MSW under time-dependent conditions relevant to landfills. To carry out the study, reactors were filled with shredded residential MSW and an anaerobic microbial inoculum to determine the roles of abiotic leaching and biological activity for 70 PFAS compounds. Reactors were periodically sampled for PFAS analysis by collecting leachate: the average total PFAS leaching was 16.7 and 2.83 nmol/kg dry refuse for biotic and abiotic leaching, respectively, indicating that biological processes were primarily responsible for leaching. The reactor gas was not analyzed for fluorinated compounds, so a future study could investigate the degree to which volatile fluorotelomers are released from MSW. In this study, biodegradation pathways were not evaluated, but from previous work, a more detailed review of biodegradation pathways can be found in the following cited publications [93; 95; 97; 51].
## 2 Theoretical Background
### Fuzzy Theory
#### 2.1.2 Determining the Factor Weight Set  
The degree of influence and importance of each parameter for the factors are different; that is, the weight of each parameter is different for each factor. The factor weight set is the set that is composed of the degree of influence of each parameter for each factor and can be expressed as \(A=\{a_{1},a_{2},\)..., \(a_{n}\}\). If \(a_{i}\) indicates the \(i\)th factor weight, the weights of all of the factors need to satisfy Equation (1). A weight set is a fuzzy subset of the factor set and can be expressed as Equation (2):  
\[\sum_{i=1}^{n}a_{i}=1,\ a_{i}\geq 0\ (i=1,2,3,\ldots,a_{n}), \tag{1}\]  
\[\begin{array}{ll}A&=\frac{a_{1}}{u_{1}}+\frac{a_{2}}{u_{2}}+\frac{a_{3}}{u_{ 3}}+\ldots+\frac{a_{n}}{u_{n}}\\ &=\{a_{1},a_{2},a_{3},\ldots,a_{n}\}\end{array} \tag{2}\]The weight of each factor can be determined by the weight coefficient method, analytic hierarchical process, paired comparison method, Likert scale, or any subjective determination according to the practical problem. It does not matter which approach is used, and human factors are involved, with the only difference being in credibility. Here, A is the fuzzy set of each factor weight. For the same factor, if different data are taken, the assessment result is different. The method of the determinant table is to carry out pairwise comparison of the importance of these evaluation targets. Scores are given after further calculation and are entered into the table. The equation for calculating \(a_{i}\) is as follows:  
\[a_{i}=k_{i}/\sum_{i=1}^{n}k_{i}, \tag{3}\]  
where \(k_{i}\) is the total score of each evaluation target and n is the number of evaluation targets.  
\[\sum_{i=1}^{n}k_{i}=\frac{n^{2}-n}{2}\times 4=2(n^{2}-n). \tag{4}\]
# Lossless Compression of Sensor Signals Using an Untrained Multi-Channel Recurrent Neural Predictor
## 1 Introduction
In this way, a bit stream of a sensor signal will be partitioned into groups before being fed into the compressor, which may break the data integrity of sensor signals. There are also compressors that are specially designed for digital signals. [PERSON] et al. proposed a lossless compression method for periodic signals based on an adaptive dictionary model which can predict the current data value according to the history [19]. [PERSON] et al. proposed a novel ECG signal prediction model that uses an autoregressive integrated moving average (ARIMA) model and discrete wavelet transform (DWT) [20]. Nonetheless, these compressors are all designed for specific signals. In this paper, we present a novel recurrent neural network (RNN) architecture that is specially designed for modeling sensor signals as the probability predictor of a context-based lossless compressor. The proposed multi-channel recurrent unit (MCRU) reads one signal point at a time, and the internal bits are re-grouped into multiple channels, each of which is assigned with a sub-recurrent unit. In this way, the intra-point features can be extracted without breaking the integrity of each signal point. Furthermore, we adopt a similar strategy as tensorflow-compress to train the network during compressing and decompressing; thus, no pre-training is needed. The effectiveness of the proposed approach is demonstrated experimentally on different types of sensor signals.
## 3 Results  
### Responses to Enhanced Drought Stress  
In our experiment, the _Q. ilex_ seedlings were treated by withholding watering from 0 to 21 days in nine different drought levels treatments. Along with the increasing days of drought and decreasing soil water content (i.e., enhanced drought stress), CO\({}_{2}\) assimilation rates (\(A\)) and maximum (\(\rm{F_{V}}/\rm{F_{M}}\)) and actual (Yield) photosystem II efficiency (\(\rm{\Phi}\)PSII) decreased from highest values at the beginning of the experiment to lowest values in severe drought conditions (Figure 1). The physiological indices of the photochemical reflectance index (PRI) (Figure 1a) and reflectance ratio (R690/R630) (Figure 1b) also decreased gradually. Both changes in PRI and R690/R630 were highly consistent with the decreases in soil water content (SWC), \(A\), \(\rm{F_{V}}/\rm{F_{M}}\) and Yield. However, in the last three extreme drought level treatments, PRI did not change significantly.  
Relationships of PRI and R690/R630 with \(A\), \(\rm{F_{V}}/\rm{F_{M}}\), and Yield under Enhanced Drought Stress  
PRI and R690/R630 were used to assess the photosynthetic response to drought stress. Both PRI (Figure 2a) and R690/R630 (Figure 2b) were significantly correlated with \(A\) and explained 82% and 86% of variance of \(A\), respectively. PRI had strong relationships with \(\rm{F_{V}}/\rm{F_{M}}\) and Yield (\(R^{2}\geq 0.85\) and \(p<0.001\) for both, Figure 2c). Similar significant correlations were found between R690/R630 and \(\rm{F_{V}}/\rm{F_{M}}\) and Yield (\(R^{2}=0.89\) and \(p<0.001\) for both, Figure 2d). In contrast, the normalized difference vegetation index (NDVI, calculated by (R900 \(-\) R680)/(R900 \(+\) R680)) showed no significant correlation with \(A\) under enhanced drought conditions (\(R^{2}=0.08\) and \(p=0.58\)).  
Figure 1: Changes in CO\({}_{2}\) assimilation rates (\(A\)), maximum (\(\rm{F_{V}}/\rm{F_{M}}\)) and actual (Yield) photochemical efficiency of photosystem II (\(\rm{\Phi}\)PSII), and soil water content (SWC) with the photochemical reflectance index (PRI) (**a**) and with reflectance ratio R690/R630 based on the red-edge region of the spectrum (**b**) in response to nine different drought levels treatment for _Quercus__lex_. Error bars denote the standard errors of the mean (\(n=9\)).
## 2 THz DFG-QCL Design and Characterization
After the DFB laser operation at \(\lambda_{\text{DFB}}\) = 6.5 \(\upmu\)m, the FP lasing takes place at around \(\lambda_{\text{FP}}\)-6.9 \(\upmu\)m. The broad FP spectra were confirmed at the pump current above 500 mA and it could be attributed to broadband gain spectrum in the DAU structure. Figure 1c shows the THz emission spectra of the DFB device at different currents in linear scale; the ultra-broadband THz emission with many longitudinal modes ranges from 1.8 THz to 3.3 THz at 78 K, which is a consequence of the frequency down conversion of mid-infrared, multi-mode emission spectra due to the FP cavity. Current-voltage and light-current characteristics of both mid-infrared and THz power outputs are depicted in Figure 2. The DFG-QCL demonstrates a mid-infrared CW power of 1.1 W as well as a CW THz power of over 8 \(\upmu\)W at 78 K, exhibiting a broadband THz emission (Figure 1c). Figure 2 also shows the temperature dependence of THz CW light-current curves in the different temperatures of 78-170 K. A THz power output of approximately 0.5 \(\upmu\)W at 170 K can be noticed. The maximum operating temperature of the present device is significantly lower than our previous papers [40, 45], in which room temperature CW operation has been achieved using the epitaxial side-up mounting configuration [45]. This indicates that thermal management of the active region of DFG-QCLs is very difficult for the epitaxial side-up mounted THz DFG-QCLs and an epitaxial side-down mounting technique [39, 46, 49] is imperative for stable CW operation at room temperature. Figure 1: (**a**) Schematic of the THz difference frequency generation quantum cascade laser (DFG-QCL) and scanning electron microscope (SEM) image of the device. Mid-infrared (**b**) and THz (**c**) spectra at different currents of the DFG-QCL at the temperature of 78 K used in this study. The cavity length is 3 mm and the ridge width is 12 \(\upmu\)m.