esa-sceva/satcom-chunk-collection huggingface dataset:
# A Study on ML-Based Sleep Score Model Using Lifelog Data
## 2 Materials and Methods
### Primary Habit Score: Good/Bad Sleep State
#### 2.2.4 Primary Sleep Habit Score Results  
The distribution of the primary sleep score calculated in this study is shown in histogram form in Figure 2.  
As can be seen from the graphs, the generated good sleep habit score (Figure 2a) is mostly distributed between 1400 and 1600 points, whereas the bad sleep habit score (Figure 2b) is distributed between 750 and 1000 points. The overall data distribution (Figure 2c) appears to follow a normal distribution, as expected. The basic statistical information of the primary sleep habit score generated in this study is summarized in Table 5.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Category** & **AUROC** & **K–S** & **Gini Coefficient** \\ \hline Train data & 0.9847 & 0.8912 & 0.9694 \\ Validation data & 0.9845 & 0.8882 & 0.969 \\ Reference & \textgreater{}0.7 & \textgreater{}0.5 & \textgreater{}0.6 \\ \hline \end{tabular}
\end{table}
Table 4: Performance metric table for training data and validation data.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Count** & **Mean** &
\begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} & **25\%** & **50\%** & **75\%** & **Max** \\ \hline
5494 & 960.889516 & 285.114076 & 163 & 748 & 1162 & 1857 \\ \hline \end{tabular}
\end{table}
Table 5: Statistical information of the sleep habit score obtained from the logistic regression model.  
The scorecard for SRI (Sleep Regularity Index) is summarized in Table 6 as follows.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature** & **Interval Value** & **Score** \\ \hline  & \(\sim\)52 & \(-\)2 \\ SRI\_2 & 52–72 & \(-\)1 \\  & 72–86 & \(11\) \\  & 86– & 29 \\ \hline  & \(\sim\)56 & \(-\)50 \\  & 56–70 & \(-\)34 \\ SRI\_3 & 70–82 & 6 \\  & 82–90 & 87 \\  & 90 & 180 \\ \hline  & \(\sim\)52 & \(-\)45 \\  & 52–62 & \(-\)19 \\ SRI\_4 & 62–70 & 2 \\  & 70–82 & 15 \\  & 82– & 29 \\ \hline  & \(\sim\)52 & \(-\)17 \\  & 52–58 & \(-\)14 \\ SRI\_5 & 58–82 & 5 \\  & 82–88 & 34 \\  & 88– & 67 \\ \hline  & \(\sim\)56 & \(-\)19 \\ SRI\_6 & 56–80 & \(-\)13 \\  & 80–86 & 9 \\  & 86– & 13 \\ \hline  & \(\sim\)56 & \(-\)17 \\ SRI\_7 & 56–78 & \(-\)8 \\  & 78–84 & 0 \\  & 84– & 6 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Scorecard for each feature.  
Figure 2: (a) Histogram of sleep habit scores for good sleep using a logistic regression model; (b) histogram of sleep habit scores generated for bad sleep data; (c) histogram of sleep habit scores for good and bad sleep data combined.  
The details for sleep duration, sleep timing, and sleep efficiency are described in the Appendix A.
## 2 Results and Discussion
### The Expression Patterns of _CsHPLs_ during N-Deficient Treatments in Six Inbred Cucumber Lines  
In the LOX-HPL pathway, HPL is one of the key enzymes that catalyzes 13-HPOD/HPOT or 9-HPOD/HPOT to produce short-chain volatiles. In cucumber, three _HPL_ genes were divided into three types by their specificities of substrate: 13-HPL which specifically catalyzes 13-HPOD/HPOT to produce C6 and C12 volatiles, 9-HPL which catalyzes 9- hydroperoxides to form C9 aldehydes, and 9/13-HPL, which can use both 9- and 13-hydroperoxides as substrates to produce C6 and C9 aldehydes [22; 25]. To study the changes in the expression patterns of _CsHPLs_ during N-deficient treatments, _HPLs_ gene expression profiles in cucumber leaves were analyzed by qPCR.  
In our study, only _CsHPL1_ (13-HPL) and _CsHPL2_ (9/13-HPL) were expressed in cucumber leaves. For the six inbred lines, as shown in Figure 9, _CsHPL1_ had a higher expression level than _CsHPL2_ and most sample points of _CsHPL1_ were significantly up-regulated during N-deficient treatments; no significantly reduced points were observed. For _CsHPL2_, 13 out of 23 sample points were significantly up-regulated during N-deficient treatments, and only one point was down-regulated significantly. For A91, all the sample points of _CsHPL1_ and _CsHPL2_ were significantly up-regulated compared to N-deficient treatments.  
Our results show that content of short-chain volatiles was significantly improved at Day 5; meanwhile, the expression pattern of _CsHPL1_ was also significantly improved in all seven inbred lines. A previous study showed that C9 aldehydes are the major volatiles in cucumber fruit [26]. The loss of 9-HPL enzyme activity and the fact that 9/13-HPL enzyme preferentially exhibits 13-HPL enzyme activity [22] in cucumber leaves might be the main reasons for the relative composition difference of C6  
Figure 9: Expression profiles of _HPL_ genes in six inbred cucumber leaves during N-deficient treatments. Leaves were sampled at 1, 2, 3, 4 and 5 d after N-deficient treatments. The map has been generated based on the fold-change values of _CsHPLs_ in the treated samples when compared with its unstressed control samples. The _CsHPL1_ expression level of control was considered as one-fold. Bars present means \(\pm\) standard deviations. * means significant difference from control at \(p\) < 0.05. Values are means of three biological replicates with three technical replicates.  
and C9 aldehydes between cucumber fruit and leaves. According to these results, we suggest that the significant improvement of short-chain volatiles in cucumber was partly caused by the up-regulated expression of _CsHPL1_ during N-deficient treatments.
## 3 Simulation Results and Discussion  
To verify the presented theoretical studies and also demonstrate the design optimization, the computer simulation of the gyrator behavior was carried out using OrCAD PSpice software. The commercially available integrated circuit LT1228 [26] was used as OTA to enable the readers the possibility of reproducing the results. This element provides transconductance \(g_{\rm m}\) controlled by an external DC current \(I_{\rm SET}\); whereas, it holds \(g_{\rm m}=10I_{\rm SET}\). An analysis of the LT1228 PSpice model parasitic properties revealed that the conductance of input pins \(G_{\rm PIN}\) is variable and depends on \(g_{\rm m}\) with the relation \(G_{\rm PIN}\)\(\approx\) 0.005\(g_{\rm m}\). It is, therefore, necessary to take into account that the functionality of the gyrator will be more affected by the parasitic conductance at higher values of \(g_{\rm m}\). The input parasitic conductance is not included in the LT1228 PSpice model, i.e., it is considered zero. The parasitic conductance \(G_{\rm POUT}\) of output pin is approximately 0.23 nS and can be neglected in comparison with \(G_{\rm PIN}\) for common values of \(g_{\rm m}\). The parasitic output capacitance is 5.2 pF. Thus, the modeled parasitic conductances \(G_{\rm PI}\) and \(G_{\rm P2}\) of the gyrator correspond to 0.005\(g_{\rm m2}\) and 0.005\(g_{\rm m1}\), respectively, and both the parasitic capacitances \(C_{\rm PI}\) and \(C_{\rm P2}\) are 5.2 pF.
## 2 System Description  
The development of the WIOBSS has benefited from the ascent of digital techniques since the 2000s. There have been a lot of changes and upgrades on the hardware and system architecture of the WIOBSS in recent years. The WIOBSS-AA is the latest system of the WIOBSS family, which is also a totally functionally-upgraded system. The most significant change is the application of the log periodic antenna array, which is located in Chongyang, Hubei province, China (29.53\({}^{\circ}\) N, 114.14\({}^{\circ}\) E), as shown in Figure 1. The antenna array was established in May 2013. The hardware and software of the radar system was accomplished in August 2014 and placed in the pink house in the center of Figure 1. The mainframe of WIOBSS-AA is displayed in the lower right corner of this figure.  
The radio system block diagram consists of three parts: the transmitting part, the receiving part and the synchronization module, as shown in Figure 2. The transmitting part is composed of five channels shown in the above schematic and the receiving part is a single channel system in the schematic below. The transmitting and receiving parts share a time-frequency synchronization module, which is based on the GPS system and used for the time and reference frequency synchronization  
Figure 1: Photo of WIOBSS-AA site showing the log-periodic antenna array. The pink building in the center of the photo is the shelter of the hardware. The mainframe of the WIOBSS-AA is displayed in the lower right corner.  
between the operating parts. The USB bus is applied in the transmitting and receiving parts, as well as the synchronization module. The core of the USB is the CY7C68013 unit, which belongs to the family of USB 2.0 transceivers.
# A New \"Doctor and Patient\" Optimization Algorithm: An Application to Energy Commitment Problem
## 2 Background
Energy commitment (EC) sets the best template for using energy carriers because the technical limitations are dealt with first and the economic challenges after. Adjusting energy carriers to the highest demand would be unnecessary and costly. Indeed, energy carriers should be used optimally, as the proper management of energy resources can save considerable money. First, the energy demand must be determined in the EC issue. Similar to the unit commitment (UC) problem, this energy demand could span 24 h. In the UC issue, the demand for electricity must be fulfilled with the appropriate unit combination for every hour of the study. UC involves adjusting thermal generators in order to meet the projected demand and minimize the cost of system operation [37]. UC is accountable in the selection of the units which can be set to operate economically [38]. UC also contributes to the power calculation of each unit based on total demand [39]. In power systems, it is important to create a table of optimum generating units with minimum fuel and transaction costs corresponding to the load requirements [40]. In order to solve the UC problem, both intelligent and classical techniques have been proposed [41]. a mixed-integer linear programming (MILP) model to figure out the transmission-constrained direct current (DC)-based unit commitment (UC) problem using the generalized generation distribution factors (GGDF) for modeling the transmission network constraints is proposed in Reference [42]. Intelligent techniques are an important choice in the engineering field due to their ability to optimize multi-range local optimal points [43]. The memetic binary differential evolution algorithm (MDPE) has been proposed to solve a profit-based UC problem [44]. An uncertain UC problem study is suggested in the presence of energy storage systems using list-based genetic algorithm-priority [45]. Quantum binary particle swarm optimization (QBPSO) algorithms are proposed to reduce operation cost in the UC problem [46]. Other algorithms, such as the whale optimization algorithm (WOA) [47], gray wolf algorithm (GWO) [48], shuffled frog-leaping algorithm [49], improved genetic algorithm [50], and simulated annealing [51], have also been suggested to find the solution of UC problem. The various studies in operation of power systems, such as energy reservation review [52], energy storage systems [53], and the impact of renewable energy sources [54], are analyzed by researchers.
## 4 Discussion  
As mentioned earlier, firstly, methods of reducing the emissions of gases that are released into the atmosphere during blasting operations are mostly based on the neutralization process. For this, solutions, fixing additives and foams are used. The overwhelming majority of these methods are used to reduce emissions of sulfur oxides and carbon monoxide, that is, to reduce nitrogen oxides, they are mainly considered only in matters of combating waste gases in metallurgy. Secondly, technological methods that can be used in open pits are not effective, do not require additional expenditures of explosives, purchasing of special equipment, or they have a limited scope, for example, in deposits represented by fractured rocks or water-cut.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**t, s** & **Concentration NO2, mg/m3** & **1** & **2** & **3** & **4** & **5** \\ \hline
180 & & 227 & 243 & 267 & 280 & 275 \\ \hline
195 & & 219 & 233 & 265 & 270 & 276 \\ \hline
210 & & 205 & 220 & 244 & 266 & 266 \\ \hline
225 & & 198 & 221 & 232 & 246 & 235 \\ \hline
240 & & 194 & 214 & 220 & 240 & 220 \\ \hline
255 & & 188 & 206 & 214 & 233 & 221 \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._  
Figure 6: The dependence of the concentration of nitrogen oxides in the explosion products in time (compiled by the author [PERSON]).  
Of special interest were those researches in which methods of improving the quality of stemming in a borehole were considered, based on the properties of stemming to retain combustion products for a longer time.  
Research shows that alkali earth metal salts, which are added to the explosive and catalyze the combustion of ammonium nitrate, reduce the explosion temperature.  
Thus, it is possible to use highly active catalysts as part of contoured stemming due to a number of advantages inherent in both methods, described above, to reduce nitrogen oxide emissions directly during blasting operations in open pits.  
According to the results of the study, it can be concluded that when an explosion of a model substance is carried out without the use of catalysts, a significant amount of nitrogen oxides is released. Over time, the nitrogen oxide concentration curve undergoes only small changes, which means that more time is required for ventilation. In the case of using catalysts, the emission of nitrogen oxide is reduced. The dependence of concentration on time in these cases is more pronounced, which indicates a high efficiency of the use of catalysts to reduce emissions of nitrogen oxides. This will reduce the risk of workers being poisoned by explosion products and reduce ventilation time, allowing more work to be done and less downtime. However, it is worthwhile to note that in order to further study the effect of highly active fillers, it is necessary to establish the most effective filler dispersion and its percentage in the contoured stemming.
## 4 Discussion
### Applicability  
The current results of our study, namely, the influencing factors of team communication in NPP emergencies, were developed for the NPP domain; thus, they are domain-specific and might not be directly applicable to other domains. However, our results also might not be directly applicable to a certain commercial NPP in operation. NPPs are designed and operated differently across countries and even across sites in the same country. In our research, aspects such as the number of operators per team, the structure of emergency teams, and other related team factors influence the applicability of our research results. In addition, the team-related settings in an NPP are prone to changes compared with design or technology changes. To address this limitation, we track and describe our analysis methods in detail whenever possible to enable practitioners to tailor our methodology to obtain results applicable to their needs.  
In this paper, we extracted the influencing factors using a literature analysis and topic modeling. We are aware that the majority of influencing factors of team communication in NPP emergencies collected in this study may represent a subset of influencing factors of team communication in common situations. However, in the literature analysis, the keywords used in collecting the reference papers contain emergency-related concepts (emergence*, critical situation, safety, accident, incident, high-risk, stressful, and adverse); thus, we conclude that the resulting influencing factors presented here are related to emergencies. (When a keyword is excluded, for example, \"emergence*\", then both emergency and normal situation communication papers will be extracted.) In topic modeling, the influencing factors obtained were derived from all domains (not only NPPs) since our intention was to develop a comprehensive list of influencing factors (similar to the studies in [23; 24]). However, we used the same keywords; thus, the resulting influencing factors are also related to emergency situations. In this paper, we focused on emergency situations because of the criticality of the situation; the lack of studies in that field; the questionable knowledge of team responses to a crisis; and the fact that many teams, including highly trained teams, perform inadequately with significant variability during emergencies [14]. As for the topic modeling results, there is a possibility that the influencing factors may be unsuitable for NPPs. Therefore, influencing factor validation from subject-matter experts is required, and we are planning this validation for future studies.  
Figure 8: Preliminary version of the team communication model in NPP emergencies.
## 3 PAZ Description
### Orbit Parameters  
PAZ will fly in a polar dawn-dusk Sun-synchronous orbit. This type of orbit allows power throughput maximization and simplifies the thermal design. TerraSAR-X and TanDEM-X satellites follow this same orbit, offering the capability for repeat-pass interferometry. PAZ will cover all of Earth with a mean revisit time of one day and a mean access delay slightly higher than 24 h (Table 3) [33].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Central frequency** & 9.65 GHz \\ \hline
**Maximum bandwidth** & 300 MHz \\ \hline
**Peak power** & 1.9 kW \\ \hline
**Pulse length** & 15 \(\upmu\)s–67 \(\upmu\)s \\ \hline
**Service factor** & \(18\%\)–\(20\%\) \\ \hline
**Pulse repetition frequency** & \(2\) kHz-\(6.5\) kHz \\ \hline \hline \end{tabular}
\end{table}
Table 2: PAZ signal parameters.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Nominal height** & 514 km \\ \hline
**Orbits per day** & 15 + 2/11 \\ \hline
**Incidence angle** & 20\({}^{\circ}\)–45\({}^{\circ}\) (full perf.) 15\({}^{\circ}\)–60\({}^{\circ}\) (accessible) \\ \hline \hline \end{tabular}
\end{table}
Table 3: PAZ orbit and incidence angle values.
## 3 Experimental Results and Analysis
### Electrical Resistivity Variation Characteristics  
The coal mass is one of solid conductor material, and the separation of internal charges and directional movement of free charges in coal mass is the basis and premise for conductivity [26; 27]. When the external force is applied to coal mass, the conductivity of coal mass will increase with directional movement of internal charges between macromolecular structure in coal mass [28]. It can be seen that the trend of the change of resistivity fluctuates slightly in steps of 1 MPa, 3 MPa, 5 MPa, 7 MPa, 9 MPa and 11 MPa from the above figure. It shows a trend of fluctuating downward with the increasing of inter-stage stress, but there is a good correspondence relationship between the resistivity curve and stress curve in the mass. The deformation and failure of internal microstructures in the coal sample are inapparent due to lower load at the initial stage of loading, while the value of electrical resistivity is relatively large. However, it is obvious that the resistivity almost maintains a constant value during the stage of constant loading. In addition, the electrical resistivity will decrease with the increase of loading not only as a result of the internal microstructures of coal sample are gradually compacted, but also because of conductive channels are better with each other. When the loading continues to increase and reach the ultimate compressive strength of coal mass, the internal microstructure of coal sample will be completely destroyed, and it is generating a large number of cracks during the process of loading, which greatly cuts off the conductive channels of coal mass. In this moment, the sample is completely destroyed because of the generation of main fracture, the electrical resistivity is abruptly increasing to a maximum.  
Figure 4: Resistivity variation curve during multi-stage loading.  
Figure 3: Schematic diagram of AE probes arrangement.
## 4 Improvements in PRI Implementation
### Different Formulations of PRI
[123]) using MODIS data, successfully removed the impact of pigment and illumination and detected the severe drought and GPP variation for a deciduous broadleaved forest that was not detected by EVI. [PERSON] et al. [109] also revised PRI by introducing the optical signal of chlorophyllic content at 550 nm to decrease the effect of the sizes of pigment pools; two revisedindices, \(\mathrm{PRI_{R1}=(R550-R531)/(R550-R570)}\) and \(\mathrm{PRI_{R2}=(R531-R570)/(2R550-R531-R570)}\), correlated better with RUE, particularly during senescence when chlorophyll content and LAI were low (\(R^{2}=0.57\) and \(0.59\) for \(\mathrm{PRI_{R1}}\) and \(\mathrm{PRI_{R2}}\), respectively; \(R^{2}=0.20\) for PRI). The residual PRI (\(\mathrm{rPRI=PRI-SPRI}\)), which removed the structural-related signal in PRI (sPRI) as a function of LAI, increased by 42% the correlation in the estimation of RUE over PRI [124]. In other studies, the calibrated PRI (\(\mathrm{CPRI=PRI-(0.645\times\ln(mNDVI705)+0.0688)}\)) adjusted the effects of the pigments and improved the correlation between PRI and NPQ [88]. PRI calculated as \(\mathrm{PRI_{norm}=PRI/(RDVI\times R700/R670)}\) (RDVI, Renormalized Difference Vegetation Index [156]) which normalizes for decreases in chlorophyll content and canopy leaf area induced by stress was better correlated with stomatal conductance, water potential and pigment content and detected water stress better than the standard PRI [75].