esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Mathematical Implementation of the Problem
### Technical and Economical Constraints  
**Total electrical and thermal equilibrium**  
\[\begin{split}\sum_{\forall n}\sum_{\forall j}\left(P_{h,e,jn}+P_ {h,e,n}^{ES_{d}}+(1-m_{h}^{Ret}).P_{h,e,n}^{Ret_{d}}\right)=\\ \sum_{\forall n}\sum_{\forall c}\left(P_{h,e,n}^{c}+P_{h,e,n}^{ES _{d}^{ch}}+m_{h}^{Ret}.P_{h,e,n}^{Ret_{h}^{ch}}\right)\end{split} \tag{6}\]  
\[\sum_{\forall n}\sum_{\forall i}\left(P_{h,ht,n}^{i}+P_{h,ht,n}^{TES_{d}} \right)=\sum_{\forall n}\sum_{\forall l}\left(P_{h,ht,n}^{k}+P_{h,ht,n}^{TES _{d}^{ch}}\right) \tag{7}\]  
Equations (6) and (7) represent the total power generated by electrical/thermal generators during each time interval; this quantity must be equal to the total demand of the electrical/thermal consumers.  
**Retailer constraints**  
Equation (8) shows the cost resulting from electrical power purchased from the retailer to the H-MG n, while Equation (9) presents the lower and upper bounds of retailers offer price for purchasing power to the H-MG n.  
\[CO_{h,e,n}^{Ret_{d}}=\pi_{h,e,n}^{Ret_{d}}\times P_{h,e,n}^{Ret_{d}} \tag{8}\]  
\[0\leq\pi_{h,e,n}^{Ret_{d}}\leq\lambda_{h}^{SSP} \tag{9}\]  
In addition, Equation (10) is the revenue resulting from selling electrical power from the H-MG n to the retailer, whereas Equation (11) shows the price bid range for sales of power by the retailer to H-MG n.  
\[U_{h,e,n}^{Ret^{h}}=\pi_{h,n}^{Ret^{ch}}\times P_{h,e,n}^{Ret^{ch}} \tag{10}\]  
\[0\leq\pi_{h,n}^{Ret^{ch}}\leq\lambda_{h,e}^{SSP} \tag{11}\]  
Equations (12) and (13) show the exchanged power constraints between H-MG n and retailer. In addition, Equation (14) represents the binary decision variable for the retailer.  
\[P_{h,e,n}^{Ret_{ch}}\leq m_{h}^{Ret}\times\hat{P}^{Ret} \tag{12}\]  
\[P_{h,e,n}^{Ret_{d}}\leq\left(1-m_{h}^{Ret}\right)\times\hat{P}^{Ret} \tag{13}\]  
\[\hat{P}^{Ret}\leq\left(P_{h,e,n}^{ESP}+P_{h,e,n}^{CHP}+P_{h,e,n}^{ES_{d}}\right) \tag{14}\]Equations (15) and (16), respectively, are the function regarding the pollution of the retailer and the cost of pollution causing by purchasing from retailers. In Equation (15), \(d_{E}^{i}\) is the retailers' pollution factor, which belongs to closed interval [0,1].  
\[E_{h,i}^{Ret}=d_{E}^{i}.P_{h,i}^{Ret} \tag{15}\]  
\[CO_{h,i}^{E,Ret}=\alpha_{i}^{Ret}\times E_{h,i}^{Ret}\times\Delta h \tag{16}\]  
In Equation (16), \(\alpha\) is calculated from the relations in Equations (17) and (18). The coefficient of price fine (i.e., Max-Max type) equals the ratio of maximum fuel cost to the maximum emitted pollution level. This coefficient, compared to other coefficients, leads to more pollution reduction. Meanwhile, the Min-Max price is equal to the ratio of the minimum fuel cost to the maximum amount of pollution released. This type of fine coefficient will minimize the cost of fuel compared to other fines.  
\[\alpha_{Max-Max,ji}=\frac{\pi^{F}\times\hat{p}_{ji}}{E_{h,ji}\hat{p}_{ji}} \tag{17}\]  
\[\alpha_{Min-Max,ji}=\frac{\pi^{F}\times\hat{p}_{ji}}{E_{h,ji}\hat{p}_{ji}} \tag{18}\]
## 2 Materials and Methods  
### Study Area  
The Sanjiangyuan National Park is located in the south of Qinghai province, between 33\({}^{\circ}\)09\({}^{\prime}\) and 36\({}^{\circ}\)47\({}^{\prime}\) N and between 89\({}^{\circ}\)50\({}^{\prime}\) and 95\({}^{\circ}\)18\({}^{\prime}\) E. It covers a total area of 123,100 km\({}^{2}\), accounting for 31.16% of the total area of the Sanjiangyuan region. The Sanjiangyuan National Park is the source of three important rivers in Asia, i.e., the Yellow, Yangtze, and Lancang Rivers. The Yellow River headwater region is located in Maduo county, Guoluo prefecture and covers an area of 19,100 km\({}^{2}\); the Yangtze River headwater region (Kekexili) is located in Zhiduo and Qumalai counties of Yushu prefecture and covers an area of 90,300 km\({}^{2}\); and the Lancang River headwater region is located in Zaduo county, Yushu prefecture and covers an area of 13,700 km\({}^{2}\). The altitude of the Sanjiangyuan National Park is 3774-6821 m. The Yangtze River headwater region has the highest average altitude, at 4772 m,  
Generally, the vegetation types in Sanjiangyuan National Park mainly include coniferous forest, shrub, meadow, prairie, and alpine vegetation. Meadow and prairie are the most important types of ecosystems, accounting for 45.42% and 37.23% of the total area, respectively, followed by alpine vegetation. Spatially, the vegetation of the studied region is differentiated along the gradient of precipitation, especially in the Yangtze River headwater region, where the precipitation gradually increases from west to east. In the western and driest part of the region (200-300 mm precipitation/year), sub-desert steppe is dominant, and gradually tends toward the east into steppe xeric grasslands dominated by Stipa purpurea. The eastern part of the region is occupied by rich-in-species mesoceric steppe grasslands (450-550 mm/year). In the Lancang area, the rich-in-species s mesosexeric steppe grasslands are predominant, but in the valleys at its eastern border, there are conifer forests dominated by Picea likiangensis, Picea purpurea, and Pinus densata. The Yellow River headwater region is dominated in the northern half by xeric steppes with Stipa purpurea, while in the south, in the context of a more humid climate, the rich-in-species and very dense mesosexeric steppes are predominant [18; 19; 20; 21]. The studied area is also, according to the Worldwide Fund for Nature(WWF), a part of the Central China eco-region that is particularly important for the conservation of large mammal fauna, containing the rare snow leopard, Panthera uncia, and many ungulate species [22; 23]. This fauna, with most of its valuable species in a critically endangered/endangered status, depends on the state of the vegetation of the region.
# A Graph-Based Approach for Modelling Quantum Circuits
## 6 Evaluation of the Proposal
### Integration in a Hybrid Classical-Quantum Development Environment  
In order to develop a true quantum software engineering discipline [5], it is necessary to define integration mechanisms between classical and quantum software development tools. In this way, the output of the execution of the quantum software can be fed back into the execution flow of the classical software. This would make it possible to take decisions and modify the models of the quantum circuit under development according to the results obtained in the previous executions, in order to generate and execute new versions of them.  
The integration of this proposal in such a hybrid development environment requires the intercommunication of modelling tools with the tools used to develop quantum software (usually, Python frameworks). In this work, the modelling environment provided by the Eclipse project, developed entirely in Java, and the Qiskit quantum software programming and simulation environment, developed in Python, have been used. This allowed the two development environments to be integrated, with one language calling the other and using its output in further computations. Solutions such as Py4], for calling Java methods from Python code, and Jython, for calling Python code from Java, could achieve such integration.
## 2 Materials and Methods
### Methods
#### 2.3.3 Correlation Analysis  
Pearson correlation coefficients (PCCs) were utilized in Matlab 9.1.0 R2016b to calculate the correlation coefficients of NPP with each meteorological factor and each image of soil water. Pearson correlation analysis is suitable for detecting the presence of a correlation between two continuous variables [65]. The equations are as follows:  
\[R=\frac{\sum\limits_{i=1}^{n}[(x_{i}-\overline{x})(y_{i}-\overline{y})]}{\sqrt{ \sum\limits_{i=1}^{n}(x_{i}-\overline{x})^{2}\biggl{[}\sum\limits_{i=1}^{n}(y _{i}-\overline{y})^{2}\biggr{]}}} \tag{8}\]  
where \(n\) is the study period (2001-2020, 20 years) and \(i\) is the number of years; \(x_{i}\), \(y_{i}\) are the values of the two variables \(x\), \(y\) in year \(i\); \(\overline{x}\), \(\overline{y}\) are the average values of the two factors in year \(n\).
# A Review on Rainfall Measurement Based on Commercial Microwave Links in Wireless Cellular Networks
## 4 Signal Processing from RSL to Rainfall Map
### Uncertainties Analysis
#### 4.6.1 Uncertainties in Each Step of Signal Processing  
* In wet/dry classification, whether adopting multiple CMLs or single CML RSL data, the empirical-based thresholds for classification will certainly lead to uncertainties and errors.  
* In baseline determination, the baseline in rain period determined by the NLA method is constant. Yet, some signal fluctuations, such as the fluctuations during the dry period, may also occur during the rain period. For shorter links or lower frequencies links, the natural fluctuation of baseline attenuation has the same order of magnitude as the quantization interval (1 dB) [1].
* In WAA compensation, the WAA value depends on the CML antenna characteristics (hydrophobicity or hydrophilicity) and the weather environment. For example, the water vapor condensation induced by the temperature drop at night, even though there is no rain, can cause the WAA value to be higher than that in light rain. In general, the WAA value increases as the rainfall intensity becomes stronger. The WAA effect on CML-based rainfall retrieval is probably the major source of errors for short links, because WAA becomes more comparable to the overall link attenuation as the length of the link decreases.
* In rain rate calculation, the _k-R_ power law relationship in Equation (1) is approximately linear in the frequency of 20-35 GHz, but when the frequency is lower or higher than that, the uncertainty caused by DSD increases [73]. Rain rates calculated by different sampling strategies and time resolution also have deviations [74]. Generally, the performance of rainfall measurement using minimum/maximum RSL with time resolution of 15 min is better than that using the instantaneous RSL. On the other hand, for different time resolutions, due to the spatial and temporal variability of rainfall, a longer sampling time interval (e.g., 15 min) will lead to a larger error, but a very short sampling time interval (e.g., 1 s) can increase the accuracy while also increasing the computational complexity.
* In rainfall map generation based on the interpolation algorithm, errors and uncertainties in the reconstructed rainfall field increase as the time aggregation decreases and the distance between two CMLs increases. The uncertainties in daily rainfall map are lower than the 15 min rainfall map, because the errors in the 15 min rainfall map are aggregated to cancel each other out over the course of a day [75]. The OK interpolation algorithm utilizes the average path link rainfall data, i.e., the mid-point rainfall data in the link, and so converting the line scale to the point scale will produce errors, which is called interpolation uncertainty [53].
## 5 Research Challenges and Future Directions
### Application Specific Transmission Protocols  
Application specific and adaptive protocols need to be developed for passive wake-up radios. This is necessary because different applications have dissimilar requirements. Similarly, passive wake-up radios are deployed in various physical environmental settings. Some of these applications have their sensor nodes in harsh environmental conditions, such as smart farm, structural monitoring, etc. Therefore, developing application adaptive and robust protocols is the best way to mitigate the high level of failure that these nodes are prone to and to improve fault tolerance in the system. For example, attempts are being made to develop passive wake-up radio adaptive protocols for medical application in [100; 101; 102]. These protocols mainly aim at improving energy efficiency as well as response time in order to achieve the medical application situations, which are, periodic/normal communication as well as random/emergency communication.
# The Calculated Circadian Effects of Light Exposure from Commuting
###### Abstract  
Light entrains human circadian rhythms, but increased time spent indoors and decreased daylight exposure may disrupt human circadian regulation and cause health problems. Much research is focused on improving indoor lighting conditions to minimize the adverse circadian impact of electric lights, and few studies investigate the circadian impact of daylight during the incidental time that people spend outdoors. For instance, when people commute from home to work, they are exposed to daylight. The purpose of this study is to investigate daylight's impact on commuters' circadian rhythms. Measurements of the illuminance and the spectral irradiance distribution (SID) of daylight were taken for three modes of commuting: driving, riding on trains, and walking; and under different weather conditions, on different days, and at different locations throughout the summer and autumn in the Sydney metropolitan region in Australia. With the SID data, three metrics were calculated to estimate the circadian impacts: \(\alpha\)-opic irradiance, circadian stimulus (\(CS\)), and equivalent melanopic lux (\(EML\)). The results suggest that driving or walking on sunny or cloudy days and riding trains on sunny days are beneficial for the commuters' circadian synchronization.  
c 2021 11 11846 11.11846 11.11846 11.1846 11.  
on the effect of this light exposure on the circadian systems of commuters. This research aims to evaluate the circadian effect of daylight from commuting.  
In this study, the illuminance and the spectral irradiance distribution (SID) of daylight were measured for three different commuting modes: driving, riding on trains, and walking, in two types of weather conditions (sunny and cloudy). The measurements were repeated during different times and days at various locations in the Sydney metropolitan area. The SID data were used to calculate three metrics: \(\alpha\)-opic irradiance [16], circadian stimulus (_CS_) [17; 18], and equivalent melanopic lux (_EML_) [19], in order to estimate the circadian effects of the different commuting modes. The average \(\alpha\)-opic irradiance for each mode of commuting in both sunny and cloudy weather conditions is reported in this study. The average _CS_ and _EML_ values are compared to their recommended minimum values.
# Improvement of Damage Segmentation Based on Pixel-Level Data Balance Using VGG-Unet
###### Abstract  
In this research, 200 corrosion images of steel and 500 crack images of rubber bearing are collected and manually labeled to build the data set. Then the two data sets are respectively adopted to train VGG-Unet models in two methods, aiming to conduct Damage Segmentation by inputting different size of data set. One method is Squashing Segmentation to input squashed images from high resolution directly into VGG-Unet model while Cropping Segmentation uses cropped image with size 224 x 224 as input images. Because the proportion of damage pixels in the data set is different, the results produced by the two data sets are quite different. For large size damage (such as corrosion) segmentation, Cropping Segmentation has a better result while for minor damage (such as crack) segmentation, the result is opposite. The main reason is the gap in the concentration of valid data from the data set. To improve the capability of crack segmentation based on Cropping Segmentation, Background Data Drop Rate (BDDR) is adopted to reduce the quantity of background images to control the proportion of damage pixels from the data set in pixel-level. The ratio of damage pixels from the data set can be decided by different value of BDDR. By testing, the accuracy of Cropping Segmentation becomes relatively higher under BDDR being 0.8.  
fullly convolutional network; damage segmentation; VGG-Unet; pixel-level data balance +
Footnote †: journal: Applied Sciences
# An Investigation of the Temperature-Drift Effect on Strain Measurement of Concrete Beams
## 2 The Temperature-Resistance Effect of the Strain Gauge
During the test, the glass ceramic with different resistance strain gauges was placed in a temperature-controlled box, and one sample was obtained per minute. The temperature changed from 5 to 55 \({}^{\circ}\)C over the course of 168 hours (7 days) as shown in Figure 5a. Figure 5b shows the strain change and temperature change relationship diagram of the PFL-20 mm strain gauge. Through linear regression analysis, the slope of the regression equation was obtained as 8.58, meaning that the temperature coefficient of resistance (\(\beta\)) of the PFL-20 mm strain gauge was 8.58 \(\mu\)/\({}^{\circ}\)C.  
Figure 4: Strain gauges attached to a glass ceramic plate with a very small thermal expansion coefficient. Figure 3: The combined temperature effect coefficient \(\gamma\) value test of a strain gauge (PFL-20 mm) attached to the concrete beam: (**a**) specimen photograph; (**b**) test results \({}^{\circ}\). The test results on the temperature changes of the PFL-20 mm strain gauge are summarized in Table 1. Column A is a hypothesized concrete thermal expansion coefficient (\(\alpha\)) of \(11\times 10^{-6}\) (\(11\)\(\mu\)\(\epsilon\)/\({}^{\circ}\)C). Column B is the temperature-resistance effect (\(\beta\)) of the strain gauge affixed on the glass ceramic. Using Equation (1), the expected combined temperature effect coefficient was obtained as \(\gamma=\alpha+\beta\) and is listed in Column C of Table 1. The experimental combined temperature effect coefficients obtained from the tests are listed in Column D. A comparison of Column C with Column D indicates that, for the PFL-20 mm strain gauge, the difference between the combined temperature effect coefficient and the expected value was 2.9%, as shown in the last column of Table 1.  
The aforementioned test was repeated on strain gauges of different lengths affixed to concrete, and the results are summarized in Table 1. The last column indicates that the differences between the combined temperature effect coefficient (\(\gamma\)) and the expected value of the various strain gauges were less than 5%. The test results were acceptable, considering the heterogeneity of concrete materials.
## 1 Introduction
### Halobacteria Diversity  
_Halobacterium_ species are obligate aerobic, rod-shaped archaea enveloped by a single lipid bilayer membrane surrounded by an S-layer made from the cell-surface glycoprotein [7]. They can use amino acids for their growth in aerobic conditions, but they can also grow in an anaerobic environment, given the correct conditions [7]. Halobacteria can be found in highly saline lakes, such as the Great Salt Lake, the Dead Sea, and Lake Magadi. Halobacteria are candidates for a life form present on Mars. These microorganisms develop a thin crust of salt that can moderate some of the ultraviolet light and make it opaque through their photosynthetic pigment bacteriorhodopsin [7].  
Halobacteria are able to survive under a wide range of salinities. According to their survival potential, they are classified as halotolerant or halophilic bacteria. Halotolerant bacteria can survive in media up to 25% NaCl, whereas halophile bacteria require salt to grow. They have been isolated from different halophyte plants across the world, i.e., the rhizosphere of _Leymus chinensis_, _Puccinella tenuiflora_, and _Suada glauca_, are highly enriched with the phyla of bacteria Actinobacteria, Acidobacteria, Bacteroidetes, Chloroflexi, Firmicutes, _Gemmatimonadetes_, Haloarchaea, and Proteobacteria [49; 50; 51]. The Firmicutes phylum consisted of genera _Bacillus_, _Virgibacillus_, _Salincoccus_, _Marinococcus_, _Halobacillus_, _Planococcus_, _Thalassobacillus_, and _Salimicrobium_; in phylum Actinobacteria, _Nocardiopsis_ was the representative genus; in phylum Proteobacteria, _Halomonas_, _Idiomarina_, and _Psychrobacter_ were representative genera [48]. Halobacteria exhibit a highly complex network of diversity than endophytic bacteria and bacteria from bulk soil. [PERSON] et al. [52] reported Actinobacteria, Bacteroidetes, Firmicutes, and Proteobacteria in the rhizosphere of _Salicornica europaea_, _Kalidium foliation_, and _Borsczowia aralocaspica_. These bacteria exhibit a variety of mechanisms that have multiple roles, e.g., safeguard halophytes from salt stress, promote their growth, and remediate soil contamination (Figure 1).
# An Investigation of the Policies and Crucial Sectors of Smart Cities Based on IoT Application
## 4 Results and Discussion
### Recognizing the Existing Obstacles in the Development of SCs
#### 4.1.6 Public Safety  
Theft of motor vehicles throughout the world, coupled with a massive loss of cash, is a disaster for insurance companies. For instance, just in the USA, in 2019 about USD 6.4 billion was lost to motor vehicle theft [52]. Likewise, every year, worldwide, 70 million smartphones are lost or stolen [53]. In these regards, IoT-based SC technologies are vital for offering real-time monitoring, enhancing public safety, and supporting proper decision-making, that will prevent a lot of harm to people [54]. For example, testing of the motorcycle antitheft system (MATS) showed that this system had 100% accuracy at speeds of up to 70 km/h and for speeds up to 80, it had 94.4% accurate [55].
# Designing Advanced Multistatic Imaging Systems with Optimal 2D Sparse Arrays
## 3 Optimization Algorithm
### Genetic Algorithm
#### 3.2.1 Custom GA Configuration  
Once the optimization tool has been selected, the optimal configuration with the smallest secondary lobes should be identified. Only configurations that meet the criteria below will be evaluated:  
* The aperture size (horizontal and vertical) cannot exceed 100 cm.
* Due to hardware limitations, the minimum distance between elements is set to 2 cm.
* The array is symmetric on both the X and Z axes.
* Transmitter and receiver elements are uniformly spaced in both horizontal and vertical dimensions.  
In this study, the GA was utilized to calculate the most suitable \(d_{rx}\) value, while \(d_{tx}\) was kept constant. Although there were previous attempts to optimize both variables at the same time, it was found that fixing the value of \(d_{tx}\) to cover the desired aperture size and concentrating on determining the optimal \(d_{rx}\) value is a more efficient method.  
This process is computationally intensive, as the GA solver must construct the appropriate architecture, compute the PSF, and evaluate the cost function for each potential \(d_{tx}\)-\(d_{rx}\) pair. However, by taking advantage of GPU acceleration and MATLAB's vector computation capabilities, we can quickly and efficiently obtain initial results. Fixing \(d_{tx}\) while optimizing only for \(d_{rx}\) yields results in less than 3 min and 18 s. If both \(d_{tx}\) and \(d_{rx}\) are optimized simultaneously, the process extends to about 15 min.