esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Setup
### In-Plane Heatwave Thermography  
For the measurements of the IPHWT, a highly sensitive _IRC906 SLS_ broadband infrared camera with a spectral response of \(2.00\,\mathrm{\SIUnitSymbolMicro m}\) to \(11.25\,\mathrm{\SIUnitSymbolMicro m}\) wavelength by _IRCCameras_ from the US is used. This camera has a high spatial resolution (\(640\times 512\) pixels), low noise-equivalent delta temperature of \(\mathrm{NETD}=35\,\mathrm{mK}\) and was operated with a frame rate of 100 fps [39]. As described in Section 3.1, the heat source is a \(40\,\mathrm{mm}\times 40\,\mathrm{mm}\times 3.9\,\mathrm{mm}\) large Peltier element by Farnell. The Peltier element is driven with its maximum input current of \(A_{max}=15.4\,\mathrm{A}\) at \(V_{int}=5\,\mathrm{V}\). The absolute temperature rise of the test specimen was not recorded during the measurements as the IPHWT is based on relative temperature changes. The Peltier element is directly clamped to the surface of the test specimen. To increase its efficiency, a brass block is attached to the backside (not attached to the panel) and serves as a thermal reservoir. As the length of the heat pulse (\(\sim\)10 s) is not crucial for later processing, the Peltier is driven by a power source that can manually be switched on/off. The recorded videos are then processed with Python 3.1 and result in single images for the covariance and local gradient method respectively. As both methods should reveal the loose rivet, the two images can be joined together to achieve a single image as a result of the IPHWT.
# Risk-Sensitive Rear-Wheel Steering Control Method Based on the Risk Potential Field
## 2 Design of Rear-Wheel Steering Control Based on the Risk Potential Field  
Figure 1 illustrates the block diagram of the 4 WS control system. The system generates a risk potential field based on the road environment information and the vehicle's state, and the reference yaw rate is calculated considering the risk potential. The desired rear-wheel steering angle is calculated using a controller comprising feedforward and state feedback controllers. The system was designed to vary the amount of assistance by changing the weighting coefficient \(w\) according to the risk level. However, as an emergency avoidance situation was assumed in this study, we set \(w=1\), and the amount of assistance was the maximum.  
In the figure, \(X\) and \(Y\) indicate the vehicle position, \(r\) denotes the yaw rate, \(V\) represents the vehicle velocity, \(\psi\) indicates the yaw angle, \(a_{x}\) denotes the longitudinal acceleration, \(\beta\) represents the vehicle sideslip angle, \(U_{risk}\) indicates the value of the risk potential, \(r^{*}\) denotes the reference yaw rate, \(\delta_{r^{*}}\)_-\(\delta_{r}\)_-\(\delta_{b}\)_ denote the feedforward and feedback terms of the desired rear steering angle, respectively, \(\delta_{r}\)\({}^{*}\) represents the desired rear steering angle, \(\delta_{sw}\) indicates the steering wheel angle, \(\delta_{f}\) denotes the front steering angle, and \(n\) represents the steering gear ratio.
## 1 Introduction  
Energy, as a topic, is gaining attention in the livestock sector because the consumption of energy in the rearing of animals will play a vital role in the future. According to UN research [1], the world population will reach 9.15 billion by 2050, thus increasing the demand for food. This will lead to an increase in the consumption of livestock products by 40-80%, compared with the consumption in 2010 [2]. As a result, the energy required to sustain the demand for animal protein will increase in the near future. Against the background of decreasing fossil energy resources due to their high demand and price, the use of renewable energy resources is an important alternative to energy from fossil fuels in the agriculture sector [3].  
The use of energy in livestock production has gained attention recently because of its harmful impact on the environment [4]. In pig housing, it is necessary to provide an adequate housing environment for optimum production, and to satisfy the legislation regarding animal welfare demands [5]. Temperature, moisture, and indoor air quality are important parameters for indoor housing environments. Temperature control is crucial for pigs, and a slight variation in the inside temperature has a negative impact on their health, growth, and performance [6]. A pig house contains a large variety of air pollutants--such as NH\({}_{3}\), CO\({}_{2}\), fine particles, microbes, and endotoxins--which cause environmental pollution and global warming [7]. Therefore, it is time to utilize renewable energy resources which reduce environmental pollution for the maintenance of indoor housing environments.  
GHP systems have been successfully utilized as alternative renewable energy sources [8] in many countries [9]. They are environmentally friendly, and can reduce electricity consumption [10]. In a GHP system, the internal air quality improves owing to the supply of fresh air, and there is also no direct combustion that produces pollutants [11]. Similarly, GHP can reduce electricity consumption by consuming one unit of electricity to provide three units of geothermal energy [12]. The use of renewable energy sources is relatively low in Korea compared to other developed countries; however, GHP systems have been gaining popularity for cooling and heating. The Korean government is taking steps to increase the use of new and renewable energy from 2.1% in 2004 to 11% in 2030. In Korea, the energy acquired will be 200 times the main energy consumption if 2% of the geothermal energy resources to a depth of 5 km are used [13]. These energy resources can be used at a depth of 100-500 m to operate a GHP for livestock [14]. Several authors [4; 7; 14; 15] have reported the beneficial effects of a heating system using a GHP on environmental pollution, energy consumption, and production performance in livestock farms. However, to the best of our knowledge, the cooling effects of GHP systems in pig farms during summer have not yet been reported. Therefore, the aim of this experiment was to assess the effects of the GHP cooling system on pig growth performance, housing conditions, and the emission of noxious gases.
# Role-Based Access Control Model for Inter-System Cross-Domain in Multi-Domain Environment
## 4 RBAC-IC Execution
### Authorization and Access Control  
The authorization and access control framework of RBAC-IC is shown in Figure 5. Figure 5 shows that the domain administrators of domain A and domain B have created a specific role associated with the same abstract role in their respective domains, and assigned different permissions. An ordinary user in domain A wants to access an object in domain B, and needs to authorize a relevant specific role in domain B. The user first applies to the administrator of the local domain, and the administrator sends a notification to domain B. If the administrator of domain B agrees, the specific role will be authorized to the user. After the user obtains the specific role, he can access resources in domain B across domains. Sections 4.2.1 and 4.2.2 describe the details of authorization and access control processing.
# A Controlled-Environment Quality Assessment of Android GNSS Raw Measurements
## 5 Conclusions and Scope for Future  
Analysis of the results of Android GNSS raw measurements under a completely controlled environment was not documented before. This represents a close match to the highest quality of raw measurements that may be achieved from the currently available Android devices in the market, barring the recently commercially available Xiaomi MI 8. Based on its comparison to real-environment-based peer research, the high stability of the \(C/N_{0}\) in Android devices and significantly reduced pseudorange noises display the vital need for solving propagation nuisances that are troubling Android measurements today. Comparisons of position measurements between the anechoic chamber and a real environment reveal the nuances behind the large errors seen in GNSS-only Android positioning solutions, such as the duty-cycle occurrence. Understanding and mitigating them is vital toward the push to achieve higher performance through such devices. As mentioned in the work, the presented atmosphere models and smoothing corrections haven't been fine-tuned to achieve maximum accuracies and, hence, a couple meters of biases remain. The low quality of the GNSS hardware on commercial Android devices also factor in these errors. Nexus 9 hardware emerges as the only viable device among those tested due to the problem of the duty cycle in others. Even under controlled conditions, there was no way to achieve an ambiguity fix after duty cycle was implemented by the devices. The limitation of the record-and-replay paradigm with one transmitting antenna is seen along with the unsuitability of the Google Toolbox PVT algorithm in such a scenario. Further controlled and real-world test comparisons need to be implemented, including more recent devices. Improvement to the positioning algorithm by refining correction models and integration with other sensors remain as future steps.  
[PERSON], [PERSON], and [PERSON] conceptualized the work; [PERSON], [PERSON], and [PERSON] curated the data and wrote the original draft; [PERSON] and [PERSON] performed formal analysis and contributed to the software development; [PERSON] and [PERSON] conceived the methodology; [PERSON] and [PERSON] performed validation of the work; all authors investigated and contributed to the writing, review, and editing; and [PERSON] supervised of the work.  
This research received no external funding.  
The authors would like to acknowledge [PERSON] for providing access to the anechoic chamber; and [PERSON] and [PERSON] for assisting during the data-collection campaign, and for supplying the hardware.  
The authors declare no conflict of interest.
## 1 Introduction
The variation of fiber coupling efficiency with scintillation index \(\sigma_{I}^{2}\) in the range of 0-1 is obtained by simulation, as shown in Figure 1. There is a reverse correlation between the scintillation index and the fiber coupling efficiency. When the height H above the ground is the same, the scintillation index increases with the increase in transmission distance, and the coupling efficiency decreases accordingly. When the transmission distance L is the same, as the height H from the ground decreases, the scintillation index begins to increase, and the coupling efficiency decreases accordingly. When \(\sigma_{I}^{2}=0.6\) and H < 600 m, the coupling efficiency will be less than 5%. During the experiment, in order to ensure that the scintillation index \(\sigma_{I}^{2}\) is in the range of 0-0.6, the fiber coupling efficiency is not less than 5% to control H above 600 m.
## 5 Case Studies
### Case Two  
#### 5.2.1 Introduction of Instrument and Model  
Based on the engineering geological conditions of the Taijiao high-speed railway coal mine goat ground, the model test was carried out. The test instrument adopts the self-developed high-speed railway coal mine goat ground pseudo-dynamic loading model test device, which is mainly composed of five parts: electric gantry, controller system, cooling system, hydraulic oil separator, and actuator, as shown in Figure 9.  
The ballasted track served as the subgrade structure for the Taijiao high-speed railway. The subgrade bed's bottom layer measured 2.3 m in height, while its surface layer stood at 0.7 m. Furthermore, the ballast layer maintained a thickness of 0.35 m. The strata of the road section in the study area were relatively gentle, showing a north-south overturning syncline distribution, and the coal rock strata were stable. According to the field drilling data, the formation lithology is mainly composed of old loess, silty clay, limestone, mudstone, sandstone and so on. The mining depth of the goal selected in the model test was 28.1 m-48.8 m, and the mining height was 1.2-6.5 m. Most of the mined-out areas were semi-filled and loose in structure.  
For the simulation, an area with a mining height of 4 m and a mining depth of 60 m was chosen to investigate the characteristics and mechanism of activation deformation in a coal mine goat site under the load of a high-speed train. From the perspective of considering the operability of the test, the formation conditions were simplified from top to bottom into 15 m loess, 10 m limestone, 20 m sandstone, and 15 m mudstone. The similarity test adopted a scale model, the geometric similarity constant \(C_{l}\) is 100, and the heavy \(C_{\gamma}\) similarity constant was 1.5. Based on the theory of similarity, the similarity constant of the basic parameters in the physical model test of the coal mine goat under the high-speed railway were obtained. The values are shown in Table 11.  
According to the similarity constants of the parameters in Table 11, the physical parameters of the model of each layer of rock and soil in the coal mine goat were calculated. In this model test, river sand, gypsum and lime were selected as similar materials. Different ratios were selected for the production of simulated materials through similar material tests. The test results show that the ratio of loess is 573, the ratio of limestone is 437, the  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Physical Quantity** & **Similitude Parameter** \\ \hline Stress & 150 \\ Elastic modulus & 150 \\ Internal friction angle & 1 \\ Time & 10 \\ Displacement & 100 \\ Cohesive force & 150 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Physical quantity similarity constant.  
Figure 9: Dynamic load loading device.
# Nutritional Values of Onion Bulbs with Some Essential Structural Parameters for Packaging Process
## 1 Introduction  
Onion (_Allium cpa_ L) belongs to the _Alliacase_ family which is mostly used as a staple food due to the presence of essential oils that gain strong flavor and taste in diet [1]. It has various forms as a whole, raw, flakes, ground, liquid, dried, or even fermented for culinary uses [2]. It has high effective nutrients as proteins, carbohydrates, sugars (glucose, fructose, galactose, arabinose), vitamins (C, g-carotenes), minerals (calcium, iron, sulfur) with some flavonoids and polyphenols components. Onion is commonly used in many medicinal purposes as a traditional medicine for the treatment of skin diseases, microbial growth, gastrointestinal problems, worms, and injuries [3, 4]. Several epidemiological studies established that onion consumption can decrease the development risks for many diseases, such as inflammatory, coronary heart, cancer, respiratory problems due to their bioactive components [5, 6, 7]. Besides, the onion amino acids link with the sensory response of the \"umami\" taste [8]. Onions contain huge amounts of arginine that acts as a nitrogen reservoir and a quantity amounts of lysine and glutamic acids [9]. Therefore, onion plays an essential role in the human diet. Physical and structural parameters of food stuff participate in a significant role in processing and packaging operations. Besides, foodsare highly complex systems that demand much ingenuity due to the data scantiness in scientific researches. Physical structural parameters of various onion varieties vary with several factors such as their proximate chemical compositions [10]. Among these structural parameters, mass, weight, volume, densities, projected area, and friction forces are the most essential in the sizing, grading, sorting, conveying, and packaging systems [11]. Therefore, the relationship between weight and both intermediate diameters and geometric attributes is needed to determine the best time for harvesting [12].  
This research established the main nutritional values, vitamins, and amino acid compositions of several onion bulbs with some essential structural parameters for providing authentic information about onion the packaging process to design and fabrication of sorting and separation equipments for the onions.
# Penetration Analysis of SAR Signals in the C and L Bands for Wheat, Maize, and Grasslands
## 2 Study Site and Dataset Description
### Dataset Description
#### 2.2.2 Sentinel-2 Images  
A total of 24 free optical images were obtained from the S2 constellation between 01/01/2017 and 31/08/2018. The S2 images were downloaded from the Theia website at the French Land Data Center ([[https://www.theia-land.fr/](https://www.theia-land.fr/)]([https://www.theia-land.fr/](https://www.theia-land.fr/))). The Theia website provides S2 images corrected for atmospheric effects and ortho-rectified [39; 40]. The NDVI maps were computed using the Near-infrared (NIR) and Red (R) bands of the S2 images (Equation (2)). From each S2 image, the NDVI pixel values within each reference  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**SAR Dates** & **Polarizations** & **Incidence** & **Pixel Size** \\
**dd/mm/yyyy** & **Polarizations** & **Angle (\({}^{\circ}\))** & **m \(\times\) m** \\ \hline
02/10/2016 & HH+HV & \(\sim\)37.5 & 6 \(\times\) 6 \\
19/02/2017 & HH+HV & \(\sim\)37.3 & 6 \(\times\) 6 \\
02/04/2017 & HH+HV+VH+VV & \(\sim\)37.4 & 3 \(\times\) 3 \\
09/05/2017 & HH+HV+VH+VV & \(\sim\)27.4 & 3 \(\times\) 3 \\
16/07/2017 & HH & \(\sim\)40.5 & 3 \(\times\) 3 \\
15/09/2017 & HH & \(\sim\)31.1 & 3 \(\times\) 3 \\
01/10/2017 & HH+HV & \(\sim\)37.5 & 6 \(\times\) 6 \\
27/05/2018 & HH+HV & \(\sim\)37.5 & 6 \(\times\) 6 \\
22/06/2018 & HH+HV & \(\sim\)30.9 & 6 \(\times\) 6 \\
01/07/2018 & HH+HV & \(\sim\)40.5 & 6 \(\times\) 6 \\
15/07/2018 & HH+HV & \(\sim\)40.5 & 6 \(\times\) 6 \\
26/08/2018 & HH+HV & \(\sim\)40.5 & 6 \(\times\) 6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of ALOS-2 images.  
plot were averaged to characterize the vegetation condition of that plot. Finally, for each reference plot, to derive NDVI values at the SAR acquisition date, a linear interpolation was performed using the NDVI values corresponding to the S2 images acquired before and after the SAR acquisition date. For the reference plots, the NDVI values ranged between 0.10 (almost bare soil) and 0.92 (vegetation height ~85 cm).  
\[NDVI=\frac{NIR-R}{NIR+R} \tag{2}\]
# BVLOS Unmanned Aircraft Operations in Forest Environments
## 5 Taking a Risk-Based Approach to BVLOS Operations in Forest Environments
### Technological Considerations
#### 5.1.6 Traffic Collision Avoidance System (TCAS)  
TCAS is widely used on commercial aircraft in combination with ADS-B. Unlike ADS-B, TCAS actively interrogates aircraft transponders to identify intruders and their range, providing both a traffic advisory (i.e., that the traffic is there) and a resolution advisory (i.e., how to respond to avoid a collision) [71; 72]. However, TCAS only works alongside ADS-B, may not be able to handle multiple intruders, and smaller unmanned aircraft may not be able to be equipped with TCAS due to payload constraints [73; 74]. Nonetheless, if flights are happening where there are commercial aviation operations present, especially if higher altitudes are being used, then TCAS can provide a useful addition. It is highly situation-dependent, but for most forestry operations, TCAS will not provide any additional safeguard; however, it is worth mentioning in case it is relevant for a particular operation based upon the operating environment.
## 4 Methods
### Imperfect Maintenance Improvement Factor  
This article uses the imperfect maintenance improvement factor to measure the effectiveness of imperfect maintenance, and the effectiveness of each imperfect maintenance varies. Specifically, this article introduces different imperfect maintenance improvement factors in each maintenance cycle, thereby affecting the number of defect occurrences within each maintenance cycle. The dynamically changing number of defect occurrences affects the cost of corrective maintenance for each maintenance cycle, thereby dynamically affecting the total maintenance cost [44; 45; 46].  
According to the theory of imperfect equipment maintenance, if the total number of \(s\)-level severity (\(s=1\) represents \"average\", \(s=2\) represents \"severe\", \(s=3\) represents \"critical\") defects that occur in a defective component of a transformer during the \(i\)-th maintenance cycle is \(n_{i}^{s}\), which needs to be rounded up after calculation, Then the calculation formula is:  
\[n_{i}^{s}=\left[\int_{T_{i}^{-}}^{T_{i}^{+}}\lambda_{i}^{s}(t)dt=\int_{T_{i}^{ -}}^{T_{i}^{+}}\frac{m^{s}}{t_{0}^{s}}\left(\frac{t-\sum\limits_{j=1}^{i} \delta_{j}\cdot T}{t_{0}}\right)^{m^{s}-1}dt\right],\;\;s=1,2,3\;\;\;;\;\;i=1,2,\ldots,N \tag{6}\]  
Among them, \(\lambda_{i}^{s}(t)\) represents the failure rate function of the \(s\)-level severe defect of the defective component in the \(i\)-th maintenance cycle, \(T_{i}^{-}\) is the start time of the \(i\)-th maintenance cycle, \(T_{i}^{+}\) is the end time of the \(i\)-th maintenance cycle, \(\delta_{i}\) is the imperfect maintenance improvement factor of the \(i\)-th maintenance cycle [47], and its expression is as follows:  
\[\delta_{i}=\left(a\cdot\frac{c_{pm}}{c_{pr}}\right)^{bi} \tag{7}\]  
In the above equation, \(a\) is the maintenance cost adjustment coefficient, \(1\leq a\leq\frac{c_{pr}}{c_{pm}}\), \(c_{pr}\) is the cost of a single major overhaul, and \(c_{pm}\) is the cost of single preventive maintenance of the component; \(b\) is an adjustment parameter for the frequency of imperfect preventive maintenance, where \(0<b<1\).  
It should be noted that since a major overhaul after every \(k\) maintenance cycle can repair the components associated with the defect mode as new, the failure rate function \(\lambda_{i}(t)\) and its integral for the severity of the defect need to be updated after every \(k\) maintenance cycle. If \(h=\left\lfloor\frac{N}{k}\right\rfloor\), the following relationship exists during the calculation:  
\[T_{1}^{-}=T_{k+1}^{-}=T_{2k+1}^{-}=...=T_{hk+1}^{-} \tag{8}\]  
\[T_{1}^{+}=T_{k+1}^{+}=T_{2k+1}^{+}=...=T_{hk+1}^{+} \tag{9}\]  
\[\delta_{1}=\delta_{k+1}=\delta_{2k+1}=...=\delta_{hk+1} \tag{10}\]  
In summary, the schematic diagram of the imperfect maintenance strategy model established in this article is shown in Figure 5.  
Optimization Model for Maintenance Interval Considering Transformer Reliability and Imperfect Maintenance