esa-sceva/satcom-chunk-collection huggingface dataset:
# Survey of Smart Parking Systems
## 4 Vehicle Detection Techniques (VDT) Implemented in SPS
### Crowdsensing  
This detection technique collects data provided by citizens. This paradigm was proposed as a type of SPS in Section 3.3, where CITS and its architecture were described as a system based on the crowd (set of system users).  
However, in this section, CS is considered a detection technique that allows the availability of parking slots to be determined. This distinction is made because this type of detection technology does not always fall within an SPS of the CITS type. For example, in Reference [125], the users (drivers) fed the system with information concerning the availability of parking spaces through images recorded from cameras installed in their vehicles. Then, the system processed this information to determine the spaces available and published the information in an application that guides users to these places; thus, it can be considered a PGIS (see Section 3.2).  
Therefore, CS detection offers advanced services through the intelligent analysis of data, which allows useful information to be extracted. Detecting vehicular activity, the urban environment, adequate road and environmental conditions, and tourist services are examples of these urban services that can be improved through the collaboration of the population [38]. This collaboration can be consolidated through a smartphone-based detection system, which allows users to accurately track the time and location of their vehicle's parking events and real-time parking events, without the need for user intervention, regardless of the location and orientation of the telephone [143].  
In addition, the detection system can be explicit, where the user indicates when a parking slot is available or vacant, or a third party (not necessarily a driver) can detect this information and upload it to the system [40].  
This type of system depends entirely on users, often called crowdsources. If there is not a sufficient population of users, the parking slot detection data will be scarce. To mitigate this risk, some implementations offer users some rewards for using the system, such as store discounts or municipal tax breaks [136].  
As mentioned in Section 4.2, beacons can be used in this type of VDT as well. This is because these devices communicate via drivers' cell phones and, hence, communicating and making sense of the data is based on user collaboration [151].  
The main advantage of this VDT is the cost of the infrastructure as, when compared to the other VDTs described so far, it is much cheaper to deploy, unlike RFID systems (ORS) that are very expensive to implement. This is because an RFID reader system must be installed in each parking space and a chip must be installed in each vehicle or in the architecture with RSU where a long-term investment is required to assemble the whole system. Crowd detection works with user smartphones, which means a lower implementation cost, but at the cost of a less accurate detection function.
## 2 Mathematical Formulation of Object Localization and Tracking Algorithms
### Object Localization and Tracking Algorithm  
When the TOFs of all sensor channels are estimated by the NRLM methods with the GN model or the DE model, the position of the target can be estimated by these TOFs. In this section, the NRLM algorithm can also be used in target position estimation. In addition, the EKF algorithm is also proposed for object localization and tracking. The performance of these two proposed methods is compared with the other methods in Section 4.
# Reactance Regulation Using Coils with Perpendicular Magnetic Field in the Tubular Core design
## 4 Results and Discussion  
### Magnetic Coupling  
Figure 6a illustrates the relationship between impedance and resistance with the current for internal and external coils. It is important to point out that the external coil remains open circuit during the internal coil measurement and vice versa. It is observed that the impedance of both coils decreases as the current increases. The peculiarity of the magnetic curve can explain the initial decrease of impedance. The magnetic saturation is also responsible for the reduction of impedance and reactance with the current amplification. Another possible factor is the appearance of high current harmonics.  
The relation between impedance and current can be approximated by the exponentiation function:  
\[Z=al^{b} \tag{8}\]  
where coefficients \(a=23563\) and \(b=-0.801\) for the inner coil, whereas \(a=5945.2\) and \(b=-0.542\) for the outer coil. The approximation coefficient (\(R^{2}\)) is more than 0.99 for both coils.  
Figure 6b presents the coupling coefficient (\(K_{\textit{couple}}\)) versus current in the internal coil. The magnetic coupling increases (\(\sim\)11%) as the current in the internal coil is enhanced. However, the coupling coefficient varies only \(\pm 5.5\%\) from the average value (\(\sim\)0.0158) and remains constant with a relatively  
Figure 5: The equivalent circuit of the experimental setup for coil controllability measurement: \(R_{1}\)—pure ohmic resistance of a working coil; \(L_{\textit{total}}\)—inductance of a leakage magnetic field; \(R_{\mu}\)—resistance representing energy losses in the core; \(L_{\mu}\)—inductance of the coil; \(L_{c}\)—coil current; \(I_{\text{loss}}\)—current representing core losses; \(I_{\mu}\)—magnetization current.  
Figure 6: (**a**) Impedance of the internal and external coils. The external coil remains open circuit during the measurement of the internal coil and vice versa. (**b**) Coupling coefficient as a function of current in internal coil. Solid lines represent the approximation.  
low magnitude. It is applicable to the controllable impedance design. The coupling coefficient can be expressed by a cubic polynomic function:  
\[K_{couple}=3\cdot 10^{-8}I^{3}-3\cdot 10^{-6}I^{2}+8\cdot 10^{-5}I+0.0148 \tag{9}\]  
The coefficient \(R^{2}\) is equal to 0.98.
## Materials and Methods
### Surface Water Storage (SWS) Estimation  
The SWS is estimated according to Equation (2), while the net surface runoff (\(\Delta\)R, unit: m\({}^{3}\)) is calculated using Equation (4). The two in-situ hydrological gauging stations with monthly observed discharge records from 2000 to 2019 represent the inflow and outflow of the study region (provided by the Yunnan Hydrological Bureau, China). The two hydrological stations use the same measurement standards, and the flow is determined using a single line method. The volume changes of the reservoir can be estimated according to Equation (5) [48]. \(\Delta\)V indicates the volume change in the reservoir (unit: m\({}^{3}\)), where SWE\({}_{t}\) (unit: km\({}^{2}\)) indicates SWE on a specific date \(t\), and \(\Delta\)WSE (unit: m) denotes the WSE difference between date \(t\) and \(t\)-1. Continued SWE and WSE data can be easily obtained from spectral and SAR imageries and multi-mission altimetry satellites (see Sections 2.4.1 and 2.4.2). A SWE-WSE curve is firstly established for Xiaowan and Nuozhadu, respectively, based on a power function shown in Equation (6).  
\[\Delta\mathrm{R}=\mathrm{\ Inflow}\ -\ \mathrm{Outflow} \tag{4}\]  
\[\Delta\mathrm{V_{t}}=\frac{\left(\mathrm{SWE_{t-1}+SWE_{t}+\sqrt{SWE_{t}}*SWE_{ t}}\right)}{3}*\Delta\mathrm{WSE} \tag{5}\]  
\[\mathrm{SWE}\ (\mathrm{WSE})=\mathrm{a}\ (\mathrm{WSE}-\mathrm{h})^{\mathrm{b}} \tag{6}\]
## 5 Experimental Results
### Channel Hole Bending  
As explained in Section 4.1, unlike other channel hole defects, it is required to exploit the various CKD patterns to detect Bending defects. Figure 13a shows \(V_{th}\) distributions of the target flash block after programming CKBD patterns. In the channel hole where Bending occurs, since the erased flash cells are not fully inhibited during a program operation, their \(V_{th}\) is slightly increased, called the _erase disturb_ phenomenon. These abnormal flash cells are sensed as a programmed state (data '0') even though their real state is an erased state (data '1'). Therefore, by performing a read operation with the appropriate read level, we can detect Bending defects. Figure 13b shows the relationship between the detection accuracy for Bending defect and the level of VCC. As VCC is raised from 2.8 V to 3.6 V (in the datasheet of commercial NAND flash memory, VCC cannot exceed 3.6 V), the detection accuracy is also enhanced because the amount of leakage current between two adjacent channel holes is proportional to VCC (i.e., voltage difference). Figure 13c shows the effect of the number of P/E cycles to screen out the Soft Bending defect. From the results, we observed that by applying just 50 P/E cycles, Soft Bending defects were efficiently activated into Hard Bending defects (i.e., no additional bad blocks during the early stage of flash lifetime).
# Combination Pattern Method Using Deep Learning for Pill Classification
## 4 Multi-Combination Pattern Labeling
### Kaze  
The KAZE algorithm employs a nonlinear scale space, which utilizes nonlinear scale filtering to make image feature blurring locally adaptive. This approach helps in reducing noise while preserving image boundaries. The KAZE detector is configured based on a scale-normalized determinant of the Hessian matrix computed at multiple scale levels. The maxima of the detector response are selected as features using a moving window. Feature description introduces the rotation invariance property by finding the dominant orientation in the circular neighborhood around each detected feature. Although KAZE features are invariant to rotation, scale, and limited affine transformations and have more distinct characteristics at different scales, they incur a longer computational time. Figure 6 illustrates the structure of KAZE-based feature detection. The process begins with image pyramid generation, creating multiple image scales. Scale extrema are identified across these scales, followed by orientation estimation represented by the colored lines, each indicating different gradient directions at specific points. Next, descriptor computation generates feature vectors that capture key point information, while feature matching compares these descriptors across different images. In the transformation computation phase, matching key points determine geometric transformations between the images. The final step, orientation estimation, refines the key points matches, ensuring accurate alignment and orientation, visualized by the connected key points and transformation overlays.  
The KAZE detector initially generates an image pyramid, wherein the input image is repeatedly down-sampled to detect features at different scales. The scale extrema step detects the maximum and minimum points in the scale space of each image based on the LoG filter. Orientation estimation is performed using the gradient orientation of the image to approximate the orientation at each key point. The descriptor computation step obtains the path of the region around the key point to extract local features from that key point. The rotation is corrected using the orientation information from the path and computed based on the SIFT descriptor. In the feature-matching step, distances are calculated between descriptors to match key points between two images via Nearest-Neighbor (NN) and the Nearest-Neighbor-Ratio (NNR) matching. To compute the transformation, the NNR matching results are used to calculate the affine transformation matrix, which is used to align features between the two images and subsequently identify the key points between them. Figure 7 presents sample results of the KAZE algorithm. The left side of the Figure 7 shows the extracted features as colored circles, and the right side shows the matching result based on the features.
## 2 Materials and Methods
### ESS Model  
For hybridization of the system, the battery is used as an ESS. The following constraints were used to model the ESS [50].  
\[0\leq P^{C}_{b,q,t}\leq b^{C}_{b,q,t}P^{C}_{b};\,0\leq P^{D}_{b,q,t}\leq b^{D}_{b, q,t}P^{D}_{b} \tag{5}\]  
\[SoC_{b,q,NT}=SoC_{b,q,1};\,SoC^{\min}_{b}\leq SoC_{b,q,t}\leq SoC^{\max}_{b} \tag{6}\]  
\[SoC_{b,q,t+1}=SoC_{b,q,t}+\Delta T(\frac{\eta^{C}_{b}P^{C}_{b,q,t}}{E_{b}}- \frac{P^{D}_{b,q,t}}{\eta^{D}_{b}E_{b}}) \tag{7}\]  
\[b^{C}_{b,q,t}+b^{D}_{b,q,t}=1;b^{C}_{b,q,t},b^{D}_{b,q,t}\in\{0,1\} \tag{8}\]  
In constraint (5), \(P^{C}_{b,q,t}\) and \(P^{D}_{b,q,t}\) are the charging and discharging power of battery \(b\) connected at bus \(q\) and at time \(t\), respectively. They are limited by maximum chargeable and dischargeable capacity of battery \(b\), i.e., \(P^{C}_{b}\) and \(P^{D}_{b}\), respectively. \(b^{C}_{b,q,t}\), and \(b^{D}_{b,q,t}\) are the binary variables and indicate the charging and discharging status of battery \(b\) connected at bus \(q\) and at time \(t\). Also, \(SoC_{b,q,t}\) in (6) indicates the state of charge (SoC) battery \(b\) connected at bus \(q\) and at time \(t\) which is limited by its boundaries. The dynamic model of energy exchange in the battery is shown in (7). In this regard, \(\eta^{C}_{b}\) and \(\eta^{D}_{b}\) are the charging and the discharging efficiency of battery \(b\), respectively. The constraint (8) prevents the battery from charging and discharging simultaneously.
# Use of SMAP Soil Moisture and Fitting Methods in Improving GPM Estimation in Near Real Time
## 3 Model and Method
### Fitting Correction Methods
#### 3.3.4 Performance Metrics  
Seven correction methods are evaluated in this study: the method using the SM information alone (SMP), the method using linear fitting alone (GPM-Linear), the method using nonlinear fitting alone (GPM-Nonlinear), the method using CDF fitting correction alone (GPM-CDF), the method combining the linear fitting correction with SMP (SMP-Linear), the method combining the nonlinear fitting correction with SMP (SMP-Nonlinear) and the method combining the CDF fitting correction with SMP (SMP-CDF). Three statistical metrics are used for the evaluation of the performance of the seven methods: the root-mean-square error (RMSE, in mm/day, or mm/month depending the temporal scale used), the Pearson correlation coefficient (R) and the mean absolute error (BIAS, in mm/day) [65]. The RMSE is applied to show the average error magnitude; R reflects the degree of agreement betweenderived products and CMPA, with values ranging from \(-1\) to \(1\); and the BIAS is used to evaluate the systematic bias in daily precipitation amount.
## 6 Validation of Results  
Monthly solar fraction curves obtained from three schemes of the current study as shown in Figure 17a was compared with a study by [PERSON] et al. [7], shown in Figure 17b and we found similarity of the trends for the months of summer season. It can be seen from Figure 17b that the solar fraction is plotted for entire year in previous published article while in current study, results of solar fraction from April to September were evaluated for the parameters of 50% PES and plotted in Figure 17a. Legends (10 to 100) in the already published paper representing different collector areas used. Lower solar fractions at the mid of the season (July) is an indication of higher auxiliary energy consumption and lowest primary energy saving month of the season justifying Equations (8) and (9) of the current study.  
Tilt angles of the collector of S-3, using optimum storage size, were plotted against seasonal collector gain obtained for ETC in the current study. As Figure 18a,b exhibited a fairly similar trend for the optimum tilt angle of the collector. Figure 18a matching with [PERSON] et al. [7]. However, the optimum value of the tilt angle of the solar collector depends upon the specific location, therefore, plots may differ in values (because of different collector areas used) and units, but trends are found to follow similar paths in both current and previous studies).  
Figure 16: Collector area per kW of cooling capacity.  
Figure 15: Monthly efficiencies of ETC and FPC for optimized flow scheme (S-3).  
Another similarity of the trend for storage size with auxiliary energy consumption for cooling system is shown in Figure 19, as minimum value of auxiliary energy consumption corresponds to the optimum tank volume. It can be clearly seen that the current study (Figure 19a) is quite similar to the [PERSON] et al. [27] study shown in Figure 19b for relation between storage size and auxiliary energy.
## 2 Results
### Spatial and Temporal Dynamic Distribution of Grassland AGB in Xinjiang  
#### 2.2.1 Descriptive Statistics of Measured Grassland AGB  
From 2000 to 2022, the annual mean values of AGB of different grassland types in Xinjiang ranged from 68.09 to 137.21 g\(\cdot\)m\({}^{-2}\) (Table 1). Among the three major grassland types, the meadow AGB was the highest, followed by steppe AGB, and then desert AGB. Furthermore, according to the grassland area, the meadow makes up 23% of the total grassland area, with the lowland meadow class accounting for the highest percentage (50.42%), whereas the mean montane meadow AGB was the highest (137.21 g\(\cdot\)m\({}^{-2}\)). The steppe accounted for 34% of the total grassland area in Xinjiang, with the temperate desert steppe (37.60%) and alpine steppe (31.91%) having relatively large areas, whereas the temperate meadow steppe AGB had the largest mean value (119.56 g\(\cdot\)m\({}^{-2}\)). The desert in Xinjiang had the most widespread distribution, accounting for 43% of the grassland area. The area was dominated by the temperate desert (78.00%), with the largest mean AGB value being in the temperate steppe desert (95.51 g\(\cdot\)m\({}^{-2}\)), and the smallest AGB was the alpine desert (68.09 g\(\cdot\)m\({}^{-2}\)). Additionally, the alpine desert with the smallest grassland area (1.17%) had the lowest mean AGB of 68.09 g\(\cdot\)m\({}^{-2}\), with a standard deviation of 2.81 g\(\cdot\)m\({}^{-2}\) and Coefficient variable (CV) of 0.04, indicating that its AGB was less volatile and less dispersed. The temperate desert with the largest grassland area (33.54%) had an AGB of 88.19 g\(\cdot\)m\({}^{-2}\), with the highest CV (0.05), indicating greater dispersion of temperate desert AGB.
## 1 Introduction
The production of bioH\({}_{2}\) from OFMSW by DF still presents several bottlenecks that must be solved, such as the variability of the feedstock, homogeneity inside the bioreactor, pH control, temperature control, and the inhibition of H\({}_{2}\)-consumers supplied through the feedstock, among other variables that may diminish the bioH\({}_{2}\) yield [21; 22]. The optimal temperature and pH values for DF are usually defined in the same way as for biogas production; namely, mesophilic (35-38 \({}^{\circ}\)C) and thermophilic (50-55 \({}^{\circ}\)C) temperatures are indicated as adequate for bioH\({}_{2}\) production, and a neutral pH is referred to as the one promoting the highest bioH\({}_{2}\) yield [3; 10]. The temperature range selected depends on the bioH\({}_{2}\) yield required and the heat available to warm up the bioreactor. Concerning the pH, a neutral pH is very difficult to use in bioreactors working with mixed populations, as it favors the development of H\({}_{2}\)-consuming methanogens. Thus, an acidic pH is preferable when the bioreactor is operated with such a complex microbial consortium to inhibit the development of H\({}_{2}\)-consuming methanogenic Archaea. A relevant operational parameter that can be used to optimize the bioH\({}_{2}\) yield is the hydraulic retention time (HRT), which corresponds to the time under which the substrate is in contact with the fermentative microbial population. As the HRT governs the contact time between the fermentative population and the organic substrate, its change also affects the organic loading rate (OLR) applied to the bioreactor. The OLR quantifies the mass of organic feedstock daily applied per unit volume of bioreactor. Hence, as the HRT increases, the OLR decreases and _vice versa_. The optimal HRT and OLR values are dependent on the feedstock composition, its biodegradability, and the ability of the fermentative population to degrade it and grow inside the bioreactor. In this context, the main aims of this study were to investigate (i) the effect of HRT on the bioH\({}_{2}\) yield obtained in the DF of OFMSW, in a lab-scale bioreactor, and consequently (ii) the effect of the OLR applied to the bioreactor when the HRT is changed. The innovation of the present work lies in the use of mixed anaerobic cultures and real OFMSW both obtained from an industrial-scale biogas plant, instead of pure cultures and a synthetic substrate as commonly reported in the literature.
# Research on Large Language Model for Coal Mine Equipment Maintenance Based on Multi-Source Text
## 4 Results and Discussion
### Experimental Results Analysis
#### 4.2.2 Coal Mine Equipment Professional Consulting  
The categories evaluated in the coal mine equipment professional consulting comparison experiment were based on the classes with the highest frequency in the coal mining exam dataset. The categories selected included coal mining systems, boring systems, transport systems, coal preparation systems, ventilation systems, and safety management. The question and candidate options were given as inputs and the text answers generated by the model were subsequently used to calculate accuracy. A sample response is shown in Table 4.  
The experimental results are shown in Figure 7, indicating that XCoalChat had an average accuracy of 77.21% and outperformed other LLMs in all evaluation categories. Specifically, XCoalChat achieved a strong performance, surpassing ChatGLM-6B, BLOOM-7B, and LLama7B's average scores of 53.59%, 47.16%, and 48.42%, respectively. It was not surprising to find that the model output the referenced normative regulations when answering questions, which was due to the ReliableCEMK-Self-Instruction methodology used in this study, based on the specific knowledge in the field of coal mine equipment maintenance and greatly improved the reliability of the model. This makes the model's answers more informative, and the model can provide coal mine equipment operators with a reasoned guide to equipment maintenance that can reduce the incidence of incorrect operation.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline \hline
**Question** & **Answer** \\ \hline How much shall the elongation not exceed in the section of the wire rope subjected to violent tension? A. 0.05\% B. 0.1\% C. 0.5\% C. 0.5\% D. 1\% & Option C should be chosen According to Article 10 of the safety regulations of the “Operating Regulations for Overhaulers of Hoisting Systems The elongation length of the wire rope subjected to violent impact cannot exceed 0.5\%. If it exceeds 0.5\%, the strength and mechanical properties of the wire rope drop sharply, which can easily cause a reduction in the coefficient of safety. This cannot guarantee the safety of lifting and needs to be replaced in time. \\ \hline \hline \end{tabular}
\end{table}
Table 4: Example of coal mine equipment professional consulting.  
Figure 7: Experimental comparison of the professional skills of coal mine equipment. **(a)** Safety management system; **(b)** mining system; **(c)** ventilation system; **(d)** driving system; **(e)** coal processing system; **(f)** transport system.