esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis and Optimization of Output Low-Pass Filter
## 2 Filter Resonance and Damping  
A current-source inverter requires a specific type of filter to connect to a standard voltage-based grid (mains). This filter is known as a second-order CL-type low-pass filter. It is used to minimize the variations in electrical characteristics between the inverter and the grid, a phenomenon known as impedance mismatch [1].  
As shown in Figure 2, this CL filter includes a damping resistor, represented by \(R_{D}\). The filter's cutoff frequency, labeled as \(f_{c}\), is the same as its resonant frequency, \(f_{R}\). This is because the filter's output signal would become infinitely large at this specific frequency if there was no damping resistance. The formula for calculating the resonant frequency is given in Equation (1).  
\[f_{R}=\frac{1}{2\,\pi\,\sqrt{L\,C}} \tag{1}\]  
To avoid this potentially harmful amplification, it is crucial to make sure the filter has sufficient damping. Some damping naturally occurs due to inherent energy losses within the inductor and capacitor components. However, additional damping is often needed.
## 2 Modeling and Problem Formulation
### Process-Planning Problem Formulation for Sustainable CPS
Suppose we want to set an upper bound \(q_{ak}\) on the maximum number of times that an operation \(k\) can be performed by each agent. The constraints \(\Pi(x,B)\) can be represented by \(\sum\limits_{j=1}^{I_{a}}x_{aj}o_{ajk}\leq q_{ak}\forall a\in A,k\in\{1, \ldots,K_{n}\}\). We define \(G(\Gamma(x,B),E(x,B))\) as an increasing function of \(\omega_{n}-\ \ \Gamma(x,B)\) and a decreasing function of \(Eng(x,B)\). For example, \(G(\Gamma(x,B),Eng(x,B))=w_{1}(\omega_{n}-\ \ \Gamma(x,B))+w_{2}Eng(x,B)\). We formulate the following problem to maximize the objective function. \[\max G(x)=G(\Gamma(x,B),Eng(x,B))=w_{1}(\omega_{n}-\ \ \Gamma(x,B))-w_{2}Eng(x,B) \tag{6}\]  
\[ \begin{array}{l}s.t.\\ \sum\limits_{a\in RA}\sum\limits_{j=1}^{I_{a}}x_{aj}o_{ajk}\geq d_{nk}\ \forall k\in\{1,\ldots,K_{n}\}\\ \omega\geq\sum\limits_{a\in RA}\sum\limits_{j=1}^{I_{a}}x_{aj}\tau_{aj}\\ \sum\limits_{j=1}^{I_{a}}x_{aj}o_{ajk}\leq q_{ak}\ \forall a\in RA,k\in\{1, \ldots,K_{n}\}\\ x_{aj}\in\{0,1\}\ \forall a\in\{1,2,3,\ldots,|RA|\},\forall j\in\{1,2,3,\ldots,J_{a}\} \end{array}  \tag{7}\]The problem defined in (6) through (10) aims to find a solution \(x_{aj}\) that maximizes the objective function \(G(x)=G(\Gamma(x,B),Eng(x,B))=w_{1}(\omega_{n}-\ \ \Gamma(x,B))-w_{2}Eng(x,B)\), where \(a\in\{1,2,3,\ldots,|RA|\}\) and \(j\in\{1,2,3,\ldots,J_{a}\}\), such that the requirement \(\Re eq_{n}\) of the process \(\Psi_{n}\) is satisfied, the total processing time of the overall process cannot exceed \(\omega_{n}\) and the maximum number of times that an operation \(k\) can be performed by each agent cannot exceed \(q_{ak}\).
## 4 Uncertainty Analysis
### Model Selection and Application
#### 4.2.2 Coupled Model
Additionally, it can reduce the risk of model overfitting. [PERSON] et al. [114] conducted a study in Mangshi City, Yunnan Province, utilizing the certainty factor (CF) method to calculate the sensitivity values of various factors. These sensitivity values were used as classification data for RF. Appropriate training data and optimized model parameters were selected for model prediction. Additionally, the CF prior model was used to select negative samples in the study area. Subsequently, a binary logistic regression model was established. This allowed for the evaluation of landslide susceptibility zoning in the study area using the ROC curve to evaluate the model. The results showed an accuracy of 91%. Coupling machine learning models with other machine learning models, also known as ensemble algorithms, is commonly employed in geological hazard susceptibility assessment. Currently, ensemble algorithms based on bagging and boosting are widely used. However, as they utilize homogeneous classifiers, their drawbacks may be amplified, leading to potential overfitting issues [115]. In contrast, heterogeneous ensemble methods can integrate different types of classifiers, leveraging the strengths of each type to compensate for weaknesses, thereby enhancing the robustness and generalization of the ensemble classifier [116]. This further improves the prediction accuracy of ensemble algorithms. The  
Figure 5: Distribution of landslide susceptibility classes based on three models: (**a**) FR-SVM; (**b**) FR-RF; (**c**) FR-ANN [79]. typical heterogeneous ensemble models mainly consist of three types: stacking, blending [117], and weighted averaging [118]. A generic ensemble learning model framework is illustrated in Figure 6. In landslide susceptibility assessment, heterogeneous ensemble models improve prediction accuracy by combining various machine learning algorithms, such as decision trees, support vector machines, and random forests. The process typically involves first training the multiple distinct base learners, then using their outputs as inputs for an ensemble strategy (such as weighted averaging or stacking) to generate the final prediction results. [PERSON] et al. [119] conducted a comparative study on landslide susceptibility assessment in the border areas between Tianshui City, Gansu Province, and Baoji City, Shaanxi Province. They employed three heterogeneous ensemble learning models: stacking, blending, and weighted averaging. RF, SVM, and BPNN were used as the base learners. The results (see Table 5) indicated that the stacking ensemble model achieved greater accuracy in landslide susceptibility assessment. [PERSON] et al. [92] combined three machine learning methods, SVM, ANN, and gradient-boosting decision trees (GDBT), to create a high-performance model. Comparing the results with three individual models, the simulation results showed that the AUC (area under the curve) value of the ensemble model was higher by 0.11-0.135 compared to the AUC value of traditional machine learning models.
## 3 Results and Discussion  
### Spatial Patterns of Phenology  
The study area experiences one growing season per year, ranging roughly from June/July to October/December, depending on the location. Figure 2 shows the spatial variability of average SOS, EOS, GSL and the proxy of seasonal biomass production, CFAPAR. A clear latitudinal gradient in all phenological variables is present: GSL increases from north to south along with CFAPAR.  
The start and end of the season are modulated by the seasonal migration of the Inter-Tropical Convergence Zone, which results in a shorter and poorer growing season in the north of the study area. The interannual variability of such variables is relevant and reflects the high climatic variability  
Figure 2: Average start (SOS) (**a**), end (EOS) (**b**) and length (**c**) of the growing season and the cumulative Fraction of Absorbed Photosynthetically Active Radiation (CFAPAR) value (**d**) during the growing season. Average values are computed over the set of phenological variables extracted from the FAPAR time series (\(n=15\), years 1998–2012). Phenological variables and CFAPAR are shown for the herbaceous and cropland land covers (other classes in white) and the five main eco-regions of the Sahel (other regions in grey).  
characterizing the region. For instance, the mean value of the coefficient of variation of GSL over the whole study area during the period of 1998-2012 is 21% and ranges from 15% in the southern agricultural band to 28% in the northern grassland band. Phenological dates and spatial patterns are similar to those found in other studies using different satellite instruments (NOAA-AVHRR, MODIS) and different phenology retrieval methods (e.g., [32,61]).
# Application Research of Multi-Mode Relay in Future Heterogeneous Networks
###### Abstract  
The fast increase of users in existing mobile networks requires more base stations (BSs) to bear more communication traffic. Future heterogeneous network is considered to be a promising candidate architecture to meet the demands of wireless networks under scarcity of radio frequency (RF) resources. In this paper, we present a multi-mode relay (MMR) model based on two-way relay technology, which is applied to heterogeneous hierarchical wireless networks (HHWN), and set up a system model of HHWN with 3 tiers, 2 users between the macrocell, and the picocell as the multi-mode relay (MMR). Specifically, we consider the new system with unequal relay emission power situation, which is usually researched in the traditional literature with equal relay emission powers. Based on this idea, we define the two-way SINR ratio, derive the mathematical formulas of outage error probability with channel estimation errors, and verify theoretical expressions by data simulations. For further comparison, several experiments are implemented to illuminate the effect on outage probability among different levels of relay emission power, noise power, and signal power. Furthermore, several conclusions are obtained, which have some meanings for implementing MMR in future heterogeneous networks.  
heterogeneous wireless networks; MMR; unequal relay power; outage probability Article
# Development and Proposal of a Novel Scoring System to Classify Dry Month Severity
## 4 Discussion
### Selection of Dry Month Parameters  
The SXI has been validated as a measure for research as a shortening of the original Xerostomia Inventory (XI) [19; 38]. The XI was originally developed because instruments to compare xerostomia across patient groups were missing, the XI and SXI have been translated and validated into several languages and are much-used tools in research [39; 40; 41]. Additionally, [PERSON] et al. argued that single-item approaches like the GXQ were insufficient due to different interpretations of a single-item question [38; 42; 43]. Accordingly, 11 items were included in the XI for a more comprehensive investigation (score range 11-55) [38]. [PERSON] et al. reduced the number of responses, resulting in a score range from 11 to 33 (the modified XI-Dutch version) [29]. Furthermore, [PERSON] et al. proposed a shortened version of the modified XI-Dutch version, with a score range from 5 to 15. As a different number of questions and response points for XI and SXI exists, care must be taken to verify that the number of questions and response points are the same if the comparison of total scores is planned. The SXI score range in the data set used in the present study was 5-15 and was kept as the score range of the SXI parameter of the DMSS.  
When CODS was evaluated by [PERSON] et al., they recommended using it in combination with a subjective measure (XI or \"bother index\") [23]. This was taken into account in the present proposal of DMSS as CODS is used together with SXI and GXQ when the DMSS is calculated. Pathological changes of the oral mucosa were one of the clinical examinations from our Dry Mouth Clinic. Mucosal pathology should naturally be notified when examining dry mouth patients but was not regarded as a principal parameter in this context. It can be discussed if pathological changes of the teeth associated with hyposulavation are assessed sufficiently in feature nine of CODS (\"active or recent restored cervical caries\"), but to narrow the number of parameters of the DMSS, it was not included as a separate parameter [31].  
Disturbances in taste and smell functions were also tested in the Dry Mouth Clinic and have been found to correlate with hyposulavation, but were not included in the DMSS because proper evaluation requires expensive equipment and is too time-consuming in this context [44]. Lastly, candida growth was also one of our previous tests and was shown to correlate with several measures of dry mouth in pSS and HNC patients, but because of the need for resources and time, evaluation of candida growth was not included as a parameter in the DMSS [13; 14].
## 2 Development of Seepage Field Prediction Methods
### Conventional Seepage Field Prediction Methods  
In order to accurately predict the seepage field and guide the actual production, researchers have established a series of models to analyze and solve the seepage field by deducing the relationship between the relevant parameters in the oil and gas development process. Traditional seepage field prediction methods Table 1 are generally divided into physical experiments and numerical simulations, which mainly rely on the understanding of the physical properties and dynamic behavior of reservoirs, and which can be subdivided into numerical simulation methods, analytical methods, and semi-analytical methods; the latter are mainly to fit the field data with empirical formulas and to predict the future seepage field based on the historical seepage field parameter data [6].  
Traditional seepage field prediction methods have some advantages: (i) they have been verified by long-term practice and have a certain degree of accuracy and reliability in traditional seepage field prediction; (ii) they are usually based on theoretical and empirical knowledge in the field of seepage mechanics, and they can provide relatively simple and intuitive explanations of seepage field prediction; and (iii) compared with the complex Deep Learning techniques, they usually do not require a large amount of data and computational resources [7]. However, there are some shortcomings in the traditional prediction methods: (i) by the method based on simulating the physical process of oil and gas reservoirs, it is difficult to accurately describe the complex characteristics of oil and gas reservoirs and the exploitation environment; in addition, the accuracy and completeness of reservoir information such as physical parameters, rock mechanical parameters, and ground stress also have a greater impact on the simulation results; (ii) it is difficult by analytical and semi-analytical methods to adequately take into account the non-homogeneous nature of the reservoir, the production, the engineering, and other factors of mutual influence, and the prediction process is based on the basic Deep Learning technique; (iii) it is difficult by analytical and semi-analytical methods to fully consider the interaction of reservoir inhomogeneity, production, and engineering factors, and the prediction process is based on basic assumptions, which makes it difficult to accurately simulate the real complex and nonlinear production process, and which does not provide enough flexibility to capture all the patterns and relationships in the data [8,9].  
Figure 2: A map of the evolution of Artificial Intelligence and seepage field prediction.
## Materials and Methods  
### Materials  
The following materials were used for the mix of the chloride-resistant spacer and HPC and the associated properties are as follows:  
1. Portland cement CEM II 42.5R was used for all mixes. The density, the specific area, and the water requirement of standard consistency were 2.97 g/cm\({}^{3}\), 380 m\({}^{2}\)/kg, and 25.2%, respectively. The soundness conformed to British standard BS EN 197-1:2011 [20]. The initial and final setting times were 183 min and 275 min, respectively. The 3 day and 28 day flexural strengths were 6.3 MPa and 9.0 MPa, respectively. The 3 day and 28 day compressive strengths were 29.7 MPa and 58.4 MPa, respectively.
2. Fly ash was produced from the burning of anthracite or bituminous coal with a density of 2.19 g/cm\({}^{3}\). The fineness was 23.6%, classified as category N, and the loss on ignition was 4.84%, classified as category A, conforming to British standard BS EN 450-1:2012 [21]. The water requirement was 98%.  
3. Ground-granulated blast slag (GGBS) was used. The density and the specific area were 2.82 g/cm\({}^{3}\) and 440 m\({}^{2}\)/kg, respectively. The 7 day and 28 day activity indexes were 87% and 102%, respectively, conforming to British standard BS EN 15167-1:2006 [22].
4. Sand with a maximum particle size of 5 mm was used as the fine aggregate. The fineness modulus of sand was 2.3, complying with British standard BS EN 12620:2002+A1:2008 [23] medium grading. The pre-dried particle density was 2754 kg/m\({}^{3}\). The loose bulk density and the percentage of voids were 1448 kg/m\({}^{3}\) and 47.4%, respectively.
5. Two types of gravel (of 5-10 mm particle size and of 10-20 mm particle size) were used as the coarse aggregate (denoted as G5-10 and G10-20, respectively). The pre-dried particle density, the loose bulk density, and the percentage of G5-10 were 2685 kg/m\({}^{3}\), 1278 kg/m\({}^{3}\), and 52.4%, respectively. The pre-dried particle density, the loose bulk density, and the percentage of G10-20 were 2738 kg/m\({}^{3}\), 1451 kg/m\({}^{3}\), and 47.0%, respectively. G5-10 and G10-20 were mixed by a 2:3 ratio by mass to comply with the British standard BS EN 12620:2002+A1:2008 [23] overall grading (denoted as G5-20).
6. Polycarboxylate superplasticiser produced by Guangdong Jiangmen Qiangli Building Material Technology Co., Ltd., was used. The solid content was 20%.
7. A retarder was used as the surface treating agent. The retarder was mainly made of the sodium gluconate retarder for concrete, which had a notable retardation effect. The solid content was 20% and the usage of the retarder for the surface treatment of the spacer was about 0.2-0.3 kg/m\({}^{2}\).
8. Tap water was used as the batch water.
# A Lightweight Crop Pest Detection Method Based on Convolutional Neural Networks
## 2 Materials and Methods  
### Dataset and Data Augmentation  
The original dataset was provided by an agricultural research institution. The dataset contains images of crop pests manually captured using a DSLR in agricultural environments. Due to duplicate images and label errors, this study recalibrated the labels of the original dataset and removed images in which no pests were visible, or pest objects were too blurred. Meanwhile, the original dataset was processed using data augmentation methods such as HorizontalFlip, VerticalFlip and Cutout [29] from the Albumentations data augmentation library to ensure the class balance of the dataset and to prevent overfitting. The data augmentation methods used in the study are shown in Figure 1.  
The crop pest images, after undergoing the above processing, comprised the dataset used in this study, named CP15. The CP15 dataset consists of 3000 selected images of crop pests. These images are divided into 15 classes, each class containing 200 images. This number balance between the classes is mainly due to the data augmentation methods we used. Each image contains a number of pests ranging from a few to several hundred. The distribution density depends on the size and species habitat. We used open-source data labeling software called labelImg to label the pests in these images. The labels contain the pest classes and the pest coordinates. Since the class names of the pests are too long, we used the class ids as the labels of the classes. The class ids start from 0. The format of the pest coordinates mainly depends on the format of the data labels. To ensure compatibility for subsequent studies, we used the most widely used VOC data label format. Additionally, we converted the data labels from the VOC format to YOLO format by encoding a conversion program to ensure support for the method proposed in this study. The full CP15 dataset consists of 3000 pest images and the text label files corresponding to these images. The detailed class information of the CP15 dataset is shown in Table 1.
## 2 Methodology
### Design of the Test Program  
The physical model is arranged with wave gauges and high-speed cameras. The location of testing points is shown in Figure 2. A total of 9 wave gauges were arranged in the river course at 3 m intervals; on the other hand, 8 cameras were arranged to monitor the whole process of the surge wave propagation.  
In order to study the influence of river course characteristics and landslide volume on the spatial and temporal distribution of landslide surge, five groups of landslide surge tests with different volumes were designed: 0.50 m\({}^{3}\), 1.00 m\({}^{3}\), 3.00 m\({}^{3}\), 4.00 m\({}^{3}\), and 5.65 m\({}^{3}\). In order to ensure consistency, for each trial, the width and thickness of the surface of the landslide were 4.00 m and 0.20 m respectively, and the depth of the river course was 1.38 m. The test MLS2 was the reference group, and the other four tests were the comparison trials. The design of the test program is shown in Table 1. The river course characteristics investigated were mainly the cross-sectional area, water depth, diversion and river course curvature. The parameter values of these characteristics at each testing point are shown in Table 2.  
Figure 1: Meilish H\({}_{3}\) landslide rock layered structure.  
The analysis of the river course characteristic parameters in Table 2 shows that the cross-sectional area of the river course changes greatly at the testing point h02, which belongs to the diversion. In contrast, the water depth of the river course between the testing points h01-h08 changes gently; nevertheless, testing point h09 changes a lot. The curvature of the river course among testing points h01-h07 ranged between 1.73 and 31.96, whereas the overall trend of the river course did not undergo a large turn, basically tracing a straight line; however, the curvature reached 84.58 at testing point h08, indicating that the river course here undergoes a large turn.