esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Conversion Models and Calibration Procedures
The data have been arranged in pairs of values: the first referring to in-situ strength, evaluated according to Equation (1) by inserting the value of core strength for the considered wall, and the second one related to the rebound index measured for the same wall on which the core was drilled. These 28 data have been combined to define a wide database, which was built as follows: firstly, the number \(m\) (\(m<28\)) of pairs of values was set and several combinations of the available 28 pairs of values are defined. In this way, each combination corresponds to a new possible experimental setup that does not consider all the effective investigated walls. Each defined combination is considered as the acquired data and utilized to calibrate the three laws in Equations (3)-(5), by minimizing Equations (6), (8) and (9), respectively. To take into consideration the accuracy of each conversion model, the compressive strength derived through the conversion model and the in-situ compressive strength based on the destructive tests of the extracted cores (see Equation (1)) have been compared and the Root Mean Square Error (RMSE) has been evaluated for each conversion model as follows:  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{N}\left(f_{c,s_{i}}-f_{c,emp_{i}}\right)^{2}}{N}} \tag{10}\]  
where \(f_{c,emp_{i}}\) is the concrete strength related to the \(i\)-th investigated point of the structure and assessed by means of the chosen empirical law.
# Identification of Industrial Heat Source Production Areas Based on SDGSAT-1 Thermal Infrared Imager
## 2 Materials and Methods  
### Study Area  
The study area has a total area of 27,754 km\({}^{2}\) and includes the cities of Ordos, Yinchuan, Wuhai, and other nearby areas (as illustrated in Figure 1). The region's latitude spans from 37\({}^{\circ}\)54\({}^{\prime}\) N to 39\({}^{\circ}\)35\({}^{\prime}\) N, and its longitude is between 105\({}^{\circ}\)39\({}^{\prime}\) and 108\({}^{\circ}\)04\({}^{\prime}\) E. It is located at the intersection of the Ningxia Hui Autonomous Region and the Inner Mongolia Autonomous Region. It is a typical industrial heat source area because of the abundance of coal, ores, and nonferrous metal resources [33].  
Figure 1: Study area. The study area is 27,754 km\({}^{2}\), including the cities of Ordos, Yinchuan, Wuhai, and other nearby areas (marked red masks) in North China. It is a typical industrial heat source area because of the abundance of coal, ores, and nonferrous metal resources.
# Efficacy of Instruments for Professional Oral Hygiene on Dental Implants: A Systematic Review
## 2 Materials and Methods
### Search Strategy  
The National Library of Medicine (PubMed(r)/MEDLINE) and Cochrane Library were used as the Internet sources to search for papers that satisfied the study purpose.  
The last search was performed on the 21 April 2021. We used the Mesh term \"dental implants\" combined using the boolean operator AND with the following search terms: \"oral hygiene\", \"curette\", \"ultrasonic scaler\", \"air polishing\", \"air flow\", \"glycine\", \"sodium bicarbonate\", \"erythritol\".  
All the original studies investigating professional oral hygiene techniques were included if they met the following inclusion criteria:  
* oral hygiene techniques on dental implants or dental implant materials,
* studies of the last ten years (since 1 January 2010),
* no patients affected by perimplantitis
* no patients with orthodontic appliances
* no patients affected by systemic pathologiesEligible articles included: comparative studies, RCT, cohort studies, case control studies, in vitro comparative studies, animal comparative studies, clinical trials, study in vitro. Restrictions in terms of language were applied: only papers written in Italian or English language were included. No publication status restrictions were imposed. Non original studies (i.e., narrative or systematic reviews, editorials, expert opinions etc.) were excluded. However, full texts of narrative and systematic reviews dealing with the topic of the present review were obtained in order to screen their reference list for possible additional studies to be included. Similarly, a hand search was performed by screening the reference list of all included publications to select potentially relevant additional studies. Redundant studies were excluded.
## 2 Theory and Method
### Time Series Forecasting Model Based on ARIMA-LSTM
#### 2.2.3 Arima-Lstm Model  
In order to deal with linear and nonlinear trends in the time series data of chemical composition content before and after weathering of cultural relics, the unique advantages of the ARIMA model in dealing with linear data and the excellent performance of LSTM in dealing with nonlinearity were used [46; 47]. First, the artifact chemical content data were processed, and linear prediction results and residual series were obtained with the help of the ARIMA model. Then, the nonlinear factors of the residual series were further analyzed by the LSTM model, and the nonlinear prediction results were obtained. Finally, the linear and nonlinear prediction results were superimposed to obtain the final prediction results for the chemical composition content. According to the decomposition principle of the time series model, it is assumed that the time series \(Y=\{y_{t},t=1,2\ldots,N\}\) consists of linear  
Figure 1: Basic structure diagram of LSTM model.  
and nonlinear components \(y_{t}=x_{t}+bx_{t}\). Therefore, the one-dimensional chemical component data are first linearly predicted by the ARIMA model to obtain the linear component \(x_{tr}\) and the residual series \(\delta_{t}=y_{t}+y_{tr}\). Then, the residual series are processed by further nonlinear prediction to obtain the nonlinear component \(bx_{tr}\). Finally, the linear and nonlinear components are combined to obtain the final prediction \(y_{tr}=x_{tr}+bx_{tr}\). Root mean square error (RMSE) [48], mean absolute percentage error (MAPE), and R\({}^{2}\) are used to evaluate the performance of the model [49; 50; 51; 52].  
R\({}^{2}\) is usually taken as [0,1]; the closer R\({}^{2}\) is to 1, the better the fit is, and the equations are as follows:  
\[\text{RMSE}=\sqrt{\frac{1}{N}\sum\limits_{i=1}^{N}(x_{i}-y_{i})^{2}} \tag{30}\]  
\[\text{MAPE}=\sum\limits_{i=1}^{N}\left|\frac{x_{i}-y_{i}}{x_{i}}\right|\times \frac{100}{N} \tag{31}\]  
\[R^{2}=\frac{\sum\limits_{i=1}^{N}(y_{i}-\overline{y})^{2}-\sum\limits_{i=1}^{ N}(y_{i}-\overline{y})^{2}}{\sum\limits_{i=1}^{N}(y_{i}-\overline{y})^{2}} \tag{32}\]  
In the above equation, \(x_{i}\) is the observed value, \(y_{i}\) is the predicted value, \(N\) is the sample size, \(\overline{y}\) is the mean of \(y_{i}\), and \(\hat{y}_{i}\) is the regression fit [32].
## 1 Introduction  
Remote sensing technologies, particularly satellite-based systems, have revolutionized various sectors, offering unparalleled data for applications like environmental monitoring, urban planning, and disaster management [1]. A significant advantage of satellite-based Earth observation is its capability to operate uninhibited by international borders, providing a comprehensive geographical coverage in a single observational pass. Such an extensive array of applications has led to an ever-increasing demand for Earth observation missions, driving the projected market value close to USD 9 billion by 2027 [2]. Synthetic Aperture Radar (SAR) sensors stand out as versatile tools within this domain. Unlike optical counterparts confined to the visible spectrum, SAR sensors offer a wider range of wavelengths, enabling high-resolution imaging across varying atmospheric conditions. This versatility allows for diverse applications, ranging from hydrological mapping to environmental monitoring [3].  
A noticeable paradigm shift in satellite deployment focuses on constellations of smaller satellites instead of a few large platforms [4]. This transition is fueled by diversified mission requirements, including the demand for higher temporal resolutions like shorter revisit times, and the inherent advantages of small satellites such as modularity, cost-efficiency, and shorter development cycles. South Korea aligns well with this global trend, planning to deploy small satellites comprising over 130 units by 2030 [5, 6]. Internationally, entities like Finland's ICEYE [7, 8, 9] and the United States' Capella Space [10, 11] have already successfully deployed SAR satellite constellations, underlining the global consensus on their utility and efficiency.  
Following this trend, there is an increased complexity and frequency in mission planning. For instance, while a Sun-synchronous orbiting large satellite may revisit the Korean Peninsula every 12 h, a constellation of 40 smaller satellites in inclined orbits could accomplish this in intervals as brief as 30 min [12]. This augmented observational capability necessitates a corresponding increase in planning intricacy. Traditional approaches of programming each satellite's mission individually are becoming impractical due to the required human resources. Furthermore, advancements in satellite attitude control technologies have led to highly maneuverable platforms, while payload enhancements enable a diverse range of imaging modes for Earth observation [13].  
In addition to the agility of these satellites, advancements in payload sensor technology now allow for more versatile observational strategies. Previously, focus was placed on observing a single target or area. However, recent developments enable multiple imaging modes, such as multi-stripmap or spotlight mode, allowing for the capture of multiple targets in a single pass as shown in Figure 1. These capabilities, combined with the aforementioned advancements, add layers of complexity to the mission planning process and emphasize the crucial need for optimized strategies for the entire satellite constellation [14].
# Fusing Appearance and Prior Cues for Road Detection
###### Abstract  
Road detection is a crucial research topic in computer vision, especially in the framework of autonomous driving and driver assistance. Moreover, it is an invaluable step for other tasks such as collision warning, vehicle detection, and pedestrian detection. Nevertheless, road detection remains challenging due to the presence of continuously changing backgrounds, varying illumination (shadows and highlights), variability of road appearance (size, shape, and color), and differently shaped objects (lane markings, vehicles, and pedestrians). In this paper, we propose an algorithm fusing appearance and prior cues for road detection. Firstly, input images are preprocessed by simple linear iterative clustering (SLIC), morphological processing, and illuminant invariant transformation to get superpixels and remove lane markings, shadows, and highlights. Then, we design a novel seed superpixels selection method and model appearance cues using the Gaussian mixture model with the selected seed superpixels. Next, we propose to construct a road geometric prior model offline, which can provide statistical descriptions and relevant information to infer the location of the road surface. Finally, a Bayesian framework is used to fuse appearance and prior cues. Experiments are carried out on the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) road benchmark where the proposed algorithm shows compelling performance and achieves state-of-the-art results among the model-based methods.  
Keywords:road detection; appearance; geometric prior; superpixel; illuminant invariant; GMM +
Footnote †: journal: Applied Sciences
# Multi-Objective Robust Optimization Reconstruction Algorithm for Electrical Capacitance Tomography
## 3 Multi-Objective Robust Optimization Imaging Model
### Proposed Multi-Objective Robust Optimization Imaging Model
#### 3.4.3 Upper-Level Optimization Problem  
The purpose of the ULOP is to estimate model parameters. Unlike conventional models, our model aims to optimize the performance in the presence of uncertainty. In order to improve the robustness of the model, our ULOP minimizes both the mean of and the variance in the performance function, which can be defined as the following multi-objective optimization problem [56; 57; 58; 59]:  
\[\min_{\mathbf{\alpha}}\{\text{mean}(R(\mathbf{\alpha})),\text{var}(R(\mathbf{\alpha}))\} \tag{11}\]  
where \(R(\mathbf{\alpha})=\sum\limits_{j=1}^{M_{g}}\Big{|}\Big{|}\mathbf{u}_{j}-\mathbf{u}_{T_{j} }\Big{|}\,\big{|}^{2}\) and is called the performance indicator; \(\text{mean}(R(\mathbf{\alpha}))\) and \(\text{var}(R(\mathbf{\alpha}))\) represent the mean of and variance in the performance indicator \(R(\mathbf{\alpha})\). By taking into account the uncertainty of the measurement data, we use noise to perturb the capacitance data and calculate the mean of and variance in the performance metric. Depending on the actual application, the capacitance noise level does not exceed 15%. The definition of the noise level is given in [16].  
In the ULOP, the mean value represents the system's expected performance under defined parameters, ensuring optimal operation across varying environmental conditions. Conversely, the variance quantifies performance fluctuations, where reduced values indicate lower sensitivity to input perturbations and consequently greater robustness. This multi-objective optimization framework provides a comprehensive solution for electrical capacitance tomography, maintaining robust performance in dynamic and complex environments.
## Materials and Methods
### Implementation Details  
#### 3.6.1 Input Data  
We chose load profiles of previous days and meteorological data as input data for the forecasting models. To build an accurate forecasting model, it is desirable to use factors that are closely related to the load. Obviously, the load profile of the targeted day is highly correlated with that of the previous day and the same day of the previous week, represented by L(\(d-1\)) and L(\(d-7\)), respectively, where \(d\) denotes the targeted day. As discussed in Section 3.3, we decided to use the temperature and forecasted temperature as features among the various meteorological data, because they were ultimately the most closely correlated with the load through EDA. The entire dataset can be seen in Table 1. The dataset is divided into two parts--the training set and the testing set. The training set contains the first 441 days of the whole period, and the testing set contains the remaining. In addition, the dataset is min-max normalized using Equation (1) before entering SOM, where \(x\) denotes the data vector or the matrix, and \(z\) is the normalized vector or matrix.  
\[z=\ \frac{x-\min(x)}{\max(x)-\min(x)}. \tag{8}\]  
The input data that were selected are listed below:  
* 24 h load profile of the previous day: L(\(d-1\));
* 24 h load profile of the same day of the previous week: L(\(d-7\));
* Average, maximum, and minimum temperature of the previous day: \(T_{\text{avg}},\ T_{\text{max}},\ T_{\text{min}}\);
* Forecasted average, maximum, and minimum temperature of the targeted day: \(T_{f,\text{avg}},\ T_{f,\text{max}},\ T_{f,\text{min}}\);  
Figure 4: The proposed framework that describes the whole process from data pre-processing to final prediction produced by clustering using SOM and the stacking ensemble learning method.
# An Advanced Physiological Control Algorithm for Left Ventricular Assist Devices
## 2 Materials and Methods
### Software Simulation Environments Protocols  
The proposed control method was implemented using MATLAB Simulink \"10.5\" software tools (The MathWorks Inc., Natick, MA, USA). This method adjusted the CVS model parameters to generate different physiological simulations (rest and exercise) for HF patients. Table 1 illustrates the baseline of the CVS model parameters to represent health and HF conditions. These parameters include the contractility of the left/right ventricle, systemic peripheral resistance, and total blood volume.  
The control method simulated a rest scenario to ensure the controller was robust enough to provide sufficient perfusion support to the HF patient. In this scenario, the CVS model parameters were varied at t = 25 s in one cardiac cycle. This includes decreases in the total blood volume with 500 mL, an increase in the left/right ventricle contractility of 15%, and systemic peripheral resistance kept within the same values.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **HF** & **Healthy** \\ \hline Systemic peripheral resistance (mm Hg \(\times\) s/mL) & 1.1200 & 0.7501 \\ \hline Left ventricle contractility (mm Hg/mL) & 0.7111 & 3.4900 \\ \hline Right ventricle contractility (mm Hg/mL) & 0.5299 & 1.7510 \\ \hline Total blood volume (mL) & 5798 & 5298 \\ \hline \hline \end{tabular}
\end{table}
Table 1: CVS model parameters.  
Figure 4: Proposed control method. CVS: cardiovascular system; LVD: Left ventricular assist device; \(H_{c\text{vs}}\): differential pressure; \(Q\): actual flow; \(Q_{p}\): estimated flow; \(Q_{r}\): reference flow; \(u_{e}(n)\): equivalent control; \(u_{c}(n)\): corrective control; \(u(n)\): control signal.  
Subsequently, the simulation was conducted to simulate the transition of system parameters from rest to one physical exertion to ensure that the control algorithm robustly automated the LVAD to provide a minimum flow to the patient. In this scenario, the CVS model parameters were varied at t = 25 s in one cardiac cycle. We keep the system for an additional 35 s in order to give the CVS system enough time to attain a steady state condition. During this scenario, the total blood volume was increased by 500 mL, the left/right ventricle contractility decreased by 20%, and systemic peripheral resistance increased by 15%.  
In (10), the parameters for the control law are given as \"\(\gamma=\begin{bmatrix}1.2&-0.95\end{bmatrix}\)\" and \"\(\tau T=0.24\)\".
# Applications of Deep Learning to Neurodevelopment in Pediatric Imaging: Achievements and Challenges
## 4 Discussion
### Challenges and Future Directions  
#### 4.2.1 Overfitting Caused by Small Sample Size  
Overfitting remains a major concern for deep learning models with deep and complex architectures, especially the models with 3D structures as the number of training parameters grows exponentially with an extra dimension [2]. The sample size should also increase to train models with many parameters to avoid overfitting. Otherwise the model might be overfitted to the training data and fail to predict new data accurately. However, neuroimaging acquisition via MRI is expensive and time-consuming. Many studies are limited to a small number of training data, experiencing the risk of overfitting [162]. In our review, some studies use cross-validation to report results while some others also report results on an independent testing dataset. The testing results are important indicators of the capability to apply the trained model on unseen new data.  
Data-sharing projects and platforms provide a vast amount of neuroimaging data, facilitating large-scale studies to train deep and complex models. We share a non-exhaustive list of available public datasets and repositories in Section 2. In common practice, supervised learning, in which the deep learning model is trained with labeled data is the most widely applied learning process [15; 163]. Open datasets and repositories prepared data and labels in pairs where labels can be disease diagnosis, clinical outcomes, and semantic segmentation ground truth. Other than labeled data, there are tons of neuroimaging data without labels or with a limited number of labels. Unsupervised learning and semi-supervised learning show great potential in dealing with such data. Unsupervised learning utilizes training data without any labels by separating the data into different categories with automatically learned patterns during training [15; 163]. Semi-supervised learning utilizes the unlabeled data to learn the feature patterns and use the labeled data to update model weights, which has yielded superior performance with a limited number of training samples in both classification and segmentation tasks [70; 110]. Transfer learning accommodates another possibility for developing deep learning algorithms with a limited number of training data. Transfer learning takes advantage of models pre-trained on large datasets and fine-tunes the system with a small number of data, providing an applicable solution for neuroimaging studies with a small sample size [60; 94; 97].
## 1 Introduction
[18] developed a kinematic error analysis method and tolerance design procedure for cycloidal gear reducers considering geometry, manufacturing, and precision performance. A review of the literature reveals that the current analytical methods for cycloid drives typically assume a two-dimensional planar problem without considering the tooth longitudinal modification effects, which fails to comply with the practical situation. In fact, the cycloid gear will be crowned  
Figure 1: The typical structure of the cycloid drive. to relieve the stress concentration occurring on the tooth edges, which will lead to serious contact failure of the cycloid drive. Therefore, the aim of this paper is to propose a computationally efficient, semi-analytical load distribution model for cycloid drives based on a three-dimensional and linear elastic solution while considering both the tooth profile and longitudinal modifications. It will have the ability to simulate quasi-statically the multi-tooth contact conditions and determine the contact stress and pattern, loaded transmission error, and gear ratio of cycloid drives. Another purpose of this paper is to propose several types of tooth longitudinal modifications of cycloid gear teeth to improve the contact stress distribution conditions by using the proposed model. The rest of this paper is structured as follows: Section 2 presents the tooth surface geometry of the cycloid gear, its generation method is introduced, and several types of tooth profile and longitudinal modification are proposed. Section 3 presents the semi-analytical load distribution model based on the unloaded and loaded tooth contact analyses. In this section, the tooth compliance model is established by combining the Boussinesq force-displacement relationships in elastic half-space with an influence coefficient method. Section 4 gives a numerical example to verify the proposed model by comparison with the prediction results of the current model based on the Hertz contact stiffness method. The effects of tooth profile, longitudinal modifications, and applied torque levels on the contact stress and pattern, loaded transmission error, and gear ratio are investigated by using the proposed model. Finally, conclusions are drawn in Section 5.