esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Reliability Evaluation for Machine Center Subsystems Using the Improved Average Rank Method  
When a subsystem fails during the operation of a machining center, the entire machine will shut down, which will result in censored time for all other subsystems. The use of the improved average rank method can effectively model censored time. Considering the time correlation, the overall reliability of subsystem modeling can be improved.  
The average rank method has been used in previous studies to calculate empirical distribution functions [12]. Its purpose is to correct the rank of failure data when the collected data contain censored data. The progress of the improved average rank method lies in that it not only considers the sorting position of the censored data but also considers the specific time of the censored data and the interval length between adjacent failure data. For the improved average rank method, the rank increment is calculated based on the above information [13]. Therefore, the reliability estimation outcome will be more accurate compared to the average rank method.  
The steps of the improved average rank method for reliability evaluation are as follows:  
1. Use the average rank method to obtain the empirical distribution function.
2. Carry out parameter estimation and obtain the preliminary model.
3. Use the improved average rank method to obtain new rank increments and updated parameters, and obtain the revised model.
## 4 Experimental Section
### Findings and Limitations in This Experiment  
This research offers significant potential in improving robots' ability to perform complex manipulation tasks. However, several limitations need to be noted. Deep reinforcement learning (DRL) and image mask models require large computational resources for training and inference, which can hinder practical implementation in hardware-limited environments. In addition, DRL often lacks the ability to generalize to scenarios that were not seen during training, so model performance can degrade when encountering objects that do not have a fixed or regular shape [45].  
Another limitation that researchers found was the reliability of observations which depended on the image mask. These masks may not always be accurate in detecting or segmenting objects, especially in poor lighting conditions or with objects that have complex textures. This can fail in the recognition and manipulation of irregular objects. This is in line with research [28; 29] that difficulties in accelerated learning may also arise because objects with irregular shapes require more time to be identified and manipulated correctly. Adaptation to dynamic environments is also a challenge, as models may struggle to adapt to changes in the surrounding environment. In various test application scenarios, these limitations can affect the efficiency and accuracy of robot manipulation.  
Research from [30; 31] states that in manufacturing plants with uniform objects, this approach can significantly increase efficiency. However, in household scenarios where the robot needs to handle a variety of objects with irregular shapes, limitations in shape recognition and manipulation may reduce the robot's performance and usability. To overcome these limitations, strategies such as multi-tasking training, data augmentation, and the integration of additional sensors can be implemented. For example, using data augmentation techniques to increase variation in the training dataset and integrating additional sensors such as LiDAR to provide more information about the shape and texture of objects can help in the manipulation of irregular objects.
# Entity-Centric Fully Connected GCN for Relation Classification
## 3 Experiment  
### DateSets  
We verify the performance of FCGCN on two widely used relation classification datasets: TACRED and SemEval 2010 Task 8.  
**TACRED:** The TACRED dataset contains 106,264 instances and 42 relation types (including 41 predefined semantic relations and a \"None\" relation, the \"None\" relation means that there is no any relationship between an entity pair) [31]. Each instance of the dataset is a sentence that contains a pair of entity mentions, and it is labeled by one of the 42 relation types.  
**SemEval 2010 Task 8:** The SemEval 2010 Task 8 is very popular recently in dealing with the problem of relation classification, which contains 10,717 instances and 9 relation types and a particular \"other\" relation type [32]. This dataset is very small, which is only 1/10 of TACRED. Each instance in this dataset expresses a sematic relation between two marked entities. 8000 instances are contained in the training set and 2717 instances are contained in the test set.  
Based on these two datasets, we employ pre-trained 300-dimensional Glove [23] to initialize the word embeddings. The pruning path K is set to 1, and the batch size is 50. The model is trained for 100 epochs. The details of the selected hyperparameters are summary in Table 1.
# Construction Industrialization in China: Current Profile and the Prediction
## 3 Construction Industrialization Profiles in China
### Prediction for the Next Five Years  
Holt method involves a forecast equation and two smoothing equations (one for the level and one for the trend), which are displayed below [9,10].  
Forecast equation:  
\[\overset{\wedge}{Y}_{t+T}=a_{t}+b_{t}T \tag{1}\]  
Level equation:  
\[a_{t}=\alpha Y_{t}+(1-\alpha)(a_{t-1}+b_{t-1}) \tag{2}\]  
Trend equation:  
\[C=\frac{\sum_{t}^{n}\left(C_{\max}-C_{i}\right)}{\max[\sum_{t}^{n}\left(C_{\max }-C_{i}\right)]} \tag{3}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Province/Municipality**} & \multicolumn{2}{c}{**Production Capacity**} & \multicolumn{2}{c}{**Actual Production**} & \multicolumn{2}{c}{**Actual Production/**} \\  & & **(million m\({}^{3}\))** & **(million m\({}^{3}\))** & **Production Capacity (\%)** \\ \hline
1 & Hunan & 11.200 & 0.544 & 4.86\% \\
2 & Anhui & 1.877 & 0.449 & 23.93\% \\
3 & Fujian & 0.460 & 0.195 & 42.43\% \\
4 & Shanghai & 0.390 & 0.159 & 40.74\% \\
5 & Zhejiang & 3.905 & 0.098 & 2.52\% \\
6 & Hebei & 0.330 & 0.098 & 29.72\% \\
7 & Hubei & 0.300 & 0.087 & 29.12\% \\
8 & Henan & 0.254 & 0.035 & 13.76\% \\
9 & Jilin & 0.130 & 0.034 & 26.19\% \\
10 & Beijing & 0.550 & 0.027 & 4.99\% \\
11 & Tianjin & 0.980 & 0.023 & 2.31\% \\
12 & Chongqing & 0.100 & 0.008 & 7.81\% \\
13 & Shaanxi & 0.100 & 0.004 & 4.29\% \\ \hline  & **Total** & **20.576** & **1.761** & **8.55\%** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Production capacity and actual production comparison.  
Figure 4: Production capacity of reinforced concrete (million m\({}^{3}\)).  
where \(\overset{\wedge}{Y}_{t+T}\) is the predicted value at time \(t+T\); \(Y_{t}\) is the observation value at time \(t\); \(a_{t}\) is an estimate of the level of the series at time \(t\); \(T\) is the elapse time from time \(t\); \(b_{t}\) is an estimate of the trend (slope) of the series at time \(t\); \(\alpha\) is the smoothing parameter for the level, \(0\leq\alpha\leq 1\); and \(\beta\) is the smoothing parameter for the trend, \(0\leq\beta\leq 1\).  
Based on the Holt theory and historical data, Eviews software is utilized to predict the industrialized construction floor area for each of the following years between 2016 and 2020. Considering that residential building and public and commercial building are the main building types for industrialized construction, three types of buildings--conventional residential buildings, advanced residential buildings, and office buildings--are predicted as the main building types in this research. The most recent twelve years of historical data (2004 to 2015) are archived from the National Bureau of Statistics of China [15,16], as listed in Table 5.  
In order to select appropriate values of \(\alpha\) and \(\beta\), multiple experiments are conducted to minimize the root mean squared error (RMSE). The prediction models and RMSE for conventional residential buildings, advanced residential buildings, and office buildings are presented below.
# SOH Estimation of Lithium-Ion Battery Pack Based on Integrated State Information from Cells
## 2 Methodology  
### Indicators of Battery  
To describe the deterioration process, the electrical parameters during charge and discharge were mostly used generally. For cells, there were numerous indicators used to monitor the status of the batteries, such as the discharge current, cut-off voltage, and open-circuit voltage. The most common assessment indicator of batteries is capacity, which is measured by calculating the electric quantity. The electric quantity of battery is always estimated by ampere-hour method. According to ampere-hour method, the capacity is calculated as:  
\[Q=\int\limits_{0}^{t}\eta ldt \tag{1}\]  
where \(Q\) represents the current capacity, \(I\) is the discharge current, and \(\eta\) is the discharge coefficient. Here are three commonly used: SOH, state of charge (SOC) and depth of discharge (DOD). SOC and DOD are used to assessment the ability at time \(t\) under one discharge cycle. DOD is the released electric quantity on a given cycle, expressed as a percentage of the total capacity of the battery:  
\[\mathrm{DOD}=\frac{Q_{released}}{Q_{rated}}\times 100\% \tag{2}\]  
SOC is the verse of DOD, which is defined as [24]:  
\[\mathrm{SOC}(t)=100\%-\mathrm{DOD}(t)=\frac{Q_{released}}{Q_{rated}}\times 100\%, \tag{3}\]  
When the deterioration of battery is not considered, \(Q_{rated}\) is constant. However, in most cases, the rated capacity of battery decreased under charge and discharge cycles. SOH is used to assess the extent of the reduction of rated capacity. The unit of SOH is percent, and 100% means it is a fresh battery [25]. Here we define the failure threshold of battery is 80% initial capacity. If \(Q_{0}\) is assumed to be the initial capacity, then the SOH of a battery is defined by:  
\[\mathrm{SOH}=\frac{Q}{Q_{0}}\times 100\% \tag{4}\]  
In our research, SOH is utilized to present the evolution of different cycles in order to assess health status of cells and battery packs.
## 2 Methods and Data
### The Developed DSSE Tool  
Here, a brief overview of the Gauss-Newton algorithm, used by the developed DSSE tool, is presented. The basic operation of the SE is shown in Figure 1.  
To run the least square based Gauss-Newton algorithm with proper configuration settings, a network must be built as a graph. This matches the part of the workflow of network modeling in a conventional simulation software. The great advantage of the pandapower library is that, on the one hand, the building process can be fully automated; and on the other hand, in the case of possible change necessities, the software is more flexible than a network modeled using specialized software. The most influential factor that affects modeling is how input data structure is available to create the current network or subnetwork. In different cases, different file formats are used to provide the essential parameters. (From Ref. [28], it can be seen that it may be advisable to apply the _dict_ format to store the data with IDs.) In addition, it is worth noting that these superstructures are primarily dependent on the current network.  
The developed tool processes the information differently on different networks, with different formats of input parameters. However, its operation can also be characterized in general, as shown in Figure 2.  
The application was designed based on object-oriented principles. The architecture of the application is hierarchical: it consists of modules and submodules in a top-down refinement manner. In this layout, the Main module controls the entire process. To show the essence of the operation, we review the above modules in the following. Configuration settings are implemented using the Configuration file [29], and a network for DSSE is created using the Network builder module. The substantive part of the DSSE is conducted in the framed part. The Measurement maker module retrieves individual measurement data and pseudo measurements from arbitrary input files. The Simulator module defines the simulation class, a mediator object controlling the entire flow of the application and coordinating object interaction [30]. The Algorithm module runs the discussed WLS algorithm, and the Validator module can check each result by running load flow. Finally, the Output writer module can save the calculated data.
## 2 Fundamentals of SAR for Antenna Sizing and Transmitting Power Consideration
### Duty Cycle  
The selection of fundamental radar parameters for a SAR satellite depends on the specific mission objectives and associated trade-offs. The duty cycle refers to the fraction of time the radar is actively transmitting pulses [37; 38; 47; 48; 53; 54]. In many SAR systems, the duty cycle is typically low, often in the range of 10% to 25%, though some systems operate with lower values. The duty cycle in operational SAR satellites like Sentinel-1 is around 10%, with Radarsat-2 and TerraSAR-X exhibiting similar ranges [11; 12; 13; 14; 15; 16; 17].
## 2 Prototype System and Methods
### Estimating the Motion Model with Volume Minimization
In addition, the large scale characteristics of the objective function highly depends on the cube  
Figure 4: The idea of volume minimization: figures show reconstructions (with red dots) assuming a constant velocity (CV) model with different velocities; green points are the raw data, black rectangles show the occupied cubes, and titles show velocities and number of cubes obtained. size, and this property can be lost if the cube size is too small. For a Cessna size airplane, a 0.2-1 m cube sizes performed well in our dataset. In the followings, the details of the SA parameterization are presented; the pseudocode of the algorithm can be found in Appendix C. A geometric cooling function is applied with \(c=0.92\) base. The initial searching boundaries (\(\mathbf{r}_{0}\)) are 100 m/s and 100 m/s\({}^{2}\) for velocities and accelerations, respectively. The search boundaries (\(\mathbf{r}_{i}\)) decrease with the cooling function and 50-500 randomly picked neighboring values within the bounds are evaluated at each iteration. The acceptance probability has an exponential distribution over the absolute difference between the candidate and current best solutions, i.e., \(|e^{\prime}-e|\). The stopping criterion is satisfied when the search bounds are smaller than 0.01 for all parameters. The overall running time depends on the cube size, the applied motion model, and the point cloud size. This algorithm typically terminates in less than 100 iterations; note that SA does not guarantee optimality. This means a roughly 1-3-s runtime on an Intel Core i7 4.4 GHz CPU. Since the current implementation is not optimized, a less than 1-s execution time is very likely to be achievable with code optimization and faster CPU, allowing for real-time applications. For the source code, see Supplementary Materials. The results provided by the SA algorithm can be assessed visually based on the consistency of the reconstruction. For instance, Figure 5 shows a reconstruction, where the edges of the reconstructed point cloud are sharp and the major parts of the airplane are clearly recognizable, indicating that the parameter estimation performed well.
## 1 Introduction
While previous research has explored various machine learning models, many rely on extensive input variables and complex feature engineering, limiting their practical application. This study proposes a streamlined approach using a deep neural network with principal component analysis (DNN-PCA) to optimize and reduce the number of necessary inputs. This not only enhances the model's accuracy and sensitivity but also makes it more practical and accessible for clinical use, especially in settings with limited resources. By improving both efficiency and accuracy, this research contributes significantly to the field of OSA diagnostics, offering a viable alternative to more cumbersome and less precise models. Also, prescreening OSA through questionnaires is crucial, even though most existing questionnaires demonstrate high sensitivity but low specificity. This study introduces an integrated screening tool designed to predict severe OSA using responses from questionnaire items. To achieve this objective, we employed a combination of scaled principal component analysis (PCA) and DNN-PCA on data from 66 participants. The questionnaires utilized include demographic items and established forms such as Berlin, NAMES, No-Apnea, No SAS, and STOP-BANG. The proposed methodology is intended to facilitate the easy and quick completion of questionnaire items by users who suspect they have sleep apnea. By doing so, it aims to minimize the time users spend in sleep laboratories or hospitals, thereby streamlining the prescreening process and making it more efficient. This study's key contribution lies in its novel integration of DNN with PCA, which not only enhances predictive performance but also simplifies the prescreening process for severe OSA. By effectively reducing the number of input variables while maintaining high accuracy, this approach makes OSA screening more accessible and practical for both patients and clinicians. Additionally, this methodology sets the stage for future advancements in cost-effective and efficient OSA diagnostics, particularly in resource-limited settings. The structure of this manuscript is organized into four sections. The proposed method of OSA prediction is introduced in Section 2, followed by Results and Discussion in Sections 3 and 4. The Conclusions are provided in Section 5. The proposed framework consists of three processing steps: (1) unsupervised preprocessing for attaining principal components, (2) feature scaling based on standardization, and (3) classifying for the prediction of sleep apnea. This study converts 38 binary or categorical variables into continuous variables, using the PCA maximum-attribute filter; it can help minimize data discretization. The proposed method would be particularly useful in identifying severe OSA with acceptable sensitivity and specificity levels, which would also reduce examination costs in sleep testing.
# A Novel Breast-Volume Self-Measurement Method with Improved Convenient and Accuracy
## 5 Conclusions  
This study successfully proposes a novel breast-volume self-measurement method that significantly enhances measurement accuracy and convenience through an innovative application of geometric models. The newly developed volume-calculation formula, based on a combination of irregular-ellipsoid and elliptical cone models, was validated experimentally, and demonstrated a relative error of only 3.8%, substantially outperforming existing methods, such as 3D scanning (4.8%) and traditional volume equations (86.3%). This high accuracy suggests that the proposed method provides reliable breast-volume data, which can improve bra sizing accuracy, fit, and comfort.  
Compared to current methods, this approach offers ease of operation and reduces reliance on specialized equipment, making it feasible for routine use in personal health monitoring, bra design, and body image assessment. Additionally, the method's simplicity enhances its applicability for a variety of health-related scenarios without the need for extensive technical expertise or expensive tools.  
Future research can build on this foundation by further refining the geometric model to accommodate different breast shapes, thereby broadening the method's applicability. Development of corresponding digital measurement systems could also expand its usability. With these optimizations, the proposed method holds promise for supporting nutritional assessments, anthropometry, and studies on psychological health, thus making it a valuable tool for comprehensive health and wellness monitoring.
# Influence of the Main Geometrical Parameters on the Design and Performance of Mixed Inflow Turbines
## 2 Materials and Methods
### New Design Theory
#### 2.3.1 A Fixed Value of the Inlet Means Diameter Case  
In the first stage of research, the average inlet diameter was chosen as constant, and the average diameter of the exducer root varied. In this stage, the average inlet diameter was preserved and--at the same time--the exducer average root diameter was varied. An iterative calculation described by Figure 7 was carried out to determine the value of the diameter ratio and to keep the same degree of the meridian plane. The ratio between the average inlet diameter and the average exducer root diameter is presented in Figure 8, and it was obtained from the following relation (Equations (12) and (13)):  
\[R=\frac{D_{2}}{D_{3-rms}}, \tag{12}\]  
\[D_{3-rms}=\sqrt{\frac{1}{n}(\sum_{i=1}^{n}D_{3,i}^{2})}. \tag{13}\]  
Figure 7: Iterative calculation of the meridian plane based on the \(D_{3-rms}\) verification.  
In order to study the same values of diameter ratio (1.4-1.45-1.5-1.55-1.6) and keep the same blade height at the outlet blade to eliminate the flow convergence effect at 2D, it is obligatory to calculate the new hub and shroud radius at the outlet.  
The outlet relative angle (Figure 9) was recalculated according to Equation (14), which maintained the same camberline slope at the rotor exit in order to preserve the same camberline shape and to eliminate its effect.  
\[a_{3}=\left[\frac{d\theta}{dx}\right]_{x3}=2\cdot\frac{\tan(\beta_{3})}{D_{3}} \tag{14}\]  
The new meridian plan geometries--with its camberline angle obtained and the different blade shapes--are presented respectively in Figures 10 and 11; all its dimension values are regrouped in Table 2.  
\begin{table}
\begin{tabular}{l l l l l l l l l l l} \hline \hline \(\mathbf{b_{2}}\) & \(\mathbf{D_{2}}\) & \(\mathbf{R0h}\) & \(\mathbf{R0s}\) & \(\mathbf{\beta_{23}}\) (\({}^{\circ}\)) & \(\mathbf{b_{3}}\) & \(\mathbf{D3h}\) & \(\mathbf{D3s}\) & \(\mathbf{\beta_{3}}\) (\({}^{\circ}\)) & \(\mathbf{A}\) (mm\({}^{2}\)) & \(\mathbf{R}\) \\ \hline
17.99 & 83.58 & 36.008 & 47.57 & 20.00 & 25.79 & 38.7970 & 59.7 & 90.3770 & \(-\)53.90 & 1046 & 1.4 \\
17.99 & 83.58 & 36.008 & 47.57 & 20.00 & 25.79 & 32.9157 & 57.6 & 84.4957 & \(-\)52.94 & 1029 & 1.45 \\
17.99 & 83.58 & 36.008 & 47.57 & 20.00 & 25.79 & 27.0700 & 58.7 & 78.650 & \(-\)52.00 & 1013 & 1.5 \\
17.99 & 83.58 & 36.008 & 47.57 & 20.00 & 25.79 & 21.1200 & 53.9 & 72.7000 & \(-\)51.08 & 998 & 1.55 \\
17.99 & 83.58 & 36.008 & 47.57 & 20.00 & 25.79 & 14.9810 & 52.2 & 66.5610 & \(-\)50.18 & 986 & 1.6 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The meridian plan geometrical parameter values for a fixed value of average inlet diameter (in mm).  
Figure 8: The inlet mean diameter and exducer mean root diameter presentation.  
Figure 9: The outlet blade angle presentation.