esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion  
Most of the publications included in our review presented high accuracy in classifying individuals with depression based on SMOTE techniques [55, 64]. Balancing data by using the SMOTE technique is not the most accurate method for predicting risk of depression. Data balanced using the bootstrapping technique is more accurate than SMOTE.  
In this study, we compared SMOTE and bootstrapping techniques. As a result, the accuracy of all the data mining classifiers dramatically increased by combining different feature selection techniques with a balancing technique, as shown in Figure 5. It was discovered that when using the SMOTE technique, the data mining technique's accuracy value was reduced compared to when using the bootstrapping technique. Figure 5c demonstrates the highest degree of accuracy when using Relief selection techniques.  
The purpose of this study was to identify and create the most significant predictor of depression in students, taking into account socio-demographics, internet addiction, alcohol use disorder, and stress factors, as shown in Table 8.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Attributes** & **Description** \\ \hline Demographic & A5 & Homeland \\ \hline  & A6 & Income adequacy \\ \hline  & A7 & Family status \\ \hline Internet Addiction & B1 & How often do you find that you stay online longer than you intended? \\ \hline  & B14 & How often do you form new relationships with fellow online users? \\ \hline  & B19 & How often do you choose to spend more time online over going out with others? \\ \hline Alcohol Usage & C3 & How often do you have 5 or more drinks on one occasion? \\ \hline  & C9 & Have you or someone else been injured as a result of your drinking? \\ \hline  & C10 & Has a relative, a friend, a doctor, or another health worker been concerned about your drinking or suggested you cut down? \\ \hline Stress & D1 & I have been afraid of making mistakes. \\ \hline  & D15 & I have felt sad and depressed. \\ \hline  & D19 & I have often felt fatigued. \\ \hline \hline \end{tabular}
\end{table}
Table 8: Selected attributes involved in each type by using the Relief technique.  
Figure 5: Accuracy of the classifiers for different feature selection techniques: **(a)** Comparing outcomes with the correlation method; **(b)** Comparing outcomes with the gain ratio method; and **(c)** Comparing outcomes with the relief method.
# An 8 kb RRAM-Based Nonvolatile SRAM with Pre-Decoding and Fast Storage/Restoration Time
## 2 RRAM-Based nvSRAM Structure and Memory Cell
### Memory Cell Design
The voltage difference between the lower and upper electrodes of RRAM2 is much greater than the reset voltage, so RRAM2 performs the reset operation. The resistance value of RRAM2 changes from low to high to complete the operation of writing 1. The upper electrode of RRAM1 is also 0, while the lower electrode is connected to storage node Q through the drain of MN5. The voltage of storage node Q is low, so that the voltage difference between the upper and lower electrodes of RRAM1 is small, and RRAM1 maintains the current resistance value. The principle of data restoration operation is shown in Figure 4. RRAM1 and RRAM2 are in the state of low resistance (L) and high resistance (H), respectively. Due to the failure of the nvSRAM system power supply, there are no data maintained in the storage node. When the power supply gradually rises from 0 to VD and the signal Restore is high, the voltage of RBL and RBLB becomes low through the control of the read drive circuit. After decoding, the RWL is high, which makes MN5 and MN6 turn on. RRAM1 and RRAM2 are in the state of low and high resistance, respectively. MN6 turns on and makes the voltage of RRAM2 gradually rise, while turning on MN5 makes the voltage of RRAM1 gradually lower. When the power supply of the nvSRAM system is restored, the voltage of the two storage nodes is restored to low and high, respectively. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Mode** & **BL** & **BLB** & **WL** & **RBL** & **RBLB** & **RWL** & **VD** \\ \hline Hold & 0.9 & 0.9 & 0 & 0 & 0 & 0 & 0.9 \\ Read(1) & 0.9 & 0 & 0.9 & 0 & 0 & 0 & 0.9 \\ Read(0) & 0 & 0.9 & 0.9 & 0 & 0 & 0 & 0.9 \\ Write(1) & 0.9 & 0 & 0.9 & 0 & 0 & 0 & 0.9 \\ Write(0) & 0 & 0.9 & 0 & 0 & 0 & 0 & 0.9 \\ Store & 0.9 & 0.9 & 0 & 1.6/0 & 1.6/0 & 0.9 & 1.6 \\ Restore & 0.9 & 0.9 & 0 & 0 & 0 & 0.9 & 0.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Memory cell truth table. When RRAM is forming, it is difficult to form all the memory cells in the same BL. This is due to the excessive load on the BL and the leakage of the device. Memory cells in a BL are divided into multiple modules to reduce the impact of the load. At the same time, memory cells are reset after forming back to HR status to reduce the leakage of the device and improve the success rate of cell forming.
## 2 Mathematical Description of the Problem
\begin{split} z(k)=\begin{bmatrix}y(k)\\ u(k)\end{bmatrix},f(k)=\begin{bmatrix}f_{a}(k)\\ f_{s}(k)\end{bmatrix},e(k)=\begin{bmatrix}w(k)\\ v(k)\end{bmatrix},\\ L=\begin{bmatrix}0&-B\\ I&D\end{bmatrix},H=\begin{bmatrix}A\\ C\end{bmatrix},F=\begin{bmatrix}B_{f}&0\\ 0&D_{f}\end{bmatrix},E=\begin{bmatrix}B_{w}&0\\ 0&D_{v}\end{bmatrix}\end{split} \]  
We pe-multiply both sides of the equation by the matrix \(N_{H}\):  
\[N_{H}Lz=N_{H}Ff+N_{H}Ee \tag{4}\]  
where \(N_{H}\bullet H=0\), and \(N_{H}Lz\) on the left side of the equation is the dynamic behavior of the system. Since the equivalent space transformation does not affect the solution of the system, Equation (4) can be used to describe the dynamic behavior of the attitude determination system shown in Equation (1). \(N_{H}Ff\) on the right side of the equation is the fault vector, which is composed of the direction matrix \(N_{H}F\) and the fault vector \(f\). \(N_{H}Ee\) is the interference vector and has a dynamic behavior. \(N_{H}Lz\) is a multivariate distribution composed of the fault vector and the interference vector. Therefore, the purpose of quantifying the detectability and isolability of the dynamic system faults shown in Equation (1) can be realized by measuring the similarity or difference in the multivariate distribution when no fault occurs or when different faults occur according to the corresponding criterion (such as the distance similarity or directional similarity).
## 2 Methods
### Experiments and Statistical Analysis  
The trained nnU-Net model and the SynthSeg model were applied to both the internal and external test sets to provide two automated ventricle segmentations in addition to the GT manual delineations per patient. Segmentation volumes were extracted and compared between the test sets. In addition, the corresponding PVS segmentation was extracted for both the nnU-Net and SynthSeg segmentations by taking a 4 mm margin surrounding the ventricle segmentation. The four segmentation metrics, DSC, HD95, surface DSC, and APL, were extracted to compare the nnU-Net and SynthSeg ventricle and PVS segmentations on both the internal and external test sets. Given the small sample sizes (18 results per segmentation metric), normality was assessed using the Shapiro-Wilk test, a widely accepted method for evaluating whether data follows a normal distribution, especially in small datasets. A threshold of 0.05 was used for significance to test normality. If the data were normally distributed, a t-test was used to compare the segmentation results. For non-normally distributed data, the non-parametric Mann-Whitney U test was employed to assess differences without assuming normality. All statistical tests were implemented in Matlab R2023a.  
The results of the models were compared against each other on the same test sets, as well as the same model on both test sets. This resulted in 24 statistical tests per structure. To account for the multiple comparisons made, we applied a Bonferroni correction to adjust the significance level which minimizes the probability of false positives when testing multiple hypotheses. The Bonferroni adjusted significance level was therefore calculated to be \(\alpha_{adjusted}=\frac{0.05}{48}\approx 0.001\) for both the t-tests and the Mann-Whitney U tests. The segmentation metric results were reported as median [range]. Boxplots were used to visualize the median scores and interquartile ranges of the DSC, HD95, surface DSC, and APL metrics.
## 3 Prototype Description
### Clustering Methods
#### 3.7.3 MiniBatch K-means  
The MiniBatch K-means algorithm is a variation of the K-means clustering technique designed to efficiently handle large datasets [70]. While standard K-means can be computationally expensive for sizable datasets, MiniBatch K-means offers a more scalable approach by using random subsets (mini-batches) of data for each iteration. This accelerates the convergence process and reduces memory requirements.  
Since the algorithm uses mini-batches, it introduces some level of randomness in each iteration. This might lead to suboptimal results if not managed properly. The quality of clusters might vary depending on the mini-batch sizes and the initialization strategy. The core mathematical formulation of MiniBatch K-means remains similar to the standard K-means algorithm, with the main differences in the assignment and update steps, which are performed on mini-batches [71].
## 3 In Situ Measurement and Analysis on Deformation Pattern
### Qualitative Analysis of the Deformation Mechanism
#### 3.3.3 Thermal Field Variation  
A comparison of bedrock temperature before and after reservoir impoundment demonstrates that the reservoir water affects not only the seepage field but also the thermal field. The valley contraction and bedrock temperature drop occur simultaneously. Based on those observed phenomena, we proposed a valley deformation mechanism driven by thermal field variation. The basic principle is shown in Figure 10.  
As the water level rises, the reservoir water with low temperature infiltrates into the rock mass, which originally has a higher temperature as described above. The cooling effect is likely to induce rock contraction at the bottom of the valley and consequently narrows the valley.
## 1 Introduction
[41] pointed out the orthogonal relationship between the maximum displacement direction and the maximum in-situ stress direction of tunnels in weak surrounding rock. [PERSON] et al. [42] proposed defining the large deformation of soft rock as a progressive plastic deformation failure of tunnel and underground engineering surrounding rock with an obvious time effect. The targeted support scheme is one of the most effective methods of controlling soft rock deformation, and previous researchers have also made good progress on the theory of large deformation support in soft rock. While support ideas are revolutionized, support measures have been put forward including retractable support and bolt-net-cable coupling support. [PERSON] et al. [43] suggested that a second lining should be applied in time to resist the deformation of surrounding rock in large a deformation tunnel, and considered that increasing the strength and stiffness of the second lining is an effective method for preventing large deformation. [PERSON] et al. [44] pointed out that the settlement of the vault is the most prominent phenomenon after excavation of a soft rock tunnel and the timely construction of a reverse arch and closed lining can significantly reduce the displacement of the tunnel. [PERSON] et al. [45] eliminated large deformation through the secondary bolt-net-cable coupling support and used a grouting bolt to control a large deformation of floor heave. However, due to the particularity of underground engineering, no theory or treatment measures can deal with the complicated and changeable geological conditions. This has resulted in the theoretical research of large deformation of soft rock continuing to lag behind engineering application and still not being thoroughly solved. Therefore, it is necessary to comprehensively and carefully consider the distribution of large deformation stress fields in soft rock, the prediction of large deformation and the selection of excavation and support methods and put forward corresponding views and solutions. This paper is based on the construction project of the diversion tunnel at the Dongsong HydropowerStation. Through theoretical study of the surrounding rock stress field, analysis of the space-time effect of numerical simulation and real-time feedback of field monitoring, the whole process of excavation and support of a large deformation tunnel in soft rock is studied. The deformation mechanism and law of soft rock mass tunnel is analyzed. The excavation scheme according to the time-space effect of soft rock mass tunnel is optimized. A method to determine the reserved deformation of soft rock mass tunnel is proposed. The use of real-time feedback monitoring technology for a large deformation tunnel is discussed. The results can provide a useful reference for the theoretical basis and technical support for the construction of large deformation tunnels in soft rock mass. At the same time, the results can further deepen and enrich the study of this kind of tunnel.
## 4 Effect of Drying on the Nutritional, Biochemical, and Physiochemical Properties of Sea Cucumbers
### Hot Air Drying  
Hot air drying is the most effective drying method to absorb water from food using the continuous flow of hot air. This process is homogenous, contamination-free, and takes less time than sun drying [37]. Even industries use this method due to the low maintenance cost and simple operational techniques [1]. Studies were conducted in hot air dryers to determine the variation of chemical composition among different drying methods such as freeze and microwave drying, where hot air was supplied at the rate of 1.5 m/s and maintained a relative humidity of 20% at 60 \({}^{\circ}\)C to obtain a moisture content of 6 to 7% [3,44]. [PERSON] et al. [37] stated that hot air drying at 60 \({}^{\circ}\)C for 48 hr was the superior processing method to obtain the nutritional and functional properties such as the total saponin, crude polysaccharide, and crude lipid percentage of sea cucumbers compared to sun drying, vacuum freeze drying, and double-distilled water cooking. [PERSON] et al. [30] reported that the percent of protein content increased by 16.4% via hot air drying over the fresh sea cucumber. [PERSON] and [PERSON] [3] reported that the percent of ash content increased by 8 and 2% in the hot air-drying method over the freeze-drying and microwave-drying methods, respectively. Moreover, the lipid percentage increased by 2% in hot air drying over freeze drying [1].  
However, hot air flow dries out the surface of sea cucumber quickly; therefore, the outer layer becomes harder than the inner layer, and the moisture from the inner layer is removed slowly. As a result, it causes product shrinkage and structure deterioration as well as hampering storage ability and damaging heat-sensitive nutritional components [27].  
Therefore, based on the above findings, it can be concluded that a range of hot air flow of 40-60 \({}^{\circ}\)C for 48-72 h could be used to optimize this method to obtain good-quality products from sea cucumbers (Figure 3).
# POU-SLAM: Scan-to-Model Matching Based on 3D Voxels
## 4 Scan-To-Model Matching with POU Implicit Surface Representation
### POU Implicit Surface Representation for Planar Features  
#### 4.2.1 POU Approach  
The basic idea of the POU approach is to break the data domain into several pieces, approximate the data in each subdomain separately, and then blend the local solutions together using smooth, local weights that sum up to one everywhere in the domain. More specifically, we assign a specific planar patch as the correspondence of a planar feature point in each correspondence voxel. The procedure of finding the specific points to form planar patches \(P_{c}\) can be found in [7]. We can calculate distances \(d\) between the planar feature point and the corresponding planar patches \(P_{c}\). Then, we project planar feature point \(x\) on the surface of planar patches defined by \(p_{s}\): \(p_{s_{i}}=x-d_{i}n_{i}\), where \(n_{i}\) is the normal of the closest point to \(x\) in each planar patch and is a good approximation of the surface normal at the projected point \(p_{s_{i}}\). Each planar patch can be regarded as a subdomain. Then, we use the weight function to blend the subdomains together.
## 4 Genome Editing for Autosomal Dominant Retinitis Pigmentosa
### Crx  
One last causative gene that merits being mentioned is _CRX_, which encodes the cone-rod homeobox protein. CRX is a key player in the development and maintenance of photoreceptors. CRX regulates the transcription of several retinal genes and, in addition, is expressed in the pineal gland, where it regulates the circadian rhythm [119]. _CRX_, like _PRPH2_, is one of the genes that cause the most variable disease phenotypes. Over 100 _CRX_ mutations have been identified, most of which cause autosomal dominant cone-rod dystrophy or LCA type 7 (Table 1 and Figure 4). Late onset _CRX_-related adRP cases have also been identified, which comprise 1.5% of all adRP [56]. In total, 5 _CRX_ mutations that cause adRP have been identified to date: four missense mutations p.Ser152 Tyr, p.Gly122 Asp, p.Arg115 Gln and p.Arg41 Gln (the latter also causes cone-rod dystrophy), and one deletion of exons 3 and 4 [56].  
Although there is variability in pathogenicity, it seems that _CRX_ mutations are dominant-negative, gain-of-function or both. Haploinsufficiency does not seem to be the cause of disease as a carrier of a null allele was reported to have a normal clinical phenotype [120]. Strikingly, in this particular case, the patient's daughter who was also heterozygous presented with LCA arguing for a more complicated mechanism. Furthermore, animal models heterozygous for null _Crx_ mutations do not have the severe symptoms displayed by patients with dominant forms of the disease [121].  
Consequently, in order to treat the adRP-related _CRX_ missense mutations, either invalidation or correction of the mutant allele would be the strategy of choice. For the reported deletion, invalidation of both alleles, followed by gene supplementation, would be the only approach. Furthermore, if such an approach were successful, it could also be applied to the _CRX_ point mutations indiscriminately.
## 1 Introduction
A random forest regression was trained to learn a quantitative precipitation estimation model directly from a four-year database [5]. It was observed that the random forest algorithm significantly reduced the error and bias of predicted precipitation intensities. Four different machine learning algorithms including decision forest regression were used to develop rainfall forecasting models [6]. Decision tree and stochastic forest algorithm were used to predict summer precipitation in Chongqing, from 2011 to 2018 [7]. A seasonal ARIMA model was applied to predict the mean temperature and precipitation in the Bhagiriathi river basin in Uttarakhand, India [8]. The annual rainfall in Hyderabad was predicted using ANN and autoregressive integrated moving average (ARIMA) approaches [9]. ARIMA was used to predict annual rainfall and maximum temperature over the Tordzie watershed in Ghana [10]. The abovementioned techniques of rainfall prediction could not account for the non-stationarity and non-linearity of rainfall data. Meanwhile, empirical mode decomposition was used as an effective tool in handling non-stationary data and was employed in many fields. Some of them are: EMD analyses of climate changes were performed with reference to rainfall data [11]. Their study suggested that EMD is an effective tool in isolating physical processes of various time scales. Ensemble EMD was used to isolate the rainfall and temperature data in different time scales and used to identify the variability [12]. Hybrid EMD models were employed in various fields for prediction. An energy time series forecasting based on empirical mode decomposition and fuzzy radial basis function and the Autoregressive model was developed [13]. EMD and support vector regression were combined to forecast financial time series [14]. Their work showed that EMD has improved forecasting, by capturing the local fluctuations of time series. In the literature, [PERSON] and [PERSON], 2008, [PERSON] et al., 2013, [PERSON] and [PERSON], 2013, [PERSON], [PERSON], and [PERSON], 2016, and [PERSON] et al., 2016 [15; 16; 17; 18; 19] had utilized hybrid EMD models for making predictions using nonlinear and non-stationary time series wind speed data. All these works have utilized EMD as an effective tool in decomposing non stationary and non-linear data into IMFs and trend signals and a suitable forecasting model is used to predict future IMFs. These hybrid models are found to be effective in improving forecasting accuracy. This paper used EMD to decompose the rainfall data and random forest regression is used to make the annual rainfall prediction. This novel combination of EMD and RF provides improved accuracy and proves to be an efficient algorithm for reducing errors in prediction.
# Generating Transparency in the Worldwide Use of the Terminology Industry 4.0
## 3 Results
### Results of the Uncorrelated Analysis  
#### 3.1.1 International Analysis  
In total, 52 countries could be identified that contributed to the research area Industry 4.0. With that number in mind, the amount of research from around the globe that contains the above-mentioned keywords becomes evident. However, it is possible to identify three main players that have published the largest amount of research and are visualized in Figure 3: the USA, China, and Europe. Each of these regions has a different research approach and focus, such as the Chinese Made in China 2025 strategy described in Section 1. It is apparent that the majority of analyzed studies related to the topic of \"CPS\" origins from the USA (50) and Europe (45), whereas 14 articles were published in China. Comparing the number of papers related to data management, the USA and Europe achieve 39 publications each, while China achieves 25. Looking at human-machine interaction, significant differences between the regions can be observed. European institutions have published 64 articles,the Americans 29, and the Chinese 7. A similar distribution can be detected when comparing the keywords linked to the category smart: Europe has published 52 papers, the USA 24, and China 11. When comparing the relative number of articles in each region, it is evident that China's focus lies in data management, with no special interest in the related topics of human-machine interaction. The research in Europe, on the other hand, is focused on human-machine interaction, followed by smart and CPS topics. Compared to these other areas, data management features the lowest number of publications. Looking at the USA, it can be seen that the American research community primarily focuses on CPS, followed by data management, human-machine interaction, and smart.  
Zooming in on a European Scale, three main countries held the highest significance in the conducted research, namely Germany, France, and the UK (see Figure 4). On the international level, Germany's focus turns out to be topics related to the categories smart, with 21, and human-machine interaction, with 19 publications, whereas data management seems to be of less interest (2). The UK, on the other hand, published most of its papers around data management (11), followed by CPS (8), human-machine interaction (7), and smart (2). In France, 10 papers were published on human-machine interaction, 7 on CPS, 6 on data management, and only 3 on topics linked to the category smart. Apart from these three main players, Spain and Italy have also conducted some research in the aforementioned fields. Eastern Europe, on the other hand, only made a small contribution to the selected topics.