esa-sceva/satcom-chunk-collection huggingface dataset:
# Radiomics in Gynaecological Imaging: A State-of-the-Art Review
## 6 Ovarian Pathologies
### Mri  
MRI is the most accurate imaging modality for the assessment of ovarian pathologies, and it may be particularly helpful for lesion characterization and stratification of the risk of malignancy. MRI is often performed as a second-line imaging modality in patients with indeterminate adrenal masses detected on other imaging exams. Radiomics analysis in ovarian pathologies has been applied to different MRI sequences, with the more promising results reported for the radiomics features extracted from the T2 WI. In a multicentric study, [PERSON] et al. [157] provided a combined model, including radiomics features extracted from the segmentation of T2 WI, which achieved a good performance (AUC of 0.86) in the external validation set for the differential diagnosis between benign and borderline epithelial ovarian tumours. [PERSON] et al. [158] applied radiomics to multiple MRI sequences to differentiate type I from type II epithelial ovarian cancers. [PERSON] and co-workers [159] proposed an MRI-based radiomics signature for the prediction of recurrence-free survival in patients with high-grade serous ovarian carcinoma. In that study, radiomics features were extracted from the T2 WI and post-contrast sequences, with similar performances. In a study including 186 patients, [PERSON] et al. [160] reported that the radiomics model based on T2 WI had the highest performance among different MRI sequences of the prediction of prognosis in patients with epithelial ovarian cancers. MRI-based radiomics models have also been tested and validated for the prediction of postoperative residual tumour, peritoneal metastases, or chemotherapy response in patients with ovarian cancers [161; 162; 163]. Radiomics can also predict genomics markers, such as BRCA mutations, proteomics, and immunological markers in patients with ovarian cancers, as explored in recent studies [164; 165; 166]. Nevertheless, [PERSON] and co-workers [167] found a low performance of the radiomics model in predicting BRCA mutation (AUC of 0.46-0.59) when applied to a multicentric cohort of 218 patients from four different centres. Optimistic results of the current radiomics studies should be balanced by the challenges for the applications of radiomics in ovarian pathologies. Most of the current radiomics research are retrospective single-centre studies lacking external validation. Heterogeneity of imaging acquisition parameters and lack of standardized protocol can limit the application of the radiomics models in external centres [168]. Moreover, inter-reader variability and reproducibility of the radiomics features are crucial for the validation of the radiomics model, and they are often missing in current studies. A recent systematic review pointed out that the methodological rigor and quality of radiomics studies are still unsatisfactory in ovarian pathologies, with a reported radiomics quality score of 6, corresponding to 16.7% of the total score, which is lower compared with other fields of research in radiomics studies [169; 170; 171].
# A Study on the Pantograph Slide Wear Model Based on Energy Dissipation
## 5 Data Analysis of Pantograph Slide Wear Model  
### Validation of Pantograph Slide Wear Model  
To verify the accuracy and reliability of the proposed wear model, an additional set of comparative validation experiments was conducted. The experimental conditions were set with a contact pressure of 90 N; contact currents of 100 A, 150 A, 200 A, and 250 A; and sliding speeds of 70 km/h, 90 km/h, 110 km/h, and 130 km/h. Under these varying conditions, the actual wear values of the pantograph slide were measured. Meanwhile,  
Figure 3: Relationship between pantograph–catenary current-carrying friction dissipation energy and wear volume.  
the corresponding predicted wear values were calculated by substituting the experimental conditions into the wear model.  
The comparative validation experiments were divided into four unit groups, numbered (1, 2, 3, 4), (6, 7, 8, 9), (11, 12, 13, 14), and (16, 17, 18, 19). Within each unit group, the sliding speed was kept constant while the contact current increased sequentially. Between the different unit groups, the test points with the same position in the sequence were set to the same contact current, while the sliding speed increased progressively. The comparison between the predicted and actual wear values of the pantograph slide is shown in Figure 4.  
As shown in Figure 4, the wear values of the pantograph slide calculated using the developed wear model closely approximate the experimentally measured values; however, there is still a certain degree of deviation between the predicted and actual wear values.  
The relative errors between the predicted and actual wear values, as well as the average relative error, are presented in Table 2.  
According to the distribution of the error data, the relative errors between the model predictions and the actual values are primarily concentrated below 10%, accounting for  
\begin{table}
\begin{tabular}{c c c} \hline
**Experiment ID** & **Relative Error (\%)** & **Mean Relative Error (\%)** \\ \hline
1 & 5.391 & \\
2 & 2.929 & \\
3 & 7.109 & \\
4 & 8.736 & \\
6 & 6.397 & \\
7 & \(-\)3.908 & \\
8 & 5.811 & \\
9 & 9.127 & \\
11 & \(-\)2.958 & 4.611 \\
12 & \(-\)7.500 & \\
13 & 6.164 & \\
14 & 11.364 & \\
16 & 5.333 & \\
17 & \(-\)4.174 & \\
18 & 11.183 & \\
19 & 12.776 & \\ \hline \end{tabular}
\end{table}
Table 2: Prediction error values of wear model.  
Figure 4: Comparison between predicted values and actual values of wear volume.  
81.25% of the validation data. The proportion of relative errors exceeding 10% is 18.75%, with the maximum relative error being 12.776% and the average relative error only 4.611%. Therefore, the model is considered effective and can be used to predict the wear of the pantograph slide.
## 4 Experiments
### Dataset  
In order to test the proposed approach, we conducted experiments on the SemEval 2015 Task 18 dataset [35], which covers three languages (English, Chinese, and Czech) and contains three different formalisms (DELPH-IN MRS (DM) [36], Predicate-Argument Structure (PAS) [37], and Prague Semantic Dependencies (PSD) [38]). For English, three formalisms (DM, PAS, and PSD) are all available; for Chinese, only PAS formalism is available; for Czech, only PSD formalism is available.  
The dataset split of three languages is shown as Table 1. For English, we use the same dataset split as in previous approaches [10; 39], with 33,964 training sentences from Sections 00-19 of the Wall Street Journal corpus [40], 1692 development sentences from Section 20, 1410 sentences from Section 21 as in-domain (ID) test data, and 1849 sentences sampled from the Brown Corpus [41] as the out-of-domain (OOD) test data. For Chinese, we use the top 3000 sentences as the development data, the remaining 25,336 sentences as the training data, and 8976 sentences as the ID test data. For Czech, we use the top 3000 sentences as the development data, the remaining 39,057 sentences as the training data, 1670 sentences as the ID test data, and 316 sentences as the OOD test data.  
For the evaluation, we use the evaluation script used in Wang et al. [8], reporting labeled F-measure scores (LF1) (including ROOT arcs) on the ID and OOD test sets for each formalism as well as the macro-average over the three of them.
# Resilient Modulus--Physical Parameters Relationship of Improved Red Clay by Dynamic Tri-Axial Test
## 4 The Dependence of Resilient Modulus on Material Properties
### The Influence of Additive Content on Resilient Modulus  
The improved red clay prepared at optimum moisture content and 93% compactness were selected to study the influence of different additives on the dynamic resilient modulus of the improved red clay. As can be seen from Figures 6 and 7, both cement and lime (3-9%) can significantly improve the dynamic resilient modulus of the red clay. In Figure 6, when the confining pressure is 60 kPa and the deviatoric stress is 30 kPa, the resilient modulus of red clay samples, prepared at a lime content of 3%, 6%, and 9% is 149.9 MPa, 166 MPa, and 180.6 MPa, respectively. Compared with 3% lime, the resilient modulus for lime content of 6%, 9% increases by 10.75%, and 20.4%, respectively. In Figure 7, when the confining pressure is 60 kPa and the deviatoric stress is 30 kPa, the resilient modulus of red clay with cement content of 3%, 6%, and 9% is 170.4 MPa, 190.5 MPa, and 202 MPa, respectively. Compared with 3% cement, the resilient modulus for cement content of 6%, and 9% increases by 11.8%, and 18.54%, respectively. By comparing Figure 6a with Figure 7a, it can be concluded that when the confining pressure is 60 kPa and the deviatoric stress is 30 kPa, an increase of 12% in the dynamic resilient modulus was observed for 3% cement, compared with that of 3% lime. By comparing other data, it can be also seen that, when the contents of cement and lime are the same, cement can significantly improve the resilient modulus of red clay.
# End-To-End Convolutional Neural Network Model for Fear Fault Diagnosis Based on Sound Signals
## 4 Experiment Result and Analysis
### Evaluation
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{4}{c}{**Accuracy (\%) on**} \\ \cline{2-5}  & **Evaluation 1** & **Evaluation 2** & **Evaluation 3** & **Evaluation 4** \\  & **Train on A** & **Train on B** & **Train on A** & **Train on B** \\  & **Test on A** & **Test on B** & **Test on B** & **Test on A** \\ \hline
**end-to-end (our method)** & **100** & **100** & **98.5** & **96.4** \\ manual feature + SVM & 98.8 & 98.6 & 94.7 & 94.5 \\ manual feature + & 93.6 & 97.5 & 87.7 & **60.3** \\ RandomForest & 96.5 & 95.7 & 93.0 & **58.9** \\ manual feature + KNN & 98.6 & 98.0 & 95.2 & 94.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The prediction accuracy on dataset A (without load) and dataset B (with load). the model on dataset B and then testing it in dataset A can be viewed as some degree of overfitting to dataset B, which led to a low performance on test samples in dataset A. On the contrary, our model did not observe such a dramatic performance decline. In these two conditions, the accuracy of our model was 98.5% and 96.4% when trained on A and tested on B and when trained on B and tested on A. Both performances were higher than 95.2% of KNN with manual features and 94.5% SVM with manual features. The diagnosis result are shown in Table 4.  
From the diagnosis result in Table 4, the recognition accuracy on gears with a tooth fracture reached 100% and 98.3% in Evaluation 3 and Evaluation 4, respectively, which meant these types of tooth fracture in gears could be easily distinguished from the other three types of gears. For normal gears, our algorithm achieved 99.0% accuracy in Evaluation 3 and 95.6% in Evaluation 4. The reason why the recognition accuracy decreased in Evaluation 4 is that the signal amplitude of the normal gears with a load and the amplitude of gears with pitting in the no-load state were similar at certain time points. As for the other two types of gears, the algorithm also achieved higher recognition accuracy in these two conditions. The reason for sample misclassifications here was due to the distribution of the amplitudes between these two types of fault signals, which were similar at certain times. This can be observed from time domain signal in Figure 7.  
The above results indicate that our end-to-end stacked CNN model has achieved significant performance in sound signal-based gear fault diagnosis and has a strong generalization capability.
# Food Waste as Feedstock for Anaerobic Mono-Digestion Process
## 1 Introduction
is important to point out that this solution constitutes a significant part of the systems used in Europe [13]. Indeed, [PERSON] et al. [34], in a recently published review paper, noted that approximately 95% of AD reactors in Europe apply single-phase AD systems. Another issue that must be considered is that AD can be carried out as a mono-digestion process or with the use of a mixture of two or more substrates. Co-anaerobic digestion (Co-AD) is known as the simultaneous digestion of two or more substrates [35]. The main aim of combining different feedstock types is to balance the ratio of C/N nutrients as well as macro- and micronutrients and to dilute inhibitors that decrease CH\({}_{4}\) production [36]. However, [PERSON] et al. [3] clearly emphasized the drawbacks of this solution. Indeed, the above-mentioned authors pointed out that in the case of Co-AD, the partial consumption of intermediate substrates decreases treatment efficiency and CH\({}_{4}\) yield. Moreover, they indicated that the reduction in the degradation of volatile fatty acids (VFAs) leads to more significant methanogenesis inhibition. As a consequence, only diluted FW may be used in the discussed strategy. In addition, choosing a suitable co-substrate with a suitable mixing ratio is of great importance [37]. According to the discussed issues, the analysis presented in the current study was limited to the single-stage anaerobic mono-digestion process. The principal objectives of this paper were to conduct a holistic investigation on the following:  
1. Physicochemical properties of FW collected from various sources, such as canteens, restaurants and cafeterias,
2. The performance profile of the single-stage anaerobic mono-digestion process with the use of FW as a feedstock. The above-presented aims of this paper have been motivated by the following facts:  
1. One of the most important challenge of the FW mono-digestion is the instability of the digester [38]. Therefore, deepening the knowledge of the FW parameters is required for ensuring high process performance. 2. The performance of the AD process with the use of FW as a feedstock in strongly influenced by several operational parameters, such as temperature, pH, ratio between food waste and inoculum (I) (FW/I), and organic loading rate (OLR). Figure 2: A bibliometric analysis of the keywords in publications of biogas (2015–2024). The analysis was performed with the use of VOSViewer software (version 1.6.20). Hence, the findings presented in the current study provide valuable insights for the management and application of FW as a feedstock in the industrial anaerobic monod digestion process.
# Methodology for the Prediction of the Thermal Conductivity of Concrete by Using Neural Networks
## 3 Methodology
### Development of the Model  
The second step is related to the general process of building the prediction model, i.e., data normalization, the selection of an appropriate neural network architecture for the prediction task, defining the learning rate of the neural network, the number of hidden layers and neurons in each layer, and the metrics to find the best model for the dataset.
## 2 Related Work
### Problems and Solutions  
Missing data and noisy data are problems encountered repeatedly in data acquisition and analysis. Especially for the analysis of spatio-temporal dynamic data, we also need to consider the characteristics of time, space and space-time. Here, periodic sensor temperature data obtained from grain storage is taken as an example to illustrate the data challenge. Due to cost and mechanized operation constraints, a large granary has only limited sensor observations (the distance between two adjacent horizontal sensors can be up to 5 m). Sensor observations may not be properly collected into the database due to unexpected reasons, such as sensor damage, communication failures, and data read/write errors. In addition, the data obtained by the sensor not only has the detection error of the sensor, but also has the systematic error caused by its principle, i.e., the temperature obtained by the sensor is the temperature in the pore inside the grains instead of the actual temperature of the grains. At the same time, this kind of data have obvious temporal and spatial relationships, i.e., its temperature changes are continuous in time and correlated in space.  
In general, there are three problems related to the automatic processing of collected data: (1) The inconsistency of periodically collected data; (2) Possible deviation between the observed value and the real value; and (3) problems related to the temporal collected data and historical data. For the above problems, the existing models require consistent observations in the spatial and temporal domains to estimate dynamic fields. Comparatively, Kalman filter needs more data for pre-processing to obtain the initial parameters of the model, so it has a better prediction structure than the state-space model. In order to simplify the calculation, the proposed model is based on the state-space model, so it does not need additional data for pre-processing.  
The FUKSS proposed in this paper provides a cyclic estimation method for spatio-temporal dynamic fields. The data selection matrix \(\mathbf{W}_{t}\) is used to realize the correspondence between the state matrix and the observation vector, so that the observation data of different sizes at different times can be directly used in the circular calculation process without additional operation. At the same time, we use the universal Kriging feature to estimate the arbitrary position and eliminate the system random error in a way similar to Kalman filter. Finally, exponential smoothing prediction is introduced to obtain quasi-observed value to simulate state input, so as to improve the prediction ability of the model.
_Applied_  
_sciences_  
**Dissolved Oxygen Control in Activated Sludge**  
**Process Using a Neural Network-Based Adaptive**  
**PID Algorithm**  
[PERSON] 1,2,3,4, [PERSON] 1,3,4, [PERSON] 2,* and [PERSON] 5  
1 College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China; 27 [EMAIL_ADDRESS] (X.D.); [EMAIL_ADDRESS] (J.W.)  
School of Engineering, Royal Melbourne Institute of Technology (RMIT) University, Melbourne 3000, Australia  
3 Key Laboratory of Gansu Advanced Control for Industrial Processes, Lanzhou University of Technology, Lanzhou 730050, China  
4 National Demonstration Center for Experimental Electrical and Control Engineering Education, Lanzhou University of Technology, Lanzhou 730050, China  
5 Department of Energy and Power Engineering, North China Electric Power University, Baoding 071003, China; [EMAIL_ADDRESS]  
* Correspondence: [EMAIL_ADDRESS]; Tel.: +61-3-9925-0810  
Received: 20 December 2017; Accepted: 6 February 2018; Published: 9 February 2018  
**Featured Application: This work is currently undergoing field testing at Pingliang Wastewater Treatment Plant situated in Gansu province, China, especially for the control of dissolved oxygen concentration in the activated sludge process of the wastewater treatment. By implementing this control algorithm, we can achieve two goals, namely improving the efficiency of wastewater treatment and reducing the aeration energy. Meanwhile, the method proposed in this work can also be extended to other large- or medium-scale wastewater treatment plants in the future.**  
**Abstract: The concentration of dissolved oxygen (DO) in the aeration tank(s) of an activated sludge system is one of the most important process control parameters. The DO concentration in the aeration tank(s) is maintained at a desired level by using a Proportional-Integral-Derivative (PID) controller. Since the traditional PID parameter adjustment is not adaptive, the unknown disturbances make it difficult to adjust the DO concentration rapidly and precisely to maintain at a desired level. A Radial Basis Function (RBF) neural network (NN)-based adaptive PID (RBFNNPID) algorithm is proposed and simulated in this paper for better control of DO in an activated sludge process-based wastewater treatment. The powerful learning and adaptive ability of the RBF neural network makes the adaptive adjustment of the PID parameters to be realized. Hence, when the wastewater quality and quantity fluctuate, adjustments to some parameters online can be made by RBFNNPID algorithm to improve the performance of the controller. The RBFNNPID algorithm is based on the gradient descent method. Simulation results comparing the performance of traditional PID and RBFNNPID in maintaining the DO concentration show that the RBFNNPID control algorithm can achieve better control performances. The RBFNNPID control algorithm has good tracking, anti-disturbance and strong robustness performances.**  
**Keywords: dissolved oxygen concentration; radial basis function (RBF) neural network; adaptive PID; dynamic simulation**
# Influence of Electrohydrodynamics on the Drying Characteristics and Volatile Components of Ginger
###### Abstract  
This article studies the electrohydrodynamic drying of ginger. In this work, drying experiments were performed in an electrohydrodynamic drying (EHD) system at various AC voltages (0 kV (control), 15 kV, 20 kV, 25 kV, and 30 kV). The drying properties and volatile components of ginger were thoroughly examined and studied using IR spectroscopy and GC-MS. The findings revealed that electrohydrodynamics significantly increased the drying rate and reduced the drying time of ginger, with a sevenfold increase in drying rate and a one-third reduction in drying time at 30 kV. The peak of the infrared spectrum of ginger remains unchanged. We found 240 volatile chemicals under different drying voltages. The primary volatile chemicals were esters and aldehydes. The ginger products dried at 30 kV had the lowest aldehyde concentration. These findings give an experimental and theoretical foundation for applying electrohydrodynamics to the subject of ginger drying.  
g +
Footnote †: journal: applied sciences
## 2 Key Technology Discussion
### Asymmetric Power Cell Designs:  
The development of asymmetric power cell designs has proven effective in maximizing efficiency [5]. By configuring the carrier amplifier to operate in Class-AB mode and the peaking amplifier in a deeper Class-C bias, the overall efficiency at PBO can be significantly improved. This configuration allows the peaking amplifier to activate only when necessary, thus conserving power during lower output scenarios. In addition, the asymmetric power cell architecture, when integrated with the PDR technique mentioned earlier, enables superior IM3 interference cancellation, leading to enhanced linearity performance.