esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This study reports on the development of an operating model for an EPB Shield TBM simulator by the correlation between operating parameters. First, four main operating parameters required for excavation are analyzed, then multiple regression analysis estimates their requirements depending on the ground conditions and the TBM's specifications. Further multiple regression analysis then determines the interactions between six operational actions and ten mechanical responses. Operating procedures are established, and interlock and warning alarm systems are designed for use in the operating model. Lastly, several simulations were tested for the operating model of the TBM simulator under various conditions.  
EPB shield TBM simulator; operating model; multi regression analysis; operating parameters +
Footnote †: journal: Applied Sciences  
Article  
An Operating Model for an EPB Shield TBM Simulator by the Correlation Analysis of Operational Actions and Mechanical Responses  
[PERSON] 1  
1 Faculty of Civil and Environmental Engineering, University of Science & Technology (UST), 217, Gajeong-ro, Yuseong-gu, Daejeon 34113, Korea; [EMAIL_ADDRESS]  
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Moreover, it is also required to determine the interaction between operational actions and the TBM's mechanical responses to inform the realistic functioning of the simulator.  
Important operating parameters that must be monitored in real-time during excavation are TBM thrust, cutterhead torque, grouting pressure, and earth pressure [6]. Here, the operator controls thrust, cutterhead torque, and earth pressure by adjusting the cutterhead rotation, shield jack speed, and screw conveyor rotation speeds. [PERSON] et al. [7] emphasized the importance of managing the core machine data values such as TBM thrust within a specific range regardless of the ground conditions. However, in the field, TBM operators control the operating parameters depending on their own experience and judgment. If some operating parameters fluctuate uncontrollably, the operator recognizes a problem and should then control the operating parameters following the procedures specified in the operation manual or the instructions suggested by the TBM manufacturer.  
This work attempts to develop an operating model for a TBM simulator. Its main contributions are as follows.  
1. Statistical analysis identified four main operating parameters required for TBM excavation.
2. Multiple regression analysis estimated each parameter's requirements depending on the soil ground conditions and the TBM's type and specifications.
3. Further multiple regression analysis determined the interactions between operational actions and mechanical responses. TBM operation procedures and interlock systems with warning systems were designed and applied in the simulator.
4. Example excavation simulations were tested in a TBM simulator based on the developed operating model.
## 1 Introduction
### The Relationship between SuDS and Health and Wellbeing  
In the late 2000s, a shift in research and environmental management occurred that linked nature with wellbeing: society was no longer considered to be a passive beneficiary of nature, but could take action to proactively protect, manage and restore natural ecosystems to address societal challenges [18]. This shift in thinking led to the emergence of a new theme of research and practice around nature-based solutions (NBS), as actors sought possibilities to bring about a more systematic approach to understanding the relationship between nature and society. The ambition of NBS is to develop management solutions that work with ecosystems to address mounting urban sustainability challenges and stimulate economic growth and employment through the green economy [19; 20].  
NBS can be defined as \"actions to protect, sustainably manage and restore natural or modified ecosystems, which address societal challenges (these may include climate change, food and water security or natural disasters) effectively and adaptively, while simultaneously providing human wellbeing and biodiversity benefits\" [19] (p. 2). One of the drivers for NBS has been the potential benefits to health and wellbeing. The relationship between health and NBS is complex, but evidence suggests that access to greenspace provided by NBS is important for healthy living and can help to mitigate public health risks associated with urbanisation and climate change [21].  
Three routes have been proposed to explain the benefits to health: (i) restoring capabilities; (ii) building capacity by enhanced physical activity, improved fitness and reduced obesity; (iii) mitigation of flooding, air pollution and reduction of the urban heat island effect [22; 23; 24]. More recently it has been suggested that the presence, accessibility, proximity and \"greenness\" of NBS influences the magnitude of the health effects [21]. Furthermore, research is beginning to identify how improvements in water environments can affect a range of socio-economic factors, including demographics [25]. When green residential space is present, this not only improves the amenity value for the existing community, but provides a key driver for migration into an area, which can lead to socio-economic change in the long term [25]. Understanding these impacts is strategically important for prioritising locations for implementing water management improvements [25].
## 3 Nonlinear Dynamic Analysis of the RBRS
### Influence of Excitation Current on Dynamic Characteristics of RBRS at Variable Speed
In order to specify the main frequency components of the system under different excitation currents of the generator \(I_{j}\), the main frequency components (where \(f_{\rm n}\) is the frequency conversion) in different speed ranges are listed in Table 3.  
Through comparison, it can be seen that the increase of the excitation current has a certain inhibitory effect on the response amplitude of the system, but it has no inhibitory effect on the motion state, which makes the motion state of the system more complex, the chaotic motion widens relatively, and the jump discontinuity is enhanced. At higher rotational speeds, the number of high-order bifurcation increases with the increase of excitation current. Therefore, it is necessary to control the excitation current within a certain range in order to enable the vibration system to operate stably. Figure 3:0 The bifurcation diagram of RBRS at \(I_{j}=1200\) A.  
Figure 3:1 3-D frequency spectrum of RBRS at \(I_{j}=1200\) A. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Excitation** & **Rotating Speed** & **Main Frequency Feature (m: Positive Integer)** \\
**Current \(I_{j}\) (A)** & \(\omega\) **(rad/s)** & **Main Frequency Feature (m: Positive Integer)** \\ \hline \multirow{6}{*}{\(I_{j}=700\)} & \(7.0\leq\omega\leq 15.6\) & \(f_{n}\) \\  & \(15.6<\omega\leq 20\) & \(f_{n}\), \(2f_{n}\), \(3f_{n}\), \(4f_{n}\), \(\ldots\) \\ \cline{1-1}  & \(20<\omega\leq 21.2\) & \(f_{n}\), \(2f_{n}\), \(3f_{n}\), \(4f_{n}\), \(\ldots\), continuous frequency components \\ \cline{1-1}  & \(21.2<\omega\leq 54.8\) & \(f_{n}\), \(2f_{n}\), \(3f_{n}\), \(4f_{n}\), \(\ldots\), \(\ldots\) \\ \cline{1-1}  & \(54.8<\omega\leq 57.6\) & \(f_{n}\)/\(4f_{n}\)/\(3f_{n}\), \(2f_{n}\), \(3f_{n}\), continuous frequency components \\ \cline{1-1}  & \(57.6<\omega\leq 71\) & \(f_{n}\), \(2f_{n}\), \(3f_{n}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Frequency feature of RBRS under different excitation current.
## 4 Conclusions  
The ability to predict the amount of settlement in sanitary landfills requires accurate knowledge of the solid particle density, bulk density, and void ratio of the landfilled waste material. Within this research, we demonstrated a strong dependence between the void ratio and the variability in the solid particle density caused by heterogeneity and decomposition. It is crucial that the solid particle density is accurately determined because false estimations of the solid particle density will most certainly lead to the erroneous prediction of landfill settlements. As a consequence, landfill capacity cannot be accurately assessed and an inefficient waste management strategy might be applied.  
In order to overcome the difficulties related to the accurate assessment of the solid particle density, a one-dimensional \(p_{d}=f(e)\) functional relationship was proposed. With the proposed relationship, it is possible to estimate the void ratio of landfilled MBT waste material without needing to know the corresponding solid particle density of the landfilled waste. However, it should also be noted that the proposed relationship was verified on MBT waste material for which closed voids were removed during the heavy treatment process. It is reasonable to assume that a similar relationship should exist for other kinds of waste materials that do not contain closed voids, or that only contain a small number of closed voids. To verify this assumption, further research on various types of waste materials is necessary. For the waste materials that contain large amounts of closed pores (for example, untreated municipal solid waste material), the proposed equation should not be used, nor should it be used in cases in which the waste in question does not share similar geotechnical properties to the waste from which the one-dimensional \(p_{d}=f(e)\) functional relationship was obtained.  
Conceptualization, I.P.; methodology, I.P.; validation, N.K., N.H. and D.B.; investigation, N.K., N.H. and D.B.; writing--original draft preparation, I.P.; writing--review and editing, I.P.; supervision, I.P.; project administration, I.P.; funding acquisition, I.P. All authors have read and agreed to the published version of the manuscript.  
Figure 6: \(p_{d}\) vs. \(e\) relationship as predicted with Equation (8).  
**Funding:** This work was supported in part by the Croatian Science Foundation under the project number UIP-2017-05-5157.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** We express thanks to [PERSON] for his work in the early stage of this study.  
**Conflicts of Interest:** The funders had no role in the design of the study, in the collection, analyses, or interpretation of data, in the writing of the manuscript, or in the decision to publish the results.
## 3 Results and Discussion
### BLUF Modules in Association with the Effector Domains
#### 3.3.17 PDZ Domain  
PDZ domains or discs-large homologous regions (DHR) are widely spread in a wide range of membrane-bound signaling proteins from bacteria, yeasts, plants, insects and vertebrates [153; 154]. The PDZ domain presents either as a single copy or as multiple copies and interacts either with the C-terminus of proteins or with internal peptide sequences [154]. Proteins with the PDZ domain are generally located at the plasma membrane, where they can directly interact with phosphatidyl 4, 5-bisphosphate (PIP2), as observed with the class II PDZ domain in syntenin [155]. PDZ domains (80-90 amino acids) are composed of compactly arranged six \(\beta\)-strands (\(\beta\)A-\(\beta\)F) and two \(\alpha\)-helices (\(\alpha\)A and \(\alpha\)B) in a globular structure. PDZ domains interact with shaker-type K\({}^{+}\) channels in several MAGUKs or bind similar ligands of other transmembrane receptors [154].
# Parametric Analysis for 3D Modeling of Consolidation-Induced Solute Transport Using OpenFOAM
## 3 Model Validation  
Due to the lack of suitable experimental data for direct validation, this study uses the one-dimensional model [19] as a reference for indirect validation. Since OpenFOAM only supports three-dimensional models, adjustments were made to adapt the model for 3D simulation while maintaining as much alignment as possible with the 1D model. The simulation covers a total duration of 20 years, with a time step of \(1\times 10^{-1}\) years to ensure numerical stability and accuracy.  
The comparison in Figure 3 shows that the 3D model and the 1D model [19] exhibit a high degree of consistency in overall trends, confirming the accuracy of the proposed model in simulating consolidation-induced solute transport. However, differences in dimensionality are the primary reason for the detailed discrepancies between the two models. The 1D solver captures only vertical processes, neglecting lateral variations and the multi-directional coupling effects in fluid flow and solute transport, where pressure and concentration gradients in different directions interact and feedback across multiple directions. In contrast, the 3D model more fully captures interactions between physical fields, accurately reflecting dynamic changes in fluid pathways and concentration distribution. This multidimensional approach brings the 3D model closer to real-world conditions, making it a powerful tool for predicting solute migration.  
Figure 3: Comparison between the present model and previous model [19]. (**a**) Pressure and (**b**) vertical displacement at the top point (\(x=10\) m) vs. depth; (**c**) solute concentration (\(c_{f}\), kg/m\({}^{3}\)) atthe bottom point (\(x\) = 10 m, \(z\) = 0 m) vs. time. Note: lines: the present model; symbols: [PERSON] and [PERSON] [19]'s model with \(S_{r}\) = 0.9, \(\mathbf{K}\) = 1.5 \(\times\) 10\({}^{-10}\) m/s, \(D_{m}\) = 2.75 \(\times\) 10\({}^{-9}\) m\({}^{2}\)/s.
## 3 Proposed Model
### Optimal Feature Selection
#### 3.5.2 Objective Function Based on Discriminant Feature  
In this research, the proposed hybrid feature selection approach is carried out in two parts, i.e., filter approach and wrapper approach. In the wrapper approach, the evaluation process is based on a classification model, which is very much efficient but computationally expensive. Thus, the filter approach is used before the wrapper approach to minimize the cost and optimized the performance. The filter approach selects some optimal subsets of features using SFFS with the help of an efficient feature subset evaluation model. In this research, we adopted Discriminant Feature Distribution Analysis Based Objective Function proposed in [27] as a feature subset evaluation model. In this evaluation model, the power of discrimination of features are measured by calculating the within-class compactness and between-class distance. The distance measurement has a significant role in feature analysis and machine learning techniques. There are several criteria to measure distribution of features, i.e., distance, correlation, entropy, information gain, and others. However, distance measurement is one of the most used techniques in data science and machine learning. Several machine learning and classification models are depending on distance measurement, i.e., k-NN, k-means, self-organizing map (SOM). Many kernel-based algorithms also use distance measurement, such as, SVM uses the distance measurement for classification. Also, the distance measurement expresses the geographical distribution in high dimension feature space efficiently. Among the several distance measurements, Euclidean distance is mostly used technique. Thus, in this measurement process, the Euclidean distance is considered to evaluate the distance of samples in a class and/or other classes. The Euclidean distance is formulated as Equation (2), where \(x\) and \(y\) are two points, \(n\) is the number of features, and \(x_{i}\) and \(y_{i}\) are features values of points.  
\[d_{x,y}=\sqrt{\left(x_{1}-y_{1}\right)^{2}+\left(x_{2}-y_{2}\right)^{2}+\ \cdots\ +\left(x_{n}-y_{n}\right)^{2}}=\sqrt{\sum_{t=1}^{n}(x_{t}-y_{t})^{2}} \tag{2}\]  
To calculate the within-class compactness, firstly, the class median is determined based on the distances of all points of a class. After that, the maximum distance is calculated from the class medium to the farthest point of a class. Finally, within-class compactness is calculated by averaging all class compactness of all classes. To calculate the between-classdistance, the minimum distance between two classes is calculated from all distances between each point from one class to each point to the other class. The minimum distance of two classes is considered as class separability. The overall between-class distance is calculated by averaging all class separability of different classes. Finally, the evaluation/objective value is calculated as Equation (3). Figure 7 presents the process of calculation of objective value.  
\[objective\_valu=\frac{between\_class\_distance}{within\_class\_compactness} \tag{3}\]
# An Empirical Study on Customer Churn Behaviours Prediction Using Arabic Twitter Mining Approach
## 3 Methodology
### Using the Model to Measure the Customer Satisfaction Rate  
The study aimed to develop a potential model for the sentiment analysis of tweets to measure customer satisfaction and predict customer churn using the real-time method. The application was aimed at Saudi Telecom companies. We developed our model to predict customer satisfaction on the AraCust corpus [84] based on the predefined companies STC, Mobily and Zain.  
In this study, our proposed model has been used based on two models: first, bidirectional Gated recurrent units (Bi-GRU) with Word2 Vec model that achieved the best result with 95.16% for accuracy [44]. Second, three transfer networks designed for Arabic language AraBERT [90], hULMonA [91] and RoBERTa [92] models were utilized on AraCust [84] to define the best performance suitable to the corpus and the dialect Arabic characteristics. Finally, the proposed model combining the AraBERT model and Bi-GRU predicted customer satisfaction for the three companies [84], Table 7.  
We noticed that the customer satisfaction percentage for the three companies STC, Mobily and Zain were 31.06%, 34.25% and 32.06%, respectively (all below 50%). Perhaps that was because customers tend to post a negative tweet rather than a positive tweet on Twitter, as previously observed.  
This study has used a sentiment analysis to design an accurate model by applying several approaches to measure customer satisfaction. Then, it has developed a questionnaire for the customers whose tweets were mined to evaluate the model by comparing the predicted customer satisfaction (using the model) with actual customer satisfaction (using the survey). From Table 7, we can see that our model achieved the goal of predicting the customer satisfaction of telecom companies based on the Twitter analysis. These results can provide insights for the decision-makers in these companies regarding the percentage of customer satisfaction and help in improving the services provided by these companies. These results should encourage the decision-makers to consider using Twitter analyses for measuring customer satisfaction and to include it as a new method for evaluating their marketing strategies.  
Next, we used these results to further predict the customer churn for the telecommunication company that provided the historical data, specifically, and compared it to the customer churn percentage that we obtained from the company.
level and the electrocardiogram (ECG) signal. In case the QT-interval of the ECG signals is larger than 445 ms and the glucose level is below 3.5 mmol/L, then the patient will be at a high risk of sudden death [4]. Wearable technologies, such as smart watches and wristbands, are some of the enabling technologies for e-health, as shown in Figure 2. An e-health system is generally composed of, among others, wireless sensors that form a body area network (BAN), wearable devices to collect data from the sensors, a gateway for internet access, a cloud server to process and store e-health data. The server is usually located in health clinics or hospitals. Wearables are battery-limited devices, which creates a stringent limit on their computational capability. To extend further the operating time of the wearables, energy-harvesting techniques, wireless power transfer along with duty cycling have been suggested in many published reports [5]. Such systems are composed of a communication module, sensors, micro controllers, an energy management unit, battery, and an energy scavenging or receiver unit. The Internet of Medical Things, IoMT, is a system in which medical devices and healthcare units are interconnected using IoT technology [6]. Wearable medical devices are cardinals in the IoMT as they can collect vital biological data to healthcare givers. The IoMT is a system of systems in which a multitude of technologies, platforms, and algorithms are used all the way from the sensor layer to the cloud computing layer. This contribution aims at reviewing latest breakthrough in the IoMT by focusing on the the following domains:  
* Semiconductor and multicore technology. * Energy harvesting and transfer. * Algorithm transformation techniques. * Implementation of deep neural networks on edge devices. * User-centered design of IoMT. The paper is organized as follows. Section 2 compares our work with existing review works. Section 3 describes the technologies, algorithms and paradigm in designing energy  
Figure 1: The three pillars for an e-health system proposed by the WHO. Figure 2: e/m-health system using IoT and wearable technologies. efficient IoMT edge devices. Section 4 focuses on the energy harvesting sources and solutions for the IoMT low-end node. Section 5 presents platforms and transformation techniques for the energy efficient implementation of DNN algorithm on IoMT edge devices. Section 6 presents approaches for a user-centered design of IoMT. Finally, Section 7 concludes the paper. Table 1 summarizes the list of abbreviations used throughout the paper. The list of symbols, their meanings and typical values are shown in Table 2.
## 4 Experiment Results
### Communication  
The communication structure is shown in Figure 30. Currently, Modbus ASCII and Modbus RTU are the most commonly used communication methods in the industry. The three systems in this study use network transmission to communicate. The first step of this system is to establish a connection between the database and the robotic arm. In the communication function of the robotic arm, you can select the model of the robotic arm and  
Figure 29: AI working area safety module system architecture.  
communication settings, such as IP and Port. The robotic arm can be selected according to the user's needs. Since the communication codes of the robotic arms of various brands are different, this study will formulate a unified communication rule--Modbus abstract class, Absmodbus--to facilitate subsequent addition and maintenance. The database is responsible for recording production records to provide the system and users with references. Therefore, the communication function includes establishing a connection with the database.