esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theoretical Framework and Hypothesis Development  
### Technology Acceptance Model  
The Technology Acceptance Model, developed and validated by [PERSON] [26; 27], is one of the most influential and widely applied theoretical frameworks for understanding the factors influencing users' behaviours toward accepting technology or information systems [25]. Grounded in the theories of Reasoned Action [65] and Planned Behaviour [66], the core TAM framework posits that users' behavioural intention to use a novel technological system is primarily determined by two cognitive beliefs: perceived ease of use and perceived usefulness [27]. BI refers to the user's readiness to exert a planned effort to use the technology in the future [67; 68], reflecting a complex interaction of both cognitive and/or affective factors that may lead to its adoption or rejection [37; 69]. Across educational and learning environments, BI typically refers to students' willingness to use innovative technology for learning and education in the future and/or their likelihood of recommending it to others [70; 71; 72]. PEU is defined as the degree to which a person believes that using a particular system would be effortless [27]. In the context of VR technology, it is typically assessed through the user's subjective perceptions of the understandability, simplicity, intuitiveness, and user-friendliness of the VR technology platform [35; 56; 73]. PU, on the other hand, is defined as the degree to which a person believes that using a particular system would enhance their job performance [27]. Within the context of a VR learning environment, PU is conceptualised as the extent to which students perceive VR technology as beneficial for enhancing their learning performance [34; 74]. In the present study, PU is assessed as the degree to which students believe training on the HMD-VR engine room simulator enhances their active learning. Active learning, as a pedagogical approach, places the learner at the centre of the training or educational process, highlighting elements such as interaction, customisation, engagement, and the application of previously acquired knowledge [75; 76]. In the context of EVEs, active learning refers to the level of autonomy afforded by a VR platform, which, in turn, enables students to actively apply theoretical knowledge in VR-simulated training scenarios, increase responsiveness and engagement, and benefit from controlled, self-paced experiential training in a risk-free environment [70; 73; 77].
# Fault Diagnosis of Railway Point Machines Using the Locally Connected Autoencoder
## 3 Methodology  
### Current Signals of Railway Point Machines  
A railway point machine consists of a motor, a transmission clutch, gearing, and a retention clutch with throw bar. The function of the point machine is to actuate the switch rail from one stock rail to the opposite position. The failure of the point machine may cause serious accidents, such as the derailment of the train with all its consequences. The health condition of the point machine is closely related to the process of the switch operation. Thus, it is particularly useful to monitor the switch operation process. The current sensors that are widely deployed in field could directly collect the electrical signals, which provides sufficient samples for the electric current analysis to solve the fault diagnosis problem. The operation of the point machine can be divided into the following four phases.  
1. The start-up phase: An activating signal transmitted from the control center is received by the point machine. The asynchronous short-circuit three-phase motor starts up. One can find a current surge in this phase.
2. The unlocking phase: At the beginning of the throwing process, the switch rail is unlocked. The current signal is observed to be gradually declined.
3. The switch phase: The actual throwing process begins. The switch rail smoothly moves on the shifting plate.
4. The locking phase: At the end of the throwing movement, when the switch rail arrives at the end position, one keep-and-detect slide engages with the end position notch and locks the retention clutch. Simultaneously, a signal is sent from the point machine to report the successful operation.  
A representative current shape which shows the four operating phases is shown in Figure 1.
## 3 Results and Discussion
### Spectra Preprocessing Method Selection  
Generally, calibration models will perform better if the noise level can be minimized either through hardware improvements or data preprocessing [39]. Table 1 shows the discrimination performance between normal and black-heart samples after different spectral preprocessing methods. All single preprocessing methods, except SGS (Savitzky-Golay smoothing), demonstrated better discriminating accuracy for calibration of 98.1%-99.1% and prediction of 96.1%-98.3% than the raw spectra. SGS had the worst performance among all preprocessing methods and was even worse than not preprocessing. This could be because SGS discarded important information for black heart detection in white radiishes. Standard normal variate (SNV) had the best accuracy with 99.1% and 98.9% for calibration and prediction, respectively, followed by mean center. SNV is commonly used to eliminate variations in spectral intensity due to light scattering. Mean center helps to enhance the differences between spectra. Their combination was also evaluated for preprocessing the raw spectra. Among all spectral preprocessing combination methods, the highest accuracies of 100% and 99.4% were achieved with calibration and prediction, respectively, using SNV and mean center. Therefore, the combination of the two methods was chosen to preprocess the spectra.
# OBU for Accurate Navigation through Sensor Fusion in the Framework of the EMERGE Project
## 3 Sensor Fusion: Loosely Coupled Algorithm Implementation  
The primary advantage of a loosely coupled integration architecture lies in its simplicity. This architecture is versatile and compatible with any INS and GNSS user equipment, making it ideal for retrofit applications. In a loosely coupled INS/GNSS system, the integration algorithm utilizes GNSS position and velocity solutions as measurement inputs, regardless of the specific type of INS correction or GNSS aiding employed. In the cascaded operation of this architecture, the GNSS user equipment, which incorporates a navigation filter [17], utilizes the Extended Kalman Filter (EKF) as the integration solution. The EKF is a recursive filter specifically designed to estimate the state of a dynamic system from multiple noisy measurements. It has evolved from the Standard Kalman Filter to address the complexities associated with nonlinear dynamic systems. The resulting integrated navigation solution consists of the INS navigation solution refined by the Kalman filter's error estimates. Specifically, within the loosely coupled integration architecture, the sequential processes can be delineated as follows:  
1. calculation of position and velocity with GNSS;  
Figure 4: Final assembled system: Navigation EMERGE OBU. Figure 3: OBU HW components: (1) Onboard Computer Raspberry Pi 4 Model B; (2) Sixdab Power Management; UPS HAT power backup system; (3) Drotek (SIRIUS RTK GNSS ROVER: F9P) with u-blox ZED-F9P chip; (4) Xsens MT630 AHRS Inertial Sensor; (5) Septentrio Polant-x MF antenna. 2. calculation of the difference between the estimated position and velocity from the GNSS and INS solutions to assess IMU errors by integrating the estimated differences in a Kalman filter;
3. correction of the INS solution using variational equations. The tests were carried out in a confined area. To simplify the spatial representation in this local context, the North East Down (NED) coordinate frame was chosen. Consequently, the position of the OBU is represented using geodetic coordinates: latitude \(L_{b}\), longitude \(\lambda_{b}\), and ellipsoidal altitude \(h_{b}\). The Earth-referenced velocity is resolved in the local navigation axis to give \(\mathbf{v}_{eb}^{n}\), and the attitude is expressed as the body-to-local navigation frame coordinate transformation matrix \(\mathbf{C}_{b}^{n}\). Here, \(e\) denotes the Earth-Centered Earth-Fixed (ECEF) reference frame, \(n\) is the NED frame, and \(b\) represents the inertial platform \"body\" coordinate frame. Figure 5 illustrates the functional diagram of the GNSS-INS loose coupling process. It is a closed-loop correction architecture; consequently, the estimated errors in the NED reference frame, \(\delta\hat{L}_{b}\) for the latitude, \(\delta\hat{\lambda}_{b}\) for the longitude, \(\delta\hat{h}_{b}\) for the height, \(\delta\hat{w}_{eb}^{n}\) for the velocity and \(\delta\hat{\mathbf{C}}_{b}^{n}\) for the attitude are fed back to the inertial navigation processor, where they are used to correct the inertial navigation solution.
# A Cost-Optimized Model for Dynamic Port Hinterland
## 3 Methodology
### Methodological Proposal  
The proposed methodology for calculating port hinterland involves an optimization exercise of logistics routes for each point on the map with respect to a specific logistics chain. This approach can be formalized as follows:  
\[H(p)=\bigcup_{(x,y)}H(p,a,o)\cdot\rho_{(a,o)}, \tag{1}\]  
\[H(p,a,o)=min\{TLC(x,y,p)\}\;\text{for all}(x,y)\in A, \tag{2}\]  
where  
* \(H(p)\) represents the hinterland of port \(p\);
* \(H(p,a,o)\) represents the hinterland of port \(p\) for a product \(a\) and from an origin \(o\);
* \(\rho(a,o)\) represents the weight of the logistic chain of the product \(a\) from the origin \(o\) into the total hinterland;
* \(TLC(p,x,y)\) is the Total Logistics Cost of point \((x,y)\) by the port \(p\);
* \(A\) is the set of all points in the area of study.  
This formulation encapsulates the essence of the hinterland calculation as a minimization problem across a geographical location, considering the total logistics cost as the key optimization criterion.  
Building upon the theoretical framework proposed by [PERSON] et al. [24], this study adopts a modified approach to cost calculation. Specifically, the Inventory Holding Cost (_IHC_) factor has been disaggregated into two distinct components: Warehouse Cost (_WHC_) and Time Management Cost (_TMC_). This refinement allows for a more nuanced analysis of the temporal and spatial aspects of inventory management within the logistics chain.  
Consequently, the Total Logistics Cost (\(TLC\)) can be expressed as (parameters are omitted for simplicity):  
\[TLC=TTC+WHC+TMC, \tag{3}\]  
where  
* \(TLC=\) Total Logistics Cost;
* \(TTC=\) Total Transportation Cost;
* \(WHC=\) Warehouse Cost;
* \(TMC=\) Time Management Cost.  
This decomposition of costs provides a comprehensive framework for analyzing the economic factors that influence hinterland formation. By separating the costs associated with physical storage (\(WHC\)) from those related to the temporal aspects of inventory management (\(TMC\)), the model offers increased granularity in assessing the factors that shape port hinterlands.  
The following sections will elaborate on each of these cost components, providing detailed explanations of their calculation methods and the underlying factors that influence their values. This comprehensive approach aims to capture the multifaceted nature of logistics costs in port hinterland analysis, offering a robust methodology for both theoretical modeling and practical application in port management and logistics planning.  
In Figure 1, each of the indicated terms is represented in a diagram. In this figure, the blue line on the left indicates maritime transit, while the black lines represent land transportation, either by road or rail.
# Creating a Remote Choir Performance Recording Based on an Ambisonic Approach
## 4 Realization of Remote Music Recording
### Recording of Musicians  
The prepared model recording should be the base to which the other members of the ensemble will add their parts. It is very convenient to put the examples on the YouTube platform as non-public recordings so that during the performance of their part, the musician will be able to plug in small headphones and keep the phone in sight (e.g., close to the music notation) and continuously control the hand movements of the conductor and the person's 'exemplary performance' for their musical part. The illustration in Figure 3 presents a recording situation under the described conditions. The recordings submitted by the musicians should be verified by the conductor and/or the producer of the project in terms of both artistic and technical aspects especially with regard to the aesthetics of the frame and the fidelity of the reproduction of the pattern, especially on the rhythmic side. The more faithful the performance, the fewer corrections need to be applied later in the recording release process.
## 3 Methodology
### Data Association--Cost Matrix Matching
Element-wise weighted mean of every cost matrix value: \[W_{M}(D_{i},P_{i})=\lambda_{IoU}\cdot IoU(D_{i},P_{i})+\lambda_{ D_{E}}\cdot D_{E}(D_{i},P_{i})+\lambda_{R}\cdot R(D_{i},P_{i}),\\ i\in D,\;j\in P,\;\lambda_{IoU}+\lambda_{D_{E}}+\lambda_{R}=1\] (12)  
To improve tracking performance in multi-class environments, cost matrices can be updated based on the match between predicted and detected object class (class gate):  
\[C^{*}(C_{i,j},D_{i},P_{i})= \begin{cases}C_{i,j}&\text{if Class }D_{i}=P_{i}\\ 0&\text{otherwise}\end{cases} ,\;i\in D,\;j\in P \tag{13}\]
# A Method for Detecting Surface Defects in Railhead by Magnetic Flux Leakage
## 4 Experimental Results and Analysis
### Physical Experiment Results and Analysis  
#### 4.2.1 Experimental System  
An experimental system is built in the laboratory, as shown in Figure 9. The system consists of an industrial computer, a signal conditioning circuit, a DAQ card, a power supply box, and a probe. The properties of the sensor, amplifier and DAQ card are listed in Table 1. An AD620 instrumentation amplifier was used in a bias amplifier circuit. The detection speed was 2 m/s. The length and width of the sensor are both 1.5 mm. When multiple sensors are closely arranged, the minimum distance between two sensors centers is 2 mm. The probe, which consists of three sensors and an excitation device, is mounted on a detection car that is placed on the rail.
## 3 Results and Discussions
### Coupled Flow and Noise Simulations for a Wind Turbine Array  
#### 3.4.1 Wake Modeling of a Wind Turbine Cluster  
Wind turbine flow are simulated for a Vestas wind turbine called NM80. The AOA and the relative velocity at all blade elements from RANS/AD simulations are inputted to the BPM model. The AD solver is much more efficient because it represents the rotor by volume force term rather than the dense body-fitted-mesh around blade surface. The flow computations are carried out with the in-house developed flow solver [24; 25; 26] which is an incompressible flow solver.  
The grid for AD simulation and a typical flowfield with multi-wake structures are depicted in Figure 9. In Figure 9a, two wind turbines, depicted with thick white lines, are used for demonstration. The inflow boundary is applied on the left and top domain edge (numbered as 1 in Figure 9), the axisymmetric boundary on bottom edge (numbered as 2) and outflow boundary on the right edge  
Figure 8: Comparisons of the RSPL in 1/3-octave bands.  
Figure 7: Sound transmission loss over 200 m at various frequencies (rigid ground and upward refraction).  
(numbered as 3). The computational domain consists of the several blocks whose number is the same as that of wind turbines to be investigated. Each block contains grid points of 64 \(\times\) 64. Using this approach, multi-wake flowfield can be efficiently modelled. A typical horizontal velocity contour plot is shown in Figure 9b.  
To show the model accuracy, in Figure 10, some results from the current simulations using RANS turbulence model are compared with the LES results under same flow conditions [27; 28]. On the modeling of wind turbine wake, many studies can be found [29; 30; 31]. In the present study, it is not of our primary interest to go into more details on this topic. Some more details of the AD modeling approach and cross comparisons can be found from our previous works [32; 33; 34].
# Advancements, Dynamics, and Future Directions in Rural Environmental Governance Research in China
## 6 Conclusions and Recommendations
### Discussion  
Chinese REM constitutes a complex and significant research domain encompassing multiple disciplines and dimensions, demanding a comprehensive examination of various elements, including nature, society, and economics. This article offers an exhaustive analysis and exploration of Chinese REM research, yet it possesses certain limitations. To begin with, the article exclusively relied on literature from two databases, CNKI and WOS, which somewhat constrained the scope of the study, potentially excluding essential publications from other international or domestic academic journals and conferences. Additionally, this article employed software tools such as VOSviewer, Bibliometrix, and ArcGIS for literature analysis, which furnished valuable insights into research topics and trends but did not engage in an extensive discourse on specific research methodologies and data quality. Lastly, the article identifies deficiencies in interdisciplinary collaboration and academic communication but refrains from conducting a thorough examination of the underlying causes. Whether these shortcomings stem from interdisciplinary barriers, inadequate academic communication mechanisms, or other factors warrants further comprehensive investigation.  
This article engages in a bold hypothesis. Firstly, we utilized ArcGIS software to create a heatmap illustrating the distribution of research institutions among the 3370 articles in this study. From a spatial standpoint, the provinces with the highest publication counts include Beijing, Jiangsu, and Hebei, while those with the fewest publications are the Tibet Autonomous Region, Hainan Province, Qinghai, and Ningxia Hui Autonomous Region. Secondly, we drew upon the '2020 List of Beautiful and Leisurely Rural Formulas in China' issued by the Department of Rural Industry Development within the Chinese Ministry of Agriculture and Rural Affairs [70]. Using ArcGIS software, we generated another heatmap. Among these regions, Jiangsu, Zhejiang, Anhui, Sichuan, and Chongqing boast the highest number of beautiful rural areas, whereas the Tibet Autonomous Region,Ningxia Hui Autonomous Region, Qinghai Province, and Hainan Province exhibit the fewest. Figure 14 illustrates that the spatial distribution of these two aspects closely aligns. Regions with fewer beautiful rural areas, such as the Tibet Autonomous Region, Ningxia Hui Autonomous Region, Qinghai Province, and Hainan Province, also demonstrate a scarcity of related studies. This article postulates that research within the field of Chinese REM appears to concentrate on regions where environmental governance methods exhibit relative effectiveness, while provinces characterized by less favorable rural environments and average governance efficiency attract comparatively less attention. Future research endeavors may delve deeper into this perspective and explore whether it influences the geographical focus of China's REM research and its correlation with beautiful rural areas in China.
## 8 Fixed and Adaptive Wave Domain Equalization
### Physical Background  
The Helmholtz integral equation (HIE) [191] provides the solution of the inhomogeneous wave equation with respect to homogeneous boundary conditions. This covers--among others--the sound field reproduced by a distribution of loudspeakers in a room. The HIE states that the sound pressure within a source and scatterer free volume \(V\) is uniquely determined by the sound pressure and its directional gradient at the boundary \(\partial V\) of the volume. This finding can be exploited for the analysis of sound fields as well as for their synthesis. For the analysis of sound fields, it is sufficient to capture the sound pressure and its gradient at the border of the volume of interest. The same holds for the synthesis of sound fields where placing loudspeakers around a listening area allows full control of the sound field within that area. However, in terms of technical complexity, it is generally not desirable to capture both the sound pressure and its directional gradient using two different types of microphones placed at the boundary of the listening area. The same also holds for the synthesis using loudspeakers. Here one would have to employ monopole and dipole loudspeakers. Microphones and loudspeakers with the properties of a monopole are desirable over their dipole counterparts. It has been shown [192] that the HIE can be reduced to a monopole-only variant under some practically feasible limitations. This lays the theoretical ground of RRE within an extended listening area. In summary, the sound field within the listening area can be analyzed and controlled by a continuous distribution of microphones and loudspeakers located on the boundary of the listening area. However, the solution of the underlying continuous problem requires the solution of integral equations derived from the HIE [193]. Operator theory provides a solution to this problem by expanding Green's function into orthogonal basis functions. A closer look onto this will be taken in the subsequent section on wave domain adaptive filtering.  
For a practical implementation of the principles outlined above, only a finite number of microphones and loudspeakers can be used. Hence, the continuous distribution of microphones and loudspeakers must be sampled spatially. The geometry and sampling is illustrated in Figure 9.  
The wave-theoretical view on RRE introduced above requires a sufficiently dense sampling of the loudspeaker and microphone contour. For typical systems, this calls for a high number of loudspeakers and microphones even when the upper frequency limit is quite low. Spatial sampling has been investigated intensively for different geometries and techniques [194; 195; 196]. The full three-dimensional coverage of the listening areas boundary by loudspeakers and microphone is often not feasible in practice. The limitations of considering only a planar listening area leveled with the listener's ears which is surround by loudspeakers are discussed in [197].
# Road Scanner: A Road State Scanning Approach Based on Machine Learning Techniques
## 5 Experiments
### Implementation  
Our experiments were practically computed on a computer dotted with an Intel Core i7-8550 Processor and 16-GB RAM capacity. To limit overfitting issues, a 4-cross validation approach was adopted. In each iteration, 1155 samples were used for training and 385 for testing. Concretely, all models and evaluation metrics were implemented using the python language and well-established data science libraries, such as Pandas, NumPy, and Scikit-learn. For all the models, several hyperparameters were tested and evaluated.  
Our empirical tests in finding optimal performances resulted in the following configurations: For ANNs, the best network topology was characterized by two hidden layers (containing each almost 15 nodes), a \"relu\" activation function, an \"adam\" solver and an iteration number of 200 for the training process. For The SVM, an \"rbf\" kernel is adopted and the value of 30 was found to be the best tradeoff for the regularization parameter C. For the Random Forests and the Extra Trees classifiers, the \"gini\" criterion is adopted and atotal number of 100 estimators was empirically validated. Similarly, 100 estimators were used for the Bagging, and the Gradient Boosting classifiers. Particularly for the Bagging, the SVM classifier was found to be the best choice as a base estimator among several other methods. For the Gradient Boosting classifier, the \"friedman_mse\" criterion was selected as a measure for the split quality, and for the stacking approach, two classifiers were chosen as the best base estimators, namely Decision Trees and SVM. In this latter approach, the final combining estimator was a Logistic Regression classifier. The results of these finely tuned models are presented in the next section.