esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Vaneless Diffuser Shape on Performance of Centrifugal Compressor
## 1 Introduction
The results confirmed that proper reduction of the width ratio can increase the stable working range of the centrifugal compressor. Although the energy loss of the system increases slightly, it has basically no effect on the overall performance of the centrifugal compressor. The effect of the change in width contraction shape at the inlet of the vaneless diffuser on the critical stall angle is within 5\({}^{\circ}\). [PERSON] [19] performed performance tests on axisymmetric and non-axisymmetric diffusers, and the results showed that compared with the traditional axisymmetric diffusers, the non-axisymmetric diffusers with varying design width along the circumference could extend the stable operation range of the centrifugal compressor by up to 28% at the design speed. [PERSON] [20] explored the influence of the shape of the vaneless diffuser on the stall inception point using the wavelet neural network method, and concluded that reducing the width ratio of the vaneless diffuser and reducing the radius ratio of the wide vaneless diffuser can improve the stability of vaneless diffusers. The highest expansion stability among the width-pinch shapes of the vaneless diffuser at the exit of the three common impellers was reported with straight and oval shapes (and round was considered to be oval). The effect of the shroud pinch on the vaneless diffuser was verified by the calculation results of the vaneless diffuser 3D incompressible model developed by [PERSON] [21], and the effect was enhanced with the increase of shrinkage rate and inlet Mach number. The study also showed that when the ratio of inlet to outlet width of the vaneless diffuser with the same radius ratio remains unchanged, for the vaneless diffuser with different contraction profiles, the more significant the inlet contraction was, the higher the stability of the centrifugal compressor would be. In this paper, steady numerical simulation based on the RANS(Reynolds-averaged Navier-Stokes equations) equation and SST(Shear-Stress-Transport) turbulence model for a centrifugal compressor with four different vaneless diffusers and three operation conditions were studied. It could be concluded that compressor stage isentropic efficiency and pressure ratio were improved by the pinch used on vaneless diffuser width. The second important finding of the study was that implementing pinch decreases the energy loss areas within compressor. Pinch on vaneless diffuser width is relatively easy to design and manufacture, giving a novel approach to energy conservation and emissions reduction.
# Securing Smart Grid Enabled Home Area Networks with Retro-Reflective Visible Light Communication
###### Abstract  
Smart appliances' run schedule and electric vehicles charging can be managed over a smart grid enabled home area network (HAN) to reduce electricity demand at critical times and add more plug-in electric vehicles to the grid, which eventually lower customers' energy bills and reduce greenhouse gas emissions. Short range radio-based wireless communication technologies commonly adopted in a HAN are vulnerable to cyber attacks due to their wide interception range. In this work, a low-cost solution is proposed for securing the low-volume data exchange of sensitive tasks (e.g., key management and mutual authentication). Our approach utilizes the emerging concept of retro-reflector based visible light communication (Retro-VLC), where smart appliances, IoT sensors and other electric devices perform the sensitive data exchange with the HAN gateway via the secure Retro-VLC channel. To conduct the feasibility study, a multi-pixel Retro-VLC link is prototyped to enable quadrature amplitude modulation. The bit error rate of Retro-VLC is studied analytically, numerically and experimentally. A heterogeneous Retro-VLC + WLAN connection is implemented by socket programming. In addition, the working range, sniffing range, and key exchange latency are measured. The results validate the applicability of the Retro-VLC based solution.  
visible light communication; heterogeneous network; backscatter communication; retro-reflective; smart grid; home area networks; key exchange; security 20232323245  
2023231245  
3
# Evaluation of Measurement Strategy for Track Side Monitoring of Railway Wheels
## 2 Wheel Profiles and Wheel Parameters  
A railway wheelset consists of an axel fixed to two sets of wheels. For the rolling stock vehicles on the track there are numerous different wheel profiles in use, and the nominal wheel shape is adapted depending on the application concerned [6]. For instance, iron ore wagon wheels used along the Iron Ore Line in Sweden are subjected to large axle loads, and the wheels generally have a profile which provides a larger contact area, thus giving a wider pressure distribution between the wheel and the rail. On the other hand, passenger trains have a particular profile which is crafted to meet the requirements for stability at higher speed. There are also different limitations for the wheel profiles depending on certain factors such as maximum allowed speed, wheel diameter, maximum allowed load, etc. Further, the wheel measures are also controlled by mandatory requirements for high and low thresholds [45].  
Figure 1 depicts the wheel profile for a new wheel (marked with a blue line) and a worn wheel (marked with a red dashed line). The wheel profile is commonly divided into two main parts: the flange and the tread. The wheel profile parameters are used to describe the condition of the wheel, and the standard wheel profile parameters are depicted in Figure 1. Flange height (Sh), flange width (Sd), flange slope (qR), and tread hollowing (Th) are the widely established wheel profile parameters in the railway industry and are described below:  
* Flange height Sh: vertical distance (in mm) from the nominal running circle (70 mm from the flange back) to the top of the flange
* Flange width Sd: lateral distance (in mm) between the flange back and the flange face measured at a height of 10 mm up from the nominal running circle
* Flange slope qR: lateral distance between the flange face position measured at a height of 10 mm up to 2 mm below the top of the flange
* Trend hollowing: difference in radius between the smallest radius of the tread (close to the running band) and the largest radius close to the field side  
Measurements of these wheel parameters are essential, as they describe different aspects of the wheel profile and give insights to the status of the wheel with respect to different requirements. For instance, the flange height cannot be too low (to avoid derailment) and cannot be too high (to avoid damages to other track components). Similarly, the flange cannot be too thin (to avoid flange failure and derailments). Measures of these parameters are hence used for maintenance decisions in the railway sector.  
In the railway sector, these parameters generally have both maintenance and safety limits, and when some of the measurements pass a high or low threshold of these limits or when a wheel failure occurs, the wheel needs to be reprofiled or replaced accordingly. The intervals between the reprofiling vary significantly depending on the application; for instance, the wheels of lighter trains usually have longer intervals between reprofiling than the wheels of trains with a large axle load.
## 4 Results
### Experiment 1 Results: One Device, Distance 7 m  
In this experiment, a large number of settings worked properly. For all settings, approximately 100% of the packets were received. However, in settings with high SF values (11 and 12), the packet payload was damaged. In those settings with a bandwidth of 125 kHz and an SF value of 10, the received data were also faulty, which suggests that at a short distance, with a high SF value, a BW greater than 125 kHz is needed to properly receive the information. Furthermore, with a low CR (4/5) packets were received at the gateway only with the lowest SF value (SF = 7); with values of 8, 9, 10, 11 and 12, all the received packets were damaged.  
Figure 7 shows the results obtained in the ten best settings of Experiment 1, where 100% of the transmitted packets were received correctly.  
Figure 8 shows a contour graph for Experiment 1. The graph shows the effect of the different evaluated parameters on the RSSI, which is ideal when closer to zero. In the graph, values closer to zero are plotted in dark blue.  
In the relationship between CR and BW, the most desirable values correspond to the lowest denominator for the CR, that is, 4/5. The BW value that is most suitable would be 250 kHz. This graph only features the denominator values of the CR in the \(y\)-axis for simplicity.  
In the graph that relates the SF and the BW, the most desirable value for SF is 7, while for BW it is close to 500 kHz.  
Finally, in the graph of the SF and CR parameters, the ideal values would be a SF of either 7 or 10, and a CR of 4/5 or 4/6. The \(x\)-axis shows only the denominator values for CR.  
The baseline setting obtained an average RSSI value of \(-\)55.53 dBm and 100% of received packets, which was close to the best setting (SF = 7, BW = 250 kHz, CR = 4/6), with an average RSSI value of \(-\)50.36 dBm and 100% packets received.  
Additionally, in this first experiment, an attempt was made to adjust the behavior of the RSSI level to a linear model combining the LoRa transmission parameters; however, the results were not satisfactory.  
Figure 7: Receive signal strength indicator (RSSI) values for the best 10 settings in Experiment 1.
## 7 Future Research Directions
### Enhancing Energy Efficiency and Sustainability in Routing
\begin{table}
\begin{tabular}{l l} \hline \hline
**Research Direction** & **Description** \\ \hline \multirow{4}{*}{Integration of real-time data and dynamic routing updates [96]} & - Incorporating real-time data such as traffic conditions, weather information, and vehicle availability \\  & into routing decisions. \\ \cline{2-3}  & Integration of real-time data and dynamic routing updates [96] & - Developing efficient data collection mechanisms and sensors for continuous data gathering \\  & - Utilizing machine learning approaches to process and utilize real-time data effectively. \\ \cline{2-3}  & - Ensuring the scalability of the algorithms to handle large-scale and complex routing problems in real time. \\ \hline \multirow{4}{*}{Hybrid approaches and integration with other optimization techniques} & - Integrating machine learning techniques with GA to enhance decision making during routing. \\ \cline{2-3}  & Hybrid approaches and integration with other optimization techniques & - Exploring the integration of swarm intelligence algorithms or other metaheuristic approaches with \\  & genetic algorithms & - Leveraging metaheuristic frameworks to evolve routing algorithms and parameters within the GA itself. \\ \cline{2-3}  & - Developing routing optimization models that explicitly incorporate uncertainty and risk management mechanisms. \\ \cline{2-3}  & - Employing robust optimization models and risk assessment methodologies to handle uncertainties and mitigate potential risks. \\ \cline{2-3}  & - Applying machine learning and data-driven approaches to improve uncertainty and risk management in routing. \\ \cline{2-3}  & - Developing efficient and scalable algorithms for large-scale routing problems, including exploring parallel and \\ \cline{2-3}  & - Investigating the utilization of alternative metaheuristic algorithms and hybrid approaches to improve scalability. \\ \cline{2-3}  & - Exploring approximation algorithms and heuristics tailored specifically for large-scale routing problems. \\ \hline \multirow{4}{*}{Enhancing energy efficiency and sustainability in routing [98]} & - Incorporating vehicle-specific energy models into the routing optimization process. \\ \cline{2-3}  & - Integrating renewable energy sources and charging infrastructure planning into the routing optimization. \\ \cline{2-3}  & - Considering environmental factors and emissions in the multi-objective routing optimization. \\ \cline{2-3}  & - Incorporating incentive mechanisms and policies to promote sustainable and energy-efficient routing decisions. \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Summary of future research directions in multi-objective message outing for EnFVs.
# Modelica-Based Energy Management of PEMFC Hybrid Power System of Vehicle
## 2 Mathematic Model of Hybrid Power System
### DC/DC Converter  
Since the voltage of the vehicle's DC bus is much higher than the PEMFC and lithium battery voltage, a DC/DC boost converter is used to stabilize and increase their output voltage. The ideal DC/DC converter has no power loss, but the operating insulated-gate bipolar transistor produces heat loss [36]. Therefore, the conversion efficiency of the DC/DC converter is defined as 98%. The relationship between the converter's input and output power can be expressed as:  
\[P_{DC,out}=P_{DC,in}\eta_{DC}=V_{bus}I_{DC,out} \tag{28}\]  
where \(P_{in}\) and \(P_{out}\) are, respectively, input and output power of the DC/DC converter, \(\eta_{DC}\) is conversion efficiency, \(V_{bus}\) is the reference voltage of DC bus, and \(I_{DC,out}\) is the outlet current of the DC/DC converter.
## 7 Discussion  
Surface charge accumulation on DC insulators has been extensively studied, yet experimental measurements remain challenging due to the need for high-voltage, high-current systems capable of sustaining coupled electrothermal fields over prolonged operation. Measuring transient charge distributions, particularly under polarity-reversal conditions, presents significant technical obstacles. As a result, short-term laboratory experiments to characterize transient surface charge distributions under electrothermal coupling are currently impractical. To address this, we experimentally measured the bulk and surface conductivity of insulators under conditions representative of practical engineering applications. These measurements enabled accurate simulations of surface charge accumulation and electric field distribution along the surface of DC GIL insulators under electrothermal coupling.  
To validate our simulation model, we referenced experimental data from [17], which investigated transient electric field distributions in a gas-solid insulation system under DC voltage, measuring surface potential distributions on a post insulator at 0 h, 2000 h, and 8000 h. Using the geometric model and parameters from [17], we computed the surface potential distributions of a cylindrical insulator at the same time intervals under a 15 kV DC voltage. The simulated results, compared with experimental data from [17], are presented in Figure 19, where M denotes the experimental data from [17] and S represents the simulation results. The close agreement between them supports the validity of our model. Future work will focus on developing advanced experimental measurement systems and refining measurement techniques to overcome current limitations and enable direct characterization of transient charge distributions under electrothermal coupling.  
Figure 18: Transient characterization of the electric field along the surface at a polarity reversal voltage: (**a**) convex surface; (**b**) concave surface.
## Materials and Methods
### Queue Length Estimation Algorithm Design
#### 2.2.3 Deep Learning to Compute the Queue Length Based on Traffic Image
If DNN gets queuing vehicles at the intersection, then it calculates the queue length as Equation (7):  
\[l_{2}(t-\tau)=\sum_{i\in k_{c}}L_{i_{c}}Q_{i_{c}}+(m_{\text{V}}-1)L_{0} \tag{7}\]  
where \(l_{2}\) is a function of time \(t\), representing the estimation of queue length from camera images. \(t\) is the time when the images are taken. \(\tau\) is the time used by DNN to process the traffic images. \(k_{c}\) is the total number of vehicle types. \(L_{i_{c}}\) is the length of this type of vehicle and \(Q_{i_{c}}\) is the number of type \(i_{c}\) vehicle in the image. \(L_{0}\) is the space between adjacent vehicles. \(m_{\text{v}}\) is the sum of all vehicles in the traffic image and can be calculated by accumulation of \(Q_{i_{c}}\).
## 3 Image-Enhancement-Related Algorithms
### Wavelet Denoising  
Wavelet denoising performs local denoising of signals or images by combining wavelet transform and thresholding. Wavelet denoising has the advantages of multi-scale adaptiveness, time-frequency localization, and non-stationary signal processing. Wavelet denoising can better preserve the detailed information of the signal or image while removing noise. Its flowchart is shown in Figure 6.  
The original signal is represented as a discrete wavelet transform (DWT):  
\[X=\sum_{i=0}^{N-1}a_{N,i}\phi_{N,i}(t)+\sum_{j=1}^{J}\sum_{k=0}^{2^{j-1}-1}d_{ j,k}\phi_{j,k}(t) \tag{4}\]  
where, \(a_{N,i}\) denotes the coefficient of the wavelet function \(\phi_{N,i}(t)\) at scale \(N\), \(d_{j,k}\) is the coefficient of the wavelet function \(\psi_{j,k}(t)\) at scale \(2^{-j}\), \(N\) denotes the lowest resolution level, and \(J\) is the total level of wavelet decomposition.  
The soft thresholding method is a thresholding technique commonly used in wavelet denoising to reduce the magnitude of the wavelet coefficients and thus reduce noise. It can effectively suppress noise while preserving the details of the signal. This method is based on thresholding the magnitude of the wavelet coefficients. Specifically, smaller wavelet coefficients are set to zero, thus suppressing the effects of noise. In the meantime, larger wavelet coefficients preserve the detailed features of the signal, so that the denoised signal is high-quality and high-definition.  
For wavelet coefficient \(d_{j,k}\), soft thresholding is expressed as follows:  
\[d^{\prime}{}_{j,k}=sign\Big{(}d_{j,k}\Big{)}\cdot max\Big{(}\Big{|}d_{j,k} \Big{|}-T,0\Big{)} \tag{5}\]  
where, \(d^{\prime}{}_{j,k}\) is the wavelet coefficients after soft thresholding, \(sign\Big{(}d_{j,k}\Big{)}\) is the sign function of \(d_{j,k}\), \(\Big{|}d_{j,k}\Big{|}\) is the absolute value of \(d_{j,k}\), and \(T\) is the soft threshold. The core of soft thresholding is that if the absolute value of \(d_{j,k}\) is less than the threshold \(T\), then it is set to zero. Otherwise, its original value is retained. The soft thresholding method helps to remove the low-amplitude noise components in the wavelet coefficients while retaining the larger-amplitude components of the signal, thus realizing signal denoising. Choosing the right threshold value \(T\) usually requires adjustments according to the specific applications and signal characteristics.  
Figure 6: Schematic diagram of wavelet denoising.  
Gradient Three-Interval Equalization Image Contrast Enhancement Algorithm Combined with Improved Adaptive Contrast Enhancement  
In order to enhance the contrast of X-ray images in melting and solidification experiments of high-temperature materials, this paper proposes the GTIE-IACE. This algorithm aims to improve overall image contrast and enhance local detail definition. The flowchart of the GTIE-IACE algorithm is shown in Figure 7.
# Research on Monitoring and Early Warning of the Mine Backfill System Based on Blockchain Technology
## 5 Discussion  
Aiming at the problems of incomplete early warning measures and low safety performance of database central management mode in the mine filling system, this paper applies blockchain technology and deep learning to the mine filling process and proposes an overlimit early warning system of mine filling equipment based on the blockchain, which achieves the functions of overlimit early warning, data storage, and retrieval of equipment operation. System experimentation and analysis evaluation show that the block generated by combining the IPFS and the blockchain to store monitoring data is reduced by more than 72% when compared to the original ordinary block, which greatly optimizes the storage space. Transfer learning is applied to the overlimit early warning model, and the correlation index of the predicted operation monitoring data reaches 0.98 in the mine filling system, which can predict the overlimit situation of different equipment more accurately and take measures to prolong the operational life of system equipment in advance.  
However, there are still limitations in the current research. It is difficult to obtain the operation data of each item of equipment in the actual mine filling system. This paper only verifies the monitoring and early warning effects of the equipment in the mine filling simulation system. The environment faced by the actual filling system equipment operation is relatively harsh, and the mutation probability of the equipment operation data is large, so it cannot be proved that the proposed model is also applicable to the actual system. The performance of the blockchain and IPFS combined storage has only been verified theoretically and experimentally, without a simulation test of the whole process of storage and retrieval. It is impossible to verify the operating performance and energy consumption analysis more intuitively during storage and retrieval.  
Future research will be based on the actual mine filling process, combined with the monitoring data of actual equipment operation, to optimize the overlimit early warning model and find a better method to achieve the prediction of overlimit data. The data management model will be based on the experimental test of operation performance and energy consumption analysis in the process of storage and retrieval to further demonstrate the scheme proposed in this paper. Additionally, the consensus algorithm applied in the alliance chain is optimized to improve the performance of data transmission and retrieval.
# Telecommunication Technologies for Smart Grid Projects with Focus on Smart Metering Applications
## 4 Smart Metering Applications--Smart Grid Projects
### Smart Metering Application Projects  
#### 4.1.1 InovGrid  
A project that involves the implementation of smart meters is the InovGrid, realised in Portugal, during which, 50,000 smart meters have been installed in Evora and other 100,000 in seven other regions. The majority of the smart meters work with the PLC technology; in particular the PLC DCSK is used. At a minor extent, the PLC PRIME and the RF mesh are used, as well as the GPRS technology. The project aims at providing with equipment that will contribute to the control and management of the distribution grid at any instant [74].
# A Method of Defect Depth Recognition in Active Infrared Thermography Based on GRU Networks
## 3 Proposed Method for Defect Depth Recognition
### Defect Depth Recognition Based on GRU  
The GRU model is originally from the RNN model, which was designed to specifically process sequence information, such as thermal sequence [23]. The raw temperature datasets processed by PCA were set as principal component datasets; these latter datasets were then fed into the GRU model. The aim here is for the learned GRU model to be able to determine whether the data come from a defective area or a sound area after a period of training.  
Compared to long short-term memory (LSTM) networks, the GRU model contains two gate structures, i.e., reset gate and update gate, for the model's merger of the input gate, the forget gate and the output gate in an LSTM network [24]. The structure of GRU network is depicted in Figure 4, where \(x_{t}\) denotes the input data at current time, \(h_{t}\) represents the hidden state of GRU unit at the current moment, and \(h_{t-1}\) is the previous hidden state.  
contains 4 nodes, and the _softmax_ activation function is applied to recognize whether the sample comes from a defect area or sound area.