esa-sceva/satcom-chunk-collection huggingface dataset:
# Residual Life Prediction of SA-CNN-BILSTM Aero-Engine Based on a Multichannel Hybrid Network
###### Abstract
However, these methods cannot adequately extract effective information from sensor monitoring data. Thus, there is still room for improvement in prediction accuracy. In this paper, to address the above problems, based on multichannel networks, a multichannel SA-CNN-BiLSTM RUL prediction method is proposed. This method targets one-dimensional time-series data from a single sensor, utilizes a 1D-CNN to mine the spatial features of the data, uses BILSTM to mine the temporal information of the data, and focuses on the useful information by using the self-attention mechanism, with each sensor monitoring parameter channel modeled individually to achieve parallel processing of different sensor data. For instance, regarding the vibration sensor data of an engine, a 1D convolutional neural network (1D-CNN) is capable of promptly capturing spatial characteristics, such as the amplitude and frequency of vibration signals within distinct time periods. Meanwhile, a bidirectional long short-term memory (BiLSTM) network analyzes the evolutionary trend of vibration features based on the time series. Moreover, the self-attention mechanism further concentrates on the crucial time periods and features that are tightly associated with the remaining life of the engine. This approach is expected to overcome the deficiencies of traditional methods and enhance prediction accuracy. Specifically, the self-attention mechanism places greater emphasis on the key periods and features relevant to the remaining life of the engine, with the aim of remedying the shortcomings of conventional approaches and improving the precision of predictions. This paper initially elaborates on the architecture design of the multichannel SA-CNN-BiLSTM model, encompassing the specific functions and parameter settings of each layer within the network. It then describes in depth how the spatial and temporal character istics of diverse sensor data can be comprehensively integrated. Subsequently, through experimental comparisons, real aero-engine monitoring data are employed to verify the superiority of the proposed model over traditional methods in terms of prediction accuracy, stability, and other aspects. Finally, the potential applications of this model, as well as the directions for future improvement, are summarized, offering novel ideas and approaches for the field of aero-engine residual life prediction. This, in turn, will contribute to enhanced aircraft flight safety and improved airline operational efficiency.
# Comparing Monitoring Networks to Assess Urban Heat Islands in Smart Cities
## 3 Choice of System Components and Programming
### Considerations of the WMO
#### 3.1.2 Centralized System for Processing the Data from the Networks  
The data collected by the AWS can be processed locally, in the AWS itself, or in a central processor of the network. In a complex meteorological network, the centralized processing system receives the data that the AWSs transmit via various means; it acquiresthem, decodes them, supervises their quality, processes and stores them, and finally transfers them, permitting their visualization. Furthermore, it can remotely control the AWS. Powerful computers integrated into the local networks are used to distribute the tasks efficiently.  
Centralized processing facilitates the transmission and management of the data through a central system that optimizes such tasks as complex calculations and codification, thus alleviating the load on the AWS; however, such a specification (functional and technical) requires multidisciplinary collaboration.
# On the Fault Detection and Diagnosis of Railway Switch and Crossing Systems: An Overview
## 3 FDD of Railway S&C Systems
### Model-Based FDD Methods for Railway S&C Systems  
A state-of-the-art review of the existing model-based FDD algorithms for railway S&C systems is presented in this subsection. Model-based FDD methods are based on constructing a mathematical model of the system--a reference model--that will be used for generating residuals \(r(k)\), as can be seen in Figure 10. These residuals are the difference between the real system measurements \(y(k)\) and the reference model output (the estimated measurements) \(\hat{y}(k)\), which represents the healthy state of the system itself. A deviation in the residuals is used to detect and diagnose different faults. Model-based FDD methods require both the real system input signal \(u(k)\) and the real system output signal \(y(k)\).  
As can be seen in Figure 10, faults can be in the S&C system actuators, sensors, or in the process itself, which represents the movable/non-movable components in the S&C system other than the actuators and sensors. By definition, a fault is an unpermitted deviation of at least one characteristic property of the system from the acceptable, usual, standard condition [56]. Literature reviews of the most common FDD techniques [57; 58; 59] indicate that if the dynamics of the system can be mathematically represented--with acceptable accuracy--model-based FDD methods can provide accurate and timely diagnostic information.  
Therefore, modeling the whole railway S&C system or part of it is the first step to achieving an accurate model-based FDD method that can monitor railway S&C system health conditions. A large number of published papers and research works report modeling of the S&C system as a whole [60; 61; 62; 63; 64], or of only a part (subsystem) of it [7; 65; 66; 67], in addition to the several research works done related to S&C fault degradation modeling [9; 68; 69; 70; 71; 72; 73], which shows good prospects for researchers in this field to develop and apply a model-based FDD technique for railway S&C systems. Thus, several, if not a dozen, papers that apply a model-based FDD method to railway S&C systems should have been published. However, only a few papers (they can actually be counted on both hands) can be found from the last decade; these applied a model-based FDD method to only detect (i.e., FD methods), to detect and diagnose (i.e., FDD methods), and/or to prognose (i.e., FP methods) a few possible fault scenarios of S&C systems to maintain their functionality, safety, and reliability.
# Internet of Things Botnet Detection Approaches: Analysis and Recommendations for Future Research
## 1 Introduction
### Research Motivation  
The IoT botnet threat is an issue facing Internet of Things (IoT) that demands efficient defense and response methods and techniques. Different approaches and technologies could provide enhancements in the detection of IoT botnets and improve the overall security of the IoT ecosystem. By probing the recent literature on IoT botnet detection, it became clear to us that there is a lack of in-depth study on solutions to IoT botnet detection, and a lack of systematization for such solutions. Thus, the research is quite undeveloped and has much potential.  
There are still some reviews and surveys related to IoT botnet detection. [PERSON] et al. [3] thoroughly surveyed IoT botnet detection solutions that applied Domain Name Space detection. Their research offers a novel classification framework for botnet detection techniques dependent on DNS and provides a detailed overview of each technique. [PERSON] et al. [4] surveyed the current methods of deep learning and forensic mechanisms for botnets in IoT, and discussed their issues. Furthermore, the researchers explored the use of deep learning algorithms in network forensics. Prospective directions of research have also been highlighted. [PERSON] et al. [5] investigated the use of machine learning in IoT botnet anomaly detection. The researchers considered the feasibility of using autoencoder algorithms for detection, and suggested future research directions for the use of machine learning algorithms in this area. Finally, [PERSON] et al. [6] presented the research most closely related to this study. They provided a demographic review on IoT-based botnets and classified the approaches into avoidance and detection, and provided recommendations for investigation into avoidance approaches.  
The formation of a botnet has several phases, and accordingly, the detection techniques differ based on the phases that are targeted. Each phase may express different activities; thus, a detailed analysis of the detection tactics in each phase is required. However, hitherto there was no comprehensive and thorough review of IoT botnet detection with botnetphases taken into account. Hence, we found the need for a thorough analysis focusing on the phases of IoT botnets in which detection is performed and the different types of attacks.
# Secure Convolution Neural Network Inference Based on Homomorphic Encryption
## 1 Introduction
### Our Contribution  
In this paper, we will use homomorphic encryption technology to protect user privacy in the inference stage, which is also the key stage of protecting user privacy information, in order to solve the privacy leakage problem of users using cloud providers. Users can use the technology in this paper to encrypt and upload personal information to the cloud, and the cloud uses a pre-trained model to infer the ciphertext. During the inference process, the cloud cannot obtain any information about the user. Through this scheme, user privacy is guaranteed. There are some schemes that use homomorphic encryption combined with a neural network reasoning stage, but we found that there is still room for improvement in combining the network structure with the ciphertext structure in these schemes, which will effectively reduce the complexity of ciphertext transmission in different layers of the model. To solve this problem, we adopt a more reasonable ciphertext allocation for the weight matrix. In the batch processing part, we make full use of the ciphertext slots, which reduces memory usage and computational complexity, thereby improving inference efficiency. In addition, we found that the parameter selection of the homomorphic encryption scheme will change the original image to a certain extent, and at the end of the inference stage, it will have a certain impact on the accuracy rate. In order to deal with this problem, we provide a new perspective, to enhance the robustness of the model by adding corresponding noise during the training phase to adapt the model to slightly \"blurred\" images. In this way, the influence of encryption scheme noise is reduced in the inference stage. At last, we use the CKKS fully homomorphic encryption scheme that supports floating-point operations, which can avoid the process of encoding floating-point numbers into integers in other schemes. Our system architecture is illustrated in Figure 1.
## 2 Materials and Methods
### Methodology
#### 2.2.1 Characterisation of Nanopowder and Nanofluid Stability  
Firstly, Transmission Electron Microscopy (TEM) was employed for the analysis of the morphological nature and the apparent size distribution of the A-TiO\({}_{2}\) nanosized particles on a JEOL JEM 1010 microscope (JEOL Ltd., Tokyo, Japan) operating at an accelerator voltage of 100 kV. For these measurements, a dispersion of A-TiO\({}_{2}\) in analytical grade 1-butanol was elaborated and subsequently deposited onto a 400-mesh copper grid, which was coated with a Formvar film and a conductive carbon layer.  
X-ray diffraction (XRD) tests were conducted to study the phase and purity of titania nanoparticles using a Malvern diffractometer, model XPERT-PRO (Malvern Panalytical Ltd., Malvern, UK), that operates at a current of 30 mA and 40 kV voltage and employs Cu K\(\alpha\) monochromatic radiation (\(\lambda\) = 1.5406 A). Scherrer equation [22] was used to determine the crystalline domain size, \(D\), of A-TiO\({}_{2}\):  
\[D=\frac{K\,\lambda}{\beta\,\cos\theta} \tag{1}\]  
where \(\lambda\) represents the X-ray wavelength, \(K\) represents the Scherrer constant--commonly taken to be 0.9 in the case of spherical crystals, \(\beta\) denotes the diffraction peak width measured at half its maximum height, and \(\theta\) indicates the specific peak diffraction angle.  
The stability of the A-TiO\({}_{2}\)/PG:W 30:70 nanofluids was examined using the Dynamic Light Scattering (DLS) method with a Malvern Zetasizer Nano ZS (Malvern Panalytical Ltd., Malvern, UK) at a 173\({}^{\circ}\) scattering angle. Specimens containing various surfactants at several surfactant-to-nanoadditive mass ratios were stored in standard cuvettes for 25 days to observe changes in their size distribution over time under both static and shaken conditions. The results were used to determine the ideal surfactant and the optimal mass ratio of surfactant to nanoadditive.
## 4 The Early Warning Model  
### Introduction to QPSO  
PSO is a meta-heuristic method that can realize the global optimization of multi-extremum functions. The particles in the population search for the global optimum of the function via cooperation and competition and share or exchange the information they obtained in their respective search processes. PSO has the following shortcomings: (1) when the number of iterations tends to infinity, the algorithm cannot converge to the global optimum with probability 1, i.e., it does not have global convergence; (2) the speed of a single particle has an upper limit, and its search space is limited, which cannot cover the whole feasible region.  
Aiming at the deficiency of PSO, [PERSON] et al. [21] proposed QPSO from the perspective of quantum mechanics, which holds that every particle in the population has quantum behavior in \(\delta\) potential wells. Thus, the particle has an uncertain search trajectory, and the algorithm has global convergence.  
The iterative optimization expression of QPSO is as follows [21]:  
\[p_{i}(t)=\varphi(t)p_{\text{best},i}(t)+(1-\varphi(t))g_{\text{best}}(t), \tag{13}\]  
where \(t\) is the number of iterations, \(p_{i}(t)\) represents the current particle position, \(\varphi(t)\) is a random number that obeys a uniform distribution in (0,1), \(p_{\text{best},i}(t)\) represents the self-optimal position in the \(i\)-th particle search process, and \(g_{\text{best}}(t)\) is the population optimal position in all particle search processes.  
\[s_{i}(t+1)=p_{i}(t)\pm\alpha(t)|s_{i}(t)-C(t)|\text{In}(1/\beta(t)), \tag{14}\]  
where \(\alpha(t)\) is a compression-expansion factor, which can be used to adjust the influences of \"self experience\" and \"group experience\" on the particle position in the next iteration. To ensure the global convergence of QPSO, \(\alpha(t)\) must be less than 1.781. Moreover, \(\beta(t)\) is a random number that obeys a uniform distribution in (0,1), and the sign before \(\alpha(t)\) is determined by \(\beta(t)\). When \(\beta(t)\leq 0.5\), the value of \(\alpha(t)\) is positive; otherwise, it is negative. Finally, \(D(t)\) is the average of the self-optimal positions in the searching process, and is defined as follows [21].  
\[D(t)=\frac{1}{N}\sum_{i=1}^{n}p_{\text{best},i}(t). \tag{15}\]  
The updating method of \(p_{\text{best},i}(t)\) and \(g_{\text{best}}(t)\) in QPSO is the same as that in PSO.
# An Adaptive Fast Incremental Smoothing Approach to INS/GPS/VO Factor Graph Inference
## 2 Materials and Methods
### Factor Graph Model of INS/GPS/VO Integrated Navigation System
#### 2.1.4 VO Factor  
When the true location of the target, as estimated by the stereo vision system, remains unknown, the vision system is limited to acquiring solely the relative position between the carrier and the target at the current time. Direct recovery of the carrier's positioning information is not possible. VO offers an indirect method to ascertain the relative relationship between the carrier's position state at two consecutive moments. This is achieved by utilizing the target as an intermediary reference position to estimate the relative position  
Figure 3: Factor graph model including factors and nodes at different times. (**a**,**b**) Factor graphs that include GPS, prior, equivalent IMU, and bias factors at time \(t_{1}\) and \(t_{2}\); (**c**) Factor graphs with VO, prior, equivalent IMU, and bias factors; (**d**) Factor graphs with GPS, VO, prior, IMU, and bias factors. Which is equivalent to the factor graph represented in (**c**), utilizing conventional IMU factors. Assuming that GPS and VO measurements are acquired every 50 IMU measurements. Additionally, bias nodes are incorporated at the same frequency as depicted in (**c**).  
concerning the same target at both adjacent moments. It is evident that the measurement equation established by the visual system is intricately connected to the temporal states during imaging. Despite the inability of this measurement equation to satisfy the prerequisites of the KF, it equivalently introduces a factor node into the system factor graph model within the factor-graph-based state estimation algorithm.  
The VO measurement equation is given by  
\[z^{VO}=h^{VO}(x_{k},x_{k+1})+n^{VO} \tag{10}\]  
where \(n^{VO}\) is the measurement noise, \(h^{VO}(x_{k},x_{k+1})\) is the measurement function, \(z^{VO}\) is the measurement, \(x_{k}\) and \(x_{k+1}\) represent the state of VO at the associated time, respectively. The VO factor equation is a binary factor defined as  
\[f^{VO}(x_{k},x_{k+1})=d\Big{(}z^{VO}-h^{VO}(x_{k},x_{k+1})\Big{)} \tag{11}\]  
Figure 3c illustrates the VO factor node added into the INS factor graph model.
## 2 Configuration of the Proposed Wireless Battery Charging System for a Quadcopter  
Figure 1 shows the proposed wireless battery charging system for a quadcopter. The system is composed of the following three parts:  
1. Wireless power transmitter, comprising transmitting coil array which can move in four directions.
2. Wireless power receiver, a receiving coil, and battery charger.
3. Control unit which can measure the terminal voltage of each transmitting coil, and align the centroid of the transmitting and receiving coil using the hill climbing algorithm.  
The proposed wireless battery charging system consists of multiple transmitting coils which can be moved in four directions (positive \(X\)-direction, negative \(X\)-direction, positive \(Y\)-direction, and negative \(Y\)-direction). The use of multiple coils can potentially allow the system to efficiently adapt to magnetic field propagation conditions, similar to the way multiple antennas are used to adapt to channel conditions in wireless communication systems [38]. Also, multiple transmitting coils decrease the time for coil alignment, providing a chance for the design of big charging stations for large drones, where coil size and design are limited due to power transmission characteristics. In this proposed system, if a drone with a receiving coil lands at any position on the multiple transmitting coils, the voltages across the transmitting coils decrease depending on the coupling of each transmitting coil with the receiver coil, and the controller knows where the drone lands by observing the change in terminal voltages of the multiple coils. When the power is transferred from the transmitting coil to the receiving coil, the terminal voltage of the transmitting coil tends to decrease due to the phenomenon called signal backscattering. By observing the backscattered signal from each transmitting coil, the controller automatically knows which transmitting coil is nearest to the receiving coil. If the controller pinpoints the nearest transmitting coil, the multiple transmitting coil array is moved to automatically align the centroid of the detected transmitting coil with that of the receiving coil using the hill climbing algorithm. The practical system used to implement the proposed system comprises a receiver circuit for battery recharging and a power transmission station.
## 5 Conclusions  
A phase conjugate system based on off-axis digital holography was designed. Based on this system, we achieved optical focusing of the light which passes through a scattering medium. The experimental results show that the phase conjugate system has good phase reduction performance, and even when the object light changes, the phase conjugate reduction also performs well. In addition, the influence of image definition as well as phase noise to time-reversed light was tested. The experiments show that the system has a certain fault tolerance. The optical phase conjugate focused on transmitting the scattering medium was successfully realized even though the diffuser was added to the system. The off-axis digital holographic phase conjugate system obtains the phase of the object light field only by acquiring one interferogram, which greatly improves the efficiency of the phase conjugate system compared with the four-step phase-shifting method. This work also promotes the practical uses of the digital optical phase conjugate system. However, since the off-axis digital holography depends on the angular difference between the object light and reference light, the range of the phase recovery point still has some limitations because of the limitation of the camera resolution. In addition, there will inevitably be some distortions in the imaging between the SLM and CMOS camera. Studying and correcting the distortion helps to further improve the reduction capability of the digital phase conjugate system.  
**Author Contributions:** [PERSON] came up with this idea. [PERSON] and [PERSON] designed the experiment. [PERSON], [PERSON], [PERSON] ([PERSON]), [PERSON], and [PERSON] ([PERSON]) performed the experiment. [PERSON] and [PERSON] analyzed the data. [PERSON] and [PERSON] wrote the original manuscript. [PERSON], [PERSON] ([PERSON]), and [PERSON] did the review and editing. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Projects from Tianjin University of Technology and Education, grant number KJ15-24; the Natural Science Foundation of Tianjin, grant number 18 JCYBJC17100; the Natural Science Foundation of Tianjin, grant number ZZSK021917.  
**Conflicts of Interest:** The authors declare no conflicts of interest.  
Figure 8: **(a) Speckle interferogram; (b) phase conjugation pattern; (c) charged couple device’s (CCD) signal with the phase of the SLM set to 0; (d) DOPC reconstrued signal with loading conjugation phase.**
# Unlocking the Potential: How Flying Taxis Will Shape the Future of Transportation
## 3 Customer Need to Use Flying Taxis
### Flying Taxis Benefit People Who Need to Travel for Business Purposes
#### Flying Taxis Benefit People Need to Travel During Rush Hour or Peak Travel Times  
Flying taxis are expected to benefit people who need to travel during rush hour or peak travel times by reducing travel times and avoiding ground-level congestion. With the ability to fly over traffic, passengers can avoid the long wait times associated with ground transportation during peak hours. This can save passengers a significant amount of time and reduce stress associated with travel delays. Additionally, flying taxis can offer a more direct route to a destination, further reducing travel times [171].