esa-sceva/satcom-chunk-collection huggingface dataset:
# Sensor Network Environments: A Review of the Attacks and Trust Management Models for Securing Them
## 6 Trust Model and Calculation Techniques
### Machine Learning Classification  
Machine learning techniques can be used to classify nodes' trust levels based on the measured trust parameters. Any type of machine learning classification technique can be used to classify trust. Choosing the right one for the application depends on the typical factors, such as access to training data, the type of data, speed, computational overhead, accuracy, etc. Machine learning models offer very good classification results; however, they come with the requirement for significant computing resources, making them unsuitable for many network types. Networks with central nodes, such as base stations, with access to the required resources, are ideal candidates for implementing a machine learning model.  
An interesting approach is taken in [23], where an unsupervised learning method, k-means clustering, is used to label a training data set. The reason for choosing this algorithm is its light computational overhead and speed. Then, a support vector machine is trained based on this labeled data set. The SVM is well suited to the sparse data set and is light computationally when compared to other supervised learning models. This process of training a model is carried out by a master cluster head node and can be updated with new data from the previous time window periodically. The trained model is then sent to cluster members, which can then use it to classify the nodes they communicate with as trustworthy or not, based on the trust parameters that they measure.  
Random forest regression is used in [49] for classifying objective and device trust. The training data set is generated through simulations of the network and the labeling of the data is based on the average values of the simulated features.  
Five types of trust evidence are split into three fuzzy levels, which are then classified using the C4.5 decision tree algorithm in [28].  
A deep neural network is used in [15] to classify temperature sensor data based on its temporal correlation. The coefficients of the sensor data discrete cosine transformation are used as inputs to the networks. Two networks are used, one with a day as the time window for the sensor data, and one which further divides the day into smaller time windows. These two predictions are then combined using DS theory. The data set for training this model is generated through a six-month trial deployment of nine temperature sensors, and attack data is also added through six simulated sensors.  
A genetic algorithm is used in [27] to optimize the trust model for detecting dishonest recommendations. The artificial bee colony (ABC) model is used and applied for detecting and isolating dishonest nodes.
## 3 Data and Methods
### Overview of Machine Learning Models
#### 3.5.2 Support Vector Machine  
Support Vector Machine (SVM) is a machine learning method based on the principle of structural risk minimization, offering a superior generalization ability compared to other conventional methods [75]. SVM possesses several unique advantages in addressing nonlinear, high-dimensional pattern recognition problems. The algorithm supports four types of kernel functions: linear, polynomial, sigmoid, and radial basis function (RBF). The literature indicates that, among these kernels, the RBF kernel provides better performancein predicting hazard susceptibility [76]. The decision function of the RBF-SVM is presented in Equation (2).  
\[f(x)=sign\left(\sum_{i=1}^{n}a_{i}y_{i}\exp(-\frac{\|x_{i}-x\|}{2\sigma^{2}})+b\right) \tag{2}\]  
In the equation, \(x_{i}\) is the influence factor vector of the training sample; \(y_{i}\) is the class label of the sample; \(a_{i}\) is the Lagrange multiplier; \(x\) is the influence factor vector of the sample to be predicted; \(\sigma\) controls the decay of the RBF kernel function, with a larger value leading to the faster decay and lower dimensionality of the feature mapping; and b is the intercept.
# Secure PBFT Consensus-Based Lightweight Blockchain for Healthcare Application
## 4 Experimental Results  
The proposed secure PBFT consensus algorithm was tested in a simulated environment, implemented using a Python programming language in a Ubuntu 16.04 operating system with configuration Intel Core i7 2.2 GHz CPU with 16 GB RAM and 1 TB hard disk.  
Figure 4: Flowchart of the proposed algorithm.  
Figure 5: Architecture of the identified healthcare use-case to demonstrate the proposed model.  
Simulation was carried in a local machine by considering a maximum of 35 nodes, initial global reputation of all the nodes as \(1/nodes\), and numerical data (assumed as health data related to a patient) required for the experiment purpose are generated manually and are fed into the system as transactions. This was performed to analyze the proposed algorithm to measure the security, reliability, and performance aspects considering parameters such as transaction throughput, latency, faulty node ratio, and communication between the nodes.
# Mixed-Degree Cubature \(H_{\infty}\) Information Filter-Based Visual-Inertial Odometry
## 3 Mixed-Degree Cubature \(H_{\infty}\) Information Filter-Based VIO  
The above keyframe management method and measurement model of intensity led to a simple frame-to-frame alignment-based egomotion estimator. However, the simple estimator with linearization-based solvers such as the Gaussian-Newton optimization and EKF performed poorly as the models were highly nonlinear and the measurement noises vary dramatically. The linear approximations and Gaussian assumptions used in the solvers were not valid practically. For a robust estimator, the previous studies on VO generally maintained a slide window of multiple keyframes or a local map. In this section, we present a nonlinear filter-based VIO method for the frame-to-frame alignment problem. The designed hybrid cubature \(H_{\infty}\) information filter used two cubature rules with different degrees to reduce the linearization error in a numerically stable way and the \(H_{\infty}\) filter to estimate the states in the presence of non-Gaussian noises.
## 4 Results and Discussion
### Comparison between Observed and Lunar Irradiance Model Predicted Radiances
This result is slightly better than but consistent with what we found earlier in this section. Another observation from the ratio plot is that the spread in the ratio values appear to become larger with increasing phase angles (away from the peak radiance) due to lower radiances at high lunar phase angles as expected. Finally, we also compared sample outputs from the GIRO and MT2009 models for the November-December 2018 lunar cycle. Figure 10 shows the ratio between the two model generated irradiances (ratio=MT2009/GIRO) for this time period. The figure shows that predictions from GIRO and MT2009 agree within \(\pm\)7% for a large range of lunar phase angles. However, differences grow in the waning phase up to \(\sim\)17% for lunar phase angles beyond 50 degrees. MT2009 underestimated the lunar irradiance near full moon while overestimated at lunar phase angles above 10 degrees in this particular study. This is expected given the intended application and accuracy estimates of the MT2009 as stated in [7].
# Towards Hybrid Energy-Efficient Power Management in Wireless Sensor Networks
## 2 Survey of Energy Optimization Approaches in WSNs
### System-Level Techniques
#### 2.5.4 Dynamic Power Management (DPM)  
Energy efficiency can be maximized by applying techniques. As energy is available, the system must also be able to adapt its consumption, if necessary by shifting the most energy-intensive tasks to more convenient periods, or even by switching the system to a less energy-intensive state when they are idle or at standby or partially used, while limiting the impact on performance [36].  
DPM to reduce the static voltage and DVFS to reduce the dynamic power have been applied together in [35; 37; 38]. Indeed, non-compliance with time constraints can affect the efficiency of the network. This solution would solve only partially the problem and would generate huge costs.
# Mechanical Faults Analysis in Switched Reluctance Motor
## 2 Materials and Methods
### Electric Motor Faults and Diagnostic Methods
#### 2.2.3 Eccentricity and Imbalance  
The two mechanical faults mentioned above were investigated in this study: dynamic eccentricity and imbalance. They were selected because they are some of the most frequent mechanical defects in electric motors, and it is possible to design appropriate tools for setting them.  
Eccentricity occurs in electric motors when there is a non-uniform air gap between the stator and the rotor. There are two types of this phenomenon: static eccentricity and dynamic eccentricity. In static eccentricity, the centerline of the shaft is at a constant offset from the center of the stator. In dynamic eccentricity, the centerline of the shaft is at a variable offset from the center of the stator (Figure 2). The causes of air-gap eccentricity might be improper mounting, a loose or missing bolt, misalignment, or rotor imbalance [19].  
Therefore, an eccentricity fault is the result of minor mechanical imperfections. The opposite can also be assumed--eccentricity generates other mechanical defects. Indeed, the intensification of this effect guides the physical contact between the stator and rotor and, consequently, damage to the motor. Thus, monitoring the early stage of this phenomenon is vital.  
In addition to the technical aspect, modifying the air gap's width contributes to the motor's magnetic field fluctuation. While at static eccentricity, it is constant in time, at dynamic eccentricity, the air gap varies periodically. Thus, a dynamic variation in the magnetic field affects the current and the torque values, which also vary in time.  
The second fault performed on the test stand was the imbalance. It occurs when the shaft's mass centerline does not coincide with its geometric centerline. There are three types of imbalance: static imbalance, couple imbalance, and dynamic imbalance (a combination of the first two). With static imbalance, only one force is involved. With a couple of imbalances, two equal forces (weights) are 180\({}^{\circ}\) from each other, causing the rotor to appear balanced at rest. In reality, almost all imbalances are dynamic. An imbalance can be caused by improper manufacture, an uneven debris build-up on the rotors/vanes/blades, or the addition of shaft fittings without an appropriate counterbalancing procedure [19].  
Figure 2: Eccentricity fault: (**a**) static (constant air-gap and angle \(\theta\)) and (**b**) dynamic (dynamically varying air-gap with rotor rotating within the variable shaft centerline offset indicated by the arrow).
# Multi-Scale Deep Neural Network Based on Dilated Convolution for Spacecraft Image Segmentation
## 3 Methods  
The SISnet network proposed in this paper is an end-to-end self-supervised learning network. The network adopts the structure of encoder+ attention+ decoder, which can effectively improve the segmentation accuracy of spacecraft objects. The network uses dilated convolution encoder to extract deep features. Through the channel attention mechanism, the network calibrates the feature information and innovatively pays attention to the calibration of the low-level features branch. Then, SISnet fuses features through a parallel-ASPP structure to enhance contextual information. Finally, the decoder of the network decodes the fused feature maps. The monocular images are input to the SISnet, which outputs spacecraft mask images.
# Uniform Linear Antenna Array Beamsteering Based on Phase-Locked Loops
## 6 Conclusions  
The principle operation of antenna arrays, in particular ULA, their theoretical foundation and understanding of beamforming, and then, scanning properties with hardware demonstration has been described by using concepts taught in undergraduate antenna theory. To promote a better understanding and visualisation of phased antenna arrays, a laboratory demonstrator involving off-the-shelf components that can be easily purchased and integrated are used in the experiments. This enables students to do some basic coding based on theory and realise the operation with a simple experimental demonstrator. In addition, the basic definitions of design parameters commonly associated with antenna arrays are described with simple mathematical equations to quantify them. The experimental hardware gives students the opportunity to explore the flexibility of the ULAs by varying other parameters of the array. The measured radiation patterns are in close agreement with the basic simulations carried out using MATLAB code. The reasons for the deviations are also discussed, such as mutual coupling, etc. The use of a conducting back-plate reflector for minimising back-radiation is also presented. By combining descriptive, analytical, and experimental teaching methods, the students can be motivated to explore further this antenna array field and reinforce their understanding of the principles of phased arrays and their use in communication and radars.  
Conceptualization V.F. Methodology V.F., U.N. and A.C.; software A.C. and A.M.; Hardware A.M.; Measurements A.M., A.C. and U.N.; formal analysis A.C.; writing--original draft preparation A.C.; writing--review and editing V.F. and A.M.; funding acquisition V.F. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Queens University of Belfast Studentship and in part by the U.K. Engineering and Physical Science Research Council (EPSRC) under Grant EP/P000673/1: Low-complexity processing for mm-Wave massive MIMO and Grant EP/N020391/1: Informed RF for 5G and Beyond.  
A repository of design files and source code is available in the cited references.  
The authors would also like to thank [PERSON] for fabrication and testing of the MMCA.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### SYNTHPTICH Dataset  
The SYNTHPTICH dataset was purposefully built for the scope of the analyses presented in this work, i.e., for testing pitch detection algorithms on arbitrarily complex signals in terms of fundamental frequency intelligibility, and to evaluate their computational complexity/speed.  
All signals were sampled at 96 ksps and represented in floating points. A typical audio setup was reproduced, so a 20 kHz low-pass filter was applied. Twelve different categories are present, with each one encompassing 99 signals with increasing fundamental frequency, starting from 100 Hz up to 5000 Hz with a 50 Hz step size.  
The categories were built with increasing pitch complexity, the simplest one being pure sine waves, to which multiple artifacts have been applied to generate sounds of increasing complexity. There are also two categories encompassing square waves; the amplitude of each starting wave is normalized to have a peak of 1. Table 1 details the characteristics of each category and its name, as well as the number of artifacts applied, with the following macroscopic characteristics:  
* Harmonics: Addition of a number of harmonic frequencies, i.e., integer multiples of F0. The amplitude of each harmonic is a random number between 0 and 1, sampled from the Gaussian distribution, and eventually re-scaled if more/less amplitude is needed;
* Partials: Addition of non-integer, random multiples of F0, with random starting amplitude linearly scaled according to the order, and random phase. The following formula explains the construction of an i-th order partial, with \(A_{i}\), \(f_{i}\) and \(\varphi_{i}\) being random amplitude (scaled according to the order), random frequency obtained by multiplying F0 by a random number between 0 and 1 (scaled according to the order) and random phase. All random quantities are obtained by sampling a Gaussian distribution with max = 1, and the formula is as follows: \[Partial_{i}(t)=\frac{A_{i}}{\mathrm{i}}\cdot\sin(2\pi if_{i}t+\varphi_{i});\] (1)
* White Gaussian Noise: Addition of white Gaussian noise of a given SNR, after measuring the power of the signal with added harmonics/partials;
* Reverb: Addition of a reverberated copy of the signal with amplitude equal to 0.1 of the measured amplitude of the starting signal.  
Figure 1 details some examples of signals found on the SYNTHPITCH dataset; notice how, with complex/dirty signals such as those present in the \"full1\" category, pitch and sinusoidal behavior become very hard to infer. The dataset is free to use for the public.
# Wireless Energy Harvesting with Cooperative Relaying under the Best Relay Selection Scheme
## 3 System Model
### Cooperative Energy Harvesting  
In the case of cooperative energy harvesting, the received signal at the relay \(i\) can be written as:  
\[y_{si}=h_{si}\sqrt{\frac{P_{s}}{d_{1}^{\,n}}}s+n_{si}, \tag{1}\]  
where \(n_{si}\sim CN(0,\sigma_{si}^{2})\) is AWGN with noise variance, \(\sigma_{Si}^{2}\). Assuming power splitting ratio scheme, \(\sqrt{\rho}y_{si}\) is used for energy harvesting to relay \(i\), where the remaining \(\sqrt{1-\rho}y_{si}\) is used for information detection. The procedure in [43] is adopted to compute the optimized values of \(\rho\). The harvested energy at relay \(i\), at time \(\dfrac{T}{2}\) can be written as  
\[ER^{i}=\eta\rho\left[\frac{P_{s}}{d_{1}^{\,n}}\left|h_{si}\right|^{2}\right]. T/2, \tag{2}\]  
Figure 2: Downlink energy harvesting (EH) Cooperative Relaying Model with best relay selection (BRS) scheme.  
where \(\eta\) is the energy conversion efficiency, that depends on EH circuitry from signal power to circuit power. In this work, the energy conversion efficiency is set to 0.6, based on the assumption that the energy harvested from the received signal is not fully consumed by the relay. After power splitting, at the input of the energy harvester, the received baseband signal with information receiver noise can be written as:  
\[{R_{input}}^{i}=\sqrt{1-\rho}\left[{{h_{si}}\sqrt{\frac{{{P_{s}}}}{{{d_{1}}^{n} }}}s+{n_{si}}}\right]+{\hat{n}_{si}}, \tag{3}\]  
where \({\hat{n}_{si}}\sim{CN(0,{\hat{\
u}_{si}})}\) is AWGN from the information receiver with noise variance, \({\hat{\
u}_{si}^{2}}\). In the second time slot, the transmitted power of the relay \(i\) is given by  
\[{P_{R}^{i}}=\frac{{ER^{i}}}{{T/2}}=\eta\rho\left[{\frac{{{P_{s}}}}{{{d_{1}}^{n} }}\left|  
\[M_{\gamma_{T}}=M_{\gamma_{sd}}(s)M_{\gamma_{brs}}(s), \tag{11}\]  
For Rayleigh fading channel, \(M_{sd}(s)=(1+s\gamma_{sd})^{-1}\), while, MGF for \(\gamma_{brs}\) in the case of BRS scheme for \(N\) relays can be expressed as [46]:  
\[M_{\gamma_{brs}}\left(s\right)=\sum_{i=0}^{N-1}\frac{N\left(-1\right)^{i}{ \left(N-1\right)\choose i}}{1+i+s\gamma_{brs}/2}, \tag{12}\]  
In the case of cooperative EH, MGF can be written as:  
\[M_{\gamma_{T}}\left(s\right)=\left(1+\gamma_{sd}s\right)^{-1}\sum_{i=0}^{N-1} \frac{N\left(-1\right)^{i}{\left(N-1\right)\choose i}}{1+i+s\gamma_{brs}/2}, \tag{13}\]  
using a partial fraction and taking Inverse Laplace transform with some algebraic manipulation, the PDF of \(\gamma_{T}\) can be expressed as:  
\[p_{\gamma_{T}}\left(\gamma\right) = N\sum_{i=0}^{N-1}\left(-1\right)^{i}{N-1\choose i}\left[\frac{ A_{i}}{\gamma_{sd}}\exp\left(-\frac{\gamma}{\gamma_{sd}}\right)+\frac{2B_{i}}{ \gamma_{brs}}\exp\left(-\frac{2\left(1+i\right)\gamma}{\gamma_{brs}}\right) \right], \tag{14}\]  
where \(A_{i}=1/\left(1+i-\gamma_{brs}/\left(2\gamma_{sd}\right)\right)\), \(B_{i}=1/\left(1-\left(1+i\right)2\gamma_{sd}/\gamma_{brs}\right)\).
## 1 Introduction
The hydrogenation process of plant oils has been used for a long time to produce structured lipids. Nonetheless, hydrogenation is considered the cause of the formation of trans isomers of fatty acids, which indicate harmful effects on human health [22]. For this reason, food manufacturers tend to replace partially hydrogenated oil products with structured lipids obtained through an intereesterification (IE) process. The IE reaction occurs in two stages: initial hydrolysis and a subsequent esterification reaction [23]. The IE process can be conducted by using two pathways such as chemical interestricification (CIE) and enzymatic intereesterification (EIE). Despite the fact that CIE is commonly applied in the food industry, EIE has received increasing attention as an emerging technique to produce structured lipids with desired technological characteristics. The EIE technique has several advantages, contrary to the CIE process. EIE is investigated as a simple and efficient bioprocess that does not involve the use of hazardous chemicals or the formation of trans fatty acids at a high level. What is more, EIE can be conducted with milder reaction conditions in comparison to CIE. During the EIE process, the thermal degradation of plant oil bioactive compounds is inhibited; hence, the final products acquire a satisfactory nutritional quality and period of shelf life [24; 25]. Taking the above into consideration, the main goal of the present study was to assess the quality and oxidative stability of structured lipids via the enzymatic intereesterification of tomato seeds oil and coconut oil mixtures catalyzed by immobilized commercial sn-1,3-specific lipase from _Rhizomucor miehei_.
# Research on Offshore Vessel Trajectory Prediction Based on PSO-CNN-RGRU-Attention
## 2 Related Works
### GRU Model  
The Gated Recurrent Unit (GRU) [22] is an improved version of the Long Short-Term Memory (LSTM) neural network and is currently a mainstream architecture for time series prediction. Compared to LSTM, the GRU network has a simpler structure, offering higher training speed and computational efficiency. Figure 3 illustrates the unit structure of the GRU, which consists of a reset gate \(r_{t}\) and an update gate \(z_{t}\). The output \(h_{t}\) is determined by the current input \(x_{t}\) and the previous state \(h_{t-1}\). The output calculation formulas for the GRU unit are shown in Equations (2)-(5).  
\[r_{t}=\sigma(W_{  
In this context, \(b_{r}\), \(b_{z}\), and \(b_{h}\) are the bias vectors for the input \(x_{t}\) and the previous state \(h_{t-1}\); \(W_{r}\), \(W_{z}\), \(W_{h}\), \(U_{r}\), \(U_{z}\), and \(U_{h}\) are the weight matrices; \(\sigma\) represents the sigmoid function; and tanh refers to the hyperbolic tangent function. The structure of the GRU unit is shown in Figure 3.