esa-sceva/satcom-chunk-collection huggingface dataset:
# Advanced Optimization Algorithm Combining a Fuzzy Inference System for Vehicular Communications
## 5 Conclusions  
This research proposes a novel approach integrating the Sugeno-FIS mechanism with Q-learning reinforcement learning (RL) to improve resource allocation efficiency in vehicular networks, named fuzzy inference reinforcement learning (FIRL). The P\({}_{\rm rk}\) value is adjusted adaptively using the Sugeno-FIS sequence, and then the Q-learning sequence will adaptively adjust the I\({}_{\rm MCS}\) each time the LTE packet is generated. The novel FIRL approach demonstrates significant performance improvements in all vehicle density scenarios presented, achieving up to a 169.83% enhancement compared to the static baseline configuration.  
The current technological challenges in C-V2X development are producing more realistic simulations that mimic real-world situations and infrastructure. More scenario configurations, such as urban and suburban, and tuning up the hyperparameters to suit more complex problems, are also worth mentioning in future works.  
Conceptualization, T.I.B., Y.-F.H. and J.-K.C.; Investigation, T.I.B.; Methodology, T.I.B. and Y.-F.H.; Software, T.I.B. and J.-K.C.; validation, Y.-F.H.; resources, S.S. and E.C.; data curation, T.I.B. and B.K.; Supervision, C.-H.H. and Y.-F.H.; Writing--Original Draft, T.I.B., C.-H.H. and Y.-F.H.; Writing--Review and Editing, C.-H.H. and Y.-F.H. All authors have read and agreed to the published version of the manuscript.  
Figure 11: PRR comparison against total number of vehicles.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & & \multicolumn{4}{c}{**50-m Distance**} \\  & \(n_{V}\) = 100 & \(n_{V}\) = 200 & \(n_{V}\) = 300 & \(n_{V}\) = 400 \\ \hline static baseline & 97.58 & 92.34 & 86.98 & 80.89 \\ FIRL & 98.84 & 98.84 & 97.7 & 96.44 \\ FIRL Improvement & 1.28\% & 7.04\% & 12.33\% & 19.23\% \\  & & \multicolumn{4}{c}{100 m distance} \\ \hline static baseline & 91.97 & 77.65 & 65.13 & 53.1 \\ FIRL & 97.18 & 96.33 & 93.48 & 88.91 \\ FIRL Improvement & 5.67\% & 24.06\% & 43.53\% & 67.46\% \\  & & \multicolumn{4}{c}{150 m distance} \\ \hline static baseline & 72.01 & 53.55 & 38.57 & 28.65 \\ FIRL & 92.11 & 92.21 & 86.84 & 77.3 \\ FIRL Improvement & 27.91\% & 72.2\% & 125.17\% & 169.83\% \\ \hline \end{tabular}
\end{table}
Table 3: PRR improvement percentage.  
**Funding:** This work was supported by the National Science and Technology Council of Taiwan under grant NSTC-112-2221-E-324-010.  
**Data Availability Statement:** Data are contained within the article.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Application of Compressive Sensing in the Presence of Noise for Transient Photometric Events
## 3 Noise in Compressive Sensing Measurements
### Measurement Noise
#### 3.2.1 Shot Noise  
Shot noise is associated with the implicit arrival of electrons at the detector. This noise is applied to the detector measurements [10, 11]. For equation purposes, we write this as additive noise to \(Ax\). However, for practical purposes, shot noise is applied to \(Ax\), as it is dependent on the signal and thus cannot be added independently to the measurements. We use Poisson distribution to depict shot noise.  
\[y=Ax+n_{s} \tag{9}\]
_Article_  
**Denoising of the Fiber Bragg Grating Deformation Spectrum Signal Using Variational Mode Decomposition Combined with Wavelet Thresholding**  
**[PERSON] 1, [PERSON] 1, [PERSON] 2, [PERSON] 2 and [PERSON] 1,*(c)**  
Received: 10 December 2018; Accepted: 31 December 2018; Published: 6 January 2019  
**Featureted Application: The main purpose of this study is to present a precise denoising algorithm to denoise the fiber Bragg grating (FBG) deformation spectra signal. The method combines variational mode decomposition and wavelet thresholding. It can be applied in signal processing in the structural health monitoring field.**  
**Abstract: Damage detection using an FBG sensor is a critical process for an assessment of any inspection technology classified as structural health monitoring (SHM). FBG signals containing noise in experiments are developed to detect flaws. In this paper, we propose a novel signal denoising method that combines variational mode decomposition (VMD) and changed thresholding wavelets to denoise experimental and mixed signals. VMD is a recently introduced adaptive signal decomposition algorithm. Compared with traditional empirical mode decomposition (EMD), and it is well founded theoretically and more robust to noise samples. First, input signals were broken down into a given number of \(K\) band-limited intrinsic mode functions (BLIMFs) by VMD. For the purpose of avoiding the impact of overbinning or underpinning on VMD denoising, the mixed signals, which were obtained by adding different signal/noise ratio (SNR) noises to the experimental signals, were designed to select the best decomposition number \(K\) and data-fidelity constraint parameter \(\alpha\). After that, the realistic experimental signals were processed using four denoising algorithms to evaluate denoising performance. The results show that, upon adding additional noisy signals and realistic signals, the proposed algorithm delivers excellent performance over the EMD-based denoising method and discrete wavelet transform filtering.**  
**Keywords: fiber Bragg grating; variational mode decomposition; discrete wavelet transform; signal processing**
## 2 Research Context
### Product Architecture
#### 2.3.2 Modular Architecture  
Modular architecture, on the other hand, consists of groups of parts called modules. These modules are common units across multiple products, catering to specific functionalities derived from market requirements [18].  
Modular structures aim to increase external variety, that is, the range of products available to the customer [13; 23; 24]. To meet such a broad product offering, modular architecture establishes a usage plan, positioning rules and usage rules [25], as well as interfaces between these modules [26]. This structure enables increased design flexibility [5; 27] and easier product changes [13], promotes module substitution and combinability, and enables the rapid incorporation of new technologies [27] and product improvement [5; 20]. To be capable of producing such a broad range of products, modular architecture requires flexibility and agility at the production process level [18; 28].  
While offering a very broad range of products, modular architecture also aims to reduce the costs associated with this product variety. It achieves product segmentation that allows for the parallel design of modules [29], thereby reducing the time to market [25]. Some of these modules can be reused [22; 29], thus reducing the time and design cost [14; 22; 25]. The reuse of modules and parts, thus, reduces internal variety [23; 24], that is, the number of parts needed to meet the product offering. Reducing this variety of parts has the effect of keeping the company's internal complexity lower even when the product offering increases [2; 26; 30; 31].  
Modular architecture shares several objectives with MC. The structure of this product architecture enables short-term gains such as increasing the product offering and reducing the variety of parts. Longer-term gains are possible upon product improvement, redesign, or an increase in product offering.
## 5 Evaluation of Model Simulation Efficiency  
The ability of a model to simulate the actual occurrence of antibiotic transport in water systems is evaluated through corroboration. Corroboration is the validation of a model prediction using monitoring data. This is achieved by comparing model simulations with observed (measured) data using different statistical metrics. A model with ease of use and closer simulation of existing reality is preferred. However, model simulation has a margin of error or uncertainty and is influenced by various factors. For instance, significant discrepancies in the predicted concentration of antibiotics are possible due to different methods of specifying degradation rates [118]. Seasonal antibiotic human consumption and WWTP operation efficiency associated with degradation processes can bring prediction discrepancies. Climate, flow, and weather variabilities affect model prediction efficiency, as far as antibiotic fate and transport are concerned. As a result of a change in the hydrological condition, the concentration of pollutants may either increase due to the lack of dilution during dry periods or work in the opposite direction, facilitating removal (natural degradation processes) by increasing the residence time [49]. During flood events, different physical phenomena may occur at the same time: first, it may contribute to the overloading of WWTPs, thus reducing removal efficiency; second, is the embolization of pollutants from sediments in the river; and third, it may washout pollutants from agricultural fields to the rivers. Therefore, incorporating seasonality (winter, spring, summer, and fall) parameters may help [42]. Different model prediction efficiency for different antibiotics has been observed in different countries (Table 2). The reviewed studies used different performance evaluation criteria. In addition to using different error metrics, most reviewed studies use graphical plot techniques to present the goodness of model prediction. Further, studies reported in GREAT-ER, WASP, and EUSES present plot comparisons due to their built-in plotting feature. One potential predictive performance is selected as their main statistical criterion for the studies that used more than one evaluation criterion. It is also important to know which model assessment criteria works best with each model [134]. Therefore, specific model evaluation criteria with the range of goodness of fit for each model are reviewed and presented in Table 2. For instance, correlation coefficient (R\({}^{2}\)) is often preferred for GREAT-ER and EUSES [9; 118; 121; 133; 135], PBIAS for WASP [2], and RMSE for QUAL-2K [101]. The outcomes of the assessment criteria, on the other hand, may change in accordance with the number of observations. For an observation below 50, suitable evaluation criteria can be chosen by comparing the weightage and bias of a respective prediction assessment criteria [134].
## 4 Experimental Results  
In order to show the viability and repeatability of the presented method, the following graphs show 35 repetitions corresponding to the estimation of the polarization angle as well as the refractive index of the two materials under test: glass and acrylic, respectively. The obtained data were compared with their nominal value, \(\mathrm{n_{g}}=1.51\) and \(\mathrm{n_{ac}}=1.49\), found in the literature. Considering these theoretical values with respect to the average of each set of measurements, the error presented when measuring the microscope slide is \(e_{rg}=\frac{1.51-1.539}{1.51}=1.92\%\). The error corresponding to the acrylic prism is \(e_{nac}=\frac{1.49-1.483}{1.49}=0.47\%\). These results show a reliable system since generally, an error rate < 5% is accepted for a good educational measurement system [13]. Therefore, according to the calculated percentage error, it is possible to conclude that the proposal presents a simple and feasible methodology to estimate the polarization angle and the refractive index of a material.  
The experimental data are shown in Figure 8, also indicating the average and the standard deviation \(\sigma\) regarding to the refractive index, as well as the average of \(\overline{\theta}_{P}\) corresponding to glass (Figure 8a) and acrylic (Figure 8b). During these tests, the entire process was followed by different physics and engineering students in order to guarantee that the whole resource was easy, interesting and practical to use (for this purpose, the \"direct observation\" technique was used as an exploratory method to qualitatively evaluate the usefulness and impact that the proposed tool had on the students. This was based on  
Figure 7: Flow diagram describing the general process.  
observing and taking notes on the students' behavior while using the tool, paying attention to their level of participation, understanding of the concepts, and any technical difficulties that might arise).  
One of the reasons the data shown in the previous figure diverge from the average is because the shaft inserted in the servomotor (which holds the test object) sometimes presents unwanted vibrations, increasing or decreasing tenths of degree in the detected polarization angle, thus affecting the calculation of \(\theta_{P}\) and \(n\).  
The device requires a prior calibration stage in which the camera must \"learn\" to detect the color of the laser spot to save its detection in the assigned variable \"ID1\". This process should be performed when the position of any mechanical component or ambient light conditions changed, which may affect the angle and color detection, respectively.
## 3 Materials and Methods
### System Design and Architecture
#### 3.2.1 Question Match Against Question-Answer Database  
When a user submits a question, DietNerD first performs a similarity search against questions in a MySQL database that stores question-answer pairs. The similarity search algorithm begins by retrieving all existing questions from the database. These questions, along with the new user query, are then converted into Term Frequency-Inverse Document Frequency (TF-IDF) vectors using scikit-learn's TfidYvectorizer. The cosine similarity is then computed between the TF-IDF vector of the user's question and the vectors of all stored questions. The top three most similar questions above a threshold are selected. As shown in Figure 2, if a match is found, the user has the option to select from the top three closest matches. Alternatively, the user can ask the system to generate an answer based on a scan of the literature.
# A Piecewise Linear Regression Model Ensemble for Large-Scale Curve Fitting
## 2 Continuous Piecewise Linear Regression
### Model Definition  
As shown at the top of Figure 1, this model can be defined as a continuous set of straight lines, linked in the knots. A model with \(K\) knots, will be made up of \(K-1\) straight lines, with the knots being placed in the coordinates \((k_{j},h_{j})\).  
These linear piecewise models can also be understood as linear B-splines and, therefore, expressed as a linear combination of triangular-shaped basis functions (see chapter 2 of [4]):  
\[\hat{f}(x_{i})=\sum_{j=1}^{K}h_{j}B_{j}(x_{i}), \tag{3}\]  
where each \(B_{j}\) is a triangular basis function, possibly non-symmetric and non-zero, in the interval \((k_{1-1},k_{3+1})\). Therefore, the output is obtained by scaling these bases by different amounts \(h_{j}\), and adding them all up. Figure 1 shows a complete example of this. The continuous piecewise linear model at the top is completely defined by the combination of the set of basis functions shown at the bottom and the \(h_{j}\) of each knot. Therefore, the model can be expressed as a set of \(2K\) parameters, the coordinates \((k_{j},h_{j})\) of all the knots. Changing these \(2K\) parameters, many different shapes can be generated, with more or less flexibility depending on the value of \(K\).
## 5 Finite Element Models Arrangement  
Different techniques are available for data validations and predictions such as artificial neural networks [29; 30; 31; 32; 33; 34], FE method [1; 35; 36], and finite strip method [37; 38]. The FE method, which is generally carried out by FE programs such as ABAQUS and ANSYS, was performed in the current study as a reliable technique for empirical data validation and response prediction. Two different FE models were used in order to simulate the experimental results of reinforcement spacing, including 200 mm, 300 mm, and without reinforcement, on the flexural strength of the upright frames about either major or minor axes. Two different arrangements were considered:  
* Bending frame: The frame was modelled to simulate the major axis test, and other parts of the set-up were simulated throughout boundary condition and interaction descriptions.
* Single beam: The beam was modelled to simulate the minor axis test, and the other components of the test were simulated using appropriate stiffeners and boundary conditions.  
Figure 22A shows the overall arrangement for the bending frame model and Figure 22B shows the same arrangement for single-uprights. The software ABAQUS/CAE v.12.1 was used to model the presented test specimens. The FE models were adjusted to replicate the tests.
## 4 Conclusions  
In this paper, a new routing approach is presented by using device multicriteria in the optimal route selection between the source and destination devices for 5G IoT networks based on D2D communications. The proposed routing scheme has overcome the challenges of energy constraints, network stability, traffic congestion, and link failure of the intermediate device for the selection of an optimal route. Therefore, to address these challenges, the proposed routing approach exploited energy consumption, mobility, queue length, and the link quality of the intermediate devices in the optimal route selection mechanism. Moreover, the MCDM technique is employed for the selection of intermediate devices in the optimal path, which provides weight to the intermediate devices according to its estimated values. Extensive simulations were conducted with various device speed, and the results have been illustrated in network QoS performance metrics. Based on the simulation results, it shows that the proposed EMBLR routing approach attains significant performance as compared to the other well-known existing routing approaches. Overall, the proposed EMBLR routing approach significantly improves EED (18.78% and 12.83%), PDR (33.67%, and 23.84%), and energy costs (20.45% and 11.56%) approximately, as compared to the MP-OLSRv2 and MEQSA-OLSRv2 routing approaches, respectively. The proposed framework is highly beneficial for society, such as in the case of a natural disaster, where the communication for survivors can be established with additional deployment of drone-based emergency management systems. In the future, with the help of the resource allocation method, the higher system data rate can be achieved, along with the security aspects. D2D communication is one of the key technologies in the 5G network, and further research in the heterogeneous network domain is imperative. Besides, as the D2D link shares the same spectrum resources with cellular links and mobile devices, it causes interference among the spectrum-sharing links, thus decreasing the overall network performance. Better spectrum utilization such as cognitive radio, coordinated multi-point joint transmissions can be used to enhance the overall network performance.  
Figure 11: Convergence time vs. various devices speed.  
**Author Contributions:** [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the simulation and results; [PERSON], [PERSON] and [PERSON] analyzed the data; [PERSON], [PERSON], and [PERSON] contributed Materials/reagents/analysis tools; [PERSON] wrote the paper. All authors have read and agree to the published version of the manuscript.  
**Funding:** The authors would like to acknowledge the EPSRC grant EP/P028764/1 (UM IF035-2017) and University Malaya Research Grant (GPF037A-2019).  
**Acknowledgments:** The authors would like to thank University of Malaya and Ministry of Social Justice and Empowerment, Government of India for their support.  
**Conflicts of Interest:** The authors declare that there is no conflict of interest regarding the publication of this paper.
## 2 Materials and Methods
### Methods  
The stress-absorbing performance can be evaluated from two aspects: crack resistance and crack expansion resistance. Crack resistance reflects the ability of the material to resist cracking under tensile stress, while crack expansion resistance reflects the ability of the material to prevent further crack expansion when cracks already exist. To evaluate these two aspects of the AR-SAMI performance, four test methods can be used: BBT, LT-SCB, CE-SCB, and OT. BBT is an important test to measure the tensile strength of the material, which can evaluate the crack resistance of the material under fatigue cracking and low-temperature shrinkage cracking conditions. The LT-SCB test can simulate the actual stress on the AR-SAMI under temperature stress and traffic load, which can evaluate the crack resistance of the material under low-temperature conditions. The CE-SCB test can evaluate the fracture characteristics of the asphalt mixture and obtain the crack propagation resistance of the mixture, which has a high correlation with the actual road performance of the AR-SAMI. The OT can simulate the basic characteristics of the reflective crack expansion in asphalt pavements. The classification of the stress absorption evaluated by these four test methods is shown in Table 5. Guangxi Province is situated in the southern part of China, between 20\({}^{\circ}\)54\({}^{\prime}\)-26\({}^{\circ}\)20\({}^{\prime}\) N. The 30-year average of the annual extreme minimum temperature in this region is \(-\)1.6 \({}^{\circ}\)C. To simulate the worst-case scenario, the minimum temperature of the test was set at \(-\)10 \({}^{\circ}\)C, while the room temperature was set at 15 \({}^{\circ}\)C. The test process is shown in Figure 3.  
Figure 3: Experimental program to study the crack resistance performance of AR-SAMI.  
\begin{table}
\begin{tabular}{c c c} \hline
**Test Name** & **Crack Resistance** & **Crack Expansion Resistance** \\ \hline BBT & \(\surd\) & \\ LT-SCB test & \(\surd\) & \\ CE-SCB test & & \(\surd\) \\ OT & & \(\surd\) \\ \hline \end{tabular}
\end{table}
Table 5: Cracking patterns in response to the test methods.
# An Overview of Key Technologies in Physical Layer Security
## 3 Secure Multi-Antenna Techniques
### Convex Optimization  
Convex optimization can be used in PLS with other secure-multi antenna techniques to find the most favorable transmit solutions that can effectively make the best out of the performance metrics of the wireless communication system. The objective function \(f(x)\) to be maximized or minimized may be considered to be the performance metric, for example, secrecy rate, SINR, and secrecy outage probability [7]. Several methods which are commonly used to solve optimization problems in PLS are widely available in the literature [51].  
One convex optimization method involves the objective function having quadratic terms, and it is called quadratic programming. Quadratic programming is used in the design problems of nonlinear programming. Some of the common quadratic problems in PLS are power minimization, secure power allocation, and beamforming. Another method used in PLS is semi-definite programming (SDP). It is used to optimize a linear function of variables under linear equality constraints and a non-negativity constraint. Most problems in PLS are usually non-convex, and they must be converted into convex problems using SDP. In turn, an efficient algorithm that is easy to implement is developed in order to obtain optimal performance metrics. One other convex optimization method is the difference of convex functions (DC) programming. In DC programming, the objective function is a subtraction of two convex functions, for instance, secrecy rate maximization. Furthermore, mixed-integer programming is one of the methods utilized. This method is applicable to problems which have discrete and continuous variables. Not least of all, fractional programming is one more method which is directed towards optimization of a ratio of two nonlinear functions. A typical example of its application in PLS is the energy efficiency maximization. Even though convex optimization offers improved secrecy performance in the physical layer than the conventional precoding techniques such as beamforming or zero-forcing, it is more computationally expensive to implement.