esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Approximated Lowpass Elliptic Response
### Coefficient Determination  
In the design of traditional integer-order analog filters, the acceptable passband and stopband attenuations are used to calculate the necessary filter order based on the desired approximation (Butterworth, Chebyshev, Inverse Chebyshev, and elliptic) [21]. This calculated order is often a real-number that is rounded-up to the nearest integer value which is necessary to be realizable using integer-order filters. From this order, the necessary coefficients can be determined through provided design equations or from tables of available coefficients, which are further used in the implementation and realization of the necessary circuits. Additionally, optimization methods have also been explored to design these types of circuits [22; 23].  
Currently, fractional-order filter design does not yet have a comprehensive set of design procedures and tables of coefficients to support their realization. However, studies are ongoing to develop methods to design these filters [5; 6; 19], which also provides the motivation behind this study. Realizing an approximated elliptic response using Equation (7) that has both passband and stopband ripple characteristics requires the appropriate selection of the transfer function coefficients \([a_{1},a_{2},a_{3},a_{4}]\). One method to determine these coefficients is through the application of optimization  
Figure 1: (**a**) Simulated magnitude responses of \((1+\alpha)\) lowpass fractional order transfer function for \(\alpha=0.1\) to 0.9 in steps of 0.1 with coefficients selected to approximate elliptic response; and (**b**) details of passband ripple.  
routines, and have been previously utilized for other filter designs [5; 18; 19]. In this work, a non-linear least squares optimization routine is applied to search for the coefficients of the fractional notch transfer function given by Equation (7) that yields the least error when compared to the second-order elliptic response given by Equation (6). This optimization problem is described by:  
\[\min_{x}\sum_{i=1}^{k}\left(\left|H_{1+\alpha}\left(x,\omega_{i}\right)\right| -\left|E_{2}(\omega_{i})\right|\right)^{2} \tag{9}\]  
where \(x\) is the vector of filter coefficients, \(\left|H_{1+\alpha}(x,\omega_{i})\right|\) is the magnitude response using Equation (7) with \(x\) at frequency \(\omega_{i}\) (rad/s), \(\left|E_{2}(\omega_{i})\right|\) is the second order elliptic magnitude response given by Equation (6) at frequency \(\omega_{i}\) (rad/s), and \(k\) is the total number of data points in the magnitude responses. The number of data points used in the fitting procedure \((k=9001)\) was selected to ensure that the ripple characteristics of the elliptic response were represented with sufficient frequency resolution within the dataset. The routine was implemented in MATLAB (v.2015b, 8.6.0.267246) using the _fminsearch_ function with the default termination tolerances. The _fminsearch_ function uses the simplex search method [24].
# A Defect Detection Method Based on YOLOv7 for Automated Remanufacturing
## 4 Case Study Validation  
To further validate the efficacy of the YOLOv7-based defect detection model, a case study was conducted on a damaged cylindrical component. This study primarily focused on the identification of wear-type defects, which are prevalent and repairable in laser-cladding processes. The NRSD dataset, notable for its high-resolution RGB images, variety of defect types, and texture that mimics real damaged components, was employed for model training.  
In this case study, a virtual replica of a damaged workpiece was created using Blender software version 3.0 (Figure 13), incorporating a metallic texture inspired by real-life damaged samples (Figure 14). This virtual replica was used to ensure controlled testing conditions and simulate specific defect types uniformly. A single wear defect, representative of common wear and tear such as dents, was introduced onto this virtual part. Subsequent image captures of this damaged part, aided by a 2D camera and a pixel stitching algorithm, facilitated the creation of a comprehensive image for analysis (Figure 15). The pre-trained YOLOv7 model, applying bounding box techniques, successfully detected these synthetic defects with an 86% accuracy on mAP. This finding underscores the potential of the model, although the expansion of validation samples and further real-life experiments are necessary for comprehensive performance verification.  
Figure 14: The actual damaged sample repaired via a laser-cladding process.  
Figure 13: Virtual Model of the damaged sample.
# Performance Analysis of Two-Way Satellite-Terrestrial Relay Network with SWIPT
## 3 System Performance Analysis
### Instantaneous BER for SPNC  
Before deriving the exact expression for average end-to-end throughput of SPNC protocol, we have to obtain the exact expressions for instantaneous error probability for PNC and SND protocols in the two-way satellite-terrestrial network for BPSK modulation. This is because the instantaneous BERs are affected by the numerical relationship of the \(S_{1}\)--\(R\) and \(S_{2}\)--\(R\) links in two cases.
# Semantic Communication on Digital Wireless Communication Systems
## 3 IER--A Novel Physical Layer Semantic Metric
### Hamming distance and BER (bit error rate)  
In most of the digital wireless system, binary coding is used. And for binary coding, Hamming distance is the commonly used metric to define the error distance between transmitted/received messages. Assume that the transmitted message S = [1,0,0, 1,1,0, 0,0,0, 0,1,1] and the received message R = [0,0,0, 1,0,0, 0,0,0, 1,1,1]. In this case, the Hamming distance is three, which is the number of the different bits between S and R. And the BER can also be defined as the ratio of the Hamming distance and the total length of the transmitted message, as shown in Equation (1), where Len(S) is the total length of the transmitted message.  
\[\text{BER}=\text{Hamming Distance}\ (\text{S},\text{R})\ /\ Len(\text{S}) \tag{1}\]
Specifically, we first analyze the steps of the Greiner-Hormann algorithm, study its data structure and algorithm flow, and find the time-consuming parts that can run independently. Second, we perform fine-grained parallel transformation of the algorithm on the basis of the CUDA programming model. The intersection calculation and insertion process, which consumes the most time in the algorithm, is optimized via CUDA multithreading. The first kernel function assigns a thread to each edge of the subject polygon to perform intersection judgment with the clipping polygon, stores the number of intersection points in a variable and returns it to the CPU. The CPU allocates corresponding memory for the intersection points and then returns the memory to the GPU; the GPU then executes the second kernel function, calculates the coordinates of the intersection points and other information in parallel, and, finally, returns this information to the CPU. Finally, we select eight subject polygons with different shape complexities to execute the serial and parallel algorithms and use different thread combination modes to analyze the best thread mode of the algorithm after parallel transformation. Moreover, on the basis of the best thread mode, we statistically analyze the running time and speedup ratio of the serial and parallel algorithms to verify the improvement in the computational efficiency achieved by the parallel algorithm proposed in this paper. The research results show that the computational efficiency of the polygon overlay analysis algorithm is greatly improved through the parallel transformation of the Greiner-Hormann algorithm, which provides a method for performing spatial analysis of complex spatial geographic data.
# The Potential of Cannabidiol in the Management of Oral Infections
## 5 CBD Delivery Methods for Oral Infections: Current Strategies and Therapeutic Potential  
To maximize the therapeutic efficacy of CBD in the context of oral infection management, the route of administration is crucial. This section discusses the primary CBD delivery methods tailored to oral infections, highlighting their pharmacological advantages and practical limitations.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Target Pathogen/Condition** & **Mechanism/Details** & **References** \\ \hline \multirow{6}{*}{_Streptococcus mutans_ (Dental caries)} & Inhibits growth, acid production, and biofilm & \\  & formation. Disrupts membrane integrity; & inhibits quorum sensing; prevents biofilm on & [20; 21; 38; 42] \\  & hydroxyapatite and enamel-like surfaces. & \\ \cline{2-3}  & _Porphyomonas gingivalis,_ & Inhibits growth and biofilm formation. MIC & \\  & _Aggregabilacter actinomycetemomitans,_ & and MBC values comparable to chlorheximide; & \\  & _Fusobacterium nucleatum_ & attenuates LPS-induced inflammation in & \\  & (Periodontal pathogens) & gingival fibroblasts. & \\ \cline{2-3}  & \multirow{4}{*}{_Enterococcus faecalis_ (Endodontic infections)} & Reduces biofilms and viable counts in denitinal & \\  & & tubules. Enhances root canal disinfection; & \\  & & potential use in irrigants or & \\  & & intracanal medicaments. & \\ \cline{2-3}  & \multirow{4}{*}{_Candida albicans_ (Oral candidiasis)} & Reduces viability, hyphal transformation, and & \\  & & biofilm integrity. Reduces adhesion to acrylic & \\  & & surfaces; potential against denture stomatitis & \\  & & and oral candidiasis. & \\ \hline \multirow{6}{*}{Anti-Inflammatory Effects in Oral Infection Models} & Synergistic effects with antibiotics (e.g., & \\  & bicatircan, erythromycincin); does not & [49; 50] \\  & & induce resistance. & \\ \hline \multirow{6}{*}{Anti-Inflammatory Effects in Oral Infection Models} & Inflammation in oral cells & Reduces TNF-\(\alpha\), IL-\(\beta\), IL-\(\epsilon\), PGE2. Inhibits & \\  & (HGFs, PDLs) & NF-\(\kappa\)B translocation and cytokine & [52; 53] \\ \cline{2-3}  & & gene expression. & \\ \cline{2-3}  & & & \\ \cline{2-3}  & & & \\ \cline{2-3}  & & & \\ \cline{2-3}  & & & \\ \cline{2-3}  & & & \\ \cline{2-3}  & & & \\ \cline{2-3}  & & & \\ \cline{2-3}  & & & \\ \hline \multirow{6}{*}{Clinical Studies} & \multirow{6}{*}{CBD Mouthwash} & Reduces plaque and gingival inflammation. & \\  & & Comparable to chlorheximide, & \\ \cline{2-3}  & & fewer side effects. & \\ \cline{2-3}  & \multirow{6}{*}{Mucositis (Oncology)} & Decreases severity and duration of oral & \\ \cline{2-3}  & & mucositis. Case series in & \\ \cline{2-3}  & & chemotherapy patients. & \\ \cline{2-3}  & & & \\ \cline{2-3}  & \multirow{6}{*}{Pain Relief} & Analesic effects via TRPV1 and adenosine & \\ \cline{2-3}  & & receptors. Reduces nociceptive signaling and & [61; 62] \\ \cline{2-3}  & & peripheral sensitization. & \\ \hline \hline \end{tabular}
\end{table}
Table 1: CBD effect in different oral infections.
## 3 Preliminaries and System Model
### System Model  
In this work, we consider a secondary system consisting of a source (S), with a large but finite buffer of size (M), and a Poisson packet arrival rate. A destination (D) and \"K\" buffer-aided relays with  
Figure 6: Three-phase CSI and buffer state exchange.  
Figure 7: Four-phase CSI and buffer state exchange.  
buffer size L in between have a single antenna. The primary system in the vicinity has a transmitter (T) and receiver (R) with an interference power constraint (interference temperature) (Q) at the primary receiver. The inter-relay interference is represented by \(\gamma_{ir}\), and the channel gain from source-to-relay is \(|g_{SRi}|\), while from the relay-to-destination is \(|g_{RDj}|\) (relay-to-destination) links, where \(i\) and \(j\) represent the \(i^{\text{th}}\) (source-to-relay) and \(j^{\text{th}}\) (relay-to-destination) links. No direct communication link between the secondary source and the secondary destination is considered. A block diagram of the system model is depicted in Figure 8.
## 3 LQ Controller
### Control: Closed Loop System
#### 3.2.2 WLTP Class 3 Driving Cycle  
The truck platoon is subjected to a WLTP Class 3 driving cycle. The nominal velocity \(v_{n}\) is set to 80 km/h, and the nominal distance \(d_{n}\) varies according to the time headway \(t_{h}\) and \(d_{0}\) settings: the latter is maintained constant at \(d_{0}=3\) m. The lead vehicle's speed  
Figure 4: Block diagram of the platooning system with LQC.  
is controlled using PID logic. The PID gains \((K_{P},K_{D},K_{I})\) are set to \(K_{P}=300\), \(K_{D}=5\), \(K_{I}=10\). The remaining control parameters (\(R_{0}\) and \(t_{h}\)) are set according to Table 1, while the truck's inertial and geometrical data are listed in Table 3. \(R_{0}\) represents the constant weight of the \([R]\) matrix (\([R]=R_{0}[I]\)), equal for both follower trucks.  
The results of the WLTP Class 3 driving cycle are divided into two parts: Figure 5 shows the \(R_{0}\) sensitivity (with constant time headway \(t_{h}=1.5\) s), whereas Figure 6 depicts the \(t_{h}\) sensitivity (with constant \(R_{0}\) set to \(10^{-5}\)).  
In Figure 5, the top charts report the velocity of follower 1 (left) and the inter-vehicle distance errors (namely \(e_{d_{12}}\)), whereas the electric motor torque is reported in the bottom charts. The green line represents the vehicle leader velocity (the disturbance), whereas the follower 1 velocity response is represented for the three different analysed values of \(R_{0}\). The results show that, even though the difference in torques is quite small, the lower the \(R_{0}\) values, the better the tracking inter-vehicle distance performance.  
In Figure 6, the effect of the time headway is more evident in the results: the higher the time headway, the less motor torque is delivered, resulting in lower longitudinal acceleration. Therefore, higher values of time headway improve passenger comfort as the RMS values of jerk and acceleration are
## 3 Results and Discussion
### Factors Affecting the Output Performance
This explains why the electrical responses in the case of tap water and 0.3 M CuSO4 solution are smaller than those obtained from DI water. Considering the case using tap water and 0.3 M CuSO4 solution, the output currents reach \(-0.675\) and \(-0.627\)\(\mu\)A, which are 89% and 83% of that generated by DI water, respectively. Similarly, the voltages produced from these two types of water are approximately 93% and 69% of that generated by DI water (\(-8.1\) V). As resumed in Figure 6a,b, the dependence of the average magnitudes of Voc and Isc on the water-flow rate verifies the highest performance of DI water in comparison with other types of water. In the same manner, silver, copper, and aluminum were selected as the material of the electrode to examine the output performance of the device. The output Voc and Isc are illustrated in Supplementary Materials Figure S2c,d; here, DI water was used as the test water. It can be seen that the silver electrode can produce a higher output voltage and current than those produced by other metal electrodes. There is a slight difference of about 10% in the average magnitude of Voc and Isc between silver and copper electrodes, whilst the output of the aluminum electrode is only about 75% of that generated by the silver electrode. Thus, the silver electrode can generate a higher output than copper and aluminum can. This arrangement in output performance could relate to the conductivity of these metals, in which silver is the best conductor. On another hand, the experimental data shown in Figure 6c,d declare the rising trend of the magnitude of the output signals with the increase of water-flow rate. Figure 6: Impacts of material selection on the output performance. (**a**) Voc and (**b**) Isc curves were measured at different flow rates when using DI water, tap water, and 0.3 CuSO4 solutions. (**c**) Voc and (**d**) Isc curves were measured at different flow rates when using silver, copper, and aluminum electrodes. Importantly, these experimental results can prove the versatility of DC-FluTENG in generating DC output from a wide range of environments. Moreover, it can conclude that, by increasing the water-flow rate, the output performance of the device will be increased. Moreover, the presence of electrolytes in water could reduce the electrical outputs, and the conductivity of the electrode has a positive relationship to output performance.
## 3 Methodology
### Fitness Function  
The fitness function is a crucial concept that plays a central role in the optimization process. A GA is an optimization algorithm inspired by the process of natural selection and is used to find solutions to complex problems. The fitness function is a key component that evaluates the quality of potential solutions, guiding the algorithm towards better solutions over successive generations.  
The fitness function essentially serves as a guide for the algorithm by quantifying how well each potential solution aligns with the desired outcome. The design of an effective fitness function is crucial for the success of a GA. It should accurately reflect the problem's objectives and constraints, providing a clear measure of solution quality. Adjusting the fitness function allows the algorithm to adapt to different domains of problems and optimization goals. The choice of performance metrics depends on the nature of the problem and the goals of the model. While accuracy is a commonly used metric, there are situations where \"recall\" may be preferred, especially in scenarios where certain types of errors are more critical than others. Detection of positive classes, i.e., phishing attacks, is more critical than overall accuracy, and the accuracy even can be misleading in imbalanced datasets. In this direction, we ovtain the \"Recall\" performance metric as the fitness value of the model in the proposed algorithm. All related calculations were conducted accordingly.  
Chromosomes are evaluated according to their fitness values, which indicate how good they are for phishing detection. Fitness values correspond to _recall_ scores that are calculated after training the ML models with selected features based on the chromosomes. In other words, an ML model needs to be trained and evaluated once for each chromosome to obtain the fitness value. The training processes differ due to the ML classifiers. For instance, while logistic regression's training time is approximately 1 s with 87 thousand URLs, random forest's training time is over 80 s in the same test machine based on our observations. The total run-time of the evolutionary progress can be too long since the fitness values of all chromosomes of the population need to be calculated for each generation. To reduce this time, we stored the fitness values of each chromosome in a hash table with their keys, which are produced using binary arrays. Then, before calculating the fitness value of a chromosome, we checked the key of the chromosome in the fitness values table so that we prevented repeated calculations of the fitness value.
###### Abstract
The hybrid SCWOA method, which is based on the WOA with SCA, is proposed for evaluating benchmark functions and engineering designs, aiming ultimately to determine the global optimal values of the functions and the associated costs of the designs [44]. The hybrid hPSO-TLBO approach, based on a combination of particle swarm optimization (PSO) and teaching-learning-based optimization (TLBO), is proposed to solve optimization problems [45]. The core concept of the hPSO-TLBO design is to merge PSO's exploitation capability with TLBO's exploration strength. As a result, the development of optimization methods is essential to provide more efficient and effective solutions from both theoretical and practical perspectives. Swarm-based methods still have potential for further development, and hybrid innovative approaches can enhance performance. Therefore, this study proposes four new hybrid approaches. This article's significant contributions are as follows:  
* Based on HHO, the WOA, and PSO, this work has presented four novel hybrid approaches dubbed HHHOWOA1, HHHOWOA2, HHHOWOA1 PSO, and HHHOWOA2 PSO. * The HHHOWOA1 and HHHOWOA2 methodologies are developed by modifying the equations utilized in the exploitation phase of the WOA and subsequently applying these modifications in the exploitation phase of Harris Hawks Optimization (HHO). The HHHOWOA1 PSO and HHHOWOA2 PSO methodologies are developed by using modified PSO equations during the final stages of the HHHOWOA1 and HHHOWOA2 algorithms. Both the WOA and HHO algorithms have been improved because of these new hybrid techniques. * The general problems of metaheuristic algorithms that often involve getting stuck in local optima, low diversity, and imbalanced exploitation capabilities have been further improved. So, the optimization search capability has been further improved, and the probability of the optimal value falling to a local minimum has been further reduced. * The proposed four novel hybrid approaches were evaluated on 23 benchmark functions and compared to the results of the publications using the same parameters. For these 23 functions, Friedman rank tests were performed based on the average optimization results of all algorithms. It was observed that the HHHOWOA2 PSO approach is ranked first, followed by the HHHOWOA1 approach, in the success ranking for the F1-F23 functions. * The HHHOWOA1, HHHOWOA2, and HHHOWOA2 PSO approaches are applied to three benchmark problems in engineering, and the optimum values obtained are compared with the literature. The structure of this manuscript is as follows: Section 2 introduces the HHO algorithm and its steps, the WOA algorithm and its steps, as well as the PSO method. Section 3 provides a detailed explanation of the proposed algorithms. Section 4 includes the benchmark sets, comparisons of the proposed algorithms with each other and with the existing literature, statistical analyses, and the application of the proposed approaches to three engineering problems. Finally, Section 5 concludes this study by summarizing the key findings.
# Modelling the Microphone-Related Timbral Brightness of Record Signals
## 2 Ratings of Brightness
### Selecting Microphones  
Microphones were selected in two stages. Firstly, microphones were identified that represented the extremes in each of seven physical/acoustic factors that are known to affect performance: transduction type, sensitivity, frequency response, directivity pattern, self-noise, diaphragm size, and transient response. This resulted in a shortlist of eight studio microphones. Five independent experts were then asked to say whether use of these microphones would be likely to result in recordings exhibiting a full range of perceptual differences, and to suggest additional microphones if not. Responses indicated that no additional microphones were required. In addition to these eight studio microphones, two MEMS (MicroElectricalMechanical System) microphones with markedly different perceived sonic qualities were chosen; outside of professional recording environments, mobile phones are probably the most common devices used to capture audio and these employ MEMS microphones. The resulting ten microphones were AKG C12, AKG C414 B-XLS, AKG C451, Coles 4038, DPA 4006-TL, Electrovoice RE20, sE 2200a, Shure SM58, Wolfson WM7131, and Knowles SPU0410 HR5H.