esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Preliminaries and Background
### Constrained Many-Objective Optimization  
Since artificial intelligence and engineering applications tend to involve more than two and three objective criteria [40], the concept of many objective optimization problems must be introduced beforehand. Literally, they involve many objectives in a conflicted and simultaneous manner. Hence, a constrained many-objective optimization problem may be formulated as follows:  
\[\begin{array}{ll}minimize&\mathbf{F}(\mathrm{x})=[f_{1}(\mathrm{x}),\dots,f _{m}(\mathrm{x})]^{T}\\ subject\ to&g_{j}(\mathrm{x})>0,\quad j=1,\dots,J\\ &h_{\mathrm{k}}(\mathrm{x})=0,\quad k=1,\dots,K\\ &\mathrm{x}\in\Omega\end{array} \tag{1}\]  
where \(\mathrm{x}=[x_{1},\dots,x_{n}]^{T}\) is a \(n\)-decision variable candidate solution taking its value in the bonded space \(\Omega\). A solution respecting the \(J\) inequality (\(g_{j}(\mathrm{x})>0\)) and \(K\) equality constraints (\(h_{\mathrm{k}}(\mathrm{x})=0\)) is qualified as attainable. These constraints are included in the objective functions and are detailed in our proposed method in Section 3.3. \(\mathbf{F}:\Omega\rightarrow\mathbb{R}^{m}\) associates a candidate solution to the objective space \(\mathbb{R}^{m}\) through \(m\) conflicting objective functions. The obtained results are thus alternative solutions but have to be considered equivalent since no information is given regarding the relevance of the others.  
A solution \(\mathrm{x}^{1}\) is said to dominate another solution \(\mathrm{x}^{2}\), written as \(\mathrm{x}^{1}\prec\mathrm{x}^{2}\) if and only if  
\[\forall i\in\{1,\dots,m\}:f_{i}(\mathrm{x}^{1})\leq f_{i}(\mathrm{ x}^{2})\] \[\bigwedge\exists j\in\{1,\dots,m\}:f_{j}(\mathrm{x}^{1})<f_{j}( \mathrm{x}^{2}) \tag{2}\]
# Boron Nitride Nanotubes for Curcumin Delivery as an Anticancer Drug: A DFT Investigation
## 2 Materials and Methods
### Loading Efficiency of Curcumin Molecules  
In comparison to carbon nanotubes, the curcumin loading efficiency of BNNTs has been reported to be as high as 98.0 percent [28]. The large surface areas of BNNTs and curcumin's molecular structure, which comprises two benzene rings and a conjugated ethylenic bond, both contribute to the high loading efficiency. Furthermore, because curcumin and BNNTs are both hydrophobic, the van der Waals forces between them will favor curcumin loading.
# DDC Control Techniques for Three-Phase BLDC Motor Position Control
###### Abstract  
In this article, a novel hybrid control scheme is proposed for controlling the position of a three-phase brushless direct current (BLDC) motor. The hybrid controller consists of discrete time sliding mode control (SMC) with model free adaptive control (MFAC) to make a new data-driven control (DDC) strategy that is able to reduce the simulation time and complexity of a nonlinear system. The proposed hybrid algorithm is also suitable for controlling the speed variations of a BLDC motor, and is also applicable for the real time simulation of platforms such as a gimbal platform. The DDC method does not require any system model because it depends on data collected by the system about its Inputs/Outputs (IOS). However, the model-based control (MBC) method is difficult to apply from a practical point of view and is time-consuming because we need to linearize the system model. The above proposed method is verified by multiple simulations using MATLAB Simulink. It shows that the proposed controller has better performance, more precise tracking, and greater robustness compared with the classical proportional integral derivative (PID) controller, MFAC, and model free learning adaptive control (MFLAC).  
Keywords:model free adaptive control; sliding mode control; model free learning adaptive control; brushless DC +
Footnote †: journal:
## 5 Comparison and Validation  
### Model Comparison  
In this section, we compare two months (April and August 2015) of post-filtered and bias corrected FOCAL v06 XCO\({}_{2}\) results with corresponding values of the CAMS v15r4 model accounting for FOCAL's column averaging kernels (e.g., [31]). Figure 13 shows 5\({}^{\circ}\)\(\times\) 5\({}^{\circ}\) monthly gridded values for both months, FOCAL, and CAMS. The main spatial and temporal patterns are similar for FOCAL and CAMS with largest and smallest values in the northern hemisphere in April and August, respectively. Differences become larger at smaller scales, e.g., FOCAL sees larger values in natural and anthropogenic source regions of Sub-Saharan Africa and East Asia in April but also above the Sahara in August. However, it shall be noted that often only few data points are in the corresponding grid boxes.  
Figure 12: FOCAL v06 total bias pattern (Equation (4)) at the example of August 2015 (**left**) and NASA OCO-2 v7.3.05b total bias pattern (**right**).  
Figure 13: _Cont._  
In grid boxes with more than 100 soundings, the standard error of the mean becomes negligible (\(\approx\)0.1 ppm). Therefore, the difference between FOCAL and CAMS in such grid boxes can be interpreted as systematic temporal and regional mismatch or bias. The standard deviation of this systematic mismatch (including also representation errors) amounts to 1.0 ppm. The standard deviation of the single sounding mismatch after subtracting the systematic mismatch amounts to 1.1 ppm which agrees reasonably well with the average reported uncertainty of 1.2 ppm.
# Exploiting Outage and Error Probability
###### Abstract  
This paper embeds a bi-fold contribution for Underwater Wireless Sensor Networks (UWSNs); performance analysis of incremental relaying in terms of outage and error probability, and based on the analysis proposition of two new cooperative routing protocols. Subject to the first contribution, a three step procedure is carried out; a system model is presented, the number of available relays are determined, and based on cooperative incremental retransmission methodology, closed-form expressions for outage and error probability are derived. Subject to the second contribution, Adaptive Cooperation in Energy (ACE) efficient depth based routing and Enhanced-ACE (E-ACE) are presented. In the proposed model, feedback mechanism indicates success or failure of data transmission. If direct transmission is successful, there is no need for relaying by cooperative relay nodes. In case of failure, all the available relays retransmit the data one by one till the desired signal quality is achieved at destination. Simulation results show that the ACE and E-ACE significantly improves network performance, i.e., throughput, when compared with other incremental relaying protocols like Cooperative Automatic Repeat reQuest (CARQ). E-ACE and ACE achieve 69% and 63% more throughput respectively as compared to CARQ in hard underwater environment.  
underwater wireless sensor network; incremental relaying; cooperative diversity; retransmission; automatic repeat request 2016161076; doi:10.3390/s16071076  
[PERSON] 1  
1 Department of Computer Science, International Islamic University, Islamabad 44000, Pakistan; [EMAIL_ADDRESS] (H.N.); [EMAIL_ADDRESS] (M.S.) 2 COMSATS Institute of Information Technology, Islamabad 44000, Pakistan; [EMAIL_ADDRESS] 2  
Cameron Library, University of Alberta, Edmonton, AB T6G 2J8, Canada; [EMAIL_ADDRESS]  
Internetworking Program, Faculty of Engineering, Dalhousie University, Halifax, NS B3J 4R2, Canada; [EMAIL_ADDRESS]  
Computer Information Science, Higher Colleges of Technology, Fujiarah 4114, United Arab Emirates  
Biomedical Technology Department, College of Applied Medical Sciences, King Saud University, Riyadh 11633, Saudi Arabia; [EMAIL_ADDRESS]  
Correspondence: [EMAIL_ADDRESS]; Tel.: +92-300-579-2728  
[PERSON] and [PERSON]  
Received: 18 May 2016; Accepted: 1 July 2016; Published: 12 July 2016
## 3 Simulation and Analysis of Microphone Array Speech  
### Simulation of Microphone Array Speech in Room Acoustic Environment  
In order to show and analyze microphone array speech simulated by the proposed MASS more intuitively, as shown in Figure 3, a common three-dimensional room acoustic environment for a meeting room is created. The simulated meeting room is 4 m long, 3 m wide, and 3 m high. In the center of the meeting room, a 2.2 m long, 1.1 m wide, and 0.75 m high rectangular conference table is placed. A uniform linear microphone array with a spacing of 4 cm is placed in the center of the conference table to collect the sound signals in the room. Around the conference table, there are 19 chairs (Target 1-Target 19) as shown in terms of the square and a TV screen that somebody is speaking (Target 20) hung on the wall to simulate target speech sources in the meeting room acoustic environment. The coordinates of microphone array and each target speech source are also shown in Figure 3. In order to simplify the simulation, we assume that: (1) Each target speech source makes sound independently without overlapping, but they can be set to overlapping if needed; and (2) the conference table and chairs are ideal and have no reflection to the incident sound wave.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Inputs** & **Outputs** \\ \hline room size & \\ source location & microphone array clean speech \\ microphone array location & microphone array clean speech with reverberation \\ signal-to-noise ratio (SNR) & microphone array noisy speech \\ absorption factor & microphone array noisy speech with reverberation \\ clean speech (for target speech source) & microphone array noise signals \\ noise signal (for diffuse noise) & \\ \hline \hline \end{tabular}
\end{table}
Table 1: The inputs and outputs of the MASS.  
Figure 3: The meeting room acoustic environment with 20 target speech sources and a uniform linear microphone array.  
So, based on such a meeting room acoustic environment in Figure 3, the room acoustic environment can be abstracted as the room shape, target speech source, and microphone array settings for simulating microphone array speech in room acoustic environment by the proposed MASS as shown in Figure 4.
# Enhanced YOLOv8 with BiFPN-SimAM for Precise Defect Detection in Miniature Capacitors
## 2 Related Work  
Increasing complexity and the miniaturization trend in electronic products have brought significant challenges to quality control, especially in detecting minute defects in capacitors. Traditional methods, such as microscopic inspection and electrical testing, are becoming less effective in this evolving context [13]. This has led to the adoption of automated visual inspection methods using deep learning and convolutional neural networks (CNNs).  
Recent advancements in this field include optimizing network structures to enhance real-time performance while maintaining detection accuracy. Techniques like depth-separable convolution and more efficient channel attention (MECA) have been utilized to reduce computational demands and improve feature extraction. Furthermore, to address the loss of detail in small targets due to pooling operations, methods using dilated convolution with varying rates (atrous spatial pyramid fast, or ASPF) have been developed. These methods extract more contextual information, which is crucial for detecting tiny defects [14; 15].  
In addition to network enhancements, new feature fusion methods have been proposed. These methods introduce shallower feature maps and employ dense multiscale weighting to fuse more detailed information, thereby improving detection accuracy. Optimization techniques such as the K-means++ algorithm for reconstructing prediction frames have also been integrated to accelerate model convergence, along with the combination of the Mish activation function and the SloU loss function to further refine model performance [16; 17; 18].  
Another critical aspect is addressing overfitting, especially in contexts with limited data. A coarse-grained regularization method for convolution kernels (CGRCKs) was introduced to maximize the difference between convolution kernels within the same layer. This method enhances the extraction of multi-faceted features and shows effectiveness compared with traditional L1 or L2 regularization, particularly in CNN applications in which dataset sizes are constrained [19; 20; 21].  
The development of high-quality test datasets, often derived from real-world production settings, remains vital. These datasets serve as practical benchmarks for training and validating models, which are essential for assessing their real-world applicability, especially in industrial settings for detecting defects in miniature capacitors [22; 23].  
In summary, the field of miniature capacitor defect detection is rapidly evolving, with deep learning technologies at the forefront. Advances in network optimization, feature fusion techniques, and regularization methods have significantly improved detection efficiency and accuracy. These developments not only enhance quality control in the electronics manufacturing industry but also set the stage for future research exploring a balance between the processing speed, accuracy, model generalization, and effective management of large diverse datasets.
# Adaptive Mutation Dynamic Search Fireworks Algorithm
## 1 Introduction  
Fireworks Algorithm (FWA) [1] is a new group of intelligent algorithms developed in recent years based on the natural phenomenon of simulating fireworks sparking, and can solve some optimization problems effectively. Compared with other intelligent algorithms such as particle swarm optimization and genetic algorithms, the FWA adopts a new type of explosive search mechanism, to calculate the explosion amplitude and the number of explosive sparks through the interaction mechanism between fireworks.  
However, many researchers quickly find that traditional FWA has some disadvantages in solving optimization problems; the main disadvantages include slow convergence speed and low accuracy, thus, many improved algorithms have been proposed. So far, research on the FWA has concentrated on improving the operators. One of the most important improvements of the FWA is the enhanced fireworks algorithm (EFWA) [2], where the operators of the conventional FWA were thoroughly analyzed and revised. Based on the EFWA, an adaptive fireworks algorithm (AFWA) [3] was proposed, which was the first attempt to control the explosion amplitude without preset parameters by detecting the results of the search process. In [4], a dynamic search fireworks algorithm (dynFWA) was proposed which divided the fireworks into core firework and non-core fireworks according to the fitness value and adaptive adjustment of the explosion amplitude for the core firework. Based on the analysis of each operator of the fireworks algorithm, an improvement of fireworks algorithm (IFWA) [5] was proposed. Since the FWA was proposed, it has been applied to many areas [6], including digital filter design [7], nonnegative matrix factorization [8], spam detection [9], image identification [10], mass minimization of trusses with dynamic constraints [11], clustering [12], power loss minimization and voltage profile enhancement [13], etc.  
The aforementioned dynFWA variants can improve the performance of FWA to some extent. However, the inhibition of premature convergence and solution accuracy improvement are still challenging issues that require further research on dynFWA.  
In this paper, an adaptive mutation dynamic search fireworks algorithm (AMdynFWA) is presented. In AMdynFWA, the core firework chooses either Gaussian mutation or Levy mutation based on the mutation probability. When it chooses the Gaussian mutation, the local search ability of the algorithm will be enhanced, and by choosing Levy mutation, the ability of the algorithm to jump out of local optimization will be enhanced.  
The paper is organized as follows. In Section 2, the dynamic search fireworks algorithm is introduced. The AMdynFWA is presented in Section 3. The simulation experiments and analysis of the results are given in detail in Section 4. Finally, the conclusion is summarized in Section 5.
## 2 Experiment Scheme
### Test Method and Parameters  
The purpose of this paper includes two aspects. One is to investigate the combined effect of corrosive solution and fatigue load on the corrosion fatigue performance of steel wire with a machine-cut notch, and another is to explore the effect of corrosive solution on the fatigue performance. The former uses the method proposed in Section 2.2.4. The surface of the steel wire is coated with cotton gauze, and the corrosive solution with different concentration and pH value is dropped on the cotton. At the same time, the fatigue load is applied on both ends of the steel wire to simulate the combination of corrosive solution and fatigue load. The latter obtains the fatigue life by applying synchronous pulsating fatigue load to steel wire, which has been corroded over 15 days and 30 days, respectively. The morphologies of the steel wire corroded over 15 and 30 days are shown in Figure 12(a),b, respectively. The service life is studied by using the test device designed in this paper under the action of synchronous pulsating fatigue load.  
Figure 11: Corrosion tests in the laboratory.  
Figure 12: Morphology of steel wire.  
With the stress ratio unchanged, different stress ranges are selected for fatigue loading. When the wire is broken, the cyclic number is defined as the fatigue life under different stress ranges, which could reflect the fatigue performance of steel wire.  
To investigate the influence of different corrosive solution and fatigue load on the life of steel wire, four kinds of corrosive solutions are used in the test, and the specimens are divided into seven groups of A-G. Fatigue test without consideration of corrosion was performed on the specimen in group A. The specimen in group B is subjected to NaCl solution with a pH of 7 and a concentration of 3.5 wt%. The specimen in group C is subjected to NaCl solution with a pH of 3 and a concentration of 3.5 wt%. The specimen in group D is subjected to NaCl solution with a pH of 7 and a concentration of 0.5 wt%. The specimen in group E is subjected to NaCl solution with a pH of 3 and a concentration of 0.5 wt%. The pH value is adjusted by acetic acid and sodium hydroxide and measured with precision paper with the accuracy of 0.1. The specimens in group F and G are corroded for 15 and 30 days, respectively, and then applied to fatigue loads.  
The test is conducted at room temperature, and the loading waveform is the sinusoidal wave. The detailed design parameters are shown in Table 3. \(\sigma_{\text{max}}\) is the maximum stress; \(\sigma_{\text{min}}\) is the minimum stress; \(\sigma_{\text{a}}\) is the stress range, which can be expressed as \(\sigma_{\text{a}}=\sigma_{\text{max}}-\sigma_{\text{min}}\); \(\sigma_{\text{m}}\) is the mean stress, which can be expressed as \(\sigma_{\text{m}}\)\(=(\sigma_{\text{max}}+\sigma_{\text{min}})/2\); \(R\) is the stress ratio, which can be expressed as \(R=\sigma_{\text{min}}/\sigma_{\text{max}}\); \(f\) is the loading frequency.  
Figure 13: Morphology of steel wire corroded in different times.
# A Numerical Study on Computational Time Reversal for Structural Health Monitoring
## 3 Computational Time Reversal for Structures
### Standard Forward Step  
For a time interval of interest \(T\), the finite element model of a system of \(N\) DOFs under transient dynamic conditions will be governed by equations of motion:  
\[\mathbf{M}\ddot{\mathbf{u}}(t)+\mathbf{C}\dot{\mathbf{u}}(t)+\mathbf{K}\mathbf{u }(t)=f(t)\mathbf{f} \tag{18}\]  
where \(\mathbf{f}\) is a load vector usually containing unit values for the components corresponding to degrees of freedom acting as probes or otherwise zeros. Furthermore, \(\mathbf{K}\) is the standard stiffness matrix, \(\mathbf{M}\) the mass matrix and \(\mathbf{C}\) the matrix for the damping. Temporal function \(f(t)\) might be given as a Ricker pulse of the form:  
\[f(t)=\Big{(}1-2\pi^{2}s^{2}(t-t_{0})^{2}\Big{)}e^{-\pi^{2}s^{2}(t-t_{0})^{2}} \tag{19}\]  
We assume a subset of the system's DOFs, \(\mathbf{u}_{\mathbf{r}}\subseteq\mathbf{u}\), where data acquisition takes place by recording the time history of response.
## 5 Supervised Classification
### Classification Experiments  
We built support vector machine (SVM) classifiers with the radial basis function (RBF) kernel for arousal and valence recognition, since it has demonstrated its effectiveness for MER tasks in previous research [52] including those based on physiological signals [37; 53]. Given that only a small proportion of samples in our dataset were labeled as neutral arousal (2.1%) or neutral valence (2.7%), all classification experiments were thus implemented as a binomial classification problem (positive valence/arousal versus negative valence/arousal), in which we excluded the samples with neutral labels in this study, resulting in 936 samples for arousal prediction and 930 samples for valence classification.  
In response to RQ1, we trained SVM models with three feature sets (i.e., audio-only, physiology-only, and audio + physiology) and compared the classification performance with [PERSON]'s robust paired-samples \(t\)-tests [54] and the Benjamini-Hochberg procedure [55] for controlling potential Type I errors introduced by multiple comparisons. Considering the imbalanced sample distribution across the positive and negative arousal/valence categories, apart from the experiments on the original imbalanced dataset, we also built prediction models with resampled balanced training data. Specifically, for each set of training folds, we undersampled the majority class via random undersampling and oversampled the minority class using the SVM borderline synthetic minority oversampling technique (SVM-SMOTE), which extrapolated minority instances along the SVM decision boundary [56]. In response to RQ2, we trained SVM classifiers on subgroups of users to investigate the role of users' personality in the customized modeling of music-induced emotion.
# Driving Safety Improved with Control of Magnetorheological Dampers in Vehicle Suspension
## 3 Control Algorithms
### Skyhook Control of Heave and Pitch Vibration Modes (Skyhook-2 DOF)  
The second variant of Skyhook, which is named Skyhook-2 DOF, emulates different damping force and torque exerted on heave and pitch velocities of the body, denoted as \(F_{\text{alg},z_{s}}\) and \(T_{\text{alg},\varphi_{sp}}\), respectively:  
\[\begin{split} F_{\text{alg},z_{s}}&=-c_{sh2,z_{s}} \cdot v_{s},\\ T_{\text{alg},\varphi_{sp}}&=-c_{sh2,\varphi_{sp}} \cdot\varphi_{sp},\end{split} \tag{28}\]  
where \(c_{sh2,z_{s}}\) and \(c_{sh2,\varphi_{sp}}\) are the parameters of the control algorithm. Similarly to Skyhook-1 DOF, they were assumed as mutually dependent, additionally taking into account different amplitudes of \(z_{s}\) and \(\varphi_{sp}\) in order to maintain a comparable desired force of both control components. It results in the following relationship: \(c_{sh2}=c_{sh2,z_{s}}=2.0\cdot c_{sh2,\varphi_{sp}}\).  
The desired force \(F_{\text{alg},z_{s}}\) and torque \(T_{\text{alg},\varphi_{sp}}\) need to be converted into desired forces that are generated by MR dampers, as follows:  
\[\begin{split} F_{\text{alg},t}&=\frac{1}{I_{f}+I_{ r}}\cdot(F_{\text{alg},z_{s}}\cdot l_{r}-T_{\text{alg},\varphi_{sp}}),\\ F_{\text{alg},t}&=\frac{1}{I_{f}+I_{r}}\cdot(F_{ \text{alg},z_{s}}\cdot l_{f}+T_{\text{alg},\varphi_{sp}}),\end{split} \tag{29}\]  
which are applied to the inverse Tanh model.  
Figure 6: Block diagram of the control algorithm, including inverse Tanh models.