esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Physiological Characterization
#### 3.3.4 Aldehyde and Ketone Compounds  
Aldehyde and ketone compounds can impart fruity, floral, and creamy notes to wine when present at appropriate levels; however, excessive concentrations may weaken the perceived intensity of the overall aroma [39]. In this study, three aldehyde and ketone compounds were identified, and different fermentation strategies significantly influenced their accumulation patterns. Among these, 6-methyl-5-hepten-2-one was successfully detected in the fermented sea buckthorn wine, with relatively high concentrations observed in both groups, though no significant difference was found between them. Studies have shown that 6-methyl-5-hepten-2-one is an aromatic compound with fruity and floral characteristics, playing an important role in the overall flavor composition of fruit wine [40]. The nonanal content increased significantly in the JH group (32.53 \(\upmu\)g/L) compared to in the JS group (24.72 \(\upmu\)g/L). Nonanal is a characteristic compound widely found in spirits and fruit wines, known to enhance floral, fruity, green (grassy), and woody notes [41]. The elevated nonanal content in the JH group may have contributed to increased aroma complexity in the wine. The concentration of damascenone, a compound known to impart fruity and sweet notes to wine, was similar in both groups [42]. These differences suggest that different yeast strains exhibit distinct selectivity in the metabolism of aldehyde and ketone compounds, ultimately influencing the aroma composition and flavor complexity of sea buckthorn wine.
# A Review of the Optimal Design of Neural Networks Based on FPGA
## 4 Neural Network Optimization Technology Based on FPGA
### Optimization of Data and Its Operations  
In the aspect of optimization of data and its operation, scholars have made many attempts and achieved certain results. Aiming at the data itself, a method to reduce the data accuracy and computational complexity is usually used. For example, fixed-point quantization is used to reduce the computational complexity with an acceptable loss of data accuracy, so as to improve the computational speed. In terms of operation, the optimization is realized by reducing the computation times of multiplication and increasing the computation speed. The commonly used optimization methods include addition calculation instead of multiplication calculation, the Winograd fast convolution algorithm, and the Im2 col convolution acceleration algorithm, among others.
###### Abstract
\begin{table}
\begin{tabular}{c c c c} \hline
**Algorithm** & **Application Scenario** & **Main Advantages** & **Limitations** \\ \hline SCE-ICA [15] & Underwater visible light communication (UVLC) & Elimination of ISI and ICI & Slow convergence of ICA algorithm, requirement of multiple iterations \\ \hline CNN-Based IBI Cancellation [21] & Mobile fronthaul network (NGFI-II) & Elimination of IBI & Requirement of IBI extraction module, complex design of input window \\ \hline DNN + NN-DD-LMS [26] & High-order UVLC (64 APSK/64 QAM) & Support for high symbol rate & Requirement of step-by-step training due to cascaded structure, sensitivity to initial weights \\ \hline \end{tabular}
\end{table} 
Table 1: Comparison of existing NN-based receiving algorithms. Figure 1: Conceptual diagram of the coordinated MISO photonic mmWave communication system using an NNWSC-based receiver with multiband multiplexing. methods typically involve cascaded processing stages, which can lead to error accumulation and reduced performance under varying channel conditions. In contrast, NNs offer a powerful alternative by enabling direct waveform-to-symbol conversion, bypassing the need for multiple cascaded processing stages, leveraging the NN's ability to uncover implicit relationships between the received waveform and the transmitted symbols, and effectively handling complex interference and adapting to dynamic channel conditions. In this paper, we propose and demonstrate a neural network-based waveform-to-symbol converter (NNWSC) in the receiver for 16 QAM communication systems using NM-CAP modulation. Integrating the NNWSC into a coordinated multiple-input single-output (MISO) photonic mmWave system with multiband multiplexing enhances flexibility in managing complex interference patterns and adapting to variable channel conditions, as shown in Figure 1. By enabling direct waveform-to-symbol conversion, the proposed receiver architecture not only simplifies the demodulation process but also addresses interference challenges more effectively than conventional DSP-based receivers, particularly under varying sub-band spacing, roll-off factors, and received optical power (ROP) conditions. Moreover, the NNWSC demonstrates its ability to mitigate symbol timing offset (STO) issues inherent in the coordinated configurations, further enhancing its suitability for complex channel environments. Experimental validations of the NNWSC's performance are conducted in coordinated MISO configurations, showing the system's adaptability to various interference conditions and achieving consistently lower BER and greater robustness compared to conventional approaches. Based on the proposed method, a coordinated MISO photonic mmWave communication system at 91.9 GHz is demonstrated at a transmission speed of 30 Gbps. The results show that the NNWSC-based receiver achieves significant bit error rate (BER) reductions compared to conventional receivers across all configurations. Overall, this study presents a new perspective on leveraging neural networks to enhance photonic mmWave communication systems, paving the way for advancements in high-frequency wireless technology.
## 1 Introduction  
Malolactic fermentation (MLF) is a secondary fermentation that takes place after alcoholic fermentation in the process of grape wine production. It is used in the majority of red wines, some white wines, and generally for wines with enhanced acidity. The process is conducted by malolactic bacteria (MLB), most often strains of _Oenococcus oeni_, and involves the decarboxylation of L-malic acid into L-lactic acid. As a result of this bioconversion, a noticeable reduction in the total acidity of the wine can be achieved. The MLB utilize not only malic acid, but also the residual sugars left by yeast after the alcoholic fermentation. This reduces the potential carbon source for spoilage microbiota, and increases microbial stabilization of the wine [1; 2; 3; 4; 5].  
MLF is also a process known to modify the aroma profile of the wine through biosynthesis or bioconversion of flavor-active compounds. Some authors suggest that MLF enhances fruity notes and buttery aromas, while reduces vegetative green and grassy aromas [4; 6]. Other researchers have postulated that MLF results in a creamier palate, less fruit intensity, and more nutty, vanilla, toasty butter, and wet leather aromas [7].  
A range of factors, including grape cultivar, the bacterial culture used, and the conditions of vnification, have been described as having an effect on the character of wines treated by MLF [8; 9]. However, little is known about the effect of different timings of malolactic bacteria inoculation on wine aroma modification. To our knowledge, only a few research groups [9; 10; 11; 12] have compared different MLB inoculation timings and determined their effect on the differentiation of the quality and quantity of the aroma compounds. However, none of these authors examined the spontaneous variant of MLF, which involves a risk of producing undesirable aroma compounds in wines.  
Our research focused on selected aroma compounds (diacetyl and its metabolic products, as well as selected ethyl fatty acid esters) synthesized during malolactic fermentation in white and red grape wines produced by three different methods of inoculation: (1) Coinoculation (COI), where the yeast and bacteria were inoculated at the same time; (2) sequential inoculation (SEQI), where malolactic fermentation was induced at the end of alcoholic fermentation; and (3) spontaneous malolactic fermentation (SPONT), where MLB inoculation was not performed. This is the first study which attempts to evaluate the effects of different timings of MLB inoculation (including the spontaneous process which involves a significant risk of producing undesirable compounds) on the bioconversion of aromatic compounds in red and white grape wines. The experiment was performed during three wine seasons.
# An Object Detection Method Based on Feature Uncertainty Domain Adaptation for Autonomous Driving
## 2 Related works  
### Adversarial Learning-Based Methods for Domain Adaptation  
The widely adopted method in domain adaptation is to enable the model to learn domain-invariant features, where the features obtained from same-class objects from two different domains are similar. To achieve this goal, a classification task is coupled with an adversarial learning approach proposed in [13]. Specifically, a domain discriminator module is added after the output of the feature extraction layer, which is used to distinguish whether the current feature map comes from the source or target domain. During backpropagation, the gradient reversal layer is utilized to pass the opposite gradient, reducing the upper bound of the feature space, which requires the obtained features to meet the requirements of the classification task while also deceiving the domain discriminator. Inspired by this work, a study [25] attempted to apply this method in the field of object detection and proposed to add a domain discriminator in each stage of the two-stage detector, achieving local and global alignment through a gradient reversal layer. During backpropagation, both source and target domain data are used to calculate the domain discriminator loss, while the detection loss is obtained only from the source domain data and corresponding ground truth labels. A study [26] pointed out that the deep layers of the feature extraction layer tend to capture global information, and direct strong alignment is not the best choice. Therefore, they proposed to implement strong alignment only in the shallow layers of the backbone and weak alignment in the deeper layers. Meanwhile, the focal loss is introduced to distinguish the difficulty of recognizing targets, allocating different alignment weights. Many subsequent works [27; 28; 29] follow the above scheme and make improvements, but these methods calculate the domain difference using the entire feature map, causing the model to pay too much attention to task-irrelevant parts. To solve this problem, reference [30] utilized the ROIs generated by the RPN as the focus center and clustered the ROIs using the K-means algorithm. Different weights are assigned to the discriminator according to the selected regions. In addition to guiding attention through RPN, there are also works [31] that partition the importance of different regions by adding a semantic segmentation module. However, for rainy and foggy weather conditions in the autonomous driving scene, RPN or segmentation networks cannot provide perfect attention guidance. Therefore, in this paper, an uncertainty alignment module is proposed by calculating the entropy of the feature channel based on the different sensitivity of each channel to different regions in the feature extraction layer.
# Predictive Path Following and Collision Avoidance of Autonomous Connected Vehicles
## 4 Simulation Results  
In this section, simulation results are presented to demonstrate the validity and assess the performance of the proposed NMPC scheme for path-following and collision avoidance of autonomous vehicles. Simulation is done in MATLAB 2017b using the static C-code generated from the ACADO toolkit [26]. These results have been obtained on a 2.8 GHz core i5 CPU with 16 GB RAM. In the simulation scenario, there are two vehicles; the own vehicle and the encountered one. It is assumed that the encountered vehicle has the priority and therefore its collision avoidance algorithm was not triggered, and it sends its future prediction states to the own vehicle. In Table 1, the parameters of the vehicle Model (1), which are obtained from [28], and the NMPC parameters are given.  
The vehicle starts from the initial position\((x,y),=(0\,\mathrm{m},0\,\mathrm{m})\) and the NMPC steers it to follow the reference quadratic Bezier path with the control points \(P_{0}=[0,0]^{T}\), \(P_{1}=[100,50]^{T}\), and \(P_{2}=[300,0]^{T}\) As shown in Figure 5, the NMPC steers the vehicle to follow the reference path, and when the slower vehicle impedes this path, the NMPC steers the vehicle to avoid collision with the encountered vehicle by overtaking it, while respecting the control input constraints and the road corridors. In Figure 6a, it is shown that the vehicle could achieve the desired speed except during the collision avoidance maneuvering. It is clear in Figure 6e,f that maneuvering of the vehicle does not have any abrupt change in the steering angle and therefore it is consistent with the steering system dynamics. In Figure 6c,d, it is shown that the maximum allowed acceleration is obeyed while avoiding any jerky actions due to the constraints on the acceleration rate of change. The distance between the centers of both vehicles at the closest point of approach is about \(3.4\,\mathrm{m}\) which is quite smaller compared to selecting circular disks, as presented in [14], which gives a minimum distance of at least \(4.4\,\mathrm{m}\). The maximum execution time of the presented scheme is about \(50\,\mathrm{ms}\) which qualifies it to be used for experimental evaluation, and the maximum number of iterations is 100.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(a_{max}=1\,\mathrm{m}/\mathrm{s}^{2}\) & \(\delta_{max}=20\,\mathrm{deg}\) & \(a_{dot_{max}}=0.4\,\mathrm{m}/\mathrm{s}^{3}\) \\ \hline \(\delta_{dot_{max}}=4\,\mathrm{deg}\,/\mathrm{s}\) & \(N=80\) & \(T_{s}=0.1\,\mathrm{s}\) \\ \hline \(T_{p}=8\,\mathrm{s}\) & \(m=1573\,\mathrm{kg}\) & \(l_{f}=1.1\,\mathrm{m}\) \\ \hline \(l_{r}=1.57\,\mathrm{m}\) & \(r_{1}=2.2\,\mathrm{m}\) & \(r_{2}=1.6\,\mathrm{m}\) \\ \hline \(Q=\mathrm{diag}(1.0,10,0.2,0.002)\) & \(R=\mathrm{diag}(0.001,0.001,0.002)\) & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Vehicle and NMPC parameters.  
Figure 5: Simulation results for collision avoidance in an overtaking scenario.  
Figure 6: Simulation results.
# An Enhanced Particle Swarm Optimization (PSO) Algorithm Employing Quasi-Random Numbers
###### Abstract  
This paper introduces an innovative Particle Swarm Optimization (PSO) Algorithm incorporating Sobol and Halton random number samplings. It evaluates the enhanced PSO's performance against conventional PSO employing Monte Carlo random number samplings. The comparison involves assessing the algorithms across nine benchmark problems and the renowned Travelling Salesman Problem (TSP). The results reveal consistent enhancements achieved by the enhanced PSO utilizing Sobol/Halton samplings across the benchmark problems. Particularly noteworthy are the Sobol-based PSO improvements in iterations and the computational times for the benchmark problems. These findings underscore the efficacy of employing Sobol and Halton random number generation methods to enhance algorithm efficiency.  
enhanced PSO; Sobol; Halton; quasi-random numbers +
Footnote †: journal: _Algorithms_
## 2 Data Analysis  
The principal component analysis (PCA) and hierarchical cluster analysis (HCA) were performed by using SPSS version 27.0 software (SPSS Inc., Chicago, IL, USA) to show the relationship among the _C. japonica_ EO from different regions based on their percentage chemical composition. The PCA was achieved selecting the three highest principal components (PCs) obtained by the linear regressions operated on scaled data. For the statistical evaluation of the complete EO composition, the covariance data was a 19 \(\times\) 14 matrix (19 EO samples \(\times\) 14 chemical compounds = 266 data). The first three PCs (PC1, PC2 and PC3) explained 46.8%, 14.7% and 10.0% of the total variance, respectively, allowing the visualization of more than 70% of the information contained within the dataset. HCA was performed using Ward's method and squared Euclidean distance.
###### Abstract  
Fluid film bearings lubricated with supercritical carbon dioxide (sCO\({}_{2}\)) eliminate the in-frrastructural requirement for oil lubricant supply and sealing in turbomachinery for sCO\({}_{2}\) power systems. However, sCO\({}_{2}\)'s thermohydrodynamic properties, which depend on pressure and temperature, pose a challenge, particularly with computational model development for such bearings. This study develops a computational model for analyzing sCO\({}_{2}\)-lubricated tilting pad journal bearings (TPJBs) with external pressurization. Treating sCO\({}_{2}\) as a real gas, the Reynolds equation for compressible turbulent flows solves the pressure distribution using the finite element method, and the Newton\(-\)Raphson method determines the static equilibrium position by simultaneously calculating forces, moments, flow rates of externally pressurized sCO\({}_{2}\), and pressure drop due to flow inertia. The finite difference method solves the energy equation for temperature distribution. The density and viscosity of sCO\({}_{2}\) are converged using the successive substitution method. The obtained predictions agree with the previous and authors' computational fluid dynamics predictions, thus validating the developed model. Hybrid lubrication increases the minimum film thickness and stiffness up to 80% and 65%, respectively, and decreases the eccentricity ratio by up to 65% compared to those of pure hydrodynamic TPJB, indicating significant improvement in the load capacity. The bearing performance is further improved with increasing sCO\({}_{2}\) supply pressure.  
Keywords:tilting-pad journal bearing; hybrid lubrication; supercritical carbon dioxide; load capacity +
Footnote †: journal: Applied Sciences
## 4 Analysis of Electromagnetic Weft Insertion Characteristics  
### Transmitting Coil Magnetic Field Analysis  
As shown in Figure 6, \(M\)(x,0) is any point on the axis of the coil, the inner radius of the coil is \(R_{1}\), the outer radius is \(R_{2}\), the total length of the coil is \(2L\), the current intensity is \(I\), the number of turns is \(N\), and \(j=NI/[2L\ (R_{2}\ -R_{1})]\) is the current density generated during continuous current distribution. The magnetic induction intensity at different positions inside the coil is shown in Figure 7. There are obvious rises and falls at both ends of the coil, and the gradient value is the largest. According to Biot-Savart law, the magnetic induction intensity on the central axis of the accelerating coil is [18]:  
\[B_{x}=\frac{\mu_{0}j}{2}\bigg{[}(x+L)\ln\frac{A}{C}-(x-L)\ln\frac{B}{D}\bigg{]} \tag{1}\]  
where: \(\mu_{0}=4\pi\times 10^{-7}\) Wb/A,  
\[A=R_{2}+\sqrt{R_{2}^{2}+\left(x+L\right)^{2}} B=R_{2}+\sqrt{R_{2}^{2}+\left(x-L\right)^{2}}\] \[C=R_{1}+\sqrt{R_{1}^{2}+\left(x+L\right)^{2}} D=R_{1}+\sqrt{R_{1}^{2}+\left(x-L\right)^{2}}\]  
Figure 5: Schematic diagram of a multi-stage segmented combined coil.  
The magnetization vector \(M\) is used to describe the magnetization state of the magnetic medium. A macroscopic volume element \(\mathbf{\Delta}V\) is taken in the magnetic medium, and this volume element contains a large number of magnetic molecules. Using the \(\sum\mathbf{m}\) molecule to represent the vector sum of the magnetic moments of all molecules in this volume element, the magnetization vector \(\mathbf{M}\) can be expressed as:  
\[\mathbf{M}=\frac{\sum\mathbf{m}_{\text{molecule}}}{\Delta V} \tag{2}\]  
After introducing the concept of magnetic susceptibility \(\chi_{m}\), the magnetization vector can also be expressed as:  
\[\mathbf{M}=\chi_{m}\mathbf{H} \tag{3}\]  
When the magnetization \(\mathbf{M}\) is known, the additional magnetic induction intensity \(\mathbf{B^{\prime}}\) can be calculated from it. Then the additional magnetic induction intensity and the magnetic induction intensity \(\mathbf{B_{x}}\) of the axis of the magnetizing field are superimposed together to obtain the magnetic induction intensity \(\mathbf{B}\) of the ferromagnetic weft clamp in the coil. Among them, \(\mathbf{B^{\prime}}\) can generally be ignored in calculation.  
\[\mathbf{B}=\mathbf{B}_{x}+\mathbf{B^{\prime}} \tag{4}\]
# A Statistical Performance Analysis of Named Data Ultra Dense Networks
## 3 Simulations and Dataset Generation
### Four-Way Factorial Design  
In experimental studies, there may be multiple factors that significantly impact the outcome. For an experiment, a quantitative variable can be designed to be qualitative, taking only a few pre-designed values, which are called levels.  
For example, in ultra-dense networks, the bandwidth of a wireless or wired link can be considered as a factor with levels such as 1 Mbps, 2 Mbps, 10 Mbps, etc. In a factorial design, two or more factors can be involved in a single experiment. The number of levels for each of the factors can be different. In general, if \(K\) factors are used to design an experiment and the \(k^{\text{th}}\) factor has \(l_{k}\) levels, then the size of the experiment is given by \(N\) as in Equation (2).  
\[N=\prod_{k=1}^{K}l_{k} \tag{2}\]  
For the generation of the dataset, a complete four-way factorial design was used [28], where the four factors were NI with values at [250, 500, 750, 1000], NN with values at [9; 25; 41; 49], RB with values at [1; 5; 10; 20], and RD with values at [10; 50; 100, 200], as shown in Table 1. Four levels of eachfactor were used to reveal the non-linearity of the data. Including all the combinations of factors in the model, the linear model was as follows:  
\[\begin{split} y_{ijknq}&=\mu+\alpha_{i}^{NI}+\alpha_{ j}^{NN}+\alpha_{k}^{RB}+\alpha_{kj}^{RD}+\alpha_{ij}^{NI\times NN}+\alpha_{ik}^{NI \times RB}+\alpha_{im}^{NI\times RD}+\alpha_{jk}^{NN\times RB}+\alpha_{jn}^{ NN\times RD}+\alpha_{kn}^{RB\times RD}\\ &+\alpha_{ijk}^{NI\times NN\times RB}+\alpha_{ijn}^{NI\times NN \times RD}\alpha_{ikn}^{NI\times RB\times RD}+\alpha_{jkn}^{NN\times RB \times RD}+\alpha_{ijkn}^{NI\times NN\times RD}+\alpha_{ijkn}^{NI\times NN \times RD}+e_{ijknq}\end{split} \tag{3}\]  
In Equation (3), \(\mu\) is the grand mean. \(\alpha\) stands for fixed effects, with the superscripts denoting the factors and the subscripts denoting their levels. The interaction effect is indicated by \(\times\) in the superscripts. Equation (3) tests the difference between levels of fixed effects since the fixed effects are considered as fixed categories.
## 2 CAD System Overview
### Adaptive thresholding and grouping  
The first step of our colon segmentation approach is an adaptive thresholding of the image, separating it into air and non-air voxels. Following [30], the threshold is computed adaptively based on the image histogram using an entropy-based measure, and clipped to ensure it is within the range \([-800,-500]\). After thresholding, all air voxels are foreground and all non-air voxels are background in the segmentation mask, as demonstrated in Figure 3(a). Next, foreground voxels are grouped into disjoint segments and labeled based on their 3D connectivity. While voxels corresponding to the air-filled colon are easily found as a result of this thresholding, often other air-filled anatomies like the lungs, small bowel, and stomach, as well as the air outside the body are included. Removal of these non-colon segments is described in the next subsection.