esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
The research results are presented in three sections: the _analysis of current modular design and prosumer methods_, the _use of modular design in the prosumer scope_ and _guidelines for the creation of the new design method_. Each one has its own subsections as be introduced at the beginning of each section.  
Section 3.1 analyzes separately the prosumer and modular design methods and tools that have been developed to date. This section does not establish a direct relationship between them but serves as a preamble to show the current context in each of the two areas. At the beginning of the section, the design process is defined for both cases.  
Section 3.2 covers the current and potential modular design-prosumer relationship. To do this, first a series of real cases where modular design has been focused on prosumer users is presented. Various design methods that meet the same characteristics are also presented, encompassing modular design and prosumer from the same perspective. The section concludes with the definition of the relationship between both terms based on the benefits of modular design and the needs of the prosumer user.  
Section 3.3 establishes a series of objectives and stages for the development of future modular design methods focused on prosumers. The objective is to make a series of recommendations that facilitate the creation and implementation of new design models in this area. This section is a preliminary result--a proposal based on the previous results that must be explored and experimented upon.
###### Abstract  
Research on new, untapped seed oil sources is receiving increased attention. In this study, 18 different seed samples of _Tilia cordata_ and _Tilia platryhyllos_ from various locations in Slovenia were collected and oil was extracted. The compositions of triglyceride fatty acids and unsaponifiable compounds were determined using GC-MS, while antioxidant activity was evaluated using the DPPH method. The oil content in the seeds varied significantly, from 9.1% to 21.7%. Linoleic acid (50-60%) was found to be the predominant fatty acid, followed by oleic acid (18-22%) and palmitic acid (8-9%). Characteristic cyclopropene fatty acids (sterculic, dihydrosterculic and malvalic acids) were present in the average range of 4-8.4%. Antioxidant activity ranged from 8.9% to 65.5%, and was higher, on average, for _T. platryhyllos_. Higher antioxidant activity was closely correlated with higher \(\gamma\)-tocopherol contents. Statistically significant correlations were confirmed between antioxidant activity and \(\gamma\)-tocopherol, between \(\Delta\)-tocopherol and phytol, between stignasterol and \(\beta\)-sitosterol and between squalene and malvalic acid. _Tilia_ oil may be of great interest for cosmetic and dermal preparations. It is, however, not considered a good source of dietary fatty acids due to the undesired, significant content of omega-6 fatty acids.  
vegetable oil; fatty acids; unsaponifiables; antioxidant activity; _Tilia_ sp.
# Multi-User Joint Detection Using Bi-Directional Deep Neural Network Framework in NOMA-OFDM System
## 3 System Model
### Signal and Channel Model
The traditional SIC methods such as LS and MMSE are used, and are also applied for CSI estimation and detection of the signal [26]. In advance, the correction coefficient \(R_{hh}\) for MMSE estimation is calculated. The traditional MMSE estimator can be expressed as follows:  
\[\hat{h}_{\text{MMSEi}}=R_{hY}R_{YY}^{-1}Y=R_{h\hat{h}_{\text{LSi}}}\bigg{(}R_ {hh}+\frac{\sigma_{w}^{2}}{\sigma_{x}^{2}}I\bigg{)}^{-1}\hat{h}_{\text{LSi}}, \hskip 28.452756 pti=1,2, \tag{11}\]  
where the MMSE estimated channel from the \(i\)th transmit antenna is \(\hat{h}_{\text{MMSEi}}\), \(Rhh=\mathbb{E}\{hh^{H}\}\) is the autocorrelation matrix, the cross correlation between the true channel and estimated channel by LS estimation is represented \(R_{h\hat{h}_{\text{LSi}}}=\mathbb{E}\{h\hat{h}_{\text{LSi}}^{H}\}\), the transmitted signal variance is \(\sigma^{2}\), and the identity matrix is \(I\). In addition, since more power is allocated to the \(U\!E_{i}\) signal, a ML detector is used to predict signals [27]. In addition, based on the UE CSI, SIC is implemented. Every UE sends pilots symbol to the BS, and these pilots are utilized for the CE and SNR inference [6; 28]. After estimation of the first UE signal, the second UE signal \(Y_{2}^{\prime}(f)\) can be estimated as follows:  
\[Y_{2}^{\prime}(f)=Y(f)-\sqrt{P}\omega_{1}(f)\hat{h}_{1}(f)\hat{s}_{1}(f). \tag{12}\]
## 2 VMD-ESN Model  
The framework of the proposed VMD-ESN model is shown in Figure 1. In the model, VMD is employed as the initial step to decompose the noise signal into a series of intrinsic mode functions. While a noise signal exhibits non-stationary behavior, its statistical features change over time. VMD handles the problem by decomposing it into modes that vary in frequency and amplitude over time by establishing a variational optimization framework that iteratively refines the decomposition to best fit the time-varying characteristics of the signal. Meanwhile, with the reconstruction constraint, VMD minimizes the sum of the squared frequency bandwidths of all modes to make the bandwidth of each mode as narrow as possible, ensuring that the modes reflect the essential features of the original noise signal, which has complex frequency structures brought by vortex-induced vibrations and turbulence-associated fluctuations. Then, each mode generated by VMD is fed into ESN as an input vector. As a special type of recurrent neural network, ESN uses a large reservoir with numerous neurons, enabling it to save information of previous inputs, which means it has a great short-term memory. Also, sparse connectivity within the reservoir facilitates its decomposition into multiple loosely coupled subsystems, enriching its echo signals and dynamic structure. With its special structure, ESN is used to capture features of all modes and predict their future evolution. Finally, the predictions from all the individual modes are combined to generate the predicted output for the original time series.
# A Survey on Theories and Applications for Self-Driving Cars Based on Deep Learning Methods
## 1 Introduction  
Recently, the rapid development of artificial intelligence has greatly promoted the progress of unmanned driving, such as self-driving cars, unmanned aerial vehicles, and so on [1, 2]. Among these unmanned driving technologies, self-driving cars have attracted more and more attention for their important economic effect [3]. However, there are lots of challenges in self-driving cars [4, 5]. For example, the safety problem is the key technology that must be solved efficiently in self-driving cars, otherwise, it is impossible to allow self-driving cars on the road. Deep learning is an important part of machine learning and has been a hot topic recently [6, 7]. Due to its excellent performances, it has been applied by scientists in the research and development of self-driving cars. More and more solutions based on deep learning for self-driving cars have been presented, including obstacle detection, scene recognition, lane detection, and so on.  
This paper provides a survey on theories and applications of deep learning for self-driving cars. We introduce the theoretical foundation of the main deep learning methods used for self-driving cars. On this basis, we focus exclusively on the applications of deep learning for self-driving cars. Other relevant surveys in the field of deep learning and self-driving cars can be used as a supplement to this paper (see e.g., [1, 6, 8, 9]).  
The main contributions of this paper are summarized as follows. (1) A comprehensive analysis and review of the development of self-driving cars are presented. In addition, the challenges and limitationsof current methods are enumerated. (2) A survey on the theoretical foundation of deep learning methods is given out, which focuses on the network structure. (3) An overview of the applications in self-driving cars based on deep learning is given out, and the details of some representative approaches are summarized. At last, some prospects of future studies in this field are discussed.  
This paper is organized as follows: In Section 2, a general introduction to the development of self-driving cars is provided. A comprehensive analysis is given out on the development of the hardware and software technologies in this field. Section 3 introduces the theoretical background of some common deep learning methods used for self-driving cars. The main applications and some representative approaches based on deep learning methods in the field of self-driving cars are summarized in Section 4. Section 5 discusses the future research directions for the deep learning methods in self-driving cars. Finally, conclusions are given out in Section 6.
## 3 The Proposed Tracker
### The KCF Tracker  
Like other CF-based methods, our proposed method is also based on the KCF tracker. Therefore, before the exhaustive discussion of our proposed method, we first review the KCF tracker [15]. The KCF tracker achieves excellent results and high-speed performance on the visual tracker benchmark [8], despite the idea and implementation of the KCF tracker being very simple. The KCF tracker collects positive and negative samples around the target using the structure of the circulant matrix, to improve the discriminative capability of the track-by-detector tracker. The circulant matrix can be diagonalized with the Discrete Fourier Transform (DFT), enabling a fast dot-product instead of an expensive Matrix algebra.  
The goal of the KCF tracker is to find a function that minimizes the squared error over data matrix \(\mathbf{X}\) and their regression target \(\mathbf{y}\),  
\[\underset{\mathbf{w}}{min}\parallel\mathbf{X}\mathbf{w}-\mathbf{y}\parallel^{2 }+\lambda\parallel\mathbf{w}\parallel^{2} \tag{1}\]  
where the square matrix \(\mathbf{X}\) contains all circulant shifts of the base sample \(\mathbf{x}\), the regression target \(\mathbf{y}\) is Gaussian-shaped, and the \(\lambda\) is a regularization parameter to ensure the generalization performance of the classifier, Equation (1) has the closed-form solution.  
\[\mathbf{w}=\left(\mathbf{X}^{\mathrm{T}}\mathbf{X}+\lambda\mathbf{I}\right)^ {-1}\mathbf{X}^{\mathrm{T}}\mathbf{y} \tag{2}\]  
The circulant matrix \(\mathbf{X}\) has some intriguing properties [47, 48], and the most useful one is that the circulant matrix can be diagonalized by the Discrete Fourier Transform (DFT) as below:  
\[\mathbf{X}=\mathbf{F}^{\mathrm{H}}diag(\hat{\mathbf{x}})\mathbf{F} \tag{3}\]  
where \(\mathbf{F}\) is the DFT matrix, and \(\mathbf{F}^{\mathrm{H}}\) is the Hermitian transpose. \(\hat{\mathbf{x}}\) denotes the DFT of \(\mathbf{x}\), \(\hat{\mathbf{x}}=F(\mathbf{x})=\sqrt{\mathrm{n}}\mathbf{F}\mathbf{x}\).  
Applying Equation (3) into the solution of linear regression (Equation (2)), we have the solution as below:  
\[\hat{\mathbf{w}}=\frac{\hat{\mathbf{x}}\odot\hat{\mathbf{y}}}{\hat{\mathbf{x }}^{*}\odot\hat{\mathbf{x}}+\lambda} \tag{4}\]  
where \(\hat{\mathbf{x}}^{*}\) is the a complex-conjugate of \(\hat{\mathbf{x}}\). The symbol \(\odot\) and the fraction denote element-wise product and division respectively.  
For detecting the new location of target in the next frame, we can compute the response \(f(z)\) for all candidate patches \(\mathbf{z}\), and diagonalize \(f(z)\) to obtain as below:  
\[\hat{f}(z)=\hat{\mathbf{w}}\odot\hat{\mathbf{z}} \tag{5}\]  
The candidate patch with the maximum response is considered as the new location of target.
# Industrial Dust Explosions. A Brief Review
## 1 Brief Review of Dust Explosion Basics
### Ignition Sources That Can Start Dust Explosions
#### 1.5.2 Open Flames, Hot Surfaces and Mechanical Impacts  
Flames of welding and cutting burners are more than sufficiently powerful to initiate explosions in most explosive dust clouds. Hot surfaces, e.g., overheated bearings, heaters in workrooms, light bulbs, and walls in dryers, can initiate dust explosions directly by contact with the dust cloud.  
Mechanical impacts are short-duration interactions between two solid bodies under the conditions of large transient mechanical forces. If the impacting metal is hard, small fragments may be torn off and start burning in air due to the initial heat absorbed by the particle in the impact. In other cases, impacts can occur repeatedly at one specific point, creating a hot surface that may be capable of igniting an explosive dust cloud contacting the hot spot.
## 2 Materials and Methods
### Tests
#### 2.3.2 Tensile Properties  
As specified in ASTM D638-14, type IV samples were prepared with a dog-bone shape. A universal tester (Instron 5566, Instron, Norwood, MA, USA) was used to measure the tensile properties of the 0.5-mm thick T/P and T/P/MA waste-blended samples, including tensile stress at break, tensile strain at break, tensile stress at yield point, and tensile strain at yield point. The speed of testing was based on the specifications of the specimen type, and T/P and T/P/MA groups were classified as rigid and semirigid groups. Moreover, type IV samples were chosen to be tested at a speed of 5 mm/min [30]. Five samples for each specification were used to obtain the average value.
## 3 Background
### Problem Formulation  
Let us assume that the server and client nodes are mobile (dynamically changes their position) and the relay node has a fully controllable dynamics, but has no control over the server or client nodes' movements. The positions of the server, relay and client nodes are \(p_{s}\), \(p_{r},\) and \(p_{c}\), respectively, in a 2D domain (\(\mathbb{R}^{2}\)), respectively. We also assume an unknown, noisy and distributed radio signal environment  
Figure 3: Relationship between the radio signal strength (RSS) and the goodput measurements. (**a**) Scatter plot depicting the measured goodput values against the RSS in various receivers at different time instants; (**b**) RSS and goodput values measured by a relay node at various positions and instants in a non-LOS (NLOS) condition.  
(RSS field) and suppose that the RSS can be measured by the relay node using on-board wireless receivers (which act as the RSS sensors). We formulate the problem statement as follows:  
**Problem 1**: Find an optimization objective function that should be maximized by optimizing \(p_{r}\), such that the network capacity (goodput) of the end-to-end server-client link is optimal.
**Problem 2**: Find a robust stochastic optimization (RSO) algorithm that maximizes the objective function and is robust to the dynamics in the environment.
**Problem 3**: Find an RSS sensing method and RSS pre-processing techniques to increase the robustness of the RSO algorithm.
**Problem 4**: Find a suitable dynamics controller for the relay node that supports the solutions of Problems 1-3 and also ensures that the relay node can reach the objective smoothly.  
The optimization algorithm is implemented on the relay node, and the inputs to the algorithm are the position (\(p_{r}\)) value of the relay node obtained using an odometer (however, we assume no knowledge on the positions of the server or client nodes) and the RSS values at the relay node for the radio signals received from the server and client nodes. The robustness of our algorithm is verified for objective maximization under sensory and environmental noises. In this regard, simulations and experimental evaluations are conducted considering various environmental conditions.
## 3 Experiment Verification
### Dataset Quality Control  
Radar echo images often contain a significant amount of noise [34] due to interactions with various obstructions in the air, such as insects and aircraft. These obstructed echoes are captured in the radar images, resulting in noise that can adversely affect algorithm performance. Consequently, quality control is typically conducted on radar echo images prior to tracking and extrapolation. Given that the echo intensity of rain clouds typically exceeds 30 dBZ, and that these cloud clusters generally do not have gaps, morphological operations are frequently utilized to eliminate holes within the radar echoes. The initial step involves binarizing the radar echo images based on the relationship between echo intensity and image grayscale values. This is followed by closing operations to effectively remove any holes within the rain cloud clusters. The specific process is illustrated in Figure 10.
## 3 Generation of the Mesh
### Mesh Boundary  
If the structure to model is complex in a sense that it contains many substructures with their own surfaces which consist of triangles of very different size and quality then the generated mesh must take into account these substructures and the joints between them, which constitutes additional problems during the mesh generation.  
The 3D mesh should be of a good quality not only in its 3D aspect but also in its 2D aspect as well. This concerns the quality of the surface of the mesh and, especially, the parts belonging to distinct areas.  
The quality of a 2D (triangular) mesh of the surface of the bouton which topologically remains similar to the 3D ball is assessed analogically to the quality of the 3D net.  
The quality measures that correspond to the ones defined for the spatial meshes are as follows: the square perimeter divided by the square root of the surface (PS2), the longest edge divided by the radius of the inscribed sphere (in that case the 2D sphere is a circle) (ER2), the longest edge divided by the smallest height (EH2) or the function of the ratio of the largest and smallest edge length, respectively, to the total perimeter of the triangle (MAXE and MINE).  
Similar as in the 3D case, the best (the smallest) attainable values of the above measures (except MINE) are:  
\(PS2_{min}=2x27^{1/4}\approx 4.55901\)  
\(ER2_{min}=2x3^{1/2}\approx 3.4641\)  
\(EH2_{min}=2/[3^{1/2}]\approx 1.1547\)  
\(MAXE_{min}=1/3\approx 0.3333\)  
and the largest MINE is  
\(MINE_{max}=1/3\approx 0.3333\)  
For the first three measures the upper bound is infinity whereas for the fourth one (MAXE) it is equal to 0.5, and the lower bound for the last parameter (MINE) is 0.
# A Low-Latency Fair-Arbiter Architecture for Network-on-Chip Switches
## 4 Implementation and Experiment
As shown in Table 5, at 250 MHz, all of the arbiters can be realized under the condition of \(N=256\), and the delay situation typically conforms to the trend under ASIC. However, only the FSA can achieve a scale of 128 with a 400 MHz clock, and the SA can only achieve a scale of 64. The FPGA results are reported in Table 6. The proposed arbiter uses 14% less LUT resources than PLA and uses 17% less flip-flop than SA. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Port** & **PPA [19]** & **SA [21]** & **PRRA [23]** & **IPRRA [23]** & **PLA [25]** & **FSA** \\
**Number** & **Unfair** & **Unfair** & & & & & \\ \hline \(N=4\) & 342 & 402 & 341 & 366 & 318 & 404 \\ \(N=8\) & 827 & 958 & 785 & 783 & 865 & 1076 \\ \(N=16\) & 1694 & 1930 & 1534 & 1540 & 1903 & 2015 \\ \(N=32\) & 3288 & 3844 & 2582 & 2864 & 3765 & 3713 \\ \(N=64\) & 6773 & 7722 & 5346 & 5977 & 7331 & 6936 \\ \(N=128\) & 13,052 & 15,559 & 10,255 & 10,793 & 14,135 & 13,761 \\ \(N=256\) & 25,113 & 30,826 & 21,121 & 21,482 & 28,231 & 26,704 \\ \(N=512\) & 50,319 & 60,844 & 40,891 & 41,940 & 54,470 & 52,608 \\ Average & 100\% & 120\% & 81\% & 85\% & 109\% & 105\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Area results of the arbiters (um\({}^{2}\)). Figure 9: Comparison of all arbiters. (**a**) Timing. (**b**) Area.
# Multi-Point Time-Synchronized Waveform Recording for the Analysis of Wide-Area Harmonic Propagation
## 4 Results  
The amount of measurement data as the outcome of this experiment is significant, giving measurement windows of 10 min for every test sequence within package B and in an average of 2 min to 3 min for testing scenarios of package A. Typically, around 30 synchronized channels of raw waveforms were recorded for each test. For representing results of this work, we chose to process only one phase (A) for every measurement in question. The signal processing method of choice is FFT with 10 cycles rectangular window. For package A, only one selected window was used for the graphical representation of the results. As it was stated in the Section 3.1 when energizing the network with MG-set, a non-distorted voltage at the terminals of Isolation Transformer is ensured. This allows for focusing on the harmonic processes originating from the downstream network on the customer side since background voltage harmonics cannot penetrate from MV upstream network via MG-set. It was therefore concluded that one 10-cycle window can be sufficient for analyzing harmonic distortion when no fluctuating background voltage harmonics are present in the test grid.  
On the contrary, with grid power supply, it was important to demonstrate time evolution of the harmonics. For this purpose, every 10 min waveform was fully processed and 10-cycle RMS magnitudes were collected into coherent plots with 600 s duration time. This technique allowed for evaluating harmonic trends being a function of fluctuating voltage background distortion. For one specific test case B.4, we introduced additionally a numerical analysis based on the estimated synchronized harmonic phasors. Only results from the selective cases will be demonstrated here.
## 5 Results and Discussion
### Instantaneous Goodput
#### 5.2.2 Malicious Network  
The malicious network shows a different trend compared to the healthy one, as depicted in Figure 15. In the initial ninety seconds, the system does not show any increase in goodput value due to the attacking node, and the system is under recovery. After 90 s, the system shows a sudden increase in goodput value due to rerouting, and the network finds the optimal path to send the packets from the source to the sink node. The value only moves up to 12,500 kbps at 20 s due to the interference of the malicious node.
## 3 Calculation Results and Discussion
### Hybrid System Off-Design Performance Evaluation under Variable Operation Parameters
#### 3.3.1 Effect of O/C Ratio Variation on the Hybrid System Performance
The analysis reveals that thrust variation is not substantial, decreasing marginally from 7.92 to 7.86 kN, with an increase in O/C ratio, as shown in Figure 8. As the overall efficiency of the GT is linked to thrust generated, it exhibits a similar variation, decreasing with an increase in O/C ratios. Despite the decrease in thrust generation, with a constant FAR, the TSFC increases proportionally, rising from 308 to 311 mg/s/N. This leads to the conclusion that the thrust generation capabilities of the GT are not significantly affected by the variable O/C ratio. Figure 8: Effect of variable O/C ratio on GT performance. Figure 7: Effect of variable O/C ratio on SOFC performance.