esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Currently, new assortments of edible oils are being introduced to the food market, such as avocado oil, blackcurrant seed oil, herbs, and various nut oils [1]. These oils usually have health-promoting properties or specific sensory and technological properties. Oils with health-promoting properties include blackcurrant or evening primrose seed oil because of the significant content of polyunsaturated fatty acids (PUFA) from the omega-6 and omega-3 groups, which are essential for human health, and monounsaturated omega-9 fatty acids [2, 3]. These acids have a beneficial effect on the functioning of the circulatory system, the structure of cell membranes, and the modulation of inflammatory reactions [4]. Oils with special technological properties include, for example, coconut oil, avocado oil, or grapeseed oil with high stability at high cooking temperatures. Oils with special sensory properties include, for example, black cumin seed oil, rose hip oil, or pepper seed oil, which are used to flavor food [5]. Although some of them are produced from waste and by-products of the food industry, such as grapeseed oil or pepper seed oil [1, 2, 5], these new oils on the market are usually more expensive than commonly used cooking oils.  
A special example of market success is grapeseed oil [5; 6; 7]. Grape pomace is waste from the production of grape juice and wine. Grape seeds contain large amounts of oil (up to 20% dry matter, depending on the variety) and are used for the production of edible oil [6; 8]. Grapeseed oil, the raw material for which is large amounts of grape pomace, is produced on an industrial scale mainly in Italy and Spain [6; 9]. This oil has gained success in the market and is now used for frying, baking, and cooking not only in Italy and Spain but also in other EU countries. Similarly, it has been found that the annual production of significant quantities of apple pomace can be utilized as a raw material in the production of a novel edible oil from apple seeds [10]. Apple seeds contain anywhere from 15% to 22% fat in dry matter, which is characterized by a nutritionally beneficial composition of fatty acids: a high content of PUFA, mainly omega-6 linoleic acid, and monounsaturated acids, mainly omega-9 oleic acid [11; 10; 12; 13; 14].  
There is a noticeable trend in the useful management of waste and by-products generated in the food industry. The industry can use them towards the development and implementation of new ranges of health-promoting food products. Because the world population is growing, and food resources are decreasing, this trend is also important in terms of food security. The aim of this paper is to outline the concept of a technological process for industrial-scale separation of seeds from apple pomace intended for production of an edible oil with health-promoting properties.
## 6 Experimental Results and Discussion
### Graphical Representations of Predicted Trajectory and Ground Truth Trajectory  
In this subsection, we present a visual contrast between the predicted trajectory and the baseline trajectory. Figure 4 illustrates the trajectories plotted on a graph, with velocity values indicated. The x-axis represents the horizontal position, while the y-axis represents the vertical position. The blue line denotes the ground truth trajectory, while the red line represents the generated trajectory. Additionally, velocity values are annotated along the trajectories, providing insights into the speed variations throughout the movement. Arrows indicating the direction of movement are also included to enhance the visualization. This comparison allows for a qualitative assessment of the model's performance in predicting trajectories and provides valuable insights into the dynamics of the system under study.  
Figure 10: Quantile loss variation across training epochs: tracking the model’s performance.
# Metrology for Measuring Bumps in a Protection Layer Based on Phase Shifting Fringe Projection
## 5 Experimental Results and Discussion
### Height Measurement of Bumps in a Polymer
#### 5.2.4 Comparison of 3D Measurement with Scanning Electron Microscopy  
Figure 15a shows the cross-section SEM results for the bump on the PI sample presented in Figure 14a. The bump height was 163 \(\upmu\)m, the PI thickness was 25.1 \(\upmu\)m on the right side to 27.1 \(\upmu\)m on the left side, and the oxide layer thickness was 5.95 \(\upmu\)m. Figure 15b shows a schematic labeling the material in Figure 15a. The cross-sectional SEM was only performed for a single measurement cut made at one site owing to limitations of time and expense. The location of the cut SEM sample was inside the BGA array measured by the fringe projection and reflectometer, but we could not exactly identify which bump. This was good as an independent method for verifying the bump height but it is not a convenient and nondestructive method for analyzing the bump height or coplanarity as required for global measurement of the full site.
## 3 Results
### Additional Remarks  
If only \(H\)s is to be exchanged among ships and marine facilities, the inverse spectral conversion may be unnecessary since Hs can also be obtained from \(4\sqrt{m_{0}}\), where \(m_{0}\) is the area of \(S_{e}(\omega_{e})\). However, there may be uncertainty in determining the \(Tp\) of \(S(\omega)\) inverted from the Tp of \(S_{e}(\omega_{e})\) when \(\omega_{e}<\frac{\beta}{4V\cos\beta}\) since it can be mapped into three different original wave frequencies. This problem can be resolved by applying the proposed ML-based method. When original wave spectra need to be informed and exchanged among surrounding vessels and facilities, the present method can also be applied, as was demonstrated in the above. In this study, the incident unidirectional random wave spectrum  
\begin{table}
\begin{tabular}{c c} \hline
**ANN Model Parameter** & **Model 4** \\ \hline Number of layers & 3 \\ Number of neurons (per each layer) & 200 \\ Activation function & ReLu \\ Optimizer & Adam (Learning rate: 0.001) \\ Loss function & MSE (Mean Squared Error) \\ Input variables & \(V,\ \beta,\ S_{e}(\omega_{e})\) \\ Output variables & \(S(\omega)\) \\ Total dataset & 384 (192 unimodal + 192 bimodal seas) \\ \hline \end{tabular}
\end{table}
Table 6: ANN Model 4 parameters.  
Figure 14: Estimated original wave spectra of bimodal sea (Model 4).  
was defined by a typical narrow-banded wave spectrum like the JONSWAP spectrum. However, more diverse patterns of incident wave spectra can be inputted and used for training the ANN algorithm to expand the overall prediction capability. The wave direction can be estimated in real-time by using the KF method and ML method with motion-sensor signals, as detailed in authors' previous papers [11,12,14,15]. In the case of multi-directional random waves, an additional directional spreading parameter can be employed with increased cases and datasets and the same ML processes can straightforwardly be applied.
# Joint Invariants of Linear Symplectic Actions
## 5 From Joint to Differential Invariants
### Symplectic Discretization  
Consider first the case \(n=1\) with coordinates \((x,y)\) on \(V=\mathbb{R}^{2}\). Let \(A_{i}=(x_{i},y_{i})\), \(i=0,1,2\), be three close points lying on the curve \(y=y(x)\). We assume \(A_{1}\) is in between \(A_{0}\), \(A_{2}\) and omit indices for its coordinates, i.e., \(A_{1}=(x,y)\).  
Let \(x_{0}=x-\delta\) and \(x_{2}=x+\epsilon\). Denote also \(y^{\prime}=y^{\prime}(x)\), \(y^{\prime\prime}=y^{\prime\prime}(x)\), etc. Then from the Taylor formula we have:  
\[\begin{array}{l}y_{0}=y-\delta y^{\prime}+\frac{1}{2}\delta^{2}y^{\prime \prime}-\frac{1}{6}\delta^{3}y^{\prime\prime\prime}+o(\delta^{3}),\\ y_{2}=y+\epsilon y^{\prime}+\frac{1}{2}\epsilon^{2}y^{\prime\prime}+\frac{1}{ 6}\epsilon^{3}y^{\prime\prime\prime}+o(\epsilon^{3}).\end{array}\]  
Therefore, the symplectic invariants \(a_{ij}=x_{i}y_{j}-x_{j}y_{i}\) are:  
\[\begin{array}{l}a_{12}=\epsilon(xy^{\prime}-y)+\frac{1}{2}\epsilon^{2}xy^{ \prime\prime}+\frac{1}{6}\epsilon^{3}xy^{\prime\prime\prime}+o(\epsilon^{3}), \\ a_{01}=\delta(xy^{\prime}-y)-\frac{1}{2}\delta^{2}xy^{\prime\prime}+\frac{1}{ 6}\delta^{3}xy^{\prime\prime\prime}+o(\delta^{3}),\\ a_{02}=(\epsilon+\delta)(xy^{\prime}-y)+\frac{1}{2}(\epsilon^{2}-\delta^{2}) xy^{\prime\prime}\\ +\frac{1}{6}(\epsilon^{3}+\delta^{3})xy^{\prime\prime\prime}-\frac{1}{2}( \epsilon+\delta)\epsilon\delta y^{\prime\prime}+o((|\delta|+|\epsilon|)^{3}). \end{array}\]  
This implies:  
\[\frac{a_{01}-a_{02}+a_{12}}{a_{01}a_{02}a_{12}}=\frac{1}{2}\frac{y^{\prime \prime}}{(xy^{\prime}-y)^{3}}+o(|\delta|+|\epsilon|).\]Thus, we can extract the invariant exploiting no distance (like \(\epsilon=\delta\)) but only the topology (\(\epsilon,\delta\to 0\)) and the symplectic area. This works in any dimension \(n\), and using the coordinates from the previous subsection we get  
\[\lim_{A_{0},A_{2}\to A_{1}}\frac{\operatorname{Area}_{\omega}(A_{0}A_{1}A_{2})} {\operatorname{Area}_{\omega}(OA_{0}A_{1})\operatorname{Area}_{\omega}(OA_{ 0}A_{2})\operatorname{Area}_{\omega}(OA_{1}A_{2})}=\frac{2(x_{t}z_{tt}-x_{tt} z_{t}+y_{tt})}{(ty_{t}-y+xz_{t}-x_{t}z)^{3}}=2I_{2}.\]  
Similarly, we obtain the invariant derivation (it uses only two points and hence is of the first order)  
\[\lim_{A_{0}\to A_{1}}\frac{\overrightarrow{A_{0}A_{1}}}{\operatorname{Area}_ {\omega}(OA_{0}A_{1})}=\frac{2\mathcal{D}_{t}}{(ty_{t}-y+xz_{t}-x_{t}z)}=2\
abla.\]  
The other generators \(I_{3},I_{4},\ldots\) (important for \(n>1\)) can be obtained by a higher order discretization, but the formulae become more involved.
# Partial Discharge Data Augmentation and Pattern Recognition Method Based on DAE-GAN +
## 3 Data Augmentation and Pattern Recognition Method Based on DAE-GAN
### Algorithm Workflow  
The DAE-GAN network has the capability to generate PD time-domain samples, and its discriminator, following transfer learning, is able to recognize partial discharge patterns. Figure 6 illustrates the framework used for partial discharge data augmentation and pattern recognition through the DAE-GAN model. The key steps involved in this process are outlined as follows:Step 1: Data preprocessing. Apply wavelet denoising to the 20 ms PD signals and segment the pulses to create the training sample library for different types of PD signals as outlined in Table 1.  
Step 2: Data augmentation. Input the training samples into the DAE-GAN network and proceed with training. Use the trained generator (G) and decoder (De) to generate varying quantities of samples based on the original data imbalance, expanding the training sample library.  
Step 3: Partial discharge pattern recognition. Fine-tune the classifier D trained in Step 2 using the augmented training sample library. Use the average value between the minimum classification score for samples of the same class and the maximum classification score for samples of other classes as the classification criterion. Input the PD pulses extracted during actual operation into the classifier D for recognition.
# Enhanced Multicast Repair Fast Reroute Mechanism for Smart Sensors IoT and Network Infrastructure
## 3 Related Works
### Problem Formulation
#### 3.1.5 Preparatory Calculations  
The analyzed FRR mechanisms work on a principle which is based on the fast detection of link failure with a neighboring router and precalculated alternative routes (precomputing). The high complexity of these precalculations is a problem area [26].  
The computational complexity of individual FRR mechanisms increases with the increasing number of routers in the network. These calculations must be repeated if there is a change in network topologies and they are typically performed on routers as specific low-priority processes when the router's Central Processing Unit (CPU) is idle [26]. Thus, the FRR mechanism calculations take up the valuable time and system resources of the router. Based on these facts, we conclude that one of the problem areas of FRR mechanisms are preparatory calculations. All existing FRR mechanisms work on this principle.  
Based on the problems thus identified and their problem areas, this document proposes the EM-REP FRR mechanism, an improved version of the M-REP algorithm. The main improvements of the EM-REP proposal focus on the protection of important unicast flows in the event of subsequent and recurring failures occurring concurrently over time. This is a unique feature, as we identified that existing IPFRR mechanisms provide the single failure protection.  
Our M-REP algorithms are not dependent on any of unicast routing protocols in general, but EM-REP is enhanced in a way that provides an advantage in specific deployment scenarios, where the area design is used (for example OSPF or IS-IS). Here, we propose the modification of the Area Border Router (ABR) router behavior. This modification adds flexibility to optimize packet delivery through an \"on-the-go\" decapsulation process, compared to the old M-REP approach where it must be performed on the last router in the delivery chain. This is the second M-REP algorithm enhancement introduced in the paper.
# Fast Planar Detection System Using a GPU-Based 3D Hough Transform for LiDAR Point Clouds
## 2 Related Works
### Clustering Methods  
Spatial distribution was a prominent judgmental information to analyse the surface shape features of a 3D point cloud in clustering methods. [PERSON] et al. [11] compared the relative values of eigenvectors and eigenvalues to locate the calibration plane in an indoor environment. Region-growing techniques, which selected a local seed plane and then expanded the plane region by adding its neighbouring points, were also utilised in plane identification. [PERSON] et al. [12] utilised seed points that were the nearest to the extracted line centres and neighbouring points to generate a planar surface incrementally. To improve the efficiency, region-growing methods based on Octrees and voxels were exploited, [PERSON] et al. [13] applied a voxel growing algorithm to detect a plane and grow a region based on the normal estimation of each point using a filtered weighted plane fitting method. [PERSON] et al. [14]adopted an Octree-based region-growing algorithm on voxelised point clouds to incrementally add the adjacent voxels into a cluster. According to the covariance matrix generated by adjacent unstructured points, subdivision planes were detected in individual sub-spaces [15].  
Considering the influence of the outliers and noise in point cloud data, [PERSON] et al. [16] firstly deleted the outlier data and then fitted the planar surface using a principal component analysis (PCA) algorithm, from which the estimated plane normals and curvatures were exploited to fit the plane. [PERSON] et al. [17] utilised a divide-and-conquer method to dispose point cloud groups with similar plane parameters based on a robust PCA to improve estimation accuracy. To promote plane detection performance, the majority of the optimisation approaches of the plane extraction algorithm enhanced the filtering efficiency of noise and outlier points in the initial phase and the robustness and stability in the peak searching process.  
In these types of plane extraction methods, the neighbouring area should be defined before computing the local geometry features, resulting in a high time consumption of the neighbouring point searching for computing Euclidean or other types of spatial distance. The accuracy rate of plane detection in these methods depends on the sub-space resolution, in which a higher resolution requires more computations. Thus, speed performance and computational complexity are the major difficulties existing in the two main types of plane detection methods.
# Practical Application of Sustainable Road Structure: Mechanical and Environmental Approach
## 5 Economic and Environmental Analysis  
### Environmental Approach  
The environmental assessment of APSE pavement was performed using LCA approach. This study utilises the ReCiPe Midpoint characterisation method [27], developed by the University of Leiden, Netherlands and uses a combination of primary and secondary data sources. Whilst some primary data were readily available (e.g., mixtures design and their production) other data proved to be unobtainable due to concerns around confidentiality and loss of competitive advantage from the industry. Other sources of secondary data used include data sets from e.g., GaBi Databases [28], Eurobitume Report [29], ICE Carbon & Price book [30] and other peer reviewed sources. All data used for the purposes of analysis are representative for the European case, with minor exceptions due to lack of comprehensive data. The following nine stages of life cycle were considered for the purpose of this study: 1--material acquisition, 2--processing, 3--transport and storage, 4--asphal production, 5--material transport to site, 6--construction, 7--service, 8--maintenance and 9--end of life.  
Within the LCA, a normalized 1 m\({}^{2}\) of road pavement was assessed, comparing reference pavement with APSE alternative. The conventional pavement design included raw aggregates and 0% RAP content, as well as use of SBS polymers in the surface layer. The analysis was performed for \"cradle to gate\" and \"cradle to grave\" stages of pavement's life cycle, considering 60 years of pavements life and under assumption of surface layer rehabilitation in years 15 and 45, as well as surface and binder layer replacement in year 30. The results of life cycle impact assessment (LCIA) are summarised in Figure 7.  
The LCIA results indicate that on average APSE pavement can lead to savings in carbon emissions of 7.9 kg CO\({}_{2}\) eq/m\({}^{2}\) (14%), considering cradle to grave system boundaries, while in the context of cradle to gate, the savings can reach 8.8 kg CO\({}_{2}\) eq/m\({}^{2}\), while inclusion of RAP and BIO-additive in binder and base layers lead to 6% and 7.6% CO\({}_{2}\). eq. emissions reduction in binder and sub-base layers. The greatest savings were attributable to sub-base layer, where CDW substituted 100% of virgin aggregates.  
Figure 7: Results of LCIA, total significant impact over 60 years.
# Data-Driven Packet Loss Estimation for Node Healthy Sensing in Decentralized Cluster
## 2 Related Work  
### Akka Cluster  
A widely-used and well-recognized solution to the problem of failure detection and troubleshooting in decentralized cluster is a framework named Akka [14]. In Akka, each node is monitored by a number of other nodes with the technology of the \(\phi\) FD. Each node in the cluster holds a reachability table and uses gossip protocol to keep consistent with other nodes. If one monitor detects that some node is faulty, it announces this event to the rest of the nodes by updating its own reachability table and make it consistent with the rest of nodes using gossip protocol. If a member is marked as faulty and has been broadcasted to all the other nodes, these nodes will then determine whether themselves shall be responsible for troubleshooting with a non-electoral leader determination algorithm. If different monitors have different outcome on whether a node is faulty, a pessimistic algorithm is adopted that nodes are only treated to be healthy if all its monitors say it is healthy, which in other words if any one of its monitors announces its faulty it will be treated as unhealthy.
# Parameter Tuning of PI Control for Speed Regulation of a PMSM Using Bio-Inspired Algorithms
## 5 Nature-Inspired Algorithms  
The optimization process refers to finding the optimal values of the parameters of a given system from all possible values, in order to maximize or minimize its performance. Optimization problems can be found in all fields of study, which makes the development of optimization algorithms essential.  
Optimization algorithms can be classified based on their nature as deterministic or stochastic algorithms. Deterministic algorithms follow a rigorous procedure, and its path and values of both design variables and functions are repeatable. Most conventional classical algorithms are deterministic, an example is the Newton Raphson algorithm.  
Stochastic algorithms always have some randomness, the strings or solutions in the population will be different each time a program is executed because the algorithms use some pseudo-random numbers, the final results may not be very different, but the paths of each individual are not exactly equal. These algorithms are divided into two main groups: heuristic and metaheuristic.  
The term heuristics means \"to find or discover by trial and error\", using these algorithms quality solutions are found for a difficult optimization problem in a reasonable time, but there is no guarantee that optimal solutions will be reached. Metaheuristic algorithms generally work better than a simple heuristic, because these use some randomization trading and local search. It is worth noting that there are no agreed definitions of heuristics and metaheuristics in literature; some use \"heuristics\" and \"metaheuristics\" interchangeably [41].  
Metaheuristics can be classified by their source of inspiration, most new algorithms today have been developed inspired by nature, most nature-inspired algorithms are based on some successful features of the biological system (biologically inspired, or bioinspired for short). Not all algorithms were based on biological systems, as many algorithms have been developed using the inspiration of physical, chemical and mathematical systems [42].