esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The overall objective of this work was to elaborate a quantitative comparison of recent works that developed EMSs for HEVs. The focus of this work was to compare the proposed EMSs with RB strategies that are used in the HEV industry instead of only doing so with the optimum. After the analysis presented, it is clear that it is difficult to draw definitive conclusions about which EMS methodology is the best since the aggregation of the results from different sources leads to errors that affect the results. Thus, this work comes to clarify the ranges of performance in which the most novel EMSs operate with regard to the technology that is commercially available. From the results, it is concluded that the improvement of these EMSs ranges roughly between 5% and 10%. Although from the review, it could be concluded that ECMS could be the EMS that offers more improvement, the works that propose it suggest that the good results obtained could be caused by the fact that some variables of the EMS were tuned with some previous information regarding the driving cycle or because the experiments proposed for the validation did not consider enough disturbances (in magnitude and frequency) to reflect a real operation scenario. Strategies that were presented a priori as the most suitable for real-time implementation, such as LB, showed slightly lower improvements and, after reviewing their methodology, it can be concluded that they could face the variability expected in real-life operation. It must be also highlighted that EMSs based on RB (considered as the most simple strategies), which have been developed from the SERCA approach, for example, offer reasonably good results with regard to the EMSs currently used in commercial HEVs.  
Finally, regarding the research questions formulated in the Introduction, the following statements can be made:  
* The most novel EMSs presented improvements roughly between 5 and 10% from those currently implemented in the HEV industry;
* Using the common RB EMSs and DP as benchmarks, it can be concluded that the EMSs that are being developed currently are nearer to the optimum than the common RB EMSs;
* Since the results of the different technologies are close, it would be interesting to focus on their capacity to be implemented in real-time controllers and encounter scenarios with frequent disturbances.  
**Author Contributions:** Conceptualization, J.P.T.; methodology, J.P.T.; literature review, J.P.T., P.G.-T., D.V., D.A.L.-G.; simulations, J.P.T., P.G.-T., D.V., D.A.L.-G.; data analysis, J.P.T., P.G.-T., D.V., D.A.L.-G.; writing--original draft preparation, J.P.T.; writing--review and editing, J.P.T., P.G.-T., D.V., D.A.L.-G. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Theoretical Derivation of Models and Generalized Modeling for Rapid Prediction
### Theoretical Derivation of Agent Model Based on PSO-SVR
#### 3.1.2 SVR Training  
SVR training is the process of using known data \((\mathbf{x}_{i},y_{i})\) (\(i=1,2,\cdots l\)) as training samples (where \(\mathbf{x}_{i}\) is the input sample and \(y_{i}\) is the output sample) to establish the mapping relationship between input and output variables.  
Figure 4: Particle swarm optimization–support vector regression implementation process.  
In SVR, the following \(\varepsilon\)-insensitive loss function is usually used, i.e.,  
\[L_{\varepsilon}(u)=\begin{cases}0,&|u|\leq\varepsilon\\ |u|-\varepsilon,&\text{else}\end{cases} \tag{4}\]  
The goal of SVR is to find a function \((\mathbf{x})=(\mathbf{w}\cdot\mathbf{\varphi}(\mathbf{x}))+b\) such that it deviates as little as possible from the actual output \(y\) at all training points by no more than \(\varepsilon\), while the function is as flat as possible. That is, when the error is less than \(\varepsilon\), it is disregarded, and only errors greater than it are considered, with the amount of excess error usually denoted by the relaxation factors \(\xi_{i}\) and \(\xi_{i}^{*}\).  
Given the training samples \((\mathbf{x}_{i},y_{i})\)\((i=1,2,\cdots l)\), where \(\mathbf{x}_{i}\in R^{n},\ y_{i}\in R\), according to the structural risk minimization criterion, the optimization objective for the SVR model is  
\[\min\frac{1}{2}\|\mathbf{w}\|^{2}+C\left(\sum_{i=1}^{l}\xi_{i}+\sum_{i=1}^{l}\xi_{ i}^{*}\right) \tag{5}\]  
The constraint is  
\[\left\{\begin{array}{l}y_{i}-\mathbf{w}^{\mathrm{T}}\varphi(\mathbf{x}_{i})-b\leq \varepsilon+\xi_{i}\\ \mathbf{w}^{\mathrm{T}}\varphi(\mathbf{x}_{i})+b-y_{i}\leq\varepsilon+\xi_{i}^{*}\\ \xi_{i},\xi_{i}^{*}\geq 0,i=1,2,\cdots l\end{array}\right. \tag{6}\]  
where \(\varepsilon\geq 0\) is the fitting accuracy of the function; \(\mathbf{w}\) is the bias parameter; \(\xi_{i}\geq 0\) and \(\xi_{i}^{*}\geq 0\) are the relaxation factors; and \(C\geq 0\) is used to control the degree of punishment for the samples exceeding the error \(\varepsilon\).  
An important concept in this model is the kernel function, which, by [PERSON]'s theorem, is satisfied by the kernel function \(\varphi(\mathbf{x})\):  
\[K\big{(}\mathbf{x}_{i},\mathbf{x}_{j}\big{)}=\varphi(\mathbf{x}_{i})\cdot\varphi\big{(} \mathbf{x}_{j}\big{)} \tag{7}\]
# Quality Control of Fried Pepper Oils Based on GC-MS Fingerprints and Chemometrics
## 3 Results and Discussion  
### Volatile Compounds of Huajiao Oils  
A total of 41 types of volatile compounds (shown in Table S1) were identified in ZBO1-ZBO16, including alkenes (26), esters (4), alcohols (6), aldehydes (1), alkanes (2), and ketones (1). Among them, alkenes (with an average content of 594.83 ug/g) and alcohols (157.23 ug/g) played a key role in the volatile profile of huajiao oil, which is consistent with the findings of [PERSON] et al. [8]. However, some oxygenated terpen, such as citronellal and geraial, which have been identified as key odorants in rip huajiao, were not detected in our study. The lack of these volatile compounds in our samples may be attributed to the different growth environments of huajiao [21], post-harvest treatments or processing methods. Moreover, our results suggested that D-limonene (297.64 ug/g, 28.91%), linalyl acetate (228.62 ug/g, 22.21%), linalool (146.138 ug/g, 14.20%), myrcene (130.25 ug/g,12.65%), and ocimene (47.73 \(\upmu\)g/g, 4.64%) accounted for 82.61% of the total flavor profile, significantly contributing to the overall flavor of huajiao oil. Notably, myrcene, limonene,linalool, and linalyl acetate have been identified as the main odorants in Hanyuan pepper oil [21; 22]. Among them, limonene (297.64 \(\upmu\)g/g) and myrcene (130.25 \(\upmu\)g/g) impart a citrus, lemony, and minty aroma to huajiao oil. Linalool (146.138 \(\upmu\)g/g) contributes to a woody, sweet, and floral fragrance, while linalyl acetate (228.62 \(\upmu\)g/g) offers a delicate and elegant scent similar to lily of the valley and lavender [23; 24].
## 1 Introduction
By giving CMs the same prediction trajectory, we can obtain the cluster's prediction trajectory, which is the same as that of the vehicles' in the cluster. * Based on the prediction trajectory of the request vehicle and clusters, as well as the vehicle's speed, the RSU proactively caches content chunks at multiple vehicles in the corresponding cluster. During the process, the RSU formulates the optimal number of content chunks to maximize the success probability of content delivery. * Based on the statistical characteristics of vehicle's speed, vehicle flow, and the number of request arrivals in RSU, we theoretically derive the success probability of content delivery. The simulation results verify the validity of the process of derivation and demonstrate that this paper improve the system's performance in terms of time delay, as well as the success probability of content delivery. In comparison with the results achieved by the authors of [19], we increase success probability by about 20%. The rest of this paper is organized as follows. The system model is presented in Section 2. In Section 3, we depict the content request and delivery process, introduce the cluster dividing algorithm, optimize the caching strategy, and derive a formula for the success probability of content delivery. Section 4 shows the simulation results and illustrates the performance of our proposed strategy. Section 5 summarizes the paper.
## 3 Results
### Fatty Acid Composition in the Tested Samples
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**Authentic Poppy**} & \multicolumn{2}{c}{**Adulterated Poppy**} & \multicolumn{2}{c}{**Canola Oil**} & \multicolumn{2}{c}{**Corn Oil**} & \multicolumn{2}{c}{**Soybean Oil**} & \multicolumn{2}{c}{**Sunflower Oil**} \\ \hline \multirow{3}{*}{Palmitic} & min\({}^{\text{a}}\)–max\({}^{\text{b}}\) & 7.73–9.31 & 9.24–12.97 & 4.23–4.40 & 11.14–12.10 & 11.09–11.28 & 4.67–4.96 \\  & mean & 8.60 & 10.55 & 4.32 & 11.70 & 11.18 & 4.80 \\  & SD\({}^{\text{c}}\) & 0.39 & 1.07 & 0.08 & 0.41 & 0.08 & 0.12 \\ \hline \multirow{3}{*}{Stearic} & min–max & 2.34–2.84 & 2.47–3.54 & 1.84–2.05 & 1.65–17.70 & 3.47–3.79 & 3.32–3.40 \\  & mean & 2.58 & 2.93 & 1.95 & 1.67 & 3.64 & 3.36 \\  & SD & 0.15 & 0.34 & 0.09 & 0.02 & 0.14 & 0.03 \\ \hline \multirow{3}{*}{Oleic} & min–max & 15.74–26.45 & 22.99–37.78 & 63.74–64.21 & 30.45–34.16 & 23.01–24.49 & 60.92–63.29 \\  & mean & 20.92 & 28.80 & 64.03 & 31.70 & 23.55 & 62.21 \\  & SD & 2.90 & 5.35 & 0.22 & 1.73 & 0.68 & 0.98 \\ \hline \multirow{3}{*}{Linoleic} & min–max & 61.87–71.96 & 46.99–63.60 & 19.59–21.16 & 50.37–53.95 & 53.57–54.12 & 27.17–29.50 \\  & mean & 66.68 & 56.59 & 20.20 & 52.01 & 53.88 & 28.12 \\ \cline{1-1}  & SD & 2.88 & 6.10 & 0.68 & 1.70 & 0.24 & 0.98 \\ \hline \multirow{3}{*}{Linolenic} & min–max & 0.50–0.81 & 0.37–0.85 & 6.53–7.44 & 1.10–1.82 & 6.45–7.14 & 0.41–0.43 \\  & mean & 0.62 & 0.64 & 7.06 & 1.41 & 6.81 & 0.42 \\ \cline{1-1}  & SD & 0.06 & 0.15 & 0.38 & 0.31 & 0.29 & 0.01 \\ \hline \hline \end{tabular}  
* \({}^{\text{a}}\) minimum, \({}^{\text{b}}\) maximum, \({}^{\text{c}}\) standard deviation.  
\end{table} 
Table 1: Major fatty acid profiles (%) of analyzed samples obtained through the fatty acid methyl ester (FAME) procedure and GC-FID analysis. Since the fatty acid content of the expected adulterant (sunflower oil) has changed over the years, and blends of oils could provide a similar fatty acid profile, there is a compelling need for non-targeted analysis in food authentication. Non-targeted screening in agricultural and food products is invaluable as it allows the detection of a wide range of potential adulterants or contaminants, even those that were not specifically targeted. This prevents overlooking incidents of adulteration with unexpected elements and points out the need for novel approaches to identify them [35]. Therefore, non-targeted analysis is an essential part of food authentication, providing a more comprehensive, unbiased, and flexible way of adulteration detection and assurance of food safety and quality. It is crucial for the protection of consumers, regulatory standards, and integrity within the food industry.
## 4 Discussion
### Comprehensive Service Life Based on Analytic Hierarchy Process
#### 4.2.3 Calculating the Weight of Each Surface Layer  
To quantify the significance or contribution of different factors to the aging of RABs, numeric scales were determined initially by repeated trial calculations, analysis of performance testing data and engineering experience. The significance of the numeric scales is listed in Table 4.  
Figure 14: Hierarchical model for determining the comprehensive service life of RABs.  
Figure 13: The structure of two-level analytic hierarchy process (AHP).  
The scale of upper surface layer to middle surface layer was 1.5, which means that when subject to the same service year, climatic condition, traffic volume, the aging of upper surface layer was more serious than middle surface layer. Similarly, the scales of the upper surface layer to lower surface layer and middle surface layer to lower surface layer were 1.7 and 1.1, respectively. By the numeric scales, the judgment matrix was given. As shown in Table 5, the composition weight of each layer to the system goal is obtained by calculating the maximum eigenvalue and the eigenvector of the judgment matrix. Thus, the contribution ratio of different layers to the aging of an example consisting of all three layers would be 45:28:27 from upper to lower layers.
## 2 Experimental Section  
### Site Description  
The experiment was conducted in Lake Taihu (Figure 1), from November 2008 to November 2010. Lake Taihu (30\({}^{\circ}\)05\({}^{\prime}\)-32\({}^{\circ}\)08\({}^{\prime}\) N, 119\({}^{\circ}\)08\({}^{\prime}\)-121\({}^{\circ}\)55\({}^{\prime}\) E) is a naturally large shallow eutrophic lake located in the delta of the Yangtze River in Eastern China. The lake has a surface area of 2.4 \(\times\) 10\({}^{3}\) km\({}^{2}\), a catchment area of 3.65 \(\times\) 10\({}^{4}\) km\({}^{2}\), a maximum depth of 2.6 m, and a mean depth of 1.9 m [24]. Lake Taihu has negligible vertical thermal stratification gradients because of its shallowness [26].  
Two buoy stations were moored in Gonghu Bay, northeastern Lake Taihu (Figure 1), which is an important drinking water source supplying for Wuxi City. To improve water quality, water is transferred from the Yangtze River to the lake through Wangyu River. The average depth of Gonghu Bay is 2.0 m, and the surface area is 147 km\({}^{2}\), half of which is covered by abundant macroplant vegetation [27]. The main species of Shangshancun station (SSC, 31\({}^{\circ}\)21\({}^{\prime}\) N, 120\({}^{\circ}\)17\({}^{\prime}\) E) are _Potamogeton malaianus_ and _Myriophyllum spicatum_. There are lots of species in Jinshu station (JS, 31\({}^{\circ}\)23\({}^{\prime}\) N, 120\({}^{\circ}\)22\({}^{\prime}\) E), including _P. maackianus_, _P. malaianus_, _M. spicatum_, _Elodea nuttalii_, _Vallisneria natans_, _Ceratophyllum demersum_, _Nymphoides pellata_, and _Trapa natans_.  
The downwelling shortwave radiation was collected at the Taihu Laboratory for Lake Ecosystem Research (TLLER, 31\({}^{\circ}\)24\({}^{\prime}\) N, 120\({}^{\circ}\)13\({}^{\prime}\) E), Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, lie in Meiliang Bay, about 8.7 km linear distance from the SSC buoy station, from June to November in 2010. Meiliang Bay is one of the most eutrophic bays in Lake Taihu, where two rivers (Liangxi and Zhihugang Rivers) empty large amounts of wastewater from factories and residential areas into the bay [28].
# Closed-Loop Robotic Arm Manipulation Based on Mixed Reality
## 3 Proposed System Architecture  
The proposed method is based on the design and development of two main software modules. The first module is responsible for the 3D representation of the robotic manipulator surroundings. The second module is responsible for the simulation of a 3D functional model of the robotic manipulator as well as the calculation of the kinematics. Essentially, the framework consists of a closed-loop control system for the robot; as the user inputs the desired position, the digital twin of the robot calculates the positions/motions of the robot joints. Then the resultant motion is sent to the robot's controller to be executed as well as to the MR application in order to generate the visualization. Finally, the robotic controller sends feedback to the backend of the application in order to confirm that the motion has been successfully executed, and thus the user can proceed with the input of a new motion. The above-mentioned process is executed recursively until the user terminatesit. In Figure 1, the flowchart of actions, describing the proposed system architecture is presented. More specifically, the framework initially relies on the successful connection of the AR/MR application with the interface of the robotic arm. As soon as the connection has been established and no errors are thrown, then the user selects whether they wish to work with a collaborative robot or just manipulate the robot. In the case of the collaborative mode, safety precautions are automatically applied, such as the maximum velocity and acceleration of the robot, as per the ISO/TS 15066, which explicitly specifies the requirements for collaborative robotic cells.  
Then, the first functional block for the manipulation of the robot is the environmental understanding by the computer(s). This task can be accomplished in two modes. The first mode requires a 3D map of the environment of the robotic arm, so the resultant 3D map in the form of a point cloud is imported into the development environment.
# A Polarity Capturing Sphere for Word to Vector Representation
## 5 Results and Discussion
### Word Similarity
Since the other approaches do not represent such composite words as vectors. * We must caution, however, that the competing methods word2 vec, GloVe, and fastText are not specifically designed to handle antonyms. Therefore, the comparison presented, which shows clear domination of our method for antonyms, may not be fully due to a particular design or algorithmic outperformance, but also partly due to the fact that the competing methods were not designed to deal with antonyms. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Word1** & **Word2** & **Sphere (Our Approach)** & **Word2 vec [17]** & **GloVe [19]** & **FastText [20]** \\ \hline happy & joyful & 0.944 & 0.424 & 0.598 & 0.701 \\ amusing & happy & * 0.778 & 0.27 & 0.387 & 0.5 \\ king & queen & * 0.83 & 0.651 & 0.76 & 0.764 \\ boy & girl & * 0.859 & 0.854 & 0.825 & 0.862 \\ male & female & * 0.702 & 0.841 & 0.938 & 0.945 \\ education & student & 0.608 & 0.401 & 0.682 & 0.547 \\ movement & motion & 0.887 & 0.231 & 0.56 & 0.583 \\ oscillate & move & * 0.828 & 0.239 & 0.166 & 0.354 \\ district & county & * 0.733 & 0.594 & 0.651 & 0.586 \\ district & commune & 0.761 & 0.164 & 0.367 & 0.464 \\ friend & partner & 0.96 & 0.37 & 0.609 & 0.619 \\ valuable & worthy & 0.849 & 0.351 & 0.537 & 0.649 \\ great & tremendous & 0.858 & 0.775 & 0.668 & 0.804 \\ joyfulness & joy & * 0.986 & 0.513 & 0.362 & N/A \\ France & Paris & 0.94 & 0.555 & 0.721 & 0.632 \\ Germany & Berlin & 0.949 & 0.554 & 0.685 & 0.673 \\ Egypt & Cairo & 0.934 & 0.602 & 0.699 & N/A \\ France & French & 0.82 & 0.633 & 0.686 & 0.68 \\ Germany & German & 0.801 & 0.681 & 0.7 & 0.742 \\ Egypt & Egyptian & 0.869 & 0.602 & 0.718 & 0.753 \\ Japan & China & * 0.738 & 0.315 & 0.697 & 0.645 \\ Japan & Greece & * 0.609 & 0.545 & 0.494 & 0.595 \\ african-country & european-country & * 0.88 & N/A & N/A & N/A \\ modern & ongoing & * 0.487 & 0.129 & 0.357 & 0.424 \\ obsolete & outdated & * 0.915 & 0.743 & 0.725 & 0.797 \\ money & shopping & * 0.599 & 0.166 & 0.487 & 0.407 \\ internet & web & 0.888 & 0.597 & 0.737 & 0.683 \\ apple & orange & * 0.93 & 0.392 & 0.493 & 0.561 \\ soccer & football & 0.926 & 0.731 & 0.819 & 0.804 \\ lion & tiger & * 0.987 & 0.512 & 0.621 & 0.67 \\ giraffe & animal & 0.954 & 0.422 & 0.436 & 0.573 \\ light & sun & * 0.612 & 0.401 & 0.525 & 0.558 \\ geology & earth & * 0.701 & 0.264 & 0.444 & 0.531 \\ tenor & opera & * 0.847 & 0.459 & 0.395 & 0.519 \\ unauthorized & simultaneously & * -0.101 & 0.175 & 0.237 & 0.3 \\ bag & country & * 0.061 & 0.085 & 0.301 & 0.367 \\ beach & truck & * 0.448 & 0.09 & 0.296 & 0.421 \\ array & again & * 0.294 & -0.011 & 0.314 & 0.317 \\ useful & wash & * 0.195 & 0.073 & 0.288 & 0.224 \\ tourism & beach & * 0.513 & 0.266 & 0.389 & 0.459 \\ decent & morning & * 0.027 & 0.041 & 0.373 & 0.253 \\ go & new & * 0.111 & 0.113 & 0.564 & 0.345 \\ yes & no & \(-1\) & 0.392 & 0.695 & 0.549 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Word similarity comparison (* means the pair is not part of the training set for our method).
# Kernel Point Convolution LSTM Networks for Radar Point Cloud Segmentation
## 3 Methodology
This paper integrates KPConv layers into an LSTM cell, which we call KPConv-based LSTM (KPLSTM). In this way, we can encode the time dependency for points individually. Additionally, we combine KPConv encoding with LSTM and ConvLSTM cells [27] in the latent space to integrate the time domain in the model on a global feature level. * _Moving Object Recognition_: In addition to the time dependency, production applications only react to moving objects, as the amount of clutter among the static points causesadditional challenges for the processing. It is desired to bring radar processing to a level of maturity to detect static objects just as with the lidar sensor. However, the research above shows that the focus is still mainly on the moving object case. Even in the simplified moving objects scenario, the detections are only reliable enough for simplified use cases, such as vehicle following in the ACC case. In this work, we evaluate our network for both static and dynamic cases and show the disadvantages of the radar for the complex static scenarios. * _One-vs-all Classification_: Due to the difficulties in creating reliant object detections from radar data, one compensation strategy is the simplification of the scenarios. In the literature, one-vs-all classification are an efficient way to simplify the training use case, while keeping a relevant task in focus. This paper shows the results for different training configurations and their impact on the segmentation performance. Radar _Data Availability_ is discussed in Section 5.2, The _Data Level_ is given as only point level data is available. _Data Sparsity_ and _Time Dependency_ are major considerations for the design of the KPConv-LSTM networks presented, which are discussed in Section 3.1. The evaluation of focusing on _Moving Object Recognition_ and _One-vs-All Classification_ is shown in Section 4.