esa-sceva/satcom-chunk-collection huggingface dataset:
# The Future of Factories: Different Trends
## 4 Characteristic of Cloud Manufacturing
Cloud manufacturing has four typical deployment modes inherited from cloud computing [33] (_public_ cloud, _private_ cloud, _hybrid_ cloud, and _community_ cloud). * In the _public_ cloud, service providers subscribe and publish their services in a multi-tenant environment. A cloud platform provides on-demand use of services to an open community of customers. * _Private_ cloud restricts the operational mode within enterprises boundaries. In a private cloud, all actors belong to the same organization. * A _community_ cloud is shared among companies which group together sharing the infrastructure. * _Hybrid_ cloud mixes the previous mode to integrate different types of cloud (e.g., public, private). Forming a bridge between different clouds requires cloud owners to select proper resources sharing models. Authors in [36] proposed a framework for the development of hybrid cloud, bridging multiple cloud platforms. The success case of cloud manufacturing in China mainly includes small- and medium-sized enterprises that have established their information systems [37]. Despite the cloud manufacturing aims to cover the entire manufacturing product life-cycle, ranging from collaborative product design down to services integration and virtualization and sharing of manufacturing resources, at present, the development of a full-featured cloud manufacturing application case is still under development [35]. Nevertheless, many industries start to experiment with developing a cloud manufacturing platform at different levels of awareness. The collaborative cloud platform proposed in [38] aims to balance uneven resources distribution and fragmentation in the integration of different services in the mold industry. The cloud platform acts as a trading platform where enterprises publish and trade their manufacturing assets. The platform's main functionalities include enterprise registration as a service provider or customer, manufacturing assets registration, service discovery, service selection, service evaluation, and transaction management. Similarly, the features of the platform are extended [39] to enable integration at the process level, as well as the tight coupling of different manufacturing management systems (i.e., MES, ERP). The collaboration model between enterprises is based on a social network model that guarantees interaction among partners in a relatively stable network environment. According to the diverse business requirements, enterprises seek new partners through the public market page, remove partners from an alliance, or join multiple networks simultaneously. The platform Tianzhi Net (www.cosimcloud.com, accessed on 20 September 2021) enables enterprises business collaboration of local industrial chains [39]. The example reported in Figure 7 shows a network alliance based on a social network model enabled by a cloud platform.
## 3 Methods and Tools
### Improvements on the TOSSIM Simulator  
Traditionally, the TOSSIM simulator has been used to evaluate well-defined, homogeneous scenarios, i.e., ones where all nodes experience the same propagation phenomena. However, in complex and heterogeneous settings like SG's substations, different visibility conditions and propagation phenomena may be experienced. These differences lead to variations in propagation losses and/or noise conditions that cannot be neglected by modern simulations. Another issue worth looking into it is the fact that TOSSIM has always been geared towards the evaluation of 2.4 GHz links; and hence, it lacks the necessary mechanisms to evaluate other WSN/IoT working frequencies.  
Therefore, to accurately evaluate the network under study, we have extended the TOSSIM simulator to: (i) be able to embrace the heterogeneous nature of a SG and (ii) allow different working frequencies for deployed IoT devices.  
The first task translates into modifying TOSSIM to permit the presence of different nodes working under various visibility conditions (e.g., the node pairs 0-4 of Figure 2a work under line-of-sight, whereas 0-3 may operate under NLOS conditions). By modifying TOSSIM's architecture, we enabled each node pair to have its own combination of \(n\) (path-loss exponent) and \(\sigma\) (standard deviation). This makes the simulation environment more flexible. Furthermore, in order to simulate different frequencies, it is also required to generate distinct BPL values according to the working frequency under use. The second issue is addressed to provide the appropriate signal modulation scheme in accordance with the frequency band. The reason is obvious; 915 MHz transceivers usually employ different modulation schemes than 2400 MHz transceivers. Hence, when the 915 MHz band is under use, the frequency-shift keying (FSK) modulation scheme is utilized (the one typically exploited by transceivers operating in such a band), whereas when transmitting in the 2400 MHz band, the OQPSK (offset quadrature phase-shift keying) modulation scheme is used. Both, FSK and OQPSK, have been implemented into TOSSIM simulator. The full modifications carried out in the TOSSIM simulator, along with the automatization scripts are at the disposal of interested readers in [25].
## 4 Materials and Experiments
### EEG Emotion Classification Experiment
The training steps are repeated for each emotional state (valence, arousal, dominance, and liking), and the classification training result graph is shown in Figure 7. All training and testing procedures proposed in this study were performed on a server with 4 Nvidia GeForce RTX2080 Ti GPUs and 256 GB RAM by using Cuda 11.2, Pytorch version 1.11. Figure 7: (**a**) Valence classification training results, (**b**) arousal classification training results, (**c**) dominance classification training results, (**d**) liking classification training results improve the training speed. Experiment 2 using the time-frequency diagrams of CSP and CWT as input data is shown in Figure 8, and the method used is referred to as CP-CNN-BiLSTM-MHSA. The images are used as data input to CNN-BiLSTM-MHSA, and the input to CNN is a \(20\times 65\times 3\) time-frequency map. To perform better feature extraction of the image, the CNN in this paper is designed as a two-dimensional convolution of two layers, the original data \(1280\times 32\times 8064\) is transformed into 7680 \(\times\) 32 \(\times\) 20 \(\times\) 65 and then sent into the CNN in the form of padding after the convolution operation to obtain the dimension \(7680\times 64\times 20\times 65\), and then after pooling down sampling it becomes \(7680\times 64\times 10\times 32\) and when sent into the next CNN layer and pooling layer becomes \(7680\times 128\times 5\times 16\). The whole training process has 102,977 training parameters. The training is based on cross-entropy function optimization and back-propagation of stochastic gradient descent for network training, and the optimizer continues to use Adam with a learning rate of 0.0001 and 50 iterations. The average accuracy of training and testing in high and low valence, arousal, dominance, and liking states was 99.98 and 98.10% respectively. Their other performance index F1 score values were 98.22%, 98.02%, 98.17%, and 98.11% respectively. The training accuracy is improved by about 4% compared to the experiments with the time series of input EEG, and the training accuracy and test accuracy shown in Figure 9.  
Figure 8: Multi-Head Self Attention structure. Figure 9: (**a**) Valence classification training results, (**b**) arousal classification training results, (**c**) dominance classification training results, (**d**) liking classification training results.
# Improved JPS Path Optimization for Mobile Robots Based on Angle-Propagation Theta* Algorithm
## 5 Simulation
### Algorithm Comparison Experiment
The AP-JPS algorithm proposed in this paper has a smaller total path turning angle, shorter path and better overall result path smoothing compared to the JPS algorithm, A* algorithm, RRT algorithm and AP-Theta* algorithm. Meanwhile, the AP-JPS algorithm has reduced algorithmic time complexity and space complexity compared to JPS, but it has substantially improved compared to the AP-Theta* algorithm based on multi-angle pathfinding. That is, the AP-JPS algorithm substantially improves the quality of the resultant path while ensuring the efficiency of the algorithm, making it more consistent with the motion process of the mobile robot in real situations.
## 1 Introduction
To obtain calculation results that meet the practical engineering needs, it is necessary to study ontology model construction and ontology-based semantic similarity calculation methods tailored for transportation infrastructure. In order to address the challenges encountered by existing methods in computing ontology-based semantic similarity within the domain of transportation infrastructure, this paper introduces a comprehensive ontology-based semantic similarity calculation method based on the SHO-BP algorithm. This method accurately computes ontology-based semantic similarity between concepts in the field of transportation infrastructure, which can effectively address the issue of heterogeneity in indicators encountered in the integration of information in the transportation infrastructure domain. The major contributions of this study are as follows:  
1. Construction of ontology models tailored for the domain of transportation infrastructure, capable of storing information from BIM models. These ontology models serve as a basis for analyzing various types of transportation infrastructure. 2. Adjustment and optimization of the existing Edge-Counting, Information-Content, and Feature-based methods to make them more suitable for analyzing the ontology models constructed in this paper. 3. Proposal of an ontology-based comprehensive semantic similarity calculation method based on the SHO-BP algorithm, addressing the absence of effective means for measuring semantic association among concepts in the transportation infrastructure domain. The remainder of this paper is organized as follows: Section 2 provides a literature review on ontology-based semantic similarity calculation. Subsequently, Section 3 elaborates on the workflow of the SHO-BP algorithm and the process of comprehensive semantic similarity calculation. Section 4 details the experimental process of ontology model construction and semantic similarity calculation, followed by an analysis of the experimental results. Finally, Section 5 presents conclusions and discusses future research directions.
# Ohmic Heating in the Food Industry: Developments in Concepts and Applications during 2013-2020
## 5 Applications of Ohmic Heating
The electrical conductivity of papaya pulp was measured at temperature ranges of 35-95 \({}^{\circ}\)C, three voltage gradients 6.66, 13.33, 20 V/cm, and for 1, 2, and 3 min. Statistical results showed that the optimal conditions for improving the papaya pasteurization process were at an electrical voltage of 13.33 V/cm and 2 min retention time depending on the low microbial load. The heated pulp using ohmic heating method was compared with fresh pulp in terms of biochemical and biological properties. The obtained results showed that the heated papaya pulp retained 86.44% of lycopene, 87.13% of beta-carotene, and 85.23% of ascorbic acid compared to the fresh samples. [PERSON] et al. [46] noted that the rate of ohmic heating of food is highly influenced by electrical conductivity based on heating tomato using ohmic heating at five different voltage gradients (6-14 V/cm), and temperatures from 26 to 96 \({}^{\circ}\)C. The results showed a linear increase in the electrical filed values with a significant increase in temperature (\(p<0.05\)) as shown in Figure 3.  
[PERSON] et al. [56] used ohmic heating for the concentration of milk by evaporation. The main problems of using conventional thermal evaporation were the loss of aromas and color change. The effect of ohmic heating was studied on the physical, chemical, sensory, and microbial properties during the concentration of cow's milk, buffalo milk, and mixed milk (50:50) and compared to conventional evaporation at 13.33 V/cm, current (5-20 Amp), temperatures (0-100 \({}^{\circ}\)C), and time 75 min. The results indicated that ohmic heating has a huge potential for rapid and homogeneous heating of liquid and semi-solid foods, resulting in a safe and high-quality microbiological product. Ohmic heating significantly increased the formation of free fatty acids (FFA) and viscosity, as well as an increase in hydroxy methyl furfural (HMF) and important color values in dye estimation such as redness (\(a^{\star}\)) and yellowish value (\(b^{\star}\)). However, the pH and brightness (\(L^{\star}\)) of concentrated milk were clearly decreased. Ohmic heating also resulted in a significant reduction in microbiological numbers, and treated milk could be preserved for a longer period. Table 1 provides a convenient summary of electrical properties, temperature, and time of recent studies of ohmic heating applications. Figure 3: Curves of ohmic heating on tomato samples using different voltage gradients (derived from [PERSON] et al., 2012) [46].
# Interferometric DEM-Assisted High Precision Imaging Method for ArcSAR
## 5 Accuracy Analysis  
We extract the DEM using the SAR images imaged on the reference plane. The defocusing of SAR image causes the larger phase error, which has the impact on the accuracy of the DEM and further affects the imaging quality. The defocusing can cause two types of phase error. The first is the random phase error due to the decrease of the SAR image's signal-to-noise ratio (SNR). The second is the phase error due to the difference in phase distortion of the SAR image pair used for the interference. In this section, we analyze the impact of the above two phase errors on DEM accuracy. Since the application background of the interferometric ArcSAR in this paper is deformation monitoring, the deformation monitoring accuracy is also analyzed.  
In addition, it should be noted that the purpose of analyzing DEM accuracy and deformation monitoring accuracy is to demonstrate the effect of DEM assist on improving the accuracy of the results. In practice, the analysis of DEM accuracy and deformation monitoring accuracy also needs to consider more complex factors such as baseline decoherence and time decoherence. Since these factors are not relevant to our topic, they are not considered in this paper.
# Linear Programming-Based Non-Probabilistic Reliability Bounds Method for Series Systems
## 3 Proposed LPNRBM
### Identification of Zero Design Variables
#### 3.2.1 Identification of Zero Design Variables for the Bicomponent System  
As shown in Figure 3a, a bicomponent system generally involves four MECE events, namely \(F_{1}\overline{F_{2}}\), \(\overline{F_{1}}F_{2}\), \(F_{1}F_{2}\), and \(\overline{F_{1}}\overline{F_{2}}\), which may theoretically be empty. However, here we only consider the cases of \(\overline{F_{1}}F_{2}=\varnothing\) shown in Figure 3b and \(F_{1}F_{2}=\varnothing\) shown in Figure 3c, where \(\varnothing\) denotes an empty set. The reasons for this is that (1) the system will completely fail when the event \(\overline{F_{1}}\overline{F_{2}}\) is empty, (2) \(F_{1}\overline{F_{2}}\) and \(\overline{F_{1}}F_{2}\) can be regarded as the same type of events due to their symmetry, and (3) \(F_{1}\) and \(F_{2}\) are non-empty and unequal, and hence, \(\overline{F_{1}}F_{2}=\varnothing\) and \(F_{1}F_{2}=\varnothing\) cannot occur simultaneously.  
Without losing any generality, it is assumed that the two performance functions of the bicomponent system are given by the following:  
\[\left\{\begin{aligned} G_{1}(\mathbf{\delta})&=\sum \limits_{i=1}^{n}a_{i}\delta_{i}+a_{0}\\ G_{2}(\mathbf{\delta})&=\sum\limits_{i=1}^{n}b_{i}\delta_{i}+b_{0} \end{aligned}\right. \tag{9}\]where \(a_{0}\), \(b_{0}\), \(a_{i}\), and \(b_{i}\) are given constants. The corresponding correlation angle \(\gamma_{12}\) is given by the following:  
\[\gamma_{12}=\arccos\left(\frac{\sum\limits_{i=1}^{n}a_{i}b_{i}}{\sqrt{\sum \limits_{i=1}^{n}a_{i}^{2}}\sqrt{\sum\limits_{i=1}^{n}b_{i}^{2}}}\right) \tag{10}\]  
According to Ref. [26], as shown in Figure 3b, the event \(\overline{F_{1}}F_{2}\) is empty when \(\gamma_{12}\) satisfies the following:  
\[0\leq\gamma_{12}\leq\left|\arccos\frac{|a_{0}|}{\sqrt{\sum\limits_{i=1}^{n}a _{i}^{2}}-\arccos\frac{|b_{0}|}{\sqrt{\sum\limits_{i=1}^{n}b_{i}^{2}}}}\right| \tag{11}\]  
Thus, the design variables whose MECE events are the subsets of \(\overline{F_{1}}F_{2}\) should be zero. Similarly, as shown in Figure 3c, the event \(F_{1}F_{2}\) is empty when \(\gamma_{12}\) satisfies the following:  
\[\arccos-\frac{|a_{0}|}{\sqrt{\sum\limits_{i=1}^{n}a_{i}^{2}}+\arccos-\frac{|b _{0}|}{\sqrt{\sum\limits_{i=1}^{n}b_{i}^{2}}}}\leq\gamma_{12}\leq\pi \tag{12}\]  
To identify more zero design variables, a total of \(\frac{\underline{m}(m-1)}{2}\) bicomponent systems need to be investigated.
## 3 Results
### Valve Kinematics  
The stages of opening and closing for the aortic valve, observed across different calcification grades under both uniform and helical flow conditions, are illustrated in Figures 6 and 7. One expected observation is that calcification disrupts the nearly symmetrical circular orifice observed in the absence of calcification. Figure 6 shows the evolution of valve kinematics over a cardiac cycle for various grades of aortic valve calcifications under uniform flow conditions. For instance, this asymmetrical behavior in Grade 3 shows itself as the restricted mobility of the heavily calcified leaflets forces the remaining leaflets to open excessively, a phenomenon readily apparent at peak systole. Conversely, in Grade 4, where two leaflets are significantly calcified, the third leaflet is forced into extreme opening, evidenced by its visibly distended shape. A comparison of calcific deposits in the coaptation zone and the belly region reveals more limited valve motion in the latter, with leaflets exhibiting convex shapes when deposits are localized to their coaptation areas. For Grades 5 and 6, where calcification is more uniformly distributed, symmetrical opening and closure were observed; however, increasing calcification severity correspondingly reduces the orifice area. As previously noted, LVOT flow can occasionally manifest as a helical pattern rather than a uniform one due to different anomalies such as HCM. Our simulations indicate that right-handed helical LVOT flow exerts a discernible influence on leaflet kinematics and morphology, as shown in Figure 7. Specifically, we observed a subtle counter-clockwise rotational tendency in the leaflets, along with occasional increases in their convex shaping.  
These alterations are likely attributed to the helical flow's additional radial components and their resultant interactions with the valve structure. Furthermore, the impact of helical  
Figure 6: Evolution of valve kinematics over a cardiac cycle for different grades of aortic valve calcification under uniform flow condition.  
flow on leaflet displacement and dynamics diminishes as the severity of calcification increases. The difference between leaflet behavior in helical and uniform inflow conditions is most pronounced in the absence of calcification and becomes progressively reduced as calcification reaches Grade 6.  
Figure 7: Evolution of valve kinematics over a cardiac cycle for different grades of aortic valve calcification under helical flow condition.
## 2 Test Analysis  
In this test, Haixia Road in Chongqing, China was chosen as the test section. The road, an urban expressway, was officially opened to traffic in 2012, and the road surface is smooth and free of potholes. It is an important node of the main road in the urban area of Chongqing. The length of the road section is 1200 m, and the width of the road is \(43.5\) (\(2.5+18.75+1.0+18.75+2.5\)) m, which is a two-way 10-lane urban road, and a median strip separates the opposing lanes.  
An unmanned aerial vehicle [16] was used to conduct a test survey on this expressway. The aerial vehicle model used was a DJI Air 2S. The vertical photography height was 269 m, and the video processing is shown in Figure 1. Based on the Matlab image recognition system detection, the vehicle position coordinates, velocity, acceleration, time headway, and space headway were collected. The test data considered the inner six lanes; lane numbers are shown on the right side of Figure 1, and the data without lane numbers were filtered. In addition, the research object of this paper is only light vehicles. Heavy vehicles such as buses and trucks were not selected in Figure 1.  
Figure 1: Processing diagram of unstructured data.  
After filtering trajectories without lane numbers and trajectories with lane number changes, a total of 306 valid track-following straight trajectory data were obtained. The relationship between the continuous driving velocity of the vehicle and the distances to the (1) right and (2) front of the vehicle were obtained by fitting, as shown in Figure 2.  
From Figure 2, it can be observed that there is an evident correlation between the velocity and the distance of the vehicle in the forward and lateral distances. During the driving process of the vehicle, as the space headway to the leading vehicle decreases, the rear vehicle will be stimulated to slow down. Similarly, the closer the vehicle is to the sideways vehicle, the greater the incentive for the driver to slow down. Simultaneously, the desired following distance of the vehicle is intruded, prompting a larger deceleration. There are also differences in the PS of different drivers, resulting in the irregular curved shape observed in Figure 2.  
Therefore, the forward and lateral distances of the vehicle are important factors affecting the vehicle's following behavior. To study the influence mechanism of this factor on traffic flow stability, the car-following model is established.
## 5 Classification
### Experiments
The comparison between the ROC-curves obtained from ANN and SVM classifiers, (as depicted in Figure 16) reveal a clear superiority of the SVM classifier, as it gives the highest accuracy. These ROC curves are generated by considering the rate at which true positives accumulate _versus_ the rate at which false positives accumulate with each one corresponding to the vertical axis and horizontal axis. The point (0,1) means perfect classification, since it gives a correct classification for all the true positive and true negative cases. Therefore, an ideal system will be initiated by identifying all the positive instances, thus, the curve will rise to (0, 1) directly, which means zero false positive rates, and then continue along to (1,1). As can be seen for SVM (blue line in Figure 16), which gives the optimal high performance, _i.e._, high classification rate with low false alarm rate. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Performance** & **ANN** & **SVM** \\ \hline Sensitivity & 94\% & 97\% \\ Specificity & 83\% & 96\% \\ Accuracy & 90\% & 97\% \\ Error & 10 & 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance of the classification methods. Figure 15: SVM separation for benign and malignant classes by using density and curvature features (strong class overlap). Red points corresponding to benign cells and blue points corresponding to malignant cells. The black line represent the margin.