esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Network Simulation and Results
### Results
#### 6.2.2 Shortest-Delay RREQ Forwarding  
As only the first RREQ is forwarded, the network tends to experience the same overhead under the shortest-delay strategy; therefore, the number of collisions is similar regardless of which type of jitter is used, as shown in Figure 18a. The number of collisions in no-jitter settings, in contrast, is obviously higher. In this situation of similar control traffic overhead, those settings using window jitter distributions have an expectedly longer average route discovery delay, as depicted in Figure 18b.  
However, because the intermediate routers simply forward the RREQ that arrives first and all of the following RREQs are ignored, the no jitter and \"standard\" jitter settings got sub-optimal routes. In the meantime, with \"window jitter\", a much shorter routes can be explored, as illustrated in Figure 18c. This is more interesting for less time-critical, but power-constrained, networks (such as sensor networks).
## 4 Experimental Research on the Multi-DOF Seat Damping Suspension  
### Experimental Prototype of the Multi-Dimensional Damping Seat and the Vibration Test System  
A multi-dimensional damping seat test prototype was developed. A multi-degrees-of-freedom vibration test was carried out, in order to test the multi-dimensional performance of the multi-DOF damping seat suspension based on the cubic Stewart platform and the MRF damper. As shown in Figure 16, the test prototype comprised a lower platform, lower joint, MRF damper, spring, upper joint, load plane, and other parts. In the test prototype, each leg contained an MRF damper and a spring to form an elastic vibration-damping system.  
Figure 16: Experimental prototype of multi-DOF seat damping suspension.  
Figure 15: The relative velocities of each leg. (**a**) The velocity of leg1. (**b**) The velocity of leg2. (**c**) The velocity of leg3. (**d**) The velocity of leg4. (**e**) The velocity of leg5. (**f**) The velocity of leg6.  
To realize a multi-degree freedom vibration test, it was necessary to excite the seat damping platform in multiple directions. A 6 DOF vibration test bed was used to generate multi-dimensional vibrational excitation. In the vibration test bed, six servo motors drove the lead screw to control the movement of six leg actuators, in order to realize the six degrees of freedom vibration. The test bed is shown in Figure 17.  
During the test, an 80 kg experimenter sat on the experimental prototype of the multi-dimensional damping seat to simulate a vehicle driver. Furthermore, two PCB three-axis acceleration sensors were installed in the center of the upper and lower plates of the damping seat, in order to measure the vibration acceleration of the upper and lower plates. In the roll direction vibration test, we calculated the roll acceleration from the vertical acceleration of the two positions. The specific method used was as follows: during the roll acceleration test, two accelerometers were installed on the upper and lower plates, and the roll acceleration measurement was obtained by subtracting the vertical acceleration values of the two points, and dividing the remaining value by the distance between the sensors [23].
# An Extended Car-Following Model Considering Generalized Preceding Vehicles in V2X Environment
###### Abstract  
Vehicle-to-everything (V2X) technology will significantly enhance the information perception ability of drivers and assist them in optimizing car-following behavior. Utilizing V2X technology, drivers could obtain motion state information of the front vehicle, non-neighboring front vehicle, and front vehicles in the adjacent lanes (these vehicles are collectively referred to as generalized preceding vehicles in this research). However, understanding of the impact exerted by the above information on car-following behavior and traffic flow is limited. In this paper, a car-following model considering the average velocity of generalized preceding vehicles (GPV) is proposed to explore the impact and then calibrated with the next generation simulation (NGSIM) data utilizing the genetic algorithm. The neutral stability condition of the model is derived via linear stability analysis. Numerical simulation on the starting, braking and disturbance propagation process is implemented to further study features of the established model and traffic flow stability. Research results suggest that the fitting accuracy of the GPV model is 40.497% higher than the full velocity difference (FVD) model. Good agreement between the theoretical analysis and the numerical simulation reveals that motion state information of GPV can stabilize traffic flow of following vehicles and thus alleviate traffic congestion.  
t 2020 12 216 10.3390/fi12120216
# Remote Sensing Image Segmentation of Marculture Cage Using Ensemble Learning Strategy
## 2 Materials and Methods
### Network Model
#### 2.1.2 Ensemble Learning  
Ensemble learning is a machine learning method that uses a series of base learners and uses some rules to integrate the individual results to achieve better performance than a single learner. The generalization ability of an ensemble approach is usually much stronger than that of a single learner, which makes it very attractive. In practice, two necessary conditions should be met for a good ensemble: accuracy and diversity [14]. The base learners should be more accurate than random guesses, and each base learner should have its own knowledge of the problem and a different error pattern from the other base learners. This paper adopts the ensemble learning method based on stacking. The stacking [15] ensemble learning framework first divides the original data set into several sub-datasets, which are input to each basic learner of the first-level prediction model, and each basic learner outputs its own prediction result. Then, the output of the first level is used as the input of the second level, the meta-learner of the second-level prediction model is trained, and the final prediction result is output by the model in the second level. The stacking learning framework generalizes the outputs of multiple models to improve the overall prediction accuracy, as shown in Figure 2.
# A Survey: Network Feature Measurement Based on Machine Learning
## 3 Network Measurement Based on ML Method
### Bandwidth
The degree of improvement varies depending on the position of the filling elements, the number of existing elements, and the position of the existing elements in some approaches that only use a small number of detection rates. Overfitting occurs when there is insufficient data for training. Because each class defines a real number range, errors will still occur even if the classifier classifies the data correctly. The maximum error happens when the available bandwidth is at the class's edge. The actual bandwidth can be converged by direct detection techniques. It cannot, however, keep up with rapid fluctuations in bandwidth. While reducing probe traffic helps to estimate the available bandwidth, it also causes network congestion. With more links, the misclassification error rose, necessitating additional training data.
## 3 Results and Discussion
### Variation of Volatile Compounds in XYMJ Green Teas
Liberated from its glycosides and giving a minty odor, methyl salicate was recognized as an important volatile compound for the tea aroma formation [23]. (Z)-3-Hexenyl butyrate was ranged from 5.85 \(\upmu\)g/L to 23.70 \(\upmu\)g/L in XYMJ green teas, which has a fruity odor [27]. These esters had fresh and fruity aroma, which would be responsible for the clean aroma notes of the XYMJ green teas. The variation of hydrocarbons in XYMJ green teas is shown in Figure 5. It is generally considered that saturated hydrocarbons, such as pentadecane, nonadecane and dodecane, have no or minor contribution to the tea flavor [28]. Naphthalene has pungent naphthyl odors [19], limonene was reported to give citrus flavor in tea leaves [29], \(\delta\)-cadinene imparts spicy and woody aroma in tea leaves [30], and \(\alpha\)-cubebene has an herby and waxy flavor [31]. \(\delta\)-cadinene and \(\alpha\)-cubebene presented the similar tendency during the spring and autumn seasons. Unsaturated hydrocarbons, including naphthalene, limonene, \(\delta\)-cadinene and \(\alpha\)-cubebene, would play an important role on the XYMJ green teas flavor formation. Figure 4: Various change trends of the esters in XYMJ green teas. All data are expressed as mean \(\pm\) S.D. (\(n=3\)). During the spring season, trans-\(\beta\)-ionone (woody, violet) and cis-geranylacetone (floral, hay-like) increased gradually from 5.90 \(\upmu\)g/L to 10.97 \(\upmu\)g/L and 2.63 \(\upmu\)g/L to 8.67 \(\upmu\)g/L, respectively. The average content of cis-geranylacetone was higher (\(p<0.05\)) in the autumn season than that in the spring season. It was reported that 2,3-octanedione had green and broccoli-like odor [32]. It is reported that dimethyl sulfide is a key volatile compound in the fresh green tea [33]. The aparagus-like dimethyl sulfide in XYMJ spring teas is significantly lower than that in XYMJ autumn teas. The animal-like indole in XYMJ spring teas was higher (\(p<0.05\)) than that in XYMJ autumn teas. Indole is a common aroma-active compound in green tea [23], it was reported that the diluted indole had sweet and floral odor and strengthened the overall green tea odor over specific content ranges [34]. The identified common volatile compounds were used as variables to accomplish the PCoA (Figure 1B) and HCA (Figure 1D). The results showed that the 8 XYMJ green teas were divided into three clusters (PERMANOVA \(R^{2}=49.5\%\), \(p<0.001\)). Cluster-1 consisted of the earlier spring teas XYMJ01 and XYMJ02, Cluster-2 consisted of the later spring teas (XYMJ03, XYMJ04) and XYMJ08, and the other three XYMJ autumn teas made up Cluster-3. Different from choosing the non-volatile compounds as variables, the XYMJ08 was divided into Cluster-2. However, the results could also reveal that production seasons have effects on the aroma quality of XYMJ green teas.
# Investigating the Performance of FixMatch for COVID-19 Detection in Chest X-rays
## 2 Literature Review
### X-ray Classification Using Deep Learning
The authors trained the model on a dataset from the Guangzhou women's and children's hospital that consisted of 5856 images, out of which 4237 were Pneumonia, and the rest were normal. For their experimentation, they randomly selected 1000 images of each class and used 70% unlabeled data for each class. The authors were able to achieve an accuracy of 94.73%, which is on par with non-SSL networks, even with unlabeled data present. Similarly, [PERSON] et al. [27] used unlabeled images to train a feature extractor instead of predicting pseudo labels. The authors used a c3 schema for feature extraction on unlabeled images and used Resnet50 for the final classification. The model was trained by using the ChestXRay2017 dataset that consisted of 5856 Pneumonia images and a mix of two sets of datasets from the US National Library of medicine to get 800 images of Tuberculosis. The class Pneumonia was trained with 1536 labels and achieved an AUC of 0.98, while the Tuberculosis class was trained with 448 labels and achieved an AUC of 0.923. Tables 1 and 2 below summarizes the work reviewed for the above section.
## 4 Evaluation
### Experimental Setup  
Table 3 shows the hardware and software configurations of our testbed, and for the experiments, we exploited HiBench Suite [15]. There are total 29 workloads in the HiBench, which are divided into six different categories including Micro, ML(Machine Learning), SQL, Graph, WebSearch, and Streaming. As representative Big Data workloads, we selected Terasort from Micro, K-means from ML, Join from SQL, and PageRank from WebSearch benchmarks. The TeraSort, K-means, and PageRank benchmarks were performed by setting the size of input data to \"huge\", and in the case of Join, it was set to \"gigantic\" due to its relatively short execution times.  
We basically conducted two types of experiments. First, in order to measure the overhead of our proposed adaptive resource allocation policy (denoted as **Adaptive**), we compared \"Adaptive\" with a single container where all of available computing resources of a host server are allocated, which can guarantee the _best_ performance in the given computing environment (denoted as **Baseline**). In this case, Adaptive starts with a single container equipped with minimal computing resources (1 vCPU, 2.5 GB of memory) and gradually adjusts allocated resources based on the policies presented in Section 3.2. Second, we ran four different containers from the Micro, ML, SQL,and WebSearch benchmarks in parallel and compared our proposed scheme with a fair and static resource allocation method, where each container is equally assigned 1/4 of available computing resources during the runtime (denoted as **Fair**).  
We used _makespan_ (time to complete a whole job) and _resource utilization_ (for CPU and Memory) as our performance metrics. Since the CPU allocations can change whenever a Big Data workload is executed, we run every workload five times and measured the average makespans.
# Security Architecture for Swarms of Autonomous Vehicles in Smart Farming
## 3 Results
### Security Architecture for Swarms of Autonomous Vehicles
#### 3.2.1 Protection of Data Confidentiality
A VPN provides a method to communicate nodes that are not physically connected to a shared network. However, data to be transmitted over a VPN is encrypted before being sent to the destiny. The IP addresses of the VPN endpoints are hidden as well. In order not to penalise communication performance, which is something especially important in the management of autonomous vehicles, it is recommendable to select VPN solutions based on secret-key encryption, usually considered as fast and low battery consumption algorithms. LogMeIn Hamachi VPN [32] is proposed as a solution to establish the VPN, a free solution for up to five nodes deployed in the same VPN network. This VPN solution is independent from the network operator, so any SIM card can be used on the vehicle side. In this case, a key exchange protocol based on the Diffie-Hellman algorithm would take place between any two VPN nodes willing to exchange messages. Once a session key has been established, AES-256 cipher would be used for data encryption. * Communication channel D: the data in the Repository can be accessed through REST APIs. As in the case of communication channel B, the best option for channel encryption would be to use TLS. A X.509 certificate must be installed on the server side (the Data Repository side in this case). The Mission Console must be configured to trust the CA of the certificate. * Communication channel E: the authors of this study consider that the encryption of this communication channel is not essential, since the components involved can be deployed in a monitored Cloud that does not offer external interfaces. By using an unencrypted interface between the SWARM Controller--responsible for supervising the operations of autonomous vehicles--and the Data Repository, the real-time requirement for managing autonomous vehicles is not compromised. * Data Repository: it is possible to encrypt data stored in a database at a number of levels: storage-level, database-level and application-level. For SQL databases, one of the most commonly used methods is column-level encryption (performed at the database level), significantly more flexible compared to entire database encryptions. Moreover, this method allows for using separate secret keys for each column within a database. For NoSQL databases, this approach may not be the most suitable, considering that not all documents define columns before inserting data. However, in general, it should be noted that encryption can cause performance degradation in all databases accessed, so it is advisable to encrypt only sensitive data (i.e., private data such as passwords, which should be stored in the database data dictionary in encrypted format).
## 5 Discussion
### Emulation of Leaf-Canopy-Atmosphere RTMs  
The first objective was to identify the driving variables of vegetated surfaces that shaped the TOA radiance reaching an optical sensor in space. To do so, RTMs of leaf, canopy and atmosphere were coupled. The coupling process of the leaf-canopy-atmosphere RTMs-PROSPECT-4, SAIL and MODTRAN-allowed to simulate LUT of TOA radiance data assuming a Lambertian surface. However, because MODTRAN is computationally expensive and takes some seconds to run a single simulation, it implies that running thousands simulations can take days to weeks. To overcome this computational burden, with emulation a bypass was found to speed up the production of simulated TOA radiance data [32]. Given the assumption that the emulator approximates the TOA radiance outputs of the original leaf-canopy-atmosphere RTMs with sufficient accuracy, it can then be safely used for RTM-based applications such as GSA studies. By having the emulator producing the simulations quasi instantly, the GSA was processed in the order of seconds. Validation against reference data showed that the emulator can reproduce TOA radiance with sufficient accuracy (NRMSE errors of 2.4%) for conducting reliable GSA studies [91]. The emulation accuracy is consistent with earlier analysis for the emulation of PROSPECT-4, PROSAIL and MODTRAN [90]. Based on this and earlier studies, the following observations can be drawn. The accuracy of the emulator depends on the type of the algorithm used, number of variables, number of training samples and complexity of the model [32; 90; 91]. From multiple machine learning methods tested such as neural networks and other kernel-based methods, in this (results not shown) and earlier studies, Gaussian processes regression (GPR) yielded the highest accuracies in approximating the outputs of the original RTM.  
Figure 7: Maps of biophysical variables obtained from L2A (**Top**) and L1C (**Middle**) data. Scatter plots of both maps with gridded color density (**Bottom**).  
Thereby, accuracies can be improved with adding more training data although that is at the expense of processing speed. Typically, training with about 1000 simulations is considered as a good trade-off between accuracy and optimizing processing speed, e.g., for GSA calculations [33; 90].
## 3 Experimental Results and Discussion
### Security Analysis
#### 3.2.1 Key Space Analysis  
The proposed image encryption algorithm uses several initial conditions to reach its goal, which is a cipher image. For this purpose, eight initial conditions were created to be used in two logistic maps and six hyperchaotic systems.  
Table 1 shows 15 state combinations from a universe of 1296 possibilities; 15 randomly selected combinations were used in the testing process. This feature allows the precision to be defined as equal to \(10^{-14}\); thus, the secret key space is at least \(15\times 10^{84}\approx 2^{283}\).  
Thus, the secret key space is large enough to resist either exhaustive or brute-force attacks. Initial conditions yield different options for the encryption process, so those conditions can be changed into a new set of credentials in the authorization system.  
Figure 8: (**a**) Plain image; (**b**) histogram of the original image; (**c**) cipher image after shuffling per bit level; (**d**) image histogram of (**c**); (**e**) ciphered image; (**f**) histogram of the ciphered image.
## 2 Background
### Virtual Commerce  
Virtual commerce describes the consumption of products and services promoted by the activities in an immersive virtual environment such as a VW [40; 41]. Virtual commerce includes virtual consumption, i.e., the purchase of virtual objects created in VWs with virtual currency [42], or a mix of virtual-real content and payments [2]. Virtual commerce has been studied from multiple disciplines including business management, consumer behaviors, and software engineering. For example, [43] discussed virtual commerce from a business operation perspective using Second Life ([[https://secondlife.com/](https://secondlife.com/)]([https://secondlife.com/](https://secondlife.com/)), accessed on 16 November 2021) as a study case, and identified critical factors for the success of virtual commerce systems regarding businesses, VW operators, and the external environment. In terms of consumer behaviors, the affordances of VWs were believed to benefit multiple consumer responses, such as brand attitudes [40], shopping enjoyment [44], and trust [45]. Furthermore, the requirements of VWs for commercial application were studied [46].  
A virtual commerce application is an ICT artifact for commercial activities such as retailing and marketing, with MR providing the immersive virtual environments that contributes to numerous improvements over existing e-commerce. For example, using low-immersion VR such as virtual try on (VTO) systems [47], product presentation in retailing is more realistic and interactive compared to the text and images presentations in web-based e-commerce. High immersion VR was perceived to bring a real-world shopping experience into e-commerce and was applied to shopping, marketing, consumer behavior research, and so on [18; 48; 49]. Meanwhile, AR can present products in the context of a user's surrounding environment to enhance product experience [50].  
Recently, transitions from existing e-commerce to virtual commerce retailing have emerged to harvest the benefits of an immersive and enjoyable shopping experience. Alibaba demonstrated VR shopping in 2016 ([[https://www.alizila.com/11-1-this-year-will-be-most-interactive-ever](https://www.alizila.com/11-1-this-year-will-be-most-interactive-ever)]([https://www.alizila.com/11-1-this-year-will-be-most-interactive-ever](https://www.alizila.com/11-1-this-year-will-be-most-interactive-ever)), accessed on 16 November 2021), and IKEA also deployed VR shops in three countries in 2017 ([[https://takeleap.com/services/virtual-reality/ikea-virtual-reality-store/](https://takeleap.com/services/virtual-reality/ikea-virtual-reality-store/)]([https://takeleap.com/services/virtual-reality/ikea-virtual-reality-store/](https://takeleap.com/services/virtual-reality/ikea-virtual-reality-store/)), accessed on 16 November 2021). Nevertheless, multiple obstacles should be overcome to realize the full potential of virtual commerce, including but not limited to consumer acceptance, business adoption, and information systems infrastructure and tools [7; 51; 52].