esa-sceva/satcom-chunk-collection huggingface dataset:
# Fog-Based CDN Framework for Minimizing Latency of Web Services Using Fog-Based HTTP Browser
## 2 Related Work
The authors in this contribution also implemented the proposed approach and evaluated the performance in [12]. These contributions rely on the ICN protocol and tackle the caching mechanisms from the perspective of the network layer. These approaches, when applied, restrict the service owner from controlling and monitoring their own data. Using the ICN protocol for adaptive caching was proposed in [13]. The browser caching of the resources also does not provide the prospective latency reduction. This is mainly due to two factors: massive content volumes and storage limitations. An architecture that supports virtual reality content delivery called SRFog is proposed in [14]. This architecture utilizes the Kubernetes-based model on both worker and master nodes. Kubernetes ([[https://kubernetes.io/](https://kubernetes.io/)]([https://kubernetes.io/](https://kubernetes.io/)) accessed on 29 November 2021) is an orchestration platform that is used for server deployment and scalability as well as management of containerized applications. Using Kubernetes in worker nodes may be feasible for the VR use case, but it is hard to utilize for more general purpose use cases due to the management effort needed to be done on each fog node. Figure 2: Fog network architecture. Peer to Peer (P2P) caching is another approach to tackling the cloud CDN limitations. In [15], a hybrid architecture of fog-supported CDN based on P2P is proposed. This approach does not enable service owners to control their data over the edge network. It also does not count for the fog nodes' storage or bandwidth. Using a cloud hierarchal cloud cache architecture equipped with replications is proposed in [16] to reduce cloud service response time. This approach does not take into consideration the fog nodes' availabilities, content offloading mechanisms, or registering new services in the fog CDN. In this paper, we propose a fog-based CDN framework that extends the cloud CDN into the fog stratum. The proposed fog-based CDN framework is built on the application layer, which comes with some advantages, including: cloud-fog transparency, straightforward deployments and updates, data control, easy installation, and fog node monitoring.
# Trust and Mobility-Based Protocol for Secure Routing in Internet of Things
## 6 Validation of the Proposed Protocol
### Routing Protocol Requirements Analysis
#### 6.1.1 Network Model  
Let SMTrust-based RPL network be defined by a Directed Acyclic Graph \(G=(V,E)\), where set of vertices or nodes is denoted by \(V\) and set of edges or links is denoted by \(E\). An edge \(e=(i,j)\) is linked with a trust index \(T(i,j)\), where \(e\in E\) and \(i,j\in V\), and \(T(i,j)\) is the trust index of the neighbouring node \(j\) as calculated by node \(i\). In the SMTrust-based RPL network, the traffic is MP2P, where data is sent from the sender or source nodes to the sink or root node. Thus, traffic is upwards from \(N_{n}\) nodes to \(N_{1}\), where \(N_{1}\) is the sink node. Let \(p\) be the path from node \(N_{n}\) to node \(N_{1}\), such that, \(p\)<\(N_{n}\), \(N_{1}\)> = (\(N_{n}\), \(N_{n-1}\),..., \(N_{1}\)).  
On the path \(p\)<\(N_{n}\), \(N_{1}\)>, let \(N_{p}\) be the next-hop node in the neighbour of \(N_{k}\), on the path \(p\)<\(N_{n}\), \(N_{1}\)>, where \(k=n\), \(n-1\),..., \(2\). Then _neighbor_(\(N_{p}\), \(N_{k}\)) represents the \(1\)-hop neighbor of node that \(N_{k}\) selected as the preferred parent, storing it in routing table and using for traffic forwarding. The path calculation algorithm in SMTrust is the Bellman-Ford algorithm, as in the standard RPL protocol, represented by the function _path_calculation_(\(G\), \(f\), \(sd\), \(sn\)), which returns the path from source node \(sd\) to the sink node \(sn\), \(p\)<\(sd\), \(sn\)>. Two types of nodes are considered in our network model: trusted and suspicious nodes. The trusted nodes are the legitimate nodes with trust index \(T(i,j)\) > = \(T\)(_threshold_), whereas the suspicious nodes have trust index \(T(i,j)\) < \(T\)(_threshold_). A sub-path is defined as any portion of the path traversed between the source-destination nodes of any given pair.
## 4 Conclusions  
Raman spectra were acquired from binary, ternary and quaternary mixture of oil samples. Non-negative least squares was used to determine the relative concentration of EVOO and other cheaper additives. The purity of EVOO was estimated from EVOO spiked with one or more adulterants using Raman spectroscopy in combination with PLS. The potential of Raman spectroscopy for determining the purity of EVOO was successfully demonstrated on the complex scenario where the spectral library was obtained with different measurement parameters and sample conditions (binary + ternary + quaternary). The chemical composition of EVOO not only varies by cultivar, region, altitude, time of harvest, soil conditions, and extraction process, but also because of ageing and oxidation associated with storage conditions such as exposure to oxygen, light, and temperature.  
Figure 6: Regression coefficients of PLS models developed from binary, ternary, quaternary and combined (binary + ternary + quaternary) dataset. The curves are scaled for better visualization.  
Figure 7: Actual concentration vs. estimated concentration of extra virgin olive oil (EVOO) calculated from binary, ternary, quaternary and combined (binary + ternary + quaternary) dataset using partial least squares (PLS) analysis. The solid line represents the zero-error curve.  
In future studies of EVOO, we plan to incorporate all the potential variations in our chemometrics model to determine the origin and the quality of EVOO. We foresee that the developed Raman sensor with PLS analysis might close the gap for the increasing demand to develop a quick, sensitive, portable, easy-to-use and cost-effective online analytical approach for quantifying either the purity or adulteration of EVOO and identifying the added adulterant. Note that the current Raman setup can easily be packed with the software which can control the entire set up and contains the calibration models to determine the quality of not only olive oil, but also other types of food real-time in the field.  
Formal analysis, S.D.; Funding acquisition, J.C.P. and M.L.; Investigation, S.D.; Methodology, S.D.; Project administration, J.C.P. and M.L.; Visualization, S.D., J.C.P. and M.L.; Writing--original draft, S.D.; Writing - review and editing, J.C.P. and M.L.  
This research was funded by EUREKA Eurostars (E10132-PASOCA) and the Danish Agency for Institutions and Educational Grants.  
There are no conflicts of interest.  
The following abbreviations are used in this manuscript:  
EVAO Extra Virgin Olive Oil  
PLS Partial Least Squares  
CCD Charge-Coupled Device
###### Abstract  
Object tracking is a computer vision task deemed necessary for high-level intelligent decision-making algorithms. Researchers have merged different object tracking techniques and discovered a new class of hybrid algorithms that is based on embedding a meanshift (MS) optimization procedure into the particle filter (PF) (MSPF) to replace its inaccurate and expensive particle validation processes. The algorithm employs a combination of predetermined features, implicitly assuming that the background will not change. However, the assumption of fully specifying the background of the object may not often hold, especially in an uncontrolled environment. The first innovation of this research paper is the development of a dynamically adaptive multi-feature framework for MSPF (AMF-MSPF) in which features are ranked by a ranking module and the top features are selected on-the-fly. As a consequence, it improves local discrimination of the object from its immediate surroundings. It is also highly desirable to reduce the already complex framework of the MSPF to save resources to implement a feature ranking module. Thus, the second innovation of this research paper introduces a novel technique for the MS optimization method, which reduces its traditional complexity by an order of magnitude. The proposed AMF-MSPF framework is tested on different video datasets that exhibit challenging constraints. Experimental results have shown robustness, tracking accuracy and computational efficiency against these constraints. Comparison with existing methods has shown significant improvements in term of root mean square error (RMSE), false alarm rate (FAR), and F-SCORE.  
Keywords:particle filter; mean shift; object tracking; multi-feature; Kalman filter +
Footnote †: journal: Applied Physics Letters
## 4 Performance Evaluation and Discussion
### LFU-Based Object Detection Performance  
Figure 5 shows the experimental setup for investigating the effectiveness of the LFU-based object detection in the proposed audio marker system. As shown in Figure 5a, the equipment employed a speaker, a microphone, a camera, and a PIR sensor, where the PIR sensor was used as a conventional marker for the performance comparison. Participants were asked to walk 4 m in front of the object detection equipment (Figure 5b). The overall layout without and with a participant is shown in Figure 5d,g, respectively. The speaker periodically produced the low frequency pulsed ultrasound, and the microphone captured audio signals that were sampled at 48 kHz. The camera was placed to provide the ground truth. In parallel, the PIR sensor values were captured and transmitted via serial communication. Figure 5e,h show typical spectrogram images without and with a person walking 4 m from the equipment, respectively. The spectrogram intensity was markedly higher when the person was present, because the LFU reached the microphone earlier. However, the spectrogram without the person was quite similar to the background image, as noticed in Figure 5c. By subtracting Figure 5e from Figure 5c, the spectrogram image in Figure 5f became small, which resulted in a low a priori SNR, \(\xi(\mathbf{p},n)\), in Equation (12). In contrast, the reflected LFU remained significantly in the spectrogram image when the person was within 4 m, as shown in Figure 5h,i before and after subtracting the background, respectively.  
Figure 6 shows the experimental results in a receiver operating characteristic (ROC) curve. As shown in the figure, the PIR-based system achieved a higher TPR, to some extent, than the proposed  
Figure 5: Experimental setup and LFU-based object detection: (**a**) experimental measurement setup; (**b**) top view of the equipment position and participant path through the experiment zone; (**c**) initial background spectrogram image (\(N=31\)); (**d**) overall experiment environment without any participants; (**e,f**) spectrograms for the situation (**d**) before and after the background subtraction, respectively; (**g**) participant walking through the experiment region; (**h,i**) spectrograms for the situation (**g**) before and after the background subtraction, respectively.  
system. However, the proposed system achieved a substantially lower FPR than the PIR-based system. In addition, the area under curve (AUC) of the proposed and PIR-based audio marker systems was measured as 0.79 and 0.74, respectively. Therefore, it could be concluded that the proposed LFU-based system yielded a better overall detection performance than the PIR-based one. According to the ROC analysis, a threshold for the log-likelihood test, \(\eta\) in Equation (13), can be chosen depending on the applications. It is expected that the decent service described in Figure 1 works well by setting \(\eta=-1.1394\), where a low FPR of 0.2 and a modest TPR of 0.78 were achieved, because numerous users are traveling frequently across the audio markers in this service.
# Development of a Simulation Model for HMT of a 50 kW Class Agricultural Tractor
## 3 Results  
### Load Analysis  
#### 3.1.1 Axle Torque  
The axles of the tractors had the same size wheels and the same weight distribution ratio, so the axle torque of the HMT was expressed as the sum of the left and right axles [39; 40]. Figure 7 provides a comparison of the measured and simulated results of the axle torque according to the gear stages. The axle torque rapidly decreased in the first gear stage, and gradually decreased as the gear stage was increased. The ranges of the measured torques were 10,030.99-20,054.67, 4801.75-10,031.64, and 2376.00-4418.15 Nm, respectively to the gear stages, and the range of simulated torque were 9938.64-20,019.61, 4544.49-9656.74, and 2271.54-4273.19 Nm, respectively, to the gear stages. The simulation results showed similar trends in all gear stages; however, the largest difference of axle torque between the measured and simulated results was 2521.67 Nm in the first gear stage. The delay for the control of the HSU speed occurred in the prototype of the HMT [24].  
The average of the simulated axle torque was smaller than the average of the measured value in all gear stages. This is because the bench test was performed in harsher conditions than the simulation analysis [41]. In the performance evaluation for the power transmission efficiency, the transmitted power was affected by the internal temperature of the transmission [14; 33]. Keeping the internal temperature of the transmission constant was difficult during the bench test, while the temperature was constant in the simulation. Table 3 lists the axle torque differences between the measured and simulated results based on the gear stages. The maximum differences of axle torque between the measured and simulated results for each gear stages were 2521.67, 375.64, and 156.86 Nm, respectively. As a result, the \(p\)-values for each of the gear stages were more than 0.05 under all gear stages, and, thus, we considered that there was no significant difference of the axle torque between the measured and simulated results. Therefore, the simulation model was available to evaluate the axle torque of the HMT tractor.
## 1 Introduction
### Author's Contribution  
The solution of the combined approach to the analysis of the needs of the A-RoF and D-RoF interfaces was not undertaken in the above-mentioned studies. The author proposed the coexistence of these interfaces in an optical network. This task is possible when the fiber optic RAN network is all-optical. Of course, we can use fiber-to-the-antenna (FFTA) or passive optical network (PON) architecture, but passive solutions do not provide such a wide scale of optical resource management. Implementation of the network in the XAhaul architecture enables the transport of digital data (backhaul) and digitized signals (fronthaul--D-RoF). The combination of A-RoF and D-RoF traffic forces proper preparation of all nodes in the network from the NG-RAN domain. Therefore, the author proposed a special O-RRH construction that can be directly or through-connected to a all-optical network. This construction was first presented by the author at an optical conference in Prague [23]. This also applies to BBU and DU nodes, which by definition have adequate computing power and will perform most of the tasks on a software basis. The method of managing nodes that carry also A-RoF traffic was presented by the author at the fiber-optic conference in Suprasl [24]. In addition, in order to formalize the baseband-over-fiber (BBoF), intermediate-frequency-over-fiber (IFoF) and radio-frequency-over-fiber (RFoF) solutions, it was proposed to extend the function of the Option 8 and to introduce new Options 9 and 10 in relation to the 3 GPP model. The most important component of the author's study are unique calculations that indicate the need to reserve optical resources when transporting digital streams fromthe D-RoF (CPRI, eCPRI) and A-RoF (BBoF, IFoF and RFoF) interfaces. Calculations were made for a variable width of radio channel according to the 5G-NR-Rel-15 baseband (CP-OFDM waveform). The results of the calculations can be of major application when scaling the resources of an all-optical network, whose task will be to transport any type of signal, including A-RoF. The calculation formulas presented below have been adapted to 5G modulation and code solutions or created from scratch as a result of appropriate transformations (applies to A-RoF interfaces). It should be noted that the effectiveness of the presented approach to handling A-RoF and D-RoF traffic will be leading when introducing O-RRH working in massive-MIMO format, where we will have to deal with handling large traffic from a large number of EUs (need to introduce IoT, which will be supported in the wireless part by 5G systems). The results of the calculations carried out will allow efficient decision-making about switching the connection to the selected type of options in the extended range by the author, i.e., Options 1-10.
## 5 Two-Way Measurement Method in CCFD
### Key Errors of the Two-Way Measurement Method in CCFD
#### 5.3.5 Ranging Noise  
The inter-satellite pseudo-range values \(\rho_{ij}\) and \(\rho_{ji}\) are extracted from the pseudo-code tracking loop of the satellite receiving device. The precision of pseudo-range measurements is related to the thermal noise error and dynamic stress error of the tracking loop. The 1\(\sigma\) tracking error of the pseudo-code loop can be expressed as follows:  
\[\sigma_{DLL}=\sigma_{LDL}+\frac{\theta_{c}}{3} \tag{46}\]where \(\sigma_{tDLL}\) is the tracking error caused by thermal noise, and \(\theta_{e}\) is the dynamic stress error caused by inter-satellite relative movement. \(\theta_{e}\) can be expressed in the following form as follows:  
\[\theta_{e}=\frac{1}{\omega_{n}^{N}}\frac{d^{N}D(t)}{dt^{N}} \tag{47}\]  
where \(N\) is the order of the loop, and \(\omega_{n}\) is the natural frequency in Hz. During actual tracking, carrier-aided code tracking technology is often used, which can largely eliminate most of the dynamic stress errors. Therefore, dynamic stress errors can be neglected, and the ranging error is primarily caused by thermal noise. \(\sigma_{tDLL}\) can be expressed as follows:  
\[\sigma_{tDLL}=\frac{1}{f_{code}}\sqrt{\frac{2d^{2}B_{n}}{C/N_{0}}[2(1-d)+\frac{ 4d}{T_{coh}C/N_{0}}]} \tag{48}\]  
where \(f_{code}\) is the pseudo-code rate in Hz, \(B_{n}\) is the equivalent noise band of the code loop in Hz, \(d\) is the correlation interval, and \(T_{coh}\) is the coherent integration time. When the inter-satellite ranging system uses the CCFD scheme, compared to the traditional TDHD scheme, the measurement time can be doubled, and the system can reduce the equivalent noise band \(B_{n}\), achieving higher-ranging accuracy by sacrificing convergence speed. Therefore, it can be theoretically verified that using the CCFD scheme can effectively reduce the pseudo-code ranging noise.
## 3 Related Work
### ASD Prediction Using ML Approach
#### 3.1.3 ASD Prediction Using ML with Optimized FS Methods  
There was only one study that used bio inspired algorithms on this data type. [PERSON] et al. [3] tried to use the Firefly feature selection algorithm to improve ASD classification by providing a minimum set of features. The dataset contains 21 features, which makes it a high dimensional dataset. They used firefly feature selection algorithm with these classifiers (NB, SVM, and KNN) with 10-fold cross-validation, and they compared the accuracy before and after applying feature selection. The results show that the firefly feature selection algorithm selected 10 feature subsets among the 21 features in the dataset as optimum and the SVM classifier provided the highest score with 97.5%.  
[PERSON] et al. [30] tried to improve the accuracy of the gene classification for ASD by using ML with geometric binary particle swarm optimization (GBPSO), which is one type of bio-inspired algorithm. They used different filters to reduce features to be 9454 features (genes). Then, they used statistical filters, which were as follows: the two-sample t-test (TT), the group correlation of features (COR) and the Wilcoxon rank sum test (WRS). The last step was choosing genes by using a GBPSO-SVM wrapper-based algorithm along with the used filters. The advantage of using this algorithm is because GBPSO starts with a random number of selected genes and searches in each iteration for the appropriate subset of genes. Then, 10-fold cross-validation with the SVM classifier was used to test the output of each candidate subset. The GBPSO algorithm contributed to the choice of an optimal subset of genes, offering the highest accuracy of classification. The combined gene subset selected by the GBPSO-SVM algorithm has been able to increase the accuracy of the classification to reach 92.1%.  
Similarly, [PERSON] et al. [31] tried to enhance the ASD prediction by using the optimal feature (genes) subsets in the classification algorithm. They used genetic algorithms (GA) and RF in the role of final gene selection. The most important genes selected by each method was used as the input features to the SVM and RF classifiers, cooperating in an ensemble. The final result of the classification was generated by RF and was about 87%.  
[PERSON] et al. [32] used the brain images dataset that contains 126 ASD samples and 126 typically developing (TD) samples to detect ASD. Three ML algorithms were implemented in this study to perform a binary classification (ASD vs. TD) using rsfMRI data. First, they used SVM in combination with particle swarm optimization (PSO) for feature selection (PSO-SVM). Second, SVM with recursive feature elimination (RFE-SVM) was used, and thirdly was RF. The diagnostic classification obtained a high accuracy of 91% with RF.