esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Evaluation of Mobile RPL-Based IoT Networks under Hello Flood Attack
## 6 Performances Analysis
### The Reference Point Group Mobility (RPGM)
#### 6.2.5 Average Power Consumption  
Figure 11 provides a detailed analysis of the APC dynamics within a network environment utilizing the RPGM mobility model, particularly under varying conditions involving the Hello Flood attack. A critical differentiation lies in the classification of nodes: mobile nodes represent scenarios without the attack, while attack nodes indicate the presence of the Hello Flood attack within the same mobility model. Across different percentages of mobile nodes, ranging from 10% to 40%, the APC remains relatively consistent in the  
Figure 10: ETX without and with attack using RPGM mobility model.  
absence of the attack. However, when the network encounters the Hello Flood attack, a notable increase in APC is observed, nearly doubling compared to scenarios without the attack. This significant rise underscores the substantial impact of the Hello Flood attack on energy consumption within the network.  
The RPGM mobility model, coupled with the Hello Flood attack, contributes to an increase in APC through several interconnected mechanisms. Firstly, the structured movements of nodes within the RPGM framework can lead to increased activity and resource utilization, resulting in higher energy consumption. This effect is further exacerbated by the Hello Flood attack, which inundates the network with excessive traffic, causing network nodes to work harder to process and transmit data, thus consuming more power. Secondly, the increased network activity and resource utilization induced by the RPGM and the Hello Flood attack can lead to higher contention for limited network resources, such as bandwidth and processing power. This contention increases the workload on network components, leading to higher power consumption as they strive to handle the increased demand. Additionally, the Hello Flood attack may introduce additional overhead in the form of malicious traffic processing, further increasing power consumption. Overall, the combination of the RPGM mobility model and the Hello Flood attack results in increased Average Power Consumption by intensifying network activity, resource contention, and processing overhead.
# Sparse Data-Extended Fusion Method for Sea Surface Temperature Prediction on the East China Sea
## 2 Problem Description and Definition  
For the observed SST data, the time series data is collected from in-site measurement points. For the satellite SST data, it provides a spatially complete SST dataset, and each grid point has a continuously long time series of SSTs.  
First, we obtain the characteristics of the time series on each grid point of satellite SST data, and calculate the association relationship of each grid point. Secondly, the grid points are clustered by taking the association relationship as the features and using the location of each in-site measurement point as the cluster center. Thirdly, based on the clustering results, the large-scale remote sensing data are expanded and fused. Finally, the SSTs are predicted based on the fusion results.  
The specific process is shown in Figure 1 below.  
Large-scale remote sensing data and sparse observation data in this paper are defined as spatio-temporal series.  
Definition of Spatio-temporal Series: Let \(D=\left\{X^{1},X^{2},\ldots,X^{n}\right\}\) be spatio-temporal series data, where \(X^{i}=\left\{x_{1}^{i},x_{2}^{i},\ldots,x_{n}^{i}\right\}\) is a piece of time-series data. Define the position information of each time data of \(i\) in \(X^{i}\), and \(x_{j}^{i}\) (\(1<j<n\)) is the data at the \(j\)th time point of the \(i\)th time series, and \(n\) means the number of grid points.  
Definition of Fuzzy Correlation: This is to analyze the relationship between the two points from the perspective of time and space. Each time series data point has multiple samples, and each sample can be divided into different categories. The relationship between the samples can be defined by the proportional relationship between the categories.  
Definition of Associated Metric: An associated metric \(m\) takes as input a tuple (\(\times 1\), \(\times 2\)), and produces an output \(r\); the larger \(r\), the higher the association.  
In previous studies, the clustering methods mainly focused on exploring the optimal clustering center on the satellite data. In this paper, we propose a fusion-based clustering method, wherein the cluster centers are determined by in-site measurement, and each grid point from satellite data will be aligned with certain cluster centers based on the association.  
Definition of Heterogeneous Clustering: Given a time series correlation matrix \(R\) of remote sensing data, the required number of clusters \(k\), which is also the number of observation points, and the clustering results are obtained by Cluster (\(k\), \(R\)) method.  
Definition of Expansion Fusion: After clustering, the sparse and uneven distributed observation data can be expanded to cover the global ocean, where the SST value from the satellite on each grid point will be replaced by its associated cluster center. Better clustering performance will lead to higher expansion ability.  
Figure 1: Data processing.
# Analysis of Volatile Components of Jasmine and Jasmine Tea during Scenting Process
## 2 Results
### Changes in the Main Aroma Components of Jasmine and Jasmine Tea  
The volatile components in jasmine and jasmine tea according to GC-MS analysis were mainly divided into terpenes, esters, alcohols, nitrogen-containing compounds, oxygen-containing compounds, and hydrocarbons in this study.
## 3 Experimental Program
### Tested Material  
The geological material tested in this study was the Callovo-Oxfordian (COx) claystone, which was investigated in France as a potential geological barrier for underground disposal of nuclear waste [16; 44]. Cores used in this study were drilled at the Underground Research Laboratory (URL) at Bure at a depth of about 500 m, operated by Andra (French National Agency for management of radioactive waste). The COx claystone was mainly composed of clay minerals (illite, montmorillonite, kaolinite and a small amount of chlorite), carbonate and quartz particles. The clay content was about 40-45%, the carbonate (mainly calcite) 25-35% and the quartz 30%. Other secondly minerals, namely feldspar, mica and pyrite, could also be found [7; 45]. The COx claystone had complex multi-scale structures. As a first approximation, two scales are generally considered as relevant for the description of mechanical behavior. At the so-called mesoscopic scale (up to mm), carbonate and quartz particles are distributed in an almost continuous clay-rich matrix [7; 27; 45]. The average size of these particles is generally smaller than 100 \(\upmu\)m. An example of 3D image by synchrotron X-ray micro-tomography is shown in Figure 4. Then at the microscopic scale (less than 1 \(\upmu\)m), the clay-rich  
Figure 3: (a) Overall view of in situ creep test system; (b) translucent polycarbonate pistons [43].  
matrix is composed of clay particles and pores between them. Therefore, the clay matrix is seen as a porous medium. Similar structures are also found in other clay-rich rocks [46; 47].  
In the present study, the emphasis is put on the measuring of creep strain field at the mesoscopic scale. At this scale, for most sedimentary clayy rocks, a layered morphology was generally observed with the presence of so-called bedding (or sedimentation) planes. Indeed, after the initial deposition, the sediment was gradually transformed into hard rock by physical (compaction, dehydration), chemical (precipitation, dissolution, epigenization) and biological (bacterial action, bioturbation) processes [48]. These diagenetic processes caused a change in the initial arrangement of sediment particles. As a consequence, most sedimentary rocks exhibited a transversely isotropy on thermal hydraulic and mechanical properties [20; 21; 22; 23; 25].
## 4 Experiments
### Results and Discussion
#### 4.4.3 Avsr  
Table 5 shows recognition accuracy of our ASR, VSR and AVSR methods in various noise environments. Note that, because the task was a 10-class classification, the accuracy in noisy environments tended to be higher compared to large-vocabulary speech recognition. The VSR accuracy was stable and unrelated to SNR since visual information is not affected by noise. As is already known, the results of VSR were lower than those of ASR in all the SNRs, because audio features are more effective and informative than visual ones. Among the models, AVSR achieved the best accuracy in all the conditions. In particular, at 0 dB, where the effect of noise was the largest, the performance was improved by 3% for city road noise and by 2.3% for expressway noise compared to ASR results. Even in the case of  
Figure 5: F1 scores in every class of our proposed multi-angle VSR model. The vertical axis indicates the F1 score, and the horizontal number means a sentence index.  
20 dB, where the effect of noise was quite small, the accuracy was slightly improved. As mentioned, we employed the decision fusion strategy, which is the simplest integration method. Similar to the ensemble approach, we believe our decision fusion method could successfully integrate ASR and VSR results, which had different recognition errors.
# Next Generation Wireless Technologies for Internet of Things
###### Abstract
Moreover, in a mesh topology, it is also feasible to obtain a higher reliability by providing more than one path to deliver data through the network. Nevertheless, in a mesh network, it is necessary to note that routing nodes usually need extra memory and processing power to establish the routing functions. A direct consequence of these requirements is that mesh networks are likewise complicated and expensive to plan. Another drawback is that, compared to a star topology, mesh networks can exhibit longer delays when routing data from a remote node through the mesh. The network size, i.e., the maximum number of connected nodes, represents also a significant problem in system design. Several technologies support up to 20 connections, while others can carry thousands of connections. Finally, the fundamental challenge in communication systems is represented by the interoperability, i.e., the capacity of devices from different vendors to interact each other. Facing this difficulty is the primary goal of the several standards organizations that establish specifications and testing methods. In fact, some standards specify one or several network layers, while others describe the whole end-to-end network specifications. The goal of this Special Issue has been to bring together a broad range of methods and technical fields to resolve open problems and hurdles typical applications of wireless technologies for the IoT, efficiently combining novel solutions, and converging on performance evaluations and comparisons with existing standards. In the next section, we present a brief review of the papers published, approximately organizing them according to thematic areas.
# A Critical Review of the Status of Current Greenhouse Technology in China and Development Prospects
## 3 Types of Greenhouses in China
### Steel Frame Shed  
Steel-frame greenhouses (Figure 9) are currently the most common type of horticultural facility in China, and by 2022, steel-frame greenhouses occupied a total area of 1,225,000 ha, accounting for 65.4% of the total area of China's horticultural facilities. The cost of each square meter of steel-frame greenhouses is around CNY 30-100. Compared with simple bamboo-frame greenhouses, steel-frame greenhouses not only maintain the advantages of simple bamboo-frame greenhouses but also overcome their shortcomings; they are also simple structures, they are strong and durable, they have a longer service life, and they have large internal spaces. Around Weifang City, where the major greenhouse area in Shandong Province is located, most of the greenhouses are steel-frame structures covered with plastic films. These greenhouses are usually unheated, and during nighttime, the roof is covered with an insulation blanket to preserve as much of the heat collected during the daytime as possible.  
Figure 8: The interior of a simple bamboo frame greenhouse.  
The advantages of steel-frame greenhouses over simple bamboo-frame greenhouses have made them the most popular type of horticultural facility in China. Figure 10 shows a cross-section of a steel-frame greenhouse, which may have variable span widths of 8, 10, 12, and 14 m, a shed edge of 0.5 m, with a height of 1.5 m. Other dimensions are shown in Table 1.
## 4 Discussion
### Effect of Different Optimization Measures  
To verify the validity of the results of identifying the factors influencing the operational effect of the green wave, we used four schemes and tested them by using simulations. Scheme 1 was the control scheme without any optimization. Scheme 2 optimized signal timing and design speed. Compared with Scheme 1, the average delay decreased to 15.4%. This indicated that the average delay was reduced by optimizing signal timing and design speed in the scheme used to control the green wave. This finding supported the view of [PERSON] et al.; that is, optimizing the signal cycle at each intersection of the green wave on arterial roads reduced overall delay [35]. Compared with Scheme 1, the average number of stops and average travel time in Scheme 3 decreased by 18.9 and 11.1%, respectively, showing that in the context of traffic management, reducing the ratio of heavy vehicles and pedestrian crossings reduced the average number of stops and average travel time. This supported [PERSON]'s view that strengthening the management of road traffic and banning large vehicles can improve the efficiency of traffic at intersections [36]. Scheme 4 optimized both the factors related to traffic management and those related to the optimization of schemes for green wave control. Compared with Scheme 1, the three evaluation indicators underwent the largest decline in Scheme 4: 39.6, 17.5, and 31.2%. This showed that the combined optimization of the scheme for green wave control and traffic management yielded the best results.  
Further comparisons of the reduction in the values of influence of Scheme 2, 3, and 4 showed that Scheme 2 underwent the smallest decrease, 10.9%. Because there was no clear linear correlation among design speed, signal timing and value of influence, improving overall efficiency in this case depended on finding the optimal scheme for green wave control. Improvements to the operational effect of the green wave are thus limited. Scheme 3 recorded the second-smallest reduction in its value of influence, 13.3%. This is because pedestrian crossings, heavy vehicle traffic, and the value of influence correlated positively. This meant that such measures of traffic management as limiting pedestrian crossings and heavy vehicles can help improve the operational effect of the green wave. Scheme 4 recorded the largest decrease in the value of influence, 29.8%. This was larger than the sum of reductions recorded by Schemes 2 and 3 (24.2%) and indicated that optimizing schemes for green wave control under the premise of implementing measures of traffic management can significantly improve the operational effect of the green wave.
## 3 Experimental Setup and Offline DSP
### Offline DSP  
The offline DSP flow chart at the Tx is shown in Figure 2c. The gray-mapped PAM-4 symbols are obtained from a random bit sequence generated by the Mersenne twister algorithm to get more convincing bit-error-ratio (BER) results [24]. For downstream transmission, the PAM-4 signal is pre-compensated by an FFE-based LUT on the OLT side. While for upstream transmission, no pre-compensation or pre-equalization is used. Since the bandwidth limitation of the transceiver devices can cause ISI, Nyquist pulse shaping based on root-raised-cosine (RRC) filter is adopted with the optimal roll-off factor 0.4. The BER performance for different roll-off factors under the back-to-back (BtB) and without SOA case is shown in Figure 3a. Figure 3b,c show the electrical eye diagrams of 50 Gbaud PAM-4 signal before and after RRC.  
The offline DSP flow chart at the Rx is shown in Figure 2d. The captured signal is resampled to 1-sps with the matched filter, and then synchronization is conducted to remove the timing offset and extract the transmitting data for BER calculating. For downstream transmission, only an FFE is applied at the ONU side for decreasing DSP complexity. For upstream transmission, a 2 nd-order VNLE is used at the OLT side to reduce linear and nonlinear impairments. The training sequence lengths of FFE and VNLE are both 1500 in our experiments. Finally, all the BER results are calculated based on 100,000 symbols after PAM-4 demodulation.  
Figure 2: (a) Experimental setup for 100 Gb/s/\(\lambda\) PAM-4 PON; Frequency response of the system (b); Tx (c) and Rx (d) side offline DSP flow, respectively.
# Robot Localisation Using UHF-RFID Tags: A Kalman Smoother Approach +
## 1 Introduction
Generally speaking, a sensor-fusion approach increases the localisation accuracy and reduces the reference tags density in the infrastructure [20]. In this paper, we propose the employment of a Rauch-Tung-Striebel (RTS) smoother based on an Extended Kalman Filter (EKF) to determine the best possible estimate of the whole trajectory followed by an agent inside a plant. This issue stems from an actual industrial application where the estimate of the set of trajectories followed by the available moving agents is of major relevance (either if they are autonomous or actually controlled by human workers). This knowledge is extremely important to the plant manager to detect points of failure or of possible collisions as well as to optimise the team trajectories as a whole. The method first proposed in [24] is here retrieved and deeply characterised through both a numerical analysis and an experimental validation with a real mobile robot in an indoor scenario. The solution robustness is discussed when measurements are missing, important feature which is hardly mentioned in the literature, showing accurate localisation of the proposed smoother with respect to filter-based approaches. After a description of the related work in Section 2, the smoother design is described in Section 3 together with the experimental setup. Then in Section 4, we discuss and report on a new set of experimental results that proves the applicability and the effectiveness of the proposed approach on an current set-up.
# ML-Based 5G Network Slicing Security:
## 1 Introduction
The idea is to partition the physical network into multiple logical networks, so that each logical network can provide specific services depending on the relevant application and its requirements [9]. Due to the enhancement of the virtualization concept in Cloud computing, there has been an increase in the expansion of physical network resources into multiple logical or virtual networks. These logical or virtual network are called \"slices\" in 5G jargon. A network slice is a free-standing virtual network with resources, flows, security mechanisms, topology, and a standard and well-defined QoS. These slices are isolated from each other and provide specific services for subscribers according to their demands [10]. Network slicing provides flexibility and scalability by allowing heterogeneous services to run over the shared physical network. It is adaptable to the changes in the requirements of the subscribers and it provides end-to-end communication. It supports the multi-service environment, on-demand network services, and multi-tenancy features within 5G.
## 5 Discussion
### Contribution 2  
To support Contribution 2, the influence of the workflow characteristics was evaluated using a specifically created test set. Here, it has been found that the choice of neural network structure, the inclusion or exclusion of a physics simulation, the number of camera positions and the number of training images per part all influenced the AM part classification accuracy on the test set. Similar articles did not provide similar insights into workflow characteristics and their influence on AM part recognition [9; 10]. The acquired knowledge was then used to optimize the proposed workflow to obtain high accuracy while maintaining sufficiently low computational costs. Currently, the results suggest that the VGG-16 network structure is best suited for image classification with the proposed workflow. This network structure achieved relatively high classification accuracy even for low numbers of training images compared to the MobileNetV2 and ResNet-50 networks. This is interesting, as all the network structures show comparable classification performance on a standard evaluation dataset [30; 37; 38]. In comparison, VGG-16 has the most network parameters by far (138.4 million), which seems to be beneficial for the proposed application. The high number of network parameters also causes the longest inference time of all the compared networks, although it still allows for 3-4 part classifications per second. In addition, including a physics simulation in the proposed workflow to only render AM part images in realistic orientations and training the network with 256 images per part from 4 camera positions for 5 epochs was found to optimize the workflow in terms of the computational resources and classification accuracy. In [12], 1000 synthetic images were used in total for 4 distinct parts (training and validation) and the training lasted 100 epochs, as comparison. As the image rendering and neural network training processes are computationally expensive, it is important to minimize the number of training images and training epochs without compromising the classification accuracy. This is particularly relevant when recognizing AM parts in non-serial production. In this scenario, parts can vary daily, and new classification networks must be trained for each new production batch. Since information on the analyzed workflow characteristics or the reasoning behind which characteristics were chosen was missing from previously published studies, the current work allows for easier recreation and optimization of automated visual AM part recognition and further research.
# A PSF-Shape-Based Beamforming Strategy for Robust 2D Motion Estimation in Ultrafast Data
## Materials and Methods
### Simulations
#### 2.3.2 Carotid Vessel Wall Displacements  
Simulations of the carotid artery vessel wall were performed using deformations patterns as obtained from a patient-specific finite element model (FEM) of the carotid artery at the bifurcation [40]. A transversal image plane, containing the internal and external carotid artery, was simulated. The amplitude of the scatterers representing the carotid artery were set at two, the surrounding tissue scatterers had an amplitude of one. Specular reflections were mimicked by positioning scatterers at the lumen-vessel wall and vessel wall-surrounding tissue transitions, which enhanced the realism of the RF data. Simulations of the vessel wall were performed in early systole, where peak inter-frame displacements are present and in late diastole, where low inter-frame displacements are present. A pre and post deformation frame were simulated for each time point, using a PRF of 50 Hz and 25 Hz (see Table 2) in systole and diastole, respectively. Band limited noise was added to the beamformed RF data resulting in an SNR of 30 dB.