esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
However, the detection and reduction of coverage holes in the network are not considered which also affects the effective transmission of data in the network. Authors in [26] focus on the deployment of wireless sensor networks using optimization techniques. Initially, the applications of WSN in various fields are mentioned and the necessity of the proper deployment of sensors in the network is addressed. The geo-spatialinformation system-based optimization is implemented in order to deploy the IoT sensors in an optimal manner. Two algorithms, namely, particle swarm optimization (PSO) and bees algorithm (BA) were experimented with, and the results show that the former algorithm has better convergence and the latter one has better fitness value computation. In spite of the deployment of sensor nodes, the coverage hole problems arise and a proper method to enhance the coverage in the network is not addressed. According to the authors in [27], a sensor deployment strategy is implemented to facilitate the effective data transmission in smart cities. The deployment of sensors is carried out in a careful manner in order to provide better coverage and a low economical budget. Initially, the Voronoi diagram is constructed and the Delaunay triangle is constructed between the randomly deployed IoT sensors. The triangle's center point is computed in order to deploy the remaining sensors in high coverage points. The clustering of nodes is executed based on k-means clustering and the sink nodes are deployed in the center of the cluster for better coverage.
## 4 Discussion
### Dependence of CO\({}_{2}\) Flux on Soil Temperature  
Our results demonstrate that decreased precipitation reduced the dependence of CO\({}_{2}\) flux components (GEP, Re, Rs, NEE) on temperature, while increased precipitation enhanced these dependencies (Table 2). The DP increased soil temperature by 0.26 \({}^{\circ}\)C and the range of soil temperature (difference between maximum and minimum values) by 1.35 \({}^{\circ}\)C during the growing season, which partly decreased the temperature limitation at the study site and thus weakened the dependencies of ecosystem processes on temperature. This occurred because the change in CO\({}_{2}\) flux per degree change in temperature in an ecosystem unlimited by temperature would be less than in a low-temperature limited ecosystem. This result was consistent with other findings that increases in temperature weakened the temperature limitation in temperate ecosystems, or those at high latitude or altitude [60; 61; 62]. In our study, IP decreased the mean value and range of soil temperature by 0.59 and 1.23 \({}^{\circ}\)C, respectively, during the growing season. The IP also enhanced the temperature limitation and, thus, increased the dependence of CO\({}_{2}\) flux on temperature. Our observation was consistent with results by [PERSON] et al. [63] that water addition increased the temperature sensitivity of soil respiration in three of four communities in the semiarid region of northern China. It was also consistent with previous findings that there is lower temperature sensitivity of soil respiration during dry periods than during wet periods [28; 54; 64; 65].  
In this study, the influencing patterns of precipitation regimes on the dependence of each CO\({}_{2}\) flux component on temperature further indicated an interaction between soil temperature and moisture on these components (Table 4. In other words, CO\({}_{2}\) flux components depended more on temperature (higher slope for the response function) when soil moisture was higher (in IP treatment) but less on temperature (lower slope for the response function) when soil moisture was lower (in DP treatment), compared with AP treatment (Table 2). This interaction could be due to the co-limitation of temperature and moisture in the ecosystem.
## 5 Experiments
### Experimental Settings and Evaluation Metrics  
Because the fine-grained air quality data were unavailable, we selected some monitoring stations as unlabeled locations and the predicted targets. Specifically, in the total 35 grids with monitoring stations, we randomly selected 15 grids as labeled grids \(G_{St}\) and the rest of grids as unlabeled grids \(G_{Un}\). We used all 8760 timestamps in a year to collect valid input data and predicted values for training and testing. All available data are partitioned into a training set, validation set, and testing set at an 8:1:1 ratio. Figure 4 is an example with k monitoring stations selected as labeled grids, and the current time is Tc. According to the problem definition in Section 3, we take Tc \(-\) 23 PM\({}_{2.5}\) data of labeled grids as input features and Tc + 24 PM\({}_{2.5}\) data of remaining unlabeled grids as a single piece of available data. In other words, the total number of all remaining unlabeled grids is the number of valid instances in time Tc (monitoring sensors sometimes have missing values). To obtain sufficient training and testing data, it is impossible to ensure that all timestamps in all k selected stations have complete historical data. Therefore, we made sure that at least one unlabeled grid will not have missing data in the future. We looked over all timestamps to collect available data and set k as 15 to calculate the amount of all available data in our experiment.  
The mean absolute error (MAE) and the root mean square error (RMSE) are used as the evaluation metrics to evaluate the predicted and the ground truth air quality value. The definitions of MAE and RMSE are as follows:  
\[MAE=\frac{1}{n}\sum_{j=1}^{n}|\beta-y| \tag{15}\]  
Figure 4: The method to collect available data in time Tc.  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(\hat{y}-y)^{2}} \tag{16}\]  
where \(\hat{y}\) and \(y\) are the predicted and observed ground truth values, respectively. We calculated average MAE and RMSE for the current time (0 h) and the future times (1-6, 7-12, 13-18, and 19-24 h).  
In order to obtain robust evaluation results, we ran the experiment 10 times and used the average MAE and RMSE scores as representative. In each run, we randomly selected labeled and unlabeled grids and made sure that the combinations of monitoring stations selections were different from the previous runs.
## 3 Results and Discussion
### Comparison of the Numerical and Optical Results
#### 3.5.1 Comparison of the Average Strain Values in Zone 1  
For this comparison, a simple plate was used. The evaluated zone was selected as Zone 1, see in Figure 6. This zone is 40 mm long and starts 25 mm away from the fixed grip. The evaluated values were organized into a table alongside the relative errors, which are given in Table 7. The relative errors were calculated using the following equation:  
\[e_{r}=\frac{v_{simulated}-v_{measured}}{v_{measured}}\cdot 100\ [\%], \tag{2}\]  
where \(e_{r}\) is the relative error, \(v_{simulated}\) is either the strain or displacement value evaluated from the simulations, and \(v_{measured}\) is either the strain or the displacement value evaluated from the optical measurements. This equation was used for the calculation of all relative error values.  
Figure 11: Strain results of the optical measurements.  
Figure 12: Deformation results of the optical measurements.  
It can be seen from these results that both the linear elastic material model with standard Young's modulus and the multilinear material model provided results with less than 5% relative errors. These results can be considered as accurate. The images of the four methods can be seen in Figure 13.
## 3 Steps of the Proposed Method
For each data set, determine the Weibull/IPL parameters (\(\beta_{i}\), \(K_{i}\), and \(n_{i}\)) defined in Equation (6). Step 4. For both sets, determine the reliability \(R(t)\) index corresponding to each observed failure time using Equation (7). Step 5. Determine the \(\eta_{i}\) values corresponding to each stress value in sets \(s_{1}\) and \(s_{2}\), using the respective \(\beta_{i}\), \(K_{i}\), and \(n_{i}\) parameters obtained from the set selected for the P-S-N analysis in Equation (5). Step 6. Using the \(\beta_{2}\), \(K_{2}\), and \(n_{2}\) parameters of \(s_{2}\), determine the equivalent failure times of set one (say \(s_{1}\)) that corresponds to the same reliability percentile (\(R(s_{1})=R(s_{2})\)) in set two (say \(s_{2}\)). They are given by:  
\[N_{eq}=(-\ln(R(t_{i})))^{\frac{1}{\beta}}\cdot\eta_{i} \tag{8}\]  
Step 7. Form the whole data set for the analysis of the P-S-N field by adding to set \(s_{2}\) the equivalent failure times determined in step 6 (s\({}_{2}\) is the set with the highest \(R^{2}\) index). Step 8. Determine the median stress from the experimental stress data. Step 9. Determine the \(\eta\) value in cycles that correspond to the median stress by using the median stress value in Equation (5) with the Weibull/IPL parameters of set \(s_{2}\). Step 10. Based on the reliability indices calculated in step 5, and the \(\eta\) value in cycles of step 9 in Equation (8), determine the predicted failure times. Then, using the maximum likelihood method, determine the Weibull parameters and the corresponding Fisher matrix. And by using the Fisher matrix data in Equation (9), determine the standard deviation of the eta \(\eta\) parameter as:  
\[\sigma_{\eta}=\sqrt{Var(\eta)} \tag{9}\]  
Step 11. Use Equations (10) and (11) to determine the upper and lower limits of \(\eta\) corresponding to a desired \(k_{\alpha}\) percentile. \[\eta_{U}=\eta\cdot\exp^{\frac{k_{\alpha}\sqrt{Var(\eta)}}{\eta}}\ (\text{upper bound}) \tag{10}\]  
\[\eta_{L}=\frac{\eta}{\exp^{\frac{k_{\alpha}\sqrt{Var(\eta)}}{\eta}}}\ (\text{lower bound}) \tag{11}\]  
where \(k_{\alpha}\) is the \(z\) value of the normal distribution that corresponds to the desired two sizes percentile determined as:  
\[\alpha=\frac{1-\delta}{2} \tag{12}\]  
which for one-sided bound is expressed as:  
\[\alpha=1-\delta \tag{13}\]where \(\delta\) is the corresponding confidence level. The numerical application is as follows.
# SDN Based Collaborative Scheme for Mitigation of DDoS Attacks
## 4 Testbed and Evaluations
### Performance of Central Control Platform
#### 4.4.3 CPU and Memory Utilization  
We also evaluated the CPU and memory utilization while measuring the effect of different AS counts. The average CPU utilization results (see Figure 15) show that the dissemination of attack definition with central control platform is not CPU intensive and it remained between 26% to 35%. Similarly, memory utilization remained constant between 24% to 25% in all the scenarios (see Figure 16).  
Figure 14: Average throughput  
Figure 12: Global AS count in November 2017 [36].  
Figure 13: Payload Dissemination Delays with varying AS Count.  
The evaluation results presented in this section show that the proposed approach is fairly lightweight in mitigating DDoS attacks near the source. The amount of time it takes to completely mitigate the attack all the way from the destination to the source is instantaneous. Our stress tests and micro benchmarks show that the overheads imposed by the additional processing (verification and forwarding of attack definition) are tolerably small on average commodity hardware. The performance can significantly improve with high-end industry scale computational devices.
## 3 Results and Discussion
### Base Force Coefficient  
Figure 15 presents the base force coefficient of models under different wind fields. For M1 in Figure 15a, a transition from sub-criticality to criticality was detected in T1, which, unlike the other wind fields, caused a variation in the mean base force coefficient. With increasing turbulence, the flow around M1 went to the supercritical regime at lower \(Re\) values [41]. Therefore, the mean base force coefficient in T2, T3, and T4 did not significantly change, and the value of \(\overline{C}_{JD}\) and \(\overline{C}_{JL}\) eventually stabilized at around 0.4 and 0, respectively. As for the fluctuating value in Figure 15b, the value of \(C\prime_{JL}\) was larger than that of \(C\prime_{JD}\). Meanwhile, \(C\prime_{JD}\) and \(C\prime_{JL}\) reached their maximum at around \(Re\) = 2.6 \(\times\) 10\({}^{5}\) in T1, and, correspondingly, one side of M1 went to the critical regime. On the other hand, as the turbulence increased, the variation of \(C\prime_{JD}\) and \(C\prime_{JL}\) tended to be at a higher \(Re\) state, and gradually became insignificant with \(Re\). For M2, the mean and fluctuating value of base force coefficient had almost no \(Re\) effect due to the stronger free end effect. With the exception that the value of \(\overline{C}_{JL}\) in T1 decreased with \(Re\) = 1.5 \(\times\) 10\({}^{5}\)-3.2 \(\times\) 10\({}^{5}\) (in Figure 15c), \(\overline{C}_{JD}\) and \(\overline{C}_{JL}\) were also stable at around 0.4 and 0, respectively. The fluctuating value \(C\prime_{JD}\), in Figure 15d, did not change with \(Re\), but increased with turbulence. The variation of \(C\prime_{JL}\) was similar with \(C\prime_{JD}\) in T2, T3, and T4, but it showed an insignificant \(Re\) effect at \(Re\) = 1.5 \(\times\) 10\({}^{5}\)-2.7 \(\times\) 10\({}^{5}\). It can be found that when turbulence is greater than 4.0%, turbulence and \(Re\) have no significant effect on the mean base force of low-aspect-ratio cylinders.  
Figure 14: Power spectrum of the local lift coefficient at 0.5 H of models under different wind fields. (a) M1; (b) M2.
###### Abstract  
With the extensive deployment and application of the Internet of Things (IoT), 5G and 6G technologies and edge intelligence, the volume of data generated by IoT and the number of intelligence applications derived from these data are rapidly growing. However, the absence of effective mechanisms to safeguard the vast data generated by IoT, along with the security and privacy of edge intelligence applications, hinders their further development and adoption. In recent years, Trusted Execution Environment (TEE) has emerged as a promising technology for securing cloud data storage and cloud processing, demonstrating significant potential for ensuring data and application confidentiality in more scenarios. Nevertheless, applying TEE technology to enhance security in IoT and edge intelligence scenarios still presents several challenges. This paper investigates the technical challenges faced by current TEE solutions, such as performance overhead and 1/O security issues, in the context of the resource constraints and data mobility that are inherent to IoT and edge intelligence applications. Using Intel Software Guard Extensions (SGX) technology as a case study, this paper validates these challenges through extensive experiments. The results provide critical assessments and analyses essential for advancing the development and usage of TEE in IoT and edge intelligence scenarios.  
T +
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**Keywords:** Trusted Execution Environment (TEE); Software Guard Extensions (SGX); Internet of Things (IoT); edge intelligence; performance evaluation; security
# The Implementation of Precise Point Positioning (PPP): A Comprehensive Review
## 2 Error Sources in PPP
### Site Displacement Effects
#### 2.8.2 Polar Tides  
The polar tide, also known as polar motion, represents the fluctuation in the earth's rotation axis's geocentric position relative to the earth's crust. The consequent disturbances to the centrifugal force linked with the earth's daily rotation deforms the earth [66]. Displacements arising from polar tides, in mm, calculated in east, north, and up directions, are given by [65]  
\[\Delta r_{e}= 9\sin\varphi[m_{1}\sin\lambda-m_{2}\cos\lambda]\] \[\Delta r_{n}= -9\cos 2\varphi[m_{1}\cos\lambda+m_{2}\sin\lambda] \tag{24}\] \[\Delta r_{u}= -33\sin 2\varphi[m_{1}\cos\lambda+m_{2}\sin\lambda]\]  
where \(m_{1}=(x_{p}-\overline{x_{p}})\) and \(m_{2}=-(y_{p}-\overline{y_{p}})\), both expressed in arc seconds (1 arc second = 1/3600 degree), represent the variations to the coordinates of the earth's mean pole in the terrestrial reference frame. These variations are determined as the difference of the polar motion variables \((x_{p},y_{p})\), which can be obtained from the IGS Earth rotation parameters product, and the mean pole \((\overline{x_{p}},\overline{y_{p}})\) from the International Earth Rotation and Reference Systems Service (IERS) model defined in Equation (7.25) in [65].  
The maximum magnitude of the polar tide displacement is 25 mm and about 7 mm in the vertical and horizontal directions, respectively [65]. The corresponding displacement in the ECEF frame can be calculated using the rotation matrix in (14)  
\[\mathbf{\Delta r_{\text{polar}}}=R_{l}^{e}\big{[}\Delta r_{e}\quad\Delta r_{n} \quad\Delta r_{u}\big{]}^{T} \tag{25}\]
## 2 Materials and Methods
### Instrumentation  
SEM images were recorded using an FEI Apreo scanning electron microscope at acceleration voltages of 5-15 kV to visualize the CNT cross-sections on the metal surfaces. An EDAX elemental analysis detector coupled with the same SEM instrument was used to analyze the atomic compositions of the samples. The Raman spectra of HD-CNTs attached to the metal surface were collected using a Renishaw inVia Raman microscope with a HeNe laser as the excitation source (633 nm). FT-IR spectra were collected using a Nicolet 6700 FT-IR instrument to identify the surface functionalization. All electrochemical analyses were performed using a PalmSens3 potentiostat/galvanostat. A three-electrode system was used with a Ag/AgCl reference electrode and a Pt wire counter electrode unless otherwise specified.
## 5 Kubernetes Capabilities for Industrial Edge Cloud
### Placement of Application Components  
A Kubernetes deployment object allows us to specify, among others, which container images to use for the application, the number of replicas for the pods, and which rules for the placement related to the nodes and pods have to be followed. For example, the number of replicas is two for each of the TSN and application components (see Figures 8 and 9).  
The placement rules are specified by affinity and anti-affinity rules related to nodes and pods, too, which is a mature feature in Kubernetes. To assure that TSN-FRER pods are deployed into nodes that are labeled to have the special NIC capability, a node affinity rule is applied in the deployment description (see, for example, Figure 12). In addition, a pod anti-affinity rule is also specified such that a TSN-FRER pod cannot be placed on a node that already runs another TSN-FRER pod to assure node level resiliency. With appropriate node and pod affinity rules, the desired placement can be specified for all of the cases, and the Kubernetes pod scheduler will place the pods accordingly by selecting among the available nodes that satisfy them.
# Ventricular Fibrillation Prediction and Detection: A Comprehensive Review of Modern Techniques
## 2 Modern ECG Signal Processing Techniques for Detection and Prediction of Ventricular Fibrillation
### Neural Networks and Deep Learning Algorithms
#### 2.2.5 Techniques Used in the Training Stage  
* Data Normalization: Data normalization is crucial in signal processing techniques because it ensures consistency and comparability between datasets, thereby reducing noise and improving the accuracy of analyses; common normalization techniques include scaling to a specific range (e.g., between 0 and 1), standardization (adjusting to zero mean and unit standard deviation), and min-max normalization.
* Data Augmentation: Since ECG data are often limited, data augmentation techniques such as adding Gaussian noise and time and frequency transformations can increase the size of the training dataset and improve the generalization of the model.
* Dropout: Dropout [36] is a regularization technique that reduces overlearning by randomly deactivating a proportion of neurons during training at a certain step and then reactivating them at the next step. Basically, neurons stopped at one step do not contribute to the calculation of the output of that layer and do not participate in the calculation of the gradient during the backward pass. This forces the network to be more robust and not rely too much on certain features. This technique, introduced by [PERSON] in [36], quickly became one of the most popular regularization methods.
# Towards Bio-Hybrid Energy Harvesting in the Real-World:
## 3 Research Progress in Energy Harvesting from Microorganisms
### Applications of MFCs
#### 3.4.3 Bioremediation  
An additional application of MFC technology has been bioremediation and this is also one of the most investigated applications of sediment MFCs [234]. In this process microorganisms are utilized to treat polluted sites to break down environmental pollutants, to regain their original condition [235]. This has long remained as an alternative natural process of waste removal from land [236]. For more details on recent developments on bioremediation of sediments, please refer to the works of [67]. There have been particular examples of this in removing in removing organic [237] hydrocarbon [238] and metal [239] based pollutants. MFCs have been reported to recover Ag(I), Au(III), Co(II), Cd(II), Cr(VI), Cu(II), Hg(II), Pb(II), Se(IV), V(V), U(VI), and Zn(II) [30]. [PERSON] et al. [240] reported their study on silver recovery from silver alkaline wastewater and simultaneous electricity production in a dual chamber bio-electrochemical cell.  
In their review, [PERSON] et al. [241] summarized the latest mechanisms of metal recovery using MFCs. In another review, [PERSON] et al. [231] classified mechanisms of metal recovery using MFCs in 4 different categories: direct metal recovery with abiotic cathodes; metal recovery with externally powered abiotic cathodes, metal conversion with bio-cathodes and metal conversion with externally powered bio-cathodes supplemented by external power sources.
# Accuracy of Different Head Movements of Intraoral Scanner in Full Arch of Both Maxilla and Mandible
###### Abstract  
This in vitro study aimed to compare the accuracy of maxilla and mandible full-arch scans from an intraoral scanner via one scan path with six different head movements. Standard maxilla and mandible models via holder were set in a dental chair to simulate position and posture. The reference models' standard tessellation language (STL) files were formatted via desktop scanner, and operative models' files were obtained via IOS TRIOS 3 Pod as superimposed by Exocad CAD software. The same scan path with six head movements (Linear, Circle, Wave, 8-figure, S-figure, and A-P) were designed to scan 10 times per jaw, and a total of 120 scan files were then compared with reference files. The data were recorded and deviations of both occlusal and B-L sides were compared, with statistical analysis being performed by one-way analysis of variance (ANOVA) and post hoc comparisons with Tukey test. The trueness of optical impression for full-arch via one path with six head movements differed from maxilla and mandible and depended on the different movements (\(p<0.05\)). In the same path, peak deviation was found at the turning points for left central incis, left first premolar, left second molar, and right second molar in maxilla, and for right first premolar, second molar, and left second molar in the mandible. The 8-figure movement showed the highest deviation (\(0.128\pm 0.086\) mm) in the maxilla and (\(0.105\pm 0.069\) mm) in the mandible. The Linear movement presented the lowest deviation (\(0.096\pm 0.07\) mm) in the maxilla while the Circle movement presented the lowest deviation (\(0.073\pm 0.041\) mm) in the mandible, with the 8-figure movement showing the worst precision among six movements. In the maxilla, the S- and 8-figure movements were not recommended, while the Linear and Circle movements showed high trueness. In the mandible, the 8-figure movement was not recommended, while the other five presented similar lower deviations.  
accuracy; head movement; intraoral scanner; full-arch scan +
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