esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Thick Ellipsoid State Estimation Algorithm  
As shown in Figure 6, the thick ellipsoid state estimation procedure consists of two stages. Prediction steps, which are based on the (uncertain) discrete-time state equation, are executed up to the point of time at which a new measurement becomes available. There, a measurement-based correction of the a-priori knowledge resulting from the previous state prediction is performed. This corrected state information then serves in a recursive manner as the input for a subsequent state prediction.
# A Study of Emergency Aircraft Control During Landing
## 6 Control System
### Preparation of Simulink Models
#### 6.2.2 Heading Control System  
The use of an external control loop in the control law discussed in Section 3.4 is necessary for two reasons. Firstly, the designed control system has to be capable of limiting the roll angle. The limiter range was initially set at \(\pm 20^{\circ}\). This constraint is dictated by the flight safety; the limited and non-linear engine operating range; and the rolling motion's effect on exciting oscillations in the longitudinal motion, which grows as the roll angle increases. Therefore, the constraint also affects the cross-coupling dynamics of the control channels. As for the second reason, the solution offers much better control over the controller's dynamics and precision. The use of the LQRI controller alone with feedback including \(\psi\) did not offer such a possibility due to the strict coupling of the aircraft roll with a given heading setpoint, which exists in such a configuration. The coupling means that small heading changes would be achieved at small roll angles with a significant increase in the settling time.  
The designed system computes the control output for the left engine thrust lever. Since the available range for increasing and decreasing the lever deflection differs, the control output is limited symmetrically with respect to the narrower range. In the prepared model of the heading control system, with a left lever deflection range of [\(-0.3\); \(0.7\)], the control limits were set to [\(-0.3\); \(0.3\)]. Table A4b in Appendix C shows the settings of the designed lateral P-LQRI controller.  
Due to the aircraft's lateral control constraints, the settling time of heading changes corresponding to prepared controller settings was not investigated. The response time was closely related to the magnitude of a given setpoint. However, other relevant system parameters were measured from the step responses. That being said, the settling time taken to achieve the maximum roll angle (\(20^{\circ}\)), was found at 12.86 s. The turn rate at the maximum roll angle reached a value of approximately \(1.55^{\circ}\)/s for both landing gear positions. Knowledge of these parameters was important for the second stage of controllers' tuning, run under the Software-In-the-Loop tests of designed control system.
## 3 System Model  
### Scenario Description  
The system model is shown in Figure 1. There is a mobile-edge computing system, including multiple mobile devices which have computation tasks and an edge cloud. Edge cloud is a relatively large data center with computation resources, which provides computing capacities in proximity to mobile devices. Its computing capacity is smaller than that of cloud computing and larger than that of mobile devices. There is a wireless base station, through which mobile devices could offload their computing tasks to the edge cloud. Suppose there are \(N\) mobile devices and \(K\) computation tasks in the MEC system. Then, we define the set of devices and tasks as \(N=\{1,2,\ldots,N\}\) and \(K=\{1,2,\ldots,K\}\). Suppose time is divided into equal-sized slots. We assume that a mobile device can only request one task during a time slot, and different mobile devices can request the same task. Suppose that the mobile devices are static in the system.  
We define \(u_{n,k}\) as the task \(k\) that mobile device \(n\) requests, and adopt three parameters representing the computation task. For task \(u_{n,k}\), its requirement is denoted as a tuple \(\{w_{k},s_{k},D_{n,k}\}\), where \(w_{k}\) is the amount of computation resource required for \(u_{n,k}\), (i.e., the number of CPU cycles required to finish the task), and \(s_{k}\) is the data size of \(u_{n,k}\), i.e., the amount of data content (e.g., the program codes and input parameters) which is transferred to the edge cloud, and \(D_{n,k}\) represents completion time requirement for the task \(u_{n,k}\). Furthermore, due to the limitation of computation resources in the edge cloud, we denote the computation capacity of the edge cloud is \(c_{s}\).  
Figure 1: A multi-user system for MEC.
## 4 Results and Discussion
### Point Cloud Accuracy
In comparison to the IMU/GPS only solution, the results with the inclusion of SfM observations show significant improvement in the horizontal component. The RMS error for the total 130 ground control targets dropped from 0.61 \(\mathrm{m}\) without SfM observations to 0.34 \(\mathrm{m}\) with SfM observations. This improvement combined with the difference in orientation shown in Figure 6(b) suggests that the SfM algorithm has resulted in an improvement in the estimation of orientation. As expected, the vertical errors within the point clouds from both solutions are similar. These results suggest that both of the assessed solutions are suitable for use in forest inventory assessment. However, the SfM observations allows the accuracy to improve to a level comparable to that achieved by modern full-scale systems (based on the values reported in [47]). This improvement will allow for direct comparison and integration of the two datasets. Furthermore, the improvement in accuracy enhances the reliability and suitability of the platform for use in multi-temporal surveys.
## 3 Results  
In the first data set, there are practically 20 blinks [49]. The ANN identifies 20 blinks in total, with an accuracy of 100%. For the second data set, that contains 20 blinks, the ANN identifies 23 blinks. There are three false alarms at 124.7, 149.3, and 300.6 s. Compared with the 100% accuracy for the first data set, it can be thought that the false alarms are caused by insufficient training data. On the one hand, the fact that it can identify all blinks implies that the method is effective. On the other hand, the false alarms indicate that it does not learn enough data features. The false alarms mainly result from the MEG signal which has similar characteristics to the blink artifacts. The non-blink 2D images we randomly selected may not be enough to cover all the MEG signal features, making it difficult for ANN to distinguish the MEG signals that similar to the blink artifacts. For the third and fourth data sets, that include 47 and 80 blink artifacts, there are one and five false alarms, respectively, and zero missing alarms. The fifth and sixth data sets contain 65 and 78 blink artifacts, respectively. The identification results show there are zero and three false alarms, respectively, and one missing alarm in each. At last, 211 and 180 blink artifacts are included in the seventh and eighth data sets, respectively. The ANN has 18 and 30 false detections and 4 and 11 missing detections, respectively, for these two data sets. The identification results for the eight data sets are summarized in Table 3.  
In addition, test accuracies of the first, third, fifth, and seventh data sets tend to be 100%, because the testing data from these data sets are similar to the training data set. For the other four data sets, we get accuracies of 86.96%, 94.12%, 95.06%, and 80.48% for the second, fourth, sixth, and eighth data sets, respectively. The eighth data set has distinctly worse results than other data sets, because the eighth MEG signal data set, measured from the fourth subject, contains more environmental noise (please see Supplementary Materials for more details). Since we only filter the original MEG signal data, different measurement environments will have a great impact on the identification results.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & **All Blinks** & **Detected Blinks** & **False Alarms** & **Missing Alarms** & **Accuracy** \\ \hline \multirow{2}{*}{**Subject 1**} & The 1 st data set & 20 & 20 & 0 & 0 & 100.00\% \\  & The 2 nd data set & 20 & 23 & 3 & 0 & 86.96\% \\ \hline \multirow{2}{*}{**Subject 2**} & The 3 rd data set & 47 & 48 & 1 & 0 & 97.92\% \\  & The 4 th data set & 80 & 85 & 5 & 0 & 94.12\% \\ \hline \multirow{2}{*}{**Subject 3**} & The 5 th data set & 65 & 64 & 0 & 1 & 98.46\% \\  & The 6 th data set & 78 & 80 & 3 & 1 & 95.06\% \\ \hline \multirow{2}{*}{**Subject 4**} & The 7 th data set & 211 & 225 & 18 & 4 & 90.39\% \\  & The 8 th data set & 180 & 199 & 30 & 11 & 80.48\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Identification results of the blink artifacts in 8 MEG signal data sets.
## 3 Results  
A total of 75 participants were recruited, of whom 46 agreed to participate voluntarily and underwent the first evaluation. Thirteen participants were excluded due to more than 25% absence, and thirteen participants were excluded because they did not complete the second evaluation (see Figure 1). Thus, 19 participants were included in this study (female = 6, male = 13), aged between 21 and 48 years (M = 32.26; SD = 9.03). Among the participants, 78.9% reported having a hybrid work regime, 15.8% reported an on-site work regime, and 5.3% reported a home office regime. The prevalence of reported pathologies among participants included 63.16% with no pathologies, 10.53% with hypertension, 5.26% with color blindness, 5.26% with myopia, 5.26% with muscle contractures and poor circulation, 5.26% with both myopia and astigmatism, and 5.26% with shoulder bursitis. Additionally, 84.2% spent between 6 to 8 h seated per day, and 15.8% spent between 9 to 12 h seated. Among the participants involved, 78.9% reported engaging in some type of physical exercise (walking, running, cycling, padel, yoga, biodanza, gym, alisthenics, futsal, soccer, and archery) with a frequency ranging from 1 to 5 times per week (M = 2.46; SD = 1.45).  
Figure 1: Participant flowchart.  
In Table 1, we can observe the results of the descriptive and inferential statistics of the quality-of-life indicators before and after the intervention. The data indicate a normal distribution as the Shapiro-Wilk test showed non-significant values (\(p<0.05\)). Additionally, the values for skewness and kurtosis were within \(-2\) and \(+2\) and \(-7\) and \(+7\), respectively. The data indicate that there are statistically significant differences in in general health (\(p<0.01\)), vitality (\(p<0.01\)), social functioning (\(p<0.05\)), and the mental health summary measure (\(p<0.04\)). Indeed, there was a significant increase in these three dimensions and the overall mental component, as observed by the increase in mean values from pre-intervention to post-intervention. The effect size was always large, given that according to the results, the effect size ranged from small for social functioning (d = 0.37) to medium for general health (d = 0.56), vitality (d = 0.60), and mental mean scores (d = 0.42).
## 2 Materials and Methods
Optical profilometry measurements were performed by ZetaTM-20 3D Optical Profiler (Zeta Instruments, KLA Instruments Group, Milpitas, CA, USA). X-ray Photoelectron spectroscopy (XPS) was used to obtain the spectra with unmonochromatized Al K\(\alpha\) (1486.6 eV) radiation in a VG ESCALAB MK II (Thermo Scientific, Waltham, MA, USA) electron spectrometer under a base pressure of 1 \(\times\) 10\({}^{-8}\) Pa. The spectrometer resolution was calculated from the Ag 3d5/2 line with analyzer transmission energy of 20 eV. The line full width at half maximum (FWHM) was 1 eV. The spectrometer was calibrated against the Au 4f7/2 line (84.0 eV), and the sample charging was estimated from C 1s (285 eV) spectra from natural hydrocarbon contaminations on the surface. The accuracy of the binding energy determination was 0.2 eV. The photoelectron spectra of C 1s, O 1s, Si 2p, N 1s, and a small amount of detected contaminations, such as F 1s and Cu 2p, were recorded and corrected by subtracting a Shirley-type background and quantified using the peak area and Scofield's photoionization cross-sections. The peak fitting was conducted using XPSPEAK4.1 software. The in-depth elemental composition analysis of the thin films was obtained by two different techniques--by sputtering with an Arbeam and by glow discharge optical emission spectroscopy (GDOES) using a GDA 750 HR GDOES (Spectruma Analytik GmbH, Hof, Germany) spectrometer. X-ray diffraction (XRD) as well as Grazing Incidence XRD (GIXRD) measurements were performed using PANalytical Empyrean apparatus in different geometries (\(\varphi\), (\(\theta\)\(-\)20), and (\(\omega\)\(-\)20) scans). The Raman spectroscopy studies were accomplished with a micro-Raman HORIBA Jobin Yvon (HORIBA Jobin Yvon, Longjumeau, France) LabRAM HR 800 spectrometer (excitation with a He/Ne (633 nm) laser). The laser beam with power of 0.5 mW was focused on a spot of about 1 \(\upmu\)m in diameter on the studied surfaces, the spectral resolution being about 1 cm\({}^{-1}\) or better. The TEM in high-resolution (HR TEM) mode and selected area electron diffraction (SAED) were carried out on an HR STEM JEOL JEM 2100 (Jeol Ltd., Akishima, Tokyo, Japan) microscope at 200 kV accelerating voltage.
# Analysis of the Effect of Temperature on the Sound Transmission Loss of a Curved Plate
## 2 Theoretical Model
### Sound Transmission Loss
 \tag{31}\]  
Substituting Equations (22) and (23) into Equation (12), with \(\Phi_{1}=\Phi_{2}=0\), the natural frequency can be calculated as follows:  
\[\omega_{mn}=\sqrt{\frac{D}{\rho h}\left[\left(\frac{n\pi}{L_{0}}\right)^{2}+ \frac{1}{R^{2}}\left(\frac{m\pi}{\theta_{0}}\right)^{2}\right]^{2}+\frac{ \frac{Eh}{R^{2}}\left(\frac{n\pi}{L_{0}}\right)^{4}}{\rho h\left[\left(\frac{ n\pi}{L_{0}}\right)^{2}+\frac{1}{R^{2}}\left(\frac{m\pi}{\theta_{0}}\right)^{2} \right]^{2}}+\frac{N_{\theta}}{\rho hR^{2}}\left(\frac{m\pi}{\theta_{0}} \right)^{2}+\frac{N_{L}}{\rho h}\left(\frac{n\pi}{L_{0}}\right)^{2}}. \tag{32}\]  
According to Equations (31) and (32), the modal coefficient (\(A_{mn}\)) can be calculated as follows:  
\[A_{mn}=\frac{2j\omega\rho_{1}I_{mn}e^{-jk_{1R}h}}{(\omega_{mn}^{2}-\omega^{2} )\rho h+j\omega^{2}(\rho_{1}/k_{1R}+\rho_{2}/k_{2R})}. \tag{33}\]  
The sound power transmission coefficient is the ratio of the radiated sound power to the incident sound power and is expressed as follows:  
\[\tau(\varphi,\xi)=\frac{\sum\limits_{m=1}^{\infty}\sum\limits_{n=1}^{\infty} \rho_{2}|\varepsilon_{mn}|}{\sum\limits_{m=1}^{\infty}\sum\limits_{n=1}^{ \infty}\rho_{1}|I_{mn}+\beta_{mn}|}. \tag{34}\]For random incidence, the azimuth and pitch intervals are integrated separately, and the sound power transmission coefficient is [31]  
\[\tau_{di}=\frac{\int_{0}^{\pi/4}\int_{0}^{\varphi_{\max}}\tau(\varphi,\xi)\sin \varphi\cos\varphi\sin\xi\cos\xi d\varphi d\xi}{\int_{0}^{\pi/4}\int_{0}^{\varphi _{\max}}\sin\varphi\cos\varphi\sin\xi\cos\xi d\varphi d\xi}. \tag{35}\]  
The literature [32] reports that the theoretical value agrees better with the measurement results when the upper limit of integration is 78\({}^{\circ}\) or 85\({}^{\circ}\). In this study, \(\varphi_{max}\) takes the value of 78\({}^{\circ}\).
## 2 Materials and Methods
### Characterization Methods
#### 2.3.6 Atomic Absorption Spectroscopy (AAS)  
The metals' residual concentrations in each culture medium were determined on Days 3 and 7 by an atomic absorption spectrophotometer (AAnalyst 800, Perkin-Elmer, Waltham, MA, USA) and the total percentage removal was calculated (compared to theinitial concentration at Day 0, also determined with AAS). A total of 10 mL from each culture was meticulously extracted to remove as few cells as possible and centrifuged for 5 min at 5000 rpm. The supernatant liquid was then removed and transferred to the AAS system. The instrument was set up for the analysis of the target heavy metals, using the appropriate wavelength for each metal (213.9 nm for Zn, 283.3 nm for Pb, 324.8 nm for Cu, 232.0 nm for Ni, and 228.8 nm for Cd). The flame (air-acetylene) was adjusted to ensure optimal atomization of the sample for accurate detection. All measurements were performed in triplicate and the results of each concentration were expressed as the respective averages.
## 2 Experiment and Methods  
### Experimental Setup and Performance Evaluation  
The dataset consisted of 1200 RGB images of pavement defects (size: 600 \(\times\) 600), 960 images of which were used for training, and 240 images were used to test the performance of the network. The defects were divided into four categories, namely transverse cracks (H_crack), longitudinal cracks (P_crack), grid cracks (N_crack), and potholes (P_groove), and the detailed data are shown in Table 1. Figure 1d shows the labelling process of polygonally categorizing a pothole with the labelling tool \"Labelme,\" and each defect of the dataset was manually annotated at the pixel level.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Class** & **H\_crack** & **P\_crack** & **N\_crack** & **P\_groove** \\ \hline Numbers & 497 & 518 & 541 & 487 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Instance segmentation dataset of the defects.  
Figure 1: Defects labelled by Labelme: (**a**) H_crack, (**b**) P_crack, (**c**) N_crack, (**d**) P_groove.  
During training, a multitasking loss (\(L\)) was defined, which included classification loss (\(L_{cls}\)), bounding-box regression (\(L_{box}\)), and mask loss (\(L_{mask}\)); the multitasking loss was calculated as Equation (1):  
\[L=L_{cls}+L_{box}+L_{mask} \tag{1}\]  
The multitasking loss was an important metric that determined whether the training process of the Mask R-CNN model was complete. When the loss converged, the training process was considered completed. The accuracy and AP were important metrics for evaluating instance segmentation models [53]. Precision represented the classification effect of the classifier, which was the ratio of true positive instances to the total positive instances (Equation (2)). The recall was the ratio of true positive instances to the sum of true positives and false negatives in the detection (Equation (3)). AP was a combined outcome of the precision and recall metrics (Equation (4)). The same was true for F1 (Equation (5)). Meanwhile, the testing time also needed to be taken into account:  
\[\text{Precision}=\frac{T_{P}}{T_{P}+F_{P}}\times 100\% \tag{2}\]  
\[\text{Recall}=\frac{T_{P}}{T_{P}+F_{N}}\times 100\% \tag{3}\]  
\[\text{AP}=\int_{0}^{1}\text{Precision}(\text{Recall})\text{ d}(\text{Recall}) \tag{4}\]  
\[\text{F1}=2\times\frac{\text{Recall}\times\text{Precision}}{\text{Recall} \ +\ \text{Precision}} \tag{5}\]  
where \(T_{P}\) was the number of positive instances that were correctly predicted, \(F_{P}\) was the number of negative instances that were predicted to be positive instances, and \(F_{N}\) was the number of positive instances that were predicted to be negative instances. The experiment was conducted on Windows 10 with Pytorch 1.10.0, and the model was trained and tested on an i5-9400F processor and NVIDIA GTX1660 graphics card.
# On the Analysis of Coverage Feedback in a Fuzzing Proprietary System
## 6 Limitations and Future Work  
In this paper, we have investigated the efficacy of coverage feedback and fuzzing results to provide a guideline for assessing the cost-effectiveness of applying coverage feedback features to closed-source systems such as proprietary firmware in IoT or UAV devices. In industry, many closed-source UAV systems, such as the DJI drone, have their dedicated SoC hardware, which requires an extensive engineering effort to apply fuzzing to [20]. Researchers trying to apply fuzzing to such systems struggle to receive feedback, such as code coverage increments and crash events. Although there are a few ways to measure codecoverage without utilizing source code via emulation or debugging features [21], applying such techniques to other proprietary systems remains challenging. Despite the evaluation and analysis conducted in this paper, quantifying the efficacy of fuzzing and the cost of altering a system (e.g., to support coverage-guided fuzzing) cannot be performed in a straightforward manner, as various factors must be considered, including CPU architecture support, operating system support, hardware interfaces, and so forth. Considering all of these factors to determine the cost of applying coverage-guided fuzzing would be a challenging future task. Although we do not provide a straightforward answer regarding the efficacy of fuzzing and the cost of coverage measurement, we hope our analysis and evaluation results can aid future research directions and be utilized as a ground-truth reference for this purpose.
## 4 Non-Destructive Test Results and Comparisons
### Comparison with Previous Equations
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**References** & **Regression Formula (MPa)** & **Strength Limit (MPa)** & **Expression Types** \\ \hline AIJ [4] & \(f_{c}^{\prime}=0.73R_{0}+10.0\) & 45 & 1 st polynomial \\ [PERSON] [34] & \(f_{c}^{\prime}=-15.034+0.9874R_{o}\) & 21.9 & 1 st polynomial \\ [PERSON] [10] & \(f_{c}^{\prime}=3.34 xxp_{0}^{0.9886}\) & 36.4 & Exponential \\ [PERSON] [35] & \(f_{c}^{\prime}=12.67R_{0}+9.7868\) & 60 & 1 st polynomial \\ [PERSON] [36] & \(f_{c}^{\prime}=2.59R_{0}-51.5\) & 65 & 1 st polynomial \\ [PERSON] [37] & \(f_{c}^{\prime}=9.5879 xxp_{0}^{0.0384R_{o}}\) & 40 & Exponential \\ [PERSON] [13] & \(f_{c}^{\prime}=1.353R_{0}-17.393\) & 42 & 1 st polynomial \\ [PERSON] [38] & \(f_{c}^{\prime}=-0.08R_{0}^{2}+8.37R_{0}-157.54\) & 52 & 2 nd polynomial \\ [PERSON] [39] & \(f_{c}^{\prime}=0.00935R_{0}^{2}+0.8R_{0}-12.06\) & - & 2 nd polynomial \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Previous equations for the rebound hammer test. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**References** & **Regression Formula (MPa)** & **Strength Limit (MPa)** & **Expression Types** \\ \hline AIJ [4] & \(f_{c}^{\prime}=21.5V_{p}-62\) & 45 & 1 st polynomial \\ [PERSON] [34] & \(f_{c}^{\prime}=0.6401V_{p}^{2.5654}\) & 21.9 & Power \\ [PERSON] [10] & \(f_{c}^{\prime}=0.0316 xxp_{p}^{1.3V_{p}}\) & 36.4 & Exponential \\ [PERSON] [40] & \(f_{c}^{\prime}=e^{i(-5.4\pm 0.8)+(0.008155.00018)V_{p}}\) & 34.0 & Exponential \\ [PERSON] [17] & \(f_{c}^{\prime}=0.5208V_{p}^{5}\) & 100 & Power \\ [PERSON] [10] & \(f_{c}^{\prime}=50.163V_{p}-178.2\) & 60 & 1 st polynomial \\ [PERSON] [11] & \(f_{c}^{\prime}=0.0136V_{p}-21.34\) & 50 & 1 st polynomial \\ [PERSON] [13] & \(f_{c}^{\prime}=32.72V_{p}-129.077\) & 42 & 1 st polynomial \\ [PERSON] [38] & \(f_{c}^{\prime}=38.05V_{p}^{2}-316.76V_{p}+681.62\) & 52 & 2 nd polynomial \\ [PERSON] et al [16] & \(f_{c}^{\prime}=0.854 exp^{2.2882V_{p}}\) & 50 & Power \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Previous equations for the ultrasonic pulse velocity method. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Test/Predicted** & **Standard Deviation** & **Root Mean Square \({}^{*}\)** \\ \hline [PERSON] [40] & 2.2 & 0.47 & 34.4 \\ [PERSON] [17] & 0.7 & 0.11 & 32.2 \\ [PERSON] [10] & 1.1 & 0.15 & 9.3 \\ [PERSON] [11] & 1.9 & 0.26 & 30.4 \\ [PERSON] [13] & 1.5 & 0.17 & 21.5 \\ [PERSON] [38] & 2.4 & 0.41 & 37.9 \\ [PERSON] et al [16] & 1.8 & 0.30 & 28.7 \\ Dual linear & 1.0 & 0.08 & 4.9 \\ regression & 1.0 & 0.08 & 4.9 \\ \hline \end{tabular}  
* calculated by using the results in Table 3.  
\end{table} 
Table 7: _Cont._  
Figure 5: Ratio of the measured strength to the estimated strength. Figure 6: Root mean square error of the compressive strength of concrete.