esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Numerical Examples
### Measurement Verification
#### 5.2.2 Dynamic Test  
The main goal of this test is to evaluate the bias of least-squares parameter estimator in a dynamic situation. The coordinates of anchors are consistent with the static positioning. The test trajectory and the experimental scene are shown in Figure 12. The tester walked along the predetermined trajectory. Besides, an additional set of test analysis is implemented by adding large noise with the standard deviation of 20 cm to the original observations. It stimulates non-line-of-sight (NLOS) environment, the error of measurements increased.  
Figure 11: The location estimation results.  
Figure 10: The hypothesis test based on Mahalanobis distance.  
The frequency histograms of parameter estimator bias are shown in Figure 13. Since the anchors and trajectory are all symmetrical in the horizontal direction, the bias in the coordinate components x and y are also basically symmetrical. The bias in the coordinate component z is also greater as before. Besides, since the dynamic motion process on the preset trajectory does not satisfy the orthogonal condition of the design matrix and the bias amount of nonlinearity. The bias in the coordinate components x and y is not unbiased and the probability of large bias in the coordinate components x and y is larger than that of small bias. Furthermore, the bias of all coordinate components in dynamic test with large noise is larger than that of original dynamic test.  
Moreover, as is shown in Figure 14, the noise will influence the observations, which in turn affects the stability of the system geometry and the calculation of DOP value. Furthermore, these factors together affect the calculation of parameter estimator bias. Figures 15 and 16 demonstrate that the large noise in NLOS environment directly affects the magnitude of the bias in the nonlinear least-squares estimator, and the Mahalanobis distance of many epochs have exceeded the threshold. We can believe that the bias estimate can be used to correct the least-squares estimator to guarantee the unbiasedness for UWB positioning. For the relatively high measurement precision and good positioning configuration, the linearization is valid and the bias can be neglected.  
Figure 14: The average PDOP, HDOP and VDOP for each set of tests.  
Figure 13: The frequency histograms of parameter estimator bias.  
Through the above analysis, we can verify that the bias in the least-squares estimator is driven by the prior precision, the design matrix and the amount of nonlinearity. In some special positioning areas like the emergency services, the localization process acts rapidly in the unknown indoor environments, so it is unrealistic to rely on a pre-existing installation with beforehand measurements, calibration, configuration and deployment. The positioning system may be ill-posed or has large ranging measurement noise. It is necessary to judge the validity of linearized model and to realize the unbiasedness for UWB positioning.
## 5 Evaluation
### ZIP Deployments
#### 5.5.2 Measurement Analysis  
The 100 deployments of each function were made in a sequential order, with an average upload speed of 21 megabits per second (mbps). For the next chart, two distinct data types are included in each function bar. The first one being the average ZIP deployment time in seconds (s), at the top of the function bar, and the second one being the ZIP archive size in kilobytes (KB), at the middle of the function bar.  
As shown in Figure 17, cloud-agnostic libraries only add a few extra kilobytes of space to ZIP archives, 16 KB for GCP and 20 KB for MsAzure to be more precise. As expected, the size difference had almost no impact on the function's deployment time. Nonetheless, as the libraries become more and more complete over time, ZIP archives will become larger in size, resulting in higher deployment times. By that time, QuickFaaS should be capable of minimizing the number of its own dependencies as much as possible, not only to lower deployment times, but most importantly, to optimize cold starts [75].  
Figure 17: ZIP deployment time.
## 3 Results
### Cantabro Test Results  
Figure 10 illustrates the Cantabro mass loss for the CBA mixture. The results demonstrate that substituting 30% coal bottom ash in an asphalt mixture decreases the Cantabro mass loss, hence enhancing the mixture's durability. The normalized Cantabro mass loss data in Table 7 show that the mixtures CBA-10-CBA-30 had better resistance to wear and tear than the control mixture CBA-0. This is evident from the fact that the Cantabro mass loss for CBA-10, CBA-20, and CBA-30 decreased by factors of 0.91, 0.90, and 0.88, respectively, in comparison to the Cantabro mass loss of the control mixture. When 30% of the aggregate is substituted with CBA, the porosity of CBA enables the mixture to absorb more bitumen. This absorption enhances the adhesion between the aggregate particles and bitumen, resulting in a stronger and more cohesive mixture. Improved bonding increases the asphalt mixture's resistance to abrasion and particle dislodging. Consequently, the reduction in Cantabro mass loss indicates better durability. Although there is a slight increase in the void content (attributed to CBA absorbing more bitumen), this increase is insufficient to negatively impact the durability of the mixture.  
However, when the CBA content exceeds 30%, the Cantabro mass loss increases significantly, indicating a decrease in durability. The observed reduction in the CBA-50 mixture due to Cantabro mass loss is 13.37%, representing a 1.17-fold increase when compared to the control mixture. Despite the observed rise in the Cantabro value for the CBA-50 mixture, it remains within the acceptable range as specified by the JKR standard. Specifically, the minimum mass loss requirement for a mixture after 300 rotations is 15% [49]. The further increase in the Cantabro mass loss of the CBA mixture was attributed to several reasons. One of the reasons is the excessive bitumen that was absorbed. As the CBA content increases, the absorption of bitumen also rises. The excessive absorption of bitumen reduces the effective bitumen content required to coat the remaining aggregates. This results in an imbalance, causing insufficient bonding among the aggregate particles. Further, the porous nature of CBA contributes to an increase in air voids, resulting in a greater number of gaps within the mixture. This phenomenon reduces the mixture's density and enhances its susceptibility to damage. Furthermore, the coal bottom ash is lighter and weaker compared to conventional aggregate, and this results in a mixture that is more susceptible to wear, disintegration, and cracking, particularly under traffic loading, further contributing to the increase in the Cantabro mass loss.  
Figure 10: Cantabro loss for different CBA contents at 300 revolutions.
## 3 Results
### Correlation Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**E**OH**} \\ \cline{2-5}  & **TPC** & **TPA** & **TFd** & **TAc** \\ \hline DPPH & 0.99 & 1.00 & 0.97 & 1.00 \\ ABTS & 1.00 & 0.99 & 0.99 & 0.99 \\ CHP & 0.98 & 0.99 & 0.96 & 0.97 \\ RP & 0.99 & 0.99 & 0.97 & 0.98 \\ XO & \(-\)0.88 & \(-\)0.86 & \(-\)0.87 & \(-\)0.82 \\ LOX & \(-\)0.93 & \(-\)0.93 & \(-\)0.91 & \(-\)0.90 \\ COX-2 & \(-\)0.72 & \(-\)0.73 & \(-\)0.70 & \(-\)0.68 \\ \hline  & \multicolumn{4}{c}{**PBS**} \\ \cline{2-5}  & **TPC** & **TPA** & **TFd** & **TAc** \\ \hline DPPH & 0.50 & 0.63 & 0.72 & 0.64 \\ ABTS & 0.96 & 0.99 & 0.99 & 0.99 \\ CHP & 0.92 & 0.87 & 0.79 & 0.83 \\ RP & 0.97 & 1.00 & 0.99 & 1.00 \\ XO & \(-\)0.97 & \(-\)0.95 & \(-\)0.88 & \(-\)0.93 \\ LOX & \(-\)0.83 & \(-\)0.77 & \(-\)0.69 & \(-\)0.72 \\ COX-2 & \(-\)0.97 & \(-\)0.98 & \(-\)0.96 & \(-\)0.98 \\ \hline  & \multicolumn{4}{c}{**DIG**} \\ \cline{2-5}  & **TPC** & **TPA** & **TFd** & **TAc** \\ \hline DPPH & 0.92 & 0.95 & 0.95 & 0.92 \\ ABTS & 0.98 & 0.97 & 0.98 & 0.92 \\ CHP & 0.97 & 0.97 & 0.96 & 0.95 \\ RP & 0.94 & 0.89 & 0.84 & 0.75 \\ XO & \(-\)0.99 & \(-\)0.96 & \(-\)0.94 & \(-\)0.89 \\ LOX & \(-\)0.96 & \(-\)0.93 & \(-\)0.89 & \(-\)0.85 \\ COX-2 & \(-\)0.97 & \(-\)0.96 & \(-\)0.99 & \(-\)0.98 \\ \hline \hline \end{tabular}  
*—wafers without raspberry pomace (control), RP-10–RP-75—wafers with 10% to 75% raspberry pomace addition, respectively.  
\end{table} 
Table 5: Pearson correlation coefficient between values of polyphenol content and antioxidant or anti-inflammatory activity of different extracts from wafers fortified with freeze-dried raspberry pomace. recorded for XO (r from \(-0.82\) to \(-0.88\)) and the lowest values were recorded for COX-2 (from \(-0.68\) to \(-0.72\)). In the case of the PBS extracts, very high correlations were found between the content of all polyphenol groups and the ability to inhibit COX-2 and XO. However, with regard to the extracts obtained after in vitro digestion, the relationships between the concentration of polyphenols and the inhibitory properties were very high for all three tested enzymes.
## 4 Conclusions
**Nomenclature**  
\(A\) area (m\({}^{2}\)), absorber  
\(b\) joint length (m)  
\(C\) conductance (W/m K)  
\(Cp\) heat capacity (kJ/kg K)  
\(D\) diameter (m)  
\(F\) standard fin efficiency  
\(F_{R}\) heat removal factor  
\(F^{\prime}\) solar collector efficiency factor  
\(h\) heat transfer coefficient (W/m\({}^{2}\) K)  
\(I\) solar radiation (W/m\({}^{2}\))  
\(k\) TC (W/m K)  
\(\dot{m}\) mass flow (kg/s)  
\(Nu\) Nussel number  
\(Pr\) Prandtl number  
\(\dot{Q}\) heat flux (W)  
\(U\) global heat transfer coefficient (W/m K)  
\(Re\) Reynolds number  
\(T\) temperature (K)  
\(W\) distance between fins (m)  
\(x\) bottom insulation thickness (m)  
Subscripts  
\(a\) ambient  
\(b\) joint between fins and pipes, lower loss  
\(bf\) base fluid  
\(c\) absorber  
\(e\) border surface  
\(g\) glass  
\(fi\) internal  
\(lmf\) hybrid nanofluids  
\(in\) inlet, internal  
\(o\) external  
\(out\) outlet  
\(nf\) nanofluids  
\(np\) nanoparticle  
\(p\) flat plate  
\(u\) useful  
Greek symbols  
\(\alpha_{0}\) absorptance  
\(\beta\) collector inclination angle (\({}^{\circ}\))  
\(\delta_{C}\) absorber thickness (m)  
\(e\) emissivity  
\(\eta\) efficiency  
\(\gamma\) average joint thickness (m)  
\(\mu\) viscosity (Pa s)  
\(\oslash\) volume fraction (\(\%\))  
\(\rho\) density (kg/m\({}^{3}\))
## 5 Result and Discussion  
The proposed strategies have been implemented and evaluated in two different scenarios. In this section, we first describe how the framework has been implemented (see Section 5.1). Then, we describe the two scenarios considered: a realistic scenario based on the adoption of a higher educational enterprise resource planning (ERP) system by some universities (see Section 5.2) and a scenario that represents a real-life registration process [33], which is executed on a daily basis by Dutch municipalities (see Section 5.3). Afterwards, in Section 5.4, we analyze the performance of the proposed strategies on a set of controlled experiments created for testing the performance of the strategies depending on both the log complexity and the model complexity.
# A New Bloom Filter Architecture
## 5 Performance Evaluation
### Performance in On-Chip Memory Accesses  
Table 5 shows a comparison of the on-chip search performance with those of other algorithms, which depends on the number of BF queries. \(A_{b}\) and \(W_{b}\) are the average number and worst-case number of Bloom filter queries, respectively. The NPT-BF and NPT-BF-chaining algorithms access the maximum level of an NPT in the worst case, and thus \(W_{b}\) is determined based on the depth of an NPT. The total number of Bloom filter queries of our proposed algorithm is determined by the summation of two Bloom filter queries. In the proposed \(l\)-BF, \(W_{b}\) is determined from the depth of the LPT, and \(A_{b}\) decreases as the Bloom filter size increases. Although the size of the \(p\)-BF is fixed at \(16N^{\prime}\), the number of queries of the \(p\)-BF depends on the size of the \(l\)-BF, since the querying to \(p\)-BF only occurs when the \(l\)-BF produces positive results.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Routing Set** & **NPT** & **NPT-BF** & **NPT-BF-Chaining** & **Proposed Algorithm** \\ \hline
10k & 682.4 & 1088.0 & 1088.0 & 272.0 \\
50k & 3372.5 & 4352.0 & 4352.0 & 1088.0 \\
100k & 6690.2 & 8704.0 & 8704.0 & 2176.0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of off-chip memory requirements (KB).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Routing Set** & \(\mathbf{\alpha}\) & **NPT-BF** & **NPT-BF-Chaining** & **Proposed Algorithm** \\ \hline \multirow{3}{*}{10k} & 2 & 16 & 16 & 276 \\  & 4 & 32 & 32 & 296 \\  & 8 & 64 & 64 & 336 \\ \hline \multirow{3}{*}{50k} & 2 & 64 & 64 & 1120 \\  & 4 & 128 & 128 & 1216 \\  & 8 & 256 & 256 & 1408 \\ \hline \multirow{3}{*}{100k} & 2 & 128 & 128 & 2240 \\  & 4 & 256 & 256 & 2432 \\ \cline{1-1}  & 8 & 512 & 512 & 2816 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of on-chip memory requirements (KB).  
It can be observed that the proposed algorithm results in significantly fewer accesses than other algorithms in the worst-case number of Bloom filter queries. When \(\alpha\) is larger than 2, the proposed algorithm shows the smallest average number of Bloom filter queries. Even though the total \(A_{b}\) of the proposed algorithm is larger than that of the other algorithms when \(\alpha\) is 2, this case is not generally used because of a large false positive rate.
## 5 Experimental Results and Evaluation
### Security and Privacy Analysis
#### 5.4.4 Privacy Preservation of PC Data  
In data acquisition, users rely on trusted private servers to protect the raw PC data by AC policies and encryption algorithms. In the data sharing process, only encrypted meta-data along with references are exposed to the public network. The decentralized data authentication framework prevents attackers from violating access privileges or inspecting any sensitive information. However, the prototype of the SAUSA presented in this paper has no integrated privacy protection module to deter data privacy breach by honest or curious users, such as dishonest data users or private servers who attempt to obtain private information from PC data without deviating from pre-defined security protocols. Therefore, a data-privacy-preserving component based on differential privacy or secure multi-party computation is needed to guarantee PC data privacy, and we leave this for our future work.
## 2 Materials and Methods
### Polymeric Tannin Content  
The polymeric tannin content (PTC) in the extracts was determined using a BSA precipitation assay [11] with slight modifications. Grape skins were manually separated from the pulp using a flat spatula and immediately soaked in deionized water. The collected skins were lyophilized for 48 h after washing, and the dried skins were pulverized using an electric grinder. Ground skin samples were stored in a desicator protected from light prior to use. For the extraction of phenolic compounds, 0.3 g of powder was added to 10 mL of an extraction solvent consisting of water, acetone, and methanol (0.36:0.48:0.16, \(\
icefrac{{v}}{{v}}\)/\(\
icefrac{{v}}{{v}}\)).  
The mixture was thoroughly vortexed, and extraction was performed twice in an ultrasonic bath (DH.WUC.D10H, DAIHAN Scientific, Wonju, Korea) at 20 \({}^{\circ}\)C for 1 h (30 min each). The extract was filtered and stored at 4 \({}^{\circ}\)C prior to analysis. In a 2 mL microtube, 0.2 mL of the extract was mixed with 1 mL of BSA solution (1 mg/mL BSA in a washing buffer). The mixture was vortexed thoroughly, incubated for 10 min, and centrifuged at 10,000 rpm for 2 min. The supernatant was removed, and the precipitate was washed with 1 mL of washing buffer (170 mM NaCl in 200 mM acetic acid, pH 4.9) to remove free acids and monomeric and dimeric tannins. Subsequently, 875 \(\upmu\)L of 8.3 M aqueous urea solution with 5% triethanolamine (pH 7.0) was added, vortexed, and incubated for 10 min at room temperature (25 \(\pm\) 2 \({}^{\circ}\)C) to isolate polymeric tannin from the protein/tannin complex. For color development, 175 \(\upmu\)L of resuspended tannin solution was mixed with 25 \(\upmu\)L of FeCl\({}_{3}\) solution (10 mM FeCl\({}_{3}\) in 10 mM HCl) using a 96-well microplate. After incubation for 10 min in a microplate reader with shaking for the first 2 min, the absorbance of the reaction mixture was measured at a wavelength of 510 nm against the blank solution. PTC was calculated using a tannic acid (TA) calibration curve. Different concentrations of TA in the urea solution (10, 50, 100, 200, and 400 \(\upmu\)g/mL) were used to prepare the calibration curve. PTC was expressed as milligram tannic acid equivalent/g dry weight of skin powder (mg TAE/g DW).
# A Denoising Method for Multi-Noise on Steel Surface Detection
## 5 Comparison of the Proposed Denoising Method with Other Denoising Methods
### Comparison of the Denoising Effect  
For the noisy image, noise reduction also used the Sobel operator [6] and Prewitt operator [7].  
The Sobel operator is a first-order differential operator. It calculates the gradient of each pixel using the gradient values of the neighboring regions of the pixel. The detection of edges is based on the phenomenon of the grayscale between the upper, lower, left, and right neighbors of an image pixel point, while the weighted difference reaches its extreme value at the edge. It is given by the following Equation:  
\[\mathrm{S}=\left(\mathrm{dx}^{2}+\mathrm{dy}^{2}\right)^{1/2} \tag{9}\]  
The Sobel operator is a \(3\times 3\) matrix used to detect horizontal and vertical edges in an image; \(-1\), \(0\), \(1\) and \(-2\), \(0\), \(2\) denote the numbers in the matrix, which represent the relative position of each pixel point in the image. Figure 7 shows the 2 convolution kernels \(\mathrm{dx},\mathrm{dy}\). The maximum value of the two convolutions is the output value at that point. The result is an edge magnitude image. The edges are picked up according to a certain threshold value. The algorithm has a smoothing effect on noise and can provide more accurate edge orientation information, but the accuracy of edge localization is not high enough.  
The idea of the Prewitt operator edge detection is similar to that of the Sobel operator. The two convolution kernels are applied to each pixel point in the image and then the two convolution results are squared to get the final edge intensity value. If the edge intensity value exceeds the set threshold, then the pixel point is considered to be an edge point. The Prewitt is given by the following Equation:  
\[\mathrm{S}_{\mathrm{P}}=\left(\mathrm{dx}^{2}+\mathrm{dy}^{2}\right)^{1/2} \tag{10}\]  
The two convolution kernels \(\mathrm{dx},\mathrm{dy}\) shown in Figure 8 form the Prewitt operator. Its differential operations are defined in an odd-sized template.  
Figure 8: Prewitt operator.  
Figure 7: Sobel operator.  
The algorithm template is convolved with the image pixel gray values from left to right and from top to bottom in order. The operator has a smoothing effect on noise, but the localization accuracy is also not high enough.  
According to the multi-noise denoising method proposed in this paper, a simulation is performed and then compared with other noise reduction algorithms. Results can be seen in Figure 8.  
Figure 9a is the image with added noise, and Figure 9b is processed by the denoising method proposed in this paper. Compared with Figure 9c,d, it can be seen that the denoising method in this paper is superior to other methods in terms of noise reduction and almost all the noise is filtered out. It shows that the multi-noise denoising method is feasible and effective.
## 6 Evaluation
### Evaluation Results
#### 6.2.3 Stray Node Detection Rate  
The stray node detection rate (SNDR) is calculated as follows.  
\[\text{SNDR}=(1-\text{MDR})\times 100 \tag{11}\]  
\[\text{MDR}=\frac{N_{\text{Miss\ Detection}}}{N_{Total\ Detection}} \tag{12}\]  
\[N_{\text{Miss\ Detection}}=\ N_{\text{Detection}}-N_{\text{Stray}} \tag{13}\]  
where \(N_{\text{Detection}}\) is the sum of the number of REPLY messages that have experienced timeout and the number of REPLY messages that returned after 2 or more hops, \(N_{\text{Stray}}\) is the number of nodes that are  
Figure 14: Packet loss rate: (**a**) Packet loss rates of the autonomous response-type stray node detection protocol; (**b**) packet loss rates of the request-response-type stray node detection protocol; (**c**) Packet loss rates of the two proposed protocols.  
outside the detection area at the time of communication, and \(N_{Total}\) Detection is the number of nodes (\(N\)) \(\times\) the number of detections.  
The node detection rate of each protocol is shown in Figure 15. Figure 15a shows the node detection rates of AR-Basic and AR-Proposal. As in the case of the packet loss rate, the detection rates of the two protocols did not differ much when the number of nodes was small. However, as the number of nodes increased, the node detection rate of AR-Basic dropped sharply. When the number of nodes was 15, the node detection rate of AR-Basic was 40% lower than that of AR-Proposal. Figure 15b shows the node detection rates of RR-Basic and RR-Proposal. Here again, the proposed protocol exhibited a higher node detection rate. The difference in the node detection rate between the two protocols was 10 to 20% irrespective of the number of nodes. Figure 15c compares the two proposed protocols. As in the case of the packet loss rate, the node detection rate of AR-Proposal was higher than that of RR-Proposal.  
These results indicate that the proposed protocols provide more efficient communication than the compared protocols do and that the autonomous response-type stray node detection is better than the request-response-type stray node detection.  
Figure 15: Stray node detection rate. (**a**) Node detection rates of the autonomous response-type stray node detection protocol; (**b**) node detection rates of the request-response-type stray node detection protocol; (**c**) node detection rates of the proposed protocols.