esa-sceva/satcom-chunk-collection huggingface dataset:
# Surface Functionalization of Polyethylene by Silicon Nitride Laser Cladding
## 3 Experimental Results
### In Vitro Osteosarcoma Testing  
Figure 8 shows the laser micrographs of sample surfaces after SaOS-2 cells treatment. The positive control Si\({}_{3}\)N\({}_{4}\) (Figure 8a) shows a large amount of bone tissue, consistent with previous observations [23]. Conversely, no bone tissue was formed on the bulk PE sample used as the negative control (Figure 8b). The Si\({}_{3}\)N\({}_{4}\)-cladded PE surface (Figure 8c) showed an almost homogeneous distribution of hydroxyapatite particles (_cf._ white areas in the micrograph).  
The microbial viability assay (WST) results provide data on the number of living cells on the surface of the different samples (Figure 9). The Si\({}_{3}\)N\({}_{4}\)-cladded PE surface showed lower cytotoxicity as compared to bulk PE and thus, higher cell proliferation. The data labeled as 2D represent cells in their medium without any substrate, as a positive control.  
Figure 8: Laser microscopy images (left side) and relative topographic data (right side) of (**a**) bulk Si\({}_{3}\)N\({}_{4}\) (positive control), (**b**) PE substrate, and (**c**) Si\({}_{3}\)N\({}_{4}\)-coated PE substrate after SaOS-2 cell treatment.  
Figure 9: Biological assay (absorbance at 450 nm) of all the investigated samples after in vitro SaOS-2 cell exposure.  
Fluorescence micrographs (Figure 10) indicate the presence of mineralized tissue by means of different stains: blue for cell nuclei; green for osteoclast, and red for osteepontin. Both red and green markers suggest the presence of mineralized bone tissue. Bulk Si\({}_{3}\)N\({}_{4}\) showed the highest amount of osteeocalcin and osteepontin in the bone tissue synthesized by cells during the treatment, while the silicon nitride particles embedded in the PE surface increased the formation of hydroxyapatite crystal as compared to bulk PE.
## 5 Experiments and Results  
### Test of the Validity of the Hybrid Algorithm  
In order to verify the effectiveness of the proposed algorithm, different datasets are designed to compare the algorithm with the CPLEX operation results. The results are shown in Table 5.  
Table 5 shows that compared with CPLEX, the algorithm in this paper is better than CPLEX in terms of time efficiency under the premise that the difference in operation results does not exceed 4%. Especially when the number of end demand points exceeds 15, the time efficiency of the proposed algorithm is significantly better than that of CPLEX, and better calculation results can be obtained.
## 2 Methods
### Quantum Proxy Signature Scheme
#### 2.3.3 Signing Phase
**Step 4** To verify the validity of the proxy warranty, we apply the model of quantum walks on circles described in Section 2.1 to transmit the random sequence \(S\) from the proxy signer [PERSON] to the arbitrator [PERSON]. Assume that the number of the walking steps is \(t\) and \(P=2^{n}\), we denote \(|I\rangle\in\{|0\rangle,\ldots,|P-1\rangle\}\) as one vertex state, \(|d\rangle\in\{|R\rangle,|L\rangle\}\) as the coin state. Using these parameters, a quantum state can be randomly generated  
\[|\varphi\rangle_{U}=U_{k}^{t}|I\rangle|d\rangle=[O\cdot(I_{p}\otimes R_{c})]^{t }|l\rangle|d\rangle, \tag{25}\]  
which is then distributed to [PERSON] and [PERSON]. **Step 5** [PERSON] transforms the random sequence \(S\) as a decimal number \(s\) (this can be easily done) and obtains the following shift operator  
\[T_{s}=\sum_{i=0}^{P-1}|(i+s)\mod P\rangle\langle i|, \tag{26}\]  
which is used to produce  
\[|\varphi(s)\rangle=(T_{s}\otimes I_{c})|\varphi\rangle_{U}, \tag{27}\]  
where \(I_{c}\) is the identity operator acting on the coin state and which is transmitted to [PERSON]. **Step 6** [PERSON] applies \(U_{k}^{-t}\) to \(|\varphi(s)\rangle\) and gains  
\[|\varphi\rangle_{S}=(T_{s}\otimes I_{c})|l\rangle|d\rangle, \tag{28}\]  
on which he performs the position measurement  
\[K=\sum_{i=0}^{P-1}|i\rangle\langle i|\otimes I_{c}. \tag{29}\]  
Denoting the measurement result as \(i_{s}\), i.e., \(i_{s}=(l+s)\mod P\), we can obtain  
\[s=(i_{s}-l)\mod P. \tag{30}\]  
That is, [PERSON] can easily recover \(S\) and obtain \(|\varphi\rangle_{M^{\prime}}\) from \(|\varphi\rangle_{M^{\prime\prime}}\) according to the recovered \(S\). **Step 7** After completing the verification of the proxy warranty, [PERSON] produces a quantum state \(|\phi_{A}\rangle=(|S_{A}\rangle,|\varphi\rangle_{M^{\prime\prime}})\) and sends it to [PERSON]. Figure 4: The encryption process of \(|\varphi\rangle_{M^{p}}\) based on \(K_{AT}\). (**a**) Quantum circuit for the encryption process of \(|\varphi\rangle_{M^{p}}\) with \(n=8\) including \(X\), \(Z\) and CNOT gates governed by \(K_{AT}\) and ID refers to the identity gate; (**b**) The probability producing the encrypted quantum state \(|S_{A}\rangle\). Figure 5: The decryption process for recreating \(|\varphi\rangle_{M^{p}}\) based on \(K_{AT}\). (**a**) Quantum circuit for the encryption and decryption processes for obtaining \(|\varphi\rangle_{M^{p}}\). It can be seen that the gates for encryption and decryption processes are symmetric; (**b**) The probability recovering the quantum state \(|\varphi\rangle_{M^{p}}\).
###### Abstract  
Slope stability is one of the crucial factors to consider in every civil engineering project. One widely used method to stabilize slope is the use of polymeric products called geosynthetics. Natural fiber geosynthetics used for geotechnical applications have attracted attention because of their environmental and economic benefits. Coir fibers made into nonwoven geotextiles are utilized in this study as an alternative material for slope stabilization. One drawback of coir fiber geotextiles is their low tensile strength and limited life span due to their susceptibility to environmental factors. This study was conducted to evaluate the effect of mercerization and bleaching treatment on the strength performance of nonwoven coir geotextiles after exposure to conditions simulating biological and chemical degradation. Microscopic images of treated coir geotextiles show the removal of surface impurities that altered the physical components in the fiber. The grab tensile strength results prove that the mercerized coir geotextiles are suitable for field conditions and groundwater exposure. The untreated coir geotextiles showed superior puncture resistance relative to the chemically treated geotextiles. The chemical treatments improved the tensile strength; however, they weakened the puncture resistance of the coir geotextile due to the decrease in thickness. A slope stability simulation conducted using Roccscience Slide2 version 9.017 software proved that coir geotextiles can effectively reinforce slopes, with strength performance almost comparable to that of synthetic geotextiles.  
slope protection; natural fiber geosynthetics; coir geotextile; mercerization; bleaching treatment; puncture resistance; grab tensile strength; slope stability analysis +
Footnote †: journal: Applied
## 5 Numerical Simulations
Simulation results are displayed in Figures 11-16, and the performance indices are summarized in Table 2. Figure 11 presents the comparisons of the control performance, from which we can find that the designed TLC method exhibited better convergence performance than backstepping and the PID method even though being perturbed by unknown dynamics and external disturbances. Figure 12 depicts that the calculated control input exceeded the operation condition of the actuators, thus causing the problem of rudder saturation. It can also be seen from Figure 12 that an auxiliary system was incorporated into the proposed controller to handle this problem. Figure 13 shows that the actual heading could track  
Figure 8: Sideslip angle estimation errors \(\tilde{\beta}\). Figure 10: Cross-track errors \(y_{c}\). Figure 9: Along-track errors \(x_{c}\). \[MIA=\frac{1}{t_{\infty}-t_{0}}\int_{t_{0}}^{t_{\infty}}\left|\pi_{e}\left(\tau \right)\right|d\tau\]0 Cross-track errors \(y_{e}\). \[MIA=\frac{1}{t_{\infty}-t_{0}}\int_{t_{0}}^{t_{\infty}}\left|\pi_{e}\left(\tau \right)\right|d\tau\]1 Sideslip angle estimation errors \(\hat{\beta}\). \[MIA=\frac{1}{t_{\infty}-t_{0}}\int_{t_{0}}^{t_{\infty}}\left|\pi_{e}\left(\tau \right)\right|d\tau\]2 Along-track errors \(x_{e}\). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{5}{c}{Value} \\ \cline{2-7}
**Method** & \(RMSE\left(x_{e}\right)\) & \(RMSE\left(y_{e}\right)\) & \(RMSE\left(E_{1}\right)\) & \(MIA\left(x_{e}\right)\) & \(MIA\left(y_{e}\right)\) & \(MIA\left(E_{1}\right)\) \\ \hline TLC + FPLOS & 0.04941 & 0.3473 & 0.06539 & 0.03204 & 0.176 & 0.0139 \\ Backstepping + FPLOS & 0.05564 & 1.501 & 0.2142 & 0.03753 & 0.6259 & 0.08243 \\ PID + FPLOS & 0.0549 & 1.354 & 0.191 & 0.0376 & 0.797 & 0.0864 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparisons of different methods.
## 2 Measurement Principle and Causes of Dynamic Error
### Causes of Dynamic Error
Experimental configuration and results are shown in Figures 3 and 4:  
As displayed in Figure 4, the maximum and minimum error at v = 30 mm/s are 32.24 ms and 28.06 ms. The time error corresponds to a periodic curve in the time-domain. The deviations at different speeds are lower than 0.05 ms and they are negligible in this research. Change the trajectory in subsequent simulations and we find that in the whole measurement zone the time error has great similarity with that of v = 30 mm/s. It is dependent on the rotation speeds of transmitters in the network. With the increase of rotation speeds, \(\delta t_{s,k}\) and \(\delta t_{m,k}\) will decrease. However, the speed and position of the receiver have little influence on dynamic time error. Figure 4: Dynamic time error in the four-transmitter network. Figure 3: Configuration of simulation experiments. In wMPS we generally set a primary transmitter. The global coordinate system will be aligned with the coordinate system of the primary transmitter. The rotation and displacement relationship between the primary transmitter and others transmitters will be calibrated during the construction of the network. Each transmitter has a certain pose and position in the global coordinate system. On the basis of the conclusion above, we divide the movement speed of the receiver into three directions: radial speed \(v_{r}\), tangential speed \(v_{t}\) and axial speed \(v_{a}\) in Figure 5. Here the radial, tangential, and axial directions mean those of each separate transmitter. According to the transmitter model, the radial speed has little influence on the dynamic error on account of the fast light speed. Therefore, the scanning angle error caused by time error can be expressed as:  
\[\delta\theta_{s\_k}+\delta\theta_{m\_k}=\left(\frac{v_{t}}{\sqrt{x^{2}+y^{2}}} +\frac{\tan\varphi\times v_{a}}{\sqrt{x^{2}+y^{2}}}\right)(\delta t_{s\_k}+ \delta t_{m\_k}) \tag{6}\]  
\(\left( \begin{array}{cc}x&y&z\\ \end{array} \right)\) is the position of the receiver in the local coordinate system. \(\varphi\) refers to the intersection angle between the rotation axis and laser plane. In this way, the dynamic error resources in wMPS are presented.
## 3 Thin Film Cathodes  
As discussed earlier, the use of epitaxial thin films offers many advantages for controlling the ORR kinetics in SOFC cathodes. Strain engineering is one of the most effective ways to tailor the ORR kinetics of perovskite and layered oxide thin films. Many studies have reported that epitaxial strain strongly influences bulk and surface properties of oxide materials including the electronic structure [55], oxygen transport [56; 57; 58; 59], oxygen defect formation [60], and oxygen surface exchange kinetics [61; 62], ultimately enhancing the high temperature oxygen electrocatalyils in ABO\({}_{3}\) and RP oxides [63; 64]. In addition, the viability of controlling the crystallographic orientation of epitaxial thin films has enabled investigation of the anisotropic nature of oxygen transport and kinetic properties in RP oxides [61; 65]. Furthermore, oxide interface engineering has brought a new concept to design highly active and stable cathode materials for intermediate SOFCs [66; 67; 68]. The growth of high quality epitaxial thin films is made possible using several deposition methods such as pulsed laser deposition (PLD), molecular beam epitaxy (MBE), atomic layer deposition (ALD), and sputtering. However, it is not the aim of this review to describe in detail those techniques; an extended review on the different techniques can be found elsewhere [69; 70; 71; 72], In the following subsections, we focus on how these factors have brought advances in the oxygen surface exchange kinetics in ABO\({}_{3}\) and RP oxide thin films.
# TA-RRT*: Adaptive Sampling-Based Path Planning Using Terrain Analysis
## 4 Experiment
### Experiment in Static Scenarios
#### 4.2.2 Memory Usage Analysis  
Table 4 presents the memory usage of different path planning algorithms across simple, intermediate, and complex environments. The proposed TA-RRT* exhibited the highest average memory usage across all environments. This higher memory consumption is attributed to the algorithm's advanced terrain-based sampling and adaptive step size strategies, which enable robust performance in diverse and complex environments.  
The memory usage of TA-RRT* exhibits consistent average values across all environments, though the standard deviation increases in more complex terrains. This variation reflects the additional computational resources required for advanced sampling and terrain analysis. Notably, this variation remains within acceptable limits, ensuring reliable performance even in environments with high obstacle density. This behavior underscores the algorithm's adaptability to diverse scenarios while maintaining predictable resource utilization.  
Overall, while TA-RRT* requires more memory than the baseline methods, its consistent performance across all environments, including effective path optimization in simple, intermediate, and complex terrains, makes it a strong candidate for applications in resource-capable systems, such as autonomous robotics and high-performance vehicles.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Simple Env.**} & \multicolumn{2}{c}{**Intermediate Env.**} & \multicolumn{2}{c}{**Complex Env.**} \\ \cline{2-7}  & **Average** & **STD** & **Average** & **STD** & **Average** & **STD** \\ \hline M1 & 62,225.48 & 74.76 & 61,597.16 & 82.76 & 61,259.84 & 237.77 \\ \hline M2 & 61,622.96 & 256.93 & 61,780.52 & 57.95 & 61,759.80 & 78.03 \\ \hline M3 & 61,674.36 & 308.50 & 61,548.76 & 58.98 & 62,195.48 & 212.90 \\ \hline M4 & 78,992.96 & 144.22 & 78,413.08 & 89.28 & 78,366.24 & 288.21 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Memory usage results of path planning algorithms across simple, intermediate, and complex environments.  
Figure 11: Box plots expressing the path length distributions of the path planning algorithms in complex environments.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Exec. Time (ms)**} & \multicolumn{2}{c}{**Path Length**} & \multicolumn{2}{c}{**No. of Nodes**} \\ \cline{2-7}  & **Average** & **STD** & **Average** & **STD** & **Average** & **STD** \\ \hline M1 & **12,554.88** & 1774.46 & 15.42 & 0.44 & 1577.81 & 146.46 \\ \hline M2 & 13,709.04 & 2844.10 & 15.77 & 0.66 & 1526.00 & 147.89 \\ \hline M3 & 19,207.87 & 6002.88 & 16.04 & 0.97 & 1647.15 & 190.97 \\ \hline M4 & 23,231.93 & 2639.77 & **15.25** & 0.20 & **2148.20** & 127.06 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Path planning results in complex environments.
## 4 Results of the Experiment
### Comparison of Inversion Results
#### 4.2.2 Accuracy Comparison  
Soil relative humidity is a crucial indicator that describes the water content in the soil, reflecting the degree of soil wetness. Relevant studies have shown a close correlation between the drought monitoring index and soil relative humidity, making it an important indicator for assessing drought conditions [30; 31; 32; 33]. In comparing the accuracy of four drought monitoring models for drought inversion, this chapter utilizes correlation analysis of 0-10 cm and 0-20 cm RSM from the China Meteorological Data Network (CMDN) as a criterion to identify the most accurate drought monitoring model.  
Select the inversion results of four drought indices in Laibin City for March and April 2022, and January 2023, and perform linear fitting with the corresponding soil relative humidity data (0-10 cm, 0-20 cm) at those times. The obtained Pearson correlation analysis results are shown in Table 4. Kendall's tau-b correlation analysis was also conducted for validation and comparison, and the results were consistent. The results are shown in Table 5. According to the test results, there is a positive correlation between RVI, NDWI, and VTCI and soil relative humidity, meaning that as the index value increases, the soil relative humidity also rises, indicating a better region. Conversely, the correlation between the TVDI index and soil relative humidity is negatively correlated, indicating that a higher index value corresponds to lower soil relative humidity, suggesting a drier condition. Compared to the relative soil moisture at 0-20 cm, all four drought monitoring indices exhibit a stronger correlation with the relative soil moisture at 0-10 cm, suggesting that the drought index model is more sensitive to the relative soil moisture in shallower layers (0-10 cm). Among all indices, the TVDI index performs the best in the correlation test, demonstrating a high correlation, which verifies the strong association between the TVDI index and the relative soil moisture. In the significance test, all four drought monitoring indices passed the 90% significance level. Based on the above analysis, the TVDI index with the best performance is selected for vegetation drought monitoring in Laibin City.
# IpDFT-Tuned Estimation Algorithms for PMUs: Overview and Performance Comparison
## 4 Discussion  
Table 8a,b summarizes the results of the performance analysis in the _P Class_ and _M Class_ testing conditions reported in Section 3. The estimation algorithms with TVE, \(|\)FE\(|\) and \(|\)RFE\(|\) values and with response times (in the case of amplitude or phase step changes) below the limits reported in the Standard in all the considered testing conditions are denoted as Compliant (C). If at least one of the TVE, \(|\)FE\(|\), \(|\)RFE\(|\) or response time limits is exceeded, an estimator is labeled as Not Compliant (NC). Again, the algorithm with the best performances in estimating each parameter is highlighted by shading the corresponding cell in Table 8a,b. The criteria adopted to identify the best estimator of each parameter for a given observation interval length are the following:  
* an algorithm able to return fully compliant TVE, \(|\)FE\(|\), \(|\)RFE\(|\) or response time values is considered to be better than another one exceeding even a single limit, regardless of the estimation errors associated with the individual tests;
* if two algorithms exhibit a different number of C and NC tests, the one with a larger number of compliant results in different tests is considered the better one, regardless of the estimation errors in the individual tests;
* if two algorithms exhibit the same amount of compliant or non-compliant tests, the algorithm with a higher number of more accurate results shown in Tables 4-7 is regarded as the better one;
* if two algorithms exhibit the same amount of compliant or non-compliant tests and they do not clearly outperform one another (e.g., because results are very similar or because the number of testing conditions in which they are better is the same), the estimators are considered to have an equivalent performance.  
From the analysis of Table 8a it results clearly that in the _P-Class_ testing conditions full compliance is achieved only with the Tuned TWLS estimator over 4-cycle intervals. However, this is also the most demanding algorithm from the computational point of view. The IpDFTc estimator requires a lower computational burden and it is globally the best one over 2-cycle intervals, although it suffers from excessive \(|\)FE\(|\) values in steady-state testing conditions. Over 4-cycle intervals instead, despite its high accuracy, the main problem of the IpDFTc algorithm is the excessive response time duration affecting synchrophasor estimation after step changes.  
In the _M-Class_ testing conditions summarized in Table 8b the IpDFTc estimator is globally the best one over 6-cycle observation intervals, but it again suffers from excessive \(|\)FE\(|\) values when out-of-band inter-harmonics are considered. On the contrary, the Tuned TWLS algorithm is confirmed to be both fully compliant and the best estimator over 8-cycle observation intervals. Quite interestingly, the DCF technique with the LSF filter over 8-cycle intervals ensures full compliance in all tests considered, despite its accuracy and responsiveness not being excellent in any test.
# A Correlation-Change Based Feature Selection Method for IoT Equipment Anomaly Detection
## 4 Methodology
### Dynamic Time-Wrapping for Data Segment Size  
As we described in Section 1, the feature correlations are time lagged, and dynamic in industrial environment. Thus, to monitor the feature correlations online, we needed to conduct curve alignment at real-time. A critical problem here is to decide the data segment used online.  
To that end, we looked into the historical data and investigated the historical time lag size. Curve alignment is a bad choice since it assumes consistent time lags for the data used for calculation. And we relied on the dynamic time-wrapping method, which considers dynamic time lags to find a time series matching which maximizes the curve similarity. With the result of dynamic time-wrapping, we could use the difference of each pair of matching time point to reflect the time lags over time.  
In this paper, we use the TADPole algorithm (Time-series Anytime DP) [16] to conduct our dynamic time-wrapping method.  
To decide an appropriate data segment size, we wanted to ensure that the data segment size was bigger than most of the time lags. Herein, we assume the time lag of a pair of correlated features is a stochastic variable following normal distribution, and conducted parameter estimation for each pair of features to generate the mean and variance of normal distribution. Following the idea of statistical process control, normal time lags range from \(\mu-3\sigma\) to \(\mu+3\sigma\) (\(\mu\) is the mean and variance is the \(\sigma\)). As a result, we choose \(\mu+3\sigma\) as the data segment size for the pair of features because it is bigger size than most of the time lags.  
Our procedure of data segmentation size works as shown in Algorithm 3. For each pair of correlated features, we used the TADPole method (line 3) to generate the time-lag sequence over the historical training data, and conducted statistical analysis (line 4) on the derived time lags sequence. Then we set \(\mu+3\sigma\) as the data segment size for correlated features.  
```
Input: feature space \(a^{(1)},a^{(2)},\ldots,a^{(d)}\);  offline historical feature sequence \(x_{1},x_{2},\ldots,x_{i}\);  correlated feature set \(C_{1},C_{2},\ldots,C_{k}\); \(T\); #the set of historical training data Output: Dsize; #the set of data segment size for correlated features
1 For each correlated sensor set \(C_{i}\)
2 For each pair of features \(a^{(i)},a^{(j)}\) in \(C_{i}\)
3\(\Delta t\gets TADPole\Big{(}T,a^{(i)},a^{(j)}\Big{)}\) # TADPole conducts TADPole algorithm on the offline historical feature sequence \((\mu,\sigma)\gets stats(\Delta t)\); \(Dsize\Big{(}a^{(i)},\ a^{(j)}\Big{)}=\ \mu+3\sigma\); End for
7 End for
```  
**Algorithm 3** DWT-based data segmentation algorithm.  
One may argue that we don't conduct dynamic time-wrapping in the previous step, which also calculated the similarity for a pair of features with a long period of historical data. This is because that dynamic time-wrapping methods merely focus on the matching of the time series but cannot be used to tell whether two time series are correlated.