esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Parameter Estimation Using the NN
Nevertheless, accuracies up to \((0.71\pm 0.01)\cdot 10^{-3}\) for relative permittivity and \((90.0\pm 0.05)\cdot 10^{-6}\) for the loss tangent were achieved with the CNN model, MSE loss function, and PCA preprocessing method. This enables the possibility of importing more varying parameters for further estimation of object material and structure estimation. Data with noise addition must be included in the data sets of synthesized UWB signals due to the fact that real life data is often influenced by the surrounding noise sources as well as hardware imperfections. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Represent.** & **SNR** & \begin{tabular}{c} **Error in** \\ **Prediction \(\times\) 10\({}^{-3}\)** \\ \end{tabular} & \begin{tabular}{c} **Training** \\ **Time (s)** \\ \end{tabular} & **Average Epochs** \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} Time \\ domain \\ (\(e_{r}\)) \\ \end{tabular} } & None & \(19.72\pm 0.62\) & 5.27 & 6.93 \\  & 0.1 & \(89.84\pm 16.46\) & 5.32 & 14.41 \\  & 0.2 & \(722.29\pm 141.22\) & 6.11 & 18.47 \\  & 0.2 (spikes) & \(69.39\pm 8.9\) & 6.55 & 20.73 \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Time \\ domain \\ (\(tan\delta\)) \\ \end{tabular} } & None & \(0.39\pm 0.04^{-3}\) & 6.1 & 19.8 \\  & 0.1 & \(1.19\pm 0.04^{-3}\) & 6.48 & 20.54 \\  & 0.2 & \(5.24\pm 0.24^{-3}\) & 6.31 & 23.47 \\ \cline{1-1}  & 0.2 (spikes) & \(1.14\pm 0.01^{-3}\) & 6.65 & 21.13 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Estimation of \(e_{r}\), \(tan\delta\) with different noise levels added.
# Robust Localization of the Mobile Robot Driven by Lidar Measurement and Matching for Ongoing Scene
## 3 Localization of Mobile Robot in Ongoing Scene
### Localization Based on Artificial Landmarks in an Ongoing Scene
#### 3.1.1 Extraction of Artificial Landmarks  
Firstly, the artificial landmarks are set in the scene, a global coordinate system of the scene is constructed. Then, the median filtering method is used for data processing. As a result, the filtered polar coordinate data \((\rho_{i},\theta_{i})\) is transformed into rectangular coordinate data \((x_{i},y_{i})\). \(F_{i}=[x_{i},y_{i}]\), that is, the formula is as follows:  
\[\left\{\begin{array}{l}x_{i}=\rho_{i}\cos\theta_{i}\\ y_{i}=\rho_{i}\sin\theta_{i}\end{array}i=1,2,3,\cdots,n.\right. \tag{2}\]  
Then, the distance \(d_{i,j+1}\) of adjacent data points is calculated based on the adaptive clustering algorithm, and it is compared with the clustering threshold t. The formula is as follows:  
\[d_{i,i+1}=dis(F_{i},F_{i+1}) \tag{3}\]  
\[t=\rho_{i-1}(\sin\Delta\varphi/\sin(\lambda-\Delta\varphi))+\sigma_{\rho} \tag{4}\]  
where \(\Delta\varphi\), the angular resolution of the Lidar sensor, is set \(0.25^{\circ}\). A constant parameter \(\lambda\) is set \(10^{\circ}\), and \(\sigma_{\rho}\) represents the error of distance value. If \(d_{i,i+1}\leq t\), then \(F_{i}\) and \(F_{i+1}\) are of the same cluster, otherwise, they are different clusters. According to the experimental tests, \(\Delta\varphi=0.25^{\circ}\), \(\lambda=10^{\circ}\), \(\sigma_{\rho}=10\) mm.  
After obtaining the clustering data, the clusters are divided into the reflector and non-reflector by the extraction of the intensity information \(I_{i}\). The diameter and intensity of the reflector are set \(D_{r}\) and \(I_{r}\), separately. When \(I_{r}\geq I_{i}\), the clustering data that extracted is the features of the reflector. Assuming the reflector 1 that in coordinate system of Lidar is \((x_{r1},y_{r1})\), the point set of the reflector is expressed as \(S_{i}=(x_{i},y_{i}),i=1,2,\cdots,n\). Then, the geometric equation of the circle is shown as:  
\[\left(x_{i}-x_{r1}\right)^{2}+\left(y_{i}-y_{r1}\right)^{2}=\frac{D_{r}^{2}}{ 4}\ (i=1,2\ldots,n) \tag{5}\]  
and the objective equation that based on the least square method is obtained as:  
\[\mathrm{F}=\sum\
olimits_{i=1}^{n}[\left(x_{i}-x_{r1}\right)^{2}+\left(y_{i}- y_{r1}\right)^{2}-\frac{\mathrm{D}^{2}}{4}\right]^{2}. \tag{6}\]  
Take the partial derivative of Equation (6) and calculate the coordinates of the central position of the reflector \((x_{r1},y_{r1})\).
## 3 Results
### Mixed Measures ANOVA Analyses
#### 3.4.2 Task Accuracy  
To examine the impact of different interaction modes (eye tracking, headset, and controller) and gaming skills on TMT-VR parts A and B Task Accuracy, repeated measures ANOVA were conducted. Results revealed a statistically significant interaction for TMT-VR Accuracy A between gaming skills and interaction modes as follows: F(2, 207) = 4.47, \(p\) = 0.012, \(\eta^{2}p\) = 0.04 (see Figure 5). Additionally, the interaction mode also presented significant results as follows: F(2, 207) = 16.59, \(p\) < 0.001, \(\eta^{2}p\) = 0.14 (see Figure 6). Participants in the high gaming skills group provided significant results in relation to the interaction modes as follows: F(1.83, 60.24) = 4.53, \(p\) = 0.02, \(\eta^{2}p\) = 0.06. Post hoc pairwise comparisons revealed a significant difference between the head and the controller (\(p\) < 0.001). Similarly, the low gaming skills group provided significant results in relation to interaction modes as follows: F(1.98, 71.42) = 15.25, \(p\) < 0.001, \(\eta^{2}p\) = 0.21, and Post hoc pairwise comparisons showed significant differences between eye tracking and the controller (\(p\) < 0.001) and between the head and the controller (\(p\) < 0.001).  
The statistical analyses regarding Accuracy B revealed a statistical significance of interaction mode as follows: F(2, 207) = 4.18, \(p\) = 0.017, \(\eta^{2}p\) = 0.04. Regarding the Accuracy TMT-VR B, the effects of gaming skill (high/low) were not statistically significant as follows: F(2, 207) = 1.09, \(p\) = 0.337, \(\eta^{2}p\) = 0.001. However, Post hoc pairwise comparisons revealed a significant result between eye tracking and the controller (\(p\) = 0.02; see Figure 7). Notably, the eye tracking mode demonstrated superior accuracy compared to the controller mode. However, there was no statistically significant difference in task accuracy between the eye and headset modes (\(p\) = 0.103), suggesting comparable performance between these two modalities. Overall, these findings highlight that the eye tracking mode emerged as the most accurate, outperforming the controller mode while exhibiting no significant differences compared to the headset mode.  
Figure 4: Task completion time of TMT-VR Task B per interaction mode and gaming skill. Task completion time is displayed as a Z score. \(p\) < 0.01 **, \(p\) < 0.001 ***.  
Figure 5: Accuracy in TMT-VR Task A per interaction mode and gaming skill. Accuracy is displayed as a Z score of the distance from the center. \(p\) < 0.01 **, \(p\) < 0.001 ***.  
Figure 6: Accuracy in TMT-VR Task A per interaction mode. Accuracy is displayed as a Z score of the distance from the center. \(p\) < 0.001 ***.
## 8 Results  
The execution time of different parts of the decomposition is measured as the elapsed wall-clock time, with measurements in between function calls to determine the relative time consumption. The final execution times are shown in Figure 13. These tests were executed using a Dell Latitude 7400 with an 8 th Generation Intel(r) Core(tm) i7-8665U Processor and 2\(\times\) 4 GiB DDR4 RAM. The program in Code Listing 1 has been used to determine the execution time and memory used by the decomposition. Unitary matrices of sizes \(U(2^{1})\) to \(U(2^{1}0)\) were randomly generated first, using QiBAM as outlined in Section 2. The matrices were stored as binary files and loaded as required for the decompositions. The decomposition was repeated 1000 times for the smaller gates and 100 times for the decomposition of the 10-qubit gate in OpenQL, with varying numbers for the intermediate sizes. The execution time and memory use as reported in this paper are the averages of these runs. To measure execution time, the Python \"time\" package was used to determine the time difference between the start and various points of the program. The time for each part of the code, as well as the resulting aggregated execution time, can be found in Figure 13 and Table 2.  
As expected, the decomposition itself took the most time--more than 10 times that of any other part. This is because of the considerable mathematical decompositions and the number of matrix operations. One of the algorithms used in the decomposition is eigenvalue decomposition, which is an iterative algorithm that requires \(O(6^{n})\) operations for an \(2^{n}\times 2^{n}\) matrix [25]. The data also show that the generation of the rotation gates and CNOTs does not contribute much to the total execution time of the algorithm, as expected. In addition, since the complete decomposition is calculated at design time, it does not influence the run-time of the final circuit when it is executed on a quantum accelerator. The same program has also been used to determine the memory allocation. This has been measured using the Python memory_profiler package. The results of this are shown in Table 3 and Figure 14. After an initial allocation of about 40 MiB, noteworthy additional allocation of memory occurs only when k.gate(...) is called. This means that the complete unitary decomposition requires much less memory than generating and storing the resulting circuit in OpenQL.
## 3 Experiments
### Intuitive Results of Combination  
The results shown in the above subsections indicate that our spatial-based texture detection and frequency-based saliency detection can both obtain location information of floating objects. However, bounding boxes from texture detection are larger than the corresponding ground truths (due to small sample training), and the results of salient detection are smaller than the real sizes (due to the loss of contour information). Through combining bounding boxes from the two methods, closer results to the ground truths can be obtained. To demonstrate the effectiveness of the combination intuitively, results in five challenging scenes are shown in Figure 7.  
In Figure 7, the first line shows that our spatial-based texture detection is able to detect floating objects correctly, though their bounding boxes are larger than the ground truths. The second line shows that correct saliency maps with no redundancies are obtained by our frequency-based saliency detection, but some contours are removed (especially in Figure 7a,c). The third line shows that the results of combination have the correct size and location information of floating objects, indicating that our combination strategy overcomes the shortcomings in different processing domains. Meanwhile, as shown in Figure 7a-e, our method is robust in the five challenging scenes, and the final results are close to the ground truths.
## 6 Conclusions  
This study presents the new results of deep time domain electromagnetic soundings (TDEM) study to gain insights into the Barbastro saline-evaporitic formation, affected by the Barbastro-Balaguer Anticline regional structure. The survey was conducted in the southern flank of the anticline in the vicinity of Graus, Spain. Resistivity data were obtained from 15 TDEM survey points with a coincident-loop 600 m \(\times\) 600 m square configuration, allowing a depth penetration of up to 3.0 km. An initial inversion model was stablished using a thickness and layered parametric constrained model based on the Monzon-1 drilling lithological logging in the rest of the TDEM survey points. The applied methodology allows for an improved detailed geological and structural view of the saline-evaporitic Barbastro Formation affected by the Barbastro-Balaguer Anticline.  
The results support the idea that that the Barbastro saline-evaporitic formation presents a complex structure because of the regional anticlinal structure, dipping SW, where the hanging wall shows a variable dip from 40\({}^{\circ}\) to close to subhorizontal (distally), ranging from 996 to 1433 m depth, while the football shows a close to flat surface between 2001 and 2101 m depth. As a result of the defined structure, the Barbastro saline-evaporitic formation shows a thickness reduction to the SW, ranging from 1103 to 601 m.  
Therefore, our study has revealed that the TDEM research method can be an important tool to study saline-evaporitic formations affected by major geological structures, such as the Barbastro-Balaguer Anticline. The new TDEM structural and geoelectrical data would be useful for allocating and designing mining drills at a region of interest for the exploitation of potassium salts in new mining projects.  
Conceptualization, D.P.; methodology, D.P., J.C. and P.C.; software, J.C. and P.C.; validation, D.P., J.C. and P.C.; formal analysis, D.P. and J.C.; investigation, D.P.; resources, D.P., J.C. and P.C.; writing--original draft preparation, D.P.; writing--review and editing, D.P., J.C., P.C. and J.L.H.-P.; visualization, J.C. and J.L.H.-P.; supervision, D.P.; project administration, D.P.; funding acquisition, D.P., J.C. and P.C. All authors have read and agreed to the published version of the manuscript.  
Figure 13: 3D representation of the hanging wall and footwall of the saline-evaporitic part of the Barbastro Formation in the survey area, including TDEM position and results.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not available.  
**Acknowledgments:** The authors sincerely appreciate the assistance of reviewers for their comments and support on this paper, especially [PERSON].  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Prediction of Total Organic Carbon Content in Shale Based on PCA-PSO-XGBoost
## 2 Materials and Methods
### Correlation Analysis  
Correlation analysis is the key to data characterization. Its main purpose is to analyze the degree of linear correlation between continuous variables and express it with statistical indicators. To more accurately describe the relationship between TOC content and logging data, quantitative correlation analysis was performed by calculating [PERSON]'s correlation coefficient and plotting the heat map.  
Assuming that any two sets of TOC content and logging data are \(X\) and \(Y\), respectively, after standardization, [PERSON]'s correlation coefficient value \(r\) can be calculated, as shown in Equation (1). The value of \(r\) ranges from \(-1\) to \(1\). An absolute \(r\) value closer to \(1\) indicates that the correlation between the two variables is higher and the correlation is better. At the same time, based on \(r\), [PERSON]'s correlation coefficient matrix can be constructed. Then, according to the size of the color filling, the intuitive correlation heat map can be generated [16].  
\[r(X,Y)=\frac{\text{cov}(X,Y)}{\sqrt{\text{var}(X)\text{var}(Y)}} \tag{1}\]  
where \(\text{cov}(X,Y)\) is the covariance of \(X\) and \(Y\); \(\text{var}(X)\) and \(\text{var}(Y)\) are the variances of \(X\) and \(Y\), respectively.
## 2 Materials and Methods
### Testing the Antimicrobial Activity of Viridiflorol  
The antifungal activity of viridiflorol was determined using a growth assay, where the plant pathogenic fungus _C. truncatum_ (syn. _Colletotrichum capsici_) was grown in liquid medium containing different concentrations of the compound. Viridiflorol, purchased by BOC Sciences (NY, USA), was initially diluted in methanol to a stock solution of 10 mg/mL. The stock solution was used to prepare the working solutions, which contained either viridiflorol in concentrations of 500, 1000, 2000 or 5000 ug/mL, or methanol in the respective concentrations that served as control. In the wells of a 96-well microtiter plate, 25 \(\upmu\)L of each working solution and 225 \(\upmu\)L Potato Dextrose Broth (PDB) were added, ending up to the test concentrations of 50, 100, 200 or 500 ug/mL viridiflorol or methanol. Hygromycin (400 ug/mL) was added in some wells instead of viridiflorol or methanol, as an antifungal drug that inhibits growth of the respective pathogens. The wells were inoculated with spores from _C. truncatum_ isolate CP2304 (4000 spores) from the fungal collection in Copenhagen. The plate was incubated on a shaker (100 rpm) for 6 days at room temperature and pictures were taken to identify fungal growth. Each treatment was performed in four replicates and the whole assay was done twice.
## 4 Analysis of Numerical Simulation Results
### Influence of Different Aircraft on Tunnel Structure
The vibration frequency of the tunnel structure varies with the taxiing position of the aircraft, as clearly shown in Figure 10. After the aircraft load is directly applied to the tunnel, the vertical acceleration abruptly changes, causing a greater impact on the structure. This indicates that the effect of aircraft taxiing on the runway substructure cannot be neglected. The top plate of the tunnel structure has the highest vertical acceleration variation of up to 259%, indicating that this section is more susceptible to impact and damage than the other parts. Because the taxiing weight of the A380-800 aircraft is maximum, and considering the most unfavorable conditions, the load of this aircraft is selected for succeeding studies.
# Research on Obstacle Avoidance Replanning and Trajectory Tracking Control Driverless Ferry Vehicles
## 5 Simulation Analysis
### Coordinated Control of Simulation Conditions
#### 5.3.2 Condition 2  
Under Condition 2, when unknown obstacles appear in the second half of the road, verification of obstacle avoidance replanning and trajectory tracking control of driverless ferry vehicles under double-shifted line conditions under coordinated control of horizontal and longitudinal controllers take place when the road adhesion coefficient is 0.8. Figure 25 illustrates the control algorithm's simulation effect.  
From Figure 25a, we can see that, when the obstacle is in the second half of the road and there is no coordinated control in the horizontal and longitudinal directions, although the driverless ferry vehicle can avoid the obstacle, the tracking accuracy of the reference trajectory is poor. In horizontal and longitudinal coordinated control, the accuracy of the obstacle avoidance replanning and trajectory tracking reference trajectory of the driverless ferry vehicle is higher. From Figure 25b,c, we can see that the stability of the coordinated control of the horizontal and longitudinal motion of the driverless ferry vehicle is better than that of the uncoordinated control, and the precision of tracking the anticipated yaw angle is also effectively improved. From Figure 25d, observably, the greatest transverse position error is already close to 1.8 m when the driverless ferry uses uncoordinated control in the transverse and longitudinal directions. In contrast, the maximum transverse position error is reduced to about 0.7 m when using transverse-longitudinal coordinated control, which  
Figure 24: Comparison of simulation results of control algorithms when obstacles are in front. (**a**) Comparison between obstacle avoidance replanning and trajectory tracking. (**b**) Comparison of front wheel angle. (**c**) Comparison of yaw angle tracking effect. (**d**) Comparison of lateral position error.  
is 61.11% lower than the tracking error with transverse-longitudinal uncoordinated control. From the above analysis, we can see that the transverse and longitudinal coordinated control strategy designed has significantly improved the tracking effect of the ferry in obstacle avoidance replanning and trajectory tracking control. This demonstrates how well the coordinated control method works on both the horizontal and vertical axes.
## 6 Results  
### Correctness Analysis  
* Correctness of the result ciphertext _res_ from \(CSP_{1}\). Given the ciphertexts of the result _res_ and the input vector \(\mathbf{c}\), for \(i\in[d]\), it holds that: \[c_{i} = (u_{i},v_{i})=(a_{i}+pe_{i}+x_{i},-a_{i})\] (14) \[\textit{res}_{i} = \left(\sum_{j=1}^{d}f_{i_{j}}u_{j},\sum_{j=1}^{d}f_{i_{j}}v_{j}\right)\] (15) \[= \left(\sum_{j=1}^{d}f_{i_{j}}a_{i}+\sum_{j=1}^{d}f_{i_{j}}pe_{i}+ \sum_{j=1}^{d}f_{i_{j}}x_{i_{j}},-\sum_{j=1}^{d}f_{i_{j}}a_{i}\right)\]Let \(\sum_{j=1}^{d}f_{ij}a_{i}=\delta_{i}\) and \(\sum_{j=1}^{d}f_{i,j}e_{i}=\delta_{i}\), such that the following holds: \[res_{i} = \left(\delta_{i}s+p\delta_{i}+\sum_{j=1}^{d}f_{ij}x_{i},-\delta_{i}\right)\] (16) \[\rightarrow Enc\left(sk,\sum_{j=1}^{d}f_{i,j}x_{i}\right)\] We can see that the result \(res\) corresponds to the multiplication of matrix \(\mathbf{F}\) and vector \(x\) in plaintext.
* Correctness of decryption for the result ciphertext \(res\). Given the ciphertext of the result \(res\), where \[res_{i}=\left(\sum_{j=1}^{d}f_{ij}u_{j},\sum_{j=1}^{d}f_{ij}v_{j}\right)= \left(\delta_{i}s+p\delta_{i}+\sum_{j=1}^{d}f_{ij}x_{i},-\delta_{i}\right)\] (17) for \(i\in[d]\), it holds that: \[y_{i} = \sum_{j=1}^{d}f_{i,j}u_{j}+\left(\sum_{j=1}^{d}f_{i,j}v_{j}\right) \cdot sk\ mod\ p\] (18) \[= \delta_{i}s+p\delta_{i}+\sum_{j=1}^{d}f_{i,j}x_{i}-\delta_{i}s\ mod\ p\] \[= \sum_{j=1}^{d}f_{i,j}x_{i}\] We can see that the decryption for the result ciphertext is correct.
* Correctness of public verification. Given the input vector ciphertext \(c\), the result ciphertext \(res\) and the public verification data \(ver=(T,\mathbf{S})=((g^{t_{1}},\ldots,g^{t_{m}}),(g^{s_{1}},\ldots,g^{s_{d}}))\), where \[c_{i}=(u_{i},v_{i})\] (19) \[res_{i}=\left(\sum_{j=1}^{d}f_{i,j}u_{j},\sum_{j=1}^{d}f_{i,j}v_{j}\right)\] (20) \[s_{i}=\sum_{j=1}^{m}t_{j}F_{j,i}\] (21) for \(i\in[d]\), it convinces the public to accept the result if and only if: \[\prod_{i=1}^{d}g^{s_{i}\cdot c_{i}}=\prod_{i=1}^{d}g^{t_{i}\cdot res_{i}}.\] (22) The left side is: \[\prod_{i=1}^{d}g^{s_{i}\cdot c_{i}} = \left(\prod_{i=1}^{d}g^{s_{i}\cdot u_{i}}\prod_{i=1}^{d}g^{s_{i} \cdot v_{i}}\right)\] (23) \[= \left(\prod_{i=1}^{d}g^{(\sum_{j=1}^{m}t_{j}f_{j,i})\cdot u_{i}},\prod_{i=1}^{d}g^{\sum_{j=1}^{m}t_{j}f_{j,i}\cdot v_{j}}\right)\] \[= (g^{\sum_{j=1}^{m}t_{j}f_{j,i}u_{j}},\ g^{\sum_{j=1}^{m}t_{j}f_{j, i}u_{j}})\]and the right side is: \[\prod_{i=1}^{d}g^{t_{i}\cdot\text{res}_{i}} = \left(\prod_{i=1}^{d}g^{t_{i}\cdot\sum_{j=1}^{d}f_{i_{j}}u_{j}}, \prod_{i=1}^{d}g^{t_{i}\cdot\sum_{j=1}^{d}f_{i_{j}}v_{j}}\right)\] (24) \[= \left(g^{\sum_{i=1}^{m}\sum_{j=1}^{d}t_{i}f_{i_{j}}u_{j}},g^{\sum _{i=1}^{m}\sum_{j=1}^{d}t_{i}f_{i_{j}}v_{j}}\right)\] If \(\mathbf{y}=\mathbf{F}\cdot\mathbf{x}\), then Equation (22) holds.
* Correctness of private verification. The correctness of private verification is consistent with the description provided in Section 3.4.