esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed Scheme
### Adjusting the Anonymity Level with \(T\)
#### 4.4.1 Definition of the Parameter \(T\)  
Formally, \(T\) can be interpreted as a tuning variable that influences the mathematical properties of the signature \(\sigma\). The choice of the value of \(T\) directly affects the operations involved in generating and verifying the signature. For example,  
* In a scenario with large \(T\), the signature becomes more difficult to correlate with the message \(M\) or the signer. This implies the use of increases the resistance against data correlation.
* In a scenario with a small \(T\), certain elements of the signature may intentionally reveal additional information, such as the identity of the signer or details about the structure of the message \(M\), depending on the application requirements.
# Joint Image Compression and Encryption Using IWT with SPIHT, Kd-Tree and Chaotic Maps
## 5 Experimental Results and Discussion
### Resistance to Statistical Attacks
#### 5.5.1 Histogram Analysis  
Figure 8 shows the \"Lena\" image histograms and, on the right, the corresponding \"Lena\" cipher image. Figure 8 shows that the histogram of the corresponding cipher image is almost uniformly distributed; therefore, this technique can withstand all types of statistical attacks. We also analyze the distribution of the pixel values of encrypted images through the chi-square test. The chi-square value was calculate using Equation (17).  
\[\chi^{2}=\sum_{i=0}^{255}\frac{\left(O_{i}-E\right)^{2}}{E} \tag{17}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & \multicolumn{2}{c}{**Decryption with**} & \multicolumn{2}{c}{**Decryption with**} & \multicolumn{2}{c}{**Decryption**} & \multicolumn{2}{c}{**Decryption**} \\  & \multicolumn{2}{c}{**Difference in the**} & \multicolumn{2}{c}{**1-bit Key**} & \multicolumn{2}{c}{**with 1-bit Key**} & \multicolumn{2}{c}{**with 1-bit Key**} \\
**Image** & \multicolumn{2}{c}{**Permutations of**} & \multicolumn{2}{c}{**Difference in the**} & \multicolumn{2}{c}{**Difference in**} & \multicolumn{2}{c}{**Difference in**} \\  & \multicolumn{2}{c}{**Lower 64 \(\times\) 64**} & \multicolumn{2}{c}{**SPIHT Output**} & \multicolumn{2}{c}{**Logistic**} & \multicolumn{2}{c}{**Quadratic**} \\  & \multicolumn{2}{c}{**Wavelet**} & \multicolumn{2}{c}{**Coefficients**} & \multicolumn{2}{c}{**with Kd-Tree**} & \multicolumn{2}{c}{**Chaotic Map**} & \multicolumn{2}{c}{**Chaotic Map**} \\ \hline \multirow{2}{*}{Lena} & PSNR & 9.5241 & 3.0214 & 2.0135 & 2.8742 \\  & MSSIM & 0.1472 & 0.0128 & 0.0214 & 0.1251 \\ \hline \multirow{2}{*}{Baboon} & PSNR & 7.3254 & 4.5210 & 2.0176 & 2.6522 \\  & MSSIM & 0.0713 & 0.0843 & 0.0421 & 0.2414 \\ \hline \multirow{2}{*}{Pepper} & PSNR & 7.3617 & 3.2412 & 2.0187 & 2.6415 \\  & MSSIM & 0.1382 & 0.0421 & 0.0342 & 0.1453 \\ \hline \multirow{2}{*}{Airplane} & PSNR & 8.3692 & 3.0124 & 2.0873 & 2.3541 \\  & MSSIM & 0.1426 & 0.0621 & 0.0462 & 0.3524 \\ \hline \multirow{2}{*}{House} & PSNR & 7.3641 & 2.0146 & 3.5214 & 2.6521 \\  & MSSIM & 0.1536 & 0.0143 & 0.0517 & 0.3245 \\ \hline \multirow{2}{*}{Lake} & PSNR & 0.7125 & 3.0241 & 2.0364 & 2.6521 \\  & MSSIM & 0.1285 & 0.0142 & 0.0394 & 0.0124 \\ \hline \end{tabular}
\end{table}
Table 3: PSNR and MSSIM of the decrypted image with wrong key.  
Here, \(O_{i}\) is the observed frequency of the ith pixel \(i\) (\(0\leq i\leq 255\)), E is the expected frequency of pixel value \(i\), and \(v_{0}=(m\times n)/256\). The results of the \(\chi^{2}\)-test for the 6 pairs of plain/encrypted test images are presented in Table 4:  
From Table 4, for the six images, the values of the \(\chi^{2}\)-test are all lower than the critical value. Thus, we conclude that the distribution of pixel values in the encrypted image is uniform. That means that the proposed scheme can resist statistical attacks.
## 3 Experimental Results
### Robotic Arm Joining/Loading Experiment  
For the above experiment, the robotic arm was in a state of no-load. In this stage, in order to test the robustness of the trajectory tracking function of each rotary axis after a load was added, a PET bottle was filled with water and installed on the upper arm and the robotic arm with a weight of 0.6 kg and 0.9 kg, respectively, to simulate the load effect generated when the arm was put on by a human, as shown in Figure 22. To test the stability of the controller parameters obtained during loading, the controller parameters of each axis were the same as those shown in Table 9 when the robot was not loaded. The tracking and error results of the experiment with loading according to Table 9 are shown in Figures 23-26.  
Figure 20: Trajectory 1—four axes—LMS-PID—curve charts of the controller parameters returned by tracking the 20 th cycle.  
Figure 21: Trajectory 2—four axes—LMS-PID—curve charts of the controller parameters returned by tracking the 20 th cycle.  
Figure 22: Schematic diagram of the robotic arm in the loading experiment.  
Figure 23: Trajectory 1—1 st-axis (load-on) ILC trial 1 and trial 12 tracking and error diagrams.  
Figure 24: _Cont._  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**Rehabilitation Trajectory**} & \multicolumn{1}{c}{**1 st Axis**} & \multicolumn{1}{c}{**2 nd Axis**} & \multicolumn{1}{c}{**3 rd Axis**} & \multicolumn{1}{c}{**4 th Axis**} \\ \hline Initial state & 11 (degree) & 33 (degree) & 17.5 (degree) & 79 (degree) \\ \hline \multirow{3}{*}{Internal controller parameters} & \(k_{p}=0.055\) & \(k_{p}=0.485\) & \(k_{p}=0.65\) & \(k_{p}=0.365\) \\  & \(k_{d}=0.0045\) & \(k_{d}=0.015\) & \(k_{d}=0.016\) & \(k_{d}=0.006\) \\  & \(k_{i}=0.003\) & \(k_{i}=0.01\) & \(k_{i}=0.02\) & \(k_{i}=0.015\) \\ \hline Learning rate & L = 0.2 & L = 0.1 & L = 0.2 & L = 0.1 \\ \hline Learning period & & & 20 & \\ \hline \hline \end{tabular}
\end{table}
Table 9: Trajectory 1 (load-on) ILC controller parameters table.  
Figure 23: Trajectory 1—1 st-axis (load-on) ILC trial 1 and trial 12 tracking and error diagrams.  
Figure 24: Trajectory 1—2 nd-axis (load-on) ILC trial 1 and trial 20 tracking and error diagrams.  
Figure 25: Trajectory 1—3 rd-axis (load-on) ILC trial 1 and trial 20 tracking and error diagrams.  
The above experimental results were compiled as shown in Table 10. From Table 10, it is observed that for the first axis, before learning control, the angle RMSE increased by 0.1203\({}^{\circ}\) after adding the load, and for the second axis, the angle RMSE decreased by 0.087\({}^{\circ}\) after adding the load. After adding the load, the trajectory of all axes of the robot arm is slightly affected, but the overall trajectory performance still maintains a certain degree of accuracy, and the trajectory results of all axes are significantly improved after applying ILC.
## 4 Evaluation of the Undrained Shear Strength Based on Empirical Relationships  
The variables from seismic dilatometer tests used in the evaluation of the undrained shear strength \(\tau_{fu}\) are presented in Table 4.  
In order to compare the undrained shear strength evaluated on the basis of empirical relationships with undrained shear strength from CIU triaxial tests obtained for a given depth, the measurements from seismic dilatometer tests were subjected to the averaging procedure. The applied procedure consisted of calculating the mean value of pressures \(p_{o}\) and \(p_{1}\), shear wave velocity \(V_{s}\), and index parameter \(K_{D}\). The mean value for each selected depth came from three to four dilatometer tests (Bielany site is an exception) located in a given test node. Because CIU triaxial tests were made by using core samples of about 0.4 m in length, the mean was taken from three, nine, or twelve readings at a given depth and \(\pm\)0.20 m.  
Evaluation of the undrained shear strength \(\tau_{fu}\) in preconsolidated cohesive soils from seismic dilatometer tests was performed using single- and multi-factor empirical relationships proposed by: [PERSON] (4) [3], [PERSON] et al. (6) [38], and [PERSON] (7) [39], as well as the three-factor relationship of [PERSON] [41] using Equation (9), the empirical coefficients determined by [PERSON] et al. [48] and the two-factor relationship of [PERSON] [42] using Equation (10). For each relationship, the maximum relative error max \(RE\) (Table 5) for the particular test site was selected from the values of relative errors \(RE\) calculated according to the formula:  
\[RE=\left|\left(d^{(p)}-y^{(p)}\right)/d^{(p)}\right|\cdot 100\% \tag{11}\]  
where \(p\) is the case number, \(p\in\{1,\ldots,P\}\), \(P\) is the number of cases, \(d^{(p)}\) is the measured value, and \(y^{(p)}\) is the calculated value. Using the max \(RE\) obtained for five locations the mean value of max \(RE\) was calculated for each relationship. Based on all test sites the mean value of the \(RE\) was also calculated for a particular relationship (Table 5). Analysis of the calculation results indicated quite high values of max \(RE\) obtained for the undrained shear strength evaluated on the basis of the single-factor empirical relationship of [PERSON] ranging between 21.3-43.9%. The max \(RE\) values calculated for \(\tau_{fu}\) evaluated from the [PERSON] et al. relationship with the bearing capacity factor \(N_{\mathrm{C}}\) determined for Pleistocene and Pliocene clays ranged between 6.0-47.6%. The max \(RE\) values calculated for undrained shear strength evaluated from the [PERSON] and [PERSON] relationship with the bearing capacity factor \(N_{D}\) determined for Pleistocene and Pliocene clays were in the range of 2.8-43.2%. The mean values of the max \(RE\) for the [PERSON], [PERSON] et al.,  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Single-Factor Relationships by:**} & \multicolumn{3}{c}{**Multi-Factor Relationship by:**} \\  & & **[PERSON] [3]** & **[PERSON] et al.
###### Abstract  
There has been very limited research conducted to predict rental prices in the German real estate market using an AI-based approach. From a general perspective, conventional approaches struggle to handle large amounts of data and fail to consider the numerous elements that affect rental prices. The absence of sophisticated, data-driven analytical tools further complicates this situation, impeding stakeholders, such as tenants, landlords, real estate agents, and the government, from obtaining the accurate insights necessary for making well-informed decisions in this area. This paper applies novel machine learning (ML) approaches, including ensemble techniques, neural networks, linear regression (LR), and tree-based algorithms, specifically designed for forecasting rental prices in Munich. To ensure accuracy and reliability, the performance of these models is evaluated using the R\({}^{2}\) score and root mean squared error (RMSE). The study provides two feature sets for model comparison, selected by particle swarm optimisation (PSO) and CatBoost. These two feature selection methods identify significant variables based on different mechanisms, such as seeking the optimal solution with an objective function and converting categorical features into target statistics (TSs) to address high-dimensional issues. These methods are ideal for this German dataset, which contains 49 features. Testing the performance of 10 ML algorithms on two sets helps validate the robustness and efficacy of the AI-based approach utilising the PyTorch framework. The findings illustrate that ML models combined with PyTorch-based neural networks (PNNs) demonstrate high accuracy compared to standalone ML models, regardless of feature changes. The improved performance indicates that utilising the PyTorch framework for predictive tasks is advantageous, as evidenced by a statistical significance test in terms of both R\({}^{2}\) and RMSE (\(p\)-values < 0.001). The integration results display outstanding accuracy, averaging 90% across both feature sets. Particularly, the XGB model, which exhibited the lowest performance among all models in both sets, significantly improved from 0.8903 to 0.9097 in set 1 and from 0.8717 to 0.9022 in set 2 after being combined with the PNN. These results showcase the efficacy of using the PyTorch framework, enhancing the precision and reliability of the ML models in predicting the dynamic real estate market. Given that this study applies two feature sets and demonstrates consistent performance across sets with varying characteristics, the methodology may be applied to other locations. By offering accurate projections, it aids investors, renters, property managers, and regulators in facilitating better decision-making in the real estate sector.  
P yTorch; neural network; machine learning; feature selection; PSO; Germany; comparative analysis; property valuation; artificial intelligence
## 2 Materials and Methods
### Methods  
The absorption spectra of two Ag nanostructures (Ag NPs and Ag NWs) and three sample films (Alq\({}_{3}\) (pure), Alq\({}_{3}\) (Ag NPs) and Alq\({}_{3}\) (Ag NWs)) were measured by a UV-visible spectrometer ([PERSON], TU-1810 PC). PL spectra of sample films were recorded by using a fiber optic spectrometer (Ocean Optics 4000) under an excitation of 405 nm (\(\pm\)5 nm) continuous laser diode (OM-12A405). Before the excitation beam reaches the sample, a switchable aperture is inserted to reduce the spot diameter to less than 2 mm, thus ensuring that the PL collection area is the same for all samples. In temperature-dependent PL measurements, the temperature was controlled over the range of 298-393 K by a temperature controller (UDIAN, AI-708). In EI-dependent PL measurements, the EI varied from 0.5 to 19.8 mW/cm\({}^{2}\) through combinations of optical filters with different transmitances (from 2.6% to 56.3%). Detection angle-dependent PL measurements of Alq(Ag NWs) were performed with the angle between the detection direction and the normal line ranges from \(-150^{\circ}\) to \(150^{\circ}\), in steps of \(15^{\circ}\).
###### Abstract  
The ideal mechanical shear properties and sliding characteristics of c-ZrO\({}_{2}\)(001)/\(\alpha\)-Al\({}_{2}\)O\({}_{3}\)(1\(\overline{1}\)02) interfaces are examined through simulated shear deformations using first-principles calculations. We investigate three types of interface models, abbreviated as O-, 2O-, and Zr- models, when shear displacements are applied along the \(<\)1\(\overline{1}\)01\(>\) and \(<\)1\(\overline{1}\)02\(>\) directions of their Al\({}_{2}\)O\({}_{3}\) lattice. The theoretical shear strength and unstable stacking energy of the ZrO\({}_{2}\)/Al\({}_{2}\)O\({}_{3}\) interfaces are discussed. In the process of the ZrO\({}_{2}\)/Al\({}_{2}\)O\({}_{3}\) interfacial shear deformation, we find that the sliding of the ZrO\({}_{2}\) atomic layers, accompanied by the shifting of Zr atoms and Al atoms near the interface, plays a dominant role; in addition, the ZrO\({}_{2}\)/Al\({}_{2}\)O\({}_{3}\) interfaces fail within the ZrO\({}_{2}\) atomic layer. Among the three models, the O- model exhibits the strongest shear resistance; whereas the Zr- model is the most prone to slip. Furthermore, their tensile and shear strengths are compared; moreover, their potential applications are provided.  
first-principles calculations  
by [PERSON] et al. [22] using first-principles calculations. The ideal mechanical strength of the ZrO\({}_{2}\)(111)/Ni(111) interface was calculated through simulated tensile and shear deformations using first-principles calculations [23]. However, despite these previous efforts, the mechanical strength and shear behavior of ZrO\({}_{2}\)/Al\({}_{2}\)O\({}_{3}\) interfaces under first-principles shear simulation are still not well examined. The difficulty of this type of research is mainly due to the complexity of the ceramic/ceramic interface structures and the large number of computations involved in the shear simulation process.  
In this study, shear simulations of c-ZrO\({}_{2}\)(001)/\(\alpha\)-Al\({}_{2}\)O\({}_{3}\)(1\(\overline{1}\)02) interfaces were carried out using first-principles calculations. The shear directions of the three models were applied along the \(<\)1\(\overline{1}\)01\(>\) and \(<\)1\(\overline{1}\)\(\overline{2}\)0\(>\) directions of the Al\({}_{2}\)O\({}_{3}\) lattice. For all three models, we examined the ideal shear strength, unstable stacking energy, and evolution of the atomic structures to clarify the sliding mechanisms of the c-ZrO\({}_{2}\)(001)/\(\alpha\)-Al\({}_{2}\)O\({}_{3}\)(1\(\overline{1}\)02) interfaces. With these results, we further compared their shear and tensile properties to clarify the overall features of the c-ZrO\({}_{2}\)(001)/\(\alpha\)-Al\({}_{2}\)O\({}_{3}\)(1\(\overline{1}\)02) system.
## 4 The Main Idea  
Equation (2) yields a non-linear response that is very high only for signals that match; in other words, there is a high fractional correlation; we exploited this characteristic to derive an edge filter for images, which will be described in the remainder of this paper. Since the core of the proposed filter is Equation (2), we have named our proposed method  
Figure 1: Comparison of the response of watermarking detection variables between the cross-correlation-based detector and the fractional-correlation-based detector. (**a**) The watermarked image. (**b**) Detection variable for the cross-correlation-based detector. (**c**) detection variable for the fractional-correlation-based detector.  
the \"Fractional Cross-Correlation Filter\" (FCCF), since the equation that computes the output image comes from the principles of fractional calculus.  
We define the FCCF as a sliding window filter, whose input is a grayscale image and which outputs the corresponding edge map. The principle of operation is similar to other sliding window approaches, as described in Algorithm 1.  
```
Data: Input image (\(I\)), Filter's Kernel (\(K\)), Parameter \(K_{p}\). // slide the window across rows and columns for\(r\) in Rows do for\(c\) in Cols do  Get the window \(x\) centered in \((r,c)\);  Compute \(\hat{x}_{m,n}\) using Equation (3);  Compute the decision threshold \(Th\) using Equation (8); if\(\hat{x}_{m,n}\geq Th\)then \(E(m,n)=1\) // the pixel at \((m,n)\) belongs to an edge  end if else \(E(m,n)=0\) // otherwise  end if  end for Result: Edge map of image (\(E\))
```  
**Algorithm 1**Computing the edge map of an image following the FCCF approach.  
One of the main components of the filter is its kernel, which we will develop and discuss in the next section.
# Interactions in Augmented and Mixed Reality: An Overview
## 3 Research Results
### Visual-Based Modality
#### 3.1.1 Gesture-Based
They accomplished these features by pre-processing the image to segment the hand from the background and matching the normalized binary vector outcome with gestures already stored in a database. Some of the issues to be further examined as regards the hand-gestures interaction method are (a) the efficiency of gesture recognition algorithms, (b) the efficiency on low contrast environments, (c) high consumption of computing resources, (d) lack of haptic feedback, and (e) hand occlusion with the augmented scene. However, the applications of hand gestures in real-life scenarios are unlimited due to the naturalness of the interaction. MixFab [89] is an example of how this interaction method can be helpful to non-expert users and allow them to perform tasks in a 3D environment with ease. The authors, [PERSON] et al., present this application prototype facilitating the manipulation of 3D objects by hand gestures and have them 3D printed without the need for any 3D modelling skills. [PERSON] and [PERSON] [90] utilized hand-gesture interactions to create interactive experiences for enhancing online language and cultural learning. The _virtual controls handling_ method of interaction refers to any interactivity between the user and a virtual control panel. In [91], [PERSON] et al. demonstrated a prototype system that projects virtual controls onto any surface, using finger-tracking techniques to understand the user's intention of interaction with the virtual control panel. One of the benefits of the virtual controls interaction method is that any control component (virtual button, display, etc.) is rearrangeable. Therefore, its ergonomic design can vary to satisfy different needs and users. Besides hand gestures, they used 3D tabletop registration to place the virtual objects at any location on their tabletop.
# Multi-Lidar System Localization and Mapping with Online Calibration
###### Abstract  
Currently, the demand for automobiles is increasing, and daily travel is increasingly reliant on cars. However, accompanying this trend are escalating traffic safety issues. Surveys indicate that most traffic accidents stem from driver errors, both intentional and unintentional. Consequently, within the framework of vehicular intelligence, intelligent driving uses computer software to assist drivers, thereby reducing the likelihood of road safety incidents and traffic accidents. Lidar, an essential facet of perception technology, plays an important role in vehicle intelligent driving. In real-world driving scenarios, the detection range of a single laser radar is limited. Multiple laser radars can improve the detection range and point density, effectively mitigating state estimation degradation in unstructured environments. This, in turn, enhances the precision and accuracy of synchronous positioning and mapping. Nonetheless, the relationship governing pose transformation between multiple lidars is intricate. Over extended periods, perturbations arising from vibrations, temperature fluctuations, or collisions can compromise the initially converged external parameters. In view of these concerns, this paper introduces a system capable of concurrent multi-lidar positioning and mapping, as well as real-time online external parameter calibration. The method first preprocesses the original measurement data, extracts linear and planar features, and rectifies motion distortion. Subsequently, leveraging degradation factors, the convergence of the multi-lidar external parameters is detected in real time. When deterioration in external parameters is identified, the local map of the main laser radar and the feature point cloud of the auxiliary laser radar are associated to realize online calibration. This is succeeded by frame-to-frame matching according to the converged external parameters, culminating in laser odometer computation. Introducing ground constraints and loop closure detection constraints in the back-end optimization effectuates global estimated pose rectification. Concurrently, the feature point cloud is aligned with the global map, and map update is completed. Finally, experimental validation is conducted on data acquired from Chang'an University to substantiate the system's online calibration and positioning mapping accuracy, robustness, and real-time performance.  
SLAM; multiple lidars; calibration +
Footnote †: journal: Applied Sciences