esa-sceva/satcom-chunk-collection huggingface dataset:
# Mission Planning of UAVs and UGV for Building Inspection in Rural Area
## 4 Simulation Studies
### Optimization Results of ACO-GA  
The locations of 46 task points are randomly generated as objects for building inspection in a certain area. The coordinates of these task points, along with the required operation time for each point, are listed in Table 3. The centroid method was employed to compute the location of the parking point. The initial location of the parking point for UGV, calculated using Equation (1), is determined as: (1206.5, 1103.9).  
Based on the coordinates of the task points and the parking point provided above, the mission planning scheme is solved using ACO-GA proposed in this study. The distribution map of the UAV task planning results obtained is shown in Figure 7. In each iteration, the UAV with the longest time consumption is selected as the fitness value for that iteration.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Parameter Values** \\ \hline Numbers of UAVs \(m\) & 5 \\ Maximum time of flight \(t_{max}\) & 900 s \\ Flight speed \(v\) & 15 m/s \\ Number of task points \(n\) & 46 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameter settings of building inspection tasks.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Task** & **Coordinates** & **Operating Time** & **Task** & **Coordinates** & **Operating Time** & **Task** & **Coordinates** & **Operating Time** \\ \hline
1 & (6.3, 928.1) & 75 s & 17 & (1155.5, 790.9) & 90 s & 33 & (1487.8, 1049.2) & 46 s \\
2 & (72.5, 708.3) & 61 s & 18  
The fitness change curve is depicted in Figure 8. The optimization rate has improved from 895.61 s to 785.88 s, resulting in a 12.25% improvement. From Figure 7, it can be observed that the route map optimized using the ACO-GA proposed in this study is highly reasonable. Each task point is assigned to a corresponding UAV for operation, demonstrating a clear division of labor and high efficiency. Additionally, each UAV does not need to return to the parking point for recharging but can directly return after completing the task. From the change curve shown in Figure 8, it can be seen that the optimization rate does not seem to be very high. This is because the operation time of UAVs at each task point is fixed, and this part of the time consumption cannot be optimized. Only the flight time of UAVs in the air can be optimized, resulting in an improvement of over one hundred seconds in flight time. Considering only the flight time, the optimization degree is quite significant.  
The operation time, flight time, and total time consumed by each UAV are presented in Table 4. From the data in Table 4, it can be observed that the proportion of flight time to the total time consumption of UAVs is relatively small. Moreover, the total time consumption of each UAV is relatively uniform. If a UAV is assigned a longer operation time at task points, its flight time is shorter, thereby achieving a relatively balanced workload distribution among UAVs.  
Figure 8: Fitness change curve of ACO-GA.  
Figure 7: Distribution of UAVs in inspection task.
# Ultra-Wideband Antenna Design for 5G NR Using the Bezier Search Differential Evolution Algorithm
## 2 Materials and Methods
### Bezier Search Differential Evolution Algorithm Description
The above algorithms competed with other algorithms and were reported as the top methods according to the CEC2014 criteria. The computational complexity of BeSD is generally superior to that of CRMLS, MVO, WA, SHADE, and LSHADE according to [3]. Furthermore, according to the numerical results presented in ([3], Table 5), the complexity of the algorithm \(Ctime\) in [3] of BeSD was better than RMLSP and MVO for problems with \(\{30,50,100\}\) dimensions. Additionally, BeSD obtained better \(Ctime\) than WA for \(\{100\}\) dimensions. BeSD also obtained better results for \(Ctime\) than LSHADE for all dimensions. In terms of computational time, BeSD has some advantages over other algorithms. In contrast to SHADE and LSHADE, BeSD does not use sophisticated statistical distributions to calculate the value of the mutation parameter, \(F\). The BeSD method for producing \(F\) is quick and effective. Unlike SHADE and LSHADE, BeSD does not use an archiving mechanism. As a result, BeSD requires comparatively little memory.
# A Reversible Data-Hiding Method with Prediction-Error Expansion in Compressible Encrypted Images
## 4 Experimental Results
### Data-Hiding Capacity and Marked-Image Quality  
The data-hiding capacity and marked-image quality of the proposed method were compared with those of the RDH-EtC [30] and RDH-MSB [29] methods. The RDH-MSB method could conduct decryption only after data extraction, so it did not derive the marked images.  
Figure 13 illustrate the hiding capacity for all the test images of each database. With the proposed method, higher hiding capacity could be attained as the value of \(L\) increased. In all databases, the proposed method achieved a high hiding capacity of around 1 bpp in the case of \(L=10\). With respect to the Kodak database, the hiding capacity of our method was around 0.82 bpp on average (\(L=10\)). In contrast, the hiding capacity of the RDH-EtC method was 0.04 bpp on average. The RDH-MSB method achieved a hiding capacity of 1.64 bpp on average, which was much higher than that of the proposed method. However, despite the high capacity, the compression performance of marked encrypted images is not considered in this method, and the order of restoration is completely fixed without any flexibility. Figure 13c,d show that the proposed method further achieved higher hiding capacity in the case of using a larger image.  
Figure 14 shows examples of marked images obtained with the proposed method with \(L=10\). The RDH-MSB method could not conduct decryption before data extraction; thus, marked images could not be obtained as described above. Figures 15 and 16 exhibit the marked-image quality of the proposed and RDH-EtC methods, respectively. It is clear that  
Figure 13: Data hiding capacity of proposed, RDH-EtC [30] and RDH-MSB [29] methods. (**a**) Kodim4; (**b**) peppar; (**c**) hic5 (1/16); (**d**) hic5 (original).  
both the PSNR and SSIM values of the proposed method decreased as the value of \(L\), i.e., the hiding capacity, increased. In addition, the values of the proposed method were lower than those of the RDH-EtC method because there was a trade-off between hiding capacity and marked-image quality. In the Kodak database, the proposed method achieved a PSNR of 27.54 dB and SSIM of 0.8960 with a hiding capacity of 0.82 bpp on average (\(L=10\)). In contrast, the PSNR and SSIM values of the RDH-EtC method were 49.14 dB and 0.9989, respectively, with a hiding capacity of 0.04 bpp. As shown in Figures 15c,d and 16c,d, the marked-image quality was affected by image size. The influence on SSIM was particularly large; the SSIM for the images with the original size was much higher than that for the 1/16 images.  
Figure 14: Marked images (\(L=10\)). (**a**) Kodim4; (**b**) pepppar; (**c**) hhc5 (1/16); (**d**) hhc5 (original).  
Figure 15: \(Cont.\)
## 6 Methodology of Sample Implementation
### Implementation of Panoramic Video Broadcast
#### 6.2.3 End-to-End System Delay Analysis  
Based on the panoramic video live broadcasting system, we conducted an end-to-end delay analysis, and the system set the duration of each frame at 30 ms.  
(1) The camera captured and cached one frame, resulting in a 30 ms delay;  
(2) The splicing box could collect up to three cached frames and generated a maximum delay of 90 ms;  
(3) The maximum splicing buffer was three frames, resulting in a maximum delay of 90 ms;  
(4) The maximum encoding buffer was two frames, resulting in a maximum delay of 60 ms;  
Therefore, the maximum delay was 270 ms at the VR panoramic video content production end.  
(5) The delay was determined by the bandwidth and the bit rate of the transmitted video. We use a wired local area network in the laboratory according to the previous implementation. In the case of the network test, when the overall minimum delay was 500 ms-1 s, the minimum delay of this part was within 500 ms, although wireless transmission would be higher;  
(6) + (7) + (8) The delay at the playback end was less than 20 ms for mainstream display devices. Network transmission delay was still the main factor causing end-to-end delay. After the above analysis, the following system (Figure 12) delay analysis composition diagram was obtained.  
Figure 11: Four pictures of one frame spliced into one plane for coding and compression.  
Figure 12: System delay analysis. The delay mainly comes from the content authoring and transmission, and the delay proportion of the player is very small.
# Lightweight and Privacy-Preserving Multi-Keyword Search over Outsourced Data
## 2 System Model and Security Requirements  
### System Model  
As shown in Figure 1, a PRMS system consists of three types of entities, namely, data owner, data user and cloud server. There is a secure communication channel between data owner and data user for key sharing. The data owner outsources a collection of documents to the cloud server. Since the documents may contain sensitive information, they cannot be directly uploaded to the cloud server. Thus, to protect the privacy of outsourced documents, they should be outsourced in ciphertext format.  
To facilitate data searching, the outsourced documents should be attached with a list of keywords or multiple types of keywords. The keywords of the same type constitutes a keyword dictionary. To guarantee that the keywords cannot leak the privacy of outsourced documents, the data processing phase would produce encrypted searchable indexes for each document. The searchable indexes are outsourced to the cloud server together with the document.  
In the search phase, data user can generate a search trapdoor of its query vector with multiple keywords to enable the cloud server to search over outsourced documents. The keywords in the query vector are also contained in the keyword dictionary, which should be transformed into search trapdoor to protect the privacy of outsourced data. Upon receiving the search trapdoor, the cloud server computes the similarity score between each encrypted searchable index and the search trapdoor, and returns the document if its similarity score satisfies the given search threshold.
# An Insightful Overview of the Wiener Filter for System Identification
## 6 Wiener Filter with the Quadratic Eigenvalue Problem
\begin{array}{cc}\mathbf{0}&\mathbf{A}_{0}\\ \mathbf{A}_{0}&\mathbf{A}_{1}\end{array} \right]\!, \tag{54}\] \[\mathbf{B} =\left[ \begin{array}{cc}\mathbf{A}_{0}&\mathbf{0}\\ \mathbf{0}&-\mathbf{A}_{2}\end{array} \right]\!. \tag{55}\]  
It can be checked that if \(\mathbf{t}\) is an eigenvector of \(\mathbf{Q}(\lambda)\), then  
\[\mathbf{u}=\left[ \begin{array}{c}\mathbf{t}\\ \lambda\mathbf{t}\end{array} \right] \tag{56}\]  
is an eigenvector of \(\mathbf{A}-\lambda\mathbf{B}\). This process corresponds to the symmetric linearization since both \(\mathbf{A}\) and \(\mathbf{B}\) are symmetric. The accuracy of this approach will depend on the conditioning of \(\mathbf{A}_{0}\) and \(\mathbf{A}_{2}\). Another possibility is to take  
\[\mathbf{A} =\left[ \begin{array}{cc}\mathbf{0}&-\mathbf{I}_{L}\\ \mathbf{A}_{0}&\mathbf{A}_{1}\end{array} \right]\!, \tag{57}\] \[\mathbf{B} =\left[ \begin{array}{cc}-\mathbf{I}_{L}&\mathbf{0}\\ \mathbf{0}&-\mathbf{A}_{2}\end{array} \right]=-\mathbf{I}_{2L}, \tag{58}\]  
where \(\mathbf{I}_{2L}\) is the \(2L\times 2L\) identity matrix. This linearization is not symmetric. Different linearizations may yield different results; therefore, choosing the right one is important. The eigenvalues of \(\mathbf{Q}(\lambda)\) can be ordered as  
\[\Re(\lambda_{1})\leq\Re(\lambda_{2})\leq\cdots\leq\Re(\lambda_{2L}), \tag{59}\]  
where \(\Re(\cdot)\) denotes the real part of a complex number. Then, the regularized Wiener filter with the QEP is  
\[\widehat{\mathbf{h}}_{\text{QW}}=[\mathbf{R}-\Re(\lambda_{1})\mathbf{I}_{L}] ^{-1}\mathbf{p}. \tag{60}\]  
Now, if the eigenvalues with the \(P\) smallest real parts are considered, another version of the Wiener filter is obtained  
\[\widehat{\mathbf{h}}_{\text{QW},P}=\big{(}\mathbf{R}-\overline{\lambda}_{P} \mathbf{I}_{L}\big{)}^{-1}\mathbf{p}, \tag{61}\]  
where  
\[\overline{\lambda}_{P}=\frac{1}{P}\sum_{p=1}^{P}\Re(\lambda_{p}). \tag{62}\]
# Analysis of Load Transfer and the Law of Deformation within a Pile-Supported Reinforced Embankment
## 4 FEM Numerical Analysis
### Soil-Arching Geometry  
The inflection point of the vertical stress curve determines the soil-arching axis. Figure 12 describes the soil-arching axis with the influence of embankment filling cohesion. In Figure 12, the ordinate is the height of the inflection point of the vertical stress curve, and the abscissa represents the separation from the pile cap's center (the star symbol in Figure 12).  
The soil-arching curve variation in Figure 12 exhibits the following traits:  
1. The height of the soil-arching axis between the diagonal adjacent piles is higher than between the horizontal adjacent piles. In tests M1, M2, M3, and M4, the height of the soil-arching crown between the diagonal adjacent piles increases by 38.5%, 32%, 30%, and 28.5% compared with that between the horizontal adjacent piles. It can be seen that there are significant differences in soil-arching morphology by comparing Figure 12a,b. That is because the net spacing \((s-a)\) between diagonal adjacent piles is larger than it is between the horizontal adjacent piles, as well as the overburden load of subsoil, which leads to sizeable pile-subsoil differential settlement, and thus the height of the soil-arching axis increases.
2. As cohesiveness rise, the height of the soil-arching crown falls. Compared with the test M1, the height of vault between horizontal adjacent piles in tests M2, M3, and M4 decreases by 3.85%, 7.69%, and 9.62%, and the height of vault between diagonal adjacent piles decreases by 8.33%, 13.89%, and 16.11%. These findings suggest that as filling cohesion increases, the effect of cohesion on the soil-arching form is gradually weakened. When the filling cohesiveness changes, the soil-arching height between the diagonal adjacent piles is more sensitive than it is between the horizontal adjacent piles.
3. The height of the soil-arching crown in Figure 12a is about 0.94 \((s-a)\sim 1.04(s-a)\), as that is about \(0.87\sqrt{2}\left(s-a\right)\sim 1.0\sqrt{2}\left(s-a\right)\) in Figure 12b. All in all, the height of the soil-arching crown is roughly 1.0 times the net spacing in the embankment.  
Figure 12: The soil-arching axis in the embankment, (**a**) the soil-arching crown between the horizontal adjacent piles; (**b**) the soil-arching crown between the diagonal adjacent piles.
# Cramer-Rao Lower Bounds on 3D Position and Orientation Estimation in Distributed Ranging Systems
## 3 Bounds on Position Estimation
### Geometric Interpretation of \(D\)  
The determinant \(\mathcal{D}_{2}\) can be interpreted in several different ways:  
1. The shortest (or perpendicular) distance between the target antenna \(h=[x,y]\) and the line joining ground node antennae \(g_{i}=[x_{i},y_{i}]\) and \(g_{j}=[x_{j},y_{j}]\) is \(d_{\perp,ij}=\mathcal{D}_{2}/d_{i,j}\), where \(d_{i,j}=d(g_{i},g_{j})\) (refer to Figure 3). The lower bound then reduces [50] to \[\sigma_{p}^{2}\geq\frac{c^{2}\sigma_{\tau}^{2}N}{2}\left[\sum_{i=1}^{N}\sum_{j =1}^{N}\left(\frac{d_{\perp,ij}}{2\,d_{i}\,d_{j}}\right)^{2}\right]^{-1}.\] (21)
2. As a higher-order generalization, interpret \(\mathcal{D}_{2}/2!\) as the volume of a 2-simplex, \(V_{i,j}\), formed by the antennae \(h\), \(g_{i}\), and \(g_{j}\) (refer to Figure 3). The bound then becomes \[\sigma_{p}^{2}\geq\frac{c^{2}\sigma_{\tau}^{2}N}{2!}\left[\sum_{i=1}^{N}\sum_{ j=1}^{N}\left(\frac{V_{i,j}}{d_{i}\,d_{j}}\right)^{2}\right]^{-1}.\] (22)
3. The quantity \((d_{\perp,ij}\,d_{i,j}/d_{i}\,d_{j})\) is called geometric conditioning \(\mathcal{A}_{i,j}\) and is a measure of the area of a parallelogram contained by vectors \(\overrightarrow{hg_{i}}\) and \(\overrightarrow{hg_{j}}\), scaled by length of those vectors. In this case, the lower bound becomes \[\sigma_{p}^{2}\geq\frac{c^{2}\sigma_{\tau}^{2}N}{2}\left[\sum_{i=1}^{N}\sum_{ j=1}^{N}\left(\frac{\mathcal{A}_{i,j}}{2}\right)^{2}\right]^{-1},\] (23) where \(\mathcal{A}_{i,j}/2\) can be envisioned as the area of triangle \(\Delta_{i,j}\) enclosed by unit vectors in the same direction. It is interesting to note that this geometric conditioning is independent of the absolute distances between the ground and target antennae.  
Figure 4: Geometric interpretation of the volume of the tetrahedron \(V_{i,j,k}(d_{i},d_{j},d_{k})\), indicated in blue.
# Universal Approach to Solution of Optimization Problems by Symbolic Regression
## 6 Results  
Symbolic regression methods are currently used to solve complex optimization problems in non-numeric spaces. To expand the area of their application and, in particular, to simplify the execution of the operations of the genetic algorithm, a universal approach was developed based on the principle of small variations of the basic solution.  
The main definitions, the distance between the codes, and the neighborhood of the code, including the concept of the inheritance property, were given. A proof of the theorem was presented for solving an optimization problem on a non-numerical space, and provided that the value of the functional is uniformly distributed over a certain interval, the use of symbolic regression methods with the inheritance property for search of optimal solution on a non-numerical space is more efficient than the random search.  
Examples of constructing small variations of the basic solution for various methods of symbolic regression were given. It is proposed that the methods which use the principle of small variations be called variational. An example of solving the synthesis problem was given, in which it was necessary to find one control function to ensure the transfer of an object from 30 initial conditions to one terminal point according to the criterion of speed and accuracy. The problem was solved by the variational Cartesian genetic programming. In addition, the classical NP-hard knapsack problem for 100 objects was solved using a variational genetic algorithm.  
The results presented in the article have both fundamental and practical importance.
## 1 Introduction
### Robotic-Assisted craniotomy  
Despite the increasing number of surgical robotic systems appearing in recent decades, only a small number of robotic assistant systems for craniotomy have appeared in the literature. The first robotic system for craniofacial surgery was proposed in 1998, adapting an industrial manipulator for the application [5]. \"Neurobot\", a robot developed for craniotomy, was reported a few years later [6]. In 2003, an anthropomorphic robot was adapted for otoneurosurgery, enhancing the control of the interactions between the drill and the patient's skull [7]. A robotic system called RobaCKa, combining a commercial CASPAR robot and an opttoronic navigation system, was the first system tested on real patients during craniotomy surgery [8]. A parallel robot based on a steward platform was also proposed for craniomy in [9] by including a prismatic joint holding the surgical drill at the mobile platform. In [10], a new dedicated mechanism decoupling position and orientation motion was used for craniotomy procedures using simulated plastic skulls. \"CRANIO\", a dedicated robotic system, was also experimentally tested, as reported in [11]. Furthermore, a robotic system for keyhole craniotomy was presented in [12], based on reconfigurable hexapod kinematics and directly fixed to the patient's head. Few mechanisms have also appeared in recent years, including a specific kinematic constraint known as the Remote Center of Motion (RCM). In [13], a hybrid mechanism decoupling two angular motions was presented. Finally, a Spherical Parallel Mechanism (SPM) for craniotomy was recently presented in [14], including optimized kinematics to guarantee highly kinematic performances.
## 4 Experimental Results and Discussion
### Experimental Environment and Dataset  
The experiment was conducted in a general medium-specification laptop environment rather than using high-end GPUs or workstations. This setup ensures that the proposed method operates effectively in standard user environments without requiring specialized hardware. The development and system specifications are summarized in Tables 21 and 22.  
To evaluate the performance of the proposed method, high-resolution orthoimages and DEM datasets were utilized, as detailed in Table 23.  
Delaunay triangulation was applied for 3D object generation, while Douglas-Peucker simplification was employed for vector-based processing. The triangulation resolution levels, edge constraints, and simplification tolerance parameters are also specified in Table 24.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Processing Stage** & **Method A (s)** & **Method B (s)** & **Proposed Method (s)** \\ \hline
1 & Vector Data Loading & 0.5 & 0.5 & 0.5 \\
2 & Mesh Generation & X & X & 6.5 \\
3 & DEM Mapping & X & X & 8 \\
4 & Total Processing Time & 45 & 30 & 15 \\ \hline \hline \end{tabular}
\end{table}
Table 20: Performance Comparison of 3D Object Generation Methods.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Category** & **Details** \\ \hline Operating System & Windows 11 (64 bit) \\ Programming Languages & C\#, Python \\ Development Tools & Visual Studio 2022 \\
3D Rendering Engine & OpenGL 2.21.0 \\ Database & PostgreSQL + PostGIS \\ Other Environment &.NET 8 \\ GPU & NVIDIA Geforce RTX3050 \\ CPU & Intel® CoreTM 5-11400H @ 2.70 GHz \\ RAM & 32 GB \\ \hline \hline \end{tabular}
\end{table}
Table 21: System Specifications.
###### Abstract  
Hundreds of thousands of tons of waste are generated from decommissioned nuclear-power facilities, and it has become a critical global issue to secure technology for reducing and recycling this waste. Concrete waste (CW) is estimated to comprise 60-80% of the total waste, and concrete-waste powder (CWP) includes enough inorganic substances used as effective materials for waste treatment. Accordingly, it can be used to produce recycled cement (RC). This study aimed to evaluate the performance of a solidification agent manufactured using recycled cement (SRC) for the safe packing of radioactive wastes, such as coarse aggregates of CW, waste soil, and metal wastes originating from decommissioned nuclear facilities. The experimental results indicated that the most relevant incineration temperature of CWP for RC was 700 \({}^{\circ}\)C. The optimum water-to-binder ratio was determined to be 0.4, and the most relevant substitution ratio of ground granulated blast furnace slag for CWP was determined to be 15%. In addition, calcium silicate hydrate is the most effective hydration product for improving the compressive strength of SRC. The maximum packing capacities of the SRC for coarse aggregates, waste soil, and metal waste, which were simulated as radioactive wastes, were determined to be 30, 5, and 7 wt%, respectively. The results of leaching tests using SRC containing radioactive wastes contaminated with Co, Cs, and Sr indicated that their leachability indices met the acceptance level for disposal. Consequently, the RC composed of CWP can be used as a solidifying agent to safely dispose of radioactive wastes, such as coarse aggregates, waste soil, and metal waste.  
concrete waste; recycled cement; decommissioned nuclear-power facilities; solidification; radioactive waste; safe disposal Article