esa-sceva/satcom-chunk-collection huggingface dataset:
# Mod2 VQLS: A Variational Quantum Algorithm for Solving Systems of Linear Equations Modulo 2
## 4 Solving the Linear System Using a Variational Quantum Algorithm
### Variational Parameter Optimization  
Along with the quantum circuits, hybrid systems like Mod2 VQLS rely on classical algorithms to optimize the variational parameters (for example, the \(\theta\) parameters in the examples so far). Parameter optimization in variational quantum circuits has become an important topic in quantum machine learning, partly because it is a signification barrier to overcome if variational quantum circuits are to deliver valuable applications on NISQ hardware.  
A considerable challenge here is that automatic differentiation (\"autodiff\") is not straightforward on quantum computers: finite-difference methods require too many nearby calculations, and even when an analytic expression for gradients is available using a 'parameter shift rule', these expressions have to be evaluated separately for each parameter ([3], SS5.3). As fresh problems are encountered, it is possible that quantum computers are not well-suited to computing gradients, and that there is no quantum approach to training variational networks that is as generally effective as classical backpropagation [17].  
Simplex-based methods are a well-established alternative to gradient-based methods. In the simple one-dimensional case, binary search is an elementary example. In more dimensions, an \(n\)-dimensional _simplex_ is the convex hull of \(n\) linearly independent vertices, and the search algorithm converges by iteratively partitioning the simplex and reducing to a smaller simplex that contains the desired solution. (For example, a triangle subdivided by a suitable line, or a tetrahedron subdivided by a suitable plane.) This is the backbone of many procedures, including the famous 1965 simplex algorithm of Nelder and Mead [18]. Nelder-Mead can sometimes be improved using interpolation based on the values of the objective  
Figure 6: A brickwork layout ansatz on 4 qubits with a depth of 5 layers.  
function at the vertices, leading to the COBYLA algorithm published in 1994 [19]. SciPy's COBYLA implementation was used in the Mod2 VQLS experiments presented below.
## 4 Data Processing
### MMS Point Cloud Georeferencing  
Since a GNSS system was not integrated with Kaarta Stencil 2, the georeferencing of the point cloud could be realized in two ways: using the control points captured by the EDM or aligning the point cloud to the CRP model, already georeferenced. As only EDM points fell within the path trajectory, the second option has been chosen and represents a completely new experiment tested by the Kaarta manufacturing experts. The filtered CRP point cloud presented in Figure 10 was thus used next as the control system for the MMS point cloud.  
After the filtering operation of the MMS point cloud, the next step consisted of roughly aligning the trajectory of Kaarta Stencil 2 with the CRP point cloud. First, the yaw angle of the starting point of the MMS close-loop trajectory in the CRP point cloud was identified and calculated. Then, the parameters of the MMS point cloud could be set up iteratively to obtain a better result in the replay operation of the trajectory line, evaluating every time the correspondence with the photogrammetry point cloud. Defined as the right alignment of the trajectory, the MMS point cloud was run following this adjusted close-loop line and automatically adapted with the same roto-translation transformation. At the end of this procedure, the two point clouds were overlapped and so they could be analyzed by executing a comparison operation between the point clouds themselves (Figure 11).  
Figure 11: MMS point cloud (false color scale) aligned to the CRP point cloud (red, green and blue (RGB) scale).  
Figure 10: Top view of the CRP (Close-Range Photogrammetry) point cloud. In this image are visible the holes due to occlusions and vegetation.
## 1 Introduction
### Background and Motivation  
Recruiters today faces an unprecedented flood of applications. The Future of Jobs 2023 report estimates that the largest job platforms process -100 million submissions every month, showing a ten-fold growth since 2016 [1]. Traditional recruitment processes, often reliant on manual keyword searches or rule-based matching systems, struggle to cope with the increasing volume and complexity of job applications [2; 14]. Empirical audits further reveal that purely keyword-based systems discard up to 15% of qualified resumes and may amplify demographic bias [15]. In response, AI-driven systems are being integrated into recruitment pipelines to enhance the precision and scalability of candidate matching.  
Siamese Neural Networks (SNNs) have gained attention for their ability to learn embeddings that capture similarity between pairs of inputs by minimizing the distance between semantically related pairs while maximizing the distance between unrelated ones. This makes them particularly suitable for tasks like job-candidate matching, where the goal is to project both job descriptions and resumes into a shared embedding space where proximity reflects semantic relevance [16; 17].  
While pretrained models or multilingual Transformers result in sufficient sentence-level embeddings [18], they may overlook contextual dependencies across document sections, since they treat input as independent segments. For example, the chronology of work experience, the interplay between skills and roles, and the presence of redundant qualifications are often distributed across multiple segments of a resume. Similarly, job descriptions may introduce role responsibilities and requirements across paragraphs or bullet points, implying temporal or logical structures that static embeddings may not fully capture.  
To address this limitation, sequential neural architectures were used as \"head\" modules added on top of static sentence embeddings. The goal of this approach was to model various dependencies across text segments that are not present in state-of-the-art solutions, resulting in more accurate candidate matching.  
The core motivation of this work is two-fold. First, we aim to augment existing sentence embedding techniques with sequential modeling to better reflect the document-level structure inherent in resumes and job descriptions. Second, we intend to conduct a systematic empirical comparison between GRU, LSTM, and Transformer head architectures to understand which model offers the best tradeoff between performance and complexity in a real-world recruitment setting. This analysis is critical for practitioners seeking to deploy intelligent candidate-matching systems that are both accurate and efficient.
# CRCSI: A Generic Block Interleaver for the Next Generation Terrestrial Broadcast Systems
## 5 Conclusions  
In this paper, we considered the design of a generic block interleaver to resist the effect of channel selective fading in terrestrial broadcast system. To tackle this problem, we formulated the block ordering problem into the design of row-column mapping rules, and proposed a generic block interleaver without extra memory space by crossing and cyclically shifting the row in order. Compared to classical block interleavers, the theoretical interleaving distance of the proposed block interleaver is obviously increased. The simulation results showed that the BLER performance in fixed reception is improved with the increasing of TBS. Moreover, the proposed block interleaver can obtain a significant performance improvement against the Doppler shift in different mobile reception scenarios.  
Figure 7: Comparison for different speed.  
**Author Contributions:** Conceptualization, R.L., J.T. and X.B.; methodology, R.L.; software, R.L.; validation, R.L. and X.B.; formal analysis, R.L.; investigation, R.L.; resources, M.L.; data curation, R.L.; writing--original draft preparation, R.L.; writing--review and editing, X.B. and M.L.; visualization, R.L.; supervision, X.B., J.T. and M.L.; project administration, X.B., J.T. and M.L.; funding acquisition, X.B. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Key R&D Program of China (grant number 2019 YFB1802703) and Shanghai Super Postdoctoral Incentive Program (grant number 2020000125).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The authors would like to thank the Shanghai Advanced Research Institute, Chinese Academy of Sciences for supporting in this investigative project.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Effect of Wheel Path in Raster Grinding on Surface Accuracy of an Off-Axis Parabolic Mirror
## 2 Materials and Research Methods  
### Experimental Details  
The off-axis paraboloid is a part of the rotationally symmetric paraboloid, which is a parabola along the meridian direction and an arc in the vertical meridian direction. In essence, it belongs to the quadric rotational aspheric surface. The general expression of the quadric rotational surface in the optical system is as follows [27]:  
\[x=\frac{c\left(z^{2}+y^{2}\right)}{1+\sqrt{1-c^{2}(k+1)(z^{2}+y^{2})}} \tag{1}\]  
\(c\) is the vertex curvature of the aspheric surface; \(k\) is an aspherical eccentricity function (parabola \(k=-1\)).  
In this test, the off-axis parabolic equation (Equation (2)) to be processed was selected, as shown in Figure 1:  
\[\left\{\begin{array}{c}x=0.286\left(z^{2}+y^{2}\right)/2\\ 5688\leq x\leq 5770\\ -25\leq y\leq 25\end{array}\right. \tag{2}\]  
The method of controlling the wheel path to complete the entire free-form surface processing is widely used. The concept of the locus shaping technique comes from turning processing [28]. The tool tip moves along the generatrix of the rotary workpiece. At the same time, the workpiece rotates around the spindle. The shape of the workpiece is realized by the tool tip depicting the dense path curve on the workpiece. There are two raster paths in the ultra-precision grinding of the off-axis parabolic surface: one is to step along the meridian direction, and the other is to step along the arc vector direction [29; 30]. as shown in Figure 2. Firstly, rough grinding was carried out to realize the rapid removal of materials, and then, finish grinding was carried out to ensure the surface accuracy of the machined surface. The size of the workpiece used was 80 mm in length, 50 mm in width, and 50 mm  
Figure 1: Schematic of off-axis parabolic surface.  
in thickness. The grinding wheel was a diamond cup wheel. The inner surface of the off-axis parabolic surface was a concave surface. To avoid machining interference, the wheel was tilted at a certain angle during the grinding process, so that the position of the tool contact point was located on the end face boundary contour of the wheel [31]. The experimental machine was a five-axis milling vertical machining center (KMC600S UMT, KEDE Numerical Control Co., Ltd., Shanghai, China). The control resolution of the X-axis/Y-axis/Z-axis was 0.0001 mm, and the C-axis was 0.0001\({}^{\circ}\). The positioning accuracy of the X-axis/Y-axis was 0.008 mm/0.005 mm, and the repeated positioning accuracy of the X-axis/Y-axis was 0.005 mm/0.003 mm. The processing parameters are shown in Table 1.
# Virtual Marker Technique to Enhance User Interactions in a Marker-Based AR System
## 6 Pilot Study
### Results  
#### 6.7.1 Task 1 Completion Time  
The time to complete task 1 is shown in the Table 1. For all participants, the average completion time (sec) of task 1 was 252.2 (SD = 93.80). The researcher observed that, in the experiment, the participants' proficiency in using the system was still insufficient after the simple training was completed. The experimenters still needed to spend some time thinking about what gestures to use during the experiment. Four of the experimenters still quickly adapted to using gestures to control programming and the operation process was relatively smooth. One experimenter, P3, did not have a good grasp of how to use leap motion to capture his own gestures and complained that the gesture control of the virtual markers was complicated. On the whole, the experimenters quickly mastered the generation of virtual markers, the merging of information, and the use of gestures, and could independently complete the complex task. In fact, despite the existence of marker recognition and some time threshold setting for confirming user input to determine the input information and other external factors, most experimenters completed task 1 within a reasonable time. Considering that the experimenters could complete complicated tasks independently in a short time after only receiving simple training, we believe that this supports experimental hypothesis 1--that is, the introduction of virtual markers will not bring significant time and operational burdens to users.
## 6 Conclusions and Future Directions  
In this paper, a heuristic scheduling algorithm based on the deep reinforcement learning and heuristic algorithm (DRL-HA) is proposed based on the historical scheduling and task characteristic information to solve the agile earth observation satellite scheduling problem (AEOSSP) with high efficiency. The final scheduling sequence is obtained by combining the DRL-HA model trained by the heuristic construction neural network (HCNN) and the task arrangement algorithm (TAA). Three kinds of experiments verify that the algorithm has outstanding performance and generalization ability. Among them, experiments of instances with different scales show that (1) the profit of the DRL-HA algorithm is 9.15% higher than that of the best heuristic algorithm, on average; (2) moreover, this algorithm model has a good generalization ability, demonstrating this algorithm process' outstanding performance in the experiments with different task scales and task distributions; (3) the algorithm is capable of providing high-quality initial solutions with a smaller number of iterations compared to results from other competitors. The model trained under a fixed scale and task distribution is able to deal with other scenarios with eminent performance and maintains the superiority of the algorithm.  
The DRL-HA model proposed in the paper exhaustively unitizes historical scheduling information and task feature information to obtain the prominent scheduling sequence. Therefore, the DRL-HA algorithm is verified to be an effective method for solving AEOSSP. In this way, the high profit and high timeliness of agile satellite scheduling can be guaranteed, and the solution of AEOSSP is further explored and improved. Although this model requires the support of hardware and much time for training, its great generalization capability renders it suitable for different scenarios, and producing a high-quality initial  
Figure 12: Interactions of GRILS at different sizes. (**a**) Interactions of GRILS at size 50. (**b**) Interactions of GRILS at size 100. (**c**) Interactions of GRILS at size 150. (**d**) Interactions of GRILS at size 200.  
solution provides this model the ability to be combined with other algorithms. In future work, we will try to combine deep reinforcement learning with the single-satellite algorithm to accomplish refined task allocation for multi-satellite scheduling problems.  
Conceptualization, J.C., M.C. and X.L.; Data curation, J.C. and J.W.; Formal analysis, J.C. and L.H.; Funding acquisition, L.H.; Investigation, J.C. and M.C.; Methodology, J.C., M.C. and L.H.; Project administration, J.C.; Resources, J.C.; Supervision, X.L.; Validation, J.C.; Visualization, J.C.; Writing--original draft, J.C., M.C. and J.W.; Writing--review & editing, J.C. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the Hunan Provincial Innovation Foundation for Postgraduate CX20210034 and the National Natural Science Foundation of China (72001212).  
Not applicable.  
The authors declare no conflict of interest.
# Wind Loads on Overhead Sign Structures: A Comparative Study
## 3 Methodology
### Computational Domain  
Figure 1 shows the computational domain that serves as a virtual wind tunnel with the boundary conditions for the airflow. We followed the approach employed in CFD analysis of low-rise buildings to determine the appropriate dimensions of the computational domain. The optimal domain size for CFD analysis is proportional to the dimensions of an object with height H, as shown in Figure 1a. The computational domain has three main parts: the inlet that represents the beginning of the wind tunnel and will simulate the source of airflow, the interior walls, and the outlet. The domain has a height of 6H, a width of 5H, and a length of 15H. The sign panel is placed 17 feet above the ground.  
A CFD analyst should define the boundary conditions after the geometric model becomes available. Boundary conditions represent a set of constraints to boundary value problems in computational fluid dynamics. They should be defined correctly. The inlet boundary condition will model the air entering the domain and describe its mean velocity and turbulence intensity profile. A user-defined function specifies the velocity and turbulence profiles over height. The mean wind velocity used in this study at a 10m is 10m/s or around 22 mph with a turbulence intensity of 20%, to be consistent with the provisions in the American Society of Civil Engineers (ASCE 7-22: Minimum Design Loadsand Associated Criteria for Buildings and Other Structures) [22]. It is worth mentioning that the ASCE 7-22 provisions utilize wind tunnel testing under a mean wind speed of 10 m/s. The inflow profile represents an open terrain with a parameter alpha of 0.178 [23]. An outlet boundary condition defines the outflow condition for the airflow pressure at the end of the enclosure. When the flow velocity and pressure information are unknown, outflow boundary conditions may model the flow at the exit [24]. The outlet assumes zero streamwise gradients for all airflow variables except the exit pressure.  
A wall boundary condition bounds regions within the enclosure to describe the flow velocity. The wall boundaries are the ground and the sign panel. There are several wall boundary conditions: no-slip, slip, moving, and rotating. We assigned the ground and sign panel to a no-slip wall boundary condition. The no-slip wall boundary condition sets all velocity components to zero at the wall.  
The symmetric boundary condition models a zero-shear slip wall and is used to reduce the extent of the computational model to an asymmetric subsection of the enclosure [24]. For this study, the sidewalls and the top of the enclosure geometry have symmetric boundary conditions.
# A Multi-Objective Path-Planning Approach for Multi-Scenario Urban Mobility Needs
## 2 Preliminaries  
To address the multi-objective optimization problem in urban path planning, this study focuses on minimizing five key objectives: total distance; congestion distance; total time; energy consumption; and risk index. These objectives are subject to various constraints, including maximum allowable time, congestion distance limits, energy consumption limits, and safety requirements. By combining the A* algorithm and the genetic algorithm and introducing a dynamic weight-adjustment mechanism, the proposed framework aims to provide efficient solutions to multi-objective optimization challenges. This section outlines the A* algorithm for multi-objective path planning, the genetic algorithm, and the dynamic weight-adjustment mechanism.
## 4 Effect of Viscoelastic Damping on Underwater Acoustic Radiation  
### Numerical Method Validation  
Prior to the analysis of underwater acoustic radiation of the ring-stiffened conical shell, natural frequency \(f_{n}\) and loss factor \(\eta\) of viscoelastic sandwich beams are simulated and compared with reported numerical results to validate the present method. Figure 7 displays the finite element model of the viscoelastic sandwich beam under clamped-free beam boundary conditions. Since the constitutive model of real viscoelastic materials varies with frequency, the polyvinyl butyral (PVB) is chosen as the viscoelastic damping material in Refs. [37; 38; 39]. The material and geometrical properties of the sandwich beam are listed in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Elastic Faces** & **Viscoelastic Core** & **Beam** \\ \hline Young’s modulus & 6.9 \(\times\) 10\({}^{10}\) N/m\({}^{-2}\) & 1.794 \(\times\) 10\({}^{6}\) N/m\({}^{-2}\) & Length & 177.8 mm \\ Poisson ratio & 0.3 & 0.3 & \\ Density & 2766 kg/m\({}^{-3}\) & 968.1 kg/m\({}^{-3}\) & Width & 12.7 mm \\ Thickness & 1.524 mm & 0.127 mm & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Material and geometrical properties of the sandwich beam.  
Figure 5: Vibration responses of the shell: (**a**) in vacuum; (**b**) in water.  
Figure 6: Comparison of responses of Point 3.  
The natural frequencies and associated loss factors of the clamped-free sandwich beam corresponding to the first four modes are presented and compared in Table 3. As shown in Table 3, natural frequencies and loss factors are close to the results in Ref. [37]. Therefore, the frequency-dependent viscoelastic model and mesh size can be used in the subsequent simulation of underwater acoustic radiation of the ring-stiffened conical shell.