esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Motion Module  
In order to be more robust against interfering sound sources in the cocktail party environment where multiple sound sources are active simultaneously, the motion module is designed to control the robot to move towards the target while maintaining a distance from other sound sources based on the estimated DoA. More specifically, the purpose of the motion module is to find an optimal direction, which is a weighted sum of the direction of the target sound source and the opposite directions of the other interfering sound sources. To calculate this direction, the directions of sound sources are firstly converted into unit vectors in the robot coordinate system by means of trigonometric functions as follows,  
\[\mathbf{V}=[(\text{cos}\theta_{t},\text{sin}\theta_{t})^{T}, \tag{8}\] \[(\text{cos}\theta_{0},\text{sin}\theta_{0})^{T},\ldots,(\text{ cos}\theta_{N_{g}},\text{sin}\theta_{N_{g}})^{T}],\]where \(\theta_{t}\) denotes the direction of the target sound source, \(N_{o}\) is the number of other sound sources in the environment and \(\theta_{i},i\in(1,N_{o})\) represents other sound source directions.  
After that, the response of the MUSIC spectrum of the target sound source and the other sound sources for the current time frame \(f\) can be written as,  
\[\overline{\mathbf{\xi}}^{\prime}=[\overline{\xi}(\theta_{t},f),\overline{\xi}( \theta_{1},f),\dots,\overline{\xi}(\theta_{N_{o}},f)], \tag{9}\]  
which indicates the intensity of the effect of each sound source on the robot.  
Thus, the direction of the robot's next movement can be straightforwardly expressed as,  
\[(x,y)^{T}=\frac{\mathbf{V}(\mathbf{W}^{T}\odot\overline{\mathbf{\xi}}^{\prime T})}{||\mathbf{V }(\mathbf{W}^{T}\odot\overline{\mathbf{\xi}}^{\prime T})||_{2}}, \tag{10}\]  
where \(\mathbf{W}=[w_{t},w_{1},\dots,w_{N_{o}}]\) indicates the weights of the directions of these sound sources, which affect the length of the path to the target, and \(\odot\) denotes the element-wise product.  
In order to instruct the robot to rotate, the direction obtained in Equation (10) is further converted into radians by the inverse trigonometric functions as,  
\[\theta^{\prime}=\arccos\frac{x}{\sqrt{x^{2}+y^{2}}}. \tag{11}\]
## 2 Related Work
### Path Planning  
#### 2.2.1 Traditional Path Planning Algorithm  
The path planning algorithm is a computational method that determines an optimal or feasible trajectory for an agent (e.g., robot, vehicle) to navigate from a start point to a goal point while avoiding obstacles and satisfying constraints [15]. Path planning algorithm development constitutes a fundamental optimization challenge in modern warehousing logistics and smart manufacturing.  
Classical graph-based algorithms include the BFS (Breadth-First Search) algorithm [16] and the Dijkstra algorithm [17], capable of determining the shortest paths between start and end points. Enhanced approaches, such as heuristic graph search algorithms [18] and the A-star algorithm [19], utilize heuristic functions for accelerated search processes. The incremental path planning method of the D-star algorithm [20] is developed for dynamic environments. Although these algorithms display their feasibilities in many problems, their finding efficiencies or results are constrained in high-dimensional complex environments, where path replanning is often required. The computational complexity increases exponentially when the problem scale becomes larger, and the generated paths typically consist of discrete connections that may not align with the actual shipping requirements.  
To mitigate these drawbacks, sampling-based path planning algorithms, exemplified by the RRT (Rapidly Exploring Random Tree) algorithm, were developed [21]. RRT, with its asymptotic optimality, approaches constructing shipping paths through free space random sampling and demonstrates strong suitability in high-dimensional spaces. But, their dynamic environment adaptability remains limited, often requiring complete path regeneration.  
Other methods include optimization approaches and learning approaches. The optimization approaches include gradient descent methods [22], energy consumption optimization [23], convex optimization [24], genetic algorithms [25], particle swarm optimization [26], ant colony optimization [27], etc. Their path planning efficiencies may be affected by complicated traffic constraints or for real-time implementation. These methods may also display high solution sensitivity when handling environmental-dependent problems and frequently converge to local optima in non-convex cases. Learning approaches, e.g., deep learning and reinforcement learning algorithms [28; 29], transform path planning problems into learning problems, enabling them to autonomously learn and generate paths. But, these intelligent methods perform sensitively to parameter selection or under complicated applications.
# A Low-Band Multi-Gain LNA Design for Diversity Receive Module with 1.2 dB NF
## 5 Conclusions  
This paper presented a low-band low noise amplifier design for a diversity receive module for 2G/3G/4G/5G multi-mode cellular handsets which covered the frequency ranges between 617 MHz to 960 MHz in five different frequency bands. The presented structure covered the gain modes from \(-12\) to 18 dB with 6-dB gain steps, where each gain mode had different current consumption. In order to satisfy the NF specifications in high gain modes, a cascode CS with a source inductive degeneration structure adopted for this design. To achieve the S\({}_{11}\) parameters and current consumption specifications, the core and cascode transistors for high gain modes (18 dB, 12 dB, and 6 dB) and low gain modes (0 dB, \(-6\) dB, and \(-12\) dB) were separated. Nevertheless, to keep the area low and keep the phase discontinuity within \(\pm 10^{\circ}\), the degeneration and load inductors were shared between two cores. To compensate the performance for PVT variations, the structure applied an LDO regulator and a corner case voltage compensator. The design was proceeded in a 65 nm RSB PDK and the supply voltage was 1 V. For 18 dB and \(-12\) dB gain modes as two examples, the NF, current consumption, and IIP3 values are 1.2 dB and 16 dB, 10.8 mA and 1.2 mA, and \(-6\) dBm and 8 dBm, respectively.  
Conceptualization, B.S.R., D.K. and K.-D.C.; methodology, B.S.R., D.K. and K.-D.C.; software, B.S.R. and Y.P.; validation, B.S.R. and K.-Y.L.; formal analysis, B.S.R., D.K. and K.-D.C.; investigation, B.S.R., D.K. and K.-D.C.; resources, B.S.R., D.K. and K.-D.C.; data curation, B.S.R., D.K. and K.-D.C.; writing--original draft preparation, B.S.R.; writing--review and editing, B.S.R. and S.A.H.A.; visualization, B.S.R.; supervision, J.-M.Y., Y.P., S.K., H.H., Y.J. and K.-Y.L.; project administration, K.-Y.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding. This work was supported by the Technology Innovation Program (or Industrial Strategic Technology Development Program-Development of Integrated Chip for Transceiver and Power Management for Ultrasonic Imaging Scanner) (20011971, Development of Integrated Chip for Transceiver and Power Management for Ultrasonic Imaging Scanner) funded by the Ministry of Trade, Industry, & Energy (MOTIE, Korea) and National Research Foundation of Korea (NRF), grant funded by the Korean government (MSIT) (no. 2021R1A4A1033424).  
The authors declare no conflict of interest.
# CAPDepth: 360 Monocular Depth Estimation by Content-Aware Projection
## 3 Method
### Overview  
Figure 1 illustrates the complete process of CAPDepth. The content-aware projection module uses optimized CAP projection to transform the ERP input image into a set of tangential images. Depth estimation for each patch is then predicted individually through a CNN and subsequently merged into the ERP depth output. The CAP projection adapts to changes in the view port's content features (e.g., straight-line bending and object stretching) and optimally adjusts the projection parameters to reduce overall distortion in the tangential projections.  
Figure 1: Overview of CAPDepth. The method includes three main parts: (1) A content-aware projection module, which calculates optimal projection parameters to project the monocular ERP input onto multiple patches; (2) A geometric-aware embedding module, which calculates spherical coordinates (\(\lambda\), \(\phi\), \(\rho\)) of sampling points and planar center coordinates (\(\lambda_{t},\phi_{t}\)), along with the iteratively updated \(\rho^{\prime}\) value as input, integrating geometric features into the image encoder; (3) An encoder–decoder network, which processes each patch to generate patch depth maps and perform global aggregation through a transformer. Finally, patches are merged into the ERP space using a mapping index map.  
To mitigate geometric differences in the overlapping regions between adjacent patches, we utilize a geometric embedding module to encode the spherical coordinates related to each pixel of the tangential images, providing additional geometric features to help integrate image geometric features. Additionally, mean filtering is applied during the merging into the ERP format to achieve smoother, more consistent depth estimation. An index map of tangential images to ERP reduces the merging time cost. To determine the spatial position of each patch in the ERP and improve the consistency of global depth estimation, we globally aggregate the deepest features from the encoder using a transformer. Finally, an iterative refinement mechanism is used to further enhance depth prediction performance.
# On the Use of Structured Prior Models for Bayesian Compressive Sensing of Modulated Signals
## 2 Proposed Method
### Group Sparse Model for Modulated Signals
#### 2.3.2 Hierarchical Model
It follows that the sparsity inside the active blocks is ensured if almost \(\gamma_{q}\) are close to zero. It is worth to note that model (5) promotes the structure of the spectrum through the dependency that it introduces between coefficients magnitudes with respect to their relative position to the carrier harmonics. One can interpret such dependency, as follows: coefficients in the same or symmetrical positions relatively to their carrier frequency contribute almost equally to the overall energy of the block where they lie. This is quite different from the Spike-and-Slab model dependency we will introduce later.
# Design of a Morphing Aircraft Based on Model Predictive Control
## 4 Numerical Simulation  
### Mathematical Model Simulation  
Since the LPV model is derived from the linearized small-disturbance model through interpolation and fitting operations, in order to verify whether the longitudinal LPV model for stable flight can effectively describe the nonlinear model's dynamic characteristics and analyze the motion trends during morphing flight, a basic reference for subsequent controller design is provided. Therefore, a simulation verification process is designed in the numerical simulation section below.  
Figure 3 shows the wingspan deformation of the longitudinal LPV model and the original longitudinal nonlinear dynamic model during the deformation simulation process. The red line in the figure represents the response curve of the nonlinear dynamic model, while the blue line represents the response curve of the LPV model. A scenario is designed where the same short-term control inputs and external disturbances are applied during the same wingspan deformation process. The variable-span aircraft is initially set to fly at a height of 5000 m and a speed of 40 m/s, with an initial wingspan morphing ratio of 0. At \(t=10\) s, wingspan morphing begins, and the aircraft's wingspan gradually transforms from the shortest (\(\xi_{b}=0\)) to the longest (\(\xi_{b}=1\)) by \(t=40\) s, after which the wingspan morphing ratio remains constant. Additionally, during the morphing flight process, from \(t=20\) to \(t=25\) s and from \(t=50\) to \(t=55\) s, a \(+1^{\circ}\) and \(-1^{\circ}\) elevator deflections are added to the initial trim control. At the same time, a periodic disturbance is applied in the lift and drag directions starting at 30 s, with a disturbance period of 6.28 s and peak values of 50 N in each direction. A periodic disturbance of 50 N-m is also applied in the pitching moment direction.  
Figure 3: Wingspan morphing ratio during simulation verification process.  
As seen in Figure 5, in the case of applying the same elevator deflection angle and throttle setting, the flight states of the nonlinear dynamic model and the longitudinal LPV model of the morphing wing aircraft are very similar, with almost identical motion trends. Over time, both the angle of attack and pitch angle motion responses gradually diverge, causing the flight speed and altitude to deviate from the reference state designed by the LPV model, resulting in an accumulation of errors. This indicates that the open-loop control performance is not satisfactory, and a suitable controller needs to be added in the subsequent design. Overall, the LPV model still effectively describes the nonlinear model's motion characteristics along the varying parameter trajectory and will be used as the benchmark model for the subsequent design.
# Biomechanical Rationale for a Novel Implant Design Reducing Stress on Buccal Bone
## 4 Discussion
In the past, high levels of insertion torque indicating high levels of primary implant stability as required for immediate loading protocols have been considered as being desirable [11,15]. However, newer literature meanwhile shows that comparably low levels of insertion torque may also result in the good long-term performance of dental implants [16,23]. In addition, recent research also applying molecular analysis has provided evidence that the compression of alveolar bone causes cell death in the surrounding of an osteotomy [18,24,25], which may have negative effects during osseointegration and may be a co-factor for bone loss frequently observed in the initial phase of implant healing [7,14,19]. Based on that, modern implant systems should not only minimize bone trauma during osteotomy preparation but should also minimize peri-implant strain development during insertion. Coinciding with these findings, implants with a triangular cross-section have been introduced not touching buccal bone walls upon final placement in order to avoid excessive stresses in esthetically relevant areas. As clinical studies did not find major benefits of such designs as compared to conventionally round implant shapes [38], the novel design investigated here may be seen as an alternative. The methodology applied in this experiment followed comparable previous studies on round vs. triangular [31] and wedge-shaped [32] dental implants. In order to benefit from standardized conditions, polyurethane foam was used as a bone surrogate material instead of using cadaver bone [30], which would have also allowed for strain measurements. For future studies, the bone surrogate materials could be adapted in order to better reflect the anatomy of the maxilla and mandible, respectively. In addition to the limited sample size and obvious limitations in simulating the mechanical behavior of alveolar bone, the alignment of the sensing elements of the strain gauges parallel to the occlusal surface of the bone surrogate material as well as the positioning of the implants with a standardized buccal wall thickness have to be seen as limitations. Due to the sensitivity of the measurement technique applied with minor positional deviations having an impact on the absolute readings, strain values can only be used for comparisons on a relative scale. Moreover, all implants were placed by one surgeon and it might be expected that variation in measurement outcomes exists among practitioners.
## 3 Results and Discussion
### Changes in the Appearance of S. aureus biofilms  
SEM images were used to further investigate the morphology of _S. aureus_ biofilms (Figure 2A-D). For the untreated _S. aureus_ biofilms, the structure of the biofilm network was dense and there were fewer holes. Meanwhile, there appeared a greater number of bacterial cells and most of the cells were single with regular shapes and smooth surfaces. After incubation with PAH for 30 min, the structure of the biofilm became loose with more and bigger holes. More bacterial cells aggregated together and the integrity of the bacteria cells was destroyed. Results suggested that PAH could lead to significant changes in the biofilm matrix, resulting in the direct exposure of the bacterial cells to PAH. The damages in bacterial cells and biofilms could be ascribed to the erosion effects of ROSs in PAH [10], which could induce damage to lipids, proteins, or DNA. Due to the good uniformity and fluidity of PAH, free radicals or reactive species can uniformly cover and fully contact the established biofilm, achieving a remarkable antibacterial effect [26].  
Figure 2: SEM images of _S. aureus_ biofilms after incubation with the plasma-activated hydrogen peroxide solution for (**A**) 0 min, (**B**) 10 min, (**C**) 20 min, and (**D**) 30 min.
# A Method for Efficient Quality Control and Enhancement of Mobile Laser Scanning Data
## 3 Methodology
### Summary  
Using our methodology, we identify deviations of an MLS point cloud with respect to another one. The reference point set \(R\) might be laser scan data acquired from the opposite driving direction, a second scanner, or a terrestrial laser scan. In summary, five processing steps are necessary to assess the coincidence of reference \(R\) and query point cloud \(M\) and to match them profile by profile.  
1. Assuming both point clouds are aligned at the centimeter level, construct a KD tree for the reference point cloud \(R\) and search for the nearest neighbor for each point in the query cloud \(M\);
2. Compute the normals for each point in \(R\) using the already constructed KD tree;
3. Segment road points, and exploit the GNSS timestamps of the MLS point cloud for profile extraction (see Section 3.2);
4. Solve the constraint optimization problem, and estimate the transformation parameters profile-wise (see Section 3.3); and
5. Smooth the estimated parameters with a moving average filter, and correct the point cloud(s) (see Section 3.5).  
For analyzing the co-registration of scanners, we suggest transforming each profile into the time-variable IMU body frame (see Section 3.4). High-frequent trajectory data are a prerequisite, though. In further consequence, the estimated parameters also relate to the body frame, which allows for a better interpretation.
## 4 Simulations
### Case 2  
To verify the flexible control performance under different desired trajectories, the velocity \(v\) of the walking motion and the amplitude \(A\) of the lifting and turning motion, i.e., \(v\in(0\,\mathrm{m/s},0.5\,\mathrm{m/s}]\), \(A\in[0.1\,\mathrm{m},0.5\,\mathrm{m}]\), in the desired trajectory were determined by industrial application requirements. Within the above range of velocity of the walking motion and amplitude of the lifting and turning motion, different desired trajectories were defined by combining different velocities with different amplitudes randomly. Then, the steady-state response performance of the control algorithm for different desired trajectories was verified. Furthermore, it affected the steady-state reaching time and tracking accuracy of UBSHR when \(v\) and \(A\) of the desired trajectory were at maximum. Owing to space constraints, the parameters of a typical desired trajectory were selected as the maximum, i.e., \(v=0.5\,\mathrm{m/s}\), \(A=0.5\,\mathrm{m}\). The desired trajectory of a UBSHR's end-effector is defined in (29)-(31) to verify the steady-state response performance of the developed control method for the typical desired trajectory. The simulation results for tracking the desired trajectory 2 under CE-SOSMC and SC-AFNN are depicted in Figure 8. Table 2 shows the numerical values of the steady-state performance and root mean square error (RMSE) for tracking the desired trajectory 2.  
\[x=0\quad(0\mathrm{s}\leq t\leq 6\mathrm{s}) \tag{29}\]  
Figure 6: The output torque curves. **(a)** Driving torque of slider 1; **(b)** Driving torque of slider 2; **(c)** Driving torque of driving wheel 1.  
Figure 7: The synchronization error curves of each component of the UBSHR’s end-effector. **(a)** Synchronization error in \(z\)-direction; **(b)** Synchronization error of \(\beta\).  
\[z=\begin{cases}0.22&(0s\leq t\leq 2s)\\ 0.345-0.125\cos[2\pi(t-2)]&(2s<t\leq 4s)\\ 0.22&(4s<t\leq 6s)\end{cases} \tag{30}\]  
\[\beta=\begin{cases}\pi-\pi\cos(0.5\pi t)&(0s\leq t\leq 2s)\\ \pi&(2s<t\leq 4s)\\ 2\pi-\pi\cos[0.5\pi(t-4)]&(4s<t\leq 6s)\end{cases} \tag{31}\]  
Analyzed from Figure 8 and Table 2, the reaching time and maximum tracking error and RMSE of SC-AFNN are smaller than that of CE-SOSMC. Thus, SC-AFNN possesses better tracking performance. The chief reason for this is that the control parameters of CE-SOSMC are obtained under optimal performance for desired trajectory \(1\). It is difficult to track different desired trajectories actively for CE-SOSMC. Nevertheless, the proposed SC-AFNN method can satisfy the flexible control of active adaption to variational desired trajectories caused by different technological requirements due to the capacity to self-learn and self-adjust control rules and control parameters.  
In conclusion, the proposed SC-AFNN method can improve the tracking performance under system uncertainties, synchronization performance, and flexible control performance of active adaption to variational desired trajectories caused by different technological requirements simultaneously.