esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion and Conclusions  
### Implications for the Field of Biomedical Image Segmentation  
The DDPM holds significant implications for the field of biomedical image segmentation, introducing novel approaches that address various challenges associated with traditional methods. DDPM excels in denoising biomedical images, contributing to enhanced image quality by effectively reducing noise and artifacts. DDPM introduces a probabilistic framework that allows for the quantification of uncertainty in segmentation predictions. Probabilistic models are valuable in medical applications where uncertainty awareness is essential for clinical decision-making and ensuring the robustness of segmentation results. DDPM can be seamlessly integrated into deep learning architectures, combining the denoising capabilities of DDPM with the representational power of deep neural networks. This integration harnesses the strengths of both approaches, providing a more robust and adaptable solution for complex biomedical image segmentation tasks. In medical applications, where transparency and interpretability are crucial, DDPM offers insights into the reliability of segmentation predictions, fostering trust among healthcare professionals. Improved segmentation accuracy is paramount in medical applications, where precise delineation of structures and anomalies is critical for diagnosis and treatment planning. DDPM, when integrated into deep learning architectures, has the potential to advance segmentation accuracy by addressing noise and uncertainties.  
DDPM's _implications for biomedical image_ segmentation are multi-faceted, encompassing noise reduction, uncertainty quantification, adaptability to diverse modalities, ethical considerations, and potential integration into clinical workflows. As research in this area progresses, DDPM stands as a promising approach contributing to the advancement of accurate and reliable biomedical image segmentation.
# Multi-Backup Beams for Instantaneous Link Recovery in mmWave Communications
## 3 System Model
### Blockage Model  
As mentioned earlier, the blockage path loss model, \(\Gamma_{bl}\), accounts for LoS-to-NLoS link transition, when obstacles of different densities are present in the direct propagation link affiliated with the main beam. This model is formulated as [18]  
\[\Gamma_{bl}=\mathbb{I}\Big{[}\mathbb{P}(d\ )\Big{]}\ \Gamma_{\text{LoS}}(d\ )+ \mathbb{I}[1-\mathbb{P}(d\ )]\ \Gamma_{\text{NLoS}}(d), \tag{9}\]  
where \(\mathbb{I}\) is an indicator function that specifies the link-blockage state, i.e., \(\mathbb{I}(x)=1\)_iff_\(x=1\), and it is set as \(\mathbb{I}(x)=0\) otherwise. Moreover, the variables \(\Gamma_{\text{LoS}}(d)\) and \(\Gamma_{\text{NLoS}}(d)\) represent the path loss for the LoS and NLoS settings, respectively, expressed as [19],  
\[\Gamma_{\text{LoS}}(d) =\ 10\log_{10}(d_{ref})\ +\ 10\ \delta_{\text{LoS}}\log_{10}(d),\ \ for\ \text{LoS}, \tag{10}\] \[\Gamma_{\text{NLoS}}(d) =\ 10\log_{10}(d_{ref})\ +\ 10\ \delta_{\text{NLoS}}\log_{10}(d),\ \ for\ \text{NLoS}, \tag{11}\]  
where the variable \(d\) represents the distance between the BS and MS, \(d_{ref}\) is close-in reference distance, and \(\delta_{\text{LoS}}\) and \(\delta_{\text{NLoS}}\) are the path loss exponents (PLE) for the LoS and NLoS settings, respectively. Moreover, the notations \(\mathbb{P}(d)\) and (\(1-\mathbb{P}(d)\)) denote the LoS and NLoS probabilities at the distance \(d\). Here the probability is \(\mathbb{P}(d)=exp(-\rho d)\), where \(\rho\) is the blockage parameter that accounts for obstacles of different dimensions and densities. Note that the higher the blockage parameter, the more blockage effects are caused to the link.
# An Efficient RRT Algorithm for Motion Planning of Live-Line Maintenance Robots
## 2 Live-Line Maintenance and Robot System
### Planning Problem Definition  
During the task process, it is necessary to plan a path from the tool position to the working position in the joint space. After the operation is completed, re-plan a path from the working position to the tool position in the changed environment. The motion planning problem can be described as:  
\[\left\{\begin{array}{l}\Theta=f(\theta_{0},\theta_{n})\\ \Psi(\theta_{i})<0,(\theta_{i}\in\Theta)\end{array}\right. \tag{1}\]  
where \(\theta_{0}\) and \(\theta_{n}\) are the joint angles of the beginning and end positions, respectively. \(\Theta\) is the planned path, which is composed of ordered path points \(\theta_{i}\). \(\Psi\) is the constraints. The constraints of this problem mainly include four aspects: obstacle constraint, preference constraint, attitude constraint and moment constraint. Obstacle constraints for robot movement include static obstacles (\(O_{S}\)) and dynamic obstacles \(O_{d}\). The position of static obstacles relative to the platform for each task is constant, while dynamic obstacles need to be measured by sensors. Obstacle constraints can be described as:  
\[f_{\text{coli}}\left(\theta_{i},O_{S},O_{d}\right)<0 \tag{2}\]  
The preference constraint is to keep the robot away from charged objects and avoid cable entanglement. The preference constraint is achieved by limiting the range of motion of the joint, described as:  
\[\theta_{imin}<\theta_{i}<\theta_{max},(\theta_{i}\in\Theta) \tag{3}\]  
The joint limits \(\theta_{imin}\) and \(\theta_{imax}\) needs to be set based on experience.  
In order to meet the requirements of the posture of the tools for the task, it is necessary to limit the robot end posture during the movement. The posture constraint can be described as:  
\[\left\{\begin{array}{l}R=f_{kin}(\theta_{i})\\ R_{min}<R<R_{max}\end{array}\right. \tag{4}\]  
where \(f_{kin}\) is the positive kinematics formula of the robot, and \(R\) is the robot posture. \(R_{max}\) and \(R_{min}\) is the attitude limit, depending on the tool requirements.  
Finally, due to the large quality of the tool ruler and the influence of the cable, the robot needs to avoid positions with poor load capacity during the movement, such as the robot being straight in horizontal. The dynamical constraints are described as follows:  
\[T(\theta_{i})<T_{\text{limit}} \tag{5}\]  
In the path planning, in order to simplify the model, we merge the attitude constraint and the moment constraint into the obstacle constraint.
# A Comprehensive Survey on Real-Time Applications of WSN
## 3 Applications of WSN
### Forest Monitoring  
The value of the forest and its importance on earth plays a vital role in human life and an unparalleled role in maintaining the biogeochemical cycle of the atmosphere. Due to urbanization, afforestation is of the utmost importance for society. Additionally, for surveillance of the forest, monitoring is required. For maintaining safety, WSNs are usually employed in the aforesaid area. Figure 5, represents real-time sensor deployment for forest monitoring.  
Monitoring the micro-climate in the woods is becoming increasingly important, given the predicted climate change. In-depth knowledge of individuals about the physical, chemical, and biological status of soil in the root system of trees makes a significant contribution to the intensive monitoring of forest ecosystems. These long-term studies are a prerequisite for the derivation of measures for forest management. Forest operation remains unaffected by the wireless transmission of sensor readings. Instead of that, wireless transmission ensures robustness, as compared to a wired installation of sensors [48; 49]. Table 6, depicts the examples of the real-time applications for forest monitoring that utilize WSNs.  
[PERSON] et al., 2006 studied and developed a Forest Fire Surveillance System (FSSS) in Daegu, Korea. Based on the literature, it was reported that no aforesaid surveillance was present. [PERSON] et al., 2006 developed the FSSS, which they incorporated and integrated into the system for surveillance in the forest. The FSSS consists of WSN, middleware, and a real-time approach. The system will detect the physicochemical parameters of the environment such as temperature, humidity, and perceived smoke. When any kind of heat and smoke is detected by the sensor, it will automatically generate an alarm regarding the possibility of a forest fire.  
Figure 5: Sensor deployment for forest monitoring [50].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**References** & **Application** & **Sensors** & **City Deployed in** \\ \hline [PERSON] et al. (2002) [46] & Monitoring Sea Bird & MICA Weather Board, Temperature, barometric & California, USA \\  & Nesting Environment & pressure, passive infrared sensors & \\ \hline [PERSON] et al. (2010) [47] & Monitor Sea Birds on & Temperature, Humidity, Movement, & \\  & Skomer Island & Radio-Frequency Identification (RFID), Weight & Wales, UK \\ \hline \hline \end{tabular}
\end{table}
Table 5: Examples of prototyped applications for habitat monitoring.  
[PERSON] et al., 2007 investigated the real-time application of sensors for the detection of the Forest Fire detection index in Columbia, Canada. The authors developed a data aggregation scheme-based Fire Weather Index (FWI) system. Additionally, the obtained data of interest were validated with other models for the system's reproducibility. The K coverage algorithm works as a function of the early detection of fire in the forest vicinity. The developed algorithm is simple, robust, and can be deployed anywhere.
# Multiscale Feature Fusion with Self-Attention for Efficient 6D Pose Estimation
###### Abstract  
Six-dimensional (6D) pose estimation remains a significant challenge in computer vision, particularly for objects in complex environments. To overcome the limitations of existing methods in occluded and low-texture scenarios, a lightweight, multiscale feature fusion network was proposed. In the network, a self-attention mechanism is integrated with a multiscale point cloud feature extraction module, enhancing the representation of local features and mitigating information loss caused by occlusion. A lightweight image feature extraction module was also introduced to reduce the computational complexity while maintaining high precision in pose estimation. Ablation experiments on the LineMOD dataset validated the effectiveness of the two modules. The proposed network achieved 98.5% accuracy, contained 19.49 million parameters, and exhibited a processing speed of 31.8 frames per second (FPS). Comparative experiments on the LineMOD, Yale-CMU-Berkeley (YCB)-Video, and Occlusion LineMOD datasets demonstrated the superior performance of the proposed method. Specifically, the average nearest point distance (ADD-S) metric was improved by 4.2 percentage points over DenseFusion for LineMOD and by 0.6 percentage points for YCB-Video, with it reaching 63.4% on the Occlusion LineMOD dataset. In addition, inference speed comparisons showed that the proposed method outperforms most RGB-D-based methods. The results confirmed that the proposed method is both robust and efficient in handling occlusions and low-texture objects while also featuring a lightweight network design.  
6D pose estimation; multiscale feature fusion; attention mechanism; point cloud feature extraction; lightweight network +
Footnote †: journal: _Algorithms_
###### Abstract  
Even though both fluid mechanics and numerical studies have considerably progressed in the past decades, experimental knowledge remains an important tool for studying the resistance to flow in fluid media where a complex environment dominates the flow pattern. After a comprehensive review of the recent literature on the drag coefficient in open channels with emergent rigid vegetation, this paper presents the results related to 29 experimental accelerated subcritical flow profiles (i.e., M2 type) that were observed in flume experiments with emergent stems in a square arrangement at the University of Calabria (Italy). First of all, we used some of the literature formulas for the drag coefficient, concluding that they were unsatisfactory, probably because of their derivation for uniform or quasi-uniform flow conditions. Then, we tested a recently proposed approach, but when we plotted the drag coefficient versus the stem Reynolds number, the calculated drag coefficients showed an inconclusive behavior to interpret. Thus, we proposed a new approach that considers the calibration of the Manning coefficient for the simulation of the free surface profile, and then the evaluation of the drag coefficients based on the fundamental fluid mechanics equations. With the help of classical dimensional analysis, a regression equation was found to estimate the drag coefficients by means of non-dimensional parameters, which include vegetation density, stem Reynolds number and flow Reynolds number computed using the flow depth as characteristic length. This equation was used to simulate all the 26 observed profiles and, also, 4 experimental literature profiles, and the results were good. The regression equation could be used to estimate the drag coefficient for the M2 profiles in channels with squared stem arrangements, within the range of vegetation densities, flow Reynolds numbers and stem Reynolds numbers of the present study. However, in the case of the three profiles observed by the authors for staggered arrangement, the regression equation gives significantly underestimated flow depths.  
river hydraulics; vegetation; flow resistance; drag coefficient; cylinder arrays +
Footnote †: journal: Applied Sciences
## Appendix B Experimental: Thz Beamline at Australian Synchrotron  
Some results obtained and ATR equipment discussed in this manuscript at at the THz/Far-IR beamline at the Australian Synchrotron (first light 2007). The polarisation of the synchroton radiation is a superposition of the linear (along the extraction mirror slit) and circular polarisations [28]. The two components originate from a constant and changing magnetic field through and at the entrance and exit of a bending magnet, i.e., the dipole- and edge-emissions, respectively. The THz/Far-IR beamline receives mainly the edge-emission, while the neighbouring IR microscopy beamline receives more of the dipole radiation. It is noteworthy that a bunch compression can be achieved to produce coherent synchrotron radiation (CSR) resulting in a flux increase of 2-3 orders in magnitude between 15 and 25 cm\({}^{-1}\) (0.45-0.75 THz; 667-400 mm) at the THz/Far-IR beamline.  
Polarisation is defined as \(E_{s}\) and \(E_{p}\), which are \(\perp\) and \(\parallel\) to the plane of incidence (or TE and TM modes), respectively (Figure 1a). For anisotropic samples, those two components are absorbed differently, according to the alignment of the absorbing dipoles. Anisotropy of absorbance in the sample can be determined. The s-pol. (TE or \(E_{y}\)) probes the orientation of dipoles in the plane of the ATR window. Possibility to change the orientation of the sample is shown in Figure 6b. In the low refractive index \(n_{1}<1.6\) (typical for bio-materials), the reflected light at a typical 45\({}^{\circ}\) incidence usually does not experience a phase change upon reflection at the sample-prism interface. However, the p-pol. (TM; \(E_{z}\)), which probes the absorbance in the direction perpendicular to the prism-sample interface (along the evanescent field) has a strong dependence of the reflected phase on \(n_{1}\). At the \(\theta_{i}=45^{\circ}\) incidence, a phase change of \(\pi\) occurs when \(\theta_{B}<\theta_{i}<\theta_{c}\); \(\theta_{B}\) and \(\theta_{c}\) are the Brewster and critical angle, respectively. The reflected \(E_{z}\) component becomes \(E_{y}\) at the output port. The consequence of the phase dependence of the TM mode makes the polarisation analysis of the ATR signals complicated, and most of the published data do not discriminate polarisation for excitation and do not carry out the polarisation analysis at the exit of the detector after the sample. Multi-reflection ATR units (Figure 2) introduce an uncertainty in the polarisation changes introduced by the sample and the rest of the setup.
# A Fast Indoor/Outdoor Transition Detection Algorithm Based on Machine Learning
## 3 Results
### Refined Classifier Performance Evaluation
#### 3.2.2 Feature Importance Analysis  
According to the accuracy on all test datasets, LightGBM performed best in all single classification model. To intuitively reflect the different kinds of features used in the LightGBM classifier training stage, we focus on the top 25 features importance. Figure 12 shows the ranking of feature importance.  
As shown in Figure 12, we found that the mean of satellite CNR feature at a current time, GS_num_proportion_90 and the skewness of satellite CNR played the most three crucial roles in the training stage. The statistical features play the most important role in the training phase that  
Figure 12: Normalized sorted features importance in LightGBM training process.  
accounts for 67.97% of the top 25 features due to a large number of statistical features were extracted. It is worth noting that the time sequence related features account for 17.78% and spatial geometry distribution related features account for 14.25%, also reflecting the effectiveness of these two kinds of related features.
## 4 APSP and Routing Tables
### Complexity
#### 4.7.1 Memory Requirements  
Although in definition 1, we make no assumption on the memory capacities of each node, the trivial \(n\)-round strategy uses at least \(2\log(n)|E|^{2}\cdot\log(W)\) memory at the leader node that solves the problem. For the APSP problem in question, using the Floyd-Warshall algorithm results in memory requirements of \(2n^{2}\log(n)\cdot\log(nW)\) at the leader node. Hence, we may ask whether the quantum APSP algorithm leads to lower memory requirements. The memory requirement is largely characterized by up to \(720n^{7/4}\log(n)\log(nW)\) needed in step 0 of the EvaluationB procedure, which can be found in the appendix. This results in a memory advantage for quantum APSP over the trivial strategy beginning in the regime of \(n>1.6\times 10^{10}\).
## 6 The Case for Cross-Layer Design
### Methodology
#### Vertical Calibration across Layers  
This type of cross-layer interaction involves the adjustment of parameters that span different layers in the protocol stack. The motivation here is that the potential performance gain at any particular layer is a function of all the parameters of the layers below it. Therefore, joint optimization undertaken in this way can yield increased performance benefits over optimization at the individual layer level. To enhance throughput, Adaptive Modulation and Coding (AMC) has been incorporated at the physical layer to match time varying channel conditions to transmission rates. Achieving high reliability at the physical layer however, requires protocols that can reduce transmission rates using either small size constellations or powerful low rate error control codes [77]. Another way to mitigate channel fading is to utilize ARQ at the MAC layer to improve system throughput and to limit the number of retransmissions. [PERSON] _et al._[77] considered AMC at the physical layer and ARQ at the MAC layer as part of a joint cross-layer design that was able to outperform AMC and ARQ working in isolation within their respective layers.
## 2 Methods
### Analysis Procedures  
The SAXS pattern is strongly dependent on the class of the sample being analyzed. Correspondingly, the methods of analysis of the SAXS data from organized systems, such as fibers and lamellar structures (Figure 1b,c), are quite different from those for disordered systems, such as solutions (Figure 1d). In the case of fibers and lamellar structures, where there are planes of atoms that diffract coherently, Bragg's law can be used to determine the spacing between these planes. The patterns are analyzed by slicing along and perpendicular to the orientation axes to obtain meridional and equatorial scans, respectively. As described in Sections 3 and 4, the full data can also be analyzed by using models derived by iterations starting with an approximate prior knowledge of the structure.  
The scattering in solutions is governed by the Debye function [42]. The 2D pattern is typically converted to a 1D pattern by azimuthal averaging. Since there are no crystalline regions and periodicity, methods based on Bragg's law are not meaningful. Instead, the diffuse scattering near \(q\) = 0 is analyzed to understand the uncorrelated structures in protein solutions and particulates embedded in a matrix and voids. These scans are analyzed by methods described in Section 5. Briefly, the small-angle region of the SAXS data is analyzed using the Guinier equation [40]. The asymptotic behavior is interpreted using Porod's law to understand the contribution of the interfaces. These are discussed in detail in Section 5.
# Reconfiguration for the Maximum Dynamic Wrench Capability of a Parallel Robot
## 1 Introduction
In (2019), [PERSON] _et al._ conducted a multi-objective optimization procedure with weighted stiffness, dexterity and workspace volume as the performance indices to determine the geometric dimensions of a reconfigurable hybrid parallel robot dubbed ReSl-Bot. Therefore, the robot can perform as needed for the task at hand. [PERSON] (2019) presents a new method for the combined topological and geometric reconfiguration of a six DOF parallel robot to achieve task-based reconfiguration. Essentially, these reconfigurable parallel robots that vary their architecture to obtain different kinematic properties only consider the kinematics of a parallel robot. It is assured that taking into account the dynamics of a parallel robot for task-based reconfiguration has real application. Especially, to consider the maximum dynamic wrench capability along a specified trajectory and promptly varying the manipulator geometry in an optimal way is practically more useful. In this paper, the architecture of the proposed Stewart-platform robot with sliding lockable base joints is determined based on the maximum allowable dynamic wrench capability. The dynamic wrench capability of a manipulator along a predefined trajectory is defined as the load which can be applied or sustained by a particular manipulator as the manipulator executes a specified task with the specified precision. Accordingly, the remainder of this paper regarding an optimal task-based reconfiguration study with the maximum dynamic wrench capability is structured as follows: in Section 2, taking into account external dynamic wrenches (applied force and/or moments), the governing equations for a six-DOF Stewart-platform robot with sliding lockable base joints are presented. In Section 3, the determination of the maximal dynamic wrench capability with respect to the locations of the links on the base is cast into the optimization problem, and then a hierarchical optimization process is proposed for the problem. Simulation results are presented and discussed in Section 4. Finally, Section 5 outlines a brief conclusion about this study.