esa-sceva/satcom-chunk-collection huggingface dataset:
# Combination Pattern Method Using Deep Learning for Pill Classification
## 1 Introduction
To accomplish this, MCPL combines features obtained from multiple established image feature-extraction algorithms: KAZE, Oriented FAST and Rotated BRIEF (ORB), Shi-Tomasi, and Harris. KAZE offers scale and rotation-invariant feature-detection robustness, capturing intricate details and broader patterns within images. ORB combines the FAST key point detector with the BRIEF descriptor for fast and efficient feature matching. Shi-Tomasi and Harris focus on corner detection, with Harris excelling under changes in rotation and illumination. Figure 1: Pentagonal pill shapes with subtle differences. By combining these algorithms, MCPL comprehensively represents pill images, capturing a wide array of features, including shapes, patterns, and details that may be missed by using a single extraction method. To effectively tackle these identification challenges, we employ the YOLO version 5 object-detection model to initially identify pills within images. This step is crucial as it sets the stage for more detailed analysis. Following this initial detection, our convolutional networks undertake a thorough analysis of the pills' shape, color, and form, focusing particularly on those with complex geometries. This targeted approach is vital for the MCPL method to refine the feature-extraction processes, significantly enhancing classification accuracy and adapting to the diverse challenges presented by different pill types. The contributions of this study are as follows. * Proposal of a new approach, Multi-Combination Pattern Labeling method, designed to enhance the accuracy and reliability of pill classification in challenging image conditions. The proposed method extracts unique and representative features from pill images, providing a rich representation of the pill's characteristics to improve classification performance. * Proposal of a pill-detection model-based MCPL to extract pills' shape, color, and form, focusing particularly on those with complex geometries. * Verification of the proposed methods through the experimental results. This paper is organized as follows: Section 2 introduces the proposed method and summarizes related studies, providing the necessary context and background for our approach. Section 3 details the deep learning-based pill-classification system developed in this study, elucidating technical details and integration of the MCPL method. Section 4 elaborates further on the MCPL method, describing how it utilizes various image feature-extraction algorithms to surmount the identified challenges. Section 5 examines the experimental results, assessing the efficacy of the MCPL method across various scenarios and pill types. Finally, Section 6 presents our concluding remarks, discussing the implications of our findings and suggesting potential avenues for future research.
# Understanding and Enhancement of Internal Clustering Validation Indexes for Categorical Data
## 2 Related Work
### Comparison of Internal and External Clustering Validation Indexes  
Internal CVIs use only internal information to identify commonalities in the data and react based on the presence or absence of such commonalities, to measure the quality of the clustering result [2; 9; 17; 23]. The internal information includes, but is not limited to, the attribute value distribution, similarities of objects or clusters, and the partition size, which are quantities and features inherited from the dataset and the clustering process.  
The avoidance of requiring external information makes internal CVIs applicable to the unsupervised scenarios, and can act as the objective functions of the clustering process. For instance, internal CVIs are used as objective functions of COOLCAT clustering [30], \(k\)-modes clustering [31], CLOPE clustering [33], and CDCS clustering [34].  
External CVIs use external information. In the literature, there are two types of overall expression of the external information: (1) Explicitly expressed as 'true partition', 'class labels', 'data division', and 'pre-specified/pre-known structure' [1; 4; 5; 23; 40; 41; 42; 43; 44; 45; 46; 47; 48]; (2) used vague expression, such as 'prior knowledge' and 'ground truth' [17; 49; 50]. In the literature adopted the first type of expression, the usage of external CVIs was representatively described as in References [5]:  
'Based on the external criteria we can work in two different ways. Firstly, we can evaluate the resulting clustering structure \(C\), by comparing it to an independent partition of the data \(P\) built according to our intuition about the clustering structure of the data set. Secondly, we can compare the proximity matrix \(P\) to the partition \(P\).'  
The external CVIs used in the references that adopted the second type of expression also required the pre-known partition, although the universal requirement was not explicitly stated. Therefore, the applications of typical external CVIs are quite limited to the scenarios where the true or designated partitions can be compared with, for instance, choosing the optimal clustering algorithm on a specific dataset.  
In the experimental section, we use external CVIs as the evaluation metrics to examine the performance of internal CVIs.
## 2 Particle Swarm Optimization
### Complexity of the PSO and Approaches for Its Parallelization in a Cluster  
The classical PSO algorithm works on the basis of iterations, during which the particles are evaluated in terms of their _fitness_ and their personal and global best positions. At the end of each iteration, the velocities and positions of all particles must be updated, which presents a challenge for the asynchronous parallel implementation of such an update.  
The use of PSO to address prediction and classification problems has the following advantages and disadvantages:  
* As a metaheuristic algorithm, no specific knowledge of the problem to be solved is required.
* As an evolutionary algorithm, it is readily adaptable to parallel computing structures.
* The ability to work with multiple solutions allows for a higher tolerance for local maxima or minima.
* The primary disadvantage of the PSO algorithm is that it requires a significant amount of time to reach an optimal solution for complex problems, particularly those with a large number of particles or dimensions. This is due to the necessity of conducting numerous evaluations of the fitness function, which can be a considerable drawback in terms of computational efficiency.  
The parallelisation of the PSO algorithm for the training of neural networks represents a pioneering strategy with multiple applications and significant benefits in the domain of machine learning and artificial intelligence. The benefits of parallelising the PSO algorithm, as outlined in reference [22], include enhanced efficiency, faster solution exploration, enhanced scalability, increased accuracy and a reduction in development time.  
The first variant considered here is the distributed synchronous PSO (DSPSO), which uses the most common form of iteration. First, the fitness of each particle is evaluated using the particle's position as input. Subsequently, the best personal position of each particle is determined to identify the best global position among all particles. Finally, the position and velocity of each particle are updated based on the best global position.  
The second variant, which was subjected to comprehensive study and subsequently implemented in its entirety, is the so-called distributed asynchronous PSO (DAPSO). It differs from the previous variant in that it updates the position and velocity of particles as soon as their _fitness_ is evaluated. Consequently, the position and velocity of each particle may not be necessarily consistent across all the nodes in the cluster during the computation of this variant of the PSO. The distributed nature of this computation will result in a faster execution time than that of DSPSO, without affecting the validity of the final result. This is due to the communication that will be established between the processes responsible for updating the position and velocity of each particle, which will ensure that the global position and velocity detected by each particle in the algorithm will be consistent, as it will adhere to a unique value.  
Figure 1: Graphical representation of a PSO algorithm’s particle with its attributes.
# Experimental Validation and Evaluation of a Coupled Twist-Camber Morphing Wing Concept
## 5 Discussion  
An experimental validation of a coupled twist and camber morphing wing concept was performed and performance was evaluated based on ground and flight tests. The concept provides roll control, improved flight efficiency, and load alleviation potential.  
The ground tests validated the target shapes of the wing for different actuation configurations. These shapes were later used to estimate roll control authority, RBM variation, and L/D for the various configurations tested. Preliminary results show that roll authority is low when compared to a general aviation aircraft, RBM reduction potential is high and the morphing concept presents benefits when adapting the configurations for different flight conditions.  
Other important challenges to overcome in order to make the concept more attractive include mass reduction and actuation energy. The current prototype mass was excessive and the continuous energy expenditure required to maintain the configuration shape needs to be avoided or at least mitigated. Nevertheless, a significant margin for improvement exists in these two areas, along with improving roll authority.  
Flight tests did not include the quantification of either the actual aerodynamic efficiency differences between configurations or the verification of the load alleviation capability in flight. However, it was possible to assess qualitatively the roll authority provided by the wing when actuated in a morphed configuration with no root actuation, which was deemed to be adequate. The preliminary flight test campaign suggested further prototype developments and quantitative testing were needed.
# Rapid-DEM: Rapid Topographic Updates through Satellite Change Detection and UAS Data Fusion
## 5 Conclusions  
The Rapid-DEM framework demonstrated the feasibility of prioritized DEM updates by combining satellite-based change detection with consumer-grade UAS mapping and open-source DEM fusion. The framework provides tools for addressing the availability of up-to-date elevation data in areas with changing topography, such as rapidly developing urban areas where the proposed priority queue helps to direct the mapping and updates to sites with the highest impact. The case study highlighted the importance of updated DEMsfor modeling surface water flow. We consider this framework a first step in developing a robust, continuous DEM updating system that can be broadly deployed to capture dynamic landscapes beyond urban regions, such as coastal areas affected by storms and sea-level rise. As the computational resources, data storage, and UAS become more affordable, open-source platforms like Rapid-DEM can enable equitable access to up-to-date DEM data reducing uncertainty in model based decision-making.  
The following are available online at [[https://www.mdpi.com/article/10.3390/rs14071718/s1](https://www.mdpi.com/article/10.3390/rs14071718/s1)]([https://www.mdpi.com/article/10.3390/rs14071718/s1](https://www.mdpi.com/article/10.3390/rs14071718/s1)), Report S1: WebODM UAS Flight Report.  
Conceptualization, C.T.W. and H.M.; methodology, C.T.W., W.R, A.P., H.M.; software, C.T.W., A.P.; validation, C.T.W., H.M., W.R. and A.P.; formal analysis, C.T.W.; investigation, C.T.W.; resources, C.T.W., W.R.; data curation, C.T.W., W.R.; writing-original draft preparation, C.T.W.; writing-review and editing, C.T.W., W.R., A.P., R.K.M., H.M.; visualization, C.T.W.; supervision, H.M., R.K.M.; project administration, C.T.W. and H.M.; funding acquisition, H.M., R.K.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Science Foundation grant number 1737563.  
Not applicable  
The data presented in this study are openly available in Open Science Foundation repository Rapid-DEM at DOI 10.17605/OSF.IO/YGGH8 [66].  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} UAS & Unmanned Aerial System \\ DEM & Digital Elevation Model \\ DSM & Digital Surface Model \\ DTM & Digital Terrain Model \\ GCP & Ground Control Point \\ SfM & Structure from Motion \\ OSM & OpenStreetMap \\ ODM & Open Drone Map \\ GBQ & Google BigQuery \\ GCS & Google Cloud Storage \\ GEE & Google Earth Engine \\ \end{tabular}
# A Novel Method for 3D Lung Tumor Reconstruction Using Generative Models
## 3 The Proposed Model
### Lung Segmentation
#### 3.1.1 Generator Architecture
\begin{cases}+1,&\text{if $a_{t}=y_{t}$ and $s_{t}\in D_{L}$}\\ -1,&\text{if $a_{t}\
eq y_{t}$ and $s_{t}\in D_{L}$}\\ +\lambda,&\text{if $a_{t}=y_{t}$ and $s_{t}\in D_{N}$}\\ -\lambda,&\text{if $a_{t}\
eq y_{t}$ and $s_{t}\in D_{N}$}\end{cases}  \tag{2}\]  
where \(D_{L}\) and \(D_{N}\) represent the minority (lung) and majority (no lung) classes, respectively. Correctly/incorrectly classifying a sample from the majority class yields a reward of \(\pm\lambda\), where \(0<\lambda<1\). The pseudocode for our proposed method is outlined in Algorithm 1. Figure 2: Architecture of the U-Ne-based generator network used for lung segmentation, illustrating the flow from input CT scan through the encoder and decoder stages to the final mask output. ```
1:Initialize generator parameters \(\theta_{g}\) and learning rate \(\alpha\). 2:Load training dataset consisting of CT scan images and corresponding ground truth masks. 3:while not converged do
4:for each image \(I_{i}\) in the dataset do
5: Process \(I_{i}\) through generator to produce mask \(M_{i}\). 6: Compute rewards \(R_{t}\) for each pixel based on its classification: \[R_{t}(s_{t},a_{t},y_{t})= \begin{cases}+1,&\text{if $a_{t}=y_{t}$ and $s_{t}\in D_{L}$}\\ -1,&\text{if $a_{t}\
eq y_{t}$ and $s_{t}\in D_{L}$}\\ +\lambda,&\text{if $a_{t}=y_{t}$ and $s_{t}\in D_{N}$}\\ -\lambda,&\text{if $a_{t}\
eq y_{t}$ and $s_{t}\in D_{N}$}\end{cases} \]
7: Calculate gradients of the policy \(\log\pi_{\theta_{g}}(a_{t}|s_{t})\). 8: Update \(\theta_{g}\) using policy gradient update rule: \[\theta_{g}^{\prime}=\theta_{g}+\alpha\sum_{t=0}^{T}\
abla_{\theta_{g}}\log \pi_{\theta_{g}}(a_{t}|s_{t})\overline{R_{t}}\]
9:endfor
10:endwhile
```  
**Algorithm 1** Pseudocode of RL-based model for lung segmentation.
# A Proposal for a Standardized Approach to Inducing Low Energy Availability in Athletes
## 2 Materials and Methods
### Methods for Measuring FFM  
FFM measurement can be done in more ways, but two of them are widely used in research setting due to accuracy or low burden and cost. Still considered the golden standard, dual energy X-ray absorptiometry, can estimate FFM with low precision errors [15]. It is not as accessible, relatively more expensive and provides small doses of radiation. The bioelectrical impedance is another way of precise FFM measurement [16]. Subjects must be instructed to be hydrated properly before measurement and must avoid physical activities several hours before. It is cheaper and is more accessible. The most important thing when observing EA is to use the same methodology when comparing inter-individual or intra-individual results, otherwise changes in FFM can be observed due to flawed methodology.
# Wireless Accelerometer Architecture for Bridge SHM: From Sensor Design to System Deployment
## 1 Introduction
### Contributions of This Paper  
In this work, to overcome the main criticism found in the previous works and to meet the requirements for SHM, a novel WSN is proposed with the following characteristics:  
* An accelerometer with a noise density of 20 \(\upmu\)g/\(\sqrt{\mathrm{Hz}}\) is adopted.
* Global navigation satellite system (GNSS) synchronization is employed in order to guarantee a maximum synchronization error of 16.8 \(\upmu\)s.
* The nodes are equipped with a battery and a PV panel to guarantee longer lifetimes with respect to the proposed state-of-the-art solution.
* The number of acquisitions per day can be increased when solar energy is more available due to seasonality.  
The nodes are then tested in a laboratory for metrological verification and then on a real bridge to verify the effectiveness of the proposed solution. This paper is structured as follows: Section 2 describes the sensor node and the gateway architecture, Section 3 defines the state machine of the sensor node by defining the different working phases, Section 4 describes the methodological validation of the sensor node through experimental tests, and Section 5 describes the case study considered for validation on a real bridge.
# The Kinematic Analysis of a Wind Turbine Climbing Robot Mechanism
## 1 Introduction
This adhesion force needs to adapt the different diameter of the wind turbine tower, as it climbs through a circular truncated cone shape by winding and unwinding the cable through the help of the step motor. Addressing the physical issues on the circular truncated cone shape of the wind turbine tower that are susceptible to environmental conditions, this paper proposed to design a climbing robot dedicated to this task or even to carry ancillary equipment to perform wind turbine maintenance. The robot can perform a tower surface visual inspection at any part of the tower structure as well as a wind turbine blades inspection, even in different weather conditions. The development of the robot has the payload capability of carrying inspection tools estimated to be at least 50% from the robot's total weight without breaking or slipping. Compared with previous studies, this proposed climbing robot utilizes a winding mechanism as an adhesion method and mecanum wheels as a locomotion method that takes advantage of maneuverability to do the different movement states up-down, rotation, and spiral to further prove the effectiveness of this robot design in performing maintenance of a wind turbine application. The comprehensive motion characteristics were defined through the kinematics analysis and innovative model to approximate the robot's position to optimize its climbing capability in a truncated cone tower. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**WT Climbing Robot** & **Image** & **Locomotion** & **Adhesion** & **Movement** & **Application Task** \\ \hline
6 & BladeBug Robot [3] & Legged & Vacuum & N/A & Inspection and resurfacing the blades. \\ \hline
7 & Magnetic & Wheeled & Magnetic & Up-Down & Blade inspection and tool handling \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._In this paper, Section 2 gives the mechanical design of a scaled-down prototype of a wind turbine climbing robot and the robot's dimension in detail. Section 3 presents a kinematic analysis of a four mecanum wheel robot with a symmetrical structure and robot kinematic equation of motion that relates wheel velocity along the tower structure. To verify the correctness of the kinematic and motion, a theoretical analysis obtained from an estimated velocity were implemented through MATLAB in Section 4. Finally, conclusions and future work are described in Section 5.
## 2 Evolution of NMR Methodologies
### Solution-State NMR for Polysaccharides
#### 2.1.1 Structural Elucidation
The HMBC spectrum provided long-range correlations between protons and carbons that are separated by two or more bonds, helping to definitively establish glycosidic linkages. For instance, a significant cross-peak between the anomeric proton at \(\delta_{\rm H}\) 5.42 and the C-4 carbon at \(\delta_{\rm C}\) 79.6 in the HMBC spectrum confirmed the \(\alpha\)-(1\(\rightarrow\)4) linkage in the glucan backbone. Additionally, the cross-peak between H-4 (\(\delta_{\rm H}\) 3.68) and the anomeric carbon (\(\delta_{\rm C}\) 102.5) further substantiated the linkages within the glucan backbone. These 2D NMR experiments allowed for a detailed and unambiguous structural elucidation of RAPS-3, revealing a backbone of \(\alpha\)-(1\(\rightarrow\)4)-D-glucan with side chains of arabinose, as well as confirming the presence of other minor polysaccharide units. The ability to distinguish between different anomeric protons (e.g., from arabinose and glucose) and identify their respective linkages to the main polysaccharide chain is a direct advantage of 2D NMR, making it indispensable for structural analysis of complex polysaccharides like those from Radix Astragali. Hence, 2D NMR techniques such as COSY, HSQC, and HMBC are critical in overcoming the challenges of structural elucidation of polysaccharides. They provide the necessary resolution to distinguish between different sugar units, determine the exact nature of glycosidic linkages, and confirm the overall architecture of polysaccharide chains, which is essential for understanding their biological activities and applications in medicinal studies.
# Three-Dimensional Processing of Reflections for Passive-Source Seismology Based on Geometric Design
## 3 Direct Migration of Reflections of 3D Waves from Passive Source Based on Geometry
### Calculation of 3D Reflections from a Passive Source Using Direct Migration Based on Geometry  
The process of calculating the 3D reflection from a passive source by using direct migration based on the geometry is similar to the method of generating virtual shot records of reflections from a 3D passive source based on the relevant geometry file, as shown in Figure 18:  
1. Having collected random noise records from the field, the geometry that satisfies the given requirements is designed by using a design software according to the known surface and information on the underground structure as well as the geological targets, and is stored in the SPS file.
2. A random noise record is set at a specified location as the noise record of the source point according to the content of the SPS.S file. The range of the corresponding receiver point is obtained from the SPS.X file according to the source point. The noise records of all receiver points are read, the one-way wave offset is calculated, stacking according to the time window is performed, and the information stored in the SPS.S file and SPS.R files is inserted into the header.
3. All of the results of the migration of virtual shot records are calculated, and the imaging section is obtained after processing the stack.  
Figure 17: Comparison of (**a**) results of migration imaging of a 2D passive source using conventional processing and (**b**) direct migration imaging.  
Figure 18: Processing flow of calculations of the direct migration method for 3D passive seismic exploration.
## 2 Wind Tunnel Test
As shown in Figure 4, four azimuth angles \(\theta_{\text{zf}}\) were considered, where an azimuth angle of 0\({}^{\circ}\) is defined as when one of the blades was vertical, and an azimuth angle of 60\({}^{\circ}\) as when one of the blades was located in front of the tower. The sampling frequency of the laser displacement sensors was set to 600 Hz, and the measurement time was adjusted such that 10 10-min samples were obtained. The natural frequencies and damping ratios in principal axes were controlled by using the spring stiffness and magnet at the bottom of the aeroelastic gimbal system. The natural frequencies were determined to be 5.6 Hz and 3.5 Hz for the tower-only case and the wind turbine case, respectively, and damping ratios were 1.1% and 0.8%, respectively, on the basis of guidelines for wind turbines in Japan [14]. The wind tunnel test conditions are summarized in Table 2.  
Figure 4: Definition of forces (**left**) and azimuth angle (**right**). Figure 3: Experimental flow. Reynolds number was defined by using reference length and reference wind speed at hub height, and Reynolds number was about \(R_{e}\) = 1.8 \(\times\) 10\({}^{4}\) for the smooth-surfaced circular tower. The maximum blockage ratio by wind turbine model was less than 2%; thus, corrections were not applied to the experimental data.