esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Analysis of a Hybrid MPPT Method for PV Systems Under Partial Shading Conditions
## 2 Materials and Methods
### Design of the Proposed MPPT Algorithms  
In this article, three different MPPT methods, including the proposed method, have been examined and compared. The first of these is the Perturb & Observe algorithm, known as a traditional algorithm, which stands out for its simple design and ease of implementation. Due to its small number of parameters, fast MPP tracking ability, and better performance in low-hardware systems, it is frequently included in studies in the literature, and the P&O MPPT algorithm has been selected as the fundamental algorithm in this study. Kalman Filter-Perturb & Observe Hybrid MPPT is a method that was created by combining the computational logic of the traditional P&O MPPT method with the Kalman Filter, which is a model- and estimation -based algorithm. In addition to the aforementioned advantages of P&O, it provides a faster and closer approach to the MPP point with its estimation ability and noise reduction ability. It processes data from voltage and current sensors and increases the MPPT ability of the P&O algorithm by calculating the estimated power. An improved hybrid algorithm has been tested by selecting the KF-P&O Hybrid MPPT method as the secondary method. In this study, a hybrid method combining the P&O algorithm and the PSO algorithm is proposed. In this hybrid method, the P&O algorithm determines the reference voltage quickly and approaches MPP. Then, the MPP is determined more accurately by using the PSO algorithm. By choosing the PSO algorithm which has the logic of algorithm inspired by nature and P&O algorithm, which is the classical method, two different algorithms that are in different categories are used. The P&O-PSO Hybrid MPPT method is chosen as the proposed method due to features  
Figure 3: Control circuit of the DC-DC boost converter.  
Figure 2: DC-DC boost converter circuit.  
such as less parameter adjustment, faster MPP convergence, high MPP tracking accuracy and ability to select global maximum search range depending on parameters.  
In this section, MPPT algorithms are used with function blocks in the MATLAB/Simulink environment. Each function block contains the MATLAB code containing the relevant algorithm. The MPPT algorithms are hybrid in structure and are created by combining more than one MPPT algorithm. In these function blocks, MPP tracking is performed, and V\({}_{\text{ref}}\), which is the reference voltage at the outputs, is produced. The reference voltages generated here are then be sent to the DC-DC boost converter control circuit. Then the current and voltage values of the PV array are measured in this structure. The measured values are processed with the MPPT algorithm and a reference voltage is produced as a result. This reference signal is used for the DC-DC boost converter control.
## 4 Discussion
### Reliability of the Measurmet  
The APA time and distance demonstrated adequate to excellent ICC in all directions and target distances except for the APA distance at 90 degrees. Since this is a novel paradigm to evaluate APA, there were no previous systems to compare with. The good reliability the APA measurement was possibly due to the reliable perturbation generated by the current testing paradigm. For the proactive balance variables, our study also showedthe adequate to excellent reliability on both path length and path time measurements. The ICC values of proactive balance variables were comparable with previous studies that utilized a stabilometric platform [9; 41]. The studies reported the ICC of COP parameters to be between 0.54 and 0.92 for incomplete SCI [9] and multiple sclerosis individuals [41], in which COP was tested during steady standing.  
There are also several other studies that use the Wii balance board as a clinical device for measuring balance and COP. For example, [PERSON] et al. [51] used a custom-design software and found that the Wii balance board for COP path length had good test-retest reliability within-device (ICC = 0.66-0.94) and between-devices (ICC = 0.77-0.89). High concurrent validity with gold standard force plate was also reported [51]. The variability possibly stemmed from the tested subjects' variability rather than the device itself.  
There are several concerns while interpreting the results of this study. First, our results showed the path time and path length were consistent in all directions. This should be due to the fact that the participants are healthy and are able to perform weight shifting symmetrically toward all directions. The testing direction could still be crucial for patients needed to be studied. Second, although the system was developed on Wii balance board, this does not exclude other COP measurement devices' probable feasibility. Any force plate system that could measure COP with adequate reliability and accuracy could be used. Third, whether the APAs measured in this paradigm could predict falls in patient population is suggested in future studies.
## 3 Results and Discussion
### Corrosion Resistance Evaluation
#### 3.2.2 The Equivalent Circuit R(C(R(QR)))
The load transfer resistance (R\({}_{\text{ct}}\)) decreases in the following order I-Zn\(>\) II-Zn / Fe\(>\) III-Mn, where I-Zn exhibits the best corrosion protection. The capacity of the electric double layer is represented by a non-ideal capacitor; therefore, it was necessary to introduce the constant phase element (CPE) because the deviation from ideal behavior is very high. In the case of phosphate and impregnated oil samples immersed in rainwater, the resistance of the surface layer and especially the resistance of the electric double layer are very high, which is why the polarization resistance is in the order of tens of k\(\Omega\cdot\)cm\({}^{2}\) (more precisely: R\({}_{\text{P}}\) = 23, 9 k\(\Omega\cdot\)cm\({}^{2}\)). Therefore, the instantaneous corrosion rate could not be measured, and the constant phase element has the significance of a Warburg impedance. The solution resistance in the case of the OPS/RW system is higher than in the case of the C45/RW system, although the geometry of the measuring cell is the same because the resistance of the oil film is added to the actual resistance of the solution.
# Dilated Multi-Temporal Modeling for Action Recognition
## 2 Related Work
### Multi-Temporal Modeling  
Accurately recognizing actions from videos can be challenging due to the diverse duration of action instances. This challenge has led to a line of research focused on multiple temporal modeling, which aims to capture different motion information in videos. Temporal modeling techniques can help to overcome the negative impact of variable action durations on recognition accuracy by capturing the temporal dependencies of action instances. By improving the ability to capture different temporal information, multiple temporal modeling has the potential to significantly improve the accuracy of action-recognition systems. Some strategies have been proposed to model diverse temporal information in videos for action recognition. Despite the recent advancements in action recognition, multi-scale temporal modeling is still an underdeveloped area of research. In recent years, there have been some efforts to address this challenge and to develop multi-scale temporal modeling techniques that can capture the temporal relationships across different scales of actions.  
For example, the temporal pyramid network (TPN) [10] models the visual tempos of different actions at the feature level, and it can be integrated with mainstream backbones to capture a variety of temporal information of action instances. SlowFast [7] employs two pathways--a slow pathway and a fast pathway--with different temporal speeds to capture spatiotemporal features. These pathways operate at different frame rates and are fused through lateral connections, allowing them to effectively model multi-temporal information. The dynamic temporal pyramid network (DTPN) [9] utilizes a pyramidal representation with varying frame sample rates to address the inherent temporal scale variation in video understanding. While these strategies--such as stacking multiple stages of features, sampling at different rates, and utilizing dual networks--can improve the ability to model multi-scale temporal context, they also incur increased, costly, multi-branch networks. Therefore, in our approach, we model a multi-scale temporal context without relying on stacked pyramids or multi-frequency sampling within a single backbone.
# Influence of the Diatomite Specie on the Peak and Residual Shear Strength of the Fine-Grained Soil
## 3 Results and Discussion
However, this improvement was more noticeable at lower vertical pressures. From the normal shear stress plots, the peak and the residual friction angles, which are indicators of the effect interlocking between particles and the variation of the friction angle, were calculated for all kaolin-diatomite specimens (see Figure 9). The cohesion for both peak and residual states was nulled in view of the contractive volumetric response, typical for normally consolidated materials. Concerning Colombian diatomite contents, the variation of the friction angle was almost negligible. In fact, as the diatomite content increased, the gap between peak and friction angles became smaller. This result suggests  
Figure 7: Relationship between peak shear stress vs. normal stress at different diatom contents. that this diatomic leads to an increase in the residual friction angle at a higher rate than the friction angle. On the other hand, the variation of the friction angle was higher as the Mexican diatomic content increased. Although the addition of Mexican diatomic may increase both peak and residual friction angles, the growth rate is greater for the peak rather than the residual one. Figure 8: Relationship between residual shear stress vs. normal stress at different diatom contents. Figure 9: Variation of the effective friction angle respect diatomic content in both species. A correlation between peak and residual friction angles was also observed for both types of diatomite, as shown in Figure 3:0 In the case of mixtures modified with Colombian diatomite, the relationship between the peak and the residual friction angle was positively direct with a R\({}^{2}\) of 0.88, whereas in the case of mixtures modified with Mexican diatomite, a good correlation with a R\({}^{2}\) value of more than 0.80 was obtained. However, while the residual friction angle tended to increase, the peak angle was getting lower values. From the graph, it can also be inferred that the addition of higher amounts of Colombian diatomite led to an increase in the interlocking effect between particles in short and large deformation levels. Meanwhile, the inclusion of Mexican diatomite also interlocked well with soil particles as its content increased. Nonetheless, at large displacement levels, it tended to dissipate them.
## 5 Experiments and Results
### Fault Detection and Classification  
In this section, we explore the comprehensive analysis, which includes a series of methodically designed experiments aimed at evaluating the effectiveness, efficiency, and adaptability of each model in identifying structural anomalies within the PFBs. Through a detailed examination of the performance metrics, computational requirements, and real-world applicability, this section aims to offer valuable insights into the capabilities of advanced neural networks in enhancing predictive maintenance strategies for wind energy infrastructure.  
Figure 19: Examples of close-up images of fan blades taken by an autonomous drone. The top row of PFBs is faulty, while the bottom row is healthy.  
Figure 18: Comparison of scenarios where the fan is much closer to or further from the drone than expected based on the distance computed.
## 2 State of The Art
### Nonlinear Video Exploration
#### 2.1.1 Hypervideos
* An authoring environment for annotations and setting up navigation paths,
* A meta-data structure for annotated data and navigation links,
* A specialized environment including but not limited to a customized video player for consuming the hypervideo. Both [PERSON] [44] and [PERSON] et al. [27] consider the complexity of hypervideo systems both in terms of production (i.e., authoring systems) and consumption environments, to be an issue that is affecting the value of such systems in exploration tasks. While hypervideos give more flexibility to the viewer in consuming the content, the flexibility is still limited to the extent to which the author has anticipated it. The viewer cannot go beyond that and while hypervideos do provide means to consume information in a multimodal manner. The multimodality comes from additional artefacts embedded by the curators instead of utilizing the potential of video as multimodal content source.
# A Graph-Cut-Based Approach to Community Detection in Networks
## 4 Results
### Example 1: A Simple Network  
A very simple network, whose community structure is obvious, is generated as shown in Figure 2a. The network is a slightly generalized version of a graph, usually called an _\(n\)-barbell_ graph, and it has two complete subgraphs that are connected by a path [67, 68, 69]. It is obvious that the network has two communities: \(C_{1}=\{0,1,2,3,4,5\}\) and \(C_{2}=\{6,7,8,9,10,11\}\).  
Figure 2: A barbell graph and the application of MCCD to the graph. **(a)** The barbell graph; **(b)** the two detected communities by MCCD; **(c)** the betweenness centrality values \(c_{B}(v)\); **(d)** the modularity values after each iteration of DIVIDE-INTO-TWO.  
The result of the MCCD algorithm applied to the network is shown in Figure 2b. As expected, the MCCD algorithm detects the two obvious communities, and they are represented as blue rectangles (\(C_{1}\)) and orange circles (\(C_{2}\)) in the figure. Therefore, the MCCD algorithm works well for the simple barbell graph.  
The betweenness centrality value \(c_{B}(v)\) for all vertices \(v\) of the barbell network is shown in Figure 2c. Vertices 5 and 6, which are boundary nodes of each community, have the highest betweenness centrality value because the shortest paths connecting nodes of the network pass through them more than any other nodes. Therefore, the MCCD algorithm selects the two vertices 5 and 6 as the source and sink nodes at the first iteration of the process. The algorithm then succeeds in finding the minimum cut between the two vertices and in detecting the obvious communities \(C_{1}\) and \(C_{2}\).  
Note that the MCCD algorithm stops its iteration after the second pass because the modularity score of the second pass, which is about 0.4527, is lower than that of the first pass, which is about 0.4565. The modularity value of every iteration is shown in Figure 2d, where the MCCD algorithm is slightly modified to perform its iterations even though the modularity value \(Q_{m}\) of each iteration starts decreasing; the lines 13-15 of MCCD Algorithm 2 are temporarily ignored to repeat the DIVIDE-INTO-TWO procedure until every edge is cut, so that each vertex forms its own community. As can be observed in Figure 2d, the modularity value quickly decreases as MCCD repeats its iteration passes.
## 3 Results
### Validation of the Effectiveness of the Proposed MSFM  
To validate the effectiveness of the proposed MSFM in multi-source feature fusion and to assess its generalization across different network architectures, we designed and conducted a comparative experiment. Specifically, we selected several state-of-the-art deep learning models, including ShuffleNetV2, ResNeXt, MobileNetV3-s, SENet, ResNet50, and ResNet101. Each model was evaluated using two fusion strategies, namely, direct data  
Figure 8: Visualization of the prediction results of different methods.  
concatenation (data fusion) and MSFM-based feature fusion, to analyze their impact on classification performance.  
As shown in Table 7, the MSFM consistently improved classification performance across different network architectures, achieving a higher accuracy, precision, recall, and \(F1\)-_score_ than conventional data fusion methods. This demonstrates that the MSFM effectively captures complementary features from multi-source data and optimizes feature fusion strategies, thereby enhancing the model's discriminative capability. Notably, for deeper networks such as ResNeXt, ResNet50, and ResNet101, the MSFM achieved substantial performance gains, improving accuracy by 3.9%, 4.3%, and 3.4%, respectively. These results indicate that, in the context of this study, feature-level fusion outperforms direct data concatenation by better leveraging complementary information from multi-source inputs, ultimately enhancing the model's ability to identify diseased regions.
# Computational Methods for Neuron Segmentation in Two-Photon Calcium Imaging Data: A Survey
## 6 Machine Learning-Based Methods
### ML Methods Applied to Summary Images
The third decoding layer has 256, 256 and 128 filters in each of its two convolutional and one deconvolutional layers, the second decoding layer has 128, 128 and 64 filters in each of its two convolutional and one deconvolutional layers, and the first decoding layer has 64, 64 and 32 filters in each of its two convolutional and one deconvolutional layers.The output layer has a concatenation, two \(3\times 3\) convolutions with 32 filters each and output softmax layer. They have validated their approach on the Neuronfinder challenge dataset ([[http://neurofinder.codeneuro.org/](http://neurofinder.codeneuro.org/)]([http://neurofinder.codeneuro.org/](http://neurofinder.codeneuro.org/)), accessed on 2 July 2022). They trained their network on the top 75% of the summary images and validated it on bottom 25%. In 2017, their pipeline stood third on the Neurofinder challenge with an F1 score of \(59\%\pm 16\%\). [PERSON] et al. proposed a shallow UNET based active neuron segmentation approach for online use purposes [79]. They apply this approach on single 2D images, which can either be individual images or summary images. First, they do motion correction by homomorphic filtering and large-scale background fluctuation removal. Then the a temporal filter is applied to each pixel to enhance the active pixels and suppress the others. Afterwards, a shallow UNET is trained on active neurons masks. The UNET has two convolution layers and two downsampling/upsampling layers and one skip connection layer. This shallow UNET allows faster processing. The temporal pixel enhancement and faster UNET deployment makes the approach capable of real-time speeds. The authors claim that this approach achieved a processing speed of 117 frames per second on 275 \(\upmu\)m ABO dataset. It achieved an F1 = 0.85 on the ABO dataset. This approach is on par with other online segmentation approaches in performance and speed while leveraging the power of both temporal signal processing and machine learning. This approach might still struggle in imaging stacks with high ration of overlapped neurons.
## 5 Virtual Robot URDF Model and Loading
### URDF Model Conversion  
The process of converting a URDF model into the URDF Robot's visual and collision model framework is shown in Figure 9. The system parses the URDF file, which defines the robot's structure and properties in XML format. Next, the system loads the STL or Collada models referenced by the URDF file, which describe the geometry of each robot component in detail. Based on these geometric data, the system creates Visual objects and Collider objects, used for rendering the robot's appearance and performing collision detection in physical simulations, respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class Name** & **Inheritance** & **Function** & **Characteristics** \\ \hline URDF Base & Object 3D & Provide Properties and Methods for Other Classes & Store URDF Node Data and Names \\ \hline URDF Collider & URDF Base & Represent the Collision Detection Part of the Robot Model & Specialized for Collision Detection \\ \hline URDF Visual & URDF Base & Represent the Visual Part of the Robot Model & Render the Robot Model \\ \hline URDF Link & URDF Base & Represent the Links in the Robot Model & Represent the Rigid Body Parts of the Robot \\ \hline URDF Joint & URDF Base & Manage the Robot’s Joints & Support and Control Various Joint Types \\ \hline URDF Mimicjoint & URDF Joint & Simulate the Behavior of Other Joints & Joint Movements for Dependency Relations \\ \hline URDF Robot & URDF Link & Represent the Complete Robot Model & Overall Management of the Robot Model, Obtain Framework and Set Joint Values \\ \hline \hline \end{tabular}
\end{table}
Table 1: Custom URDF class functions.  
Then, the system constructs the URDFRobot object, which contains all the robot's parts and their relationships. To simulate the connections and movements between the robot's parts, the system creates URDFLink and URDFJoint objects, which reflect the physical properties of the links and the motion constraints of the joints. The system also processes and generates the data needed for the interactive interface and analyzes the motion range of the robot joints to ensure accurate reflection of the robot's motion limits in the simulation.  
Finally, the entire robot model is converted and exported in a Three.js-compatible format, allowing the robot model to be rendered on the WEB platform using the Three.js library, and thereby providing users with a visual operation and interaction experience.
## 2 Materials and Methods
### Focused-Laser Irradiation Process  
The FLI process was performed in the home-built system equipped with a multichannel gas panel, a vacuum chamber, a dry pump system, a microscope, and a high-precision XY stage. The vacuum chamber was pumped to \(\sim\)10\({}^{-4}\) Torr and then filled with Ar gas maintaining the chamber pressure at \(\sim\)400 Torr during the FLI process. A highly stable continuous wave 532 nm laser (diode-pumped solid-state laser, Lighthouse Photonics, San Jose, CA, USA) was focused onto a sample at the laser power of 250 W for 1 min. The laser beam was delivered through a 10\(\times\) objective lens (Plan Apo, Mitutoyo, 0.28 NA, Kanagawa, Japan) with a laser spot size of \(\sim\)2.3 \(\upmu\)m. The laser irradiation conditions set in this work are the optimal parameters for maximizing the PL modulation given in the previous study [32]. No irradiation effect was activated at the laser power below 200 W. While the ambient PL intensity of ML-MoS\({}_{2}\) increased as the laser power increased in the range of 200-250 W, it decreased at the laser power above 250 W. Finally, significant decomposition was detected on ML-MoS\({}_{2}\) irradiated at the laser power of 300 W by the contact-mode AFM [32]. Further, to understand the irradiation process on MoS\({}_{2}\) and other 2D materials more deeply, the effects of laser process parameters on laser absorption should also be considered carefully [51].