esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Overvoltage Simulation Model Including Variable Capacitance
### Overvoltage Simulation Model Including Variable Capacitance
#### 3.3.1. Pantograph-Catenary Distance Control Module  
The function of the pantograph-catenary distance control module is to simulate the gap distance variation during the pantograph dropping process. As shown in Figure 7, the module's input parameters include dropping speed \(V\), time \(t\), and delay time \(\Delta t\). The real-time calculation formula for the pantograph-catenary distance is \(L=Vt\). The actual situation is that pantograph-catenary distance does not infinitely increase, but remains at a constant value after the pantograph dropping process ends.  
Therefore, a selector is designed to implement the above-mentioned logic: when \(L_{t}>L_{0}\), \(L_{t}\) is the pantograph-catenary distance; otherwise, \(L_{0}\) is output indicating the end state of the pantograph dropping process. Another output of this module is the signal \(C_{1}\), which indicates whether arc calculation can begin. Since the pantograph-catenary distance starts from 0, there is a risk of simulation divergence when calculating the arc at very small distances. A threshold is set to avoid this situation: the arc calculation proceeds only when the pantograph-catenary distance exceeds the threshold value (1 \(\upmu\)m).
# A Review of Fabrication Technologies for Carbon Electrode-Based Micro-Supercapacitors
## 5 Fabrication Technologies of Carbon Electrode-Based Micro-Supercapacitors
### Coating Methods
#### 5.2.1 Spray Coating  
Spray coating can be applied using a spray gun or air brush, and it has been used commonly for the fabrication of microelectrodes or film coating [152; 153; 154; 155; 156; 157]. The homogeneity and mass loading of active materials depends on the substrate temperature, the distance between the substrate and the spray nozzle, and the deposition time. Another key aspect in this method is that the droplets generated during spraying should not block the nozzle.  
The interdigitated architecture of an asymmetric micro-supercapacitor with titanium carbide MXene and reduced graphene oxide electrode materials were fabricated using a custom-made mask and scalable spray coating technique onto a flexible, transparent substrate. Hence, this study did not require photolithography but only simple sequential spray coating of MXene and reduced graphene oxide to produce a 2D micro-supercapacitor [158]. In this process, each sprayed layer was dried completely using a hot air gun, so the coffee ring effect was avoided and uniform coatings were obtained. A custom-made Kapton mask was essential to get the interdigitated pattern electrodes [158]. The spray coating method was also employed to make electrodes of multi-walled carbon nanotubes for high-performance, planar micro-supercapacitors with patterned ionogel electrolyte [159], presented in Figure 8. Ultrasonic spray coating, where droplets are created by ultrasonic vibration, was used to deposit electrode paste with active carbon and one-dimensional silver nanowires onto substrates. A 28 kHz ultrasonic spray nozzle was utilized with a 3 mL/min feed rate at 8.7 psi gas-guiding pressure and a distance of 8 cm between the nozzle and the substrate, with the nozzle moving at 10 mm/s. Thin film electrodes produced in this manner had a higher specific surface area and pore volume, and ion adsorption and desorption were also improved, resulting in better charge storage [160]. In a similar study, ultrasonic spray coating was achieved at the same parameters to make activated carbon thin films doped with silver nanowires, where the effect of the nanowire diameter on the performance of the micro-supercapacitor was investigated [72].
###### Abstract  
Automation in agriculture is revolutionizing the management of field tasks. This paper focuses on identifying the essential needs and preliminary technical requirements for designing an autonomous robot specifically for fruit harvesting. The study examines the integration of advanced machine vision technologies and neural networks for the accurate detection of ripe fruits and obstacles, optimizing harvesting efficiency. Key features such as proximity and touch sensors are also evaluated to ensure the safe handling of fruits, minimizing damage during harvesting. The technical specifications, including traction, speed, and the robot's ability to operate on uneven terrain, are analyzed to ensure adaptability across various crops and ground conditions. Perception systems, incorporating cameras and sensors, are crucial for crop inspection and route planning. The main findings of this review establish a set of technical requirements that serve as a foundation for developing efficient and economically viable fruit-harvesting robots. These systems offer significant potential to transform agriculture by addressing labor shortages and improving harvesting precision. However, challenges remain, particularly in adapting to diverse crops and environments, emphasizing the need for further research and development.  
agricultural robotics; fruit-harvesting; autonomous harvesting +
Footnote †: journal: Accepted: 14 October 2024  
damaging them, adapting to different crop varieties, and ensuring timely harvesting--crucial factors for maintaining both yield and quality.  
This study focuses on identifying and analyzing the key technical requirements necessary for the design and construction of an autonomous robot specifically optimized for fruit harvesting. Understanding these requirements is essential for creating a robot capable of addressing the pressing needs of modern fruit production while ensuring efficiency and sustainability in the agricultural sector.
## 5 Experimental Results
### Performance Analysis for Multiple Cameras Fall Dataset (MCFD)
#### 5.3.2 Result from OpenPose Module  
The effectiveness of the OpenPose module in capturing and analyzing body poses, even in the presence of blurring, is demonstrated in Figure 6. These results showcase the key points extracted from subjects in various videos, providing valuable insights into body movements and postures. Such accurate extraction enables further analysis and the subsequent prediction of falls. Although the OpenPose module successfully extracted most of the key points from the frames, there were instances where it failed to detect the person or generated key points randomly within the frames. To address this issue, we applied data preprocessing techniques and employed linear interpolation to manage these occurrences.
# TD-DNN: A Time Decay-Based Deep Neural Network for Recommendation System
## 5 Experimental Evaluation
### Experimental Results with Discussion  
To evaluate the efficiency of the proposed method, an experiment has been carried out on a variety of datasets, i.e., ML-100k, ML-1M and Epinions datasets. The performance of the proposed method TD-DNN is compared with the eight existing techniques: ACOS, MPIP, SVD, DLCRS, DeepEDU, NNNB, NCF, and NMF, through MAE and RMSE metrics. Every dataset is split into a 20-80% ratio, where 80% of the data is utilized for training and the remaining 20% is used for testing purposes. This method is performed five times, and the final result is the average of all.  
Unlike other Deep Learning models, where a raw matrix is inserted as input, a non-noisy time weighted rating matrix is inserted as input in the proposed work. To acquire this, firstly, noisy ratings from the dataset and then a time decay function is applied to give weightage to the most recent ratings. The steps of removing the noisy ratings from the dataset are depicted in Algorithm 1, and a list of time decay functions are given in Table 8. Since there are lots of time decay functions, to identify which one is best, all of them are applied to the ML-100k dataset. The findings displayed in Table 11 show that the power decay function produces better outcomes for all k values (neighbors); hence, it has been used in the proposed model.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Values** \\ \hline Activation Function & ReLU \\ Loss Function & Huber \\ Optimizer & Adam \\ Regularizer & L2 (le\(-\)6) \\ Learning Rates & 0.001 \\ Total Hidden Layers & 4 \\ Neuron in Layers & 512-256-64-8 \\ Epochs & 250 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Hyper Parameters.  
Figure 6: Comparison between Learning Rates.  
Since power is an efficient time decay function according to the results displayed in Table 11, therefore, using it, the weights are calculated and multiplied with the respective non-noisy ratings. Then, resultant non-noisy time-weighted ratings are inserted as input to the NN model for the training purposes, which produces the predicted ratings as output. The performance of each model in terms of MAE and RMSE is shown in Table 12, clearly indicating that the proposed TD-DNN method obtains a lower MAE and RMSE than each comparable method; hence, it is superior.  
On the ML-100k dataset, the performance of the TD-DNN approach is best, while item-based CF (using ACOS) is the worst, and our method gives almost 20% better results than MAE and RMSE in comparison. After TD-DNN, the performance of NCF is better, and our method yielded about four better results than that in terms of MAE and RMSE comparison. Similarly, on the ML-1M dataset, our method achieved 20% better results than the IBCF method. On the high-sparsity Epinions dataset, the TD-DNN method gives lower MAE and RMSE values than all other methods. It produces better results than IBCF in MAE (\(\approx\)25%) and RMSE comparison (\(\approx\)30%). On this dataset, the performances of NCF and Deep EDU methods are almost similar.
# Density Self-Adaptive Hybrid Clustering Routing Protocol for Wireless Sensor Networks
###### Abstract  
Energy efficiency is of major concern in wireless sensor networks, especially in difficult node deployment environments. Clustering is an effective method to save energy consumption and prolong the network lifetime. There are two kinds of clustering methods in hierarchical routing protocols, namely distributed control and centralized control. In the distributed strategy, the algorithm has good scalability, but it can easily lead to an uneven distribution of cluster heads. When the centralized strategy is used, the network is balanced and robust, but the overhead of clustering is large and the network latency is increased. In our proposed protocol, named DAHC, a mixture algorithm of distributed and centralized control is used to select a cluster head (CH). Simultaneously, we propose a novel approach to judge the cluster density and adjust according to the preset threshold. The simulation results show that our methods can evenly distribute the energy load on all nodes, save the energy consumption of communication and extend the network lifetime concurrently.  
wireless sensor networks; routing protocol; clustering; cluster density +
Footnote †: journal:
## 3 Results  
The implemented four channels generator is shown in Figure 6, which is enclosed in a grounded 19\" stainless steel box, with air cooling. The generator was tested using resistive loads. Unless described otherwise, voltage waveforms were recorded with a Tektronix TDS3012B 100 MHz 1.25 GS/s oscilloscope, using PMK PHY1000 100:1, 400 MHz, 50 M\(\Omega\)/7.5 pF high voltage probe. The current measurements were recorded using a Bergoz CT-D0.5-B current transformer.  
Figure 6: Top view of the 19” rack of the four channels generator, 506 length, 430 width, and 174 mm height: (**A**) The four Marx modulators; (**B**) main control board; (**C**) capacitor charging power supply; (**D**) auxiliary circuits, power supplies, and trigger signal generation board; (**E**) voltage pulses output, protections, current, and voltage measurement.
# GIS-Based Multi-Objective Routing Approach for Street-Based Sporting-Event Routing
## 4 Discussion  
Mega events in cities can have positive social and cultural effects. Local governments and planners attempt to make public spaces more enjoyable through events held in cities. Events generate pleasure by facilitating direct and enjoyable experiences for citizens. However, it is inappropriate to degrade the public nature of a city for the sake of pleasure. Street-based sporting events that block roads for a period of time have a significant negative effects on host cities.  
It is unrealistic to expect the complete elimination of the negative effects of mega events. However, events should be designed and managed such that public spaces can reach the common goal of inducing citizens' participation and protecting their public nature. Currently, many policymakers and event planners are attempting to reduce inconvenience for citizens, but many cities are continue to experience this inconvenience. Policymakers can improve the planning process (which has been limited to traditional event planning methods) with an approach based on massive data from cities, such as GIS.  
In this study, we confirmed the applicability of the framework proposed in previous GIS-based routing studies for distance-based sports events. Furthermore, it was confirmed that by integrating the GIS-based routing process with the BIM environment, it is possible to effectively evaluate the impact on the downtown area. In addition, an algorithm that derives different routes according to the adjustment of environmental variables can search for suitable routes according to the needs of policymakers and event planners, and can quickly create various routes. Through the application of research on evaluation- and visualization-model creation through the integration of the current GIS and BIM, it was confirmed that this is an effective process for route evaluation. The technological-triad framework of GIS-routing-BIM proposed in this study can be applied to existing GIS-based routing research, and it is likely that an effective decision-making model can be implemented by integrating the research results in the BIM environment. Moreover, through the linkage of  
Figure 8: Example of interference between route and facilities.  
BIM-based digital-twin technology, which is not covered in this study, it will be possible to implement a more powerful decision-making model.  
The GIS- and algorithm-based event-path-selection process created using the proposed methodology can be utilized simply and effectively in the initial planning stage of an event. In addition, the technological-triad framework of GIS-routing-BIM proposed in this study can facilitate rapid and accurate decision making by policymakers. This can be used to address the negative effects of street-based mega-events by proactively identifying and preventing damage to public spaces. This methodology is meaningful because it presents an effective and rational digital method that breaks away from the existing planning process.
###### Abstract  
Haze and mist caused by air quality, weather, and other factors can reduce the clarity and contrast of images captured by cameras, which limits the applications of automatic driving, satellite remote sensing, traffic monitoring, etc. Therefore, the study of image dehazing is of great significance. Most existing unsupervised image-dehazing algorithms rely on a priori knowledge and simplified atmospheric scattering models, but the physical causes of haze in the real world are complex, resulting in inaccurate atmospheric scattering models that affect the dehazing effect. Unsupervised generative adversarial networks can be used for image-dehazing algorithm research; however, due to the information inequality between haze and haze-free images, the existing bi-directional mapping domain translation model often used in unsupervised generative adversarial networks is not suitable for image-dehazing tasks, and it also does not make good use of extracted features, which results in distortion, loss of image details, and poor retention of image features in the haze-free images. To address these problems, this paper proposes an end-to-end one-sided unsupervised image-dehazing network based on a generative adversarial network that directly learns the mapping between haze and haze-free images. The proposed feature-fusion module and multi-scale skip connection based on residual network consider the loss of feature information caused by convolution operation and the fusion of different scale features, and achieve adaptive fusion between low-level features and high-level features, to better preserve the features of the original image. Meanwhile, multiple loss functions are used to train the network, where the adversarial loss ensures that the network generates more realistic images and the contrastive loss ensures a meaningful one-sided mapping from the haze image to the haze-free image, resulting in haze-free images with good quantitative metrics and visual effects. The experiments demonstrate that, compared with existing dehazing algorithms, our method achieved better quantitative metrics and better visual effects on both synthetic haze image datasets and real-world haze image datasets.  
Keywords:image dehazing; generative adversarial network; contrastive learning +
Footnote †: journal:
## 2 Basic Definition of Navigation System  
In an IMU system, the signal measured using inertial sensors contains all movement information including carrier motion, earth rotation and gravity. However, these parameters are defined in various coordinate systems; the navigation algorithm involves the transformation of several coordinate systems. For instance, the gyroscope and accelerometer measure the angular and linear acceleration increment in absolute inertial coordinate (i-frame). Since the carrier moves on the earth's surface, the earth's rotating navigation coordinate system should also be taken into consideration. In addition, the carrier's motion and attitude change throughout the whole navigation process, so the body frame is a constantly changing coordinate compared to inertial navigation. In this article, several reference coordinates are introduced, and the descriptions are defined as follows:  
\(\rm o_{i}x_{i}y_{i}z_{i}\) (i-frame): Earth-centered initially fixed (ECIF) orthogonal reference frame. Inertial frame has the center of the Earth as its origin; axis \(\rm o_{i}z_{i}\) coincides with the Earth's rotation axis; axis \(\rm o_{i}x_{i}\) points to the vernal equinox, while axes \(\rm o_{i}x_{i}\) and \(\rm o_{i}y_{i}\) are in the plane of the Earth's equator. Inertial frame is also the reference coordinate system for gyros and accelerometers.  
\(\rm o_{e}x_{e}y_{e}z_{e}\) (e-frame): Earth-centered earth-fixed (ECEF) orthogonal reference frame has the Earth's center as its origin; axis \(\rm o_{e}z_{e}\) is the Earth's rotation axis; \(\rm o_{e}x_{e}\) points to the prime meridian, while axes \(\rm o_{e}x_{e}\) and \(\rm o_{e}y_{e}\) are in the plane of the Earth's equator.  
\(\rm o_{n}x_{n}y_{n}z_{n}\) (n-frame): Navigation frame is an orthogonal reference frame aligned with actual east-north-up (ENU) geodetic axes. The navigation frame takes the center of gravity of the carrier device as the origin; axis \(\rm o_{n}x_{n}\) points to the east direction; axis \(\rm o_{n}y_{n}\) points to the north direction, and axis \(\rm o_{n}z_{n}\) points vertically to the up direction. The navigation coordinate system is the reference coordinate system for inertial navigation solving.  
\(\rm o_{p}x_{b}y_{b}z_{b}\) (b-frame): The body frame is the orthogonal reference frame aligned with right-forth-up (RFU) axes of carrier. Body frame takes the center of the carrier as the origin; axis \(\rm o_{b}\)\(\rm x_{b}\) points to the right side of the carrier; axis \(\rm o_{b}y_{b}\) points to the forward direction of the carrier, and axis \(\rm o_{b}z_{b}\) is perpendicular to the carrier to the up.
# A Multiclassification Model for Skin Diseases Using Dermatoscopy Images with Inception-v2
## 1 Introduction
Nevertheless, machine learning is a pervasive tool in numerous domains. In dermatology, for instance, machine learning techniques such as support vector machines (SVMs) [12], Bayesian (NB) classifiers [13], and decision trees (DTs) [14] are employed in classification. Convolutional neural networks (CNNs) have gained considerable traction in recent years, largely due to their capacity to automate feature extraction and their extensive applicability in research contexts [15; 16; 17]. These techniques facilitate the expeditious and efficacious detection of malignant cells, with a survival rate exceeding 95% among patients who are promptly identified and treated [18; 19]. This paper proposes a multiclassification model for dermatological images based on the Inception-v2 architecture, with the objective of addressing the aforementioned issue. Firstly, the dataset is subjected to preprocessing operations, including data enhancement and hair removal. Subsequently, the efficacy of various loss functions is evaluated, and finally, focal loss is selected as the loss function. Subsequently, experimental tests are conducted on three distinct networks, namely Resnet-50, Densenet, and Inception-v2. During the testing phase, confusion matrices and Grad-CAM heat maps are employed as visualisation methods. The optimal classification model is identified through ablation experiments. This paper makes the following contributions:  
1. The data were enhanced and hair was removed, among other things, to address issues such as an uneven distribution of the original dataset and images. The results of these processes were demonstrated to improve the performance and classification accuracy of the model through the use of ablation experiments. 2. Testing on three different networks, Resnet-50, Densenet, and Inception-v2, and comparing the NNLLoss and focal loss functions, the results show that the focal loss function has a better performance for the classification model, particularly for diseases with limited original datasets. 3. The utilisation of Grad-CAM heat maps provides insight into the model's classification methodology for skin disease images, facilitating a deeper comprehension of the underlying processes.