esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time Hybrid Multi-Sensor Fusion Framework for Perception in Autonomous Vehicles
## 4 Hybrid Sensor Fusion Algorithm Overview
### Object Classification and Road Segmentation using Deep Learning  
For road semantic segmentation, we use camera images as well as depth information from the LiDAR. We combine these raw data at the depth channel, which results in an RGBD image with a depth channel of size four. We then send this information to our proposed Fully Convolutional Network (FCNx). FCNx is an encoder-decoder-based network with modified filter numbers and sizes in the downsampling and upsampling portions and improved skip connections to the decoder section. The model is trained and tested on actual sensor data on our embedded NVIDIA GPU-powered computers. Finally, the model performance is evaluated by comparing its predictions with baseline benchmark networks and the ground truth labels.
## 2 Network Compression  
Network compression includes two parts. On the one hand, some observable buses are merged with their neighbors by pre-configuring a part of PMUs; on the other hand, based on existing ZIB measurements, some buses which can become an observable bus are merged with their neighbors. Network compression can reduce the PMU candidate locations, increase network measurement redundancy and improve the optimization effect.
## 4 Application Classification of Intelligent Algorithms for Hydrodynamic Noise  
### Classification Basis of Intelligent Algorithms for Hydrodynamic Noise  
The implementation of intelligent algorithms in the domain of hydrodynamic noise research encompasses a range of disciplines, including the optimization of noise sources, the prediction of noise, and the formulation of control strategies. To establish a systematic framework for analysis, it is essential to categorize these algorithms based on a well-defined criterion.  
In this review, intelligent algorithms are classified according to their primary application objectives, which define their role in addressing hydrodynamic noise-related challenges. In comparison to classifications that are based solely on algorithmic principles, such as evolutionary computation, deep learning, or reinforcement learning, a function-oriented approach is adopted in this study. The classification system is comprised of three major categories:  
Optimization Design--Algorithms dedicated to optimizing structural parameters to minimize noise generation.  
Noise Prediction--Methods employed to model and predict hydrodynamic noise fields using numerical and data-driven techniques.  
Control Strategy Optimization--Approaches focusing on active and passive noise control to achieve real-time noise suppression.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm Type** & **Advantage** & **Application Example** & **Technical Difficulties** & **Applicable Scenarios** \\ \hline \multirow{4}{*}{Genetic Algorithm} & Strong global search & \multirow{2}{*}{Wing shape optimization for low data and low noise optimization} & Sensitive to parameter selection, easy to fall into local optimum & Multivariable shape optimization, material property optimization \\ \hline \multirow{2}{*}{Ant Colony Algorithm} & Path planning & \multirow{2}{*}{Optimize the arrangement of noise sources to improve the system response speed} & Slow convergence and sensitive to parameter variation & Real-time control and path optimization \\ \hline \multirow{2}{*}{Particle swarm optimization algorithm} & Strong parallel computing capability, suitable for geometric structure optimization & UWAC noise optimization for design efficiency & Computational complexity is high & Flow field prediction and geometry optimization \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._This classification framework has been selected to align with the practical demands of hydrodynamic noise research, emphasizing the specific contributions of intelligent algorithms in solving engineering and scientific problems. The subsequent sections provide a detailed analysis of each category, discussing the methodologies, advantages, and recent developments.
## 2 Methods
### Interpreting 3D Information from 2D Images  
There are two steps to calculate the real coordinates of the geological objects in an image. First, perspective transformation is applied to change the scene of an image to a new viewing plane, which is parallel to the camera lens. The calculation is shown in Equation (1)  
\[\left[x^{\prime},y^{\prime},w^{\prime}\right]=\left[u,v,w\right]\left[\begin{array} []{ccc}a_{11}&a_{12}&a_{13}\\ a_{21}&a_{22}&a_{23}\\ a_{31}&a_{32}&a_{33}\end{array}\right] \tag{1}\]  
This section may be divided into subheadings. It should provide a concise and precise description of the experimental results, their interpretation, and the experimental conclusions that can be drawn. \(u\) and \(v\) are the coordinates of the raw image; \(x\) and \(y\) are the calculated coordinates, \(x=x^{\prime}/w^{\prime}\) and \(y=y^{\prime}/w^{\prime}\); the transformation matrix \(\left[\begin{array}{ccc}a  
coordinate systems, we can calculate coordinate \(z\) values in the image and extract 3D geological information.
## 3 Results and Discussion  
### Traditional Sensory Analysis  
Sensory indicators of color, taste, and odor are the key evaluation indexes of TCMs, which are considered to be important components and intuitive attributes in the identification of traits [13; 14; 15]. [PERSON], the book of Origins of the Materia Medica, emphasized the significance of color, taste, and odor as identifiers of high-quality medicinal herbs [16].  
There is a significant difference between different qualities of TCM samples. For color, a fresh and normal Lycii fructus sample is true red and that of a poor and deteriorated sample is dark red; the cross-section of Scutellariae radix is yellow and an abnormal one is green; and the normal color of Armeniaceae semen amarum is pale yellow and an abnormal one is bright or dark yellow. For taste, the normal Piperis kadsurae caulis is bitter and spicy; the normal Sinomenii caulis is not spicy; and the natural Bovis calculus is bitter first and then sweet with a cool feeling and no fishy smell. For odor, the normal Ferulae resina has a particularly foul smell, Moschus has a particularly fragrant smell, and Ginseng radix and rhizoma have a slight fragrant. These significant characteristics are regarded as an important basis of initial assessment of quality.  
In this study, the normal and rancid ZSS samples were subject to traditional sensory analysis by artificial methods including seeing, tasting, and smelling. The results of color, taste, and odor are shown in Table 1. Compared with the normal samples, it was found that the color became darker, the taste became bitter and sourer, and the odor became irritating and unpleasant for rancid samples, which were the results of changes in the properties of ZSS itself. In detail, the degree of rancidity of fatty oils is accelerated due to unfavorable conditions such as high temperature, humidity, and oxygen concentration. More peroxides and free fatty acids are produced in whole process, which continue to decompose into some aldehydes and ketones with short carbon chains. Then, these chemical changes result in ZSS developing a special pungent odor and darker color.  
During the storage link of ZSS, the rancidity phenomenon results in a series of changes in chemical components, especially volatile compounds that have a unique odor and smell, including olefins, terpen, aldehydes, ketones, and acids. These changes are finally reflected in the color, taste, and odor. Consumers can identify the quality according to these characteristics, eliminate rancid samples, and pick high-quality samples.
## 4 Digitalization of MGs  
The use of digital technologies in electric power systems has been evolving over several decades, and there have been several milestones in this journey. Table 4 presents some key milestones in the history of digital technologies in electric power systems. In parallel to advances in electric power systems, the digitalization of MGs has enabled the integration of various digital technologies, such as DERMSs, MEMSs, IoT devices, big data analytics, blockchain technology, AI, digital twin technology, cloud computing, and augmented reality, allowing for the optimization of microgrid performance and the enhancement of their resilience and stability. Additionally, one technological trend that aligns with several of these paradigms is open-source technology, encompassing both hardware and software components that are freely available for modification and distribution. Open-source technology offers several advantages in the context of microgrid development and digitalization. Firstly, it fosters collaboration and knowledge sharing within the microgrid community, enabling researchers, practitioners, and developers to exchange ideas, best practices, and innovative solutions. This collaborative environment promotes the rapid advancement of microgrid technologies. Moreover, open-source hardware and software tools provide cost-effective options for implementing and maintaining microgrids [116]. By reducing the financial barriers associated with proprietary systems, open-source technology makes microgrid solutions more accessible and affordable. This affordability factor is especially significant in developing regions and remote communities, where the adoption of microgrids can greatly impact energy access and sustainability goals. To this end, supervisory and remote monitoring systems are improved with the hardware and software technologies enabling IoT-based communication mediums in RESs such as wind and solar PV systems, and in battery ESSs [116, 117, 118].
###### Abstract  
Alarms generated by a wind turbine alarm system indicate the need for emergency action by operators to protect the turbine from running into risky conditions. However, it can be challenging for operators to identify the fault types that trigger alarms, particularly with few labeled fault samples. This paper proposes a novel fault diagnosis method for wind turbines with alarms that collaboratively uses labeled and unlabeled alarms to improve diagnosis accuracy. First, the proposed method distinguishes different alarm sequences using a designed Siamese convolutional neural network with an embedding layer (S-ECNN) model. Then, the fault category of an unknown alarm sequence is diagnosed based on similarity scores. Specifically, the Skip-gram model is used to mine potential relationships among alarms in unlabeled alarm sequences, and pretrained alarm vectors are obtained. In the S-ECNN model, the pretrained alarm vectors are further optimized and trained using labeled alarm sequences. The similarity scores are calculated based on the distance between the extracted discriminative features of alarm sequences. The effectiveness of the proposed method is validated using actual alarm data from a wind farm.  
wind turbines; alarms; fault diagnosis; Siamese convolutional neural network; word embedding +
Footnote †: journal: Applied  
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# Fast 50 Hz Updated Static Infrared Positioning System Based on Triangulation Method
## 1 Introduction  
The use of Global Navigation Satellite Systems (GNSS) for civilian purposes has had a significant impact on the logistics industry. The ability to accurately locate people or objects has created enormous opportunities [1]. However, materials or objects in front of the receiver may scatter GNSS signals, making them unsuitable for indoor positioning. Scattering can also be an obstacle when using Bluetooth [2], Wi-Fi [3], RFID [4] and ultra-wideband (UWB) [5] technologies, which are also used for positioning. The availability of positioning solutions is expected to increase interest in this technology, which can be used for navigation, tracking, marketing, entertainment, safety, and security. Accuracy and speed are crucial in all areas of positioning. Meeting these requirements in a limited space can be challenging due to the multiple surfaces that reflect the usable signal, as well as the saturation of the space with electromagnetic waves that may interfere with the positioning system signals. Indoor and outdoor positioning differ significantly in the likelihood of encountering obstacles in the signal path. The negative effects of these obstacles can be limited by the use of other technologies. Non-line-of-sight (NLoS) refers to the obstruction of a wave's path between the source and receiver by an object, while line-of-sight (LoS) indicates an unobstructed path [6]. Current positioning systems use information from reference points to determine the position of an object. The most common technique is distance measurement. The trilateration method requires knowledge of at least three distances, while GNSS positioning is based on measuring the difference in arrival times between two satellites, and therefore, the difference of two distances. Another method is angle measurement, which uses the triangulation method and requires knowledge of atleast three angles. A technique is employed where reference points transmit a signal, either by radio or infrared, and the object's position is determined by measuring the strength of the received signal. When talking about positioning systems, it is essential to mention those based on mobile phone technology and, in particular, on the ever-evolving 5G technology. It is expected to soon be accurate enough to allow autonomous cars to navigate with centimetre precision [7]. Recent developments and research indicate that this positioning technique, called 5G New Radio (NR), is increasingly valuable for precise indoor and outdoor positioning services. Depending on the method used, these measurements can vary from a few metres [8] to centimeters [9].  
This paper describes a high-speed positioning system that uses infrared transmitters and receivers to determine position through triangulation. The paper presents the operating principle and design of the system, as well as the results of outdoor tests. The system has a measurement refresh rate of 50 Hz and achieves positioning accuracy in the centimeter range.
## 1 Introduction
Although they have some disadvantages, these models are often referred to as a 'black box' because it is often not clear which factors are used to predict the outcome; nonetheless, on-going research seeks to better explain this. Training time for the models is costly because of the time involved, but the increasingly high accuracy achieved by them makes their use widespread in the field of solar radiation forecast. This study shows the benefits of using advanced artificial neural networks for solar forecasting (ST_Dilated_CNN considerably outperformed conventional methodologies) and highlights the appropriateness of applying a spatio-temporal approach to GHI data (considering the multidimensional influences of meteorological and astronomical variables). Dilated CNNs can preserve the resolution and dimensionality of the data at the output layer while providing a larger receptive field, are more computationally efficient, and have less memory consumption. This is because of the dilation of the layers rather than pooling of the layers, so the order of the data is preserved. For example, the structure of the convolution helps to preserve the order of the data in 1D dilated causal convolutions where the prediction of the output depends on previous inputs. The main contributions of this work are as follows:  
* The proposed network ST_Dilated_CNN succeeds in capturing representations of the complex spatio-temporal patterns present in solar data. * The proposed architecture ST_CNN_v2 is able to simulate the dilated range, with the difference between the two models being less than one percentage point. * The proposed models outperform the benchmark models, with a difference of up to three percentage points in forecast skill. The paper continues with the Section 2, where the dataset used as well as the selection of the stations are described. Section 3 presents the proposed methods, while Section 4 presents the training methodology applied. The results and the associated discussion and analysis can be found in Section 5. The article ends with the Section 6.
## 1 Introduction
Fourth, most of these systems suffer from performance deterioration across different datasets; hence, they are dataset-dependent to some degree [14; 15; 16]. With these issues in mind, the primary purpose of this research is to propose an automated CAD system that can identify, differentiate and classify several types of skin cancers and improve the performance of the existing CAD system. A classification model for skin lesions can assist dermatologists in accurately identifying the type of skin cancer, which can facilitate early detection and improve patient outcomes. Automated classification can also help reduce subjectivity in diagnosis, enable faster and more accurate triage, and improve access to care, particularly in areas where dermatology specialists are scarce. Therefore, developing a skin lesion classification model for skin cancers is a significant motivation for advancing the field of dermatology and improving patient outcomes. This motivated our study to propose a genetic algorithm that could generate the best hyperparameters from a well-known CNN architecture. The optimized CNN was trained and tested on different datasets to detect and distinguish various skin lesions. The training images are pre-processed to remove hair, blood vessels, and other factors interfering with identifying boundaries and improving low contrasts between the lesion area and the surrounding skin causing blurry boundaries. Pre-processed techniques are also applied to smooth weak, irregular, fuzzy borders and remove unwanted small regions. This process can assist in extracting deep features and producing more discriminative and robust representations. Second, a normalization technique is applied since skin lesion images usually have different sizes, shapes, and colors depending on skin conditions, which could hamper achieving high classification accuracy. The genetic algorithm developed here is used in a unique way to find the optimal hyperparameters of the CNN. To our knowledge, this use of a genetic algorithm is new to this type of methodology. The aim of the optimized CNN model presented here is to create a fully automatic computer-aided diagnosis system for skin lesion detection and classification that: (a) reduces the computational cost, and (b) provides efficient extraction of skin lesion features even in the case of limited training datasets.
## 3 Stiffened Shell Structures
### Geometric Imperfection and Sensitivity of Ring Stiffened Cylindrical Shells  
Some investigations show that the imperfection sensitivity of stiffened cylindrical shells is still clearly recognizable but probably much lower than that of unstiffened shells. Theoretically, the reduced sensitivity can be explained by the lack of clustering in the eigenvalues. For unstiffened cylinders, while many different possible buckling shapes can initiate failure at the identical load, ring stiffness limit the possible failure modes. Moreover, more recent measurement studies [44; 45] using 3D laser scanning technology have shown that the initial geometric imperfections of ring-stiffened cylindrical shells are related to the ring stiffener spacing. The results show that the sensitivity of the geometric imperfection is dependent on whether the ring spacing is in multiples of the half-wavelength of the axial buckling of the cylindrical shell. As research continues, the understanding of the stability of stiffened cylindrical shells is increasing among researchers and engineers, but due to the complexity of the structure of the stiffened cylindrical shell, the simplified analytical model for the stiffened cylindrical shell and the sensitivity to initial imperfections still need further improvement.
## 2 Analysis Methods
### Shock Reflection in the Dual Solution Domain
#### 2.3.3 Transition from Regular Reflection to Mach Reflection  
Theoretically, shockwave reflections in the dual solution domain could be regular or Mach reflections, but the mechanisms for determining the reflection characteristics are not fully understood. However, experiments were performed while changing either the distance between the wedges, the angle of the wedges, or inflow conditions, which showed that the probability of transition from regular reflection to the Mach reflections is high when there is a disturbance in the pressure or density upstream [28; 29].  
In the present study, the transition occurred when the fluctuation of the pressure in the freestream region was intentionally applied to the wedges with angles less than 23\({}^{\circ}\). The fluctuation is about 20% of its undisturbed value, which is similar to the value from previous research [4]. Comparing Figure 5 to Figure 6 shows that the regular reflection changed to Mach reflection for the same wedge angle of 23\({}^{\circ}\). Once the transition occurred, the Mach reflections were maintained, even after the disturbance passed through the computational domain. Figure 6 shows the results of the transition for wedge angles of 22\({}^{\circ}\) and 23\({}^{\circ}\). As the wedge angle increases, the height of the Mach reflection also increases because the interaction of the shock waves occurs upstream as the angle of the oblique shock wave increases.  
The reflection characteristics of oblique shock waves in the dual solution domain are influenced by the angle of the wedge and the freestream conditions [18; 30]. However, few studies have been performed on the effect of the distance between wedges. To observe the effect of the distance between wedges, the same amount of artificial pressure disturbance was applied upstream of the domain and the distance between the wedges was increased by 5 mm (\(\sim\)0.2D). In Figure 7, the same transition phenomena due to the disturbance were observed, however the position of the Mach reflection moved downstream due to the crossing position of the oblique shock wave moving downstream. Furthermore, the height of the Mach reflection was decreased as the distance between the wedges increased. It is expected that the transition from Mach reflection to regular reflection by a disturbance could be obtained more easily when the distance between two wedges is larger because the height of the Mach reflection is reduced. Therefore, wedges with a larger distance were implemented in this study.  
Figure 5: Regular reflection (**left**, 23\({}^{\circ}\) wedge), Mach reflection (**right**, 24\({}^{\circ}\) wedge).