esa-sceva/satcom-chunk-collection huggingface dataset:
# A Generic Framework for Enhancing Autonomous Driving Accuracy through Multimodal Data Fusion
## 2 Background and Motivation  
### Overview about Sensors  
Figure 1 depicts the overall architecture of the autonomous driving (AD) system, and Figure 2 illustrates the functionality of the AD system.  
Figure 1: Architecture of an autonomous driving (AD) system.  
Figure 2: A functional perspective that describes four main functional blocks.  
Further, this section discusses the basics of sensor technology, how AVs perceive their environment data using the sensors, various types of sensors, and a brief introduction to data fusion. While AV systems differ slightly from one another, they are usually complex systems with several sub-components. The layout of an AV model may be divided into two stages, according to several perspectives: the hardware and software tools in the initial step, and the functional perspective required for the AD system's operating units in the second stage. From a technical perspective, the two main tiers are hardware and software, with individual components representing various facets of the entire AV platform in each layer. Communication infrastructure is provided by a number of AD sub-components that operate between the hardware and software levels. Clarification, agility, automobile controller, system tracking, organization, and policy-making are the main operational components of AVs. These operational units' duties center on information flow from perceived data to vehicle control, data collection, and data analysis.  
As discussed in the introduction the AVs' surrounding environment data are collected using advanced sensors such as LiDAR and radar also using thermal and stereo cameras. This section highlights the functioning and characteristics of sensors and cameras. Sensors convert events or changes in the environment into mathematical computations that may be processed later. Sensors are classified into two major categories based on their functionality. The first category denotes internal state sensors, also known as proprioceptive sensors, which store and detect the states of a mobile system, such as force, angular rate, wheel load, battery voltage, and other variables. Inertial measurement units (IMUs), encoders, inertial sensors (gyroscopes and magnetometers), and position sensors (global navigation satellite system (GNSS) receivers) are examples of proprioceptive sensors. Complete localization refers to the vehicle's location with respect to a global reference frame, whereas relative localization addresses the vehicle's coordinates that align with the surrounding landmarks (world). The external sensors detect information about the external world that is relevant to the AVs. For precise and accurate object detection and to conduct dependable and safe operations, individual and relative sensor orientation is critical. In general, getting enough data from a single reputable source in AD is challenging. The internal health state of the components, as well as the exterior surrounding information of the AVs, are critical for successful decision-making to ensure safety.
## 3 Materials and Methods
### Evaluation Metrics  
To measure the performance of the models and compare the effect of preprocessing on them, we will use the metrics accuracy, precision, recall, and AUC.  
_Accuracy._ It is a general measure that quantifies the proportion of hits in the classification. Given the number of true positives (_TP_), false positives (_FP_), true negatives (_TN_), and false negatives (_FN_), the accuracy is defined as:  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN}\]  
_Precision._ Quantifies actual positives versus all images that have been classified as positive.  
\[Precision=\frac{TP}{TP+FP}\]  
_Recall._ It measures the number of well-classified positives versus actual positives.  
\[Recall=\frac{TP}{TP+FN}\]  
_AUC._ In addition, we will use the area under the curve. Both the ROC curve and the _AUC_ are a measure of the overall performance of a binary classification model. It measures the ability of the model to correctly classify positives at different thresholds. The higher the _AUC_ the better the model is at predicting. A perfect model would have an _AUC_ of 1 and a random classifier would have a value of 0.5 [80].
# Transfer Learning Analysis for Subvisible Particle Flow Imaging of Pharmaceutical Formulations
###### Abstract  
Subvisible particles are an ongoing problem in biotherapeutic injectable pharmaceutical formulations, and their identification is an important prerequisite for tracing them back to their source and optimizing the process. Flow imaging microscopy (FIM) is a favored imaging technique, mainly because of its ability to achieve rapid batch imaging of subvisible particles in solution with excellent imaging quality. This study used VGG16 after transfer learning to identify subvisible particle images acquired using FlowCam. We manually prepared standards for seven classes of particles, acquired the image information through FlowCam, and fed the images over 5 \(\upmu\)m into VGG16 consisting of a convolutional base of VGG16 pre-trained with ImageNet data and a custom classifier for training. An accuracy of 97.51% was obtained for the test set data. The study also demonstrated that the recognition method using transfer learning outperforms machine learning methods based on morphological parameters in terms of accuracy, and has a significant training speed advantage over scratch-trained CNN. The combination of transfer learning and FIM images is expected to provide a general and accurate data-analysis method for identifying subvisible particles.  
Keywords:image analysis; protein formulation; convolutional neural network; particle identification; machine learning +
Footnote †: journal:
###### Abstract  
The point source superposition method based on the Rayleigh integral model is time-consuming for calculating the three-dimensional spatial sound field. In this paper, the look-up table method is introduced into the calculation of the acoustic field to reduce the computational effort. Based on the region of synchronous vibration on the transducer, a sub-regional look-up table method is proposed. Simulations demonstrate that both the acoustic field look-up table (ALUT) method and the acoustic field sub-regional look-up table (ASLUT) method have the same acoustic field calculation results as the point source method. Regarding the cost of time, the acoustic field look-up table method takes only one third of the time of the point source method, and the acoustic field sub-region look-up table method takes only one eighteenth of the time of the point source method, with the possibility of further reduction. Both the ALUT and ASLUT methods significantly reduce the calculation time for different types of transducers, which is beneficial for the study of planar sound source devices.  
acoustic field; planar acoustic source; look-up table method Article  
Fast Calculation of Acoustic Field Distribution for Ultrasonic Transducers Using Look-Up Table  
Hefei 230009, China 3
# TBRNet: Two-Stream BiLSTM Residual Network for Video Action Recognition
## 3 Proposed Approach  
In this section, we describe our proposed TBRNet and its main components. The TBRNet can be divided into two modules: the two-stream CNN-based encoder and the BiLSTM-based decoder. The overall architecture of TBRNet is shown in Figure 3. Specifically, residual learning, as well as its key idea, is described in the first subsection, since it is applied to three places in our model. Then, we introduce the proposed Res-C3D model which is used to construct the two streams. After that, several types of interaction between the two pathways are discussed. Subsequently, we describe the decoder modeled with the BiLSTM network. Finally, the attention mechanism for BiLSTM and the integration of overall global spatiotemporal representations using residual connections are described in the last subsection.  
Figure 3: The overall architecture of our proposed encoder–decoder model Two-Stream BiLSTM Residual Network (TBRNet) for video action recognition. First, we divided a video into \(N\) segments over time and sample \(t\) RGB frames and corresponding \(t\) optical flow frames for each segment with a fixed-time interval. Then, those stacked frames were fed into the encoder with two-stream networks to extract visual features, where each network had residual connections from the motion stream to the appearance stream for earlier interactions. Each stream was constructed using the proposed Residual Convolutional 3D (Res-C3D). In addition, “A-” and “M-” stand for the appearance stream and motion stream, respectively. After that, the short-term spatiotemporal features from the encoder at every time step were fed into attention-based BiLSTM to obtain the bidirectional temporal dependencies, and the “F-” and “B-” stand for the forward and backward directions of LSTM, respectively. Those temporal dependencies were integrated with the short-term spatiotemporal features in another merged LSTM (M-LSTM) for global spatiotemporal representations. Finally, a softmax layer was applied to obtain the final result of classification.
## 3 Results and Discussion
### Electrochemical Chloride Ion Detection by the Cu (II) Complex  
For extensive electrochemical application, the prepared mixed Cu (II) complex was used for the detection of chloride ions by adding a fixed amount of NaCl in a standard buffer solution (pH = 7) via cyclic voltammetry (CV). The electrochemical properties of Cu (II) complex were investigated by using the three-electrode system in the potential range from +0.80 V to \(-\)0.80 V in a standard buffer (pH = 7) at various scan rates. The CV plots (Figure 7) of Cu (II) complex have shown two peaks in the standard buffer (pH = 7) electrolyte. The cathodic peak was obtained in the forward scan and anodic peak during the reverse scan of CV measurements.  
Figure 5: SEM images: (**a**) low resolution, and (**b**) high resolution of the mixed ligand Cu (II) complex.  
Figure 6: Cyclic voltammograms of the Cu (II) complex in CH\({}_{3}\)CN (0.1 mol L\({}^{-1}\) TBAPF\({}_{6}\)), glassy carbon working electrode, Ag/AgCl reference electrode and platinum wire electrode with scan rate 100 mV s\({}^{-1}\).  
The cathodic and anodic currents increased with the increase in scan rate from 50 mVs\({}^{-1}\) to 150 mV/s. Furthermore, the cathodic potential (E\({}_{\text{pc}}\)) is shifted towards more negative potentials, and the anodic potential (E\({}_{\text{pa}}\)) moved to more positive potentials with increasing scan rates towards the detection of chloride ions [37]. The plot of anodic and cathodic peaks versus scan rates (Figure 7b) has shown a linear dependence relation and hence suggests a quasi-reversible nature and diffusion-controlled transfer of electrons during the electrochemical reaction. The electrochemical system shows excellent retention coefficients (r\({}^{2}\)) of 0.99246 and 098866, respectively, which again deduces the good detection of chloride ions in neutral buffer electrolytes [38; 39].
## 2 Literature Review
### Modern Applications and Enterprise Management in the Era of Industry 4.0
#### 2.4.1 Artificial Intelligence in an Industry 4.0  
Figure 3 below explains how companies applied artificial intelligence to enterprise resource planning and key planning indicators in the era of Industry 4.0. As the world advances so too do the areas of technological applications to advance to achieve and enhance better customer satisfaction.  
Figure 3, explain how the digital era help companies use artificial intelligence applications to influence and easily understand customers through enterprise resource planning  
Figure 3: Artificial intelligence for industrial use.  
(ERP) and key performance indicators (KPI). With the advances in technology, companies do not longer rely on human resources to measure their sales rate as AI embedded in systems such as key performance indicators helps companies plan better using enterprise resource planning. AI with the help of professional software skills such as ERP and cognitive systems such as KPI has changed the state of affairs within business [57]. Since ERP depends on AI software for business planning, it has also tremendously supported KPI. To understand key performance indicators within a business, there's a need for effective evaluation and monitoring. Due to large data acquisition within nowadays business activities AI is trying to assist. Nowadays, businesses use AI to predict sales and forecast the return on investments of their products. Since KPI is to evaluate how well or not a product is doing, AI has shown that it can effectively manage these services according to Figure 3 above. AI tools, with the support of the right data, have a far-reaching benefit on sales than average sales personnel. ERP systems or digital platforms are the best AI-built systems nowadays that help to manage a global supply chain, enabling understanding of customers' behaviours.AI tools have been largely incorporated in enterprise software [58]. An AI provides the following services to industrial enterprises through ERP software. To predict asset conditions and predict maintenance industries require AI technology [59]  
* User assistance and augmentation
* Process improvement
* Planning and forecasting  
In aid of more productive and profitable business, AI is being incorporated to make more critical business decisions and solve complex issues nowadays. AI tools have access to a larger amount of data and are capable of processing more than any person can determine. AI provides services and products for business needs and sales leads will generate the most business gains. With AI, decisions go beyond basic process automation. Examples of AI already used with ERP are Consumer digital assistants Siri, Alexa, and the Google digital assistant. AI assists businesses with customer insights by examining social media, customer service interactions, and product quality systems.
## 2 Current Status of Development of Geophysical Exploration Techniques
### Exploration Techniques Based on Elastic Wave Velocity
#### 2.2.1 Surface Wave Method  
Surface waves are Rayleigh waves generated during the propagation of seismic waves in seismic exploration using refraction and reflection methods. With the advancement of technology, [PERSON] et al. [34] studied the dispersion characteristics of Rayleigh wave propagation and applied it to geophysical exploration technology. The surface wave method is essentially based on the dispersion characteristics of surface wave propagation, where various frequency components of the surface waves are used to determine the variation in the wave velocity with the frequency in the subsurface medium. The core problem is to accurately obtain the phase velocity of surface waves of different frequencies, and the commonly used methods for extracting surface wave dispersion curves are the spatial autocorrelation (SPAC, spatial autocorrelation) and frequency-wave velocity method (F-K). Since the surface waves are more energetic and have a lower velocity and frequency than body waves and are not constrained by the difference in the wave impedance, the detection resolution is higher, which is conducive for detecting stratigraphic structures. Figure 5 shows the principle of the surface wave method.  
Based on the different excitation methods, surface wave methods can be divided into steady-state and transient surface wave methods, and there are mainly two types of seismic sources: artificial sources and natural sources. The artificial source generally adopts a linear type, which has the drawback of relatively shallow exploration depth, whereas the natural source can adopt a linear type, L type, and multiple circular type. The multiple circular types should be used when the surrounding interference level is high, and the natural source has the disadvantages of long data collection cycles and difficulty in field operation. [PERSON] et al. [35] introduced pseudo-random seismic sources as surface wave exploration sources and conducted an experimental analysis on three sources to demonstrate the superiority of pseudo-random seismic sources in terms of detection depth, resolution, and acquisition time. The high fitting of the depth-velocity curves with artificial and natural sources proved the validity of the method. [PERSON] et al. [36] proposed a comprehensive surface wave method, which is an analysis method for shallow S-wave velocity structures, and a natural surface wave method under H/V constraint. The results showed that the obtained profile matches the known borehole information by detecting the actual project, thus verifying its higher accuracy and stability. However, the detection depth of the surface wave method is approximately half a wavelength, which is unsuitable for large- and medium-sized dikes with deep seepage points. Figure 6 shows the inverse model section of the surface wave method.
# Trade-Off Analysis for Array Configurations of Chipless RFID Sensor Tag Designs
## 5 Simulation Study of Array Configurations
### RCS Analysis
In the unit cell scenario, as shown in Figure 14a, the structural mode RCS component forms a Gaussian spectrum, indicating a uniform distribution of reflected energy across frequencies. Conversely, the antenna mode RCS component exhibits a pronounced peak, emphasising the resonant behaviour of the tag resonator at a specific frequency. This resonance is a crucial feature harnessed for encoding information. Transitioning to the 4 \(\times\) 4 array configuration, as shown in Figure 14b, the structural mode RCS displays deviations from the Gaussian spectrum, revealing the emergence of spectral dips, particularly around 5.20 GHz. This phenomenon results from the collective resonance and interference effects induced by the increased number of reflective elements in the array. The spectral dips in the structural mode RCS signal can be misinterpreted as a data bit. On the other hand, the antenna mode RCS component shows a prominent peak, highlighting the resonant behaviour of the tag at a designed frequency. Therefore, when designing chipless RFID arrays, it is necessary to consider the trade-off between the structural and antenna mode RCS.
# Multifeature Fusion Neural Network for Oceanic Phenomena Detection in SAR Images
###### Abstract  
Oceanic phenomena detection in synthetic aperture radar (SAR) images is important in the fields of fishery, military, and oceanography. The traditional detection methods of oceanic phenomena in SAR images are based on handcrafted features and detection thresholds, which have a problem of poor generalization ability. Methods based on deep learning have good generalization ability. However, most of the deep learning methods currently applied to oceanic phenomena detection only detect one type of phenomenon. To satisfy the requirements of efficient and accurate detection of multiple information of multiple oceanic phenomena in massive SAR images, this paper proposes an oceanic phenomena detection method in SAR images based on convolutional neural network (CNN). The method first uses ResNet-50 to extract multilevel features. Second, it uses the atrous spatial pyramid pooling (ASPP) module to extract multiscale features. Finally, it fuses multilevel features and multiscale features to detect oceanic phenomena. The SAR images acquired from the Sentinel-1 satellite are used to establish a sample dataset of oceanic phenomena. The method proposed can achieve 91% accuracy on the dataset.  
SAR; deep learning; oceanic phenomena; multifeature fusion; CNN +
Footnote †: journal: Accepted: 16 December 2019; Published: 30 December 2019
# Influence of Material on the Density of a Point Cloud Created Using a Structured-Light 3D Scanner
## 5 Discussion  
The applicability of the 3D structured-light scanner to different types of cultural heritage has been proven in many studies. [PERSON] et al. [41] and [PERSON] et al. [42] describe methods of digitizing important cultural heritage. [PERSON] [43], [PERSON] et al. [44] and [PERSON] [45] deal with the digitization of archaeological finds using a 3D structured-light scanner. [PERSON] et al. [46] used a structured-light 3D scanner to create a digital model of exposed historical clothing. [PERSON] et al. [47] describes structured-light 3D scanning of heritage objects in a museum environment. Also, our own research [11] was focused on the creation of a digital model of [PERSON]'s monuments using structured light. The mentioned studies have been solving the point cloud quality issue in various ways. [PERSON] [45], [PERSON] et al. [46], and [PERSON] et al. [47] modify some parts of the digital model using software. [PERSON] [43] changes the lighting conditions for scanning. [PERSON] et al. [44] do not need such high precision (hundredths of a millimeter). [PERSON] et al. [42] use a bleaching spray.  
This article focuses on evaluating the effect of surface properties of the selected materials on the density of the point cloud. The density of the point cloud is an important indicator of the quality of the point cloud, but the quality is also affected by other phenomena, such as holes in the point cloud, point noise (especially horizontal), or the distribution of points in the measured area (some parts may have very densely located points and others very sparsely located points). Research has shown certain repeating elements in certain types of material surfaces or their deformations.
## 3 Results and Discussion
### The Advantages, Disadvantages, and Potential Applications of High Resolution LULC Classification
The high-resolution LULC classification at different times allows the identification of particular, small-scale processes and temporal dynamics too. Two small-scale processes are exemplarily described in the following: (1) The urban encroachment into the river source area and only remaining urban recreational space of high ecological value in Figure 7. The construction of a group of buildings on both sides is approximately through the remaining vegetation separating the urban areas. (2) A forest (high vegetation) surrounding an important tributary of the Pochote river is disappearing due to increasing urban development east and west of it (Figure 8). Figure 7: Zooming-in on a tributary in the Urban area shows a decrease of high vegetation in 2018 (right) in comparison to 2015 (middle) and 2009 (left). These two examples show how the developed LULC classification can serve to identify dynamic and detailed processes of environmental degradation in order to plan tailor-made counteractions. The classification results allow not only a qualitative but also a quantitative assessment of these processes. The detailed high-resolution quantification can also be the basis for the quantification of population densities (based on building densities), degrees of surface sealing, and abundance of green spaces of different quality (high or low vegetation). This information is essential for sustainable urban development.