esa-sceva/satcom-chunk-collection huggingface dataset:
# A Method for the Calibration of a LiDAR and Fisheye Camera System
## 4 Discussion  
The results of the experiments show that the proposed method is able to calibrate the system composed of a fisheye camera and a LiDAR sensor with a high degree of accuracy. The method has been tested both in a simulated environment and in a real environment. First, in the case of the simulated environment, the accuracy of the method has been measured through the mean rotation error, the mean translation error, and the MPE. Second, in the case of the real environment, the actual transformation between the camera and the LiDAR is not known, so the results obtained with the proposed method cannot be compared with this actual transformation. For this reason, we have used the reprojection error as a metric to evaluate the quality of the resulting calibration. In all the cases, these metrics indicate that the error is small enough to be used in applications where a wide field of view is needed. To clearly prove the validity of the approach, Figures 12 and 13 show a point cloud projected onto the corresponding fisheye image using the estimated calibration parameters to perform this projection.  
The results obtained are of great importance for the development of autonomous vehicles or mobile robots where the priority is to obtain a wide field of view without the need for a great deal of image sensors since the proposed method permits the fusion of the information from both sensors with a high degree of accuracy. These fused data can subsequently be useful for processing by neural networks or any other algorithms that require a wide field of view to work properly and provide complete information regarding the surroundings of the sensors. Additionally, it is worth highlighting that the time needed to calculate the transformation with the proposed method is much lower than the minimization method, which is an advantage for the proposed method.  
Figure 13: Image from Figure 12 zoomed in.  
The limitations observed during the experiments are related to the quality of corner detection in the image, which depends on the specifications of the sensor and the calibration of the fisheye lens. This has an influence on the quality of the fusion of the information from both sensors, which is lower in the case of the real environment than under simulation, as expected.
# Implementation and Security Test of Zero-Knowledge Protocols on SSI Blockchain
## 1 Introduction
Despite the growing popularity of ZKPs in recent years, and the consequent increase in the research of its applications, its study is still not common. Therefore, we present the objectives after the development of an application that implements three ZKP protocols, oriented to the study of these methods and their comparison. The general objective of this work is to develop an application with a graphical interface aimed at students in areas related to computer security, such as computer engineering and mathematics, who are interested in the study of Zero-Knowledge Proof methods and, specifically, in their application for authentication in public-private key systems. It will focus on showing the Feige-Fiat-Shamir, Guillou-Quisquater, and Schnorr protocols step-by-step, allowing the user to modify the necessary parameters in each method to gain a better understanding of their role in the security and efficiency of these. More specifically, the contributions of this work are:  
* Studying the operation of the Feige-Fiat-Shamir, Guillou-Quisquater, and Schnorr protocols for their correct implementation in an authentication system;
* Creating an application that manages the Verifier and Prover roles and allows the user to observe the authentication protocols step-by-step, including the verification calculations performed by the Verifier and the communication between the two;
* Testing the security of the protocols against brute-force attacks that try to take advantage of the vulnerability of the ZKP methods against an adversary who manages to guess the challenge that the Verifier is going to demand from the Prover;
* Allowing the application to be executable in as many environments as possible. As it is a desktop application, this is achieved by its implementation in an executable environment in the main operating systems: Windows, OS, and Linux. The paper has the following structure: Section 1 is an introduction to the paper and to the problem of Self-Sovereign Identity. It justifies the motivation of the work in the context of today's hyper-connected society where privacy has taken greater importance in the consciousness of citizens and presents the approach of the work. Section 2 consists of a deep dive into the current state of online identity management technologies and protocols, the role of blockchain technology in this field, and Zero-Knowledge protocols. Section 3 presents the methodology followed to test the security of Zero-Knowledge protocols. Thus, an application to test the security of ZKPs is proposed. Section 4 presents the use-case and requirement analysis for the proposed application prior to its development. Section 5 shows its main classes and interfaces. Section 6 executes the security tests using the developed software and presents a final reflection to summarize everything learned and the challenges encountered. Section 7 presents the main conclusions of the obtained results. Finally, Section 8 considers possible future lines of work.
# Energy Efficiency in Short and Wide-Area IoT Technologies--A Survey
## 4 Classification of Energy Efficiency Methods
### Mechanisms for RFID Active Tags
#### 4.1.3 Bluetooth and BLE Bluetooth
An ideal polling scheme would be achieved by keeping the slaves in a sleep state and waking them up via dedicated polling packets by the master only if there is data for them. In real cases, arrival times of data packets cannot be predicted exactly due to traffic load fluctuations. The rate of transmission of polling packets is thus the result of a trade-off between latency in data delivery and energy consumption: the aim of ASP is indeed to optimize this trade-off by specifically addressing two sources of inefficiency: (a) unsuccessful polling procedures, where the slave replies to polling packets with a NULL packet, in order to maintain a constant bit rate, and (b) useless receptions, in which the slaves receive many access codes and headers for packets that are not meant for them. In ASP, the master schedules the traffic so to keep the successful transmission exchange rates between each slave and the master itself within a predefined range. The master also maintains a timer for each slave, in order to poll the slave at the optimal moment in terms of data delivery latency. ASP performance was evaluated using IBM's BlueHoc Bluetooth extension for the NS-2 simulator [134]. Simulations were executed with the assumption that there were no lost packets and the traffic was unidirectional (slave-to-master). Results for a single-slave piconet (i.e., a piconet with one master and one slave) were used as a baseline in order to evaluate the power consumption of both master and slave. The analysis was then carried out for piconets including up to seven slaves. Results showed that the energy consumption using the ASP algorithm is dependent on the data rate. When both master and slaves use low data rates, the energy consumption is low. If the data rate of the slaves increases, the energy consumption also increases.
# Thermal Modelling and Experimental Validation in the Perspective of Tool Steel Laser Polishing
## 3 Results and Discussion
### Moving Experiment: Microstructure Observation and Adjustment of the Absorption  
#### 3.3.1 Microstructure Observations and Adjustment of the Absorption  
One of the critical material parameters for simulating laser processes is the light absorption of the given material at the laser wavelength. Indeed, the absorption of light gives the source term in the heat equation and so determines the thermal behavior of the material. For the tool steel used in this study with the EDM rough surface (\(Ra=3.15\)\(\upmu\)m), the absorption was measured at 35%. This value is dependent on the roughness and evidence of this is given by the fact that the absorption was measured at 30% for the same material but with mirror polish finish. This is consistent with the study of [PERSON] et al. [22] who found that an increase of the roughness leads to an increase of the absorption. It is unlikely that a constant absorption is the best solution in simulation since several effects can occur such as Drude absorption [23], oxidation [24], or changes of surface roughness [25].  
[PERSON] et al. [26] demonstrated that the absorption of the liquid tool steel is also unknown due to the complexity of measuring this property. As a consequence, for the simulations, the absorption of the liquid was fitted based on the experimental measurements of the size of the melt pool and HAZ observed during the experiments.
# A Bibliometric Analysis and Visualization of In-Vehicle Communication Protocols
## 2 Materials and Methods
### Sources of Information  
Using the Scopus database maintained by Elsevier, this study follows the methodological approaches provided in [10]. Scopus, which was launched in 2004, is now one of the largest bibliographic databases in the world, with more than 77.8 million entries compiled since 1969. Its coverage comprises more than 23,000 peer-reviewed journals, 294 trade journals, over 852 book series, and proceedings from more than 120,000 international academic conferences. This makes Scopus an authoritative source for accessing the global research landscape. As [11] indicates, the database is comprehensive on a global scale and comes equipped with sophisticated bibliometric research tools. These tools allow for advanced filtering and segmentation by the year of publication, document type, author names, institutional affiliation, and citation counts, enabling more precise analysis of scholarly trends.  
In combination with these functions, this study takes a systematic approach to finding and analyzing the literature about in-vehicle communication protocols. Scopus was chosen because of its credibility, data trustworthiness, and capability to conduct detailed scientific analyses. The database provided relevant publications for analysis, such as CANand LIN, FlexRay, MOST, and Automotive Ethernet, which aided in exploring existing research gaps, publication patterns, and significant contributions in this research domain.
# Bearing Anomaly Recognition Using an Intelligent Digital Twin Integrated with Machine Learning
## 2 Proposed Methodology  
Figure 1 illustrates the intelligent DT integrated with SVM for FPRCI. Based on this figure, the combination of the proposed signal approximation algorithm, adaptive neural-fuzzy integrated with variable structure Kalman filter, and SVM is recommended for FPRCI. Thus, the intelligent DT (signal approximation and estimation), residual signal generation, and classification are the three main stages in the proposed algorithm.  
Figure 1: Proposed intelligent digital twin integrated with machine learning for bearing anomaly classification.  
In the first stage, the proposed intelligent DT is designed. This part has two sub-blocks, the signal approximation unit and the signal estimation unit. For normal signal approximation, the mathematical vibration signal modeling is integrated with the data-driven algorithm. The data-driven-based regression is designed based on the combination of the autoregression technique, the Laguerre method, and the machine learning-based (support vector regression (SVR)) approach (henceforth called SAL). Therefore, from now on the combination of the mathematical-based modeling (MAT) and SAL algorithm is called MS. For vibration signal estimation, the hybrid robust Kalman filter (HRKF) is proposed. The HRKF is the combination of the Kalman Filter (KF), the high-order variable structure technique (HVS), and the adaptive neural-fuzzy method (ANF). Thus, first, the KF is developed based on the normal signal state-space signal modeling. Next, The HVS technique is suggested to reduce the error of signal modeling in uncertain conditions. After that, the combination of the KF and the HVS with the ANF method is used to increase the reliability and flexibility of the normal signal estimation.  
Thus, the proposed intelligent DT is developed by MS integrated with the HRKF. The second stage is the residual signal generation based on the difference between the original RAW signals and the estimated ones. The level of residual signals in different patterns is completely different. In addition, the different crack sizes have different signal levels. Thus, the level of residual signals for normal (NS), roller fault state (RFS), inner fault state (IFS), and outer fault state (OFS) are different, which from now on is called fault pattern recognition. Moreover, the level of these signals in the various crack sizes (0.007, 0.014, and 0.021 inch) are different, which from now on is called crack size identification.  
The third stage is used for the classification of residual signals based on the machine learning approach. Thus, the SVM is used for FPRCI.
## 2 Materials and Methods
### Measurements
#### 2.2.3 Health Indicators (Dependent Variables)  
All health indicators of the respondents were self-reported. A self-assessment of health status was examined with the question \"How do you assess your health status on a 5-point scale?\" and was divided into three categories: (1) good/very good (4-5 points); (2) average (3 points); or (3) bad/very bad (2-1 point). Perception of the impact of the environment on individual health was assessed by the question \"What, in your opinion, negatively affects your health--the environmental status, psychological stress, harmful habits, improper nutrition, the lack of physical activity, or inattention to yourself?\" The presence of chronic diseases was recorded according to the answer to the question \"Do you have any chronic or long-term diseases?\" (yes/no).  
Body mass index (BMI) is based on an individual's height and weight parameters (\"How many kilograms do you weigh?\" and \"What is your height in centimetres?\") and was calculated as kg/m\({}^{2}\). Depending on their BMI, the respondents were divided into three categories: (1) normal weight (BMI \(\leq\) 24.9), (2) overweight (BMI 25.0-29.9), and (3) obese (BMI \(\geq\) 30) [22].  
PA was examined with the question \"How many hours or minutes per week do you engage in physical exercises of at least average intensity (consider not only activities at the gym, but also walking, cycling, planting, etc.--i.e., activities that make you pant and/or sweat)?\". The indicated hours and minutes were calculated as minutes per week. According to the WHO recommendations on PA, the obtained data were dichotomised into two groups--sufficient (\(\geq\)150 min per week) or insufficient PA (<150 min per week).  
The presence of cardiovascular and chronic diseases was examined with questions \"Have you ever had a heart attack (myocardial infarction)?\" and \"Do you have any of the following diseases: (1) hypertension (high blood pressure) (yes/no), (2) diabetes (yes/no), or (3) a stroke (stroke consequences) (yes/no)?\". Systolic and diastolic blood pressure (BP) was recorded according to the question \"What blood pressure (mmHg) do you usually have?\". The obtained parameters were dichotomised as hypertension (if systolic BP was \(\geq\)140 mmHg and/or diastolic BP was \(\geq\)90 mmHg) or non-hypertension.
## 3 Routing
### Routing in WSN  
Generally, protocols in WSNs are application-based. Hence, universal communication protocols do not exist because all applications consider varying factors that influence their design. Routing in a WSN is not similar to routing in other networks because most WSNs are data centric and require the flow of data from many sources to a sink. Hence, due to the intrinsic nature of the network, combined with its unique constraints and self-organizing nature, multi-hopping is employed to send data to the sink. Based on the underlying network structure, WSN routing protocols can generally be classified as flat, hierarchical, location-based or QoS-based. Ref [13, 19] have attempted to provide details about the available protocols in this respect.
# A Rigid Motion Artifact Reduction Method for CT Based on Blind Deconvolution
## 2 Methods
### Iterative Blind Deconvolution  
#### 2.2.1 Image Degradation Model  
The generation of CT images can be modeled as an image degradation system. The motion-degraded image is formed by a convolution between the ideal image and the point spread function (PSF) [23].  
\[g(\mathbf{s})=f(\mathbf{s})*h(\mathbf{s}), \tag{2}\]  
where \(g(\mathbf{s})\) is the degraded image, \(f(\mathbf{s})\) is the ideal image, \(h(\mathbf{s})\) is the impulse response of the system or PSF, \(\mathbf{s}\) denotes the spatial coordinates, and \(*\) is the convolution in spatial domain.  
In practice, the PSF is not precisely known. Hence, an iterative blind deconvolution approach is needed to restore the real image without complete knowledge of the associated system PSF.
# Process-Oriented Stream Classification Pipeline: A Literature Review
## 4 Data Processing
### Preprocessing  
Data preprocessing is nearly all knowledge discovery processes' first and most critical step. Frequently, this step takes more time than the actual analysis step [99]. Raw data is usually low quality and full of inconsistencies, missing values, noise, or redundancies. This challenge could cause the performance of classification models to fall short of their capabilities. By proper preprocessing, the quality and reliability of automated classification can be improved significantly. Working with stream data is accompanied by specific properties, e.g., that it cannot be stored in its entirety and data points often arrive at high speed. The typical preprocessing steps have to be treated differently than in classical offline settings [99]. Time is a critical component of stream classification problems compared to the offline setting. Typical offline methods benefit efficiency because some approaches can iterate over the data multiple times to identify essential features or align feature spaces for better comparability with enough processing time. Regarding the requirements of online  
Figure 4: Overview of data preparation methods applicable for data stream classification purposes. Here, the handling of data of the respective _sources_ is specified.  
learning, one iteration should be enough. However, this is not always precisely handled in practice. For example, multiple iterations over a selection of data points are also possible. Nevertheless, not all data points can be stored permanently, and preprocessing steps should be kept in time and short. In addition, the emergence of new classes and features or the change of feature value spaces makes accurate preprocessing difficult. An example is an image or video data, as in surveillance applications or gesture recognition scenarios. Usually, processing pixels is a complex task due to a large data quantity and the fact that only a small amount of pixels captures essential information. The data needs to be reduced before the analysis for efficient computations.  
While most summarizing papers in the field of stream data preprocessing cope with sub-aspects, the work of [91, 99] give a broad overview of tasks and techniques. The authors elaborate on the current status, such as challenges and research directions. Within their extensive survey, ref. [99] review the mentioned preprocessing algorithms and evaluate them against several synthetic and real-world benchmarking datasets by comparing the accuracy of classification results. As a basis, they use the datasets, as well as generators of the MOA framework. In the following, we will structure typical preprocessing steps according to the work of [99].
# Augmented Reality in Precision Farming: Concepts and Applications
## 1 Introduction
discuss, upwards of 600 cows may be present [13] (although the number depends on the country) and moving in a dispersed fashion over a significant areas, is a different challenge to crop management precision farming applications, where the objects, in this case crops, are static organised or condensed and the AR is often sensor-driven. In this article, the investigation put forward highlights the different challenges concerning the development of AR applications for precision farming. Whilst AR is often present in works related to this article, for example by [PERSON] et al. [9], existing discussions of the technology typically fall under a wider survey of precision farming-based technologies, of which AR is proposed as a communication medium. Therefore, this article stands apart from existing literature, as few works within the precision farming research domain place the main emphasis of the investigation on AR hardware frameworks. As such, the following three research questions (RQ) are identified. RQ1: _How is AR currently deployed within an agricultural setting?_ RQ2: _What technologies is AR coupled with to provide a service for farmers?_ and RQ3: _What hardware frameworks are used to deploy AR technologies in an agricultural setting?_  
To address the research questions, a snowballing approach was adopted as a review protocol for considering applied AR solutions within the agricultural domain, focusing on articles from November 2016 to November 2021. The five year range is selected due to the advancing pace of AR technologies and SDK solutions within this time frame; for example Microsoft released the HoloLens in 2016, which can be recognized as a landmark in AR tech (as discussed in [18] by [PERSON] et al.) sparking an interest in wider developments in this area. For the review process, digital libraries involved in the search included Scopus, IEEE Xplore, MDPI, with Google Scholar employed for checking any missed articles from the repositories. Findings and recommendations can be used to support those aiming to develop AR applications within a precision farming setting. The remainder of the paper is organised as follows. Section 2 provides a background discussion on the software and hardware requirements for AR deployment in a precision farming setting. Section 3 discusses AR deployment models and Section 4 discusses the findings. The paper is concluded in Section 5.