esa-sceva/satcom-chunk-collection huggingface dataset:
# Variable Structure PID Controller for Satellite Attitude Control Considering Actuator Failure
## Nomenclature
(2020) [PERSON]; [PERSON]; [PERSON] Finite-Time Controller for Flexible Satellite Attitude Fast and Large-Angle Maneuver. _Symmetry_**2020**, _14_, 45. [CrossRef]
* [PERSON] et al. (2021) [PERSON]; [PERSON]; [PERSON]; [PERSON] Dynamic Sliding Mode Controller with Variable Structure for Fast Satellite Attitude Maneuver. _Math. Probl. Eng._**2021**, _2021_, 5539717. * [PERSON] et al. (2021) [PERSON]; [PERSON]; [PERSON]; [PERSON] Prescribed-time attitude tracking control of spacecraft with arbitrary disturbance. _IEEE Trans. Aerosp. Electron. Syst._**2021**, _10_, 1557-9603. [CrossRef]
* [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON] Robust fixed-time attitude stabilization control of flexible spacecraft with actuator uncertainty. _Nonlinear Dyn._**2020**, _100_, 2505-2519. [CrossRef]
* [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON]; [PERSON] Robust tracking control of robot manipulators with joint velocity measurement uncertainty and actuator faults. _IEEE/ASME Trans. Mechatron._**2020**, _25_, 1354-1465. [CrossRef]
* [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON] Reconfigurable tolerant control of uncertain mechanical systems with actuator faults: A sliding mode observer-based approach. _IEEE Trans. Control Syst. Technol._**2018**, _26_, 1249-1258. [CrossRef]
# Artificial Intelligence-Based Semantic Internet of Things in a User-Centric Smart City
## 3 AI-SIoT Architecture
### Service Management Layer
#### 3.2.1 IoT Platform  
The IoT platform is the accessible entrance of the IoT for the smart devices. Examples include but are not limited to the oneM2M platform, the Alljoyn platform, the Google Android Things and the Apple HomeKit. The oneM2M is an international standardization organization in the field of IoT and the oneM2M platform provides a universal resource framework, which allows oneM2M devices to register to the resource pool. Alljoyn is an open-source software framework originally developed by Qualcomm and the Alljoyn platform provides a complete and comprehensive P2P solution, which does not depend on a specific communication protocol. The Google Android Things is an IoT operation system and it can speed up the development of IoT devices based on Android system and related applications. The Apple HomeKit focuses on the SH field and the Intelligent devices in SH can be managed by Apple's virtual assistant Siri. These IoT platforms control the accessed devices and collect device data. The IoT platform is usually divided into two associated submodules. One submodule is deployed around the smart devices to ensure their access to the IoT, and it also provides the access points. As a submodule of IoT platform, a smart gate can help the smart devices to access the IoT, and it is usually seen as the bridge between the infrastructure layer and the service management layer. The other submodule is deployed in the Cloud, and it provides the remote management, data analysis, and other extended services. In our AI-SIoT architecture, an AI interface is constructed to link the IoT platform and the AI module. All platforms can leverage the AI interface to access the AI module for data analysis. Although the IoT standards tend to be unified, a variety of different standards and platforms will still coexist for a relatively long time in the years to come. To this end, it is necessary to provide the same interfaces for the intelligent interactions between different platforms.
## Vision-Based Sensing
### Coordinate Transformation  
From the identified image coordinates of the markers the joint angles, that is, the generalized coordinates, have to be computed in a second step. To do so, lens distortions, which make straight lines in real world look curved in the image, have to be eliminated first. For this purpose, the Matlab function undistortPoints from the Computer Vision Toolbox is available. Then, with the Matlab function pointsToWorld, the \(x\)- and \(y\)-coordinates of the markers given in the checkerboard coordinate system from calibration are obtained. Coordinate transformation to the desired world coordinate system is then realized by simple translation and rotation. Taking the offsets between the markers on the robot's surface and the joint center points into account, the center point coordinates \([x_{1},y_{1}]\), \([x_{2},y_{2}]\), \([x_{3},y_{3}]\) of the joints equipped with markers are obtained in the world coordinate system. The joint angles \(\alpha\) and \(\beta\), see Figure 5, are finally computed by the trigonometric relations  
\[\alpha = \text{atan2}(x_{2}-x_{1},y_{2}-y_{1})\, \tag{32a}\] \[\beta = \alpha-\text{atan2}(x_{3}-x_{2},y_{3}-y_{2})\, \tag{32b}\]  
where \(atan2\) is the four-quadrant inverse tangent function. The indices represent the numbers of the markers according to Figure 1.  
Note that for tracking a spatial movement with more than two degrees of freedom, at least a second camera is necessary to set up a stereo vision system. Then, the transformation of marker coordinates from an image coordinate system to the checkerboard coordinate system or world coordinate system, respectively, is done by triangulation. In case of 3D motion, there is a risk for markers to be hidden behind the robot's parts depending on its configuration and the camera positions. Therefore, these markers would be invisible for one or more cameras making a localization of the respective robot parts impossible. To address this problem, more cameras and/or markers are required to capture the mechanical components' position for the complete workspace and all possible robot configurations. To do so, a motion capture system like OptiTrack might be used as it is done in Reference [43] to capture human motion in a driver-in-the-loop simulator or in Reference [44] to capture the motion of a flying robot. However, using a motion capture system contradicts the low-cost approach of slim hardware requirements pursued in this contribution.
## 3 Results
### Historical Comparison Cohort and Analysis of Outcomes
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Patient Characteristics (\(u=66\))** & \(n\) **(\%)/Mean (\(\pm\)SD)** & \(\beta\) **(\%) \(\%\) CD** & \(p\)**-Value** \\ \hline \multicolumn{4}{c}{Calculated Intra-tumoral dwMRI Metrics (Mean \(\pm\) SD)} \\ Quantitative Anisotropy (QA) & 1.19 (\(\pm\)0.42) & 139.85 (\(-\)253.72, 533.41) & 0.480 \\ Fractional Anisotropy (FA) & 0.19 (\(\pm\)0.06) & \(-\)663.42 (\(-\)3433.88, & 0.634 \\ Mean Diffusivity (MD) & 1.25 (\(\pm\)0.34) & 504.75 (28.20, 981.29) & **0.038** \\ Axial Diffusivity (AD) & 1.47 (\(\pm\)0.36) & 474.17 (30.69, 917.65) & **0.037** \\ \hline \multicolumn{4}{c}{Generalized Q-Space Imaging (QSI)-Derived Tractography Features} \\ Identified Anatomic White Matter & 51 (77.3\%) & – & 0.965 \\ Structure Involvement (\%) & 34 (51.5\%) & – & 0.091 \\ Corpus Callosum & 6 (9.1\%) & – & 0.461 \\ Corticothalamic Tracts & 9 (13.6\%) & – & 0.691 \\ Superior Longitudinal Fasciculus & 19 (28.8\%) & – & 0.824 \\ Inferor Longitudinal & & & \\ Fasciculus/Inferor Frontal-Occipital & 20 (30.3\%) & – & **0.008** \\ Fasciculus & 4 (6.1\%) & – & 0.589 \\ Mean Tract Length (mm) & 29.1 (\(\pm\)11.7) & \(-\)7.79 (\(-\)21.88, 6.31) & 0.274 \\ Mean Projecting Tract Length (mm) & 64.2 (\(\pm\)19.6) & \(-\)5.63 (\(-\)13.98, 2.72) & 0.183 \\ Number of Projecting Tract Bundles & 5.0 (\(\pm\)2.1) & 12.21 (\(-\)47.47, 71.88) & 0.684 \\ \hline \hline \end{tabular} Age, tumor location, volume, calculated intra-tumoral diffusion-weighted MRI (dwMRI) metrics, and generalized q-space imaging (QSI)-derived tractography features are reported in terms of proportions or means and standard deviations. Association of continuous variables with overall survival was assessed using linear regression with report of \(\beta\) coefficient and \(p\)-values, while binary variables were assessed using two-sided \(t\)-tests with reported \(p\)-values. The bolded numbers indicate \(p<0.05\) which is below significance threshold.  
\end{table} 
Table 2: _Cont._  
Figure 3: Survival analysis of tumor-associated quantitative imaging features. On Kaplan-Meier survival analysis stratifying the historical glioblastoma cohort from MDACC (\(n=66\)) by quartile groups  
in terms of select demographic and radiomic metrics (\(n=66\)): age (**A**), tumor volume (**B**), intratumoral mean diffusivity (**C**), number of projecting tract bundles observed (**D**), mean total tract length (**E**), and mean projecting tract length (**F**). Statistically significant negative effects on overall survival were seen with older age (\(p=0.013\)), longer mean total tract length (\(p=0.039\)), and longer mean projecting tract length (\(p=0.022\)). Larger tumor volume and lower mean diffusivity were non-significant but trended towards negative effects on survival as well.
# Naturally Occurring Flavonoids and Isoflavonoids and Their Microbial Transformation: A Review
## 2 Isoflavonoids and Flavonoids from Actinomycetes and Fungi
### Isoflavonoids
#### 2.1.3 Complex Isoflavones  
An antifungal luteone, 5,7,2\({}^{\prime}\)\(A^{\prime}\)-tetrahydroxy-6-(3,3-dimethylallyl) isoflavone (**38**), was obtained from the cultures of _Aspergillus flavus_ and _Botrytis cinerea_. At the level of 7-14 \(\mu\)g/cm\({}^{2}\), the growth of _Chlamydia herbarum_ was completely inhibited by compound **38**[44]. Four compounds, luteone hydrate (**39**), luteone metabolite AF-2 (**40**), BC-1 (**41**), and BC-2 (**42**), were obtained from the co-cultures of _A. flavus_ and _B. cinerea_. Five isoflavonoids, M-1-1 (**44**), M-1-2 (**45**), M-2 (**46**), M-3-1 (**47**), and M-3-2 (**48**) were isolated from the fungus _A. flavus_ or _B. cinerea_ by converting liciosoflavone A (**43**). When their inhibitory ability to grow _Cidasporium herbarum_ was tested, compounds **38** and **43** were found to completely inhibit growth at the level of 25-100 \(\mu\)g/cm\({}^{2}\), while compound **44** had only a small inhibition zone at the level of 100 \(\mu\)g/cm\({}^{2}\)[45]. A new isoflavone, genisteinin-4\({}^{\prime}\)-(6\({}^{\prime\prime}\)-methyl)-salicylate (**49**) was obtained from _Streptomyces_ sp. GW27/2506 [35].
# Optimal Control of an Ultraviolet Water Disinfection System
## 2 A Disinfection UV System
### The Dynamic Model of the UV Disinfection System
#### 2.2.2 Bacterial Abatement Calculation
An outline of the computation of bacterial abatement is presented in Table 1.  
Generally speaking, the values of \(k_{1}(I_{0},Q_{0})\) and \(k_{2}(I_{0},Q_{0})\) can be deduced by an interpolation procedure that processes the available experimental measurements. In our work, these data were not available. Therefore, we exploited a peculiar aspect of the characteristic curves (presented in paper [5] and Appendix B) that depict the bacterial reduction versus flow rate. An additional hypothesis can be made: the curves are partial branches of parabolas. Because the determination of the gain values (\(k_{1}(I_{0},Q_{0})\) and \(k_{2}(I_{0},Q_{0})\)) is based upon this hypothesis, one may consider that our approach has an empirical characteristic. Hence, only a few details are given in Appendix A concerning the determination of the gain values. **Remark 4**. : _The procedure that calculates \(A(I_{0},\,Q_{0},\,t)\) is essential for solving the optimization problems of the following sections. Nevertheless, the high degree of accuracy of this calculation is not crucial to define and solve the optimization problems, which address the UVDS under consideration. Finally, our main objective is to study these problems and propose good approximative methods to solve them. Obviously, for real-time implementation of optimal solutions, high calculation accuracy is needed._  
The procedure implementing the calculation \(A(I_{0},\,Q_{0},\,t)\) was written using the MATLAB language (and system), like all of the algorithms presented in this paper. Figure 4 depicts the static gains generated using procedure \(A(I_{0},\,Q_{0},\,t)\), which adopted the additional hypothesis. Compared with experimental measurements, these curves reveal high accuracy of the calculations. Hence, we have an appropriate tool to address the nonlinear dependence from Figure 3. \begin{table}
\begin{tabular}{c c} \hline \hline
**start** \(A(I_{0},\,Q_{0},\,t)\) \\ \hline  & \(/^{*}y1,\,y2,\) and \(y\) are vectors with real elements \({}^{*}/\) \\ \hline  & \(/^{*}k_{1}(I_{0},\,Q_{0})\) and \(k_{2}(I_{0},\,Q_{0})\) are real functions \({}^{*}/\) \\ \hline
1. & \(y_{1}\leftarrow\) step response of \(T_{1}(s)\) ; \\ \hline
2. & \(y_{2}\leftarrow\) step response of \(T_{2}(s)\) ; \\ \hline
3. & \(A(I_{0},\,Q_{0},\,t)\gets k_{1}(I_{0},\,Q_{0})\cdot y_{1}+k_{2}(I_{0},\,Q_ {0})\cdot y_{2}\) \\ \hline
4. & _return_\(A(I_{0},\,Q_{0},\,t)\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Outline of function \(A(I_{0},\,Q_{0},\,t)\). Figure 4 confirms the physical behavior of the UVDS: the bacterial abatement diminishes with the increase in the flow rate and increases with the increase in the UV intensity.
# Balanced Loss Function for Accurate Surface Defect Segmentation
###### Abstract  
The accurate image segmentation of surface defects is challenging for modern convolutional neural networks (CNN)-based segmentation models. This paper identifies that loss imbalance is a critical problem in segmentation accuracy improvement. The loss imbalance problem includes: label imbalance, which impairs the accuracy on less represented classes; easy-hard example imbalance, which misleads the focus of optimization on less valuable examples; and boundary imbalance, which involves an unusually large loss value at the defect boundary caused by label confusion. In this paper, a novel balanced loss function is proposed to address the loss imbalance problem. The balanced loss function includes dynamical class weighting, truncated cross-entropy loss and label confusion suppression to solve the three types of loss imbalance, respectively. Extensive experiments are performed on surface defect benchmarks and various CNN segmentation models in comparison with other commonly used loss functions. The balanced loss function outperforms the counterparts and brings accuracy improvement from 5% to 30%.  
surface defect inspection; semantic segmentation; loss imbalance; balanced loss function +
Footnote †: journal: Medical Imaging
# Data and Knowledge-Driven Bridge Digital Twin Modeling for Smart Operation and Maintenance
## 4 Spatiotemporal Data Collection and Processing for Bridge Digital Twin
### Time Series Sensory Data Collection and Processing
#### 4.3.3 Sensor Data Processing Using Long Short-Term Memory
When unexpected variations, such as sudden increases in stress or displacement, are detected, the LSTM model generates an alert, prompting immediate investigation and further analysis. This early warning mechanism is crucial for mitigating the risk of structural failures. By detecting early signs of damage or stress, LSTM models enable maintenance teams to undertake corrective measures before the situation escalates. Such a proactive approach helps prolong the lifespan of the structure while simultaneously reducing the likelihood of sudden and catastrophic failures.
## 4 Results  
Following the method described in Section 3, we show the results corresponding to: (1) the conducted GSA of the leaf-canopy-atmosphere RTM (Section 4.1), (2) the performance assessment on the retrieval of biophysical variables from synthetic S2 surface reflectance and TOA radiance (Section 4.2), and (3) the proof-of-concept results for the retrieval of biophysical variables from real S2 L1C and L2A data.
## 2 Materials and Methods
### Monitoring of Residual Oil and MEL Content  
During the incubation period, the culture was sampled to determine residual oil and MEL content at different time intervals. The culture broth was centrifuged at 8000 rpm for 15 min and the supernatant was extracted with hexane. To quantify residual oil, hexane extract was evaporated at 60 \({}^{\circ}\)C and the quantity of residual oil was measured. For the quantification of MEL, the crude product was analyzed by the anthrone method [32]. The anthrone method was chosen to quantify MEL due to its simplicity, cost-effectiveness, and ability to handle multiple samples quickly. Unlike HPLC or GC, which require sophisticated equipment, extensive sample preparation, and trained personnel, the anthrone method is a straightforward colorimetric assay. It can be performed with basic laboratory equipment, making it accessible for many labs. Additionally, the anthrone method is effective in quantifying carbohydrate components in MEL, providing reliable results without the complexity and higher costs associated with HPLC or GC techniques. Mannosylerythriol lipid (Figure 1) content was calculated by multiplying mannose values by 3.6 as per the average molecular weight of fatty acids from the fatty acid profile of mannosylerythriol lipids and molecular weight of mannose [13; 14].  
The anthrone method quantifies mannosylerythriol lipids (MELs) by reacting them with anthrone reagent. First, MEL samples and glucose standards are prepared, with glucose used to create a calibration curve. Each solution is reacted with anthrone, heated in a boiling water bath for 10 min, and then cooled. Absorbance at 620 nm is measured to determine carbohydrate concentration. A standard curve is plotted using glucose standards and the carbohydrate content in the MEL sample is calculated by comparing the sample's  
Figure 1: General structure of mannosylerythriol lipids produced by _P. antarctica._  
absorbance to the curve. The MEL concentration is estimated based on the carbohydrate-to-lipid ratio.
## 1 Introduction
\begin{table}
\begin{tabular}{c c} \hline \hline
**Keyword** & **Meanings** \\ \hline ITS & Intelligent Transportation System \\ VANETs & Vehicular AdHoc Networks \\ V2V & Vehicle-to-Vehicle \\ V2I & Vehicle-to-Infrastructure \\ V2X & Vehicle-to-Everything \\ RSU & Road Side Units \\ EEE & End-to-End \\ PDR & Packet Delivery Ratio \\ RR & Route Reliability \\ P2P & Peer-to-Peer \\ TRPs & Topological Routing Protocols \\ GRPs & Geographic Routing Protocols \\
5G & Fifth Generation \\ SDN & Software-Defined Network \\ DDoS & Distributed Denial of Services \\ MANETs & Mobile AdHoc Networks \\ CA & Certification Authority \\ LCG & Linear Congruential Generator \\ FYS & Fisher-Yates Shuffle \\ API & Application Programming Interface \\ OTP & One-Time Password \\ HTTP & Hypertext Transfer Protocol \\ UR & Ultra-Reliability \\ IoT & Internet of Things \\ AI & Artificial Intelligence \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Indexed Terms Meanings. We proposed secure cluster-based authentication and communication protocols for Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communications. Our work covers the following phases for V2I and V2V communications and resource sharing in a VANET. * Authentication and registration using third-party certification authority. * V2I and V2I Communication Channel. * Graph-Based Resource Sharing in Vehicular Communication. We took up assistance from the third-party certification authority for registration and authentication, and demonstrated the V2V and V2I communication mechanism in a 400 m diameter. There is much distortion in the moving vehicle, so we set the 400 m cluster for a specific VANET. We addressed primary security goals such as integrity, authenticity, and availability of messages in vehicular communication on a specific cluster. Figure 1 simplifies the Vehicle-to-Vehicle communication in a 400 m cluster. CH means the Head of the Cluster, and CM denotes the other Cluster Member. We proposed two algorithms based on baseline and greedy approaches for resource allocation in vehicular communications. Section 1 addresses the introduction and research contribution. In Section 2, we discuss previous studies relevant to the research. The various past research contributions on VANET are exhibited by a table. Section 3 includes the research methodology, implementation, \begin{table}
\begin{tabular}{c c} \hline \hline
**Keyword** & **Meanings** \\ \hline MD5 & Media-Digest Algorithm for Encryption \\ SUMO & Simulation of Urban Mobility \\ API & Application Program Interface \\ TCP & Transfer Control Protocol \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._  
Figure 1: Vehicular communication in a 400 m diameter. performance evaluation, results, and discussions. The methodology section demonstrates the proposed protocol, implementation, and graph-based resource sharing in vehicular communications. This section further addresses notations and assumptions, research results, and performance evaluation. In the implementation subsection, we have discussed the utilized tools and technologies. Ultimately, the paper ends with a conclusion section.
## 2 Related Works
In contrast, the ICP method does not rely on extensive training datasets, making it more adaptable to smaller or less annotated datasets. The ICP algorithm is simple and easy to implement and understand. It performs well, especially when dealing with dense point clouds and good initial alignments, allowing for rapid convergence. It also has lower computational overhead and is relatively more straightforward to implement. Given these factors, this article employs the ICP method for point cloud registration. In summary, parking space recognition, also referred to as free space detection, encounters numerous challenges. Firstly, the parking space detection method based on images struggles to maintain effectiveness in complex, unknown environments. Secondly, the pure Lidar-based parking space detection method cannot ensure the accuracy and complexity of mapping and parking space recognition in the AVP system. Therefore, we proposed an automatic parking space recognition approach that tightly couples Lidar with IMU based on the geometric relationship between the vehicle and the parking space, leveraging accurate 3D pose information from point clouds. The IMU compensates for Lidar pose, enabling precise vehicle localization, local map construction of unknown parking scenes, and identification of vacant parking spaces. This approach enhances the robustness of parking space recognition and annotation. Compared to existing methods, our approach offers the following contributions:  
1. We propose a parking space detection method based on tightly coupled Lidar and pre-integrated IMU. By integrating Lidar point clouds with IMU data tightly, we construct a high-precision localization and grid map. This method identifies free space  
Figure 3: Modern ecological parking lots where parking lines cannot be detected. (**a**) Top-view image of a parking lot; (**b**) Image of an ecological parking lot. between obstacle vehicles using convex hull detection and the Hough transform on the grid map, and finally determines parking spaces based on vehicle size constraints. Therefore, our method achieves high-precision perception of parking scenes. 2. We rely solely on Lidar for perceiving the parking scene, eliminating the need for prior maps or parking space information at the input data source. In contrast to current Lidar parking space detection methods that rely on prior information from maps or images, our approach enables precise parking space detection in unknown and complex environments. 3. We propose a polarized Lidar matrix transformation method in Lidar SLAM. Our approach converts 3D point clouds into 2D matrices and integrates BEV projections of point cloud grid maps to achieve parking space recognition. By compressing 3D data into 2D, we reduce data storage requirements without sacrificing critical features, thereby addressing the high complexity associated with 3D point cloud processing.