esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods and Principles
### Equivalent Linear Dynamic Time-History Analysis Method
#### 2.2.1 Viscoelastic Artificial Boundary Method  
Considering that the dynamic artificial boundary is the critical point for studying the seismic performance of an underwater shield tunnel [30], the basic principle of the viscoelastic artificial boundary method [27] is that a new physical element formed by a parallel viscous damper and a spring is imposed on the boundary of the foundation to simulate the infinite foundation during seismic analysis and calculation. Firstly, the seismic wave at the boundary of the model is converted into the equivalent load of each node of the boundary. With the scattered wave propagating to the boundary, which is produced by the calculation, part of the model is absorbed by the viscoelastic artificial boundary. At this time, the node corresponding to the boundary would generate free-field motion under the action of the seismic load. Therefore, the seismic wave input issue should be equivalently transformed into the free-field motion issue under the action of the nodes corresponding to the viscoelastic boundary through the viscoelastic artificial boundary, and these loads are finally applied to the foundation boundary of the model in the form of an equivalent nodal force. The boundary model is displayed in Figure 5. When calculating the model, the normal and tangential damping coefficients can be obtained by the following equations:  
Normal damping coefficients:  
\[C_{t}=\rho\cdot V_{s}\cdot\Delta A_{i} \tag{3}\]  
Figure 4: Schematic diagram of equivalent linearization method.  
Figure 5: Schematic diagram of artificial boundary model.  
Tangential damping coefficients:  
\[C_{n}=\rho\cdot V_{p}\cdot\Delta A_{i} \tag{4}\]
## 2 Quadcopter Modeling  
A quadcopter is subjected to torques and forces (Figure 1). Quadcopter dynamics are examined in an inertial frame, B and an earth frame, E. The quadcopter model consisted of two counterclockwise and two clockwise rotating motors. The arm length is denoted by \(L\). The dynamics of the quadcopter can be described as follows [37; 38; 39; 40; 41; 42; 43; 44]:  
\[\left\{\begin{array}{l}\ddot{\phi}=\big{(}U_{\phi}+(I_{y}-I_{z})\dot{\phi} \dot{\psi}-\overline{J}_{m}\dot{\theta}\Omega-K_{1}\dot{\phi}\big{)}/I_{x}\\ \ddot{\theta}=\big{(}U_{\theta}+(I_{z}-I_{x})\dot{\phi}\dot{\psi}-\overline{J} _{m}\dot{\phi}\Omega-K_{2}\dot{\theta}\big{)}/I_{y}\\ \ddot{\psi}=\big{(}U_{\psi}+(I_{x}-I_{y})\dot{\phi}\dot{\theta}-K_{3}\dot{\psi }\big{)}/I_{z}\\ \ddot{x}=\big{\{}U_{T}(\cos\phi\sin\theta\cos\psi+\sin\psi\sin\psi)-K_{4}\dot{ x}\big{\}}/m\\ \ddot{y}=\big{\{}U_{T}(\cos\phi\sin\theta\sin\psi-\sin\phi\sin\psi)-K_{5}\dot{y }\big{\}}/m\\ \ddot{z}=-g+\big{\{}U_{T}(\cos\phi\cos\theta)-K_{6}\dot{z}\big{\}}/m\\ \end{array}\right. \tag{1}\]  
where \(I_{x},\ I_{y},\ I_{z}\) are the moments of inertia along the \(x,\ y,\ z\) directions, respectively; \(K_{1},\ K_{2},\ K_{3},\ K_{4},\ K_{5}\) and \(K_{6}\) are drag terms; \(\overline{J}_{m}\) is the moment of inertia of an individual motor, and with \(\omega_{i}\) being the rotational speed produced by the \(i\)th motor; \(m\) is the total mass; \(\phi,\ \theta\), and \(\psi\) are the Euler angles including roll, pitch, and yaw, respectively; \(x,\ y\), and \(z\) denote the position of the quadcopter; and the four control inputs can be presented as:  
\[\left\{\begin{array}{l}U_{T}=F_{1}+F_{2}+F_{3}+F_{4}\\ U_{\phi}=(F_{4}-F_{2})L\\ U_{\theta}=(F_{3}-F_{1})L\\ U_{\psi}=\tau_{1}-\tau_{2}+\tau_{3}-\tau_{4}\\ \end{array}\right. \tag{2}\]  
where \(\tau_{i}=d\omega_{i}^{2}\) and \(F_{i}=b\omega_{i}^{2}\) represent the torques and thrust forces, respectively, induced by the \(i\)th motor; \(b\) and \(d\) are positive constants, and \(U_{T}\) is the total thrust; \(U_{\phi},\ U_{\theta}\) and \(\ U_{\psi}\) are the torques in the roll, pitch, and yaw directions.  
Figure 1: Quadcopter configuration.  
The relationship between thrust force and control inputs (Equation (2)) can be rewritten as:  
\[\left[\begin{array}{c}U_{T}\\ U_{\phi}\\ U_{\theta}\\ U_{\psi}\end{array}\right]=\left[\begin{array}{cccc}1&1&1&1\\ 0&-L&0&L\\ -L&0&L&0\\ d/b&-d/b&d/b&-d/b\end{array}\right]\left[\begin{array}{c}F_{1}\\ F_{2}\\ F_{3}\\ F_{4}\end{array}\right] \tag{3}\]  
The bound of control inputs can be calculated as:  
\[\left\{\begin{array}{l}0\leq U_{T}\leq 4 bw_{\max}^{2}\\ -bL\omega_{\max}^{2}\leq U_{\phi}\leq bL\omega_{\max}^{2}\\ -bL\omega_{\max}^{2}\leq U_{\theta}\leq bL\omega_{\max}^{2}\\ -a\omega_{\max}^{2}\leq U_{\psi}\leq a\omega_{\max}^{2}\end{array}\right. \tag{4}\]  
where \(\omega_{\max}\) is the maximum velocity of each actuator.
# Preface of Special Issue on Laser Scanning
## 1 Introduction  
A laser is a spatially coherent light that can travel through space with very little diffraction. This distinctive feature makes lasers an ideal signal carrier for measuring distances. The special issue of \"Laser Scanning\" (also known as LiDAR--Light Detection and Ranging) provides several interesting reports on the wide range of applications that this technology supports. Laser scanning is so defined because it uses laser technology to detect objects and calculate distances between the sensor and the reflective surfaces. Reflected laser pulses provide a means for measuring time-of-flight, phase differences, and other properties of the reflected pulse and of the surface that causes a full or partial reflection. A strong added value of laser scanning is that it can provide multiple returns from a single emitted pulse, because of the partial obstruction of the laser beam. For several decades, accurate distance measures have been done with laser scanners as described above, and the technology is ever actively being improved. Recent developments include multi-wavelength scanners, solid-state LiDAR, single-photon LiDAR, and scanners with increased pulse frequency, thanks to solutions related to concurrent pulses in the air (multiple time around--MTA). Also the decreased weight of components allows sensors to be assembled into unmanned vehicles (e.g., Unmanned Aerial Vehicles--UAVs) [1; 2].  
Laser scanning applications covered in this special issue can be divided into the following categories: multi-wavelength LiDAR [3; 4], mobile mapping support for indoor [5; 6; 7], outdoor, and other applications [8], object tracking and navigation [9; 10; 11], deformation monitoring [12; 13], modelling and detection [14; 15; 16], geometric accuracy assessment [17; 18; 19], and hybrid technologies like LiDARgrammetry [20], which are summarized in more detail in the following section.
# Quality Analysis of Bonded Joints in the Renovation of Plastic Automotive Parts
## 3 Results
### Assessment of the Impact Strength of Bonded Joints  
The bonded joints for the impact strength evaluation were made following the methodology described in Section 2.6. The bonded joints were evaluated after curing and exposure in different environments, namely in an environment with the presence of SO2 at total moisture condensation, during immersion in a 20% salt solution and under realistic atmospheric conditions for one year. A thickness of 0.1 mm was chosen to compare the effect factors of bonded joints after curing and exposure in different environments.  
The measured values of impact strength of bonded joints for different types of adhesives of different thicknesses are shown in Table 15 and graphically processed in Figure 12.  
The highest value of impact strength after curing was achieved by ChS EPOXY 1200 adhesive, a lower value was achieved by MEGABOND 2000, and the lowest value of impact strength was achieved by DINITROL 860 adhesive.  
In terms of adhesive thickness applied, it was observed that the highest strength values were achieved by adhesives with thicknesses of 0.33 mm, except for the DINITROL 860 type adhesive, where the highest strength was achieved at 0.1 mm adhesive thickness. Table 16 shows the characteristic fracture surface appearances for each adhesive type and thickness. For all specimens bonded with the same type of adhesive of a given thickness, adhesive failure of the bonded joint occurred. In several cases, it was possible to evaluate the fracture failures as an adhesive.  
Figure 11: Comparison of strength properties for different environments.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Adhesive**} & **Adhesive Thickness** & **Average Impact Strength** \\  & **[mm]** & **[kg.cm\({}^{-1}\)]** \\ \hline \multirow{3}{*}{Megabond 2000} & 0.1 & 32.16 \\ \cline{2-3}  & 0.25 & 32 \\ \cline{2-3}  & 0.33 & 38.4 \\ \hline \multirow{3}{*}{ChS EPOXY 1200} & 0.1 & 23.84 \\ \cline{2-3}  & 0.25 & 34.08 \\ \cline{2-3}  & 0.33 & 41.44 \\ \hline \multirow{3}{*}{DINITROL 860} & 0.1 & 15.68 \\ \cline{2-3}  & 0.25 & 13.76 \\ \cline{1-1} \cline{2-3}  & 0.33 & 12.8 \\ \hline \hline \end{tabular}
\end{table}
Table 15: Measured loads at joint failure–impact strength.
# A Review of Advanced CMOS RF Power Amplifier Architecture Trends for Low Power 5G Wireless Networks
## Advanced RF PA Architectures
### Millimeter Wave RF PA  
Millimeter wave RF PAs are intended to work at frequencies above 25 GHz. The published papers related to mm-wave PA research reveal an overall tendency of architectures which are used in frequency ranges above 25 GHz. Papers [63; 64; 65] present detailed mm-wave CMOS PA reviews distinguishing architecture types alongside their research results. According to the review tables in the latter papers, advanced PA architectures, such as DPA, ET/EER PA, TWA or outphasing PA, are rarely implemented at frequencies above 25 GHz in CMOS process node. The most common architectures in the mm-wave range are single- or two- stage stacked approaches in both single-ended and differential forms and often operate in nonlinear regions (ex. class-E, class-F). Papers [64; 66; 67] propose mm-wave DPAs although deep nanometer CMOS processes (ex. 45 nm, 28 nm) are utilized. Concluding the results presented in the above papers, complex architecture solutions (such as envelope tracking) are irrelevant in mm-wave PAs which are usually kept as simple as possible, close to the classic arrangement. Moreover, according to [68] high-efficiency mm-wave PAs designed using silicon on insulator (SOI), Gallium Arsenide pseudomorphic high electron mobility transistors (_GaAs_ pHEMT), Silicon Germanium heterojunction bipolar transistors (_SiGe_ HBT's) or Gallium Nitride (_GaN_) processes provide superior performance compared to CMOS. Due to the fact, that a small number of different architecture solutions in CMOS has been published, mm-wave PAs are not further elaborated in this article.
## 3 Results
### Performance Analysis by Anomaly Type
TranAD achieves a UCR score above or equal to 0.5 for the classes \"outlier\", \"smoothed increase\" and \"steep increase\", but for the latter, the corresponding low F1 Score indicates a high number of false positives. For the \"sampling_rate\" anomalies, the opposite is true, as the F1 Score is 0.61 here, but the UCR score is 0.0. We will discuss these differences in Section 4 in more detail. TMDI and MERLIN together detect the anomalies of more than two-thirds of the annotated anomaly types. For the classes \"flat\", \"reversed\", \"time shift\" and \"unusual pattern\", no method achieved a UCR score above or equal to 0.5.
# A Survey of OCR in Arabic Language: Applications, Techniques, and Challenges
###### Abstract  
Optical character recognition (OCR) is the process of extracting handwritten or printed text from a scanned or printed image and converting it to a machine-readable form for further data processing, such as searching or editing. Automatic text extraction using OCR helps to digitize documents for improved productivity and accessibility and for preservation of historical documents. This paper provides a survey of the current state-of-the-art applications, techniques, and challenges in Arabic OCR. We present the existing methods for each step of the complete OCR process to identify the best-performing approach for improved results. This paper follows the keyword-search method for reviewing the articles related to Arabic OCR, including the backward and forward citations of the article. In addition to state-of-art techniques, this paper identifies research gaps and presents future directions for Arabic OCR.  
Keywords:optical character recognition; Arabic OCR; preprocessing; segmentation; classification; postprocessing +
Footnote †: journal: Medical  
online services that can be used for various purposes such as mail sorting, bank cheque reading, signature verification, utility bill processing, and insurance applications. Digital pens help blind or illiterate people by reading text in audio form. Many online recognition systems are implemented in different fields such as number-plate recognition [4]. Similarly, an online OCR system is capable of receiving and processing real-time input images. For offline recognition, multiple models are used with different datasets for different algorithms to get better recognition accuracy [5].
# Measuring Gas Flaring in Russia with Multispectral VIIRS Nightfire
## 2 Method
### Cloud-Free Aggregation of Radiative Heat from Flares
Normalization in (3) for the \(N_{det}/N_{ob}\) ratio is needed to compensate for the short-time variability of flowrate and cloud-free coverage in the long-term averaging. For example, if two identical flares were observed by satellite 60 and 30 times in one month, but one was detected 6 times and another only 3 times, the normalized \(RH\) and the estimated flared volumes will be equal. Otherwise, the accumulated \(RH\) and flared volume will bias to the more frequently detected flares.
## 3 Proposal for Smart Energy Management
### Software Description
#### 3.3.4. Local Decision-Making  
This module is the main core of the software. It is based on a decision tree and supported by other parts of the system. Some of these parts provide it with the information inputs required for its operation: collected data from the electricity market; sensors data from the WSAN with information about energy consumption; and the specifications established by users through a group of constraints and rules to be met by the system. This module considers all collected data to make the best decision that ensures the compliance with the specifications set by users. As a result of the decision-making, the system manages the operation times of each device, and decides when and how the energy is consumed (Figure 8). The algorithm finds the best combination to carry out all the tasks, processingthem with the lowest possible energy consumption. The system is continuously running and updating its input data so that any change at any instant can cause variations in the decisions.  
The decision-making process starts with the analysis of the available data from the different sources (electrical market, WSAN and specifications set by users and machinery), at each moment. Based on this analysis, decisions are made to make the most of the available energetic resources at each moment. The algorithm is continuously doing these operations to evaluate whether it is possible to enhance the actual consumption scenario, while performing an on-line check of all conditions and applying the required conditions for them to be met.  
For example, if six hours are needed to charge an electric forklift and there are twelve hours available to do it, the system will distribute the charging process throughout this time, to make the most efficient use of the available energetic resources and reduce its cost. Of course, being the main constraint, the system will make sure that the forklift gets charged by the specified time. This module also sends the required instructions to the control part (hardware) of the system, which executes them through the actuators at the nodes.
# Privacy Auditing in Differential Private Machine Learning:
## 4 Privacy Auditing Schemes
### Privacy Audits
#### 4.3.3 Differential Privacy Auditing with Model Inversion
[PERSON] et al. [202] align the gradients of dummy data with the actual data, making the dummy images resemble the private images. The paper describes in detail how gradient inversion attacks work by recovering training patterns from model gradients shared during federated learning. [PERSON] et al. [203] use gradient compression to reduce the effectiveness of gradient inversion attacks. [PERSON] et al. [204] introduce a novel generative gradient inversion attack algorithm (GGI) where the dummy images are generated from low-dimension latent vectors through the pre-trained generator. **Empirical privacy loss approach** calculates the difference between theoretical and empirical privacy losses in inversion scenarios. Auditors measure the privacy loss, \(\epsilon\), by performing a model inversion on a DP-protected model and comparing the result with the theoretical privacy budget. Large deviations indicate a possible weakness of DP in protecting against inversion. [PERSON] et al. [205] investigate defense mechanisms against model inversion and propose prediction purification techniques, which involves modifying the outputs of the model toobscure sensitive information while still providing useful predictions. It shows how adding additional processing to predictions can mitigate the effects of inversion attacks. [PERSON] et al. [206] apply DP to software defect prediction (SDP) sharing models and investigate privacy disclosure through model inversion attacks. The authors introduce class-level and subclass-level DP and use DPRF (differential private random forest) as a part on enhancing DP mechanism. **Embedding and reconstruction test:** It examines whether latent representations or embeddings could be reversed to obtain private data. The auditors question whether embeddings of DP models are resistant to inversion by attempting to reconstruct data points from compressed representations. [PERSON] et al. [207] show that stricter privacy restrictions can lead to a strong bias in inferential, affecting the statistical performance of the model. They propose an approach to improve the data privacy in regression models under heteroscedasticity. In addition, there are some methods such as Graph Model Inversion (GraphMI) that are specifically designed to address the unique challenges of graph data [208]. [PERSON] et al. [139] recover the training images from the predictions of the model to evaluate _the privacy loss_ of a face recognition model and measure the success of model inversion attacks based on the _performance of an evaluation model_. The results have shown that even a high privacy budget \(\epsilon=8\) can provide protection against model inversion attacks.
# Analysis of Ruggedness of 4H-SiC Power MOSFETs with Various Doping Parameters
###### Abstract  
This work investigates the effect of the doping concentration of SiC power metal-oxide-semiconductor field-effect transistors (MOSFETs) under an unclamped inductive switching (UIS) condition. Switching circuits such as inverters and motor-drive circuits often face unexpected operating conditions; therefore, a UIS test is performed to assess the avalanche ruggedness of the device, and design parameters such as the doping concentration should be considered to improve the UIS characteristics. Technology computer-aided design circuit simulation results, such as the current flows during failure and electrical changes, were obtained by changing the doping concentration of each region in the SiC power MOSFET.  
power MOSFET; silicon carbide; unclamped inductive switching; avalanche ruggedness
###### Abstract
The InSAR technique is based on the processing of at least two complex SAR images covering the same area and acquired from slightly different points of view [32]. InSAR allows the extraction of three-dimensional information using the phase difference and the along- and cross-track location of targets on the image and can be used for measuring the topography of a surface and its changes over time [33; 34; 35]. The generation of DEMs from SAR imagery using InSAR techniques is not new; for example, the well-known Shuttle Topography Mission (SRTM) provides near-global DEM data produced by InSAR [36]. However, there is a need for higher temporal and spatial resolution DEM data, particularly for assessing landslide events. In this study, Sentinel-2 optical data and Sentinel-1 SAR data were used for geomorphological feature mapping. Sentinel-2 is a multispectral optical sensor from the European Union's Earth Observation programme, Copernicus, with 13 spectral bands combining different spatial resolutions (up to 10 m for Red, Green, Blue (RGB) and Near-infrared (NIR) bands) and a repeat cycle of 5 days [37]. The Sentinel-1 A SAR instrument was the first satellite of the Copernicus Sentinel missions [38], which provides radar data following the retirement of the ERS-2 and Envisat missions. The Sentinel-1 mission comprises a constellation of two polar-orbiting satellites (A and B). The Sentinel-1 instrument provides high-resolution C-band (wavelength of 5.6 cm) data with a short revisit time of 12 days for each satellite, and six days with Sentinel-1 A and B [39]. The satellite has a new type of ScanSAR mode, known as Terrain Observation with Progressive Scan (TOPS) SAR [40]. It has a similar pixel resolution (about 3.5 m in the range direction and 14 m in the azimuth direction) to ScanSAR but with an enhanced signal-to-noise ratio distribution [41]. The high temporal and spatial resolution of the Sentinel-1 A and B SAR has been used in various InSAR applications [42]. However, using Sentinel-1 data for generating DEMs and its application in landslide analysis is still in the early stages [43; 44]. In this research, we focused on a large landslide that occurred on the 7 July 2018 on the eastern side of the Fagraskogarfjall mountain in Hitardalur valley, western Iceland, leading to a landslide-dammed lake and changes in the watercourse [45]. The main objectives of this research are to semi-automatically assess the landslide-induced geomorphological changes in Hitardalur valley by jointly using Sentinel-1 and Sentinel-2 data within an OBIA workflow and to assess the potential of post-event DEMs generated from Sentinel-1 image pairs for the estimation of the Hitardalur landslide volume.
###### Abstract  
Accelerating the development of the 5G network and Internet of Things (IoT) application, multi-access edge computing (MEC) in a small-cell network (SCN) is designed to provide computation-intensive and latency-sensitive applications through task offloading. However, without collaboration, the resources of a single MEC server are wasted or sometimes overloaded for different service requests and applications; therefore, it increases the user's task failure rate and task duration. Meanwhile, the distinct MEC server has faced some challenges to determine where the offloaded task will be processed because the system can hardly predict the demand of end-users in advance. As a result, the quality-of-service (QoS) will be deteriorated because of service interruptions, long execution, and waiting time. To improve the QoS, we propose a novel Fuzzy logic-based collaborative task offloading (FCTO) scheme in MEC-enabled densely deployed small-cell networks. In FCTO, the delay sensitivity of the QoS is considered as the Fuzzy input parameter to make a decision where to offload the task is beneficial. The key is to share computation resources with each other and among MEC servers by using fuzzy-logic approach to select a target MEC server for task offloading. As a result, it can accommodate more computation workload in the MEC system and reduce reliance on the remote cloud. The simulation result of the proposed scheme show that our proposed system provides the best performances in all scenarios with different criteria compared with other baseline algorithms in terms of the average task failure rate, task completion time, and server utilization.  
multi-access edge computing; fuzzy logic; collaborative task offloading; small-cell network +
Footnote †: journal: Applied Soft