esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 The Potential of Computer Vision Research in UAV Datasets
### Leveraging UAV Datasets for Computer Vision Applications
#### 5.1.7 Urban Scene Segmentation and Forestry Analysis  
UAVid provides annotations for eight classes and 30 high-resolution video sequences in 4K resolution to address segmentation challenges in urban scenes. It uses models such as Multi-Scale-Dilation net to support tasks like population density analysis and traffic monitoring. FOR-instance provides UAV-based laser scanning data for tree instance segmentation and is intended for use in point cloud segmentation in forestry. It facilitates benchmarking and method development by supporting both instance and semantic segmentation.
## 4 Trajectory Tracking via Attitude Stabilization
### Trajectory Tracking Using the Reference Model Method (TTRM)  
The controller described in the previous section is composed of a block of PD/PID controllers that stabilize the quadcopter attitude, consequently maintaining the position.  
As described in Section 3.1, by setting some specifications for our reference system, the quadcopter UAV must behave in the same way. We aim to have a stabilization time for all four quadcopters \(T_{s}=0.5\ s\), without overshoot.  
To perform this design, we consider the attitude and altitude transfer functions given in (14) which are rewritten in (29).  
\[\left\{\begin{array}{l}F_{\phi}(s)=\frac{\phi(s)}{U_{2}(s)}=\frac{l}{L_{s}s^ {2}}\\ F_{\theta}(s)=\frac{\theta(s)}{U_{3}(s)}=\frac{l}{l_{y}s^{2}}\\ F_{\psi}(s)=\frac{\psi(s)}{U_{4}(s)}=\frac{1}{l_{z}s^{2}}\\ F_{z}(s)=\frac{z(s)}{U_{1}(s)}=\frac{k_{z}}{s(\tau_{z}s+1)}.\end{array}\right. \tag{29}\]  
A PD/PID controller is introduced for each flight parameter, \(\phi\), \(\theta\), \(\psi\), and \(z\) (30,31).  
\[C_{PD_{j}}(s)=\frac{U_{i}(s)}{j_{d}-j}(s)=k_{p_{j}}+k_{d_{j}}s \tag{30}\]  
\[C_{PID_{j}}(s)=\frac{U_{i}(s)}{j_{d}-j}(s)=k_{p_{j}}+\frac{k_{i}}{s}+k_{d_{j}}s \tag{31}\]  
\(k_{p_{j}}\), \(k_{i_{j}}\), and \(k_{d_{j}}\) are the proportional, integral, and derivative gains, respectively, with \(j=\phi,\theta,\psi,z\). The characteristic polynomials of the controlled systems are represented by Equations (20) and (21) and \(D_{z}(s)\) in Equations (18) and (19). The obtained parameters are summarized in Table 3.
# Analysis of Recent Deep Learning Techniques for Arabic Handwritten-Text OCR and Post-OCR Correction
## 4 Deep-Learning Techniques
### Generative Adversarial Networks (GANs) [46]  
GANs consist of two parts: the generator and the discriminator. The generator and the discriminator compete against each other, which is why they are referred to as adversarial. The generator tries to produce examples that seem as real as the ground-truth examples. On the other hand, the discriminator tries to distinguish whether the image is false or true. In other words, it establishes whether the image belongs to the ground-truth dataset or whether it was generated by the generator. The generator is fed by a random input vector, and it generates an example accordingly. The discriminator, on the other hand, takes as inputs both the generated example and the real example, and then determines which is real and which is generated. Through this classification, the loss is computed, and both the generator and discriminator models are updated. The GANs are usually used to augment the data and generate multiple samples that appear similar to the real dataset, which can help to prevent overfitting and reduce generalization errors in deep-learning models. Both parts of GANs can be made of CNNs. An example of a GAN is shown in Figure 8. The green indicates the generator and the dark pink indicates the discriminator. The dark blue shows random seeding, while variations in the torque show the confusion between the real and false samples.
# Mitigation of Electro Magnetic Interference by Using C-Shaped Composite Cylindrical Device
## 2 Experimental and Simulation Setup
### Preparation of the C-Shape Composite Device  
The C-shaped cylindrical device combined with the passive shielding method and the active shielding method was used to counteract the environmental electromagnetic field. The device structure was made by using an electric coil surrounded by an electromagnetic-shielding material. A high-permeability material and a permalloy material that absorbed the majority of electromagnetic waves were used for electromagnetic shielding. Electric coils served as the active shielding system, depending on the input current. The C-shaped opening was cut by a wire-cutting machine for the perfect edge. For a C-shaped device, it was expected that C-shape could gather magnetic field lines around it and emit magnetic field lines to the opening. With such a lens, the magnetic field lines were collected by the C-shape with a high-permeability material and emitted through the focus.
## 1 Introduction
However, likely for computational simplicity, these prior studies focused primarily on populations of a few hundred or few thousand cells. To our knowledge, this represents the first biophysical model designed to study budding colonies that considers populations of more than 10,000 cells and emphasizes the impact of biophysical properties of cells on colony organization. In this paper, we study the impact of budding cell division on overall shape, size and spatial organization of growing yeast colonies. To do this, we develop a 2D ABM that explicitly includes the mechanical interactions that arise when the daughter cell is growing and physically connected to its mother. Spatial rearrangement of cells in our synthetic colonies depends on cell-cell interaction and our model incorporates several other important biological processes including, asymmetric cell cycle lengths between the mother and daughter cell and the impact of nutrient limitation. In addition, we develop specific metrics to quantify the spatial organization of cells that emerges due to different biophysical properties in budding and non-budding colonies. We then adapt our model to simulate colony growth in a nutrient-rich and nutrient-limited environment and discuss how nutrient limitation impacts global colony organization as it relates to sector-like regions formed by individual subcolonies. In Section 2.1 we analyze the impact of budding alone by considering colonies grown in an environment with an inexhaustible supply of nutrients. In Section 2.2 we study how a more realistic nutrient-limited environment acts in concert with biophysical forces created by budding division to further impact colony organization. In Section 3 we discuss the implications of our work in understanding the appearance of sectoring patterns in growing yeast colonies and more broadly outline extensions of our model to further study prion sectors in _S. cerevisiae_. In Section 4, a detailed description of our model and the metrics we developed to quantify spatial organization of cells are given. Finally, Section 5 offers our concluding remarks.
## Abbreviations  
The following abbreviations are used in this research article:  
SST Solid State Transformer  
RESs Renewable Energy Resources  
THD Total Harmonic Distortion  
PI Proportional Integral  
PWM Pulse Width Modulation  
SVPWM Space Vector Pulse Width Modulation  
VSC Voltage Source Converter  
VSI Voltage Source Inverter  
HVDC High Voltage Direct Current  
FACT Flexible Alternating Current Transmission  
SVC Static VAR Compensator  
STATCOM Static Synchronous Compensators  
UPFC Unified Power Flow Controllers  
IGBT Insulated Gate Bipolar Junction Transistor  
VFD Variable Frequency Drive  
DAB Dual Active Bridge  
VOC Vector (oriented) Current Control  
OCC Outer Current Control  
ICC Inner Current Control  
MIMO Multi-Input Multi-Output  
PCC Point of Common Coupling  
PLL Phase Locked Loop  
IMC Internal Model Control  
PFI Power Factor Improvement  
HFT High Frequency Transformer  
VAR Volt Ampere Reactive  
**Notations**  
The following notations are used in this research article:  
\(Vx\) DC voltage at DAB end/input of SST-VSI  
\(V_{DC,mes}\) DC voltage at SST-VSC end/input of DAB  
\(V_{out}\) output voltage of the SST  
\(f_{out}\) output frequency of the SST  
\(pc\) point of common coupling  
\(v_{S}\) voltage source at converter side  
\(P_{mes}\) measured active power  
\(P_{ref}\) reference of active power  
\(Q_{mes}\) measured reactive power  
\(Q_{ref}\) reference of reactive power  
\(V_{AC,mes}\) measured AC voltage  
\(V_{AC,ref}\) reference of AC voltage  
\(i_{S}\) current from grid to the SST  
\(i_{d}\) direct component of current  
\(i_{q}\) quadrature component of current  
\(i_{d,ref}\) reference for \(d\)-axis component of current  
\(i_{q,ref}\) reference for \(q\)-axis component of current  
\(e_{c}\) grid voltage  
\(e_{d}\)\(d\)-axis component of grid voltage  
\(e_{q}\)\(q\)-axis component of grid voltage  
\(V_{d,ref}\)\(d\)-axis component of reference voltage from inner current loop  
\(V_{q,ref}\)\(q\)-axis component of reference voltage from inner current loop  
\(V_{abc,ref}\) three phase voltage reference signals
# Research on Intraparticle to Interparticle Entanglement Swapping Protocols
## 2 The Distinctions and Similarities between the Two Kinds of Entangled States
### Practical Application  
This section compares the practical application of the two types of entangled states. Interparticle entangled states can be used in many state-of-the-art technologies, including quantum key distribution (QKD), quantum dense coding, quantum secure direct communication (QSDC), and QT. Intraparticle entangled states can be applied to quantum-dense  
Figure 2: Experimental setups for generating and separating intraparticle and interparticle entangled states. In (**a**), the experimental setup for generating and separating intraparticle entangled states is shown [16]. QWP represents the quarter wave plate, and the Q-plate is an optical instrument (modulating the polarization state of light). PBS is the polarization beam splitter, DP is the Michelson interferometer, and HWP represents the half-wave plate. CBS represents the circularly polarized beam splitter. In (**b**), the experimental setup for generating interparticle entangled states is shown. PPKTP is an efficient non-linear artificial crystal (generating entangled photon sources), DM is the reflector mirror, IF is 1-nm interference filter [36]. In (**c**), the experimental setup for separating interparticle entangled states is shown. D\({}_{\text{HH}}\), D\({}_{\text{2H}}\), D\({}_{\text{IV}}\), and D\({}_{\text{2V}}\) are the detectors, and BS represents the beam splitter.  
encoding, entanglement switching, QT, and QKD. Intraparticle entanglement states exhibit stronger robustness and a greater capacity for information carrying than interparticle entangled states [15] in communication processes. However, separating and detecting intraparticle entangled states is a relatively challenging task. High levels of security are demonstrated for the two types of entanglement states [15; 17; 38].  
All in all, this section compared and analyzed the distinctions and similarities between the two kinds of entangled states. These findings show that interparticle and intraparticle entanglement both have a correlation and that interparticle entanglement is non-local; however, non-locality has no meaning for intraparticle entanglement. Intraparticle entanglement has a stronger robustness and higher capacity to carry information, and it is easier to generate and more convenient to detect and separate than interparticle entanglement in real-world applications.
## 6 Conclusions  
Hazard investigation education is very important for equipping learners with adequate knowledge and skills to reduce potential hazards at construction jobsites. Therefore, research efforts have studied and adapted the state-of-the-art technologies such as 360 VP and 3D-VR to promote construction hazard education in recent years. Despite the significant benefits of providing an engaging and immersive learning environment, 360 VP does not have 3D object anatomization tools to support learning, while the 3D-VR limitation is a lack of providing a real-world environment. Meanwhile, it is a fact that a lack of research integrates these technologies in order to eliminate limitations and maximize advantages for enhancing learning outcomes. Thus, this research objective focuses on developing a construction hazard investigation system entitled iAPR, which integrates 360 VP and 3D-VR technologies by leveraging 3D object anatomization on a 360 VP platform. The proposed iAPR system consists of three key learning modules, namely HUM, HRM, and SPM, which adopt the revised [PERSON]'s taxonomy theory to improve hazard education. The iAPR prototype were developed, and the usability of the system was evaluated by users including educators, construction professionals, and learners. Moreover, a comparison between before-exam and after-exam results is carried out objectively in order to evaluate the effectiveness of the iAPR platform. Preliminary findings prove that the iAPR has significant pedagogic methods to improve the learner's construction hazard investigation knowledge and skills, which improves safety performance.  
Despite the proposed advantages of the iAPR system, future works need to further investigate the following perspectives:  
* Regarding an adaptability and reality, an extended study of deploying the iAPR system on various wearable devices should to be conducted, e.g., head-mounted-displays, Microsoft Hololens, Google glass, etc.
* From usability and popularity perspectives, cost efficiency should be considered in terms of initial investigation, and maintenance as well as human labor.
* In application and utilization points of view, it is necessary to conduct an in-depth investigation of how much improvement in learning outcome the learners would obtain in different types of construction such as bridges, tunnels, damps, etc.
* In terms of systematical implementation and elasticity, since this research proposes and validates the iAPR system in a prototype, a full-scale system of iAPR should be deployed in order to comprehensively validate the system performance for a large number of learners.  
Figure 6: Usability evaluation.  
**Author Contributions:** Conceptualization, H.C.P. and A.-T.P.-H.; Data curation, H.C.P. and A.-T.P.-H.; Methodology, H.C.P.; Software, N.-N.D., S.C. and A.-T.P.-H.; Supervision, H.C.P. and A.-T.P.-H.; Validation, H.C.P., P.T.N. and A.-T.P.-H.; Writing--original draft, H.C.P., N.-N.D. and A.-T.P.-H.; Writing--review & editing, S.C.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results  
### Growth Parameters of _E. coli_ Chemostat Cultures  
Chemostat cultures of the _E. coli_ W3110 and the isogenic _xrel_A mutant were run at different dilution rates (0.05, 0.1 and 0.2 h\({}^{-1}\)) and the determined growth parameters are shown in Table 1.  
Overall, the biomass yields increased with the dilution rate, but the mutant strain exhibited a slightly higher biomass yield than the W3110 strain in the same conditions. In turn, the W3110 strain produced higher amounts of acetate than the mutant strain when growing at a higher dilution rate (0.2 h\({}^{-1}\)). Residual concentrations of glucose were also detected in the chemostat cultures, but only at higher dilution rates.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline  & & \multicolumn{3}{c}{**W3110**} & \multicolumn{3}{c}{**\(\Delta\)_xrel_A mutant**} \\ \hline
**Dilution rate (h\({}^{-1}\))** & **0.05** & **0.10** & **0.20** & **0.05** & **0.10** & **0.20** \\
**Biomass yield (g\({}_{\text{Biomass}}\)-g\({}_{\text{Glucose}}\)\({}^{-1}\))** & 0.36\(\pm\)0.056 & 0.44\(\pm\)0.15 & 0.55\(\pm\)0.10 & 0.46\(\pm\)0.063 & 0.46\(\pm\)0.064 & 0.67\(\pm\)0.3 \\
**Biomass (g\({}_{\text{Biomass}}\)-L\({}^{-1}\))** & 1.8\(\pm\)0.28 & 2.2\(\pm\)0.34 & 2.7\(\pm\)0.43 & 2.3\(\pm\)0.31 & 2.3\(\pm\)0.32 & 3.3\(\pm\)0.45 \\
**Glucose (g\({}_{\text{Glucose}}\)-L\({}^{-1}\))** & (1) & 0.029\(\pm\)0.0086 & 0.040\(\pm\)0.0033 & (1) & (1) & 0.023\(\pm\)0.010 \\
**\(q_{\text{Glucose}}\) (g\({}_{\text{Glucose}}\)-g\({}_{\text{Biomass}}\)-h\({}^{-1}\))** & 0.14\(\pm\)0.021 & 0.23\(\pm\)0.076 & 0.36\(\pm\)0.063 & 0.11\(\pm\)0.015 & 0.22\(\pm\)0.030 & 0.30\(\pm\)0.13 \\
**Acetate (g\({}_{\text{Acetate}}\)-L\({}^{-1}\))** & (1) & (1) & 0.34 & (1) & (1) & 0.02 \\
**\(q_{\text{Acetate}}\) (x10\({}^{3}\))(g\({}_{\text{Acetate}}\)-g\({}_{\text{Biomass}}\)-h\({}^{-1}\))** & - & - & 25\(\pm\)3.8 & - & - & 1.1\(\pm\)0.15 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Growth parameters of the W3110 and _xrel_A mutant _E. coli_ strains in aerobic glucose-limited chemostat cultures.
# A Design of a Time Synchronization Protocol Based on Dynamic Route and Forwarding Certification
## 3 Design of a Time Synchronization Protocol Based on Dynamic Routing and Forwarding Certification
### Solution for the Reference Node Void Problem  
Since the parts of nodes with a poor performance are disqualified from forwarding synchronization information, there is probably a problem that some nodes lack synchronization references; that is, all of the previous hop nodes within the communication range of a certain node are disqualified from forwarding time synchronization information, so that the node cannot synchronize because time synchronization information is not received in this round.  
To tackle the reference node void problem, \(T_{wait}\) is defined as the maximum waiting time. During \(T_{wait}\), the nodes to be synchronized passively wait for time synchronization information. If there is no information received after \(T_{wait}\), the node is considered to have the status of a reference node void. In this case, the node will initiate a synchronous request to its neighbors. Then, all nodes that have received this request will reply. The node will be synchronized with the best performance reference node.
## 3 Multivariate Statistical Techniques and Data Treatment
### Data Treatment and Multivariate Statistical Methods  
PCA is sensitive to outliers, missing data, and poor linear correlation among variables due to insufficient assigned variables. Thus, the data treatment needs to be performed for missing data and outliers in the monitored water quality data before executing multivariate statistical analysis. There might be a real shift in the value of an observation that arises from non-random causes. In this study, outliers were detected according to [PERSON] [40] test method using XLSTAT 2016. On the other hand, data collection and analysis were conducted with great prudence to minimize the amount of missing data. However, the incidence of missing data is inevitable and was handled by the multiple imputation of missing values technique using Markov Chain Monte Carlo (MCMC) [41].  
The raw water quality parameters were standardized to a mean of 0 and variance of 1 using Z-scale transformation to examine the normality of the distribution of data sets and to ensure that the different variables were equally weighted in the statistical analyses [36]. The data were further checked for normality using Kaiser-Meyer-Olkin (KMO) and [PERSON]'s sphericity tests to determine if our measured variables may be factorized efficiently. KMO is the degree of sampling adequacy, which shows the percentage of variance that is likely attributable to the underlying factors. Generally, the KMO index ought to be greater than 0.5 for satisfactory factor analysis. When the KMO index is close to 1, the PCA of the variables is suitable; however, when it is close to 0, the PCA is not relevant. In this study, the KMO had a value of 0.68. [PERSON]'s test of sphericity shows whether the correlation matrix is an identity with variables that are unrelated. The significance level, which is 0 in this study (less than 0.05), indicates that there are significant relationships among the variables.
## 5 Potential Challenges
### Development of End-to-End Framework  
DL might be implemented over the entire end-to-end system. When creating an end-to-end DL-assisted architecture, a large amount of labeled data is required to train the network, and each model must be connected to the next while taking channel impairments into account [185]. The recommended DL algorithms might alter based on the type of application. The algorithms can be monitored, unsupervised, or based on reinforcement learning, and power utility requirements depending on training should be considered.
# Research on a Decoupling Algorithm for the Dual-Deformable-Mirrors Correction System
###### Abstract  
Wavefront distortion caused by atmospheric turbulence can be described as different types of aberrations, such as piston, tilt, defocusing, astigmatism, coma and so on. The operation of dual deformable mirrors can have mutual coupling effects, which affect the correction effect of wavefront distortion. This study combines a fast-steering mirror (FSM) and a deformable mirror (DM) to form a dual-deformable-mirrors wavefront correction system, and proposes a decoupling algorithm that can correct any specified aberration. In this decoupling algorithm, both the FSM and the DM are controlled using the mode method, and the specific corrected aberrations are obtained based on a limited matrix. The compensation ability of the DM is directly characterized by the mode coefficients of the aberrations, which can achieve independent correction of any order of aberrations and effectively reduce the coupling effect of the dual-deformable-mirrors wavefront correction system. An adaptive optical dual-deformable-mirrors wavefront correction system experiment was built to verify the decoupling algorithm. When the DM corrects the 3 rd-, 10 th-, and 25 th-order aberrations, and the FSM only corrects the 1 st- and 2 nd-order aberrations, the coupling coefficients are approximately \(1.17\times 10^{-3}\), \(1.814\times 10^{-2}\) and \(7.81\times 10^{-3}\), respectively, and their magnitude reaches \(10^{-2}\) and below \(10^{-2}\), respectively. The experimental results show that the decoupling algorithm can effectively suppress the coupling effect between the FSM and the DM.  
FSM; DM; dual-deformable-mirrors wavefront correction system; decoupling algorithm; coupling coefficient 2023  
1312112[[https://doi.org/10.3390/app132212112](https://doi.org/10.3390/app132212112)]([https://doi.org/10.3390/app132212112](https://doi.org/10.3390/app132212112))