esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Power electronic transformers (PETs), as the core devices of the energy internet, are the key to achieve both effective consumption for renewable energy and the safe and coordinated operation for AC/DC hybrid system. In order to overcome the shortcomings of the existing PETs, a novel PET with an improved structure that applicable for multi-voltage level AC/DC hybrid distribution network is proposed. The topology of the proposed PET is analyzed, and the corresponding control methods are suggested for different parts. The input stage utilizes the modular multilevel converter structure and applies the virtual synchronous machine control strategy to enhance the inertia and damping of the system. The power of the output stage is adjusted flexibly and that enables the PET to provide certain power support to the upper grid and participate in its primary frequency regulation. A combined connection of input-series output-series and input-series output-parallel is applied for the dual-active-bridge modules of the isolation stage to enable network interconnection and electrical isolation of AC/DC grids with significantly different voltage levels. A power coordinated control method is then proposed to meet the power demand of the distribution networks connected to the output stage and ensure stable operations of PET simultaneously. The reliability and efficiency of the proposed PET topology and control strategy for AC/DC hybrid distribution network are finally verified via PSCAD/EMTDC simulation.  
Keywords:power electronic transformer; modular multilevel converter; virtual synchronous machine; input-series output-series; power coordinated control +
Footnote †: journal: Applied Sciences
# Lip-Reading Classification of Turkish Digits Using Ensemble Learning Architecture Based on 3 DCNN
## 3 Material and Method
### Deep Learning Architectures  
The subject of lip reading, which has been studied for many years in the field of image processing, was initially tried to be realized with machine learning algorithms, but today it is tried to be realized with deep learning architectures [32]. When we look at the deep learning architectures used in lip reading studies, it is seen that convolutional neural networks are used extensively. In lip-reading studies, it is seen that both 2 DCNN-based methods, 3 DCNN-based methods, and 2 DCNN and 3 DCNN-based models are used together [33]. Models based on 3 DCNN seem to handle spatio-temporal problems in a more qualified manner. On the other hand, 2 DCNN models are seen to be better in fine-grained operations in feature extraction. In the use of 2 DCNN models for video data, concatenating the frames into a single frame is one of the methods applied in order not to lose the time context.The choice of models (CNN, LSTM, 3 DCNN) was based on their proven effectiveness in previous lip-reading studies [4; 32]. While CNNs are effective for spatial feature extraction and LSTMs handle temporal dependencies well, 3 DCNNs capture spatio-temporal features better which makes them suitable for lip reading task. The selection of models was based on successful examples in the literature, with consideration given to the suitability of each model (CNN, LSTM, C3D, I3D) for the data type.
## 1 Introduction
### Connectivity-Based Proposals
The authors study the complexity of the optimal relay placement problem and propose methodologies to create chains or trees of relays as required by different static scenarios. By contrast, in changing environments static solutions cannot be successfully applied because the location optimality does not hold over time. In [36] the exploration problem is addressed ensuring a time-varying connected topology in \(3D\) cluttered environments but following a decentralised control strategy which enables simultaneous multi-task exploration. Another centralised but asynchronous strategy is followed in [19; 37] in order to address the problem of multi-robot exploration under recurrent connectivity. In these works, the authors leverage a variant of the Steiner tree problem that appears as a particular case of different known graph optimisation problems. Robot placement is treated as an optimisation problem through Integer Linear Programming. Exact and approximated algorithms are compared on particular scenarios.
## 2 Operation of the BB84 Protocol
### Preparation and Measurement  
During the protocol operation, [PERSON] prepares and sends signals to [PERSON], who measures them in a random sequence of Pauli bases, either the \(Z\) or \(X\) basis. [PERSON] records her signal choices, while [PERSON] records his basis choice, including the respective measurement outcomes. During the classical step, [PERSON] and [PERSON] utilize the authenticated public channel to conduct a classical communication protocol that employs a classical record of the quantum phase as the input.
## 5 Numerical and Simulation Results  
Numerical results via computer simulations are provided to evaluate the proposed trajectory optimization technique. For the simulations, trajectory optimization is considered for one UAV that provides aerial wireless communication for IoVs including four IVs. The navigation time interval of interest is discretized into 10 time steps with a sampling time of 5 s, i.e., a total time of 50 s. The maximum horizontal and vertical speeds are 10 m/s and 8 m/s, respectively.  
A 3D occupancy map of a typical urban area is simulated in the MATLAB platform, as shown in Figure 5a. Also, the map routes of four IVs are illustrated in a 2D plot of the occupancy map in Figure 5b, where IVs move in the arrow directions.  
We assessed the results of the optimization algorithm and its running time using the developed linear kinematic model. Also, we compared it with the sampling-based RRT-like technique that is used in complex situations. Computations were performed on a laptop computer with the following processor: Intel(R) Core(TM) i7-8665U CPU @1.90 GHz, 2112 MHz, 4 Core(s), 8 Logical Processor(s).  
Figure 5: _Cont_.
###### Abstract  
Background: Approximately 20-50% of prostate cancer patients experience biochemical recurrence (BCR) after radical prostatectomy (RP). Among them, cancer recurrence occurs in about 20-30%. Thus, we aim to reveal the utility of machine learning algorithms for the prediction of early BCR after RP. Methods: A total of 104 prostate cancer patients who underwent magnetic resonance imaging and RP were evaluated. Four well-known machine learning algorithms (i.e., k-nearest neighbors (KNN), multilayer perceptron (MLP), decision tree (DT), and auto-encoder) were applied to build a prediction model for early BCR using preoperative clinical and imaging and postoperative pathologic data. The sensitivity, specificity, and accuracy for detection of early BCR of each algorithm were evaluated. Area under the receiver operating characteristics (AUROC) analyses were conducted. Results: A prediction model using an auto-encoder showed the highest prediction ability of early BCR after RP using all data as input (AUC = 0.638) and only preoperative clinical and imaging data (AUC = 0.656), followed by MLP (AUC = 0.607 and 0.598), KNN (AUC = 0.596 and 0.571), and DT (AUC = 0.534 and 0.495). Conclusion: The auto-encoder-based prediction system has the potential for accurate detection of early BCR and could be useful for long-term follow-up planning in prostate cancer patients after RP.  
machine learning; prostate cancer; biochemical recurrence +
Footnote †: journal: Medical  
Imaging Reporting and Data System (PI-RADS) version 2, which was released in 2012, also showed promising results in terms of the risk stratification for predicting BCR [12].  
Thus, the purpose of this study is to reveal the utility of machine learning algorithms for developing prediction models, facilitating postoperative follow-up and aiding in the clinical management of patients with prostate cancer after RP.
## 2 System Model
### Task Model  
We assume that each piece of UE \(k\) has the computation task at time slot \(t\) as \(\phi_{k}^{t}=\{C_{k}^{t},M_{k}^{t},t_{k}^{max}\}\), which can be executed on the smart UE (SUE) by employing local computing resources or offloading either to the E-AP or cloud computing tier to accomplish the computation. \(C_{k}^{t}\) denotes the required computing resources (total number of CPU cycles) to accomplish the computations task \(\phi_{k}^{t}\), \(M_{k}^{t}\) is the size of input data for computing \(\phi_{k}^{t}\) including the input parameters and program codes, and \(t_{k}^{max}\) represents the tolerable maximum latency for the task completion. Furthermore, we assume that the task of each UE \(k\) will be intact and impossible to break into partitions. As a result, each request will be executed on only one layer. Hereafter, the task execution decision can be described as \(S_{k}^{m}\in\{0,1\}\) where \(S_{k}^{m}=1\) shows how the task will either be carried out locally by the SUE, at the edge using the E-AP, or on the cloud computing tier.
# CD44 Depletion in Glioblastoma Cells Suppresses Growth and Stemness and Induces Senescence
###### Abstract  
Glioblastoma multiforme (GBM) is a lethal brain tumor, characterized by enhanced proliferation and invasion, as well as increased vascularization and chemoresistance. The expression of the hyaluron receptor CD44 has been shown to correlate with GBM progression and poor prognosis. Here, we sought to elucidate the molecular mechanisms by which CD44 promotes GBM progression by knocking out (KO) CD44, employing CRISPR/Cas9 gene editing in U251 MG cells. CD44-depleted cells exhibited an impaired proliferation rate, as shown by the decreased cell numbers, decreased Ki67-positive cell nuclei, diminished phosphorylation of CREB, and increased levels of the cell cycle inhibitor p16 compared to control cells. Furthermore, the CD44 KO cells showed decreased stemness and increased senescence, which was manifested upon serum deprivation. In stem cell-like enriched spheres, RNA-sequencing analysis of U251 MG cells revealed a CD44 dependence for gene signatures related to hypoxia, the glycolytic pathway, and G2 to M phase transition. Partially similar results were obtained when cells were treated with the \(\gamma\)-secretase inhibitor DAPT, which inhibits CD44 cleavage and therefore inhibits the release of the intracellular domain (ICD) of CD44, suggesting that certain transcriptional responses are dependent on CD44-ICD. Interestingly, the expression of molecules involved in hyaluron synthesis, degradation, and interacting matrix proteins, as well as of platelet-derived growth factor (PDGF) isoforms and PDGF receptors, were also deregulated in CD44 KO cells. These results were confirmed by the knockdown of CD44 in another GBM cell line, U2990. Notably, downregulation of hyaluron synthase 2 (HAS2) impaired the hypoxia-related genes and decreased the CD44 protein levels, suggesting a CD44/hyaluronan feedback circuit contributing to GBM progression.  
CD44; glioblastoma; RNA-sequence analysis; PDGF family members; hyaluron biology; HAS2; stem cell-like enriched spheres +
Footnote †: journal: _cancers_
## 5 Data Imbalance
### Data Imbalance Handling Techniques Future Research Directions
#### 5.5.3 Deep Learning and Traditional Methods Integration  
Integrating deep learning techniques like GANs and LSTMs with traditional methods such as decision trees can enhance model interpretability and stability [127]. This hybrid approach improves the detection of minority classes and adapts better to evolving network threats [123].
# Design of Novel Laser Crosslink Systems Using Nanosatellites in Formation Flying: The VISION
## 5 Formation Flying Nanosatellite Bus
### Subsystems Design
#### 5.2.4 Command and Data-Handling Subsystem (CDHS)  
The OBC has the following capabilities and features: low power consumption within 0.5 W, 400 MHz clock speed with the ARM cortex A5, embedded RT-patched Linux OS, and docking for the GNSS receiver, which supports multiple channels for parallel interfaces such as CAN-bus and I2C. The FSW is based on the core flight system (cFS) developed by NASA to be used as the main platform for the FSW. Thus, the FSW has a simplified architecture and is robust, providing multitasking, such as the computation of the formation flying GNC algorithms. With the basic functions in the cFS, the software bus (SB) provides an interface between each module, enhancing the robustness of the FSW and reducing the development cost. In addition, the back-up OBC integrated with the UHF transceiver was adopted to handle on-orbit contingencies, acting as a hardware watchdog timer. The FSW architecture and the configurations of the two computers are shown in Figure 19.  
\begin{table}
\begin{tabular}{l c r r r r r r} \hline \hline \multirow{2}{*}{**Elements**} & \multirow{2}{*}{**Unit**} & \multicolumn{3}{c}{**Downlink (1)**} & \multicolumn{3}{c}{**Uplink (1)**} & \multicolumn{3}{c}{**Crosslink (2)**} \\ \cline{3-8}  & & **UHF** & **S-Band** & **X-Band** & **UHF** & **S-Band** & **S-Band** \\ \hline Modulation & - & GMSK & QPSK & 8-PSK & GMSK & QPSK & QPSK \\ Frequency & MHz & 437.0 & 2200.0 & 8250.0 & 437.0 & 2100.0 & 2200.0 \\ Data Rate & Kbps & 4.8 & 1000.0 & 1,000,000.0 & 4.8 & 500.0 & 10.0 \\ Tx Power & W & 1.0 & 1.0 & 2.0 & 27.0 & 27.0 & 1.0 \\ Tx Gain & dBi & 0.0 & 8.0 & 13.0 & 18.9 & 36.0 & 8.0 \\ EIRP & dBm & 29.5 & 37.8 & 41.7 & 57.8 & 76.3 & 7.4 \\ Path Loss & dB & \(-153.2\) & \(-163.1\) & \(-169.5\) & \(-153.2\) & \(-162.7\) & \(-160.1\) \\ Rx Gain & dB & 18.9 & 36.0 & 51.0 & 0.0 & 8.0 & 8.0 \\ Eb/N0 & dB & 17.3 & 14.8 & 14.0 & 41.0 & 37.6 & 18.6 \\ \hline
**Link Margin** & dB & 9.6 & 7.0 & 4.2 & 33.2 & 29.8 & 6.7 \\ \hline \hline \end{tabular}
\end{table}
Table 10: RF communication link budget for each communication module.  
Figure 18: Configurations of the RF communication subsystem.
# Yeast-Based Magnetic Biocomposite for Efficient Sorption of Organic Pollutants
## 3 Result and Discussion
### Methylene Blue Adsorption
#### 3.2.2 Equilibrium Studies
Based on the fitting of the D-R adsorption model, the average adsorption potential (E\({}_{\rm P}\)) was calculated for the MB sorption process on YL-M-L-AC. E\({}_{\rm P}\) represents the mean potential energy for adsorption at the periphery of the adsorption region, and its magnitude indicates the adsorption mechanism, delineating between physical adsorption (E\({}_{\rm P}\) < 8.0 kJ/mol), ion-exchange (8.0 kJ/mol \(\leq\) E\({}_{\rm P}\) < 16.0 kJ/mol), and chemical adsorption (E\({}_{\rm P}\)\(\geq\) 16.0 kJ/mol) to a certain extent [23; 76]. The E\({}_{\rm P}\) value is 8.54 kJ/mol and is, therefore, in the range of 8.0-16.0 kJ/mol, so the adsorption of the dye on the biocomposite was dominated by ion exchange. The performed equilibrium studies suggest that all types of adsorption processes may be involved in the MB removal process on YL-M-L-AC. This observation is identical to other studies using biocomposites consisting of various types of ingredients. Figure 8: Nonlinear fitting plots of isotherm models, t = 24 h, T = 25 \({}^{\circ}\)C.
## 2 Methods
### Mixed Attention Module (MAM)
#### 2.3.2 Channel Attention Mechanism  
The operation of the channel dimension is similar to the above, and the four feature maps \(F_{i},i\in[1,2,3,4]\) are first expanded into one-dimensional vectors along the width and height dimensions and transposed to obtain \(\mathbf{X}\in\mathbf{R}^{WH\times\mathbb{C}}\) through three fully connected layers, and the outputs are \(\mathbf{Q}\) = \(\mathbf{X}\mathbf{W}_{q}\), \(\mathbf{K}\) = \(\mathbf{X}\mathbf{W}_{k}\), and \(\mathbf{V}\) = \(\mathbf{X}\mathbf{W}_{v}\). As dimensionality reduction would bring about too much information loss, the algorithm proposed in this article does not reduce the dimensionality of the channel. The correlation matrix is then obtained by \(\mathbf{B}\) = \(\mathbf{K}^{T}\mathbf{Q}\), where \(\mathbf{B}_{ij}\) represents the inner product of column \(i\) in \(\mathbf{K}\) and column \(i\) in \(\mathbf{Q}\), i.e., the correlation of the two different channel vectors. Similarly, each column of the correlation matrix \(\mathbf{B}\) needs to be normalized using the Softmax function, constrained to be within (0,1). Finally, after multiplying \(\mathbf{V}\) with \(\mathbf{B}\) and passing through a fully connected layer \(\mathbf{W}_{s}\), the channel significance-enhanced feature map \(\mathbf{X}^{C}\) = \(\mathbf{V}\mathbf{B}\mathbf{W}_{s}\) is obtained, and the final feature expression is:  
\[\mathbf{X}^{C}=\mathbf{X}\mathbf{W}_{v}\sigma((\mathbf{X}\mathbf{W}_{k})^{T}\mathbf{X}\mathbf{W}_{q})\mathbf{W} _{s} \tag{2}\]  
where \(\mathbf{W}_{q}\), \(\mathbf{W}_{k}\), \(\mathbf{W}_{v}\mathbf{W}_{s}\in\mathbf{R}^{\mathbb{C}\times\mathbb{C}}\). Finally, the outputs of these two branches are merged. Considering the effect of the residual structure, the merged features are summed with the input \(\mathbf{X}\) to generate the final feature map \(\mathbf{Y}\in\mathbf{R}^{WH\times\mathbb{C}}\):  
\[\mathbf{Y}=\mathbf{X}^{C}\oplus\mathbf{X}^{S}\oplus\mathbf{X} \tag{3}\]  
where \"\(\oplus\)\" denotes the summation of the feature maps at the element level. After transposition and recovery of the dimension expansion, \(\mathbf{Y}\) is fed into the subsequent module.
## 3 Resonant Converter Families
### Structure of Resonant Power Converter  
As shown in Figure 17, the structure of a resonant converter consists of three stages [16; 26; 78; 79]; the first stage is the control switching network (_CSN_), the second step is the resonant tank network (_RTN_), and the third is the network of the rectifier with a low-pass filter (_DR_LPF_).  
Each stage is assigned a specific task to accomplish the major goal of the resonant converter; for instance, the _DC_ source enables the quick mode switching of the controlled switching network (_CSN_) based on the working frequency to generate the output voltage to be fed into the next step. To reduce _THD_ in the second stage, sinusoidal voltages and  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Advantages** & **Disadvantages** \\ \hline \multirow{8}{*}{_IBDC_} & (1) & There are almost equivalent current switch stresses on both sides. & (1) \\  & (2) & Soft switching can be achieved without the need for active or passive components. & (2) \\  & (3) & Has a simple construction that simplifies the process of design and maintenance. & (3) \\  & (4) & Has a fast-dynamic action feature due to the lack of additional passive components. & (4) \\  & (5) & Has average current mode control or peak current mode control. & (4) \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 17: Structure of resonant converters [16].  
current signals are generated using the stage of the high-frequency resonant tank network (_RTN_) that consists of two or more reactive components [80]. This stage will be defined by the frequency selective network as energy buffering between the load and the _CSN_. The impedances of both inductance and capacitance are the same in resonance conditions particularly, which will produce the resonant frequency. Then, a rectifier network rectifies and filters the incoming signal, then passes the filter to generate the correct _DC_ output voltage [81].
## 4 Conclusions  
In this study, software CONVERGE was used to numerically model the gas flow, spray process and combustion process of a supercharged gasoline direct injection engine under practical engine  
Figure 11: Heat release rate of knock combustion.  
Figure 12: Flame propagation of knock combustion (intake port on the right).  
Figure 13: Temperature cloud and instantaneous velocity field (top view).  
operating conditions. For combustion modeling, the normal combustion and knock combustion were analyzed, respectively. The conclusions could be drawn as follows:  
(1) During the gas flow process, the tumble in the cylinder was gradually formed with the increase of the valve lift, enhanced in the compression stroke and finally was broken due to the extrusion of the piston. The tumble ratio in the combustion chamber by the G-RNG \(k-\varepsilon\) model had the same trend to that of the RNG \(k-\varepsilon\) model.  
(2) During the spray process, the results show that the fuel droplets gradually evaporated and moved to the intake side under the turbulent and high temperature in the cylinder. Nevertheless, fuel and air distribution was relatively dispersed at the \(-20\)\({}^{\circ}\)CA, and a certain amount of fuel was attached to the cylinder wall.  
(3) During the normal combustion process, most of the mixture equivalence ratio was near to 1.05, it was favorable for rapid combustion, so that the flame propagated faster on the intake side. During the knock combustion process, the correct amplitude of pressure oscillations generated by a knock could be captured. At 14 \({}^{\circ}\)CA, an auto-ignition point was formed near the exhaust valve, and the flame gradually spread around. Combined with the related findings, it could be concluded that the propagating detonation wave derived from multiple hot-spots auto-ignition indicated vital effects on knock intensity of in-cylinder pressure at 24 \({}^{\circ}\)CA.  
[PERSON], [PERSON] and [PERSON] conceived and developed the paper; [PERSON] conducted the simulation and the draft; [PERSON] conducted the typesetting of the paper; [PERSON] provided guidance and reviews throughout the development of this paper.  
This research is funded by \"State Key Laboratory of Engines at Tianjin University (K2018-09)\", \"Research Center of Manufacturing Development of Wuhan City Circle (WZ2016Y15)\" and \"Research fund of Center for Materials Research and Analysis, WHUT (2018 KFJ07)\".  
The authors would like to thank CSSC Huangpu Wenchong Shipbuilding Company Limited for financial support.  
The authors declare no conflict of interest.
## 2 Data Acquisition, Preparation and Feature Selection
### Data preparation and feature selection
at level 5 the signal was filtered 4 times. possible), and the remaining data would be processed automatically by the computer. The other clicks within the sets, and the eighth set, were then used for validation. Figure 2 shows a combination of two graphics with the four strongest features, measured using Fisher's power of discrimination with Equation (2), from five animals that allows some insight in the feature space. The combination of just these four features already shows some possibility of separating the animals. Three animals are already separated in the left figure, while two are completely mixed. Combination with the two other characteristics in the right figure allows these two to be separated as well. An important observation in these figures, and one that we used to design the classifier, was that the data showed a strong clustering tendency. This suggested the application of a RBF network which has a natural way of modelling these clusters in its hidden layer, or a SVM which does not model the clusters themselves but their borders. Another reason we decided to use RBF and SVM based classifiers is because these methods have a local response defined by the distance of a sample to the cluster centres or support vectors. This is different from, for example, multi-layer perceptron networks, where a node in the first layer will give the same response for all points on a specific hyperplane. Although, as a result, perceptron networks may have better generalisation in areas of the feature space that are poorly sampled,  
Figure 3: Variability in the two strongest features (first feature on top, second on bottom) from Table 1 during the dive. Figure 2: Scatter plots of the four most discriminating features for five animals. The combination of these four characteristics already shows possible separation of five animals. Moreover, the features show a strong clustering tendency that suggests the use of a Gaussian model. we preferred the local properties that allowed more accurate modelling of the feature space as it is presented in Figure 2. To have an idea about the variability of the features, Figure 3 shows the two strongest features during the whole dive. From this figure it can be suspected that 50 consecutive samples may not always be sufficient to characterise the variance of the feature.