esa-sceva/satcom-chunk-collection huggingface dataset:
# Facile Synthesis of Highly Conductive Vanadium-Doped NiO Film for Transparent Conductive Oxide
## 4 Conclusions  
In conclusion, pristine NiO and 3 at.% vanadium (V)-doped NiO were successfully synthesized by a solvothermal decomposition method and deposited on an FTO substrate. The 3 at.%V:NiO film exhibited a lower crystalline size when compared to pristine NiO film. A systematic crystallographic, optical, and morphological investigation showed that the V content in the NiO lattice would be beneficial for optoelectronic device applications. Also, AFM investigations of the 3 at.%V:NiO showed that the root mean square (RMS) surface roughness was lowered in comparison to that of the pristine NiO film. Finally, I-V curves showed that the conductivity of the V:NiO film was effectively enhanced by about 34.4%. The improvement of electrical conductivity can promote better charge transport and a reduction of interfacial charge accumulation.  
A.K. performed the experiments and wrote the manuscript. H.K.S. contributed to the preparation and revision of the manuscript. All authors have read and agreed to the published version of the manuscript. This work was supported by a National Research Foundation of Korea (NRF) Project #2018 R1 AA4102552813 and Korea Basic Science Institute (National research Facilities and Equipment Center) grant funded by the Ministry of Education (grant 2020R1A6C101A204). The authors declare no conflict of interest.
# Complexity Modeling of Steel-Laser-Hardened Surface Microstructures
## 4 Statistical Analysis of the Data and Results  
As mentioned in Section 3, the sets of tested specimens can be grouped into four sets of six units each. The specimens of each group were tested at the same surface temperature, at the same speed of the laser beam in absolute value, and for equivalent impact angle values.  
An accurate statistical analysis was carried out, repeating the same measurements five times on different specimens in order to better understand the results found.  
Table 4 shows the mean value and the standard deviation relative to the six values of each set. A good level of repeatability of the calculated complexity method is proved by the low values of the standard deviation (St. Dev.) compared with the mean values.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Set of Specimens** & _CC Values_ & **Mean Value** & **St. Dev.** \\ \hline Set 1 (1373 K; 2 mm/s; 30\({}^{\circ}\)) & & & \\ \hline P1 & 1.8681 & & \\ P5 & 1.8916 & & \\ P9 & 1.8809 & 1.8826 & 0.0090 \\ P13 & 1.8924 & & \\ P17 & 1.8793 & & \\ P21 & 1.8832 & & \\ \hline Set 2 (1473 K; 3 mm/s; 60\({}^{\circ}\)) & & & \\ \hline P2 & 1.8524 & & \\ P6 & 1.8365 & & \\ P10 & 1.8923 & 1.8450 & 0.0316 \\ P14 & 1.7952 & & \\ P18 & 1.8552 & & \\ P22 & 1.8382 & & \\ \hline Set 3 (1573 K; 4 mm/s; 75\({}^{\circ}\)) & & & \\ \hline P3 & 1.8275 & & \\ P7 & 1.8921 & & \\ P11 & 1.7253 & & \\ P15 & 1.8893 & & \\ P19 & 1.8614 & & \\ P23 & 1.8718 & & \\ \hline Set 4 (1673 K; 5 mm/s; 90\({}^{\circ}\)) & & & \\ \hline P4 & 1.8735 & & \\ P8 & 1.8863 & & \\ P12 & 1.8771 & & \\ P16 & 1.8846 & 1.8632 & 0.0449 \\ P20 & 1.7721 & & \\ P24 & 1.8857 & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Mean values and standard deviation of \(D\) parameter.  
Maximal complexity (1.8924) occurred for specimen P13. Specimen P13 was hardened with 2 mm/s, 1373 K under laser beam angle of 30\({}^{\circ}\). Minimal complexity (1.7253) occurred for specimen P11. Specimen P11 was hardened with 4 mm/s, 1573 K under laser beam angle of 75\({}^{\circ}\).  
Table 5 shows the comparison between calculated and predicted values, highlighting the percentage error and taking the calculated method as reference. It has to be noted that in both methods the highest values of complexity were obtained by operating under the two extreme conditions: 1373 K, 2 mm/s, 30\({}^{\circ}\) and 1673 K, 5 mm/s, 90\({}^{\circ}\), respectively.  
In addition, the percentage error made with the complexity prediction method was found to be less than 1% under each working condition.  
The prediction of microstructure in an arbitrary forming process must be correlated with the level of phase transformation; in particular, the ferritic-austenitic transformation. The impact of martensitic and ferritic structures on the mechanical and topographical properties of materials was observed. It follows that once the microstructural geometry of the laser-hardened materials is known, the mechanical and topographical properties of the materials can be determined.
## 4 Experiments
### Comparative Analysis  
Firstly, an image group consisting of a reference image and a source image is input, which is a feature extracted by the model. The feature map is divided into three different scales, large, medium, and small, through the composite U-Net feature extraction network and the improved DRE-Net feature enhancement network, and the small-scale feature map is processed by the attention mechanism to enhance its features; then the feature map extracted from each view is transformed to the hypothesis plane through the microsimplex responsive transform to construct the feature body, and the feature bodies of the different views are fused together to form a three-dimensional cost body; regularize the cost body to get a single-channel probabilistic body; then recover the depth map from the probabilistic  
Figure 9: Scale filter training process.  
body, and finally optimize and fuse the depth map until all layers are constructed, and finally output to get the 3D model of the scene.  
In order to compare the traditional 3D reconstruction algorithms, deep learning-based 3D reconstruction algorithms, and many other algorithms, this paper has carried out a comparison test with them, taking into account the cost of resource training, the dataset used for the comparison test is the 3D reconstruction universal dataset DTU [33], which is categorized in accordance with the training set, the validation set and the test set. There are 119 different scenes or objects in the dataset, which contains 79 scenes for training, 18 scenes for validation, and 22 scenes for testing. The test results are shown in Table 1.  
From Table 1, we can see that compared with the traditional supervised 3D reconstruction methods, such as Gipuma [33], CAMP [34], COLMAP [18], etc., the method used in this paper ranks second in terms of accuracy, and the first one is the Gipuma method, which cannot be surpassed by other deep learning-based methods at present. However, deep learning-based methods can not only achieve significant results in reconstructing Comp and Overall but also have a huge advantage in terms of running time and memory consumption.  
Comparing the unsupervised 3D reconstruction methods based on deep learning, such as MVSNet, D2 HC-MVSNet [20], etc., the MVSNet + Co U-Net + DRE-Net_SA method proposed in this paper also shows significant improvement in Acc and Comp. Cascade-MVSNet achieves the best performance in Acc. However, it cannot simultaneously consider the reconstruction accuracy and completeness, and the model in this paper achieves the best performance in terms of reconstruction accuracy, completeness, and comprehensive evaluation indexes.
# Coordinated Dispatch of Multi-Energy Microgrids and Distribution Network with a Flexible Structure
## 2 Mathematical Model Formulation
### Multi-Energy Microgrids
#### 2.1.5 Absorption Chiller (AC)  
The absorption chiller (AC) can convert steam absorbed from WH into cooling power. The refrigeration cycle of the AC is performed through various circulation processes by utilizing characteristics such as absorption of the solution and generation of refrigerant steam. The mathematical model can be expressed as follows:  
\[\begin{array}{l}H_{AC}(t)=H_{C}(t)COP_{AC}\\ 0\leq H_{C}(t)\leq H_{WH}(t)\\ H_{AC}^{\min}\leq H_{AC}(t)\leq H_{AC}^{\max}\end{array} \tag{8}\]  
where \(H_{AC}(t)\) is the output cooling power of AC. \(COP_{AC}\) is the coefficient of performance of AC. \(H_{C}(t)\) is the heat gained from WH by AC. \(H_{AC}^{\min},H_{AC}^{\max}\) are minimal and maximal output cooling power of AC, respectively.
# Elderly Fall Detection in Complex Environment Based on Improved YOLOv5s and LSTM
## 4 Fall Detection with WiFi Signal Based on LSTM Model  
### The Sensing of WiFi Signal  
At present, CSI is basically used to study WiFi-based perception tasks, and CSI represents the channel attributes of communication links in each subcarrier. The Orthogonal Frequency Division Multiplexing (OFDM) technique is commonly used in WiFi devices. Multiple Input Multiple Output (MIMO) technology regulates the multipath transmission effect. Based on OFDM technology, the spatial channel is divided into multiple subcarriers with different frequencies, and multiple antenna pairs can transmit WiFi data simultaneously with the support of MIMO technology. CSI records each subcarrier's data information, describes the signal characteristics in the propagation process, and also considers the influence of environmental factors and propagation path on the signal. The received signal can be expressed as follows:  
\[M=HX+\theta \tag{9}\]  
where \(M\) represents the receiver signal vector, \(H\) is the channel state matrix of the wireless signal, \(X\) denotes the signal vector of the wireless transmitter, and \(\theta\) represents Gaussian white noise. The current method of collecting CSI is to extract information from the physical layer through special network cards, with the mainstream network cards being Intel 5300 chipset and Atheros 9k series. In this study, an Intel 5300 network adapter is mainly used to obtain CSI information, as CSI information is extracted from the network adapter through the 802.11n CSI tool, where each antennae pair generates 30 groups of subcarrier information and then the obtained CSI data, that is, the wireless signal channel state matrix, are expressed as follows:  
\[H=\left[H_{1},H_{2},...,H_{i},...,H_{N}\right]^{T},i\in[1,N] \tag{10}\]  
where \(N\) represents the number of subcarriers, and the CSI signal of the \(i\) subcarrier can be expressed as follows:  
\[H_{i}=\left|H_{i}\right|e^{(j\angle H_{i})} \tag{11}\]  
where \(|H_{i}|\) and \(\angle H_{i}\) represents the amplitude and phase of the \(i\) subcarrier, respectively. CSI can effectively respond to multipath effects and record the information in the propagation process. As a kind of fine-grained channel information, CSI can be effectively applied to activity recognition and perception tasks such as fall detection [32].
## 2 Materials and Methods
### Dataset
#### 2.2.6 Wind Energy Calculation  
Wind power density is a comprehensive index used to evaluate the status of wind energy resources in a certain area. It is precisely defined as the wind energy vertically passing through a unit area of airflow within a unit of time. Wind energy is influenced by  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **RMSE (m/s)** & **Bias (m/s)** & **MAPE** & **WDAR (\%)** \\ \hline
54953 & 2.73 & 2.13 & 0.15 & 78.57 \\
58264 & 1.63 & 1.35 & 0.18 & 89.28 \\
58265 & 2.69 & 1.96 & 0.18 & 96.43 \\ \hline \end{tabular}
\end{table}
Table 4: WMO station validation results.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **RMSE** & **Bias** & **MAPE** \\ \hline HY-2C & 1.19 & 0.95 & 0.06 \\ CFOSAT & 1.25 & 0.97 & 0.06 \\ \hline \end{tabular}
\end{table}
Table 5: Comparison of HY-2C, CFOSAT, and WRF.  
factors such as the magnitude of wind speed, the frequency distribution of wind speeds, and air density. The following is an expression for wind power density:  
\[D_{WP}=\frac{1}{2n}\sum_{i=1}^{n}\rho v_{i}{}^{3} \tag{7}\]  
where \(n\) represents the number of records within the designated period, \(\rho\) denotes air density, and \(v_{i}{}^{3}\) represents the cube of the wind speeds recorded for the \(i\)th observation. DWP is the wind power density expressed in units of \(\mathrm{W/m^{2}}\). The air density \(\rho\) is measured in units of \(\mathrm{kg/m^{3}}\), and its calculation method is as follows:  
\[\rho=\frac{P}{RT} \tag{8}\]  
In the equation, \(P\) represents atmospheric pressure, \(R\) denotes the gas constant, and \(T\) represents the temperature in Kelvin. The value of \(R\) used in this paper is 287 J/kg\(\cdot\)K.
# An Unsupervised Anomaly Detection Based on Self-Organizing Map for the Oil and Gas Sector
## 3 The Research Approach
### Tuning Phase  
Choosing the parameters that characterize the network, such as the neighbourhood size and the value of the learning rate, is a delicate operation. Indeed, these values cannot be chosen at first glance. Still, optimal values must be looked for in the space of possible variables to find a set of variables that will afford the algorithm the most desirable results [44]. Among the many hyper-parameterization techniques in the literature, the hyperopt.fmin function is chosen to find the optimal values of the SOM [45]. The task performed by this function is to find the best value of one or more scalar functions among a set of possible arguments. This method causes the user to describe the objective function space in which to search precisely in the optimal parameters of the network [46]. To use this function, it is necessary to define:  
* The space on which to search.
* The objective function to be minimized.
* The database in which to store all search evaluations (optional).
* The search algorithm to be used (optional).  
Equation (13) defines the search space consisting of the two linear distributions \(\Phi\) and A. The first \(\Phi\) establishes a set of possible variable values the neighbourhood radius can take. At the same time, the A distribution indicates a potential deal to be assigned to the learning rate (Equations (15) and (16)). Finally, the sequence of values presented within a linear distribution is defined by a step (Equation (14)).  
\[\mathrm{X}(\Phi,\mathrm{A}) \tag{13}\]  
\[\mathrm{step}=10^{-\mu}\mathrm{\ with\ }\mu\in\mathbb{N}\mathrm{\ and\ step}\in(0,1] \tag{14}\]  
\[\Phi=\left\{\forall\ \mathrm{s}_{\mathrm{i}}\in\mathbb{R}:\mathrm{s}_{ \mathrm{i}}=\mathrm{i}\cdot\mathrm{step},\mathrm{i}\in[1,5\times 10^{\mu}], \mathrm{i}\in\mathbb{N}\right\} \tag{15}\]  
\[\mathrm{A}=\left\{\forall\ \mathrm{a}_{\mathrm{i}}\in\mathbb{R}:\mathrm{a}_{ \mathrm{i}}=\mathrm{i}\cdot\mathrm{step},\mathrm{i}\in[1,5\times 10^{\mu}], \mathrm{i}\in\mathbb{N}\right\} \tag{16}\]  
The task performed by hyper-parameterization is to test the SOM network with different learning rate values and neighbourhood size to find the best combination that minimizes an objective function (Equation (17)). In the case of SOM networks, it is necessary to  
Figure 5: Different discrete neighbourhood functions with different grid types. (**a**,**b**) Rectangular SOM Grid; (**c**,**d**) Hexagonal SOM Grid.  
to have a low Quantization Error (QE). Only in this way can the map return outputs that preserve the topological relationships of the source data.  
\[\mathrm{s}^{\prime}_{\mathrm{i}},\mathrm{a}^{\prime}_{\mathrm{i}}=\mathrm{ argmin}(\mathrm{QE}) \tag{17}\]
# A Deep Learning Framework for Enhanced Detection of Polymorphic Ransomware
## 1 Introduction
### Research Questions and Objectives  
This work introduces an enhanced framework for early ransomware detection. Our approach addresses critical challenges in current detection methods by (i) generating synthetic data to overcome scarcity in the ransomware's pre-encryption phase, (ii) employing incremental learning for dynamic updates to feature relevance, and (iii) utilizing a Bayesian approach to estimate uncertainty for optimized training adjustments.
# Robotized Mobile Platform for Non-Destructive Inspection of Aircraft Structures
## 3 Testing and Experimental Validation  
### ECA Testing System  
The two most frequently used testing methods in aviation using portable flaw detectors are the ultrasonic method and the eddy current method. Due to the physics of the phenomena used in detection, the ultrasonic method requires the use of a coupling agent like water or gel. This introduces an additional difficulty that influenced the decision to abandon the UT system at this stage of work. Moreover, due to the fact that the surfaces of fuselages and wings in currently operated aircraft are made mainly of Al alloys, cracks are the most expected type of damage. Therefore, it was decided to use the ET method as the most frequently used crack detection method. The Omniscan MX system with the EC Array SAB-067-005-03 head was selected for testing. This system is distinguished by the use of an array probe that allows scanning a large area. As in the case of using single transducers, currents are induced in the conductive material. Eddy current disturbances caused by defects or geometry changes cause a change in the signal received by the coil, affecting the reading on the flaw detector screen. An important advantage is the possibility of recording signals collected at a larger number of frequencies during a single scan and further post-processing of the collected signals, which allows for a reduction in the number of scans performed.  
The probe SAB-067-005-03 selected for initial tests allows scanning with eddy current frequencies in the range of 1-25 kHz. The choice was dictated by the fact that it was the largest available, which made it possible to collect the signal from a relatively large area. It was also important that low frequencies allow for obtaining responses from greater depths, which in the case of multi-layer structures facilitates the interpretation of the results. The scans show not only the rivets, but also the internal elements. At this stage of the research, the aim was to assess whether the collected result was legible rather than to look for damage.
## 2 Kaggle's Airline Passenger Satisfaction Data
The accuracy represents the closeness of the generated non-dominated solutions to Pareto-optimal solutions (if they are available) or to reference solutions. The spread of solutions in the solution set--measured by the distance between extreme solutions in the set--represents how well the generated solutions arrive at the extrema of the Pareto-optimal or reference solution set. In turn, the distribution of solutions in the solution set represents how evenly the solutions are distributed along the approximation of Pareto-optimal/reference front in the objective space. Therefore, a set of solutions characterized by a higher accuracy, a higher spread, and a better-balanced distribution outperforms the alternative sets of solutions. An appropriate MOEOA for our experiments should have the ability to generate solution set of possibly high accuracy, spread and distribution balance. It is directly related to the analysis of possible \"overlapping\" of some of input attributes over the other ones in the airline passenger satisfaction data (see the next section for a detailed presentation). Among the traditional MOEOAs, SPEA2 and NSGA-II (Nondominated Sorting Genetic Algorithm II) [26] are the best known. Since SPEA2 has a higher ability to generate solution sets of better-balanced distribution ([22; 26]), we selected SPEA2 for further improvement of the spread and distribution of generated solutions. The essence of the proposed SPEA2's generalization (referred to as SPEA3) consists in replacing its environmental selection procedure by our original algorithm, which improves the spread, distribution balance and diversity of generated solutions. The environmental  
Figure 2: Implementation of three-fuzzy-set SFP. selection, in general, creates a collection of the best solutions out of all solutions obtained so far and keeps them in an external archive. Table 2 presents differences between environmental selection procedure of SPEA2 and our original environmental selection procedure implemented in SPEA3. Concluding:  
1. in our original approach implemented in SPEA3, the complementary operations of increasing and reducing the archive lead to obtaining the best available distribution balance and spread of solutions belonging to Pareto-front approximation (see [19] for a detailed presentation and [20] for a discussion), whereas
2. in SPEA2 only the truncation procedure (if activated) contributes to improving the distribution balance and diversity of the final set of solutions (not addressing, however, the problem of improving the spread of solutions) and giving, in general, worse results than its counterpart of SPEA3.
# Convergence Analysis of the Phase-Scheduled-Command FXLMS Algorithm with Phase Error
## 1 Introduction
Based on the PSC-FXLMS algorithm, several modifications and improvements have been proposed to address the plant model stability under errors, the convergence speed and the influence of control effort for phase error between the disturbance and command signals [13; 14]. To address a limitation of these ASP algorithms, that they are always effective for stationary noise conditions, [PERSON] et al. [15] proposed an algorithm for nonstationary noise profiling application. This algorithm inherits the advantages of minimum control effort and insensitivity to the magnitude error of a plant model. A detailed investigation is important and useful to understand the behavior of ANE-based and ASP-based algorithms, and stimulate and inspire the development of more advanced and practical ANC system structures and algorithms for sound quality control. Detailed investigations of ANE-based algorithms have been widely reported in references [13; 16; 24; 25]. They have drawn the following conclusions: when the gain factor is small, the influence of the secondary path phase estimation error is dominant; however, when the gain factor is larger than one, the influence of the secondary path amplitude estimation error becomes dominant. For the ASP-based algorithm, [PERSON] et al. [13] provided a qualitative guide of properties, including the plant model stability and the influence of control effort with phase error between the disturbance and command signals. However, the convergence time constant and convergence performance of the influence of phase error between disturbance and command signal has not been particularly considered. Therefore, in this paper, for both stationary and nonstationary disturbance cases, a detailed analysis of the classical ASP-based algorithm (PSC-FXLMS) considering the convergence time constant and convergence performance with phase error between disturbance and command signal is conducted. The rest of this paper is organized as follows. Section 2 provides a brief introduction to the PSC-FXLMS algorithms. Section 3 conducts the performance analysis of this system with phase error between the disturbance and command signal. Section 4 presents some simulations to demonstrate the validity of the analytical findings. Conclusions are provided in Section 5.