esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Wind Direction
As shown in Figure 7b, under the \(0^{\circ}\) horizontal wind direction, wind turbine 2 is completely in the wake of wind turbine 1, resulting in obvious wake interference and a sharp decrease in the conversion efficiency of wind turbine 2. This is similar to the wake flow of a HAWTs array, which is an axial layout. In order to improve the efficiency of downstream axial wind turbines, they need to be arranged far away, which wastes land resources and does not increase the power density of the entire wind farm. Similarly, \(45^{\circ}\) and \(-45^{\circ}\) wind directions have interference from the wake flow to some extent, making it difficult to arrange wind turbine arrays, as can be seen in Figure 7c,d. When the wind direction is not horizontal, as shown in Figure 7a,c,d, there are gaps between the wind turbines. As indicated by the Venturi effect, when the gap between two wind turbines is small, a high-speed airflow is generated, shown as the circled purple areas, which creates a gain effect between the entire VAWTs. However, interference from the wake flow of upstream wind turbines exists for \(45^{\circ}\) and \(-45^{\circ}\) wind directions, while there is no wake interference for the \(90^{\circ}\) wind direction. Therefore, the transverse arrangement of wind turbine arrays can improve the average power coefficient of the entire system, which explains the results in Figure 6.  
Figure 6: (a) Efficiency comparison curves of different wind directions under different TSR; (b) polar coordinate efficiency comparison curves of different wind directions under optimal TSR.
# Wake-Up Receiver with Equal-Gain Antenna Diversity +
## 4 Experimental Results
### Antenna Port Impedances  
To verify the impedances of both antennas, a frequency sweep from 820 Mhz to 920 Mhz was performed and the reflections were measured with a network analyzer. The measurement results are plotted in Figure 8 that illustrates the reflection of the straight antenna in yellow and the reflection of the angular antenna in green. As can be seen, the minimum reflection is at approximately 870 MHz for the straight antenna and at approximately 878 MHz in the case of the angular antenna. With a reflection below \(-15\) dB, both antennas perform very well in the frequency region of interest that is around 870 MHz.  
Figure 6: Photo of the wireless sensor node with equal gain diversity.  
Figure 7: Schematic of the diversity system.
# Clustering Using an Improved Krill Herd Algorithm
## 5 Simulation and Experiment
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Data Set** & **Criteria** & **K-means** & **ACO** & **PSO** & **KHCA I** & **KHCA II** & **IKHCA** \\ \hline \multirow{4}{*}{Iris} & Best & 98.5 & 97.4 & 97.1 & 96.4 & 96.65 & 96.66 \\  & Worst & 117.4 & 99.2 & 100.5 & 103.1 & 97.68 & 96.67 \\  & Mean & 104.7 & 97.8 & 98.8 & 98.6 & 96.67 & 96.66 \\  & Rank & 6 & 3 & 5 & 4 & 2 & 1 \\ \hline \multirow{4}{*}{Wine} & Best & 16,562.6 & 16,510.3 & 16,336.4 & 16,328.1 & 16,293.01 & 16292.12 \\  & Worst & 17,995.9 & 16,535.8 & 16,426.4 & 16,430.9 & 17,710.16 & 16,589.23 \\ \cline{1-1}  & Mean & 17,101.5 & 16,528.5 & 16,396.3 & 16,384.2 & 16,490.17 & 16,305.51 \\ \cline{1-1}  & Rank & 6 & 5 & 3 & 2 & 4 & 1 \\ \hline \multirow{4}{*}{Glass} & Best & 225.3 & 219.8 & 271.1 & 216.2 & 210.85 & 210.30 \\  & Worst & 263.1 & 258.1 & 286.8 & 255.9 & 246.16 & 223.03 \\ \cline{1-1}  & Mean & 248.2 & 241.3 & 279.3 & 238.3 & 215.86 & 215.90 \\ \cline{1-1}  & Rank & 5 & 4 & 6 & 3 & 1 & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Objective function values obtained by the algorithms. In Table 3, the clustering accuracies of IKHCA and other clustering algorithms are given, and part of the results were obtained directly from [30], with the bold font indicating the best results. At a glance, one can easily see that the last three clustering algorithms (KHCA I, KHCA II, and IKHCA) by using KHA are obviously better than the K-means, ACO, and PSO algorithms. It can be seen that the introduction of KHA into the clustering problem is reasonable and effective. Based on these results, IKHCA is proved to be the best algorithm with respect to objective function value and accuracy.
# A Review of Transformer-Based Approaches for Image Captioning
## 5 Vision-Language Pre-Training in Captioning
### VLP in Image Captioning
#### 5.2.7 \"One For All\" (OFA) Framework  
[PERSON] et al. [106] present a \"One For All\" framework that aims to be task-agnostic and modality-agnostic. This work also pursues the objective of task comprehensiveness, in which a single model can generalize such that it performs well on a large variety of tasks, including vision-language, vision only and language only tasks. They also demonstrate their model's performance in zero-shot learning and task transfer.  
Their approach unifies a variety of multimodal and unimodal vision and language tasks in a sequence-to-sequence learning framework. Their model has the advantage that it yields state-of-the-art performance despite pre-training on a significantly smaller dataset than other models pre-trained on large-scale datasets; they pre-train on a dataset of 20 million image-text pairs. For comparison, the pre-training dataset sizes for CLIP [53] and ALIGN [54] are 400 million and 1.8 billion, respectively.  
The architecture is transformer-based and consists of an encoder-decoder model without task-specific layers added. The image encoder is similar to that of SimVLM [103] and is based on CoAtNet [104]; it extracts fixed-size patches from images. For the textual encoding, a given text sequence is transformed into a subword sequence which is then embedded into features. The inputs from the different modalities are represented in a unified embedding space. To do this, they discretize data from the visual and textual domains using vector quantization and byte-pair encoding, respectively, and then represent them using a unified vocabulary. The downstream tasks and modalities of interest are then represented in a sequence-to-sequence setting for training. They rely on handcrafted instructions (instruction-guided pre-training) to differentiate between the different tasks. To optimize the model, they use a cross-entropy loss that takes into account the input and an instruction. The training objective (loss function) to be minimized is given by:  
\[\mathcal{L}=-\sum_{i=1}^{|y|}\log P_{\theta}(y_{i}|y_{<i},x,s|) \tag{6}\]  
where \(\theta\) refers to the model parameters, \(x\) is the input, \(s\) is the handcrafted instruction and \(y\) is the output. This model achieved state-of-the-art performance and in the case of the downstream task of captioning, it topped the publicly available MS COCO captioning leaderboard [69] (as of 31 May 2022).
# An Online Detection Method for Coal Dry Screening Based on Image Processing and Fractal Analysis
## 5 Conclusions  
An algorithm was developed for extracting the edge image of coal particles. We found that the fractal behaviour exists in the edge image of coal particles by comparing the fractal dimension of the coal-edge image from different heights. Ulteriorly, we made clear that the smaller the particle size of coal, the higher the fractal dimension of the coal image by analysing the fractal dimension of coal particles with different particle size compositions. Therefore, fractal dimensions can be employed to detect the screening efficiency of the same production line. Along with a fractal method to describe coal-screening efficiency and coal PSD indirectly based on image processing is proposed. The results proved that the fractal dimension of the coal-edge image becomes smaller with the increase in the screening efficiency; the maximum and minimum fractal dimensions are 2.6223 and 2.5454. Therefore, our detection method greatly reduces the difficulty of real-time screening efficiency detection and opens a new idea. Although we have developed an image-processing technology using fractal dimension to measure screening efficiency, the reliability of this method still depends on more experiments to verify, and this technology cannot directly obtain the PSD of coal.  
In the future, it is worthwhile to investigate the relationship between fractal dimension and coal particle size distribution (PSD), and this method has the potential to be applied to the screening of other ones.  
Data curation, D.H.; Formal analysis, D.H.; Funding acquisition, C.L.; Methodology, D.H. and C.L.; Resources, D.H.; Software, D.H.; Writing--original draft, D.H.; Writing--review & editing, D.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China [51775544] and Xuzhou XCMG Mining Machinery COLTD [2020050049-07].  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflict of interest.  
Figure 10: Relation of coal-screening efficiency and fractal dimension for coal-edge images.
## 1 Introduction  
A failure mode causes and effects analysis (FMCEA) is an analytical technique that combines technology and the knowledge of experts. It is used to identify the possible failure modes (FMs) of a product or a process and manages to eliminate their negative effects. The traditional FMCEA method prioritizes the FMs based on the decreasing values of their risk priority numbers (RPNs), which are equal to the product of the risk factors (occurrence, O; severity, S; and detection, D) [1].  
However, the conventional FMCEA has been criticized in the literature due to several deficiencies, including (i) the assignment of equal weights to the risk factors in the RPN computation. Additionally, another drawback of the FMEA method is that (ii) attention is not paid to the time and cost spent to solve the FMs; sometimes, the time and cost spent to fix the riskiest FM can be used to fix many other different FMs in lower ranks. Another issue that may be faced while applying the FMCEA is (iii) that some FMs (parent failures) may have sub-failure modes (SFMs); those sub-failures are most likely to have different risk priorities with large gaps, which puts a big question mark on the overall rank of the parent FM. Moreover, (iv) the efficiency notion is not considered in the risk assessment in the conventional RPN method. Furthermore, (v) hidden or latent risks may remain underestimated or inaccurately estimated due to the possibility of various combinations of risk factors resulting in the same RPN value. Consequently, these undisclosed risks might eventually cause unforeseen errors or issues. Additional shortcomings associated with the FMCEA method, along with some of the methods suggested to address them, can be found in [2; 3; 4; 5] and are summarized in Appendix A.
## 2 Materials and Methods
### Pilot Test Procedures
#### 2.3.2 Biorecirculation Testing
Approximately 997.9 kg (2200 pounds) of whey along with 283.9 L (75 gallons) of concentrate nutrient solution was blended with groundwater extracted from HSE-6, and injected into HSI-8. The solution (whey/nutrient/groundwater) were injected over an approximate 2.0 h period at an approximate rate of 9.5 L per minute (2.5 gallons per minute). Recirculation was reinitiated immediately following the direct injection of the amendments and nutrients. Recirculation was stopped 15 days after the direct injection. About three months after ceasing recirculation, the test cell received a supplemental dose of EVO and nutrient solution. This supplemental dose added to HSI-8 consisted of approximately 946.4 L (250 gallons) of EVO solution added over a 6.0 h period and 227.1 L (60 gallons) of nutrient solution over a 2.0 h period. Recirculation, which began immediately after the EVO injection, operated continuously for a month. Ethyl Lactate Test Cell (HSI-14/HSE-11): Approximately 1457.4 L (385 gallons) of EL and 246.1 L (65 gallons) of nutrient mix were injected simultaneously as a solution at an approximate feed rate of 2.7 L per hour (4.7 gallons per hour) over 4 days into the recirculation flow between HSE-11 and HSI-14. During and after the injection, recirculation was continued for total of two weeks. About 4 months after ceasing recirculation, a supplemental injection of nutrient mixture and EVO was added to the test cell. The supplemental injection at this test cell consisted of approximately 946.4 L (250 gallons) of EVO solution over a 6.0 h period and 227.1 L (60 gallons) of nutrient mix injected over a 2.0 h period. Recirculation, which began immediately after the supplemental EVO injection, lasted for a month. Biorecirculation sampling occurred from the beginning of the recirculation period and 1 month after stopping recirculation flow. Table 1 summarizes biorecirculation sampling analysis and test methods. The primary sampling during this phase was from the test cell extraction wells at the manifold; however, in the source zone dual completed monitoring wells located 0.9 m (3 feet) from the extraction well and screened in the lower and upper portions of the shallow aquifer were also sampled. These wells were designated SMW-3S and SMW-3D, where the 'S' and 'D' designated the shallow and deeper zones of the water-table aquifer.
# Estimation of Compressible Channel Impulse Response for OFDM Modulated Transmissions
## 3 DFT-Based LS Channel Estimation Methods  
### General Principle  
The classical LS estimate \(\hat{\mathbf{h}}_{LS}\) of the CIR can be an entry point to development of various DFT-based methods. It is represented in matrix notation by the following \(N\times 1\) vector:  
\[\hat{\mathbf{h}}_{LS}=\frac{1}{N}\mathbf{F}^{H}\mathbf{X}_{p}^{-1}\mathbf{Y}_{p}, \tag{4}\]  
where \(\mathbf{F}\) is \(N\times N\) DFT matrix with entries \(\mathbf{F}(n,k)=e^{-j2\pi nk/N}\), \(\mathbf{X}_{p}\) is the diagonal matrix of block pilot tones, and \(\mathbf{Y}_{p}\) is vector of the received training symbol. According to the common rule of thumb, the pilot tones are unit amplitude.  
The DFT-based methods are developed under the assumption that in a properly designed OFDM system, the length of the channel impulse response (\(L\)) should not be longer then length of the cyclic prefix (\(M\)). Thus, the number \((N-M)\) of remaining, nonzero amplitude coefficients in the reconstructed impulse response \(\hat{\mathbf{h}}_{LS}\) can be interpreted as a noise-induced distortion. Therefore, to improve the channel estimation, only \(M\) successive elements of \(\hat{\mathbf{h}}_{LS}\) that have the largest total energy should be considered as the ones used to determine the pure channel part of impulse response. In this article, special attention is directed toward methods where the estimation quality improvement is obtained by simple selection of the appropriate LS coefficients. The basic assignment complies with the following formula [9]:  
\[\hat{\mathbf{h}}_{C}=\mathbf{I}_{N\left[M\right]}\hat{\mathbf{h}}_{LS}, \tag{5}\]  
where \(\mathbf{I}_{N\left[M\right]}\) is a \(N\times N\) diagonal matrix with the entries defined as follows:  
\[\mathbf{I}_{N\left[M\right]}(n,n)=\left\{\begin{array}{ll}1&\text{if}\quad n \in\mathbb{M}\\ 0&\text{otherwise}\end{array}\right.. \tag{6}\]  
Figure 3: System model of the OFDM baseband transmission.  
Due to possible time offset in frame synchronization, taking into account the circular shift property of FFT, the set:  
\[\mathbb{M}=\{m,m+1,...,(m+M-1)\mod N\}, \tag{7}\]  
contains \(M\) successive integers modulo \(N\) and  
\[m=\arg\max_{n}\left(\sum_{i=n}^{(n+M-1)}\sum_{i=n}^{\mod N}|\hat{\mathbf{h}}_{LS }(i)|^{2}\right) \tag{8}\]  
assures maximum energy of \(\mathbf{h}_{C}\) out of all other shifts. The last processing step of the channel reconstruction is Fourier transform:  
\[\hat{\mathbf{H}}_{C}=\mathbf{F}\hat{\mathbf{h}}_{C} \tag{9}\]  
to obtain the channel estimate \(\hat{\mathbf{H}}_{C}\equiv\hat{\mathbf{H}}\) in the frequency domain (each entry of the vector \(\hat{\mathbf{H}}\) corresponds to the particular subchannel characteristic).
# Configuration Optimization of Mobile Photovoltaic-Diesel-Storage Microgrid System Based on CPS-MOEA
## 5 Experiments
### Results Analysis
#### 5.2.1 Optimization Results Analysis  
The achievement of an optimal Pareto front, as illustrated in the study's results, underscores the efficacy of the CPS-MOEA in navigating the trade-offs and compromises that are inherent in the multi-objective optimization of mobile photovoltaic-diesel-storage microgrid systems. This Pareto front serves as a visual representation of the optimal solutions achieved, showcasing the balance between different objectives such as reliability and maneuverability. This balance is critical for the practical application and decision-making processes involved in the planning and operation of such microgrid systems.  
To properly evaluate the performance of CPS-MOEA, PICEA-g and MOPSO were introduced as comparative algorithms. To further validate the effectiveness of CPS-MOEA, MAPSO was used to generate a reference Pareto optimal frontier by running single objective optimization multiple times. If the solution obtained by the algorithm is close to the reference Pareto optimal frontier, then the algorithm is good. In this article, the Pareto optimal frontier is composed of 100 non dominated solutions obtained from 100 independent runs. As shown in Figure 4, the reference Pareto optimal frontier and PFs obtained from CPS-MOEA PICEA-g and MOPSO are presented.  
It can be easily observed from Figure 3 that the PF of CPS-MOEA is superior to that of PICEA-g and MOPSO. This reflects that the curve obtained by optimizing CPS-MOEA is closest to the two coordinate axes. Meanwhile, the PF obtained by CPS-MOEA is  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Battery Parameters**} & \multicolumn{1}{c}{**Diesel Generator Parameter**} \\
**Nominal Capacity (Ah)** & **Voltage (V)** & **Rated Power (W)** \\ \hline
100 & 12 & 2000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters.  
closer to the reference Pareto optimal frontier than that obtained by PICEA-g and MOPSO. This confirms the effectiveness and superiority of the proposed method in terms of the optimization capability in the first step. In addition, it can be seen that optimizing CPS-MOEA can achieve more uniform PF. Therefore, it can be concluded that CPS-MOEA has a better optimization ability in distribution than PICEA-g and MOPSO.
## 3 Results and Discussion
### Adsorption and Photocatalytic Removal of MB  
#### 3.2.1 Effect of TiO\({}_{2}\) Amount in the SA/PVP Matrix  
As the catalyst loading in the SA/PVP/TiO\({}_{2}\) nanocomposite has a key role in dye decay efficiency, the effect of the photocatalyst concentration on MB degradation was investigated by increasing the TiO\({}_{2}\) amount in the SA/PVP matrix from 1 to 5 wt.%. As seen in Figure 4, the decay efficiency rose when the TiO\({}_{2}\) concentration increased from 1 to 3 wt.%, which can be justified by the fact that at low concentrations, more porous empty sites and polymer functional groups, such as COO, are accessible on the beads' external surface to absorb cationic dye molecules via electrostatic attraction. However, the active sites available for the photocatalytic reaction are limited. Thus, by increasing the catalyst loading to 3 wt.%, additional active sites become available for the photocatalytic reaction. This leads to an increase in the hydroxyl ions' adsorption onto the surface of the beads to produce OH\({}^{\bullet}\) radicals. On the other hand, the photocatalytic activity decreased at a high concentration of the catalyst, as it hampers the dye decay rate as a result of light penetration shortage inside the beads. A second possibility is the agglomeration of the catalyst nanoparticles, resulting in a decrease in the operative surface area of the catalyst, and consequently, a decrease in the decolorization efficiency.
# Direct Numerical Simulation of Turbulent Boundary Layer over Cubical Roughness Elements
## 2 Numerical Details
The domain size and the number of grids in each direction are denoted by \(L_{i}\) and \(N_{i}\), respectively. \(\Delta x\), \(\Delta y\), and \(\Delta z\) are the grid resolutions in streamwise, wall-normal, and spanwise directions, respectively. Figure 1 depicts schematic views of cubic roughness elements. The 3D cubes are periodically arranged in both the streamwise and spanwise directions in a staggered manner. The height of each cube is \(k=1.59\delta_{0}\), and the streamwise and spanwise sizes of the cube are \(l_{x}=l_{z}=1.54\delta_{0}\), representing \(l_{x}\approx l_{z}\approx k\). The streamwise and spanwise spacing is \(p_{x}/k=2\) and \(p_{z}/k=2\), respectively. As illustrated in Figure 1, \(\varepsilon\) represents a virtual origin, and \(y^{\prime}\) indicates \(y-\varepsilon\). The virtual origin is obtained from the moment \(M\) due to the forces in the downstream direction, where \(M\) is defined as \(\sum_{y=0}^{y=k}\left(C_{p}y\right)+C_{f}k\). Here, \(0.5C_{f}\) is the frictional drag, and \(0.5C_{p}\) is the form drag. Consequently, \(\varepsilon\) is calculated from \(\varepsilon=M/(C_{f}+C_{p})\), interpreted as the wall-normal location where the drag acts on the roughness [45]. Four locations indicated as P\({}_{0}\), P\({}_{1}\), P\({}_{2}\), and P\({}_{3}\) are chosen for analyzing the turbulence statistics. A smooth wall is applied to the region \(x/\delta_{0}<700\), and the 3D cubic roughness elements are implemented on the wall over \(x/\delta_{0}=700\). Inflows are developed over the smooth wall, and turbulent flows are fully developed beyond \(x/\delta_{0}\) = 600. The flow characteristics suddenly change from smooth to rough near \(x/\delta_{0}=700\). After a transitional state, the flows become stable and enter an equilibrium region [46; 47; 48]. The turbulence statistics are obtained at \(x/\delta_{0}>840\), far from the step change, where \(Re_{\tau}\) is 816. For comparison, the DNS dataset for a ZPG TBL with a smooth wall at \(Re_{\tau}\) = 825 is employed [42; 44], referred to as \"Smooth\". The present data set is referred to as \"Rough\".
## 3 Numerical Validations of 3D Embedded Flaws Simulations  
In a first step prior to this study, numerical validations were performed in a wide range of classical 2D NDT configurations by comparing simulations from the proposed hybrid FEM method, the asymptotic PTD model [10] and another coupling method (2D CIVA ATHENA). Such numerical validations showed that the PTD approach, which has a lower computation time, breaks downs for small flaw heights compared to the wavelength as shown in [10]. Preliminary comparisons between PTD and hybrid FEM have been reported by some of the present authors in two simple 2D configurations [42] and lead to the same conclusions. The hybrid FEM method appears thus be a solution to overcome the limitation of analytical models for small flaws.  
The proposed 3D hybrid FEM model is evaluated here by comparing numerically it with existing analytical 3D models as the 3D PTD model. In these validations, crack inspections in ferritic steel components are simulated using several different configurations: TOFD [28] configurations, which are usually used to detect and characterize cracks from their edge diffraction echoes, and pulse echo configurations with various incidences to study both the reflection (normal incidence) and diffraction (oblique incidence) from the crack. The influence of the flaw height and extension is also evaluated notably for small sizes to determine the improvement provided by the FEM model in 3D configurations. For the simulations, the following mechanical parameters have been considered: longitudinal and shear sound velocities of 5900 and 3230 m/s and a density of 7.9 g/cm\({}^{3}\). All the input data for simulation are exactly the same for all the used models. These data describe the inspection configuration [1]: the characteristics of the component, the used probes, the inspection scanning, the inspected flaws and the simulation settings.