esa-sceva/satcom-chunk-collection huggingface dataset:
# Patch-Wise Infrared and Visible Image Fusion Using Spatial Adaptive Weights
###### Abstract  
In infrared (IR) and visible image fusion, the significant information is extracted from each source image and integrated into a single image with comprehensive data. We observe that the salient regions in the infrared image contain targets of interests. Therefore, we enforce spatial adaptive weights derived from the infrared images. In this paper, a Generative Adversarial Network (GAN)-based fusion method is proposed for infrared and visible image fusion. Based on the end-to-end network structure with dual discriminators, a patch-wise discrimination is applied to reduce blurry artifact from the previous image-level approaches. A new loss function is also proposed to use constructed weight maps which direct the adversarial training of GAN in a manner such that the informative regions of the infrared images are preserved. Experiments are performed on the two datasets and ablation studies are also conducted. The qualitative and quantitative analysis shows that we achieve competitive results compared to the existing fusion methods.  
infrared and visible image fusion; Generative Adversarial Network; patchGAN; dual-discriminator; spatial adaptive weights +
Footnote †: journal: Applied Sciences  
14 September 2021  
10 September 2021  
1
# Demonstration of Triband Multi-Focal Imaging with Optical Coherence Tomography
## 5 OCT Imaging of Cleared Murine Brain  
The focal shift between separate imaging bands may also be useful for volumetric imaging for samples that allow larger penetration depth such as lipid-cleared tissues. It has been demonstrated that the intact brain sample can be imaged at optical resolution with clearing assisted scattering tomography (CAST) [29], a method combining OCT with a controlled clearing of lipid. While the lipid removal can be optimized to provide sufficient contrast throughout the entire depth of the sample, the shallow DoF determined by the high NA objective lens mandates step-wise adjustment of the focal plane to enable the 3D imaging for a large volume. We examined application of the multi-band source in CAST. The murine brain tissue (from a wild type C57 BL/6 mouse, aged 6-8 weeks) was prepared following a customized clearing method based on SWITCH [30] protocol. Before and during imaging, the sample was submerged in a refractive-index matching solution (23.5% (\(w/v\)) \(N\)-methyl-D-glucamine, 29.4% (\(w/v\)) diatrizoic acid, and 32.4% (\(w/v\)) iodixanol). In general, this solution aided in suppressing scattering and achieving adequate SNR. However, the absorption loss in the 1550 nm imaging spectrum was too large to obtain a meaningful contrast, and the imaging was only performed using the two shorter wavelength subsystems. The sample was placed with superior cortex facing the objective. A set of volumetric data was acquired from individual interferometers and processed independently with depth-scale matching. A set of 29 discrete end-face sections were obtained using MIP, over a range of 105 \(\upmu\)m for each plane. Figure 4a,b shows three selected examples of such MIP planes (horizontal cross-sections) that are 0.75 mm apart. The reduced collection efficiency due to the focal shift between imaging sources is apparent as individual sources capture different features along the depth. Figure 4c-e compares the OCT XY-tomogram (coronal section) of a single source (c) \(\lambda_{c}\) = 1060 nm and (d) \(\lambda_{c}\) = 1310 nm with the equivalent cross-section taken from the merged stack (using two bands).  
Figure 3: The vascular projections acquired from the bands centered at (**a**) 1060 nm, (**b**) 1310 nm, and (**c**) 1550 nm are shown. Projections were obtained using a colormap to encode the depth of the vessel, in (**d**) imaging band at 1310 nm, and (**e**) combining all three imaging bands. The magenta arrows mark the superficial vessel that is best captured by the 1060 nm system. The blue arrows mark the deeper vessel that is best captured by the 1550 nm system. Scale bar = 1 mm.
## 2 Related Work
In [9], the temporal graph network (TGN) is employed for classifying fraudulent transactions and events, along with a methodology for asynchronous training, which is particularly useful for handling large-scale graphs. TGN involves using a GNN in continuous-time scenarios, where vertices and edges are linked to interaction events via timestamps. The TGN also incorporates a memory module, which stores information about past interactions of each vertex with its neighbors. Additionally, the TGN integrates other modules, such as attention mechanisms, to enhance performance. In addition, previous works, such as [22], have only addressed fraud detection in online payment platforms by incorporating a limited number of events and static graphs, without adding the temporal aspect or using an ETG (Event-Based Temporal Graph). Other works that have approached fraud detection from the dynamic point of view do so using a discrete dynamic perspective; the graph is divided into snapshots in discrete time intervals [5; 7; 20; 21; 23]. Meanwhile, the current work uses an ETG, which is a dynamic graph for the continuous case, rather than a discrete/snapshot-based graph. Furthermore, these works consider the temporal aspect as an extra parameter of the algorithm, for example, combining different features with timestamps of the graph or encoding the temporal parameter produced by a neural network, as the temporal aspect is seen as recurrent layers of information.
# Superhydrophobic Surfaces Enabled by Femtosecond Fiber Laser-Written Nanostructures
## 4 Summary and Conclusions  
We developed an fs fiber laser surface-processing technique to achieve superhydrophobicity. Under optimal conditions (i.e., 200 kHz PRR, 16 \(\upmu\)J pulse energy, 100 mm/s scanning speed, 5 \(\upmu\)m pitch), a contact angle of 171\({}^{\circ}\) and a rolling angle of less than 3\({}^{\circ}\) were achieved. Moreover, it took only five minutes to process a \(1\times 1\) inch sample, which was over 10 times faster than an fs solid state laser and involved much simpler preparation than coating methods. In a recent study, scientists achieved a contact angle of 167\({}^{\circ}\) using a nano-Bi\({}_{2}\)O\({}_{3}\) coating, which was kept almost stable for 180 days after exposure to the natural environment [34]. However, coatings have the possibility of being subject to durability issues, such as weak bond quality, peeling, and erosion.  
We have demonstrated that fs fiber lasers can create hierarchical structures on metals for the surface to exhibit multifunctional properties, such as superhydrophobicity, self-cleaning, and light-trapping. The homogeneity of the surface structure plays a critical role in superhydrophobicity, and there is a strong correlation between superhydrophobicity and reflectance, the latter providing a quick indicator of hydrophobic quality.  
The present works lay a solid foundation to further optimize the process to achieve a higher speed as well as better quality, stability, and durability of superhydrophobic surfaces for any type and morphology of materials (e.g., metals, semiconductors, silicon, and glass). With these key advantages  
Figure 10: Reflectance as a result of various scanning speeds (left) and pitches (right).  
and prospective advancements, these surfaces can upgrade the performance of biomedical devices, the efficiency of solar cells, the water-repellent abilities of infrared sensors, aircraft operation in harsh environments, and more critical applications in a wide array of industries.  
[PERSON] contributed towards the design, execution, and manuscript for this research. [PERSON] contributed to the execution and manuscript revision. [PERSON] contributed to the design and manuscript revision. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 3 Materials and Methods
### Materials
#### 3.1.4 Data Pre-Processing  
Data pre-processing is a crucial step in ensuring the quality and consistency of the input data. For the DCAT model, the following pre-processing steps were implemented:  
1. **Image Pre-Processing:** * _Resizing_: All images were resized to a uniform size of \(224\times 224\) pixels to ensure consistency across datasets. * _Normalization:_ Pixel values were normalized to the range [0, 1] by dividing by 255. Additionally, mean subtraction and division by standard deviation were performed for each color channel to align with pre-trained ViT model standards.
2. **Text Pre-Processing:** * _Tokenization_: Captions were tokenized into individual words using a tokenizer trained on the LDC-IL Tamil Text Corpus. * _Stop-word Removal:_ Commonly occurring Tamil stop-words were removed to reduce noise and focus on more informative words. * _Length Normalization:_ Captions longer than a predetermined maximum length were truncated, while shorter captions were padded with a special <pad> token.
# Adjustable Pheromone Reinforcement Strategies for Problems with Efficient Heuristic Information
## 4 MAX-MIN Ant System for TSP and ATSP  
It is important to note that ACO is a general metaheuristic that has different variants, and among them, the important variants are the ant colony system (ACS) [9; 17], MAX-MIN ant system (MMAS) [8], and three bound ant system (TBAS) [35; 36]. Since MMAS is very successful and the most popular for these kinds of optimization problems, in this study, we describe and use the MMAS variant. The general description of the MMAS metaheuristic is given in Algorithm 1. In the initialization phase, the algorithm loads the problem instance and parameter settings and creates and initials necessary variables and data structures such as pheromone trails, solutions, etc.  
```
INPUT: problem instance, parameter settings (including stopping criteria and local optimization choice) Initialize (); FirstIteration = true; While stopping criteria are not met do  For each ant k in the colony do  Sk = ConstructSolution();  If local optimization is enabled then  Sk = LocalOptimization (Sk);  EndIf  If fist ant in the colony then  Sb = Sk;  EndIf  If FirstIteration is true then  Solution = Sb;  FirstIteration = false;  Else if Sb is better than Solution then  Solution = Sb;  EndIf  EvaporatePhenormeTrails();  ReinforcePhenormeTrails(); EndWhile  OUTPUT: Solution
```  
**Algorithm 1**A high-level description of MMAS.  
Our implementation of MMAS for ATSP and TSP uses a common design and associates each arc with distance \(d_{ij}\) to pheromone trail \(\tau_{ij}\) and organizes it in a matrix. After that, in the while loop, the colony of ants constructs new solutions. If some kind of local optimization is used, which is optional, then solutions are possibly improved according to the implemented method. After that, pheromone trails are evaporated by using the equation:  
\[\tau_{ij}=(1-\rho)\cdot\tau_{ij}, \tag{3}\]  
i.e., by multiplying each pheromone trail with \(1-\rho\), where \(\rho\) is a parameter of the algorithm that must be between 0 and 1. If the pheromone value drops below the lower limit \(\tau_{\text{MIN}}\), then the pheromone trail is set to \(\tau_{\text{MIN}}\). After the pheromone trails are evaporated, the trails associated with components of some kind of best solutions are reinforced in the ReinforcePheromoneTrails () procedure. These pheromone trails that are reinforced are increased in MMAS for TSP and ATSP by using Equation (4):  
\[\tau_{ij}=\tau_{ij}+\frac{1}{f(s_{b})}, \tag{4}\]  
where \(f(s_{b})\) is the cost of some kind of best solution, which is normally the best solution in the current iteration or best solution so far (in all previous and current iteration). If the case of TSP when pheromone trail \(\tau_{ij}\) is reinforced, then by symmetry is also set \(\tau_{ji}=\tau_{ij}\).
## 4 Experimental Results
### Results for the Samson Dataset  
The spectral resolution of the Samson dataset is 3.13 nm. Each pixel was recorded on 156 spectral bands ranging from 0.401 um to 0.889 um in terms of wavelength. A region encompassing 95 \(\times\) 95 pixels was selected for the experiment. In this region, the hyperspectral data were not degraded by blank bands or serious noise bands. It contained three endmembers: \"Soil\", \"Tree\" and \"Water\".  
Finally, experiments were conducted on the Samson dataset. Because this dataset was not degraded by blank bands or serious noise bands, it can more directly reflect the performances of the different unmixing methods. Table 5 shows the abundances and endmembers estimated for the Samson dataset.  
As can be seen in Figure 7, all the methods captured the approximate shape of the endmembers and their change, but the reflectance values were different. The performances of the FCLSU and GLNMF algorithms on this dataset were relatively general. There was some obvious noise in the GLNMF abundance map, and the abundance estimation for  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Methods**} & **FCLSU** & **GLNMF** & **GraphL** & **gtvMBO** & **DAEU** & **EGU-pw** & **EACNN** \\ \hline \multirow{4}{*}{SAD} & Road & 0.6121 & 0.7670 & 0.3344 & 0.1152 & 0.1235 & **0.1082** & 0.1135 \\  & Soil & 0.1672 & 0.1141 & 0.0788 & 0.1460 & 0.1187 & **0.0786** & 0.0816 \\  & Water & 0.2286 & **0.0629** & 0.3918 & 0.3915 & 0.1565 & 0.1832 & 0.1273 \\  & Tree & 0.1642 & **0.0983** & 0.2170 & 0.2434 & 0.1583 & 0.1089 & 0.0988 \\ \hline \multicolumn{2}{c}{Mean SAD} & 0.2931 & 0.2606 & 0.2555 & 0.2240 & 0.1392 & 0.1197 & **0.1053** \\ \hline \multicolumn{2}{c}{RMSE} & 0.1481 & 0.1115 & 0.1453 & 0.1360 & 0.1215 & 0.0896 & **0.0700** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experimental results for the Jasper Ridge dataset.  
Figure 6: The endmember extraction results of the Jasper Ridge dataset.  
GraphL was 0.04 higher than those of the FCLSU and GLNMF algorithms, but the estimated endmember results were not very ideal. In addition, because the gtvMBO method imposes non-negative constraints on the endmembers via its hard threshold operators, it had many endmember estimation results close to zero. In contrast, because the distribution of the dataset was simple and there were no complex SVs, it was very obvious that compared with the traditional unmixing methods, the DL-based model obtained very excellent unmixing results. Compared with DAEU and EGU-pw, the EACNN further improved the abundance estimation for the Samson dataset by nearly 0.016 and 0.006, respectively. In other words, compared with the unmixing method based on an AE, the EACNN puts more emphasis on the spatial and spectral information contained in the HSI in the unmixing process and guides the network in obtaining the best results through its convolution operation and attention mechanisms.  
Figure 7: _Cont._
# Improving Deep Learning Anomaly Diagnostics with a Physics-Based Simulation Model
###### Abstract  
Deep learning algorithms often struggle to accurately distinguish between healthy and anomalous states due to the scarcity of high-quality data in real-world applications. However, these data can be obtained through a physics-based simulation model. In this research, the model serves a dual purpose: detecting anomalies in industrial processes and replicating the machine's operational behavior with high fidelity in terms of a simulated torque signal. When anomalous behaviors are detected, their patterns are utilized to generate anomalous events, contributing to the enhancement of deep neural network model training. This research proposes a method, named Simulation-Enhanced Anomaly Diagnostics (SEAD), to detect anomalies and further create high-quality data related to the diagnosed faults in the machine's operation. The findings of this study suggest that employing a physics-based simulation model as a synthetic-anomaly signal generator can significantly improve the classification accuracy of identified anomalous states, thereby enhancing the deep learning model's ability to recognize deviating behavior at an earlier stage when more high-quality data of the identified anomaly has been available for the learning process. This research measures the classification capability of a Long Short-Term Memory (LSTM) autoencoder to classify anomalous behavior in different SEAD stages. The validated results clearly demonstrate that simulated data can contribute to the LSTM autoencoder's ability to classify anomalies in a peripheral milling machine. The SEAD method is employed to test its effectiveness in detecting and replicating a failure in the support element of the peripheral milling machine.  
Keywords:Simulation-Enhanced Anomaly Diagnostics (SEAD); physics-based simulations; diagnostics; anomaly classification; synthetic training data; deep learning; LSTM +
Footnote †: journal: Applied Sciences
# Experimental Investigation of the BIPV System under Sanluurfa Meteorological Conditions
## 2 Materials and Methods
### Experiment Procedure and PVsyst Simulation Study
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline  & \multicolumn{1}{c}{**Bifacial**} & \multicolumn{1}{c}{**Error Rate**} & \multicolumn{1}{c}{**South**} & \multicolumn{1}{c}{**Error Rate**} & \multicolumn{1}{c}{**East**} & \multicolumn{1}{c}{**Error Rate**} & \multicolumn{1}{c}{**Roof**} & \multicolumn{1}{c}{**Error Rate**} \\  & \multicolumn{1}{c}{**Panel (kWh)**} & \multicolumn{1}{c}{**of PVsyst**} & \multicolumn{1}{c}{**Monofacial**} & \multicolumn{1}{c}{**of PVsyst**} & \multicolumn{1}{c}{**Monofacial**} & \multicolumn{1}{c}{**of PVsyst**} & \multicolumn{1}{c}{**Monofacial**} & \multicolumn{1}{c}{**of PVsyst**} \\  & \multicolumn{1}{c}{**Analysis**} & \multicolumn{1}{c}{**Panel (kWh)**} & \multicolumn{1}{c}{**Analysis**} & \multicolumn{1}{c}{**Panel (kWh)**} & \multicolumn{1}{c}{**Analysis**} & \multicolumn{1}{c}{**Panel (kWh)**} & \multicolumn{1}{c}{**Analysis**} \\ \hline February & 35.74 & 11.8\% & 32.20 & 6.37\% & 14.71 & \(-\)11\% & 25.88 & 2\% \\ March & 31.41 & \(-\)19\% & 28.13 & \(-\)27.6\% & 17.43 & \(-\)47.2\% & 33.10 & \(-\)23.1\% \\ April & 30.31 & 2.8\% & _26.69_ & \(-\)5.9\% & 25.14 & \(-\)14.3\% & 45.08 & \(-\)9.4\% \\ May & 27.89 & 13.2\% & 24.01 & 7.7\% & 30.28 & \(-\)8.2\% & 52.28 & \(-\)6.8\% \\ June & 24.78 & 11.3\% & 20.21 & 6.6\% & 33.06 & \(-\)8.3\% & 57.24 & \(-\)9.5\% \\ July & 26.44 & 12.3\% & 22.21 & 8.2\% & 35.65 & \(-\)2\% & 58.58 & \(-\)6.5\% \\ Total & 176.57 & & 153.45 & & 156.27 & & 272.16 & \\ \hline \end{tabular}
\end{table} 
Table 2: Amounts of energy produced in the experimental setup in kWh for February, March, April, May, June, and July, and error rates (%) of the PVsyst analysis. Figure 3: Preparation of the experiment in the PVsyst software.
## 4 Models and Methods
### Calculation of Angle between Two Helices  
To analyze conformational changes in the relative orientations of any two helices, the program interhlx (written by [PERSON], available at ([[http://www.nmr.uhnres.utoronto.ca/ikura/interhlx/](http://www.nmr.uhnres.utoronto.ca/ikura/interhlx/)]([http://www.nmr.uhnres.utoronto.ca/ikura/interhlx/](http://www.nmr.uhnres.utoronto.ca/ikura/interhlx/)))) was used to calculate the angle between two helices, including the sign in a structure or a family of structures. The program calculates the sign of the angle between two helices by following this convenient role: The two helices are taken to be positioned by helix I being in front of helix II. Helix I (from N to C) is used to define first vertical vector. A second vertical vector is defined with its tail at the \(C\)-terminus of helix II. The angle between helices I and II is the rotation required to align the head of the second vector with the \(N\)-terminus of helix II. The vector is rotated in the direction that produces an angle of less than 180 degrees with the clockwise or counterclockwise rotation represented by positive or negative sign. This program can also provide other geometry-based parameters such as interhelical distances [66, 67].
# Rotating Lorentz Force Magnetic Bearings' Dynamics Modeling and Adaptive Controller Design
## 2 Design of Lorentz Force Universal Magnetic Levitation Configuration
### RLFMB Structure and Principle  
#### 2.2.1 RLFMB Structural Design  
Figure 4 depicts the RLFMB's structural layout. The RLFMB's stator is made up of an exterior magnetic conducting ring, an outside magnetic steel, an inner magnetic steel, and an inner magnetic conductive ring, which are arranged radially from outside to inside. The rotor is made up of a coil bracket and a coil. The two groups of radially symmetrical inner and outer magnets have the same magnetization direction, and the outer magnet and the inner magnet are both permanent magnets that are radially magnetized. For the magnetic circuit to be closed, the outer and inner magnetic conductive rings are made of magnetically conductive materials. The coil bracket considers the good thermal conductivity of metal materials while choosing materials, but it will generate induced electromotive force to impede the rotor rotation. Non-metallic materials frequently result in rotor output attenuation at high temperatures because of their poor heat conductivity. Because RLFMB achieves stable pointing, deflection swing movement, no continuous rotation, and less heat generation, the coil bracket can be made of polyimide, a non-metallic material. The coil is made of copper wire.  
Figure 3: Force diagram of orthogonal, linear Lorentz force magnetic bearings. (**a**) Force diagram of orthogonal magnetic bearings. (**b**) Force diagram of linear magnetic bearings.  
To ensure that RLFMB does not maneuver uncontrollably at a great angle of the load compartment/frame compartment due to instability, the inner and outer magnets of the machine are left open under this structure. This also restricts the direction of the load to a specific range, creating a safe working environment. The magnet's axial thickness can be raised while maintaining the same mass due to the structure's simultaneous reduction in the magnet's radial size, which ensures that the magnetic circuit is closed and minimizes magnetic leakage.