esa-sceva/satcom-chunk-collection huggingface dataset:
# Underwater Image Mosaic Algorithm Based on Improved Image Registration
###### Abstract
In addition, in terms of underwater image mosaic, [PERSON] et al. [19] combined SIFT and wavelet transformation, but the image registration speed was slow. [PERSON] et al. [20] applied the SURF algorithm to underwater images and obtained a good mosaic effect, but only for images of shallow water with good illumination. [PERSON] et al. [21] proposed a seamless underwater image mosaic technology based on alpha clipping. Due to degradation problems such as low contrast and color deviation of underwater images, traditional image mosaic algorithms cannot be directly applied to underwater image mosaic. In the above-mentioned studies, there exist three problems. Firstly, the algorithms have high computational complexity. Secondly, the algorithms focus on improving the effectiveness of a single index and less on improving the performance of two or three indicators. Thirdly, they are seldom used for low contrast and low definition in image processing. Based on these problems, in this paper, we proposed an improved image registration (IIR) algorithm to realize the rapidity of calculation and accuracy of image processing, by optimizing the solution of the Homography matrix according to the number of inner points. In addition, the proposed algorithm was tested on the images from the Oxford Buildings Dataset [22] to verify that it has high feasibility since this dataset is recognized by the academic community for image processing research and is highly representative, containing thousands of images. Finally, the IIR algorithm was implemented in underwater image processing to improve the quality of underwater image mosaic.
## 2 Materials and Methods
### Apparent Resistivity Tensor Analysis  
#### 2.1.1 The Concept of Apparent Resistivity Tensor  
Following [14], a generic 3D resistivity structure buried beneath a plane-free surface is examined. Different bipole current sources are assumed to be used to conduct a resistivity study inside area S. Following [20], using the notion of apparent resistivity tensor \(\mathbf{\rho}^{(a)}\), the law relating the measured electrical field vector \(\mathbf{E}_{i}\) to the current density vector \(\mathbf{J}_{i}\) for a uniform half-space is introduced for the generic \(i\)-th bipole source:  
\[\mathbf{E}_{i}=\mathbf{\rho}^{(a)}\mathbf{J}_{i} \tag{1}\]  
vector \(\mathbf{J}_{i}\) for a uniform half-space, using the concept of apparent resistivity tensor \(\mathbf{\rho}^{(a)}\). \(\mathbf{\rho}^{(a)}\), is obtained using two bipoles (\(i=1\), \(2\)) as in [4]:  
\[\mathbf{\rho}^{(a)}=\begin{pmatrix}\rho_{11}^{a}&\rho_{12}^{a}\\ \rho_{21}^{a}&\rho_{22}^{a}\end{pmatrix}=\frac{\begin{pmatrix}E_{1x}I_{2y}-E_ {2x}I_{1y}&E_{2x}I_{1x}-E_{1x}I_{2x}\\ E_{1x}I_{2y}-E_{2y}I_{1y}&E_{2y}I_{1x}-E_{1y}I_{2x}\\ I_{1x}I_{2y}-I_{2x}I_{1y}\end{pmatrix}}{I_{1x}I_{2y}-I_{2x}I_{1y}} \tag{2}\]  
This form can be used to define rotational invariants, which are independent of both the individual current source bipoles and the direction of the electrical field. The invariant P associated with the trace of \(\mathbf{\rho}^{(a)}\)[4] is taken into account, which has the crucial characteristic of offering anomalies that are closely contained to the sources. P is provided as  
\[P=\frac{1}{2}[\rho_{11}^{a}+\rho_{22}^{a}]=\frac{1}{2}\frac{E_{1x}I_{2y}-E_{2x }I_{1y}+E_{2x}I_{1x}-E_{1x}I_{2x}}{J_{1x}I_{2y}-I_{2x}I_{1y}} \tag{3}\]  
A reference coordinate system (x, y, z) where the \(z\)-axis is positive downward and S is on the \(xy\)-plane is considered. The coordinates of two fixed current electrode pairs, \(A_{i}\) (positive) and \(B_{i}\) (negative) (\(i=1\), \(2\)), are put with \(\mathbf{r}_{A_{i}}\equiv\left(x_{A_{i}},\ y_{A_{i}},0\right)\) and \(\mathbf{r}_{B_{i}}\equiv\left(x_{B_{i}},\ y_{B_{i}},0\right)\). The intensity of the energizing current through the \(i\)-th current bipole \(A_{i}B_{i}\), the \(j_{lx}\) and \(I_{ij}\) (\(i=1\), \(2\)) terms in Equation (3), is expressed with \(I_{i}\) (\(i=1\), \(2\)) at the generic variable point \(\mathbf{r}\equiv\left(x,\ y,0\right)\), where the electrical field components are measured. These are given as  
\[J_{ix}=\frac{I_{i}}{2\pi}\left(\frac{\mathbf{r}-\mathbf{r}_{A_{i}}}{\left|\mathbf{r}-\mathbf{r} _{A_{i}}\right|^{3}}-\frac{\mathbf{r}-\mathbf{r}_{B_{i}}}{\left|\mathbf{r}-\mathbf{r}_{B_{i}} \right|^{3}}\right)\cdot\mathbf{i}, \tag{4a}\] \[J_{iy}=\frac{I_{i}}{2\pi}\left(\frac{\mathbf{r}-\mathbf{r}_{A_{i}}}{\left|\mathbf{r}-\mathbf{r} _{A_{i}}\right|^{3}}-\frac{\mathbf{r}-\mathbf{r}_{B_{i}}}{\left|\mathbf{r}-\mathbf{r}_{B_{i}} \right|^{3}}\right)j, \tag{4b}\]  
in which \(\mathbf{i}\) and \(\mathbf{j}\) are the unit vectors describing the \(x\)-axis and \(y\)-axis, respectively.
## 4 Performance Analysis of Optimized Model
### Performance Analysis of Anti-Demagnetization  
Due to the use of multiple layers and different magnetization directions in the permanent magnet of the model, there may be a risk of demagnetization. Therefore, the authors carried out demagnetization analysis on the permanent magnet of the optimized model. Demagnetization analysis of permanent magnets is performed by analyzing the changes in the residual number of magnets before and after current excitation. The demagnetization ratio definition formula [32] is shown in Equation (10).  
\[\delta= \left(1-\frac{B_{r}^{\prime}}{B_{r}}\right)\times 100\% \tag{10}\]  
where, \(B_{r}^{\prime}\) is the remanent magnetism of the permanent magnet after overcurrent, and \(B_{r}\) is the initial remanent magnetism of the permanent magnet.  
Under overcurrent conditions, the demagnetization rate of the permanent magnet of the optimized model is shown in Figure 17. Demagnetization simulation was performed under overcurrent working conditions, operating at a maximum of 10 times the rated current. The permanent magnet inside the optimized model rotor had hardly any demagnetization. When the current continued to increase to 11 times the rated current, the permanent magnet in the top magnetic barrier of the rotor of the optimized model began to demagnetize, as shown in Figure 18, with a maximum demagnetization rate of 6.95%. In a word, the optimized model exhibited good anti-demagnetization performance and can still operate stably under overcurrent conditions.
# Measurement of Visible Radiation through a _Sansevieria cylindrica_-Based \"Living Sensor\"
## 3 Discussion
### Analysis and Spatial Distribution of Applied Illuminance  
From the perspective to characterize the living sensor based on _Sansevieria cylindrica_, an analysis of the illuminance with spatial distribution studies and the response of the specific measurand adopted during characterization was accomplished. First, the spectral power distribution of the source applied to the _Sansevieria cylindrica_ is measured using a Konica Minolta CS-1000 Spectroradiometer (Figure 3).  
By varying the distance d between the source and the living sensor, shown in Figure 2b, the illuminance trend with distance shown in Figure 4 was then obtained. This last series of measurements is necessary because the characterization of the sensor was performed at various light intensity levels obtained just by varying the distance between the light source and the plant.  
As can be observed, the illuminance shows a decreasing trend with distance in agreement with what is expected. The graph shows the values obtained with an illuminance meter (Konica-Minolta model T-10) in the distance range of 10-60 cm and the fit function that best approximates the experimental data (y = \(a+b\cdot x^{-2}\)).  
The analytical trend corresponding to the illuminance \(I\) as a function of distance d can be expressed as follows:  
\[I(d)=1727+876,162\cdot x^{-2} \tag{1}\]  
Although the source is not point-like, the experimental data follow the trend as a function of the square of the distance. Moreover, the known term reproduces well the light level corresponding to the ambient background.  
Figure 3: Spectral relative power distribution as a function of wavelength for the light source used.  
The distribution of illuminance values within the square area with sides of 20 cm, on which the plant is placed during the measurements, is shown in Figure 5 in the form of 3D graphs for six values of distance d, from 10 cm to 60 cm. Each graph represents the illuminance level as a function of the spatial co-ordinates. A decrement of the illuminance level as a function of d is clearly visible. In particular, considering the minimum distance d of 10 cm, a corresponding maximum illuminance level of about 10,270 lux was measured at the center of the spatial grid, while the minimum level was measured at the final part of the grid and corresponds to about 4700 lux. Considering the maximum distance d of 60 cm, the maximum illuminance level decreased to about 1438 lux, while a minimum level of about 1080 lux was observed. Table 1 summarizes the minimum and maximum illuminance values, and the corresponding co-ordinates, measured for each distance.  
Figure 4: Illuminance level as a function of the distance d from source and detector.  
Figure 5: Analysis of the illuminance distribution applied to the _Sansceivia cylindrica_ for various distances between source of light and plant.
# Adaptive Noise Exploration for Neural Contextual Multi-Armed Bandits
## 1 Introduction
We propose an adaptive dynamic noise exploration framework to address the general contextual bandit problem. Based on this framework, we develop two novel neural contextual multi-armed bandit algorithms: EAD and EAP. * EAD algorithm: Adjust the variance of the added noise adaptively based on the arms' selection frequencies and incorporate this adjusted noise into the rewards of the selected arms. With adaptive noise, these rewards are used to construct the loss function. * EAP Algorithm: This algorithm introduces adaptive dynamic noise directly into the network parameters at the neural network's hidden layers. Continuously interacting with the environment using these noisy parameters achieves more stable exploration. 2. To validate the effectiveness of our framework, we conducted empirical evaluations on two recommendation system datasets and six classification datasets. Compared to existing baseline methods, our adaptive dynamic noise exploration strategies demonstrate significant advantages in improving long-term cumulative rewards and exploration time.
# Study on Noise Model of an Automotive Axial Fan Based on Aerodynamic Load Force
## 3 Noise Model of Automotive Axial Fan
### Time-Domain Solution of Fan Noise Model  
The position of each point on the blade is in the blade coordinate system, whereas the receiving point is in the fixed coordinate system. In order to describe the distance between them, the position of each point source must be transformed from the blade coordinate system to the fixed coordinate system, including the conversion between the blade coordinate system and the rotating coordinate system, and the rotation coordinate system and the ground coordinate system.  
The fan blade movement produces a pressure disturbance in the fluid (air) and forms aerodynamic noise. The aerodynamic noise of the fan is the result of the superposition of sound waves radiating from all points on the surface of the fan blade. The sound pressure at the receiving point is the sum of all pressure waves emitted by all sound sources at different times but arriving at the same receiving point at the same time, so it needs to be solved by the delay equation.
## 4 GA Application in Goods Distribution Systems
### Initialization  
In the first step, the vector of initial stock levels is calculated according to (15), i.e., assuming the worst-case conditions of a persistent maximum uncertainty (demand). The simulation of network performance using this vector allows one to determine the largest holding cost, i.e., \(HC_{\max}\), and establish the initial population. At the same time, the calculated vector of RSLs constitutes the upper bound of the search space with respect to the chromosome-related operations, i.e., \(RSL\in[0,\)\(RSL_{\max}]\) (component-wise). Therefore, in contrast to the general GA-based computing problems, the initialization is not random, but rather expert-guided.  
In order to determine a suitable number of individuals in the population, various population sizes were examined. Table 2 contains the results of over \(10^{3}\) simulations performed for the same distribution scenario assuming different sizes. In the considered class of systems, a 10-individual population allowed for obtaining a high value for the fitness function in a relatively short time.  
The performed numerical experiments showed that the fitness function value changed quickly in the initial phase of the optimization run, but less frequently afterward. Then, a sudden improvement of the fitness value may cause a short uptrend; however, the longer the time without changes, the higher the probability that the optimal solution has already been obtained. For this reason, two stop conditions were applied: an overall limit on the number of generations and a limit on the number of generations without fitness value improvements.
# A Hybrid Exact-Local Search Approach for One-Machine Scheduling with Time-Dependent Capacity
## 1 Introduction  
Scheduling problems are interesting due to their practical usefulness and hardness. Such problems emerge in various domains, including manufacturing, computing, project management, and many others. Since scheduling problems are typically NP-hard, several approaches compete to attain the often unreached optimal solutions. Metaheuristics, heuristics, constraint programming, and mathematical programming often yield excellent results. The latter two are especially sensitive to problem sizes since their exact nature implies that all solutions must be checked or intelligently pruned.  
Scheduling is a thoroughly studied subject that is considered a discipline on its own [1]. Scheduling problems are classified using the commonly accepted (\(\alpha|\beta|\gamma\)) notation [2], where \(\alpha\) refers to the machine environment, \(\beta\) refers to the constraints, and \(\gamma\) refers to the objective function. A valuable asset for appreciating the variety of scheduling problems under the (\(\alpha|\beta|\gamma\)) notation can be found at [3].  
In this work, we study a variation in the one-machine scheduling with time-dependent capacity problem that [PERSON] et al. introduced in [4; 5; 6]. The problem emerged in the contextof scheduling charge times for a fleet of electric vehicles and is an abstraction of the real problem. It is classified as \((1,Cap(t)||\sum t_{i})\), meaning that it involves a single machine with capacity that fluctuates through time, with no other constraints, and the objective is minimizing the accumulated tardiness from all jobs. The problem piqued our interest due to its precise definition, the publicly available datasets, and the excellent results that were already published and which we used for comparisons.  
We propose a novel way to approach the problem based on a model with an embedded rule (the due times rule) that helps constraint programming solvers reach good solutions fast. We also propose three improvement procedures that are local search routines. We combine the exact and local search parts in our approach to the problem, and we manage to achieve results equal to or better than the best-known results for 91 and 48 cases, respectively, out of 190 public problem instances.
# Commonsense-Guided Inductive Relation Prediction with Dual Attention Mechanism
## 3 Methodology
### Experimental Results
#### 4.2.2 Ablation Study  
The ablation study explored the impact of each component on the overall model performance, and we conducted the ablation experiment using the WN18 RR dataset. This experiment mainly consisted of five parts: (1) removal of the dual attention mechanism (w/o Att), (2) removal of the neighboring relational feature module (w/o NRF), (3) removal of the neighboring relational path module (w/o NRP), (4) removal of the commonsense-based re-ranking module (w/o CSR), (5) removal of the comparative learning (w/o CL). Table 5 shows the results of the ablation experiment, and we were able to find that the CNIA removal of any of the modules was worse than the original model, thus proving the effectiveness of each module.  
* **CNIA w/o Att**: Remove the dual attention mechanism and directly splice to obtain the neighboring relationship features. The Hits@10 was reduced by 3.72%, 1.25%, 5.37%, and 0.63%. This showed that ignoring the effect of different edges on nodes and the effect of different relations on the target relation reduces the accuracy of inference.
* **CNIA w/o NRF**: Remove the neighbor relational features and predict directly from the node features obtained by initialization. Ignoring the neighbor relationship makes the node features less expressive, which results in losing the effective information, and it also cannot completely portray the nodes.
* **CNIA w/o NRP**: Remove the neighboring relational path feature and ignore the message propagation path feature from the head node to the tail node. This makes the performance degradation obvious, thereby indicating that the neighboring relational path feature plays an important role in dealing with sparse subgraphs.
* **CNIA w/o CSR**: Remove the commonsense re-ranking module that fails to filter out the predictions that do not conform to commonsense. The experiments verified that the absence of the commonsense re-ranking will produce relationships that do not conform to commonsense, thus degrading model performance.
* **CNIA w/o CL**: Remove the contrastive learning and do not perform this operation of MI maximization. This resulted in the Hits@10 being reduced by 4.24%, 4.99%, 7.57%, and 9.24%. The results showed that, without contrastive learning, the results of most of the metrics dropped, thus demonstrating that global information helps to better model neighboring relational features.
## 2 Materials and Methods
### Remotely Sensed Measurements
#### 2.3.3 Estimating ANHP from iAPAR  
We fit a linear mixed effects regression model using R (version 4.3.2) and the package lme4 [37] to estimate ANHP from iAPAR for the 20 pastures in the CARM experiment. We fit a single model designed to test whether the relationship between iAPAR and ANHP varied by grazing management and ecosite, using the following formula (in R language notation, where '-' denotes a function, '-' denotes an interaction in addition to the additive fixed effects, and '11x' denotes a random intercept for the variable _x_):  
\[ANHP\ \sim\ iAPAR*GM*ES+(1|Year)+(1|Plot\ ID)\]  
where GM is the grazing management category (one of season-long, pulse grazing, or ungrazed), ES is the ecosite (one of Loamy Plains, Sandy Plains, or Salt Flats), and random intercepts are fit for each year and plot ID. We asked whether the slope of the iAPAR to ANHP relationship varied by grazing management treatment, which would be indicated by coefficients significantly different from zero for one or more of the GM:iAPAR interaction variables, with the ungrazed treatment set as the reference. Ecosite was included because we also expected the relationship between iAPAR and ANHP to vary with plant community type [12]. By including ecosite as a fixed effect, we were able to assess the temporal  
Figure 4: Flow chart of data processing to develop iAPAR model. (PAR) Photosynthetically active radiation. (APAR) Plant-absorbed photosynthetically active radiation, (NDVI) normalized difference vegetation index.  
relationship between iAPAR and ANHP while also accounting for the primary source of spatial variation in the dataset (i.e., soil type). We included the three-way interaction among iAPAR, grazing management, and ecosite because the effect of grazing management on the iAPAR to ANHP relationship could differ by ecosite. Including year and plot ID terms allowed intercepts to vary randomly across plots and years and accounted for the repeated measures design of the data collection. Model assumptions were checked using residual diagnostic plots. We explored square-root transforming the data to achieve more normally distributed residuals, but the improvements to fit were negligible and we would have lost information on high and low values in the dataset, so data were not transformed. All multiple comparisons were adjusted using the Tukey HSD method with a significance level of 0.05.  
To determine whether season-long grazing with a heavier stocking rate affected the iAPAR to ANHP relationship, we also fit a model comparing season-long heavy grazing management to other grazing management treatments for the subset of data available from the corresponding years (2019-2022) and ecosite (Loamy Plains). We fit the same model as before but without the ecosite (ES) term since all data came from the Loamy Plains ecosite. We tested the same model assumptions and asked whether the GM:iAPAR interaction was significantly different for the ungrazed vs. season-long heavy grazing treatment.
# Delamination Localization in Multilayered CFRP Panel Based on Reconstruction of Guided Wave Modes
## 3 Results and Discussion  
### Guided Wave Interaction with Delamination  
For reference, the defect-free signal is required to be compared with the signal in case of delamination. Then, the reference location of delamination is estimated by calculating the ToF between \(t_{1}\) and \(t_{2}\) as shown in Figure 9.  
The low amplitude reflected and overlapped converted modes can be reconstructed by considering the estimated reference location using the defect-free signal and phase velocity from the phase spectrum approach. Using the analytical model, targeted modes can be reconstructed at any distance with an accurate time of flight. By comparing the analytical signal with the numerical or experimental signal, the defect location can be estimated.  
After interaction with delamination, guided waves are reflected, transmitted, and converted to other modes, as shown in Figure 10. Part of excited A\({}_{0e}\) mode is reflected at the leading edge of the delamination, i.e., A\({}_{0r}\) mode, and another part of the excited A\({}_{0e}\) is converted into S\({}_{0}\) mode. i.e., S\({}_{0c}\) mode.  
In this study, the A\({}_{0}\) mode is dominant, and the S\({}_{0}\) mode has a lower amplitude. Therefore, the out-of-plane displacement component of guided waves is extracted for analysis, as shown in Figure 11.  
Figure 10: Guided wave propagation scenario while interacting with delamination.  
Figure 9: Reference signal and envelope of the defect-free and delamination samples to estimate the location of the delamination.  
In the case of defect-free, both S\({}_{0}\) and A\({}_{0}\) modes are reflected from the leading and trailing edge of the plate. In the case of delamination, the A\({}_{0}\) mode is reverberated multiple times within the delamination due to the displacement profile of the A\({}_{0}\) mode being perpendicular to the delamination. The reflection coefficient of the S\({}_{0}\) mode is lower compared to the A\({}_{0}\) mode because the displacement profile of the S\({}_{0}\) mode is parallel to the delamination.