esa-sceva/satcom-chunk-collection huggingface dataset:
# Total Internal Reflection Lens for Optical Wireless Receivers
###### Abstract  
This work considers the use of a freeform total internal reflection (TIR) lens for optical concentration and provides for the first time experimental results in the context of optical wireless communications (OWC). The lens is placed on a surface-mounted device (SMD) avalanche photodiode (APD) to minimize position tolerances and simplify assembly. The lens achieves a concentration ratio of \(g_{\mathrm{o}}=44.7\) (16.5 dB) within the FOV center and exhibits an acceptance angle of \(\pm 5^{\circ}\). The TIR lens approach is validated by comparing eye diagrams and bit error ratios (BER) of a receiver with and without a TIR lens. For the measurements, non-return-to-zero (NRZ) on-off keying (OOK) signals are transmitted with a data rate of 1.289 Gbit/s.  
free-space optics; PSO; freeform; LiFi; optical concentrator; optical wireless communications; OWC; ray tracing; receiver lens; TIR lens +
Footnote †: journal: _Photonics_
## 5 Performance Analysis
### Ser  
For the sake of simplicity, assume that all AWGNs are normalized and have the same distribution with zero mean and variance \(\sigma^{2}\) for both LUs and Eves throughout the paper. Meanwhile, all the baseband symbols are normalized.  
In the single-carrier case, using Equation (17), the SNR per symbol of the LU can be written as  
\[r^{\text{c},LU}=\frac{P_{s}\beta_{1}^{2}(1-\xi)^{2}}{\sigma^{2}}. \tag{31}\]  
Likewise, in the multi-carrier case, by means of Equation (26), the SNR per symbol of the LU can be expressed as  
\[r^{\text{m}\epsilon,LU}=\frac{P_{s}\beta_{1}^{2}}{\sigma^{2}}. \tag{32}\]  
By contrast, the SNR per symbol of the traditional single antenna array AN-aided DM schemes can be given by  
\[r^{sa}=\frac{P_{s}\beta_{1}^{2}}{\sigma^{2}}. \tag{33}\]In this paper, quadrature phase-shift keying (QPSK) modulation is used by the proposed schemes. A closed-form expression for the SER of QPSK modulated signals in AWGN channel can be given by [45]  
\[P_{e} =1-(1-\frac{1}{2}erfc\sqrt{r/2})^{2}\] \[=erfc\sqrt{r/2}-\frac{1}{4}erfc^{2}\sqrt{r/2}, \tag{34}\]  
where \(erfc(x)=2\big{/}\sqrt{\pi}\cdot\int_{x}^{\infty}\exp(-t^{2})dt\) is the complementary error function of the standard normal distribution.  
Meanwhile, the received signals for the Eve are scrambled by the eavesdropper steering vector.  
At this time, for the single-carrier case, the SNR per symbol for the Eve can be transformed into  
\[r^{sc,E}=\frac{P_{s}\beta_{1}^{2}\mathbf{h}^{H}(\theta_{el})\mathbf{h}(\theta_ {dl})\mathbf{h}^{H}(\theta_{dl})\mathbf{h}(\theta_{el})}{\sigma^{2}}. \tag{35}\]  
Likewise, the SNR per symbol for the Eve in the multi-carrier case can be rewritten as  
\[r^{mc,E}=\frac{P_{s}\beta_{1}^{2}|\mathbf{h}^{H}(\theta_{e})\mathbf{h}(\theta _{dl})|^{2}}{\sigma^{2}}. \tag{36}\]  
It is easy to see that the SER for the LU for the proposed DM schemes is primarily affected by the information power splitting factor \(\beta_{1}\), while the SER for the Eve is dependent on the information power splitting factor \(\beta_{1}\) and the eavesdropper steering vector both.
## 2 Materials and Methods
### Linear Programming  
Linear programming, or linear optimization, is a method whose objective is to find the best outcome of a mathematical model using linear constraints. Linear programming was already used in the past for channel allocation in large and cellular and radio networks [24]. Even though this is a powerful tool, learning linear programming is time consuming, especially for people with no programming/mathematical background.  
There are multiple linear programming solvers available, although most of them are either paid or have proprietary licenses. We analyzed some Copyleft licensed software, including Coin-or linear programming (CLP), GNU Linear Programming Kit (GLPK), Cassowary constraint solver, Lp_Solve, and Qoca. Even though GLPK appeared to be one of the least efficient (especially when compared to commercial solvers) [25; 26], it had the advantage of an already available layer for AWS Lambda [27]. There are also multiple input formats. For GLPK in particular, there are four:  
* **CPLEX-LP**: Used in IBM ILOG CPLEX Optimization Studio, a commercially available optimization software package. More user-friendly than the others.
* **MPS**: Mathematical Programming System is a file format used in both linear programming and mixed integer programming problems. There are some limitations though; for example, numeric fields limited to 12 characters. Modern extensions of this format, without those limitations, are also accepted by GLPK.
* **GLPK**: A custom format, similar to DINAMICS format, a format created by the Center for Discrete Mathematics and Theoretical Computer Science;  
\begin{table}
\begin{tabular}{c c c} \hline
**Geohash Length** & **Cell Width [km]** & **Cell Height [km]** \\ \hline
1 & 5000 & 5000 \\
2 & 1250 & 625 \\
3 & 156 & 156 \\
4 & 39.1 & 19.5 \\
5 & 4.89 & 4.89 \\
6 & 1.22 & 0.61 \\
7 & 0.153 & 0.153 \\
8 & 0.0382 & 0.0191 \\
9 & 0.00477 & 0.00477 \\
10 & 0.00119 & 0.000595 \\ \hline \end{tabular}
\end{table}
Table 1: Geohash length and its precision with geographic coordinates.  
**MathProg--**High level modelling language. GLPK MathProg translator is not appropriate for large models, due to its taking a considerable amount of time with big problems. This happens because the translator is not able to recognize shortcuts on the optimization.  
We ended up using the MathProg format, since the models are not that big and it is possible to structure it, cluing the solver in on optimization paths for faster optimization. In case someone finds a model to take a long optimization time, they can use GLPK to convert a MathProg format in any of the other formats. There are cases where converting first and optimizing later is more efficient.
## 4 Experimental Results
### Effectiveness of Unity Feedback  
In our study, we designed a unity feedback to narrow the search domain of the SR function, so our loss function contains two parts, as shown in Equation (20). We used the SR loss and unity feedback loss to train our DMFN, and we used \(w\) to represent the weight assigned to the unity feedback loss. In this experiment, we increase the weight of unity feedback loss \(w\) from 0 to 1 to obtain the best trade-off. Then, we compare the SR results of DMFN with different weights in Table 1. The unity feedback improved the performance of our DMFN, and performed best when \(w=0.1\). Therefore, we set the weight of unity feedback loss to 0.1 to supervise the training of our methods.
## 3 Material and Methods
### Comparison of Similarity Scores between the Architectures  
The primary aim of this study was to compare the performance of two different GAN architectures--one with six layers and the other with eight layers--for both the generator and the discriminator. The study aimed to vary the number of epochs between 25, 50, 100, 250, 500 and 1000, with the objective of determining the optimal performance and investigating the relationship between the considered parameters, such as the number of epochs and the number of layers in the GAN model.  
The results, as detailed in Section 4, demonstrated that the eight-layer GAN architecture was the most effective in achieving the highest similarity score between the real and generated data samples, especially after being trained for 1000 epochs, as depicted in Figure 19. The results suggested that the eight-layer GAN model may perform better, as the additional layers provide more capacity for the model to learn complex patterns in the data. Furthermore, it was observed that the number of epochs significantly affected the efficiency of the GAN model, as the respective increase allowed the model to learn more complex patterns in the data, generating higher-quality synthetic samples. However, it should be noted that while we understand the increased complexity of the eight-layer model, in this study, we considered the similarity performance between the original and the generated samples as the primary evaluation metric. More specifically, the quality of the generated samples was the primary evaluation metric, and the results indicated that increasing the number of epochs led to better performance of the GAN model as the  
Figure 18: Absolute log mean and standard deviation for 8 layers, 1000 epochs.  
architecture increased in depth. Additionally, regarding the six-layer architecture, a gradual increase in the similarity score is observed, which is stable from 100 to 1000 epochs without any optimization. This fact may lead to the conclusion that this model cannot produce better results for this type of dataset. On the other hand, the eight-layer model presented a variation in its performance during the different epochs selected. This may indicate that the specific GAN model architecture may not have been properly optimized or that it may need further tuning of its hyperparameters.  
Another interesting observation, based on Figure 19, was the decrease in the similarity score between iterations 50 to 250 for the eight-layer architecture, compared to the corresponding six-layer architecture. This indicates that for this combination of iterations and architecture, a better selection of hyperparameters may be required, as the model yielded a lower similarity score for the generated datasets compared to the real ones. Another reason could be the specific sampling used, which may have led to these lower results in terms of similarity.
## 4 Implementation
### Exploration Algorithms
#### 4.1.6. Correlation Filter Exploration  
This algorithm maximizes the use of the global coverage area construction by each agent. It is based on a binary image of the environment, where the pixels marked with black are positions that have been covered or where there are obstacles (see Figure 7). Using the region observed by the agent sensor as a correlation filter, it is possible to find different points where the maximum coverage is achieved, and that position is chosen since it has a lower cost to reach.  
The basic idea in this approach is the correlation implementation in 2D. Given a square filter, the results of correlation can be computed by aligning the center of the filter with a  
Figure 6: Example case and decision for less covered and farthest cell exploration.  
Figure 7: Example case and environment binary image for correlations filter exploration.  
pixel. Then, all overlapping values are multiplied together and add up the result, as shown in Equation (6) as  
\[Correlation\;Image(x,y)=\sum_{j=-N}^{N}\sum_{i=-N}^{N}(CF(i,j)*EBI(x+i,y+j)), \tag{6}\]  
where \(CF\) is the Correlation Filter of size 2 N \(\times\) 2 N and \(EBI\) is the Environment Binary Image. Figure 8 presents an example in the simulation environment and the correlation levels between the environment and the sensor area.
# Simulating Slosh Induced Damping, with Application to Aircraft Wing-like Structures
## 3 Numerical Methodology
### Numerical Setup  
In this study, the behaviour of the cantilever beam and the sloshing inside the tank are modelled by the CSM and CFD parts of the multiphase FSI framework independently. A set of meshes are, therefore, needed for both domains. A quadratic Lagrange tetrahedral-based mesh is generated for the structural domain [49], while a hexahedral-based structured mesh is generated for the fluid domain. Following a grid convergence study, 12 million hexahedral cells and 12.7 thousand DOF were adopted for the CFD and CSM meshes, respectively. It was found that using double the number of cells/DOF produced values within a few percent of the coarser solutions. The meshes used for the simulations of this study are shown in Figure 3. The time-step size and the OpenFOAM keyword nAlphaSubCycles of the simulation was carefully chosen per case to ensure the Courant number and the interface Courant number per VoF step in the fluid domain all remain below 0.5.  
The numerical setup for the simulations within this study follow that of the validation and the demonstrative cases in [PERSON] et al. [31, 33], except for the turbulence model, where the \(k-\omega\quad SST\) is used according to [PERSON] et al. [50] and [PERSON] et al. [51]. A no-slip boundary condition was employed for all six boundaries of the fluid domain. For the structural domain, a fixed boundary was set for the \(x=0\) m plane for the cantilever beam, while other boundaries were set as free, with zero traction by default. The Aitken's method was employed to achieve a tight coupling between the fluid and structural domains.  
Figure 3: Meshes used for the simulation of the free decay of an oscillating aircraft-wing-like structure with a sloshing tank with (**a**) fluid mesh and (**b**) structure mesh.
## 3 The Proposed Rolling Bearing Fault Feature Extraction Method under Variable Speed Conditions
### The Joint Sparsity and Low-Rankness of the Fault Feature in OFSC  
In this subsection, a specific example is given to illustrate the sparsity and low-rankness of the faulty rolling characteristics in OFSC. According to the previous study, the faulty rolling bearing signal with variable speed is  
\[z(t)=A(t)\sum_{i=1}^{L_{m}}e^{-\beta(t-T_{i})}\sin[2\pi f_{\text{resonance}}\left( t-T_{i}\right)]u(t-T_{i})+n(t), \tag{6}\]  
\[A(t)=1+\mu\cos\left(2\pi\int_{0}^{t}f_{r}(\tau)d\tau\right), \tag{7}\]  
where \(L_{m}\) represents the impulses count, \(A(t)\) is the amplitude modification with \(\mu\) the amplitude modification magnitude, \(\beta\) is the coefficient related to damping, \(T_{i}\) is the duration of \(i\)-th impulse, \(u(t-T_{i})\) denotes the step signal with \(T_{i}\) as the starting point, and \(f_{\text{resonance}}\) is the resonance frequency.  
Here, a simulated signal of the outer race fault in a speeding up condition and its OFSC are displayed in Figure 1a,b. The detailed parameters are introduced in Section 4. It can be seen that the intervals between two fault impulses are variable with time. The feature of the fault is sparse in the obtained OFSC. Meanwhile, the singular value decomposition (SVD) is applied on the simulated OFSC, as seen in Figure 1c. It can be seen that the faulty characteristic is relatively large on only several singular values. Hence, the joint sparsity and low-rankness can be used for the extraction of the fault feature.  
Combined with Equation (2), the optimization problem of the sparsity and low-rankness jointly enforced \(\mathbf{X}\) is  
\[\underset{\mathbf{X}}{\text{minimize}}\ \lambda_{0}\lVert\mathbf{X}\rVert_{*}+ \lambda_{1}\lVert\mathbf{X}\rVert_{L1},\ \text{subject to}\ \lVert\mathbf{Z}-\mathbf{X}\rVert_{F}\leq\delta, \tag{8}\]  
where \(\lambda_{0}\) and \(\lambda_{1}\) can be used to adjust the weight of the low-rank and sparse component, \(\lVert\cdot\rVert_{*}\) is the nuclear norm, \(\lVert\cdot\rVert_{L1}\) denotes the \(L1\) norm, \(\lVert\cdot\rVert_{F}\) is the Frobenius norm and \(\delta\) is the upper bound of the noise \(\mathbf{N}\left(\lVert\mathbf{N}\rVert_{F}\right)\). Once the optimazation problem (8) is solved, the joint sparsity and low-rankness fault feature \(\mathbf{X}\) in the OFSC can be extracted.
# Replacement of Fault Sensor of Cutter Suction Dredger Mud Pump Based on MCNN Transformer
## 3 The Model Framework and Data Preprocessing
### Feature Selection
Through conducting initial MPWSS key index selection filtering, relevant characteristic parameters can be preliminarily identified. Afterward, two intelligent algorithms Random Forest and XGBoost were combined, and the weights were set to 0.5 respectively [34,35]. This stacked model provides a more comprehensive assessment of feature importance and resolves the overfitting problem that may exist in a single model. Based on this stacked model, the 25 features with the highest comprehensive importance ranking are extracted, as shown in Figure 9. As can be seen from the figure, the parameter \"Current shift output\" has the highest correlation coefficient. This parameter indicates the current working status of the motor. Since there is a mechanical or hydraulic transmission relationship between the underwater pump and the motor, the greater the load on the motor, the greater the load on the pump and the output pressure may be higher, resulting in greater sealing water pressure required. The second most relevant parameter is \"tide level\". The tidal water level changes periodically, so the output pressure of the underwater pump may also change periodically. When the tidal water level is high, the underwater pump requires a higher speed. To pump mud from deeper waters, higher sealing water pressures may need to be generated. Figure 8: Mud transportation process. Figure 9: Feature importance. This study selected the 25 parameters with the highest correlation and conducted comparative analysis with other parameters with lower correlation coefficients. The location distribution of related sensors on the ship and their mutual influence are shown in Figure 10. It can be seen from this figure that the underwater pump has a physical connection with some sensors, which means a certain mathematical formula, and also has a data connection with some sensors, which means there is a certain correlation calculated through Random Forest and XGBoost algorithms.
## 3 Results and Discussion
### Thermal Properties  
#### 3.2.1 Degradation Temperature  
The degradation temperature of the compound can be increased by adjusting the introduction of mineral filler (Figure 5). The overall degradation temperature can be increased up to 6% with the correct amount of coarse particles, with a higher single-particle volume, and fine filler particles. If the coarse filler fraction is reduced or the fine filler fraction increased significantly, the effect on the degradation temperature is reduced. An explanation for this increase in the degradation temperature can be the higher thermal conductivity of calcium carbonate. The surrounding energy input is taken up quicker by the filler particles, but less is stored due to their lower thermal capacity than the polyamide 12.
# Microplastic Pollution: Chemical Characterization and Impact on Wildlife
## 3 Microplastics and Wildlife
### Microplastics in Birds
The color of the microplastics found was transparent (56.2%) followed by red (18.7%), black (12.5), yellow, and blue (6.2% each). Microplastic color is important due to many animals discriminating against their food choices by color. Micoplastic pollution is truly a growing global crisis and even remote areas like the Arctic are also polluted by microplastics. A recent and first investigation into plastic ingestion by glaucous gulls (_Larus hyperboreas_) in the Arctic region reveals information about the health status of those species in the arctic region [86]. The authors investigated and found microplastics occurred in 14.3% of the bird's intestines. MPs sampled were mostly polypropylene and polystyrene. This prevalence is less than what was observed in other environments; however, the migration of microplastics to sparsely populated environments is remarkable.
# Deep Learning for Medical Image Cryptography: A Comprehensive Review
## 4 Deep Learning for Medical Image Cryptography
### Medical Image Resolution Improvement or Denoising for Deep Learning-Based Medical Image Analysis  
Along with the rapid advancement in the digital image processing technology, improving the resolution of the medical images or medical image denoising remains a fundamental challenge to meet the better diagnosis in such a heterogeneous IoMT environment today. Here are some recently published works related to enhancing the resolution of medical images. [PERSON] et al. [121] presented a CNN-based sparse aware noise reduction strategy for enhancing the quality of reconstructed MR images in order to produce high quality pictures. Additionally, authors have included encryption and decryption in the suggested IoT architecture using the ABE algorithm. [PERSON] et al. 's [24] research focus on developing a lightweight cloud infrastructure for processing medical data while maintaining the privacy of the data's contents. The pseudo-predictive deep denoising network (PPDD) was developed using deep learning techniques. The dynamic data are unpacked and a denoise procedure is used at the edge devices to reduce the complexity of the storage system. A comparison of the proposed architecture's validity with earlier related work published in the literature is also included. [PERSON] et al. [108] proposed the privacy-preserving DNN-based image denoising on the cloud for IoT multimedia healthcare applications. According to the suggested system design, the model owner initially stores an encrypted trained DNN model in the cloud. The encrypted DNN model that was previously stored is used to perform image denoising in the encrypted domain after the encrypted images have been transmitted to the cloud. Thanks to the suggested security strategy, the DNN model and private picture content are all kept private throughout the entire cloud-based service flow. [PERSON] et al. [122] proposed a hybrid encryption approach that encrypts the medical pictures, first using AES first and then a deep learning model (autoencoder). By using autoencoders as a feature extraction method, this hybrid model enables to have control over the size and structure of the medical images being encrypted and then communicated. During decryption, the hybrid encryption approach that has been proposed can also denoise the medical images. [PERSON] et al. [123] suggested the implementation of the CADTra model for automatic detection of disorders associated with pneumonia. The constructed model uses autoencoder techniques with a modified loss function to denoise the images and deep learning algorithms to classify data. They have applied transfer learning techniques and a four-layer convolutional neural network to the categorization of pneumonia. With regard to chest X-ray pictures, this model supports the multi-class classification while supporting the binary classification for CT images. Additionally, 98% and 99%, respectively, of the proposed model's correctness have been attained.