esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Evaluation of Scatter Correction on True CXRs  
A set of 10 real CXRs acquired without an anti-scatter grid were taken from the MIMIC-CXR database [53; 54] to test the single-energy model for high-energy CXRs on true data. To introduce these images as the input of the neural network, some operations must be performed. First, the CXR in Dicom format is loaded in a Python environment as a numpy array. Then, the edges of the images that do not contain information are cropped so that they are focused on the lung region. The original size of the CXRs selected from the database is \(2544\times 3056\), so they are rescaled to \(96\times 128\), as previously done with the simulated images (see Section 2.3). Finally, the values of the pixels in the true images must be converted to the same scale as in the training, simulated images. The values of the real CXR represent the logarithm of the initial beam intensity (\(I_{0}\)) divided by the intensity that arrives at the detector (\(I\)), while the values depicted in the training images are \(I/I_{0}\), as explained in Section 2.2. So the transformation of pixel values is made following the Beer-Lambert law [55]: \(I=I_{0}\times e^{-\mu\times x}\), where \(\mu\) is the linear attenuation coefficient of the material, and \(x\) is the path length of the beam through the material; and then adjusting the scale so that the minimum and maximum values corresponding to bone and lungs are similar to the simulated images, as well as the ratio between them.  
The output of the CNN, i.e., the scatter fraction, is multiplied by the downsized and scale-transformed CXR, yielding the estimation of the scatter contribution in low resolution. To obtain the final scatter-corrected CXR, the native resolution is recovered in the image of scatter by means of bilinear interpolation. Then, the high-resolution scatter image is subtracted from the high-resolution, scale-transformed CXR, giving the high-resolution scatter-corrected image.  
The performance of the single-energy model on real data is quantitatively evaluated by means of the ratio between a region of the lung with and without ribs on it:  
\[Ratio=\frac{\frac{1}{L}\sum_{m=1}^{L}img(m)}{\frac{1}{R}\sum_{n=1}^{R}img(n)} \tag{8}\]  
where \(L\) and \(R\) stand for the total number of pixels of the rib-free lung region and the rib lung region, respectively, and \(img(m)\) and \(img(n)\) represent the values of the given image \(img\) in pixel \(m\) (rib-free region) or in pixel \(n\) (rib lung region). The comparison of the ratio obtained in the original CXR (affected by the scatter) and in the estimation of the scatter-corrected image determines if the NN algorithm provides images with better contrast between different tissues.
# An Improved YOLOX Model and Domain Transfer Strategy for Nighttime Pedestrian and Vehicle Detection
###### Abstract
By increasing the detection scale of YOLOv3 from 3 to 4 and establishing the feature fusion target detection layer down-sampled by 4\(\times\), [PERSON] et al. [13] were able to extract more object attributes and improve tiny target recognition. [PERSON] et al. [14] improved the Cascade R-CNN algorithm. The outline of the object is estimated in the first step using a Locally Sliding Line-based Point Regression (LocSLPR) method, which is defined as the intersection of the sliding lines with the object's bounding box. to fully utilize information The performance of our system is then further enhanced in the second step by gradually regressing the target object using a Rotated Cascade Region-based Convolutional Neural Network (RCR-CNN). Although the performance of these detection algorithms has been improved, there are still problems of target misses and false detections in the face of small, dense targets, line-of-sight occlusions, and low-illumination conditions at night. In addition, although the detection accuracy of the two-stage detection algorithm is higher, it is difficult to meet the real-time requirements of intelligent driving systems in terms of detection speed, while the YOLOv3 [13] and YOLOv4 [15] models of the one-stage algorithm have larger files and lower detection accuracy, which are not suitable for the deployment of low-performance storage devices. To address the above issues, a lightweight YOLOX [16] is chosen as the baseline model to adapt to the nighttime vehicle/pedestrian detection task by simultaneously improving the algorithm and training strategy. The main contributions of this paper are as follows:  
1. An improved algorithm based on YOLOX was proposed for small target pedestrian and vehicle detection at night. The main improvements include 1. reparameterization of the model structure using the Re-parameterization Visual Geometry Group (RepVGG) technique; 2. the introduction of a coordinate-based attention mechanism; 3. the addition of a new feature scale fusion branch; and 4. improvement of the loss function. 2. A domain transfer training strategy was proposed that allows the model to be trained more efficiently using daytime datasets. The large-scale daytime dataset is fused with the much smaller night dataset after low-illumination degrading. The models were then trained and tested separately after unified low-illumination enhancement to fully extract the data features of the existing daytime dataset and remedy the nighttime data deficiencies problem. 3. The proposed improved YOLOX and domain transfer training strategies were validated on a real-world dataset. The experimental results showed that the improved YOLOX algorithm produced fewer errors than the original algorithm, and it was more accurate for nighttime vehicle/pedestrian detection when combined with a domain transfer training strategy.
## 3 Methodology
### Rotation Angle Interval Control  
Control of the rotational angle is as essential as the control of the rotational speed in maximizing the directional sensation conveyed to the user through force concentration. The impact of the directional sensation can vary depending on the rotational angle. When the different acceleration-deceleration interval is set to span _Ps_ to _Pe_, the length of the interval, the actual motor movement will behave differently depending on the length of the interval (_Pe-Ps_) (Figure 4). In theory, if the rotation angle interval is short, the force concentration will be rendered with more impact than the long interval since the motor will accelerate and decelerate for the shorter term. The actual movement of the rotational angle also behaves differently from the programmed movement. If the interval is set to be wide enough, sufficient time is given for the acceleration and deceleration such that the movement may closely follow the programmed movement, but this may in turn deteriorate the directional sensation due to force dispersion. If the interval is set to be narrower, the directional sensation can be maximized, but may result in a large discrepancy between the programmed movement and the actual motor movement. In this study, we developed a control model and tested how well the movement pattern of the motor mimics the programmed movement over three different rotation angle intervals (\(\pm 30^{\circ}\); \(\pm 60^{\circ}\); \(\pm 90^{\circ}\)). The three rotation angle intervals in 30-degree increments were used as it is inefficient to test rotation angle intervals per degree. In this study, we aimed to identify the direction of correlation between the rotation angle interval and impact of the directional sensation.  
Figure 3: Graph of programmed motor velocity (filled orange circles) vs. actual motor velocity (unfilled blue circles).
# Topology Optimisation Using MPBILs and Multi-Grid Ground Element
## 3 Performance Enhancements of Multi-Objective, Population-Based Incremental Learning
### Multi-Learning Rate  
The second approach to enhance the performance of MPBIL is the use of multi-learning rate. This question arises from the previous method, when it is using two learning rates that are of an opposite quantity. The question remains whether the performance of OMPBIL can benefit from the opposite concept or by using two learning rates. MPBIL with multi-learning rate (MPBILMLR) is proposed to solve topological optimization and to compare with the opposition-based concept. This algorithm differs from the traditional MPBIL by using three learning rates (\(L_{R}\) = 0.25, 0.5, 0.75). The procedure of MPBILMLR algorithm is slightly different from OMPBIL. Therefore, the procedure of MPBIL with multi-learning rate algorithm is shown in Algorithm 2.  
```
Initialization Probability matrix \(\mathbf{P}=[\mathbf{0.5}]_{l\times nb}\), Probability matrix \(\mathbf{P}i=[\mathbf{0.5}]_{l/\text{M}\times nb}\) where \(i=1,\,\ldots,\,\text{M}=3\), external Pareto archive \(\mathbf{Pareto}=\{\}\).
1: Generate a binary population \(\mathbf{B}\) from \(\mathbf{P}\).
2: Decode the binary population to be \(\mathbf{x}_{n\times Np}\) and find the objective values \(\mathbf{f}_{m\times Np}\).
3: Update \(\mathbf{Pareto}\) by replacing it with non-dominated solutions of union set \(\mathbf{Pareto}\cup\mathbf{x}\).
4: If the number of members in Pareto exceeds the predefined archive size \(N_{A}\), remove some of them by using an archiving technique.
5: If the termination criterion is fulfilled, stop the procedure. Otherwise, go to step 6:
6: Update \(\mathbf{P}\) and create a binary population
6:1: Set a binary population \(\mathbf{B}=\{\}\).
6:2: For \(i=1\) to \(l/\text{M}\).
6.2:1: Select \(n_{0}\) binary solutions from Pareto randomly.
6.2:2: Use \(L_{\text{RK}}\) = 0.25, 0.5, 0.75, for each \(k=1,\,\ldots,\,\text{M}\). (For this research M = 3)
6.2:3: Update the \(i\)th row of \(\mathbf{P}\) by using (1).
6.2:4: Generate the \(i\)th row of probability matrix \(\mathbf{P}i\) using (2) and each \(\text{L}_{\text{RK}}\).
6.2:5: Generate \(rand\in[0,1]\) a uniform random number.
6.2:6: If \(rand<\) the predefined mutation probability, update the \(i\)th row of \(\mathbf{P}\), \(\mathbf{P}\) and \(\mathbf{P}3\) using similar equation in [3].
6.2:7: Generate binary subpopulations \(\mathbf{SB1}\), \(\mathbf{SB2}\) and \(\mathbf{SB3}\) from the \(i\)th row of \(\mathbf{P}\), \(\mathbf{P}2\) and \(\mathbf{P}3\), respectively.
6.2:8: Set \(\mathbf{B}=\mathbf{B}\cup\mathbf{SB1}\cup\mathbf{SB2}\cup\mathbf{SB3}\)
6.3: Next \(i\).
7: Go to step 2.
```  
**Algorithm 2** MPBIL with multi-learning rate.
## 4 Results
### Dynamic Setup
#### 4.2.5 TTFF Cold Start  
Among the last results obtained during this study was the TTFF, in this case, performed using the cold start startup mode. When a cold start is performed, the information that the receiver may have stored is considered erroneous: current position, visibility of any of the GPS satellites, velocity, and time. As a result, the receiver is required to look for any satellites [3]. After receiving a signal from a satellite, the device can begin calculating the approximate location of the other satellites.  
Figure 17 illustrates the results obtained from the TTFFs performed depending on the position of the two devices inside the vehicle. The data obtained in each of the various positions in which the device was arranged in every location of the vehicle were averaged.  
Figure 16: Summary of all the results of TTFF measured with the warm start method when moving following the path in Figure 4. For TTFF, the lower the value, the better.  
As can be seen, for each of the positions, discrimination was made between the average of the city and the total average of all the data obtained. The general result shown in Figure 17 is that, in all positions, the linearly polarized antenna showed better results, both in general and in a city environment. This proves the robustness of the linear polarization technology since it takes less time to perform a fix.
## 3 Results and Discussion
### Minerals and Heavy Metals Composition
The FAO/WHO has established a PTWI of 5 \(\mu\)g/kg, 5.6 \(\mu\)g/kg and 25 \(\mu\)g/kg body weight per week, respectively, for total mercury, cadmium and lead. Arsenic content of the algae analysed in this study was 0.83-0.88 mg/kg DW of _Spirulina_ and _Chlorella_, respectively, while levels of cadmium were the highest in _Chlorella_ 0.19 mg/kg DW. Lead content of both _Spirulina_ (2.97 mg/kg DW) and _Chlorella_ (1.85 mg/kg DW) were particularly high which raises questions about the safety of these currently available microalgae supplements. However, these values do not pose any risk within the reasonable consumption of any microalgae supplement which is usually declared on the package and most of these supplements are served as tablets of 500 mg, which can be taken twice a day maximum. For example, to exceed the PTWI of lead, weekly consumption amount by an adult weight (60 Kg) of 0.5 Kg of _Spirulina_, 0.81 kg of _Chlorella_ and 16.6 Kg of C. _reinhardtii_. The poisonous potential or effect of the heavy metal is also dependent on its physical state. While arsenic is most toxic in its inorganic form, mercury is most toxic in its organic form methylated mercury, MeHg [56].
## Methods  
### Data Processing  
Figure 3 shows the process to train and test the SSI retrieval model from GK2A/AMI data in this study. We preprocessed the input data and constructed matchups between the satellite data and ground-based in situ SSI. The matchups were classified into training datasets and testing datasets based on the acquisition date. Over approximately a year,  
Figure 2: The distribution of in situ measurement stations over the Korean Peninsula, where the red rectangles indicate the Korea Meteorological Administration (KMA) Automated Surface Observing System (ASOS) stations; blue star and green stars denote the leodo Ocean Research Station operated by the Korea Hydrographic and Oceanographic Agency (KHOA) and flux towers operated by the National Institute of Forest Science (NIFoS), respectively.  
from 25 July 2019 to 31 July 2020, the matchups were used as the training dataset for training the ML model. For training the model, we conducted five-fold cross-validation to optimize the ML model; 80% of the training datasets were used for the model training by adjusting parameters in the ML model, and 20% were used to validate the SSI derived from GK2A/AMI based on the ML model for minimizing the loss function and preventing the overfitting of the ML model. For the matchups from 1 August 2020 to 31 January 2022, the testing datasets were used to assess the ML model's performance. Because the objective of this study was to build an ML model for estimating the operational SSI in real time, the ML model could estimate SSI for a longer period based only on the data for previous training periods. Thus, we did not select the random training and testing dataset for the entire period, but sequentially selected the training and testing dataset.  
To estimate SSI from GK2A/AMI data, we used sixteen channels, two background channels, and two static data as input variables. The spectral characteristic of cloud changes depending on the season, surface type, surface temperature, and environmental conditions. By accumulating satellite data for a specific period, it is possible to extract the spectral characteristics of the area under a clear sky. Thus, when using satellite data to detect cloud, it is common to use the background channel that accumulates and produces data for a specific period [25]. Because SSI dramatically depends on the cloud cover, we used two background channels, one visible channel (VIS0.6), and one thermal infrared channel (IR10.5) over 30 days as input variables to improve cloud detection. Furthermore, since SSI varies according to solar radiation, we used extraterrestrial solar radiation (ESR) and SZA as input variables.  
Figure 3: Flowchart describing the process of training and testing the surface solar irradiance (SSI) retrieval model using a machine learning (ML) approach.
## Appendix B Back Propagation Neural Network (BP)  
This feed-forward neural network based on the error back propagation algorithm is a supervised learning neural network. The core idea of the BP algorithm is to divide the learning process into two stages, the forward transmission of signal and the back transmission of error. It mainly uses the mean square error and gradient descent method to modify the network connection weight in order to achieve the minimum sum of error squares. The forward propagation formula is as follows:  
\[Y=\ f(\sum_{i=1}^{n}w_{i}X_{i}+b)\]  
where f is the activation function, \(X\) is the input data, \(w_{i}\) is the connection weight, \(b\) is the bias term, and n is the number of input data (neurons in the front layer).  
1. In the incentive transmission, the input layer transmits the received normalized input variables to the hidden layer. Then, the hidden layer neurons process the received data by weighted summation, and then substitute them into the activation function to transmit the activated value to the output layer. The output layer obtains the final result after weighted summation and activation again. We define that the input layer, the hidden layer and the output layer as follows, respectively: \[\text{Input layer}:X_{i}=x_{i}\] Hidden layer : \[A_{j}=f(\sum_{i=1}^{n}w_{ji}X_{i}+b_{j})\] Output layer : \[O_{k}=g(\sum_{j=1}^{n}w_{kj}A_{j}+b_{k})\]where \(w\) is the connection weight, \(b\) is the offset, \(f\) is the activation function, the common activation functions are sigmoid, tanh, Relu, etc., and \(g\) is the linear transfer function.
2. During the weight updating phase, in order to minimize the value of error function, the gradient descent algorithm is used to gradually modify the connection weight and offset value of output layer and hidden layer: \[\begin{split}\text{Output layer}:&\ d=(Y_{k}-O_{k})g ^{\prime}(w_{kj})\\ w_{junew}&=w_{kj}+\eta_{1}dA_{j}\\ \text{Hidden layer}:&\ h=\sum\limits_{k=0}^{s}dw_{kj }f^{\prime}(w_{ji})\\ w_{junew}&=w_{ji}+\eta 2 hx_{i}\end{split}\] where \(d\) and \(h\) represent the gradient term of corresponding layer neurons, and \(\eta_{1}\) and \(\eta_{2}\) represent the learning rate. Offsets updating is similar to weights updating.
3. Specific parameters of the BP neural network: In this paper, the ReLU nonlinear function is used in the hidden layer and purelin linear function is used in the output layer. Linear fitting model is selected to predict trend term in EEMD decomposition results. RMSProp and Adam are selected as optimization functions. Other hyperparameters are mostly set by default.
## 2 Related Work
### Attention Mechanism
#### 2.3.1 Channel Attention Module  
The structure of the channel attention module (CAM) is shown in Figure 5, whose core purpose is to highlight those feature channels that are more important to the task. We assume an intermediate feature map \(F\in\mathbb{R}^{B\times W\times H\times C}\) as input, and then global average pooling and global max pooling are applied to F to generate two feature vectors, \(f_{i}\in\mathbb{R}^{B\times 1\times 1\times C}\) and \(g_{i}\in\mathbb{R}^{B\times 1\times 1\times C}\), representing the global maximum feature and average feature of each channel. This step aims to concentrate the information on the feature map of the channel so as to better capture it. Using these two features can greatly improve the representation capability of the network.  
\[f_{i}=AvgPool(F) \tag{7}\]  
\[g_{i}=MaxPool(F) \tag{8}\]  
Figure 4: The map of 2D-DCAtention.  
Figure 5: The structure of the channel attention module.  
\(f_{i}\) and \(g_{i}\) pass through a weighted shared multilayer perceptron (MLP). The shared MLP is composed of two convolution functions and a ReLU activation function, which firstly reduces the dimension and then raises the dimension. Because the weights in MLP are shared, the weight matrix of inputs used is the same, and this operation can reduce the parameters.  
\[S_{i}=MLP(f_{i})=W_{1}(W_{0}(f_{i})) \tag{9}\]  
\[T_{i}=MLP(g_{i})=W_{1}(W_{0}(g_{i})) \tag{10}\]  
where \(W_{0}\in\mathbb{R}^{C\times C/r}\) and \(W_{1}\in\mathbb{R}^{C\times C/r}\); \(r\) is the reduction ratio in the hidden layer. After applying the shared MLP to each feature, the element-wise sum is used to merge the output feature vectors \(S_{i}\) and \(T_{i}\). Then, the output feature vector is generated by the sigmoid function to generate the weight vector \(M_{c}(F)\).  
\[M_{c}(F)=\sigma(S_{i}+T_{i}) \tag{11}\]
# Integrated Earthquake Catalog of the Ossetian Sector of the Greater Caucasus
###### Abstract
The network comprises 25 permanently operating short-period and broadband digital seismic stations (21 transmit data in real-time). It continuously monitors seismic events in Georgia and adjacent regions [14]. The Greater Caucasus is one of the most active segments of the Alpine-Himalayan collision belt. The Ossetian sector, considered in this paper, is characterized by a fairly high level of seismicity [15; 16; 17; 18; 19; 20; 21; 22]. So, [PERSON] identified 11 possible earthquakes source (PES) zones [23]: 10 zones on the northern slope of the Greater Caucasus, and the Racha-Java zone on the southern slope. A significant number of the strong earthquakes in the Greater Caucasus are confined to the Racha-Java PES zone. For these zones, a general increase in the maximum magnitude potential from north to south is observed [23; 24]. The strongest earthquake, both within the considered sector and in the entire Greater Caucasus, was the Racha earthquake. It occurred in the territory of the Republic of Georgia within the Racha-Java PES zone [23] on 29 April 1991 at 09:12 (UTC) with a magnitude of \(MW=7.0\) (GCMT--Global Centroid-Moment-Tensor). According to [25], the earthquake was followed by more than 3500 aftershocks. The strongest aftershocks are considered to be the following events: 29 April 1991 at 18:30 (UTC) with \(MW=6.1\) (GCMT) 15 km northeast of the epicenter of the main shock and 15 June 1991 with \(MW=6.2\) (GCMT) 15 km east of the village of Java. On 23 October 1992, 120 km east of the epicenter of the Racha earthquake, the Barisakho earthquake with \(MW=6.4\) (GCMT) occurred on the southern slope of the Greater Caucasus [26; 27]. Speaking about events with \(M\geq 6.0\) that occurred in the considered segment of the Greater Caucasus, we have to mention the Chernogorsk earthquake with \(MW=6.2\) (GCMT), which occurred on 28 July 1976 in the Chernye Gory region, 17 km southwest of Grozny [28; 29].