esa-sceva/satcom-chunk-collection huggingface dataset:
# Genetic Algorithms to Maximize the Relevant Mutual Information in Communication Receivers +
## 2 Preliminaries
### The Information Bottleneck Method  
The information bottleneck method is an information theoretical framework introduced by [PERSON] et al. in [1]. It originates from machine learning and considers three discrete random variables \(\mathsf{X},\mathsf{Y}\) and \(\mathsf{T}\) which form a Markov chain \(\mathsf{X}\to\mathsf{Y}\to\mathsf{T}\). \(\mathsf{X}\) is termed the relevant random variable. The idea is that \(\mathsf{Y}\) is observed and shall be compressed to a more compact representation \(\mathsf{T}\). It is well-known from the famous rate-distortion theory that in this context a compression corresponds to minimizing the so-called compression information \(\mathsf{I}(\mathsf{Y};\mathsf{T})\). However, it shall be guaranteed that also the mutual information \(\mathsf{I}(\mathsf{X};\mathsf{T})\) is maximized. As a result, one can conclude that \(\mathsf{X}\) defines which features of \(\mathsf{Y}\) are considered to be relevant and shall be preserved under compression. The compression rule that maps a realization \(y\in\mathcal{Y}\) onto its compressed representation \(t\in\mathcal{T}\) is typically considered as a conditional probability distribution \(p(t|y)\). This allows us to cover probabilistic and also deterministic mappings of \(y\) onto \(t\). In this article, however, we will restrict ourselves to deterministic mappings \(p(t|y)\in\{0,1\}\,\forall(y,t)\) that, of course, fulfill the law of total probability. In this situation, \(t\) is a deterministic function of \(y\), i.e., \(t=f(y)\).  
There exist many information bottleneck algorithms [26; 27; 28; 29; 30] that can construct the desired compression mapping \(t=f(y)\) for a given cardinality of \(\mathcal{T}\). A popular information bottleneck algorithm in communications is the KL-means algorithm from [26; 27]. Due to the fact that \(\mathsf{Y}\) is discrete, it is possible to store the mapping \(t=f(y)\) in a lookup table with size \(|\mathcal{Y}|\) by just storing each \(t\) for the respective \(y\). The mapping \(t=f(y)\) then clusters the event space of \(\mathsf{Y}\) into several clusters \(\mathcal{Y}_{t}\) which, mathematically, are the preimages of \(t=f(y)\).
## 3 Results and Discussions
### Artificial Neural Network (ANN)
#### 3.4.1 Dataset  
The dataset includes 34 radiation dose values (input values) and corresponding TL glow curves (output values), which are processed and normalized before training. Each of the output values consist of 221 response values. The data were normalized using min-max scaling to improve model convergence and ensure a consistent range of values. The normalized dataset was then split into training, validation, and test sets using stratified partitioning techniques, allocating 70% for training and the remaining 30% for validation and testing. The stratified splitting ensures representative sampling across all dose levels, enhancing the generalization capability of the model.
## 3 Results
### Improving Image Quality by Removing Compression Artifacts
Some examples of the outcomes are presented in Figure 5.  
The comparison of computational time presented in Table 2 shows the average, standard deviation, and median time per image calculated on the BreCaHAD dataset. The tests were conducted on a workstation equipped with a 12 th Gen Intel Core i9-12900 KF processor (16 cores, 24 threads, 800-5200 MHz; Intel Corporation, Santa Clara, CA, USA) and an NVIDIA GeForce RTX 3060 GPU (NVIDIA Corporation, Santa Clara, CA, USA). It can be concluded that the developed methods are slightly slower compared to classical filters, such as the Gaussian and average filters. Additionally, the Sobel variant is slower than the EPIIC method. However, they perform significantly faster than the FBCNN method, even when a graphics card is used for the inference, which makes the proposed methods better suited for processing large WSI images. For each artifacts removal method, we estimated which parameters have the highest impact on image quality by calculating two metrics: PRCC (Figure A1) and variable importance (Figure A2). Additionally, we included the Quality Factor (\(QF\)) in the analysis to show if the achieved gain in image quality compared to JPEG compressed images varies with the compression ratio. In all methods, \(QF\) was negatively correlated with gain in \(PSNR\) and \(SSIM\), meaning a higher need for artifacts removal for low \(QF\) values. Also, the highest variable importance value for this factor was observed in most cases. For EPIIC and EPIIC Sobel, window size and the standard deviation of the Gaussian filter have a higher influence on the results than the two other parameters. Similarly, in the other methods, changing window size resulted in different image quality, so it is an important  
Figure 3: Results of the best methods for the BreCaHAD dataset. Images present: original image, compressed image \(QF=30\), EPIIC, EPIIC Sobel, FBCNN, image after Gaussian smoothing, Guided filtering.
## 3 Results  
### The Results of PNN and Other Rain Area Delineation Methods  
In this study, 16 rain events over the Yangtze River Basin through June, July and August 2009, were observed by the rain gauge network. The detailed information of these rain events is presented in Table 2. In this section, the results of the PNN-based rain area delineation method (termed PNN) were validated by calculating six categorical statistics described in Section 2.2.3.  
The rain event of 26 July 2009, at 9 am was taken as an example to present the results of the Korean, TRMM 2A12, SI threshold and PNN rain area delineation methods. Figure 3 shows the rain areas  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & **Rain/No Rain Area Delineation by the Models** \\ \cline{2-4}  & & **Rain** & **No Rain** \\ \hline Ground observations & Rain & h & m \\  & No rain & f & z \\ \hline \hline \end{tabular}
\end{table}
Table 1: The contingency table of rain areas detected by rain gauges and delineated by rain area delineation methods.  
detected by rain gauge network and delineated by the Kmean, TRMM 2A12, SI_threshold and PNN, respectively. Although the rain areas shared a similar spatial pattern, there were numerous differences among them. For example, the Kmean, TRMM 2A12 and SI_threshold seriously under-estimated the rain areas in the rectangle shown in Figure 3. In contrast, the PNN can detect the rain areas in this region more accurately compared with the other three methods.  
Figure 4 presents the spatial distribution of the misdetected (the red circles) and the false alarm (the green circles) precipitation of the rain area delineated by the Kmean, TRMM 2A12, SI_threshold and PNN, respectively. The false alarm precipitation of the Kmean and TRMM 2A12 was 201 and 225, while the misdetected precipitation of them was 295 and 231, respectively. The SI_threshold produced 489 misclassified precipitations, including 239 misdetected precipitations and 250 false alarm precipitations. Compared with the three rain area delineation methods mentioned above, the PNN produced the smallest amount of false alarm precipitation (129) and misdetected precipitation (180). Besides, the CSI, ETS, HK and HSS of PNN (0.63, 0.53, 0.68 and 0.69) were obviously higher than the Kmean (0.42, 0.32, 0.45 and 0.49), TRMM 2A12 (0.48, 0.35, 0.52 and 0.52) and SI_threshold (0.46, 0.33, 0.51 and 0.49). Therefore, it can be concluded that the PNN could provide more reasonable rain area delineation compared with the other three methods.
## 2 Energy Tracking Methodology
### Battery Degradation Model  
The degradation model of energy storage batteries is based on Li-ion batteries, as the most used resource in medium-size energy storage systems. Li-ion batteries are losing storage capacity with calendar aging and cycling. The methodology is not taking into account the calendar aging, which will not affect the short-term planning horizon. Cycle life loss is related to temperature, charge and discharge rate and energy throughput [34]. The effects of discharge rates and capacity loss of a battery cell induced by cycling can be captured by an exponential function (12).  
\[\begin{array}{l}Q_{loss,\%}=B_{1}\cdot e^{B_{2}\cdot I_{rate}}\cdot Ah\\ B_{1}=a\cdot T^{2}+b\cdot T+c\\ B_{2}=d\cdot T+e\end{array} \tag{12}\]  
\(Q_{\text{loss}}\) is the percentage of capacity loss induced by cycling. Both factors \(B_{1}\) and \(B_{2}\), are functions of temperature. \(I_{rate}\) is the charge/discharge rate expressed as a C-rate. \(Ah\) is the total \(Ah\)-throughput for a given period. Coefficient values and units are given in [34].  
After the determination of all necessary components, the multi-attribute objective function can be composed.
## 4 Methods for Mitigating Jamming
### Scenario 1  
When the jammer is located at the destination, we can obtain the value of the n-th receiving aperture after photoelectric conversion as follows:  
\[y_{n}=Ra_{n}h_{in}\sqrt{P_{s}}s+R\sqrt{N_{J}}h_{J}\Lambda s_{J}+n_{n} \tag{29}\]  
where \(h_{in}\) represents the attenuation coefficient of the n-th channel; \(a_{n}\) represents the weighted value of the n-th channel; For the purpose of simplifying calculations, we assume that \(a_{n}=\frac{1}{N}\); \(n_{n}\) is the AWGN with zero mean and variance \(N_{0}\); \(\frac{1}{N}\) represents the jamming from N receiving apertures at the receiver, which follows a uniform distribution. Therefore, the probability of jamming for any one of them is \(\frac{1}{N}\).  
The receiver combines the received electrical signals by using the equal gain combining (EGC) [33] as follows:  
\[y_{D}=\sum\limits_{n=1}^{N}y_{n}=R\sqrt{P_{s}}s\sum\limits_{n=1}^{N}a_{n}h_{in }+R\sqrt{N_{J}}h_{J}\Lambda s_{J}+\sum\limits_{n=1}^{N}n_{n} \tag{30}\]  
From (30), we can obtain the received SIR as follows  
\[\gamma_{SJR-D}=\frac{P_{s}\left(\sum\limits_{n=1}^{N}a_{n}h_{in}\right)^{2}}{N _{J}\left(h_{J}\right)^{2}}=\frac{\rho\overline{\gamma}_{D}\left(\sum\limits _{n=1}^{N}a_{n}h_{in}\right)^{2}}{\left(h_{J}\right)^{2}} \tag{31}\]  
We can also obtain from (30) the received SNR as follows  
\[\gamma_{SNR}=\frac{P_{s}\left(\sum\limits_{n=1}^{N}a_{n}h_{in}\right)^{2}}{NN _{0}}=\frac{\overline{\gamma}_{D}\left(\sum\limits_{n=1}^{N}a_{n}h_{in}\right) ^{2}}{N} \tag{32}\]
# EUV-Induced Plasma: A Peculiar Phenomenon of a Modern Lithographic Technology
## 3 Bulk Materials Facing EUV Photon Induced Plasma
### Identification of Reaction Mechanisms
#### 3.3.1 Carbon Removal  
The reduction of carbon layers in a hydrogen atmosphere or hydrogen plasma has been extensively studied [64; 73; 83; 84; 85; 86; 87; 88; 89]. The applications range from plasma processing [83; 84; 85], to cleaning in EUV related applications [64; 84; 87; 90; 91], and to nuclear fusion, where carbon is a plasma facing material candidate [85; 86; 88].  
Carbon etching mechanisms depend on the involved particles. For example, Hydrogen atoms impact surfaces and react with the carbon surface, forming volatile hydrocarbons [73; 92]. This process is especially sensitive with respect to the radical and surface temperatures [83; 92]. Bombardment with hydrogen ions at moderate energies (<60 eV) results in synergetic chemical and physical etching. Initially, incident ions damage the surface, creating dangling bond sites that are available for hydrogenation. These sites can be passivated by additional ions, which leads to (volatile) hydrocarbon formation. Molecular ions below 60 eV are not dissociated when undergoing C-C bond breaking [88]. Measured sputtering yields are about 0.01 C/ion between 10-60 eV/atom [86; 88].  
Figure 10: Time-averaged ion energy distribution functions for H\({}^{+}\), H\({}_{2}{}^{+}\), and H\({}_{3}{}^{+}\) ions from an EUV-induced plasma produced in H\({}_{2}\) gas at 5 Pa. Figure produced using data from [63].  
Although the separate radicals and ions are capable of etching, the etch rate is significantly increased when both of the species are present. Combined erosion by low-energy ions and additional atomic hydrogen is described by [PERSON]'s model [84], which has been extended for H\({}^{+}\) impact [86]. Again, the ions break C--C bonds, passivated by the atomic hydrogen instantaneously. This process is known as reactive ion etching.  
Carbon removal by EUV-induced plasmas has been numerically and experimentally investigated [12; 91][64; 91]. The carbon samples were negatively biased from 0-200 V in both of the studies. Experimentally, no etching was measured at a grounded sample in 3 Pa H\({}_{2}\) while using a photon fluence of \(5\times 10^{19}\) cm \({}^{-2}\)[64]. This agrees with the experimental ion energy distributions that were measured in [59] (<8 eV) and the threshold of 10 eV. By increasing the bias voltage, and thereby the ion impact energy, yields between 0.1 C/ion and 1 C/ion were found. The difference with the ion sputtering rates that are mentioned before are attributed to the increased sputtering yields in reactive ion etching.  
EUV-irradiation of the surface generates activated carbon (up to 4% of the surface carbon atoms), which is able to react with H\({}_{2}\) molecules, and hence increases the sputter yield. The reproducibility [91] of the experiment yielded slightly higher sputter rates, peaking at 1.9 C/ion at a \(-200\)V sample bias. No evidence was found for the increase in sputter yield due to EUV-irradiation of the surface, but the margin of error was such that it could not be excluded.
# Dual-Neighborhood Tabu Search for Computing Stable Extensions in Abstract Argumentation Frameworks
## 2 Preliminary Definitions
### Stable Labelings  
We incorporate a labeling-based algorithm in the proposed method. A labeling is a function \(L\): \(Arg\rightarrow\{in,out,undec\}\) that assigns a label to each argument to indicate the current state of the argument. For the classical 3-valued labelings approach, in signifies acceptance of the argument, out denotes rejection of the argument, and undec indicates the argument's status as undecided.  
**Definition 8**.: _Given an \(AF=(A,R)\) and \(L\) is considered as a complete labeling, if for any \(a\in A\) satisfy:_  
1. \(L(a)=in\) _s.t._ \(\forall b\in\{a\}-:L(b)=out\)_;_
2. \(L(a)=out\) _s.t._ \(\exists b\in\{a\}-:L(b)=in\)_;_
3. \(L(a)=undec\) _s.t._ \(\forall b\in\{a\}-:L(b)\
eq in\wedge\exists c\in\{a\}-:L(c)=undec\)_._  
With Definition 8, it follows that \(a\) is legally labeled in if and only if all its attackers are labeled out. If \(a\) is legally labeled as out if there is at least one attacker labeled as in.  
Figure 3: Example of grounded extension. Directed edges represent attack relations. (**a**) there is a relationship of defense, with \(a\) defending against \(c\); (**b**) there is a relationship of self-attack; (**c**) there is a relationship of mutual attack.  
is legally labeled as undec then it has at least one attacker that is labeled as undec and it does not have an attacker that is labeled as in.  
It has been proved in the literature [11] that complete labeling and stable labeling are related as follows in Theorem 1.  
Given an \(AF=(A,R)\) and a labeling \(L\). The following statements are equivalent:  
1. \(L\) is a complete labeling such that \(undec(L)=\emptyset\);
2. \(L\) is a stable labeling.  
Therefore, the solution for stable labelings relies only on the use of the labels in and out. A stable labeling is a set of labels that all arguments are legally labeled. Then the corresponding in(\(L\)), the set of arguments labeled as in, is also called the corresponding stable extension.  
Figure 4 illustrates an example of stable labelings. In the left abstract argumentation framework, the labels of the arguments \(a,b,c,d\) are incorrect according to the first rule in Definition 8; the labels of the arguments \(f,g\) are also incorrect according to the second rule in Definition 8. Thus, only the label of parameter \(e\) is identified as correct. On the right-hand side, the labels of all arguments satisfy Definition 8, i.e., it is a stable labeling. Arguments \(b,d,f\) labeled in form a stable extension.  
Based on Definition 8 and Theorem 1, we can summarize the overall idea of our algorithm. First, we use only in and out labels in our algorithm to label the argument states. Then, we keep modifying the mislabeled arguments by the first and second provisions of Definition 8 until there are no mislabeled arguments. At this point, all the labels of the arguments are legal and no undec label satisfies the criteria of Theorem 1, and we can obtain a stable labeling. Then we output the set labeled in.
# Image Motion Measurement and Image Restoration System Based on an Inertial Reference Laser
###### Abstract  
Satellites have many high-, medium-, and low-frequency micro vibration sources that lead to the optical axis jitter of the optical load and subsequently degrade the remote sensing image quality. To address this problem, this paper developed an image motion detection and restoration method based on an inertial reference laser, and describe edits principle and key components. To verify the feasibility and performance of this method, this paper also built an image motion measurement and restoration system based on an inertial reference laser, which comprised a camera (including the inertial reference laser unit and a Hartmann wavefront sensor), an integrating sphere, a simulated image target, a parallel light pope, a vibration isolation platform, a vibration generator, and a 6 degrees of freedom platform. The image restoration principle was also described. The background noise in the experiment environment was measured, and an image motion measurement accuracy experiment was performed. Verification experiments of image restoration were also conducted under various working conditions. The experiment results showed that the error of image motion detection based on the inertial reference laser was less than 0.12 pixels (root mean square). By using image motion data to improve image quality, the modulation transfer function (MTF) of the restored image was increased to 1.61-\(1.88\) times that of the original image MTF. The image motion data could be used as feedback to the fast steering mirror to compensate for the satellite jitter in real time and to directly obtain high-quality images.  
Keywords:inertial reference laser; micro vibrations; image motion; image restoration; remote sensing satellite +
Footnote †: journal: Sensors
## 2 Materials and Methods
### GPS Equipment  
Each athlete wore a GPS unit with a MEMS motion sensor for each game. The GPS was sampled at 10 Hz, and the motion sensor, including the accelerometer, gyroscope,and magnetometer, was sampled at 200 Hz. The sensor was 36 mm \(\times\) 68 mm \(\times\) 13 mm with a mass of 39 g. The sensor was attached to each player using a strap around the chest, positioned as recommended by Polar Pro instructions. The GPS unit communicates wirelessly via Bluetooth to an iPad located on the sidelines. Players wore the GPS unit through the duration of their warm-up routine, allowing the units to be running for at least 30 min before data used for this study were recorded.
## 2 Methods
### Enhanced Feature Module
The process of EFM can be defined as:  
\[X=Conv^{1\times 1}(L_{i}\otimes CA(F))\oplus C_{5} \tag{1}\]  
where \(L_{i}\) denotes the \(i\)-th layer of MSLA, \(CA\) denotes the process of CAU, \(Conv^{1\times 1}\) denotes a \(1\times 1\) convolution layer, \(\otimes\) denotes element-wise multiplication, and \(\oplus\) denotes element-wise summation. The context information of feature maps obtained by a single convolution kernel is limited. Dilated convolution [35] can expand the receptive field of the convolution kernel. Thus, we apply dilated convolution to generate multi-scale spatial attention and obtain the local attention description by recoding critical information of feature maps. The MSLA module consists of five parallel branches, namely a \(1\times 1\) convolution layer \(L1\), three parallel dilated convolution layers \(\{L2,L3,L4\}\) with different dilated rates of [3; 6; 12], and a global average pooling layer \(L5\). The \(1\times 1\) convolution and global average pooling are used for achieving cross-channel information integration and generating global feature description, respectively. The process of MSLA is defined as:  
\[F=contact(L1(C_{5}),L2(C_{5}),L3(C_{5}),L4(C_{5}),L5(C_{5})) \tag{2}\]  
\[L5=UpSampling\Big{(}C^{1\times 1}(AVP(C_{5}))\Big{)} \tag{3}\]  
where \(C^{1\times 1}\) represents the convolution operation and AVP represents the global average pooling. Subsequently, a global average pooling and a global max pooling independently are applied on the concatenated multiscale features \(F\in\mathbb{R}^{C^{\prime}\times H\times W}\) to aggregate two different forms of context information. Then, the global average pooling descriptor \(D_{gap}\) and the global max pooling descriptor \(D_{gap}\) undergo squeeze and expansion, to learn the interdependence among the features of \(X\in\mathbb{R}^{C^{\prime}\times H\times W}\). The operations of squeeze and expansion are inspired by SENet [30], which can retain the essential information of multiscale feature maps. Next, the \(D_{gap}\) and \(D_{gap}\) undergo two convolution layers followed by the element-wise summation operation and the sigmoid function. The CAU is computed as:  
\[CA=sigmoid\big{(}Conv_{2}\big{(}\delta\big{(}Conv_{1}(D_{gap}(F))\big{)} \big{)}\big{)}\oplus Conv_{2}(\delta(Conv_{1}(D_{gap}(F))))\big{)} \tag{4}\]  
where \(\delta(\cdot)\) represents the ReLU activation function, and \(Conv_{1}\), \(Conv_{2}\) represent \(1\times 1\) convolution layer for channel squeeze and expansion, respectively. Finally, the enhanced feature map X is assigned to the feature pyramid layers [P3, P4, P5, P6, P7] in Figure 2 for improving the feature representation of each layer. Figure 3: The architecture of EFM. The EFM contains of two parts: a multi-scale local attention module and a channel-guided attention unit.