esa-sceva/satcom-chunk-collection huggingface dataset:
# Risk-Aware Wireless Positioning in
## 2 Preliminary: Various Wireless Positioning Schemes
### Fingerprinting  
The fingerprinting scheme is the positioning scheme that estimates the user position from a database, which has already been measured [22; 23]. The implementation of the fingerprinting scheme is divided into the training phase and the positioning phase. In the training phase, all BSs make a database by collecting the propagation characteristics of received signals (e.g., RSSs) from RF devices at known positions. Then, in the positioning phase, the BSs estimate the position of an RF device by comparing the propagation characteristic of the received signal with the database. The fingerprinting scheme is widely investigated especially for indoor environment, where the signal propagation model based distance estimation is hard. Compared to other positioning schemes, the fingerprinting scheme is more robust, accurate and cost-efficient for indoor positioning [6]. The fingerprinting scheme also has accurate position estimation for multipath issues and NLoS propagation in challenging environment [7]. In recent years, the fingerprinting scheme has also been widely utilized for outdoor positioning due to its accuracy, energy efficiency and low latency. For outdoor positioning, the fingerprinting scheme used, varies with the operating environment and performance. These schemes can be classified as visual fingerprinting, motion fingerprinting, signal fingerprinting and hybrid fingerprinting. Each scheme has pros and cons for different situations [8].
# Trepenoid Biosynthesis Dominates among Secondary Metabolite Clusters in _Mucoromycotina_ Genomes
## 3 Results and Discussion
### Non-NRPS Siderophore Synthases Are Conserved in Early-Diverging Fungi  
Fungal siderophores are ferric ion-specific chelators usually secreted under iron-stressed conditions and they are supposed to play crucial roles in several biological processes, including virulence or oxidative stress tolerance. The majority of fungi produce hydroxamate and carboxylate types of siderophores [60]. They can be synthesised in a non-ribosomal peptide synthetase (NRPS)-dependent pathway and another NRPS-independent pathway (NRPS-independent siderophore, NIS) [61] and the first one seems to be more common and better studied (e.g., [62]). The presence of NIS enzymes was predicted in _Basidiumycota_, _Muconales_, _Ascomycota_ and _Kickxellales_ representatives [61]. However, in our study, the non-NRPS siderophore synthases were shown to be conserved in early-diverging fungi.  
Siderophore SMCs are characterised by a single gene coding an enzyme with a conserved protein architecture with an \(N\)-terminally localized lucA_lucC (PF04183) domain followed by a FluF (PF06276) domain. The identified clusters are evolutionarily related and an approximate maximum likelihood reconstruction of the siderophore evolutionary history performed with FastTree largely recovered the species tree of fungi (Supplementary Figure S3). Interestingly, we did not observe SMCs with the lucA_lucC hallmark using AntiSmash in any of _Umhelopsis_ strains analysed, leading to the hypothesis of siderophore loss in this lineage. However, a BLASTP search with _M. alpina_ CCTCC-M207067 GCA_001021685_6_glim-encoded siderophore synthase against the NR database led to the identification of siderophore synthetases in two recently sequenced _Umhelopsis_ isolates [63] (_U. isbellina_ protein acc. KAG2182392.1, _U. vinacea_ KAG2188916.1), which were not included in this dataset. Siderophore SMCs seem to be vertically inherited and are present in every single clade of the early-diverging fungi sequenced so far, including Holomycota representatives such as _Fonctilula alba_ protein XP_009497925.1.
# EUV-Induced Plasma: A Peculiar Phenomenon of a Modern Lithographic Technology
###### Abstract  
After a long period of relatively low interest, science related to effects in the Extreme Ultraviolet (EUV) spectrum range experienced an explosive boom of publications in the last decades. A new application of EUV in lithography was the reason for such a growth. Naturally, an intensive development in such area produces a snowball effect of relatively uncharted phenomena. EUV-induced plasma is one of those. While being produced in the volume of a rarefied gas, it has a direct impact onto optical surfaces and construction materials of lithography machines, and thus has not only scientific peculiarity, but it is also of major interest for the technological application. The current article provides an overview of the existing knowledge regarding EUV-induced plasma characteristics. It describes common, as well as distinguishing, features of it in comparison with other plasmas and discusses its interaction with solid materials. This article will also identify the gaps in the existing knowledge and it will propose ways to bridge them.  
Extreme Ultraviolet; lithography; EUV; EUV-induced plasma; photon-induced plasma; MCRS; Electron density; Ion energy distribution function; IEDF; EUVL
# A Novel Deep Learning Segmentation and Classification Framework for Leukemia Diagnosis
## 2 Materials and Methods
### The Proposed System
These outline cells are analyzed further to extract the needed features and perform the categorization process based on these characteristics. The proposed model extracts 23 features, including area, diameter, shape, location, mean, and variance. Figure 2 depicts the detail of the internal structure of the developed segmentation model and Figure 3 offers a complete internal description of the implemented categorization method. In Figure 2, the segmentation is performed based on the deep learning architecture, which is U-shaped. To obtain a final segmentation result, any input is required to travel through various levels. These levels represent different resolutions. The input is represented in a size of H \(\times\) W \(\times\) D, where H refers to the height, W denotes the width, and D refers to the depth. In this study, depth denotes the number of applied neurons. The dimensional size of the inputs is projected inside the projection layer into a constant dimensional space. Then, a patch size of 32 is applied. The Multi-Layer Perceptron (MLP) encompasses three Gaussian error Linear Units (GeLU). Each GeLU contains a convolutional block with a size of 2 \(\times\) 2 and one downsampling operator. In addition, to enhance the segmentation accuracy, two tools are deployed, which are Gated Position-Sensitive Axial Attention (GPSAA) and the Local-Global training method (LoGo). The GPSAA tool determines the interaction relationship between the features and the efficiency of the occurred computations, while the LoGo tool assists in extracting local and global features. Furthermore, a learnable positional embedded Epos is applied in this study to maintain the retrieved spatial information between inputs. Figure 1: The flowchart of the proposed algorithm. In Figure 3, the feature extraction and categorization operations are performed using the UNET structure. This structure includes three levels. Each level contains layers of convolutional, max-pooling, and fully connected. In addition, the ReLU activation function is used to predict the classification and classification of the features. Figure 3: The complete architecture of the developed classification model. Figure 2: The internal structure of the segmentation stage. takes place at each level. The proposed approach categorizes results as healthy or cancerous cells. Cancerous cells are classified into two groups, which are ALL or AML. No public datasets that included all four types were found. In every convolutional block, a dedicated number of kernels is assigned to ensure that diverse features are achieved. The utilized number of kernels is 5 \(\times\) 5 \(\times\) 4. Three fully connected layers with 200 neurons in each are deployed. The last fully connected block includes three nodes to represent the total number of obtained findings by the proposed framework, which are healthy cells, ALL cells, and AML cells. Table 4 shows the applied configuration hyperparameters inside the feature extraction and classification stage.
# Secrecy Control of Wireless Networks with Finite Encoding Blocklength
## 1 Introduction
[19] employed directional antennas and evaluated the secure secrecy performance in millimeter wave ad hoc networks. While for the wireless multi-hop networks, [PERSON] [20] proposed a dynamic control algorithm in multi-hop wireless network with untrusted relays. While in [21], [PERSON] et al. considered a multi-hop network with random and independent node distribution. To guarantee the secrecy transmission, they developed a strategy to encode the confidential message with multi-path transmission and infinite coding block length. By using stochastic network optimization [22], they developed a control policy to stable the network and maximize the network utility, which combines end-to-end secrecy encoding, routing scheme and the resource allocation algorithm. However, most of the works mentioned above only focused on cellular networks security or multi-hop wireless networks with multi-path transmission strategy to guarantee the security. In this paper, we consider the secrecy control problem in multi-hop wireless network, which is extended work of our previous work [23]. In [23], we have developed a secrecy control algorithm to maximize the network utility, while the blocklength of secrecy encoding is infinite. For the case of finite blocklength, it would be much more complicated since perfect secrecy is not possible. To deal with this scenario, We exploit an independent randomization encoding strategy to guarantee the security and define a secrecy outage probability. Given a constraint on the probability of secrecy outage, we develop an improved control algorithm, which is decomposed into end-to-end secrecy encoding, flow control and routing scheduling such that the network stability and secrecy outage constraint are satisfied. Finally, we prove that the performance of proposed control policies can close to the optimal utility result asymptotically. The rest of the paper is organized as follows. In Section 2, we introduce the network model and problem formulation. Section 3 proposes an improve control policy with finite secrecy codewords. Section 4 evaluates the proposed policy with various network conditions. Finally, the paper is concluded in Section 5.
# Rotating Machinery Fault Detection Using Support Vector Machine via Feature Ranking
## 3 Data Collection and Processing
### Data Observation
#### 3.4.2 Frequency Domain Data  
All accelerometer data was converted into the Frequency Domain using a Fast Fourier Transform. By learning the characteristics of these signals in the frequency domain, the optimal sample window and different power bands could be determined. To evaluate data effectively, all the data needs to remove offset or DC components before converting data into a frequency domain. A high-pass Filter was applied to all accelerometer data before converting it into the Frequency Domain.  
Figure 6 presents six distinct frequency zones, labeled Zone 1 to Zone 6, each representing a group with similar frequency characteristics when comparing the frequency behavior across each axis of the accelerometers. These zones effectively capture different frequency patterns and can be used as distinct power bands for further analysis.
# High-Speed Visible Light Communications: Enabling Technologies and State of the Art
## 6 Conclusions  
Owing to the various inherent advantages, such as spectral and bandwidth relief, no healthy concern, high security, low cost and low interference with RF waves, VLC have emerged as one of the most energy efficient and promising wireless communication approaches. How to achieve a high-speed data transmission is a key problem to be solved in the VLC system. In this review, the possible solutions to improve the data rate, including novel transmitter architecture, advanced modulation and equalization technology and blue filter were elaborately discussed elaborately. And recent progresses show that the type of transmitter is an important factor that affects the data rate. Modulation and equalization technologies can improve the system performance to a certain extent. Blue filter has a marginal effect for the ps-LED based VLC system, and can improve its performance without increasing the computational complexity at the receiver. Till now, the highest data rate achieved in a commercial phosphorescent white LED-based VLC system is 2.08 Gb/s over a 1 m free-space transmission distance. And the data rate is up to 17.6 Gb/s transmission in a GaN LD based VLC system with impedance-matched transmission-line circuit board and UFMC additive 16-QAM OFDM. In the gradually mature and practical path, there are still some issues that need to be addressed by in-depth study in future researches, such as LED thermal management, bi-directional transmission, data transmission in lights off mode and so on. Despite this, with the development of new devices and related technologies, VLC is foreseen to be readily implemented into general lighting infrastructures to achieve various exciting and beneficial applications.  
This work was partially supported by the Shanghai Science and Technology Foundations (15 DZ2282100, 16 DZ2290600, 18590712600), Youth Innovation Promotion Association CAS and New Teachers Research Funding Project of Northwestern Polytechnical University (3102017 QQD076). All authors contributed to the research work. [PERSON] proposed the frame of the paper, [PERSON] wrote the paper, [PERSON] contributed to the figures and table, [PERSON] revised the paper.  
The authors declare no conflict of interest.
# Substitute Seed Nodes Mining Algorithms for Influence Maximization in Multi-Social Networks
## 2 Influence Maximization in Multi-Social Networks
### Multi-Social Networks Influence Maximization  
In the background of various social networks coexisting on the Internet, information can spread between multiple social networks \(G_{1}(V_{1},E_{1}),\ G_{2}(V_{2},E_{2}),\)\(G_{3}(V_{3},E_{3}),\)\(\ldots,\)\(G_{n}(V_{n},E_{n})\). Each node in \(G_{i}\) has two states: activated or inactivated. The activated nodes are those who have affected by the information and will spread the information to their neighbors in the next time period. The inactivated nodes are those who have not heard of the information or rejected to adopt it [19]. A node whose state is inactive can be activated by activated nodes, and a node that is in an activated state cannot be changed into an inactivated state, that is, the activation process is irreversible.  
The problem of Multi-Social Networks Influence Maximization is to find \(k\) (\(k\) is an integer and satisfies \(k>=1\)) seed users from all the users \(U=\left\{u^{1},u^{2},u^{3},\ldots,u^{m}\right\}\) of \(G_{1}(V_{1},E_{1}),\)\(G_{2}(V_{2},E_{2}),\)\(G_{3}(V_{3},E_{3}),\)\(\ldots,\)\(G_{n}(V_{n},E_{n})\), to constitute the initial seed node set S, such that by activating these users in S, the number of users that are ultimately activated is the most. \(R_{i}(S)\) indicates the number of users that are ultimately activated on \(G_{i}\). Hence, the problem of multi-social networks influence maximization can be defined as  
\[\textit{Seed}=\underset{S\subseteq U}{argmax}\sum\
olimits_{i=1}^{n}R_{i}(S) \tag{2}\]  
where _Seed_ is expected set of the initial nodes and \(|\textit{Seed}|=k,\ Seed\subseteq U\).
## 4 Results
### Data Filtering  
As observed in the previous subsection (see Figure 4), raw \(\Delta\)C values and calculated \(\Delta V_{RMS}\) values show a significant ripple due to internal chemical changes of the cell [43], mainly due to the recovery effect. The recovery effect is due to an overhang of the negative electrode over the positive electrode due to a design feature of the cells. This is a common effect found in aging tests, although it complicates the design of RUL forecasting tools [44; 45]. In order to improve the modeling accuracy and to reduce the dependence of the results on the internal chemical changes, these data are filtered by discrete wavelet transform (DWT). Although other filtering approaches are possible [22], in this paper, a three-stage DWT was applied to the calculated \(\Delta V_{RMS}\) values because it offers several advantages, such as low computational complexity, a simultaneous localization in the time and frequency domains, and the ability to separate the fine details of the signal. The applied process is summarized in Figure 5, which shows the original \(\Delta V_{RMS}\) signal, the approximated or filtered signal, and the three details (high-pass filters) removed from the signal corresponding to the successive application of low-pass and high-pass filters made by the three-level DWT. It is noted that the discrete Meyer mother wavelet has been applied because it is commonly used in fault diagnosis approaches [46].  
Figure 4: Capacity fade \(\Delta\)C and evolution of the proposed estimator \(\Delta V_{RMS}\) for cell B0005 at a C-rate of 1.  
Figure 6 shows the results of the three-level DWT filter. It compares the original values of the \(\Delta V_{RMS}\) with the filtered ones vs. the number of charge and discharge cycles of the cells.  
As can be seen in Figure 6, the DWT process applied to the \(\Delta V_{RMS}\) signal can significantly reduce the noise of the time series while preserving the overall trend, thus also increasing the quality of the data used during the subsequent forecasting stage.
# Cross-View Outdoor Localization in Augmented Reality by Fusing Map and Satellite Data
## 4 Results
### Ablation Studies  
In this section, we examine the different ablation studies we conducted. We ensure that the model makes use of the ground-level image for the final position estimation and look at the effectiveness of the satellite data, OSM data, and starting position. The goal of this is to provide an argument for the effectiveness of each part of the model, which we will also show with a contribution ablation study comparing the original LM model with our AlignNet.
## 3 Data and Methodology
### Survey and Data Acquisition  
A total of 15 deep TDEM survey points were executed on a close to flat area covered mainly by vegetable crops. TDEM data were acquired using a TerraTEM system (Monex GeoScope Ltd.; The Basin, VIC, Australia) together with a TerraTX-50 transmitter (Figure 3). The Terra TX-50 transmitter delivers an output voltage of 96 volts and 50 A transmitter current, with a GPS synchronization module. A coincident-loop 600 m \(\times\) 600 m square configuration was selected to obtain higher penetration and better signal to noise levels at late points, allowing a more reliable apparent conductivity estimation at depth [44]. Loops were laid out using a compass for correct placement. TDEM locations were determined using a handheld 3 m accuracy GPS while topography heights were extracted from 5 m resolution DEM (Instituto Geografico Nacional, [[https://centrodedescargas.cnig.es/CentroDescargas/index.jsp](https://centrodedescargas.cnig.es/CentroDescargas/index.jsp)]([https://centrodedescargas.cnig.es/CentroDescargas/index.jsp](https://centrodedescargas.cnig.es/CentroDescargas/index.jsp))) (accessed on 11 October 2022).  
TDEM survey points were distributed in a self-regular grid covering the selected survey area, where there was sufficient spacing for the loop laying and avoiding the close presence of anthropogenic interferences. TDEM-13 was laid out in the vicinity of the Monzon-1 borehole. The presence of power lines, plot fencing and lack of spacing in some points, especially in the southern part of the study area, generated a deformation of the recording grid, creating a final non regular grid with an important gap between southern survey points (TDEM-7, 12, 13 and 15) and the northern ones (TDEM-5, 6, 8 and 11) (Figure 2). To improve data quality during acquisition and decrease the interference noise, a strong signal and enhanced signal-to-noise ratio proceeding was established based on up to 4000 stacks per channel [45] and a high current external transmitter (up to 50 amperes)0020(Figure 4).
# Ciphered BCH Codes for PAPR Reduction in the OFDM in Underwater Acoustic Channels
## 3 System Architecture
### Peak to Average Power Ratio  
PAPR refers to the ratio between the peak power and average power of the system. OFDM adds a PAPR of \(10\log K\) dB to the original PAPR of the system, where \(K\) is the number of subcarriers used. The PAPR of an OFDM system [61] is calculated as:  
\[\text{PAPR }(\text{dB})=10\log_{10}\frac{max[|x_{n}|^{2}]}{E[|x_{n}|^{2}]} \tag{9}\]  
where \(x_{n}\) is the discrete time domain OFDM signal, \(max\)\([|x_{n}|^{2}]\) is the peak signal power and \(E[|x_{n}|^{2}]\) is the average signal power. The PAPR performance is usually evaluated using a complementary cumulative distribution function (CCDF). A CCDF is a plot that shows the probability of exceeding a certain value of PAPR. Additionally, in this work, we use \"PAPR gain\" to evaluate the effectiveness of a certain ciphered BCH code. We define \"PAPR gain\" as the amount of reduction in PAPR compared with the maximum possible PAPR of an OFDM signal. The maximum possible PAPR of an OFDM signal with \(N\) subcarriers is given by \(10\log_{10}N\).