esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In recent years, the advancement of deep learning technology has led to excellent performance in synthetic aperture radar (SAR) automatic target recognition (ATR) technology. However, due to the interference of speckle noise, the task of classifying SAR images remains challenging. To address this issue, a multi-scale local-global feature fusion network (MFN) integrating a convolution neural network (CNN) and a transformer network was proposed in this study. The proposed network comprises three branches: a CovNeXt-SimAM branch, a Swin Transformer branch, and a multi-scale feature fusion branch. The CovNeXt-SimAM branch extracts local texture detail features of the SAR images at different scales. By incorporating the SimAM attention mechanism to the CNN block, the feature extraction capability of the model was enhanced from the perspective of spatial and channel attention. Additionally, the Swin Transformer branch was employed to extract SAR image global semantic information at different scales. Finally, the multi-scale feature fusion branch was used to fuse local features and global semantic information. Moreover, to overcome the problem of poor accuracy and inefficiency of the model due to empirically determined model hyperparameters, the Bayesian hyperparameter optimization algorithm was used to determine the optimal model hyperparameters. The model proposed in this study achieved average recognition accuracies of 99.26% and 94.27% for SAR vehicle targets under standard operating conditions (SOCs) and extended operating conditions (EOCs), respectively, on the MSTAR dataset. Compared with the baseline model, the recognition accuracy has been improved by 12.74% and 25.26%, respectively. The results demonstrated that Bayes-MFN reduces the inter-class distance of the SAR images, resulting in more compact classification features and less interference from speckle noise. Compared with other mainstream models, the Bayes-MFN model exhibited the best classification performance.  
synthetic aperture radar (SAR); speckle noise; ConvNeXt; Swin Transformer; Bayesian hyperparameter optimization algorithm +
Footnote †: journal: Applied Sciences
###### Abstract  
Oil spill cleanup in water remains a critical challenge due to the harmful secondary pollution from conventional methods such as burning or chemical degradation. Herein, we present a facile method to fabricate a superhydrophobic and superoleophilic polyurethane (PU) foil for efficient and environmentally friendly oil/water separation. More specifically, micropillar arrays were embedded onto the foil surface through a nanoimprinting process. Microporous structures were generated at the foil cross-section by a supercritical carbon dioxide (CO\({}_{2}\)) saturation method. The dimensions of pillar and pore structures were optimized with the aim of boosting selective wetting (i.e., water repellency and oil attraction) properties. As a result, the developed PU foil shows an oil absorption efficiency nearly 4 times higher than a pristine reference. Moreover, the structured PU foil stably retains the absorbed oil for over a week, demonstrating an absorption capacity of nearly 400%, which is also much superior than the unstructured sample. Our concept of combining both topographical micropillars and cross-sectional micropores onto PU foil provides a novel approach for achieving efficient and environmental friendly oil/water separation.  
superhydrobobicity; oil/water separation; micropillar; microporous; foil +
Footnote †: journal: Applied sciences
# A Survey on Energy Drainage Attacks and Countermeasures in Wireless Sensor Networks
## 4 Recent Review Based on State-of-the-Art Works for Energy Drain Attack
### Data Link Layer
To detect unauthorized events, reward and penalty values were introduced. For example, if a received packet is repetitive, then the penalty increases. A transmission counter was used to control transmission. Every sensor node checks the counter prior to transmitting any packet. If this counter is less than the value, then the packet is sent. If that counter becomes the predetermined limit, the packet cannot be sent until the counter value reaches zero. In summary, energy drain attacks at the data link layer focus on denial-of-sleep attacks. Different from jamming attacks in the physical layer, there is no ML approach in this \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Reference Key Features** & **Advantage** & **Disadvantage** \\ \hline
[42] & Cryptography and authentication & High adaptability & Maintenance overhead for clustering \\ \hline
[43] & Threshold limit & Specified WUR networks & Certain training period \\ \hline
[44] & Distributed cooperation model & Load balancing & Overhead for data collection \\ \hline
[45] & Cryptography and authentication & Low computational cost consumption & High memory usage \\ \hline
[46] & Secure trigger frame & Supporting multi links & Increased latency \\ \hline
[47] & Event aggregation and controlled transmissions & Low computing overhead & Low adaptability \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of recent anti energy drain attack at data link layer. research area. Based on data processing, it will be feasible to identify attack patterns and classify them as normal or attack.
## 2 Spitz Tumors
### MAP2K1 Mutated Spitz Tumors  
Mitogen-activated protein kinase kinase 1 (_MAP2K1_) is a proto-oncogene that resides on chromosome 15q. It encodes MEK1, a serine-threonine kinase downstream of RAF in the RAS-RAF-MEK-ERK pathway, which in turn activates the MAPK pathway in cell proliferation and differentiation [76; 21]. Although rare, _MAP2K1_ mutations, particularly in-frame deletions in exons 2 and 3, have been reported in Spitz neoplasms [24]. These deletions frequently lead to the inactivation of the autoinhibitory domain of the MEK1 protein resulting in unopposed activation of the kinase domain [24].  
Clinically, _MAP2K1_-mutated Spitz neoplasms are mostly present on the lower extremities of young females as small, pigmented, flat, or mildly elevated lesions [21; 24; 25; 26]. No distinctive histomorphological features have been established for these lesions given the small number of cases. However, recurring histologic characteristics among the cases described include a wedge-shaped compound or intradermal melanocytic proliferation composed of large epithelial cells with vesicular nuclei and moderate to severe nuclear pleomorphism arranged in nests and showing a plexiform growth pattern, poor maturation and a tendency to converge around the adnexal structures and neurovascular bundles [21; 24; 25; 26]. Typically, marked accumulation of melanin pigment and melanophages in the stroma and an absence of epidermal hyperplasia are evident [24; 25]. Some cases can also show overlapping features between Spitz nevi and deep penetrating nevi [76] or even dysplastic spitz nevi, also termed SPARK nevi [25]. The morphologic heterogeneity seen in _MAP2K1_-mutated Spitz neoplasms is thought to be related to the prevalence of additional secondary genetic alterations in these tumors [24]. NGS can be reliably used to detect _MAP2K1_ mutations [3].
## 4 Hybrid Control Based on Extension Theory
### Extension Theory
#### 4.1.2 Extension Sets  
In order to extend a specific set into a continuous value range \((-\infty,\infty)\) and calculate the correlation degree, the extensive set is established [37]. The extensive set \(E(T)\) is required which are defined as  
\[E(T)=\{(x,y,y^{\prime})\big{|}x\in U,y=k(x)\in I,y^{\prime}=T(x)\in I\} \tag{18}\]  
where \(y\) is the correlation function, \(y^{\prime}\) is extension function, \(T\) is the extension transformation based on the control strategies for extension set \(E(T)\), \(I\) is the real number field.  
Extension set \(E(T)\) is composed of several parts as  
\[E(T)=A_{+}\cup A_{-}\cup dA_{+}\cup dA_{-}\cup J_{0}(T) \tag{19}\]  
Figure 4: Relationship between fuzzy sets and extension sets.  
where \(dA_{+}\), \(dA_{-}\), \(A_{+}\), \(A_{-}\), and \(J_{0}(T)\) are positive extension domain, negative extension domain, positive classical domain, negative classical domain, and zero boundary of extension set respectively, which are shown as  
\[dA_{+}=\{(x,y,y^{\prime})\big{|}x\in U,y=k(x)<0,y^{\prime}=T(x)>0\} \tag{20}\]  
\[dA_{-}=\{(x,y,y^{\prime})\big{|}x\in U,y=k(x)>0,y^{\prime}=T(x)<0\} \tag{21}\]  
\[A_{+}=\{(x,y,y^{\prime})\big{|}x\in U,y=k(x)>0,y^{\prime}=T(x)>0\} \tag{22}\]  
\[A_{-}=\{(x,y,y^{\prime})\big{|}x\in U,y=k(x)<0,y^{\prime}=T(x)<0\} \tag{23}\]  
\[J_{0}(T)=\{(x,y,y^{\prime})\big{|}x\in U,y=k(x)=0\} \tag{24}\]  
Figure 5 illustrates the relationship between correlation function, extension function and extension set. The value of the correlation function determines the control field of the suspension system and extension function decides the trend of the control field. If extension function \(T(x)<0\), the system will tend to non-domain; If extension function \(T(x)>0\), the system will tend to the classical domain.
## Abbreviations  
The following abbreviations are used in this manuscript:  
BC broadcast  
CDH Command and Data handling  
CIRESCubeSat Imaging Radar for Earth Science  
COTS Commercial-Off-The-Shelf  
CRC Cyclic Redundancy Check  
ECSS European Cooperation for Space Standardization  
EGSE Electrical Ground Segment Equipments  
ESA European Space Agency  
FCT Flow Control Token  
FDIR Fault Detection Isolation and Recovery  
FPGA Field Programmable Gate Array  
In BCB In Broadcast Channel Buffer  
InSAR Interferometric SAR  
IP Intellectual Property  
JAXA Japan Aerospace eXploration Agency  
JPL Jet Propulsion Laboratory  
LEO Low Earth Orbit  
LUT Look-Up-Tables  
MAC Medium Access Controller  
NASA National Aeronautics and Space Administration  
Out BCB Out Broadcast Channel Buffer  
QoS Quality of Service  
Reg register  
SAR Synthetic Aperture Radars  
VC Virtual Channel  
VCB Virtual Channel Buffers
## 3 Results and Discussion
### Emissions Module  
The inventory includes qualitative and quantitative information on industrial pollutant emissions into air and water. It is implemented with details on waste production and management.  
This kind of information is a fundamental requirement to better understand environmental problems and identify the most effective emission reduction policies. Therefore, comprehensive emission inventories are useful both for scientific applications and political processes.  
The _emissions module_ of the web platform is divided in subsections according to the different environmental compartments. Stakeholders can consult all stored data, but they cannot modify or enter information directly. Data are updated periodically and are always specific for each pollutant, source and emission point. In particular, the following subsections are available in the web platform:  
* _Industrial wastewater_ (Figure 4). Here, there is the list of the authorized discharges of each industry, the related technical specifications, the coordinates of each discharge point, the information on the receptors, the treatment plant's details (if required) and the authorized emission limits.
* _Urban sewage_. Technical specifications of each purification plant, coordinates of the discharge point, information on the receptors and authorized emission limits are some of the available data.
* _Atmospheric emissions_. There is the list of the authorized emission points of each industry, the related technical specifications, the coordinates of each emission point, the treatment plant's details (if required), the authorized emission limits, the information on the putting into operation and commissioning.  
The _waste management subsection_ are still being designed. Here, the following information will be provided: technical specifications and coordinates of the plant, types of waste treated, description of the waste treatment activities, types of disposal and recovery codes and details of the environmental management actions.  
The _emissions module_ is strictly interconnected with the _environmental inspections module_, where analytical data are available.  
Figure 4: Example of the _industrial wastewater subsection_. (**a**) General information on a discharge point of an installation and the related receptor; (**b**) Technical specifications of the discharge; (**c**) Authorized emission limits; (**d**) List of the analytical checks carried out.
# Analysis of Slope Stability in Ion-Adsorption Rare Earth Mine Under In Situ Leaching Condition
## 2 Methodology
### Model Parameter Selection  
In this study, the physical and mechanical parameters and permeability coefficients of rock and soil layers of the studied slope were comprehensively selected based on the results in Table 1 and relevant specifications. Firstly, for the rock and soil layer above the water level, the physical and mechanical parameters are determined under the saturation conditions. The rock and soil layer below the water level is described based on the unsaturated conditions. Then, the permeability coefficient of the rock and soil layer was estimated by combining the results in Table 1 and the volume water content equation provided by the SEEP/W module, by estimating the permeability coefficient equation. The SEEP/W module provides typical moisture content functions for various types of soils. Simply input the residual moisture content and saturated moisture content, and then select the corresponding soil sample to establish an unsaturated soil calculation model.  
During the process of the in situ shallow well leaching method, leaching solutions are injected through injection wells, reducing the shear strength parameters of the weathered ore layers. Relevant studies [36] have shown that in situ leaching reduced the internal friction and cohesive force of the weathered ore layer within 15 days, and after this period, it remains essentially unchanged. Therefore, the cohesive force and the internal friction of the slope materials need to be reduced in the limit equilibrium analysis. The 1.0-2.0% magnesium sulfate aqueous solution is used as a leaching agent in this leaching process. The parameter reduction scheme in this simulation is to reduce the cohesive force and internal friction of the fully weathered layer to 0.85 and 0.7 times before injection on the 5 th and 10 th days of injection, respectively.
## 1 Introduction
The HI-SLAM [27] proposes a real-time monocular dense mapping framework based on neural implicit fields, constructs neural field maps through multi-resolution grid encoding and Signed Distance Function (SDF) representation, and enhances depth accuracy with monocular depth priors, achieving high-precision real-time SLAM. This work offers an enhanced ORB-SLAM3 technique to increase positioning accuracy and dense map creation in difficult open-pit mining situations. This article improves the feature-matching process inside the ORB-SLAM3 framework by implementing point-line feature fusion, increasing matching resilience in low-texture and low-contrast settings. Moreover, it integrates a dense point cloud mapping component that produces intricate dense maps by amalgamating camera depth data and converting them into effectively stored octree maps, substantially minimizing storage demands and enhancing three-dimensional query efficiency. The experimental results indicate that the enhanced algorithm outperforms existing approaches in positioning accuracy, map creation quality, and navigation performance, exhibiting significant robustness, especially in complicated terrains and open-pit mining situations.
## 2 Dataset, Models and Method
### Dataset
#### 2.1.2 AMSR-E Brightness Temperature Data  
AMSR-E is a total power passive microwave radiometer that measures horizontally and vertically polarized brightness temperatures at separate frequencies of 6.925, 10.65, 18.7, 23.8, 36.5, and 89.0 GHz. Its individual measurements have spatial resolutions varying from \(\sim\)0.05\({}^{\circ}\) at 89.0 GHz to \(\sim\)0.5\({}^{\circ}\) at 6.925 GHz. Because of spatial resolution differences, we used the nearest neighbor interpolation method to resample the data to fit the horizontal resolution of our model (\(\sim\)0.05\({}^{\circ}\)). We used vertical polarizations of 6.925 and 10.65 GHz to retrieve soil moisture information, because the atmosphere can be considered transparent at these frequencies. The 89-GHz band of the AMSR-E shows high sensitivity to cloud water content and precipitation compared with other channels. Consequently, the 89-GHz vertical polarization was used to assimilate cloud information over the land surface. The motivation for using vertical polarization is that it is less sensitive than horizontal polarization to land surface roughness and vegetation. The calibrated brightness temperature (Level 1B) data were obtained from the Japan Aerospace Exploration Agency (JAXA).
# Multi-Winner Election Control via Social Influence: Hardness and Algorithms for Restricted Cases
## 2 Background
### Linear Threshold Model  
We are given a directed graph \(G=(V,E)\). Each edge \((u,v)\in E\) has a weight \(b_{u,v}\in[0,1]\). The sum of the incoming weight to each node \(v\in V\) is at most one, i.e., \(\sum_{u\in N_{v}^{i}}b_{u,v}\leqslant 1\), where \(N_{v}^{i}\) is the set of incoming neighbors of \(v\). Furthermore, each node \(v\in V\) has a threshold \(t_{v}\in[0,1]\) which is generated uniformly at random.  
In this model, the diffusion will start from a set of nodes \(S\subseteq V\) known as seed nodes. At the first step, just the seed nodes will become active/influenced/infected, and all other nodes are inactive. Let us show \(A_{i}\) as the set of nodes that are active at step \(i\), i.e., \(A_{1}=S\). The activation process, for each step \(i>1\), is as follows: all nodes in \(A_{i-1}\) will remain active at step \(i\), i.e., \(A_{i-1}\subseteq A_{i}\); moreover, each inactive node \(v\in V\setminus A_{i-1}\) will become active if the sum of the weight from its incoming activated neighbors is not less than its threshold, i.e., for each node \(v\in V\setminus A_{i-1}\), it will be in \(A_{i}\) if \(\sum_{u\in N_{v}^{i}}b_{u,v}\geqslant t_{v}\). The diffusion process will proceed in utmost \(|V|\) discrete steps, and it will stop as soon as no extra node becomes active, i.e., it stops at step \(k>1\) if \(A_{k}=A_{k-1}\). We use \(A_{S}\) as the set of activated nodes after the diffusion process started from the set of seed nodes \(S\). In what follows, to increase the readability of this article, when we say after \(S\), it means after the diffusion process started from a set of seed nodes \(S\). Note that the thresholds are not a part of the input, and they will be generated uniformly at random and independently when we run the process. Furthermore, the process is random, and several executions on the same graph may get different results for \(A_{S}\).  
[PERSON] et al. [7] defined the IM problem as: Given a graph \(G=(V,E)\) and a budget \(B\leqslant|V|\). Find a set of seed nodes \(S\subseteq V\), (\(|S|\leqslant B\)) so that the expected \(|A_{S}|\) is maximized. They proved that the problem is _NP_-hard under LTM. Moreover, they showed that a greedy algorithm can solve the problem approximately within a factor of \(1-\frac{1}{\epsilon}-\epsilon\), where \(\epsilon\) is any small constant and fixed number.
## 2 Three Dimensional Printing Technologies
### Selective Laser Sintering (SLS)/Melting (SLM) and Electron Beam Melting (EBM)  
Selective laser sintering/melting (SLS/SLM) aims to print 3D objects using powder materials (e.g., polyamides, polycaprolactone, hydroxyapatite, stainless steel, titanium, and Co/Cr) [23]. This powder is heated above the glass transition or melting temperature using a high energy CO\({}_{2}\) beam, thus sintering/melting the particles together in a specific pattern. The curing of the powder proceeds according to a layer-by-layer step, until the entire material is printed. The post-printing processing consists of the removal of the excess powder particles for plastic materials [6], while it is far more challenging for metals, for which specific post-processing is required to remove support structures (saving, CNC machining etc.).  
Electron beam melting (EBM) technology is based on the use of metal powder (stainless steel, titanium, etc.) as a printing material as well. In this case, the powder is submitted to sintering by a computer-controlled electron beam in a vacuum (differently from SLS which uses a laser beam) [17].
## 2 State of the Art  
### Microgears and the Monitoring of Their Manufacturing Process  
There is no universal definition of microgears, but according to the standard VDI 2731, microgears are defined as gears that have two of the following three characteristics (see Figure 1) [3]:  
* Characteristic external dimensions (e.g., diameter or edge length) \(<\) 20 mm
* Module \(<\) 200 \(\upmu\)
* Structural details \(<\) 100 \(\upmu\)  
Monitoring the production of micro components requires special, highly sensitive measurement technologies [9]. In addition to traditional measurement technologies, indirect signals can also be utilized for process monitoring. The utilization of current signals from the spindle and feed motors, as described by [PERSON] et al. [10], has the advantage of being simple and cost effective to measure in comparison to the use of force signals, which contain high levels of noise [11]. Additionally, structure-borne sound signals can  
Figure 1: Characteristic gear parameters.  
provide useful information regarding tool wear [12]. [PERSON] [13] further uses structure-borne sound signals for the diagnosis of damaged types of microgears within microdrive systems.
# Magnetic Field Effect of Near-Field Radiative Heat Transfer for SiC Nanowires/Plates
###### Abstract  
The SiC micro/nano-scale structure has advantages for enhancing nonreciprocal absorptance for photovoltaic use due to the magneto optical effect. In this work, we demonstrate the near-field radiative transfer between two aligned SiC nanowires/plates under different magnetic field intensities, in which Lorentz-Drude equations of the dielectric constant tensor are proposed to describe the dielectric constant as a magnetic field applied on the SiC structure. The magnetic field strength is qualified in this study. Using local effective medium theory and the fluctuation-dissipation theorem, we evaluate the near-field radiation between SiC nanowires with different filling ratios and gap distances under an external magnetic field. Compared to the near-field heat flux between two SiC plates, the one between SiC nanowires can be enhanced with magnetic field intensity, a high filling ratio, and a small gap distance. The electric field intensity is also presented for understanding light coupling, propagation, and absorption nature of SiC grating under variable incidence angles and magnetic field strengths. This relative study is useful for thermal radiative design in optical instruments.  
magnetic field effect; near-field radiation; SiC nanowires +
Footnote †: journal: Applied Physics Letters