esa-sceva/satcom-chunk-collection huggingface dataset:
# Inverse Halftoning Methods Based on Deep Learning and Their Evaluation Metrics: A Review
## 2 Inverse Halftoning Technology for Digital Halftoning
### End-to-End Learning Method Based on Training of Multiple Sub-Networks (EEBMN)  
Because there are two tasks of removing halftone noise dots and restoring fine details in inverse halftoning, the mapping from halftoning images to continuous-tone images is a complex nonlinear transformation. Thus, it is difficult for inverse halftoning with a signal network to address these two tasks well. On the basis of the idea that the transformation can be considered as a compound of multiple nonlinear transformations, many researchers proposed inverse halftoning methods based on multiple sub-networks, where each sub-network was designed for a different task [19; 20; 21; 22; 23]. Depending on whether the subnetwork is pre-trained or not, the methods in references [20] are called deep learning without pre-training (DLOPT), whereas the other methods in references [19; 20; 21; 22; 23] are called deep learning with pre-training (DLWPT). Experiments show that these methods can achieve better performance than the method with a single network.
# Vision-Guided Autonomous Robot Navigation in Realistic 3D Dynamic Scenarios +
## 5 Three-Dimensional Navigation
### Discussion and Limitations  
Our research endeavors in 3D navigation, while promising, are accompanied by inherent limitations that warrant consideration.  
Conversion Discrepancies: One notable limitation arises during the conversion process from triangular meshes to navigation meshes (NavMeshes). This transformation can occasionally introduce gaps or holes in the NavMesh, creating discrepancies between what is perceived as walkable by our navigation system and the physical reality. For instance, as depicted in Figure 13, while the white mesh represents the constructed 3D environment,  
Figure 14: The wheeled mobile robot equipped with an RGB-D camera. A note should be made that 3D LiDAR and 2D LiDAR are not used in this study.  
certain walkable areas, such as the floor, may not be accurately converted into the NavMesh (depicted in blue). These conversion discrepancies, although infrequent, can potentially affect the robot's navigation by misinterpreting certain regions as navigable when they are not.  
Outdoor Navigation Challenges: Another significant limitation pertains to outdoor navigation, particularly in the context of varying light conditions. The performance of our system's relocalization capabilities can be susceptible to perturbations caused by changing illumination, with outdoor environments exhibiting a heightened sensitivity to this effect. Addressing this limitation is an ongoing effort, with potential strategies under exploration to enhance the system's robustness under varying outdoor lighting conditions.  
Indoor Navigation Stability: Conversely, our research indicates a higher degree of navigation stability in indoor environments. This stability is attributed to the relatively consistent mapping conditions that remain largely unaffected by changes in illumination. Such indoor navigation stability finds practical utility in controlled environments like warehouses or factories, where lighting and mapping conditions are more predictable.  
In summary, our 3D vision-based autonomous navigation system exhibits great promise, yet we acknowledge these inherent limitations. We are actively engaged in ongoing research to address these challenges and further refine the capabilities of our system, aiming to bridge the gap between theoretical advancements and practical solutions in the field of autonomous navigation.
## 3 Methods
### A2C2f-FRFN
#### 3.1.1 Feature-Refinement Feedforward Network  
The conventional Feedforward Network (FFN) processes information at each pixel location individually, playing a crucial role in improving feature representation through self-attention mechanisms. Therefore, designing an effective feedforward network to enhance features and drive potential high-quality image recovery is critical.  
To overcome this issue, we introduce a Feature-Refinement Feedforward Network (FRFN), which performs feature transformation through an enhancement-relief paradigm. Specifically, we strengthen the information elements within the features by incorporating a partial convolution (PConv) operation and use an adaptive gating subsystem to automatically suppress feature channels with high redundancy coefficients, thereby achieving dynamic computation allocation during information processing. The FRFN can be represented as:  
\[\hat{X}^{\prime}=\text{GELU}(W_{1}\text{PConv}(\hat{X})),\quad[\hat{X}^{ \prime}_{1},\hat{X}^{\prime}_{2}]=\hat{X}^{\prime}, \tag{1}\]  
\[\hat{X}^{\prime}_{r}=\hat{X}^{\prime}_{1}\otimes\text{F}(\text{DWConv}(\text{ R}(\hat{X}^{\prime}_{2}))), \tag{2}\]  
\[\hat{X}^{\prime}_{\text{out}}=\text{GELU}(W_{2}\hat{X}^{\prime}_{r}), \tag{3}\]  
where \(W_{1}\) and \(W_{2}\) represent linear projection matrices; the operator \([.]\) denotes channel-wise slicing; \(\text{R}(\cdot)\) and \(\text{F}(\cdot)\) correspond to the Reshape and Flatten operations, respectively, transforming sequential inputs into 2D feature maps (and vice versa), a key mechanism for introducing locality into the architecture; \(\text{PConv}(\cdot)\) and \(\text{DWConv}(\cdot)\) indicate partial convolution and depthwise convolution operations; and the symbol \(\otimes\) denotes matrix multiplication. The input features (\(\text{HW}\times\text{C}\)) are first normalized through Layer Normalization (LN), then processed by partial convolution (PConv) and linear transformation for dimension adjustment. Subsequently, the features are split into two parallel branches: one branch extracts local patterns through depthwise separable convolution (DWConv), while the other preserves the original information flow. These two branches are fused via element-wise multiplication, followed by another linear transformation to restore the original dimensionality. Finally, the processed features are combined with the initial input through residual connection, yielding refined output features. The architecture is illustrated in Figure 3.  
The Feature-Refinement Fusion Network (FRFN) enhances representation capability by distilling discriminative features from information flow while suppressing redundant components, effectively implementing feature selection at the channel level. This architecture further provides an explicit pathway for the model to eliminate uninformative features along channel dimensions through its dual-branch design.
###### Abstract
It can effectively extract the rich spatial-spectral information of HSI. 3. A joint attention feature extraction strategy was proposed. For the HSI data, the network is only allowed to learn its useful bands for HU. On the other hand, the endmember bundles aggregate spatial information to a certain extent, and the amount of data is less than that of the original HSI; therefore, it is more efficient to extract spatial information from the endmember bundles. The rest of the paper is structured as follows. Section 2 briefly introduces the relevant category models for advanced HU, while Section 3 provides details for related methods and the proposed EACNN network framework. Next, Section 4 validates the proposed method and evaluates the experimental results from different datasets. Section 5 discusses the above experiments. Finally, conclusions are reached in Section 6.
# A Realistic, Flexible and Extendible Network Emulation Platform for Space Networks
## 3 Architecture of the Network Emulation Platform
### Physical Implementation Model
#### 3.2.1 Web UI  
Researchers interact with the proposed platform through the Web UI. The Web UI stores network model parameters configured by researchers in the database, and shows network models in real time to allow researchers to observe the emulation process intuitively.
## 3 Results and Discussion
### General Characteristics of De Novo Bacidia rubella Genome
#### 3.1.3 Other Biosynthetic Genes Identified In Silico in the Two _Bacidia_ Species  
Using further in silico analyses with antiSMASH, we were able to identify genes encoding enzymes to synthesize secondary metabolites such as clavaric acid (100% similarity) and squalestatin S1 (40% similarity). These terpen have been identified in both _Bacidia_. Moreover, we identified a monascorubrin biosynthetic gene in _B. rubella_ confirmed by 100% BLAST identity to the monascorubrin biosynthetic gene from _Talaromyces_ (_Penicillium_) _marneffei_ (PKS3: HM070047) (2004) (2004). Apart from monascorubrin biosynthesis, PKS3 genes were suggested to be involved in the production of the well-known toxin citrinin, as well as a yellow pigment, anakfalvin (2004). Monascorubrin and its related compounds are polyketides used as natural red colorants for food (2004). In _B. rubella_, monascorubrin could be  
\begin{table}
\begin{tabular}{l l l l l l l} \hline
**Tree** & **logL** & **Deltal** & **p-KH** & **p-SH** & **c-ELW** & **p-AU** \\ \hline
**Bacidia + Cladoniaceae** & \(-\)28,054.52811 & 2.062 \(\times\) 10\({}^{-8}\) & 0.495\(+\) & 0.814\(+\) & 0.496\(+\) & 0.566\(+\) \\ \hline
**Cladoniaaceae + Parmeliaceae** & \(-\)28,108.54142 & 54.013 & 0.0126\(-\) & 0.0126\(-\) & 0.00853\(-\) & 0.0081\(-\) \\ \hline \end{tabular}
\begin{tabular}{l l l l l l} \hline  & deltaL: logL difference from the maximal logI in the set. p-KH: \(p\)-value of one-sided Kishino–[PERSON] test (1989). p-SH: \(p\)-value of Shimodaira–Hasegawa test (2000). c-ELW: Expected Likelihood Weight ([PERSON] and [PERSON] 2002). p-AU: \(p\)-value of approximately unbiased (AU) test ([PERSON], 2002). \\ \end{tabular}
\end{table}
Table 3: Parameters for the topology test. Topology 1, recovering _Bacidia_ + Cladoniaceae as monophyletic; Topology 2, recovering Cladoniaceae + Parmeliaceae as monophyletic. Value for the Kishino–Hasegawa test ([PERSON] and [PERSON], 1989), [PERSON] and [PERSON], 1989), expected likelihood weights ([PERSON] and [PERSON], 2002) and approximately unbiased (AU) test ([PERSON], 2002) are given.  
responsible for the characteristic orange to the orange-brown coloration of the apothecia. However, these substances have not yet been reported from _B. rubella_.  
In the genome of _B. gigantensis_, we identified genes possessing high sequence similarity with genes involved in the production of pyranonigrin E (100% similarity to BGC0001124), naphthopyrone (100% similarity to BGC0000107), and melanin (100% similarity to BGC0001265). However, in most cases, only a part of the sequences showed a high percentage of similarity; therefore, our in-silico based report is provisional. Additional detailed studies are necessary to confirm these first functional assignments.
# Bearing Fault Identification Using Machine Learning and Adaptive Cascade Fault Observer
## 5 Experimental Results
### CWRU Dataset  
In this section, the SVM is directly applied to FOR-ACO, FOR-SPIO, and FOR-PIO that further are referred to as SVM + FOR-ACO, SVM + FOR-SPIO, and SVM + FOR-PIO, respectively. These methods are used to test single-type fault identification in two different conditions: crack-variant and load-variant conditions.
# Surface Electromyography-Based Action Recognition and Manipulator Control
## 2 Method and Mathematical Background  
### System Architecture  
The acquisition electrodes were attached to the forearm of the subject. After being collected, 4-channel sEMG signals were simply amplified by the integrated programmable gain amplifier (PGA) in the acquisition instrument, and transmitted to the host computer through wireless local area network (LAN). After preprocessing, noise was got rid of to further raise the signal-noise rate (SNR) and the signals were divided into overlapping sliding windows. Then, features were extracted and later the classifier learned the eigenvalues of different actions and carried out the classification. Finally, the classifier output the label and the corresponding control command was transmitted to the manipulator to realize the control. The manipulator was kept under observation to see whether it followed the movement of subjects consistently and synchronously, which provided a feedback. Figure 1 illustrates the schematic diagram of the sEMG detection and the action recognition system.
## 5 Conclusions  
In this paper, a novel adaptive state detector-based post-filtering active control (DMPFxNLMS) algorithm is proposed to achieve ANC in both non-impulsive and impulsive noise environment. An adaptive Gaussian core is introduced that allows the convergence rate of the weight vector to be raised and lowered more quickly than conventional methods when the value of the feedback signal \(e\left(n\right)\) increases and decreases in fast tracking mode. A novel state detector is also developed to keep robust for the adaptive filter when the divergence of \(e\left(n\right)\) is barely acceptable, and its feasibility is proved by theoretical analysis. Simulations and experimental results show that the proposed DMPFxNLMS algorithm can provide faster tracking than the PFxNLMS algorithm using optimal  
Figure 11: Performance comparison of noise in error learning curves.  
Figure 12: ANR performance comparison of the real-time experiment.  
parameter in non-impulsive interference environment and have better robust performance than FxRMC and FxlogMLS algorithms. Moreover, the DMPFxNLMS algorithm also has less computational load than FxRMC algorithm. Therefore, the proposed DMPFxNLMS algorithm is able to provide better ANC performance in Gaussian environment with impulsive interference.  
Conceptualization, W.Z.; methodology, W.Z. and L.L.; software, W.Z.; validation, W.Z. and L.L.; formal analysis, W.Z. and L.L.; investigation, W.Z.; resources, S.S. and J.S.; data curation, W.Z. and L.L.; writing--original draft preparation, W.Z.; writing--review and editing, W.Z. and L.L.; visualization, W.Z. and L.L.; supervision, W.Z. and L.L.; project administration, L.L.; funding acquisition, S.S. and J.S.  
This research was funded by National Science Foundation of China, grant number 61171183, 61471140. This research is also funded by Aerospace Support Fund in Harbin Institute of Technology, grant number 01320214.  
The authors declare no conflict of interest.
# Deep Contrastive Learning-Based Model for ECG Biometrics
## 4 Conclusions  
In this research work, we proposed an end-to-end deep learning system for biometric identification using ECG biometrics signals. The ECG signal inherited by an individual is highly secure and very hard to be forged. The proposed system includes three main blocks. The first block is used to convert a 1D ECG signal into a 2D image suitable for feature extraction via convolutional networks and to extract the representing features using EfficientNet backbone. The second block is a contrastive learning module used to boost the dissimilarity between the different classes. The last block is a classification layer located at the head of the network. The experimental results reveal the promising capabilities of the proposed solution in terms of classification accuracy. For future developments, we plan to investigate advanced fusion strategies based on deep learning methods to improve the classification results, especially for the cross-session scenario. In addition, we plan to rely on domain adaption methods to reduce the discrepancy between training and testing sessions obtained from different long periods.  
Methodology, N.A. ([PERSON]); software, R.M.J.; formal analysis, M.S.I. and Y.B.; investigation, Y.B.; resources, H.A.; data curation, M.S.I. and H.A.; writing--original draft preparation, N.A. ([PERSON]); writing--review and editing, R.M.J. and Y.B.; supervision, N.A. ([PERSON]); funding acquisition, N.A. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Science, Technology and Innovation Plan, King Abdulaziz City for Science and Technology, Saudi Arabia, grant number 13-INF2168-02.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Macro** & **Weighted by Prevalence** \\ \hline One-vs-One ROC AUC scores & 0.999976 & 0.999979 \\ One-vs-Rest ROC AUC scores & 0.999976 & 0.999979 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The accuracy of the proposed system on the first session dataset.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{5}{c}{**Over All Accuracy**} \\ \hline
**Dataset** & & **Session 1** & **Session 2** & **Session 1 to 2** & **Session 2 to 1** \\ \hline \multirow{2}{*}{Contrastive learning} & Without & 96.61 & 95.35 & 39.61 & 41.96 \\  & With & 98.20 & 97.40 & 46.06 & 47.9 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Sensitivity with respect to the contrastive loss module.  
**Institutional Review Board Statement:** The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Board of King Saud University (E-22-6748).  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Breath Analysis: A Promising Tool for Disease Diagnosis--The Role of Sensors
## 3 Types of Nanomaterial-Based Sensors in Breath Analysis
### Selective Applications  
Nanomaterial-based hybrid materials have been used for optical fiber modification as sensing films for selective VOC detection. The use of graphene, commonly combined with MNPs [188; 189], is a representative example. Pt NPs-GO [188] and Ag NPs-GO [189] functionalized optical fibers have been developed for efficient and selective NH\({}_{3}\) detection (NH\({}_{3}\) is a common biomarker for renal and liver diseases as well as _Helicobacter pylori_ infection) [7]. Remarkably, Pt NP-functionalized increased the sensitivity in comparison to pristine GO [188], while the concentration of Ag NPs was inversely correlated with sensitivity [189]; this reveals the benefits as well as the vulnerability of the synergistic effects of hybrid materials. An additional hybrid material, namely a thin film of poly (allylamine hydrochloride) and Si NPs infused with tetrakis (4-sulfophenyl) porphine, has been used as an optical-fiber coating for the selective detection of methanol in the presence of water and other alcoholic vapors [190].
# Polymeric and Crystalline Materials for Effective and Sustainable CO\({}_{2}\) Capture
## 3 Petroleum-Based Polymers
### Polymeric Ionic Liquids  
Polymeric ionic liquids (PILs) are defined as polyelectrolytes containing ionic liquid species (cations or anions) and thus, forming a large molecular structure [128]. Ionic liquids (ILs) are liquids at room temperature due to weak intermolecular forces, whereas PILs possess strong intermolecular forces between polymer chains and are, therefore, solid. In addition, PILs, synthesized from ionic liquid monomers (ILMs), offer improved processability, stability, and control over structure. For PILs to be successful candidates for CO2 capture, the effect of structural variation in PILs (i.e., choice of cation, anion, backbone, alkyl chain length, porosity, and cross-linking) needs to be evaluated. For a complete overview of the CO2 capture capacity of PILs up to 2015, the reader is referred to the work of [PERSON] and coworkers [129]. For the reader's benefit, typical structures of PILs for CO2 sorption are illustrated in Figure 4. Depending on the structure and the choice of the ion, CO2 capture capacity ranging from 0.03 to 0.3 mmol g\({}^{-1}\) at 298 K and 1 bar could be obtained [129].  
Recently, [PERSON] and coworkers reported the preparation of benzimidazole-based hypercross-linked poly(ionic liquid) (HPLs) [130]. The HPLs were obtained by Friedel-Crafts alkylation reaction between the ionic liquid monomer (i.e., benzimidazole) and benzyl halide moieties. A CO2 uptake of 1.47 mmol g\({}^{-1}\) at 298 K and 1 bar were obtained. Interestingly, the polymeric substrate also showed catalytic activity for CO2 cycloaddition (i.e., epoxides to cyclic carbonates).  
Figure 3: Recent examples of polymeric amines for CO2 capture [122; 124; 125].
###### Abstract  
In most of the realistic measurement device-independent quantum key distribution (MDI-QKD) systems, efficient, real-time feedback controls are required to maintain system stability when facing disturbance from either external environment or imperfect internal components. Traditionally, people either use a \"scanning-and-transmitting\" program or insert an extra device to make a phase reference frame calibration for a stable high-visibility interference, resulting in higher system complexity and lower transmission efficiency. In this work, we build a machine learning-assisted MDI-QKD system, where a machine learning model--the long short-term memory (LSTM) network--is for the first time to apply onto the MDI-QKD system for reference frame calibrations. In this machine learning-assisted MDI-QKD system, one can predict out the phase drift between the two users in advance, and actively perform real-time phase compensations, dramatically increasing the key transmission efficiency. Furthermore, we carry out corresponding experimental demonstration over 100 km and 250 km commercial standard single-mode fibers, verifying the effectiveness of the approach.  
measurement-device-independent quantum key distribution; reference frame calibration; machine learning; transmission efficiency; biased basis choice +
Footnote †: journal:  
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