esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Image Inpainting Methods Based on Deep Learning
## 4 Deep-Learning-Based Image Inpainting Algorithm
### Computational Component Optimization
#### 4.2.3 Partial Convolution  
To distinguish between valid pixels and defective pixels, the convolutional layer should only operate on valid pixels that meet certain conditions. [PERSON] et al. [58] introduced partial convolution and the mask updating strategy. Here, X represents the input feature map, M is the mask, and W is the convolution kernel. Unlike standard convolution, where the input feature map (X) is directly multiplied by the kernel (W), then summed, in partial convolution, M is introduced as a mask. The mask (M) consists of elements 0 and 1, where 0 indicates a damaged region. This involves computing the pixel values of the undamaged region with X and M, then applying the convolution operation with kernel W. The computation process of partial convolution is shown in Figure 5.  
Figure 4: Dilated convolution.  
Figure 3: Regular convolution.  
The specific formula for this operation is given by Equation (7):  
\[x^{\prime}=\begin{cases}\mathrm{W}^{T}(\mathrm{X}\odot\mathrm{M})\frac{\mathrm{ sum}(1)}{\mathrm{sum}(\mathrm{M})}+b,&\text{if $\mathrm{sum}(\mathrm{M})>0$}\\ 0,&\text{otherwise}\end{cases} \tag{7}\]  
When elements are filled in the damaged region, M updates as convolution progresses according to Equation (8).  
\[m^{\prime}=\begin{cases}1,&\text{if $\mathrm{sum}(\mathrm{M})>0$}\\ 0,&\text{otherwise}\end{cases} \tag{8}\]  
where \(m\) represents the elements of M. Based on the mask update rule, the mask is automatically updated until all values in the mask are 1. The introduction of partial convolution allows the image repair network model to repair any irregular area.
## 3 Analysis and Discussion
### Semi-Arid Lands
#### 3.1.2 Monitoring and Evaluation of Ecological Restoration  
In the western US, disturbances from past land use and invasive species (e.g., cheatgrass; _(Bromus lectorum L._ [Poaceae]) pose significant challenges to sustainable rangeland management. Many lower-elevation basins in the Intermountain West, previously dominated by big sagebrush (_Artemisia tridentata_ Nutt.) or salt desert shrub ecosystems, are now threatened by annual grass invasions [41]. Recognizing the need for science-based solutions to combat these invasions and their impact on rangeland sustainability, substantial restoration efforts have been made. However, success has been limited, and annual grasses continue to spread beyond the Intermountain West [35].  
To monitor the impact of restoration efforts, various performance metrics are employed, including canopy cover (using field-based measurements and also estimates extracted using remotely sensed imagery and machine learning techniques), floristic composition, riches and abundance [42], and soil moisture (SM) [43]. The SM is a crucial metric due to its role in soil-plant-water interactions and land-atmosphere interaction systems [10]. Given the significance of SM in evaluating ecological restoration impacts, this review focuses on analyzing and systematizing literature related to the main data sources available (remote sensing and in situ measurement), mapping techniques, and modeling approaches (algorithms) for effective SM measurement and monitoring in semi-arid ecosystems.
# Experimental Evaluation of Graph Databases: JanusGraph, Nebula Graph, Neo4j, and TigerGraph
## 3 Graph Databases
### TigerGraph  
TigerGraph is an open-source graph database that was released in 2017. It is a system designed to perform multiple computations simultaneously based on parallelism [12]. It is also a distributed database capable of analyzing web-scale data in real-time. Its query language is GSQL, and as the name suggests, it is a direct extension of SQL intended for graph databases, and it enforces strict declaration. TigerGraph uses C++ as its programming language.  
TigerGraph has the following features [21]:  
* A database capable of handling graph grids and a workload in a natural production environment where tens of terabytes of data are connected and constantly updated;
* It has a high availability cluster that uses replication to provide continuous service when one or more servers are unavailable, or some service components fail. The main advantages of TigerGraph are as follows [21]:
* In terms of elasticity, users often do not know what hardware or computing power they will need. Elasticity eliminates the need to plan for the capacity;  
Figure 3: The Neo4j interface.  
It includes a flexible, high-performance data loader that can transfer tabular or semi-structured data while online.  
On the other hand, its limitations are [21]:  
It only runs on Linux servers;  
It has an expensive cloud service;  
It incurs an expensive annual subscription price for the storage it provides.  
TigerGraph's interface is also very intuitive; it is shown in Figure 4.
## 3 Data Visualization and Analysis Tool
### Overview  
Upon loading a spectrum, the user can opt to display the LIBS spectra recorded by the instrument in three different ways:  
1. **Average over all measurement points and shots:** This provides a spectrum that is more representative of the bulk composition.
2. **Shot averages per position:** Emphasizes differences between sampling locations.
3. **Individual display of each shot's spectrum:** Reveals possible shot-to-shot variations, allowing for the derivation of chemical trends with depth.  
Figure 6a illustrates the application's main window after opening a measurement, displaying the averages from five different sampling positions.  
Once a set of LIBS spectra is loaded, they can be selected and manipulated individually or collectively from the Spectrum Selector on the top right, where each loaded spectrum is listed with its measurement ID. All spectra are initially displayed exactly as recorded by the spectrometer. To account for the ambient lighting conditions and the instrument's response function, dark spectra can be subtracted and a calibration curve can be applied. For the removal of the background emission, which is usually undiagnostic, the user can choose between a moving minima algorithm [37] and a polynomial fit. In both cases, the fitted background signal is displayed, and parameters can be fine-tuned so that the fit quality can be assessed visually. Finally, the LIBS spectra can be normalized using the sum, maximum, and mean of the spectrum intensity.  
Underneath the Spectrum Selector pane, the Line Browser allows the user to display the positions of various rock-forming and trace element's emission lines as color-coded  
Figure 6: Screenshots of the application used to visualize and analyze the recorded LIBS data. In panel (**a**), the shot averages per position of a LIBS measurement are shown just after loading the data. Panel (**b**) shows the GUI after using the automatic line identification feature. For a better overview, the titanium (Ti) lines are hidden.  
lines in the spectrum. The intensity of each emission line is represented by the opaqueness of the element marker line. This feature is enabled by the NIST LIBS database [38] and helps in the identification of elements in the spectrum.
## 3 Results and Discussions
### Deformation Mechanism and Failure Mechanism
The deformation mechanism in the densification stage is mainly due to plastic collapse causing the cell walls to wrinkle along the compression direction, leading to the cell walls pressing together and the pores being compacted. At this point, the compressive properties of foam aluminum are equivalent to those of solid metal, with stress increasing rapidly within a small strain range. From the above analysis, it can be known that the deformation mechanism of foam aluminum is that stress concentration first occurs at the weak pore walls, forming deformation bands. These deformation bands continuously develop and collapse layer by layer. The micro-deformation is irregular and is the result of the combined action of pore wall bending and folding. It is precisely because of the unique compressive behavior of foam aluminum materials, that is, when subjected to external impact, they are prone to deformation and can maintain stress at a low level while deforming. During the compressive deformation process, a large amount of work is consumed and transformed into various forms of energy dissipation such as plastic deformation, collapse, and rupture of pores in the structure, thereby effectively absorbing external impact energy. The deformation process of closed-cell aluminum foam is shown in Figure 16.  
Figure 15: Static compression and dynamic deformation process of the stress-strain curve of closed-cell aluminum foam. As analyzed above, the deformation mechanism of foam aluminum begins with stress concentration at the weak pore walls, leading to the formation of deformation bands. These bands continuously evolve and collapse layer by layer. The micro-deformation is irregular, resulting from the combined effects of pore wall bending and folding. It is the unique compressive behavior of foam aluminum materials that makes them prone to deformation when subjected to external impacts, while maintaining stress at a low level during deformation. This behavior allows for the consumption of a large amount of work during the compressive deformation process, which is then transformed into various forms of energy dissipation, such as plastic deformation, collapse, and rupture of pores within the structure, thereby effectively absorbing external impact energy.
# A Photonic Label-Free Biosensor to Detect _Salmonella_ spp. in Fresh Vegetables and Meat
## 1 Introduction
Photon transduction provides the basis for detection. It uses resonant cavities in the form of ring resonators constructed using silicon nitride technology. A variety of applications have been suggested for the examination of refractive indices from these ring resonators. Within these transducers, the refractive index of light is modified when the analyte of interest forms a binding interaction with the bioreceptor that is anchored to the ring's surface. Consequently, the concentration of the analyte attached to the ring can be correlated with the observed signal. Biosensors to detect _Salmonella_ still depend on laborious manual operations, so their use in the laboratory is limited. Although existing optical microfluidic biosensors have acceptable levels of sensitivity, their high cost and complicated assembly are weaknesses [43].
# Knowledge-Aware Enhanced Network Combining Neighborhood Information for Recommendations
## 4 Experiments
### Influence of Different Modules  
In this part, we discuss the impact of the URL module, the IRL module, and the knowledge-aware attention network on KCNR, as shown in Figure 6. As can be seen from the figure, the results of KCNR outperform \(KCNR_{w/o\ URL}\) without URL and \(KCNR_{w/o\ URL}\) without IRL. This indicates that the KCNR model adds more supplementary information to users and items through URL and IRL, which makes the model describe users and items more accurately. By processing in this way, we can better understand users' personalized preferences and thus provide them with more accurate recommendation results. In addition, the results of KCNR outperform \(KCNR_{w/o\ ATT}\) without knowledge-aware attention networks, and it has the most significant performance improvement. This shows that, when the model selects neighbor entities for users, it takes into account entities that are more relevant to the user, by using the knowledge-aware attention network, and combines the information of these entities with the original embedding of the user, which significantly improves user representational ability. The main purpose of a recommendation system, is to model the user's preferences to provide higher-quality recommendation results to the user. Therefore, the improvement of the model by knowledge-aware attention networks is relatively significant.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**MovieLens-1M**} & \multicolumn{2}{c}{**Book-Crossing**} & \multicolumn{2}{c}{**Last.FM**} \\ \cline{2-7}  & **AUC** & **F1** & **AUC** & **F1** & **AUC** & **F1** \\ \hline LibFM & 0.892 & 0.763 & 0.685 & 0.618 & 0.777 & 0.710 \\ PER & 0.706 & 0.639 & 0.624 & 0.562 & 0.632 & 0.596 \\ CKE & 0.801 & 0.703 & 0.671 & 0.611 & 0.744 & 0.673 \\ Wide\&Deep & 0.898 & 0.791 & 0.712 & 0.645 & 0.756 & 0.654 \\ RippleNet & 0.912 & 0.812 & 0.725 & 0.650 & 0.766 & 0.702 \\ MKR & 0.911 & 0.838 & 0.727 & 0.665 & 0.795 & 0.729 \\ KGCN & 0.908 & 0.834 & 0.690 & 0.634 & 0.798 & 0.718 \\ FairGo & 0.907 & 0.838 & 0.716 & 0.661 & 0.796 & 0.700 \\ CAKR & 0.919 & 0.844 & 0.744 & 0.648 & 0.800 & 0.725 \\ \hline
**KCNR\({}_{\tt Bi}\)** & **0.926** & **0.852** & **0.750** & **0.666** & **0.811** & **0.732** \\ KCNR\({}_{sum}\) & 0.926 & 0.851 & 0.743 & 0.661 & 0.808 & 0.732 \\ KCNR\({}_{concat}\) & 0.925 & 0.851 & 0.741 & 0.662 & 0.805 & 0.734 \\ KCNR\({}_{neighbor}\) & 0.924 & 0.851 & 0.738 & 0.654 & 0.805 & 0.734 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The results of AUC and F1.
# Energy Sources of Mobile Robot Power Systems: A Systematic Review and Comparison of Efficiency
## 2 Power Requirements for Mobile Robots
### Critical Issues: Powering of Medical Robots  
Rechargeable batteries and batteries for medical devices are mainly made of NiMH, Li-ion, or Li-polymer cells, while the sale of Ni-Cd batteries for all models of medical devices is prohibited throughout the EU [43; 44; 45]. The eHealth paradigm constitutes a response to the challenges of the healthcare system, including medical staff shortages.  
Robotic assistive devices, primarily mobile (feeders, intelligent wheelchairs, independent mobile, and assistive robots), are increasingly used to improve the independence and quality of life of people with disabilities. They are becoming more and more clinically relevant, and social awareness of their role and availability is growing [46].  
Surgical power tools (SPTs), including surgical robots, are in common use in many surgical specialties [47]. The da Vinci Surgical Robot, for safety, includes an emergency battery that allows the system to function for twenty minutes during a general power outage, allowing surgery to continue once power is restored. Lithium-thionyl and lithium-manganese cells and batteries are used. They provide reliable, long-lasting power for robot control systems, with a service life of 5 to 10 years. This allows critical configuration information to be retained in the event of loss of external power. In addition to having to endure between servicing, they should offer high safety, low weight, long life, environmental suitability, and fast charging. It should be noted that in these types of robots, there may be multiple batteries supporting various consoles and functions. The solutions used here are similar to those in surgical robots due to the safety requirements of patients and medical staff [48].  
Exoskeleton gait training is equivalent to traditional therapy for patients with chronic stroke, but it will be a long time before powered exoskeletons can be turned into a clinical tool for gait rehabilitation after stroke [49]. The active work cycle (the time between charges) of exoskeletons is their biggest problem, so advances in the area of batteries could translate into the fastest technological success in this area.  
Future technologies for medical robotics may include lithium-sulfur (Li-S) batteries sodium-ion (Na-ion) batteries (which do not consume any scarce resources, just salt, but are approximately three times heavier and are less powerful), and solid-state batteries (with graphite-based anodes or metal lithium anodes).
## 2 Related Work
### Methods of Combining GAN with DRL  
The performance of Deep Reinforcement Learning (DRL) relies on extensive, high-quality, and diverse interaction data. However, acquiring such data in real-world scenarios often proves costly, inefficient, or even infeasible. For instance, in communication anti-jamming problems, obtaining specific jamming information before encountering jamming is challenging, and DRL exhibits prolonged convergence times in complex jamming environments. The core functionality of a GAN lies in learning from data distributions to generate realistic samples, effectively alleviating data scarcity bottlenecks. [PERSON] et al. [22] designed a GAN-based spectrum completion network to reconstruct missing spectral data, which was subsequently used to train DRL-based channel networks, effectively addressing spectrum access issues caused by incomplete sensing information. [PERSON] et al. [23] employed a GAN to generate synthetic network traffic data, integrating synthetic and real data into DRL models to enhance the overall performance of intrusion detection systems. [PERSON] et al. [24] proposed a GAN-based algorithm to recover traffic data from adjacent intersections, addressing the problem of insufficient traffic data caused by limited communication bandwidth, and they introduced a value-decomposition-based multi-agent DRL algorithm for cooperative traffic-light control; the proposed method significantly improved traffic efficiency. [PERSON] et al. [25] developed a DRL approach that integrates a GAN with DDPG, constructing a DDPG model together with an environment-predictive GAN module to facilitate efficient agent-environment interaction; compared with conventional DRL algorithms, the method achieved substantial performance gains in dormitory environment scenarios. [PERSON] et al. [26] introduced a GAN-augmented Meta-DRL intelligent routing framework for dynamic network environments, employing a learnable GAN feature encoder to generate high-quality rare traffic features that markedly accelerate convergence and improved reward performance. [PERSON] et al. [27] presented a DRL-based risk-averse stochastic dynamic scheduling method to enhance both the robustness and economic efficiency of power system operation in critical risk scenarios. They further proposed KEG-GAN to synthesize additional critical scenario samples, which not only elevated the economic efficiency of power system operation but also reduced the potential high costs associated with critical scenarios.  
In summary, GAN-enhanced DRL approaches have been widely adopted across diverse domains, leveraging synthetic data to boost the performance of DRL algorithms. Our work follows this paradigm by integrating GANs into a DQN-based anti-jamming decision framework for communications: we employ the GAN to generate a richer variety of jamming samples for pre-training the DQN, thereby substantially accelerating and improving the learning process of the agent.
## 7 Future Directions
### Energy-Efficient Systems  
However, the big power consumption is a shortcoming of THz-FSO systems that is being constantly addressed from time to time. Improving signal processing algorithms to optimize the energy consumption and power-saving hardware components of these systems could greatly extend their life. Another study could embrace an energy harvester focusing on the extraction of energy from the ambient environment, like solar or oscillator energy, in creating self-powered hybrid wire-line/wireless communication nodes.
# Multispectral Target Detection Based on Deep Feature Fusion of Visible and Infrared Modalities
## 6 Conclusions  
In this study, YOLO-MEDet was proposed as a bimodal object detection framework tailored for multispectral fusion of visible and infrared imagery. The model employs a dual-stream midway fusion backbone alongside two custom modules: the Attention-Enhanced Feature Fusion (AEFF) module for cross-modal adaptive integration and the C3-PSA module for efficient multiscale spatial attention. These components were jointly designed to address the challenges of detecting small, occluded, and distant targets in complex road environments.  
Extensive experiments on the FLIR, KAIST, and M\({}^{3}\)FD datasets validated the effectiveness of YOLO-MEDet, with consistent performance improvements over state-of-the-art multispectral detectors. For instance, YOLO-MEDet achieved a +2.5% [EMAIL_ADDRESS] improvement on the M\({}^{3}\)FD dataset compared to the strongest baseline. The ablation results further confirmed the complementary benefits of the AEFF and C3-PSA modules.  
While the model introduces additional computational complexity, it remains scalable for high-accuracy applications. Future work will focus on designing lightweight variants, integrating alignment-invariant fusion mechanisms, and exploring domain-adaptive training strategies to enhance deployment across diverse operational conditions.  
Conceptualization, Y.G.; Formal analysis, Y.Z.; Funding acquisition, Y.Z.; Investigation, X.Y.; Methodology, Y.G.; Resources, X.Y. and Y.G.; Software, Y.G.; Supervision, X.Y.; Validation, X.Y.; Visualization, Y.G.; Writing--original draft, Y.G.; Writing--review and editing, L.Y. All authors have read and agreed to the published version of the manuscript.  
This study is the Liaoning Provincial Department of Science and Technology's pilot project (2022)H24/10200029).  
Not applicable.  
The data are contained within the article.  
The authors would like to thank the editors and reviewers for their insightful suggestions. We are grateful to [PERSON] for their constructive suggestions on the statistical analysis.  
The authors declare no conflicts of interest.  
Figure 14: Training loss comparison on the M\({}^{3}\)FD dataset.
## 3 Test Results, Analyses, and Discussion
### Rheological Properties
#### 3.1.2 MSCR Analysis
Figure 7: MSCR rutting evaluation of the repetitively rejuvenated asphalt-binders: (**a**) Recovery; (**b**) non-recoverable creep compliance (\(J_{\rm nr}\)) (Red bars: the asphalt-binders rejuvenated with Type I; blue bars: the asphalt-binders rejuvenated with Type II; green bars: the asphalt-binders rejuvenated with Type III). Comparing Figures 7b and 8b, it is observed that the non-recoverable creep compliance of the aged asphalt-binder declined significantly. This indicates that aging partly provided an improvement in elasticity and that the elasticity of the asphalt-binders after rejuvenation was obviously restored. With an increase in the aging cycles, the asphalt-binders with Type I and Type III rejuvenators exhibited an increasing trend in \(I_{\rm irr}\), whilst the aged asphalt-binder with Type II exhibited a decreasing trend at 0.1 kPa and 3.2 kPa, respectively (see Figure 8b). Under the same stress level, the superiority ranking of the non-recoverable creep compliance of SBSMA in the equivalent aging cycle was Type I \(\approx\) Type III \(>\) Type II, and that of SBSMA in the equivalent rejuvenation cycle was Type III \(>\) Type I \(>\) Type II, respectively.