esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In this paper, the vibration attenuation effects of the oil film gap (\(C_{\mathrm{S}}\)), elastic support stiffness (\(K_{\mathrm{a}}\)) and the lubricating oil viscosity (\(\mu_{\mathrm{s}}\)) of SFD selected by using the multi-objective optimization method on the dynamic responses of the faulted system were analyzed. Some conclusions drawn from the study can be summarized as follows:  
1. For the vibration responses caused by the system with misalignment fault, three variable parameters are strongly nonlinear, in which \(K_{\mathrm{a}}\) has obvious impact on fundamental, \(2\times\) fundamental and \(3\times\) fundamental frequency, \(C_{\mathrm{S}}\) and \(\mu_{\mathrm{s}}\) are weak., For the vibration responses caused by a misalignment-rubbing coupling faults system, in which \(C_{\mathrm{S}}\) has a remarkable effect on fundamental frequency, \(\mu_{\mathrm{s}}\) and \(K_{\mathrm{a}}\) are sensitive to \(2\times\) fundamental and \(3\times\) fundamental frequency, respectively.
2. Optimizing the parameters of SFD have an effect on vibration control and stability of the rotor system with varied faults by using NLPQL and NSGA-II algorithm. The vibration responses of the system with the optimization parameters are shown as: With no fault, the amplitude of fundamental frequency decreased by \(23\%\). With the misalignment fault, the amplitude of the fundamental frequency decreased by \(43.4\%\), the amplitude of \(2\times\) fundamental frequency decreased by \(27.5\%\), while the amplitude of \(3\times\) fundamental frequency decreased by \(66.7\%\). With the misalignment-rubbing coupling fault, the amplitude of fundamental frequency decreased by \(7.4\%\), the amplitude of \(2\times\) fundamental frequency decreased by \(51.5\%\), and the amplitude of \(3\times\) fundamental frequency decreased by \(16.8\%\). Overall, the feasibility of the optimization method of the variable-structured SFD operational parameters for the faulted rotor system was verified.
3. The research results presented in the paper can provide helpful guidance for the design of the SFD and will be validated by experiments in the next planned step.  
[PERSON] developed the model and conducted the analyses; [PERSON] wrote and revised the paper; [PERSON] contributed analysis tools.  
This work is supported by the National Natural Science Foundation of China (No. 51705355).  
The authors declare no conflict of interest.
# Quantum-Secure Coherent Optical Networking for Advanced Infrastructures in Industry 4.0
## 2 State of the Art  
Optical network security has its foundation at the intersection of classical cryptography, quantum information theory, and optical physics. The intersection of these multiple disciplines offers the promise of increasing performance and solving the unique challenges of maintaining data confidentiality and integrity while simultaneously providing the high-performance characteristics of optical communications. Contemporary research has evolved along three critical dimensions: the quantum vulnerability assessment of traditional encryption schemes, the security implications of integrating legacy protocols, and the construction of quantum-resistant frameworks. With the advances of coherent optical technologies such as 400G ZR+ and the sophistication of quantum computing threats, these dimensions are becoming more and more important. Furthermore, to build on the added complexities provided by integrating Media Access Control Security (MACsec) and Optical Transport Unit level 4 (OTU4) framing, a new set of vulnerabilities in these environments was created. This work explores the current state of the research in this problem, highlights the available solutions' gaps, and correspondingly suggests the role of a novel framework.
## 4 Results
### Implementation Details
#### 4.1.4 Evaluation Metrics  
To evaluate the model performance and generalization ability more objectively and comprehensively, we selected five evaluation metrics for the experiments:  
1. Dice coefficient (_Dice_) \[Dice=\frac{2\times|P\bigcap G|}{|P|+|G|}\] (10)
2. Precision \[Pre=\frac{TP}{TP+FP}\] (11)
3. Recall \[Recall=\frac{TP}{TP+FN}\] (12)
4. Volumetric overlap error (_VOE_) \[VOE=1-\frac{P\bigcap G}{P\bigcup G}\] (13)  
Figure 7: Comparison before and after data preprocessing.  
Relative volume error (_RVD_)  
\[RVD=\frac{|P|-|G|}{|G|} \tag{14}\]  
where _TP_ is true positive, indicating that the liver region was correctly segmented; _TN_ is true negative, which indicates that other organ regions were correctly segmented as the background; _FP_ is false positive, which means that other organ regions were incorrectly segmented as the liver; _FN_ is false negative, implying that the liver regions were incorrectly segmented as the background; \(P\) indicates the target pixel of the predicted label; and \(G\) represents the target pixel of the ground truth.
# Weighted Similarity-Confidence Laplacian Synthesis for High-Resolution Art Painting Completion
## 2 Related Work  
In recent years, advancements in image completion techniques have been driven by a dual reliance on both deep learning and traditional methods. Researchers have navigated challenges common to both, necessitating robust generalization capabilities for effective image completion. A spectrum of techniques has been explored, from deep neural networks [4; 6; 7; 8; 9; 10; 11; 12] to traditional methods [13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25].  
Deep learning methods, particularly those utilizing Generative Adversarial Networks (GANs), have shown promise in producing credible completions for complex images. [PERSON] et al. [12] employed partial convolutions, but challenges included blurriness in repairing large missing regions and limited effectiveness for images with simple structures. [PERSON] et al. [26] and [PERSON] et al. [27] introduced GAN-based methods emphasizing the importance of vanishing points and adversarial training, respectively, in capturing structural edges during the inpainting process. However, these approaches raised concerns about extended running times, necessitating further optimization for deep learning-based inpainting methodologies.  
Traditional methods, such as exemplar-based image inpainting [28] and structure propagation [29], have emerged as efficient alternatives for restoring single damaged images. Despite their efficacy, these methods faced challenges in handling complex structures and textures, sensitivity to patch size, and vulnerabilities to variations in illumination. [PERSON] et al. [18] and [PERSON] et al. [19] presented distinct image inpainting techniques, each with unique weaknesses related to computational constraints, distortions in perspective, and struggles with detailed scenes and fine elements.  
Several researchers have explored synergies between deep learning and traditional approaches for art painting completion. [PERSON] et al. [30] combined deep learning with manual sliding windows, facing challenges with detailed textures and unique artistic expressions. [PERSON] et al. [31] introduced user line drawings for inpainting guidance, excelling in simpler paintings but proving time-consuming for complex art paintings. Building upon these insights, this paper proposes the Weighted Similarity-Confidence Laplacian Synthesis algorithm, addressing challenges in structural and textural completion in high resolution for various paintings, even in irregularly missing areas encountered in the painting process.
## 4 Petrography  
### Dayheen Ring Complex  
#### 4.1.1 Peralkaline Granites  
Petrographically, the peralkaline granites in the Dayheen complex can be classified into two main types: hornblende-biotite granite and aegirine-riebeckite granite. The hornblende-biotite granite is primarily composed of quartz (28-35%), alkali feldspar (32-38%), plagioclase (20-30%), and 5-10% hornblende and biotite (Figure 3c,d). It exhibits a dark-gray to reddish-brown color and has a porphyritic texture. Poikilitic inclusions are common within granite and consist of plagioclase laths and hornblende. In some areas, quartz forms a myrmekitic texture with K-feldspar. Perthite and minor microcline represent the main K-feldspar minerals. Locally, perthite may be partially replaced by sercite. Plagioclase occurs as tabular crystals and exhibits simple albite twinning. It occasionally contains irregular fluorite crystals. Locally, plagioclase may undergo alteration to sercite and epidote. Subhedral prismatic crystals of dark blueish-green hornblende are present (Figure 3d). It shows pleochroism and contains inclusions of magnetite and titanite. The biotite crystals have a pale reddish-brown color and weak pleochroism. The granite contains several accessory minerals, including apatite, zircon, allanite, magnetite, and titanite.  
The aegirine-riebeckite granite within the Dayheen complex is primarily composed of quartz (35-40%), K-feldspars (20-30%), albite (25-30%), and 7 to 10% aegirine and amphibole. It exhibits a porphyritic texture, where the phenocrysts consist of microcline, quartz, riebeckite, aegirine, and biotite. In the groundmass, the major minerals present are quartz and plagioclase. Quartz occurs as large, anhedral rounded grains within the rock. These quartz grains contain numerous inclusions of fine laths of albite, aegirine, and riebeckite. Albite is present as subhedral crystals in the groundmass of the rock. The K-feldspar crystals are primarily microclines and occur as microphenocrysts. Occasionally, the microcline crystals may have a rim of lath-shaped albite. In some instances, subhedral crystals of riebeckite can also be observed. They are characterized by their deep-blue color and exhibit varying degrees of pleochroism (Figure 3e). Aegirine is present as subhedral to euhedral prismatic crystals (Figure 3f). The color of the aegirine ranges from deep-green to pale olive-green. The accessory minerals mainly include apatite, zircon, fluorite, allanite, pyrochlore, samarskite, and hematite.
## 4 Results and Analysis
### Network Parameter Evaluation  
The network parameters, such as delay, jitter, instantaneous throughput, bytes transmitted in the payload, and total bytes transmitted, were measured. This allowed for the analysis of the algorithm's efficiency and the network's responsiveness to the encryption policy.  
Figure 14: Histogram of correlation coefficient of bit pairs for 31 different messages.  
Figure 15: Density plot of the correlation coefficient of bit pairs of encrypted messages.
# Assessment of MicroPET Image Quality Based on Reconstruction Methods and Post-Filtering
## 3 Results  
### Image Accuracy  
Phantom images are shown in Figure 1A. Image quality, sharpness, accuracy, and intensity were different following the reconstruction methods. We confirmed the image accuracy of reconstruction methods in hot rods of 5, 4, 3, and 2 mm diameter for the NEMA NU 4 mouse image quality phantom (Figure 1B). In the image, the rod size was measured as FWHM. In 2, 3, and 4 mm rods, the OSEM2D reconstruction method showed the highest accuracy among all tested methods. The accuracy of OSEM3D in the 2 mm rod was higher than that in other rods. In the 5 mm rod, 3 DRP with Hann filter was the most accurate reconstruction method. In the iterative reconstruction methods (OSEM2D and OSEM3D), the measured rod size followed the number of iterations and was not changed by the specific pattern.
# Closing Connectivity Gap: An Overview of Mobile Coverage Solutions for Not-Spots in Rural Zones
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} A2G & Air-to-Ground \\ AMPS & Advanced Mobile Phone System \\ APs & Altitude Platforms \\ BS & Base Station \\ CAPEX & Capital Expenditure \\ CDMA & Code Division Multiple Access \\ D2D & Device-To-Device \\ FANET & Flying Ad-Hoc Network \\ FFTx & Fiber-To-The-x \\ GBS & Ground BS \\ GEO & Geostationary Orbit Satellite \\ GT & Ground Terminal \\ HAPs & High-altitude Platform \\ ISP & Internet Service Provider \\ KPI & Key Performance Indicator \\ LAPs & Low-Altitude Platform \\ LDC & Least-Developed Country \\ LEO & Low-altitude Earth Orbit Satellite \\ LOS & Line-of-Sight \\ LTE-NR & LTE-New Radio \\ MANET & Mobile Ad-Hoc Network \\ MCI & Mobile Connectivity Index \\ MEO & Medium-altitude Earth Orbit Satellite \\ ML & Machine Learning \\ MNO & Mobile Network Operator \\ MTC & Machine-Type Communication \\ NLOS & Non-Line-of-Sight \\ NOMA & Non-Orthogonal Multiple Access \\ PL & Path Loss \\ QoS & Quality Of Service \\ RDP & Rural Population Density \\ SNR & Signal-to-Noise Ratio \\ SCPs & Stratospheric Communication Platforms \\ SUAV & Small UAV \\ SWAP & Size, Weight, and Power constraint \\ TBD & To-Be-Developed \\ TVWS & Television White Space \\ UAS & Unmanned Aerial System \\ UAV & Unmanned Aerial Vehicle \\ UE & User Equipment \\ UHF & Ultra High Frequency \\ UTM & UAS Traffic Management \\ VANET & Vehicular Ad-Hoc Network \\ VDSL & Very-high-bit-rate Digital Subscriber Line \\ \end{tabular}
# A Comprehensive Survey on Knowledge-Defined Networking
## 7 Application Plane
### Application Scenarios
#### 7.3.5 Big Data  
Big data refers to extremely large (in volume, velocity, and variety) and complex data sets that cannot be easily managed or processed using traditional data processing methods. The ability to analyze and make sense of big data has become increasingly important in various fields such as healthcare, business, scientific research, etc. KDN has been helpful in solving issues that exist with big data applications such as data processing in cloud data centers, data delivery, scheduling, etc. by efficiently managing the network [524]. On the other hand, big data can help KDN with traffic engineering, mitigation of security attacks, cross-layer design, etc. An efficient workload slicing scheme processes big data in order to handle data-intensive applications in a multi-edge cloud environment, where inter-data center data transfer is handled by an application policy based on an energy-aware traffic flow scheduling technique [525]. A real-time big data streaming framework called Typhoon, which operates based on a policy of partially offloading application layer data routing and control to the network layer in KDN in order to achieve on-the-fly programmability and high-performance data routing, has been studied in [526]. A big data simulation tool to integrate big data applications with SDN is called BigDataSDNSim, which contains the big data management system: Yet Another Resource Negotiator (YARN) and SDN-enabled networks in a cloud computing environment has shown superior performance in Map-Reduce applications [527]. BigDataSDNSim implements application policies such as the Map-Reduce application selection policy and the Hadoop Distributed File System (HDFS) replica placement policy. A service called AmoebaNet has been proposed to overcome limitations existing in big data science, such as the last mile problem, scalability, and programmability problems, by applying KDN to provide QoS-guaranteed services [528]. AmoebaNet allows application programs to program networks at runtime to optimize for optimum performance. With the help of big data analytics applications, by using multi-dimensional analysis of key performance indicators and machine learning to generate knowledge regarding traffic congestion, QoS can be managed in knowledge-defined networks [529]. A web service manager called AWESoME is an application that identifies and prioritizes the traffic of important web services by using big data algorithms to build models describing the traffic of a large number of web services while installing flow rules related to the services [530]. In KDN, big data can be analyzed using machine learning techniques for clustering, forecasting, and managing traffic behaviors with the aid of big data applications that define policies for network function virtualization [531]. Big data, machine learning, KDN, and application policy-driven network function virtualization can be integrated together to build a framework for self-organizing networks and for network slicing [532].
# Traffic Signal Control with State-Optimizing Deep Reinforcement Learning and Fuzzy Logic
###### Abstract  
Traffic lights are the most commonly used tool to manage urban traffic to reduce congestion and accidents. However, the poor management of traffic lights can result in further problems. Consequently, many studies on traffic light control have been conducted using deep reinforcement learning in the past few years. In this study, we propose a traffic light control method in which a Deep Q-network with fuzzy logic is used to reduce waiting time while enhancing the efficiency of the method. Nevertheless, existing studies using the Deep Q-network may yield suboptimal results because of the reward function, leading to the system favoring straight vehicles, which results in left-turning vehicles waiting too long. Therefore, we modified the reward function to consider the waiting time in each lane. For the experiment, Simulation of Urban Mobility (SUMO) software version 1.18.0 was used for various environments and vehicle types. The results show that, when using the proposed method in a prototype environment, the average total waiting time could be reduced by 18.46% compared with the traffic light control method using a conventional Deep Q-network with fuzzy logic. Additionally, an ambulance prioritization system was implemented that significantly reduced the ambulance waiting time. In summary, the proposed method yielded better results in all environments.  
deep reinforcement learning; deep Q-network; fuzzy logic; traffic signal control Article
## 3 Results
### Structure of the Groundwater Microbial Community  
Sequencing of clones, which contained 165 rRNA gene inserts from the groundwater sample, resulted in a total of 93 sequences. After checking their quality and end-trimming, the sequences were identified by comparisons with bacteria and archaea libraries from NCBI, according to the threshold of 95% and 98.65% of similarity for genus and species identification, respectively [37, 38]. No archaeal clones were obtained. This study constitutes a first view of the most abundant bacteria that inhabit the groundwater environment of the Petrola lake-aquifer system. It is far from being considered a complete characterization of the bacterial community, which must be done with other modern techniques such as next-generation sequencing. However, the results obtained show the presence of certain bacteria that have been reported to be involved in natural bioremediation processes.  
The sequences clustered into two phyla, Proteobacteria and Firmicutes, with 97.8% and 2.2% of relative abundance, respectively. Within Proteobacteria, the subphylum Rhodobacteria is completely represented with its members of Gammaproteobacteria, Betaproteobacteria, and Alphaproteobacteria classes, covering 49.4%, 29.2%, and 19.1% of relative abundance, respectively. In contrast, within Firmicutes only the class Bacilli is identified with 2.2% representation.  
The comparison of the sequences from the 93 clones resulted in 36 species identified, which were clustered into 18 genera and 10 families, listed in Table 2. At the family level, the dominant taxa were Pseudomonasaceae and Comamonadaceae, covering together a 58.4% of the global abundance. These families, which together with Caulobacteraceae, Xanthomonadaceae, Bacillaceae, and Rhizobiaceae accounted for 69.7% of relative abundance over the total sequences analyzed, are closely reported to nitrogen cycling, including the nitrate removal by denitrification processes [39, 40, 41, 42, 43]. Chromatiaceae and Rhodobacteraceae were found to cover 11.2% and may constitute a sulfur turnover consortium. The members of these families were found to be deeply involved in sulfur and carbon biogeochemical cycling, with competitive advantages in sulfur metabolic mechanisms, and symbiosis with aquatic micro- and macroorganisms [44, 45]. In addition to carbon cycling, Sphingomonadaceae, Moraxellaceae, and some specific genera (i.e., _Phenylobacterium_) have enzyme specificity and kinetics involved in the degradation of a wide range of xenobiotics, which can include pesticides such as atrazine [46, 47, 48]. At the genus level, _Pseudomonas_ was the most abundant Proteobacteria found in the groundwater sample analyzed, accounting for 29.2% of the total bacterial sequences in the sample with 11 different related species. Also, genera from the above-mentioned consortium of families with xenobiotic abilities, _Sphingopyxis_, _Acinetobacter_, and _Phenylobacterium_, showed eight different species, becoming the third most abundant group.
# Review on the Electrical Resistance/Conductivity of Carbon Fiber Reinforced Polymer
## 5 Necessity and Methods to Increase the CFRP Conductivity
### CNTs/CB
#### 5.1.1 Carbon Nanotubes  
CNTs-polymer composites have gained popularity recently over metals [177; 178]. After CNTs were first reported by [PERSON] in 1991 [179], they have been extensively explored [180; 181; 182].  
CNTs are new advanced materials, especially in EMI shielding and electronics of aircrafts. CNTs may exist as a fabric-like format and can be integrated into composite materials. CNTs in the coating form can realize the better shielding performance. The tensile strength of CNTs is higher than that of carbon fiber, but CNTs are more flexible. The greatest challenge in the CNTs technology is scaling and cost reduction. CNTs can reduce 69% weight of pipelines, but CNT formats and manufacturing cost should be thoroughly considered [6]. CNTs can improve the EMI shielding effectiveness via absorption and reflection. Adding CNTs significantly improved the absorption, which becomes the main shielding mechanism [183]. CNT films are popular among different types of CNTs. Upon CNT film interleaving, the in-plane and out-of-plane electrical conductivities of the laminates were enhanced significantly, as shown in Figure 7. This is mainly due to the fact that the interleaved CNT film could effectively enhance the conductivity of resin-rich area [184]. CNT buckypaper as well as insulating adhesives were developed for the purpose of protecting the CFRP laminate during the lighting strike. [PERSON] et al. proved that CNT buckypaper could facilitate the lightning current to the ground and dissipate the energy by promoting the electrical conductivity of CFRPs [185]. The major challenge of these approaches is the preparation of large scale and high quality CNT dispersion [184].