esa-sceva/satcom-chunk-collection huggingface dataset:
# Multidisciplinary Optimization of Aircraft Aerodynamics for Distributed Propulsion Configurations
## 4 Multidisciplinary Analysis and Optimization in Aero-Propulsive Design
### Propulsion Controlled Aircraft  
To achieve complete wake filling, almost the entire control surface (rudder, aileron, elevator) of an aircraft (BWB) should be replaced by a DP system. This would make control operations very difficult. A thrust modulation control system, namely, propulsion controlled aircraft (PCA), has to be applied to manipulate the magnitude of forwarding drive forces acting on the aircraft to achieve the effect of propulsion control. PCA could also manipulate the thrust vector on each propulsor to produce the required moments and accelerations. As the number of propulsors increases, the force and moment of distributed PCA acting on the aircraft also increases [78].  
The use of DP to control the direction and thrust magnitude of aircraft to substitute conventional control surfaces would increase energy consumption. Additionally, the stability and controllability of airworthiness must be satisfied, particularly in the case of BWB, which initially has stability problems. It is widely accepted that redundant propulsors would help. They would address stability issues in the BWB design and may improve control with thrust vectoring. However, the interaction between this coupling system and the aerodynamics of the aircraft configuration would increase sophistication. It requires more robust intelligent engine control systems with adaptive thrust modulation and thrust vector to provide sufficient stability and controllability [79]. As of today, no one has studied propulsion control as an MDO factor in the BWB-DP system. It is conceivable that the level of complexity is the current difficulty.
# Combining Unsupervised Approaches for Near Real-Time Network Traffic Anomaly Detection
## 4 Experiment
### Results
Times shown in the table are measured in milliseconds. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Algorithm** & **Precision** & **Recall** & **F1-Score** & **\(\sigma\) (F1-Score)** & **AUROC** & \(\sigma\) **(AUROC)** & **Accuracy** \\ \hline EC & 0.6264 & 0.5961 & 0.6053 & 0.0381 & 0.5961 & 0.0318 & 0.7804 \\ EIF & 0.7063 & 0.7063 & 0.7063 & 0.0019 & 0.7063 & 0.0019 & 0.8142 \\ DAGMM & 0.6934 & 0.6934 & 0.6934 & 0.0078 & 0.6934 & 0.0078 & 0.8060 \\ DAGMM-EIF & 0.7083 & 0.7040 & 0.7056 & 0.1198 & 0.7040 & 0.1200 & 0.8182 \\ DA & 0.6774 & 0.6774 & 0.6774 & 0.0020 & 0.6774 & 0.0020 & 0.7990 \\ DA-EIF & 0.7051 & 0.6912 & 0.6975 & 0.0046 & 0.6912 & 0.0022 & 0.8148 \\ DSEBM & 0.6800 & 0.6800 & 0.6800 & 0.1879 & 0.6800 & 0.1879 & 0.7975 \\ MemAE & 0.7146 & **0.7146** & **0.7146** & 0.0015 & **0.7146** & 0.0015 & 0.8194 \\ MemAE-EIF & **0.7431** & 0.6972 & **0.7146** & 0.0001 & 0.6972 & 0.0056 & **0.8350** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results obtained from testing unsupervised algorithms on the CIC-IDS2017 dataset. \begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithm** & **Prediction Time** \\ \hline EC & 1198.630 \\ EIF & 246.960 \\ DAGMM & **1.830** \\ DAGMM-EIF & 141.140 \\ DA & 11.013 \\ DA-EIF & 401.161 \\ DSEBM & 14.010 \\ MemAE & 12.710 \\ MemAE-EIF & 476.712 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Prediction times for analyzing 1000 records of the NSL-KDD dataset. The times shown in the table are measured in milliseconds. Forest algorithm. In addition to identifying connections that are likely to be anomalous, the algorithms of Deep Learning generate new features which, together with the original characteristics of the input, constitute the sample to provide an input to the algorithm Extended Isolation Forest. It is interesting to evaluate the contribution in the new features generated by the Deep Learning algorithms and how useful these features are to the Extended Isolation Forest algorithm in generating the anomaly score for the suspect samples. Considering the datasets used, it is now desired to highlight which features most influence the prediction process of the EIF model used by the MemAE-EIF algorithm. Through the following experiment, we aim to verify if the new features introduced by the MemAE-EIF algorithm affect the prediction process of the EIF algorithm and thus contribute to the increase in metrics observed in the experiments conducted.
# A Stay Cable Icing Identification Method Based on the Fusion of U-Net and ResNet50
###### Abstract
However, it is necessary to label the cable icing image before image classification. It is challenging to quantitatively evaluate the icing based on the complex icing growth characteristics [17]. A method should be proposed to quantify the classification of icing degree. In recent years, much of the literature has adopted a two-stage deep learning method. [PERSON]. [18] developed a two-stage deep learning method for missing electrical insulator string detection. In the first stage, the insulator string components in the detection area were segmented by semantic segmentation. In the second stage, the segmented mask was input into the CNN for detection to improve the model's accuracy. [PERSON]. [19] adopted a two-stage deep learning method. In the first stage, DeepLabV3+ was used to segment the leaves from the background, and the image obtained after segmentation was used as the input for the second stage. In the second stage, U-Net was used to segment the diseased leaves. Finally, the ratio of the lesion's pixel area to the leaf's pixel area was calculated to classify the leaves. The research on cucumber leaf disease segmentation and insulator string segmentation in complex backgrounds has progressed. However, there are few studies on cable icing detection in deep learning frameworks. Based on the existing research, this study proposes a two-stage cable icing detection model combining U-Net and ResNet50 [20] for specific angle cable icing images. The main contributions of this paper are as follows:  
1. A stay cable icing identification model with a two-stage architecture was proposed based on deep learning. This model achieved the accurate segmentation of the surface ice and icicles and the classification of icing degree grade, which provided a basis for the precise deicing of the stay cable deicing robot. 2. An icing degree grade classification method was proposed. The method calculates the mask area and uses it as a label to input into the classification network. Compared with the direct use of mask area to classify the icing level, this method improved the generalization ability [21]. 3. The cable icing data set was made, which makes up for the vacancy in this field. The remaining structure of the paper is outlined as follows: Section 2 provides an overview of the establishment and preprocessing of the stay cable icing data set. Section 3 presents the framework for icing identification and quantification. Section 4 details the experimental procedures and examines the test results. Finally, Section 5 summarizes the new method and evaluates the feasibility of this study.
## 5 Key 6G Technologies
### Other Perspectives
#### 5.3.15 Edge Computing  
Enhancing network service performance, optimizing the use of physical and virtual resources, lowering mobile operator costs, and streamlining network complexity in both the control and user planes are all made possible by edge computing [232]. However, the diverse needs of numerous end users challenge network operators to explore alternative solutions, utilizing advanced AI tools and modern ML methods to overcome edge computing limitations. This results in the introduction of edge intelligence (EI), which introduces automation and intelligence at the edge of the mobile network by integrating AI and ML techniques. It is predicted that EI will play a key role in enabling networks that go beyond 5G and 6G, satisfying the need for automation in the rapidly changing fields of user equipment, smart devices, the Internet of Intelligent Things (IoIT), and intelligent services [233].  
With regard to the next-generation radio access network (NG-RAN) architecture, EI finds major use in automating management and orchestration tasks pertaining to virtual resources, with a focus on the RAN network slice subnet management function (NSSMF) and network function management functions (NFMFs). Initiatives like ETSI's ENI ISG are addressing this, investigating and recommending solutions to streamline management and orchestration complexity [234]. In a number of use cases, such as the previously stated one, EI comprises a network of connected devices that gather, organize, handle, and evaluate data. The processed data are then relayed to assisted systems as recommendations and/or orders to execute tasks or functionalities, facilitating automation [234].  
While EI has many benefits, there are still a number of unanswered research questions regarding its realization in mobile networks beyond 5G. Extensive research efforts are needed to identify and address these challenges, which include problems with data consistency, scarcity at the edge, adaptability of statically trained models, and data privacy and security. For EI in networks beyond 5G and 6G to be fully realized, these issues must be resolved [232].
# Tunable Mid-Infrared Graphene Plasmonic Cross-Shaped Resonator for Demultiplexing Application
## 4 Conclusions  
In the present study, we have proposed, and analyzed new tunable graphene plasmonic cross-shaped resonator and a two-channel demultiplexer in mid-infrared (MIR) region. It is shown that it is possible to tune the transmission spectrum of the proposed structures by alternating of the applied bias voltage (Fermi energy) of graphene which is an advantage of the proposed structures. Our proposed structures are single mode in the wavelength range of 5-12 \(\upmu\)m. Based on the proposed plasmonic cross-shaped resonator, two-channel demultiplexer is designed and studied in MIR region. The minimum value of crosstalk for the proposed demultiplexer is -48.30 dB. The maximum transmission ratio of 55% and the minimum FWHM of 220 nm are obtained by our proposed resonator structure. The proposed structures are analyzed by use of three-dimensional finite-difference time-domain (3D-FDTD) method and coupled mode theory (CMT). Our work could be used for further development of nano meter sized active and passive optical devices and structures in MIR region.  
Software and simulation, S.A.; writing--original draft preparation, S.A. and T.F.; writing--review and editing, S.A. and T.F.; All authors discussed the results and contributed to the final manuscript. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
# Lip-Reading Classification of Turkish Digits Using Ensemble Learning Architecture Based on 3 DCNN
## 3 Material and Method
### Deep Learning Architectures
#### 3.3.3 3 Dimensional Convolutional Neural Networks  
3 DCNN stands for 3D Convolutional Neural Network. It is a deep learning structure created using layers of neurons and used to recognize patterns from data. Although CNNs are successful in image processing, it is seen that 3 DCNN models are more successful for spatio-temporal data. The main function of the 3 DCNN architecture is to extract deep-level features from the given data [40]. 3 DCNN exhibits higher performance than classical convolutional neural network models [41; 42], evaluates spatio-temporal data [43], and has better performance compared to long-short-term memory architectures. It is seen that it is especially useful for video data in terms of low processing and memory requirements.  
In the 3 DCNN architecture, another dimension is added to the filters and step values such as \(3\times 3\times 3\) or other variations are applied as \(1\times 1\times 1\) or different variations [43]. 3 DCNNs also receive spatio-temporal information in time-dependent images [27]. Thus, 3 DCNNs can also deal with temporal data on video data [33,44]. [PERSON] et al. [45] showed that spatio-temporal information in video data can be extracted using 3 DCNN architectures more successfully than 2 DCNN architectures. In this study, models containing both 2 DCNN and 3 DCNN were used. One of the 3 DCNN models used is the C3D [45] model. The C3D model can obtain more successful results in video analysis, as it can extract spatio-temporal information better.  
Another 3 DCNN-containing model used in this study is the Two-Stream Inflated 3D ConvNet (I3D) model.The I3D model is presented by researchers from DeepMind and Oxford University in a paper entitled \"Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset\" [46].
## 3 Optimum TMD Parameters Based on AFSA
### Comparison Study  
The classic Den Hartog method [29] has a wide application in TMD design; however, it does not consider the damping of the primary system, which may lead to a defective TMD. The optimum formulas of the Den Hartog method are shown in following, which is also devoted to minimizing the maximum of the structural acceleration response under external harmonic excitations.  
\[f_{opt}=\frac{1}{\sqrt{1+\mu}} \tag{9}\]  
\[\zeta_{opt}=\sqrt{\frac{3\mu}{8(1+\mu/2)}} \tag{10}\]  
[PERSON] and [PERSON] [37; 38] proposed an improved design method considering structural damping, whose optimization goal is to minimize the maximum acceleration dynamic amplification factor of primary structure under external harmonic excitations. The optimum formulas of the [PERSON] and [PERSON] method are shown in the following.  
\[f_{opt}=\sqrt{\frac{1}{1+\mu}}+(0.096+0.88\mu-1.8\mu^{2})\zeta_{p}+(1.34-2.9 \mu+3\mu^{2})\zeta_{p}^{2} \tag{11}\]  
\[\zeta_{opt}=\sqrt{\frac{3\mu(1+0.49\mu-0.2\mu^{2})}{8(1+\mu)}}+(0.13+0.72\mu+0.2\mu^{2})\zeta_{p}+(0.19+1.6\mu-4\mu^{2})\zeta_{p}^{2} \tag{12}\]  
The comparison study between the optimization method based on AFSA proposed in this paper and the classic Den Hartog method is shown in Table 2. The comparison study between the optimization method based on AFSA proposed in this paper and the Ioi and Ikeda method is presented in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \(\mu\) & 0.010 & 0.020 & 0.030 & 0.040 & 0.050 & 0.060 & 0.070 & 0.  
It is presented in Table 2 that the novel optimization method based on AFSA proposed in this paper has a smaller maximum acceleration dynamic amplification factor than the classical Den Hartog method, and in general, the reduction ratio increases with the increasing of the TMD mass ratio and the structural damping ratio. As can be seen in Table 3, the novel optimization method proposed in this paper has a smaller maximum acceleration dynamic amplification factor than the Ioi and Ikeda method, and in general, the reduction ratio increases with the decreasing of the TMD mass ratio and structural damping ratio. Therefore, the novel optimization method based on AFSA proposed in this paper has a better effect in controlling the maximum acceleration dynamic amplification factor.
# An Interactive Real-Time Locating System Based on Bluetooth Low-Energy Beacon Network +
## 4 Implementation and Experiments
### Experiment 3  
In this experiment, we check the overall operations of the prototype system. First, we let a user with the Mobile App walk from location \(A\) to location \(B\), and then return to location \(A\). As expected, the smartphone user was able to see the correct locating results in the Mobile App which switched from location \(A\) to location \(B\), and then switched back to location \(A\). Then we checked the moving pattern recorded in the Cloud Database. Figure 11 is a screenshot from the Google Firebase, in which the top-level key refers to the encrypted ID of the user and the second-level keys refer to the locating records for this user. In each record there are values of Major and Minor, the associated location name, and the timestamp of the locating record. From Figure 11 we can see three consecutive records with the determined location \(A\), \(B\), and then \(A\). The order of the determined locations in the Cloud Database apparently matches the route taken by the user, and the timestamps correctly indicate the time that the user was physically located at those locations.  
Next, we test the use case of two simultaneous users. Specifically, to ease the cross check of the locating results, in this test two smartphones were held by a single person to emulate two users taking the same route simultaneously. The route starts from location \(B\) to location \(D\) via location \(C\), then takes the reverse direction back to location \(B\). Table 2 summarizes the observed locating results recorded in the Cloud Database, from which we can compare the timestamps of the two emulated users at these locations. Since the test was carried out by a single person, ideally the locating results of the two emulated users should be exactly the same. However, we can see that there exist time differences in most of the locating results. Again, we believe this is due to the natural uncertainty of multi-hop wireless transmissions and possibly the contention of wireless resource. Fortunately the time differences are limited to only a few seconds and would not be a problem.  
Figure 11: A snapshot of the Firebase database showing the moving pattern of a user.
## 3 Results
### Detection of THz Radiation in GaSe:S Crystals
#### 3.3.2 The THz Waves Detection Efficiency  
After assembling and adjusting the experimental setup, the GaAs crystal detector (Figure 4) was successively replaced by the studied GaSe:S samples, for each of which the spectrometer signal was recorded (Figure 5). As can be seen, the sample with \(x=0.12\) has the highest signal amplitude.  
Figure 4: Layout of the experimental setup—terahertz time-domain spectrometer. FI—Faraday isolator, \(\lambda/2\)—half-wave plate, PBS—polarization beamsplitter, ODL—optical delay line, GP—Glan polarizer, AOM—acousto-optical modulator, LPF—low-pass filter, THz—terahertz radiation, EOC—electro-optic crystal, \(\lambda/4\)—quarter-wave plate, WP—Wollaston prism, BPD—balanced photodetector, PC—personal computer.  
Figure 5: THz waveforms detected with GaSe:S crystals samples.  
For clarity, Figure 6 shows the dependence of the peak value of the THz signal amplitude on \(x\). The dependence is in good agreement with the work in which a similar study was carried out at the wavelengths of a titanium-sapphire laser [60]. Unfortunately, there were no samples provided with \(x\) in the range of 0.04 to 0.12; thus, we could not accurately determine the optimal sulfur content for maximum detection efficiency. According to [60], this value \(x\) should be in the vicinity of 0.11.
## 2 Experimental Methods
### Construction of WL-VLC System  
As presented in Figure 1d, the main part of the VLC system consisted of blue InGaN-uLED, G-PQDs and R-PQDs, a transmitter lens (Tx lens), a receiver lens (Rx lens), and a photodetector. The blue-emitting uLEDs were employed as light sources to excite different PQDs which could adjust the color component of light required by the VLC system. Using a non-return-to-zero on-off key (NRZ-OOK) modulation scheme, the signals were modulated to the uLEDs by the bias tee. The modulated light propagated through the free-space transmission link. Color converters were put into the light path to fulfill the experimental requirements. The Tx lens and Rx lens were used to collimate and focus the modulated light generated from the light-emitter. The light was received and recorded by a photodetector, and then the light signals were converted to electric signals for further analysis of the frequency response, BER, and eye diagrams. Based on the above VLC link, a network analyzer (Agilent, N5225A, 10 MHz-50 GHz) was used to measure the frequency response to obtain the modulation bandwidth under different currents. The BER was measured by an error-detector module built into the signal quality analyzer (Anritsu, MP1800A, 0.1-14 GHz). A wide-bandwidth oscilloscope (Agilent 86100A, 14 GHz) was used to capture eye diagrams. All measurements were carried out at ambient temperature in air.
## 4 Applications
### Biomedical Applications
developed a series of shape-memory PLA/Fe\({}_{3}\)O\({}_{4}\) composite scaffolds utilizing 4D printing technology, enabling remotely controlled actuation for bone tissue engineering [286]. These scaffolds, composed of PLA/Fe\({}_{3}\)O\({}_{4}\) composite filaments with 15% Fe\({}_{3}\)O\({}_{4}\) NPs, were initially compressed to a smaller size while retaining the memory of their original, larger dimensions for injection into the implant site. Upon exposure to a magnetic field at 27.5 kHz, the compressed scaffolds expanded in situ to their predetermined shape and size, facilitating precise and minimally invasive implantation (Figure 27g). Bone fracture implantation was achieved using 4D-printed porous honeycomb structures, fabricated from a composite of PLA, poly-methyl methacrylate (PMMA), and Fe\({}_{3}\)O\({}_{4}\) NPs, as demonstrated by [PERSON] et al. [287]. [PERSON] and coworkers created a bone morphogenetic protein-2 (BMP-2) loaded, porous smart nanocomposite scaffold, utilizing chemically cross-linked poly(\(\varepsilon\)-caprolactone) (c-PCL) and hydroxyapatite nanoparticles (HANPs), designed for the controlled release of growth factors [288]. This shape-memory scaffold is capable of returning from a compressed to its original shape within 10 min. The extended recovery time observed in vivo, a result of reduced heat transfer in air compared to water, provides a practical advantage for the implantation process of the scaffold. [PERSON] and colleagues developed polyurethane/hydroxyapatite-based SMPNCs as a bone repair foam, characterized by its exceptional self-fitting capacity [289]. They demonstrated that the foam could perfectly match the defect site upon in vivo implantation, showcasing its potential for precise bone repair applications [289]. SMPCs offer a platform for developing controllable drug delivery systems, capable of targeting therapeutic drugs to specific sites and ensuring optimal therapeutic effects. [PERSON] et al. engineered magnetically responsive SMPCs by incorporating iron oxide magnetic nanoparticles (MNPs), enabling the activation of SMEs via an alternating magnetic field for controlled drug release [290]. These materials not only demonstrated favorable shape-memory properties and cytocompatibility but also allowed for the tunable release of drugs such as doxorubicin, 6-mercaptoethanolurine, and rhodamine, with over 75% cytocompatibility. The ability to adjust polymer chemistry, MNP content, and shape-memory properties facilitated the customization of drug release profiles, illustrating the system's versatility in delivering dual drugs with enhanced precision.
## 3 Results and Discussion
### Image Pre-Processing Results
To avoid the influence of the pre-training weights on the results, each model was trained by starting from scratch, with the batch size set to 16 and the number of iterations set to 150 epochs. Figure 12: HE-proposed THz image and its corresponding grey scale distribution histogram. (**a**) Original THz image; (**b**) grey scale distribution histogram of (**a**); (**c**) HE-processed image; (**d**) grey scale distribution histogram of (**c**).