esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
The permeability loops, measured by the MAT method on samples 23-27, are shown in Figure 5a,b with the slope of the magnetizing current being 0.05 and 0.5 A/s, respectively. Changing the speed of the magnetization obviously has an effect on the recorded permeability loops. However, the surface roughness alters the obtained curves in different ways: the ratio of maximum permeability (reference sample/sample having largest surface roughness) is different for these two \"speeds\" of measurement. The maximal permeability was determined simply as the peak value of permeability loops, shown in Figure 5a,b.  
The most visible difference between the differently machined samples appears as the peak values of the permeability curves, as shown in Figure 5. However, MAT process generates a big data pool, and those descriptors should be chosen from this, which result in the best correlation between magnetic characteristics and the investigated independent variable. In case of 0.05 A/s slew rate measurement the optimally chosen MAT descriptor is the \(1/\mu(h_{a},h_{b})\) descriptor, where the magnetizing field, \(h_{a}\), is 450 mA and the corresponding minor loop amplitude, \(h_{b}\), is 1300 mA. In case of 0.5 A/s slew rate measurement the optimally chosen MAT descriptor is the \(\mu(h_{a},h_{b})\) descriptor, where the magnetizing field, \(h_{a}\), is 500 mA and the corresponding minor loop amplitude, \(h_{b}\), is 950 mA. In Figure 6a, the optimally chosen normalized elements of permeability matrix are given as a function of \(R_{a}\) for two different slopes of magnetizing current. These parameters are taken from the area, indicated by lines in Figure 5a,b. It can be seen in Figure 6a that the magnetic parameters are modified by about 40%, compared with the reference sample, if 0.05 A/s slope of magnetizing current is applied. In the case of the higher slope, this ratio is about 20% smaller, but is still significant.  
Figure 5: Series of minor permeability loops measured by MAT on the investigated samples. (**a**) with 0.05 A/s slope of magnetizing current, and (**b**) with 0.5 A/s slope of magnetizing current.  
The result of Barkhausen noise measurement (RMS BN responses) is shown in Figure 6b, for two cases of direction of magnetizing field with respect to the sample orientation (transversal and longitudinal). For the better comparison, the values of BN signals are also normalized here by the value obtained on reference sample.  
Similarly to the MAT measurement, a clear tendency of modification of magnetic parameters as a function of surface roughness characteristics is also seen. Moreover, if the MAT descriptor obtained by the faster slew rate of magnetizing current is compared with Barkhausen noise signal measured in transversal direction, a remarkably good correlation can be found between their parameters (see in Figure 6c).
# Molecularly-Imprinted SERS: A Potential Method for Bioanalysis
## 2 Molecularly Imprinted Polymer (MIP)
### MIP Application in Analytical Chemistry  
MIP has been widely used for chemical analysis and drug delivery in the pharmaceutical industry. In analytical chemistry, MIP is used particularly for sample preparation. In chemical analysis, sample preparation is critical. The application of MIP can improve the analytical method's selectivity. This is due to the MIP cavity's ability to preferentially attach to the same or analogous analytes as the template utilized during the fabrication process. As a result, MIP is commonly utilized as a sorbent in solid phase extraction. This is due to the use of less selective sorbents in solid-phase extraction. MIP may also be used as a stationary phase in chromatographic separations, which is a sort of chemical analysis. It may also be utilized as a chemical and biological sensor and probe.  
MIP's application as a sorbent is not confined to its usage as a sorbent for solid phase extraction (SPE). dSPE (dispersive solid phase extraction), dSPE (magnetic solid phase extraction), and dSPME are further separation techniques that use MIP as a sorbent (solid phase microextraction) [98; 99]. The results of sample preparation using MIP are usually followed by detection using various methods. Some of the analytical methods that are often used are spectrophotometry [98; 99; 100], spectrofluorometry [101; 102; 103; 104], liquid chromatography [105; 106], gas chromatography [107], Raman spectroscopy, electrophoresis, electrochemical [108; 109; 110; 111; 112; 113; 114; 115; 116] methods.
# Chaos Based Frequency Modulation for Joint Monostatic and Bistatic Radar-Communication Systems
## 2 Contributions of Our Work  
In this work, we explore and demonstrate the potentials of chaos for dual radar-communication functionality. To the best of the authors' knowledge, this is the first series of experiments to combine the chaotic radars and communication systems using chaos. In particular, this will be the first work to present the joint bistatic radar-communication system using chaos. To that end, we:  
1. Generate a chaos-based frequency-modulated (CBFM) waveform for dual functionality. First, the chaos shift-keying approach is used to encode digital information onto the chaotic system and thereby using it as an instantaneous frequency to transmit CBFM pulses.
2. Show that the proposed waveforms perform better in terms of bit-error rate and imaging capabilities than the current RadComm waveforms.
3. Demonstrate that the CBFM waveform in bistatic configuration solves two issues. Firstly, reconstruct the transmitted waveform at the receiver via a direct synchronization scheme and a simple response chaotic oscillator. Secondly, to obtain the high-resolution imagery of targets.
4. Illustrate the high-resolution imaging capabilities of the CBFM waveform for a monostatic radar mode.  
The remainder of the paper is as follows. Section 3 will show a way to generate a CBFM waveform with digital information embedded in it. Section 4 describes the architecture to decode the information at the communication receiver. Section 5 shows a systematic procedure to reconstruct the transmitted waveform and simulate cross-ambiguity functions. [PERSON] will also characterize CBFM bistatic radar performance by simulating complex target imagery and analyze cross-ambiguity function in the presence of noise. Finally, in Section 6, we will show the high-resolution imaging capabilities of chaotic monostatic radar.
## 6 Advancement, Challenges and Future Directions
Comparing the average precision values of cars and pedestrians in Table 6, it is clearly visible that detection of the thin surface of pedestrians achieved far less precision. Hence, how to increase precision for thin object detection with point cloud methods is open research question;
* Data scarcity is one of the main constraints in 3D object detection-related research. Due to the support and sponsorship of automobile companies in their pursuit of building self-driving cars, some enriched benchmark datasets such as KITTI, Waymo, nuScenes, etc. are widely available. A few indoor benchmark datasets are also available for robot vision research, such as SUN RGB-D and ScanNet. But there is scarcity of open benchmark datasets for other fields. Specially, 3D object detection is becoming popular in precision agriculture, but the conducted research works are found to be using self-collected datasets. However, these datasets are not publicly available. This is a constraint on conducting 3D object detection research in agriculture. To reduce the issue of data scarcity, the 2D object detection sector is getting benefit from the new technology Generative Adversarial Networks (GAN). GAN is a type of augmentation technique that can generate new synthetic data. In the near future, 3D object detection techniques can be benefitted by GAN as well. To enhance 3D datasets, more 3D CAD model datasets (e.g., ShapeNet) can emerge in the near future.
# Mechanical and Dielectric Properties of Aligned Electrospun Fibers
## 4 Applications of Aligned Fibers
### Influence of Aligned Fibers on Mechanical Properties of Nanofiber [PERSON] et al. (2005) showed that well-aligned, multi-walled carbon nanotubes (MWCNT) can improve the mechanical properties of a PAN-based nanofiber mat [177]. [PERSON] et al. (2007) demonstrated that electrospun polymer/CNT leads to nanocomposite fibers with embedded CNTs orienting parallel to the nanofiber axis [178]. Moreover, alignment of CNTs in the fiber direction can improve thermal conductivity [179]. [PERSON] et al. (2016) reported that semi-aligned electrospun carbon nanofiber composites show excellent bending strength and interlaminar shear strength [180]. High-performance aramid copolymer fibers underwent four treatment factors. Among them, the degree of stretching after coagulation resulted in high degree of molecular orientation. The increased tensile strength of aramid fibers improved the cut resistance of aramid fibers, and therefore can be used in cut protection [181]. Ultra-high molecular weight polyethylene fibers with the tensile strength of 1.5 GPa were successfully prepared and structure and tensile property were studied. The increase in draw ratio improved the crystallinity of ultra high molecular weight polyethylene fibers. The molecular orientation degree increased, and tensile property also improved [182]. Increasing the draw-ratio resulted in an increased molecular orientation, Young's modulus, and tensile strength of poly(amide-block-aramid) fibers comprised of alternating rigid aramid blocks of poly(p-phenylene terephthalamide) (PPTA) and flexible blocks of polyamide 6,6. Heat treatment at 300 \({}^{\circ}\)C of the fibers resulted in an increase of Young's modulus and minor increase of strength [183].  
Aligned micro scale fibers (7 um diameter) have application in composite reinforcement. There is increased need to manufacture complex composites for light weight applications. Carbon/epoxy composites have greater application in aircraft, sports cars, and space crafts because of a better strength to weight ratio than that of metals like aluminum alloys. They are thermally stable because of the lower coefficients of thermal expansion properties of carbon fibers. [PERSON] et al. (2014) showed that short carbon fiber composites can be used in places where complex shapes and ductile properties are required [184]. Short carbon fibers, with an aspect ratio of 400, resulted in composites with a tensile modulus of 119 GPa and strength of 1211 GPa.  
[PERSON] and [PERSON] (2014) reported that cellular composites with controlled alignment of multi-scale and high aspect ratio fibers can result in reinforcement of hierarchical structures [185]. They demonstrated the first 3D printed cellular composites composed of oriented fiber-filled epoxy with exceptional mechanical properties. [PERSON] et al. (2017) developed a new carbon fiber reinforced epoxy for 3D printing which resulted in printing materials with longitudinal Young's modulus up to 57 GPa [186].
# Bond Fatigue of TRC with Epoxy Impregnated Carbon Textiles
## 2 Materials and Methods
### Load Regime and Experimental Program  
In order to define the load levels of the cyclic tests, first the reference strength had to be determined. As in the cyclic tests, the influence of cyclic loading on the necessary anchorage length ought to be investigated. The static reference tests were done with different anchorage lengths as well, which were defined as a multiple of the fiber strand distance, \(a\). There were four different anchorage lengths varying between \(1a\)-\(4a\). The minimum number of specimens per length was 10. The average value per length was set as the reference strength for the cyclic tests.  
The load regime for the cyclic tests was the following. First, the specimen was loaded path-controlled up to a defined mean stress. Thereby, it was important that the crack in the middle of the specimen had completely developed. Afterwards, the cyclic loading was started, force-controlled with a sinusoidal oscillation and constant amplitude. The load frequency was 12 Hz. The maximum number of load cycles was set to \(2\times 10^{6}\) to limit the duration of the test, and because this is a usual choice for cyclic tests with concrete or steel. Afterwards, runouts that withstood the cyclic loading were tested path-controlled until failure to determine their residual strength.  
To reduce the amount of data, measurements, e.g., the force and deformation during the cyclic loading, were done in intervals. After a defined period of time, 800 measuring values were recorded with a measuring rate of 400 Hz. The duration of this period increased over the course of the cyclic testing: during the first load cycles, every measuring point was recorded, until approximately the 5000\({}^{\text{th}}\) load cycle, the period was 10 seconds; until 30,000 load cycles, it was three minutes, and afterwards, it was 15 min.  
The experimental program is shown in Table 1. Per anchorage length, two different minimum stresses \(\sigma_{min}\) were investigated with different related maximum stresses \(\sigma_{max}\). The load levels are specified as a percentage of the reference strength. As one of the goals of the research project C\({}^{2}\)-V\(2.1\) is to create stress-number of cycles to failure (S-N) diagrams for TRC, load levels had to be chosen that lead to the failure of the specimens. Since preliminary tests showed that only high amplitudes cause failure, a maximum \(\sigma_{min}\) of 50% was set. The second \(\sigma_{min}\) of 30% was chosen as a compromise between the distance of the two investigated minimum stresses and the limit of the testing machine (regarding the combination of amplitude and frequency). The related maximum stress was increased after each test series, with the aim to force the failure of the specimens. Each test series contained four specimens. However, especially in test series with short anchorage lengths, some specimens failed already during the static loading up to the mean stress. So, these specimens could not be taken into account in the evaluation in the following section.
# An Improved Method for Evaluating Image Sharpness Based on Edge Information
## 3 Experimental Results and Analysis
### Content-Independent Experiment  
The experiment was designed to show that the evaluation method proposed in this paper was superior compared to the evaluation method proposed in reference [14] and the traditional Tenegrad function evaluation method.  
As in the second experiment in the previous subsection, we still selected images from the LIVE database in this experiment; the difference was that the selected images were 29 undistorted reference images with different contents. First, the order of these 29 images was disrupted and randomly ordered. After that, a Gaussian blur was added to these images sequentially according to the order of measured images, with a Gaussian blur standard deviation from 0.1 to 2.9 in steps of 0.1, resulting in 29 blurred measured images. Finally, the measured images were evaluated by the method in this paper, the method in reference [14], and the Tenegrad function method, respectively; the evaluation results are shown in Figure 20.  
As can be seen in Figure 20, with the increase in the Gaussian blur standard deviation of the images, both the evaluation methods in this paper and reference [14] show an increasing trend while the Tenegrad function evaluation method shows a decreasing trend, which are caused by their respective calculation principles. In order to compare the accuracy of the three evaluation methods more comprehensively, this paper adds a comparison of the root mean square error (RMSE) and mean absolute error (MAE) of the three evaluation methods in addition to the calculation of the Pearson linear correlation coefficient (CC); the final results are shown in Table 2.  
Larger CC values and smaller RMSE and MAE values in the above table indicate the better validity of the method. Therefore, it is clear from the above table that the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **CC** & **RMSE** & **MAE** \\ \hline Proposed & **0.9684** & **0.6534** & **0.4746** \\ Reference [14] & 0.9627 & 0.7158 & 0.5275 \\ Tenegrad & \(-\)0.7817 & 1.944 & 2.9083 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance of the three evaluation methods.  
Figure 20: Comparative experiment of image content irrelevance (red represents the method of this paper, blue represents the method of reference [14], green represents the Tenegrad method).  
evaluation method proposed in this paper had significant superiority compared to the other two methods.
# A Novel Traffic Classification Approach by Employing Deep Learning on Software-Defined Networking
## 5 Conclusions  
This research develops a model for traffic classification by application types and network attacks using DL techniques to enhance QoS and security in SDN. The classifiers identify application types and attacks in five classes: Multimedia, VoIP, Instant message, File transfer, and Attacks. The SEMMA methodology was conducted to develop accurate models and obtain reliable results. We trained the models with four derived RNN algorithms, GRU, BiGRU, LSTM, and BiLSTM, which were evaluated during this process. Each model performed traffic classification using two open datasets, VPN-nonVPN and InSDN, extracting flows with a 15 s timeout with the CICFlowMeter tool. It resulted in a dataset of 50,000 samples, with 10,000 samples per class representing different types of applications and attacks. We selected five features to train the models based on analyzing the features obtainable from the SDN controller's northbound interface and conducting mutual information analysis. We determined the length of the sequence through several experiments. The accuracy results for each model were as follows: GRU (99.65%), BiGRU (98.60%), LSTM (99.63%), and BiLSTM (99.32%). These results underscore the remarkable effectiveness of the GRU model in traffic classification. These findings provide great potential for improving the security and QoS in SDN employing traffic classification, contributing to network administration.  
Although deep learning has been used to develop the classifiers, developing new applications or attacks could affect their effectiveness. In addition, the datasets used may only partially reflect the complexity of traffic in real environments. Therefore, as future work, we will seek to implement the proposed classifiers in a real-time SDN environment to instantaneously classify incoming flows based on the statistical information of the flows and apply corresponding QoS, and security policies.  
Conceptualization, D.N.-A. and E.B.-A.; methodology, D.N.-A., W.F. and L.M.; software, D.N.-A., C.C.-G. and F.P.; validation, D.N.-A.; formal analysis, W.F. and L.M.; investigation, D.N.-A.; data curation, D.N.-A., C.C.-G. and F.P.; writing--original draft, D.N.-A. and W.F.; writing--review and editing, D.N.-A. and W.F.; supervision, W.F. and L.M.; funding acquisition, D.N.-A. and W.F. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: Data are contained within the article.  
**Acknowledgments:** During the preparation of this work, the authors used the Grammarly tool to check spelling, grammar, punctuation, and clarity errors. After using this tool, the authors reviewed and edited the content as needed and took full responsibility for the publication's content.
###### Abstract  
In this study, for the application of visible-light wearable eye trackers, a pupil tracking methodology based on deep-learning technology is developed. By applying deep-learning object detection technology based on the You Only Look Once (YOLO) model, the proposed pupil tracking method can effectively estimate and predict the center of the pupil in the visible-light mode. By using the developed YOLOv3-tiny-based model to test the pupil tracking performance, the detection accuracy is as high as 80%, and the recall rate is close to 83%. In addition, the average visible-light pupil tracking errors of the proposed YOLO-based deep-learning design are smaller than 2 pixels for the training mode and 5 pixels for the cross-person test, which are much smaller than those of the previous ellipse fitting design without using deep-learning technology under the same visible-light conditions. After the combination of calibration process, the average gaze tracking errors by the proposed YOLOv3-tiny-based pupil tracking models are smaller than 2.9 and 3.5 degrees at the training and testing modes, respectively, and the proposed visible-light wearable gaze tracking system performs up to 20 frames per second (FPS) on the GPU-based software embedded platform.  
deep-learning; YOLOv3-tiny; visible-light; pupil tracking; gaze tracker; wearable eye tracker +
Footnote †: journal: Applied Sciences
# Efficient Massive MIMO Detection for M-QAM Symbols
## 4 Simulation Results
At BER=\(1.3\times 10^{-5}\), the average complexity of the INL-BOXDCD (with \(N_{u}=10^{3}\) and \(V=3\)) is \(0.16\times 10^{5}K_{t}\), and the average complexity of the BOXDCD (with \(N_{u}=10^{5}\)) is \(4\times 10^{5}K_{t}\). When \(N_{u}=10^{3},V=1\), the computational complexity of the INL-BOXDCD  
Figure 1: BER performance of the INL-BOXDCD as a function of SNR for different ratios \(\rho\) of receiving to transmitting antennas; \(V=1\), \(N_{u}=300\), 16 QAM and \(K_{t}=60\). detector is twice as high as that of the BOXDCD detector, while the INL-BOXDCD detector has a BER that is approximately 100 times lower after SNR\(=24\) dB. Figure 2: Effect of \(V\) on the BER performance of the INL-BOXDCD detector for a \(60\times 60\) MIMO system with different QAM constellations; **(a)** 4 QAM; **(b)** 16 QAM; **(c)** 64 QAM. The average complexity of the proposed INL-BOXDCD algorithm for a \(100\times 100\) MIMO system is listed in Table 1 and compared with that of the MMSE, iterated MMSE \((V=10)\), \(\text{BOXDCD}(N_{u}=10^{5})\) and INL-BOXDCD(\(N_{u}=10^{3},V=3\)) when BER = \(10^{-4}\). For \(K_{t}=100\), the MMSE detector approximately requires \({K_{t}}^{3}=10^{6}\) complex multiplication and addition operations. The average complexity of the iterated MMSE detector with \(V=10\) is \({K_{t}}^{3}(V-1)=9\times 10^{6}\) complex multiplications and additions. With a given \(N_{u}=10^{5}\), the average computational complexity of BOXDCD is \(4K_{t}N_{u}=40\times 10^{6}\) real additions. For a given \(N_{u}=10^{3}\) and \(V=3\), the average computational complexity of the INL-BOXDCD detector is \(4K_{t}N_{u}(V+1)=1.6\times 10^{6}\) real additions. From Table 1 and Figure 4, we notice that the MMSE detector shows poor performance, while iterative MMSE has a better performance by increasing \(V\). When BER = \(5\times 10^{-5}\), the iterated MMSE requires 2 dB more than BOXDCD and INL-BOXDCD. The matrix inversion of iterated MMSE (and MMSE) requires high complexity \(\mathcal{O}({K_{t}}^{3})\), which is calculated by counting the cost of the addition and multiplication operations. BOXDCD and INL-BOXDCD require only addition operations. The results also show that the proposed algorithm, INL-BOXDCD, has a much lower computational cost compared to BOXDCD by reducing \(N_{u}\), while the two algorithms achieve comparable performance. Figure 4: BER performance comparison among BOXDCD, MMSE and INL-BOXDCD: 16 QAM, \(100\times 100\) MIMO configuration. Figure 3: The effect of \(N_{u}\) on the BER performance for different MIMO configurations; 16 QAM, \(V=2\). The modulation error ratio (MER) performance of MMSE, iterated MMSE, BOXDCD and INL-BOXDCD over 1000 16 QAM symbols in a \(100\times 100\) MIMO system are shown in Figure 5. The results show that for a large-scale system, box-constrained detectors offer better performance than MMSE-type detectors. The INL-BOXDCD shows a better performance than BOXDCD with \(N_{u}=10^{3}\) after 15 dB, which is consistent with the BER verifications.