esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Finally, in the recent years, novel high-performance computational tools, based on the isogeometric analysis (IGA) [31] and the generalized differential quadrature (GDQ) method [32], have been developed. In particular, in [33; 34] the delamination in composites was deeply investigated by using isogeometric analysis. In [35], a generalized large deformation contact algorithm able to take into account both frictional contact and mixed-mode cohesive debonding was developed, based on IGA. Moreover, in [36] the delamination process in multi-layered composite specimens was investigated, while in [37], the mixed-mode delamination in moment-loaded double cantilever beam (MLDCB) specimens were studied, both analytically and numerically. The differential equations of the problems in [36; 37] were solved in a strong form by using the GDQ method. In general, the delamination growth phenomena are usually investigated by means of numerical technique based on non-linear algorithms, which are characterized by high computational costs. Despite this, such relevant computational costs could be acceptable in a detailed verification stage, less expensive procedures are desired in a preliminary design stage or during optimization procedure. In this work, a _fast_ and linear procedure, able to predict the delamination growth initiation under mixed fracture mode I and II in stiffened composite panels is presented. The proposed procedure is expected to be applicable in the frame of a preliminary design/optimization stage on composite structures subjected to instability loads. Actually, the state of art of _fast_ procedures for the determination of mode I based delamination growth initiation under compression load for delaminated stiffened panels [38; 39] has been improved, by extending the applicability and increasing the accuracy when mixed mode I and II growth is expected. In Section 2, the theoretical background of the _Fast_ numerical procedure is introduced, and the improvements to the existing _Fast_ approach of [38; 39] are described from a theoretical point of view. In Section 3, numerical analyses focused on the validation of the described method are introduced, and the effectiveness of the proposed novel approach is assessed by comparisons with standard non-linear numerical techniques.
## 4 Results
### DWT and 1D-CRNN Feature Ranking  
Random forest is a regression, classification and feature ranking ensemble learning approach. It is made up of decision trees that have been trained on random subsets of data and features. The relevance score is calculated to rank the importance of features by measuring the decrease in data impurity when a feature is used for tree splitting. Impurity can be measured using a variety of metrics such as Gini impurity or entropy. To obtain a more consistent assessment, the relevance score of each feature is averaged across all trees in the forest. This ranking can assist in identifying the most relevant features, which can then be used for feature selection or data reduction. The feature ranking for DWT and 1D-CRNN features is shown in Figure 11. The feature ranking is analyzed for DWT and 1D-CRNN on the combined dataset. The ranking of features shows the significance of both DWT and 1D-CRNN features, as well as the combination of these features.
# Sub-Band Backdoor Attack in Remote Sensing Imagery
## 9 Case Study
### Case Study 2: STRIP  
Table 5 shows that STRIP failed for most of the cases. STRIP uses the entropy of the predicted probabilities for classes in the dataset to detect poisoned samples. For a backdoored model, poisoned samples will show a low entropy and the embedded trigger makes the prediction strongly towards the target class, while benign samples will show a larger entropy. We visually compare the entropy performances of STRIP in all-band, important-band, and less-important-band BadNets in Figures 6-8. It was observed that the all-band BadNets attack has a lower entropy distribution in Figure 6 compared with important-bands attack (Figure 7) and less-important-bands attack (Figure 8), indicating that STRIP faces more challenges in detecting sub-band BadNets backdoor attacks.
# Advances in Remote Sensing and Propulsion Systems for Earth Observation Nanosatellites
## 4 Propulsion Systems in Nanosatellites
### Taxonomy Nanosatellites' Propulsion Systems  
Nanosatellite propulsion systems can be categorized into non-electric and electric propulsion systems. Specifically, the non-electric propulsion systems can be classified into liquid, cold gas, and solid rocket systems. In contrast, electric propulsion systems can be classified into resistojets, electrospray, ion, Hall, and pulsed plasma systems. Table 8 shows a comparison of nanosatellites' propulsion systems.
## 4 Conclusions  
We conducted a theoretical analysis of the intensity distribution and normalized M\({}^{2}\) factor for the partially coherent flat-topped beam rectangular arrays propagating both forwards and backwards through atmospheric turbulence and plasma. The numerical results show that, in forward transmission, partially coherent flat-topped beam rectangular arrays experiences less impact from atmosphere turbulence and plasma compared to that caused by reverse transmission. With other conditions remaining constant, an increase in beam order, a decrease in transverse spatial coherence width, and an enlargement of the beam waist all contribute to mitigate the influence of atmospheric turbulence and plasma on this beam. These findings highlight the significant implications for FSO communication systems operating through turbulent atmosphere and plasma links. This optimization can lead to reduced signal degradation and improved transmission quality, making FSO communications more viable and effective in challenging environments.  
Conceptualization, Y.C. and B.G.; methodology, X.M., C.Y. and Y.Z.; software, X.M. and L.Y.; validation, Y.Z., L.Y. and Y.Y.; formal analysis, X.M., C.Y. and H.Z.; writing--original draft preparation, X.M. and Y.Y; writing--review and editing, H.Z., Y.Z. and Y.Y; supervision, Y.C. and B.G.; funding acquisition, H.Z., Y.G., Y.Y. and Y.C. All authors have read and agreed to the published version of the manuscript. Funding was provided by the National Key Research and Development Program of China (2022 YFA1404800), the National Natural Science Foundation of China (NSFC) (12192254, 12174227, 92250304, 12447150, W2441005, 12474295), and the Key Research and Development Program of Shandong Province (2024)MRH0105). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Data are contained within the article. Conflicts of Interest: The authors declare no conflicts of interest.
## 2 Contribution of Proposed Structure  
There is a trade-off between the device size, the number of operating bands, the average gain and the undesirable distortion in the radiation characteristics. Although the antennas reported in the literature have a high gain and are multiband, there is a trade-off among many factors. Incorporating metamaterial and slots into the design of a miniaturized multiband antenna to achieve acceptable gain across all of the operating bands is the overarching goal of the study, which has been presented to solve the design problems that have been outlined above. The following is an outline of the primary contributions, novelty and advantages of this work:  
* The proposed simple structure helps to attain the multiband response to cover a broad range of applications.
* A metamaterial slot antenna with a high gain and the ability to operate on six different bands is presented for use in WLAN and earth observation applications.
* To achieve a multiband response, optimization of the feed point and changing the superstate layer's height are carried out.
* The simulation and fabrication were performed using the low-profile FR4 substrate material.
* The antenna consists of a straightforward patch construction and a superstate design influenced by metamaterial rings.  
* The addition of metamaterial rings in the patch gives a better response to the superstrate antenna design.
* The varying size of metamaterial rings carries out the optimization of the reflection coefficient.
* The study includes comparing the findings of simulation and measurements and the fabrication of an antenna construction employing low-profile FR4 material.
* The structure provides the minimum reflection coefficient of \(-49\) dB with a bandwidth of \(400\) MHz.
* To determine the extent of the improvement, a comparison of the work being conducted here with that of other articles is included.
# Numerical Investigation of the Relationship between
## 4 Discussion
### Optimal Anastomosis Angle  
The results show that local hemodynamics in the anastomosis region is heavily influenced by the anastomosis angle, so optimizing the angle is a potential strategy for improving bypass graft patency. The optimal design must display reduced pressure drops, smaller recirculation regions, and minimized areas affected by abnormally high or low WSS. Suggestions from the literature also indicate the potential benefits of spiral flow, so higher secondary velocity magnitudes could indicate better bypass graft performance.  
Figure 13 summarizes the results observed for the pressure drop, secondary velocity, recirculation area, and areas of abnormally high and low WSS according to anastomosis angle. The values were normalized to the 60\({}^{\circ}\) graft values. The chart was arranged so that more favorable values were projected outwards; thus, the optimal graft would occupy the largest area in the chart.  
The 30\({}^{\circ}\) graft outperformed the other grafts on three variables. Significant reduction in areas of recirculation and abnormally high WSS resulted from using a 30\({}^{\circ}\) anastomosis angle. The pressure drop between the inlet and outlet was also slightly reduced when using the smallest angle. In these variables, clear, negative relationships between the angle and graft performance were observed.  
Looking at the areas affected by abnormally low WSS, the 30\({}^{\circ}\) graft was slightly outperformed by the 60\({}^{\circ}\) graft. However, as previously discussed, any improvement based on this variable may not be important due to the minuscule difference between the highest and smallest values observed and because little to no blood flow is expected in these areas anyway.  
Lastly, the 30\({}^{\circ}\) graft fared the worst in terms of flow spirality, as indicated by the secondary velocity magnitude. However, it must be noted that the secondary velocity does not directly indicate superior graft performance. It is just suggested to enhance other hemodynamic metrics such as WSS, so although the 30\({}^{\circ}\) graft had the weakest spiral character, no direct detrimental biological effects arose from it. Increased spiral character, even if it raised WSS as observed from other anastomosis angles, may have hurt graft performance due to increased pressure drop and larger areas affected by abnormally high WSS.  
Therefore, the use of smaller anastomosis angles seemed to have enhanced the hemodynamic performance of ridged, spiral-flow-inducing graft design. This is congruent with studies in the literature which state that smaller anastomosis angles are beneficial to bypass graft performance. This benefit is often attributed to less \"disturbed flow\" caused by smaller anastomosis angles which are characterized by reduced WSS peaks and gradients, flow separation, recirculation area, and secondary flow components [47].
# A Sensitivity Study of POD Using Dual-Frequency GPS for CubeSats Data Limitation and Resources
## 1 Introduction
Due to the limitations in the antenna system, reports of short data pieces and frequent cycle slips, which hampers the high-accuracy POD, can also be found for the triple-CubeSat CanX-2 [20]. As such, not only for the planning purpose but also to get prepared for the possible sub-optimal real data collection, investigations of the CubeSat POD accuracy with diverse constraints on data and resources are important. A proper compromise is very possibly needed between the limiting resources, the suboptimal data conditions, and the requirements on orbital accuracy. For the dual-frequency CubeSat POD, which is the focus of this study, it was pointed out by previous studies that the discontinuity of the observation data with a low duty-cycle may significantly degrade the orbital accuracy in both the real-time and the postprocessing modes [21; 33]. In this study,  
Figure 1: (**a**) Distribution of the tracked satellite numbers per epoch and (**b**) the time history of the satellite numbers having both the L1/L2 observations and at least the L1 observations. The data was collected from the CubeSat STF-1 from 15:39:37 in GPS time (GPST) on 27 February 2019 to 3:55:10 on 28 February 2019.  
we restrict focus on postprocessing reduced-dynamic dual-frequency CubeSat POD, which relieves the strong constraints of the computational load onboard the CubeSat and allows for the usage of good dynamic models. In the experiments of CanX-2 [20] and STF-1 [21], geodetic grade receivers NovAtel OEM4-G2 and OEM615 tracking dual-frequency GPS signals were mounted onboard the CubeSats. With the expectation that in future CubeSat missions, dual-frequency measurements can be properly tracked in each duty-cycle by geodetic receivers with a mean average satellite number of at least 5 to 6, this study performs a sensitivity analysis of different limitations on the data availability, continuity, resources, latencies, and arc lengths with respect to the orbital accuracy. The overlapping effect of different constraints is investigated, and a compromise between these factors and the orbital accuracy is discussed. In addition to CubeSats, the analysis should also benefit the POD of other small satellites that need to bare similar constraints on data, power, and resources.
## 3 Methods
### Oscillation Frequency Estimation  
The fluid oscillation frequencies were evaluated starting from the wall-to-fluid heat flux distributions (Section 3.1), since the local thermofluidic interactions were believed to be strictly linked to the heat dissipation process underlying the FPPHP operation [13; 32]. As already stated, the oscillatory behavior quantification was based on the wavelet method; in particular, according to [PERSON] et al. [6], the Morlet wavelet, the shape of which is defined by Equation (2) for dimensionless time \(\eta\) and the characteristic parameter \(\omega_{0}\), was adopted.  
\[\psi(\eta)=\frac{1}{\sqrt{\pi}}e^{-\frac{\eta^{2}}{2}}e^{i\omega_{0}\eta} \tag{2}\]  
The wavelet transform of a time-dependent signal \(g(t)\) reads as [33]  
\[W_{\psi}(a,\tau)=\frac{1}{\sqrt{a}}\int_{-\infty}^{\infty}g(t)\psi^{*}\bigg{(} \frac{t-\tau}{a}\bigg{)}dt \tag{3}\]  
where \(a\) and \(\tau\) are the wavelet scale (dilation of the wavelet function) and time shift, respectively, and the superscript \({}^{*}\) denotes the complex conjugate. The term \(1/\sqrt{a}\) is due to the adopted L1 normalization of the wavelet, used for comparison purposes between different scales. The output of the wavelet analysis is the so-called magnitude scalogram, defined as the absolute value of the wavelet transform, plotted as a function of frequency and time. In particular, it represents the power related to each frequency over the sample duration. Such a power is strictly linked to the oscillation amplitude of the signal; in fact, high-amplitude oscillations result in high powers in the scalogram. On the other hand, the power spectrum referred to as the considered signal is defined as the time integral of the magnitude scalogram, for every frequency. While the scalogram describes the time evolution of the characteristic frequencies underlying the considered oscillatory phenomenon, the power spectrum allows for the identification of the dominant frequency related to the considered sample (identified by a peak in the power spectrum).  
The wavelet parameters adopted in the present analysis are listed in Table 2. In particular, these parameters were chosen to guarantee a good trade-off between the frequency and time resolution of the method.
## 1 Introduction  
The sCO\({}_{2}\)(supercritical carbon dioxide) Brayton power cycle has the characteristics of small size, compact structure and strong environmental adaptability. Compared with the commonly used Rankine cycle, it has the advantage of high efficiency in the medium and high temperature zone [1]. Compressors are the \"heart\" of various power cycles. sCO\({}_{2}\) compressors are also the key to improving cycle efficiency and operational stability in sCO\({}_{2}\) Brayton cycle generation systems. A sCO\({}_{2}\) Brayton power cycle less than 100 Mwe can choose a single-stage or a multi-stage centrifugal compressor as the main compressor [2]. It can be expected that for a long time to come, centrifugal compressors will be the main form of sCO\({}_{2}\) compressors.  
The sCO\({}_{2}\) centrifugal compressors in 100 kWe scale have accumulated lots of experimental test results. The experimental cycle of Sandia laboratory in the United States includes the main compressor and the recompression compressor; the main compressor inlet condition is near the critical point, and the recompression compressor inlet temperature is much higher than the critical point. Therefore, the main compressor is mainly tested and the maximum experimental speed is 65,000 RPM, which is 92.8% of the design speed; the highest pressure ratio reached 1.65, reaching 90% of the design pressure ratio. The efficiency is 60-70% [3, 4]. The TIT (Tokyo Institute of Technology) centrifugal compressor uses a closed impeller, but the maximum experimental speed only reaches 70% of the design speed, and the efficiency of the compressor is only 48% [5]. The experimental speed of SCIEL (the Supercritical CO\({}_{2}\) Integral Experimental Loop) shunt back-to-back compressor is only 50% of the design speed, and the compressor efficiency (including external losses) is 25% [6; 7; 8].  
The above-mentioned sCO\({}_{2}\) centrifugal compressors show that the influence of rotor windage loss cannot be ignored, and should be treated carefully to improve the efficiency of the compressor.  
In this study, a single-stage centrifugal compressor with labyrinth seal and oil lubrication is designed, and a low-speed motor is connected through a high-speed gearbox as the power to effectively avoid high wind resistance losses caused by high-pressure density fluid in the high-speed shaft. Based on the above design, the single-stage compressor is expected to have better performance than the existing sCO\({}_{2}\) compressor.
# Marine Bioactives and Their Application in the Food Industry: A Review
## 2 Marine Sources of Bioactive Molecules
### Marine Cyanobacteria
#### 2.1.2 Proteins and Bioactive Peptides from Microbiota
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Type of Enzymes** & **Responsible Microorganism** & **Applications** & **References** \\ \hline \multirow{3}{*}{Protease} & _Aureobashium pullulans, Bacillus mojavensis, Psychobracter, Clostridium, Rhizopus, Penicillium, and Aspergillus_ & Pharmaceutical (digestive drugs and anti-inflammatory drugs), detergent, and leather industries & [34; 36] \\ \hline \multirow{3}{*}{Lipase} & _Penicillium Oxalicum, Aspergillus flavus, Moraxella, Candida intermedia, Pichia guillermondii, Lodderomyces elongisporus, Candida parapsellosis, Candia rugosa, Candida quercetin, and Yarrovia lipolytica_ & Detergents, cosmetic production, paper production, and food flavoring & [33; 34; 37] \\ \hline \multirow{3}{*}{Chitinase} & _A Aspergillus, Penicillium, Rhizopus Myxobacter, Sorocytoplaspa, Bacillus, Enterobacteriaceae, Flavobacterium, Arthobacter, Vibrio parahaemolyticus, S Vibrio fluvialis, Vibrio antimicrobials, Vibrio alginolyticus, Listonella anguillarum, Klebsiella, Pseudomonas, Ceratia, Chromobacterium, Clostridium, and Aeromonas hydrophilic_ & [33; 38; 39] \\ \hline \multirow{3}{*}{_Aganases_} & _Pseudomonas galatica, Cytoplasm, Alternomonas, Bacillus, Vibrio, Pseudoalteromonas, Streptomyces, and Pseudomonas_ & Food industry (beverages, bread, cookies, and some low-calorie food) & [33; 34; 40] \\ \hline \multirow{3}{*}{Cellulase} & _Cytoplaspa, Cellulomonas, Vibrio, and Clostridium, Nocardi, Straptomyces, Trichoderma, Aspergillus, Fusarium, Chaetomium, Phoma, Sportotrichum, and Penicillium_ & Cotton and linen product processing, bio-textileile auxiliaries, and bio-fertil processing & [33; 34; 38; 41] \\ \hline \multirow{3}{*}{Xylanases} & _Apergillus niger_ and _Penicillium chrysogenum_ & Paper and pulp industries and beverage clarification & [33; 42] \\ \hline \end{tabular}
\end{table} 
Table 2: Enzymes derived from marine microorganisms and their applications.
## 2 Materials and Methods
### Model-Specific Parameters
Other parameters such as green vegetation fraction, fractional cover, and effective leaf width are simply derived from satellite-based NDVI and land cover maps where vegetation parameters are assigned based on the look-up table (Table 2) in [PERSON] et al. (2020) [36].
# From Stationary to Nonstationary UAVs: Deep-Learning-Based Method for Vehicle Speed Estimation
## 3 Data and Methods  
The proposed methodology enables robust traffic analysis using computer vision and geospatial data analysis techniques to detect, track, and map objects. It begins with preparing a video and an orthomosaic reference image. The orthomosaic was developed using the WebODM (version 2.5.0) application of OpenDroneMap17. The experiment footage was collected in the daytime with clear sky conditions using a DJI mini 3 pro (Shenzhen, China) at 4K (3840 \(\times\) 2160) resolution with a frame rate of 30 FPS. For the template-matching algorithm to function effectively, conducting the experiment in daylight is essential. For validation, Pro Laser III speed gun manufactured by Kustom Signals Inc. (Owensboro, KY, USA) was used. To synchronize the video with the speed gun, measurements were taken five times (as discussed in Table 3), and parts with speed gun measurements served as a benchmark for validation. Subsequently, SIFT (scale-invariant feature transform) is used for feature recognition on the reference image and georeferencing. The key difference between stationary and moving drone footage lies in the frequency of the pixel calibration. For stationary drone footage, georeferencing is performed on the first frame and remains constant throughout the entire video. In contrast, for moving drone footage, pixel calibration must occur for each frame, because the homography of the input frames changes with the drone's movement and variations in altitude. By replicating the homography of the template image, it is possible to dynamically adjust the calibration of the cells despite the drone's movement and shifts in altitude. YOLOv8 model is then applied to detect objects within each video frame. The next crucial step involves using a transformation matrix to translate pixel-based coordinates into real-world geographical locations, connecting visual data with physical geography. The method tracks object movements across frames, logging their geographical positions and other key parameters, such as ID, class, and frame of appearance, which enables the real-time velocity measurement. Figure 1 provides a step-by-step illustration of the workflow.  
Footnote 17: [[https://github.com/jml/](https://github.com/jml/)]([https://github.com/jml/](https://github.com/jml/))