esa-sceva/satcom-chunk-collection huggingface dataset:
# Multidisciplinary Optimization of Aircraft Aerodynamics for Distributed Propulsion Configurations
## 3 Performance and Coupling Effects of Aero-Propulsion
### Aero-Propulsive Coupling Effects
Other factors that might affect the structure or mechanical design of small engines include high rotational speed, which may exceed 2 million r.p.m. A rotational speed this high not only demands non-lubricated air bearings but also introduces numerous new challenges in the fields of aerodynamics, materials, and manufacturing. The forenamed various aero-propulsive configurations (listed in Table 2) can be combined to improve further propulsion efficiency and flight performance. NASA N3-X is a model that precisely integrates the BWB, BLI, jet wing, and parallel trailing edge. Its merits are extracted and evolved from three former models of N2A, SAX-40, and cruise-efficient short takeoff and landing (CESTOL). It has the characteristics of high controllability, high wake filing, low noise emission, and high fuel efficiency. From the predictions of NASA, its reduction of NOx in the take-off and landing phases could be up to 85% of the Tier 6-CAEP/6 standard. Furthermore, the cumulative noise can be reduced by a minimum of \(12 EPNdB\) compared with their N+3 environmental goal [44; 67]. The dilemma is that this model is an all-electric-driven aircraft, and the current development situation of all-electric-driven aircraft is still immature. Additionally, the storage space and electric current conversion between AC and DC do not yet have the necessary performance to convert the output instantaneously. On the other hand, DP consists of multiple propulsors, which means the conventional energy loops that should be integrated with DP might also face the supply-demand problem on multiple propulsors instantaneously. From the comparison and detailed description in Table 2, it can be found that the DP and BLI systems can also be integrated with TWB. A parametrization study on the power system of propulsive fuselage indicates that the potential of energy-saving in an identical fan inlet area is up to 10% compared to a conventional podded power system. The fuel combustion of an economic and design mission of the turbo-electric concept can be reduced by 7% and 12%, respectively [63]. However, it is not easy to implement the characteristics of small engines into the TWB since it is a concentrated tube body. The only possibility is to place the DP at a position with low thrust-drag areas (wing), but this may result in excess thrust.
## 2 Materials and Methods
### Nonlinear Parameters Calculation
#### 2.2.3 The Lyapunov Exponent (LyE)  
The LyE was computed using an algorithm that estimates the dominant Lyapunov exponent of a 1D timeseries by monitoring orbital divergence. The algorithm was distributed for many years by [PERSON] et al. [40] in Fortran and C language. In this case, the same code was adopted into MatLab [41].
## 3 Experimental Results and Discussion  
In order to evaluate the performance of the proposed approach, we utilize the collected dataset, described in Section 2.1, to develop three evaluation scenarios based on the number of unobserved video subsequences in the testing videos, including: fully-observed video sequences, single unobserved video subsequence with random length and two unobserved video subsequences with random lengths. Moreover, we utilize the LOVSO-CV procedure to evaluate the performance of each scenario. In particular, the classifiers in each scenario are trained using all of the video sequences except one video sequence that is used for testing. This evaluation scheme is repeated for all possible combinations, and the overall result is computed by averaging the results obtained from each repetition. For all evaluation scenarios, the window size \(W\) and the overlap size \(O\) of the motion profile are selected experimentally and are equal to three frames and one frame, respectively. In order to train the SVM classifiers in the second and third evaluation scenarios, we divided each video sequence into overlapping segments. The size of each segment is equal to 20% of the total number of frames in the video sequence, and the overlap between any two consecutive segments is equal to 50%. Moreover, as the lengths of the unobserved video sequences are random, we evaluate the performance of the proposed approach for the second and third evaluation scenarios by repeating the LOVSO-CV procedure ten times, such that in each repetition, we generate unobserved video subsequences with different random lengths in the testing sequences. Then, the average values of the recognition accuracy, precision, recall and F1-measure are computed as performance evaluation metrics over the ten LOVSO-CV train-test repetitions. These metrics are defined as follows:  
\[Accuracy=\frac{(TP+TN)}{(TP+TN+FP+FN)}, \tag{23}\]  
\[Precision=\frac{TP}{(TP+FP)}, \tag{24}\]  
\[Recall=\frac{TP}{(TP+FN)}, \tag{25}\]  
\[F_{1}-measure=\frac{2 TP}{(2 TP+FP+FN)}, \tag{26}\]  
where \(TP\) is the number of true positive cases, \(TN\) is the number of true negative cases, \(FP\) is the number of false positive cases and \(FN\) is the number of false negative cases. Next, we discuss the results of our proposed approach for each evaluation scenario.
# Approximate Networking for Universal Internet Access
## 5 Discussion Issues
### Zero Rating and Net Neutrality
While Facebook's Free Basics is probably not the right solution, it may be the case that better-designed \"good enough\" solutions offered at a convenient and affordable price will disrupt the currently established markets.
## 3 Results and Discussion
### Marshall Parameters
Included are the percentages of bitumen binder corresponding to the minimum percentage of void content in mineral aggregate (VMA), and the minimum VMA is given in parenthesis. contents studied. The AC-PA mix achieves a minimum VMA with a low bitumen content (Table 5), which indicates that it may suffer from plastic deformation at bitumen dosages larger than 3.91%. In any case, the AC-WC and AC-MT mixes achieve a minimum VMA at a larger bitumen dose. Figure 4 gives the values for the Marshall Stability and the Marshall flow as a function of bitumen content for each of the studied mixes. Once again, the data represents the mean bitumen content and standard deviation computed from measurements made on three specimens. Additionally, the graph shows the best fit second-order polynomial along with its R\({}^{2}\) value that verifies a good fit. As Figure 4 shows, the AC-PA mix shows a drop in stability with increasing bitumen content. However, the AC-WC and AC-MT mixes show an increase in stability with increasing bitumen content up to a point where both reach a maximum value of about 4.7%, after which both slowly decrease for a higher bitumen content. The results show that the mixes made with mine wastes are less sensitive to binder content. As may be seen in Figure 4, the deformations that were produced in the AC-WC and AC-MT mixes were less than that of the control mix (AC-PA). The AC-MT mixes showed the least deformation, probably because the effective bitumen content of these mixes was less than those of the other analyzed mixes. Figure 5 shows the Marshall quotient and its best second-order polynomial fit as a function of bitumen content for the studied mixes. The Marshall quotient may be used to evaluate the rigidity of the mixes, with high values representing greater rigidity, corresponding to a more resistant material [34; 35; 36]. The AC-PA mix had a higher Marshall quotient with a low bitumen content than the AC-WC and AC-MT mixes, indicating that these mixes are more rigid and more resistant than the mixes made with mine wastes. Figure 4: (**a**) Marshall stability and (**b**) flow data and best second-order polynomial fit as a function of bitumen content for the studied mixes.
## 3 Experiment Results and Discussion
### Image Fusion Results of the Discussion  
In the air craft image fusion result, seven indicators point out that the method in this study derived the best fusion result. From the subjective human eye observation, it was deemed that the fusion result using the DctVar method was clearly the poorest, while the other results were more approximate. In the clock image fusion result, the seven indicators showed the study derived the best fusion result, while the subjective human eye observation deemed the DctVar and DctVarCv results to be the poorest. The study and the SAIF methods were equally matched in terms of the details. In the disk image fusion result, the five indicators showed the DctVarCv method derived the best fusion result, while the subejctive human eye observation deemed the DctVar and DctVarCv reuslts to be the poorest. The square effect clearly existed in the images. On the other hand, the SAIF method derived the best fusion result. There was a considerable difference between the indicator reuslts and the human eye condition. It was speculated that the square effect exerted less influence on the ratings of the indicators. In the lab image fusion results, the five indicators showed the DctVarCv derived the best fusion results. The  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Leopard**} & \multirow{2}{*}{ \begin{tabular}{c} **Proposed** \\ **Method** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **DetVar** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **DetVarCv** \\ \end{tabular}  } & \multirow{2}{*}{\ \begin{tabular}{c} **SAIF** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **Optimum** \\ \end{tabular}  } \\ \hline Qtri & 1.4509 & 1.4708 & 1.471 & 1.4631 & DctVarCv (4) \(>\) DctVar (3) \(>\) SAIF (2) \(>\) This study (1) \\ \hline Qtri & 0.4598 & 0.4524 & 0.4539 & 0.4601 & SAIF (4) \(>\) This study (3  
subejective human eye observation deemed DctVar and SAIF results to be the poorest, with suqare effect and halo lines in the head region. The DctVarCv and the study derived the best fusion results. In the leopard image fusion results, the seven indicators showed SAIF derived the best fusion result, but the respective indidactors were basically quite approximate. The subjecitve eye observation deemed the respective methods derived the same results. In the toy image fusion results, the six indicators sshowed DctVarCv derived the best fusion result. The subejctive eye observation deemed the study and SAIF derived the best fusion result, while the square effect existed in the DctVar and DctVarCv in the details. Therefore, it was speculated that the indicators were unable to determine the influence of the square effect.
# The CIPCA-BPNN Failure Prediction Method Based on Interval Data Compression and Dimension Reduction
## 6 Conclusions  
In this paper, we introduce the concept of interval data into fault prediction. Based on actual UAV flight data, a CIPCA-BPNN fault prediction model was established. CIPCA can achieve sample compression and dimensionality reduction of flight data on the basis of retaining the vast majority of data features and can reduce the data imbalance ratio to a certain extent. It can greatly shorten the modeling time of a fault prediction model, improving the modeling quality. By comparison, it has more advantages than the traditional PCA method. The experimental results show that the prediction effect of CIPCA-BPNN was better than the traditional PCA-BPNN model and could accurately predict the occurrence of a fault 9 to 7 s before the fault occurred. In the future, the model can be loaded into UAVs for practical application. The prediction results of the model can give the UVA operator precious time to deal with the failure before it happens, which has a strong practical significance.  
Conceptualization, L.Y. and S.Z.; data curation, L.Y.; formal analysis, L.Y.; investigation, L.Y.; methodology, L.Y. and C.L.; project administration, G.J., F.W., and S.Z.; resources, C.L.; software, L.Y.; supervision, L.Y., G.J., F.W., W.C., and S.Z.; validation, L.Y., G.J., F.W., and W.C.; visualization, L.Y.; writing--original draft, L.Y.; writing--review and editing, L.Y. All authors have read and agreed to the published version of the manuscript.  
Figure 8: Failure prediction effect at each moment before failure by CIPCA-BPNN.  
**Funding:** This research was supported by the National Natural Science Foundation of China (Grant No.71971013 & 71871003) and the Technical Research Foundation (Grant No.JSZL201660 IA004). The study was also sponsored by the Fundamental Research Funds for the Central Universities (Grant No.YWF-20-BJ-J-943) and the Graduate Student Education & Development Foundation of Beihang University.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Restrictions apply to the availability of these data. The data in this research came from Volitation (Beijing) Technology Co., Ltd. Please contact [PERSON] ([EMAIL_ADDRESS]) to inform about the data availability.  
**Acknowledgments:** All authors would like to thank the data support by Volitation (Beijing) Technology Co., Ltd.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 2 Materials and Methods
### In Vivo Activity
#### 2.6.3 Intra-Brain Distribution Experiment  
To investigate the distribution of LA-1 and loganin within the brain, the experimental protocol was as follows: First, the rats were subjected to 2 h of induced ischemia, after which the filament was removed to allow for reperfusion. Subsequently, LA-1 (10 mg/kg) and loganin (30 mg/kg) were administered via tail vein injection. Based on pharmacokinetic data, the concentrations of LA-1 and loganin in the blood were found to be negligible 3 h post-injection. At the 3 h time point, the rats were anesthetized, perfused with saline via the heart, and then euthanized to collect brain tissue. The brain tissue was divided into left and right hemispheres, with the right hemisphere representing the ischemic region. The right hemisphere was placed into a 5 mL centrifuge tube, and 600 \(\mu\)L of pre-chilled PBS was added, followed by homogenization on ice. A 200 \(\mu\)L aliquot of the homogenate was combined with 200 \(\mu\)L of chromatography-grade methanol and 200 \(\mu\)L of chromatography-grade acetonitrile, and subjected to ultrasonic treatment for 5 min. The mixture was then centrifuged at 13,000 rpm for 10 min at 4 \({}^{\circ}\)C, and the supernatant was collected for concentration. After concentration, 200 \(\mu\)L of chromatography-grade methanol was added for reconstitution, followed by another round of high-speed centrifugation to obtain the supernatant. Finally, the target molecules were quantified using LC-MS/MS (ACQUITY UPLC I-Class/Xevo TQ-XS, Waters, Milford, MA, USA). This series of steps allowed for a detailed assessment of the distribution of LA-1 and loganin in the rat brain.
# Optimized Deployment of Generalized OCDM in Deep-Sea Shadow-Zone Underwater Acoustic Channels
## 4 Simulation
### Impact of Doppler Shift  
In this subsection, we evaluate the impact of Doppler effects on OFDM, OCDM, and GOCDM using the deep-sea shadow zone channel described in the previous subsection. First, a normalized Doppler shift of 0.1 is introduced, and no equalization is applied to observe the direct impact of the Doppler shift on the different modulation schemes. The simulation results are presented in Figure 7. It is evident that theDoppler shift severely affects all modulation schemes, with OCDM and GOCDM exhibiting similar levels of degradation. However, compared to OFDM, both OCDM and GOCDM maintain superior performance under Doppler-induced distortions.  
Next, we investigate the impact of different Doppler shifts on communication performance. The results, shown in Figure 8, indicated that, as the Doppler shift increases, all modulation schemes experience significant degradation. The degree of impact is similar across all schemes, confirming the theoretical conclusions presented in Section 2.  
Figure 6: The constellation diagrams of (**a**) OFDM, (**b**) OCDM, and (**c**) GOCDM for \(K=2048\) and SNR of 25 dB.
## 3 Results and Discussion
### Advantages of Steel Casing in Pile Foundation Engineering  
In this study, an increase of 8 m steel casing can increase 452 kN (ultimate bearing capacity) compared to conventional reinforced concrete piles without steel casing, equivalent to the gravity of 9.2 trucks whose quality is 5000 kg, in which it can be seen that the presence of steel casing can enhance the ultimate bearing capacity of the pile foundation.  
In addition to the above advantage, steel casing has lots of other advantages. For example, it can position the pile, stabilize the borehole wall and so on.  
Moreover, there are lots of loess caves and karst caves in China [37]. When a cave is encountered during an impact into a hole before forming a bored pile, if the cave is small and the leakage is less, it can be backfilled through the stone and clay, re-impact into the hole. If the cave is huge and the leak is serious, it is necessary to use a steel casing to clog the cave [38], as shown in Figure 18.  
In a word, the steel casing is very beneficial to the pile foundation; it can not only improve the bearing capacity of the pile foundation, but also solve the slurry leakage problem of huge caves in the project. The most important point is to choose a reasonable length of steel casing according to the engineering needs.
# Smart Nanocomposites for Nanosecond Signal Control: The Nano4 waves Approach
## 2 Materials and Methods
### Microwave Characterization  
The novel setup developed during Nano4 waves for the microwave characterization of nanoinclusions powders is reported in Figure 3a. It shows the VNA (vector network analyzer) and a particular test cell developed to perform the EM characterization directly on the as-obtained nanopowders, without any type of sample preparation. Figure 4 shows the topology of the two different test cells (microstrip and coplanar) that were tested in this work. As detailed in [21], both configurations allow to retrieve the constitutive electromagnetic parameters \(\varepsilon\) and \(\upmu\) for each type of nanoinclusion over the considered frequency range.  
The VNA is the basic instrument for the microwave characterization of nanoinclusions and resulting devices conceived during this work. It measures the transmission of a microwave signal through the materials and devices under scope, noted S\({}_{21}\), as well as the reflection at their input, noted S\({}_{11}\). Three VNAs were used during the project: Wiltron 360 and 37397C from Anritsu, and N5247A PNA-X 67 from Agilent/Keysight. They operate in the 40 kHz-65 GHz frequency range. For FMR measurements, the VNA was coupled with an electromagnet supplying DC magnetic field values ranging from 0 to 9 kOersteds. The whole setup is described in ref. [22].  
The measurement of thin film absorbers developed during the work is illustrated in Figure 3b. Each sample is inserted between the two waveguide flanges connected to the VNA through coaxial cables. Using this configuration, the reflection at input of the absorber and absorption through it are obtained from the reflection S\({}_{11}\) and transmission S\({}_{21}\) coefficients measured by the VNA as:  
\[\mathrm{R}=|\mathrm{S}_{11}|^{2} \tag{1}\]  
\[A=1-|\mathrm{S}_{11}|^{2}-|\mathrm{S}_{21}|^{2} \tag{2}\]
## 2 Methods and Materials
### Experimental Data  
The presented methods were applied to measurements taken with the liquid-filled ionization chamber presented in [13]. This array has 2041 detectors with 2.5 \(\times\) 2.5 mm\({}^{2}\) effective area and 0.5 mm thickness. A total of 43 \(\times\) 43 detectors cover a central area of 107.5 \(\times\) 107.5 mm\({}^{2}\). The operation voltage is +1500 V, yet, despite this operation voltage, recombination is still important for large dose rates [13,25]. The readout is done with VLSI electrometers (Sens-Tech Ltd, Egham, UK), which communicate with a computer through an Ethernet interface. The readout can be performed at rates \(>\)1000 Hz, even though in practice we usually employed values of 20-250 Hz (4 to 50 ms integration times). The readout software includes an option (_sequence mode_) that allows saving every single acquisition, in addition to the total integrated signal.  
Measurements were performed at Hospital General de Valencia-ERESA in a True-beam linear accelerator (Varian Medical Systems, Palo Alto, CA, USA). We analyzed the verification of a complex treatment, a lymph node tumor treatment consisting of 15 fields. The treatment was delivered at 600 MU/min. The acquisition was saved in _sequence mode_ and analyzed offline (the online correction method was not implemented in the readout software) to correct for recombination with the proposed method.
# Research on a Measurement Method for the Ocean Wave Field Based on Stereo Vision
## 5 Conclusions  
This paper presents a comprehensive processing algorithm for wave measurement that is based on stereo vision.  
1. Compared with the conventional siftGPU, based on 6280 \(\times\) 4210 image processing, the matching time can be controlled below 6 s and the number of matching points can be kept above 20,000, indicating much better performance than that of the traditional siftGPU algorithm.
2. In this paper, the least squares method and wave theory have been used to normalize the sea surface base plane calibration, plane error point fitting and wave parameter inversion. This design method is simple and feasible. Compared with RBR wave height measurement, the wave height range is 0.2 m-1 m, the measurement error is less than 10%, and the periodic measurement error is less than 0.5 s.
3. In this paper, the Hough transform and least squares method were used to calculate the wave direction value. Because the RBR wave height instrument does not have the function of wave direction measurement, compared with the original wave image, the detection results and original direction are basically the same.  
**Author Contributions:** Conceptualization, H.S.; methodology, H.S.; software, W.C.; validation, Q.Z.; formal analysis, X.W.; investigation, G.W.; resources, P.Z.; data curation, T.Z.; writing--original draft preparation, H.S.; writing--review and editing, H.S.; visualization, G.W.; supervision, T.Z.; project administration, X.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Key Special Project for Introduced Talents Team of Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), grant number GML2019 ZD-0502, and the High Tech Ship Scientific Research Project of the Ministry of Industry and Information Technology, grant number [2019]357.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data that were used to support the findings of this study are included within the article.  
**Acknowledgments:** The authors would like to thank the authors of SiftGPU and SIFT for making their algorithms available as open-sources, this was really helpful to the research that is described in this paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.