esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Simulation of Flapping Airfoil Aerodynamic Characteristics
## 3 Results and Analysis
### Effects of Motion Parameters on the Aerodynamic Characteristics of Flapping Airfoils
#### 3.1.1 Research on Flapping Airfoil Lift Mechanism
The cloud diagram of the flapping airfoil flow field is displayed in Figure 7, where the 0.6 T moment corresponds to stage \(\
u\) of the first stroke in Figure 6. The separation of leading edge vortices during this process is caused by the fact that the leading edge and trailing edge positions on the flapping airfoil are situated far from the leading edge vortices, while the middle position is situated close to them. The flow velocity at the front and trailing edge positions of the upper airfoil cannot be continuously increased by the leading edge vortices. Consequently, the low-pressure areas on both sides are reduced, and as the strength of the leading edge vortices is weakened, the low-pressure areas on the upper airfoil are further diminished. Stages \(\lambda\), \(\
u\) indicate that both the leading edge vortex and trailing edge vortex originated by instantaneous rotation will detach in the subsequent leading edge vortex delayed stall mechanism. The reasons for its formation are not the same, one is mainly caused by rotational motion, and the other is mainly induced by translational motion. Due to different formation mechanisms, the leading edge vortex and trailing edge vortex caused by rotation cannot obtain energy from translational motion, they will be separated during linear Motion. The leading edge vortex shedding caused by rotation is indicated in the cloud image of 0.35 T, and the leading edge vortex shedding (or separation) produced by translation is displayed in the cloud image of 0.60 T. There is also a trailing edge vortex capture mechanism in the flapping airfoil flow field, as shown by 0.45 T. The enhancement of the low-pressure area and the reduction in lifton the lower airfoil surface are caused by the trailing edge vortex capture. Therefore, in the design of flapping airfoil aircraft, this trailing edge vortex capture should be avoided, and if it cannot be avoided, this mechanism should also be debilitated. There are two leading edge vortices in the figure. One is separated, and energy cannot be acquired again, therefore, the low-pressure region on the upper airfoil surface cannot be further enhanced to raise the lift of the flapping airfoil, and its energy will be dissipated by the flow field. The other is a new leading edge vortex, where energy is absorbed by it, resulting in an enhancement of the low-pressure region on the upper airfoil surface and an increase in flapping airfoil lift. The rotation directions of the leading edge vortex and the trailing edge vortex in the flow field are different, and they will have a mutual influence on the flow field area of the flapping airfoil surface. The adherence of leading edge vortices to the upper airfoil surface is connected to the angle of attack of the airfoil. The inability of trailing edge
# Future Industrial Applications: Exploring LPWAN-Driven IoT Protocols
## 5 Industrial Application Perspective
### Manufacturing and Automated Industries  
LPWAN technologies play a vital role in developing cost-effective solutions in predictive industrial maintenance. Predictive maintenance involves the monitoring of the industrial equipment [116] and predicting the required maintenance [117] when necessary. It can reduce the possibility of unwanted shutdown of the equipment and reduce costs. LPWAN also provides solutions for effective and cost-efficient asset monitoring [13] due to its broad range of communication and minimal power consumption capabilities. LPWAN enables continuous and real-time tracing of the assets that help in optimizing asset utilization and improving overall resource allocation [118; 119]. It also improves the supply chain visibility [10] by tracing and monitoring the shipment of goods and optimizing the supply chain operations. LPWAN is suitable for deployment in the factories as remote controls and industrial sensors to monitor processes (temperature, pressure, flow rate, asset health monitoring) and industrial environments. This will enable safer working environments in plants and factories [34]. Through a case study, [PERSON] et al. [120] tried to identify a suitable technology for product traceability in the metal fabrication sector. The authors found Sigfox to be the most appropriate technology for tracking products during manufacturing due to its ability to transmit/receive data over a more extended range. On the other hand, RedCap is suitable for creating private networks in factories without interference, maintaining privacy and reliability [121].
# Research on the Influence of Loading Frequency on the Dynamic Response of Concrete Sleopers
## 3 Dynamic Responses of Sleepers at Different Loading Frequencies
### Effect of Loading Frequency on Sleeper Stress  
The train is in direct contact with the rail during operation, with the train load getting transmitted to the sleeper through the rail, and the sleeper bears a part of the load and continues to transmit the load to the tracked. To analyze the distribution of the load transmitted to the sleeper inside it, measuring circles were added to the sleeper center and the sleeper at the position under the left and right strand rails. The stress changes of the sleeper under the left rail, at the center of the sleeper, and under the right rail corresponding to different loading frequencies within the total loading time (1 s) were recorded. The results are shown in Figure 11.  
Figure 11 shows that during the initial loading, the sleeper had a large stress due to the load, and the sleeper model was not very stable. After continuing to cyclically load the sleeper, the stress change trend in the sleeper tended to be stable. The stress of the sleeper area under the rail enlarged significantly, while the stress peak at the center of the sleeper was smaller. Under different loading frequencies, the peak values of the sleeper stress under the left and right strand rails were close to each other, approximately 0.6 MPa, and the maximum compressive stress at the center of the sleeper was not much different, with both being approximately 0.002 MPa. The variation law of the stress in the sleeper is similar to the variation of the applied load, and both show a sinusoidal distribution. Due to the symmetry of the geometric distribution of sleopers and rails and the symmetry of the load action, the stress change and stress peak value of the sleeper under the left and right strand rails are the same, and the stress change curves of the two almost coincide.  
Figure 11: Distribution of stress in sleeper. (**a**) 5 Hz. (**b**) 10 Hz. (**c**) 20 Hz. (**d**) 50 Hz.
## 4 Discussion
### Histological Changes  
Abnormalities in the histology of plant roots that were treated with NP were described for many plant species, including crops [41]. In _Gossypium hirsutum_, ZnO NP caused anatomical alterations in the roots, such as the vacuolisation of the cortical cells [42]. The treatment of _Asparagus officinalis_ with the ZnO NP resulted in an increased size of its cells and the number of cell files in specific root tissues [41]. In _Capsicum annuum_ roots, selenium NP (nSe) resulted in the development of secondary tissues and fibres, a restriction in the differentiation of vascular tissues or the repression of the vascular tissues after a higher dose of nSe [43]. Studies on maize (_Zea mays_ L.) and cabbage (_Brassica oleracea_ var. capitata L.) that were treated with Ag NP and ZnO NP exhibited cellular alternation in the root apical meristem and elongation zone as well as a diverse metaxylem development compared to the control roots [29]. In the roots of _Lolium preenne_ that was treated with ZnO NP, collapsed cortical cells and the destroyed continuity of the rhizodermis and root cap were described [44]. A similar reaction to NP was described for _Brassica juncea_[45] and _Brassica napus_[46]. In the roots of _Hordeum sativum_ that was treated with CuO NP, abnormalities in the root histology were also detected, and it was shown that the rhizodermis had undergone a disruption and that the cortical cells had collapsed [47]. The presented examples indicate that histological changes are an important aspect of the plant-NP interaction (for a review, see [14]). This knowledge is essential for investigating the impact of NP on the growth and development of crops, especially in terms of their nanotoxicity. A previous study showed that positively charged AuNPs modified the root morphology, inhibited the differentiation of rhizodermal cells into hair cells and caused qualitative changes in the components of the cell walls [27]. The results presented in this paper are the first (to the best of our knowledge) showing the influence of negatively charged AuNP on the histology of barley roots, although our research has shown that AuNP do not enter the root cells.
# N-Trans: Parallel Detection Algorithm for DGA Domain Names
## 4 Experiment and Analysis
### Experimental Results and Analysis  
To validate the results of the proposed parallel detection model based on the N-gram algorithm and Transformer to identify malicious domain names, four sets of experiments were designed based on the same dataset.  
1. The optimal parallel model was selected to detect malicious domain names by changing the parallelism and combining N-gram algorithms in the parallel detection model;  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**N-Gram** & **Digital Features** & **Legitimate** & **Malicious** \\ \hline \multirow{2}{*}{\(N=1\)} & Mean & 2007.154 & 4190.865 \\  & Variance & 2146.498 & 2152.575 \\  & Skewness & 1.0524 & \(-\)0.87408 \\  & Kurtosis & 0.1087 & \(-\)1.19031 \\ \hline \multirow{2}{*}{\(N=2\)} & Mean & 62.24157 & 110.4813 \\  & Variance & 127.3123 & 87.4684 \\  & Skewness & 3.588488 & 0.18239 \\  & Kurtosis & 16.4035 & \(-\)1.62526 \\ \hline \multirow{2}{*}{\(N=3\)} & Mean & 6.768033 & 4.553918 \\  & Variance & 14.68101 & 3.503929 \\  & Skewness & 6.177821 & 0.848823 \\  & Kurtosis & 55.35392 & 0.086178 \\ \hline \multirow{2}{*}{\(N=4\)} & Mean & 2.286315 & 1.13609 \\  & Variance & 4.317711 & 0.428017 \\  & Skewness & 12.90284 & 1.241187 \\  & Kurtosis & 303.786 & 2.659457 \\ \hline \multirow{2}{*}{\(N=5\)} & Mean & 1.519426 & 1.024079 \\  & Variance & 2.262212 & 0.249301 \\  & Skewness & 16.31627 & 11.2395 \\  & Kurtosis & 445.1127 & 128.8666 \\ \hline \hline \end{tabular}
\end{table}
Table 3: N-gram algorithm results analysis. The mean value is an indicator of the concentration trend of the distribution of the phrase elements; the variance shows the trend of the number of phrase elements; the skewness shows whether the distribution of the phrase elements of the domain name is symmetrical; the kurtosis can get the difference of the data distribution of the domain name phrase elements data compared with the normal distribution.  
By changing the head value in the Transformer model, the appropriate number of heads was selected as a parameter in the model training;
3. In order to verify the detection effect of the model proposed in this paper, the Naive Bayesian and XGBoost machine learning algorithms and RNN, LSTM, and Bi-LSTM models in deep learning were selected to compare with the model designed in this paper;
4. Through ablation experiments, we compare the number of features, training time, and evaluation metrics to analyze the effects of adding sign bits and L1 regularization in a single detection model and compare the effects of adding sign bits in a parallel detection model.  
The following experiments are implemented in the _keras_ deep learning framework, _Tensorflow_ back-end, and _sklearn_ module.
## 1 Introduction
### Multifunctional Reflective Metasurfaces Combining Distinct Functionalities
In designing these devises, people noticed that an arbitrary anisotropic meta-atom might exhibit undesired polarization cross-talking (i.e., varying one parameter of the meta-atom can influence its EM responses for both polarizations). Such an effect hinders the fast designs of meta-devices with complex functionalities, which typically require 2D parameter searching to realize the desired phase profiles. To overcome such cross-talking and to enlarge the working bandwidth, [PERSON] et al. proposed a new type of anisotropic meta-atom [95] which consists of multilayers of planar resonators coupled with a metallic ground plane (Figure 5a). A crucial improvement is to add a wire loop to surround each planar resonator (see Figure 5a), which can significantly degrade the polarization cross-talking due to the screening effect [95]. Figure 5: **(a)** Topology of the dual-layer anisotropic meta-atoms using composite cross bar and cross loop. The meta-atom contains two identical composite metallic resonators and a continuous metal plate (yellow) separated by two dielectric spacers (blue). **(b)** Phase distribution required on the metasurface to achieve quad-beam emissions. Schematics of a bifunctional metasurface which behaves as (c) a lens or (d) a beam splitter to generate quad large-angle pencil beams when excited by incident waves with polarizations \(E//x\) (yellow light) and \(E//y\) (blue light), respectively. **(e,f)** Simulated (red lines) and measured (blue dashed lines) radiation patterns on \(x\)-\(z\) plane for \(x\) polarization and \(y\) polarization, demonstrating the bi-functionality possessed by the fabricated device. Reproduced from [95] with permission. Based on such meta-atoms, the authors successfully realized two bifunctional metasurfaces combining complex wave-control functionalities [95]. One device is shown in Figure 5 which can achieve the functionalities of focusing (Figure 5c) and large-angle multi-beam emissions (Figure 5d) under external excitations with two polarizations. While the phase distribution responsible for the focusing functionality is easy to obtain (Equation (5)), that for another one needs to be carefully determined (see Figure 5b). The structural parameters of all meta-atoms can be quickly determined from two phase profiles, thanks to the low polarization cross talking property of the meta-atoms. Measured radiation patterns (Figure 5e,f) demonstrated clearly the bi-functionalities possessed by the fabricated device. Another device realized in [95] can deflect beam anomalously and achieve small-angle multi-beam emissions for two polarizations. With such a unique meta-atom structure at hand, people can realize other multifunctional meta-devices exhibiting powerful and complex wave-manipulation functionalities.
## 3 Incremental Nonlinear Dynamic Inversion Control Law Design
### Synchronization Filter  
To ensure the effective operation of the INDI controller [37], it is essential to synchronize the angular acceleration and control surface deflection. Failure to do so may result in unsynchronized delays between the actuator and angular rate estimations, which can cause oscillatory behavior [32] and even lead to control divergence. For further clarification, please refer to Equation (23) and Figure 6.  
\[\
u_{f}=\frac{S^{\prime}(s)}{s}L(s)\
u_{cmd}+T^{\prime}(s)\
u_{cmd} \tag{30}\]  
It is important to take into account the angular rate sensor model, which is denoted by \(L(s)\) and can be mathematically represented as a first-order lag system with a time constant of \(\tau_{s}\). Additionally, it is crucial to consider the dynamics of the actuator. To this end, a second-order linear actuator model is utilized in the synchronization filter, as outlined below:  
\[\hat{H}_{a}(s)=\frac{{\partial_{a}}^{2}}{s^{2}+2\hat{\zeta}_{a}\hat{\omega}_{a }s+\hat{\omega}_{a}^{2}} \tag{31}\]  
where \(\hat{\omega}_{a}\) and \(\hat{\zeta}_{a}\) are the estimated natural frequency and damping ratio of the actual actuator used, respectively. Finally, the synchronization filter can be formulated as  
\[\hat{v}=\hat{H}_{a}(s)\bigg{[}\frac{S^{\prime}(s)}{s}L(s)+T^{\prime}(s)\bigg{]} \
u_{cmd} \tag{32}\]  
It is worth highlighting that the angular acceleration input to the ESO, which is computed using the mathematical model, also needs to be synchronized. To achieve this, Equation (23) can be referenced, and the response delay caused by the physical actuator must be taken into account. To this end, the estimated actuator model is applied to the mathematically calculated results, as shown below:  
\[a_{m}=\hat{H}_{a}(s)a_{mc} \tag{33}\]  
where \(a_{mc}\) is the calculated result.
## 4 Development of Optical Sensing Techniques for Red Meat Quality Prediction
### Data Analytics and Chemometrics
#### 4.2.2 Modelling Approaches  
Quantitative methods used in spectral analysis of meat include MLR, principal component regression (PCR), and partial least squares (PLS) regression [120]. MLR relates theconcentration as a function of the absorbance, incorporating information on target analyte concentrations and other contributing components [172]. PCR, like MLR, is an inverse calibration method and an extension of PCA, where the PCs derived from the PCA serve as variables in an MLR model [190]. PLSR follows the same mathematical model as PCR but differs in terms of data compression, utilising spectral and concentration data [120]. In cases where the relationship between x and y variables is highly non-linear, techniques like artificial neural network (ANN) and support vector machine (SVM) are useful [191]. ANN is a computing system comprising interconnected processing elements that mimic the brain's parallel processing to generate meaningful outputs from input variables [191]. SVM, on the other hand, is a method for non-linear modelling of spectral data [112]. Its distinct characteristics include efficient modelling of non-linear or complex data structures using kernels, determining model coefficients through a complex criterion instead of least squares minimisation, and representing the model in terms of support vectors rather than a single regression coefficient vector [192].
## 2 Methods
### Principles Involved in the Method
#### 2.2.2 Landslide Detection  
Following the fusion of gradients to yield the final result, G, variations emerge due to disparities in the number of differential interferograms and temporal baselines. This discrepancy hinders the stabilization of G, prompting the necessity for normalization. Peristalic landslides typically exhibit prolonged, subtle deformations, fostering the persistence of stable deformation signals across numerous differential interferograms. In contrast, noise remains stochastic and can be effectively eliminated through coherence analysis. Consequently, within the result G, regions depicting landslides manifest significantly heightened gradient values compared to other areas. In addition, there is a spatial aggregation tendency in these regions. These areas are defined as landslides.
###### Abstract  
With the development of commercial space technology and the proposal of concepts such as \"Black Jack\", the Space Transport Layer (STL), and the commercial space-based Internet, large-scale low-orbit satellite constellations have become a research hotspot in the aerospace field. Large-scale low-orbit satellite constellations consist of a huge number of satellites, which makes the networking control and operation management of the constellations more complicated. It also increases the difficulty of achieving the economical and efficient networking of the constellations as well as ensuring their safe and stable operation. In this study, aiming at the problem of large-scale constellation phase control in low orbit, strategies for constellation station holding were examined. First, aiming at the problem of station keeping of large-scale constellations in low orbit, the characteristics of satellite phase drift and phase keeping were analyzed, and absolute and relative station-keeping strategies were proposed. Second, a phase-holding loop control method combining semi-major axis overshoot control and passive control was proposed, and a relative phase-maintenance scheme based on a dynamic reference satellite was designed. Then, the absolute and relative station controls of different low-orbit constellations were simulated. The simulation results showed that in order for all satellites in the constellation to maintain a phase angle deviation within \(\pm 0.1^{\circ}\) in a low-solar-activity year, about 13 days were required on average to adjust the semi-major axis of the satellites by about 71 m. The relative position control of small-scale constellations was simulated, and only four orbital maneuvers were needed to achieve the phase angle maintenance within the threshold of \(\pm 5^{\circ}\) for all satellites in the constellation within 300 days. Finally, it was concluded that absolute control was suitable for large-scale constellation phase preservation, and relative control was more suitable for small-scale constellation phase preservation. This paper can provide a reference and suggestions for future large-scale constellation deployment and maintenance control strategies of low-orbit constellations.  
Starlink constellation; position keeping; track maintenance; absolute control; relative control +
Footnote †: journal: Applied Sciences  
Article
## 7 Limitations of the Study  
### Model Simplification  
Two major simplifications were made when creating the numerical model, which included simplifying the geometric features of the tested cable and rock bolts and omitting bolt grouting. For this parametric study, all bolts were modelled as smooth cylinders, which does not represent the true nature of bolts. Geometric properties omitted include general cross-sectional shapes, indented wires, angle of twist, bulbed strands, anchors and threaded sections.  
Furthermore, when creating the model, the diameter of the hole created in the concrete box was equal to the diameter of the bolt under investigation, where grouting of the bolt was replaced using surface-to-surface contact properties. When installing a bolt in the field, the bored hole is greater than the diameter of the bolt. The bolt is anchored into the excavation face by grouting with a resin or cementitious grout. Thus, this model excludes the bolt-grout and grout-rock interactions that occur in the field.
# Distributed Kernel Extreme Learning Machines for Aircraft Engine Failure Diagnostics
## 2 Review of Basic and Kernel Elm
### Elm  
For the structural form of SLFNs, [PERSON] et al. proposed ELM to improve the network training speed, and then extended the hypothesis of ELM from neuron hidden nodes to other hidden nodes. Training samples can be represented as \(\{\mathbf{x}_{i},t_{i}\}_{i=1}^{n}\), where \(n\) is the training samples number, \(\mathbf{x}_{i}\) denotes input of \(ith\) sample with \(m\)-dimension and \(t_{i}\) is output of \(ith\) sample. Then, given the input vector \(\mathbf{x}\), the output of SLFNs with \(L\) hidden nodes can be written as  
\[f(\mathbf{x})=\sum_{i=1}^{L}\beta_{i}h_{i}(\mathbf{x})=\mathbf{h}^{T}(\mathbf{x})\mathbf{\beta} \tag{1}\]  
where \(\mathbf{h}(\mathbf{x})=[h_{1}(\mathbf{x})\cdots h_{L}(\mathbf{x})]^{T}\) denotes the hidden output, and \(\mathbf{\beta}=[\beta_{1}\cdots\beta_{L}]^{T}\) denotes the output weights. Assume that outputs of these \(n\) training samples can be approximated with zero errors, the compact formulation is as follows  
\[H\mathbf{\beta}=\mathbf{t} \tag{2}\]  
where \(H=[\mathbf{h}(\mathbf{x}_{1})\cdots\mathbf{h}(\mathbf{x}_{n})]^{T}\) is named as hidden output matrix. The solution of output weights \(\mathbf{\beta}\) only involves a simple linear equation, and the solution may be equivalent to the minimization of training error i.e., \(\min\|H\mathbf{\beta}-\mathbf{t}\|\). The optimal estimation of output weights may be represented by the Moore-Penrose generalized inverse [35]\(H^{\ddagger}\) as follows  
\[\mathbf{\hat{\beta}}=H^{\dagger}\mathbf{t} \tag{3}\]  
Generally, the orthogonal projection can be used to solve the generalized inverse \(H^{\ddagger}\). If \(H^{T}H\) is nonsingular, \(H^{\ddagger}=(H^{T}H)^{-1}H^{T}\), or if \(HH^{T}\)is nonsingular, \(H^{\ddagger}=H^{T}(HH^{T})^{-1}\).