esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
A magnetic field sensor based on MZI fabricated by filling-splicing is proposed in this paper. The SHDCF is fused after filling MF in the air hole to ensure that the MF is in a completely enclosed space, which makes the sensor stable. The sensitivity of the RI change of the suspension core waveguide of this structure is simulated based on Rsoft. The SMF connected to the optical spectrum analyzer is used to receive the transmitted light from core 1, core 2, air hole, and cladding of the SHDCF, respectively, to verify the sensing theory and determine the optimized length of SHDCF. Finally, the sensor was fabricated with the appropriate splicing parameters, and the experimental results show that the sensitivity of the sensor is \(-\)116.1 pm/Gs in the range of 0-200 Gs with a good linearity. The sensor has the advantages of a simple structure, low cost, fast response, and high structural strength, and the structure is expected to be used in magnetic field measurement of industrial production.  
Conceptualization, M.C. and Y.Z.; methodology, M.C.; software, Q.L.; validation, M.C. and Y.Z.; investigation, Q.L.; resources, Y.Z.; data curation, Q.L.; writing--original draft preparation, Q.L. and M.C.; writing--review and editing, M.C.; visualization, M.C.; supervision, Y.Z.; project administration, Y.Z.; funding acquisition, Y.Z. and M.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China under Grant 61933004; the Fundamental Research Funds for the Central Universities under Grants N2123012, N2123014, and N160408001; Natural Science Foundation of Hebei Province (F2021501011, F2020501040); and the Science and Technology Commission of Shanghai Municipality (STCSM) (SKLSFO2020-03) in China.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors acknowledge the Hebei Key Laboratory Micro-Nano Precision Optical Sensing and Measurement Technology at the Northeastern University.  
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.
## 3 Bond Behavior and Bond Strength
### Influence of Embedded Length  
The bond strength reduced with an increase in embedded length. This reduction can be attributed to the calculation of bond strength based on the assumption of a uniform bond stress distribution. However, a more pronounced non-uniform distribution of bond stresses typically occurs with greater embedded lengths and the Poisson effect [23; 25; 30]. [PERSON] et al. [25] reported that a greater load is needed to pull out the BFRP bar with increased embedment length. However, due to the Poisson effect, this extended embedment length results in a significant decrease in confinement and mechanical interlocking, subsequently reducing bond strength. Additionally, it has been reported that a linear relationship can effectively characterize the correlation between average bond strength and embedment length. Figure 8 illustrates the experimental findings regarding the influence of embedment length on the average bond strength of the BFRP bar.  
[PERSON] et al. [22] studied the bond characteristics between braided AFRP and PVA FRCC and proposed a trilinear model for the bond stress-slip relationship, as shown in Figure 9. Pullout tests with varying bond lengths, fiber-volume fraction in FRCC, and cross-sectional dimensions were performed. The bond strength improved as both the amount of fiber and the size of the cross-section of FRCC increased, due to the fiber's bridging effect along the cracks. The proposed model was checked using numerical analysis for longer bond lengths, and the results showed that the average bond stress-slip relationship matched well with the experimental results.  
Figure 8: Effect of embedment length on average bond strength of BFRP bar [25].  
Figure 9: Trilinear model [22].
# Continuous-Thrust Circular Orbit Phasing Optimization of Deep Space CubeSats
## 1 Introduction
In the context of a rapid orbit transfer in which the performance index to be minimized is the total flight time, a classical circle-to-circle (planar) orbit raising and lowering is firstly considered (see Section 3.2) with the aim to test the effectiveness of the proposed simplified thrust model. Then, the phasing maneuver along a circular orbit with a ra  
Figure 1: Artistic concept of the CubeSat M-ARGO approaching the target asteroid. Image: © ESA-Jacky [PERSON]. dius of one astronomical unit is analyzed in detail as illustrated in Section 4. The second mission scenario, in fact, models the orbital repositioning of a MTC along the Earth's heliocentric trajectory when the eccentricity of the planet's orbit around the Sun is neglected. In particular, the MTC optimal performance results are studied in that scenario by using a parametric approach, and considering the typical CubeSat design constraints as, for example, the maximum propellant mass expenditure and the maximum value of the flight time. The results of the optimization of a generic (circular) orbit phasing maneuver are finally used to study, in a simplified trajectory design framework, the transfer of a MTC towards one of the two triangular Lagrangian points of the Sun-Earth system [24] as discussed in Section 4.1. In particular, considering a phasing ahead (or behind) maneuver of 60 deg with respect to the initial CubeSat position, which is assumed to be coincident with that of the starting planet, the Earth-\(L_{4}\) (or the Earth-\(L_{5}\)) optimal transfer of a MTC is analyzed by considering the constraints on the maximum admissible value of the required propellant mass. In this context, numerical simulations will show that a (rapid) transfer towards a triangular Lagrangian point of the Sun-Earth system, in which the flight time reach its minimum value, requires a propellant expenditure greater than the maximum admissible value. However, a trade-off study, in which the (optimization) condition on minimum flight time is relaxed in favor of decreasing the required propellant mass, reveals that a transfer towards a triangular Lagrangian point is still possible with a flight time slightly higher than the global minimum value. This aspect is illustrated at the end of Section 4.1, which precedes the Conclusions section, and constitutes a new mission application of the MTC.
# Sharpening of Worldview-3 Satellite Images by Generating Optimal High-Spatial-Resolution Images
## 2 Materials and Methods
### Index for the Quality Evaluation of Sharpened Images
#### 2.5.2 Spectral Angle Mapper (SAM)  
The SAM is an evaluation index for spectral information distortion that calculates the angle of pixels between the sharpened and original images to quantify the angle difference between two vectors [27]. Values approaching zero indicate greater similarity between the original and sharpened images. The SAM is defined by Equation (23):  
\[\text{SAM}(v,\hat{v})\ =\ \text{arccos}(\frac{\langle v,\hat{v}\rangle}{\|v\| _{2}\|\hat{v}\|_{2}}) \tag{23}\]  
where \(v\) refers to the spectral vector of the original image's pixel, \(\hat{v}\) refers to the spectral vector of the sharpened image's pixel, \(\langle v,\hat{v}\rangle\) refers to the inner product of the two vectors, and \(\|v\|_{2}\) and \(\|\hat{v}\|_{2}\) refer to the sizes of the two vectors.
# Method for Recognition of Communication Interference Signals under Small-Sample Conditions
###### Abstract  
To address the difficulty in obtaining a large number of labeled jamming signals in complex electromagnetic environments, this paper proposes a small-sample communication jamming signal recognition method based on WDCGAN-SA (Wasserstein Deep Convolution Generative Adversarial Network-Self Attention) and C-ResNet (Convolution Block Attention Module-Residual Network). Firstly, leveraging the DCGAN architecture, we integrate the Wasserstein distance measurement and gradient penalty mechanism to design the jamming signal generation model WDCGAN for data augmentation. Secondly, we introduce a self-attention mechanism to make the generation model focus on global correlation features in time-frequency maps while optimizing training strategies to enhance the quality of generated samples. Finally, real samples are mixed with generated samples and fed into the classification network, incorporating cross-channel and spatial information in the classification network to improve jamming signal recognition rates. The simulation results demonstrate that under small-sample conditions with a Jamming-to-Noise Ratio (JNR) ranging from \(-10\) dB to \(10\) dB, the proposed algorithm significantly outperforms GAN, WGAN and DCGAN comparative algorithms in recognizing six types of communication jamming signals.  
communication jamming signal recognition; small-sample recognition; data augmentation 2024145869. [[https://doi.org/10.3390/app1435869](https://doi.org/10.3390/app1435869)]([https://doi.org/10.3390/app1435869](https://doi.org/10.3390/app1435869))
## 2 Materials and Methods
### Grid Independence Verification  
Five different grids are selected for each of the two models in the validation experiment. The corresponding grids for the cable room model with an electric heat wire are as follows--Grid 1: 1,177,001, Grid 2: 1,225,717, Grid 3: 1,358,326, Grid 4: 1,533,790, and Grid 5: 1,600,172. The cable room model with cables includes Grid 6: 782,880, Grid 7: 816,663, Grid 8: 831,633, Grid 9: 919,621, and Grid 10: 935,666. Six monitoring points are positioned along the XYZ coordinate axis in the cable room, as shown in Figure 2d. The measurement points A (169.51, 810, 65) and B (549.51, 810, 65) are located near the rear of the cable room at the top. Measurement points C (169.51, 415, 185) and D (549.51, 415, 185) are situated at the midpoint of the walls on opposite sides of the cable room. Measurement points E (169.51, 20, 285) and F (549.51, 20, 285) are situated near the main door at the bottom. The velocity variation trend of monitoring points in different grids is shown in Figure 4a,b. It can be observed that the velocity change trend is consistent across all detection points in various grids. In Figure 4a, when utilizing Grid 4 and Grid 5, and in Figure 4b on the right, using Grid 9 and Grid 10, the numerical stability is adequate to fulfill the calculation requirements. This confirms that the calculation results are independent of the grid.  
\begin{table}
\begin{tabular}{c c c} \hline
**Boundary Condition** & **Type** & **Value** \\ \hline Solution domain & Fluent/Maxwell & / \\ Fluid & Air & / \\  & & Thermal conductivity: 387.6 W/(m K); \\ Solid 1 & Copper & Bulk conductivity: \\  & & 58,000,000 siemens/m; \\ Solid 2 & Ni-Cr & Thermal conductivity: 15 W/(m K); \\ Solid 3 & Epoxy resin & Thermal conductivity: 0.276 W/(m K); \\ Solid 4 & Steel & Thermal conductivity: 10.27 W/(m K); \\ Solid 5 & Cross-linked polyethylene & Thermal conductivity: 0.29 W/(m K); \\ Mixture & Water-vapor; & / \\  & Air & / \\ Wall & Convection & heat-transfer coefficient: 9.445 \\  & & W/(m\({}^{2}\)K) \\  & & Velocity: 1.5 m/s, \\  & & Temperature: 29 \({}^{\circ}\)C, \\  & & Component mass fraction (H\({}_{2}\)O): \\ Inlet & Velocity-inlet 1; & 0.01204; \\  & Velocity-inlet 2 & Velocity: 0.4 m/s, \\  & & Temperature: 28 \({}^{\circ}\)C, \\  & & Component mass fraction \\  & & (H\({}_{2}\)O):0.01791/0.01904/0.02038/2387 \\  & & \(-\)0.91 Pa, \\ Outlet & Pressure outer & backflow temperature: \\  & & 28 \({}^{\circ}\)C \\ Target residual & / & 10\({}^{-4}\) \\ \hline \end{tabular}
\end{table}
Table 1: Boundary conditions are set.
## 4 Discussion
### Color Values of SSEFs in Different Conditions
#### 4.2.2 Color Values at Different Soaking Times in Different Conditions  
The increase in the SWE concentration increased the* value of the film, while the L* and b* values decreased. The prolonged soaking duration was also shown to affect the color values of the SSEF significantly (\(p<0.05\)). The increased soaking duration in the neutral conditions increased the a* value while decreasing the L* and b* values. Unlike the neutral condition, the acidic and alkaline environments resulted in similar results. The L* value increased with the increase in the SWE concentration, while the a* and b* values decreased. Extended soaking times were also shown to affect the color values of the SSEF significantly (\(p<0.05\)). The prolonged soaking durations in the acidic and alkaline conditions increased the a* value while decreasing the L* and b* values.  
As can be seen from the results, the gap in the color values between the soaking periods became increasingly comprehensive with the increases in the pH of the soaking condition. The largest gap was observed with the SSEF soaked in alkaline conditions. According to [20], brazilin exhibits stronger stability characteristics at lower pH values and reduced stability at higher pH values. These color variations are caused by the protonation and deprotonation of the hydroxyl (OH) group in brazilin. Similar results were noted for other polyphenic pigments discovered by different researchers. According to some studies [47; 48; 49; 50], the anthocyanins' most prevalent form in acidic conditions (pH 3) is the flavylium cation, which is more stable than the forms that exist at higher pH (such as quinoidal base, carbinol pseudo base, and chalcone). Because phenolic chemicals produce a quinone intermediate that is immediately harmed by oxidation, their stability decreases at higher pH levels [20]. Anthocyanin also plays a role in the color changes in SSEF. Anthocyanins are sensitive to pH fluctuations, since they lose color above 3.0. At pH levels below 3, anthocyanins primarily reside in the form of highly stable red flavylium cations. The rapid hydration of the flavylium cation causes the colored carbinol pseudo base to be produced when the pH rises from 4 to 5. At pH 6-7, a neutral quinoidal base (purple to violet in color) results from the deprotonation of the flavylium cation; at pH 7-8, an anionic quinoidal base is produced (blue color) [51; 52; 53; 54; 55].
# Faster R-CNN-LSTM Construction Site Unsafe Behavior Recognition Model
## 4 Discussion  
In this study, we introduced and tested a dual-stream convolutional model for hazardous behavior recognition based on Faster R-CNN-LSTM. The primary advantage of this model lies in its ability to holistically consider spatiotemporal features by integrating the spatial characteristics of Faster R-CNN with the temporal properties of LSTM. This integration provides a more comprehensive perspective for behavior recognition. The experimental results affirmed its high accuracy in recognizing unsafe behaviors, especially in classifying whether construction workers wear safety helmets. Additionally, the model is equipped with the capability to process video streams in real time, offering instantaneous safety monitoring on construction sites.  
However, due to the amalgamation of two network structures, the computational complexity of the model is relatively high, which might lead to increased computational resource demands. The model could also face the risk of overfitting when the dataset is limited, potentially diminishing its generalization capacity in practical applications.  
Two pivotal areas emerge for future improvements: (1) The model's structure can be further refined to reduce the computational resource requirements and enhance its efficiency. (2) There is potential in integrating other types of sensor data, such as audio or temperature, to furnish the model with richer contextual information, thereby further improving its recognition accuracy.  
In terms of practical applications, this model boasts significant potential. It can be implemented on construction sites to provide the real-time monitoring of workers' behaviors, allowing for early detection and warnings of potential unsafe actions. By analyzing and evaluating workers' behaviors, the model can also serve as a robust data support for safety training initiatives on construction sites.
# Simulation of Wind-Battery Microgrid Based on Short-Term Wind Power Forecasting
## 2 Modeling of System Components
### Forecast Corrections Using Probability of Misclassification  
Similarly to the credit scoring theory that uses the probability of default to measure the uncertainty of a company to meet its debt obligations within a time period [51], this work proposes the use of probability of misclassification to measure the likelihood that the wind turbine output is within the limits of the forecasted power class [52]. Once the ANN model is trained, the confusion matrix \(M\) of the \(N\) power classes is generated using the test sample. The confusion matrix represents, for each pair of classes \(\{C_{i},C_{j}\}\), how many predictions from class \(C_{i}\) were incorrectly assigned to power class \(C_{j}\). Given that the power classes follow an ordered relation as the power output is smaller in the lower classes than in the upper classes, i.e., \(C_{1}<C_{2}<\ldots<C_{N}\), only the elements below the diagonal of \(M\) have a negative effect on the forecast.  
Assuming an n-square confusion matrix \(A=(a_{ij})\) and its decomposition \(A=LU\), where \(L=(l_{ij})\) is the lower triangular matrix and \(U=(u_{ij})\) is the upper triangular matrix, the PLM of matrix \(M\) is defined as the vector \(p=(p_{ij})\) with components:\[p_{i}=\left(\sum_{j=1}^{n}l_{ij}\right)/\left(\sum_{j=1}^{n}u_{ij}\right). \tag{12}\]  
When considering that the wind speed follows the Weibull distribution [49], the wind turbine power curve is monotonically increasing in accordance with the defined intervals of the power classes, and a large sample (data of approximately one year) is available, it can be assumed without a loss of generality that there is an order-preserving relation between the components of vector \(p\), i.e., \(p_{1}<p_{2}<\dots<p_{n}\) (note: the order-preserving relation is not a necessary condition for the proposed method). Interpolating the Fisher-Jenks intervals vector and the vector of PLM, a function describing the probability of negative misclassification of each power class is obtained. This function helps to mitigate the negative effects of a poor rating based on the estimation of wind power, penalizing the upper classes where the risk of misclassification is higher due to the inherent volatility of wind at high speeds, as shown in Figure 1, for the \(N=8\) power classes obtained by the Fisher-Jenks method.
## 3 The Proposed System
### Feature Fusion  
The most straightforward way to combine feature categories is to stack them in a single feature vector and verify their effect by the same classifier. However, irrelevant and redundant features in the stacking process not only increased the need for extra processing complexity but also had the potential to decrease the model's accuracy. Therefore, this paper mainly considers the following feature fusion methods. One approach is the best subset selection technique. This technique starts with a subset comprising only one feature. Retain the subset with the highest objective function value (such as accuracy, loss function, etc.) by assessing the performance of the trained classifier. For each subgroup with \(f\) features, the procedure is repeated, with \(f\) growing by one at each step. The forward stepwise selection method is one more method we consider. By gradually adding features to the model one at a time, this technique starts with a feature-less model and gradually expands its feature set. Comparing this feature selection method to the best subset selection method, it is more computationally efficient. because the best subset selection method considers only \(\sum_{i=1}^{f}(f-k)=\frac{f(f+1)}{2}\) subsets, while the latter uses a greedy algorithm to consider all \(2^{f}\) possible models. The third approach we consider is early fusion. Multiple layers of features are fused, and then the predictor is trained on the fused features. The concat operation is used to connect the two features directly. If the dimensions of the two input features \(x\) and \(y\) are \(p\) and \(q\), the dimension of the output feature \(z\) is \(p+q\). At the same time, the parallel strategy is used to combine the two feature vectors into a complex vector. For the input features \(x\) and \(y\), \(z=x+iy\), where \(i\) is an imaginary number unit. The representative algorithms are Inside-Outside Net and HyperNet. The opposite of this approach is late fusion. To enhance the detection performance, it integrates the findings of multiple layers of detection; that is, the characteristics of different dimensions are tested separately, and then the test results are integrated.  
Considering the efficiency and practicability of modeling, this paper adopts the forward stepwise selection algorithm for feature fusion. In each iteration, the feature category that yields the smallest logloss is added to our model. Stop the process until the value of logloss does not reduce during the addition process.
## 2 Methods  
### Artificial Neural Networks  
Artificial Neural Network (ANN) is a system of data processing, describing, predicting, and clustering which is inspired by the biological nervous system [26]. The nerve cell is the smallest component of this system. The data gained from experiments, or the measurements, are inputs of this system. The relative importance of the inputs is assigned by weights, and the weighted input values are combined by the summing function. Evaluation of information takes place by processingthe obtained data from the summing function and the outputs of the activation unit. The nature of the relationship between artificial neural cells and biological neurons is illustrated in Figure 1. As shown in this figure, a similar manner of processing collected data from the outside world exists for both an artificial neural cell and a biological neuron.  
For this purpose, the input layer takes variables from resources like experimental data to do some mathematical operations. Then these values are sent to the hidden layers, in which the number of hidden layers can be specified regarding the complexity of problems. The estimation of output value for all ANN structures can be provided as below:  
\[Y_{i.\ pred}=f\left(\sum_{i=1}^{n}w_{i}x_{i}+b\right) \tag{1}\]  
where the output value (\(Y_{i.\ pred}\)) is computed using a function\(f\) and the summation of the multiplication of weight \(w_{i}\) and input \(x_{i}\) to the bias \(b\).  
In general, ANN models predict the output parameters according to the following procedure:  
* Training trends are determined based on datasets.
* The architecture of the neural network is defined.
* The network parameters are determined.
* The feedforward back-propagation program is run.
* Data are compared and analyzed.  
In this study, the pressure drop, as the output, is essentially the target, and is predicted by compiling the testing inputs data. Inputs 1 to 6 are hydraulic diameter of channel, critical temperature  
Figure 1: Schematic structure of the artificial neural network for estimating pressure drop.  
of fluid, critical pressure of fluid, acentric factor of fluid (a concept referring to the molecule's shape), mass flux, and quality (the mass fraction of vapor in a saturated mixture), respectively. Table 1 presents the values of these input parameters.  
Various models of the artificial neural network can be utilized in the prediction and comparison of experimental data results. Classification of these networks is dependent on their application. In this paper, four specific types of neural networks, such as multilayer perceptron, radial basis function, cascade feedforward, and general regression, are employed for estimations.