esa-sceva/satcom-chunk-collection huggingface dataset:
# Authentication Securing Methods for Mobile Identity: Issues, Solutions and Challenges
## 2 Mobile Authentication Method and Issues
### Static Password Authentication  
The static password is the earliest and most widely used means of authentication [2]. The system sets up two tuples of information for each legitimate user during registration: a commonly named username and password. User information provided during the registration will be tied to the username. Each login will require the user to self-introduce by entering the username and authenticating it using a secret password. Figure 1 represents the idea of how registration is performed.  
This type of authentication purely depends on the user to memorize. Commonly, users will select a word that they can easily remember, potentially exposed to attack. Cybercriminals have created many tools that can help in guessing passwords. On the other hand, a complex password may be forgotten, and the user will have to reset that. This will take away the convenience of the authentication.  
Static passwords are easy to forget, lose, or leak [11]. They are simple to crack using tactics such as guessing, dictionary attacks, brute force cracking, theft, replay assaults, Trojan horse attacks, and other methods [7]. They are also too risky to use independently, especially for the purpose of mobile identity authentication.
## 1 Introduction
After a failure occurs, it incurs not only downtime costs but also failure risk costs, such as losses caused by failure to complete production tasks on time, equipment damage, or more serious personnel injuries. Some scholars develop maintenance policies from a failure risk perspective. [PERSON] et al. [24] proposed a preventive maintenance policy pertaining to the corrosion conditions and future risks. A random risk assessment model was established by considering the failure probability with respect to the random corrosion depth and failure consequence with respect to the random corrosion length. [PERSON] et al. [25] optimize the maintenance frequency during the life cycle of steel bridges to minimize risk costs. [PERSON] et al. [26] arranged maintenance strategies for sewage pumping station subsystems based on risk ranking. [PERSON] et al. [27] considered the impact of failure risk when setting strategy selection criteria and developing maintenance alternatives. [PERSON] et al. [28] regarded the operation and maintenance departments of electric multiple units as two sides of the game, and regarded failure risk and maintenance costs as two mutually constraining factors, with the reliability threshold of preventive maintenance as the bargaining object. [PERSON] et al. [29] proposed a risk-based maintenance schedule and unit commitment coordination model, with the optimization objective of minimizing the sum of maintenance costs, generation costs, and risk costs. Considering risk costs can effectively control operational risks and significantly improve reliability, it is more reasonable to consider risk costs when arranging a maintenance strategy, which can better ensure operation and improve reliability. In summary, this article focuses on the reasonable modeling of the reliability of the machining center subsystems, considering opportunistic maintenance in maintenance scheduling, and taking risk costs into account. This article attempts to use the improved average rank method to model the machining center subsystems and then consider the opportunistic maintenance strategy of the entire machine based on the optimal maintenance time arrangement for each subsystem. The remaining part of this article is arranged as follows. Section 2 models the reliability of the machining center subsystems using the improved average rank method. Section 3 includes the arrangement of subsystem maintenance plans and the designation of opportunistic maintenance strategy, and provides the solution process for the optimization model of the maintenance strategy. Section 4 provides a numerical example. Section 5 provides the conclusions.
## 4 Model Verification
### Prototype Model Test  
Secondly, the actual prototype is fabricated as aforementioned design model with drag springs. It is tested by Dynamic Signal Test System DH8303 based on the full-mode vibration exciter method [29] by Jiangsu Donghua Test Technology Co. Ltd. Thereinto, the micro exciter has the maximum exciting force 100 N and the frequency range from 0.1 Hz to 1 kHz. The signal generator can provide all sorts of signal with the frequency range from 0.01 Hz to 10 kHz. The power amplifier has rated output power 200 W with the frequency range from direct current to 40 kHz. The micro exciter and sensors are set on the petals as shown in Figure 6. The sensors are located at two ends of the petal for acquisitions of vibration signal by the micro exciter from different excitation locations. Finally, the first six natural frequencies limited by the sensor and exciter number are obtained in Table 4 to make a comparison between the test and simulation for Model 1.  
From the results above, the first frequency results of test and simulation are close to the theoretical result and in the same level. It is reasonable that the theoretical result is little lower because it only concerns one petal without the effect of adjacent petals. The simulation almost coincides with test. For each frequency order, the relative error is less than 3.9%. These errors between the test and simulation in Table 4 may be caused by the test system and simulation model refinement.  
The petal has the revolution DOF along the pin axis locked by the torsion spring and rotation limiter so that the structure is more flexible at the revolute joint than other components. In the test, the structure is hardly excited in a very lower frequency domain by the micro exciter so as to show that the test frequencies of first four orders are little bigger than the simulation frequencies in Table 4. Moreover, the exciter and sensors as the extra mass on the petal lead to enlarge the structural flexible as showing the smaller test frequencies of last two orders in Table 4. These two aspects together may be the main factors to slightly narrow down the lower frequency domain and lead to these errors in the test. But these errors are allowable, and the results are credible.  
Therefore, it is stated that the simulation of CRM is practicable, and this numerical model is verified by both theoretical method and prototype model test for the following discussions.
## 5 Conclusions  
In practical engineering, in order to solve the problem that S & I data reduce the accuracy of fault diagnosis and interfere with the stability of model training, this paper presents an ACGAN-SN data synthesis model. The method uses synthetic high-quality fault data to solve the fault diagnosis problem under S & I data. Through the analysis of the experimental results, the following conclusions can be drawn:  
* By introducing kernel norm regularization into the ACGAN model, the training stability of the model can be effectively improved, and gradient disappearance and model collapse can be avoided.
* Compared with traditional SMOTE and RO-Sampling data synthesis algorithms, ACGAN-SN can synthesize high-quality fault sample data. The similarity between the synthetic data and real data can reach 95.84%.
* The data synthesized by ACGAN-SN effectively improve the fault diagnosis accuracy under S & I data.  
Conceptualization, Q.T. and F.L.; methodology, F.L.; software, F.L.; validation, Z.F. and Q.W.; formal analysis, Q.W., T.G. and J.C; investigation, Z.F.; resources, G.A.; data curation, G.A.; writing--original draft preparation, F.L.; writing--review and editing, Q.T.; visualization, Z.F.; supervision, T.G. and J.C.; project administration, J.C. and T.G.; funding acquisition, Q.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Beijing Natural Science Foundation (grant no. L211010, 3212032) and the National Railway Administration (grant no. AJ2021-043). The authors wish to extend their sincere thanks for the support from the Beijing Municipal Science and Technology Commission of China.  
Not applicable.  
Figure 12: The t-SNE results of (**a**) ACGAN-SN, (**b**) ACGAN, (**c**) ACGAN, (**d**) RandomOS, and (**e**) SMOTE.
# A New Real-Time Detection and Tracking Method in Videos for Small Target Traffic Signs
## 2 Materials and Methods
### The Improvement of YOLOv3  
To balance the real-time performance and high accuracy of the detection architecture, a new modified YOLOv3 structure is presented (shown in Figure 3), which using Darknet-53 [14] as the backbone network for feature extraction. As shown in Figure 4a, YOLOv3, compared with YOLOv2, refers to the residual structure of ResNet [49], which consists of 2 DBL (Darknetconv2d BN Leaky) units, aiming to increase identity mapping to relieve the model degradation by introducing shortcut. Where the DBL unit shown in Figure 4b, includes CONV (Convolution layer), BN (Batch Normalization), and the activation function LeakyReLU. The predicted output in Figure 3 mimics FPN (Feature Pyramid Networks) and upsampling feature fusion and uses multi-layer feature fusion to acquire a richer feature representation. By continuous subsampling, the 608 \(\times\) 608 pixel image is capable of dividing into grid cells, corresponding to anchor boxes of different scales that are available from the K-means clustering algorithm. The specific grid cell is responsible for detecting the target when the center of the target's ground truth falls within it.  
Figure 4: The constituent structural unit of Residual Networks. (**a**) Residual unit. (**b**) DBL unit.  
Figure 3: The structure of improved YOLOv3 (Red is the deleted part, blue is the newly added part).  
As shown in Figure 3, the YOLOv3 turns the original image into 19 \(\times\) 19, 38 \(\times\) 38, 76 \(\times\) 76 feature maps after feature extraction. The larger the size of the output feature map, the more grid cells in the feature map there are and the smaller the receptive field is. The receptive field is the size of the region mapped by each grid cell in the feature map in its corresponding input image. The larger the receptive field is, the larger the range in the initial image mapped by it will be, and the features contained will be more global and have a higher semantic level. The smaller the receptive field, the smaller the range in the image of the corresponding mapping, and the features contained tend to be local and more detailed [50]. Thus, the original three-scale feature maps of YOLOv3 are improved due to the limited extraction capability for small target features of 8-times subsampling. The output feature map with four-times subsampling is employed as an alternative to 32-times subsampling. Somehow, the ability to detect small targets can be strengthened. In contrast, extra features can be turned off to save computing resources.
## 3 Analysis of Contrast Extraction Algorithms
### Calibration with the Zero-Contrast Baseline
The contrast level spanned over two orders of magnitude for our eight data sets and covered the typical contrast levels for recent speckle visibility spectroscopy measurements at FELs. Note here that by calibrating using the unfocused beam, it will also automatically eliminate the effect of the slow intensity change in the chosen ROI, as shown in Figure 1a, due to the structure factor of the sample. A detailed derivation can be found in the supplementary material of Ref. [25]. Estimated from the summed unfocused beam scattering, this gave an additional \(\sim\)0.3% contrast. While the general agreement is encouraging, the \(\sim\)1% level deviations were still larger than statistical uncertainties considering the sufficient number of frames, detector pixels and speckles taken into the calculation [26]. This suggests additional sources of systematic bias beyond both the calibration of the GG algorithm and the correlation analysis, e.g., the cross-correlation could be susceptible to the shot-to-shot beam spatial property fluctuations of the focused FEL pulses. Further investigation is needed to improve the accuracy of the speckle visibility measurement at FELs, and this certainly will benefit from a more stable FEL source with minimized intensity and spatial fluctuations adding to the error of the visibility extraction. Our result shows that Equation (4) is not the sole optimization criterion for an XPCS measurement at FELs. The absolute magnitude of contrast should also be taken into consideration in the experiment design. A low starting contrast \(\leq\)1% will make the measurement more susceptible to source and optics instabilities, which  
Figure 4: Comparison of the contrasts extracted from the cross-correlation of the neighboring frames and the calibrated contrasts using the GG photon assignment algorithm. The dashed line is a guide of the eye denoting when the two methods perfectly agree. mostly likely requires a more rigorous analysis procedure to produce a meaningful result. Thanks to the nearly fully transverse coherent X-ray beams, the typical XPCS measurements at X-ray FELs have a high starting contrast (\(\geq\)5%) [9; 22; 25; 27; 28]. The geometry of the scattering setup, i.e., detector to sample distance, generally relies on focused beams to achieve an oversampling ratio close to or bigger than 1, while the unfocused beam will lead to an oversampling ratio \(\ll\) 1. These factors therefore allow the implementation of this calibration protocol based on unfocused beams and provide on-the-fly feedback to experiments.
## 2 Materials and Methods
### Remote Sensing Data and Analysis
#### 2.5.2 Satellite Image Pre-processing  
The HJ-1A/B images acquired from July to November 2013 underwent pre-processing. The atmospheric correction consisted of three steps. First, the digital number (DN) values were converted to at-sensor spectral radiance (\(L_{sat}\)) by radiometric calibration. G (Wm\({}^{-2}\cdot\)sr\({}^{-1}\cdot\)um\({}^{-1}\)) is the calibration factor and L\({}_{\rm o}\) (Wm\({}^{-2}\cdot\)sr\({}^{-1}\cdot\)um\({}^{-1}\)) is the calibration offset. G and L\({}_{\rm o}\) were restored in the corresponding header file and are displayed in Table 5. Second, the resulting images (\(L_{sat}\)) were converted into exo-atmospheric top-of-atmosphere (TOA) reflectance.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No.** & **Satellite** & **Sensor** & **Date** & **Quality** & **Field Observation Date** \\ \hline  
\\ \hline \hline \end{tabular}
\end{table}
Table 4: Dates of the selected HJ-1A/B imagery and field observations.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**Band 3**} & \multicolumn{2}{c}{**Band 4**} \\ \cline{3-6}
**Satellite Sensor** & & **G/L\({}_{\rm o}\)** & **E\({}_{\rm sun}\)** & **G/L\({}_{\rm o}\)** & **E\({}_{\rm sun}\)** \\ \hline HJ-1A-CCD1 & G & 1.0673 & \multirow{2}{*}{1542.664} & 1.0429 & \multirow{2}{*}{1073.826} \\  & L\({}_{\rm o}\) & 3.6123 & & & 1.9028 & \\ \hline HJ-1A-CCD2 & G & 1.0464 & \multirow{2}{*}{1549.824} & 1.0519 & \multirow{2}{*}{1078.317} \\  & L\({}_{\rm o}\) & 3.7360 & & & 0.7385 & \\ \hline HJ-1B-CCD1 & G & 1.0555 & \multirow{2}{*}{1566.714} & 1.1042 & \multirow{2}{*}{1077.085} \\  & L\({}_{\rm o}\) & 3.2144 & & & 2.5609 & \\ \hline HJ-1B-CCD2 & G & 1.0245 & \multirow{2}{*}{1553.201} & 1.0146 & \multirow{2}{*}{1074.544} \\  & L\({}_{\rm o}\) & 5.2537 & & & 6.3497 & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Coefficients used for radiometric calibration and mean extraterrestrial solar irradiance (E\({}_{\rm sun}\), Wm\({}^{2}\cdot\)um\({}^{-1}\)) used for TOA reflectance of HJ-1A/B CCD 2013 images.  
Third, the exo-atmospheric TOA reflectance (\(P_{TOA}\)) was converted into surface reflectance (\(P_{\lambda}\)) using the atmospheric code of the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) model, a radiative transfer model used to process visible and near-infrared multi-angle data [(69; 70)].  
After the surface reflectance conversion, the images were geometrically corrected using a WGS-84 projection using the geometric corrected HJ-1A/B based image as it is recommended by the CSRS center [(50)]. The original of the based image was referenced to the topographic map 1:50,000 of RTSD [(51)]. The GCPs (at least 20 points over the sub-subset image scene with coverage of about 800 km\({}^{2}\)) were selected throughout using apparent features such as buildings and crossroads. To minimize the statistical properties of image alteration, nearest neighbor resampling was applied for all geometrical transformations. The polynomial transformation method was used to re-project the images with an RMSE of less than 0.3 pixel.
# The Electric Vehicle Routing Problem with Time Windows, Partial Readers, and Parcel Lockers
## 5 Experimental Results
### Results on EVRPTW-PR-PL
The search process of our ALNS is also analyzed by observing an example of solving instance c101, as shown in Figure 6. The blue line represents the values of the best solution \(f_{b}\) over 30,000 iterations, while the red line shows a linear increase in computational time over iterations. It can be seen that the ALNS algorithm rapidly improves the solution quality at the beginning of the search and achieves convergence after approximately 10,000 iterations. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \(\delta_{L}\) & **0** & **0.1** & **0.2** & **0.3** & **0.4** & **0.5** & **0.6** & **0.7** & **0.8** & **0.9** & **1** \\ \hline \(\tilde{f}_{b}\) & 723.31 & 721.27 & 720.07 & 715.98 & 718.06 & 718.58 & 716.85 & 718.13 & 719.28 & 722.99 & 722.32 \\ \(\Delta\tilde{f}_{b}\)[\%] & 1.02 & 0.74 & 0.57 & 0.00 & 0.29 & 0.36 & 0.12 & 0.30 & 0.46 & 0.98 & 0.89 \\ \(\tilde{f}(s)\) & 45.13 & 43.43 & 43.56 & 49.98 & 44.28 & 44.20 & 43.93 & 56.60 & 58.64 & 56.04 & 56.58 \\ \hline \end{tabular} Note: \(\min(f_{b})=715.98\).  
\end{table} 
Table 4: Results of EVRPTW-PR-PL obtained from our ALNS algorithm with different values of \(\delta_{L}\). Figure 5: Impact of locker removal and insertion heuristics on the performance of ALNS.
## 4 Discussion
### Dominant Driving Factors of GPP  
When exploring the spatial distribution of GPP in the BTH, we selected three natural driving factors (surface temperature, precipitation, and downward shortwave radiation) and two human driving factors (nighttime light data and land use data) [40]. With these five factors, we quantitatively calculated the explanatory credibility and interaction between factors. The results show that land surface temperature is the most important natural factor that influences the spatial distribution [41] of GPP in the BTH. It is speculated that land surface temperature may affect the gross primary productivity of vegetation photosynthesis, leading to spatial differentiation within the region. Precipitation [42] and the downward surface shortwave radiation effects on the spatial distribution are small. It can be speculated that if the northern regional precipitation difference is not big enough, the vegetation survival demand is small, and cultivated land types of irrigation may affect the distribution of GPP. Downward surface shortwave radiation was not a large factor, with its value changing little due to a small difference in elevation in the BTH, showing little influence on spatial heterogeneity. Both nighttime light data and land use data showed significant contributions. Land use type directly determines the presence and type of vegetation, so it has a direct contribution to the spatial distribution of GPP, which is similar to the results of previous studies [43]. As one of the characteristics of human activities, the contribution of nocturnal light data indicates that the spatial distribution of GPP is affected by human activities, and the gross primary productivity of vegetation is lower in areas with frequent human activities [44].  
The results of interaction factor analysis shows that the synergistic effect of two factors increases the explanatory power of the spatial differentiation of GPP compared with the single factor effect. According to Table 5, the interaction between LST and precipitation, downward surface shortwave radiation, and night light all showed nonlinear enhancement, indicating that precipitation, shortwave radiation, night light, and other factors increased the explanatory power of spatial differentiation of GPP due to their combination with LST. The interaction between land type and the four factors showed that all the four factors enhanced the explanatory power of land use on the spatial differentiation of GPP. Therefore, we should pay more attention to the positive and negative effects of environmental factors interaction on vegetation productivity and regional carbon storage while focusing on the impact of single factors on GPP [45].  
In this paper, the spatial distribution and temporal variation of GPP in the past 20 years were analyzed, and the correlation of natural factors was explored. The spatial differentiation of GPP in the BTH was analyzed using geographic detectors, and the driving factors and influencing mechanisms of spatial analysis of GPP in the BTH were studied.