esa-sceva/satcom-chunk-collection huggingface dataset:
# Parameter Design for Inter-Satellite Laser Link Acquisition Under Weak Light Conditions
## 4 Acquisition Camera Integration Time Selection  
In Section 3, we derived a theoretical formula for the acquisition failure probability. Before using simulation to validate the theoretical results, we need to study and select an  
Figure 6: Schematic of the calculation of the maximum acquisition radius.  
important parameter. The integration time of the camera has an important influence on the calculation of the error of laser spot location. With known acquisition camera noise parameters, we can calculate the spot positioning error at the current noise level. However, the camera noise does not vary linearly with integration time. The acquisition camera we use is Tekwin SH640 (TWKWIN, Xi'an, China). The main parameters are listed in Table 2, and the mean and standard deviation of the noise of the camera at each integration time measured in the absence of light are shown in Figure 7.  
In light of the above, we can analyze the centroid error using the camera's actual noise data in lightless environments, where the number of photoelectrons can be generated through simulation. The amplitude distribution on the acquisition camera can be expressed as follows:  
\[E(x_{i},y_{i})=\frac{E_{l0}}{j\lambda f}\text{exp}\left[jk_{0}\left(f+\frac{x _{i}^{2}+y_{i}^{2}}{2f}\right)\right]\iint_{D}A(\alpha,\beta)\text{exp}\left[-j \frac{k_{0}}{f}(x_{i}\alpha+y_{i}\beta)\right]d\alpha d\beta, \tag{23}\]  
where \(E_{l0}\) is a complex constant caused by phase delay of the imaging system, \(k_{0}\) is the wave number,\((x_{i},y_{i})\) is the position of the \(i\)th pixel, \((\alpha,\beta)\) is the coordinate at the acquisition camera, and \(A(\alpha,\beta)\) is the corresponding complex amplitude distribution within the integration area \(D\). \(A(\alpha,\beta)\) can be expressed in polar coordinates:  
\[A(\alpha,\beta)=A(r,\theta)=E_{a}\text{exp}\{-jk_{0}[r\phi\text{cos}(\theta- \theta_{0})]\}, \tag{24}\]  
where \(E_{a}\) represents the complex amplitude of the receiving beam, which can be calculated by the power of the edge of the effective radius of the scanning beam. \(\phi\) denotes the beam tilt in the yaw direction, and \(\sigma_{0}\) is caused by the tilt in the pitch direction.  
According to Equations (5), (23) and (24), we can perform the simulation in MATLAB R2021b (Mathworks, Natick, MA, USA). The simulation result is shown in Figure 8. As shown in the figure, there are very few integration times available to choose from. For the subsequent simulation process, we select an integration time of 800 ms to make sure the centroid error is less than 0.1 pixel.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Tekwin SH640** \\ \hline Resolution & \(640\times 512\) \\ Pixel size (\(L_{pixel}\)) & \(15\)\(\mu\)m \\ Conversion efficiency (\(Q_{e}\)) & 0.7 A/W@1064 nm \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of the SH640 CMOS camera.  
Figure 7: The mean and standard deviation of the noise of Tekwin SH640.
The exact coordinates of the selected pixel reflect the precise localization of the feature in the image plane. As a result, the relation between feature maps and actual image content is much more direct than in the case of classic CNNs. This greatly simplifies the semantic analysis of features and feature maps, which originally required a specific approach such as deconvolutional neural networks [3]. Naturally, the aforementioned features have to be non-trivial. If visual features consisting of a single pixel were allowed, the trained feature extractor could take the form of an identity function and the feature maps would correspond to the original image. In order to select semantically meaningful features, our method ensures that the feature maps at a selected level are sparse matrices, where the neighborhood of each activated (non-zero) element is zeroed. The resulting image representation, based on the visual features of the image, should contain sufficient information to ensure that the goals of the analysis are met. The encoding is obviously lossy, but the most common patterns found in the training set are expected to be preserved. Because of its ubiquity in the field of machine learning, the MNISTdata set of handwritten digits [7] was employed for the purpose of the present study. Not only does it allow for comparisons of the obtained results with those produced by similar techniques, but it is also, due to its simplicity, easily interpretable. The latter asset enables understanding of the meaning of the generated local features. The remaining part of this paper is organized as follows. Section 2 discusses related works. In Section 3, the concept of convolutional neural networks is outlined and our novel contributions are defined. Section 4 describes the experimental framework for providing as sparse an image representation as possible, without losing the key information. The results are discussed in Section 5. A detailed analysis of the neural network models is illustrated with various visualizations. Finally, Section 6 presents the conclusions and directions for further research.
## 4 Bibliographic Analysis
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**R** & **Organization** & **CL** & **MP** & **MP (\%)** & **NC** & **CR** & **CR (\%)** \\ \hline
1 & Beijing Univ. Posts and Telecom. (BUPT) & U & 66 & 1.92 & 47 & 345 & 1.38 \\
2 & Beijing Jiaotong Univ. (BUUT) & U & 60 & 1.74 & 50 & 455 & 1.83 \\
5 & Univ. Politecnica de Valencia (UPV) & U & 39 & 1.13 & 27 & 350 & 1.41 \\
6 & Hanyanying Univ. & U & 39 & 1.13 & 12 & 95 & 0.38 \\
7 & Electronics and Telecom. Research Institute (ETRI) & I & 38 & 1.10 & 21 & 156 & 0.63 \\
8 & Tsinghua Univ. & U & 38 & 1.10 & 43 & 303 & 1.22 \\
15 & DeutschesZentrum fur Luft-Und Raumfahrt (DLR) & I & 31 & 0.90 & 29 & 131 & 0.53 \\
25 & ConsiglioNazionaledelleRicerche (CNR) & I & 24 & 0.69 & 18 & 200 & 0.80 \\
27 & Qatar Mobility Innov. Center (QMIC) & I & 24 & 0.69 & 15 & 233 & 0.94 \\
30 & Instituto de Telecomunicacoes (IT) & I & 22 & 0.64 & 29 & 428 & 1.72 \\ \hline \hline \end{tabular}  
* R: Ranking position; CL: Classification; U: University; I: Institution; MP: Manuscripts published; NC: Collaborations with other organizations; CR: Citations received.  
\end{table} 
Table 2: Most productive institutions in I2X, V2X, and P2X research from 1996 to 2018. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**R** & **Organization** & **MP** & **MP (\%)** & **NC** & **CR** & **CR (\%)** \\ \hline
3 & General Motors (GM) & 49 & 1.43 & 56 & 641 & 2.57 \\
22 & Volvo & 26 & 0.76 & 54 & 322 & 1.29 \\
40 & Toyota Info Tech. Center & 19 & 0.55 & 21 & 235 & 0.94 \\
45 & Volkswagen AG & 18 & 0.52 & 23 & 137 & 0.55 \\
57 & Forschungszentrum Telekom. Wien (FTW) & 16 & 0.47 & 16 & 500 & 2.01 \\
60 & DENSO Corporation & 16 & 0.47 & 8 & 9 & 0.04 \\
61 & NXP Semiconductors & 16 & 0.47 & 4 & 30 & 0.12 \\
64 & Renault & 15 & 0.44 & 20 & 46 & 0.18 \\
81 & Ford Motor Company & 13 & 0.38 & 33 & 49 & 0.20 \\
93 & Huawei Technologies Co., Ltd. & 12 & 0.35 & 18 & 89 & 0.36 \\ \hline \hline \end{tabular}  
* R: Ranking position; MP: Manuscripts published; NC: Collaborations with other organizations; CR: Citations received.  
\end{table} 
Table 3: Most productive enterprises in I2X, V2X, and P2X research from 1996 to 2018. Figure 7: Bibliographic evolution of the contributions on I2X, V2X, and P2X research per source type. (15), \"ZigBee\" (15), \"4G\" (14), \"LTE-V\" (11), \"V2R\" (11), \"IEEE 1609\" (10), \"V2P\" (8), \"RFID\" (21) and \"V2G\" (7), among others. The frequency in which the terms appear allows us to assess the weight of research in this field.
# An Experimental Study on Ice Accretion under Bridge Cable in Different Conditions
## 3 Results and Discussion
### Formation of Ice on the Surface of the Cable
Unfrozen water was forced to flow and freeze along the upper edge of the spiral fillets, strengthening the interconnections between the ice ribs and forming irregular ice structures at the helix. It can be seen from Figure 3 that the ice structure shape at the helix varied according to the position of the helix on the cable surface. Ice edges are more likely to form on the side of the helix that obstructs water flow. On the smooth surface, water condensed into ice ribs on both sides and then continued to flow downward along the bottom of the stay cable away from the surface. It is worth noting that the ice ribs at the bottom of the stay cable converged to form banded ice at ambient temperatures of \(-25\)\({}^{\circ}\)C, \(30^{\circ}\), and \(45^{\circ}\) angles, and the top of the ice was zigzag, as shown in Figure 6. The conditions of low temperature and appropriate inclination angle contributed to this phenomenon. Figure 4: Icing on the side of the cable under different temperature and inclination angle conditions. Figure 5: \(-25^{\circ}\), Ling conditions of the bottom surface under different diameters and inclination angles. Figure 6: Ice rib. During the dynamic accretion process of ice, the change of the cable surface (smooth plane-spiral fillet-smooth plane) dramatically influences the dynamic accretion process of ice. It was found that the ice accumulation in the lower part is closely related to laboratory temperature, cable diameter, and angle. Through the subsequent quantitative analysis of these conditions, we can have a clearer understanding of the bottom ice accumulation and provide a constructive reference for optimizing the stay cable ice accumulation model and the subsequent installation of deicing facilities.
## 2 Materials and Methods
### Data Processing  
Kinematic data were captured using a motion analysis system that comprised eight infrared cameras (Vicon Motion Analysis, Oxford, UK) by tracking 28 infrared reflective balls (reflective markers) with a diameter of 14 mm at 100 Hz. The infrared reflective balls were attached to participants' corresponding parts following the scheme suggested by the CGM 23 lower-limb model. Kinetic data were captured using a 3D force plate (Kistler, Winterthur, Switzerland) at 1000 Hz, which was synchronized with a motion analysis system. The raw data collected via the Vicon system were subjected to kinematic and inverse dynamic analysis using the Visual3D software (Version 6, C-Motion, Inc, Germantown, MD, USA). To enhance the data quality, a fourth-order low-pass Butterworth filter was applied, targeting three-dimensional coordinates at 10 Hz and force platform data at 50 Hz. Lower-limb joint angles were determined using the Euler angle method [28,29]. The initial ground contact (IC) was identified as the first instance when the vGRF exceeded 10 N [30]. The following data were analyzed: the joint angles of the hip, knee, and ankle in the sagittal and frontal planes at the IC and the peak vertical ground reaction force (vGRF) moments, in degrees (\({}^{\circ}\)); the weight-normalized peak vGRF (BW); the time to peak vGRF (T-vGRF), measured in milliseconds (ms); and the vertical loading rate (LR), expressed as body weight per millisecond (BW/ms). The LR was calculated using formula (1) [31]:  
\[\mathrm{LR}=\mathrm{peak}\ \mathrm{vGRF}/\mathrm{T}-\mathrm{vGRF}. \tag{1}\]
# HinPhish: An Effective Phishing Detection Approach Based on Heterogeneous Information Networks
## 5 Experiments Evaluation and Comparing
### Experiment 3: Comparison of HinPhish with Search Engine-Based Methods  
Phishing detection approaches based on search engines leverage the site reputations provided by various search engines. Generally, search engines prefer to collect more information about normal sites than phish sites. In this respect, the three approaches  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Approach**} & \multirow{2}{*}{**Algorithm**} & \multicolumn{4}{c}{**Performance**} \\ \cline{3-6}  & & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline phishpedia & \multirow{2}{*}{NN} & \multirow{2}{*}{0.8121} & \multirow{2}{*}{0.9714} & \multirow{2}{*}{0.6478} & \multirow{2}{*}{0.7773} \\ (cleaned data) & & & & & \\ \hline LDP & Heuristic & 0.7816 & 0.8815 & 0.6525 & 0.7499 \\ \hline Jail-phish & Heuristic & 0.8667 & 0.8692 & 0.8875 & 0.8783 \\ \hline HHP & Heuristic & 0.9835 & 0.9788 & 0.9882 & 0.9835 \\ \hline [PERSON] et al. [45] & RF & 0.9154 & 0.9546 & 0.8740 & 0.9125 \\ \hline [PERSON] et al. [46] & RF & 0.9797 & 0.9772 & 0.9834 & 0.9803 \\ \hline [PERSON] et al. [18] & GB & 0.9510 & 0.9429 & 0.9608 & 0.9518 \\ \hline HinPhish & RF & 0.9856 & 0.9834 & 0.9881 & 0.9858 \\ \hline \hline \end{tabular}  
* NN, Neural Network. 2 GB, Gradient Boosting.  
\end{table}
Table 6: Descriptions and performances of different approaches.  
Figure 7: The experimental results with Phishpedia and HinPhish.  
we chose each had certain advantages. LDP [32] is a search engine-based lightweight phishing detection system, which only chooses the top-level domain and page title as search keywords, having the advantages of fast response and less redundant checking, among others. Jail-phish [34] advances the search keywords, through a dynamic generation algorithm designed for search keywords. Moreover, Jail-phish has the ability to detect phishing sites hosted on compromised servers. The difference of HHP [47] is that HHP applies advanced search engine search syntax, allowing it to collect more accurate site data. In addition, two decision strategies for HHP were also proposed, in order to detect phishing sites hosted on compromised servers. Table 6 shows the performance of these approaches on the phishpedia-cleaned data set. As can be seen, the effects of LDP and Jail-phish did not meet our expectations, which may have been caused by the unstable site data provided by the search engines. However, HHP could deal with this issue through use of the advanced search syntax. Therefore, among these three methods, HHP had the best performance. In Figure 8, we show a histogram of the performance of these three methods and HinPhish. From the figure, we can see that HinPhish still had a comparative advantage, compared with the most advanced search engine-based methods.
# Current Progress in Femtosecond Laser Ablation/Ionisation Time-of-Flight Mass Spectrometry
###### Abstract  
The last decade witnessed considerable progress in the development of laser ablation/ionisation time-of-flight mass spectrometry (LI-TOFMS). The improvement of both the laser ablation ion sources employing femtosecond lasers and the method of ion coupling with the mass analyser led to highly sensitive element and isotope measurements, minimisation of matrix effects, and reduction of various fractionation effects. This improvement of instrumental performance can be attributed to the progress in laser technology and accompanying commercialisation of fs-laser systems, as well as the availability of fast electronics and data acquisition systems. Application of femtosecond laser radiation to ablate the sample causes negligible thermal effects, which in turn allows for improved resolution of chemical surface imaging and depth profiling. Following in the footsteps of its predecessor ns-LIMS, fs-LIMS, which employs fs-laser ablation ion sources, has been developed in the last two decades as an important method of chemical analysis and will continue to improve its performance in subsequent decades. This review discusses the background of fs-laser ablation, overviews the most relevant instrumentation and emphasises their performance figures, and summarizes the studies on several applications, including geochemical, semiconductor, and bio-relevant materials. Improving the chemical analysis is expected by the implementation of laser pulse sequences or pulse shaping methods and shorter laser wavelengths providing current progress in mass resolution achieved in fs-LIMS. In parallel, advancing the methods of data analysis has the potential of making this technique very attractive for 3D chemical analysis with micrometre lateral and sub-micrometre vertical resolution.  
chemical composition; elemental imaging; sub-micrometre resolution; laser ablation; time-of-flight; mass resolution; atomic ions +
Footnote †: journal: Applied Sciences  
1
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))
## 1 Introduction
Reference [7] presented a genetic algorithm to obtain optimum random number seeds to generate wind speeds of correlated wind farms and used MCS to study the effect of wind penetration and correlation on system risk. Customized simulation programs and expertise in the field would generally be required to implement simulation techniques in practice, and therefore, would not be readily applied. The analytical method, on the other hand, is a relatively simple method, and should therefore be more attractive to system operators and planners. Reference [4] introduced an analytical model of a WECS using conditional probabilities to obtain joint model of correlated WECSs. References [7] and [8] used this conditional method to build an analytical wind model. It becomes important to develop simple models to be readily acceptable in real world reliability evaluation applications. There have been studies done to reduce the number of states in multi-state models to simplify the evaluation. Reference [10] suggested a simple seven-state analytical wind model. The correlation between multiple wind farms was, however, not considered in the study. The methods used in [6; 7; 8; 9] to generate correlated random number seeds are quite complex. This paper uses a simple algorithm using Cholesky decomposition [11] for the generation of correlated random numbers for recognizing correlation between two wind farms. This paper then focuses on simplifying the wind models incorporating the wind correlations and wind penetration levels.
###### Abstract  
(1) Background: Recent studies report high accuracies when using machine learning (ML) algorithms to classify prostate cancer lesions on publicly available datasets. However, it is unknown if these trained models generalize well to data from different institutions. (2) Methods: This was a retrospective study using multi-parametric Magnetic Resonance Imaging (mpMRI) data from our institution (63 mpMRI lesions) and the ProstateX-2 Challenge, a publicly available annotated image set (112 mpMRI lesions). Residual Neural Network (ResNet) algorithms were trained to classify lesions as high-risk (hrPCA) or low-risk/benign. Models were trained on (a) ProstateX-2 data, (b) local institutional data, and (c) combined ProstateX-2 and local data. The models were then tested on (a) ProstateX-2, (b) local and (c) combined ProstateX-2 and local data. (3) Results: Models trained on either local or ProstateX-2 image data had high Area Under the ROC Curve (AUC)s (0.82-0.98) in the classification of hrPCA when tested on their own respective populations. AUCs decreased significantly (0.23-0.50, \(p<\) 0.01) when models were tested on image data from the other institution. Models trained on image data from both institutions re-achieved high AUCs (0.83-0.99). (4) Conclusions: Accurate prostate cancer classification models trained on single-institutional image data performed poorly when tested on outside-institutional image data. Heterogeneous multi-institutional training image data will likely be required to achieve broadly applicable mpMRI models.  
machine learning; prostate cancer; magnetic resonance imaging; artificial intelligence Article
## 5 Conclusions  
The accurate estimation of cropland ET is important for crop irrigation, fertilization, and other management measures. In this study, we proposed an ANN-PM model based on ML and the PM equation to estimate cropland ET. At the same time, we optimized the Medlyn-PM model (uses ANN-derived GPP along with Medlyn's stomatal conductance to compute _Gs_, and the computed _Gs_ is used to estimate ET). We compared the two models to get a better method for estimating ET based on the ML approach. Specifically, we used ANN to estimate _Gs_ in ANN-PM and GPP that was used to estimate _Gs_ in conjunction with [PERSON]'s _Gs_ model in Medlyn-PM. We have the following conclusions.  
1. The optimal ANN architecture to estimate _Gs_ in ANN-PM consists of two hidden layers with 48 neurons in each layer, and that to estimate GPP in Medlyn-PM, two hidden layers and one neuron in each layer was optimal. The optimized _g\({}_{0}\)_ and _g\({}_{1}\)_ values in [PERSON]'s _Gs_ model are 0.06 and 3.94, respectively.
2. The ANN-PM model can reasonably estimate the ET of cropland (RMSE = 19.09-19.50 W m\({}^{-2}\), MAE = 13.27-13.41 W m\({}^{-2}\), and R\({}^{2}\) = 0.83 for training, validation, and test datasets) and is proven to perform better than Medlyn-PM with a smaller RMSE and MAE and larger R\({}^{2}\).
3. The ANN approach can represent the water stress impacts on ET well, as ANN-PM can reasonably capture the seasonal variations in ET at the dry sites (AI < 0.5). Additionally, the performances of the ANN-PM model at the dry sites were as good as at the wet sites.  
Conceptualization, Y.L. and Y.B.; Methodology, Y.L. and Y.B.; Software, Y.L. and Y.B.; Validation, Y.L.; Formal analysis, Y.B.; Investigation, Y.L. and Y.B.; Resources, Y.B.; Data curation, Y.L. and Y.B.; Writing--Original Draft, Y.L.; Writing--Review and Editing, Y.B., S.Z., J.Z., and L.T.; Visualization, Y.L. and Y.B.; Supervision, Y.B.; Project administration, Y.B.; Funding acquisition, Y.B. and J.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant Nos. 41,901,342, 31,671,585), \"Taishan Scholar\" Project of Shandong Province, and Key Basic Research Project of Shandong Natural Science Foundation of China (Grant No. ZR2017 ZB0422).