esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiments  
### Study Areas and Data Sources  
Two regions in the Shanxi Province, China, were selected as the experimental areas: Yulin and Taiyuan (Figure 6). Based on pre-temporal and post-temporal remote sensing images from the fourth quarter of 2019 and 2020 in the experimental areas, a deep Siamese CNN change detection model was used to execute the automatic feature extraction of change information of the new bare land. The image data used in the experimental area are a mosaicking dataset of Chinese multi-sensor satellite remote sensing images (Figure 7),  
Figure 5: Schematic diagram of overlapping buffer areas between parcels.  
including ZY3-02/03 optical stereo mapping satellites and the 2 m/8 m optical satellite constellation (GF-1 B, C, and D). The spatial resolution of the images is approximately 2 m.  
Figure 6: Location map of experimental area.  
Figure 7: Bi-temporal multi-sensor mosaic images of the experimental area: (**a**) Images from the fourth quarter of 2019; (**b**) Images from the fourth quarter of 2020.  
As a large dataset is required to train the deep CNN change detection model, based on domestic multi-sensor satellite data with 2 m resolution, the training samples were collected and semi-automatically labeled based on the historical archives of land-use change parcels. The sample comprises a pre-temporal image, a post-temporal image, and a label.  
As this study focuses on the automatic extraction of newly constructed bare land, the pre-temporal sample can be agricultural land, garden, forest, grass, buildings, or any other land type, whereas the post-temporal is fixed as constructed bare land. To maintain domain adaptation between the sample and test data, this experiment mainly selected sample data from the fourth quarter of recent years. In terms of sample diversity and balance, factors such as the target scale, image radiation, and distribution area were accounted for. The samples were sliced into 512 \(\times\) 512 pixels, with the total number being 20,052, as shown in Figure 8.
# A New Algorithm for Simultaneous Retrieval of Aerosols and Marine Parameters
## 2 Data and Methods
### Radial Basis Function Neural Network (RBF-NN)-Training and Evaluation
#### 2.3.2 Training and Evaluation
To determine optimized values for the coefficients \(a_{ij}\), \(b\), \(c_{jk}\), and \(d_{i}\) in Equation (24) for the ranges of the five input/retrieval parameters given above, we used the MATLAB radial basis function 'newrb', which iteratively creates a RBF-NN by adding neurons one at a time until the mean-squared error (MSE) defined as [48; 49]  
\[\mathrm{MSE}=\frac{1}{N_{s}}\sum_{i=1}^{N_{s}}\Bigl{(}R_{i}^{T}-R_{i}^{P} \Bigr{)}^{2}, \tag{25}\]  
falls beneath an error goal or a actual number of neurons after optimisation \(N\) in Equation (24) has been reached. Here \(R_{i}^{T}\) is the target value of the TOA radiance computed by a well-tested RTM for the \(i\)th MODIS channel, and \(R_{i}^{P}\) is the corresponding TOA radiance value predicted by the RBF-NN in Equation (24), and \(N_{s}\) is the number of instrument channel used. The call for the MATLAB function 'newrb' includes the parameters \(p_{k}\), \(R_{i}\), \(goal\), \(spread\), and \(N\), where \(p_{k}\) (\(k=1,2,\ldots,N_{p}\)) are different sets of the five input/retrieval parameters and \(R_{i}\) are corresponding sets of RBF-NN predicted TOA radiance values, whereas \(goal\), \(spread\), and \(N\) are design parameters. As noted above, \(N\) is the actual number of neurons after optimisation in Equation (24). Further, \(goal\) is equal to the mean-squared error MSE, and \(spread\) is equal to the width of the RBF-NN. We used 90% of the 10,000 sets of input parameters (9000 parameter sets) for training and the remaining 10% (1000 parameter sets) for validation. After testing different combinations of values for goal, spread, and \(N\), we found the combination of settings \(goal=0\), \(spread=1.5\), and \(N=300\) to give very accurate results. Figure 2 shows a comparison between the TOA radiances obtained from the RBF-NN and the RTM. The correlation coefficients between the two models for SZAs 45\({}^{\circ}\), 53\({}^{\circ}\), 63\({}^{\circ}\), and 75\({}^{\circ}\) are 0.9999996, 0.9999910, 0.9999967, and 0.99999984, respectively, with average deviations of 0.011%, 0.047%, 0.029%, and 0.015%, respectively. The RBF-NN did not achieve the targeted value of \(goal=0\), but at the point of termination with \(N=300\) the value was \(goal=1.0\times 10^{-7}\). In addition to predicting the TOA radiances very well, the RBF-NN is faster than the RTM by a factor of 200.
# A Hidden Fingerprint Device on an Opaque Display Panel
## 2 Theory  
A prototype of the proposed OFR that can be integrated into a display is presented in Figure 1a. Light from the collimated light source system [12] is reflected into the cover glass by the reflective surface above it, as shown in Figure 1b. The light area is projected onto the reflecting surface and changed from A to A\({}^{\prime}\). The formulation is shown in Equation (1). The light is then again projected by the reflecting surface onto the surface of the cover glass, as shown in Figure 1c, and the relationship is formulated as in Equation (2). For example, if the width of A is 1 mm and the angle of the reflective surface is 38 degrees, the length of A\({}^{\prime\prime}\) is about 4.13 mm. This means that we can inject a narrow light width (A) and get a larger light width (A\({}^{\prime\prime}\)) after reflection. Based on this concept we can obtain an effective high screen ratio design. After the light is reflected by the reflecting surface, it is transmitted into the cover glass with total internal reflection (TIR). When the finger is pressed on the surface of the cover glass, the placement of the ridges will destroy the total internal reflection due to changes in the refractive index. This principle is usually called frustrated total internal reflection (FTIRIR). In order to allow the light to have total internal reflection in the cover glass and be partially destroyed when a fingerprint is placed on the cover glass, the angle of the reflective surface must satisfy the condition in Equation (3), where n\({}_{\text{skin}}\), n\({}_{\text{cover glass}}\) and n\({}_{\text{air}}\) are the refractive indices of fingerprint skin, cover glass and air, set to 1.5, 1.517 and 1 respectively. Thus, we get the design value of the angle of the reflection surface between 20.6 degrees and 40.7 degrees. The remaining total internally reflected light will continue to be reflected to the end of the cover glass where there is a reflective surface with the same angle, which will change the width of A\({}^{\prime}\) back to the width of A. After this, a third reflective surface will be placed under the cover glass; the function of this reflecting surface is to enlarge the beam to the width of A\({}^{\prime}\) and project it onto the Complementary Metal Oxide Semiconductor (CMOS) sensor. It is through the inclusion of this third reflective surface that we can obtain fingerprints on the CMOS sensor with the same area as on the cover glass. Through this design, we can create a fingerprint recognition device that can be used in opaque displays while meeting the requirement of high screen ratios.  
Figure 1: (**a**) The design concept which combines with the display panel. (**b**) Light area enlarges from A to A\({}^{\prime}\) due to \(\theta\) angle. (**c**) Light area enlarges from A\({}^{\prime}\) to A\({}^{\prime\prime}\) due to \(\theta\) angle.  
\[A^{\prime}=\frac{A}{\cos(\theta)} \tag{1}\]  
\[A^{\prime\prime}=A+A^{\prime}\cdot\sin(\theta)\cdot\tan(2\theta) \tag{2}\]  
\[n_{skin}>n_{cover\ glass}\cdot\sin 2\theta_{cover\ glass}>n_{air} \tag{3}\]
# Application of Absolute Nodal Coordinate Formulation in Calculation of Space Elevator System
## 5 Discussion
### Computational Efficiency  
The ANCF method and LM model use [PERSON]'s method for iterative solutions. Although the Jacobian matrix needs to be solved numerically, the calculation cost is relatively high but the total number of calculation steps is reduced, thereby reducing the calculation time.  
FEM obtains the result through the solution of multi-step linear equations, but when the displacement change is close to the force change, the speed of convergence will be very slow or the convergence will fail. The stability is worse than that of the ANCF method and the LM model.
# Machine Learning and Deep Learning Methods for Intrusion Detection Systems: A Survey
## 4 Research on Machine Learning-Based IDSs
### Log-Based Attack Detection
#### 4.4.2 Log Feature Extraction-Based Detection  
This method involves extracting log features according to domain knowledge and discovering abnormal behaviors using the extracted features, which is suitable for most machine learning algorithms. Using a sliding window to extract features is a common approach. The sliding window makes use of the contextual information contained in logs. In addition, the sliding window is a streaming method that has the benefit of low delay.  
Intrusion behaviors may leave traces of system calls, and analyzing these system calls with classification algorithms can detect intrusions. [PERSON] et al. (2009) proposed a CNN method to analyze system calls. Every underlying operation that involves the operating system will use system calls; thus, analyzing the system call path can reproduce the complete intrusion process. They conducted experiments on the NGIDS-DS and the ADFA-LD datasets, which include a series of system calls. First, they extracted features with a sliding window. Then, they applied a CNN model to perform classification. The CNN was good at finding local relationships and detecting abnormal behaviors from system calls.  
Model interpretation is another important research direction, which has attracted extensive attention. [PERSON] et al. [62] proposed an interpretable deep learning detection method using data from the CERT Insider Threat dataset, which consists of system logs. They first extracted 414-dimensional features using a sliding window. Then, they adopted a DNN and an RNN to classify logs. The DNN detected attacks based on the log contents, and the RNN detected attacks based on the log sequences. The proposed methods reduced the analysis workload by 93.5% and reached a detection rate of 90%. Furthermore, they decomposed the abnormal scores into the contributions of each behavior, which was a helpful analysis. Interpretable models are more convincing than are uninterpretable models.  
Some logs lack labeled information; consequently, supervised learning is inappropriate. Unsupervised learning methods are usually used with unlabeled logs. [PERSON] et al. [63] proposed an unsupervised learning detection method in the enterprise environment. They conducted experiments on the VAST 2011 Mini Challenge 2 dataset and extracted features from the host and network logs. Due to the different influences of each feature, they selected features using the Pearson correlation coefficient. Then, they clustered the logs with the K-means and DBSCAN algorithms. By measuring the salient cluster features, the clusters were associated with abnormal behaviors. Finally, they analyzed the abnormal clusters manually to determine the specific attack types.
## 6 Security and Feature Analysis
### Feature Analysis
Service \(u_{v}\) only computes once for generating the secret key and the token. During the Authentication and Key Agreement phase, \(\delta_{d}\) only needs to operate two AES encoding/decoding algorithms. \(u_{v}\) takes three and \(\zeta_{z}\) takes two encoding/decoding algorithms. Hence, the lightweight feature is achieved in the proposed protocol. * Session key: This is a temporary cryptographic key used during a single communication session between two entities, ensuring secure and confidential data exchange through a key exchange or key establishment protocol. This protocol involves a series of cryptographic algorithms and techniques to securely generate and exchange the session key between the entities. After the Authentication and Key Agreement phase, edge device \(\delta_{d}\), and fog node \(\zeta_{z}\) will have the same session key \(K_{\delta_{d}\zeta_{z}}\). In the Authentication and Key Agreement presented in Figure 6, line 5, \(\delta_{d}\) receives a response message \(R_{\delta_{d}}\) from service provider \(u_{v}\). Then, \(\delta_{d}\) retrieves \(K_{\delta_{d}\zeta_{z}}\) by performing \(D(R_{\delta_{d}},K_{\delta_{d}})\). In line 10, \(\zeta_{z}\) obtains \(K_{\delta_{d}\zeta_{z}}\) by using its secret key \(K_{\zeta_{z}}\). Then, \(\delta_{d}\) receives the message \(M_{\zeta_{z}\delta_{d}}\) from \(\zeta_{z}\), and computes its own ID and fog node \(ID\), \(\zeta_{z}ID\), by using \(K_{\delta_{d}\zeta_{z}}\). If the session keys between \(\delta_{d}\) and \(\zeta_{z}\) are not the same, the secure communication channel will not be built. * Scalability and compatibility of the system: The authentication protocol's scalability requirements include user count, concurrent requests, network traffic volume, and system response time. It leverages 5G networks' capabilities to handle large user requests efficiently. The protocol's high degree of scalability ensures optimal processing without delays, and adding more edge users or fog nodes does not compromise system efficiency. In the IoT environment, numerous users utilize a variety of devices that often originate from different manufacturers. In the designed protocol, edge users and fog nodes access the 5G network through the eSIM card. This approach ensures that, despite the diversity of devices used by edge users, they can rely on the eSIM card for seamless data transmission. Consequently, this significantly mitigates compatibility conflicts, data synchronization issues, and the requirement for additional middleware components to facilitate communication between different tiers. By leveraging the standardized eSIM technology, the protocol promotes enhanced compatibility and streamlined communication within the IoT ecosystem.
## 2 Literature Review
In [13], the three-dimensional perfect difference code (3D-PD) code is proposed in order to remove the effect of PINN, where this code needs two photo-detectors in order to detect the optical signal at the receiver, which allows the system becomes more complex. In [14], the three-dimensional hybrid code called the perfect difference/multi-diagonal (3D-PD/MD) based on two sequences and one sequence of 2D-PD and 1D-MD code, respectively, has been studied. The suggested code also needs two photo-detectors at the receiver, which allows the system becomes more complex and needs higher power consumption.
# Impact of Die Radius in a Streamlined Die during Wire Drawing
## 6 Conclusions  
Based on the findings presented in this study, the following conclusions are drawn:  
1. The distribution of design factors such as strain, stress, temperature rise, damage value, and drawing force of the wire fabricated by the streamlined die was different from that of the wire manufactured by the conventional die during wire drawing. Furthermore, the behavior of the design factors of the wire fabricated by the streamlined die was highly dependent on the die radius.
2. The different behaviors of the design factors in the wire manufactured by the streamlined die can be explained by the frictional work and redundant work with die angle. The temperature rise and drawing force were high at a higher die radius (small \(I_{\text{DR}}\) value) because of the great frictional stress and heating effect stemming from the high contact length of the wire and die. Additionally, the higher redundant work at the surface area with decreasing die radius (large \(I_{\text{DR}}\) value) led to a higher \(S\!I\) value, effective stress at the surface area, damage value at the center area, temperature rise, and drawing force due to the abrupt change in the metal flow of the wire stemming from the high die angle.
3. After the optimization of several design factors, it is concluded that the optimum \(I_{\text{DR}}\) values range from 0.14 to 0.18, indicating that a streamlined die with a radius of 70-90 mm was the most suitable in the present process condition. In particular, the damage value of the wire was reduced in this range of die radii compared to the conventional die. The proposed design concept can provide process engineers with practical seeding ideas to select the optimal die design for high-quality wires.  
Figure 14: Schematic of redundant work at the surface area of the drawn wire with the die angle.  
Figure 15: Schematic description of variations in various design factors with \(I_{\text{DR}}\).  
**Author Contributions:** [PERSON] designed and performed the experiments, analyzed data, and wrote the paper. The author has read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT, South Korea) (Grant No. 2018R1D1A1B07050103).  
The author declares no conflict of interest.
## 4 Results  
### Model Performance and Comparison  
The Informer model was compared with the four other methods for the end-of-season prediction using the leave-one-year-out strategy. \(R^{2}\), \(RMSE\) (t/ha), \(MAPE\) (%), and \(d\) of rice yield prediction in each of the past 8 years by the five models are shown in Figure 4. Overall, the Informer model showed a better predictive capability than all other models, with an average \(R^{2}\) of 0.81, \(RMSE\) of 0.41 t/ha, \(MAPE\) of 15.47%, and \(d\) of 0.74 from 2009 to 2016. Moreover, the Informer model performed best in multiple testing years. Additionally, the machine learning (i.e., RF and XGBoost) and deep learning (i.e., AtLSTM) models provided better results than the traditional linear regression model (i.e., LASSO) in rice yield prediction in India, demonstrating that the machine learning methods could handle the nonlinear relationship between yield and input variables. The scatter plot showed that the observed and predicted rice yield had a linear and positive pattern for the five models in the testing years from 2009 to 2016 (Figures 5 and S1). Overall, we found that the Informer model showed the best performance in predicting rice yield in multiple testing years. In addition, the AtLSTM model had the most satisfactory performance in 2013. Meanwhile, we found the lowest performance in the LASSO model compared with the other models (Figures 5 and S1).  
The spatial patterns of the predicted yield for all testing years are presented in Figures 6 and S2. Overall, the spatial patterns of the predicted yield were in good agreement with the observed yield, especially for the four machine learning models. The Punjab State, the northwest of the study area, had high-yield districts. In contrast, the Uttar Pradesh State had low-yield districts in the southeast of the study area. The proposed Informer model best agreed with the observed yield for multiple testing years, while the  
Figure 5: Scatter plots of reported yields vs. predicted yields of (**a1–a4**) LASSO, (**b1–b4**) RF, (**c1–c4**) XGBoost, (**d1–d4**) AttLSTM, and (**e1–e4**) Informer in 4 testing years: (1) 2013; (2) 2014; (3) 2015; (4) 2016.  
LASSO performed worst, especially in the high-yield area. The relative error maps of the predicted rice yield in all testing years are shown in Figures 7 and S3. Comparing all five models, the Informer model outperformed the other models in multiple test years, such as 2012, 2014, 2015, and 2016, indicating that the Informer model can effectively improve the performance of yield prediction. In contrast, the LASSO model performed the worst, especially in 2010 and 2015, compared with the other models. The three baseline methods, RF, XGBoost, and AtLSTM, were more advantageous than the linear LASSO but not as well as the Informer model.
# A Linear Strong Constraint Joint Solution Method Based on Angle Information Enhancement
###### Abstract  
High-precision indoor positioning technology is increasingly prominent in its application value in emerging fields such as the Industrial Internet of Things, smart cities, and autonomous driving. 5G networks can transmit large-bandwidth signals and have the capability to transmit and receive signals with multiple antennas, enabling the simultaneous acquisition of angle and distance observation information, providing a solution for high-precision positioning. Differences in the types and quantities of observation information in complex environments lead to positioning scenarios having a multimodal nature; how to propose an effective observation model that covers multimodal scenarios for high-precision robust positioning is an urgent problem to be solved. This paper proposes a three-stage time-frequency synchronization method based on group peak time sequence tracing. Timing coarse synchronization is performed through a group peak accumulation timing coarse synchronization algorithm for multi-window joint estimation, frequency offset estimation is based on cyclic prefixes, and finally, fine timing synchronization based on the primary synchronization signal (PSS) sliding cross-correlation is used to synchronize 5G signals to chip-level accuracy. Then, a tracking loop is used to track the Positioning Reference Signal (PRS) to within-chip accuracy, obtaining accurate distance information. After obtaining distance and angle information, a high-precision positioning method for multimodal scenarios based on 5G heterogeneous measurement combination is proposed. Using high-precision angle observation values as intermediate variables, this algorithm can still solve a closed-form positioning solution under sparse observation conditions, enabling the positioning system to achieve good positioning performance even with limited redundant observation information.  
5G; synchronization; fine tracking; fusion positioning solution +
Footnote †: journal: Applied sciences  
Article