esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comprehensive Real-Time Traffic Map for Geographic Routing in VANETs
## 4 The Proposed Solutions
### CRT Map Based Routing  
In this section, we describe how to make routing decisions based on the CRT Map. Each vehicle is aware of not only street organizations, provided by the traditional digital road map, but also the real-time traffic density information. Once a relaying vehicle reaches an intersection, it chooses a connected road segment and forwards the delivery packet on it until the next intersection. The process is repeated until the packet reaches its destination. Here we design an evaluation function \(Score()\), to give each connected road segment a score and select the one with the highest score to forward packets. In the evaluation of a connected road segment, \(r_{c}\), we consider not only its traffic density and distance to the destination, but also the following road segments. We make each of the road segments contribute to the final score of \(r_{c}\), based on their traffic densities, reliability of traffic density, road lengths, and distances to the destination. We make a road segment further away from \(r_{c}\) to contribute less. It is because the traffic has rapidly changed and may become different when it is actually delivered to the road. Note that, the shortest path from \(r_{c}\) to the destination is assumed when evaluating \(r_{c}\). It does not mean that the shortest path is actually the delivery path, but rather a way of evaluation. In addition, all necessary information can be extracted locally from the CRT Map and the digital road map of the relaying vehicle. Hence, a local decision can be made without extra cost and delay. A flowchart of CBR is shown in Figure 5.  
Below, we formally define the evaluation function \(Score()\). Let \(v_{i}\) be the relaying vehicle at an intersection and \(r_{c}\) is one of the connected road segments of the intersection. When evaluating \(Score(r_{c})\)  
Figure 5: A flowchart of CRT Map Based Routing (CBR).  
\(v_{i}\) first calculates the shortest path \(\left\{r_{c_{1}},\ r_{c_{2}},\cdots,r_{c_{k}}|r_{c_{1}}=r_{c},r_{c_{j}}\in R,\ 1\leq j\leq k\right\}\), where \(r_{c_{1}},\ r_{c_{2}},\cdots,r_{c_{k}}\) are the consecutive road segments started at \(r_{c}\) and the destination vehicle is located at \(r_{c_{k}}\). The total length of the shortest path started at \(r_{c}\) is denoted as \(PL_{r_{c}}=\sum_{j=1}^{k}L_{c_{j}}\), then \(Score(r_{c})\) is defined as:  
\[Score(r_{c})=\sum\
olimits_{j=1}^{k}\left(D_{c_{j}}^{i}\times P_{c_{j}}^{i} \times\frac{L_{c_{j}}}{PL_{r_{c}}}\right)\times\left(\frac{\sum_{j=j+1}^{k}L_{ c_{l}}}{PL_{r_{c}}}\right) \tag{3}\]
# Effect of Nonlinear Hysteresis Details of Isolation System on In-Structure Response Spectra
## 2 Consideration When Developing ISRS of Fixed and Isolated Structures
### ISRS of Seismically Isolated Structure Through Equivalent Linear and Nonlinear Analysis  
Because isolated structures normally behave nonlinearly due to the bilinear type of hysteresis behavior of isolators, nonlinear analysis needs to be performed to analyze the dynamic response. However, due to the complexity of the nonlinear model and the computing resource required for nonlinear analysis, elastic analysis is sometimes performed by replacing it with an equivalent linear isolator model. The accuracy of linear analysis using the equivalent linear model has been confirmed through many studies [25; 26; 27; 28; 29; 30], and equivalent linear models are more efficient in calculating the member forces of the structure because the equivalent models predict the maximum force of nonlinear models comparably accurately [30]. In terms of the ISRS, as can be seen in Figure 2, when a linear analysis is performed using an equivalent linear model, the peak of the ISRS occurs only at the isolation frequency. In addition, because the entire structure behaves as a rigid body in the elastic region, all floors show almost the same response. However, in the case of nonlinear model, as the isolators began to behave nonlinearly beyond the yield point, an additional peak occurred at frequencies other than the isolation frequency, and it can be seen that all floors show different responses. The equivalent linear analysis is more efficient for calculating the structural force response, but when evaluating the floor acceleration response, it may not be suitable for evaluating the ISRS because the response at all floors is the same and the peak occurs only at the isolation frequency. Therefore, in this study, a nonlinear analysis was performed to analyze the ISRS variability according to variables that may affect the hysteresis behavior.  
Figure 1: The ISRS derived from analysis and the design ISRS with broadened frequency (ASCE 4-16).
# Radiometric Cross-Calibration of GF-1 PMS Sensor with a New BRDF Model
## 5 Discussion
### Total Uncertainty of Cross-Calibration Using MODIS as Reference Sensor  
The total uncertainty of cross calibration is summarized in this part. There are five influence factors in cross-calibration.  
1. SBAF: In some cases, ground-measured parameters such as spectral reflectance, surface meteorological range, and water vapor column are not available for most cross calibration. Therefore, empirical ground-measured parameters could be used in cross calibration. In this article, the ground measured parameters of Dunhuang are used to calculate the SBAF coefficient of Golmud. The uncertainty caused by SBAF was 4.85%, 0.67%, 1.06%, and 1.59%, respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Influence Factor** & **Blue** & **Green** & **Red** & **NIR** \\ \hline Ground spectrum & 4.79\% & 0.67\% & 1.05\% & \(-1.40\%\) \\ Meteorological range & \(-0.03\%\) & \(-0.04\%\) & \(-0.06\%\) & \(-0.75\%\) \\ Water vapor column & \(-0.76\%\) & 0.01\% & \(-0.14\%\) & \(-0.02\%\) \\ Aerosol type & 0.06\% & \(-0.03\%\) & \(-0.01\%\) & \(0.04\%\) \\ Total uncertainty of SBAF & 4.85\% & 0.67\% & 1.06\% & \(1.59\%\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Total uncertainty caused by ground spectrum, atmosphere, and aerosol type.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type** & **Blue** & **Green** & **Red** & **NIR** \\ \hline Simulated TOA reflectance & 0.2535 & 0.2623 & 0.3116 & 0.3615 \\ Measured TOA reflectance & 0.2520 & 0.2590 & 0.3080 & 0.3650 \\ Relative error & \(-0.60\%\) & \(-1.27\%\) & \(-1.16\%\) & \(0.97\%\) \\ \hline \hline \end{tabular}
\end{table}
Table 8: Relative error between simulated TOA reflectance calculated by BRDF and measured TOA reflectance calculated by official calibration coefficients.  
Figure 7: Spectra used for SBAF calculation, where Dunhuang is raw spectrum and sand is new spectrum.  
2 BRDF model: The construction of BRDF model in this paper is based on time-series of MODIS image data and has high accuracy. The uncertainty of BRDF model is \(-0.60\%\), \(-1.27\%\), \(-1.16\%\), and 0.97%, respectively.
3 Radiative transfer model: The simulation software MODTRAN is used in this paper, which might have some effects on the TOA reflectance, and the uncertainty of MODTRAN is about 1%.
4 Reference sensor: MODIS and Landsat 8 OLI were used as reference sensor in this paper. The uncertainty of the MODIS and OLI on board calibration was within 2%, which was the basic uncertainty for the PMS1 cross-calibration.
5 Image uncertainty: The image uncertainty mainly comes from geometric mismatching between PMS image and reference image. In our study, the DNs were mean value of homogenous areas, so the image uncertainty of geometric mismatching was within 0.5%.  
Table 9 shows the uncertainties caused by various factors and the total uncertainties. The total uncertainties are calculated by square root methods, which are 5.40%, 2.70%, 2.78%, and 2.95% of four bands, respectively.
# Finite Element Analysis of Thermal Stress and Thermal Deformation in Typical Part during SLM
###### Abstract  
Selective laser melting (SLM) constitutes an additive manufacturing (AM) method. Many issues such as thermal strain and macro-thermal deformation, which are caused by the thermal stress of different process parameters, are not clear. In this paper, an efficient and fast manufacturing simulation method was researched based on a moving heat source model and an elastoplastic theory of welding simulation, which was studied based on the thermodynamic coupling algorithm with a software-developed application for the SLM process. Subsequently, typical case results of thin and hollow plate part formation and the corresponding performances were simulated in detail. The results demonstrated that the effective thermal stress increased as the layer height increased from the surface layer to the substrate, while the thermal strain followed an approximate change rule. In addition, the stress was released from the underlying substrate when the support was removed. Moreover, the largest single axis plane stress was changed from tension to compression from the edge to the center, finally reaching equilibrium. In particular, maximum macro thermal deformation occurred at the printed support structure to the samples, displaying similar results in other locations such as the corners. Finally, the effectiveness of the simulation could be verified from the realistic printed part, which could provide proof for the quality prediction of the part that is actually forming.  
additive manufacturing; moving heat source; elastoplastic theory; thermodynamic coupling algorithm; experimental verification Article
# Multi-Objective Gate Allocation Problem Based on Multi-Commodity Network Flow Model
## 2 Literature Review
[PERSON], [PERSON], and [PERSON] [36] established the quadratic objective function. They used the adaptive search algorithm to solve it, which reduced the towing cost and improved the robustness of the aircraft to a certain extent. [PERSON] et al. [37] proposed an adaptive improved genetic algorithm combining parallel advantages and elite strategies to improve the decision-making ability of large-scale handling data on the real airport surface. Some studies guarantee the interests of airlines by reducing the waiting time and consumption cost [38; 39; 40], and passengers' overall satisfaction in the waiting process is also a part of the consideration [41]. During the process of surface control, the gate allocated to the aircraft will be temporarily busy, so the aircraft and passengers need to spend time waiting on the ground until the corresponding gate is free. Moreover, some scholars also consider more practical effects, such as aircraft fuel consumption [21].
# Analysis of GNSS Displacements in Europe and Their Comparison with Hydrological Loading Models
## 1 Introduction
The first method is based on a WLS fit using an optimized covariance matrix obtained from an MLE (maximum likelihood estimation) of a PL noise model [36]. The interannual signal is considered as time-correlated noise by the estimator and is directly propagated in the parameter uncertainties given by the WLS. The second method is a WLS method where only white noise is accounted for in the covariance matrix, but in which we modified the classic deterministic model (linear velocity and seasonal signal) to account for interannual variation as polynomials. Adding polynomials in the estimated model should reduce the parameter uncertainties given by WLS as it should better fit the data. However, given these polynomials, we can compute the instantaneous velocity and deduce a statistical uncertainty on the linear velocity, which reflects the variability of the instantaneous velocity around its average value. This method should provide a more realistic empirical velocity uncertainty than that directly given by the WLS in the case which is known to be far too small when only WH noise is accounted for [15; 23]. Then, the fundamental difference between these two approaches (respectively, WLS + MLE and WLS + polynomials + WH) is the presumed nature of the interannual variation (respectively, stochastic and deterministic) and, consequently, the estimation of the parameter uncertainties. Moreover, we highlight the need to estimate both draconitic and solar periods to prevent biasing estimates of the annual cycle. Finally, in order to search for the true geophysical signals in interannual variations, the extraction of spatial common modes from the signal is usually carried out [37]. This can be conducted using different techniques: principal component analysis (PCA) [38; 39; 40], independent component analysis (ICA) [41], or robust statistical methods [42]. Since the sources are not necessarily independent, it is reasonable to choose PCA in order to compare the interannual signal from GNSS with the signals derived from hydrological models, GRACE (Gravity Recovery and Climate Experiment), and the GRACE Follow-On time variable gravity field [43]. Although recent studies on interannual variations show correlations between GNSS and some global hydrology models [44], we show here that models can also differ significantly from each other and that the interannual signal from GNSS should be interpreted very carefully.
# Multiscale Content-Independent Feature Fusion Network for Source Camera Identification
## 2 Methodology
### MCIFFN Structure
#### 2.1.1. Squeeze and Excitation (SE)  
The convolution kernel, as the core of the CNN, is typically used to aggregate spatial information and channel-wise information in a local receptive field and finally obtain global information. A convolutional neural network is composed of a series of convolution layers, nonlinear layers, and down-sampling layers. These layers capture the image features from the global receptive field to describe the image. However, it is very difficult to learn a network and exhibit strong performance. SENet starts from the relationship between feature channels, hoping to explicitly model the interdependence between feature channels. In addition, instead of introducing a new spatial dimension to fuse feature channels, it adopts a new \"feature recalibration\" strategy. Specifically, it automatically obtains the importance of each feature channel through learning, enhances the key features, and suppresses the useless features according to importance. Generally, it allows the network to use global information to selectively enhance useful feature channels and suppress useless feature channels to realize the adaptive calibration of feature channels. Squeeze-and-Excitation is shown in Figure 3.  
Figure 3 illustrates the working principle of the SE module. Given an input \(x\), the number of characteristic channels is \(C_{1}\). After a series of convolutions and other general transformations, a feature with the number of characteristic channels \(C_{2}\) is obtained. Different from the traditional CNN, the following three operations are used to recalibrate the previous features:  
* The first is the squeeze operation, which compresses the features along the spatial dimension, turning each two-dimensional feature channel into a real number that has a global receptive field to some extent. The output dimension matches the input feature channel number. It represents the global distribution of the response on the feature channel and makes the layer close to the input while also obtaining the global receptive field, which is very useful in many tasks.  
Figure 3: A Squeeze and Excitation block.  
Figure 2: The schema of the MCIFFN structure.  
The second is the exception operation, which is similar to the gate mechanism in recurrent neural networks. A parameter \(W\) is used to generate weights for each feature channel, where the parameter \(W\) is learned to explicitly model the correlation between feature channels.
* The last is a reweight operation, which regards the weight of the output of exception as the importance of each feature channel after feature selection and then weighs the previous feature channel by channel through multiplication to complete the recalibration of the original feature on the channel dimension.
# A Low-Overhead Countermeasure against Differential Power Analysis for AES Block Cipher
### Existing Solutions with Respect to Side-Channel Attacks and Their Countermeasures
The method employs logic gates with power consumption, which is independent of the data signals, and therefore, the technique removes the foundation for DPA. **Additional security issues with respect to commercial devices. ** Cryptography devices are extremely vulnerable when it comes to industrial applications. DPA attacks could lead toward monetary losses, data integrity and sensitive data leakage. These security threats have a great impact on the national or community level if we consider the broad spectrum of users. We present two different case studies that express the true need for the countermeasure and it leads toward the shortcoming as described in the forthcoming section. Case-study#1: In [30], [PERSON] first provided the hardware demonstration of DPA attacks on a smart card. He analyzed the power traces of the smart card and then revealed the key of encrypted data. This attack embarked on a new career of security threats, which led to different countermeasures. Case-study#2 is a recent example of the side-channel attack on AMD (advanced micro devices) processors. The side-channel attack provides access to a malicious application on the attacked system to exploit the hidden flaws inside the CPU (central processing unit). Their attack makes use of memory associated with apps (means applications) to obtain sensitive information, e.g., encryption keys, and passwords. They leverage power and timing measurements of prefetch instructions. Their analysis highlights the greater leakage of AMD processors as compared to a prefetch-based attack on Intel processors [31].
# Dynamic Graph Representation Learning for Passenger Behavior Prediction
## 3 Preliminaries
### Problem Formalization  
Passenger Behavior Prediction with a dynamic graph aims to predict the subway station that passenger \(u_{i}\in\mathcal{U}\) will enter or leave in the future. Specifically, given the passenger node \(u_{i}\), and the historical passenger-station interaction sequence before \(t_{k}\), i.e., \(\mathcal{G}^{t_{k}}\), we solve such a problem by predicting whether \(u_{i}\) will pass through station \(s_{j}\in\mathcal{S}\) at timestamp \(t_{k}\). The process can be formalized as below  
\[\hat{p}_{u_{i},s_{j}}^{t_{k}}=f(\mathcal{G}^{t_{k}})\]  
where \(\hat{p}_{u_{i},s_{j}}^{t_{k}}\) is the probability that passenger \(u_{i}\) will pass through the target interaction station \(s_{j}\) at time \(t_{k}\).
## 2 Data and Study Area
### Remote Sensing Data  
The China Environment Satellite is an Earth observation, small satellite constellation containing three satellites (HJ-1A, HJ-1B and HJ-1C) for environment and disaster monitoring. The technical specifications of the on-board cameras on the HJ-1A and HJ-1B satellites are listed in Table 2. Images and orbital characteristics are available on the web page of the China Centre for Resources Satellite Data and Applications (CRESDA) [33].  
\begin{table}
\begin{tabular}{l l  
HJ-1A/B charge-coupled device (CCD) images were acquired for late-March and early May for both 2010 and 2011 based on the winter crop phenology. Seven images were needed to cover the whole study area, and a total of 28 cloudless images were collected. Image processing, which included geo-correction, radiance calibration and atmospheric correction, used the ENVI 4.8 software. An average root mean square error (RMSE) of 0.87 pixels was achieved for these images. NDVI images for each CCD image were generated and mosaicked into a single CCD NDVI image for the entire study area. Based on the acquisition dates, a total of four CCD NDVI images for the NCP study area were generated for late-March and early May in 2010 and 2011.  
Time series of 16-day composite MODIS NDVI products and reflectance products from Terra and Aqua satellites at 250-m resolution were acquired for October to June for both 2010 and 2011 using NASA's Reverb tool [34]. Five tiles (h26v04, h26v05, h27v04, h27v05 and h28v05) were used for crop condition assessment.
## 4 Statistical Properties
### The Identifiability Property  
The IP is a very worthwhile statistical property that ensures precise inference. In this subsection, we calculated the IP of the NGLog-X family of the distributions. Let \(\delta_{1}\) and \(\delta_{2}\) be the two additional parameters having the CDFs \(G(x;\delta_{1},\Theta)\) and \(G(x;\delta_{2},\Theta)\). The parameter \(\delta\) is identifiable if \(\delta_{1}=\delta_{2}\). Mathematically, the IP is derived as:  
\[G(x;\delta_{1},\Theta)=G(x;\delta_{2},\Theta) \tag{25}\]  
Inserting Equation (7) in Equation (25), we obtain  
\[\frac{e^{\delta_{1}}\;F(x;\Theta)}{\big{(}e-\log F(x;\Theta)\big{)}^{\delta_ {1}}}=\frac{e^{\delta_{2}}\;F(x;\Theta)}{\big{(}e-\log F(x;\Theta)\big{)}^{ \delta_{2}}} \tag{26}\]  
After simplifying Equation (26), we obtain  
\[e^{\delta_{1}}\;F(x;\Theta)(e-\log F(x;\Theta))^{\delta_{2}}=e^{\delta_{2}}\; F(x;\Theta)(e-\log F(x;\Theta))^{\delta_{1}}\]  
\[e^{\delta_{1}}\;(e-\log F(x;\Theta))^{\delta_{2}}=e^{\delta_{2}}\;(e-\log F(x; \Theta))^{\delta_{1}} \tag{27}\]  
By taking logarithm of Equation (27), we obtain  
\[\delta_{1}+\delta_{2}\log(e-\log F(x;\Theta))=\delta_{2}+\delta_{1}\log(e- \log F(x;\Theta))\]  
\[\delta_{1}(1-\log(e-\log F(x;\Theta)))=\delta_{2}(1-\log(e-\log F(x;\Theta))) \tag{28}\]  
From Equation (28), we observed that \(\delta_{1}=\delta_{2}\). Hence, the parameter, \(\delta\), is identifiable.