esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Approach of GPS/GALILEO RT PPP with Ionospheric Constraint
### RT Ionosphere Products and Post-Processing GIM Products  
The RT ionospheric products using spherical harmonic expansions are broadcasted with an updating rate of 60 s in CLK92/CLK93 RT stream from the CNES caster [3]. A spherical harmonic expansion allows a global and continuous model of the ionosphere, but can also be applied to regional representation [3]. Based on these products and ionospheric mapping function, the slant TEC of each satellite can be used for positioning in real time. In contrast to RT ionospheric products, the post-processing GIM products from CODE agency are maps that contain a globally-distributed grid [16]. The spatial resolution of latitude and longitude is 2.5\({}^{\circ}\) and 5\({}^{\circ}\) in these maps, respectively, and the map is updated at an interval of one or two hours [16]. When the GNSS users obtain the vertical total electron content value \(VTEC\) from the ionosphere products, the slant ionosphere delay can be computed as follows:  
\[I_{product}^{sys,s}=m\cdot\left(40.3\cdot\frac{VTEC}{f_{1}^{2}}\right) \tag{6}\]  
\[m=\cos^{-1}\left(\arcsin\left(\frac{R_{E}}{R_{E}+H}\cdot\sin z\right)\right) \tag{7}\]  
where \(I_{product}^{sys,s}\) is the ionospheric delay of the pseudo-observable, \(m\) is the ionospheric mapping function as expressed in Equation (7), \(z\) is the zenith angle from the satellites to the receiver, \(R_{E}\) is the radius of the Earth (m) and \(H\) is the height of the ionosphere shell (m), where the value of \(H\) is 450 km for the products of CNES and CODE agency. Since the post-processing GIM products form CODE agency, that are computed by the stations distributed globally, has the highest accuracy (about 2\(\sim\)4 TECU) compared with those of other agencies [17], we use the post-processing GIM product as a reference to assess the RT ionosphere products. Figure 1 shows the slant TEC of GPS/GALILEO satellites for stations GMSD on day of year (DOY) 045, 2018. The slant TEC variation of RT ionosphere products and post-processing GIM products are quite consistent for a whole day, but there are obvious offsets with different products at some times, and the maximum offsets can be up to about 10 TECU, which is equivalent to a range error of about 1.6 m of the \(f_{1}\) frequency of GPS. If the imprecise and unreliable variances of ionospheric constraint are used, these offsets have a negative impact on the accuracy of positioning.
# Privacy-Friendly Datasets of Synthetic Fingerprints for Evaluation of Biometric Algorithms
## 2 Background and Related Work
### Data-Driven Fingerprint Generation
#### 2.2.1 Basics of GAN  
GANs were originally introduced in [18]. GANs belong to the family of deep neural networks as they consist of two deep neural networks called generator (\(G\)) and discriminator (\(D\)). In traditional GANs, \(G\) obtains a random latent vector and produces a synthetic sample that should resemble the training samples, while \(D\) decides whether the given instance is original or synthetic. The training of \(G\) and \(D\) is done interchangeably based on an objective function. Here, the objective of \(G\) is to produce synthetic instances that are able to fool \(D\). The objective of \(D\) is to make no errors in discriminating between real and synthetic instances. The weights of \(G\) and \(D\) are updated via back propagation.  
An input of \(G\) is a random latent vector \(Z\) sampled from the uniform or normal distribution. Given \(Z\), \(G\) generates a synthetic instance. During GAN training, \(Z\) is not controlled. After training, the latent space can be explored by modifying inputs and observing the changes in outputs. For producing more diverse outputs, the authors of [5] propose to add noise.  
The role of \(D\) is in guiding \(G\) to produce more realistic data. \(D\) acts as a traditional binary classifier. It obtains either a training sample (labeled as real) or a synthetic sample generated by \(G\) (labeled as fake). The outcome of \(D\) is the adversarial loss used to adapt both \(G\) and \(D\). After the GAN training is finished, \(D\) is dropped and \(G\) is used as a standalone tool.  
In the initial work [18], \(G\) contains only fully connected layers. As suggested in [19], the original GAN struggles to process high dimensional data such as images because \(D\) always dominates the scene. In order to work with images, the authors of [20] proposed to use deep convolutional neural networks (DCNNs) in the GAN architecture. Recently, NVIDIA research group proposed several advanced architectures such as PGGAN [4], StyleGAN [5], StylaGAN2 [6], and StylaGAN3 [21] that still dominate the field.  
In order to control the GAN generated output, the conditional GAN (cGAN) architecture is introduced in [22]. Instead of feeding a random latent vector to the generator, the GAN is fed with some meaningful information called \"condition\". This is how GAN is guided to generate specific data. CycleGAN [23] and pix2 pix [24] are two popular and effective cGAN architectures for image-to-image translation.
# Present and Future Marine Microwave Satellite Missions in China
## 2 Development of Marine Satellites in China
### Progress History of China's Marine Dynamic Environmental Satellites
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Parameter** & & & **Value** & & \\ \hline Frequency (GHz) & 6.6 & 10.7 & 18.7 & 23.8 & 37.0 \\ Polarization & V H & V H & V H & V & V H \\ Scan width & & & 1600 km & & \\ Footprint size(km) & 100 & 70 & 40 & 35 & 25 \\ Sensitivity (K) & \textless{}0.5 & \textless{}0.5 & \textless{}0.5 & \textless{}0.5 & \textless{}0.8 \\ Dynamic range & & & 3–350 K & & \\ CAL precision & & 1 K (180\(\sim\)320 K) & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Main parameters of the HY-2 RM. successfully launched on 21 September 2020 and 19 May 2021, respectively [[[http://www.nsoas.org.cn](http://www.nsoas.org.cn)]([http://www.nsoas.org.cn](http://www.nsoas.org.cn))]. At the present time, China has constructed the first batch of its marine dynamic environmental satellite constellations. The joint observations of three satellites were adopted. The observational efficiency of the sea surface wind fields is detailed in Figure 1. The sea surface wind field observation range from the HY-2 series satellites reaches nearly 70% of the global sea area in a three-hour period; 86% in six hours; and 95% in 12 h. It is considered that if combined with the Meteorological operational satellite (MetOp) satellite, the wind field observational effectiveness will be further improved. Coupled with the OceanSat-3 equipped with scatterometer to be launched by (Indian Space Research Organisation) ISRO in 2022, the wind field observation efficiency will be further improved. The FY-3E satellite, launched by the National Satellite Meteorological Center (NSMC) of the China Meteorological Administration on 5 July 2021, carries the world's first dual frequency and dual polarization conical scanning radar, which obtains high-precision wind field measurements through on-board internal calibration and on orbit active external calibration [30]. At present, the satellite is in orbit undergoing testing. FY-3E wind radar works in C and Ku bands at the same time, which can ensure the high-precision measurement of low and medium wind speed field, but also measure the high wind speed wind field of typhoon level. The sea surface wind field data obtained by FY-3E wind radar is worth looking forward to. Similarly, the observation efficiency of radar altimeter using the HY-2 constellation is analysed in this paper. Figure 2 details the maximum radius of at least one observation point in a circle if we randomly find a point on the earth and draw a circle around the point. The blue and orange curves in the figure show that the coverage of China's three altimetry satellites is slightly lower than that of the four international altimetry satellites. Figure 1: Global sea-area coverage of the dynamic satellite constellation scatterometer.
# A Two-Stage Co-Evolution Multi-Objective Evolutionary Algorithm for UAV Trajectory Planning
## 3 General Framework of MOUTP Based on TSCEA
### B-Spline Curve  
MOUTP yields a set of key trajectory points through the TSCEA, but some of the key trajectory points are widely spaced, and the fitted trajectory segments have a large folding angle with the trajectory segments, which is not in line with the maneuvering performance of the UAV. The UAV trajectory can be smoothed using the B-spline fitting method, which can fine-tune the trajectory in a specific region without affecting the overall trajectory. The order of the B-spline can be adjusted according to the actual needs of the UAV so as to balance the accuracy of the fitting and the computational efficiency. This is suitable for fitting the uneven distribution of the key trajectory points [26]. The mathematical expression of the UAV 3D m-order B-spline curve equation is as follows:  
\[\left\{\begin{array}{l}X(s)=\sum\limits_{i=0}^{m}X_{i}B_{i,m}(s)=X_{0}B_{0,m}( s)+X_{1}B_{1,m}(s)+\ldots+X_{m}B_{m,m}(s)\\ Y(s)=\sum\limits_{i=0}^{m}Y_{i}B_{i,m}(s)=Y_{0}B_{0,m}(s)+Y_{1}B_{1,m}(s)+ \ldots+Y_{m}B_{m,m}(s)\\ Z(s)=\sum\limits_{i=0}^{m}Z_{i}B_{i,m}(s)=Z_{0}B_{0,m}(s)+Z_{1}B_{1,m}(s)+ \ldots+Z_{m}B_{m,m}(s)\end{array}\right. \tag{11}\]  
where \(0\leq s\leq 1\), \(i=1,\ldots,m\); \(X_{i}\), \(Y_{i}\) and \(Z_{i}\) denote the position vectors of each key trajectory point; and \(B_{i,m}(s)\) is the \(m\)-times Bernstein basis function, also known as the B-spline segmentation mixing function, whose expression is as follows:  
\[\begin{array}{l}B_{i,m}(s)=\frac{1}{m!}\sum\limits_{j=0}^{m-i}(-1)^{j}.C_{m+ 1}^{j}(s+m-i-j)m\\ \\ =\frac{1}{m!}[(s+m-i)m-(m+1)(s+m-i-1)m+\ldots\\ \ldots+(-1)^{m-i}.C_{m+1}^{m-i}s^{m}]\end{array} \tag{12}\]  
where \(C_{m}^{n}=\frac{m!}{m!(m-m)!}\), \(C_{m}^{0}=1\).  
When \(m=3\), the basis function of the B-spline can be expressed as follows:  
\[\left\{\begin{array}{l}B_{(0,3)}(s)=\frac{1}{6}(-s^{3}+3s^{2}-3s+1)\\ B_{(1,3)}(s)=\frac{1}{6}(3s^{3}-6s^{2}+4)\\ B_{(2,3)}(s)=\frac{1}{6}(-3s^{3}+3s^{2}+3s+1)\\ B_{(3,3)}(s)=\frac{1}{6}s^{3}\end{array}\right. \tag{13}\]  
When the UAV key trajectory points are unevenly distributed, i.e., the key trajectory points are not equidistant from the key trajectory points, they can be further iteratively updated based on the following basis function:  
\[B_{i,1}(s)=\left\{\begin{array}{l}1,\ \ \tau_{i}\leq s\leq\tau_{i+1}\\ 0,\ \ \text{elsewise}\end{array}\right. \tag{14}\]  
\[B_{i,m}(s)=\frac{s-\tau_{i}}{\tau_{i+m-1}}B_{i,m-1}(s)+\frac{\tau_{i+m}-s}{
# Segmented Hybrid Impedance Control for Hyper-Redundant Space Manipulators
## 1 Introduction
* Introduction of an integrated object control strategy that actively manages the forces arising from the object mass and motion during manipulation, ensuring stable control under varying inertial conditions. * Presentation of a complete control architecture enabling HRSMs to wrap around, stabilize, and manipulate free-floating objects. Through mathematical modeling using [PERSON]'s method, we derive the complete dynamic equations governing the HRSM system. This theoretical foundation supports our control design and stability analysis, ensuring robust performance across various manipulation scenarios. Using Figure 1 as an illustration, the subsequent sections of this paper delve into the development and application of segmented hybrid impedance control. Here, the manipulator is tasked with tracking the force at the contact point of the second segment and exhibiting impedance behavior using joint 15 to joint 24. Conversely, joint 1 to joint 14 of the manipulator are solely focused on achieving object position control, obstacle avoidance and self-avoidance, thereby allowing meticulous movement of the object. The structure of this paper is as follows. Section 2 presents the system design of the segmented hybrid impedance control and outlines the system description of the HRSM. In Section 3, the dynamic equations of the HRSM are derived using [PERSON]'s method, alongside an introduction to a contact collision model. Section 4 introduces the segmented hybrid impedance control strategy, object stabilization system and real-time collision avoidance. Section 5 demonstrates the numerical simulation results and validates the goals of the control strategy concerning the contact state, behavior of intermediate joints, object behavior and collision avoidance. Finally, the concluding section summarizes the paper and draws conclusions.
## 2 System Model and Problem Formulation
### Problem Formulation
#### 2.2.2 Joint Delay Cost with Power Consumption Optimization  
In this subsection, we will give the joint optimization for time delay and energy cost. Due to the different impact of time and energy cost, we introduce a weight factor, denoted as \(\omega\), which indicates the emphasis on either time or energy cost. Thus, minimizing the time and energy cost of system can be specified as the following problem:  
\[P2:\;\;arg\ \min_{K}\bigl{(}T_{Aver}-T_{opt}\bigr{)}^{2}+\omega*P_{Sys} \tag{19}\] \[\text{s.t.}\ C_{b}\leq C_{bmax}\] \[\sum\
olimits_{f=1}^{K*C_{s}}f\leq F\] \[K=\left|\rho\right|_{1}\]  
The joint optimization problem is hard to solve, so we decouple \(\mathcal{P}2\) problem in two stages as follows:  
Stage 1: We consider the system power allocation is fixed, i.e., \(P_{MEC}\) is already known. We find that the optimization of \(P2\) can be decoupled into \(K\) sub-problems with regard to the number of active MECSs, and the series of problems \(G\{K\}\), in order to find the minimum delay cost for each sub-problem \(K\), which follows the solution for the \(P1\).  
Stage 2: In this stage, we focus on minimizing the energy cost in the JCO algorithm.  
```
1:Set \(T_{opt}=0\), \(P_{MEC}=0\), and \(T_{Aver}=0\)
2:while
3:Calculate \(T_{opt}\), \(P_{MEC}\), \(T_{Aver}\), \(P_{Sys}\), then set \(P2=P2\)
4: If (6) is satisfied for active a new MECS
5: If (8) is satisfied for inactive a new MECS
6: If \(\bigl{(}T_{Aver}-T_{opt}\bigr{)}>\theta\) then
7:\(G\{K\}=G\{K+1\}\),and use genetic algorithm minimum \(P1\)
8: else then
9: obtain \(P_{Sys}^{\prime}\), \(T_{Aver}^{\prime}\)
10: update \(P2\),and if \(\frac{P2}{T_{Aver}^{\prime}}>\delta\) then reset \(G\{K\}\)
11: End If
12: Until \(P2\) - \(P2<\varphi\), end while
13: Output \(T_{Aver}\),\(P_{Sys}\),\(K\), \(P2\)
```  
**Algorithm 1** Joint Cost Optimal (JCO) algorithm
## 3 Results
### Performance Metrics for ORR Prediction  
The model trained to predict ORR had a promising LOOCV performance with all four metrics: accuracy, AUC, F1 score, and MCC. The accuracy of predicting ORR was 75%. The AUC value (0.71) suggests a decent quality level of model performance. The F1 score (0.58) indicates that the model was feasible in predicting the treatment response, which was supported by the MCC value (0.40). In the confusion matrix for all 63 LOOCV ORR predictions can be seen a rather good sensitivity (0.58) and a high specificity (0.82) of the model (Figure 2). The false negatives (8/63 samples) were identified as the cases where the prediction model did not predict a presence of objective treatment response for a test dataset sample which was a true positive, i.e., CR or PR was present. The false positives (also 8/63 samples), on the other hand, were the cases where the model predicted the presence of CR or PR for the sample but the sample was a true negative, i.e., the response was SD or PD.
# Response of Global Terrestrial Carbon Fluxes to Drought from 1981 to 2016
## 5 Conclusions  
With the remote-sensing-driven BEPS model, this study investigated the responses of global carbon fluxes to drought, which were indicated by the maximum correlation coefficients between terrestrial carbon fluxes and SPEI, and by the sensitivity of terrestrial carbon fluxes to drought. The main findings were:  
1. The maximum correlation coefficients between terrestrial carbon fluxes and SPEI varied spatially. In southern North America, central Eurasia, East Asia, most of South America, most of Africa, and Australia, terrestrial carbon fluxes responded strongly to drought. In northern North America, northeastern Eurasia, the Tibetan Plateau, the Amazon Basin, and the tropical rainforest regions of Southeast Asia, the response of carbon fluxes to drought was weaker, and in abnormal wet years, carbon fluxes might decrease. Overall, with the increase in positive annual water balance, the response of carbon fluxes to drought became weaker.
2. The time scales at which the strongest responses of carbon fluxes to drought were discovered were various in the different regions. Drought had short-term lagged effects on carbon fluxes in arid and humid climate areas. In contrast, carbon fluxes in semi-arid and semi-humid areas responded to drought at relatively longer time scales.
3. The sensitivity of monthly carbon fluxes to drought differed spatially. In areas with a negative annual water balance, the sensitivity of terrestrial carbon fluxes in response to drought increased with increasing annual water balance. In areas with a positive annual water balance, there was a slight decrease trend with increasing annual water balance.  
All of the authors contributed significantly to this manuscript. Conceptualization, W.J.; data curation, Q.H. and W.J.; methodology, Q.H. and W.J.; writing--original draft, Q.H., W.J., and X.L. 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 (42141005, 41801075) and the Jiangsu University Philosophy and Social Science Research Fund (2018 SJ1611).  
**Institutional Review Board Statement:** Not applicable.
## 4 Bionic Morse Coding Method
### Information Modulation
#### 4.1.2 Step 2: Synchronization and Bionic Morse Elements Waveforms  
Identification of bionic Morse elements in a sea natural carrier is a crucial procedure in this proposed technique. These elements are used to encode and extract the covert information. All four elements and the synchronization signal should be different from each other with respect to time and frequency but lie within a same sound source. If an enemy detects the signal, it should assume it as a sea natural carrier due to its signal characteristics and imperceptibility with the real ocean noise. A whole communication signal including a synchronization header should be analogous with the noise present in the ocean.  
In this research we used a humpback whale song as a bionic carrier. The song phrase of whale has unique segments with respect to amplitude and frequency. Figure 3 shows the time frequency graph of all segments distinctly. It is visibly observed that each waveform is distinguishable and has a unique frequency that lies within the bandwidth of 4 kHz. One segment is used for synchronization and other segments represents bionic Morse elements termed as club, diamond, heart and spade.  
All the bionic Morse elements and synchronization signals should be non-identical to each other in this proposed mimicry communication technique. They should be dissimilar because they are used to represent covert information. Figure 4 shows the cross and auto correlation between synchronization signal and bionic Morse elements which proves that these waveforms are non-analogous to each other. In figure we represent C and S with correlation and synchronization signal and numbered 1,2,3,4 to club, diamond, hearts and spades respectively. In Figure 4 CSS means that it is an auto correlation of synchronization signal and C21 represents the cross correlation between diamond and club bionic Morse elements.  
Figure 4a-e shows the auto and cross correlation of a synchronization signal with bionic Morse elements. It can clearly witness the high correlation peak only in (a) which dictates that the synchronization signal is only identical to its signal and different from other bionic Morse elements. Similarly Figure 4f-o shows the auto and cross correlation between bionic Morse elements. We can notice that C11, C22, C33, and C44 have high correlation peaks which are not uniform in other correlation outputs. This proves that the waveforms of bionic Morse elements and synchronization signal are dis similar and they could be easily distinguishable at the receiver. The waveform can be decoded at the receiver through correlation technique. The demodulation process is briefly elaborated in Section 4.2.  
Figure 3: **(a)** Synchronization signal, (**b–e**) bionic Morse elements as club, diamond, heart and spades in time domain, and (**f**) shows single sided Fourier transform of synchronization signal and (**g–j**) bionic Morse elements respectively.
## 4 ITS Threats Analysis and Classification
### Classification of ITS Attacks and Cryptographic Countermeasures
#### 4.4.4 Attacks on Confidentiality and Countermeasures  
The confidentiality of ITS messages can be required by some specific applications, for example to provide secure toll payments and Internet services by encrypting the messages transmitted between vehicles and RSUs [18]. However, if the exchanged messages do not contain any sensitive information (e.g., ITS safety messages), the confidentiality is not necessary [47, 79]. Several attacks can affect the network during the absence of confidentiality protection mechanisms [51]. In the following, several examples of these attacks are described briefly with their countermeasures.  
* Eavesdropping attacks: An eavesdropping attack affects only the network confidentiality and does not impact the network resources and availability [18]. This kind of attack enables the attacker to extract sensitive information from the transmitted packets, such as the location information of the vehicles. To provide resistance against this kind of attack, all sensitive data that have crucial importance should be encrypted so as to ensure the privacy of the involved ITS entities and their communications;
* Data interception attacks: This attack affects the user privacy in addition to confidentiality. This attack is dangerous and consists of listening to the network for a certain duration and then tries to analyze the collected traffic data to extract the maximum amount of useful information. The same countermeasure that permits providing resistance to eavesdropping can be adopted to resist traffic analysis attacks;
* Brute force attacks: The brute force attack can be performed against the confidentiality of exchanged messages or the authentication process. For example, this attack can reveal the network identifier of the vehicle by using an extensive dictionary search approach. However, due to the dynamic nature of the ITS network, connection times are relatively short, and hence, a brute force attack is not easy to conduct, since it is time consuming and resource intensive. Furthermore, this attack can become harder when using stronger encryption and key generation algorithms [51].
# Symbol Error-Rate Analytical Expressions for a Two-User PD-NOMA System with Square QAM
## 4 BER and SER for PD-NOMA Square QAM Signal  
Now, let us derive analytical expressions for SER and BER against the \(E_{s}/N_{0}\) ratio in the PD-NOMA scenario. We consider that the first layer receives the largest power portion \(p_{1}\) of the total transmit power \(E_{s}\), and the second layer receives the remainder \(p_{2}=E_{s}-p_{1}\). According to the SIC, the first layer is decoded in the first instance, considering that the second layer and AWGN are interferences. The second layer is decoded under the conditions of AWGN only after the perfect interference cancellation of the first layer; however, the incorrect cancellation causes the additional signal corruption. Further, we describe the signal decoding process in both PD-NOMA layers and get BER and SER expressions for them.