esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparison Analysis on the Accuracy of Galileo PPP Using Different Frequency Combinations in Europe
###### Abstract  
The Galileo constellations are characterized by transmitting GNSS signals on multi-frequencies, which can benefit the robustness and accuracy of the solutions. However, the dual-frequency E1/E5a combinations are generally used for precise point positioning (PPP). In this paper, the performance of Galileo static and kinematic PPP using different dual- and multi-frequency combinations are assessed using observations from the European region. Overall, the accuracy of daily PPP achieved by the dual-frequency GPS, Galileo, and BDS is better than 5 mm in the horizontal direction and better than 10 mm in the vertical direction. Though the number of observed Galileo satellites is less than GPS, the horizontal accuracy can reach 1.6 mm/2.3 mm/5.7 mm on North/East/Up component, which is improved by 59.0% and 12.3% compared to the GPS in the north and up direction. Then, the accuracy of Galileo static PPP is analyzed using different dual- and multi-frequency combinations. Results indicate that the Galileo E1/E5b PPP can degrade the accuracy due to the inter-frequency clock biases between the E1/E5a and E1/E5b combinations. Best accuracy can be achieved for the triple- and four-frequency PPP, which is 4.8 mm in the up direction. The hourly accuracy for the static PPP can reach 5.6 mm/9.2 mm/12.6 mm in the north/east/up direction using the GPS/Galileo/GLONASS/BDS combinations. Finally, a positioning convergence ratio (PCR) indicator, which represents the accuracy of PPP over a period, is used to analyze the convergence time of kinematic PPP. Results indicated that the multi-frequency Galileo observations contribute minorly to the convergence of kinematic PPP. However, Galileo shows the best convergence performance for the single GNSS positioning, and the GPS/Galileo combined PPP achieved the best performance for the PPP using different GNSS combinations.  
Galleo; multi-frequency; precise point positioning (PPP); hourly PPP +
Footnote †: journal: Applied Sciences  
1
## 4 Numerical Examples
### Validation on Homogeneous Layered Velocity Model
#### 4.1.1 Ablation Experiments on the Homogeneous Layered Model
The incorporation of cosine loss encourages the model to learn distinctly different directional features for the upgoing and downgoing waves, thereby improving the separation accuracy. The results demonstrated significantly enhanced wavefield separation with cleaner separation outcomes. Figure 9: Comparison of separation results for homogeneous-layer VSP records under three conditions. (**a–d**) show the knowledge representation of upgoing waves and the separated upgoing wavefields in three conditions. (**e–h**) show the knowledge representation of downgoing waves and the separated downgoing wavefields in three conditions.
###### Abstract  
To improve the accuracy of predictions and simplify the difficulty with the algorithm, a simplified empirical model is proposed in developing a long-term predictive approach in determining the ionosphere's F2-layer critical frequency (\(f_{o}\)F\({}_{2}\)). The main distinctive features introduced in this model are: (1) Its vertical incidence sounding data, which were obtained from 18 ionosonde stations in east Asia between 1949 and 2017, used in reconstructing the model and verification; (2) the use of second-order polynomial and triangle harmonic functions, instead of linear ones, to obtain the relationship between the seasonal vs. solar-cycle variations of \(f_{o}\)F\({}_{2}\) and solar activity parameters; (3) the flux of solar radio waves at 10.7 cm and sunspot number are together introduced in reconstructing the temporal characteristics of \(f_{o}\)F\({}_{2}\); and (4) the use of the geomagnetic dip coordinates rather than geographic coordinates in reconstructing the spatial characteristics of \(f_{o}\)F\({}_{2}\). The statistical results reveal that \(f_{o}\)F\({}_{2}\) values calculated from the proposed model agree well with the trend in the monthly median statistical characteristics obtained from measurements. The results are better than those obtained from the International Reference Ionosphere model using both the CCIR and URSI coefficients. Furthermore, the proposed model has enabled some useful guidelines to be established for a more complete and accurate Asia regional or global model in the future.  
Keywords:ionosphere; F2-layer critical frequency; simplified regional model; long-term prediction; east Asia +
Footnote †: journal: Applied Physics Letters B
# SCATTER PHY: An Open Source Physical Layer for the DARPA Spectrum Collaboration Challenge
## Appendix A Two-Stage Detection Algorithm
### Step # 3: Detection of the Synchronization Sequence  
Finally, after the calculation of the noise-power reference, \(Z_{\text{REF}}\), and the synchronization sequence detection threshold level, \(\alpha\), the decision about whether the synchronization sequence is present or not is evaluated through the following hypothesis testing:  
\[\text{PDP}(i)\geq\alpha Z_{\text{REF}},\forall\;i=0,1,\cdots,N_{\text{ Synch}}-1. \tag{10}\]  
If the hypothesis testing is true, then the synchronization sequence plus noise hypothesis, \(H_{1}\), is selected. Otherwise, if the hypothesis testing is false, then the noise-only hypothesis, \(H_{0}\), is selected, and consequently, the received signal buffer is proclaimed as not having the presence of the synchronization sequence.
# Towards Mitigating Jellyfish Attacks Based on Honesty Metrics in V2X Autonomous Networks
## 4 Secure and Efficient Routing Protocol for Jellyfish Attack Prevention in V2X (SecE-V2X)
### Honesty Metrics
#### 4.2.3 Packet Reordering  
To deal with Jellyfish reordering attacks and capture a packet's displacements from their original locations, this metric is defined as the percentage of packets that are late relative to their predicted position, as determined by the received index. The packet reordering, \(PR\), is calculated using Equation (3), proposed in [49]:  
\[\left\{\begin{array}{l}{PR=\sum\limits_{i=i+1}^{i=D}RD[i]}\\ {D[j]=RI[j]-AS[j]}\end{array}\right. \tag{3}\]  
where \(D[j]\) is the displacement of the packet, \(j\), \(RI[j]\) is the received index of the packet, \(j\), and \(AS[j]\) is the arrived sequence of the packet, \(j\). Reordering density (\(RD\)), proposed in [50], refers to the distribution of packet displacements from their sending positions. As a result, \(PR=0\) corresponds to the case that all of the packets are in the correct order and \(PR>0\) for the reordered packet sequence.
## 2 Global Open Geo-Information and Integrated Framework
### Available Global Open Dataset
The GlobaLand30 dataset is a global open dataset extracted from more than 20,000 Landsat and HJ-1 images at a 30-m resolution for 2000 to 2010. Artificial surfaces and cultivated land types can be used for disaster impact estimations [26]. Disaster information is used to evaluate disaster conditions and to further determine physical damage and the severity of damages incurred via administrative reporting, remote sensing, ground surveying, and crowdsourcing. The international disaster database (EM-DAT) is a widely used statistic ground-based disaster dataset that includes essential data on over 18,000 mass disasters occurring around the world from 1900 to the present. The database is updated weekly on its website [27]. Relief Web is a specialized digital service of the United Nations Office for the Coordination of Humanitarian Affairs (OCHA) through which crisis and disaster information is collected, updated, and analyzed around the clock from more than 4000 global information sources. It is offered on the web in the form of reports, maps, and data on conditions and assessments [28]. Since the turn of the century, earth observation technologies have increasingly been used to map impacts and to support emergency responses around the globe. As only a few countries own remote sensing satellites, the International Working Group on Satellite-based Emergency Mapping (IWG-SEM) [29] has successfully provided mapping services for major disaster emergency responses (e.g., International Charter Space and Major Disasters, European COPERNICUS Emergency Management Service, UN-SPIDER, UNOSAT, and Sentinel Asia) [30]. Post-disaster data provided through the above SEM mechanisms are offered in the form of damaged delineation and degree maps derived from satellite imagery. Crowdsourced mapping tools are innovative and rapidly developing technologies generated by volunteers worldwide who are working together to create free maps through online mapping platforms. The tools have been widely used for global emergency support as they are updated in real time. Of the increasing number of crowdsourcing platforms that are becoming available, OpenStreetMap (OSM) is the most widely used, offering a free, accurate, detailed, and up-to-date digital global map platform [31] collected through mobile tracing and satellite imagery interpretation. OSM has become a baseline dataset used to deliver major emergency mapping support for international humanitarian organizations. The Humanitarian OpenStreetMap Team (HOT) launched in January 2009 coordinates the creation, production, and distribution of free mapping resources in support of humanitarian relief efforts in many places around the world through OpenStreetMap. It proved successful when applied to the 2010 Haiti Earthquake [32] and 2011 Great Japan Earthquake [33]. Ushahidi, MapAction, GDACS live map, and UNOSAT live map are other crowdsourced mapping platforms that provide post-disaster and disaster map information at close to real time.
## 3 Multiple RFI Sources Geolocation Method
### RFI Source Number Estimation  
After detecting the presence of RFI signals in the echo signal, estimating the number of interference sources is a key issue for the next steps. The minimum description length (MDL) criterion is a very effective method of estimating the number of signal sources in blind source separation based on information theory [22], which applies the minimax entropy principle and does not need to set a threshold [23]. Further, the MDL criterion can estimate the number of signal sources quite accurately under the condition of a high signal-to-noise ratio [23]. Because RFI makes the ISR of the SAR echo very high, it is extremely suitable for estimating the number of RFI sources from the contaminated multi-channel SAR echo signal based on the MDL criterion. Thus, this paper applies the MDL criterion to calculate multiple sets of echo signals of the SAR system and obtains the estimated value of the number of RFI sources. Specifically, for the estimation of the number of RFI sources, the MDL criterion searches the number of RFI signals by minimizing the following equation:  
\[MDL(n)=L\cdot(N-n)\cdot\ln p(n)+0.5\cdot n\cdot(2N-n)\cdot\ln L, \tag{19}\]  
Figure 3: Flowchart of the proposed multiple RFI sources geolocation method.  
where \(n\) is the number of RFI sources to be estimated, \(N\) is the number of SAR channels, \(L\) is the sampling number, \(p(n)\) is the likelihood function, which can be written as:  
\[p(n)=\frac{\frac{1}{N-n}\sum\limits_{i=n+1}^{N}\lambda_{i}}{\left(\sum\limits_{ i=n-1}^{N}\lambda_{i}\right)^{\frac{1}{N-n}}}, \tag{20}\]  
where \(\lambda_{i}\) denotes the i-th eigenvalue of the covariance matrix of SAR echo signal in Equation (17).  
The procedure for estimating the number of RFI sources is to first calculate the covariance matrix \(\mathbf{R_{S}}\) of the SAR echo signal by using Equation (17) and compute the eigenvalues of \(\mathbf{R_{S}}\). Then, the likelihood function and the MDL function are constructed according to Equations (20) and (19). Finally, all the function values of the MDL function are calculated by setting \(n\) in the range of \([1,N]\). The \(n\) corresponding to the minimum of the function values is the estimated value of the number of RFI sources.
## 3 Results and Discussion
### Experiments on the Common Benchmark
#### 3.2.3 Evaluation on the UAV123 Dataset  
The UAV123 dataset comprises 123 challenging video sequences captured exclusively by unmanned aerial vehicles (UAVs). Each UAV123 video sequence, recorded from low altitudes, consists of an average of 915 frames. There are major challenges for trackers due to the inherent instability of the UAV perspective and the frequent variations in target distance. Consequently, the low resolution of many objects in the video leads to particularly high tracking requirements for this dataset.  
UAVs are widely employed in the field of intelligent inspection. Taking advantage of this similarity, we further validate the effectiveness of our method on the UAV123 dataset. Figure 6 shows the tracking results compared to six other Siamese trackers including, SiamRPN [35], SiamMASK [40], SiamRPN++ [42], SiamCAR [44], SiamBAN [45], Ocean [46], SiamDW [48] and ADMT [49]. Our tracker achieves a success score of 0.625 and a precision score of 0.827, which still outperforms recent competitive Siamese trackers.
## 4 Melnikov Analysis
This distance is related to the first order to the Melnikov function \(M(\tau)\) in the following manner \(d(\tau)=\varepsilon(M(\tau)+O(\varepsilon))\), where \(M(\tau)\) is given by:  
\[M(\tau)=\int_{-\infty}^{\infty}h_{0}(\phi^{*},v^{*}_{\phi})\wedge h_{1}(\phi^ {*},v^{*}_{\phi})\mathrm{d}\tau, \tag{17}\]  
where \(\wedge\) defines the wedge product (\(\mathrm{d}\phi\wedge\mathrm{d}v_{\phi}=-\mathrm{d}v_{\phi}\wedge\mathrm{d}\phi\), \(\mathrm{d}\phi\wedge\mathrm{d}\phi=\mathrm{d}v_{\phi}\wedge\mathrm{d}v_{\phi }=0\)), \(h_{0}\) is the gradient of the unperturbed Hamiltonian  
\[h_{0}=(-\sin\phi^{*}+\
u)\mathrm{d}\phi+v^{*}_{\phi}\mathrm{d}\
u, \tag{18}\]  
and \(h_{1}\) is a perturbation form to the same Hamiltonian which can be written as  
\[h_{1}=(-\alpha v^{*}_{\phi}-\gamma\omega^{2}\sin\omega\tau)\sin\phi^{*} \mathrm{d}\phi. \tag{19}\]  
It is important that \(h_{0}\) and \(h_{1}\) are defined on the homoclinic orbits \((\phi,v_{\phi})=(\phi^{*},v^{*}_{\phi})\). Figure 8: Computation of the heteroclinic (for the torque \(\
u=0.0\)) and homoclinic (for the torques \(\
u=0.2\), \(0.4\), \(0.6\)) orbits in (a), and \(\
u=0.02\), \(0.04\), \(0.06\)) in (b). Thus, Melnikov function \(M(\tau)\) reads:  
\[M(\tau)=\int_{-\infty}^{\infty}v_{\phi}^{*}(\tau-\tau_{0})\big{(}\alpha v_{\phi} ^{*}(\tau-\tau_{0})-\gamma\omega^{2}\sin\omega\tau\big{)}\sin\phi^{*}(\tau-\tau _{0})\mathrm{d}\tau. \tag{20}\]  
The condition for the intersection between the stable and unstable manifolds to happen is clearly \(d(\tau)=0\), what implies that \(M(\tau)=0\). Hence, we can write that the condition for the global homoclinic transition can be written as:  
Figure 7:0  
Lastly, for \(\
u=0\), the mathematical expression for the critical parameter is:  
Figure 7:1 Figure 7:2  
We represent the corresponding Melnikov critical curves in Figure 7:3 In particular, Melnikov critical curves \(\eta_{c}^{\prime}=f/\alpha\) versus frequency \(\omega\) for the applied torques \(\upsilon=0\), 0.02, 0.04, 0.06 for the periodically driven pendulum with a torque, are represented in Figure 9a-d. On the other hand, Melnikov critical curves \(\eta_{c}^{\prime\prime}=\gamma/\alpha\) versus frequency \(\omega\), for \(\upsilon=0\), 0.2, 0.4, 0.6, for the vertically excited parametric pendulum with a torque are plotted in Figure 9e-h. For values of the parameters above the critical values represented in the curve, the motion are chaotic, while become periodic otherwise. Interestingly, the curves that are shown in Figure 9c,f-h, for \(\
u\
eq 0\), could be caused by the more important influence of longer homoclinic orbits comparing to the heteroclinic orbits. In fact, the asymmetric potential makes the phenomenon of the resonance due to external perturbations are more probable. Therefore, the motion of the system could suffer a direct transition to a rotation regime, instead of showing chaotic solutions. The curves are reliable in the limit of small forcing and damping parameters.
# Smart Nanocomposites for Nanosecond Signal Control: The Nano4 waves Approach
## 3 Results and Discussion
### Realization of Efficient Microwaves Absorbers
#### 3.3.2 Multilayered Gradient Absorber  
A second type of absorber is schematized in the inset of Figure 17b. It uses a stack of polymer composite films similar to those studied in the previous section, with increasing concentration of mixed MWCNTs and GNPs across the film thickness, according to column 2 of Table 4. The \(\varepsilon\) and \(\sigma\) parameters corresponding to each composite layer were measured using the method described in Section 2.4 and Figure 3b. As shown in columns 4 and 5 of Table 4, both \(\varepsilon\) and \(\sigma\) increase with overall concentration of nano-inclusions. This graded increase of the conductivity and permittivity allows a progressive penetration of the microwave signal as well as its progressive absorption through the 6-layered composite. As a consequence, the reflection can be minimized and the absorption maximized over a wide frequency range, as obtained experimentally at Figure 17 and confirmed by simulation. The operation of such kind of graded MWCNTs-GNPs nanocomposite is detailed in [18] and its behavior was announced in Section 2.1.2.
###### Abstract
In order to comprehend such vital tasks, sensors are required to provide assistance to operators, technicians, and engineers [46; 47; 48; 49; 50]. The characteristics of the sensor are directed to provide real-time, meaningful information to increase productivity, efficiency, and flexibility in various sectors, including commercial and academic research. An urgent need to apply sensors in both the maritime industry and research activities, as well as a tendency to do so, has been shown since early 2010, and it is important to quantify the frequency with which they are applied to predict the trends and developments of the new decade initiated in 2021. The current development of sensor technology will provide an excellent opportunity to improve the efficiency and safety of marine structures and environments. Real-time monitoring and analysis strategies will be vital to improving commercial automation in shipping, logistics, and marine and offshore activities in Maritime 4.0. IoT devices and machine learning algorithms will contribute a certain level of intelligence for industrial control systems and answer all problems that may arise. By collecting high-quality data with reliable sensor technology, the possibility of extending the life cycle of marine structures can be improved according to the highest standards  
Figure 1: An outline of the industrial revolutions. of operation and maintenance. The current sensor technology and the development of a new generation of sensors and robust networking architectures will be on the cusp of revolutionary changes in environmental monitoring and data collection in Maritime 4.0. This work aims to present a schematic review of sensor-based technology and its applications that is thoroughly focused on maritime industry and research. Contemporary subjects, including logistics and shipping, hull hydrodynamics, machinery performance, Arctic expedition, vibration and corrosion-based damage, and explosion analysis, are discussed and summarized to provide a charted trend of sensor application as an observational and measurement instrument. Predictions related to the technology's applications in the new decade based on the state and development of the previous decade are presented.
## 4 Transmission Channel Analysis  
This Section is devoted to provide a brief review of basic and fundamental concepts related to wireless links. Such notions will be applied in Section 5 for the application scenario and taking into account the hardware components that were adopted for the measurement campaign.  
The outcome of wireless transmissions is mainly conditioned by two key factors: LoS between the transmitter and the receiver and enough link margin at the receiver side. The former ensures the best propagation thus enlarging the probability of a successful communication. Such chances are furthermore enhanced if the prolate ellipsoidal volume wrapping the direct LoS path between the transmitter and the receiver (i.e., the first Fresnel zone) is free from obstacles. On the other hand, enough link margin at the receiver side is fundamental for the feasibility of the transmission. In other words, it is mandatory in order to achieve the broadcast that the RSSI of the received signal is higher than the receiver sensitivity. This requirement is assessed by evaluating the link budget of the communication.
## 1 Introduction
### Related Work  
When researching the capability of spread spectrum systems to counter single-frequency interference, it is commonly assumed that the spread spectrum sequence is a completely random and infinite binary sequence. This implies that the length of the spread spectrum sequence is significantly larger than the number of chips in each bit, and different spread spectrum sequences are employed for spreading each information bit. Based on this assumption, [PERSON] et al. analyzed the influence of single-frequency interference on the BER of a system [5; 6; 7], while [PERSON] et al. examined the processing gain of a DSSS system [8]. However, due to the necessity of pseudo-code synchronization, practical underwater acoustic DSSS systems often use short-period PN sequences. In these systems, each information bit employs the same periodic sequence for spreading, with the code period matching the duration of the bit. The anti-interference capabilities of short-code systems differ from those of long-code systems, which employ infinite long PN sequences. Various studies have explored the anti-interference performance of short-code systems. For example, some authors [9; 10] have presented the output power of single-frequency interference and the processing gain of the spread spectrum system against single-frequency interference. Others [11; 12; 13] have investigated the additive interference sequence of the system's output. [PERSON] et al. demonstrated the impact of single-frequency interference on the performance of spread spectrum systems through simulations [14; 15]. Additionally, some authors [16; 17; 18; 19; 20] have provided the BER of the system when the frequency of single-frequency interference matched the carrier frequency of the signal.  
Current research on the anti-single-frequency interference capability of short-code systems faces several issues. Firstly, the system's anti-jamming performance cannot be quantitatively analyzed, and there is no quantitative expression regarding the BER under single-frequency interference in relation to the SIR, spreading ratio, and frequency bias coefficient. Secondly, the BER performance of the system is only discussed when the single-frequency interference and the signal carrier are at the same frequency, without delving into the study of deviation in interference frequency from the signal carrier.