esa-sceva/satcom-chunk-collection huggingface dataset:
# An Enhanced Non-Coherent Pre-Filter Design for Tracking Error Estimation in GNSS Receivers
## Appendix B Derivation of Estimation Noise Variance of ATAN2 FL Discriminator  
In order to derive the estimation noise variance of the ATAN2 discriminator, we assume this estimation noise is white noise. Hence, Equation (11) is established as follows:  
\[\begin{array}{l}\text{E}(n_{\text{ATAN}})=0\\ \text{D}(n_{\text{ATAN}})=\text{E}([n_{\text{ATAN}}-\text{E}(n_{\text{ATAN}})] ^{2})=\text{E}(n_{\text{ATAN}}{}^{2})=\sigma_{\text{ATAN}}{}^{2}\\ \text{E}[n_{\text{ATAN}}(n)n_{\text{ATAN}}(n-1)]=0\end{array} \tag{23}\]where \(\mathrm{E}(n_{\mathrm{ATAN}})\) is the mean value of \(n_{\mathrm{ATAN}}\). \(\mathrm{D}(n_{\mathrm{ATAN}})\) is the variance of \(n_{\mathrm{ATAN}}\). According to Reference [9], the ATAN2 algorithm is equivalent to Equation (A13):  
\[\delta f_{\mathrm{ANAN2}}(n)=\frac{\delta\varphi_{\mathrm{ANAN}}(n)-\delta \varphi_{\mathrm{ATAN}}(n-1)}{T_{\mathrm{coh}}}\] (A13)  
Plug Equation (15) into Equation (A13), the \(\delta f_{\mathrm{ANAN2}}(n)\) can be reformulated as follows:  
\[\begin{array}{l}\delta f_{\mathrm{ANAN2}}(n)=\frac{\delta\varphi(n)-\delta \varphi(n-1)}{T_{\mathrm{coh}}}+\frac{n_{\mathrm{ATAN}}(n)-n_{\mathrm{ATAN}}( n-1)}{T_{\mathrm{coh}}}\\ =\delta f+\frac{n_{\mathrm{ATAN}}(n)-n_{\mathrm{ATAN}}(n-1)}{T_{\mathrm{coh}} }=\delta f+n_{\mathrm{ATAN2}}\end{array}\] (A14)  
where \(n_{\mathrm{ATAN2}}=\frac{n_{\mathrm{ATAN}}(n)-n_{\mathrm{ATAN}}(n-1)}{T_{ \mathrm{coh}}}\).  
This conclusion gives us an instruction that we can derive \(\mathrm{D}(n_{\mathrm{ATAN2}})\) according to the statistical property of \(n_{\mathrm{ATAN}}\). \(\mathrm{D}(n_{\mathrm{ATAN2}})\) is calculated as follows:  
\[\mathrm{D}(n_{\mathrm{ATAN2}})=\mathrm{E}([n_{\mathrm{ATAN2}}-\mathrm{E}(n_{ \mathrm{ATAN2}})]^{2})=\mathrm{E}(n_{\mathrm{ANAT2}}{}^{2})-\mathrm{E}^{2}(n _{\mathrm{ATAN2}})\] (A15)  
The first term of \(\mathrm{D}(n_{\mathrm{ATAN2}})\) can be derived as:  
\[\mathrm{E}(n_{\mathrm{ANAT2}}{}^{2})=\mathrm{E}[(\frac{n_{\mathrm{ATAN}}(n)- n_{\mathrm{ATAN}}(n-1)}{T_{\mathrm{coh}}})^{2}]=\frac{2}{T_{\mathrm{coh}}{}^{2}} \mathrm{E}(\varphi^{2}{}_{\mathrm{ATAN}})\] (A16)  
The second term of \(\mathrm{D}(n_{\mathrm{ATAN2}})\) is  
\[\mathrm{E}^{2}(n_{\mathrm{ATAN2}})=\mathrm{E}^{2}(\frac{n_{\mathrm{ATAN}}(n)- n_{\mathrm{ATAN}}(n-1)}{T_{\mathrm{coh}}})=0\] (A17)  
Plug Equations (A16)-(A18) into Equation (A15), we can get the analytical solution of \(\mathrm{D}(n_{\mathrm{ATAN2}})\) as Equation (A18).  
\[\mathrm{D}(n_{\mathrm{ATAN2}})=\frac{2}{T_{\mathrm{coh}}{}^{2}}\mathrm{E}( \varphi^{2}{}_{\mathrm{ATAN}})=\frac{2}{T_{\mathrm{coh}}{}^{2}}\sigma_{ \mathrm{ATAN}}{}^{2}=(\frac{1}{2\pi})^{2}\frac{1}{c/n_{0}\cdot T_{\mathrm{coh} }{}^{3}}(1+\frac{1}{c/n_{0}\cdot T_{\mathrm{coh}}})\] (A18)
## 3 Results and Discussion
### Constellation Diagrams
It is important to note that the integration of optical pumping with GaAs mixers was not possible because of their high-demand LO power requirements compared to InGaAs mixers.
# Mitigating the Scintillation Effect on GNSS Signals Using MP and ROTI
## 2 Materials and Methods
### Kr  
KF as the core algorithm during the GNSS positioning process is used to increase the accuracy of positioning by integrating more measurements. There are two major procedures in KF, prediction and update, which iterate for all the measurements [32]. Figure 1 shows the flowchart on how to apply KF step by step.  
As shown in Figure 1, the state and covariance matrix were initialized first, where the covariance matrix is the estimation of state error. Then, following inputting of the dynamic model and process noise, the state and covariance matrices were predicted based on the initialized values or the values updated at the previous epoch. After that, the innovation vector was calculated as the difference between the input values and values generated from the previous epoch. Next, the Kalman gain was obtained by introducing the measurement error. Afterwards, the state and covariance matrix were updated using the Kalman gain as the weight of the innovation vector. The output was used to predict the state and covariance  
Figure 1: Flowchart of KF.  
matrix for next iteration until all the measurements were processed. Further details of the Kalman Filter approach are provided in the electronic supporting material.
# Strategies for Dielectric Contrast Enhancement in 1D Planar Polymeric Photonic Crystals
## 6 Conclusions and Perspectives  
This review focuses on the role of the dielectric contrast in polymer planar photonic crystals. The research interest in these systems arises from the necessity of reducing costs and using milder fabrications than those employed for inorganic structures. On the other hand, despite their ease of processing, even at the large scale, and their low cost compared to inorganics, polymeric systems suffer from low dielectric contrast for mutually processable materials. Several new macromolecular media with modified refractive indexes were proposed in the literature, but only a few have been demonstrated to be suitable for the fabrication of DBRs. We reviewed these systems and envisaged a new possible structure with a dielectric contrast close to that in inorganic media. This structure uses a perfluorinated polymer as a low refractive index medium and a titania-based polymer nanocomposite as a high refractive index counterpart. Spectral response calculations allowed us to compare inorganic systems with materials that were both reported for the fabrication of multilayered structures but not coupled together and to envisage the possibility of employing them in achieving radiative rate enhancement in light-emitting devices, such as lasers and LEDs.  
**Supplementary Materials:** The following are available online at [[http://www.mdpi.com/2076-3417/10/12/4122/sl](http://www.mdpi.com/2076-3417/10/12/4122/sl)]([http://www.mdpi.com/2076-3417/10/12/4122/sl](http://www.mdpi.com/2076-3417/10/12/4122/sl)), Figure S1: Spectral dispersion of the refractive index of commonly used polymers for the fabrication of DBRs [5; 6; 7]. Figure S2: (a) Spectral dispersion of the refractive index of engineered polymers and (b) relative spectra reported in the literature normalized by R\({}_{\text{max}}\)[8; 9; 10; 11].  
**Author Contributions:** P.L. conceptualization, data, elaboration and original draft prreparation, D.C. supervision, P.L, D.C., H.M., P.S., M.A., M.P. writing, review and editing. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This project was funded by the Bank Foundation Compagnia di San Paolo Project \"Sulfur-based Polymers from Inverse Vulcanization as high refractive index materials for all-polymer planar phOTonic crystals--PIVOT\" (IDROL 20583).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Optimized Polar Codes as Forward Error Correction Coding for Digital Video Broadcasting Systems
## 2 Preliminaries
### Polar Codes Decoder
The adaptive SCL decoder presented in [36] is summarized in the flowchart shown in Figure 3. it initially uses list size (\(L=1\)) which is equivalent to simplified SC decoder, and then iteratively doubles \(L\) (if there is no correct path passing CRC), until \(L\) reaches a predefined number \(L_{max}\), and outputs the path with the highest probability and exits the decoding process.
# Current Trends on Green Wireless Sensor Networks
## 2 Energy Conservation
### Energy-Efficient Routing
#### 2.4.1 Hierarchical Routing Protocols
The ACO algorithm is studied for finding an optimal trajectory for the mobile sink. In [158] a special clustering method called Energy Centers searching using PSO (EC-PSO) is proposed for clustering and CH selection. In [159] a firefly algorithm is developed for selecting the CH optimally. [PERSON] and [PERSON] [160] propose the evolutionary multipath energy-efficient routing protocol (EMER) using a cuckoo search algorithm to optimally select the CH considering energy efficiency. Recently, [PERSON] [161] proposes an optimal CH selection by considering energy, delay, distance, and security using a new algorithm that hybridizes the concept of dragon fly and firefly algorithms. Additionally, recently, the Optimized QoS-based Clustering with Multipath Routing Protocol (OQoS-CMRP) has been proposed [162] by applying the Modified Particle Swarm Optimization to form clusters and select CHs. Ref. [163] introduces an algorithm that uses fuzzy logic for cluster construction and CH selection, and ACO for inter-cluster routing to mitigate the hot spot problem and extend network lifetime. In [164] an interesting PSO-based unequal and fault tolerant clustering protocol (PSO-UFC) is presented. In [165] the authors use a cuckoo optimization algorithm (COA) for clustering and selection of optimal CHs, considering four criteria such as the remaining energy of nodes, distance to the base station, within-cluster distances, and between cluster distances. In [166] a multihop LEACH protocol is optimized by means of an ACO algorithm, using a CH close to the BS. Other recent works that propose LEACH  
Figure 4: Classification of hierarchical routing strategies. optimizations are the proposal in [167] using a Fuzzy C-means clustering (FCM) Algorithm, the work in [168] that uses a PSO algorithm or the optimization made in [169] by means of a Genetic Algorithm. Another interesting work is that of [PERSON] and [PERSON] [170] where fuzzy sets and fuzzy decision rules have been used for intelligent selection of CHs and to setup multihop routes to BS. Although LEACH is the preferred protocol for using as basis for optimization, other cluster-based protocols are also used. Therefore, several improvements of PEGASIS has been recently proposed. In [171] an Enhanced PEGASIS (EPEGASIS) protocol is proposed to mitigate the problem of hot spots from four directions. The work in [172] combines PEGASIS with Hamilton Loop algorithm, through a mixture of single-hop and multihop mechanisms, inserting a mobile agent node that is responsible for receiving and merging packets from the CHs. The authors in [173] also combines PEGASIS with a genetic algorithm to build the chain instead of the greedy algorithm. The problem of CH selection in APTEEN using artificial intelligence has also attracted the interest of researchers in recent years: using PSO [174], a combination of genetic algorithms and fruit fly optimization algorithm [175], or ACO [176,177].
## 2 Materials and Methods
### Design of the DC-DC Controllers  
The purpose of the control in the DC-DC stage is to capture the maximum energy from the solar panel. With the solar panel model obtained in Section 2, the power curve at different irradiance points can be obtained (Figure 7). It is observed that the output power is at its maximum only in a single voltage and current point. In addition, this point can change under different atmospheric conditions and solar irradiance.  
Therefore, the maximum power point tracking (MPPT) of the panel must be monitored. For this, various MPPT techniques have been developed [21; 22; 23; 24; 25; 26]. In this research, the incremental conductance algorithm is implemented to achieve this objective, and the scheme is shown in Figure 8. The control loop guarantees that the solar panel current is regulated to the current reference \(i_{ref}\) delivered by the MPPT algorithm. This is accomplished  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline \(L_{i}\) & 5.184 mH \\ \(L_{g}\) & 940 uH \\ \(R_{i}\) & 0.93 \(\Omega\) \\ \(R_{g}\) & 0.26 \(\Omega\) \\ \(C_{f}\) & 0.2 uF \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of the dual-buck inverter.  
by adjusting the duty cycles of the flyback converter's power MOSFETs. In addition, the proposed scheme includes digital filters for eliminating noise in the current and voltage measurements of the solar panel.
## 3 Results and Discussion
### Effect of the DESs and the PEF Usage on the TPC of the Extracts
Despite the fact that there are quite a few reports on the use of PEFs for polyphenol extraction [21; 46], the combination of DESs with PEFs is an underdeveloped field with relevant reports being scanty and  
Figure 1: TPC (mg GAE g\({}^{-1}\) dw) in _Cistus creticus_ extracts prepared with water, an ethanol-water mixture, and the four examined DESs, with and without the PEF pretreatment; Statistically significant differences between the same solvent used are denoted with one asterisk (*) for \(p<0.05\) and two asterisks (**) for \(p<0.01\). Statistically significant differences (\(p<0.05\)) for samples without the PEF treatment are denoted with small letters, and differences for samples prepared with the PEF pretreatment are denoted with capital letters; Ethanol-water mixture was used as a positive control. sparse [47]. In an effort to further enhance the TPC of the extracts, we examined whether the use of PEFs could be beneficial. The results are presented in Figure 1. As regards the use of PEFs, it is obvious that in all cases, the DES-PEF prepared extracts contained more polyphenols, compared to the water-PEF extracts, as occurred in the case without the PEF treatment. When the PEF method was used as a pretreatment step, the TPC of the extract prepared with water was increased by 16%, compared to the extract prepared without the PEF treatment. The extracts prepared with DES-1 and DES-2 and the use of PEFs, exhibited a 12 and 14% increase, respectively, in their TPC, compared to the extracts prepared without PEFs. Finally, the extracts prepared with DES-3 and DES-4 and the PEF pretreatment, contained 4-8% more polyphenols, compared to the extracts prepared without PEFs. According to the results, it can be concluded that the use of PEFs was beneficial in all cases, and with a short (20 min) treatment step, a notable increase was achieved. A ~20% increase in the flavonoid content of the extracts prepared from non-processing wastes, using DESs and PEFs was also reported by [PERSON] et al. [47]. The increase in the extraction yield can be attributed to the electroporation that takes place, making it more feasible for the DESs to come in contact with the polyphenols, resulting in an increase. Moreover, the pH of each DES may further affect the extraction process. The pH of DES-1, DES-2, DES-3, and DES-4 were 5.2, 4.3, 2.2, and 10.0, respectively. It can therefore be inferred that the highly acidic and alkaline pH of DES-3 and DES-4 are less suitable for the extraction of polyphenols, since the compounds may be more unstable at that pH. Therefore, DES-1 and DES-2, which have moderate acidic pH values, are more suitable.
## 2 Control Strategies
### Linear Quadratic Lateral Tracking Control
#### 2.1.3 Brake Pressure Splitting Logic  
This logic aims to ensure that the total moment required is reached: firstly, selecting if the right or left wheel side needs to be actuated in accordance to the sign of the input moment; and then defining how much each wheel has to be braked facing the problem of braking pressure saturation limit.  
To identify the wheel to be actuated, a selector based on the understeering and oversteering behavior is adopted: when the vehicle is understeering, it goes to a greater trajectory than that set by the driver. To compensate for the error in the trajectory and linearise the vehicle behavior to the driver's input, a braking force must be applied on the inner rear wheel. On the other hand, when the vehicle is oversteering, to compensate for the development of the vehicle on a lower trajectory, the outer front wheel must be braked (31)-(34). In these equations, the \(M_{bij}\) symbols represent the brake moment, where index \(i\) inherits the same convention described in the previous Section 2, while \(j\in\{l,r\}\) indicates the left or right wheel, respectively.  
\[M_{bfl}=\begin{cases}0,&right\quad turn\quad and\quad understeering\\ \frac{|M_{bur}|R_{wf}}{\frac{l}{2}\cos\delta},&right\quad turn\quad and\quad oversteering \end{cases} \tag{31}\]  
\[M_{brr}=\begin{cases}\frac{|M_{bur}|R_{wr}}{\frac{l}{2}},&right\quad turn\quad and \quad understeering\\ 0,&right\quad turn\quad and\quad oversteering\end{cases} \tag{32}\]  
\[M_{bfr}=\begin{cases}\frac{|M_{bur}|R_{wf}}{\frac{l}{2}},&left\quad turn\quad and \quad oversteering\\ \frac{l}{2}\cos\delta},&left\quad turn\quad and\quad oversteering\\ 0,&left\quad turn\quad and\quad understeering\end{cases} \tag{33}\]  
\[M_{brl}=\begin{cases}0,&left\quad turn\quad and\quad oversteering\\ \frac{|M_{bur}|R_{wf}}{\frac{l}{2}},&left\quad turn\quad and\quad understeering \end{cases} \tag{34}\]  
Thus, it is possible to reduce the lateral slip by adding a longitudinal slip component.  
From the torques found, through the rotational balance (36), the braking pressure delivered to each wheel \(P_{bij}\) is defined (35).  
\[P_{bij}=\frac{M_{bij}}{(A_{c}tR_{wi})} \tag{35}\]  
\[I_{wij}\dot{\omega}_{ij}=-M_{bij}-F_{xij}R_{wi} \tag{36}\]  
The BBW system saturates the brake caliper pressures to a maximum of 150 bar. This constraint could limit the braking force of the wheel, not ensuring achieving the moment requested. For this reason, a logic that redistributes the pressure on the wheels is developed, consisting of the following steps:  
1. Checking if the wheel reaches the saturation limit;
2. Definition of the pressure quantity by which the saturation limit is exceeded;
3. Subtraction of this quantity from the wheel of the other side ensures the allocation of the control yaw moment.  
In addition, the EBD-ABS logic explained in [37] has been added to ensure that the wheels do not lock up.
# A Hybrid Scheme for Disaster-Monitoring Applications in Wireless Sensor Networks
## 2 Related Work
### Multiple Wireless Interfaces MAC  
In general, a sensor node has only one wireless communication interface. If sensor nodes have multiple wireless communication interfaces, this may naturally improve the throughput and delay performance. MMAC-HR has two wireless interfaces; one is used as the control channel to exchange control information, and the other shifts dynamically among the data channels [37]. Therefore, MMAC-HR resolves the multiple-channel exposed terminal problem and can enhance the throughput and delay performance. The DSP is a MAC protocol based on multiple-interface nodes and fast- and slow-hopping approaches with multiple-channel access [38]. To avoid the busy receiver problem, the DSP protocol utilizes a multiple rendezvous approach to reduce the delay, because the slow-hopping interface always follows the same hopping sequence. Multiple wireless interface MAC protocols require hardware support and consume more energy.
###### Abstract
The CS algorithm also provided a computationally efficient solution for focusing bistatic forward-looking SAR [18]. Subsequently, researchers focused on addressing image distortion in near-field bistatic SAR imaging caused by using equivalent monostatic approximation algorithms [19], such as the extended NLCS algorithm proposed to improve Doppler resolution and mitigate azimuth ambiguities [20]. A controlled singular value decomposition (SVD) algorithm was proposed to enhance imaging precision and reduce the complexity of processing bistatic SAR data with nonlinear trajectories [21]. Currently, new algorithms and methods to enhance imaging capabilities and phase preservation are being continuously explored to address the challenges posed by more complex satellite constellations. In the context of InSAR technology, phase unwrapping stands as a pivotal core step in the InSAR processing pipeline. Phase unwrapping can be categorized into single-baseline phase unwrapping and multi-baseline phase unwrapping techniques. Single-baseline phase unwrapping relies on a single interferogram for phase unwrapping [22; 23; 24]. However, it suffers from a limitation wherein the assumption of phase continuity is utilized, rendering phase unwrapping challenging in regions with significant terrain variations, such as mountains and hills. These areas with phase discontinuities pose difficulties in correctly identifying phase gradients, leading to unwrapping errors. On the other hand, multi-baseline phase unwrapping techniques leverage multiple interferograms to perform phase unwrapping. By combining interferograms corresponding to different baseline lengths, these techniques can accurately estimate the gradients of phase-discontinuous areas, thereby enhancing the accuracy of phase unwrapping [25]. However, acquiring interferograms with different baseline lengths requires multiple acquisitions. Given the constraints of satellite imaging capabilities, storage capacity, and data transmission capabilities, additional acquisitions significantly prolong the overall data production time. A constellation of multiple satellites in formation flight can acquire multiple sets of spatial baselines with varying lengths simultaneously while also mitigating errors caused by temporal baselines on terrain, making it a preferable solution. The PIESAT-01 constellation is the world's first multi-baseline distributed InSAR satellite system. This paper focuses on the imaging of the PIESAT-01 constellation and proposes a technical workflowfor multi-satellite interferometric reconstruction of DSM. Subsequently, interferometric imaging experiments and processing are conducted using PIESAT-01 constellation data. Analysis of the imaging and interferometric processing results validates the focusing and phase-preserving characteristics of the imaging algorithm, as well as the accuracy of the interferometric processing.
# A Blockchain Solution for the Internet of Vehicles with Better Filtering and Adaptive Capabilities
## 7 Simulation Experiments and Analysis of Results  
We conducted simulations of vehicular communication within the IoV environment to evaluate the performance of various consensus algorithms. For simplicity, we assumed that the IoV operating environment is sufficiently open, and the distances between vehicles and related infrastructure are large enough, allowing the vehicles to be modeled as point objects in a relatively static state. In our experimental setup, consortium blockchain nodes were used to represent the vehicles for simulation purposes. The experiments were implemented using Python 3.8 on the PyCharm platform, running on a machine equipped with a 12 th-generation Intel Core i5-1240P processor and 16 GB of RAM. We simulated the performance of four consensus algorithms: PBFT, GPBFT, CBFT, and AE-PBFT. GPBFT and CBFT are two benchmark algorithms used for experimental comparison. GPBFT is a Byzantine fault-tolerant consensus algorithm that leverages short group signatures with a two-level administrative structure. A certification authority classifies node identities based on credit values and employs group signature schemes to trace the true identities of malicious nodes, thereby enhancing the system's security and stability. The CBFT algorithm, on the other hand, divides the consensus process into inter-group and intra-group consensus based on the network nodes' response speed to management nodes. It further categorizes nodes into different types using a credit model to minimize the likelihood of the master node being  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Consensus Algorithm** & **Communication Complexity** \\ \hline PBFT & \(2n(n-1)\) \\ GPBFT & \(n^{2}+n-1\) \\ CBFT & \(\left(\frac{n}{n}\right)^{2}-\frac{3n}{n}+2n\) \\ AE-PBFT & \(\approx\left(\frac{2n}{5}-1\right)\left(\frac{2n}{5}+1\right)\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Communication complexity of four algorithms.  
malicious. Compared to PBFT, these two algorithms achieve significant improvements in communication overhead and security, making them more robust and suitable for experimental comparison. Successful program execution, indicated by correct output, signified that consensus was achieved. To assess the performance of these algorithms, we varied the number of nodes in the network from 0 to 100 and evaluated key metrics, including consensus latency, throughput, communication overhead, and fault tolerance.