esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In short, most works [5; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21] have considered demodulating the chaotic secure communication systems with the assumptions that: (i) the transmitter's structure isknown, and even that the parameters are given; or (ii) chaotic maps and their time series are only affected by white noise, in particular Gaussian noise. In this study, the particle filtering technique is employed to solve the problems mentioned above. The particle filter (PF) is the state-of-the-art solution to nonlinear and non-Gaussian problem, following the Bayesian filtering framework, which uses a sequential Monter Carlo method to approximate the optimal filtering by representing the probability density function with a swarm of particles [22; 23; 24]. Due to this sample-based representation, particle filters are able to represent a wide range of probability densities, allowing online, real-time estimation of nonlinear, colored or non-Gaussian dynamic systems. [PERSON] and [PERSON] [25] prove that the PF has better performance in convergence rate and estimation accuracy than the extended Kalman filter (EKF) and unscented Kalman filter (UKF). In this paper, a novel PF-based algorithm is proposed to blindly demodulate the CDS-SS communication system without any knowledge of the transmitter's chaotic map, parameters, or initial value, and even under colored or non-Gaussian noise conditions. To begin with, the formulation of this problem is introduced. Then, autoregressive moving average (ARMA) models are utilized to describe the colored noises, so that the demodulation could be formulated as a mixed parameter and state estimation problem. Accordingly, the message signal can be estimated by using two alternate PFs, and the state variable of one PF is the chaotic state, and the other one is the message signal. Finally, the proposed algorithm is implemented to blindly demodulate the CDS-SS signals.
# Fusion of Land-Based and Satellite-Based Localization Using Constrained Weighted Least Squares
## 5 Proposed Method
### Proposed Method for Land-Based Station
#### 5.1.2 Covariance Matrix of DOA Result  
The DOA localization error is defined as \(\hat{\mathbf{\eta}}_{a}\). Thus, \(\Delta\eta_{a}=\hat{\mathbf{\eta}}_{a}-\eta_{a}^{o}\) is the optimal solution to the following problem:  
\[\left\{\begin{array}{l}\min_{\Delta\eta_{a}}{(G_{a}\Delta\eta_{a}-\varepsilon_ {a})^{\mathrm{T}}W_{a}(G_{a}\Delta\eta_{a}-\varepsilon_{a})}\\ \mathrm{s.t.}\ {(\Delta\eta_{a})^{\mathrm{T}}\tilde{\mathbf{\Lambda}}_{1}\eta_{a}^{o}=0} \\ {(\Delta\eta_{a})^{\mathrm{T}}\big{(}2\tilde{\mathbf{\Lambda}}_{2}\eta_{a}^{o}+ \gamma_{1}\big{)}=0}\end{array}\right. \tag{64}\]  
where \(\tilde{\mathbf{\Lambda}}_{1}=blkdiag(\mathbf{\Lambda}_{1},0)\). The constraints in (64) are derived from (2) and (29), respectively. \(\Delta\eta_{a}\) can be represented as [5]  
\[\Delta\eta_{a}=\Big{(}\mathbf{I}_{4}-\tilde{G}_{a}^{-1}\mathbf{\Psi}_{a}\Big{(}\mathbf{\Psi }_{a}^{\mathrm{T}}\tilde{G}_{a}^{-1}\mathbf{\Psi}_{a}\Big{)}^{-1}\mathbf{\Psi}_{a}^{ \mathrm{T}}\Big{)}\tilde{G}_{a}^{-1}\mathbf{G}_{a}^{\mathrm{T}}\mathbf{W}_{a} \varepsilon_{a} \tag{65}\]  
where \(\mathbf{\Psi}_{a}=\big{[}\mathbf{\psi}_{a1},\mathbf{\psi}_{a2}\big{]}=\big{[}\tilde{\mathbf{ \Lambda}}_{1}\eta_{a}^{o},2\tilde{\mathbf{\Lambda}}_{2}\eta_{a}^{o}+\gamma_{1} \big{]}\).  
The covariance matrix of \(\Delta\eta_{a}\) is expressed as  
\[\mathbf{cov}(\Delta\eta_{a})=\tilde{G}_{a}^{-1/2}\Big{(}\Pi^{\perp}\Big{[}\tilde{ G}_{a}^{-1/2}\mathbf{\Psi}_{a}\Big{]}\Big{)}\tilde{G}_{a}^{-1/2} \tag{66}\]  
The covariance matrix of the source estimate can be obtained as  
\[\mathbf{cov}(\hat{\mathbf{u}}_{a})=[\mathbf{I}_{3},\mathbf{0}_{3\times 1}]\tilde{G}_{a}^{-1/2} \Big{(}\Pi^{\perp}\Big{[}\tilde{G}_{a}^{-1/2}\mathbf{\Psi}_{a}\Big{]}\Big{)} \tilde{G}_{a}^{-1/2}[\mathbf{I}_{3},\mathbf{0}_{3\times 1}]^{\mathrm{T}} \tag{67}\]
## 6 Results, Analysis and Discussion
### Statistical Analysis of Performance Metrics
#### 6.1.3 Tries with Box and Whiskers Representation
For AODV, the remaining 25% of nodes has five tries, and the tries' average value increases to 1.95. In the DSR case, the remaining 25% has five tries, and the tries' average value increases to 1.87. In contrast, for MPH, another 24% of nodes has four tries and the remaining 1% five tries, and the tries' average value is 1.76. Finally, in the reactive jamming case, AODV and DSR have 50% of nodes between one and three tries, while MPH values remain unchanged (between one and two tries). The AODV and DSR cases have a very similar behavior, where the remaining 25% of nodes has five tries, and the tries' average value increases to 1.97. On the other hand, for MPH, the remaining 25% of nodes has four tries, and the tries' average value is 1.82. This MPH behavior describes that although the random (\(\rho=80\) packets/s) and reactive jammings affect MPH nodes similarly, MPH exposes better performance and link connectivity than AODV and DSR protocols.
# Development of Parameters towards Voice Bifurcations
## 1 Introduction
### Main Concept
Non-zero_ BI _strongly indicates biphonic Type-II voice if observed across multiple observation windows._  
The HDF value cannot be near 1.0 as it is computed from two distinctive peaks, i.e., \(f_{0}\
eq f_{1}\). The proximity depends on the spectral resolution of the window function. The modulo operators in the HDF guarantees not to penalize \(f_{1}\) if it is any of the valid harmonic frequencies. On the other hand, the BI guarantees two possible cases (\(\gamma\) representing \(F_{0,1}/F_{0,2}\) vs. \(F_{0,2}/F_{0,1}\)) to yield the same BI measure. Table 2 lists examples of the expected HDF and BI values for different Type-II voicings: 2 types of subharmonics and 3 biphonia cases. The latter cases are specified with a ratio of integers, which signifies the ratio of the \begin{table}
\begin{tabular}{l c c c} \hline
**Voice Type** & \(f_{0}\) & \(f_{1}\) & \(\gamma\) \\ \hline Type I & \(F_{0}\) & \(nF_{0}\), \(n\in\{2,3,\ldots\}\) & \(n\geq 2\) \\ Type II—Period-\(m\) Subharmonic & \(F_{0}\) & \(nF_{0}\)/\(m\), & \(n/m\
ot\approx 1\) \\ Type II—Biphonia & \(F_{0,1}\) & \(F_{0,2}\), near \(F_{0,1}\) & \(\sim 1.0\) \\ Type III & \(F_{0}\) if prevalent otherwise random & Random & Random \\ \hline \end{tabular}  
* \(F_{0}\): fundamental frequency, \(F_{0,1}\), \(F_{0,2}\): fundamental frequencies of two competing harmonic series.  
\end{table} 
Table 1: Expectation of Frequency Estimates 1.  
vibration rate. For example, 2:3 biphonia indicates that one part of the vocal folds vibrates two cycles while the other vibrates three cycles. The HDF and BI could be measured for any form of voice signal that contains the oscillatory patterns of the vocal folds; for example, acoustic waveforms, electroglottogram, and glottal area waveforms obtained from high-speed videeondoscopy. The acoustic signals, however, require additional processing to account for the vocal tract effect.
## 2 Methods and Materials
### Online Correction Method: Pulsed Radiation
#### 2.1.3 Considerations Regarding the Application to Liquid-Filled Ionization Chambers  
In order to apply [PERSON]'s Equation (1) (and correction methods based on it), there must be no overlapping between charges ionized by consecutive pulses, i.e., charge collection must take less than the time between consecutive pulses. This constraint is easily satisfied in air-filled chambers, where the mobilities of charge carriers are large. However, this may not be the case in liquid-filled detectors, where charge collection takes \(\sim\)10 ms operating under standard conditions, while the time between pulses may be below that value in clinical LINACs.  
However, even if [PERSON] theory cannot be formally applied, it was shown in [25] that the functional dependence of the collection efficiency on the dose per pulse does not change with overlapping. In this situation, in the near-saturation region we can write:  
\[f=\frac{1}{u^{\prime}}\log(1+u^{\prime}) \tag{15}\]  
where \(u^{\prime}\sim D_{0}\).  
Because the functional dependence of the collection efficiency on the dose per pulse does not change, correction methods based on this dependence, like the two-dose-rate method [14], can still be employed. This is not the case with correction methods based on the functional dependence of the collection efficiency on the voltage (like the three-voltage method [23]). The online correction method developed in the previous section can also be applied, with the particularity that the two-dose-rate method is used to obtain the value of \(u^{\prime}_{ref}\).  
A further consideration needs to be taken into account in LICs, regarding the extended collection time of the charge released by a pulse and the integration time. As mentioned in the above discussion, the charge collection time in LICs is not negligible in comparison with the time between pulses (in fact, it may be larger). In addition, due to the high density of the ionization medium, the readout signal is large, and integration times may be limited to values of the order of ms (depending on the readout electronics). As the integration time, the collection time, and the time between pulses are of the same order of magnitude, fluctuation on the readout signal may appear which are not caused by variations of the dose per pulse (this is easily noticeable in Section 3.1 for measurements taken with the detector presented in [13]). Such fluctuations, which are not caused by variations of the dose per pulse, should be minimized to improve the accuracy of the online correction method. As discussed in Section 2.1.2, one should set \(\Delta t>>T\) to avoid this issue. If this is not technically possible, replacing the readout current (\((I_{\text{col}})\)) with a moving average, \(\langle I_{MA,n}\rangle\) (emulating a longer integration time), will reduce the undesired fluctuations:  
\[\langle I_{MA,n}(t_{i})\rangle=\left(\sum_{j=i-n+1}^{i}\langle I_{\text{col}}( t_{j})\rangle\right)/n \tag{16}\]  
This moving average will be used in the analysis of experimental data of a LIC in Section 3.1.
# A Multi-Layer Secure Sharing Framework for Aviation Big Data Based on Blockchain
## 8 Conclusions  
This paper analyzes the practical data-sharing requirements in aviation operations and establishes a mathematical model for secure aviation big data sharing. Building on this foundation, we propose a multi-layer security sharing architecture that addresses the storage and timeliness constraints in aviation data sharing through blockchain optimization deployment and consensus mechanisms. By implementing a dual-layer sharing framework combining data sharing and model sharing, we achieve comprehensive aviation big data sharing while providing targeted support for ATM operations.  
Based on real-world aviation operational scenarios, this study constructs a mathematical model for secure aviation big data sharing and innovatively develops a multi-layer security architecture that effectively resolves the critical challenges of data security and real-time performance in aviation data sharing. At the technical implementation level, we employ a dynamic deployment strategy to significantly reduce storage pressure on lightweight nodes, design a clustering-based grouped PBFT consensus mechanism to meet the timeliness requirements of critical business data, and establish a dual-layer architecture integrating data sharing with model sharing to ensure security, efficiency, privacy protection, and sharing incentives for ATM data sharing. This solution provides secure and efficient data-sharing support for ATM operations while offering a viable technical pathway for the digital transformation of air traffic management systems.  
The proposed approach demonstrates three key technical innovations: the dynamic deployment strategy effectively alleviates storage constraints for resource-limited nodes, the optimized consensus mechanism ensures timely processing of critical aviation data, and the integrated sharing framework simultaneously addresses security, privacy, and efficiency requirements. Together, these advancements establish a comprehensive solution for aviation big data sharing that meets the stringent demands of modern air traffic management systems while supporting their ongoing digital transformation.  
Conceptualization, Z.W. and Q.W.; Methodology, Q.W.; Software, Q.W.; Validation, Q.W., Z.W. and Y.L.; Formal analysis, Q.W.; Investigation: Q.W.; Resources, Z.W.; Data curation, Q.W.; Writing--original draft preparation, Q.W.; Writing--review and editing, Z.W. and Y.L.; Visualization, Q.W.; Supervision, Z.W.; Project administration, [PERSON]; Funding acquisition: Z.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Key R&D Program of China (No. 2023 YFB4302901), National Natural Science Foundation of China (No. 62172418), Tianjin Natural Science Foundation Project (No. 24 JCZDJC01000), and Fundamental Research Funds for the Central Universities of China (No. 3122022081).  
Not applicable.  
The dataset used in this study is available from the corresponding author upon request.
# Data-Driven Control Algorithm for Snake Manipulator
## 3 Simulation of DDPG Control Based on 2-Link Model
### DDPG Agent Establishment  
(1) Q network design. The Q network uses a deep convolution neural network with three inputs and one output. The three inputs are the observed value (joint angle value \(\begin{bmatrix}\theta_{1}&\theta_{2}\end{bmatrix}\); the angular velocity value \(\begin{bmatrix}\theta_{1}&\theta_{2}\end{bmatrix}\)), and the action value (torque value \(\begin{bmatrix}\tau_{1}&\tau_{2}\end{bmatrix}\)), and the output is the evaluation reward value. The input parameter of the observed value input is eight. The network is set to the first full connection layer to set the number of nodes as 128. The activation function is ReLU. The number of nodes in the second layer of the full connection layer is 200. The input parameter of the action value is four. The number of nodes in the fully connected layer is 200. The final output parameters of the full connection layer of the three inputs are added through the ReLU activation function. The setting parameter of the third layer is one. The output value can be obtained. The Q network configuration is shown in Figure 5. The network optimizer is set to the adaptive matrix estimation (adam) optimizer. The learning rate is set to 0.0005.  
(2) Action network design. The action network adopts a deep convolution network with one input and one output. The input is the observed value. The output end is the torque value \(\left[\begin{matrix}\tau_{1}&\tau_{2}\end{matrix}\right]\). The input parameter is eight. The network is set to the first full connection layer to set the number of nodes to 128. The activation function is ReLU. The second full connection layer sets the number of nodes 200. The activation function is ReLU. The number of nodes in the full connection layer of the third layer is set to two. The activation function is tanh. The magnification of the scale layer is five times. The action network configuration is shown in Figure 6. The network optimizer is set to the adaptive matrix estimation (adam) optimizer. The learning rate is set to 0.001.  
(3) Parameter setting of agent training: Training is set at 1800 episodes with 200 samples per episode. The training end mark is the point at which the total value of a certain episode reward is zero or the required number of training episodes is completed.
## 4 Analysis Program for Liner Corrosion
### Validity of Gusset Sub-Model  
After the gusset sub-model is established, its spatial coordinates are readjusted to match those of the corresponding region in the overall model. Thereby, the boundary conditions of the gusset sub-model are determined based on the corresponding region in the overall model. Utilizing the sub-modeling analytical interface offered by ABAQUS, the node-based sub-modeling technique is applied to transfer the node displacements from the overall model to the boundaries of the gusset sub-model. It is important to note that the sub-modeling analysis also involves the definition of the temperature field. Figure 9 depicts the distribution of the temperature field in the gusset region of the NCS under LOCA conditions. It can be seen that the maximum temperature of the inner wall of the gusset is approximately 145 \({}^{\circ}\)C, and the temperature exhibits a nonlinear distribution along the thickness direction of the wall. Furthermore, the temperature of the inner wall decreases as time increases, and the temperature spreads farther in the direction of the wall thickness. Based on the heat transfer results, a LOCA temperature of 145 \({}^{\circ}\)C is applied as a thermal load to the gusset region. Meanwhile, it is assumed that the ambient temperature outside the NCS is 20 \({}^{\circ}\)C.  
Figure 8: Gusset sub-model and delineation of the corrosion area.  
Figure 7: Strain of liner in the gusset region of the NCS.  
According to the loading procedure described above, nonlinear FE analyses are performed on the overall model and the gusset sub-model to validate the effectiveness of the gusset sub-model. To facilitate the comparison of the analysis results, the mesh density of the gusset sub-model is set to be the same as that of the overall model. As shown in Figure 10, the deformation distributions of both the overall model and the gusset sub-model are similar under the same internal pressure (2.1 MPa), and the difference in the maximum displacement is less than 5%. Moreover, the strains of the liner demonstrate a high degree of similarity throughout the whole pressurization process. Generally, the developed gusset sub-model is effective.
# Dynamic Thermal Rating and Development of Renewable Energy Zones in Sicily
## 4 Methodology  
At present, there are no studies describing the presence, configuration, and types of REZs [8] in the Sicilian territory; therefore, the first objective of this study was to develop a methodology capable of identifying the REZs in the region. The specific technical contribution of this study lies precisely in the fact that, for the first time in the world, the identification of REZs is carried out according to a simple and direct procedure. The main reason for the need to identify REZs in Sicily is to reduce grid congestion, particularly the problem of Wind Power Curtailment (WPC). To achieve this, the basic characteristics of a REZ were defined. The different REZs were then identified using software developed by Terna that allows for detailed power flow analysis. Once the REZs in Sicily were identified, the application of the Dynamic Thermal Rating in each of them was simulated; an algorithm proposed by the IEEE was used [14]. Subsequently, based on the data collected in the early stages, the most critical power lines were identified. The use of Dynamic Thermal Rating in the study of Renewable Energy Zones also represents an innovative technical contribution. Finally, economic analyses were carried out, and a verification comparison was made with the devices currently installed in Sicily.
## 2 Materials and Methods
### Programming and Calibration of the Device
Only the characteristics considered were changed for obtaining the minimum error for each calibration. Data collection by the sensor device and the KIMO equipment was carried out at regular intervals of 5 min (which provided 108 measurement points each). A shorter time between measurements was beneficial to obtain more data, helping to obtain a more accurate and successful calibration line. Additionally, this time could be linked to the resolution of the IAQ monitoring device. A longer time between data collection meant that changes in CO\({}_{2}\) concentration were neglected. Calibrations compare the experimental data from the KIMO equipment with the prototype estimates. To validate these comparisons, different error rates are used. This allows to observe how well the prototype device reproduces the behavior of the reference standard equipment. Several indices for validating models are reported in the literature, which can be classified according to their dependence on the scale of the compared signals. To begin with, errors that depend on the scale [92] such as the mean absolute error (MAE) used by [93] which measures the average magnitude of the error between the measured data by the equipment and the data estimated by the device, the mean error (ME) used by [94] which measures whether the device measures whether the device overestimates or underestimates the measured data, and the root mean square error (RMSE), used by [95] which weights the forecasts that are farthest away from the measured value by the equipment. Additional examples include the errors that do not depend on the scale [96] such as the mean absolute percentage error (MAPE) used by [97] which measures the average percentage error of the estimates and the best fit index (FIT) used by [98] which compares the measured and estimated data with respect to the average of the measured data. It can be noted that the coefficient of determination (R\({}^{2}\)) only measures the proportion of the variance in the dependent variable that is predictable from the independent one [99]. In addition, R\({}^{2}\) is invariant for linear transformations of the distribution of the independent variables [100], so that an output value close to one does not always produce a good prediction regardless of the scale on which these variables are measured. In this context, indicators as MAPE are recommended when it is more important being sensitive to relative variations than to absolute variations [101]. However, although scale-independent errors are widely used in the literature, since they are used when comparing data with different scales, the weighting of the larger-scale error causes adjustments with larger deviation the higher the CO\({}_{2}\) concentration to penalize against others. For instance, RMSE gives a relatively high weight to large errors since the errors are squared before they are averaged, so that it is useful when large errors are particularly undesirable [102].
## 2 Evolution of Maritime Communication Networks
### Communication Links and Technologies
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Link** & **Technology** & **Frequency Band** \\ \hline Satellite \(\leftrightarrow\) Ground & SATCOM, Ka/Q/V-band, & L, Ku, Ka, Q/V, Optical \\  & Optical Comm & \\ \hline Satellite \(\leftrightarrow\) HAP/UAV & LaserComm, mmWave, & Ka/Q/V bands, Optical \\  & Backhaul & \\ \hline HAP/UAV \(\leftrightarrow\) Ships/Sea BS & mmWave, THz, Sub-6 GHz, & 3.5 GHz, 28 GHz, \\  & IRS-assisted links & \textgreater{}100 GHz (THz) \\ \hline Ground BS \(\leftrightarrow\) Ships (Nearshore) & 5G NR, LTE, & \\  & MIMO & Sub-6 GHz, 24–28 GHz \\ \hline UAV \(\leftrightarrow\) UAV, & mmWave/THz, & \\ UAV \(\leftrightarrow\) IRS UAV & RIS beam coordination & 60 GHz, \textgreater{}120 GHz \\ \hline Ship \(\leftrightarrow\) Ship/Buoy (S2S) & LTE, Wi-Fi 6, VHF/UHF & 156–174 MHz (VHF), 2.4/5 GHz, LTE bands \\ \hline Ship/Buoy \(\leftrightarrow\) AUV (Underwater) & Acoustic, Optical, & kHz (acoustic), \\  & Magnetic Induction & 450–550 nm (optical), LF EM \\ \hline Buoy \(\leftrightarrow\) Satellite & SATCOM, Ka/Q/V & Ka, Q/V \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Communication links and operating bands for different MCN interfaces.