esa-sceva/satcom-chunk-collection huggingface dataset:
# Public Key-Based Authentication and En-Route Filtering Scheme in Wireless Sensor Networks
## 4 The Proposed Scheme
### Scheme Overview
#### 4.1.1 Design of Mechanism for Filtering False Report  
The basic idea of the mechanism for filtering a false report is to generate a cluster signature for each event report which is cosigned by \(t\) cluster nodes in that event area so that any forwarding node with the cluster public key can verify the signed event report and drop the false one.  
To reduce the length of a signed report, the elliptic curve Pintsov-Vanstone signature scheme (ECPVSS) [21; 22] is adopted, which is a message recovery signature scheme based on ECC (elliptic curve cryptography). Using ECPVSS, the signer will pick a random \(k\) and use \(kP\) for encrypting part of the report. A different random \(k\) is required for a different signature, otherwise, the adversary might easily infer the private key by capturing any two signatures generated from the same \(k\). PDF [19] adopts the joint Shamir random secret sharing scheme [23] to share a different random \(k\) among the group of sensors each time, which will incur high communication overhead and time overhead. To address this problem, this study let each participating node directly pick a random \(k_{j}\) and provide \(k_{j}P\) to jointly construct \(kP\). Compared to PDF, this method is simpler and incurs less communication consumption and time overhead.  
Due to the threat of node compromise, any single node cannot be trusted to hold the private key used for signing the event reports. Therefore, this study adopts [PERSON]'s secret sharing technique [23] to share the private key. To confine the impact of a compromised node to its vicinity, the authors let each cluster share a unique private key (called a cluster private key) among the sensor nodes in that cluster (each cluster node holds a unique share of the cluster private key). Any \(t\) sensor nodes in a cluster can reconstruct the cluster private key collaboratively. Therefore, each event report will be cosigned by \(t\) cluster nodes in that event area. As long as less than \(t\) sensor nodes in a cluster are compromised, the adversary cannot obtain the cluster private key and cannot generate a valid cluster signature.
## 5 Conclusions  
We examined the driving factors for the spatial heterogeneity and change trends of vegetation in the MHRB. The following are the primary conclusions. (1) Annual LAI and GPP increased significantly from 2001 to 2018 at the regional scale (\(p<0.05\)). The areas with significantly increased (\(p<0.05\)) LAI and GPP accounted for 81.4% and 91.5% of the MHRB, respectively. (2) Land use was the most influential element for the spatial heterogeneity of LAI and GPP, and the humidity index was the most crucial among climate indicators. (3) Non-linear enhancement or bivariate enhancement was discovered between any two factors, and the strongest interaction was from land use and humidity index. (4) The dry areas with annual precipitation below 407 mm and a humidity index under 0.41 had the lowest values of LAI and GPP. With an increase in slope, LAI and GPP values rose. LUM and forests both had higher LAI and GPP values. (5) The areas with unaltered land use accounted for about 87% of the areas with significantly increased vegetation. The increasedprecipitation and humidity index and the reduced VPD facilitated vegetation growth in the MHRB from 2001 to 2018. (6) The sensitivity of GPP to climate change was stronger than that of LAI. Even though more factors need to be taken into account, our study provides a comprehensive overview of the vegetation distribution, variations, and their driving forces in the MHRB. The methodology and results are distinctly important for ecological conservation and eco-hydrological research in the MHRB or other vegetated areas.  
Conceptualization, B.C. and Y.-J.S.; Data Curation, B.C.; Formal Analysis, B.C.; Funding Acquisition, Y.-J.S.; Methodology, B.C. and Y.W.; Supervision, X.Z. and Y.-J.S.; Validation, B.C. and Y.-J.S.; Visualization, B.C. and X.Z.; Writing--Original Draft, B.C.; Writing--Review and Editing, B.C., Y.W. and Y.-J.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China (2021 YFD1700500), the Natural Science Foundation of Hebei Province (D2021503001), and the Hebei Provincial Key R&D Programme (22377001D).  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to permissions issues.  
The authors declare no conflicts of interest.
## 3 Results
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & & & & **Parameter Variation (*) (\%)** \\ \hline H\({}_{2}\) Participation into the Combustible (\%) & 0 & 10 & 20 & +20 \\ \hline Stoichiometric flue gases volume [Nm\({}^{3}\)/Nm\({}^{3}\)] & 10.67 & 9.90 & 9.12 & \(-\)14.5 \\ \hline Stoichiometric air volume [Nm\({}^{3}\)/Nm\({}^{3}\)] & 9.52 & 8.81 & 8.10 & \(-\)14.9 \\ \hline Net calorific value [J/Nm\({}^{3}\)] & 35,800 & 33,299 & 30,798 & \(-\)14.0 \\ \hline Water vapor specific volume [Nm\({}^{3}\)/Nm\({}^{3}\)] & 2.15 & 2.04 & 1.93 & \(-\)10.2 \\ \hline Carbon dioxide specific volume [Nm\({}^{3}\)/Nm\({}^{3}\)] & 1 & 0.9 & 0.8 & \(-\)20 \\ \hline Water vapor partial pressure in the flue gases [bar] (excess air 1.2) & 0.171 & 0.175 & 0.180 & +5.26 \\ \hline Carbon dioxide partial pressure in the flue gases [bar] (excess air 1.2) & 0.0795 & 0.0772 & 0.0745 & \(-\)6.29 \\ \hline Adiabatic theoretical burning temperature [\({}^{\circ}\)C] (excess air 1.2) & 1770 & 1776 & 1782 & \(<\)1 \\ \hline Dew point temperature in the flue gases [\({}^{\circ}\)C] (excess air 1.2) & 56.77 & 57.24 & 57.77 & +1.76 \\ \hline \hline \end{tabular}  
* Calculated for the entire domain of H\({}_{2}\) concentration and referred to the initial value (corresponding to H\({}_{2}\) = 0%).  
\end{table} 
Table 3: Burning process characteristics when hydrogen is infeed in pure methane. variation), the final water vapor flow will also increase. This will generate a larger available water vapor flow for condensation. Considering that the water vapor's partial pressure also increases by approx. 5%, general beneficial condensing conditions are to be expected when H\({}_{2}\) concentration increases in mixture with CH\({}_{4}\). The dew point temperature in the flue gases with a 1.2 excess air coefficient converges to this conclusion, its value is rising with 1 \({}^{\circ}\)C.  
The adiabatic theoretical burning temperature rises marginally, by less than 1%, having practically no influence over the heat transfer conditions or the burner temperature field (no dangerous high temperatures that might damage the burner's stabilization surface). For the computational results, a very large number of parameters were determined and consequently analyzed; the entire process of heat and mass transfer for each separate boiler surface being investigated and presented via the computational modelling of each boiler. A total of 270 functional parameters were investigated for different functioning regimes (different temperatures, velocities, heat fluxes, and heat and mass transfer coefficients) for CORCOND, and 130 parameters for FINCOND modelling program, respectively. Nevertheless, the interest of the current study being the functional behavior of the boiler from the user's point of view, and mainly focusing on the condensing process, only a narrow selection of the most defining parameters is presented and further commented on. The functioning parameters for the corrugated coil and finned pipes condensing boilers are plotted in Tables 4 and 5, respectively.
## 2 Methodology
### Reliability Evaluation of the Model  
After tuning the hyperparameters of the LSTM and SVR models with the fitting datasets, the training datasets and optimal hyperparameters were adopted to train the LSTM models and SVR models. To verify the prediction accuracy of the LSTM models, SVR models and the ensemble models, the root mean square error (RMSE) (Equation (13)) and mean absolute percentage error (MAPE) (Equation (14)) were calculated. The overall process is shown in Figure 3.  
\[RSME=\sqrt{\frac{1}{N}{\sum_{i=1}^{N}\left(\hat{x}_{i}-x_{i}\right)^{2}}} \tag{13}\]  
\[MAPE=\frac{1}{N}{\sum_{i=1}^{N}\left|x_{i}-\frac{\hat{x}_{i}}{x_{i}}\right|} \tag{14}\]where \(x_{i}\) is the measured value; \(\hat{x}_{i}\) is the predicted value; \(N\) is the number of predicted values.
# Precise Orbit Determination of BDS-2 and BDS-3 Using SLR
## 4 Results
### The Success Rate of Multi-day Solutions  
In Table 4, we find that the percentage of success solutions increases dramatically with the increase in the arc length, except for the comparison with WUM orbit of GEO C01. Although the success rate of C01 improves from 19.8% of the 3-day solution to 37.2% of the 9-day solution for the orbit overlaps, it decreases from 12.7% of the 3-day solution to 8.1% of the 9-day solution for the comparison with WUM orbit. Among the three BDS IGSO, the success rate of C13 in the 9-day success solution can reach 89.0% for the orbit overlaps and 85.5% for the comparison with WUM orbit. In particular, from the 3-day solution to the 9-day solution, the success rate of C11 increases from 53.2% to 100% for the comparison with WUM orbit, as well as 43.0% to 99.4% for the orbit overlaps. The success rate is also more than 80% for the orbit overlaps of BDS-3 MEO C21 and C29; however, it is only 58.7% for BDS-3 MEO C20, caused by the interruption of nearly a month of SLR observation data for C20, and a similar situation appears in C30 (as seen in Figure 2).  
Figure 5: The SLR validation time series for BDS-3 satellites derived from WUM in the half-year time series since 2019. The definition of different colour dots and the red line are the same as in Figure 4.
## 2 Formulation of the Problem
The displacement components \(u_{0}\), \(v_{0}\) and \(w\) and shear rotations \(\psi_{r}\) and \(\psi_{\theta}\) are assumed in the form  
\[ \begin{array}{c}u_{0}(r,\theta,t)=U(r)\ e^{n\theta}e^{i\omega t},\\ v_{0}(r,\theta,t)=V(r)\ e^{n\theta}\ e^{i\omega t},\\ w(r,\theta,t)=W(r)\ e^{n\theta}\ e^{i\omega t},\\ \psi_{r}(r,\theta,t)=\Psi_{r}(r)\ e^{n\theta}\ e^{i\omega t},\\ \psi_{\theta}(r,\theta,t)=\Psi_{\theta}(r)\ e^{n\theta}\ e^{i\omega t},\end{array}  \tag{8}\]  
where \(r\) and \(\theta\) are polar coordinates which describe the radial and circumferential direction, \(\omega\) is the angular frequency of vibration, \(t\) is the time and \(n\) is the circumferential node number. Figure 2: Plate of variable thickness. Substituting Equation (8) into the equilibrium equation and replacing the coefficients \(A_{16},\ A_{26},\ A_{45},\ B_{11},\ B_{12},\ B_{22},\ B_{66},D_{16}\) and \(D_{26}\) identically with zero for antisymmetric angle-ply laminates, the resulting equation becomes, in the matrix form,  
\[\left[ \begin{array}{ccc}L_{11}&L_{12}&L_{13}&L_{14}&L_{15}\\ L_{21}&L_{22}&L_{23}&L_{24}&L_{25}\\ L_{31}&L_{32}&L_{33}&L_{34}&L_{35}\\ L_{41}&L_{42}&L_{43}&L_{44}&L_{45}\\ L_{51}&L_{52}&L_{53}&L_{54}&L_{55}\end{array} \right]\ \left\{ \begin{array}{c}U\\ V\\ W\\ \psi_{R}\\ \psi_{\Theta}\end{array} \right\}\ =\ \left\{ \begin{array}{c}0\\ 0\\ 0\\ 0\end{array}
# Design and Implementation of a Multi-Function Hydrophone for Underwater Acoustic Application
## 3 Results and Analysis
### Experimental Results in Mode 2
In order to avoid error propagation, the training symbols are periodically inserted in the transmitted signals, and low-density parity-check (LDPC) coding with a rate of 3/4 is utilized. Figure 12: Structure of CE-DFE. Figure 13 shows the results in mode 2. Figure 13a shows the transmitted Sonar picture1. The transmitted picture is compressed with three colors, where the green part denotes no target, and the blue and yellow colors denote detected targets with different strengths. The SNR of the signals in each hydrophone is 21.5 dB, 29.3 dB, and 25 dB, respectively. Figure 13b shows the picture using the first hydrophone, one may see that, although there is much noise, the outline can be observed. The BER after LDPC decoding in Figure 13b is 0.1721. Looking at Figure 13c, the picture is clearly recovered, and the noise level is extremely low. The BER in Figure 13c is \(2.7778\times 10^{-4}\). Since the multiple reception is used, the vertical diversity is used to enhance received SNR. Finally, Figure 13d shows the recovered picture using three hydrophones, one may see that the recovered picture is the same with in Figure 13a, and the BER in Figure 13d is 0. Mode 2 builds a bridge of signal transmission between the underwater and non-underwater, providing a new solution when some urgent information needs to be recovered in time. Moreover, mode 2 can connect with Cloudy. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Notation** & **Value** \\ \hline Sample frequency (kHz) & \(f_{s}\) & 64 \\ Center frequency (kHz) & \(f_{c}\) & 15.5 \\ Bandwidth (kHz) & \(B\) & 4 \\ Modulation system & - & QPSK \\ Channel length & \(L\) & 25 ms (100 samples) \\ Length of feedforward filter & \(N_{ff}\) & 75 ms (300 samples) \\ Length of feedback filter & \(N_{fb}\) & 24.8 ms (99 samples) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parameters setting in mode 2. Figure 13: Image transmission results. (**a**) The transmitted picture. (**b**) Recovered image through one channel. (**c**) Recovered image through two channels. (**d**) Recovered image through three channels.
## 5 Experimental Data Analysis Based on Linear Regression
### Error Analysis of Linear Regression  
After the training data have been trained using linear regression, the test data in other experiments are obtained to verify the predicting error of this method. Predicting scatter images are shown in Figure 9.  
The horizontal axis represents the real disturbance value of the robot measured by the force/torque sensor; the vertical axis represents the disturbance value predicted by the model; the red line is the y = x line; each blue point represents a set of artificial lateral-line data; its abscissa is the corresponding real value of the disturbance; and the ordinate is the predicted value of the disturbance output by the model. Theoretically, the closer the blue point is to the y = x line, the closer the disturbance value predicted by the model is to the real disturbance value, and the better the prediction effect of the model is. On the contrary, the farther the blue point is from the y = x line, the more the disturbance value predicted by the model deviates from the real disturbance value, and the worse the prediction effect of the model is. The distance from the blue point to the red line in the \(Y\)-axis direction directly reflects the model error.  
Predicting-error analysis of the regression models obtained by this method is shown in Table 2.  
SSE is the sum of squares due to error, namely the sum of the squares of the differences between the dependent variable and the regression values of the dependent variable; MSE is the mean squared error which reflects the accuracy of regression estimation; MAE is the mean absolute error, the average value of the absolute error between the observed value and the real value.
# Physics-Based Noise Modeling and Deep Learning for Denoising Permanently Shadowed Lunar Images
## 3 Methods
### Evaluating Indicator  
To evaluate image quality, we employed a combination of reference and non-reference metrics. For the simulated noiseless images, we used Mean Absolute Error (MAE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index Measure (SSIM) [45]. MAE quantifies pixel-level discrepancies between denoised and original images, while PSNR measures the disparity between the processed and original images, with higher values indicating better quality. SSIM offers a more holistic evaluation by comparing structural similarities, including luminance and contrast, and aligns well with human visual perception. For real lunar PSR images, which lack ground-truth references, we used three non-reference metrics: Integrated Local Natural Image Quality Evaluator (IL-NIQE) [46], Non-Reference Quality Metric (NRQM) [47], and Multi-Scale Image Quality (MUSIQ) [48]. IL-NIQE assesses image naturalness by comparing it to typical natural scene statistics, making it useful for evaluating natural feature preservation. NRQM provides a general image quality score based on perceptual quality, while MUSIQ simulates human visual perception using multi-scale information, reflecting subjective image texture across different spatial scales.  
As non-reference image quality metrics, the specific values of IL-NIQE, NRQM, and MUSIQ are generally based on the image quality characteristics. The quality of denoised images is typically assessed by the difference in these metrics before and after denoising. Using the original image as a baseline, a lower IL-NIQE value for the denoised image compared to the original is considered better, while higher NRQM and MUSIQ values for the denoised image, relative to the original, are preferred [46; 47; 48].
# A Survey on LoRaWAN Architecture, Protocol and Technologies
## 2 LoRa & LoRaWAN Technologies
### MAC Layer--LoRaWAN
#### 2.4.7 Channel Access  
LoRaWAN controls how devices access to the medium by duty-cycle limitation. Each sub-band has to wait for \(Toff_{subband}\) duration to transmit the next frame. Nodes can still use different sub-band while waiting for previously available sub-band.  
\[Toff_{subband}=\frac{FrameAirTime}{DutyCycle_{subband}}-FrameAirTime \tag{8}\]  
For 868 MHz duty-cycle limitation, 1% and it corresponds to 36 s. The first three channels 868.1, 868.3 and 868.5 reserved for control commands and should be implemented by all end nodes.  
The protocol has two activation processes for nodes to participate in the network; (i) Activation By Personalization (ABP) and (ii) Over The Air Activation (OTAA) which will be discussed in Section 2.6.
## Abbreviations  
AWOAAdvanced whale optimization algorithm BERBit error rate GWOGray wolf optimization FLOPsFloating point operations per second MIMagnetic induction MIMOMulti-input-multi-output MMSEMinimum mean square error MLMaximum likelihood MRCMaximal ratio combining MISOMulti-input-single-output OFDMOrthogonal frequency division multiplexing PSOParticle swarm optimization algorithm QPSKQuadrature phase shift keying QAMQuadrature amplitude modulation SVGSingular value decomposition WOAWhole optimization algorithm ZFZero forcing
## 4 Discussion
### Protein Analysis  
According to the results of the DSC analysis of swine meat, it can be stated that myosin, _which is the most abundant myofibrillar protein_, is sensitive to high hydrostatic treatment even in lower pressure ranges [29; 70].  
The results showed that the proteins present in the samples were fragmented under the effect of a lower pressure treatment, and during the storage they were broken down as a consequence of the activity of proteolytic enzymes. This clearly indicated the maturation of the meat, as a result of which the amount of denaturable proteins decreased.  
To quantify the denaturation of proteins, the values of the denaturation enthalpy were determined. This thermodynamic property allowed the quantitative characterization of proteins by determining the areas under the denaturation peaks. In the case of swine, the heat flow values did not show any large change either due to changes in pressure values or storage time [70]. In contrast, a remarkable decrease could be observed in the wild boar meat at the end of storage. This indicated that the cohesive force between proteins was weakened by the proteolytic enzymes released, which meant that less energy would be sufficient to denature the proteins. However, the effect of enzymes appeared to decrease at 250 MPa, thanks to which a notable difference was not visible between the fresh and aged samples, which indicated partial or complete denaturation of the enzyme system [32].  
Overall, it can be stated that the observed meat proteins were affected even by low hydrostatic pressure values (150-300 MPa) [31]. Actin and myosin were found to be more sensitive to HHP treatment than collagen and sarcoplasmic proteins.
# A Survey on Video Big Data Analytics: Architecture, Technologies, and Open Research Challenges
## 5 Open Research Challenges and Future Directions
### Scalability and Real-Time Processing  
The exponential rise in video data from surveillance, healthcare, and smart city infrastructure has increased the demand for scalable, low-latency analytics. Cloud-centric architectures often suffer from latency and network bottlenecks [108; 112], making them less suitable for real-time inference. While hybrid edge-cloud systems offer partial solutions, key challenges remain in adaptive inference scheduling, task offloading, and dynamic resource orchestration. Emerging strategies, such as reinforcement learning-based scheduling and predictive offloading, leverage context-aware optimization using network states and device profiles, but require further refinement to meet the stringent latency demands of live video analytics.  
Edge device constraints further complicate deployment. Running deep models in resource-limited environments necessitates lightweight variants. Techniques such as quantization, pruning, and knowledge distillation [30] show promise, but need to be optimized for high-dimensional video streams without sacrificing performance. While next-generation infrastructure such as 6G may alleviate bandwidth limitations, coordinated co-design of AI models and network protocols will be essential. To this end, future versions of ViMindXAI will incorporate reinforcement learning-based schedulers and predictive offloading into the DAL and DaAL layers for context-aware resource management. ONNX-compatible lightweight models enhanced with quantization and distillation will support efficient edge inference under practical constraints.
## 3 Importance Measure Index and Calculation Method  
The failure uncertainty of component level will propagate to the failure uncertainty of system level, leading to the uncertainty risks of the system during operation. To reduce system-level uncertainty effectively in the design stage, this section proposes an importance measure index to measure the contribution of the uncertainty of the component failure rate to the uncertainty of the power supply reliability as well as an algorithm for computing the index presented on the basis of the AEPS reliability calculation method described in Section 2. The ranking of the obtained index can quickly lock the key components with high contribution to system-level reliability uncertainty and provide theoretical guidance for designers to revise the system design: priority to improve the performance of the key components can more effectively reduce the uncertainty of system reliability.