esa-sceva/satcom-chunk-collection huggingface dataset:
# Investigation of Data Quality Assurance across IoT Protocol Stack for V2I Interactions
## 6 Findings and Discussion
### Discussion
Paralleling the features of UDP, the QUIC protocol offers superior performance under various metrics TCP is known for [112], making it a viable choice for a plethora of V2I applications. Leveraging the strengths of both TCP and UDP, SCTP uniquely combines these traditional protocols to create a distinctive transport layer solution. The efficacy of SCTP among its peers in addressing five of our DQ dimensions is largely attributed to its inherent multipath, multi-homing, and multi-streaming capabilities [113], which can significantly enhance the reliability, efficiency, and robustness of V2I communications. The multipath capabilities ensure uninterrupted data flow between V2I nodes, even under challenging network conditions. This feature is particularly important for real-time traffic management, enabling smooth data transmission. The multi-homing feature allows vehicles to maintain a sustained connection with multiple infrastructures simultaneously, increasing reliability. This is especially beneficial for safety-critical applications like collision detection and avoidance. Finally, the multi-streaming capability can enable the transfer of diverse data types in separate streams, enhancing data delivery efficiency. This capability can significantly improve the performance of use cases that involve the exchange of a variety of data, such as traffic information, vehicle telemetry, and multimedia data. V2I communications necessitate high availability and timely and reliable packet delivery, even under challenging network conditions. These are key characteristics the transport layer protocols should effectively support that are beneficial to the numerous use cases and are particularly beneficial to smart traffic signal coordination [114, 115]. Finally, as earlier discussed in Section 3, 6 LoWPAN is specifically designed to enable IPv6 packets to be transmitted while RPL provides routing functionalities for IPv6 packets over constrained networks. These protocols underscore their critical role in maintaining robust V2I communication by satisfying three critical DQ dimensions: availability, confidentiality and reliability.
# DIFFBAS: An Advanced Binaural Audio Synthesis Model Focusing on Binaural Differences Recovery
## 2 Methodology
### Overall Loss Function  
In addition to the ITD mentioned above, the human ear's ability to localize sound sources also depends on the ILD (interaural level difference) and the spectral cues generated by the pinna. Under ideal circumstances, by accurately modeling the time delays and energy attenuation during the sound source propagating to both ears, the synthesized binaural audio can have ITD, ILD and spectral cues consistent with the real audio, thereby achieving higher sound localization accuracy. [PERSON] et al. [15] underscored the significance of accurate phase estimation for binaural audio by introducing a phase loss term \(\mathcal{L}_{\textit{pls}}\). They also revealed that the \(\mathcal{L}_{2}\) loss on waveforms combined with \(\mathcal{L}_{\textit{pls}}\) loss can effectively supervise the time delays and energy reductions in sound propagation. However, these two losses are only used to supervise the training of propagation models for each ear. Since these two losses cannot be completely optimized to 0, even if the loss is well optimized, it cannot ensure that the difference between the two ears is fully restored. Therefore, additional loss is needed to compensate for this deficiency to intuitively compute disparity cues between ears  
According to Sections 2.3 and 2.4, we infer that the IPD of binaural audio contains spatial information and can be quantified by calculating the phase spectral difference. Ideally, the predicted IPD of binaural audio should be highly consistent with the actual IPD of binaural audio to obtain a consistent spatial experience. Therefore, we introduce an additional loss named \(\mathcal{L}_{IPD}\) to better supervise the model to learn more accurate binaural differences. According to Equation (12), \(\mathcal{L}_{IPD}\) can be defined as  
\[\mathcal{L}_{IPD}=\frac{1}{F_{s}\cdot T_{s}}\sum_{w=0}^{F_{s}}\sum_{t=0}^{T_{ s}}\lvert IPD(\mathbf{\hat{y}}(t))-IPD(\mathbf{y}(t))\rvert \tag{13}\]  
where \(\mathbf{\hat{y}}(t)\) is the predicted binaural audio signal and \(\mathbf{y}(t)\) is the actual binaural audio, and \(F_{s}\) and \(T_{s}\) are the frequency and time resolutions of STFT. According to Equation (7), we calculated that the binaural phase difference may exceed \(2\pi\) at high frequencies, causing confusion in human positioning. Therefore, it is necessary to focus on the low-frequency range and filter the frequency distribution with lower energy when calculating IPD, since real sound is usually concentrated at specific frequencies with high energy.  
The backbone network is trained by minimizing the following loss:  
\[\mathcal{L}(\mathbf{\hat{y}}(t),\mathbf{y}(t))=\lambda_{1}\mathcal{L}_{2}+\lambda_{2} \mathcal{L}_{\textit{phs}}+\lambda_{3}\mathcal{L}_{\textit{IPD}} \tag{14}\]  
where \(\lambda_{1}\), \(\lambda_{2}\) and \(\lambda_{3}\) are the hyperparameters during training.
# Estimation of Wind Turbine Blade Icing Volume Based on Binocular Vision
## 2 Principle and Method
### Stereo Matching
#### 2.1.3. Disparity Computation and Disparity Optimization  
In the disparity computation, the Winner-Takes-All (WTA) method is employed to select the disparity value with the lowest cost as the optimal disparity, thereby generating the initial disparity map. However, due to differences in camera viewpoints, there may be invalid occlusion regions in the image pair where disparities cannot be reliably computed. Therefore, the initial disparity map requires multi-step optimization, including consistency checking, iterative support region voting, appropriate interpolation, and sub-pixel refinement.  
For each point in the initial disparity map, the left-right consistency is checked using the following formula:  
\[|D_{L}(p)-D_{R}(p-D_{L}(p))|\leq\delta \tag{13}\]  
where \(D_{L}(p)\) is the disparity value at pixel \(p\) in the left disparity map, \(D_{R}(p-D_{L}(p))\) is the disparity value at the corresponding pixel \(p-D_{L}(p)\) in the right disparity map, and \(\delta\) is the error tolerance, typically set to \(\delta=1\). Points that pass this check are marked as valid, while those that fail are labeled as outliers.  
An iterative support region voting process is then applied to the outliers. The conditions for updating an outlier's disparity value are:  
\[\begin{cases}|R(p)|>\tau_{n}\\ \frac{n_{d_{R}(p)}}{|R(p)|}>\tau_{r}\end{cases} \tag{14}\]  
where \(|R(p)|\) is the total number of pixels in the support region \(R(p)\) around pixel \(p\); \(n_{d_{R}(p)}\) is the number of pixels within \(R(p)\) that share the most frequently occurring disparity value, i.e., the number of votes for the mode disparity value; \(\tau_{n}\) and \(\tau_{r}\) are predefined thresholds.  
When the support region of an outlier satisfies the above conditions, the value \(d_{R}(p)\) is used to replace the outlier's disparity, and it is marked as valid. This process is iteratively repeated to handle additional outliers.  
Appropriate interpolation is utilized to address the remaining outliers. First, outliers are classified into occluded points and mismatched points. For occluded points, the nearest valid disparities in the left or right direction are used for replacement. For mismatched points, the nearest valid disparities in both the left and right directions are found, and the mismatched point is replaced with the smaller of the two disparity values.  
Finally, sub-pixel refinement is applied to the disparity map to obtain the final disparity map.
# An ML-Based Solution in the Transformation towards a Sustainable Smart City
## 4 Results
In machine learning algorithms, several key hyperparameters have been tuned to optimise model performance:  
* LightGbm Regression: The number of boosting iterations (num_iterations), learning rate (learning_rate) and maximum tree depth (max_depth).These control the number of trees built, the step size of model updates and the complexity of each tree;
* FastTreeTweedie Regression: the number of leaves (num_leaves), the minimum number of examples per leaf (min_leaf_count) and learning rate (learning_rate).These affect the granularity of the tree, the minimum data needed in a leaf for further splitting, and the speed of convergence;
* LbfgsPoissonRegression: the number of iterations (num_iterations), the strength of regularisation (l2_regularization) and the tolerance (convergence_tolerance).These govern the maximum number of optimisation steps, the penalisation of large coefficients and the convergence criteria of the algorithm;
* FastTreeRegression: number of trees (num_trees), minimum split gain (min_split_gain) and learning rate (learning_rate).These control the ensemble size, the threshold for splitting nodes and the rate of model adaptation;
* FastForestRegression: number of trees (num_trees), the number of features to consider per split (num_features_per_split) and the minimum number of samples per leaf (min_samples_per_leaf).These control the diversity and depth of the trees, as well as the minimum data required to create a leaf;
* SdcaRegression: L1 and L2 regularisation terms (l1_regularization and l2_regularization) and convergence tolerance (convergence_tolerance). These parameters control the sparsity of feature selection, the model complexity penalty and the stopping criterion of the optimisation process. Cross-validation showed the superiority of the cascade of simple algorithms over the concurrent solution described in [56] (Figure 6). It is worth considering that for a small sample of smart city transportation data, this deep learning approach achieved 0.99978 [56].
## 1 Introduction
Building on this foundation, the current study developed and verified a finite element model (FEM) to replicate the interaction between a seat cushion with an embedded pneumatic spring and a simplified human body model under static conditions. Finite element analysis (FEA) has proven invaluable in studying human-seat interactions, yet it presents significant challenges [8; 9; 10; 11; 12]. These challenges include accurately modeling the geometry and material properties of human structural layers (bones, muscles, and skin) and capturing the unique characteristics of the pneumatic spring. Furthermore, defining realistic boundary conditions and validating simulation results against experimental data are essential yet complex tasks. Previous studies on FEM-based seat cushion modeling have often relied on oversimplified material models, neglecting the viscoelastic and hyperelastic behavior of seat materials, which has led to discrepancies between simulations and real-world performance. Additionally, many works have been constrained by limited experimental validation, relying on outdated pressure distribution data or insufficient participant demographics, reducing the generalizability of findings. Furthermore, incomplete boundary conditions in prior research have failed to accurately replicate realistic seating scenarios, limiting the applicability of FEM in optimizing seat comfort. Addressing these limitations, this study integrated experimentally validated material models, refined boundary conditions, and an adaptive pneumatic spring system to enhance the accuracy and practicality of FEM-based seat design. This paper proposes a novel solution: a simplified half-symmetric FEM framework designed utilizing MSC.Marc 2018 software. The model effectively replicates the static contact pressure distribution between a human body and a car-seat cushion integrated with a pneumatic spring. By maintaining generality and minimizing computational complexity, the model incorporates realistic boundary and initial conditions derived from experimental setups, ensuring its reliability and applicability. The paper is organized as follows: Section 2 offers an in-depth analysis of the seat cushion design and the operational principles of the integrated pneumatic spring. Section 3 describes the key steps involved in performing finite element analysis (FEA) of the human-seat interaction. Section 4 discusses the findings from both the simulations and experimental evaluations, highlighting key findings and offering insights for future research. Finally, Section 5 concludes the study, emphasizing the broader implications of the proposed FEM framework. This work aimed to establish a robust and innovative simulation model that bridges experimental and computational approaches. The validated model serves as a foundation for optimizing seat designs, reducing the reliance on costly physical prototypes, and accelerating the development of advanced, ergonomic seating solutions for enhanced driver comfort and health.
## 3 Definition of GS-MR Scheme and Security Model  
### Definition  
A GS-MR scheme contains four probabilistic polynomial time (PPT) algorithms:  
1. \(KeyGen(1^{\lambda},1^{N}):\) this takes the security parameter \(\lambda\) and maximum group members \(N\) as the inputs, and outputs the group public key \(gpk\), group member's signing key \(gsk\), and group manager's tracking key \(gtk\).
2. \(Sign(gpk,M,gsk_{\pi},\{\mathbf{ID}_{i}\}_{i=1}^{N}):\) this takes the group public key \(gpk\), a message \(M\), the signing key \(gsk_{\pi}\), and a group member's identity set \(\{\mathbf{ID}_{i}\}_{i=1}^{N}\) as the inputs, and outputs a signature \(SIG\) of \(M\) under \(gsk_{\pi}\).
3. \(Verify(gpk,SIG,\{\mathbf{ID}_{i}\}_{i=1}^{N}):\) this takes the public key \(gpk\), a signature \(SIG\), and a group member's identity set \(\{\mathbf{ID}_{i}\}_{i=1}^{N}\) as the inputs, and outputs \"Valid\" and complete message \(M\) if the signature \(SIG\) is a valid signature on the message \(M\), or \"Invalid\" otherwise.
4. \(Open(gpk,SIG,gtk):\) this takes the group public key \(gpk\), a signature \(SIG\), and the tracking key \(gtk\) as the inputs, outputs the member identity \(\mathbf{ID}_{\pi}\) of the signer if the signature \(SIG\) is \"Valid\", checked using \(Verify\), or \(\bot\) otherwise.
# A Unified Point Multiplication Architecture of Weierstrass, Edward and Huff Elliptic Curves on FPGA
## 5 Implementation Results and Comparison
### Implementation Results

Table 3: Implementation results of our unified accelerator architecture over \(GF(2^{233})\) on FPGA. If we consider the throughput and hardware area at the same time for comparison, i.e., \(FoM\) from Equation (7), it can be observed that the unified design in this article performs better on Virtex-6 FPGA than the Virtex-7 implementation. It is essential to mention that the higher value of \(FoM\) guarantees the higher performance of the architecture. As we can see in Equation (7), the defined \(FoM\) metric is a ratio of throughput with slices, so the lower hardware resources (area) achieved for Virtex-6 result in higher \(FoM\).
# Study on the Early Warning Methods of Dynamic Landslides of Large Abandoned Rockfill Slopes
## 2 Project Overview
### Ideas on Early Warning  
(1A) Based on the results in [2] and the 26 landslides monitored in the previous period (the remaining five landslides were used to verify the early warning method), the surface characteristics of waste rockfill slopes can be identified, and a preliminary judgment can be made about the possible landslide patterns in the excavation process. During the accumulation of waste rockfill, the density of some accumulation layers and areas caused by rainfall, the rolling of construction vehicles, etc. is quite different, or there might be a certain amount of abnormally large-sized rubble on the slope surface and in the shallow layer. Both conditions play a great role in supporting the rubble particles above the dense zone. Therefore, combined with the surface characteristics of the rockfill slope, a possible landslide pattern can be determined through the analysis of the characteristics of the cross-sectional area of different displacement bodies over time.  
(1B) For possible landslides being monitored, based on the real-time constant displacement cross-sectional area data obtained from slope deformation monitoring, a time series prediction model such as ARIMA-SVR [28] (Autoregressive Integrated Moving Average model-Support Vector Regression: a hybrid ARIMA and SVR model of time series prediction mode) can be used. Based on the existing data and the prediction of future data, the future trend of constant displacement cross-sectional areas can be predicted.  
(2) Real-time monitoring data can be extracted, the real-time changing process of the cross-sectional area of equal displacement can be studied and analyzed, and the mode of slope deformation can be determined according to the change trend of the cross-sectional area of the equal displacement body.  
(3) According to the measured (1A) and predicted values of the cross-sectional area of the equal displacement body (1B), the relationship between the cross-sectional area change of the equal displacement body in different modes and landslides of the rockfill slope can be analyzed, and an early warning judgment of such landslides can be determined. Then, the change process of the cross-sectional area of different landslides and equal displacement bodies can be independently analyzed.  
(4) On the basis of the determined early warning criteria of the rockfill slope, an early warning grade division standard of the rockfill slope can be established.  
The detailed process is shown in Figure 10.
# Path Segmentation-Based Hybrid Caching in Information-Centric Networks
## 3 Path Segmentation-Based Hybrid Caching
### Caching in Non-Edge Area
#### 3.4.2 Caching in the Migration Node  
In Section 3.3, we mentioned that we maintain a cache value for each cached content, and the placement of incoming content, the replacement, and migration of cached content are all decided based on cache value. Therefore, the caching decision made by the migration node \(j\) on migrated content \(m\) should also be based on this mechanism. The calculation of cache value \(CV_{j}(m)\) for the incoming migrated content should be different. We mainly consider the popularity of the content, and the calculation formula is as follows:  
\[CV_{j}(m)=f2*P_{j}(m) \tag{5}\]  
where \(f2\) is the weighting constant and \(P_{i}(k)\) is the content popularity normalized with the maximum popularity \(p_{max}\) of cached content. As described in Algorithm 3, the migration node \(j\) calculates the cache value for each incoming migrated content (Step 3) and makes caching decisions according to the cache value. When the replacement occurs, the replaced content is directly expelled (Steps 4-9).
```
current node: \(j\) remaining cache space: \(B_{j}\)
1: for any incoming migrated content \(m\)
2:if content \(m\) not in \(j\)then
3:\(CV_{j}(m)=f2*P_{j}(m)\)
4:if\(B_{j}==0\)then
5:if\(CV_{j}(m)<\min\left\{CS_{j}(n)\ \ \forall n\right\}\) then
6: do not cache m
7:else
8: find \(c\) where \(CS_{j}(c)==\min\left\{CS_{j}(n)\ \ \forall n\right\}\)
9: content \(c\) is replaced with \(m\)
10:endif
11:else
12: cache content \(m\)
13:endif
14:endif
15:if any content expires then
16: expel the content
17:endif
```  
**Algorithm 3**Caching in the Migration Node
# Secure Watermarking Schemes and Their Approaches in the IoT Technology: An Overview
## 2 The Concept of Watermarking
### Embedding Domain
#### 2.1.1 Singular Value Decomposition  
The SVD technology converts an object from an array into three arrays that are similar in their sizes to the original matrix. Assume that \(A\) is an object and that the SVD is defined by Equation (1) [36; 37; 38].  
\[A=USV^{T} \tag{1}\]  
where the \(U\) and \(V\) components denote the orthogonal metrics with small singular values and the \(S\) denotes the diagonal matrix with larger singular value entries as given in Equation (2).  
\[S=\left[\begin{array}{ccc}\sigma_{1}&\dots&1\\ \vdots&\ddots&\vdots\\ 2&\dots&\sigma_{n}\end{array}\right] \tag{2}\]  
where, \(\sigma\)'s (diagonal elements) denote the singular values that satisfy Equation (3) as follows:  
\[\sigma_{1}\geq\ \sigma_{2}\geq\dots\ \geq\ \sigma_{n}\ \geq 0 \tag{3}\]  
Typically, the methods that rely on the SVD technology can explore the decomposition of a single value, which is related to an object. After that, the singular values are replaced by its surrounding watermark.
## 4 Results and Discussion
### WM-OA-ICOS Combined with White Noise Perturbation
#### 4.2.1 Data Calibration for WM-OA-ICOS  
During the further calibration, the CH\({}_{4}\) standard gas was mixed with nitrogen by the gas mixing system to prepare seven gas mixtures with concentrations in the range of 700 ppm to 1200 ppm. The gas mixtures were used to measure the 2f signal amplitude with the original signal and the white noise perturbation. Each concentration was measured 20 times. It could be concluded that the 2f signal absorption peaks of the original signal and the white noise perturbation at different concentrations were significantly different, and these differences could be used for concentration inversion analysis. By fitting the experimental data with the quadratic polynomial, the data fitting curves for the original signal and the white noise perturbation were obtained. The fitting curves between CH\({}_{4}\) concentration and the 2f signal absorption peaks under original signal and white noise perturbation are shown in Figure 7, and the relationship can be expressed by Equation (7). It could be concluded that the curve fitting of the original signal (black) was 0.99942, while the curve fitting of the white noise perturbation (red) was 0.99983. The average relative deviation of the original signal and the white noise perturbation were 0.010-0.012 V and  
Figure 6: (**a**) Raw concentration data collected by the sensor under the original signal (black) and the white noise perturbation of 30% modulation depth (red); (**b**) The Allan deviation of the original signal (black) and the white noise perturbation (red).  
0.003-0.004 V, respectively. The results showed that the optimization method with the white noise perturbation of 30% modulation depth significantly improved the curve fitting and enhanced the accuracy of the concentration inversion.  
\[\left\{\begin{array}{l}C_{Original-signal}=-211.58828+2099.98086A_{da}-508.76 947A_{da}^{2}\\ C_{White-Noise-Perturbation}=-214.80374+2259.80136A_{da}-653.43824A_{da}^{2}\end{array}\right. \tag{7}\]