esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Preliminaries
### Dual-Verifiable Encryption  
Motivated by convergence encryption [23], which offers solely data authenticity validation and does not provide multi-data assurance, we build two provable encryption methods for multi-data verification. In the proposed method, we use two homomorphic hash functions [17] to achieve double validation. The protocol we propose comprises the algorithms listed below:  
* \(\text{Key}(n,d_{1},\ldots,d_{n},h_{1})\rightarrow\{sk_{i}\}_{i\in[1,\mu]}:\) Enter the files amounts (\(n\)) for \(n\) files \(\{d_{i}\}_{i\in[1,\mu]}\), and generate key \(\{Y_{i}\}_{i\in[1,\mu]}\) using homomorphic hash \(h_{1}\).
* \(\text{AggKey}(n,\{Y_{i}\}_{i\in[1,\mu]})\to Y_{i_{\text{agg}}}\): This algorithm generates the aggregate key (\(Y_{i_{\text{agg}}}\)) after receiving as inputs the amount of files (\(n\)) and key \(\{Y_{i}\}_{i\in[1,\mu]}\).
* \(\text{Encrypt }(d_{1},\ldots,d_{n},h_{1},h_{2})\rightarrow(\{E_{1},\mu_{2}\}_{i\in[1, \mu]},a_{M}^{\prime})\). This algorithm generates \(n\) ciphertexts (\{(a_{1},a_{2},\mu_{j})}_{i\in[1,\mu]}\) and an aggregated ciphertext (\(a_{M}^{\prime},a_{2}^{\prime}\)), obtaining \(n\) files (\(\{m_{j}\}_{i\in[1,\mu]}\)) and two homomorphic hashes (\(h_{1}\) and \(h_{2}\)).  
Verify \((sk_{agg},\alpha^{\prime}_{M},\alpha^{\prime}_{2})\to M^{\prime}\): Initially, this method determines if \(h_{2}(\alpha^{\prime}_{M}\cdot sk_{agg})=\alpha^{\prime}_{2}\). Environmental data ciphertexts \((\alpha^{\prime}_{M},\alpha^{\prime}_{2})\) are accurate if this equation is true. Then, it decrypts \(\alpha^{\prime}_{M}\) using key \(sk_{agg}\), yielding decrypted data \((M^{\prime})\). Additionally, it determines whether \(h_{1}(M^{\prime})=sk_{agg}\). This equation indicates that several files have not been altered if it is correct. It returns the original data \((M^{\prime})\) at the end.
# Recursive Least Squares for Near-Lossless Hyperspectral Data Compression
## 3 Experimental Results
### Compression Performance Analysis
#### 3.2.2 Reconstruction Quality Analysis  
In the near-lossless compression scheme, the peak absolute error \(\Lambda\) introduced by the compression process directly affects the quality of the reconstructed image. Figure 5 shows the original image of the AIRS Grand16 image and the reconstructed image at \(\Lambda=\{0,1,10,20,30\}\). It can be noted that the visual performance of each image is extremely similar.  
To objectively evaluate the quality of the reconstructed images, we compare the proposed scheme with the best-known near-lossless compression scheme CCSDS-123-AC, including two metrics, namely the peak signal-to-noise ratio and spectral angle mapper.  
If D is the dynamic range (in bits) of the original image, the maximum pixel value is \(2^{D}-1\). The reconstructed image quality is evaluated in terms of the peak signal-to-noise ratio (PSNR), which is measured in dB and defined as:  
\[PSNR\Big{(}S,\widetilde{S}\Big{)}=10\log_{10}\frac{\big{(}2^{D}-1\big{)}^{2}}{ MSE\Big{(}S,\widetilde{S}\Big{)}}(dB)\, \tag{10}\]  
Figure 4: The relative gain of our scheme on CCSDS-123-AC at different peak absolute errors for AC, AU, and AIRS datasets.  
Figure 5: Visual comparison for the “AIRS Grand16 1256 th band” image: (**a**) original, (**b–e**) reconstructed image at \(\Lambda=\{1,10,20,30\}\), respectively.  
where \(MSE\left(S,\widetilde{S}\right)\) is the mean square error between the original image S and the reconstructed image \(\widetilde{S}\).  
\[MSE\left(S,\widetilde{S}\right)=\frac{\sum_{1}^{NX}\sum_{1}^{NY}\sum_{1}^{ NZ}\left(S_{z}(x,y)-\widetilde{S}_{z}(x,y)\right)^{2}}{NX\times NY\times NZ}, \tag{11}\]  
Spectral Angle Mapper (SAM) treats the spectrum of each image element as a high-dimensional vector and measures the similarity between two spectra by calculating the angle of the vector. The SAM of each pixel spectrum in the original and reconstructed image is denoted as  
\[\alpha(x,y)=\cos^{-1}\left(\frac{\sum_{Z}^{NZ}S_{z}(x,y)\times\widetilde{S}_{ z}(x,y)}{\sqrt{\sum_  
The experimentally obtained data points have been highlighted by marked dots, and linear interpolation is used between the markers. It can be seen that under the same compressed bit rate, the proposed scheme has a higher peak signal-to-noise ratio and a smaller average spectral angle. Further, for image quality, as measured by the PSNR and SAM, our approach is competitive compared to the best-known CCSDS-123-AC scheme.
## 2 Classical ANC Algorithms
### Classical FxRLS Algorithm  
The classical FxLRS algorithm considers the following deterministic least squares (LS)-based cost function [42]  
\[\xi_{\text{LS}}(n)=\sum_{i=1}^{n}\lambda^{n-i}\hat{e}^{2}(i), \tag{6}\]  
where \(\lambda\) is the exponential forgetting factor. Using (3), \(e(i)\) is the a posteriori error given as  
\[e(i)=d(i)-\mathbf{x}_{f}^{T}(i)\mathbf{w}(n), \tag{7}\]  
where \(\mathbf{w}(n)\) is the currently available estimate of the ANC control filter. Computing the gradient of \(\xi_{\text{LS}}(n)\) and equating it to zero  
\[\frac{\partial\xi_{\text{LS}}(n)}{\partial\mathbf{w}(n)}=-2\sum_{i=1}^{n}\lambda^{ n-i}\mathbf{x}_{f}(i)e(i)=0, \tag{8}\]  
and then following the derivation for the standard RLS algorithm as in [42], we arrive at the following computations for the classical FxRLS algorithm for ANC systems:  
\[\mathbf{u}(n)=\mathbf{\Phi}(n-1)\mathbf{x}_{f}(n), \tag{9}\]  
\[\mathbf{k}(n)=\frac{\mathbf{u}(n)}{1+\mathbf{x}_{f}^{T}(n)\mathbf{u}(n)}, \tag{10}\]  
\[\mathbf{\Phi}(n)=\lambda^{-1}\mathbf{\Phi}(n-1)-\lambda^{-1}\mathbf{k}(n)\mathbf{u}^{T}(n), \tag{11}\]  
\[\mathbf{w}(n)=\mathbf{w}(n-1)+\varepsilon(n)\mathbf{k}(n), \tag{12}\]  
where \(\mathbf{k}(n)\) is the Kalman gain vector which, in addition to (10), can be equivalently expressed as \(\mathbf{k}(n)=\mathbf{\Phi}(n)\mathbf{x}_{f}(n)\)[42], and \(\varepsilon(n)\) is the a priori estimation error expressed as  
\[\varepsilon(n)=d(n)-\mathbf{x}_{f}^{T}(n)\mathbf{w}(n-1), \tag{13}\]  
which requires the previous coefficient vector \(\mathbf{w}(n-1)\) and information about \(d(n)\). Unfortunately, the latter is present only in the acoustic domain and is not directly accessible. Another important issue to circumvent is the poor numerical stability of the classical FxRLS algorithm for ANC systems [3,18]. Nevertheless, it is well-known that RLS-based adaptive filters exhibit faster convergence compared to the stochastic-gradient-based LMS adaptive filters, though at a cost of increased computational complexity. Considering that modern digital hardware allow implementing complex algorithms, it is worth developing FxRLS-based adaptive filtering solutions for practical applications. In the following- we explore developing a new FxRLS algorithm based an an objective function well suited for impulsive ANC systems.
## 4 Simulation Experiment  
The multi-agent collaborative target search problem refers to multi-agents cooperating with each other and avoiding collisions and obstacles to search for targets. Agents can communicate with each other to avoid collisions by sharing location information. In order to verify the effectiveness of our MADDPG-E algorithm, we used the Multi-agent Particle Environment (MPE) [43] provided by OpenAI. The multi-agent cooperative search scenario is shown in Figure 3. In the two-dimensional plane, the red balls represent the three agents, and the speed is relatively slow. The green ball represents the target to be searched for, and its speed is relatively high. Because the target moves faster, it is difficult for a single agent to search for it, so multi-agents need to cooperate to fulfill the task. The black balls represent the two obstacles.  
Figure 3: The multi-agent collaborative target search scenario.  
The multi-agent collaborative target search task is for the agents to avoid collision and search for the target. Therefore, the distance between the agents, the distance between the agents and obstacles, and the distance between the agents and targets are needed and used as evaluation indicators to measure the multi-agent search strategy. The multi-agent search strategy evaluation index is as follows:  
\[\begin{cases}\|D_{i}-D_{n}\|\geqslant R_{i}+R_{n}\\ \text{and }\|D_{i}-D_{o}\|\geqslant R_{i}+R_{o}\\ \text{and }\|D_{i}-D_{t}\|\leqslant R_{i}-R_{t}\end{cases},\;n\in\{1,2,\ldots,N\} \tag{15}\]  
\(D_{i}\), \(D_{n}\), \(D_{o}\), and \(D_{t}\) are the central coordinates of the agent \(i\), the \(n\)-th agent, the obstacle, and the target, respectively. \(R_{i}\), \(R_{n}\), \(R_{o}\), and \(R_{t}\) are the the radius of the agent \(i\), the \(n\)-th agent, the obstacle, and the target, respectively. Through the quality evaluation index, the status of the agent in the current position and the quality of the current search strategy can be judged.  
The hyperparameters in the experiment are shown in Table 2.  
Two experiments are designed in this paper, which are the fixed initial position and random initial position.
# Protecting Hybrid ITS Networks: A Comprehensive Security Approach
## 4 Experimental Evaluation and Results Analysis
### Computation Time
#### 4.1.1 Performance Analysis: DLAPP  
According to the DLAPP's proposal [13], its signature and verification simulation took 0.0167 ms (each operation). However, the study only measured the time of the HMAC cryptographic operation, thus being a theoretical estimation. It is essential to include all the computation associated with protecting and deprotecting a message to have a realistic measure of the computation time. The experimental performance results of the DLAPP protocol are shown in Table 7 (from a total of \(\sim\)500 messages, one per second).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Node** & **Protection Latency [ms]** & **Deprotection Latency [ms]** \\ \hline Smartphone X & 0.158 & 0.162 \\ OBU & 0.366 & 0.327 \\ RSU & 0.127 & 0.084 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Median security CT latency using DLAPP in each node.  
Figure 12: Total and security computation times extraction representation for transmission (tx) and reception (rx).  
It can be concluded that, when using hardware (OBU and smartphones), the actual performance falls short (\(\sim\)90 to 95%) of what was initially projected in the protocol proposal. This can be attributed to the initial projections being based on simulations and not considering the entire protection and deprotection process.  
Calculating the total operations per second as the DLAPP's proposal [13] does, the smartphone can protect up to 6311 and deprotect 6165 messages per second (Figure 13). Conversely, the OBU has a lower capacity, protecting 2733 and deprotecting 3058 messages per second. This performance difference (\(\sim\)54%) may be attributed to the inherent limitations of OBUs as a resource-constrained device [14], as can be seen by its specifications in Table 6. Furthermore, the relation between protection and deprotection times exhibits similarity across all nodes. This is because the primary time-consuming factor is the HMAC, which is common in both operations.  
Assuming a similar high-vehicle-density scenario as [13], i.e., 180 vehicles within communication range, sending a packet every 100 ms, this would result in 1800 messages needing to be verified per second. Based on the results (Figure 13), the DLAPP protocol is computationally light enough to manage such a type of high-node-density scenario.
## 2 Materials and Methods
### Ground Survey Data Collection and Processing  
Our sample plots data were collected from the National Forestry Inventory in Yunnan in 2016, with a total of fifty-two sample plots, each with an area of (30 m \(\times\) 30 m), distributed as shown in Figure 1. Each sample plot was recorded with a diameter at breast height, coordinates, canopy density, volume, and tree height [24]. In this study, the biomass of _Quercus aquifolioides_ sample sites was calculated according to the model suggested in the industrial standard published by the State Forestry Administration. The model can be expressed using Equation (1). The product of individual plant biomass and the number of trees is the aboveground biomass of the small class. And the carbon storage is acquired by multiplying the biomass with carbon coefficients, as seen in Equation (2). Here, we set the carbon coefficients to 0.48, which was referenced by the _Quercus aquifolioides_ industry standard [25]. The information on the carbon storage of _Quercus aquifolioides_ in the observation point was obtained after calculation, as presented in Table 1; the maximum was 131.98 t/hm\({}^{2}\), and the minimum was 5.36 t/hm\({}^{2}\).  
\[\mathrm{M_{Quercus}}=0.07806\mathrm{D}^{2.06321}\mathrm{H}^{0.57393} \tag{1}\]  
\[\mathrm{C}=\mathrm{M}\times\mathrm{C_{C}} \tag{2}\]  
where M is the aboveground biomass (kg/m\({}^{2}\)), D is the diameter at breast height (cm), H is the standing height (cm), C is the carbon storage (t/hm\({}^{2}\)), and C\({}_{\mathrm{C}}\) is the carbon conversion index.  
Figure 1: Location of Shangri-La.
## 1 Introduction
### Knowledge Gap  
Based on the literature review, it can be seen that in the context of modeling the deterioration pattern of track geometry condition for the prediction of damping maintenance demands, stochastic process methods provide more direct bases for track maintenance decisions, such as maintenance cycles and remaining useful life, compared to machine learning models, and have stronger applicability. However, existing studies based on stochastic process methods mostly adopt the Markov process, assuming that the deterioration process of track geometry conditions is memoryless, which overlooks the exponential characteristic that the deterioration process within a tamping cycle starts at a slow rate and accelerates over time [15; 30; 31], making it difficult to ensure the accuracy of the prediction results.
## 3 Performance Analysis
### Outage Probability  
Outage probability is a crucial performance indicator for wireless communication systems. Outage probability is defined as the probability of the system's instantaneous SNR dropping below a predetermined threshold \(\Psi_{th}\). Therefore, the outage probability of the system under consideration is computed as [39]  
\[P_{o}=\mathrm{Prob}\{\Psi_{c2e}<\Psi_{th}\}=F_{\Psi_{c2e}}(\Psi_{th}), \tag{15}\]  
where \(F_{\Psi_{c2e}}(\Psi_{th})\) represents the cumulative distribution function (CDF) of \(\Psi_{c2e}\) given as  
\[F_{\Psi_{c2e}}(\Psi_{th})=\prod_{m=1}^{M}F_{\Psi_{SRmD}}(\Psi_{th}). \tag{16}\]  
Here, \(F_{\Psi_{SRmD}}(\Psi_{th})\) is the CDF of the \(\Psi_{SRmD}\) link, which is computed as  
\[F_{\Psi_{SRmD}}(\Psi_{th})=1-[1-F_{\Psi_{SRm}}(\Psi_{th})][1-F_{\Psi_{SRmD}}( \Psi_{th})], \tag{17}\]  
where \(F_{\Psi_{SRm}}(\Psi_{th})\) and \(F_{\Psi_{SRmD}}(\Psi_{th})\) are the CDFs of the \(SR_{m}\) and \(R_{m}D\) links evaluated at \(\Psi_{th}\) and are given by  
\[F_{SR_{m}}=1-Q\bigg{(}\frac{\ln(\Psi_{th})-\mu_{\Psi_{SR}}}{\sigma_{\Psi_{SR}}} \bigg{)}, \tag{18}\]  
and  
\[F_{R_{m}D}=1-Q\bigg{(}\frac{\ln(\Psi_{th})-\mu_{\Psi_{RD}}}{\sigma_{\Psi_{RD}}} \bigg{)}. \tag{19}\]  
Here, \(Q(x)=\frac{1}{\sqrt{2\pi}}\int_{x}^{\infty}e^{-t^{2}/2}dt\). Using Equations (15) to (19) with iid channels, the final expression for the outage probability of the considered system is computed as  
\[P_{o}=\bigg{[}1-Q\bigg{(}\frac{\ln(\Psi_{th})-\mu_{\Psi_{SR}}}{\sigma_{\Psi_{ SR}}}\bigg{)}Q\bigg{(}\frac{\ln(\Psi_{th})-\mu_{\Psi_{RD}}}{\sigma_{\Psi_{RD}}} \bigg{)}\bigg{]}^{M}. \tag{20}\]
###### Abstract  
Accurate wind speed prediction is extremely critical to the stable operation of power systems. To enhance the prediction accuracy, we propose a new approach that integrates bidirectional long short-term memory (BiLSTM) with fully adaptive noise ensemble empirical modal decomposition (CEEMDAN), the RIME optimization algorithm (RIME), and a multi-head self-attention mechanism (MHSA). First, the historical data of wind farms are decomposed via CEEMDAN to extract the change patterns and features on different time scales, and different subsequences are obtained. Then, the parameters of the BiLSTM model are optimized using the frost ice optimization algorithm, and each subsequence is input into the neural network model containing the MHSA for prediction. Finally, the predicted values of each component are weighted and reconstructed to obtain the predicted values of wind speed time series. According to the experimental results, the method can predict the short-term wind speeds of wind farms more accurately. We verified the effectiveness of the method by comparing it with different models.  
wind speed prediction; adaptive noise; modal decomposition; RIME optimization; multi-head self-attention mechanism; bidirectional long short-term memory network; deep learning 2020 14 8337 (10.3390/app1488337) Article  
CEEMDAN-RIME-Bidirectional Long Short-Term Memory Short-Term Wind Speed Prediction for Wind Farms  
1
# Lip2 Speech: Lightweight Multi-Speaker Speech Reconstruction with Gabor Features
## 4 Gabor-Based Speech Reconstruction Network
### Model II: GaborFea2 Speech
#### 4.2.2 GaborFea2 Speech Model Structure  
The final structure of our proposed GaborFea2 Speech model is shown in Table 3. Input to the network is a sequence of Gabor features, denoted as \(F_{n}\), extracted from silent video frames. To increase the length of the input sequence, video frames are upsampled to 130 fps, resulting in a 3-s sample video of 390 frames. This length is then divided into \(K\) non-overlapping slices \(L_{p}\). Gabor features are augmented with first and second-order temporal derivatives to enrich the input representation, tripling the dimension of Gabor features. This fusion results in a 2D tensor with dimensions (\(L_{p}\), \(3^{\mathrm{r}}F_{n}\)). The target bottleneck spectrogram feature vector, which acts as the desired output, is also partitioned into \(K\) slices of length \(L_{a}\) without overlap. By reserving the spatial aspect of the preprocessed input, an LSTM can be directly used to model time dependencies. A single-layer LSTM network with 512 units is utilised to capture temporal patterns. LSTM outputs are then flattened and passed through a single-layer fully connected network before reaching the final output layer. A similar procedure is followed in the output process as with the GaborCNN2 Speech model. A sigmoid nonlinearity is used at the output layer, while the LSTM and fully connected layers use ELU activation functions. The audio waveform is then reconstructed using the auditory spectrogram toolbox [57], which estimates the phase that corresponds to the magnitude of the auditory spectrogram and performs an inverse transformation.
## 5 Ground-Based Calibrations
### Sensitivity Calibration  
Sensitivity refers to the minimum space environment change that can cause a one-count change in the payload. The main determinant is the geometric factor G. Next, the lowest flux change that the detector can respond to is 1/G, and the detector sensitivity is,  
\[\frac{1/\mathrm{G}}{\mathrm{N}}\times 100\% \tag{6}\]  
The geometric factor of the high-energy proton detector G = 0.30 cm\({}^{2}\)sr. According to the aforementioned detection sensitivity test method, the flux increase that causes a count change in the sensor is \(\Delta\)N = 3.3 cm\({}^{-2}\)sr\({}^{-1}\). When the flux is \(>\)66 cm\({}^{-2}\)s\({}^{-1}\)sr\({}^{-1}\), the sensitivity of the detector is better than 5%. Similarly, the geometric factor of the high-energy electron detector G = 0.046 cm\({}^{2}\)sr. Therefore, the flux increase that causes a count change in the sensor is \(\Delta\)N = 21.7 cm\({}^{-2}\)sr\({}^{-1}\). When the flux is \(>\)434 cm\({}^{-2}\)s\({}^{-1}\)sr\({}^{-1}\), the sensitivity of the detector is better than 5%.
# Secure Encapsulation Schemes Using Key Recovery System in IoMT Environments
## 4 Proposed Scheme-I (Key Recovery System Using Signcryption)
### Setup Phase  
In this phase, the KGC takes the security parameters as an input the security parameter \(1^{\lambda}\) and generates public parameters.  
* **Step 1:** The KGC selects \(\lambda\)-bit large prime \(p\), where \(q\) is a large prime factor of \(p-1\) and group \(\mathbb{G}\) of prime order \(p\). In addition, a random generator \(g\in\mathbb{G}\) is selected.
* **Step 2:** A master private key \(sk_{M}\in\mathbb{Z}_{p}^{*}\) is randomly selected and a master public key \(pk_{M}\in g^{sk_{M}}\) is computed.
* **Step 3:** KGC selects Hash function \(H\).
* **Step 4:** Then, public parameters \(params=(G,n,p,q,g,S,H)\) are published.
# Efficient Dynamic Cost Scheduling Algorithm for Financial Data Supply Chain
## 3 Data Batch Algorithm
### Dynamic Cost Scheduling Algorithm for Data Batch Processing (DCSDBP)
#### 3.1.2 Problem Parameters  
\begin{tabular}{l l} \hline \(I\) & Set of data files. \\ \(I_{I}T^{T}\) & Subset of the data files that are available for processing at any time T. \\ \(n_{i}\) & Required processing time for data file i. \\ \(SLA\) & Batch process time as agreed on in the SLA. \\ \(K\) & Set of all leased processors. \\ \(R\) & Set of rented processors. \\ \(V^{T}\) & Number of rented extra processors that can be acquired at any discrete time T. \\ \(I_{ij}^{T}\) & Binary parameter equal to 1 if data file j immediately precedes data file i, and 0 \\ \(a_{i}^{T}\) & Data file weight based on precedence/dependency matrix. \\ \(\beta_{i}\) & Data file scheduling priority provided by the client. \\ \(I_{ij}^{T}\) & Precedence/dependency matrix at each period T. \\ \(BW\) & Available batch process window. \\ \(Csf\) & Leased processor software fixed leasing cost. \\ \(Csv\) & Variable leasing cost per unit time of a processor software. \\ \(Ch\) & Leased processor hardware cost. \\ \(Csf\) & Fixed rental cost per unit time of additional processor software. \\ \(Ccsv\) & Variable rental cost per unit time of additional processor software. \\ \(Cch\) & Rental cost per unit time of additional processor hardware. \\ \(Cp\) & Penalty cost per unit time for failing to execute the batch process as per the SLA. \\ \(e_{i}\) & Number of times file i can be multi processed. \\ \(TCp\) & Delay time total penalty cost. \\ \(T_{H}\) & Total cost of renting one additional processor at any time unit T. \\ \(D^{T}\) & Available files total multiprocessing ei at any time T. \\ \(T^{r}\) & Clock discrete time at which extra processor r is rented. \\ \(END\) & End of batch process. \\ \(TBC\) & Total batch process cost. \\ \hline \end{tabular}
## 3 Materials and Methods
### Methodology
#### 3.4.3 Predictive Model: Gradient Boosting  
Gradient Boosting is a boosting ensemble learning model which uses the decision tree model as the individual model. The individual model learns from the previous models, but unlike AdaBoost, gradient boosting calculates residual errors made by the earlier models. Finally, Gradient Boosting makes predictions by simply adding up the prediction values of all decision trees [38; 39].