esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Denoising Transformer-Based Audio Music Genre Classification
### Preprocessing and Noise Injection  
The preprocessing is employed in the pre-training and fine-tuning stages, as illustrated in Figure 3. Initially, audio data are converted into Mel spectrograms with dimensions \(W\) and \(H\), corresponding to time and frequency, respectively. These spectrograms are then resized using the librosa library to new dimensions, \(H^{\prime}\) and \(W^{\prime}\), which are determined  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Research Contents** & [19] & [20] & [7] & [13] & [16] & **The Proposed Method** \\ \hline Data Rep. & Spec. \& MFCC & Spec. & MFCC & Spec. & Spec. & Spec. \\ \hline \multirow{2}{*}{Model} & KNN, GMM, & \multirow{2}{*}{SVM} & \multirow{2}{*}{CNN} & ResNet- & Swin & \multirow{2}{*}{Deformer} \\  & SVM & & & BiGRU & Transformer & \\ \hline Pre-training & - & - & - & - & \(\surd\) & \(\surd\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Differences between existing approaches and the proposed method.  
Figure 1: Overview of the method: pre-training and fine-tuning stages.  
based on the experimental hardware. It is worth noting that the values of \(H^{\prime}\) and \(W^{\prime}\) should be carefully chosen; excessively large dimensions may incur a larger calculation resource usage. Finally, the resized Mel spectrograms are normalized by scaling the values to fit within a range from zero to one.  
Noise injection is operated additionally only in the pre-training stage. They are divided by \(N\) equal-sized patches through a matrix-division operation. Each patch is derived by dividing the Mel spectrogram into \(N\) sections, resulting in patches with dimensions of \(W^{\prime}/\sqrt{N}\times H^{\prime}/\sqrt{N}\), where \(W^{\prime}\) and \(H^{\prime}\) are the width and height of the transformed spectrogram, respectively. Patches \(\{p_{1},p_{2},p_{3},...,p_{n},\ldots,p_{N}\}\), follow the order from left to right and top to bottom. The noise ratio, \(\beta\%\), dictates the fraction of patches that receive noise. The \(\beta\%\) of patches are injected with noise \(z\) with a Gaussian distribution \(\mathcal{N}(0,\;1)\); otherwise, they remain unchanged. Finally, a noisy Mel spectrogram, which is the combination of \(\{p_{1}{}^{\prime},p_{2}{}^{\prime},p_{3}{}^{\prime},...,p_{n}{}^{\prime}, \ldots p_{N}\}\), is obtained:  
\[p_{n}{}^{\prime}=\left\{\begin{array}{l}p_{n}+z\;if\;\beta\%\\ p_{n}\;else\end{array},\;z\sim\mathcal{N}(0,\;1)\right. \tag{1}\]  
Figure 3: Preprocessing and noise injection of data.  
Figure 2: Flowchart of pre-training and fine-tuning under the proposed method.
# Modular Robotic Design and Reconfiguring Path Planning
## 2 Robotic Design and Modeling
### Building of the Module Reconfiguration Platform  
#### 3.1.1 Reconfiguration Platform  
The reconfiguration platform is mainly used to complete the mutual transformation between modules and configurations. Within the platform, modules are assembled into cellular robot configurations with different functions through reasonable obstacle avoidance strategies and path planning. The reconfiguration planning flow chart is shown in Figure 3. Firstly, the target configuration is set according to the task and a mathematical model is built. After determining the number of modules, connection interfaces, and connection relationships, the system transmits the information to the module warehouse. Then, the module warehouse performs the screening of modules and releases them to the module reconfiguration platform. Within the platform, the motion path map of the module is firstly constructed and the obstacle module is set up at the same time. Subsequently, a reasonable path planning of the 2D map is carried out, specifically including the development of obstacle avoidance guidelines and the design of innovative algorithms. Finally, the formation of the target configuration is completed within the platform. The completed reconfigured cell robot is transported to the robot's work area by the robotic transport system. In addition, the completed robots are again transported via the robotic transport system directly to the reconfiguration platform for disassembly of the conformation so that it can participate in the next stage of reconfiguration or be stored.  
Based on the above analysis, we divide the functions of the platform. The overall distribution of the module reconfiguration system is shown in Figure 4. It consists of three parts: module warehouse, module reconfiguration platform, and robot transport platform. The module warehouse is mainly used for storing reconfiguration function modules. It screens and releases the corresponding number of modules to the reconfiguration platform according to the configuration requirements and transports the modules to the reconfiguration position following the configuration connection requirements through the three-DOF drive device to complete the docking between modules. The reconfigured robot that has completed its task can also be returned to the reconfiguration platform for de-configuration. The robot is deconstructed into separate functional modules and rejoined to the new robotic configuration tasks. The reconfigured robot on the platform is transported to the working position via a conveyor platform. The various parts of the platform are closely connected and work together to accomplish the task of reconstructing the platform through information communication and collaborative drive.  
Figure 3: Module reconstruction planning flowchart.
# Large Eddy Simulation of Film Cooling with Bulk Flow Pulsation: Comparative Study of LES and RANS
## 3 Numerical Methods
### Governing Equations and Turbulence Models
#### 3.1.2 LES Approach  
LES resolves large eddies directly, but small eddies are modeled. It filters out the eddies that are smaller than the grid spacing and resolves large eddies. This approach is reasonable because mass, energy, and momentum are mostly transported by large eddies [25]. The filtered Navier-Stokes equations for LES are as follows [17]:  
\[\frac{\partial\rho}{\partial t}+\ \frac{\partial}{\partial x_{i}}(\rho\overline {u}_{i})=0 \tag{8}\]  
\[\frac{\partial(\rho\overline{u})}{\partial t}+\frac{\partial}{\partial x_{j}} \bigg{(}\rho\overline{u}_{i}\overline{u}_{j}\bigg{)}=\frac{\partial}{\partial x _{j}}\bigg{[}\mu\bigg{(}\frac{\partial\overline{u}_{i}}{\partial x_{j}}+\frac{ \partial\overline{u}_{j}}{\partial x_{i}}\bigg{)}-\frac{2}{3}\mu\frac{ \partial\overline{u}_{i}}{\partial x_{l}}\delta_{ij}\bigg{]}-\frac{d\overline {p}}{dx}+\frac{\partial\tau_{ij}}{\partial x_{j}} \tag{9}\]  
The subgrid-scale turbulent stress (\(\tau_{ij}\)) is modeled using the Boussinesq hypothesis as follows [17].  
\[\tau_{ij}-\ \frac{1}{3}\tau_{kk}\delta_{ij}=\ -\mu_{l}\bigg{(}\frac{\partial \overline{u_{i}}}{\partial x_{j}}+\frac{\partial\overline{u_{j}}}{\partial x_ {i}}\bigg{)} \tag{10}\]
# A Survey of Adversarial Attacks: An Open Issue for Deep Learning Sentiment Analysis Models
## 2 Adversarial Examples and Deep Learning Models
### Measurement and Evaluation Metrics
#### 2.5.2 Attack Evaluation  
During the development of an adversarial attack, it is necessary to measure and control the perturbations or modifications to keep their size to a minimum and ensure that they are imperceptible. Afterwards, the effectiveness of the attack must be evaluated.  
* **Success rate.** The success rate is the most direct and effective evaluation criteria [27]. The attack success rate indicates the percentage of successful adversarial examples and the percentage of unsuccessfully attacked inputs. This measure provides insight into the susceptibility of a model to the designed adversarial examples.
* **Model Robustness.** Adversarial attacks are designed to affect the performance of models concerning the correct classifications. The robustness of DL models is related to the classification accuracy (_before-attack accuracy_, or BA) and how it is affected by adversarial examples (_after-attack accuracy_, or AA).
## 6 A Practical Example for GDM with IVIFPRs
### Comparative Analyses
#### 6.2.2 Comparison with Other Existing Methods  
Except for the above quantitatively comparative analysis, this subsection implements a qualitatively comparative analysis by using the existing GDM methods [6; 18].  
1. The concerns of these methods are different. Method [6] studied the consistent and consensus in GDM with IFPR, whereas Method [18] and the proposed algorithm concentrate on the multiplicative consistency of IVIFPR. The discrepancy is that Method [18] focused on the multiplicative transitivity of IVIFPR and improving the consistency of an inconsistent IVIFPR, while the proposed algorithm is devoted to judging and measuring the multiplicative consistency degree of an IVIFPR and address the GDM with IVIFPRs.
2. Method [18] investigated an acceptable property of multiplicative consistency of IVIFPR and introduced some associated concepts of IVIFPR (i.e., the approximate, the perfect and the acceptable multiplicative consistent IVIFPR), but the multiplicative consistency degree of an IVIFPR cannot be obtained in Method [18]. Following the work of Method [18], based on two principles of decision making (the majority and minority principles), this paper proposes a new definition of consistency index of an IVIFPR, which can measure the multiplicative consistency degree of an IVIFPR.
3. Regarding to priority weight determination, Methods [6; 18] cannot provide any tools or methods determine priority weights. However, this paper extends error-analysis-based approach in IVIFPR to determine priority weights, and it is worth noting that the proposed algorithm can reduce the complexity of the calculation.
## 3 Theoretical Formulation
### Discrete Dynamic Equation
The solution can be expressed as  
\[\boldsymbol{\eta}=\boldsymbol{P}\boldsymbol{\hat{\eta}} \tag{36}\]  
where \(\boldsymbol{P}\) is a \(2N\times(2N-m)\) projection matrix. Multiplying both sides of Equation (29) by \(\boldsymbol{P}^{\text{T}}\), the global governing equation becomes  
\[\hat{\mathcal{M}}\boldsymbol{\hat{\eta}}+\mathcal{C}\boldsymbol{\hat{\eta}}+ \mathcal{\hat{K}}\boldsymbol{\hat{\eta}}=\mathcal{\hat{F}} \tag{37}\]  
where  
\[\mathcal{\hat{M}}=\boldsymbol{P}^{\text{T}}\mathcal{M}\boldsymbol{P},\quad \mathcal{\hat{C}}=\boldsymbol{P}^{\text{T}}\mathcal{C}\boldsymbol{P},\quad \mathcal{\hat{K}}=\boldsymbol{P}^{\text{T}}\mathcal{K}\boldsymbol{P},\quad \mathcal{\hat{F}}=\boldsymbol{P}^{\text{T}}\mathcal{F} \tag{38}\]  
Solving the above equation, the static and dynamic response of non-uniform AFG beams resting on variable foundations and subjected to non-uniformly distributed loads can be obtained. The standard implicit Newmark time integration method is chosen to solve the differential dynamic equilibrium equations. Therefore, the dynamic equilibrium equation can be discretized as  
\[ \begin{split}\Big{(}\mathcal{\hat{M}}+\gamma\Delta t\mathcal{ \hat{C}}+\delta\Lambda^{2}\mathcal{\hat{K}}\Big{)}\boldsymbol{\tilde{\eta}}_ {t+\Delta t}=\\ \mathcal{\hat{F}}_{t+\Delta t}-\mathcal{\hat{C}}\big{(}\boldsymbol{ \hat{\eta}}_{t}+\Delta t(1-\gamma)\boldsymbol{\tilde{\eta}}_{t}\big{)}- \mathcal{\hat{K}}\Big{(}\boldsymbol{\hat{\eta}}_{t}+\Delta t\boldsymbol{\hat {\eta}}_{t}+(0.5-\delta)\Delta t^{2}\boldsymbol{\tilde{\eta}}_{t}\big{)}\end{split}  \tag{39}\]  
The values of displacements \(\boldsymbol{\hat{\eta}}_{t+\Delta t}\) and velocities \(\boldsymbol{\hat{\eta}}_{t+\Delta t}\) at the time \(t+\Delta t\) can be gradually deduced according to the time step \(\Delta t\), respectively, as  
\[\boldsymbol{\hat{\eta}}_{t+\Delta t}=\boldsymbol{\hat{\eta}}_{t}+\Delta t \boldsymbol{\hat{\eta}}_{t}+(0.5-\delta)\Delta t^{2}\boldsymbol{\tilde{\eta}} _{t}+\delta\Delta t^{2}\boldsymbol{\tilde{\eta}}_{t+\Delta t} \tag{40}\]  
\[\boldsymbol{\hat{\eta}}_{t+\Delta t}=\boldsymbol{\hat{\eta}}_{t}+\Delta t(1- \gamma)\boldsymbol{\tilde{\eta}}_{t}+\gamma\Delta t\boldsymbol{\tilde{\eta}}_ {t+\Delta t} \tag{41}\]  
The coefficients \(\delta=0.25\) and \(\gamma=0.5\) provide the weight of the contribution of acceleration to the change in velocity and displacement.
# Improving Passive Time Reversal Underwater Acoustic Communications Using Subarray Processing
## 1 Introduction
The outputs of subarray time reversal equalizers are then processed by a low-order multichannel DFE to produce the final estimate of the transmitted data. TR-DFE can be considered a special case of TR-MC-DFE, where \(P=1\). Consequently, TR-MC-DFE can be viewed as a generalized time reversal equalizer. It is important to recall that TR alone usually requires a minimum four-element array to achieve acceptable performance. It is also important to note that the base station underwater can support a minimum number of receivers (e.g., \(M=8\)) [11]. Many works have demonstrated that exploiting the channel sparsity leads to better receiver performance [16; 17; 18; 19; 20; 21]. In TR communications, accurate channel estimation is critical for performance. Different spare channel estimation algorithms, including least squares (LS), orthogonal matching pursuit (OMP) and improved proportionate normalized least mean squares (IPNLMS), have also been investigated. The contributions of this paper include the following aspects: (1) A TR-MC-DFE method is proposed to improve the performance of conventional passive TR communication; (2) The performances of TR-DFE and TR-MC-DFE are compared and analyzed using the data collected from one lake experiment; (3) The influence of three channel estimation algorithms on the performance of the TR-MC-DFE method is analyzed and compared. The rest of this paper is organized as follows. System model is introduced in Section 2. Section 3 first reviews the multichannel receiver algorithms, including MC-DFE and TR-DFE, and then presents the receiver structures of TR-MC-DFE. The performance of the proposed TR-MC-DFE algorithms is analyzed using the simulated data in Section 4. Section 5 illustrates the lake experiment and the communication results. Conclusions are given in Section 6.
# Portable Smart Spectrometer Integrated with Blockchain and Big Data Technology
## 2 Structure of the Portable Smart Spectrometer
### Digital Signature Module  
Digital signatures are mainly used to sign digital messages in case of impersonation or falsification of messages, and can also be used for identity authentication of both parties. Applying digital signatures to spectral data can prevent it from being tampered with. The digital signature module is applied to digitally sign the measured spectral data. The CPU speed of the STM32F407 is up to 168 MHz; sufficient to act as a cryptographic module for PSS-1.0. The microcontroller supports the SHA512 encryption algorithm and the Ed25519 digital signature algorithm. They are used to generate public and private keys and to digitally sign data. SHA512 is a kind of hash function. A hash function is any function that can be used to map data of arbitrary size onto data of a fixed size [17]. Ed25519 is currently the fastest elliptic curve signature algorithm with better performance than the NIST series and higher security than NIST P-256 [18]. Digital signatures generated by Ed25519 only account for 64 bytes, making it well-suited to embedded devices [19]. Digitally signing the measured data directly makes the signing take too long and the generated signature data is too large, because the private key can only encrypt a certain length of data at a time. However, the PSS-1.0 first hashes the spectral data with SHA512 and then signs the hash value with Ed25519, which shortens the signing time and reduces the amount of signature data.  
Figure 5: Temperature measurement module of the portable smart spectrometer.  
Figure 6: Data transmission module of the portable smart spectrometer.
## 5 SCODE_plus_: Enhanced Secure Routing and Key Management Scheme
### Secure Data Dissemination Scheme to Mobile Sinks
#### 5.3.1 Secure Neighboring Discovery  
After deployment, each node \(A\) broadcasts a _HELLO_ message in order to discover its neighborhoods. This message is encrypted by _A_'s cluster key _KCH_:  
\[\text{A}\to broadcast:\ \{\mathit{ID}_{A}\mid CID_{A}\mid N_{0}\}K_{CH}\]  
Each receiving node \(B\) decrypts the message and checks if it is a neighbor of \(A\). If yes, it replies to \(A\) node ID and cell ID along with a _nonce_ value \(N_{0}\) encrypted by the pair-wise shared key _KAB_.  
\[B\to A:\ \{\mathit{ID}_{B}\mid CID_{B}\mid N_{\sigma}\!+\!1\}K_{BA}\]  
Receiving node \(A\) decrypts the message using the pair-wise shared key. It then checks if the _nonce_\(N_{0}\) is the one it has broadcasted. If it is, \(A\) accepts \(B\) as a neighbor and updates its neighborhood table.  
The above two-way handshake protocol can avoid (or defend against) the unidirectional link problems (or attacks) [16]. For example, if an attacker uses node \(A\) which is a more powerful node such as a laptop with longer transmission range than \(B\), then \(A\) can send a message to \(B\) directly, but \(B\) cannot send a message to \(A\) within one-hop. However, node \(B\) still thinks that \(A\) is a one-hop neighboring node and various problems may arise. For example, \(B\) tries to packets to \(A\), since A is out of _B_'s communication range, the packets will be dropped.
## 4 Experiment and Results
### Binary Classification
#### 4.1.2 Performance Evaluation
Figure 3 showcases the confusion matrices for all models, and Figure 4 presents the ROC AUC scores. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Actual Traffic Class** & **Predicted as Attack** & **Predicted as Legitimate** \\ \hline Attack & TP & FN \\ Legitimate (Normal) & FP & TN \\ \hline \hline \end{tabular}  
* True Positive (TP): The model correctly predicts attack traffic.
* False Negative (FN): The model incorrectly predicts attack traffic as legitimate traffic.
* False Positive (FP): The model incorrectly predicts legitimate traffic as attack traffic.
* True Negative (TN): The model correctly predicts legitimate traffic.  
\end{table} 
Table 5: A binary classification confusion matrix [51]. Figure 4: ROC-AUC scores for all employed models (binary). \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & \begin{tabular}{c} **F1-** \\ **Score** \\ \end{tabular} & \begin{tabular}{c} **ROC-** \\ **AUC** \\ \end{tabular} & \
\begin{tabular}{c} **Searching Training** \\ **Time (s)** \\ \end{tabular} &
\begin{tabular}{c} **Testing** \\ **Time (s)** \\ \end{tabular} \\ \hline RF & 0.95 & 0.95 & 0.95 & 0.95 & 0.99 & 2280.81 & 6.96 & 0.45 \\ LR & 0.95 & 0.95 & 0.95 & 0.95 & 0.99 & 221.58 & 11.27 & 0.99 \\ DT & 0.95 & 0.95 & 0.95 & 0.95 & 0.99 & 74.98 & 0.12 & 0.01 \\ GB & 0.95 & 0.95 & 0.95 & 0.95 & 0.99 & 3051.20 & 43.08 & 0.45 \\ XGBoost & 0.95 & 0.95 & 0.95 & 0.95 & 0.99 & 1252.83 & 12.38 & 0.04 \\ MLP & 0.89 & 0.91 & 0.89 & 0.89 & 0.95 & 3551.45 & 264.61 & 0.20 \\ DNN & 0.81 & 0.85 & 0.81 & 0.80 & 0.92 & 5602.99 & 722.57 & 6.44 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Model evaluation: Performance metrics and computational times (binary). Figure 3: Confusion matrix for all employed models (binary).
# Uncertainty Detection: A Multi-View Decision Boundary Approach Against Healthcare Unknown Intents
## 3 A Multi-View Decision Boundary Learning Approach for Unknown Intent Detection
### Second-Stage Training for Unknown Intent Detection  
We adopt a decision boundary learning strategy to outline the boundary of each intent based on the transformed query representations obtained in Section 3.2. Assume that the decision space is the high-dimensional feature space where the new representations lie. In this feature space, we aim to determine the decision boundaries, the irregular sphere corresponding to each intent, to enclose query representations with the same intent label. With an adaptive mechanism [13], boundary-based decision methods have proven effective in unknown intent detection tasks.  
Here, we first calculate the centroid for each intent category. The query dataset \(\{(z_{0},\ y_{0}),\ (z_{1},\ y_{1}),\ \ldots,\ (z_{i},\ y_{i}),\ldots,(z_{N-1},\ y_{N-1})\}\) is \(N\) query samples with their intent labels. Let \(S_{j}\) denote the set of known-intent query representations labeled with the class \(j\). Its centroid \(c_{j}\) is computed as follows:  
\[c_{j}=\frac{1}{\big{|}S_{j}\big{|}}\sum_{(z_{i},y_{i})\in S_{j}}z_{i} \tag{1}\]  
Let \(\Delta_{j}\) denote the radius of the spherical decision boundary concerning centroid \(c_{j}\). It is obtained through the second-stage model training. For each query representation \(z_{i}\), the strategy aims to satisfy the following constraint:  
\[\forall(z_{i},\ y_{i})eS_{j},\ \left\|z_{i}-c_{j}\right\|_{2}\leq\Delta_{j} \tag{2}\]  
\[\Delta_{j}=log\Big{(}1+e^{\widehat{\Delta_{j}}}\Big{)} \tag{3}\]where \(\widehat{\Delta}_{j}\) is the adaptive boundary parameter, learned using a machine-learning optimization method proposed by [PERSON], [PERSON], [PERSON], and [PERSON] [13]. This optimization method can approximate a balanced decision boundary with the boundary loss function as follows:  
\[\mathcal{L}_{b}=\frac{1}{N}\sum_{i=0}^{N-1}\big{[}\delta_{i}\big{(}\big{\|}z_{i} -c_{y_{i}}\big{\|}_{2}-\Delta_{y_{i}}\big{)}+(1-\delta_{i})\big{(}\Delta_{y_{i} }-\big{\|}z_{i}-c_{y_{i}}\big{\|}_{2}\big{)}\big{]} \tag{4}\]  
where \(\delta_{i}\) is defined as follows:  
\[\delta_{i}:=\begin{cases}1,&\quad if\ \big{\|}z_{i}-c_{y_{i}}\big{\|}_{2}>  
We randomly set 25%, 50%, or 75% proportions of intents in the training dataset as unknown and used the remaining query samples to train the first-stage known intent classification model. All intent classes of the test dataset were included in the second stage to simulate the unknown intent detection task and test the performance, following the processing procedure by [PERSON] and [PERSON] [11] and [PERSON], [PERSON], [PERSON], and [PERSON] [13].
# The BER Performance of the LDPC-Coded MPPM over Turbulence UWOC Channels
## 6 Conclusions  
In this paper, we evaluated the BER performance of LDPC-coded MPPM UWOC systems over turbulence-induced fading channels. For iterative decoding, the initial bit LLR of MPPM was derived and simplified, based on Gaussian distribution and Jacobian logarithm. Furthermore, a closed-form expression of the BER without FEC was presented for using the Gauss-Laguerre integration and GG distribution fading models. MC simulation was adopted to verify the correctness of the closed-form analytical results and to demonstrate the effectiveness of LDPC code with simplified LLRs on turbulence mitigation for different modulation formats. LDPC code with simplified LLRs can significantly improve BER performance in fading channels. LDPC offered code gains of 11.8 dB, 12.1 dB and 12.3 dB at BER = \(10^{-4}\) for (6, 3), (5, 2) and (4, 1) MPPM scheme in the GG fading channel with a scintillation index of \(\sigma_{l}^{2}\) = 0.2073. Furthermore, LDPC code with simplified LLRs is suitable for practical implementation because of less computational complexity and no requirement for CSI.  
Conceptualization, H.J. and S.R.; methodology, H.J. and S.R.; software, H.J. and S.R.; validation, H.J. and S.R.; investigation, H.J., S.R., N.H. and X.L.; writing--original draft preparation H.J. and S.R.; writing--review and editing, S.R. and W.P.; funding acquisition, N.H. and H.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the National Natural Science Foundation of China (grant number 61961008), and in part by the Dean Project of Guangxi Key Laboratory of Wireless Broadband Communication and Signal Processing (grant number GXKL06200127). [PERSON] works at the DSP Centre, which has been part-funded by the European Regional Development Fund through Welsh Government and also by the North Wales Growth Deal through Ambition North Wales, Welsh Government, and UK Government.  
The authors declare no conflict of interest.