esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Bistatic Anisotropic Polarimetric Two-Scale Model
However, around the Bragg resonant wavenumber, it can be approximated as in (1) with  
\[W(\kappa)=\frac{S_{0}}{\kappa^{\alpha}} \tag{2}\]  
\[\Phi(\kappa,\phi)=1+\Delta\cos[2(\phi_{0}-\phi)] \tag{3}\]  
where for sea surfaces \(\alpha=3.5\) and \(S_{0}\), \(\Delta\) and \(\phi_{0}\) are parameters depending on wind speed and direction [33]. More specifically, \(S_{0}\) is the amplitude coefficient of the omnidirectional part of the power-law PSD of small-scale roughness; \(\Delta\) regulates the amplitude oscillationsof the angular spreading function; \(\phi_{0}\) is directly related to wind direction. The actual expression and definition of such parameters are of no concern here and, therefore, are omitted for the sake of brevity. However, they can be found in [33]. Large-scale roughness slopes are modeled as jointly Gaussian zero-mean random variables, whose variances and correlation coefficient, again, depend on wind speed and direction as detailed in [26]. More specifically, such wind-driven variances are modeled according to the Katzberg model in [34] properly extended to a wider frequency range. According to polarimetric scattering theory (see e.g., [35]), under BA-PTSM, the scattered field is described by means of the polarimetric covariance matrix. Each matrix entry \(R_{pq,rs}\) is then expressed as the sum of the large-scale roughness scattering contribution evaluated via GO \(R_{pq,rs}^{CO}\) and the small-scale roughness scattering contribution \(\langle R_{pq,rs}^{SPM}\rangle_{s_{x},s_{y}}\), evaluated as the SPM term relevant to the generic tilted facet averaged over the large-scale slopes. The SPM term for the generic facet is calculated assuming the small-scale roughness PSD defined in (1)-(3). Accordingly,  
\[R_{pq,rs}=R_{pq,rs}^{GO}+\langle R_{pq,rs}^{SPM}\rangle_{s_{x},s_{y}} \tag{4}\]  
where the subscripts \(p\), \(q\), \(r\) and \(s\) may each stand for horizontal and vertical polarization, and \(\langle\cdot\rangle_{s_{x},s_{y}}\) denotes the ensemble average over the large-scale slopes \(s_{x}\) and \(s_{y}\). The rationale for deriving \(R_{pq,rs}^{CO}\) and \(\langle R_{pq,rs}^{SPM}\rangle_{s_{x},s_{y}}\) and their detailed expressions are reported in [26] and are here omitted for the sake of brevity. Finally, it is worth mentioning that an intrinsic feature of TSM and its generalizations, including BA-PTSM, is the cutoff wavenumber, i.e., the surface wavenumber separating the small-scale roughness from the large-scale one. The choice of the cutoff wavenumber has a certain degree of arbitrariness and might heavily influence the scattering evaluation. Therefore, it is commonly regarded as an intrinsic limitation of TSMs. Nevertheless, it has been demonstrated that BA-PTSM is only slightly affected by the choice of the cutoff wavenumber, whose effects on scattering predictions are non-negligible only for scattering directions in the very narrow angular region where GO and SPM results are comparable [26].
## 3 Path Optimization
### Obstacle Model  
The environment consists of \(M\) obstacles, and the \(m\)-th obstacle (\(m=1,\ldots,M\)) is modeled as a compact convex set \(O_{m}\). For example:  
\[O_{1}=\begin{bmatrix}1&\cdots&1\\ \vdots&1&\vdots\\ 1&\cdots&1\end{bmatrix} \tag{31}\]  
A rectangular obstacle can be represented as \(O_{1}\in R^{w\times l}\), where \(w\) and \(l\) are the width and length of the obstacle, respectively. When the obstacle is another polyhedron, we assume that the obstacle \(O_{m}\) is a compact convex set with a non-empty relative interior and can be expressed as:  
\[O_{m}=\{y\in R^{n}|a_{m}y\leq\kappa b_{m}\} \tag{32}\]  
where \(a_{m}\in R^{w\times l}\), \(b_{m}\in R^{w}\), and \(\kappa\subset R^{w}\) is a closed convex cone with a non-empty interior. The representation in (31) is fully general because any compact convex set admits a conic representation of the form (32) ([26], p. 15). The partial ordering with respect to a cone K is defined by \(x\leq xy\Longleftrightarrow y-x\in\kappa\). Specifically, polyhedral obstacles can be represented in the form of (32) by choosing \(\kappa=R_{+}^{l}\); for example, \(\left\{(x,t)\in R^{w\times l}||x||_{2}\leq t\right\}\), the obstacle is an ellipsoid. For detailed information, please refer to [27]. For static obstacles, the set \(O\) is a constant; for dynamic obstacles, the set \(O_{m}\) can be represented as \(O_{m}^{t}\), which changes over time.
# Improved Low-Depth SHA3 Quantum Circuit for Fault-Tolerant Quantum Computers
## 2. Preliminaries
### Secure Hash Algorithm (SHA) 3  
In 2015, the National Institute of Standards and Technology (NIST) released Secure Hash Algorithm 3 (SHA3) [29] to replace SHA1 [30] and SHA2 [31]. The SHA3 hash function family consists of four hash functions (SHA3-224, SHA3-256, SHA3-384, and SHA3-512) and two extendable-output functions (XOFs) (SHAKE128 and SHAKE256). The input data output hash results through 'absorbing' and'squeezing' steps using the sponge structure, as shown in Figure 2. SHA3 has a sponge structure, so it outputs a hash value of a constant length regardless of the input length. When absorbing, a message block is transformed through XOR and permutation functions, and the transformed message block is updated by repeating the function \(f\) (i.e., Keccak-f[1600, 24]) composed of five steps : \(\theta\), \(\rho\), \(\pi\), \(\chi\), and \(\iota\). The inner operation of function \(f\) is explained in detail in Section 3 for implementation in the SHA3 quantum circuit.  
Figure 1: Decomposed Toffoli gate. \(\tau\) indicates reversible gates.  
Figure 2: Operation process for SHA3 hash function. (\(r\): block size, \(c\): capacity, \(f\): Keccak-f[1600]).
## 2 Materials and Methods
### Cell Cultures for Ex Vivo Assays  
Murine macrophages (RAW 264.7, Sigma Aldrich, St. Louis, MO, USA) were cultured in standard conditions as previously reported (Bir
## 5 Conclusions  
This paper offers a unique method of bearing fault diagnosis based on 2D-image- and cross-domain few-shot learning with quick adaptive capabilities, focusing on issues with limited fault data and variable bearing conditions. Firstly, a 2D image conversion method based on the MEMTF multiscale morphological transformation is proposed to address the problem that some commonly used time-frequency analysis methods rely on expert knowledge in converting 1D vibration signals into a 2D image. Secondly, small samples from the target domain are used to fine-tune the extractor trained on the natural image dataset. Additionally, a distance-based classifier was employed to enhance the limited sample capacity for categorization. Finally, the improved cross-domain few-shot learning is used to implement bearing fault diagnosis. To demonstrate the proposed method's effectiveness and superiority in the presence of few failure data, this paper performs experiments utilizing data from the CWRU and data from the production line, respectively. The findings demonstrate that the proposed method performs better at fault diagnosis than the current methods and is more in line with real-world production applications. In future research, the method should be used in more work, including diagnosing other bearing compound faults, gear faults, etc.  
Conceptualization, T.W. and C.C.; methodology, T.W.; software, T.W. and X.D.; validation, T.W. and X.D.; formal analysis, T.W.; investigation, H.L.; resources, C.C.; data curation, T.W.; writing--original draft preparation, T.W.; writing--review and editing, T.W., X.D. and H.L.; visualization, H.L.; supervision, C.C.; project administration, C.C.; funding acquisition, C.C. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Natural Science Foundation of China (No.51675350).  
Not applicable.  
This paper uses the rolling bearing fault diagnosis datasets of CWRU, which can be obtained from [[https://engineering.case.edu/bearingdatacenter/download-data-file](https://engineering.case.edu/bearingdatacenter/download-data-file)]([https://engineering.case.edu/bearingdatacenter/download-data-file](https://engineering.case.edu/bearingdatacenter/download-data-file)). Last accessed on 10 January 2023. Additional detailed data supporting the results of this study are available from the corresponding authors upon request.  
The authors declare no conflict of interest.
# A Novel Adaptive Gain of Optimal Sliding Mode Controller for Linear Time-Varying Systems
## 3 Illustrative Examples
### Example 2--Proposed Controller with Equation (12)  
The model of Example 1 is used, but the gain matrix function shown in Equation (12) is utilized where the parameters are used as follows; \(t\in[0,300]\), \(M_{L\xi}(t)=1.5,N_{L\xi}(t)=1.2\), \(\gamma=10\). The cost function is expressed as follows:  
\[J_{2}=\frac{50\big{|}x_{e}(T)\big{|}}{\gamma}+\frac{1}{2}\int\limits_{0}^{T} \big{(}u^{2}(t)+x_{e}^{2}(t)\big{)}dt \tag{26}\]  
The main input control is the given by:  
\[u^{*}(t)=-\gamma Q_{L\xi}{}^{*}(t)sign(x_{e}(t)) \tag{27}\]  
where \(Q_{L\xi}{}^{*}(t)\) is found from Equation (12) as follows:  
\[\hat{Q}_{\Delta L}(t)=\big{|}x_{e}(t)\big{|}-\gamma x_{e}(t)Q_{\Delta L}(t) \tag{28}\]  
The proposed control in this simulation is named \"the proposed control 1\" for the convenience to distinguish from the other optimal controller used in other examples. In this simulation, the observer used in [19] is applied to evaluate the proposed control performances. Simulation results are shown in Figure 2a-f. The gain matrix \(Q_{\Delta L}(t)\) in Figure 2a is always changed depending upon its value, and the values are positive. This result has point out in Equation (20). Based on the variation of \(Q_{\Delta L}(t)\), the main input control \(u^{*}(t)\) is also changed with the values in positive and negative areas as shown in Figure 2b. The response of the system is shown in Figure 2c-f. Because the original system given by Equation (21) is to be vibrated due to the main input control \(u^{*}(t)\), the state variable are also vibrated as shown in Figure 2e,f. The results clearly show that the proposed control with gain matrix function given in Equation (12) can provide better performance than the compared control shown in Figure 1. In Figure 3, the response of the system following the power spectral density (PSD) is presented including the gain matrix \(Q_{\Delta L}(t)\) and the state variable \(x_{e}(t)\). The value of the gain matrix is stable with the increasing frequency as shown in Figure 3a. Hence, the position \(x_{e}(t)\) is also stable as shown in Figure 3b. This performance directly indicates that the optimal controller with the new gain function given by Equation (12) can guarantee the stability of the system with better performances.  
Figure 2: _Cont._Figure 2: Simulation results of the proposed control 1 with Equation (12): (**a**) gain function \(Q_{\Delta L}(t)\), (**b**) main control \(u^{*}(t)\), (**c**) state variable \(x_{e}(t)\), (**d**) state variable \(\dot{x}_{e}(t)\), (**e**) large view of state variable \(x_{e}(t)\), (**f**) large view of state variable \(\dot{x}_{e}(t)\).
## 4 Control Scenarios
### Total Flow Chart of Service
#### 4.2.1 **[Service Package #1]** Detection of the Fire Occurrence Place  
Figure 8 and Table 5 show a real-time fire detection package by smart sensors, which detects fire points from smart sensors and provides real-time AR-based fire occurrence monitoring service to users, administrators, and rescuers. The following describes each step in detail:  
* **Smart sensor-based real time fire detection:** Collecting real-time information from smart sensors, including gas leak detector sensors, temperature sensors, electric leak detector sensors, and multi-gas detector sensors, which monitor the fire in real time and report the situation to the server immediately when a fire is detected from the smart sensor;
* **Send the sensing data:** Real-time sensing data is transmitted to the server through Smart ZigBee gateway;
* **AR-based fire occurrence monitoring:** Sensing data sent to the server is processed through a collection and analysis algorithm model, and displays the location information of sensor data, status information, etc. on the AR-based user interface (UI) screen of smart phones, smart pads, and so on. Users, administrators, and rescuers identify evacuation and rescue guidelines through this AR-based UI screen so that they can be safely rescued.
## 3 Kernel Density Estimator-Bayes Discriminant  
The KDE-Bayes discriminant, based on kernel density estimation, was proposed by [PERSON] in 2015 [15]. The advantage of the KDE-Bayes discriminant over the traditional Bayesian discriminant is that it overcomes the condition that the data should follow a normal distribution [16].  
For the KDE-Bayes discriminant, the unitary kernel function is defined as:  
\[\hat{f}(x)=\frac{1}{nb}\sum\limits_{i=1}^{n}K(\frac{x-x_{i}}{b}) \tag{1}\]  
In this equation, \(b\) is the set bandwidth, \(n\) is the sample size, \(K()\) is the kernel function, and \(x\) is the independent variable.  
For data with large sample sizes, whichever form of kernel function is actually adopted, theoretically, it must eventually be possible to find a reliable result that converges to the density; generally speaking, a Gaussian kernel is used:  
\[K(u)=\exp{(0.5u)^{2}}/\sqrt{2\pi} \tag{2}\]  
When the Gaussian kernel is chosen, according to Silverman's Rule of Thumb ([PERSON], 2024 [17]), the bandwidth \(b\) is:  
\[\hat{b}=1.06s\cdot n^{-1/5} \tag{3}\]  
In this equation, \(s=\sum\limits_{j=1}^{n_{i}}\sqrt{\left(x_{ji}-\overline{x}_{i}\right)^{2}/(n_ {i}-1)}\).  
When the sample size is insufficient, if the number of samples in a certain category is small, the KDE may fail to effectively distinguish the densities between different categories, resulting in a decline in classification performance. Moreover, kernel density estimation depends on the section of smoothing parameters (e.g., bandwidth \(b\)). A small sample size may lead to an inappropriate bandwidth choice, which can affect the quality of the density estimation and negatively impact the classification results.  
After the kernel function \(K()\) and bandwidth \(b\) are determined, the joint probability density of the samples \(x=(x_{1},x_{2},\ldotsldots,x_{m})^{T}\) can be estimated. Assume that each of its components \(x_{1},x_{2},\ldotsldots,x_{m}\) is a one-dimensional random variable, its probability density function takes the following form:  
\[\hat{f}_{j}(x)=\frac{1}{(\sqrt{2\pi})2n_{j}b_{1}b_{2}\ldots b_{m}}\cdot\sum \limits_{i=1}^{n_{j}}\exp\left\{-\frac{1}{2}\left[\frac{\left(x_{1}-x_{i1}^{ (j)}\right)^{2}}{b_{1}^{2}}+\frac{\left(x_{2}-x_{i2}^{(j)}\right)^{2}}{b_{2}^ {2}}+\ldots+\frac{\left(x_{m}-x_{im}^{(j)}\right)^{2}}{b_{m}^{2}}\right]\right\} \tag{4}\]  
In this equation, \(i=1,2,\ldots,n;\ldots j=1,2,\ldots,k;n=n_{1}+n_{2}+\ldots+n_{j}\).  
The criterion for the KDE-Bayes discriminant is as follows:  
\[\text{If }P(\,G_{i}|x)=\max\limits_{1\leq i\leq k}\left\{q_{i}\hat{f}_{i}(x) \right\},\;x\in G_{i} \tag{5}\]  
In this equation, \(i=1,2,\ldots,k\), and then \(x\in G_{i}\).
# IoT IP Overlay Network Security Performance Analysis with Open Source Infrastructure Deployment
## 2 Related Works
It focuses on improving network performance by allowing edge devices to choose the best paths dynamically. Networked agents learn from changing conditions to enhance metrics like latency, bandwidth, and reliability, ensuring efficient and reliable data transmission. The article [22] examines the impact of imperfections in successive interference cancellation (SIC) on the security of downlink cognitive radio networks using Non-Orthogonal Multiple Access (NOMA). It investigates how these imperfections affect the system's ability to protect data from eavesdropping and explores factors like channel conditions and power allocation to optimize secure communication despite these challenges. The article [23] explores how imperfections in successive interference cancellation (SIC) affect the security of downlink cognitive radio networks using Non-Orthogonal Multiple Access (NOMA). It assesses the impact of these imperfections on maintaining data secrecy and examines strategies to optimize secure communication despite the challenges posed by SIC flaws. The article [24] explores an AI-driven SDN (Software-Defined Networking) intrusion-detection system for identifying and mitigating DDoS (Distributed Denial of Service) attacks. It proposes an innovative approach to improve the effectiveness and efficiency in detecting such attacks. The article [25] addresses security measures for MQTT (Message Queuing Telemetry Transport) communications in Machine-to-Machine (M2M) systems within the food retail distribution sector. It proposes strategies to protect data transmission and ensure the integrity and confidentiality of communications in this context. The article [26] discusses how 6G mobile networks can enhance AI applications through an intelligent user plane, which optimizes network resources and supports advanced AI-driven services. It explores the integration of AI with network infrastructure to improve performance and enable new functionalities in future mobile networks. The article [27] examines how SDN (Software-Defined Networking) can be used for the dynamic deployment of Intrusion-DetectionSystems (IDSs) with load balancing to support drones during emergency scenarios. It focuses on optimizing network management and enhancing security for drone operations in critical situations.
## 5 Model Validation and Results  
While the previous case studies demonstrate the ability of DxT to provide detailed insights into both tactical dynamics and individual decision-making, they remain illustrative examples. As such, they do not allow for broader conclusions regarding the model's overall performance.  
To rigorously validate DxT, we used a distinct subset of the StatsBomb 360 Open Data that was not included in the 210 matches used for the empirical derivation of xT and DxT. This new dataset consists of 85 matches, containing 151,827 actions of type pass, carry, and shot. This ensures that our validation is conducted on a separate set of matches, allowing us to assess how well DxT generalizes to unseen game situations.  
The main challenge in validating a complex model like DxT lies in the computational resources required to process a sufficiently large number of cases for validation. Given the structure of DxT, the validation process involves handling large-scale probability matrices, making the calculations computationally expensive.  
We had to compute and manipulate through 10 iterations three key probability matrices:  
* Shot probability matrix of size \((8,\ 12,\ 151827)\)
* Move probability matrix of size \((8,\ 12,\ 151827)\)
* Transition probability matrix of size \((8,\ 12,\ 8,\ 12,\ 151827)\)  
The transition probability matrix computation was even more demanding. For each of the 151,827 actions, it was necessary to compute:  
* The hypothetical xG for the actions themself.
* The hypothetical xG for every grid cell on the pitch--considering the positioning of players and requiring an additional \(8\times 12\times 151\),827 xG computations.  
Despite vectorizing our code to optimize performance, we encountered significant computational bottlenecks when attempting to run these calculations.  
To overcome these limitations, we performed our experiments on MARWAN's high-performance computing (HPC) infrastructure, which provided the necessary computational power to process large-scale probability matrices. The hardware configuration was as follows:  
* CPU: 2\(\times\) Intel Xeon Gold 6148 (2.4 GHz, 20 cores each)
* RAM: 1 TB
* GPU: 2\(\times\) NVIDIA Tesla P100 (12 GB) with CUDA v10.1  
After computing the DxT grids for each of the 151,827 evaluated actions (implemented in Python 3 [33]), we assessed the model's predictive performance using the Brier Score and the Area Under the Receiver Operating Characteristic Curve (AUC-ROC). These metrics were selected for their specific advantages: the AUC-ROC quantifies the model's ability to discriminate between positive and negative outcomes, which is particularly relevant given the class imbalance in our dataset, where only 1.03% of sequences lead to goals; meanwhile, the Brier Score evaluates both the calibration and accuracy of probabilistic predictions by measuring the mean squared error between predicted probabilities and actual outcomes.  
Our model achieved a Brier Score of 0.0811 and an AUC-ROC of 0.7283, significantly outperforming xT models, which yielded a Brier Score of 0.4260 and an AUC-ROC of 0.6038 (see Table 5).
## 2 Related Work
However, the similarity to the original training samples may be reduced when generating electronic medical records due to the need for more medical knowledge in pre-trained language models such as BERT. In addition, the generated model will include redundant words, increasing the ambiguity of the enhanced data. A pre-trained model can bring training data to downstream tasks but requires data augmentation approaches for \begin{table}
\begin{tabular}{c c c} \hline \hline
**Interpretation-Based** & **Advantage(s)** & **Limitation(s)** \\ \hline \multirow{4}{*}{Synonym replacement} & \multirow{4}{*}{1. Easy to use.} & 1. The scope and parts of the augmented words’ speech are limited. \\  & & 2. This method cannot solve the ambiguity problem. \\  & & 3. Sentence semantics may be affected if there are too many substitutions. \\ \hline \multirow{4}{*}{Semantic embeddings} & 1. Easy to use. & 1. This method cannot solve the ambiguity problem. \\  & 2. Higher replacement hit rate and more comprehensive replacement range. & 2. Sentence semantics may be affected if there are too many substitutions. \\ \hline \multirow{4}{*}{Machine translation} & 1. Easy to use. & 1. This method has poor controllability and limited diversity because of the fixed machine translation \\  & 3. This approach guarantees correct syntax and unchanged semantics. & models employed. \\ \hline \multirow{2}{*}{Masked language model} & 1. Wide range of applications. & 1. There is a need to mask training data. \\  & 2. Strong application. & 2. High training difficulty. \\ \hline \multirow{2}{*}{SimBERT} & \multirow{2}{*}{1. No need to mask training data.} & 1. Lack of medical domain knowledge. \\  & & 2. This model reduces similarity to the original training sample. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of various data augmentation models. specific tasks. A knowledge graph contains rich, structured knowledge, which can capture low-frequency entity information to solve the problem of the long tail effect naturally existing in natural languages. A knowledge graph forms a triad of entities, relationships, and attributes by extracting and storing large-scale medical knowledge in a knowledge base. It can solve the problems of knowledge dispersion, heterogeneity, redundancy, and fragmentation in big medical data and provide an effective way of integrating and organizing medical knowledge [24].