esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Comparison with Experimental Results
### Suggestion for Design Formula  
To derive an empirical design formula, the expression of [PERSON]'s formula with the revised coefficient with the same general form as the [PERSON] and [PERSON] expressions was used [15,16]. Based on the above results, the ultimate strength of a curved plate under longitudinal compression was empirically derived by curve fitting via the experimental results as follows.  
\[\frac{\sigma_{UX}}{\sigma_{Y}}=\left(\frac{2.25}{\beta}-\frac{1.25}{\beta^{2}} \right)\times C_{f}\qquad\begin{array}{c}for\ \ 2.0\leq\beta\leq 2.8\\ for\ \ 3.0\leq\theta\leq 11\end{array} \tag{2}\]  
\[C_{f}=\frac{C_{a}}{\beta^{2}}+\frac{C_{b}}{\beta}+C_{c} \tag{3}\]  
\[C_{a}=-299.06\Big{(}\frac{b}{R}\Big{)}^{2}-46.45\frac{b}{R}-15.65\]  
\[C_{b}=-272.81\Big{(}\frac{b}{R}\Big{)}^{2}+32.30\frac{b}{R}+13.62\]  
\[C_{c}=-59.43\Big{(}\frac{b}{R}\Big{)}^{2}-4.77\frac{b}{R}-2.03\]  
Similar to the work of [PERSON] and [PERSON], the formula only works at a specific range of slenderness ratios and curvatures. However, it can be said that the present design formula leads to a very meaningful outcome in the sense that the design formula was derived on the basis of the experimental investigation of the various parameters of the curved plate, which is widely used in the ship and offshore industries. Figure 15 shows the variation in the ultimate strength plotted against the results predicted using the empirical formulae shown in Equations (2)-(4), as well as the experimental results. In the present formula, a correlation ratio of 0.5174 and a standard deviation of 0.3790 were obtained against the experimental results. The proposed empirical formula can be a good indicator for estimating the behavior of the curved plate.
## Appendix A  
### Butterworth Filter  
Since filter order n = 5, i.e., is an odd number, the transfer function of the normalized Butterworth filter is written as follows [38]:  
\[\mathrm{H(s)}=\frac{1}{s+1}\cdot\prod_{\mathrm{k=1}}^{(n-1)/2}\frac{1}{s^{2}+ (2\cdot\mathrm{sin}\theta_{\mathrm{k}})\cdot s+1} \tag{10}\]  
In Equation (10), \(\theta_{\mathrm{k}}\) is determined as follows:  
\[\theta_{\mathrm{k}}=\tfrac{2\cdot k-1}{2\cdot n}\cdot\pi_{\mathrm{r}},\ \ \ \mathrm{k}=1,2,\ldots,n/2. \tag{11}\]  
For the fifth-order filter (n = 5), we get:  
\[\mathrm{H(s)}=\frac{1}{s+1}\cdot\frac{1}{s^{2}+0.309\cdot s+1}\cdot\frac{1}{s^ {2}+0.809\cdot s+1} \tag{12}\]  
After transformations, we get:  
\[\mathrm{H(s)}=\frac{1}{s^{5}+2.118\cdot s^{4}+3.368\cdot s^{3}+3.368\cdot s^{ 2}+2.118\cdot s+1} \tag{13}\]  
Filter scaling is performed as follows. When transitioning from a normalized filter to a high-pass filter, the variable \(s\) in Equation (13) should be replaced with the corresponding expression:  
\[s\rightarrow\frac{\omega_{\mathrm{cut}}}{s} \tag{14}\]  
where \(\omega_{\mathrm{cut}}\) is the high-pass filter cut-off frequency.  
The amplitude-frequency response (AFR) of the filter was determined as follows:  
\[\left|\mathrm{H(j}\omega)\right|=\sqrt{\left(\mathrm{Re}(\omega)\right)^{2}+ \left(\mathrm{Im}(\omega)\right)^{2}} \tag{15}\]  
where \(\mathrm{Re}(\omega)\) and \(\mathrm{Im}(\omega)\) are real and fictitious parts of the normalized Butterworth filter transfer function.  
The AFR of the Butterworth high-pass filter is shown in Figure 14a.  
As can be seen from Figure 8, the AFR of the Butterworth high-pass filter has a smooth character in both the passband and the stopband.
# Blockchain-Based Multistage Continuous Authentication for Smart Devices
## 6 Scheme Analysis
### Security Evaluation
#### 6.1.1 Security Analysis  
Decentralization: There is no longer a reliance on a single central authority in the structure of the blockchain. As a result, there will be no single point of failure of the central server that could bring down the blockchain network system. At the same time, the distributed structure prevents the central server from monopolizing and tampering with the data. This feature can protect the legal rights of users effectively.  
User Anonymity: In order to ensure the security of the user's identity information, the identity of the user in this scheme is transmitted as the result of a hash operation. The blockchain credentials store the user's signed identity information \(signInfo\). The unidirectional nature of the hash function makes it computationally challenging to deduce the user's identity plaintext \(Info\) by backtracking \(signInfo\). In addition, the encrypted public information \(Hash(P_{i})\) is stored in the IPFS. Even if a malicious person obtains the encrypted message, it cannot be decrypted in reverse. The public message \(P_{i}\) does not leak too much information about the random string \(R_{i}\). As a result, this solution can prevent the leakage of user identity information to a certain extent and ensure the privacy and security of the identities of the users on the chain.  
Conditional Traceability and Nonrepudiation Property: Due to the traceability feature of the blockchain, the communication information between the user and the server is permanently recorded in the blockchain in the form of blocks. This means all data can be traced. And, given that all of the user's transactions are digitally signed with a private key known only to the server, the server cannot deny the act of signing the trust credentials; thus, the user cannot deny the interaction with the server.  
Resistance to Other Attacks: In common forgery attacks, the attacker publishes a malicious message by forging the identity of the signatory. In this scheme, it is very difficult for an attacker to forge an identity. It is assumed that the attacker intercepts the authentication request message sent by the user to the service node. However, it is not possible to decrypt the request message, because the private key of the service node is not available. Even if the private key of the service node is leaked, only the server side has the corresponding key pair and writes the public key to the blockchain ledger. Consequently, the attacker is unable to obtain a legitimate digitally signed message. This shows that the scheme is resistant to man-in-the-middle (MitM) attacks.  
Figure 17: User authentication process.
# Fused Node-Level Residual Structure Edge Graph Neural Network for Few-Shot Image Classification
## 2 Related Work
### Graph Neural Network  
Graph Neural Network (GNN) is a deep learning architecture designed for graph-structured data, initially introduced by [PERSON] et al. [20]. By combining information from nearby nodes and working iteratively through a message-passing mechanism, it makes it possible to depict complex interactions between data objects. GNN has enormous promise for solving Few-Shot Learning (FSL) problems [21; 22; 23] because it has been shown that few-shot learning algorithms must take use of the connections between the support and query sets [11; 12; 13; 14]. [PERSON] et al.'s [17] extension of many proposed few-shot learning models to the GNN framework was the first to incorporate GNNs into few-shot learning. EGNN, a deep neural network that operates on edge-labeled graphs, was introduced by [PERSON] et al. [19]. Through a completely connected structure, EGNNs combine an edge-focused GNN and a feature extractor. The output of edge prediction is used to assess the performance of the model. On the other hand, [PERSON] et al. [14] used a node-pair-based technique, where nodes are considered as pairs, constituting single nodes in the relational graph, to explicitly characterize relationships between samples. They assessed the similarity of neighboring relation nodes, which indicate support or query nodes, and used these similarity scores as aggregation weights to aggregate the attributes of neighbor relation nodes. In order to investigate potential hierarchies inside classes while learning from the class level, [PERSON] et al. [15] proposed hierarchical GNNs. The three fundamental components of a hierarchical GNN are skip-connection layers, bottom-up inference, and top-down inference. In order to extract shared characteristics inside each class, the bottom-up inference module uses a GNN to explore inter-node correlations and class-wise k-nearest neighbor pooling to maintain the nearest k nodes to each class.  
According to [PERSON] et al.'s research [19], the EGNN is a GNN-based graph neural network created for graph-structured data. The key idea behind it is conditional updateson graph edges, which makes it easier for nodes to communicate with one another and aggregate features. By taking into account each edge that connects nodes at each time step and updating nearby nodes' attributes based on edge conditions, EGNNs enable nodes to learn more about their neighbors. This data is then combined and integrated to create fresh node feature representations, which are then used to modify node states. The EGNN has excellent performance in a variety of applications, including image categorization and social network analysis. This is accomplished by numerous iterations of node and edge updates.
# Detection of Electromagnetic Seismic Precursors from Swarm Data by Enhanced Martingale Analytics
## 4 Quantitative Experiment
### Croatia 2020  
On 29 December 2020, Croatia experienced a seismic event with significant implications for the region. The earthquake, with a magnitude of 6.4 and located within central Croatia, underscored the country's susceptibility to seismic activity despite not being as seismically active as other Mediterranean regions. While the event's magnitude may not have been unprecedented, its impact on local communities and infrastructure was notable. Croatia, situated in southeastern Europe, experiences seismic activity due to its location at the boundary of the Eurasian Plate and the Adriatic microplate. While not as seismically active as some other Mediterranean regions, Croatia still faces occasional earthquakes resulting from the collision and subduction of these tectonic plates, along with the presence of fault systems within the region. The seismicity in Croatia, though generally of lower magnitude, poses risks to local communities and infrastructure. In this case study, we delve into a specific earthquake event that occurred in Croatia on a particular date and location. Our focus lies on analyzing the electromagnetic field (EMF) intensity data collected before and after this seismic event.  
Like our study in Section 4.2, the area of investigation time-series extracted for Beta contains a zero error, with the values for \(b_{x}\), \(b_{y}\), and \(b_{z}\) equaling zero. This is a common occurrence in long-form satellite telemetry and sensing.
# Suppressing Salt Transport through Composite Pervaporation Membranes for Brine Desalination
## 1 Introduction
The long-term performance of composite pervaporation membranes is crucial for evaluating their feasibility for practical application, especially for complex brine treatment. Thus, in this study, a series of composite PVA/PVDF membranes were fabricated by coating a thin layer of PVA onto commercial hydrophobic PVDF membranes. Different cross-linking density was investigated to understand their effect on the membrane performance. In addition, GO nanosheets were blended into the PVA layer to further suppress salt transport through the membrane. To better understand the properties of the GO/PVA composite membrane, freestanding GO/PVA membranes were fabricated to investigate the interactions between GO and PVA, as well as the water/salt diffusion process within the composite layer. Furthermore, the composite membrane's anti-fouling performance was explored using highly concentrated brine solutions containing humic and calcium salts. Lastly, the pervaporation rig optimization was carried out to promote the operational flux for the composite membranes.
## 1 Introduction
### Review Methodology  
The papers included in this review were collected in January of 2022 using various online databases including Science Citation Index Expanded, Emerging Sources Citation Index, Conference Proceedings Citation Index, BIOSIS Citation Index, Data Citation Index, MEDLINE, Derwent Innovations Index, KCl-Korean Journal Database, SciELO Citation Index, and the Russian Science Citation Index. We searched the databases for titles matching the query: (cardiac OR pericardial OR pericardial OR pericardial OR epicardial OR heart) AND (adipose tissue OR fat) AND (segmentation OR quantification). The search resulted in 70 total results consisting of papers that mention EAT segmentation and quantification. We only include the papers for those that describe methods for automatic EAT or PAT segmentation or quantification (as opposed to papers that measure EAT manually e.g., for clinical trials and similar), resulting in a total of 23 papers. As this is a clear inclusion criterium, there were no disagreements between the authors about which studies to include. The PRISMA flow diagram is shown in Figure 1.  
We review semi- and fully-automatic methods for direct EAT segmentation and quantification, EAT segmentation and quantification via pericardium delineation, and PAT segmentation and quantification. We further classify each category based on used image processing approaches into four groups: traditional image processing methods, atlas-based methods, machine learning-based methods, and deep learning-based methods.  
The first computer-assisted methods for EAT and PAT segmentation start to appear around 2006. Until 2010 most methods rely on traditional image processing techniques like active contours and region growing. From 2010 onwards, applications of learned model parameters including atlas-based segmentation emerged as a new standard. From 2015 onwards, machine learning is commonly used in combination with traditional image processing methods. In recent years, deep-learning-based approaches started to appear, first by extracting pre-determined features from the images, and later directly using CT images as input. A relationship between the number of published papers and used methods over time is presented in Figure 2.  
Figure 1: PRISMA flow diagram of our review methodology.
# Research on Fault Diagnosis Algorithm of Ship Electric Propulsion Motor
## 2 Theoretical Background
### Constructing Multi-Scale Features  
The features extracted by traditional methods cannot efficiently render the state features of the signal, which affects the diagnostic accuracy. Considering the non-stationarity of the signal, the features at a single scale may overlap in the feature space, and it is difficult to fully reveal the signal characteristics. By referring to the multi-scale concept and considering the characteristics of the signal at multiple scales to ensure the overall integrity and local details of the signal, this paper proposes a multi-scale feature index that can reveal the performance of the signal in multi-scale space. Meanwhile, based on the traditional time-domain index, combined with the advantages of multi-scale space on feature space overlap and signal cross-scale complexity, this paper constructs a multi-scale index as the basis for fault classification. Firstly, the time-domain signal is coarse-grained, which makes it difficult to obtain its distribution at different scales. For the time-domainsequence \(x=\{x_{1},x_{2},\cdots,x_{n}\}\), a signal segmentation of length \(\tau\) is performed, where the \(jth\) signal segment can be expressed as:  
\[\Big{\{}x_{p,(j-1)\tau+1},x_{p,(j-1)\tau+2},\cdots,x_{p,j\tau},\cdots\Big{\}}(1 \leq j\leq\frac{n}{\tau}) \tag{15}\]  
The corresponding coarse-grained sequence \(y^{(\tau)}\) can be expressed as:  
\[\left\{y^{(\tau)}=\left\{y_{1}^{(\tau)},y_{2}^{(\tau)},\cdots,y_{j}^{(\tau)}, \cdots\right\},1\leq j\leq\frac{n}{\tau}\right. \tag{16}\]  
where \(\tau\) is the scale factor. By extracting the time domain features of the coarse-grained sequence \(y^{(\tau)}\) of different scales, the required multi-scale features can be obtained. Since the multi-scale sequence \(y^{(1)}\) is the original time-domain sequence \(x\) when \(\tau=1\), the calculated result is the traditional time-domain feature.
## 3 Flight Control
### Attitude Controller
Thus, the derivative of this Lyapunov function \(\dot{V}_{2}\) is defined by:\[\dot{V}_{2} =\delta_{1}^{T}A_{1}\dot{\delta}_{1}+\delta_{2}^{T}J\dot{\delta}_{2}+ \bar{\boldsymbol{\alpha}}^{T}\mathbf{T}\dot{\bar{\boldsymbol{\alpha}}} \tag{18}\] \[=\delta_{1}^{T}A_{1}(\hat{\boldsymbol{\eta}}_{1d}-(\boldsymbol{ \eta}_{2d}-\boldsymbol{\delta}_{2}))+\delta_{2}^{T}J\dot{\boldsymbol{\delta}}_{ 2}+\bar{\boldsymbol{\alpha}}^{T}\mathbf{T}\dot{\bar{\boldsymbol{\alpha}}}\] \[=-\delta_{1}^{T}A_{1}A_{2}\delta_{1}+\delta_{2}^{T}(\boldsymbol {A}_{1}\delta_{1}+\boldsymbol{J}(\hat{\boldsymbol{\eta}}_{2d}-\hat{\boldsymbol {\eta}}_{2}))+\bar{\boldsymbol{\alpha}}^{T}\mathbf{T}\dot{\bar{\boldsymbol{ \alpha}}}\]  
Substituting Equation (11) into Equation (18), Equation (18) can be rewritten as  
\[\dot{V}_{2}=-\delta_{1}^{T}A_{1}A_{2}\delta_{1}+\delta_{2}^{T}(\boldsymbol{A}_ {1}\delta_{1}+\boldsymbol{J}\hat{\boldsymbol{\eta}}_{2d}-(\boldsymbol{\tau}+ \boldsymbol{\tau}_{0}-\boldsymbol{\omega}\times\boldsymbol{J}\boldsymbol{ \omega}))+\bar{\boldsymbol{\alpha}}^{T}\mathbf{T}\dot{\bar{\boldsymbol{ \alpha}}} \tag{19}\]  
To make this system asymptotically stable, we design the control law as:  
\[ \begin{array}{l}\boldsymbol{\tau}=\boldsymbol{A}_{1}\delta_{1}+\boldsymbol{A }_{3}\delta_{2}+\bar{\boldsymbol{\eta}}\hat{\boldsymbol{\eta}}_{2d}-\hat{ \boldsymbol{\tau}}_{0}+\boldsymbol{\omega}\times\boldsymbol{J}\boldsymbol{ \omega}\\ =\boldsymbol{A}_{1}\delta_{1}+\boldsymbol{A}_{3}\delta_{2}+\boldsymbol{Y}\dot{ \boldsymbol{\alpha}}\end{array}  \tag{20}\]  
where \(\boldsymbol{A_{3}}\in\boldsymbol{R}^{3\times 3}\) is a positive diagonal gain matrix, and the matrix \(\boldsymbol{Y}\) is:  
\[\boldsymbol{Y}=\left[ \begin{array}{ccccc}\hat{\boldsymbol{\eta}}_{2d\chi}&- \omega_{y}\omega_{z}&\omega_{y}\omega_{z}&1&0&0\\ \omega_{x}\omega_{z}&\hat{\boldsymbol{\eta}}_{2 dy}&-\omega_{x}\omega_{z}&0&1 &0\\ -\omega_{x}\omega_{y}&\omega_{x}\omega_{y}&\hat{\boldsymbol{\eta}}_{2 dz}&0&0&1 \end{array} \right] \tag{21}\]  
Then, Equation (19) becomes  
\[\dot{V}_{2}=-\delta_{1}^{T}A_{1}A_{2}\delta_{1}-\delta_{2}^{T}A_{3}\delta_{2}+ \bar{\boldsymbol{\alpha}}^{T}\Big{(}\mathbf{T}\dot{\bar{\boldsymbol{\alpha}} }+\mathbf{Y}^{T}\delta_{2}\Big{)} \tag{22}\]  
So we can design the adaptive law as  
\[\dot{\bar{\boldsymbol{\alpha}}}=-\mathbf{\Gamma}^{-1}\mathbf{Y}^{T}\delta_{2} \tag{23}\]  
Thus, the derivative of the Lyapunov function \(\dot{V}_{2}\) is negative. According to the Invariant set theorem, the system will be asymptotically stable. That is to say, the tracking error \(\boldsymbol{\delta}_{1}\) and the estimation error of the model parameters \(\hat{\boldsymbol{\alpha}}\) will converge to \(0\), and the state of FMAV can track the command value. This designed controller can not only stabilize the system through the state feedback control loop but also adapt the parameters of the model in real-time according to the state error.
# Signature Split Method for a PQC-DSA Compliant with V2V Communication Standards
## 1 Introduction
### Related Work  
PQC migration research in the V2V communication environment has been recently conducted [5]. Research on the hybrid mode design with PQC based on IEEE 1609.2 was conducted in [5], and benchmark research on protocol-specific PQC was conducted in [7] using the simulation tool V2 Verifier. Here, we concentrate on applying PQC to the V2V communication mentioned in existing research, considering that specific PQC algorithms with large signature and key sizes are difficult to mount in protocols. For example [5; 7], it was reported that the signature size of Crystals-Dilithium [8] for PQC-DSA is generally too large, making it difficult to apply to V2V communication. In [5], a new hybrid protocol was designed that satisfies the IEEE 1609.2 standard and quantum security, even if PQC-DSA with a larger signature size was later proposed to solve this. The benchmark study [7] suggests that research is needed to apply PQC to V2V protocols with small BSM packet sizes, such as the WAVE protocol.
# A Compact Broadband Planar Inverted-F Antenna with Dual-Resonant Modes
## 3 Principle and Analysis
### Parametric Studies  
To verify the above design principles, the effect of Slots 1 needs to be analyzed in Antenna III. Figure 5a shows the simulated frequencies of TM\({}_{3/2,0}\) and TM\({}_{1/2,2}\) modes with different slot lengths (\(Cp\)). It can be observed that as \(Cp\) increases, the higher resonant mode TM\({}_{3/2,0}\) significantly shifts down, while the lower resonant mode TM\({}_{1/2,2}\) stays almost fixed, which is consistent with the previous analysis that the frequency of the TM\({}_{3/2,0}\) mode  
Figure 4: Input impedance of (**a**) PIFA with Slots 1; (**b**) PIFA with Slots 1 and Slots 2.  
(_f_2) can be heavily affected by the loading of Slots 1, while that of the TM\({}_{1/2,2}\) mode (_f_1) is hardly affected. Once an appropriate slot length (_Cp_ = 19.25 mm) is selected, the TM\({}_{3/2,2}\) mode can be combined with the TM\({}_{1/2,0}\) mode to realize a dual-mode operation. Figure 5b clearly depicts the merging process of the two modes from the perspective of the reflection coefficient.  
The effect of Slots 2, including the position (_Ls_) and the size (_Ws_), is also studied in Antenna III. As shown in Figure 6a,b, it can be found that both the position and the size of Slots 2 have significant effects on the impedance matching. By properly choosing _Ls_ and _Ws_, for example, _Ls_ from 0 to 4.5 mm and _Ws_ from 6.5 to 11.5 mm, the simulated \(|\,\)S\({}_{11}\,|\) gradually reduces to smaller than \(-10\) dB, so that the impedance matching can be progressively improved. With the final parameters (_Ls_ = 4.5 mm, _Ws_ = 11.5 mm) chosen, the reflection coefficient is below \(-10\) dB over a desired wide frequency under dual-mode resonance.
# Concrete Dam Deformation Prediction Model Research Based on SSA-LSTM
## 2 LSTM and SSA Fundamentals
### LSTM Principles
To summarize, the current hidden layer output \(h_{t}\) and cell state \(C_{t}\) of the LSTM model are influenced by the previous hidden layer output \(h_{t-1}\) and cell state \(C_{t-1}\), as well as the current input \(x_{t}\) at the given moment. According to several references, the LSTM model containing more than two hidden layers is constructed. This model can better train the complex nonlinear relationship between dam deformation and impact factors [4; 13].