esa-sceva/satcom-chunk-collection huggingface dataset:
# Neural Graph Similarity Computation with Contrastive Learning
## 3 Methodology
### Graph Similarity Prediction Network
#### 3.2.2 Attention-Based Graph Pooling  
The node-level embeddings generated by each convolutional layer are then aggregated to produce the graph-level embedding. Common approaches include the mean pooling or degree-aware pooling. To better characterize the weight of each node, we adopt the attention-based pooling strategy to generate the graph embedding [7].  
Specifically, given the node embedding matrix \(\mathbf{X}^{l}\) generated by the \(l\)-th convolution layer, we first calculate the global context embedding \(\mathbf{c}^{l}\) using mean pooling operation \(mean\_pool\), followed by a nonlinear transformation:  
\[\mathbf{c}^{l}=\sigma_{p}(mean\_pool(\mathbf{X}^{l})\mathbf{W}^{l}_{p}), \tag{2}\]  
where \(\mathbf{W}^{l}_{p}\) is the learnable weight matrix in the pooling layer, and \(\sigma_{p}\) is the activation function. The global context embedding captures the graph structure information, which is then used to calculate the attention of each node, as nodes with more similar embeddings to the global context embedding theoretically should receive higher attention weights. Formally, the attention weight \(att_{i}\) assigned to node i is calculated as \(sigmoid(\mathbf{c}^{\intercal}\mathbf{X}^{l}_{i})\), where \(\mathbf{X}^{l}_{i}\) is the node embedding, \(\mathbf{c}\) refers to \(\mathbf{c}^{l}\) and \(\intercal\) is the transpose function. Finally, the graph-level embedding produced by the attention-based pooling is denoted as \(\mathbf{g}^{l}=\sum_{i}att_{i}\mathbf{X}^{l}_{i}\).
# Pose Tracking and Object Reconstruction Based on Occlusion Relationships in Complex Environments
## 3 Experiments  
### Datasets  
To train and comprehensively evaluate the proposed method, we not only utilize the HO-3D dataset but also data specifically recorded using RealSense.  
HO-3D [18] is a dataset containing RGB-D videos of interactions between human hands and YCB objects [30] captured at close range using RealSense cameras. The ground truth is generated through multi-view registration. We used the latest version, **HO-3D_v3**, and conducted testing on the official evaluation set. It includes four different objects, 13 video sequences, and a total of 20,428 frames.  
While the HO-3D dataset is the most widely used dataset for object pose tracking-related research, it contains relatively few instances where the foreground object is blocked by moving occlusion. Therefore, we captured RGB-D video data using RealSense cameras, which include 6 different objects, 12 video sequences, and a total of 10,265 frames. The videos such as _video_switch_, _video_firm_cushion_, and _video_box_ involve a large amount of interaction with objects, and the surface textures of the objects are weak. To test the robustness of the method when occlusions change frequently and when the object's own texture information is unreliable, we also recorded long-duration RGB-D video data: _video_cup_. In this video, the target object is only fully exposed in the frame for 20% of the time, with less than 50% of its surface visible for the rest of the time. The data aim to assess whether the method can rapidly and accurately perceive changes in occlusion relationships, maximize the use of limited information, and make reliable pose predictions. Finally, to highlight the method's ability to withstand moving occlusions, we also recorded a video, _video_boxes_, where the camera and the target object remain relatively stationary, but there are randomly moving occluding objects between them. While ground truth for pose estimation is not available for these datasets, the target objects can be fully reconstructed in advance via scanning. Therefore, to provide a comprehensive and objective analysis of the results, this section will focus on evaluating the effectiveness of the model reconstruction.
## 3 System Design Configuration
### Procedure Used to Determine the ISL Frequency Bands  
The frequency bands for the water vapor mission are limited to the available meteorological UHF communication frequencies in the international telecommunication union (ITU) region 3 where the Kyushu Institute of Technology, Japan is located [49; 50]. To probe  
Figure 5: Time detection procedure.  
atmospheric water vapor in near-Earth regions using the ISL network, two available options (available band pairs) of frequencies are listed as shown in Table 2.  
From the available options, only one frequency from each of the two pairs can be selected. Considering the mission requirements, lower frequency bands give better estimations of _TWVC_ and _TEC_. The reason being that lower bands are influenced much more than higher bands. As a result, two low frequency bands \(f_{1}=400.15\) MHz and \(f_{2}=460.00\) MHz have been selected from each of the two available pairs. These two frequencies are known and automatically detected by each satellite in the ISL network. Furthermore, the two frequencies have a relatively smaller frequency gap making it feasible to implement with a single antenna that could be tuned to operate at both frequencies while either transmitting or receiving SS signals. Implementation of one antenna to perform both the transmission and reception of the SS signal is proposed to reduce complexity of the design configuration as well as to save on the occupied volume and mass of the satellite. Also, this frequency gap is wide enough to determine and differentiate variation of the _TEC_ from the _TWVC_ data.
# Review of Fast Computation Methods for Finite-State Predictive Control of Multi-Phase Drives
## 2 MPC for Current Control of MPS
### Drive Control  
FSMPC for variable-speed drives builds upon the Indirect Field-Oriented Control (IFOC) scheme [17]. This approach incorporates an outer feedback loop for speed regulation and an inner feedback loop dedicated to stator current control, as shown in Figure 1.  
For MPS, phase stator currents are transformed using the Clarke projection into the \(\alpha\)-\(\beta\) and \(x\)-\(y\) planes. This transformation enables the decoupling of control variables, simplifying the control strategy. According to the IFOC approach, the torque-generating currents should track a reference value set by the torque/speed control loop. The \(x\)-\(y\) current referencesare maintained at zero to minimize energy losses. In this manner, rotor flux and torque are regulated independently. The rotor flux target is given by \(i_{d}^{*}\), while \(i_{q}^{*}\) serves as the reference for electrical torque. These references are projected onto the \(\alpha\)-\(\beta\) plane via the Park transformation, resulting in a reference stator current in the \(\alpha\)-\(\beta\) space as \(I_{s\alpha-\beta}^{*}=D\left(i_{d}^{*},i_{q}^{*}\right)^{\mathsf{T}}\), where matrix \(D\) is defined as  
\[D=\begin{pmatrix}\cos\ \theta_{a}&\sin\ \theta_{a}\\ -\sin\ \theta_{a}&\cos\ \theta_{a}\end{pmatrix}. \tag{1}\]  
The rotor flux position \(\theta_{a}\) is estimated as \(\theta_{a}=\int\omega_{e}\,dt\), where \(\omega_{e}=\omega_{sl}+P\omega\), with \(P\) being the number of pairs of poles of the IM and \(\omega_{sl}\) the slip. This last quantity is related to rotor flux linkage (\(\psi_{r}\)) as \(\omega_{sl}=i_{qs}/(\tau_{r}\psi_{r}/L_{m})\), where \(\tau_{r}\) is the rotor time constant and \(L_{m}\) the mutual inductance. This expression can be simplified so that direct stator current is used instead of rotor flux, as it is the usual approach for IFOC. With this, one obtains \(\omega_{sl}=i_{qs}/(i_{ds}\hat{\tau}_{r})\), where \(\hat{\tau}_{r}\) is an estimation of the rotor time constant \(\tau_{r}=L_{r}/R_{r}\), with \(L_{r}\) being the rotor inductance and \(R_{r}\) the rotor resistance, respectively. As a result, the set point for stator current tracking \(I_{s}^{*}(k)\) has an amplitude given by \(I_{s}^{*}=\sqrt{i_{d}^{*2}+i_{q}^{*2}}\). Finally, the \(\alpha\)-\(\beta\) references can be expressed as  
\[I_{s\alpha}^{*}(t)=I_{s}^{*}\sin(\omega_{e}t),\quad I_{s\beta}^{*}(t)=I_{s}^{* }\cos(\omega_{e}t),\quad I_{sx}^{*}(t)=0,\quad I_{sy}^{*}(t)=0. \tag{2}\]  
At this point, the role of FSMPC is the generation of stator currents by issuing voltages through a VSI. This process is influenced by the specific VSI configuration and the number of phases, as described below.
# Enhancing Traceability of Italian Almonds through IR Spectroscopy and Chemometric Classifiers
## 3 Results  
In this section, the outcome of the SIMCA and SPORT analyses is discussed. Regardless of the classifier employed, distinct pretreatments were tested on data: first derivative [24], second derivative [25], and Standard Normal Variate (SNV) [26]. Clearly, in the case of SIMCA, a distinct classification model was built for each tested pretreatment; on the other hand, SPORT allows for ensemble preprocessing, so a single model was calculated. More details can be found in Sections 2.2, 2.3, 3.1 and 3.2. All data were mean-centered before the creation of any model.  
To ensure robust model evaluation, the spectra were divided into two distinct sets: one for training and one for validation. Specifically, 80 samples were allocated for training, and 80 were reserved for validation. The dataset was divided into two mutually exclusive subsets using the Duplex algorithm [27]. Regardless of the classifier used, all models were created in order to predict the geographic origin of the samples. Consequently, three classes were taken into consideration: Calabria, Apulia, and Sicily.  
Prior to the classification analysis, Principal Component Analysis (PCA) was used to explore the data to evaluate the presence of suspicious samples or outliers; this did not highlight any anomaly.
## 5 Experiment Results and Analysis
### Ablation Study for the Template Utility Evaluation (Answer to RQ2)  
This section preliminarily explores the utility of prompt templates for the BERT model. In this experiment, we investigate the proposed prompt template \(P=f_{p}(R,T,O,QA+[MASK])+X\), with the dataset being the \"Social networking\" subset from AWARE. F1-Score is the evaluation metric that reflects the model's performance more comprehensively and accurately. Moreover, BERT has a multi-head attention mechanism. According to previous evidence-based conclusions, the position of \(MASK\)[39] and the relative position between the template and sentence [11] are not within the experiment consideration scope.  
Table 5 shows the final results taken from five experiments to minimize errors. Obviously, the model relies solely on \(MASK\) tokens without context information, obtaining the worst performance. The performance improved with a single template, and the \(T+[MASK]\) template achieved the highest average F1-Score. This suggests that a well-defined task description significantly influences prediction accuracy. The B4 MLM model's performance with the binary template reached parity with B4 SC, and combining range prompts \(f(R)\) within well-defined task descriptions achieved the best results. The triple template experiment results prove that question-answer (Q&A) prompt mode \(f(QA)\) (e.g., _\"what is...? Answer is [MASK].\"_) assists the model in better understanding contextual information and task intentions, which can further improve the performance of the B4 MLM model. In addition, the last three results have proven that the lexicon-enhanced and dynamic weighting method obviously improved the performance of BERT. Although Hard-prompt templates benefit from stability and interpretability, they face issues such as the lack of  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Categories** & **Models** & **Acc \%** & **F1-Score \%** \\ \hline \multirow{4}{*}{**Syntax-based**} & PD-RGAT [46] & 77.31 & 75.68 \\  & SentiGCN [32] & 75.69 & 73.71 \\  & DualGCN [31] & 77.01 & 74.97 \\  & dotGCN [30] & 78.47 & 76.31 \\ \hline \multirow{3}{*}{**Attention-based**} & APEKCG [29] & 79.64 & 78.55 \\  & FT + BERT (SOTA) [10] & 78.62 & 76.70 \\  & **LePB-SA4 RE** & **80.51** & **79.17** \\ \hline \hline \end{tabular} The bold means high-performance results.  
\end{table}
Table 4: Performance of LePB-SA4 RE on the entire AWARE dataset compared to other methods.  
Figure 4: Confusion matrix of LePB-SA4 RE prediction results.  
In summary, designing templates with clear contextual information, well-defined task descriptions, reasonable prediction ranges, and Q&A structures can guide the PLM to achieve higher efficiency in app reviews mining-based requirements elicitation tasks.
## 2 Model Formulation
The Laplace inversion formula used to compute the behavior of the physical quantities of interest \(f(t)\) (e.g., the pipeline current or voltage) in the time domain from \(\tilde{f}(s)\), i.e., the corresponding quantity in the frequency domain, is:  
\[f(t)=\frac{1}{2\pi j}\int_{\alpha-j\infty}^{\alpha+j\infty}\tilde{f}(s)e^{st} \mathrm{d}s, \tag{6}\]  
with \(t>0\) and, defining \(\alpha_{0}\) as the abscissa of convergence, \(\alpha>\alpha_{0}\). The numerical procedure for computing Equation (6) is obtained by discretizing the domain \(|\alpha-j\infty,\alpha+j\infty[\) with a step amplitude \(\Delta\omega=\pi/T\) and integrating the expression by means of the trapezoidal rule. The resulting discrete expression for computing Equation (6) is:  
\[f(t)=\frac{e^{st}}{T}\operatorname{Re}\left\{\frac{\tilde{f}(\alpha)}{2}+\sum _{k=1}^{\infty}\tilde{f}\bigg{(}\alpha+j\frac{k\pi}{T}\bigg{)}e^{j\frac{k\pi t }{T}}\right\}, \tag{7}\]  
with \(0<t<T\). Equation (7) is affected by a discretization error due to the trapezoidal rule. An additional error is introduced when the infinite sum in Equation (7) is arrested at the \(N\)th term. The inversion is computed using the algorithm described in [25]. While the abscissa of convergence \(\alpha_{0}\) must be provided by the user (\(\alpha_{0}=0\) for this work), the values of the \(T\) and \(N\) parameters are computed internally in order to satisfy the tolerance required by the user. The actual value of \(\alpha>\alpha_{0}\) is also computed internally, as a function of the time \(t\) at which the inverse transform has to be evaluated. The process is repeated for the set of complex frequencies \(s\) at which Equation (3) is solved. Further details on the implementation of the numerical inversion algorithm can be found in [26]. A particularly relevant piece of information that can be obtained with the proposed methodology is the impulse response of the system. The convolution of the impulse response allows the behavior of the considered system to be evaluated as a response to any forcing term \(J_{0,z}(t)\), avoiding the need to Laplace transform the forcing term itself.
## 2 Preliminaries
### Problem Formulation  
In the attitude-tracking problem, we define the target attitude as \([\xi_{d},\omega_{d},\dot{\omega_{d}}]\), so the relative attitude is  
\[\xi = \xi\otimes\xi_{d}^{-1}=\frac{\xi-\xi_{d}+S(\xi)\xi_{d}}{1+\xi^{T} \xi_{d}} \tag{6}\] \[\tilde{\omega} = \omega-R\omega_{d} \tag{7}\]  
Figure 2: Three working modes of NQTAUV.  
where \(\tilde{R}\) is the relative attitude matrix, and the detailed derivation of \(\tilde{R}\) can be found in Reference [32] as  
\[\tilde{R}=\frac{(1-\xi^{T}\xi)I+2\xi_{5}^{\xi T}-2S(\xi)}{1+\xi^{T}\xi} \tag{8}\]  
where the derivative of \(\tilde{R}\) is  
\[\hat{R}=-S(\tilde{\omega})\tilde{R} \tag{9}\]  
and the derivative of \(\tilde{\omega}\) is  
\[\dot{\tilde{\omega}}=\dot{\omega}-(\hat{R}\omega_{d}+\tilde{R}\dot{\omega}_{d })=\dot{\omega}-[-S(\tilde{\omega})\tilde{R}\omega_{d}+\tilde{R}\dot{\omega}_ {d}] \tag{10}\]  
We then obtain the attitude-tracking error system model  
\[\dot{\xi} = H(\xi)\tilde{\omega} \tag{11}\] \[\dot{\omega} = J^{-1}[-S(\omega)J\omega-I_{A}\dot{\omega}-C(\omega)\omega-D( \omega)+\tau]-(-S(\tilde{\omega})\tilde{R}\omega_{d}+\tilde{R}\dot{\omega}_{d}) \tag{12}\]  
where \(\omega=\tilde{\omega}+\tilde{R}\omega_{d}\).  
We use feedback linearization to cancel the nonlinearities of the system dynamics  
\[\tau=v+[S(\omega)J\omega+I_{A}\dot{\omega}+C(\omega)\omega+D(\omega)]+J(-S( \tilde{\omega})\tilde{R}\omega_{d}+\tilde{R}\dot{\omega}_{d}) \tag{13}\]  
and System (12) then becomes  
\[\dot{\omega}=J^{-1}(v+d) \tag{14}\]  
where \(d\) is the disturbance, and \(d\) comes from various sources. First, the tilting servo generates a disturbance torque to the pitch axis. Second, when the NQTAUV flies in the water, the hydrofoil generates extra force and torque for the vehicle. Without a linear velocity sensor, both of the above torques cannot be calculated, we treat those and other outside disturbances as one combined disturbance. We then estimated combined disturbance \(d\). Noting that \(d^{\prime}=J^{-1}d\), System (14) becomes  
\[\dot{\omega}=J^{-1}v+d^{\prime} \tag{15}\]
## 4 Proposed Scheme
### Scheme Properties  
The proposed method is characterized by a series of features that make it suitable for a subclass of applications with low computing power, such as Internet of Things (IoT) applications where it is not possible to implement a centralized model.  
First, the method offers an adjustable level of anonymity--an essential feature in scenarios where the confidentiality of the entities participating in the secure communication process has different degrees of security. This flexibility is ensured by the parameter \(T\), which allows the degree of anonymity to be adjusted.  
Second, the method is characterized by the possibility of setting the computational efficiency to minimize resource consumption, but if the degree of resistance to attacks is desired to be increased, the parameters are modified, depending on the computing power available in the system in which it is implemented. Critical operations, such as anonymization and signing, are performed in a simplified way, which makes them suitable for implementation on devices with limited resources, such as IoT systems.  
Another notable feature is the compatibility of the method with certain subspaces. This is due to the fact that its mathematical structure is adapted to operate in finite vector subspaces, which facilitates its application without the need for mathematical primitives that require complex calculations to determine the working fields, for example, primitive polynomials of high degree. This compatibility allows the creation of versions for areas such as signal processing and cryptographic data analysis.  
Last but not least, the procedure benefits from an adjustable degree of control parameters, of security by choosing the fields over which the control vectors of the cryptographic primitives are defined, making the implementations resistant to brute force attacks or those based on statistical analysis.  
```
Input:\(M\), \(T\), \(K_{\text{priv}}\), \(K_{\text{pub}}\) Output:\(\sigma\)
1 Key Generation: Generate keys \(K_{\text{priv}}\), \(K_{\text{pub}}\) such that \(e\cdot d\equiv 1\mod\varphi(n)\);
2 Signing: Generate a random factor \(r\leftarrow\text{Random}()\);
3 Compute \(M^{\prime}\gets M\cdot r\mod n\);
4 Compute \(\sigma^{\prime}\leftarrow(M^{\prime})^{K_{\text{priv}}}\mod n\);
5 Return\(\sigma^{\prime}\);
6 Unblinding: Compute \(\sigma\leftarrow\sigma^{\prime}\cdot r^{-1}\mod n\);
7 Return\(\sigma\);
8 Verification: Validate if \(\sigma^{e}\mod n=M\cdot T\);
9 ifValidthen
10 Signature Accepted;
11 else
12 Signature Rejected;
13 Adjusting Anonymity with \(T\): if\(T\rightarrow\infty\)then
14\(r\leftarrow\) Complex value (high confidentiality);
15
16 else
17\(r\leftarrow\) Simplified (enhanced transparency);
18
19 Blockchain Integration: foreach node \(i\in\) Blockchaindo
20 Verify \(\sigma^{e}\mod n=M\);
21 ifValidthen
22 Store \((M,\sigma)\);
```  
**Algorithm 1**Proposed Scheme for Tempered Signatures
## 2 Improved Homotopy Analysis Method
### Traditional Homotopy Analysis Method  
#### 2.2.1 Zero-Order Deformation Equation  
For comparison, nonlinear boundary value Equations (3) and (4) are solved firstly by HAM. When \(w(\xi)\) is expanded into a power series of elementary functions in terms of \(\xi\), we have  
\[w(\xi)=\sum_{k=1}^{+\infty}a_{k}\xi^{k} \tag{8}\]  
where \(a_{k}\) is elementary coefficient. Equation (8) serves as the approximation solution to the current problem.  
With reference to the boundary condition  
Denoting a non-zero auxiliary parameter as \(\hbar\), a more general form of homotopy is then constructed as  
\[H[\Psi(\xi,q)]=(1-q)L[\Psi(\xi,q)-w_{0}(\xi)]-q\hbar\mathrm{N}[\Psi(\xi,q)] \tag{13}\]  
Setting \([\Psi(\xi,q)]=0\), a family of equations are derived as follows  
\[(1-q)L[\Psi(\xi,q)-w_{0}(\xi)]=q\hbar\mathrm{N}[\Psi(\xi,q)] \tag{14}\]  
Equation (14) is subject to the boundary conditions  
\[\Psi(0,q)=0,\;\Psi^{\prime}(1,q)=0 \tag{15}\]  
where \(q\in[0,1]\) is an embedded parameter.  
When \(q=0\), Equations (14) and (15) yield  
\[\Psi(\xi,0)=w_{0}(\xi) \tag{16}\]  
When \(q=1\), Equations (14) and (15) are exactly the same as Equations (3) and (4) provided that  
\[\Psi(\xi,1)=w(\xi) \tag{17}\]  
Therefore, as the embedded parameter \(q\) increases from \(0\) to \(1\), \(\Psi(\xi,q)\) evolves from the initial guess \(w_{0}(\xi)\) to the solution \(w(\xi)\). For brevity, Equations (14) and (15) are defined as zero-order deformation equations.  
Assume that \(\Psi(\xi,q)\) is analytic in the domain of \(q\in[0,1]\), so that the deformation derivatives exist:  
\[w_{0}^{[n]}(\xi)=\frac{\partial\Psi^{n}(\xi,q)}{\partial q^{n}}|_{q=0} \tag{18}\]  
\(\Psi(\xi,q)\) can then be expanded in the Maclaurin series of \(q\) as follows:  
\[\Psi(\xi,q)=w_{0}(\xi)+\sum_{n=1}^{+\infty}w_{n}(\xi)q^{n} \tag{19}\]  
where  
\[w_{n}(\xi)=\left.\frac{1}{n!}\frac{\partial^{n}\Psi(\xi,q)}{\partial q^{n}} \right|_{q=0} \tag{20}\]  
It is worth mentioning that the auxiliary parameter \(\hbar\) in Equation (14) determines the convergence regions. Assuming that \(\hbar\) is properly chosen, so that all of these Maclaurin series are convergent at \(q=1\). Thus, at \(q=1\) Equation (17) gives  
\[w(\xi)=w_{0}(\xi)+\sum_{n=1}^{+\infty}w_{n}(\xi) \tag{21}\]  
Equation (21) bridges the initial guess solution \(w_{0}(\xi)\) to the exact solution \(w(\xi)\). The result for the \(n\)th-order approximation is given by:  
\[w(\xi)\approx w_{0}(\xi)+\sum_{m=1}^{n}w_{m}(\xi) \tag{22}\]