esa-sceva/satcom-chunk-collection huggingface dataset:
# Sensor Technologies for Transmission and Distribution Systems: A Review of the Latest Developments
## 1 Introduction
Finally, the recent research attempts to describe wireless sensing networks and machine learning-based models for fault classification and fault forecasting in power grids and smart grids [1; 2; 6; 23; 24; 25; 26; 27; 28; 29]. However, these studies are more focused on the overall architecture and topology of the wireless sensing networks with various communication protocols and machine learning algorithms for outage management rather than the sensors which are solely responsible for the fault data acquisition. Keeping these points in mind, this review paper attempts to provide an in-depth analysis of all the latest developments in different types of sensor technologies and how they are responsible for fault detection in various transmission and distribution system equipment. This paper also demonstrates the evolution, development, and practical deployment of smart sensors [30] and wireless sensor networks for real time measurements, condition-based health monitoring, and predictive maintenance of the smart grid [2; 31; 32]. The remainder of this paper is organized as follows. Section 2 serves as an introduction to this paper. This section introduces the challenges faced by the transmission and distribution systems of an electrical power grid and the importance of the deployment of sensors on them. This section also describes some of the unique characteristics of modern-day sensors which have made them an essential part of the operations and maintenance of power grids. Section 3 lists different types of sensors and power grid components. It reviews some of the latest sensors that have been deployed in various power system components. This section discusses the unique characteristics and functioning of these state-of-the art sensors and how they are effective in mitigating major fault conditions occurring in various power grid components. It is also explained how these challenges can serve as future research directions. Finally, Section 6 concludes the entire work.
## 4 Experiment and Discussion
### Flight Test
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Controller**} & \multicolumn{4}{c}{**RMS Error**} \\ \cline{2-5}  & **Roll (\({}^{*}\))** & **Pitch (\({}^{*}\))** & **Yaw (\({}^{*}\))** & **Altitude (cm)** \\ \hline Adaptive controller & 1.363 & 1.484 & 0.352 & 0.171 \\ PID controller & 6.752 & 1.726 & 0.635 & 2.483 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of the RMS attitude and altitude errors from the adaptive controller and the PID controller.
## 3 Fluctuation Correlations and Thermodynamics of Binary Symmetric Electrolyte Solutions
### Positive-Negative Ionic Correlations  
The positive-negative ion concentration-fluctuation correlation is also derived from Equation (61). The result becomes  
\[\begin{split}\langle\delta\mathcal{E}_{+}&(\mathbf{k _{1}},\omega_{1})\delta\mathcal{E}_{-}(\mathbf{k_{2}},\omega_{2})\rangle=- \frac{D^{2}\kappa^{4}}{4(ze)^{2}}\frac{\langle\delta\rho_{e}(\mathbf{k_{1}}, \omega_{1})\delta\rho_{e}(\mathbf{k_{2}},\omega_{2})}{(i\omega_{1}+Dk_{1}^{2}) (i\omega_{2}+Dk_{2}^{2})}\\ &+\frac{D\kappa^{2}}{2 ze}\Bigg{[}\frac{i\mathbf{k_{1}}\cdot \langle\delta^{\dagger}_{\mathbf{j}+}(\mathbf{k_{1}},\omega_{1})\delta\rho_{e }(\mathbf{k_{2}},\omega_{2})\rangle}{(i\omega_{1}+Dk_{1}^{2})(i\omega_{2}+Dk_ {2}^{2})}-\frac{i\mathbf{k_{2}}\cdot\langle\delta^{\dagger}_{\mathbf{j}-}( \mathbf{k_{2}},\omega_{2})\delta\rho_{e}(\mathbf{k_{1}},\omega_{1})\rangle}{( i\omega_{1}+Dk_{1}^{2})(i\omega_{2}+Dk_{2}^{2})}\Bigg{]}.\end{split} \tag{71}\]  
The correlation between the positive and negative ionic fluxes is zero because of Equation (39). In addition, \(\langle\delta\mathcal{E}_{+}(\mathbf{k_{1}},\omega_{1})\delta\mathcal{E}_{-}( \mathbf{k_{2}},\omega_{2})\rangle=\langle\delta\mathcal{E}_{-}(\mathbf{k_{1}},\omega_{1})\delta\mathcal{E}_{+}(\mathbf{k_{2}},\omega_{2})\rangle\). The inversion of Equation (71) at \(t_{1}=t_{2}=t\) yields  
\[\langle\delta c_{+}(\mathbf{r_{1}},t)\delta c_{-}(\mathbf{r_{1}},t)\rangle=c^ {2}[g_{+-}(r)-1]. \tag{72}\]  
The radial distribution function and pair interaction energy between oppositely charged ions are  
\[g_{+-}(r)=1+\frac{(ze)^{2}}{4\pi\varepsilon\varepsilon_{0}k_{B}T}\frac{e^{- \kappa r}}{r}, \tag{73}\]  
and [11]  
\[w_{+-}(r)=-\frac{(ze)^{2}}{4\pi\varepsilon\varepsilon_{0}}\frac{e^{-\kappa r} }{r}. \tag{74}\]  
The sign of the interaction energy \(w_{+-}(r)\) is negative, which is in agreement with the fact that positive and negative ions attract each other.
## 3 Application and Illustration of the Proposed CRITIC-GRA Model
### Stability Index of Open Stoping  
#### 3.2.1 Stability Probability with the Updated Mathews Graph Method  
An engineering geological investigation and evaluation of the geological structural planes of the rockmass around the field stopes in the iron mine was carried out. The diamond drilling and coring of the rockmass were performed to collect rock samples for lab testing of the rock's physical and mechanical properties. Based on the specific properties of the rockmass in the illustrated iron mine, the evaluated input data for the updated Mathew graph method are presented in Table 2.  
\begin{table}
\begin{tabular}{l l l l l l l l l l l l l} \hline \hline \multirow{2}{*}{**Present**} & \multicolumn{3}{c}{**Stope Dimensions**} & \multicolumn{3}{c}{**Stability Probability**} & \multicolumn{3}{c}{**Simulated Safety Index with FLAC3D**} & \multicolumn{3}{c}{**Mining Economic Index**} \\ \cline{2-13}  & **Length** & **Width** & **Height** & **Wall** & **Root** & **Wall** & **Root** & **Wall** & **Root** & **Max.** & **Cost** & **Capacity** \\  & \(L\) & \(W\) & \(H\) & **Prob.** & **Prob.** & **Disp.** & **Disp.** & **Plastic Vol.** & **Plastic Vol.** & **Stress** & **(CNV/0)** & **(t/Day)** \\ \hline
1 & 40 & 15 & 60 & 0.91 & 0.90 & 0.70 & 0.80 & 38,357 & 10,846 & 37.65 &  
According to Equation (1), the Stability Index \(M\) of the above illustrated rockmass properties was calculated to be a fixed value of 8.2 and 18.1 for the roof and wall of the considered open stopes, respectively. It should be mentioned that the rockmass was assumed to be homogeneous and isotropic here which led to a set of constant values of rockmass properties, and thus a constant Stability Index \(M\) in all the preset schemes. In addition, the Shape Factor \(S\) for the side walls and roof of the open stopes with different preset dimensions were directly calculated with Equation (2) considering different combinations for the roof with stope length and width and for the wall with stope length and height.  
From Table 3, it can be seen that the roof is stable when the stope length \(L\) = 40-60 m and width \(W\) = 15-20 m. For the investigated rockmass quality and properties, the roof reached a critical stable state at a length of 91.3 m when the stope width was 20 m.  
The side wall of the single stage stope (i.e., height \(H\) = 60 m) is stable when the stope length \(L\) = 40-60 m, which reaches the critical stable state at the length of 91.3 m.  
The side wall of the double stage stope (i.e., height \(H\) = 120 m) reaches the critical stable state at the stope length of 51.8 m which implies that the stope wall fails when the stope length is greater than this critical length (Scheme No. 14, 16, 18, 20 of Table 3).  
And then, the probability _f(z)_ of the stability conditions for exposed side walls or roof (abbreviated as wall probability and roof probability) around the excavated stope void can be assessed with Equation (5) and shown in Table 3. These probability values can be used as basic samples for further synthesis with the stability results from FLAC3D simulations and mining economic indexes.
# A Novel Equivariant Self-Supervised Vector Network for Three-Dimensional Point Clouds
## 3 The Equivariant Vector Network with Self-Supervised Learning
### Information Theory in Masked Autoencoder
As the mask rate increases and the subset \(x_{masked}\) expands, the latent feature \(z\) becomes more capable of learning information specific to the network's target subset \(x_{masked}\). This results in better learning outcomes. Simultaneously, it is worth noting that both the input and output of the network constitute a subset of the original sample data distribution \(x\), and the goal of the MAE can be regarded as the optimal estimation of \(E(x_{masked}|x_{vis})\). The general form of the MAE loss function can be easily obtained, and this general form also has better generalization. Its expression is as follows:  
\[\mathbb{E}[x_{masked}|x_{vis}]=||x_{masked}-x_{vis}||_{2}^{2} \tag{7}\]If the encoder and the decoder in the MAE network framework have equivariance, the latent feature \(z\) can be equally rotated with the rotation of the input data \(x_{vis}\), and the MAE of features learned in this way can realize the equivariant reconstruction. When encoder \(f^{\prime}(x,\theta)\) and decoder \(g^{\prime}(z,\eta)\) have equivariance, so that it meets \(f^{\prime}(xR,\theta)=f^{\prime}(x,\theta)R\), \(g^{\prime}(zR,\eta)=g^{\prime}(z,\eta)R\), the equivariance is as follows. For any rotation matrix \(\mathbf{R}\in\text{SO}(3)\), there is  
\[f^{\prime}(xR,\theta)=f^{\prime}(x,\theta)R=z(\theta)R \tag{8}\] \[g^{\prime}(zR,\eta)=g^{\prime}(z,\eta)R=\widehat{x}R \tag{9}\]  
In this case, the overall framework of the MAE is equivariant:  
\[x_{vis}R\xrightarrow{f^{\prime}(x,\theta)}z(\theta)R\xrightarrow{g^{\prime}( z,\eta)}\widehat{x}_{masked}R \tag{10}\]  
The rotation \(R\) performed on the input data is passed through the network to the latent feature \(z\), and then to the final reconstruction result. Finally, the network can be implemented for equivariant feature learning and rotated data reconstruction. The above theory illustrates that the internal representation learned through the MAE framework can regenerate the data if it is rich enough. Of course, this is also well demonstrated by the original MAE experiment. At the same time, if the encoder and the decoder are equivariant, the MAE framework will be equivariant. The network architecture design in this paper extends the MAE. While retaining its original excellent internal representation learning ability, it is endowed with the learning of equivariant features. Following the above ideas, if constructed in an equivariant way, the network can be applied to a wider range of learning tasks, achieving better generalization and robustness. It can realize the equivariant reconstruction of point clouds and the learning of equivariant features.
# Discerning Reality through Haze: An Image Dehazing Network Based on Multi-Feature Fusion
## 3 Multi-Feature Fusion Image Dehazing Network
### Lightweight Gated Channel Attention
\[\ell_{1}=|\sum_{i=1}^{H}\sum_{j=1}^{W}\chi_{c}^{i,j}| \tag{8}\]  
\[\ell_{2}=\left\{\sum_{i=1}^{H}\sum_{j=1}^{W}\left(\chi_{c}^{i,j}\right)^{2}+ \epsilon\right\}^{\frac{1}{2}} \tag{9}\]  
\[S_{c}=\alpha_{c}\ell_{1}+(1-\alpha_{c})\ell_{2} \tag{10}\]  
where \(\ell_{1}\) is the absolute value of the channel features of each pixel coordinate in the image and \(\ell_{2}\) is the square root of the sum of squares of the channel values of the image pixel coordinates. Moreover, a constant \(\epsilon\) is set to prevent the derivative from being zero. Channel Normalization Layer: Normalization layers are employed to accentuate channels with high feedback values and suppress those with lower feedback values, thus achieving a dynamic adjustment of features [40, 41, 42]. LGCA utilizes \(\ell_{1}+\ell_{2}\)-norm formulations to normalize the channels, optimizing the weight distribution among them. The formula for channel normalization is as follows:  
\[S_{c}^{*}=\frac{\sqrt{c}S_{c}}{||S_{c}||_{2}}=\frac{\sqrt{c}S_{c}}{\left[( \sum_{c=1}^{C}S_{c}^{2}+\epsilon)\right]^{\frac{1}{2}}} \tag{11}\]  
Figure 5: Structure of lightweight gated channel attention mechanism. where \(S=[S_{1},S_{2},...,S_{c}]\); to avoid excessively small values of \(S_{c}\) when \(c\) is too large, we introduces a constant \(\sqrt{C}\), and a small constant \(\varepsilon\) is introduced to prevent abnormal algorithm performance when the denominator is zero. Notably, the channel normalization method proposed in this study (\(O(C)\)) involves much less computation than FC (\(O(C^{2})\)). Gated Adaptive Layer: The gating mechanism is capable of enhancing the overall performance of the algorithm by inducing competition and cooperation among channel features [43]. Concurrently, the gating mechanism is instrumental in preventing the potential loss of features within deep networks, augmenting the algorithm's flexibility and robustness and enabling adaptability to various task requirements. The LGCA augments each original channel feature \(\widehat{\chi}\) by integrating a gating mechanism, thereby not only increasing the algorithm's flexibility but also ensuring the integrity of the original feature \(\widehat{\chi}\). The specific definition of the gated adaptive layer is as follows:  
\[\widehat{\chi_{c}}=\widehat{\chi}[1+tanh(\gamma_{c}S_{c}^{*}+\beta_{c})] \tag{12}\]  
The operation of the gating mechanism allows the network to dynamically adjust. When a channel's gating weight is positively activated, the channel is encouraged to compete with other channels. Conversely, if the gating weight is negatively activated, it encourages the channel to cooperate with other channels. That is, when both weights \(\gamma\) and biases \(\beta\) are zero, the original features are passed to the next layer without modification.
# Weibo Text Sentiment Analysis Based on BERT and Deep Learning
## 3 Preliminary Knowledge
### Lstm  
Although the RNN can process sequence information, it is prone to problems such as gradient disappearance or gradient explosion. LSTM can effectively solve these problems. Compared with RNN, LSTM introduces a memory unit \(\mathbf{C}_{t}\) and three logic gates: input gate \(\mathbf{i}_{t}\), output gate \(\mathbf{o}_{t}\) and forget gate \(\mathbf{f}_{t}\). The network determines the output information at the time through the output information at the previous time and the input information at the time, and uses part of the output information at the current time as the input information at the next time. Through the memory unit and logic gates, LSTM can decide how much historical information and the input information at the current moment to keep, which can better transmit and abandon the information. However, LSTM can only process information in a single direction, and the words in the text will be affected by its context. Therefore, it is necessary to process information in both the forward and reverse directions. Then, the BiLSTM is proposed which the information in both directions can be processed at the same time. It means the output of the BiLSTM network contains both contextual information before and after. The structure of BiLSTM is shown in Figure 3:  
In one direction, suppose that \(\mathbf{h}_{t}\) and \(\mathbf{x}_{t}\) are the hidden state vector and input vector at time \(t\), respectively, \(\mathbf{U}\) and \(\mathbf{W}\) represent the weight matrix to be trained, and \(b\) represents the bias term to be trained.  
The forget gate expresses how much information to forget by outputting a number in the interval \([0,1]\) according to the following formula, as shown in Formula (4):  
\[\mathbf{f}_{t}=\sigma\Big{(}\mathbf{W}_{f}\mathbf{h}_{t-1}+\mathbf{U}_{f} \mathbf{x}_{t}+\mathbf{b}_{f}\Big{)}. \tag{4}\]  
Figure 2: Convolutional neural network structure.  
The input gate determines how much new information to store by calculating \(\mathbf{i}_{t}\) and \(\mathbf{C}_{t}\) and combining them according to the following equations, as shown in Formulas (5)-(7):  
\[\mathbf{i}_{t}=\sigma(\mathbf{W}_{i}\mathbf{h}_{t-1}+\mathbf{U}_{i}\mathbf{x}_{ t}+\mathbf{b}_{i}) \tag{5}\]  
\[\mathbf{C}_{t}=\tanh(\mathbf{W}_{c}\mathbf{h}_{t-1}+\mathbf{U}_{c}\mathbf{x}_{ t}+\mathbf{b}_{c}) \tag{6}\]  
\[\mathbf{A}_{t}=\mathbf{f}_{t}\odot\mathbf{A}_{t-1}+\mathbf{i}_{t}\odot \mathbf{C}_{t}. \tag{7}\]  
The output gate determines which parts of the current existing information are output, as shown in Formulas (8) and (9):  
\[\mathbf{o}_{t}=\sigma(\mathbf{W}_{o}\mathbf{h}_{t-1}+\mathbf{U}_{o}\mathbf{x} _{t}+\mathbf{b}_{o}) \tag{8}\]  
\[\mathbf{h}_{t}=\mathbf{o}_{t}\odot\tanh(\mathbf{A}_{t}). \tag{9}\]  
BiLSTM combines the forward and reverse hidden states as the final hidden state representation at time \(t\). In this way, it is conducive to the flow of time information in two directions, and context information can be better learned.
###### Abstract  
Predicting student academic success is a significant task in the field of educational data analysis, offering insights for personalized learning interventions. However, the existing research faces challenges such as imbalanced datasets, inefficient feature transformation methods, and limited exploration data integration. This research introduces an innovative method for predicting student performance by transforming one-dimensional student online learning behavior data into two-dimensional images using four distinct text-to-image encoding methods: Pixel Representation (PR), Sine Wave Transformation (SWT), Recurrence Plot (RP), and Gramian Angular Field (GAF). We evaluated the transformed images using CNN and FCN individually as well as an ensemble network, EnCF. Additionally, traditional machine learning methods, such as Random Forest, Naive Bayes, AdaBoost, Decision Tree, SVM, Logistic Regression, Extra Trees, K-Nearest Neighbors, Gradient Boosting, and Stochastic Gradient Descent, were employed on the raw, untransformed data with the SMOTE method for comparison. The experimental results demonstrated that the Recurrence Plot (RP) method outperformed other transformation techniques when using CNN and achieved the highest classification accuracy of 0.9528 under the EnCF ensemble framework. Furthermore, the deep learning approaches consistently achieved better results than traditional machine learning, underscoring the advantages of image-based data transformation combined with advanced ensemble learning approaches.  
academic performance prediction; ensemble learning; machine learning; educational data mining; deep learning Article
## 3 Dynamic Object Identification and Tracking
### Evaluating Tracking Performance Using the MOTA Metric
```
1:Initialize tracker with \((d_{th},max\_skip,max\_trace)\)
2:Initialize MOTA metrics: \(fp,miss,mme,gt\gets 0\)
3:while ROS running do
4: Receive detections \(D\) and states \(S\)
5:if no active tracks then
6:for\(d\in D\)do
7: Create new track with \(d,s\)
8:endfor
9:else
10: Compute cost matrix \(C\) between predictions and \(D\)
11: Solve assignment problem using Hungarian algorithm
12:for each assignment \((p,d)\)do
13:if\(C(p,d)>d_{th}\)then
14: Mark \(p\) as unmatched, increment \(fp\)
15:else
16: Update track with \(d\)
17:endif
18:endfor
19: Remove tracks with skipped frames \(>max\_skip\)
20:for unmatched \(d\in D\)do
21: Create new track for \(d\)
22:endfor
23:endif
24: Update \(fp,miss,mme,gt\)
25: CalculateMOTA
26:endwhile
27:procedureCalculateMOTA
28:\(MOTA\gets 1-\frac{fp+miss+mme}{gt}\times 100\)
29: Print MOTA
30:endprocedure
```  
**Algorithm 3** Object tracking and MOTA calculation
## 3 Exploring Speaker-Independent Dysarthric Speech Recognition
### Datasets and Data Split
#### 3.2.5 Summary  
The various training data used in the experiments are summarized below:  
* **C_rich**: Rich-resource canonical speech from five languages in **Common Voice**;
* **C_rich_tgt**: Rich-resource canonical speech for target language in **Common Voice**;
* **C_indm**: In-domain canonical speech from the dataset of target dysarthric speakers;
* **D**: Dysarthric speech from source speakers across all dysarthric datasets, including in-domain dysarthric data;
* **D_indm**: In-domain dysarthric speech from source speakers within the dataset of target dysarthric speakers.  
All datasets used in this work are publicly available and have been extensively utilized in numerous published studies. During preprocessing, mislabeled utterances (as identified in related works) and recordings shorter than 0.2 s were excluded. These preprocessing steps, combined with the datasets' widespread use, ensure robust annotation accuracy and reliable sample representativeness.  
We utilized multiple canonical and dysarthric speech datasets for training and testing to increase variability in our test sets, aiming to ensure a representative sample for practical applications. However, potential biases may still exist in the data, particularly related to age, disorder type, language, and accent. To mitigate these effects, we have consistently reported recognition results averaged across six dysarthric datasets, covering a broader range of variations compared to most recent studies. Consequently, the potential negative impact of this variability has been effectively minimized, ensuring a more robust and generalized evaluation of our approach.
## 3 Modal Parameter Identification
### Identification of the Hankel Matrix  
In this section, the solutions of the state matrix \(A\) and the output matrix \(C\) are calculated. As the Hankel matrix of a vibrating structure includes modal information, the output state space model was constructed, and the Hankel matrix was obtained from displacement data [27]:  
\[H_{0/2i-1}=\frac{1}{\sqrt{j}}\left(\begin{array}{cccc}y_{0}&y_{1}&\ldots&y_{ j-1}\\ y_{1}&y_{2}&\ldots&y_{j}\\ \ldots&\ldots&\ldots&\ldots\\ y_{i-1}&y_{i}&\ldots&y_{i+j-2}\\ y_{i}&y_{i+1}&\ldots&y_{i+j-1}\\ y_{i+1}&y_{i+2}&\  
Further, the singular vector decomposition of Equation (17) leads to:  
\[T_{1/i}=USV^{T}=\left(\begin{array}{cc}U_{1}&U_{2}\end{array}\right)\left( \begin{array}{cc}S_{1}&0\\ 0&0\end{array}\right)\left(\begin{array}{c}V_{1}^{T}\\ V_{2}^{T}\end{array}\right)=U_{1}^{T}S_{1}V_{1}^{T} \tag{18}\]  
where \(U\) and \(V\) are orthogonal matrices and \(S\) is a diagonal matrix composed of singular values.  
Hence, the observability matrix and the state sequence can be expressed as:  
\[O_{i}=U_{1}S_{1}^{1/2} \tag{19}\]  
\[\Delta_{i}=S_{1}^{1/2}V_{1}^{T} \tag{20}\]  
Therefore, the modal parameters of the system can be analyzed by calculating the state matrix \(A\) and the output matrix \(C\) based on the matrices \(O_{i}\) and \(\Delta_{i}\).