esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Deep Learning and Symmetrized Dot Pattern to Detect Surge Arrester Status
###### Abstract  
Surge arresters primarily restrain lightning and switch surges in the power system to avoid damaging power equipment. When a surge arrester fails, it leads to huge damage to the power equipment. Therefore, this study proposed the application of a convolutional neural network (CNN) combined with a symmetrized dot pattern (SDP) to detect the state of the surge arrester. First, four typical fault types were constructed for the 18 kV surge arrester, including its normal state, aging of the internal valve, internal humidity, and salt damage to the insulation. Then, the partial discharge signal was measured and extracted using a high-speed data acquisition (DAQ) card, while a snowflake map was established by SDP for the features of each fault type. Finally, CNN was used to detect the status of the surge arrester. This study also used a histogram of oriented gradient (HOG) with support vendor machine (SVM), backpropagation neural network (BPNN), and k-nearest neighbors (KNN) for image feature extraction and identification. The result shows that the proposed method had the highest accuracy at 97.9%, followed by 95% for HOG + SVM, 94.6% for HOG + BPNN, and 91.2% for HOG + KNN. Therefore, the proposed method can effectively detect the fault status of surge arresters.  
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Footnote †: journal: Applied  
**Citation:** [PERSON]; [PERSON]; [PERSON] Application of Deep Learning and Symmetrized Dot Pattern to Detect Surge Arrester Status. _Appl. Sci._**2022**, _12_, 650. [[https://doi.org/10.3390/app12020650](https://doi.org/10.3390/app12020650)]([https://doi.org/10.3390/app12020650](https://doi.org/10.3390/app12020650))
## 3 Results and Discussion
### Desalination Effect  
Figure 10 recorded the desalination degrees with the ED running times for three cation exchange membranes, and Table 3 summarized their desalination effects, by employing the same homogeneous anion exchange membrane (CMX) to match with the above-mentioned three membranes under similar test conditions. The results indicated that the CAM alloy membrane had the shortest desalination time (16 min), but could obtain a desalination degree (96.8%) close to the CMX homogeneous membrane (99.5%). For an ED process, the maximum desalination degree should arrive at the place where the ion transfer flux from the desalination side under the electric field is equal to the ion diffusion flux from the other side (or concentration side) under the difference of two-side salt concentrations. From the  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Membrane** & **Thickness (mm)** & \(P\) **(MPa)** & _W\({}_{\text{c}}\)_ **(wt\%)** & _IEC_ **(mmol/g)** & \(r\) **(\(\Omega\cdot\text{cm}^{2}\))** & _t\({}^{\star}\)_ **(-)** \\ \hline QHC\({}^{\text{a}}\) & 0.43 & 0.65 & 50.2 & 2.06 & 10.12 & 0.908 \\ CAM\({}^{\text{b}}\) & 0.38 & 0.51 & 35.5 & 2.18 & 4.75 & 0.962 \\ CMX\({}^{\text{c}}\) & 0.19 & 0.42 & 28.9 & 1.73 & 2.78 & 0.987 \\ \hline \end{tabular} \({}^{\text{a}}\) The heterogeneous cation exchange membrane from Qianqiu Water Treatment Co.; \({}^{\text{b}}\) The custom-made cation exchange alloy membrane (DVB content = 5%, _R_*DVBVPVP = 0.8); \({}^{\text{c}}\) The homogeneous cation exchange membrane from Tokuyama Co. Ltd.  
\end{table}
Table 2: The properties of cation exchange membranes.  
Figure 9: _I-V_ curves of the cation exchange membranes.  
viewpoint of the relationship between the structure and the property, the lower area electric resistance could contribute to a stronger electric driving force and, thus, a larger ion transfer flux, while the denser membrane's inner structure would limit and decrease the ion diffusion. Comparing these three kinds of cation exchange membranes, we can agree that the area electric resistance and the inner compact degree of the prepared alloy membrane were closer to those of the homogeneous membrane rather than the heterogeneous membrane. On the whole, the alloy membrane exhibited a remarkably superior desalination effect to the commercial heterogeneous membrane. Though there is a long way to gain the same area electric resistance and compaction degree as the homogeneous membrane, the result still illustrated that our cation exchange alloy membrane had shown great potential for applications in electro-driven membrane separation processes.
## 2 Experimental Study on Adhesion Properties of Raw Coal
### Research Results and Discussion
#### 2.3.3 Influence of Compaction Force on Raw Coal Adhesion Properties  
Figure 10 shows the changes in cohesion at the coal-coal and coal-wall interfaces under varying compaction forces. The results indicate that cohesion increases with compaction force at both interfaces, with the coal-wall interface consistently exhibiting higher cohesion than the coal-coal interface under the same conditions. Taking raw coal with a 3% moisture content as an example, for every 100 kPa increase in compaction force, the cohesion at the coal-coal interface rises by about 9.5%, displaying an almost linear trend. However, the increase in cohesion at the coal-wall interface is different; a rise in compaction force from 300 kPa to 400 kPa results in a 37.3% increase in cohesion, but a further increase to 500 kPa only yields a 3.8% increase. This pattern is due to the interlocking of coal particles under compaction, which becomes tighter as compaction force increases, leading to a consistent rise in cohesion at both interfaces. The coal-coal interface, with its numerous voids between particles, requires a greater compaction force for adherence, making its cohesion more sensitive to compaction force. In contrast, the smoother surface of the coal bulker wall allows for better adhesion at the coal-wall interface; thus, as compaction force continues to rise, its influence on cohesion gradually decreases.  
Figure 11 depicts the changes in internal friction angle at the two interfaces under different compaction forces. The internal friction angle at both interfaces shows a similar  
Figure 10: Variation of raw coal cohesion with respect to compaction force.  
Figure 9: Variation of raw coal internal friction angles with respect to moisture content.  
trend of gradual increase with compaction force, and the coal-wall interface has a consistently higher internal friction angle than the coal-coal interface under the same conditions. For raw coal with a 1% moisture content, an increase in compaction force from 300 kPa to 400 kPa results in a 3\({}^{\circ}\) rise in the internal friction angle at the coal-coal interface and a 3.3\({}^{\circ}\) rise at the coal-wall interface. Higher compaction forces lead to tighter interfacial contact, and with low moisture content, dry friction contributes to the increase in the internal friction angle.
# A Construction Method for a Dynamic Weighted Protein Network Using Multi-Level Embedding
## 2 Dynamic Weighted Protein Network Construction
### Multi-Level Embedding Method Construction
#### 2.3.2 Deep Attributed Network Embedding
\(p_{ij}\) denotes the joint probability between the representations of two different nodes in two modalities, and \(E_{ij}=0\) denotes that the \(i\) node and \(j\) node are not connected topologically. At this point, if left unconstrained, \(H_{i:}^{M}\) and \(H_{j:}^{Z}\) may be relatively similar, thus leading to the confusion of expressions in different nodes, so they need to be pushed apart, and minimizing \(1-p_{ij}\) can achieve this. In addition, without a direct connection between two nodes, our processing logic underpins distinguishing the functional and spatial locations of different protein nodes, which is one of the prerequisites for constructing an accurate model in dynamic protein network analysis. The joint optimization objective loss function is obtained by combining the semantic similarity, first-order similarity, higher-order similarity, and consistency. \[L=L_{s}+L_{h}+L_{f}+L_{c}. \tag{19}\]  
By minimizing the loss function \(L\), the feature representations learned by all nodes from the network structure and node attributes can be obtained. This approach integrates attributes and structural features while directly mapping the functional relationships and dynamical properties of connected nodes to the embedding of each protein node. After obtaining the embeddings \(B_{0},B_{1},\dots,B_{n}\) of multiple protein subnetworks, the protein embeddings can be represented by concatenating the embeddings learned from all protein subnetworks as matrix \(B\). For instance, the final embedding of protein \(u\) is represented as  
\[B(u)=[B_{0}(u),B_{1}(u),\dots,B_{n}(u)], \tag{20}\]  
where \(B_{\cdot}(u)\) is the protein subnetwork to which protein \(u\) belongs. Next, the weighted PPIN is constructed by calculating the cosine similarity (CS). Given the embedding vectors \(B(u)\) and \(B(v)\) for protein \(u\) and protein \(v\), the cosine similarity between proteins is defined as  
\[C=\left\{ \begin{array}{cl}\frac{B(u)\cdot B(v)}{\|B(u)\|\times\|B(v)\|}& \text{if }A_{uv}>0\\ 0&\text{if }A_{uv}=0\end{array} \right.\quad. \tag{21}\]The refined adjustment of the reconstructed weighted adjacency matrix is achieved by calculating the cosine similarity and updating the original PPIN to obtain the final weighted adjacency matrix \(C\). Each element of the matrix \(C\) reflects the importance of the existing connections and the potential similarity between nodes. The resulting weighted matrix \(C\) contains not only the structure of the original network but also emphasizes the information embedded in the nodes by the cosine similarity of each pair of connected nodes, which will be next applied to the protein-complex recognition algorithm. The general description of the DWPNMLE is presented in Algorithm 1.
## 4 Concept
### Planning and Installation
#### 4.1.3. Design of Measurement System and Its Integration  
In the task \"Design of measurement system and its integration\", measurement systems and their integration into the IT environment are designed according to the concept. This means that the measurement and data transmission chain from the sensor to the data memory is planned in detail. For example, measuring ranges, sampling times, and protection classes are specified. The handling of the measurement technology is worked out, for example, in instructions for calibration or measured variable calculation. With the concrete design of the IT structure, it is determined which operations in the data pre-processing take place at the control PC, edge PC, Fog PC, or in the cloud.  
For the data quality, this means that the selection of the specific sensors and IT components determines the measurement ranges, signal patterns, performance, and susceptibility to faults.
# Efficient Solution Criteria for a Coupled Fractional Laplacian System on Some Infinite Domains
## 3 Main Results
### Existences and Uniqueness Result
\begin{split}&\left\|\frac{(\mathbb{Y}_{1}\mathcal{V})(\mathfrak{t})}{ (1+\varphi^{\theta_{1}}(\mathfrak{t};c))}-\frac{(\mathbb{Y}_{1}\bar{\mathcal{V }})(\mathfrak{t})}{(1+\varphi^{\theta_{1}}(\mathfrak{t};c))}\right\|\\ &\leq\frac{1}{\Gamma(\theta_{1})(1+\varphi^{\theta_{1}}( \mathfrak{t};c))}\int_{c}^{\mathfrak{t}}\varphi^{\prime}(\mathsf{v})\varphi^{ \theta_{1}-1}(\mathfrak{t};\mathsf{v})\big{\|}(\mathbb{Y}_{10}\mathcal{V})( \mathsf{v})-(\mathbb{Y}_{10}\bar{\mathcal{V}})(\mathsf{v})\big{\|}d\mathsf{v} \\ &\quad+\frac{|\Lambda_{1}|\varphi^{\theta_{1}-1}(\mathfrak{t};c)}{ (1+\varphi^{\theta_{1}}(\mathfrak{t};c))}\sum_{j=1}^{n}\eta_{j}\frac{1}{ \Gamma(\theta_{1}-1)}\int_{c}^{\epsilon_{j}}\varphi^{\prime}(\mathsf{v}) \varphi^{\theta_{1}-2}(\epsilon_{j};\mathsf{v})\big{\|}(\mathbb{Y}_{10} \mathcal{V})(\mathsf{v})-(\mathbb{Y}_{10}\bar{\mathcal{V}})(\mathsf{v})\big{\|} d\mathsf{v}\\ &\leq\frac{(\mathfrak{q}-1)(\mathcal{A}_{2})^{\mathfrak{q}-2} \big{\|}\mathcal{V}-\bar{\mathcal{V}}\big{\|}_{\mathbb{Y}_{1}}}{\Gamma( \theta_{1})(1+\varphi^{\theta_{1}}(\mathfrak{t};c))}\int_{c}^{\mathfrak{t}} \varphi^{\prime}(\mathsf{v})\varphi^{\theta_{1}-1}(\mathfrak{t};\mathsf{v}) \mathcal{I}_{c^{+}}^{\theta_{1};\varphi\prime}\Big{(}\mathcal{N}_{1}( \mathsf{v})(1+\varphi^{\theta_{1}}(\mathsf{v};c))\Big{)}d\mathsf{v}\\ &\quad+\frac{|\Lambda_{1}|\varphi^{\theta_{1}-1}(\mathfrak{t};c)} {(1+\varphi^{\theta_{1}}(\mathfrak{t};c))}\sum_{j=1}^{n}\eta_{j}\frac{( \mathfrak{q}-1)(\mathcal{A}_{2})^{\mathfrak{q}-2}\big{\|}\mathcal{V}-\bar{ \mathcal{V}}\big{\|}_{\mathbb{Y}_{1}}}{\Gamma(\theta_{1}-1)}\\ &\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\times \int_{c}^{\epsilon_{j}}\varphi^{\prime}(\mathsf{v})\varphi^{\theta_{1}-2}( \epsilon_{j};\mathsf{v})\,\mathcal{I}_{c^{+}}^{\theta_{1};\varphi\prime}\Big{(} \mathcal{N}_{1}(\mathsf{v})(1+\varphi^{\theta_{1}}(\mathsf{v};c))\Big{)}d \mathsf{v}\\ &\leq\frac{(\mathfrak{q}-1)(\mathcal{A}_{2})^{\mathfrak{q}-2} \big{\|}\mathcal{V}-\bar{\mathcal{V}}\big{\|}_{\mathbb{Y}_{1}}}{(1+\varphi^{ \theta_{1}}(\mathfrak{t};c))}\,\mathcal{I}_{c^{+}}^{\theta_{1}+\theta_{1}; \varphi\prime}\Big{(}\mathcal{N}_{1}(\mathfrak{t})(1+\varphi^{\theta_{1}}( \mathfrak{t};c))\Big{)}\\ &\quad+\frac{|\Lambda_{1}|\varphi^{\theta_{1}-1}(\mathfrak{t};c) }{(1+\varphi^{\theta_{1}}(\mathfrak{t};c))}(\mathfrak{q}-1)(\mathcal{A}_{2}) ^{\mathfrak{q}-2}\big{\|}\mathcal{V}-\bar{\mathcal{V}}\big{\|}_{\mathbb{Y}_{ 1}}\sum_{j=1}^{n}\eta_{j}\,\mathcal{I}_{c^{+}}^{\theta_{1}+\theta_{1}-1; \varphi}\Big{(}\mathcal{N}_{1}(\epsilon_{j})(1+\varphi^{\theta_{1}}( \epsilon_{j};c))\Big{)}\\ &\leq\mathcal{A}_{1}\big{\|}\mathcal{V}-\bar{\mathcal{V}}\big{\|} _{\mathbb{Y}_{1}}.\end{split} \]  
Also,  
\[
## 3 Results and Discussion
### Classification of Transient Conditions  
The prior classification outcomes were executed under stationary operating conditions. However, in practical applications, alterations in speed and torque occur. Specifically, variations in speed complicate the detection of damage due to shifts in the gearbox-specific frequencies, which are predominantly utilized for damage detection purposes. Consequently, the following damage classification was conducted using speed ramps. These ramps extend from \(\pm\)2000 rpm, \(\pm\)1300 rpm, and \(\pm\)300 rpm at the high-speed shaft, with a slope of 150 rpm/s, at a torque of 55 Nm.  
The classification employing rotational angle sensors involves the self-transformation of the time-sampled IAS to an equidistant angle-sampled IAS, with the measured rotational angle of the sensors. This process enables the evaluation of damage-specific frequencies in the order-frequency domain with equidistant rotational angle sampling [5; 22]. Due to the order-frequency spectrum, the damage-specific orders do not shift depending on the speed, thus allowing for an exact analysis of the damage-specific components. The speed ramp is segmented into intervals of a one-second duration, with an overlap of 50% for the purpose of damage classification. The five-fold cross validation is used for the damage classification.  
As illustrated in Figure 8, the confusion matrices reveal that the accuracy of the classification is significantly reduced under transient operating conditions, with all sensors exhibiting an accuracy of less than 70%, in comparison to the accuracy of almost 100% observed under stationary operating conditions. This decline can be attributed to the more intricate nature of the classification problem, compounded by a reduction in data points by more than a fifth. The gear wheel sensor demonstrated the highest level of accuracy within this classification, with a percentage of 69%, as illustrated in Figure 8a. The pole ring sensor exhibited an accuracy of 58% (see Figure 8b), while the acceleration sensor demonstrated an accuracy of 41% (see Figure 8c). These findings underscore the efficacy of the gear wheel sensor in facilitating precise damage predictions. Moreover, the utilization of rotational angle sensors in transient conditions offers a distinct advantage over the use of acceleration sensors as it enables the incorporation of rotational angle information into order-domain frequency analysis. However, it is crucial to acknowledge that enhancing the prediction accuracies remains a significant challenge, particularly for industrial applications. To achieve this, the development of a more extensive database is imperative, along with the investigation of authentic damage cases. A thorough examination of the confusion matrices, depicted in Figure 8, reveals that misclassification predominantly occurs along the main diagonal. This observation serves to reinforce the findings derived from stationary operating conditions, indicating that the classification algorithms are contingent on the extent of damage, or the frequency feature utilized exhibits a correlation with the damage.
## 5 Conclusions  
We have proposed a quantum network with entanglement in the middle scheme, which provides secure conferencing communication for an arbitrary number of users. An entanglement source in the middle generates an multipartite GHZ state and distributes each mode of the state to the network users, respectively. The legitimate users receive the entangled modes, and perform homodyne measurement on them to distill mutual information for generating secret conferencing keys for each pair of users. Our security analysis shows that the quantum conferencing scheme can generate secret keys against collective attacks on both the untrusted entangled source and all of the quantum links.  
The aim of this paper is to establish a secure conferencing network for dozens of users within a community or a factory area. Simulation results show that our scheme can provide high rate secure conferencing keys for more than 100 users in a quantum network with a radius of hundreds of meters. The security analysis of our scheme is against collective attacks, whereas the analysis against coherent attacks can be achieved by applying a Gaussian de Finetti reduction [20]. Further research should be carried out to establish the finite-size composable security of the quantum conferencing with entanglement in the middle scheme.  
Figure 4: The maximum transmission distance of the network with different detection methods of the users. The number of users is set to 10 in (**a**) and 100 in (**b**), respectively. The blue solid (red dashed) lines show the conferencing key rate when all the users perform homodyne (heterodyne) detection on their received modes.  
Figure 3: The maximum distance from the source to a user with conferencing key rate \(K\to 0\), in terms of different number \(N\) of network users. The thermal noise is set to \(\tilde{n}=0.05\) in (**a**) and \(\tilde{n}=0\) in (**b**), respectively.  
**Author Contributions:** Conceptualization, Z.Z.; Methodology, Z.Z.; Formal Analysis, Z.Z.; Resources, R.S.; Writing--Original Draft Preparation, Z.Z.; Writing--Review and Editing, Y.G.; Visualization, Z.Z.; Supervision, Y.G.; Project Administration, Y.G.; Funding Acquisition, Y.G.  
**Funding:** This research was funded by the National Natural Science Foundation of China (Grant No. 61572529).  
**Conflicts of Interest:** The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
## 3 Results
### AIGC Input-Output Classification
#### 3.3.8 Text to Molecule or Molecular Structure  
Generative AI techniques have expanded beyond visual content generation and are now being applied to drug discovery as well. Text2 Mol [60] combines multimodal molecule retrieval by employing two submodels: a text encoder (SciBERT) and a molecule encoder (MLP-GCN). These submodels work together to create an embedding space for efficient retrieval of relevant molecules. Instead of retrieving information from existing molecules [1], researchers proposed MoI75 (molecular T5) [58], a self-supervised framework designed for molecule captioning and text-based de novo molecule generation. MoI75 utilizes a transformer architecture and achieves its desired results by scaling the pretrained data. Generative AI has pushed boundaries with the introduction of JT-VAE (junction tree variational autoencoder) [57]. This approach allows for the generation of molecular graphs with specific chemical properties. By leveraging the power of variational autoencoders and junction trees, it was designed on two encoders and two decoders which are used to decompose and encode the latent space and generate new moleulcar graphs. Furthermore, MoFlow [76] presents another approach to molecular graph generation. This technique focuses on modeling the underlying distribution of molecular graphs and employs flow-based generative models to generate new molecules. The goal is to optimize the chemical structure generation process and enhance the efficiency of drug discovery. In terms of optimizing chemical structures, researchers have proposed Mol-CycleGAN [125]. This model utilizes the CycleGAN architecture to perform cyclic transformations between two domains: real and generated molecules. By leveraging the power of generative adversarial networks, Mol-CycleGAN [125] aims to refine and optimize chemical structures, thereby aiding in the drug discovery process.
## 3 Panoptic Segmentation
### Top-Down Approach to Panoptic Segmentation
#### 3.3.1 Improvement in PS on Predicted Output Using Top-Down Approach  
The conflicts that arise among the outputs of both semantic and instance segmentation branches should be studied comprehensively. It is seen that the top-down approach needs a refinement step to harmonize the results from the two branches seamlessly. But the conflicts are mainly noticed during the post-processing step as discussed in the previous section. The internal disagreements within the instance branches stem from overlapping and occlusion.  
Figure 8: The Network architecture of Joint Semantic and Instance Segmentation [40].  
Figure 7: Mask R-CNN approach to perform Instance Segmentation [38].  
Overlapping and Occlusion  
As already stated, in PS each pixel is labeled by the category to which it is assigned, and then segmentation is performed on each object instance. This is a challenging task as the current approaches make use of two independent models which do not share features and therefore the pipeline implementation is labor intensive. Since a heuristic approach is applied to merge the results, it is difficult to determine the extent of overlapping between object instances in the absence of sufficient contextual information at the time of merging. As instance segmentation is basically object detection, an overlap can be seen between the different instance masks being predicted. However, in the case of PS, a class label and an instance id are assigned to prevent this overlap. To avoid overlapping, non-maximum suppression (NMS) is utilized that primarily sorts the predicted segments with respect to its confidence scores. The bounding box with the highest scores is chosen and added to the list of outputs and is subsequently extracted from the candidate list. The IoU among all the candidate boxes is computed by the network and boxes whose IoU is greater than the threshold is discarded.  
While resolving the overlapping issue, it is also important to view the instances sorted in descending fashion using detection scores and then placing the objects on some material canvas such that higher-score objects are placed higher. This approach can fail because of occlusion. To address this, a spatial sorting module named Occlusion Aware Network (OANet) [41] for PS is proposed at the post-processing step that can predict the'stuff' and instance segmentation in a single network. This network maps the output of instance segmentation to a tensor; a large convolution is then applied to get a ranking score map. The ranking score map is optimized by using a cross-entropy loss and then OANet obtains the ranking score of each object instance and then executes spatial sorting. This way, the occlusion problem between the predicted instances is resolved.  
Some improvements on PS include incorporating the information exchange module, incorporating certain methods based on attention, and improvements on the loss function to name a few, as seen in the literature.