esa-sceva/satcom-chunk-collection huggingface dataset:
# FSM-BC-BSP: Frequent Subgraph Mining Algorithm Based on BC-BSP
## 3 Proposed Method
### Detailed Design
#### 3.2.2 Obtaining Edge Frequent Subgraphs  
In the data preparation phase, which corresponds to the frequent 1_subgraph mining stage, the algorithm takes the following steps.  
1. [label=(0)]
2. Infrequent Edge Removal: While scanning the entire graph database, infrequent edges are removed, retaining only the frequent subgraphs.
3. Vertex Renumbering: After removing infrequent edges, the vertices on the entire large graph are renumbered. It is essential to ensure that vertices and edges maintain consistent labeling.
4. Initialization for Frequent 1_Subgraph Mining: BC-BSP determines the current number of super steps. When it is identified as super step 0, the process for frequent 1_subgraph mining is initiated.
5. Static Data Generation: The static data needed for each iteration are generated after the 0 th iteration. Specifically, an Edge Extend Map (EEM) is created. The EEM stores information about which vertices each vertex can extend to during the mining process.
6. Message Transmission: During message transmission between super steps, in addition to the graph information of the current mode, the EEM needs to be sent. This facilitates efficient communication and information sharing between different super steps.
7. Support Calculation Optimization: In the first super step, when receiving the message sent by the 0 th super step, support calculation is optimized. Since non-frequent edges have been removed while reading the graph data, support calculation is no longer necessary. Instead, the algorithm focuses on expanding the current edge based on the data structure of the EEM.  
As depicted in Figure 3, the algorithm, after reading the adjacency list of the original graph, performs the 0 th iteration to generate an edge code and the EEM data structure. The unique minimal DFS code for each graph serves as the key during message sending. Subgraphs with the same specification encoding are sent to the same node along with the EEM data structure for all vertices included in the message. This structured approach ensures efficient processing and communication throughout the frequent subgraph mining process.  
Figure 3: Example of data preparation.
## 5 Experimental Results
### RQ2: How Efficient Is LinearTGAN in Improving Wafer Measurement and Inspection Performance?
#### 5.2.2 Inspection Performance  
Balancing high-fidelity image reconstruction with computational efficiency is a critical requirement for real-world deployment in semiconductor inspection workflows. The proposed LinearTGAN architecture was specifically designed to address this trade-off, incorporating approximately 16 million parameters, enabling real-time inference on standard commercial GPUs without compromising output quality. This computational parsimony facilitates seamless integration into inline wafer inspection pipelines, where both accuracy and throughput are essential. As illustrated in Table 2, the integration of LinearTGAN with the R-SAM system yields significant performance gains. Specifically, the USI RSAM 300 system operating at a 50 \(\upmu\)m step size achieves a substantial reduction in scanning duration from 2727 s with the conventional TSAM-400 system to 763 s, while preserving the original image resolution of 6000 \(\times\) 6000 pixels. This configuration requires no additional  
Figure 5: Schematic of the workflow for defect segmentation evaluation.  
Figure 6: Defect segmentation performance comparison.  
post-processing, resulting in a scanning efficiency of 72%. These results underscore the effectiveness of LinearTGAN in enabling high-precision inspection with markedly improved operational efficiency, thereby demonstrating its suitability for deployment in advanced semiconductor manufacturing environments.  
Scanning performance further improves when LinearTGAN is applied to the USI RSAM 300 system at a 100 \(\upmu\)m step size. Here, the initial resolution is reduced to 3000 \(\times\) 3000 pixels to accelerate data acquisition. SR reconstruction using LinearTGAN enhances the resolution to 6000 \(\times\) 6000 pixels, enabling effective restoration of HR images. This advanced approach dramatically reduces the scanning time to 190 s, with an additional 16 sec allocated for SR processing, resulting in a total acquisition time of just 206 s. Integrating LinearTGAN with R-SAM enables the system to achieve an exceptional scanning performance of 92%, representing a substantial breakthrough over conventional scanning methodologies. These findings highlight the capability of LinearTGAN in enhancing wafer inspection and measurement performance, demonstrating its effectiveness as an efficient and scalable solution for a wide range of semiconductor applications.
## 2 Backdoor Attack  
Attackers can embed the backdoor by poisoning the training data. Training data poisoning is currently the most direct and common method to encode the backdoor into the weight of the model through the training process [1]. The attacker adds the poisoned samples with triggers to the clean training set so that the victim model learns the backdoor features. Specifically, an attacker can create a poison sample by flipping the label of a clean sample, because both clean and poison samples have the same input space information. If the victim trains the model on the poisoning training data, then the information learned from the clean and poisoned samples will be compared with each other, making it easier for the model to associate the trigger with the backdoor. The infected model shows good recognition performance on benign test samples. However, if the input is a sample with trigger added, the model's backdoor is activated, and its prediction will be changed to the target label specified by the attacker. Figure 1 shows the overview of backdoor attack.  
**Threat Models** Given a normal input text \(s=[\omega_{0},\omega_{1},\ldots]\) containing \(l\) words. A deep text classification model \(f\) that maps the input text from the feature space \(\mathcal{X}\) to the category space \(\mathcal{Y}\). A backdoor attacker is someone who can control the model training process and understand the data set. The attacker hopes to add a trigger to \(s\) (its real label is \(y\in\mathcal{Y}\)) and generate an attack sample \(s_{b}\), so that the model will classify \(s\) into a specified class, that is, \(f(s_{b})=t(t\
eq y)\). \(t\) is the prediction label of model \(f\) for input \(s_{b}\). \(y\) is the real label of input \(s_{b}\).  
**Attack strategy** Backdoor attacks on text data are different from tasks such as images and videos. For text data, attackers have multiple attack strategies to create text triggers [10; 21; 23]. For instance, an attacker can create triggers by adding, replacing, etc. The short trigger can be a word, and the long trigger can be a sentence. Multiple attack strategies have brought huge challenges to textual backdoor defense.  
Character-level triggers [10]: A character is the basic component of input text data. For a single word, we can choose to insert, delete, exchange or replace one or more characters to generate a new word. The new word will act as a trigger.  
Word-level triggers [10; 23]: Compared with character triggering, word-level triggers provide a wider range of modifications. The simplest and most straightforward way is to add a new word to the sentence as a trigger. Another simple attack strategy is to replace the original words with words of the same meaning.  
Sentence-level triggers [23]: The sentence-level trigger changes the original sentence the most. We can add a new sentence anywhere, and we can also choose to modify the sentence or multiple words, such as modifying the tense of the sentence.
## 4 Results and Discussion
### Case Study 2: Flexible Beam-Rigid Wheel Coupling
Figure 23 compares \(H_{y_{2}y_{2}}^{AC}\), \(H_{y_{2}\theta_{2}}^{AC}\), and \(H_{\theta_{2}\theta_{2}}^{AC}\) for a higher frequency range up to 6000 Hz to cover two modes. While the magnitude of these components varies due to the nature of the system and the excitation applied, all three receptance components align at the natural frequency peaks. This alignment indicates that the resonance frequencies predicted by the three receptance components, despite different excitations, remain consistent, as seen in the previous case. This consistency suggests that whether considering translational or rotational excitations, the predicted natural frequencies are stable, as natural frequencies are intrinsic properties of the system. Figure 24 provides a comparative analysis of the receptance component at the tip, specifically translational displacement due to an excitation force, for two distinct configurations: the beam-beam setup and the beam-rigid wheel configuration. Using the GRCFBS method, this comparison reveals notable differences in dynamic behavior between the two systems in spite of similar mass between substructures B and C. In the beam-beam configuration, the system exhibits four resonance peaks, indicative of a more complex dynamic response due to the combined stiffness contributions of both beams. In contrast, the beam-rigid wheel configuration demonstrates only two natural frequencies, reflecting a simpler dynamic behavior due to the contribution of only one beam stiffness and the rigid wheel's inertia contribution. The peak magnitudes in the beam-beam setup are higher compared to the beam-rigid wheel configuration, suggesting that the beam-beam coupling results in a softer system with reduced overall stiffness. This indicates that the beam-beam coupling experiences greater flexibility and higher peak responses under excitation compared to the beam-rigid wheel setup. It is worth noting that the frequency range up to 2000 Hz is generally considered high for large mechanical systems, such as those typically analyzed in automotive engineering  
Figure 24: Comparison between receptance component at the tip: translational displacement to excitation force for two cases. where NVH focuses on frequencies from 0 to 1000 Hz, with some analysis extending to around 2000 Hz. However, for smaller, high-speed systems such as turbines and motors, higher frequencies can be more relevant. Table 2 provides a comparative analysis of dominant modes of vibration for case studies 1 and 2 (systems AB and AC) in terms of resonance and anti-resonance frequencies below 2000 Hz. The receptance components and the associated percentage errors are listed to evaluate the accuracy of the GRCFBS method against the FEA benchmark. The results highlight the strengths and limitations of GRCFBS in predicting translational and rotational motions across different dynamic coupling scenarios.
# DLMFCOS: Efficient Dual-Path Lightweight Module for Fully Convolutional Object Detection
## 4 Experiment Results and Discussion
### Comparison with Other Networks
#### 4.2.2 Ms Coco2017  
MS COCO2017 has 80 detailed categories, which is four times the number of classifications, and small objects (e.g., scissors and toothbrushes). Therefore, when applying the same detection method used with the PASCAL VOC07 dataset, P and R were both low. Table 4 lists the comparison results for the MS COCO2017 validation dataset. In this case, the proposed method used the same input resolution as the baseline FCOS detector. Compared with the existing two-step method, our method shows 0.5% higher detection accuracy with approximately six times fewer computations. Compared with the one-stage YOLOv3, the proposed method has nearly three times fewer parameters and a 5.4% higher accuracy. We confirmed that the detection accuracy improved by 1.5% with 2.5M fewer computations than the baseline FCOS.  
Figure 10 shows the detection results of the occluded objects in the MS COCO2017 minival dataset. The baseline FCOS detector has difficulty with this, owing to the loss of low-level information, as illustrated in Figure 9a. The proposed method minimizes false detections by improving the FPN and transferring low-level information to deeper layers with high-level information. As shown in Figure 9b, the proposed FPN structure improves occluded object detection.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Detector** & **Backbone** & **Input Resolution** & **AP** & **AP50** & **AP75** & **Params (M)** & **FPS** \\ \hline \multicolumn{8}{c}{Two-stage} \\ \hline CoupleNet [36] & Resnet-101 & \(\sim\)800 \(\times\) 1024 & 34.4 & 54.8 & 37.2 & - & 8.2 \\ MASK R-CNN [9] & ResNet-101 & \(\sim\)800 \(\times\) 1024 & 38.2 & 60.4 & 41.7 & - & - \\ Faster R-CNN [38] & ResNet-50 & \(\sim\)800 \(\times\) 1024 & 38.4 & - & - & 134.7 & 2.4 \\ Libra R-CNN [39] & ResNet-50 & \(\sim\)800 \(\times\) 1024 & 38.5 & 59.3 & 42.0 & - & - \\ \hline \multicolumn{8}{c}{One-stage} \\ \hline YOLOv3 [40] & DarkNet-53 & 608 \(\times\) 608 & 33.0 & 57.9 & 34.4 & 65.2 & 20 \\ RetinaNet [24] & ResNet-50 & 512 \(\times\) 512 & 36.3 & 55.3 & 38.6 & 37.7 & - \\ FSAF [41] & ResNet-50 & 800 \(\times\) 1333 & 37.2 & 57.2 & 39.4 & - & - \\ RetinaNet \(+\) & ResNet-50 & 800 \(\times\) 1333 & 36.4 & 56.2 & 38.7 & - & - \\ Foveabox [42] & ResNet-50 & 800 \(\times\) 1333 & 37.4 & 56.1 & 40.3 & 32.2 & 58 \\ \hline Proposed method & ResNet-50 & 800 \(\times\) 1333 & 38.9 & 58.0 & 41.6 & 29.7 & 62 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of MS COCO2017 minival dataset detection results.  
Figure 9: Detection results with the PASCAL VOC07 testing dataset by (**a**) the baseline FCOS and (**b**) the proposed method.
# Stacked Ensembles Powering Smart Farming for Imbalanced Sugarcane Disease Detection
## 3 Results
### Timing Results with Feature Extraction Methods  
Figures 10-12 provide the timing results for the different models, highlighting the feature extraction times in orange for InceptionV3, SqueezeNet, and Deeploc. The timing results indicate that stacking, SVM, KNN, the ANN, and RF are significantly faster than the CNN, RNN, LSTM, and GRU. For instance, the KNN requires only 3 s (Deeploc), 1 min and 17 s (SqueezeNet), and 1 min and 38 s (InceptionV3), compared to over 40 min for the CNN (multi-class). Pre-trained feature extractors like SqueezeNet, InceptionV3, and Deeploc, which take 47, 48, and 24 s, respectively, further emphasize the efficiency of conventional methods. In this context, stacking has demonstrated competitive timing (e.g., 10 min with Deeploc) and achieved the best F1-score and CA values, highlighting its robustness among all methods.  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Class** & **Model** & **AUC** & **CA** & **F1-Score** & **Precision** & **Recall** & **MCC** \\ \hline \multirow{8}{*}{Sett Rot} & Neural Network & 0.9997 & 0.9972 & 0.9869 & 0.9869 & 0.9869 & 0.9853 \\  & KNN & 0.9987 & 0.9974 & 0.9879 & 0.9862 & 0.9896 & 0.9864 \\  & Random Forest & 0.9997 & 0.9940 & 0.9729 & 0.9575 & 0.9888 & 0.9697 \\  & Stack & 0.9999 & 0.9983 & 0.9921 & 0.9931 & 0.9912 & 0.9911 \\  & SVM & 0.9939 & 0.9542 & 0.8170 & 0.7224 & 0.9400 & 0.8003 \\ \hline \multirow{8}{*}{Viral Disease} & Neural Network & 0.9917 & 0.9713 & 0.8711 & 0.8705 & 0.8718 & 0.8550 \\  & KNN & 0.9868 & 0.9690 & 0.8596 & 0.8675 & 0.8519 & 0.8423 \\  & Random Forest & 0.9851 & 0.9632 & 0.8395 & 0.8163 & 0.8639 & 0.8191 \\  & Stack & 0.9930 & 0.9751 & 0.8888 & 0.8834 & 0.8944 & 0.8749 \\  & SVM & 0.9244 & 0.8974 & 0.5529 & 0.5366 & 0.5703 & 0.4953 \\ \hline \multirow{8}{*}{Yellow Leaf} & Neural Network & 0.9920 & 0.9802 & 0.9505 & 0.9518 & 0.9492 & 0.9381 \\  & KNN & 0.9851 & 0.9695 & 0.9211 & 0.9524 & 0.8918 & 0.9029 \\ \cline{1-1}  & Random Forest & 0.9915 & 0.9721 & 0.9292 & 0.9412 & 0.9176 & 0.9119 \\ \cline{1-1}  & Stack & 0.9947 & 0.9824 & 0.9559 & 0.9586 & 0.9533 & 0.9450 \\ \cline{1-1}  & SVM & 0.7739 & 0.7759 & 0.4286 & 0.4374 & 0.4201 & 0.2894 \\ \hline \multirow{8}{*}{Smut} & Neural Network & 0.9785 & 0.9593 & 0.6180 & 0.6115 & 0.6246 & 0.5965 \\ \cline{1-1}  & KNN & 0.9702 & 0.9624 & 0.5981 & 0.6847 & 0.5310 & 0.5839 \\ \cline{1-1}  & Random Forest & 0.9686 & 0.9536 & 0.5425 & 0.5654 & 0.5214 & 0.5186 \\ \cline{1-1}  & Stack & 0.9890 & 0.9809 & 0.8275 & 0.7891 & 0.8698 & 0.8185 \\ \cline{1-1}  & SVM & 0.9189 & 0.8992 & 0.3793 & 0.2808 & 0.5841 & 0.3582 \\ \hline \hline \end{tabular}
\end{table}
Table 6: _Cont._  
Figure 10: Timing results for InceptionV3 feature extraction and various ML and DL methods.
## 2 Materials and Methods
### Monitoring Methodology
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multicolumn{2}{c}{**Number of**} & \multirow{2}{*}{**Internal**} & \multirow{2}{*}{**Room/Orientation**} & \multirow{2}{*}{**Outer Shading**} & \multirow{2}{*}{**Inner Shading**} & \multirow{2}{*}{**Venting**} & **Additional** \\  & \multicolumn{1}{c}{**Inhabitants/Age**} & & & & & **Measures to** \\  & \multicolumn{1}{c}{**During**} & & & & & **Prevent** \\  & \multicolumn{1}{c}{**Monitoring**} & & & & & **Overheating** \\ \hline \multirow{6}{*}{A1\({}^{*}\)} & \multirow{6}{*}{1/84} & \multirow{6}{*}{5.8} & Room/NNE & No & No & \(1\) window & \multirow{2}{*}{No} \\  & & & & & & & \\ \cline{1-1}  & & & & & & & \\ \cline{1-1}  & & & & & & & \\ \cline{1-1}  & & & & & & & \\ \cline{1-1}  & & & & & & & \\ \cline{1-1}  & & & & & & & \\ \hline \hline A2 & 1/55 & 11.0 & Bedroom/SSW & Yes (balcony, trees) & Curtains, continuously & Trickle ventilation & No \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Basic features of the observed apartments: inhabitants and behaviour. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Logger**} & \multicolumn{6}{c}{**Type of Measurements**} \\ \cline{2-6}  & \multicolumn{1}{c}{**Temperature**} & \multirow{2}{*}{**Humidity**} & \multicolumn{2}{c}{**Atmospheric Pressure**} & \multirow{2}{*}{**Lighting**} & \multirow{2}{*}{**Window Opening**} \\ \hline \multirow{3}{*}{Termio-15} & Range \(-30\)\({}^{\circ}\)C to +70\({}^{\circ}\)C, & & & & \\  & +70 \({}^{\circ}\)C, & \(-\) & \(-\) & \(-\) & \(-\) & \(-\) \\ \cline{1-1}  & precision \(\pm 0.3\)\({}^{\circ}\)C & & Range 10\% to 95\%, & Range 700 hPa to 100 hPa, precision & Range 0 lux to 180,000 lux, 400 nm to 700 nm & \(-\) \\ \cline{1-1}  & precision \(\pm 0.2\)\({}^{\circ}\)C & precision \(\pm 3\%\) & \(\pm 1\) hPa & \(\pm 1\) hPa & Range 0 lux to 180,000 lux, 400 nm to 700 nm & \(-\) \\ \hline Reed switch Efento & \(-\) & \(-\) & \(-\) & \(-\) & \(-\) & \(-\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Data logger characteristics. During monitoring, the occupants evaluated their general perception of internal conditions using the thermal sensation vote (TSV) ASHRAE seven-point scale (\(-3\) cold, \(-2\) cool, \(-1\) slightly cool, \(0\) neutral, \(+1\) slightly warm, \(+2\) warm, or \(+3\) hot). They were also asked what percentage of monitoring time they would assign on a scale from \(0\) to \(+3\).
_Article_  
**Microbial Community and Atrazine-Degrading Genetic Potential in Deep Zones of a Hypersaline Lake-Aquifer System**  
**[PERSON] \({}^{\spadesuit}\)\({}^{\spadesuit}\), [PERSON], [PERSON] \({}^{\spadesuit}\) and [PERSON]\({}^{\spadesuit}\)**  
Group of Hydrogeology, Biotechnology and Natural Resources Laboratory, Institute for Regional Development, University of Castilla-La Mancha, 02071 Albacete, Spain; [PERSON].A.); [PERSON]] (J.G.-A.)  
\({}^{\spadesuit}\) Correspondence: [PERSON]S] (Y.E.); [PERSON]] (M.A.-O.)  
Received: 7 September 2020; Accepted: 10 October 2020; Published: 13 October 2020  
**Abstract: The chemical composition of groundwater and related surface water ecosystems can be modified by intensive agricultural activities. This is the case of the Natural Reserve of Petrola saline lake (Albacete, SE Spain), which constitutes the discharge area of an unconfined aquifer. The extended use of fertilizers and pesticides poses a threat to ecosystem sustainability. One of the most applied herbicides worldwide has been atrazine. Despite being prohibited in Spain since 2007 by European regulations, atrazine can still be detected due to its high persistence in the environment. Atrazine degradation pathways are mediated by biological processes performed by microorganisms with adapted metabolic mechanisms that make in situ bioremediation possible. To evaluate the presence of such microorganisms in the unconfined aquifer, groundwater was collected from a flowing 37.9 m deep piezometer. DNA was extracted, and the bacterial 16S rRNA gene was amplified and cloned. Later, 93 clones were sequenced, providing the first molecular assessment of bacterial community structure in the deep zones of the aquifer. Some of these bacteria have been previously described to be involved in atrazine degradation. In addition, 14 bacteria were isolated from the groundwater samples and identified by 16S rRNA gene sequencing. DNA from these bacteria was subjected to PCR assays with primers designed for the genes involved in the atrazine degradation pathway. Positive results in the amplification were found in at least three of these bacteria (_Arthrobacter_ sp., _Nocardioides_ sp. and _Pseudomonas_ sp.). The atrazine-degrading genetic potential was shown to be dependent on the _trzN_ and _atzA,B,C_ gene combination. These results suggest for the first time the adaptation of the bacterial population present in deep aquifer zones to atrazine exposure, even after more than 15 years of its ban in Spain. In addition, this study provides the baseline data about the bacterial communities found in deep aquifer zones from the hypersaline lake-aquifer system.  
**Keywords: Groundwater contamination; 16S rRNA gene; _Atz_ and _trz_ genes; Bacterial bioremediation
## 5 Simulation Result  
The system parameters and control parameters of different controllers for simulation are shown in Table 1. MATLAB/Simulink is used to simulate the compensation results of the different current controllers for the three-phase four-wire \(LC\)-HAPF. The simulation results before and after HCC, PCC, LQRC and LQRIC-controlled \(LC\)-HAPF under 50 V and 40 V DC-link voltage conditions are shown in Figures 5 and 6, and the values are summarized in Tables 2 and 3.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multirow{2}{*}{**Before Compensation**} & \multicolumn{4}{c}{**After Compensation**} \\ \cline{3-5}  & & **HCC** & **PCC** & **LQRC** & **LQRC** \\ \hline THD\({}_{\text{ss}}\) (\%) & 33.7 & 15.4 & 14.4 & 8.2 & 6.1 \\ THD\({}_{\text{sb}}\) (\%) & 33.7 & 15.6 & 15.0 & 7.9 & 6.3 \\ THD\({}_{\text{isc}}\) (\%) & 33.7 & 15.9 & 14.3 & 8.0 & 7.1 \\ PF & 0.76 & 0.99 & 0.99 & 1.00 & 1.00 \\ QTotal (var) & 615.1 & 10.0 & 9.7 & 3.6 & 2.9 \\ Total SW loss & / & 12.3 & 13.8 & 15.9 & 15.9 \\ (W) & / & 12.3 & 13.8 & 15.9 & 15.9 \\ \(i_{sa}\) (Arms) & 3.28 & 2.49 & 2.46 & 2.46 & 2.46 \\ \(i_{sb}\) (Arms) & 3.28 & 2.49 & 2.45 & 2.46 & 2.46 \\ \(i_{sc}\) (Arms) & 3.28 & 2.48 & 2.45 & 2.46 & 2.46 \\ \(i_{sn}\) (Arms) & 2.97 & 0.79 & 0.77 & 0.41 & 0.36 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation results before and after the _LC_-HAPF compensation under 40V DC-link voltage.  
Figure 5: Simulation results of three-phase PCC voltage, source current and neutral current before and after _LC_-HAPF compensation with (**a**) HCC; (**b**) PCC; (**c**) LQRC; and (**d**) LQRIC under 50 V DC-link voltage. (For each graph, the blue lines are the waveforms of source voltage, the red lines are the waveforms of source current, and the peacock blue lines are the waveforms of neutral current.)