esa-sceva/satcom-chunk-collection huggingface dataset:
# Tailhook Recognition for Carrier-Based Aircraft Based on YOLO with Bi-Level Routing Attention
## 4 Experiments
### Ablation Study  
To verify the validity of the designed and introduced modules in our method, ablation studies were conducted on D_C3, BRA, and Gloss separately on the VOC dataset to analyze the impact of each module on overall model performance. The findings of the ablation study are presented in Table 6. From the comparison of the ablation study results, it is evident that the YOLOv5s model using the D_C3 module improved by 3.8% in mAP50 and 1.5% in mAP50-95. This indicates that the D_C3 module with deformable convolution can dynamically adjust the shape of the convolution kernels to adapt to geometric variations in the input features, thus extracting more effective spatial features. The YOLOv5s model using the BRA module showed significant improvements in both mAP50 and mAP50-95, demonstrating that the introduction of the bi-level routing attention mechanism effectively enhances the effectiveness and accuracy of feature extraction, laying a solid foundation for subsequent classification and bounding box regression. The YOLOv5s model with the Gloss loss function achieved a 5.6% improvement in mAP, indicating that the \(L_{geiou}\) loss function improved the bounding box regression speed while considering classification, localization, and confidence losses and introduced weight balancing for hard and easy samples.  
To verify the role of the BRA module in feature extraction, Grad-CAM [29] was used to visualize the heatmaps of the 13 th and 14 th layers of the proposed model, respectively, showing the activation intensity of various parts of the image before and after applying BRA processing, and analyzing which regions are more critical for the prediction results. The heatmap visualization results are displayed in Figure 13, where the first column represents the original image, the second column shows the heatmap without BRA module processing, and the third column presents the heatmap processed by the BRA module. It can be noted that the model's focus is now more intensely directed toward the target object in the image, producing heatmap contours that closely align with the shape of the original object. The focus points are notably more concentrated and have a certain degree  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **D\_C3** & **BRA** & **Gloss** & **mAP50** & **mAP50-95** \\ \hline YOLOv5s & \(\times\) & \(\times\) & \(\times\) & 0.717 & 0.446 \\ YOLOv5s+D\_C3 & ✓ & \(\times\) & \(\times\) & 0.755 & 0.461 \\ YOLOv5s+BRA & \(\times\) & ✓ & \(\times\) & 0.792 & 0.473 \\ YOLOv5s+Gloss & \(\times\) & \(\times\) & ✓ & 0.773 & 0.459 \\ Ours & ✓ & ✓ & ✓ & **0.814** & **0.495** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Ablation study results.  
Figure 12: Comparison of experimental results on the VOC dataset.  
of suppression of background information. This indicates that the optimized model has improved its ability to locate targets and refined the sensitive areas for feature extraction.
# Prediction of Carbonate Reservoir Porosity Based on CNN-BiLSTM-Transformer
## 3 Results
### Ablation Study  
To better validate the effectiveness of the different layers in the proposed network structure, this section conducts ablation experiments on the components of the proposed method using the same dataset. To ensure the independence of other variables in each ablation experiment, the training parameters, such as sample size, initial learning rate, and training epochs, are kept consistent with the original setup. Ablation of the Feature Extraction CNN Layer: The first experiment removes the CNN layer responsible for extracting local features from well logging data. Instead, the model directly predicts using the preprocessed well logging data without the CNN feature extraction step. Ablation of the Global Dependency Transformer Layer: The second experiment excludes the Transformer layer, which builds global dependencies using the Transformer encoder. This setup uses only the sequence information extracted by the CNN and BiLSTM layers. Simultaneous Ablation of CNN and Transformer Layers: The third experiment removes both the CNN layer and the Transformer layer, leaving only the BiLSTM layer to process the data. The results of each ablation experiment are compared with the CNN-BiLSTM-Transformer model using the test set. The comparison results, shown in Figure 8, illustrate the effectiveness of each network layer and its contribution to the overall model. The results of the three ablation experiments indicate that the feature extraction CNN layer and the global dependency Transformer layer significantly enhance the predictive performance of the network structure. These layers improve the model's accuracy and robustness. The CNN-BiLSTM-Transformer model achieves the lowest RMSE and MAE and the highest R\({}^{2}\), demonstrating that the CNN-BiLSTM-Transformer network structure outperforms single deep learning networks and delivers improved prediction capabilities.  
Figure 7: CNN-BiLSTM-Transformer framework structure.
## 5 Conclusions  
This study clearly demonstrates the feasibility of using VI-based remote sensing information derived from Planet imageries to assess the grain-filling process of winter wheat. Three indicators of the grain-filling stage, i.e., dry thousand-grain weight, fresh thousand-grain weight and water content, had significant correlations with most VIs. The dry thousand-grain weight obtained the best inversion results in the overall model, and its R\({}^{2}\) exceeded 0.8. Wheat growers may be able to utilize satellite remote sensing to manage their areas remotely and invert the fields that entered the grain-filling period first, making the most accurate management decisions using spatial variability maps. However, future studies are needed to explore the efficiency of remote sensing in monitoring winter wheat maturity across more different cultivars and geographic locations, especially from an economic return perspective. In addition, the use of a thermal sum measure is recommended, such as growing degree days (GDDs), in combination with remote sensing, thus strengthening the potential to evaluate the monitoring of the grain-filling process considering the local environmental conditions.  
Conceptualization, X.Z. and Y.L. ([PERSON]); formal analysis, X.Z. and Y.S. ([PERSON]); investigation, X.Z., Y.L. ([PERSON]) and Y.Y.; data curation, X.Z. and Y.S. ([PERSON]); writing--original draft preparation, X.Z.; writing--review and editing, Y.L ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was financially supported by the project financed through the research and development of plant variety testing technology, the Development Center of Science and Technology, MARA of the People's Republic of China.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## Experiments
### Ablation Study  
#### 4.4.1 Pre-Traning with Contrastive Learning  
The finding that pre-training a network on a rich source set can help boost performance once fine-tuned on the target set has been key to the success of many applications. To evaluate the impact of pre-training process on model performance, we select ST-GCN and ST-GCN as the baseline for further ablation study. As shown in Figure 5, for all baselines, the performance of the scratch-trained model lags behind that of pre-trained. From the table, we can see that pre-trained models require more training to achieve optimal performance than models trained from scratch. A recent study suggests the gap between pre-trained and scratch-trained can be closed simply by training more epochs. To this end, We perform additional experiments, training the network with \(2\times\) and \(3\times\) epochs. We notice that the recognition accuracy does not improve with more extended training, probably because both baselines suffer from over-fitting. The results illustrate the effectiveness of pre-training and the advantages over scratch training.  
We pre-train the model with the joint level contrastive loss, bringing corresponding joint features closer and pushing the non-corresponding features farther apart, and Figure 6 shows affinity matrices between joints after pre-training. Most of the previous work uses an attention mechanism to distinguish the specificity of the joint. However, we focus on the uniqueness of the joint and embed it in the features of the joint itself. Joint features more dispersed in the feature space help predict heads to obtain more distinguishable high-order representations for classification.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & \(\mathbf{K_{1}}\) & \(\mathbf{K_{2}}\) & **CS acc (\%)** \\ \hline (a) & 2 & 1 & 93.3 \\ (a) & 2 & 2 & 93.8 \\ (b) & 3 & 1 & 94.1 \\ (c) & 3 & 2 & 94.1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison in terms of interaction recognition accuracy of \(K_{1}\), \(K_{2}\).  
Figure 5: Comparison in terms of interaction recognition accuracy of models w/ and w/o pre-training on the NTU60 dataset. schedules means the training epochs.
# Intelligent Reflective Surfaces for Wireless Networks:
## 3 IRS-Assisted Wireless Networks
### Channel Estimation
#### 3.2.1 THz Systems  
In [35], the channel estimation is realized by beam training in NLOS conditions, i.e., there is no direct path between the communication ends. A hierarchical codebook design is provided as the basis of beam training to reduce the estimation complexity in the THz MIMO systems. The authors leverage the narrow beams characteristic of THz systems and employ the hierarchical beam sweeping to find the strongest beam power. The overall channel estimation procedure is performed in a cooperative way between both communication ends. The results are presented in terms of the spectral efficiency (SE) and compare the system performance obtained with the proposed channel estimation algorithm versus that obtained with perfect CSI. The results exhibit a small gap between the imperfect CSI and the perfect CSI approaches.  
The work in [36] combines the IRS and the MIMO technologies, and develops a cooperative beam training scheme to facilitate the channel estimation in a multiuser analog-digital hybrid IRS-assisted THz MIMO system. In particular, two different hierarchical codebooks for the proposed training procedure are designed. Based on the training results, the authors also propose two hybrid beamforming designs for both single-user and multi-user scenarios, respectively. In order to reduce the computational complexity of the real-time implementations, an efficient ternary-tree search is proposed for the BS and users, which is proved to be more efficient compared to the binary-tree search. The results are presented in terms of the sum-rate showing a small gap between the system performance with perfect and imperfect CSI when employing the proposed procedure for channel estimation.
# Radiochromic Films for the Two-Dimensional Dose Distribution Assessment
## 3 Instruments for Dose Reading  
The instruments for the measurement of the RCF darkness (densitometers, spectrometers, flatbed scanners, etc.) are all equipped with a light source and a system of light detectors. Before performing effective reading measurements with RCFs, some precautions should be taken. The scanning process induces polarization effects on RCFs [63], implying a change in the response which is a function of their orientation on the flatbed scanner. The RCF behaves as a polarizer upon scanning because of the needle-like-shape of the RCF polymers [16]. A difference of up to 15% in the optical density response has been observed for HF Gafchromic(tm) films (discontinued) [63]. A variation up to 9% in net optical density has been observed for EBT-2 Gafchromic(tm) films [64], whereas the newest EBT-3 Gafchromic(tm) films have shown to have a less difference up to 4.5% in net optical density between landscape and portrait orientation [10]. As a result, the orientation of the RCF pieces to measure must be kept fixed during the scanning. It is recommended to cut RCFs with sharp scissors or guillotine cutter and to handle them wearing gloves in order to avoid fingerprints. It is good practice to number all RCF pieces to be irradiated and further analyzed.  
It is well-known that RCFs are sensitive to UV light, temperature changes and humidity [17]. EBT Gafchromic(tm) films have been successfully proved to operate as UV radiation meter with a reproducible color change during exposure [65]. Some RCF types, such as EBT-3 Gafchromic(tm) films can be immersed in water and used for measurements in water phantoms [66; 67]. However, evidence suggest that the optical properties of the cover layer of EBT-3 Gafchromic(tm) films change as a result of the interaction with water; thus, it is recommended to build new calibration curve for specif measurements in water phantom [68]. The accuracy of the RCF measurement can be improved by using a control RCF piece which serves to take into account any change in color due to the environmental causes. However, the use of an RCF control piece should be considered as a trade-off between accuracy and uncertainty as its use can affect measurement uncertainties [69]. The two following subsections deal with the three main RCF reading instruments, namely flatbed scanners, densitomers, and detection systems (spectrophotometers and spectrometers) for the RCF spectral response.
## 2 Materials and Methods
### Workflow and Model Implementation
#### 2.4.2 Label Masks  
In this study, we defined four classes for the semantic segmentation task, namely floating, emergent, water, and land. As described above, the floating class encompassed most types of floating or lightly rooted and submerged aquatic vegetation, with _Ceratophyllum_ spp. and _Potamogeton_ spp. among others, and the emergent class identified mixed or uniform stands of mostly _Typha_ spp. or _Phragmites_ spp. vegetation, along with shorter emergent vegetation such as grasses and sedges at the water's edges. The water class was  
Figure 4: U-net architecture reproduced from [11]. The black boxes correspond to a multi-channel feature map. The number of channels is denoted on top of the box. The x-y-size is provided at the lower left edge of the box. Gray boxes represent copied feature maps. The colored arrows denote the separated operations. The final segmentation layer outputs 4 classes of labels for this study. Details refer to [11].  
simply areas devoid of either floating or emergent vegetation, with muddy bottom. The land class was non-water habitat and could be farmland, villages, terrestrial vegetation, or bare ground.  
Technicians with extensive on-the-ground training and who were working directly with the identification and surveillance of aquatic vegetation at the sampling sites manually annotated 50 sub-images of WorldView-2 satellite imagery to create a binary mask for each class to use as training data. The manual annotation was completed via visual interpretation. The higher-resolution drone imagery was used to confirm the presence of each class on satellite data (Figure 2d) in the training set. Floating vegetation appears distinctly dark blue on drone and satellite imagery, which can help distinguish it from the green color of emergent vegetation. Therefore, for each image, four binary masks representing the four classes described above were produced. For example, a pixel of the floating mask has a value of 1 (white color) if it belongs to the floating class or a value of 0 (black color) if it belongs to any other class (Figure 5). There are no overlaps in the 256 \(\times\) 256 labeled pixels for each class. Of the 50 \(\times\) 4 masks, 80% were used as training/test set and 20% as hold-out validation/out-of-bag (OOB) set. Within the training set, 86% of the pixel area was water or land, 4% was floating vegetation, and 10% was emergent vegetation.
# An Intelligent DOA Estimation Error Calibration Method Based on Transfer Learning
## 4 Simulation and Result Analysis
### Model Structure and Parameters  
The intelligent DOA estimation model is constructed based on CNN. The structure and parameters of the model are shown in Table 2.  
Before transfer learning, the batch size is set to 360, and the learning rate is set to 0.001. We use the early stop strategy for training. When the validation error does not decrease for five epochs, the training will be stopped. When transfer learning, we fine tune the model with a smaller learning rate of 0.0001, other parameters remain unchanged.
## 3 Results and Discussion
### Optimize Parameters Using a Chemometric Approaches
As shown in Tables S2-S4, the independent variables, A, B, and D; interaction terms, AB and BD; and quadratic of A\({}^{2}\) and B\({}^{2}\) had a significant linear effect on the EE% response (\(p\) < 0.05) for the selected steroids. However, the effects of independent variable C, interaction terms AC, AD, BC, and CD, and quadratic of C and D were not significant (\(p\) > 0.05). Based on the coefficients of the terms in the model (refer to Equations (2)-(4)), it can be observed that the interaction between the number of HDE-SBME devices and extraction time (AB) holds the most significant main effect in the model. To elaborate, elevating the values of these two variables from lower to higher values increases the EE% of the chosen steroids. The three-dimensional graph of RSM for the positive significant interaction effect of the extraction time and number of HDE-SBME devices on the EE% response is shown in Figure 6A. It depicts that increasing the extraction time and number of HDE-SBME devices enhances the performance of the microextraction process. The maximum response was obtained in 30 min using three HDE-SBME devices. Meanwhile, by increasing the extraction time to more than 30 min, all selected analytes start to deplete from the HDE-SBME devices [38]. Figure 6B shows the positive effect of decreasing the number of HDE-SBME devices and volume of methanol as an elution solvent on the efficiency of the developed method. The EE% of the process increased with an increase in the volume of methanol used as the elution solvent from 100 to 200 \(\mu\)L; however, by increasing the volume to more than 200 \(\mu\)L, the response state decreased, which may be attributed to the dilution effect of the analytes. The interaction effect of the number of HDE-SBME devices and elution time positively affects the EE% of the developed method. As shown in Figure 6C, by increasing the ultrasonic time from 2 to 5 min, the EE% of selected steroids were increased. Then, the EE% started to decrease with an increase from 5 to 8 min, and this could be attributed to the chemical reaction of ultrasound on methanol during prolonged times resulting in produced free radicals, which may lead to the oxidation of selected steroids and the reduction in their EE% [39]. Figure 6: \(Cont\). As a result, the RSM based on the CCD optimization model recommended the following experimental conditions, which were adopted. Three HDE-SBME devices, 30 min extraction time, 200 \(\upmu\)L methanol as elution solvent, and sonication for 5 min as elution time.
# Detecting Fake Reviews Using Aspect-Based Sentiment Analysis and Graph Convolutional Networks
## 4 Methodology
### Model Evaluation  
To evaluate the model's performance, we look at two measures: validation loss and accuracy. We implement early stopping to prevent overfitting. Early stopping means we stop the training process when the validation loss does not improve for a certain number of epochs. This number is called 'patience'. For our experiments, we set the patience to 10 epochs. So if the validation loss does not improve for 10 epochs in a row, the training automatically stops. This helps ensure the model does not start overfitting to the training data.  
To evaluate the performance of our model, we compute the following metrics [32]:  
* **Accuracy**: This metric evaluates the overall correctness of the model. It is defined as the ratio of correctly predicted observations to the total observations. \[\text{Accuracy}=\frac{\text{True Positives}+\text{True Negatives}}{\text{Total Observations}}\] (2)
* **Precision**: This measures the accuracy of positive predictions. \[\text{Precision}=\frac{\text{True Positives}}{\text{True Positives}+\text{False Positives}}\] (3)
* **Recall** (sensitivity): This metric quantifies the ability of the model to find all the relevant cases (positive examples). \[\text{Recall}=\frac{\text{True Positives}}{\text{True Positives}+\text{False Negatives}}\] (4)* **F1 score**: The F1 score is the harmonic mean of precision and recall, providing a balance between them. \[\text{F1 Score}=2\times\frac{\text{Precision}\times\text{Recall}}{\text{ Precision}+\text{Recall}}\] (5)
* **Area under the curve (AUC)**: The AUC represents the degree or measure of separability. It reflects the model's ability to distinguish between classes. \[\text{AUC}=\int_{0}^{1}\text{TPR}(t)\:dt\] (6) where \(\text{TPR}(t)\) is the true positive rate at threshold \(t\).
## 3 Method
### FP-Growth Algorithm
#### 3.2.2 Computation Process  
The core principle of the FP-Growth algorithm involves reconstructing the transaction database into a frequent pattern tree (FP-Tree), which has a single root labeled as \"null\". The FP-Tree is subsequently divided into several conditional databases, from which frequent itemsets are mined. This structure compresses the original transaction database while retaining associated information, significantly reducing the search space, preventing the explosion of association combinations, and enhancing the efficiency of landslide monitoring data mining. The essential computation process of the FP-Growth algorithm is outlined as follows:  
1. First, the transaction database is scanned overall, calculating the support for each item separately. The minimum support is set to filter the frequent itemset, which will be sorted in the frequent-item header table \(L\) in descending order of support count (Figure 7a).
2. Second, scan the transaction database, and construct the FP-Tree based on all frequent itemset. The root labeled as \"null\" will be built first, and then the frequent itemset is inserted into the children of the root by following the order of the frequent-item header table \(L\).  
3. The head of the node-link is created to facilitate searching FP-Tree (Figure 7b) by connecting each frequent itemset to the tree node of the FP-Tree. 4. Based on the links between the frequent-item header table \(L\) and the tree nodes, a bottom-up approach is adopted to mine the strong association rules for the priority tree.
## 5 Application of ZPLTS-ELECTRE II Method in Ontology Ranking
### Discussion
#### 5.4.3 Strengths of ZPLTS-ELECTRE II  
The advantages and strengths of the proposed ZPLTS-ELECTRE II method that make it superior to existing ELECTRE II enhancement methods are as follows:  
1. The method can model both quantitative numerical and qualitative linguistic criteria, thereby providing decision-makers with more flexible and realistic expression of their preferences.
2. Individual discordance thresholds can be specified for each criterion. This provides the decision-makers with more flexibility in expressing their preferences.
3. Decision-makers are able to express their level of confidence or credibility when providing their evaluations, thereby improving the ability of the model to capture the cognitive nature of the decision-making process.
4. The applications of the ZPLTS-ELECTRE II method are not constrained to ontology ranking, but rather, it can be applied to any decision-making problem in any domain.