esa-sceva/satcom-chunk-collection huggingface dataset:
# Short Text Aspect-Based Sentiment Analysis Based on CNN + BiGRU
###### Abstract  
This paper describes the construction a short-text aspect-based sentiment analysis method based on Convolutional Neural Network (CNN) and Bidirectional Gating Recurrent Unit (BiGRU). The hybrid model can fully extract text features, solve the problem of long-distance dependence on the sequence, and improve the reliability of training. This article reports empirical research conducted on the basis of literature research. The first step was to obtain the dataset and perform preprocessing, after which scikit-learn was used to perform TF-IDF calculations to obtain the feature word vector weight, obtain the aspect-level feature ontology words of the evaluated text, and manually mark the ontology of the reviewed text and the corresponding sentiment analysis polarity. In the sentiment analysis section, a hybrid model based on CNN and BiGRU (CNN + BiGRU) was constructed, which uses corpus sentences and feature words as the vector input and predicts the emotional polarity. The experimental results prove that the classification accuracy of the improved CNN + BiGRU model was improved by 12.12%, 8.37%, and 4.46% compared with the Convolutional Neural Network model (CNN), Long-Short Term Memory model (LSTM), and Convolutional Neural Network (C-LSTM) model.  
short text; aspect-level; sentiment analysis; convolutional neural network (CNN); bidirectional gating recurrent unit (BiGRU) +
Footnote †: journal: Applied Sciences
## 1 Introduction
One channel was replaced by only adding a source, a mixer, a splitter, and a combiner in the novel structure, which helps to avoid the matching problem between the two channels in the classical structure with acceptable system performance. In addition, in the novel structure, the sequential control of the output pulses is simplified for the later digital sampling and storage. The structure and detailed principle of the classical CTS is described in section two. The description and principle of the novel single-channel CTS system is shown in section three. The simulation verification with ideal and nonideal devices and the experiment analysis of the obtained results are presented in section four and five, respectively. Finally, a brief conclusion is drawn in the last section. Figure 1: Schematic diagram of pulse compression procedure in radar system. SAW—surface acoustic wave.
# An Integrated Safety Monitoring and Pre-Warning System for Fishing Vessels
## 5 Conclusions  
This study successfully developed an integrated safety monitoring and early warning system for fishing vessels, addressing key safety concerns through real-time data acquisition and intelligent hazard assessment. The system's sensing terminal subsystem, incorporating a nine-axis sensor, temperature and humidity sensors, a GPS module, and a surveillance camera, demonstrated its capability to collect and transmit critical operational and environmental data. Utilizing fuzzy logic algorithms, the decision support subsystem effectively processed these data to predict potential hazards and trigger timely alerts, as evidenced by the field trials in the Zhoushan Sea area.  
The findings confirmed the system's effectiveness in enhancing vessel safety by providing accurate, real-time monitoring and early warning functions. Its modular and cost-efficient design ensures scalability and adaptability for different fishing vessel sizes and operational scales, while the integration of multi-sensor data fusion and fuzzy logic algorithms offers a significant advancement in maritime safety technology. The system's ability to send alerts via SMS and web-based notifications ensures that crew members are promptly informed of potential risks, enabling timely interventions to prevent accidents.  
In conclusion, this research contributes a robust and practical solution to improve the safety of fishing vessels, with potential for widespread application in the fishing industry. Future work could focus on expanding the system's functionality and further optimizing its performance.  
Figure 15: Alert warning interface of web application.  
**Author Contributions**: K.Y.: Writing--original draft, Conceptualization, Supervision, Resources, Project administration. J.L.: Writing--original draft, Software, Formal analysis, Data curation. J.D.: Software, Validation. B.Z.: Writing--review and editing. L.Q.: Writing--review and editing. Methodology, Visualization, Funding acquisition. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by Fund Project of Key Laboratory of Marine Environmental Survey Technology and Application, Ministry of Natural Resources [grant number MESTA-2023-B005]; Ningbo Natural Science Foundation [grant number 2023][090]; \"Pioneer Leading Goose + X\" Science and Technology Program of Zhejiang Province [grant number 2025C02018]; the National Natural Science Foundation of China [grant number 62341127]; and Zhoushan Science and Technology R&D Project [grant number 2024C03007].  
**Data Availability Statement**: The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
**Conflicts of Interest**: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
## 3 Results  
Out of 2573 tests conducted during the study period, 212 were confirmed as _Salmonella_-positive based on the Version 1.11 criteria, while 185 were positive under the Version 1.12 criteria. A total of 27 cases were identified as discrepant, where the results were positive according to Version 1.11 but negative under Version 1.12. Reflex cultures were selectively performed at the discretion of clinicians, primarily in patients with a clinical suspicion of _Salmonella_ infection.  
In Version 1.11, the distribution of results was as follows: 49.1% (\(n=104\)) were both HMMD-positive and culture-positive, 40.6% (\(n=86\)) were HMMD-positive but culture-negative, and 10.3% (\(n=22\)) were HMMD-positive only. In comparison, under Version 1.12, the results were 55.7% (\(n=103\)), 38.4% (\(n=71\)), and 5.9% (\(n=11\)), respectively (Figure 2). While the adjustment of the _Salmonella_ detection probe threshold in the HMMDs reduced the number of false positives, it also resulted in one culture-confirmed positive case (3.7%) being incorrectly reported as negative.  
When examining the clinical symptoms, those who were culture-confirmed for _Salmonella_ did not exhibit distinct characteristics of infection exposure or key symptoms of _Salmonella_ enteritis, such as abdominal pain, diarrhea, and fever, in the groups where the culture was not positive (Figure 3).  
Figure 3: Clinical characteristics of patients positive for _Salmonella_ on xTAG Gastrointestinal Pathogen Panel Version 1.11, classified by culture testing status and results. Excluding discrepant cases between Versions 1.11 and 1.12, patients were categorized as: (**A**) culture-positive, (**B**) culture-negative, (**C**) culture not performed, and (**D**) discrepant cases.  
Figure 2: Overview of the study. Out of 2573 cases tested with the xTAG Gastrointestinal Pathogen Panel, 212 individuals were identified as _Salmonella_ positive under Version 1.11 criteria. Following the revised threshold in Version 1.12, 27 of these patients were reclassified as negative.  
For the 185 samples that tested positive for _Salmonella_ in both Version 1.11 and Version 1.12 of the Luminex xTAG GPP, the mean and standard deviation of the MFI values for probe 1 and probe 2 were analyzed based on whether stool cultures were performed and their results. Statistically significant differences were observed between groups (Table 2). Specifically, for probe 1, which is crucial for _Salmonella_ detection, the MFI values were 2264.96 \(\pm\) 1273.77 for stool culture-positive cases, 1232.49 \(\pm\) 860.18 for stool culture-negative cases, and 1247.09 \(\pm\) 1209.37 for cases where stool culture was not performed (\(p\)-value < 0.005).
## 4 Experiments
### Ablation Study  
We conducted a series of experiments comparing the performance of our method with and without key components:  
1. \(BERT_{url}\): This scheme removes perspectives other than the URL and utilizes only our adversarially trained BERTurl model.
2. \(BERT+Multi\)-_views_: This scheme utilizes the original English BERT to obtain the initial representation of the URL and combines it with several other perspective features used in this study.
3. \(FinedBERT+Multi\)-_views_: This scheme utilizes the fine-tuned BERT for the URL, in conjunction with other perspective features used in this study.
4. \(Full\).  
The ablation study was conducted on the imbalanced dataset. By analyzing the ROC curves and corresponding AUC values, we can assess the relative effectiveness of each ablation model.  
Based on Figure 5, we observed several key findings regarding the performance of different models. Firstly, the Full model consistently outperformed all the ablation models across various evaluation metrics. Notably, as the amount of training data decreased, the performance gap between the Full model and the ablation models widened. This finding highlights the importance of retaining all the components and features in the Full model, as it consistently demonstrated superior performance in handling the complexities of the task.  
Secondly, we compared the performance of Ablation Model 1 (\(BERT_{url}\)) and Ablation Model 2 (\(BERT+Multi\)-_views_) across three different datasets. Interestingly, we observed that as the dataset size decreased, the importance of the multi-per  
Figure 4: Comparison between proposed method and baseline method in data drift experiment.  
Figure 5: Comparison of several ablation models.  
increased. Specifically, the performance of Ablation Model 1, which retained the smile feature, was better than Ablation Model 2 on smaller datasets. This suggests that the inclusion of multiple perspectives becomes more critical when the dataset is limited, as it provides additional discriminative information for accurate classification.  
Overall, these results provide valuable insights into the effectiveness of the Full model and highlight the significance of multiple perspectives in capturing the complexities of the task, particularly in scenarios with limited training data. These findings contribute to the understanding and improvement of models for the task at hand, with implications for further research and development in related fields.
## 4 Case Studies
### Case Study 2: Anomaly Detection for Maritime Safety  
* Problem: Identifying anomalous behavior in vessel movements can be crucial for maritime safety and security. However, given the sheer volume of vessels in operation, manual monitoring is neither feasible nor efficient [38].
* Solution: A study used unsupervised learning techniques, specifically a one-class support vector machine (SVM), to detect anomalies in vessel movements.
* Implementation: The algorithm was trained on historical AIS data, and it learned to recognize typical vessel behaviors. Any deviation from these typical patterns was flagged as an anomaly [38].
* Results: The algorithm successfully detected a variety of anomalies, including vessels deviating from their usual routes and vessels moving at unusual speeds. This early detection allowed for timely interventions and improved maritime safety and security.
# Design and Optimization of Origami-Inspired Orthopyramid-Like Core Panel for Load Damping
## 6 Result and Discussion
### Optimize the Best Geometric Parameters  
Section 6.3 finds that the influence of similarity parameter to 4-edged elements' maximum equivalent stress, in compression simulations, are different than the other two. The stress value does not decrease with the increasing of the similarity parameter as shown in Table 5. The GA [46; 47; 48; 49] integrated with the FEA model (using ANSYS software) was employed to find its optimal similarity parameters corresponding to the minimum equivalent stress. The individual fitness was increased with the decrease of the stress value. The basic flow chart of the algorithm is shown in Figure 20, which inherited the advantages of GA and the strong solution ability of FEA software.  
The calculation steps are as follows:  
Step 1: The running parameters of the Genetic Algorithm are set. The optimization variables were the population size; the cross probability; and the evolution termination parameter.  
Step 2: Generate initial populations according to the selection strategy.  
Step 3: The ANSYS parameter design language is used to calculate the maximum stress.  
Step 4: The stress is used to calculate the individual fitness value and evaluate the population. If the iterative termination principle is satisfied, the iterative will stop. Otherwise, the process is switched to the next step.  
Step 5: Operate the GA operation and get a new population. Go to Step 3.  
Figure 19: The FEM results of the maximum plastic strain of the panels.  
Figure 18: Comparison of the maximum produced force.  
In this specific implementation of the algorithm, our goal was to minimize the maximum stress. The optimization variable is the similarity parameter \(w\), whose range is set to 1/2,5/6. Using the algorithm above, we find that the optimal similarity parameter \(w\) is 0.8104. The corresponding tip angle \(\beta\) is 72.94 \({}^{\circ}\), the bottom surface side length \(L\) is 21.95 mm, and the side length \(S\) is 19.56 mm. The comparison of the non-optimal and optimal solutions is shown in Figure 21.
## 2 Continuous Shear-Flexural Beam Model
\[u(\zeta,t)=\sum\limits_{i=1}^{\infty}u_{i}(\zeta,t) \tag{3}\]  
Considering classical damping, the contribution of each mode to the response, in terms of lateral displacement (u\({}_{i}\)(\(\zeta\),t)) and inter-story drift ratio (IDR\({}_{i}\)(\(\zeta\),t)), is determined as follows:  
\[u_{i}(\zeta,t)=\Gamma_{i}\phi_{i}(\zeta)D_{i}(t) \tag{4}\]  
\[IDR_{i}(\zeta,t)=\frac{1}{H}\Gamma_{i}\phi^{\prime}_{i}(\zeta)D_{i}(t) \tag{5}\]  
Here, the modal participation factor (\(\Gamma_{i}\)) for the continuous model with uniform mass distribution is given by  
\[\Gamma_{i}=\frac{\int\limits_{0}^{1}\phi_{i}(\zeta)d\zeta}{\int\limits_{0}^{1 }\phi_{i}^{2}(\zeta)d\zeta} \tag{6}\]  
By substituting Equation (4) into Equation (1) and solving for the free vibration case using the separation of variables method, two partial equations dependent on location (\(\phi_{i}\)(\(\zeta\))) and time (D\({}_{i}\)(t)) are obtained:  
\[\frac{d^{2}D_{i}(t)}{dt^{2}}+\omega_{i}^{2}D_{i}(t)=0 \tag{7}\]  
\[\frac{d^{4}\phi_{i}(\zeta)}{d\zeta^{4}}-\alpha^{2}\frac{d^{2}\phi_{i}(\zeta)}{ d\zeta^{2}}+\frac{mH^{4}}{EI}\phi_{i}(\zeta)=0 \tag{8}\]  
The general solution of Equation (8) provides the mode shape function (\(\phi_{i}\) (\(\zeta\))):  
\[ \begin{gathered}\beta_{i}=\sqrt{\gamma_{i}^{2}+\alpha^{2}}\\ \phi_{i}(\zeta)=B_{1}\sin(\gamma_{i}\zeta)+B_{2}\cos(\gamma_{i} \zeta)+B_{3}\sinh(\beta_{i}\zeta)+B_{4}\cosh(\beta_{i}\zeta)\end{gathered}  \tag{9}\]  
where B\({}_{1-4}\) represents the integration constants obtained based on the boundary conditions. The eigenvalue (\(\gamma_{i}\)) is derived from the solution of the circular frequencies (\(\omega_{i}\)) using Equation (10). \[\omega_{i}^{2}=\frac{EI}{mH^{4}}\gamma_{i}^{2}(\gamma_{i}^{2}+\alpha^{2}) \tag{10}\]  
In this case, the period ratios (T\({}_{i}\)/T\({}_{1}\)) for the continuous beam model are determined as follows:  
\[\frac{T_{i}}{T_{1}}=\frac{\gamma_{1}^{2}(\gamma_{1}^{2}+\alpha^{2})}{\gamma_{ i}^{2}(\gamma_{i}^{2}+\alpha^{2})}. \tag{11}\]  
The mode shape function (\(\phi(\zeta)\)), drift mode shape function (\(\phi^{\prime}(\zeta)\)), modal participation factor (\(\Gamma_{i}\)), and period ratios (T\({}_{i}\)/T\({}_{1}\)) solely depend on the lateral stiffness ratio \(\alpha\)[5; 6]. Accurate dynamic properties and structural response prediction can be achieved by selecting the appropriate value of \(\alpha\)[4]. In addition to these parameters, information regarding the structure's fundamental vibration period and damping ratios is required for modal analysis. [PERSON] and [PERSON] [5] suggested using the period equations proposedby [PERSON] and [PERSON] [1] or [PERSON] [34] to calculate the fundamental vibration period of the continuous model.
## 3 Results
### Scenario II  
In Scenario II, we used different shares (10%, 20%, 30%, 40%, and 50%) of the Manual labels to fine-tune the models pretrained with the OBIA labels from Scenario I. The results reveal that the fine-tuning approach can produce a comparable or even better performance than the models trained with purely Manual labels. For example, the IoU value of the Manual-OCOS model for the Kule Dry is 0.5857. Fine-tuning the OBIA-OCOS model with 20% of the Manual labels improves the IoU value to 0.6033. The fine-tuned models produced all of the highest IoU values (highlighted in red) for each camp during each season. Based on the existing results, it is not found that intermixing training strategies are influential in the fine-tuning results. Additionally, most of the highest IoU values occur in between 20% and 40% of cleaned labels. However, no such stable percentage was found to perform the best in all cases. More research is required to dissect the further relations between these factors.
# Supporting Privacy of Computations in Mobile Big Data Systems +
## 4 Secure and Verifiable Cloud Computing for Mobile Systems
### Recovery and Verification of Outputs
The client can translate each of the garbled values from the evaluator \(p_{e}\) into a plaintext bit using the \(\lambda_{\omega}\) values of the output wires in order to recover the result of the requested computation. Only if the garbled output from the evaluator matches with either \(\omega_{0}\) or \(\omega_{1}\), for each output wire \(\omega\) of the circuit, the client declares that the output verification is successful. Collusion Resistance: Notice that the evaluator can return one of the two expected garbled outputs for every output wire in the circuit without performing any computation at all, if and only if: (i) it colludes with all the \(n\) servers, \(\{p_{1},p_{2},\ldots,p_{n}\}\), that participated in creating the garbled circuit; or (ii) it factorizes \(N\) into the prime factors, \(p\) and \(q\), which is infeasible. Furthermore, note that in order to cheat successfully without any collusion, the evaluator would have to correctly guess one of the two expected garbled outputs for each output wire in the circuit. Each garbled output value is \((nk+1)\) bits long. Therefore, for any reasonable value of the number of servers (\(n\geq 2\)) and security parameter (\(k=128\), for example), the evaluator would have to correctly guess hundreds of bits for each output wire in the circuit. Hence, it is very highly unlikely for the evaluator to be successful. Unpredictability: Even with the knowledge of all the previous/future bits, one cannot predict the next/previous bit output from the Blum, Blum, Shub pseudorandom number generator [13; 14]. Therefore, through the observation of garbled values during evaluation, the evaluating server, \(p_{e}\), cannot predict the preceding or subsequent garbled values, or the \(\lambda\) values for every wire in the circuit.
# A Geospatial Framework of Food Demand Mapping
## 2 Materials and Methods  
### Food Demand Mapping Framework  
The food demand mapping framework is designed to synthesize individual dietary preferences with geospatial data, offering a multidimensional view of food accessibility and consumption patterns (see Figure 1). This innovative approach aims to bridge the gap between micro-level dietary choices and macro-level spatial analytics, enabling a comprehensive understanding of food demand. By integrating detailed surveys on eating habits with advanced spatial mapping techniques, the framework seeks to identify and address disparities in food access, ultimately guiding more targeted and effective policy interventions. This introduction to the framework sets the stage for a nuanced exploration of the complex interplay between personal food choices and geographic food availability.
## 2 Natural Polyphenols Directly Inhibiting Transmembrane Glucose Transport
### Other Natural Polyphenols with Direct GLUT1-Binding Activities  
Besides the well-studied green tea extracts, several other compounds have also been reported to show inhibitory effects on GLUT1. Genistein, a soybean-derived isoflavenoid compound with well-known preventive activity against breast and prostate cancers, can block GLUT1 activity by attaching to its external glucose-binding site [57]. Like green tea polyphenols, genistein behaved as a competitive inhibitor of uptake transport and a non-competitive inhibitor of net sugar output of human red cells, as demonstrated by kinetic analyses [57]. Interestingly, genistein and green tea polyphenols could bind to GLUT1 in different conformations. While genistein attached to GLUT1 with its external surface associating with glucose, green tea polyphenols could bind to this transporter with its cytoplasmic side bound by glucose [57].  
Resveratrol is a kind of stilbene extracted from grapes and has attracted strikingly increased attention due to its potent antioxidant, radical scavenging, chemopreentive and anticancer activities [59]. Resveratrol has been considered as a bona fide inhibitor of GLUT1 in multiple studies [60]. [PERSON] et al. reported that resveratrol could block GLUT1-mediated glucose transport through directly binding to one of its internal domains in a non-competitive manner to reduce glucose uptake in human leukemic cells [51]. In human red blood cells, nordihydroroguaiaretic acid (NDGA), a natural compound with structural similarity to resveratrol, could also inhibit glucose uptake in a non-competitive manner by attaching to the non-substrate binding site of GLUT1 [13].
# Balloon Design for Mars, Venus, and Titan Atmospheres
## 4 Balloon Design for Different Atmospheres
### Balloons on Venus
There are  
Figure 6: Altitude variation of the SP balloons for different shapes with varying fineness ratio \(f\) in order to find the stable fineness ratio on Venus (**a**) oblate (\(f\): 2–8) (**b**) prolate (0.1–0.8) (**c**) airship (0.1–0.8). Figure 7: Altitude variation of the OZP balloons for different shapes with varying fineness ratios \(f\) in order to find the stable fineness ratio on Venus (**a**) oblate (\(f\): 2–8) (**b**) prolate (0.1–0.8) (**c**) airship (0.1–0.8). almost no altitude excursions for these shapes after the balloon reaches the stable altitude and, hence, the velocity is almost zero. To summarise the design concept of the balloon on Venus, the following two criteria can be used: mass efficiency and time to reach the float altitude. If one wants to use prolate shape in the case of ZP balloons, then only certain configurations are stable (finiteness above 0.3). For SP and OZP balloons, while the flight performance is almost similar for all shapes, the total mass of the balloon system varies. The oblate shape balloons with a fineness ratio above 4 and prolate and airship shape balloons with a fineness ratio below 0.3 are much heavier in comparison to the sphere. If the mission needs the balloon to ascend slowly and have no constraints on the mass of the balloon system, the oblate shape can be a preferred option; if the mission demands a faster ascent, then the prolate shape can be a preferred choice; if the mass efficiency is the major requirement than the spherical balloon of ZP, SP, or OZP can be used.