esa-sceva/satcom-chunk-collection huggingface dataset:
# Cell Response to Oxidative Stress in Antarctic Filamentous Fungi
## 5 Oxidative Stress and Antioxidant Defence
### Antioxidants and Antioxidant Defence
#### 5.2.1 Enzymatic Antioxidants
These enzymes typically exist as dimers or tetramers, with each subunit having an approximate molecular mass of 80 kDa, and they contain heme, which serves as a prosthetic group with iron. Catalase-peroxidases have been identified in a variety of organisms, including bacteria, archaea, fungi, and even more complex eukaryotes. The catalytic mechanism they employ mirrors that of conventional CATs, comprising the same two distinct stages. * The third category encompasses manganese-containing CATs. In contrast to the previous two categories, these catalases lack heme as a prosthetic group and instead utilize manganese ions at their active sites. Mn-CATs are structured as hexamers, with each subunit exhibiting a molecular mass ranging from 28 to 35 kDa, and they contain two manganese ions within their active center. They operate through a two-step mechanism that is similar to that of heme-containing enzymes [Reactions 6 and 7]. During the first phase, H2O2 acts as the oxidizing agent, while in the subsequent phase, it serves as the reducing agent. Glutathione peroxidase (GPx) is an enzyme that contains selenium and is classified under the peroxidase category (EC 1.11.1.19). This enzyme utilizes glutathione (GSH) to facilitate the conversion of hydrogen peroxide (H2O2) into water and to reduce organic peroxides to their corresponding alcohols in mammalian systems. The GPX family comprises eight distinct isoforms (GPX1 to GPX8), among which GPX1, GPX2, GPX3, GPX4, and GPX6 are recognized as selenoproteins with significant antioxidant properties. Notably, GPX6 is uniquely expressed in humans. Despite the importance of these enzymes, comprehensive investigations into the functions of the entire GPX family in the context of oxidative stress responses remain insufficient. To date, only a limited number of studies have systematically explored the involvement of GPX in oxidative stress [142]. Reductase enzymes, including glutathione reductase (GTR) and thioredoxin reductase (TRR), are integral components of the secondary antioxidant defense system. These enzymes facilitate the continuous production of NADPH, which is essential for counteracting reactive oxygen species (ROS) [143]. The presence of NADPH is crucial for mitigating the harmful effects of toxic substances that may enter aerobic cells. Additionally, NADPH can also be generated through the pentose phosphate pathway, aided by the enzymes glucose-6-phosphate dehydrogenase and 6-phosphogluconate dehydrogenase [144; 145].
# State-of-the-Art Survey on Deep Learning-Based Recommender Systems for E-Learning
## 3 Classification Method
### Classification Based on Recommendation Techniques
#### 3.1.7 Deep Learning-Based RS  
Traditionally, RSs are based on clustering, nearest neighbor, and matrix factorization methods. Deep learning is a subset or type of machine learning method based on artificial neural networks that teach computers or machines by imitating how the human brain gains certain knowledge. Thus, deep learning is a neural network with more than three layers. In recent years, deep learning has seen tremendous growth in its popularity and usefulness, largely due to more powerful computers, larger datasets, and techniques to train deeper networks [34]. The influence of deep learning demonstrates its effectiveness when applied to RSs, as it has been dramatically changing recommendation architecture and providing more opportunities to improve the performance accuracy (e.g., recall, precision, etc.) of RSs [35]. Deep learning methods mainly used for RSs are deep belief networks (DBN), multilayer perception (MLP), auto-encoder (AE), convolutional neural networks (CNN), recurrent neural networks (RNN), restricted Boltzmann machine (RBM), neural autoregressive distribution estimation (NADE), and adversarial networks (AN) [35].
## 2 Materials and Methods
### Data Collection Protocol: Various Scenarios of RF Transmitters on Marine Vessels and Reasons  
Under a normal operation condition, the ship will have the two radars (X-band and S-band), the AIS and the Sat Comm C; all transmitting continuously. The VHF and the MF/HF radios operate intermittently, in addition to the other navigation equipment transmitting continuously. By considering all of these situations, we developed a data collection protocol and performed various scenarios to observe and measure the radiation emissions from all of the transmitters. The protocol and reasons for the various scenarios are described in Table 3.  
\begin{table}
\begin{tabular}{p{34.1 pt} p{34.1 pt}} \hline \hline
**Scenario No.** & **Scenario Details** \\ \hline A & Measured with the VHF fixed (IC-M505) switched ON and all other transmitting antennas switched OFF \\ \hline B & Measured with the VHF fixed (Sailor 6006) switched ON and all other transmitting antennas switched OFF \\ \hline C & Measured with the MF/HF transceiver (TU 6360 TX/Tx and CU6301 control unit) switched ON and all other transmitting antennas switched OFF \\ \hline D & Measured with the satellite communication, Sat-Com C (6006), switched ON and all other transmitting antennas switched OFF \\ \hline E & Measured with the AIS navigation (R5 AIS) switched ON and all other transmitting antennas switched OFF \\ \hline F & Measured with the radar X-band (VMFT ECAT2 253/8/MK/VM2) switched ON and all other transmitting antennas switched OFF \\ \hline G & Measured with the radar S-band (VMFT ECAT2 253/8/MK/VM2) switched ON and all other transmitting antennas switched OFF \\ \hline H & Measured with the Group Z (radar X-band, radar S-band, satellite communication (Sat-Com C) and AIS navigation) switched ON and all other transmitting antennas switched OFF (under the normal operation condition, the ship will have two radars, the AIS and Sat Comm C; all transmitting continuously; besides, Sat Comm C does not continuously transmit; however, it operates all of the time; the VHF and MF/HF radios operate intermittently, in addition to the other navigation equipment transmitting continuously) \\ \hline I & Measured with the Group Z + VHF fixed (IC-M505) switched ON \\ \hline J & Measured with the Group Z + VHF fixed (Sailor 6006) switched ON \\ \hline K & Measured with the Group Z + MF/HF transceiver (TU 6360 TX/Tx and CU6301 control unit) switched ON \\ \hline \hline \end{tabular}
\end{table}
Table 3: Data collection protocol: the various scenarios of marine ship transmitters for measuring RF-EF levels.
## 7 Discussion
One question could be whether the effect we observed could be caused by a lexical priming (seeing a word written once primes the reader to expect it again in the future) [38; 39] or a topical effect (the knowledge of the information about to be given helps decipher the unfamiliar text). In our experiment, the topics were similar between the two conversations, but no restrictions were imposed on the exact words being used in the experimenter's replies as long as the information given remained consistent ([PERSON] could not have two siblings in one conversation and none in another for example, but the experimenter could express it in different ways depending on the question asked). This question could likely be answered in a future experiment by designing a protocol that would independently test the influence of lexical priming and of the topical effect. All of this indicated that deciphering textisms without prior information about the context of the conversation is a task that requires a significant amount of cognitive effort and that deciphering textisms becomes easier when participants know about the semantic content of the messages, as we expected. It is also important to note that in our case, participants were young adults who, by definition, while not being experts in textisms, had a higher probability of knowing how they worked. We would expect the differences to be even stronger between traditional writing and textisms if participants were older people who were not used to textisms at all. It is yet unknown how much of a decrease in response times would be observed when older participants would encounter textisms in the second conversation compared to when they would encounter them in the first conversation. Indeed, while older participants might find deciphering textisms to be even more complicated than our younger participants(thus, we would expect a higher \(\Delta d\) than we observed for textisms in the first conversation), we do not know if they would be able to use their knowledge of the first conversation to decipher textisms as well as the younger participants in this study. Two alternative patterns of results could then be considered for such an experiment: older participants would have a higher \(\Delta d\) than we observed in the first conversation with textisms (because of the higher cognitive cost required), but in the second conversation, \(\Delta d\) would also come close to the baseline (around 0 s) if the prior information was enough to fully disambiguate the textisms, or alternatively, \(\Delta d\) could reach a middle level between the value observed in the first conversation and the baseline if the prior information only partially helped to decipher the textisms.
## 3 Results and Discussion
### Volatile Compounds Analysis Using GC-qMS
#### 3.4.6 Terpenes and Norisoprenoids  
Terpenes have been confirmed to be one of the most important aromatic compounds in fruit wine since these volatiles possess a relatively low odor threshold. Terpenes could provide wine with fruity, rose, floral and sweet aromas [21]. In this study, these wine samples were found to contain 17 terpenes (Table 3). Among these terpenes, \(\beta\)-myrcene, \(\alpha\)-terpineol, linalool, citronellol, geranyl acetone, cis-rose oxide, trans-rose oxide and geraniol appeared to be the dominant individual terpen in the passion fruit wines. \(\beta\)-Myrcene was reported to possess a lychee flavor and its concentration was much higher than its odor threshold in these passion fruit wine samples, indicating that its flavor notes could significantly contribute to the passion fruit wine aroma [9]. These different strain-fermented wine samples contained different \(\beta\)-myrcene concentrations. The higher linalool concentration was found in the CY and VI-fermented wines, whereas the BV and ES wines contained a lower linalool level. The lowest linalool concentration was found in the BV-fermented passion fruit wine. It should be noted that the concentrations of linalool in these wine samples were all higher than their threshold. This suggested that its floral and musky aromas were significantly incorporated to produce the finest expression of the passion fruit wine. Additionally, these wine samples exhibited different \(\alpha\)-terpineol concentrations, and their concentrations were much higher than their odor threshold. The VI-fermented passion fruit wine contained the highest level of \(\alpha\)-terpineol, followed by the ES and CY wine. The BV passion fruit wine showed the lowest content. \(\alpha\)-Terpineol was described as having lilac, floral and sweet notes [25]. It should be noted that nerolidol, 4-terpineol and nerol showed concentration differences in these different strain-fermented wines. However, their low concentration restricted their flavor notes being incorporated into the overall aroma of these passion fruit wines [36].  
Similar to terpenes, norisoprenoids also possess low odor thresholds in fruit wine, and these volatiles can always enhance the wine aroma with flower and fruit notes. Based on their structure, norisoprenoids can be divided into megastigmane and non-megastigmane norisoprenoids [3]. In the present study, \(\beta\)-ionone and \(\alpha\)-ionone were two major megastigmane norisoprenoids in these wine samples (Table 3). The concentration of \(\beta\)-ionone was significantly higher than its odor threshold in these passion fruit wines. These different strain-fermented passion fruit wines contained a comparable \(\beta\)-ionone concentration. Therefore, rose and violet could be substantially incorporated into the passion fruit wine aroma [21; 37]. Similarly, the flavor feature of \(\alpha\)-ionone could also be enhanced in these wines since its OAV was above 1 in these wine samples. \(\alpha\)-Ionone and \(\beta\)-ionone exhibited the highest level in the CY passion fruit wine.
# Stock Market Prediction Using Microblogging Sentiment Analysis and Machine Learning
## 5 Conclusions
### Limitations  
A few factors might jeopardize the validity of our findings. Firstly, Python packages such as TextBlob and VADER produce incorrect sentiment scores for a number of tweets. For example, in the presence of sarcasm, they frequently misinterpret a bad tweet as a good one and vice versa, resulting in an improper training set.  
Another risk to the accuracy of our results is the presence of spam accounts, false accounts, and bots that generate Twitter and StockTwits data. As a result, the dissemination of false information and an incorrectly estimated sentiment may be stored in our dataset. Furthermore, it was discovered that for a specific day, although sentiment might be positive, stock movement might be moving downwards.  
Lastly, the presented assessment metrics may be deemed limiting. Nonetheless, due to our preliminary findings in [45], we excluded publishing further accuracy metrics. There is a class imbalance problem in the data, as seen in Table 5. In all four analyzed examples, the number of buyers outnumbers the number of sellers, indicating that most individuals prefer to purchase Microsoft stocks and just a few choose to sell them. For unbalanced data, F-score and AUC are suitable metrics [46].
# Study on Few-Shot Object Detection Approach Based on Improved RPN and Feature Aggregation
## 3 Definitions and Improved Network Framework
### Basic Network Framework
#### 3.2.2 Improved RPN Module  
RPN is a fully convolutional network that can simultaneously predict the target detection box and score of the target, generate high-quality region proposals through end-to-end training, and share the same convolutional features with the detection network. Its essence is a classless object detector based on sliding windows, which can input images of any scale and output a series of rectangular candidate regions. The structure of the original RPN is shown in Figure 3.  
Figure 2: The framework of IFA-FSOD with a feature extractor, an improved RPN, RoI Align, and a newly added feature aggregation module.  
Figure 3: The original RPN structure.  
where \(\varphi_{Add}\) and \(\varphi_{Mult}\) consist of a CNN layer and a Relu layer, \(\varphi_{Add}\) can display the distance between two input features, and \(\varphi_{Mult}\) shows the correlation between features. The combination of the two achieves a stronger effect. Finally, input \(f_{q}\) into the binary classification layer and regression layer to predict the proposal. By improving PRN, the precision of positioning proposals for new categories can be enhanced, and candidate regions with high IoUs can be obtained.
## 2 Gaussian Processes
### Computational and Memory Requirements  
Gaussian processes regression (GPR) requires \(\mathcal{O}(n^{3})\) operations and \(\mathcal{O}(n^{2})\) storage [15], making it an intractable method for datasets that contain over a few thousand training points. Over the past few decades, a considerable amount of work has been done by the community to overcome this problem. A thorough review can be found in [14]. Big datasets are not an issue in our research, as we aim to gain a maximum amount of information about a large number of test points on a mesh or point cloud by using as few training points as possible. We typically restrict ourselves to \(n<100\). This leads to a matrix \(K_{X,X}\) of moderate size \(n\times n\). Contrarily, in this work, we study the challenge of having \(n_{*}>1000\) test points, resulting in a large matrix \(K_{X_{*},X_{*}}\) of size \(n_{*}\times n_{*}\) and a matrix \(K_{X,X_{*}}\) of size \(n\times n_{*}\) (and its transpose). The predictive mean \(\mathbb{E}(\mathbf{f}_{*})\) and variance \(\mathbb{V}(\mathbf{f}_{*})\) of a trained GP cost \(\mathcal{O}(n)\) and \(\mathcal{O}(n^{2})\) operations per test point [15], resulting in \(\mathcal{O}(nm_{*})\) and \(\mathcal{O}(n^{2}n_{*})\). Practical applications generally do not require the full predictive covariance matrix, but rather the predictive variance along its diagonal. This reduces the calculation of the \(n_{*}\times n_{*}\) values of \(K_{X_{*},X_{*}}\) to only the \(n_{*}\) diagonal values. The main contribution of this work is the feasibility of implementing a kd-tree to approximate the set of test points \(X_{*}\) and thus reducing \(n_{*}\) all together. In sequential analysis, this computational gain adds up quickly. For every new incoming data point, \(n\) increases by one and both \(K_{X,X}\) and \(K_{X,X_{*}}\) have to be recalculated again.
# Surface Defect Detection of Rolled Steel Based on Lightweight Model
## 4 Experiment and Results
### Comparison Experiment of Different Algorithms  
This paper picks three popular target detection methods, YOLOv3, SSD, and Faster R-CNN, and conducts experiments on the same dataset to further validate the detection performance of the technique presented here. The results are shown in Table 2 and Figure 9.  
From the analysis in Table 2 and Figure 6, the results can identify that YOLOv3 and Faster R-CNN have similar detection accuracy. However, Faster R-CNN has far more parameters and calculations than YOLOv3, and the detection speed is also low. The detection accuracy of SSD is the lowest, but the detection speed is higher than that of  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Model** & **Parameters** & **GFLOPs** & **Weight/MB** & **[EMAIL_ADDRESS]/\%** & **fps** \\ \hline YOLOv3 & 61,545,000 & 65.6 & 235.1 & 73.3 & 34.1 \\ SSD & 26,285,000 & 62.7 & 92.6 & 42.2 & 52.6 \\ Faster R-CNN & 137,099,000 & 370.2 & 108.3 & 73.6 & 7.9 \\ YOLOv5s-GCE & 3,719,490 & 8.1 & 7.6 & 85.7 & 58.8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance comparison of different algorithms.  
Figure 8: Comparison of detection effects.  
Figure 9: Detection results of various defects by different algorithms.  
YOLOv3 and Faster R-CNN. The mAP value of the YOLOv5s-GCE algorithm in this paper is 12.4%, 43.5%, and 12.1% higher than that of YOLOv3, SSD, and Faster R-CNN, respectively. The detection accuracy of YOLOv5s-GCE for defect patches is slightly lower than that of Faster R-CNN. However, the detection accuracy of other defects is higher than the other three algorithms, which can meet the high requirements of model detection accuracy in actual defect detection. While the YOLOv5s-GCE algorithm maintains high precision, the model's amount of parameters and calculations are far lower than the other three algorithms. The model size is only 7.6 MB, which is more suitable for mobile applications, and the detection speed also reaches 58.8 fps. Based on the above analysis, the comprehensive detection performance of the YOLOv5s-GCE algorithm in this paper is better than that of the mainstream target detection algorithms such as YOLOv3, SSD, and Faster R-CNN. The model is small in size and high in detection speed. It can meet the real-time detection requirements of rolled steel in industrial defect detection.
## 3 Experiments and Simulations
### Test Methods  
The STL values of the structures were measured using an impedance tube of 30 mm diameter (according to standards outlined in the document ASTM E2611-09). Figure 4 shows a photograph of the impedance tube used (model ZT13, SKC Acoustics Technology Co., Ltd., Beijing, China) which covers the frequency band 1.6-6.3 kHz. Each sample must be sealed tightly and flexibly in the tube for testing, as the presence of gaps around the edge of a sample can greatly affect the resulting STL values obtained [31]. In our experiments, therefore, samples were fixed in position using Teflon tape, in order to avoid sound leaking around the sample via surrounding cracks or gaps. Considering that the seals around the samples can have a significant effect on the test results, three separate samples were prepared for each specimen test, in order to check the reproducibility of the procedure and thus guarantee the accuracy of the final test result. In addition, in order to verify that the samples were properly sealed and the tubes were seamless, each sample was subjected to the same test twice. When measuring the STL of a sample using the impedance tube, it is necessary to decompose the steady-state broadband signal into incident and reflected waves in front of the specimen (i.e., in the tube containing the sound source). A similar decomposition, into transmitted and reflected waves, must be performed at the other end of the apparatus (i.e., in the receiving tube), as in  
The sound source generates an acoustic signal, which propagates inside the tube as plane waves. The acoustic pressure produced by these plane waves are measured using the four microphones mounted in the wall of the impedance tube. As shown in Section 2, when the sound waves (\(P_{i}\)) generated by the sound source impinge onto the surface of the specimen, a portion of the sound (\(P_{r1}\)) is reflected. The remaining portion (\(P_{t}\)) is transmitted to the other side and propagates within the receiving tube. In practical conditions, the sound-absorption end cannot completely absorb the acoustic wave energy. Therefore, a portion of the transmitted wave (\(P_{t}\)) is reflected. Figure 5. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Test Types** & **Honeycomb Type** & **h\({}_{\text{g}}\)/mm** & **l/mm** & **h\({}_{\text{d}}\)/mm** \\ \hline A\({}_{1}\) & PJ-NH1-1.83-48 & 1 & 1.83 & 20 \\ A\({}_{2}\) & PJ-NH1-2.75-48 & 1 & 2.75 & 20 \\ B\({}_{1}\) & PJ-NH1-2.75-48 & 0.5 & 2.75 & 20 \\ B\({}_{2}\) & PJ-NH1-2.75-48 & 1 & 2.75 & 20 \\ B\({}_{3}\) & PJ-NH1-2.75-48 & 1.5 & 2.75 & 20 \\ C\({}_{1}\) & PJ-NH1-1.83-48 & 1 & 1.83 & 40 \\ C\({}_{2}\) & PJ-NH1-1.83-48 & 1 & 1.83 & 30 \\ C\({}_{3}\) & PJ-NH1-1.83-48 & 1 & 1.83 & 20 \\ C\({}_{4}\) & PJ-NH1-1.83-48 & 1 & 1.83 & 10 \\ D\({}_{1}\) & PJ-NH1-1.83-48 & 1 & 1.83 & 40 \\ D\({}_{2}\) & PJ-NH1-1.83-48 & 1 & 1.83 & Doubled-layer structures \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The types of honeycomb sandwich structures used in the tests and their corresponding parameters. Figure 4: The impedance tube used in the experiments.