esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Confirmatory Analysis Results  
#### 3.2.1 Selectivity and Sensitivity  
The samples showing signals below the ELISA detection limit (3 ug L\({}^{-1}\) and 75 ug kg\({}^{-1}\) in matrix) were considered to be \"blank\" and then analyzed to verify the selectivity of the UPLC/FLD method. After UPLC analysis, no interfering compound was found in the retention time windows of interest: \(\pm\)2.5% of T-2 and HT-2 chromatographic peak retention times. Thus, all samples were characterized by the absence of interfering peaks, demonstrating method selectivity. The chromatograms reported in Figure 2B,C show an example of a blank maize sample and the same spiked sample with the T-2 and HT-2 toxins at 250 ug kg\({}^{-1}\).  
Method sensitivity, linearity, and limits of detection were evaluated by using the regression line approach [24]. The calibration curve determination coefficient (R\({}^{2}\)) was higher than 0.995 for the T-2 toxin and 0.999 for the HT-2 toxin. The linear regressions were obtained by injecting six standard solutions of the T-2 and HT-2 toxins at concentrations of 15, 50, 100, 250, 500, and 1000 ug L\({}^{-1}\). By means of the least squares method, the pairs of values were interpolated (concentration of the standard and area of the chromatographic peak). The limits of detection and quantification were 9.0 and 29 ug kg\({}^{-1}\) for the T-2 toxin, respectively, and 5.7 and 19 ug kg\({}^{-1}\) for the HT-2 toxin, respectively (Table 4). The obtained LODs were comparable with those reported by [PERSON] et al. [25] in their improved method by ultra-performance liquid chromatography with photodiode array detection. Though these sensitivity levels were obviously lower that those achievable by using mass spectrometry [26], they can be considered satisfactory with respect to the indicative legal limits reported for the sum of the T-2 and HT-2 toxins in the European Commission Recommendation of 27 March 2013 [27]. Thus, given its higher simplicity and cheapness compared to mass spectrometry, the UPLC/FLD approach may represent a valid alternative for reliable confirmatory analysis.
## 5 Findings  
### Model Validation  
To validate the model, we look at sections not used during training as mentioned in Section 3.2. The error of the voltage prediction is shown in Figure 7. Most sections have a RMSE below 5 mV, which is very accurate. The error distribution of the validation and training sections have the same mean and variance. This shows that the model is not overfitted and can extrapolate to unseen operation sections. Here and in the following figures, we use different colors for each vehicle. Nevertheless, the main intention of the plots is to illustrate the spread between vehicles. No correlation between a specific vehicle and the RMSE could be observed, i.e., the fleet model works well for all vehicles. (A lower sample rate in the first half of 2022 resulted in higher errors. The sections with high RMSE after May 2022 were individually analyzed and found to be anomalous, i.e., they were corrupted by measurement or transmission errors).
## 3 Results and Discussion
### Effect of Liquid Nitrogen Pulsation on DCM Stability
#### 3.2.1 Frequency Impact
In the design of the liquid nitrogen cooling system, efforts should be made to reduce pipeline resistance to lower the excitation amplitude brought by flow pulsation, for example, by polishing and grinding to reduce the roughness of the pipeline, increasing the diameter of the pipeline or reducing the length of the pipeline to decrease along-the-path resistance, increasing the curvature at pipe bends, and adopting equal or gradual diameter pipeline designs to reduce local resistance in the system. Considering the impact of excitation frequency on system vibration, measures should be taken to reduce high-frequency excitation to ensure system stability. In addition, the operating range of the pump frequency should be positioned after the first-order frequency to reduce the amplification factor when the crystal is subjected to forced vibration. Choosing a pump with high flow and low speed is advisable. A lower speed pump causes lower pulsation frequency, and lower frequency ensures flow stability. Reciprocating pump types can be chosen, since under constant flow conditions the pulsation frequency generated by reciprocating pumps is often lower than that of centrifugal pumps. The influence of external pipelines should be given special attention in system design. The second and third modal vibrations of the DCM are the bending vibrations of the outlet and inlet bends of the U-shaped circuit. When the system experiences second and third modal vibrations, the external pipelines generate displacement components in the z direction, causing vibration in the crystal. The natural frequency of external pipelines is relatively high, making it prone to resonance when the pump is running. Therefore, it is necessary to strengthen the rigidity of external pipelines, increase their modal frequency, and keep them away from the frequency range of flow pulsation excitation.
In this paper, our first motivation is to modify the structure of Faster R-CNN to improve the performance of multi-scale object detection in infrared images. Our second motivation is to develop a data augmentation method that can solve the issue of non-uniform class distribution while augmenting a dataset. The general structure of the network is shown in Figure 1. Our main contributions are summarized as follows:  
1. We designed a double-layer RPN pyramid, in which the receptive field of the top feature map is more suitable for the prediction of large objects, while the receptive field of the bottom is denser and the resolution is higher for the prediction of small objects. 2. We introduce a multi-scale pooling method plus the inception4 module to project proposals on outputs of different scales simultaneously. A 1 \(\times\) 1 convolution is then utilized to synthesize coarse and fine features. In this way, the network can capture more abundant multi-scale information. 3. By adding the inception4 module after the backbone, the receptive fields of feature maps generated by different size convolution kernels are various. This makes the extractor more sensitive to objects with different sizes. At the same time, the shortcut makes network training smoother. 4. Because of the background noise and the scarcity of foreground details, we used PSalign pooling to extract the local features of objects. It can fully explore the foreground information of objects. 5. Since the existing data augmentation methods cannot deal well with the imbalance of categories, we elaborately designed an instance-level data augmentation method. Taking account of the proportion of different categories and the spatial relationships between objects, the algorithm ensures that the original data is not damaged while expanding the dataset. Figure 1: The complete structure of our frame. The structure of this paper is as follows: Sections 2 and 3 introduce the details of the network modification. Section 4 describes our data augmentation method. Section 5 presents the experimental results and analysis. We draw the conclusion in Section 6.
## 5 Conclusions  
Our research presents a pioneering approach to blind and robust watermarking for medical images, which capitalizes on the Discrete Wavelet Transform (DWT) in conjunction with gradient analysis. By integrating the LL sub-band, gradient magnitude, and gradient direction, our method achieves a balance between imperceptibility and robustness, ensuring the protection of sensitive medical data. Through comprehensive evaluations, including metrics like the PSNR, SSIM, NC, and BER, we have demonstrated the superiority of our technique over existing methods, promising enhanced security without compromising image quality. Moreover, simulations on real healthcare data involving various common attacks, such as median and noise addition, further underscored the resilience of our method, highlighting its efficacy in maintaining watermark integrity under adverse conditions. This innovative solution holds significant potential for practical applications in medical image authentication and secure data transmission within healthcare systems.  
Conceptualization, K.H.; Software, K.H. and A.B.; Validation, K.H., O.A. and A.B.; Formal analysis, O.A.; Investigation, B.K.; Data curation, A.B.; Writing--original draft, K.H. and O.A.; Writing--review & editing, B.K.; Visualization, B.K.; Supervision, B.K. and O.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Figure 5: The impact of watermark size on the watermarking process in terms of calculation time.  
Data Availability Statement: Publicly available dataset was analyzed in this study. This data can be found here: [[https://www.kaggle.com/datasets/fusicfenta/chest-xray-for-covid19-detection](https://www.kaggle.com/datasets/fusicfenta/chest-xray-for-covid19-detection)]([https://www.kaggle.com/datasets/fusicfenta/chest-xray-for-covid19-detection](https://www.kaggle.com/datasets/fusicfenta/chest-xray-for-covid19-detection)) (accessed on 15 January 2024).  
The authors declare no conflicts of interest.
# Sentiment Analysis of Students' Feedback with NLP and Deep Learning: A Systematic Mapping Study
## 2 Sentiment Analysis and Related Work
### Overview of Sentiment Analysis
From the learners' perspective, there are a number of papers [10; 11; 12] that have applied sentiment analysis to investigate the correlation of attitude and performance with learners' sentiments as well as the relationship between learners' sentiments and drop-out rates in Massive Open Online Courses (MOOCs). Regarding teachers' perspectives, sentiment analysis has been widely adopted by researchers [13; 14; 15] to examine various teacher-associated aspects expressed in students' reviews or comments in discussion forums. These aspects include teaching pedagogy, behavior, knowledge, assessment, and experience, to name a few. Sentiment analysis was also used in a number of studies [16; 17] to analyze student's attitudes towards various aspects related to an institution; i.e., tuition fees, financial aid, housing, food, diversity, etc. Regarding courses, aspect-based sentiment analysis systems have been implemented to identify key aspects that play a critical role in determining the effectiveness of a course as discussed in students' reviews and then examine the attitudes and opinions of students towards these aspects. These aspects primarily include course content, course design, the technology used to deliver course content, and assessment, among others.
# Vector Control of PMSM Using TD3 Reinforcement Learning Algorithm
## 2 PMSM Model and RL  
### PMSM Model  
The essential components of a PMSM are a permanent magnet pole rotor and a three-phase winding fixed stator. A rotating magnetic field is produced when a three-phase AC power supply is applied to the stator winding [19]. The magnetic poles on the rotor interact with the revolving magnetic field to produce an electromagnetic torque that excites the rotor to rotate synchronously. The principle of PMSM is shown in Figure 1.  
\[u_{d}=R_{s}i_{d}+L_{d}\frac{d_{i_{d}}}{dt}-\omega L_{q}i_{q} \tag{1}\] \[u_{q}=R_{s}i_{q}+L_{q}\frac{d_{i_{q}}}{dt}+\omega(L_{d}i_{d}+\phi_ {f})\]  
where \(u_{d}\)--D-axis voltage; \(u_{q}\)--Q-axis voltage; \(R_{s}\)--stator resistance; \(i_{d}\)--D-axis current; \(i_{q}\)--Q-axis current; \(L_{d}\)--D-axis equivalent inductance; \(L_{q}\)--Q-axis equivalent inductance; \(\omega\)--rotor angular velocity; \(\phi_{f}\)--Number of flux linkage.  
The principle of vector control of PMSM servo system, when \(i_{d}\) is equal to 0, the D-axis voltage \(u_{d}\) is transformed into:  
\[u_{d}=-\omega L_{q}i_{q}. \tag{2}\]  
When the D-axis current reaches zero, the D-axis current no longer contributes to the torque voltage. Electromagnetic torque is generated by the current of the motor. We can change the torque of the motor by changing the Q-axis current \(i_{q}\). To map the change in force, the frequency of the current can also be adjusted to control the speed. Finally, the duration of the speed is used to control the motor speed.  
This paper uses RL controller to control the current of PMSM current loop. A typical FOC architecture was developed in Simulink, where the outer loop controller controls the speed, while the inner loop PI controller controls the D-axis and Q-axis currents (Figure 2). The RL module is created in the current loop, which replaces the current loop PI controller for this architecture.  
The whole FOC control system consists of three parts, PMSM model, inverter model, and reinforcement learning model, in which the FOC control framework has two control loops: the external loop uses PI controller to change the speed, and the internal loop uses RL agent to change the D-axis and Q-axis current.  
Figure 1: PMSM rotation schematic.  
Figure 2: RL-FOC for PMSM.
## 3 Applications of Single Molecule Measurement: Disease Diagnosis, Monitoring
### Drug Screening and Environmental Monitoring  
Single-molecule drug screening is one of the major targets due to its featuring inexpensive measurement systems and rapid evaluation of various candidate chemical substances.  
Figure 5: Virus detection by single-molecule/particle electrical detection with a nanopore device. Sample viruses are collected from liquid biopsy samples, such as nasal swabs, saliva, blood, and urine, etc. As targets, there are virus whole-particles, RNA and/or its cDNA converted from the virus RNA, and parts of the virus particles and its proteins such as spikes that constitute the virus. The presence of viruses has been detected by identifying the shape of the virus, sequencing nucleic acid base chains, detecting known target nucleic acid base sequences by complementary strand sequences, and detecting target markers such as proteins by using probe molecules that selectively bind to them.  
The detection of drugs by single-molecule detection methods has been reported [89; 90; 91; 92; 93; 94]. For example, drugs such as ibuprofen [95], doxorubicin [99], and trifuridine (FTD) [100] were detected. It is expected that further integration of these single-molecule detection technologies enables comprehensive drug screening by using the Total Analysis System (TAS), which consists of various kinds of probe molecules immobilized sensor array.  
In the application of environmental monitoring, these single-molecule measurements by nanodevices are expected to provide stable, long-term measurements of a wide variety of target molecules with low costs. The application of environmental monitoring includes the constant monitoring of pollutants in the air, aqueous solutions, and soil, such as toxic and hazardous substances. Among them, metagenomic sequencing has recently become interesting [107; 110]. Metagenomic sequencing does not target a single microorganism or bacterium, but a mixture of genomes from various microorganisms or bacteria. For example, it can be used to classify bacterial populations in clinical samples of patients without purification. In addition to such medical applications, the environmental impact of genetically modified organisms (GMOs) by the monitoring of the rate of genetic modification in plants is a potential important target. The monitoring of hazardous substances such as bacteria, viruses, and explosives are also important targets. There have been reports on the detection of harmful ions [44; 47; 48] and explosives such as TNT [112].
# Cooperative Admission Control with Network Coding in 5G Underlying D2D-Satellite Communication
## 5 Conclusions  
In this paper, we presented cooperative admission control with network coding for cellular/IoT devices supported by the D2D-satellite network. The problem formulated is divided into subproblems, and the heuristic solution is developed for each subproblem to keep the complexity of the proposed algorithm low. First, the joint admission control, which includes mode assignment, power allocation, and user selection, is formulated. The problem is MINLP, which is NP-hard. The linearization of the formulated problem is proposed, which ensure convergence. Then it is solved by GAMS branch cut, NOMAD, and branch bound solvers. ATSPSN-GAMS branch cut outperforms other techniques in terms of capacity, admitted user selection, energy efficiency, and complexity. Second, a novel network coding for multiple interfaces (MANC) has been developed for the stream and batch decodability techniques. The stream is used for instant decodability while the batch is used to lower packet completion time, where devices can wait for all linear combinations of the packets to arrive for decodability. Three sets are developed, categorizing the random linear network code for transmission from multiple interfaces, i.e., cellular, D2D, and satellite. The aim is to achieve packet completion in minimum time. For comparative study, the lower and upper bounds are derived for each technique in a lossless and lossy second stage channel. A bi-directional matching algorithm named JAMANC is introduced to find optimal mode selection and network codes utilizing the cooperative admission with MANC preference list. JAMANC is developed by coordinating the joint admission with multiple interface network coding with a supported D2D-satellite network. The simulation proves that JAMANC performs better than standalone techniques, i.e., ATSPSN and MANC, and standalone interface, i.e., cellular, D2D, and satellite. Overall, the packet completion time decreases for JAMANC-stream and JAMANC-batch with low complexity and lesser link budget cost. Our scheme proves that the cooperation between admission control based on joint channel conditions with network codes based on packets available in multiple interfaces is an effective technique to improve performance, especially at adverse channel conditions or higher packet loss.  
**Author Contributions:** [PERSON] experimented and contributed significantly to analysis and manuscript preparation; [PERSON] contributed to the conception of the study; [PERSON] performed the data analyses and wrote the manuscript; [PERSON] helped perform the analysis with constructive discussions. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The Fundamental Research Funds support this research for the Central Universities (No. YWF-20-BJ-197).  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 14: Packet Completion time versus probability loss factor.
# Effect of Corrosion Characteristics on Long-Term Aging of Austenitic 304 Steel
## 3 Results and Discussion
### Corrosion Properties
We need to evaluate quantitatively the Cr depletion, size, and distribution of carbides for further research to investigate the influence of carbide on the corrosion resistance. In addition to the Cr depletion near the grain boundary, the ferrite islands along the grain boundary decomposed into \(\sigma\)-phase. For the formation of \(\sigma\)-phase, additional Cr must be supplied from ferrite and \(\gamma\)-austenitic matrix. Therefore, in the vicinity of \(\gamma\)/\(\delta\) interface, the Cr depletion may be accelerated, causing also decrease in corrosion resistance with long-term aging. As a concluding result of the polarization test, the corrosion resistance of AISI 304 stainless steel subjected to long-term thermal aging decreases with aging time. The microstructural analysis results well support this result.
## 4 Experiment and Results
### Results
#### 4.4.3 Random Iterative Mixup Data Augmentation during Fine-Tuning  
The results in Figure 11 show that the proposed data augmentation strategy (RIM-ixUp) during fine-tuning improves the robustness of features extracted from the deep model. The robustness of feature vectors is at par in contrast to including all low-quality food images during fine-tuning. For the VireoFood-172 dataset, our data augmentation strategy improves performance by 16.81% for Gaussian noise, 44.46% for the Gaussian blur, and 48.47% for the defocus blur on low-quality test images as compared to model fine-tuned with pristine food images.  
To further understand how the inclusion of low-quality images during transfer learning improves the robustness of features, we employed Gradient Explainer an explainable AI method to visualize the regions in an image and their contribution towards the top 3 most likely classes. We set the parameter rank_output to 3. Figures 12 and 13A shows the visualization of a randomly selected image. The visualizations show that fine-tuning CNN without RIMixUp data augmentation strategy causes the same pixels or regions in the photo to activate for multiple classes especially Gaussian blur and defocus blur. It means that the quality distortions in the food photo increase overlapping amongst feature vectors extracted from CNN. However, Figures 12 and 13C shows that the same set of pixels or regions are not activated for multiple classes when CNN has employed the RIMixUp data augmentation method during fine-tuning. It reduces overlapping among the features from images of various quality. To validate this, we extract the feature vectors from the model fine-tuned by using our data augmentation strategy, with low-quality images and model fine-tuned with pristine food images. t-SNE visualization of feature vectors in Figure 14 shows that the proposed data augmentation method for food images significantly reduces the overlapping among the feature vectors. Hence, making the feature extractor invariant to quality distortions. The results are similar when we include all low-quality images during transfer learning, even though fine-tuning with low-quality images increases the training time due to an increase in the size of the training dataset.  
Figure 11: Proposed data augmentation strategy RIMixUp during fine-tuning improves the robustness of the features.  
Figure 12: Visualization of pristine and food photo with Gaussian noise shows our method activates the relevant pixels for correct class.  
Figure 13: Visualization of the food photo with Gaussian and defocus blur shows that using our strategy prevents the same set of pixels in the image to activate multiple classes.
## 3 Results
### Comparison among Results from Different Models  
#### 3.3.1 Forest Sites  
In this study, AVM, GR, TG, and VPM generally provide better estimates than MOD17/MOD_ALG in different ecosystems, and the VPM and TG models produce the highest estimating accuracy (Table 5). Higher R\({}^{2}\) values at MF for both models suggest that a combination of EVI and temperature can better capture the temporal change of GPP_EC, and the TG model produces lower RMSE and bias. Similar results have been reported by [PERSON] _et al._[11] for EBF sites: they used air temperature as a proxy of LST in the TG model and found that correlations were significantly improved. Because meteorological variables are not often available at the required temporal/spatial resolution, a TG model entirely based on remote sensing data shows great potential for estimating GPP at a regional or global scale. Meanwhile, the lower R\({}^{2}\) values for all models in EBF suggest that this linear regression relationship may not be suitable for EBF sites.  
Apart from EVI and temperature, water stress is another important environmental factor influencing the actual LUE under unfavorable conditions for ENF. AVM brings a good GPP estimation (R\({}^{2}\) = 0.67), and VPM produces a better estimation at ENF with R\({}^{2}\) of 0.68, which may be due to water stress. GR and TG models provide lower values of RMSE and bias, but R\({}^{2}\) (0.61) is obviously lower than those for AVM and VPM.