esa-sceva/satcom-chunk-collection huggingface dataset:
# Correlations between SSQ Scores and ECG Data during Virtual Reality Walking by Display Type
## 1 Introduction
This confirms that the acclimation time is important when designing VR-based training. In the case of [PERSON] et al. (2020), 10 [PERSON] patients in their 60s performed treadmill walking with track walking/ treadmill walking/VR, and we tried to compare the walking variables at that time [13]. As a result, VR walking has no difference in stride time variability (i.e., coefficient of variation, CV) or fractal dynamics (i.e., \(\alpha\) value of detailed function analysis, DFA) under different walking conditions, but the step length and gait cadence were improved compared to treadmill walking. These studies show that VR can be used to implement gait rehabilitation. When VR is used to perform rehabilitation, side effects such as cybersickness are considered a major limitation. To use VR in various fields in addition to rehabilitation, there is a need for research on cybersickness during VR use that is accompanied by movement, namely, walking. Many studies have been conducted on cybersickness that occurs when viewing virtual environments; however, research on cybersickness that occurs when viewing virtual environments accompanied by regular movements is lacking. Therefore, this study aimed to use a subjective evaluation (SSQ) and an objective evaluation (ECG) to determine the cybersickness that occurs when subjects are presented with a virtual straight path on two types of displays (screen and HMD) and walk at a constant speed on a treadmill. In addition, this study analyzed the difference in HR and HRV (root mean square of the successive differences (RMSSD), LF/HF ratio) between the motion-sickness group and no-motion-sickness group, as classified using the SSQ, and analyzed the correlation between the SSQ score that determined the presence of motion sickness and HR and HRV to find reliable variables that allow motion sickness to be evaluated via ECG.
# Reviewing CAM-Based Deep Explainable Methods in Healthcare
## 4 Results and Discussion of Selected Articles
### Analysis of Dataset Size  
The size of the dataset has a significant and far-reaching impact on research, affecting several key aspects including credibility, interpretability, generalization ability, and computational resource requirements. Through in-depth analysis of the dataset sizes used in each article, we can gain profound insights into the data scale adopted in current medical field research on activation mapping methods. In Figure 3 below, the sizes of the datasets used in each article are presented, ranging from a minimum of 68 samples to a maximum of 107,038 samples.  
Figure 4 displays the results of a categorized analysis of the dataset sizes in 45 articles related to the application of activation mapping in the medical field.  
From Figure 4, it is evident that there are a total of 22 articles using datasets larger than 5000 and they are concentrated in recent years. As shown in Figure 5, a total of 18 articles were published primarily in 2022 and 2023. Over time, the scale of medical datasets available to researchers is increasing, and this development trend holds significant importance for the application of activation mapping methods in the medical field.  
Figure 2: Annual publication count.  
Specifically, the size of the dataset has the following four important impacts:  
(1) The analysis of dataset size reflects the data resource situation in the medical field when applying activation mapping methods.  
(2) In research, the choice of dataset size can directly influence the credibility of research results. Larger datasets typically offer more robust results while capturing the data distribution better, thereby enhancing research credibility.  
(3) Additionally, dataset size is related to interpretability since larger datasets may contain more rich features and patterns, aiding in a deeper understanding of model behavior.  
(4) Furthermore, dataset size is crucial for model generalization. Larger datasets can typically support more complex models that can generalize better to various clinical scenarios, thereby improving model efficacy in practical applications.  
Despite the advantages of larger datasets mentioned above, they also require more computational resources and time to process. Balancing the benefits and complexities of large datasets is, therefore, an important consideration for researchers and practitioners.
## 4 Optimization and Analysis of Multi-Blade Centrifugal Fan  
### Reliability Verification of the Numerical Simulation  
In order to eliminate the impact of the number and quality of meshes on the calculation results and minimize the number of meshes to ensure efficiency, mesh independence verification needs to be carried out first. The total pressure and efficiency are selected as verification indicators, and eight sets of calculation examples are calculated (Figure 7). Taking into account the accuracy and speed of the solution, a \(5.99\times 10^{6}\) mesh scheme is selected to carry out the subsequent simulations (Figure 8).  
Figure 6: Optimization process of prototype fan.  
Figure 7: Result of the mesh independence verification.  
To verify the reliability of the simulation model, the total pressure and efficiency under different operating conditions are calculated and compared with experimental data, as shown in Figure 9.  
Comparative analysis between numerical simulations and experimental measurements (Figure 9) shows that the maximum relative errors of full pressure and efficiency are 2.1% and 9.6%, respectively, which all appear under Q = 100 m\({}^{3}\)/h (not the typical working interval of the fan), while in the typical operating flow interval of the fan, good consistency is shown between the simulation results and the experimental data, which indicates that the simulation model has good prediction accuracy.  
The noise experiment of the fan is conducted in a semi-anechoic room (Figure 10a). The background noise sound pressure level is 15 dB, which is far lower than the centrifugal fan noise. The microphone is located at the fan outlet and is offset by 45\({}^{\circ}\) (Figure 10b) to reduce the impact of outlet airflow and motor noise on the experimental results. Aerodynamic noise calculations and experiments in this paper are conducted at a flow rate of 500 m\({}^{3}\)/h, and the noise spectrum obtained is shown in Figure 11.  
By comparing the experimental and simulation results of fan noise, it can be seen that there are some differences between the two. The reason for the error is that the noise generated by the mechanical part of the fan and the electromagnetic noise generated by the motor during operation are not considered in the simulation. Additionally, the original structure is simplified somewhat during the simulation. Under rated operating conditions, the simulated noise at the measuring point is 57.75 dB, while the experimental is 58.85 dB, with an error of 1.1 dB.  
Figure 8: Mesh of the fan.  
Figure 9: Comparison of total pressure and efficiency results. (**a**) Total pressure. (**b**) Efficiency.  
Overall, the consistency between simulation and experiment is in good coincidence, indicating the rationality of model simplification, mesh partitioning, and simulation parameter settings during simulation, which can be used for subsequent calculations and optimization.
## 4 The Proposed Framework
### Decision Rule  
MobileNetV2 and ShuffleNet showed excellent performance in classifying medical images or thermal images [42; 44]. In addition, both of the pre-trained models were developed based on the same fundamental architecture, namely, MobileNetV1. MobileNetV2 proposes inverted residuals and linear bottlenecks to improve MobileNetV1, while ShuffleNet proposes channel shuffling and group convolution to perform the task. ShuffleNet and MobileNetV2 were selected as the baseline classifiers based on the performance of the sensitivity and specificity rate of the mode itself prior implementing the fusion strategy. As neither of the models achieved a 100% accuracy rate, we performed the fusion strategy at the decision stage. In addition, ShuffleNet and MobileNetv2 have shown a reliable performance in classifying the medical images [20; 44; 42]. Other pre-trained benchmark models also showed very promising performances, but ShuffleNet and MobileNetv2 has less learning parameters and smaller model sizes.  
Figure 10: The proposed framework.  
In the present study, it was found that transferring the pre-trained model weight from ImageNet to the DFU dataset did not improve the classification performance significantly. Using the single-baseline CNN models did not achieve a 100% accuracy rate; therefore, to enhance the performance of CNN models in classifying DFUs, we fused the results of MobileNetV2 and ShuffleNet. ShuffleNet had the ability to classify the negative DFU image precisely (100% accuracy) but not for positive DFU images. On the other hand, MobileNetv2 had the ability to classify the positive DFU image precisely (100% accuracy) but not for negative DFU images. The proposed decision fusion is formulated as follows:  
\[R(x)=\begin{cases}S(x),&\text{ if }S(x)=\text{POSITIVE}.\\ M(x),&\text{ otherwise}.\end{cases} \tag{3}\]  
where \(R(x)\) is the decision fusion result of positive or negative patients; \(S(x)\) is the classification results using ShuffleNet; \(M(x)\) is the classification result using MobileNetV2. Decision fusion determines which result will be chosen as the output. Based on Equation (3), if a patient is found to have a positive result by ShuffleNet, the patient will be considered as a positive patient. In contrast, if a patient is found to have a positive result by MobileNetV2, the patient will be considered as a negative patient.  
```
input :A thermal DFU image output :\(M(x)\) is the label result of the MobileNetV2 model; \(S(x)\) is the label result of the ShuffleNet model; \(R(x)\) is the label result of decision fusion; The input image is classified using MobileNetV2 and ShuffleNet; if\(S(x)==\text{POSITIVE}\)then \(R(x)=S(x)\); else \(R(x)=M(x)\) ;  end if
```  
**Algorithm 1**Decision Fusion Schema  
Figure 11: The transfer learning concept using a pre-trained model.
# Service Life Prediction and Life Cycle Costs of Light Weight Partitions
## 3 Methodology
### Typical Deterioration Patterns  
The development of deterioration pattern curves enables statistical analysis to explore different deterioration pathways. Graphically recording partition survey data over time showcases the real-time component performance (CP) and allows these curves to be formulated for each partition type. The curves evaluate partition deterioration under standard conditions and in response to specific failure mechanisms. This methodology thoroughly assesses partition durability and performance across various service conditions.  
Through statistical analysis based on the general linear model, it is possible to determine, for deterioration pattern curves that allow it, a typical deterioration path (TDP) that graphically and quantitatively expresses the expected average decline in component performance over time. Additionally, it is possible to establish a prediction interval that will contain, with a given statistical probability, future observations of similar components at different stages of wear. The results of this analysis will make it possible to predict the continued deterioration pattern of partitions according to the wear conditions they are exposed to, and estimate the period when their performance level will fall below the required minimal level.  
In cases where a specific failure mechanism influences the deterioration pattern, the reduction in component performance may not follow a defined path, and instead display a dispersed array of points over time. This irregular pattern often does not arise from the component's age but rather from inherited flaws rooted in defective construction or design. The impact of the failure factor varies in severity, is closely tied to the quality of construction, and can be quantified by referring to the average decrease in performance itcauses. By incorporating this failure influence into the component's inherent deterioration pattern, we can delineate a typical deterioration pathway and predict service life in such failure conditions.
# Human Factors Analysis of the Improved FRAM Method for Take-Off Quality Lateral Shift
## 4 Take-Off Model Construction  
The evaluation of aircraft take-off quality examines the following main points: lateral offset, airspeed above the ground, climb rate, pitch angle, flap position, etc. [20; 21; 22]. In this paper, the lateral offset, an important take-off quality measure, is analyzed. Take the common three-point aircraft take-off taxiing as an example: During the take-off process, once the tower provides the take-off command to the pilot, the pilot brakes and pushes the throtttle to increase the engine speed. The aircraft take-off phase is shown in Figure 4. When the speed reaches a certain value, the pilot releases the brake so that the aircraft gains longitudinal acceleration, putting the aircraft into a three-point taxiing acceleration phase. When the aircraft taxiing speed reaches \(V_{R}\) (speed to lift the front wheel), the pilot operates the aircraft to lift the front wheel in a two-point taxiing phase. When the aircraft speed is over \(V_{\mathit{lof}}\) (ground speed), the aircraft, due to \(L\) (lift), overcomes its own \(G\) (gravity) and undergoes vertical acceleration into the climb phase. The take-off process is completed when the aircraft climbs to a safe altitude and retracts the landing gear. Therefore, the aircraft take-off can be roughly divided into two stages, ground taxiing and air climbing, as shown below.  
Based on the principle of dynamics, the calculation model of the aircraft taxiing distance is [23]:  
\[S=\frac{1}{g}\int_{0}^{1.15 Vs}\frac{V\mathrm{d}V}{\frac{P\cos\phi_{P}}{G}-f- \frac{\rho V^{2}A}{2G}(c_{x}-fc_{y})}\]  
\(S\) is the take-off taxiing distance, \(P\) is the thrust of the engine, \(V_{S}\) is the stall speed, \(V\) is the taxiing speed of the aircraft, \(\phi_{P}\) is the angle between the thrust line of the engine and the baseline of the angle of attack, \(C_{x}\) is the drag coefficient, \(C_{y}\) is the lift coefficient, \(G\) is the weight of the aircraft, \(\rho\) is the atmospheric density, \(A\) is the wing reference area, \(g\) is the gravitational acceleration, and \(f\) is the sliding friction coefficient. Consider the effect of wind direction and speed on take-off taxiing:  
\[\left\{\begin{array}{c}V_{x}=V_{T}+V_{W_{x}}\\ V_{y}=V_{W_{y}}\end{array}\right.\]  
\(V\) is the taxiing speed, \(V_{T}\) is the airspeed, \(V_{W}\) is the wind speed, and \(x\) and \(y\) represent the longitudinal axis direction and transverse axis direction, respectively.  
Thus, the offset of the aircraft on the \(y\)-axis can be expressed as:  
\[y_{s}=V_{y}\cdot t\]  
Figure 4: Schematic diagram of the take-off stage.  
According to the structure of the above function modules, each function module in the take-off phase can be connected to the others to form a functional network, As shown in Figure 6, where the solid lines represent the relationships between the pilot's direct operation modules, and the dashed lines represent the relationships between the impact factor modules.
# Types and Performances of Polymer Gels for Oil-Gas Drilling and Production: A Review
## 2 Types of Polymer Gels Used for Oil-Gas Drilling and Production
### In Situ Polymer Gels Crosslinked
In situ crosslinked polymer gels have the following advantages: (a) in situ crosslinked polymer gels used for formation plugging can generate a high-strength bulk gel in in situ pores and fractures to achieve high-strength plugging; (b) in situ crosslinked polymer gels used for oil-gas profile control and displacement have a continuous phase with suitable accessibility to formation pores and fractures of different sizes; thus, they can displace oil and gas in pores and fractures in a wide range with high sweep and displacement efficiencies; (c) in situ crosslinked polymer gels have a simple preparation and injection process but still have the following problems: (i) during the injection process, the polymer gel solution will be affected by the shearing effect of the pores in the formation, and the macromolecular chains break easily, leading to a significant reduction in the solution viscosity, which affects gelation; (ii) due to multiple components, the gel solution will exhibit chromatographic separation between the components and formation water dilution  
Figure 3: Preparation and mechanism of physically crosslinked GP-A gel as a lost circulation control agent (**a**) Schematic diagram of gel plugging in fracture; (**b**) schematic of the interaction between the GP-A gel and crosslinking agent; (**c**) gelation time of the GP-A gel under the organic chromium crosslinker [33]. (Reprinted/adapted with permission from Ref. [33]. 2021, [PERSON], et al.). and viscosity reduction during the injection and formation migration processes, resulting in a non-uniform distribution of component concentrations, which will affect the gelation effect. In addition, (iii) compared with gels after gelation, the gel solutions before gelation are more susceptible to the influence of various factors such as high temperature and high salinity, resulting in a reduction in gel strength, which makes it challenging to achieve high-strength plugging of the formation pores and fractures. Therefore, in situ crosslinked polymer gels must be studied in depth for increasingly complex and harsh formation environments to assess their resistance to temperature, salinity, shear, and dilution.
# R&D of an Innovative OFDM Communication Payload for Small-Size AUV
###### Abstract  
With its superiorities of low cost, high flexibility and deployment convenience, small-size autonomous underwater vehicles (AUVs) have been extensively applied to perform a variety of undersea missions. While underwater acoustic (UWA) communication provides a practical way to establish a wireless link, it still poses a significant challenge due to the strict limitations of a small-size AUV platform in terms of load capacity, energy supply and cost. Orthogonal frequency division multiplexing (OFDM) has drawn extensive attention due to its high data rate capability and relative robustness to multipath, the performance of which is unfortunately sensitive to the widespread Doppler effect. While efficient Doppler compensation is significantly crucial for UWA OFDM mobile communication, most of the conventional approaches are conducted using software resampling, thus rendering a huge burden on memory and calculation capability as well as a considerable processing delay. In this paper, from the perspective of hardware completion an UWA OFDM communication payload based on STM32F407 processor is designed and implemented to facilitate agile Doppler compensation with low computational overhead. In particular, after estimating Doppler by calculating the time compression or extension of the preamble signal, Doppler compensation is performed by directly adjusting the direct digital frequency synthesizer (DDS)-driven sampling rate of the analog-to-digital converter (ADC). As the Doppler is compensated parallel to the ADC acquisition, processing delay and memory requirement can be avoided. Finally, hardware-in-loop (HIP) simulation is performed to demonstrate the effectiveness and superiority of the proposed system. The results show that the designed system has the potential to achieve an effective communication rate of 3.19 kbps with the admissible implementation overhead. Future work will entail the integration and performance evaluation of the proposed UWA OFDM communication payload on a practical small-size AUV platform.  
Doppler compensation; orthogonal frequency division multiplexing (OFDM); direct digital frequency synthesizers (DDS); underwater acoustic (UWA) communication +
Footnote †: journal: Marine Science and Engineering  
0000-0002-2301-11029-22320][PERSON]  
0000-0002-1105-2232][PERSON]  
0000-0002-1105-2232][PERSON]  
0000-0002-1105-22320][PERSON]  
0000-0002-1105-22320][PERSON]
## 3 Experiment
### Compare Models and Experimental Design Details  
In this section, the performance of GLFFNet on the Potsdam and GID datasets is compared with that of six representative semantic segmentation models in recent years to prove the superiority of GLFFNet proposed in this paper. The networks selected are as follows: DeepLabv3+ [8] that is a classical semantic segmentation network using deeply separable convolution, CAE(ViT-L, UperNet) [31] that uses a new mask image modeling and context auto encoder, ABCNet [32] that uses Bezier curves to make a new concise parametric representation of the curved scene text to accurately locate the oriented and curved scene text in the image, BANet [33] that proposed a new bilateral structure consisting of a convolutional layer and Transformer block with a view to capturing both local texture information and full-length dependency information in the feature network, SegFormer [34] that used a novel positional-encoding-free and hierarchical Transformer encoder, and UNetFormer [19] that uses a Transformer-based decoder for real-time urban scene segmentation. In order to ensure fairness, this paper chooses the configuration of each network as closely as possible.  
In our experiments, we fully train all models on the same dataset as described above and ensure the fairness of the experiments by setting up the same training environment. The three most important parameters in the training process are batch size, learning rate, and number of training iterations. Since some models are so large that the training process requires a lot of GPU memory, we uniformly set the batch size to 8. Then we use \(1\times 10^{-5}\) as the initial learning rate to start the training, and the learning rate decays with the training process. Finally, after completing the training of 100 K iterations, the model with the best results is selected for testing, and the model's performance is evaluated using accuracy and inference speed as criteria, respectively. Beyond the initial, we use Adam [35] as the optimization strategy during the training process. All code is based on the MMSegmentation implementation, and all training is done on NVIDIA RTX2080 TI GPUs.
## 1 Introduction
This paper is organized as follows. Section 2 provides a brief review of relevant LEO orbital design studies. Section 3 describes the methods used in the LEO-LEO occultation event simulation and the constellation performance assessment. The design and results of the numerical experiment are described and analyzed in Section 4. The results and their application significance are discussed in Section 5, and some conclusions are drawn in Section 6.