esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
This paper investigates the balance between gravity field orders and system resources in spaceborne GNSS receivers by analyzing the on-orbit data from the Gaofen-7 satellite. The results reveal that selecting the appropriate gravity field order allows for sub-meter accuracy in orbit determination despite limited system resources. Specifically, setting the gravity field order to 60 is found to be the optimal choice. This setting significantly improves orbit determination accuracy while still reserving enough system resources to support the execution of other programs, thereby enabling additional functionalities. These findings are important for spaceborne GNSS receiver platforms with limited resources and provide both practical strategies and a theoretical foundation for real-time precise orbit determination of LEO satellites.
# Combining Facial Expressions and Electroencephalography to Enhance Emotion Recognition
## 3 Results
### Comparison with Other Literature  
In the literature, based on the MAHNOB-HCI data set, the authors of [10] developed a method of mapping face action units to high-level emotional states for facial feature extraction and used SVM-RFE to select EEG features before classifying them using a Gaussian naive Bayes classifier. Following fusion, they achieved 73.0% valence detection and 68.5% arousal detection rates. The authors of [24] proposed a novel emotion recognition method using a hierarchical Bayesian network to accommodate the generality and specificity of emotions simultaneously and achieved 56.9% and 58.2% accuracy for valence and arousal, respectively. In the literature, based on the DEAP data set, the authors of [25] addressed the single-trial binary classification of emotion dimensions (arousal, valence, dominance, and liking) using EEG signals and achieved 76.9% and 68.4% accuracy for valence and arousal, respectively. The authors of [24] also tested their model on the DEAP data set and achieved 58.0% and 63.0% accuracy for valence and arousal, respectively. Our performance for both valence and arousal space surpassed that of previous results
###### Abstract  
Patient-reported outcomes (PROs) digitalization and integration into clinical practice has widened its purpose, which makes it relevant to reconceptualize PRO accordingly. Therefore, this study aims to describe and critically discuss the purposes of PRO and to suggest an interdisciplinary definition of PRO aligned with current applications. The findings in this study are based on a formerly conducted scoping review on PRO and patient participation; hence, a sub-study focusing on the purpose of PRO. The purposes of PRO pertain to research and drug testing; quality and economy; patient-centered care; politicization and democratization; and organization and culture. The suggested definition describes PRO as _a validated questionnaire; developed_ in collaboration between patients, clinicians, and other pertinent stakeholders; _systematically_ applied; _mediated_ digitally or paper-based; _completed_ directly by the patient, with assistance or by a qualified proxy; composed of generic, disease-specific, condition-specific or preference-based _measures_; consisting of _content_ pertaining to the patient's physical and mental health condition, functioning, symptoms, well-being or health-related quality of life (HRQoL); providing objective and/or subjective outcomes, and individual and/or population _data_. An alternative understanding of PRO is meant to enhance the link between purposes and definitions of PRO, facilitating interdisciplinary stakeholder discussions on PRO, potentially improving future PRO interventions.  
Patient-reported outcome measures (PROM); patient-reported outcome (PRO); quality of life; patient-centered care; definition of PRO; purpose of PRO
###### Abstract  
The propagation model is an essential component in the design and deployment of a wireless sensor network (WSN). Although much attention has been given to near-ground propagation models, few studies place the transceiver directly on the ground with the height of antennas at the level of a few centimeters, which is a more realistic deployment scenario for WSNs. We measured the Received Signal Strength Indication (RSSI) of these truly near-ground WSNs at 470 MHz under four different terrains, namely flat concrete road, flat grass and two derived scenarios, and obtained the corresponding path loss models. By comprehensive analysis of the influence of different antenna heights and terrain factors, we showed the limit of existing theoretical models and proposed a propagation model selection strategy to more accurately reflect the true characteristics of the near-ground wireless channels for WSNs. In addition, we implemented these models on Cooja simulator and showed that simplistic theoretical models would induce great inaccuracy of network connectivity estimation.  
Wireless sensor networks (WSNs); near-ground propagation model; path loss; terrain factor +
Footnote †: journal: Accepted: 18 April 2019; Published: 22 April 2019
# A Study of Gender Bias in Face Presentation Attack and Its Mitigation
## 5 Discussion  
During this research many experiments were completed in order to investigate the existence of bias in presentation attack detection methods and to explore the effect of the DB-VAE on mitigating bias in face spoofing detection. In this subsection, the results and findings obtained by those experiments are summarized and discussed.  
In summary, we concentrate on the basic ideas and facts that were inferred from our experiments. First, the results of both phases indicate that the differences between females and males in detecting face presentation attacks using the two CNN-based models are minor. In addition, contradicting to some studies such as [8] that showed that iris presentation attack detection methods are biased toward females, our results shows that CNN-based models have better performance for females than males. In all the experiments done in this research, the ratios of females' false positive predictions out of the total images were either 0% or higher than that of males. Moreover, the results in Sub Section 4.2 demonstrated that the probabilities of detecting spoof faces using VGG16 were slightly higher for females than males (See Figure 14).  
Second, some claims suggested that the unfairness of facial data-based technology is caused by the unbalanced data in the training dataset. However, the experiments in this research showed that even though our training data are unbalanced, and include only 20% to 28% of females, the models' performance is better for females than for males. Therefore, our findings refute the claim that unbalanced data in trainings data can lead to a bias in performance. We think algorithms can be bias toward some demographic groups even if the distribution of these groups is high in the training data. See Figure 4 for gender distribution in the training data.  
Third, ResNet50 experiments with transfer learning demonstrated that transfer learning has not reduced the reported gender bias. It only improved the overall performance of the model compared to VGG16.  
Fourth, the results of experiment with the DB-VAE showed that the DB-VAE is effective in mitigating the minor bias by improving the average of the probabilities of detecting spoof faces and increasing the accuracy.  
Finally, some researchers suggested that if one demographic group or gender was tested more than the other group or gender, this will normally lead to an increase in the number of false predictions of this group. After conducting many experiments using ResNet50 and VGG16 with different sizes of test datasets, we think that imbalance in test data has no strong link to bias in results.
# Enhanced Quasi Type-1 PLL-Based Multi-Functional Control of Single-Phase Dynamic Voltage Restorer
## 3 Reference Signal Generator
### Small-Signal Modeling and Tuning  
A small-signal model of the proposed PLL can be obtained by considering the signal flow in Figure 3. For this purpose, first, the small-signal model of the pre-loop filters needs to be developed. The first pre-loop filter is the delayed signal cancellation block, which is given by transfer function (11). This transfer function can be converted into synchronous reference frame by substituting \(s=s+j\omega_{n}\).  
\[\text{DSC}_{dq}(s)=\frac{1+e^{-t_{d}s}}{2}. \tag{28}\]  
The transfer function of the APF in synchronous reference frame can be obtained by applying the Park transformation to single-stage APF and is given by the following [PERSON] et al. [26].  
\[\text{APF}_{dq}(s)=\frac{s+2\omega_{n}}{2(s+\omega_{n})}. \tag{29}\]  
In our study, a two-stage APF is considered. As such, the effective transfer function is given by the following.  
\[\text{APF}_{dq}^{2}(s)=\bigg{(}\frac{s+2\omega_{n}}{2(s+\omega_{n})}\bigg{)}^{ 2}. \tag{30}\]  
Considering the pre-loop filters, the small-signal model is shown in Figure 4, where \(\gamma=k_{dc}+k_{\phi}\) is the overall phase compensation gain.  
The proposed PLL has only one parameter to tune, which is the frequency estimation gain \(k_{f}\). This gain can be tuned in several ways. Two of the popular approaches are based on open-loop phase margin and settling time. The later is considered in this study. In order to tune gain \(k_{f}\) using this method, a \(+2\) Hz frequency step is considered. Considering a 2% settling time, settling time versus the gain \(k_{f}\) is given in Figure 5. From this figure, \(k_{f}=89\) has been found to provide the fastest settling time. This value has been considered as the optimal gain for \(k_{f}\). Considering this value of \(k_{f}\),validation of the small-signal model is provided in Figure 6.  
Figure 4: Small-signal model of the proposed PLL.  
Figure 5: Settling time versus \(k_{f}\) for a \(+2\) Hz frequency step change.  
Once the phase of the grid voltage fundamental component is estimated, the reference compensation voltage can be calculated as follows:  
\[v_{c}^{\text{ref}}=V_{p}^{\text{ref}}\sin\bigl{(}\dot{\theta}_{g}\bigr{)}, \tag{31}\]  
where reference magnitude is provided by \(V_{p}^{\text{ref}}\). The design of the control signal \(u\) based on the reference grid voltage (31) is detailed in the next section.
# Robust Visual Localization with Dynamic Uncertainty Management in Omnidirectional SLAM
## 5 Results
### Robust Localization Results  
Initially, we produced localization results only focusing on the proposed robust localization, aiming at validation. Firstly, Figure 13 compares performance results between a general localization technique based on our former approach [33] ('standard') and with the proposed robust localization presented here. It is worth mentioning that the experiments are run under Dataset 1, for a 100-times execution setup, in order to get consistent average results to model the error. Poses and associated images are randomly extracted from the dataset for localization computation, so that no specific localization structure is presented rather than a sample, as shown in Figure 13.  
Figure 13a presents a comparison between the % of matched points out of the total number of feature points detected in two images (\(Y\)-left-axis), with the time consumed in the process (\(Y\)-right-axis) and with the total number of matched points (\(X\)-axis). The proposed localization technique provides a \(\sim\)30% average increase of feature points matched, with respect to the standard. This implies that larger numbers of feature points are now matched correctly, and it increases the probability of obtaining a robust localization. It also produces a considerable reduction of false positives due to the delimited search area on the image. Obviously, this comes at the cost of computation. Notwithstanding that, the time results are proven to keep a stable and bounded solution for real-time purposes, regardless of the number of matchings.  
Secondly, Figure 13b presents accuracy results in angular terms for the localization. The robust localization proposal is demonstrated to outperform the standard localization technique \(\sim\)40% on average. Note that these results show the average error on the estimated localization (\(\beta\), \(\phi\)), in Equation (15). In general, this is a desirable output, especially when dealing with the parallax effect associated with angular measurements. Then, subtle improvements of the angular localization suffice to ensure convergence on a generic visual SLAM application.  
Finally, Figure 13c presents the RMSE (Root Mean Square Error) in the final \(XY\) pose and the time consumption with the total number of feature points detected in two images. It is worth highlighting that an acceptable tradeoff solution is also fulfilled in order to assure real-time operation with a sufficient precision, considering the requirements of the final application. Figure 13d presents an example of the estimated localization structure from a sample path in Dataset 1.  
Figure 12: Main details for Dataset 3 Bovisa 2008-10-04, which is used for comparison purposes. (**a**) Layout for Part 1; (**b**) layout for Part 2.
## 3 Experimental Data Analysis
Table 1 shows the logging parameters and porosity statistical analysis. These logging parameters were obtained through logging tools, but accurate porosity values needed to be obtained through indoor experiments and coring. The main task of this article was to test the porosity prediction algorithm based on indoor porosity measurement data. Figures 2 and 3 present distribution histograms and violin plots of the data, respectively, revealing the normal distribution characteristics of the data. This distribution is crucial for regression prediction, as it lays a scientific foundation for parameter estimation and statistical inference, and ensures the reliability and effectiveness of regression analysis. Following a normal distribution of data allows us to more accurately identify the correlation between independent and dependent variables, and predict future development trends. We allocated the dataset into training and testing subsets with a ratio of 6:4, ensuring ample data for model training to capture features and trends. This method also facilitated assessing the model's predictive performance on unseen test data. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Logging Parameters** & **Max** & **Min** & **Mean** & **Median** &
\begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} & **Skewness** \\ \hline PE & 6.10 & 3.31 & 3.39 & 4.17 & 0.14 & 1.97 \\ DEN & 2.62 & 2.53 & 2.57 & 2.58 & 0.02 & 0.16 \\ AC & 62.56 & 55.06 & 57.30 & 57.26 & 1.18 & 1.27 \\ GR & 67.98 & 39.24 & 46.94 & 47.69 & 5.36 & 1.87 \\ R25 & 148.38 & 111.04 & 125.88 & 126.56 & 5.18 & 0.78 \\ CNL & 11.81 & 5.08 & 7.73 & 7.80 & 3.30 & 0.46 \\ M2R1 & 787.56 & 143.71 & 349.97 & 67.25 & 67.25 & 0.98 \\ POR & 12.97 & 2.32 & 6.30 & 6.56 & 1.71 & \(-\)0.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Well-logging parameters and porosity statistics table.
# Research on Rollover Stability and an Anti-Rollover Warning System for the Vibroseis Truck
###### Abstract
It is necessary to develop an anti-rollover warning system for vibroseis trucks to accurately judge the driving states and avoid rollover accidents. Based on the structural characteristics and working field of the vibroseis truck, this paper carried out an analysis of the influence of different factors on the rollover stability of a vibroseis truck. Treating speed as a monitoring value and in comparison with the critical steering speed, a rollover warning index and an anti-rollover warning system were established, which can achieve a multi-level judgment of different driving conditions. A simulation analysis of the anti-rollover warning system by MATLAB/Simulink software 2016b was conducted, and results showed that this system can provide an effective stability analysis and alarms for the vibroseis truck.
# Convolved Feature Vector Based Adaptive Fuzzy Filter for Image De-Noising
## 4 Proposed Methodology: Convolved Feature Vector Based Fuzzy Filter
### Noise Detection Mechanism
#### 4.1.1 Divide and Conquer Strategy  
Existing noise removal techniques use a typical style of noise detection, i.e., central pixel is judged based on its neighboring pixels and some threshold. We have introduced a new mechanism in which the central location of each kernel is changed all together by adding more neighbors to existing kernel and dividing the new (large) kernel to produce more kernels of the same size of original kernel. For example, if we have a 3 \(\times\) 3 kernel then by adding one neighbor along its each side will produce a 5 \(\times\) 5 kernel with the same center as shown in Figure 2a. Now, by diving 5 \(\times\) 5 kernel to the same size as of the original kernel will produce 9 subsets of size 3 \(\times\) 3 as shown in Figure 2b. It can be seen from the Figure 2b that the position of central pixel has changed and is no more at central location in eight kernels except one kernel lying at center. This strategy can be further extended to 7 \(\times\) 7, 9 \(\times\) 9 and 11 \(\times\) 11 based on noise ratio and noise type but we have restricted our algorithm to just 5 \(\times\) 5 to avoid computational complexity. Furthermore, in noise detection mechanism, the most critical task is to differentiate the noisy and edge pixels as they possess similar attributes. This division strategy will provide us an extra check to evaluate the noisy and edge pixel lying at central location. Kernels with no or less traces of noise are best candidates for noise estimation.
## 1 Introduction
Adjusting the temperature and light intensity can consume a considerable amount of energy, while adjusting the humidity consumes relatively little energy. Additionally, there is a solution based on water vaporization under high pressure, but this solution has a risk of infection because the vapor condenses on the leaf [18]. However, to our knowledge, there is a report studying the effects of a fluctuating VPD condition on plant growth performance [19]. The report examined the effects of VPD fluctuation on the plant growth in lettuce. There have been reports studying the relationships between the VPD condition and the growth performance of a plant. The VPD condition affects the soil moisture regime, and the soil moisture regime is linked to the leaf water potential and plant growth [11]. There are reports considering the relationships among the VPD condition, soil water availability, and growth performance [14; 20]. The growing medium holds water and nutrients and provides a zone for gaseous exchange and anchorage for plant roots. There is research on the relationships between the growing medium and growth performance [21; 22; 23; 24]. These studies examined the condition of the soil water content when polymer [21] or differently treated soils [22; 23; 24] were used as growing media. The growing medium does not affect the VPD condition, but it affects the growing conditions. As the VPD condition affects the growing conditions, the growing medium can be related to the effects of VPD regulation. Therefore, it is necessary to deeply explore the effects. In this paper, we monitored the trend of the VPD and observed the effect of VPD regulation by irrigating the water using VPD control equipment that automatically inserts fogging water when the VPD deviates from the recommended range. To observe the effectiveness of VPD regulation depending on the growing medium, tomato crops were planted in three different growing media, i.e., soil, soil above poly vinyl chloride (PVC) trays, and coconut fiber above PVC trays. Our hypotheses are that, in these conditions, VPD regulation improves the growth performance of tomato crops, and the improvements are relevant to the growing media. The first hypothesis of our study is \"H1. _VPD regulation influences crop growth performance_\", and the second hypothesis is \"H2. _The growing medium influences the effect of VPD regulation._\" The objective of this paper was to observe any significant variation in the growth parameters and yields of tomato crops grown in different growing media.
## 2 Principle of FCM Clustering Algorithm Based on Local Regression Feature
### Process of Local Regression FCM Algorithm  
The algorithm flow is shown in Figure 1.  
Local regression FCM algorithm determines the location and frequency of abnormal wind speed values, selects the cluster number by Xie-Beni index after smoothing the original data, and determines each cluster's cluster center and sample number. The local regression FCM algorithm meets the requirements of intelligent ventilation; determining the position and number of abnormal values is equivalent to choosing the position and number of abnormal wind speed values, which staff can use to check sensor faults. Smoothing the original data is equal to reducing the noise of the wind speed data set based on maintaining the data characteristics so that it can participate in the ventilation network solution; the determination of the number of clusters is equivalent to the conclusion of the number of ventilation state fluctuations; the cluster center and the sample number in the class belong to different fluctuation states.
## 4 Results and Discussion
### Results Comparison and Analysis
#### 4.2.2 Optimized Hyperparameter Settings  
In this study, 5-fold cross-validation (\(K=5\)) is combined with the grid search method to optimize the hyperparameters. In 5-fold cross-validation, 20% of the training dataset is used as a validation dataset in each iteration, while the remaining 80% of the training dataset is used to train the model. The final results of hyperparameter tuning are shown in Table 9.
###### Abstract  
To solve the problem of location service interruption that is easily caused by incomplete visual satellite environments such as occlusion, urban blocks and mountains, we propose an altimeter + inertial navigation system (INS) + giant low earth orbit (LEO) dual-satellite (LEO2) switching integrated navigation algorithm based on a similar ellipsoid model and unscented Kalman filter (UKF). In addition to effectively improving the INS error, for the INS + LEO dual-satellite switching algorithm without altimeter assistance, our algorithm can also significantly suppress the problem of excessive navigation and positioning error caused by this algorithm in a long switching time, it does not require frequent switching of LEO satellites, and can ensure navigation and positioning functions without affecting LEO satellite communication services. In addition, the vertical dilution of precision (VDOP) value can be improved through the clock error elimination scheme, so, the vertical accuracy can be improved to a certain extent. For different altimeter deviations, we provide simulation experiments under different altimeter deviations; it can be found that after deducting the fixed height deviation, the algorithm can also achieve good accuracy. Compared with other typical algorithms, our proposed algorithm has higher accuracy, lower cost and stronger real-time performance, and is suitable for navigation and positioning scenarios in harsh environments.  
LEO; altimeter; double star; similar ellipsoid; integrated navigation; switching navigation and positioning +
Footnote †: journal: Remote Sensing, 2021, 13, 4099.  
[[https://doi.org/10.3390/rs13204099](https://doi.org/10.3390/rs13204099)]([https://doi.org/10.3390/rs13204099](https://doi.org/10.3390/rs13204099))