esa-sceva/satcom-chunk-collection huggingface dataset:
# Merging-Squeeze-Excitation Feature Fusion for Human Activity Recognition Using Wearable Sensors
## 7 Conclusions and Future Work  
In this work, we proposed a feature fusion method called the merging-squeeze-excitation (MSE) feature fusion for wearable-sensor-based HAR using multibranch architectures. The MSE feature fusion will calibrate the feature maps during the fusion. Each feature map will be emphasized or suppressed according to its importance measured from both itself and the corresponding pre-merged feature map. In addition, we presented the following four extensions of the MSE feature fusion: the MSE feature fusion with local skip connections, the MSE feature fusion with a global skip connection, the MSE feature fusion with global channel attention, and deep MSE feature fusion. LeNet5, AlexNet, and VGG16 were applied as feature extractors. The addition, maximum, minimum, and average merging were used in the pre-merging and post-merging steps. According to the experimental results, the MSE feature fusion with a global skip connection (using the average merging and AlexNet), the MSE feature fusion with local skip connections (using the minimum merging and AlexNet), and the original MSE feature fusion (using the average merging and AlexNet) achieve the highest accuracy scores of 99.24%, 98.59%, and 98.04% on the PAMAP2, DaLiAc, and DSAD datasets, respectively. For future work, in addition to the channel-attention mechanism, other attention techniques such as spatial attention, modal attention, convolutional block attention, and selective kernel convolution can be applied to feature fusion in order to combine feature maps from different branches effectively.  
This research was funded by the SIIT Young Researcher Grant, under a contract No. SIIT 2019-YRG-SL04, the Sirindhorn International Institute of Technology, Thammasat University, Thailand.  
Not applicable.  
The datasets used to support the findings of this work are available in [36; 37; 38].  
The authors declare no conflict of interest.
# Using Virtual Soundwalk Approach for Assessing Sound Art Soundscape Interventions in Public Spaces
## 3 Results
### Results of the Qualitative Data Analysis  
Ranked data was used in the statistical analysis, rather than raw results obtained from the SSQP. The results of the tests made to investigate the presence of certain types of sounds are shown in Table 3 for all three locations. The results of the tests made to investigate the change in the perception of sound environments are shown in Table 4, again for all three locations. The tables show mean ranks for each variable on location \"1\" and \"2\", and the estimated true difference of these mean ranks, as well as the lower and the upper bound of the 95%-confidence interval of that estimation. Finally, the obtained \(p\)-value is shown in the right-most column. The difference is significant at the 0.05 confidence level if the confidence interval does not contain 0. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Mean Value**} \\ \hline
**Location** & **Soundscape Descriptor** & **1** & **2** & **Difference** & \multicolumn{1}{c}{**\(p\)-Value**} \\ \hline  & Pleasant & 33.03 & 45.97 & \(-\)21.83 & \(-\)12.95 & \(-\)4.07 & **0.0043** \\  & Calm & 36.35 & 42.65 & \(-\)16.08 & \(-\)6.31 & 3.47 & 0.2059 \\  & Uneventful & 41.40 & 37.60 & \(-\)5.74 & 3.79 & 13.33 & 0.4355 \\  & Monotonous & 38.00 & 41.00 & \(-\)12.71 & \(-\)3.00 & 6.71 & 0.5447 \\  & Unpleasant & 41.32 & 37.68 & \(-\)5.84 & 3.64 & 13.12 & 0.4515 \\  & Chaotic & 39.73 & 39.27 & \(-\)9.12 & 0.46 & 10.05 & 0.9248 \\  & Evertful & 39.36 & 37.68 & \(-\)7.73 & 1.69 & 11.10 & 0.7256 \\  & Exciting & 36.24 & 42.76 & \(-\)16.20 & \(-\)6.51 & 3.18 & 0.1877 \\  & Pleasant & 42.61 & 38.39 & \(-\)5.51 & 4.22 & 13.96 & 0.3950 \\  & Calm & 47.01 & 33.99 & 3.60 & 13.03 & 22.45 & **0.0068** \\  & Uneventful & 40.49 & 40.51 & \(-\)9.95 & \(-\)0.03 & 9.90 & 0.9961 \\  & Monotonous & 39.65 & 41.35 & \(-\)11.55 & \(-\)1.70 & 8.15 & 0.7352 \\  & Unpleasant & 35.25 & 45.75 & \(-\)20.09 & \(-\)10.50 & \(-\)0.91 & **0.0320** \\  & Chaotic & 36.31 & 44.69 & \(-\)17.98 & \(-\)8.38 & 1.23 & 0.0875 \\  & Evertful & 39.29 & 41.71 & \(-\)12.31 & \(-\)2.43 & 7.46 & 0.6308 \\  & Exciting & 42.26 & 38.74 & \(-\)6.33 & 3.53 & 13.38 & 0.4833 \\  & Pleasant & 44.85 & 36.15 & \(-\)0.75 & 8.70 & 18.15 & 0.0712 \\  & Calm & 48.06 & 32.94 & 5.37 & 15.13 & 24.88 & **0.0024** \\  & Uneventful & 43.48 & 37.53 & \(-\)3.78 & 5.95 & 15.68 & 0.2308 \\  & Monotonous & 45.05 & 35.95 & \(-\)0.73 & 9.10 & 18.93 & 0.0696 \\  & Unpleasant & 38.68 & 42.33 & \(-\)13.25 & \(-\)3.65 & 5.95 & 0.4560 \\  & Chaotic & 37.18 & 43.83 & \(-\)16.47 & \(-\)6.65 & 3.17 & 0.1845 \\  & Eventful & 38.28 & 42.73 & \(-\)14.30 & \(-\)4.45 & 5.40 & 0.3759 \\  & Exciting & 30.67 & 48.33 & 27.40 & \(-\)17.67 & \(-\)7.93 & **0.0004** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Statistical significance in change of the perceived dominance in sound source types with (2) and without (1) the designed sound source introduced
# Airfoil Optimization Design Based on the Pivot Element Weighting Iterative Method
## 3 Aerodynamic Optimization Design of Airfoil
### Radial Basis Functions Neural Network Model  
The Kriging [23], RSM [24], and RBF [25] methods are widely used in the surrogate model. The disadvantage of the Kriging is that model construction can be very time-consuming and complex [26]. The RSM is not suitable to create a global metamodel for a highly nonlinear response [27]. The RBF is suitable for the highly nonlinear problems and these problems can be modelled with limited number of samples [27]. And when BPO is 14, the least samples of the quadratic RSM and the Kriging with the quadratic regression model are 496. It is a huge computation for the airfoil optimization.  
Figure 16: Range of the S1223 airfoil at \(k_{\text{lupper}}=0.04\) and \(k_{\text{2 lower}}=0.04\).  
The neural network consists of three layers: the input layer, the hidden layer and the output layer, which has the ability to approximate the complex nonlinear function. So the RBF neural network model [5] is used to establish the approximation model of the airfoil aerodynamic data.  
Firstly, the RBF neural network is trained by the sample data of DOE. The approximation relationship between aerodynamic coefficients and design parameters can be obtained. Secondly, it is necessary to evaluate the prediction precision, in order to ensure the adaptability of the approximation model. There are two indicators to assess the precision of the approximation model and they are the root mean square error (RMSE) and correlation coefficient (\(R^{2}\)). The closer the RMSE is to 0, the higher the precision of the approximation model is. Generally, the range of \(R^{2}\) is from 0 to 1. The closer \(R^{2}\) is to 1, the better the fitting precision is. The value of \(R^{2}\) is larger than 0.9 [28]. In this paper, the thresholds of the RMSE and \(R^{2}\) are set as 0.03 and 0.98. The number of initial sample data is 100 and 50 random samples are used to analyze the errors of the approximation model. If the RMSE and \(R^{2}\) cannot reach the requirements of thresholds, 100 new samples are added into the initial samples until the RMSE and \(R^{2}\) satisfy the setting values. When the number of the samples is increase to 300, the RMSE and \(R^{2}\) of the lift coefficients are 0.00485 and 0.99955. And the RMSE and \(R^{2}\) of the drag coefficients are 0.03 and 0.98619.  
In the paper the experimental data of the Opt LHD are 300 samples for the S1223 airfoil. The approximation model based on the RBF neural network is established to calculate aerodynamic coefficients of the S1223 airfoil. 50 samples are used to analyze the errors of the approximation model.
# Antibacterialarial Agents Used in Modifications of Dental Resin Composites: A Systematic Review
## 4 Discussion
### Limitation of the Review and Future Insights  
The collected studies show that there is a large group of different modifiers showing antibacterial properties. The antibiocidal character of materials are tested against different, not standardized microorganisms like _Streptococcus mutans_, _Staphylococcus aureus_, _Porphyromonas gingivalis_, _Lactobacillus acidophilus_, _Enterococcus faecalis_, _Staphylococcus aureus_, _Streptococcus sanguinia_, _Streptococcus mitis_, _Escherichia coli_, and _Helicobacter pylori_. Therefore, it is worth proposing at this point to limit pathogen testing to the use of cultures that participate to the greatest extent in the development of caries, i.e., _Streptococcus mutans_ and _Candida albicans_, as well as to establish specific, standardized/unified test procedures.  
Comprehensive analysis to determine whether a modifier can be used in a resin-based dental composite examining at least the basic mechanical (hardness, flexural strength, and modulus) and chemical properties (water sorption) and their long-term stability is crucial. Unfortunately, in some of the cited studies, the methodology was not enriched with any basic strength tests or tests related to the biocompatibility and cytotoxicity of the modifications used, which, on the one hand, indicates potential opportunities for development, but, on the other hand, makes it difficult to select the most promising antibacterial substances. Additionally, some of the cited works took into account only the composition of pure resins, so there is a concern about whether these modifications will also be effective in filled materials, although only such materials are used in dentistry due to the required functional properties.
# Design of Tracking, Telemetry, Command (TT&C) and Data Transmission Integrated Signal in TDD Mode
## 5 Discussion
### Ranging and Doppler Measurement Performance for Integrated Signals  
When the simulation parameters are \(T_{sp}=10\) s, \(\sigma_{\alpha c}=10\) m and \(\sigma_{Doppler}=780\) Hz, compared with the pure BOC signal, the measurement accuracy is reduced mainly due to the interference of the APSK's subpeak, which will reduce the CNR of the TT&C signal. The degree of the decrease in the signal is the same as that during the acquisition process. The measurement results are shown in Figure 7.  
Figure 7: The ranging and doppler measurement performance of an integrated signal and pure signal: (**a**) Doppler frequency measurement results of integrated signal and pure BOCcos signal; (**b**) Ranging results of integrated signal and pure BOCcos signal.
# A Spatio-Temporal Schedule-Based Neural Network for Urban Taxi Waiting Time Prediction
## 4 Results and Discussion
### Experiment Analysis and Discussion
#### 4.2.3 Effect of Weather Features on the Predictive Models  
According to the provisions of Section 3.1, this paper divides the weather into 10 types. Under different weather conditions, the minimum MAE value of waiting time for taxi passengers predicted by this system is 24.05 and the maximum is 63.5. Moreover, the MAE value of predicted passenger waiting times for different weather is shown in Figure 10 below.  
Figure 9: Prediction effect of different functional areas.  
It appears that the more deeply the weather affects people's lives, the greater the impact on the waiting time of taxis. There may be three reasons for this pattern. First of all, weather that causes less impact, such as sunny and cloudy days, will not bring major changes to the flow of taxis. Secondly, terrible weather is likely to increase taxi waiting times, so it is easier to predict with larger errors which cause the larger MAE value. In addition, extreme weather has less data, so its influence rules are difficult to learn by neural networks.
## 3 Results and Discussion
### Numerical Relationship of Plasma's Influence on Electromagnetic Waves  
In the interaction between plasma and electromagnetic waves, the WKB method can be used for numerical simulation. The WKB method is an important tool for solving problems in mathematical physics, suitable for obtaining approximate solutions of linear differential equations with spatially varying coefficients.  
During the propagation and interaction of electromagnetic waves in plasma, the characteristics of the plasma significantly affect the electromagnetic waves, including refraction, absorption, and other effects, such as Faraday rotation caused by magnetized plasma. From the perspective of wave absorption, in plasma, due to the constant movement and collisions of its particles (such as electrons and ions), the propagation of electromagnetic waves undergoes two main stages: firstly, upon encountering the plasma, refraction occurs, altering their path; secondly, the electromagnetic waves are absorbed by the plasma, resulting in a gradual decrease in energy.  
The absorption of electromagnetic waves by plasma is mainly achieved through two basic mechanisms: anomalous absorption and normal absorption. Anomalous absorption occurs during the interaction between electromagnetic waves and plasma, causing collective oscillations of electrons and ions in the plasma, thereby reducing the energy of the electromagnetic waves. Normal absorption occurs when electrons absorb energy under the influence of the electromagnetic field, converting the energy of the electromagnetic waves into their kinetic energy, and transferring it through collisions with neutral particles or ions. Normal absorption is the primary pathway for the attenuation of electromagnetic wave energy and is a key point in the study of plasma stealth technology.  
For non-magnetized plasma, the dielectric constant is given by:  
\[\varepsilon_{p}=\varepsilon_{0}\varepsilon_{r}=\varepsilon_{0}(1-\frac{ \omega_{p}^{2}}{\omega^{2}+v^{2}}-j\frac{v}{\omega}\frac{\omega_{p}^{2}}{ \omega^{2}+v^{2}}) \tag{14}\]  
Further, according to the wave propagation equation of electromagnetic waves:  
\[\
abla^{2}\overset{\rightharpoonup}{E_{r}}+k_{r}^{2}\overset{\rightharpoonup}{E_{r}}=0 \tag{15}\]  
For the propagation of electromagnetic waves in an isotropic plasma, the WKB method can be used to obtain the solution as follows:  
\[\overset{\rightharpoonup}{E_{z}}=e^{-j\int_{0}^{z}k(z^{\prime})dz^{\prime}} \overset{\rightharpoonup}{E_{0}} \tag{16}\]  
If the electromagnetic wave is incident from \(z=0\) and reflects at \(z_{0}\) (where the energy is \(P_{0}\)) and is absorbed again, the energy of the electromagnetic wave when it exits the plasma is:  
\[P(z_{0})=e^{-4 Im(\int_{0}^{z}k(z)dz)}P_{0} \tag{17}\]  
Thus, the expression for the two-way energy attenuation of electromagnetic waves propagating in plasma can be derived as:  
\[Att=\left|10 lg\frac{P_{z_{0}}}{P_{0}}\right|=\left|17.37 Im(\int_{0}^{z}k(z)dz )\right| \tag{18}\]
## 2 Global Sensitivity Analysis Method  
Global sensitivity analysis, also known as importance measure analysis, quantifies the impact of input uncertainty on output uncertainty from a global perspective. Consequently, it is widely applied in engineering design and reliability assessments [16]. Among these methods, the variance-based global sensitivity analysis method proposed by [PERSON] [17] is currently the most widely used approach. This method evaluates the influence of input variables on output responses by quantifying their contribution to the variance of the output relative to the total input variance [18].
# Novel Real-Time Compensation Method for Machine Tool's Ball Screw Thermal Error
## 6 Conclusions  
In this paper, a real-time thermal error compensation method of a ball screw feed system is proposed. A new iterative prediction method of the machine tool's screw thermal error based on a finite difference equation is developed. Compared with the existing method of screw thermal error prediction, the proposed method in this paper has a better calculation efficiency while ensuring accuracy. The real-time compensation function of the screw thermal error based on the above method is integrated into the CNC system.  
A simple measurement verification experiment shows that the compensation function can reduce the screw thermal error of the feed axis to within 10 \(\upmu\)m. A comparative analysis of a test piece, machined with and without compensation, resulted in a reduction of the maximum machining error from 71 \(\upmu\)m to 13 \(\upmu\)m after compensation, which verifies the efficacy of the proposed screw thermal error prediction method and compensation function. The proposed model overcomes some of the challenges of traditional error compensation models, including high computational expense and difficult parameter identification.  
Currently, our work is based on the no-load or light load of the machine tool. The future research will focus on the influence of complex load on the thermal error of the lead screw and consider the research through the electronic control data in the processing process.  
Conceptualization, H.Z. and Y.H.; methodology, H.Z. and R.R; software, R.R.; validation, R.R. and Y.H.; formal analysis, J.Y.; investigation, Y.H. and R.R.; resources, H.X. and J.Y; data curation, H.Z.; writing--original draft preparation, R.R.; writing--review and editing, Y.H.; visualization, R.R.; supervision, H.Z.; project administration, J.Y.; funding acquisition, H.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Major science and technology project of Hubei Province (2021 AAB001) and High Grade CNC System and Servo Motor Project (TC210H03A-04).  
Not applicable.  
The data that support the findings of this study are available on request from the corresponding author, [[PERSON]] upon reasonable request.  
The authors declare no conflict of interest.
# Spacecraft and Asteroid Thermal Image Generation for Proximity Navigation and Detection Scenarios
## 3 Results
### Asteroid Case
#### 3.2.3 Asteroid Case Conclusions  
As for the spacecraft case, the goal of the method was to produce a thermal image retaining all the details of the terrain for an asteroid geometry. The reported results show that the pipeline is able to create images with a very high level of detail, producing realistic thermal images of complex terrain geometries. The pipeline therefore enables us to start testing navigation algorithms in such scenarios. As for the spacecraft case, it must be pointed out that the method is still not validated; thus, the accuracy of the generated images is not guaranteed. Nonetheless, given the ability to deal with such a level of geometry complexity and the qualitative correctness of the results, this is a good starting point. In particular, as for the spacecraft case, the ASTRA Polimi team is focused on the application of such images in combination with VIS images for covering the proximity phases, in which VIS images have low or adverse illumination conditions.  
In order to validate the thermal images of Ryugu, the thermal model must be tuned and run in transient mode to recreate the exact temperature field, and the thermal images must be recreated with the exact thermal camera parameters and noise levels used in the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**N Faces** & **Face Size [m]** & **Min [K]** & **Max [K]** & **Note** \\ \hline
3.7M & 0.4 & \(-\)340 & 1280 & Oscillation \\
4.3M & 0.36 & 77 & 800 & Oscillation \\
5.7M & 0.33 & 40 & 418 & Stable \\
6.4M & 0.31 & 40 & 416 & Stable \\
8.9M & 0.26 & 42 & 419 & Stable \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ryugu simulation stabilizes at 6M faces on the asteroid surface.  
Figure 19: Ryugu temperature field rendering. In the right image, the object is rotated by 180\({}^{\circ}\) to show the other side of the asteroid. Light and shadows are inserted to better visualize the terrain; they do not represent any temperature feature.  
Hayabusa mission. The pipeline is perfectly able to do that, but it is out of the scope of this article which focuses entirely on the method.  
Figure 21: Ryugu temperature field (**left**), and radiance field \(q_{f}c\) (**right**).  
Figure 20: Temperature field (**left**), radiance field \(q_{f}c\) (**right**).
## 3 Experimental Results
### Facial Synthesis Results  
We test the synthesis of facial images using our model as described in Section 3.4. In Figure 6, the right-most image in each row represents the synthesis output of the two left-side images. The front module of our decoder takes a latent vector encoded from the left image, and the back module takes a latent vector from the right image. The output image has the characteristics of both images but different scale-specific features. In other words, the output has the coarse-scale characteristics of the first image (e.g., the overall structures or locations of facial attributes) and the fine-scale features of the second image (e.g., the eyes or the skin color). Note that facial synthesis is achieved without requiring additional information, such as binary attribute labels for each image.
# A Survey on IoT-Edge-Cloud Continuum Systems: Status, Challenges, Use Cases, and Open Issues
## 3 Indicative Use Cases
### Maritime Applications  
A maritime environment is generally characterized by multiple and diverse entities dispersed over large geographical areas, including, among others, ships, vessels, ports,  
Figure 7: Data collection and process optimization in a smart manufacturing environment [67].  
unmanned surface vehicles (USVs), unmanned underwater vehicles (UUVs), sensors, and actuators [71; 72]. A challenging task in such a heterogeneous environment is proper data collection and processing for the optimization of various tasks related to the maritime sector, such as just-in-time arrival for vessels and ships in ports [17], pollution monitoring, search and rescue (SAR) operations, etc. Therefore, IoT devices can collect and transmit data to edge servers for proper ML training and process optimization. With respect to Figure 8, for example, a distributed ML model can be integrated into the hardware of each vessel (continuously trained with its individual operational data) and can provide recommendations for the optimized ship's speed and route planning, taking into account the time of arrival (as scheduled by the port based on the availability of its services) and the environmental weather conditions. In the same context, a centralized ML algorithm deployed at the port will indicate the optimized scheduling for the just-in-time arrival provision of port services. In this case, the cloud server collects the aggregated data and updates the corresponding models that are periodically inferred to the hardware of the vessels.  
FL can be also applicable in the maritime sector for security and privacy protection during process optimization. To this end, various critical processes can be optimized with local vessel data, such as fault diagnosis, reduction of CO\({}_{2}\) emissions, etc. [73; 74].
## 2 Methods
### Data Acquisition  
A field campaign was carried out between 7 and 14 January 2014. The purpose of the fieldwork was to collect leaf hyperspectral data and SPAD-Chlorophyll meter readings. Multispectral Landsat 8 data were acquired on 28 January to measure the association between CC and satellite-derived VIs. Landsat 8 was selected in this study given that its medium spatial resolution (30 m) enables capture of the heterogeneity of the mangrove landscape while its spectral resolution enables the computation of broad band indices highly correlated with CC [1, 57]. In addition, Landsat 8 is the most recent instrument of the Landsat mission and has a similar spatial resolution to the future Sentinel 2 MSI sensor; therefore, it allows the continuity of mangrove chlorophyll concentration monitoring.  
The leave-one-out cross-validation (LOOCV) method was used to validate the relationship between CC at the ESU level and Landsat 8 NDVI green. Figure 2 provides a schematic overview of the methodology followed in this paper.