esa-sceva/satcom-chunk-collection huggingface dataset:
# SLR, GRACE and Swarm Gravity Field Determination and Combination
###### Abstract
We shortly introduce the representation of the gravity field in a spherical harmonic expansion and exemplify the problem of signal loss due to leakage or filter attenuation in Section 2. In the following, all monthly gravity fields are truncated to the same spherical harmonic degree for the sake of comparison of the different techniques. To illustrate the capability of the different satellite gravimetric techniques to track temporal variations of the mass distribution within the system Earth, we transform the gravity variations to variations in equivalent water height (EWH) [30] or mass variations in Greenland and at the coast of West Antarctica and compare their spatial distribution and development with time. Moreover, we fit deterministic signal models of secular and seasonal variations to consecutive five-year time-periods and compare the derived mass trends. Comparable studies were already performed by [PERSON] et al. [31], [PERSON] et al. [32] and [PERSON] et al. [33]. The latter concluded that truncated at spherical harmonic degree 5 gravity field models are not able to correctly separate mass loss in Greenland and Antarctica and that, while the inter-annual variations in none of the SLR time-series are realistic, long-time mass trends are well captured in the regularized AIUB SLR time-series truncated at degree 10 [19] as opposed to other series. [PERSON] et al. [33] suggest to either reduce the temporal resolution of the SLR-derived gravity fields to reduce their scatter, which in our eyes is counter-intuitive because it also reduces the separability of mass loss signal in Greenland and Antarctica, or to combine SLR with other data. We do not continue our regularized SLR-only degree 10 time-series beyond 2014 because in combination with Swarm the full sensitivity of SLR can be exploited without regularization. The combination approach is favorable because it is independent from external information not based on the original observations. We provide an unconstrained SLR-only degree 6 time-series and a combined SLR + Swarm time-series where SLR entered to degree and order 10. The decorrelation of the individual spherical harmonic coefficients is possible by the Swarm data. Due to the consistent processing of Swarm and SLR, we are able to perform the combination on the normal equation level, taking correlations between reference frame (co-estimated in the case of SLR), force model and orbit parameters into account [34]. Combinations of SLR with CHAllenging Minisatellite Payload (CHAMP), GRACE or Gravity field and steady-state Ocean Circulation Explorer (GOCE) for gravity field determination were also studied by [PERSON] et al. [35], [PERSON] et al. [36], [PERSON] et al. [37] and [PERSON] et al. [38].
## 2 System Model  
We have considered a cooperative relaying EH scenario as shown in Figure 2, where a source has to transmit its information data to the destination. Due to fading or weak link between a source-destination pair, the source node seek the help of IoT relay node (\(IoT_{R}\)) for relaying its information data. Here, the source node may be an IoT node which has abundant energy supply from the other sources. Cooperative communication with single relay is a simple but effective communication scheme especially for energy constrained networks such as IoT networks [34]. Furthermore, using more than one relay increases the complexity of the systems greatly [25]. Therefore, we have considered a single \(IoT_{R}\) node for our system model. However, it can be extended to multiple \(IoT_{R}\) node scenario as well.  
\(IoT_{R}\) is rather power constrained node that acts as a DF relay. It first harvests the RF energy from source signal using either time switching protocol or power splitting protocol in the first stage and then transmits the source information data along with its own data using NOMA protocol in next subsequent stage. The dual purpose of energy harvesting and forwarding the information data is thus served by \(IoT_{R}\). The receiving end for source and \(IoT_{R}\) node serves as the destination for data transmission. Unlike several of the previous works, here the information data forwarded by \(IoT_{R}\) node is the source node information data and its own data.
## 3 Results and Discussion
### Droplets Deposition Characteristics
#### 3.2.3 Prediction Model of the Drift Distance of the Droplets Deposition Center  
According to the above analysis and Table 2, simulation experiments of droplet deposition were designed for three factors, including the V-shaped wind speed, the crosswind wind, and the spray pressure. The design and range analysis of the experiments are shown in Table 3.  
Figure 13: Droplet spatial distribution and the droplet concentration in the calibration line with different spray pressure: 0.4 MPa (**a**); 0.5 MPa (**b**); 0.6 MPa (**c**).  
Therefore, the prediction model of the drifting distance of the droplet's deposition center is as follows:  
\[y=5.98+0.37Z_{1}(x_{1})+0.02Z_{2}(x_{1})-2.85Z_{1}(x_{2})+0.41Z_{2}(x_{2})-0.02Z_{1 }(x_{3})-0.03Z_{2}(x_{3}) \tag{7}\]  
By plugging Equation (6) into Equation (7), the prediction model is as follows:  
\[y=15.57+0.06(x_{1}-3)^{2}+0.37x_{1}+0.0492(x_{2}-20)^{2}-0.57x_{2}-9(x_{3}-0.5) ^{2}-0.2x_{3} \tag{8}\]  
When the spray conditions are the same, the correlation between the prediction model and the simulation is shown in Figure 14.  
As shown in Figure 14, the correlation coefficient is 0.998, which indicates that the model can be used to predict the drifting distance of the droplet deposition center.
# Research on Feature Fusion Method Based on Graph Convolutional Networks
## 4 Results
### Experimental Analysis  
#### 4.5.1 Statistical Significance Analysis  
To assess the statistical significance of the experimental results, we conducted t-tests on the accuracy of the BertGCN model and the BGF model on each dataset. T-tests are a statistical method used to compare the means of two samples to determine if they are significantly different. We employed an independent-samples t-test to compare the accuracy  
Figure 8: Comparison chart of MR datasets. (**a**) This is a comparison plot of the accuracy of the BertGCN and BGF models on the MR datasets, where the blue line indicates the accuracy of the BertGCN, and the red line indicates the accuracy of the BGF model. (**b**) This is a comparison plot of the loss functions of the BertGCN and BGF models on the MR datasets, where the blue line indicates the loss function of the BertGCN, and the red line indicates the loss function of the BGF model.  
Figure 7: Comparison chart of R52 datasets. (**a**) This is a comparison plot of the accuracy of the BertGCN and BGF models on the R52 datasets, where the blue line indicates the accuracy of the BertGCN, and the red line indicates the accuracy of the BGF model. (**b**) This is a comparison plot of the loss functions of the BertGCN and BGF models on the R52 datasets, where the blue line indicates the loss function of the BertGCN, and the red line indicates the loss function of the BGF model.  
of the BGF model with that of the BertGCN model. T-values were calculated using the following formula:  
\[\mathfrak{t}=\frac{\overline{X}_{1}-\overline{X}_{2}}{\sqrt{\frac{s_{1}^{2}}{n_{1 }}+\frac{s_{2}^{2}}{n_{2}}}} \tag{16}\]  
where \(X_{1}\) and \(X_{2}\) denote two independent samples, \(\overline{X}_{1}\) and \(\overline{X}_{2}\) are their means, \(s_{1}\) and \(s_{2}\) are standard deviations, and \(n_{1}\) and \(n_{2}\) denote the number of samples. The \(\mathfrak{t}\)-value is used to find the \(p\)-value in the \(\mathfrak{t}\)-distribution table to determine the significance of the results.  
With the \(\mathfrak{t}\)-test, we obtain the following \(p\)-values:  
* On the R8 dataset, the BGF model improves accuracy by 0.28% over BertGCN, yielding a \(p\)-value of 0.03, indicating statistical significance.
* On the R52 dataset, the BGF model improves accuracy by 0.9% over BertGCN, yielding a \(p\)-value of 0.045, indicating statistical significance.
* On the MR dataset, the BGF model improves accuracy by 0.03% over BertGCN, with a corresponding \(p\)-value of 0.87, indicating an improvement but not reaching statistical significance.  
These results demonstrate that the improvement of the BGF model is statistically significant on the R8 and R52 datasets. However, on the MR dataset, the improvement does not reach statistical significance.
# Evaluation of Multi-Stream Fusion for Multi-View Image Set Comparison
## 3 Solution
### Key-Point Feature Matching  
In addition to DeepRanking, we use an alternative processing stream to obtain image pair similarity. Hand-crafted features (keypoints and their local descriptors) are extracted from the images. We apply the standard SIFT approach. Other schemes which are computationally more efficient and of similar quality such as SURF or ORB could also be used [10; 11]. According to [11], SIFT gives the best image matching in most of the tested cases. In real-time applications, binary descriptors would be preferred [12].
## 2 Methods  
### NDVI Dataset  
The NDVI is the normalized difference between red and near infrared reflected radiation from the Earth's surface as measured by the space-borne sensors, and it represents the plants/ecosystems photosynthetic activity [3,21]. Furthermore, seasonal rise and fall in NDVI represent changes in the growing season in response to environmental and biophysical conditions [22]. The National Oceanographic and Atmospheric Administration (NOAA) polar orbiting environmental satellites have acquired more than three decades of Earth observations using the advanced very high-resolution radiometer (AVHRR) sensors [23]. This data set has been updated periodically at different temporal and spatial resolutions since 1982. It is processed consistently to develop a long-term record by the Global Inventory Monitoring and Modeling System (GIMMS) project at the NASA Goddard Space Flight Center [24]. The spatial and temporal resolution of GIMMS data is 8 km \(\times\) 8 km and 15 days, respectively.
# Spatial Perception Correntropy Matrix for Hyperspectral Image Classification
## 2 Spatial Perception Correntropy Matrix
### Construction of Correntropy Matrix  
The optimal window is calculated by Equation (2), and then this window is used to construct the correntropy matrix. \(K-1\) similar pixel vectors selected by spatial perception are used to construct a correntropy matrix with a target pixel vector. Define \(\textbf{q}_{i}\) and \(\textbf{q}_{j}\) as two different spectral bands, \(\textbf{q}_{in}\) and \(\textbf{q}_{jn}\) as the n-th spectral value on the \(\textbf{q}_{i}\)th and \(\textbf{q}_{j}\)th spectral bands, then the correntropy between \(\textbf{q}_{i}\) and \(\textbf{q}_{j}\) can be expressed as:  
\[C(\textbf{q}_{i},\textbf{q}_{j})=\frac{1}{K}\sum_{n=1}^{K}k_{\sigma}(\textbf{ q}_{in},\textbf{q}_{jn}) \tag{3}\]  
where  
\[k_{\sigma}(\textbf{q}_{in},\textbf{q}_{jn})=\frac{1}{\sqrt{2\pi}\sigma}exp(- \frac{\|\textbf{q}_{in}-\textbf{q}_{jn}\|_{2}^{2}}{\sqrt{2\sigma^{2}}}) \tag{4}\]  
where \(\sigma\) is the width parameter of the function, which controls the radial scope of the function. Set \(\sigma\) to \(\sigma=0.05\) in the experimental section. Then the following correntropy matrix can be obtained:  
\[M_{c}=\{C(\textbf{q}_{i},\textbf{q}_{j})\}_{ij=1}^{N} \tag{5}\]  
where \(N\) denotes the number of spectral channels. The nondiagonal element of the correntropy matrix is the correntropy between different bands, reflecting the relationship between different bands. Through the above process, each pixel in the HSI can be represented by a correntropy matrix. The obtained correntropy matrix not only contains spectral information, but also reflects the relationship between similar spectra. So SPCM can provide more distinguishing features for classification and improve classification performance.
# An Efficient Routing Scheme Based on Node Attributes for Opportunistic Networks in Oceans
## 3 The Proposed Scheme: RSNA
### The Description of RSNA
#### 3.2.2 Forwarding Willingness Mechanism  
Forwarding willingness consists of the device capacity and movement behaviors of nodes. We define it into two categories as follows.  
One category is related to the energy and buffer of node called device capacity (\(DC\)), which can be given by  
\[DC=(\lambda\cdot\frac{B_{r}(t)}{B_{init}}+\mu\cdot\frac{E_{r}(t)}{E_{init}}+1 )^{-1}, \tag{5}\]  
where \(B_{r}\) and \(B_{init}\) are residual and initial buffer size of node. Similarly, \(E_{r}\) and \(E_{init}\) are residual and initial energy of node at time \(t\). \(\lambda\) and \(\mu\) are influence factors of buffer and energy on device capacity, respectively.  
The second category involves the movement behaviors of nodes (\(BN\)), which means the more movement relations of nodes, the higher forwarding probability. \(BN\) is defined as follows:  
\[BN =\frac{O_{l}}{I_{l}+O_{l}}\] \[\left\{\begin{array}{l}I_{l}=\frac{\sum N_{l-1}}{\sum\limits_{ teT}N_{t}}\\ O_{l}=\frac{\sum N_{l+1}}{\sum\limits_{teT}N_{t}}\end{array}\right., \tag{6}\]  
where incoming degree (\(I_{l}\)) is defined as the ratio of the number of upstream nodes (\(l-1\)) that forward message directly to itself to the total number of nodes. Similarly, the outgoing degree (\(O_{l}\)) represents the ratio of the number of direct downstream nodes (\(l+1\)) that receive forwarded message to the total number of nodes. Here, a forwarding willingness metric \(FW\) is given by  
\[FW=\zeta\log_{2}{(DC+1)}+\eta\log_{2}{(BN+1)}, \tag{7}\]  
where \(\zeta\) and \(\eta\) are the influence factors, respectively.
## 2 Dataset Construction  
This section describes the preconditions and the dataset construction.  
As discussed in the introduction, _Aedes_, _Anopheles_, and _Culex_ are the human blood-sucking mosquitoes. The most infectious species among the _Aedes_ genus are _Aedes albopictus_ and _Aedes aegypti_. The most infectious species among the _Anopheles_ genus is _Anopheles stephensi_. The most infectious species among the _Culex_ genus is _Culex pipiens pallens_.  
The time zone, behavior habits, and infectious diseases of _Aedes albopictus_ and _Aedes aegypti_ are similar. Experts may find challenges in determining the difference in appearance even with the aid of a microscope. Therefore, we targeted _Aedes albopictus_ out of the two species because of its large global population.  
We utilized the images obtained by using a single-lens reflex camera to learn, while images obtained using a smartphone were used for testing and further validation. In addition, we assumed that the illuminance varied from 380 to 1340 lx and the background was white herein.  
Table 1 presents the details of the shooting equipment. Figure 1 depicts an example of the captured images. The region of the mosquito was manually clipped from the captured images. Figures 2 and 3 demonstrate the clipped mosquitoes from the images captured using a single-lens reflex camera and a smartphone, respectively.  
Each clipped image was rotated by 90\({}^{\circ}\), and all the rotated images were added to the dataset; the number of images had a four-fold increase. Consequently, we obtained the images of each mosquito species, consisting of 4000 images for training, 500 images for testing, and 300 images for validating. In general, the dataset with three types of mosquitoes comprised 12,000 images for training, 1500 images for testing, and 900 images for validating.
# Review of Soft Actuators Controlled with Electrical Stimuli: IPMC, DEAP, and MRE
## 2 Recent Progress in Development and Applications of Soft Actuators  
### Ionic Polymer Metal Composite (IPMC)  
Ionic polymer-metal composite is composed of an ion-exchange (mostly cation-exchange) polymer membrane (ionomer) and metallic electrodes deposited on both surfaces of the membrane. The ionomer provides channels for selective ion migration and is mostly responsible for the mechanical properties of the IPMC. The metallic electrodes provide the applied voltage to the membrane surface and work as capacitor electrodes that can accumulate a certain amount of charge. The accumulated charge forces the ion migration in the membrane, which causes the bending of the IPMC actuators. Electroactive ionic polymers usually work in a humid environment and are driven by low voltages of 1-5 V. Inside the polymer structure, anions are clustered together to provide channels for the flow of the cations. When the material is hydrated, the hydrated cations diffuse under the influence of an electric field toward an electrode applied to the outer surface of a membrane. The accumulation of hydrated cations on the negative electrode side causes mechanical and electrostatic stress, which ultimately translates into the deformation of the IPMC actuator, as shown in Figure 2. The parameters of ion-exchange membranes, such as Young modulus, size of the ion-conducting channels, size of the counter ion (together with a solvation sphere), and the amount of absorbed solvent, as well as the insertion of different modifiers into the structure of the membrane, play a significant role on the final properties of IMPC. On the other hand, the conductivity of the electrode layer, their crack resistance during bending, and adhesion to the membrane surface are also crucial. Because the ion-exchange membrane and electrode properties mostly influence the IPMC parameters, two main routes of the IPMC modification can be distinguished. The first path focuses on the modification of the ionomer, and the second path focuses on the modification of electrodes.  
Figure 1: Recent trends in modification of electrical stimuli soft actuators.
## 3 Results
### TNF-$x Tolerance Was More Inducible by MSF Than by K8 Lysates in LPS-Treated THP-1 Cells  
To compare the anti-inflammatory effects of MSF and _L. plantarum_ K8 lysates (K8 lysates), THP-1 cells were treated with each lysate at the indicated concentrations and stimulated with LPS. At a concentration of 200 \(\upmu\)g/mL, the inhibition of TNF-$x expression was 60% higher with MSF than with K8 lysates (Figure 3A). In addition, the synergistic effect of IL-1$ expression was 10% lower with MSF than with K8 lysates (Figure 3B). These results show that MSF has high anti-inflammatory efficacy, specifically for TNF-$x$.  
Figure 3: Comparison of anti-inflammatory responses induced by MSF or _L. plantarum_ K8 lysates. (**A**,**B**) THP-1 cells were treated with MSF and _L. plantarum_ K8 lysates, respectively, at the indicated concentration for 18 h, and cells were retreated with 500 ng/mL LPS for 4 h. The levels of cytokines in the culture supernatants were measured by ELISA. Statistical analysis was conducted with two-way ANOVA (G). * \(p\) < 0.05; **_p_ < 0.01; ***_p_ < 0.001.  
Figure 2: IL-1$ expression in THP-1 cells by MSF and LPS treatment. (**A**) THP-1 cells were treated with LPS at the indicated concentration for 4 h. (**B**) THP-1 cells were treated with MSF at the indicated concentration for 18 h. (**C**) THP-1 cells were pretreated with MSF at the indicated concentration for 18 h and then were retreated with 500 ng/mL LPS for 4 h. The level of interleukin (IL)-1$ in the culture supernatants was measured by ELISA. Statistical analysis was conducted with a one-way ANOVA followed by [PERSON]’s HSD post hoc test. ***_p_ < 0.001.  
Mitogen-Activated Protein Kinase (MAPK) Pathways Were Involved in MSF-Mediated TNF-\(\alpha\) Tolerance  
The phosphorylation of MAP kinases (e.g., p38, ERK, and JNK and NF-kB p65 subunits) by LPS and/or MSF was examined by western blot. The phosphorylation of p38, JNK, and p65 was increased by LPS treatment. Among these, p38 and JNK were deactivated by the combined treatment of LPS and MSF. On the other hand, phosphorylation of p65 was stronger when using the combined treatment (Figure 4A). When cells were treated with the indicated inhibitors, TNF-\(\alpha\) production was inhibited by p38 and JNK inhibitors, suggesting that both signaling pathways were involved in LPS-mediated TNF-\(\alpha\) production (Figure 4B). Taken together, these findings indicate that MSF induces TNF-\(\alpha\) tolerance in LPS-treated THP-1 cells by down-regulation of the p38 and JNK pathways. On the other hand, IL-1\(\beta\) production was inhibited by PI3K, NF-kB, and JNK inhibitors (Figure 4C). Considering these data together with Figure 3A, the NF-kB pathway might be involved in IL-1\(\beta\) production in THP-1 cells treated with MSF followed by LPS.
# Emotion Recognition from ECG Signals Using Wavelet Scattering and Machine Learning
## 2 Materials and Methods
### Feature Extraction
#### 2.4.1 Time Domain Features  
Commonly extracted features in emotion recognition include time domain features [7]. In this study, 17 features were extracted, corresponding to the root mean square successive differences (RMSSD) in interbeat intervals (IBI); the proportion of total IBI that are longer than 20 ms and 50 ms (pNN20, pNN50); 2 Poincare coefficients [34]; and 6 statistical parameters of heart rate (HR) and heart rate variability (HRV), namely, mean, standard deviation, skewness, kurtosis of the raw signal over time, and number of times the signal value is above/below the mean \(\pm\) 1 standard deviation.
# Downdraft Assessment of Glass Curtain Wall Buildings with a Radiant Floor Heating System
## 6 Conclusions  
This study performed a downdraft assessment on glass curtain wall buildings with respect to the radiation condition of the RFHS and the thermal performance of the window. The results obtained are as follows.  
For the downdraft assessment, the RFHS applied to perimeter zones with different window types was modeled using CFD simulation. The simulation result showed that most values, such as airflow velocity, temperature, and draft rate, satisfied the downdraft assessment criteria because of the ascending airflow caused by the radiant heat from the floor during the RFHS application and the radiant heat transfer effect between the RFHS and the window.  
In addition, the temperature rise of cold air through heat radiation along the floor and the related cold airflow reduction phenomena in the descending air flow were derived based on the mechanism of preventing the downdraft with the RFHS application. The temperature difference between the air supplied through the RFHS and the air descending along the cold surface of the window increased when the RFHS was applied to a narrow area with a high temperature. The airflow velocity also increased with the heat exchange. The airflow re-entered the window side, and the downdraft in the occupied area exceeded the strict value specified by the standards. Conversely, if heat were applied according to the indoor thermal output by widening the radiant heat area of the RFHS, a downdraft could occur as a result of a blocking failure prior to the influx of the cold air into the occupied area caused by low surface temperatures. Therefore, applying the RFHS is advantageous in a perimeter zone without causing airflow inducement at the window side with acceptable surface temperatures.  
If the vertical height of the window were larger, the downdraft phenomenon was more influenced not by the cold air from the window, but by the velocity of the airflow descending along the window. Thus, the velocity of the cold airflow must be reduced as the vertical height of the window increases. To this end, the temperature difference between the air from the window and the radiating air from the RFHS should be reduced. Moreover, the cold air influx phenomenon at the window should be minimized by heated air ascending toward the window by designing a wider radiation area.  
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2015R1D1A1A09061467).  
**Author Contributions:** [PERSON] and [PERSON] conceived and designed the simulation; [PERSON] and [PERSON] performed the simulation; [PERSON], [PERSON] and [PERSON] analyzed the data; [PERSON] wrote the paper; [PERSON] and [PERSON] supervised the paper writing.  
**Conflicts of Interest:** The authors declare no conflict of interest.