esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulation Results
### Rates for Different Indoor Percentage  
The parameter determining the percentage of users in an indoor condition could highly impact the rate of the users. This is of course caused by the higher losses encountered by the signal propagation and the absence of the LOS component whenever a certain UT is inside a building. This harmful effect is visible in Figure 11, where the CDF curves represent percentage of indoor UTs ranging from 0% to 100%. The number of UTs below the acceptable SINR threshold rises for higher indoor percentages. It is interesting to observe how two inflection points arise in the CDFs as the percentage of indoor users decreases. Additionally, in this case, the results for the two possible polarizations of the antenna elements show the same behavior.  
Figure 10: CDF of UT rates for different transmitting powers at the BS, \(N_{t}=72\) with \(N_{ty}=36\), \(N_{tz}=2\), \(N_{r}=1\), traffic density = 2500 users/km\({}^{2}\).
# Cloud-Based Single-Frequency Snapshot RTK Positioning
## 3 Experiment Setup and Results
### Results and Discussions  
Before presenting the RTK performances, the number of satellites and DOP values are shown in Figure 7 as an overview of the snapshot data scenario geometry, an elevation mask of 10 degrees was set to filter out the low-elevation satellites.  
Note that this work focuses on single frequency applications and only GPS L1 C/A and Galileo E1C signals wereused for the following RTK process. Although BDS B1C signals are also in the L1 frequency band; their measurements were not provided by the VRS service used in this research at the time of data collection, and instead only the BDS B1I signal which is centered at a slightly different frequency (1561.098 MHz) was available, thus the BDS constellation is not included in the current SRTK engine analysis.  
To present the fix rate and mean LRF results, two plots can be generated for each integration time used, with the X axis representing the varying settings of baseline distance and Y axis representing the SRTK fix rate and mean LRF value, respectively. As an example, Figures 8 and 9 are generated for all the bandwidths used when the integration time is set to 100 ms.  
Figure 7: Number of satellites used (**top**) and the DOP values (**bottom**) of the collected snapshot data.  
As expected, the two plots follow a similar pattern, since the LRF directly impacts the possibility of obtaining a RTK fix. For other integration time settings, similar trends can be found in the results.
# Cross-Modal Image Registration via Rasterized Parameter Prediction for Object Tracking
## 4 Experiment Settings
### Evaluation Metric  
For image registration evaluation, we computed the Root Mean Squared Error (RMSE) of the corner point coordinates, which indicates the matching degree between transformation image and reference image, as follows:  
\[RMSE=\frac{1}{4}\sum_{i=0}^{3}[(x_{0}^{i}-x_{1}^{i})+(y_{0}^{i}-y_{1}^{i})] \frac{1}{2}. \tag{8}\]  
In addition, various conventional image quality metrics can be used to evaluate the effect of image registration in different views, including the following:  
* **Correlation Coefficient (CC)**: describes the linear correlation between images; a larger correlation coefficient between the target and reference images indicates higher similarity between the two, i.e., a better registration result, and is calculated as follows: \[CC_{XY}=\frac{Cov(X,Y)}{\sqrt{D(X)}\sqrt{D(Y)}}\] (9)
* **Structural Similarity Index Measure (SSIM)**: constructs the quality loss and distortion of images, including the correlation loss, grayscale distortion, and contrast distortion; a larger SSIM indicates that the quality of the target image is closer to that of the reference image. SSIM is calculated as follows: \[SSIM(x,y)=[ luminance(x,y)]^{\alpha}\cdot[contrast(x,y)]^{\beta}\cdot[ structure(x,y)]^{\gamma}\] (10)
* **Peak Signal-to-Noise Ratio (PSNR)**: computes the ratio of peak energy to noise energy for measurement of distortion in the image registration process; the larger the PSNR value, the closer the target image is to the reference image. PSNR is calculated as follows: \[PSNR=10\times lg(\frac{MaxValue^{2}}{MSE})\] (11)
* **Mutual Information (MI)**: describes the mutual information contained in each image. The higher the similarity or overlap between two images, the higher their correlation, and the smaller their joint entropy, which means that the mutual information is greater. MI is calculated as follows: \[MI(X,Y) =H(X)+H(Y)-H(X,Y)\] (12) \[H(X) =-\sum_{j=1}^{N}p(a_{j})logp(a_{j})\] (13) \[H(X,Y) =-\sum_{x,y}P_{XY}(x,y)logP_{XY}(x,y)\] (14)
## 9 Future Research Directions and Challenges
### Combination of Multiple Estimators  
Combining multiple estimators for range-based localisation or fingerprinting could compensate for each method's disadvantages, improve accuracy, and introduce resilience to failure cases. This technique is well established in the general machine-learning task and is known as ensemble learning [120]. The ensemble method combines different estimators and often yields much more accurate results than individual methods into four main paradigms Bayesian averaging, error-correcting output coding, Bagging, and boosting [121].
# Adsorption of Lead and Nickel on to Expanded Graphite Decorated with Manganese Oxide Nanoparticles
## 3 Results and Discussion
### Thermodynamic Study  
The effects of temperature on the adsorption capacities of MONPs-EG for Pb\({}^{2+}\) and Ni\({}^{2+}\) were investigated through a series of experiments at 298 K, 308 K, and 323 K in single metal solutions. The results are shown in Figure 6. The adsorption capacities were found to decrease with the increase of temperature, suggesting that the adsorption of metal ions onto MONPs-EG was favorable at low temperature. At higher temperature, the attractive forces between MONPs-EG and metal ions could be weakened, leading to a decrease in adsorption capacity. Thermodynamic studies were performed to determine the nature of the adsorption processes; the calculated parameters are summarized in Table 5. The negative values of \(\Delta\)H\({}^{0}\) confirm the exothermic nature of the adsorption of both metal ions onto the prepared adsorbent in single metal systems at 298-323 K [46; 47], which is supported by the results of the temperature effect on the adsorption processes. Negative values of \(\Delta\)S\({}^{0}\) reflected that the randomness decreases at the MONPs-EG and solution interface during the adsorption of metal  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{\
\begin{tabular}{c} **Metal Ion** \\ **(System)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **q\({}_{e,  
ions [48; 49]. The higher magnitude of \(\Delta\)S\({}^{0}\) obtained in the Pb\({}^{2+}\) system signified that it had a better affinity toward and was more stable on the MONPs-EG compared to Ni\({}^{2+}\). Furthermore, negative values of \(\Delta\)G\({}^{0}\) at all investigated temperatures indicated that the adsorptions of both metal ions onto the prepared MONPs-EG were thermodynamically favorable and spontaneous in nature [28; 50].
# Investigation on the Measurement Method for Output Torque of a Spherical Motor
## 2 Electromagnetic Theory and Simulation of the Output Torque of a Spherical Motor  
### Motor Structure  
This paper studies a 3-DOF PMSM, which is mainly composed of a stator spherical shell, stator coils, a rotor spherical shell, rotor permanent magnets, and a rotor output shaft [12; 13; 14]. In the PMSM, the upper and lower hemispheric shells of the stator of the spherical motor are evenly arranged with 12 copper coils without the iron core. The latitude angle between the factors on the upper and lower spherical shells is 45 degrees, and the longitude angle between the adjacent factors is 30 degrees. The spherical surface of the rotor is inlaid with 40 NdFeB permanent magnets. The N and S pole magnets are distributed along the warp and weft directions. The latitude angle between the upper and lower adjacent permanent magnet is 30 degrees. The longitude angle between the left and right adjacent permanent magnets is 36 degrees. The structure of the stator and rotor is shown in Figure 1.
## 4 Performance Evaluation
As the transmission interval increases or the value of k decreases, the data delivery ratio increases. Figure 6: Data delivery ratio with increasing the transmission interval. **(a)** 30 hops-sized network topology **(b)** 50 hops-sized network topology **(c)** 70 hops-sized network topology. In general, the collision domain size is a major consideration to determine the effective throughput. However, since pole transformers stand in a row in our system, the power quality data is forwarded in a peer-to-peer manner except at branch nodes with more than one child node. The collision domain size is three. As shown in the figure, the effective throughput is acceptable for providing the desired power quality monitoring service. Figure 7 shows the distribution delay of the power quality data when the transmission interval of the data is varied. The distribution delay of the power quality data represents the average time from when a data source sends its power quality data until the substation receives the data. Only the cases of the substation successfully receiving data are counted in. It is well known that the transmission delay in a wireless _ad hoc_ network increases as the network diameter increases [23]. Besides, the offered load which can be controlled by the transmission interval and the value of k affects on the transmission delay. However, as shown in the figure, the distribution delay between a pole transformer and the substation is less than 0.1 seconds. The distribution delay is an acceptable range to provide the monitoring service. As we can see in Figure 2, the last hop from six feeders to the substation in the collection subsystem can be the performance bottleneck because all data are gathered at a single collecting point (substation). We have experimented with the performance on the last hop in Figure 8. The value of k is set to 1.0. Contrary to our concern, the bottleneck problem does not occur in the last hop. It is because the  
Figure 7: Distribution delay of power quality data with increasing the transmission interval. **(a)** 30 hops-sized network topology **(b)** 50 hops-sized network topology **(c)** 70 hops-sized network topology. collision domain size is still small and the data is not concentrated to the substation simultaneously due to the relatively large data measurement interval and transmission interval.
## 1 Introduction
### Problem Statement and Motivation  
Beam steering based on the continual scanning of the troposphere without determining the spatio-temporal dynamics of beam pointing will be inefficient. Beam pointing dynamics provide knowledge of favorable propagation heights that give the highest received power at different times of the day during different seasons. This helps achieve focused, fast, and efficient beam steering, saving the time taken in continual scanning. Beam pointing dynamics also determine the maximum height for a link, beyond which the received power is always low due to significant increases in the path loss. This sets the useful height range for beam steering, thus saving time and resources wasted in scanning at unfavorable heights.  
Methods for the determination of the beam pointing dynamics of a tropospheric link are not available in the present literature. Hence, it is required to develop a method that can help ascertain the individual powers received by the receiver due to the propagation of a transmitted signal at various heights at different times of the day in different seasons. An analysis of these received power values will lead to an understanding of temporal variations in the ideal beam steering heights and the useful height range required for beam steering on a tropospheric link.
## 3 Results
### Independent Testing  
To evaluate the model's performance on an independent dataset, we collected E4 data on a real-life APS event from a healthy subject during a 40-minute oral presentation in front of an audience. This independent dataset was not used in the training or validation of the model. The data collection involved a healthy individual wearing an Empatica E4 wristband during an APS inducement event. The collected data were preprocessed similar to the training data, including standardization using the mean and standard deviation of the training set. The model was able to detect APS accurately during the entirety of the 40-minute presentation. This high accuracy is attributed to the pronounced changes in physiological variables, specifically, an increase in GSR and HR and a reduction in ST. These changes were more significant compared to the mild APS inducement in the clinical experiments. The significant physiological changes observed in the real-life scenario underscore the model's robustness and its ability to generalize to different APS events. Figure 7 shows the beeswarm SHAP summary plot, which shows that the ranking of feature importance is identical to the order of importance deduced using the testing dataset of the clinical experiment.
# A Pseudo-3D Model for Electromagnetic Acoustic Transducers (EMATs)
###### Abstract  
Previous methods for modelling Rayleigh waves produced by a meander-line-coil electromagnetic acoustic transducer (EMAT) consisted mostly of two-dimensional (2D) simulations that focussed on the vertical plane of the material. This paper presents a pseudo-three-dimensional (3D) model that extends the simulation space to both vertical and horizontal planes. For the vertical plane, we combines analytical and finite-difference time-domain (FDTD) methods to model Rayleigh waves' propagation within an aluminium plate and their scattering behaviours by cracks. For the horizontal surface plane, we employ an analytical method to investigate the radiation pattern of Rayleigh waves at various depths. The experimental results suggest that the models and the modelling techniques are valid.  
Analytical solutions; FDTD; EMATs; beam directivity +
Footnote †: journal: Applied Mechanics and Physics Letters
# An Efficient Algorithm for De-Interleaving Staggered PRI Signals
## 3 Simulation and Analysis
### Comparative Experiments
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**PRI Jitter Rate**} & \multirow{2}{*}{**Missing Rate**} & \multirow{2}{*}{**Input Radiation Source Conditions**} & \multicolumn{3}{c}{**De-Interleaving Results**} \\ \cline{3-5}  & & & **Sequence** & **CMM** & **Ours** \\ \hline \(\pm\)4\% & 5\% & 1 staggered PRI & 1/1 & 1/1 & 1/1 \\ \(\pm\)4\% & 5\% & 1 staggered PRI and 1 fixed PRI & 1/2 & 1/2 & 2/2 \\ \(\pm\)4\% & 5\% & 1 staggered PRI and 2 fixed PRI & 1/3 & 1/3 & 3/3 \\ \(\pm\)4\% & 5\% & 2 staggered PRI and 2 fixed PRI & 2/4 & 2/4 & 4/4 \\ \(\pm\)4\% & 5\% & 2 staggered PRI and 3 fixed PRI & 2/5 & 2/5 & 5/5 \\ \hline \(\pm\)8\% & 5\% & 1 staggered PRI & 1/1 & 1/1 & 1/1 \\ \(\pm\)8\% & 5\% & 1 staggered PRI and 1 fixed PRI & 1/2 & 1/2 & 2/2 \\ \(\pm\)8\% & 5\% & 1 staggered PRI and 2 fixed PRI & 1/3 & 1/3 & 3/3 \\ \(\pm\)8\% & 5\% & 2 staggered PRI and 2 fixed PRI & 2/4 & 2/4 & 4/4 \\ \(\pm\)8\% & 5\% & 2 staggered PRI and 3 fixed PRI & 2/5 & 2/5 & 5/5 \\ \hline \(\pm\)12\% & 5\% & 1 staggered PRI & 1/1 & 1/1 & 1/1 \\ \(\pm\)12\% & 5\% & 1 staggered PRI and 1 fixed PRI & 1/2 & 1/2 & 2/2 \\ \(\pm\)12\% & 5\% & 1 staggered PRI and 2 fixed PRI & 1/3 & 1/3 & 3/3 \\ \(\pm\)12\% & 5\% & 2 staggered PRI and 2 fixed PRI & 2/4 & 2/4 & 4/4 \\ \(\pm\)12\% & 5\% & 2 staggered PRI and 3 fixed PRI & 2/5 & 2/5 & 5/5 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison results of three algorithms under different PRI jitter rates. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Missing Rate**} & \multirow{2}{*}{**Input Radiation Source Conditions**} & \multicolumn{3}{c}{**De-Interleaving Results**} \\ \cline{3-5}  & & **Sequence** & **CMM** & **Ours** \\ \hline \(5\%\) & 1 staggered PRI & 1/1 & 1/1 & 1/1 \\ \(5\%\) & 1 staggered PRI and 1 fixed PRI & 2/2 & 2/2 & 2/2 \\ \(5\%\) & 1 staggered PRI and 2 fixed PRI & 3/3 & 3/3 & 3/3 \\ \(5\%\) & 2 staggered PRI and 2 fixed PRI & 4/4 & 4/4 & 4/4 \\ \(5\%\) & 2 staggered PRI and 3 fixed PRI & 5/5 & 5/5 & 5/5 \\ \hline \(10\%\) & 1 staggered PRI & 1/1 & 1/1 & 1/1 \\ \(10\%\) & 1 staggered PRI and 1 fixed PRI & 2/2 & 2/2 & 2/2 \\ \(10\%\) & 1 staggered PRI and 2 fixed PRI & 3/3 & 3/3 & 3/3 \\ \(10\%\) & 2 staggered PRI and 2 fixed PRI & 3/4 & 4/4 & 4/4 \\ \(10\%\) & 2 staggered PRI and 3 fixed PRI & 4/5 & 4/5 & 5/5 \\ \hline \(15\%\) & 1 staggered PRI & 1/1 & 1/1 & 1/1 \\ \(15\%\) & 1 staggered PRI and 1 fixed PRI & 2/2 & 2/2 & 2/2 \\ \(15\%\) & 1 staggered PRI and 2 fixed PRI & 3/3 & 3/3 & 3/3 \\ \(15\%\) & 2 staggered PRI and 2 fixed PRI & 2/4 & 3/4 & 4/4 \\ \(15\%\) & 2 staggered PRI and 3 fixed PRI & 3/5 & 3/5 & 4/5 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison results of three algorithms under different pulse missing rate.
# First Vegetation Optical Depth Mapping from Sentinel-1 C-band SAR Data over Crop Fields
## 5 Discussion
### Possible Limitations of WCM Modelling?  
In this study, it is shown that VOD retrievals for rapeseed are probably not logical (not correlated to NDVI). [PERSON] et al. [24] reported that an important double bounce scattering mechanism may occur over a well-developed rapeseed crop after a heavy rainfall. The double bounce scattering could be due to soil moisture increase in the underlying soil and to the presence of water standing in the canopy. The observations of [PERSON] et at. [24] fit particularly well with our case study since between 24 March 2018 and 12 May 2018 and between 1 March 2019 and 1 June 2019, when the rapeseed was well-developed (NDVI between 0.6 and 0.7), frequent rainfall events occurred (Figure 2). Thus, the inaccurate VOD estimation obtained over our study area may be related to the double scattering mechanism which is not considered in the WCM modelling.  
In our method we assume that there is no difference in the vegetation contribution to total backscatter between two images up to 18 days apart. However, substantial fresh biomass and structural changes can occur within 18 days, particularly during the vegetative stages [41; 42]. The choice of 18 days is the compromise to have enough images (four S1 images), to robustly estimate VOD while assuming that the vegetation has changed a little. In some cases, this assumption may not be valid. As a reminder, currently, VOD is calculated using only low spatial resolution data but with a revisit time of about 1 day (SMOS, SMAP, and ASCAT). The 1-day revisit time allows combining several data to estimate VOD once a year or once a month.  
It is worth mentioning that for the regions in the world where the revisit time of S1 is 12 days (region outside Europe), VOD computed using the present method may be useless for crop monitoring. In the case of 12 days revisit time, the use of four images (as in the present method) will lead to one VOD value each 36 days, which is longer than one crop growth stage's period, and thus the assumption about vegetation condition stability considered in order to resolve Equation (3) will be no longer valid. Finally, the approach developed in this study is not applicable for irrigated plots.
# A Survey on Wireless Body Area Networks for eHealthcare Systems in Residential Environments
## 3 Discussion
### Communication Range  
ZigBee is considered to be a wireless Local Area Network (LAN) technology, thus it covers a greater range, whereas BLE is a WPAN protocol and its range is more limited. In a typical health monitoring system, there are scenarios in which collected data is required to be transferred to an access point within a room distance. In these scenarios, both BLE and ZigBee are considered as suitable protocols. However, in scenarios where data needs to be transmitted to a local station located in the other side of the house, if no other home networking infrastructures such as WiFi, PLC or Ethernet is employed, ZigBee is regarded as the better solution, simply because BLE is unable to cover the required distance by itself.
# Programmable Data Plane Applications in 5G and Beyond Architectures: A Systematic Review
## 3 Background in Programmable Data Plane and 5G and Beyond
### Network Function Virtualization, Network Slicing, and Multi-Access Edge Computing
#### 3.3.2 Network Slicing (NS)  
Network slicing (NS) [40] is a technique employed in data networks that enables the establishment of multiple virtual networks, known as slices, atop a single physical network infrastructure. These slices are distinct and self-contained network instances capable of being tailored to satisfy the specific demands of an application or service.  
NS finds significant utility in 5G networks [41], in which slices are utilized to deliver differentiated services for diverse applications, such as IoT (mMTC), autonomous vehicles (uRLLC), and audio/video streaming (eMBB). Each of these applications possesses unique network requirements pertaining to bandwidth, latency, and reliability. This facilitates the provision of individual virtual networks for each application, accompanied by their own QoS assurances.
# Composite Kernel Method for PolSAR Image Classification Based on Polarimetric-Spatial Information
## 5 Conclusions and Future Work  
In this paper, a feature fusion approach that exploits both polarimetric and spatial information of PolSAR images is proposed. The polarimetric information is captured by collecting polarimetric signatures. The spatial information is captured by using the morphological transformation. Inspired by the complementarity between spatial and spectral features producing significant improvements in optical image classification, performance can be improved by fusing polarimetric and spatial information in the classifier. Traditionally, the method is based on a \"stacked\" approach, in which feature vectors are built from the concatenation of polarimetric and spatial features. In this paper, we propose a new method called the \"composite kernel\" method, which tunes the contribution of different type of features. Compared with one signal feature classification and traditional feature fusion classification via vector stacking, the composite kernel method possesses several excellent properties, making fusion more effective and leading to better performance. The proposed approach has been tested on the Flevoland data set and the San Francisco Bay data set. The obtained results confirm the benefit of combing different types of information via composite kernels for PolSAR image classification.  
In the future, we will continue studying PolSAR image classification with polarimetric and spatial features. Other possible kernel distances could be investigated via, for example, the spectral angle mapper. More advanced feature combination methods that solve the problems of feature extraction and combination simultaneously could also be researched. One such framework is multiple kernel learning, which could be utilized as a platform for developing effective classification approaches.  
This study was supported by the Fundamental Research Funds for the Central Universities A03013023601005 and the National Nature Science Foundation of China under Grant 61271287 and Grant U1433113.  
[PERSON] led the study. [PERSON] designed the experimental and wrote the paper. [PERSON] arranged the data sets. [PERSON] analyzed the data and checked the paper.  
The authors declare no conflict of interest.