esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Classification Model Training  
The eXtreme Gradient Boosting (XGBoost Version: 1.7.1) classifier was used for this analysis. XGBoost is a boosted tree machine-learning classification algorithm designed to be scaled for use on large datasets and is computationally more efficient than other machine-learning algorithms such as Random Forests [53]. XGBoost is also able to utilise GPU hardware acceleration, increasing model training speed [53].  
The XGBoost classifier was used to generate two separate binary classifications: (1) for the detection of Inundated Forest, and (2) Open Water. In both cases, all other land cover types are lumped into a collective, other landcover class. For the inundated forest classifier, the Open Water and other landcover training datasets were concatenated to create a single negative case class (totalling ~24.1 million training samples), and Inundated Forest training data was used as a true case dataset (totalling ~12.3 million samples).  
For the Open Water classifier, the training data is prepared in a similar way: the Open Water training dataset is used as the true case data, with other landcover and Inundated Forest training data concatenated to form the false case data.  
Table 2 shows Jeffries-Matsusita (JM) Distances calculated between the training datasets for the detection of each class. JM-Distances less than 0.5 are generally considered to show poor class separability [54]. Consequently, classification predictor variables with a value greater than 0.5 were selected, as shown in bold in Table 2. Further classification predictor variables were also included which showed a JM-Distance less than 0.5, in the case of object mean Incidence Angle for the detection of Inundated Forest. This was specifically included as the incidence angle of the radar pulse can directly influence the strength of the double-bounce backscatter. It was expected that including incidence angle as a classifier predictor variable would allow the XGBoost classifier to learn from this interaction.  
In the case of Open Water detection, object mean, and standard deviation of slope angle were also included as it would be expected that areas of open water would exist in areas of very low or zero slope. The standard deviation of HAND for each segment was also included, as it would be expected water objects would all have similar HAND values.  
The training data were split, with 75% being used to train the classifier and the remaining 25% reserved for internal model evaluation. Bayesian Optimisation was used to select the optimal hyperparameter set for the XGBoost classifier. The two binary classifiers were saved as trained models and applied to individual input ALOS-2 PALSAR-2 \(1^{\circ}\times 1^{\circ}\) tiles.
## 3 Experiments and Analysis  
To conduct the numerical experiment, the forward and backward filtering were used to process the observations based on the PPP method and obtain the precise STD and residuals. Then the results were saved as a SINEX_TRO 2.0 correction format, which supports conventional tropospheric parameters, including ZTD, ZWD, ZHD, carrier phase residuals, and multipath information. Finally, the correction files were introduced for simulating the real-time kinematic PPP process, and the effects of the troposphere on the PPP were comprehensively evaluated. Note that the specific processing strategies for the troposphere estimation are shown in Table 1.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Item** & **Strategies** \\ \hline Estimator & Forward Kalman/backward smoothing \\ Satellite orbits & Fixed \\ Satellite clock offsets & Fixed \\ Observations & Carrier phase and pseudo-range observations \\ Observation weighting & Elevation-dependent weight \\ Elevation mask angle & Five degrees \\  & ZHD: Saastamoinen model \\ Zenith troposphere delay & ZWD: estimated with random-walk \\  & Mapping function: Global Mapping Function (GMF) \\ Troposphere gradient & Estimated, epoch-wise random-walk \\ \hline \hline \end{tabular}
\end{table}
Table 1: Processing strategies for the troposphere estimation.  
Figure 1: Carrier phase observation residual from CUT0 station by forward filtering (SMT00) and backward filtering (SMT24). There are four kinds of satellite systems: (**a**) CUT0-GPS. (**b**) CUT0-Galileo. (**c**) CUT0-BDS-GEO. (**d**) CUT0-BDS-GEO/IGSO.
## 2 Problem Description and Formulation (INS Motion Model)
### Neural Network Localisation Scheme Set-Up  
We propose a displacement estimation model to minimise the effect of the noise in the accelerometer, as illustrated in Figure 1. The proposed model, which is analogous to the functioning of a closed-loop or feedback control system, operates in prediction mode by feeding back the output of the neural network at \(t-1/t-2\) and the vehicles' acceleration at time window \(t/t-1\) into the neural network in order to estimate the distance covered by the vehicle within the current window \(t/t-1\), as shown in Figure 2.  
However, as presented in Figure 1, during the training phase, the NN is fed with the GNSS estimated displacement rather than the output of the NN. Both models (training and prediction) are structured this way due to the availability of the GNSS signal during the training phase and its absence in the prediction phase. The NN's output is thus setup to mimic the functionality of the GNSS resolved displacement at window \(t-1/t-2\) during the prediction operation.  
Figure 1: Training set-up of the proposed displacement estimation model.  
Figure 2: Prediction set-up of the proposed displacement estimation model.  
Howbeit, as the NN's output never matches the GNSS displacement, the challenge becomes one of minimising the effect of the inexactness of the previous NN's estimation on the performance of the prediction model. We set about to address this by introducing a controlled random white Gaussian noise \(N\) with a normal distribution \(N\sim N\) (\(\mu\), \(\sigma 2\)) to one of the inputs of the NN; the GNSS resolved displacement within the previous time window, during the training phase. Where the mean \(\mu\), and the and the variance \(\sigma 2\), are determined experimentally from a sample displacement resolving of the GNSS and INS signals. This approach attempts to aid the NN to account for the impreciseness in the prediction output. Figures 1 and 2 shows the training and prediction set-up of the displacement model, respectively.  
Furthermore, we adopt a much simpler approach towards the estimation of the vehicles orientation rate as we found no performance benefit in utilising the feedback approach presented in the previous paragraphs. On the orientation rate estimation, the NN is made to learn the relationship between the yaw rate as provided by the gyroscope and the ground truth (yaw rate) calculated from the information provided by the GNSS.
# Non-Iterative Cluster Routing: Analysis and Implementation Strategies
## 3 Methods
### Cluster Routing Implementation
#### 3.3.4 Cluster Routing with Multiple Channels  
Cluster routing can also be implemented with multiple channels. Each capsule layer consists of **C** channels and, within each channel, there are **N** clusters. As each channel contributes a vote cluster, there are **C** vote clusters per channel, making **N** equal to **C** in this case. For simplicity, this variation involves multiple channels, with each channel representing the single-channel version of cluster routing, as previously presented.
# A Novel K-Means Clustering Algorithm with a Noise Algorithm for Capturing Urban Hotspots
###### Abstract  
With the development of cities, urban congestion is nearly an unavoidable problem for almost every large-scale city. Road planning is an effective means to alleviate urban congestion, which is a classical non-deterministic polynomial time (NP) hard problem, and has become an important research hotspot in recent years. A K-means clustering algorithm is an iterative clustering analysis algorithm that has been regarded as an effective means to solve urban road planning problems by scholars for the past several decades; however, it is very difficult to determine the number of clusters and sensitively initialize the center cluster. In order to solve these problems, a novel K-means clustering algorithm based on a noise algorithm is developed to capture urban hotspots in this paper. The noise algorithm is employed to randomly enhance the attribution of data points and output results of clustering by adding noise judgment in order to automatically obtain the number of clusters for the given data and initialize the center cluster. Four unsupervised evaluation indexes, namely, DB, PBM, SC, and SSE, are directly used to evaluate and analyze the clustering results, and a nonparametric Wilcoxon statistical analysis method is employed to verify the distribution states and differences between clustering results. Finally, five taxi GPS datasets from Aracaju (Brazil), San Francisco (USA), Rome (Italy), Chongqing (China), and Beijing (China) are selected to test and verify the effectiveness of the proposed noise K-means clustering algorithm by comparing the algorithm with fuzzy C-means, K-means, and K-means plus approaches. The compared experiment results show that the noise algorithm can reasonably obtain the number of clusters and initialize the center cluster, and the proposed noise K-means clustering algorithm demonstrates better clustering performance and accurately obtains clustering results, as well as effectively capturing urban hotspots.  
K-means clustering; noise algorithm; unsupervised evaluation; non-parametric Wilcoxon statistical analysis; urban road planning; taxi GPS data +
Footnote †: journal: Medical Imaging
## 2 Finite Element Modeling
### Heat Source Modeling
#### 2.3.1 Step 1: Node Selection Procedure  
\(r_{0}\) represents the radius of the laser beam. \(r_{1}\) is is set to \(\frac{1}{3}r_{0}\), since more than 50% of the energy is distributed within this small area. If we take the moment \(t_{0}\) as example, the center and height of the heat source are \((0,y_{t_{0}},0)\) and \(\eta_{t_{0}}\). A control volume of cylindrical shape is defined by \(r_{1},h\) and the center of the cylinder (0, \(y_{t_{0}}\), \(\eta_{t_{0}}\)), where \(r_{1}\) is the radius and \(h\) represents the height of cylinder (see Figure 7). We calculate an average temperature from all the nodes included in control volume, and this average temperature is used as the criterion for adjusting \(\eta_{t}\).  
Figure 8 shows the general procedure for selecting the nodes. A program (SIL) will loop on all the nodes present on the laser path, and the selected nodes should satisfy two conditions: (i) \(x^{2}+(y-y_{t_{0}})^{2}\leqslant\tau_{1}^{2}\); (ii) \((|z|-\eta_{t_{0}})^{2}\leqslant(h/2)^{2}\), where \((x,y,z)\) are the coordinates of the node. One should note that \(h/2\) should be larger than the minimal element size in the depth direction; otherwise, no nodes could be selected for Step 2. However, \(h/2\) should not be so large as to induce the instability due to the adjustment of \(\eta_{t}\). In this study, \(h\) is set to 20 \(\upmu\)m. Finally, the green nodes shown in Figure 8 are automatically selected for step 2.  
Figure 8: Schematic diagram of node selection.  
Figure 7: Schematic diagram of the control volume.  
Compared to the strategy of selecting only one node at the bottom center of the laser beam, selecting numerous nodes can provide more representative results, and the average temperature is supposed to be less sensitive to oscillations related to time steps.
# COVID-Related Misinformation Migration to BitChute and Odysee
## 6 Discussion  
This paper provides evidence on the diffusion of COVID-related disinformation videos from YouTube, a mainstream video platform, to BitChute and Odysee, which are low content-moderation platforms. Starting from a dataset of 7220 videos that were banned from YouTube after being flagged as spreading COVID-19 misinformation, we collected a dataset of 4096 BitChute and 1810 Odysee videos.  
Our analysis indicates that COVID-related misinformation videos on YouTube are shared on low content-moderation platforms soon after their original posting. The analysis showed that, for both BitChute and Odysee, most videos were shared at most within a week after their upload on YouTube. One might hypothesize that users are aware that their content will not have a long lifespan on the mainstream platforms, and they choose to post them on platforms with low content moderation. This finding is also in line with another study, which shows that the number of shares of the \"Plandemic\" on BitChute reached a peak just the day after the video was banned from YouTube [57].  
As for the popularity of migrated videos on BitChute and Odysee, the number of views on both platforms is relatively low. The number of views and other viewer interaction metrics are also used in other studies of misleading health-information videos in online video platforms such as YouTube [37]. However, the extremely low number of views of videos in our study does not permit the study of more complex correlations as in [78]. Furthermore, we analyzed the number of likes and dislikes, which is also relatively low on both platforms. Yet, it is worth mentioning that videos in BitChute that feature the keywords \"banned\" or \"deleted\" in the title tend to gather more likes, which is in agreement with previous work on YouTube that found a tendency for negative tone videos to be liked more by the viewers than videos with positive or ambiguous tone [50].  
Finally, emotion detection has been investigated in relation to the spread of misinformation. Authors in [84] proposed an emotion-based misinformation detection framework to learn content- and comment-emotion representations. They found that the proportion of anger on fake news content is 9% higher than in real news, while the percentage of happiness is 8% lower. In our analysis, we detected more negative emotions on BitChute, especially fear, compared to Odysee, where we found more positive emotions.
## 5 Results and Discussion
### Global Performance Evaluation
Hence, power inputs lower than 100 W are here referred as \"low\", while higher power inputs are referred as \"average-high\". For every heat load, the vertical BHM presents lower values of \(R_{eq}\) with respect to the horizontal orientation, denoting better thermal performances within the gravity-assisted mode in agreement with [PERSON] et al. [28]. In addition, for both configurations, \(R_{eq}\) strongly decreases until about \(Q_{net}\) = 100 W, to approach an almost asymptotic value at high power inputs. Such abrupt change in trend is because the device experiences a significant variation in terms of working behavior from low power inputs (\(Q_{net}\) < 100 W) to higher heat loads. Specifically, for \(Q_{net}\) < 100 W, intermittent flows occur (i.e., flows characterized by frequent stopovers which undermine the device performance), while, at high power inputs, the device is fully activated (i.e., the fluid regularly flows in every channel, ensuring a proper and constant heat dissipation to the condenser). Such a transition in terms of working regime is due to stronger phase-change phenomena at the evaporator, which could not be regularly sustained at power inputs lower than 100 W. In fact, a relevant part of the provided heat loads was transferred  
Figure 7: Equivalent thermal resistances of the studied FPPHP. to the condenser by conduction through the copper walls of the device, i.e., the dissipated heat did not take part in fluid evaporation.
## 2 Materials and Methods
### Data  
#### 2.3.1 Flux Data  
FLUXNET2015 provides gap-filled EC-GPP and corresponding meteorological data on a daily time scale. We excluded sites without V\({}_{\rm cmax}\) data. A total of forty sites in EuroFLUX and one site in AmeriFLUX were selected from FLUXNET 2015 [60]. Time series LAI data were extracted from the GLASS LAI product for the pixels in which the flux towers were located. Because of the potential for uncertainties in both GPP and LAI, we excluded flux data with inconsistent temporal profiles of EC-GPP and LAI. This screening can reduce the amount of noise from the spatial mismatch between the remotely sensed data and field observations. In addition, nighttime flux data were removed. A total of 188 site years were selected for model calibration and validation purposes. The site-level daily EC-GPP, shortwave radiation, temperature, VPD, and LAI were used to drive the FGM.
# Criterion-Related Validity and Reliability of the Front Plank Test in Adults: The ADULT-FIT Project
## 3 Results
### Reliability  
Test-retest reliability of the FPT is presented in Table 3. Significant mean differences between T1 and T2 were found in the total sample (4.07 \(\pm\) 1.59 s; \(p=0.012\)) and in the female sample (7.27 \(\pm\) 3.12 s; \(p=0.027\)). The effect size (Cohen's \(d\)) of the mean differences was 0.001 in both groups. Non-significant differences were found between T1 and T2 in the male sample (2.12 \(\pm\) 1.68 s; \(p=0.213\)), and the ICC reported a high reproducibility, being 0.99 (0.98-0.99). All the analyzed error measurements showed low values (%Error = 4.68; %CV = 6.33; SEE = 13.97 s), and similar results were obtained when the sample was divided by sex and age groups. The MDC\({}_{90}\) values were approximately 0.00 for each measure, indicating that no real change occurred between T1 and T2. Figure 4 shows the Bland-Altman plots of the FPT for the whole sample and divided by sex and age groups. The random error was close to 0 and narrow LoA (14.39 s (95% LoA = \(-32.27\) to 24.14; \(p=0.002\))) in the whole sample, in males (\(-2.41\) s (95% LoA = \(-26.01\) to 21.18; \(p=0.023\))) and females (\(-6.79\) s (95% LoA = \(-42.28\) to 28.70; \(p=0.020\))) (Figure 4B,C). According to age groups, the random error also was close to 0 and narrow LoA (\(-2.99\) s (95% LoA = \(-31.73\) to 25.75; \(p<0.001\))) in young adults and adult groups (\(-8.18\) s (95% LoA = \(-36.42\) to 20.07; \(p=0.899\))) (Figure 4D,E). Heteroscedasticity of errors was observed (\(r^{2}>0.10\); \(p<0.05\)) between T1 and T2 in all groups, excepts in adults (\(r^{2}=0.0011\); \(p=0.899\)) (Figure 4). Figure 3: Electromyographic activity of trunk muscles during the front plank test. Difference in the EMG data of the trunk muscles (URA, LRA, EO, MF, and ES). MVIC = maximum voluntary isometric contraction; RA = rectus abdominis. Significance level *** = statistically significant (\(p<0.001\)) for the erector spinae and multifidus muscles.
## 6 Advanced Considerations: Environmental Factors in Underwater VLC
### Water Salinity  
Salinity, or the concentration of salt in water, is another critical environmental factor affecting underwater VLC performance. Salinity changes the refractive index of water, which in turn affects light propagation. Higher salinity can increase the absorption and scattering of light, thus degrading communication performance. Ref. [55] presents that salinity has a direct effect on the statistical properties of link coefficients in VLC systems underwater, where higher salinity increases optical turbulence. Ref. [8] emphasizes that the models should be capable of adapting to various water types, including salinity variation, for the purpose of predicting VLC performance with accuracy. Ref. [56] again supports this argument by citing a change in salinity where turbulence degrades communication indicators because of signal attenuation and bit error rates.  
It is also reflected in various studies on vertical VLC systems. Salinity changes with depth, as demonstrated in [60], and the different layers result in non-mixing effects, thus making variable signal propagations possible. Ref. [62] extends this discussion by highlighting that salinity stratification can cause variations in the light signal as it travels through different layers, contributing to fading effects. Ref. [63] also comments that salinity alters the refractive index of seawater and hence light absorption and scattering, affecting path loss and communication performance. According to [64], with the increment of salinity level, the reliable estimation of channel state information (CSI) becomes harder; hence, the error probability is higher, which deteriorates the system performance. Such results emphasize salinity as one of the main factors during the design and optimization of VLC underwater systems.
## 3 Data and Empirical Results
### Empirical Results  
#### 3.2.1 CD-Vine COPAR Models  
The dependence of economic data was explored within: (1) manufacturing; (2) agriculture; and (3) the service sector. Macro-economic time-series data are usually non-stationary ([PERSON] and [PERSON], 1982). Hence, the KPSS test was utilized within the null hypothesis of a stationary against a unit root alternative. All variables were stationary at an alpha level of 0.01 in all cases (for example, in the manufacturing sector for the GPP, it gives KPSS level of 0.166, truncation lag parameter of 2 and p-value of 0.121; for the FDI, it reports KPSS level of 0.168, truncation lag parameter of 2 and \(p\)-value of 0.152; and for the TRADE, it informs KPSS level of 0.199, truncation lag parameter of 2, \(p\)-value of 0.136). Consequently, the VAR specification in level was sufficient. Table 1 summarizes that the dependencies between two variables belong to several copula families (Gaussian, Clayton, Frank, Joe, Rotated Joe, rotated Clayton, and Rotated Gumbel). The pair-copula constructions decompose a multivariate probability density towards bivariate copulas for a good selection choice to build more powerful tree sequences. We then informed the bivariate copula families serving as building blocks for vine copulas. For instance, the optimal pair-copula constructions based on the C-Vine COPAR between the GPP (1) and FDI (2), the GPP (1) and TRADE (3), and the FDI-TRADE (23) and GPP (1) in the manufacturing sector were [PERSON], rotated Joe and [PERSON] families, respectively. The Kendall's tau reported both positive and negative dependencies. For tail dependencies in manufacturing sector (D-Vine COPAR), the GPP and FDI had a great dependence in the upper tail, indicating that GPP is likely to raise together with FDI; GPP and TRADE were similar. For the agriculture sector (C-Vine COPAR), the GPP and TRADE had dependence in the lower tail. Furthermore, we can see that the AIC to penalize copula families with more parameter for D Vine-COPAR model (59.125) produced a better performance in the case of the manufacturing sector, while the C Vine COPAR models (100.422 and 111.162) provided properly good fit models for the agriculture and service sectors. After choosing the independence copula, the optimal lag of the Vine COPAR(p*) models were D-Vine COPAR (3), C-Vine COPAR (3) and C-Vine COPAR (2) for the manufacturing, agriculture and service sectors, respectively. Figure 1: The Regional Economic performances in the Southern Thailand SEZ.