esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussions
### Hydrogen Bonding Analysis  
In this study, we primarily calculated the number of hydrogen bonds formed between interfacial water and the C-S-H surface to investigate the effect of SCA alteration on the hydrophilicity of the C-S-H surface. As shown in Figure 14, the hydrogen bonds at the interface mainly form between water molecules and the silicate chains, as well as between water molecules and SCA.  
Figure 11: The fracture surface of RCA after binarization: (**a**) no modification under dry conditions; (**b**) SCA modification under dry conditions; (**c**) no modification under moist conditions; (**d**) SCA modification under moist conditions.  
Figure 14: Type of hydrogen bond: (**a**) water-silicate chains; (**b**) water-SCA (KH550).  
Figure 12: The surface microstructure of the RCA: (**a,b**) no modification; (**c,d**) SCA modification.  
Figure 13: The interface microstructure of the RCA/asphalt: (**a**) no modification; (**b**) SCA modification.  
The number of hydrogen bonds formed between C-S-H and interfacial water is shown in Figure 15. The results indicate that there are fewer hydrogen bonds between C-S-H and interfacial water after SCA modification. Specifically, unmodified C-S-H forms 213 hydrogen bonds with interfacial water. The more hydrogen bonds form between C-S-H and interfacial water, the stronger the hydrophilicity of the C-S-H surface. Due to the hydrophobic nature of asphalt, stronger hydrophilicity of the C-S-H surface leads to weaker adhesion between asphalt and the C-S-H. After SCA modification, the number of hydrogen bonds at the C-S-H interface decreases by 19.7%, indicating a decrease in the hydrophilicity of the C-S-H surface after SCA modification, thereby enhancing the interaction at the asphalt/C-S-H interface. This is mainly because the C-S-H surface is composed of silicate chains and calcium ions, which have strong hydrophilicity. After SCA modification, the distance between interfacial water and the C-S-H surface is increased, thereby reducing the formation of hydrogen bonds and decreasing the hydrophilicity of the C-S-H surface. Compared to the previous SCA-modified SiO\({}_{2}\) interface, the SCA modification can be very effective in reducing the hydrophilicity of both SiO\({}_{2}\) and C-S-H surfaces, thus improving the effect of water molecules on the aggregate/interface [60]. The results show that SCA modification has a positive effect on enhancing the water damage resistance of the aggregate/asphal interface.
## 2 Methods and Data
### Model  
#### 2.3.1 K-Means  
Clustering not only can be used as a separate process to find the internal structure and regularity of data but also can be used as a precursor process for supervised learning tasks such as classification. K-means algorithm is one of the widely utilized clustering algorithm [27]. In this study, the K-means algorithm was used to for clustering and analyzing the similarities and differences of the mechanical ventilation operation behavior of occupants.  
Given a sample set \(X=\{x_{1}\), \(x_{2}\),..., \(x_{n}\}\), the K-means algorithm divides \(X\) into \(K\) clusters \(C\). Each cluster has its \"centroid\" \(\mu_{j}\) (\(j=1\), \(2\),..., \(K\)) obtained by averaging the samples in this cluster. The K-means algorithm aims to select the optimal cluster partition by minimizing the squared error.  
\[\sum_{i=0}^{n}min(\left\|x_{i}-\mu_{j}\right\|^{2}) \tag{1}\]  
The algorithm is composed of three steps. Firstly, \(k\) centroids are initialized, where a naive method is to randomly choose \(k\) samples from \(X\). Secondly, each sample in \(X\) is assigned to its nearest centroid. Thirdly, the mean value of the samples for each cluster as the new centroid is recalculated. The differences between the old and the new centroids are computed, and the algorithm repeats the last two steps unless the difference is less than a threshold.  
The Silhouette Coefficient is usually used to evaluate the performance of a cluster analysis without knowing the true cluster label [28]. A higher Silhouette Coefficient score indicates a model with better defined clusters:  
\[S=\frac{\sum_{i=1}^{n}\frac{b(i)-a(i)}{max\{a(i),b(i)\}}}{n} \tag{2}\]  
where \(S\) is the Silhouette Coefficient score, \(a(i)\) is the average distance between sample \(i\) and all other samples within the same cluster and \(b(i)\) is the minimum average distance between \(i\) and all samples in any other clusters.
# Evaluation of Low-Temperature Performance of Recycled
## 3 Results and Discussion
### Fracture Strength, Fracture Temperature, and Transition Temperature  
The fracture strength and fracture temperature results of the mixture obtained through the TSRST are illustrated in Figures 9 and 10. In this study, it is revealed that as the RAP content increases, both the fracture strength and fracture temperature of RAMs gradually decrease. The observed order was OR \(>\) UV 0.6 \(>\) TH 0.6 \(>\) UV 0.4 \(>\) TH 0.4 \(>\) UV 0.2 \(>\) TH 0.2, indicating that the addition of RAP diminishes the low-temperature crack resistance of RAMs, consistent with previous research findings [30; 31]. This decrease is attributed to the harder and more brittle nature of the aged asphalt present in RAP, in contrast to the original asphalt. Furthermore, a higher RAP content leads to poorer asphalt miscibility within the mixtures, resulting in an increased number of microcracks and micropores. These defects render the RAM more susceptible to fracture at lower temperatures due to thermal stress during the cooling process.  
The transition temperature is a crucial indicator of the rheological properties of an asphalt mixture at low temperatures. It separates the temperature-stress curve into two distinct segments; the first segment reflects the stress relaxation performance of the mixture, while the latter segment's stress relaxation properties disappear. As the temperature decreases, the asphalt mixture shifts from viscoelastic behavior to elastic behavior. The transition temperature signifies the point at which the viscoelastic behavior of the mixture changes [32]. The transition temperature is determined graphically, using a specific method  
Figure 8: SEM images of virgin aggregate and binder interface (\(\times\)1000).  
that involves marking the tangent lines of the viscoelastic and elastic stage curves on the temperature-stress curve of the asphalt mixture. The temperature corresponding to the intersection of these two tangent lines is the transition temperature. Analysis of Figure 10 reveals that the transition temperature of the RAM with UVRAP is higher than that of the original asphalt mixture. Conversely, the transition temperature of the RAM with THRAP is lower than that of the original asphalt mixture. The viscoelastic temperature range of the RAM with UVRAP is narrowed by 10.79%, while the elastic temperature range is extended by 1.30%. On the contrary, the elastic temperature range of the RAM with THRAP is extended by 2.16%, while the elastic temperature range is narrowed by 18.75%. This suggests that UVRAP accelerates the shift from viscoelasticity to elasticity within a narrow temperature range and extends the temperature range for elastic stage failure of RAMs. In contrast, THRAP accelerates the destruction of the asphalt mixture during the elastic stage. RAPs with varying thermal histories have different effects on the stress relaxation capacity of RAMs.
## 2 Materials and Methods
### Model
#### 2.3.2 Model Training and Evaluation  
The values of each element of the flux site data used in this study have large differences. To ensure that model learning and training are not affected by this issue, each element of the data is standardized and normalized using the following formula:  
\[\mathbf{x}^{\prime}=\frac{x-\mu}{\sigma} \tag{6}\]  
In Equation (6), \(\mu\) and \(\sigma\) are the mean and standard deviation of the corresponding element, respectively. The processed element data have a mean of 0 and a variance of 1, which prevents the model from being biased toward elements with large value ranges and ensures the accuracy of the model. After data pre-processing, the learning and training steps start. Since the observation data from the nine flux sites are not large in scale (a total of 1421 records), ten-fold cross-validation was applied to make full use of the data and to ensure the model's prediction performance on the whole data set. That is, for each fold, 10% of the data were taken as the test set, and the remaining 90% were used for learning and training. Then, the data used for learning and training were divided into training and validation sets at a ratio of 9:1, and the data order was randomly shuffled to avoid over-fitting. The model uses Mean Squared Error (MSE) as the loss function and the Adam optimizer to update model parameters. This process was repeated ten times to complete the predictions on all the data and evaluate the results.  
This study used three common statistical indicators to evaluate model prediction accuracy: mean squared error, mean absolute error, and R-squared. The relevant formulas are as follows:  
\[R^{2}=(\frac{\sum_{i=1}^{n}\left(y_{i}-\overline{y}\right)(y_{i}^{\prime}- \overline{y}^{\prime})}{\sqrt{\sum_{i=1}^{n}(y_{i}-\overline{y})^{2}}\sqrt{ \sum_{i=1}^{n}(y_{i}^{\prime}-\overline{y}^{\prime})^{2}}})^{2} \tag{7}\]  
\[RMSE=\sqrt{\frac{1}{n}{\sum_{i=1}^{n}\left(y_{i}^{\prime}-y_{i}\right)^{2}}} \tag{8}\]  
\[MAE=\frac{1}{n}{\sum_{i=1}^{n}\left|y_{i}^{\prime}-y_{i}\right|} \tag{9}\]  
where \(y_{i}^{\prime}\) and \(y_{i}\) are the predicted and observed values of GPP, respectively; \(\overline{y}^{\prime}\) and \(\overline{y}\) are the mean values of \(y_{i}^{\prime}\) and \(y_{i}\), respectively; and n is the number of observation samples.
## 4 The Updated IoT Network  
An IoT network with changes at the device level is presented in Figure 2. The updated IoT system implements the following in the device layers to enhance the fault tolerance of the network [11; 42].  
1. Implement a fault detection system in the device layer that detects possible power-related faults and then isolates the faulty devices.
2. Establishing a crossbar network between the cluster heads and the device clusters to provide alternative redundant communication paths.
3. Develop redundant networks using different topologies connecting base stations and cluster heads.
4. Connecting the base stations to the microcontrollers in peer-to-peer mode.
5. A method to compute fault tolerance considering linear and probability models.
6. Connecting several controllers to a single services server
7. Connecting a services server to a gateway en route to the internet connecting the cloud.  
The devices in a cluster are linearized and connected to a cluster head free from sensing or actuating function. A crossbar network connects the linearized clusters' outputs to the cluster heads. The cluster heads are connected to a base station in a parallel computing mode, and the base stations communicate with multiple microcontrollers in a peer-peer computing mode.  
The microcontrollers are connected to the server of the service in many-to-one mode. The services server receives the requests from the devices or the users, executes the service-related code, and transmits the results back to controllers or the user.  
The revised IoT network with changes made in the device layer is shown in the Figure 2.  
For developing a fault tree of the sample IoT network, the crossbar network is replaced by a single device whose success rate is computed using probability models connected with the crossbar network [11]. The success rate of such a network is computed as 0.842. The fault tree of the residual network and a table showing the success rate computations are generated through the algorithms presented by [PERSON] et al. [11]. The fault tree generated for the updated network is shown in Figure 3.  
From the sample network, the single base station is the real bottleneck and forms the most vulnerable areas of failure of the entire IoT network. Any fault accruing in this patch will disconnect the entire network, and the fault tolerance of such a network becomes zero. The need for implementation of redundancy of the base stations thus arises.  
Figure 2: Sample IoT network with a single base station.
## 2 Materials and Methods
### 3 dB Quadrature Hybrid Coupler and T/R Switch  
A 3 dB quadrature hybrid coupler was designed with an axisymmetric structure and fabricated using chip capacitors, trimmer capacitors, inductors, and a 2 mm thick copper shield for isolation. The 3 dB quadrature hybrid coupler was implemented to transmit a signal to both ports of the PFT \({}^{13}\)C birdcage RF coil with a 90\({}^{\circ}\) phase difference and 3 dB attenuation [30]. The values of the capacitors and inductor at the resonance frequency corresponding to \({}^{13}\)C in 3.0 T (32.13 MHz) were as follows: C\({}_{1}\) = 99 pF, C\({}_{2}\) = 41 pF, and L = 175 nH. Even and odd mode analyses of the designed circuit diagram indicated a magnitude error of 0.0032 V and the phase difference corresponded to 89.94\({}^{\circ}\). Additionally, the analysis of practical implementation using the FieldFox RF analyzer indicated that the mean absolute difference of the magnitude was 0.087 dB and the mean absolute phase difference was 89.2\({}^{\circ}\).  
A transmit/receive (T/R) switch was designed and fabricated to protect the receive preamplifier from the high-power signal in transmission and to transmit the signal to the reception port while it disconnects from the transmission port [31]. The T/R switch was constructed using equivalent lumped-LC networks (L = 247.67 nH and C = 97.65 pF) and crossed PIN diodes instead of using \(N\)4 transmission lines. The crossed PIN diodes exhibited behavior similar to short circuits via a high-voltage signal during the transmit mode and open circuits during the receive mode. To prevent damage to the switching diodes and short circuits from high RF energy, a high-capacitance coupling capacitor was installed in the T/R switching circuit.
## 2 Materials and Methods
### Sentinel-1 and Sentinel-2 Data Pre-Processing  
A time series of 80 Sentinel-2 Level-2A images corresponding to our study area (\(110\times 110\) km tile: T30 SNE) and acquired from September 2020 to August 2021 were downloaded from the Theia website \"[[https://www.theia-land.fr/](https://www.theia-land.fr/)]([https://www.theia-land.fr/](https://www.theia-land.fr/)) (accessed on 15 October 2021)\". These data are atmospherically corrected using the MAJA processing chain [68] and correspond to ortho-rectified surface reflectance. They cover 10 bands ranging from visible to near-infrared wavelengths and are provided with a mask for clouds and their shadows [69].  
After applying the cloud mask, we selected the 26 images with low cloud cover (\(\leq\)5%) for our study area. We then resampled bands with 20 m spatial resolution to 10 m using the nearest-neighbor assignment method. Following analysis of NDVI profiles, the reflectance of bare soil was extracted using an NDVI threshold [29; 70; 71]. Pixels with an NDVI <0.25 were considered as bare soils. Only 58 of the 83 sampling points were bare soils on at least one date during the 2020-2021 agricultural season.  
The dual-satellite constellation of Sentinel-1A and Sentinel-1B was launched in April 2014 and April 2016, respectively. It provides C-band (frequency = 5.4 GHz) SAR images with a revisit time of 6 days in dual-polarization VV (vertical-vertical) and VH (vertical-horizontal) modes. In this study, we used Level-1 ground-range-detected products acquired from September 2020 to August 2021. Sentinel-1 images were downloaded using the Google Earth Engine application, which provides radar signal images processed for thermal noise removal, radiometric calibration, terrain correction, and speckle filtering.
# Statistical Characterization of Supraharmonics in Low-Voltage Distribution Networks
## 2 Materials and Methods
### Probability Distributions for the Statistical Characterization of Supraharmonics
#### 2.3.2. Lognormal Distribution  
For the analyzed phenomena, either the TSHD or individual supraharmonics can be zero or positive values; then, it is appropriate to include distributions having positive-real-valued support. The lognormal (LogN) distribution is among them, and it assumes that the logarithm of the random variable is normally distributed. The PDF is:  
\[f_{LogN}(x|m,s)=\frac{1}{xs\sqrt{2\pi}}e^{-\frac{\ln{(x)}-x|^{2}}{2\delta^{2}}}, \tag{7}\]  
where \(m\) is the mean of the logarithm of the random variable (i.e., a location parameter) and \(s\) is the standard deviation of the random variable (i.e., a scale parameter). The MLE of the parameters on a dataset \(\mathbf{x}=\{x_{1},\dots,x_{N}\}\) of observed samples is:  
\[\left\{\begin{array}{c}\hat{m}=\frac{1}{N}\sum_{n=1}^{N}\ln(x_{n})\\ \hat{s}=\sqrt{\frac{1}{N}\sum_{n=1}^{N}[\ln(x_{n})-\hat{m}]^{2}}\end{array}\right. \tag{8}\]
## 4 Results and Discussion
### Lstm--Word2 Vec/GloVe Embeddings
These embeddings are subsequently fed as inputs into an LSTM model comprised of dropout regularization and sigmoid output units for binary spam/nonspam predictions. As evidenced in Figure 9, the model achieves strong performance, with 79.74% validation accuracy on the YouTube dataset after five training epochs. The convergence of training and validation loss curves indicates minimal overfitting. The model begins with a training loss of 0.5581 and an accuracy of 69.53%. On the validation set, it achieves a loss of 0.4681 and an accuracy of 77.78%. The final epoch shows training loss at 0.4316 and accuracy at 79.73%, with validation loss and accuracy improving to 0.4309 and 79.74%, respectively. The close performance on both training and validation sets suggests that the model is convergent well and learning from the training data without significantly overfitting. In Figure 9, the blue line (training accuracy) and the orange line (Validation accuracy) show the accuracy of the model for each epoch on the training and validation datasets, respectively. There is a noticeable dip in validation accuracy in the second epoch, which could be indicative of some instability in the training process at that point. However, from the third epoch onwards, both training and validation accuracy improved consistently. The final epoch shows the training and validation accuracy converging, which is a sign of good generalization. The blue line (training loss) and the orange line (validation loss) indicate the loss on the training and validation datasets across epochs. Similar to the accuracy plot, there is an increase in the validation loss during the second epoch. However, in subsequent epochs, both training and validation losses decrease, indicating improvement in the model's performance. The convergence of the training and validation losses by the fifth epoch suggests that the model is not overfitting significantly. The precision for classification is 76% of the predicted comments. The recall is higher, at 87%, which means that the model can capture 87% of the actual data. The F1-score, which is a harmonic mean of precision and recall, is 81% for comments, indicating a balance between precision and recall. For spam, precision is higher at 0.85, suggesting that when the model predicts spam, it is correct 85% of the time. The recall for spam is lower at 0.72, meaning the model misses around 28% of actual spam tweets. The F1-score for spam is 78%, slightly lower than for nonspam, due to a lower recall. Overall, the accuracy model correctly identified 80% of the tweets and 87% of the comments, as shown in Table 4. In essence, the role of GloVe in encoding semantics explained the convergence plots, interpreted the precision/recall trade-off, and contrasted against Word2 Vec--all in service of enhancing our approach design.
## 3 Study Area, Dataset and Evaluation Metrics  
### WRF Domain Configuration  
Two entirely different study areas (as shown in Figure 3) are involved in this study to validate the forecasting ability of the proposed hybrid model, as summarized in Figure 2. One is the area that covers the site of Maizuru located in the middle of Japan; the other one is located in Qinghai, China.  
The WRF domain configuration follows the steps recommended by [PERSON] [33]. For the Maizuru site, it consists of a parent domain and two nested domains with horizontal resolutions of 6.0 km, 1.5 km and 0.5 km, respectively, in order to obtain high-resolution predictions. For the Qinghai site, there are four domains with horizontal resolutions of 12.5 km, 4.5 km, 1.5 km, and 0.5 km, respectively.  
Figure 2: A schematic diagram of the hybrid system of forecasting day-ahead solar radiation.  
The resolutions of the parent domains for both sites are based on GFS (Global Forecast System) real-time forecasts.
## 4 Methodology
### Error Resilience
The probability of channel byte loss (\(BL\)) serves to predict the amount of redundant data to be added to the payload. In implementation, \(BL\) is found through measurement of channel conditions, which is mandatory anyway in a WiMAX mobile station. If the original packet length is \(L\), then the redundant data is given simply by  
\[R=L\ \times BL+(L\ \times BL^{2})+(L\ \times BL^{3})...=L/(1-BL)-L \tag{2}\]  
which is arrived at by adding successively smaller additions of redundant data, based on taking the previous amount of redundant data multiplied by \(BL\). The statistical model of Equation (1) in Section 2 was utilized to determine the Raptor code failure probability. Figure 10: Percentage contributions of data partitions (DPs) A, B and C over a range of QPs for (**a**) \(Paris\) and (**b**) _Stefan_ video clips. Packets with \"piggybacked\" repair data are also sent. Suppose a packet cannot be decoded despite the provision of redundant data. It is implied from Equation (1) that if less \(k\) symbols (bytes) in the payload are successfully received, a further \(k-m+e\) redundant bytes can then be sent to reduce the risk of failure. This reduced risk arises because of the exponential decay of the risk that is evident from Equation (1), which gives rise to Raptor code's low error probability floor. In practice, \(e=4\), reduces the probability of failure to decode to 8.7%. Only one retransmission over a WiMAX link is allowed to avoid further increasing latency. If that retransmission fails to allow reconstruction, that packet is abandoned.
## 5 BER and Data Rate Calculation of the Proposed Network  
This section calculates the system's SINR, BER, and data rate, and performs packet interarrival time analysis. These calculations assume practical asynchronous communication systems. In addition, the network estimations include synchronous communication systems.
## 2 Numerical Model and Simulations
### Governing Equations  
#### 2.2.1 Assumptions  
1. Heat transfer and fluid flow is simplified to one dimension along the axial direction of the regenerator;
2. The wall of the regenerator is adiabatic;
3. Axial conduction in the solid matrix is neglected;
4. Heat conduction between the wall and solid matrix in the regenerator is negligible;
5. Density of the solid matrix is assumed to be constant and thermal expansion of the solid is neglected.