esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Hardware Design  
EMMNet is composed of data collectors, data concentrators, hand-held devices, a centralized server, and clients. The hardware design of smart sensors will be detailed in subsequent subsections.  
Figure 1: Architecture of EMMNet.
## 3 Evolution of Smart Grids
The major drawback of AMR technology is that it does not solve demand-side management (DSM) problems, as shown in Figure 3. Based on the data collected from the meter, utilities cannot take corrective action with AMR technology, which means that the transition to a smart grid is not feasible. Given all these limitations, utility spending shifted to advanced metering infrastructure (AMI). Figure 1: The current electricity utility grid. Figure 2: The evolution of the smart grid. AMI is a two-way communication system that allows utilities to both collect meter data and change parameters at the customer service level. The fundamental goal of load management and securing utility revenues can be achieved through AMI.
# A Robust Sharding-Enabled Blockchain with Efficient Hashgraph Mechanism for MANETs
## 7 Performance Evaluation
### Experimental Evaluation  
#### 7.2.1 Experimental Setting  
Opnet [38] is utilized to simulate the communication of consensus and disclose the effects of mobility on consensus. It is a popular network simulator of discrete events, providing a realistic and reliable simulation environment in most network types. Some models are pre-built in Opnet and easily deployed for MANET simulation, including a traffic model, statistical model, mobility config, failure config, routing protocol, etc.  
The scenario map is 4 km \(\times\) 4 km. Nodes are identified by their IP address, and they move in a random direction with a power of 0.05 dB and a speed of 50 m/s. The number of failure nodes is less than 1/3 of all nodes. Packets are propagated through the aodv routing protocol in the channel bandwidth of 11 Mb/s without being reassembled during transmission. The traffic is generated in an exponential distribution. Each simulation under the same condition is replicated ten times, and the average of these 10 data points is considered.
# A Feature-Weighted SVR Method Based on Kernel Space Feature
## 3 Feature-Weighted Support Vector Regression
### The Necessity of Feature Weighting
Therefore, the similarity of the sample points generated by the classical algorithm may be influenced greatly by the unimportant features with a large value range, resulting in the inconsistency of the similarities in the kernel space and in the actual situation. We can deduce that the kernel element is used to simplify the computation of the inner product in the kernel space in solving the convex quadratic programming problems from Formulas (4)-(5). If the similarity cannot reflect the actual rule of the dataset and is dominated by unimportant features, the solution to the optimization problem by applying the structural risk minimization principle is unreasonable. In the paper, the feature-weighting method is used to match the effect of the feature on the kernel space feature with its contribution to the model output. Regarding the k-th feature, if a weight value \(w_{k}\in\left[0,1\right]\) is given, the kernel element will be changed as:  
\[K_{w}(x_{i},x_{j})=\exp\left(-\gamma\left(\sum_{k=1}^{n}\left(w_{k}\left(x_{ ik}-x_{jk}\right)\right)^{2}\right)\right) \tag{12}\]  
Likewise, the weighted elements of the linear kernel can be rewritten as follows:  
\[K_{w}\left(x_{i},x_{j}\right)=\sum_{k=1}^{n}w_{k}^{2}x_{ik}x_{jk} \tag{13}\]The weighted sigmoid kernel can be expressed as follows:  
\[K_{w}\left(x_{i},x_{j}\right)=\tanh\left(\gamma\sum_{k=1}^{n}w_{k}^{2}x_{ik}x_{jk }+R\right) \tag{14}\]  
If the contribution of each feature to the output can be confirmed before model training and an appropriate weight value is assigned, the role played by the feature in the kernel space matches its contribution. When all the weights of the feature are one, the FW-SVR is degraded to the classical SVR. When the weight of a feature tends to be zero, it shows that the input feature has little influence on the output and means a dimension reduction. Moreover, the distance between the sample points is shortened, and the distribution of the samples is more compact.
# Application of Machine Learning Models to Multi-Parameter Maximum Magnitude Prediction
## 3 Methods
### Machine Learning Models
#### 3.1.2 SVM  
SVM is a supervised learning model for classification and regression that was proposed by [PERSON] and his colleagues in the 1990s. SVM classifies data by finding an optimal hyperplane in a high-dimensional space, aiming to maximize the margin between classes, thereby improving classification accuracy and robustness [34]. The specific mathematical description is as follows:  
Let the training set be \(T_{i}=\Big{(}\text{x}_{i},\text{y}_{j}\Big{)}\), where \(i=1,2,\ldots,l\). The hyperplane is \(y=\omega^{\text{T}}x+b\), where \(\omega\) and \(b\) represent weight vector and bias, and denotes the matrix transpose operation. Maximize the sample distance of the hyperplane to different classes according to the SVM principle, which is expressed as:  
\[arg\;max\bigg{\{}\frac{1}{\parallel\omega\parallel}\cdot min[(\omega^{\text{ T}}x+b)\cdot y_{i}]\bigg{\}} \tag{14}\]  
where \(\parallel\omega\parallel\) represents the L2 norm of the weight vector, used to balance the model's complexity and classification accuracy.  
of information in and out of the memory cell, determining which information should be remembered and which should be forgotten (Figure 4).
# A Novel Method for Ocean Wind Speed Detection Based on Energy Distribution of Beidou Reflections
## 4 Results, Analysis and Discussion
### Wind Speed from the Meteorological Station  
During the coastal experiment, the sea surface wind data from the Zhapo meteorological station was collected every 5 minutes in two periods, i.e. from August 3 to August 5 and from August 13 to August 14. Since these two periods include two typical wind states: gentle wind (1-10 m/s) and high wind (10 m/s-40 m/s), respectively, we chose them to verify the performance of the new observables for wind retrieval. From the afternoon of August 3 to the early morning of August 5, the trend of the wind shows a decreasing evolution ranging from 10 m/s to 2 m/s as shown in Figure 6a. To the  
Figure 5: Power waveforms at 12:00 on August 13, 2013. (**a**) 1 ms power waveforms; (**b**) Averaged power waveform (coherent integration time = 50 ms, incoherent integration: \(N\) = 5000).  
contrary, the wind speed shows an increasing evolution from noon on August 13 to the early morning on August 14. The wind speed ranged from 12.7 m/s to 37.3 m/s during the observation time.
## 2 Materials and Methods
### Proposed Methodology
#### 2.2.1 Category of Image Dataset  
Under adversarial machine learning (AML), we run each adversarial attack individually on the DNN model, ResNet-34, using the test set in the CIFAR-100 dataset, which contains 10K images. The attack success ratio of each of the adversarial attacks varies, as illustrated in Table 1. During the categorization process, as represented in Algorithm 1,  
Figure 1: Proposed adversarial detection system design, which is composed of a primary DNN classification decision model and a secondary classical ML model for adversarial attack detection and verification.  
Figure 2: Classification accuracy over Top_k before and after different adversarial attacks using the CIFAR-100 dataset by two classical ML models: **(a)** random forest model and **(b)** the _k_-NN model. The accuracies under different adversarial attacks are almost identical; thus, those resulting curves override each other and make a single purple-colored curve.  
each image (x) is first checked by the DNN model for the correct label. The mispredicted result from DNN(x) adds x directly to the SETmis set. In contrast, the correct prediction of x passes to the AML(x) algorithm for a trial (e.g., FGSM), and the successfully applied attack output is added to the SETadv set. The unsuccessful attack moves x to the SETrc set. In summary, we categorize the outputs into three sets as follows:  
* SETrc: The set of images that the DNN can correctly identify;
* SETmis: The set of images that the DNN misidentifies (misclassification);
* SETadv: The set of images produced by AML that can successfully and deliberately make the DNN misidentify as another object the attacker wants.  
The percentage of misclassified images (SETmis) is maintained at 22.54 across various attacks. However, the percentages for the other categories vary depending on the strength of each attack and its parameters. Generally, the adversarial generated samples (SETadv) or the attack success ratio receive the highest percentage among other sets in all four adversarial attacks.  
```
1[SETrc, SETmis, SETadv] = Category (Image dataset, DNN classification results) Input {\(x\), \(right\) label} \(\in\) CIFAR-100(\(test\) set), \(DNN\) model \(DNN(x)\), \(adv\_attack\)\(AML(x)\) Output: The three categories of image dataset according to DNN model classification and AML results. Initialize SETcrc, SETmis, SETadv to be all empty for image \(x\in\) CIFAR-100 do if\(DNN(x)\) is mispredict then  SETmis \(\leftarrow\) x else if\(DNN(x)\) is correct and AML(x) fail then  SETcrc \(\leftarrow\) x else  SETadv \(\leftarrow\) x endif endif endfor return SETcrc, SETmis, SETadv]
```  
**Algorithm 1** Categorize Image Dataset.
# Simulation of Fidelity in Entanglement-Based Networks with Repeater Chains
## 7 Results
(**a**) Fidelity by varying the number of QRs (4 memory positions) equally distributed across \(L=1000\) km. (**b**) Fidelity by varying the number of memory positions in 2-hops with one QR and an internode distance of 500 km. Error bars represent the standard deviation of multiple simulations. between the nodes by simply incrementing the generated pairs per round. On the contrary, one round of DEJMPS (Figure 8) only uses one extra EPR pair in order to increase fidelity, although \(R_{X}(\theta)\) and CNOT gates are involved, as well as a measurement, which are very resource intensive, especially in terms of time. Furthermore, there is an intrinsic probabilistic nature associated with this method, as both measurements registered by the two parties must coincide in order to ensure a successful purification, as well as an asymptotic behavior that limits the effectiveness of both high fidelity inputs and an increase in the number of rounds. As we see in Figure 9, the fidelity largely increases after a second distillation round, whereas with a third iteration, the asymptotic behavior of the method enters into place and decreases the gain in the parameter. This only strengthens the argument in favor of the application of DEJMPS distillation in order to increase the fidelity to feasible limits for a successful communication, as the application of two rounds of distillation only consumes two additional EPR pairs, whereas if we were to apply the method in Figure 7b, each of the nodes would consume between 12 and 14 memory slots to land in, the higher percentiles of the fidelity. Circling back to Figure 9, we can extract an intuitive threshold for the tolerance of the fidelity around \(F_{thr}\approx 0.85\), as two rounds of purification take it close to \(F>0.95\), which, for small and medium messages is a tolerable value of the parameter. This threshold needs to be changed if we are dealing with a larger quantity of data and should be moved towards \(F_{thr}\approx 0.90\). However, taking into account the asymptotic nature of the protocol, a more complex fine- tuning must be implemented in the communication scheme in order to transmit large packages of data. Figure 8: Simulation for a three-node network applying one round of the DEJMPS distillation protocol.
## 4 The Experimental Results Discussion
### The Graphical Distinction Between Bug-Holes and Cracks  
The feature of the graphical distinction between bug-holes and cracks is obvious; that is, the cracks on the concrete surface are narrow and long. The bug-holes tend to be round, although the shape of the bug-holes is irregular. So, a sharp characteristic coefficient \(\lambda\) can be used to distinguish the bug-holes and the cracks, and the coefficient \(\lambda\) can be obtained by Formula (2).  
\[\lambda=\frac{L^{2}}{A} \tag{2}\]  
where \(L\) is the perimeter of shape and A is the area of shape, unit is pixel. In special cases, for positive circles, \(\lambda=4\pi\approx 12.56\), unit is pixel. Some partial representative shape coefficients of holes and cracks are shown in Figure 4. The shape coefficient was applied to filter the image to successfully remove the cracks and eliminate the interference of the cracks when detecting the holes. The filtering effect is shown in Figure 5.  
The perimeter and the area of the feature profile can be obtained by calculating the number of pixels in the contour periphery or the number of pixels in the contour whole. The calculation formula is as follows:  
\[L=\delta\times L_{pix} \tag{3}\]  
\[S=\delta^{2}\times S_{pix} \tag{4}\]  
where \(\delta\) is the size of individual pixel point, \(L_{pix}\) is the number of pixels of the feature edge profile, unit is pixel, and \(S_{pix}\) is the number of all pixels within the feature region, the unit of \(S\) and \(S_{pix}\) is pixel square.  
Three parameters can be used to evaluate the characteristic value of concrete surface bug-holes, such as the equivalent diameter \(\overline{D}\), the area rate \(A_{r}\), and the pixels quantity of bug-holes.  
\[\overline{D}=\sqrt{\frac{4S}{\pi}} \tag{5}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Crack Widths** & \multicolumn{3}{c}{**The Recommended Zoom Ratio in Different Distance (Times)**} \\ \cline{2-5}
**Range (mm)** & **0–0.5 (m)** & **0.5–1 (m)** & **1–1.5 (m)** & **1.5–2 (m)** \\ \hline
0–0.1 & \(\geq\)9 & - & - & - \\
0.1–0.5 & \(\geq\)5 & \(\geq\)7 & \(\geq\)9 & - \\
0.5–1 & \(\geq\)3 & \(\geq\)3 & \(\geq\)5 & \(\geq\)7 \\
1–3 & \(\geq\)3 & \(\geq\\[A_{r}=\frac{S}{S_{c}} \tag{6}\]  
where \(S_{C}\) is the concrete surface area in the image, pixel square
## Appendix A Analysis of the Impact of Pressure Wave Damping on the Wave Speed  
In the above-mentioned two methods of determining the pressure wave speed in each of the pipes connected in series, the impact of wave damping was neglected or significantly reduced. Damping, according to the theory of vibrations, reduces their frequency. Treating pressure waves as damped vibrations, it is possible to estimate the impact of the damping ratio on the frequency reduction, i.e., reduction of wave velocity [56; 57].  
The circular frequency of damped vibrations is determined from following formula:  
\[\omega=\frac{2\pi}{T} \tag{10}\]  
Assuming that the pressure wave observed during the laboratory tests is an example of linearly damped vibrations, it can be assumed that the circular frequency of these vibrations is related to the circular frequency of undamped vibrations, \(\omega_{0}\), according to the following relation:  
\[\omega=\sqrt{\omega_{0}{}^{2}-h^{2}} \tag{11}\]  
where \(h\) is the damping coefficient constituting the exponent of the function describing the change in time of the amplitude of linearly damped vibrations:  
\[A(t)\propto e^{-ht} \tag{12}\]  
It follows from (8) and (11) that the circular frequency of undamped vibrations can be determined as follows:  
\[\omega_{0}=\sqrt{\omega^{2}+h^{2}}=\sqrt{\left(\frac{2\pi}{T}\right)^{2}+h^{2}} \tag{13}\]  
The damping coefficient \(h\) in the exponent of the function (12) can be defined as follows:  
\[h=\delta\frac{2}{T}=\delta\frac{\omega}{\pi} \tag{14}\]  
The constant logarithmic damping decrement, \(\delta\), characteristic for this type of vibration, is defined as follows:  
\[\delta=\ln\frac{|A_{i}|}{|A_{i+1}|} \tag{15}\]  
The presented definitions (11)-(15) show that the influence of damping on the circular frequency can be calculated as follows:  
\[\omega_{0}-\omega=\frac{2}{T}\Bigg{(}\sqrt{\pi^{2}+\ln^{2}\frac{|A_{i}|}{|A_{ i+1}|}}-\pi\Bigg{)} \tag{16}\]  
An example analysis of the developed phase of the water hammer course presented in Figure 5 (main text of the paper) showed that the ratio of successive oscillation amplitudes is on average \(\sim\)1.1, which means that for the wave period \(T\) = 0.3043 s, the damping share in the circular frequency is:  
\[\frac{\omega_{0}-\omega}{\omega_{0}}\cong 0.05\% \tag{17}\]
## 4 Results
In the last two years of the observation period (2017-2018), the seasonal component was higher than at the beginning of the observations (2014-2016) but lower than in the 2016-2017 period. The InSAR time series, which was projected from LOS to vertical, show a very similar seasonal subsidence pattern as the GNSS observations (Figure 7). We calculated the difference between the GNSS and the InSAR time series using Root Mean Square Error (RMSE) analysis. The RMSE between the GNSS and the Sentinel-1 observations is 7.13 mm and between the GNSS and the COSMO-SkyMed is 4.21 mm. These results suggest that the accuracy of the InSAR positioning is in the range of 4-7 mm. The groundwater level time series indicates an overall decline in groundwater level with a strong seasonal component (grey line in Figure 7). The accumulated groundwater level during the four-year observations period is 12 m, suggesting an average water level drop of 3 m/yr. The ground level seasonal component varies from one year to another in the range of 3-7 m. The highest seasonal decline occurred in 2016-2017 (7 m), and the lowest decline occurred in 2015-2016 (3 m). The vertical displacements obtained from GNSS, COSMO-SkyMed, Sentinel-1, and groundwater level observations show very similar temporal patterns of an overall decline with seasonal components. In order to quantify these relations, we conducted cross-correlation analyses between the four independent time series (GNSS, COSMO-SkyMed, Sentinel-1, and groundwater level). The cross-correlation analysis provides a comparison between two-time series and objectively finds how they match. In this analysis, the lag refers to time-shift between two series (vertical displacement and groundwater level). The cross-correlation analysis results revealed that there is no offset between groundwater level and land subsidence derived from Sentinel-1 and COSMO-SkyMed data. However, the analysis detected a month lag between the groundwater level and the land subsidence derived from GNSS (Figure 8). The cross-correlation coefficient between groundwater level data and vertical displacements obtained from GNSS, COSMO-SkyMed, and Sentinel-1 have been found 0.83, 0.78, and 0.88 respectively. Figure 7: Vertical displacement time series at the KAPN GNSS station derived from GNSS, COSMO-SkyMed and Sentinel-1 observations. The figure also displays groundwater level measured at a well (well no: 52258), which is located 3 km southwest of the KAPN station.
## 3 Physical Simulation
As can be known from Figure 10b that when the mining width of the working face was 102 cm, KS1, KS2, KS3, and PKS subsided by 1.8, 1.1, 0.8, and 0.7 mm, respectively. According to Figure 10d, when the mining width of the working face reached 204 cm, KS1, KS2, KS3, and PKS subsided by 4, 3.4, 3, and 2.3 mm, respectively. In the 2 cm mining height model, due to an increase of the mining width, the subsidence of each stratum increased to some extent, but various overlying stratum had good integrity and uniformly showed slight bending subsidence. The subsidence trend of overlying strata in the 4 cm mining height model was the same as that of the model with 2 cm mining height. The subsidence curves are in Figure 11.  
Similarly, in the 4 cm mining height model, the curves of strata movement with mining widths of 102 cm and 204 cm were compared. As can be known from Figure 11c, when the mining width of the working face was 102 cm, KS1, KS2, KS3, and PKS subsided by 2.7, 1.7, 1.3, and 1.1 mm, respectively. According to Figure 11d, when the mining width of the working face reached 204 cm, KS1, KS2, KS3, and PKS subsided by 6.3, 4.6, 4.3, and  
Figure 8: Mining and backfilling process in the physical model: (a) 2 cm mining height; (b) 4 cm mining height. Figure 9: Measuring points layout for observation of strata movement. 3.8 mm, respectively. In the 4 cm mining height model, due to an increase in the mining width, the subsidence of each overlying stratum increased to some extent. It is thus clear that mining width has an obvious influence on overburden subsidence control in longwall pier-column backfilling. Figure 11: Overburden subsidence curves of 4 cm mining height physical model: (**a**) 102 cm mining width; (**b**) 204 cm mining width; (**c**) subsidence curves of 102 cm mining width; (**d**) subsidence curves of 204 cm mining width. Figure 10: Overburden subsidence curves of 2 cm mining height physical model: (**a**) 102 cm mining width; (**b**) 204 cm mining width; (**c**) subsidence curves of 102 cm mining width; (**d**) subsidence curves of 204 cm mining width. As can be known from a comparison of Figures 10 and 11 that when strata movement stabilized, the subsidence values of various key strata in the 4 cm mining height model were uniformly greater than those in the 2 cm mining height model. To be specific, the maximum subsidence values of KS1, KS2, and KS3 increased by 2.3, 1.2, and 1.3 mm, respectively. The maximum subsidence value of PKS increased by 1.5 mm. Furthermore, in the 4 cm mining height model, when strata movement stabilized, KS1 contained a vertical crack (Figure 12); in contrast, in the 2 cm mining height model, various strata had good integrity. It can be concluded that mining height has a significant influence on the overburden subsidence control effect in longwall pier-column backfilling and that the larger the mining height, the weaker the control effect of overburden subsidence.
## 2 Data Acquisition and Pre-Processing
### Data Fundamental Analysis  
#### 2.4.1 Subjective Data Analysis  
The drivers were categorized into three categories: aggressive, normal, and cautious by calculating each driver's subjective question score. In order to test the reliability of the results of the questionnaire, [PERSON]'s alpha reliability analysis was conducted on the scores of DSQ and RQP. The reliability of the two questionnaires was 0.841 and 0.815, respectively. Generally, a reliability of 0.70 is acceptable, and the range between 0.70 and 0.98 indicates high reliability. The analysis results show that the designed questionnaire contents were reasonable and reliable. According to the comprehensive score of each driver, the K-means clustering method was applied to cluster the scores of 33 drivers. The higher the score, the more aggressive the driving style and the greater the driving risk. Finally, 16 normal drivers, seven cautious drivers, and 10 aggressive drivers were obtained, as shown in Table 2.
# A Review of Deep Learning Based Speech Synthesis
## 3 Statistical Parametric Speech Synthesis
### Parameter Prediction  
Parameter prediction is used to predict acoustic feature parameters based on the result of the text analysis module and the trained acoustic model. For the SPSS, there are usually two kinds of parameter prediction methods: HMM-based parameter prediction and DNN-based parameter prediction. This paper will give a review of these methods in the following.