esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Empirical Methodology  
Apart from standard deviation, coefficient of variation, conditional variance, and other classical methods that were evaluated by [PERSON] and [PERSON] [49], we measure volatility by using a range of percentages, mean change in percentage, moving average, and Coppock index. However, this technique is considered insufficient to provide accurate information on the volatility of food, which is very volatile. Logarithmic standard deviation and conditional variance are still considered the most efficient [50; 51]. This study performed a log-return series of rice prices because it is considered a complete summary and a free scale and more comfortable understanding returns. The augmented Dickey-Fuller (ADF) test is applied to examine the unit root [52] and determine the magnitude or size of differentiation needed to generate the stationarity of the measured variables. The ADF test is also a part of measuring volatility with autoregressive conditional heteroscedasticity ARCH or generalized autoregressive conditional heteroscedasticity (GARCH). For processes that cannot pass the first differential with ADF, further analysis can be carried out by finding the autoregressive integrated moving average (ARIMA) model's best integration order.  
Before building the ARCH-GARCH model, a stationarity test of the data used for volatility analysis must be conducted. Time series data problems arise if the range and mean value are constant continuously, and the covariance value depends on the lag value in that period. A differentiation process can be undertaken from the first time series value or by transforming \(\ln\) or the square root to solve the non-stationarity problem.  
The method used in this research is the ADF test, comparing the \(\tau\) statistic with the MacKinnon \(\tau\) statistic, as in Equation (2), using the regression Equation (1):  
\[\Delta y_{t}=\ \alpha_{0}+\ \gamma y_{t-1}+\sum_{i=2}^{p}\beta_{i}\Delta y_{t-i+1 }\ +\ \epsilon_{t} \tag{1}\]  
\[\tau=\ \frac{\hat{p}}{Se(\hat{p})} \tag{2}\]  
The hypothesis used in the ADF test is as follows:  
**H\({}_{0}\):**: \(\gamma\) = 0 (\(y_{t}\) _not stationary_)
**H\({}_{1}\):**: \(\gamma\) < 0 (\(y_{t}\) _stationary_)  
If the \(\tau\) statistic is greater than the MacKinnon \(\tau\) statistic, then H\({}_{0}\) is rejected.  
The ARCH and GARCH models are widely used to measure volatility in the stock market [53; 54]. Although this method has been used for a long time, it is still considered the most effective way, primarily when heteroscedasticity problems are found, which often show varying but low volatility values. Both ARCH and GARCH models can calculate volatility and are considered the most efficient [55]. However, other methods such as the standard deviation from return [56] and the normal distribution and distribution of Student's-t [57] are also used.
# Application of the KINEROS 2 Model to Natural Basin for Estimation of Erosion
## 3 Erosion Estimation Models Used
### Usle
#### 3.1.2 Determination of Soil Erodibility Factor (\(K\))  
Soil erodibility factor (\(K\)) indicates the facility with which the soil suffers material losses from erosion. [PERSON] and [PERSON] (1978) designed a nomograph and developed a regression algorithm to calculate \(K\) value. There is an alternative regression algorithm by [PERSON] et al. (1978) [1,13,20]:  
\[K=\left[2.71\times 10^{-6}M^{1.14}(12-a)\right]+4.2\times 10^{-2}(b-2)+3.2\times 1 0^{-2}(c-2) \tag{3}\]  
where \(a\) is the organic matter by weight percentile, \(b\) is the structure number given as graph, \(c\) is the profile permeability class given as graph, and \(M\) is the texture representation factor, expressed as follows:  
\[M=(100-clay\%)\cdot(sil\%+fine\ sand\%) \tag{4}\]  
\(K\) value is the average amount of annual soil lost per unit of the energy factor \(R\) of rainfall when the soil in issue is permanently bare, carved in favor of a 9% slope and 22.1 m in length [13]. Aggregate soil and dense vegetation cover reduce the vulnerability of soil to erosivity. Soil properties influencing the \(K\) factor include soil texture, organic matter, soil structure, and permeability of the soil profile [1]. \(K\) value was calculated using the results of granulometric analysis of soil samples made.
## 3 Results and Discussion
### Dragline Excavator
With such settings, the nature of AE activity became synchronized with the cycle of operation of the dragline excavator (Figure 26). The greatest activity was observed with excavation of the ground and with unloading the bucket, that is at the loading of the structure, when cracks could grow and at structure unloading, when the crack edges can rub each other. Figure 25: Tested dragline excavator. Figure 26: Cont. Measurement of the acoustic properties of the monitored excavator was carried out using the Hsu-Nielsen source. The following values were obtained: velocity was 3200-3700 m/s and attenuation was 1.5-2.8 dB/s. The recommended maximum distances between AE sensors that ensure a linear location are from 4.5 to 9 m.  
At the second stage, within 2 days, the AE data was collected directly for subsequent detailed analysis. The distribution of the AE signal amplitudes was analyzed. For all structural elements except for the struts, the differential distribution of the amplitudes was exponential, that is corresponding to a random noise process. On the left and right struts in the range of amplitude values of 80-85 dB\({}_{\text{AE}}\), the distribution was significantly different from the exponential, which indirectly indicated the presence of a defect. The results of linear location analysis were as follows (Figure 27). On the lower boom belt, there were no sources of AE. Several sources were found on the column and the upper boom belt, which turned out to be noise, since they were localized near the winch attachment points, or were inactive. On the left and right struts, a linear location revealed 3 sources of AE on each, near which there were no potential sources of noise. Signals appeared at the stages of loading and unloading. The amplitude distributions presented above also make it possible to classify these sources as defects. Figure 26: AE activity when using 30–500 kHz (**a**) and 150–500 kHz (**b**) frequency filters. Correlation of AE hit count with the cycle of operation of the dragline excavator when using 150–500 kHz frequency range (**c**). In addition, the spectrograms of the signals on the struts were analyzed, which made it possible to determine the arrival times of different frequency components (Figure 28). It was confirmed that the signals were emitted by impulsive sources and propagated along the walls of the object as Lamb waves. With the help of the spectrogram analysis, the distances between the AE sources and AE sensors were obtained, which coincided with the results of the usual linear location based on the analysis of the arrival times difference [27; 28]. Figure 27: Results of acoustic emission location. At the points on the struts revealed as defective by the AE testing, an additional ultrasonic testing was carried out. On the left strut, 1 crack was found with the size of 20 mm, on the right--1 crack with the size 30 mm. At present, the question of the SHM-system design for the testing of draglines is under consideration.
# Sentence Compression Using BERT and Graph Convolutional Networks
## 4 Experiments
### Datasets and Experimental Setup  
Training a sentence compression model based on deep learning requires a parallel corpus consisting of pairs of original and compressed sentences.  
We use the Korean sentence compression corpus [6] consisting of 144,987 pairs, of which 117,252 are part of the training set, 13,130 are used as the validation set, and 14,605 are used as a testing set. This corpus was built by a sentence compression algorithm that deletes nodes from a syntactic dependency tree of an original sentence while preserving the grammaticality of a compressed sentence. The algorithm chooses nodes to be deleted using the structural constraints and semantically required information on a sentence. The Korean sentence compression corpus was successfully built by applying the algorithm to headlines and the first sentences of news articles. The average number of words in source sentences is 16.5, and that of words in compressed sentences is 12.0. The average compression ratio (CR) for word-based is about 72.5, and that for character-based is about 75.5.  
This study used the Google sentence compression dataset for English ([[https://github.com/google-research-datasets/sentence-compression](https://github.com/google-research-datasets/sentence-compression)]([https://github.com/google-research-datasets/sentence-compression](https://github.com/google-research-datasets/sentence-compression)), accessed on 16 September 2021). The first 1000 pairs of \"comp-data.eval.json\" were used as a testing set, and the last 1000 pairs were used as a validation set. Of the 200,000 pairs in sent-com.train*.json, 199,915 pairs in which the original sentence lengths were less than 512 tokens were used as a training set. The average CR for the dataset is 42.3.  
The hyperparameters used in the experiments are depicted in Table 1. The version of BERT we used is BERT-BASE ([[https://huggingface.co/bert-base-uncased](https://huggingface.co/bert-base-uncased)]([https://huggingface.co/bert-base-uncased](https://huggingface.co/bert-base-uncased)), accessed on 16 September 2021) for English and KorBERT ([[http://aiopen.etri.re.kr](http://aiopen.etri.re.kr)]([http://aiopen.etri.re.kr](http://aiopen.etri.re.kr)), accessed on 16 September 2021) for Korean.
# An Intelligent DOA Estimation Error Calibration Method Based on Transfer Learning
All the error factors in the environment are taken into consideration. (ii) This requires only a small number of actual samples to fine-tune the model, saving a lot of time and manpower. (iii) It is environmentally adaptable. In a new environment, only a small number of actual signal samples need to be collected. After approximately a few dozen epochs of fine-tuning, the model is ready for practical application. Figure 1: Traditional error calibration methods. The main contributions of this paper are as follows:  
1. We propose an intelligent DOA estimation error calibration method based on transfer learning, which learns error knowledge of the actual environment from a small number of signal samples and improves the DOA estimation accuracy in practical application. 2. We generate a large number of ideal simulation signal samples to train an intelligent DOA estimation model constructed based on CNN. Then, we fine tune the model with a small number of actual signal samples collected in the actual environment. Before and after transfer learning, the model's tasks are the same, and the working environment is transferred from the ideal condition to the actual environment. 3. We experimentally show that transfer learning can effectively improve the DOA estimation accuracy in practical application. We further discuss the impact of different fine-tuning approaches (freezing different network layers) and different numbers of actual samples used for fine-tuning. The experimental results indicate that the intelligent DOA estimation model performs better when freezing the first convolutional layer. The more actual signal samples are used for fine-tuning, the better the model performs.
# Advances and Future Perspective on Detection Technology of Human Norovirus
## 2 Detection Techniques
### Molecular Detection Methods
#### 2.3.2 Thermal Cycling Amplification
Finally, the concentration or copy number of the target can be calculated according to the Poisson distribution principle and the proportion of positive droplets. dPCR has low detection limits, high accuracy and is not affected by the complex matrix of the samples. However, RT-qPCR requires expensive instrument, which hindered its widespread application [72; 73; 74; 75]. Commercial kits for the detection of HuNoVs based on RT-qPCR were developed [76; 77]. At present, the US Food and Drug Administration (FDA) has permitted three commercial kits that can be used for GI/GII HuNoV detection, including FilmArray GI panel (BioFire Diagnostics, Salt Lake City, UT, USA), Luminex xTag GI pathogen panel (GPP; Luminex Corporation, Toronto, Canada) and Nanosphere Verigene enteric pathogen test (Nanosphere, Inc., Northbrook, IL, USA) [78]. [PERSON] et al. evaluated the performance of three commercial kits including Biofire's Gastrointestinal Panel (FilmArray, BioFire Diagnostics, Salt Lake City, UT, USA), Luminex xTAG\({}^{\bigotimes}\) Gastrointestinal Pathogen Panel (GPP), and the TaqMan Array Card (TAC) for the detection of HuNoVs (GI/GII) in stool samples [79]. The sensitivities of FilmArray, GPP, and TAC system for norovirus GI and GII detection were 87.8%, 78.0%, and 87.8%, respectively. [PERSON] et al. evaluated the Luminex xTag GPP. The results indicated that this kit was not sensitive to GII.2 and GII.3 HuNoVs [80]. Therefore, commercial kits were insufficient for the detection of multiple HuNoV genotypes. Furthermore, applicable kits may need to be updated on the basis of the HuNoVs epidemic virus survey.
## 2 Literature Review
They discussed strategies to improve intrusion detection systems and emphasize different types of intrusion detection systems. The authors explained the core principles of cyber security, including DDoS attacks, data anomalies, and intrusion detection. They also highlighted the introduction of fuzzy logic solutions to address DDoS attacks. The survey's findings offered benefits for businesses and governments seeking business sustainability. [PERSON] et al. in [32] proposed a security system consisting of two parts. In the first part, the authors explained how to compromise the network by infecting some IoT devices, and through them, the infection can be spread to the entire network. Second, the authors provided a set of methods that includes filtration, abnormal traffic created by IoT device identification, screening, and publishing the abnormal traffic patterns to the other home routers on the network. The proposed system blocks the connection received from Malicious nodes for a certain period of time without causing any delay for normal traffic. [PERSON] et al. in [33] evaluated the effectiveness of DDoS detection through multiple experimental scenarios. They analyzed traffic flow in transmission sessions, including regular and retransmission scenarios. The study's main contribution was its ability to predict DDoS attacks by analyzing transmission behavior variability. Sensor nodes can transmit signals simultaneously, and the study used a tablet computer as the primary communication hub. The optimal transmission interval is 23 milliseconds. The study highlighted the correlation between transmission session saturation and DDoS attack success. Based on the previous literature review, and by analyzing a diverse range of sources, this section has highlighted the evolution of concepts, methodologies, and key findings for the use of predictive tools to analyze the behavior of a network, especially in the IoT domain. However, two questions arise: What are the impacts of a DDoS attack over M2M traffic? Are LTE-M networks resilient toward this type of attack? To answer these questions, we study, hereinafter, the impact of DDoS attacks on LTE-M networks.
# Dispersive FSO Performance Estimation with Gaussian Pulses and Laplace Modeled Time Jitter
###### Abstract  
FSO communications tend to be one of most convenient, wireless, high-data-rate communications technologies of global telecom networking, and they are implemented and operated with low-cost resources. Despite their advantages, FSO systems' performance is delimited by several physical phenomena, which act on propagating signal beams through the atmospheric path. Among other effects, chromatic dispersion and time jitter affect the shape and the detection instant of the incoming optical pulse, respectively. This results in signal fading and probable misdetections, and the signal fades along the propagation path due to power losses. Particularly, chromatic dispersion affects the width of the longitudinal information pulse, while the stochastic nature of the time jitter effect is treated with the use of a statistical model for the instantly received irradiance of the detecting pulse at the corresponding time slot. In this study, the symmetrical Laplace distribution was chosen for weak time jitter effect emulation because of its symmetry in pulse detection before or after the center of the specific timeslot. Thus, the joint influence of all three effects could considered, including all the parameters involved. Moreover, new-closed-form mathematical expressions were derived in order to accurately estimate the availability and the reliability of the FSO links under consideration. Next, using the derived mathematical forms, performance outcomes were presented for typical parameter values for realistic FSO links. FSO links; Laplace-modeled time jitter; chromatic dispersion; chirped gaussian pulses; power losses; expected irradiance; outage probability; average bit error rate +
Footnote †: journal: Computer Phys.
# The Preparation of Active Support-Based Sealing Material and Sealing Effect Analysis
## 4 Engineering Application
### The Project Examples of borehole Areas under Active Support Force  
The borehole sealing test was conducted in the 2404 belt bottom drainage roadway of Shunhe Coal Mine. The bag-type borehole sealing device employed in this test was featured with \"two plugging and two injections\"; at the same time, the expansive cement-based material developed for this test was also used. In total, 28 boreholes were drilled, 24 of which were effective. Through the analysis of underground gas drainage data, it was found that the gas drainage concentration gradually decreases with the passage of the drainage time. Therefore, the drainage time with a high gas concentration and the negative pressure in the borehole were used to evaluate the sealing effect of the borehole. The 90-day gas drainage concentration and the negative pressure in the 24 boreholes were statistically analyzed, and then the analysis results were compared with the data for sealing with ordinary cement slurry. The results are shown in Figures 13 and 14, respectively.  
Through the analysis and comparison of Figures 13 and 14, it can be determined that, with the application of the bag-type borehole sealing device, both the gas drainage concentration and negative pressure in the borehole are higher than those obtained with the original sealing technology. When the bag-type borehole sealing device is applied, the average drainage concentration during the first 15 days is 84.11%, displaying a relatively high stability and continuity; by contrast, the average drainage concentration when using the original sealing technology is 73.2%. When the drainage lasts for 90 days, the gas drainage concentration with the bag-type borehole sealing device is 47.5%, while that of the original sealing technology is only 25.7%. The gas drainage concentration is increased by 84.8%. It shows that the bag-type borehole sealing device featured with \"two plugging and two injections\" has a good sealing effect when using the newly developed expansive sealing material. Similarly, when the bag-type borehole sealing device is applied, the negative pressure is also higher than that when the original sealing technology is used. In addition, with the original sealing technology, the negative pressure decreases as the drainage time increases, indicating the existence of a gas leakage channel in the process of drainage.  
Figure 13: The comparison of gas drainage concentration.
###### Abstract
The waveguide performance in the range of 0.1 THz to 0.5 THz has been analyzed, including the Q factors, resonant frequency (\(f_{R}\)), full width half max (FWHM), \(-60\) dB bandwidth, and loss.
# Millimeter Wave Attenuation Due to Wind and Heavy Rain in a Tropical Region
## 4 Results and Analysis  
### The Distribution of Rain Rate and Wind Speed  
The results in Figures 7 and 8 show rain and wind distributions over the 8-month period during the rainy season in 2022, at Chiang Mai University, Thailand. The data may be used for annual statistical prediction since the climate outside the period is dry. At the measurement location, which is a tropical location, the weather characteristics include heavy rain. This is seen in the cumulative distribution of the measured rain rates, as in Figure 7. The measured rain rate, which exceeded 0.01% of the time \(R_{0,01}=83.20\) mm/h is used for analysis, and which is higher than the prediction given using the ITU-R P.837-7 recommendation [16] of \(R_{0,01}=67.27\) mm/h.  
Figure 7: Cumulative distribution of the measured rain rates compared to the ITU-R P.837-7 model.  
The cumulative distribution function of the wind speed during the same period was plotted in Figure 8. As described in [27], the wind speed distribution can be modelled using a Weibull distribution:  
\[F(v)=1-\exp\left[-\left(v/\eta\right)^{\beta}\right], \tag{20}\]  
where \(v\) is the wind speed (m/s), \(\eta\) is the scale factor and \(\beta\) is the shape factor. The value of \(\beta\) and \(\eta\) for the measured data are 1.1911 and 1.3016, respectively. The values of the wind speeds at 50%, 99.9% and 99.99% of the time were 0.8989 m/s, 5.2597 m/s and 6.5603 m/s, and are used as the low-wind threshold, the high-wind threshold and the value used in the link budget calculation, respectively.
## 4 Discussion
### Study Limitations  
This study had a convenience sample, which increased selection bias and only evaluated immediate changes in kinematic and electromyographic variables. Therefore, the implementation of randomized samples and a prospective study with mammoplasty subjects should be considered to fully understand the long-term changes and mitigate the selection bias. A wider range of implant volumes should be considered in future research, given the new trends towards larger breasts, mainly outside of Europe. To increase external validity, future research should include larger and more diverse sample sizes, such as different age groups, body types, and those with pre-existing postural control conditions.  
The Hawthorne bias is worth mentioning because of the possible change in behavior of the participants due to their awareness of being observed and the potential to perform better or differently than they would in real-life conditions. Blinding techniques can be used to reduce awareness and obtain more typical behavior.  
Another possible limitation of this study was the potential influence of confounding variables in the analysis performed. The sample characterization data, such as the PA level, age, BMI, scapular dyskinesia, and MV, could have been confounding variables that biased the results. Future research should include methods to control and adjust for these confounding variables to isolate the effects of implant weights in the kinematic and electromyographic data.  
It should also be noted that the present study did not address all the physical effects and symptoms of a real mammoplasty. The analysis focuses only on the effect of different implant weights on postural control. However, a comprehensive approach including surgical technique, soft tissue healing, scar adhesions, etc., must be considered when evaluating future postural control problems associated with mammoplasty.