esa-sceva/satcom-chunk-collection huggingface dataset:
# Impact Performance of Helmholtz Self-Excited Oscillation Waterjets Used for Underground Mining
## 2 Research Method  
### Nozzles and Specimen  
A Helmholtz self-excited oscillating waterjet (HSEOW) nozzle was used in the present study, which is one of the typical pulsed nozzles used in engineering practice. A Helmholtz oscillator consists of an upstream nozzle, an upstream collision wall, an oscillation chamber, a downstream collision wall, and a downstream nozzle [21], while upstream nozzle diameter is \(d_{1}\), downstream nozzle diameter is \(d_{2}\), collision wall angle is \(\alpha\), oscillation cavity length is \(L_{\mathrm{c}}\), oscillation cavity is \(D_{\mathrm{c}}\). The profile and photo of nozzles is shown in Figure 1. The upstream nozzle and downstream nozzle are made of stainless steel and connected by thread, thus achieving the purposes of pressure-bearing and sealing. In the present study, the upstream nozzle remained unchanged (\(d_{1}=2.6\) mm) and nozzle structures are only adjusted by changing the size of the downstream nozzles. The upstream and downstream nozzles are strictly centered in processing, and all of the inner wall roughness is 0.8. The structure parameters are shown in Table 1.  
As for the specimen, pure aluminum (1060A Chinese Industry Standard, \(100\times 100\times 10\) mm) was employed for the tests for its good elongation, high plasticity and corrosion resistance, and reasonable price. The chemical compositions and physical properties of the specimen are shown in Tables 2 and 3. Before the tests, the hardness of the aluminum plate specimen involved was measured to ensure that the hardness is at the same level and would not cause interference to the test results. Hvst-1000z microhardness tester was selected, with an average hardness value of 23.95 and an error range of  
\begin{table}
\begin{tabular}{c c c c c} \hline \(d_{1}\)/mm & \(d_{2}\)/\(d_{1}\) & \(L_{c}\)/\(d_{1}\) & \(D_{c}\)/\(d_{1}\) & \(\alpha\)/\({}^{o}\) \\ \hline
2.6 & 1.2 & 1, 2, 3, 4 & 4, 6, 8, 10 & 120 \\ \hline \end{tabular}
\end{table}
Table 1: Structure parameters of nozzles.  
Figure 1: (**a**) Profile and (**b**) photo of HSEOW nozzles.  
\(-\)3.9-4.1%. The error of test results was affected by the system error of hardness tester, the error of reading indentation data, and the uneven density distribution of aluminum plates.
## 6 Conclusions  
This study proposes a new technique for recovering multivariable data from 5G-based IoTs by combining TRPCA and TC approaches. The proposed approach can recover missing values, multiple noises, and outlier issues. By using the correlation between multi-attributes and solving problems using tensor data, this method can improve system performance. This uniqueness arises from the incorporation of WTSVT into TC and the TRPCA to handle singular values, which increases reconstruction accuracy by retrieving essential components. This combination overcomes the limitations of other models, such as WTC, the TRPCA, and the TRPCA-TNN, and significantly improves the ability to reduce mixed noise and outliers and reconstruct missing values. The proposed method is highly resistant to data corruption, including mixed noise, outliers, and missing values. Its NMAE consistently outperformed other metrics across two datasets, the NDBC-TAO and U.S. Climate Normal datasets. This method can be useful in various applications, particularly those related to the 5G-based IoT in real-world scenarios. Thus, these findings bridge the gap in the literature regarding better ways to handle extensive data analysis within the IoT environment.  
Our objective in the near future is to automate the process of choosing the most appropriate weight for a particular dataset. This will allow us to utilize the most efficient and precise decision-making methods. Furthermore, we will explore situations in which there is a correlation between errors or noise, such as when one type of error is dependent on another. In addition, we will consider ways to reduce the complexity of the proposed algorithm.  
Conceptualization, H.H.-P.V.; methodology, H.H.-P.V.; formal analysis, H.H.-P.V.; writing--original draft preparation, H.H.-P.V.; writing--review and editing, M.Y. and T.M.N.; visualization, H.H.-P.V.; supervision, M.Y.; funding acquisition, M.Y. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by MSIT under the Information Technology Research Center (ITRC) Support Program Supervised by IITP under grant IITP-2023-2021-0-02046.  
**Data Availability Statement**: Publicly available datasets were analyzed in this study (Section 5.1). This data can be found here: [[https://www.ncdc.noaa.gov/cdo-web/datasets](https://www.ncdc.noaa.gov/cdo-web/datasets)]([https://www.ncdc.noaa.gov/cdo-web/datasets](https://www.ncdc.noaa.gov/cdo-web/datasets)), (accessed on 16 June 2023), [[https://tao.ndbc.noaa.gov/tao/data_download](https://tao.ndbc.noaa.gov/tao/data_download)]([https://tao.ndbc.noaa.gov/tao/data_download](https://tao.ndbc.noaa.gov/tao/data_download)) (accessed on on 10 June 2023).  
**Conflicts of Interest**: The authors declare no conflicts of interest.
## 2 Materials and Methods
### GPS System--External Load Variables  
During training sessions, a global positioning system (18 Hz, GPEXE, Exelio srl, Udine, Italy) was used to collect the external load data. The players wore a sports vest with a pocket on the back (between the scapulae) where the devices were placed. This equipment was used in the past by other authors and validated as reliable to determine movement patterns [60]. The following variables were used for analysis: acceleration actions of 2-3 m/s\({}^{2}\) (ACC2), high acceleration actions \(>\) 3 m/s\({}^{2}\) (ACC3), deceleration actions of \(-\)2--3 m/s\({}^{2}\) (DEC2), high deceleration actions \(<\) \(-\)3 m/s\({}^{2}\), total distance covered (DIST), distance covered within speed zone 14.4-19.7 km/h (DIS4), distance covered within speed zone 19.8-25.2 km/h (DIS5), distance covered within speed zone \(>\) 25.2 km/h (DIS6), number of actions \(>\) 19.8 km/h (SPEF), metabolic power distance covered at an intensity of 15-25.5 w/kg (MPW4), metabolic power distance covered at an intensity of 25.5-50 w/kg(MPW5), metabolic power distance covered at an intensity of \(>\)50 w/kg (MPW6), number of metabolic power actions at an intensity of \(>\)25.5 w/kg (MPWEF).
## 3 Results
### Storage Stability of the MDCR-Modified Asphalts  
The stability of modified asphalt during storage is crucial for its practical application in engineering projects. To assess this, the compatibility of the modifiers with asphalt was evaluated using a segregation test, which measured the softening point difference (SPD) over time. The SPD of the modified asphalt decreased as storage time increased, indicating a change in compatibility. Figure 8 gives the SPDs of the modified asphalts with different DCR and MDCR contents. Specifically, with a 20% DCR content, the modified asphalt exhibited an SPD of 11.5 \({}^{\circ}\)C. This relatively high SPD suggests a lower compatibility of the DCR within the asphalt matrix, indicating potential issues with stability and homogeneity over time. When the same proportion of MDCR-modified asphalt was incorporated, the segregation of the MDCR within the asphalt was significantly reduced by 54.8%, with an SPD of 5.2 \({}^{\circ}\)C. This reduction is considerably greater than the 35% decrease in SPD observed in an asphalt binder containing acrylamide-grafted rubber, as determined through the cigar tube test sample, in comparison to a control asphalt with conventional rubber [22]. Furthermore, when the MDCR content reached more than 40%, the results of the phase separation test could meet the requirements of 2.2 \({}^{\circ}\)C outlined in ASTM D5892. Therefore, this achievement marks a significant improvement in the stability of the MDCR-modified asphalt, qualifying it as suitable for engineering applications.  
Figure 7: The road performance of the MDCR-modified asphalt with different MDCR contents: (**a**) penetration at 25 \({}^{\circ}\)C, (**b**) softening point, (**c**) ductility at 5 \({}^{\circ}\)C, and (**d**) elastic recovery at 15 \({}^{\circ}\)C.
## 3 Results
### Definition and Discussion of the Coupling Coefficient
#### 3.2.2 The Response Relationship Between the SATDA and SCA  
Figure 6 illustrates the response between the _SCA_ and _SATDA_ in the four typical marine areas. Just as expected, the results in Figure 6 demonstrate that the values of the coupling coefficient \(k_{satha}\) and the correlation coefficients between the _SCA_ and _SATDA_ over the four typical marine areas are all obviously larger than those in Figure 5. For example, the value of the coupling coefficient in the ARC region increased from 0.34 to 0.55, while the correlation coefficient increased from 0.60 to 0.72. This result indicates that the _SCA_ is more sensitive to the _SATDA_ than the _SSTA_.  
Figure 6: The map of the scattering coefficient anomaly overlaid as contours on the sea–air temperature difference anomaly at the (**a**) ARC, (**c**) BMC, (**e**) KE, and (**g**) GS, and the scatter plot distribution of data for the (**b**) ARC, (**d**) BMC, (**f**) KE, and (**h**) GS sea areas.  
In order to further demonstrate the influence of \(SATD\) and \(SST\) on the scattering coefficient in the four typical sea areas, the long-term variation characteristics of coupling coefficients \(k_{satta}\) and \(k_{ssta}\) from January 2002 to June 2009 are shown in Figure 7. From the comparison between \(k_{ssta}\) and \(k_{sstda}\), one can find that the values of \(k_{sstda}\) are significantly larger than those of \(k_{ssta}\). On the other hand, we also find that the values of \(k_{sstda}\) and \(k_{ssta}\) exhibit seasonal variation. In the four sea regions, the coupling coefficients \(k_{sstda}\) and \(k_{ssta}\) reach the maximum values in summer (December and January in the Southern Hemisphere, and July and August in the Northern Hemisphere) and the minimum values in winter (July and August in the Southern Hemisphere, and December and January in the Northern Hemisphere).
## 2 Overview of the Basic Concepts of Wind Farm Design and Optimization
### Adverse Factors and Conflicting Objectives
#### 2.2.1 Wind Turbine Wakes  
The wake of a WT [57, 58, 59] is a volume of affected flow that is characterized by a reduced streamwise velocity, high vorticity and increased levels of turbulence compared to the free stream airflow. The consequences of operating WTs in the wakes of obstacles (e.g., WTs, meteorological masts or buildings) have been reported based on field experience: (1) Overall power losses up to 41% may occur, where the precise loss figures depend on the inter-WT distances, wind direction, site complexity (e.g., onshore, offshore), turbulence intensity and WT type, among other relevant factors [60; 61; 62; 63; 64; 65; 66; 67; 68]; (2) Enhanced fatigue loads of up to 80% [58] that drastically reduce the lifespan of the WT and significantly increase the Operation and Maintenance (O&M) costs. In particular, operating a WT under partial wake conditions (_i.e._, only a fraction of the rotor of the downwind WT operates in the wake of upstream WTs) results in higher fatigue loads in contrast with operating a WT under full wake conditions as pointed out by [PERSON] _et al._[69], where the blade root bending flatwise, the main shaft torque and the tower head bending moment-based fatigue loads are the most affected. In addition, the WT induced turbulence intensity may not only reduce the power output of the downstream WTs but can also cause dynamic loading and increased levels of noise which, in the long run, lead to blade damage and higher O&M costs. Moreover, wind farm wakes produce important disturbances in the atmospheric boundary layer, thus they can potentially affect neighboring wind farms [70; 71; 72; 73].  
WT wakes have special relevance for the wind farm design process, as it is the single factor that accounts for the largest energy losses [58; 60; 61; 63; 68]. Consequently, the adequate positioning of each WT within the wind farm site is one of the factors that most influences the profitability of a project throughout its operational lifetime.
## 1 Introduction
While the beam is fully compressed and the beam current is increased, space charge and CSR effects limit the bunch compressor performance and distort the current profile. To satisfy our goal for THz SASE FELs in the simulation, for instance, a 2-nC beam current profile with booster phase of \(-\)39 degrees can be compressed (in under-compression mode) into an average bunch current of \(\sim\)200 A with normalized vertical emittance under 20 \(\upmu\)m.  
A commissioning method was developed to achieve the chicane dipole magnet currents to guide the electron beam throughout the reference path. Moreover, the two commissioning objectives are to ensure proper electron beam transport through the vacuum chamber of the chicane and to acquire the vertical bending angle closest to 19 degrees by each chicane dipole magnet, thereby achieving minimum energy (or momentum) dispersion. Figure 2 shows a concept of the first commissioning method based on the dispersion measurements at the screen downstream of the chicane. The method uses two cerium-doped ytterbium aluminium garnet (Ce:YAG) screens called HIGH2.SCR2 and HIGH2.SCR3, located upstream and downstream of the chicane, respectively. The chicane dipole magnets are named CHICANE.D1, CHICANE.D2, CHICANE.D3, and CHICANE.D4, respectively. Figure 1: Brief layout of PITZ for the THz SASE FEL experiments.
## 4 Performance Evaluation and Discussion
### Performance Evaluation
In addition, the more data, the higher the accuracy, so if the data is obtained from fewer cases such as \"timeout\" or \"PG error\", the accuracy of the data will be as high as 94% due to the abundance of data, and it will be possible to create a model that shows a higher level of accuracy.
# A Low Sampling Rate Receiver Design for Multi-Antenna Multi-User OFDM Systems
## 3 Proposed Low Sampling Rate Design  
In the conventional system shown in last section, all users have to use an ADC with a sampling rate that is at least equal to the Nyquist rate. However, the actual frequency bandwidth occupied by each user is only a fraction of the total system bandwidth. This observation inspired the design shown in this section. With the aid of precoding, we significantly reduced the sampling rate of the receiver using this model.  
The system of the proposed design is shown in Figure 2 and was essentially similar to Figure 1, except for the following points:  
1. In the proposed scheme, the sampling rate of the ADC of the receiver was reduced to \(f_{\rm s}=f_{0}/K=V/T\);
2. An overall ZF precoder was used at the BS, rather than the per subcarrier MRT precoder as in (12), in order to enable the low sampling rate of the receiver;
3. The FFT module was no longer required at the receiver.
## 6 Experimental Results
### Performance Evaluation of CLAC-KHM
As we can recognize in Figure 4A,B, the behavior of CLAC-KHM is similar to LAC and k-means while enjoying better performance on the Iris topology and Aarnet topology. In contrast, spectral clustering does not have a rather normal behavior. From the attained results, it is concluded that the CLAC-KHM leads to better efficiency thanks to utilizing learning automata and KHM.
## 4 Results
### When Will Employees Respond to Spear-Phishing Emails Sent to Them?  
The analysis of the timing of phishing attacks reveals a new perspective. Upon analyzing the timing results of the last experiment at the technical university, it was observed that employees took an average of 8.5 h to find and open the phishing email (see Figure 8, left boxplot diagram). We should consider that the waiting time was 72 h (3 days), but during the period the longest time was 42.4 h. This might be related to the requirement for employees to respond to received emails within 3 working days. Half of the emails were read within 4.88 h, while the quickest time to open them was 15 s.  
The average time to click on the link in the email and open the web page was 86 s (see Figure 8, middle boxplot diagram). The average is highly affected by the five outlier values. These values aim to reach up to 10 min, while the upper quartile values do not reach 1.5 min. Data filling in the form requires some time, but the values usually fit into 5 min.  
By analyzing the timing of those 57 employees who filled in the form with the requested data, we found that the average data entry time from the moment when the email was sent was 808 s. Taking the standard deviation values (std = 478) and comparing them to the 300 s interval estimated in our second hypothesis, we see that between those values, there are significant differences (\(p<0.0001\)). The time from the moment when the phishing email was sent to when the user filled in the form with the requested data with a 95% confidentiality had a very broad range. Considering that the maximum mean time was 934 s and the standard deviation was 478, the lowest value of the range in a 95% confidentiality interval was lower than 300 s. Hence, hypothesis (H02) that no employee  
Figure 7: Distribution of employees’ credulity scores.  
will reveal their data in 5 min or sooner is rejected. The research results indicate that eight data forms were submitted within 5 min faster than before. This was about 14% of the cases. Meanwhile, the fastest submitted form was received 45 s after the phishing email was sent. The fastest form filling could be even faster as the sum of the lowest values for each step would be 27 s. This highlights the importance of automated email filtering and employee awareness regarding social engineering attacks as they become more prevalent. If at least one employee provides all the required data for the hacker in less than 5 min, there will be no time for the cybersecurity management team to react. In this experiment, the first incident registration was received less than 6 min after the email had been read.  
The development of new, usually AI-based solutions for phishing websites [44] and emails [45] indicate that we are approaching a high accuracy (reaching 96-99% F-score) in regard to identifying phishing attacks and potentially could achieve results with even higher accuracy in the future. However, at the same time, it is important to use the up-to-date phishing filtering solutions and guarantee that legal user emails will not be blocked.
# Intelligent Environment-Adaptive GNSS/INS Integrated Positioning with Factor Graph Optimization
## 2 System Architecture of Adaptive GNSS/INS Integration by FGO
### Kalman Filter  
For each epoch \(t\), the basic KF formulation [24] mainly consists of an INS state transition in Equation (9) and GNSS measurement update in Equation (12). Additionally, the error covariance matrix \(\mathbf{P_{t}}\) in Equation (10) introduces the system noise covariance \(\mathbf{Q_{t}}\), and the GNSS measurement in Equation (11) is assumed to satisfy Gaussian distribution with the measurement noise covariance matrix \(\mathbf{R_{t}}\), which represents exactly the GNSS solution uncertainty to be tuned in this study. The process noise, given GNSS information, can be calculated by Equation (13).  
\[\mathbf{\hat{S}_{t}^{-}}=\mathbf{F_{t-1}}\mathbf{\hat{S}_{t-1}^{+}}+\mathbf{U_{t-1}} \tag{9}\] \[\mathbf{\hat{P}_{t}^{-}}=\mathbf{F_{t-1}}\mathbf{\hat{P}_{t-1}^{+}}\mathbf{F_{t-1 }^{T}}+\mathbf{Q_{t-1}}\] (10) \[\mathbf{Z_{t,GNSS}}=\mathbf{H_{t}}\mathbf{\hat{S}_{t}^{-}}+\mathcal{N}(0,\bm {R_{t}})\] (11) \[\mathbf{\hat{S}_{t}^{+}}=\mathbf{\hat{S}_{t}^{-}}+\mathbf{K_{t}}\Big{(}\mathbf{Z_ {t,GNSS}}-\mathbf{H_{t}}\mathbf{\hat{S}_{t}^{-}}\Big{)}\] (12) \[\mathbf{\hat{P}_{t}^{+}}=(1-\mathbf{K_{t}}\mathbf{H_{t}})\mathbf{\hat{P}_{t}^{-}} \tag{13}\]  
(\"+\" and \"\(-\)\" means before and after GNSS measurement update, while \"\(\,\)\" highlights estimated variables. \(\mathbf{F_{t-1}}\) and \(\mathbf{H_{t}}\) stand for the state transition matrix and measurement matrix, respectively. \(\mathbf{K_{t}}\) is the Kalman gain, calculated using \(\mathbf{H_{t}}\), \(\mathbf{\hat{P}_{t}^{-}}\), and \(\mathbf{R_{t}}\).)  
Because this study focuses on the post-processing of the positioning solutions, a Rauch-Tung-Striebel (RTS) smoother [24; 41] has been employed after the filter to improve the smoothness of trajectories. The backward process has been expressed in Equation (14)-(16), where the overscript \"\(s\)\" stands for the smoothed variables.  
\[\mathbf{K_{t}^{s}}=\mathbf{\hat{P}_{t}^{+}}\mathbf{F_{t}^{T}}\Big{(}\mathbf{\hat{P}_{t+1}^{-} }\Big{)}^{-1} \tag{14}\]  
\[\mathbf{\hat{S}_{t}^{s}}=\mathbf{\hat{S}_{t}^{+}}+\mathbf{K_{t}^{s}}\Big{(}\mathbf{\hat{S}_{t+ 1}^{s}}-\mathbf{\hat{S}_{t+1}^{-}}\Big{)} \tag{15}\]  
\[\mathbf{\hat{P}_{t}^{s}}=\mathbf{\hat{P}_{t}^{+}}+\mathbf{K_{t}^{s}}\Big{(}\mathbf{\hat{P}_{t+1 }^{s}}-\mathbf{\hat{P}_{t+1}^{-}}\Big{)}(\mathbf{K_{t}^{s}})^{T} \tag{16}\]
# Multi-Augmentation-Based Contrastive Learning for Semi-Supervised Learning
## 3 Results
### Ablation Experiment  
Additional ablation experiments were conducted to examine the roles of the different components in MAC-SSL and evaluate the impacts of certain parameters. Specifically, we measured the effects of the following:  
1. Investigating the effects of different strong augmentation strategies: directly using RandAugment (MAC-SSL) and using RandAugment after applying moderate augmentation (MAC-SSL-II)
2. Using different sample ratio hyperparameter \(\mu\) values ranging from 1 to 9
3. Removing temperature sharpening (i.e., setting \(T=1\))
4. Performing MixUP between labeled examples only, unlabeled examples only, and without mixing across labeled and unlabeled examples
5. Using the mean class distribution over two augmentations (i.e., weak and moderate augmentations) or using the class distribution for a single augmentation (i.e., only weak augmentation)
6. Employing weak augmentation, moderate augmentation, and strong augmentation, as well as the scenario where only weak augmentation and strong augmentation were utilized (with the class distribution being used only for weak augmentation)  
The ablation experiments were conducted on the CIFAR-10 and SVHN datasets, and the results are shown in Tables 1-3. It was observed that each component contributed to the performance of MAC-SSL, and incorporating MixUP solely on labeled samples resulted in the largest performance loss, even surpassing the impact caused by not using MixUP at all. This indicated that unlabeled samples contributed significantly to the training process, as the information contained within a large volume of unlabeled samples exceeded that of a small number of labeled samples. MAC-SSL was able to fully utilize the information embedded within the unlabeled samples, resulting in remarkable performance.  
Figure 9: Plots of the different methods as training progress on CIFAR-10 with 1000 labeled samples. (**a**) Accuracy on the test data. (**b**) Loss on the test data.  
Applying RandAugment in addition to moderate augmentation as the strong augmentation strategy proved to be more effective on the SVHN dataset. MAC-SSL also yielded superior results with a small number of labeled samples and an increased number of unlabeled samples (an increased \(\mu\)). Notably, it outperformed the conventional strong augmentation strategy on the CIFAR-10 dataset with 40 labeled samples, as well as on the CIFAR-10 dataset with 1000 labeled samples when \(\mu=9\).