esa-sceva/satcom-chunk-collection huggingface dataset:
# BVLOS Unmanned Aircraft Operations in Forest Environments
## 6 Directions for Future Research
### Future Pathways for BVLOS in Forested Environments  
Although BVLOS is possible with UAV operations in forested environments, especially when building a model for a Hard BVLOS scenario, which would likely carry greater financial support, for the average foreser looking to conduct Soft BVLOS operations to collect operational data over a small stand, the reality is that, without closing airspace or hiring a MEWP, the technology and compliance costs prevent significant barriers for entry. In order for BVLOS to become as simple as conducting typical VLOS operations, the technology needs to become cheaper, more robust, be rigorously tested, certified, and off-the-shelf. Airspace integration must also be addressed, thoroughly proven, and put in place. With these aspects addressed, we foresee that UAV operations with BVLOS, in forested areas, could become as routine as capturing photogrammetric data over a cornfield.  
In the near term, two novel ways that these barriers are being overcome can be observed in Australia and the United States. In Australia, CASA have implemented regulations for BVLOS in remote areas, which enable CASA to grant clearance for BVLOS operations, if they fit within certain bounds and if it can be proven that they are in a remote area. In the United States, the FAA makes it relatively simple to apply for Temporary Restricted Areas (TFRs) using special use airspace in an effective manner to de-conflict airspace for BVLOS operations.
## 4 Discussion
Similar findings have also been presented in the studies by other investigators; thus, we confirmed the role of EZR in the prognosis prediction of PDAC [50; 51]. Furthermore, given the relationship between SATB1 and \(\beta\)-catenin [40], SATB1 and SMAD3 [54], \(\beta\)-catenin and SMAD3 [55], SMAD3 and Erzin [56], we next evaluated whether there was any possible added value of combining SATB1, SMAD3, Erzin and \(\beta\)-catenin to the prognostic value of each of the proteins alone. Kaplan-Meier survival analysis demonstrated that the subset of patients whose tumors co-expressed high levels of SATB1c, SMAD3, Erzin and \(\beta\)-catenin had extremely poor OS, and the combined expression of these markers better predicted patient survival than looking at each marker individually. Moreover, this combined 4-protein panel proved to be a powerful independent prognostic factor associated with worse outcome. In turn, a particularly improved OS was experienced by patients with tumors expressing SATB1n-highSMAD3 lowEzrinlow\(\beta\)-cateninlow, and this expression panel appeared as an independent positive prognostic factor in the multivariate Cox analysis. Likewise, in the TCGA dataset, a combined 4-gene panel of _SATB1/SMAD3/EZR/CTNNB1_ better predicted patient survival than looking at each marker individually, and it emerged as a powerful independent prognostic factor associated with poorer patient outcomes. Thus, our analyses showed that examining the combined expression of SATB1, SMAD3, Erzin and \(\beta\)-catenin may be more helpful in predicting the prognosis of PAC patients than single markers. This could be because the predicted biology related to their co-expression included significant interconnectivity with certain master regulators associated with cancer phenotype, as shown by our functional enrichment analysis. Likewise, the biological processes and signaling pathways that emerged from the PPI network are known or are suspected to have a tight cause-and-effect relationship with carcinogenesis. However, the answer to whether these are implicated in the mechanisms linking SATB1/SMAD3/Ezrin/\(\beta\)-catenin co-expression to PDAC will require further study. As a major limitation, we want to emphasize that the small cohort size may hamper the statistical power of our study. Specifically, when we focused the subsequent analyses on the combined expression of the studied proteins, the sample size in certain subgroups became relatively small, but the number of cases was still within the range accepted in other studies [57; 58]. Nevertheless, our findings should be validated with a larger sample size. It is appreciated that a more robust multivariate analysis should include at least 10 events per variable.
## 2 Materials and Methods
### Types of Attacks, Exploited Vulnerabilities, and Countermeasures
#### 2.1.3 Countermeasures  
Often, in the context of cybersecurity training within a workplace, organizations send fake phishing emails to their employees to test their awareness and response to such threats. These attacks are called **phishing simulations** and mimic real-world phishing tactics, including deceptive links and attachments, to assess how likely employees are to fall for phishing scams. The primary goal is to educate employees on recognizing and avoiding phishing attempts by providing immediate feedback and training to those who fall victim on the simulated phishing attacks. This proactive approach helps enhance the organization's overall security posture by reducing the risk of successful phishing attacks [23]. Additionally, phishing simulations can identify vulnerable individuals or departments, allowing for targeted training, tailor made awareness programs and improved cybersecurity measures.  
**Previous experience** with phishing can significantly impact a user's susceptibility to phishing emails. Users who have encountered phishing attempts in the past and learned from these experiences are generally more vigilant and better equipped to identify phishing cues. This experiential learning can lead to improved detection skills and a heightened sense of caution when dealing with suspicious emails. On the other hand, users who have previously fallen victim to phishing emails may become more cautious and skeptical of email communications. However, if the previous experience was negative and led to significant losses, it could also result in heightened fear and anxiety, potentially impairing their judgment and making them more susceptible to future attacks [2]. Thus, both the nature of the previous experience and the user's response to it play critical roles in shaping their vulnerability to phishing.  
**User awareness training** is a crucial cybersecurity strategy aimed at educating individuals on recognizing and responding to cyberthreats, particularly phishing emails. This kind of training involves structured programs that teach users how to identify deceptive emails, malicious links, and fraudulent websites. Organizations implement user awareness training through interactive workshops, e-learning modules, and phishing simulations to reinforce learning and improve retention. The primary goal is to enhance employees' ability to detect and mitigate social engineering attempts by fostering a security-conscious culture. Well-designed training programs include real-world examples, periodic assessments, and adaptive learning techniques to ensure continuous improvement in users' threat awareness [24; 25]. Additionally, targeted training can be provided to individuals or departments identified as more vulnerable based on phishing simulations, leading to more effective risk reduction and an overall stronger cybersecurity ecosystem.
# Steel Surface Defect Diagnostics Using Deep Convolutional Neural Network and Class Activation Map
## 5 Conclusions  
In this paper, we proposed a steel surface defect detection based on a convolutional neural network with a class activation map. It was clear that the proposed model outperformed other shallow machine learning algorithms, i.e., support vector machine and logistic regression in terms of accuracy and F-1 metrics. Our detection model was comparable in comparison with similar previous deep learning-based models, e.g., CNN and PLCNN concerning accuracy metric. Besides, the explainability of the deep learning model was also discussed by providing a localized region within the steel defect image to support decision making. This ability bridged significantly between human-expert decisions and the black-box model of deep learning. Among the possible ways to extend this study, future work might consider other large-scale steel defect datasets and more precisely, the use of an advanced technique that could classify multiple defect categories within an image would be desirable.  
Conceptualization, S.Y.L.; methodology, S.Y.L. and B.A.T.; writing-original draft preparation, S.Y.L. and B.A.T.; writing-review and editing, B.A.T.; supervision, S.L.; funding acquisition, S.L. and S.J.M.  
This work was supported in part by the Technology Innovation Program of the Ministry of Trade, Industry and Energy (MOTIE) under Grant 10080729, in part by the Korea Institute for Advancement of Technology (KIAT) grant funded by the Korea Government (N0008691, The Competency Development Program for Industry Specialist) and in part by the High-Potential Individuals Global Training Program of Institute for Information and Communications Technology Planning Evaluation (IITP) under Grant 2019-0-01589.  
Figure 7: Class activation maps of steel surface defects localized by the proposed method.
# Analysis of Multi-Antenna GNSS Receiver Performance under Jamming Attacks
## Appendix A  
Tracking performance of different PRNs with CW and BWGN interference are provided in Tables A1 and A2.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & \multicolumn{2}{c}{PM} & \multicolumn{2}{c}{MPDR} \\ \cline{2-5}
**PRN** & **Mean (mm)** & **Standard Deviation (mm)** & **Mean (mm)** & **Standard Deviation (mm)** \\ \hline PRN 3 & \(-0.11\) & 0.34 & \(-0.06\) & 0.55 \\ PRN 6 & \(-0.13\) & 0.28 & 0.01 & 0.62 \\ PRN 10 & - & - & 0.05 & 0.81 \\ PRN 16 & \(-0.10\) & 0.37 & \(-0.06\) & 0.63 \\ PRN 18 & \(-0.14\) & 0.36 & 0.03 & 0.65 \\ PRN 21 & \(-0.09\) & 0.25 & 0.01 & 0.57 \\ PRN 24 & \(0.01\) & 0.49 & 0.05 & 0.70 \\ PRN 25 & \(0.07\) & 0.53 & \(-0.04\) & 0.61 \\ PRN 29 & \(-0.06\) & 0.45 & 0.04 & 0.69 \\ \hline \end{tabular}
\end{table}
Table 3: Pseudorange measurement errors for PM and MPDR in the presence of BWGN interference.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & \multicolumn{2}{c}{PM} & \multicolumn{2}{c}{MPDR} \\ \cline{2-5}
**PRN** & **Mean (mm)** & **Standard Deviation (mm)** & **Mean (mm)** & **Standard Deviation (mm)** \\ \hline PRN 3 & \(-0.11\) & 0.33 & \(-0.06\) & 0.55 \\ PRN 6 & \(-0.13\) & 0.29 & 0.01 & 0.62 \\ PRN 10 & \(-0.19\) & 1.73 & 0.05 & 0.80 \\ PRN 16 & \(-0.11\) & 0.37 & \(-0.06\) & 0.63 \\ PRN 18 & \(-0.14\) & 0.34 & 0.03 & 0.65 \\ PRN 21 & \(-0.09\) & 0.25 & \(-0.01\) & 0.57 \\ PRN 24 & \(0.01\) & 0.49 & 0.05 & 0.70 \\ PRN 25 & \(0.07\) & 0.52 & \(-0.03\) & 0.61 \\ PRN 29 & \(-0.06\) & 0.43 & 0.03 & 0.69 \\ \hline \end{tabular}
\end{table}
Table 4: Carrier phase measurement errors using PM and MPDR with BWGN interference.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & \multicolumn{2}{c}{PM} & \multicolumn{2}{c}{MPDR} \\ \cline{2-5}
**PRN** & **Mean (mm)** & **Standard Deviation (mm)** & **Mean (mm)** & **Standard Deviation (mm)** \\ \hline PRN 3 & \(-0.11\) & 0.33 & \(-0.06\) & 0.55 \\ PRN 6 & \(-0.13\) & 0.28 & 0.01 & 0.62 \\ PRN 10 & - & - & 0.05 & 0.81 \\ PRN 16 & \(-0.10\) & 0.37 & \(-0.06\) & 0.63 \\ PRN 18 & \(-0.14\) & 0.36 & 0.03 & 0.65 \\ PRN 21 & \(-0.09\) & 0.25 & \(-0.01\) & 0.57 \\ PRN 24 & \(0.01\) & 0.49 & 0.05 & 0.70 \\ PRN 25 & \(0.07\) & 0.53 & \(-0.04\) & 0.61 \\ PRN 29 & \(-0.06\) & 0.45 & 0.04 & 0.69 \\ \hline \end{tabular}
\end{table}
Table 5: Pseudorange measurement errors for PM and MPDR in the presence of CW interference.
# The Effects of Concussions on Static Postural Stability
## 3 Results
### Unilateral Stability Test  
During this test, significant differences in sway between CONC and NO CONC groups were evident in the DOM EC and ND EC conditions. In DOM EC condition, COP-X max (\(p=0.002\); \(t=3.41\); \(d=1.26\)), COP-Y max (\(p=0.045\); \(t=2.09\); \(d=0.77\)), Avg. Displacement along X (\(p=0.011\); \(t=2.72\); \(d=1.00\)), Avg. Displacement along Y (\(p=0.032\); \(t=2.26\); \(d=0.83\)), and 95 EA (\(p=0.024\); \(t=2.38\); \(d=0.88\)) demonstrated significant differences with greater postural sway in the CONC group compared to the NO CONC group (Figure 3). Similarly, in ND EC condition, COP-Y max (\(p=0.042\); \(t=2.14\); \(d=0.79\)), COP-Y min (\(p=0.046\); \(t=2.09\); \(d=0.77\)), Avg. Displacement along Y (\(p=0.009\); \(t=2.80\); \(d=1.03\)), and 95 EA (\(p=0.043\); \(t=2.12\); \(d=0.78\)) demonstrated significant differences with greater postural sway in the CONC group compared to the NO CONC group (Figure 4). The descriptive statistics for all variables demonstrated higher values for the CONC group than the NO CONC group in both conditions. No significant differences between CONC vs. NO CONC groups were observed in DOM EO or ND EO conditions.  
Figure 2: Significant postural sway variables between the individuals with a history of concussion (CONC) and those without concussion (NO CONC) when standing on a foam pad placed on the force platform on both feet with eyes opened (foam BL EO) condition of Clinical Test of Sensory Interaction and Balance (CTSIB) Test. (**Top left**) COP-X max; maximum center of pressure (COP) excursion in the anterior-posterior direction. (**Top right**) Avg. Displacement along X; average COP displacement in the anterior-posterior direction. (**Bottom**) 95 EA; 95% ellipsoid area. Bars represent standard errors.  
Figure 4: _Cont_.  
Figure 3: Significant postural sway variables between the individuals with a history of concussion (CONC) and those without concussion (NO CONC) in eyes closed (EC) dominant condition of the unilateral stability test. (**Top left**) COP-X max; maximum center of pressure (COP) excursion in the anterior-posterior direction. (**Top right**) COP-Y max; maximum COP excursion in the medial-lateral direction. (**Bottom left**) Avg. Displacement along X; average COP displacement in the anterior-posterior direction. (**Bottom right**) Avg. Displacement along Y; average COP displacement in the medial-lateral direction. Bars represent standard errors.  
The secondary analysis of ONCE vs. MULTIPLE groups revealed a significant difference in postural sway between groups in the COP-Y average (\(p\) = 0.019; \(t\) = 2.62; \(d\) = 1.27) during the ND EO condition. The descriptive statistics demonstrated higher values for the ONCE group than the MULTIPLE group. No significant differences between ONCE vs. MULTIPLE groups were observed in the DOM EO, DOM EC, and ND EC conditions.
# Development of a Novel Concrete Curing Method Using Induction Heating System
## 4 Conclusions  
In this study, a novel accelerated concrete curing method using an induction heating system was developed and its efficiency was demonstrated by FEM simulation and experimental investigations. The following conclusions are drawn within the scope of this study:  
1. The effects of induction heat curing on the increase in the temperature of the metallic forms with different materials were evaluated using FEM-based thermal analysis. The results revealed the superior heating performance of steel form by induced magnetic field compared with those for aluminum and stainless steel forms. Therefore, it is concluded that the steel form is most appropriate for concrete curing using an induction heating system.
2. The results from both FEM-based thermal analysis and formwork heating experiment verified that the temperature uniformity across the height of the formwork surface was higher when an equally spaced three-turn coil, rather than a one-turn coil, was used.
3. Concrete cured in the low-temperature chamber by using an induction heating system achieved sufficiently high compressive strength even at a very early age. This demonstrates the effectiveness of using an induction heating system for concrete curing in cold weather.
4. Early strength development of induction heat-cured concrete is proportion to the value of the maturity factor. This indicates that both the maximum temperature and duration of induction heat curing affect the early age strength of concrete.
5. The temperature distributions in the concrete cured using the induction heating system were not uniform and the early-age strength decreased when the cross-sectional dimension of the specimen increased to approximately 300 mm because the heat generated in the steel form could not be sufficiently transferred to the center of the concrete.
6. The proposed method using induction heating is possibly applicable for curing both precast concrete and in-situ concrete (particularly in cold weather) in practice. Performing a preliminary thermal analysis, considering the size and shape of the concrete members, is required for determining the appropriate arrangement of the induction coils. Nevertheless, further investigations that account for practical member size, environmental conditions, and construction process are required for the utilization of the induction heat-curing method in construction fields.  
Conceptualization, C.-H.A. and H.-O.S.; Funding acquisition, H.-O.S.; Investigation, C.-H.A., J.L., D.-J.K. and H.-O.S.; Supervision, C.-H.A. and H.-O.S.; Writing--original draft, C.-H.A. and H.-O.S.; Writing--review & editing, H.-O.S. All authors have read and agreed to the published version of the manuscript.  
This research was supported by a grant (20 CTAP-C152012-02) from Technology Advancement Research Program (TARP) funded by Ministry of Land, Infrastructure and Transport of Korean government and also supported by research fund of Chungnam National University.  
The authors declare no conflict of interest.
# Bearing Fault Diagnostics Based on the Square of the Amplitude Gains Method
## 5 Summary  
The parametric method of bearing failure detection presented in the article is based on auto-correlation and power spectral density functions. The signal is analyzed in two separate periods. When the space between the analyzed intervals is close to each other, the influence of external disturbances is eliminated. Therefore, each change in the parameters of the diagnostic model should be interpreted as a change in the machine's technical condition.  
Markings of bearing damage are decipherable and presented in the form of characteristic color maps, where dominant _green_ and _blue_ indicate an undamaged bearing or very low wear, _red_--increased wear, or _black_--bearing damage. The exact location of the colors on the damage maps may vary as the diagnostic thresholds are determined by the mean and standard deviation, i.e., statistical parameters. Therefore, it is not the exact locations that are important but the predominance of a particular color. It should also be noted that the described parametric method should not be used on small datasets because it requires the determination of diagnostic thresholds based on statistical operations.  
The experimental results confirmed that the method can reliably detect bearing damage. Furthermore, the method is simple and uses only measured vibration data (signal from one sensor is sufficient). Therefore, preliminary preparation of the rotor for testing is not required. Furthermore, displacement is measured on the shaft, so the bearing could be inaccessible or difficult to reach. In addition, the machine can be continuously monitored online. This conclusion allows for the future practical implementation of the method.  
At the current stage of development, the method can only warn about the detection of bearing damage (the method cannot indicate the type of damage). Therefore, informing the maintenance staff about the need to carefully look at the bearing (when _blue_ and _red_ colors dominates) or to replace it (when the damage map is mostly _red_ and _black_) becomes a possibility.  
The next stages of the method developed in diagnosing bearings will be continued in subsequent articles.  
R.G.: Conceptualization, Investigation, Methodology, Project administration, Validation, Visualization, Writing--Original Draft, Funding acquisition, Resources, Supervision. B.B.: Data curation, Investigation, Resources, Software, Validation, Visualization, Writing--Reviewing and Editing, Funding acquisition. J.E.M.: Reviewing and Editing, Visualization, Funding acquisition. All authors have read and agreed to the published version of the manuscript.  
The presented work was supported by the Ministry of Education and Science in Poland (research project No. WZ/WM-IIM/2/2022), by the Polish National Agency for Academic Exchange as part of the Academic International Partnerships (PPI/APM/2018/1/00033/U/001 project) and Erasmus+ Programme fund (partnership agreement 2017-3475/001-001).  
Not applicable.  
The authors declare that they have no conflict of interest.
# Application-Specific Integrated Circuit of an Inter-IC Sound Digital Filter for Audio Systems
## 2 Materials and Methods
### Digital Filter Architecture
#### 2.2.3 FPU (M3)  
Figure 3 shows the signal processing data path for a deterministic mono filter. Once the data are deserialized to a 16-bit word, they enter the first stage, which consists of a 16-stage shift register, and the previous data move forward in the register chain when there is a positive edge from the left-right data selector.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Name** & **Description** \\ \hline Input & I2C\_SCL & I2C bus clock \\ Input & I2S\_CLK & I2S bus clock \\ Input & I2S\_ADC & I2S serial data from the ADC \\ Input & I2S\_LR & I2S bus data left/right selector \\ Input & rst & Global reset \\ Input & I2C\_SDA & I2C bus serial data \\ Output & I2S\_DAC & I2S bus serial data to the DAC \\ \hline \hline \end{tabular}
\end{table}
Table 1: Pinout of the filtering module.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name** & **HPF** & **LPF** \\ \hline LPF0 & - & 1.894 kHz \\ LPF1 & - & 3.057 kHz \\ BPF0 & 1 kHz & 20 kHz \\ BPF1 & 4 kHz & 10 kHz \\ \hline \hline \end{tabular} Note: The order for all the filters is 16.  
\end{table}
Table 2: Cutoff frequencies of deterministic filters.  
Figure 2: Deserialization module block diagram.  
When the left-right data selector is low, a single step of the convolution between the input data in the shift register and the filter bank coefficient is performed in the multiply-accumulate (MAC) unit. This filtered signal, after 16 clock edges, will be again serialized in the next stage. Figure 4 describes the complete stereo filtering diagram constructed with two mono filtering modules, where the right and left channels are multiplexed based on the value of the I2S LR signal. It is important to note that, with these features, the stereo filtering module is I2S-compliant.