esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Description of the Study Area  
The Eastern Beaufort Sea region was selected as our study area. This area of sea is located between northern Alaska and the northwestern Canadian coast, north of Banks Island, west of the Arctic islands, and east of the Chukchi Sea. In the summertime, the northern part of the sea remains heavily covered with multi-year ice that is conveyed from the central Arctic by anticyclonic Beaufort circulation (Baufort, 2016). In the southern part of this sea, there are areas of widely covered open sea, as well as areas of newly frozen sea ice.  
During the navigable window of the Arctic shipping lanes (early August to late October), the sea ice in the Beaufort Sea area undergoes a melting, drifting, and then freezing process, producing many different types of sea ice, which can be classified using deep learning remote sensing semantic segmentation methods. The types of sea ice in this sea are complex and more typical, with almost all the WMO-defined sea ice development stages represented throughout the summer period. As shown in Figure 1, the sea ice types in this zone are mixed and disordered, but mainly comprise multi-year ice and thin ice. We classified the sea ice into four stages based on the sea ice development process defined by the WMO, which are open ocean, one-year ice, multi-year ice, and thin ice. Here, thin ice refers to the group of sea ice stages with a thickness of less than 30 cm, including new ice, Nilas ice, young ice, and gray and gray-white ice. Multi-year ice refers to the group of sea ice stages with a thickness of more than 120 cm, mainly consisting of old ice, second-year ice, and multi-year ice. Due to the low distribution of one-year ice during the summer ice melt period, we grouped both unknown developmental stages and iceberg regions into the one-year ice category. Different types of sea ice show significant differences regarding their representation in SAR images. Because the microwaves emitted by SARare scattered more diffusely on rough surfaces, causing the sensor to receive returning microwaves from multiple directions, such surfaces appear brighter. Multi-year ice tends to have a rougher surface than one-year ice due to a variety of factors, such as sea winds and multi-year melting. Thin ice, on the other hand, is also affected by both melting and freezing during its formation, making the surface smoother, but with more widespread coverage and distributions, such as in inter-ice channels, compared to one-year ice. These factors account for the differences in color and texture between different types of sea ice.  
The most typical sea ice types in each category are gray and white ice, multi-year ice, and thin one-year ice. In terms of time scale, our study focuses on the summer ice melt period, during which we found fewer one-year ice examples, and therefore, the one-year ice type was considered as a background category in our experiments.
## 5 Simulations
### Simulation Results  
Figure 8 shows the total delay duration for the detection and recovery of the failure occurrence for the proposed TFR approach and the standard PTP under RSTP. The simulation results show that the total delay duration was reduced by about 100% in comparison to the standard PTP because the standard PTP does not include a fault detection and recovery mechanism. However, the total delay duration was reduced by about 79-99%, in the case of fault occurrence between the MC and the SC.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number of Failed Links** & **PTP with RSTP** & **TFR Best-Case Scenario** & **TFR Worst-Case Scenario** \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 1: The delay values (seconds) of PTP with RSTP, and the TFR approach with the best-case and worst-case detection scenarios. PTP: precision time protocol; RSTP: Rapid Spanning Tree Protocol; TFR: Timing Fault Recovery.  
Figure 9 shows the local clock timing comparison of the SC with respect to MC in the same communication path for the TFR approach, the standard PTP, and the standard PTP protocol with RSTP.  
The simulation results show that the TFR approach significantly reduced the local clock drifting of the SC in comparison to the standard PTP by about 90% and the standard PTP with RSTP by about 75%, in the case of fault occurrence between the MC and the SC.  
Figure 8: A simulation comparison of fault recovery durations in the TFR and PTP under RSTP approaches. TFR-Timing Fault Recovery; RSTP: Rapid Spanning Tree Protocol.  
Figure 9: Slave clock timing values with TFR and PTP with RSTP with respect to MC timing. MC: Master Clock.
## 4 Discussion  
For long distance infrared signature of targets in the field, the inversion uncertainty is mainly affected by (1) the uncertainty of the radiometric calibration, (2) the uncertainty of the estimation for the atmospheric transmittance and atmospheric radiation, and (3) the uncertainty of the environmental condition changes in the field, such as temperature, wind, etc., other than that in the laboratory. Among these factors mentioned above, the uncertainty in the radiometric calibration, i.e., values of \(k\) and \(G_{0}\) in Equation (13), under current measuring conditions is approximately 6% for a medium-wave infrared thermal imager [22]. The uncertainty in the estimation of the atmospheric transmittance and atmospheric radiation, depending on meteorological equipment and MODTRAN, is about 20-30%; and the environmental differences between the field and the laboratory is usually 10%. As a result, the uncertainty of the measurement for the infrared thermal imager can be calculated by the root mean square of the three factors listed above, approximately 23.2-32.2%. In our experiments, the inversion uncertainty derived from the analytical expression and by MODTRAN is 2.56%, 10.2% and 5.7%, 16.7%, respectively, both of which are acceptable according to the above analysis. It is obvious that the uncertainty derived from the analytical expressions is lower than that by MODTRAN for cooperative targets, i.e., targets that can be easily placed a blackbody or an object that served as a blackbody nearby. However, for non-cooperative targets such as an enemy vehicle, we can only employ MODTRAN to estimate the radiation of targets theoretically.
# A Review on Mixed Reality: Current Trends, Challenges and Prospects
## 3 Concept of Mixed Reality
### Evaluation  
After the development of each MR application, evaluation of the implemented system to understand its potential shortcomings is important. This evaluation is conducted from several perspectives, e.g., based on a user survey, a comparison with a common system, or based on an environmental reference. Evaluation based on user surveys shows the user satisfaction level with the system output. A comparison of results with a common system helps to determine the potential advantages or disadvantages of the MR application in practice, and evaluating the system output by observing environmental references is a practical MR-based navigation application.  
**Evaluation based on user survey**. To improve MR systems, evaluating user satisfaction is critical; this process has been considered in several ways. A common method used in several studies is to conduct surveys based on questionnaire items. Ref. [74] used questionnaires incorporating the Likert scale to evaluate different items, such as fantasy, immersion, and system feedback in MR game applications. Ref. [75] created MR matrices comprising cognitive load, user experience, and gestural performance for surveys, and used variance analyses to identify inconsistent views during the evaluation of MR learning applications. Ref. [76] employed a questionnaire with statistical tests such as the t-test to assess user feedback in learning applications. Ref. [77] employed a combination of the creative product semantic scale (CPSS) -based method and the Likert scale in MR storytelling applications. This evaluation uses different items, such as novelty, resolution, and elaboration for evaluation. Ref. [37] proposed the use of surveys with the ANOVA test in learning applications to detect inconsistent views. Ref. [78] proposed the questionnaire approach to evaluate MR applicationsin construction jobs using parameters such as accuracy, efficiency, ease of use, and acceptability. This method was based on many analytical methods, such as the use of the Likert index for scoring the questionnaire, assessing the reliability of parameters with the coefficient of Cronbach Alpha, and evaluation of the agreement of the groups' views using a statistical inference model such as the students t-test. **Evaluation based on common system**. Ref. [79] used surveying to compare the MR guidance output with that of a common system, such as computed tomography imaging for surgery. This evaluation was performed on the basis of different parameters, such as visualization, guidance, operation efficiency, operation complexity, operation precision, and overall performance. **Evaluation based on environmental reference checking**. Ref. [80] discussed the evaluation of navigation systems using the precision term, and proposed using the reference position in order to achieve increased precision. In this example, the user moves from the reference point or reads a visual marker in his/her path using a camera.
## 4 Dynamics and Isolation Performance of the Vibration Isolator  
### Dynamic Equation  
Based on [PERSON]'s second law and the structure in Figure 6, the motion equation of the vibration isolation system exposed to the base displacement excitation \(z(t)=Z\cos(\omega t)\) with amplitude \(Z\) and frequency \(\omega\) is established as:  
\[m\frac{\mathrm{d}^{2}u}{\mathrm{d}t^{2}}+c\frac{\mathrm{d}u}{\mathrm{d}t}+4a^{ 2}k_{\rm n}u^{3}-4 ak_{\rm n}(a\Delta y^{2}+\delta_{\rm n})u+k_{\rm p}u=ma^{2}Z \cos(\omega t) \tag{11}\]  
Figure 7: Non-dimensional stiffness characteristics of QZS vibration isolator. (**a**) \(r=0.5\); (**b**) \(\overline{a}=0.4\).  
where \(c\) is the damping coefficient in the vertical direction.  
Considering the QZS condition in Equation (9), the non-dimensional form of Equation (11) is obtained as:  
\[\frac{\mathrm{d}^{2}\overline{u}}{\mathrm{d}\tau^{2}}+2\xi\frac{\mathrm{d} \overline{u}}{\mathrm{d}\tau}+4\overline{a}^{2}r\overline{u}^{3}=\Omega^{2} \overline{Z}\cos(\Omega\tau) \tag{12}\]  
where the new non-dimensional transformation parameters are defined as \(\Omega=\frac{\omega}{\omega_{0}}\), \(\omega_{0}=\sqrt{\frac{k_{\mathrm{P}}}{m}}\), \(\tau=\omega_{0}t\), \(\xi=\frac{c}{2m\omega_{0}}\), \(\overline{z}(\tau)=\overline{Z}\cos(\Omega\tau)\) and \(\overline{Z}=\frac{Z}{\Delta y}\).  
Here, the harmonic balance method (HBM) [30], an approximate analytical algorithm, is used to acquire dynamic characteristics of the isolation system. Since the primary resonance response is of interest, the steady solution to Equation (12) for \(\overline{u}\) can be written as:  
\[\overline{u}=\overline{A}\cos(\Omega\tau+\theta) \tag{13}\]  
where \(\overline{A}\) and \(\theta\) are the amplitude and phase of the response, respectively. Substituting Equation (13) into Equation (12) and making the coefficients of \(\sin(\Omega\tau)\) and \(\cos(\Omega\tau)\) equal to zero, the following relations can be obtained:  
\[\left\{\begin{array}{l}2\xi\Omega\overline{A}+\Omega^{2}\overline{Z}\sin \theta=0\\ 3\overline{a}^{2}r\overline{A}^{3}-\Omega^{2}\overline{A}-\Omega^{2}\overline {Z}\cos\theta=0\end{array}\right. \tag{14}\]  
By the nature of trigonometric function, Equation (14) can be combined as the implicit amplitude-frequency equation:  
\[(\overline{A}^{2}-\overline{Z}^{2})\Omega^{4}+(4\xi^{2}\overline{A}^{2}-6 \overline{a}^{2}r\overline{A}^{4})\Omega^{2}+9\overline{a}^{4}r^{2}\overline {A}^{6}=0 \tag{15}\]
## 2 GNSS-Derived Height Determination
In addition, the official elevations of island stations might refer to a locally determined vertical datum. Therefore, if control stations referring to different vertical datum are discounted, it is common for unknown stations not to be surrounded by fixed stations. In Korea, therefore, the guidelines were updated by adding an exception for coastal regions or islands in 2017 [14]. According to the guidelines, the elevations of unknown stations located in the coastal region or islands can be determined by fixing only two inland stations. At least two stations are required so as to maintain the stability of the network. Additionally, the maximum baseline distance between the control stations should be shorter than 20 km. During processing, two cases of adjusted ellipsoidal height should be calculated, using half of the total observation time; they should then be compared to each other to check the consistency according to the time. The differences in the cases should be smaller than 3 cm or 5 cm, depending on the target precision. This means that the adjusted ellipsoidal height from 4 h of the first day, and on the second day, should be smaller than 3 cm. Figure 1: Concept of GNSS-derived height determination [10]. The most recent model should be applied to the geoid model. In Korea, KNGeoid18, which has a 2.3 cm degree of fit (DOF), was developed [15] so that it should be applied to GNSS-derived height determination. The detailed surveying conditions in Korea are summarized in Table 1. For reference, the latest version of the guidelines was re-announced in January 2020 because the public surveying rules were amended by officially adopting GNSS-derived height determination in December 2019 [16] and the development of KNGeoid18. However, the principles and conditions are identical to the previous guidelines, so the contents were not changed.
## 4 Testbed Results
### Modbus and DNP3  
The testing for Modbus and DNP3 followed the same test pattern as the GOOSE protocol's 5G TCP configuration. Each protocol was tested for 1000 cycles for statistical reliability, with the test coordinator generating packets to be encapsulated by the Core Network, forwarded to the gNB in GTP format, and transmitted over the wireless interface for UE reception. The same requesting packet was generated for each protocol: a Modbus \"Write Registers\" packet with a 78-byte payload and a DNP3 \"Direct Operate\" packet with an 87-byte payload. These packets were chosen due to their small packet size for the protocol. The \"Write Registers\" packet for Modbus contains a single address, a size byte, and 2 data bytes to be written, whereas the \"Direct Operate\" packet for DNP3 contains a single 16-bit item payload. Both packets represent the smallest supported packets according to each protocol's TCP specification. The results shown in Figure 12 show that each protocol achieved similar results, but DNP3 exhibited a slightly lower latency compared to Modbus/TCP and GOOSE. Due to this discrepancy, additional testing was conducted for multiple packet sizes for both Modbus and DNP3 packets. Within the Modbus and DNP3 standards, individual packet sizes are defined for TCP communications. For Modbus, the maximum size packet is 260 bytes [3], whereas DNP3 allows for 292 bytes [4]. For testing the impact of packet size on latency, individual packets with lengths of 103, 145, 221, and 255 bytes were tested in the same manner as the smaller packet sizes used in the initial testing.  
The results of these packet size tests, once again comparing the measured latency, can be seen in Figure 13. The results in this figure indicate that the latency did not correlate with the limited packet size of Modbus and the DNP3 protocol. Since each packet exhibited a similar average latency and similar latency distribution, we can conclude that there was  
Figure 11: Network optimization attempt.  
no linear relation between the packet size and the measured latency. Instead, we posit that packets adhering to the Modbus and DNP3 standards would always exhibit a similar latency for delivery. This implies that the gNB scheduling of wireless packets is the main constraint on packet latency, and packet scheduling for these studied protocols is expected to fall within a similar latency range for similar network topologies and technologies.
# Adaptive Constant False Alarm Detector Based on Composite Fuzzy Fusion Rules
## 6 Conclusions  
CFKLQ-CFAR improves target detection performance by improving information utilization during the detection process. The environmental information collected by two sensors is fused using fuzzy logic, and composite decision-making is applied to the multiple fuzzy fusion results. CFVI-CFAR, designed with a variability index, effectively improves the detection performance of VI-type constant false alarm rate detectors. The simulation results show that CFKLQ-CFAR improves the detection probability in a multiple-target environment. CFVI-CFAR demonstrates better detection performance than VIHECMOS-CFAR in a multiple-target environment and exhibits superior false alarm control ability in acclutter edge environment compared with VIHECMOS-CFAR. In complex noise environments, such as wireless communications, satellite communications, or audio signal processing, the CFVI-CFAR detector can effectively separate signals from noise and detect the presence of target signals. It can also be used in environmental monitoring, sensor data fusion, and other applications to detect abnormal patterns or targets from multi-source sensor data. However, if there are many targets in the application scenario or if the amount of data is extremely large, it may not meet the real-time requirements due to the increased computational complexity. Compared with AI-based detection systems, the CFVI-CFAR detector has lower computational complexity, does not rely on large amounts of data, and has good interpretability. However, CFVI-CFAR also faces some limitations and potential challenges in practical implementation. It may be less adaptable to extremely complex and highly dynamic environments and relies on manually designed rules, and the computational complexity may increase in complex scenarios. In the future, the determination method of CFVI-CFAR will be optimized and improved, and parallel computing and hardware acceleration will be used to optimize the computational complexity to further improve the detection performance of CFVI-CFAR.  
Figure 8: False alarm probability of clutter edge environment.  
**Author Contributions:** Conceptualization, C.X. and Y.Y.; methodology, C.X.; software, Y.Y.; validation, C.X. and Y.Y.; formal analysis, Y.Y.; investigation, C.X.; resources, C.X.; data curation, Y.Y.; writing--original draft preparation, Y.Y.; writing--review and editing, C.X.; visualization, Y.Y.; supervision, C.X.; project administration, C.X. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 3 Proposed Algorithm
```  
**Algorithm 1**Propagator Direct Data Acquisition (PDDA) for 1D and 2D Direction Estimation \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Number of Multiplications** & **Number of Additions** \\ \hline Capon & M \(\times\) (M + 1) & (M - 1) \(\times\) M + (M - 1) \\ MUSIC & (M - D) \(\times\) (M + 1) & (M - D) \(\times\) (M - 1) + (M - D - 1) \\ Pisarenko & M & M - 1 \\ Propagator & M \(\times\) (M + 1) & (M - 1) \(\times\) M + (M - 1) \\ PDDA & M & M - 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Showing the computational operations required to construct the spatial spectrum for the PDDA and popular AOA methods. The overall computational cost of the mentioned above algorithms are calculated and described below in Table 4. It is obvious from Tables 1-4 that the PDDA method has lower computation complexity than the other AOA methods presented.
## 3 Results
### Short-Time Fourier Transform  
PGA (0-0.53 g) and intensity (II to VI) variations were observed in Odisha. Periodogram of the spectral density of PGA and intensity variations were plotted for the study area. In Figure 6a, the amplitudes of PGA values are shown against frequency, where the amplitudes vary from 0-10 m, while frequencies vary from 0-0.5 Hz. Similarly, in Figure 6c, the amplitudes of intensities vary from 0-12 m, while frequencies vary from 0-0.5 Hz. However, in both cases, the amplitude level at the frequency of 0.01 is 8.76 Hz, while the amplitude level at the frequency of 0.05 is 7.129 Hz. This indicates that the power of the amplitude increased when frequency decreased. Therefore, the highest PGA and intensity can be expected in Cuttack, Bhubaneswar, Kendrapada, Jagatsngpur, Puri, Khodrha,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & **Predicted** & \\ \cline{3-4}  & & **Positive** & **Negative** \\ \hline
**Actual** & **Positive** & 1750 & 50 \\  & **Negative** & 100 & 800 \\ \hline  & & Accuracy & 0.94 \\  & & Precision & 0.95 \\  & & Recall & 0.97 \\  & & F1 score & 0.96 \\  & & Threat score (TS) or critical success index (CSI) & 0.92 \\  & & Fowlkes–Mallows index (FM) & 0.95 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Confusion matrix of prediction based on RNN.  
Anugul, Dhenkanal, and some parts of Bhadrakh and Baleswar. STFT simulation was conducted using the Blackman-Harris window, which had a window size of 512 mm (rectangular window = 512). STFT analysis for PGA and intensity in the simulated graph have a stretched magnitude. In Figure 6b, the frequencies of PGAs are plotted against 32 expected locations. The locations were chosen randomly. PGA variation in Odisha was highest between the location of 10-18, where frequency varied from 0-0.1 Hz. STFT simulation for intensity variation was projected for 32 locations in Odisha, as shown in Figure 6d. The highest intensity could be expected at the locations from 7-14 and 17-25, having a frequency range of 0-0.15 m. The highest PGA and intensity were observed in Cuttack, Bhubaneswar, Anugul, Dhenkanal, and some parts of Bhadrakh and Baleswar. This analysis did not count external factors of sub-surface lithology and complicated tectonic structures. Therefore, SSA was conducted to identify the expected locations for high ground shaking.  
Figure 5: Periodogram for PGA and intensity (1800 events) at a radius of 1700 km where Odisha is the center. (**a**) periodogram for PGA; (**b**) periodogram for intensity.  
Figure 6: Short time Fourier transform (STFT) analysis for PGA and intensity in Odisha. (**a**) Periodogram for PGA in Odisha, (**b**) STFT simulation for PGA, (**c**) periodogram for intensity in Odisha, and (**d**) STFT simulation for intensity. (Frequency: Hz and amplitude: m). Red amplitude levels were plotted to mark the highest peak.