esa-sceva/satcom-chunk-collection huggingface dataset:
# A Method for Managing ADS-B Data Based on a 4D Airspace-Temporal Grid (GeoSOT-AS)
## 3 A 4D Airspace-Temporal Grid Framework (GeoSOT-AS) for ADS-B Data Organization
### GeoSOT-AS Time Dimensional Profiling Rules and Methods  
GeoSOT-T dissection theory [42] describes the segment-by-segment dichotomy and multiscale encoding of time, and relies on encoding clustering for fast retrieval of data containing temporal information. This info primarily takes the forms of the expansion of 1 y into 16 mo, 1 mo into 32 d, 1 d into 32 h, 1 h into 64 min, 1 min into 64 s, 1 s into 1024 millisec, and 1 millisec into 1024 microsec in a limited time domain (65536 B.C.-65535 A.D.) by co-primary temporal binary expansion. Through these expansions, the binary tree structure of year, month, day, hour, minute, and second is thus realized, forming a 64-level binary  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Level** & **Height Interval (m)** & **Level** & **Height Interval (m)** \\ \hline
1 & 76,800 & 6 & 2400 \\
2 & 38,400 & 7 & 1200 \\
3 & 19,200 & 8 & 600 \\
4 & 9600 & 9 & 300 \\
5 & 4800 & 10 & 150 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Recursive scales for height dimensional profiling.  
Figure 6: Height division and coding method: (**a**) flight height level division; (**b**) binary tree encoding structure of height.  
tree with time scales as large as 130,000 years after B.C. (level 0), and as small as microsecond (level 63). The time slice binary tree constitutes a multi-scale, uniform discrete-time coding system.  
Based on the flow form of ADS-B data, it was proposed to use three expansions, whereas for the data cycle by day, it was proposed to use the extraction of partial layers for coding representation, three expansions way time expansion (Table 3), in order to fit the binary integer profile (i.e., 0-24 h daily time span was expanded to 0-32 h, 0-60 min was expanded to 0-64 min, 0-60 sec was expanded to 0-64 s. For example, first set the time range, based on the form of ATC data, for example, set to [2020-04-26 00:00:00, 2020-04-26 24:00:00), that is, based on the form of data flow of a day to do the analysis. To describe the entire day span, three different expansions are utilized. These expansions employ the bifurcation dissection principle of GeoSOT-T, using the Table 3 expansion format to achieve the goal of integer bifurcation dissection.  
The time code format is shown in detail below:  
\[\underset{\text{unsigned}}{\overset{0}{\omega}}\quad\underset{\text{hour}}{ \overset{10010}{\omega}}\quad\underset{\text{minute}}{\overset{100101}{\omega}} \quad\underset{\text{sec}\,\text{cond}}{\overset{110011}{\omega}}\]  
where the advantage of integer bifurcation is that the data of the same time period can be given the same prefix, whereas the data requests within a time period can be based on the prefix index of the database, instead of retrieving the timestamp information of the whole cross-domain in turn. This process ultimately simplifies the time period query in database retrieval to a simple binary-coded matching query, which greatly reduces the retrieval cycle.
## 6 Conclusions  
The utilization of a smartphone's onboard microphone for determining \(g\) via the free fall method has proven to be successful, showcasing a noteworthy alignment between the calculated \(g\) and the locally accepted reference value for \(g\). The time measurements exhibited some degree of variability, with the smallest relative error amounting to 3.27%. The experiment's reported value of \(g\), accounting for associated uncertainty, is 9.8274 \(\pm\) 0.01 m/s\({}^{2}\), with an accuracy of 99.82%. The proposed method can enhance the accuracy of gravitational acceleration measurements in free fall experiments in various ways, building upon previous presentations. Additionally, the method is simple to implement, with the system being easily installed at a low cost. Moreover, it offers flexibility to adjust the experimental design according to the specific environmental conditions. This versatility makes it suitable for various applications, including educational purposes, research, and practical experimentation. Notably, the approach employing the smartphone's onboard microphone sensor yielded favorable outcomes, offering enhanced convenience compared to alternative sensor-based methods, as it obviated the need for supplementary equipment. Additionally, the integration of SPARKvue streamlined the process by directly displaying sound level, sound intensity, and period values. In summation, this study represents a valuable contribution to the refinement of current methodologies for the analysis of mechanics, particularly in the context of the free fall phenomenon, and is applicable across educational institutions at both university and secondary levels.  
Conceptualization, S.H.; Methodology, S.H.; Software, S.H.; Validation, S.H.; S.S. and P.K.; Formal analysis, S.H.; Investigation, S.H. and T.R.; Resources, S.H.; Data curation, S.H.; Writing--original draft, S.H.; Writing--review & editing, S.H.; Visualization, S.H.; Supervision, S.H.; Project administration, S.H.; Funding acquisition, S.H. All authors have read and agreed to the published version of the manuscript. This research project was financially supported by Mahasarakham University. This research project was financially supported by Mahasarakham University.  
Data Availability Statement: The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 4 Discussion  
### Artificial Intelligence Ethics  
Based on the outcomes of the program proposed in this research, we will explore the influence of the Artificial Intelligence Ethics Education Program on middle school students' awareness of artificial intelligence ethics. The artificial intelligence ethics survey sample is as follows: \"I believe that in the event of an unavoidable accident, an autonomous car driven by artificial intelligence should prioritize the protection of the driver over pedestrians\", \"I believe that most artificial intelligence technologies can be trusted and are safe\", \"I believe that another form of discrimination or injustice can be caused by strong artificial intelligence that thinks and judges like a human\", and \"I believe that even when collecting data and developing artificial intelligence technology for the benefit of the nation, organization, society, and all people, personal information and privacy must be protected\". In this study, the statistical analysis of the Artificial Intelligence Ethics Education Program's effectiveness was conducted using SPSS software. The primary method of analysis was a paired-sample \(t\)-test, which was applied to the results of the pre and post-test questionnaires to assess the impact on the participants' ethical awareness of artificial intelligence. This approach aimed to determine any significant changes in the mean scores between the two groups, reflecting the level of improvement in AI ethics awareness, with the results indicating a notable increase. The level of artificial intelligence ethics awareness improved by 0.773 points (\(t=-14.235\), \(p<0.000\)). A significant difference was identified, as displayed in Table 10 below. In this regard, the results of learners' awareness of artificial intelligence ethics after implementing the Artificial Intelligence Ethics Education Program of this study align with the discussion from previous research [5; 36; 39; 40; 41] that emphasized the cultivation of ethical awareness through the application of core competencies in artificial intelligence literacy education. The qualitative analysis of learners' understanding of artificial intelligence ethics after the application of the Artificial Intelligence Ethics Education Program is as follows. The learners' ethical sense of responsibility pertains to ensuring that the artificial intelligence system adheres to the rules and that, in the event of an accident, it is possible to trace back the condition to explain the specific design and outcomes of artificial intelligence technology. This is consistent with the notion of accountability examined in previous studies [42; 43; 44]. In addition, the ethical consciousness of developers aligns with existing research findings, indicating that the ethics of AI developers are essential for learners to internalize and practice AI ethics [25]. Learners stressed the importance of transparency, insisting that sources should be cited for images or videos that contain artificial intelligence technology. This demonstrates that the learning process around deepfakes acknowledges
# An Optimization Methodology for Adapting Legacy SGX Applications to Use Switchless Calls
## 4 Approach  
This section describes the problem scope and key challenges of utilizing switchless calls to existing SGX-ported applications. Then, we present our key insight and propose an optimization strategy to optimize the performance of an existing SGX application by selectively adopting switchless calls depending on ECALL/OCALL characteristics.
# Investigation of Roller-Tape Contact Pair Used in Precision Mechatronic System
## 2 Research Object  
The study analyses the raster generation and control system. The system was designed to have a constant linear relationship between the rotation angle of the drum and the linear displacement of the bar. This was achieved by minimizing the kinematic and geometric errors of the measuring system and the tape, which directly affects errors of the raster generation system. A focused laser beam raster is formed based on the number of pulses in the angle measuring system.  
The raster generation bench consists of a massive fine-structured granite base and the tape movement devices mounted thereon presented in Figure 1, the laser raster generation components and the raster scale error management system components. Figure 1 illustrates the raster generation bench: 1--tape unwinding mechanism, 2--granite base, 3--tape displacement using angle measuring system, 4--deflection node, 5--error monitoring unit, 6--pulling--stretching system, 7--control panel, 8--tape winding mechanism, 9--mechanism for covering tape in a protective band, 10--indoor partition, 11--microscopes with CCD camera, 12--scanner, 13--optical laser beam deflection system, 14--laser, 15--constant belt tension mechanism, 16--device computing control set and laser and scanner freezer, 17--tape stretch control and management system, 18--vibration isolation supports. The length of the granite base is 2 m in the Y direction.  
The main factors that adversely affect the accuracy of the dynamic calibration process are vibrations from external and internal sources. Vibrations from external sources--technological devices, vehicles, fans, etc. are usually transmitted through the floor. Internal sources of vibration include motors, reducers, rotary motion transmitter and laser system scanners.  
One of the most important components of the device is the tape displacement measuring system (Position 3 in Figure 1). Figure 2 presents its 3D images in cross-sections: 1--angle transducer base, 2--angle transducer limbo, 3--optical angle transducer heads, 4--precision tape displacement measurement shaft, 5--system base, 6--hood, 7,8--rotary system bearings, 9--tape clamping shaft, 10--tape clamping mechanism. The circumference of the steel band tape measured in the precision tape displacement measurement shaft is 200 mm. Accordingly, the diameter of this shaft at the point of contact with the tape is 63.662 mm.  
The value of the standard deviation with respect to the means of dissemination of calibration results of the 6 realizations is \(\mathrm{S_{D}}=0.087\) (see Formula 2). In pursuit of the maximum accuracy, a shaft was mounted in special, extra-high-precision bearings, using the method of four-head primary electrical signal generation. Calibration was performed autonomously by attaching a polygon to the tape displacement using an angle measuring system (polygon calibrated with PTB and its known errors), measuring it using an autocollimator, and entering an error correction.
## 3 Materials and Methods
### Establishment and Evaluation Indicators of Model  
Object detection is an important task in the field of computer vision, and evaluation indicators play a vital role in object detection. It provides an objective and quantitative evaluation of the performance of the model, which can help researchers understand the performance of the model in identifying and locating targets. Through the analysis of evaluation indicators, problems and deficiencies in the model can be identified, and guidance can be provided for further improvement of the model. This study focuses on the detection accuracy and detection speed of tomato ripeness. After the model is built, use several commonly used evaluation indicators for object detection, such as Precision, Recall, mAP, and other indicators to evaluate the performance of the model. These evaluation indicators are calculated as shown in Formulas (5)-(9) [36]:  
\[Precision=\frac{TP}{TP+FP}\cdot 100\% \tag{5}\]  
\[Recall=\frac{TP}{TP+FN} \tag{6}\]  
Precision measures the proportion of boxes that are actually correct among all the boxes predicted to be positive. Recall measures the proportion of true positive boxes that the model can correctly predict, where TP (True Positive) indicates the correctly predicted target box, FP (False Positive) indicates the incorrectly predicted target box, and FN (False Negative) indicates the missed target box.  
\[AP=\int_{0}^{1}P(R)dR \tag{7}\]  
\[mAP=\frac{1}{\mathcal{C}}\sum_{c=1}^{C}AP_{c} \tag{8}\]  
AP is an indicator for a comprehensive evaluation of Precision and Recall. It is obtained by calculating the area of the Precision-Recall curve. Generally speaking, the higher the AP, the better. AP is generally calculated for each category. mAP is the average of APs of all categories. It is the most common evaluation criterion in object detection tasks, especially in multi-category detection tasks. For each category, its AP is first calculated, and then the average of APs of all categories is calculated. The higher the mAP, the better the overall performance of the model, where P(R) is the Precision at Recall R. C represents the number of categories, and \(AP_{c}\) is the average Precision of category C.  
\[FPS=\frac{Num}{Time} \tag{9}\]  
FPS (Frames Per Second) is also an important evaluation metric. where Num indicates the number of frames and Time indicates the time.  
In addition to the above evaluation metrics, the confusion matrix is also a common metric for evaluating the performance of target detection models.  
It can realistically present the relationship between real categories and predicted categories. In the multi-classification problem, the confusion matrix is a matrix of N \(\times\) N, whose structure is shown in Figure 6. Each row represents a true category, and each column represents a predicted category. Values on the diagonal represent the number of correctly categorized samples, and values on the off-diagonal represent the number of incorrectly categorized samples.
## 3 Simulation Results
### Simulation Results According to the Number of Training Datasets  
Figure 2 shows the performance of each algorithm with respect to the size of the training dataset used in the NN optimization process. Only the NN-based methods show variation in their performance with the size of the training dataset.  
Despite the large size of the training dataset, such as \(10^{5}\), the conventional deep-learning-based methods are not able to surpass the performance of the nonlocal patch tensor-based method.  
This can be attributed to the challenge of extracting valuable information from the raw and unrefined training dataset. Remarkably, the T-DAE-EDMR technique outperforms the other techniques even when \(M\) is just \(10^{2}\). In addition, DAE-EDMR shows a noticeable improvement when \(M\) is increased to \(10^{3}\). It is noteworthy that when \(M\) is \(10^{5}\), DAE-EDMR shows a slightly better performance than that of T-DAE-EDMR. This observation implies that extracting information from \(N\) contaminated eigenvalues, instead of solely relying on the \(k+2\) highest values among them, can yield advantages when a large training dataset is available.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithms \(\backslash\) Variations** & \(\mathbf{P=5}\) & \(\mathbf{P=10}\) & \(\mathbf{P=15}\) & \(\mathbf{P=20}\) & \(\mathbf{P=25}\) \\ \hline None (undenoised) & 0.532\(\backslash\)31.69 & 0.554\(\backslash\)26.41 & 0.539\(\backslash\)25.28 & 0.535\(\backslash\)25.01 & 0.538\(\backslash\)24.31 \\ Semi-definite relaxation & 0.466\(\backslash\)24.70 & 0.458\(\backslash\)20.21 & 0.457\(\backslash\)18.69 & 0.470\(\backslash\)18.31 & 0.477\(\backslash\)18.10 \\ Nonlocal patch tensor-based method & 0.321\(\backslash\)24.31 & 0.326\(\backslash\)19.46 & 0.328\(\backslash\)18.12 & 0.330\(\backslash\)17.78 & 0.325\(\backslash\)17.36 \\ Deep-learning-based method A & 0.530\(\backslash\)29.65 & 0.527\(\backslash\)25.65 & 0.522\(\backslash\)22.82 & 0.533\(\backslash\)22.91 & 0.539\(\backslash\)22.91 \\ Deep-learning-based method B & 0.334\(\backslash\)24.64 & 0.323\(\backslash\)19.71 & 0.328\(\backslash\)18.29 & 0.325\(\backslash\)18.04 & 0.326\(\backslash\)17.90 \\ DAE-EDMR & 0.180\(\backslash\)17.57 & 0.176\(\backslash\)15.72 & 0.178\(\backslash\)13.92 & 0.177\(\backslash\)13.81 & 0.172\(\backslash\)12.03 \\ T-DAE-EDMR & 0.183\(\backslash\)18.62 & 0.183\(\backslash\)14.89 & 0.183\(\backslash\)13.59 & 0.178\(\backslash\)12.94 & 0.176\(\backslash\)12.42 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The performance of each scheme under varying cardinalities of the sensor network (left: NMSE between ground-truth and denoised EDMs; right: Localization error in meters).  
Figure 2: Performance comparison according to the number of training datasets. (**a**) NMSE between ground-truth and denoised EDMs and (**b**) localization error in meters.
# The Effect of Apple Juice Concentration on Cider Fermentation and Properties of the Final Product
## 3 Results and Discussion
### Assimilation of Malic Acid  
At the start of fermentation, apple juice contained 32.07 \(\pm\) 2.24 mM of malic acid while the reconstituted concentrate 56.68 \(\pm\) 4.47 mM. The higher malic acid content in the concentrate was most likely caused by removal/loss in other constitutes contributing to the Brix value during clarification and evaporation.  
The concentration of residual malic acid in cider samples fermented under different conditions in comparison to initial amounts are shown in Figure 3. In general, malic acid consumption by the yeast was rather low. Temperature and nutritional supplementation had no stimulating effect on malic acid assimilation. The main difference in malic acid consumption can be noticed only when comparing the juice and reconstituted concentrate (_p_ < 0.05). Thus, malic acid consumption was higher in the environment with higher initial malic acid content. This can be tied to previous reports of a more efficient enzymatic conversion of malic acid to pyruvate at higher extracellular malate concentrations [15; 32; 33]. The K\({}_{\mathrm{m}}\) of Mae1p enzyme responsible for malic acid decarboxylation is reported at 50 mM which is close to the concentration of malic acid in juice concentrate [32].
# Multi-Media and Multi-Band Based Adaptation Layer Techniques for Underwater Sensor Networks
## 8 Implementation Setup and Results
### Multi-Media/Multi-Band Modem Setup and Testing  
The 'a' part of Figure 13 shows the modem setup for the multi-media/multi-band approach; the 'b' part of Figure 12 shows the operation selection and working of the multi-media/multi-band device. The modem setup was developed using a Xilinx zynq board with multi-media/multi-band techniques embedded inside it. This modem is applicable for real time applications such as diver network monitoring, tsunami monitoring, etc. as shown in Section 7.4.2.2. In this approach, the modem was developed with: (1) Two transducers supporting bandwidths of 70 kHZ and 140 kHZ, as used for acoustic communication; (2) a single bandwidth for infrared (IR) communication at a wavelength of 700 nm to 1 mm; and (3) Visible light (VL) communication using the blue wavelength at a range of 450 to 485 nm. So, this modem setup is combines of multiple bands for different media, such as acoustic, VLC, and IR. The detail specifications of the modem used for the multi-band/multi-media approach is shown in Table 5.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Specification Used** & **Acoustic** & **Visible Light** \\ \hline MCU & ATmega & ATmega \\ Protocol/Interface & Serial & Serial \\ Modulation & BPSK & OOK \\ Operational frequency & 30 KHz & - \\ Communication range & 50 m & 1.5 m \\ Power consumption & 15V, 4.5 Watt & 12V, 2.4 Watt \\ Baud rate & - & 38,400 \\ Dimension & 70 mm \(\times\) 40 mm & 44 mm \(\times\) 18 mm \\ \hline \hline \end{tabular}
\end{table}
Table 4: Multi-media modem specifications.  
Figure 13: Multi-media and multi-band modem setup and operations.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Specification** & **Description of Modem** \\ \hline XILINX(tm) ZYNQ ApSoC & Dual-core ARM Cortex-A9 MPC(tm) with CoreSight, the operation is up 667 MHz, \\ XC7Z020-CLG484-1 & NEON(tm) \& Single/Double Precision Floating Point for each processor and 85K \\  & Programmable Logic Cells \\ \hline Memory &
\begin{tabular}{c} 512 MB DDR3, 256 Mb Quad-SPI Flash, Full size SD/MMC card cage and 16 GB SD Card \\ (UHS-1) \\ \end{tabular} \\ \hline Connection type & 10/100/1000 Ethernet, USB OTG (Device/Host/OTG), USB UART \\ \hline Expansion & 2.54 pitch Box Header, Pmod(tm) headers (2 \(\times\) 6) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Multi-media/multi-band modem specification.
# Enhancing Communication Security in Drones Using QRNG in Frequency Hopping Spread Spectrum
## 2 Related Works
By employing secure key exchange mechanisms such as RSA or ECC for the initial distribution of synchronization information and AES for encrypting the frequency hopping sequences, we address the challenges associated with synchronization and security in true random FHSS systems. This integration enhances the unpredictability of the hopping sequences and provides a robust defense against jamming and interception attempts. Furthermore, the implementation of a concurrent access protocol and a shared memory system for random number distribution ensures that multiple drones can efficiently share a single QRNG device without compromising the quality of randomness. This approach optimizes resource utilization in scenarios where equipping each drone with its own QRNG may be impractical due to size, weight, or power constraints. By combining quantum randomness with established cryptographic practices, our methodology offers a significant advancement in securing drone communications. It addresses the vulnerabilities associated with deterministic PRNGs and enhances the overall resilience of the communication network against sophisticated attacks.
## 5 Simulations  
A real dataset, which is the BBC Dataset [20], is adopted in the simulations to evaluate the performance of the proposed CIMS scheme. The dataset consists of documents in typical news areas that were collected from 2004 to 2005. There are 2250 documents in the dataset. The proposed scheme is implemented on Windows 10 with an Intel Core(TM) i7 CPU running at 3.40 GHz and 16 GB RAM. The search quality and efficiency of the proposed scheme are evaluated.