esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Collision Warning Algorithm Alarm in Fishing Vessel's Waterway
## 2 Collision Warning Algorithm for Small Fishing Vessels
### Overview of the Collision Warning Algorithm  
To reflect the steering characteristics of small fishing vessels, a previous work applied the minimum criteria for safety based on millimeter-wave communication using a transmission cycle of 100 ms to develop a collision warning algorithm [23]. The parameters used in identifying ships subject to a high risk of collision include distance, DCPA, TCPA, and the PARK model risk value. Figure 2 shows the overview of the algorithm for small fishing vessels.  
The algorithm developed in the study supplements the noncontinuous alarms that result from the course-keeping performance and steering characteristics of a ship by combining DCPA, TCPA, and PARK model risk value criteria and configuring the alarm so that it occurs at three different stages depending on the distance. The distance criteria for the alarm are classified based on the results of a survey of 19 fishing vessel captains. Pre-alarm is the alarm stage when the ship operator can identify the risk in advance when two ships approach each other at a high speed \(\geq\)24 kts. From the survey, it is considered that the average distance from which the fishing boat's ship operator perceives another ship is 2713.13 m. The first main alarm is the alarm stage when the ship operator can identify a collision risk and determine the situation when there is a distance of 0.5-1 miles between two ships. This is also based on the survey, where an average of 1700.11 m was determined to be the distance at which a captain begins to constantly observe other ships. The second main alarm is the alarm stage that is set to generate the alarm in a situation when the distance is closer to within 0.5 miles. This step is based on an average distance of 563.58 m at which a fishing vessel captain normally initiates a collision avoidance maneuver.  
Figure 2: Overview of the collision warning algorithm for small fishing vessels.
###### Abstract  
Security is one of the major concerns while designing robust protocols for underwater sensor networks (UWSNs). The underwater sensor node (USN) is an example of medium access control (MAC) that should control underwater UWSN, and underwater vehicles (UV) combined. Therefore, our proposed method, in this research, investigates UWSN combined with UV optimized as an underwater vehicular wireless network (UVWSN) that can completely detect malicious node attacks (MNA) from the network. Thus, MNA that engages the USN channel and launches MNA is resolved by our proposed protocol through SDAA (secure data aggregation and authentication) protocol deployed in UVWSN. SDAA protocol plays a significant role in secure data communication, as the cluster-based network design (CBND) network organization creates a concise, stable, and energy-efficient network. This paper introduces SDAA optimized network known as UVWSN. In this proposed SDAA protocol, the cluster head (CH) is authenticated through the gateway (GW) and the base station (BS) to guarantee that a legitimate USN oversees all clusters deployed in the UVWSN are securely established for providing trustworthiness/privacy. Furthermore, the communicated data in the UVWSN network guarantee that data transmission is secure due to the optimized SDAA models in the network. Thus, the USNs deployed in the UVWSN are securely confirmed to maintain secure data communication in CBND for energy efficiency. The proposed method is implemented and validated on the UVWSN for measuring reliability, delay, and energy efficiency in the network. The proposed method is utilized for monitoring scenarios for inspecting vehicles or ship structures in the ocean. Based on the testing results, the proposed SDAA protocol methods improve energy efficiency and reduce network delay compared to other standard secure MAC methods.  
SDAA; MNA; UWSN; UVWSN; UV; MAC; CBND; USN; BS; trustworthiness/privacy; energy efficiency; GW +
Footnote †: journal: Sensors
###### Abstract  
The accuracy and fluency of a handover task affects the work efficiency of human-robot collaboration. A precise and proactive estimation of handover time points by robots when handing over assembly parts to humans can minimize waiting times and maximize efficiency. This study investigated and compared the cycle time, waiting time, and operators' subjective preference of a human-robot collaborative assembly task when three handover prediction models were applied: traditional method-time measurement (MTM), Kalman filter, and trigger sensor approaches. The scenarios of a general repetitive assembly task and repetitive assembly under a learning curve were investigated. The results revealed that both the Kalman filter prediction model and the trigger sensor method were superior to the MTM fixed-time model in both scenarios in terms of cycle time and subjective preference. The Kalman filter prediction model could adjust the handover timing according to the operator's current speed and reduce the waiting time of the robot and operator, thereby improving the subjective preference of the operator. Moreover, the trigger sensor method's inherent flexibility concerning random single interruptions on the operator's side earned it the highest scores in the satisfaction assessment.  
Keywords:human-robot collaboration; handover task; waiting time; handover time prediction method; Kalman filter; MTM +
Footnote †: journal: Applied Sciences
# A Pulse Shaping Based Optical Transmission System of 128 QAM for DWDM with N \(\times\) 904 Gbps
###### Abstract  
In this paper, we propose an optical transmission system of 128 quadrature amplitude modulation for dense wavelength division multiplexing. In such a system, Gaussian optical filtering is used to get an appropriate photonic carrier. Theoretical analysis and simulation computation show that the modulated multi-carrier photonic signals with the wavelength spacing of 0.7 nm can transmit for over 80 km with the standard single mode fiber. Using digital signal processing algorithms to compensate the transmission impairments, the transmission rate of the single-carrier photonic signal can reach up to 904 Gbps and the spectral efficiency of the transmission can reach up to 10.33 bps/Hz. When this technology is applied to a dense wavelength division multiplexing system with N channels, the huge message capacity of N \(\times\) 904 Gbps can be realized. Furthermore, we find that the bit error rate and the error vector magnitude are similarly influenced by the optical signal-to-noise ratio and the bandwidth of the Gaussian optical filter. The influence presents mostly a synchronization trend with the change of the optical signal-to-noise ratio and the bandwidth of Gaussian optical filter.  
coherent optical communication; pulse shaping; DWDM; high spectral efficiency Article  
A Pulse Shaping Based Optical Transmission System of 128 QAM for DWDM with N \(\times\) 904 Gbps
## 4 Description of Different Deployed VPN Solutions
### System Tools for Performance Analysis: Iperf and Atop  
Iperf allows to measure the bandwidth available for IP networks. It is available in two versions, Iperf2 and Iperf3, released as open source and thanks to the support for all the most popular operating systems (including mobile ones such as Android and iOS) and the ability to adjust numerous parameters in the analysis of network performance for various protocols, is undoubtedly one of the reference tools for network diagnostics.  
Atop is conceived to be a system performance monitor, which works through CLI (Command Line Interface) and can record and report the activity of all operating system processes and its HW/SW components. It allows to analyze the server at runtime and automatically in the long term. It shows the resource usage of all processes and devices and provides the ability to monitor threads within processes and highlights critical lines using colors (red). Moreover, it allows to analyze disk I/O and network usage. Thanks to the kernel module netatop it can acquire TCP, UDP data, and network bandwidth. For each established VPN connection, an instance of atop was launched to collect CPU and network statistics data. These data was passed to _atoppar_ to generate readable and archival reports via CSV. With special scripts, these CSVs were tested to create successive graphics using regex and Gnuplot. Subsequently, the aggregated graphs were produced using Excel. All the figures relating to the following topologies have a double scale and display respectively the average throughput (left axis) and the standard deviation (right axis), for wired and wireless endpoints (left figure and right figure).  
Without using VPN protocols, the average throughput of the network is 94.1 Mbps, in the fast ethernet sections, with an RTT of 1417 ms. In the Gigabit sections, the throughput is 760 Mbps, with an average RTT of 0.730 ms.
_Appl. Sci._**2015**, \(5\), 1221-1234; doi:10.3390/app5041221  
_applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
Effect of Ta\({}_{2}\)O\({}_{5}\) and Nb\({}_{2}\)O\({}_{5}\) Dopants on the Stable Dielectric Properties of BaTiO\({}_{3}\)-(Bi\({}_{0.5}\)Na\({}_{0.5}\))TiO\({}_{3}\)-Based Materials  
**[PERSON] \({}^{\dagger,\star}\), [PERSON], [PERSON] and [PERSON]**  
Department of Materials and Mineral Resources Engineering, National Taipei University of Technology, Taipei 106, Taiwan; E-Mails: [EMAIL_ADDRESS] (Y.-F.H.);  
[EMAIL_ADDRESS] (Y.-W.H.); [EMAIL_ADDRESS] (Y.-X.L.)  
\({}^{\dagger}\) Current Address: Department of Materials and Mineral Resources Engineering, National Taipei University of Technology, 1, Sec. 3, Chung-Hsiao E. Rd., Taipei 106, Taiwan.  
\({}^{\star}\) Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +886-2-2771-2171 (ext. 2735).  
Academic Editor: [PERSON]  
_Received: 4 September 2015 / Accepted: 2 November 2015 / Published: 16 November 2015_  
**Abstract:** In this study, BaTiO\({}_{3}\)-(Bi\({}_{0.5}\)Na\({}_{0.5}\))TiO\({}_{3}\) ceramics with various amounts of Ta\({}_{2}\)O\({}_{5}\) dopant were investigated for their ability to enhance high-temperature stability to meet X9R specifications. The results were compared to those for ceramics with the common Nb\({}_{2}\)O\({}_{5}\) additive. The best composition appeared to be 0.9 BaTiO\({}_{3}\)-0.1(Bi\({}_{0.5}\)Na\({}_{0.5}\))TiO\({}_{3}\) with 2 mol% Ta\({}_{2}\)O\({}_{5}\) dopant sintered at 1215 \({}^{\circ}\)C, which had a dielectric constant of 1386, a tan\(\delta\) value of 1.8%, temperature coefficients of capacitance (TCCs) of \(-1.3\%\) and 1.2%, and electrical resistivities of \(2.8\times 10^{12}\) and \(1.5\times 10^{10}\)\(\Omega\cdot\)cm at 25 \({}^{\circ}\)C and 200 \({}^{\circ}\)C, respectively. Its microstructure consisted of fine equiaxed grains with a perovskite structure and an average grain size of 0.46 \(\upmu\)m and some rod-like grains of second-phase Ba\({}_{6}\)Ti\({}_{17}\)O\({}_{40}\) with a size of approximately 3.2 \(\upmu\)m. The Ta\({}_{2}\)O\({}_{5}\) dopant caused a reduction in the grain size and a slight increase in trapped pores. The temperature dependence of the dielectric constant flattened and the Curie point was dramatically suppressed with the addition of Ta\({}_{2}\)O\({}_{5}\) dopant, leading to smooth dielectric temperature characteristics over a relatively broad temperature range. The X9R formulations and their dielectric properties were highly repeatable in this study.  
**Keywords:** dielectric properties; X9R; formulation; microstructure
# A Novel Measurement Standard for Surface Roughness on Involute Gears
###### Abstract  
Although manufacturers of coordinate measurement systems and gear measurement systems already provide instruments that enable an end-of-line-monitoring of the roughness properties of gears, the roughness measurement on gear flanks still lacks traceability with respect to the standardised SI-units. There is still a gap between well standardised roughness measurements on planar surfaces and gear measurements on involutes. This gap is bridged by a novel physical measurement standard (PMS), also referred to as material measure, for roughness measurements on involute gears that has been developed at the Physikalisch-Technische Bundesanstalt (PTB). The necessary transformations between the systems of roughness and gear measurements have been implemented. The measurement standard itself represents calibrated roughness values for the parameters \(R_{\mathrm{a}}\), \(R_{\mathrm{z}}\), \(R_{\mathrm{q}}\), \(R_{\mathrm{k}}\), \(R_{\mathrm{pk}}\) and \(R_{\mathrm{vk}}\) and Mr1 and Mr2. Furthermore, the PMS can be measured both with classic profilometers as well as gear measurement systems with integrated roughness probes.  
surface roughness metrology; gear metrology; traceability +
Footnote †: journal: Applied sciences
# On Channel Estimation in LTE-Based Downlink Narrowband Internet of Things Systems
## 4 NB-IoT Downlink Channel Estimation  
At first, we calculate the estimates of the channel by considering the subcarriers allocated in a PRB of the symbols (\(l=3\), \(8\)) of a subframe having 15 kHz subcarrier spacing and containing NRS sequences. Thereafter, we acquire the knowledge of the estimated channel at other symbols in a PRB by applying linear time dimensional interpolation to get the total channel estimation. NPDSCH and NRS hopping are ignored in the current work to make this technique commonly usable in all multicarrier communication schemes. In this work, the NRS (pilot) assisted channel estimation is performed by a well-known and efficient LMMSE channel estimation algorithm. Let us consider that the transmitted and received NRSs are \(\mathbf{A}\) and \(\mathbf{B}\), respectively, and the frequency response of the channel for the LMMSE estimate is \(\mathbf{\Upsilon}\). We divide the entire channel estimation process into four steps for ease of explanation. A simplified LMMSE estimate is achieved by exploiting the properties of transmitted NRS as the first step; to reduce the number of complex multiplications, optimal rank reduction formulae are applied in the second step; in the next phase, the autocorrelation matrix of the channel is split into a few submatrices for efficient understanding. Lastly, we overlap the submatrices to optimize the performance of the channel estimation technique in the fourth step.
# Magnetic Moment Estimation Algorithm Based on Convolutional Neural Network
## 4 Results
### Adding Noise  
During aeromagnetic operations, noise inevitably affects the accuracy of the measurements. The performance of the model is evaluated under different noise levels by applying varying signal-to-noise ratios (SNRs) to the dataset. The noise primarily originates from the aeromagnetic environment, with the sensor carrier playing a crucial role as the dominant factor influencing the noise level. This influence is particularly evident on moving platforms, where vibrations, orientation changes, and equipment onboard generate additional noise. As a result, noise affects both the training data and the test set.  
Gaussian white noise with an SNR ranging from \(-30\) to \(50\) dB is added to the dataset. The model is then trained using the data segmentation and training methods described above. The following results are obtained: the test results of the model trained with samples at an SNR of \(-5\) dB are shown in Figures 11-13.  
Figure 11: The estimation diagram of the magnetic moment magnitude, declination, and inclination under noise conditions with SNR \(=-5\) dB.  
Figure 12: Fitting errors of the magnetic moment magnitude, declination, and inclination under noise conditions with SNR \(=-5\) dB.  
From Figures 14 and 15, it can be observed that as the SNR increases, the error generally decreases and eventually stabilizes. When the SNR = \(-5\) dB, the errors in the three components of the magnetic moment are approximately 7%. The relative error in the magnetic moment magnitude is 4.92%, while the absolute errors in the magnetic moment declination and inclination are 4.73\({}^{\circ}\) and 1.54\({}^{\circ}\), respectively. The vector angle difference is 2.7\({}^{\circ}\).  
Figure 14: Errors in different components of the magnetic moment under different SNRs.  
Figure 13: The angular difference between the test magnetic moment vector and the predicted magnetic moment vector with SNR = \(-5\) dB.  
As the model is trained with different samples, the prediction error gradually stabilizes, demonstrating the ability of the model to perform effectively in harsh noise environments. These samples are augmented with noise of gradually increasing SNR, further validating robustness and adaptability of the model to varying levels of noise interference. This behavior highlights potential of the model for reliable performance in real-world scenarios where noise conditions are unpredictable and challenging.
## 3 Materials and Methods
Figure 4: The front view of the oZm v3. This study used the OMPM to generate the UALM2 dataset, which includes measurements from six meters associated with the aggregate and five applications. The UALM2 dataset demonstrates the OMPM's capability to provide high-quality data for NILM applications, highlighting its potential for enhancing energy efficiency and load disaggregation accuracy. The evaluation methodology in this study focused on ensuring reproducibility, leveraging open-source hardware, and conducting comprehensive comparative analysis. An extensive pipeline was followed, encompassing the entire process from raw data conversion to the evaluation of disaggregation results. The datasets were consistently split into training, validation, and testing phases, using an 80/20 split in some scenarios to enable consistent comparisons across algorithms and environments. Trained models were saved in H5 format to optimize efficiency and reusability. During validation, the optimal model was applied, and its predictions were compared with the actual ground truth data. A key part of the strategy involved comparing performance with publicly available and widely used NILM datasets, such as REDD, UK-DALE, and ECO, to demonstrate improvements in accuracy and efficiency under various conditions. This study focused on the disaggregation of typical household appliances. The custom datasets (DSUALM, DSUALMH, UALM2, DSUALM10, and DSUALM10H) included devices such as fans, freezers, televisions, vacuum cleaners, kettles, lamps, laptops, and more, eventually covering up to 10 appliances. For the new OMPM hardware, low-consumption devices were prioritized to increase the complexity of disaggregation. Figure 5: OMPM electrical schematic (Source: own elaboration). The primary software tool used was NILMTK v0.4.0, extended with NILMTK-Contrib. Custom data converters were developed to process measurements from oZm v1, oZm v3, and OMPM into a format compatible with NILMTK, enabling seamless integration of custom data into the evaluation framework. Both traditional algorithms (CO, FHMM, Mean, and Hart85) and deep learning approaches (DAE, RNN, Seq2 Point, Seq2 Seq, and WindowGRU) were evaluated. The analysis used a broad set of standard metrics provided by NILMTK, including F1-score, MAE, RMSE, NDE, MNEAP, and EAE, to gain a comprehensive understanding of each model's performance. Furthermore, this study investigated the impact of several factors on disaggregation performance, such as the sampling rate (ranging from 125 ms to 30 min), the presence of harmonic content (up to the 50 th order), and the application of power filters with different thresholds (10 W, 50 W, and 100 W). For deep learning models, due to their high computational cost, evaluations were mainly conducted under a specific configuration: 1 s sampling, with harmonic content, and without power filters.
# Calculation of the Insertion Loss of Barriers on Rigid Ground in the Time Domain
## 3 Simulation  
It is efficient and acceptable to approximate the infinite barrier with a finite one, as proved in Figure 3. For better comparison, the sound pressure level (SPL) for frequencies \(f=1\,\)kHz and \(f=2\,\)kHz in Figure 3 are artificially raised to 28 dB and 14.5 dB, respectively. The sound pressure level at the receiving point \(R_{3}(0.5,0.5,0)\,\)m from the source \(S(0.5,-0.5,0)\,\)m converges with the ratio \(L/\lambda\), where \(L\) is the length of the barrier and \(\lambda\) is the wavelength. Lower frequencies require longer barriers. When the barrier length \(L\) increases to \(10\lambda\), the convergence is sufficient for the whole frequency range. Thus, the barrier length \(L\) is set to 20 m, with its middle edge point located at the origin \(O\) of the coordinate systems in the following simulations using MATLAB. The barrier has a height of 1 m.  
To check the validity of the method described in the previous section, the results are compared with those derived from the MacDonald method. Three receiving points corresponding to regions I, II, and III are selected as \(R_{1}(-0.6,0.1,0)\) m, \(R_{2}(-0.3,0.2,0)\,\)m and \(R_{3}\). Figure 4 presents the insertion loss at three different receivers from the same source. The time-domain method agrees reasonably well with the MacDonald method in the frequency range of interest in different regions. The Mean Absolute Error (MAE) values corresponding to the three receiving positions \(R_{1}\), \(R_{2}\), and \(R_{3}\) in Figure 4 are 0.1, 0.2, and 0.8, respectively. Moreover, the calculation with the proposed method is more efficient compared with the MacDonald method. The frequency-domain MacDonald method take 78 s to calculate 397 frequency points using a computer with 1.60 GHz main frequency and 16 GB random access memory. By contrast, the proposed method takes 3 s for the whole frequency range as it operates in the time domain with a sampling frequency of 48 kHz.  
Figure 3: The sound pressure level (SPL) at \(R_{3}\) versus the ratio \(L/\lambda\) with different frequencies.  
Figure 4: The insertion loss (IL) at (**a**) \(R_{1}\), (**b**) \(R_{2}\), and (**c**) \(R_{3}\).