esa-sceva/satcom-chunk-collection huggingface dataset:
# Improving Passband Characteristics in Chebyshev Sharpened Comb Decimation Filters
## 4 Results
### Potential Future Research Work  
Compensated Chebyshev sharpened decimation filters can become even more versatile and effective in modern signal processing domains by focusing on various applications and research areas. A key aspect of this is ensuring robustness to various input signal conditions, a challenge that is crucial for the practicality of the research. Other key challenges for future work include achieving optimal trade-offs between computational complexity and performance, and addressing hardware constraints in real-time systems.  
_Integration with Machine Learning_. Exploring the synergy of compensated Chebyshev sharpened decimators with machine learning algorithms to dynamically adjust parameters based on historical or expected input signal statistics--training models to fine-tune filter coefficients in real time for non-stationary signals.  
_Advanced Compensation Techniques_. The need for filters that dynamically adapt their compensation based on real-time signal characteristics or varying decimation factors is crucial. Exploring methods to handle signals with nonlinear distortions or dynamics is a key aspect of this research.  
_Implementation Optimization_. Design hardware-efficient algorithms for FPGA or ASIC implementations to optimize power and speed. Investigate approaches for real-time, high-throughput system implementation.  
**Author Contributions:** Conceptualization, G.J.D., and A.F.-V.; methodology, G.J.D.; software, G.J.D.; validation, G.J.D. and A.F.-V.; formal analysis, G.J.D.; investigation, G.J.D.; resources, G.J.D. and A.F.-V.; data curation, G.J.D. and A.F.-V.; writing--original draft preparation, G.J.D.; writing--review and editing, G.J.D., and A.F.-V.; visualization, G.J.D. and A.F.-V.; supervision, G.J.D. and A.F.-V.; project administration, G.J.D.; funding acquisition, G.J.D. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are openly available in [MATLAB] at [[https://www.mathworks.com/help/gads/particleswarm.html](https://www.mathworks.com/help/gads/particleswarm.html)]([https://www.mathworks.com/help/gads/particleswarm.html](https://www.mathworks.com/help/gads/particleswarm.html)).  
The authors declare no conflict of interest.
# Study on the Ozone Gas Sensing Properties of rf-Sputtered Al-Doped NiO Films
); [EMAIL_ADDRESS] (G.K.); [EMAIL_ADDRESS] (G.K.); [EMAIL_ADDRESS] (G.K.)  
[PERSON]  
1 Institute of Electronic Structure & Laser, Foundation for Research and Technology (FORTH-IESL), 71100 Heraklion, Greece; [EMAIL_ADDRESS] (A.P. ); [EMAIL_ADDRESS] (V.K. ); [EMAIL_ADDRESS] (G.K.); [EMAIL_ADDRESS] (G.K.); [EMAIL_ADDRESS] (G.K.)  
[PERSON]  
1 Institute of Electronic Structure & Laser, Foundation for Research and Technology (FORTH-IESL), 71100 Heraklion, Greece; [EMAIL_ADDRESS] (A.P. ); [EMAIL_ADDRESS] (V.K. ); [EMAIL_ADDRESS] (G.K.); [EMAIL_ADDRESS] (G.K.)  
[PERSON]  
1 Institute of Electronic Structure & Laser, Foundation for Research and Technology (FORTH-IESL), 71100 Heraklion, Greece; [EMAIL_ADDRESS] (A.P. ); [EMAIL_ADDRESS] (V.K. ); [EMAIL_ADDRESS] (G.K.); [EMAIL_ADDRESS] (G.K.); [EMAIL_ADDRESS] (G.K.)  
[PERSON]  
1 Institute of Electronic Structure & Laser, Foundation for Research and Technology (FORTH-IESL), 71100 Heraklion, Greece; [EMAIL_ADDRESS] (A.P. ); [EMAIL_ADDRESS] (V.K. ); [EMAIL_ADDRESS] (G.K.); [EMAIL_ADDRESS] (G.K.); [EMAIL_ADDRESS] (G.K.)  
[PERSON]  
1 Institute of Electronic Structure & Laser, Foundation for Research and Technology (FORTH-IESL), 71100 Heraklion, Greece; [EMAIL_ADDRESS] (A.P. ); [EMAIL_ADDRESS] (V.K. ); [EMAIL_ADDRESS] (G.K.); [EMAIL_ADDRESS] (G.K.)  
[PERSON]
thermoelectric [18], and gas sensing devices for the detection of gases, such as H\({}_{2}\)[19; 20], CH\({}_{4}\)[21], NO\({}_{2}\)[22], ethanol [23], O\({}_{3}\)[24], etc. Furthermore, the introduction of Aluminum (Al) atoms in the NiO structure seems to enhance its electrical as well as optical properties [25], while not affecting its gas sensing performance. As a result, Al-doped NiO films have been already tested against NO\({}_{2}\)[26; 27], ethanol [28], CO [29], H\({}_{2}\), and CH\({}_{4}\)[30]. However, NiO:Al films have not been examined for ozone detection, according to our literature research. In this work, rf-sputtered Al-doped NiO films were tested, for first time, against ozone. The effect of Oxygen content in Ar-O\({}_{2}\) plasma as well as the operating temperature on the gas sensing performance were studied. It was found that NiO:Al films have the ability to detect ozone even at room temperature, while those prepared with 4% O\({}_{2}\) in plasma successfully detected ozone at an ultra-low concentration of 10 ppb at 80 \({}^{\circ}\)C. The latter is one of the lower operating temperatures for metal oxide gas sensors.
# A Digitalization Algorithm Based on the Voltage Waveform of the Multifunction Vehicle Bus
## 3 Experimental Results
### Sampling Data at Different Sampling Rates  
The first stage of the experiment was the analysis and comparison of sample numbers under multiple sampling rates. Due to the specifications of the oscilloscope, the sampling rates of the physical-layer voltage waveform involved in the comparison were 6.25 MHz, 12.5 MHz, 31.25 MHz, 62.5 MHz and 125 MHz. Figure 10 and Table 2 present the waveforms and information extracted at different sampling rates.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \begin{tabular}{c} **Sampling** \\ **Rate/MHz** \\ \end{tabular} & \begin{tabular}{c} **Package Loss** \\ **Rate/\%** \\ \end{tabular} & \begin{tabular}{c} **Average** \\ **State** \\ **Amplitude/V** \\ \end{tabular} & \begin{tabular}{c} **Average** \\ **Rate/(mV\(\cdot\)ns\({}^{-1}\))** \\ \end{tabular} & \begin{tabular}{c} **Average** \\ **Overshoot/\%** \\ \end{tabular} & \
\begin{tabular}{c} **Average** \\ **Edge** \\ **Distortion/\%** \\ \end{tabular} &
\begin{tabular}{c} **Time Stamp** \\ **Score** \\ \end{tabular} \\ \hline
12.5 & 0 & 1.81 & 22.98 & 95 & 1.06 & 4507 & 70.9 \\
31.25 & 0 & 1.795 & 28.43 & 91.68 & 0.4 & 4307 & 81.6 \\
62.5 & 0 & 1.84 & 32.5 & 94.4 & 0.2 & 4735 & 98.3 \\
125 & 0 & 1.845 & 34.48 & 94.9 & 0.2 & 2689 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Waveform features of tested sampling rates.  
Figure 9: Diagram of terminator using EMD.  
We obtained several indicators from Table 2:  
* \"Package loss\": This is obtained by calculating he percentage of data frames during the sampling period for which correct decoding information could not be obtained, measured against all data frames during the sampling period. It serves as a metric to assess the reliability of information transmission.
* \"Timestamp\" represents the starting point of the waveform collected. The numbers represent the sequence of data frames collected at the link layer.
* \"Scores\": These represent the similarity between waveform features at the corresponding sampling rate and the waveform at a 125 MHz sampling rate. They are obtained by calculating the ratios of the waveform features at the corresponding sampling rate to those at a 125 MHz sampling rate (with the \"Average edge distortion\" inverted).
* The description and the calculation procedures for the remaining variables can be found in Section 2.2.2 on feature extraction.  
Figure 10: (**a**) Waveform at 6.25 MHz. (**b**) Waveform at 12.5 MHz. (**c**) Waveform at 31.25 MHz. (**d**) Waveform at 62.5 MHz. (**e**) Waveform at 125 MHz.
## 2 Related Work
### Wireless Sensor Network with Mobile Sink  
In wireless sensor networks, considering the limited capacity of battery in sensor nodes, energy efficiency is considered as a main issue. With regard to the convergent nature of methods for sending the common wireless sensor network package causing the concentration of data traffic towards the sink, nodes likely discharge their batteries before other nodes when coming close to static sinks. This results in issue of energy holes [15; 16], and a disruption in topology. In addition, this issue may separate the sinks preventing the delivery of sensor data traffic. Thus, mobile sinks have been proposed and provided as a solution for solving this problem [17; 18; 19; 20].  
Load balancing implicitly by the movement of the sink assists in shifting the hotspots around the sink and the development of drainage, attaining even energy consumption and increasing the lifespan of the network [21].  
In addition, mobile sinks increase network connection by accessing to compartments separated from the network for data retrieval; otherwise, in a static sink, it might be inaccessible [22; 23].  
Noticing the situation of the mobile sink in relation to the network is the main issue for any routing protocol. For resolving this problem, the hierarchical routing protocols have been proposed.  
Such an approach removes the need for an extended network of sink notices and considerably reduces the overhead of position notification, as well as by an increase in the energy efficiency of the network.  
Some of the most influential hierarchical routing protocols of mobile sink include Two-Tier Data Dissemination (TTDD) [24], Grid-based Energy-Efficient Routing [25], Honeycomb Tessellation (HexDD) [26], Hierarchical Cluster-based Data Dissemination (HCDD) [27], Quad-tree Based Data Dissemination Protocol (QDD) [28], Line-based Data Dissemination [29], Railroad [30] and Ring Routing [31].  
Moreover, three selected methods with a proposed method have been introduced and compared to review their advantages and disadvantages.
## 5 Quantitative Analysis of Pore-Throat Network
### Quantitative Analysis of Throat
#### 5.2.4 Coordination Number  
The coordination number refers to the number of throats connected to a single pore in the pore network. The larger the coordination number, the better the connectivity. As can be seen from Table 4, for the carbonate samples, the number of pores was approximately equal to the number of throats, leading to an average coordination number of 1. For the sandstone sample, the number of pores amounted to 2708 while the number of throats was merely 264, leading to an average coordination number of 0.1. Thus, the connectivity between pores is much worse for sandstone compared to carbonate.  
In Table 4, the average volume of pore was larger than the average volume of throat for both sandstone and carbonate. The average volume of throat was approximately one order of magnitude smaller than the average volume of pore. For all the REVs in Table 4, the number of throats was either almost equal to or outnumbered by the number of pores. It can be thus inferred that the porosity of the REVs is majorly related to the volume of the pores.  
In Table 4, the average radius of the pores was similar between sample A and C, as was the average volume of the pores. However, the REV of sample C contained only 1042 pores, in contrast to the 4335 pores in the REV of sample A. As a result, the porosity was higher for the REV of sample A. As for the carbonate samples B, D and E, their average radius of pore and average volume of pore were relatively small compared to their counterparts in sample C. However, samples B, D and E contained a larger number of pores compared to sample C. In the end, the porosity for the REV2s of samples B, C, D, E turned out to be similar (see Table 3). In summary, the number of pores, the radius of pore and the volume of pore are important factors affecting the porosity of a sample.  
The average coordination number of the test rock samples and pore-throat network in the 3D model (Figure 11) are not contradictory. In Figure 11, in both of the pore-throat network models of marine carbonate rocks exist isolated pores, as well as the situation of multiple throats connected by a single pore. Therefore, the average coordinate number of marine carbonate rocks in Table 4 are accurate and reasonable. Owing to the number of throats being far fewer than the number of pores in the sandstone sample, therefore the average coordination number of it was 0.1, and the connectivity of the sandstone sample in Figure 11 was the worst, with the existence of a large number of isolated pores.
## 1 Introduction
### Motivation  
The present work aims to (1) introduce and validate an improved version of the tabulated chemistry solver initially presented in [33]; (2) assess the solvers' combustion and emissions predictions under Diesel and gasoline engine conditions within the 0-D SRM framework; (3) demonstrate the computational efficiency of the method, which allows large design optimization studies while taking detailed chemistry effects into account. With respect to the analyses discussed in [33], the present work includes numerous performance and accuracy refinements on both the tabulation and the engine simulation codes, phenomenological formulations of the turbulence models for CI and SI engines, a novel approach for thermal NO source terms tabulation and a method validation under SI engine conditions in addition to a broader CI engine simulation campaign. To the authors' knowledge, a comprehensive SRM-based tool chain featuring tabulated chemistry, physical turbulence models and computational efficiency comparable to multi-zone models (i.e., [7]) has not been demonstrated before.  
The paper is structured as follows: In Section 2, a concise description of the SRM framework is given together with a description of the turbulence models and progress variable-based solver. In Section 3, an accuracy assessment investigation of the refined storage/retrieval strategy under zero-dimensional constant pressure reactors in shown. The validation of the development is demonstrated in Sections 4-6. Online and tabulated chemistry-based solvers are compared using the SRM under Diesel (Section 4) and spark ignited engine (Section 5) conditions. Finally, the accuracy of the introduced solvers is discussed with focus on combustion and emissions predictions along with remarks on computational performances in Section 6. For validation, the following criteria were defined: (1) Is the solver accuracy consistent with the general model accuracy? (2) Is the CPU time of the developed software acceptable for engine development, engine optimization and driving cycle analysis?
## 3 Experimental Setup and Uncertainty Analysis  
### Experimental Apparatus and Procedure  
In order to evaluate the performance of the test pump experimentally, a test facility was designed and constructed. Figure 4 shows the schematic diagram of the test facility for the microbubble pump. The water tank was designed to circulate the water by the suction pressure generated by the operation of the test pump, and the water- level in the water tank was kept constant during the pump performance test. The water flowrate was measured by an electronic flow meter (VVX25, Sika, Kaufungen, Germany) with a range between 7 to 150 LPM (liters per minute). Two different pressure sensors were installed at the inlet (PSAN-(L)V01C(P)A, Autonics, Busan, Korea) and outlet (PSAN-(L)1C(P)A, Autonics, Busan, Korea) of the pump as well.  
The range of the inlet pressure sensor is from \(-101.3\) kPa to 0 kPa, whereas the outlet pressure sensor can measure up to 1 MPa. A ball valve was installed at the outlet of the  
Figure 4: Schematic diagram of the pump test facility.  
pump to control the water flowrate. During the pump performance test, the rotational speed of the pump impeller was kept constant with the help of a servo motor (FMA-CN10-AB00, Higen, Changwon, Korea) with a rated capacity of 1.0 kW. Shaft torque and rotational speed were measured by a torque meter having a tachometer (SS-100, [PERSON], Yokohama, Japan) [5]. Figure 5 shows a picture of the pump's experimental setup.  
In order to evaluate the pump performance experimentally, pressure data, \(\mathrm{P_{in}}\) and \(\mathrm{P_{out}}\) (total 300 data at the rate of 1 data/s), were recorded automatically using a data acquisition system. The flowrate and torque data were measured 10 times at 30-s intervals.  
Table 2 shows the pump performance data in the design flow condition and the off-design flow conditions. It is noted that the flow coefficient at the design flow condition is 0.39.
# The Internet of Drones: Requirements, Taxonomy, Recent Advances, and Challenges of Research Trends
## 3 Requirements of Internet of Drones (IoD)
### Security Requirements  
Researchers have created several security and privacy approaches to secure the Internet of drones (IoD) network to protect the location of unmanned aerial vehicles (UAVs) and the privacy and security problems that come with using the IoD network. Due to these localization errors, drone positioning was previously unreliable, which had disastrous implications for the whole IoD network. Another critical aim of the IoD network is to increase the level of security and privacy to a point where it cannot be compromised [21]. Therefore, the main requirements of security and privacy of the IoD network include authenticity, confidentiality, availability, integrity, and non-repudiation [4; 11; 22]:
## 4 Experimental Validation
### Results of Experimental Test  
#### 4.2.1 Results of Experiments  
Figure 6 shows the correlation of the rotation speed and the torque obtained from the experimental test. The rotation speed is set in the range of 150-750 rpm with an interval of 100 rpm. As can be observed in Figure 6, the rotation speed influences the required braking torque, and a higher rotation  
Figure 4: Picture of the used experimental test table.  
Figure 5: Schematic flow of the experimental test.  
speed tends to require a higher braking torque. However, the required braking torque is maximum at 600 rpm and tends to decrease slightly when the rotation speed is increased. This phenomenon is considered due to the skin effect, which influences the sensing during measurement. The characteristics of the ECB using aluminum conductor material are such that, if the air gap changes, there will be a shift in the value of the critical velocity caused by changes in current density. When the critical speed point is exceeded, brake torque is reduced. This is caused by the emergence of the magnetic field concentration on the outside (skin effect).
## 5 Numerical Results
Recall that higher average BER values translate into more erroneous bits arriving at the receiver's side, which aggravates the detection capability, and thus degrades the quality of the received signal. As is the case of terrestrial FSO in [2; 46] results in Figure 3 are now obtained for the examined TOW with 4-PPM system by using the corresponding (26) and (27), (30), and (31) expressions. Figure 3  
Figure 2: ABER versus electrical SNR for SISO and SIMO TOW system configurations with OOK and various NZB pointing errors conditions. illustrates the corresponding to Figure 2 ABER evolution, but under the assumption that TOW employs 4-PPM modulation format instead of OOK. The ABER performance comparison between results of Figures 2 and 3 illustrates that decreased corresponding ABER values are obtained by using 4-PPM instead of OOK. Thus, it is proved that PPM modulation format can be used as an effective solution to address more drastically than OOK the ABER performance degradation of TOW links due to the joint impact of skin-induced attenuation and NZB pointing errors. As is the case of terrestrial FSO in [2; 46] results in Figure 4 are now obtained for the examined TOW with 8-PPM system by using the corresponding (26) and (27), (30) and (31) expressions. Figure 4 illustrates, in turn, the corresponding to Figure 3 ABER evolution, but assuming that TOW system employs 8-PPM modulation format instead of 4-PPM. It is highlighted that TOW system outperforms when IM/DD with PPM scheme based on greater number of time slots is employed. Although greater time slots PPM ensures more power efficiency than lower one, as well as OOK, this happens at the expense of both an increased bandwidth and a more complex system's design. Indeed, when 4-PPM format is employed the laser is 'on' during a specific quarter of the 4-PPM bit interval (slot) and 'off' during the rest bit intervals (slots). Thus, the detection is a more complex procedure than that for OOK, while also the lower power consumption at the expense of the increased bandwidth requirement stems from the fact that a 4-PPM symbol consists of a pulse of constant power where information occupies one slot duration within four possible time slots with the rest of the slots being empty. Figure 4: ABER versus electrical SNR for SISO and SIMO TOW system configurations with 8-PPM and various NZB pointing errors conditions. Figure 3: ABER versus electrical SNR for SISO and SIMO TOW system configurations with 4-PPM and various NZB pointing errors conditions.
## 3 Proposed ESM Block-DNN Detector
### Data Pre-Processing
#### 3.1.2 Feature Vector Generator  
Given that the raw data consist of multiple matrices containing complex-valued IQ components, it is crucial to convert them into vector form. In this pivotal phase, we employ a sophisticated technique called separated FVG (SFVG) to handle raw data. The quintessence of SFVG lies in its capability to decompose the complex-valued IQ data into real-valued vector components. For instance, we input channel matrix \(\mathbf{H}\) into SFVG and the output is given by the following expression:  
\[\begin{array}{c}\mathbf{f}_{\text{SFVG}}(\mathbf{H})=\big{|}|\Re(h_{1,1})|,| \Im(h_{1,1})|\\,|\Re(h_{1,2})|,|\Im(h_{1,2})|\\,...,|\Re(h_{n,\phi})|,|\Im(h_{n,\phi})|\\,...,|\Re(h_{N_{r},N_{t}})|,|\Im(h_{N_{r},N_{t}})|\big{|}^{T}.\end{array} \tag{9}\]
# GNSS Multipath Detection Using Continuous Time-Series C/N0
###### Abstract  
The reduction of multipath errors is a significant challenge in the Global Navigation Satellite System (GNSS), especially when receiving non-line-of-sight (NLOS) signals. However, selecting line-of-sight (LOS) satellites correctly is still a difficult task in dense urban areas, even with the latest GNSS receivers. This study demonstrates a new method of utilization of C/N0 of the GNSS to detect NLOS signals. The elevation-dependent threshold of the C/N0 setting may be effective in mitigating multipath errors. However, the C/N0 fluctuation affected by NLOS signals is quite large. If the C/N0 is over the threshold, the satellite is used for positioning even if it is still affected by the NLOS signal, which causes the positioning error to jump easily. To overcome this issue, we focused on the value of continuous time-series C/N0 for a certain period. If the C/N0 of the satellite was less than the determined threshold, the satellite was not used for positioning for a certain period, even if the C/N0 recovered over the threshold. Three static tests were conducted at challenging locations near high-rise buildings in Tokyo. The results proved that our method could substantially mitigate multipath errors in differential GNSS by appropriately removing the NLOS signals. Therefore, the performance of real-time kinematic GNSS was significantly improved.  
GNSS; multipath; NLOS; survey; DGNSS; RTK +
Footnote †: journal: Sensors