esa-sceva/satcom-chunk-collection huggingface dataset:
# Advances in the IoT and Smart Cities
## 1 Introduction  
The proliferation of the Internet of Things (IoT) and the rise of smart cities are revolutionizing how societies operate and urban ecosystems are managed. This Special Issue of Applied Sciences, \"Advances in the IoT and Smart Cities\", brings together contributions that explore the dynamic intersection of data-rich technologies, real-world applications, and emerging challenges in urban environments. IoT technologies are at the heart of smart city development, enabling automated and real-time decision-making for infrastructure, energy, mobility, and governance. However, as cities strive toward intelligence and sustainability, they encounter critical issues related to interoperability, privacy, performance, and system integration.  
The aim of this Special Issue is to offer insights into the recent scientific and technological advances supporting the next generation of smart and connected urban infrastructures. These contributions demonstrate how innovations in data management, edge computing, behavior-driven development, and secure communication protocols are shaping the future of IoT in smart city applications. More importantly, the topics addressed resonate with global efforts to foster open smart city platforms and adopt frameworks that support data sovereignty and platform interoperability.
## 3 Results
### Growth Indexes and Photosynthesis of Maize Leave  
Maize plant height increased with time during the growth season (Table 4). The plant height of the U+FA treatment was significantly advanced by 8.47% and 8.19%, at the V12  
Figure 1: Effects of different fertilization treatments on the endogenous hormones of auxin (IAA, (**A**)) and zeta nucleoside (ZR, (**A**)) and the concentrations of nitrogen metabolism-related enzymes of nitrate reductase (NR, (**B**)), nitrite reductase (NiR, (**B**)), glutamine synthetase (GS, (**C**)), glutathione reductase (GSR, (**C**)), glutamate synthase (GOGAT, (**D**)), and glutamate dehydrogenase (GLDH, (**D**)) in maize root at the 12-leaf stage, in 2018. The treatments include: no nitrogen fertilizer (CK); application of urea (U); combined application of urea and fulvic acid solution (U+FA). The values are means of four replicates, and the error bars are standard errors. Different letters indicate significant differences between treatments for a same parameter according to SNK’s test at \(p<0.05\).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{5}{c}{**Maize Growth Stages**} \\ \cline{3-6}
**Variable (mg kg\({}^{-1}\))** & **Treatment** & **Seedling** & **Joiting** & **12-Leaf** & **Silking** \\ \hline \multirow{4}{*}{\(\mathrm{NH_{4}}^{+}\)-N} & CK & 13.78 a & 8.92 b & 3.31 ab & 4.11 b \\  & U & 18.56 a & 8.09 b & 3.13 b & 3.38 b \\  & U+FA & 15.58 a & 19.37 a & 3.51 a & 16.65 a \\  & CK & 26.32 b & 12.89 c & 12.97 b & 7.81 ab \\ \multirow{2}{*}{\(\mathrm{NO_{3}}^{-}\)-N} & U & 126.48 a & 22.65 a & 13.83 b & 6.89 b \\  & U+FA & 132.03 a & 18.79 b & 15.52 a & 8.09 a \\ \multirow{2}{*}{Available P} & CK & 54.65 c & 83.31 a & 108.23 a & 31.50 b \\  & U & 63.87 b & 75.85 b & 81.40 b & 42.93 a \\ \multirow{2}{*}{Available K} & U+FA & 76.96 a & 65.74 c & 60.66 c & 42.49 a \\  & CK & 343.29 a & 116.46 b & 244.38 a & 144.41 b \\ \multirow{2}{*}{Available K} & U & 319.98 a & 189.41 a & 159.23 b & 134.29 b \\  & U+FA & 345.42 a & 169.48 a & 149.02 b & 235.57 a \\ \hline \hline \end{tabular}
\end{table}
Table 3: Effects of different fertilization treatments on soil nutrients in 2017. one-way ANOVAs followed by SNK’s test at \(p<0.05\).  
and silking stage, respectively, compared to the U treatment. Maize stem diameter first increased and then decreased during the growth season. At the jointing stage, maize stem diameter was 17.58% greater than in the U+FA treatment than in the U treatment. At the seedling, V12, and silking growth stages, there were no great disparities in stem diameter between the U and U+FA treatment.  
At the jointing stage, the SPAD value and Pn of the U+FA treatment were significantly higher by 18.63% and 12.69%, respectively, and Gs was significantly lower by 19.63%, compared to the U treatment (Figure 2). There was no notable change in Tr between the U and U+FA treatments at the jointing stage (Figure 2B). The results of SPAD value, Pn, Gs, and Tr indicated improved CO\({}_{2}\) assimilation and photosynthate accumulation in the U+FA treatment as compared with the U treatment.
# Effect of Environmental Temperature on the Insulating Performance of Epoxy/MgO Nanocomposites
## 3 Results and Discussion  
### SEM Imaging  
Figure 1 shows the SEM images of the cross-section of the samples with different contents of MgO nanoparticles. Figure 1a-e represent the SEM images of composites with an MgO content of 0.5%, 1%, 3%, 5%, and 7%, respectively. The MgO nanoparticles can be observed as more brightly colored in the sample. As the doping concentration increased, the distribution density of the MgO nanoparticles gradually increased, maintaining a uniform distribution with no obvious reunion phenomenon in the early stage. The epoxy resin composite has good interfacial properties. With the increase in the doping amount of the filler, the distance between nanometer magnesium oxide particles decreases, and the surface of the composite has large protrusion, which indicates that there is agglomeration in the material. When the packing mass fraction reaches 5%, the composites begin to agglomerate, but the degree of agglomeration is relatively light. With the increase in the packing mass fraction, the agglomeration became more and more obvious when the packing mass fraction reached 7%. The presence of aggregates is conducive to the formation of the thermal conductivity network chain, which increases the thermal conductivity of epoxy resin. Compared with pure epoxy resin, the volume resistivity of the composite material with a mass fraction of 7% was lower than that of pure epoxy resin, and the dielectric loss and breakdown performance of the composite material were significantly increased.
## 5 Heat Transfer Analysis of Internal Meshing Screw Mixer
### Effect of Process Parameters on Heat Transfer
Figure 10: Comprehensive heat transfer index curves at different speeds in two modes: (**a**) average temperature and temperature uniformity curves of Mode 1 at different rotational speeds; (**b**) comprehensive index curve of heat transfer in Mode 1 at different speeds; (**c**) average temperature and temperature uniformity curves of Mode 2 at different rotational speeds; and (**d**) comprehensive index curve of heat transfer in Mode 2 at different speeds. In Mode 2, a detailed examination of the \(T_{m}\), \(MI_{T}\), and \(CI\) curves presented in Figure 10c,d reveals intriguing insights. Across all three rotational speeds, the \(T_{m}\) associated with \(\omega\) = 60 rpm surpasses that of the other two speeds, and the contrast between the \(T_{m}\) of \(\omega\) = 30 rpm and the other two average temperatures continues to widen. Regarding Figure 10c, it is noteworthy that solely the \(\omega\) = 60 rpm curve maintains dynamic stability, while the \(MI_{T}\) curves for the other rotational speeds remain predominantly stable with no observable trends. Figure 10d provides further clarity by illustrating a conspicuous trend: the \(CI\) value for \(\omega\) = 30 rpm is significantly smaller than that of the other two speeds, and this gap progressively widens. The \(CI\) value for \(\omega\) = 60 rpm generally exceeds that of \(\omega\) = 90 rpm. Additionally, the \(CI\) curve for \(\omega\) = 60 rpm maintains a distinct peak period of 0.5 s, while the growth of \(CI\) for the other two speeds follows a relatively slower and more stable trajectory. Consequently, within Mode 2, \(\omega\) = 60 rpm consistently exhibits the highest heat transfer efficiency. A comparative analysis of the \(CI\) diagrams for Mode 1 and Mode 2 reveals notable distinctions. Notably, the \(CI\) curve of Mode 1 exhibits a distinct periodic pattern, while this characteristic is observed solely in the \(CI\) curve for \(\omega\) = 60 rpm in Mode 2. Furthermore, the \(CI\) for Mode 1 consistently outperforms Mode 2 in terms of both growth rate and \(CI\) values, thus reaffirming the superior convective heat transfer efficiency of Mode 1. This leads to the conclusion that an increase in rotational speed has  
Figure 11: Heat transfer comprehensive index curves for different structural parameters under two modes: (**a**) comprehensive heat transfer index curve of Mode 1 under different eccentricity; (**b**) comprehensive heat transfer index curve of Mode 2 under different eccentricity; (**c**) comprehensive heat transfer index curve of Mode 1 under different clearances; and (**d**) comprehensive heat transfer index curve of Mode 2 under different clearances. a positive effect on heat transfer efficiency. In a broader context, higher rotational speeds correspond to enhanced heat transfer efficiency. Nevertheless, among the three rotational speeds, \(\omega\) = 60 rpm stands out as the optimal parameter with the highest convective heat transfer efficiency.
# A Multi-Band Circularly Polarized-Shared Aperture Antenna for Space Applications at S and X Bands
## 5 Conclusions  
This paper showcases a patch antenna operating in the X-band frequency range with 12.8 dbic gain, positioned between two substrates, with another S bands slotted on a crossed-strip microstrip patch located above, generating a gain of 7.7 dBic. A notable reduction in the size was observed, replacing two antennas next to one antenna system, which could be handy in a CubeSat limited in space. This antenna can be used for S-band uplink/downlink and X-band high-speed data downlink in CubeSats. As more CubeSat projects are looking at high-frequency operations for deep space communication with increased data download requirements, this antenna can be used in CubeSats for use with NASA's Near-Earth Network (NEN) and Deep Space Network (DSN) for high-speed data transmission in the X-band, and TT&C operation in the S-band.  
Conceptualization, S.S.K. and S.I.L.; methodology, S.S.K.; software, S.S.K.; validation, S.S.K., M.H.K. and S.I.L.; formal analysis, S.S.K.; investigation, S.S.K.; resources, S.I.L.; data curation, S.S.K.; writing--original draft preparation, S.S.K. and M.H.K.; writing--review and editing, S.I.L.; visualization, S.S.K. and S.I.L.; supervision, S.I.L.; project administration, S.I.L.; funding acquisition, S.I.L. All authors have read and agreed to the published version of the manuscript.  
This research has received partial external funding provided by the National Science Foundation (Award Number: AGS 1936537).  
Not Applicable.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Ref.**} & \multirow{2}{*}{**Freq. Bands**} & \multirow{2}{*}{**AR BW (\%)**} & \multirow{2}{*}{**Gain (dBic)**} & \multicolumn{2}{c}{**Antenna**} \\  & & & & **Volume (mm\({}^{3}\))** & **Polarization** \\ \hline
[26] & S-/X-band & NA & 8/11.5 & 140 \(\times\) 140 \(\times\) 9.4 & Linear \\
[27] & S-/X-band & 1/1.4 & 6.55/12.5 & 82 \(\times\) 82 \(\times\) 4 & RHCP \\
[28] & S-/X-band & NA & 7.2/12.4 & 100 \(\times\) 100 \(\times\) 1.6 & Linear \\
**Proposed** & **S-/X-band** & **0.35/0.8** & **7.7/12.8** & **60 \(\times\) 60 \(\times\) 20** & **RHCP** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance comparison of the proposed antenna with other related work.
# Accelerated Correction of Reflection Artifacts by Deep Neural Networks in Photo-Acoustic Tomography
## 2 Data Generation
### Reflection Artifacts  
Applying the conventional PAT reconstruction directly to the ultrasound signals generated by the forward model (1) could result in artifacts. This is because the conventional reconstruction methods do not take into consideration of the acoustic reflections. We provide a numerical example in this section to illustrate the effect of the acoustic reflections.  
We choose the Shepp-Logan phantom (see Figure 1a) as the initial pressure \(p_{0}(r)\) to produce ultrasound signals based on the forward model (1) with \(c(r)=1\). The recorded acoustic signal is shown in Figure 1b. To demonstrate the reflection artifacts by conventional algorithms, we adopt one of the conventional time reversal (TR) methods proposed in [8]. The sound speed in the TR is again \(c(r)=1\). Then the TR method is mathematically equivalent to the 2D universal back-projection formula. The stoppage time is again \(T=4\). The reconstructed image is illustrated in Figure 1c. It is clear that direct application of the TR method leads to numerous artifacts in the reconstructed image, especially near the boundary of the ellipses in the Shepp-Logan phantom. The generation of these artifacts can actually be well understood from the mathematical point of view; see the analysis in [12] for details.  
The failure of the conventional TR method indicates that some procedures must be introduced to correct the reflection artifacts in the presence of reflections. There have been some iterative correction algorithms, for instance [10; 11; 12; 13; 14; 39] as well as methods based on deep learning [25]. In the next section, we propose a novel neural network to remove the reflection artifacts. Its efficacy is compared with two of the popular iterative correction algorithms as well.
# Online Algorithm for Deriving Heart Rate Variability Components and Their Time-Frequency Analysis
## 2 Materials and Methods
### Wavelet Transforms  
The essence of the wavelet transform (WT) is the use of a local mother wavelet with a shape similar to the sought features in the non-stationary signal together with daughter wavelets of increasingly smaller scale (closely related to the frequency band), which are moved along the signal and high- and low-pass filter it, producing the subsequent levels of decomposition. The effects are the approximation and detail coefficients obtained for each of the levels, from which, using the inverse transform, the components of the analyzed signal with a specific frequency content can be obtained. The WT is applied in a continuous (CWT) or discrete (DWT) form. A special case of the second one is the wavelet package decomposition (WPD). In this work context, WPD is the better option than the original DWT due to the ability to precisely determine the frequency ranges of the extracted components.  
In this work, the CWT was used to decompose HRV in a similar manner as in [42], using the Morlet wavelet. The resulting scalogram (2D) was then transformed into the HRV components by applying the inverse transform (ICWT) and summing its samples over four standard frequency ranges (see Introduction) for each time instant.  
Similarly, in the case of WPD, an analogous approach was used as in the compared works [40; 41]. Since the analyzed HRV is sampled at a frequency of 2 Hz, a 10-level decomposition with the biorthogonal wavelet (_bior4.4_) was applied, which for the spectrum down to 1 Hz allowed obtaining band boundaries with an accuracy of \(1/2^{10}\approx 1\) mHz. Finally, the individual HRV components were obtained by summing the inverse transform (IWPD) results for the levels: 1-3 for ULF (including the HRV mean value), 4-40 for VLF, 41-153 for LF, and 154-410 for HF.
## 4 Discussion
In addition, due to the errors caused by the device type, noise interference, scale effect, travel time extraction, and other reasons in the process of detection and inversion [42], as well as considering the heterogeneity of underground rock and soil layers and the influence of groundwater, the geophysical parameters such as wave velocity and resistivity of each rock and soil layer and karst vary widely. For the abnormal geological body and karst range, it mostly depends on personal engineering experience to speculate and draw circles by comparing the results of various detection methods. Future research should also pay attention to the quantitative interpretation of various geophysical exploration methods to increase the reliability and applicability of the results.
# A Controlled-Environment Quality Assessment of Android GNSS Raw Measurements
## 4 Results and Discussion
### Positioning Solutions
Panel (**b**) depicts a similar low-elevation satellite scenario of real-environment measurements of P10, and it can be seen that, in general, such a minor shift should be drowned out in the noise. On exploring further, it was seen that the problem lay with the use of a WLS-based PVT algorithm of the Google Toolbox while processing the anechoic-chamber measurements. In the toolbox, the quality of measurements is weighted based on the receiver-clock uncertainty of an incoming satellite measurement [17]. In the anechoic setup of this work, this uncertainty was the same for all satellite measurements due to the replay paradigm through one antenna, which makes weight matrix \(\mathbf{W}\) an identity matrix in the WLS algorithm, leading to nonoptimized handling of the appearance and disappearance of signals. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Before \(t^{*}\)**} & \multicolumn{3}{c}{**After \(t^{*}\)**} \\ \hline
**Device** & **North (m)** & **East (m)** & **Height (m)** & **North (m)** & **East (m)** & **Height (m)** \\  & \(\mu\pm\sigma\) & \(\mu\pm\sigma\) & \(\mu\pm\sigma\) & \(\mu\pm\sigma\) & \(\mu\pm\sigma\) & \(\mu\pm\sigma\) \\ \hline Huawei P10 & \(-2.6\pm 0.2\) & \(-1.3\pm 0.1\) & \(-20.2\pm 0.2\) & \(-3.4\pm 0.6\) & \(-1.3\pm 0.7\) & \(-20.1\pm 1.1\) \\ Huawei P10 Plus & \(-2.5\pm 0.2\) & \(-1.3\pm 0.1\) & \(-20.1\pm 0.1\) & \(-3.6\pm 0.7\) & \(-1.1\pm 0.8\) & \(-20.1\pm 1.2\) \\ Nexus 9 & \(-2.8\pm 0.4\) & \(-1.6\pm 0.3\) & \(-19.9\pm 0.5\) & \(-3.1\pm 0.4\) & \(-1.1\pm 0.2\) & \(-20.7\pm 0.3\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Statistics of the smoothed-positioning solutions obtained from smartphones inside the anechoic chamber before and after the event. Figure 14: Clustering effect due to the presence of a low-quality signal (PRN 15) in a controlled environment.
# Investigating the Poisson Ratio of 3D Printed Concrete
## 3 Results  
### Poisson's Ratio  
The results of the Poisson ratio testing are given in Table 3 for the CAST-CM, OI and OIII groups. An average value was determined for each group based on all five specimens tested within the group. It was found that the conventionally cast printable concrete (CAST-CM) had a Poisson ratio of 0.18, which is within the typical range of 0.15-0.25 for hardened cast concrete. The 3D printed specimen tested in OI orientation, i.e., load perpendicular to print direction yielding oblate voids, was found to have a higher ratio of 0.21. Furthermore, the 3D printed specimen tested in OIII orientation, i.e., load parallel to print direction yielding prolate voids, was found to have the highest ratio of all groups at 0.24. Thus, the Poisson ratios of 3D printed concrete in this study are 17% and 33% higher in OI and OIII, respectively, compared to its cast equivalent. The one-way ANOVA performed between all three groups tested, shown in Table 4, yielded a \(p\)-value of 0.0047. This value is approximately an order of magnitude less than the significance value of 0.05, thereby confirming the null hypothesis that there is a statistical difference between the means of the three groups.  
Figure 7: The General Electric Nanotom S CT scanner used to determine the porosity characteristics of the 3D printed and cast concrete in this study.
# Receiver-Initiated Handshaking MAC Based on Traffic Estimation for Underwater Sensor Networks +
## 8 Simulation and Analysis
### Performance Comparison  
To verify the advantages of the proposed system, we compared TERI-MAC with RIPT. A brief introduction on RIPT was presented in Section 3.1. Furthermore, we also ran RIPT with the traffic predictor to validate the effectiveness of the adaptive data polling mechanism based on the traffic estimation.  
Figures 13 and 14 demonstrate the performance of three methods (i.e., TERI-MAC, RIPT MAC and RIPT with traffic estimation) in terms of the energy efficiency and hop-by-hop communication delay. As revealed in Figure 13, TERI-MAC achieved the best energy efficiency as revealed in Figure 13, at the cost of a relatively long hop-by-hop communication delay, as shown in Figure 14. In the TERI-MAC simulation with \(E_{th}=0.2\), a roughly stable control packet overhead was achieved with a wide range of network traffic loads, which was much lower than the RIPT MAC, especially at low traffic loads. However, a longer hop-by-hop communication delay was introduced in the TERI-MAC, due to the less frequent data polling when the network had a light traffic load. In addition, we observed the heavy reliance of the performance of RIPT on the network traffic load. When the traffic load was low, there were only few data packets available in the senders for each handshaking communication.  
Figure 12: Tradeoff between channel utilization and the hop-by-hop communication delay performance.  
This overfrequent data polling in RIPT MAC resulted in a relatively high control packet overhead and thus a poor energy efficiency. The efficiency performance improved at a high traffic load but was still worse than that of TERI-MAC.  
We also implemented the proposed traffic estimation for RIPT MAC in order to verify the efficiency of the traffic estimation scheme. The modified RIPT achieved a significant improvement in energy efficiency, as shown in Figure 13. With an estimation of the number of packets at each sender, the polling receiver was able to detect the low traffic load of the network and tended to slow down the handshaking process to accumulate packets. Even though a longer delay resulted from the adaptive data polling scheme, the substantial energy efficiency improvement is more promising for its use in power constraint UWSNs. The efficiency enhancement to RIPT reveals the effectiveness of our traffic estimation-assisted adaptive data polling scheme for receiver-initiated MAC protocols.
## 3 Methodology
### Transmission  
This part focuses on the transmission of the signal to guarantee efficient, reliable, and secure communication through the optimization of system settings, device and channel authentication, and the prevention of connection failures. The transmission process is delineated as follows:
# Autonomous Wireless Sensor Networks in
## Materials and Methods
### Communication Module  
#### 2.3.1 RF Communication Module  
The communication board contains a fully integrated ISM Band Sub-GHz Transceiver (MRF89 XA). The MRF89 XA is a single chip, multi-channel FSK/OOK transceiver capable of operating in the 863-870 MHz license-free ISM frequency bands. The MRF89 XA module is optimized for very low power consumption and supports data rates up to 200 kbps. It incorporates a baseband modem with data handling features that include a 64-byte FIFO, packet handling, automatic CRC generation, and data whitening. The size and compact architecture allows for flexible integration.  
The RF (radio frequency) communication parameters are made programmable and most of them may be dynamically set. The MRF89 XA uses several low-power mechanisms to reduce overall current consumption and extend battery life. The MRF89 XA complies with European (ETSI EN 300-220) and United States (FCC Part 15.247 and 15.249) regulatory standards.  
The RF communication module is equipped with a wide-band half-duplex transceiver, and it supports proprietary sub-GHz wireless protocols and power-saving modes. The operating voltage is 2.1-3.6 V with low current consumption typically \(-\)3 mA in receiver mode; \(-\)25 mA at +10 dBm in transmitter mode, and 1-2 mA in Sleep mode. It is operational in temperature ranges from \(-\)40 \({}^{\circ}\)C to 85 \({}^{\circ}\)C which make it well suited for IPM deployment in harsh environments. Furthermore, the communication module supports high data rates of up to 200 kbps and NRZ coding. It has a wide receiver signal strength with dynamics range (70 dB from RX noise floor). The module houses a built-in synchronizer for incoming data, and clock synchronization and recovery and 64-byte transmit/receive FIFO with preload in standby mode