esa-sceva/satcom-chunk-collection huggingface dataset:
# Advanced Sensor Integration and AI Architectures for Next-Generation Traffic Navigation
## 1 Introduction
### Simulation Tools for Traffic Studies  
Numerous simulation tools have been created to examine traffic behaviors, optimize signal timings, and assess infrastructure designs. Among these, VISSIM, Simulation of Urban MObility (SUMO), and RegTraffic are particularly notable for their distinct features and applications in traffic research.  
VISSIM is a highly regarded microscopic traffic simulation software that facilitates a detailed representation of traffic flow and interactions at intersections. It has been utilized in various studies to assess infrastructure design solutions aimed at reducing congestion. For example, [PERSON] et al. [61] underline the effectiveness of a novel intersection model using VISSIM, demonstrating its capability to convert field data into practical insights for urban planners. The software's stochastic microscopic simulation features allow it to replicate complex traffic operations, making it an invaluable tool for analyzing and enhancing urban traffic dynamics [62]. Additionally, VISSIM's compatibility with other technologies, such as vehicle-to-everything (V2X) communication, boosts its functionality in developing intelligent urban traffic systems [63].  
SUMO has been employed in numerous studies to develop microscopic simulation models that mirror real-world traffic conditions. For instance, [PERSON] et al. [64] used SUMO to evaluate bicycle highways in urban environments, illustrating its ability to model multiple transportation modes within a single platform. Integrating automated planning techniques into SUMO has proven effective in centralizing traffic routing, thereby enhancing traffic management in city areas [65]. SUMO's capacity to incorporate real-time data further improves its performance in simulating dynamic traffic scenarios.  
RegTraffic is a regression-based traffic simulator that addresses certain limitations of traditional simulation tools by incorporating spatiotemporal traffic modeling. [PERSON] et al. emphasized its practical uses in traffic analysis, including measuring traffic flow and simulating congestion [66]. This simulator's ability to model traffic congestion based on interactions between neighboring road links represents a significant progression in traffic simulation methods. Regression techniques allow RegTraffic to deliver more precise predictions of traffic behavior, especially in heterogeneous road networks where real-time data might be limited.
# Winter Wheat Mapping in Shandong Province of China with Multi-Temporal Sentinel-2 Images
## 2 Materials and Methods
### Methodology
#### 2.3.1 Image Collection and Pre-Processing  
The data processing in this research is mainly based on the GEE platform because it not only provides a variety of remote sensing image data, including Sentinel-2, but also pre-processes most of the raw data in bulk [42]. The Sentinel-2 Level-2A (L2A) image provided by GEE has already completed the spatial and atmospheric corrections [43; 44]. It can be used directly through the catalogue \"COPERNICUS/S2_SR\" for the next step of processing and analysis. This significantly reduced the amount of computation and increased the rate of data use. On the basis of Sentinel-2 L2A, only de-clouding, clipping and splicing are needed to build the corresponding time-series datasets.  
It is known that winter wheat in Shandong is usually sown in October of the previous year and harvested in June of the following year. Thus, images covering the entire growth period of winter wheat in 2020 (October 2019-June 2020) were selected for winter wheat identification in this research. Firstly, a total of 1702 Sentinel-2 L2A images with less than 50% cloud cover for this period were filtered on the GEE platform. Then, the effects of clouds and other poor observations were excluded by the QA60 band, which is specially designed for storing cloud obscuration information [10; 45]. On this basis, a monthly image collection (9 images in total) was composited on a monthly basis to characterize the temporal variability of the winter wheat. In this process, the monthly images that still have gaps due to the influence of clouds are complemented with the images of that month in the adjacent year [46; 47]. At the same time, the nearest-neighbor resampling method was used to resample six bands with a 20 m resolution to a 10 m resolution to ensure a consistent spatial resolution. A high-quality Sentinel-2 time-series image collection with a complete coverage of Shandong was finally created.
# Point of Interest Recognition and Tracking in Aerial Video during Live Cycling Broadcasts
## 2 Related Work
### Conclusions  
Numerous studies have explored landmark recognition and object tracking as separate areas of research. Next to that, several user interfaces exist that employ segmentation techniques to facilitate object tracking in videos with minimal user input. However, most existing landmark recognition approaches do not locate the landmark within the image or through a video sequence. Therefore, to the best of our knowledge, no comprehensive solutions have been proposed to recognize and track points of interest in real time during cycling race broadcasts.
# Analysis and Investigation of Dual-Polarized Color LED Based Visible Light Communication System
## 4 Conclusions  
We proposed and investigated 2 \(\times\) 2 PDM G- and B-LED VLC transmission systems with orthogonal-polarization to double the data traffic. Here, to obtain the dual-polarized VLC signals, the two orthogonal POLs at the VLC-Rx side could be controlled for rotation automatically to match the dual-polarized VLC signals from the VLC-Tx site. The presented dual-polarized VLC system could accomplish the total modulation rate of 1200 and 1120 Mbps (VLC(Green+Blue)) passing after 3 and 4 m free-space transmission lengths, respectively, when the corresponding illuminances of the G- and B-LEDs were 2.4 and 0.5 lux and 5.4 and 2.3 lux. To produce the more flexible proposed dual-polarized VLC system, we also can employ paired G-LEDs and B-LEDs acting as the optical Tx to deliver dual-polarized VLC(Green) and VLC(Blue) signals for alternative indoor applications. Hence, the proposed 2 \(\times\) 2 PDM G- and B-LED VLC system did not need to use a color filter for decoding signals. In addition, we also executed and analyzed the influence of different polarization offsets at a fixed VLC rate through a wireless link length of 3 and 4 m, respectively. Based on the observed results, the largest offset angle of the two POLs should be less than 50\({}^{\circ}\) to maintain the optimal VLC modulation rate below the forward error correction (FEC) level. Furthermore, if there were many clients with only one Rx detector in the same  
Figure 8: Obtained corresponding modulation rates of VLC(Green) and VLC(Green) within the FEC level under the different rotation angles from 0\({}^{\circ}\) to 50\({}^{\circ}\).  
Figure 9: Schematic diagram of the POL’s rotation control for decoding dual-polarized VLC traffics.  
room for receiving dual-polarized VLC traffic, the polarization of the POL for each client could be controlled properly to share and adapt the total capacity. Hence, the proposed dual-polarized LED VLC system not only doubled the data rate but also provided signal security according to the PDM method.  
Conceptualization, C.-H.Y.; methodology, S.-K.L.; validation, Y.-C.Y., W.-H.H. and B.-Y.W.; resources, C.-H.Y. and C.-W.C.; data curation, Y.-C.Y., W.-H.H. and B.-Y.W.; writing--original draft preparation, C.-H.Y. and Y.-C.Y.; writing--review and editing, C.-H.Y.; supervision, C.-H.Y. and S.-K.L. All authors have read and agreed to the published version of the manuscript.  
This paper was supported by the Ministry of Science and Technology, Taiwan, under Grant MOST-109-2221-E-035-071.  
Not applicable.  
The authors declare no conflict of interest.
# Physics-Based Noise Modeling and Deep Learning for Denoising Permanently Shadowed Lunar Images
## 2 Datasets
### Simulated Data of Lunar PSRs
Figure 3 illustrates the distribution of incident angles for the selected training data and their corresponding range [29]. In summary, the clean image \(\widetilde{S}\) represents the average photon signal, and the relationship between it and the noisy image \(\widetilde{I}\) is given by Equation (2). Figure 4 provides a schematic illustration of the synthetic samples: Figure 4a shows a noise-free image captured under adequate lighting, while Figure 4b displays the noisy image generated by adding noise from the physical noise model to the image in Figure 4a. Notably, the image in  
Figure 3: Original image selection. Figure 2: Compression function. Figure 4b exhibits prominent horizontal and vertical stripes caused by dark noise, significant color variations due to flat-field and nonlinear responses, as well as Poisson noise mixed with photon noise, Read noise, and companding noise. \[\widetilde{I}=N\bigg{(}F\times\bigg{(}\widetilde{S}+N_{p}\bigg{)}\bigg{)}+D+N_{c} \tag{2}\]  
We selected 1093 original lunar surface images, spanning from 75\({}^{\circ}\) to \(-\)75\({}^{\circ}\) lunar latitude, which meet the physical noise model requirements as baseline inputs for noise synthesis. After systematic cropping and noise addition, the synthetic dataset consisted of 77,310 pairs of aligned images. Data partitioning followed a stratified distribution, with 10% retained for evaluation, while the remaining 90% was split 4:1 for model training and validation. To validate the effectiveness of our training using the simulated dataset, we selected real lunar PSR experimental data from the unmodified Moon's South Pole as the real-world dataset. Figure 5 presents some representative samples from this test set.
## 3 Proposed Bamboo Forest Growth Optimization Algorithm
### Energy Consumption Model  
The energy consumption of the network mainly comes from the wireless communication between nodes. According to the first-order radio model, this study uses the energy loss formula to calculate the energy consumption of nodes. The model diagram is shown in Figure 7, The sensor node consists of a transmitter and a receiver [40]. The left module is the transmitter of the sensor node, and the right module is the receiver. \(l\) is the length (in bits) of the information received or transmitted by the sensor node. \(d\) is the distance between the transmitter and the receiver.  
Figure 6: The working process of the protocol based on BFGO-C in HWSN.  
As shown in Figure 7, the data are transmitted from the transmitting electronics to the amplifier. IThey are transmitted to the receive electronics at a distance of \(d\) meters through wireless communication [41]. When sending data, the energy consumption produced by the node is the sum of the energy consumption produced by the transmitting electrons and the amplifier circuit [42]. The calculation of transmitting volume of routing protocol based on BFGO-C is shown in Equation (11).  
\[E_{tx}(l,d)=\begin{cases}E_{elec}\times l+\epsilon_{fs}\times l\times d^{2},d<d _{0}\\ E_{elec}\times l+\epsilon_{mp}\times l\times d^{4},\text{else}\end{cases} \tag{11}\]  
where \(E_{elec}\) represents the power used by the transmitter and receiver, and \(\epsilon_{fs}\) and \(\epsilon_{mp}\) are the power amplifier coefficients under the free space and multipath fading models, respectively. \(d_{0}\) represents the distance threshold from the transmitter to the receiver, as shown in Equation (12).  
\[d_{0}=\sqrt[]{\frac{\epsilon_{fs}}{\epsilon_{mp}}}. \tag{12}\]  
If \(d>d_{0}\), it is a multipath fading model; conversely, it is a free space model. The received data energy consumption \(E_{rx}\) is shown in Equation (13).  
\[E_{rx}(l)=E_{elec}\times l. \tag{13}\]  
The energy consumption of data fusion is shown in Equation (14).  
\[E_{m}(l)=E_{DA}\times l\times(1+n), \tag{14}\]  
where \(E_{DA}\) is the unit energy consumption of data fusion.
# Random Fiber Array Generation Considering Actual Noncircular Fibers with a Particle-Shape Library
## 3 Results and Discussion
### Comparison of the Results between Actual Noncircular Fibers and Circular Fibers
#### 3.2.1 Comparison of the Effective Elastic Properties
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(V_{f}\) & **Algorithm** & **Fiber Shape** & \(E_{1}\)**(GPa)** & \(E_{2}\)**(GPa)** & \(G_{23}\)**(GPa)** & \(G_{12}\)**(GPa)** \\ \hline \multirow{4}{*}{60\%} & RFR & Circular (reference) & 138.924 & 3.675 & 1.348 & 1.687 \\  & & Noncircular (error in \%) & (\(-\)0.48\%) & (\(-\)0.93\%) & (\(-\)1.11\%) & (4.33\%) \\  & RSE & Circular & 138.913 & 3.537 & 1.297 & 1.534 \\  & & Noncircular & 138.283 & 3.528 & 1.292 & 1.556 \\  & & Noncircular (\(-\)0.45\%) & (\(-\)0.25\%) & (\(-\)0.39\%) & (1.43\%) \\ \hline \multirow{4}{*}{55\%} & RFR & Circular & 127.057 & 3.235 & 1.167 & 1.463 \\  & & Noncircular & 126.946 & 3.283 & 1.162 & 1.528 \\  & & (\(-\)0.09\%) & (1.48\%) & (\(-\)0.43\%) & (4.44\%) \\ \cline{1-1}  & RSE & Circular & 127.043 & 3.011 & 1.083 & 1.231 \\  & & Noncircular & 126.745 & 3.018 & 1.085 & 1.254 \\  & & (\(-\)0.23\%) & (0.23\%) & (0.18\%) & (1.87\%) \\ \hline \multirow{4}{*}{45\%} & RFR & Circular & 103.321 & 2.516 & 0.879 & 1.077 \\  & & Noncircular & 104.041 & 2.564 & 0.887 & 1.128 \\ \cline{1-1}  & & (\(0.70\%\)) & (1.91\%) & (0.91\%) & (4.74\%) \\ \cline{1-1}  & & (\(103.310\) & 2.365 & 0.825 & 0.930 \\ \cline{1-1}  & & (\(104.007\) & 2.394 & 0.836 & 0.954 \\ \cline{1-1}  & & Noncircular & (\(0.67\%\)) & (1.23\%) & (1.33\%) & (2.58\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of the effective elastic properties of T300/PR319. be considered because of the transverse tensile strength accompanying crack propagation. The reason is that, according to our previous work [18], it was reported that the result of the transverse crack propagation simulation showed a different crack pattern from the test result when the circular fibers were assumed instead of the actual fibers. In addition, as \(\mathbf{RR}_{T}\) of the composite is larger, the deviation of \(E_{2}\), \(G_{23}\), and \(G_{12}\) values of RFR are larger than those of RSE. This is because the minimum distance between the fibers of the RFR is closer than that of the RSE, as reported in [24]. To check randomness using the effective elastic properties of RVEs with noncircular fibers in the transverse direction, the symmetry of the compliance matrix was checked as follows:  
\[\frac{E_{2}}{\
u_{23}}=\frac{E_{3}}{\
u_{32}}\ \ \rightarrow\ \frac{E_{2}\
u_{32}}{E_{3}\
u_{23}}=1 \tag{5}\]  
In addition, the anisotropic ratio was confirmed as follows:  
\[G_{23}=\frac{E_{2}}{2(1+\
u_{23})}\ \rightarrow\ \frac{E_{2}}{2(1+\
u_{23})G_{23}}=1 \tag{6}\]  
Table 6 shows the results for Equations (5) and (6). It can be seen that all values are close to unity and are sufficiently random in the transverse direction.
## 2 Experimental Outline  
### Model Test Similarity Criteria and Material Preparation  
The basic project for this study is Qingdao Metro Line 6, with a tunnel depth of 10.3-18.8 m. The tunnel's lining measures 6 m in OD and 5.4 m in ID. It primarily travels through weak and unstable fully to moderately worn diorite, where the creep effect is clearly visible. Consequently, the medium-weathering diorite is chosen as the prototype surrounding rock in this paper's physical model test. Similar materials for the surrounding rock and lining are then prepared and chosen based on a similarity criterion. Finally, research is conducted on the time-dependent characteristics of the surrounding rock and lining structure of the operational tunnel stress and deformation.  
The fundamental similarity ratios used in the model test are the geometric similarity ratio (\(C_{L}=30\)) and the unit weight similarity ratio (\(C_{\gamma}=1\)), and the similarity ratio between the prototype value and the model value of each physicochemical parameter is derived according to the principle of the similarity theory: the similarity ratio of Poisson's ratio, strain, and angle of internal friction is 1 (\(C_{\mu}=C_{\varepsilon}=C_{\varphi}=1\)), and the similarity ratio of the strength, cohesion, and elastic modulus is 30 (\(C_{\sigma}=C_{c}=C_{E}=30\)). According to the similarity ratio calculation, the physical and mechanical parameters of the model material must be obtained to meet the parameter requirements [31; 32]. The similar material ratios of the surrounding rock and its physicochemical parameters [33] are shown in Tables 1 and 2.  
The ratios \(C_{L}=C_{\sigma}=C_{E}=30\) were taken into consideration when formulating lining structural materials. For this lining concrete, the proportion of comparable ingredients was 1:0.9 for water to gypsum. For this test, a monolithic lining model with a 20 cm outside diameter and a 1 cm wall thickness was utilized. Table 3 displays the model and prototype materials for the lining [34].  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Iron Powder** & **Quartz Sand** & **Barite Powder** & **Water** & **Cement** \\ \hline
0.204 & 0.305 & 0.254 & 0.136 & 0.101 \\ \end{tabular}
\end{table}
Table 1: Each constituent material accounted for the mass percentage of similar material in surrounding rock (%).  
The reinforcement of the lining model is built of wire mesh with a diameter of 1.1 mm, which is combined with the design papers of the dependent project tunnels and relevant literature [34; 35]. The lining model is developed as a monolithic tube sheet since it is difficult to build in pieces due to its tiny size and brittle gypsum composition. Figure 1 displays the tunnel lining model as well as the lining structure specimens made of similar materials.
# 3D-printed super-Wideband Spidron Fractal Cube Antenna with Laminated Copper
###### Abstract  
In this paper, a 3D-printed super-wideband (SWB) Spidron fractal cube antenna is proposed. The Spidron fractal configuration is utilized as a self-complementary structure on each face of a 3D frame to attain SWB characteristics. The antenna is excited through a tapered microstrip balun for both mode transforming and impedance matching. A prototype of the proposed antenna, including the 3D frame fabricated with the help of a 3D printer and Spidron fractal patches made of copper tape, is experimentally verified. The measured \(-10\) dB reflection ratio bandwidth is 34:1 (0.44-15.38 GHz). The peak gain varies from 3.42 to 9.29 dBi within the operating frequency bandwidth. The measured radiation patterns are nearly omnidirectional at all operating frequency bands.  
3D-printed antenna; self-complementary; Spidron fractal; super-wideband antenna
# Hierarchical Phoneme Classification for Improved Speech Recognition
## 4 Experimental Results
### Various Window Sizes  
In order to find the optimal window sizes for individual phoneme groups, we tried various window sizes in the MFCC feature extraction. Table 6 shows performance variations by choosing different window sizes with six phoneme group models. For fricatives and stops, 10 ms window size was the best, both 15 ms and 25 ms were the best for affricates ([jh] and [ch]) and 20 ms was the best for nasal. This is because fricative and stop sounds are very short consonants, and nasal sounds are voiced and close to vowel sounds. Generally, consonant models showed lower phoneme error rates with smaller window sizes because the actual vocalization length is shorter for consonants, so it is possible to obtain more information by using a small window length. Surprisingly, for vowel/semivowel and closes, window analysis size did not affect performance. Unlike consonants, they have very stable characteristics over time, so a change in analysis sizes  
Figure 8: The error rates using mel-frequency cepstral coefficients (MFCCs) and log filter bank energy according to change window size. (**a**) shows fricatives and afficates. (**b**) shows stops and nasals. In all phoneme groups, the error rate of MFCC is lower than that of log filter bank energy.  
made no difference. We used the best window sizes for each of the phoneme group models to maximize the performance of the proposed hierarchical phoneme classification model.
# Drone SAR Imaging for Monitoring an Active Landslide Adjacent to the M25 at Flint Hall Farm
## 5 Discussion
This will be derived from either single-pass drone InSAR or third-party products from suppliers such as AW3D, who provide sub-metre resolutions. \(\varphi_{orbit}\) is the phase change arising from deviations in the satellite orbit, which is corrected using the precise orbit data. For drone InSAR, flight paths will be corrected using the fixed GNSS data with millimetric accuracy. \(\varphi_{tropo}\) (troposphere) and \(\varphi_{iono}\) (ionosphere) are atmospheric phase contributors, which are typically corrected using spatial filtering. For drone InSAR, the shorter atmospheric range will reduce the attenuation potential and thus the effect of these two variables. Furthermore, spatial filtering will take place over smaller areas and potentially with higher frequency radar signals with different attenuation potentials. It is anticipated that the atmospheric contribution will be negligible. \(\varphi_{scat}\) (scattering) and \(\varphi_{noise}\) are negligible phase contributors associated with the target characteristics and signal processing. \(\varphi_{defo}\) (deformation) is the phase signal of interest, which informs the detection of relative changes in distance between the sensor and target, once all other phase contributors are accounted for. Once developed, this adapted InSAR processing chain will inform the detection of any landslide activity and movement, which is typically subsidence. For other geotechnical hazards and infrastructure, multi-pass SAR data collection will be required for the InSAR processor to output meaningful deformation data.