esa-sceva/satcom-chunk-collection huggingface dataset:
# Technologies Trend towards 5G Network for Smart Health-Care Using IoT: A Review
## 4 Technology Trends to Achieve the Requirements in the 5G Network  
From the above-mentioned applications and scenarios, the essential requirements are summarised in Table 4, which are required for the future generation of the cellular network (5G); ultra-high connection density, traffic capacity and high data rate, ultra-high reliability, ultra-low latency, extremely high energy efficiency, and communication at large distances. For the realisation of these requirements, many technology trends can be identified and how to utilise these technologies to deliver the presented requirements for 5G network. Table 4 show the summary of numerous scenarios for smart health-care and its requirements.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Scenario** & **Drivers** & **Communication Technologies** & **Required Latency** & **Required Data Rate** \\ \hline \multirow{6}{*}{M2M Wearables} & & NB-IoT & & \\  & & (interconnected devices) & & \\  & & LoRa & & \\  & & (sensor applications) & & \\  & & gathering & Zigbee & & \\  & & (data collection) & & \\  & & Bluetooth & & & \\  & & (D2D sensors) & & & \\ \hline \multirow{2}{*}{Digital Hospital} & Communication inside building & Wi-Fi & 10–100 ms & Few Mbps \\ \hline \multirow{2}{*}{Emergency Medical Services} & Emergency Communication and High-speed reply & LTE-A LTE-A Pro & 20–100 ms & From 100 Mbps to 3 Gbps \\ \hline \multirow{2}{*}{Remote Surgery} & URLLC service between many locations & 5G & 20–30 ms & Few Gbps \\ \hline \multirow{4}{*}{Tactile Communication} & URLLC (Ultra-reliable and low latency communications), eMBB (enhanced Mobile Broadband) & 5G, 4G, Wi-Fi, Bluetooth & sub-ms & Few Gbps \\ \hline \multirow{2}{*}{Combination of all scenarios} & Communication, latency, bandwidth, applications & 5G, 4G, Wi-Fi, Bluetooth & up-to few ms & Few Mbps to 3 Gbps \\ \cline{1-1}  & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary of numerous scenarios for smart health-care.
# sRrsR-Net: A New Low-Light Image Enhancement Network via Raw Image Reconstruction
## 4 Experiments
### Comparison Analysis on the sRGB Domain Datasets  
In this part, three sRGB domain datasets were considered: LOL-v1, LOL-v2-real, and LOL-v2-synthetic, each presenting unique challenges. The main quantitative experimental results for ten models are shown in Table 1. Our proposed method achieves state-of-the-art performance on most datasets, demonstrating excellent results in terms of the PSNR and SSIM metrics.  
The LOL-v1 dataset, with its small size and high dynamic range lighting conditions, poses challenges for generalization. Despite this, our model achieved a PSNR of 29.49 dB and an SSIM of 0.861, surpassing the second-best method, SNR, by 2.77 dB in PSNR and 0.221 in SSIM, effectively handling the diverse and challenging real-world scenes. For the LOL-v2-real dataset, which contains a wider variety of lighting environments, our model achieved a PSNR of 29.48 dB and an SSIM of 0.889, improving by 2.27 dB in PSNR and 0.018 in SSIM over the second-best method, showcasing robustness in realistic conditions.  
On the LOL-v2-synthetic dataset, our method delivered a 2.13 dB improvement in PSNR compared to the competitive method SNR, but the SSIM dropped slightly by 0.008. The slight decrease in SSIM can be attributed to the design of our ISP enhancement module, which prioritizes improvements in color and lighting. This may trade off some structural consistency for better subjective visual quality. This trade-off likely contributed to the slight decrease in SSIM. Nonetheless, the overall performance demonstrates the robustness and effectiveness of our method across diverse datasets.  
Qualitatively, here we offer a visual comparison of the proposed model's performance against four mainstream low-light enhancement methods on the LOL-v1 and LOL-v2 datasets, they are MIRNet, SNR, Retinexformer, and Zero-DCE, respectively. The enhancement results of five image examples from these two datasets are illustrated in Figure 6 highlights how our model, sRrsR-Net, produces enhanced images with more consistent color representation and natural brightness compared to the other methods. For instance, in the enlarged portions of the images (line 4 in Figure 6), our model sRrsR-Net effectively eliminates shadows caused by uneven lighting, while maintaining the natural color of the green lake water, closely resembling the real-world scene. This is due to the RAWFormer module's sensitivity to green tones, which helps in producing more natural colors during image conversion. In contrast, Zero-DCE tends to produce images with a grayish tint due to its lightweight nature. Retinexformer does not perform as well in maintaining color fidelity, leading to less natural-looking images. The SNR model exhibits inconsistent color reproduction, particularly in the enlarged lake region, where darker patches in the lower-right corner give the appearance of fragmentation.
## 3 Results and Discussions
### Simulation Results

Table 2: The main lobe magnitude direction comparison for three states. Figure 10: Radiation efficiency of the proposed frequency-reconfigurable antenna for each switching state. valuable insights for sub-6 GHz applications. The existing literature emphasizes the importance of reconfigurable antennas and presents various approaches and applications, including WLAN, sub-6 GHz 5G, and vehicular communications. The proposed frequency-reconfigurable antenna contributes to the advancement of research in this field. Confirming the electrical wavelength through surface current distribution analysis is a fundamental step in validating the resonance behavior of antennas, especially in frequency-reconfigurable designs. Surface current distributions for each state are presented in Figure 11a-d. The results comply with the expected electrical length, particularly in the context of sub-6 GHz applications. In this study, the proposed antenna resembles the conventional monopole antenna structure. However, the working principle of the proposed design is different from the monopole antenna. If the antenna's working principle is based on the monopole design, the corresponding lengths should be 31.25 mm (for 2.4 GHz), 21.43 mm (for 3.5 GHz), and 25 mm (for 3 GHz), respectively. It is also deduced from Figure 11 that the overall antenna dimension is reduced by incorporating the add-drop ring resonator due to coupling and resonance control.
## Materials and Methods
The first inversion step estimates the residual height and displacement velocity of the study area, and is applied to flatten the interferograms. Reference points are used in this step (one or more) and are automatically selected (based on high coherence and minimal deformation) for the removal of the offset phase from all interferograms. For the processing of large areas, the scene is split into sub-areas where individual processing is undertaken. A mosaicking operation is then conducted to merge all smaller subset areas to produce the final velocity map, and in this instance several reference points are selected.
## 2 Circuit Design and Analysis
### Proposed Switched-Resonator Triple-Band Load Network
The impedance can be changed from capacitive \(C_{x}\) to inductive \(L_{in}\)[19] and can be derived as  
\[\text{I}_{\text{in}}=G_{m2}\bigg{(}G_{m1}V_{in}\times\frac{1}{sC_{X}}\bigg{)} \tag{6}\]  
and  
\[\text{Z}_{\text{in}}=\frac{\text{V}_{\text{in}}}{\text{I}_{\text{in}}}=\frac{ sC_{X}}{G_{m1}\times G_{m2}}=sL_{in} \tag{7}\]  
In Equation (7), \(\text{Z}_{\text{in}}\) is with an inductive loading \(L_{in}\) with an inductance \(C_{x}/(G_{m1}\times G_{m2})\). The proposed input matching network is presented with an additional switched resonator \(Z_{L_{c},sw}\) between point \(X\) and \(V_{dd}\). Therefore, we added a resonator \(Z_{L_{c},sw}\) parallel to the capacitor \(C_{x}\) (here, \(Z_{c}=1/sC_{x}\)), as shown in Figure 3e. The impedance \(Z_{\text{in}}\) can be written as  
\[\text{Z}_{\text{in}}=\frac{\text{V}_{\text{in}}}{\text{I}_{\text{in}}}=\frac {1}{G_{m1}\times G_{m2}(Z_{c}\|Z_{L_{c},sw})} \tag{8}\]  
\(Z_{\text{in}}\) will be an inductive loading with an inductance \(C_{x}/(G_{m1}\times G_{m2})\) if \(Z_{L_{c},sw}\) is merely an open loading. As shown in Figure 4a, with a gyrator-based design, \(\text{Z}_{\text{in}}\) can be inductive around the resonant frequency \(\omega_{o}\) when the switch of \(Z_{L_{c},sw}\) is off. Contrarily, the impedance at the output node \(X\) is capacitive when the operating frequency is far from the resonant frequency. When the switch of \(Z_{L_{c},sw}\) is on, \(Z_{L_{c},sw}\) is changed to a dual resonator loading, as shown in Figure 4b, with dual inductive points found at the resonated frequencies \(\omega_{1}\) and \(\omega_{2}\). By contrast, the triple-band input matching network can be accomplished when the feedback mechanism is provided with the proposed switched resonator. Figure 2: Load impedance versus frequency with different C\({}_{d}\) parameters. (**a**) A dual-band load network when the switch is on. (**b**) A single-band load network when the switch is off. \[Z_{L\_sw(off)}=\frac{-j\omega L_{d}}{\omega^{2}L_{d}C_{d}-1} \tag{2}\]0 _Cont._  
\[Z_{L\_sw(off)}=\frac{-j\omega L_{d}}{\omega^{2}L_{d}C_{d}-1} \tag{2}\]1 Conventional and proposed gyrator-based topology. **(a)** Schematic of regulated cascode active inductor, **(b)** Smith chart, **(c)** equivalent circuit, **(d)** circuit topology and input impedance, **(e)** proposed gyrator-based circuit topology and its impedance with a switched resonator \(Z_{\text{L-SW}}\).
## 4 Experimental Design and Results Analysis
### Dataset Construction  
An obstacle dataset was constructed for common objects that may be encountered by unmanned construction machinery during driving in complex working conditions in open-pit mines. The dataset is mainly collected from the Internet and field cameras. Seven types of targets, including cars, loaders, excavators, mining trucks, pedestrians, falling rocks, and potholes, were selected for research experiments. These targets are likely to cause risks of collision, rollover, jamming, casualties, etc., during the driving of unmanned engineering machinery. In order to improve the robustness of the dataset, the acquired images are cleaned, and duplicate, blurred, formatted, and overly similar images are deleted. The cleaned images are used as the final obstacle dataset. Figure 9 displays a few photos from the dataset.  
The seven different types of obstacles in the Yolo format dataset images were annotated using the image annotation tool LabelImg v1.8.6. The annotation data were saved in a Txt file that corresponded to the image name. A training set, validation set, and test set were created using the annotated dataset photos and the Txt files containing the annotation data in an 8:1:1 ratio. In order to simulate the harsh environment of a real open-pit mine and enhance the dataset's capacity for generalization, rain noise, fog noise, Gaussian noise, and geometric transformation (flip, rotation, random cropping) were added to the training set images for enhancement processing. The specific dataset construction process is shown in Figure 10.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Configuration Name** & **Parameters** \\ \hline CPU & Intel(R)Core(TM)i7-13650 HX, Intel, Santa Clara, CA, USA \\ GPU & NVIDIA GeForce RTX 4060 Laptop, NVIDIA, Santa Clara, CA, USA \\ Python & v3.8 \\ Deep Learning Framework & Pytorch 2.2.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Running configuration.  
Figure 8: The LAMP method.  
After processing, the obstacle dataset has a sum of 7242 pictures. In the annotation results, the quantity of annotation boxes of the seven obstacle categories is slightly different, as shown in Figure 11. Since people are distributed in many types of work in open-pit mines and falling rocks are also ubiquitous and widely distributed, the number of annotations is large, but the overall annotations are relatively balanced, guaranteeing that the quantity does not significantly change from each category in the sample, thereby balancing the data distribution between categories, strengthening the dataset's robustness by preventing the model from focusing more on some categories while neglecting others.  
Figure 10: Dataset construction flowchart.  
Figure 9: Partial dataset images.
# In-Orbit Results and Attitude Analysis of the SNUGLITE CubeSatellite
## 5 Conclusions  
The SNUGLITE CubeSat was successfully launched and settled in orbit, and it has since been broadcasting its beacon data continuously. Unfortunately, being that the CubeSat is unable to pick up commands from the ground station, it cannot receive the expected datasets that are excluded from beacon data. Still, though raw data measurements were unavailable, analyses were performed based on the available datasets. Particularly, the results of attitude estimation were verified by evaluating the operation.  
In this paper, the novel LQG-based attitude system using low cost sensors and actuators for the CubeSat platform was presented, with focus on the in-orbit real-time attitude estimation performance and analysis using a new method for comparing two sun-pointing vectors. The sun-pointing vector calculated from attitude estimation was compared to the reference sun-pointing vector generated usingon-board parameters, such as the power generation levels at each side of the CubeSat. The calculated RMS error between two sun-pointing vectors was approximately 15\({}^{\circ}\), but it reduced to 6.1\({}^{\circ}\) for a high power generation situation, which is more reliable.  
When the uplink functionality of tele-commands from the ground station to the satellite is restored, SNUGLITE will be able to download continuous raw data, including sensor data and control input for the attitude system, as well as measurements from GPS receivers for scientific missions. This will allow for deep performance analyses of the SNUGLITE CubeSat, including the analysis of the attitude control.  
CubeSat development, O.-J.K., H.S., S.Y., and Y.B.; methodology, C.K. and O.-J.K.; formal analysis and validation, O.-J.K. and H.S.; resources and data curation, Y.B. and S.Y.; writing--original draft preparation, O.-J.K.; writing--review and editing, O.-J.K.; onboard GPS receiver development, H.K., J.L., and J.H.; supervision, C.K.; project administration, S.H. and Y.C. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (No. NRF-2017M1A3A3A02016230), contracted through by the Institute of Advanced Aerospace Technology at Seoul National University. The Institute of Engineering Research at Seoul National University provided research facilities for this work.  
The authors would like to thank the amateur operators' efforts to receive the data from the SNUGLITE CubeSat and upload for sharing ([[https://db.satnogs.org/satellite/43784/](https://db.satnogs.org/satellite/43784/)]([https://db.satnogs.org/satellite/43784/](https://db.satnogs.org/satellite/43784/))). The lists of operators are as below: DK3 WN, YC3 BVG, SV3 CIX, JE9 PEL, AD7 NP, VK2 FAK, EU1 XX, UX5 UL, JA1 GDE, W2 RTV, VK3 HBF, and CX8 AF.  
The authors declare no conflict of interest.
# Tracking Method of GM-APD LiDAR Based on Adaptive Fusion of Intensity Image and Point Cloud
## 2 Intensity Image KCF Target Tracking Method Based on the Muti-Feature Fusion  
KCF is a classical tracking model based on kernel function and correlation filtering theory. By expanding training samples through cyclic matrix, discriminant classifier is trained to judge the target, achieving rapid detection and recognition of the target [16].  
The main process is as follows:  
Step 1: The HOG feature of the target is extracted from the first frame for training and learning to build the correlation filter.  
Step 2: The target position of the previous frame is taken as the search area, of which features are extracted. The edge effect is reduced by cosine window function, and the response map in frequency domain is obtained by fast Fourier transform.  
Step 3: By implementing the inverse Fourier transform to generate the response map in time domain, the max peak position in this response map is the optimal target prediction position of the next frame.  
Step 4: The HOG features of the target are extracted from the current frame, which are used for training and learning together with the expected output, while the correlation filter is updated.  
Step 5: Repeat Step 2 to Step 4 to achieve continuous target tracking.  
The overall flow of the KCF algorithm is shown in the figure below. \(\odot\) in Figure 1 means the multiplication of two outcomes in the frequency domain.  
However, the HOG feature alone cannot fully describe the target, especially when the target is partially obscured or the image quality is blurred due to rain, snow and other environmental factors, which is prone to problems such as reduced tracking accuracy and even tracking failure. In order to overcome this shortcoming of KCF algorithm, this paper adopts multi-feature fusion method to fuse HOG feature and Fourier descriptor feature of target in LiDAR intensity image to improve tracking accuracy. The overall flow of the improved algorithm is shown in the figure below. \(\odot\) in Figure 2 means the multiplication of two outcomes in the frequency domain.  
Figure 1: Overall flow of KCF.
# Zero-TCE: Zero Reference Tri-Curve Enhancement for Low-Light Images
## 3 Materials and Methods
### Tri-Curve Enhancement for Contrast (TCE-C)  
Analogous to the TCE-L module, we introduce the TCE-C module, which is designed to augment the contrast of low-light images, thereby enhancing the finer details within darker regions. The framework of TCE-C is illustrated in Figure 4.  
Figure 3: LE curves with different adjustment parameters \(\alpha\) and numbers of iteration \(n\). (**a**) \(n\) is equal to 1; (**b**) \(n\) is equal to 4, and \(\alpha_{1}\), \(\alpha_{2}\), and \(\alpha_{3}\) are all equal to \(-1\).  
We observe that, while TCE-L is generally effective in brightening images and preserving details, it does not significantly enhance contrast, and its effectiveness may be unsatisfactory under extreme low-light conditions. Therefore, we propose a third-order contrast-enhancement curve (CE curve), which can be expressed as follows:  
\[CE(I(\mathbf{x});\beta)=I(\mathbf{x})-\beta I(\mathbf{x})^{2}[1-I(\mathbf{x})]. \tag{5}\]  
The formula for high-order curves can be expressed as follows:  
\[CE_{n}(\mathbf{x})=CE_{n-1}(\mathbf{x})-\beta_{n}[CE_{n-1}(\mathbf{x})]^{2}[1-CE_{n-1}(\bm {x})]. \tag{6}\]  
It is evident that this curve meets the requirements of the network. Moreover, by increasing the order of the curve compared to the original, the enhancement effect becomes more pronounced. Figure 4(a) displays CE curves with different parameters. Compared to Figure 2(a), the CE curve exhibits stronger adjustment capabilities.  
Similarly, to address more complex low-light environments, we introduce a high-order adjustment mechanism for the CE curve by defining \(\beta\) as a pixel-wise parameter:  
\[CE_{n}(\mathbf{x})=CE_{n-1}(\mathbf{x})-C_{n}[CE_{n-1}(\mathbf{x})]^{2}[1-CE_{n-1}(\mathbf{x} )]. \tag{7}\]  
We also set \(n\) to 8. When \(n\) equals 1, Equation (6) is reduced to Equation (5). Figure 4(b) displays various high-order curves. Compared to the curves in Figures 4(a) and 2(b), they offer superior adjustment capabilities for enhancement effects. The enhancement effects of the TCE-L and TCE-C modules are shown in Figure 6. It is evident that TCE-L effec  
Figure 4: The architecture of the proposed TCE-C module.  
Figure 5: CE curves with different adjustment parameters \(\beta\) and numbers of iteration \(n\). (**a**) \(n\) is equal to 1; (**b**) \(n\) is equal to 4, and \(\beta_{1}\), \(\beta_{2}\), and \(\beta_{3}\) are all equal to \(-1\).  
tively increases the brightness of low-light images, while TCE-C significantly enhances their contrast.
# Synergistic Effect of Nonthermal Plasma and ZnO Nanoparticles on Organic Dye Degradation
## 3 Results and Discussion  
### Optical Emission of Ar Plasma Jet  
A typical emission spectrum of atmospheric pressure Ar plasma is depicted in Figure 1. The discharge parameters were at a fixed Ar flow rate of 3 L/min, discharge voltage of 7 kV\({}_{\rm{PP}}\), and AC frequency of 18 KHz. The Ar plasma plume was in stable laminar Ar flow mode. The plasma emission was collected with an optical fiber facing the plasma plume. As evident from Figure 1, in addition to the Ar I line (4p \(\rightarrow\) 4s) in the range of 690-870 nm, other spectral bands/lines, such as at 309 nm (hydroxyl (OH)), in the 315-400 nm range (SPS of N\({}_{2}\), and at 777 nm (atomic oxygen OI), can be observed. The high intensity of the Ar I spectral lines indicates the presence of high-energy electrons (\(\geq\)11 eV), which play a crucial role in the dissociation of dye molecules. The dissociation energy of the H\({}_{2}\)O molecule H\({}_{2}\)O \(\rightarrow\) O + H\({}_{2}\) and/or H\({}_{2}\)O \(\rightarrow\) OH + H is 5.0 and 5.1, respectively [32; 33]. In addition,metastable argon atoms Ar* cause stepwise excitation via electron collisions, which increases plasma reactivity. The presence of OH-, N\({}_{2}\)-, and OI-excited species are inevitable, as the Ar plasma plume is diffuse in the surrounding air; hence, energetic electrons interact with the surrounding air molecules and generate such excited species. Interestingly, Figure 1 confirms the presence of a moderately strong UV emission band (<400 nm), which induces additional pathways to generate OH radicals through ZnO nanoparticles that eventually improve the catalytic activity of the plasma-ZnO nanoparticle system.
# In-Band Medium-Frequency R-Mode Signal Quality Estimation +
###### Abstract  
Assessing the quality of a received signal is of fundamental importance to predict the performance of the receiver. In general, the signal to noise ratio (SNR) or the carrier to noise density \(C/N_{0}\) ratio is used as an indicator to describe the signal quality of most receivers. For the medium-frequency (MF) R-Mode, a terrestrial positioning navigation and timing (PNT) system, the knowledge of the SNR or \(C/N_{0}\) is important for monitoring purposes, helping the service operator to assess the healthiness of the transmitted signals, as well as for the users to optimize the receiver algorithms and performance. In this paper, we present how the \(C/N_{0}\) can be estimated from the output of the discrete Fourier transform (DFT). The DFT is already used in the receiver to perform the phase estimation; hence, the receiver computational load is reduced. Theory is first presented and discussed, followed by the definition of the estimator, which is tested with Monte Carlo simulation as well as with real data to validate the approach. The results show good agreement between theory, simulation, and in-field measurements, which proves that the estimated \(C/N_{0}\) is a good indicator to measure the received signal quality under optimal propagation condition.  
Keywords:signal quality estimation; positioning; navigation; R-Mode _Ense._Proc. 2025, 88, 50