esa-sceva/satcom-chunk-collection huggingface dataset:
# Evolution of Access Network Sharing and Its Role in 5G Networks
## 6 Summary and Conclusions  
This paper has highlighted the critical role that sharing of optical networks will play in the evolution of future networks, beginning with 5G. We reviewed the largely regulatory-driven models that are currently used for access networks sharing, and we highlighted their major challenges. We see that the major shortcoming of current sharing models is their inability to support diverse requirements when the network is utilized by multiple operators that want to provide a wide range of services to their customers. This limitation is a bottleneck for 5G networks, one of whose main novelty is their ability to support highly heterogeneous applications. We conducted a case study of more recent multi-service PON sharing and highlighted current challenges and limitations and tentative state-of-the-art solutions that overcome these limitations. Our findings suggest that functional virtualization, along with SDN, could play a vital role in overcoming the challenges, since these technologies enable flexible management for operators, which is especially needed in the network access. In Section 5, we speculated on how the 5G evolution might affect the current network ownership models, challenging the conventional roles of InP and VNOs and demanding new ownership models. We investigated some of the challenges for network operators sharing a common PON infrastructure and discussed the possibility of new ownership models. We briefly introduced the state-of-the-art solutions to some of these challenges, including a market model to provide trading incentives through monetization of the excess resources and then modelling the multi-tenant PON as a bilateral trade market. Multi-tenancy could potentially facilitate new partnership and co-investment models for network operators. In this report, we addressed one such model in which a trusted infrastructure provider is the sole provider of the resources. However, more complex sharing models are yet to be addressed as new network ownership/operation models emerge thanks to the network virtualization. Finally, we conclude this paper with a few insightful remarks on possible future research:  
* Designing and regulating new access network ownership models should be a key policy priority to ensure smooth deployment of 5G networks. * More research is needed to determine the business implications of the new ownership models, presumably utilizing novel approaches such as blockchain and smart contracts to assure economic robustness and trust. * Further studies should assess the potential for other network component/function virtualization opportunities to enhance the flexibility of the shared access. Conceptualization, N.A. and M.R. ; investigation, N.A., H.G. and F. S.; writing N.A., H.G., F.S. and M.R. ; editing: M.R. ; visualization: N.A. ; supervision: M.R. ; funding acquisition: M.R. Financial support from Science Foundation Ireland grants 14/IA/252 (O'SHARE) and 13/RC/2077 is gratefully acknowledged. The authors declare no conflict of interest. The following abbreviations are used in this manuscript:
# Self-Supervised Remote Sensing Image Dehazing Network Based on Zero-Shot Learning
## 3 Methodology
### Functionality of Modules in the Dehazing Framework  
In this section, we describe the functionality of the proposed dehazing framework's various modules. The dehazing network consists primarily of two components: a \(DCP\) module and two RefineNets. The RS hazy image is initially preprocessed by the \(DCP\) module, and its outputs are then refined by the RefineNets module.  
The \(DCP\) module is implemented with two advantages: (1) The \(DCP\) module separates the hazy input into three components (dehazed image, transmission map, and atmospheric light) based on the haze imaging model [37], which guarantees the design of the reconstruction loss function. (2) The initial dehazed image contributes to the design of the lightweight RefineNet architecture and improves the efficiency of subsequent zero-shot training.  
Although the \(DCP\) is effective at removing haze, it is inapplicable to white regions and produces undesirable artifacts. To rectify the defects, the CNN-based RefineNets is implemented. The well-designed loss function facilitates the network's zero-shot learning. Ultimately, the proposed dehazing framework offers three benefits: (1) effective haze removal, (2) excellent generalizability, and (3) rapid zero-shot training speed.
## 5 Experiments
### Knowledge Generation Process and Distribution
In addition, it also avoids the broadcast storm problem during the knowledge distribution process. Basically, to avoid this problem, a forwarder candidate suppresses the rebroadcast of low-priority candidates forwarders [42]. For this purpose, every time that a vehicle receives knowledge to be distributed, it checks if it is within the zone of preference [43; 44] (Legend (G)), and if so, it transmits first (Legend (H)) because it has the shortest waiting time. Due to the broadcast suppression mechanism implemented (zone of preference), as soon as the neighbouring vehicles outside the zone of preference receive the same scheduled knowledge, they cancel the retransmission (Legend (I)), thereby avoiding the traffic of redundant knowledge in the network.
# Continuous Variable Quantum Secret Sharing with Fairness
## 2 CVQSS Scheme with Fairness
### Chinese Remainder Theorem  
Let \(n\geq 2\), \(m_{1},...,m_{n}\geq 2\) and \(s_{1},...,s_{n}\in\mathbb{Z}\). The system of congruence equations  
\[\begin{cases}S\equiv s_{1}\bmod m_{1},\\ \quad\vdots\\ S\equiv s_{n}\bmod m_{n},\end{cases} \tag{1}\]  
has solutions in \(\mathbb{Z}\), when \(gcd(m_{i},m_{j})=1\), for all \(i,j\in[1,n]\). It has been proved that this solution can be calculated as  
\[S=\sum_{i=1}^{n}s_{i}T_{i}M_{i}\bmod M, \tag{2}\]  
where \(M=\prod_{i=1}^{n}m_{i}\), \(M_{i}=M/m_{i}\) and \(T_{i}\times M_{i}\bmod m_{i}=1\).  
According to the CRT equations described above, secret \(S\) can be divided to \(n\) shares named \(s_{i}\) for \(n\) participants and also can be recovered, when shares are all collected, which means the \((n,n)\)-threshold secret sharing scheme can be achieved. Of course, the \((t,n)\)-threshold scheme also can be similarly designed, where \(n\geq 2,t\leq n\) as long as the moduli \(m_{i}\) are prime numbers and secret \(S\in[h,H]\), where \(h=\prod_{i=n-(t-1)+1}^{n}m_{i},H=\prod_{i=1}^{n}m_{i}\)[11]. Thus, whichever threshold scheme is demanded in practice, it can be designed by the CRT. What's more, compared with other traditional methods, such as the polynomial interpolation method of [PERSON], whose key recovery interpolation formula requires \(O(t\log^{2}t)\) operations, the CRT-based scheme requires only \(O(t)\) operations [24].
# 8-W 2-Stage GaN Doherty Power Amplifier Module on 7 \(\times\) 7 QFN for the 5G N78 Band
## 2 Design of 2-Stage DPAM  
### The Circuits for the 2-Stage DPAM  
Figure 1 illustrates the circuit diagram of the proposed 2-stage DPAM implemented using IPD. The proposed DPAM employs a 2-stage single driver structure to achieve high gain and a compact form factor. The main stage incorporates two GaN-HEMT bare dies, serving as the carrier and peaking amplifiers, respectively. The driver stage consists of a single GaN-HEMT bare die. The input and interstage matching networks are compact, achieved through simplification methods and exhibit low loss due to high-Q IPD. These small-sized networks are connected to the GaN-HEMT dies using bond wires for integration into a compact QFN package. The load network is implemented with high-Q surface-mountable passive components on a PCB to ensure high output power with minimal loss. The input matching network for the driver stage, designed using an IPD, features a low-pass structure complemented by an L-section high-pass circuit, transitioning an input impedance from approximately 2 \(\Omega\) to 50 \(\Omega\).
## 4 Conclusions  
Through the processing of aerial remote sensing images, the effectiveness of the proposed processing method was verified in the present study, and the wide-view angle processing speed also reached quasi-real-time. Therefore, the following conclusions can be drawn from the results of the current study:  
1. In the geometric transformation model of two adjacent images, the transformation model of \"scale + rotation\" was adopted instead of the perspective projection model of an area array camera, which adapts to the geometric transformation model between adjacent images of wide-field aerial scanning images, and better solved the whole transformation problem between adjacent images.
2. In the calculation of the whole transformation model, the homonymous pointing on both sides of the scanned image was used to calculate the scale and rotation parameters between adjacent images, and the rough geometric stitching between adjacent images was realized through the whole scale and rotation transformation processing.
3. On the basis of the whole transformation model, local coordinate transformation and adjustment were carried out on the scanning direction of images according to a large number of locally matched points, which were obtained by normalized cross-correlation matching. They could solve the local geometric non-uniformity deformation between adjacent images with better ways and results.  
Conceptualization, L.D.; Methodology, L.D. and F.L.; Software, L.D.; Validation, F.L. and H.Y.; Formal analysis, L.D.; Resources, H.Y.; Writing--original draft preparation, L.D.; Writing--review & editing, F.L., M.H. and H.Y.; Visualization, F.L; Supervision, H.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 10: Zoom in on the local area of the mosaic image.
# Preparation of Nanoparticle-Loaded Microbubbles via an Electrohydrodynamic Atomization Process
## 3 Results and Discussion
### Effect of Nanoparticle Concentrations on the Microbubbles
We analyzed the size distribution of microbubbles prepared by introducing nanoparticles with different concentrations. The results are shown in Figure 8. As can be seen from Figure 8a, when nanoparticles were not loaded, the size of BSA microbubbles was between 40 and 160 \(\upmu\)m. It is also worth noting that more than 40% of the microbubbles were larger than 200 \(\upmu\)m. When silica nanoparticles were added into the shell layer, the size distribution of the microbubbles was significantly narrowed. The proportion of microbubbles with sizes greater than 200 \(\upmu\)m decreased sharply. When the concentration of silica nanoparticles was 0.5 mg/mL, microbubbles showed an ideal size distribution. More than 60% of microbubbles were distributed in the 120-160 \(\upmu\)m size range (see Figure 8b). It can be seen from Figure 8c that when the concentration of silica nanoparticles increased to 1 mg/mL, more than 35% of microbubbles were distributed in the 40-80 \(\upmu\)m size range. The microbubbles ranging from 120-200 \(\upmu\)m accounted for more than 50% of the total, while the microbubbles with sizes greater than 200 \(\upmu\)m accounted for \(\sim\)8%. As can be seen from Figure 8d, when the concentration of silica nanoparticles increased to 2 mg/mL, the proportion of microbubbles ranging in size from 40-160 \(\upmu\)m was more than 95%. Basically, the breakage and dissolution behavior of microbubbles is the result of the coordinated influence of interfacial tension and size. The classical Laplace equation (\(\Delta\)P = 2 \(\sigma\)/R) suggests that the microbubble surface pressure difference is inversely proportional to the microbubble radius R and directly proportional to the microbubble surface interfacial tension. The smaller the microbubble, the greater the pressure difference between the inside and outside, which means that the microbubble is easier to break. This is consistent with the findings shown in Figures 2 and 3. When the size change of microbubbles is negligible (i.e., assuming that R is constant), the smaller interfacial tension leads to the smaller pressure difference. As a result, more stable microbubbles can be achieved. According to the experiment results, we can preliminarily deduce that the introduction of nanoparticles has an impact on the interfacial tension of the surface of microbubbles. The introduction of nanoparticles may reduce the interfacial tension at the interfaces of microbubbles, which improves the stability of microbubbles and prolongs the survival time. In addition, it also worth noting that the stability of the nanoparticle-loaded microbubbles may not simply be dominated by size and surface tension. Other parameters, such as the mechanical strength of the shell layer, may also be key factors affecting the stability of the microbubbles, and need to be further studied.
# Spintronic RF-Direct on-off Keying Modulation Using a Frequency Division Multiplex
## 6 Conclusions  
We demonstrated a new communication technique with spintronics technology. It is the first report to implement wireless communication by applying STNO array to modulate frequencies and amplitudes simultaneously. The obtained results demonstrate the possibility of increasing data rates remarkably by using an STNO array, which features a wide band range even exceeding 100%, low DC consumption at the micro-watt level, and nano-sized realization. However, many barriers must still be surmounted. In particular, the linewidth of STNO's oscillation signal was 130 MHz, so in this paper, only two STNOs could be transmitted using the on-off Keying modulation to secure twice the transmission speed. As research is currently under way to improve linewidth to \(\sim\) MHz, it is believed that RF-Direct on-off Keying Modulation can be performed on a number of STNOs. It would be easy to increase the transmission speed. Increased transmission speed in the big data environment is an essential requirement for communication. This paper presents a technology that can easily speed up transmission data in the array structure of STNO.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref.** & **LC VCO [23]** & **LC VCO [24]** & **Ring VCO [25]** & **STNO (This Work)** \\ \hline Oscillation frequency (GHz) & 4.0\(\sim\)4.34 & 13\(\sim\)19 & 0.11\(\sim\)0.55 & 2.1\(\sim\)4.9 \\ \hline Tune range (\%) & 8.2\% & 37.5\% & 133\% & 80\% \\ \hline Phase noise & \(-113.6\) dBc/Hz @ 1 MHz & \(-113\) dBc/Hz @ 1 MHz & \(-71\) dBc/Hz & \(-63\) dBc/Hz @ 130 \\ \hline Core Pdc (mW) & 1.8 & 24 & 3.69 & 0.18 \\ \hline Size & 0.19 mm\({}^{2}\) & 0.14 mm\({}^{2}\) & 0.01 mm\({}^{2}\) & 0.009 \(\upmu\)m\({}^{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The STNO Performance comparison with other Works.  
Furthermore, as STNOs are capable of implementing nano-size in low power consumption in \(\mu\)W and array structures, the proposed plan is expected to run competitively on high-speed, low-power, and small-scale near field communication (NFC) at relatively low cost. In addition, STNO's output level is expected to increase even at low power consumption in the future, so it can be applied not only to proximity applications but also to various communication applications.  
Conceptualization, I.-Y.O.; validation, I.-Y.O.; formal analysis, M.-S.K.; Writing--review and editing, M.-S.K. and K.-S.K.; Funding acquisition and supervision, C.-H.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Sun Moon University Research Grant of 2021.  
The authors are also pleased to acknowledge a valuable co-operation work with KIST and KBSI.  
The authors declare no conflict of interest.
# In-Vehicle Data for Predicting Road Conditions and Driving Style Using Machine Learning
## 6 Evaluation Metrics  
The results of the detection are analyzed by showing the classification confusion matrix for each algorithm. The confusion matrix shows the true positives, false positives, true negatives and false negatives for each of the classification problems in this study. From the confusion matrix, accuracy, recall, precision and F-score are calculated.  
Accuracy represents the number of the correct prediction as a ratio to the number of total predictions. Precision shows how many are predicted correctly from all the classes predicted as positive. Recall shows how many are predicted correctly from all the positive classes. F-score is the harmonic mean of precision and recall.  
The above measures are given as follows:  
\[Accuracy=\frac{TP+TN}{TP+FP+TN+FN} \tag{1}\]  
\[Precision=\frac{TP}{TP+FP} \tag{2}\]  
\[Recall=\frac{TP}{TP+FN} \tag{3}\]  
\[F\text{-}score=2\frac{Precision*Recall}{Precision+Recall} \tag{4}\]  
The permutation feature importance approach is implemented to show the importance of the used features to the accuracy of detection. This algorithm works by shuffling the data of a single feature at time to destroy its quality while maintaining the rest of the features. If the quality of prediction is highly impacted, it means the feature is very important for the predictor. Feature ranking helps in understanding how the ML algorithms work and what data are more important to them. [PERSON] et al. [33] showed more information about the permutation importance and implementation.
## 4 Databases and Evaluation Parameters
### Evaluation Parameters
#### 4.2.2 Contrast Determination  
There are many methods for calculating the contrast in images, and the measurement of contrast is a critical parameter in the analysis of biomedical images [29]. The absolute mean intensity difference between the white foreground and the black background of the image is used to calculate the contrast in retinal fundus images and MR brain images. Figure 3 shows the contrast determination of the retinal fundus images and the brain MRI. The left hand image in Figure 3 shows the contrast between the blood vessel and its background on the retinal fundus image. The right hand image in Figure 3 shows the contrast between the white foreground and the black background on the brain MRI.  
In Figure 3, the blue dots represent the blood vessel pixel value from the retinal fundus images, and the red represent the background pixel values. The blue dots in Figure 3 represent the pixel value in the foreground white planes of the brain MRI, and the red dots in Figure 3 represent the black plane of the brain MRI. The mathematical representation of the contrast is shown in Equation (3).  
\[\mathrm{C}_{|\mathrm{[fn-bn]}|}=\left|\frac{1}{n}(\sum_{i=1}^{n}\mathrm{I}_{\mathrm{ fini}}-\sum_{i=1}^{n}\mathrm{I}_{\mathrm{bini}}\right|. \tag{3}\]  
The contrast of the brain MRI is measured between the foreground (colored white, and it is also called foreground white) and the background (colored black, and it is also called background black). The retinal image contrast is measured between the retinal blood vessels (in the foreground) against the background of the retinal vessels. The contrast in the two images (retinal and brain) is represented by \(\mathrm{C}_{|\mathrm{[fn-bn]}}\). The intensities of the retinal blood vessels or the white foreground of the MR brain image and the intensities of the background against the blood vessels or intensities of the black background of MR brain images are represented as \(\mathrm{I}_{\mathrm{Ini}}\) and \(\mathrm{I}_{\mathrm{bini}}\), respectively. The variable n indicates the number of pixels, where \(n\) = 200. The pixel locations for measuring the contrast were chosen at random.
# Aerostat-Based Observation of Space Objects in the Stratosphere
## 2 Astronomical Observation Conditions in Stratosphere
### Available Observing Time  
The aerostat-based detection platform working in the stratosphere without the limit of clouds, snow, rain, and other weather can detect space debris in all weather compared with the ground-based observation station, which means it can carry out observation tasks throughout the year. On the other hand, daytime observation is available because the stratospheric airspace has a good continuous sky background in the day and night and excellent transmittance in visible and infrared bands without the effect of atmospheric turbulence. In summary, this method has the advantage of the availability of observable hours and can carry out the observation of space objects all day and in all weather.  
Figure 5: Atmospheric humidity as measured in situ by a meteorological balloon.
## 3 Results and Discussions
### Optical Properties
Moreover, the shift in wavelength in the visible region further supported our findings as productive in DSSCs, because the photocatalytic efficiency of TiO\({}_{2}\) is affected by its ability to respond to UV radiation along with visible light, which could be improved by shifting its optical absorption from UV to the visible region. Ba doping increased the absorption in the visible region. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample** & **Eg (eV)** & **n Coefficient** \\ \hline TiO\({}_{2}\) & 3.21 & 1.72 \(\pm\) 0.003 \\ Ba/TiO\({}_{2}\) & 3.26 & 1.61 \(\pm\) 0.003 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: n coefficient determination and band gap. Figure 4: UV-visible absorption spectra of synthesized TiO\({}_{2}\) and Ba/TiO\({}_{2}\) photoanodes with N3 dye molecule. Figure 3: (**a**) Tauc plots of synthesized TiO\({}_{2}\) and Ba/TiO\({}_{2}\) photoanodes (**b**) Graphical representation of Equation (7), inset relates to Ba/TiO\({}_{2}\).
# Data Collection in Areas without Infrastructure Using LoRa Technology and a Quadrotor
## 4 Instrumentation and Construction
### System Operation  
The system's operation is divided into two modules: the monitoring station and the reception system mounted in the UAV. The station's operation mainly focuses on programming the embedded card, since the program is dedicated to carrying out the process of reading, packaging, and transmitting data autonomously. The steps of the station's operation are as follows:  
* Generation and loading of the program.
* Battery connection for independent power.
* Verification of communication between the card, sensors, and transmission module.
* Positioning of the sensors on the surface to be monitored.
* Waiting for the UAV to enter the transmission range with the station.  
The operation of the reception system is similar to that of the monitoring station since the reception, storage, and uploading of the data are carried out automatically using the Raspberry Pi Zero programming code. However, the navigation of the UAV must be carried out through remote control in a conventional mode.  
* Program generation and programming.
* Battery connection.
* Verification of communication between the card and the transmission module.
* Start of the UAV flight.
* Bringing the UAV closer to the monitoring station (at least 400 m) from which the information is to be collected.
* Finishing the UAV flight and uploading the collected data. The ground station is required to have Internet access for automatic synchronization. Otherwise, it can be performed manually.