esa-sceva/satcom-chunk-collection huggingface dataset:
# Conditional GAN-Based Two-Stage ISP Tuning Method: A Reconstruction-Enhancement Proxy Framework
## 6 Conclusions  
This paper proposes an ISP tuning method based on two-stage proxy. By decoupling the ISP process into a reconstruction phase (physical signal recovery) and an enhancement phase (visual quality and color optimization), the two-stage proxy can better fit different modules in ISP. By using different network structures and loss functions for different targets, the two-stage proxy can efficiently fit the reconstruction and enhancement modules in ISP. In the Section 4, firstly, this article compared the performance differences between the two-stage proxy and end-to-end proxy in terms of proxy performance and downstream CV tuning tasks and demonstrated the importance of the two-stage proxy. Secondly, by comparing the performance differences of different tuning methods on the downstream CV tasks of denoising and object detection, the performance improvement of proxy tuning compared to manual tuning and the performance improvement of the two-stage proxy tuning method compared to the end-to-end proxy tuning method were verified. Thirdly, by comparing different loss functions, the significance of improving the loss function for performance enhancement in addition to the two-stage proxy was verified in this paper. Those experiments have shown that this method outperforms traditional ISP and end-to-end proxy methods on public datasets (SIDD, KITTI) and outperforms hand-tuning by more than 21% in performance metrics.  
Conceptualization, J.Y. and P.Z.; methodology, P.Z.; software, P.Z.; validation, P.Z.; investigation, P.Z.; writing--review and editing, P.Z. and J.Y.; supervision, J.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ISP & Image Signal Processor \\ CV & Computer Vision \\ HV & Human Vision \\ PPM & Pyramid Pooling Module \\ BLC & Black Level Correction \\ BNR & Bayer Noise Reduction \\ AWB & Auto White Balance \\ CSE & Color Saturation Enhancement \\ LDCI & Local Dynamic Contrast Improvement \\
2 DNR & Two-dimensional Noise Reduction \\ \end{tabular}
## 3 Results
### Radiation Dose Received by Organs at Risk (OARs)
Comparison of the maximum radiation dose received by the spinal cord under the three plans (Figure 7) revealed that a significant difference was only observed between the MF Plan and the 3D Plan (\(p\) = 0.046). For the 3D Plan, 4D Plan, and MF Plan, the median values (and ranges) were 1422 cGy (3630-258 cGy), 1270 cGy (4389-39 cGy), and 1179 cGy (3865-34 cGy), respectively. Figure 3: Radiation dose distribution of (**a–c**) transverse, (**d–f**) coronal, and (**g–i**) sagittal sections of the chest computed tomography (CT) image. The dose distributions shown here are for the 3D Plan (**a**,**d**,**g**), 4D Plan (**b**,**e**,**h**), and MF Plan (**c**,**f**,**i**). Figure 4: Box plot of the relative volume of the lung receiving a dose above 5 (V\({}_{5}\)), 10 (V\({}_{10}\)), and 20 (V\({}_{20}\)) Gy for the three treatment plans. Figure 5: Box plot of the mean radiation dose received by the lung in the three treatment plans. Figure 6: Box plot of the mean radiation dose received by the heart for the three treatment plans. Figure 7: Box plot of the maximum radiation dose received by the spinal cord under the three treatment plans. This study also investigated the relationship between the percentage difference in the PTV and the mean dose of the lung. As shown in Figure 8, the percentage difference in the PTV and the percentage difference in the V\({}_{20}\) exhibited positive correlations between the 3D-Plan versus 4D-Plan, 3D-Plan versus MF-Plan, and 4D-Plan versus MF-plan (Figure 8a). In addition, the percentage difference in the PTV was also positively associated with the percentage difference in the mean dose of the lung (Figure 8b). In a comparison of the V\({}_{20}\), the 3D-Plan versus 4D-plan, 3D-Plan versus MF-plan, and 4D-Plan versus MF-plan had Pearson correlation coefficients of 0.714 (\(p\) = 0.006), 0.866 (\(p\) < 0.001), and 0.779 (\(p\) = 0.002), respectively; and in a comparison of the mean dose, the Pearson correlation coefficients were 0.819 (\(p\) = 0.001), 0.898 (\(p\) < 0.001), and 0.753 (\(p\) = 0.003), respectively.
# UAV Path Optimization for Precision Agriculture Wireless Sensor Networks
## 6 Conclusions  
In this work, we proposed a method for data collection from SNs by an UAV for use in precision agriculture applications. The introduced Prohibition List strategy used by the UAVs allowed us to save 63% in flight time with respect to the case where this strategy was not used, guiding the UAV efficiently among SNs during the reconfiguration phase. This fact makes the system highly adaptable to changes in wireless sensor networks, thus favoring the decision-making process by guaranteeing the freshness and continuity of the gathered data. Regarding the distance covered by the UAVs, we obtained a reduction of more than 20% in their flight path, due to their decision-making in terms of going or not to not yet visited SNs, which made the process more accurate. In relation to the SN, we obtained a significant reduction of 89% over its active time with the proposed strategy, minimizing it to only the reading time. This was done by positioning the UAV only a few moments before the SN becomes active and forces it to hibernate until its next cycle after the data collection.  
Future work may consider, for instance, the impact of SN battery depletion, so that the UAV must detect that a device is no longer operational and therefore recalculates the optimized route. A dual problem is the detection of new devices and consequential route recalculation. Other interesting issue is to consider more than one UAV simultaneously flying over the SNs, which allows the design of a collaborative system, exchanging messages among UAVs for discovering SNs. Another extension is to consider the effect of optimizing transmission rate and power at each SN, which may considerably impact the battery lifetime and flight time.  
Conceptualization, G.E.J.J., M.E.P. and L.A.d.P.L.J.; methodology, G.E.J.J., L.A.d.P.L.J. and M.E.P.; software, G.E.J.J. and L.A.d.P.L.J.; validation, G.E.J.J., M.E.P. and L.A.d.P.L.J.; formal analysis, M.E.P., B.S.C., R.D.S. and S.M.-S.; investigation, L.A.d.P.L.J. and G.E.J.J.; resources, M.E.P. and S.M.-S.; data curation, B.S.C., R.D.S. and S.M.-S.; writing--original draft preparation, G.E.J.J., M.E.P. and L.A.d.P.L.J.; writing--review and editing, M.E.P., B.S.C., R.D.S. and S.M.-S.; visualization, M.E.P., B.S.C., R.D.S. and S.M.-S.; supervision, M.E.P. and L.A.d.P.L.J.; project administration, M.E.P. and L.A.d.P.L.J.; funding acquisition, M.E.P., B.S.C., R.D.S. and S.M.-S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Conselho Nacional de Desenvolvimento Cientifico e Tecnologico--CNPq, grant numbers 308240/2016-2 and 401710/2016-5), Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior-Brasil (CAPES), grant number 001, FONDECYT Iniciacion, grant number 11200659 and SCC-PIDi-UTEM FONDECQUIP-EQM180180.  
The authors declare no conflict of interest.
## 1 Introduction  
During the COVID-19 epidemic, people have become increasingly reliant on digital services like online shopping, food delivery, digital payments, education, and video streaming. In contrast to fifth-generation (5G) systems, sixth-generation (6G) systems are expected to meet high-performance requirements: spectrum efficiency increases to 100 b/s/Hz; connections are number of times higher (10\({}^{7}\) devices per km\({}^{2}\)), and low latency (0.1 ms) [1; 2; 3]. Increasing user connections in 6G wireless systems can be accomplished with the non-orthogonal multiple access (NOMA) technique [4; 5]. The evolution of wireless communication systems, an overview of 6G enabling technologies, new applications and various open research challenges related to 6G are briefly discussed in [6].  
The base station (BS) of a downlink (DL) NOMA system allocates different power factors to multiple users who share the symmetrical orthogonal resources (i.e., space, time, frequency, codes) [7]. NOMA can be divided into two groups: Fixed NOMA (FNOMA) and Ordered NOMA (ONOMA). User decoding order in FNOMA is fixed, whereas it is based on channel gains in ONOMA. While the weak user (_WU_) with the lower channel gain is decoded first in ONOMA, the distant user is decoded first in FNOMA [8]. The receiver in NOMA uses the successive interference cancellation (SIC) approach to decode the superposed information.  
To improve the quality of service (QoS) of wireless environment, there has been a surge in interest in managing the propagation environment. With the aid of reconfigurable antennas or scatterers, schemes like spatial scattering modulation [9], and beam index modulation [10], take advantage of the variations in the signatures of received signals to transmit extra information bits in environments with a lot of scattering. On the other hand, reflector arrays, meta-surfaces and substantial intelligent surface are intelligent tools that manage the propagation environment in order to increase coverage and quality of signal [11]. In contrast to multiple-input-multiple-output (MIMO), amplify-and-forward relaying, and backscatter communication, the reconfigurable intelligent surface (RIS) only reflects the incident signal with a phase shift and does not require separate energy source for processing, encoding, decoding, or retransmission [12]. RIS technology is intended to be utilized in addition to algorithms to build the hardware framework for artificial intelligence (AI) in the next-generation of wireless networks [13]. Table 1 provides a list of symbols used in this work.  
The remainder of this paper is organized as follows: Section 2 conducts a literature study, and Section 3 derives the closed-form outage probability and optimal power fractions for stronger user (\(SU\)) and \(WUI\). The performance of the proposed schemes is evaluated in Section 4 in terms of probability of outage, sum capacity, throughput, and probability of error. Finally, Section 5 concludes the work and offers directions for future research.
# Outdoor Node Localization Using Random Neural Networks for Large-Scale Urban IoT LoRa Networks +
## 6 Conclusions and Future Work  
In this paper, we proposed a LoRa RSSI-based localization system using the RNN algorithm. The proposed method uses 0.001, 0.01, 0.1 learning rates and 1000, 3000, 5000, and 10,000 data samples to evaluate the performance of the proposed RNN-LoRa RSSI-based localization system in a large, dense urban area of Antwerp in Belgium. The proposed localization system is trained using 0.001, 0.01, 0.1, and 1 learning rates; 1000, 3000, 5000, 10,000, and 130,343 data samples; and about 1000 data samples were enough to evaluate our system's performance with minimum training time. Furthermore, through extensive simulations and analysis, the proposed system tested and achieved a minimum mean localization error of 0.29 m. We plan to evaluate the performance of our proposed localization system in an indoor environment.  
Writing--original draft, W.L; writing--review and editing, H.L., R.M.G. and A.-U.-H.Q. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Commonwealth Scholarships in the UK in partnership with the Government of Rwanda.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Materials and Methods
### Machine Learning Technique
#### 3.4.2 Logistic Regression  
Logistic regression (LR) is a parametric classification model with a certain fixed number of parameters that depend on input features and their output categorical predictions. It is a binary classification model. The LR model is performed based on a logistical function that is defined as follows [23]:  
\[f(X)=\frac{1}{1+e^{-X}} \tag{2}\]  
Figure 1: Feature importance score.  
where \(X\) is a weighted sum of the input feature, which is defined as \((X=w_{1}x_{1}+w_{2}x_{2}+...+w_{n}x_{n})\), here, \(w\) is the weight, and \(n\) is a number of input features.  
Now, the logit form of the logistic model can be obtained by the following formula [24]:  
\[y=logit(p)=ln\bigg{(}\frac{p}{1-p}\bigg{)}=w^{T}x+b \tag{3}\]  
where logistic (\(logit\)) is the ratio of class probabilities, \(x\) is the data feature vector, and \(b\) is the bias of the model.  
Therefore, the benefits of using LR include its flexibility, reliability, and the ability to resist over-fitting without any hyper-parameter tuning in small-scaled data sets.
## 2 Materials and Methods
### Isolation of MuSCs  
All the muscles of the lower limb were removed from both limbs and stored on ice in wash buffer, consisting of 5% horse serum and 1% penicillin-streptomycin in Dulbecco's Modified Eagle Media (DMEM). The muscles were minced into a slurry, containing pieces approximately 3 mm in diameter [49]. The minced tissue was transferred to dissociation buffer (3.3 mg/mL collagenase II in wash buffer) and incubated at 37 \({}^{\circ}\)C for 1 h while being mixed every 5-10 min. This was followed by a wash step and centrifugation at 500\(\times\)_g_ for 5 min before removing the dissociation buffer and adding stock collagenase II (4.3 mg/mL) (Worthington, Lakewood, CA, USA) and stock dispase (6.0 mg/mL) (Gibco, Gaithersburg, MD, USA) to the solution. This solution was incubated for an additional 30 min at 37 \({}^{\circ}\)C while mixing. A 10 mL, 20-gauge needle was used to triturate the solution. This was followed by an additional wash before the solution was run through a 40 \(\upmu\)m filter. Two washes through the filter were performed. The cells were pelleted by centrifugation at 500\(\times\)_g_ for 5 min at 22 \({}^{\circ}\)C and all of the media was aspirated off. MuSCs were isolated from cell suspension via magnetic separation using the Satellite Cell Isolation Kit from Miltenyi Biotec (Bergisch Gladbach, Germany). We chose this method as previous studies have verified this kit results in a highly purified MuSC population [50; 51; 52].
## 2 Results and Discussion
### Ftir  
FTIR was used to acquire advanced information of the chemical bonding in TP0, washed TP2 and unwashed TP2 films, as shown in Figure 5. The spectra showed significant differences, which were due to the rising bands from the addition of PEG at 3389.77 and 3375.15 cm\({}^{-1}\). All spectra showed broad bands, which indicated the presence of O-H stretching vibrations of water absorbed with the hydroxyl group on the photocatalyst surface. The O-H bond was also noticed in both spectra at 1637.32 and 1637.45 cm\({}^{-1}\). The peak at 2900 cm\({}^{-1}\) was detected in all spectra corresponding to C-H bonds.  
Figure 4: N\({}_{2}\) adsorption–desorption isotherms of the washed immobilized TiO\({}_{2}\)/PEG (TP2).  
Figure 5: FTIR spectra of TP0, TP2 (washed TP2), unwashed TP2 and double-sided adhesive tape (DSAT) samples.  
Interestingly, washed TP2 spectra showed a strong absorption in the region of 1705.02 cm\({}^{-1}\), which justified that the carbonate species C=O existed. On the other hand, a new peak detected at 1160.06 cm\({}^{-1}\), which is assigned to C-O species, was also present in the spectra. The presence of C=O affirmed that TiO\({}_{2}\) has reacted strongly with PEG, which could potentially lead to the high photoactivity of TP2. The C=O bonds detected were due to the oxidation of PEG in the irradiated TP2 film. As shown in Figure 5, the carbonaceous species C-C bond was detected in all samples except TP0. Generally, the IR spectrum for DSAT also produced the C=O bonds, but this factor can be discarded because during FTIR analysis of all samples, each sample's powder was carefully scratched and taken for analysis, excluding the DSAT. This is further supported by the IR spectrum of unwashed TP2, where the formation of C=O bonds was absent in Figure 5. In brief, even though the composition and structure of the investigated TP2 film do not greatly change, the photoactivity efficiency of TP2 in Table 1 was indirectly attributed to the formation of C=O bond that promotes more reactions to take place.
###### Abstract  
The paper presents an experimental security assessment within two widely used open-source 5G projects, namely Open5 GS and OAI (Open-Air Interface). The examination concentrates on two network functions (NFs) that are externally exposed within the core network architecture, i.e., the Access and Mobility Management Function (AMF) and the Network Repository Function/Network Exposure Function (NRF/NEF) of the Service-Based Architecture (SBA). Focusing on the Service-Based Interface (SBI) of these exposed NFs, the analysis not only identifies potential security gaps but also underscores the crucial role of Mobile Network Operators (MNOs) in implementing robust security measures. Furthermore, given the shift towards Network Function Virtualization (NFV), this paper emphasizes the importance of secure development practices to enhance the integrity of 5G network functions. In essence, this paper underscores the significance of scrutinizing security vulnerabilities in open-source 5G projects, particularly within the core network's SBI and externally exposed NFs. The research outcomes provide valuable insights for MNOs, enabling them to establish effective security measures and promote secure development practices to safeguard the integrity of 5G network functions. Additionally, the empirical investigation aids in identifying potential vulnerabilities in open-source 5G projects, paving the way for future enhancements and standard releases.  
5G security; Open5 GS; Open-Air Interface; NF vulnerability assessment Article
# Multi-Source Data-Driven Terrestrial Multi-Algorithm Fusion Path Planning Technology
## 2 Preliminaries
### Conflict Resolution in Multi-Source Data Fusion  
In scenarios where information from different sources conflicts--e.g., GPS data indicates a free road while traffic cameras detect congestion--effective conflict resolution mechanisms become essential. Our current model prioritizes GPS- and OSM-based traffic data, but we acknowledge the need for integrating real-time video surveillance or transport authority reports to improve trustworthiness. In future work, we will explore data reliability scoring models and fusion consistency checks to enhance the robustness of multi-source  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Travel Scenario** & **Time** & **Congested Distance** & **Total Distance** & **Safety** & **Avoidance Zone** \\ \hline Daily Commute & 0.30 & 0.30 & 0.25 & 0.10 & 0.05 \\ \hline Long-Distance Travel & 0.25 & 0.15 & 0.30 & 0.15 & 0.15 \\ \hline School Pickup/Drop-off & 0.20 & 0.15 & 0.10 & 0.30 & 0.25 \\ \hline Night-time Delivery & 0.25 & 0.10 & 0.10 & 0.35 & 0.20 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Weighting of transport demand in multi-scenario cities.  
decisions. Potential techniques include confidence-weighted fusion, historical consistency verification, and Bayesian inference to better evaluate and reconcile conflicting inputs.
## 4 Results and Discussion
### Experiment 2: Manipulation of Deformable Fluid Bags  
The automation of the task of transferring infusion bags as in Experiment 2 will reduce the possibility of contamination as well as the variability in production. However, modification of the cabling, robot body, gripper, etc. is necessary to comply with GMP. The robot and the attached peripherals cannot be easily sanitised in their current form, and a lot of parts of the robot provide space for collecting dust and microbes. However, low particle emission collaborative robots that suit cleanroom requirements are available on the market [102].  
Gripping from the top, where, the robot grasps the bag from the top side where ports are not present, seemed to be the most successful method for manipulating sealed bags. For a filled and sealed bag, the structure at the inlet allows the robot to grasp the bag firmly, whereas in the case of the unfilled infusion bags shown in Figure 5, tubes at the inlet portion will hinder the grasping. Filled bags can also be grasped from the side where ports are not there, provided fluid will not flow out through the ports. However, this is not an ideal point as the bag can slip if not held firmly. Similarly, the robot, with the help of the 3-finger adaptive gripper could hold the bag just like humans do. This is possible only if the bag is appropriate for grasping and not too large. There is also a possibility of the bag being punctured if it is trapped between the joints of the gripper finger.
# Phase Balancing and Reactive Power Support Services for Microgrids
## 4 Microgrid Central Controller
### Real-Time Simulation-Base Scenario
The active power (\(P_{grid}^{+1}\)) and reactive power (\(Q_{grid}^{+1}\)) at the MG's PCC during the case when the MGCC was disabled are also shown in Figure 9. The positive values of active and reactive power denote that energy is flowing from the main network to the MG while negative values denote that the MG is exporting energy to the distribution grid. As can be seen, a great amount of positive sequence reactive power is required by the main grid to cover the reactive power needs of the buildings causing reduction of the available capacity for transmitting active power.
## 4 Experiments and Analysis
### Experimental Results
#### 4.4.1 Anhui Province
Figure 7: The segmentation results of building areas using different methods on Anhui Province images: (**a**) SegNet, (**b**) DeepLabV3-Plus, (**c**) MAFF-HRNet, (**d**) UNet++, (**e**) SegFormer, (**f**) PSPNet, and PSANet (**ours**).
## 4 Results and Discussion
### Membrane Performance  
#### 4.2.1 Effect of Absolute Pressure Difference  
This section presents the experimental results of the effects of absolute pressure difference on the water vapor permeability of a dense membrane. At the start of the experiment, the absolute pressure difference on both sides of the membrane formed by pressurization or decompression was the same (60 kPa). During Test C, both nitrogen and water vapor were introduced into the cell, while in Test D the nitrogen flow was in the opposite direction to the water vapor. The data measured under 50% relative humidity are shown in Table 4.  
As nitrogen permeated through the membrane the cell pressure in Test C increased, while the pressure in Test D decreased. These data were converted into performance factors and compared to that of the cup method in Table 5. The relative humidity was 70% for the cup method measurement.  
The selectivity is not indicated for Test D because it is the ratio of the substance permeated in the same direction, and also not for the cup measurement because it was not possible to measure the permeance of nitrogen in the cup method. The measured water  
Figure 5: Pressure variation in the test cells over time.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Test A**} & \multicolumn{2}{c}{**Test B**} \\ \hline
**Time (h)** & _p(abs) (kPa)_ & **Absorbent Mass (g)** & _p(abs) (kPa)_ & **Absorbent Mass (g)** \\ \hline
0 & 42.22 & 77.31 & 41.98 & 79.92 \\
12 & 58.78 & - & 57.36 & - \\
24 & 69.02 & - & 67.68 & - \\
36 & 76.76 & - & 75.19 & - \\
48 & 82.48 & 80.60 & 80.97 & 83.21 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Real-time data for repeatability test.  
vapor permeability was the highest in Test C with the same direction of permeation of nitrogen and water vapor. Hence, the measured permeance depends on the direction of nitrogen flow through the test cell. In addition, the results suggest that the presence of an absolute pressure difference affects the water vapor permeance.