esa-sceva/satcom-chunk-collection huggingface dataset:
# Context-Encoder-Based Image Inpainting for Ancient Chinese Silk
## 4 Experiments
### Evaluation Index  
Objective Evaluation Metrics Selected and Their Justifications:  
L1: Predominantly utilized in image restoration, the L1 norm quantifies per-pixel reconstruction accuracy by computing the Mean Absolute Error (MAE) between the restored image and the original.  
Peak Signal-to-Noise Ratio (PSNR): A classical metric in image quality assessment, an elevated PSNR value indicates superior image quality.  
Structural Similarity (SSIM): This index evaluates the restored images by comparing their brightness, contrast, and structural attributes to those of the original images. An increase in SSIM values correlates with enhanced image restoration effectiveness.  
Frechet Inception Distance (FID): Employed for assessing the plausibility of image representations, FID measures the distance between the feature distributions of authentic and synthesized images. Lower FID scores suggest a closer approximation to the distribution of real images, generally implying higher-quality generated images.  
Universal Quality Image Index (UQI): This index gauges the degree of distortion between the content of generated images and real images. Higher UQI values denote improved image quality.  
Visual Information Fidelity (VIF): Based on a natural scene statistics (NSS) model, VIF evaluates the extent of image distortion and the fidelity of human visual perception. Higher VIF values indicate better image quality.
## 4 Results and Discussion
### Behaviour of the Residue  
Figure 6 is evaluated over a varying number of users for \(K\), \(N=10\), \(E_{b}/N_{0}=0\) [dB]. In Figure 6, it is noticed that the behaviour of the residue after two iterations for various system loads. The system under test had a processing gain of 10 dB, and the number of users was varied from 1 to 25. The SNR was set at 0 dB. Otherwise, the system parameters were the same as described in Section 4.1.  
We see in Figure 6 that while the residue may be trivial at small loads, the residue builds to the substantial value of nearly 0.4 at a reasonably modest load of \(K/N=15/10\), and continues to grow as the load increases. Figure 6 demonstrates that the residue is significant for high system loads and therefore must be taken into account when determining PIC output statistics for systems with APP feedback.  
Figure 7 is evaluated for a low SNRs, it is seen that the weighted delay filter (WDF) and the MRC filter shows the identical performance. It is also observed that WDF enters the waterfall region earlier with single user bound for a low value of SNR as compared to MRC filter. Further noted that WDF outperforms MRC giving a value of 0.8 dB.  
Another simulation result is evaluated in Figure 8 considering the different number of users, i.e., 15 and 20. It was seen from the results that convergence for the unfiltered case is unlikely happen for 15 users, while for the filtered case, it is unlikely happen for 21 users.  
Figure 6: Residue Behaviour with \(K\), \(N=10\), \(E_{\rm b}/N_{0}=0\) [dB].  
Figure 7: Weighted delay and MRC filter performance vs. SNR, \(K/N=20/10\), \(L=1000\), Iterations = 10.  
The conclusion can be made that for a given values of SNR = 6 dB, and a processing gain of 10 dB, the system load increases to approximately 42% using the weighted delay filtering.  
Figure 7 shows the simulation for the optimal filter's system capacity. The results show that WD performs better for larger SNR values. However, MRC and WD show the same performance for lower SNR values. Similarly, Figure 8 is plotted to show the contribution from different user points of view. It shows that we can see the system capacity achieved using the WD filtering for a fixed value of SNR, varying the iterations.
# Position Accuracy Improvement by Implementing the DGNSS-CP Algorithm in Smartphones
## 4 Experiments and Results
### Dynamic Test
First, the local line-of-sight unit vector was calculated using the elevation and azimuth angle provided in the position-related output of Android's LocationManager, and this was transformed to Earth-centered, Earth-fixed coordinates for use by the correction method. In addition to GPS, data from the GLONASS and BeiDou satellite constellations were used for positioning. Thus, to achieve position-domain correction, the line-of-sight vector was transformed to an observation matrix suitable for multiple constellations. We identified the likely structure of the algorithm in the GNSS module of the smartphone, and demonstrated that this assumption was correct via several preliminary tests. This was an essential step in modifying the DGNSS-CP algorithm for use on the target smartphones. The results of static and dynamic tests confirmed that the standalone Android GPS accuracy could be improved by 30%-60% using the proposed approach, and showed that the maximum error could also be reduced. In addition, the proposed algorithm does not require the infrastructure of DGPS correction to be modified, and the rover corrects its positional error based on the standard NMEA position output format, which can be acquired directly from the smartphone. Thus, the proposed method is very simple to implement. Therefore, if used to create simple programs, the proposed algorithm could significantly improve the position accuracy of current smartphones using software alone. This research was supported by a grant (15 CTAP-C097385-01) from the Infrastructure and Transportation Technology Promotion Research Program funded by the Ministry of Land, Infrastructure, and Transport of the Korean government. [PERSON] modified the DGPS-CP algorithm for smartphone applications and performed the experiments. [PERSON] provided significant algorithmic comments and critical feedback. [PERSON] gave important suggestions for the contents and organization of the manuscript. [PERSON] suggested the original algorithm of DGPS-CP and supervised its development and the direction of the research. All authors participated in formulating the idea and in discussing the proposed approach and results. All authors read and approved the final manuscript. The authors declare no conflict of interest.
# Detecting Green Mold Pathogens on Lemons Using Hyperspectral Images
## 4 Conclusions  
Spectral imaging is not only a noninvasive, but also a very efficient method, of identifying and quantifying the lemons infected by green mold pathogens. The spectral range of 540 nm to 560 nm and 570 nm to 600 nm are good for identifying the states of lemon infected by the pathogens. The spectral range of 550 nm and 580 nm, which is found to be the dominant wavelengths of the green mold pathogen and the lemon's skin color, is especially sensitive for quantifying the infection degrees, and discriminating between the fresh and infected lemons, respectively: the quantification can be done by calculating the normalized coefficient \(\alpha\) at 550 nm and discriminating by spectral intensity variations at 570 nm and 580 nm. The \(\alpha\) value at which the lemon can be considered fresh will be less than 1.3.  
The proposed method for determining the parameter \(\alpha\) and calculating the spectral intensity variations can be useful in practice for detecting pathogens in other citrus fruits and foods too. Defining the \(\alpha\) value range, which can help to discriminate the fresh and the infected lemons more accurately, and comparing this range with those for other citrus fruits will be the subject of our further research.  
Figure 14: Normalized spectral intensity distribution of Figure 13 in the spectral range of 500 to 560 nm.  
Figure 15: Microscope image of the surfaces with 200 times magnification: **1**: Fresh lemon (Sample 1), and **2**: Sample 6 with the \(\alpha=1.51\).  
The determined value range will enable the automatization of the process of inspecting the large amounts of citrus fruits in real-time.  
Y.V., Setting experimental setup and data collection; G.H., Total Planning and sample preparation; Y.K., Software design; T.V., Formatting, assisting manuscript preparation and English corrections; J.-Y.S., Full manuscript preparation and corresponding. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Priority Research Centers Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (Grant Number: NRF-2018R1A6A1A03025542).  
The authors declare that they have no conflict of interest.
# A PMSG Wind Energy System Featuring Low-Voltage Ride-through via Mode-Shift Control
## 3 System under Consideration
### Pmsg  
The mechanical power from wind turbines is translated into electrical power using a PMSG. The PMSG voltage and current equations can be defined as in (3) and (4):  
\[v_{dgen}=(R_{a}+pL_{d})i_{dgen}-\omega_{e}L_{d}i_{dgen} \tag{3}\]  
\[v_{qgen}=\left(R_{a}+pL_{q}\right)i_{qgen}+\omega_{e}L_{d}i_{dgen}+\varphi_{f }\omega_{e} \tag{4}\]  
where \(v_{dgen}\) and \(v_{qgen}\) are the PMSG stator dq axis voltages, and \(i_{dgen}\) and \(i_{qgen}\) represent the PMSG stator currents, respectively. \(L_{d}\), \(L_{q}\) and \(R_{a}\) indicate the stator dq axis inductances and resistance, respectively. \(\omega_{e}\) is the electrical angular frequency.  
The electromechanical developed torque \(T_{e}\) is obtained as given by (5):  
\[T_{e}=\frac{3}{2}\left(\frac{P}{2}\right)\left(\varphi_{f}i_{qgen}+\left(L_{ d}-L_{q}\right)i_{qgen}i_{dgen}\right) \tag{5}\]  
where \(\varphi_{f}\) is the amplitude permanent magnet flux linkage and \(p\) is the number of pole pairs.  
Figure 2: Schematic diagram of considered PMSG-based WECS.  
The torque and mechanical speed can be related using (6), where \(J\) is the moment of inertia, \(\omega_{r}\) is the rotor mechanical angular frequency and \(T_{L}\) is the load torque.  
\[T_{e}=J\bigg{(}\frac{2}{P}\bigg{)}\frac{d\omega_{r}}{dt}+\omega_{r}\bigg{(} \frac{2}{P}\bigg{)}+T_{L} \tag{6}\]
# Post-Quantum Key Exchange and Subscriber Identity Encryption in 5G Using ML-KEM (Kyber) 1
## 7 Conclusions and Future Directions
Weprovide a comprehensive review of side-channel attacks on NIST's third-round PQC candidates, highlighting real-world vulnerabilities and their implications for hardware implementation. We also provide an expanded specification of Subscription Concealment Protection Schemes, including a Post-Quantum Profile C using Kyber-512. We propose a concrete extension to the 5G SUCI format that supports quantum-resistant key encapsulation via ML-KEM (Kyber-512). We also include more results in the performance evaluation. The authors declare no conflict of interest.
# Network Slicing for Beyond 5G Systems: An Overview of the Smart Port Use Case
## 3 Enabling Technologies
### ONF SDN Architecture  
ONF SDN architecture is ideal for 5G network slicing because it meets the application services requirements in an effective manner, as the CP dynamically configures the data plane resources to carry out customized services to the application plane. As described in [16], controllers and resources (e.g., infrastructure resources) are envisioned as key elements. A controller is defined as a programmable centralized unit, which manages the resources in an effective manner and delivers services to the underlying data plane. As shown in Figure 3, controllers are an essential part of the architecture, as they form a link between clients and resources via client and server contexts. A controller needs all the details to mediate with a respective client. The client context consists of the resource group and support function. Moreover, the resource group provides responsive approach and enable its connection with the controller. Comparisons of different SDN controllers has been shown in Table 1. Client operations are supported by the client support function,  
Figure 2: Existing network (**left**) vs. SDN (**right**).  
based on protocols that enable the clients to interact with the controller. A controller needs all the details from the server context in order to mediate with the respective resources. The controller performs the virtualization and the clustering of the underlying resources. Orchestration enables the controller to dynamically forward the respective resources to different resource groups. Controllers are connected to applications and resources via northbound interface and southbound interface, respectively. These interfaces help users and applications to connect with the network.  
The administrator in the architecture enables the configuration of the controller and also establishes the client and server contexts. Openflow allows the transmission between infrastructure resources and the controller, and it also helps to procure the traffic management. Openflow forwards the traffic between layer 2 and layer 3 networks and optimally updates the forwarding protocols, acting as a interface between data plane and control plane. Although, it heavily depends on the controller to establish the status of the transmitted packets [17]. Some of the 5G network slicing challenges, such as scalability and security, can be raised by Openflow due to the occurrence of a processing delay between controller and layer 2/layer 3 devices [17]. It can be overcome by advanced stateful forwarding technologies such as OpenState as it provides flexibility by increasing the network virtualization ability.
# Experimental Evaluation of a LoRa Wildlife Monitoring Network in a Forest Vegetation Area
## 6 Conclusions  
This paper presented the behaviour of the LoRa in the forest vegetation with extensive experimental measurement. The measurement operated in the 433 MHz and 868 MHz frequency bands. The experiment carried out pushed the limits of the modulation in terms of the obtainable coverage and thus analyzed the impact of parameters such as spreading factor, code rate and bandwidth and the effects of a slow fading channel. According to these results and analysis, we can conclude that the LoRa signal stability greatly depends on the environment, and it is more stable in not so dense forest environments than in highly dense forest environments.  
The impact of payload length on the \(PDR\) was demonstrated. It was demonstrated that packets with the longest payload have been shown to suffer more from the effects of a slow fading, however leading to a lower \(PDR\) due to CRC discarding of the corrupted one. The conclusions obtained show that the proposed tool provides useful guidelines for future developers on the design of smart repelling systems and the performance of LoRa in forest vegetation areas. In addition, our results can provide insight to geolocation and hikers in a natural park, for obtaining the maximum achievable coverage in both not so dense and highly dense forest environments. As future work, further tests would be carried out to study the impact of the environment, in terms of atmospheric conditions (air humidity, pressure, rainfall etc.), on the LoRa performance, and propose a suitable propagation model for forest environments from the obtained results.  
The presented work was carried out in collaboration with all of the authors. Conceptualization, M.O.O. and D.A.; methodology, M.O.O. and D.A.; investigation, M.O.O.; visualization, M.O.O.; validation, D.A.; resources, S.G.; funding acquisition, S.G.; formal analysis, M.O.O.; writing--original draft preparation, M.O.O.; writing--review and editing, M.O.O. and D.A.; supervision, D.A. and S.G.; All authors have read and agreed to the published version of the manuscript.  
This work was jointly supported by Regione Toscana, Italy, under the program POR FESR 2014-2020 (ULTRA-DEFENDER and ULTRREP projects) and by the Italian Ministry of Education and Research (MIUR) in the framework of the CrossLab project (Departments of Excellence).  
Not Applicable, the study does not report any data.  
The authors would like to thank [PERSON] for his conduct in the experimental tests.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analysis, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 2 Materials and Methods
### Methods
#### 2.2.4 Colorimetric EIA  
First, STI-NP was adsorbed at the concentrations 0.1-5 \(\upmu\)g/mL in 50 mM PBS, pH 7.4, into the wells of a transparent microplate and incubated overnight. Then, the wells were washed three times with 50 mM PBST containing 0.05% Tween-80, pH 7.4 (washing buffer). The solution of 50 mM PBS, pH 7.4, with 0.1% gelatin (blocking buffer) was dropped by 150 \(\upmu\)L per well and incubated for 30 min at 37 \({}^{\circ}\)C. After this procedure, microplate wells were washed again with the washing buffer, and rabbit antiserum solutions were added at the volume of 100 \(\upmu\)L at different dilutions (from 1:100 to 1:1,000,000) and incubatedat 37 \({}^{\circ}\)C for 1 h. The anti-NP rabbit antiserum was diluted in the blocking buffer. The resulting immune complexes were observed with the goat anti-rabbit IgG labeled with horseradish peroxidase (GARI-HRP) diluted 1:3000 in 50 mM PBST with 0.05% Tween-80 at 37 \({}^{\circ}\)C. Then, 100 \(\mu\)L of the substrate solution was added to develop the color in the wells for 15 min. After this, the reaction was stopped by the addition of 50 \(\mu\)L 0.1 M H\({}_{2}\)SO\({}_{4}\).  
For competitive immunoassay, NP solutions with different concentrations (from 100,000 \(\mu\)g/mL to 0.01 ng/mL) in methanol: water (1:4) were added to the wells and then anti-NP rabbit antiserum in chosen dilution in equal volume. The incubation was provided for 1 h at +37 \({}^{\circ}\)C. The development of complexes was provided as described above.
## 3 Results
### Forest Growth Simulations
#### 3.2.2 Model Calibration Forest Growth Simulations
Results of both simulations proved that the ability of the FEST-WB integrated with the FOREST module could be implemented to study both processes in a reliable way. The model at a first step was calibrated for the hydrological part. Subsequently, the calibration of forest growth was carried out. Both processes are interconnected through many parameters such as soil water content, water and light interception, and evapotranspiration. Simulation results reflect the ability of the model as a whole and through the integration of hydrological and forest growth to reproduce an accurate response of the catchment to eco-hydrological dynamics. Figure 8: Model performance (NSE) projected onto the parameter space (for different pairs of parameters). Low model performance is represented with dark-red color while high model performance is shown with light-blue color (DBH calibration). Figure 10: The comparisons of tree height and DBH between simulations and observations. Figure 9: Results of DBH and H simulations using FEST-WB Vs measurements (simulation period 1976–1993-before the thinning). A similar study was carried out by [PERSON] [46] in the same area, in which the 3D-CMCC-CNR model was calibrated and validated against forest growth measurements, and without any consideration of hydrological data. The performances reached using this model are comparable to the results of the newly developed FOREST module integrated in FEST-WB model that, in addition to the 3D-CMCC-CNR model, can properly simulate the hydrological components. FEST-FOREST model is considered as sophisticated model, similarly to some existing models such as SWAT, with the difference that FEST-FOREST model is fully distributed simulated.
# Short-Term Photovoltaic Power Generation Based on MVMD Feature Extraction and Informer Model
## 3 Informer Forecasting Model
### Loss Function  
In this paper, we use the Huber loss function to train the PV power generation model. Its calculation formula is as follows:  
\[\eta_{\text{Huber}}=\left\{\begin{array}{ll}\frac{1}{2}(y_{i}-\hat{y}_{i})^{2 },&|y_{i}-\hat{y}_{i}|\leq\delta\\ \delta|y_{i}-\hat{y}_{i}|-\frac{1}{2}\delta^{2},&|y_{i}-\hat{y}_{i}|>\delta\end{array}\right. \tag{9}\]  
Figure 1: The overall framework of informer.  
Here, \(y_{i}\) is the actual power value, \(\hat{y}_{i}\) is the predicted value from the model, and \(\delta\) is the hyperparameter of the Huber loss function, set to 0.5 in this model's training.  
When predicting the total PV power of a large city, two key factors need to be considered when handling large-scale training data. First, the training accuracy must be ensured, and second, the training time required is also an important factor that cannot be ignored. Therefore, to improve the training accuracy without sacrificing training speed, this paper proposes using the Huber loss function in an improved Informer model for PV power generation [32]. The Huber loss function has a hyperparameter, \(\delta\), which can be adjusted to accommodate different types of power prediction model training.  
When the prediction deviation is less than \(\delta\), a loss calculation method similar to MSE is used to improve the model's convergence speed when the gradient of the loss is small. When the prediction deviation is greater than \(\delta\), a linear error similar to MAE is used to reduce the impact of large losses caused by outliers on model training, thereby improving the model's robustness to outliers.
## 3 Results & Discussion
### In Silico Evaluation of the Antibacterial Potency of LRE Phytoconstituents  
Computational tools are widely used for the prediction of the antimicrobial activity of diverse groups of antimicrobials. The most common target predictions are based on individual molecules. Ligand-based methods include methods that are based on the principle of chemical similarity, which states that similar chemical structures tend to present similar antimicrobial activities more often than not, although even structurally similar compounds may interact with a protein target in different ways [54; 55]. In this study, 51 phytochemicals were identified in LRE. The computational screening was applied to phytochemicals with content above 0.5% \(w/w\), as well as to triterpenoids, whose content was not calculated for technical reasons. The application of computation tools is expected to pinpoint the antimicrobial components of extracts, avoiding laborious and time-consuming fractionation and purification procedures. Thus, the antibacterial activities of LRE phytochemicals against _Listeria monocytogenes_ were determined using the online tool AntiBac-Pred of the Way2 Drug platform. Antibac-Pred analyzes growth inhibitors or non-inhibitors of _L. monocytogenes_ based on antibacterial activity data available in ChEMBL, a database of bioactive molecules with drug-like properties (Figure 1). The score for each compound is expressed as confidence in its activity, which represents the difference between the probabilities that a chemical compound inhibits or does not inhibit the growth of a given bacteria. As confidence increases, the chances of the prediction being true are greater [56]. The calculated confidence scores (Table 3) show that the most promising inhibitor of _L. monocytogenes_ is liquiritin-apioside, followed by licorice glycoside C1/C2. Interestingly, similar confidence scores were calculated for well-established antibiotics such as ampicillin and gentamicin. There is no data yet for the antibacterial potency of both pure phytochemicals. Isoliquiritin-apioside, glycyroside, and kanzonol W also appear to have the potential to act as possible anti-Listeria agents. Surprisingly, the typical triterpenoids of licorice roots, namely, glycyrrhizin and glycyrrhetinic acid, do not exert inhibitory effects against _Listeria_ according to the confidence scores. However, previous works demonstrate their strong antimicrobial effects against bacteria such as _Basillus subtilis_, _Escherichia coli_, _Staphylococcus aureus_, and _Salmonella typhi_[51; 52; 53; 57; 58]. The low confidence scores do not preclude the potent activity of compounds, since their structures are not typical of the \"active\" structures of the training set. Overall, the computational screening revealed the presence of many compounds with anti-Listerial potential that act in an additive or synergistic way. In addition, further studies are required to investigate the potency of LRE triterpenoids as inhibitors of _Listeria_ bacteria.