esa-sceva/satcom-chunk-collection huggingface dataset:
# Sending-or-Not-Sending Twin-Field Quantum Key Distribution with Light Source Monitoring
## 4 Conclusions  
In summary, we analyze the security of the SNS protocol under UPC, and propose an LSM scheme to solve the unknown PND problem in the source part. An important problem for the SNS protocol is whether its security analysis is still valid without assuming the prepared state is an ideal coherent state. In this paper, we calculate the form of the quantum state sent from [PERSON] and [PERSON] in [PERSON]'s view, and show that the single photon part of the state has the same form as the ideal source condition under UPC, thus the security analysis remains valid. We apply the LSM scheme proposed previously to the SNS protocol to estimate the probabilities \(p_{n}(\mu)\) precisely to eventually obtain a tight bound of the secret key rate. Through simulation, we show that, with the proposed LSM scheme, the performance of the SNS protocol under UPC can be almost the same as that of the original SNS protocol with an ideal source. Moreover, the LSM scheme improves the performance of the SNS protocol when considering source fluctuation, indicating that the SNS protocol can still have a long transmission distance with a fluctuated source in real-life QKD systems.  
**Author Contributions:** Conceptualization, Y.Q., Z.C. and Y.Z.; methodology,Y.Q., Z.C. and Y.Z.; validation, Y.Q. and B.X.; formal analysis, Y.Q.; investigation, Y.Q. and Z.C.; resources, Y.Z. and H.G.; writing-original draft preparation, Y.Q.; writing--review and editing, Y.Q., Z.C. and B.X.; visualization, Y.Q.; funding acquisition, H.G. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by National Natural Science Foundation of China (Grant Nos. 61531003, 61771439) and the National Science Fund for Distinguished Young Scholars of China (Grant No. 61225003).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Discussion
### Security and Safety  
The reliability, data security and machine safety aspects were considered in a study of the operability of a SICS controller. An analysis and determination of the protective measures required for SICS was carried out. In addition, the required methods and measures for securing the process data access to networked sensors/actuators were investigated and tested using the example of encrypted access (HTTPS or WSS).  
Critical aspects considered include: Disturbance of the process data communication, disconnection, runtime error in the SICS controller, connection delay and/or connection interruption, breaking into the cloud and modifying the files for SICS services and breaking into the client and/or into the cloud and disruption of the SICS operation.  
In general, it can be estimated that there are deficits compared to a conventional PLC, especially with regards to machine safety, but these depend on the respective operating and application environment. However, the use of edge computing or the local connection of the sensors and actuators to a SISC controller can certainly provide workable solutions.  
Interested readers are welcome to ask the authors about the topic further documents and research reports (in German).
## 4 Discussion
### Effects of Vegetation on CUE
The distribution of alpine vegetation, which is mainly found in the high-altitude areas of the western Three Rivers Source region, exhibited disparate correlation patterns with a range of climate variables. The data indicated a positive correlation with temperature, a negative correlation with precipitation, and a positive correlation with evapotranspiration. A comprehensive analysis of climatic trends over two decades was conducted, with a particular focus on the comparison between these trends and the spatial distribution patterns of vegetation, as well as their responsiveness to climatic shifts. This analysis revealed that differential vegetation responses to climatic variations have resulted in the creation of spatial heterogeneities in CUE across the Three Rivers Source region.
## 4 Conclusions  
Measuring the resolution of an optical system is commonly performed using an ISO 12,233 resolution chart. This method requires the resolution chart to be fixed at a specific position. This measurement method requires a lot of space. For this reason, a small resolution chart is conventionally placed at a short distance, and a collimator system is placed between the resolution chart and test lens. When this is performed, a virtual image with a resolution chart enlarged by a collimator is created. This method can save a lot of measurement space compared with the method that directly uses a resolution chart. However, because the resolution chart is also fixed in this method, measuring the change in the resolution according to the change in the object distance is not possible. Therefore, in our study, a focus-adjustable collimator system was designed. By using this developed optical system, a method of measuring the resolution according to the change in the object distance while reducing the measurement space was proposed. In addition, we confirmed that the MTF performance of the collimator itself could be sufficiently ensured even if the lens inside the collimator moved.  
Conceptualization, H.C., S.-w.C. and J.R.; methodology, H.C. and J.R.; formal analysis, J.R.; writing--original draft preparation, H.C., S.-w.C. and J.R. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2020R1A2C4001606). This research was also supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF2020R11A3052712). This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2019R1F1A1062397).  
Not applicable.  
The data presented in this study are included within the article.  
Figure 9: Manufacturing result of the optical system shown in Figure 5.
## 2 Related Works
### Literature Review  
The rapid integration of the Internet of Things (IoT) has highlighted the urgent need for identity management solutions that are robust and scalable and protect user privacy. Traditional centralized identity systems often struggle in IoT environments due to vulnerabilities such as single points of failure, limited scalability and reduced user control [7]. In addition, this type of architecture is more vulnerable to common attacks (e.g., a DDoS attack) as there is a centralized server that can be targeted. There are also scalability issues in an IoT ecosystem due to the same centralization problem [8]. Finally, most of these systems operate in a non-transparent way, as the control of identities is given to a third party and therefore control and ownership of these identities is denied to the owners [9].  
Decentralized Identity (DID) frameworks, supported by distributed ledger technologies (DLTs) such as blockchains and Directed Acyclic Graphs (DAGs), are increasingly recognized as transformative solutions for secure identity management. By eliminating intermediary organizations, these systems enable users to independently create and manage their unique digital identities stored on a decentralized network. This approach enhances data sovereign, privacy, and interoperability across services while minimizing reliance on centralized entities [10]. DLT-based identity frameworks use cryptographic mechanisms to secure personal information and ensure data integrity. Each transaction is immutably recorded in a transparent and chronological ledger that is accessible to all participants through a consensus mechanism. Unlike traditional models that rely on intermediaries such as identity providers (IDPs) or service providers (SPs), DLT systems allow participants to maintain synchronized copies of data without centralized control. This decentralized structure not only prevents single points of failure but also ensures identity portability, a critical requirement for IoT ecosystems [9]. For IoT devices, DLTs address specific challenges by providing scalable and secure identity solutions. They mitigate the risks associated with traditional centralized systems, such as vulnerability to cyber attacks and loss of control over identity data. In addition, DLT-based systems enhance interoperability, enabling the seamless integration of services in a decentralized environment [11].
## 2 Numerical Modeling and Validation
### Numerical Models  
#### 2.2.1 Modeling Assumptions  
Since the test walls were in-plane loaded and constrained in the out-of-plane direction during testing, a two-dimensional simulation scheme was deemed to be sufficient, and was subsequently adopted for the modeling in this study. Figure 3 shows the schematic of the GSCCW wall models developed in DIANA software [11]. As shown, an eight-node quadrilateral isoparametric plane stress element was used to model the infill concrete, steel faceplates, loading beam and foundation beam. A bar reinforcement element was used for the steel tie plates. No consideration for the interface between concrete and steel plates was modeled, since no data on this have been reported for test wall specimens. The mesh size for all of the elements was 50 mm, and the translation of the bottom of the foundation beam was constrained. The FE models were loaded in two steps following the same protocol as in the tests: the vertical loading was applied first to the top of the loading beam at uniform pressure, and then cyclic lateral displacement-controlled loading was applied to the centroid of the loading beam. The modified Newton-Raphson method was used for the incremental iterative solution. The convergence criteria combine both force norm and displacement norm. In addition, geometrical nonlinearity was considered in the analysis.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Wall ID**} & **Wall Dimension** & **Steel Plate** & \multirow{2}{*}{**Steel Channel**} & **Axial Load** & **Concrete** & **Steel Plate** \\  & **W \(\times\) T \(\star\)** & **Thickness** & **(mm \(\times\) mm \(\times\) mm)** & **Ratio** & **Strength** & **Strength** \\  & **(mm \(\times\) mm)** & **(mm)** & **(mm)** & **(MPa)** & **(MPa)** & **(MPa)** \\ \hline GSCCW-1 & 1400 \(\times\) 160 & 4 & [16 (160 \(\times\) 64 \(\times\) 5) & 0.1 & 26.6 & 263.2 \\ GSCCW-2 & 1400 \(\times\) 160 & 4 & [16 (160 \(\times\) 64 \(\times\) 5) & 0.4 & 26.6 & 263.2 \\ \hline \hline \end{tabular} Note: \(\star\) W is wall width, and T is wall thickness.  
\end{table}
Table 1: Design parameters for the wall specimens [9].  
Figure 2: Tested GSCCW walls (adapted from [9]): (**a**) cross-section; (**b**) front view; (**c**) side view; (**d**) photo.
## 1 Introduction
Several reviews and research studies have shown that trans-resveratrol, due to its pharmacological activities, is an important anti-carcinogenic and anti-aging/anti-inflammatory agent, with neuroprotective and antioxidant proprieties, and that it could protect, to some extent, against cardiac and metabolic disorders. Therefore, a growing demand for trans-resveratrol is expected in areas such as nutraceuticalals, health, cosmetics, and food, due to its pharmacological importance [14]. The majority of resveratrol supplements available on the market are made of extracts obtained from the roots of Japanese knotweed (_Polygonum cuspidatum_) and the degree of purity can vary widely. Research studies have shown that emodin, a compound that could have laxative effects, is found in unpurified (or partially purified) resveratrol extracts [15]. The Japanese knotweed is an invasive plant that grows in heavily polluted environments and is contaminated with heavy metals. Additional concerns about the safety of resveratrol supplements have been raised, due to the possibility of the root tissue of Japanese knotweed to cellular uptake of these contaminants [16; 17]. [PERSON] et al. studied the biosynthetic origins of trans-resveratrol accumulation in grape canes during postharvest storage and they assayed the structural genes PAL, C4H, 4 CL, and STS (forming E-resveratrol) through a quantitative real-time PCR. They demonstrated that, due to transcript abundance of the structural genes, the resveratrol content increases during storage time. In the first four weeks of storage, the STS gene was induced; during the 6 weeks of storage, the three genes (PAL, 4 CL, and C4H) of the general phenylpropanol pathway were constitutively expressed. These results confirm that vine shoots during the storage time is still transcriptionally active [14]. The results can vary widely, despite recent advances in the determination of stilbenoid compositions of vine shoots [18]. This variability can be attributed to the various factors influencing the stilbenoid composition, especially the provenance and the variety of the grape cane [19]. [PERSON] et al., (2017) reported a resveratrol content ranging from 0.95 \(\pm\) 0.08 to 3.94 \(\pm\) 0.21 g/100 g D.W. for 10 samples of vine waste of different varieties extracted with 60% ethanol [20]. The aim of this study was to characterize trans-resveratrol accumulation in pruned vine shoots, and particularly to determine the optimal storage period for vine variety FeteascaNeagra, to identify which vine waste contains a higher amount of trans-resveratrol, to be exploited economically and to study possible correlations among the kinetics of resveratrol increase, total polyphenol content, and antioxidant activity of vine waste.
# Automated Aviation Wind Nowcasting: Exploring Feature-Based Machine Learning Methods
## 3 Materials and Methods
### Methods
#### 3.2.5 ML Model Evaluation  
To evaluate each trained model, a comparative analysis was employed to identify the best set of features and the selection technique that delivered superior results. The assessment was performed using MAE, MSE, and MAPE as evaluation and comparison metrics [59]. These metrics are defined as  
\[MAE=\frac{1}{m}{\sum_{i=1}^{m}}|X_{i}-Y_{i}| \tag{16}\]  
\[MSE=\frac{1}{m}{\sum_{i=1}^{m}}(X_{i}-Y_{i})^{2} \tag{17}\]  
\[MMAPE=\frac{1}{m}{\sum_{i=1}^{m}}\bigg{|}\frac{|X_{i}-Y_{i}}{Y_{i}}\bigg{|} \tag{18}\]  
For the feature selection and training procedures, all the data from the years 2018 and 2019 were used with a 5-fold cross-validation approach to ensure statistical robustness and mitigate the risk of overfitting in the model's predictions. The shuffling procedure divided the dataset into five distinct folds, with each fold serving as a testing set rotationally, whereas the remaining folds formed the training set. While not directly ensuring statistical relevance, this method facilitated the assessment of the model's performance across different data subsets, thereby reducing overfitting and improving the generalizability of the model's results [60].  
The three assessed targets were the subsequent 2, 10, and 20 min for the MID sensor, as this sensor is considered the most important in the airport.
# LO and Calibration Signal Distribution in a Multi-Antenna Satellite Navigation Receiver +
###### Abstract  
Due to the low signal power of global navigation satellite signals, the receivers are prone to radio frequency interference. Employing multi-antenna arrays is one method to mitigate such effects, by incorporating spatial processing techniques. The large size of the uniform rectangular arrays prevents their use in applications where installation space is limited. Therefore, we proposed a new approach, namely to split one full array into a number of smaller, spatially distributed, sub-arrays to reduce their size and exploit available installation spaces. This concept challenges the distribution of the local oscillator and calibration signals to the respective sub-arrays. This paper compares qualitatively different design concepts for a satellite navigation receiver with two two-element sub-arrays, installed multiple wavelengths apart from each other, in support of establishing an optimal choice for our intended applications in the automotive sector in terms of electrical performance and required hardware and software efforts. In general, weighing the pros and cons of the different concepts, as discussed in the paper, will assist in optimizing the system design approach for a specific application.  
compact distributed array; GNSS receiver frontend; LO signal distribution; system calibration; satellite navigation; interference mitigation +
Footnote †: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
## 3 Experiments and Results
### Experiments in a Noisy Setup  
To evaluate the impact of the proposed ADL contribution, our core experiments assessed the algorithms' robustness to noise in the annotations. Table 2 specifically examines the effect of the ADL strategy. 'Proportion' represents the percentage \(\beta\) of noisy labels introduced into the training dataset, with various other proportions also tested beyond those shown in the table (but omitted here to avoid clutter). The 'Network' column demonstrates that different networks were employed for training separately, and the presence of a module implementing the ADL strategy (annotated with check marks) improves performance across the board. Whilst in Table 1, the various networks performed similarly, it is evident from Table 2 that our proposed ADL strategy mitigates the impact of noisy labels, bringing the segmentation performance to levels that can be considered acceptable in many practical applications. Eight example predictions by NR-UNet under different data situations (i.e., different ratio \(\beta\) of noisy labels) are sketched in Figure 7. In this setup, NR-UNet is noticeably more effective than its counterparts. By comparing the outcomes of different algorithms and varying the proportion of noisy labels introduced into the training dataset, we gain valuable insights into the robustness and adaptability of our method in addressing the challenges posed to medical image segmentation tasks by noisy annotations.  
Figure 5: Box plot for the Dice coefficient distribution of prediction by each model.  
Figure 6: History of training loss (in blue) and validation loss (in red) against the number of epochs for (**a**) the CNN encoder–decoder, and for (**b**) the ViT-improved encoder and CNN decoder.
## 1 Introduction
### IoV  
IoV is a network of vehicles and allows the utilization of data generated by linked automobiles and Vehicular Ad Hoc Networks (VANETs). IoV is a decentralized network of vehicles outfitted with sensors, software, and technologies linking and exchanging data over the Internet following agreed-upon standards [11; 12]. IoV emerged from Vehicular Ad Hoc Networks [13] (\"VANET,\" a type of mobile ad hoc network used for communication between automobiles and roadside equipment), and is predicted to grow into an \"Internet of autonomous vehicles\" in the future. The IoV is predicted to be one of the facilitators of autonomous, connected, shared, and electric (ACES) Future Mobility [14]. Figure 4 shows an illustration of the IoV.
## 4 Experiments
### Experimental Results
Table 4 further demonstrates that for thresholds of 0.7, 0.8, 0.85, 0.9, and 0.95, the proportion of samples above the threshold diminishes across different datasets. While higher thresholds correlate with increased Top1-\(t\) accuracy, the accuracy may drop to zero when the threshold approaches 1. This occurs because fewer samples exhibit maximum similarity scores close to 1, potentially resulting in no qualifying samples and thus zero accuracy. In summary, although higher thresholds improve accuracy, they simultaneously reduce the proportion of samples satisfying the threshold condition. The practical implications of threshold selection are evident in applications such as dragonfly species identification. Higher thresholds enhance identification accuracy but impose stricter requirements on photographers, necessitating optimal shooting angles, higher-quality images, and potentially more time investment.
# Methods, Progress and Challenges in Global Monitoring of Carbon Emissions from Biomass Combustion
###### Abstract  
Global biomass burning represents a significant source of carbon emissions, exerting a substantial influence on the global carbon cycle and climate change. As global carbon emissions become increasingly concerning, accurately quantifying the carbon emissions from biomass burning has emerged as a pivotal and challenging area of scientific research. This paper presents a comprehensive review of the primary monitoring techniques for carbon emissions from biomass burning, encompassing both bottom-up and top-down approaches. It examines the current status and limitations of these techniques in practice. The bottom-up method primarily employs terrestrial ecosystem models, emission inventory methods, and fire radiation power (FRP) techniques, which rely on the integration of fire activity data and emission factors to estimate carbon emissions. The top-down method employs atmospheric observation data and atmospheric chemical transport models to invert carbon emission fluxes. Both methods continue to face significant challenges, such as limited satellite resolution affecting data accuracy, uncertainties in emission factors in regions lacking ground validation, and difficulties in model optimization due to the complexity of atmospheric processes. In light of these considerations, this paper explores the prospective evolution of carbon emission monitoring technology for biomass burning, with a particular emphasis on the significance of high-precision estimation methodologies, technological advancements in satellite remote sensing, and the optimization of global emission inventories. This study aims to provide a forward-looking perspective on the evolution of carbon emission monitoring from biomass burning, offering a valuable reference point for related scientific research and policy formulation.  
keywords: biomass combustion; carbon emissions; satellite remote sensing; bottom-up method; top-down method +
Footnote †: journal: _Annosphere_  
_Accepted: 16 October 2024_  
Published: 18 October 2024_