esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Evolution and Trends of 6G Network Architecture
### The Design Fundamentals of 6G Networks
#### 2.1.5 Future-Proofing Emerging Technologies  
As novel technologies like quantum, distributed ledger technologies (DLTs), artificial intelligence (AI), and post-quantum cryptography (PQC) emerge, there is a growing need for efficient and seamless integration into telecom architecture. The 6G network must include mechanisms that facilitate the integration of emerging technologies into its overarching functional architecture, including technologies that have not yet been invented.  
Specific technology prospects such as quantum, DLT, AI, and PQC are critical to ensuring that 6G is equipped to address future challenges effectively.  
Quantum technology has the potential to render 6G infrastructure tamper-proof and bolster security through cryptographic key exchanges. In addition, quantum computing can solve NP-hard optimization problems in linear time, enabling the rapid execution of network optimization tasks that would otherwise be computationally prohibitive.  
Post-quantum cryptography (PQC) plays a crucial role in safeguarding 6G networks against potential quantum computing threats. While quantum computing offers numerous benefits, it also poses significant risks to traditional cryptographic systems like RSA and ECC, which are vulnerable to quantum attacks. PQC algorithms, such as lattice-based, code-based, and multivariate-quadratic cryptography, provide robust resistance against such threats while maintaining compatibility with current communication protocols. The National Institute of Standards and Technology (NIST) has been actively leading the standardization of PQC algorithms, emphasizing their importance for future-proofing critical infrastructures like 6G [11].  
Distributed ledger technologies (DLTs) facilitate data provenance by storing and distributing data, transactions, and contracts in an immutable manner. This is particularly beneficial in multiparty systems with limited trust among involved parties, which is a scenario mirrored in the telecom industry's complex ecosystem involving suppliers, vendors, operators, and consumers. By enhancing the efficiency of relationships within this network, DLTs streamline processes such as feature approval by one operator on a DLT, which then instills trust among other operators without requiring lengthy procurement procedures. Furthermore, as part of the telecom subscriber plan, users can create their own marketplaces to exchange data plans or other resources.  
AI has long been employed in telecoms to optimize consumer-facing and network-related issues. However, as networks become more dispersed and atomized, new types of AI are needed. These must operate in a distributed manner, and as consumer-facing decisions become more critical, Explainable AI (XAI) will become essential to comply with strict legal and ethical specifications.  
By incorporating quantum technology, DLTs, AI, and PQC, the 6G network can ensure robust security, operational efficiency, and adaptability, laying the foundation for a highly secure, intelligent, and future-proof telecom infrastructure.
# AOA-Based Three-Dimensional Positioning and Tracking Using the Factor Graph Technique
## 7 Conclusions  
This paper provided an AOA-based tracking technique in 3D with an FG algorithm. The system accuracy was proven via simulations even with large measurement variances. It can be found that the introduction of elevation angels not only realized 3D detection, but also enhanced the detection accuracy in 2D. Moreover, the convergence performance of the proposed FG algorithm was evaluated by simulations, which was shown to be achieved within around 10 iterations despite a large measurement variance. Finally, the predicted target state by EKF was used to eliminate sudden  
Figure 8: Performance with dynamic measurement variance. ACRB, approximated Cramer–Rao bound.  
Figure 7: RMSE with and without side information (SI) in false alarms.  
sensing errors and estimate the observation variance through calculating the ACRB, which significantly enhanced the robustness of the tracking performances.  
**Author Contributions:** Introduction, H.Z.; system model, H.Z.; outage analysis, H.Z.; simulation and numerical results, H.Z.; simulation program checking, Z.Z.; writing, original draft preparation, H.Z.; writing, review and editing, Z.Z.; project administration, H.Z.; funding acquisition, H.Z. All authors read and agreed to the published version of the manuscript.  
**Funding:** This research was in part funded by the Key Project of Natural Science Foundation of Department of Education of Anhui Province Grant Numbers KJ2018A0449 and KJ2014 ZD31, in part funded by the Key Project of Education Quality of Anhui Province Grant Number 2017 jyxm0510, and in part by the Anhui Provincial Science Foundation under Grant 1908085 QF283.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Route Prefix Caching Using Bloom Filters in Named Data Networking
## 1 Introduction
Thus, the name lookup time can be greatly reduced if the route prefix is cached, which is called route prefix caching or prefix caching. A few prefix caching schemes were proposed to perform the name lookup efficiently [18; 19]. Prefix caching is now challenging because there is a problem that needs to be solved. In the FIB, a leaf prefix is a prefix that has no descendant prefix, whereas a non-leaf prefix has some descendant. Non-leaf prefix caching may incur a problem not found for leaf prefix caching. If a matching prefix is non-leaf, its descendants can also be matched. Thus, non-leaf prefix caching may result in a wrong LPM result if there are some descendants which are not cached yet. In this paper, we present an elaborate method to avoid such an incomplete prefix caching problem that occurs when a non-leaf prefix is cached. Our technique caches non-leaf prefixes together with the branch information of their children. The branch information is represented by a Bloom filter [20] which is used to check whether a child exists on the branch. The rest of this paper is organized as follows: we describe the name lookup for the packet forwarding in NDN in Section 2. We also overview the route prefix caching and the related works. Section 3 explains the proposed route prefix caching scheme using Bloom filters in NDN. In Section 4, the performance is evaluated in terms of several variables. Finally, Section 5 concludes the paper.
###### Abstract  
_Caparis zyellanic_ leaf extract was employed in this work to create iron oxide nanoparticles (\(\alpha\)-Fe\({}_{2}\)O\({}_{3}\)) using anhydrous ferric chloride. The UV spectrum, XRD, FT-IR, and FE-SEM with EDX methods were used to characterize the fabricated nanoparticles. The iron oxide nanoparticles obtained were spherical in form, with an average crystallite size of 28.17 nm determined by XRD. The agar well diffusion method was used to assess the antimicrobial activity of the \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\) nanoparticles created in this study against pathogenic organisms, Gram-negative bacteria (_Escherichia coli_ and _Pseudomonas aeruginosa_), Gram-positive bacteria (_Staphylococcus aureus_ and _Streptococcus pyogenes_), and fungi (_Candidata albicans_ and _Apergillus niger_). Among the pathogens tested, _S. pyogenes_ had the highest zones of inhibition (25 \(\pm\) 1.26 mm), followed by _S. aureus_ (23 \(\pm\) 0.8 mm), _E. coli_ (23 \(\pm\) 2.46 mm), _P. aeruginosa_ (22 \(\pm\) 1.86 mm), _C. albicans_ (19 \(\pm\) 2.34 mm) and _A. niger_ (17 \(\pm\) 3.2 mm). The substance was further tested for anticancer activity against A549 (lung cancer) cells using the MTT assay. The cytotoxic reaction was found to be concentration-dependent. The present study, therefore, came to the conclusion that the bio-effectiveness of the manufactured \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\) nanoparticles may result in applications in biomedical domains.  
bio-fabrication; _Carparis zyellanic_; \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\); antimicrobial activity; lung cancer (A549)
# A Data-Driven Approach for University Public Opinion Analysis and Its Applications
## 1 Introduction
Within the framework of the machine learning method, the sentiment features in the text need to be extracted manually (e.g., using principal component analysis, the information gain method, the frequency feature extraction method, etc. ); then, the extracted features are assigned weights (e.g., entropy weights, Boolean weights, etc. ), and the text sentiment tendency is derived using classification models (e.g., decision tree, Bayesian classifier, support vector machine, etc.). When appropriate text features are selected and fully trained, machine-learning-based content sentiment analysis outperforms lexicon-based methods. However, machine learning-based methods strongly rely on handcrafted features, mapping of raw Chinese text to abstract features requires extensive human experience and workload, which restricts the further development of this method. As a branch of machine learning, deep learning methods have attracted considerable attention from researchers due to automatic feature engineering capabilities [11]. Using deep networks, raw text data can be represented as progressive, layer-by-layer features, and these features can be learned simultaneously to form an end-to-end content sentiment analysis model, which considerably simplifies the training process. Motivated by the limitations of sentiment-lexicon-based methods and machine-learning-based methods, in this paper, we propose a deep-learning-based model for analysis of university online public opinion. First, a data crawler is used to automatically acquire data from social media frequently used by college students. Following data preprocessing, word embedding and Chinese word segmentation, raw data can be transformed into numerical word vectors. Finally, a CNN-based deep learning model is used to automatically extract semantic features and output the sentiment tendency of public opinion data. The main contributions of this paper are as follows:  
(1) A data crawler is applied to collect public opinion data on social media. In contrast to manual collection methods, data crawlers simultaneously collect data from frequently used Apps to form a high-quality database;(2) An LSTM network-based Chinese word segmentation model is proposed. Compared with traditional lexicon-based or statistically based word segmentation methods, this method can effectively capture long-term semantic dependencies in sentences and can be self-updated based on new versions of the corpus;  
(3) A CNN-based sentiment analysis model is proposed for university public opinion. Compared with traditional machine learning models, the deep learning model does not require tedious feature engineering, and it is capable of automatically extracting high-dimensional features using a deep network structure to form an end-to-end model.
# Operational Resilience Metrics for Complex Inter-Dependent Electrical Networks
## 3 The RecSIM Tool
Specific damage functions implemented to assess the expected damage level or damage frequency of different EDN components can be seen in [27; 35; 36] for assessing earthquake-induced damage and damage induced from heat waves. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**EDNs** **Exposed Components** & **Type** & **Vulnerability Factors** & **Damage Metric** \\ \hline Generators & Node & Building characteristics (geometry, node typology, earthquake resistant design (ERD). Internal elements characteristics (i.e., type of anchoring). & Damage level \\ \hline Primary and Secondary Substations & Node & Dedicated or multipurpose structure. Building characteristics (geometry, typology, earthquake resistant design (ERD)). Internal elements characteristics (i.e., type of anchoring). & Damage level \\ \hline Buried power lines & Line & Cables and conductors: material, geometrical characteristics (diameter and length measured between joints). Joints: constructive characteristics. & Damage frequency \\ \hline Aerial power lines, cables and electric conductors & Line & Cables and conductors: material, geometrical characteristics. Poles: material, height, foundations type. Support structures: material, type. Insulators: terminal block type. & Damage frequency \\ \hline Aerial power lines and switch disconnectors & Node on aerial line & Height above ground. Switch disconnectors mass. Anchor type. Aerial line shelf type. & Damage level \\ \hline Aerial power lines and transformer/substations located on a pole & Node on aerial line & Height above ground. Transformer weight. Anchor type. Shelf type. & Damage level \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Proposed taxonomy for assessing vulnerability of EDNs [35]. CIPCast-DSS was used to evaluate the resilience of urban infrastructure networks at systemic level. For example, in [28], the authors simulated a realistic earthquake event occurring in the city of Florence (Italy) by predicting disruptions on buildings and critical infrastructure and by designing a reliable scenario, accounting for road obstructions due to building collapse, to be used to design efficient contingency plans for infrastructure networks using an approach similar to that described in [17].
## 5 Discussion  
1. In the raw hazard event dataset experiment, the overfitting problem aroused due to the limited dataset. Comparing the performance of the trained network based on the raw dataset, 2\(\times\) augmented dataset, and 6\(\times\) augmented dataset, as shown in Table 6, the augmentation of the hazard event dataset led to a significant improvement in the accuracy and reduction in the standard deviation on the validation dataset.
2. The trained network exhibits robust stability in estimating risks associated with new hazard events similar to the existing ones in the training dataset, achieving a maximum accuracy rate of 100% and a minimum of 91.43%, with a median value of 97.14%. Considering that an acceptable rate should ideally reach up to 100%, with a median value of 88.57%, the trained network demonstrates trustworthiness in estimating the risk of hazard events complexly different from the existing ones.
3. It is found that nearly 80% were small deviations (\(\mid Rl_{deviation}\mid\leq 2\)) and the discrimination factor \(Rl_{dis}\) is close to 1. That means the proposed method has a high likelihood of reaching a smaller deviation in the case of risk estimation deviation with no obvious bias, which proves the effectiveness of the proposed method in risk estimation.
4. Among the CNN-based, bi-LSTM-based and LSTM-based risk estimation methods, the LSTM-based one demonstrates no significant bias when deviations occur, effectively balancing accuracy and stability in risk estimation of new hazard events completely different from the existing ones.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Accuracy**} & \multicolumn{4}{c}{**Training Dataset**} & \multicolumn{4}{c}{**Validation Dataset**} \\ \cline{2-9}  & **Max** & **Min** & **Median** & **Max** & **Min** & **Median** & **Standard Deviation** \\ \hline raw dataset & 100\% & 97.33\% & 98.67\% & 77.78\% & 33.33\% & 55.56\% & 0.118 \\
2\(\times\) & 100\% & 100\% & 100\% & 91.89\% & 64.86\% & 78.38\% & 0.065 \\
6\(\times\) & 100\% & 100\% & 100\% & 99.23\% & 93.85\% & 96.92\% & 0.015 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of the trained network based on the raw dataset and augmented dataset.  
Figure 18: Heatmap of the activations output by the LSTM layer for each time step of the sequences in estimating the risk index of new hazard event.
# Optimal Power Dispatch of an Offshore Wind Farm under Generator Fault
## 4 Optimal Power Dispatch with Fault Accommodation
### Proportional Dispatch Strategy  
As a baseline wind farm control strategy, PDS was introduced in [18]. In this strategy, the WF controller distributes the power demand to the WTs based on the proportion of each WT's available power. It can be expressed as (11).  
\[P_{\mathrm{r}\mathrm{i}}=\frac{P_{\mathrm{a},i}}{\sum_{i=1}^{n}P_{\mathrm{a},i}} P_{\mathrm{dem}} \tag{11}\]  
where \(P_{\mathrm{a},i}\) is the available power of the \(i\)th WT.  
Certain characteristics of PDS can be determined. First, power demand can be easily distributed to the individual WTs. Power reference depends on the proportion of the WT's available power. Second, this strategy does not consider the aerodynamic interaction among all the WTs, so it is not an optimal way to dispatch the reference. In other words, WFs still have the ability to produce more power. Finally, PDS does not consider the fault condition. The faulty WT can only be controlled to continue running or shut down by the WF operator manually.
## 5 Conclusions and Future Work  
We proposed a method, Adacomp, to adaptively adjust learning rate by only exploiting the values of the loss function. Therefore, Adacomp has higher computational efficiency than other gradient-based adaptive methods, such as Adam and RMSprop. From a high abstract level, Adacomp penalizes large learning rates to ensure the convergence and compensates small learning rates to accelerate the training process. Therefore, Adacomp can help escape from local minima with a certain probability. Extensive experimental results show that Adacomp is robust to network architecture, network initialization, batch size, and learning rate. The experimental results show that Adacomp is inferior to Adadelta and others in the maximum validation accuracy over learning rate. Thus the presented algorithm cannot be an alternative to the state-of-the-art. However, the adaptive methods use different learning rates for different parameters and Adacomp determines only the global learning rate.  
In future work, we will validate Adacomp using more tasks (besides convolutional neural networks) and extend Adacomp to a per-dimension first-order algorithm to improve the accuracy of SGD. Additionally, we will apply Adacomp to the distributed environments. Note that Adacomp only uses values of loss functions. Therefore, it is suitable for distributed environments where communication overhead is a bottleneck. Furthermore, we will study how to set the parameters of Adacomp in an end-to-end mode. This may be achieved by introducing feedback and control modules.  
This research received no external funding.  
Not applicable.  
Figure 6: Stability comparison of eight methods on three datasets, KMNIST, Fashion-MNIST, and CIFAR100. Horizontal axis denotes the average prediction value of each method when learning rate was set as \(0.001,0.01,0.1,1\), and vertical axis denotes the corresponding standard variance. (**a**) Comparison on KMNIST Dataset; (**b**) Comparison on Fashion-MNIST dataset; (**c**) Comparison on CIFAR-100 dataset.  
**Acknowledgments:** This work was supported in part by the National Key Research and Development Program of China under Grant 2020-YFA0713900; in part by the National Natural Science Foundation of China under Grant 61772410, Grant 61802298, Grant U1811461, and Grant 11690011; in part by the China Postdoctoral Science Foundation under Grant 2020T130513 and Grant 2017M623177; and in part by the Fundamental Research Funds for the Central Universities under Grant xjj2018237.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results and Discussion
### Cytotoxicity Assay  
The cytotoxicity of GNPs in human keratinocytes cells for two different exposure periods (12 and 24 h) and concentrations from 0 (control) to 10 \(\upmu\)g/mL were studied. Thus, at 12 h of exposure, higher concentrations of 0.05 \(\upmu\)g/mL GNPs showed significant cytotoxic effects in the HaCaT cell line (Figure 3).  
The cytotoxicity results at 24 h-exposure of GNPs in HaCaT cells showed also significant non-cytotoxicity for concentrations \(\leq\) 0.05 \(\upmu\)g/mL (Figure 4).  
These results performed at different exposure times clearly shows that the principal cytotoxic effect occurs during the first 12 h. The cell viability of the HaCaT cells after exposure for 12 and 24 h to a GNP concentration of 0.1 \(\upmu\)g/mL also showed cell viability higher than 70% indicating non-cytotoxicity according to the ISO-10993 standard (Figures 3 and 4). Studies based on bronchial epithelial cell line (BEAS-2B) cytotoxicity showed similar results at 24 h of cell exposure with GNPs [27].  
Figure 2: Rama spectrum of graphene nanoplatelets.  
From these results performed at 12 and 24 h of exposure (Figures 3 and 4), the EC\({}_{50}\) was calculated for the GNPs (Table 3).  
Table 3 shows that these non-oxidized CBNs are about five times higher cytotoxic than the oxidized GO nanosheets [13]. This fact can be attributed to smaller sizes of the GO nanosheets used in that study and the hydrophilic functional groups present on their basal planes and at the edges, which decreases toxicity according to previous studies performed with other graphene-based materials [28]. Thus, small and hydrophilic graphene-based materials are not considered in the literature.  
Figure 4: Human keratinocyte HaCaT cell viability after 24 h exposure to GNPs at different concentrations (from 0 ug/mL (control sample) to 10 ug/mL) measured by the (3-(4, 5-dimethylthiazol-2-yl)-2, 5-diphenyl tetrazolium) (MTT) assay. Results are expressed as % of the control sample and as the mean \(\pm\) standard deviation of six replicates. The results of the statistical analysis with respect to control are indicated in the graph: ****\(p<0.0001\); n.s: not significant. The limit is 70% of cell viability for the materials to be considered non-cytotoxic according to the ISO-10993 standard.  
Figure 3: Human keratinocyte HaCaT cell viability after 12 h exposure to GNPs at different concentrations (from 0 ug/mL (control sample) to 10 ug/mL) measured by the (3-(4, 5-dimethylthiazol-2-yl)-2, 5-diphenyl tetrazolium) (MTT) assay. Results are expressed as % of the control sample and as the mean \(\pm\) standard deviation of six replicates. The results of the statistical analysis with respect to control are indicated in the graph: ****\(p<0.0001\); n.s: not significant. The limit is 70% of cell viability for the materials to be considered non-cytotoxic according to the ISO-10993 standard.  
nanomaterials form more stable dispersions significantly reducing aggregation, which can render easier the internalization and excretion cell processes.
# An Improved Back-Projection Algorithm for GNSS-R BSAK Imaging Based on CPU and GPU Platform
## 3 Methods  
### Review on BPA  
The BPA is a typical time-domain imaging algorithm [37], which comes from computer tomography. Firstly, the imaged plane is divided into grids, and the center of each grid is determined as the pixel point. Secondly, range compression used matched filtering to achieve pulse compression in the range. Thirdly, in the range-azimuth time domain, the coherent accumulation method is used to realize the azimuth focusing of each pixel point by point. Each pixel in the imaged plane is traversed to calculate the position of the target in each echo, and the phase is compensated for coherent accumulation to obtain the pixel value of the current point.