esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Implementation and Discussion
### Limitation and Future Work
#### 4.3.5 Empirical Validation Comparison with Studies  
One of the major limitations of this study was that it only provided a qualitative comparison among the proposed study and existing studies. This version of the study only tested with a prototype of the PoF based collaborative medical decision-making system with the help of third party (multichain) platform [72]. This work is an ongoing work, that includes the development of an independent blockchain framework followed by its performance analysis. Upon completing the whole system, this study plans to include real-time network behavior analysis of a PoF based collaboration system and existing medical decision gathering platforms. Because of this limitation, this study only includes theoretical comparison with existing platforms. At the same time, the study found strong qualitative advantages that encourages us to build the whole system in future. In the future version, the study plans to include critical blockchain system analyzing factors (network delay, block creation time, block finality time, energy consumption, etc.).
# Electrochemical Biosensors Based on Conducting Polymers: A Review
## 4 Conducting Polymer-Based Electrochemical Biosensors
### Conducting Polymer-Based Whole Cell Biosensors
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Active Layer** & **Detection Mode** & **Linear Range** &
\begin{tabular}{c} **Detection** \\ **Limit** \\ \end{tabular} & **Ref.** \\ \hline Polyaniline–MoS\({}_{2}\) & voltammetry & \(10^{-15}\)–\(10^{-6}\) M & \(10^{-15}\) M & [124] \\ \hline Polypyrrole–Au and Ag NPs & voltammetry & 7–150 nM & 7 nM & [125] \\ \hline poly [3–acetic acid \\ pyrrole,3–N–hydroxphthalimide pyrrole] & impedometry & 0.05–5.5 nM & 1 pM & [126] \\ \hline Polypyrrole–Polyaniline– & impedometry & \(10^{-13}\)–\(10^{-6}\) M & \(10^{-13}\) M & [127] \\ \hline Polypyrrole–PEDOT–Ag NP & impedometry & \(10^{-15}\)–\(10^{-11}\) M & \(5\times 10^{-15}\) M & [128] \\ \hline Polypyrrole–CNT-COOH & impedometry & \(10^{-12}\)–\(10^{-7}\) M & \(5\times 10^{-12}\) M & [129] \\ \hline Polyaniline & voltammetry & \(10^{-15}\)–\(10^{-12}\) M & \(10^{-15}\) M & [130] \\ \hline Polyaniline–methylene blue & voltammetry & \(10^{-12}\)–\(10^{-10}\) M & \(10^{-12}\) M & [131] \\ \hline Polyaniline–graphene & voltammetry & \(10^{-13}\)–\(10^{-7}\) M & \(3\times 10^{-14}\) M & [132] \\ \hline Polyaniline–graphene & voltammetry & \(10^{-9}\)–\(10^{-6}\) M & \(8\times 10^{-7}\) M & [133] \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of conducting polymer-based DNA biosensor performances. conductometric urea biosensor was fabricated which used the change in resistivity generated by an increase of the pH due to the catalytic action of urease contained in the whole _Brevibacterium ammonigenes_ cells previously immobilized in a polystyrene sulphonate-polyaniline (PSS-PANI) conducting film [146].
## 3 Results
### Relationships between Carbon Emissions and Hydrothermal Factors  
The relationships between the CO\({}_{2}\)-C emission fluxes and soil water contents under different biochar application amounts all conformed to an exponential decay equation. According to the fitted equations, the soil water content could explain 21.1-34.3% of the variations in the CO\({}_{2}\)-C emission fluxes. The coefficient of determination (R\({}^{2}\)) for the fitted equation increased as the amount of added biochar increased (Table 1). However, the relationships between the CO\({}_{2}\)-C emission fluxes and soil temperature under different treatments all conformed to an exponential growth equation. According to the fitted equations, soil temperature could explain 72.6-75.8% of the variations in the CO\({}_{2}\)-C emission fluxes. The R\({}^{2}\) value for the fitted equation decreased as the amount of added biochar increased (Table 1).  
Figure 7: Variations in field CH\({}_{4}\)-C uptake fluxes under different amounts of added biochar during the winter wheat growing seasons in 2018–2019 and 2020–2021. Note: The circular cone height represents the CH\({}_{4}\)-C uptake flux value, and the Akima spline curve represents the trend of CH\({}_{4}\)-C uptake fluxes with time.  
There was no significant correlation between the CH\({}_{4}\)-C uptake flux and soil water content under each treatment (Table 2). However, the relationships between the CH\({}_{4}\)-C uptake fluxes and soil temperatures under different biochar application amounts all conformed to an inverse U-shaped quadratic equation. According to the fitted equations, the soil temperature could explain 46.3-72.6% of the variations in the CH\({}_{4}\)-C uptake flux. The application of biochar increased the R\({}^{2}\) value for the fitted equation (Table 2).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Equation** & **Treatment** & **a** & **b** & **R\({}^{2}\)** & \(p\)** \\ \hline \multirow{3}{*}{F = a \(\times\) e(b\(\times\)SWC)} & B20 & 320.212 & \(-\)0.124 & 0.343 & 0.011 \\  & B10 & 276.467 & \(-\)0.115 & 0.235 & 0.042 \\ \cline{1-1}  & B0 & 294.764 & \(-\)0.106 & 0.211 & 0.055 \\ \cline{1-1}  & B20 & 20.019 & 0.103 & 0.726 & 0.000 \\ \cline{1-1} F = a \(\times\) e(b\(\times\)ST) & B10 & 19.963 & 0.104 & 0.745 & 0.000 \\ \cline{1-1}  & B0 & 27.301 & 0.099 & 0.758 & 0.000 \\ \hline \hline \end{tabular} Note: B20, B10, and B0 represent 20, 10, and 0 Mg biochar ha\({}^{-1}\), respectively.  
\end{table}
Table 1: Equations of the relationships between the CO\({}_{2}\)-C emission fluxes (F) and soil water content (SWC) and soil temperature (ST) under different amounts of added biochar in wheat fields.  
Figure 8: Cumulative CH\({}_{4}\)-C uptake under different amounts of added biochar during the winter wheat growing seasons in 2018–2019 (**A**) and 2020–2021 (**B**). Note: The column length represents the mean \(\pm\) standard error of newly added cumulative CH\({}_{4}\)-C uptake in each stage, and different lowercase letters indicate significant differences between treatments (Duncan’s test, \(p<0.05\)).
# Wiener Filter Using the Conjugate Gradient Method and a Third-Order Tensor Decomposition
###### Abstract  
In linear system identification problems, the Wiener filter represents a popular tool and stands as an important benchmark. Nevertheless, it faces significant challenges when identifying long-length impulse responses. In order to address the related shortcomings, the solution presented in this paper is based on a third-order tensor decomposition technique, while the resulting sets of Wiener-Hopf equations are solved with the conjugate gradient (CG) method. Due to the decomposition-based approach, the number of coefficients (i.e., the parameter space of the filter) is greatly reduced, which results in operating with smaller data structures within the algorithm. As a result, improved robustness and accuracy can be achieved, especially in harsh scenarios (e.g., limited/incomplete sets of data and/or noisy conditions). Besides, the CG-based solution avoids matrix inversion operations, together with the related numerical and complexity issues. The simulation results are obtained in a network echo cancellation scenario and support the performance gain. In this context, the proposed iterative Wiener filter outperforms the conventional benchmark and also some previously developed counterparts that use matrix inversion or second-order tensor decompositions. system identification; Wiener filter; conjugate gradient method; tensor decomposition; Kronecker product; low-rank approximation; echo cancellation +
Footnote †: journal: Applied sciences  
==ERROR: No output for this page==
At this point, we need to introduce the auxiliary matrices (for \(i=1,2,\ldots,L_{2}\) and \(j=1,2,\ldots,P\)):  
\[\mathbf{M}_{12,11}^{ij(0)} =\mathbf{I}_{L_{2}}\otimes\Big{[}\ \ \epsilon\ \ \ \mathbf{0}_{L_{12-1}}^{T}\ \Big{]}^{T}\otimes\Big{[}\ \ \epsilon\ \ \ \mathbf{0}_{L_{11-1}}^{T}\ \Big{]}^{T},\] (23) \[\mathbf{M}_{11}^{ij(0)} =\mathbf{I}_{L_{22}}\otimes\Big{[}\ \epsilon\ \ \ \mathbf{0}_{L_{11-1}}^{T}\ \Big{]}^{T},\] (24)  
which will further facilitate the definition of matrices \(\overline{\mathbf{\Xi}}_{12,11}\) and \(\overline{\mathbf{\Xi}}_{2,11}\). Hence, in each main iteration \({}^{(n)}\) of the algorithm, we first construct using (23):  
\[\mathbf{G}_{12,11}^{ij(n)} =\mathbf{M}_{12,11}^{ij(n-1)},\ i=1,2,\ldots,L_{2},\ j=1,2,\ldots,P,\] (25) \[\overline{\mathbf{G}}_{12,11}^{i(n)} =\sum_{j=1}^{P}\mathbf{G}_{12,11}^{ij(n)},\ i=1,2,\ldots,L_{2},\] (26) \[\overline{\mathbf{G}}_{12,11}^{(n)} =\Big{[}\ \overline{\mathbf{G}}_{12,11}^{1(n)}\ \ \ \overline{\mathbf{G}}_{12,11}^{2(n)}\ \ \ \ldots\ \ \ \overline{\mathbf{G}}_{12,11}^{L_{2}(n)}\ \ \Big{]}.\] (27)  
These allow us to compute  
\[\mathbf{R}_{2}^{(n)} =\Big{(}\overline{\mathbf{G}}_{12,11}^{(n)}\Big{)}^{T}\mathbf{R} _{\mathbf{x}}\overline{\mathbf{G}}_{12,11}^{(n)},\] (28) \[\mathbf{r}_{2}^{(n)} =\Big{(}\overline{\mathbf{G}}_{12,11}^{(n)}\Big{)}^{T}\mathbf{r} _{\text{xd}}.\] (29)
## 2 Route Generation Methodology
### Graph Formulation  
To use the proposed methodology, the targeted area can be modeled as a graph. Any method can be used in this process; however, the resolution has an impact on the quality of the graph produced. As a result, the optimal graph resolution can be selected through trial and error. Based on previous experience, the resolution can be set to twice the length of the vehicle.  
The graph should consist of many edges such that the optimizer has the freedom to select the most direct path to nodes that are far from the current node. Therefore, the graph can be strongly connected for all nodes that allow a direct connection. In some cases, the direct path between a pair of nodes crosses over a boundary or static obstacle. In these cases, a direct connection cannot be made. The graph can be modeled as an undirected graph where all nodes are strongly connected if the direct path does not cross a boundary or static obstacle. To generate the graph within the serviceable area (used by the lower-levelpath generation), a grid of nodes spaced at the specified resolution was created within each sub-area. Then, nodes inside of obstacles were removed, and direct connections (edges) between all remaining nodes were made. Edges that passed through obstacles or outer boundaries were omitted since these direct routes were infeasible. By doing so, a strongly connected graph with direct connections between all nodes was created. Then, each sub-area's graph can be connected to neighboring sub-areas to create the total graph used by the higher-level path generation. By connecting all sub-areas, the higher-level path generation algorithm can find the shortest path between sub-areas.  
Additionally, there should be some deadhead (non-serviceable) distance between the outer boundary of the targeted area and static obstacles. The buffer distance reduces the possibility of collisions and creates some space to allow the vehicle to turn (if necessary). An example of a graph created in a small area can be seen in Figure 1. In Figure 1, the graph representation of the serviceable area does not extend directly to the border. This is the effect of the buffer distance.  
For the following methodology, a large area can be divided into several smaller areas. Since several vehicles are dispatched to cover the entire area, it can be divided into several smaller areas that can be assigned to each vehicle. This can be carried out manually or automatically; however, it is recommended that there is at least the same number (or more) of sub-areas as vehicles available.
## 3 Results and Discussion  
### Compressive Strength and Setting Time of Aasr  
The compressive strength of each specimen is shown in Figure 3. Overall, the compressive strength of AASR increases with age for all mixes, and their strength for a given age decreases with the increase of RM. Interestingly, as 30% RM was added, the compressive strength of specimens developed faster than that of the control mix (especially in the first 7 days), and the 28-day compressive strength reaches to about 77.5 MPa. However, when the amount of RM was increased to 50%, the increase rate of compressive strength became lower, and the 28-day compressive strength decreased to about 57.5 MPa; and when the amount of RM admixture increased to 70%, the development of the compressive strength of the specimens slowed down further, and the compressive strength decreased at all ages significantly, as only 20.2 MPa was obtained after 28 days. This could mainly be attributed to the following two aspects: the introduction of an appropriate amount of RM keeps AASR system at a higher alkalinity, which is helpful to the development of compressive strength, and the excessive amount of RM with low-activity results in less hydration products being formed in AASR due to the dilution effect, which leads to a poor development of compressive strength.  
The initial and final setting times of AASR with different RM content is shown in Figure 4. From Figure 4, it can be seen that the setting time of AASR is prolonged with the increase of red mud admixture, which is mainly because the dissociation of red mud is more difficult and slows down the hydration process of the whole system, (which can be confirmed from Section 3.2), so the setting times are prolonged.  
To further explore the mechanisms behind these findings, the porosity, reaction process, reaction products and micro structure were carefully examined, and the results are presented in the following sections.
## Case Studies
### Case Study 2: Duolingo  
#### 5.3.1 Background  
Duolingo is a leading platform in language learning, renowned for its user-friendly interface and innovative approach to teaching languages through gamified experiences [46]. Established in 2011, Duolingo has grown to serve millions of users worldwide, offering courses in over 30 languages. Its mission is to make education free, fun, and accessible to all, and it achieves this through a technology-driven learning environment that adapts to individual learners' needs.  
The selection of Duolingo as a case study for implementing the Data-Driven Personalized Learning Model (DDPLM) is predicated on its extensive data generation and processing capabilities. Duolingo collects a vast array of data points with each user interaction, ranging from quiz responses and lesson completions to time spent on exercises and streaks maintained over days [47]. This rich dataset provides an ideal setting for applying advanced data analytics to enhance personalized learning experiences.  
DDPLM's integration into Duolingo aims to leverage the platform's existing infrastructure to introduce a deeper level of personalization. The primary objective is to transform how users engage with language learning by dynamically adjusting content based on individual progress and learning habits. By doing so, Duolingo can offer a more tailored  
Figure 3: Trends in Learner Engagement on Edmodo Over Six Months.  
educational experience that aligns with each learner's pace and learning style and addresses specific linguistic challenges they face.  
This case study examines how DDPLM can enhance the effectiveness of language learning through sophisticated data analytics, thereby providing insights into learner behavior and preferences that can be used to optimize the learning process. The overarching goal is to demonstrate how a well-established learning platform like Duolingo can be further enhanced by integrating a model that prioritizes personalization based on empirical data, thereby setting a new standard in educational technology for language acquisition.
## 3 Results
### Spatial Mismatch Analysis of Population, Economy, and GPP  
From 1991 to 2020, Guangdong Province experienced a rapid increase in the overall spatial mismatch index, indicating that the degree of spatial mismatch has intensified, and the coordination between food production and the levels of population and economic development has declined (Figure 10). In terms of the directions of the mismatch, the positive mismatch areas are primarily located in the PRD and EG regions, whereas the negative mismatch areas are mainly found in the NG and WG regions. Specifically, 10 prefecture-level cities have consistently been in the negative mismatch zone, with Zhanjiang showing the highest negative mismatch index. By the end of the study period, Zhanjiang's mismatch index reached \(-23.15\), showing a change range of 6.57% over the period. Most prefecture-level cities have moved towards high negative mismatch values, indicating that food production capacity is increasingly outpacing the demands of population growth and economic development. Only Jiangmen and Huizhou maintained stable spatial mismatch indices. By contrast, 11 prefecture-level cities have long been in the positive mismatch zone. Guangzhou, Shenzhen, and Dongguan quickly reached high positive mismatch values, indicating that regional food production increasingly lags behind population growth and economic development. Shenzhen's spatial mismatch index increased by 14.86%, primarily due to its strong attraction to the external population and rapid economic growth over recent decades. Meanwhile, food production lagged significantly behind, resulting in poor spatial coordination. Conversely, Jieyang and Shantou gradually decreased to low positive mismatch values. Only three prefecture-level cities, Zhaoqing, Yunfu, and Jieyang, experienced fundamental changes in their spatial mismatch indices, shifting from positive to negative values. This indicates a shift from food production being relatively advanced to relatively lagging behind population and economic development, with low levels of spatial mismatch. Due to differences in geographical location, cropland resources, population density, and socioeconomic levels among cities, their contributions to the overall spatial mismatch index of Guangdong Province vary. Most prefecture-level cities have low contributions to the overall spatial mismatch, maintaining around 3%. Only Zhanjiang, Guangzhou, and Shenzhen have contributions of 20.98%, 14.14%, and  
Figure 9: Balance analysis of added and lost GPP in each prefecture-level city from 1991 to 2020.  
12.78%, respectively, with Guangzhou and Shenzhen experiencing significant human-grain relationship conflicts.
# Inverse Modeling of Seepage Parameters Based on an Improved Gray Wolf Optimizer
## 6 Conclusions  
Aiming at balancing the local and global search of GWO, along with uniformity and stochasticity, IGWO was proposed in this paper. The improvement of IGWO in accuracy and running time was indicated in numerical experiments. IGWO was used in the inverse modeling of the hydraulic conductivity of the Kakiwa dam. The hydraulic head and leakage were used to set up the objective function. Errors in the hydraulic head and leakage were calculated. The steady seepage field was analyzed in the application case. The main conclusions of this paper are given as follows.  
(1) The performance of IGWO is improved due to the three strategies. A novel approach for initialization contributes to populations of semi-uniform and semi-random. The polynomial-based nonlinear convergence factor is selected to keep the equilibrium of the local and global search. Weighting factors based on Euclidean norms and hierarchy helps to update the position of the wolf dynamically.  
(2) A numerical experiment was conducted to demonstrate the performance of IGWO. The optimal values obtained by IGWO are closer to the theoretical solution than the results of the other two algorithms. The results of the experiment demonstrate the feasibility and efficiency of IGWO.  
(3) The target combination of hydraulic conductivity was obtained by IGWO. The values of hydraulic conductivities were substituted into the finite element model. The values of the hydraulic head and leakage quantity at the corresponding measurement points were obtained. The maximum values of absolute and relative errors of the hydraulic head were--1.41 m and 0.95%, respectively. The absolute and relative errors of the seepage volume were--0.02 m\({}^{3}\)/s and 5.00%, respectively. The results of the application case show that the inversion model and the algorithm are reliable and efficient.  
Conceptualization, Y.S. and L.X.; methodology, Z.S.; software, Y.S.; validation, J.D. and L.J.; formal analysis, L.X.; investigation, J.D.; resources, Z.S.; data curation, L.J. and Q.L.; writing--original draft preparation, Y.S.; writing--review and editing, Y.S.; visualization, J.D. and Q.L.; supervision, Z.S.; project administration, Z.S.; funding acquisition, L.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the \"National Key R&D Program of China, grant number 2019 YFC1510802\".  
Not applicable.  
The authors declare no conflict of interest.
# A Systematic Survey on 5G and 6G Security Considerations, Challenges, Trends, and Research Areas
## 2 Related Works  
### Security Focuses in 5G  
To enhance the security of 5G cellular networks, 3 GPP released an updated Technical Standard (TS) 33.501 [2] for security architecture and procedures. This document outlines the security architecture through three different strata and six separate security domains, as shown in Figure 2. These security domains and the sections of this paper that discuss their respective research areas are listed below:  
Figure 1: High-level scope and structure of our 5G security survey.  
1. Network Access Security focuses on UE authentication and secure network services access with a particular focus on attacks against the physical/radio interface. This domain includes both 3 GPP and trusted non-3 GPP access and the access security between the Serving Network (SN) and Access Network (AN). In the context of this paper, sections covering authentication, 5G access, RAN, and UE physical layer security will review the current state of research in each of these areas. These sections will outline current research enhancements, vulnerabilities, and future research directions to align with the TS 33.501 standard.
2. Network Domain Security encompasses security for network nodes that enables the secure exchange of signaling data and user plane data. Sections covering the topics of non-3 GPP co-existence, SDN, NFV, and MEC will discuss research in this domain later in this paper.
3. User Domain Security contains the security features that secure the user's access to mobile equipment. Discussed later are general resources and security measures listed by the National Institute of Science and Technology (NIST) on identifying areas of concern for user security at the device end.
4. Application Domain Security entails security for the user domain to the application provider domain in order to be able to exchange messages securely. Application domain security is not within the scope of TS 33.501, but this paper will discuss blockchain technology and post-quantum cryptography for applications specific to 5G networks.
5. Service-Based Architecture (SBA) Domain Security is the set of security features that enables network functions to securely communicate within the serving network domain and with other network domains. Such features include network function registration, discovery, and authorization security aspects as well as the protection for the service-based interfaces. SBA domain security is a new security feature compared to TS 33.401 [3]. These security features will be discussed in the SBA section with specific subsections corresponding to the various network functions in a 5G system.
6. Visibility and Configurability of Security spans the set of features that inform the user whether a security feature is in operation. Visibility and configurability are mentioned within this survey in the sections discussing the user equipment, the Core Network, and O-RAN (Open-RAN).