esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhanced Quasi Type-1 PLL-Based Multi-Functional Control of Single-Phase Dynamic Voltage Restorer
## 3 Reference Signal Generator
### DC Offset Rejection  
Measurement offset \(v_{0}\) causes estimation error in the estimated phase. Thus, rejection of this offset is essential in order to eliminate the steady-state error. For this purpose, the half-cycle delayed signal cancellation (DSC) method is a popular choice in the literature [34,35]. The same approach is considered here. For this purpose, let us consider half-cycle delayed version of the signal \(v_{g}\) as follows:  
\[v_{g}^{t_{d}}(t) =v_{0}+V_{p}\sin\bigl{(}\omega_{g}(t-t_{d})+\phi\bigr{)},\] \[=v_{0}-V_{p}\sin\bigl{(}\theta_{g}\bigr{)}, \tag{9}\]  
where \(t_{d}=\frac{T}{2}\) with \(T=\frac{2\pi}{\omega_{g}}\) being the period of the grid voltage signal. Then, the DC offset can be eliminated by the following operation.  
\[\tilde{e}_{g}^{\varnothing}=\frac{1}{2}\Bigl{(}v_{g}-v_{g}^{t_{d}}\Bigr{)}. \tag{10}\]  
In implementing Equation (10), the actual period of the grid voltage is required. In the off-nominal frequency condition, the amount of required dely could be a fraction, thereby increasing computational complexity. A potential solution is to use the nominal period; however, this will introduce amplitude and phase attenuation in the off-nominal condition. In this study, only extracting the phase is required. thus, appropriate compensation of the phase delay is necessary to eliminate the error. For this purpose, let us consider the transfer function of the DSC operation (10) as provided by the following:  
\[\frac{\tilde{e}_{g}^{\varnothing}}{v_{g}}(s)=\mathrm{G_{dc}}(s)=\frac{1-e^{-t_ {d}s}}{2}, \tag{11}\]  
where the estimated value is indicated by \(\hat{\ }\). The discrete-time version of the transfer function (11) is given by the following:  
\[\mathrm{G_{dc}}(z)=\frac{1-z^{-N_{d}}}{2}, \tag{12}\]  
where the required delay is given by as \(N_{d}=t_{d}f_{s}\) with \(f_{s}\) being the sampling frequency. By substituting \(s=j\omega_{g}\), the phase angle of the transfer function (11) is given by the following.  
\[\angle\mathrm{G_{dc}}(s) =\tan^{-1}\biggl{\{}\tan\biggl{(}\frac{\pi}{2}-\frac{T\omega_{g} }{4}\biggr{)}\biggr{\}},\] \[=\frac{\pi}{2}-\frac{T\omega_{g}}{4},\] \[=\frac{\pi}{2}-\frac{T}{4}(\omega_{n}+\delta_{\omega}),\] \[=\frac{\pi}{2}-\frac{T}{4}\frac{2\pi}{T}-\frac{T}{4}\delta_{\omega},\] \[=-\underbrace{\frac{T}{4}}_{k_{k_{k}}}\delta_{\omega}. \tag{13}\]In calculating (13), it is assumed that \(\omega_{n}=2\pi/T\). Equation (13) shows that the use of nominal period in (10) causes a phase delay of \(-k_{dc}\delta_{\omega}\) in the off-nominal frequency case. This phase needs to be compensated in the loop-filter to eliminate the phase error in the off-nominal frequency condition.
## 3 Materials and Methods
### Selection and Cost Estimation of Retrofit Strategies
#### 3.3.2 Cost Estimation of the Selected Strategies  
The cost of each strategy was obtained by contacting various companies to gather pricing information, facilitating a cost-effectiveness analysis. The proposed strategies were evaluated with a primary emphasis on reducing energy consumption and associated costs. This evaluation primarily involved comparing the strategies based on their financial efficiency, using the payback period as a key indicator. The payback period calculation determines when the initial investment for a retrofit alternative will be recouped through savings in cooling energy consumption. Equation (1) is employed to calculate the payback period as follows:  
\[\text{Ni}=\text{R}/(\text{Eb}-\text{Ec})*\text{x} \tag{1}\]  
where Ni is payback period (years), R is total retrofit cost (SAR), Eb is energy consumption prior to investment (kWh.year), Ec is energy consumption after investment (kWh.year), and x is electricity cost (SAR per kWh). This equation illustrates that the Simple Payback Period (SPP) is calculated by dividing the cost of implementing energy conservation measures by the annual energy savings. The Saudi Electricity Company has established varying electricity tariffs for different building sectors in Saudi Arabia. In the specific case of the discussed study, the monthly cooling energy consumption exceeds 6000 kWh, leading to an associated energy cost of 0.30 SAR/kWh [54]. Table 4 shows a cost estimation for the various proposed facade retrofit strategies.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Strategy Code** & **Strategy Description** & **U-Value** [W/m2.K]** & **SHGC** \\ \hline S-W-1 & Smart glass (electrochromic) & 1.7 & 0.155 \\ \hline S-W-2 & Low-e coating & 1.761 & 0.38 \\ \hline S-W-3 & Triple-glazed & 1.726 & 0.422 \\ \hline S-W-4 & Double-glazed with Argon & 2.51 & 0.495 \\ \hline S-W-5 & Smart glass with Argon & 1.493 & 0.144 \\ \hline S-W-6 & Low-e coating with Argon & 1.493 & 0.373 \\ \hline S-W-7 & Triple-glazed with Argon & 1.593 & 0.42 \\ \hline \hline \end{tabular}
\end{table}
Table 3: U-value and SHGC of the different window retrofit strategies (compared to a U-value of 2.66 W/m2.K and SHGC of 0.497 in the base case).
# Enhancing Kidney Disease Diagnosis Using ACO-Based Feature Selection and Explainable AI Techniques
###### Abstract  
Kidney disease is a global health concern, impacting a substantial part of the overall population and contributing to high morbidity and mortality rates. The initially diagnosed phases of kidney disease are often present without noticeable indications, leading to delayed diagnosis and treatment. Therefore, early detection is crucial to reducing complications and improving the lives of those impacted. However, the performance of previous automated approaches has often been hindered by suboptimal feature selection and algorithms \"black-box\" nature, which adversely affect their interpretability and clinical applicability. This paper aims to address these limitations by creating an effective machine-learning-based approach that integrates ant colony metaheuristic optimization algorithms for feature selection and explainable artificial intelligence techniques such as SHAP and LIME for model interpretation. The ant colony optimization method identified the most relevant feature subsets using a clinical dataset, reducing model complexity while preserving predictive accuracy. Performance evaluation shows that the extra trees classifier, when using optimized selected features, achieved the highest performance with an accuracy of 97.70% and an area under the curve of 99.55%, outperforming previous models trained on raw and complete processed feature sets. To enhance interpretability, the SHAP and LIME explainable techniques were employed, providing detailed insights into the contribution of key features such as _TimeToEventMonths_, _HistoryDiabetes_, and _Age_. This comprehensive framework, combining advanced feature selection with explainable models, improves clinical decision-making and fosters trust in machine learning applications for healthcare.  
Keywords:explainable machine learning; model interpretation; feature optimization; kidney disease diagnosis; transparent artificial intelligence +
Footnote †: journal: Applied sciences
## Appendix B Gap Amplification via Randomized Graph Product
\[\Pr[\exists S^{\prime}\subseteq L^{\prime},|S^{\prime}|=N^{ \epsilon}\wedge\mathcal{F}(S^{\prime})<\delta n] =\Pr[\exists S\subseteq L,|S|<\delta n\wedge|S^{k}\cap L^{\prime}| \geqslant N^{\epsilon}]\] \[\leqslant\sum_{S\subseteq L,|S|<\delta n}\Pr[|S^{k}\cap L^{ \prime}|\geqslant N^{\epsilon}]\] \[=\sum_{S\subseteq L,|S|<\delta n}\Pr\left[\sum_{i\in[N]}\mathds{1 }\{U_{i}\in S^{k}\}\geqslant N^{\epsilon}\right].\]  
Observe that, for each \(i\in[N]\), \(\mathds{1}\{U_{i}\in S^{k}\}\) is simply an independent Bernoulli random variable with mean \((|S|/n)^{k}<\delta^{k}=1/N\). Hence, by Chernoff bound, we have  
\[\Pr[\exists S^{\prime}\subseteq L^{\prime},|S^{\prime}|=N^{\epsilon}\wedge \mathcal{F}(S^{\prime})<\delta n]\leqslant\sum_{S\subseteq L,|S|<\delta n}2^{ -\Omega(N^{\epsilon})}\leqslant\sum_{S\subseteq L,|S|<\delta n}2^{-\Omega(n^{ \epsilon})}=2^{-\Omega(n^{2})}\]  
as desired. Analogously, we also have \(|\mathcal{F}(T^{\prime})|\geqslant\delta n\) for every subset \(T^{\prime}\subseteq R^{\prime}\) of size \(N^{\epsilon}\) with high probability, thereby concluding the proof of Proposition A1. With Proposition A1 ready, let us proceed with our soundness proof. Suppose that the event in Proposition A1 occurs. Consider any subset \(S^{\prime}\subseteq L^{\prime}\) of size \(N^{\epsilon}\) and any subset \(T^{\prime}\subseteq R^{\prime}\) of size \(N^{\epsilon}\). Since \(|\mathcal{F}(S^{\prime})|\geqslant\delta n,|\mathcal{F}(T^{\prime})|\geqslant\delta n\) and \(G\) does not contain \(K_{\delta n,\delta n}\) as a subgraph, there exists \(u\in\mathcal{F}(S^{\prime})\) and \(v\in\mathcal{F}(T^{\prime})\) such that \((u,v)\
otin E\). From the definition of \(G^{\prime}\), this implies that \(S^{\prime}\) and \(T^{\prime}\) do not induce a biclique in \(G^{\prime}\). As a result, \(G^{\prime}\) does not contain \(K_{N^{\epsilon},N^{\epsilon}}\) as a subgraph. From this and from Proposition A1, \(G^{\prime}\) does not contain \(K_{N^{\epsilon},N^{\epsilon}}\) as a subgraph with high probability, concluding our soundness argument. Since Lemma 2 asserts that distinguishing between the two cases above are NP-hard (assuming SSEH) and the above reduction takes polynomial time, we can conclude that, assuming SSEH and NP \(\
eq\) BPP, no polynomial time algorithm can distinguish the two cases stated in the lemma.
# Thermostable D-Allulose 3-Epimerase for Long-Term Food-Compatible Continuous Production Systems
## 2 Materials and Methods
### Gene Cloning, Expression, and Sequence Analysis of TcDAEase  
The gene sequence encoding TcDAEase (NCBI ID: WP_052889376.1) was cloned into the pET22b (+) expression vector to construct the recombinant plasmid pET22b-TcDAEase [23]. The plasmid was transformed into E. coli BL21(DE3) for heterologous expression. Pre-cultures were grown in 100 mL Luria-Bertani (LB) medium supplemented with 50 \(\upmu\)g/mL ampicillin at 37 \({}^{\circ}\)C 200 rpm overnight. The seed culture was then inoculated (2% \(\upsilon/\upsilon\)) into 100 mL auto-induction Terrific Broth (15 g/L tryptone, 25 g/L yeast extract, 10 g/L NaCl, 2 g/L glucose, 3 g/L lactose) [24]. Cultures were incubated at 37 \({}^{\circ}\)C until OD\({}_{600}\) reached 0.6, then shifted to 25 \({}^{\circ}\)C for 24 h to enhance protein yield. Cells were harvested via centrifugation (8000 rpm, 4 \({}^{\circ}\)C, 20 min). Multiple sequence alignment of TcDAEase with other reported D-allulose 3-epimerases was conducted using Clustal Omega (version 1.2.4, EMBL-EBI, UK) [25] to identify conserved catalytic residues and structural stability. A phylogenetic tree was constructed using MEGA version 11.0.13 (Molecular Evolutionary Genetics Analysis, Pennsylvania State University, University Park, PA, USA) via the maximum likelihood method with 1000 bootstrap replications to assess evolutionary relationships and statistical confidence.
# A Game Theory-Based Model for the Dissemination of Privacy Information in Online Social Networks
## 3 A Game-Based Model for Privacy Information Dissemination
### Decision on Forwarding Privacy Information
#### 3.1.1. Intimacy  
Intimacy is expressed as the degree of closeness between two users in an OSN. Studies have shown that intimacy plays a vital role in terms of with which users would share their privacy information. 95.24% of people will choose whether to communicate personal privacy information based on perceived intimacy. The most intuitive example is that users with a high degree of intimacy, such as parents and children, would tend to protect the privacy of each other, i.e., to stop the spreading of privacy information. On the other hand, users with a low degree of intimacy, such as ordinary colleagues or even strangers, would not care much about the protection of each other's privacy and would, thus, have the tendency of spreading each other's privacy information. These examples indicate that  
Figure 1: The procedure of privacy information dissemination.  
intimacy has a great influence on whether a user will be interested and participate in the dissemination of privacy information.  
Intimacy is defined as the emotional experience when an individual gets along well with others according to \"The Dictionary of Psychology\". It includes, but is not limited to, the affection between parents, brothers, and sisters, the love between men and women, the friendship between friends, and other different emotions. To facilitate our study, we classify intimacy into four levels in increasing order as shown in Table 1.
# Artificial Intelligence-Based Autonomous UAV Networks: A Survey
### Summary of Existing Surveys
A recent study [19] provides an in-depth survey of various applications being developed for UAV networks in current years. While the survey provides information on the recent work in this area, it also provides a detailed taxonomy of multiple UAV networks based on nine classes. Another vital highlight of the paper is proposing the three levels of autonomy based on how multiple UAVs work. Moreover, a detailed discussion of challenges and future trends is also provided. Researchers from both industry and academia have lately been proposing and designing various technologies and solutions for UAV networks. One of the recent survey studies [20] explores UAV networks from both security and privacy perspectives. In this study, the authors discuss issues such as sensor malfunctioning, unreliable communication between aerial and ground stations, and possible leakage of aerial-view images as critical factors in the success of UAV networks. The study suggests borrowing some techniques from MANET and vehicular technologies and molding them to apply to UAV networks to be used to address said concerns. A survey similar to our work was conducted in 2019 [21]. The survey provides a comprehensive discussion of the various machine learning (ML) techniques being used in UAV networks. The authors provide a classification of ML techniques based on the aspects of networks and communications. The survey proposes the use of ML techniques to achieve optimized performance for UAV networks. The authors proposed using a large amount of multi-source data and applying various deep-learning techniques to achieve a highly effective UAV network. Another recent study covering the ML techniques to aid the UAV networks is presented in [22]. The adaptive nature of UAV networks along with their context-changing awareness make UAVs a suitable candidate for future communication technologies. With the self-adaptive techniques, machine learning (ML) is regarded as one of the best technologies to aid UAV networks in achieving their communication and reliability goals. Various studies focus on UAV communications perspectives between ground and aerial stations. It was suggested that the use of mobile-edge computing (MEC) in tandem with UAV networks makes it more reliable and effective for future communications. An interesting study exploring the experimental setup, prototyping, and communication for UAV networks is reported in [23]. In this study, the authors have provided step-by-step techniques for establishing a prototype and experimental setup for UAV networks. This setup includes all stages, i.e., from the selection of aircraft to communications protocols. The authors also explored various protocols that can be used to provide effective and reliable communication in the case of UAVs. Insight into future trends and technologies is also provided, including recommendations for the use of machine learning, 5G cellular UAV networks, and aerial relays. Table 1 provides a summary of related surveys conducted in this area.
# Effects of On-Site PV Generation and Residential Demand Response on Distribution System Reliability
###### Abstract  
In the last few decades, there has been a strong trend towards integrating renewable-based distributed generation systems into the power grid, and advanced management strategies have been developed in order to provide a reliable, resilient, economic, and sustainable operation. Moreover, demand response (DR) programs, by taking the advantage of flexible loads' energy reduction capabilities, have presented as a promising solution considering reliability issues. Therefore, the impacts of combined system architecture with on-site photovoltaic (PV) generation units and residential demand reduction strategies were taken into consideration on distribution system reliability indices in this study. The load model of this study was created by using load data of the distribution feeder provided by Bosphorus Electric Distribution Corporation (BEDAS). Additionally, the reliability parameters of the feeder components were determined based on these provided data. The calculated load point and feeder side indicators were analyzed comprehensively from technical and economic perspectives. In order to validate the effectiveness of the proposed structure, four case studies were carried out in both DigSILENT PowerFactory and MATLAB environments.  
Keywords:distribution system; PV generation; reliability; residential demand response programs +
Footnote †: journal: Applied Sciences
# Biomedical Applications of Polymeric Cryogels
## 3 Cryogels in Medical Applications
### Controlled Drug Delivery
They synthesized MMC imprinted poly(hydroxyethyl methacrylate-N-methacryloyl-(l-)-histidine methyl ester)-Cu(II) [PHEMAH-Cu(II)] and MMC imprinted PHEMAGA cryogel membranes by free-radical bulk polymerization under partially frozen conditions. They synthesized these materials in different molar ratios of HEMA and MBAA at fixed amount of the template molecule for showing the effect of cross-linker in drug release rate. The increase of cross-linker ratio in the cryogel network resulted in the construction of more rigid and crosslinked polymers. They studied the release rate of MMC with different amounts of template and showed an increasing cumulative release of MMC with the increasing amount of MMC. [PERSON] et al. synthesized poly(hydroxyethyl methacrylate-N-methacryloyl-l-phenylalanine poly(HEMA-MAPA) membranes used for the controlled release of curcumin via UV-polymerization technique. Curcumin is used as a folk remedy and a drug for some certain diseases and cancers. They reported 7.4 optimal pH and 37 \({}^{\circ}\)C temperature for controlled release of curcumin. They obtained optimum values which were very compatible with in-body usage without any extreme release factors [50]. [PERSON] and [PERSON] have prepared 5-fluorouracilc (5-FU) imprinted cryogel discs using a coordination complex between metal-chelate monomer MAH and 5-FU with the assistance of Cu(II) ion [51]. The complex was copolymerized with HEMA to produce PHEMAH cryogel discs. It was observed that the cumulative release of 5-FU decreased by the increasing amount of cross-linker, and 5-FU transport mechanism was found to be non-Fickian. In another study, [PERSON] et al. prepared an implantable and degradable molecularly imprinted cryogel for pH-responsive delivery of doxorubicin (DOX) [36]. The release rate of DOX from cryogel discs increased in more acidic conditions. Kinetic studies have shown that the general release behavior of molecularly imprinted cryogel discs was a combination of diffusion and erosion control. In another study, [PERSON] et al. prepared temperature-sensitive poly(ethoxyrtiethyleneglycol acrylate) cryogels for controlled releasingof the hydrophilic drug verapamil hydrochloride. The controlled releasing of verapamil hydrochloride reported over a period of more than 8 h [52].
## 4 Experiments and Analysis
### Experimental Setup
#### 4.1.2. Baselines  
The baselines for our experiments include three state-of-the-art methods: one NLP-based method, SAFE [7], and two GNN-based methods, FIT [10] and GMN [9]. All baseline methods are implemented according to the original papers. The evaluation metrics used in the experiments are listed in Table 6. We define TP as the number of correctly detected similar pairs, FN as the number of dissimilar pairs incorrectly detected as similar, and FN as the number of similar pairs incorrectly detected as dissimilar.
## 3 Background Theory
### Lightweight Cryptography Final Candidate
#### 3.4.2 Tiny JAMBU  
Tiny JAMBU was one of the finalists in the lightweight cryptography algorithm selection contest organized by NIST. Tiny JAMBU is designed based on the JAMBU block cipher by providing data authentication features. Tiny JAMBU has three key variants, namely 128-bit (Tiny JAMBU-128), 192-bit (Tiny JAMBU-192), and 256-bit (Tiny JAMBU-256). Tiny Jambu was designed by [PERSON] and [PERSON] by adding security parameters in the form of a 64-bit tag and a 96-bit nonce [23].
## 7 Conclusions  
Optimizing DL TS to estimate CSI in m-MIMO systems in a general situation with single-stage precoding and when users exhibit different channel covariance matrices is extremely challenging. Even though there are sophisticated algorithms available to deal with the issue of optimizing the TS to accurately estimate CSI, they tend to have slow convergence speeds and high computation complexity. To address this issue, this paper proposed a feasible DL TS to estimate the CSI with a low-computational requirement. The proposed TS method works with limited CCT aiming to maximize the DL sum rate performance. The performance of the proposed training design is assessed in terms of the maximum rate that can be achieved, and it is compared with the most advanced methods available. The results illustrated that the proposed TS method outperforms the existing methods in the medium and high SNR regimes. The enhancement in the system performance is achieved by maximizing the rate of the FDD m-MIMO system, without increasing system complexity and overhead. Specifically, this paper demonstrated that a DL TS design that is practical and not overly complex can be attained by taking into account the sum of the eigendecomposition of the channel's covariance matrices. The obtained results signify the effectiveness of using the proposed TS solution for practical consideration in comparison with the existing DL TS solutions.  
Figure 11: Rate as a function of the number of BS antenna based on OR model with different spatial channel correlations, i.e., \(\omega_{\rm H}=\{5^{\circ},15^{\circ}\}\), SNR = 15 dB, and \(\tau_{\rm c}=100\). (a) \(\omega_{\rm H}=5^{\circ}\); (b) \(\omega_{\rm H}=15^{\circ}\)[36; 41].  
**Author Contributions:** Conceptualization, M.A.N. and M.A.; methodology, M.A.N., A.M.A.-H. and M.A.; validation, B.M.M., S.H.A. and M.A.; investigation, A.M., M.A. and A.M.A.-H.; resources, A.M.A.-H., A.M., B.M.M. and S.H.A.; writing--original draft preparation, M.A.N., A.M.A.-H., M.A. and A.M.; writing--review and editing, B.M.M., S.H.A., M.A. and M.A.N.; visualization, M.A.N., M.A., S.H.A., B.M.M. and A.M.; supervision, M.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
All data are available within the manuscript.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} m-MIMO & Massive multiple-input multiple-output \\ TS & Training sequence \\ FDD & Frequency-division-duplex \\ CCT & Channel coherence time \\ URLLC & Ultra-reliable and low-latency communications \\ BS & Base station \\ CSI & Channel state information \\ TDD & Time-division-duplex \\ UL & Uplink \\ SNR & Signal-to-noise ratio \\ LTE & Long-term evolution \\ MMSE & Minimum-mean-square-error \\ ZFBF & Zero-forcing \\ OFDM & Orthogonal frequency-division multiple \\ DPC & Dirty paper coding \\ CCM & Channel covariance matrix \\ \end{tabular}