esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Neutron Talbot-Lau Grating Interferometry at HZB  
Talbot-Lau neutron interferometry experiments were performed at the HZB-CONRAD2 beamline using polychromatic beam at an interferometry design wavelength of 0.35 nm [40; 41]. The \(L_{1}\) distance was 4.78 m and the intergrating distance (\(D\)) was 2.27 cm. The G2-detector (\(L_{2}\)) was 4 mm. The source grating, G0, had a period of 790 \(\upmu\)m with 20 \(\upmu\)m Gd thickness, the phase grating, G1, a period, (\(p_{g1}\)), of 7.96 \(\upmu\)m with 38 \(\upmu\)m thick Si for a \(\pi\)-phase shift at 0.35 nm, and the analyzer grating, G2, a period of 4 \(\upmu\)m with 10 \(\upmu\)m thick Gd. The instrument operated in the first order Talbot distance with a \(p_{g1}\) = 7.97 \(\upmu\)m, \(z\) = 5.0 cm, and \(D\) = 2.27 cm, yielding \(\xi\) = 1.97 \(\upmu\)m.  
Batteries were charged, imaged, discharged, and imaged as described in Section 2.1. Li-po batteries of differing capacities (15 mAh, 43 mAh, 50 mAh, and 90 mAh) were attached to an aluminum plate. Batteries were first imaged at their initial resting voltage (around 3.7 V) with 20 s exposures at three different angles (0\({}^{\circ}\), 10\({}^{\circ}\), and 20\({}^{\circ}\)) with 14 steps over 1.2 mm, slightly above the 0.8 mm period of the grating. The 15 mAh batteries were charged up to 4.2 V at 15 mA and left to rest for 20 min, following by \"charged\" imaging. After imaging, the batteries were discharged at 15 mA to 2.7 V and left to rest for 20 min. At this point, \"discharged\" imaging took place.
## 1 Introduction
The system exhibited a visible color change from yellow to colorless, attributed to a redox reaction between the AgNPs and mercury ions, enabling direct visual detection [16]. Collectively, these studies demonstrate that the integration of silver nanoparticles into polymer matrices not only improves colorimetric properties, but also imparts multifunctionality, including sensing capability, antimicrobial activity, and enhanced thermal stability. These features position AgNP-based polymer composites as promising candidates for active packaging, smart textiles, and environmental monitoring systems. The key scientific question we address, therefore, is whether anthocyanin-derived, plasmonically active AgNPs can simultaneously enhance color stability and moisture management in starch/PVA films, thereby transforming a fragile biopolymer into a multifunctional, intelligent packaging material. To answer this question, we synthesize AgNPs using anthocyanin extracts from pigmented native potatoes and embed them into potato-starch/PVA matrices. The effects of this incorporation on color stability and moisture of the resulting materials are evaluated. This work not only highlights the valorization of underutilized Andean agrobiodiversity, but also contributes to the development of sustainable, functional materials with potential applications in active packaging and biomedical systems.
## 4 Experiments and Analysis
### Comparative Experiment
At a prediction window of 96, the CT-Transformer achieved relative reductions in mean absolute error (MAE) of 45.9%, 26.9%, 51.3%, 26.4%, 49.6%, and 8.70% compared with Autoformer, ETSformer, FEDformer, NHiTS, SCINet, and GBT, respectively. It also reduced the mean squared error (MSE) by 65.2%, 37.2%, 70.2%, 38.3%, 67.6%, and 14.4%, respectively. At prediction windows of 192, 336, and 720, the CT-Transformer similarly demonstrated significant reductions in MAE and MSE, showcasing superior accuracy and robustness over advanced time-series forecasting models. These comparisons are depicted in bar charts in Figures 7 and 8, comparing the MAE and MSE values of the proposed fusion model with those of the baseline models across the four prediction lengths. To more clearly illustrate the superiority of the proposed model, Figure 9 presents comparison plots between our model and the baseline models, showcasing the valid values across various prediction window sizes. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Source**} & \multirow{2}{*}{**Metrics**} & \multicolumn{4}{c}{**PeMS Traffic Dataset**} \\ \cline{3-6}  & & & **96** & **192** & **336** & **720** \\ \hline \multirow{2}{*}{CT-Transformer Module} & & MAE & 0.231 & 0.236 & 0.234 & 0.258 \\ \cline{3-6}  & & MSE & 0.137 & 0.142 & 0.138 & 0.156 \\ \hline \multirow{2}{*}{Autoformer} & \multirow{2}{*}{NeurIPS2021} & MAE & 0.427 & 0.450 & 0.513 & 0.551 \\ \cline{3-6}  & & MSE & 0.394 & 0.425 & 0.509 & 0.573 \\ \hline \multirow{2}{*}{ETSformer} & \multirow{2}{*}{arXiv2022} & MAE & 0.316 & 0.325 & 0.324 & 0.329 \\ \cline{3-6}  & & MSE & 0.218 & 0.233 & 0.220 & 0.238 \\ \hline \multirow{2}{*}{FEDformer} & \multirow{2}{*}{PMLR2022} & MAE & 0.474 & 0.392 & 0.477 & 0.511 \\ \cline{3-6}  & & MSE & 0.460 & 0.326 & 0.452 & 0.510 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparative experimental results of different models at various prediction window sizes. Figure 9: Comparative prediction results of different models at prediction window sizes of 96, 192, 336, and 720.
# On Mathematical Modelling of Automated Coverage Optimization in Wireless 5G and beyond Deployments
###### Abstract  
The need to optimize the deployment and maintenance costs for service delivery in wireless networks is an essential task for each service provider. The goal of this paper was to optimize the number of service centres (gNodeB) to cover selected customer locations based on the given requirements. This optimization need is especially emerging in emerging 5G and beyond cellular systems that are characterized by a large number of simultaneously connected devices, which is typically difficult to handle by the existing wireless systems. Currently, the network infrastructure planning tools used in the industry include Atoll Radio Planning Tool, RadioPlanner and others. These tools do not provide an automatic selection of a deployment position for specific gNodeB nodes in a given area with defined requirements. To design a network with those tools, a great deal of manual tasks that could be reduced by more sophisticated solutions are required. For that reason, our goal here and our main contribution of this paper were the development of new mathematical models that fit the currently emerging scenarios of wireless network deployment and maintenance. Next, we also provide the design and implementation of a verification methodology for these models through provided simulations. For the performance evaluation of the models, we utilize test datasets and discuss a case study scenario from a selected district in Central Europe.  
network coverage capacity; optimization; wireless networks; 5G; set covering problem +
Footnote †: journal: Applied Sciences
## 3 Methods
### Solution Finding of MNSTLA Model  
The optimization method based on the ADMM is used to work out Formula (16). Formula (16) can be rewritten as an augmented Lagrange function:  
\[L(D,B,T,E,L,Z,Y,\mu)\]  
\[=\left|\left|Z\right|\right|_{\gamma,*}+\lambda_{1}\left|\left|L\right|\right| _{\gamma}+\lambda_{2}\left|\left|T\right|\right|_{w,1}+\left\langle Y_{1},Z-B \right\rangle+\left\langle Y_{2},L-V(B)\right\rangle+\left\langle Y_{3},D-B-T-E\right\rangle\]  
\[+\frac{\mu}{2}\left(\left|\left|Z-B\right|\right|_{F}^{2}+\left|\left|L-V(B) \right|\right|_{F}^{2}+\left|\left|D-B-T-E\right|\right|_{F}^{2}\right)+ \lambda_{3}\left|\left|\left|E\right|\right|_{2,1}\]\[s.t.\ D=B+T+E,Z=B,L=V(Z) \tag{17}\]  
where \(Y_{*},\ \mu\) are an Lagrange multiplier and a positive penalty scalar, \(\langle*\rangle\) represents the inner product, and \(||*||_{F}\) is the norm for Frobenius.  
The ADMM method is used to iteratively update the \(Z\) and \(L\) by the Formula (17), respectively:  
\[Z^{k+1}=\underset{Z}{arg\ min}||Z||_{\gamma,*}+\frac{\mu^{k}}{2}||Z-B^{k}+ \frac{Y_{1}{}^{k}}{\mu^{k}}||_{F}^{2} \tag{18}\]  
\[L^{k+1}=\underset{L}{arg\ min}||L||_{\gamma}+\frac{\mu^{k}}{2}||L-V(B^{k})- \frac{Y_{1}{}^{k}}{\mu^{k}}||_{F}^{2} \tag{19}\]  
Find their solutions by t-SVD [20] operation and unit contraction operator, respectively:  
\[Z^{k+1}=D_{W/\mu^{k}}(B^{k}-\frac{Y_{1}^{k}}{\mu^{k}}) \tag{20}\]  
\[L^{k+1}=Th_{\lambda_{1}/\mu^{k}}\Big{(}V(B^{k})-\frac{Y_{2}{}^{k}}{\mu^{k}} \Big{)} \tag{21}\]  
where \(D(^{*})\) represent the t-SVD operation and \(Th(^{*})\) represent the unit contraction operator.  
Extract the term containing \(B\) from the Formula (17):  
\[B^{k+1}=\frac{\mu^{k}}{2}(||D-B-T^{k}-E^{k}+\frac{Y_{1}^{k}}{\mu^{k}}||_{F}^{ 2}+||Z^{k+1}-B+\frac{Y_{2}^{k}}{\mu^{k}}||_{F}^{2}+||L^{k+1}-V(B)+\frac{Y_{3}^ {k}}{\mu^{k}}||_{F}^{2}) \tag{22}\]  
The Formula (22) is equivalent to the following linear equations:  
\[(2I+V(B))B^{k+1}=D-T^{k}-E^{k}+\frac{Y_{1}^{k}}{\mu^{k}}+Z^{k}+\frac{Y_{2}^{k }}{\mu^{k}}+V^{T}(V_{B}^{k}+\frac{Y_{3}^{k}}{\mu^{k}}) \tag{23}\]  
The closed form of the Formula (23) can be obtained by 3D Fast Fourier Transform:  
\[B^{k+1}=ifftn(\frac{fftn(D-T^{k}-E^{k}+\frac{Y_{1}^{k}}{\mu^{k}}+Z^{k}+\frac{ Y_{2}^{k}}{\mu^{k}}+V^{T}(V_{B}^{k}+\frac{Y_{3}^{k}}{\mu^{k}}))}{2\mu^{k}I+\mu^{k} \big{|}fftn(V(B)\big{|}^{2}}) \tag{24}\]  
where \(fftn\) is the fast 3D Fourier transform and \(ifftn\) is the inverse transform of the \(fftn\).  
Variables \(T\) and \(E\) are corrected:  
\[T^{k+1}=\underset{T}{arg\ min}\lambda_{2}||T||_{W,1}+\frac{\mu^{k}}{2}||D-B^{k +1}-T-E^{k}+\frac{Y_{3}^{k}}{\mu^{k}}||_{F}^{2} \tag{25}\]  
\[E^{k+1}=\underset{E}{arg\ min}\lambda_{3}||E||_{2,1}+\frac{\mu^{k}}{2}\Big{|} \Big{|}D-B^{k+1}-T^{k+1}-E\Big{|}_{F}^{2} \tag{26}\]  
By using the element-by-element shrinkage operation method in references [29; 36], we obtain:  
\[T^{k+1}=Th_{\lambda_{W/\mu^{k}}}(D-B^{k+1}-E^{k}-\frac{Y_{3}^{k}}{\mu^{k}}) \tag{27}\]  
\[E^{k+1}=\frac{\mu^{k}(D-B^{k+1}-T^{k+1}-\frac{Y_{3}^{k}}{\mu^{k}})+Y_{3}^{k}}{ \mu^{k}+2\lambda_{3}} \tag{28}\]
# The Effect of Cement Aging on the Stability of a Cement-in-Cement Revision Construct
## 2 Materials and Methods  
We analyzed the influence of cement aging on a cement-in-cement revision construct using the bending strength as the evaluation criterion. For the study, we used Palacos R+G (Heraeus, Hanau, Germany) and SmartSet MV (DePuy Synthes, Warsaw, IN, USA) bone cement. The monomer of both cement types contains 98% of methyl methacrylate and 2% N,N-dimethyl-p-toluidine. The cement powder of the Palacos R+G bone cement contains 81.9% of poly (methylacrylate, methyl methacrylate), 15% zirconium dioxide, 1% hydrous benzooyl peroxide, and 2.1% gentamicin sulphate. The compositions of the SmartSet MV powder contain 67% Polymetyl Metheacrylate, 21.1% Methyl Methaacrylamide/Styrene Copolymer, 1.9% Benzoyl Peroxide, and 10% Barium Sulfate. A total amount of 112 samples were prepared and tested from both types of cement. The samples were divided into four groups, and each group was differed into four examination times. In two of the four groups, cement-in-cement revision constructs were applied. This resulted in the groups Palacos-Revision and Smartset-Revision. The groups, Palacos-Reference and SmartSet-Reference, served as aged-only reference groups. The time of examination results from four different aging states. According to ISO 5833:2002-05, the first point in time was 24 \(\pm\) 2 h after sample preparation [17]. The further examinations were 4 weeks plus 1 day, 4 weeks plus 3 days, and 4 weeks plus 10 days. A set of 7 specimens was tested at each investigation time point for each group.
# An Edible, Decellularized Plant Derived Cell Carrier for Lab Grown Meat
## 3 Results
### Sample Decellularization
#### 3.1.1 DNA Quantification of Decellularized Florets  
The DNA quantification assay demonstrated that florets, having been submitted to the decellularization protocol, lost nearly all DNA content (Figure 2a). Native samples maintained an average of 2.045 \(\pm\) 0.833 ug DNA/mg tissue, while decellularized samples retained an average of 0.0037 \(\pm\) 0.00961 ug DNA/mg tissue.
# Cooperative Standoff Tracking of Moving Targets Using Modified Lyapunov Vector Field Guidance
## 5 Experiments and Results
### Numerical Simulation
#### 5.1.2 Estimation of Target States Using IMM-UKF  
In this section, numerical experiment is carried out to verify the validity of the state estimator. When estimating the target state using IMM-UKF, the motion models mentioned in Section 4 are used. The target trajectory is designed as shown in Figure 12. A gaussian noise of which \(\mu=0\) and variance \(\sigma^{2}=10\) is imposed on the position of the target in the direction x and y, respectively.  
The estimation error in position is shown in Figure 13. Based on the IMM-UKF state estimation algorithm, the position error is reduced from 10.4 m to 5.35 m. It can be seen that when the target is turning around \(t\in(180,280)\cup(500,600)\), the error is relatively significant, owing to the fact that the motion model is not accurate enough.  
The same trend can be seen in the velocity estimation (Figure 14a,b), where \(t\in(180,280)\cup(500,600)\), the velocity estimation is relatively inaccurate.  
Figure 11: Experiments results with respect to an initial UAV position near the standoff circle. (**a**) Trajectories of UAVs; (**b**) Distance error versus time; (**c**) Course rate versus time.  
This section presents the experimental results of target state estimation. The estimator works well when the target moves along a straight line, while the estimation error increases when the target changes its moving direction. The results are consistent with the notion that it is harder to estimate the state of nonlinear motion.
## 5 Experiments and Results
### Fault Detection and Classification
#### 5.2.3 Adaptation and Application of Deep Learning Model Architectures  
This study incorporates a refined methodology rooted in our previous work [65]. In that foundational research, we evaluated the application of diverse deep learning architectures for effectively distinguishing between _healthy_ and _faulty_ WTBSs using thermal imagery. Leveraging the insights gained, we selectively adapted those architectures that previously demonstrated exceptional performance for this particular task.  
Our approach focused on a curated assortment of deep learning models, each fine-tuned for the binary classification of WTBs as either _healthy_ or _faulty_. The architectural spectrum ranged from early convolutional networks like AlexNet and the VGG series to more advanced frameworks such as ResNet and DenseNet, extending to the latest innovations with the Vision Transformer (ViT). This selection was guided by the successful outcomes and efficacy of these models in our earlier investigations, facilitating a robust examination of various image classification strategies tailored to the unique requirements of WTB condition assessment.  
Crucial to our strategy was the specific customization of each model for binary classification, building on their proven capabilities. This entailed detailed adjustments to their terminal layers, categorizing them according to the nature of these layers--either as classifiers or fully connected layers. Such modifications were essential for precise tuning towards binary classification of PFB health. Employing a structured configuration approach, we utilized a _base_config_ dictionary as a pivotal organizational tool. This facilitated a systematic arrangement of models based on architectural characteristics and the requisite end-layer modifications, ensuring the application of our previous knowledge for the optimal adaptation of each architecture to the task at hand, as documented in our prior work [65].
# A Mathematical Study of COVID-19 Spread by Vaccination Status in Virginia
## 3 Results
### Effect of Waning Immunity and Delta Variant on Vaccine Efficacy  
Recent studies have suggested that the COVID-19 immunity conferred by vaccination wanes over time [29; 30; 31] and that the Delta variant may be more likely to produce breakthrough cases [38]. To test for these effects in the VDH data, we introduce the following piecewise-constant vaccination efficacy rates \(\alpha_{1}(t)\) and \(\alpha_{2}(t)\) to the two-stage vaccination  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Interval** & **Dates** & **\% Part Vax** & **\% Full Vax** & \(\beta(t)\) & \(R_{t}\) \\ \hline
1 & 1/23–2/21 & 4.4\% & 0.5\% & 0.153 & 0.786 \\
2 & 2/22–3/23 & 14.0\% & 6.1\% & 0.200 & 0.978 \\
3 & 3/24–4/22 & 26.7\% & 14.7\% & 0.251 & 1.106 \\
4 & 4/23–5/21 & 44.3\% & 29.1\% & 0.241 & 0.906 \\
5 & 5/22–6/20 & 53.2\% & 42.7\% & 0.238 & 0.762 \\
6 & 6/21–7/20 & 58.2\% & 50.3\% & 0.464 & 1.312 \\
7 & 7/21–8/19 & 60.7\% & 53.7\% & 0.539 & 1.404 \\
8 & 8/20–9/18 & 64.2\% & 56.1\% & 0.453 & 1.119 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Vaccination level, effective transmission rate, and the effective reproduction number for the spread of COVID-19 in Virginia over the period 23 January through 18 September 2021.  
model (2) with piecewise-constant transmission rate \(\beta(t)\) (3) and vaccination uptake curves \(V_{i}(t)\) (4):  
\[\alpha_{i}(t)=\left\{\alpha_{i,j},\quad\frac{j-1}{4}L\leq t<\frac{j}{4}L,j=1, \ldots,4\right. \tag{12}\]  
where \(L\) is the number of days the simulation period. That is, we divide the interval of consideration into four parts of equal length, each with a constant rate reduction of partial or fully vaccinated individuals becoming infected with COVID-19. For the simulation period of January 23 through September 18, 2021, this corresponds to \(L=238\) and \(L/4=59.5\) so that the transmission reduction rates is allowed to change roughly every second month.  
In order to establish population-level waning immunity in the model (2) with time-dependent \(\alpha_{1}(t)\) and \(\alpha_{2}(t)\) (12), we expect to see \(\alpha_{i,1}>\alpha_{i,2}>\alpha_{i,3}>\alpha_{i,4}\). We also expect that the fit would be significantly better than the one obtained with the model (2) and the parameter values in Table 2. In fact, we do not observe any significant trend in the best-fitting \(\alpha_{i,j}\) values (\(\alpha_{1,1}=0.363\), \(\alpha_{1,2}=0.518\), \(\alpha_{1,3}=0.583\)\(\alpha_{1,4}=0.750\), \(\alpha_{2,1}=1.00\), \(\alpha_{2,2}=0.879\), \(\alpha_{2,3}=0.880\), \(\alpha_{2,4}=0.908\)). We also do not observe a significant reduction in the error function (7). Our analysis consequently does not support including waning immunity or changes in the efficacy of the vaccines against the Delta variant at the population level. We caution, however, against drawing the conclusion that waning immunity is not occurring since the model (2) is only able to estimate vaccine efficacy values at the population rather than individual level.
###### Abstract  
Transitioning a Terrestrial Trunked Radio (TETRA) network to a Long-Term Evolution (LTE) network in public protection and disaster relief (PPDR) systems is a path to providing future services requiring high radio interface throughput and allowing broadband PPDR (BB-PPDR) radio communications. Users of TETRA networks are currently considering how to deploy a BB-PPDR network in the coming years. This study offers several radio planning considerations in TETRA to LTE migration for such networks. The conclusions are obtained from a case study in which both measurements and radioelectric coverage simulations were carried out for the real scenario of the Murcia Region, Spain, for both TETRA and LTE systems. The proposed considerations can help PPDR agencies efficiently estimate the cost of converting a TETRA network to an LTE network. Uniquely in this study, the total area is divided into geographical areas of interest that are defined as administrative divisions (region, municipal areas, etc.). The analysis was carried out using a radio planning tool based on a geographic information system and the measurements have been used to tune the propagation models. According to the real scenario considered, the number of sites needed in the LTE network--for a specific quality of service (90% for the whole region and 85% for municipal areas)--is a factor of 2.4 higher than for TETRA network.  
radio planning; TETRA; LTE; geographical information systems (GIS)
# Wavelet-Based Estimation of Damping from Multi-Sensor, Multi-Impact Data
## 3 Damping Extraction Methods
### Frequency-Domain Damping Extraction Methods  
Frequency-domain damping extraction methods are techniques used to determine the damping characteristics of a structure by analyzing its response in the frequency domain. These methods convert time-domain signals into the frequency domain using techniquessuch as the Fourier transform. This transformation allows for a detailed analysis of the system's resonant frequencies and their associated damping characteristics. A variety of methodologies have been developed for this purpose, ranging from simple curve-fitting strategies to more sophisticated algorithms that leverage discrete Fourier. These methods are highly effective in handling complex signals and provide clear insights into the damping behavior of different modes. This section delves into the mathematical formulations and practical applications of these well-established methods, highlighting their importance in the accurate assessment of damping properties in various structural systems.
## 3 NB-IoT Architecture
Both IP data packets and non-IP data packets are supported by this sequence [21; 29]. A more detailed view of the architecture of NB-IoT based on the used protocol stacks shows 6 layers of protocols (Figure 7), which are the physical, Medium Access Control (MAC), Radio Link Control (RLC), Packet Data Convergence Protocol (PDCP), Radio Resource Control (RRC), and Non-Access Stratum (NAS) layers, in order from the lower to upper layers. The upper layers such as NAS provide security based on LTE. Additionally, the PDCP and MAC layers are intended to enable security with different schemes for access control and resource distribution. The RLC also provides security in addition to its responsibility to support the mobility of the devices. The RRC layer in NB-IoT functions similarly to in LTE. There are real-time limitations regarding NB-IoT applications that can be better managed using tailored protocols such as the Constrained Application Protocol (CoAP) and IPv6, which goes over WPAN or 6 LoWPAN [5; 27].
# A Review of Plant Disease Detection Systems for Farming Applications
## 2 Literature Review: Precision Agriculture Research Developments
### Feature Extraction  
One of the foundational elements of computer-vision-based image recognition is the extraction of features [65]. A feature is data that are utilized to solve a particular computer vision problem and is a constituting part of a raw image [64]. The feature vectors include the features that have been retrieved from an image [66]. An extensive range of techniques is used to identify the items in an image while creating feature vectors [62]. Edges, image pixel intensity, geometry, texture, and image modifications such as Fourier, Wavelet, or permutations of pixels from various color images are the primary features [46; 66]. Use as a set of classifiers and machine learning algorithms is feature extraction's ultimately purpose [66]. The feature extraction in plant leaf disease-monitoring systems is subdivided into three spheres that include texture, color, and shape [20; 21; 46; 65].