esa-sceva/satcom-chunk-collection huggingface dataset:
# B5 GEMINI: AI-Driven Network Digital Twin
###### Abstract  
Network Digital Twin (NDT) is a new technology that builds on the concept of Digital Twins (DT) to create a virtual representation of the physical objects of a telecommunications network. NDT bridges physical and virtual spaces to enable coordination and synchronization of physical parts while eliminating the need to directly interact with them. There is broad consensus that Artificial Intelligence (AI) and Machine Learning (ML) are among the key enablers to this technology. In this work, we present B5 GEMINI, which is an NDT for 5G and beyond networks that makes an extensive use of AI and ML. First, we present the infrastructural and architectural components that support B5 GEMINI. Next, we explore four paradigmatic applications where AI/ML can leverage B5 GEMINI for building new AI-powered applications. In addition, we identify the main components of the AI ecosystem of B5 GEMINI, outlining emerging research trends and identifying the open challenges that must be solved along the way. Finally, we present two relevant use cases in the application of NDTs with an extensive use of ML. The first use case lays in the cybersecurity domain and proposes the use of B5 GEMINI to facilitate the design of ML-based attack detectors and the second addresses the design of energy efficient ML components and introduces the modular development of NDTs adopting the Digital Map concept as a novelty.  
digital twin; network digital twin; artificial intelligence; machine learning; telecommunications
# Extended Geometry and Probability Model for GNSS+ Constellation Performance Evaluation
## 5 Discussion  
This article aims to provide a convenient way of assessing the global distribution of DOP and satellite visibility for the arbitrary navigation satellite constellation design. To do so, we replace the conventional point-by-point simulation of the constellation by statistical considerations of the probability of finding in certain viewing directions. We extend an earlier work of [PERSON] et al. (2015) and [PERSON] et al. (2016) to include non-circular orbits in analysis and present results for various sets of mixed-orbit constellations. Unfortunately, a disadvantage is also existing that EGAPM is not suitable to assess the ambiguity dilution of precision (ADOP), which can predict when ambiguity resolution can be expected to be successful ([PERSON] et al., 2017; [PERSON] et al., 2018; [PERSON] et al., 2019). Applying EGAPM, if a large number of grids or points can be viewed regarded as satellites and their ambiguities are estimated, the computation burden will be too heavy. What is worse, the calculation results of ADOP are not coincident with corresponding results calculated by GFZ precise ephemerides. However, in our next research, we will commit to finding a more ingenious way to solve this problem.
# Computational Investigations and Control of Shock Interference +
## 3 Model Validation  
A backpressured isolator with a Mach 2.5 inflow was utilized as a configuration to perform model validation. The measurements of wall pressure at multiple axial locations were obtained at the Isolator Dynamics Research Laboratory (IDRL) at NASA Langley Research Center (NASA LaRC) [22]. Specifications of this facility, as well as a detailed description of the setup used (for the computational and experimental studies), can be found in the work by [PERSON] et al. [22]. These data were utilized for model validation in the current paper. In the previous work of [PERSON] et al. [9], a comparison between computed wall pressures and NASA LaRC IDRL calculations done by [PERSON] et al. [22], for this experimental set, has been presented. It should be noted that multiple boundary conditions for this setup have been published, but in this work, the boundary conditions for the computational investigations were obtained from those published in [PERSON] et al. [9].  
In the published experimental work, a backpressure of 304.51 kPa was obtained from measurements (by averaging the 20 surface pressure measurements taken around the periphery of the isolator duct at the x = 22 h station) when a pressure plug was used at the isolator exit. Likewise, the time-averaged values of wall pressure obtained from sensors mounted at multiple locations were reported, which have been utilized for comparison with the time-averaged RANS calculations of the same setup. It is likely that the implementation of a pressure plug to generate backpressure at the isolator exit could have contributed to initial transience and low-amplitude oscillations throughout the steady-state operation and resulted in experimental uncertainty. However, the uncertainty in experimental results and experimental error were not reported in the published data on the experimental case. Since time-averaged experimental data without error and uncertainty were reported, RANS was utilized for the simulation of the time-averaged flow field in this isolator.  
Figure 5: Code Verification: Comparison of numerical results with exact solution at \(t=0.035\) s.
## 4 Discussion
### Discussion of the Model Training and Model Behavior  
Each parameter set was calculated with ten different random initializations. The scores are the mean of these ten calculations. The best runs of each parameter set yield 36% lower RMSE on average than the corresponding mean over all ten runs. All models are far less suited for the validation sand-binder system D. Interestingly, models that do not achieve the best RMSE for the test data have better scores for the validation data. This difference suggests that despite the ten randomly initialized runs, there was some overfitting toward the test data. Figure 15 summarizes this behavior. Despite the lower prediction quality, this graph shows a clear learning process by the models. The best models score a mean RMSE for the validation data of 0.166. This is 78% higher than the best model score for the test data. Nevertheless, they score a similar mean RMSE for both test (0.148) and validation data (0.167), suggesting that these models are less suited for the test data but are similar and partially valid for other sand-binder systems.  
The models abstracted some information suitable to predict the validation system D. However, they lacked relevant parameters during the training phase to gain similar prediction values as they gained for the test data. Figures 17-22 show robust behavior for the test data without significant outliners. The models can be summarized as robust and usable, but only for sand-binder systems known through the training data. Newsand-binder systems can contain variations in the features not modeled during the training, which might change by including more sand-binder systems in future works.  
Figure 14 summarizes R\({}^{2}\) for the models shown in Figure 13. The R\({}^{2}\)-scores support the previously derived model behavior. DS 9 shows the most reliable scores of all datasets with a low difference between the highest achieved score and the best mean model. All datasets were able to score R\({}^{2}\) values above 0.75, indicating that models could learn a significant part of the cause-effect correlations between the input data and the target value DM. Furthermore, it shows the mean MAE achieved by the models in Figure 13 divided by the standard deviation of the target value. The mean MAE is 34% to 57% of the standard deviation of DM. Thus, the prediction is below the datasets' variations, indicating functioning models.  
Interestingly, the XGB models yielded better scores than the ANN in most cases, which can be explained by the features contained in the dataset. These can be used directly for predicting the target features. No complex model building or meta-feature calculations are needed, which is confirmed by Figures 17-20. Only the very low number of neurons have slightly worse scores than the more complex ones. Starting with 20 neurons in each layer, the mean RMSE does not improve significantly with more neurons.
## 3 Results and Discussion  
### Soil Characterization  
The grain size fractions used in the experiment were separated by dry sieving a sandy soil collected from a sand pit in Ottendorf, Germany. The total pore volumes of the resulting fractionsvaried from 37.4% to 44.3% (Table 2). These values correspond to pore volumes of sandy soils [11], which are in the range of 36% to 56%. The largest pore volume (44.3%) was observed for the fraction 125 um to 200 um while the smallest pore volume (37%) was observed for the fraction 1000 um to 2000 um. This corresponds to the theory that the smaller the grains, the greater the deviation from the ideal spherical shape that creates larger pore spaces [11].  
The PSD index parameters n and \(\lambda\) estimated by fitting the experimental data with the van [PERSON] and Brooks/Corey models indicate that the fraction 630 um to 1000 um has the widest range of pore sizes while the fraction 125 um to 200 um has the most uniform pore size.  
The pore diameters are directly proportional to the grain sizes of the different fractions, which is also confirmed by the investigations of [56]. The pore size distribution curve of the fraction 125 um to 200 um shows a peak at 47 um, with 90% of the pores between 30 um and 70 um; the fraction 200 um to 630 um shows a peak at 95 um with 90% of the pores between 40 um and 150 um; the fraction 630 um to 1000 um shows a peak at 230 um with 90% of the pores between 87 um and 320 um and the fraction 1000 um to 2000 um shows a peak at 425 um with 85% of the pores between 230 um and 800 um.  
The pH values of the individual fractions (Table 2) fall slightly in the acidic range but are within the interval of bacterial optimal growth conditions (pH 6-8) [57].
## 3 DI-distributed Localization (DIL) Algorithm
### Localization Algorithm  
In the localization algorithms, first the distance measurement mechanism of the normal nodes is introduced from the received signal strength indicator (RSSI) value of the beacon nodes. The algorithms are proposed to compute the coordinate of each normal node based on the angle information from an anchor node and distance information from at most three beacon nodes taking the fading and shadowing effects due to noise and obstacles, as described below.
# Mechanistic Insights into the Bornyl Diphosphate Synthase from _Lavandula angustifolia_
## 3 Results
### The Predicted Ligand-Binding Sites of LaBPPS-GPP Complex  
The structural prediction of LaBPPS using AlphaFold2 [35; 36; 49] demonstrated high reliability (Figure 2; Table 3). The structural model included both the terpene synthase and terpene cyclase domains (Figures 1 and S1). Based on this model, molecular docking was performed using AutoDock software, AutoDock 4.2.6 (La Jolla, CA 92037, USA) to generate a protein-substrate complex (Figure 4). The binding energy, which served as an indicator of docking success, was calculated to be \(-\)5.79 kcal/mol, significantly exceeding the threshold of \(-\)1.2 kcal/mol, thereby confirming the validity of the docking results. In the predicted protein-substrate complex, the substrate geranyl diphosphate (GPP) was positioned securely within the binding pocket, with electrostatic interactions involving magnesium ions (Mg\({}^{2+}\)), the phosphate group of GPP, and the side chains of aspartic acid and glutamic acid facilitating the binding (Figure 4).  
Figure 4: **The structural model of LaBPPS-GPP binary complex.** The substrate GPP is depicted in stick representation, and magnesium ion (Mg\({}^{2+}\)) is shown as yellow spheres. (**a**) The surface representation of the model, with color coding indicating charge distribution: red for negative charges, blue for positive charges, and white for neutral charges. (**b**) The ribbon representation of the model, with a close-up view of the active sites on the right. Polar and charged interactions are represented by dashed lines.  
Figure 3: **Dynamic light scattering (DLS) spectrum of LaBPPS.** The analysis revealed that LaBPPS has a hydrodynamic radius of 5.9 \(\pm\) 0.3 nm.  
The results of mutational experiments showed that substituting alanine for the amino acids D356, D360, R497, D501, or E508 resulted in a 50- to 100-fold reduction in enzymatic activity (Figure 5). This suggested that these negatively charged residues are strategically positioned to interact electrostatically with the positively charged magnesium ion (Mg\({}^{2+}\)), thereby stabilizing and neutralizing the phosphate group of the substrate GPP. This evidence underscores the critical role of these conserved negatively charged residues (D356, D360, R497, D501, and E508) in maintaining the bornyl diphosphate synthase activity of LaBPPS.
## 4 Results
### Ground Reflection  
All antenna measurements were performed in an open-area test site (OATS), i.e., on a beach after more than 15 rainless days. The calibration of the electrically short  
Figure 8: Antenna input characteristics, with and without balun: (**a**) resistance; (**b**) reactance; (**c**) VSWR.  
Figure 7: Insertion loss of the balun terminated on the folded dipole.  
dipole configuration to be installed on the drone was performed through S-parameter measurements between two identical antennas, including baluns.  
The antennas were placed at 2.5 m away one from the other, face-to-face, in horizontal and vertical positions (Figure 9). Both tripods were adjusted to a height of 1.25 m.  
Dipole mode and loop mode gain figures were computed by using (15) and (16) and are shown in Figure 10.  
Figure 9: Antenna setup for ground reflection measurements in an OATS: (**a**) horizontal coplanar orientation; (**b**) horizontal face-to-face; (**c**) vertical face-to-face.  
The ground reflection coefficient (Figure 11a) and the gain figures corrected for ground reflection (Figure 11b,c) were computed by using (13), (15), and (17).  
Figure 11: Ground effect: (**a**) ground reflection coefficient; (**b**) gain: horizontal ‘face-to-face’ orientation, with and without ground reflection correction; (**c**) gain: horizontal ‘coplanar’ orientation, with and without ground reflection correction.  
Figure 10: Gain: (**a**) dipole mode; (**b**) loop mode.  
We found that the ground reflection in the current measuring site did not critically impact the accuracy, which was slightly better for a 'face-to-face' orientation.
# Theoretical Description of Carbon Felt Electrical Properties Affected by Compression
## 3 Interpretation of Experimental Data
Assuming the density of the interconnections between the carbon filaments is the main felt conductance factor, its relative increase (K\({}_{\mathrm{inc}}\))\({}_{\mathrm{rel}}\) was calculated as:  
\[\left(K_{inc}\right)_{\mathrm{rel}}=\frac{\left(ASC\right)_{\mathrm{D}i}}{ \left(ASC\right)_{\mathrm{D}0}}\cdot\frac{\left(V_{0}\right)}{\left(V_{i}\right)} \tag{4}\]  
where (ASC)\({}_{\mathrm{D}i}\) and (ASC)\({}_{\mathrm{D}0}\) are average specific conductance for CF having an initial width D\({}_{0}\) and the width D\({}_{i}\) at time t. V\({}_{0}\) designates initial felt and V\({}_{i}\) its volume at time t. A felt width (or equivalently, \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{\(\varepsilon_{\mathrm{r}}\)} & \multirow{2}{*}{**ASC**} & \multicolumn{3}{c}{**ASC=a*(1-exp(-b D)) +c**} & \multirow{2}{*}{**Coefficient R\({}^{2}\)**} \\ \cline{3-4}  & & **a** & **b** & **c** \\ \hline
1 & dry & 0.5071 & 0.02488 & 0.0933 & 0.969 \\
2.02 & cyclohexane & 0.5024 & 0.02008 & 0.2116 & 0.988 \\
21.6 & ethanol & 0.4269 & 0.02377 & 0.322 & 0.982 \\
42.5 & glycerol & 0.9134 & 0.01278 & 0.0996 & 0.967 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Coefficients of the approximating function of the relative increase of filaments interconnections for ASC. Figure 5: Average surface conductivity (ASC) of a dry CF versus relative felt decrease. -felt volume) decrease was observed as a result of felt compression. Data of interconnection increase of the CF ASC were calculated in accordance with expression (4) and are placed in Appendix C. The persistent increase of interconnection density can be observed that is witnessing the correctness of the proposed model of CF increased conductivity versus its compressing. K\({}_{\text{incr}}\) could be approximated rather exactly by the expression (5) for all liquids. The choice of such exponential function was dictated by its good correlation to measured data. The typical graphic interpretation of (5) is given in Figure 6.  
\[\left(K_{inc}\right)_{rel}=A\cdot\left(e^{B\cdot D}-1\right)+1 \tag{5}\]  
Comparison of the measurements results with those obtained for a K\({}_{\text{incr}}\) from the approximation function (5) for each liquid are shown in Table 5.
# Markov Chain Model-Based Optimal Cluster Heads Selection for Wireless Sensor Networks
## 4 The Proposed Clustering Protocol
### Settling Phase
#### 4.4.3 Association of Cluster Members (ACM) and Cluster Formation (CF)
The proposed model also adopts clustering due to the following reasons:  
* Clustering facilitates in reducing the cost of topology maintenance as a reaction to dynamic topology changes. * In a clustered structure, the topology reconfiguration is only performed on the CH level and it does not affect root nodes. * Clustering also minimizes the overhead generated due to dynamic topology adaptation. * Clustering allows resource utilization optimization which is successfully used to save time and energy. **Lemma 3**. : _The time and data packet exchange complexity of MOCHs for cluster formation in a round is \(O(1)\). While the time and the message exchange complexity of the proposed scheme for dividing the whole network into random clusters during a round with \(N\) number of nodes is \(O(N)\)._  
Proof. : After the selection of optimal number of CHs, the election process is completed. Each of the sensor nodes either sends or receives a message. The nodes which are selected as CHs for this round advertise their status as CHs, while the \(C_{MNs}\) send a _Joint-Req-Msg_ to their associated CHs merely. Therefore, the data packet exchange complexity of a node during a round is \(O(1)\). Thus, the information exchange complexity of the proposed model for developing clusters in a round is \(O(N)\). In the cluster formation procedure, each sensor node in the sensing field needs to process \(N-1\) nodes despite the fact that in severe cases to become a \(C_{MN}\). This entire process is completed in a predefined time slot. The time complexity of a round for selecting a CH is the \(O(1)\). Consequently, the time complexity of cluster formation in a round of the proposed scheme is \(O(N)\). **Lemma 4**. : _The time complexity of MOCHs for selecting a CH in a round is \(O(1)\). While the time complexity of the proposed scheme for the whole network with \(N\) number of nodes is \(O(N)\)._  
Proof. : In a random CH selection process of MOCHs, all the nodes in the network generate a random number \(R_{and}\) between 0 and 1. Then these nodes compare this generated number \(R_{and}\) with the threshold value \(T_{h}\), pre-computed by employing the Equation (10). If the randomly generated number \(R_{and}\) of a node is less than the threshold value computed by Equation (10), in this case that node elects itself as a CH for this current round. This entire course of action for random CH selection is completed during a predefined time slot. Consequently, the time complexity of a single node for selecting it as a CH is \(O(1)\). Accordingly, the time complexity of selecting CHs in a round for the proposed model is \(O(N)\).
# Assessment of Water Consumption Behavior in Single Households Using Smart Water Meters
## 2 Materials and Methods
### Data Collection  
The Chibata district water distribution plant provided 1871 households with data from smart water meters for this study, including hourly and daily water readings. The data covered a monthly period from 1 to 30 September 2022. Furthermore, additional details, such as the ID numbers related to each household, were shared, facilitating the analysis of smart water meter data and hourly water consumption volume. However, information on the number of households and types per household is unavailable.
## 3 The Procedure of TC Center Estimation
### Optional Stage: NRCS-Adjusted Estimation  
Centered on the precisely estimated TC center position, a sub-SAR image with a coverage area of \(L_{2}{}^{\circ}\times L_{2}{}^{\circ}\) is extracted from the original SAR image. Subsequently, the position corresponding to the lowest NRCS value in this sub-image is designated as the NRCS-adjusted TC center position. In this stage, \(L_{2}\) is set to 0.6\({}^{\circ}\).  
It should be noted that \(L_{1}\) and \(L_{2}\) were separately chosen as 1.2\({}^{\circ}\) and 0.6\({}^{\circ}\) based on two main criteria: first, to ensure that the sub-SAR images cover the TC eye area as completely as possible, and second, to optimize computational efficiency. These parameters are suitable for other TCs as well. Additionally, these values are not fixed at 1.2\({}^{\circ}\) or 0.6\({}^{\circ}\) and can be adjusted based on the criteria.  
Figure 4: Coarse estimation for centers of (**a**,**d**) TC Noru; (**b**,**e**) TC Doksuri; (**c**,**f**) TC Koinu. The coarsely estimated TC center positions are denoted by red points, while the visually located TC center positions are denoted by cyan triangles in (**a**–**c**). The sub-SAR images inside the green boxes are used to precisely estimate the TC center position. (**d**–**f**) represent the heatmaps corresponding to the compensation angles of 0.5\({}^{\circ}\), \(-\)16.5\({}^{\circ}\), and \(-\)17.5\({}^{\circ}\), respectively. The position of the maximum value in (**d**–**f**) represents the coarsely estimated TC center positions (red points in (**a**–**c**)).
## 7 Performance Analysis: Reliability and Scalability
### Evaluation Approach and Tools  
The experiment to access the performance of the proposed framework was separated into two categories. The first was from the normal connected IoT devices' viewpoint. The second was from the simulated requests to capture the system behaviors. For the normally connected sensor node, the measurements were also acquired. Testing was done to ensure the experimental testbed was feasible. The simulated experiment was done to test the performance while system scaling was conducted.  
Figure 12: Docker Swarm with duplicated container services at the scale up purpose.  
During the testing phase, the function of sending requests was executed to simulate virtual IoT devices published in the specified time interval. The simulated IoT devices sent requests or messages with an increment of \(10^{3}\) (1000, 100,000, 1,000,000) to the IoT platform. In this experiment, the platform ran under the MQTT publishing operation with Quality of Service (QoS) level 0--the client simply published the message, and there was no acknowledgment by the broker. The reason for choosing level 0 was to avoid the system overhead created by acknowledgment messages. The data used for the system ingestion were simulated and represented an observation of a unique and random phenomenon. The estimated average size of each datum generated was 240 bytes. These implied the amount of data ingested to the platform backend services from sensor nodes and container controller.  
The experiment aims to collect CPU, throughputs, and memory metrics usage is conducted by using cAdvisor as container resources usage analysis platform and Prometheus in combination with Grafana for monitoring the proposed platform as shown in Table 4.