esa-sceva/satcom-chunk-collection huggingface dataset:
# A Modular Experimentation Methodology for 5G Deployments: The 5 GENESIS Approach
## 4 Formalization of the Experimentation Methodology
### Experiment Execution  
The experiment workflow is depicted in Figure 1, on top of the 5 GENESIS reference architecture. In terms of execution flow, labels 1 to 7 represent the steps for describing an experiment (1), to execute it (2 to 6) and to report results (7). Next, we illustrate the execution of an experiment with an example.  
Assume that a service provider would like to assess the delay perceived at the application level in a certain device. First, the experiment is configured via the Portal. The chosen parameters will be used in order to fill in the fields of the ED. In the example, the experiment requests the execution of a Round Trip Time (RTT) test in a static scenario with LoS and good coverage, and the usage of an URLLC slice. Subsequently, the ED is delivered to the Coordination Layer first and MANO Layerthen, where the Slice Manager deploys and configures the entire slice indicated by the experimenter in the ED. In the third step, the scenario is configured via the configurations that are specified in the corresponding scenario template selected by the experimenter. Among other, in this specific example, a ping application is installed in the device under test. The scenario can potentially affect the radio access technology (e.g., the signal strength of the base station), as well as the backhaul and core networks (e.g., the amount of background traffic in the backhaul). In the fourth step, the ELCM initiates the ping client, as well as the monitoring probes collecting all of the complementary measurements specified in the test case (or at least the ones available). The activation of the ping client triggers the 3 GPP signaling in order to establish the data connection. In this example, the experiment test case defines a ping between the UE and the Packet Gateway (P-GW) located at the core network, so we can skip steps five and six in the execution flow, devoted to the testing of services deployed outside of the testbed (e.g., at the vertical facility). Finally, M&A post-processes the measurements and reports the obtained KPI statistical indicators to the experimenter.  
The source code for the components described in this section (Portal, ELCM, Slice Manager, etc) is available under Apache license 2.0 in [8]. The license allows modifying and using the software without restrictions. The main purpose of this open initiative is to provide a general testing framework for 5G infrastructures. The framework is flexible enough to cover the testing requirements of 5G verticals; moreover, it enables the comparability and reproducibility of experiments and results across 5G infrastructures having similar features.  
The repository in [8] also includes the probes that were used in Android devices to collect the radio information, the iPerf and ping agents for Windows PCs and Android devices, and a streaming agent for video streaming tests. A manual for installing and using all of the components is also available [44].
## 3 Optimization with Consideration of the Reliability Criteria
### Objective Functions and Constraints for Market Settlement with Reliability Criteria
#### 3.1.2. Mathematical Model of the Revenue Stream from a Resource Portfolio with a PV and BESS  
The objective function for profit maximization of the portfolio with the PV generator and the BESS is modified as follows:  
\[M_{t}^{S-B}=RSB_{t}^{SMP}+REC_{1}^{S}\cdot X^{S}+RSB^{REC_{5}}-P_{sb,t}^{loss} \cdot c_{sb},\quad\forall\;sb,t \tag{25}\]  
As presented in Equations (26) and (27), the revenue streams from the SMP and the REC with the multiplier, represented by \(RSB_{t}^{SMP}\) and \(RSB^{REC_{5}}\), respectively, include the additional incentive for the energy discharged from the BESS, denoted by \(P_{sb,t}^{B2C}\), considering the factor, \(\tau^{o}\), and noting that the BESS is installed outside.  
\[RSB_{t}^{SMP}=\left(PCC_{t}^{S}+\sum_{sb}P_{sb,t}^{B2C}\cdot\tau^{o}\right) \cdot SMP_{t},\quad\forall\;t \tag{26}\]  
\[RSB^{REC_{5}}=\omega^{S}\cdot\left(REC_{5}^{S}+\sum_{sb}P_{sb,t}^{B2C}\cdot \tau^{o}\right)\cdot X^{S} \tag{27}\]  
To enable flexible operation responsive to the SMP, as well as opportunities during the multiplier periods, the variables \(P_{sb,t}^{S2B}\) and \(P_{wb,t}^{W2B}\) were added to the BESS in fixed operation, as presented in Equations (28) and (29).  
\[REC_{5}^{S}=\sum_{t\
eq TC}\left(\sum_{sb}\left(P_{wb,t}^{B2C}-P_{wb,t}^{S2B} \right)\right) \tag{28}\]  
\[REC_{5}^{W}=\sum_{t\in TD}\left(\sum_{wb}\left(P_{wb,t}^{B2C}-P_{wb,t}^{W2B} \right)\right) \tag{29}\]
## 3 Experiments and Results
### Scenario 3: Pre-College and Academic Features  
The third scenario includes pre-college and academic features. Performance of the obtained prediction models is reported in Table 8. The best performance by the end of term 1, is again the LR method with term performance represented _by courses_, whether single or cumulative, with 63.2%. When the prediction is performed later, we observe similar results with the previous two scenarios, in other words the best performance is mainly obtained with the LR method, with term performance represented _by cumulative GPA_. The GNB shows a slightly superior performance for the term 6. Figure 7b,d shows that the performance of the cumulative models are improving from 63.2% (term 1) to 93.7% (term 6) with the decreased prediction window size. Again, same as for previous two scenarios, the models using only one past term reach a performance pick for prediction performed by the end of term 4 (see Figure 7a,c).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & **ML Algorithm** & **1 st Term** & **2 nd Term** & **3 rd Term** & **4 th Term** & **5 th Term** & **6 th Term** \\ \hline \multirow{4}{*}{**Single**} & \multirow{4}{*}{**Course**} & LR & **0.632** & 0.688 & 0.759 & 0.822 & 0.817 & 0.817 \\ \cline{3-8}  & & RFC & 0.611 & 0.660 & 0.725 & 0.814 & 0.788 & 0.793 \\ \cline{3-8}  & & GNB & 0.551 & 0.635 & 0.765 & 0.815 & 0.791 & 0.793 \\ \cline{2-8}  & & LR & 0.622 & 0.679 & 0.753 & 0.837 & 0.810 & 0.829 \\ \cline{3-8}  & & RFC & 0.597 & 0.668 & 0.731 & 0.821 & 0.801 & 0.804 \\ \cline{3-8}  & & GNB & 0.574 & 0.663 & 0.738 & 0.806 & 0.798 & 0.800 \\ \hline \multirow{4}{*}{**Cumulative**} & \multirow{4}{*}{**Course**} & LR & **0.632** & **0.709** & 0.783 & 0.858 & 0.912 & 0.931 \\ \cline{3-8}  & & RFC & 0.611 & 0.679 & 0.757 & 0.838 & 0.891 & 0.904 \\ \cline{3-8}  & & GNB & 0.551 & 0.652 & 0.732 & 0.795 & 0.860 & 0.892 \\ \cline{3-8}  & & LR & 0.622 & 0.699 & **0.799** & **0.874** & **0.922** & 0.931 \\ \cline{3-8}  & & RFC & 0.597 & 0.693 & 0.775 & 0.862 & 0.905 & 0.912 \\ \cline{3-8}  & & GNB & 0.574 & 0.682 & 0.754 & 0.836 & 0.903 & **0.937** \\ \hline \hline \end{tabular}
\end{table}
Table 8: Accuracy results of the the ML Models with the academic and pre-college features.  
Figure 6: Accuracy plots along the terms of the ML Models with the academic and demographic features.
# BDS-3 Triple-Frequency Timing Group Delay/Differential Code Bias and Its Effect on Positioning
## 3 Results and Analysis
### Analysis of SPP Positioning Accuracy
#### 3.1.2 UC123  
The positioning errors in the horizontal and vertical components for UC123-0 SPP of the AREG station on DOY 182, 2022, are presented in Figure 6. Comparing Figures 4 and 6, the non-corr of UC has significantly smaller error fluctuations than IF. Because UC does not require a linear combination, it does not amplify the observation noise substantially. Moreover, the TGD/DCB correction improves the error distribution concentration and positioning accuracy and decreases the error fluctuation. This is the same situation as the IF combination performance.  
Figure 5: Box plot of IF123 and IF1213 SPP positioning accuracy.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**non-corr/m**} & \multicolumn{3}{c}{**tgd-corr/m**} & \multicolumn{3}{c}{**dcb-corr/m**} \\ \cline{2-10}  & E & N & E & E & N & U & E & N & U \\ \hline IF123 & 6.90 & 7.98 & 0.82 & 0.91 & 0.91 & 2.84 & 0.82 & 0.84 & 2.79 \\ IF1213 & 7.71 & 8.69 & 0.87 & 0.96 & 0.94 & 2.79 & 0.87 & 0.87 & 2.74 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average positioning accuracy of SPP for IF123 and IF1213 models.  
For visual verification and analysis, the box plots of the SPP positioning accuracy for UC combinations are plotted in Figure 7. This shows that the accuracy of the E component is between 1-2.5 m, the N component is between 1.2-3 m and the U component is between 3-6 m for UC123. The tgd-corr and dcb-corr have higher positioning accuracy for all combinations, 75% of which reach 1.5 m or better in the horizontal component and 3.5 m or better in the vertical component, and the deviation distributions of UC123-0-UC123-3 are the same.  
The average positioning accuracies of SPP for the UC model are shown in Table 4. Comparing Tables 3 and 4, we find that the average positioning accuracy of UC without DCB correction is about 3 m in the horizontal component and 5 m in the vertical component, which is much higher than the accuracy of IF. The positioning accuracy of UC123 in the E, N and U components after TGD correction is 1.00 m, 1.32 m and 2.84 m, respectively. These accuracy values are comparable to that of IF123 and IF1213 in the E and U components but are slightly worse in the N component. The tgd-corr has an accuracy improvement of 27-30% compared with non-corr, and the DCB correction effect is basically the same as TGD.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**non-corr/m**} & \multicolumn{3}{c}{**tgd-corr/m**} & \multicolumn{3}{c}{**dcb-corr/m**} \\ \cline{2-10}  & **E** & **N** & **U** & **E** & **N** & **U** & **E** & **N** & **U** \\ \hline UC123 & 1.39 & 1.86 & 3.90 & 1.00 & 1.32 & 2.84 & 0.98 & 1.31 & 2.82 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Average positioning accuracy of SPP for UC123 model.  
Figure 6: SPP positioning error of UC123-0 at AREG.  
Figure 7: Box plot of UC123 SPP positioning accuracy.
## 4 Proposed Method
### Predicting Average Response of Sub-system Using Inference Function--Step 2  
The panel excited by a unit force in the vertical direction up to a 500 Hz range and the vibration response were averaged of all panel nodes. For cavity average response, the cavity was excited by the acoustic source at the measurement point and response average of the pressure from all nodes where the panel coupled.  
The average response function of the panel and cavity was predicted using the inference function as an input of the neural network. The inference function is a type of wave function which was devised by the physical knowledge of the structure response in the frequency domain. The frequency of major response peaks from the panel/cavity are caused by the panel/cavity modes and the corresponding natural frequency of them. The inference function for this study is shown in Figure 4, and it is expressed with Equation (4).  
Figure 3: Graph convolutional network for panel and cavity natural frequency prediction: (**a**) for panel; (**b**) for cavity. Numbers in each layer is the number of outputs.  
\[\begin{array}{ll}0\leq f\leq f_{1}&y=\frac{1}{f_{1}}f\\ f_{i}<f\leq(f_{i+1}-f_{i})/2&y=-\frac{2}{(f_{i+1}-f_{i})}f+1+\frac{2f_{i}}{(f_{i+ 1}-f_{i})}\\ (f_{i+1}-f_{i})/2<f\leq f_{i+1}&y=\frac{2}{(f_{i+1}-f_{i})}f+1-\frac{2f_{i+1}}{( f_{i+1}-f_{i})}\\ f_{5}<f&y=1&i=1,2,3,4\end{array} \tag{4}\]  
System response peak location in frequency domain depends on the natural frequency, and the magnitude of response depends on the radiation efficiency of each mode.  
Figure 5 shows the neural network for predicting average response. The frequency location is guided by the inference function as input data, and the level of response highly depends on the mode shape and the neural network trained to predict the level of response according to the order of the peaks. The number of input and output of the neural network was 62. This is the number of the 1/12 octave band from approximately 10 to 500 Hz.
## 4 Discussion
### Interpretation of Interaction Effects  
Due to the significance of the locating schemes on the overall displacements after welding, the interaction of the locating scheme with the other factors is of significance. The two main factors in Assembly I were the locating scheme and welding sequences. Figure 6e visualizes how these two interact for the N-O, N-I, and mean and median solutions for the locating scheme and the welding sequences. As can be seen, a near-optimal solution for the welding sequence resulted in lower displacements for the mean and N-O locating scheme solutions. However, in a non-ideal locating scheme, a non-ideal welding sequence resulted in minimum displacement, specifying the close interconnection between the locating scheme and the welding sequences. For Assembly II, the N-O locating scheme and N-O and N-I welding sequences resulted in similar displacement behavior; however, in the case of the N-I locating scheme, the N-O welding sequence helped to dampen the displacement by 56%, Figure 7e.  
The interaction between Path F's direction, which had the highest significance among the three segments for both of the assemblies, and the welding sequences are studied in Figures 6f and 7f. This study shows that a backward pass resulted in lower displacement when the N-O (FGH) and median (HGF) welding sequences were utilized in Assembly I. For the N-I welding sequence in Assembly I (HFG), the forward weld path resulted in lower displacements. For Assembly II, the weld path F forward pass resulted in lower displacement for the N-O welding sequence (FHG), and for the N-I welding sequence (GHF), the backward weld sequence resulted in lower displacement. This indicates that the effect of path direction on displacement varies depending on the welding sequence employed. This interplay underscores the necessity of considering combined factor effects rather than isolated influences when optimizing welding parameters.
## 2 Materials and Methods  
### Presenting the Catenoy Site  
The Catenoy (Oise) experimental site is located about 50 km north of Paris in the Paris sedimentary basin (Figure 1). The coordinates of the center of the site are as follows: latitude 49\({}^{\circ}\)22\({}^{\prime}\)05\" N, longitude 2\({}^{\circ}\)30\({}^{\prime}\)26\" E, altitude \(\sim\)60 m asl. The geology corresponds to a few meters of Quaternary deposits (colluviums, loess) and Tertiary formations, lying over a hundred meters of Senonian chalk that is only visible in the thalwegs (see also Figure 2b). Under the site, the underground geology of the first 25 m is 3 m of colluvium, 4 m of Thanetian sands, and 18 m of chalk. At a few hundred meters on the south of the site, Ypresian and Lutetian layers form a hill. There are no major tectonic accidents nearby and the bedding of the chalk formation is horizontal. This chalk encloses an aquifer with a static level at a depth of 13 m which flows in the WSW-ENE direction [4].  
Located in a former agricultural field that has been fallow for more than a decade, the site is equipped with 8 piezometers aligned in the direction of flow of the aquifer over a distance of 80 m, referenced PZ1 to PZ6 (including supplementary piezometers PZ2 BIS and PZZTER), plus a technical shed housing the monitoring material (Figure 2). The piezometers are about 25 m depth and are screened in the aquifer starting from 13 m depth.  
The hydrodynamic characteristics of the chalk aquifer at the site were determined during a pumping test carried out in 2013 [4]. Depending on the piezometer considered, the following values were reported:  
* Storage coefficient (porosity): \(1.1\times 10^{-2}\) to \(6.5\times 10^{-2}\)
* Hydraulic conductivity (permeability): \(6.4\times 10^{-4}\) to \(1.4\times 10^{-3}\) m\(\cdot\)s\({}^{-1}\)  
In addition, a previous tracing test made it possible to estimate the flow rate of the aquifer at 3 m\(\cdot\)d\({}^{-1}\) at PZ3 and PZ5, and 10 m\(\cdot\)d\({}^{-1}\) at PZ4, the latter being situated in a preferential flow path (fissured area). This test, therefore, demonstrates the double porosity of the aquifer studied.  
There is also a meteorological station on-site to measure the following parameters at hourly time intervals: atmospheric temperature and pressure, rainfall.  
Figure 1: Location and geological context of the experimental site.  
Figure 2: Diagram of the Catenoy experimental site: (**a**) planimetry; (**b**) cross-section.
## 2 Problem Description
### Objective Function  
The transportation route formation model's objective function in post-disaster areas with uncertainty in humanitarian logistics is:  
1. Location of emergency facilities The emergency facility location model generates an objective function based on the shortest distance between open distribution centers and demand points (\(min\Sigma_{ij}\)_distance_).
2. Predicting location and forming transportation routes Based on open distribution centers, the location prediction model forecasts locations. The model is then trained by displaying an increase in accuracy, indicating that the model is still optimizing its internal parameter adjustments to improve performance, which has the potential to provide reliable predictive results and can be used effectively in relevant contexts such as predicting locations or data classification. The formation of transportation routes yields a destination function, which is a combination of routes based on demand point clusters \(\left(\bigcup\limits_{i=1}^{i=n}L\right)\).
3. Distribution route in uncertainty The forbidden route model, which is used to optimize route planning in routing problems with forbidden route restrictions, is used for distribution routes that are subject to uncertainty. Every vehicle used in this model will not take the forbidden route. The model's objective function is to minimize the total cost (\(h\)) and arrival time (\(w\)) of the vehicle at the demand point \(\left(\binom{Min}{x,y,z,q}\right)\mathbb{1}_{1}(D)=\{w,h\}\) of a route that is not prohibited.
## 2 Materials and Methods
### Field Sampling Strategy and Water Content Indicator Measurements  
Field data for the wet season were collected between 27 and 31 March 2022. This was meant to capture the peak water content period after the grass had responded to the maximum rainfall occurring in January. The dry season data were collected between 16 and 20 June when the GWC was low due to senescence. Although the driest month with the lowest temperatures is July, the communities in Vulindella initiate seasonal burning of grass in June to stimulate new growth for livestock, making it impossible for data collection to be conducted in July.  
One hundred and thirteen 10 m \(\times\) 10 m quadrats were established 150 m apart using a purposive sampling strategy. A plot size of 10 m by 10 m was chosen in this study to ensure that all sampled areas were within the confines of Sentinel-2 MSI bands. Consequently, the 150 m distance was chosen to account for possible ground truth satellite image geolocation errors. Within each plot, two 1 m \(\times\) 1 m sub-quadrats were randomly established within the 10 m plots for clipping grass. A handheld Trimble Global Positioning System (GPS) with a sub-meter accuracy was used to collect the centre co-ordinates of the sub-quadrats. The co-ordinate points were stored as waypoints and later used for navigation in the dry season data collection. Grass was clipped within each sub-quadrat, placed in brown paper bags, and correctly labelled. The fresh weight (FW) of sampled biomass was instantly weighed using a calibrated scale with a 0.5 g measurement error. The samples were taken to the laboratory and placed in the oven at 105 \({}^{\circ}\)C. For dry weight (DW), the samples were weighed consistently until a constant dry weight was reached (48-72 h).  
LAI was also measured within each sub-plot using a portable handheld ground-based and non-destructive LAI 2200 plant canopy analyser (LI-COR Biosciences, Lincoln, NE, USA). Specifically, in measuring LAI, five readings were conducted: one above the grass canopy and four below the grass canopy. Field measurements for LAI were conducted between 12:00 noon and 14:00 as this is the optimal period of the day for vegetation photosynthetic activity [68].
## 3 System Architecture
### General System Architecture
As far as the information exchange is concerned, the platform is connected with a cloud database. In the database, there is dedicated space where certain 3D geometries are saved. These geometries are imported in the AR scene only by the expert engineers. The collection of these tools can be realized as a digital toolbox of the most common tools used in  
Figure 2: Proposed system architecture and steps sequence. MRO. However, due to increasing machine tool complexity, specialty tools might be needed. As such, the expert engineer is capable of adding a digital copy of the specialty tool through the \"Create tool\" Graphical User Interface (GUI). In Figure 3, the GUI for joining an online maintenance session is depicted. On the top left corner of the GUI, the user through the provided dialog can select a session to join. In case the session has reached its end, then the user has to leave the session. On the lower left corner, the provided buttons assist the master engineer to retrieve from the Cloud platform the required tools by performing a \"drag and drop\" action to position them in the technician's FOV. However, if the desired tool does not exist in the Cloud toolbox, then the engineer is capable of uploading a 3D geometry. More specifically, in Figure 3, the \"plus\" icon when hit triggers the \"Create Tool\" function, so that the engineer adds the geometry in a suitable filetype along with relevant info and a description of the tool. The filetypes accepted by the Cloud Database have been limited to the filetypes accepted by the AR tool. Therefore, since the framework is developed in the Unity 3D game engine, the supported model file formats for 3D geometries are,.fbx,.dae (Collada),.3 ds,.dxf,.obj. Then the algorithm running in the background is responsible for uploading the geometry on the Cloud Database as well as to create a new record in the SQL Database. The SQL database is created and used in an ontological manner, so that a smart-searching algorithm can automatically generate suggestions to the engineer during the AR scene creation phase. In Figure 4, the set of the initial tools used for populating the Cloud Toolbox is presented. For this manner, the most common technical tools have been utilized. Figure 3: GUI for online maintenance session.