esa-sceva/satcom-chunk-collection huggingface dataset:
# The Role of PIXE and XRF in Heritage Science:
## 6 Comparison between PIXE and XRF Techniques at the INFN-LABEC  
As a practical example, here, the same area of the parchment irradiated at the PIXE beamline described in Section 3 is reported and with the XRF spectrometer presented in Section 5. The scan was conducted with the motor system shown in Figure 2. Experimental parameters are reported in Table 2 and the areas measured are presented in Figure 6.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Technique** & **Probe** & **Energy** & **Current** & **Diameter** \\ \hline \multirow{2}{*}{PXE} & Proton beam & 4 MeV & tens of pA & 0.5 mm \\  & X-rays Mo & & 26 keV max. & 0.8 mA & 0.5 mm \\ \hline \end{tabular}
\end{table}
Table 2: Experimental parameters of PIXE and XRF techniques.  
Figure 5: Spectra of a katana treated with acid (**a**) and of a well-polished katana (**b**). Two anode voltages (9 kV in blue and 25 kV in red) were used for each measuring point.  
On the contrary, with the PIXE technique, only the ink on the front side was clearly detected, whereas the letter on the other side is not clearly visible from the map of mercury.  
This result is a consequence of the higher penetration depth of XRF in comparison with PIXE. This can be considered an example of the advantage of matching the two techniques; indeed, the detection of the materials on the verso of the folio together with those on the recto may complicate the reading. On the other hand, two materials can be detected with only one measurement (easily distinguishable thanks to the mapping methods) and, moreover, the eventual interaction with these materials may be observed.
## 5 Simulation Method Uncertainties
### Angular Error
#### 5.2.1 Geometric Approximations  
Over shorter ranges, it is always simpler and often sufficient to use a local coordinate system that can be treated as Cartesian for the purpose of calculating range and incidence and scattering angles. Whether this is sufficient in any particular case will depend on the ranges and directions involved, the location on the globe, the local coordinate system used, and the sensitivity of the simulations to these errors. We therefore restrict our analysis to our particular case, where we deal with objects in and near Switzerland and ranges of up  
Figure 14: Comparisons between the single-iteration MoM simulation received-power values and the basic and full SBR+ simulation received-power values: (**a**) Basic SBR+; (**b**) Full SBR+.  
Figure 13: Comparison between the frequency-mean simulated received-power values (Equation (7)) computed with the single-iteration MoM method and the two different SBR+ methods. The grey box indicates the times during which the roll exceeded \(18^{\circ}\), and is intended as an approximate indication of the times where the aircraft was turning.  
to 65 km (transmitter to target range). We use the CH1903 (Swiss grid) coordinate system as our approximately-Cartesian coordinate system, using the conversion scheme in [35] to convert the WGS84 position and altitude values reported by the aircraft's IMU. For a more exact method, we calculate the great-circle azimuth and range angles between the transmitter/receiver and the target position on the surface of the WGS84 ellipsoid. The resultant range and azimuthal incidence angle errors are shown in Figure 15 along our flight track.  
The range error can be up to around 5 m in our case, but since this is over a range of many kilometers, and since, from a simulation point of view, it affects only the scaling of the reflected signal from the aircraft, not the aircraft orientation, it is highly unlikely to be a significant factor.  
The mean azimuthal error is similar for both the transmitter and receiver, at approximately 0.93\({}^{\circ}\), which is potentially significant. This error would be less if we were operating closer to the center of the coordinate system, but it is clear that it cannot be assumed that a Cartesian approximation is sufficient.  
Similarly, the incidence elevation angle can be computed either from a simple Cartesian calculation in the Swiss grid, or less approximately by computing the distances on a locally spherical (with the radius of the WGS84 ellipsoid at that point) earth. The resultant errors of these two methods are shown in Figure 15 along our flight track. The difference in our  
Figure 15: The error in ground range, azimuth and elevation between the transmitter and aircraft and receiver and aircraft resulting from using a Cartesian approximation along the ground range compared with an ellipsoidal earth approximation: (**a**) ground range; (**b**) azimuth; (**c**) Elevation.  
case comes close to 0.3\({}^{\circ}\), but we note that this is only around one-third of the error from the Cartesian azimuth approximation.
## 2 Materials and Methods
### Complexity Analysis  
The computation of the compound eye structural weights involves matrix operations, and we primarily analyze the computational complexity of the iterative optimization process. Assuming the constructed infrared image tensor is \(D\in\mathbb{R}^{m\times n\times F}\), where \(F\) is the temporal dimension, the computation involves \(B=U\times_{3}V^{T}\), where \(U\in\mathbb{R}^{(\,mn\,\,)\times r}\) and \(V\in\mathbb{R}^{F\times r}\). Updating \(G_{1},\ G_{2}\) requires soft thresholding calculations, with a combined computational complexity of \(O\)\((2 mnr)\). Updating \(U\) involves FFT calculations, resulting in a computational complexity of \(O\)\((mnr\ log\ (mn))\). Updating \(T\) requires soft thresholding calculations, with a complexity of \(O\)\((mn\ F)\). Updating \(V\) necessitates SVD calculations, leading to a computational complexity of \(O\)\((Fr\ ^{2})\). The complexity of updating ww and other parameters is \(O\)\((1)\). Therefore, the overall computational complexity of the iterative solution is \(O\)\(\big{(}1+mnr\ log\ (mn)+Fr\ ^{2}+mn\ F\big{)}\).  
For an input image of size \(640\times 640\), the detection time per frame is approximately \(0.1821\) s. This includes all preprocessing steps applied to the original image, such as retaining only the valid imaging regions and computing the weighting operator. These steps involve various matrix operations, and there is room for further optimization in future implementations to improve computational efficiency.
## 3 Position Estimation of the Occluded Parts
### Smoothing Contours  
There are random mutation points caused by noise in contour detection. In Figure 8, the four typical mutation instances are depicted. These mutations increase the complexity of the contour description and lead to errors in the geometric feature extraction process. Therefore, the contour and chain code are restored by the first-order differential information of the chain code.  
The first-order difference process records the number of changes in the adjacent two chain code elements in the eight counterclockwise direction, which can be expressed as follows:  
\[a_{i}^{\prime}=(a_{i}-a_{i-1})\text{mod}M \tag{12}\]  
In Equation (12), \(a_{i}\) denotes the chain code element, mod denotes the modulo operation, and \(M\) denotes the number of chain code directions. The chain code and the chain code differential sequence are analyzed. When \(a_{i}^{\prime}=2,6\), \(a_{i-1}=a_{i}\
eq a_{i+1}\), and \(a_{i-2}=a_{i+1}=0,2,4,6\), and then \(a_{i-1}\) and \(a_{i}\) are assigned. The mutation point is found by traversing the contour along the counterclockwise direction, and the contour information and chain code sequence are smoothed.
# Real-Time Identification of Look-Alike Medical Vials Using Mixed Reality-Enabled Deep Learning
## 5 Evaluation and Discussion of the Proposed Model's Results
### Class-Wise Detection Accuracy  
Figure 5 presents a comparative analysis between the YOLOv4-Tiny model and the proposed Lightweight YOLOv4 model in terms of detection accuracy, training convergence, and inference performance. The evaluation results in Figure 5a,b highlight key metrics such as mean average precision (mAP), precision, recall, and F1 score. The proposed model achieves a higher mAP of 98.76% compared to 95.19% for YOLOv4-Tiny, demonstrating an improved detection accuracy, especially for small and visually similar medical vials. In addition, the class-wise evaluation shows that the proposed model achieves 100% accuracy in multiple vial categories, while YOLOv4-Tiny exhibits misclassification errors due to overlap and occlusions.  
Figure 5c,d illustrates the training loss and mAP convergence over multiple iterations. The proposed model demonstrates a faster and smoother convergence, indicating improved learning stability and generalization. The mAP progression curve in Figure 5d shows that the proposed model achieves higher accuracy earlier in the training process, while YOLOv4-Tiny exhibits more fluctuations and slower improvement. Furthermore, the inference speed of the proposed model is 20 ms per frame, maintaining real-time processing capabilities, which is essential for Mixed Reality (MR)-assisted vial detection applications.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Metric** & **Proposed Model** & **YOLOv4 Tiny** \\ \hline \hline
**Class-Wise Accuracy** & & \\ \hline
**EpinephrinPocket (Class 0)** & 95.78\% & 90.15\% \\
**Midazolam (Class 1)** & 100.00\% & 95.83\% \\
**Adenosis (Class 2)** & 98.89\% & 98.10\% \\
**AmidadorneHCI (Class 3)** & 100.00\% & 100.00\% \\
**Amidoron (Class 4)** & 100.00\% & 96.00\% \\
**Atropine (Class 5)** & 93.89\% & 91.00\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._
# Applications of Machine Learning Algorithms in Geriatrics
## 3 ML Models Used in the Diagnosis and Monitoring of
### The Most Studied ML Models in Recent Literature
Analyzing the works in the literature, it is found that the RF algorithm is the most studied in modeling geriatric problems through AI components. In Table 1, one can observe the large volume of works disseminated in the field. The authors' preference for this model stems from its ability to handle incomplete data that contains noise or does not conform to a standard template. This algorithm is preferred for tasks such as identifying frailty, estimating postoperative mortality, detecting falls, or making clinical decisions due to its ease of implementation, especially since many modern tools include it in their list of automatic training algorithms, as well as its ability to train with a small volume of data. The RF model is followed by XGBoost, which is predominantly used in clinical predictions \begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithm** & **References** \\ \hline RF & [4,12,13,24– \\
26,28,30,32,39,40,45,50,52,53,55,57] \\ XGBoost & [13; 26; 27; 39; 40; 50; 53; 57; 58] \\ SVM & [5; 11; 13; 14; 25; 27; 28; 23; 39; 53; 57] \\ LR & [4; 13; 26; 27; 30; 34; 39; 50; 53; 57; 59] \\ KNN & [5; 11; 28; 32; 37; 53; 59] \\ DT & [11; 27; 28; 32; 45; 53] \\ NN & [5; 12; 13; 26; 27; 31; 32; 37; 40; 42; 45; 52; 53; 57] \\ NB & [11; 28; 32; 37; 59] \\ LightGBM & [26; 40] \\ CatBoost & [4; 40; 42] \\ GBM & [4; 45] \\ Bayesian Models & [11; 32] \\ King Lu, Tariff & [11] \\ HMM & [32] \\ Autoencoders & [32] \\ LSTM/Bi-LSTM & [32] \\ Transfer Learning & [32] \\ Stochastic Gradient Descent & [26] \\ Bagging Classifier & [31] \\ Voting Classifier & [31] \\ Regularized Models & [4; 40; 42] \\ OneClassDetection & [44] \\ GradientBoostingRegressor & [44] \\ LinearRegressor & [44] \\ SupportVectorRegressor & [44] \\ K-means & [21] \\ Threshold-based & [33] \\ Hybrid Systems & [33] \\ Vision Systems & [33] \\ SGB & [4] \\ Elastic-Net PLR & [4] \\ \hline \hline \end{tabular} Note: GBM—Gradient-Boosting Machine; HMM—Hidden Markov Model; LSTM—Long Short-Term Memory; NN—Neural Network; PLR—Penalized Logistic Regression; SGB—Stochastic Gradient Boosting.  
\end{table} 
Table 1: Distribution of ML algorithms in relation to the models investigated in the analyzed specialized literature. that require a multivariable approach. Additionally, this model can handle situations where values are missing from the training dataset. Another model that has been extensively studied is the KNN and NB models, which are traditional models for classification using similarities in clinical features. Table 1 highlights the comparison between the models studied with a geriatric focus. The choice of an algorithm depends on the nature of the available data, the objective of the study, the extent to which the model can be explained to the medical team, and the quality of the data used in training. In Table 1, it can be observed that RF and XGBoost stand out considerably from the other models investigated. The two models are predominantly used in prediction problems.
## 3 Materials and Methods
### Data and Variables  
In order to answer the research questions and assess the hypothesis, the data for this paper were gathered from four neighborhoods in the two cities, i.e., two neighborhoods in each city, one representing sprawled districts and one representing compact districts. The local language of Urdu was used to conduct face-to-face interviews in Lahore and Rawalpindi, though the questionnaires were prepared in English. Samanabad and the Pak Arab Housing Scheme were selected as neighborhoods in Lahore; Samanabad is located within the central part of Lahore. It has a regular street pattern with a mixed land use structure. In contrast, Pak Arab is a new development area located in the suburbs of Lahore. It also has a regular street network dominated by residential development. Five hundred participants answered the questionnaire in autumn 2018, and then 461 questionnaires remained after validating the data. Satellite Town and Pindora were chosen in Rawalpindi. Satellite Town is a comparatively new development district, with a regular street pattern. Pindora is slightly older than Satellite Town and has an irregular street pattern and a more mixed land use structure. Four hundred respondents filled out questionnaires, which were then validated in Rawalpindi in summer 2020. Our sample size was altogether 861 for the two cities. This sample size is representative at the neighborhood level according to [PERSON] ([PERSON], 1963). The confidence interval for Samanabad and Pak Arab is 6.59 and 6.38, respectively, based on a confidence level of 95% and a population of 45,518 and 3233, respectively. The figures are 6.91 and 6.6 based on a population of 39,693 and 21,119 for Satellite Town and Pindora, respectively, in Rawalpindi. Thus, the sub-samples are representative at the district level. Each questionnaire included two maps: the map of the neighborhood and the map of the city. Participants were asked to determine the nearest intersection to their homes and workplaces on these maps. These intersections were defined as \"home\" and \"work\" locations to respect the participants' privacy. Then all the points were pinned in Google Maps and converted to shape files in Arc Map 10.3. To calculate disaggregated street-length density, we set up 600-m catchment areas based on the street network and obtained street-length density by dividing the sum of the lengths of all streets by the total catchment area. We calculated commuting distance according to the shortest distance between home and workplace for each participant who indicated these locations. Next, other travel-pattern and economic information came from questionnaires and included the number of commuting and non-commuting trips per week, the use of private motor vehicles in commuting and non-commuting trips, the frequency of public transport use, the use of active mobility modes, age, daily activity, and monthly household income. Variables are presented in Table 1. Figures 2 and 3 show selected neighborhoods in Lahore and Rawalpindi.
Although computational fluid dynamics (CFD), the finite element method (FEM), and measurement methods have been individually used by different scholars to study the flow dynamics and vibration of Francis runners, there have been relatively few studies on the use of a fully three-dimensional fluid-structure interaction (FSI) method in the analysis of large-scale prototype Francis turbines, which can more accurately capture the flow-induced dynamic behavior. The numerical calculation method and computational model of the turbines have been carefully calibrated by comparing them with measurement results [32]. The verified computational method is adopted for this investigation. To avoid the severe flow-induced dynamic problems on Francis turbines, this investigation analyzes the flow-induced stresses of a large prototype Francis turbine rated at 46 MW. Section 2 of the paper presents the mathematical physics equations of the unsteady CFD analysis and flow-induced structural stress analysis. The CFD calculation model, pressure distributions, and flow velocity distributions under four different load conditions (25% load, 50%, 75%, and 100% load) are described in Section 3. The flow-induced structural stress analysis of the Francis turbine runner and the structural stress distributions at four load conditions are compared and discussed in Section 4. The conclusions are summarized in Section 5.
# Novel Semantic-Based Probabilistic Context Aware Approach for Situations Enrichment and Adaptation
## 5 The Prototype
### Possible Scenarios  
We used our prototype to assist the user in his daily life situations. We want to meet the user's needs in different places using the rule recommendation and service adaptation process based on the device's context information. We assume that the prototype has a registry of all his situation rules. As shown in Figure 7, the user has three domains (i.e., home, car and office domains). Figure 6: GUI for specifying the values of preferences. Figure 7: Possible scenarios. If we assume that user's preferences are more important than situation, thus, preference is expressed with high priority and assigned to value \(\in\) [0.7, 1] situation is expressed with low priority and assigned to value \(\in\) [0, 0.3]). For example, W\({}_{\text{P}}\) = 0.7 and Ws = 0.3. Since we have two types of preferences, so W\({}_{\text{EP}}\) = 0.35 and W\({}_{\text{IP}}\) = 0.35 as shown in Figure 8. These weights cannot be directly specified by the user, but we need an expert to prioritize such criteria. For the threshold between 0.6 and 1, we apply a threshold of 0.6. It permits to recommend wide range of situations rules to achieve high number of situation rules correctly recommended compared to other thresholds based on several test results. **First Scenario. ** The first scenario consists of responding to the user, [PERSON], who is at home. The system recommends suitable rule items according to the home's location, explicit and implicit preferences. The user sets his explicit preferences as shown in Figure 9. The PP vector (Preference Pair) of information presented in Section 4 are calculated, based on the provided profile configuration. Table 2 shows a list of relevant home situation rules (e.g., house_ intrusion_ alarm, house_ fire_ alarm, etc.) for every field (security, domotics and study), where each rule item \(r_{i}\) is a recommended rule where the final score \(Score_{r_{i}}\geq\)  
Figure 8: Similarity weights for all scenarios. Figure 9: Explicit preferences for the first scenario. sorting the suitable rules by field according to the score reached through the ontology-based context-aware recommendation process. However, selecting the top-10 rules is sufficient. Moreover, due to the constrained resources of mobile devices, compatible rule items were automatically defined using SWRL language. **Second Scenario. ** A second scenario is when it is time to go to work; the system loaded the user's outdoors preferences to recommend new rule items and ensure the dynamic context changes (location is outside home and the available device is smartphone and car's navigator). The new recommended rules items are automatically updated and adapted to new current context. Table 3 shows a list of relevant home situation rules (e.g., _al_otahim_Market_, _car_map_weekday_, etc.) for every field (shopping and driving). Figure 10 illustrates the user explicit preferences for the second scenario.
## 4 Simulation Results and Discussion
### Scenario 1: A Two-Area Power System (TAPS) Integrating Hydropower Plants (Figure 2)
The simulation scenario, which includes a load variation of 0.05 pu at 1 s, along with the integration of wind power with time-varying wind speeds, has led to strong oscillations in the initial phase. After t = 10 s, the frequency deviations gradually decrease and move towards a stable state. Around 50-60 s, the frequency deviation in all three areas fluctuates slightly around the equilibrium value, with an error of less than \(\pm 0.01\) Hz, indicating that the system has been effectively regulated. Regarding the tie-line power, during the first 5 s, power exchanges between the areas exhibit strong oscillations, particularly between areas 2 and 3, with an amplitude of approximately \(-0.032\) pu. After t = 10 s, these fluctuations significantly decrease and gradually stabilize. From t = 50 s onward, tie-line power between the areas no longer exhibits major variations and remains stable with an error of less than \(\pm 0.002\) pu. This demonstrates that the control algorithm has successfully balanced power among the areas, ensuring system stability (Figure 12). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Area** & \(\mathbf{K_{p}}\) & \(\mathbf{K_{i}}\) & \(\mathbf{K_{d}}\) \\ \hline
1 & \(-2.9374\) & \(-2.8921\) & \(-0.8143\) \\
2 & \(-2.9457\) & \(-2.9763\) & \(-0.4858\) \\
3 & \(-2.8532\) & \(-2.9016\) & \(-2.9845\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: PID parameters for scenario 3. Figure 11: Wind speed fluctuation scenario. Figure 12: Frequency deviation and tie-line power simulation result of scenario 3. The convergence plot (Figure 13) demonstrates the efficiency of the PSO algorithm, as the objective function value experiences a sharp decline during the initial iterations. Within the first five iterations, there is a significant decrease, indicating rapid progress in finding better solutions. By approximately the seventh iteration, the value stabilizes, signifying convergence. This pattern confirms the algorithm's effectiveness in optimizing the given problem. Remark 3: In contrast with the other approaches proposed in [9; 15; 23], the WP is integrated into the PS, which is used fully for the real PS. In addition, the simulation results reveal that the planned regulator built on PSO algorithm tuning remains suitable; subsequently, there are no fluctuations in the progression variable beforehand it spreads a steady state. This stay is exposed by the detail that the regulator output leads the WP through vacillation. This shows that the PID regulator, adjusted by algorithm expressions, has a fast and effective response to load changes and WP variations, which is beneficial in improving the performance of the PS.