esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The aim was to predict engine core size analysis based on pressure ratio, bypass ratio, and sea-level take-off thrust. The results showed that the binary classification model predicted core engine sizes with 92% accuracy, whereas the 3-class predictive models, i.e., acceptable, acceptable with improved manufacturing technologies, and unacceptable core sizes, have an overall accuracy of 75%, which predicts undesirable engine core sizes. [PERSON] et al. [10] developed a data-driven method to predict the pressure on a cylindrical body from the velocity distribution in its wake flow. They proposed a deep-learning Neural Network constituted with CNN. The RMSE of the predicted results and the CFD results was less than 0.004. Earlier, a time-marching throughflow method for the off-design performance analysis of axial compressors was analyzed on rotor-67. The method was based on the Euler equations, and an inviscid blade force model was proposed to achieve desired flow deflection. The flow discontinuity problems at the leading and trailing edges were tackled by automatic correction of the blade mean surface using cubic spline interpolation. The flow discontinuity issues at the leading and trailing edges were addressed by using cubic splitting to automatically fix the blade mean surface [11; 12]. Earlier authors [13] investigated the flow field in the tip clearance region of the transonic compressor at non-uniform flow conditions under the design and off-design RPM. The results found that hub radial pressure distortion and co-swirl flows improve the stability range of turbofan. Furthermore, compressor performance alleviated the stall at higher RPM combined with distorted inlet pressure. To the best of the author's knowledge, limited work has been published that covers a hybrid analysis of both deep learning Neural Network and ML regression learners on transonic compressor prediction with multiple inlet variable parameter effects on compressor outcomes. In this work, supervised learning regression learner algorithms and ANN are employed to train data archived from CFD analysis. The current research presents a novel predictive approach based on Regression Learner, ANN, and CFD to predict the effects of pressure ratio, temperature ratio, and mass flow rate distribution on transonic compressor performance. A significant aspect of this work is prediction analysis with high accuracy at different flow conditions with lower computational costs and time. Furthermore, the deep learning Neural Network produces substantially more accurate results when compared with the results of CFD analysis.
# Survey on Q-Learning-Based Position-Aware Routing Protocols in Flying Ad Hoc Networks
## 4 Q-Learning-Based Position-Aware Routing Protocols in FANETs
### QL-Based Cross-Layer Routing Protocol (QCLCRP)  
[PERSON] et al. [51] proposed the QL-based cross-layer routing protocol (QCLCRP), which utilizes the upper confidence bound (UCB) to determine the balance between exploration and exploitation. They proposed carrier-sense multiple access with multi-channel automatic synchronization (CSMA/MAS) to reduce collisions in the MAC layer by maintaining transmission in a round-robin fashion with synchronization. The MAC delay statics are sent to the QL module for better routing decision-making. The Q-value is adaptively updated for better decision-making by updating the learning rate and discount factor according to the mobility of the node and variance of the MAC delay. The reward function includes the difference between the distances between two UAVs projected to the destination and the MAC delay.  
_Advantages_: QCLCRP uses the cross-layer concept with an exploration strategy using UCB that enhances the performance of both the MAC and routing layers.  
_Limitations_: In QCLCRP, the hello interval frequency is not adaptive. It does not consider mobility control and energy-efficient routing, although the exploration rate was defined by calculating the UCB for each source node considering the number of times a node is selected as a forwarding node and the number of times a source node makes the routing decision.
# Dielectric Spectroscopy Studies and Modelling of Piezoelectric Properties of Multiferroic Ceramics
## 3 Results and Discussion
### X-ray Diffraction Studies  
As an example, an X-ray diffraction pattern of 67 BFBT ceramics sintered at 970 \({}^{\circ}\)C is shown in Figure 2. The search-match process was performed using Match! software (Crystal Impact, Bonn, Germany) [26]. Phase analysis was performed, and it was found that the reference pattern of Ba\({}_{0.3}\)Bi\({}_{0.7}\)FeO\({}_{2.85}\)[27] matches all peaks shown within the measuring range 2\(\Theta\) with Figure-of-Merit (\(FoM\)) parameter \(FoM\) = 0.96.  
It is worth noting that the reference pattern (source of entry: Crystallography Open Database; COD ID 4341652) exhibited a tetragonal structure (space group \(P4 mm\)) with the following unit cell parameters: \(a\) = 3.9963 A and \(c\) = 4.0032 A. Based on Archimedes' method and calculations of theoretical density, it was found that 67 BFBT ceramics reached a relative density of 94.5%.  
Line profile analysis was performed, and the resulting Williamson-Hall plot is shown in Figure 3.  
One can see from the Williamson-Hall plot that the average size of the crystallites is <\(D\)> = 801 A. The average strain (<\(\varepsilon\)>), which is a measure of micro-deformations, is <\(\varepsilon\)> = 0.01%.  
Figure 1: (**a**) Morphology of 67 BFBT ceramics sintered at 970 \({}^{\circ}\)C; (**b**) grain size distribution.
## 2 Results and Discussions
### Comparing UV and ELSD in Quantitative Determination of Saponins in PV
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**ELSD**} & \multicolumn{6}{c}{**UV**} \\ \cline{2-7}
**Saponin** & \multicolumn{2}{c}{**Intra-Day Precision**} & \multicolumn{2}{c}{**Inter-Day Precision**} & \multicolumn{2}{c}{**Intra-Day Precision**} & \multicolumn{2}{c}{**Inter-Day Precision**} \\ \cline{2-7}  & \begin{tabular}{c} **Contents** \\ **(mg/g)** \\ \end{tabular} & \% R.S.D. & \begin{tabular}{c} **Contents** \\ **(mg/g)** \\ \end{tabular} & \% R.S.D. & \begin{tabular}{c} **Contents** \\ **(mg/g)** \\ \end{tabular} & \% R.S.D. & \begin{tabular}{c} **Contents** \\ **(mg/g)** \\ \end{tabular} & \% R.S.D. & \
\begin{tabular}{c} **Contents** \\ **(mg/g)** \\ \end{tabular} & \% R.S.D. &
\begin{tabular}{c} **Contents** \\ **(mg/g)** \\ \end{tabular} & \% R.S.D. \\ \hline N-R1 & ND & NA & ND & NA & \(1.5\pm 0.03\) & \(2.01\) & \(1.5\pm 0.11\) & \(2.24\) \\ M-R1 & ND & NA & ND & NA & ND & NA & ND & NA \\ G-Rg1 & \(35.80\pm 0.2\) & \(0.75\) & \(36.6\pm 1.1\) & \(3.03\) & \(37.3\pm 0.1\) & \(0.27\) & \(37.2\pm 0.14\) & \(0.378\) \\ G-Re & ND & NA & ND & NA & \(0.89\pm 0.01\) & \(1.53\) & \(0.9\pm 0.05\) & \(5.91\) \\ MR2 & \(62.8\pm 0.3\) & \(0.51\) & \(64.1\pm 1.5\) & \(2.43\) & \(65.1\pm 1.3\) & \(1.99\) & \(66.8\pm 1.4\) & \(2.20\) \\ p-RT4 & \(16.8\pm 0.2\) & \(1.4\) & \(17.6\pm 0.6\) & \(3.78\) & ND & NA & ND & NA \\ VRI1 & \(6.70\pm 0.2\) & \(3.03\) & \(7.70\pm 0.18\) & \(2.37\) & ND & NA & ND & NA \\ VR2 & \(1.50\pm 0.05\) & \(3.77\) & \(1.70\pm 0.06\) & \(3.87\) & ND & NA & ND & NA \\ G-Rb1 & \(10.1\pm 0.00\) & \(0.09\) & \(10.5\pm 0.3\) & \(2.96\) & \(10.1\pm 0.02\) & \(0.2\) & \(10.1\pm 0.07\) & \(0.75\) \\ G-Rd & \(5.60\pm 0.06\) & \(1.14\) & \(5.60\pm 0.12\) & \(2.26\) & \(5.6\pm 0.01\) & \(0.27\) & \(5.6\pm 0.026\) & \(0.46\) \\ \hline \hline \end{tabular} ND—not detected; NA—not available.  
\end{table} 
Table 1: Calibration curve for ten saponins in the ELSD and UV detectors.
# Traffic Signal Optimization for Multiple Intersections Based on Reinforcement Learning
## 3 Methods  
### Learning Process  
Figure 1 shows the learning process of the reinforcement learning-based traffic signal optimization model. The microscopic simulation environment was implemented by Simulation of Urban MObility (SUMO). The model received the realtime traffic flow of the intersection as a state. Based on this, the action determined by the model was implemented at the intersection by SUMO's signal controller. Traffic flow changed due to signal control was delivered to the model as a reward. By repeating this process, the model learned an optimized signal pattern that minimized traffic congestion, such as the vehicles' delay and the number of vehicles stopped. The reinforcement learning model was designed based on Deep Q-network (DQN). Since the given problem was a classification that selected an action appropriate for the situation, the SoftMax Function was applied to the activation function of the output layer.
# Hg Content in EU and Non-EU Processed Meat and Fish Foods
## 4 Discussion  
The mercury content was determined by DMA-80, a rapid technique that permitted a direct analysis of processed foods investigated. Hg levels were variable. In general, aquatic products showed a higher content than the other samples. This trend was expected, given the higher mercury accumulation capacity of aquatic species [21,37], and is comparable to other studies in the literature [3]. Furthermore, the different mercury content observed in fish and shellfish samples is related to the different accumulation capacities of the various species. This capacity is strongly correlated with the age and size of the product [37]. Several studies exist in the literature explaining the occurrence of a strong correlation between the mercury content and the size of fish and shellfish, especially in big predators, such as tuna fish and sharks [37,38]. This is coherent with our study, where the highest mercury concentration was obtained for several types of canned tuna. In addition, the mercury content in the investigated fish samples was lower than in one study in the literature [39]. The data obtained shows a higher mercury content in the samples of canned tuna in olive oil, which had a higher fat content than natural canned tuna. In general, this trend seemed to be present in most of the fish and shellfish samples analyzed in our study. Species containing less than 10 percent fat showed the lowest mercury levels. This trend could depend on several factors, including the different accumulation capacities of the species under study. In addition, consideration must also be given to the types of samples examined, namely processed foods which are characterized by a different composition than the corresponding raw materials. In other studies reported in the literature, fat content and mercury concentration followed the opposite trend. In fact, in one such research, the highest percentage of fat (11.49%) was shown by the herring sample, which nevertheless contained low mercury concentrations (0.021 mg/Kg) [40]. Further specific studies will therefore be necessary to demonstrate a possible correlation between fat content and mercury content and to obtain more significant data to confirm our hypothesis. Relative to the processed meat samples, mercury contents were remarkably low. The range was from <LOQ to 3.73 \(\pm\) 0.18 ug/Kg. These levels were either comparable to some studies [16,25] in literature or even lower than others [14]. This demonstrates the safety and quality of the meat products analyzed. European Regulation No 1881/2006 and subsequent amendments (Regulation (EU) No 2022/617) [27,28] specify maximum mercury levels in three food classes: fish, salt, and food supplements. There are no guidelines for the other food types. Comparison with the maximum levels set by European regulations is of fundamental importance to be able to assess the safety of products. In turn, these could provide further information and be used as sentinel organisms for assessing the degree of environmental contamination [41,42].
# Camera-Based Continuous Heart and Respiration Rate Monitoring in the ICU
## 2 Materials and Methods
### Video Dataset  
In total, this study included 36 ICU patients from the Catharina hospital (Eindhoven, The Netherlands). For 25 patients, informed consent was obtained prior to elective surgery with planned postoperative admission to the ICU. The remaining 11 patients had unscheduled (emergency) admissions to the ICU, and informed consent was obtained from the patient if they were conscious and otherwise from a family member. Each patient participated in the trial for up to 24 h or until discharged from the ICU, whichever occurred first. For one postoperative patient, a failure in the recording laptop caused the RGB camera to not store any video data, and therefore, this patient was not included in any further analysis. The summarized characteristics of the patients can be found in Table 1. Furthermore, the operation of the NIR camera was dependent on the operation of the NIR light source. In some cases, the NIR light source or the whole cart was moved or unplugged from the outlet, rendering any video recording from the NIR camera inadequate for further analysis. This situation occurred for two postoperative patients in total.  
\begin{table}
\begin{tabular}{l r} \hline \hline
**Description** & **Mean (Standard Deviation)** \\ \hline Number of patients & 35 \\ Total recording time (h) & 699 \\ Age (years) & 68.0 (8.7) \\ Height (cm) & 173.0 (8.4) \\ Weight (kg) & 86.5 (15.2) \\ BMI & 28.8 (4.7) \\ \hline Male & 22 \\ Female & 13 \\ \hline Fitz–Patrick scale I/II & 35 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summarized patient characteristics. One patient of the total of thirty-six was excluded from further analysis due to missing video data.  
Figure 2: Complete measurement setup for RGB and NIR video monitoring in the ICU.  
Each video was annotated with a number of parameters that would impede the measurement completely, i.e., the patient was not in the field of view of the camera, was (partially) occluded by staff or equipment, or when the camera was covered by staff for privacy reasons. For the purpose of annotation, a second set of videos was created from the raw video recordings, which were downsampled to a frame rate of 1 frame/minute. This resulted in a reasonable number of frames to be annotated as compared to the original 699 h of recording per camera. Furthermore, the frame rate of 1 frame/minute is considered sufficiently high such that most patient occlusions by staff and equipment can be detected, as they typically take 1 min or more to complete. These annotations were then interpolated to the frame rates of the recordings by copying the nearest annotation of each video frame in the original recording.
## 2 Materials and Methods
### Optimization Approach  
Optimization involves solving mathematical problems to achieve the optimal result of a set of variables that will allow the objective function to be maximum or minimum [15; 16; 19; 20; 21]. Generally, the purpose of optimization is to find the maximum value or the minimum value of a given objective function which has the following components:  
1. Designed Variable  
The designed variable refers to the variable that is the answer to a problem solved, and the designed variables are defined to clearly describe the nature of the engineering system, such as size, weight, and quantity.
2. Objective Function  
The objective function is a function in which we want to find the minimum or maximum value by defining the objective function in the form of mathematical equations.  
Figure 4: The accuracy of prediction equation for profit/type.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Variables**} & \multicolumn{2}{c}{**Unstandardized**} & \multirow{2}{*}{**Standardized**} & \multirow{2}{*}{**Sig.**} & \multicolumn{2}{c}{**Colllinearity Statistics**} \\  & & & & **Coefficients** & & **VIF** \\ \hline \multicolumn{1}{c}{(Constant)} & \(-\)384,364.26 & 229,043.285 & & 0.119 & \\ \multicolumn{1}{c}{Land cost/type} & 1.486 & 0.186 & 0.688 & 0.000 & 1.423 \\ \multicolumn{1}{c}{House building cost/type} & 0.919 & 0.197 & 0.402 & 0.001 & 1.423 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of model variables.  
Therefore, we can write the objective function in the form of a mathematical equation as \(Z=f(x)\), where \(X=x_{1}\), \(x_{2}\),..., \(x_{m}\).  
3. Constraints  
Constraints are conditions or constraints of an objective function relating to variables selected in the objective. The objective function must correspond to given constraints, and it can be classified according to two types of signs: _equality constraints_ (=) and _inequality constraints_ (\(\leq,\geq\)).  
4. Sign Restrictions  
The sign restriction is associated with each variable; each variable must be non-negative. Linear Programming  
Linear programming is an optimization technique that is used for problems with objective functions and constraints as linear functions, where the equations of constraints are in the form of equality constraints or inequality constraints [15; 16; 19; 20; 21].  
The objective function is defined as the maximization problem, which has the standard form of Equation (4):  
\[\text{Maximize}\ Z=\sum_{j=1}^{n}C_{ij}x_{j}\leq b_{i}\ =\ C_{1}\ X_{1}\ +\ C_{2}\ X_{2}\ +\...\ +\ C_{n}\ X_{n} \tag{4}\]
## 1 Introduction
### AguaClara Technology  
The AguaClara (AC) group, launched in 2005 at Cornell University's School of Civil and Environmental Engineering, aims to reinvent the conventional coagulation process and develop context-appropriate water treatment solutions for underserved communities in the Global South. Working in partnership with Agua Para el Pueblo, a Honduran NGO, AC focuses on improving the condition of the Water, Sanitation, and Hygiene (WASH) sector in rural Central America through the deployment of off-grid, gravity-powered treatment plants [34; 35].  
AC builds on CEPIS designs, following a user-centric, low-maintenance approach to design. The group developed an off-grid, gravity-powered water treatment method that relies on passive components and novel flow channel geometries to accomplish the same process goals conventionally achieved by mechanical components and external power sources, such as mixing, flow monitoring, maintaining the sludge blanket, or performing the filter backwash. This type of implementation eliminates the need for electricity and therefore reduces operational costs and possible failure modes typically associated with conventional water treatment plants.  
AC made four main improvements to the original CEPIS technology:  
1. Creating a novel off-grid system for flow monitoring and reliable dosing of the chemical agents [36].
2. Redesigning the baffled flocculator to generate a more uniform collision potential and allow for shorter hydraulic retention times [37].
3. Adapting the sludge blanket process, which conventionally requires vacuum pumps and large valves, for passive operation only driven by gravity [38].
4. Designing a stacked rapid sand filter, which reduces the head loss over the filtration media and minimizes the need for backwash water [39; 40].  
These improvements aim to deliver reliable, efficient water treatment with minimal maintenance, enabling off-grid operation. Deployed in various locations in Central America, AC plants have improved access to safe water, fostered community engagement, and built local capacity through community empowerment and training operators. This section is only meant as an introduction to the design approach, presenting the main improvements to the conventional treatment process developed by the AC group. The exact information about the designs, such as flow rates, dimensions, and other parameters necessary to recreate the system, can be found in the documents published by AC, as well as in the other materials available in the public domain [41].
## 2 System Model
: _Table 1 lists the positions and the erasure probabilities in a topology with \(K=2\) satellites, when either \(s=0\) (both satellites orbit together) or \(s=1\) (both satellites are separated by a single position; i.e., in the next iteration the second satellite occupies the current position of the first satellite). Moreover, we consider a set of \(n=10\) different erasure probabilities, \(\epsilon\in\{0.01,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9\}\). These erasure probabilities could be binuivocally related to the elevation angle of the satellite with respect to the cluster, from the zenith (\(90^{\circ}\)) to the horizon (close to \(0^{\circ}\)) with intervals of \(\Delta\theta=10^{\circ}\), as the severity of fading and shadowing is a function of the elevation angle [39; 40; 41]. Note that with \(s=0\) there are in total \(19\) positions, and when \(s=1\) there is one more position with at least one satellite being visible by the IoT cluster._  
It is easy to check that in the general case the number of positions in each lap is given by  
\[M=2n-1+(K-1)s. \tag{1}\]  
Note that this interpretation of \(M\) is valid as long as \(M\leq\frac{360^{\circ}}{\Delta\theta}\), but in constellations with too many satellites or where the satellites are too far apart from each other, such that \(M>\frac{360^{\circ}}{\Delta\theta}\), \(M\) still coincides with the number of iterations associated with each lap of the satellite constellation. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c} \hline \hline \(s\) & **SAT** & **P1** & **P2** & **P3** & **P4** & **P5** & **P6** & **P7** & **P8** & **P9** & **P10** & **P11** & **P12** & **\(\cdots\) & **P18** & **P19** & **P20** \\ \hline \multirow{2}{*}{0} & \(Sat_{1}\) & 0.9 & 0.8 & 0.7 & 0.6 & 0.5 & 0.4 & 0.3 & 0.2 & 0.1 & 0.01 & 0.1 & 0.2 & \(\cdots\) & 0.8 & 0.9 & x \\  & \(Sat_{2}\) & 0.9 & 0.8 & 0.7 & 0.6 & 0.5 & 0.4 & 0.3 & 0.2 & 0.1 & 0.01 & 0.1 & 0.2 & \(\cdots\) & 0.8 & 0.9 & x \\ \hline \multirow{2}{*}{1} & \(Sat_{1}\) & 0.9 & 0.8 & 0.7 & 0.6 & 0.5 & 0.4 & 0.3 & 0.2 & 0.1 & 0.01 & 0.1 & 0.2 & \(\cdots\) & 0.8 & 0.9 & x \\  & \(Sat_{2}\) & x & 0.9 & 0.8 & 0.7 & 0.6 & 0.5 & 0.4 & 0.3 & 0.2 & 0.1 & 0.01 & 0.1 & 0.2 & \(\cdots\) & 0.8 & 0.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Erasure probabilities at each position (P1, P2, P3,..., P20) considering a constellation with \(K=2\) LEO satellites (\(Sat_{1}\), \(Sat_{2}\)), where the satellite spacing is either \(s=0\) or \(s=1\), and the erasure probabilities \(\epsilon\in\{0.01,0.1,0.2,0.3,0.4,0.5,\) 0.6, 0.7, 0.8, 0.9\(\}\). **Example 2**. : _To facilitate the understanding of the system model, we present in Figure 1 a snapshot of the 17 th positions of the first satellite in a constellation of \(K=5\) satellites, with a fixed satellite spacing of \(s=4\) positions, and assuming that \(\varepsilon\in\{0.01,\,0.1,\,0.2,\,0.3,\,0.4,\,0.5,\,0.6,\,0.7,\,0.8,\,0.9\}\).