esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of pH and Optical Properties of Dual Ringe HEDP Irrigating Solution
Active irrigation methods, such as ultrasonic activation and laser-activated irrigation (LAI), have been shown to achieve superior antimicrobial and chelating effects compared to passive irrigation [1; 23]. LAI is effective in eliminating bacterial infection and significantly contributes to the enhanced long-term success of endodontic therapy when compared with conventional techniques [24]. The effectiveness of LAI is based on a phenomenon called cavitation. This phenomenon occurs when the static pressure in a liquid descends below its vapor pressure, leading to the formation of vapor-filled voids. The subsequent collapse of these bubbles generates shock waves, thereby augmenting the cleaning efficacy through the propulsion of the irrigants within the canal. This irrigation flow expels debris from the canal, eliminating the smear layer and biofilm from the dentin surface [25; 26]. Currently, wavelengths ranging from 1000 to 3000 nm are predominantly employed in LAI [26; 27; 28]. However, other groups of lasers are gaining popularity in endodontics such as diode lasers which operate at wavelengths in the visible and near-infrared (NIR) region [29]. As of now, the literature on the application of diode lasers in LAI is limited [30]. Additionally, the literature on the application of diode lasers in combination with Dual Rinse HEDP in endodontics is notably scarce. The primary objective of our study was to investigate the pH value of the independent solution of Dual Rinse HEDP or in combination with NaOCI, and compare it with other irrigants used in endodontics. Additionally, we aimed to examine the optical characteristics of these solutions within the ultraviolet, visible and NIR wavelength range up to 1100 nm. The null hypothesis that was tested assumes that there is no difference in pH values and optical characteristics between Dual Rinse HEDP solutions and other tested irrigants.
# A Proactive Approach to Extended Vehicle Routing Problem with Drones (EVRPD)
## 3 Problem Statement and Mathematical Model
### Mathematical Model
\begin{table}
\begin{tabular}{l l} \hline \hline
**Constraints** & **Description** \\ \hline (1a) & Delivery cost (for Q2 and Q4, it is an objective function (FC1) and is minimized) \\ (1b) & Number of drones used (for Q6, it is an objective function (FC2) and is minimized) \\ (2) & Determined the arrival and departure of the drone at and from the delivery point (customer) \\ (3) & If no items were to be carried on the route, then a drone did not travel that route. \\ (4) & If a drone did not travel along a route, no items were to be carried on that route. \\ (5) & In no route segment could a drone carry more parcels than its payload. \\ (6) & Items were delivered to and from mobile points, h\(\in\)H. \\ (7) & Ensured a single run of the drone \\ (8) & Items were picked up or delivered to customers. This constraint prevented sub-tours. \\ (9) & Runs were executed within the required time. \\ (10) & Each drone picked up or delivered parcels from or to a source (MH); the first component of the constraint was responsible for the delivery of parcels to the customer, while the second was responsible for the pick up from the customer. \\ (11) & Ensured the drone was at a point only once (no returns). \\ (12) & Ensured the drone took off and returned to the MH point. (We excluded the possibility that the drone left the parcel with a given customer and another took it to the next customer). \\ (13) & Determined the value of the decision variable Z\({}_{h}\). This variable determined mobile point h\(\in\)H from the drone take-off point (in this version of the model, we did not introduce capacity limits for trucks, so Z\({}_{h}\) = 1 determined both the MH point and the truck). \\ (14) & Drone take-offs could not take place from more mobile points h\(\in\)H than the number of vehicles (trucks). \\ (15) & Binarity \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Indices, sets, parameters, and decision variables. The proposed version of the model (Q1-8, (1a)-(15)) supported a wide range of decisions related to last-mile logistics. The most important included: (a) the optimal route determination for drones (due to the routes traveled or costs), (b) the optimal selection of the set of mobile points (MH) at which vehicles (trucks) parked and drones took off, (c) the optimal selection of the set of drones used, (d) checking whether the set of resources (drones and vehicles) allowed the execution of a given set of orders (packages), (e) determining the actual needs for drones and trucks to fulfill a given set of orders, (f) determining the actual delivery time for a given set of orders, (g) determining the optimal cost of the implementation of a given set of orders, and (h) identifying those shipments (parcels) that prevented the implementation of a given set of orders using available resources (vehicles and drones). Moreover, decisions (a)-(h) could be considered with an additional condition for a given delivery time T.
## 3 Results
### Temperature Distribution within the Process Zone
#### 3.4.2 Global, Average, and Maximum Temperature Distribution
As a result of this, the maximum temperature for static beam shaping in set #1 (Figure 16a) with 2250 \({}^{\circ}\)C is the lowest compared to set #2 (Figure 16b) with 2665 \({}^{\circ}\)C and set #3 (Figure 16c) with 2600 \({}^{\circ}\)C. Particularly highlighted is set #4 (Figure 16d) with 2910 \({}^{\circ}\)C as the highest measured value in this evaluation. It is noticeable that the reversal point directly located at the cut front (left-hand side of the images) is more emphasized than the reversal point further back in the cut kerf (right-hand side of the images). Figure 14 depicts higher peak temperatures of beam oscillation compared to static beam shaping for single measurement moments. Figure 16 confirms this as a global effect over the entire cut front. The position of the peak temperatures, marked by the square in Figures 15 and 16, are combined in one chart in Figure 17.  
The location of the average and maximum peak temperatures for set #1 (Figure 17a) differ slightly with 15 \(\upmu\)m. Observing beam oscillation, the position of the highest maximum temperature is not necessarily congruent with the position of the highest average value. In set #2 (Figure 17b), both temperature peaks shifted toward the beginning of the cut front. This results in 95 \(\upmu\)m distance of the peak positions for set #2. The two temperature peaks differ in a distance of about 10 \(\upmu\)m for set #3 (Figure 17c). A difference in peak locations is especially evident for set #4 (Figure 17d). The considered positions differ by about 235 \(\upmu\)m. The highest average temperature significantly shifted to the rear part of the cut front. Figure 17: Spatially resolved temperature profiles along the symmetry axes of the cut front for average temperature \(\overline{T}_{i}\) and maximum temperature \(T_{i}^{max}\) calculated with e = 1 from all single images within the complete measurement period of 500 ms of 12 mm stainless steel cut with the following parameters: (**a**) Set #1: Static beam shaping; and different values of the longitudinal, linear beam oscillation parameters: (**b**) Set #2: 300 \(\upmu\)m amplitude and 500 Hz frequency; (**c**) Set #3: 550 \(\upmu\)m amplitude and 500 Hz frequency; (**d**) Set #4: 300 \(\upmu\)m amplitude and 2000 Hz frequency.
## Case Studies
### Hurst Exponents Fitting with Crossovers  
Second, in Figures 6-8 the red line represents single memory scale estimation, i.e., one Hurst exponent fitting without crossover \(H_{1}\). Green line represents two memory scales \(H_{1,2}\) with one crossover. Blue line represents three memory scales \(H_{1,2,3}\) with two crossovers. All the crossover points are marked in circles. The phenomenon of the multiple memory scales in Var1 and Var2 are observed in Figures 6 and 7. Therefore, the variation of the memory scales with crossovers cannot be captured  
Figure 4: Histogram of Var1 with PDF fitting.  
Figure 5: Histogram of Var2 with PDF fitting.  
by the conventional Hurst R/S fitting method with the constant value. After processing data with MFDFA, the multiple Hurst exponents, along with crossover point position might carry on information of multifractality of the control performance.  
Var3 is the variable of errors from coolant level loop, which in reality to keep the coolant away from the alarm levels. It should be noted that the control priority is comparatively lower in the design of the MIMO system. Compared with Var1, Var2, however, the monofractal behavior of Var3 can be validated through Figures 8-10.  
There might be done further conclusions on the control quality of the loops. Var1 and Var2 show multi-persistence behaviour with different Hurst exponents in different memory scales. In contrary Var3 is clearly mono-persistent. It means that this loop is rather not coupled with the other ones. Its Hurst exponent is approximately equal to 1. Its tuning is highly sluggish, almost not existent. It confirms initial observation done using the histogram. The other two loops are the most probably close coupled. Observation of the R/S plots and the shortest Hurst exponent also reveals dynamically sluggish control, however in the longer memory scale control improves and is back to the neutral one.  
Figure 8: R/S plot Var3 with crossovers.  
Figure 9: Histogram of Var3 with PDF fitting.
# Double-Bend and Semi-Spherical Energy Deflectors' Blast Load Efficiency--A Comparative Study
## 3 Numerical Modelling
### Variants of the Deflector Geometry  
Numerical analyses, especially in the case of a relatively low level of model complexity, made it possible to easily and quickly check many variants of the proposed solution. In the case of the following study, a whole range of double-bend deflector and semi-spherical deflector variants were selected for numerical analyses. Two approaches were adopted for the double-bend deflector. In the first, the A-dimension (the distance in the horizontal plane from the edge of the deflector to the second bend) was varied from 60 to 130 mm (in 10 mm increments). This variation allowed deflectors with both a more \"straight\" shape (Figure 11a) and a more \"pointed\" shape (Figure 11b) to be subjected to testing while maintaining identical external dimensions. In addition, the effect of changing the second critical dimension, the B-dimension (the distance in the vertical plane from the edge of the deflector to the second bend) while keeping A-dimension constant was also tested. For this  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Description** & **Unit** & **Value** \\ \hline \(\rho\) & Density & kg/mm\({}^{3}\) & 1.2 \(\times\) 10\({}^{-9}\) \\ C\({}_{4}\) & Constant & - & 0.4 \\ C\({}_{5}\) & Constant & - & 0.4 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Equation of state parameters for air [46].  
Figure 10: Non-reflecting boundaries of the air domain.  
purpose, a deflector was chosen with an A-dimension equal to 80 mm and B-dimensions varying from 15 to 45 mm with 5 mm increments. The designations of these deflectors consist of two numbers (e.g., \"60-15\"), the first being the A-dimension and the second being the B-dimension. It is worth noting that with the presented double-bend sizing sizing, the 80-15 deflector is the same as the V-shaped deflector (Figure 12a).  
Semi-spherical deflectors have only one leading dimension: the radius of the semi-spherical part. For the study, we selected deflectors whose radius values varied from 10 to 50 mm in 5 mm increments (Figure 13). In this case, the most convex semi-spherical deflector (indicated as \"semi-spherical 50\" because of radius equal to 50 mm) had the same height as the double-bend deflector (see Figure 4).
## 3 Proposed System
### Design of Likelihood Functions
#### 3.3.3 Likelihood Function  
Let \(id\) be the BLE ID, \(R^{id}_{(x,y)}\) be the RSSI value measured by \(BLE^{id}\) at point \((x,y)\), \(\mu^{id}_{(x,y)}\) be the mean, and \(\sigma^{id}_{(x,y)}\) be the variance; the distribution of \(R^{id}_{(x,y)}\) is  
\[fn^{id}_{(x,y)}(R^{id}_{(x,y)})=\frac{1}{\sqrt{2\pi\sigma^{id}_{(x,y)}}}\text {exp}\left\{\frac{-\Big{(}R^{id}_{(x,y)}-\mu^{id}_{(x,y)}\Big{)}^{2}}{\sigma^ {id}_{(x,y)}}\right\}. \tag{9}\]  
Let \(R^{id}_{t}\) be the RSSI value measured from the BLE beacon at time \(t\). The following equation is used to determine the upper probability of each \(R^{id}_{t}\).  
\[F^{id}_{mix}(R^{id}_{(x,y)})=\int_{R^{id}_{t}}f^{id}_{mix}(R^{id}_{(x,y)})\; \text{d}R^{id}_{(x,y)}. \tag{10}\]  
Let \(D\) be the set of id's ordered by decreasing the value of \(F^{id}_{mix}(R)\). Furthermore, let \((xp^{n}_{t},yy^{n}_{t})=p(n)\). The distribution of \(R^{id}_{(x,y)}\) is used to calculate the likelihood of the particles in \(p(n)\). When the particle is within a radius \(r\) of the BLE beacon  
\[fc_{\big{(}c^{j}_{p(n)}\big{)}}=\sum_{D}fn^{D}_{\big{(}c^{j}_{p(n)}\big{)}}R^ {D}_{t}\left\{F^{i}_{mix}(R^{id}_{(x,y)})\right\}^{-1}, \tag{11}\]  
otherwise  
Figure 5: Graph of Equation (7).  
\[fc_{\left(\binom{j}{p(n)}\right)}=\sum_{D}fn_{\left(\binom{j}{p(n)}\right)}^{D}(R_ {t}^{D}), \tag{12}\]  
Thus, the likelihood function \(fp(xp_{t}^{n},yp_{t}^{n})\) of the particle corresponding to the coordinate \((xp_{t}^{n},yp_{t}^{n})\) is expressed as follows:  
\[fp(xp_{t}^{n},yp_{t}^{n})=\sum_{j}\omega^{j}(xp_{t}^{n},yp_{t}^{n})fc_{\left( \binom{j}{c_{(xp_{t}^{n},yp_{t}^{n})}}\right)} \tag{13}\]  
The mean \(\mu_{(x,y)}^{id}\) and variance \(\sigma_{(x,y)}^{id}\) are used to calculate the likelihood of the fingerprint points around the particle, and then the distance weighting function is used to process the estimates to calculate the likelihood of the particle, resulting in a location estimate. Figure 6 shows the flow of calculating the likelihood function A.  
The likelihood function B is more accurate, as it uses more reference points around the particle. An interpolated positional fingerprint with a weighting function is generated based on distance. By using the mean \(\mu_{(x,y)}^{id}\) and variance \(\sigma_{(x,y)}^{id}\) in the interpolated data, the likelihood of the mixed normal distribution of the location points can be calculated. Figure 7 shows the flow of calculating the likelihood function B.  
The following equation can be used to generate an interpolated fingerprint with a distance weighting function.  
\[\mu_{(x,y)}^{id}=\sum_{i}\left\{\omega_{(x,y)}^{i}\right\}\mu_{ \left(\zeta_{(x,y)}^{i}\right)}^{id} \tag{14}\] \[\sigma_{(x,y)}^{id}=\sum_{i}\left\{\omega_{(x,y)}^{i}\right\} \sigma_{\left(\zeta_{(x,y)}^{i}\right)}^{id} \tag{15}\]
# Real-Time Wave Energy Converter Control Using Instantaneous Frequency
## 3 Comparison of Methods  
To evaluate the performance of the frequency estimation techniques, four different wave scenarios were analyzed:  
* Case 1: Sinusoidal fluctuation in frequency centered around a 9 s dominant period.
* Case 2: Step change in wave amplitude, maintaining the frequency condition of Case 1.
* Case 3: A 50% step increase in frequency from the baseline 9 s condition.
* Case 4: JONSWAP sea condition with a 9 s peak period and a peak enhancement factor \(\gamma=3.3\).  
These scenarios, with simulation examples illustrated in Figures 3-6, provide a comprehensive basis for evaluating the accuracy and robustness of each frequency estimation method. In each case, the true instantaneous frequency is compared against the estimates obtained using the CHT and PLL approaches.  
Figure 2: Block diagram of the PLL used in this study. The block diagram consists of three main components: the PD, which extracts the phase difference between the input wave signal and the internally generated VCO output; the LF, which smooths the phase error signal and determines the system’s bandwidth and transient response; and the VCO, which dynamically adjusts its frequency to match the estimated instantaneous frequency of the incoming wave. The control gains within the loop filter are tuned for optimal tracking performance in varying wave conditions.  
Figure 4: Case 2: Instantaneous frequency comparison for step change in amplitude, evaluated using CHT and PLL.  
Figure 5: Case 3: Instantaneous frequency comparison for frequency step-down, showing performance of CHT and PLL estimations.  
Figure 3: Case 1: Instantaneous frequency comparison between the true value, CHT estimation, and PLL-based estimation.
# Interior Sound Quality Prediction of Pure Electric Vehicles Based on Transfer Path Synthesis
## 2 Materials and Methods  
### PEV Transfer Path Analysis  
Many factors contribute to the noise in a PEV. As each noise source has its own transfer path and transmission mode, the transfer path of the noise in a PEV is particularly complicated. However, the noise transmission is divisible into only two categories: structure-borne noise and airborne noise. Structure-borne noise is caused by vibrations of a component in the vehicle, which are transmitted to the car body through the connected parts. The resulting vibrations of the vehicle body panel radiate into the cab. Airborne noise arises from the vibrations of each noise source or from internal noise excitation, which radiates noise into the air. The airborne noise is transmitted to the surroundings and then reaches the vehicle through other paths such as body panels, holes, gaps, or windows. If the PEV is operating under linear time-invariant conditions, the sound pressure around the human ear in the vehicle can be obtained by superposing the energy of each noise source transmitted to the vehicle. Before reaching the ear, the energies of the excitation sources are attenuated by the structures and air transmissions along the different transmission paths. The sound pressure in the PEV is; thus, calculated as  
\[P_{interior}=\sum P_{i,air}+\sum P_{i,structure}=\sum(H_{i,air}\times P_{i,source })+\sum\bigl{(}H_{i,structure}\times F_{i,source}\bigr{)}, \tag{1}\]  
where \(P_{interior}\) denotes the total noise pressure in the vehicle; \(i\) and \(j\) are the indices of the airborne and structure-borne noise sources, respectively; \(P_{i,air}\) and \(P_{i,structure}\) are the pressures of the \(i\)th airborne noise sources and the \(j\)th structure-borne noise sources, respectively; \(H_{i,air}\) and \(H_{j,structure}\) are their corresponding transfer functions; \(P_{i,source}\) is the \(i\)th airborne noise source excitation; and \(F_{i,source}\) is the \(j\)th structure-borne noise source excitation.  
The noise composition in a PEV is quite different from that in an ICEV. The noise in a PEV is mainly contributed by electromagnetic noise of the driving motor, the tire noise, oil pump noise, and fan noise of the battery-cooling ventilation system. The structure-borne noise is formed from vehicle body vibrations caused by road surface excitations and vibrations of the powertrain through the mount, suspension, and chassis. The total interior noise in a PEV includes airborne and structure-borne noise as described above. Figure 1 schematizes the noise transfer paths through a PEV.
# A Novel 2D Micro-Displacement Measurement Method Based on the Elliptical Paraboloid
## 4 Discussion  
### Manufacturing Error of the Elliptical Paraboloid  
Several factors influence the accuracy of the 2D displacement measurement system. The first error that needs to be considered is the manufacturing error relating to the surface of the elliptical paraboloid, which has a major impact on the measurement system. The surface profile of the elliptical paraboloid is measured by an optical 3D surface profiler, and a parabola is selected from the test results to fit the quadratic curve. The fitting results of parabola and the fitting error are shown in Figures 10 and 11. As can be seen from Figures 10 and 11, the measured data are parabolic and can be effectively fitted by quadratic curves with a fitting error within \(\pm\) 0.3 \(\upmu\)m in the range of 80 \(\upmu\)m height.  
Figure 8: Comparison results in the \(x\) direction.  
Figure 9: Comparison results in the \(y\) direction.  
In order to verify all of the processing characteristics of the elliptical paraboloid, several parabolas passing through its vertex in four different directions were selected and fitted in this paper. The fitting results are shown in Table 1. According to Table 1, the elliptical paraboloid has ideal processing precision because the quadratic coefficient (\(\frac{1}{2\pi^{2}}\)) of each parabola has high repeatability. It is worth mentioning that these quadratic coefficients are consistent with the design value of 0.0032.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parabola** & **Quadratic Coefficient** & **First-Order Coefficients** & **Constant Term** \\ \hline
1 & 0.003157 & –38.4374 & 1.1681 \\
2 & 0.003157 & –39.3118 & 1.2224 \\
3 & 0.003160 & –35.9953 & 1.0234 \\
4 & 0.003158 & –36.7723 & 1.2039 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Fitting results of multiple parabola.  
Figure 11: Parabola fitting error.  
Figure 10: Parabola measured data and fitting results.  
The perfect processing quality of the elliptical paraboloid is the basis and key aspect of micro-displacement. Therefore, high machining accuracy of the elliptical paraboloid in the manufacturing process is required, and its cleanness needs to be guaranteed in the measurement conditions.
# Optimizing the Parameters of Long Short-Term Memory Networks Using the Bees Algorithm
## 2 Long Short-Term Memory (LSTM)
### LSTM Definition and Process  
The LSTM is one of the DL networks that deals with time series or sequence problems. It is an extension of RNNs that can remember long-term dependencies, as it retains the information for a long period. The normal RNN is not able to do so, thus resulting in a vanishing gradient problem [9]. This is a situation where the RNN is not able to propagate useful information from the end of the network back to the beginning of the network, as the information is stored only for a short period [12].  
The structure of the LSTM consists of three gates: the first one is the forget gate that decides if the information coming from the previous time scale is relevant to be remembered or irrelevant to be forgotten; the second one is the input gate that tries learning new information from the cell input; and the last gate is the output gate, wherein the cell transfers the updated information from the previous time cycle to the next time cycle. In addition, it has a cell state that carries the information along with all cycles as it stores the information for a long period and has a hidden state for short-term memory; this cell state is present in RNN as well [9]. Reference [9] presented the following Figure 1, which illustrates the basic structure of the LSTM, where the first part is the forget gate, the second part is the input gate, and the last one is the output gate. In addition, \(\text{C}_{\text{t-1}}\) is the cell state for the previous time cycle, \(\text{C}_{\text{t}}\) is the cell state for the current time cycle, \(\text{H}_{\text{t-1}}\) is the hidden state for the previous time cycle, and \(\text{H}_{\text{t}}\) is the hidden state for the current time cycle.  
The LSTM network starts with the forget gate to decide if the information coming from the previous time scale is relevant to be remembered or irrelevant to be forgotten. Then, the input gate is used to quantify the importance of the input of new information. After that, the cell transfers the updated information from the previous time cycle to the next time cycle in the output gate.  
Reference [8] presented a more intuitive architecture of the LSTM network, as is shown in Figure 2.