esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Biocompatibility Issues  
Any implantable device within the human body must meet regulatory standards for biocompatibility and prove to be nontoxic. The Food and Drug Administration (FDA) is among the regulators that approve any medical device before reaching the market. A classification of 3D-printed devices includes products with low or no risks, such as bandages and surgical tools and implantable devices [148]. The issues related to their biocompatibility, sterility, cleaning, reproducibility must be solved in order to obtain approval. Ideally, the selection of the printing formulations should consider water-based and biocompatible raw materials. The presence of organic solvents, mostly toxic, requires supplementary purification steps in order to prevent damage to bioprinted living cells and tissues. SLA is, to some extent, compatible with cell-laden resins, but printing must be performed under sterile conditions [149]. With respect to the polymer matrix, either synthetic or natural polymers may be suitable, and some of them have been featured in Section 4.1.  
After a survey of the literature, with a focus on the cytotoxicity tests performed on graphene-based materials, some contradictory results were revealed. Some authors concluded that graphene might have potential cytotoxicity risks at even low concentrations [150]. Conversely, other research works have presented the ability of graphene to stimulate cell proliferation [151]. The results showed that the addition of small concentrations (less than 1 wt%) of graphene improved human adipose-derived stem cells (hADSCs) proliferation to poly(\(\varepsilon\)-caprolactone) (PCL)/graphene scaffolds [151]. It has been reported that graphene provides the requirements to develop successful scaffolds for nerve restoration. This is due to graphene's strong \(\pi\) bond and large surface, which improve cell bioelectricity between scaffolds and cellular membrane [152]. Moreover, the cells showed good survival after the fabrication of the construct. Other studies reported the effect of graphene or graphene oxide on stem cell differentiation into neurogenic [153], chondrogenic [154], myogenic [155] and osteogenic cells [156].  
Regarding the polymers used in SLA, acrylates are cytotoxic and cannot be used as scaffold materials but can be replaced with methacrylates, thiol-ene systems, and other photoreactive monomers. For the preparation of cell-laden scaffolds, it is essential that the resin is water-soluble. Up to date, the range of photopolymerizable biomaterials for tissue engineering and regenerative medicine by SLA is quite limited.
# Advanced Construction of the Dynamic Matrix in Numerically Efficient Fuzzy MPC Algorithms
## 2 Efficient Fuzzy MPC Algorithms with the Advanced Construction of the Dynamic Matrix
### Generation of the Free Response  
The method of advanced free response generation, based on a nonlinear process model, proposed in [12], is reminded in this subsection. Define the following vectors:  
\[\mathbf{u}_{k+i|k-1}=\left[u_{k+i|k-1}^{1},\ldots,u_{k+i|k-1}^{n_{u}}\right]^{T}, \tag{20}\]  
where \(u_{k+i|k-1}^{m}\) are elements of control signal trajectories obtained in the last \((k-1)\)st time step.  
First, the components of the free response are derived iteratively using the nonlinear model (17) and the vectors (20):  
\[\begin{array}{c}\widehat{\mathbf{y}}_{k+1}=\mathbf{f}(\mathbf{y}_{k},\mathbf{y}_{k-1},\ldots,\mathbf{y}_{k-n_{k}+1},\mathbf{u}_{k|k-1},\ldots,\mathbf{u}_{k-n_{k}+1})\ ;\\ \widehat{\mathbf{y}}_{k+2}=\mathbf{f}(\widehat{\mathbf{y}}_{k+1},\mathbf{y}_{k},\ldots,\mathbf{y} _{k-n_{k}+2},\mathbf{u}_{k+1|k-1},\mathbf{u}_{k|k-1},\ldots,\mathbf{u}_{k-n_{k}+2})\ ;\\ \vdots\\ \widehat{\mathbf{y}}_{k+i}=\mathbf{f}(\widehat{\mathbf{y}}_{k+i-1},\widehat{\mathbf{y}}_{k+i -2},\ldots,\mathbf{y}_{k-n_{k}+i},\mathbf{u}_{k+i-1|k-1},\mathbf{u}_{k+i-2|k-1},\ldots,\bm {u}_{k-n_{k}+i})\ ;\\ \vdots\end{array} \tag{21}\]  
Note that the values \(\widehat{\mathbf{y}}_{k+1}\) are used when calculating the output values \(\widehat{\mathbf{y}}_{k+2}\), and in general, in the \(i\)th iteration, the values \(\widehat{\mathbf{y}}_{k+1},\ldots,\widehat{\mathbf{y}}_{k+i-1}\) are used to obtain \(\widehat{\mathbf{y}}_{k+i}\).  
Next, the final form of the free response is obtained, after taking into account the estimation of unmeasured disturbances \(\mathbf{d}_{k}=\mathbf{y}_{k}-\widehat{\mathbf{y}}_{k|k-1}\):  
\[\widetilde{\mathbf{y}}_{k+i|k}=\widehat{\mathbf{y}}_{k+i}+\mathbf{d}_{k}. \tag{22}\]  
Note that the free response can be iteratively improved using the nonlinear model. Moreover, it can be modified to include information about measured disturbances. These topics are detailed in [12; 43].
## 3 Methodology for Change Detection
### Tree Growth Monitoring  
Tree height and tree growth are both of great importance in forest monitoring, as they can indicate and predict forest biomass productivity [42]. In this paper forest growth is assessed using periodic annual increment (PAI) values [37].  
According to our investigation, the maximum height from the CHMs is the best predictor for H100 (Experiment I). The PAI is computed using the difference between the maximum heights of two CHMs within each inventory plot region (500 m\({}^{2}\)), divided by the number of years between the data acquisition of both datasets:  
\[PAI=\frac{InvenHeight_{After}-InvenHeight_{Pre}}{Year\ Difference} \tag{3}\]  
The LiDAR2001 CHM was used as the initial height for each inventory plot, and represented as \(InvenHeight_{Pre}\) in Equation (3).  
Due to the storm event, many inventory plots have negative changes instead of positive tree growth change. Therefore, in a preprocessing step, all inventory plots with negative height changes were removed, which reduced the number of inventory plots for tree growth evaluation to 137. The PAIs calculated using LiDAR2010 as \(InvenHeight_{after}\) are recorded as reference.  
We assume that tree heights have a positive correlation with tree ages, which influences the PAIs [31]. Thus the obtained tree growth can be further analyzed by calculating the relationship between tree height and PAIs.
## 2 Materials and Methods
### Regression Analysis and Modeling
#### 2.5.2 MSE Modeling  
As performed for STICKRATIO modeling, Table 8 presents the MSE model-related coefficients. It is possible to see that the linear term X\({}_{2}\) and the second-order interaction term X\({}_{1}\).X\({}_{2}\), are associated with \(p\)-values of 19.53 [%] and 74.15 [%], respectively, which is greater than 5 [%] and therefore considered to be non-significant.  
Therefore, a new regression for the MSE was conducted (Table A2--Appendix A) without the second-order interaction term X\({}_{1}\).X\({}_{2}\), while keeping the linear term X\({}_{2}\), as the second-order interaction X\({}_{2}\).X\({}_{3}\) is still within the confidence interval. Table 9 details the coefficients and also the \(p\)-values, where the linear term X\({}_{2}\) has a \(p\)-value greater than 5 [%]. It remains in the model due to its significant second-order interaction with the term X\({}_{3}\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variables** & **Coefficients** & **Standard Error** & \(p\)**-Value** \\ \hline X\({}_{1}\) & 31.0383 & 9.8979 & 0.0017 \\ X\({}_{2}\) & 13.9064 & 10.7356 & 0.1953 \\ X\({}_{3}\) & 261.4604 & 10.7232 & 0.0000 \\ X\({}_{1}\)\({}^{2}\) & 178.7858 & 12.1278 & 0.0000 \\ X\({}_{2}\)\({}^{2}\) & 207.4768 & 21.2280 & 0.0000 \\ X\({}_{3}\)\({}^{2}\) & 444.6921 & 17.1241 & 0.0000 \\ X\({}_{1}\).X\({}_{2}\) & \(-8.2976\) & 25.1533 & 0.7415 \\ X\({}_{1}\).X\({}_{3}\) & 320.5658 & 28.2518 & 0.0000 \\ X\({}_{2}\).X\({}_{3}\) & 375.3703 & 31.8360 & 0.0000 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Effect of factors on MSE.  
Figure 2: Real (black line) versus modeled STICKRATIO (red line) for the present analysis.  
With the obtained coefficients, the model is finished up and is formulated as per Equation (4). MSE (\(Y_{2}\)) is presented as a function of RPM [rpm] (\(X_{1}\)), FLOW [L/min] (\(X_{2}\)), and WOB [tf] (\(X_{3}\)).  
\[\begin{split} Y_{2}=31.9602X_{1}+15.2027X_{2}+260.5446X_{3}+181.32 81X_{1}^{2}+213.1123X_{2}^{2}\\ +444.5563X_{3}^{2}+315.2538X_{1}X_{3}+369.1122X_{2}X_{3}\end{split} \tag{4}\]  
A plot comparing the real versus the modeled MSE can be seen in Figure 3. It can be observed that in various parts of the graph, especially at the lower MSE values, the comparison between the model and the real dataset is quite close, suggesting that the model trend curve follows the real one. It is interesting to note that, according to R\({}^{2}\) obtained (approx. 90.34 [%]), the model fits very well.
## 4 Evaluation and Results  
The purpose of a system test is to evaluate the end-to-end system specifications and to verify that an application performs tasks as designed. In the Dr. Barea system, we ensured the effective performance of the system using unit, integration, performance, and user acceptance testing. In addition, we measured anxiety levels through the CFSS-DS.
# Enhanced Sensitivity of FeGa Thin-Film Coated SAW Current Sensor
## 4 Sensor Experiments and Discussions
### Sensor Performance Evaluation
#### 4.2.4 Fatigue Characteristics  
Magnetostrictive strain usually leads to fatigue and aging of magnetic-sensing films. Thus, the fatigue characteristics of the FeGa thin film were investigated by cycle testing of the sensitivity of the developed sensing devices. The number of cycles was set to 100. In each cycle test, the current of 0-10 A was applied to the sensing device coated with various FeGa thin-film patterns to obtain the detection sensitivity. The cycle interval was set to 10 min. Table 3 shows the statistical analysis of the relationship between the sensor sensitivity decrease rate and the number of cycle runs. It can be seen that the sensitivity of sensors with different patterns decreased slightly with the increasing cycle runs. However, the performance decline was less than 4% after 100 cycle runs, which is  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**SAW Sensing Devices** & **Magnetosrictive Film** &
\begin{tabular}{c} **Sensitivity** \\ **(kHz/A)** \\ \end{tabular} & **Hysteresis Error** \\ \hline membranous -film & FeGa & 23.1 & 2.57\% \\ grate-film & FeGa & 32.2 & 0.92\% \\ dot-film & FeGa & 37.9 & 0.81\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: The sensing performance of the proposed sensors in this work.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Year** & **Material** & **Frequency** & **Sensitivity** & **Reference** \\ \hline
2017 & FeNi & 150 MHz & 10.7 kHz/A (5.35 kHz/mT) & [11] \\
2020 & FeCo & 150 MHz & 10.7 kHz/mT & [30] \\
2021 & PVA bound Fe & 433 MHz & 678 kHz/120 mT & [14] \\  & FeGa & 150 MHz & 37.9 kHz/A (18.95 kHz/mT) & our work \\ \hline \hline \end{tabular}
\end{table}
Table 2: The sensing performance comparison of the proposed sensor in this work with the existing sensor prototype with similar structures.  
Figure 9: (**a**) The hysteresis of the proposed sensors; and (**b**) the hysteresis loop of patterned FeGa thin film.  
acceptable. Obviously, thanks to the good ductility and strong impact resistance of FeGa thin film itself, excellent long-time stability and weak fatigue were observed (Figure 10a), which was far better than that of the FeCo thin film [30]. Moreover, The SEM views of the FeGa thin film after 0, 50, 100 cycle testing runs are depicted in Figure 10b-d. It can be seen that the unloaded FeGa thin film was perfectly uniform. Although a few cracks appeared after 100 runs, most areas of the film remained uniform. It means excellent stability was achieved from the FeGa thin film coated sensing device in long-term runs.
###### Abstract  
With the increasing integration of rooftop photovoltaic (PV) generation and plug-in electric vehicles (EVs) into the households at user level, household consumers become prosumers. The coordination between the household prosumers and distribution network (DN) becomes essential to the energy management and optimal operation for both entities. In this paper, the residential prosumer cluster is considered as a residential microgrid (RMG) and a hierarchical DN integration method for the multi-RMGs is presented. A two-level hierarchical distributed optimization model is established based on the analytical target cascading to coordinate the RMGs and DNs. At the RMGs level, each RMG is required to individually optimize the energy consumption scheduling in every household by taking into account the effect of time-of-use electricity price on the demand response of EVs and flexible loads. At the DN level, the optimally coordinated operation problem is formulated as a relaxed optimal power flow model based on the second order cone programming by considering the power flow balance constraints. Case studies on the modified IEEE 33-bus system demonstrate the feasibility and effectiveness of the proposed method by achieving coordinated economic optimality as well as coordinated operating points for all entities.  
cluster of prosumers; residential microgrid; distribution network; optimal operation; distributed coordination +
Footnote †: journal: Applied Sciences
## 5 Methodology
### Comparative Overview of the Symbolic Regression Techniques  
The four SR methods employed in this study follow different paradigms, offering complementary strengths. PhySO relies on deep reinforcement learning with physical constraints, ensuring unit consistency and physically meaningful equations. PySR, in contrast,uses a high-performance evolutionary algorithm focused on accuracy-simplicity trade-offs, with explicit constant optimization. MOSR is a multi-objective genetic programming approach, optimizing simultaneously for accuracy and complexity while encouraging diverse solutions. PGSR integrates symbolic regression with physics-guided heuristics, such as dimensional analysis and low-degree polynomial fitting. While PhySO and PGSR prioritize physical consistency through embedded constraints or analytical strategies, PySR and MOSR focus more on evolutionary discovery and computational efficiency. This methodological diversity enables a robust evaluation of SR capabilities and allows for contrasting equation forms and variable sensitivities in the predictive models. To complement the comparative overview, Figure 6 provides a schematic representation of the core computational structure of each symbolic regression method discussed.  
Figure 6: Flowchart synthesizing the core mechanisms of the symbolic regression algorithms applied in this study: (**a**) PhySO, (**b**) PySR, (**c**) MOSR, and (**d**) PGSR.
## 2 Materials and Methods
### Surface Analysis
#### 2.3.2 SEM-EDS  
SEM and EDS were used for recording high magnification images and analyzing the chemical composition of tribofilms, respectively. A low accelerating voltage of 3 kV was employed to resolve the thin tribofilms using a Magellan 400 FEG-SEM (FEI Company, Eindhoven, The Netherlands). The samples were not coated with any conductive coating prior to inspection. In addition, compositional mapping was performed by EDS using an X-Max 80 mm\({}^{2}\) X-ray detector (Oxford Instruments, Abingdon, UK). EDS analysis was performed at an accelerating voltage of 5 kV, which was sufficient to detect the elements contained in the tribofilms (i.e., C, O, Si, P and S) while limiting beam penetration inside the steel substrate.
## 3 Results and Discussion
### Elementary Model Compression Analysis  
In the study, simulation and experimental methods were used to analyze the compressive behavior of the elementary model. Compression simulations of an elementary model (T = \(-\)522 and SQF = 0.25) with load along the X/Y/Z-axis were completed using an FEA tool in the INVENTOR software. PLA's mechanical properties were first applied to the model. The model was then meshed before compression simulation. Constraints that act as rigid surfaces were applied under the model. Loads of 0.1 MPa loading level perpendicular to the X/Y/Z-axis were applied on the top of the elementary models. The X/Y/Z-axis displacement of the top surface of the elementary model under compression was then calculated. Visual results of simulated compression along the X/Y/Z axes on the elementary model are shown in Figure 4a. Displacements of stressed surfaces along each axis are graphed in Figure 4b. Simulation results showed that under the same compression load, the smallest compression occurred in the Z-axis among the three axes, while displacements along the X and Y axes were comparable. These simulation results implied that the model's stiffness was greater in the Z direction than the X and Y directions, which is consistent with the compressional behavior of wood. In other words, the model's cross sections were more resistant to compression than its longitudinal sections as wood is highly anisotropic, meaning that its stiffness along the grain is much higher than its stiffness across the grain [7; 19].  
Figure 4: (**a**) Elementary model compression simulation (using PLA’s mechanical properties; T factor of the model was -522 and SQF factor was 0.25); (**b**) Displacement of stressed surfaces in the direction of each axis; (**c**) Plot of force vs. displacement for each axis from printed elementary model compression experiments.  
Figure 3: Effects of SQF (**a**) and Threshold (**b**) on surface area and porosity of the reconstructed model.  
In order to verify whether the 3D-printed elementary model shared its digital counterpart's anisotropic compressive behavior, elementary models were magnified to a scale of 1:3 and underwent compression experiments with loads along the X/Y/Z-axis. Figure 4c shows results of the printed elementary model's compression experiment where, under the same stress, the smallest compression again occurred in the Z-axis, which agrees with the simulated results as well as the mechanical properties of wood. Therefore, the compressive behavior of the digital and printed elementary model was anisotropic with greater compressional resistance in its cross sections.  
Comparing Figure 4b,c, the printed model was less resistant to compression in the Z-axis than the digital model. This might be the result of structural defects introduced by FDM printing technology in the printed model such as intralayer cavities [17], fiber debonding [20], and interlayer voids [21].
## 2 Methodology
### Bayesian Network Improvement
#### 2.7.3 Interval Analysis of FR-BN Variables Based on the Cloud Model  
In FR-BN, there are a large number of symptom root nodes \(v_{i}\) and their corresponding component monitoring information nodes \(m_{i}\). This study leverages the advantages of cloud model interval analysis to address the issue of state interval diversity caused bythe \"multivariate heterogeneity\" characteristic of state monitoring information, thereby improving FR-BN into pR-BN. The specific steps are as follows:  
* Define a set of random variables \(\xi=\{\xi_{1},\xi_{2},\ldots,\xi_{n}\}\) and determine the initial state space, where \(\xi_{i}(i=1,2,\ldots,n)\) represents the state of the monitoring information node \(m_{i}\) in FR-BN;
* Define the number of discrete states for the interval as y, and partition the qualitative domain D of the random variables into y subdomains \(\left[D_{1},D_{2},\ldots,D_{y}\right]\). Each variable generates a corresponding cloud model \(\left[C_{1},C_{2},\ldots,C_{y}\right]\), where \(D_{i}(i=1,2,\ldots,y)\) represents the \(i\)-th subdomain corresponding to a specific state interval of the state variable, and \(C_{i}(i=1,2,\ldots,y)\) represents the \(i\)-th interval cloud model characterizing the state interval of \(D_{i}\);
* For the state information cloud model with multiple variables and multiple intervals, a cloud generator is used. The numerical characteristic parameters of each interval cloud model \(C_{i}\), denoted as \([x_{Exi},x_{Eni},x_{Hei}](i=1,2,\ldots,y)\), generate variable values that satisfy the requirements of each interval. Here, \(x_{Exi},x_{Eni}\), and \(x_{Hei}\) represent the expectation, entropy, and hyper-entropy of \(C_{i}\), respectively;
* Substitute the cloud model parameters into the membership function for interval analysis to obtain the membership degrees of different discrete states, denoted as \(\mu_{i}(i=1,2,\ldots,N)\).
# Multiple Correspondence Analysis of Emergencies Attended by Integrated Security Services
###### Abstract  
A public safety answering point (PSAP) receives thousands of security alerts and attends a similar number of emergencies every day, and all the information related to those events is saved to be post-processed and scrutinized. Visualization and interpretation of emergency data can provide fundamental feedback to the first-response institutions, to managers planning resource distributions, and to all the instances participating in the emergency-response cycle. This paper develops the application of multiple correspondence analysis (MCA) of emergency responses in a PSAP, with the objective of finding informative relationships among the different categories of registered and attended events. We propose a simple yet statistically meaningful method to scrutinize the variety of events and recorded information in conventional PSAPs. For this purpose, MCA is made on the categorical features of the available report forms, and a statistical description is achieved from it by combining bootstrap resampling and Parzen windowing, in order to provide the user with the most relevant factors, their significance, and a meaningful representation of the event grouping trends in a given database. We analyzed the case of the 911-emergency database from Quito, Ecuador, which includes 1,078,846 events during 2014. Individual analysis of the first-response institutions showed that there are groups with very related categories, whereas their joint analysis showed significant relationships among several types of events. This was the case for fire brigades, military, and municipal services attending large-scale forest fires, where they work in a combined way. Independence could be established among actions in other categories, which was the case for specific police events (as drug selling and distribution) or fire brigades events (as fire threats). We also showed that a very low number of factors can be enough to accurately represent the dynamics of frequent events.  
public-safety answering point; first-response institutions; multiple correspondence analysis; ECU-911; Parzen windows; bootstrap resampling 2019 9 1396 10.3390/app9071396