esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
Few works in the literature address the evaluation of AM adoption in early product design. Even fewer are the works that implement multi criteria decision processes to systematically validate the results obtained. Furthermore, a gap that has been highlighted  
Figure 8: (**a**) One of the geometries obtained by TO in Altair Inspire 2019. (**b**) Redesigned PPC geometry modelled in Solidworks 2020.  
is the absence of a clear distinction between considerations and information that can be extrapolated or calculated in an early phase from those that require a more in-depth study.  
The main objective of this paper is to provide a procedural approach that supports the designer to understand the potential offered by additive technologies in a given design context. This paper focuses on the early phase, consisting of the Preliminary Analysis and the Potential Analysis, where multicriteria decision approaches have been leveraged. Since the detailed redesign phase turns out to be the most onerous task, it is limited to only promising situations resulting from a potentiality assessment.  
The application of these stages has been exemplified by analysing a propeller pitch controller test case. The proposed flow has proved to be a useful approach as the designer is able to evaluate whether a product can be produced through AM, leveraging the limited information available at an early stage. A new optimized product geometry has then been presented to be realized by PBF. Furthermore, the new part has benefits over the initial design, thus confirming the assumptions of the previous phases. In conclusion, it can be affirmed that the case study is suitable to AM production as foreseen by the application of the preliminary evaluations.  
In future work, the approach, as conceived in an algorithmic way, is suitable to be embedded in a software tool to operatively support and speed up the designer activity, especially in the context of configuring customized products [42]. Following the phases described in this work, such a tool would aim at automating the process, such as indices computation and geometry evaluation, also verifying the geometric and production limits of AM and traditional technologies. Finally, a cost assessment phase based on geometric product features needs to be added to complete the analysis for an effective application in companies.  
Conceptualization and methodology, R.R.; validation and data curation, J.L.; writing--original draft preparation, J.L., J.S.; writing--review and editing, M.P. ([PERSON]); supervision and funding acquisition, M.P. ([PERSON]). All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
All data is contained within the paper.  
The authors declare no conflict of interests.
## 2 Materials and Methods
### Determination of Photosynthetic Pigment Contents
#### 2.4.2 Determination of and Chlorophyll-a, Chlorophyll-c, and Carotenoids  
Cells of each sample were filtered through GF/F glass microfiber filters (Whatman, 25 mm diameter) at day 12 to determine the contents of chlorophyll-a (Chl-a), chlorophyll-c (Chl-c), and carotenoids (Caro). Pigments underwent extraction using 90% acetone (5 mL) at 4 \({}^{\circ}\)C in the darkness for 24 h, then centrifuging for 5 min at 3000\(\times\)\(g\) (Eppendorf AG, Hamburg, HH, Germany). The optical densities (OD) of supernatant were measured by a spectrophotometer (Shanghai sunny hengping, Shanghai, China). Calculations for Chl-a, Chl-c, and Caro were performed using these equations [49; 50]:  
\[\text{Chl-a}=(11.4902\times\text{A}_{664}-0.4504\times\text{A}_{630})\times \text{V}_{1}/\text{V}_{2}\]  
\[\text{Chl-c}=\text{Chl-(c}_{1}+\text{c}_{2})=(22.6792\times\text{A}_{630}-3.40 41\times\text{A}_{664})\times\text{V}_{1}/\text{V}_{2}\]  
\[\text{Caro}=7.6\times\text{(A}_{480}-1.49\times\text{A}_{510})\times\text{V}_{ 1}/\text{V}_{2}\]  
In this context, A\({}_{664}\), A\({}_{630}\), A\({}_{480}\), and A\({}_{510}\) correspond sequentially to the optical densities at the wavelengths of 664, 630, 480, and 510 nm. V\({}_{1}\) represents the quantity of 90% acetone utilized, while V\({}_{2}\) denotes the volume of the algal liquid. When performing the final data processing, the units were converted to ug mL\({}^{-1}\).
# Doped Zinc Oxide Nanoparticles: Synthesis, Characterization and Potential Use in Nanomedicine
## 4 Use of Doped ZnO NPs in the Biomedical Field
### Biological Behavior
Moreover, undoped ZnO nanoparticles were tested as well, which are toxic for any cell line. On the other hand, 10 wt.% Fe-doped nanoparticles were biocompatible for both the cell typologies. Fe-doped ZnO nanoparticles obtained with a similar synthesis method were tested in another work in vivo [205]. Hatching zebrafish embryos were taken as an indication of the toxicity of Fe-doped and undoped ZnO nanoparticles. Zebrafish were bred so as to achieve fertilization and the embryos were collected. Then, the embryos were treated with ZnO nanoparticles doped with different Fe levels, and the hatching rate was evaluated. The results showed that there were significant improvements with increased doping levels. Again, this was related to a decreased release of zinc cations. In the same  
Figure 7: Zinc release in an aqueous solution at pH 7 by the dissolution of Fe-doped ZnO nanoparticles at different doping levels. Adapted from ref. [128]. work, it was also shown that iron doping can reduce the pulmonary inflammation and oxidative stress in rats and mice. Despite the previous work being focused on the ability of a single dopant (iron) to stabilize the ZnO NPs stability, very promising results are found in terms of an increased cytocompatibility by using Fe-doped ZnO nanoparticles. This is corroborated by in vitro and in vivo tests, attributing the increased biocompatibility to a decreased level of zinc cations being released.
# Incremental Formula-Based Fix Localization
## 5 Results of the Experiments  
### Result of RQ1: Effectiveness in Finding Angelic Fix Candidates  
To evaluate the effectiveness of our method in finding angelic fix candidates, we report and compare (1) the number of MFCs that each technique found for each run and (2) the number of which are feasible angelic fix candidates. A technique is said more effective in finding angelic fix candidates if it can find more feasible fix candidates.  
Table 1 shows the experimental results for 41 buggy versions of the TCAS program in terms of effectiveness in finding MFCs. The first section of the table shows the version name (_Ver_), the number of faults (_Faults_), and the number of failing test cases (_Ftc_) for each buggy version. The second and third sections of the table show, on average, the total number of MFCs that each technique found in a fault localization run for each buggy version, as well as the number of these found MFCs that are feasible (i.e., MFCs with valid explanation), respectively.  
For example, version \(v1\) has one injected fault, and there are 131 failing test cases. Thus, each fault localization method was run on version \(v1\) 131 times. On average, both AgxFaults and PF found 44 minimal angelic fix candidates for each run, while those of FS and FI are 52 and 3, respectively. After examining the MFCs generated by all methods, we found that all MFCs generated by AgxFaults, PF, and FI are feasible, while only 35 in a total of 52 MFCs generated by FS are feasible. Thus, the number of feasible MFCs generated by AgxFaults, PF, FS, and FI is 44, 44, 35, and 3, respectively.  
In total, each method was run 2156 times over 41 program versions. On average, the number of MFCs generated for each run by the AgxFaults, PF, FS, and FI are 87, 87, 45, and 3, respectively. Of which, the number of generated MFCs that are feasible of AgxFaults, PF, FS, and FI are 87, 87, 23, and 3, respectively. As the result showed, AgxFaults produced similar results as the PF approach, which is based on a more complex error trace formula encoding the entire program semantic, in all buggy versions. All MFCs generated by AgxFaults, PF, and FI approaches are feasible (i.e., provide valid explanations), while only 23 (51%) out of 45 MFCs generated by FS are feasible. AgxFaults outperform single-path  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multicolumn{3}{c}{**Program Info**} & \multicolumn{5}{c}{**Num. MFC Found**} & \multicolumn{5}{c}{**Num. Feasible MFC**} \\ \hline
**Prog** & **Faults** & **Ftc** & **Agx** & **PF** & **FS** & **FI** & **Agx** & **PF** & **FS** & **FI** \\ \hline v1 & 1 & 131 & 44 & 44 & 52 & 3 & 44 & 44 & 35 & 3 \\ v2 & 1 & 69 & 48 & 48 & 44 & 3 & 48  
formula approaches (both flow-sensitive and flow-insensitive techniques) in finding more feasible MFCs.
# Multi-Objective Optimization Study on Production of AlSi10 Mg Alloy by Laser Powder Bed Fusion
## 1 Introduction
The meticulous control of production parameters is the key to the production of high-quality and high-performance parts. [PERSON] et al. emphasize that since additive manufacturing is still a developing technology, the optimum processing parameters have not yet been fully determined in the literature [4]. In this study, the aim was to increase the efficiency of material, machine, and design processes by determining the right parameter combinations. For this purpose, the effects of parameters such as laser power, hatch distance, and scan speed on relative density and surface roughness were analyzed with a full factorial experimental design using AlSi10 Mg alloys produced by the SLM method. The most suitable parameters for two basic performance properties, such as surface roughness and relative density, were determined by TOPSIS, a multi-criteria decision-making method used to select the best parameter combination among alternatives. This study presents a comprehensive methodology for improving the quality and performance of AlSi10 Mg parts produced by the SLM method.
###### Abstract  
Insulating materials can be classified into solid, liquid, and gaseous forms. Solid insulation materials are divided into different types such as organic, inorganic, and polymer types. In electrical circuits, solid insulation materials are generally used as components that provide insulation and mechanical support. In recent years, as a result of developing technologies, the production of participation insulation materials with 3D printing technology has become widespread. Three-dimensional printing technology enables the rapid creation of objects by combining materials based on digital model data. It is important to evaluate the materials produced with 3D printing in terms of insulation coordination. Studies have shown that the electrical breakdown strength of solid dielectrics varies depending on factors such as sample type, thickness, the magnitude of applied voltage, and the temperature of the physical environment. According to IEC-60243 standards, there are various methods to measure the breakdown strength of solid insulators applied to different voltage types. In this study, the behavior of PLA, ABS, ASA, PETG, and PC/ABS materials produced with 3D printing and having the potential to be used as insulation materials when exposed to high voltage within the scope of insulation coordination was investigated. The breakdown strengths of solid insulation materials produced with 3D printing were measured in the high-voltage laboratory within the scope of IEC-60243. Breakdown strength was statistically evaluated with the Weibull distribution. Damage analysis of the breakdowns in the test specimens was examined in detail with ImageJ software. With the comparative analysis, the behaviors of PLA, ABS, ASA, PETG, and PC/ABS solid insulation materials were revealed and their superiority over each other was determined.  
high voltage; breakdown voltage; breakdown strength electric field distribution; 3D printer; ABS; PLA; ASA; PC-ABS; PETG; insulation coordination
## 3 Current Signal Processing and Feature Extraction  
### Characteristic Processing of High-Frequency Current Signal  
The time domain characteristics of arc current are mainly manifested as the \"flat shoulder\" of the current waveform at zero crossing, the high-frequency components of arc current being rich, and the current waveform being asymmetric. In view of the above characteristics, the maximum rise rate and skewness of the current are selected as the time domain characteristics of arc current.
# Impact of Aerodynamic Interactions on Aeroelastic Stability of Wing-Propeller Systems +
## 1 Introduction
The present study aims to enhance the understanding of the flutter mechanism in wing-propeller models, investigating the influence of aerodynamic interference between the propeller and the wing. To make this happen, the pylon mounting stiffness of the wing-propeller system presented in this work is modified to induce wing flutter. Aerodynamic interference effects between the wing and the propeller are captured using a vortex-particle method (VPM). Aeroelastic analyses are performed by coupling the VPM with a multibody dynamics tool, which takes into account the nonlinear dynamics of the interconnected rigid and flexible bodies representing the aircraft components. The paper proceeds as follows: the first section outlines the methodology and the numerical tools used for the aeroelastic analyses. Subsequently, the dynamic model setup of the wing-propeller system is presented, along with the initial validation of the two sub-components. Stability results are then presented and discussed, comparing the flutter diagrams of the wing-propeller system with and without the aerodynamic interference effects. Finally, a section of concluding remarks ends the paper.
# Glucose Data Classification for Diabetic Patient Monitoring
## 3 Proposal Description  
This section presents a detailed description of the proposed blood glucose monitoring system. The aim of this project is to monitor the blood glucose level of the diabetic patient. The measured data is updated in the cloud every day. This collected data is used by the doctor or another medical expert to monitor the patient's blood glucose variation visually and send the response in real-time to protect the patient's health from a critical condition.  
The designed blood glucose monitoring system consists of three distinct subsystems (Figure 1). The first is dedicated to capturing glucose level in the blood with the glucose sensor. This sensor captures the level of glucose in the blood, displays its exact value, and transmits it via the communication module (Bluetooth, Wi-Fi, 4G) to the Android mobile phone. This is necessary because current mobile phones do not include means of direct interface with the analog signals of the outside world. The second subsystem includes the storage and data processing service received by the smartphone. The third subsystem is data analysis by domain specialists and decision-making.  
For a better diabetes management strategy, the glucose sensor will provide the diabetic patient and health professionals with the necessary information about diabetes. The monitoring of the blood glucose levels can help to reduce a person's risk with a range of diabetes-related complications. Structured self-monitoring involves checking the blood glucose levels at certain times of the day for a given period (i.e., two weeks), and then working with the diabetes healthcare team to determine how food, physical activity, and treatment have an impact on the blood glucose levels.
## 2 Materials and Methods
### Specim Preparation  
Cylindrical composite specimens measuring 8 mm in height and 10 mm in diameter were fabricated using a black Teflon mold with a slightly tapered cylindrical cavity (Figure 1).  
Sonic fill specimens were dispensed into the mold using their proprietary ultrasonic delivery system to adjust viscosity. Ten specimens were prepared for each of the six composites. The mold was placed over a celluloid strip, covered with a glass slide, and filled with composite resin, and then another celluloid strip and glass slide were placed on top. This assembly was compressed using a hydraulic press to eliminate excess material and minimize internal porosity. The glass slide was removed, and the composite resin was polymerized for 20 s using the respective LCU. After polymerization, specimens were scraped to remove uncured material, and therefore they did not possess the same length. They were then embedded in a cylindrical mold filled with epoxy resin and allowed to set for 24 h. Specimens were then sectioned longitudinally using a low-speed diamond saw under water cooling (Buehler Isomet, Diamond Cut-off Wheel MOD10, Struers, Copenhagen, Denmark) to produce rectangular slabs containing the specimens cut longitudinally, maintaining perpendicularity to the top surface and staying parallel  
Figure 1: The Teflon mold used for the fabrication of the specimens.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**LCU** & **Code** & \begin{tabular}{c} **Power** \\ **Manufacturer** \\ \end{tabular} & **Wavelegth** & **Serial Number** & **Manufacturer** \\ \hline Elipar Trilight & HA & 800 mW/cm\({}^{2}\) & 460 nm & & 3M/ESPE, St. Paul, MN, USA \\ Demi Ultra & De & 1100 mW/cm\({}^{2}\) & 450–570 nm & 786010009 & Kerr, Brea, CA, USA \\ Bluephase Style & BS & 1100 mW/cm\({}^{2}\) & 385–515 nm & 1100017240 & Ivoclar Vivadent, Vivadent, AG, \\  & & & & & Schaan, Liechtenstein \\ Valo & VA & 1000 mW/cm\({}^{2}\) & 395–480 nm & \
\begin{tabular}{c} VO 4582 \\ Lot 86020 \\ \end{tabular} &
\begin{tabular}{c} Ultrader Products Inc., South \\ Jordan, UT, USA \\ \end{tabular} \\ \hline \end{tabular}
\end{table}
Table 3: Light-curing units used in the present study.  
to the specimen's long axis (Figures 2 and 3). The slabs were polished using 600-grit metallographic abrasive paper under water cooling (Dap V, Struers) to create a flat surface with minimal roughness, suitable for accurate indentation measurements. Specimens were stored at 37 \({}^{\circ}\)C for 24 h before testing.  
Figure 3: The cylindrical rod of the cured specimen is embedded in epoxy resin and sectioned longitudinally to produce a slab containing half of it. The illustrated red points represent the surface, 0.5 mm, 2 mm, 3 mm, and 4 mm depth points where the microhardness measurements were performed.  
Figure 2: Three SonicFill specimens polymerized with Blue Style, embedded in epoxy resin, sectioned, and polished.
## 1 Introduction
It involves evaluating the contributions from both direct and reflected paths, considering the physical characteristics of the reflecting surface. GO and PO methods have also been extended to the geometrical theory of diffraction (GTD) and physical theory of diffraction (PTD) by explicitly incorporating diffraction phenomena to enhance analytical accuracy [24; 25; 26]. However, these methods require analyses of the electric field integral equation (EFIE) or magnetic field integral equation (MFIE), the induced current distributions on surfaces, ray tracing, the reflection/refraction/diffraction phenomena of electromagnetic waves, boundary conditions, etc. This paper presents a simple method for analyzing the radiation characteristics of a defocus-fed parabolic antenna in high-frequency regimes. The presented method is a simplified approach to the PO technique and refers to the analysis of antennas using discrete components studied in [27; 28; 29]. The presented method is based on the discrete division of a parabolic reflector surface and considers only simplified wave propagation theory and the effect of the scalar function pattern from the feeder. Additionally, array theory is exclusively applied for the analysis of radiation characteristics. Therefore, a very simplified formula is used to calculate the radiation characteristics of a defocused parabolic reflector antenna. The performance of the presented simplified method was validated by comparing its results with those obtained from two representative commercial tools: GRASP (General Reflector Antenna Design Software Package) and CST (Computer Simulation Technology) Studio Suite. These tools utilize PO/GTD and FIT techniques, respectively. The validation process covered a range of configurations for defocus-fed parabolic antennas. The details of the presented method and its validation through simulation experiments are discussed in the following sections.
## 2 Materials and Methods
### The Attention Mechanism with CNN-BiLSTM
#### 2.3.1 The Convolutional Block Attention Module (CBAM)  
The convolutional block attention module (CBAM) [17] is an effective attention module to use for convolutional neural networks. Instead of directly computing the attention map, CBAM infers the attention map along the channel and the spatial dimensions with the channel attention module and the spatial module as shown in Figure 5. In the channel attention module as Figure 6 showed, for the input feature in each channel, the average-pooled features and the max-pooled features are calculated simultaneously. Then, the MLP (Multi-Layer Perceptron) and sigmoid function are used to fuse the two features and generate channel attention map.  
In the spatial attention module as shown in Figure 7, in order to generate the spatial attention map, the spatial attention module uses average pooling and max-pooling operations on the channel attention feature to generate the average-pooled features and the max-pooled features across the channel. Then, the concatenation and convolution are used to produce the spatial attention map.  
Finally, the attention feature F \(\in\)\(\mathbb{R}^{\text{L-F}}\) after CNN can be calculated with channel attention features and spatial attention features, wherein L is the length of the ECG and F is the number of convolutional filters on the last convolutional layer.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**CNN** & \\ \hline Convolutional layer & Filter = 32, kernel size = 200, stride = 8 \\ Rectified Linear Unit & \\ Batch normalization + dropout (0.70) & \\ Max pooling & Pool size = 8, stride = 8 \\ First BiLSTM & \\ LSTM layer + dropout (0.70) & Step size = 25, LSTM size = 40 \\ The stitching method of LSTM & Adding \\ Second BiLSTM & \\ LSTM layer + dropout (0.70) & Step size = 25, LSTM-size = 40 \\ The stitching method of LSTM & Adding \\ \hline \hline \end{tabular}
\end{table}
Table 1: The structure of the CNN (Convolutional Neural Networks) and BiLSTM (Bi-directional Long Short-Term Memory).