esa-sceva/satcom-chunk-collection huggingface dataset:
# Study of Drilled Holes after a Cryogenic Machining in Glass Fiber-Reinforced Composites
## 2 Materials and Methods
### Experimental Setup  
Two composites, manufactured by Ensinger (Ensinger, Nufringen, Germany), were evaluated. PEEK-GF30 plates were made by a VictrexTM (Victrex, Thornton, UK) polyether-ketone polymer and 30% E-glass fiber, and produced by extrusion and machined; it is a high purity material with values of total mass loss, collected volatile condensable material, and regained water vapor close to 0.2, 0, and 0.08%, respectively. PA66-GF30 with 30% of E-glass fiber was also manufactured by extrusion and machined; it had 2.4% total mass loss, 0% collected volatile condensable material, and 0.26% of water vapor regained. All these data were provided by the supplier. PEEK-GF30 and PA66-GF30 composite plates (see main proprieties in Table 1) that had a thickness of 6.5 mm were drilled in a Manga Tongtai TMV-510 (Tong-tai Machine and Tool Co., Ltd., Kaohsiung Hsien, Taiwan) machining center, using a 6 mm diameter drill bit provided by FMT Tooling Systems (FMT Tooling Systems Company, Trofa, Portugal), specifically designed for composites and high feed rates. The main characteristics of the drill bit can be observed in Table 2. This type of drill bit is considered adequate to achieve low roughness in drilling composites [23].  
The cutting conditions were 5000, 6000, and 7000 rpm for the drill rotation speed (N) and 0.5, 0.75, and 1 mm/rev for its feed rate (f). These cutting conditions were selected because they had been previously used for these composites in dry drilling and drilling by cooling compressed air with good outcomes.  
The tests were performed in a cryogenic environment impacting liquid nitrogen directly on the tool through a nozzle with a 2 mm diameter outlet; the liquid nitrogen provided by AirLiquid in refillable cylindrical tanks of 158 L, reached \(-\)128 \({}^{\circ}\)C and 0.2 MPa pressure. The academic literature uses the term cryogenic to refer to temperatures below 0 \({}^{\circ}\)C [10]. The temperature was controlled by an infrared pyrometer, Optris (Optris Infrared Sensing, Portsmouth, NH, USA). Thrust forces developed during the drilling process were measured using a Kistler 9257B piezoelectric dynamometer connected to a Kistler 5070A multichannel amplifier (Kistler Group, Winterthur, Switzerland). Figure 1 shows a scheme of experimental setup.  
The superficial quality was measured in roughness using a Mitutoyo SJ-400 surface roughness tester and variations in input diameter by a coordinate measuring machine, Mitutoyo BX 303 (Mitutoyo Corp., Kawasaki-shi, Japan). The diameters were measured using the least squares method and according to ISO 4291 standard [24]. The difference between the nominal value and the average of the eight measured values, taken along the circumference of each hole, was established as the variation under study; therefore, this deviation was a circularity error.
## 2 Materials and Methods  
### Sample Preparation  
Fresh strawberries (_Fragaria \(\times\) ananassa_) were sourced from a local market. The strawberries were uniform in size, color, and ripeness stage to ensure experimental consistency. They were washed thoroughly with tap water to remove any surface impurities and gently dried with paper towels. The initial humidity of the strawberries was 88.58 \(\pm\) 1.71% (wet basis). The strawberries were cut into 10 \(\pm\) 0.5 mm-thick slices, each weighing 7.5 \(\pm\) 1.2 g.  
For the osmotic solution preparation, an aqueous infusion of chokeberry and food-grade glycerol (Honeywell Specialty Chemicals Seelze GmbH, Seelze, Germany) were used. The food-grade glycerol was sourced from a local supplier, and the antioxidant-rich chokeberry infusion was prepared fresh for each experiment. Glycerol, also known as sugar alcohol (E 422, group I), is considered a non-toxic and safe additive according to Regulation (EC) No. 1333/2008 and was chosen as the osmotic agent due to its water binding capacity, which significantly reduces the water activity of the product and extends its shelf life. Regarding infusion preparation, dry chokeberry fruits were crushed and homogenized, then added to boiling water at a ratio 1:5 g/mL (\(w/v\)), and, after that, left for 1 day at room temperature (25 \({}^{\circ}\)C). The solution was afterward filtered under reduced pressure. To prevent textural deterioration during the dehydration process, 1.5% \(w/w\) calcium chloride (CaCl\({}_{2}\)) (Chem-Lab NV, Zedelgem, Belgium) was added to the osmotic solution. CaCl\({}_{2}\) is known for maintaining firmness and structural integrity by forming chemical bonds with the plant tissue matrix, thereby preserving the texture of the dehydrated product [23]. It also aids in the formation of a compact surface layer, which facilitates higher rates of water loss and solid gain [22].
## 4 System Model Based on Power Splitting and NOMA
### Stage 2  
In this stage, the \(IoT_{R}\) node transmits a superimposed composite signal \(Z_{I_{C1}}\) which consists of source information \(x_{s}\) and \(IoT_{R}\) information \(x_{IoT_{R}}\) to the respective destination node i.e., source user and IoT user using NOMA protocol. The superimposed composite signal \(Z_{I_{C1}}\) following NOMA protocol is given as:  
\[Z_{I_{C1}}=\sqrt{\phi_{1}P_{IoT_{R}}}x_{s}+\sqrt{\phi_{2}P_{IoT_{R}}}x_{IoT_{R}} \tag{24}\]  
where \(\phi_{1}+\phi_{2}=1\) and \(\phi_{2}=1-\phi_{1}\).  
Now, the received signals at the respective source user and IoT user can be given as:  
\[y_{s_{src}}=\sqrt{P_{IoT_{R}}}h_{s_{src}}Z_{I_{C1}}+n_{s_{src}}, \tag{25}\]  
\[y_{IoT_{src}}=\sqrt{P_{IoT_{R}}}h_{IoT_{src}}Z_{I_{C1}}+n_{IoT_{src}}, \tag{26}\]
## 1 Introduction
[PERSON] et al. [24] proposed an adaptive mesh criterion based on time-averaged quantities, producing meshes that are suitable for capturing the unsteady flow field at any instant and do not evolve rapidly over time. The combined RANS-LES method adopted in this study is an improved delayed DES-based methodology derived from the classical DES method [25]. To this end, the shear stress transport (SST) \(k\)-\(\omega\) two-equation turbulence model and its corresponding DDES version are employed. In combination with AMR technology, a novel method of omega vortex identification as an adaptive mesh criterion is introduced. In the first step, preliminary test calculations are performed on the flow around a cylinder. Subsequently, the method is applied to calculate the flow around the vertical plane of a submarine at high angles of attack and is validated on the basis of the experimental data. Finally, the influence of different angles of attack and rolling angles on the lateral force, rolling moment, and yawing moment of the submarine have been discussed and analyzed.
## 4 Mitigating the Data Protection Risks
### Protection against Model Inversion Attacks  
With respect to the risk of model inversion attacks, any access to the output module needs to be restricted to \"black-box\" access, and the output of the model needs to be limited to reduce the knowledge that a malicious user can gain from it. For example, the success rate of such attacks was significantly reduced when the classification algorithms presented approximate confidence values, as shown in [44], or only the predicted class label, according to [45].  
Again, model inversion attacks can be seen as a specific instance of reidentification attacks (see Section 4.1), and thus, techniques like DP are also good candidates for alleviating the relevant risks.
## 4 Bearing Capacity Analysis  
### Determining the Bearing Capacity of Pile Base and Shaft  
The test results were used to produce diagrams of pile settlement versus applied force for the standard static load tests and of shaft lifting and base settlement versus applied force in the case of the bi-directional tests. The Brinch-Hansen 80% method [21] was used to approximate the test results as it enables a good fitting of \(Q\)-\(s\) curves to the results, and to the estimation of the boundary bearing capacity of the pile base and shaft [22]. A reversed parabolic shape of the \(Q\)-\(s\) curve given by formula (1) is assumed in this method. The shape of the \(Q\)-\(s\) curve and the ultimate capacity \(Q_{tilt}\) can be determined from the formulae (1) and (2).  
\[Q=\frac{\sqrt{s}}{s\times A+B} \tag{1}\]  
\[Q_{tilt}=\frac{1}{2\sqrt{A\times B}} \tag{2}\]  
Figure 9: Example of pile head load and displacement vs. time plot for the traditional static load test.  
where \(A\) and \(B\) are the constants for the Brinch-Hansen method, depending on the system of units adopted.
# Fostering Sustainable Aquaculture: Mitigating Fish Mortality Risks Using Decision Trees Classifiers
## 5 Discussion
### Research Contribution  
The current research proposes a pioneering data-driven strategy, leveraging advanced data mining classifiers and incorporating DT classifiers to forecast and classify examples of caged fish mortality. The project, embedded within the overarching initiative \"Improving Competitiveness of the Greek Fish Farming through Development of Intelligent Systems for Disease Diagnosis & Treatment\", addresses the significance of mortality rate escalation in cageed fish populations. The focus expands to unsustainable fish farming paradigms and environmental factors which impact this issue [37].  
The key contribution lies in the development and evaluation of predictive models showcasing highly accurate results. By employing efficient data mining classifiers for numeric data, the research identifies DTs as one of the most suitable methods for predicting fish mortality. The robustness of the model pinpoints the efficacy of data-driven practices in cage aquaculture management [37].  
Furthermore, the incorporation of a comprehensive dataset from diverse aquaculture sources, encompassing factors like the geographical locations, husbandry methods, and key parameters including the weather conditions and water quality, adds significant depth to the research. The dataset forms the basis for a thorough analysis, including a feature importance assessment and the prediction of factors which influence fish mortality [38].  
The outcomes of this research contribute significantly to sustainable management efforts. The deployment of focused conservation and operational strategies, informed by the feature importance analysis, offers valuable insights for stakeholders. The proactive measures advocated in the strategies aim to mitigate fish mortality and enhance the protection of farmed fish stocks [38].
# Research on Fleet Size of Demand Response Shuttle Bus Based on Minimum Cost Method
###### Abstract  
Demand-responsive connector services (DRC) are an important means to improve the current mobility connection problem. In this study, we develop a hybrid model for the minimization of total system cost for demand response shuttle buses, which includes operating cost and user cost, with fleet size per hour as the optimization variable of the model. The relevant variables are analyzed and numerically modeled by Matlab, and the relationship between fleet size, vehicle capacity and demand density and waiting time, onboard time, vehicle travel distance, and total system cost is analyzed. The results indicate that introducing financial subsidies markedly lowers the critical demand density necessary to ensure system viability. Moreover, subsidy intensity is positively associated with the service's operational robustness. Through parametric examination, we observe a strictly monotonic relationship between subsidy magnitude and demand thresholds: as subsidy levels increase, the minimum demand requirements for sustainable operation decrease in a consistent, progressive manner; meanwhile, the optimal fleet size exhibits an approximately linear relationship with travel demand per unit area across varying vehicle capacities. Notably, an increase in vehicle capacity corresponds to a decrease in the growth rate of the required fleet size. This model demonstrates robust adaptability across diverse operational scenarios and serves as an effective tool for evaluating the efficiency of resource allocation in demand-responsive transit (DRT) services. Furthermore, it provides valuable theoretical support for the scheduling and planning of public transportation systems, particularly in low-density urban environments.  
urban transportation; bus cost; fleet size; shuttle bus; demand response Article
###### Abstract  
Previously synthesized and tested water-dispersible photoactive polymeric microparticles have been employed as heterogenous photosensizers to evaluate their performance in generating singlet oxygen through direct solar irradiation. This study utilizes these photocatalysts for the degradation of Acetamiprid in IWWTP wastewater effluents from the Agri-food industry, exploring, in addition to direct or simulated solar irradiation, the influence of pH on the photooxidation process. Over a thousand emerging pollutants, including pesticides like Acetamiprid, have been detected in aquatic environments in recent years, posing challenges due to the limitations of current wastewater treatment technologies. The developed method is particularly effective under basic or slightly basic conditions, aligning with the natural pH of wastewater and addressing a limitation of conventional Acetamiprid degradation methods, which typically require medium acidification to be effective. Polymers P3 and P4 exhibited high photocatalytic activity, achieving over 99% degradation of Acetamiprid through oxidation via singlet oxygen generated by Rose Bengal supported on the polymer matrix, while maintaining catalytic efficiency across multiple cycles. The results confirm that Acetamiprid removal from industrial wastewater via direct solar irradiation is feasible, though constrained by the availability of sufficient effective sunlight hours.  
Keywords:photochemistry; solar photocatalytic treatment; industrial wastewater; singlet oxygen; emerging pollutants; acetamiprid; Rose Bengal Article
## 2 Materials and Methods
### Characterization of Powders  
2.5.1 Surface Oil Content Analysis  
Surface oil content was analysed by its extraction from powders with n-hexane added at a ratio of 1:10 (\(w/v\)) and shaken for 2 min at a temperature of 20 \(\pm\) 2 \({}^{\circ}\)C. The solvent was then filtered, and the collected solid residue was rinsed three more times with 20 mL of n-hexane. The collected filtrates were evaporated using a rotary evaporator (Buchi Labortechnik AG, Flawil, Switzerland). Then residue was weighted, and the surface oil content was calculated as a percentage of the powder.  
2.5.2 Encapsulation Efficiency Analysis  
The encapsulation efficiency (EE) of emulsion was calculated by dividing the amount of encapsulated oil (determined as the difference between the total and surface oil content) by the total oil content of the powder and then multiplying by 100%. The total oil content was the theoretical amount of oil in the anhydrous fraction of the prepared emulsion.  
2.5.3 Powder Morphology Analysis  
Powder morphology was analysed using scanning electron microscopy (SEM). SEM Quanta 200 (FEI Company, Hillsboro, OR, USA) was used for the analysis. Powder was attached to the microscope state using two-sided adhesive tape, mounted on SEM tubs, and coated with palladium in a sputter coater. Then the powder surface was analysed at an accelerating voltage of 30 kV and \(\times\)400 magnifications.  
2.6.1. Colour Analysis  
The colour of juices was measured using digital image analysis (DIA), which consisted of a computer with a monitor, a digital camera (DXM-1200, Nikon Inc., Melville, NY, USA), a light set (colour temperature about 5400 K, Kaiser Fototechnik GmbH and Co., KG, Buchen, Germany), and LUCIA G v. 4.8 software (Laboratory Imaging, Praha, Czech Republic). The parameters of the CIEL*a*b* colour model were determined for each juice sample (20 mL in a 50-mL beaker) at a temperature of 20 \(\pm\) 1 \({}^{\circ}\)C [30]. The L* value represents lightness (from black to white for values in the range from 0 to 100, respectively), the a* value represents the green or red colour (negative or positive values, respectively), and the b* value represents the blue or yellow colour (negative or positive values, respectively). Additionally, the chroma (C*, the degree of colour saturation) was calculated with the following formula: C* = \(\surd\)(a*2 + b*2), and the total colour difference (\(\Delta\)E) comparing fortified samples to control sample was calculated with the following formula: \(\Delta\)E = \(\surd\)(\(\Delta\)L*2 + \(\Delta\)a*2 + \(\Delta\)b*2).  
2.6.2. Physical Stability Analysis  
The physical stability of fresh and stored (1, 2, and 6 days) juices was determined using the Turbiscan Classic 2 (Formulation Co., Toulouse, France) by measuring the backscattering of light (\(\Delta\)RW). The profile of changes in the backscattering coefficient (RW) over time was analysed, and the slope coefficient of the curve was calculated.
# A Miniaturized and Fast System for Thin Film Thickness Measurement
## 5 Experiments and Discussion
### Thin-Film Thickness Uncertainty Verification
#### 5.1.2 Germanium-Based 1 \(\upmu\)m SiO\({}_{2}\) Standard Film Measurement  
Germanium is the most widely used infrared optical material and often covered with a thin film to enhance the transmittance and durability in harsh environments such as high temperature and acid. In this article, a 1 \(\upmu\)m thickness SiO\({}_{2}\) film sample on a germanium substrate with a stepped structure is measured to verify the accuracy of thickness control during the coating process. In Figure 11a, the stepped structure consists of an uncoated area, and a coated area is designed for the measurement benchmark and measured by white light interference (WLI) 12 times. At the same time, the PPS performs a 12 points measurement on the coating part of the WLI measurement area.  
Figure 10: Comparison of the result of CIFFT and LSP algorithm for standard film (**a**) Single point repeatability measurement; (**b**) \(4\times 4\) grid points measurement.  
After leveling and denoising, the vertical distance between the upper and lower fitting surfaces of the step shown in Figure 11b, is calculated to represent the WLI measurement result. The detailed measurement result is shown in Table 1. Comparing the PPS and WLI measurement data, the mean thickness of PPS is 0.892 and the standard deviation is 0.076, while the mean thickness of WLI is 0.954, and the standard deviation is 0.053. The mean of PPS measurement results is about 50 nm smaller than WLI, but the measurement speed is faster than WLI.
## 2 Performance Support System
### Definitions, Principles, and Theoretical Foundations
#### 2.1.3 Key Performance Indicator (KPI)  
The key performance indicators in the cellular network management literature are measures that can be used to measure the quality of a network at different levels. Some of these indicators depend on time or user behavior, such as the number of downlinks to the user. Other indicators, such as the channel quality indicator (CQI), random channel access attempts (RCATs), or the percentage of dropped calls, due to lack of dependency on the user program used on the user side, are less related to the user's behavior, so the indicators of this category are more suitable for evaluating the network condition. Telecommunication networks are changeable, and there are several key performance indicators to assess network condition. Using manual methods to solve network problems does not meet the management needs of these networks.  
Many indicators are used to evaluate the quality of radio networks, and by saving the fault-free behavior of each KPI, its deviation from its normal behavior can be identified. The high number of these indicators, and the related counters to each one, have made it impossible to check all the indicators and the impact of each one on the quality of the network. In the following, we examine the steps of establishing a call in a cell network. Then we introduce the most important indicators affecting the network mode, submitted by experts in this field.
###### Abstract  
Considerable effort has been put over the last few decades into clarifying the correct design and analysis of split-plot factorial experiments. However, the information found in the literature is scattered and sometimes still not easy to grasp for non-experts. Because of the importance of split-plots for the industry and the fact that any experimenter may need to use them at some point, a detailed and step-by-step guide collecting all the available information on the fundamental methodology in one place was deemed necessary. More specifically, this paper discusses the simple case of an unreplicated split-plot factorial experiment with more than one whole-plot (WP) factors and all factors set at two levels each. Explanations on how to properly design the experiment, analyze the data, and assess the proposed model are provided. Special attention is given to clarifications on the calculations of contrasts, effects, sum of squares (SS), parameters, WP and sub-plot (SP) residuals, as well as the proper division of the proposed model into its sub-designs and sub-models for calculating measures of adequacy correctly. The application of the discussed theory is showcased by a case study on the recycling of molybdenum (Mo) from CIGS solar cells. Factors expected to affect Mo recovery were investigated and the analysis showed that all of them are significant, while the way they affect the response variable was also revealed. After reading this guide, the reader is expected to acquire a good understanding of how to work with split-plots smoothly and handle with confidence more complex split-plot types.  
split-plot experiment; analysis of split-plot; unreplicated split-plot; residuals; split-plot calculations +
Footnote †: journal: Applied Sciences
# Application of Machine Learning Models to Multi-Parameter Maximum Magnitude Prediction
## 3 Methods
### Machine Learning Models
#### 3.1.6 BiLSTM  
BiLSTM is an extension of LSTM that simultaneously considers forward and backward information in a sequence. Specifically, BiLSTM consists of two independent LSTM layers: the forward LSTM layer performs forward propagation in the order of the input sequence, while the backward LSTM layer performs backward propagation in the reverse order of the input sequence (Figure 5). In this way, the output at each time step of BiLSTM contains information from the current and previous steps as well as the current and subsequent steps. Therefore, BiLSTM is highly effective for tasks requiring considering both past and future information within a sequence.
# Numerical Simulation of Seakeeping Performance on the Preliminary Design of a Semi-Planing Craft
## 3 Data Analysis
### Full-Scale Experiments
#### 3.2.2 Medium Sailing Speed (U = 14 Knots, \(H_{1/3}\) = 1.3 m, \(\overline{T}\) = 4.2 s):  
Figure 4a presents a comparison of results from the simulation and on-board measurement (in terms of heaving motion) for all wave heading angles at medium sailing speed, for which the simulation results were sufficiently consistent with the experimental results. In terms of rolling motion (Figure 4b), the simulation results were consistent with the experimental results, except for the wave angles of 0\({}^{\circ}\)-90\({}^{\circ}\), for which the pitching motion was overestimated.