esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of Magnetic Concrete for Inductive Power Transfer System in Rail Applications
## 4 Results and Discussion
### Comparative Analysis of Transmitter Coupler Designs in IPT Systems  
This section primarily focuses on evaluating changes in the coupling coefficient between Coupler Design 1 (using W-I ferrite cores) and Coupler Design 2 (utilizing magnetic concrete). As shown in Table 6, the simulation results from Ansys Maxwell reveal a slight decrease in the coupling coefficient from 0.236 for W-I ferrite cores to 0.232 for magnetic concrete. Additionally, Table 6 indicates a corresponding simulation-based power efficiency of 90.85% for W-I ferrite cores and 89.71% for magnetic concrete, with normal concrete significantly lower at 84.3%. The experimental validation conducted in a full-scale setting, as presented in Table 7, demonstrates a slight efficiency drop from 89.17% for W-I ferrite cores to 88.12% for magnetic concrete. These comprehensive evaluations confirm magnetic concrete's capability to maintain effective magnetic coupling and energy efficiency comparable to traditional ferrite cores, thereby validating its potential as a suitable alternative material in IPT systems.  
The change in coupling coefficient correlates with overall system efficiency. In high-power applications such as railway systems, efficiency losses at elevated power levels can significantly impact performance and energy consumption. By enhancing power transfer efficiency, magnetic concrete helps to keep high power transfer while minimizing energy losses, making it an attractive material for IPT system designs. These findings suggest that magnetic concrete is a viable alternative to ferrite cores in the development of transmitter couplers for IPT systems. Its robustness, cost-effectiveness, and efficiency improvements are particularly advantageous in environments where durability and economic feasibility are critical.
# The Application of an Impedance-Passivity Controller in Haptic Stability Analysis
## 5 Experimental Results
### Case III: High Stiffness  
From Figure 4, the maximum stiffness is 14,200 N/m in order to stay in the passive stable region. However, due to the force limitation of the haptic device, the maximum allowable stiffness was set to be \(K=4000\) N/m, representing the characteristics of hard objects. From Figure 4, the case of \(K=4000\) N/m and \(B=1\) Ns/m is in the boundary of the stable region. Interestingly, from Figure 12, it is shown that a high stiffness setting did cause instability, where the stylus position continuously bounced up and down. With the proposed controller, the oscillation behavior was eliminated in 1 s, and the effectiveness of proposed scheme was clarified.  
Table 1 displays the summary of the time needed to reach the stabilizing equilibrium for all cases. In this study, the IPC was used to select the available controller parameters in the stability range through a prestabilization analysis. In a DOF adhesion, it is clearly observed that the IPC starts compensating the system upon entering the virtual environment. Thus, the effect of feedback and energy convergence can be achieved quickly and steadily, while ensuring that the haptic feedback generated by the controller parameters does not exceed the hardware limits of the device. It is clear from Table 1 that the addition of the IPC to the haptic system does reach stability faster than a controller-less system and a basic-passivity controller in the cases of negative damping coefficients and high stiffness constants, respectively. Finally, it can be demonstrated from the experimental results that the proposed IPC in this study fully meets the research objectives.  
Figure 12: Experimental results at \(K=4000\) N/m, \(B=1\) Ns/m, \(\alpha=1\) Ns/m: (**a**) controller free, (**b**) the basic passivity controller, and (**c**) the IPC.
# Finsformer: A Novel Approach to Detecting Financial Attacks Using Transformer and Cluster-Attention
## 4 Results and Discussion
### Ablation Study on Transformer Feature Extractor  
This section is dedicated to evaluating the performance of different feature extractors in the task of detecting financial attack behaviors. By comparing the performance of manual feature extraction (represented by support vector machine, SVM), recurrent neural network (RNN), convolutional neural network (CNN), and Transformer feature extractors across key metrics such as precision, recall, and accuracy, the experiment conducted an in-depth analysis of the advantages and limitations of each feature extractor and explored their effectiveness in handling financial data.  
As shown in Table 3, the performance of manual feature extractors was relatively lower. This was mainly attributed to the inability of manually selected features to fully capture complex and non-linear patterns in financial data, limiting the model's accuracy in identifying attack behaviors. While SVM performs well in linear problems, its efficacy diminishes in the face of complex financial data. The RNN feature extractor, suitable for processing sequential data, showed better performance than manual feature extraction. Its ability to capture temporal dependencies is particularly important for financial transaction data analysis. However, RNNs may face challenges in handling long-term dependencies due to the issue of vanishing gradients. The performance of the CNN feature extractor was slightly inferior to that of RNN, possibly because temporal dependencies in financial transaction data are more critical than spatial patterns. Nonetheless, CNN showed certain effectiveness in capturing local patterns, especially in data with strong spatial correlations. The Finsformer feature extractor exhibited the best performance across all metrics, demonstrating its strong capability in global feature extraction. The Finsformer, through its attention mechanism, processes all elements of a sequence simultaneously, effectively capturing the global dependencies in financial transaction data. This mechanism allows the Finsformer to consider all elements in a sequence and capture the complex relationships between them, especially in complex and non-linear financial data.
## 7 Conclusions  
The polymer-based hybrid integration technology is reviewed in this work. The polymer materials can be synthesized and tested prior to the fabrication. The low-temperature layer formation process is simple and fast, leading to a substantial fabrication cost reduction. Deep grooves, trenches and slots are etched with oxygen-based RIE with a metal-mask selectivity exceeding 500. Fibers, TFEs and PDs can be passively inserted in/attached on the chip, reducing also the assembly cost.  
Furthermore, multi-layer polymer waveguides and multi-step 45 \({}^{\circ}\)mirrors enable the 3D construction of the photonic integrated circuits. Silicon nitride has been introduced as core material, and heterogeneous waveguide components are realized, opening up possibilities to incorporate inorganic materials in polymer and combine their properties for multi-functional devices.  
For practical devices, 40-channel OLTs have been assembled, also proving the feasibility of integrating large arrays of LDs and PDs on the polyboard. Equipped with a thin-film filter, an on-chip fiber groove and a 45 \({}^{\circ}\)mirror, a compact ONU device is realized, allowing bi-directional transmission in the C band and detection in the L band. For applications in coherent detection, a 90 \({}^{\circ}\) hybrid is demonstrated with integrated LO and VOAs.  
The polyboard has been further incorporated into a larger hybrid integration platform together with EO polymer modulators, InP electronics and ceramic RF interconnects. This powerful platform has yielded an all-in-one, 22-nm wavelength tunable, 100 Gbaud transmitter module that not only delivers state-of-the-art performance but also allows direct plug-and-play deployment in the optical network systems. With more advanced, multi-flow transmitter modules under way, this polymer-based hybrid integration platform is only starting to unleash its full potential and is bound to bring photonic integration to a new, unprecedented level.  
Comparing to other integration platform like SOI or InP, the advantage of polymer platform lies in the low-cost fabrication technology, the possibility to realize multi-layer optical circuits, the flexibility and capability to integrate a large variety of optical functions. For applications, where several functionsmust be integrated on space smaller than a couple of mm\({}^{2}\), other integration technologies may be more appropriate.
## 3 Results and Discussion
### Effect of Organic Grinding Aids on the Grinding Property of Converter Steel Slag
#### 3.4.2 Effect of GA on the Particle Size Distribution of Steel Slag Powder  
The uniformity coefficient of steel slag powder can reflect the extent of the width of particle size distribution, and the smaller the uniformity coefficient, the wider the particle size distribution. From Figure 10, it can be seen that the uniformity coefficient of steel slag powder is firstly increased and then gradually decreased with the increasing of grinding time. By comparison, the uniformity coefficient of steel slag powder with GA is larger than that without GA for the same grinding time, indicating that the particle size distribution of steel slag powder can be narrowed by GA. The effect is the strongest at 50 min and 60 min grinding times.  
Figure 10: Uniformity coefficient of steel slag powder with GA vs. grinding time.  
Figure 9: Sieving residue of steel slag powder with grinding aid (GA) vs. grinding time.  
As shown in Table 6, it can be seen that the effect of GA on the particle size distribution of steel slag powder is relatively small before reaching 40 min grinding time. The reason is that particles are coarse and have a good dispersion at this stage, basically having no agglomeration of fine particles. The grinding efficiency of steel slag is basically not influenced by dispersion of particles, so the optimization role of GA on the particle size distribution is not obvious. During 40 min, 50 min, and 60 min grinding time, the proportions of particles in the range of 3-32 \(\upmu\)m are obviously increased by 7.24% (absolute value, same below), 7.22%, and 10.63%, respectively, after adding GA, while the proportions of particles in the range of more than 32 \(\upmu\)m are also obviously decreased, indicating that GA can efficiently weaken agglomeration of fine particles, then improve the dispersion of steel slag powder. Meanwhile, many researches indicate that 3-32 \(\upmu\)m particles have the greatest contribution on the property of cement-based materials, so the improvement of 3-32 \(\upmu\)m particles proportion after using GA shows that GA can significantly optimize particle size distribution of steel slag powder. At 70 min grinding time, as the agglomeration of particles is very serious, GA can not completely resist it due to the fixed dosage (0.05%), resulting in the weakening of GA's optimizing role. It shows that the optimizing role of the fixed dosage GA on the particle size distribution of steel slag powder is different under different grinding times, and the role of 0.05% dosage of GA is best during 40-60 min grinding time.
## 3 Results and Discussion
### Sensory Evaluation of the Intensity of the Odor-Active Compounds in the Fermented seaweeds
The compounds that contributed to the strong unpleasant odor intensity included 1-octen-3-one, (E)-2-heptenal, and (E)-2-decenal. The note of 1-octen-3-one was described as fishy and metallic in this study, and [PERSON], [PERSON], [PERSON], and [PERSON] [22] described the odors of 1-octen-3-one as metallic, dirty, and dusty in brown seaweeds. As shown in Table 4, BS and SC fermentations could relieve the intensity of the unpleasant odors in the fermented _Laminaria_ sp. suspensions. The note intensity of octanal, (E)-2-heptadienal, and 2,4-heptadienal, which contributed to fatty notes, were reduced or eliminated by BS and SC. Notably, the note of 1-penten-3-one (pungent and fresh) [43], categorized in the neutral odors, was weakened during fermentation with BS and SC, and a similar result was observed in Table 2. Modifying the aroma profile for food ingredients could be investigated and applied in the food industry. For example, in mitigating the beamy aroma in the legume-based beverage, fermentation with lactic acid bacteria is applied [44]. In this study, based on our GC-O analysis, BS and SC have shown application potential in enhancing pleasant odors and reducing unpleasant odors for seaweeds _Ulwa_ sp. and _Laminaria_ sp.
# An Adaptive Fast Incremental Smoothing Approach to INS/GPS/VO Factor Graph Inference
## 2 Materials and Methods
### Factor Graph Model of INS/GPS/VO Integrated Navigation System
#### 2.1.3 GPS Factor  
The GPS measurement equation is  
\[z_{k}^{GPS}=h^{GPS}(x_{k})+n^{GPS} \tag{8}\]  
where \(n^{GPS}\) represents the measurement noise, and \(h^{GPS}\) stands for the measurement function, which establishes the relationship between the measurement \(z_{k}^{GPS}\) and the carrier's position.  
The GPS factor equation is a unary factor defined as  
\[f^{GPS}(x_{k})\doteq d\Big{(}z^{GPS}-h^{GPS}(x_{k})\Big{)} \tag{9}\]  
which is solely connected to the node at time \(t_{k}\). The GPS factor \(f^{GPS}\) can be integrated into the graph along with an equivalent IMU factor \(f^{IMU}\).  
Figure 3b illustrates examples of factor graphs encompassing GPS measurements and data from various sensors operating at different rates. Additionally, it presents prior factor nodes associated with navigation and bias factor nodes.
# Total Ionizing Dose Effects on a Delay-Based Physical Unclonable Function Implemented in FPGAs
## 4 Experimental Results and Discussion
### Delay Variation of the RO Loop  
RO frequencies play a key role in the RO-PUF authentication scheme, therefore they have been subjected to exhaustive analysis during the whole experiment. For each RO, we have carried out 100 frequency acquisitions in order to improve the measurement error by averaging the values. At pre-irradiation conditions, the average RO frequencies of FPGA1 and FPGA2 are 196.12 MHz and 199.16 MHz respectively. At the end of the experiment [500 krad(Si)], the average RO frequencies of FPGA1 and FPGA2 have decreased to 195.1 MHz (0.5%) and 197.92 MHz (0.6%) respectively. Figure 7 shows how the 512 RO average frequencies of FPGA2 changed during the experiment. It can be appreciated that after a first sharp decrease at 10 krad(Si), all the ROs follow the same tendency. It is also worthy of note that there are not many intersections between the different lines, which indicates a good frequency stability. These results are very similar to those reported in [16], where the effects of ageing in a RO-PUF were studied.  
Figure 8 shows the distribution of the average frequencies at pre-irradiation conditions and at the end of the experiment. Once again, the frequency stability can be highlighted. The results for FPGA1 are analogous to those depicted in Figures 7 and 8.  
Figure 6: Internal Core Current vs. radiation.  
Figure 7: RO frequencies vs. radiation.
# Elderly Fall Detection in Complex Environment Based on Improved YOLOv5s and LSTM
## 6 Experimental Results and Analysis
### Performance of Fall Detection Algorithm Based on Improved YOLOv5s
#### 6.1.3 Experimental Settings  
The hardware and software environments used in the experiment are shown in Tables 4 and 5.
## 3 Results and Discussion
### Extracts' Characterization by HPLC  
#### 3.4.1 Evaluation of PEF Effects Based on Extracts' Total Area  
Following the results regarding the estimation of Y\({}_{\mathrm{TP}}\) and oxidation temperature, the maximum percentage increase in total area between the PEF and reference extracts was reached by using 25% EtOH for both pulse durations. In particular, for pulse duration 10 \(\upmu\)s, the increase was 72.83%, while for 100 \(\upmu\)s it was 78.72%, both significant at \(p<\) 0.05 (see Figures S1 and S2, respectively, in Supplementary Materials). For pure water, there were minor changes (not significant--\(p>\) 0.05). In the case of 50% EtOH, significant increases (\(p<\) 0.05) of 19.42% for 10 \(\upmu\)s and 13.44% for 100 \(\upmu\)s appeared. Further addition of EtOH (75%) increased the percentage difference, in the case of the 10 \(\upmu\)s pulse duration reaching a greater increase (significant at \(p<\) 0.05) in total areas than that of 100 \(\upmu\)s (72.72% versus 25.46%, respectively). Finally, at 100% EtOH (pure EtOH), a significant increase in PEF-treated samples took place. The percentages were 52.73% versus 36.33% for 10 \(\upmu\)s and 100 \(\upmu\)s, respectively. It is worth mentioning at this point that there seems to be a clear trend indicating a gradual rise in the extraction yield when increasing the ethanol solvent content from 0% up to 75% EtOH, after which a drop takes place resulting in lower yields when pure ethanol was used.
# A Deep Learning Approach to Analyze Airline Customer Propensities: The Case of South Korea
## 3 Materials and Methods
### Data Collection  
For data collection, we conducted a survey of the effect of airline servicescape on customer churn risk and satisfaction. A total of 340 Korean adults, who have used airplanes at least once within the last five years, responded to the 50 questions related to the physical and social environment of the airlines, brand experience, brand loyalty, and customer satisfaction. Due to the decrease in air travel caused by COVID-19 pandemic, the specific time range required for empirical research was limited to customers who boarded an aircraft from 1 January 2016 to 28 February 2021. We conducted the survey for 24 days, from 8 March to 31 March 2021, and used a self-filling questionnaire through Google Docs. For the survey, we considered various characteristics of responders, including gender, age, occupation, number of airlines used within the last five years, frequently used airlines, seat class used for air travel, flight time required for air travel, and purpose of flight. To avoid biases in data analysis, we removed data from 28 responders who did not complete their survey properly. Table 1 shows the details of responders' characteristics. From the table, we can make initial observations. For example, we can observe that responders frequently take Korean Air and Asiana Airlines over other foreign airlines. We can also observe that despite the higher prices, responders prefer FSC (i.e., Korean Air and Asiana Airlines) over LCC (i.e., Jeju Air, Jin Air, T'way Airlines, Air Busan, or Air Seoul). Figure 1: Overall flow of the proposed methodology. We divided the survey questions into five categories to make it easier for responders to differentiate them. Specifically, we used 16 factors for the physical services escape category (e.g., airplane design and cabin environment), 12 factors for the social services escape category (e.g., cabin crew, number of passengers on board and their behaviors), nine factors for the brand experience category (e.g., feelings regarding the airline and in-flight meals, and hospitality of cabin crew), five factors for the brand loyalty category (e.g., attachment to the airline, intention to continue to use, and airline recommendation) and eight factors for the customer satisfaction category (e.g., satisfaction with cabin crew, flight, and overall service). The score for each factor was measured as 1, 2, 3, 4, and 5, which represent \"very bad\", \"somewhat bad\", \"neutral\", \"somewhat good\" and \"very good\", respectively. Table 11 shows a summary of all factors. The table also shows a statistical summary (i.e., mean and standard deviation values) of factors, which enables us to make further observations. For example, we can observe that passengers are generally satisfied with the cabin crew of air carriers of South Korea. We can also observe that most of the passengers feel that there is a lack of diversity of in-flight games and readings.
## 2 Materials and Methods  
### Plasma System  
A schematic of the plasma system adopted in this study, Plasma Assisted Sanitation System (PASS), developed by AlmaPlasma srl, is shown in Figure 1. PASS is composed of an SDBD (surface dielectric barrier discharge) plasma source, a treatment chamber, a cooling system, and a high-voltage generator. The SDBD consists of a mica dielectric layer (2 mm thick) interposed between an air-cooled high-voltage (HV) aluminum electrode and a grounded electrode (AISI 316L rhomboid mesh). The treatment chamber is made of poly(methyl methacrylate) (PMMA) and has two optical accesses (quartz windows) to allow optical spectroscopy; when the SDBD is placed on the top of the treatment chamber a closed volume is defined. This treatment volume can be reduced by means of PMMA fillings, resulting in three different treatment volumes (Table 1).  
CAP was homogenously generated on the surface of the mesh on an area of around 551 cm\({}^{2}\). The device, operating in environmental air (relative humidity in the range 20-40%), was driven by a micropulsed generator (AlmaPULSE, AlmaPlasma srl, Bologna, Italy), allowing the application of 1-20 kV, with a frequency in the 1-20 kHz range, and  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Volume** & **Volume \(\times\) 10\({}^{3}\) (cm\({}^{3}\))** \\ \hline V1 & 2.60 \\ V2 & 5.21 \\ V3 & 6.51 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Available treatment volumes defined using different PMMA fillings.  
Figure 1: 3D schematic of the SDBD plasma source used in the experimental activities: (**a**) exploded and (**b**) assembled views of the plasma source  
a tunable duty cycle (1-100%). Two discharge times, considered the time for which the generator was kept on, were tested: 10 and 20 min. To evaluate the surface power density (SPD) delivered to the plasma source, voltage and current waveforms were recorded by means of a high-voltage probe (Tektronix P6015A) and a current probe (Pearson 6585) connected to an oscilloscope (Tektronix DPO40034). The power density was evaluated using the following formula:  
\[SPD=\frac{1}{A}.\frac{1}{T}\int V\cdot Idt \tag{1}\]  
where \(V\) is the applied voltage, \(I\) is the current, \(T\) is the period, and \(A\) is the active surface of the mesh. The SPD was calculated by averaging three measurements, each taking into account 400 microseconds of plasma discharge (i.e., two full voltage periods) with an acquisition rate of 0.25 giga samples per second.  
The plasma source was operated imposing a voltage of 6.00 kV at a frequency of 5.00 kHz with a fixed duty cycle (DC = 100%).  
The mesh temperature was measured during the discharge time by means of a fiber optical temperature sensor (opSense, OTG series), placed inside the investigated volume, and data were recorded every 0.05 s.