esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiments and Results
### Calibration Experiments  
According to the measurement principle described above, in theory, the straightness error and the angle error have a linear relationship with the measured value of the detector. Therefore, the system measurement error can be obtained through calibration experiments.  
Figure 8 shows a diagram of the device location for the calibration experiment. A cube prism with five plane mirrors is placed on a six-dimensional moving platform. An interferometric laser encoder (RLE, Britain, accuracy of \(\pm 1\) ppm ), an industrial theodolite (Leica TM5100A, Switzerland, accuracy of \(\pm 0.5\) arcsec), and a standard autocollimator (Taylor Hobson DA400, Britain, accuracy of \(\pm 0.2\) arcsec) are used to calibrate various system errors.  
As shown in Figure 9, the calibration results are linearly good. In the range of \(\pm 360^{\prime\prime}\), the linear-fit determination coefficient of the pitch and the yaw of the provided system are 1.0000 compared with the Leica TM5100A, the standard deviation of the pitch and the yaw are 0.58\({}^{\prime\prime}\) and 0.83\({}^{\prime\prime}\), and the range of fluctuation is from \(-1^{\prime\prime}\) to \(1.4^{\prime\prime}\) and \(-1.2^{\prime\prime}\) to \(1.2^{\prime\prime}\), respectively. In the range of \(\pm 360^{\prime\prime}\), the linear-fit determination coefficient between the roll of the provided system and the autocollimator is 0.9999, the standard deviation is 2.31\({}^{\prime\prime}\), and the range of fluctuation is from \(-5.0^{\prime\prime}\) to \(2.8^{\prime\prime}\). In the range of \(\pm 100\)\(\mu\)m, the linear-fit determination coefficient of horizontal and vertical straightness of the provided system are 0.9996 and 0.9995 compared with RLE, the standard deviations are 1.33 \(\mu\)m and 1.4 \(\mu\)m, and the ranges of fluctuation are from \(-1.6\)\(\mu\)m to 2.8 \(\mu\)m and \(-1.9\)\(\mu\)m to 3.1 \(\mu\)m, respectively.  
Figure 8: Calibration experiments. (**a**) Setup for the angle calibration. (**b**) Setup for the straightness calibration.  
Figure 7: Experimental setup for the five-degree-of-freedom motion error measurement.
# MPC for Optimized Energy Exchange between Two Renewable-Energy Prosumers
## 1 Introduction
### Model Productive Control  
Model predictive control (MPC) refers to techniques that utilize models of a process and incorporate future predictions of performance and external variables to systematically determine control signals by minimizing an objective function at each time step [16]. These algorithms predict future process behavior and choose a control sequence to optimize an objective function based on these predictions. The initial value of this control sequence is retained before advancing the prediction window into the future for the next time step. Applications of MPC include industrial control of robots, servo motors, steam generators, and control of anesthesia delivery [17]. As well as having the ability to utilize available forecasting data, MPC can efficiently incorporate constraints and manage multiple control variables. MPC does have a large computational burden (the optimization) at each time step, although this is only a problem for applications that require high-speed sampling. A generic MPC schematic is illustrated in Figure 1, where a process model predicts future outputs based on previous inputs/outputs and optimized future control signals.  
MPC is frequently applied to power delivery systems, especially those that involve renewable sources and distributed generation. For example, MPC is used in maximum power point tracking for DC power systems, to manage the fluctuating output from a PV array connected to a constant DC bus [18]. Active and reactive power disturbances that occur within combined wind-turbine and battery microgrids can also be controlled by MPC, while maximizing power delivery from the turbines [19]. These algorithms have also been used to mitigate disturbances caused by errors in weather forecasting models in a distributed generation system [20].
## 7 Conclusions
#### Limitations of This Study and Questions for Possible Future Research  
By analyzing the impact of BI using a model encompassing multilayered feedback, this study focused on emergent phenomena not previously considered by either the proponents or the opponents of BI. Therefore, the parameter settings used assumed values to confirm emergent phenomena. We also incorporated a hypothetical function of work motivation designed to decline when external income is earned. Therefore, for future research, it will be necessary to introduce more realistic work motivation functions and to incorporate research findings on relative wage disparities. As research on relative work motivation functions has been conducted by statistically analyzing survey data, future studies should use the results of these surveys to establish precise parameters.  
Additionally, the current model does not incorporate the full scope of the labor market and does not address issues such as voluntary resignations, company layoffs, and job hunting. The labor market is a variable that interacts with work motivation and the expansion of production capacity through price and output adjustments and capital investment. Therefore, introducing a fully modeled labor market could lead to emergent effects that would further destabilize the economy due to the impact of BI. Therefore, further extending the model to analyze the impact of BI is something to consider in the future.  
However, the results of this study reveal that analyzing the impact of BI from a macro, long-term perspective, with a multilayered feedback system structure and a macro, long-term perspective, results in the emergence of a unique phenomenon: an increase in GDP and a contraction in economic activity. Although this study focused on labor supply, this multilayered feedback could significantly impact overall market product volume, prices, and even technological progress. Thus, there is a concern that price fluctuations may cause the welfare, education, and health of people with low incomes to not improve with the introduction of BI, as anticipated by proponents. Furthermore, major economic system changes can significantly impact government finances and create further destabilizing behavior if the government adopts corrective policies. Therefore, based on the findings of this study, it can be argued that in examining the impact of BI, it is necessary to consider the multilayered feedback that each decision-making entity in society produces in response to change, such as the impact of BI on corporate activities.  
Conceptualization, K.T. and I.Y.; methodology, K.T.; software, K.T.; validation, K.T. and I.Y.; writing--original draft preparation, K.T.; writing--review and editing, K.T. and I.Y. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by JSPS KAKENHI Grant Number JP22K04591.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Design of a Double-Matched Cross-Polar Single Antenna Harmonic Tag
## 5 Prototype Realization and Measurements
### Measurements of the Realized Doubler Circuit  
The reflection coefficient of the doubler circuit is carried out using the Anritsu MS46122B vector network analyzer. A 30 dB attenuator has been placed between the VNA and the port of the circuit. This setup allowed for the measurement of the circuit's behavior under conditions similar to those assumed during simulation (i.e., with input power of \(-30\) dBm). In fact, the nonlinear behavior of the Schottky diode makes its impedance change with the variation of the input power. The comparison between the measured reflection coefficient and the simulated one is presented in Figure 15, which also shows reflection coefficients of the antenna. As evident, measurements agree with simulations: both bands at 1.7 GHz and at 3.4 GHz have a reflection coefficient lower than \(-10\) dB. The measured reflection coefficient of the circuit appears a little bit better than that of the simulated model likely because the prototype has losses greater than that of the model.  
In order to asses the capabilities in re-transmitting the second harmonic, a continuous wave (CW) signal at a specific frequency has been sent to the input port of the doubler circuit and the power returned in the second harmonic is measured. To achieve this, the circuit has been connected to the Anritsu MS2830A signal generator/spectrum analyzer using two band-reject filters and the Anaren 42,100 power splitter. The transmission filter is inserted to further attenuate the second harmonic of the transmitter while the receiving filter was added to prevent receiver saturation at the frequency of the first harmonic. The setup is sketched in Figure 16a. Figure 17 presents the measured reflection and transmission coefficients of the two band-reject filters used in this measurement. A CW signal at 1.7 GHz is transmitted with a variable power to the doubler circuit while the power received at 3.4 GHz is measured. Figure 16b displays the measurement results, after removing the losses introduced by various components in the system (cables, filters, power splitter). That measurement permits us to retrieve the conversion loss of the circuit, which is variable with the input power--in fact, it is lower for high input power and higher for low input  
Figure 14: Fabricated prototypes: (**a**) matching circuit for Schottky zero-bias diode HSMS-2850; (**b**) annular patch antenna (final layout).  
power (e.g., the conversion loss is about 18 dB for \(P_{Tx}=-10\) dBm while it is 35 dB for \(P_{Tx}=-30\) dBm).  
Figure 16: Measurement of conversion loss of doubler circuit. (**a**) Measurement setup: transmitted CW signal at 1.7 GHz with different power levels, received power at 3.4 GHz; (**b**) Received power at 3.4 GHz vs. transmitted power at 1.7 GHz (insertion loss removed).  
Figure 17: Reflection and transmission coefficients of band-reject filters used in the measurement setup.  
Figure 15: Reflection coefficient for both circuit and antenna: comparison between measurements and simulations.
# Cascaded Dual-Inpainting Network for Scene Text
## 4 Experiments and Discussion
### Datasets and Evaluation Metrics  
Dataset: To evaluate the performance of the proposed network, experiments were conducted on the TII-ST database [30], a synthetic scene text database containing 80,000 training images and 20,000 test images. Each sample comprised a corrupted color image paired with its ground truth segmentation grayscale mask. All images were resized to \(64\times 256\) pixels to maintain uniform input dimensions during evaluation. As this dataset was built for text image inpainting, it only contains English texts. It uses corrosion forms to simulate real-life corrosion, i.e., a convex hull, irregular region, and quick draw. It contains various fonts and colors, except for graphic styles.  
Metrics: Since the output of the proposed CDINST model is a foreground grayscale image of restored text, conventional metrics like PSNR and SSIM are significantly affected by grayscale intensity variations. They prove inadequate for accurately evaluating the model's actual restoration capability. To establish a more precise assessment of restoration performance, text recognition accuracy was adopted as the primary evaluation metric while still reporting PSNR and SSIM values as supplementary references. The text recognition accuracy metric consists of two components: word-level accuracy \(A_{w}\) and character-level accuracy \(A_{c}\), defined as follows:  
\[A_{w}=\frac{N_{correct\_rec\_words}}{N_{total\_words}}\times 100\% \tag{12}\]  
\[A_{c}=\frac{N_{correct\_rec\_chars}}{N_{total\_chars}}\times 100\% \tag{13}\]  
\[Recall_{c}=\frac{N_{correct\_rec\_chars}}{N_{correct\_chars}}\times 100\% \tag{14}\]  
where \(N_{correct\_rec\_words}\) is the number of correctly recognized words, \(N_{total\_nends}\) denotes the total words count, \(N_{correct\_chars}\) indicates the correctly recognized characters, \(N_{total\_chars}\) represents the total character count, and \(N_{correct\_chars}\) represents the total correct character count. \(Recall_{c}\) denotes character-level recall.  
Implementation setting: The proposed model was implemented using PyTorch 2.6 on an NVIDIA RTX 4080 GPU. The backbone network requires no pre-training on external datasets. During initialization, the Adam optimizer was employed with a base learning rate of \(2\times 10^{-4}\). The loss balancing coefficients were set to \((\alpha_{1},\alpha_{2})=(1,10)\), \((\beta_{1},\beta_{2},\beta_{3},\beta_{4})=(10,10,10,10)\), \((\gamma_{1},\gamma_{2})=(1,1)\).
## 7 Discussion
4. Adopting Decentralized Inventory Strategies  
For air operators managing large or geographically dispersed fleets, decentralizing inventory can be more effective than centralized systems. By strategically positioning spare parts in regional hubs based on MTBF-driven forecasts, operators can significantly reduce lead times and enhance responsiveness to maintenance needs. 5. Dynamic Supplier Collaboration for Consistent Lead Times  
Variability in supplier lead times was a recurring challenge identified in this research. Air operators should establish dynamic partnerships with suppliers, including consignment or vendor-managed inventory agreements, to mitigate this. 6. Regular Validation and Adaptation of Forecasting Models  
The research underscores the importance of continuously refining predictive models to adapt to the aviation industry's dynamic nature. Air operators should institutionalize quarterly reviews of model parameters, including MTBF adjustments, seasonal demand patterns, and safety stock thresholds. 7. Addressing Data Gaps and Cross-Operator Collaboration  
One significant limitation of this research was the lack of comprehensive data across multiple air operators, driven by competitive concerns. Operators should collaborate on anonymized data-sharing initiatives to establish industry benchmarks and share insights into achieving optimal MTBF and safety stock levels. Additionally, investing in IoT-enabled systems to capture real-time operational data can fill existing gaps, enhancing the scalability and accuracy of forecasting models. This approach would improve individual operator performance and provide an improved management system for collective industry advancement in managing AOG challenges effectively.
## 3 Results and Discussion
### Plugging of Porous Rock by Aggregates of Colloidal Dispersions  
To investigate plugging of porous rock by particle aggregates, we performed two core flooding experiments; we injected NP/PSS-co-MA colloidal dispersions combined with the zwitterionic (TPS) or cationic (DTAB) surfactant. NP dispersions that included the zwitterionic surfactant exhibited a strong electrostatic repulsive force during core flooding, associated with high injectivity (0.13 mL/psi) and high-level oil recovery (74.2%) (Figure 6a). On the contrary, when colloidal dispersions with the cationic surfactant were injected, they gelled (aggregated) at the sandstone core inlets, thereby plugging the porous rock (Figure 6b). Injection of colloidal dispersions containing the zwitterionic surfactant was therefore optimal.  
Figure 4: Schematic diagram of adsorption of zwitterionic surfactant onto negatively charged silica nanoparticle with PSS-co-MA.  
Figure 5: Zeta potential measurement of samples 1–6 in Figure 3.
## 4 Case Study of Data Collaboration
### Visual Traffic Flow Detection Based on DFA
#### 4.3.3 Duplicate Removal Based on Similarity  
For the frame-by-frame images in the above detection area, we compared the similarity of the images based on the hash perception algorithm, removed the images whose similarity is greater than the threshold, compressed data, and cleaned the data to form a new image dataset. Its specific content is: First, reduce the image size, the high-frequency details are removed, and the light and dark information is retained. Second, the discrete cosine transform (DCT) is performed on the binarized image after the above image expansion processing, and the influence of noise on the image is ignored. Third, convert the DCT value to a hash value. Next, images are paired to generate Hamming distance. Then, images are filtered according to the threshold calculated by Hamming distance, and the images with high similarity were eliminated. Finally, data cleaning, retrieving the original data according to the image number and updating it.
## 3 Results and Discussion
### Fatty Acid Analysis
\begin{table}
\begin{tabular}{l l l l} \hline \multirow{2}{*}{**Free Fatty Acids**} & **FCK** & **FCC** & **FNP** \\ \cline{2-4}  & & \multicolumn{2}{c}{**mg/100 g**} \\ \hline Erucic Acid & 1.52 \(\pm\) 0.29 \({}^{\text{a}}\) & 1.37 \(\pm\) 0.14 \({}^{\text{a}}\) & 1.36 \(\pm\) 0.02 \({}^{\text{a}}\) \\ Nervonic Acid & 0.41 \(\pm\) 0.05 \({}^{\text{a}}\) & 0.34 \(\pm\) 0.14 \({}^{\text{ab}}\) & 0.23 \(\pm\) 0.03 \({}^{\text{b}}\) \\ Linoleic Acid & 299.71 \(\pm\) 7.21 \({}^{\text{a}}\) & 216.81 \(\pm\) 7.01 \({}^{\text{c}}\) & 238.00 \(\pm\) 9.29 \({}^{\text{b}}\) \\ Linoleic Acid (trans) & 0.16 \(\pm\) 0.01 \({}^{\text{a}}\) & 0.11 \(\pm\) 0.02 \({}^{\text{b}}\) & 0.16 \(\pm\) 0.01 \({}^{\text{a}}\) \\ \(\gamma\)-Linolenic Acid & 1.16 \(\pm\) 0.10 \({}^{\text{a}}\) & 0.79 \(\pm\) 0.37 \({}^{\text{a}}\) & 0.88 \(\pm\) 0.07 \({}^{\text{a}}\) \\ Alpha-Linolenic Acid & 21.50 \(\pm\) 2.19 \({}^{\text{a}}\) & 15.06 \(\pm\) 0.94 \({}^{\text{b}}\) & 16.89 \(\pm\) 0.92 \({}^{\text{b}}\) \\ Eicosadienoic Acid & 20.38 \(\pm\) 1.35 \({}^{\text{a}}\) & 13.04 \(\pm\) 2.45 \({}^{\text{b}}\) & 14.47 \(\pm\) 0.61 \({}^{\text{b}}\) \\ Dihomo-gamma- & 2.69 \(\pm\) 0.19 \({}^{\text{a}}\) & 1.82 \(\pm\) 0.68 \({}^{\text{b}}\) & 2.09 \(\pm\) 0.03 \({}^{\text{ab}}\) \\ Enucic acid & 1.52 \(\pm\) 0.29 \({}^{\text{a}}\) & 1.37 \(\pm\) 0.14 \({}^{\text{a}}\) & 1.36 \(\pm\) 0.02 \({}^{\text{a}}\) \\ Eicosatrienoic Acid & 3.63 \(\pm\) 0.38 \({}^{\text{a}}\) & 2.13 \(\pm\) 0.85 \({}^{\text{b}}\) & 2.44 \(\pm\) 0.28 \({}^{\text{b}}\) \\ Arachid Acid & 9.93 \(\pm\) 0.21 \({}^{\text{a}}\) & 8.04 \(\pm\) 1.27 \({}^{\text{b}}\) & 8.81 \(\pm\) 0.15 \({}^{\text{ab}}\) \\ Eicosapentaenoic Acid & 0.26 \(\pm\) 0.02 \({}^{\text{a}}\) & 0.18 \(\pm\) 0.02 \({}^{\text{b}}\) & 0.19 \(\pm\) 0.01 \({}^{\text{b}}\) \\ Docosadienoic Acid & 0.29 \(\pm\) 0.04 \({}^{\text{a}}\) & 0.18 \(\pm\) 0.08 \({}^{\text{b}}\) & 0.19 \(\pm\) 0.01 \({}^{\text{ab}}\) \\ SFAs & 779.29 \(\pm\) 34.73 \({}^{\text{a}}\) & 546.63 \(\pm\) 15.60 \({}^{\text{b}}\) & 579.88 \(\pm\) 17.63 \({}^{\text{b}}\) \\ MUFAs & 631.83 \(\pm\) 29.89 \({}^{\text{a}}\) & 444.96 \(\pm\) 10.67 \({}^{\text{b}}\) & 477.55 \(\pm\) 16.33 \({}^{\text{b}}\) \\ PUFAs & 361.23 \(\pm\) 10.92 \({}^{\text{a}}\) & 259.54 \(\pm\) 12.69 \({}^{\text{c}}\) & 285.49 \(\pm\) 10.64 \({}^{\text{b}}\) \\ UFAs & 993.06 \(\pm\) 40.61 \({}^{\text{a}}\) & 704.50 \(\pm\) 23.07 \({}^{\text{b}}\) & 763.04 \(\pm\) 26.89 \({}^{\text{b}}\) \\ UFAs/SFAs & 1.27 \(\pm\) 0.01 \({}^{\text{b}}\) & 1.29 \(\pm\) 0.02 \({}^{\text{ab}}\) & 1.32 \(\pm\) 0.01 \({}^{\text{a}}\) \\ Total FFAs & 1772.35 \(\pm\) 75.34 \({}^{\text{a}}\) & 1251.13 \(\pm\) 36.90 \({}^{\text{b}}\) & 1342.92 \(\pm\) 44.36 \({}^{\text{b}}\) \\ \hline SFAs: saturated fatty acids; MUFAs: monounsaturated fatty acids; PUFAs: polyunsaturated fatty acids; UFAs: unsaturated fatty acids. Different letters under the same parameter indicate significant differences (\(p<0.05\)). & \\ \hline \end{tabular}
\end{table} 
Table 2: _Cont._
## 5 Application to Case Study and Discussion
### The Guadalfeo River and Morti-Salobrena Aquifer Interaction  
The Morti-Salobrena coastal aquifer is in the province of Granada (Southern Spain) (Figure 13). One of its main water inflows is the recharge from the Guadalfeo River, and this volume of water is very important to maintain the high quality of groundwater in the aquifer [18]. The aquifer is formed by sediments deposited by the Guadalfeo River and other ephemeral streams. The associated hydraulic conductivities vary from normal values for clays and silts to clean gravels. The depth of the aquifer is also highly variable, varying between 30 and 50 m in the northern zone (alluvial sedimentary medium) and reaching more than 250 m in the areas near the coast (deltaic sedimentary medium).  
To estimate the recharge flow of the Guadalfeo River to the Motril-Salobrena aquifer, we used a temperature profile recorded with a piezometer in April 2002; it was located close to the river (70 m from its center) so that we could be sure that the borehole was within the thermal characteristic length. The profile was dimensioned in order to use the universal abacus in Figure 7 (see Figure 14). The half-width of the river is 15 m in this area.  
the aquifer in this zone is 0.126 m\({}^{3}\)/s. The estimate made by [PERSON] et al. [18] was around 0.145 m\({}^{3}\)/s; thus, the difference between the two is less than 15%, which is a more than acceptable result.
# Optimized Water Distillation Layout for Detritiation Purpose
## 3 Process Simulations
### Standalone Distillation  
The Standalone Distillation (STD) concept has been simulated in Aspen Plus assuming geometrical data and boundary conditions presented in Table 1. Furthermore, the reboiler is supposed to be fed on the hot side with water coming from the CVCS. The total condenser option was considered, accounting for the condensation of the whole distillate.  
The scheme of the configuration, along with the main results of the process simulation in terms of temperature (green), pressure (magenta), flow rate (red), and tritium specific activity (blue), are presented in Figure 1. Tritiated water at 7.4 \(\times\) 10\({}^{10}\) Bq kg\({}^{-1}\) passes through the economizer and the pressure reducer, reaching the operative conditions of the water distillation column (WD-CLM). Distillate, depleted in tritium, is condensed into the condenser and collected in the reflux drum. Part of this is refluxed into the column and the rest comes back to the CVCS recovering heat through the economizer. A small fraction of the bottom product, enriched in tritium, is sent to the Water Detritiation System (WDS), whereas the major portion is boiled inside the reboiler and sent back to the WD-CLM. The  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Unit** & **Value** \\ \hline Number of columns & – & 1 \\ Number of theoretical stages, including & – & 130 \\ condenser and reboiler & – & 48 \\ Feeding stage, from top & – & CY Gauze, by [PERSON] \\ Packing & – & 14.17 \\ Column height & m & 1.52 \\ Feed flow rate & kg h\({}^{-1}\) & 110 \\ Bottom flow rate & kg h\({}^{-1}\) & 1.72 \\ Bottom tritium specific activity & Bq kg\({}^{-1}\) & \(3.7\times 10^{12}\) \\ Distillate flow rate & kg h\({}^{-1}\) & 108.28 \\ Distillate tritium specific activity & Bq kg\({}^{-1}\) & \(1.628\times 10^{10}\) \\ Boil-up flow rate & kg h\({}^{-1}\) & 2281 \\ Reflux flow rate & kg h\({}^{-1}\) & 2172 \\ Pressure drops & kPa & 4.25 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Preliminary design and operative parameters of the EU-DEMO CPS water distillation column assuming target tritium specific activity of 7.4 \(\times\) 10\({}^{10}\) Bq kg\({}^{-1}\) into PHTS.  
tritium removal rate is given by the product of the flow rate and the composition of the stream sent to the WDS and equals the tritium permeation rate at the BB. The detritiation efficiency of the system (\(\eta_{CPS}\)), defined in Equation (1), where \(x\) is the tritium specific activity in Bq kg\({}^{-1}\) and _CVCS-CPS_ and _CPS-CVCS_ stand for the streams coming from and returning the CVCS, is equal to 82.5%. All the results agree with the outcomes of previous design activities [3; 10].  
\[\eta_{CPS}=\frac{x_{CVCS-CPS}-x_{CPS-CVCS}}{x_{CVCS-CPS}}\times 100 \tag{1}\]