esa-sceva/satcom-chunk-collection huggingface dataset:
# Manufacturing of Micro-Lens Array Using Contactless Micro-Embossing with an EDM-Mold
## Materials and Methods
### Fabrication of Micro-Lens Array  
The micro-embossing process includes presetting, heating, embossing, cooling, and demolding (see Figure 3). The processing parameters, such as embossing temperature, embossing pressure, keeping pressure, embossing time, and cooling velocity, have great influence on the friction characteristic, deformation behavior, and replication quality of hot embossing micro structures.  
At the beginning of embossing, the chamber of the embossing apparatus was vacuumed using a vacuum pump. Then, the mold and the PMMA material were heated to a temperature between glass transition and melting point, and soaked for about 100 s to make temperature uniformly distributed. Furthermore, the lower mold was moved by the servomotor drive, controlled by load cell and position sensor. The embossing pressure was in the range of 200-800 N. Finally, nitrogen flowed through the embossing chamber to cool the molds and substrate to a certain temperature. When the mold cooled to room temperature, the PMMA substrates were demolding.  
The main influencing factors, including embossing temperature, embossing pressure, and holding time, were performed with sensitivity analysis by investigating orthogonal experiment. In our case, the orthogonal experiment was composed of three factors with three levels, namely embossing temperature, embossing force, and holding time (see Table 2).
## 4 Analyses of Numerical Simulation Results  
### Flow Physics on Aerodynamic Performance Under Design Conditions  
#### 4.1.1 Aerodynamic Performance  
The unique incidence curves and mass average aerodynamic performance parameters at the outlet plane (under design conditions) of the baseline and improved cascades are shown in Figure 12 and Table 4, respectively. The parameter extraction position of the numerical results is consistent with that of the experimental results, that is, 50% of the vane chord upstream from the leading edge and 75% of the chord downstream from the trailing edge. Both curves of unique incidence for the baseline and improved cascades show that the unique relationship between inlet Mach number and inlet flow angle is consistent. This indicates that the negative-curvature ramp would just change the local flow field and could not affect the aerodynamic parameters at the cascade inlet. In addition, the decrease in outlet flow angle \(\beta_{2}\) shows more attached fluids around the improved cascade with the decrease in flow loss. The total pressure loss coefficient of the improved cascade declines  
Figure 11: Vane isentropic Mach number distributions at an inlet Mach number of 1.75 with a static pressure ratio of 2.52.  
Figure 10: Experimental schlieren photographs of the cascade flow for \(M=1.75\) and different static pressure ratios.  
by 4.6% under design conditions, as shown in Table 4. The total pressure loss coefficient is defined as  
\[\omega=\frac{P_{\text{in}}^{*}-P_{\text{out}}^{*}}{P_{\text{in}}^{*}-p_{\text{in }}} \tag{9}\]  
To show the difference between the baseline and the improved cascade aerodynamic parameters in detail, the distributions of Mach number and total pressure loss coefficient normalized by the vane pitch under design conditions are shown in Figure 13. It is noteworthy that the increase in Mach number at the outlet plane of the improved cascade occurs in the wake and main flow near the vane suction side surface (the blue arrow in Figure 13). The corresponding total pressure loss is reduced in this regard, which may result from the profile modification on the suction surface of the cascade. The minor modifications to the baseline cascade near the first-passage shock impingement position on the suction surface achieve great improvements in aerodynamic performance.
# Design of Multi-Cell Cooperative Control Algorithm Based on Fuzzy Brain Emotional Learning
## 5 Simulation Results
### Control Single Cell
Figures 8 and 9 show the trajectory errors of \(X\) axis and \(Y\) axis when using different controllers for single-cell manipulation, so that the control differences of different controllers can be seen more clearly. The trajectory errors of PI and PID controllers in both directions jitter violently before convergence. When BP, RBFNN, and FCMNN are used as controllers, the errors in the \(X\) and \(Y\) directions can converge to around 0 quickly, but with the change  
Figure 7: The comparison of single-cell trajectory using each controller: (**a**) Use PI as a controller. (**b**) Use PID as a controller. (**c**) Use BP as a controller. (**d**) Use RBFNN as a controller. (**e**) Use FCMNN as a controller. (**f**) Use FBELNN as a controller. of the trajectory, there will still be slight jitter, which cannot fully fit the desired trajectory. The cell manipulation controller based on FBELNN proposed in this paper can converge the trajectory error to 0 at a faster speed, and still maintain stable control during cell manipulation, with a small trajectory error. Table 2 shows the root mean square error values (RMSE) of single cell manipulation when using each controller. The error is converted into RMSE and used as the performance evaluation index, which can more clearly and objectively reflect the performance difference of different controllers. It can be seen that under the same experimental parameters, the cell manipulation controller based on FBELNN proposed in this paper has higher control accuracy than other controllers in all controllers, and the RMSE values on \(X\) axis and \(Y\) axis are 2.8583 \(\times\) 10\({}^{-6}\) and 2.1381 \(\times\) 10\({}^{-6}\) respectively. The FCMNN controller has the second smallest RMSE value. Compared with that, the RMSE of the cell manipulation controller proposed in this paper on the \(X\) axis and \(Y\) axis is reduced by 13% and 19% respectively, which can move the single cell to the desired trajectory in a timely and accurate manner, and better complete the single cell manipulation experiment. Figure 8: The trajectory error on the \(X\)-axis (single-cell manipulation): (**a**) Use PI as a controller. (**b**) Use PID as a controller. (**c**) Use BP as a controller. (**d**) Use RBFNN as a controller. (**e**) Use FCMNN as a controller. (**f**) Use FBELNN as a controller.
# A GNSS-Cellular Network Hybridization Strategy for Robust Positioning
## 2 Materials and Methods
### Study Area and Observation Data
In addition, Table 3 shows the root mean square (RMS) positioning errors computed directly from the raw field data used in Figures 5-7. These values allow for a quantitative comparison of the GNSS and 4G performance across the three test environments: indoor, urban canyon, and outdoor. The root mean square (RMS) error was calculated using the following expression:  
\[RMS=\frac{\Sigma_{i}{\left({{e_{i}}}\right)}^{2}}{N}\]  
where \(N\) is the number of epochs and \(e_{i}\) is the positioning error at epoch \(i\). The mathematical models for the GNSS-only, 4G-only, and hybrid positioning solutions were developed in Python 3.8.10 64-bit, structured into four main processing stages:  
1. Data acquisition and parsing. 2. Preprocessing and filtering. 3. Construction of the WLS adjustment model. 4. Estimation of the final solution, including the following:  
* Computation of the compensated user coordinates;
* Elimination of high-residual observations based on a \(p\)-value threshold of 2.5, as defined in Equation (10);
* Estimation of the variance-covariance matrix to assess solution quality. All the analyses were performed using the WLS method, which enabled consistent position estimation and facilitated a quantitative comparison across all three positioning strategies. Both hybridization methods were implemented in Python for testing purposes. The computational load was minimal: the WLS adjustment per epoch required approximately 30-50 milliseconds on a standard laptop (Intel i5, 16 GB RAM), and the algorithm's structure is well suited for optimization and real-time implementation on mobile processors or embedded devices. No recursive filtering or Doppler-based updates were used; the approach relies entirely on matrix operations and thresholding techniques.
# Short-Term Power Prediction of Wind Turbine Applying Machine Learning and Digital Filter
## 3 Time Series Forecasting Experiments
### Pre-Processing of Signals  
For the SG filter, there are two degrees of freedom that can be adjusted to suit the actual situation: the data frame length, and the polynomial order. From the filtering effect of the SG filter on the power signal demonstrated in Figure 10, it can be seen that the lower the polynomial order, the higher the degree of smoothing when the data frame length is the same (a); and the greater the data frame length, the higher the degree of smoothing when the polynomial order is the same (b).  
The purpose of using filters is to degrade the noise, while the signal edge definition should be guaranteed, which is an advantage of the SG filter over the linear Moving Average (MA) filter [36]. To degrade the noise and ensure the signal edge definition, a lower polynomial order and a slightly larger data frame length are desirable. The literature [33] has set the frame length of the SG filter to 5, and the polynomial order to 2. Filters of order 2 but with progressively increasing frame length (3-31) were used for comparison in this study. The over-smoothed signal does not guarantee clear edges, which leads to poor performance of the trained model. The data frame length of 5 and the polynomial order of 2 are also valid in this case. The specific comparative test results are in Section 3.5.2.  
Importantly, the input in the training phase was raw unfiltered data, and the output of the training set used filtered data, so the LSTM network that completed the training was able to overcome the noise of the actual signal and predict effectively.  
Figure 10: Diagram of the effect of digital filtering. (**a**) The processing effect of filters with the same frame length but different orders. (**b**) The processing effect of filters with the same order but different frame lengths.
# Implementation and Evaluation of HTTP/3 Connectivity Check Using Happy Eyeballs Algorithm
###### Abstract  
The HTTP Alternative Services (Alt-Svc) method is defined as an application to check connectivity in HTTP/3. This method is designed based on the fact that communication with old HTTP is guaranteed and the HTTP/3 adoption rate is not necessarily dominant, and it is considered effective in the early stages of transition. However, once HTTP/3 has reached its peak and the transitional period has passed, the uncertainty and redundancy of the Alt-Svc procedure become detrimental. In Alt-Svc, the procedure involves first completing the old HTTP connection to use HTTP/3, and then migrating to HTTP/3 if possible; however, because HTTP/3 is a protocol that eliminates the waste of the old HTTP handshake (TCP handshake followed by TLS handshake), HTTP/3 does not fully benefit from the rapid connection establishment of HTTP/3. Therefore, we propose a method to apply the Happy Eyeballs algorithm, which is used for IPv4 and IPv6 connectivity checks, to the old HTTP and HTTP/3 connectivity checks. The Happy Eyeballs algorithm performs the two selections in parallel to eliminate the delay that occurs in sequential processing, but the proposed method differs from the conventional Happy Eyeballs algorithm in that, even if the old HTTP is adopted once, it switches to the HTTP/3 connection if it is possible to connect using HTTP/3. The proposed method differs from the conventional Happy Eyeballs algorithm by introducing a mechanism to switch to HTTP/3 connections when HTTP/3 connections are available, even when the old HTTP is adopted. Results of the evaluation experiments demonstrated that the adoption rate of HTTP/3 increases in environments with high communication latency because the old HTTP performs the TLS handshake after the TCP handshake, but with this improvement, HTTP/3 is preferentially selected even in low latency environments when it is selectable.  
QUIC; HTTP/3; connectivity check; Happy Eyeballs 2022  
24 June 2022  
28 June 2022
# Secure Encapsulation Schemes Using Key Recovery System in IoMT Environments
## 7 Conclusions  
This paper proposed key recovery systems based on key encapsulation secured from various attacks in IoMT environments in schemes II and II.  
In the key recovery system, the session key used in the ciphertext is recovered via the KRF and used. However, the KRF can be forged and KRF owners can deny the fact that they generated the KRF. Furthermore, unauthorized KRAs can access the M-KRA and interfere with key recovery. To solve this problem, the key recovery system using signcryption includes the session key hash in the KRF. Therefore, the KRF data cannot be forged. In addition, this system includes the private key of a device in special value of the KRF. A device cannot deny that it generated the KRF. Furthermore, the system ensures the security requirements mentioned in Section 3, including KRF integrity and non-repudiation, are fulfilled.  
Additionally, there is a problem that the key can be recovered by collusion attacks and key or message leakage among the Med, KRC, and KRA. To solve this problem, the Med must have the help of the KRC and KRA to recover the key by a proxy re-encryption function. In addition, the KRC or KRA would also need mutual help to recover a complete session key. That is, by limiting the information and processing capabilities of the three participants, the key recovery system can be expected to be secure against various attacks. Furthermore, because the KGC generates the private keys of all participants, there is the problem that the KGC's authority is strong. To solve this, a partial private key generation scheme is used. The KGC generates a partial private key and sends it to the participants. Participants who receive partial private keys use them to generate complete private keys and solve the KGC key escrow problem.  
Future research is to check whether unexpected problems occur when the proposed schemes are implemented in actual systems. Furthermore, additional research is needed that can examine the amount of computations, time, and cost incurred when recoveringkeys. In addition, further research is needed to determine whether the proposed schemes are secure against other types of security threats.  
Conceptualization, T.K., W.K. and I.L.; methodology, T.K., W.K., D.S. and I.L.; data investigation, T.K., W.K.; analysis and validation, T.K., W.K. and I.L.; writing--original draft, T.K.; writing--review and editing, T.K., W.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education(NRF-2019R1A2C1085718) and the BK21 FOUR (Fostering Outstanding Universities for Research)(No. 5199990914048) and the Soonchunhyang University Research Fund.  
The authors declare no conflict of interest.
###### Abstract  
Wastewater produced during the wine-making process often contains an order of magnitude greater chemical oxygen demand (COD) concentration than is typical of domestic wastewater. This waste stream is also highly variable in flow and composition due to the seasonality of wine-making. The recent growth of small-scale wineries in cold climates and increasing regulations present a need for low-cost, easily-operable treatment systems that do not require large amounts of land, yet maintain a high level of treatment in cool temperatures. This research investigates the use of a subsurface vertical flow constructed wetland (SVFCW) to treat winery wastewater. In this study, clinoptilolite, tire chips, and a nano-enhanced iron foam were used to enhance bench-scale gravel cells to adsorb ammonia, nitrate, and phosphorus, respectively. The treatment systems, without nitrogen adsorption media, performed well, with \(>\)99% removal of COD and 94% removal of total nitrogen. Treatment systems with the nitrogen adsorption media did not enhance nitrogen removal. Equilibrium was reached within two weeks of start-up, regardless of prior inoculation, which suggests that microbes present in the winery wastewater are sufficient for the start-up of the wastewater treatment system; therefore, the seasonality of winery wastewater production will not substantially impact treatment. Operating the treatment systems under cool temperatures did not significantly impact COD or total nitrogen removal. Further, the use of nano-enhanced iron foam exhibited 99.8% removal of phosphorus, which resulted in effluent concentrations that were below 0.102 mg/L P.  
winery wastewater; constructed wetland; cold weather wetland; vertical flow wetland; nutrient adsorption Article
# A Heterogeneous Robotic Swarm for Long-Term Monitoring of Marine Environments
## 2 Distributed Marine Monitoring System
### aPad
#### 2.2.1 Modules  
The aPad is equipped with a docking mechanism realised as a mechanical lever system which forces the aMussel into a funnel-shaped slot, as seen in Figure 8. The aMussel itself has a specially designed docking section shaped as two inverted cones to provide some tolerance for vertical displacement to the docking mechanism and also provide levelling with the charging dock while being forced with the motorised shutter.  
Figure 8: Illustration of docking mechanism mounted on aPad, consisting of two immovable delrin levers (1) mounted on the aPad which act as a guiding rail funnelling the aMussel towards the charging dock, a motorised aluminium shutter (2) that pushes on the aMussel while closing, and a charging dock (3).  
Figure 7: Members of the subCULTron swarm operating in real-world conditions, Venice, Italy.  
The charging dock contains three charging coils encapsulated in waterproof resin which provide wireless energy transfer, and is also shaped in a way to provide tolerance in vertical aMussel angle at approach, as well as mechanical locking once the aMussel is in the slot. The docking principle is purely mechanical with quite high tolerances to offsets with the approaching aMussel, including vertical shift tolerance of \(\pm 50\) mm, and horizontal shift tolerance of \(\pm 130\) mm.  
For surface communication purposes, the aPads are equipped with an Ubiquitin UniFi Mesh wireless router. The router operates on two different frequency bands (2.4 GHz and 5 GHz) using a separate antenna for each.  
For underwater communication purposes, the aPads contain acoustic modems identical to the ones installed in each aMussel.
## 2 Protocols for Synchrotron X-Ray Experiments
### XAFS
#### 2.2.3 Data Analysis  
XAFS data analysis involves processing spectral and metadata, such as the coordination number, bond distance, Debye-Waller factor, and R factor, to derive detailed information about the local atomic structures and bonding characteristics. Data are processed using the Demeter package (v0.9.26) [9].  
The recorded spectra are normalized, and the extended XAFS (EXAFS) oscillations are extracted using the Athena software within the Demeter package. The normalized spectra are calculated as follows:  
\[\mu_{\text{norm}}=\frac{\mu(E)-\mu_{\text{pre}}(E)}{\Delta\mu(E_{0})} \tag{1}\]  
where \(\mu_{\text{pre}}(E)\) represents a regression line fitted to data typically between \(-150\) eV and \(-50\) eV, and \(\Delta\mu(E_{0})\) denotes the edge jump. The EXAFS oscillation, represented by the following formula, was extracted using the AUTOBK algorithm [9]:  
\[\chi(k)=S_{0}^{2}\frac{\sum_{i}\frac{N_{i}F_{i}(k_{i})}{k_{i}R_{i}^{2}}\text{ exp}\Big{(}-2k_{i}^{2}\sigma_{i}^{2}\Big{)}\text{exp}(-\frac{2R_{i}}{\lambda_{i}}) \text{sin}\{2k_{i}R_{i}+\phi_{i}(k_{i})+\phi_{c}(k_{i})\} \tag{2}\]  
where \(k=\sqrt{\frac{2m}{\hbar^{2}}(E-E_{0})}\), and the parameters \(S_{0}\)\({}^{2}\), \(N\), \(F\), \(R\),\(\sigma^{2}\), \(\lambda\), \(\phi\), and \(\phi_{c}\) represent the intrinsic loss factor, coordination number, backscattering factor, bonding distance, Debye-Waller factor, mean free path, and phase shifts due to scattering and absorption, respectively. The radial structure function is derived from the Fourier transformation of the oscillation. An example of the resulting plot is shown in Figure 4.  
The EXAFS data are further analyzed using Artemis, another tool in the Demeter package. Curve fitting is performed in \(R\)-space with weighting using the parameters calculated by FEFF6L [10]. FEFF calculations are based on CIF files from the Crystallography  
Figure 4: Pt L\({}_{3}\)-edge XAFS spectra of TEC10E50E, a hydrogen-reduced sample supplied by TANAKA Precious Metals. (**a**) X-ray absorption near edge structure spectrum at the Pt L\({}_{3}\)-edge, indicating the electronic state of Pt. (**b**) EXAFS spectrum in k-space, showing oscillations related to local structures. (**c**) Fourier-transformed EXAFS spectrum in R-space, illustrating the radial distribution of neighboring atoms around Pt.  
Data curation is conducted to organize the processed data and metadata, ensure consistency, and facilitate their reuse. This involves standardizing formats, associating relevant metadata, and systematically archiving datasets for future analysis and references.
# Light-YOLO: A Study of a Lightweight YOLOv8n-Based Method for Underwater Fishing Net Detection
## 4 Experimental
### Model Visualization  
In order to highlight the excellent performance of the Light-YOLO model in terms of modelling and recognition capabilities, a detailed comparative analysis was conducted. During the analysis, the specific pictures of which are shown in Figure 11. It was first observed that the YOLOv7-tiny model, although showing high confidence, suffers from omission and misdetection problems. Similarly, compared with the YOLOv3-tiny model, the same problem is encountered, and the phenomenon of a single vulnerability being repeatedly detected several times is found. Meanwhile, we randomly selected a set of validation sets, including output block diagrams and corresponding labels, to observe Light-YOLO in detail. The results show that Light-YOLO is extremely accurate in identifying various vulnerabilities and is optimized for problems in the YOLOv7-tiny and YOLOv3-tiny models. This proves that Light-YOLO possesses superior recognition ability and fully meets this study's requirements in terms of accurate recognition.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Models** & **[EMAIL_ADDRESS] (\%)** & **[EMAIL_ADDRESS]–0.95 (\%)** & **Params** & **GFLOPs** & **FPS** \\ \hline YOLOX [30] & **87** & 50.7 & 8.05 & 21.8 & **227.69** \\ Gold\_YOLO [31] & 85.3 & 48.9 & 46 & 21.5 & 148.6 \\ Cascade RCNN [32] & 79.6 & 42.0 & 69.152 & 209.92 & 50.8 \\ RTDETR [33] & 84.6 & 47.8 & 32.80 & 108.0 & 116.32 \\ DDQ [34] & 83.9 & 48.7 & 48.31 & 236.2 & 46.15 \\ Light-YOLO & 86.7 & **51.8** & **4.4** & **6.1** & 121.95 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Other family comparison experiments.  
Figure 11: Visualization of underwater target recognition with different models: (**a**) YOLOv3-tiny; (**b**) YOLOv7-tiny; (**c**) Light-YOLO.