esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Lessons Learned and Limitations
### Limitations  
Despite the overall success of the architecture, several limitations became evident:  
* Integration Overhead: While OPC UA and native protocols facilitated broad compatibility, legacy controllers required dedicated configuration and testing efforts, especially when using intermediate gateways (e.g., IBHLink S7++).
* Latency Tuning: Maintaining real-time responsiveness across all sources proved challenging during multi-source operation. The system required per-source buffer adjustments to ensure latency below 50 ms (see Table 1).
* UI Prototyping: The visualization layer was realized using ibaQPanel. A full implementation of the KAVA concept is planned.
With this, claimed water from wastewater must have adequate quality to be reused, which sometimes is only possible when the wastewater is depurated with additional or complementary treatments to the traditional approaches [3].  
Compounds that are continuously detected in water sources at low concentrations (\(\mu\)g/L or ng/L) are called contaminants of emerging concern (CEC). This category includes several compounds such as endocrine disruptors (ED), pharmaceutical and personal care products (PPCP) [4], pesticides [3], illicit drugs, synthetic and natural hormones [2], and heavy metals [5], among others.  
These pollutants enter and accumulate in water resources due to continuous effluents discharges [6,7] and the fact that existent WWTPs are not designed to remove these types of contaminants, mainly due to their recalcitrant character [8].  
One group of compounds that are detected in different water sources are parabens [9]. They have already been detected in rivers [10], tap water [11], pool water [12], drinking water [13], and effluents [14].  
Parabens are used in industries (cosmetic, food, and pharmaceutical) mainly due to their good preservative and anti-microbial properties but also due to their chemical stability, low toxicity, and low production costs [7,15,16]. These compounds are endocrine disruptors [17,18], which can present estrogenic [19] and antiandrogenic activity [20]. Furthermore, they are related to male infertility [17] and tumors [21], so parabens can negatively affect human health [17]. In animals, the use of parabens can have negative impacts, since endocrine disruptors compounds can cause feminization, imposex, abnormalities, and a decrease in fecundity of species [22].  
Due to their continuous detection and negative impacts on different organisms, special measures must be taken to control and remove parabens from ecosystems. The use of advanced technologies in wastewater treatment can help to decrease the discharge of parabens and other CECs present in treated wastewater into rivers and other water resources. With this, advanced oxidation processes (AOPs) can present the most suitable solution since these technologies can efficiently promote the CECs abatement.  
AOP technologies such as ozonation [23, 24, 25]; catalytic ozonation [26]; photocatalytic ozonation [27, 28]; Fenton's process [29, 30]; photo-Fenton [31]; photocatalysis with UV radiation [7, 32], visible radiation [6, 33], and solar radiation [34]; photolysis using UV radiation [35]; photoelectrocatalysis [36]; photo-sonochemical degradation [37]; or sonochemical oxidation [38, 39] were already used for parabens abatement.  
This paper overviews the application of AOPs in the removal of parabens from water mixtures. The effect on treated water ecotoxicology is considered, as well as the costs associated with the treatment.
# Digital Workflow to Fabricate Complete Dentures for
## 1 Introduction  
Complete dentures have been manufactured using a conventional workflow, which requires multiple visits of the patients to a dental clinic and complex laboratory procedures [1, 2]. With recent advances in computer-aided design and computer-aided manufacturing (CAD-CAM) technology, the digital design and fabrication of complete dentures have become an active area of research. The advantages of this workflow include the elimination of cumbersome denture tooth arrangement and subsequent resin injection or packing and reproducibility of the dentures, which can be attributed to the use of a digital workup; the designed data file can be stored in a backup device and reused later to manufacture the same denture [3].  
Moreover, the performance of intraoral scanners has also dramatically increased; several studies and clinical reports have described the feasibility of intraoral scanners for obtaining digital scans in dentulous arches [4, 5]. This direct digitization reduces chair time for impression making and enables real-time verification of the accuracy of the obtained impression through a monitor connected to the intraoral scanner. Nevertheless, a barrier for complete digitization is the inability to establish a three-dimensional (3D) internal relationship using intraoral scanners. When using intraoral scanners, the interarch relationship is established by registering the bilateral buccal surfaces of the teeth; however, in cases of complete edentulism, this approach is not applicable since there are no teeth present to be registered [6].  
The transformation of a negative imprint into a positive object is a fundamental concept of dentistry; for example, a definitive cast is fabricated by pouring dental stone into a negative dental impression. Commercial reverse engineering software programs have a similar function in a digital world; they enable free transformation of the 3D scan data between positive and negative shapes. This paper presents a digital workflow to register the interarch relationship by using a reversing and superimposing (RAS) technique.
## 4 Conclusions and Discussion  
The set of the stage-augmented reality performance system designed by the authors realized the functions of remote triggering, real-time tracking and virtual-real fusion. UWB technology provides high accuracy in positioning and low power consumption, which is more suitable for large stage performances. In practical applications, it is necessary to pay attention to the inconvenience caused by some influencing factors. For example, the system is highly affected by metal. When setting up the base station, it needs to be away from large-area metal. The base station can be set in the form of a suspension, and the antenna tag also needs to avoid contact with metal jewelry.  
Compared with the previous spatial augmented reality interactive system in stage application, this system has the characteristics of a high degree of freedom and high concealment, which allows the performers to be free from technical constraints during the performance, and focus on the stage performance, while the high concealment of the module brings a higher sense of immersion to the spatial augmented reality, and improves the quality of the stage performance. It also has received high evaluation in the practical application of some Chinese dramas. The system not only provides a better  
Figure 12: Augmented reality on stage.  
viewing experience for the audience, but also offers more possibilities for the combination of rising technology and traditional art.  
Conceptualization, Y.Z.; methodology, Y.Z.; software, Y.S., W.Z. and Z.Z.; formal analysis, Z.Z.; investigation, Y.S. and W.Z.; data curation, Y.S., W.Z., Z.Z. and M.P.; writing--original draft preparation, Y.S. and W.Z.; writing--review and editing, Y.S., Y.Z. and W.Z.; visualization, Y.S.; project administration, Y.Z.  
The work is supported by the China National Key Technology Support Program under Grant No.: 2014 BAH15F02  
The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# An Enhanced Feature Extraction Network for Medical Image Segmentation
## 1 Introduction
[12] proposed DoubleU-Net, which is a combination of two U-Net structures. The first U-Net uses pre-trained VGG-19 as the encoding network, and the second U-Net uses ASPP to capture context information. It achieves better segmentation results than U-Net in colonoscopy, dermatoscope, and microscope images. In addition, due to the good performance of U-Net in the field of medical image analysis, many scholars combine it with other models to further improve its performance. [PERSON] et al. [13] proposed an image fusion algorithm based on dual-tree complex wavelet transform (DTCWT) and frequency domain U-Net, which improves the accuracy of tumor segmentation. [PERSON] et al. [14] combined the local difference method with U-Net. [PERSON] et al. [15] proposed a method for combining level set and U-Net. [PERSON] et al. [16] presented a combination of random walk and U-Net. [PERSON] et al. [17] proposed a combination of the graph partition method and U-Net. [PERSON] et al. [18] proposed a combination of deep reinforcement learning and U-Net. Semantic segmentation tasks and medical image segmentation tasks are used to classify each pixel in the image. All segmentation tasks face two problems. The first problem is how to improve the feature extraction ability of the network. If the feature extraction ability is improved by increasing the depth of the network blindly, a lot of detailed information will be lost, and detailed information is extremely important for image segmentation. Second, most methods based on U-Net using skip-connection add the features extracted by the encoder to the output of the corresponding layer of the decoder indiscriminately, and this will inevitably introduce noise information, thus interfering with the subsequent segmentation results. Almost all network improvements are essentially designed to address these two problems. Promoting the feature extraction ability of the network and compensating for lost information to the greatest extent is very important for the segmentation of medical images. The main contributions of this paper are:  
1. Based on U-Net, we propose an improved network EFEN, which further enhances the feature extraction ability of the network by adding feature extraction processes. In this paper, the skip-connection is improved. Each skip-connection uses positional encoding and a cross-attention mechanism. By embedding positional information, absolute information and relative information between organs can be captured. Each skip-connection uses a cross-attention mechanism to select information so that the network can automatically give useful features a larger weight while suppressing useless information. Then, the target boundaries or small targets can be better segmented. 3. For medical image segmentation tasks, the proportion of target pixels in the whole image may be much less than that of background pixels, thus causing a class imbalance problem. In this paper, we adopt binary cross-entropy and dice loss to jointly optimize the training learning.
## 1 Introduction
[18] described in 2021 the development and commissioning of a software application to optimize the ETS positioning and to overcome the time demanding process proposed by [PERSON] and collaborators [21]. The latest development was focused on the use of a robotic manipulator (Mitsubishi Industrial Robot model RV-2F-D, Mitsubishi Electric Europe, Agrate Brianza, Italy) for the definition and successive repetition at each irradiation session of the patient-specific position and orientation of the ETS [18]. An infrared optical-tracking system (OTS) is used to track the position of radio-opaque reflective spherical markers (BrainLab, 1 cm diameter) on the ETS surface and to compare these markers' positions to the reference configuration generated using the application developed by [PERSON] and Colleagues [18]. Currently, the ETS alignment procedure takes place twice in clinical rooms, namely the CT bunker and the treatment room, with non-negligible impact in terms of room occupancy. Therefore, the need to standardize and streamline the workflow in order to reduce the time spent in clinical rooms is emerging with particular importance. The aim of this work is to present an alternative method for ETS alignment out of the CT bunker and to compare the results achieved with the two procedures (the conventional procedure in the CT room and the method proposed in this study). Furthermore, in this study, we focused on finding a solution to overcome the limitations highlighted in previous studies, such as in [PERSON] et al. [18], and related to the need to exclude from treatment patients for whom a low external gaze direction was required, due to the unfeasibility of the ETS configuration. Our study includes a wider and more heterogeneous clinical dataset than the one described in [PERSON] et al. [18], including configurations that required the use of a specific extender attached to the robotic manipulator, in order to reach the planned position. In the proposed study, the initial alignment of the ETS device in the CT bunker (Figure 2b) was replaced by a corresponding procedure, performed in a dedicated room and supported by a commercial general purpose optical-tracking system (Smart-DX motion analysis system, BTS Bioengineering, Italy) (Figure 2c,d). The position of the three cameras equipping the system was optimized to ensure the optimal visibility of the markers mounted on the ETS chassis and used to localize the ETS critical components (fixation light, monitoring cameras, etc.). The study focused on the quantitative assessment, in controlled conditions and exploiting retrospective clinical data, of eventual deviations in the ETS setup between the conventional CT bunker-based procedure and the novel one, as the initial step for proposing a change in the clinical procedure with consequent lower occupancy of the CT bunker.
# Numerical Simulation of the Tip Leakage Vortex Characteristics in a Semi-Open Centrifugal Pump
## 2 Computational Model and Method
### Test Rig  
As shown in Figure 3, experimental measurements were conducted on a closed test rig for a centrifugal pump in the State Key Laboratory of Eco-hydraulics in the Northwest Arid Region, Xi'an University of Technology. The comprehensive measurement errors of the head and efficiency were \(\pm\)1.41% and \(\pm\)1.57%, respectively. The flow rate could be measured using the electromagnetic flowmeter with an accuracy lower than 1%. A frequency inverter manufactured by Asea Brown Boveri Ltd. was used to regulate the rotating speed and ensure that the pump operating at the nominal rotating speed of 980 r/min. The fluctuation of rotating speed at different flow rates was approximately 3 r/min, indicating that the uncertainty of the speed was less than 0.31%.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Value** & **Parameters** & **Value** \\ \hline Impeller inlet diameter, D\({}_{1}\) & 100 mm & Impeller outlet diameter, D\({}_{2}\) & 232 mm \\ Impeller outlet width, b\({}_{2}\) & 16 mm & Blade number, Z & 6 \\ Blade inlet angle, \(\beta_{1}\) & 16.6\({}^{\circ}\) & Blade outlet angle, \(\beta_{2}\) & 18.6\({}^{\circ}\) \\ Wrag angle, \(\Phi\) & 99.4 & Blade thickness, T & 4 mm \\ Impeller radius of suction & 88 mm & Impeller radius of pressure & 92 mm \\ surface & 88 mm & surface & 92 mm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Geometrical parameters of the pump.  
Figure 1: Computational domain of the pump.  
Figure 2: Impeller meridional shape.
## 3 Results
### Optimizing the Model
#### 3.1.5 Random Forest  
The hyperparameters of the random forest model that were specified in the study are the number of estimators, maximum depth, minimum number of samples split, and minimum number of samples leaf. The number of estimators represents the number of trees to be generated in the random forest. Increasing the number of trees can potentially improve prediction performance, but it can also slow down the model's training and prediction speed [81]. The maximum depth limits the maximum depth of each tree in the random forest [81]. Setting a maximum depth helps control the risk of overfitting, as deeper trees can capture more specific patterns in the training data but may struggle to generalize well to new data. The minimum number of samples split is the minimum number of samples required to split an internal node, while the minimum number samples leaf is the minimum number of samples required to be at a leaf node [81]. These parameters also contribute to controlling the model's complexity and generalization ability. The hyperparameter  
Figure 2: Optimal model of decision tree.  
ranges specified in the study are as follows. The number of estimators is set to 100 and 200; maximum depth as 0, 5, and 10; minimum number of samples split as 2, 5, and 10; and minimum number samples leaf set to 1, 2, and 4.  
The analysis of each CV value for the optimal hyperparameter values in terms of R\({}^{2}\), RMSE, MAE, and the Delta RMSE revealed similar results between CV values of 3 and 10. However, there was a noticeable difference when the CV was set to 2. For CV as 2, the test set had an R\({}^{2}\) of 0.88, an RMSE of 5.69 MPa, and an MAE of 4.16 MPa, with a Delta RMSE as 2.35 MPa. On the other hand, for CV values between 3 and 10, similar performance metrics were obtained. Especially for CV values as 3, the R\({}^{2}\) was 0.89, RMSE was 5.59 MPa, and MAE was 3.98 MPa, and Delta RMSE was 2.97 MPa. Comparing the two CV values, 2 and 3, it can be observed that the model performs better when the CV is set to 3. Although the Delta RMSE value was higher for CV = 3 compared to CV = 2, the higher R\({}^{2}\) and lower RMSE and MAE values indicate better overall performance when the CV is set to 3. Therefore, the optimal model for the random forest is obtained when the CV is set to 3, with 200 estimators, no maximum depth, a minimum samples split of 2, and 1 minimum samples leaf, as summarized in Table 3.
# A Technological Framework to Support Asthma Patient Adherence Using Pictograms
## 2 Materials and Methods
### Evaluation Process of Pictogram Comprehension by Users Diagnosed with Bronchial Asthma
#### 2.4.1 First Evaluation of Pictogram Comprehension by External Expert Judgement  
In this phase, we assessed the comprehension of pictograms by users with the assistance of two external experts, both kinesologists. They evaluated the responses of 50 users (24 from the Valparaiso Region and 26 from the Biobio Region) for each of the 18 pictograms and reported their level of understanding.  
To conduct this assessment, we created an Excel-based evaluation matrix, including:  
Instructions for the evaluator;  
The medical indication;  
The associated pictogram;  
The participant's response;  
A field for the expert judge's evaluation.  
Each evaluator independently determined whether, in their opinion, the participant's response corresponded to the medical indication conveyed by the pictogram. They used two response categories:  
The pictogram is adequately understood;  
The pictogram is not adequately understood.
# Simplified Two-Dimensional Generalized Partial Response Target of Holographic Data Storage Channel
## 3 Proposed Model  
Figure 3 shows the simulation model of the proposed scheme exploiting two 1D Viterbi detections based on the two target vectors \(\mathbf{v}\) and \(\mathbf{b}\).  
In this model, using a spatial light modulator (SLM), the original data \(u[k]\) are encoded into the data \(a[j,k]\) and stored on the holographic medium. To read back the data, we apply the reference beam onto the holographic medium. The data page is displayed and read by a CCD [23]. Then, the read-back signal suffers from blurring effects (2D ISI), additive noise, and misalignment. Thus, the signal \(y[j,k]\) is presented as follows [23; 24; 25]:  
\[y[j,k]=a[j,k]\otimes h[j,k]+w[j,k], \tag{29}\]  
where \([j,k]\) is the position on the data page, \(w[j,k]\) is the additive Gaussian noise with zero mean and variance \(\sigma^{2}\), and the discrete point spread function (PSF) \(h[j,k]\) is given by:  
\[h[j,k]=\int\limits_{j-1/2}^{j+1/2}\int\limits_{k-1/2}^{k+1/2}h(x,y)dxdy. \tag{30}\]  
The continuous PSF is modeled by:  
\[h(x,y)=\frac{1}{\sigma_{b}^{2}}\text{sin}\,c^{2}\left(\frac{x-m_{x}}{\sigma_{ b}},\frac{y-m_{y}}{\sigma_{b}}\right), \tag{31}\]  
where \(\text{sinc}(x,y)=(\text{sin}(\pi x)/\pi x)\) (\(\text{sin}(\pi y)/\pi y\)), \(\sigma_{b}\) is the grade of blur in the resultant diffracted signals, and \(m_{x}\) and \(m_{y}\) are \(x\) and \(y\) axis misalignments, respectively.  
Then, the read-back signal \(y[j,k]\) goes through the 2D equalizer, which is updated by the MMSE algorithm. The output of the equalizer \(z[j,k]\) is close to the desired signal from the GPR target (i.e., \(z[j,k]\approx d[j,k]=a[j,k]\otimes\mathbf{G}\)). With the GPR target estimated earlier, the signal \(z[j,k]\) is detected by the serial detection and restored into the signal \(\bar{a}[j,k]\). Finally, the signal \(\bar{a}[j,k]\) is decoded into the original signal \(\bar{u}[k]\).  
Figure 3: Proposed simulation model.
## 2 Basic Concepts and Work Scope
### Spatial Side Information
In this case, one receiver or sensor from a positioning device collects a set of fingerprints at one position, and then it is moved to a neighboring or adjacent position to collect another set of fingerprints. The set of fingerprints is mapped to a position estimate at each sampled position using a selected pattern-matching method. This process may be repeated for a certain number of positions or indefinitely. Finally, the current position is estimated using a filter (e.g., a smoothing filter), relying on the past position estimates. That is, each target position estimate is the result of the mapping of a set of fingerprints associated with the target position, plus the past position estimates. Certainly, this approach uses spatial side information to estimate a position. However, we argue that such an approach to estimating a position is the result of the aggregation of the past position estimates in the position domain, as depicted in Figure 7b, and not in the fingerprint domain, as depicted in Figure 7c. Here, we are interested in using the fingerprints from the target, as well as from adjacent spatial positions, entirely in the fingerprint domain, that is, before the fingerprints from the target and adjacent positions are mapped to a target position estimate. At the end of this article, we will benchmark a pattern-matching method operating in the position domain against two pattern-matching methods operating in the fingerprint domain and draw conclusions from the results obtained.
## 3 Results  
### Adhesives Characterization  
Adhesive samples exhibited viscosity values between 994.67 and 3171.67 cP. The range of pH fluctuated from 2.5 to 3.3. Density varied between 1.09 and 1.12 g/cm\({}^{3}\). The solids content range fluctuated from 40% to 52%. Finally, electrical conductivity exhibits values between 8.12 and 12.97 mS (see Table 3).  
In general, PVAc-D3 and PVAc-D4 adhesives showed similar physical and chemical properties as those reported on their technical manufacturing data sheet.  
The PVAc-D4 adhesive presented higher values of viscosity, total solids and electrical conductivity.  
Also, the D3-MWCNT and D3-LIGNO adhesive blends showed a lower viscosity compared to the neat PVAc-D3 adhesive.  
Figure 5: Image process to obtain EP and AP parameters. (**A**) Fluorescence Leica DM 2000 LED microscope picture. (**B**) Studied section. Variables definition: (\(Ai\)), (\(Yi\)), (\(Xo\)). (**C\({}_{1}\)**) Areas of the section studied. (**C\({}_{2}\)**) Image (**C\({}_{1}\)**) converted by ImageJ software.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Samples ID**} & **Viscosity** & \multirow{2}{*}{**pH**} & **Density** & \multirow{2}{*}{**Solid (\%)**} & **Electrical** \\  & **(cP)** & & **(g/cm\({}^{3}\))** & & **Conductivity (mS)** \\ \hline D3 & 2668.33 & 3.2 & 1.10 & 45 & 8.12 \\ D3-MWCNT 0.05\% & 1549.00 & 3.1 & 1.10 & 43 & 8.52 \\ D3-MWCNT 0.5\% & 994.67 & 3.3 & 1.09 & 40 & 8.33 \\ D3-LIGNO 0.5\% & 1486.00 & 2.9 & 1.10 & 44 & 8.37 \\ D3-LIGNO 2.5\% & 1719.00 & 3.3 & 1.10 & 45 & 8.97 \\ D4 & 3171.67 & 2.5 & 1.12 & 52 & 12.97 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Adhesive samples characterization.