esa-sceva/satcom-chunk-collection huggingface dataset:
# Interactive Airfoil Optimization Using Parsec Parametrization and Adjoint Method
## 3 Parsec Parametrization
\begin{table}
\begin{tabular}{c c c c} \hline \multicolumn{4}{c}{**Basic Parsec Parametrization [10]**} & \multicolumn{4}{c}{**Parsec Variant Used for Optimization [13]**} \\ \hline
**Parameter** & **Meaning** & **Parameter** & **Meaning** \\ \hline
**—** & **—** & \(r_{lo}\)[1] & Lower surface leading edge radius \\ \(x_{lo}\)[1] & Lower surface crest & \(x_{lo}\)[1] & Lower surface crest location—horizontal position \\  & location—horizontal position & \(y_{lo}\)[1] & Lower surface crest location—vertical position \\ \(y_{xxlo}\)[1] & Lower surface crest curvature & \(y_{xxlo}\)[1] & Lower surface crest curvature \\ \(r_{te}\)[1] & Leading edge radius & \(r_{up}\)[1] & Upper surface leading edge radius \\ \(x_{up}\)[1] & Upper surface crest & \(x_{up}\)[1] & Upper surface crest location—horizontal position \\ \(y_{up}\)[1] & Upper surface crest location—vertical position \\  & location—vertical position & \(y_{xxup}\)[1] & Upper surface crest location—vertical position \\ \(y_{xxup}\)[1] & Upper surface crest curvature & \(y_{xxup}\)[1] & Upper surface crest curvature \\ \(\alpha_{te}\)[\({}^{\circ}\)] & Trailing edge direction & \(a_{te}\)[\({}^{\circ}\)] & Trailing edge direction \\ \(\beta_{te}\)[\({}^{\circ}\)] & Trailing edge wedge angle & \(\beta_{te}\)[\({}^{\circ}\)] & Trailing edge wedge angle \\ \(y_{te}\)[1] & location—vertical position & \(y_{te}\)[1] & Trailing edge location—vertical position \\ \(\Delta y_{te}\)[1] & Trailing edge thickness & \(\Delta y_{te}\)[1] & Trailing edge thickness \\ \hline \end{tabular}
\end{table} 
Table 2: Parsec parameters and their meaning. Figure 3: Parsec parameters of an airfoil and their geometrical meaning. It has been assumed that the trailing edge thickness (\(\Delta y_{te}\)) is always equal to zero (sharp trailing edge) and is therefore not a design variable. This may limit the applicability in some cases, but the benefits in terms of the robustness of the algorithm were deemed to outweigh this drawback. The sharp trailing edge makes it easier to properly enforce the Kutta condition and prevents the optimization from making illegal changes to the trailing edge geometry. The area around the trailing edge is where the viscous effects are usually the most profound since the wake tends to start forming there. Because the optimization cannot account for those viscous effects (and especially the wake formation), it was decided to restrain it in this area as much as possible. On top of that, the trailing edge can be tweaked in many ways, most of them far exceeding the capabilities of the parsec parametrization, as the lack of trailing edge control is deemed to be one of its main weaknesses [10]. For the trailing edge fine-tuning, methods capable of properly capturing viscous phenomena must be used. Vector \(\mathbf{F}\) of the \(11\) design variables based on parsec parametrization is given as follows. \[\mathbf{F}=\left[\tau_{lo},x_{lo},y_{lo},y_{xxlo},\tau_{up},x_{up},y_{up},y_{ xxup},\alpha_{te},\beta_{te},y_{te}\right]^{T} \tag{16}\]
## 6 Conclusions  
Indoor positioning services apply to museums offer new and promising ways to study visitor behaviors. It can help the management of such facilities, in order to monitor visitor paths and see what are the pieces they admire the most and what are the ones they miss. Indeed, managers of museums are always looking for new ways to improve the visiting experience, and are often worried that we will miss something during the tour.  
But those kind of place also bring architectural constrains in sensor placement, and as we saw, sometime making algorithms such as the triangulation less effective. The MZS  
Figure 11: Visual comparison between triangulation and MZS, with data from tag number 8 in **DS2**.  
algorithm in other hand, as promising results as the position of sensors seems to have less impact, making it specifically useful in tight spaces. This kind of indoor positioning technique is still in an early stage of development, and improvement can still be made. An area for improvement can be the use of additional technique, such as trilateration, to reduce the tracking error. As the MZS algorithm seeks to get a zone and thus can be taken into account with another method, but if used alone, only return a zone, making accurate positional representation difficult. Sensor placement can indeed have a huge impact on accuracy and performances on various techniques, and it's something that should be considered more carefully as studies shown [25; 26].  
BLE remains a reference signal in the field of indoor positioning and make experimentation as the one described in this paper easier and do not need a lot of materials to be efficient and can alone give a rough idea of the position of a visitor, even in real time.  
Finally, we can also add that indoor positioning systems in most cases do not need a complex algorithm, often too heavy to be processed by a smartphone or too difficult to implement. Studies such as [27; 28; 29],  
shows in different ways the use of the architecture and room's layout of a building. Thus, the search for maximum accuracy become less relevant, when we know where the user can and cannot be, and what path could he follow. Therefore, the robustness of a method should be more valuable than its accuracy.  
Conceptualization, R.J.; methodology, R.J.; software, R.J.; validation, B.K. and F.C.; formal analysis, R.J.; investigation, B.K., F.C.; resources, R.J., B.K., F.C.; data curation, R.J.; writing--original draft preparation, R.J.; writing--review and editing, B.K.; visualization, R.J.; supervision, B.K., F.C.; project administration, B.K., F.C.; funding acquisition, B.K., F.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Berger-Levrault and the Nouvelle-Aquitaine region, under the DA3T project number 2018-1R40225 And The APC was funded by the L3I Laboratory.  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Public dataset (DS1) used in the article: 10.1109/JIOT.2017.2712560 The other dataset (DS2) come from an experimentation.  
The authors declare no conflict of interest.
## 8 Conclusions  
The system design and experimental results demonstrated that BARMS was capable of providing an illumination function in darkness, showing a safe maintenance route, projecting BIM models, confirming relative locations, and illustrating maintenance using animation. BARMS was very effective when working outdoors at night or in similar indoor low light situations by seamlessly integrating the existing conditions with the overall scope of the setting and using the readily available smartphone illumination function. The smartphone application provided a maintenance route that avoided unanticipated obstacles, and offered a viewing angle orientation with the optimal nearby. The system projected BIM models into an environment for maintenance workers and confirmed the relative locations of pipes on the other side of walls. A straightforward indication of the valve location was provided with a clear and simple maintenance animation to lessen work burden and increase operational efficiency.  
The successful application of BARMS and its integration with BIM in facility management were confirmed. The PSSUQ results demonstrated that the application could identify the correct power switch and cooling tower that were connected to the leaking pipe, and assist with the proper shut down of the valve. In general, maintenance efficiency could also be improved in low illumination environments.  
Future studies should address the drifting problem through better positioning accuracy by developing a well-planned network of application-anchoring locations to reduce accumulated drifting errors. It is reasonable to believe that technical solutions to these problems will be achieved by computer scientists and AR engine developers in the future.  
Software was programmed by [PERSON]. Most of the conceptualization, methodology, validation, formal analysis, investigation, resources, data curation, visualization, and writing to original draft preparation were conducted by both [PERSON] and [PERSON]. In particular, writing to review and editing, supervision, and project administration were mainly conducted by [PERSON].  
This research received no external funding.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Hardened Properties  
#### 3.2.1 Compressive Strength  
The density of concrete samples oscillated between 2270 kg/m\({}^{3}\) and 2300 kg/m\({}^{3}\). The density of the tested mixes was in the range typical of regular concrete, namely 2000-2600 kg/m\({}^{3}\). Compressive strength was determined after 7 and 28 days of curing. Compressive strength was calculated based on the actual dimensions of the samples. The average values are represented in Figure 4. The minimum required compressive strength of C12/15 concrete is shown with a dashed line (19.0 MPa). The values obtained after 7 and 28 days of curing are represented by plain and crosshatched bars, respectively.  
After 28 days of curing, all the tested concrete mixes achieved the minimum compressive strength class. The mix containing fly ash obtained the highest compressive strength in all the tests. However, the range in compressive strength, compared to concrete with ground glass, of up to 2.9% was considered insignificant.  
When comparing the compressive strength after 28 days and after 7 days of curing, we observed the greatest increase in the case of concrete with the addition of glass powder (by 43-52% depending on the quantity of glass added to the mix). For mixes with fly ash, compressive strength increased in this period by 30% to 40%. The compressive strength increased with the quantity of additions.
# Optimization of Microalgae-Bacteria Consortium in the Treatment of Paper Pulp Wastewater
## 2 Materials and Methods  
### Characterization of Aerobic Biomass, Species of Microalgae, and Wastewater  
The microalgae strain used in this study was _Scenedesmus_ sp., collected in Portugal and supplied by Aqualgae (Viana do Castelo, Portugal). The culture was adapted in 1 L Erlenmeyer, with a constant air supply (2 L/min) and photosynthetic radiation of 100 \(\upmu\)mol/m\({}^{2}\).s, an average temperature of 25 \(\pm\) 2 \({}^{\circ}\)C, and a photoperiod of 12 h of light and 12 h of darkness. The culture medium was GoldMedium Fresh-Water Species (GM-FWS), also supplied by Aqualgae (Viana do Castelo, Portugal), with a concentration inoculum of volatile suspended solids (VSS) of 735.00 \(\pm\) 70.36 mg/L. The activated sludge used for inoculation and the development of the microalgae-bacteria consortium came from the bottom disposal of an aerobic reactor from a paper pulp industry WWTP located in Portugal. The average sludge VSS concentration was 3045.00 \(\pm\) 210.99 mg/L and the VSS concentration in the paper pulp industry wastewater was 365.00 \(\pm\) 20.21 mg/L. The experimental setup is shown in Figure 1.
## 3 Results
### The Calculation of Mission Priority  
According to the seven types of mission priority impact factors discussed in Section 2.2.1, the relevant information for the five newly arrived missions after pretreatment is shown in Table 8. The observation time window of the satellite for each point target was calculated by calling STK software. Firstly, the qualitative descriptions in the impact factors table were transformed into quantitative descriptions. The visible light, microwave and infrared image types were assigned values of 1, 2, and 3, respectively, and the maritime moving target, maritime static target, land moving target and land static target were assigned values of 1, 2, 3, and 4, respectively. For the impact factors, F1, F3, F6 and F7 are positive indicators, and F2, F4 and F5 are negative indicators. Therefore, the reciprocal method was used to convert all negative indicators into positive indicators. Then, the range transformation method was used to convert the transformed impact factor matrix into the standard matrix (Table 9). The distance between each mission and the ideal positive solution and the ideal negative solution was calculated respectively, and the close-degree between each mission and the ideal solution was obtained. The close-degree set is {\(C_{26}\),\(C_{27}\),\(C_{28}\),\(C_{29}\),\(C_{30}\)}, and the corresponding values are {0.376, 0.613, 0.217, 0.510, 0.466}. Through the steps above, the quantification values of each mission priority were calculated. The mission priority values from mission T26 to mission T30 are 3, 6, 2, 5 and 4, respectively. The higher the value is, the higher the priority of the mission. The impact factors of 25 initial missions were calculated via the same method, and the calculation process is not listed due to space limitations.
## 4 Results of Research on the Accuracy of Marking Objects with Eye-Tracking
Figure 6 shows the distribution determined for the average error values \(\overline{\Delta}l\) for all 170 analyzed points on the screen. The distribution (Figure 6) confirms that unfavorable results were obtained. There is a low probability (<0.1) that the error value will be below 50 px. For this value (50 px), obtaining a precision with a probability of at least 0.95 would be desirable. This would guarantee a high solution efficiency. A marking efficiency of at least 95% would be obtained for at least 95% of the prohibited item types. Analyzing the detailed data, it is essential to note that each person had a different accuracy of gaze focus points. The detailed data show that the range of error \(\Delta l\) for a single gaze is between 1.0 and 175.1 px among all subjects. The range of error \(\Delta l\) is, therefore, 174.1 px. This is even greater than when considering the average error values. The results show that direct application of the eye-tracking method to the issue at hand is impossible and that an area of permissible error will be required. The value of the total error obtained is influenced by the error resulting from the accuracy of the measuring device as well as natural human movements performed during the test, e.g., body movement, head rotation. Figure 5: Summary of the maximum, average and minimum values of the error with respect to the size of the example forbidden item. Figure 6: The distribution of average error values for the analyzed 170 points of gaze focus. The results obtained in the research are closely related to the size of the screens and the resolution displayed. This research is based on 19\({}^{\prime\prime}\) monitors and 1600 \(\times\) 1200 px screen resolution. Changing the system configuration requires further research in this area.
# CarrDet: Attention-Symmetry-Based Microscopic Carrier Detection for Wastewater Treatment
## 5 Discussion
### Impact of Carriers Similarity Threshold \(\theta\)  
In formula (6), the threshold \(\theta\) plays a crucial role in assessing the level of similarity between pairwise image blocks. If \(\theta\) is set too low, the defective carriers are regarded as normal carriers, while if \(\theta\) is set too high, the normal carriers are regarded as defective carriers. Therefore, it is very important to choose an appropriate threshold \(\theta\). The importance of threshold selection has been emphasized in various studies, highlighting its significant impact on the performance of image processing and pattern recognition tasks [37; 38]. To thoroughly investigate its impact, we conduct four sets of comparative experiments on Carrier600. In each set, the cosine distance threshold \(\theta\) is set to 0.6, 0.7, 0.8, and 0.9, respectively. These experiments aim to explore its influence on carrier detection performance.  
As shown in Figure 5, when the cosine distance threshold \(\theta\) is 0.8, the highest mAP value of 94.32% is obtained, which is 2.69%, 1.83%, and 0.53% higher than the thresholds of 0.6, 0.7, and 0.9, respectively. Based on the trend of the mAP value as the cosine distance threshold \(\theta\) changes from 0.6 to 0.9, we observe that CarrDet performs best when the cosine distance threshold \(\theta\) is around 0.8. The experimental results demonstrate that the performance of our proposed framework is strongly influenced by the cosine distance threshold \(\theta\). This underscores the potential constraints of our framework that should be considered during practical use.  
Figure 5: The mAP comparison with different carrier image blocks similarity threshold \(\theta\) on Carrier600.  
Future research could explore automated methods for threshold selection, such as using machine learning algorithms to dynamically adjust \(\theta\) based on the dataset characteristics and real-time feedback. This could eliminate the need for manual threshold setting and improve detection accuracy across varied datasets.
## 3 Optimization Design
### Design Parameter
#### 3.1.2 Dimension Parameter  
The optical angle nano-positioning mechanism is utilized within the vacuum chamber of the monochromator device at the SRF, which also accommodates other components including coarse adjustment mechanisms and cooling systems. Consequently, the dimensional requirements for the nano-adjustment mechanism are extremely stringent; an oversized nano-adjustment mechanism could interfere with adjacent devices. The specific geometric dimensions are detailed in Table 2.
## 8 System Design
### Open Spacecraft Design  
The OpenOrbiter spacecraft implements the OPEN layout which places the subsystem/payload boards around a central payload area. This configuration provides room for four boards with a volume of 154.5 cm\({}^{3}\) each, a payload area with an approximate volume of 250 cm\({}^{3}\) and 57.5 cm\({}^{3}\) of battery space.  
Figure 10 shows an exploded view of an initial CAD model of the spacecraft, which is useful for illustration purposes, as it shows the notion of the board mounts around the central payload area. This model depicts 'dummy' boards that might be used to support a component that didn't interconnect in to the PC/104-compliant bus. The exterior structure has been changed somewhat in the final OPEN version; the cross-supports have been determined to be extraneous and have been removed. This is difficult to interpret, visually, so this diagram has been shown to illustrate the base concept (in addition to demonstrating the design process that was undertaken). The payload area will be used to house an optical camera payload, for the OpenOrbiter Mission. Figure 11 shows the assembled version of the early CAD model of the OpenOrbiter spacecraft.  
In addition to innovation in structure and layout, the spacecraft will feature an electronic stacking scheme (without a requirement for physical stacking) which allows the computer boards and components to be placed anywhere within the spacecraft and still correctly interface with each other. This removes the limitations placed on the layout by previous physical stacking methods while maintaining a level of backwards compatibility, should the designer desire to use boards designed for stacking (with an adapter) or utilize a more traditional stacking method in one area of a multi-unit spacecraft. The board supports for this stacking scheme are shown in Figure 1.  
Figure 10: Exploded View of Computer Board and Payload Support Structure.
## 3 Results and Discussion
### Optical Properties of Coated Paper  
Figure 11 compares the gloss of coated papers treated with coating colors containing crosslinkers with different zirconium contents and a polyamine-based crosslinker, showing that the types and concentrations of crosslinkers in the coating colors do not significantly affect gloss. However, it should be noted that KZC20, with the highest zirconium content, showed a slight decrease in gloss. Higher zirconium content in KZC20 may lead to a higher  
Figure 10: Images demonstrating minimal change in print mottle of the coated paper: (**a**) print mottle images at 0.5 part (indicating lower crosslinker concentration); (**b**) print mottle images at 0.9 part (indicating higher crosslinker concentration).  
crosslink density within the coating layer. While increased crosslinking can enhance certain properties, such as wet rub resistance [44; 45], it can also create a more rigid and less smooth surface, which scatters light more diffusely and reduces gloss [28].  
Gloss is primarily influenced by factors such as coating composition (types and proportions of pigments, binders, and additives), surface smoothness, drying and curing conditions, coating thickness, base paper quality, and the application method [46; 47; 48]. These elements collectively determine the smoothness and uniformity of the coating layer, which are crucial for achieving high gloss levels in coated paper products.  
Figure 12 compares the brightness of coated papers treated with coating colors containing crosslinkers with different zirconium contents and a polyamine-based crosslinker. The results indicate that the brightness of the coated paper was hardly affected by the types and concentrations of the crosslinkers in the coating colors. However, similar to the gloss results observed in Figure 11, the use of KZC20 led to a slight decrease in brightness. This suggests that while most crosslinkers do not significantly impact brightness, the higher zirconium content in KZC20 may introduce surface irregularities or increase crosslink density, resulting in reduced light reflectance and a slight decrease in both gloss and brightness.  
Despite these minor reductions in gloss and brightness with KZC20, it is important to note that these differences do not significantly affect the marketability or usability of the paper. The observed trade-offs are minimal and within acceptable ranges for most applications. Therefore, adjustments to the formula to mitigate these effects may not be necessary unless specific applications require exceptionally high gloss and brightness. Understanding these effects is crucial for optimizing coating formulations to achieve the desired optical properties in coated paper products, and further exploration of these parameters can help in fine-tuning the balance between optical properties and functional performance.
## 6 Experimental Setup and Results
### Experimental Settings
* **ST-ResNet**[19]: a deep learning model considering periodic features and external weather data. * **T-DenseNet**[20]: a deep learning model for spatial data and temporal features with DenseNet. * **CNN-RNN**[13]: a model with CNN and LSTM, but without multi-data. * **TSMixer**[17] excels by blending operations across time and feature dimensions, achieving success on benchmarks such as M5. * **SAMformer**[18] extends TSMixer by integrating sharpness-aware optimization and channel-wise attention, enhancing the performance and efficiency in multivariate long-term forecasting.