esa-sceva/satcom-chunk-collection huggingface dataset:
# Simultaneous SAXS/SANS Method at D22 of ILL: Instrument Upgrade
## 2 Materials and Methods
This was essential in order to enable a combined SAXS and SANS measurement in a synchronous mode. SANS data were collected using the D22 instrument at a neutron wavelength of 6 A (\(\Delta\lambda/\lambda\) = 10%), an aperture of 10 mm and SDD\({}_{\rm Neutron}\) of 1.4, 5.6, 8, 17.6 m to cover a \(q\)-interval ranging from 3 \(\times\) 10\({}^{-3}\) to 0.7 A\({}^{-1}\), corresponding to sizes 2R \(\sim\pi/q\) varying between 5 and 1050 A. Aiming at examining the feasibility of acquiring an acceptably high signal-to-noise ratio for SAXS data at certain experimental conditions, two different sized silica NPs (100 nm and 26 nm) were tested, as these samples are of typical sizes (covering \(q\)-ranges) measured at various experimental conditions (collimation lengths 17.6,8.0 and 5.6 m). The first sample was a powder of monodisperse silica NPs with a nominal size of 100 nm. The second sample was an aqueous dispersion of commercial silica NPs (Ludox TM50, 50 wt %) with an average diameter of approximately 26 nm, purchased from Sigma-Aldrich, Germany. A 5 mL solution (2 wt %) was prepared from each silica sample in a deuterated solvent (D\({}_{2}\)O). Both SAXS and SANS data were simultaneously collected at different SDD\({}_{\text{X-ray}}\), SDD\({}_{\text{neutron}}\) and neutron collimation lengths. Both SAXS and SANS data sets were processed using Grasp software [32]. In the case of generating large numbers of SAXS and SANS data (for instance during in situ investigations), easy online data reduction and qualitative analysis of scattering data using this common software assists with the interactive optimization of experimental analysis.
# The Influence of Automated Machining Strategy on Geometric Deviations of Machined Surfaces
###### Abstract
[PERSON] and [PERSON] [35], in their research, analyze the measurement of flatness with seven single- and multi-point measuring methods. The results show that the value of flatness is also affected by the orientation of the profile lines with respect to the machining path and data filtration. [PERSON] and [PERSON] [36] investigate the influence of the number of measurement points on the accuracy of the CNC machine tool. The numerical investigation uses from 4 to 200 points, and the results show that the minimum number of points to achieve the minimum deviation is 50. [PERSON] [37] presents the factors affecting the measurement of flatness (machining method, strategy, milling parameters, etc.) and the influence of the number of measuring points. The results show that the flatness values are in the range from 0.124-0.0572 mm. [PERSON] et al. [38] present, in their research, the impact of the depth of cut on surface flatness. Results show the flatness deviation was 6.7 \(\upmu\)m between the cutting strategies. The maximum deviation was reported at the beginning and end of the cutting process. The milling strategy, known as raster, and the use of offset paths, as claimed by [PERSON] [17], have their advantages and disadvantages. When milling with raster, a shorter toolpath is created, but it is not possible to completely remove traces known as scallop height. By contrast, using a strategy known as offset, it is possible to remove the remaining scallop height to obtain a smooth surface. [PERSON] [39] also described a comprehensive overview of the most common strategies used in the machining process, offset and zig-zag, in his publications. The aim of the research, conducted by the authors of this work, was to provide the result of a comparison of milling strategies in two different programs and evaluated geometric characteristics of the produced test specimens. The article presents the results of the evaluation of the deviation of flatness, perpendicularity and parallelism. In two different programs, two parts were produced with the same cutting conditions. The main difference was only in the overlap of the tool. The question is the following: What is the relationship among the influence of the program, the control system of the accuracy of part production (dimensional accuracy) and tool overlap in selected geometric deviations? As part of our results, the flatness was determined based on 1945 points by least square (LS). The aim of the article was to verify the effect of the combination of the tools for finishing operations and their overlap.
## 4 Design and Electrical Performance Characterization
### Design Procedure  
The filter design procedure was implemented through five sequential steps, each validated by full-wave simulations of the Device Under Simulation (DUS) as follows:  
1. Eigen-mode frequency and intrinsic quality factor analyses: An eigen-mode frequency analysis and external quality factor (\(Q_{e}\)) evaluation of the selected resonator were performed in a representative environment incorporating specific boundary conditions. To distinguish between the resonator's eigen-modes and those of the cavity, the conductivity of the cavity housing was deliberately reduced by four orders of magnitude relative to silver \(\sigma_{Ag}\). Specifically, the lateral boundaries were assigned a conductivity of \(\sigma\) = 1000 S/m, while the top and bottom faces maintained infinite conductivity to preserve a high-quality ground plane and accommodate Z-axis variations in the circular dielectric resonator modes.
2. Resonant mode selection: The cylindrical resonator geometry was chosen to enable the excitation of non-hybrid modes, consistent with typical satellite application requirements [23]. To optimize the operational bandwidth while avoiding unwanted mode interactions, the design employs the resonator's fundamental mode. This mode selection specifically maximizes the fundamental mode's single-mode bandwidth, ensuring a spurious-free frequency response.
3. Parametric analysis of the dielectric resonator aspect ratio and boundary conditions: A parametric study of the resonator's aspect ratio (AR) was conducted to evaluate its behavior and identify the optimal design point for maximizing the quality factor  
Figure 7: Even–odd analysis. (**a**) Circuit model for inter-resonator coupling between two identical resonators. (**b**) Even-mode analysis. (**c**) Odd-mode analysis.  
when implemented on Rogers-family substrates. Additionally, the minimum distance between the resonator and lateral boundaries was systematically varied to ensure a robust design with minimal sensitivity to boundary effects. This methodology maintains consistent resonant frequency and performance characteristics while preventing unwanted cavity-mode excitation. The cavity height was fixed to allow for manual frequency tuning.
4. Input-output coupling and inter-resonator coupling characterization: Various feeding methods for exciting the target mode (input-output coupling) were evaluated. After identifying the optimal configurations, their performance was thoroughly characterized across multiple parameters. In parallel, inter-resonator coupling techniques were investigated and parametrically analyzed to determine the nominal operating point (NOP).
5. Cross-coupling characterization: The final design stage involves analyzing cross-coupling mechanisms to synthesize complex filter responses, incorporating transmission zeros and/or frequency response equalization techniques.
# NZEB Renovation Definition in a Heating Dominated Climate: Case Study of Poland
## 4 Cost-Optimal Heat Transfer Coefficients of Renovated Building Envelope
Change of heat source during the renovation process along with an increase of energy prices can promote more ambitious solutions. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**W1—4.7 EUR per 1 GJ**} & \multicolumn{4}{c}{**W2—14.3 EUR per 1 GJ**} \\ \cline{2-9}
**Building Element** & **d** & **U-Value** & **Renovation** & **Unit** & **d** & **U-Value** & **Renovation** & **Unit** \\ \cline{2-9}  & **cm** & **W/(m\({}^{2}\) K)** & **Cost** & **cm** & **W/(m\({}^{2}\) K)** & **Cost** & **U-Value** \\ \hline External wall & 12 & 0.206 & 26.63 & EUR/m\({}^{2}\) & 24 & 0.118 & 32.29 & EUR/m\({}^{2}\) \\ Floor above an unheated cellar & 7 & 0.247 & 12.60 & EUR/m\({}^{2}\) & 10 & 0.187 & 18.00 & EUR/m\({}^{2}\) \\ Suspended floor & 14 & 0.235 & 30.82 & EUR/m\({}^{2}\) & 26 & 0.144 & 36.74 & EUR/m\({}^{2}\) \\ Flat roof & 12 & 0.217 & 20.42 & EUR/m\({}^{2}\) & 26 & 0.121 & 26.50 & EUR/m\({}^{2}\) \\ Pitched roof & 20 & 0.177 & 33.73 & EUR/m\({}^{2}\) & 35 & 0.097 & 39.02 & EUR/m\({}^{2}\) \\ Windows & - & 0.9 & 133.90 & EUR/m\({}^{2}\) & - & 0.9 & 133.90 & EUR/m\({}^{2}\) \\ External doors & - & 1.3 & 973.27 & EUR/pcs. & - & 0.9 & 1035.08 & EUR/pcs. \\ \hline \hline \end{tabular}
\end{table} 
Table 12: The cost-optimum renovation variants (depending on the energy cost) of building’s envelope used in the stage II of the study (discount rate 4%). Figure 2: The discounted, cumulative cost (K\({}_{\text{Rd}}\)) per m\({}^{2}\) for renovated external wall depending on U-value and the energy cost. In addition sensitivity analysis was performed for two different discount rates. The changes in the results by the modified discount rate of 3% were shown in Table 13. The discount factor changed from \(\Sigma\)Rd(i) = 25.26 (discount rate: 4.0%) to \(\Sigma\)Rd(i) = 29.20 (discount rate: 3.0%). The changes in the results by the modified discount rate of 8% were shown in Table 14. The discount factor changed from \(\Sigma\)Rd(i) = 25.26 (discount rate: 4.0%) to \(\Sigma\)Rd(i) = 15.31 (discount rate: 8.0%). The energy price increase: 1.6%, and the year-on-year inflation: 1.8% were the same for both cases. The change of the discount rate has a visible influence on the calculation results. Increase of the discount rate causes the increase of cost-optimal U-vales. It happens because future energy savings are more discounted. With a decrease of the discount rate the situation is opposite. Based on the literature review [36; 45; 46] and historical data from the Polish Office of Competition and Consumer Protection further analysis was performed for the discount rate equal to 4%.
## 4 Results and Discussion
### Micro-Tests after the Addition of B. pasteurii to CG and FA
#### 4.1.5 Effect of Solid Surface Roughness on _B. pasteurii_  
SEM and OM images (Figure 8) showed different sizes among CG, FA and _B. pasteurii_. As can be seen in Figure 8a,b, CG is much larger compared to FA. In order to observe _B. pasteurii_ clearly and compare its size with FA, OM is used (Figure 8c,d). And it shows that the size of _B. pasteurii_ is close to some FA particles. Therefore, the _B. pasteurii_ we selected has a size comparable with that of FA, whereas CG has the largest size.  
In Figure 9, the CG particles have a rough surface, whereas most of the FA has a smooth surface. Surface roughness is a major factor affecting adhesion [60], because if the surface is smooth and the spacing between the filaments is smaller than the size of the bacteria, the adhesion of bacteria is reduced [61]. With a larger anchoring area, bacterial adhesion of the rough surface can be enhanced. Hence, it is easier for _B. pasteurii_ to adhere to the CG surface (Figure 9a). In other words, the presence of CG promotes, more than FA (Figure 9b), the _B. pasteurii_ adhesion and growth. The microorganisms in the system are able to reach a stable growth phase and start their biological processes more rapidly [62]. For this reason, the CG environment contains a greater _B. pasteurii_ biomass to hydrolyze urea, resulting in a faster increase in conductivity than in FA. As a consequence, mineralization occurs in a short time.
## 11 Application Examples  
### State at Rest, Active State, and Passive State  
An application example showing the variation of \(1/f_{m}\), \(K_{oe}^{c-\varphi}\), \(K_{ae}^{c-\varphi}\), and \(K_{pe}^{c-\varphi}\) and \(\sigma_{o}\), \(\sigma_{a}\) and \(\sigma_{p}\) with depth \(z\) for both static and dynamic conditions is given in Figure 13. It is mentioned that the curves drawn are typical for any \(c^{\prime}-\varphi^{\prime}\) soil. The example in question refers to a cohesive-frictional soil with \(c^{\prime}=20\) kPa, \(\varphi^{\prime}=\)30\({}^{\circ}\), and \(\gamma=18\) kN/m\({}^{3}\). Solid lines denote static conditions (\(k_{h}=k_{v}=0\)), whilst dashed lines denote dynamic conditions with \(k_{h}=\)0.3 and \(k_{v}=\)\(k_{h}/2\). The purpose of this section is not to compare the proposed solution with existing ones. This has already been done in the \"discussion\" section.  
From Figure 13 some very interesting observations can be made. First, the increase (active state and state at rest) or decrease (passive state) in dynamic earth pressure is due to the decrease of the mobilized shear strength (expressed, herein, by the \(1/f_{m}\) factor). In cohesive soils, a neutral zone exists near the soil surface (zone of no lateral pressure). The depth of this neutral zone is smaller than the respective tension zone in the active state for the same soil. Moreover, the depth of both neutral and tension zones reduces as the seismic loading increases. Cohesion is also responsible for the curvature of the horizontal earth pressure distribution near the surface. This effect weakens as \(z\) increases (that is, the earth pressure distribution increases linearly with depth for greater \(z\) values). Finally, in the active and passive state under static conditions, as expected, the full strength of soil is mobilized (\(1/f_{m}=1\)).  
In addition, an example chart giving the required displacement of a smooth translating wall for the active state to be fully developed with respect to depth \(z\) is given in Figure 14 for the following conditions: \(c^{\prime}=\)20 kPa, \(\varphi^{\prime}=\)30\({}^{\circ}\), \(\gamma=\)18 kN/m\({}^{3}\), \(E_{Young}=\) 5000 or 10,000 kPa, \(\mu=0.3\), \(k_{h}=0.3\), \(k_{v}=k_{h}/2\), and wall height \(H=\)3 m. Equation (30) was used with \(\Delta K=K_{oe}^{c-\varphi}-K_{ae}^{c-\varphi}\). From this figure it is confirmed that the active state is not developed simultaneously at all points. From Equation (30) it is also inferred that \(\Delta x_{\max}\) is inverse analogous to both \(\mu\) and \(E_{young}\).  
Figure 13: Application example diagrams showing the variation of \(1/f_{m}\), earth pressure coefficients (\(K_{ee}^{<\varphi}\), \(K_{ae}^{<\varphi}\), and \(K_{pe}^{<\varphi}\) ), and earth pressure (\(\alpha_{o}\), \(\alpha_{a}\), and \(\alpha_{p}\) ) with depth \(z\) for all states (at rest, active, and passive) and for static and dynamic conditions.
# Multigraphene Prepared by One-Pot Pyrolysis of Diatomite/Polypropylene Composites
## 2 Materials and Methods  
### Materials  
Commercial polypropylene chips containing 0.1 wt.% diatomite were provided by Shanghai Rizhisheng Technology Co., Ltd. (Shanghai, China) Pure polypropylene (PP) pellets were provided by the China Petroleum & Chemical Corporation (Shanghai, China). Raw diatomite powders with a median particle diameter (D\({}_{50}\)) of 22 \(\upmu\)m (\(\geq\)85 wt.% SiO\({}_{2}\)), hydrofluoric acid (HF, 40%), sulfuric acid (H\({}_{2}\)SO\({}_{4}\), 98%), nitric acid (HNO\({}_{3}\), 65%), ethanol (95%), and acetone (99.5%) were purchased from Sinopharm Chemical Reagent Co., Ltd., (Shanghai, China). The diatomite was purified with a mixture of nitric acid and sulfuric acid in a molar ratio of 2:1, followed by washing, filtrating, and drying [30].
# SPDepth: Enhancing Self-Supervised Indoor Monocular Depth Estimation via Self-Propagation
## 3 Methods
### Local Window Self-Propagation  
To better utilize local self-similarities provided by the pixels with solid supervision, a local window self-propagation strategy is well-designed. The strategy guarantees that the propagation is performed in a reliable range. A detailed diagram of the self-propagation module is provided in Figure 3.  
The split key feature windows together with the projected query features are used for computing correlations. Figure 4 gives a description of the windows split process. Each key feature window is centered on the pixel \(P_{ij}\) to be predicted. The total number of windows is \(H\times W\). The window radius is \(R\). Let \(K=2R+1\), then the windows are of the size \(K\times K\). \(F_{KWIN}\in\mathbb{R}^{H\times W\times C\times K^{2}}\) denotes the partitioned and resh  
Figure 3: Details of the self-propagation module. The projected key features are split into feature windows. The window attention scores are calculated through the self-attention operation. The depths are then weighted in respective windows.  
\(F_{Q}\in\mathbb{R}^{H\times W\times 1\times C}\) are the projected and reshaped query features. The correlations are computed through a simple matrix dot-product operation. The normalized window attention scores \(S\in\mathbb{R}^{H\times W\times 1\times K^{2}}\) are calculated as follows:  
\[S=\text{softmax}(\frac{F_{Q}F_{KWIN}}{\sqrt{C}}) \tag{4}\]  
where \(S\) reveals the similarity distribution for each feature vector with respect to its \(K^{2}\) neighboring feature vectors.  
The depth \(D\in\mathbb{R}^{H\times W\times 1}\) is partitioned into overlapped windows \(D_{WIN}\in\mathbb{R}^{H\times W\times K^{2}\times 1}\) in the same way as key features. The propagated depth \(\widetilde{D}\in\mathbb{R}^{H\times W\times 1}\) is calculated as the following:  
\[\widetilde{D}=SD_{WIN} \tag{5}\]  
As the outcome of the self-attention module, the propagated depth is weighted with local feature self-similarities. The operation helps propagate high-accuracy depths from supervised regions to those not supervised. Combined with the above calculation process, the computational complexity of the local window self-propagation is  
\[\Omega_{WIN}=2 HWC^{2}+(1+C)HWK^{2} \tag{6}\]
## 2 Devised 24 h-Ahead Prediction Approach  
The devised WPP technique is based on the integration of WT, ACO, and FFANN. The WT is employed to extract the most important and healthy time subseries (data elements) from the target variable (wind power) original data time series. That means WT is applied in this paper to expand the initial target data into subseries. Using the target data subseries instead of the original data series increases the forecast accuracy of the devised WPP method. Therefore, the target data subseries is employed as the training target for the FFANN model at the second stage. That means the second stage FFANN models the existing affiliation between the wind speed actual measurement data at the farm spot and the wind generation actual measurement data subseries (obtained from the WT decomposition), while the first stage FFANN models the existing affiliation between the weather variables and the actual wind speed measurement.  
Hence, through data-driven training of the FFANN models in these two stages, a trained wind power forecast model is obtained. The future (forecasted) weather variables from NWP models (weather stations) are given to the first-stage trained model to estimate the looming wind speed at the farm spot. This forecasted speed in the initial step is then given to the second step trained model to estimate the future wind generation subseries. The ACO algorithm is applied for the FFANN model in each stage of the devised approach for finding the global best values of the FFANN connecting weights. The ACO algorithm searches the optimal values of the FFANN weight parameters to ensure that the possible lowest error of the wind generation forecast is achieved. Finally, the desired wind generation forecast is found using the inverse WT of the obtained future wind power subseries.  
The devised dual-step WPP approach in this paper is shown schematically in Figure 1. As aforementioned, the ACO-FFANN at the first step is implemented to estimate the farm spot and the turbine hub elevation. In this step, a predictor set consisting of historical weather forecasts as the input and prior wind speed actual record as the target are employed to train the FFANN model. In the second step, the WT-ACO-FFANN is implemented to fit the wind speed versus power relationship using actual measurement data. Then, the estimated speed by the ACO-FFANN in the first step is given to the WT-ACO-FFANN in the second step to estimate the wind generation for the time ahead.
## 2 Materials and Methods
### AFM Experiments--AFM Scanning  
The mica AFM substrates with surface-adsorbed HRP particles were scanned with a Titanium atomic force microscope (NT-MDT, Zelenograd, Russia; the microscope pertains to the equipment of \"Human Proteome\" Core Facility of the Institute of Biomedical Chemistry, supported by Ministry of Education and Science of Russian Federation, agreement no. 14.621.21.0017, unique project ID: RFMEF162117X0017). NSG10 AFM probes (\"TipsNano\", Zelenograd, Russia) with tip curvature radius of 10 nm (resonant frequency: 47 to 150 kHz; force constant: 0.35 to 6.1 N/m) were employed for AFM scanning. At least ten scans were obtained for each AFM substrate studied. Regarding the blank experiments, no objects with heights exceeding 0.5 nm were observed.  
Microscope operation, treatment of the AFM images obtained (flattening correction, etc.) and exporting the AFM data to ASCII format were performed using a NOVA Px software (NT-MDT, Moscow, Zelenograd, Russia) supplied with the microscope. The number of the visualized particles in the obtained AFM images was calculated with a specialized AFM data processing software developed in IBMC.  
The relative distribution of the AFM-visualized objects with height \(\rho(h)\) (density function), and the number of particles visualized by AFM (normalized per 400 \(\upmu\)m\({}^{2}\)) were calculated for each sample studied as described by [PERSON] et al. [55].  
Namely, \(\rho(h)\) was calculated as follows:  
\[\rho(h)=(N_{h}/N)\times 100\%, \tag{1}\]  
Figure 1: Experimental setup’s key unit. The inset shows an enlarged picture of the \(\sim\)1-cm diameter test tube. Thin lines indicate the distances between the coil and the test tubes with working and control HRP solutions.  
where \(N_{h}\) is the number of distinct particles, imaged by AFM, with height \(h\); and \(N\) is the total number of imaged distinct particles [55].  
The number of particles \(N_{400}\), normalized per 400 \(\upmu\)m\({}^{2}\) area of the substrate surface, was calculated as:  
\[N_{400}=N\times 400/(n\times d^{2}), \tag{2}\]  
where \(N\) is the number of visualized objects for one and the same sample; \(n\) is the number of AFM scans performed for one and the same sample; and \(d\) is the linear dimension of the AFM scan (\(\upmu\)m).