esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Image Preprocessing  
For both tasks, we cropped and resized original fundus images into \(512\times 512\) to reduce memory usage when training. The resolution of \(512\times 512\) was chosen to be a power of 2 that was shown to maintain the performance of fundus analyses [39]. For each image, the ocular area of a fundus image was spotted by first detecting the largest blob at the center. The blob was defined as a group of connected pixels with intensities above 20 in the red channel. For blob detection, _measure_ module in _skimage_ package in Python was used. If there exists no blob in the center, the image was excluded from further processing. The resized image was saved in an 8-bit color format. Finally, we normalized the pixel intensities to a scale between 0 and 1 and neither used standardize per-instance statistics to preserve color information nor w e standardized the images with population statistics as they do not affect the true data distribution.
## 3 Results
### Determination of the Tire Steering Resistance Torque of an Unlocked Steered Wheel in Static State
#### 3.2.2 Study of the Radius of the Trajectory of the Point of Contact of a Rigid False Wheel  
Experimental studies of the radius of the movement trajectory of the contact point of the rigid false wheel were carried out on the bench (see Section 6) with the simultaneous measurement of the angle of rotation of the rigid false wheel about the kingpin axis \(\theta\) and its spin angle about the knuckle axis \(\theta_{w}\).  
On the one hand, the path \(S_{w}\) that passes through the contact point of a rigid false wheel when rotating about the kingpin axis is defined by the expression  
\[S_{w}=R\theta \tag{54}\]  
where \(R\) is the radius of the movement trajectory of the rigid false wheel contact point, point D, m; and \(\theta\) is angle of rotation of the false wheel about the kingpin axis, rad.  
On the other hand, the path \(S_{k}\) passing through the contact point of the rigid false wheel when spinning about the knuckle axis is determined by the formula  
\[S_{k}=r_{w}\theta_{w} \tag{55}\]  
where \(r_{w}\) is the rolling radius of the wheel, m; and \(\theta_{w}\) is the angle of spin of the about the knuckle axis, rad.  
Having equated the paths (54) and (55), we obtain an equation, from the analysis of which we determine the radius \(R\) of the trajectory of the contact point of the rigid false wheel as follows:  
\[R=\frac{r_{w}\theta_{w}}{\theta} \tag{56}\]  
Figure 5 shows the graph of the dependence of the radius of the movement trajectory of the contact point of the rigid false wheel on the rotation angle in the presence of only the kingpin inclination angle \(\alpha_{k}\), calculated according to dependence (34) (solid curve) and experimentally obtained values of this radius (red dots).  
Figure 6 shows that the experimental and calculated data coincide. The maximum error does not exceed 1.74%.  
Figure 6 shows the graph of the dependence of the radius of the movement trajectory of the contact point of the rigid false wheel on the rotation angle with a combined kingpin angle, calculated according to dependence (40) (solid curve) and experimentally obtained values of this radius (red dots).  
Figure 6 shows that the experimental and calculated data coincide. With the combined kingpin angle, the error does not exceed 3.89% and is within the accuracy of the measurements, which confirms the reliability of the obtained dependencies (40).  
From the analysis of the above results of experimental and theoretical studies, it follows that the center of the transport motion of the unlocked rigid false wheel contact point with the support surface is the projection onto the support surface of the center of rotation of the rigid false wheel. A dependence was obtained for determining the radius of the movement trajectory of the point of contact of the false wheel when it is rotated in static state.  
Figure 5: Dependence of the radius of the trajectory of the contact point of the rigid false wheel on the angle of rotation with \(\alpha_{k}=9.50\), \(\beta_{k}=0\), \(\gamma_{k0}=-0.50\), \(l_{k}=0.225\) m.
# The Holistic Perspective of the INCISIVE Project--Artificial Intelligence in Screening Mammography
## 3 Data Preparation and Integration  
### Data Integration and Quality Check Tool  
During its lifetime, INCISIVE collects data divided in two categories: (a) clinical and biological data in a structured form, including demographic and medical history data, histological and blood markers, and treatment and tumor details, and (b) imaging data, coming from various modalities, including mammography, CT, and MRI. These data correspond to distinct timepoints during the patients' treatment: baseline/diagnosis, after first treatment, first follow-up, or second follow-up.  
A multifaceted strategy for data integration was followed which aimed to address multisite data collection challenges as regards non-imaging data and imaging data, including steps for the homogenization of data structures and semantics [26].  
Following an iterative procedure to reach a consensus among data providers, a template was created, and data providers were asked to collect the structured data following this template. The data schema is depicted in detail in Figure 1. The template included, among other things, the type of information and the terminology to use in specific fields, specifically adopted for the specific type of cancer. A small subset of fields was considered as mandatory, and their absence was considered to significantly decrease the data usability. The imaging data were used in studies and series per person in a folder hierarchy following various conventions and were accompanied by segmentations of the volumes of interest.  
All data providers were given instructions on the steps to follow to make data collection as uniform as possible. In addition, a data quality check was also performed on a local level to detect possible problems and support the data provider in correcting them before uploading data to the INCISIVE repository [27]. These included checks of the structured data (for structural and semantic compliance with the template, the existence of duplicates, and the absence of mandatory values), as well as checks of the imaging data hierarchy and Digital Imaging and Communications in Medicine (DICOM) files. The data preparation workflow, along with the several components of the data integration quality check tool, is described in Figure 2.
## 2 Materials and Methods
### Confocal Microscopy  
RAW264.7 cells were seeded on coverslips (Marienfeld, Lauda-Konigshofen, Germany) in 24-well plates at a density of 20 \(\times\) 10\({}^{3}\) cells/well in complete DMEM medium and incubated for 24 h under SC to adhere. THP-1 cells were seeded on coverslips in 24-well plates at a density of 100 \(\times\) 10\({}^{3}\) cells/well in complete RPMI medium supplemented with 200 nM PMA to obtain human M\(\varphi\) as described above. Afterwards, RAW 264.7 cells and human M\(\varphi\) were incubated in the corresponding complete media in the presence of 5 \(\upmu\)M of the respective lipid-oligonucleotide conjugates under SC for 24 h. After incubation, coverslips with cells were washed twice with PBS, fixed with 3.7% formaldehydehyde in PBS buffer (15 min, 37 \({}^{\circ}\)C), and washed twice with PBS. The cells were stained with the Phalloidin-iFluor532 (Abcam, Cambridge, UK) or Phalloidin-TRITC (ECM Biosciences, Versilles, KY, USA) according to the manufacturer's protocols. After staining, the cells were washed twice with PBS and placed on slides in a drop of ProLongTM Glass Antifade Mountain with NucBlueTM (Thermo Fisher Scientific, Waltham, MA, USA). Mounted samples were allowed to cure on a flat, dry surface for 18-24 h at room temperature in the dark. Intracellular localization of lipid-oligonucleotide conjugates was assessed by confocal fluorescent microscopy on an LSM710 (Zeiss, Oberkochen, Germany) using an \(\upalpha\)Plan-Apochromat 100x/1.46 Oil DIC M27 objective. Analysis of intracellular accumulation of LONs and Z-stacks was performed using ZEN software (Zeiss, Oberkochen, Germany). Confocal microscopic analysis was performed in three channels (blue, green, red). Fluorescence in the blue channel corresponded to DAPI (nuclei staining); the green channel corresponded to fluorescence of LONs labeled with fluorescein (Flu), and the red channel corresponded to Phalloidin-iFluor532 or Phalloidin-TRITC (cytoskeleton staining).
# Method for Measuring Absolute Optical Properties of Turbid Samples in a Standard Cuvette
## 3 Results  
### Investigation of Difference Measurements  
#### 3.1.1 Variation over Optical Properties  
The chief measurements which we consider are \(\ln|\text{SR}\{\widetilde{T}\}|\) and \(\angle\text{SR}\{\widetilde{T}\}\) (or \(\ln|\text{DR}\{\widetilde{T}\}|\) and \(\angle\text{DR}\{\widetilde{T}\}\) considering coupling; (Again we note that the Dual-Ratio of the \(\widetilde{T}\) (DR\{\widetilde{T}\})) and a Single-Ratio of the \(\widetilde{T}\) (SR\(\{\widetilde{T}\}\)) are the same when not considering optode coupling (shown in Section 3.1.2))). Figure 3 shows these measurements (Equations (5) and (6)) over a large range of optical properties, specifically \(\mu_{a}\) and \(\mu_{s}^{\prime}\) (Figure 3a,c) or \(n_{i}\) and \(n_{o}\) (Figure 3b,d).  
Since the intention is to convert these measurements of \(\ln|\text{SR}\{\widetilde{T}\}|\) and \(\angle\text{SR}\{\widetilde{T}\}\) to \(\mu_{a}\) and \(\mu_{s}^{\prime}\), the desire is for the measurements to vary significantly more as \(\mu_{a}\) and \(\mu_{s}^{\prime}\) are varied as compared to varying \(n_{i}\) and \(n_{o}\). The iso-lines (white lines) in Figure 3a,b consider the same values (the same is true for Figure 3c,d). From this we see that varying \(\mu_{a}\) and \(\mu_{s}^{\prime}\) varies \(\ln|\text{SR}\{\widetilde{T}\}|\) across 4 more iso-lines than varying \(n_{i}\) and \(n_{o}\) (and about 2 times more for \(\angle\text{SR}\{\widetilde{T}\}\)). This suggests promise in the goal of retrieving \(\mu_{a}\) and \(\mu_{s}^{\prime}\).  
To recover \(\mu_{a}\) and \(\mu_{s}^{\prime}\) from \(\ln|\text{SR}\{\widetilde{T}\}|\) and \(\angle\text{SR}\{\widetilde{T}\}\) we must also have significantly different information in \(\ln|\text{SR}\{\widetilde{T}\}|\) and \(\angle\text{SR}\{\widetilde{T}\}\) so that the recovered variables (\(\mu_{a}\) and \(\mu_{s}^{\prime}\)) have a unique solution and little cross-talk. There is also promise along these lines as the iso-lines in Figure 3a versus Figure 3c are qualitatively orthogonal. This suggests a fit to \(\mu_{a}\) and \(\mu_{s}^{\prime}\) from \(\ln|\text{SR}\{\widetilde{T}\}|\) and \(\angle\text{SR}\{\widetilde{T}\}\) should be possible. This is further investigated in Section 3.2.  
One final insight that can be drawn from Figure 3 is the effect of \(n_{r}\) which is constant along diagonal lines with positive slopes in Figure 3b,d. From this we see that \(\ln|\text{SR}\{\widetilde{T}\}|\) is little effected by \(n_{r}\). Further, in the upper left portion of the plots where \(n_{r}<1\angle\text{SR}\{\widetilde{T}\}\) is only significantly effected by \(n_{i}\). Therefore we may be able to optimize the design of the cuvette boundary to reduce cross-talk with the \(n\)s which is discussed further in Section 4.
# Identical Parallel Machine Scheduling Considering Workload Smoothness Index
## 3 Problem Statement and Mathematical Model
### Mathematical Model  
The objective is to minimize the \(SI\), as shown in Formula (1).  
\[\textbf{[P1]}:\min SI = \sqrt{\sum_{i\in\mathcal{M}}(W_{\max}-W_{i})^{2}}. \tag{1}\]  
subject to  
\[\sum_{i\in\mathcal{M}}x_{ij}=1,\hskip 14.226378 pti\in\mathcal{J} \tag{2}\] \[W_{i}=\sum_{j\in\mathcal{J}}x_{ij}\cdot p_{j},\hskip 14.226378 pti\in \mathcal{M}\] (3) \[W_{i}\leq W_{\max},\hskip 14.226378 pti\in\mathcal{M}\] (4) \[x_{ij}\in\{0,1\},\hskip 14.226378 pti\in\mathcal{M},j\in \mathcal{J}\] (5) \[W_{i}\in\mathbb{S}^{+},\hskip 14.226378 pti\in\mathcal{M} \tag{6}\]Constraint (2) guarantees that each job can only be processed in a machine. Constraint (3) calculates the workload of machine \(i\in\mathcal{M}\). Constraint (4) ensures that the workload of each machine cannot exceed the maximum workload constraint. Constraints (5) and (6) give the ranges of the variables, where \(\mathbb{S}^{+}\) is the set of positive real numbers.
# Digital Twin-Driven Stability Optimization Framework for Large Underground Caverns
###### Abstract  
With rapid urbanization, the utilization of underground space has become an important part of infrastructure. However, the stability of underground spaces such as large caverns remains a key challenge in civil engineering throughout the lifecycle of a project. Traditional methods of stability assessment rely on static models and periodic monitoring and often fail to capture real-time changes in rock behavior, leading to potential safety risks and, in severe cases, even the collapse of underground infrastructure. To address this challenge, this study introduces a digital twin (DT) framework to improve stability assessments and monitor deformations in underground structures. The framework enables the continuous monitoring and adaptive optimization of rock support systems by combining real-time sensor data with virtual simulations. A five-dimensional DT framework comprises physical objects, virtual objects, service systems, DT data, and their interconnections. It incorporates six key modules, which are structure, geology, material, behavior, performance, and environment, to enhance the understanding of cavern stability. The framework is based on Industry Foundation Classes standards to ensure seamless data exchange, interoperability, and the standardized representation of geotechnical and structural data. A seven-step methodology is developed for this framework, encompassing geological assessment, virtual modeling, Building Information Modeling (BIM)-based design, construction processes, real-time monitoring, and optimization strategies. To evaluate its effectiveness, the framework is applied to a case study, demonstrating improvements in deformation monitoring and rock support efficiency. The findings highlight the potential of integrating DT with BIM to enhance safety, reliability, and long-term stability in underground construction projects.  
digital twin (DT); building information modeling (BIM); underground cavern stability; real-time monitoring; rock support optimization
## 2 Numerical Model
### Model Verification  
The results for cone penetration resistance and excess pore pressure dissipation were compared with the published cone resistance factor and consolidation coefficient solution. Figure 2 compares the numerical cone resistance factors (\(N_{c}\)) of this work with previous studies under OCR = 1, \(I_{\rm r}\) = 67 and \(I_{\rm r}\) = 91. There was a significant agreement with [PERSON] et al.'s formula [6]. Due to the rigidity index \(I_{\rm r}\) being different, there was a slightly different outcome from [PERSON] et al.'s [21] FE calculation result. In Figure 2, \(w/D\) was the normalized penetration displacement.  
**3. Effect of OCR on Penetration Resistance**  
_3.1. Effect of OCR on Cone Resistance Factor \(N_{c}\)_  
The cone tip resistance \(q_{c}\) under different OCRs is shown in Figure 3. According to Figure 3, the OCR had a significant influence on \(q_{c}\).  
**Figure 3. \(Cont.\)**  
Figure 2. Comparison of calculation results [6].  
The cone tip resistance \(q_{\rm c}\) is related to the undrained shear strength in the form:  
\[N_{\rm c}\ =\ \frac{q_{\rm c}-\sigma_{\rm v}}{s_{\rm u}} \tag{11}\]  
It is widely recognized that the cone resistance factor must vary with soil rigidity, owing to the accommodation of the cone volume by 'elastic' strains in the soil outside the plastic zone [6; 18]. [PERSON] et al.'s [6] cone resistance factor \(N_{\rm c}\) is widely used in clay:  
\[N_{\rm c}\ =\ 3.4+1.6\ln I_{\rm r}-\frac{1.9(1-K_{0})}{2s_{\rm u}}\sigma_{\rm v} \tag{12}\]  
Equation (11) is adopted to normalize the calculated results. The undrained shear strength is theoretically derived from the Modified Cambridge Clay model:  
\[s_{\rm u}\ =\ \frac{M}{2}\exp(\frac{e_{\rm N}-(\lambda-\kappa)\ln 2-e}{\lambda}) \tag{13}\]  
where \(e\) is the void ratio of soil under the current state, and \(s_{\rm u}\) is the undrained shear strength of the soil. For normally consolidated soils, \(e\) is on the \(K_{0}\) line, and for overconsolidated soils, \(e\) is on the swelling line. The relationship between the undrained strength of normally consolidated soil and over-consolidated soil under the same stress conditions is as follows:  
\[\frac{s_{\rm u(OC)}}{s_{\rm u(NC)}}\ =\ OCR^{\Lambda} \tag{14}\]  
Figure 3: Penetration resistance under different OCR at \(\sigma_{\rm v}\) = 30 kPa. (**a**) Kaolin silty clay; (**b**) Yellow River Delta silty clay.  
The cone resistance factor \(N_{\rm c}\) is shown in Figure 4. It shows that the OCR almost does not affect \(N_{\rm c}\). The reason is that \(N_{\rm c}\) mainly depends on plastic zone size and \(I_{\rm r}\) is outside the plastic zone. The cone resistance factor \(N_{\rm c}\) tends to be stable between 8-8.9 in kaolin silty clays and 8.9-9.9 in Yellow River Delta silty clay.  
Figure 4: Cone resistance factor \(N_{\rm c}\)—normalized penetration depth curve. (**a**) Kaolin silty clay; (**b**) Yellow River Delta silty clay.
## 3 Results
### Acetate Pigment  
Neutral copper acetate, i.e., verdigris, corresponds chemically and structurally with the mineral hoganite (Cu(CH3 COO)2H2O) [79], as detected by XRD. Based on the literature, the main ATR-FTIR characteristic bands that can be associated to this pigment were detected (Table 6, Figure 13a). However, the study of the VER-S by DRIFT modality showed differences, specially related to hydroxyl groups (-OH bonds) of the acetate ion. Whilst in ATR-FTIR we observed three characteristic bands at 3447, 3362 and 3268 cm\({}^{-1}\), the DRIFT spectrum showed a broad band between 3700 and 3200 cm\({}^{-1}\) likely due to the interaction with atmospheric moisture (Figure 13b). The acetate group was also detected by DRIFT modality, though at higher wavenumbers and with lower intensity (marked with a black rectangle in Figure 13).  
Figure 12: Absorbance spectra of natural pigment (OR-N) obtained by ATR-FTIR (**a**) and DRIFT (**b**) modalities. See Table 1 for the explanation of the pigment identification codes.  
Figure 13: Absorbance spectra of synthetic verdigris (VER-S) obtained by ATR-FTIR (**a**) and DRIFT (**b**) modalities. See Table 1 for the explanation of the pigment identification codes.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Pigment**} & \multicolumn{2}{c}{**ATR-FTIR**} & \multicolumn{2}{c}{**DRIFT**} \\ \cline{2-5}  & **Wavenumber (cm\({}^{-1}\))** & **Assignment 1** & **Wavenumber (cm\({}^{-1}\))** & **Assignment 1** \\ \hline \multirow{8}{*}{VER-S} & 3447, 3362, 3268 & \(\
u\)(–OH), acetate ion [80; 81; 82] & 3800–3200 & \(\
u\)(–OH) [26], acetate ion (?) \\ \cline{2-5}  & 2990, 2942 & \(\
u\)(C–H), acetate [83] & 3080, 3027 & \(\
u\)(C–H) [26], acetate (?) \\ \cline{2-5}  & & & 2863 & \(\
u_{\mathrm{s}}\)(CH\({}_{3}\)) [26], acetate (?) \\ \cline{2-5}  & & & 2778–1935 & Not assigned \\ \cline{2-5}  & 1652 & C=O [26] & & \\ \cline{2-5}  & 1595 & \(\
u_{\mathrm{s}}\)(COO\({}^{-}\)), acetate [80; 81; 82] & 1710, 1690 & \(\
u_{\mathrm{s}}\)(COO\({}^{-}\)) [26], acetate (?) \\ \cline{2-5}  & 1441, 1417 & \(\
u_{\mathrm{as}}\)(COO\({}^{-}\)), acetate [80; 81; 82] & 1577,1508 & \(\
u_{\mathrm{as}}\)(COO\({}^{-}\)) [26], acetate (?) \\ \cline{2-5}  & & 1468 & (COO\({}^{-}\)) [26], acetate (?) \\ \cline{2-5}  & 1260 & \(\
u_{\mathrm{s}}\)(C=O) [80; 81; 82] & 1289 & \(\
u_{\mathrm{s}}\)(C=O) [26] \\ \cline{2-5}  & 1155 & -CH\({}_{3}\)[80; 81; 82] & 1171 & -CH\({}_{3}\) (?) \\ \cline{2-5}  & 1355, 1056, 1036 & \
\begin{tabular}{c} C–H from the CH\({}_{3}\) in the \\ acetate group [80; 81; 82] \\ \end{tabular} & 1392, 1060 &
\begin{tabular}{c} C–H from the CH\({}_{3}\) in the \\ acetate group (?) \\ \end{tabular} \\ \hline \multirow{2}{*}{690} & \multicolumn{2}{c}{8(O–C–O), acetate ion} & \multirow{2}{*}{725, 662} & \multirow{2}{*}{O–C–O [26]} \\  & [80; 81; 82; 83] & & & \\ \end{tabular}
\end{table}
Table 6: Acetate-based pigment analysed by ATR-FTIR and DRIFT modalities. Bands (cm\({}^{-1}\)) and assignment (vibration mode and mineral associated) are included when possible. See Table 1 for the explanation of the pigment identification codes.
## 3 Results and Discussion
### PM2.5 Samples Chemical Composition and Conductance Measurements

Table 3: The mean mass surficial concentrations for the overall the inorganic ionic fraction (calculated as the sum of F\({}^{-}\), Cl\({}^{-}\), NO\({}_{3}\)\({}^{-}\), SO\({}_{4}\)\({}^{2-}\), Na\({}^{+}\), NH\({}_{4}\)\({}^{+}\), K\({}^{+}\), Ca\({}^{2+}\), Mg\({}^{2+}\)), elemental carbon (EC) and organic carbon (OC) are presented. Weight percentages (wt%) for the inorganic ionic, EC and OC are reported, as well. Average values are reported alongside the corresponding 99% confidence intervals. The whole dataset is split into detected and not-detected samples, in relation to the conductivity measurements in the AEC. \(n\) indicates the number of samples in each subset. \(\mu\)g cm\({}^{-2}\) for not-detected ones; for OB samples, the mean PM\({}_{2.5}\) loading is 174.2 \(\pm\) 87.8 \(\mu\)g cm\({}^{-2}\) for detected ones and 130.4 \(\pm\) 27.6 \(\mu\)g cm\({}^{-2}\) for not-detected ones. It is worth highlighting that only 25% of OB samples showed an electrical conductance response (3 out of 12), while this percentage was 78% for MI-TS samples (39 out of 50). To better understand this different behavior of the samples from the two different sites, the chemical composition of the two subsets has to be considered (Figure 4). First of all, the average ionic contents of the samples from both the rural and the urban site are comparable and do not statistically differ for detected and not-detected samples. Particularly they are 88.5 \(\pm\) 27.0 and 53.1 \(\pm\) 11.3 \(\mu\)g cm\({}^{-2}\) (MI-TS detected and not-detected samples) and 83.4 \(\pm\) 37.2 and 62.9 \(\pm\) 12.9 \(\mu\)g cm\({}^{-2}\) (OB detected and not-detected samples). Therefore, if the sole ions determined the electrical conductance properties of these two different types of aerosols, the discrepancies in the percentages of detected and not-detected samples observed for the two different sampling sites could not be explained. When the EC concentrations are compared, they are statistically different at the MI-TS site for samples with electrical conductivity (27.9 \(\pm\) 3.7 \(\mu\)g cm\({}^{-2}\)) compared to samples that did not show it (17.0 \(\pm\) 1.8 \(\mu\)g cm\({}^{-2}\)). The poor estimated EC contents for rural samples are therefore responsible for the fact that a majority of OB samples didn't show any electrical response and they are respectively 7.3 \(\pm\) 6.2 \(\mu\)g cm\({}^{-2}\) for detected samples and 5.9 \(\pm\) 0.8 \(\mu\)g cm\({}^{-2}\) for not-detected ones. Accordingly, the two different subsets collected in the two different sampling sites and with distinct chemical composition present a different electrical response. This result highlights once again the aforementioned synergy between the inorganic ionic components and the conductive elemental carbon fraction in the determination of aerosol conductance properties.