esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results
### Tumor Clearance Induced by Opsonization and CD47 Deletion Prolongs Survival, but Some Tumors Escape
Antibody-Engineered Marrow Macrophages/Monocytes Are Less Effective When Pre-Loaded with Microparticles  
We hypothesized that pre-loading the A'PB macrophages with micron-sized particles will impact their tumor access and/or activity, further modulating their phagocytic ability. Engulfment of polystyrene particles reportedly enhances subsequent phagocytosis of particles and bacteria in vivo [35], but pre-engulfment might also undermine motility including anti-cancer phagocytic activity. [29] To test this possibility, A'PB cells were incubated with IgG-opsonized polystyrene microparticles, with \(\sim\)40% of cells associated with \(\sim\)1-10 microparticles (Figure 7A). Microparticle pre-loaded A'PB preparations were injected at day 4 (per Figure 5C) and at day 6 per the '2\(\times\) A'PB' dosing therapy (plus anti-Tyrp1 per Figure 5B) in the B16F10 lung metastasis model. Mice sacrificed on day 14 showed that the 2\(\times\) A'PB + microparticles had no significant effect on the tumor area relative to untreated controls (Figure 7B). The result nonetheless shows that A'PB activity can be effectively modulated. Figure 6: A’PB therapy represses growth in human metastatic WT CD47 tumors. (**A**) (i) Table of human xenograft mouse model details. Human lung A549 carcinoma is surgically implanted at an orthotopic metastatic site in the livers of NSG mice. Anti-SIRP\(\alpha\) is used to block CD47 signaling on adoptively transferred marrow cells and anti-human is the source of tumor-opsonizing antibody (APB). (ii) Schematic of tumor induction and treatment. NSG mice were surgically implanted with liver metastases. Mice were treated with A’PB marrow on the indicated day 0, and 3 times weekly for 2 weeks with anti-human i.v. (**B**) (i) Representative whole-body fluorescence image of tdTomato signal in surgically implanted liver metastases (left). Images of explanted liver with liver metastasis (middle top, A549 tumor circled, scale: 1 cm) and tdTomato signal of A549s infiltrating stroma (middle bottom). (ii) Sectioned liver tissue images showing tdTomato, \(\alpha\)SMA, DNA, and collagen of A549 metastases relative to normal liver tissue (top right) and H&E staining (bottom right). (ii) In vivo growth curve of liver metastatic human lung adenocarcinoma A549 tumors. A’PB and tumor-opsonizing antibody repress tumor growth by 50% over several days relative to untreated control. Tumor burden is monitored by fluorescent imaging with an IVIS Lumina II (fluorophore: tdTomato). Without additional treatment to continue tumor cell clearance, metastases begin to expand again after about 10 days. Error bars represent s.e.m. Exponential decay for Tumor area in the regression phase of the treated tumor fits to \(\exp(-t/1.55)\) for \(0<t<8\).
# Segmented Hybrid Impedance Control for Hyper-Redundant Space Manipulators
## 3 Equations of Motion
### Contact Model  
In order to articulate the force applied where the manipulator interacts with the truss structure, we establish models for both the vertical contact force and friction. The contact model used in this manuscript follows the same logic used in reference [15]. Two algorithms were developed to address the detection and analysis of collisions between a link and an obstacle, both modeled as cylinders. The first algorithm based on computer-graphics-based methods identifies whether a collision has occurred, determines the collision point, classifies the collision as either dynamic or static, and assesses the direction of the resulting forces using a friction model.  
The closest distance between the two cylindrical axis segments, as shown in Figure 3, \(\mathrm{P_{A1}P_{A2}}\) and \(\mathrm{P_{B1}P_{B2}}\) is determined by:  
\[\left\|\mathrm{I_{AB}}\right\|^{2}=(\mathrm{P_{A1}-P_{B1}+v_{A0}(P_{A2}-P_{A1} )-v_{B0}(P_{B2}-P_{B1})})^{2}, \tag{8}\]  
where the coordinates of \(\mathrm{P_{A}}\) and \(\mathrm{P_{B}}\) are calculated as \(\mathrm{P_{A}}=\mathrm{P_{A1}+v_{A0}(P_{A2}-P_{A1})}\), \(\mathrm{P_{B}}=\mathrm{P_{B1}+v_{B0}(P_{B2}-P_{B1})}\); while \(\mathrm{v_{A0}}\) and \(\mathrm{v_{B0}}\) are the parameters for evaluating the collision state. The outputs of this first algorithm are then utilized by a second algorithm, which calculates the normal and tangential forces for each link involved in the contact based on the Flores contact force model.  
\[\mathrm{f_{n}}=\mathrm{K_{C0l}}\delta^{\mathrm{R_{C0l}}}+\mathrm{X_{C0l}} \delta^{\mathrm{R_{C0l}}}\dot{\delta}, \tag{9}\]  
Figure 3: Cylinder contact illustration.  
The hysteresis damping factor is as follows:  
\[X_{\text{Col}}=\frac{8(1-C_{\text{r}})K_{\text{Col}}}{5C_{\text{r}}\delta^{(-)}}, \tag{10}\]  
where \(K_{\text{col}}\) is the contact stiffness coefficient, \(\delta\) is the embedded depth of the two contact rigid bodies, and Cr is the restitution coefficient \(\dot{\delta}^{(-)}\) indicating the speed at the beginning of the collision.
# Multi-Satellite Task Parallelism via Priority-Aware Decomposition and Dynamic Resource Mapping
## 2 Related Work
### Intelligent Beam Matching and Cross-Layer Resource Optimization  
While focusing on the decomposition and allocation of complex tasks in satellite networks, resource mapping has also become a core challenge in achieving efficient service provision. In order to address the challenges of non-uniform traffic demand and dynamic topology adaptation in satellite networks, researchers have solved the problems of improving spectral efficiency and dynamic resource allocation through flexible user beam mapping, multi-objective resource optimization, and cross-layer modeling, improving the responsiveness of satellite systems. [PERSON] et al. [51] proposed an intelligent beam user matching scheme that breaks through traditional cell boundaries and combines dynamic beam coverage with adjustable bandwidth allocation, achieving efficient resource adaptation in non-uniform traffic scenarios; [PERSON] et al. [52] introduces an iterative optimization framework based on TDM, which combines time score and power allocation to reduce resource fragmentation while meeting differentiated requirements. In addition, [PERSON] et al. [27] proposed a multi-objective modeling method that combines region decomposition and resource allocation, using the NSGA-II algorithm to balance coverage and resource consumption, providing a cross-domain reference for resource mapping. However, existing research lacks theoretical support for scenarios such as real-time reconstruction and multi-track layer resource collaboration in dynamic environments, and breakthroughs need to be sought in stochastic optimization models and distributed dynamic decision-making mechanisms.  
In conclusion, existing methods have significant bottlenecks in real-time performance assurance, non-convex optimization solution efficiency, cross-domain collaboration capability (multi-orbital layer resource linking), and lack systematic solutions to policy consistency and resource fragmentation issues in heterogeneous network environments. In the future, breakthroughs are needed in low-complexity joint optimization theory, lightweight distributed decision-making mechanisms, and virtual accurate linkage digital twin modeling technology to address the challenges of high dynamics and multi-constraint satellite network collaboration. To address these issues, this paper proposes hierarchical adaptive decomposition and reinforcement learning-based resource mapping (HADRM), which models the complex dependencies between tasks and employs deep reinforcement learning-based resource mapping to cope with real-time changes in task dependencies and on-board resources. Ultimately, timing scheduling ensures the optimization of the execution order of the final tasks without affecting global optimality.
# Flexural Behavior of Self-Prestressed RC Slabs with Fe-Based Shape Memory Alloy Rebar
## 3 Experiment Results and Discussion
### Load-Deflection Relationship
#### 3.4.2 Effect of the Amount of Fe-SMA Rebar
Therefore, by assuming that the flexural stress in the outermost concrete fiber below the neutral axis in Equation (4) is equal to the flexural tensile strength of the concrete, and that the moment generated by an external load is equal to the initial cracking moment, the initial crack moment and the initial crack load (\(P_{cr}\)) can be obtained from Equations (7) and (8), respectively:  
\[f_{cr}=0.63\sqrt{f_{c}} \tag{6}\]  
\[M_{cr}=\frac{f_{cr}I_{g}}{y}+\frac{P_{rec}I_{g}}{A_{c}y}+P_{rec}\times e \tag{7}\]  
Figure 11:0  
where \(f_{c}\) is the compressive strength of concrete and \(a\) is the distance from the support point to the load point. Figure 12 shows a comparison between the theoretical initial crack load (\(P_{cr,theory}\)) calculated from Equations (1)-(8) and the experimental initial crack load (\(P_{cr,exp}\)). The theoretical initial crack loads of SL-2A, SL-3A, SL-4A, and SL-5A calculated by these formulas were 47.87 kN, 55.59 kN, 63.30 kN, and 71.02 kN, respectively. The ratio of the theoretical value to the experimental value was calculated to have an average value of 0.81. The experimental value was smaller than the theoretical value for the following two reasons. This first was the thermal expansion of the Fe-SMA rebar interfacial concrete during activation of the Fe-SMA rebar. [PERSON] et al. [27] reported that when thermal expansion of the matrix occurred, the recovery stress of Fe-SMA was reduced by about 50% compared with that of Fe-SMA under the ideal constraint condition. In addition, they argued that the reduction in the recovery stress of the Fe-SMA due to thermal expansion of the matrix should be minimized by heating the Fe-SMA as soon as possible during Fe-SMA activation. Although the Fe-SMA rebar heating time was as short as 30 s in this study, it was considered that thermal expansion occurred in the concrete surrounding the interface of the Fe-SMA rebar. Therefore, additional studies are required in the future to quantitatively evaluate the thermal expansion of the interfacial concrete and the corresponding decrease  
in the recovery stress of Fe-SMA during activation of the Fe-SMA rebar. The second cause may be some activation of the Fe-SMA rebar by the heat of hydration of concrete during curing of the concrete. According to ACI, concrete with a compressive strength of 41.4 MPa (6000 psi) or more is classified as high-strength concrete [33]. High-strength concrete is known to have a relatively high heat of hydration due to the use of a large amount of cement [34]. The compressive strength of the concrete used in this study was 46.2 MPa, and it was thus classified as high-strength concrete. Therefore, it was judged that a relatively high heat of hydration occurred during concrete curing. Accordingly, when Fe-SMA rebar is applied to high-strength concrete members, appropriate measures are required to reduce the concrete hydration heat.
## 3 Results and Discussions  
### Stability of ACM Stabilized O/W-Type Nanoemulsion  
The particle size data measurements from Cryo-TEM imaging studies were identical to the measurements of particle size made on the Zetasizer. The Sauter mean diameter (D\({}_{(3,2)}\)) was calculated from the particle size data using Equations (1)-(3) using methods developed by [49; 50]. Table 1 shows the constituents used in formulating the sets as determined by the three-component D-optimal mixture design and the stability as a function of (D\({}_{(3,2)}\)). Higher concentrations of ACM led to a decrease in D\({}_{(3,2)}\) of oil globules for all investigated ACM stabilized O/W-type nanoemulsions. When ACM concentration was highest regardless of oil concentration the D\({}_{(3,2)}\) of emulsions was low. For instance, the average D\({}_{(3,2)}\) of stabilized O/W nanoemulsions containing an ACM-emulsifier oil ratio (EOR) of 5:3.0, 5:4.6, 5:7.3 and 5:10.0% were 6.6 \(\pm\) 0.1, 6.9 \(\pm\) 0.1, 7.0 \(\pm\) 0.1, and 7.4 \(\pm\) 0.2 nm, respectively (Table 1). Overall, the trend was higher the EOR the lower the D\({}_{(3,2)}\) (particle size) i.e., higher stability as seen in Table 1. The literature supports this claim as revealed by a study conducted by [40] which investigated the key elements that influenced the formulation of the formulation of non-ionic surfactants-stabilized fish oil nanoemulsions using high-intensity ultrasound. The authors observed that when the EOR and HLB were high the particle size was low and concluded that EOR and HLB influenced particle size [53]. Hence, the results obtained on the (D\({}_{(3,2)}\)) of ACM stabilized nanoemulsions are consistent with results reported by [53] that the higher the EOR the lower the particle size of non-ionic surfactants-stabilized fish oil nanoemulsions.  
The particle size distribution of emulsions stabilized with 5% ACM was between 0.1 and 0.2 nm while that of emulsions stabilized with 1% was between 0.5 and 0.6 nm (Table 1). This signifies that as the ACM-emulsifier oil ratio (EOR) increased the particle size distribution of ACM stabilized nanoemulsions (ASE) decreased (Table 1). Hence, the higher the ACM-EOR the lower the D\({}_{(3,2)}\) and PSD and the higher the stability of the ASE. Authors have reported that increases in emulsifier concentration octenyl succinin anhydride concentration lead to a decrease in D\({}_{(3,2)}\) and PSD [49; 50]. Hence, the higher the EOR the lower the D\({}_{(3,2)}\) and PSD i.e., the higher the stability of the emulsion [49; 50]. Thus, the lower particle size D\({}_{(3,2)}\); and PSD enhance the structural stability of the nanoemulsions as the ACM is better able to adsorb at the oil/water interface of the nanoemulsion. This implies that the nanoemulsion formulated by encapsulation with ACM could be an alternative means of incorporating functional components into food-grade, personal care, and drug delivery products for health benefits
# A Configurable Dependency Model of a SCADA System for Goal-Oriented Risk Assessment
## 6 Illustrative Example: Water Distribution System
### Introducing Countermeasures into the Dependency Model
The three-point sensitivity chart is regenerated for the updated version of the dependency model and is presented in Figure 12. It shows a new range of paragons to which the core goal is sensitive together with the levels of sensitivity to each leaf paragon (between the right and left sides of each bar). For example, for the leaf paragons where the corresponding bars in Figure 12 have no green section, the conclusion is that the increase of the success probabilities would not lead to any increase of the success probability of the root paragon. This indicates that the current state of these leaf paragons is adequate, and they do not require any further actions on them. For the leaf paragons where the corresponding bars have a green segment, it means that increasing the success probability for these paragons will lead to the increase of the probability of the core goal success. Based on this outcome, a cost-benefit analysis may be conducted to establish what actions and improvements in terms of risk management for these sets of leaf paragons is optimal. Figure 12 also shows that the success of the root goal critically depends on those leaf paragons that have a large red segment stretching on the sensitivity axis from the nominal probability (0.61) to 0. For the paragons presented by the top ten bars, which exhibit a stretch of sensitivity from 0 to the nominal probability, the graph indicates that the drop of the success probability for each of these paragons leads to the overall failure of the core goal. Figure 10: Access Control and Other Security Policies are introduced as Countermeasures. Figure 11: Updated Top-Level DM for the Water Distribution System. Figure 12: Three-Point Sensitivity Graph for the Water Distribution SCADA System with Countermeasures. Above, we presented some exemplary conclusions that may be derived from a three-point sensitivity chart. A sensitivity chart forms a basis for a systematic and more detailed risk assessment for each dependency in a model.
# Implementation of Constrained Swept Synthetic Aperture Using a Mechanical Fixture
## 2 Materials and Methods
### Simulation  
Field II [30; 31] was used to simulate transducer motion for mixed target phantoms to study the optimization of estimation parameters. This simulated the P4-2v phased array transducer with center frequency 3 MHz, 0.3 mm element pitch, 64 elements and 80% bandwidth. The simulations were performed at a 120 MHz sampling rate, and the channel data were stored at a 20 MHz output sampling frequency. A 120 mm (axial) \(\times\) 200 mm (lateral) \(\times\) 2.5 mm (elevation) speckle-generating material at 15 scatterers per resolution cell was simulated at the expected SSA resolution (approximately equivalent to that of a 7 cm array) with a 4 mm radius anechoic lesion at a 6 cm depth and point target at a 10 cm depth. Simulations were repeated for 10 different realizations of randomly positioned and weighted scatterers. Four sets of simulations were performed:  
1. Reference image--Multistatic acquisition (each transmit/receive element pair) from a single array position generates a synthetically focused baseline image without the SSA technique.
2. Subsample estimation sweep--Simulations of plane wave transmission and single-element diverging transmission (from the center of the array) were produced from fine displacements up to a single element pitch in 0.03 mm steps.
3. Large displacement estimation sweep--Simulations of plane wave transmission and single-element diverging transmission were produced from larger displacements as would be found in a 5 cm SSA sweep in steps of 0.1 mm.
4. Equivalent image--Full synthetic aperture acquisition from a fully populated 7 cm long array produces a similar image to the 5 cm SSA approach when the active transmit aperture is limited to 5 cm.
# Mass Estimation-Based Path Tracking Control for Autonomous Commercial Vehicles
## 4 Algorithm Verification
### Real Vehicle Verification
#### 4.2.1 Controller Effect Comparison Experiment  
To test the control effects of LQR, MPC, and MFFRLS + MPC controllers on real vehicles with load changes and execution delay issues, a complex circular path was chosen, with a test speed of 20 km/h. The weight parameters of each controller are the optimal parameters for real vehicles, and the test results are shown in Figure 10.  
Figure 8: EV18 experimental platform.  
Figure 9: Vehicle software architecture diagram.  
Figure 10a represents the tracking effect of autonomous commercial vehicles with three different controllers. The tested road is a public road, consisting of straight roads and roundabouts. Under the control of the controllers, the vehicle first passes through the straight road and then enters the roundabout, exiting at the third intersection of the roundabout. From the figure, it can be seen that at the positions where the straight road enters and exits the roundabout, the MFFRSL + MPC controller has the best tracking effect, with the least deviation from the reference path. The LQR controller has the largest deviation. Figure 10b shows the lateral error of the three controllers. The lateral error of the MFFRSL + MPC controller is better compared to the other two controllers, with the maximum lateral error occurring at the entrance to the roundabout being 0.13 m. The MPC controller's maximum lateral error is 0.15 m, while the LQR controller performs the worst, with a maximum lateral error reaching 0.23 m. Figure 10c represents the heading error of the three controllers. The heading error of the MFFRSL + MPC controller is relatively better, with the maximum heading error being 0.085 rad. The initial lateral and heading errors of the vehicle are not zero because the starting position of the vehicle does not perfectly align with the reference path.  
Figure 10d describes the output angles of the three controllers: LQR, MPC, and MFFRSL + MPC. The trends in angle changes during the control process in real vehicle experiments are basically the same, with differences in smoothness. MFFRSL + MPC has better smoothness than LQR and MPC. MFFRSL + MPC can correct model deviations and  
Figure 10: Comparative diagram of algorithms: (**a**) Path tracking effect comparison; (**b**) Lateral error; (**c**) Heading error; and (**d**) Steering angle.  
predictive functions and can converge errors through minor angle adjustments, avoiding overshoot and improving smoothness.
# VITA-D: A Radionic Web Tool for Predicting Vitamin D Deficiency Levels
## 4 Results
### Variables and Machine Learning Model Selection
#### 4.1.3 Random Forest  
Among the evaluated algorithms, the Random Forest model demonstrated superior performance in classifying vitamin D levels. The ROC curve presented in Figure 11a illustrates the model's discriminative ability in identifying subjects with vitamin D levels categorized into two binary classes: \(\leq\)20 ng/mL (insufficient) and \(>\)20 ng/mL (sufficient). The AUC score of 0.986 indicates exceptional performance, signifying that a positive instance is highly likely to be correctly classified compared to a negative instance. This result highlights the model's effectiveness in addressing binary classification tasks.  
Figure 11b presents a comparison of the primary evaluation metrics, including precision, recall, F1-score, Matthews Correlation Coefficient (MCC), Kappa coefficient, and AUC. The values obtained for these metrics indicate stable model performance, achieving an overall accuracy exceeding 87%, particularly excelling in the majority class. For the class representing sufficient vitamin D levels, the recall reached 100%, indicating that all actual cases were correctly identified by the model. Additionally, the weighted F1-score exhibited an optimal balance between precision and recall, reaching 93%.  
The overall test accuracy of the model was 87.73%, demonstrating that most predictions were correctly classified. However, challenges were encountered in identifying the minority class (vitamin D deficiency). The classification report revealed that the precision, recall, and the F1-score for this class were 0.00%, indicating the necessity of applying techniques to address class imbalance, such as oversampling or adjusting class weights during training. Conversely, for the majority class, the model achieved 88% precision and 100% recall, resulting in an F1-score of 93%, further corroborating its reliability and consistency in classification for this category.  
The Random Forest algorithm outperformed other analyzed models, including Logistic Regression and Support Vector Machine (SVM). Logistic regression exhibited an average performance of 70%, while Support Vector Machine achieved approximately 60%. The Random Forest model significantly surpassed these models and was identified as the optimal choice for this task. Its improved performance is attributed to its ability to capture non-linear relationships among variables and reduce overfitting by averaging predictions across multiple decision trees. These advantages and comparative results are visually summarized in Figure 12.  
Figure 11: (**a**) ROC curve result for predictions with the Random Forest model; (**b**) results of metric comparisons.  
Based on these findings, the Random Forest algorithm was selected as the most suitable machine learning predictive model, leveraging statistical indicators. Consequently, this model was implemented for predictions within the online web application.