esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Main Effect Parameter Analysis
#### 3.2.4 Effect of Reaction Time  
Figure 3 outlines the impact of reaction times ranging from 60 to 120 min on biodiesel output. Initially, the reaction of biodiesel yield was very fast after 60 min of reaction time, and therefore, the biodiesel yield increased. This occurs due to retention time for the reaction occurring up to 80 min in duration and then remaining relatively constant until 90 min; thereafter, the biodiesel yield started decreasing as the reaction time further increased. This occurred as the reaction reached equilibrium, and hence a reverse reaction started to occur. A longer reaction time decreases the biodiesel yield, which might be due to the increased probability of biodiesel hydrolysis under alkaline conditions [86; 87]; therefore, it is of great importance to immediately separate the produced biodiesel from the reaction mixture once reaching the required reaction time. A higher reaction time decreases the biodiesel yield, which has negative effects, such as increased energy consumption and  
Figure 3: Main effect plot for biodiesel yield.  
reduced process efficiency [37]. Similar trends were observed with the transesterification process carried out for biodiesel conversion [38].
## 7 Conclusions  
A nonlinear observer for the ROV was investigated, and a 4-DOF nonlinear sliding state observer was designed in this study. A 6-DOF dynamic model was set up under the condition of ocean current.  
Figure 24: Observed error of velocity and position: (**a**) velocity error; (**b**) position error.  
Figure 23: Observed result of ROV position: (**a**) position in the X direction; (**b**) position in the Z direction.  
Figure 25: Cable disturbing force: (**a**) cable force in the X direction; (**b**) cable force in the Z direction.  
The lumped mass method was adopted for the dynamic simulation of the umbilical cable and verified by a comparison with the experimental data. A coupling dynamic model of the ROV and the umbilical cable was established. Further, a 4-DOF nonlinear sliding mode observer for the ROV system was set up, and the ocean current model and the simplified flexible cable disturbance force model were established. The convergence and the stability of the observer were proven, and the applicability and the performance of the observer were verified by simulation under different working conditions. Unmeasured states such as the velocity state, current velocity, and cable disturbance were observed with the designed observers. We concluded that establishing a simplified disturbance model that could effectively estimate the actual disturbing force, and adopting the disturbing force compensation method effectively improved the observation precision and reduced the chattering of the observer outputs; doing so can also provide a basis and a reference for the design and application of other tethered ROV systems.  
[PERSON] designed the disturbance estimator and the integration with the main observer, and wrote the manuscript. [PERSON] conceived and designed the nonlinear observer. [PERSON] analyzed the results and wrote the manuscript.  
This work was supported by the Natural Science Foundation of China (Grant No. 51779139), the National Key Research and Development Program of China (Grant No. 2016 YFC0300700), the 12 th Five-Year Plan Project of International Sea Area Resources Survey and Development of China Ocean Mineral Resources R & D Association of the People's Republic of China (DY125-21-Js-06).  
The authors declare that there is no conflict of interest.
## 2 Methodology
### Static Modal Derivatives  
To compute SMDs, the generalized eigenvalue problem in which the nonlinear elastic force is linearized and is cast in the form \(\mathbf{\mathrm{f}}_{\mathrm{int}}=\mathbf{\mathrm{K}}\mathbf{\mathrm{u}}\), is differentiated with respect to the amplitude of \(j_{\mathrm{th}}\) mode and evaluated around the system-static equilibrium position. Ignoring the inertia terms leads to the SMDs that can also be obtained through the differentiation of the nonlinear static equation [21,22,24] as follows:  
\[\theta_{\mathrm{ij}}^{\mathrm{f}}=-\bar{\mathbf{\mathrm{K}}}^{-1}\mathbf{\mathrm{K}}^ {\prime}\mathbf{\mathrm{X}}_{\mathrm{i}}^{\mathrm{c}}\ ;\ \ \mathbf{\mathrm{X}}^{\mathrm{c}}=[\mathbf{\mathrm{\Phi}}^{\mathrm{f}},\mathbf{\mathrm{\Psi}}^ {\mathrm{r}}] \tag{14}\]  
\[\theta_{\mathrm{ij}}^{\mathrm{c}}=-\bar{\mathbf{\mathrm{K}}}  
The SMDs do not feature orthogonality to linear modes, so a Gram-Schmidt process is employed to provide a linearly independent basis. The size of a complete set of SMDs (\(\mathbf{\Theta}\)) that can be computed from n modes is quadratic to n. However, due to their symmetry, \(N_{\text{d}}=n(n+1)/2\) unique SMDs can be computed.  
\[\mathbf{\Theta}=[\theta_{11},\theta_{12},\dots,\theta_{N_{\text{d}}}N_{\text{ d}}] \tag{17}\]  
The number of SMDs grows quadratically with the number of linear modes. So, it is important to initially find the modes with higher participation in the structure response and calculate their corresponding SMDs. Subsequently, the most relevant SMDs are selected. To achieve this, the following steps are considered as shown in Figure 1.  
The suggested steps have an offline computational cost, but this procedure leads to a high accuracy through the proper selection of the SMDs and to a faster convergence.
## 2 Materials and Methods
### 2.4. Statistical Analysis  
Application of the Kolmogorov-Smirnov test, in conjunction with the Lilliefors test, showed that the sample distribution was normal, linear, and homoscedastic. The relative reliability analysis was carried out by calculating the Intraclass Correlation Index (ICC), by single measures, 2-way mixed effects model and absolute agreement [25]. Values below 0.5 indicate poor reliability, between 0.5 and 0.75 moderate reliability, between 0.75 and 0.9 a good reliability, and above 0.9 indicates excellent reliability [26]. The coefficient of variation (CV) for raw data were calculated and used as an absolute reliability indicator [27]. A value above 10% has been considered insufficient absolute reliability [28]. The standard error of measurement (SEM, %SEM) and the minimum detectable change (MDC, %MDC)have also been used as complementary absolute reliability measures [28]. The SEM can be estimated as the square root of the mean square error term from the repeated measures ANOVA [29] SEM = \(\sqrt{\text{MSE}}\), where MSE is the mean square error term from the repeated measures ANOVA [29]; % SEM was calculated as SEM/M \(\times\) 100 [30], where M is the mean of the two intra-day measurement. MDC was calculated as SEM \(\times\) 1.96 \(\times\)\(\sqrt{\text{2}}\)[29], %MDC was expressed as MDC/M \(\times\) 100 [28] where M is the mean value of the two intra-day measurements. Analyses were carried out for all data and according to the players' specific playing position (Guards vs. Bigs).
## 3 Result and Analysis
### Parameter Extraction in 2D Plane  
The images of the fixed damage area are acquired with the same number of frames, as shown in Figure 7.  
The ROI of damage images were captured in Figure 7. The number of image acquisition interval frames is 10 images. The direction of the arrow indicates the direction of damage development. As fatigue loads continue to accumulate, damage continues to expand and develop. A nonlinear texture change is formed on a 2D plane. The characteristic parameters of each image were acquired and data recorded.  
Figure 7: Development of damage in 2D space.
## 4 Fluid Dynamic Performance
### Experimental Techniques
#### 4.2.2 \(\mu\)-Piv  
\(\mu\)-PIV is a flow diagnostic technique that quantifies the velocity field in microfluidic devices [78; 79]. This technique measures the local velocity components from the trajectories of tracer particles in an interrogation region over a known time. In these kinds of measurements, the microchannel is placed on the imaging stage of an inverted confocal microscope equipped with objective lenses. The confocal microscope is able to deliver extremely thin images by true means of depth-wise optical slicing, and allows the gathering of 3D reconstructed information from the line of sight depth-wise resolved imaging without the need for physical slicing of specimens. A CCD (Charge-Coupled Device) camera connected to the microscope is used to acquire the images. A double-pulsed 532 nm wavelength Nd:YAG laser system is normally used for volume illumination and 0.5-2 \(\upmu\)m diameter fluorescent particles at a concentration of \(\sim\)20-300 ppm for the solids fraction are used as tracer particles.  
Figure 4: Example of the formation of a wake in a Newtonian fluid around an ellipsoid. The flow direction is from left to right.  
Figure 3: Scanning Electron Microscopy images of a simplified 3D microbot prototype in a microchannel.  
An epifluorescent filter cube will also be used to prevent the laser light from reaching the sensor of the camera, then receiving only the light emitted by the fluorescent particles. The time lapse between two consecutive frames is adjusted depending on the flow rate in order to ensure that the frame by frame particle displacement is optimal for subsequent analysis using an adaptive cross-correlation PIV algorithm with interrogation areas typically ranging from 16 \(\times\) 16 pixels to 32 \(\times\) 64 pixels, depending on the lens used. For each experiment, a minimum of 100 image pairs should be recorded and processed to perform an averaged cross-correlation analysis using a specific software in order to obtain the velocity field in the test section.  
Images in Figure 5 show the velocity vectors of the fluid flow around an ellipsoid of 1:2 for a Newtonian (up) and for a viscoelastic (down) fluid. The differences between these two kind of fluids are clear. In the case of the fluid with elasticity and shear-thinning, the wake is sharper than for the Newtonian case at the same _Re_, which is in agreement with the region where the highest normal stresses are expected [77].
## 3 Results and Discussions
### Mechanism Analysis of RAP Emulsification-Separation
#### 3.7.2 Influence of Process Parameters  
The separation of asphalt and aggregate depends on the balance between the force separating the asphalt and aggregate and the force combining the asphalt and aggregate. This step is influenced by various factors, including temperature, processing time, stirring speed, etc. Figure 17 shows the mechanism of the process affecting the separation effect.  
The temperature significantly affects the separation of oil from the solid matrix, and an increase in temperature leads to a gradual decrease in the adhesion between the solid and asphaltene. Because of the strong bonding between the RAP aggregate at room temperature and the aggregate asphalt, it is necessary to soften the asphalt by heating. The ionic surfactant is weakened by temperature and becomes the formulation of choice. The hot washing process significantly increases ionic surfactant activation energy in the solution system and completely ionizes it, thus stabilizing the system. Surfactant molecules in ionic form are more likely to react with the strong electrolytes in asphalt, which, in turn, promotes the formation of gels. The ionic surfactant generates the gel molecule, and its hydrophobic group moves toward the inner gel; the inner core of the gel is attracted to the asphalt and draws it to the inner gel to exhibit a solubilizing effect.  
The processing time and stirring speed can also affect the separation effect. If the stirring speed and time are insufficient, the surfactant and RAP will not mix evenly. However, at the same time, excessive rotation speed and time can lead to the destruction of aggregates and generate higher energy consumption. Through this experimental study, it was found that the processing time mainly affects the asphalt content and agglomeration degree, while the mixing speed mainly affects the FMR and agglomeration degree. As the processing time increases, the degree of wear between aggregates intensifies and the asphalt content decreases. The longer the time, the higher the mechanical collision efficiency between aggregates and the more clusters are opened. Therefore, the emulsification-separation is usually completed within 60 min, and it is not recommended to extend the processing time. At the same time, as the stirring speed increases, the mechanical force becomes stronger, and the effect of agglomeration opening improves. However, this study found that, from  
Figure 16: Mechanism of surfactant action.  
Figure 17: Mechanism of the process affecting the dispersion effect.  
the perspective of the separation effect, the collision effect between aggregates is insufficient when the rotation speed is low, and all three indicators are poor. Increasing the rotation speed to 1800 RPM can achieve the best effect.
## 4 Case Study
### Actual Power Grid System in a Certain Region
#### 4.3.2 Analysis of Planning Scheme
On the one hand, to simplify computational complexity, we employed the K-means clustering method to handle the uncertainty of renewable energy and load demands. Yet, this method of selecting typical days may overlook some low-probability but high-impact extreme scenarios, potentially limiting its application in future power systems with high proportions of renewable energy. On the other hand, the study only considers a single generic model for energy storage resources, whereas future grids may incorporate a variety of storage technologies such as hybrid storage, electrolytic hydrogen, dispatchable and load-shifting capabilities, and other generalized energy storage options. By coordinating and optimizing various types of generalized energy storage resources, it is possible to further smooth out fluctuations in renewable energy generation, enhance the system's capacity to integrate renewable energy, and improve overall economic efficiency. In the future, we aim to conduct further research addressing these two issues, with the goal of enhancing the power grid's ability to accommodate renewable energy through multi-stage coordinated planning of transmission networks and energy storage resources, thereby advancing the progress of power systems towards low-carbon solutions. Conceptualization, Y.L., H.L. and Z.M. ; Data curation, J.L. and M.Z. ; Formal analysis, Y.L. and H.L. ; Investigation, Y.L. ; Methodology, Y.L., H.L., Z.M. and J.L. ; Resources, H.L. and J.L. ; Software, H.Z. and J.L. ; Supervision, J.L. and M.Z. ; Validation, H.Z. ; Visualization, M.Z. All authors have read and agreed to the published version of the manuscript. This research was funded by Project \"Research on key technologies of power grid integrated planning to enhance generalized adequacy\" of State Grid Shanxi Electric Power Company Economic and Technological Research Institute, grant number 520533230006. Not applicable. The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors. Authors [PERSON], [PERSON] and [PERSON] were employed by the State Grid Shanxi Electric Power Company Economic and Technological Research Institute. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The authors declare that this
# Single-Shot Compressed Imaging via Random Phase Modulation
## 4 Compressed Imaging Using Random Phase Modulation
### Single-Shot Compressed Imaging with Spatial-Domain Single Random Phase Mask (CI-SSRPM)
The subspace pursuit (SP [14]) algorithm is used as the recovery algorithm. The reconstruction quality is quantified using the signal-to-noise ratio (SNR) and shown in Table 1. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Phase Mask** & **Measurements** & **SNR (dB)** \\ \hline Gauss & 2163 & 26.87 \\ Gauss & 2336 & 28.91 \\ Gauss & 2595 & 29.94 \\ [PERSON] & 2163 & 28.13 \\ [PERSON] & 2336 & 29.07 \\ Toeplitz & 2595 & 30.18 \\ Circulant & 2163 & 27.73 \\ Circulant & 2336 & 29.26 \\ Circulant & 2595 & 30.21 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: SNR of Lena image reconstruction with different number of measurements.
## 4 Discussion
Another drawback is the fact that the quality of the precalibration, respectively, the co-registration of the US probes was not sufficiently assessed for each case. When reflecting the results of the accuracy measurement in a phantom, which showed an offset of 1.3 mm, compared to the Euclidean distance of the geometric centers of gravity (mean \(\pm\) SD: \(3.7\pm 2.5\) mm) and the partially small tumor volumes, we see the need for further improvement in future applications. However, we believe the influence of the precalibration error to be within a just acceptable range. Summing up the results of this study, we found the segmented tumor objects in i3D US clearly delineated the tumor boundaries and was comparable to preMRI segmentations. Dice coefficient and Euclidean distance of geometric centers of gravity indicated a moderate brain shift even before dural opening, and the Hausdorff distance of 8.1 mm suggested a good resemblance of the objects with only moderate deformation. Both the measured shift and deformation might be partially affected by the pressure applied with the US probe during image acquisition and calibration inaccuracy to some extent. An additional feature of i3D US, which was not evaluated in this study, is the possibility of updating the navigation during surgery based on i3D US. The simplest way to do so is to rely solely on the i3D US data sets rigid co-registration of preoperative and intraoperative imaging is limited by brain deformation or distortion. Approaches addressing this problem utilize deformable methods [49], different mathematical algorithms [7], and deep learning with convolutional neural networks to improve registration and segmentations [50]. Yet, all of these share the common feature of an increase in computation time and registration uncertainty [51], making them less suitable for routine use in brain tumor resection when compared to fully implemented rigidly co-registered US. Taken together, even under optimal conditions, we found differences in object appearances in both modalities. Nevertheless, we conclude that our study contributes to the body of literature showing that the rigid co-registration of i3D US utilizing a precalibrated trackable transducer offers a valuable supplement to multimodal neuronavigational set-ups with high imaging quality allowing a precise depiction of pathologies, whereby it is straightforward in use and allows convenient integration in preexisting systems and workflows. **Author Contributions**: Conceptualization, M.B., C.N., and B.S. ; methodology, M.B. and B.S; validation, B.C., C.N., and M.P. ; investigation, M.B. and B.S. ; resources, M.B., B.C., C.N., M.P., and B.S. ; data curation, M.B. and B.S. ; writing--original draft preparation, B.S. ; writing--review and editing, M.B. and C.N. ; visualization, M.B. and B.S. ; supervision, M.B., C.N., and B.S. ; project administration, M.B. All authors have read and agreed to the published version of the manuscript. **Funding**: This research received no external funding. **Conflicts of Interest**: The authors declare no conflict of interest.
## 5 Feasibility Study Through System Simulations
### Simulation Process  
A system simulator was constructed to generate the training and testing datasets of fingerprints, execute different fingerprinting pattern-matching methods, and produce the performance results. The simulations are based on Monte Carlo methods, relying on basic pseudo-random number generators, following the uniform and normal distributions.
## WT1 Immunohistochemical Expression in Human Embryonal/Fetal and Neoplastic Tissues
### WT1 Expression in Malignant Rhabdoid Tumors  
Malignant rhabdoid tumors are highly aggressive tumors that usually occur in the kidneys of children. Other sites are rarely affected, such as somatic soft tissues, liver, gastrointestinal tract, pelvis, retroperitoneum, abdomen, heart and central nervous system [138]. Interestingly, soft tissue localization prevails in fetuses, newborns and young children, while in adolescents, renal and nervous system sites, they are more frequent [138]. In some cases, tumors are metastatic at presentation and have a fatal course [138]. Histologically, they show solid or trabecular growth patterns composed of round/epithelioid to polygonal cells. Typically, neoplastic cells have abundant, deeply eosinophilic cytoplasm with a paranuclear eosinophil, PAS-positive inclusion and large, round, vesicular nuclei with finely dispersed chromatin, containing a prominent eosinophilic nucleolus. Mitoses and necrosis are commonly seen. In some cases, minor tumor components consisting of smaller, round, undifferentiated cells with a scant cytoplasmic rim may be present or even prominent. Similarly to desmoplastic small round cell tumors, malignant rhabdoid tumors exhibit polyphenotipic profiles, with variable co-expression of different markers, including vimentin, cytokeratins, epithelial membrane antigen (EMA) and CD99 [138]. However, the complete absence of nuclear immunoreactivity for INI1 protein is the most useful diagnostic clue [139; 140; 141; 142]. Tumors can express additional markers, such as muscle specific actin, alpha-smooth muscle actin, S100 protein, synaptophisin, and CD56. Occasionally, WT1 (clone WT-C19) can be expressed at nuclear or nucleo-cytoplasmic levels (Figure 9) [33; 34; 138]. We have experience of similar results also by using antibodies against the _N-terminal_ portion (clone WT 6F-H2) [143].  
Figure 8: Desmoplastic small round cell tumor showing neoplastic cells arranged in variably-sized nests, separated by a prominent fibro-sclerotic stroma (**A**). Neoplastic cells showing diffuse nuclear expression with WT1 (_C-terminus_) (**B**) and cytoplasmic expression with WT1 (_N-terminus_) (**C**).
## 3 Simulation Results and Analysis
### Macro-Micro Analysis  
The process evolves through three stages by combining macro and micro simulation results. In the initial 0-2 mm stage, particle compression begins, intensifying with deeper penetration and affecting primarily surface particles. This stage observes a notable increase in contact force distribution due to stronger forces elevating the average contact force, whilst most existing forces remain stable. The XOY plane's variance is lesser than the XOZ's, with the Z-directional distribution gradually diminishing due to the initial Z-axis particle orientation from the rainfall method. Particles undergo compression and lateral dispersion with the increase in pressure, enhancing transverse forces, with YOZ and XOZ plane distributions mirroring each other. In the 2-3 mm stage, contact force changes slow down due to the stress distribution amongst more particles, maintaining a stable average contact force distribution. This stage's uniformity results from the pressing plate's consistent pressure, minimising distribution variations, with YOZ closely reflecting XOZ distributions. During the 3-5 mm stage, smaller forces decrease, establishing a new equilibrium and increasing particle motion around the pressing plate, forming a cyclic pattern. This phenomenon results in reduced contact forces, particularly weaker ones, whilst larger force distribution becomes more pronounced with minimal subsequent changes, primarily due to spatial constraints slowing internal particle movement, causing a synchronous overall contact force increase with stable distribution patterns.