esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Framework for Evaluation of Functional Recovery for
### In-Story Repair Strategy Prioritizing Vertical Access Recovery  
Resilience (\(R\)) is essentially and fundamentally the quality of being able to return quickly to a previous good condition after problems have occurred [39]. The quantification of resilience must be able to address both the loss of functionality (which can be quite sudden in the case of a disastrous event, such as an earthquake or extreme hazards), and the path of this recovery of functionality both in time and space.  
Functionality-based assessment approaches assess the resilience of a system of any type using a functionality curve, as shown in Figure 3. The functionality \(Q(t)\) of a system varies between 0% and 100%, with 100% meaning that the system is fully functional, and 0% meaning that the system malfunctions with zero service availability. Mathematically, \(R\) can be calculated by integrating \(Q(t)\) from the occurrence of the event (\(t_{2}\)) over a control time for the period of interest (\(\Delta t\)) [40]. In comparison with indicator-based assessment,  
Figure 2: A functional tree for a building system.  
functionality-based assessment provides more details on the behavior of a system over time after being attacked by disruptions.  
\[R=\frac{\int_{t_{2}}^{t_{2}+\Delta t}Q(t)dt}{\Delta t} \tag{1}\]  
The post-earthquake recovery process is determined by both the mobilization time and the repair duration, as illustrated in Figure 3. Several causes of delay can prolong the mobilization period required to initiate recovery. During this time, the functional loss of the building remains unchanged. The repair duration depends on the repair scheme applied to the building's internal components, which can be divided into two dimensions: the sequence of component repairs within each story and the sequence of repairs across stories. In particular, the restoration of the structural system, which consists of beams, columns, and MT connections, is treated as a prerequisite for the recovery of dependent systems such as vertical access, HVAC, and interior partitions.  
Following the REDi guideline and the Chinese code GB 38591-2020 [41], components within each floor are categorized into seven groups: structural components (S), stairs (F), interior components (A), exterior components (B), mechanical components (C), electrical components (D), and elevators (E). The repair sequence on each floor is divided into two stages, as illustrated in Figure 4. Components in each stage can be repaired concurrently, while components in the second stage (A, B, C, D, E) must wait until the first-stage repairs (S, F) are completed. Notably, in the REDi guideline, stairs are classified under the second stage.
###### Abstract  
Colorectal cancer (CRC) continues to be a primary contributor to the global health burden, and early detection is vital for optimal outcomes. Standard detection techniques, including colonoscopy and fecal occult blood tests, have been confirmed effective yet their invasiveness and poor sensitivity are a limitation. MicroRNA (miRNA) are now recognized as stable non-invasive biomarkers with differential expression in cancerous tissues, but reports have been heterogeneous and studied under different settings. This study, completed based on PRISMA guidelines, was a systematic review of miRNA signature diagnostic accuracy to detect early CRC. Case-case control studies, cross-sectional, and cohort research published between 2014-2024 were identified through the Scopus, PubMed, and Cochrane databases. We extracted diagnostic metrics such as sensitivity and specificity while assessing bias with the ROBINS-e tool and the Newcastle-Ottawa Scale. Meta-analyses showed that miRNA panels have high diagnostic accuracy with a pooled sensitivity of 1.84 (95% CI: 1.48-2.19) and a pooled specificity of 1.43 (95% CI: 1.01-1.85). The accuracy of miRNA-139-3p was the highest among all panels. Meta-regression did not reveal any significant confounders, while publication bias was not detected. These results highlight the miRNA panels' potential as non-invasive biomarkers in early CRC detection, providing a promising alternative to conventional screening methods, with miRNA-139-3p as the most diagnostically accurate biomarker.  
Keywords:miRNA biomarkers; colorectal cancer detection; diagnostic accuracy; non-invasive screening +
Footnote †: journal: Applied sciences
# Biological and Technical Challenges Related to Cultured Meat Production
## 2 Cultured Meat Production Process
According to [PERSON] et al., there is the perspective to develop methods and technologies to achieve the goal of clean in vitro meat in the near future [28]. However, beyond industrial requirements, small-sized laboratory meat cultures also face several common challenges. Cells used for cultured meat production require anchor sites in order to grow and proliferate. A solution to this problem, especially within 3D printing and bioprinting, is the optimal formulation of biomaterials and bioinks, and mixing products such as polystyrene, gelatine, collagen, or protein-based additives to provide adhesion points for the cells, preventing them from remaining suspended within the construct [29]. Such anchoring sites are critical to allow cells to grow on scaffolds and structures specifically textured to ensure muscle cell differentiation. Such scaffolds are responsible for ensuring cultured meat products with multiple structural properties, including cell shape and arrangement [14]. Scaffolds can be created from many materials, which may be of natural origin (cellulose, decellularized plants, alginate, chitosan, collagen, and gelatine) or synthetic (polyethylene glycol or polyacrylamide) [18]. To date, a great concern is that animal-derived products are the most suitable and efficient for cell affinity and attachment [29], but they are inadequate in the view of an animal-free method for the cultured meat process. Moreover, since the final product must be edible, tissue scaffolds, if not edible, must be at least biodegradable and non-toxic or alternatively designed to be removable before consumption [14,18]. Once it has reached maturity, the final product must be harvested, which adds a step in the process, increasing the complexity of the procedure. This step can be performed using enzymatically, chemically, or manually, with the first two being preferred [30]. Hence, the cultured meat and its industrial process scale-up for commercial purposes is a complex and multidisciplinary matter that requires a synergic effort from the biological, chemical, technical, and industrial field. The main research focus must be the development and improvement of cell lines available to set up cell culture and culture media, bioreactors, cell lines, scaffolding, and biofabrication.
## 3 Results
### Population-Specific Method Results  
The architecture used in the population-specific method consists of two LSTM layers with 50 neurons each, using sigmoid for gate activation and hyperbolic tangent tanh for state activation, followed by a Fully Connected Layer and a Regression Layer, optimized using the Adam optimizer. Figure 2 shows the comparison between LSTM-estimated MAP versus real MAP values for three patients with worst fitting (Figure 1(a)), average fitting (Figure 1(b)) and best fitting (Figure 1(c)).  
For Patient 15 (Figure 1(a)), the LSTM model fails to predict MAP accurately, resulting in a modest \(R^{2}\) value of 0.38, due to the weak correlation between inputs (CeProp, CeRemi) and output (MAP) (Table 2). As depicted in Figure 1(a), similarly, Patient 28 (Figure 1(b)) shows a less accurate estimation with an \(R^{2}\) value of 0.58. By contrast, Patient 63 (Figure 1(c)) shows an accurate estimated MAP profile with an \(R^{2}\) value of 0.7. The strong correlation between drug concentrations and MAP enhances the ability of the model to accurately capture and predict MAP dynamics.
## 3 Signalized Intersection Traffic Flow Model for Intelligent Connected Environment  
Building upon the previously established multi-factor car-following model for mixed traffic flow, this study aims to analyze the impact of CAV on intersection efficiency within mixed traffic. Considering the characteristics of HDV when passing through intersections and the real-time traffic information reception capabilities of CAV, a decision-making model for intersection passage in mixed traffic flow is developed. The study scenario is subject to the following constraints:  
1. The research scope is limited to straight-through vehicles at a single intersection with fixed signal timing;
2. The effects of adjacent intersections, roadside parking, pedestrians, and bicycles are not considered;
3. CAV can obtain real-time road traffic information;
4. The wireless communication between vehicles and between vehicles and traffic signals is assumed to be reliable, with no delay in traffic information transmission.
## 4 EEG Signal Acquisition and Motor Imagery Training
### Patient Training on Motor Imagery Tasks  
The Graz training paradigm is a powerful method for teaching individuals to control their EEG signals through motor imagery (MI). In essence, it is a dedicated training program for the brain, analogous to how athletes train their muscles to perform specific tasks. Through repeated practice, users learn to produce distinct EEG patterns associated with different imagined movements, like clenching a fist or moving a foot [44; 45].  
Here is a breakdown of the Graz training process:  
1. Calibration Phase: * Warm-up (t = 0 s): The user focuses on a fixation point or cue to settle their mind and prepare for the upcoming task. MI Task: A visual cue, such as an arrow, instructs the user to perform a specific MI task (e.g., imagine left-hand movement). * Data Collection (t = 3 s): During the task period, EEG sensors capture the user's brain activity, recording the unique electrical signature of the imagined movement. * Machine Learning (t = 3-7 s): The collected data are then analyzed by a machine learning algorithm. This algorithm identifies the key features that distinguish different MI tasks from the EEG signals. * No Feedback: In the initial phase, no feedback is provided to the user. This allows the machine learning algorithm to focus solely on understanding the user's individual EEG patterns.
2. Feedback Phase: * MI Task Repetition: Once the system has been calibrated, users repeat the MI tasks. Real-time Feedback: This time, the system provides feedback in real time. For example, a bar might grow longer or change color based on the system's confidence in recognizing the current MI task. * Refinement and Repetition: With each trial, the user receives feedback and can adjust their mental strategies to produce clearer EEG patterns. This iterative process strengthens the connection between the imagined movement and the corresponding EEG signature.
3. Multiple Sessions: * Gradual Improvement: The Graz training paradigm involves multiple training sessions, spread over days or weeks. With each session, the user's ability to generate distinct EEG patterns for different MI tasks improves, leading to more accurate recognition by the system. * Customization: The training protocol can be adapted to individual needs and goals. The specific MI tasks, cues, and feedback types can be tailored to suit different applications, such as controlling a prosthetic limb or navigating a virtual environment.  
Figure 3 illustrates the timeline of a single Graz training trial, highlighting the key stages and timings [45]. By actively engaging in this training process, users can unlock a  
Figure 2: International 10-20 electrode placement system.  
powerful tool for brain-computer interaction. The Graz training paradigm empowers individuals to harness the potential of their own minds, opening doors to exciting possibilities in various fields.
## 3 Quality Control of Silicon Carbide Printed Part Formation
### The Impact and Control of Key Printing Parameters on Green Part Formation Quality
#### 3.1.3 Elimination of the Stair-Step Effect
**(c)** Excessive binder jetting volume. of the MDI device and the inherent structural parameters and established an equivalent model between the displacement field and the electric field, substituting the electric field for the displacement field. By establishing the relationship between the excitation pulse parameters and droplet volume, the piezoelectric printhead is driven to accurately eject predetermined binder droplets. To enhance the formation accuracy of precision casting sand molds, in [PERSON]'s latest research [133], he employed a precise ink droplet control process to drive the printhead to eject droplets of accurate volume and conducted a preliminary analysis of the penetration and diffusion phenomena of binder spray volume within the sand powder layers (as shown in Figure 16). In this study, the spreading area of droplets on the powder bed was determined using a machine vision system coupled with a high-resolution camera, while the penetration depth was measured by cross-sectional analysis with a profilometer. [PERSON] [133] believes that the motion between the powder and the binder is a complex coupling relationship, and that this relationship can vary significantly due to differences in powder materials and binder types. This study provides important guidance for the control of layer thickness and binder spray volume to improve formation quality in form printing, and it offers empirical guidelines for the adaptive allocation of binder spray volume at different layer thicknesses within adaptive slicing strategies. Currently, BJ 3D printing technology in SiC material formation is in its infancy, and the relationship of the binder within the SiC powder bed is still unclear. Figure 16: Diffusion and penetration of binders with different volumes in the powder bed [133]. (**a**) Diffusion areas of binder droplets of 55 pl, 70 pl, and 83 pl in the powder layer. (**b**) Penetration depths of binder droplets of 70 pl, 140 pl, and 210 pl in the powder layer. In light of the aforementioned research perspectives, studying the deposition patterns of binder in SiC powder beds is urgently needed. To improve the formation quality of BJ 3D printing in the field of SiC manufacturing, related studies on deposition experiments must provide more scientific and precise guidance for process control. As mentioned at the beginning of this section, conducting more in-depth and detailed studies on the effects of various printing parameters on BJ 3D print quality, in conjunction with conventional BJ formation processes, and innovating based on the unique material properties of SiC powder are key to enhancing the formation quality of binder 3D printing of SiC materials.
# Computational Fluid Dynamics-Aided Simulation of Twisted Wind Flows in Boundary Layer Wind Tunnel
## 2 Procedural Framework
### Development Stage  
_Step B_ validates the CFD setup for the TWF simulations by comparing the results obtained from the numerical wind tunnel with those from an actual one. The validation involves the selection of the turbulence model, the computational domain, the grid scheme, and the solution strategy in CFD. Only the CFD setup that leads to a simulation error, \(er_{\rm WT,CFD}\), below the preset threshold, \(er_{\rm lim}\), will be adopted for further steps (i.e., \(er_{\rm WT,CFD}<er_{\rm lim}\)). To ensure sufficient accuracy of the CFD simulations, this study sets the threshold \(er_{\rm lim}\) at 20%. Notably, this step aims only to validate the CFD setup, and therefore the TWF generated at this step is generated without the characteristics of ABL winds.  
_Step C_ optimizes the design of the guide vane system to generate the desired twisted wind field. The design includes two aspects, including (1) the geometrical shape of the  
Figure 1: Procedural framework for CFD-aided wind tunnel simulation of twisted wind flow.  
vane and (2) the spatial configuration of the vanes. The shape of a guide vane is quantified by two terms, namely, the variation in the twist angle with height, \(\theta_{\text{vane}}(z)\), and the total height of the guide vane, \(H_{\text{vane}}\). As for the spatial configuration, the location of the \(i\)-th guide vane is denoted by \(\left(x_{i},y_{i}\right)_{\text{vane}}\) using a Cartesian coordinate system. By adjusting the above parameters of the guide vanes in the numerical wind tunnel, a TWF over the entire model test region, typically the turntable area, can be generated.  
_Step D_ incorporates the ABL wind characteristics into the TWF generated at _Step C_. This is achieved by adding the conventional passive devices (e.g., grids, spires, and ground roughness) into the numerical wind tunnel. Multiple parameters, such as the amount, dimensions, and locations of these conventional devices, are carefully adjusted to generate the desired TWF. The setting up of the conventional devices can usually be accomplished with ease based on the experience gained from the actual wind tunnel.  
_Step E_ evaluates the performance of the experimental setup established at the previous step. If the error for numerical simulation (i.e., the difference between the simulation results and the target values), denoted by \(e\sigma_{\text{obj,CFD}}\), is lower than the preset threshold of \(e\sigma_{\text{lim}}=30\%\) (proposed based on practical experience), the current experimental setup will be adopted for the next step in the closeout stage. Otherwise, the procedures in _Steps_ C and \(D\) will be carried out again until the requirement \(e\sigma_{\text{obj,CFD}}<er_{\text{lim}}\) is met.
# An Enhanced Auxiliary Information-Based EWMA-t Chart for Monitoring the Process Mean
## 3 Proposed Control Charts
### Generally Weighted Moving Average (GWMA-t) Control Charts  
This study further extends the EWMA-t chart by adding design and adjustment parameters to enhance detection capability. The statistic \(T_{i}\) in Equation (1) is used instead of the statistic \(X_{i}\) in Equation (10); hence, the novel statistic for the GWMA-t chart is \(G_{i}\), defined as follows:  
\[G_{i}=\sum\limits_{j=1}^{i}{(q^{(j-1)^{\alpha}}-q^{j^{\alpha}})T_{i-j+1}+q^{j ^{\alpha}}G_{0}} \tag{11}\]  
where the initial value of \(G_{i}\) is set to zero (i.e., \(G_{0}=0\)). For the in-control case, we express the mean and variance of \(G_{i}\) as:  
\[E(G_{i})=0 \tag{12}\]  
\[Var(G_{i})=Q\cdot\frac{n-1}{n-3} \tag{13}\]  
where \(Q=\underset{i\rightarrow\infty}{\lim}\left(\sum\limits_{j=1}^{i}{(q^{(j-1)^{ \alpha}}-q^{j^{\alpha}})}^{2}\right)\) is an asymptotic value. We use the asymptotic control limits instead of the time-varying control limits in this study to simplify the control chart. Assuming that \(L_{g}\) denotes the width of the control limit, the GWMA-t chart is:\[\left\{\begin{array}{c}\mathit{UCL}=0+L_{g}\sqrt{Q\cdot\frac{n-1}{n-3}}\\ \quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad CL=0\\ \quad\quad\quad\quad\quad\quad\quad\quad LCL=0-L_{g}\sqrt{Q\cdot\frac{n-1}{n-3}} \end{array}\right. \tag{14}\]  
When the statistic \(G_{i}\) remains inside the control limits \((\mathit{LCL},\mathit{UCL})\), the process stays in control. However, if \(G_{i}>\mathit{UCL}\) or \(G_{i}<\mathit{LCL}\), the process mean shifts. We can determine the value of \(L_{g}\) using a numerical simulation to achieve the desired in-control \(\mathit{ARL}\). Since the design parameter \(q\) is a constant, we can use parameter \(\alpha\) to adjust the kurtosis of the weighting function slightly. In particular, the EWMA-t chart is a special case of the GWMA-t chart when \(\alpha=1\) and \(q=1-\lambda\).
## 5 Conclusions  
The improved anisotropic rock physics modeling method considers the effects of connectivity between different shapes of pores and the anisotropy caused by fractures. Our approach can estimate shear-wave velocity, pore aspect ratio and fracture density more accurately from the conventional well logging data, as compared with the traditional rock physics model. It provides the key parameters to other workflows, such as logging evaluation, seismic anisotropy inversion and fracture development in tight sandstones. In addition, our modeling workflow achieved good results in the application of the fine characterization of the deep-buried tight sandstone reservoirs with strong heterogeneity.  
It should be noted that this model can achieve good application results under the condition that the geological situation conforms to the assumption of effective mediumtheory. However, it may be that not all the tight sandstones are the same as in our study area. Therefore, the applicability of this rock physics model should be carefully verified before applying it to other regions.  
The theoretical model cannot fully explain the elastic properties of real tight sandstones underground because the simplified pore and fracture geometry models using effective theory are still far away from the structure of actual rock. Therefore, we still have more work to do to develop more accurate and practical rock physical models in the future.  
Conceptualization, Y.Z. and G.Z.; methodology and validation, Y.Z.; formal analysis and investigation, Y.Z.; resources, J.L.; writing--original draft preparation, Y.Z.; writing--review and editing, Y.Z.; visualization, Y.Z.; supervision, G.Z.; project administration, G.Z. and J.L.; funding acquisition, J.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Nature Science Foundation of China (Grant Nos. 42074136 and U19B2008) and the Innovation Project of China University of Petroleum (YCX2020014).  
Not applicable.  
The authors declare no conflict of interest.