esa-sceva/satcom-chunk-collection huggingface dataset:
# The Effectiveness of Unimodal and Multimodal Warnings on Drivers' Response Time: A Meta-Analysis
## 1 Introduction
### Multimodal Warning Systems and Related Theories  
The discussion on the characteristics and effectiveness of each unimodal warnings has always been a major debate. Nevertheless, the development of multisensory warning systems has enlightened another conversation on understanding the relationship between the effectiveness of warning signals and the number of applied sensory modalities within the system.  
In the field of neuroscience and ergonomics, researchers found that individuals tend to respond faster to a signal that is presented through multiple sensory modalities rather than one sensory modality; this facilitatory effect on response time is called the _multisensory integration effect_ (MIE), also known as multisensory enhancement ([23; 24; 25; 26]). Many driving-related research showed that drivers' response time to warning signals improves as the number of applied modalities increases in the driving warning system, as suggested by the MIE. Specifically, [PERSON] and colleagues (2017), and [PERSON] and [PERSON] (2020) both found multimodal warning showed better performance in response time than unimodal warnings, with visual-only warning considered as the worst in driving take-over request situations [10; 27]. However, some studies suggested that the comparisons between the two did not showed substantial evidence in supporting the benefits of multimodal warnings relative to unimodal warnings. [28; 29]. Particularly, [PERSON] and colleagues (2019) found that there was no significant difference in drivers' response time between multimodal and unimodal warnings (except for visual warning); despite that the response time showed a decreasing trend as the number of modalities increases [29]. These thus raised the ambiguity that whether MIE is robust enough in improving the effectiveness of driving warning systems, and what the relationship is between MIE and the number of applied modalities.  
To solve this ambiguity, a meta-analysis is necessary to integrate the findings on the relationship between the observed response time and the number of applied warning modalities. The current study also raised two interpretations detailed below to illustrate this relationship and tried to provide a more concrete understanding of MIE with driving as the exemplar.
# Movable Platform-Based Topology Detection for a Geographic Routing Wireless Sensor Network
## 3 Workflow  
As the technology faces specific application scenarios, WSN employs short-distance wireless communication between nodes. Due to limited node memory, power, computing capacity, single channel and weak anti-interference capacity, nodes within network do not connect each other all the time, but connect where necessary. Nodes that are not working will stay asleep. Within a short time, the platform is not able to go through all nodes to find out their connecting status. Besides, nodes should reduce the complexity of all protocols to cope with the hardware and cost. In the WSN using location-based routing protocol, nodes select routing nodes according to the geological position of neighbor nodes, and the network topology is consistent with the selected node-routing. Hence, the cost parameters for the targeted network to select routing nodes are calculated according to the connection of part of the nodes, and then the topology of the whole network is calculated on the basis of cost parameters. The method is roughly divided into the following steps:  
* Capture node couples. The platform captures air data packets, determines the position of two nodes according to the direction and intensity of the electromagnetic wave that they send out and platform position in the process of data packet sending and receiving, and determines upstream nodes.
* Base address seeking. For the node couples detected, the downstream node and upstream node extension lines (defined as the direction line of node couple) should point at the station in at least one reference direction. The platform moves along the direction line of node couple and should move along the direction of new node couple after it captures node again. It will find station through changing direction line for many times.
* Global sampling. Upon arriving at station, the platform detects node couples and nodes of the whole network centering on the station according to the pre-determined design route.
* Construction of cost parameters. After the platform finishes detection, it will compare the neighbour nodes of midstream, upstream and downstream nodes according to nodes, calculate the range of cost parameter and determine the valuation of cost parameter.
* Reconstruction of network topology. Calculate all node couples of the whole network according to the cost parameters calculated, and re-calculate cost parameters and iterate as per the first result.
* Iteration and correction. Iterate the number of node hops again according to the estimated topology, and update node connection according to detection results and obtain the number of new node hops, iterate cost parameters and construct a new topology.  
System structure is as shown in Figure 5.
# A Short Review of Through-Silicon via (TSV) Interconnects: Metrology and Analysis
## 2 TSV Metrology
### Profile
The total equivalent stress and deformation increase significantly with increasing TSV diameter. The aspect ratio has no effect on the total equivalent stress, but has a significant effect on the total deformation, especially for larger diameters. In addition, the larger the diameter, the more pronounced the effect. The equivalent stresses and deformations (from the center of the TSV to the edge of Si layer) increase with increasing diameter. The maximum stress is located at the Cu/SiO interface, and the maximum deformation is concentrated at the edge of Si layer. [PERSON] et al. [29] have proposed an innovative optical method for non-invasive detection of TSV geometry, called the high-frequency signal analysis (HFSA) method. This method involves irradiating the TSV hole with a laser beam and collecting the scattered light in the pupil plane. The scattered light carries information about the hole, and the high-frequency spectral signal in the pupil plane can be analyzed to derive the 3D profile of the hole. However, as this technique heavily relies on the diffracted high-frequency spectral signal obtained from the pupil data, accurate physical modeling methods are required to simulate the interaction between the incident point and the TSV hole. To accomplish this, the researchers used finite-difference time-domain (FDTD) analysis as a rigorous solver to calculate the pupil data for TSV diffraction. The HFSA method has several advantages, including being a non-destructive and in-line method of measuring TSV geometry. Moreover, it offers high sensitivity and resolution, making it ideal for measuring small TSVs with high aspect ratios. Additionally, the method is fast, allowing for real-time monitoring and feedback during TSV fabrication processes. Although the HFSA method is promising, it also has some limitations. For instance, the accuracy of the method depends on the accuracy of the physical modeling used to simulate the interaction between the laser beam and the TSV hole. Additionally, the method may not work well in the presence of surface roughness or defects. Further research is necessary to improve the accuracy and applicability of this method for TSV metrology. Overall, the HFSA method offers a potentially valuable tool for the non-invasive and accurate measurement of TSV geometry, enabling the development of more reliable and high-performance 3D semiconductor packaging processes (Figure 3).
# The Prediction Performance Analysis of the Lasso Model with Convex Non-Convex Sparse Regularization
###### Abstract  
The incorporation of \(\ell_{1}\) regularization in Lasso regression plays a crucial role by inducing convexity to the objective function, thereby facilitating its minimization; when compared to non-convex regularization, the utilization of \(\ell_{1}\) regularization introduces bias through artificial coefficient shrinkage towards zero. Recently, the convex non-convex (CNC) regularization framework has emerged as a powerful technique that enables the incorporation of non-convex regularization terms while maintaining the overall convexity of the optimization problem. Although this method has shown remarkable performance in various empirical studies, its theoretical understanding is still relatively limited. In this paper, we provide a theoretical investigation into the prediction performance of the Lasso model with CNC sparse regularization. By leveraging oracle inequalities, we establish a tighter upper bound on prediction performance compared to the traditional \(\ell_{1}\) regularizer. Additionally, we propose an alternating direction method of multipliers (ADMM) algorithm to efficiently solve the proposed model and rigorously analyze its convergence property. Our numerical results, evaluated on both synthetic data and real-world magnetic resonance imaging (MRI) reconstruction tasks, confirm the superior effectiveness of our proposed approach.  
L 2025 18 195
## 2 The Rotary Dryer System
### The Regulatory Control System  
Rotary dryers are an important type of equipment for drying cut tobacco. Although there are many process parameters in the dryer control system, in real case scenarios, the operator only controls the temperature of dryer according to the intended process. In the plant where we studied the tobacco drying process, the Assessment of the entire procedure is mainly based on the control of the moisture content of the product. Therefore, from the financial side, achieving the intended level of moisture is considered the riskiest part of the business.  
The regulatory control system of the plant is shown in the Figure 2 below. The regulatory control system is based on the dryer proportional-integral-derivative (PID) control system, which contains feedforward control, PID moisture controller, and PID temperature controller. Before starting the production, the dryer needs to reach a temperature of 124 \({}^{\circ}\)C and keep it for 20 min.  
In the first stage of drying process, the belt keeps feeding the drum, consequently the amount of cut tobacco varies and it will stay inside the dryer few minutes. The operator sets the temperature value based on several parameters (as shown by Figure 2) such as inlet moisture, the value of the previous batch, and the target value of outlet moisture. At this stage, the temperature control of the dryer is mainly a feedforward control.  
In the central stage of drying process, the online outlet moisture measurement instrument detects cut tobacco. Consequently, the PID temperature controller considers the results of the feedforward control operation and the PID moisture controller as input parameters. As a matter of fact, the moisture content control relies mainly on the PID controller at this stage. In this phase, the operator pays attention to the mean and SD of the outlet moisture content of cut tobacco, and carries out appropriate intervention to meet the assessment target.  
In the last stage of the drying process, the steam valve is closed. The cut tobacco is mainly dried through the residual heat of the dryer without manual intervention.  
This is the regulatory control system already existing in the plant. The main role of our proposed controller is to replace operator and achieve intelligent control, to be described in the following section.
## 3 Results and Discussion  
### Sensory Evaluation Analysis on Tea Samples Exposed to YLDI  
The sensory quality of tea samples subjected to YLDI are detailed in Table S3 through systematic sensory analysis. With respect to the taste attribute, the tea samples exposed to 6000 Lux, 1000 Lux, and 4000 Lux attained scores exceeding 90. Notably, the samples under 6000 Lux attained the highest score of 93, exhibiting a fresh taste with floral flavor. Regarding the aroma attribute, all five tea samples scored above 90, further confirming that yellow light significantly improved the aroma profile of green tea. Specifically, the tea samples under 500 Lux and 2000 Lux predominantly displayed a tender chestnut-like aroma, whereas the tea samples under 1000 Lux demonstrated a chestnut-like aroma accompanied by floral notes. The samples exposed to 4000 Lux and 6000 Lux were distinguished by a pronounced floral aroma, with the 6000 Lux samples exhibiting a particularly strong and enduring floral fragrance. According to their aroma types, the tea samples under YLDI could be categorized into three groups: tender chestnut-like aroma (500 Lux and 2000 Lux), floral aroma (4000 Lux and 6000 Lux), and chestnut-like aroma with floral aroma (1000 Lux). In terms of overall quality, the tea samples processed under 6000 Lux achieved the highest score, followed by those under 1000 Lux, 4000 Lux, 2000 Lux, and 500 Lux, respectively. These results indicate that higher light intensities are beneficial for enhancing the sensory quality of green tea, particularly in promoting the development of floral notes.
# A Systematic Review of Cybersecurity Risks in Higher Education
## 8 Vulnerabilities in HE
### Administrative and Cultural Vulnerabilities in HE
#### 8.1.3 Insufficient Risk Management and Communication  
Appropriate risk management is at the center of all infosec work [11]. FireEye writes that when faced with the balance between openness and mitigating cybersecurity risks, universities often err on the side of openness [5]. In the security compliance check of the Turkish universities, all of the participants were found severely lacking in both vulnerability (weakness) management and software development security [4], the compliance check of the Jordanian University produced similar results [52]. UNIT reported that while some risk assessments were being conducted, comprehensive risk management was not implemented on any of the surveyed universities [6]. A significant issue was that there was no follow up on identified and planned risk treatments. Furthermore, [PERSON] et al. [45] points to insufficient risk reporting and communication channels as critical vulnerabilities in the analyzed university. The UNIT report [6] also found that the GDPR had sparked an initiative to map PII within the universities. However, there was still a lack of overview of sensitive research data and other mission-critical data. This finding did not necessarily indicate that all research data had insufficient secure storage; however, the report addressed that it was unclear regarding the details of how data was secured.
## 2 Materials and Methods
### Proposed Feature Extraction Method
#### 2.1.1 The 2.5D Image Transformation  
An input 3D local image patch of 32 \(\times\) 32 \(\times\) 32 voxels is transformed into a 2.5D image patch consisting of three-channeled, two-dimensional (2D) images reflecting 3D information. The three channels are the axial, coronal, and sagittal slices through the 3D image patch center point. The 2.5D image transformation significantly reduces the number of pixels in the image input to the CAE, which is expected to avoid overfitting on the CAE training data due to the effect of the curse of dimensionality [15].  
Four types of 2.5D images are used in this method: center slice extraction (CentSE) 2.5D images [12], maximum-intensity projection (MIP) 2.5D images [13], minimum-intensity projection (MinIP) 2.5D images, and mean-intensity projection (MeanIP) 2.5D images. The centSE-2.5D image, first proposed by [PERSON] et al. for lymph node detection on CT images, consists of three images from which the axial, coronal, and sagittal center slices of the 3D patch image are extracted, as shown in Figure 2a.  
Figure 1: Flowchart of the proposed feature extraction method.  
MIP-2.5D image is obtained by applying a three-directional MIP process to a 3D patch image, as shown in Figure 2b. The MIP method, a method of observing the voxel values of a 3D image in any axial direction and projecting the largest of the values onto a 2D image, is often used when diagnostic radiologists read 3D medical images. In this study, MIP processing is applied in three directions, as in the CentSE-2.5D image, and a set of three MIP slices (axial, coronal, and sagittal) was used as the MIP-2.5D image. The MinIP-2.5D image and MeanIP-2.5D image are also sets of axial, coronal, and sagittal projection slices with minimum and mean voxel value projections in a similar way to the MIP-2.5D image, as shown in Figure 2c,d. The use of four 2.5D images with different properties is required to perform generalized local image feature extraction, regardless of the lesion or image type of interest.
## 3 Graph Theory and G-Pre Preconditioning Algorithm Design
### G-Pre Preconditioning Algorithm Design  
The speed of the solution iteration significantly determines the overall efficiency of the simulation and analysis. However, the iterative convergence process is influenced by various factors, with the preconditioning effect on the coefficient matrix being one of the key determinants. To enhance the preconditioning effect, this section introduces a preconditioning algorithm called G-Pre. It consists of the following three main stages:  
1. Preconditioning division and reordering stage: This stage divides and reorders the matrix using a graph partitioning algorithm. 2. Preconditioning factorization stage: In this phase, the ILU algorithm is employed to factorize the matrix. 3. Parallel solving stage: The preprocessed matrix is then solved through parallel iterations. By integrating graph theory algorithms with ILU factorization, the matrix structure can be effectively improved, facilitating parallel computation and enhancing the efficiency of matrix solving. These improvements will be demonstrated through numerical experiments in Section 4. An efficient preconditioning algorithm named G-Pre has been developed for solving systems of finite element linear equations by integrating three methodologies--graph partitioning, graph reordering, and ILU factorization. The G-Pre algorithm comprises three main stages--a partitioning stage, a matrix reordering stage, and a factorization stage; these are followed by an iterative solving stage, where the final solution to the original matrix is obtained through iterative computations. The partitioning stage utilizes a graph partitioning algorithm to divide the computational domain into cohesive subdomains, minimizing edge cuts between different clusters. This step ensures balanced workload distribution across processors in parallel computing environments. The matrix reordering stage applies a graph reordering algorithm to sequentially renumber nodes within each subdomain, aiming to reduce fill-in during the subsequent factorization process. Finally, the factorization stage employs ILU factorization to prepare the matrix for efficient iterative solving. Algorithm 3 illustrates the code implementation process of the G-Pre preconditioning algorithm, using the GMRES algorithm as an example to demonstrate how these stages are integrated to solve the system of linear equations iteratively. Figure 4: Schematic diagram for dividing and rearranging matrices. (**a**) Original matrix; (**b**) full graph rearrangement; (**c**) graph division and rearrangement. Figure 3: Graph reordering schematic. ```
**Algorithm 3**G-Pre code process.
###### Abstract
The above research mainly focuses on the principle of SC-CO\({}_{2}\) jet fracturing and the influence of relevant parameters on fracturing results. The effect of beddings on the propagation of fractures induced by SC-CO\({}_{2}\) jet have been studied by [PERSON] et al. [24], but the influence of geological factors on the propagation of fractures induced by SC-CO\({}_{2}\) jet have not been studied clearly. Figure 1: A schematic of hydra-jet fracturing. The initiation and propagation behavior of fractures induced by fracturing technology is complicated and difficult to predict. Various factors influence the initiation and propagation behavior of fractures, including engineering factors and geological factors. The engineering factors are controllable, and understanding the effects of engineering factors, such as fracturing fluid viscosity and injection rate, on the fracture initiation and propagation are important to maximize the stimulated reservoir volume (SRV) [25; 26; 27; 28; 29; 30]. The geological factors, such as in situ stress and discontinuities, have an impact on fractures initiation and propagation, which is uncontrollable, but the fractures propagation can be predicted through investigating the influence of geological factors on fracture propagation [31; 32; 33; 34; 35; 36; 37]. Previous studies mainly focus on the engineering factors on the propagation of fractures induced by SC-CO\({}_{2}\) jet, and few studies investigate the influence of geological factors on the propagation of fractures induced by SC-CO\({}_{2}\) jet. For most reservoirs, the formations of reservoirs are layered, and the mechanical properties of different layers can be different, which can affect the propagation of fractures. It is significant to investigate the propagation behavior of fracture induced by SC-CO\({}_{2}\) jetting in the layered reservoirs with different mechanical properties which, here, is called hard and soft layered rock. In this paper, four groups of artificial sandstones were prepared to investigate the fracture propagation behavior in hard and soft layered rocks by SC-CO\({}_{2}\) jet fracturing. A strain collection device was employed to monitor the strain changes of the specimens during the experiments. After the experiments, a three-dimensional scanner was used to get the morphologies of the main fractures.