esa-sceva/satcom-chunk-collection huggingface dataset:
# New Insights on Mechanical Stimulation of Mesenchymal Stem Cells for Cartilage Regeneration
###### Abstract  
Successful tissue regeneration therapies require further understanding of the environment in which the cells are destined to be set. The aim is to structure approaches that aspire to a holistic view of biological systems and to scientific reliability. Mesenchymal stem cells represent a valuable resource for cartilage tissue engineering, due to their chondrogenic differentiation capacity. Promoting chondrogenesis, not only by growth factors but also by exogenous enhancers such as biomechanics, represents a technical enhancement. Tribological evaluation of the articular joint has demonstrated how mechanical stimuli play a pivotal role in cartilage repair and participate in the homeostasis of this tissue. Loading stresses, physiologically experienced by chondrocytes, can upregulate the production of proteins like glycosaminoglycan or collagen, fundamental for articular wellness, as well as promote and preserve cell viability. Therefore, there is a rising interest in the development of bioreactor devices that impose compression, shear stress, and hydrostatic pressure on stem cells. This strategy aims to mimic chondrogenesis and overcome complications like hypertrophic phenotyping and inappropriate mechanical features. This review will analyze the dynamics inside the joint, the natural stimuli experienced by the chondrocytes, and how the biomechanical stimuli can be applied to a stem cell culture in order to induce chondrogenesis.  
mesenchymal stem cells; tissue engineering; chondrogenesis; osteoarthritis; bioreactor; mechanical stimuli; physical stimulation; compression; shear stress; hydrostatic pressure
## 4 Results and Discussion
### Correlation Analysis  
Based on the distribution of the variables, the coefficients between the Pearson correlation and direction and strength of the monotonic relationship between the two parameters were calculated pairwise and shown as a heat map (Figure 13). All data (1610) regarding distance from the corner (xH), nail length (L/H), excavation depth (H), wall angle (\(\alpha\)), slope angle (\(\beta\)), and plane-strain ratio (PSR) data were evaluated together.  
The correlation effect rates and their meanings are provided in Table 5. There is a very strong positive relationship between PSR and xH. There is a negative relationship between PSR and \(\beta\), a negative relationship between \(\beta\) and H, and a weak, positive relationship between \(\beta\) and L/H.
# Development of a New Sound Quality Parameter for Road Noise Perception Inside Vehicle Cabinet
###### Abstract  
Road noise significantly impacts how customers perceive vehicle noise, especially in electric vehicles, where it becomes more noticeable with the lack of the masking effect of the internal combustion engine. In this study, a novel sound quality (SQ) metric to capture the perception of road noise was established with the help of both objective measurements and subjective evaluations on six different vehicles under smooth and rough road conditions. A jury of 50 individuals participated in subjective evaluations in controlled settings, experiencing road noise on six vehicles under both smooth and rough conditions. The same vehicles were also objectively measured in these conditions. Using subjective responses and objective measurements, this study identified key sound quality parameters influencing perception. These parameters were used to develop a new regression model predicting customer perception of road noise, considering both aspects of comfort and satisfaction to follow as a key indicator for road noise, particularly in electric vehicles. While an R\({}^{2}\) of 0.312 was obtained with SPL, R\({}^{2}\) of 0.972 and 0.999 were obtained with the new comfort and satisfaction metrics, respectively. The effectiveness of the newly created SQ metrics was further validated across various vehicles.  
sound quality; road noise; acoustics; multi-variable regression Article
## 3 Results and Discussion
### Geobia
#### 3.2.1 Segmentation  
By visual interpretation of the generated models, a model with values of spectral detail 18, spatial detail 11, and minimum segment size 15 was selected (Figure 4). The stated values are similar to the values used in research in which the GEOBIA approach to vegetation mapping was applied [13; 63; 68].
## 3 Proposed Dehazing Method  
In this section, we present our proposed image dehazing system overcoming the inherent limitations of existing dehazing methods, aiming to improve the method's efficiency, the accuracy of the estimation of both transmission map and atmospheric light, and the quality of recovered results. Figure 2 illustrates the general process of the proposed method.  
The proposed approach has four key components: (1) atmospheric light estimation; (2) transmission map calculation; (3) transmission map refinement; and (4) image reconstruction via the degradation model in Equation (1). For the atmospheric light estimation, we propose a new algorithm based on both image blurriness. Both transmission map and its refined map are generated by a cascaded multi-scale CNN model as two subnetworks (\(\text{CMCNN}_{t}\) and \(\text{CMCNN}_{t}\)). The details of each step are explained in detail in the following subsections.
## 2 Materials and Methods
### Transport Emissions Model  
As part of the aim of this model, it is important to take into account the following considered and not considered criteria for the variables and for the model.  
Figure 3: Location of vehicle volume measurement (VVM) points. Each segment between points is defined by vertical lines and represented letters from A to J. Elaborated by the authors (ARCGIS 10.3).  
* Considered criteria: 1. The model worked only for GHG vehicle emissions, (as proposed in the methodology); 2. The emission factors proposed by the IPCC were used for road transport models; 3. Within the emissions, CO\({}_{2}\), CH\({}_{4}\) y N\({}_{2}\)O are already quantified and are presented as CO\({}_{2}\)e, considering their respective global warming potentials and the respective types of vehicles; 4. No adjustments were made to the uncertainties of GHG emission factors (EF) stipulated by the IPCC; 5. Regarding the field information used to calculate the activity data (DA), a statistical error of 1% was considered in such a way that the \(p\)-value calculation was smaller than that error.
* Not considered criteria: 1. The model does not consider other polluting sources, besides the GHG specified by the IPCC methodology, for example, criteria pollutants for smog-control measurements, which were carried out in closed environments.  
The transport model used in this research is presented in Figure 4, for the estimation of GHG emissions based on the IPCC methodology; Table 1 presents the equations associated with the model. This was applied to each of the BLC segments, considering their specific length data (L).  
Figure 4: Emissions estimation model (own elaboration) based on IPCC [35].  
The proposed model in Figure 4 was adopted from the IPCC methodology for GHG calculations (E = EF \(\times\) AD), designed for open environments and consisting of the application of the defined emissions factors for each GHG [35]. The variable \"activity data\" (AD) utilizes parameters to quantify emissions, which were obtained from field information, such as travels (T) and vehicle performance (Pf) and were used to obtain fuel consumption (F); the EF variable is based on a previous study [36].  
The IPCC model contains three application tiers, which depend on available information. This case study considers Tier 3 [35], which requires highly specific activity data and emission factors, as well as kilometers traveled and vehicle efficiency.  
Based on the previous description and using weighted average volume by type of vehicle, a 20 year GHG emissions projection was made, considering historical vehicle data from 1980 to 2019. One of the simulation's essential variables was vehicle engine performance (Pf), which is a function of the capacity, age, and mechanical conditions of each type of vehicle [36].
# Poka Yoke in Smart Production Systems with Pick-to-Light Implementation to Increase Efficiency
## 4 Results
### Evaluation of the Pick-to-Light System Introduction  
When evaluating the process's efficiency, we focused on several factors. The introduction of the PTL system can bring many advantages for operations that ensure the picking of goods. Figures 8 and 9 show the assembly times of individual workers for one piece and a production batch.  
Results for research question 2: When assembling one piece of the extruder, we observed the greatest reduction in time for worker number 14, where the total time was reduced from 145 s to 95 s, which represents a reduction in time of 35%. It was a 25-year-old man with a high school education who worked as an assembly worker. His assembly experience contributed to a significant reduction in time. In general, he praised the system tested by us. The smallest time saving was achieved by worker number 9. From the 126 s of classic extruder assembly, his time was reduced to 113 s, representing a shortening of the operation by 11%. Worker 25 also achieved the same shortening by 11% from 140 s to 125 s. In the first case, it was a 43-year-old woman and a service worker, in the second case, a 54-year-old man and a production worker. Both of them answered after one test that the designed device seemed small and difficult to operate.  
Results for research question 3: During the production batch assembly, we can state that the preparatory operations did not change to the production batch, but we recorded a time saving during the production-batch assembly. The greatest time savings of up to 33% were achieved by worker number 14. The smallest savings of only 7% were achieved by worker number 16, and his total time was reduced from 859 s to 798 s. All tested workers said that they liked the introduction of PTL system and could imagine its deployment in the company in the future.  
Another evaluated factor that we focused on was the error occurrence before and after the PTL introduction shown in Tables 4 and 5.  
Results for research question 4: From the mentioned measurement of errors, we can conclude that 30 workers made 34 errors during the assembly of 10 extruders before introducing the PTL system. After presenting our PTL system, only 22 errors occurred, representing a decrease in the error rate of 35%. Table 5 shows that errors significantly decreased at seven, eight and nine repetitions. We assume that we can substantially reduce the error rate by implementing several repetitions and getting used to the PTL system.
## 3 Materials and Methods
ArcSWAT divided the basin into multiple subbasins based on the DEM raster and drainage pattern. In the ArcSWAT window, we selected the commands \"Watershed Delineator\" > \"Automatic Watershed Delineation\". For stream definition, we selected \"DEM-Based\", and then pressed \"Flow direction and accumulation\". ArcSWAT has a threshold for the area required to define the beginning of a stream and therefore the area of the corresponding subbasin. In this study, ArcSWAT automatically selected a threshold area of 320,781 hectares. The SWAT model applies the water balance equation to simulate the hydrologic cycle:  
\[\text{SW}_{\text{t i}}=\text{SW}_{0\;\text{i}}+\sum_{i=1}^{t}\left(R_{\text{day i}}\;-\;Q_{\text{surf i}}-E_{\text{a i}}-W_{\text{seep i}}-Q_{\text{gw i}}\right)\]  
where \(\text{SW}_{\text{t i}}\), \(\text{SW}_{0\;\text{i}}\), \(\text{t}\), \(\text{R}_{\text{day i}}\), \(\text{Q}_{\text{surf i}}\), \(\text{E}_{\text{a i}}\), \(\text{W}_{\text{seep i}}\), and \(\text{Q}_{\text{gw i}}\) represent final soil water content on day i (mm), initial soil water content on day i (mm), time (days), precipitation on day i (mm), surface runoff on day i (mm), evapotranspiration on day i (mm), water entering the vadose zone from the soil profile on day i (mm), and return flow on day i (mm), respectively. In the current study, we applied the Soil Conservation Service (SCS) curve number method in the SWAT model to calculate \(\text{Q}_{\text{surf i}}\):  
\[\text{Q}_{\text{surf}}=\frac{\left(R_{\text{day}}-\text{I}_{\text{a}}\right)^ {2}}{\left(R_{\text{day}}-\text{I}_{\text{a}}+\text{S}\right)}\]  
where S and I\({}_{\text{a}}\) represent retention parameters (mm) and initial abstraction (mm), respectively. \[\text{S}=25.4\,\left(\frac{100}{\text{CN}}-10\right)\]  
where CN represents the Curve Number. The CN is attained from tables with correlations with soil type, soil moisture, and land cover. ArcSWAT uses the Penman-Monteith equation to calculate evapotranspiration, which takes into account various factors such as solar radiation, air temperature, wind speed, and relative humidity. Moreover, ArcSWAT applies the Green Ampt equation to calculate infiltration. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data Type** & **Resolution** & **Data Description** & **Data Source** \\ \hline DEM & 30 m \(\times\) 30 m & Elevation and slope & [60] \\ Soil type & 250 m \(\times\) 250 m & Soil feature classification & [61,62] \\ LULC & 30 m \(\times\) 30 m & Land use and land cover classifications & [63] \\ Weather data & Monthly data & Precipitation, relative humidity, temperature, solar radiation, and wind speed & [65] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Input data used for the Soil and Water Assessment Tool (SWAT) model.
## 3 Results
### Mann-Whitney U Test  
As the assumption of normality was violated for attack duration data in both open-play and set-play groups, a non-parametric Mann-Whitney U test was chosen for the analysis. The findings presented in Table 7 indicate a statistically significant difference (\(p\) < 0.001), confirming that final attempts arising from open play had significantly longer attack durations than those stemming from set plays.
# A Compact Triple Band Antenna Based on Multiple Split-Ring Resonators for Wireless Applications
## 1 Introduction
Section 3 contains simulation results and discussions, including a comparative analysis of multi-SRR loaded antenna configurations, real and imaginary input impedance, VSWR, current distribution, gain performance and parametric study, as well as a comparison between the analytical model, equivalent circuit and numerical simulations. In Section 4, the proposed antenna is fabricated and experimentally measured to validate the design. Finally, in Section 5, the proposed antenna is compared with the state of the art, highlighting its performance and demonstrating its superiority.
# Lightweight Detection Method of Obfuscated Landing Sites Based on the AST Structure and Tokens
## Proposed System
### Lightweight AST  
In the conventional AST, some nodes serve as noise by reducing AST similarities. Some other nodes do not affect the AST similarity computation. Hence, we define below two types of nodes.  
**Definition 1**.: _An AST nonstructural node is a node whose AST structure is not affected._  
For example, identifier and literal nodes in the ECMAScript standard [21] do not affect the AST structure. They only change their attribute values, e.g., 'name' in the identifier node and 'value' in the literal node. Other examples of an AST nonstructural node are nodes whose attributes have a single type only. These nodes do not generate children nodes; rather, they include 'type' attributes only to specify their node type. However, if the type attributes of these nodes are different, the similarity is calculated to be a lower value, even though the AST structures are the same. The token mentioned in Figure 1 belongs to the AST nonstructural node.  
**Definition 2**.: _An AST structural node is a node that is not an AST nonstructural node._  
All nodes except the AST nonstructural nodes are structural nodes. These nodes can have children nodes when they obtain other attribute values in addition to the attribute type. In other words, if the attribute types of these nodes are different, the AST structure can be differentiated by connecting with the other children nodes. The structure mentioned in Figure 1 belongs to the AST structural node.  
**Definition 3**.: _A lightweight AST is an AST that excludes AST nonstructural nodes._  
According to the above definition, an AST consists of an AST structural node and an AST nonstructural node. Hence, we can set up a lightweight AST that consists of AST structural nodes only by pruning out AST nonstructural nodes. A lightweight AST is helpful for calculating similarity because it represents the structure.  
Figure 2 shows an algorithm that extracts a lightweight AST and tokens from JavaScript. The data structures are initialized, such as _NS_NODE_ for AST nonstructural nodes, \(S_{i}\) for storing AST structural nodes and trees, and \(T_{i}\) for AST nonstructural nodes (line 3). Parse trees are generated and AST nonstructural nodes are extracted from input JavaScript (lines 4-14). First, parse trees are generated and stored at \(S_{i}\) per input JavaScript (lines 4-6). Next, each node is visited on the parse trees, and whether it is an AST nonstructural node is checked. If 'yes', it is added to \(T_{i}\) and removed from \(S_{i}\). This process is repeated for all nodes in the parse trees (lines 7-14). The function ast tree () takes \(S_{i}\) as input and stores the resulting output, AST without AST nonstructural node, to \(S_{i}\).
## 11 Hardware and Testbeds
### Hardware Testbeds
The main design principles of the proposed system are mapping each task to the lowest powerful tier that has the sufficient resources to accomplish the needed tasks reliably within the required latency, exploiting the wake-up on-demand and triggering of the higher tier nodes only when necessary in order to save energy, and exploiting the information redundancy from overlaps in cameras coverage to improve energy-efficiency and performance (e.g., overlaps camera coverage information can be used in object localization for intelligently wake up the correct nodes in the higher tier). The paper shows a practical example of the proposed system where the nodes in the first tier are always turned on or duty-cycled (woke up periodically) and used for object detection (through simple frame differencing) and then, in case of detecting an object, the nodes try to localize the detected object exploiting the information redundancy from overlapping camera coverage and using triangulation techniques for localization. After that, the nodes in Tier 1 woke up the nodes in Tier 2, which are close the detected object (within their FoV), that in turn perform object recognition by capturing photos of the object, identifying object features, and searching the database for a match. Finally, the corresponding nodes in the Tier 3 are woken up to perform object tracking with the help with the other tiers as the detected object is moving. The WMSN-testbed [17] at the Broadband Wireless Networking (BWN) Laboratory at Georgia Tech is based on commercial off-the-shelf (COTS) advanced devices and has been built to demonstrate the efficiency of algorithms and protocols for multimedia communications through wireless sensor networks. The testbed is integrated with the scalar sensor network testbed, which is composed of a heterogeneous collection of Imote2 and Micaz motes from Crossbow. The testbed allows the integration of heterogeneous devices in experimental testbeds and includes three different types of multimedia sensors: low-end imaging sensors, medium-quality webcam-based multimedia sensors attached with Stargate boards, and pan-tilt cameras mounted on Acroname GARCIA mobile robots. The testbed uses both IEEE 802.15.4 and IEEE 802.11b for wireless communication and it is capable to deliver JPEGvideo streaming in QCIF format (176 \(\times\) 144) at 15 fps.
## 2 Theoretical Background
### Design Variables and Contact Geometries  
In ultra-high-pressure hydrogen valve design, the geometric characteristics of contact geometries critically determine sealing performance and maximum equivalent stress. We define these contact geometries through three principal design variables: plunger curvature radius (\(R_{p}\)), seat curvature radius (\(R_{s}\)), and eccentricity of the plunger curvature center (\(e\)).  
Both the conventional rectangular contact geometry and our proposed concave contact geometry incorporate identical edge rounding with radius (\(R_{r}\) = 0.2 mm), conforming to manufacturing and machining standards. This rounding dimension represents an optimal value widely applied in precision fluid control components, mitigating stress concentration at edges while ensuring consistent precision machining.  
The key difference between the two designs lies in their geometric configuration. The rectangular contact geometry features a flat plunger face with edge rounding \(R_{r}\), while the concave contact geometry incorporates curved surfaces defined by \(R_{p}\) and \(R_{s}\), as illustrated in Figure 2a,b. In both configurations, initial contact occurs at the edge-rounding portion. However, as load increases, their behaviors differ significantly. In the rectangular contact geometry, contact remains concentrated near the initial contact region, intensifying localized stress concentration. In the concave contact geometry, the curved configuration allows the contact area to gradually expand across the curved surfaces, effectively distributing stress over a wider region as pressure increases.  
As shown in Figure 3, \(e\) determines the position of the plunger curvature center relative to the plunger axis, significantly affecting initial contact position and subsequent pressure distribution. Ultra-high-pressure valve design must simultaneously satisfy the conflicting requirements of low-pressure sealing performance and high-pressure durability. In the valve structure, the seat consists of aluminum bronze alloy (685 MPa compressive strength), while the plunger uses PAI material with relatively lower strength (186 MPa), making structural optimization of the plunger especially critical. Therefore, the key challenge is to derive optimal contact geometry by analyzing both contact pressure at low pressure conditions and maximum equivalent stress at high pressure conditions.