esa-sceva/satcom-chunk-collection huggingface dataset:
# Self-Assembled MoS2/ssDNA Nanostructures for the Capacitive Aptasensing of Acetamiprid Insecticide
## 3 Results and Discussions
### Analytical Performances
#### 3.4.3 Selectivity  
The selectivity is an important parameter for analytical sensing devices, which characterizes the ability of the aptamer to detect the specific analyte in a sample containing other interfering molecules. The aptasensing platform was challenged by testing it with different interferents such as chlorotluron and copper (II) for copper-based pesticides as only few analytical method could discriminate the nature of remained contaminant residue [45]. Therefore, the biosensor response obtained with acetamiprid alone was compared with those obtained in presence of interferents under the same experimental conditions. The results collected from capacitance curves are presented Figure 5. The response recorded in the presence of aforementioned interferents did not show any increase of the capacitance  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Platforms** & **Detection Method** & **Dynamic Range** & **LOD** & **Ref** \\ \hline GCE 1/AuNPs 2  & CV & 0.1 pM–10 nM & 0.077 pM & [42] \\ PtNPs 3 microstrips modified Au IDEs 4  & EIS & 10 pM–10 nM & 1 pM & [43] \\ SiC/AGNPs 7/PB-AuNPs 8  & CPV & 1 pM–6.5 \(\mu\)M & 1.53 pM & [16] \\ GCE /FO-AgNPs 7/PB-AuNPs 8  & CV & 1 pM–10 nM & 0.3 pM & [15] \\ GCE/Ag NPs anchored on nitrogen-doped graphene & EIS & 100 pM–5 nM & 17 fm & [17] \\ SPCE/LA-MoS\({}_{2}\) & ECS & 50 fM–450 fM & 33 fm & [44] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Analytical performances of various aptasensors.  
signal. A blocking effect was observed with chlortoluron, leading to decrease in capacitance while Cu2+ induced a minor perturbation of the transduction signal leading also to decrease of ECS response. This study evidences the high selectivity of the aptasensor toward acetamiprid knowing that the interferents have concentrations 10-fold higher than that of the target.
## 3 2D Numerical Simulation Results and Discussion  
### Simulation of Air Curtain Formation Time and Its Shape  
Figure 6 represents the complete formation of an air curtain within 60 s in the middle plane of the unmodified cabinet. The velocity of airflow out of the honeycomb is 0.6 m/s. The curtain air velocity is reduced to approximately 0.4 m/s near the return air grille (RAG, Figure 1) at the bottom of the cabinet. Due to the small area of the intake grille, the maximum intake air velocity value of 1.2 m/s is reached in this zone (Figure 6).  
Figure 5: Refrigerated cabinet with thermocouples.  
Figure 7 represents the complete formation of the air curtain in the middle plane of the modified cabinet with a changed honeycomb angle and length of wall shelves (Figure 1 (right), Table 1). The honeycomb was turned 15 degrees clockwise (Table 1, Figure 1). It can be seen from Figure 7 that unlike the case of an unmodified cabinet (Figure 6), the air curtain is formed in the outside contour of the cabinet, which is represented by a solid black line in Figures 6 and 7. The air curtain is formed after 20 s only, that is, three times faster than in the unmodified cabinet case (Figure 6). The airflow velocity values obtained are similar to those obtained for the unmodified cabinet (Figure 6). A significant difference is observed only in the formation time and position of the air curtain. The ambient air infiltration ratio is less than in the case of an unmodified cabinet due to the shorter air curtain and the cold air travel time.  
The warm air in the room has an impact on the air curtain of the refrigerated cabinet. In the case of the unmodified cabinet (Figure 6), the airflow radius is greater than in the modified cabinet (Figure 7). A larger radius means a weaker airflow and makes it easier for the warm air from the surroundings to infiltrate inside the cabinet. Under real conditions in retail stores, customers and hall workers who take out and put in food can also damage the airflow bubble. The air curtain may not even be restored during the opening hours of the retail store due to frequent interruptions. Therefore, the proposed design changes will create a stronger and shorter air curtain with a shorter formation time, which will also allow one to reduce energy consumption.  
Figure 6: Air velocity (m/s) distribution in the middle plane of the unmodified cabinet.
# Towards a Glass New World: The Role of Ion-Exchange in Modern Technology
## 2 Glass Strengthening: The Ion-Exchange Contribution
### Thermal Strengthening  
Annealed, heat strengthened and chemically strengthened (or tempered) glass are the three main types of glass for general use. Annealed glass is the \"regular\" glass, as it comes from industrial glass manufacturing (float glass). Thermal strengthening involves a rapid cooling in air of the annealed glass surface after it has been heated to a temperature above the glass transition temperature \(\mathrm{T_{g}}\) and close to the melting point of the material. At the beginning of this cooling process the glass surface cools down and contracts quickly, while its internal core remains hot in order to compensate dimensional changes with small relaxation stress. In this condition, the inner region of the glass is subject to compression while the external one is in tension. When the glass interior cools and contracts, the surfaces are already rigid and therefore residual tensile stresses are created inside the glass while compressive stresses occur at the surface. In comparison to a simple annealed glass, this thermal tempering process increases the strength of the pristine material because the applied stresses must overcome the residual compressive ones on the surface, thus preventing the breaking event [13; 113; 114]. This allows to increase the mechanical strength of the glass by about 4-5 times (up to a few hundred of MPa) and, at the same time, guarantees a better behaviour of the material in terms of safety when it is subject to a breakage [115]. In fact, when this event occurs, this kind of glass generally shatters into many small non-sharp and quite harmless fragments unlike what happens for annealed glasses which break in larger pieces, with a dangerous dagger-like profile. This particular response of tempered glass to a breakage event can be explained by the high strain energy present within these materials. The main limitation of the thermal strengthening process is linked to the minimum thickness obtainable for a tempered glass, which cannot reach values lower than a few millimetres (around 2-3 mm, typically) due to the rapid cooling process and the substantial difference in cooling rate from the glass core to its surface. This inevitably imposes further limits on the geometry and shape of the final products made with this type of glass, which must be simple and not very elaborate [11; 12; 13; 67]. For all these reasons, thermally tempered glass finds its widest use in all those applications where human safety becomes an issue, such as in the automotive sector, for the development of side and rear windows in the vehicles, in the architectural one for the realisation of safety glass doors in private and public buildings, in the domestic environment (see, for instance, the glass for showers or microwave ovens), and the military/civil sector for the development of bulletproof glass where tempered glass is an essential component.
## 3 Results
### Mechanical Properties  
The MIMC composites exhibit different mechanical properties because of the difference of reinforcement materials. Figure 9 shows the mechanical properties change with different reinforcement materials. Compared with AZ31 magnesium alloy, the MIMC composites have a significant improvement in the yield strength (YS), ultimate tensile strength (UTS), and elongation (E). The improvement in mechanical performance is attributed to the following as (1) superior mechanical properties of the reinforcement itself; (2) the reinforcements have a good interface with the matrix; (3) effective load transfer can be carried out from matrix to reinforcement; and (4) fine and evenly distributed grain are produced during coordination deformation of the reinforcement and matrix.  
Figure 6: The optical microstructure and the grain size distributions of magnesium matrix composite in three kinds of reinforcement as-cast states: (**a**,**b**) MISC; (**c**,**d**) MITC; (**e**,**f**) MIAC.  
Figure 8: The [0001] basal pole figures: (**a**) cast states of MIMC; (**b**) extruded state of MISC; (**c**) extruded state of MITC; (**d**) extruded state of MIAC.  
Figure 7: The SEM and the grain size distributions of magnesium matrix composite in three kinds of reinforcement as-extruded states: (**a,b**) MISC; (**c,d**) MITC; (**e,f**) MIAC.  
Among the three kinds of composites, the MISC composites have the maximum values of UTS (Figure 9a), YS (Figure 9b), and E (Figure 9c), which are 355 MPa, 241 MPa, and 13%. For each parameter, comparing with the AZ31, the increase is 47.9%, 60.7%, and 85.7%, respectively, while the MITC composites followed, the mechanical properties are 340 MPa, 220 MPa, and 12%, increasing by 41.7%, 46.7%, and 71.4%, respectively. The MIAC composites are the minimum, the mechanical properties of which are 280 MPa, 175 MPa, and 8%, increasing by 16.7%, 16.7%, and 14.3%, respectively. These changes are ascribed to two factors. One is the influence of reinforcement itself, and the mechanical properties of the three network reinforcements are shown in Table 2. Another is the influence of the grain refinement degree of matrix alloy.
## 4 Results and Discussion
### Isotherm, Kinetic, and Thermodynamic Studies
#### 4.4.2 Kinetic Models  
The kinetic mechanism that occurred during the adsorption process of Cr(VI) and MB on CPW, APWP, APWH, and APWN was calculated using the data from the effect of time. In this study, the pseudo-first-order model (PFOM), pseudo-second-order model (PSOM), and intraparticle diffusion model (IPDM) were determined to predict the best-fitting model. The values of the non-linear correlation coefficients (R\({}^{2}\)) were used as the determining factor, and the values closer to unity (1) were considered. Considering the data obtained from the results, it can be concluded that the PSOM was the best-fitting model as it had higher R\({}^{2}\) values. The PSOM is based on chemical adsorption, while the PFOM is based on physical adsorption. This indicates that the adsorption mechanism between the adsorbents (APWN, CPW, APWH, and APWP) and the pollutants (Cr(VI) and MB) was either through electrostatic interactions, \(\pi\)-\(\pi\) bonding, or hydrogen bonding. The IPDM was investigated to find out whether the uptake of the pollutants (Cr(VI) and MB) occurred on the surface or in the pores of the adsorbent. The result in Table 3 shows that adsorption occurred both on the surface and within the pores for all adsorbents [37].
## 4 Conclusions  
In this paper, the influence law of integral panel structure parameters and crack size on SIF at crack tip is studied, the influence degree and trend of integral panel structure parameters on SIF are given, and the regression model of SIF at crack tip of integral panel is established. Through this study, the following conclusions can be drawn:  
(1) among the integral panel structure parameters, rib height has the most obvious influence on the SIF at the crack tip, followed by rib thickness, and rib spacing has the least influence on the SIF, and this sequence does not change with the crack growth;  
(2) increasing the height and thickness of the integral panel is conducive to improving the crack resistance of the entire wall plate, slowing down the crack growth rate and improving the damage tolerance. However, this lifting is limited; when the height and thickness of the panel increased to a certain size, the crack resistance of integral plate cannot continue to be improved.  
(3) The predicted regression model result of SIF based on the integral plate structure parameters and crack size shows an acceptable agreement with the finite element results, with the determination coefficient of 0.955, indicating that the established regression model had a high prediction accuracy for this problem.  
Conceptualization, G.H., Y.B., W.Y. and L.Z.; Formal analysis, G.H., Y.B., W.Y., L.Y. and D.F.; Investigation, G.H., Y.B., L.Z., L.Y. and D.F.; Supervision, W.Y.; Validation, G.H., Y.B., L.Z., L.Y. and D.F.; Writing--review & editing, G.H., Y.B. and L.Z. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China (Grant Number 51327902, U1637601). The authors declare no conflict of interest.
###### Abstract  
Research on the multi-field coupling effects in rocks has been ongoing for several decades, encompassing studies on single physical fields as well as two-field (TH, TM, HM) and three-field (THM) couplings. However, the environmental conditions of rock masses in deep resource extraction and underground space development are highly complex. In such settings, rocks are put through thermal-hydrological-mechanical-chemical (THMC) coupling effects under peak temperatures, strong osmotic pressures, extreme stress, and chemically reactive environments. The interaction between these fields is not a simple additive process but rather a dynamic interplay where each field influences the others. This paper provides a comprehensive analysis of fragmentation evolution, deformation mechanics, mechanical constitutive models, and the construction of coupling models under multi-field interactions. Based on rock strength theory, the constitutive models for both multi-field coupling and creep behavior in rocks are developed. The research focus on multi-field coupling varies across industries, reflecting the diverse needs of sectors such as mineral resource extraction, oil and gas production, geothermal energy, water conservancy, hydropower engineering, permafrost engineering, subsurface construction, nuclear waste disposal, and deep energy storage. The coupling of intense stress, fluid flow, temperature, and chemical factors not only triggers interactions between these fields but also alters the physical and mechanical properties of the rocks themselves. Investigating the mechanical behavior of rocks under these conditions is essential for averting accidents and assuring the soundness of engineering projects. Eventually, we discuss vital challenges and future directions in multi-field coupling research, providing valuable insights for engineering applications and addressing allied issues.  
Keywords:fragmentation rock; multi-field coupling; damage evaluation; rock creep; constitutive model +
Footnote †: journal: applied sciences
## 4 Implementation and Experiments
### Datasets  
#### 4.2.1 NutritionCLIP Datasets  
To effectively fine tune the CLIP model for the specific needs of the food domain, this study utilizes large, publicly available food image datasets such as Food 101 [58], CNFOOD-241 [59], and Taiwanese Food 101 [60] as image inputs. The Food 101 dataset primarily includes a collection of popular food items, encompassing 101 food categories. Considering the highly diverse dietary habits in different regions, the CNFOOD-241 and Taiwanese Food 101 datasets are specifically introduced to enrich the model's regional adaptability. CNFOOD-241 comprises 241 types of Chinese cuisine, while Taiwanese Food 101 contains 101 kinds of Taiwanese snacks, ensuring that the classification results better align with local dietary cultures.  
In Table 3, \"Train\", \"Validation\", and \"Test\" represent the number of data points divided from each dataset into training, validation, and testing sets, respectively.  
GPT-3.5 [44] is also employed to augment the textual data input to enhance the model's language understanding capabilities. Using the names of food categories as inputs, GPT-3.5 generates detailed textual descriptions of various foods guided by prompts. These descriptions cover aspects such as the color, appearance, and standard serving containers of the foods, providing rich and semantically meaningful data as textual input for the model. This enhancement improves the accuracy and applicability of the model in food classification, better meeting specific application requirements.
## 3 Bisection Constraint Method for Topology Optimization Considering Multiple-Loading Conditions
### Termination Criterion  
The most commonly used termination criterion in optimization algorithms occurs when the reduction in the objective function is sufficiently small. Hence, in this study, the reduction in the objective function is checked at every outer loop to decide whether the optimization process should be continued or terminated. This condition is expressed as follows (Step 9 in Figure 1):  
\[|\overline{c_{m_{k}}}-\overline{c_{m_{k+1}}}|\leq\epsilon, \tag{16}\]  
where \(\overline{c_{m^{k}}}\) is the maximum compliance at step \(k\), \(\overline{c_{m_{k+1}}}\) is the maximum compliance at step \(k+1\); and \(\epsilon\) is a small positive scalar. As shown in Figure 2, the reduction in the objective function is represented by the reduction in the purple dotted line, which is the line crossing maximum compliance at every outer loop.  
Notably, the algorithm of the proposed method can also be terminated even before the reduction in the maximum compliance becomes sufficiently small. As explained in Section 3.1, during the optimization process, the most critical compliance is treated as the objective function and is reduced when all the trade-off compliances are relaxed. When the two largest compliances meet each other at the end of a sub-TO problem, it is not possible to reduce one of them without increasing the remaining one. Thus, this case creates a stable equilibrium position of the compliances. In other words, the algorithm converges.  
In summary, two termination criteria are set for the proposed method: (1) the reduction in the maximum compliance is sufficiently small; and (2) the two most critical compliances are the same at the end of a sub-TO problem. The algorithm can be terminated when either condition is reached.
## 3 Case Study
### Prediction Analysis after Clustering
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Prediction DaysGroup** & **K = 1** & **K = 2** & **K = 3** & **K = 4** & **K = 5** & **K = 6** & **K = 7** & **K = 8** \\ \hline First day (mm) & 0.0714 & 0.0762 & **0.0712** & 0.0750 & 0.0735 & 0.0743 & 0.0763 & 0.0742 \\ Second day (mm) & 0.1969 & 0.1975 & **0.1946** & 0.2006 & 0.1989 & 0.1992 & 0.1982 & 0.2002 \\ Third day (mm) & 0.3256 & 0.3246 & **0.3230** & 0.3293 & 0.3281 & 0.3289 & 0.3251 & 0.3281 \\ \hline \hline \end{tabular} Note: The best results are highlighted in boldface.  
\end{table} 
Table 7: Comparison of RMSE for different number of clusters for different days of prediction on the test set. Figure 12: Clustering results at K = 6. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Prediction DaysGroup** & **K = 1** & **K = 2** & **K = 3** & **K = 4** & **K = 5** & **K = 6** & **K = 7** & **K = 8** \\ \hline First day (mm) & 0.0478 & 0.0524 & **0.0475** & 0.0495 & 0.0491 & 0.0506 & 0.0508 & 0.0490 \\ Second day (mm) & 0.1388 & 0.14
## 3 Results
### Decision-Tree-Based Crop Classifications  
The crop classification maps obtained using decision trees are shown in Figure 12, with the confusion matrices calculated using the validation dataset. Although the overall accuracy (OA) is slightly different between the two classifications (44\(\%\) and 42\(\%\) for TSX + S2 and S2, respectively), the producer accuracy for each crop showed some considerable differences.  
Figure 12: Classification maps and confusion matrices. (**a**) classification resulting from the use of S2; (**b**) classification resulting from the integration of S2 and TSX.  
In particular, the crop-classification map obtained using TSX + S2 data (Figure 12b) shows a greater accuracy than the classification based only on S2 data (Figure 12a). Tree crops showed a producer accuracy of 70% for TSX + S2 and of 57% for S2, with an increase of 13%. Also, the Vetch class showed an increase of the accuracy using the TSX + S2 approach, from 34% to 39% for S2 and TSX + S2, respectively.  
In general, four out of seven classes showed an increase in the producer accuracy with the TSX + S2 classification approach, with the exception of winter cereals, maize, and soybean.
# PCB Network Analysis for Circuit Partitioning
## 3 Circuit Analysis of PCB Netlists
### Network Attribute Analysis  
The network in the netlist refers to the connection between components. The connection can be between nodes or among multiple nodes. The main networks in the PCB netlist are usually divided into the following types according to the signal properties: signal network, power network, ground network, and clock network. The signal network can be subdivided into three types: data network, address network, and control network. Networks with different attributes also show different characteristics. Among all networks, the largest is the ground network, which has the largest number of connected components; the second largest are various power networks, because IC chips are generally active chips powered by power supplies; then there are various clock networks and signal networks. The data network and the address network are multiple parallel lines connected from one chip to another or a multi-pin connector. Chips also need clock signal. The attributes of networks are summarized as follows:  
\[\left\{\begin{array}{ll}\text{GND}&\text{nets}\\ \text{Power}&\text{nets}\\ \text{Signal}&\text{nets}\end{array}\right.\left\{\begin{array}{ll}\text{ Data}&\text{signal}&\text{nets}\\ \text{Address}&\text{signal}&\text{nets}\\ \text{Control}&\text{signal}&\text{nets}\end{array}\right.\]
# Combined Channel Estimation with Interference Suppression in CPSS
## 4 Complexity Analysis and Performance Simulation  
In this section, we will evaluate the computational complexity and performance of the proposed channel estimation with traditional channel estimation. For easy description, the proposed channel estimation scheme is abbreviated as Discrete Fourier transform-KNN (DFT-KNN). One of the comparative schemes is the frequency domain windowed scheme, which is a typical scheme of traditional transform domain channel estimation, and abbreviated as DFT-WF [10]. The other comparative scheme is a discriminant analysis scheme, which is one of the earliest channel estimation schemes based on machine learning techniques, and abbreviated as DFT-DA [11].