esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The dynamic action of groundwater in the water-rich sand stratum carries away injected cement slurry before it becomes solidified, which seriously affects the determination of the diameter and strength of the column. Grouting additives and information-based construction are two main actions to control the quality of jet grouting construction. This study introduced a solution of grouting additives and information-based construction of jet grouting in the water-rich sand stratum. First, high-polymer cement grout (HPCG), red mud and phosphogypsum cement grout (RPCG) and metakaolin cement grout (MKCG) additives were screened with a series of laboratory tests on solidification time and permeability; moreover, the mix proportion of grouting fluids was developed in order to adapt for water-rich sand strata. Secondly, information-based construction of jet grouting was conducted to control grouting quantity with real-time monitoring of drill rotational velocity, drill lifting speed and injection pressure equipped with the monitoring system on the double fluid jet grouting systems. Lastly, the validity of grouting additives and information-based construction in the water-rich sand stratum was verified via a test pile in situ, and a series of material tests on drilling core samples on permeability with SEM observation. The results indicate that the high polymer is the preferred additive of grouting fluids because the solidification time can be controlled in the range of 10 min to 20 min; the permeability of drilling core samples can reach the order of \(10^{-7}\), with the mix proportion being A:B = 2:1, high-polymer additive:water > 1:3, with a water-cement ratio of 0.8. The specifications of information-based construction are a drill rotational velocity of 10 r/min, a drill lifting speed of 0.2 m/min, an injection pressure of 20 MPa and a grouting quantity of 40 L/min.  
jet grouting; grouting additives; information-based construction; water-rich sand stratum; permeability +
Footnote †: journal: Applied  
Accepted: 2 December 2022  
Published: 8 December 2022
## 6 Results  
As detailed described during the simulation in Section 5, a total of 8 conventional servers were used, each capable of hosting up to 150 users simultaneously, resulting in a maximum capacity of 1200 users in total. Additionally, 4 fog nodes were implemented, with each capable of supporting up to 50 users connected simultaneously, adding an additional capacity of 200 users. Therefore, the total capacity for concurrent connected users reached 1400.  
It is crucial to note that the simulation considered several factors, including user and server locations. This consideration is essential as latency, the network response time, is directly influenced by the distance between users and servers.  
The simulation spanned a period of 10 min, during which 1300 users were simulated interacting with different servers based on their location and server load. Each user was assigned a gameplay plan consisting of various actions with associated probabilities. Specifically, these were adjusted such that 60% of actions involved server connections, 30% involved disconnections, 5% involved server migrations, and another 5% involved staying on the same server. These parameters are detailed in Table 1.  
To demonstrate the effectiveness of the proposed model, this research conducted simulations in four distinct scenarios: one without load balancing, one with load balancing implemented, another with fewer servers and fog nodes, and the last with more servers but fewer fog nodes.
# A Novel Drone Design Based on a Reconfigurable Unmanned Aerial Vehicle for Wildfire Management
## 3 Materials and Methods
### Detail Design  
Following the estimation process outlined above, the next stage involved detailed design. In this phase, all the equipment was precisely determined. Correctly dimensioned 3D models of the components, complete with their mass properties, were imported into the CAD software (SOLIDWORKS 2023). The position of each component on the drone was carefully determined, with a focus on satisfying the following requirements:  
* Ensuring that every component is properly installed on the frame in a manner that does not obstruct its own function or the function of adjacent components. This entails careful placement to prevent interference or conflicts that could affect the drone's performance;
* Ensuring that components requiring frequent inspection and/or manual operation are easily accessible;
* Minimizing the distances between equipment that will be connected with cables. Reducing cable length helps to maintain a tidy and organized layout, minimizing the risk of interference and signal degradation;
* Ensuring that the total centre of gravity is kept close to the geometric centre of the drone. Maintaining a balanced centre of gravity is essential for stable flight and control.
Considering the factors of weather information and electricity pricing, a maximized risk income model of the virtual power plant (VPP) is established based on the conditional value-at-risk (CVAR) with DR participation in Ref. [20]. [PERSON] [21] proposes a two-stage, low-carbon economic scheduling model considering the characteristics of wind, photovoltaic, thermal power units and demand response at different time scales, reducing the total system scheduling cost and ensuring contributors' obligations to system operation. Most existing studies have focused on the response capacity constraints of DR resources. Currently, the participation of DR resources in grid dispatch is actively increasing and has different types of response characteristics; therefore, their significant differences in response forms, response interval time, response duration and other characteristics should be fully considered in the dispatch process. The categories of DR operation characteristics are listed in Table 1. Thus, we further explore the multivariate response characteristics of DR resources in this paper. Based on the multiple response modes, we propose a flexible demand response dispatch strategy in which the impacts of wind power uncertainties are considered. The remainder of this paper is organized as follows. Section 2 formulates the DR resource operation decision model based on multiple response characteristics of DR resources. Section 3 proposes the flexible demand response dispatch strategy by combining wind power scenario generation and the reduction method. Section 4 verifies the effectiveness of the proposed strategy with simulation results. Finally, Section 5 draws conclusions from the presented study.
# Authentication-Based Secure Data Dissemination Protocol and Framework for 5G-Enabled VANET
## 3 Interactive Model and Architecture of IoV
### IoV Architecture
#### 3.2.4 Communication Layer  
Communication layer is mostly accepted in cloud/edge hybrid architecture. It is associated with the wireless communication layer (such as Wi-Fi, DSRC, LTE). For the most part of driving data, the intra-vehicular network requires reasonable local dispensation and computing using the actual-time communication among intelligent devices on the cloud. The major purpose of this layer is resource optimization. The actual-time data communication can be recognized across self-establishing network among vehicles and RSUs. At significant level, the cloud's requirement is to perform centralized control across the entire traffic information, and to authenticate the feature model for network topology, road situation information and space-time service of autonomous moving pieces of the entire IoV.
## 3 Results and Discussion
### Adsorption Kinetics and Isotherm  
#### 3.3.1 Adsorption Kinetics  
The main objective of the kinetic study of the removal of organic pollutants is to know the control mechanisms of the adsorption process as well as the mass transfer and chemical reactions. In this research on the removal of Direct Red 81 textile dye by CS, the following kinetic models were examined: pseudo-first-order, pseudo-second-order, and interparticle diffusion.  
The pseudo-first-order (PFO) model was based on the sorption capacity of dye on the particle surface. The form was as follows [43]:  
\[\frac{\text{d}\text{q}}{\text{dt}}=\text{k}_{1}(\text{q}_{\text{e}}-\text{q}_ {\text{t}}) \tag{4}\]  
After integration using the initial conditions at t = 0, q = 0, we can obtain the following expression [44]:  
\[\text{Ln}(\text{q}_{\text{e}}-\text{q}_{\text{t}})=-\text{K}_{1}\text{t}+ \text{ln}\text{q}_{\text{e}} \tag{5}\]  
where K\({}_{1}\) represents the pseudo-first-order adsorption rate (t\({}^{-1}\)), q\({}_{\text{e}}\) represents the adsorption capacity at equilibrium (mg/g), and qt represents the adsorption capacity at time t (mg/g).  
The pseudo-second-order (PSO) model was proposed by [PERSON] and [PERSON]. This model implies that the adsorption process between the surface of the bio-adsorbent and the pollutant is of the chemical adsorption type. The expression is as follows [45]:  
\[\frac{\text{d}\text{q}}{\text{dt}}=\text{k}_{2}(\text{q}_{\text{e}}-\text{q}_ {\text{t}}) \tag{6}\]  
After integration using the initial conditions t = 0, q = 0, and a t, q = q\({}_{\text{t}}\), we obtained the following expression [24]:  
\[\frac{1}{\text{q}_{\text{t}}}=\frac{1}{\text{K}_{2}\text{q}_{\text{e}}{}^{2}} +\frac{1}{\text{q}_{\text{e}}}t \tag{7}\]  
where K\({}_{2}\) is the pseudo-second-order adsorption constant (mg/g.min).  
The intraparticle diffusion model (IPD) allows the diffusion mechanisms of the aqueous phase to be met in the internal pores of the adsorbent, since it is a slow process. The expression below represents the linear form of the model [46]:  
\[\text{q}_{\text{t}}=\text{K}_{\text{int}}\text{t}^{\frac{1}{2}}+\text{C} \tag{8}\]  
where K\({}_{\text{int}}\) is the inter-articular diffusion constant (mg/g\(\cdot\)min\({}^{1/2}\)), and C is the thickness of the thin layer (mg/g).  
The parameters of the PFO, PSO, and IPD kinetic models for the adsorption of DR-81 onto cockleshells are presented in Table 2.  
The results of the adsorption kinetics of DR-81 by CS for the PFO, PSO, and DIP models are shown in Table 2. The linear models give the correlation coefficients of the kinetic model PSO (R\({}^{2}\geq\) 0.9999), which are better compared to those of the PFO model (R\({}^{2}\leq\) 0.936) and the IPD model (R\({}^{2}\leq\) 0.942). In addition, the theoretical adsorbed amounts in the PSO model are closer to the experimentally adsorbed amount compared to the other kinetic models. This equality indicates that the adsorption of DR-81 by CS occurs via chemisorption [24] and via electron exchange between the dye and the bio-adsorbent surface through covalent forces [46].
# Proxy-Based Adaptive Transmission of MP-QUIC in Internet-of-Things Environment
## 4 Performance Analysis by Experimentations
### Analysis of Packet Priority and Number of Clients  
The proposed adaptive MP-QUIC is performed by considering the priority of data packet. To analyze the impacts of packet priority on performance, we compare response Malays for candidate schemes when all data packet have the same priority (priority = 1), as shown in Figure 9. From the results, we see that the existing SP-QUIC gives the worst performance. The proposed adaptive MP-QUIC scheme provides the best performance among all candidate schemes. This is because in the proposed scheme the candidate path with a lower hit value or a lower RTT value will be preferred for data transmission. This tends to induce traffic load balancing and performance improvement.  
Figure 8: Total transmission delays for different sizes of data files transmitted.  
Figure 7: Total transmission delays for different packet loss rates.  
Figure 10 shows the performance comparison for experimens with different numbers of clients, in which the number of clients is ranged from 10 clients to 50 clients. From the results, we see that the proposed adaptive MP-QUIC scheme still provides the best performance. This is because in the proposed scheme the proxy tries to keep the response delays as low as possible by using adpative transmission and packet priority, even when the number of clients gets larger in the network. In the meantime, in the existing SP-QUIC and MP-QUIC schemes, the response delays tend to get larger, as the number of clients increases.
## 3 Approach
### Collision Avoidance Algorithm
#### 3.2.3 Starting Collision Avoidance  
When judging whether the USV will collide with an obstacle, the distance between the USV and the obstacle is the smallest at the collision time, and their respective positions are called CPA(Close Point Approach). The time required for the USV to reach its CPA point is called \(t_{CPA}\). As shown in Figure 8, the red and green solid ellipses are the positions of the obstacle and USV at the time \(t_{0}\), \(V_{obs}\) and \(V_{usv}\) are the velocity vectors of the obstacle and USV at time \(t_{0}\), the red and green dashed ellipses are the positions of the obstacle and USV at time \(t_{CPA}\), and the \(CPA_{obs}\) and \(CPA_{USV}\) points are the positions of the center points of the obstacle and USV at time \(t_{CPA}\). The \(t_{CPA}\) is calculated from Equation (14):  
\[t_{CPA}=\frac{(P_{USV}-P_{obs})(V_{USV}-V_{obs})}{\|(V_{USV}-V_{obs})\|^{2}} \tag{14}\]  
Figure 7: RV(Reachable Velocity)and RAV (Reachable Avoidance Velocity).  
Assuming that the speed of the USV is kept constant, i.e., the USV can turn at a constant angular acceleration. In Figure 8, in the process of sailing to \(CPA_{USV}\) point, the USV turns left with a constant angular acceleration at point \(B\), which is tangent to the obstacle at point \(P_{L}\) and just along the path \(B-P_{L}\). The time required for the USV to sail from point \(B\) to point \(P_{L}\) is \(t_{L}\). Then the USV starts to turn left to avoid the obstacle at the time \(t_{CPA}=t_{L}\), which is just tangent to the obstacle at point \(P_{L}\). \(P_{L}\) is calculated from Equation (15):  
\[\left\{\begin{array}{l}x=x_{B}+\int_{0}^{t_{L}}v\cos\bigl{(}\theta_{0}+ \omega_{0}t+\frac{1}{2}\alpha t^{2}\bigr{)}dt\\ y=y_{B}+\int_{0}^{t_{L}}v\sin\bigl{(}\theta_{0}+\omega_{0}t+\frac{1}{2}\alpha t ^{2}\bigr{)}dt\\ P_{L}C_{1}+P_{L}C_{2}=2a,\end{array}\right. \tag{15}\]  
where \((x,\ y)\), \((x_{B},\ y_{B})\) are the coordinates of point \(P_{L}\) and point \(B\) in the obstacle coordinate system, respectively, and \(v,\ \theta_{0}\,\ \omega_{0}\) and \(\alpha\) are the current velocity, heading, angular velocity, and angular acceleration of the USV, respectively. \(P_{L}C_{1}+P_{L}C_{2}\) is the sum of the distances from \(P_{L}\) to the ellipse in the obstacle coordinate system. The \(a\) is the semimajor axis of the ellipse. The USV turning right is similar to turning left. The time of starting collision avoidance is \(t_{avoid}\) that can be calculated by Equation (16):  
\[t_{avoid}=kmax(t_{L},t_{R}) \tag{16}\]  
where \(k\) is a coefficient that is greater than \(1\) to ensure the safety and smooth turning of the USV. When \(t_{CPA}=t_{avoid}\), the USV starts to avoid the obstacle.
# Early Detection of Residual/Recurrent Lung Malignancies on Post-Radiation FDG PET/CT
###### Abstract
reported that five of six physicians (including three thoracic radiation oncologists and three thoracic radiologists) had a 100% false negative rate using a follow-up CT image to assess benign injury or recurrence [11]. The false negative rate was greatly reduced to 23.1% by a radiomics-based model consisting of five imaging features. Meanwhile, deep learning-based approaches have demonstrated remarkable success in image-based classification problems. Unlike radiomics, which depends on hand-crafted features, deep learning approaches automatically learn underlying representations that differentiate malignant from benign lesions. This ability to autonomously identify and utilize complex patterns in data makes deep learning a powerful tool in medical imaging and diagnostic applications. However, deep learning-based modeling requires a large  
Figure 1: Four examples, with two each representing benign inflammation (**a**,**b**) and residual/recurrent malignancy (**c**,**d**), as shown on PET/CT scans. The colorful contours overlaid on the images are the regions of interest drawn by nuclear medicine physicians. amount of training data. Unlike applications based on natural images, where training samples are often abundant, many medical applications face limitations in the availability of cases. To address this challenge, instead of relying solely on radiomics or deep learning, a hybrid approach has been developed. This approach integrates radiomics and deep learning-based modeling, leveraging the advantages of both handcrafted features and learned features. Specifically, our approach uses two distinct models based on radiomics and Convolutional Neural Networks (CNNs), with the results of each model being fused through an evidential reasoning (ER) approach [12; 13]. By integrating the predictive probabilities from these two models, we capitalize on the handcrafted features identified by radiomics and the deep, abstract representations learned by CNNs. This fusion facilitates a more comprehensive and nuanced analysis, enhancing overall predictive accuracy and reliability. Furthermore, this integrative approach mitigates the individual limitations of each model, resulting in a synergistic effect that enhances the robustness of the predictive outcomes.
# The Accurate Measurement of Students' Learning in E-Learning Environments
## 2 Background
### Item Response Theory
In the figure, different locations of and distances between Item 2 and Item 3 in the two metrics indicate that the two metrics have their own origins and units. The advantage of locating both the persons' abilities and the items' difficulties on the same latent continuum becomes clearer by contrasting IRT and classical test theory (CTT) [20]. In CTT, raw total scores are used to represent persons' abilities. Easier items would produce higher total scores, and harder items would produce lower total scores, when the same abilities are assumed. That is, in the total scores, the persons' abilities are confounded with the items' difficulties. Because of this confounding, it is difficult to compare persons' abilities from different tests in CTT. On the contrary, IRT estimates person and item parameters separately. Therefore, the estimated person parameters (or persons' abilities) are not confounded with the item difficulties. This separate estimation of person and item parameters in IRT enables us to compare persons' abilities on different metrics from different tests. For example, in Figure 2, we can compare the ability of Person 2 in IRT Calibration 1 and the ability of Person 3 in IRT Calibration 2 based on their relative locations to the common items, Item 2 and Item 3, in both metrics: Person 2 has  
Figure 1: Item characteristic curves (ICCs) for three different sets of item parameters. Figure 2: Illustration of two separate IRT calibrations having their own metrics. a higher ability than Person 3. The common items in two separate calibrations are often called anchor items connecting two metrics. In sum, the separate calibration of persons' abilities and items' difficulties in IRT provides desirable features: item parameters are not sample-dependent (item parameter invariance); scores describing persons' ability are not test-dependent (person parameter invariance).
# Common Molecular Mechanisms and Biomarkers in Breast, Colon and Ovarian Cancer
## 2 Materials and Methods
### Data Mining and Protein Screening  
The data mining was performed in the Search Tool for the Retrieval of Interacting Genes (STRING) database ([[https://string-db.org/](https://string-db.org/)]([https://string-db.org/](https://string-db.org/))) (version 12.0) (Accessed on 30 January 2025) [21]. All human proteins involved in breast, colon, and ovarian cancer, obtained from curated databases, were downloaded. To ensure the accuracy and relevance of the analyzed data, a rigorous criterion for the inclusion and exclusion of proteins was applied. Only proteins described in curated databases within STRING were considered, ensuring their support through experimental evidence or systematic reviews. Proteins reported exclusively in scientific literature without validation in curated databases were excluded, avoiding potential biases caused by unverified data. Additionally, only proteins corresponding to the search terms specified in Table 1 were selected, ensuring their direct relevance to the biological processes of breast, colon, and ovarian cancer. This approach enabled the identification of a highly reliable and functionally relevant set of proteins for studying shared mechanisms among these cancer types.  
STRING was selected for its reliability and widespread use in molecular biology research. It provides access to multiple curated databases containing protein interactions supported by both experimental data and in-silico predictions, ensuring a robust foundation for network analysis.  
For statistical analysis, STRING automatically applies False Discovery Rate (FDR) correction using the Benjamini-Hochberg procedure. This method controls the expected proportion of false positives among the rejected null hypotheses, ensuring a balance between sensitivity and specificity in biomarker selection. Compared to more conservative approaches like Bonferroni correction, FDR provides greater statistical power while maintaining control over Type I errors. This ensures that the reported associations reflect meaningful biological relevance in the context of cancer research.  
While other databases such as CPTAC, TCGA, or HPA provide valuable information on protein expression and clinical data, the objective of our study was not to evaluate expression differences at the tissue level or within clinical cohorts, but rather to identify proteins with strong evidence of interactions across multiple cancer types while considering biological information.  
Finally, a protein list for each tumor type was obtained, after removing duplicate proteins. The three protein lists were then compared to identify the common proteins across the different tumor types. This comparison was performed using a Venn diagram ([[https://bioinfogp.chb.csic.es/tools/venny/](https://bioinfogp.chb.csic.es/tools/venny/)]([https://bioinfogp.chb.csic.es/tools/venny/](https://bioinfogp.chb.csic.es/tools/venny/))) (Venny 2.1.0) (Accessed on 7 February 2025) [22].  
Figure 1: Diagram of the in-silico analysis process and databases used.