esa-sceva/satcom-chunk-collection huggingface dataset:
# Anti-Cancer Drug Screening with Microfluidic Technology
## 2 Monodrug Screening on a Microscale Sample
### Three-Dimensional Cell Cultures
This hybrid microchip combining paper-based microfluidic and cell culture reservoir is more cost-effective than traditional cell-based bioassays using multi-well plates, and is a promising platform for high-throughput drug screening. [PERSON] et al. developed a paper-based microfluidic for anticancer drug screening on MCF-7 and HepG2 cells [30]. This platform was composed of two layers: the upper layer for cell culture and the lower layer for medium self-driven perfusion supply. Moreover, cells were directly seeded in the patterned hydrophilic culture area without hydrogel encapsulation. Integration of microfluidics and microelectrodes in a 3D tumor microenvironment is a powerful platform to accurately and rapidly monitor the response of cancer cells to different drugs. [PERSON] et al. engineered a microfluidic platform integrated with the microsensors to measure the electrical response of cancer cells seeded in a 3D gel matrix to a chemotherapeutic drug [48]. This device can delineate three cancer cells, including drug-susceptible, drug-tolerant, and drug-resistant cell, in less than 12 h. They tested five different drug concentrations (carboplatin for B16-F10 and 4T1 cancer cell lines and paclitaxel for prostate cancer cell lines). [PERSON] et al. developed a microfluidic platform mimicking drug transport to the tumor cell lines and patient-derived breast cancer organoids through the vascular network [49]. Utilizing this tumor on-a-chip model, different features of tumor progression including cell proliferation, angiogenesis, cell migration, and tumor cell intravasation can be studied. Furthermore, chemo- and anti-angiogenic drugs can be tested on individual tumors for precision medicine. The device has three parallel tissue chambers which are separated by a porous membrane. The central tissue chamber is for the microvasculature and the two side chambers are for loading tumor samples. The fluid flows through the vasculature chamber due to the pressure difference between two fluidic lines. [PERSON] et al. also presented a diffusion-based microfluidic device to screen primary human prostate cancer cells, grown in 3D as a heterogeneous culture from biopsy-derived tissue. As proof-of-concept screening, a panel of prostate anti-cancer drugs (cisplatin, docetaxel, and enzalutamide) with eight different dosages were tested on spheroids derived from two patients biopsies [35]. [PERSON] et al. described a microfluidic device for the expansion of patient-derived circulating tumor cells (CTC) without pre-enrichment procedures [50]. In this platform, CTCs are co-cultured with immune cells on inverted dome-shaped elliptical microwells that provide the formation of CTC clusters within 2 weeks. The addition of inlets in the tree-like gradient generator facilitated the efficient screening of drugs as a combinatorial treatment [51].
## 3 Experimental Results
### PDT Results
#### 3.2.3. Deterioration Behavior
Figure 12c,d shows the relationship between the maximum roof drift ratio and PGA, the relationship between the base shear force and PGA, respectively. The coefficient \(k_{\text{det}}\) can be reflected by the slope of the relationship curves. As shown in Figure 12c,d, three phases including linearity, slight hardening and softening were observed. The softening branch occurred when PGA was larger than 0.60 g, where the stiffness reduced to 51.7% of its initial value.
## 4 Conclusions  
This article systematically investigated the effect of laser power on the phase composition, densification behavior, microstructure, and mechanical properties of Inconel 718 alloy formed by SLM. The results are similar to those of [PERSON] et al. [34]. Compared to others, the current study further limits the influence of other process parameters, for example, hatch distance and laser speed. Therefore, the influence of various laser powers is more evident. The main conclusions are as follows:  
As the laser power increased, the peak intensity of XRD gradually decreased, and auxiliary strengthening phases such as \(\gamma^{\prime}\) precipitated from the matrix. At the same time, the increase in working temperature in the molten pool promoted the evaporation of gas and the vaporization of the low-melting-point alloy components, and the relative density gradually increased from 99.31% to 99.79%.  
As the laser energy density increased, the greater laser energy irradiated the material surface, resulting in a stronger Marangoni convective flow and a deeper molten pool formation. The microstructure gradually coarsened, transforming from columnar dendrites to cellular crystals.  
As the laser power increased from 310 W to 360 W, little change was observed in the average grain size and texture strength of the Inconel 718 alloy samples formed by SLM.  
As the laser power increased, the nano-hardness of the material gradually decreased. The nano-hardness of the material decreased with the increase in laser power. The nano-hardness of the four groups of samples from 285 W to 360 W decreased form 3.43 GPa to 2.09 GPa, and the elastic modulus decreased form 205.72 GPa to 199.91 GPa.  
However, an increase in laser power could promote the formation of the auxiliary strengthening phase \(\gamma^{\prime}\). The increase in the fraction of phases and low-angle grain boundaries hindered the movement of dislocations, thereby enhancing the strengthening effect through the interaction of grain boundaries and strengthening phases.  
Methodology, T.L. and Y.Z.; Resources, S.Z.; Data curation, L.Y.; Writing--original draft, Y.W.; Writing--review & editing, L.G. and S.Z.; Project administration, J.M. and S.Z. All authors have read and agreed to the published version of the manuscript.  
The authors gratefully acknowledge the financial support from the Scientific and Technological Innovation Foundation of Foshan, USTB (No. BK21 BE015).  
The original contributions presented in the study are included in the article material, further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.
# A Distributed Multicast QoS Routing Construction Approach in Information-Centric Networking
## 3 System Design
### Tree Construction Algorithm  
The construction process of the delay-constrained multicast tree is divided into four phases: data source registration, tree node selection during multicast member joining, join message propagation, and grafting process. The source node initiates the multicast service by creating and registering a Multicast Identifier (MID). Receivers interested in specific multicast services acquire tree nodes by interacting with the resolution system and initiating join requests. The path delay is estimated during the join message propagation process, and the tree nodes are constructed hop by hop based on link states during the grafting process.
## 4 Analytical Methods  
### Zircon U-Pb Dating  
The process of individual mineral separation involves crushing the rock to a powder finer than 0.3 mm and using methods such as heavy liquid separation, magnetic separation, and manual observation under a microscope to select individual mineral grains. The target preparation of zircon and the acquisition of cathodoluminescence images, reflected light images, and transmitted light images were all completed at the Analytical Testing Center of the Inner Mongolia Autonomous Region Geological Survey Institute. The LA-ICP-MS zircon U-Pb isotopic dating was also performed at the same center. A 193 nm laser ablation system and Neptune Plus multi-collector inductively coupled plasma mass spectrometer (MC-ICP-MS) were utilized, with a laser ablation spot size of 32 \(\upmu\)m, an energy density of 10 J/cm\({}^{2}\), and a frequency of 6 Hz. The standard zircons GJ-1 and 91500 were used for calibration. Data processing was carried out using the ICPMSDataCal program developed by Professor [PERSON] from China University of Geosciences and the Isoplot program by [PERSON] for analysis and plotting, with common lead correction using \({}^{208}\)Pb. The Pb, U, and Th contents of the zircon samples were calculated using the external standard NIST 610. In this study, chronological analysis was conducted on the pillow basalt (DDC2019729-1), and the test results are presented in Table 1.
# Bidirectional Recurrent Neural Network Approach for Arabic Named Entity Recognition
## 3 Approach
### Bidirectional Recurrent Neural Networks (B-RNNs)  
Despite its simplicity, B-RNN is a powerful way to improve the neural network ability to learn, especially for NLP problems. The need for B-RNN helps us consider it in the context of NLP tasks, especially NER in which we try to extract NEs, such as people, location, organization, date, and time.  
We consider the three sentences below:  
* \"Saba\" in the three sentences will be tagged with something else in each sentence. This word represents a location in the first sentence, a company name in the second sentence, and a proper noun in the third sentence. The problem is that the word \" appears at the beginning of each sentence. This condition indicates that the RNN network will not detect any other word in the sentence before it has the opportunity to make a prediction on the word \",\", resulting in a possible incorrect prediction. B-RNN solves this problem by traversing the sequence in both directions. The backward RNN will calculate \(\overset{\leftarrow}{h_{T}}\) in reverse direction, starting from \(h_{T}\) and then going backward until \(h_{1}\) for ease of prediction of the right label for the given NE. The idea of going backward helps accurately mark the NEs for the word \" that appears at the beginning of the sentence.  
The bidirectional unit contains two LSTM/GRU series: one is propagating in the forward direction, and the other is propagating toward the back direction. We concatenate the outcome from the two series to establish a joint representation of the word and its context.  
\[h_{t}=[\overset{\rightarrow}{h_{T}},\overset{\leftarrow}{h_{1}}], \tag{14}\]  
where \(\overset{\rightarrow}{h_{T}}\) is the last word in the forward chain and \(\overset{\leftarrow}{h_{1}}\) is the last word in the backward chain.
# The Efficient Processing of Moving \(k\)-Farthest Neighbor Queries in Road Networks +
## 3 Notation and Formal Problem Description  
**Definition 1**.: \(k\)_FN query [1; 2; 3; 4; 5; 6; 7; 8]. Given a set of facilities \(F\), positive integer \(k\), and query point \(q\), the query point \(q\) retrieves the set of \(k\) facilities farthest from \(q\), which is denoted as \(\Phi(q)\)._  
**Definition 2**.: _MKFN query. Given a set of facilities \(F\), positive integer \(k\), and moving query point \(q\), the moving query point \(q\) constantly retrieves the set of \(k\) facilities farthest from \(q\). We assume that \(\overline{q_{s}q_{e}}\) is a query segment in which \(q\) moves. Then, the MKFN query result can be represented as \(\Phi(\overline{q_{s}q_{e}})=\{\langle q,\Phi(q)\rangle|q\in\overline{q_{s}q_{e}}\}\)._  
**Definition 3**.: _Road network and network distance [22; 23; 24; 25; 26]. A road network \(G\) is modeled as a weighted undirected graph \(G=\langle V,E,W\rangle\), where \(V\) is a set of road intersections, \(E\) is a set of road segments, and \(W\) represents the distance matrix. The network distance \(d(q,f)\) between \(q\) and \(f\), where \(q\) and \(f\) are two points on \(G\), is the sum of the lengths of the road segments along the shortest path between \(q\) and \(f\). The terms \"network distance\" and \"length of the shortest path\" are used interchangeably._
## 3 Materials and Methods
### Normalization
#### 3.2.3 Regularization  
Regularization is a method used in ML and DL to stop overfitting. Overfitting happens when a model learns to perform well on training data but struggles with new unseen data. Regularization helps by adding extra rules or penalties during training, which prevents the model from fitting too closely to the training examples. This approach allows the model to learn broader patterns, leading to better performance on test data [27].  
L1, L2, and dropout are the three main types of regularization [27]: there is L2 regularization (also known as Ridge Regression or Weight Decay), L1 Regularization (or Lasso Regression), and dropout.  
L2 regularization adds a penalty to the loss function that is equal to the sum of the squared weights. This helps prevent the model from learning excessively large weights, which can lead to an overly complex model that is likely to overfit training data.  
L1 Regularization introduces a penalty equal to the sum of the absolute values of the weights in the loss function. This approach can result in sparse models, where some weights become exactly zero, effectively selecting important features by eliminating those that are not significant.  
Dropout is a technique where a portion of the neurons in a layer is randomly \"dropped out\" or ignored during training. This practice helps to prevent the model from relying too heavily on any single neuron and encourages it to learn more robust and general features. Typically, dropout is applied with a probability parameter (e.g., 0.5), indicating that half of the neurons drop out during training. In our methodology, we used L2 regularization to stop the overfitting for the four DL models.
## 2 Model Problem of the Allen-Cahn Equation and Finite Difference Approximation
### Discretization  
In this subsection, we present the discretization of the Allen-Cahn Equation (1) using the Crank-Nicolson finite difference method for the linear term and the forward Euler method for the nonlinear term. The resulting discretized system is given by  
\[\frac{u(x_{i},t_{j+1})-u(x_{i},t_{j})}{\Delta t} = \frac{\epsilon}{2}\left(\frac{u(x_{i+1},t_{j})-2u(x_{i},t_{j})+u( x_{i-1},t_{j})}{(\Delta x
## 4 Experimental Results and Analysis
### Analysis of Algorithm Performance  
#### 4.3.1 Influence of Probability Correction Coefficient on PBIL  
In the real-coding PBIL algorithm for multi-satellite scheduling, the value probability matrix affects the direction of evolution, and the probability correction coefficient will affect the change of the value probability matrix so as to have a certain impact on the solution accuracy and calculation efficiency.  
In this section, we analyze the influence of different probability correction coefficients on the performance of the real-coding PBIL algorithm. For 50 targets with different probability correction coefficients (\(X\in[0,1]\)), 10 simulation experiments are carried out. The statistical results of the real-coding PBIL algorithm are shown in Table 3 and Figure 11. The following data are the average results of 10 simulation experiments.  
It can be seen from the above table and figure that when the probability correction coefficient \(X\) is 0, the value probability matrix has no role in guiding the evolution direction but a random optimization process, so the average running time is long, and the quality of the solution is low. After adding the probability correction coefficient for guidance, the optimization ability of the algorithm is significantly enhanced, and the average running time is greatly reduced.  
Figure 11: The influence curve of different probability correction coefficients in the PBIL algorithm.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Probability Correction Coefficient \(X\)** & **Average Running Time (s)** & **Average Optimal Fitness Value** \\ \hline
0 & 1501.9 & 2457 \\
0.1 & 857.2 & 3779 \\
0.2 & 790.3 & 3616 \\
0.3 & 791.2 & 3481 \\
0.4 & 785.3 & 3348 \\
0.5 & 782.7 & 3469 \\
0.6 & 788.6 & 3257 \\
0.7 & 775.7 & 3233 \\
0.8 & 770.5 & 3266 \\
0.9 & 765.2 & 3395 \\
1 & 769.3 & 3313 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Statistical results of different probability correction coefficients in PBIL.  
With the increase of coefficient \(X\), the average running time is basically stable, but the average optimal fitness value generally shows a downward trend, especially between 0.1 and 0.5. The possible reason is that the increase of the correction probability coefficient leads to a rapid evolution process falling into a local optimum, and at the same time, a large probability correction coefficient also makes the algorithm's optimization and exploration ability low, thus affecting the quality of solutions.  
Hence, we can know that the real-coding PBIL algorithm can achieve optimal performance when \(X=0.1\).
## 2 Materials and Methods
### Statistical Analysis  
From the three trials exported, the trial with median ball velocity for both preferred and non-preferred leg kicks was selected for analysis [24; 25]. Normality and variance homogeneity were evaluated using the Shapiro-Wilk and [PERSON]'s tests, respectively. Figure 2: The key events of kicking. The kicking leg is highlighted in orange. TO: toe-off of kicking leg; SLTG: support leg touch-down; BI: ball impact; MHF: maximum hip flexion. Kinematic differences of the support leg during kicking were assessed using two-way ANOVA or the Scheirer-Ray-Hare (SRH) test, with \"Group\" (competition level: MaleU19, EliteU19, MalePro, FemalePro, as defined in Table 1) and \"Leg Preference\" (preferred vs. non-preferred leg) as independent variables. Effect size (partial eta squared, \(\eta^{2}\)) was used to interpret the impact of variables. An \(\eta^{2}\) value of 0.01 indicates a small effect, 0.06 indicates a medium effect, and 0.14 or higher indicates a large effect [27]. Post-hoc analyses with Bonferroni correction were applied to significant results. The SRH test was performed in Python 3.11.7 (Python Software Foundation, Wilmington, DE, USA), and other analyses were conducted using SPSS(r) 27.0 (IBM Corp., Armonk, NY, USA). A simplified a priori power analysis was also conducted using G*Power (version 3.1) for a two-way ANOVA with four group levels and two leg conditions (preferred vs. non-preferred). Assuming a medium effect size (f = 0.25), \(\alpha\) = 0.05, and power = 0.80, the required sample size was estimated at 52. The actual sample size (N = 78) therefore provided sufficient power to detect both main and interaction effects. A linear mixed-effects model (LMM) was used to assess the relationship between lower limb joint kinematics and kicking performance. Before running the models, the variance inflation factor (VIF) was used to check for multicollinearity, and all values were below 5, indicating no severe multicollinearity issues (Tables 3 and 4). Ball velocity and ball spin were the dependent variables, while joint ROM and peak angular velocity metrics served as independent variables. \"Group\" was included as a fixed effect to control for baseline differences, and \"player\" was treated as a random effect to account for individual variability. Separate models were created for preferred and non-preferred leg kicks. The marginal R\({}^{2}\) (R\({}^{2}\)m) ranged from 0.169 to 0.261, indicating that the fixed effects explained 16.9% to 26.1% of the variance. The conditional R\({}^{2}\) (R\({}^{2}\)c) ranged from 0.295 to 0.725, showing that the full model, including random effects, explained 29.5% to 72.5% of the variance. Residuals vs. fitted value plots were examined to assess model assumptions (Figure A1). No systematic patterns or severe heteroscedasticity were observed in most models. Slight increases in residual spread were noted at higher fitted values in some non-preferred leg models, but overall, model assumptions were considered adequately met. Regression coefficients and \(p\)-values were calculated, with a 0.05 confidence interval. LMM was performed with Python 3.11.7.
# Analyzing Meta-Heuristic Algorithms for Task Scheduling in a Fog-Based IoT Application
## 2 Related Work
### Heuristic Algorithms  
The security-aware and budget-aware (SABA) algorithm [18] is defined for scheduling in a multi-cloud. SABA contains three main steps: clustering, prioritizing tasks, and assigning data to specific data centers based on the constant workflow datasets. SABA's objectives are makespan, security, and budget.  
The Multi-objective Heterogeneous Earliest Finish Time (MOHEFT) [19] scheduling algorithm is known as a model of HEFT. This heuristic algorithm is based on Pareto solutions. Makespan and cost optimization are based on workflow applications on the Amazon commercial cloud. The flexibility provided by MOHEFT as a multi-objective algorithm is very attractive. Tests have shown that in some cases, costs can be reduced to half by a small increase of 5% in makespan.  
The enhanced IC-PCP with replication (EIPR) algorithm [20] is a scheduling and provisioning solution that uses the idle time of VMs and a budget surplus to repeat the tasks to meet the deadline. This method improves the performance of the network. Additionally, in [21], tasks are scheduled by a heuristic algorithm such that the objective function includes the makespan and the execution cost of the tasks. The results show higher efficiency and lower mandatory costs than other methods.
## 2 Problem Formulation
### Spatial Extended Source Model  
Given the above, and as shown in Figure 1, the spatially extended sources are remodeled by multiple monopole sources arranged in a certain shape, amplitudes, and the phase relationship between adjacent monopoles. Thus, multiple monopoles can be arranged as continuous linear sources with constant or linear amplitudes and random uniform phases. According to the significant grouping characteristics of spatially extended sources, its model can be established:  
\[s(t)=\left[\underbrace{s(t)_{1}\cdots s(t)_{g^{\prime}}}_{s[1]}s(t)_{g+1} \cdots s(t)_{2g^{\prime}},\cdots\underbrace{s(t)_{N_{s}-g}\cdots s(t)_{N_{s}}} _{s[G]}\right] \tag{2}\]  
where \(g\) is the grouping length and \(G\) depicts the number of groups, \(N_{s}=G\times g\). Further considering the correlation of monopoles in the groups, taking the first group \(s[1]\) as an example and the first monopole source \(s(t)_{1}\) in the group as a reference, the \(k^{th}\) monopole source \(s(t)_{k}\) in the group can be expressed as:  
\[s(t)_{k}=\beta_{k}e^{i\Delta\varphi_{k}}s(t)_{1} \tag{3}\]  
where \(\beta_{k}\) is the amplitude fading factor \(k=1,\cdots,g\) and \(\Delta\varphi_{k}\) represents the phase difference of \(s(t)_{k}\) related to \(s(t)_{1}\), without losing the generic form, taking \(\beta_{k}=1\) and \(\Delta\varphi_{k}=0\).
# Application of Predictive Maintenance Concepts Using Artificial Intelligence Tools
## 3 Application Example
### Application of Models in the Validation Set  
In this first application, the validation set is used in order to understand how a wide variety of models behave, as well as to look for the tuning of the hyper-parameters of certain models. Such an approach is due to the fact that it is not possible, at the outset, to determine which algorithm is most suitable for a given problem. When training and evaluating a wide variety of models at an early stage, it is possible to see which ones have the greatest potential, however, for this step to be successful, it is necessary that the metrics for evaluating the models have been chosen in accordance with the established objectives.  
The models tested were K-Nearest Neighbors, Decision Tree, Random Forest, Naive Bayes and Artificial Neural Networks. The best results were obtained by Random Forest and Artificial Neural Networks models [32].