esa-sceva/satcom-chunk-collection huggingface dataset:
# The Design and Investigation of Hybrid a Microfluidic Micromizer
## 4 Results and Discussion
### Acoustic Field
#### 4.1.2 Effect of Micropillar Gaps  
This section describes the effect of micropillar gaps on acoustic fields, such as acoustic pressure and acoustic velocity. Figure 9 describes the graphical representations of the acoustic pressure and acoustic velocity with different micropillar gaps. It can be seen in the graphical representations that the acoustic pressure and acoustic velocity increased with the decrease in micropillar gaps. Furthermore, the blade-shaped micropillar has larger values of acoustic pressure and acoustic velocity than the other designs. Moreover, the maximum and minimum values of acoustic pressure and acoustic velocity are noted at micropillar gaps of 0.15 mm and 0.25 mm, respectively.  
The main reason behind this increase in acoustic pressure and acoustic velocity with decreasing gaps is due to a larger surface area and more boundaries to hit. Thus, denser micropillars intensify the field strength, resulting in an enhancement of mixing performance. It was also noted that compared to the other micropillars, the blade-shaped micropillars with a 0.150 mm gap generate a stronger acoustic field and, according to the computed results, the magnitude of acoustic pressure and acoustic velocity is approximately three and four times that of the case with no micropillars, respectively. The acoustic pressure and acoustic velocity distribution with different micropillar gaps are also shown in Figures 10 and 11.  
Figure 8: Acoustic velocity for different micropillars shapes: (**a**) no micropillars; (**b**) circular-shaped micropillars; (**c**) hexagonal-shaped micropillars; (**d**) blade-shaped micropillars.  
Figure 9: Variation of acoustic pressure and acoustic velocity with different micropillar gaps.  
A stronger acoustic pressure field can be observed with a 0.150 mm micropillar gap compared to the others. Similarly, a less acoustic pressure field can be observed in the case of a 0.250 mm micropillar gap. The reason behind this higher field strength is because of a  
Figure 11: Distribution of acoustic velocity with different micropillar gaps: (**a**) 0.150 mm, (**b**) 0.175 mm, (**c**) 0.200 mm, (**d**) 0.225 mm, (**e**) 0.25 mm.  
Figure 10: Distribution of acoustic pressure with different micropillar gaps: (**a**) 0.150 mm, (**b**) 0.175 mm, (**c**) 0.200 mm, (**d**) 0.225 mm, (**e**) 0.25 mm.  
larger surface area and more boundaries to hit, which results in increasing the diffusion of fluids and mixing performance. The distribution of acoustic velocity can also be seen in Figure 11. Higher small vertexes and acoustic velocity fields generated in the case of a 0.150 mm micropillar gap compared to the other cases. It can also be noted that fewer acoustic fields are generated near the wall in the case of a 0.150 mm gap compared to the other cases. The reason behind this strong acoustic field near the micropillars is due to a larger surface area and more boundaries in the microchamber than in the other cases.
## 3 Results and Discussions  
### Aggregate Quality Tests  
Based on the tests conducted to evaluate the suitability of mudstone aggregate as material for asphalt pavement, it was found that mudstone aggregates satisfy the qualifications of the aggregate quality in accordance with the standards of the Ministry of Land, Infrastructure and Transport (MOLIT) of South Korea. The results and criteria are summarized in Table 6.  
Among the aggregate qualities investigated, it can be observed that the abrasion rate, stability, and coating rate of the mudstone aggregate after conducting dynamic immersion  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Aggregate Property** & **Criteria [18]** & **13 mm Coarse Aggregate** & **10 mm Coarse Aggregate** \\ \hline Absolute dry density & \textgreater{}2.5 & 2.64 & 2.63 \\ Absorption rate (\%) & \textless{}3.0 & 1.41 & 1.9 \\ Coating rate after dynamic immersion test (\%) & \textgreater{}50 & 67 & 67 \\ Flatness rate (\%) & \textless{}10 & 4.0 & 3.7 \\ Abrasion rate (\%) & \textless{}35 & 14.3 & 14.1 \\ Stability (\%) & \textless{}12 & 4.1 & 4.5 \\ Percentage of fractured particles in coarse aggregate (\%) & \textgreater{}85 & 100 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results of the aggregate property evaluation.  
are excellent based on the criteria of the standards. The abrasion rate of the mudstone aggregate was found to be less than the criteria for both 13 mm and 10 mm aggregates. This implies higher durability and reduced deformation for the asphalt pavement. Moreover, the stability of mudstone aggregate was found to be 4.1% and 4.5% for 13 mm and 10 mm aggregates, respectively, indicating high resistance to freezing, thawing, and weathering. Furthermore, the excellent coating rate after dynamic immersion test, with a value of 67%, implies that the asphalt mixture with mudstone aggregate has a significant resistance to moisture damage and aggregate stripping. Therefore, it was determined that the use of lime or liquid anti-stripping agents is unnecessary for colored asphalt pavement.
# Approximation Algorithms for Sorting \(\lambda\)-Permutations by \(\lambda\)-Operations
## 2 Preliminaries and Basic Facts
### Inversions  
An _inversion_ is defined as a pair of elements \((\pi_{i},\pi_{j})\) such that \(i<j\) and \(|\pi_{i}|>|\pi_{j}|\). The number of inversions in \(\pi\) is denoted by \(\mathrm{Inv}(\pi)\).  
For example, the permutation \(\pi=(1\;5\;4\;3\;2)\) has 6 inversions. The 3-reversal \(\rho(3,5)\) transforms \(\pi\) into \(\pi^{\prime}=(1\;5\;2\;3\;4)\) that has 3 inversions.  
**Lemma 1**: _For all \(\lambda\)-permutations \(\pi\
eq i\) and all \(\lambda\geq 2\), we have \(d^{\lambda}_{r}(\pi)\geq\frac{\mathrm{Inv}(\pi)}{\lambda(\lambda-1)/2}\), \(d^{\lambda}_{t}(\pi)\geq\frac{\mathrm{Inv}(\pi)}{\lambda^{2}/4}\), and \(d^{\lambda}_{rt}(\pi)\geq\frac{\mathrm{Inv}(\pi)}{\lambda(\lambda-1)/2}\)._  
Proof.: A \(\lambda\)-reversal of length \(k\) can only change the inversions between elements of the segment affected by it. Therefore, the maximum number of inversions that a reversal of length \(k\) can remove is equal to \(\binom{k}{2}\), since this is the maximum number of inversions between the elements of a segment of length \(k\). By definition, the length of a \(\lambda\)-reversal is less than or equal to \(\lambda\), so a \(\lambda\)-reversal can remove, at most, \(\binom{\lambda}{2}\) inversions. Since the identity permutation is the only one with \(\mathrm{Inv}(\pi)=0\), any sorting sequence has to remove all inversions of the permutation, which results in the lower bound \(d^{\lambda}_{r}(\pi)\geq\frac{\mathrm{Inv}(\pi)}{\lambda(\lambda-1)/2}\).  
A \(\lambda\)-transposition \(\tau(i,j,k)\) of length \(k^{\prime}\) also affects only the inversions between elements affected by it. Moreover, the inversions between elements on the segment \(\pi_{i},\ldots,\pi_{j-1}\) are not changed. The same is valid for inversions between elements of the segment \(\pi_{j},\ldots,\pi_{k-1}\). So, if an inversion \((\pi_{x},\pi_{y})\), with \(x<y\), is removed, then \(\pi_{x}\) is in \(\pi_{i},\ldots,\pi_{j-1}\) and \(\pi_{y}\) is in \(\pi_{j},\ldots,\pi_{k-1}\). Therefore, the maximum number of inversions that a transposition can remove is equal to \((j-i)(k-j)\leq(k^{\prime}/2)^{2}\), since \((j-i)+(k-j)=k^{\prime}\). This bound is maximum when \(k^{\prime}=\lambda\), which results in \(\lambda^{2}/4\). As before, this results in the lower bound \(d^{\lambda}_{t}(\pi)\geq\frac{\mathrm{Inv}(\pi)}{\lambda^{2}/4}\).  
Using a similar argument, we have \(d_{\pi}^{\lambda}(\pi)\geq\frac{\operatorname{Inv}(\pi)}{\lambda(\lambda-1)/2}\).
# When 5G Meets Deep Learning: A Systematic Review
## 3 Results
### What are the Main Problems Deep Learning Is Being Used to Solve?
#### 3.1.7 Handover Prediction  
The handover process ensures continuous data transfer when users are on the move between call towers. For that, the mobile management entity (MME) must update the base stations where the users are connected. This procedure is known as location update. The handover delay is one of the main problems in wireless networks [49]. Conventionally, a handover is carried out based on a predefined threshold of the Reference Signal Receiver Power (RSRP), the Reference Signal Receiver Quality (RSRQ), among other signal strength parameters [50]. Predicting the handover based on the nearby stations' parameters can be a fruitful strategy to avoid handover errors, temporary disconnections and improve user experience [49].  
In this systematic review, we located two papers that addressed handover prediction. In [51], [PERSON] et al. proposed a deep learning model to avoid handover failures. For that, the deep learning model was trained to detect if the handover will fail or be successful based on the historical signal condition data.  
In [52], the handover prediction was tested to provide uninterrupted access to wireless services without compromising the expected QoS. The authors proposed both analytical and deep learning-based approaches to predict handover events in order to reduce the holistic cost.
## 5 Simulation Results and Discussion
### Throughput  
Throughput is a crucial metric for evaluating network performance, as it measures the amount of data that can be successfully transmitted within a given time frame. The results of the simulation comparison demonstrate that our protocol exhibits superior network data transmission efficiency and reliability.  
Figure 10 shows the throughput comparison. At the scale of a 100 m \(\times\) 100 m network, in terms of throughput, compared with OAFS-IMFO, FC-RBAT, FRNSEER, and BOA-ACO, FQA has increased by 38.89%, 32.765%, 26.165%, and 8.72% on average, respectively. These data show that our protocol maintains the effectiveness of data transmission rates as the network expands. FQA has increased by 22.01%, 22,79%, 3.58%, and 2.04% on average at 200 m \(\times\) 200 m, respectively.  
Figure 9: Network energy consumption comparison in different scenarios: (**a**) scenario 1; (**b**) scenario 2; (**c**) scenario 3; (**d**) scenario 4.
## 5 Experiment Results  
### Experimental Instances  
To evaluate the effectiveness of the proposed IIG, 56 instances were generated based on the well-known Solomon benchmark instances for the VRPTW. These instances included three types, i.e., random instances, cluster instances, and semi-cluster instances and each instance contained 100 rescue sites. The expanded instances maintain the same number of customers, locations, service times, and time windows as the Solomon instances. Additionally, for each rescue site, a random number of survivors and their corresponding initial life strength were generated. All algorithms were implemented in MATLAB R2022a and run on an Intel Core i5-12490 PC with 16 GB of RAM. To ensure fair comparisons, a uniform maximum CPU time of 100 s was used as a stopping criterion for all of the algorithms. The final solutions, obtained after 30 independent runs for each comparison, were collected for performance analysis.
## 2 OGSMP-MIMO System Modeling
### OGSMP-MIMO System Principle and Detection Algorithm  
Assume an OGSMP-MIMO system with an \(N_{t}\) transmit antenna and \(N_{r}\) receiving antenna, as illustrated in Figure 2; in each symbol time, only \(N_{a}\) (\(1<N_{a}<N_{l}\)) antennas are activated, and there are \(N=C_{N_{t}}^{N_{t}}\) possible combinations of activated antennas. In contrast, the OGSMP-MIMO system requires that the number of active antenna combinations must be an integer power of two; hence, the actual number of antenna combinations used is \(N_{c}=2^{\lfloor\log_{2}N\rfloor}\), where \(\lfloor\cdot\rfloor\) represents a round-down function.  
Figure 1: VLC communication link model.  
The OGSMP-MIMO system sends independent modulation signals through an activated antenna combination. Assuming that the modulation order is \(L\), the number of transmitted bits at the same time is as follows:  
\[R_{OGSMP}=\left\lfloor\log_{2}C_{N_{t}}^{N_{t}}\right\rfloor+N_{a}\log_{2}L \tag{3}\]  
As illustrated in Figure 2, the transmission bit of the OGSMP-MIMO system comprises two parts: the modulated signal and bit signal mapped by the antenna combination. The modulated bit stream is:  
\[\mathbf{x_{u}}=[0,\cdots,0,A_{u},0,\cdots,0]^{T} \tag{4}\]  
where \(A_{u}\) denotes the modulation symbol transmitted by the \(u\)-th antenna. The modulated symbol vector is transmitted through the \(N_{r}\times N_{t}\) dimensional channel \(H\), where \(\eta\) is the photoelectric conversion efficiency, which is usually taken as \(1\). The received signal model can be expressed as:  
\[\mathbf{y}=\eta Hx_{u}+n\ y\in C_{N_{r}\times N_{t}} \tag{5}\]  
where \(n\in C_{N\times N_{t}}\) represents the additive white Gaussian noise. At \(A_{t}\) the receiving end, the sequence-number combination of the activated transmit antenna and the modulation symbol is discriminated by the maximum likelihood detection algorithm, and the original bit information is obtained by demapping [30]. The idea of the maximum likelihood (ML) algorithm is to obtain \(H_{x}\) by multiplying the activated antenna combination by the channel matrix \(H\), then calculate the Euclidean distance between \(y\) and \(H_{x}\), and determine the activated antenna serial number and constellation symbol, according to the minimum value of the Euclidean distance. The formula for the maximum likelihood detection algorithm is  
\[\left(\hat{k},\hat{s}\right)=\underset{k,s}{\mathrm{argmin}}\|y-\eta Hx\|_{F}^ {2} \tag{6}\]  
where \(\left\|\bullet\right\|_{F}\) represents the F-norm, \(\hat{k}\) represents the estimated activated transmit antenna index, \(\hat{s}\) represents the estimated value of the transmitted modulation symbol, and \(\eta\) denotes the photoelectric conversion efficiency. \(k\) and \(s\) represent the index of the activated transmit antenna and exact value of the transmitted modulation symbol, respectively.
## 4 Principles and Solving Steps of the Multiplier Path Following Method  
### Principles  
The multiplier method, an algorithm for solving optimization problems with equality constraints, was proposed by [PERSON] [33] and [PERSON] [34] simultaneously and independently in 1969. Its basic principle is that through the introduction of a Lagrange function and an appropriate penalty function, the original problem can be transformed into a series of unconstrained optimization problems. Due to the introduction of the Lagrange function and the appropriate penalty function, this method can surmount the \"sick\" imperfection in the Lagrange function with an exterior penalty function method.  
The path following method, an interior point algorithm for solving linear programming problems, was proposed by [PERSON] in 1984. Then, it was developed to solve quadratic programming problems [35]. This algorithm is a polynomial-time algorithm, insensitive to the problem size. Furthermore, the iterations always search within the feasible region towards the optimal solution, exhibiting small computational demands andgood convergence. The solution methodology leverages relaxed Karush-Kuhn-Tucker conditions (KKT conditions, necessary and sufficient conditions for the existence of the optimal solution) and the concept of a central path to maintain an approximately optimal search direction in each iteration, thereby transforming the problem of the optimal solution search into a problem of a central path search [36; 37; 38; 39].  
To address the mixed-constraint quadratic programming problem presented in Equation (14), this article combines the solution strategies of the two aforementioned methods to develop an improved optimization algorithm: the multiplier path following optimization algorithm. This method's core idea is as follows: First, the multiplier method is used to transform the mixed-constraint quadratic programming model into a quadratic programming model with only inequality constraints; then, the path following method is employed to solve this modified model, effectively integrating the two algorithms.  
4.1.1 Conversion of the Compound Constraints into Inequality Constraints Using the Multiplier Method  
For the quadratic programming problem in Formula (14), the augmented Lagrangian function can be defined as follows:  
\[\Phi(\mathbf{T},\mathbf{v},\beta)=f(\mathbf{T})-\mathbf{v}^{\mathrm{T}}(\mathbf{C_{\mathrm{e}}}\mathbf{ T}-\mathbf{f})+\frac{\beta}{2}(\mathbf{C_{\mathrm{e}}}\mathbf{T}-\mathbf{f})^{\mathrm{T}}(\mathbf{C_{ \mathrm{e}}}\mathbf{T}-\mathbf{f}) \tag{15}\]  
where \(\beta\) represents the penalty parameter; \(\mathbf{v}=(v_{1},v_{2},\cdots,v_{1})^{\mathrm{T}}\) represents the Lagrange multiplier vector, in which \(l\) is the number of rows in the coefficient matrix of equality constraint equation \(\mathbf{C_{\mathrm{e}}}\).  
Then, Formula (14) can be converted into  
\[\left\{\begin{array}{l}\min\Phi(\mathbf{T},\mathbf{v},\beta)\\ \mathrm{s.t.}\ \mathbf{AT}\geq\mathbf{b}\end{array}\right. \tag{16}\]
## 3 Evaluation Methods of the Performance of GSP
### Fatigue Life Performance
#### 3.2.2 Fatigue Correction Factor  
The correction factor or shift factor of fatigue life is an experiential effective coefficient expressing the relationship between the fatigue life in test and that in field. According to complex test conditions including material types, test methods, loading mode, and testing temperatures, the shift factor is difficult to be unified and determined through different studies.  
[PERSON] et al., calculated the fatigue correction coefficient of GSP as follows [119]:  
\[N_{f}=\frac{1}{5}\times\frac{1}{3}\times 0.50\times\frac{60}{365}\cdot N_{e}=5.48 \times 10^{-3}N_{e} \tag{2}\]  
Specifically, the intermittent time coefficient was selected as 5; the stress-reduction multiple of the fatigue life was 3 times; the transverse distribution coefficient was adopted as 0.5; and the unfavorable season days was 60 [119].  
Similarly, in another test, [PERSON] et al., considered different factors from [PERSON]: the intermittent time coefficient was 7; the crack propagation coefficient was 20; the Days number of the unfavorable season was 60; and the transverse distribution coefficient was 0.5. The result was expressed as follows [109]:  
\[N_{e}=\frac{1}{0.5}\times 7\times 20\times\frac{365}{60}\cdot N_{f}=1.703\times 1 0^{3}N_{f} \tag{3}\][PERSON] et al., indicated the fatigue life correction factor of GSP should be calculated based on IIFT data, considering the intermittent time and loading mode, as shown in Table 12 [117; 118].  
Due to an additional factor of 1.1 for the lateral load distribution, 45 was finally determined as the correction factor [117; 118]. However, this factor was still thought to be a conservative value, because the loading intermission in field was much longer than that in experiment.
## 2 Materials and Methods  
### VLE Imaging System  
The Nvision VLE Imaging System (NinePoint Medical, Inc., Bedford, MA, USA) integrates a second-generation form of OCT technology, termed optical frequency-domain imaging (OFDI) [16; 17; 18; 19]. The VLE system was composed of a disposable optical probe with an inflation system and an imaging console, which incorporates a swept light source, optical receiver, interferometer, and a data-acquisition computer. The light source consisted of a near infrared light (\(\lambda\) = 1250-1350 nm) that was transmitted into the catheter. At the distal end of the optical probe, a non-compliant balloon allowed a correct alignment in the esophagus for in-vivo imaging. During an automatic pullback of the optical probe, a 6 cm circumferential segment of the esophagus is scanned in 90 seconds. A VLE pullback acquires 1200 cross-sectional images with a sampling density of 50 um (voxel dimensions, 5.9 um \(\times\) 16.2 um \(\times\) 50 um). The axial and lateral resolution of a VLE image were approximately 7 um and 40 um, respectively. The penetration depth reached approximately 3 mm into tissue. For more comprehensive technical details we refer to previous publications [16; 17; 18; 19].
## 2 Methodology  
### Numerical Model Description  
In this research the OpenFOAM (Open Field Operation and Manipulation) model was utilized, which is an open-source computational fluid dynamics (CFD) program widely used in academia and industry to solve complex fluid dynamics problems through numerical methods. It provides a versatile collection of libraries that reproduces the simulation of fluid flow, turbulence, heat transfer, and other related physical processes. OpenFOAM offers various solvers and utilities that can be customized to address specific problems, making it a flexible and powerful tool in the field of computational engineering. For turbulent flow modeling by OpenFOAM, one of the primary approaches that is used frequently is solving the Reynolds-Averaged Navier-Stokes (RANS) equations. The RANS methodology simplifies the complex, instantaneous Navier-Stokes equations under turbulent conditions by decomposing the velocity and pressure fields into mean and fluctuating components. This results in a set of equations that describe the flow in terms of these averaged quantities. The RANS model adopts the Reynolds approximation of continuity and momentum equations as the following.  
\[\frac{\partial U_{i}}{\partial x_{i}}=0, \tag{1}\]  
\[\frac{\partial U_{i}}{\partial t}+U_{j}\frac{\partial U_{i}}{\partial x_{j}}= -\frac{1}{\rho}\frac{\partial P}{\partial x_{i}}+\frac{\partial}{\partial x_{j }}\left[v\left(\frac{\partial U_{i}}{\partial x_{j}}+\frac{\partial U_{j}}{ \partial x_{i}}\right)\right]-\frac{\partial}{\partial x_{j}}\overline{u_{i} u_{j}}, \tag{2}\]  
where \(U_{i}\) is the time -averaged velocity in the Cartesian coordinate; \(P\) is the pressure; \(\rho\) is the fluid density; \(v\) is the kinematic viscosity; \(u_{ij}\) is the velocity fluctuation; and \(\overline{u_{i}u_{j}}\) is the Reynolds stress. The \(k\)-\(\varepsilon\) model has been widely used in previous turbulence modeling, but it known to struggle in accurately simulating flows with adverse pressure gradients and leading to imprecise results in separated flows or strong curvature flows. Later, the \(k\)-\(w\) model addressed the limitations of the \(k\)-\(\varepsilon\) model by providing more accurate simulations of boundary layers. However, both the \(k\)-\(\varepsilon\) and \(k\)-\(w\) models are inadequate in modeling separated flows under strong adverse pressure gradients [31; 32; 33]. To address these shortcomings, the \(k\)-\(\omega\) SST model proposed by [PERSON] [34] has been used for the turbulence model, which uses the robust capabilities of the \(k\)-\(\omega\) model for the near-wall regions and the \(k\)-\(\varepsilon\) model for the free flow regions. It has been shown that in complex geometric arrangements such as the subway model in this research, the \(k\)-\(\omega\) SST model can reliably calculate flow characteristics which was the main reason for its utilization in this study.