esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Strategy Improved Flamingo Search Algorithm for Global Optimization
## 4 Experiments and Analysis  
### Test Functions and Evaluation Criteria  
The experimental environment of this paper adopts: a Windows 10 64-bit operating system, Intel Core i5-6300 HQ CPU, 8 GB memory, Python programming language, and MATLAB R2018a graphing software. To evaluate the performance of the algorithm, three sets of test experiments were conducted. These included the use of 23 general benchmark functions [11] for performance testing, which effectively verified the merit-seeking ability of IFSA. Table 1 shows that the tested functions can be classified into single\(-\)peaked functions (F1-F9), multi-peaked functions (F10-F16), and fixed-dimension functions (F17-F23). The algorithm's performance was comprehensively measured using different types of test functions. To ensure fairness, the population size was uniformly set to 50, the termination condition was set as 300 iterations, each benchmark problem was run independently for 30 times, and the performance evaluation criteria were based on the mean, standard deviation and optimal value of the average results of 30 iterations. The mean value reflects the algorithm's ability to find the best, the standard deviation reflects the algorithm's robustness, and the optimal value reflects the algorithm's best search accuracy. The optimal results are bolded in the experiment section.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Function Formula** &
\begin{tabular}{c} **Region of** \\ **Search** \\ \end{tabular} & **Best** & **Dimensionality** \\ \hline \(F_{1}(x)=\sum_{i=1}^{n}x_{i}^{2}\) & \([-100,\,100]\) & 0 & 20 \\ \(F_{2}(x)=\sum_{i=1}^{n}[x_{i}]+\prod_{i=1}^{n}[x_{i}]\) & \([-10,\,10]\) & 0 & 20 \\ \(F_{3}(x)=\sum_{i=1}^{n}\left(\sum_{j=1}^{n}x_{j}\right)^{2}\) & \([-100,\,100]\) & 0 & 20 \\ \(F_{4}(x)=\max_{i}\{|x_{i}|,1\leq i\leq n\}\) & \([-100,\,100]\) & 0 & 20 \\ \(F_{5}(x)=\sum_{i=1}^{n-1}\left[100\times\left(x_{i+1}-x_{i}^{2}\right)^{2}+ \left(x_{i}-1\right)^{2}\right]\) & \([-2.048,\,-2.048]\) & 0 & 20 \\ \(F_{6}(x)=\sum_{i=1}^{n}\left([x_{i}+0.5]\right)^{2}\) & \([-100,\,100]\) & 0 & 20 \\ \(F_{7}(x)=\sum_{i=1}^{n}i\times x_{i}^{4}+\text{random}[0,1)\) & \([-1.28,\,1.28]\) & 0 & 20 \\ \(F_{8}(x)=\sum_{i=1}^{n}|x_{i}|^{i+1}\) & \([-1,\,1]\) & 0 & 20 \\ \(F_{9}(x)=\sum_{i=1}^{n}ix_{i}^{2}\) & \([-10,\,10]\) & 0 & 20 \\ \(F_{10}(x)=\sum_{i=1}^{n}-x_{i}\times\sin\left(\sqrt{|x_{i}|}\right)\) & \([-500,\,500]\) & \(-418.9829\,\times\) & 20 \\ \(F_{11}(x)=\sum_{i=1}^{n}\left[x_{i}^{2}-10\times\cos(2\pi x_{i})+10\right]\) & \([-5.12,\,5.12]\) & 0 & 20 \\ \(F_{12}(x)=-20\times\exp\left(-0.2\sqrt{\frac{1}{\pi}}\sum_{i=1}^{n}x_{i}^{2} \right)-\exp\left(\frac{1}{\pi}\sum_{i=1}^{n}\cos(2\pi x_{i})\right)+
# Optimization of Active Distribution Network Operation with SOP Considering Reverse Power Flow
## 3 Actual Maximum Transfer Capacity of Reverse Power Flow in the Distribution Network
### Constraints  
#### 4.3.1 Power Distribution Grid Flow Constraint  
To model the distribution network using the \(Distflow\) branch model [18], the power flow constraints of distribution network m can be expressed as:  
\[\sum_{j\in\Omega_{b}}\left(P_{m,j,t}^{*2}\ -I_{m,ji,t}^{*2}r_{m,ji}^{*}\right)+P_{m,i,t}^{*}=\sum_{i\in\Omega_{b}}\left(P_{m,ik,t}^{*}\ \right) \tag{38}\]\[U^{*2}_{m,i,t}=P^{*DG}_{m,i,t}+P^{*SOP}_{m,i,t}-P^{*L}_{m,i,t} \tag{42}\]  
\[Q^{*}_{m,i,t}=Q^{*DC}_{m,i,t}+Q^{*SOP}_{m,i,t}+Q^{*CB}_{m,i,t}-Q^{*L}_{m,i,t} \tag{43}\]  
\[U^{*2}_{m,i,t}=U^{*2}_{m,i,t}+Q^{*2}_{m,i,t}+Q^{*2}_{m,i,t} \tag{44}\]  
\[I^{*2}_{m,i,t}=I^{*2}_{m,i,t}+Q^{*2}_{m,i,t} \tag{45}\]  
In the distribution network \(m\), \(P^{*}_{m,i,t}\) and \(Q^{*}_{m,i,t}\) denote the active and reactive power injected at node \(i\), respectively, while \(P^{*}_{m,i,t}\) and \(Q^{*}_{m,i,t}\) represent the active and reactive power flowing out from node \(i\). The resistance and reactance on line ij are \(r_{m,ji}\) and \(x_{m,ji}\), respectively. The active power injection at node i during time period t for the DG, SOP, and load in distribution network m are \(P_{m,DC,i,t}\), \(P^{*}_{m,SOP,i,t}\), and \(P^{*}_{m,Load,i,t}\), respectively. The reactive power injections at node i during time period t for the DG, SOP, CBs, and load are \(Q^{*}_{m,DC,i,t}\), \(Q^{*}_{CB,i,t}\), \(Q^{*}_{SOP,i,t}\), and \(Q^{*}_{Load,i,t}\), respectively. The node voltage at i is \(U^{*}_{i,t}\), with \(U^{*}_{min}\) and \(U^{*}_{max}\) as the minimum and maximum voltage values, respectively. The branch current ij and the maximum allowable current on the line are \(I^{*}_{m,ij,t}\) and \(I^{*}_{max}\), respectively.
## 4 Discussion
Therefore, these results may suggest that PTE reduces the inflammatory actions in LTA-induced RAW cells via suppressing the activation of the ERK and NF-\(\kappa\)B pathways, as it stated above that ERK is associated with NF-\(\kappa\)B activation [36]. HO-1, an inducible stress protein, has antioxidant properties and plays an essential role in protection of oxidative stress in chronic disease [39]. In addition, the cellular protective role of HO-1 was reported via stopping a variety of inflammatory responses [40]. Various antioxidants can induce HO-1 to prevent oxidative damage and, therefore, compounds that can induce HO-1 expression may be favorable in the treatment of oxidative damage. [PERSON] et al. [41] found quercetin stimulates HO-1 protein expression in LPS induced bv-2 microglia cells. Similarly, we found PTE obviously raised HO-1 expression in LTA-induced cells. Moreover, the effect of PTE on antioxidant enzyme catalase (CAT) and glutathione was also established in LTA-induced cells. It shows that PTE elevates the activity CAT and the level of GSH. CAT enzyme is catalytically reacting on \(\mathrm{H}_{2}\mathrm{O}_{2}\) to form \(\mathrm{H}_{2}\mathrm{O}\)[42]. A previous study revealed that curcumin, a natural anti-inflammatory compound, raises the activity of CAT to recover RAW cells from the LPS-induced ROS formation [43]. Glutathione (GSH), a cellular antioxidant found in various natural sources, contains L-cysteine, L-glutamic acid, and glycine [44]. Earlier investigations have shown that GSH inhibits oxidative stress-induced cell damage, and its decline contributes to the development of several diseases [45]. In addition, [PERSON] et al. [46] found that liquiritin apioside, a main flavonoid component of licorice, had antioxidant properties by increasing the level of GSH against cigarette smoke-induced oxidative stress in lung epithelial cells. Here, the observed elevation of antioxidant defense molecules HO-1, CAT and GSH by PTE treatment indicates that PTE may at least be partially associated with the antioxidant mechanisms against LTA-induced inflammation in RAW cells.
# Task-Driven Virtual Machine Optimization Placement Model and Algorithm
## 3 System Model and Problem Description
### Estimation of User Rental Costs  
The allocated CPU resources of a VM are closely related to its ability to execute tasks efficiently. The greater the CPU resources available, the quicker a task can be completed, thereby reducing the actual execution time. However, for cloud service providers, a higher CPU performance inevitably leads to higher operational costs owing to the increased power consumption and cooling requirements. This results in cloud service providers charging a higher rental fee. Thus, to balance the efficiency of the cloud data center with the interests of users, this study also examined user rental costs.  
Inspired by [17], the execution costs for user tasks mainly include: the rental cost of VM computing resources, transmission cost of bandwidth, and rental cost of physical resources consumed by the running VM.  
The rental cost of VM computing resources per unit of time can be estimated using Equation (4):  
\[C_{j}^{exec}=\left(v_{j}^{cpu}+v_{j}^{mem}\right)\times\left(c_{j}^{cpu}+c_{ j}^{mem}\right) \tag{4}\]  
where \(v_{j}^{mem}\) represents the memory capacity of \(v_{j}\), \(c_{j}^{cpu}\) denotes the rental prices for CPU of \(v_{j}\) and \(c_{j}^{mem}\) indicates the rental prices for memory of \(v_{j}\).  
The transmission cost of bandwidth can be estimated using Equation (5):  
\[C_{j}^{tran}=\sum_{j=1}^{M}\left(\frac{\sum_{i=1}^{L}x_{i,j}(sf_{i}+so_{i})}{v _{j}^{bw}}\times c_{j}^{bw}\right) \tag{5}\]  
where \(x_{i,j}\) is the same as that in Equation (3), \(sf_{i}\) and \(so_{i}\) represents the input and output file size of \(t_{i}\), respectively. \(v_{j}^{bw}\) represents the memory capacity of \(v_{j}\) and \(c_{j}^{bw}\) expresses the rental prices for bandwidth of \(v_{j}\).  
The rental cost for the physical resources consumed by the running VM on PM is estimated using Equation (6):  
\[C_{k}^{ent}=y_{j,k}c_{k} \tag{6}\]  
where \(y_{j,k}=1\) indicates \(v_{j}\) is deployed on \(h_{k}\), \(y_{j,k}=0\) otherwise. \(c_{k}\) represents the rental price of \(h_{k}\). Therefore, the user rental costs can be estimated using Equation (7):  
\[\begin{array}{l}\text{cost}\\ =\sum\limits_{i=1}^{L}\sum\limits_{j=1}^{M}\Big{(}x_{i,j}E_{i,j}C_{j}^{exc} \Big{)}+\sum\limits_{i=1}^{L}\sum\limits_{j=1}^{M}C_{j}^{tran}+\sum\limits_{k= 1}^{N}C_{k}^{ rent}\\ =\sum\limits_{i=1}^{L}\sum\limits_{j=1}^{M}\Big{(}x_{i,j}E_{i,j}C_{j}^{exc}+C_ {j}^{tran}\Big{)}+\sum\limits_{k=1}^{N}C_{k}^{ rent}\end{array} \tag{7}\]
## 7 The Influence of the Constraint Range on the Optimization Results  
This section provides a description of the optimization parameter setting process and an analysis of the sensitivity of the constraint ranges during optimization. The Xiangshan Port bridge is examined using the sum of the bending energies of the girder and the pylon as the objective function. Notably, preliminary calculations indicate that the displacement and cable force constraints exhibit higher sensitivity, while the allowable stress range in the steel box girder is comparatively large, rendering the structural response insensitive to it. Therefore, this section employs the multiplier path following optimization algorithm to optimize the finished state of the cable-stayed bridge, using the constraint on the main girder's vertical displacement, the constraint on the horizontal displacement of the pylon, the constraint on the stay cable force, and the constraint on the cable force distribution parameter as the variables. This analysis investigates the influence of the constraint ranges on the optimization results. The finite element model and the parameter values used in this optimization process are consistent with those presented in Section 5.  
The Influence of the Constraint Range for the Main Girder's Vertical Displacement on the Optimization Results  
Holding the other constraints constant, the constraint range for the main girder's vertical displacement was varied from \(\left|\delta_{\mathrm{gi}}\right|\leq 500\,\mathrm{mm}\) to \(\left|\delta_{\mathrm{gi}}\right|\leq 50\,\mathrm{mm}\) to assess its influence on the optimization results. Figure 17 depicts the variations in the structural states corresponding to decreasing the constraint ranges for the main girder's vertical displacement. Variations in the constraint range for the main girder's vertical displacement significantly affect the vertical displacement of the main girder, the maximum tensile stress in the main  
Figure 16: Cable force under dead load.  
girder top chord, the horizontal displacement of the top of the pylon in the bridge, and the maximum positive and negative bending moments in the bridge pylon. The changes in the other structural states are less than 5%. The maximum vertical deflection of the main girder and the horizontal displacement of the top of the pylon in the bridge both increase as the constraint range for the main girder's vertical displacement decreases. Conversely, the maximum upward deflection of the main girder, the maximum tensile stress in the main girder's top chord, and the maximum positive and negative bending moments in the bridge pylon all decrease with a narrowing constraint range. Upon comparing the various results, a constraint on the main girder's vertical displacement of \(|\delta_{\text{gi}}|\leq 150\) mm leads to smoother structural alignment and lower maximum positive and negative bending moments in both the main girder and bridge pylons, as well as reduced cable force distribution parameters. Consequently, this constraint range is deemed appropriate.
## 3 Methods
### Cross-Platform Gait Analysis and Fall Detection Wearable Device  
In modern medical, the gait analysis, such as knee joint rehabilitation, and fall detection were getting important, especially for elders [1]. In the existed gait analysis, some of them could be executed at home to save time and money for patients and medical staff. Hence, how to combine health care with technology for residential gait analysis becomes an important issue gradually [3; 4; 5; 13; 14].  
Although the residential gait analysis could reduce the time money, and manpower, how to judge the correct motion of gait analysis without any help of medical staff becomes an important issue [15; 16]. In the same condition, to detect the fall accident correctly was another important issue. Hence, we integrated the Arduino board with gyroscope, three-axis accelerometer, and Bluetooth to develop a gait analysis and fall detection wearable device, GAFDWD, to address the above issues. Compared with [9; 10; 11; 12], the existed fall detection wearable device only focused on fall detection without considering gait analysis. They often aimed to improve the accuracy of fall detection, low power, and low cost. None of them addressed the APP in mobile devices. Moreover, they addressed nothing for cross-platform in mobile devices, such as Android and iOS. Compared with other software, there is no way to call the emergency contact directly, and the emergency contact cannot receive the notification in time. Hence, the above mention was the advantages of CPGAFDWD compared to other software. Since CPGAFDWD included wearable, cross-platform APP, and cloud database, the remote accessing by the Internet was needed. It is also the limitation of CPGAFDWD.  
To upload the detected data from GAFDWD to cloud database in real time, to design an APP for GAFDWD was required. Since the APP was depended on the platform of mobile device to be used, how to ensure the APP we designed could be applied for different platforms was an important issue. Hence, a cross-platform APP, CPAP, was proposed for GAFDWD in this paper. In CPAP, it received the sensed data captured from CPAP via Bluetooth, and then sent these data to cloud database, such as MongoDB, via the Internet in real time. The users could track the status of gait analysis and receive the alarm of fall detection by CPAP immediately.  
In GAFDWD, the hardware included gyroscope, three-axis accelerometer, and Bluetooth, as shown in Figure 3. The gyroscope and three-axis accelerometer were used as GY-521. The captured signal from GY-521 was transferred into the sensed data of gait analysis and fall detection by fusion calculation, such as rotation matrix, quaternion, and Euler angle format, respectively. The Bluetooth was used as HM-10 to transmit the sensed data after fusion calculation to CPAP via Bluetooth. Then CPAP uploaded the data to cloud database as MongoDB in real time. Figure 4 showed the mechanism for GAFDWD.  
Figure 3: Hardware of CPGAFDWD.
## 4 Trajectory Planning
### Trajectory Planning Mehthod
The position of the trajectory points could be obtained directly from the trajectory analytical formula; the velocity and acceleration between every point are planned to make the speed smooth. The mechanical joint motions could be calculated from the position array of points by inverse solving. Then, the motor control commands could be determined from mechanical joint motions. The semi-close loop position control is selected in the trajectory tracking experiment, and it is more suitable than other controls in this accurate trajectory training. To guarantee the patient's safety, a human joint check function is built. It could verify and display the human joint angle through the kinematic calculations. If the angle exceeds the preset or limitation value, it would stop the training to avoid the secondary damage. Training trajectories provided by this robot include the circle, straight, curve, helix and other spatial trajectories. Because the methods are almost same, no more details are shown in this section.
# Fusion of Bilateral 2 DPCA Information for Image Reconstruction and Recognition
## 4 Experiments
### The Effect of Reshaping Images  
As discussed in Section 3.6, DB2 DPCA is closely related to other PCA algorithms under some extreme conditions when images are reshaped. Therefore, we proceed to test the effect of reshaping images on classification performances. When an image is reshaped, its structure information is partially lost. In order to preserve the original image structure as much as possible, pixels in the image matrices are stacked column-wise as in Figure 1. By changing the image height \(p\), a series of new databases can be generated. We only  
Figure 3: Sample images from the Extended Yale B face database and their variants. The first column shows the sample images and the remaining columns show their variants.  
consider the cases when the image height \(p\) satisfies \(1\leq p\leq\sqrt{d}\) and \(d\) is divisible by \(p\). In this way, we can avoid introducing noises in the reshaping procedure.  
Since a \(p\times q\) image can be reshaped into a \(h\times w\) image in many different ways, it is necessary to consider the mixed effects of reordering and reshaping. To this end, we first generate two randomly reordered databases as in Figure 3 and then reshape the reordered databases by stacking pixels column-wise as in Figure 1 to generate two series of new databases. The original database and its reordered and reshaped variants are called variants 0-2 in order. Note that the same reordering and reshaping rule should be applied to all images in the original database in order to keep the image structure consistent. Figure 4 shows the reordered and reshaped images of a sample image from the Extended Yale B face database. The image heights in the eight columns of the figure are 28, 32, 36, 42, 48, 56, 63 and 72 in order.  
For illustration, we focus on the Extended Yale B face database with CRC as the classifier again. Since there are three series of reordered and reshaped databases, we fix the feature number \(k\) in the each algorithm as in [21]. Specifically, we choose the smallest \(k\) satisfying that the percentage of total variance explained by the first \(k\) principal components is larger than a preset threshold \(\theta\). The threshold \(\theta\) is set to 0.5, 0.6, 0.7 and 0.8 in the experiments. For the two bilateral algorithms, i.e., (2D)\({}^{2}\)PCA and DB2 DPCA, we choose \(k\) in the same manner for two laterals separately. Then, we conduct image classification tasks on the reordered and reshaped databases in order to examine the effect of reshaping images.
## 4 Proposed Scheme
### Analysis of Transmission Probability
The probability of such event is  
\[\prod_{k=1,k\
eq i}^{i+1}\left(\frac{y_{i}-t}{y_{k}}\right) \tag{6}\]  
Thus the probability of Sensor \(i\) transmission being successful during \([y_{i}-y_{i+1},y_{i}-y_{i+2}]\) is  
\[\int_{y_{i}-y_{i+2}}^{y_{i}-y_{i+2}}\prod_{k=1,k\
eq i}^{i+1}\left(\frac{y_{i }-t}{y_{k}}\right)\frac{dt}{y_{i}} \tag{7}\]  
By making the similar change of variables \(s=t/y_{i}\), (7) is given by  
\[\left(\prod_{k=1}^{i+1}\frac{y_{i}}{y_{k}}\right)\int_{1-z_{i+1}}^{1-z_{i+2}} (1-s)^{i}ds=\left(\prod_{k=1}^{i+1}z_{k}^{-1}\right)\left\{\frac{z_{i+1}^{i+1} -z_{i+2}^{i+1}}{i+1}\right\} \tag{8}\]  
By continuing the similar computations for the probability of successful transmission during intervals \([y_{i}-y_{i+2},y_{i}-y_{i+3}],[y_{i}-y_{i+3},y_{i}-y_{i+4}],\ldots\), we obtain the expression (3). Let us consider a special case such that \(y_{1}\geq y_{2}=y_{3}=\ldots=y_{n}\). Let us denote the probability of Sensor 1 winning the frame by \(p_{1}\). From Proposition 1 it is given by  
\[p_{1}=1-z_{2}+\frac{1}{z_{2}^{n-1}}\frac{z_{2}^{n}}{n}=1-\left(\frac{n-1}{n} \right)z_{2} \tag{9}\]where \(z_{2}=y_{2}/y_{1}\). The probability of a Sensor other than Sensor 1 becoming the winner of frame is given by \((1-p_{1})/(n-1)=z_{2}/n=y_{2}/(ny_{1})\) due to the symmetry among \(n-1\) sensors except Sensor 1. Thus these sensors gets the probability proportional to their score \(y_{2}\). Thus this can serve as a good approximation when \(n-1\) sensors have small scores which are similar to each other, and there is one sensor with a large score, thus the probability is approximately \(y_{i}/(ny_{1})\). Note the analysis is based on the continuous approximation, in particular the collision has been ignored. The actual probability of successful transmission and Equation (3) will be compared in Section 5 via numerical methods.
# Three-Dimensional Multifaceted Attention Encoder-Decoder Networks for Pulmonary Nodule Detection
## 5 Conclusions  
This paper proposes a multi-task deep learning algorithm, the MAED model, for pulmonary nodule detection and false positive nodule screening. We combined the self-attention module with the LRA block to form the multifaceted attention block, which enabled the model to establish irregular long-range dependencies based on the features of the nodules, making the model better able to deal with the irregular lesions of pulmonary nodules. We integrated the multifaceted self-attention block with the encoder-decoder structure in the CNN with each other, which enabled the proposed model to model the local texture features of nodules while establishing an effective long-range dependency, making the model better able to identify the fine nodules. The performance of the model was further improved using methods, such as multiscale modules and focal loss functions. We conducted extensive experiments on the LUNA16 dataset to validate that the MAED model had the effectiveness of each component, and the overall performance of the model had an average sensitivity of 89.1% at seven predefined FPs/scans. The model proposed in this paper has better performance compared to state-of-the-art single- or multi-stage detection methods.  
Representativeness/racial bias will occur when the datasets used to develop AI algorithms are not sufficiently diverse to represent many different population groups and/or characteristics. Therefore, in future work, we will collect more clinical data from different regions/countries to avoid the over-concentration of data leading to representativeness/ethnicity bias of the model. In addition, we will further expand the pulmonary nodule detection model into a pulmonary nodule segmentation model to provide more help to radiologists in their diagnostic work.  
Conceptualization, K.C.; Methodology, K.C. and Z.W.; Validation, Z.W.; Data Curation, H.T. and Z.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 6: Visualization of the results of LRA-generated masks.