esa-sceva/satcom-chunk-collection huggingface dataset:
# Nature-Inspired Optimization Algorithms for the 3D Reconstruction of Porous Media
## 2 Material and Methods
### Nature-Inspired Optimization Algorithms
#### 2.2.5 Artificial Bee Colony (ABC)  
The artificial bee colony (ABC) algorithm is a heuristic optimization algorithm based on the intelligent behavior of a honey bee swarm, which was proposed by [PERSON] and [PERSON] [28]. In the ABC algorithm, the colony of artificial bees contains three groups of bees: employed bees (bees going to the food source visited by themselves previously), onlookers (bees waiting to decide on a food source), and scouts (bees carrying out a random search). In the ABC algorithm, the first half of the colony consists of employed artificial bees and the second half consists of onlookers. For every food source (a possible solution), there is only one employed bee (the number of employed bees is equal to the number of food sources around the hive). Each cycle of the search consists of three steps: sending the employed bees to the food sources and then measuring their nectar amounts, selection of the food sources by the onlookers after the information is shared by the employed bees and determining the nectar amount of the foods, and determining the scout bees and then sending them to possible food sources [28; 42]. The colony has to optimize the overall efficiency of nectar collection, where each possible solution has a specific amount of nectar. An artificial employed or onlooker bee probabilistically produces a modification on the position (solution) in her memory for finding a new food source and tests the nectar amount (fitness value) of the new source (new solution).  
An onlooker bee \(i\) evaluates the nectar information taken from all the employed bees and chooses a food source with a probability related to its nectar amount according to the following definition:  
\[P_{i}=\frac{fit_{i}}{\sum_{n=1}^{NP}fit_{n}}, \tag{8}\]  
where \(fit_{i}\) is the fitness value of the \(i\)th solution. This probabilistic selection is equal to a roulette wheel selection mechanism.  
Each employed bee \(x_{i}=\{x_{i1},x_{i2},\ \ldots,x_{in}\}\) generates a new candidate solution (food position) in the neighborhood of its present position according to the following rule:  
\[x_{ij}^{t+1}=x_{ij}^{t}+\varphi_{ij}\Big{(}x_{ij}^{t}-x_{kj}^{t}\Big{)}, \tag{9}\]  
where \(k\in\{1,2,3,\ldots,BN\}\) and \(j\in\{1,2,3,\ldots,n\}\), \(j\
eq i\) are randomly chosen indexes, \(BN\) is the number of employed bees, and \(\varphi_{ij}\in[-1,1]\) is a random number.
# Concept Drift Adaptation Methods under the Deep Learning Framework: A Literature Review
## 6 Conclusions
**Abbreviations**  
The following abbreviations are used in this manuscript:  
OCDD  
one-class drift detector  
CDT_MSW  
concept drift type identification method based on multi-sliding windows  
KSWIN  
Kolmogorov-Smirnov test detector  
LD3  
label dependency drift detector  
STUDD  
student-teacher approach for unsupervised drift detection  
CDCMS  
concept drift handling based on clustering in the model space  
DMDDM  
diversity measure drift detection method  
I-LSTM  
improved long short-term memory  
DDM  
drift detection method  
MLP  
multilayer perceptron  
CNN  
convolutional neural network  
RNN  
recurrent neural network  
DNN  
deep neural network  
SEOA  
selective ensemble-based online adaptive deep neural network  
BODL  
bilevel online deep learning framework  
NADINE  
neural network with dynamically evolved capacity  
CIDD-ADDONN  
Adadelta optimizer-based deep neural networks with concept drift detection  
ADWIN  
adaptive sliding-window drift detection technology  
OARNN  
online adaptive recurrent neural network  
TPE  
tree-structured Parzen estimator  
ONU-SHO  
opposition-based novel updating spotted hyena optimization  
ONU-SHO-RNN  
ONU-SHO-based RNN  
adaptive behavioral-based incremental batch learning malware variant  
detection model  
SPC  
statistical process control  
MUSE-RNN  
multilayer self-evolving recurrent neural network  
LSTM  
long short-term memory  
CI  
continuous integration  
GA  
genetic algorithm  
MOMBD-CDD  
multi-objective metaheuristic optimization-based big data analytics with concept drift detection  
STEPD  
Statistical Test of Equal Proportions method  
GSO  
glowworm swarm optimization  
Bi-LSTM  
bidirectional long short-term memory  
CUSUM  
cumulative sum  
EWMA  
exponentially weighted moving average  
AD-LSTM  
adaptive LSTM framework  
SDWIN  
sliding-window algorithm  
TP-ALS  
two-phase adaptive learning strategy  
ECNN  
evolutive convolutional neural network  
online CNN-based model selection using performance gradient-based saliency maps  
DIH  
deep incremental hashing  
ROC  
region of competence
# Mobile Robot for Security Applications in Remotely Operated Advanced Reactors
## 9 Introduction to Mobile Dog Robots
### Technical Capabilities
#### 9.3.2 Communication  
The SPOT robot uses a gigabit connection for real-time communication and has an ethernet connector, a low-voltage communications connector (TTL Serial), a GPS antenna, and a USB. In contrast, the Ghost Robotics robot uses Wi-Fi-6, 4G, 5G, and FirstNet radios for communication. The ANYmal Robotics robot uses Wi-Fi and 4G LTE for communication. The SPOT robot dominates communication, as real-time communication is essential, especially for security and surveillance monitoring.
# Online and Offline Model for Housekeeping Order Assignment Based on Dynamic Programming Algorithm
## 6 Conclusions  
For order assignment, this paper establishes offline and online order assignment models based on dynamic programming, both of which can achieve optimal order assignment. For the online order assignment model, an order backlog mechanism is added to prevent  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & \(Sum\) & \(m\) & \(a\) & \(b\) \\ \hline Online assignment model & 1405.14 & 709 & 56.19\% & 95.22\% \\ Nearest distance assignment model & 810.25 & 1200 & 28.85\% & 56.76\% \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparison with the nearest distance assignment model.  
the algorithm from falling into the local optimization dilemma. The online assignment model has strong applicability and can be applied to other resource assignment problems.  
In order to demonstrate the stability of the model, a sensitivity analysis is conducted. By analyzing the effects of the two variables, household travel speed and order update time, it is concluded that the household's moving speed is sensitive from 1 km/h to 10 km/h and insensitive from 10 km/h to 60 km/h. The order update time is insensitive in the range of [0.1 h, 0.5 h] and [0.6 h, 2 h], but sensitive in the range of [0.5 h, 0.6 h], thus proving the superiority and stability of the model proposed in this paper. Furthermore, the online order assignment model is compared with the nearest distance assignment model. The results show that the total weighted score of the nearest distance assignment model is 810.25, while the proposed online assignment model is 1405.14. The higher the total weighted score, the better the service quality of the company. Therefore, the online assignment model is superior to the nearest distance assignment model.  
However, the order assignment models do not address practical issues such as temporary order cancellations and temporary leave requests by housekeeping, which require further research.  
Conceptualization, J.C.; methodology, J.C. and C.L.; software, Y.S. and J.L.; validation, Y.S. and J.S.; formal analysis, J.C and J.S.; investigation, C.L. and J.L.; resources, J.C.; data curation, J.C. and Y.S.; writing--original draft preparation, J.C., J.S. and J.L.; writing--review and editing, J.C., Y.S. and C.L.; visualization, Y.S.; supervision, C.L., X.S. and J.C.; project administration, C.L. and J.C.; funding acquisition, X.S. and J.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (grant number: 52005435), the Natural Science Foundation of Jiangsu Province (grant number: BK20190873), the Undergraduate Education Reform Project of Yangzhou University (Special Funding for Mathematical Contest in Modeling) (grant number: xkjs2023049), and the Shuangchuang Program of Jiangsu Province of China (grant number: (2019)30756).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
# Tolerance-Aided Interference Degradation for Optical OFDM in Power-Constrained Systems
## 2 Problem Description
### Clipping Effects of the Transmitted Signals  
In OWC systems, the inherent characteristics of light sources dictate the utilization of only intensity modulation and direct detection (IM/DD) techniques. This necessitates the modulation of transmitted signals onto the input current of light sources, thereby confining the signals to non-negative values. Owing to stringent peak radiation power constraints, any signals surpassing these prescribed limits must undergo a process of hard clipping, inevitably engendering clipping distortions.  
OFDM signals are generated through inverse fast Fourier transform (IFFT), a process known to yield a relatively high peak-to-average power ratio (PAPR). Consequently, this inherent property of OFDM signals results in the presence of both negative and high amplitude values, which invariably fall within the zones of zero clipping and peak clipping, as visually depicted in Figure 1. Therefore, the transmitted signals experiencing zero and peak power clipping are severely distorted. Figure 1 further demonstrates the nonlinearity in the light source's photocurrent-optical power response, introducing distortion during the modulation of OFDM signals. To rectify this nonlinear behavior and establish a linear relationship between output optical power and input current, pre-distortion emerges as a viable solution, as outlined in prior research [15]. Through the application of pre-distortion techniques, a linearized relationship between input current and output power is attained, as evidenced in Figure 1. In this paper, it is assumed that pre-distortion methods are employed in the system under consideration. Note that this pre-distortion is applied to linearize the current-optical power response, ensuring the accurate transmission of the desired waveform.
# On Combining DeepSnake and Global Saliency for Detection of Orchard Apples
## 3 Target Fruit Detection Based on Fusion Improved Saliency Calculation
### Saliency Map Generation  
The Lab model consists of three elements, brightness (L) and two color channels (a and b). The a includes colors from dark green (low brightness value) to gray (middle brightness value) to bright pink (high brightness value); b is from bright blue (low brightness value) to gray (medium brightness value) to yellow (high brightness value). Therefore, this fusion of color will provide with a bright effect. The transformation formulas from RGB to the Lab color space are as follows [24]:  
\[\left\{\begin{array}{l}L=0.607R+0.174G+0.201B\\ a=1.4749(0.2213R-0.339G+0.1177B)+128\\ b=0.6245(0.1949R+0.6057G-0.8006B)+128\end{array}\right. \tag{4}\]  
Inspired by [25; 26] and according to the characteristic of the Lab image, the saliency value of each pixel can be calculated as the sum of the euclidean distances from other pixels in the image. The \(L\) channel in the Lab color space was separated, and the image data under the obtained Lab color space is detected by the global \(L\) component contrast calculation method to obtain the saliency map \(S(l_{k})\) under each condition. Thus a saliency map S was generated by the formula as follows:  
\[S(I_{k})=\sum_{\forall I_{k}\in I}\|I_{k}-I_{i}\| \tag{5}\]  
where \(I_{i}\) is the \(L\) channel value of the pixel i, \(I_{i}\in[0,255]\), \(I_{k}\) is the pixels in the image.  
A final saliency map was obtained as follows:  
\[S(I_{k})=\sum\limits_{i=1}^{n}S(I_{k}) \tag{6}\]  
where \(S(I_{k})\) to the right of the equal sign is a salient map corresponding to the Lab color space image, i is the number of fused iterations. \(n\) is the number of iterations, and the best effect can be achieved when \(n\) is 18 through experiments. Image fusion processing can enhance the brightness of the fruit part, which is more distinct between the fruit part and the background.  
The RGB color channel is separated, and the color difference is calculated as the pixel value of the result \(\text{Se}(I_{k})\) of this step:  
\[\text{Se}(I_{k})=|I_{kR}-I_{kG}| \tag{7}\]  
where \(I_{kG}\) is the G channel value of the pixel \(I_{k}\), and \(I_{kR}\) is the R channel value of the pixel \(I_{k}\).  
The results of color difference method were further fused with the results of saliency detection (\(S(I_{k})\))) (from Equation (6)) and color difference (\(\text{Se}(I_{k})\)) (from Equation (7)) according to the following formula:  
\[S(I_{k})=S(I_{k})+\text{Se}(I_{k}) \tag{8}\]  
where \(S(I_{k})\) to the right of the equal sign is the results of saliency detection of pissel \(I_{k}\), and \(\text{Se}(I_{k})\) is the result color difference of the pixel \(I_{k}\).  
We do an open operation on the OTSU [27] result. In this paper, a square with a side length of 27 is used as a structural element for open processing.
# A Study on the Surrogate-Based Optimization of Flexible Wings Considering a Flutter Constraint +
## 2 Methodology
### Surrogate Model Training, Validation, and Testing
#### 2.2.2 Testing  
After training and validation, the model is tested on a dataset it has not seen before, known as the test set, to gauge the model's effectiveness. Learning curves further evaluate the model's generalization. The final surrogate models used for testing and the later optimization process are based on the optimal hyperparameters obtained for each model after tuning. The suitability of surrogate models for the aeroelastic analyses required for the optimization process was visualized using parameters from the testing dataset. The results for each model are shown in Figure 2.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Method** & **R\({}^{2}\) (-)** & **RMSE (\%)** \\ \hline Extra Tree Regressor & 0.921 & 8.92 \\ Decision Tree Regressor & 0.749 & 15.84 \\ Linear Regression & 0.245 & 27.5 \\ Bayesian Ridge & 0.245 & 27.5 \\ Lasso & 0.244 & 27.5 \\ ARDR Regression & 0.243 & 27.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Flutter speed performance metrics.  
From the results, the surrogate models accurately predict the mass and lift coefficient. Discrepancies in the drag coefficient suggest complexities in aeroelastic interactions in certain regions of the design space. The flutter speed showed the greatest difference, with an R\({}^{2}\) of 0.921. The flutter model might have been overfitting, as can be seen from the learning curves in Figure 3, and more data might improve its performance. Challenges in flutter modeling could arise from estimating flutter speed using the iterative p-k method.  
Figure 3: Learning curves of flutter velocity model.  
Figure 2: Visual comparison of the results obtained using the SHARPy simulations (in blue) and the surrogate model (in orange) in the testing phase of the SMs.
## 4 Result and Discussion
### Pipeline Stratum Model under 3D Effect
#### 4.1.4 Influence Distance  
To find out the possible influence range of the 3D effect in the pipeline stratum, this study used the model that set \(n\) as 0.1. The section size of the pipeline was 1.5 (W) \(\times\) 2 (D) m, _dep_ = 2 m, _dy_ = 3 m, _dx_ = 3 m, \(L\) = 42 m, and the L1-L5 lines were 1.5, 0, 3, 6, and 9 m away from the boundary of the pipeline.  
As shown in the image on the left in Figure 7, the profiles at L1 and L3 had a significant 3D effect, whereas the 3D effect of the L4 line profile gradually became less obvious, and in the L5 line profile, there was no 3D effect. Therefore, the 3D effect influence distance was 6 m, and gradually faded after 6 m.
# Secrecy Enhancing Scheme for Spatial Modulation Using Antenna Selection and Artificial Noise
## 3 Secrecy-Enhancing Spatial Modulation Scheme
### Optimum Selection of the Transmit Antenna Combination  
Assuming that the data are transmitted by the \(j\)-th antenna, the received signal power at Eve is \(\|\hat{\mathbf{g}}\mathbf{T}_{k}\mathbf{e}_{j}\|^{2}\), which is defined as a quantity, called leakage for Bob [16]. Generally, we can assume that the power of the received signal at Bob, i.e., \(\|\hat{\mathbf{h}}\mathbf{T}_{k}\mathbf{e}_{j}\|^{2}\), is sufficiently large compared to the power of the AWGN, (i.e., \(\sigma^{2}\)) and that \(\|\hat{\mathbf{h}}\mathbf{T}_{k}\mathbf{e}_{j}\|^{2}\) is sufficiently large compared to \(\|\hat{\mathbf{g}}\mathbf{T}_{k}\mathbf{e}_{j}\|^{2}\). Based on these two assumptions, we define the signal-to-leakage noise ratio (SLNR) for the \(j\)-th channel of the \(k\)-th combination as:  
\[\varphi_{j}(\mathbf{T}_{k})=\frac{\|\hat{\mathbf{h}}\mathbf{T}_{k}\mathbf{e}_{ j}\|^{2}}{\|\hat{\mathbf{g}}\mathbf{T}_{k}\mathbf{e}_{j}\|^{2}+\sigma^{2}}. \tag{14}\]  
Assume that the selected \(N_{t}\) antennas are activated with equal probability to transmit the information symbols. The optimum matrix \(\mathbf{T}_{k}\) can be found by maximizing SLNR as follows:  
\[\max\ \sum_{j=1}^{N_{t}}\varphi_{j}(\mathbf{T}_{k}) \tag{15}\] \[\text{subject to}\ \mathbf{T}_{k}\in\{\mathbf{T}_{1},\mathbf{T}_{2}, \ldots,\mathbf{T}_{P}\}.\]  
However, the above optimum solution of \(\mathbf{T}_{k}\) with exhaustive search requires a high complexity. We propose the following complexity reduced method.  
Assume all of the transmit antennas are independent. Then, the SLNR at the \(l\)-th, \(\varphi_{l}\), can be represented by:  
\[\varphi_{l}=\frac{\|\hat{h}_{l}\|^{2}}{\|\hat{g}_{l}\|^{2}+\sigma^{2}}, \tag{16}\]  
where \(\hat{h}_{l}\) and \(\hat{g}_{l}\) denote the \(l\)-th elements of the channel \(\hat{\mathbf{h}}\) and \(\hat{\mathbf{g}}\), respectively. In this way, we calculate the SLNR values for all transmit antennas, and the calculated SLNR values are sorted in descending order such that:  
\[\underbrace{\varphi_{\pi_{1}}\geq\varphi_{\pi_{2}}\geq\cdots\geq\varphi_{\pi_ {N_{l}}}}_{N_{t}\ \text{selected antennas}}\geq\cdots\geq\varphi_{\pi_{N_{l}}}, \tag{17}\]  
where \(\{\pi_{1},\pi_{2},\cdots,\pi_{N_{a}}\}\) is an ordered permutation set of \(\{1,2,\cdots,N_{a}\}\). Therefore, the optimization problem in (15) is equivalent to selecting \(\varphi_{\pi_{d}}\) from (17) for \(1\leq d\leq N_{t}\).
# Retrofitting of a Multi-Span Simply Supported Bridge into a Semi-Integral Bridge
###### Abstract  
Thousands of multi-span, simply supported beam bridges with short or medium spans have been built in China. They often suffer from problems of cracks in the link slabs over piers, and the deterioration and damage of deck expansion joints at abutments. To address these problems, one approach is to retrofit them by converting the simply supported box beams into continuous structures over the piers and jointless bridges over the abutments. This paper discusses the design methodology and details for retrofitting the Jinpu Bridge in Zhangzhou, Fujian, China, from a simply supported bridge into a semi-integral bridge, in which semi-fixed dowel joints are used to connect the superstructure and the substructure, including piers and abutments. Simultaneously, the finite element software is used to calculate the internal forces and displacements of the structure. The analysis reveals an 11.1% reduction in the maximum positive moment at the midspan of the main beam in the semi-integral bridge compared to the simply supported bridge. However, the shear forces at the interior pier increase by 6.4%. According to the response spectrum analysis, the maximum longitudinal displacement of the semi-integral bridge's main beam is 11.6 mm, reduced by 80.1% compared to the simply supported bridge under a dead load and earthquake effects. The maximum bending moment and shear force on the pier of the semi-integral bridge are 984.7 kN\(\cdot\)m and 312.6 kN, respectively, both below their ultimate bearing capacities. The maximum displacement at the top of the pier is 7.7 mm, which is below the allowable 52.4 mm displacement. The calculated results conform to the design requirements specified by the code.  
keywords: retrofitting; semi-fixed dowel joints; semi-integral bridge; finite element model; response spectrum +
Footnote †: journal: Applied sciences
## 2 Instrument Overview  
### Geometric Structure of the Microwave Antenna  
The subject, a GEO microwave radiometer (i.e., GeoMWR in [18]), adopts the classical offset Cassegrain reflector antenna attached with a planar folding mirror (Figure 1). It is mainly composed of a reflector system and a multi-band quasi-optical feed (QOF) system.  
The reflector system consists of three reflectors (i.e., a main reflector (MR), a first sub-reflector (FSR), and a second sub-reflector (SSR)). The MR is an offset parabolic reflector (\(f\) is the focal length of the parent paraboloid), which is cut from one side of the rotationally symmetric parabolic reflector by a cylinder with an aperture of \(D_{0}\) and an offset height of \(h_{0}\). The FSR is an offset hyperbolic reflector (\(2a\) and \(2c\) are the major axis length and inter-focal distance of the parent hyperboloid, respectively), which is cut from the concave hyperboloid by a cylinder with an aperture of \(D_{1}\) and an offset height of \(h_{1}\). The aperture of the FSR is much smaller than that of the MR (i.e., \(D_{1}\ll D_{0}\)), and its virtual focus and focal axis coincide with those of the MR. The MR and the FSR form the classical offset Cassegrain antenna. The SSR is a planar folding mirror (\(D_{2}\) is the aperture of the SSR), which is used to fold the beam propagation path to achieve a compact system layout.  
The QOF system consists of a lightweight moving part, frequency-selective surfaces, wire grid polarizers, a series of planar, hyperbolic, and ellipsoidal mirrors, and corrugated feed horns used at different frequency bands. The lightweight moving part makes a rapid circular rotation around its rotating shaft, generating conical scanning of the antenna beam. After the frequency and polarization separations by the frequency-selective surfaces and wire grid polarizers, the incoming broadband signal beam is split into separate beams and transmitted by a series of planar, hyperbolic, and ellipsoidal mirrors to the input feed horns working at different frequency bands [18; 27]. Compared with the traditional multi-beam focal plane array of the horns, the QOF system can receive and transmit multiple frequency bands simultaneously and therefore allow the electrical boresights of different frequency bands to be coaxially aligned on a common boresight [28].