esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### 3D Interframe Displacement Estimation
#### 2.4.1 Single-Probe Zero-Degree  
In the single-probe zero-degree method, the axial (\({d_{0}}^{ax}\)), lateral (\({d_{0}}^{lat}\)), and elevational (\({d_{0}}^{el}\)) inter-frame displacements estimates (\(d\)) based on either \(A_{0}\) or \(B_{0}\) were used as basis for strain estimation. Please note that the superscript denotes the displacement estimation direction relative to the corresponding probe.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**3D Interframe Displacement Estimation**} & \multicolumn{4}{c}{**3D Strain Estimation**} \\ \cline{2-7}  & \multicolumn{2}{c}{**Window Size \(\pm\) Search Range**} & \multicolumn{2}{c}{**Regularization**} & \\ \cline{2-7}
**Axis Direction** & \multicolumn{2}{c}{**(Beamforming Grid Samples)**} & \multicolumn{2}{c}{**(Displacement Grid Samples)**} & \\ \cline{2-7}  & \multicolumn{2}{c}{**Median Filtering**} & \multicolumn{2}{c}{**Least Square Window Sizes (Ax, Lat, Ele)**} & \\ \cline{2-7}  & \multicolumn{2}{c}{**Sample Iteration**} & \multicolumn{2}{c}{**Sub-Sample Iteration**} & \multicolumn{2}{c}{**(Kernel Sample Size Ax, Lat, Ele)**} & \\ \cline{2-7}  & \multicolumn{2}{c}{**Inter-Frame**} & \multicolumn{2}{c}{**Tracked**} & \multicolumn{1}{c}{**Ax**} & \multicolumn{1}{c}{**Lat**} & \multicolumn{1}{c}{**Ele**} \\ \hline Axial & \(81\pm 15\) & \(33\pm 5\) & \(7,11,3\) & \(7,11,3\) & \(17,7,3\) & \(5,25,3\) & \(5,7,15\) \\ Lateral & \(13\pm 4\) & \(7\pm 4\) & \(7,11,3\) & \(7,11,3\) & \(17,7,3\) & \(5,25,3\) & \(5,7,15\) \\ Elevational & \(3\pm 0\) & \(3\pm 2\) & \(28,20,5\) & \(7,11,3\) & \(33,11,7\) & \(9,49,7\) & \(9,11,61\) \\ \hline \end{tabular}
\end{table}
Table 2: 2-step cross-correlation-based displacement and strain estimation settings.  
Figure 5: The axis definition and nomenclature for the probe related directions: axial, lateral, and elevational. Vertical, horizontal, and longitudinal directions are related to the phantom orientation.
## 4 Results and Discussion
### HIL Experimental Platform and Parameter Setting  
A HIL experimental platform of a single-leg servo system is established to assess the proposed energy management strategy's performance. As shown in Figure 6, the SC and battery packs are connected to a DC/DC converter. Two converters supply the total power of the legged robot (the two motors). The NI PXI 1082 (NI, TX, USA) is used for data collection, sending control signals to the converters from the Matlab/Simulink environment (the energy management strategy). The robot is controlled by a PC to achieve different moves. The legged robot is designed as a one-degree-freedom system for safety purposes. The parameters of the single-leg servo system are listed in Tables 1 and 2.  
The robot motions on a single-legged servo platform and performs current acquisition by jumping to different heights to obtain original data. The original data are combined and extended with gait information and height information to generate data on different operating conditions, which are used to train the LSTM and predict future power. These  
Figure 6: The hardware-in-the-loop experimental platform of a single-leg servo system.  
data are also used to verify the effectiveness of the energy management strategy proposed in this paper. Note that the single-legged servo platform is part of a four-legged robot, e.g., a robot dog. The movement of the single-legged servo platform is similar to the load power when the robot dog is walking, running, or jumping. The difference lies in the total power demand: for the robot dog, the load power is the sum of four alternating legs, which can fluctuate more violently. However, the periodic pattern of the load power is similar. Thus, the validation of this study can be extended to the four-legged robot when the coordination control is added.  
The energy component of the experimental setup consists of a Li-ion battery pack and a supercapacitor pack. The batteries and supercapacitors are both connected to separate DC/DC converters, which are, in turn, connected to the DC bus and supply power to the legged robot. The operation of the DC/DC converters is managed by a STM32 series microcontroller (Geneva, Switzerland), which receives the current reference from the NI PXI 1082. The parameters of the HESS are listed in Table 3.
## 3 Proposed Method
### Improved MLP Model
#### 3.1.1 Model Network Structure  
The improved MLP model mainly consists of an input layer, four fully connected layers, two MAGU modules, an output layer, and some activation functions. Four fully connected layers and two MAGU modules constitute the hidden layer of the improved MLP model. The hidden layer mainly performs a series of linear transformations and feature extractions on the input data. The sigmoid activation function is applied in the MAGU module. The fully connected layer uses the rectified linear unit (ReLU) activation function, which can improve the nonlinear ability of the model and speed up its training.  
In Figure 3, assuming that the input of the presented model is \(x\), its output after passing through the first fully connected layer and the first ReLU activation function is  
\[x_{1}=F_{1}(W_{1}x+b_{1}) \tag{3}\]  
where \(W_{1}\) and \(b_{1}\) denote the weight matrix and bias vector of the first linear layer, \(x\) means the input data, and \(F_{1}\) expresses the ReLU activation function. The ReLU activation function [33] is  
\[F_{1}(x)=\left\{\begin{array}{ccc}x&if&x\geq 0\\ 0&if&x<0\end{array}\right. \tag{4}\]  
The output of the second linear layer can be expressed as  
\[x_{2}=W_{2}x_{1}+b_{2} \tag{5}\]  
After inputting \(x_{2}\) into the first MAGU, the output is  
\[x_{3}=g_{1}\odot u_{1}+(1-g_{1})\odot x_{2} \tag{6}\]  
where \(\odot\) means elementwise multiplication, \(g_{1}\) represents the output of the gating operation in MAGU, and \(u_{1}\) denotes the output of the MHA after linear transformation in MAGU.  
The output of \(x_{3}\) after the third linear layer and the second ReLU activation function can be expressed as  
\[x_{4}=F_{1}(W_{3}x_{3}+b_{3})\]  
Figure 2: Overall flow chart of the presented scheme.  
The output of the data after the fourth linear layer is  
\[x_{5}=W_{4}x_{4}+b_{4} \tag{8}\]  
After taking \(x_{5}\) as the input of the second MAGU module, the output can be expressed as  
\[x_{6}=g_{2}\odot u_{2}+(1-g_{2})\odot x_{5} \tag{9}\]  
The final output of the proposed model is  
\[y=F_{2}(W_{5}x_{6}+b_{5}) \tag{10}\]  
where \(F_{2}\) denotes the linear activation function. The linear activation function is adopted in the output layer of the improved MLP model. The final estimation result is obtained through the identity mapping of the linear activation function.  
The number of neurons in the input layer of the presented model is set to be consistent with the number of feature vectors in the dataset. The first and second fully connected layers have 128 neurons. The number of fully connected layer neurons of the first MAGU module is 128. The third and fourth fully connected layers have 64 neurons. The number of fully connected layer neurons of the second MAGU module is 64. The manuscript is studying a regression problem, and the output is a scalar. Therefore, the number of neurons in the output layer is 1.
# Three Diverse Applications of General-Purpose Parameter Optimization Algorithm
## 6 Application to CFD Simulation
### Overview  
Nowadays, _air conditioners (ACs)_ are equipped in many rooms in houses, schools, factories, and offices to offer comfortable environments for humans and machines. On the other hand, global warming has been escalated due to overconsumption of fossil fuels. Therefore, the proper use of ACs has become more important around the world.  
Then, the estimation or prediction of the distributions of the temperature or humidity in a room using a simulation model will be useful to properly control ACs. By estimating the room environment changes under various actions, it will be possible to decide when ACs be turned on or off. Even, the timing to open or close windows in the room can be selected.  
ACs rely on a limited number of sensors for measuring the temperature and humidity in the room. Therefore, to obtain the distribution of the temperature or humidity in a room, additional sensors should be used together by externally allocated in the room, which is not practical. Moreover, the sensors cannot predict future changes of them.  
To estimate or predict the distributions in a room together with sensors, we are investigating the CFD simulation using _OpenFOAM_ software [26]. Then, the optimization of the parameters in _OpenFOAM_ is critical in order to fit well the simulation results with the corresponding measured ones.
## 1 Introduction  
Food, dairy, maritime, water systems, oil, paper, opticians, dentistry, hospitals, and domestic households are several examples of industries that are affected by the biofilms [1; 2; 3; 4; 5; 6; 7; 8]. The occurrence of biofilms leads to serious problems, e.g., product spoilage, reduced production efficiency, corrosion, unsightliness, infection, pipe blockages, and equipment failure [9; 10]. A biofilm is defined as an organized aggregate of microorganisms living within an extracellular polymeric matrix that they produce. This is irreversibly attached to either a material surface or a living surface, which cannot be easily removed due to its complex structure [11; 12]. This complex layer of extracellular polymeric substances (EPS) protects them from antibiotics, disinfectants, and dynamic environments [13; 14]. The intercellular communication within the biofilm rapidly stimulates the up and down-regulation of gene expression. This property enables temporary adaptations, such as phenotypic variation and the ability to survive in nutrient deficient conditions. These pitfalls and challenges create an industrial interest in developing materials, methods, and tools that can prevent or remove the formation of biofilms.  
The food and beverage industries operate their production units under stringent hygiene standards to verify high-quality products. However, the presence of biofilms can cause hygienic problems in the case of pathogenic organisms. Biofilms consist of complex microbial communities that adhere to the surfaces fencing processes, like cleaning and disinfection [15]. Biofilms are observed in processing equipment and open surfaces, resulting in food safety problems or a weakening of the efficiency of the production lines [16]. It is important to control the formation of biofilms to avoid corrosion and contamination. This review provides a brief of the biofuling process, including the production mechanisms and control of microbial adhesion.
## 1 Introduction
The method obtained images suitable for the learning model by employing super-resolution (SR) for normalization and data augmentation. The technique enhanced the resolution of the images, and through proper labeling, could detect small objects, achieving similar or superior performance to the existing bridge damage detection models. In this study, we utilized that method and proposed a new model of combination training with bridge members. Subsequently, by integrating the two approaches, we proposed the bridge damaged-object-detection deep-combination framework (BDODC-F). In Step 1, to ensure diversity in the type of damaged objects and model performance improvement, a DL-based SR module was employed to improve the performance of individual models. In Step 2, for better performance and optimization of the integrated models, we proposed the bridge damage-detection DL combination model by individually training the models with bridge members. To accurately reflect the similarities and differences of shapes and features between the classes to be classified, the model was trained with bridge members such as abutment and slab for the damage type. Then, a combination model structure was designed, resulting in a single DL combination model. Consequently, to maximize the model detection performance, a framework was proposed to encompass the entire process of data input, dataset construction, training, model deployment, and detection. The proposed framework exhibited improved accuracy, i.e., the mean average precision (mAP), by 191.6% in image quality enhancement and 110.6% in the combined model. The contributions of this study include:a framework capable of automated bridge damage safety diagnosis through image capture, - an SR-based image pre-processing process suitable for identifying bridge damaged objects, - a damaged-object-detection model framework optimized for each member of the bridge, - research and development of a damaged-object-detection model with high accuracy. The features of the proposed framework can be used to construct a system that can automatically identify damaged objects in situ in real time by collecting images through mobile devices based on the proposed framework. The structure of the rest of the paper is organized as follows. In Section 2, the extant literature related to this work is reviewed. Section 3 presents the theoretical and experimental aspects of the proposed technology. Section 4 describes the results of the experiments conducted to evaluate the performance of the technology. Section 5 concludes the research with a summary of the study, limitations, and outlook.
## 1 Introduction
At present, the seismic research of underground engineering mostly focuses on urban subway shield tunnels, subway stations, comprehensive pipe galleries and mountain tunnels [16]. For a large-diameter double-layer pipe jacking tunnel in complex soil and rock combination stratum, the difference in the stratum conditions and interaction between the pipe jacking tunnels may make its internal force more complex. At present, there are few relevant studies that reasonably consider the above factors. To study the seismic response characteristics of large-diameter vertical double-layer pipe jacking tunnels in complex soil and rock strata, a dynamic nonlinear analysis model was established according to a real power electric pipe jacking project in Jinan. The numerical method was first verified using a shaking table test. Subsequently, the acceleration, displacement and stress response characteristics of the vertical double-layer pipe jacking tunnel under earthquake motion were studied by numerical calculation; then the influences of interlayer soil depth and soil-rock composite stratum were analyzed. This research provides a reference for the seismic design and performance evaluation of large diameter vertical double-layer overlapping pipe jacking and similar tunnel engineering.
# Optimal Pattern Synthesis of Linear Array Antennas Using the Nonlinear Chaotic Grey Wolf Algorithm
## 5 Array Antenna  
This section presents the model and array factor of LAAs and planar arrays. The problem of optimizing the PSLL and objective function is detailed in [32].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Objective Function** & **Algorithm** & **Min** & **Mean** & **SD** \\ \hline \multirow{4}{*}{\(f_{1}(x)\)} & NCGWO & \(7.6893\times 10^{-99}\) & \(2.6133\times 10^{-100}\) & \(1.4032\times 10^{-99}\) \\  & GWO & \(2.60\times 10^{-73}\) & \(3.55\times 10^{-71}\) & \(1.18\times 10^{-70}\) \\  & PSO & \(5.01\times 10^{-17}\) & \(5.96\times 10^{-15}\) & \(2.54\times 10^{-14}\) \\  & GA & \(2.23\times 10^{-31}\) & \(1.3\times 10^{-3}\) & \(0.105\) \\ \hline \multirow{4}{*}{\(f_{2}(x)\)} & NCGWO & 27.10 & 26.01 & 0.59 \\  & GWO & 34.45 & 36.40 & 0.75 \\  & PSO & 10.574 & 54.55 & 56.00 \\  & GA & 23.89 & 284.04 & 255.57 \\ \hline \multirow{4}{*}{\(f_{5}(x)\)} & NCGWO & 0 & 0 & 0 \\  & GWO & 0 & \(2.27\times 10^{-15}\) & \(1.11\times 10^{-14}\) \\  & PSO & 21.89 & 77.89 & 24.37 \\  & GA & 11.94 & 35.97 & 12.26 \\  & NCGWO & 0 & 0 & 0 \\  & GWO & 0 & \(6.24\times 10^{-4}\) & \(2.52\times 10^{-3}\) \\  & PSO & 0 & \(1.48\times 10^{-2}\) & \(2.38\times 10^{-2}\) \\  & GA & 0 & \(1.7\times 10^{-3}\) & \(1.46\times 10^{-2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Result comparison with 50 independent runs.  
Figure 6: Illustration of the two-dimensional versions of the test functions. (**a**,**b**) are unimodal functions; (**c**,**d**) are multimodal functions.
# Two Interval Upper-Bound \(Q\)-Function Approximations with Applications
## 3 Two Novel Interval Upper-Bound \(Q\)-Function Approximations  
**Lemma 1**.: _There exists a unique \(x_{1}\in[0,+\infty)\) such that \(Q_{1}^{[7]}(x)\leq Q_{2}^{[7]}(x)\) if and only if \(x\leq x_{1}\) (\(x\in[0,+\infty]\))._  
**Proof.** Inequality \(Q_{1}^{[7]}(x)\leq Q_{2}^{[7]}(x)\) is equivalent to  
\[(3\pi-8)x+\pi\sqrt{x^{2}+8}\leq 4\sqrt{(\pi-2)^{2}x^{2}+2\pi}.\]  
Squaring both sides and regrouping factors yields  
\[x\sqrt{x^{2}+8}\leq x^{2}+\frac{4(4-\pi)}{3\pi-8}.\]  
Again, squaring both sides of the inequality and rearranging terms gives  
\[x\leq x_{1}:=\frac{4-\pi}{\sqrt{2(\pi-3)(3\pi-8)}}\]  
That finishes the proof of the lemma.  
Let us define the following composition of the \(Q\)-function approximations, \(Q^{com\_1}(x)\), such that \(Q^{com\_1}(x)=Q_{1}^{[7]}(x)\) for \  
novel approximations of the \(Q\)-function are presented as well. Although it is not very visible from Figure 1, we show below that the proposed composite approximation of the \(Q\)-function, specified for \(x_{1}\) = 1.3514, that is, for intervals determined from Lemma 1 as [0, 1.3514) and [1.3514, \(\infty\)), outperforms in terms of accuracy the \(Q\)-function approximations used for comparison purposes. The results of approximating the \(Q\)-function for different values of \(x\) are presented in Table 2.  
Figure 2 presents the comparison of the absolute relative error calculated for the proposed first interval upper-bound approximation of the \(Q\)-function and selected ones from the literature [7; 11; 12; 13; 14]. As visible from Figure 2, the approximation specified by Lemma 1 for \(x_{1}\) = 1.3514 has a minimal absolute relative error and is more accurate than other considered \(Q\)-function approximations in almost the entire observed range of argument values.  
Figure 1: Comparison of several \(Q\)-function approximations.  
Figure 3 presents the comparison of the absolute relative errors of the two novel interval upper-bound approximations of the \(Q\)-function with the one from [13]. Although with the first interval upper-bound approximation of the \(Q\)-function, \(Q^{com,1}(x)\), a better approximation (lower values of the absolute relative error) has been obtained in a wide range of argument values compared to the \(Q\)-function approximation from [13], there is still an interval in which better results in terms of accuracy can be obtained by using a slightly more complex second interval upper-bound approximation of the \(Q\)-function, \(Q^{com,2}(x)\). In other words, we ascertained that an additional improvement of the accuracy in the whole range of argument values could be obtained by prudently forming the second composite interval upper-bound approximation of the \(Q\)-function specified by Lemma 2.
# Which Local Search Operator Works Best for the Open-Loop TSP?
## 2 Local Search
### The Uniqueness of the Operators  
All operators create a large number of neighbor solutions. The set of solutions generated by one operator can be similar to the set of another operator. Given the same initial solution, if all the candidate solutions generated by one operator were a subset of the solutions generated by another operator, then this approach would be redundant. Next, we study the uniqueness of these methods.  
**Relocate:** The highlighted target in the start tour in Figure 8 can swap to six different positions. By analyzing these six results, we can observe that the tours 1 to 3 are also possible outcomes of 3-permute. Tours 1 and 2 are also possible outcomes of 2-opt and tour 1 can be an outcome of link swap. However, the rest of the tours are unique, and only possible to produce by the relocate operator. Only the relocate operator can produce the best result in this example (tour 4).  
Figure 7: Original tour and three alternatives created by link swap (left). In each case, the link x is replaced by a new link connecting to one or both terminal nodes. In the O–Mopsi example (right), the tour is improved by 137m.  
**2-opt:** In this method, two links are removed and two new ones are created. An example is shown in Figure 9, where the first removed link is fixed as the red one, which is highlighted, and the second one is varied. The resulting six new tours are shown. The other operators can generate four of them as well; however, two of them are unique. The best outcome is uniquely generated by the 2-opt operator.  
**3-permute:** The example in Figure 10 reveals that the operator does not produce even a single unique result. Therefore, if both relocate and 2-opt are used, this operator is redundant. Relocate can find four out of the five solutions, and 2-opt can find three.  
A potential benefit of the 3-permute operator is that it needs to explore only a smaller neighborhood, O(\(N\)), in comparison to O(\(N^{2}\)) of relocate and 2-opt. However, this smaller neighborhood is not extensive enough, as it misses the solutions shown in Figures 8 and 9. Our experiments also show that the local search using 3-permute is consistently outperformed by the other operators. We, therefore, do not consider this operator further.  
**Link swap:** This operator creates three alternatives for a selected link. Two of these are redundant with 2-opt, but the one which connects the two previous terminal points is unique (Figure 11). It is  
Figure 8: Six neighboring solutions created by relocate, of which three are unique.  
Figure 10: Six neighboring solutions created by 3–permute, but none of them is unique.  
also the best one. Link swap is therefore worthwhile in cases when the terminal points are close to each other.
## 5 Characterization of Isolated-Aperture Surfaces  
GSTCs are initially derived for single-layer metasurfaces consisting of isolated scatterers [18], which is named as metafilms. However, GSTCs based on surface susceptibilities are not appropriate for metascreens composed of periodic isolated apertures. Starting from [PERSON]'s equations, another form of GSTCs specially for metascreens is derived in [36], where the EM field relations across surfaces are described by not only surface susceptibilities but also other parameters called surface porosities. Different from GSTCs for metascreens in [36], an alternative characterization method for zero-thickness metascreens is given in this paper, which is directly derived from GSTCs for metafilms combined with [PERSON]'s principle. Furthermore, only the surface susceptibilities are required to compute scattering coefficients of given metascreens through the method introduced below.