esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
Figure 1: The average percentage of antibiotic-resistant bacteria in the water of Large Rudnickie Lake. The frequency of occurrence of the \(qnrS\) gene in the individual samples at the same location did not differ significantly. The average frequency of the gene was higher in mesophilic bacteria isolated from water samples taken at the bathing site located on the northern shore of the lake compared to the bathing site on the eastern shore (Figure 3). This suggests that the elevated presence of this gene was unlikely to be related to the inflow of river water feeding the lake from the east. At present, we do not yet have complete data to provide a meaningful estimate of the magnitude of the threat posed by antibiotic resistance agents in recreationally used water bodies. The quantitative studies conducted use both culture-based methods and molecular techniques. In the case of molecular techniques, an important consideration is the proper selection of target genes. As reported by [PERSON] et al. (1988), such genes could  
Figure 3: Comparison of the frequency of occurrence of \(qnrS\) gene in the bathing areas of the Great Rudnickie Lake. Figure 2: Image of agarose gel showing PCR amplification of detected _bla_TEM genes in the water samples. be, for example, \(int\)I, \(mcr\)-1, \(su1\), \(tet\)W, \(bla\)TEM, \(qnr\)A, and a number of others. The results obtained in this study confirm the suitability of the application of \(bla\)TEM and \(qnr\) genes as indicators of recreational water contamination with resistance factors. The results of this study conducted on the Great Rudnickie Lake showed that recreational waters in Central Europe may be a site for the occurrence and the spread of bacteria showing resistance to frequently used antibiotics, and thus help to signal another serious problem related to the phenomenon of drug resistance. Commonly isolated resistance genes have been detected in antibiotic-resistant strains, which may suggest that bacteria present in the bathing area have a predisposition to both fluoroquinoline and \(\beta\)-lactam antibiotics. It can also be mentioned that parallel studies of our own (not yet published) on the occurrence of bacteria in one of the wastewater treatment plants located in the area in question showed that there were significant amounts of antibiotic-resistant bacteria in the treated wastewater discharged into the environment. Their abundance for some antibiotics, including \(\beta\)-lactam antibiotics, reached as high as \(10^{3}\) CFU/cm\({}^{3}\), while resistance was related to such pharmaceuticals as amoxicillin, rifampicin, vancomycin, and nitrofurantoin, among others. In this context, the fact that treated wastewater from one of the nearby treatment plants is discharged into the river that feeds with its waters into the studied water body cannot be overlooked. Thus, it can be suggested that the occurrence of antibiotic-resistant microorganisms in the waters of Great Rudnickie Lake may be part of a broader phenomenon resulting from the possibility of the spread of bacteria due to the overlap of their various sources, including municipal and agricultural.
# Efficient Resource Allocation for Beam-Hopping-Based Multi-Satellite Communication Systems
## 4 Online Resource Allocation Scheme
### Problem Formulation  
First, let us define the available power of satellite \(j\) at the \(k\)th time-slot as \(B_{k}^{j}\) which is given as  
\[\begin{split} B_{1}^{j}&=P_{max},\\ B_{k}^{j}&=B_{k-1}^{j}-\sum_{m,n}p_{m,n(k-1)}^{j}(k=2,3,\ldots K).\end{split} \tag{20}\]  
The online problem with \(K\) time slots is to design \(K\) resource allocation schemes \((\mathbf{A}_{1},\ldots,\mathbf{A}_{K})\) and \((\mathbf{P}_{1},\ldots,\mathbf{P}_{K})\). For the first time slot, we facing the optimization problem:  
\[\begin{split}\max_{\mathbf{A}_{1},\mathbf{P}_{1}}\sum_{j,m,n}& B_{sc}\log_{2}\Bigl{(}1+\gamma_{m,n1}^{j}\Bigr{)}\\ &+\mathbb{E}\biggl{\{}\sum_{k=2}^{K}\sum_{j,m,n}B_{sc}\log_{2} \Bigl{(}1+\gamma_{m,nk}^{j}\Bigr{)}\biggr{\}}\\ s.t.& R1:\sum_{m,n}p_{m,n1}^{j}\leq B_{1}^{j},\forall j,\\ R2:& p_{m,n1}^{j}\geq 0,\forall j,\forall m,\forall n,\end{split} \tag{21}\]  
In the above optimization problem, the first item represents the system throughput achieved in the first time slot, and the second item represents the expected system throughput achieved in the future \(K-1\) time slots. Therefore, the optimization problem is to balance the throughput between the first time slot and the future \(K-1\) time slots. Constraint \(R1\) indicates that the total power allocated in the first time-slot is not greater than the current remaining allocatable total power.  
Under instant channel information, throughput balancing for multiple time slots is a dynamic process. Over time, the channel gain information gradually changes from previously unknown to known, and at the same time, online optimization problems continue to evolve forward:  
\[\begin{split}\max_{\mathbf{A}_{k^{\prime}},\mathbf{P}_{k^{\prime} }^{j}}&\sum_{j,m,n}B_{sc}\log_{2}\Bigl{(}1+\gamma_{m,nk^{\prime}}^ {j}\Bigr{)}\\ &+\mathbb{E}\Biggl{\{}\sum_{(k=k^{\prime}+1)}^{K}\sum_{j,m,n}B_{sc }\log_{2}\Bigl{(}1+\gamma_{m,nk}^{j}\Bigr{)}\Biggr{\}}\\ s.t.& R1:\sum_{m,n}p_{m,nk^{\prime}}^{j}\leq B_{k^{ \prime}}^{j},\forall j,\\ R2:& p_{m,nk^{\prime}}^{j}\geq 0,\forall j,\forall m, \forall n,\\ R3:& B_{k^{\prime}}^{j}=& B_{k^{\prime }-1}^{j}-\sum_{m,n}p_{m,n(k^{\prime}-1)}^{j},\forall j,\end{split} \tag{22}\]  
which is a subproblem of (15). Due to the fact that the future channel gains are random variables, this problem is inherently stochastic, and the current channel gains will have an impact on resource allocation schemes for different time-slots in the future, thus coupling optimization problems for different time-slots.
The in-process capability and suitability of the speckle photography method for two-dimensional deformation measurement in the image plane with resolutions of less than 20 nm [11] has already been demonstrated in several manufacturing applications [12; 13; 14; 15]. However, since mechanical machining causes at least a biaxial stress state and, thus, a triaxial strain state in the workpiece via the machining forces, a reconstruction of three-dimensional deformation fields is required for a complete metrological description of the loads. Even though original speckle photography is only suitable for two-dimensional in-plane measurements, various method derivatives exist to obtain out-of-plane deformation information as well. In this context, [PERSON] et al. [16] propose a combination of speckle photography and speckle interferometry. The robust setup uses only one camera and can determine three-dimensional displacements with a resolution of 0.6 \(\upmu\)m. The method introduced by [PERSON] [17] uses the decorrelation of the speckle patterns associated with the out-of-plane motion, i.e., the speckle correlation functions changing with the motion are evaluated. The determined measurement uncertainties here are significantly lower than in the approach of [PERSON] et al. [16] and are in the range of one tenth of the wavelength. Therefore, the aim of this paper is the realization of a solely speckle-photographic in-process measurement system for the acquisition of three-dimensional load fields. In a first practical implementation, the deformation fields in a manufacturing rolling process are to be measured. The open questions in the context of speckle-photographic 3D deformation measurement in manufacturing processes are to what extent such a measurement system is suitable for use in the harsh manufacturing environment and to what extent the proposed algorithms are sufficient for in-process measurement. Furthermore, it must be clarified in which functional relationship the speckle decorrelation is related to displacements in the out-of-plane axis and how a calibration of this axis needs to be performed. Accordingly, this paper is organized as follows: Section 2 describes the challenges of three-dimensional deformation measurement by speckle photography, especially in deformation evaluation according to Fricke-Begemann [17]. Moreover, an alternative approach and its implementation in the context of sub-pixel interpolation is presented. After the demonstration of calibration strategies, the first in-process measurements during deep rolling follow in Section 3. Section 4 discusses the reconstruction of the three-dimensional deformation or displacement field, followed by a summary and an outlook in Section 5.
## 2 State of the Art
### Methods for Optimal Layout Problems  
#### 2.1.1 Exact Methods  
The exact methods stand for methods that ensure the optimality of the solution. In that respect, many exact methods have been deployed in order to solve the mathematical models developed to define several optimal layout problems. The very first approach to solve layout problems is the Quadratic Assignment Problem (QAP) model, first introduced in [29]. QAP is developed in order to find the optimal positions of equal-area componentson discrete locations [65; 66]. An alternative way to formulate layout problems is Mixed Integer Linear Programming (MILP), first introduced in [30]. The MILP model uses a continuous/discrete representation and is thus more representative of the layout problem structure than the QAP model [67; 68; 69]. Branch and Bounds (B&B) resolution methods have also been used in order to solve the aforementioned mathematical formulations and have been successfully applied to optimal layout problems [23; 24; 25]. Finally, Dynamic Programming techniques have been used in order to solve instance cutting or packing problems [26; 27; 28].
## 3 Results
### Speech Stress Recognition  
Parameters of the BP network structure must be appropriately adjusted to obtain satisfactory prediction accuracy. The values of parameters used in the BP network structure are listed in Table 1. The model used in the present study consisted of an input layer, two hidden layers, and an output layer. The tanh function was set as the activation function (Figure 7) that connected input and hidden layers and was also used as the connection between two hidden layers. We did not set up the activation function between hidden and output layers to obtain the linear output.  
If the learning rate is excessively high, then the results become unstable. However, if the learning rate is excessively low, then the network convergence becomes slow. The learning rate was set to 0.005 in this study, and the loss converged to approximately 0.03 after calculating 5000 epochs. An Adam optimizer was developed from the stochastic gradient descent (SGD) algorithm and applied to the model. The Adam optimizer uses momentum methods to combine historical descent information to the new descent direction and adaptive learning rate methods to help the learning rate automatically decay during the training process [21]. Therefore, the Adam optimizer can improve the convergence rate. The results of the loss value generated using the BP neural network are shown in Figure 8.  
\begin{table}
\begin{tabular}{c c} \hline Number of layers & 4 \\ Neurons & Input: 20 \\ Hidden: 15 \\ Hidden: 8 \\ Output: 1 \\ Activation function & tanh Function \\ Learning rate & 0.005 \\ Optimizer & Adam \\ \hline \end{tabular}
\end{table}
Table 1: The Parameters of The Model.  
Figure 7: tanh Function.  
We should input audio features of test samples after achieving the desired loss error of training data to verify whether the model presents overfitting. The relative error for test data was calculated using Equation (2) to assess the ability of the model. Figure 9 depicts the re-error curves of the model. The optimal performance of test samples was stable at around 0.044 in the end, thereby indicating that the accuracy of the model reached approximately 95%. We randomly selected 20 test samples for comparative analysis. Figure 10 presents the comparative results of the true and predicted values. As shown in the figure, the difference between the predicted and true values of the 20 samples is small.  
\[\text{Relative error}=\frac{1}{\text{N}}\sum_{i=1}^{\text{N}}\frac{\hat{y}_{i }-y_{i}}{y_{i}} \tag{2}\]  
Figure 8: The Loss of Training Data.  
Figure 9: The Re-error of Test Data.  
The results of speech stress recognition indicated that speech features (i.e., MFCC) can be used as effective indicators to distinguish various stress states during the stimulated nuclear power plant mission.
# Skin Lesion Segmentation in Dermoscopic Images with Combination of YOLO and GrabCut Algorithm
## 3 Materials and Methods
### Training Yolov3  
Yolov3 [70] was trained with the ISBI 2017 dataset as the lesion detection part of our system. The dataset was separated as training and validation sets. Then the final system detection performance was evaluated using two different datasets (the ISBI 2017 and the PH2). There are studies about the effectiveness of transfer learning in deep nets [71; 72]. So, in the training phase, we used pretrained weights of ImageNet dataset [73]. Afterwards, Yolov3 was fine-tuned and re-trained with the skin lesion images. The training parameters of Yolov3 are set as follows: batch size = 64, subdivisions = 16, momentum = 0.9, decay = 0.0005, learning rate = 0.001. Yolov3 was trained through 50,000 epochs and the network weights were saved every 10,000 epochs. Test result showed that the weights saved at 10,000 th epoch were the most successful at detecting the location of lesion in the image. The whole implementations and computations were performed on a PC with two Intel Xenon processors, 64 GB RAM, NVIDIA GTX 1080 Ti GPU and Ubuntu 14.04 operating system. Python and C programming languages, OpenCv image processing framework were used in the development of the system.  
Figure 10: A demonstration of a minimum cut operation in image segmentation [69].  
Figure 11: An illustration of the suggested skin lesion segmentation pipeline architecture.
## 3 Methodology
### Traffic Preprocessing  
Preprocessing of the data was usually carried out to make the data compatible with the desired ML models. The raw data were synthesized and converted to a form which could be easily utilized by our proposed ML models. The real traffic trace is used to test the performance of different VNFs. From the on-campus interconnection link, which was 10 Gbps (both incoming and outgoing), 2 traffic traces, each 5 min long, were captured at two different moments. These traffic traces were then used to reproduce the traffic in experimental environment, which was further sent to virtual network functions, and the respective CPU consumption was monitored.  
For synthesis of the data in the test environment, the speed of the link as considered too high. Therefore, we scaled it by dividing the rate by 10 for day time and 40 for the night time, which resulted in approximately 2000 packets/s. The traffic was described by a set of 86 features from the network to transport layers.  
Components needed for the completion of our experiments were deployed in the single physical machine monitored by hypervisor. All the virtual machines (VMware ESXi v5.5) deployed under the hypervisor were connected with virtual links (gigabit). Trafficwas generated from one virtual machine and sent to another VM where the VNF was installed. TCPreplay (v 3.4.4) was used to replay the traffic. Each VNF (Firewall, OVS, Snort) was installed on separate virtual machines, and CPU consumption was monitored with the performance monitoring tool of the hypervisor. Another VM was installed as SDN controller, which receives the traffic from VNF working as OVS, when necessary. Figure 6 shows the complete configuration setup.  
The hypervisor performance monitoring tool (Virtualization manager v2020.2.1) was used to manage and analyze the VMs performance under a single umbrella. Virtualization manager is a very powerful tool developed by SolarWinds which can monitor the CPU, memory, and storage needs in real time with good accuracy [27]. Out of various output parameters available, only the CPU load was used because the results need to be compared and analyzed with the state-of-the-art, which uses the same parameter. This will help us to adopt the best ML model in the current scenario.  
The data set was generated by collecting traffic features as an input while the average central processing unit utilization as the output. A total of 755 data points were collected for firewall, 1172 for OVS, and 1359 for Snort VNF. Our collected data set showed exactly the same behavior when applied to ML models, with the one that is publicly available at [23], which further validates the data set, but for fair comparison with the state-of-the-art, we used the same data set publicly available at [23].
# Energy-Efficient and QoS-Aware Computation Offloading in GEO/LEO Hybrid Satellite Networks
## 4 Discussion
### Limitations and Future Works  
The partial computation offloading problem discussed in this paper only considers that missions can be decomposed. In future work, we will pay attention to the partial computation offloading problem in GEO/LEO hybrid satellite networks considering the interdependencies among missions. Moreover, the selection of mission offloading nodes can be further enriched. In the future, consideration can be given to the terrestrial networks and offloading missions of LEO satellites to their adjacent and idle LEO satellites to enhance the computing capabilities for GEO/LEO hybrid satellite networks.
## 1 Introduction
In reality, linearity is an ideal situation, and pure linear systems do not exist in the real world. Because of the use of large displacements or limiting devices, TMDs can have nonlinear characteristics during vibration, even if they are not considered in the design. This nonlinearity has historically been ignored in the design process, and [PERSON] and [PERSON] confirmed that doing so can adversely affect control performance [23]. [PERSON] et al. proposed a scheme for a hybrid control structure of NES and TMD and obtained the theoretical energy prediction equations using the multiscale method [24]. [PERSON] et al. investigated the nonlinear behavior generated by the cable under TMD control, obtained the modulation equations of the system using the multiscale method, and explored the nonlinear characteristics of the system through the modulation equations based on different key parameters [25]. [PERSON] focused on the effect of wind load on the dancing of ice-covered conductors, solved the model using an improved multiscale method, investigated the nonlinear characteristics generated by the model, and improved the TMD based on the analysis results obtained to enhance its control performance [26]. In this paper, we mainly derived the analytical approximate solution of the primary structural frequency response of a nonlinear tuned mass damper with hardening enhancement characteristics and obtained the modulation demodulation equation related to the structural amplitude. This equation can obtain the structural amplitude-frequency curve related to the TMD frequency, and we then analyzed the calculation results to obtain the optimal design parameters under the corresponding conditions and compared them with the numerical method to prove the correctness of this equation. Comparing the computational speeds of the complex variable averaging method with those of the numerical method, the results showed that the frequency response curve generated based on this analytical method was faster than that of the conventional numerical method. The previous paragraph described the classification, working principle, history, and development of dampers. Section 2 introduces the equation of motion and its dimensionless form in this paper. Section 3 derives the approximate analytical solution for the equations of motion for this system. Section 4 verifies the accuracy of the analytical formulation using numerical methods. The optimal TMD frequency for the structure considering the nonlinear coefficients was obtained analytically, and the control performance of the TMD at this frequency was verified. After that, the control performance of linear TMD and nonlinear TMD under different design methods was compared using the obtained optimal design parameters. Section 5 compares the time of generating the amplitude-frequency response curves by the complex variable averaging method with that of the conventional numerical method, and the results show the superiority of the complex variable averaging method in terms of computational speed. Section 6 summarizes the main contents and research results of this study.
## 3 Results
### Evolution of Mechanical Properties
#### 3.4.3 Strengthening Parameters Measurements  
Apparently, strain hardening leads to increased hardness, modulus, and overall strength, while still maintaining good ductility. In this case, the achieved mechanical property may be compared with Al\({}_{3}\)BC/Al-Cu composites containing 13% and 26% Al\({}_{3}\)BC particles in the Al matrix, which possess high tensile strength (300 MPa-427 MPa) but lower elongations (2-4.5%) [53]. This justifies further investigation of strengthening parameters.
* Load-bearing contribution (\(\Delta\sigma_{Load}\)): The strength increment due to load-bearing capacity of Al\({}_{3}\)BC reinforcement was determined from the expression [59]: \[\Delta\sigma_{Load}=\frac{A+1}{4}v_{p}\sigma_{m},\] (6) where \(v_{p}\) denotes the volume fraction of Al\({}_{3}\)BC particles (\(v_{p}\) = 0.65) and \(\sigma_{m}\) refers to the yield strength of the composite and the particle aspect ratio denoted by \(A\) (-1) [18; 19]. Hence, the contribution \(\Delta\sigma_{Load}\) of Al\({}_{3}\)BC reinforcement was predicted to be \(\Delta\sigma_{Load}\) = 12 MPa, comparable to \(\Delta\sigma_{Load}\) = 16 MPa in the AlSi\({}_{10}\)Mg/SiC (10 wt.%) matrix composite, exhibiting UTS 350 MPa and elongation to failure up to \(\sim\)3% [60].
* Strengthening capability contribution (\(R\)): The contribution of a specific volume fraction of Al\({}_{3}\)BC reinforcements to the strengthening of the Al matrix in Al\({}_{3}\)BC/Al composite, \(R\), was evaluated from [61]: \[R=\big{(}\sigma_{yc}-\sigma_{ym}\big{)}/f_{p}\sigma_{ym}\] (7) The strengthening capability of the Al\({}_{3}\)BC/Al composites, calculated as \(R\) = 5.6 is impressive. The value is close to FeSiB-reinforced Cu matrix composites (\(R\) = 5.9) [62] and even surpasses SiC-particle-reinforced (\(R\) = 2.5) [63] and Al\({}_{2}\)O\({}_{3}\)-particle-reinforced (\(R\) = 2.3) [48] Cu matrix composites by a factor of two. This finding highlights the remarkable strengthening efficiency achieved in Al\({}_{3}\)BC/Al composites.
* Furthermore, [PERSON]'s ratio, which is the ratio of the calculated bulk modulus (\(G\) = 80 GPa) to shear modulus (\(B\) = 196 GPa) (\(B\)/\(G\)), can be used to determine the ductility or brittleness of the material. A Pugh's ratio greater than 1.75 indicates that the material is more ductile in nature [64]. Based on the calculated Pugh's ratio, a \(B\)/\(G\) = 2.45 is observed for the as-developed Al\({}_{3}\)BC/Al composite. It can be concluded that the as-fabricated Al\({}_{3}\)BC/Al composite has superior ductility while considering the poison's ratio of \(v\) = 0.32. Furthermore, if the Vickers hardness of a material falls within the range of 10-20 GPa, it is typically referred to as hard [65]. According to this information, the composite (\(H_{Th}\) = 15 \(\pm\) 5 GPa) is categorized as a hard material.