esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Results and Analysis  
To evaluate the performance of the TMD passive control system, dampers with different control strategies were designed and implemented in the laboratory. The controlled modes of the structure were first confirmed by modal identification. Under the same harmonic load, the peak value of the measured response was obtained.  
The experimental results are summarized in the following Sections 5.1 and 5.2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Excitation Condition** & **Incoming Signal** &
\begin{tabular}{c} **Excitation** \\ **Band/Hz** \\ \end{tabular} & **Amplitude/\(G\)** & **TMD Position** \\ \hline STMD-B & Harmonic wave & 111.3 & 0.2 & Top of bottom structure \\ DTMD-B & Harmonic wave & 111.3 & 0.2 & Top of bottom structure \\ MTMD-B & Harmonic wave & 111.3 & 0.2 & Top of bottom structure \\ MTMD-B & Harmonic wave & 111.3 & 0.2 & Top of two-layer structure \\ STMD-T & Harmonic wave & 111.3 & 0.2 & Top of two-layer structure \\ MTMD-T & Harmonic wave & 111.3 & 0.2 & Top of two-layer structure \\ MTMD-T & Harmonic wave & 111.3 & 0.2 & Top of two-layer structure \\
4 MTMD-T & Harmonic wave & 111.3 & 0.2 & Top of two-layer structure \\ Spatial multi-point TMD & Harmonic wave & 111.3 & 0.2 & Top and bottom of structure \\
4 MTMD-B & Harmonic wave & 111.3 & 0.2 & Top of bottom structure \\ \hline \hline \end{tabular}
\end{table}
Table 2: Vibration reduction test conditions.  
Figure 4: Forms of TMDs in the structure: (**a**) single-tuned mass damper (STMD)-B; (**b**) dual-tuned mass damper (DTMD)-B; (**c**) multiple-tuned mass damper (MTMD)-B; (**d**) multiple dual-tuned mass damper (MDTMD)-B; (**e**) STMD-T; (**f**) DTMD-T; (**g**) MTMD-T; (**h**) MDTMD-T; (**i**) quadruple TMD-T; (**j**) spatial multi-point TMD; and (**k**) quadruple TMD-B.
## 2 Identification of Slip Pathways in Dam Slope
The elastoplastic finite element strength reduction method (FEM-SRM) progressively reduces the shear strength parameters of dam materials, causing the dam slope to gradually yield until reaching the ultimate equilibrium state. This process reveals the evolution of slope instability and the final slip mode. In this study, the FEM-SRM is employed to identify slip pathways in earth rock dam slopes. The main steps are as follows:  
1. Establishing a Finite Element Model of the Earth-Rock Dam and Foundation: A global finite element model is constructed for both the dam body and its foundation. The constitutive model for dam materials and the overburden layer of the foundation adopts the ideal elastoplastic model, which is commonly used in engineering applications, with the Mohr-Coulomb criterion employed for yield and failure conditions. The bedrock is modeled using a linear elastic approach. The Newton-Raphson method is used for iterative elastoplastic finite element calculations. 2. Simulating Construction and Reservoir Operation Conditions: Based on different computational scenarios, the dam filling process, reservoir impoundment, and water level drawdown are simulated to obtain the corresponding stress distributions. These stress states serve as the initial conditions for subsequent strength reduction analyses. 3. Progressively Reducing Shear Strength Parameters: The shear strength parameters (cohesion and internal friction angle) of dam materials and the foundation's overburden layer are gradually reduced. During this process, elements with lower strength or higher stress levels fail first. The stress in failed elements is then redistributed to adjacent elements, increasing their stress levels and triggering progressive failure in the slope. When the material strength is reduced to a critical level and the elastoplasticfinite element iterations fail to converge, the slope is considered to have reached overall instability. By identifying the elements experiencing shear failure, slip pathways can be determined. These pathways consist of continuous failure zones that penetrate the dam body or extend through both the dam body and foundation. Finally, the element numbers in each slip pathway are recorded, and the reliability of each slip pathway is computed using a parallel system approach.
# Application of Neural Networks in Distribution of the Load in Cluster-Based Web Systems
## 4 Test Bed Used in the Experiments
* Least load (LL)--assigns HTTP requests to the servers with the lowest number of serviced HTTP requests (this strategy works very well in practical solutions and it is a kind of reference line);
* Fuzzy-neural request distribution (FNRD)--intelligent strategy using fuzzy-neural approach [6];
* Neural network (NN)--a new solution presented here, using neural networks. The load balancer was modeled as a single queue in the simulation. Each of the web server modules contained separate CPU and SSD modeling queues. RAM acted as a cache for the file system. The database server has been modeled as a single queue. The response times for the above elements were obtained by measuring them on a real server with an Intel Xeon E5-2640 v3 processor. The experiments were carried out for a cloud system with 20 WWW servers and 1 database server. The number of clients simulating different loads on the web system ranged from 100 to 2500. In each experiment, 40 million HTTP requests were processed, the warming phase took about 10 million requests, and the response time for 30 million was measured.
## 5 Experiments
### Evaluate Performance
#### 5.2.2 Speed of Convergence Comparison  
Next, we want to show the convergence of the estimation by MC simulation and the proposed IS simulation. To perform this, we simulate the parameters in Case 4 by changing a variety of numbers of cycles. For MC simulation, the number of cycles is \(10^{6}\) and \(10^{7}\) cycles, and for the proposed IS simulation, the number of cycles is \(10^{2},\ 10^{3},\ \text{and}\ 10^{4}\) cycles. In both of the two methods, we set the queue threshold as \(q=20[packets]\). Figure 7 shows the convenience of the simulations. We can see that the speed of convergence of the proposed IS is faster than the speed of convergence of MC. The results demonstrate that the proposed IS significantly reduces simulation time compared with MC simulation.
## 3 Proposed Method
### Improve the Loss Function
#### 3.5.2 Bounding Box Gaussian Distribution Modeling  
Small target defects, such as damage and black spots, are mostly not strictly rectangular, and their bounding boxes often contain some background pixels. The foreground pixels and background pixels are concentrated in the center and boundaries of the bounding box, respectively. To better describe the weights of different pixels within the bounding box,the bounding box is modeled as a two-dimensional (2D) Gaussian distribution, where the central pixel of the bounding box has the highest weight and the pixel weights decrease gradually from the center to the boundary. For a horizontal bounding box, where, and represent the center coordinates, width, and height, respectively, its formula is shown in Equation (9).  
Modeled as follows:  
\[\frac{\left(x-\mu_{x}\right)^{2}}{\sigma_{x}^{2}}+\frac{\left(y-\mu_{y}\right) ^{2}}{\sigma_{y}^{2}}=1 \tag{9}\]  
\(\left(\mu_{x},\mu_{y}\right)\) is the center coordinate of the ellipse, \(\left(\sigma_{x},\sigma_{y}\right)\) is the length of the semi-axis along the x and y axes.  
The probability density function of the two-dimensional Gaussian distribution is shown in Equation (10):  
Modeled as follows:  
\[f\big{(}x\big{|}\mu,\sum\big{)}=\frac{\exp\left(-\frac{1}{2}\left(x-\mu\right) ^{T\sum^{-1}}\left(x-\mu\right)\right)}{2\pi\big{|}\sum^{\frac{1}{2}}} \tag{10}\]  
\(x\) represents the Gaussian distribution coordinates, \(\mu\) represents the mean vector, \(\sum\) represents the covariance matrix.  
When these conditions are satisfied, the horizontal bounding box can be modeled as a two-dimensional Gaussian distribution, as shown in Equation (11).  
Modeled as follows:  
\[\mu=\begin{bmatrix}c_{x}\\ c_{y}\end{bmatrix},\sum=\begin{bmatrix}\frac{\pi^{2}}{T}&0\\ 0&\frac{\pi^{2}}{4}\end{bmatrix} \tag{11}\]
## 3 Results and Discussion
### Performance Testing
#### 3.2.2. COD Removal Performance in the \(\mathrm{Cat-2/H_{2}O_{2}}\) System
As the total oxidant concentration increased to 1.2 mM, the removal efficiency decreased to 23.4%. When [Fe(VI)] + [H\({}_{2}\)O\({}_{2}\)] was further increased to 1.5 mM and 1.8 mM, the COD removal efficiencies were 32.8% and 34.4%, respectively. These findings suggest that increasing the total concentration of Fe(VI) and H\({}_{2}\)O\({}_{2}\) does not enhance COD removal significantly. Therefore, considering cost-effectiveness, a total oxidant concentration of 0.9 mM was selected for subsequent experiments. The concentrations of H\({}_{2}\)O\({}_{2}\), potassium ferrate, and catalyst were 0.8 mM, 0.1 mM, and 0.5 g/L, respectively. 2.5 Effect of Molar Ratio of Fe(VI) and H\({}_{2}\)O\({}_{2}\) on COD Removal Performance in the Cat\(-\)2/H\({}_{2}\)O\({}_{2}\)/Fe(VI) System  
Figure 7 illustrates the effect of the [Fe(VI)]:[H\({}_{2}\)O\({}_{2}\)] ratio on COD removal. The results show that as the [Fe(VI)]:[H\({}_{2}\)O\({}_{2}\)] ratio increases from 1:1 to 1:7, the COD removal efficiency increases from 17.5% to 45.1%. However, when the ratio is further increased to 1:8, the COD removal efficiency only slightly increases to 46.2%. Therefore, a [Fe(VI)]:[H\({}_{2}\)O\({}_{2}\)] ratio of 1:8 was selected as the condition for the small-scale continuous flow reactor. Fe(VI) will spontaneously decompose under acidic to neutral conditions to generate Fe(III) and O\({}_{2}\). H\({}_{2}\)O\({}_{2}\) may also be directly catalyzed by Fe(VI) to decompose into H\({}_{2}\)O and O\({}_{2}\), resulting in oxidant waste. High concentrations of H\({}_{2}\)O\({}_{2}\) will capture -OH to generate less active superoxide radicals (HO\({}_{2}\)), which will reduce the oxidation efficiency. If Fe(III) generated by the reduction of Fe(VI) is not reduced to Fe(II) in time, Fe(OH)\({}_{3}\) precipitation will form, covering the active sites and hindering the Fenton cycle. \begin{table}
\begin{tabular}{c c c c} \hline
**pH** & **[H\({}_{2}\)O\({}_{2}\)] + Fe(VI)](mM)** & **[H\({}_{2}\)O\({}_{2}\)]:[Fe(VI)]** & **Removal Rate (\%)** \\ \hline
8.1 & 0.9 & 5:1 & 31.8 \\
8.1 & 1.5 & 5:1 & 32.8 \\
8.1 & 1.8 & 5:1 & 34.4 \\ \hline \end{tabular}
\end{table} 
Table 4: COD removal rate under different conditions in the Cat\(-\)2/H\({}_{2}\)O\({}_{2}\)/Fe(VI) system. (The reaction condition: pH = 8.1, [Fe(VI)]:[H\({}_{2}\)O\({}_{2}\)] = 1:5). \begin{table}
\begin{tabular}{c c c} \hline
**pH** & **Removal Rate** & **COD (mg/L) (\(\pm\)1)** \\ \hline
4.0 & 7.7\% & 65 \\
5.0 & 0.0\% & 70 \\
6.0 & 0.0\% & 70 \\
7.5 & 11.8\% & 62 \\
8.1 & 31.3\% & 48 \\ \hline \end{tabular}
\end{table} 
Table 3: COD removal rate under different conditions in the Cat\(-\)2/H\({}_{2}\)O\({}_{2}\)/Fe(VI) system. (The reaction condition: [Fe(VI)] = 100 \(\upmu\)M, [H\({}_{2}\)O\({}_{2}\)] = 1 mM, \(C_{\mathrm{Catalyst}}\) = 0.5 g/L, pH = 4.0). Furthermore, the Fe leaching experiments were performed. As a result, the leaching rate of Fe was only 1.0% during the reaction in the \(\mathrm{Cat{-}2/H_{2}O_{2}/Fe(VI)}\) system, which indicated a low leakage of heavy metals.
## 3 Indoor Positioning Systems
### Hybrid Indoor Positioning Systems  
A hybrid positioning system, by definition, combines two or more systems to improve the performance of each individual system [116]. In a hybrid system, one of the technologies is generally considered more relevant for estimating the user's location, while the others are considered complementary and are used to improve the system's resources, such as accuracy and coverage area [43]. In some cases, technologies and techniques are fused to address the limitations of one of the components used in the positioning system. Currently, numerous hybrid positioning systems in the literature combine the different IPSs reviewed so far. A complete classification of hybrid positioning systems is not feasible due to the large number of possible combinations of IPSs that can form such systems.  
IPSs that use a single type of sensor or algorithmic approach have certain limitations in the indoor environment due to their various characteristics, requiring the application of more physical or processing resources and time for data manipulation. For example, methods based on angle and time require additional hardware, influencing the cost of the system. Also, they cannot be applied to wireless networks if they are not synchronized with the network nodes. Fingerprinting methods require extensive data collection and calibration of results in all indoor environments.  
In this section, we will review the most exciting hybrid systems for locating and navigating visually impaired people. This review focuses on the following groups: RSSI-IMU, RSSI-Vision, IMU-Vision, RSSI-IMU-Vision, and others. The mapping models adopted to improve the performance of the IPS will be described for each of them. Additionally, we will comment on the possibility of using smartphones as sensors and computational devices.
## 2 Theoretical Analyses on the Torsional Wave-Damage Interaction
### Guided Waves in a Cylindrical Structure  
The elastic wave equation in a cylindrical structure is derived from the Navier's equation as [20]:  
\[\mu\
abla^{2}\mathbf{u}+(\lambda+\mu)\
abla\
abla\mathbf{u}=\rho\frac{\partial^{2 }\mathbf{u}}{\partial t^{2}} \tag{1}\]  
where \(\mathbf{u}\) denotes the displacement vector. \(\lambda\) and \(\mu\) are Lame constants and \(\rho\) is the mass density. Generally, there are three types of wave modes propagating in a cylindrical structure as longitudinal, torsional, and flexural modes which are denoted with L, T, and F modes, respectively. Taking a solid rod as an example, the stress-free boundary conditions for three different modes write  
\[\left\{\begin{array}{c}\mathrm{L}:\sigma_{rr}=\sigma_{rz}=0\\ \mathrm{T}:\sigma_{r\theta}=0\\ \mathrm{F}:\sigma_{rr}=\sigma_{rz}=\sigma_{r\theta}=0\end{array}\right. \tag{2}\]  
where the coordinates are illustrated in Figure 1a. As an example, a steel solid rod is examined with a radius of 3.5 mm, Young's modulus of 200 GPa, mass density of 7850 kg/m\({}^{3}\), and Poisson's ratio of 0.29. Combining the boundary conditions and the governing equation, the dispersion curves are obtained and shown in Figure 1a. Multiple wave modes are observed and identified. Detailed derivations and the characteristics of the wave modes are well documented in the textbook [33]. Among all these modes, the T(0,1) mode is highlighted which is non-dispersive with respect to the frequency. The mode shape of the T(0,1) mode wave in terms of displacement is then examined at a representative 50 kHz in Figure 1b. Only the circumferential displacement component \(u_{\theta}\) exists which is linearly proportional to the radius. As the angular displacement \(\varphi\) is related to \(u_{\theta}\) by the radius \(r\) as \(u_{\theta}=\phi_{r}\), it is deduced the \(\varphi\) of the T(0,1) mode wave remains to be a constant across the radius. This mode shape feature of the T(0,1) waves is tenable at any frequency.
# Deep Learning-Based Segmentation of Various Brain Lesions for Radiosurgery
## Materials and Methods
### Deep Learning Models
#### 2.4.5 V-Net  
V-Net is the adaption of U-Net for 3-dimensional data to capture the relationships in consecutive slices, which were omitted in the 2D models, addressing contiguity problems and yielding better results in the segmentation of various 3D images [20]. It replaces the convolution and pooling operations with 3D versions.
## 2 Materials and Methods
### Analytical Methods  
#### 2.3.1 OSL Dating  
Pre-treatment of samples and OSL measurements were carried out at the Luminescence Dating Laboratory of East China Normal University. For OSL dating, quartz grains (38-63 \(\upmu\)m) were purified and extracted, then etched with H\({}_{2}\)SiF\({}_{6}\) in a dark room under red light and prepared according to the method of [PERSON] et al. [28]. Quartz grains were used to determine equivalent doses by the single-aliquot regenerative dose method [29]. Neutron  
Figure 1: (**a**) Location of the Chenqianshan loess section at Shengshan Island in the East China Sea (modified from Cheng [22]) and (**b**) the alternation of loess and weak paleosol units in the Chenqianshan section.  
activation analysis (NAA) of uranium (U), thorium (Th) and potassium (K) was used to determine the radionuclide concentrations of all samples. The water content of the samples was the actual measured water content. Considering the changes in water content during the geological stage, the given absolute error is 7%.
## 4 Results
### Thermal Maps Generated through Path Tracking  
One of the possible applications for the Soft Jellyfish Robot proposed is to monitor not only temperature but also other environmental variables (pH, contamination (turbidity)). Temperature was chosen to demonstrate this due to its ease of management and the fact that it is reversible, not contaminating the water and thus allowing us the opportunity to repeat the experiments without having to change it; additionally, this is a low-cost robot. Thermal maps are a representation of the monitored data.  
During the trajectory journey, through the virtual points given by means of the interface shown in Figure 11, temperature measurements were carried out at different points along the path that traveled with a sampling interval of 500 milliseconds, to subsequently generate a thermal map of the area, based on the temperature gradient and the focused position of each measurement in an instant. To carry out these tests, boiled water (around 90 \({}^{\circ}\)C) poured from the test pool top was used, so that a heat source was generated that disperses and allows the water temperature to be modified uniformly.  
The average time required to travel a trajectory at an average speed of 2 cm/s is 52.3 s; during this time, a constant flow of water was maintained from the top with a manually reduced flow, which is why it reaches the lower part with a temperature of around 33 \({}^{\circ}\)C in the lower areas.  
The movement of the jellyfish bell affects in part the uniform distribution of the water, but the sensor has been placed on top just where the movement of the water is greatly affected.  
Thus, Figure 16a refers to a map generated from real measures taken by Robotic Jellyfish with a thermal focus on the upper central part, while for Figure 16b, it has two thermal sources of dispersion in the upper corners. Water was poured in each spot with a high temperature at 90 \({}^{\circ}\)C, and the graph shows the temperature transfer tendency to the lower temperature zones, reaching a maximum of 50 degrees and a minimum of 25 in the areas where there was no temperature transfer.  
Figure 15: Experimental kinematic parameters based on horizontal displacement. (**A**) Shows the 3D traveled trajectory; (**B**) Shows the jellyfish positions along the path; (**C**) Shows x position; (**D**) Shows y position; (**E**) Corresponds to x speed and red rectangles are the electrical pulses given; (**F**) shows y acceleration.
# The Modified Void Nucleation and Growth Model (MNAG) for Damage Evolution in BCC [PERSON]
## 3 Results and Discussion
### Parameterization of the MNAG Model
#### 3.3.1 Single-Crystal Ta
Additionally, Figure 3 shows that the (001) orientation had a different time evolution for void nucleation/growth compared to the (110) and (111) orientations. As can be seen from Table 2, compared to the other two orientations, (001) had a (1) higher void nucleation stress \(\sigma_{n}\), which corresponded to a lower void nucleation rate; (2) lower void growth threshold stress \(\sigma_{g0}\), which corresponded to a higher void growth rate; and (3) higher base void coalescence rate \(V_{c0}\), which corresponded to a higher void coalescence rate. These observations were in line with the trends shown in the right-hand panels in Figure 3.  
These differences were also highlighted in Figure 4 that shows snapshots of the single-crystal Ta along the above three orientations at 50 ps. The average size of voids along the (001) orientation (Figure 4a) was observed to be larger than that along the other two orientations (Figure 4b,c). This arose from the void-growth-and-coalescence-dominated failure along with the (001) orientation, as compared to void-nucleation-dominated failure along the other two orientations. This had previously been linked to the propensity of each orientation to nucleate twins under shock, which could act as void nucleation sites [66]. Since the parameters were in line with the actual snapshots highlighting the physics of damage and failure in Ta, this example clearly showed that the parameters in the MNAG model could be used to obtain mechanistic information about the damage evolution and how it depended on the microstructure. Figure 3: MNAG model fitted to single-crystal Ta along: (**a**) (001), (**b**) (110)], and (**c**) (111) orientation. The left-hand panels show the evolution of the void number density and damage based on MD and the MNAG model parameters, and the right-hand panels show the evolution of stress (\(\sigma_{m}\)) and the contribution from void nucleation, growth, and coalescence terms over time based on the MNAG model.
## 2 Materials and Methods
### Data Reduction Equations
#### 2.2.2 The Effect of Latent Heat on the Performance  
The primary importance of the desiccant dehumidification system is its capability to reduce the moisture content of the humid air. Taking into account the handling of the moisture content, the performances were evaluated regarding the latent heat ratio and latent effectiveness. The latent effectiveness (\(\varepsilon\)) was obtained as:  
\[\varepsilon=\frac{X_{ads,in}-\ X_{ads,out}}{X_{ads,in}-\ X_{ideal}} \tag{5}\]  
where \(X_{ideal}\) is the ideal humidity ratio that is calculated by considering the isenthalpic line of the humidity ratio inlet of the adsorption process under the constant's relative humidity of the regeneration inlet conditions (kg/kg). If the water content was dehumidified completely, the ideal humidity ratio would reach the zero value. This value can be considered for the optimization of the desiccant dehumidification system [36]. The differences between the humidity ratio inlet of the adsorption process and ideal humidity ratio were referred to as the maximum dehumidification capacity of the system.  
The performance of the desiccant dehumidification system, which showed the energy amount, was evaluated by using the latent heat ratio (\(LHR\)). \(LHR\) can be described as the ratio of the latent heat to the total energy (latent and sensible heat) required by the system [37]. It was given by the following equation:\[LHR=\frac{Q_{lat}}{Q_{lat}+Q_{sen}} \tag{6}\]  
Latent heat (\(Q_{lat}\)) was given by  
\[Q_{lat}=Q_{ads}\ =\ d_{ads}\ \dot{m_{a}}\ \gamma\ =\dot{m_{a}}\ (h_{in}\ -\ h_{out}) \tag{7}\]  
and Sensible heat (\(Q_{sen}\)) was given by  
\[Q_{sen}=Q_{des}=\dot{m_{a}}\ c_{p}\left(T_{des,in}\ -\ T_{des,out}\right) \tag{8}\]  
where \(Q_{lat}\) is the latent heat (W), \(Q_{sen}\) is the sensible heat (W), \(Q_{ads}\) is the average of enthalpy exchange in the adsorption process (W), \(Q_{des}\) is the average of heat exchange in the desorption process (W), \(\dot{m_{a}}\) is the air mass flow rate (kg/s), \(\gamma\) is latent heat of vaporization (kJ/s), \(c_{p}\) is specific heat of the air at bulk temperature (kJ/kg-K), \(T_{des,in}\) is air inlet temperature of the desorption process (K), and \(T_{des,out}\) is the air outlet temperature of the desorption process (K).