esa-sceva/satcom-chunk-collection huggingface dataset:
## 9 Seismic Isolation and Energy Dissipation Devices for Composite Structures
### Seismic Isolation and Energy Dissipation Devices for Composite Structures: State-of-the-Art
A macroscopic finite element model showed that buckling-restrained braces provide lateral stiffness and resistance under small earthquakes and dissipate energy under severe earthquakes. This model is part of the OpenSees program ([[https://opensees.berkeley.edu/](https://opensees.berkeley.edu/)]([https://opensees.berkeley.edu/](https://opensees.berkeley.edu/))). [PERSON] et al. (2024) [154] conducted a comprehensive investigation of a cable-stayed bridge featuring concrete-filled steel tube pylons and an innovative seesaw system for seismic protection. The geometry of the problem is shown in Figure 8. Through detailed finite element modeling and extensive parametric studies, they evaluated the system's effectiveness in mitigating seismic demands under various earthquake scenarios. Their analysis incorporated soil-structure interaction effects across different soil conditions and foundation types, demonstrating the system's capacity to reduce deck displacements, pylon base shear, and cable forces. Furthermore, [PERSON] (2025) [155] investigated the seismic performance of two-, four-, and six-story composite buildings with viscous wall dampers using concrete-filled steel tubular columns and steel beams. Their work assessed dampers' effectiveness through nonlinear time history analyses. The results showed reduced inter-story drift ratios and peak floor accelerations, validating the dampers' integration in mid-rise structures. The behavior and bearing capacity of composite members were based on Ref. [156]. Furthermore, the influence of multiple earthquakes and soil flexibility on the structural response have been examined in Ref. [157]. The results show that multiple ground motions increase demands compared to single events. Incorporating soil-structure interaction reduces drift and accelerations but increases displacements. [PERSON] and [PERSON] (2023) [158] examined the effectiveness of buckling-restrained braces and viscous dampers in enhancing the seismic performance of composite buildings during earthquakes. Steel-concrete composite frames were analyzed with and without these devices. The results showed that viscous dampers were more efficient, especially when placed in center bays for regular buildings and corner bays for C- and L-shaped composite buildings, reducing period, maximum story displacement, base shear, and maximum inter-story drift significantly, and making them a great option for mid-rise composite buildings. Figure 8: Innovative seismic protection system for composite bridges utilizing a seesaw-type energy dissipation mechanism, demonstrating advanced structural resilience techniques (adapted from [PERSON] et al. (2024) [154]).
# A Review on the Prospects of Mobile Manipulators for Smart Maintenance of Railway Track
## 4 Robotic Maintenance Technology in Other Industries
### Mobile Manipulators for Inspection and Repair Tasks
#### 4.2.2 Maintenance in Nuclear Power Plants & Hazardous Places  
Nuclear materials, the mining industry, or any container with radioactive material are incredibly hazardous to humans. Short exposure may create cancerous cells, while prolonged exposure will create severe health effects such as skin burns or radiation sickness, leading to death. Although many protective measures are in place to minimize exposure, a remote mobile manipulator is the ultimate form of protection.  
A wheeled mobile manipulator, called radiation protection assistant robot (RPAR-I), was designed for surveillance and monitoring of radioactive material [15] with a camera for safe navigation and this was controlled remotely to protect the worker from exposing themselves to radiation. Figure 13a shows 4 DoF manipulators attached to a 2-wheel drive robot base. Instead of teleoperation, many researchers took a dual approach to design the control system, considering both autonomous and remote-controlled modes [93; 94; 95]. A semi-autonomous system, CERNbot [94], was designed with few predefined standard operating procedures, as shown in Figure 13b. CERNbot used an autonomous mode for inspection and navigation and an operator-friendly human-machine interface (HMI) for teleoperation. As all these robots had teleoperation capability, these robots were also equipped with computer vision systems and had data transfer capabilities.  
The multifunctional inspection and rescue robot (MIRR) was engineered to execute inspection, maintenance, and rescue operations, such as the remote handling (RH) system in the tokamak fusion facility [96]. Equipped with two manipulators, operation robot arm (ORA) and inspection robot arm (IRA), on an omnidirectional mobile vehicle (OMV), MIRR navigated in a narrow space by avoiding multiple obstacles and performing different tasks. Another omnidirectional mobile manipulator was MIRA SPS, which detected and navigated through a narrow passage in a semi-structured environment by leveraging a computer vision strategy [97].  
In many cases, manipulation was required underwater [98], in the air [99], or inside a steam generator [100]. For example, the underwater mobile manipulators designed in [98] detected and removed unwanted objects from the bottom of nuclear reactor vessels. In contrast, the lightweight aerial mobile manipulator proposed in [99] was experimented with for accessing hard-to-reach and hazardous locations. The aerial mobile manipulator was equipped with a manipulator and deposited material through a small-scale extruder to repair a crack, as shown in Figure 13c.  
Figure 13: Mobile manipulator in the nuclear industry: (**a**) RPAR-I (radiation protection assistant robot) [13]; (**b**) CERNbot with single- and dual-arm configuration [92]; (**c**) aerial mobile manipulator extruding PU foam to block a crack in surface [97].
## 5 Results and Discussion
### Effect of Leachate Level  
To illustrate the effect of the leachate level on the failure mechanism, the failure surfaces and corresponding factors of safety are displayed in Figure 10 with various leachate levels of \(H_{\text{W}}\) = 15, 20, 25, and 30 m. Other parameters were kept constant. Through a comparison with Figure 10a-d, it can be found that all the failure surfaces developed along the interface between the new and existing berm. The leachate level had a significant impact on the factor of safety of the berm--the higher of the leachate level was, the lower the factor of safety.  
The effect of the leachate level on the stability and the maximum tensile force of the geotextiles is displayed in Figure 11. It is apparent that with the increasing leachate level, the factor of safety decreased and the maximum tensile force of the geotextiles increased. Once the factor of safety approached \(\text{FOS}=1.0\), the tensile force of the geotextile dramatically increased. A significant effect of the leachate level was also observed in slope stability studies by [PERSON] et al. (2018), [PERSON] and [PERSON] (2018). [PERSON] et al. (2018) indicated that when there was no leachate in the landfill, the FOS levels were 2.22 and 2.14 for landfill units I and II, respectively. When the leachate level saturated, the FOS for units I and II were 1.14 and 0.15, respectively. [PERSON] and [PERSON] (2018) illustrated a case history where the leachate level was known to be high, i.e., 16-30 m above the liner, and they showed how the FOS decreased as the leachate level increased. Clearly, cases of high leachate levels on liner systems can lead to stability failure, as illustrated in the two cases.  
Figure 9: FOS and \(T_{max}\) versus geotextile strength.  
Figure 10: Cont.  
Figure 10: Effect of leachate level: (**a**) Leachate level I (\(H_{w}\) = 15 m), (**b**) Baseline (\(H_{w}\) = 20 m), (**c**) Leachate level II (\(H_{w}\) = 25 m), and (**d**) Leachate level III (\(H_{w}\) = 30 m).
## 3 Results
### Experimental Results and Comparisons
#### 3.2.2 Reflected Component Adaptive Gamma Correction Results  
The effect of adaptive gamma correction is shown in Figure 10. As could be seen from the reflected component maps, the internal contrast of the corrected defective area was significantly increased, and the texture features were clearer. From the observation of the gray intensity surface map (the gray intensity of the image was inverted, and then the gray intensity surface map was produced), the corrected defective area was richer in each segment of the internal gray intensity, and the enhancement effect was obvious.  
Figure 10: Adaptive gamma correction effect. (**a**) Reflected component figure; (**b**) adaptive gamma correction result; (**c**) reflected component gray intensity surface figure; (**d**) adaptive gamma correction resulting in gray intensity surface figure.  
Figure 9: Luminance decomposition effect. (**a**) SSR result; (**b**) MSR result; (**c**) improved SSR decomposition result.
## 4 Experimental Validation
### Simulation Results: Control Scenario I  
The first control scenario simulates the AUV in an open environment and focuses on the path-following implementation, not including environmental disturbances in the system. From the coverage path algorithm, a series of waypoints in NED coordinates form the predefined path. Figure 11 illustrates the results for this simulation control scenario, including the north, east, and down positions and the yaw orientation. Moreover, Figure 12 illustrates the cross-track error \(e(t)\) and the yaw orientation error for the LOS-based, path-following algorithm in the simulation environment. These plots represent the previously discussed cascade switching mechanism, with the sphere of acceptance to show when the AUV has arrived at the desired waypoint and the requirement of less than 1 degree yaw angle error before moving to the next waypoint.  
This scenario uses both the mathematical model developed of the AUV in Section 2 and the GNC architecture introduced in Section 3. The simulation tool for this control scenario uses the MATLAB-Simulink software, which can interactively simulate the AUV system model and show the results on scopes and graphical displays. The MATLAB-Simulink model includes the reduced-order AUV mathematical model to calculate the AUV position and velocities based on the total wrench generated from the PID controllers. The mathematical model uses the Equation (3), which contains the hydrodynamic coefficients from the parameter estimation approach shown in Table 2. Additionally, the AUV position and orientation act as navigation feedback for the LOS-based path-following algorithm, using a similar architecture to Figure 9.  
Figure 10: System overview for the Girona500 AUV with high- (ROS computer), and low-level (sensors and actuators) control.  
This test validates the GNC algorithm using the LOS-based and position PID controllers approach, including the switching mechanism of the sphere of acceptance and reached angle. The AUV precisely generates the required trajectory for the LOS-based, path-following algorithm and reaches the final waypoint of the coverage path.
# Review of Automatic Estimation of Emotions in Speech
## 4 Models to Use Measures of Analysis for Speech Emotion Classification
### Support Vector Machines (SVMs)  
A common general binary classifier called SVM [59; 60] is simple and efficient in discriminating between two classes (e.g., emotions) in very high dimensional feature spaces. For SER, an SVM can be trained to estimate a hyperplane in representation space, to separate utterances with a specific emotion from all other utterances [58]. SVMs use low-dimensional feature vectors (e.g., average cepstral vectors) that are transformed into separable high-dimensional vectors by use of kernel transformations (also known as tricks).
## 3 Case Study: Purse Seiner in the Adriatic Sea
### LCCA Models for Different Power Systems
#### 3.3.10. The LCCA of an All-Electric Ship  
The battery represents the main element of a fully electrified ship. Its cost is calculated by multiplying the required battery capacity by a battery price of EUR 250/kW [36]. The investment cost of an all-electric ship constitutes 45% of the battery cost, while the remaining 55% is spent on on additional equipment [70]. The battery is replaced once in a ship's lifetime, and its price declines by 25%. Maintenance represents 5% of the investment cost, while electricity cost is calculated by multiplying energy consumption by electricity prices from Table 5.
## 2 Materials and Methods
### Error-Correction Coding  
Channel coding is implemented in the OFDM system to protect information bits from errors after transmission through the communication channel. Two classical block coding schemes are used, namely Bose-Chaudhuri-Hocquenghem (BCH) codes, and Reed-Solomon (RS) codes. During the inland-water experiment, the codes of different parameters were tested to find the maximum coding rate that allows reliable transmission with a BER less than \(10^{-4}\) to be achieved.  
Figure 1: Pilot tones pattern [7].
# Multi-Object Tracking with Grayscale Spatial-Temporal Features
## 4 Results
### Evaluation Comparison with Other State-of-the-Art Trackers  
We compare the performance of our tracker with other trackers on the MOT16 benchmark. Table 3 lists the performance of our method on MOT16; also, comparisons with other models are listed.  
We can find that the performance of trackers based on private detectors is better than those based on public detectors. Detectors are divided into public detectors provided by the dataset and private detectors customized by the algorithm. The public detector provided by the MOT16 dataset is the deformable parts model (DPM). These private detectors can be trained by yourself or the state-of-the-art detectors proposed by others. The reason is that these private detectors obtain more accurate detection results compared to public detectors. Tracking performance tends to improve with better detection quality; thus, tracking performance can be improved by using state-of-the-art detectors. But data association is the main part of tracking, so we use public detector DPM. Compared to the methods using a public detector in Table 3 on the MOT16 benchmark, our method achieved  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Module** & **MOTA \(\uparrow\)** & **IDF1 \(\uparrow\)** & **MT \(\uparrow\)** & **ML \(\downarrow\)** & **TFPS \(\uparrow\)** \\ \hline Baseline & 20.5 & 13.6 & 7.4 & 51.6 & 15.4 \\ Baseline + GSP & **46.7** & **52.0** & **21.5** & **34.1** & **17.6** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ablation experiment results on MOT16. \(\uparrow\) indicates that higher is better, \(\downarrow\) indicates that lower is better. The best results are shown in **bold**.  
the best score in metric ML and metric MT. The ML and MT metrics reached 21.5 and 34.1, respectively. This means that we successfully tracked more targets and lost fewer targets than the other methods. In metric TFPS, the tracking algorithm of ours was performed using a CPU without GPU accelerating, but it was better than the other trackers, which reached 17.6. We only used traditional methods for feature extraction and ID matching, and the MOTA and IDF1 scores were comparable to these state-of-the-art trackers. Our metric MOTA was only 0.5 lower than the highest metric MOTA of AMIR in Table 3 but higher than the other methods. Our metric IDF1 was only lower than the metrics of DMAN and DM but higher than the other methods. Therefore, it can be concluded that our method maintains tracking accuracy while improving operational efficiency.
# Nitrogen Oxide Removal by Coal-Based Activated Carbon for a Marine Diesel Engine
## 3 Results and Discussion
### Effect of AC on \(No+\text{NO}_{2}+\text{NH}_{3}\)  
When the NO/NO\({}_{2}\) molar ratio is 1, SCR denitration mainly takes place according to the fast SCR reaction Equation (2). At present, the catalytic effect of AC on Equation (2) at different temperatures is not clear. Therefore, this section mainly studies the catalysis of AC for Equation (2) at 15-300 \({}^{\circ}\)C.  
The inlet gas is 500 uL/L NO + 500 uL/L NO\({}_{2}\) + 1000 uL/L NH\({}_{3}\). As shown in Figure 7, the experiment is divided into six stages, namely, 15 \({}^{\circ}\)C without AC (1 st stage), 15 \({}^{\circ}\)C with AC (2 nd stage), 15-100 \({}^{\circ}\)C with AC (3 rd stage), 100-200 \({}^{\circ}\)C with AC (4 th stage), 200-300 \({}^{\circ}\)C with AC (5 th stage), and 300 \({}^{\circ}\)C with AC and without NH\({}_{3}\) (6 th stage). It can be seen from Figure 7 that NO\({}_{2}\) can react with NH\({}_{3}\) to generate NO and N\({}_{2}\) at room temperature without AC in the 1 st stage. In the 2 nd stage, there is almost no NO\({}_{2}\) at the outlet when the gas is introduced into the activated carbon reactor. The concentration of the outlet NO is also reduced to some extent. The denitration efficiency is about 59.5% at 15 \({}^{\circ}\)C. In the 3 rd stage, when the temperature is raised from 15 \({}^{\circ}\)C to 100 \({}^{\circ}\)C, the chemical denitration of AC is not significantly improved, but its physical adsorption ability is greatly reduced, resulting in an increase in outlet NO, and the denitration efficiency is about 53%. In the 4 th stage, when the temperature is raised from 100 \({}^{\circ}\)C to 200 \({}^{\circ}\)C, the chemical denitration of AC is greatly enhanced, around 69.2%, and the outlet NO is greatly reduced. In the 5 th stage, when the temperature is further increased from 200 \({}^{\circ}\)C to 300 \({}^{\circ}\)C, the denitration ability of AC does not change significantly, and the near-stable denitration efficiency reaches 70.8%. In the 6 th stage, when NH\({}_{3}\) is turned off, most NO\({}_{2}\) is converted to NO and then desorbed, resulting in a rapid NO concentration increase at the outlet, eventually reaching 920 uL/L. This indicates that NH\({}_{3}\) plays an important role in the denitration ability of AC at high temperatures. It is found that NH\({}_{3}\) increases the denitration efficiency of AC from 8% to 70.8% at 300 \({}^{\circ}\)C.  
Figure 7: Effect of AC on NO + NO\({}_{2}\) + NH\({}_{3}\).
# Adaptive Visual Saliency Feature Enhancement of CBCT for Image-Guided Radiotherapy
## 3 Experiments and Analysis  
### Dataset and Experimental Setting  
The experimental data are shown in Figure 6, and all of them are acquired from the Affiliated Hospital of Yunnan University, decrypted, and anonymized. Both CBCT and CT images are acquired for each sample of the same patient. The CBCT images were obtained by linac (XVI, Elekta Solutions AB, Stockholm, Sweden), and the corresponding CT images were acquired by Philips Brilliance Big Bore CT equipment, with a slice thickness of 3 mm. All images in our experiment were resized to 256 \(\times\) 256 pixels. For this work, we have acquired CBCT and CT image sequences of three patients, totaling 120 pairs of images. Since there is little difference between the different sequence images of the same patient, four sections with large differences were selected as experimental data for analysis.  
To illustrate the superior performance of the proposed enhancement algorithm, six classical and effective enhancement algorithms are used for comparison in our experiment, including the multi-scale retinex (MSR) method [33], the multi-scale retinex with color restoration (MSRCR) method [19], the dark channel prior (DCP) method [17], the color balance and fusion (CBF) method [34], the robust retinex model (RRM) [35], and statistical methods with image processing concepts (SMIPC) [21]. For quantitative evaluation, the enhancement performance was summarized with four imaging quality evaluation metrics: average gradient (AG) [36], spatial frequency (SF) [37], edge intensity (EI) [38], and human visual system inspired quality measures (UIQM) [39]. Among them, AG represents the gradient information, and the higher the gradient value, the higher the structural contrast of the image. SF denotes the frequency information, and a higher SF value represents the more image information and abundant imaging frequency. EI is used to measure the edge intensity of a given image; the higher the EI value, the sharper and clearer the texture of the edge is. UIQM is an often-used image evaluation metric based on the human visual system for measuring the color, clarity, and contrast of the given image. We also use the structural similarity index measure (SSIM) values [40] before and after registration of CBCT and CT images to objectively measure the registration performance. The larger these metrics, the better the quality of the enhanced images. In the experiments, all the codes are used in the version provided by the corresponding authors, and the parameters are set the same as the authors. All the experiments are performed under the Matlab R2020a, on a PC with an AMD Ryzen 7, 16 G RAM, and a 3.2 GHz processor.
## 2 Numerical Simulator
### Amplitude and Intensity Distributions in the Plane of the CCD Sensor  
A second generalized Huygens-Fresnel transform gives the expression of the electric field in the CCD plane: \(G_{2}\). It is given by the following relation:  
\[\begin{split} G_{2}(x,y)&=\frac{\exp(i\frac{2\pi}{ \lambda}\ell_{2})}{i\lambda\sqrt{B_{2}^{x}B_{2}^{y}}}\int_{\mathbb{R}^{2}}G_{1 }(\xi,\eta)T(\xi,\eta)\exp\left[i\frac{\pi}{\lambda B_{2}^{x}}(A_{2}^{x}\xi^{2 }-2\xi x+D_{2}^{x}x^{2})\right]\\ &\exp\left[i\frac{\pi}{\lambda B_{2}^{y}}(A_{2}^{y}\eta^{2}-2 \eta y+D_{1}^{y}y^{2})\right]d\xi\ d\eta\end{split} \tag{4}\]  
where \(\ell_{2}\) is the optical path between the object plane and the CCD plane. \(A_{2}^{x,y}\), \(B_{2}^{x,y}\), and \(D_{2}^{x,y}\) are the coefficients of the matrices \(M_{2}^{x,y}\) which describe the second part of the optical system (see Figure 1). We have \(M_{2}^{x}=M_{2}^{y}=M_{2}\) for an axisymmetric system. \(T\) is the transmittance of the object. \(x\) and \(y\) are the transverse coordinates in the sensor plane. This second integral is evaluated numerically. The intensity distribution recorded by the CCD sensor is then obtained from:  
\[I(x,y)=G_{2}(x,y)\cdot\overline{G_{2}(x,y)} \tag{5}\]  
where the overline refers to the complex conjugate.
## Nomenclature  
\begin{tabular}{l l l l l}
**Magnitude** & **Meaning** & **Units** \\ \(r\) & Dimensionless geocentric distance of RGV & \\ \(\phi\), \(\varphi\) & Longitude and Latitude of RGV & rad \\ \(v\) & Dimensionless velocity of RGV & \\ \(R_{e}\) & Radius of earth & m \\ \(\theta\) & Flight path angle of RGV & rad \\ \(\psi\) & Flight heading angle of RGV & rad \\ \(L\), \(D\) & Dimensionless lift and drag force of HGV & \\ \(\rho_{0}\) & Atmospheric density constant & kg/m\({}^{3}\) \\ \(\rho\) & Atmosphere density constant & kg/m\({}^{3}\) \\ \(H_{0}\) & Atmospheric altitude constant & m \\ \(C_{L}\), \(C_{D}\) & Lift coefficient and Drag coefficient & \\ \(v_{c}\) & Dimensional velocity & m/s \\ \end{tabular}  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Method** & \(E_{I}\) **(km)** & \(E_{h}\) **(km)** & **Waste Time (s)** & **Iteration** & **Number of Grid Points** \\ \hline Our method & 0.016 & 0.001 & 110.67 & 21 & 200 \\ Gaussian Spectral method & 0.010 & \(1\times 10^{-4}\) & 154.20 & 25 & 341 \\ \hline \end{tabular}
\end{table}
Table 4: Generation effect of different methods.