esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Result of Seismic Capacity  
The seismic capacity of a member with nonlinear behavior can be expressed as the energy absorption and ductility factor during hysteretic behavior. The absorbing energy according to the ductility factor was calculated as the area of the hysteresis loop at each loading step. The energy absorption of each specimen is shown in Figure 17. For the case of the longitudinal loading, the energy absorption of the RH-SC-L specimen is higher because the displacement at the failure is slightly larger. For the case of transverse loading, the RH-SL-T specimen is larger than those of any other specimens. It is almost the same until 5.0, before failure occurs at the bottom of the column. After the bottom part of the column fractured, the energy absorption capacity at the top of the column contributed to the seismic performance. It is also confirmed in the failure mode that the top of the column withstands the load in the transverse direction even after damage. The energy absorption capacity at the top of the column is determined by the transverse reinforcing bar ratio at the plastic hinge region. Therefore, even if the specimen RH-SC-T without lap-splicing at the bottom of the column has the largest displacement when the initial damage occurred, the specimen RH-SL-T, which has larger transverse reinforcing bar ratio, has better seismic performance.  
The test results are summarized in Table 3. The displacement ductility of the RH-SL-L specimen is smaller than that of the RH-SC-L specimen. There is a difference in ductility between the loading directions. For the longitudinal direction, the pier behaves like a single column. For the transverse direction, the pier behaves as multiple columns which generate multiple plastic hinges. This is consistent with the design value of strength reduction factor defined as three for the single column pier and five for the multi-column pier in the design code. It was expected that there would be a large difference in seismic performance according to the details of the reinforcing bar, but the bond failure in the lap-splice did not occur, so all the specimens were found to have a seismic performance, although there are slight differences. The reason why the lap-splice failure did not occur may be that the plastic hinge region is small because the cross-sectional diameter of the multi-column pier is smaller than single-column pier.  
Figure 16: Load-displacement envelope curve of transverse loading specimens.
# Improved ADRC-Based Autonomous Vehicle Path-Tracking Control Study Considering Lateral Stability
## 4 The Path-Tracking Controller Design  
For the autonomous vehicle path-tracking problem described in this paper, the path-tracking controller is designed based on the IADRC proposed in Section 3, and the direct yaw moment controller is designed to improve the lateral stability of the vehicle; the whole control system structure is shown in Figure 4. The controller adopts the idea of state tracking control; that is, the actual state of the vehicle tracks the desired driving state, so that the lateral deviation and heading error of the vehicle tend to zero, and the yaw rate tracks the desired value, which guarantees the lateral stability of the vehicle during path tracking.  
The controller takes \(x=\big{[}y_{\sigma},\dot{y}_{e},\varphi_{e},\dot{\varphi}_{e}\big{]}^{T}\) as the controlled variable, based on the linear quadratic regulator method to design the state error compensator to make decisions and output the initial front-wheel steering angle \(\delta_{f0}\) and the initial external yaw moment \(\Delta M_{z0}\). Based on the IESO proposed in Section 3, the disturbance state observer is designed to estimate the total disturbance value in real time, calculate the compensation value, and compensate the front-wheel steering angle and external yaw moment output from the state error compensator, thus obtaining the final front-wheel steering angle \(\delta_{f}\) and external yaw moment \(\Delta M_{z}\). To achieve the optimal distribution of the external yaw moment, the braking  
Figure 4: Control scheme structure principle.  
force control distributor is designed based on the concept of control allocation (CA), and eventually, the problem of CA can be transformed into the constrained weighted least squares (CWLS) problem.
## 3 Methods  
In our study, we developed and compared three strategies: the basic one (using only the Ki67 dataset) and two that use the H&E and Ki67 datasets. The idea of using the H&E and Ki67 datasets was inspired by the routine work of pathologists, who observe HE and KI67 stained slides to evaluate the specimens. In routine practice, pathologists have 2 separate slides--one stained with HE and one stained with KI67; based on this, the pathologists then perform an analysis. This approach is modeled as the second method. In the third developed method, we used the computational power and deconvolutional technique to merge HE and KI67 stained slides into one artificial \"HEKI\" image. All approaches are based on Deep Learning and were developed to explore the possibilities of combining information from several stains. The proposals follow a three-step workflow: (a) tissue area segmentation, (b) hot-spot detection, and (c) Ki67 LI evaluation. All methods were developed using Matlab [37], and the calculations were performed on a computer with an Nvidia Quadro 400 graphics card and Cuda version 8.0.  
Figure 3: Example of tiles extracted from the KI-67 stained images: (**I**) non-tumor class, (**II**) lymphocytes, and (**III**–**XI**) tumor classes with several progressive tumor proliferations. The length bars shown in the tiles are 50 \(\upmu\)m.
## 2 Reinforcement Learning Task for Truss Layout Design  
### Problem Statement  
Truss optimal layout design problems involve three kinds of optimization aspects: topology, shape, and size, aiming to find the optimal node positions, bar connections, and bar sectional areas, respectively. This optimal design is usually aimed at finding the layout with the minimum structural weight under linear and nonlinear constraints that limit design variable range, such as structural performance and other design requirements. To make this design task clear, the constraints used in this paper are listed in Table 1, which can be seen as a set to form different constraint combinations \(G\) in different experiments. Thus, the layout design problems involved in this paper can be expressed as:  
\[minimize\ W(P,E)=\sum_{i}\rho l_{i}A_{i}, \tag{1}\]  
\[subject\ to\ G=\{G|G\in\{g_{1},g_{2},g_{3},g_{4},g_{5},g_{6},g_{7}\}\}, \tag{2}\]  
where \(W(P,E)\) is the weight of the structure, \(P\), and \(E\) are the node set and the edge set of the structure, \(\rho\) is the material density, \(l_{i}\) and \(A_{i}\) are the length and cross-sectional area of the \(i^{th}\) bar. \(\sigma_{i}\) is the stress of \(i^{th}\) bar; \(\delta_{j}\) is maximum displacement in all directions of the \(j^{th}\) node; \(\sigma_{i}^{c}\) is the stress of the \(i^{th}\) bar in compression, in general, \(\sigma_{buckle}=\pi^{2}EI_{i}/(A_{i}l_{i}^{2})\), where \(l_{i}\) is the moment of inertia of the \(i^{th}\) bar; \(\lambda_{i}\) is the slenderness ratio of the \(i^{th}\) bar. The subscripts \(min\) and \(max\) represent the upper and lower bound of the corresponding variable, respectively.  
For constraint \(g_{1}\), the design domain \(\Omega\) defines the boundaries for the position of the truss element [21]. Constraint \(g_{2}\) defines the upper and lower bounds for the cross-sectional area of bars. For constraint \(g_{3}\), the stress of each truss member should be lower than the allowable stress [9]. The displacement constraint \(g_{4}\) is formulated by considering that the nodal displacement must be lower than the allowable displacement [22]. Constraint \(g_{5}\) represents the buckling instability of the bar. All bar sections used in this paper are circular solid sections unless otherwise stated [23]. Constraints \(g_{6}\) and \(g_{7}\) define the limits of slenderness ratio and length of bars, respectively.  
Note that the search-based method is a kind of \"black box\" optimization model; the algorithm is applicable for different objectives. Here, the most common objective, the minimum weight, is selected as the model objective for the convenience of comparing the experimental results with other literature. The design domain \(\Omega\) and specific load case information will be given in the specific experiment.
# Advanced Hysteretic Model of a Prototype Seismic Isolation System Made of Polymeric Bearings
## 2 Polymeric Bearings and Previous Shaking Table Investigation  
Precise description of PBs, detailed material testing (i.e., static tension and compression tests, Dynamic Mechanical Analysis, etc.), and finally the results of the extensive shaking table investigation, which was conducted in order to verify the effectiveness of this prototype base isolation system in reducing structural vibrations due to ground motions, has already been published (see [27; 28]). To briefly outline the key results it should be mentioned that PBs were constructed with the use of a specially prepared for this purpose polymeric material with increased energy dissipation potential. Energy dissipation potential of PBs was confirmed by a relatively high value of the loss factor obtained from the Dynamic Mechanical Analysis test. Moreover, static tension and compression results explicitly demonstrated that the tested polymer is considerably nonlinear and its mechanical behaviour is strongly dependent on the strain rate.  
In the final step, an extensive shaking table investigation was conducted. This experimental research program was performed with the use of a middle-sized one-directional shaking table platform, which was employed to simulate the lateral forces and displacements caused by seismic excitations.  
Figure 1: Shaking table investigation: fixed-base two storey experimental model (**a**) close-up view of PB used in base-isolated model (**b**).  
Dynamic response of two experimental structure models with dynamic characteristics typical for low-rise and medium-rise buildings (i.e., a 1.20 m high single-storey, and a 2.30 m high two-storey structures with fundamental frequencies of 3.34 Hz and 1.94 Hz, respectively), both fixed-base and base-isolated with the use of PBs, to a number of different ground motions and a mining tremor (as an example of so-called induced seismicity) was deeply analyzed. As an example, the 2.30 m high two-storey experimental model mounted on the shaking table platform, together with the close-up view of PB, is presented in Figure 1. The results of this experimental research explicitly confirmed the effectiveness of seismic isolation system made of PBs in mitigating seismic-induced structural vibrations. Peak lateral accelerations recorded at the top of the tested structures subjected to a number of different ground motions were decreased by approximately 11-39% and 12-57% for the single-storey and two-storey structure models, respectively. Also, it is worth mentioning that a full scale version of the PB to be used for real structures was developed, submitted to the Polish Patent Officeand patented in 2017.
## 3 Numerical Test Method and Scheme Design
The inflow edge and the upper surface are set as pressure boundaries, allowing for either a fixed or time-dependent water head. The rear boundary of the model is a liquid water head outflow boundary, which means that fluid can flow out from and back into this boundary. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Name**} & **Average** & **Density** & **Critical Shields** & **Entrainment** & **Bed Load** & **Angle of** \\  & **Particle Size** & **(kg/m\({}^{3}\))** & **Parameter** & **Coefficient** & **Transport Rate** & **Repose** \\  & **(mm)** & **(kg/m\({}^{3}\))** & **(m\({}^{3}\))** & **(m\({}^{3}\))** & **(m\({}^{3}\))** \\ \hline \hline \(<\)0.005 mm & 0.003 & 2970 & 0.3 & 0.003 & 8 & 28.7\({}^{\circ}\) \\ \hline \hline \(0.005\)–\(0.01\) mm & 0.007 & 2970 & 0.3 & 0.003 & 8 & 28.7\({}^{\circ}\) \\ \hline \hline \(0.01\)–\(0.03\) mm & 0.02 & 2970 & 0.3 & 0.003 & 8 & 34.7\({}^{\circ}\) \\ \hline \hline \(0.03\)–\(0.05\) mm & 0.04 & 2970 & 0.29 & 0.003 & 8 & 34.7\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Properties of fine tailings mixed with 5% fly ash. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Name**} & **Average** & **Density** & **Critical Shields** & **Entrainment** & **Bed Load** & **Angle of** \\  & **Particle Size** & **(kg/m\({}^{3}\))** & **Parameter** & **Coefficient** & **Transport Rate** & **Repose** \\  & **(mm)** & **(kg/m\({}^{3}\))** & **(m\({}^{3}\))** & **(m\({}^{3}\))** & **(m\({}^{3}\))** & **(m\({}^{3}\))** \\ \hline \hline \(<\)0.01 mm & 0.005 & 2970 & 0.3 & 0.003 & 8 & 28.7\({}^{\circ}\) \\ \hline \(0.01\)–\(0.074\) mm & 0.04 & 2970 & 0.29 & 0.003 & 8 & 34.7\({}^{\circ}\) \\ \hline \hline \(0.074\)–\(0.25\) mm & 0.12 & 2970 & 0.28 & 0.003 & 8 & 34.7\({}^{\circ}\) \\ \hline \hline \(0.25\)–\(0.5\) mm & 0.37 & 2970 & 0.26 & 0.003 & 8 & 34.7\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Properties of prototype tailings. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Name**} & **Average** & **Density** & **Critical Shields** & **Entrainment** & **Bed Load** & **Angle of** \\  & **(mm)** & **(kg/m\({}^{3}\))** & **Parameter** & **Coefficient** & **Transport Rate** & **Repose** \\  & **(mm)** & **(kg/m\({}^{3}\))** & **(m\({}^{3}\))** & **(m\({}^{3}\))** & **(m\({}^{3}\))** & **(m\({}^{3}\))** \\ \hline \hline \(0.005\) mm & 0.003 & 2170 & 0.3 & 0.003 & 8 & 24.3\({}^{\circ}\) \\ \hline \(0.005\)–\(0.01\) mm & 0.008 & 2170
# Effects of Sand-Cement Columns on Primary Consolidation Settlement
## 2 Materials and Methods
### Consolidation Test  
Cylindrical saturated clay specimens were prepared using specimen rings measuring 20 mm in height and 60 mm in diameter. The rings were pressed into the clay to extract the specimen, and the ends were trimmed with a spatula to produce smooth, even surfaces. This method was repeated to create three specimens representing the following configurations: one without sand-cement columns, one with a single sand-cement column, and one with two sand-cement columns. Column cavities were formed for specimens requiring sand-cement columns by extracting clay from the designated positions using a 6 mm diameter plastic straw. The sand-cement mixture was prepared by blending dry sand and cement in a mortar mixer for five minutes to ensure uniformity. The prepared mixture was then poured into the cavities, ensuring they were filled evenly to the full 20 mm height of the specimen. Water was added to the dry mix in a controlled proportion of 25% by volume of sand, measured with a syringe to achieve a consistent mortar paste. Porous stones were first saturated by immersing them in distilled water to prepare the consolidation setup and ensure full saturation. In each consolidation mold, a lower porous stone was positioned at the base, followed by a filter paper that had been pre-soaked in distilled water to prevent air entrapment. The saturated clay specimen ring was carefully placed on the filter paper. This assembly was completed by layering another wet filter paper and an upper porous stone with a loading cap on top to ensure uniform load distribution during testing. This procedure was systematically repeated for all three specimens, ensuring consistent preparation across configurations.  
The sand-cement columns were cured for seven days to facilitate adequate hydration and hardening. During this phase, the consolidation units containing the prepared specimens were submerged in water, with levels meticulously monitored to ensure continuous immersion, consistent with standard soil-cement stabilization procedures [26]. Each consolidation unit was placed in a one-dimensional oedometer test apparatus capable of  
Figure 3: Schematic diagram of the experimental setup.  
conducting three simultaneous tests. Incremental weights of 1 kg, 2 kg, 4 kg, 8 kg, 16 kg, 32 kg, and 64 kg were sequentially applied using a loading hanger, adhering to established protocols for consolidation testing [18]. Transducers affixed to the loading caps measured the deformation of the saturated clay specimens, both with and without sand-cement columns, under the applied stress. The deformation data were transmitted to a data logger, which processed and displayed the results as deformation versus the square root of time, facilitating a detailed analysis of the consolidation behavior.
## 5 Simulation Results and Analysis
### Simulation Settings  
We considered a single-cell scenario. BS is located in the center of the region. The simulation setup we used was based on the urban case in 3 GPP TR 36.885 [30]. We followed the main simulation setup in [22]. The main simulation parameters are summarized in Table 1, and the channel models of V2V and V2I links are shown in Table 2.  
The double DQN for BS consists of three fully connected hidden layers, containing 1200, 800, and 600 neurons, respectively. The activation function used by the hidden layer of double DQN networks is \(\text{ReLu}\,f(x)=\max(0,\,x)\). The RMSProp optimizer [37] is used to update network parameters. The discount factor of the double DQN algorithm is set to 0.05, and the learning rate is set to 0.001. Moreover, \(\mathbf{\theta}^{\text{target}}\) is updated with \(\mathbf{\theta}^{\text{train}}\) every 500 steps.  
We trained the whole neural network for 4000 episodes and fixed the payload size for V2V links during the training phase to be 1060 bytes, which is varied during the test phase.
## 2 Materials and Methods  
### Spectrometers  
Two different spectrometers were used in the study:  
* A bench-top Fourier Transform Infrared Spectrometer (Spotlight 400 FTIR, Frontier ATR; PerkinElmer, Waltham, MA, USA), working in the range of 650-4000 nm, equipped with an attenuated total reflection.
* An ultra-compact device MicroNIR 1700 ES (Figure 1), working in the range of 908-1676 nm, distributed by Viavi Solutions--Milpitas, CA, USA.  
The MicroNIR dimensions are 45 mm in diameter and 42 mm in height, weighing about 60 g, and it is equipped with a 128-pixel detector array, which records data with a nominal spectral resolution of 6.25 nm. The system is composed by two small tungsten light bulbs as the radiation source and a linear-variable filter (LVF) directly connected to a linear indium gallium arsenide (InGaAs) array detector. The MicroNIR can be directly connected to a USB port of any laptop.  
FTIR was used to ground-truth the applicability of a pocket NIR spectrometer for plastic type identification, as it is the most commonly used method for chemical characterization of potential plastics [11; 18].
## 3 Results  
### Demographics  
The demographic results were as shown in Table 1. Among the 190 participants, 65% were in the PSU group and 35% were in the SUC group. Overall, 47% were men, 52% women, 33% were 19 to 29 years old, 37% were 30 to 39 years, and 30% were over 40. The most frequently used applications in the past year (self-reported) were 20 web surfing in the SUC (20) and Social Network Service (SNS) and web surfing in PSU group (37). The \(p\) value of all variables in both groups was greater than 0.05. The \(p\) value of gender between the groups was 0.822, age group was 0.593, marital status was 0.171, education was 0.901, occupation was 0.884, and most used application was 0.944. Hence, there was no statistically significant difference.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Characteristics**} & \begin{tabular}{c} **SUC** \\ **(\(n\) = 66)** \\ \end{tabular} & \begin{tabular}{c} **PSU** \\ **(\(n\) = 124)** \\ \end{tabular} & \
\begin{tabular}{c} **X\({}^{2}\) Value** \\ \end{tabular} &
\begin{tabular}{c} _p-Value_ \\ \end{tabular} \\ \hline \multirow{2}{*}{Gender} & Male & 32 (35.6) & 58 (64.4) & \multirow{2}{*}{0.051} & \multirow{2}{*}{0.822} \\ \cline{2-2} \cline{5-6}  & Female & 34 (34.0) & 66 (66.6) & & \\ \hline \multirow{4}{*}{Age group} & 19 to 29 & 24 (38.7) & 38 (61.3) & \multirow{4}{*}{1.045} & \multirow{4}{*}{0.593} \\ \cline{2-2} \cline{5-6}  & 30 to 39 & 25 (35.2) & 46 (64.8) & & \\ \cline{1-1} \cline{2-2} \cline{5-6}  & Over 40 & 17 (29.8) & 40 (70.2) & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Demographic data of SUC and PSU.
## 4 Results  
The experimental framework employed a multi-modal analytical approach integrating statistical hypothesis verification, machine learning assessment, and safety-critical system validation. Rigorous performance evaluation leveraged internationally recognized testing protocols across three dimensions of analysis:  
First, spatiotemporal feature evaluation utilized advanced neural architectures to achieve precise temporal synchronization, verified through statistically robust hypothesis testing methodologies. Second, cross-sensor fusion analysis demonstrated enhanced feature coherence across heterogeneous data sources, employing decentralized learning strategies to address data distribution challenges while maintaining feature integrity. Third, safety validation protocols certified for critical applications confirmed the system's capacity to minimize unintended operational triggers during operational use, with multi-modal sensing architectures outperforming conventional single-sensor configurations in responsiveness and reliability.  
Machine learning evaluation incorporated multi-dimensional performance diagnostics, including classification matrix assessments, latency-accuracy equilibrium optimization, and nonlinear feature discrimination analysis. Safety verification extended to dynamic environmental stress testing, with topological risk models revealing distinct operational boundaries between nominal and critical states. Comprehensive validation procedures confirmed adherence to international functional safety standards while substantiating measurable advancements in system robustness compared to traditional design paradigms.  
This integrative validation strategy harmonized quantitative technical benchmarks with qualitative safety assurance objectives, establishing a replicable framework for developing intelligent mobility systems that balance technological sophistication with human-centric safety requirements.