esa-sceva/satcom-chunk-collection huggingface dataset:
# Parametric Analysis of the Shear Lag Effect in Tube Structural Systems of Tall Buildings
## 5 Parametric Analysis
### The Impact of Beam Height on the Shear Lag Effect  
In this chapter, the impact of the beam height in a tube structure on the shear lag effect will be analyzed. While the beam height will be varied, the storey height will stay the same with the value of H\({}_{1}\) = 3.5 m. The layout dimensions have constant values of A/B = 24 m/24 m, and the column spacing has a constant value of s = 2.4 m. The beam height will be varied five times with values of h = 1.2 m (H\({}_{1}\)/h = 2.92), h = 1.1 m (H\({}_{1}\)/h = 3.18), h = 1.0 m (H\({}_{1}\)/h = 3.50), h = 0.9 m (H\({}_{1}\)/h = 3.89) and h = 0.8 m (H\({}_{1}\)/h = 4.4). The height of the building stays the same. The results of the analysis are shown in Figure 10.  
Figure 9: The change in the shear lag effect with the variation of column spacing (\(\beta\)—s/c diagram).  
According to the results (Figure 10) the following can be concluded:  
1. All storeys bellow the 12 th storey are in the positive shear lag effect area (\(\beta<1\)). For these storeys, by decreasing the beam height, the positive shear lag effect increases. Examining the curves in Figure 10 it can be concluded that the positive shear lag effect is more prominent for lower storeys of the building and that coefficient \(\beta\) is smallest for the ground storey.
2. The transition from the positive to the negative shear lag effect area for all beam height variants is at the 12 th storey (\(\beta=1\)), which is approximately at 33% of the building's height measured from the ground.
3. All storeys above the 12 th storey can be found in the negative shear lag effect area (\(\beta>1\)). As the building rises, so does the coefficient \(\beta\). Furthermore, the increase of coefficient \(\beta\) in one storey due to the variation of beam height is more and more prominent as the building rises. While on the 18 th storey this increase is mild, on the 36 th storey the increase of coefficient \(\beta\) with the decrease of beam height is quite notable.  
It can be deduced that the decrease in beam height results in the increase of positive and negative shear lag effect in tall tube buildings.
## 1 Introduction  
Recently, fiber-reinforced composite materials have attracted considerable attention in various industrial fields, such as automobiles, aerospace, vessels, sports, military, and electronics, owing to their characteristics, such as good mechanical properties, damping, low density, and formability [1, 2, 3]. Fiber-reinforced composites consist of reinforcing fibers, such as carbon fibers, glass fibers, or aamid fibers, and a polymer matrix made of thermoset or thermoplastic materials. The reinforcing fibers have intrinsic physical properties that provide high strength, modulus, and low density, whereas the polymer matrix transfers the internal force between one fiber and another in composite materials.  
Theroplastic materials are particularly advantageous in terms of impact resistance, reusability, and high productivity owing to their physical and chemical structures. Among thermoplastic materials, polyamide 6 (PA6) has often been used as the matrix of composites because it can provide not only a low cost but also advantageous mechanical properties at relatively high temperatures. Composites composed of PA6 and glass fibers have been used in research and industrial fields [4, 5, 6, 7]. However, the intrinsic tensile and flexural properties of thermoplastic materials are inferior to those of thermoset materials. The relationship between the fiber length and mechanical properties has been studied for decades to solve these problems [8]. Long-fiber-reinforced thermoplastic (LFT) composites are widely utilized because they have better mechanical properties than short fibers, which are utilized in short-fiber-reinforced thermoplastics [9; 10]. Furthermore, LFT can have significantly high fiber content [4]. Increasing the fiber content is expected to improve the mechanical properties of discontinuous fiber composites.  
In this study, the manufacturing methods of LFT composites were classified into pultrusion and direct processes to study their effects on the fiber length of PA6/GF composites. The pultrusion process typically leads to improved mechanical properties because it induces a longer fiber length than the direct process for reinforcing the fibers. On the other hand, the direct process is less affected by the melt viscosity than the pultrusion method. Therefore, there are many types of resins that can be manufactured using the direct process, including high melting point viscous polymers such as Polyether ether ketone (PEEK), Polyphenylene Sulfide (PPS), Polyesterimide (PEI), etc. These polymers can be economically produced through the direct process. However, composites prepared via the direct process show notably poor mechanical properties, such as flexural strength, tensile strength, and impact strength. This study aimed to characterize the mechanical properties of PA6/GF composites in terms of the fiber content, resin type, and process.
###### Abstract  
An active self-interference (SI) cancellation technique for SAW-less receiver linearity improvement is proposed. The active canceler combines programmable gain and phase in a single stage and is co-designed with a highly-linear LNA, achieving low noise and low power. A cross-modulation mechanism of the SI canceler is identified and strongly suppressed thanks to the introduction of an internal resistive feedback, enabling high effective receiver IIP3. TX leakage of up to \(-4\) dBm of power is suppressed by over 30 dB at the input of the LNA, with benefits for the entire receiver in terms of IIP3, IIP2, and reciprocal mixing. The design was done in a 40 nm CMOS technology. The system, including receiver and active SI canceler, consumes less than 25 mW of power. When the canceler is enabled, it has an NF of 3.9-4.6 dB between 1.7 and 2.4 GHz and an effective IIP3 greater than 35 dBm.  
blocker; cross-modulation; distortion cancellation; diversity; FDD; full-duplex; IIP3; SAW-less; self-interference
## 3 Resource Allocation under Total Power Constraint  
Although joint optimization of physical layer, medium layer, and application layer can achieve better performance [23], the successful establishment of communication depends first on the connection of the physical layer. Hence, this section focuses on the optimization of the physical layer. In this section, we prepare to seek for the maximum value of the sum rate of the secondary network subject to the available power budget and the tolerable interference threshold at the primary user. This problem is formulated by  
\[\max_{\left\{p_{s,i},p_{r,j},\rho_{i,j}\right\}}\sum_{i=1}^{N} \sum_{j=1}^{N}\rho_{i,j}r_{i,j} \tag{3}\] \[s.t.\sum_{i=1}^{N}\sum_{j=1}^{N}\left(p_{s,i}+p_{r,j}\right)\leq P _{T}\] \[\sum_{i=1}^{N}p_{s,i}c_{i}\leq Q_{1},\sum_{j=1}^{N}p_{r,j}d_{j} \leq Q_{2}\] \[\sum_{i=1}^{N}\rho_{i,j}=1,\sum_{j=1}^{N}\rho_{i,j}=1,\forall i,j\]  
where \(c_{i}=\left|\tilde{h}_{s,i}\right|^{2}/N_{0}\), \(d_{j}=\left|\tilde{h}_{d,j}\right|^{2}/N_{0}\), \(P_{T}\) is the total power budget at the secondary network, \(Q_{1}\) and \(Q_{2}\) denote the tolerable interference threshold that \(PD\) can maintain at the first phase and the second phase, respectively. \(\rho_{i,j}\in\left\{1,0\right\}\) is a binary variable indicating whether the subcarrier pairing \((i,j)\) is successful. If subcarrier pairing is formed, then \(\rho_{i,j}=1\); otherwise, \(\rho_{i,j}=0\). Although our problem is a mixed integer programming problem due to the existence of discrete variables, the dual gap is asymptotically zero according to the time sharing property of OFDM [24].
## 3 System Design and the Proposed HPR-DCC Methodology  
### System Model and Assumptions  
The proposed system model aims to estimate vehicular parameters by emphasizing the adjustment of transmission power and transmission rate. This is achieved through the assimilation of received messages and distance information from neighboring vehicles, as illustrated in Figure 2. This model incorporates critical parameters, such as neighboring vehicles' transmission power, distance of received messages, and estimated path loss (PL), which are instrumental in determining the optimal transmission power necessary for achieving the desired level of awareness within the target vehicle's awareness range. The system model operates on several fundamental assumptions. Primarily, it assumes that the transmission power is modulated based on the estimated PL value, with the goal of achieving the target awareness percentage within the awareness range, while not considering the impact of frame error rate. Subsequently, the model presumes that the PL value estimation relies on messages from a sufficient number of neighboring vehicles, enabling target-aware transmission for vehicles that have not received any messages. Lastly, under extreme circumstances where the distance between vehicles is minimal and path loss is substantial, the transmission power will be maintained at a prominent level to ensure the preservation of the target awareness range. The control mechanism embedded within the proposed system model incorporates the calculation of CBR, received messages, and distance information, as well as the computation of transmission power necessary to attain the target awareness percentage. Moreover, the estimation of the PL value is employed to adjust the transmission power for target-aware transmission within the awareness range. This ensures that the vehicle maintains awareness of the target vehicle throughout the communication process.
# Ni/Zn Layered Double Hydroxide (LDH) Micro/Nanosystems and Their Azorubine Adsorption Performance
## 4 Results and Discussion
### Adsorption Process from Aqueous Solutions  
#### 4.2.1 Adsorbent Dosage Effect on the Azorubine Removal  
One of the parameters that significantly affect the adsorption capacity is the adsorbent dosage [45]; Figure 4 illustrates theNi/Zn LDH dosage effect on Az adsorption. The removal ratio of azo dye was increased when increasing the amount of Ni/Zn LDH adsorbent that was used. High Az removal is observed, with the maximum reachinga value of 97% with 30 mg L-1 adsorbent. Thiscan be attributed to the larger pore diameter (around 24 nm), which favors Az removal at lower adsorbent dosages [45]. In addition, the higher sorbent removal with Ni/Zn LDHs at low adsorbent dosage demonstrates the high affinity between this adsorbent and the Az. Therefore, we fixed the adsorbent amount to 30 mg L-1 forall subsequent experiments.  
Figure 4: Ni/Zn LDH adsorbent dosage effect on Carmoisine (Az) removal.  
Figure 3: XPS spectra of the Ni/Zn LDH-based adsorbent in the Ni2p, Zn2p, C1s, O1s energy regions.
## 1 Introduction
SO-DRL implements Deep Deterministic Policy Gradient (DDPG)-based path planning by piggybacking the search-based algorithm onto the training platform and collecting training experience through the use of known maps for path planning reinforcement learning. The experience is played back according to priority. Eventually, the UGV/UAV equipped with lidar is capable of quickly learning path planning in unknown environments (as shown in Figure 1). The validity of SO-DRL can be verified in a real UGV and UAV without the need to adjust the reference. The main contributions of this paper are the following:  
* A unique end-to-end DRL-based path planning algorithm (SO-DRL) was proposed to enable the air-ground unmanned system to perform path planning obstacle avoidance in unknown environments using only laser perception without the need for known maps and complex path and optimization designs. * A priori global optimization of the search-based algorithm was constructed, which alleviated the sparse reward problem at long destination distances and allowed the algorithm to converge quickly and efficiently with better success rate performance. * A highly simulated real physical environment was built and a multi-stage learning process based on course learning was designed to quickly and effectively guide the planning of UGV/UAV in more complex environments. The results show that our method has faster, better convergence, and stronger problem-solving ability than classical reinforcement learning methods. * Our algorithm has a good generalization capability of simulation to reality. We designed a realistic lidar-only UAV and UGV for obstacle avoidance experiments, which show that the obtained strategies can be mounted on a real UGV or UAV for navigation in unknown environments without optimization. The remainder of this paper is organized as follows. Section 2 introduces related work. Section 3 models the path planning for reinforcement learning, and the architecture of the developed algorithm. Section 4 discusses the simulation experiment results. Section 5 presents the real-world experiments, and we draw a conclusion and present some potential future work in Section 6.
## 3 Influence of Boundary Distance in the Integral Response Deformation Method
### Influence of the Lateral Boundary Distance
#### 3.1.2 Soil Stress  
Under seismic loading, the deformation of underground structures is primarily controlled by the surrounding geotechnical medium. Accordingly, the internal forces and deformations of a structure are closely related to the stress and deformation distributions within the surrounding soil or rock. Studies have demonstrated that the maximum shear stress in a geotechnical medium can, to some degree, reflect the constraints exerted on underground structures. Figure 6 shows the maximum shear stress contours in the geotechnical medium computed by the IRDM under varying lateral boundary distances.  
As the \(R_{S}\) increases, the shear stress in the geotechnical medium surrounding the structure gradually decreases. When \(R_{S}\)\(\leq\) 1.0, the lateral boundaries are too close to the structure, imposing excessive constraints and causing elevated stress levels in the surrounding area. For \(R_{S}\) = 2.0, the boundary influence on the surrounding region weakens significantly, although localized horizontal shear stress concentrations persist near the intersections of the sidewalls and floors. When \(R_{S}\)\(\geq\) 3.0, boundary effects on adjacent regions become negligible.  
Figure 6: Shear stress distributions computed via the IRDM under different \(R_{S}\) values, where \(R_{S}\) is a dimensionless lateral boundary distance ratio \(R_{S}\) = \(L_{S}/L\).  
Therefore, when the IRDM is applied for seismic analysis of underground structures, lateral boundaries should be placed far enough to minimize boundary effects on computational accuracy. These findings suggest that setting the lateral boundary distance to three times the structural width effectively controls the boundary effect while maintaining high precision.
# English Speech Emotion Classification Based on Multi-Objective Differential Evolution
## 4 Experimental Results and Analysis
### Discussion  
The running time of MODE-NSF using the KNN classifier in the four datasets was 268.8662, 295.3447, 178.7590, and 543.2745 respectively, while the running time on the RF classifier was 4008.0458, 11,584.3484, 3997.2735, and 16,545.7497. It is also reported in [30, 31] that RF has a large time complexity, while KNN has a small workload. Ref. [31] achieved a recognition accuracy of 41% for eNTERFACE05, while MONDE-NSF acquired an accuracy of 49%. In [32, 33], they obtained an accuracy of 75% using MFCCs on RAVDESS, while MODE-NSF only exhibited an accuracy of 53%. Both the results reported in [34] and those of MODE-NSF exhibited an accuracy of 76% for SAVEE. Ref. [15] achieved a 97% accurate classification for TESS, compared to 98% for MODE-NSF. The algorithms acquired a high recognition accuracy using TESS and a low value for eNTERFACE05. MODE-NSF showed  
Figure 4: Pareto optimal solutions acquired by the algorithms.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Dataset** & **MOGA** & **MODE** & **NSGA-II** & **MODE-NSF** \\ \hline eNTERFACE05 & 18,608.8552 & 4885.9528 & 11,366.0398 & **4008.0458** \\ RAVDESS & 61,563.0978 & 16,457.0307 & 33,405.7332 & **11,584.3484** \\ SAVEE & 18,906.203 & 5643.6755 & 12,084.5362 & **3997.2735** \\ TESS & 72,422.1074 & 19,536.5901 & 41,187.0129 & **16,545.7497** \\ \hline \hline \end{tabular}
\end{table}
Table 8: The average running time of the algorithms (second).  
excellent performance for eNTERFACE05, SAVEE, and TESS, which illustrates that the NSF-guided method is suitable for speech emotion recognition.
# Analysis of Controllability in Cyber-Physical Power Systems under a Novel Load-Capacity Model
## 4 Case Study and Discussion  
The load redistribution model proposed in this paper was validated, and its controllability changes during the process were analyzed using the IEEE standard 39-node system and the Chinese 132-node system. This paper assumes the assurance of complete synchronization between the power system and the information system, and unless otherwise specified, the system capacity parameters \(\alpha\) and \(\beta\) are 0.5, respectively. The horizontal axis in the graph of this section, times of attacks, represents the system's change in terms of controllability after enduring \(x\) instances of node attacks. The simulations in this paper were performed using MATLAB 2020b on a personal computer equipped with an Intel Core i5 2.4 GHz CPU and 16 GB RAM.  
In general, the network coupling methods of CPPS can be categorized into a \"one-to-one\" coupling strategy and \"one-to-many\" coupling strategy. This paper adopts the \"one-to-one\" coupling strategy to analyze the cascading failure in CPPS. Meanwhile, the coupling methods between nodes in two networks can be divided into the following four methods:  
* **DDM**: High-degree nodes in the power network are connected to high-degree nodes in the information network.  
Figure 4: An example of the cascading failure process. The nodes represent partial locations in the network, the dashed lines indicate the connections between nodes, and the solid lines represent the direction of load redistribution after node failures. The colors of the nodes represent their respective states, where green, yellow, and red indicate nodes in underloading, heavy-loading, and overloading states, respectively. The darker the color, the heavier the load on the node. In (**a**), node \(N_{1}\) is in an overloading state, and after the failure, the load should be distributed among neighboring nodes. In (**b**), it represents the state of this set of nodes after the load redistribution of \(N_{1}\). Nodes \(N_{2}\) and \(N_{5}\) also become overloading, and \(N_{4}\) becomes heavy-loading. This process will be repeated until the network stabilizes or collapses.  
* **BBM**: High-betweenness nodes in the power network are connected to high betweenness nodes in the information network.
* **DBM**: High-degree nodes in the power network are connected to high betweenness nodes in the information network.
* **BDM**: High-betweenness nodes in the power network are connected to high-degree nodes in the information network.
## 3 Simulation Results
### Minimization of the Active Losses Criterion  
Relying on the load-flow method proposed in this study, the calculation of the active power losses in each branch was achieved. In this case, the ultimate aim was to reduce the power losses by applying the proposed approaches.  
The results presented in Table 2 show that the active power losses decreased by 63.1258 kW compared to the base case value of 202.6774 kW. The percentage reduction was 31.15% compared with the initial topology presented in Figure 5. The open branches are 7-9-14-  
The simulation results show that, for the IEEE 33-bus test system, all methods have found the optimal network configuration with minimum power loss. However, the number of runs reaching out to the optimal configuration of PSO is superior to the remaining methods. Specifically, the success rate, which is defined as a result obtained in percent by dividing the number of runs finding out the optimal configuration by the total of runs, of PSO is 85% while this value of GA and exhaustive approaches are 61 and 8%. Figure 11 shows the comparisons of the successful rate of the proposed methods for IEEE 33-bus test system.  
The evolution of the active power losses during the searching process using the GA and PSO algorithms for the test distribution system is shown in Figure 12. The difference in terms of iterations in achieving an optimal solution was 14 iterations. Although GA is superior to the exhaustive approach, it is still inferior to PSO in terms of speed to reach the optimal solution. In addition, this method can solve, in a satisfactory manner the problem of finding the optimal configuration for a large network in a reasonable time.  
The PSO-based reconfiguration solution proposed in this paper provides significant improvements in active power losses and reliability of the IEEE 33-bus distribution network. Indeed, the PSO approach is a population-based evolutionary technique that has many key advantages over other optimization techniques. First, it is a derivative-free algorithm, unlike many conventional techniques. Second, it has the flexibility to be integrated with other techniques to form a hybrid tool such as a load flow algorithm. Then, it  
Figure 11: The comparison of the successful rate of the proposed methods for IEEE 33-bus test system.  
Figure 12: GA and PSO convergence characteristic for the minimization of the active power losses criterion.  
has fewerparameters to adjust, unlike many other competing evolutionary techniques. It also has the ability to escape from local minima. Finally, it does not require a good initial solution to start its iteration process.
# Face Recognition and Tracking Framework for Human-Robot Interaction
## 5 Experiments and Analysis
### Results
#### 5.3.1 Dataset Evaluation  
We use the ChokePoint dataset [54] to evaluate the proposed framework. This dataset is a video dataset that was collected and designed for experiments on person identification/verification under real-world surveillance conditions. It contains videos of 25 subjects (six female and 19 male). In total, the dataset consists of 48 video sequences and 64,204 face images with variations in terms of illumination conditions, pose, sharpness, as well as misalignment due to automatic face localization/detection.  
The experimental results show the performance of tracking for 25 subjects of the ChokePoint dataset. To show recognition refinements, we have tested the proposed face recognition framework with tracker-assisted and without. The average results are shown in Table 1. Furthermore, the Receiver Operating Characteristic (ROC) curve is obtained in Figure 5, which shows that the tracking approach improves the recognition rate for high false positive rates and reduces the false classification rate.
## 2 Materials and Methods
### Data
#### 2.1.2 MODIS Data  
MODIS has multispectral bands and provides visible light and thermal infrared data, with a higher resolution from 250 to 1000 m. MODIS data are widely used in fields such as monitoring global climate change, vegetation ecology, air pollution, land-use cover change, urban development, and environmental monitoring. In this study, MOD09A1 was used to provide land surface reflectance to calculate the vegetation/water index, and MOD15A2H was used as \(fPAR\) in 8-day and 500 m spatiotemporal resolution during the period of ground-site observations. MOD17A2H, as a popular GPP product for research worldwide, was used as a reference value to compare with the results of both individual LUE models and the models fused by machine learning. ORNL DAAC (Oak Ridge National Laboratory's Distributed Active Archive Center, Oak Ridge, TN, USA) provides the download link and the Fixed Sites Subsets Tool for global users to match ground sites and corresponding MODIS pixel data. All missing or poor-quality data in MODIS products were removed based on the MODIS quality control flags. The MODIS products were interpolated temporally to daily time scale from composite products with a linear interpolation to match with ground-site observations.