esa-sceva/satcom-chunk-collection huggingface dataset:
# Pedestrian and Cyclist Detection and Intent Estimation for Autonomous Vehicles: A Survey
## 7 Sensors Fusion Techniques Using Deep Learning
### DL for Sensor Fusion
In [128], investigation for the optimal fusion technique for CNN-based pedestrian detection was undertaken. Fusion architectures were tested using a Faster R-CNN. Two types of fusion techniques were examined; feature-level and decision-level. Pixel-level techniques were not considered in this study. In another study [86], the performance of pixel-level fusion with early and late fusion techniques were considered. In [86], fusion architectures were implemented with an SSD. The results indicate that the Pixel-level Fusion does not perform well, but for early and late fusion, multispectral information can achieve better performance for pedestrian detection. Based on the KAIST dataset, using the wavelet transform for pixel-level fusion provided a lower Miss Rate (MR) for the early and late fusion of 9% and 5% respectively. In [97], assessment of the gain of accuracy fused visible and thermal images was conducted. To achieve this, the results from visible images, thermal images and then a combination of the two was compared. An early fusion technique approach was used for the study. Evaluation and the results were compared using the KAIST [142] multispectral dataset. The study found that a combination of features of visible and thermal images produces a better detector than with visible images or thermal images alone in the daytime. This result was not what was expected as it was believed that thermal images would not improve the features in the visible images. There is also a slight improvement in the night-time for the combination of the images.
## 5 Analysis of Cracking and Damage in Wharf Structures under Impact Loading
### Damage Identification Based on Modal Flexibility Using Finite Element Simulation
#### 6.2.4 Results of Numerical Simulation for Damage Identification  
The mode shape is defined as the distribution pattern of relative displacements of all points in a structure when it vibrates at a specific natural frequency. It represents the structural vibration form and can be regarded as the shape of the structure's vibration at that frequency. The primary vibration modes of the pile foundation under study are longitudinal for the first mode and transverse for the second mode. Figure 35 presents the mode shape cloud diagrams for the first two modes of the undamaged pile. Figure 36 shows the corresponding normalized displacement curves. By substituting the frequency and mode shape values of the intact state into Equations (9) and (10), the modal flexibility of each node in the intact state is calculated. The frequency and mode shape values under various damage conditions are then substituted into Equations (11) and (12) to compute the modal flexibility of each node under those conditions. Equation (13) is used to calculate the modal flexibility differences for the four damage scenarios, as shown in Figure 37.  
Figure 37 demonstrates that modal flexibility effectively identifies the damage locations in the pile foundation, indicated by \"spikes\" at the damaged positions. The x-axis of the modal flexibility plot represents the node numbers, with nodes 6 and 7 corresponding to the endpoints of unit 6. Node 6 exhibits the greatest change in modal flexibility, followed by node 7. This is because node 6 has a larger primary mode shape amplitude than node 7, leading to a greater change in mode shape and consequently a larger change in modal flexibility after damage. Since nodes 6 and 7 show the largest changes in modal flexibility among all nodes, it can be inferred that unit 6 is damaged. Similarly, nodes 12 and 13 correspond to the endpoints of unit 12. The changes in modal flexibility at these  
Figure 35: Displacement mode shape cloud diagram of the finite element model in the undamaged state. (**a**) First mode shape; (**b**) Second mode shape.  
Figure 36: Mode shape curves for the first two modes of the pile in an undamaged state.  
nodes indicate damage in unit 12. Additionally, it is evident that greater damage severity corresponds to larger changes in modal flexibility, allowing the extent of structural damage to be qualitatively assessed.
# A State-of-the-Art Review on Robots and Medical Devices Using Smart Fluids and Shape Memory Alloys
## 2 Robots Using ERF and MRF
### MRF-Based Robots
On the other hand, [PERSON] and [PERSON] [31] applied MRF technology to control unwanted oscillations of a spherical robot. In general, the inner suspension platform of a spherical robot undergoes a severe oscillation when the robot is rolling forwards, which could cause instability of the robot system. Thus, in order to reduce oscillations during the rolling motion, they used an MR damper integrated with a robust sliding-mode controller, which was robust against disturbances and parameter insensibility. One potential smart material, with material characteristics controllable by external magnetic fields, is the magneto-rheological elastomer (MRE). A controllable MRE, which belongs to the MR-material family, is a composite material with magnetic sensitive particles suspended, or arranged, within a non-magnetic elastomer matrix [32; 33]. The operational modes of an MRE are quite different to those of MRFs in that the iron particles are locked within the polymeric matrix, and under external excitations. This restricts the particle movement around their original locations, and thus the direction of the chain structures no longer coincides with the magnetic field. The three operational modes of an MRE are classified as shear, squeeze/elongation, and field-active modes. Depending upon the dynamic motion of MRE applications, an appropriate operational mode needs to be selected to maximize actuating  
Figure 5: Schematic of parallel planar robot using MR damper [29]. performance. Recently, a new concept for locomotion of miniature robots, based on the periodic electromagnetic actuation of the MRE body structure, was introduced by [PERSON] et al. [34]. Figure 6 presents a basic configuration of two different micro-robots with MRE body structures. The left one incorporates an inelastic-polymeric frame with an integrated micro-coil, and the movement is bidirectional, controlled by the driven frequency. The right one consists of only a symmetric MRE body with six embedded micro-coils, and thus, is applicable to compliant planar-locomotion systems. In this work, prototypes of the two MRE activated micro-robots were manufactured, and their dynamic motions were investigated by observing the driven frequency-dependent movement for two cases, uniaxial- and the planar-locomotion systems. It was seen from the experiments that the maximum average speed of the uniaxial locomotion system was 4.1 mm/s at a driving frequency of 20 Hz, while that of the planar locomotion system was 5 mm/s, at the same driving frequency.
## 3 Experimental Investigation  
### Test Specimens and Materials  
In order to gain a better insight into the behaviour of the joint, an extended series of experimental tests was carried out. In total, 84 specimens were taken into account in the laboratory investigation, with 72 \"half-size\" and 12 \"standard\" specimens. Among the half-size specimens, 36 samples were tested parallel and 36 perpendicular to the grains of timber. Furthermore, 12 full-size specimens tested to confirm the observed correlation between half-size and standard specimens (Figure 1).  
Half-size specimens (with dimensions \(B=120/W=60/L=60\) mm) were drilled in their full height \(L\) with a concave-notch diameter equal to \(d_{h}=14\) mm. This hole was placed at the centre of the cross-section of each timber log, in order to introduce both the rod and the adhesive bond. Standard type specimens were characterized by double size (with dimensions \(B=120/W=120/L=60\) mm) and prepared with a similar approach. Each timber log was drilled in the full height (60 mm), and a \(d_{h}=14\) mm hole was drilled at the centre of the wood cross-area.  
To get the results for the highest service class, Siberian larch (_Larix sibirica_) wood was used for the timber components [27]. After three weeks in a climate enclosure room with a controlled atmosphere, the moisture content was around 12% (Figure 2). The measured average density was close to 600 kg/m\({}^{3}\), with a standard deviation of 25 kg/m\({}^{3}\).  
Figure 1: Preparation of bonded-in rod (BiR) connections with M10 steel rod (examples of polyurethane adhesive bonding): (a) half-size and (b) full-size specimens.  
Figure 2: Preparation of specimens under controlled atmosphere.  
One standard metrically threated steel rod with 8.8 strength, nominal diameter \(d\) = 10 mm and total length of 200 mm was bonded in each wooden specimen (\(L\) = 60 mm the bonded length). The bonding effect was investigated with two different adhesive types, being represented by a two-component epoxy (KGK EPOCON '88) and a two-component polyurethane (LOCTITE PUREBOND CR 821). Table 2 summarizes the nominal mechanical properties of materials.  
Based on the M10 rod in use and the \(d_{h}\) mm hole, an annular bond-line thickness of 2 mm was created for each sample, and the anchorage length was set equal to \(H\) = 60 mm. The bonding stage was performed under controlled laboratory conditions (9% humidity in wood and a room temperature of 20 \({}^{\circ}\)C, see Figure 1).
# A Study on Failure Analysis and High Performance of Hydraulic Servo Actuator
## 3 Design of Hydraulic Servo Actuators
### Control System Equipped with the Servo Actuator  
The design of the hydraulic servo actuator of tensile compression fatigue test equipment has been designed with low friction hydrostatic bearings to solve failure modes. Misalignment was designed with a double bush design for concentricity, and the thermal compression of gas was designed to allow air emissions.  
In order to minimize the loss of fluid power in the static pressure bearing, the optimal parameters for the land and groove parts were set through the simulation and the prototype was made according to the specification of the maximum load of 2 ton and maximum displacement of 200 mm.  
The proportional integral (PI) controller in a typical Windows operating system (OS) has a limitation of control as the phase difference between the reference signal and feedback signal occurs and increases at frequencies up to 7 Hz. Thus, in order to reduce the phase difference between the reference signal and the feedback signal of the hydraulic servo actuator, a real-time OS-based controller for the operation of the PI controller can be built to implement the computation speed of the reference signal and the feedback signal with at least 100 us to control up to 100 Hz. The control system of the hydraulic servo actuator is a PI controller based on a real-time OS, which can be controlled in two modes of displacement and force and is configured as shown in Figure 11. As shown in Figure 12, the PI controller is a control technique that uses integral control, which creates control signals by integrating error signals, in parallel to the proportional control.  
Figure 11: Real-time system block diagram.  
Figure 10: Pressure drop of the fluid flow through 21 radial grooves.  
The transfer function of the PI controller, which is displayed as a transfer function between the error signal and the control signal, is shown in Equation (9). Here, \(K_{p}\) is called a proportional factor, and \(K_{i}\) is called an integral coefficient.  
\[\text{C(s)}=\frac{U(s)}{E(s)}=K_{p}+\frac{K_{i}}{s}=\frac{K_{p}\Big{(}s+\frac{K _{i}}{K_{p}}\Big{)}}{s} \tag{9}\]
# Secure ECDSA SRAM-PUF Based on Universal Single/Double Scalar Multiplication Architecture
### Contributions and Structure  
The main contributions of this paper are as follows:  
1. An RM code soft-decision attack algorithm for SRAM-PUFs is proposed. The attacker simply needs to modify the parameter \(P_{\text{attack}}\) in the ROM to clone the SRAM-PUF.
2. We propose a secure ECDSA SRAM-PUF based on custom signature and verification schemes. The computationally expensive modular inversion operation present in standard ECDSA is omitted in the custom schemes. The custom schemes enhance the difficulty of the proposed RM code soft-decision attack algorithm.
3. We propose a universal computing hardware architecture for ECSM and elliptic curve double scalar multiplication (ECDSM) based on the differential addition chains (DAC) to enhance the overall performance of the design.
4. A secure ECDSA SRAM-PUF architecture is proposed in this paper. The hardware architecture for RM code soft-decision emphasizes lightweight design, while the ECDSA architecture is performance-oriented. Our design is implemented on both an ASIC and FPGA to compare with the existing literature in terms of bit error rate (BER), reliability, uniqueness, and area-time product (ATP).  
The remaining sections of the paper are structured as follows. Section 2 provides an introduction to the related background and preliminaries covering the fundamentals of SRAM-PUF and ECDSA. Section 3 presents the ECDSA-based SRAM-PUF scheme along with the corresponding fast algorithm. In Section 4, the paper delves into the hardware architecture of the proposed algorithm, accompanied by an exploration of hardware evaluation and optimization. Section 5 offers insights into the hardware implementation results of the proposed architecture. Finally, Section 6 serves as the conclusion, summarizing the key findings and contributions of the paper.
## 2 Study Area and Data
### Data
#### 2.2.4 Gross Primary Productivity Data  
To facilitate a comparison between the downscaled SIF result and GOSIF data, 8-day gross primary productivity (GPP) data (MOD17A2H) with 500 m resolution were obtained. These data will be used for the correlation analysis involving both the downscaled SIF result and GOSIF.
## 5 Comparison with the Previous Studies  
The comparison between the proposed design and the previous studies was shown in Table 4. Metamaterial enables the extreme miniaturization of existing devices and applications, as well as the customization of novel characteristics not available in current satellite and radar applications. Higher resonance frequencies are typically easier to produce when using a smaller metamaterial structure. As stated in Table 4, the previous studies used a larger size of unit cell and an array metamaterial structure. For example, [PERSON] et al. [PERSON] _et al._ (2018) used 10 \(\times\) 10 mm\({}^{2}\) size of FR-4 unit cell, which resulted in only a single frequency band, and the characteristic of the proposed design is ENZ. The same goes for [PERSON] et al. [PERSON] _et al._ (2018) and [PERSON] et al. [PERSON] _et al._ (2018); they proposed double-negative metamaterial structures for a single frequency band, which is X-band. Besides the size of the metamaterial structure, other parameters such as the design of the metamaterial must be considered to produce the desired novel properties. The proposed design consists of five rings of resonators, resulting in a dual-frequency band. [PERSON] et al. [PERSON] _et al._ (2018) designed a double T-U-shaped metamaterial, which resulted in triple-frequency bands. Thus, it was shown that a multiple number of ring resonators will result in multiple resonance frequencies. Furthermore, the proposed design also exhibited the ideal properties of the artificial material, which is a left-handed metamaterial. When compared to other studies, they only managed to produce ENZ and DNG metamaterials. The proposed metamaterial also uses different types of substrate. Even though the dielectric constant of Rogers RO4003C is lower than that of FR-4 and nickel aluminate, the proposed metamaterial achieved better results compared to the previous studies. With the highlighted performance of the novel metamaterial structure, it can be deduced that the designation of compact oval-square-shaped SRR using Rogers RO4003C substrate is suitable for current satellite and radar applications. On a side note, the proposed metamaterial also needed to be improved in terms of design structure. Although it may provide some benefits to the targeted applications, the complicated design of the proposed SRR may result in additional work for the researchers.  
Figure 13: The comparison of S-parameters for design A and design B.
## 2 Complex Natural Resonances Methods
### Cauchy's Method  
In the frequency domain, this method was presented in 1821 by [PERSON], approximating a function by a ratio of two polynomials [20]. After that, it has been applied to the solution to electromagnetic problems modeling the system frequency response in the same way [11]. It assumes that the response is completely linear-time-invariant (LTI); thus, the transfer function can be written as in Equation (11) [10]. Equation (12) can be constructed with the coefficients \(a_{k}\) and \(b_{k}\) and then having \([\mathbb{C}]\) as in Equation (13). As in the MPM approach, the SVD is applied to \([C]\) and a threshold to have the smallest singular value greater than or equal to the number of significant decimal digits of data in (14) and (15), having \(w\) as the number of accurate significant decimal digits in the data from the response.  
\[H(f_{i})\approx\frac{A(f_{i})}{B(f_{i})}=\frac{\sum_{k=0}^{P}a_{k}f_{i}^{k}}{ \sum_{k=0}^{Q}b_{k}f_{i}^{k}},\quad i=1,2,\ldots,N \tag{11}\]  
Figure 5: Input signal \(y(t)=\sin 5t+\sin 10t\) and its reconstruction using the CNRs calculated through MPM.  
Figure 6: Poles \(s_{i}=\alpha_{i}+j\omega_{i}\) calculated with MPM from the benchmark signal \(y(t)=\sin 5t+\sin 10t\). These poles come in conjugated pairs in the complex plane.  
\[\begin{bmatrix}A&-B\end{bmatrix}_{N\times(P+Q+2)}\begin{bmatrix}a\\ b\end{bmatrix}_{(P+Q+2)\times 1}=[C]_{N\prod(P+Q+2)}\begin{bmatrix}a\\ b\end{bmatrix}_{\times(P+Q+2)\times 1}=0 \tag{12}\]  
\[[C]=\begin{bmatrix}1&f_{1}&\cdots&f_{1}^{p}&-H_{f_{1}}&-H_{f_{1}}f_{1}&\cdots&-H_ {f_{1}}f_{1}^{Q}\\ 1&f_{2}&\cdots&f_{2}^{p}&-H_{f_{2}}&-H_{f_{2}}f_{2}&\cdots&-H_{f_{2}}f_{2}^{Q}\\ \vdots&\vdots&\ddots&\vdots&\vdots&\ddots&\vdots\\ 1&f_{N}&\cdots&f_{N}^{p}&-H_{f_{N}}&-H_{f_{N}}f_{N}&\cdots&-H_{f_{N}}f_{N}^{Q} \end{bmatrix} \tag{13}\]  
\[[U][\Sigma][V]^{H}\begin{bmatrix}a\\ b\end{bmatrix}=0 \tag{14}\]  
\[\frac{\sigma_{R}}{\sigma_{max}}\approx 10^{-w} \tag{15}\]  
Now, returning to (11), the TLS solution approach can be used to solve the equation and find the residues [19], which lead to the general steps for the method from the input signal to the reconstructed one in Figure 7.  
A straightforward validation process occurs in the same way as the ones for MPM and Prony's methods, but this time for a frequency domain benchmark signal \(Y(\omega)=\frac{0.5}{\omega+1}+\frac{5}{\omega+5}+\frac{7}{\omega+6}+\frac{9} {\omega+8}\). The signal reconstruction comparison and the poles calculated with this method are shown in Figures 8 and 9.
## 4 Conclusions  
Using post-classification correction (PCC) based on ancillary data and knowledge-based logic rules, the overall accuracies for NDVI-NDMI and TCT across both sets of years for the Houston site are consistently between 81% and 84%, with Kappa coefficients varying a little more, between 45% and 52%. For the South Texas site, overall accuracies are much lower, ranging between 58% and 65%, with the Kappa coefficients, also lower, between 43% and 51%. However, these accuracies are higher than the NDVI-BI calculation, which has an overall accuracy and Kappa coefficient for the first and second year pairs of 73% and 21%, and 71% and 22% respectively. Overall accuracy and Kappa coefficient for the South Texas site for the first and second year pairs of NDVI-NDMI is 73% and 43%, and 73% and 46%, respectively. For the 2009 to 2010 years, the TCT overall accuracy is 75% with a kappa coefficient of 47% while the 2010 to 2011 years has an overall accuracy of 76% and a Kappa coefficient of 51%. For the South Texas site, the accuracies based on PCC are also higher than the NDVI-BI calculation, which indicated an overall accuracy and Kappa coefficient for the first and second year pairs of 65% and 21%, and 61% and 24% respectively. Generally speaking, the PCC results were more likely to identify changed cells within the sampled points, indicating a greater sensitivity towards change. We are calculating lower producer's accuracy for changed cells indicating that the CVA results were not as sensitive to change as the PCC. This may have been because the selected threshold was too high, thus excluding certain land use/cover changes from the analysis. The high overall accuracy and good kappa coefficients indicate the CVA was effective in detecting vegetation dynamics caused by drought. These are the summaries. For full accuracy assessment, contact the authors. Our results are also in line with the publicly available National Land Cover Databases (NLCD) (mrlc.gov). The U.S. Geological Survey (USGS) who produce NLCD updates also use Landsat sensor imagery but their methodology is based on multi-sourced training data and decision-tree land cover classifications. Given, their wide definitions of land cover, they report accuracies for the conterminous U.S. between 71% and 97% [32]. Remote sensing is crucial for environmental monitoring. Data on land use/cover at various scales and for various dates contribute to models of environmental change, and in return, fuel many local and federal environmental policies. Sudden sort-term drought events decimate ecosystems and threaten agricultural livelihood. By developing a methodology based on remote sensing data, we are contributing to research on how to measure more precisely the dryness of land use/cover using