esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Conventional Detection Scheme
### Reduced-complexity Sequential Detection (RCSD) Scheme  
Recalling from (4) that \(P_{i}(k)=P_{i}(-k)\), the property of \(D_{i}(k)=D_{i}^{*}(-k+1)\) is valid for any \(i\) and \(k\in[2,N_{p}/2]\). Using the symmetric feature of the PSSS, the sequential detection of the IFO and SI is proposed in [27]. A normalized PSSS by the channel magnitude is used to remove the effect of channel fading, taking the expression \[\Omega_{a}(v)=\frac{1}{N_{p}/2-1}\sum_{k=2}^{N_{p}/2}\frac{\bar{Y}_{l}(k+v)\bar{Y}_ {l}(-k+v+1)}{|\bar{Y}_{l}(k+v)||\bar{Y}_{l}(-k+v+1)|}, \tag{9}\]  
which approaches to one when signal-to-noise ratio (SNR) increases at a true IFO trial \(v=\epsilon\). Based on this property, the IFO is detected by minimizing the quantity \(|\Omega_{a}(v)-1|\)  
\[\hat{\epsilon}= \arg\min_{v}\left|\Omega_{a}(v)-1\right|, \tag{10}\]  
which is performed independent of the PSS. Based upon the estimation of \(\epsilon\), the SI is obtained via one dimensional search  
\[\hat{n}= \arg\max_{n}\left|\Omega_{b}(n)\right|, \tag{11}\]  
with  
\[\Omega_{b}(n)=\sum_{k=2}^{N_{p}/2}\left\{\frac{\bar{Y}_{l}(k+\hat{ \epsilon})}{|\bar{Y}_{l}(k+\hat{\epsilon})|}+\frac{\bar{Y}_{l}^{*}(-k+\hat{ \epsilon}+1)}{|\bar{Y}_{l}^{*}(-k+\hat{\epsilon}+1)|}\right\}D_{n}^{*}(k). \tag{12}\]  
This decoupled strategy can avoid two-dimensional search, leading to substantial complexity reduction. Therefore, the RCSD scheme achieves good detection performance with low complexity when there is no residual STO, while the presence of residual STO has a negative effect on the detection accuracy.
## 2 Modeling of the DC Sub-Microgrid
### Modeling of AC Sub-Grid  
AC Sub-grid configuration is shown in Figure 5. Utility grid and AC loads are supplied through the voltage source inverter (VSI). The currents and voltages of the voltage source inverter are as follows: \(V_{d}\), \(V_{q}\), \(I_{d}\), and \(I_{q}\) are the output voltages and currents of the dq axis; \(I_{gd}\), \(I_{gq}\), \(V_{gd}\), and \(V_{gq}\) are the utility grid dq axis currents and voltages, respectively. Operating frequency and voltages for the AC loads and utility grid are the same throughout the grid-connected operation. Parks and Clark's dq transformation is used for VSI mathematical modeling and can be expressed by the following equations [45]:  
\[\frac{dI_{d}}{dt} = -\frac{V_{d}}{L_{f}}-\frac{R_{f}I_{d}}{L_{f}}+\omega_{o}I_{q}+ \frac{U_{d}V_{dc}}{L_{f}} \tag{21}\] \[\frac{dI_{q}}{dt} = -\frac{V_{q}}{L_{f}}-\frac{R_{f}I_{d}}{L_{f}}-\omega_{o}I_{d}+ \frac{U_{q}V_{dc}}{L_{f}}\] (22) \[\frac{dV_{d}}{dt} = -\frac{V_{d}}{RC_{f}}+\omega_{o}V_{q}+\frac{I_{d}}{C_{f}}-\frac{ I_{gd}}{C_{f}}\] (23) \[\frac{dV_{q}}{dt} = -\frac{V_{q}}{RC_{f}}-\omega_{o}V_{d}+\frac{I_{q}}{C_{f}}-\frac{ I_{gq}}{C_{f}}\] (24) \[\frac{dI_{gd}}{dt} = \frac{V_{d}}{L_{g}}-\frac{R_{g}I_{gd}}{L_{g}}+\omega_{o}I_{gq}- \frac{V_{gd}}{L_{g}}\] (25) \[\frac{dI_{gq}}{dt} = \frac{V_{q}}{L_{g}}-\frac{R_{g}I_{gq}}{L_{g}}-\omega_{o}I_{gd}- \frac{V_{gq}}{L_{g}} \tag{26}\]  
Figure 5: AC subgrid configuration.  
where variable expressions of \(C_{f},\ R_{f}\), and \(L_{f}\) are the capacitance, equivalent resistance and inductance of the filter, respectively. \(R_{g}\) and \(L_{g}\) are the equivalent resistance of the inductor, respectively. Output voltages of VSI have angular frequency denoted by \(\omega_{o}\).
# Non-Autoregressive Sparse Transformer Networks for Pedestrian Trajectory Prediction
## 4 Experiments
### Experimental Settings  
Our proposed NaST network was trained on the Pytorch deep learning framework. In our experiment, the original coordinates data were first embedded into dimension 32 by a fully connected layer, followed by ReLU activation. During the training stage, the encoder received each frame embedded and extracted the features for the time steps observed. The sparse spatial and temporal transformers accepted inputs with a feature size of 32. The number of encoder layers in the sparse spatial and sparse temporal encoders were two and one, respectively. The number of self-attention heads was set to eight in each encoder layer. The number of layers in the decoder transformer was set to two, with four multi-heads.  
The model was trained with the Adam optimizer [9] for 300 epochs with a batch size of 16. The learning rate was 0.0015. The sparse spatial threshold value \(\gamma\) and sparse temporal value \(\delta\) were, empirically, set to 0.1 and 0.5. The model was trained by minimizing the Mean Square Error (MSE) loss. During the inference stage, 20 samples were generated for each sample and the closest one to ground truth was used to compute the ADE and FDE metrics.
## 5 Results of the Proposed Power Allocation Scheme
Figure 8 investigates the DASR performance with \(N\) numbers of antennas comparing the proposed power optimization method with the conventional method when the coherence time was increased \(\tau_{\mathrm{c}}=100\) but with various \(N\) and different SNR values. The correlation coefficient was 0.1 and \(K=10\). The results indicate that increasing the SNR would significantly enhance the DASR. A peak DASR was achieved at \(N=300\), and an enhancement of 23 bits in the DASR was achieved when \(\mathrm{SNR}=10\) dB in comparison with \(\mathrm{SNR}=0\) dB. The results show that for both of the SNR values considered, the proposed power allocation optimization method improved the DASR performance outcomes, especially as \(N>200\). The results presented in the section clearly show the effectiveness of the proposed power optimization approach in improving the DASR performance. Figure 6: DASR with coherence time comparing different power allocation methods, with \(N=200\). Figure 7: DASR with coherence time comparing different power allocation methods, with \(N=500\).
## 2 Research Progress in In Situ Road Health Monitoring Technology
### Resistance-Type Strain Gauges
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Brand** & **PAST** & **ASG** & **ASG—3000 HT** & **KM100 HAS** \\ \hline Country & Denmark & America & America & Japan \\ City & Copenhagen & Texas & Texas & Tokyo \\ Manufacturer & Dynatest & CTL & Jewell & TML \\ Monitoring & \(\pm\)1500 & \(\pm\)1500 & \(\pm\)3000 & \(\pm\)5000 \\ range/\(\mu\epsilon\) & \(-\)30–150 & \(-\)34–204 & \(-\)34–200 & \(-\)20–180 \\ Temperature & \(-\)30–150 & \(-\)34–204 & \(-\)34–200 & \(-\)20–180 \\ range/\({}^{\circ}\)C & 120 & 350 & 350 & 350 \\ Resistance value/\(\Omega\) & 1/4 Bridge & Wheelstone All & Wheelstone All & Wheelstone All \\  & & Bridge & Bridge & Bridge \\ Modulus/MPa & 2200 & 2340 & Not provided & 40 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of parameters of resistive asphalt strain gauges. Figure 7: Longitudinal strain response under different running tracks and speeds [52]. In addition to asphalt strain gauges, in order to improve the durability and detection accuracy of strain gauges, scholars are also constantly exploring new encapsulation technologies for resistance strain gauges. According to the different packaging structures, these can be divided into tube encapsulation, substrate encapsulation, clamp encapsulation, embedded encapsulation, surface metalized encapsulation, etc. According to the different encapsulation materials, they can be divided into single-material encapsulation and mixed-material encapsulation [5]. [PERSON] et al. [59] proposed a simple, cost-effective strain sensor based on synergetic conductive networks constituted by double-layer microstructures with multi-walled carbon nanotubes and elastomers. However, the above technologies lack research on asphalt pavement monitoring in engineering applications.
## 5 Major Challenges
### Hardware and Energy Consumption for Blockchain Applications Are a Major Challenge  
Blockchain is used widely on the Internet for a variety of purposes. Due to its decentralized nature can be viewed as a viable alternative to many traditional transaction systems. Blockchain systems must be offloaded to an accelerating system for scalability, interoperability, and sustainability (scalability, interoperability, and sustainability). Public key operations such as signature generation and verification can be offloaded as one of the solutions, a secure public key infrastructure engine. They have combined a load dispatcher with our Public Key Crypto Engine (BA414 EP) to design our blockchain hardware accelerator. Because the transaction load is distributed among several components, the overall transaction speed and output accuracy increase. The architecture provides a high-performance offloading architecture and support for all cryptographic algorithms, such as ECC. The ECDSA operations supported by us include those used by Ethereum, Ripple, Bitcoin, Hyperledger, and EdDSA using the Edwards 25519 curve for the Libra blockchain [18]. In terms of electricity consumption, Bitcoin is estimated to consume 127 terawatt-hours (TWh) per year.  
This usage exceeds Norway's entire annual electricity consumption. The average energy consumption of Bitcoin per transaction is 707 kilowatt-hours (kWh), 11 times more than Ethereum. Bitcoin requires computers to solve ever more complicated math problems to verify transactions. The miner who completes the mathematical equation the fastest certifies the transaction and receives a small reward in the form of Bitcoins in return. The proof of work consensus mechanism uses much more energy than many people realize. This is accomplished by having many competitors race to the finish line to see who can package the transactions quickly and solve the small mathematical problem, says [PERSON], global blockchain leader at EY. In addition, Bitcoin transactions take upwards of 10 min, so this process takes a considerable amount of time. This is the length of time it takes to mine a new block. Compared to other digital transactions, such as those powered by Visa, Visa transactions are faster and require less energy.  
Compared to Bitcoin's 4 transactions per second (TPS), Visa can handle around 1700 transactions per second (TPS). Bitcoin's huge energy consumption problem cannot be solved by returning to centralized networks such as Visa. After all, the central promise of Bitcoin is to eliminate intermediaries such as the card networks and their concentrated power over finance. Bitcoin's advocates, however, have several other options as well. We can switch to renewable energy, transitioning to proof of stack systems, embrace pre-mining and introduce carbon credits [30].
# [PERSON] Coding-Based Medical Image Data Hiding Method against Salt and Pepper Noise
### Contributions of the Proposed Work  
For the vast majority of medical data hiding methods in the literature, no method against salt and pepper noise is recommended. Moreover, methods that apply salt and pepper noise to the Stego image generally yield results for 1% density noise. This does not show full resistance to salt and pepper noise. The study [14], which offers results of salt and pepper noise in different densities, is only recommended for the MRI images. Moreover, in addition to noise filtering, this study can also get good results by hiding the same secret message eight times with repetition code. Although there are studies using different error control coding for non-medical images, these encodings are binary compatible and not used in decimal numbers.  
Considering the existing works, the advantages of the proposed study and its contributions to the literature are:  
* Results tested for CT, MRI, US, and X-ray images, against salt and pepper noise at several densities.
* It is proposed to use more effective RS coding instead of complex data hiding algorithms to remove salt and pepper noise.
* To the best of our knowledge, this is one of the first data hiding studies of medical images-based on Reed Solomon coding.
* Since the proposed data hiding method directly hides decimal numbers, the hiding capacity per pixel is high.
* Noise-resistant spatial plane data hiding becomes more resistant to noise with the proposed method.  
The rest of this paper is as follows; in Section 2, Reed Solomon encoding and decoding processes are described. The proposed method is presented in Section 3. Experimental test results are evaluated in Section 4. Section 5 contains the conclusion.
###### Abstract  
Based on micro-scale casting thin sections, nano-scale SEM images, and the pore distribution map identified through a binary image in Matlab, the pore size distribution and pore throat coordination number of the strata of Upper Paleozoic He8 section tight sandstone in the southeastern Ordos Basin were quantitatively analyzed with the above experimental data. In combination with a high-pressure mercury injection experiment, the pore throat distribution, the pore throat ratio, and the relationships between the characteristics, parameters, and pore permeability were investigated clearly. The results show that the tight sandstone pore space in the study area is dominated by micron-sized intergranular pores, dissolved pores, and intragranular pores. The nano-scale pore throat consisted of clay minerals, intercrystalline pores, and the flake intergranular pores of overgrowth quartz grains. Kaolinite and illite intercrystalline pores occupy the pore space below 600 nm, while the ones above 800 nm are mainly dominated by the intergranular pores of overgrowth quartz grains, and the 600-800 nm ones are transitional zones. The permeability of tight sandstone increases with the average pore throat radius, sorting coefficient, median pore throat radius, and average pore throat number. The porosity is positively correlated with the average pore radius and the average pore throat coordination number, and negatively correlated with the median pore throat radius.  
Ordos Basin; tight sandstone; pore throat structure; Matlab; quantitative analysis; porosity and permeability 1
## 2 Materials and Methods
### Skeletonization  
The mesh skeletonization is performed using the mean curvature skeleton algorithm [30]. Unlike other approaches, such as the algorithm used in [18], the mean curvature skeleton output is a graph representing the mesh skeleton and there is no need for additional processing. The skeletonization algorithm works really well with a biomimetic optimization output despite the jagged surface of the resulting mesh. There are no disconnected areas or undesirable skeleton curves. Such artifacts are often presented in skeletons obtained by techniques based on morphological filtering, as seen in [10; 31]. As shown during preprocessing, the skeletonization algorithm is sensitive to mesh density. The resolution of the skeletonization can be increased by increasing the mesh density using a loop subdivision algorithm [32]. The performed tests showed no improvement of the initial solution's quality when using higher resolution meshes, but the skeletonization time increased greatly. Hence, the mesh returned by a mesh generator from a structural optimization system is used as-is for the skeletonization algorithm input. Sample meshes with matching skeletons are shown in Figure 4.  
Figure 2: Example of feature recognition algorithm input. **(a,b)** Input of the structural optimization algorithm, as seen in the CAD system. **(a)** Optimized object and optimization domain boundary. **(b)** Optimized object and three boundary condition areas, which in this particular case are also areas excluded from optimization. Bottom: structural optimization output. **(c)** Raw surface mesh. **(d)** Surface mesh smoothed, only for visualization purposes.  
Figure 4: (**a**,**c**) Example output meshes from topology optimization system. (**b**,**d**) Skeletons generated for corresponding meshes.  
Figure 3: Impact of mesh density on skeletonization algorithm. (**a**) Mesh preprocessed with CGAL algorithms to perform removal of areas excluded from optimization. (**b**) Skeleton generated for (**a**) mesh. One of the structural elements completely collapsed. (**c**) Mesh with removed areas excluded from optimization using mesh generator from structural optimization system. Mesh retained vertex density. (**d**) Skeleton generated for (**c**) mesh.  
There was no attempt to improve the skeleton alignment inside the mesh. Although this would improve the initial solution quality, for proof of concept work, this part is considered redundant. The refinement step, performed next, should yield similar results.
## 3 Power Efficient Wake-Up Receiver for WBAN
This part of the circuit first decodes the PWM encoded signal and generates SPI compatible signals for data transfer to the processor. The architecture is power efficient since most components constituting it are passive. The only active components are the data slicer (comparator) and the SPI adapter. Due to the simplicity of this circuit, the power consumption is kept very low. The basic functionality is achieved by using an envelope detector as a simple and low power wireless receiver, and building simple active circuitry around it to raise the voltage levels, reject false wake up signals, and make the output SPI compatible. Measurements show that the static current consumption of our WUR, while listening on a wireless channel, is \(180\) nA at \(1.5\) V. Therefore, the static power consumption (when listening on a channel) is \(270\) nW. This, with the dynamic power of \(1.75\) nJ/bit and sensitivity of \(-51\) dBm for \(5.5\) kbit/s data rate, satisfies the energy consumption/range requirements for WBAN. The data rate is sufficient for a 10-15 byte wake up packet length containing information for addressing the sensor node to be woken as well as some additional information and a CRC field.
# Optimal Design and Hybrid Control for the Electro-Hydraulic Dual-Shaking Table System
###### Abstract  
This paper is to develop an optimal electro-hydraulic dual-shaking table system with high waveform replication precision. The parameters of hydraulic cylinders, servo valves, hydraulic supply power and gravity balance system are designed and optimized in detail. To improve synchronization and tracking control precision, a hybrid control strategy is proposed. The cross-coupled control using a novel based on sliding mode control based on adaptive reaching law (ASMC), which can adaptively tune the parameters of sliding mode control (SMC), is proposed to reduce the synchronization error. To improve the tracking performance, the observer-based inverse control scheme combining the feed-forward inverse model controller and disturbance observer is proposed. The system model is identified applying the recursive least squares (RLS) algorithm and then the feed-forward inverse controller is designed based on zero phase error tracking controller (ZPETC) technique. To compensate disturbance and model errors, disturbance observer is used cooperating with the designed inverse controller. The combination of the novel ASMC cross-coupled controller and proposed observer-based inverse controller can improve the control precision noticeably. The dual-shaking table experiment system is built and various experiments are performed. The experimental results indicate that the developed system with the proposed hybrid control strategy is feasible and efficient and can reduce the tracking errors to 25% and synchronization error to 16% compared with traditional control schemes.  
Keywords:dual-shaking table; system optimal design; synchronization and tracking control; adaptive sliding mode control; feed-forward inverse control; observer-based compensation +
Footnote †: journal: Applied
# A Comparative Study of the CMT+P Process on 316L Stainless Steel Additive Manufacturing
###### Abstract  
Adopting the cold metal transfer plus pulse (CMT + P) process, 316L stainless steel wire was treated with a single channel multi-layer deposition experiment under different linear energy. The microstructures of different regions on the deposited samples were observed by optical microscope and scanning electron microscope, and the element distribution in the structure was analyzed by energy dispersive spectrometer. The mechanical properties and microhardness were measured by tensile test method and microhardness tester, respectively, and the anisotropy of tensile strength in horizontal and vertical directions were calculated. Finally, the fracture morphology of the tensile samples were observed by SEM. Experiment results showed that when the difference between the actual and the optimal wire feeding speed matching the specific welding speed was too large, this led to an unstable deposition process as well as flow and collapse of weld bead metal, thus seriously deteriorating the appearance of the deposition samples. The results from metallographic micrograph showed that rapid heat dissipation of the substrate caused small grains to generate in the bottom region of deposition samples, then gradually grew up to coarse dendrites along the building direction in the middle and top region caused by the continuous heat accumulation during deposition. Tensile test results showed that with the increase of linear energy, the horizontal and vertical tensile strength of the as-deposited samples decreased. In addition, the higher linear energy would deteriorate the microstructure of as-deposited parts, including significantly increasing the tendency of oxidation and material stripping. The microhardness values of the bottom, middle and top regions of the samples fluctuated along the centerline of the cross-section, and the values showed a trend of decreasing first and then rising along the building direction. Meanwhile, the yield strength and tensile strength of each specimen showed obvious anisotropy due to unique grain growth morphology. On the whole, the results from this study prove that CMT+P process is a feasible MIG welding additive manufacturing method for 316L stainless steel.  
additive manufacturing; cold metal transition; austenitic stainless steel; microstructure; anisotropy; second-phase particles +
Footnote †: journal: Applied Physics Letters
# Study on the Influence of a Rigid Wall on Cavitation in Underwater Explosions Near the Free Surface
###### Abstract  
A two-fluid, phase transition-based multiphase flow model is employed to simulate the dynamics of phase transition between liquid and vapor phases during shock wave and rarefaction wave propagation in underwater explosions. The aim is to understand the influence of a rigid wall on the cavitation evolution process and the cavitation collapse load, considering various charge quantities and water depths. The evolution of crucial physical qualities, such as the density, pressure, and the cavitation domain, within the flow field are analyzed and summarized. The presence of a rigid wall is found to significantly impact the cavitation evolution process in underwater explosions. It affects the shape, size, and dynamics of the cavitation domain, as well as the interaction between the explosion and the surrounding fluid. Specifically, the reflected wave on the wall influences the cavitation collapse load, leading to notable differences in the collapse time and collapse pressure compared to free-field conditions. Under different operating conditions, the size and position of the cavitation domain exhibit distinct changes. The proximity of the rigid wall results in unique patterns of cavitation domain evolution, which in turn lead to variations in the pressure distribution and the emergence of new cavitation regions. The findings of this study provide valuable insights into the behavior of cavitation and atomization induced by underwater explosions near the free surface. The understanding gained from these investigations can contribute to the development of effective safety measures and protective strategies in marine and underwater engineering applications. By accurately predicting and mitigating the effects of cavitation, it is possible to enhance the design and operation of underwater structures, ensuring their integrity and minimizing the potential risks associated with underwater explosions.  
underwater explosion; rigid wall; phase transition; cavitation bubble; cavitation collapsed pacs: 1