esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results  
As described above, in this work, two optimisation cases, with regards to different excitations, are studied in two optimisation scenarios where the focus is on tyre and suspension design, respectively. In this section, the results of the two multi-objective optimisation scenarios are displayed. More specifically:  
* Scenario 1 investigates the tyre optimisation in a vehicle being employed with a passive suspension and driven over an S-Path, in which road surfaces of Class A (Case 1) and B (Case 2) are assigned. The optimisation results are illustrated in Figures 8 and 9, which display the Pareto fronts with regards to the objectives (Figure 8a,b) and the design variables (Figure 9a-e). Having obtained the optimal solution alternatives for the two cases, common solutions are sought between them, requiring to provide similar wear in the two cases (\(<\)8% difference) and have close design variable values (\(<\)9% difference). The common solutions identified with the above characteristics are illustrated in Figures 8 and 9 alongside the alternatives, while their values are listed in Table 5. In addition, in Figure 10, the final circumferential tread profiles for the optimum designs are compared with the initial tyre design, after driving over the Class B S-Path twenty times back and forth.
* Scenario 2 explores the suspension optimisation for two road cases and various suspension types (PS, SH-2, ADD, SH-ADD-2, GH-2, PDD and SH-PDD). The optimisation results are illustrated in Figure 11, which presents the Pareto fronts of the optimum solutions for the two cases. For each suspension type, a common solution is identified among the optimal alternatives provided by the two optimisation cases, requiring them to have close design variables values (\(<\)3-15% difference according to the cases). Finally, the values of the identified common solutions are presented in Table 6 alongside with the threshold of difference that was allowed.  
The goal of these scenarios is for conclusions to be extracted regarding: \((a)\) how tyre wear behaves with regards to passenger comfort and vehicle handling; \((b)\) where optimum tyre and suspension parameters have converged trying to compromise among the above objectives under different road types; and \((c)\) which suspension could compromise among all of them optimally.  
Figure 8: Comparison of the optimum solutions obtained for the two optimisation cases (class A and B road profiles) in Scenario 1: (**a**) TW vs. RC; and (**b**) TD vs. RC.
# Multistage Smoothing Compressor for Multistep Pulse Compressors
## 2 Beam Smoothing Owing to the Spatial Chirp
### Beam Smoothing in the Far Field  
In large-sized ultrahigh peak-power laser systems, the primary risk for optical damage is a relatively high LSIM at the middle/high frequencies of the laser beam in the near field. Additionally, some of the wavefront aberrations at middling/high spatial frequencies can cause small-scale beam focusing in the far field during free propagation. These wavefront aberrations frequently occur due to imperfect optics, especially for large-sized optics. As for the wavefront distortion at low spatial frequencies, which affects the final focal diameter and then the focal intensity, deformable mirrors are commonly utilized to correct these wavefront distortions to achieve a small focal diameter. For wavefront aberrations with high spatial frequencies, a spatial filter based on a 4f optical system, with a small pinhole at the focal point, is usually used to filter out the wavefront aberration laser beam. This spatial filter can also be used to magnify the laser beam. To ensure that most of the laser beam can pass through the pinhole easily, the size of the pinhole is usually set to be approximately ten times the diffraction-limited size. As a result, some wavefront aberrations with high spatial frequencies and most of the wavefront aberrations with middling spatial frequencies remain in the output laser beam, which may self-focus or induce hotspots in the far field and damage important optics.  
The wavefront aberrations at middling/high spatial frequencies cannot be completely eliminated by the induced spatial chirp. However, the induced spatial chirp can significantly reduce the damage risk of wavefront aberrations in the far field. Similarly to the principle of spatial intensity smoothing [22], the principle of wavefront aberration smoothing with spatial chirp can be explained using Figure 3. Without spatial chirp, the wavefront aberration with a high/middling spatial frequency induces a hotspot in the far field during free propagation, as shown in Figure 3a. Even if a suitable one-dimensional spatial chirp width is induced in the laser beam, the primary hotspot is smoothed into a line with \(D\), as shown in Figure 3b. To experimentally show this property, a kHz Ti:sapphire femtosecond laser is used. A lens with 2 m focal lens is located in front of a four-grating compressor to simulate the wavefront aberration in the laser beam; a small round spot is achieved at the focal point or far field, as shown at the bottom of Figure 3a. However, if the laser beam is reflected out between G2 and G3, it becomes a line at the focal position, as shown at the bottom of Figure 3b. Since the diameter of the hotspot is usually about 1 mm, even for a spatial chirp width of 10 mm, the hotspot will be reduced ten-fold. From a different perspective,the spatial chirp actually smooths the wavefront aberrations with middling/high spatial frequencies because the wavefront at every position is an average of all wavelengths.
## 4 Simulation Results
### Case Study 1: Comparison with Centralized Method  
In this case study, the focus is on comparing the optimal values between the proposed method and the Lagrangian relaxation method [4]. The initial values can be obtained by Equation (13). The total electrical load demand is 750 (p.u.) with five electrical loads of 150 (p.u.) and the total heat load demand is 800 (p.u.) with five heat loads of 160 (p.u.). Then, the proposed method is used to calculate the optimal solutions. The estimated electrical and heat power incremental cost, mismatch and outputs are shown in Figure 5. It can be seen that all of the electrical and heat power mismatch converge to zeros, which means the supply-demand balance are satisfied. After about 250 iterations, all of the electrical power incremental cost converge to a common value and all of the heat power incremental cost converge to another common value, meanwhile all agents are running in their feasible operating regions and the electrical and heat power outputs are listed in Table 2. In order to further verify the effectiveness of the proposed method, the Lagrangian relaxation method is used to solve this problem in a centralized fashion. The calculation results are also listed in Table 2. It can be seen that the solutions of the two methods are very close, which means the proposed method can also model the optimal values with distributed fashion.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Agent** & \(\mathbf{a}\) & \(\mathbf{b}\) & \(\mathbf{\alpha}\) & \(\mathbf{\beta}\) & \(\varepsilon\) & **Min** & **Max** \\ \hline EOA1 & 0.0174 & 5.5 & - & - & - & 60 & 180 \\ EOA2 & 0.1880 & - & - & - & - & \(-75\) & 75 \\ EOA3 & 0.0124 & 6.4 & - & - & - & 50 & 150 \\ EOA4 & 0.0004 & 0.106 & - & - & - & 0 & 140 \\ EOA5 & 0.0146 & 5.8 & - & - & - & 40 & 120 \\ EOA6 & 0.0009 & 0.02 & - & - & - & 0 & 180 \\ CGA1 & 0.0069 & 2.9 & 0.0060 & 0.84 & 0.0062 & - & - \\ CGA2 & 0.0087 & 5.0 & 0.0054 & 0.12 & 0.0022 & - & - \\ HOA1 & - & - & 0.0102 & 3.3 & - & 40 & 500 \\ HOA2 & - & - & 0.0146 & 2.42 & - & 30 & 300 \\ HOA3 & - & - & 0.0001 & 0.084 & - & 0 & 250 \\ HOA4 & - & - & 0.1660 & - & - & \(-200\) & 200 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the 16-bus system.  
Figure 4: Feasible operating region of CGA1 and CGA2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Agent** & **Case Study 1: Proposed Method** & **Case Study 1: Centralized Method** & **Case Study 2: Proposed Method** \\ \hline EOA5 & 66.1650 & 66.2368 & 66.2062 \\ EOA6 & 130.0000 & 130.0000 & 130.0000 \\ CGA1-E & 215.1412 & 215.0000 & 215.0399 \\ CGA2-E & 110.2749 & 110.2000 & 110.2529 \\ CGA1-H & 179.2058 & 180.0000 & 179.7754 \\ CGA2-H & 134.9488 & 135.6000 & 135.1400 \\ HOA1 & 150.9958 & 150.1772 & 150.5662 \\ HOA2 & 135.6291 & 135.0553 & 135.32714 \\ HOA3 & 180.0000 & 180.0000 & 180.0000 \\ HOA4 & 19.2180 & 19.1675 & 19.1914 \\ Total cost & 5811.3 & 5806.9 & 5809.3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison for the 16-bus system.  
Figure 5: _Cont._
###### Abstract  
Power transmission lines frequently face threats from lightning strikes, severe storms, and chemical corrosion, which can lead to damage in steel-aluminum-stranded wires, thereby seriously affecting the stability of the power system. Currently, manual inspections are relatively inefficient and high risk, while drone inspections are often limited by complex environments and obstacles. Existing detection algorithms still face difficulties in identifying broken strands. To address these issues, this paper proposes a new method called SL-YOLOv8. This method incorporates an improved You Only Look Once version 8 (YOLOv8) algorithm, specifically designed for online intelligent inspection robots to detect broken strands in transmission lines. Transmission lines are susceptible to lightning strikes, storms, and chemical corrosion, which is leading to the potential failure of steel- and aluminum-stranded lines, and significantly impacting the stability of the power system. Currently, manual inspections come with relatively low efficiency and high risk, and Unmanned Aerial Vehicle (UAV) inspections are hindered by complex situations and obstacles, with current algorithms making it difficult to detect the broken strand lines. This paper proposes SL-YOLOv8, which is a broken transmission line strand detection method for an online intelligent inspection robot combined with an improved You Only Look Once version 8 (YOLOv8). By incorporating the Squeeze-and-Excitation Network version 2 (SENet_v2) into the feature fusion network, the method effectively enhances adaptive feature representation by focusing on and amplifying key information, thereby improving the network's capability to detect small objects. Additionally, the introduction of the LSKblockAttention module, which combines Large Selective Kernels (LSKs) and the attention mechanism, allows the model to dynamically select and enhance critical features, significantly enhancing detection accuracy and robustness while maintaining model precision. Compared with the original YOLOv8 algorithm, SL-YOLOv8 demonstrates improved precision recognition accuracy in Break-ID-1632 and cable damage datasets. The precision is increased by 3.9% and 2.7%, and the recall is increased by 12.2% and 2.3%, respectively, for the two datasets. The mean average precision (mAP) at the Intersection over Union (IoU) threshold of 0.5 is also increased by 4.9% and 1.2%, showing the SL-YOLOv8's effectiveness in accurately identifying small objects in complex situations.  
broken strand detection; YOLOv8; accurate recognition; SENet v2; attention module +
Footnote †: journal: Applied Sciences
###### Abstract  
Reconfigurable Intelligent Surfaces (RISs) not only enable software-defined radio in modern wireless communication networks but also have the potential to be utilized for localization. Most previous works used channel matrices to calculate locations, requiring extensive field measurements, which leads to rapidly growing complexity. Although a few studies have designed fingerprint-based systems, they are only feasible under an unrealistic assumption that the RIS will be deployed only for localization purposes. Additionally, all these methods utilize RIS codewords for location inference, inducing considerable communication burdens. In this paper, we propose a new localization technique for RIS-enhanced environments that does not require RIS codewords for online location inference. Our proposed approach extracts codeword-independent representations of fingerprints using a domain adversarial neural network. We evaluated our solution using the DeepMIMO dataset. Due to the lack of results from other studies, for fair comparisons, we define oracle and baseline cases, which are the theoretical upper and lower bounds of our system, respectively. In all experiments, our proposed solution performed much more similarly to the oracle cases than the baseline cases, demonstrating the effectiveness and robustness of our method.  
localization; reconfigurable intelligent surface (RIS); representation learning; domain generalization; domain adversarial neural network (DANN) +
Footnote †: journal: _Sensors_  
1
Footnote 1: email: {[[https://doi.org/10.3390/s23020984](https://doi.org/10.3390/s23020984)]([https://doi.org/10.3390/s23020984](https://doi.org/10.3390/s23020984))}
# Analysis of Soil-Water Characteristic Curve and Microstructure of Undisturbed Loess
## 3 Results and Discussion
### SWCCs of Undisturbed Loess Samples
#### 3.1.1 SWCCs of the Undisturbed Loess Samples Buried in the Same Layer  
Samples NO. 1 and NO. 2 represent undisturbed loess samples buried at different depths within the same layer. The comparison of their SWCCs, as depicted in Figure 4, highlights the significant influence of the burial depth on the slope of the transition zone. This slope is indicative of the soil's water retention capacity, with a smaller slope indicating better water retention.  
Table 1 reveals that deeper soil burial results in a higher dry density. However, it is noteworthy that the slope of sample NO. 1 is larger than that of sample NO. 2, suggesting that the slope of the SWCC transition zone is not solely determined by the dry density. Factors such as the soil's plasticity index and fine particle content play a crucial role in shaping the SWCC morphology [59; 60]. The plasticity indices of samples NO. 1 and NO. 2 are 13.24% and 15.30%, respectively (Table 1). It is observed that the slope of the SWCC decreases with an increase in the plasticity index [59]. Additionally, the fines contents are 73.22% and 88.50%, respectively. A higher fines content enhances the soil's water retention ability and reduces the slope of the SWCC transition zone. Despite the slight difference in the dry density between NO. 1 and NO. 2 and their similar pore ratios, the saturated volumetric water contents of the SWCC samples remain essentially the same.
# An Optimal Authentication Scheme through Dual Signature for the Internet of Medical Things
## 1 Introduction
### Motivation and Contributions  
Several dual-signature schemes have been presented in the published research; however, the majority of these schemes relied on RSA, ECC, etc., which resulted in significant computational and communication costs, or some of them were not evaluated using any formal security validation tool, which is very necessary to test the authenticity of the scheme. To overcome these issues, we propose a scheme entitled \"the optimal authentication scheme through a dual signature for the IoMT\". We have listed below the research contributions of the proposed scheme:  
* We propose a dual-signature scheme using hyperelliptic curve cryptography (HECC), an advanced form of elliptic curve cryptography (ECC) that provides the same level of security as ECC with a key size that is half that of ECC.
* We provide an informal security analysis study in terms of authentication, integrity, and non-repudiation, and demonstrate that the proposed scheme is resistant to these attacks.
* In addition, we evaluated the security criteria using the security validation tool Scyther, and the results indicate that the proposed scheme is secure against man-in-the-middle attacks.
* Finally, we compared the performance of the proposed scheme to that of related schemes from the literature and observed that the proposed scheme lowered computation and communication costs.  
The rest of the article is organized as follows. The preliminaries are detailed in Section 2. The related work and network model are included in Sections 3 and 4. Sections 5 and 6 contain the proposed dual signature scheme and informal security analysis. Sections 7 and 8 provide a performance evaluation in terms of the computational cost and communication overhead with existing schemes. Section 9 contains the concluding remarks and is followed by Appendices A and B.
# Distributed Routing and Channel Selection for Multi-Channel Wireless Sensor Networks +
## 5 Towards a Completely Distributed and Dynamic Approach  
However, implementing a distributed coalition formation game in a WSN environment has several limitations in terms of its applicability in practical scenario. First, such a game requires significant amount of information exchange in between the sensor nodes due to its iterative nature. Also they need to be in common channel at the time of this information exchange. Second, the information exchange in between the sensor nodes need to be _completely reliable_, i.e., the convergence criteria requires no packet loss. This is hard to obtain in lossy wireless networks. Third, such a game theoretic scheme is suitable for static environments. In a _rechargeable_ sensor networks, due to the varying energy availability, such coalition formation game needs to be repeated to take into account the network dynamics which is onerous in terms of additional information exchange. Fourth, the assignment will be repeated again if some nodes will join or leave the network, which is common in a rechargeable environment. To cope with this, we propose a completely distributed and dynamic routing and channel assignment scheme in this section.
# A Novel Decomposed Optical Architecture for Satellite Terrestrial Network Edge Computing
## 4 Experimental Demonstration of Decomposed Optical Architecture  
### Experimental Setup  
The implemented experimental computing prototype with decomposed hardware is depicted in Figure 3. There are four FPGA-based hardware cubes (two processor cubes and two memory cubes) with one four-port-count NOS in the prototype. Four hardware cubes are designed via Vivado utilizing Xilinx Vertex VU095 [29], and the controller for the four-port-count optical switch is implemented with Xilinx Vertex-7 VC709 [30]. In this work, we use DDR4 memory as the on-board resource of memory cubes. All the four hardware cubes of NOS interconnections are based on two parallel commercial 10 Gb/s/SFP+ transceivers (TRX). One TRX is to connect hardware cube and the NOS input/output port for the optical packet sending plane, while another TRX is for the optical packet control plane to process the packet tag. All the components in the hardware cube (including MMU, flow controller, and NI) are designed by FPGA circuit with a 322.3 MHz reference clock for data processing. In the processor cube, all the transferred data are handled via 32 bit-width format, and the payload length of each packet is set to 512 bits, as this value is the classical size of a cache line in existing computing structures. Before sending the optical payload, the packet has an eight-bit size preamble code to set up the link between hardware cube and NOS. Meanwhile, there is also an eight-bit size check code after the optical payload. Between two continuous optical packets, it has a slot to identify the start/end of packets, including 16 ns slack and 6 ns rise/fall times. The transmitter has an output of 1.2 dBm by utilizing the SFP+ transceiver, while a 2 m-length fiber is applied to connect the hardware cubes and NOS. Each SOA-based optical gate is interconnected to the input/output port of the NOS with a 1 m-length fiber. In the packet sending plane of the four-port-count NOS, there are four 1-to-4 optical signal splitters, sixteen SOA-based optical gates, and four 4-to-1 optical signal couplers in total. The switch controller distributes its reference clock to all the hardware cubes for time synchronization, and the processing of the optical packet sending plane and control plane is synchronized by utilizing the same clock and periodic calibration. At the same time, blank packets are sent to keep the link alive during periods of no data packets.  
Figure 2: Functional diagram of (**a**) processor cube and (**b**) memory cube.
## 3 Results
### Body Sway around the Mean CoP Position  
The mean variances (assessed by the SD) of the CoP traces along the frontal and the sagittal plane during the 1 min quiet-stance periods pre- and post-stimulation were calculated to evaluate the possible effect of vision and stimulation on the body sway around its mean CoP position (Table 1). Although the mean SDs of the CoP traces on the frontal and sagittal planes were larger with EC than with EO (by about 4% for ML and about 13% for AP, all conditions collapsed), the main vision effect was not significant for the ML (F(1,19) = 0.0017, \(p\) = 0.97) and just significant for the AP direction (F(1,19) = 4.59, \(p\) = 0.045, d = 0.98, \(\pi\) = 0.53). The main pre-/post stimulation effect was significant, both for the ML (F(1,19) = 11.5, \(p\) < 0.01, d = 1.54, \(\pi\) = 0.89) and the AP direction (F(1,19) = 13.0, \(p\) < 0.05, d = 1.65, \(\pi\) = 0.93), but there were no interactions with either vision (ML: F(1,19) = 0.82, \(p\) = 0.37; AP: F(1,19) = 3.41, \(p\) = 0.08) or stimulation profiles (ML: F(1,19) = 0.0004, \(p\) = 0.98; AP: F(1,19) = 0.24, \(p\) = 0.63). The post-hoc test showed no pre-post differences in the AP SDs except for the FR and FL condition under EC (\(p\) < 0.01). Instead, for the ML direction, the difference in the SDs between the pre- and post-stimulation was always significant (post-hoc, \(p\) < 0.05 for all comparisons), likely because the variance of the ML sway incorporated the slow changes in the CoP displacement determined by the asymmetric stimulation. There was no main effect of translation cycle profile, either for the ML (F(1,19) = 0.8, \(p\) = 0.38) or the AP direction (F(1,19) = 2.82, \(p\) = 0.11). Across the subjects, there was no relationship between the pre-/post-stimulation difference of ML sway and the CoP positions, either for the FL or FR condition (EC and EO collapsed) (regression lines, FL: y = \(-\)0.05x + 0.066, R\({}^{2}\) = 0.036, \(p\) = 0.24, FR: y = 0.03x + 0.045, R\({}^{2}\) = 0.013, \(p\) = 0.48).  
Closing the Eyes at the End of the Stimulation with EO did not Modify the Post-Effect on the CoP Position  
All participants also performed an additional trial (condition FR-EO/EC) in which they were instructed to close their eyes at the end of the asymmetric perturbation sequence administered with vision (FR-EO) (this trial was not systematically repeated for the FL-EO condition). The mean ML CoP displacement in the post-stimulation condition FR-EO/EC (mean \(\pm\) SD, 0.54 cm \(\pm\) 1.28) was similar to the displacement recorded in the EO condition (0.57 cm \(\pm\) 0.77) (FR-EO vs. FR-EO/EC, paired \(t\)-test, \(p\) = 0.88).