esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Summary and Perspectives
If adaptability is an issue, but power source and weight are not, then it would be rather preferable to choose the field-active configuration. The advent of new applications of complex fluids in energy dissipative devices will strongly depend on the new scientific findings yet to come. The rheological characterization of active-fluids beyond shear and squeeze flows with the external field perpendicular to the bottom plate of the rheometer is a research line in its embryonic stage [76]. Thus, it would be possible to find applications considering other flow/field configurations different from the current ones. Additionally, the combination of microfluidics and active fluids, as in the CorkSTFluidics composites [178], may result \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Application**} & \multicolumn{2}{c}{**Active-Fluids**} & \multicolumn{2}{c}{**Passive-Fluids**} \\ \cline{2-5}  & **+** & **-** & **+** & **-** \\ \hline \multirow{2}{*}{**Dampers**} & \multirow{2}{*}{Versatility} & Settling & \multirow{2}{*}{Low cost} & \multirow{2}{*}{Unadaptability} \\  & & Redispersibility & & \\ \hline \multirow{2}{*}{**Sandwich structures**} & \multirow{2}{*}{Versatility} & Settling & \multirow{2}{*}{Low cost} & \multirow{2}{*}{Unadaptability} \\  & & Heavy-weight & & \\ \hline \multirow{2}{*}{**Body armor**} & \multirow{2}{*}{Versatility} & Power dependence & \multirow{2}{*}{Power independence} & \multirow{2}{*}{Unadaptability} \\  & & Heavy-weight & & Light-weight \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of the most relevant applications of complex fluids in energy dissipating systems and their pros and cons. in a solution for one of the intrinsic problems regarding the use of active-fluids, i.e., weight. In the case of passive-fluids, VEFs have not been explored intensively yet, neither body armor applications nor for sandwich structures. Regarding STFs, the development of new constitutive models able to capture all their characteristic rheological features will allow performing accurate numerical simulations and, therefore, the development of new applications. The author would like to acknowledge [PERSON] for stimulating and fruitful discussions, as well as the financial support from FCT, COMPETE and FEDER through grant IF/00190/2013 and project IF/00190/2013/CP1160/CT0003. The author would also like to thank Vicoda GmbH, who graciously provided the pictures used in Figure 3 and for fruitful discussions regarding the viscoelastic dampers. The author declares no conflict of interest. The following abbreviations are used in this manuscript:  
ERFs: Electrorheological fluids  
FFs: Ferrofluids  
MRFs: Magnetorheological fluids  
EMRFs: Electro-magneto-rheological fluids  
STFs: Shear thickening fluids  
VEFs: Viscoelastic fluids  
PPEs: Protective Personal Equipments  
MMOD: Micro Meteor Orbital Debris  
GER: Giant Electrorheological effect  
WBV: Whole Body Vibration  
HAV: Hand-Arm Vibration  
SAOS: Small Amplitude Oscillatory Shear  
MAOS: Medium Amplitude Oscillatory Shear  
LAOS: Large Amplitude Oscillatory Shear
# Preventing Black Hole Attacks in AODV Using RREQ Packets
## 2 Black Hole Attack
### Methods of Defense against Black Hole Attacks  
Existing studies of preventing black hole attacks can be divided into two main categories, namely, methods based on opinion and methods based on sequence numbers. Opinion-based defense methods focus on the characteristics of black hole node behavior. A typical example is called Statistical Ad hoc On-demand Distance Vector (SAODV) routing [12]. In SAODV, a given node has an appropriate asymmetric signature-key pair, which provides integrity, authentication, and non-repudiation security features to the route discovery mechanism via digital signature and hash chain mechanisms. Sequence number-based defense methods focus on unnatural sequence numbers set by black hole nodes when they receive packets, and use a threshold value to defend against black hole nodes. One typical example is called Secure Route Discovery for the AODV protocol (SRD-AODV) routing [13]. Sequence number-based methods sometimes identify a normal node as a black hole node. Therefore, hybrid methods that can restore normal nodes to the network by applying an opinion-based redecision mechanism have been developed as well. In a study employing this method, [PERSON] et al. [14] applied a dynamic sequence number threshold to RREP packets received by a node to determine whether the sender of the RREP packet is a black hole node. Other studies have aimed to establish secure routes without focusing on the behavior of black hole attacks or sequence numbers. Using a genetic algorithm, [PERSON] et al. proposed ETSAODV, which calculates a confidence value from the previously collected distances between neighboring nodes and determines the routing destination based on this value [15,16] Other studies have aimed to extend the route establishment process of AODV to select a more secure route. Sluw-Aodv, proposed by [PERSON] et al., extends AODV to establish three routes by implementing a route confirmation process with Route Confirm packets and using challenge-response authentication. Sluw-Aodv extends AODV by implementing a route confirmation process using Route Confirm packets and then using challenge-response authentication [17].  
Figure 1: Black hole attack.
# Energy and Delay Aware Data Aggregation in Routing Protocol for Internet of Things
## 6 Result and Discussions
### Simulation Results
#### 6.2.7 Reconstruction MSE Value  
Figure 11 depicts the reconstruction of Mean Square Error (MSE) value for the number of observations. It is noted that reconstruction MSE of LA-RPL and EDADA-RPL are 0.02 and 0.01 respectively for the 120 th iterations. In that, the RPL always the MSE values are 0 because there is no compression process during the data transmission. Reconstruction of the MSE value has been observed to decrease as the number of observations increases.It is due to the variation in node energy and data traffic between the nodes of the network.
## 1 Introduction  
Technological advances in industry are in different production sectors of the supply chain. Today, many optical, electronic, and mechatronic technologies can be applied to the industrial scenario, improving the Industry 4.0 framework based on the digital transformation process. Some recently advanced technologies are in metastructures oriented to optical computing [1], nanoparticles in metasurface applications [2], Epsilon-Near-zero (ENZ) metamaterials [3, 4], graphene-based transistors for biomedical applications [5], plasmonic devices [6, 7], and deep learning that supports decision-making as seen in COVID-19 risk management processes [8, 9]. These technologies could be integrated into industrial processes or considered as innovative products for manufacturing industries. An upscale in production was gained by Industry 4.0 and by innovative facilities of Industry 5.0. The Industry 5.0 era has been characterized by a full digitalization and transformation process which contributed to fully changing organizational and production processes. This change is mainly focused on the implementation of Artificial Intelligence (AI) algorithms, which tailor production and processes in a self-adaptive modality. Specifically, the new advances in optoelectronic and mechatronic technologies show a possible evolution in the management of industrial processes, thus optimizing resources according to the dynamic market. Today, a strategic market plan should be designed first in the short term, to follow better the market's trends and trajectories. The goal of this proposed short review is then to define production models based on advanced technology discussed in the paper, and offer an approach to designing a possible future framework which includes Industry 5.0 facilities that can start an analysis of the scientific state of the art profiled here to serve actual industry technology needs, and to orient further research. Figure 1 illustrates a block diagram sketching a basic Industry 5.0 framework, which will be discussed in this paper. The diagram, deduced from the analysis of the state of the art and from emergent topics in research, consists of the following blocks or grouped modules:  
* A sensing module composed of innovative optoelectronic sensors (block 1), industrial mechatronic sensors (block 2), and detection algorithms (block 3);
* Control and actuation facilities (block 4) which address production, quality, safety, process management, raw materials management, and Process Mining (PM);
* An AI engine managing production line actions (blocks 1, 2, 3, 4, 5);
* An Advanced robotic industrial platform (block 5). The proposed auto-consistent framework of Figure 1 highlights the relationships between all mentioned blocks. Particular attention is given to new advanced sensors such as optoelectronic sensors suitable for high-velocity data transfer, industrial mechatronic sensors supporting the control and actuation mechanisms of advanced production machines and industrial robots, and engineered intelligent processes requiring AI decision-making procedures.
# Multi-Scale Aggregation Stereo Matching Network Based on Dense Grouping Atrous Convolution
## 3 Experiments
### Experimental Results
#### 3.4.2 Accuracy Analysis of Disparity Map  
For intelligent driving tasks, we chose to evaluate the disparity map on the KITTI 2015 dataset with street scenes. Figure 5 shows the disparity map effect of PSM-Net, GWC-Net, and DenseGASPP. These results were reported by the KITTI evaluation server.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Dilation Rate**} & \multirow{2}{*}{**Scale of Cost Volume**} & **Scene Flow** & **KITTI 2015** \\  & & **EPE** & **EPE** \\ \hline \multirow{3}{*}{(2,3) (4,5) (6,7)} & 1/2 1/4 1/8 & 0.86 & 0.69 \\  & 1/3 1/6 1/12 & 0.67 & 0.44 \\ \cline{1-1}  & 1/4 1/8 1/16 & 0.92 & 0.74 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experimental evaluation of the cost volume in different scales.  
Figure 5: Disparity map effect under the KITTI 2015 dataset. The first and third rows are the input image of the stereo image pairs, (**a**) PSM-Net, (**b**) GWC-Net, (**c**) DenseGASPP are the disparity maps obtained in the network for the input pairs.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Serial Number**} & \multirow{2}{*}{**Interval of Dilation**} & \multirow{2}{*}{**Dilation Group**} & \multirow{2}{*}{**Scene Flow**} & **KITTI 2015** \\  & & **Rate** & **Group 1** & **Group 2** & **Group 3** & **EPE** & **EPE** \\ \hline
1 & 1 & (2,3) & (4,5) & (6,7) & 0.86 & 0.69 \\
2 & 2 & (2,3) & (5,6) & (8,9) & 0.93 & 0.77 \\
3 & 2 & (3,4) & (6,7) & (9,10) & 0.92 & 0.75 \\
4 & 3 & (2,3) & (6,7) & (10,11) & 0.98 & 0.71 \\
5 & 4 & (2,3) & (7,8) & (12,13) & 1.02 & 0.94 \\
6 & 5 & (2,3) & (8,9) & (14,15) & 1.14 & 0.96 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental evaluation of different dilation rates of the DenseGASPP module.  
As shown by the regions we selected with white boxes, our network showed a more accurate environmental perception in both the weak texture area of the object and the object edge compared with the state-of-the-art networks.
# Singular Phenomenon Analysis of Wind-Driven Circulation System Based on Galerkin Low-Order Model
## 5 Discussion
The model's ability to simulate bifurcations and transitions between stable states can help explain how such external perturbations lead to observable changes in circulation patterns. The relative stability of phenomena like the Gulf Stream, ENSO, and other large-scale ocean-atmosphere structures stems from their inherent dynamical properties. As discussed, these systems often exhibit stable and predictable behaviors due to the interplay between multiple equilibrium states and external forcing. Our model provides a theoretical foundation for understanding this stability by demonstrating how wind-driven circulation can transition between states while maintaining overall system coherence. The methodologies presented in this study have practical applications in climate modeling and prediction. By incorporating real-world datasets, the model can be adapted to simulate specific oceanic phenomena and forecast potential changes due to varying wind stress conditions. This capability is crucial for improving weather predictions and developing strategies for climate adaptation and mitigation. In conclusion, our study advances the understanding of wind-driven ocean circulation's complex dynamics, offering a framework to explore the multifaceted interactions between atmospheric winds, ocean currents, and climate variability. Future research should aim to integrate extraterrestrial influences and real-world data to further refine and validate the model's predictive capabilities.
## 2 Data Processing and Analysis Procedure
### Data Processing Methods  
To investigate the impact of solar storms on low-frequency signal strength, this study employs the Savitzky-Golay filter [21] to smooth raw signal strength data and extract more accurate trends in signal variation. Compared to other commonly used smoothing methods, such as moving average filters, the Savitzky-Golay filter applies local polynomial fitting to achieve data smoothing. This approach not only effectively reduces high-frequency noise but also maximally preserves the local features and critical characteristics of the signal, such as abrupt changes and peaks. Previous studies have demonstrated that this method exhibits significant advantages in time-series analysis, particularly in retaining data trends while minimizing distortions to lag-dependent features [21; 22; 23]. Furthermore, empirical research has verified that Savitzky-Golay filtering does not introduce notable distortions to the key dynamic characteristics analyzed in lag and correlation studies [24; 25]. Additionally, this method has proven particularly effective in capturing genuine signal variation trends in high-noise environments and has been widely applied in analyzing dynamic changes caused by solar flares [26; 27]. In this study, the filter parameters, including window size and polynomial degree, were carefully optimized to adapt to the complex dynamic relationship between low-frequency time-code signals and X-ray flux (XRA).
## 4 Result Analysis and Comparison with FTA
### FTA of Escalator Breaking System  
As mentioned earlier, FTA is a typical top-down risk analysis method. It adopts a logical method and carries out hazard analysis work vividly. Its features are intuitive, clear, clear-cut, and logical, and can be used for qualitative analysis and quantitative analysis. In general, the development of safety system engineering is also based on fault tree analysis. In 1974, the US Atomic Energy Commission published a report on the risk assessment of nuclear power plants, the \"Rasmussen Report (WASH-1400 report)\", which effectively and extensively applied the FTA, thereby rapidly promoting its development of the FTA [8]. The fault tree is composed of various event symbols and logic gates. The relationship between events is represented by logic gates. Based on the FTA, a fault tree with the escalator reversal as the top event was established as shown in Figure 14.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline \hline
**Unsafe Control (Incentives)** & **Example Scenario** \\ \hline Unreasonable installation location; signal from sensor is disturbed; sensor damaged & The sensor does not measure the change effectively when overspeed occurs, which resulting in the PLC not making correct overspeed judgments. \\ \hline Component of PLC damaged & Velocity judgment can be wrong and command mistake because of the component of PLC damaged. \\ \hline Unreasonable overspeed judgment algorithm; Trigger threshold of overspeed judgment is too high or too low & The misjudment of overspeed can lead to frequent starts of emergency brake system, which would influence the operation of escalator and aggravating the wear of mechanical components. \\ \hline Command mistake from PLC & The brake command is sent during normal operation or the command is not sent when the speed exceed the safe threshold, or there is a delay in the command, increasing the response time of brake system. \\ \hline Component of brake damaged & The mechanical component of brake system can’t afford the instantly impact force and energy when it needs to work. \\ \hline Component of Driving spindle damaged & The risk of losing power would be increased when components of driving spindle are damaged \\ \hline High traffic flow & (1) the escalator is running under a heavy load, which would increase the wear of components and risk of component damage; (2) high traffic flow can easily lead to chain reaction and butterfly effect of unsafe behavior \\ \hline Unsafe behavior and mental state of passenger & The passengers did not grasp the handrails. When an accident occurred, it caused panic, stampede, and increased brake load. \\ \hline Response time of brake system is too long when accident occurs & When the accident occurs, the overspeed judgment time is long, the PLC does not issue the instruction in time, or the braking time is too short, or the passengers have a long reaction time, all of which cause the time course of the brake action process to be longer than the time course of the accident \\ \hline \hline \end{tabular}
\end{table}
Table 7: Specific example Scenarios of hazardous unsafe control.
## 4 Stability Evaluation
### Operating Pressure Scheme and Evaluation Method  
#### 4.2.1 Operating Pressure Plan  
Operating pressure plays a pivotal role in determining the storage capacity and ensuring the cavity's safety. According to Canadian standards for underground gas storage, the maximum operating pressure should not exceed 80% of the cap-rock fracture pressure [30]. In China, safety regulations stipulate that the upper-limit pressure of salt-cavern gas storage must not be higher than 80% of the formation fracture pressure at the production casing shoe, nor can it exceed 80% of the overlying formation pressure at the casing shoe [31].  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Lithology** & **Elastic** & **Poisson’s** & **Cohesion** & **Friction** & **Tensile** & **A** & **n** \\  & **Modulus (GPa)** & **Ratio** & **(MPa)** & **Angle (\({}^{\circ}\))** & **Strength (MPa)** & & & \\ \hline Upper and lower & & & & & & & & \\ mudstone & 11.2 & 0.26 & 4.24 & 26.5 & 3.24 & / & / \\ \hline Rock salt & 7.71 & 0.3 & 3.77 & 34 & 1.34 & \(1.08\times 10^{-7}\) & 3.17 \\ \hline Mudstone interlayer & 11.2 & 0.24 & 4.24 & 26.5 & 3.24 & \(2.4\times 10^{-8}\) & 1.7 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Mechanical parameters of salt formation [29].  
Figure 9: 3D geomechanical model of BWSC with a large height difference.  
The minimum operating pressure must ensure that no significant sidewall spalling, cavity roof collapse, or ground subsidence occurs in the cavity-surrounding rock during long-term injection and production operations. The established Jintan gas storage facility uses 0.007 MPa/m as the lower-limit pressure. Some studies also take 20-30% of the overlying rock pressure at the casing shoe as the minimum allowable pressure [32; 33]. Here, 75% of the overlying rock pressure at the casing shoe is adopted as the upper-limit pressure and 30% as the lower-limit pressure. Due to the special characteristics of the large-height-difference BWSCs, the design of their operating pressure needs to consider the safety and stability of both the shallow and deep cavities. According to the aforementioned pressure-design method, the casing shoe is located 50 m above the cavity roof. For the deep-cavity casing shoe, the designed upper-limit pressure is 29 MPa, and the lower-limit pressure is 12 MPa. For the shallow-cavity casing shoe, the designed upper-limit pressure is 25.5 MPa, and the lower-limit pressure is 10.5 MPa. To explore the deformation characteristics of the surrounding rock, a constant-pressure rheological simulation analysis is conducted for these four pressures.
# Low-Profile Harmonic Transponder for IoT Applications
## 2 Paper Framework and Prototype Description  
The paper is divided into five sections as explained in Figure 1. Accordingly, the bullet points of each section are illustrated in Figure 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Ref.** & **Single Layer** & **Electrical Size** & \(\mathbf{f_{0}}\) (GHz) & \(\mathbf{P_{t}}\) (dBm) & **Coverage (m)** \\ \hline
[2] & N & \(0.119\lambda_{0}\times 0.088\lambda_{0}\) & 9.4 & 33 & 60 \\
[10] & N & \(0.406\lambda_{0}\) (Wire) & 5.8 & 28 & 6 \\
[11] & N & \(0.267\lambda_{0}\times 0.133\lambda_{0}\) & 1 & 16 & 2 \\
[12] & N & \(-\) & 1.04 & 0 & 0.5 \\
[13] & N & \(0.68\lambda_{0}\times 0.68\lambda_{0}\) & 3.5 & 25 & 7 \\
[15] & Y & \(1.28\lambda_{0}\times 0.123\lambda_{0}\) & 2.45 & 10 & 0.7 \\
[16] & N & \(0.3\lambda_{0}\times 0.26\lambda_{0}\) & 1.2 & 16 & 4 \\
[17] & Y & \(0.267\lambda_{0}\times 0.117\lambda_{0}\) & 1 & 16 & 5 \\ This Work & Y & \(0.245\lambda_{0}\times 0.163\lambda_{0}\) & 2.45 & 20 & 3 \\ \hline \end{tabular}
\end{table}
Table 1: Recently published harmonic transponders.  
The proposed transponder consists of two bowtie dipoles placed over an Ro4350 substrate with 0.508 mm thickness, and the antennas are matched to the diode, using the T-matching network, as shown in Figure 2. The two dipoles serve as transponder antennas, one for receiving 2.45 GHz and the other, smaller one for transmitting the converted second harmonic from the diode. The realized gain of the fundamental and harmonic antennas are 1.39 dBi and 3.8 dBi, respectively. The transponder circuit model is driven to evaluate the matching performance at different receiving powers and frequencies.  
One major design challenge is that the diode impedance is frequency and power dependent, similar to a conventional UHF RFID chip. An additional design challenge is to simultaneously match the diode impedances at two harmonically separated frequencies. In this regard, the diode is assumed to be zero biased in order to have optimized matching at the lower values of the input power, i.e., less than \(-20\) dBm. Thus, the zero-biased diode impedances are calculated to be \((19.35-j227.35)\)\(\Omega\) at 2.45 GHz, and \((15.2-j79.3)\)\(\Omega\) at 4.9 GHz. After that, a T-matching network is utilized for simultaneously matching the diode to the dipoles at the targeted frequencies.  
Due to the symmetry of the T-matching network, the equivalent circuit is transformed to the single-ended equivalence shown in Figure 3, which is complete and reasonably ac  
Figure 1: Paper framework.  
Figure 2: Harmonic transponder physical layout on the left and the corresponding radiation pattern at 2.45 GHz on the right.