esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Postulated Models Describing the Mechanism of Very High Cycle Crack Initiation and FGA-Formation
### Hydrogen-Assisted Crack Growth of Short Cracks  
[PERSON] et al. [9; 35; 51; 86] postulated a hydrogen-induced short crack propagation as a result of the hydrogen preferentially attached to non-metallic inclusions during the heat treatment. [PERSON] et al. [113] observed trapped hydrogen around the non-metallic inclusions via secondary ion image analysis. The localized increased hydrogen content is supposed to lead to hydrogen embrittlement in the immediate vicinity of the inclusion and thereby suppresses the crack-closure effects, which usually determine the fatigue strength. To evaluate the influence of hydrogen on the FGA-formation a test with specimens containing different hydrogen contents due to their heat treatments was performed. [PERSON] et al. observed smaller FGAs for the specimens with lower hydrogen content. The specimens containing the least hydrogen showed crack initiation at non-metallic inclusions but also at large bainitic areas. Since FGA was only observed for crack initiation around non-metallic inclusions and not for bainitic areas-induced crack initiation [PERSON] et al. attributed the FGA-formation to the hydrogen trapped around the inclusion [51].  
According to [PERSON] et al. [114], the hydrogen in the crack tip leads to an increase in the SIF, which promotes short crack growth and the microstructural barriers are therefore no longer sufficient to arrest the crack. The influence of hydrogen and the threshold condition for the hydrogen-induced crack propagation are described by [PERSON] et al. [95]. Since the trapped hydrogen around non-metallic inclusions only has an effect within the plastic zone forming in the respective cycle it is stated that hydrogen-induced crack propagation only takes place as long as the crack propagation per cycle is smaller than the plastic zone in front of the crack tip. Therefore, the postulated hydrogen-induced crack growth changes into a long crack growth as soon as the crack propagation per cycle exceeds the size of the plastic zone [95].
## 3 Results
### Gene Expression Analysis  
Six growth-related genes containing _fatty acid synthase (Fas)_, _Lipase_, _carnitine palmitoyltransferase (Cpt-1)_, _cytochrome C oxidase (Cox)_, _ATP synthase (ATPase)_ and _NADH dehydrogenase (NADH)_ were examined for molecular responses in shrimp among commercial pellet feeding, and 5% and 10% commercial pellet reduction with ex-situ biofoc supplement. The expression levels of all six genes were significantly higher in both shrimp fed with 5% and 10% commercial pellet reduction with ex-situ biofoc supplement (95%C+BF and 90%C+BF, respectively) than the control (100%C) (Figure 6).  
For genes involved in lipid metabolism and the digestion process, _Fas_ (2.15 and 3.40 folds), _Lipase_ (2.21 and 3.13 folds), and _Cpt-1_ (1.79 and 1.57 folds) transcripts exhibited slightly significant increases in shrimp-fed with 95%C+BF and 90%C+BF, respectively compared to the control (100%C). Comparing between shrimp-fed with 95%C+BF and 90%C+BF, _Fas_ and _Lipase_ expression levels in 90%C+BF-fed shrimp were significantly higher than those in 95%C+BF-fed shrimp for 1.58 and 1.42 folds, respectively. In contrast, there was no significant difference for _Cpt-1_ transcript.  
For genes involved in the energy metabolic process, the expression levels of _ATPase_ (25.06 and 20.51 folds) and Cox (19.19 and 19.01 folds) were dramatically significantly higher in shrimp-fed with 95%C+BF and 90%C+BF, respectively, whereas _NADH_ expression level was slightly significant higher (2.91 and 2.34 folds, respectively). Comparing between shrimp-fed with 95%C+BF and 90%C+BF, _ATPase_ and _NADH_ expression levels in 90%C+BF-fed shrimp were significantly lower than those in 95%C+BF-fed shrimp for 1.22 and 1.01 folds, respectively, while there was no significant difference for the _Cox_ transcript.  
Figure 5: Fatty acid profiles analysis of shrimp-fed with commercial pellet (100%C, blue) and shrimp-supplemented with ex-situ biofoc in different commercial pellet reduction ratios (95%C+BF, red; 90%C+BF, yellow). Error bars represent a value of \(\pm\)standard deviation. Different letters above bars (a and b) represent a significant difference among the three groups.
# 3D Monitoring of Buildings Using TerraSAR-X InSAR, DInSAR and PolSAR Capacities
## 3 Measuring the Height by Interferometry  
### Methodology  
#### 3.1.1 SAR Interferometry Principle  
Across-track SAR interferometry is based on the measurement of the phase difference \(\phi\) between two SAR images acquired from different point of views, separated by a baseline \(B_{\perp}\). This phase difference is linked to the height of the scatterers \(h\).  
Single-pass interferometry enables to avoid temporal decorrelation effects and thus to limit the noise on the phase difference estimation. To perform single-pass acquisitions, two antennas have to be used conjointly, one emitting and the two receiving. This type of image can be acquired by satellite constellation composed of TSX and TDX, such as for the PolInSAR images used in this study. On the contrary, the same satellite can be used to acquired two images at two different moments. In this case, the acquisition corresponds to repeat-pass interferometry.  
Single-pass interferometry is needed to guaranteed a high degree of coherence over vegetation and soils that decorrelate rapidly at X-band. Nevertheless, single-pass interferometry is very costly since the two satellites have to acquire conjointly. Due to operational constraint, TSX and TDX do not always flight in close formation. Moreover, the degree of coherence \(\gamma\) on urban structures and buildings is very high, even for acquisitions separated by more than four years: buildings are very stable structures leading to a very steady phase difference. Thus, it is possible to monitor buildings using repeat-pass interferometry, taking advantage of the long time series available.  
In repeat-pass mode, the unwrapped phase difference \(\phi\) can be linked to the height using the following equation [22]:  
\[\phi\equiv-\bar{k}_{z}h=-2\frac{2\pi}{\lambda_{0}}\frac{B_{\perp}}{R\sin( \theta)}h\quad[2\pi] \tag{1}\]  
where \(\bar{k}_{z}\) is the repeat-pass interferometric factor. The minus sign account for the fact that the buildings are imaged in layover.
## 4 Results
### ML, FR and MS Variables  
Table 6 shows the degree of relationship between the ML, FR and MS variables. There are significant associations between these variables, regardless of ML, with a high strength association. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Relationship**} & \(\mathbf{X^{2}}\) & \(\mathbf{df}\) & \(\mathbf{p}\)-Value & \(\mathbf{Vc}\) & \multicolumn{1}{c}{**Strength**} \\ \hline \multicolumn{2}{c}{Tying * FR * IRPSG} & 636.875 & 4 & 0.00 * & 0.88 & High \\ \multicolumn{2}{c}{Winning * FR * IRPSG} & 262.008 & 4 & 0.00 * & 0.51 & High \\ \multicolumn{2}{c}{Losing * FR * IRPSG} & 262.008 & 4 & 0.00 * & 0.51 & High \\ \hline \multicolumn{2}{c}{Note: \(X^{2}\) = Chi-square test; \(\mathbf{df}\) = Degrees of freedom; \(Vc\) = Cramer’s V coefficient; MS = Match Status; FR = Final Result; IRPSG = Impact of the Repercussion of the Player Substitutions on Goals. * \(p\) < 0.05.} \\ \end{tabular}
\end{table} 
Table 4: Degree of relationship between the MS, FR and IRPSG variables. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**RPSG**} & \multirow{2}{*}{**IRPSG**} & \multicolumn{3}{c}{**Tie**} & \multicolumn{3}{c}{**Win**} & \multicolumn{3}{c}{**Loss**} \\ \cline{3-10}  & & \(\mathbf{n}\) & \% & \(\mathbf{ASRs}\) & \(\mathbf{n}\) & \% & \(\mathbf{ASRs}\) & \(\mathbf{n}\) & \% & \(\mathbf{ASRs}\) \\ \hline \multirow{3}{*}{Immediate} & Positive impact & 12 & 50.00 & 0.00 & 25 & 75.80 & 3.70 * & 8 & 24.20 & \(-3.70\) * \\  & Negative impact & 12 & 50.00 & 0.00 & 8 & 24.20 & \(-3.70\) * & 25 & 75.80 & 3.70 * \\ \cline{2-10}  & Positive impact & 15 & 50.00 & 0.00 & 85 & 90.40 & 10.40 * & 9 & 9.70 & \(-10.30\) * \\ \cline{2-10}  & Negative impact & 15 & 50.00 & 0.00 & 9 & 9.60 & \(-10.40\) * & 84 & 90.30 & 10.30 * \\ \cline{2-10}  & Positive impact & 17 & 50.00 & 0.00 & 58 & 89.20 & 8.20 * & 7 & 10.60 & \(-8.20\) * \\ \cline{2-10}  & Negative impact & 17 & 50.00 & 0.00 & 7 & 10.80 & \(-8.20\) * & 59 & 89.40 & 8.20 * \\ \cline{2-10} Medium–long-term & Positive impact & 8 & 50.00 & 0.00 & 31 & 88.60 & 5.90 * & 4 & 11.40 & \(-5.90\) * \\ \cline{2-10}  & Negative impact & 8 & 50.00 & 0.00 & 4 & 11.40 & \(-5.90\) * & 31 & 88.60 & 5.90 * \\ \cline{2-10} Long-term & Positive impact & 17 & 50.00 & 0.00 & 82 & 91.10 & 10.20 * & 8 & 8.90 & \(-10.20\) * \\ \cline{2-10}  & Negative impact & 17 & 50.00 & 0.00 & 8 & 8.90 & \(-10.20\) * & 82 & 91.10 & 10.20 * \\ \hline \hline \end{tabular} Note: \(n\) = Frequency; RPSG = Representation of the Player Substitutions on Goals; FR = Final Result; IRPSG = Impact of the Repercussion of the Player Substitutions on Goals. * \(ASRs>1.96\).  
\end{table} 
Table 3: \(ASRs\) of the crossover of the RPSG, FR and IRPSG variables.
# SDN Architecture for 6 LoWPAN Wireless Sensor Networks
## 1 Introduction
The controller uses SD6 WSNP messages to send rules dictated by network applications to the nodes, which in turn define the forwarding of data plane packets. The maintenance of the RPL communication stack at the control plane allows the coexistence of SD6 WSN nodes with conventional 6 LoWPAN devices, allowing gradual migration, without discontinuity of the operating services. SD6 WSN allows network applications to implement routing algorithms in addition to RPL, overcoming the lack of flexibility in 6 LoWPAN. A unified view enables network applications to solve complex problems. Traffic can be balanced through the routing nodes, allowing the optimized use of power and reducing interference and contention regions in the transmission medium. Algorithms can also control the radio interface, changing the transmission power to prioritize specific data streams, building routes that meet special quality requirements. Network applications can also build virtual networks to isolate traffic among private nodes, including mechanisms that enable them to meet application-specific security requirements. In addition, due to the unified view, centralized control enhances the planning and management capacity, facilitating the construction of management systems. We evaluate SD6 WSN in two experiments. The first was chosen due to the prominence of 6 LoWPAN in the construction of AMI networks and neighbor area networks (NAN) of companies in the electric power sector [8], supporting bidirectional communication for a more effective control of the electricity distribution network. Among others, this includes measurement, load control (of consumers) and distributed photovoltaic generation. The second experiment evaluates communication between peer devices, a scenario that becomes increasingly important in the so-called Internet of Things (IoT). Our experiments confirm that the proposed approach provides performance gains when compared to 6 LoWPAN. The remaining of this paper is organized as follows: Section 2 discusses the related work, including the limitations pointed out in the literature to the 6 LoWPAN-RPL approach and the differences of our approach with respect to others that adopt SDN techniques for sensor networks. In Section 3, we describe the architecture, extending the presentation of the SD6 WSN control plane presented in [9]. In Section 4, we provide a detailed description of how we have implemented SD6 WSN in the Contiki operating system, including the software options to adapt a constrained device to act as an SD6 WSN node, and the hardware options for real implementations. Section 5 presents the two experiments that compare the performance of SD6 WSN with respect to the 6 LoWPAN-RPL approach. Section 6 presents the findings and final remarks.
## 2 Problem Description and Modeling
### Communication Model  
In an SEC network, data transmission between satellites \(s_{i}\) and \(s_{j}\) at time \(a\) is accomplished through a _route_\(G_{i,j}^{a}=<\!\!s_{i}\to s_{1}\!^{\prime}\to s_{2}\!^{\prime}\to\ldots s_{k} \!^{\prime}\to s_{j}\!\!>\), where intermediate satellites \(s_{i}\!^{\prime}\) and \(s_{i+1}\!^{\prime}\), \(i\in\mathbb{N}_{k-1}\), are adjacent. To minimize routing delay, the shortest route \(GS_{i,j}^{a}\) is adopted, whose length is denoted as \(\mid\!GS_{i,j}^{a}\!\mid\), determined by the sum of ISL distances. Obviously, \(GS_{i,j}^{a}\) can be obtained from matrix \(L_{a}\) using the Dijkstra algorithm [PERSON] (1986). To simplify, we assume that route in an SEC network remains fixed during the transmission of one task but may change across different tasks.  
Then, the communication delay \(\partial_{i,j}^{a}(d)\) can be obtained, which consists of two components, propagation delay and transmission delay:  
\[\partial_{i,j}^{a}(d)=\frac{\left|GS_{i,j}^{a}\right|}{c}+\frac{d}{r} \tag{1}\]  
where \(c\) represents the speed of light with a value of 3 \(\times\) 10\({}^{5}\) km/s, \(d\) is the amount of transmitted data from \(s_{i}\) to \(s_{j}\), and \(r\) is the communication capability of the ISL. Note that the communication delay is negligible when data are transmitted locally, i.e., \(\partial_{i,j}^{a}(d)=0\) for \(i=j\).
# Interest Flooding Attacks in Named Data Networking and Mitigations: Recent Advances and Challenges
## 4 State of the Art Detection and Mitigation Strategies
### Blockchain-Based Approaches  
Blockchain-based defenses against IFAs in NDN are an emerging area of research that uses the decentralized and tamper-proof nature of blockchain to improve trust and coordination among network entities [45; 46]. The core idea involves using blockchain to maintain a secure, immutable ledger of network activities or reputation data, enabling NDN routers to make informed decisions about traffic management.  
In an IoT-based NDN scenario, a blockchain (REF) could be employed to record the behavior of nodes, effectively creating a shared \"blacklist\" or reputation ledger accessible to all network participants. A data-driven trust mechanism was produced in which trust values of the IoT sensor nodes are stored on a blockchain [47], which facilitates the detection and mitigation of attacks, including IFAs. In this framework, sensors and NDN forwarders can update the ledger with evidence of malicious activity, and the consensus mechanism ensures that no single compromised node can falsely accuse others without collective agreement.  
Smart contracts, self-executing contracts with terms directly written in code, can be utilized to automate mitigation actions (REF). For example, a smart contract could be programmed to trigger specific responses, such as instructing edge routers to block a particular consumer identity, once predefined conditions are met, like multiple routers reporting an abnormally high rate of unsatisfied interests from that identity. A smart contract approach ensures timely and coordinated responses to potential threats.
## 4 Proposed Optimizations
### Streamlining Cryptographic Operations for Enhanced Efficiency  
Optimizations shown in Table 3 streamline complex computations. This effectively reduces the total number of instructions required for a complete differential addition law from 15 to 12 (as evident in Columns 3 and 4 of Table 3). It contributes to code elegance and carries substantial implications for computational efficiency. In addition to reducing instruction count, this optimization profoundly impacts the utilization of storage elements. With a decrease from \(15\times m\) to \(11\times m\) storage elements, it ensures that the cryptographic system operates more economically, particularly in resource-constrained environments. It causes a reduction of m clock cycles when applied to the PM computation of the BEC, as defined in Algorithm 1. It leads to faster operations, a crucial requirement in securing communications and data across various applications.
## 3 Proposed Method
### Adaptive Update Model  
In the tracking process, the attributes of objects change constantly. Furthermore, most objects maintain continuity with the previous frame. We propose that the adaptive learning rate is applied using the reliability of the response map. The peak-to-sidelobe ratio (PSR) [19] value of the response regards the reliability of the response map. It reflects the relationship between the main lobe and the surrounding side lobe by:  
\[\rho_{k}=\psi\left(\frac{R_{k}^{p}-R_{k}^{u}}{R_{k}^{\sigma}}\right) \tag{17}\]  
where \(R_{k}^{u}\), \(R_{k}^{\sigma}\) and \(R_{k}^{p}\) are the mean, standard deviation and peak value of the response. \(\psi\) is a regularization parameter. The weighting function calculates the adaptive learning rate \(\hat{\rho}_{k}\).  
\[\hat{\rho}_{k}=\left(\frac{\rho_{m}}{1+e^{-\
u_{1}\cdot(\rho_{k}-\
u_{2})}} \right)^{\gamma} \tag{18}\]  
where \(\
u_{1}\), \(\
u_{2}\) and \(\gamma\) are the parameters of the weighting function and \(\rho_{m}\) controls the maximum learning rate.  
Figure 4 shows the response maps for two different values of the adaptive learning rate. The reliability of the response map determines the adaptive learning rate value. Specifically, the higher the rate value, the more it influences the update model, and vice versa. The adaptive update model is defined by:  
\[\hat{\alpha}_{t}=(1-\hat{\rho})\hat{\alpha}_{t-1}+\hat{\rho}\cdot\alpha_{t}. \tag{19}\]  
The number of \(\hat{\rho}\) depends on the number of blocks. \(\hat{\alpha}_{t}\) means the update model for the current frame, and \(\hat{\alpha}_{t-1}\) means the update model for the previous frame. \(\alpha_{t}\) is the calculated kernel regression solution in the current frame. The adaptive update model is performed to learn the translation estimation and scale estimation, identically.  
For real-time tracking, we assume that the peak position of the response is the center, and we do not need to calculate the update model for the next frame. Then, we employ the sparse update for real-time object tracking. The sparse update is given by:  
\[\delta=\begin{cases}\text{{true},~{}~{}|\Delta x+\Delta y|\leq\hat{\tau}}\\ \text{{false},~{}~{}otherwise}\end{cases} \tag{20}\]  
where \(\hat{\tau}\) is the parameter for the sparse update; when \(\delta\) is true, we can skip the model update for efficiency.
# An NFC on Two-Coil WPT Link for Implantable Biomedical Sensors under Ultra-Weak Coupling
## 4 Minimizing Interactions between NFC and WPT
### Minimizing Interactions between NFC Uplink and Power Link
#### 4.1.1 Avoiding the Out-Body Receiver from Being Saturated by the Power Carrier  
In the out-body receiver, we introduce the RC voltage divider to prevent the uplink filter from being saturated. The divide ratio of the RC divider is calculated according to the ratio between the power carrier voltage on \(L_{\text{o}}\) and the maximal input voltage limit of the uplink filter. Since the PDL (\(P_{\text{load}}\)) should be kept constant to about 5 mW (the total power requirement of our device for the future integrate circuit (IC) design), the power carrier amplitude on \(R_{\text{load}}\) can be derived in Equation (7), where \(\omega_{\text{dl}}=2\pi\times 2\) MHz is the angular frequency of the power carrier. By using Equation (7) and the transfer function in Equation (3), \(V_{\rm o}({\rm j}\omega_{\rm dl})\) (the resonated power carrier amplitude on \(L_{\rm o}\)) can be derived. Through calculating, \(V_{\rm o}({\rm j}\omega_{\rm dl})\) could vary from 220 V to 22 V in the operating range (\(0.01<k<0.1\)).  
\[V_{\rm load}\left({\rm j}\omega_{\rm dl}\right)=\sqrt{2P_{\rm load}R_{\rm load}} \approx 4.5\;{\rm V}, \tag{7}\]  
The maximal input voltage allowed for the uplink receiving filter without being saturated is 2 V. Thus, the divide ratio of the RC voltage divider is required to fulfill Equation (8), where ABS() is a function to calculate the absolute value. After simplifying Equation (8), we can derive the constraint of \(R_{\rm d}\) and \(C_{\rm d}\), which is shown in Equation (9).  
\[{\rm ABS}\left(\frac{1}{1+{\rm j}\omega_{\rm dl}R_{\rm d}C_{\rm d}}\right)\leq \frac{2}{220}, \tag{8}\]  
\[R_{\rm d}C_{\rm d}\geq 8.8\times 10^{-6}\;\Omega{\rm F}. \tag{9}\]
# An Efficient RRT Algorithm for Motion Planning of Live-Line Maintenance Robots
## 2 Live-Line Maintenance and Robot System  
Robotic live-line maintenance refers to the maintenance of overhead cables by robots using special tools without cutting off the power supply. Common maintenance for live lines includes the disconnection of drain wires, replacement of insulated terminals, and removal of foreign objects (tree branches, kites, etc.), as shown in Figure 1. Working at heights causes difficulties in terms of observations and operations. The threat of charged objects to the robot must be considered during operation tasks. Therefore, reliable and efficient robot operation methods are needed to help operators complete tasks. Therefore, reliable and efficient robot operation technology is needed to help operators complete tasks.
# Planetary-Gearbox Fault Classification by Convolutional Neural Network and Recurrence Plot
## Experiments and Discussion
### Data Preprocessing and Model Design  
#### 5.2.1 Data Preprocessing  
First, taking the sun gear as the reference shaft, a raw vibration with 600 s was converted into those in the angular domain by the COT, which eliminates speed fluctuations. Sampling points per revolution in the angular domain were selected to be 1024.  
Subsequently, the data series in the angular domain were divided into several segments. According to Equation (7), \(i_{cs}=28/99\) data length corresponds to 99 circles of the sun gear. Then, each segment was divided in this way. To reduce computation time, 200 resampling points were selected in a revolution of the sun gear. Thus, the number points of the resampled data series were \(1.6\times 10^{6}\), and each segment was \(1.98\times 10^{4}\) points in the condition that the rotating speed of the sun gear is 800 r/min. In the time domain, we could calculate the time of a complete circle under different rotating speeds. For example, when the rotating speeds of the output shaft were 800, 700, and 600 r/min, the lengths of the data series in the time domain were 7.425, 8.486, and 9.9 s, respectively, when resampling ratewas 2000 points per second. Thus, each segment was randomly selected as 7.5, 8.5, and 10 s of the total 600 s of observed vibration.  
Figure 3: Kinematic schema of experiment planetary gearbox.  
Figure 2: Experiment planetary gearbox: (**a**) test rig; (**b**) sun gear with tooth-root crack; (**c**) planet gear with tooth-root crack; (**d**) planetary carrier with crack.  
Then, all segments were processed by recurrence-plot analysis to obtain \(4\times 3\times 500\) 2D images as the training sample. The recurrence plots in the angular domain of the four conditions with \(n_{s}=800\) r/min are presented in Figure 4. In order to show the obtained recurrence-plot differences between multifault categories, only 8000 of 19,760 points are shown. Angle delay \(\delta\) and embedding dimension \(d\) were determined by the FNN and mutual-information function, respectively. Threshold \(\varepsilon\) was determined by the standard deviation of the observed angular-domain signal [11]. The vertical distance between these lines can be seen in Figure 4, signals' intermittent behavior with a characteristic angle scale [21] of components caused by faults. Small fluctuations could also be seen for all considered conditions, but these differences were slight and not conducive to quantitatively distinguish. Moreover, for the same condition, they may have been slightly different, so it was hard to determine the planetary-gearbox failure type only on the basis of recurrence plots.