esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Lightning Transient Voltages
#### 3.1.2 Typical Measurement Results  
In 2017, a total of 832 pieces of data were recorded by the two measuring systems. 650 pieces of data were recorded by the system in Z-substation and 184 pieces of data had been recorded by the system in the Y-substation. 307 pieces of data can be synchronized with a ground flash recorded by the LLS.  
Once a lightning stroke was recorded by two measuring systems, the double-end traveling wave location method (DTLM) is activated to determine the position along the Yan-Zhuang line and nearest to the lightning stroke point.  
On 13:58:27, May 14 th, 2017, the two measuring systems were triggered in succession and the original recorded voltage waveforms from the sensors of phase C are shown in Figure 7. Obvious voltage fluctuations can be seen in the dotted box. After filtering out the power frequency signal, the lightning transient voltage waveforms are shown in Figure 8. As shown in the figures, the arriving time of lightning transient voltage for the Y-substation and Z-substation are estimated to be 341,862.1 \(\upmu\)s and 341,911.1 \(\upmu\)s, respectively. The time difference is 49.0 \(\upmu\)s. According to DTLM, the initial position (which is usually the position nearest to the lightning stroke point on the transmission line) of the lightning transient traveling wave is about 12.41 km away from the Y-substation, which is between No. 25 tower and No. 26 tower. Meanwhile, LLS indicated that there was a lightning stroke with a return stroke current of \(-\)49.5 kA near No. 23 tower. The location error could be caused by the sag of transmission lines, the distortion of transient voltage during transmitting, the nonideal traveling velocity, and the randomness of the lightning stroke.
## 3 Theoretical Background  
As compared to the reflected intensity measurements, the SE is a model-based approach where one compares the experimental data by using accurate and methodical calculations (Sections 3.1-3.3). The SE parameters (\(\Psi\), \(\Delta\)) are generally related to the ratio \(\rho\)of complex Fresnel reflection coefficients \(\widetilde{\overset{\sim\text{P}}{\tau}}\) and \(\widetilde{\overset{\sim\text{S}}{\tau}}\) of incident light polarized parallel (\(\,\parallel\,\)) and perpendicular (\(\perp\)) to the plane of incidence [22; 23]:  
\[\rho\equiv\frac{\widetilde{\overset{\sim\text{P}}{\tau}}}{\widetilde{\overset{ \sim\text{S}}{\tau}}}=\tan\Psi\exp(i\Delta). \tag{1}\]  
Measurements of film thickness by in-line SE methodology have an important role in monitoring the epitaxial growth process. To extract the optical constants and thickness of the layer structured materials, one needs to establish reasonably accurate models for simulating the complex dielectric functions \(\widetilde{\overset{\sim\text{}}{\epsilon}}(\omega)\) [refractive indices \(\widetilde{\overset{\sim\text{}}{\epsilon}}(\omega)\)] for both the epifilms and substrates in the appropriate FIR \(\rightarrow\) MIR \(\rightarrow\) UV energy E [\(\equiv\) h\(\omega\)] regions.
# Performance of Time Reversal Based Underwater Target Detection in Shallow Water
## 3 Target and Clutter Channel Modeling  
The objective of this study is to detect a cylindrical object that is bottoming at the seabed as shown in Figure 1. For computer simulation and analysis, the target and clutter channel models are required, and the models employed in this study are presented in this section.
## 2 Methodology
### Numerical Scheme  
In the present simulations, the governing equations are discretized by the finite volume method (FVM). The PISO-SIMPLE (PIMPLE) algorithm is employed for the pressure-velocity coupling. The time derivatives are solved by the second-order implicit Crank-Nicolson scheme. Second-order Gauss with a limited linear scheme is employed for the convective terms. Second-order Gauss with a linear scheme is employed for the diffusive terms. The generalized geometric-algebraic multi-grid (GAMG) numerical method with Diagonal-based Incomplete Cholesky (DIC) preconditioner is employed to solve the pressure equations. For more details information of the above numerical scheme, please refer to Open FOAM [21].
## 2 Compact Model and Physics
### Discussion on Fin Width  
The special 3D structure of FinFETs results in a different spatial distribution of traps, thus causing degradation relying on structural parameters, especially the device width. The diffusion of hydrogen produced during bond dissociation in the three-dimensional FinFET structure can be effectively modeled, showing a clear relationship between trap distribution and the fin width-to-length ratio. In the modeling process, it is assumed that traps are generated at fixed locations without redistribution over time.  
As illustrated in Figure 5, there are several different forms of diffusion of H at the oxide interface. Considering the different diffusion forms, the equations for the corresponding diffusion forms are substituted into Equations (3) and (1) and solved to obtain the distribution equation of the trap as follows [18]:  
\[N_{\text{IT}}(t)\propto\left[\frac{(D_{\text{II}}t)^{1/2}}{2}+\frac{\pi(D_{ \text{II}}t)}{6(W_{\text{Fin}}+2H_{\text{Fin}})}+\frac{\pi(D_{\text{II}}t)}{12( L_{c}-\Delta L)}+\frac{\pi(D_{\text{II}}t)^{3/2}}{24(L_{c}-\Delta L)(W_{\text{Fin} }+2H_{\text{Fin}})}\right]^{1/2} \tag{13}\]  
When the stress voltage \(V_{gs}=V_{ds}\) is low, the channel cutoff region length \(\Delta L\) approaches zero, and (13) can be simplified. The semi-empirical relationship between AR and fin width is written as \(AR\propto N_{\text{IT}}\propto(1/W_{\text{eff}})^{a_{g}}\).
## 4 Simulation and Analysis
### Simulation Results
In coupling control tests, the initial errors are somewhat large but gradually approach zero in later stages. This indicates that the coupling algorithm plays a role in MMS. The CE is greatest during DM regardless of which control method is used. This  
Figure 10: Angular velocity curves of the four off-center arms during motion mode switching: (**a**) cross motion; (**b**) in-place rotation; (**c**) diagonal motion; (**d**) steering motion. may be caused by the asymmetric kinematic characteristics and wide steering angles used by off-center arms 2 and 4 during this motion. Overall, the CE performance improves substantially under the proposed control methodology. Figure 12 shows simulated mode switching longitudinal and lateral acceleration changes. In all motion modes, acceleration values are greater than simulation values and fluctuations occur. Meanwhile, acceleration is smaller under coupling control than under distribution control. In CM mode switching, the maximum absolute longitudinal acceleration rates under coupling control and distribution control are \(-0.033\) m\(\cdot\)s\({}^{-2}\) and \(-0.011\) m\(\cdot\)s\({}^{-2}\), respectively. In contrast, the maximum absolute lateral acceleration rates under coupling and distribution control are \(0.031\) m\(\cdot\)s\({}^{-2}\) and \(0.010\) m\(\cdot\)s\({}^{-2}\), respectively. The four values in the above order are \(0.028\) m\(\cdot\)s\({}^{-2}\), \(-0.008\) m\(\cdot\)s\({}^{-2}\), \(-0.023\) m\(\cdot\)s\({}^{-2}\), and \(-0.006\) m\(\cdot\)s\({}^{-2}\) during IR mode switching, \(0.029\) m\(\cdot\)s\({}^{-2}\), \(0.008\) m\(\cdot\)s\({}^{-2}\), \(-0.035\) m\(\cdot\)s\({}^{-2}\), and \(0.007\) m\(\cdot\)s\({}^{-2}\) during DM mode switching, and \(-0.028\) m\(\cdot\)s\({}^{-2}\), \(0.004\) m\(\cdot\)s\({}^{-2}\), \(0.028\) m\(\cdot\)s\({}^{-2}\), and \(-0.006\) m\(\cdot\)s\({}^{-2}\) during SM mode switching. From these results, we can see that the longitudinal and lateral disturbances are smaller under coupling control than under conventional distribution control. The maximum CE reduction reaches \(50\%\). Therefore, MMS occurs with better stability under coupling control. This further demonstrates that the method proposed in this study is feasible and effective. Figure 11: Angular velocity coupling errors of motion mode switching: (**a**) cross motion; (**b**) in-place rotation; (**c**) diagonal motion; (**d**) steering motion.
## 6 Proposed Cross-Domain Authentication Method
### Cross-Domain Device Mutual Authentication  
When Device \(D_{i}^{A}\) moves into Domain \(B\), Device \(D_{i}^{A}\) and Edge Gateway \(EG_{j}^{B}\) should perform mutual authentication.  
At this time, as shown in Figure 3, the authentication process is as follows:  
**(1) Initial authentication message from the device to the edge gateway of another edge gateway.** Device \(D_{i}^{A}\) generates a random number \(c_{i}\) and a timestamp \(T_{4}\) and then computes  
\[W_{i}=T_{c_{i}}(x)\]  
\[M_{5}=h(EID_{j}^{B}||PDID_{i}^{A}||TID_{i}||T_{4})\]  
Afterwards, Device \(D_{i}^{A}\) sends \((TID_{i},W_{i},M_{5},T_{4})\) to Edge Gateway \(EG_{j}^{B}\).  
**(2) Authentication verification by the edge gateway.** After receiving the message, Edge Gateway \(EG_{j}^{B}\) checks the validity of the timestamp \(T_{4}\) by verifying C where \(T\) is the current timestamp. If the timestamp is valid, Edge Gateway \(EG_{j}^{B}\) then retrieves the authentication record \(TID_{i}\), \(EID_{i}^{A},Y_{t},T_{3}\) from the blockchain through the blockchain anchor node using \(TID_{i}\) and computes  
\[h(TID_{i}||EID_{i}^{A}||S||T_{3})\oplus Y_{t}=PDID_{i}^{A}\]  
Edge Gateway \(EG_{j}^{B}\) verifies whether \(M_{5}=h(EG_{j}^{B}||PDID_{i}^{A}||TID_{i}||T_{4})\). If they are equal, Device \(D_{i}^{A}\) is successfully authenticated. Then, Edge Gateway \(EG_{j}^{B}\) generates \(c_{m}\) and a timestamp \(T_{5}\) and then computes  
\[X_{m}=T_{c_{m}}(x)\]  
\[Y_{im}=T_{c_{m}}(W_{i})\]  
\[SK_{im}=h(Y_{im}||TID_{i}||PDID_{i}^{A})\]  
\[M_{6}=h(SK_{im}||T_{5})\]  
Edge Gateway \(EG_{j}^{B}\) sends \(\{M_{6},X_{m},T_{5}\}\) to Device \(D_{i}^{A}\).  
**(3) Authentication verification by the device.** After receiving the information, Device \(D_{i}^{A}\) checks the validity of the timestamp \(T_{5}\) by verifying whether \(|T-T_{5}|<\Delta_{t}\). If the timestamp is valid, Device \(D_{i}^{A}\) then computes  
\[Y_{im}=T_{c_{i}}(X_{m})\]  
\[SK_{im}=h(Y_{im}||ID_{i}||PDID_{i}^{A})\]  
Device \(D_{i}^{A}\) verifies whether \(M_{6}=h(SK_{im}||T_{5})\). If they are equal, the device stores the session key \(SK_{im}\).
## 3 Results
### Accumulation and Transfer of the Combined Pollutants in Amaranth
#### 3.1.2 BDE-153 Content in the Amaranth as Affected by Cd  
When the concentration of BDE-153 in the nutrient solution was 5 \(\mathrm{\SIUnitSymbolMicro g}\)/L, with the increase in Cd from 0 to 5 mg/L, the accumulation of BDE-153 in the leaves gradually decreased from 62.5 ng/g to 39.0 ng/g, and the accumulation of BDE-153 in the stems gradually decreased from 69.8 ng/g to 25.3 ng/g. At the same time, the accumulation in the roots increased first and then decreased, and the transfer coefficient also gradually decreased from 0.09 to 0.05. The same pattern was observed when the concentration of BDE-153 in the nutrient solution was 20 \(\mathrm{\SIUnitSymbolMicro g}\)/L. The accumulation of BDE-153 in the root system also decreased. This indicated that the migration of BDE-153 at a low concentration of Cd was influenced by a synergistic effect, but the presence of a high concentration of Cd inhibited the accumulation of BDE-153 in the aboveground parts of the amaranth. This may be because low concentrations of Cd stimulated the growth of amaranth and promoted the absorption of BDE-153 by the amaranth roots, but high concentrations inhibited the growth and metabolism of the plants, thereby reducing the absorption of BDE-153 by the plants.  
The accumulation of BDE-153 in the roots, stems, and leaves of the amaranth increased significantly with an increase in the BDE-153 concentration in the nutrient solution when Cd was held at the same pollution level as used under the single treatment of BDE-153 and the interaction of Cd/BDE-153.  
In addition, the transfer coefficient of BDE-153 in all treatment groups was very low (0.05-0.10), and the transfer coefficients gradually decreased from 0.09 and 0.1 to 0.05 with an increase in the BDE-153 concentration, indicating that the BDE-153 taken up by the roots of the amaranth mostly accumulated in them, and the transfer ability to the shoots was relatively low (Figure 2). The transport ability of PBDEs in plants may be related to phloem transporters because there are fewer phloem transporters in amaranth, which affects the transfer of BDE-153 from the ground [31].
# Experimenting with Routing Protocols in the Data Center: An ns-3 Simulation Approach
## 2 Background and Related Work
### Fat Tree Networks  
Fat-tree networks are topologically partially ordered graphs, and \"level\" denotes the set of nodes at the same height in such a network, where the nodes of level zero (the lowest) are called Leaves, those of level one are Spines, and the ones of level two are Top of Fabric (ToF) or Cores. The subset of Leaf and Spine nodes that are fully interconnected is called a Point of Delivery (PoD). Level two is called the aggregation level and has the responsibility of connecting different PoDs.  
Following the notation described in [12], a fat-tree topology can be specified by three parameters: \(K_{LEAF}\), \(K_{TOP}\) and \(R\). \(K_{LEAF}\) and \(K_{TOP}\) describe the number of ports pointing north or south for the leaf and spine nodes, respectively. Finally, the number of links from a ToF to a PoD are denoted by \(R\) and called \"redundancy factor\". As an example, the Figure 1 shows a fat-tree with \(K_{LEAF}=2\), \(K_{TOP}=2\) and \(R=1\). For simplicity, from now on we assume \(K_{LEAF}=K_{TOP}=K\).  
Observe that there are two types of fat trees: single-plane and multi-plane. In a single-plane topology, each ToF is connected to all the Top of PoD (ToP). This topology has the maximum value of redundancy factor, with \(R=K\). In these topologies, the number of ports for each ToF is at least \(P\times K\), which might be unfeasible if \(P\) and/or \(K\) are too large.  
On the other hand, in a multi-plane topology, ToFs are partitioned into planes: \(N=K/R\) sets, each with the same number of nodes. All the ToFs of the same plane are connected to the same set of spines of each PoD. The topology shown in the Figure 1 can be described as a multi-plane fat-tree with \(K=2\) and \(R=1\) and \(N=K/R=2\) ToF planes. It is worth noting that in this configuration, redundancy is sacrificed to increase the number of PoDs.
# Frequency Response Extension Method of MET Vector Hydrophone Based on Dynamic Feedback Network
## 3 Materials and Methods  
### Online Update and Adaptive Strategy of Dynamic Feedback Network  
The dynamic feedback network has the ability to be updated online and be adaptively adjusted while extending the low-frequency cutoff frequency of METH in order to cope with the frequency characteristic drift problem that may occur during the long-term operation of METH. This section will detail the online update and adaptive strategy of the dynamic feedback network.  
Algorithms 1 and 2, respectively, describe the adaptive compensation strategies for low-frequency cutoff frequency and bandwidth sensitivity.  
Among them, Algorithm 1 requires the ability to update the feedback system parameters online. As shown in Figure 6, the feedback system \(W_{\text{fb}}\) is implemented by the DSP, so it can be controlled through programming.  
```
1:Switch the system to open-loop mode, turn off all compensation, run the excitation coil self-test to obtain the open-loop transfer function of the system at the current time.
2:Calculate the feedback system \(W_{\text{fb0}}\) according to the expected low-frequency cutoff frequency.
3:Update the feedback system parameters online and switch the system to closed-loop mode.
```  
**Algorithm 1** Compensation of low-frequency cutoff frequency  
The key to the adaptive strategy is the calculation of the compensation system parameters. The following sections will detail the calculation methods of the compensation system for low-frequency cutoff frequency, bandwidth sensitivity, and high-frequency cutoff frequency.
## 3 Constrained Adaptive Tracking Control
### Adaptive PID Controller Design with Control Input Constraints
 \tag{36}\]  
From the update law of (36), we can obtain:  
\[\Delta W_{2}=\eta_{w2}e(k)\frac{\partial y(k)}{\partial u(k)}\frac{\partial u _{c}(k)}{\partial W_{2}} \tag{37}\]  
where:  
\[\frac{\partial u_{c}(k)}{\partial W_{2}}=\ln\alpha[k_{1}x_{c1},k_{2}x_{c2},k_{ 3}x_{c3}]^{T}\]  
**Theorem 2**. : _Define \(\bar{\Psi}_{2}=\max_{k}\left\|\frac{\partial y(k)}{\partial u(k)}\right\|\), \(\bar{\Psi}_{3}=\max_{k}\left\|\frac{\partial u_{c}(k)}{\partial W_{2}}\right\|\). The tracking error \(e(k)\) converges asymptotically to zero if \(\eta_{w2}\) is chosen as:_  
\[0<\eta_{w2}<\frac{2}{\bar{\Psi}_{2}^{2}\bar{\Psi}_{3}^{2}} \tag{38}\]  
Proof of Theorem 2.: Define a Lyapunov function as \(V_{2}(k)=\frac{1}{2}e^{2}(k)\); from (37), we get:  
\[ \begin{split}\Delta V_{2}(k)&=V_{2}(k+1)-V_{2}(k) \\ &=\Delta e(k)\left(e(k)+\frac{\Delta e(k)}{2}\right)\\ &=-\frac{\partial y}{\partial u}\left(\frac{\partial u_{c}(k)}{ \partial W_{2}}\right)^{T}\Delta W_{2}\times\left[e(k)-\frac{1}{2}\frac{ \partial y}{\partial u}\left(\frac{\partial u_{c}(k)}{\partial W_{2}}\right)^ {T}\Delta W_{2}\right]\\ &=-\eta_{w2}e(k)\left\|\frac{\partial y(k)}{\partial u(k)}\right\| ^{2}\left\|\frac{\partial u_{c}(k)}{\partial W_{2}}\right\|^{2}\times\left[e (k)-\frac{1}{2}\eta_{w2}e(k)\left\|\frac{\partial y(k)}{\partial u(k)}\right\| ^{2}\left\|\frac{\partial u_{c}(k)}{\partial W_{2}}\right\|^{2}\right]\\ &=-\eta_{w2}e^{2}(k)\left\|\frac{\partial y(k)}{\partial u(k)} \right\|^{2}\left\|\frac{\partial u_{c}(k)}{\partial W_{2}}\right\|^{2}+\frac{ 1}{2}\eta_{w2}^{2}e^{2}(k)\left\|\frac{\partial y(k)}{\partial u(k)}\right\| ^{4}\left\|\frac{\partial u_{c}(k)}{\partial W_{2}}\right\|^{4}\\ &\leq-\eta_{w2}\left(1-\frac{\eta_{w2}\bar{\Psi}_{2}^{2}\bar{ \Psi}_{3}^{2}}{2}\right)\times e^{2}(k)\left\|\frac{\partial y(k)}{\partial u(k )}\right\|^{2}\left\|\frac{\partial u_{c}(k)}{\partial W_{2}}\right\|^{2} \end{split} \]  
We can find that \(\Delta V_{2}(k)\) can be negative definite in the variables \(e(k)\) when \(\eta_{w2}\) satisfies Equation (38). Since \(V_{2}(k)\) is a decreasing and non-negative function, the function is converging to a constant of zero, i.e., \(V_{2}^{\infty}\geq 0\), as \(k\rightarrow\infty\); therefore, \(\Delta V_{2}(k)\to 0\). We can infer that \(\lim_{k\rightarrow\infty}e(k)=0\)To provide a direct view impression of the presented control strategy, the structure chart is shown in Figure 5.
## 3 Literature Review
### Infrastructure Inspection and Maintenance  
Drones play a significant role in inspection and maintenance operations, acting as a game changer in examining and maintaining infrastructure. The study discusses integrated detection and surveying operations with drones, such as wall-sticking drones for inspecting crude oil storage tanks, detecting concrete crack damage, fatigue crack detection in steel bridges, deflection detection in bridges, corrosion analysis in towering chimneys, and visual inspection of concrete dams [20; 21]. The mention of thermal cameras, X-ray cameras, and wireless detectors incorporated with drones highlights their potential in inspecting hard-to-reach parts of infrastructures.
## 4 The Proposed Framework
### The Methodological Framework
#### 4.1.2 Preprocessing  
The datasets (LST and NDVI) were then subjected to geometric correction operations to eliminate distortions caused by satellite acquisition or instrumentation errors and ensure that all data share the same projection coordinate system. This allowed for a consistent overlay of geospatial information. Once prepared and corrected, the dataset was subjected to a resampling process to obtain a uniform resolution of 30 m \(\times\) 30 m, thus facilitating a correct comparison of the data without discrepancies related to different spatial resolutions. This step is crucial for maintaining consistency across the datasets, allowing for accurate integration and analysis of LST and NDVI values.
# Study About the Performance of Ascon in Arduino Devices
## 4 Security Analysis
### Algebraic Attacks
#### 4.1.1 Differential and Linear Cryptanalysis
Therefore, while this does not imply a general probabilistic weakness in the full-round Ascon design, it clearly demonstrates that nonce reuse severely compromises confidentiality by enabling probabilistically guided algebraic inference over internal secrets. Also, the number of blocks of the plaintext and associated data should not be close to the limit protected by the encryption algorithm, which is bounded at \(2^{64}\) blocks per key. Recently, machine learning (ML) techniques have been used to develop differential distinguishers. In this way, in [24] (previously published in [24]), the construction of a differential distinguisher is presented with the objective of being used in the Ascon permutation. Authors show an accuracy of 99.4% for the 4-round distinguisher with \(2^{18}\) data complexity.
###### Abstract  
Long-time exposure to the microgravity conditions experienced during space flights induces alterations in the homeostasis of organs and tissues, including skeletal muscles, which undergo atrophy with the loss of mass and strength due to decreased size and altered composition of myofib. Microgravity conditions can also affect the functionality of satellite cells, i.e., the adult stem cells providing the muscle precursors that are responsible for the growth and maintenance of muscle mass in adult life, as well as for muscle regeneration following a damage. The MyoGravity project, funded by Agenzia Spaziale Italiana (ASI), aimed to send human muscle precursor cells (huMPCs) on board the International Space Station (ISS) in order to study the effects of real microgravity on the differentiation capacity of this cell type. To this end, it was necessary to use a methodology to cultivate huMPCs inside dedicated space bioreactor devices (Experiment Units, EUs) specifically designed to cultivate cell cultures and perform scientific protocols in the space environment of the ISS. Here, we report the setting of several cell culture parameters to convert the EUs into suitable devices for biomedical experiments using huMPCs for space flight purposes.  
microgravity; human muscle precursor cells; satellite cells; muscle atrophy; space flights; international space station +
Footnote †: journal: Medical Physics Letters