esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on the Time Domain Semi Analytical Method for Horizontal Vibration of Pile in Saturated Clay
## 2 Mechanical Model  
### Fractional Order Kelvin Model  
The classic Kelvin model is composed of a spring element K and a [PERSON] dashpot N in parallel, as shown in Figure 1. The fractional Kelvin rheological model is obtained by replacing the [PERSON] dashpot with the [PERSON] dashpot in the classical Kelvin model [23], and the constitutive equation is described as:  
\[\sigma(t)=E_{s}\varepsilon(t)+\eta\frac{\partial^{a}\varepsilon(t)}{\partial t^{ a}} \tag{1}\]  
where \(\sigma\) and \(\varepsilon\) are stress and strain, as functions of the time coordinate \(t\); \(E_{s}\) is the elastic modulus of spring element K; and \(\eta\) and \(a\) are the viscosity coefficient and viscous order of Abel dashpot N, respectively. Equation (1) shows that the dashpot N will degenerate into a linear elastic solid in the case of \(a=0\) and degenerate into an ideal Newtonian fluid in the case of \(a=1\). In the general case of \(0<a<1\), dashpot N represents a viscoelastic solid, and the value of a represents the proportion of solid properties to fluid properties [24].  
The Laplace transform is introduced for a time-domain function \(f(t)\) as follows:  
\[\left\{\begin{array}{l}\hat{f}(s)=\int_{0}^{\infty}f(t)e^{-st}dt\\ f(t)=\frac{1}{2\pi i\int_{\zeta}^{\zeta+i\infty}\hat{f}(s)e^{st}ds}\end{array}\right. \tag{2}\]  
where \(\hat{f}(s)\) is the Laplace transform function corresponding to the function \(f(t)\), \(s\) is the transform parameter of the time coordinate \(t\), \(\zeta\) is any real number, and \(i=\sqrt{-1}\) is the imaginary unit.  
Using Equation (2) to perform the Laplace transform on Equation (1), we obtain  
\[\hat{\sigma}(s)=(E_{s}+\eta s^{a})\hat{\varepsilon} \tag{3}\]  
Equation (3) reflects the stress-strain relationship in the Laplace transform domain with a complex modulus \(\hat{E}=E_{s}+\eta s^{a}\). Equation (3) is expanded to a three-dimensional stress state and written in tensor form as  
\[\hat{\sigma}=\lambda(s)\hat{\theta}I+2\mu(s)\hat{\varepsilon} \tag{4}\]  
where \(\hat{\sigma}\) and \(\hat{\varepsilon}\) are stress and strain tensors, \(\hat{\theta}\) is volumetric strain, \(I\) is a three-order identity matrix, and \(\lambda\) and \(\mu\) are the Lame constants. According to the definition, the expression of \(\lambda\) and \(\mu\) can be obtained as  
\[\left\{\begin{array}{l}\lambda(s)=\frac{v(E_{s}+\eta s^{a})}{(1+v)(1-2v)}\\ \mu(s)=\frac{E_{s}+\eta s^{a}}{2(1+v)}\end{array}\right. \tag{5}\]  
where \(v\) is the Poisson's ratio.
# Design and Analysis of Adaptive Hierarchical Low-Power Long-Range Networks
## 2 A Hierarchical Communication Scheme
[24] analyzes the available protocols based on their use for routing data throughout the network in three major categories: Block, Grid and Chain organization and represent how efficient each protocol is in terms of energy, delivery delay, scalability and stability. In the Wireless Sensor Networks domain, the problem of data aggregation and collection is a major topic for research. Various solutions have been proposed [25] dealing with the problem of forwarding data to facilitate the aggregation of the information using multiple techniques:  
* Tree-based solutions like TinyAGgregation [26] or Directed Diffusion [27] are based on the generation of a tree like structure in the whole network to forward the data to a single node that handles the aggregation of the whole dataset. * Cluster-based approaches like LEACH [28] are based on the formation of smaller structures in the network that can locally aggregate the data in multiple nodes. * Multipath routing solutions like Synopsis Diffusion [29] or Ring Routing [30] are closer to the tree-based solutions but provide a backup path in the event of a single node failure. * Hybrid approaches like Tributaries and Deltas [31] tries to overcome the problems of both tree and multipath structures by combining the best features of both schemes. They form multiple data structures inside the network and use the best one in terms of communication cost and latency. The solution presented here is based on the combination of the tree-based and cluster-based approaches. Network structures formed behave like clusters inside the network and inside each cluster a tree-based solution is used for forwarding data to the controller of each structure. Finally, the controller device of each group is directly connected with the Internet through the LPWAN to finally deliver the aggregated data.
# Performance of Time Reversal Based Underwater Target Detection in Shallow Water
## 2 Target Peak Enhancement Using Time Reversal Processing
### Concept of the Time Reversal Scheme
\tag{5}\]  
According to the time-varying property of the clutter channel, that is, \(H_{c}(t_{o},\omega)\
eq H_{c}(t,\omega)\) for \(t_{o}\
eq t\), in case of \(\mathrm{H_{0}}\), the expectation of \(R_{tr}(t_{o},\omega)\) may be a small value, while in case of \(\mathrm{H_{1}}\), the expectation of \(R_{tr}(t_{o},\omega)\) may have a high value due to the time-invariance of the target channel. When the time reversal technique is applied to the signal scattered by the clutter, the focusing of the waveform does not perform well due to the time-varying characteristics. However, in the case that the time reversal technique is applied to the section where the target exists, the pulse can be focused at the target position and it has a higher value than that without the time reversal technique.
# Numerical Study on the Yaw Control for Two Wind Turbines under Different Spacings
## 2 Physical Models and Numerical Approach
### Control Strategy of Wind Turbine  
Due to the influence of the yaw and wake velocity deficit, the turbines cannot maintain the rated working state, so it is necessary to adopt autonomous control to adjust the angular speed of the turbines. The speed control of the rotor is carried out by using the Five-Region Method [27], as shown in Figure 1, in which the relationship between the angular rotor speed and the generator torque is specified.  
\[\frac{d\Omega}{dt}=\frac{1}{I_{Drivetrain}}(T_{Aero}-N_{Gear}T_{Gen}) \tag{5}\]  
where \(\Omega\) is the angular velocity of the rotor, \(T_{Aero}\) is the torque of the turbine, \(T_{Gen}\) is the torque of the generator, which is obtained through the control of the Five-Region method, \(N_{Gear}\) is the speed ratio of the gearbox, \(I_{Drivetrain}\) is the total rotational inertia of the transmission system.  
When the wind speed is greater than the rated wind speed, the wind turbine turns on the pitch control based on the PID algorithm. In this study, the change of pitch angle during the control process is as follows:  
\[\Delta\theta=K_{P}N_{Gear}\Delta\Omega+K_{I}\int_{0}^{t}N_{Gear}\Delta\Omega dt \tag{6}\]  
where \(K_{P}\) is the proportional gain, \(K_{I}\) is the integral gain, \(\Delta\Omega\) is the speed deviation value, and \(N_{Gear}\) is the gear speed ratio.
## 4 The Proposed Method
### Extraction Malicious Function Identifiers
#### 4.4.2 Attention Map  
Attention Rollout [24] is used to generate a heatmap of the areas of the dex image, on which the model focuses its attention [20]. In Section 4.3, the model produces a detection result for the input DEX image, and the attention weight is calculated using Equation (6). Here, a heatmap is created using the Attention Rollout method with the attention weight calculated by the model. In each Transformer Encoder layer, the MSA creates an attention weight for each head. Equation (9) shows how these attention weights are combined into an attention matrix at the layer.  
\[W_{k}[x,y]=max(Wh_{0}^{k}[x,y],\cdots,Wh_{n}^{k}[x,y]),\ k=1\cdots L \tag{9}\]  
where \(W_{k}\) is attention matrix at the layer \(k\). \(L\) is the total number of encoder layers in the model. \(x\), \(y\) are coordinates. \(n\) is the index number of attention heads. \(Wh_{i}^{k}\), (\(0\leq i\leq n\)) is the attention weights of head \(i\) at the layer \(k\). The Attention Rollout method uses the average value, but we use the maximum value to obtain more emphasized values. \(W_{k}\) is generated by taking the maximum value of each coordinate in the attention weights of the \(n\) heads. Then, we keep the top \(p\) percent of elements in \(W\) and reset the rest to zero. Each Transformer Encoder layer produces an attention matrix. Next, the following Equation (10) is used to obtain the raw attention \(A_{k}\). The Attention Rollout adds an identity matrix to the attention matrix and renormalizes the weights to account for residual connections.  
\[A_{k}=0.5W_{k}+0.5I,\ k=1\cdots L \tag{10}\]  
where \(L\) is the total number of encoder layers in the model. \(k\) is the layer number. Therefore, we add the identity matrix to the attention matrix, as in Equation (10), to obtain the raw attention \(A_{k}\). There is a \(A_{k}\) for each Transformer Encoder layer [24].  
The attention rollout matrix is created using the following Equation (11). The attention rollout matrix of the previous layer is multiplied by the current layer's raw attention. By repeating this process for all layers from 0 to \(L\), we can obtain the attention rollout matrix \(AR\) at the last layer.  
\[AR_{i}=\left\{\begin{array}{cl}A_{i}\times AR_{i-1}&\text{if }i>0\\ A_{i}&\text{if }i=0\end{array}\right. \tag{11}\]  
where \(AR_{i}\) is the attention rollout matrix. \(A_{i}\) is the raw attention at the layer \(i\), and the multiplication operation is matrix multiplication [24].  
In ViT, images are divided into patches, and when image patches are entered into the model, they are prefixed with a class token, as shown in Equation (3). Since detection is performed using class tokens, an attention map needs to be generated based on the attention of the remaining image patches to the class token. Therefore, in the attention rollout matrix, we subtract the first row corresponding to the class token and use the rest of the attention rollout matrix for subsequent processing. Finally, to obtain the attention map, we convert and resize it to fit the image size.
# A Radiation-Hardened Low-Power SRAM with Enhanced Write Capability for Space Applications
## 3 Implementation Results and Analysis
### Hold Power Comparison  
In SRAM cells, the hold mode is defined as the state in which the WL is deactivated and both BL and BLB are precharged to a high voltage level. During this mode, the proposed EWS-16T cell minimizes leakage paths by completely isolating the storage nodes from the bitlines--achieved by turning off both the access transistors (N7/N8) and the additional PMOS transistors (P7/P8). Moreover, the cross-coupled structure adopted in the EWS-16T enhances storage node stability and effectively suppresses loop-induced leakage current. Additionally, the storage nodes retain weak logic levels ('1' at Q and '0' at S0), which helps reduce the voltage difference across internal nodes, thereby further minimizing leakage.  
As shown in Figure 11, the EWS-16T cell achieves a hold power of 53 nW, which is 2.13\(\times\), 3.20\(\times\), 1.07\(\times\), 2.48\(\times\), 2.37\(\times\), and 1.20\(\times\) lower than those of QCCS, SCCS, SAW16T, HP16T, RHSCC16T, and S8P8N, respectively. These results confirm that the EWS-16T cell outperforms conventional radiation-hardened SRAM designs in terms of power efficiency during idle states. Through these structural and electrical optimizations, it can be concluded that the EWS-16T cell effectively suppresses leakage current even in hold mode, achieving significantly improved low-power operation suitable for space applications.
## 2 Mathematical Basis  
A graph with \(d\) parts (\(d\geq 2\)) is a graph whose set of vertices can be divided into d disjoint subsets \(V_{1}\), \(\ldots\), \(V_{d}\), such that there is no edge connecting vertices belonging to the same subsets \(V_{i}\), \(V(G)=\bigcup_{i\in 1\ldots d}V_{i}(G)\), and such that \(\forall_{u,v\in V_{i}}\{u,v\}\
otin E(G)\). The graph \(G_{d,m}\) = (\(V\), \(E\), \(W\)) can be partitioned into m maximum cliques \(K_{i}\) (\(i\) = 1, \(\ldots\), \(m\)), where \(m\) is the number of vertices in \(V_{i}\). If there are \(d\) parts of the graph, then each of the maximum cliques \(K_{i}\) should have \(d\) vertices, where \(v_{i}\in V\) and \(i\) = 1, \(\ldots\), \(d\). Each edge where \(e_{ij}\in E\) has its own weight \(w_{ij}\in W\), and each edge \(e_{ij}\) connects two vertices, \(v_{i}\) and \(v_{j}\), which belong to different parts of the graph, \(i\) = 1, \(\ldots\), m, \(j\) = 1, \(\ldots\), m. Each clique \(K_{i}\) has \(d\)\({}^{*}\) (\(d\) - 1) edges, so for each clique, one can compute the sum of the weights of all of its edges. There are \(m\) maximal cliques. However, if the graph does not have all of the edges, it is considered noncomplete; then, there are no \(m\) maximum cliques; their number is less than \(m\), but this number is not known. If the graph does have all of the edges, it is considered complete. The partitioning of a weighted graph with m vertices in each of its parts into m maximum cliques with a minimum total weight can be represented as a multidimensional assignment problem (i.e., matching elements from \(V\) = {\(V_{1}\), \(\ldots\), \(V_{d}\), \(d\) > 2} disjoint sets of equal size d with the smallest total weight) and can be formulated as a binary integer problem. The formulas expressing the multidimensional assignment problem are as follows:  
\[min\sum_{i\in V1}\cdots\sum_{id\text{ }Vd}c_{i1,i2,id}X_{i1,i2,id} \tag{1}\]  
\[s.t.\sum_{i2\in V2}\cdots,\sum_{im\text{ }Vd}X_{i1,i2,\ldots,id}=1,\text{ }i_{1}\in V_{1}\]  
\[\sum_{i1\in V1}\cdots\sum_{is-1 Vs-1}\sum_{s+1 Vs+1}\cdots\sum_{id\text{ }Vd}X_{i1,i2,id}=1,\text{ }i_{s}V_{s},\text{ }s=2,\ldots,d-1 \tag{2}\]  
\[\sum_{i1\in V1}\cdots\sum_{id-1\in Vd-1}X_{i1,id}=1,\text{ }i_{d}\in V_{d},\text{ }X_{i1,i2,id}\text{ }0,\text{ }1,\text{ }i_{s}V_{s},\text{ }s=1,\ldots,d \tag{3}\]  
where for every \(d\)-vertex clique (\(i_{1}\), \(i_{2}\), \(\ldots\), \(i_{d}\)) \(V_{1}\) x \(V_{2}\) x, \(\ldots\), x \(V_{d}\), the variable \(x_{i1,i2,\ldots,id}\) takes the value of one if the elements of this clique belonging to the partitioned graph; otherwise, it is zero. The total cost of partitioning into \(m\) cliques with \(d\) vertices (Equation (1)) is calculated as the cost of matching elements from different sets \(V_{i}\). For example, a clique with \(d\) elements (\(i_{1}\), \(i_{2}\), \(\ldots\), \(i_{d}\)) would cost \(w_{i1,i2,\ldots,id}\).  
\[w_{i1,i2,\ldots,id}=\sum_{s=1}^{d}\sum_{t=s+1}^{d}w_{isit} \tag{4}\]
## 2 Average Power Scaling of Modelocked Thin-Disk Lasers: Challenges and Milestones
### Conclusion and Outlook  
Further average power scaling of modelocked TDLs will require larger laser spot sizes on the disk and on the SESAM. Power scaling in fundamental-mode operation with larger spot sizes on the thin disk is feasible by using state-of-the-art contacting methods and standard gain materials such as Yb:YAG. However, we believe these power-scaling capabilities will be extended and most likely outperformed in the near future by novel materials such as Yb:LuO, when contacting methods and growth are perfected.  
Following the guidelines for high-damage threshold SESAMs presented here, using larger spot sizes on the SESAM should be straightforward. In particular, the suggested upside-down SESAMs with improved thermal management will be ideally suited for future multi-kW level intracavity average powers.  
Currently, the main limitation to higher average powers is thermal effects and even thermal damage observed on intracavity components, in particular in the dispersive mirrors required to achieve stable modelocking. Designs with improved substrates and higher damage threshold material composition for better heat removal will be an important step towards reaching the next milestones in average power and pulse energy.  
Given the advantages discussed in paragraph 2.1 and latest power scaling results, it seems likely that the next step in power and energy scaling of modelocked TDLs will be achieved by operating the oscillator in a vacuum environment (Figure 14).  
Figure 13: Schematic of the fabrication steps of future generation upside-down high-power SESAMs.  
With the suggested improvements, we expect further average power scaling in the near future. Furthermore, reaching pulse energies in the 100 \(\upmu\)J range already appears feasible by increasing the length of the resonator using a Herriott-type passive multi-pass cell [32, 58].
# Optimization of DG-LRG Water Extraction Algorithm Considering Polarization and Texture Information
## 3 Optimized Local Region Growth Algorithm Considering Polarization and Texture Information
### Automatic Selection of Seed Points Based on DWI
#### 3.2.2 Automatic Selection of Seed Points  
Due to the presence of noise in the results of rough extraction of water bodies mentioned above, in order to ensure that seed points are selected in non-noisy areas, we use gradient amplitude and DWI variance as constraints, considering the nearest mean point to select seed points. The specific method is as follows: first, the initial water extraction result is extended by \"a\" unit from top, bottom, left, and right, then a square window with a side length of 2a + 1 is set, sliding in steps of \((2a+1)/2\); next, the DWI variance and center pixel gradient amplitude within the window is calculated, as follows:  
\[\sigma=\frac{1}{(2a+1)^{2}}\sum(\text{dwi}(\text{i},\text{j})-\text{u})^{2}, \tag{3}\]\[\text{G}(\text{x},\text{y})=\sqrt{\left(\text{d}\text{l}/\text{d}\text{x}\right)^{2 }+\left(\text{d}\text{l}/\text{d}\text{y}\right)^{2}}, \tag{4}\]  
where \(\sigma\) is the variance, dwi(i,j) is the DWI value of the pixel, u is the mean of the DWI within the region, G(x,y) is the gradient amplitude of the pixel, dl/dx is the rate of change of the pixel value in the x direction, and dl/dy is the rate of change of the pixel value in the y direction. The variance reflects the overall degree of change in pixel values within the window, while the gradient amplitude reflects the rate of change of the center pixel compared to surrounding pixels. If the gradient amplitude is large, it indicates that a pixel may be located in the edge area of an object. Generally, seed points should be selected in areas with smaller variance and gradient amplitude. Finally, pixels within the window with the closest pixel value to the mean are selected as candidate seed points. The candidate seed points outside the coarse water range are then removed to generate the final seed point set.
## 4 Experimental Setup
### Laboratory Tests  
Laboratory tests were performed exploiting a plastic container filled with \(150\,\mathrm{L}\) of water, with dimensions \(70\,\mathrm{cm}\times 60\,\mathrm{cm}\times 53\,\mathrm{cm}\)[40]. These dimensions allowed to have a thinner layer of water above the transmitter box with respect to all the other sides: this aspect was crucial to evaluate the actual transmission performance only as a function of the transmitter depth since the attenuation along the other directions was by far higher than along the perpendicular one. The reader was positioned above a desk near the plastic container, at an approximate height of \(1\,\mathrm{m}\). The distance between the reader and the water surface was approximately \(2\,\mathrm{m}\). All these distances were measured using a measurement tape with \(\pm 0.1\,\mathrm{cm}\) of uncertainty: however, due to the difficulty of identifying the exact point above the transmitter in the water surface, a \(\pm 0.5\,\mathrm{cm}\) uncertainty is assumed. Figure 2 schematizes the experimental setup, while the plastic container with the transmitter can be seen in Figure 3.  
Tests were performed positioning the transmitter at two different depths: \(6\,\mathrm{cm}\) and \(12\,\mathrm{cm}\), for both these depths a \(\pm 0.5\,\mathrm{cm}\) measurement uncertainty can be assumed. This distance was measured from the upper surface of the box, in the spot where the transmitter antenna was fixed. In order to keep the box suspended in the water, it was positioned on a plastic structure and anchored to it using ropes and concrete ballasts.  
Figure 1: Architecture of the transmitter node.  
Figure 2: Laboratory test setup.  
Together with the tests at different depths, also different salinities for each depth were tested. In particular, since salinity can be expressed in g/L, different levels were achieved by adding increasing amounts of sodium chloride (NaCl) to still water: indeed, NaCl represents around 80% of all dissolved salts. While seawater salinity ranges between 31 g/L and 39 g/L, with a global average value of 35 g/L, lower values were also tested, with the aim of studying LoRaWAN performance also in case of lagoon environments (for example, the Venice lagoon [41]), where salinity can be notably lower than in the open sea. The overall quantity of water was 150 L, thus a 10 g/L salinity could be obtained by dissolving in it 1.5 kg of salt. A total of 9 salinity levels were set up by consequently adding 1.5 kg for the first two levels and then 0.5 kg of salt at a time for the other 7 levels: 0 g/L, 10 g/L, 20 g/L, 23.3 g/L, 26.7 g/L, 30 g/L, 33.3 g/L, 36.7 g/L, 40 g/L and 43.3 g/L. The salt was weighed using a laboratory scale with uncertainty of 2 g. The tested operational conditions are reported in Table 1.
# Vehicle Delay Model Applied to Dynamic and Static Traffic Impact Analysis of Large Parking Lots
###### Abstract  
With the surge of motor vehicle ownership and land intensification, plenty of large parking lots emerge as the times demand. Although it solves the problem of insufficient parking spaces, it intensifies the interaction between dynamic and static traffic. This paper presented an impact assessment method for the interaction of dynamic and static traffic flow in parking lots. Firstly, the average vehicle delay was selected as the evaluation index. The delay effect caused by the interaction of dynamic and static traffic flow was determined according to the driving path of vehicles. Then, the average vehicle delay models of arrival vehicles, departure vehicles, and road vehicles in the parking lot were established. Finally, for the parameters difficult to determine directly in the delay model, this paper proposed a method to calibrate the model parameters by using the simulation experimental data on the VISSIM platform. The results showed that the errors of the three models are within the controllable range, and the delay model parameters had high reliability and feasibility. The delay models can provide a quantitative basis for the parking lot management department to formulate regulation strategies and realize more refined information guidance and navigation in the parking lot.  
large parking lots; entrance and exit; dynamic and static traffic; delay; VISSIM