esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Theoretical Analysis
### Search Delay  
This section studies the search delay of TS strategy, and compare with that of ERS. The search delay is defined as the duration from the initiation of the search phase to the moment that the actor receives the search packet. When the actor is discovered in the search phase, the performance of TS is the same as that of ERS. Thus we only evaluate their performance when the actor is discovered in the chase phase. To clarify the analysis, we assume that the second search attempt in ERS will flood the search packets all over the network.  
In TS strategy, the search delay consists of two parts: the delay in the search phase \(T_{s}\) and that in the chase phase \(T_{c}\). The \(T_{s}\) is the time for the packet that is forwarded by \(S\) hops, such that,  
\[T_{s}=S\cdot\tau_{P} \tag{11}\]  
In the chase phase, the packet moves along the trail to chase the actor, the delay can be estimated by  
\[T_{c}\leq\frac{\varepsilon/\tau_{A}}{1/\tau_{P}-1/\tau_{A}}=\frac{\varepsilon \cdot\tau_{P}}{\tau_{A}-\tau_{P}} \tag{12}\]  
where \(\varepsilon/\tau_{A}\) is the maximum distance between the packet and the actor at the beginning of the chase phase, and \(1/\tau_{P}-1/\tau_{A}\) indicates the velocity difference of them.  
Then we have the search delay of the TS strategy,  
\[D_{TS}=T_{s}+T_{c}\leq S\cdot\tau_{P}+\frac{\varepsilon\cdot\tau_{P}}{\tau_{A }-\tau_{P}} \tag{13}\]  
In ERS, when the source initiates a search attempt, it also generates a waiting timer \(T_{w}\) to wait for the ACK packet. The waiting timer should satisfy \(T_{w}\geq 2\cdot T_{s}\) to ensure that the second search attempt will not be initiate before the ACK packet is received. Therefore, when the first search attempts fails, the search delay of ERS can be estimated by,  
\[D_{ERS}=T_{w}+M\cdot\tau_{P}\geq 2\cdot S\cdot\tau_{P}+M\cdot\tau_{P} \tag{14}\]  
where \(M\) is the number of hops the packet is transmitted to the actor, and \(M>S\). In this equation, the first item represents the delay for the source to wait until it initiates another search attempt, and the second item is the delay in the second search attempt.  
The difference of search delay between ERS and TS is,  
\[D_{ERS}-D_{TS}\geq S\cdot\tau_{P}+M\cdot\tau_{P}-\frac{\varepsilon\cdot\tau_ {P}}{\tau_{A}-\tau_{P}} \tag{15}\]  
According to Equation (15), when the following condition is satisfied, the delay of TS is shorter than that of ERS,  
\[M+S>\frac{\varepsilon}{\tau_{A}-\tau_{P}} \tag{16}\]  
Generally, the packet forwarding is much faster than the actor movement, i.e., \(\tau_{A}>>\tau_{P}\). With reasonable setting of \(\varepsilon\), the search delay in TS is smaller than that in ERS.
# A Flexible FPGA-Based Stochastic Decoder for 5G LDPC Codes
## 4 Proposed Architecture  
The suggested FPGA-based partially parallel stochastic decoder's top-level design is presented in this section. It has runtime flexibility and the capability of decoding received messages corresponding to the set of seven code rates \(R=1/3,2/5,1/2,2/3,3/4,5/6\), and \(8/9\) and codeword length \(N=3808\) for QC LDPC code compliant to the 5G NR standard. Initially, the architecture determines the maximum matrix dimensions, such as the number of block columns \(N_{B}=68\), the number of block rows \(M_{B}=46\), lifting size \(Z_{c}=56\), column-weight \(W_{v}=30\), and row-weight \(W_{c}=19\), for this decoder from the supported code rate set. The architecture comprises various basic modules such as a stochastic variable node decoder (SVND), a stochastic check node decoder (SCND), BGM read-only memory (ROM), a controller unit, an intrinsic message memory unit (IMMU), and a routing network between modules SVND and SCND. These modules of the design are explained in the following subsections.
# Mechanical Behavior of Steel Fiber-Reinforced Lightweight Concrete Exposed to High Temperatures
## 2 Experimental Study  
### Raw Materials  
Portland cement (P.O. 42.5), according to the Chinese standard (GB175-99) [29], was used for all mixtures. Commercial fly ash and silica fume were applied as an admixture for the LWC and SFLWC. The main physical properties and chemical compositions are presented in Table 1. Crushed limestone (4-20 mm diameter) was used as coarse aggregates of NWC. Crushed shale ceramicsite and continuous graded shale ceramicsite sand were employed as ALWC coarse and fine aggregates, respectively. Spherical sintered fly ash ceramicsite was employed as a SLWC coarse aggregate.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multicolumn{5}{c}{**Chemical Composition (\%)**} & \multirow{2}{*}{**Blaine Fineness**} & \multirow{2}{*}{**Density**} & \multirow{2}{*}{**LOI**} \\ \cline{2-2} \cline{6-7}  & **SiO\({}_{2}\)** & & & & & \\ \hline Cement & 21.66 & 5.42 & 63.15 & 2.62 & 2.89 & 3110 & 3.14 & 1.63 \\ Fly ash & 49.10 & 36.70 & 4.96 & 3.67 & 0.37 & 3871 & 2.21 & 2.08 \\ Silica fume & 95.28 & 0.28 & 0.35 & 0.14 & 0.13 & 200,000 & 2.20 & 1.40 \\ \hline \hline \end{tabular}  
* Continuous graded natural river sand was applied as NWC and SLWC fine aggregate. The properties of the aggregates used for the LWCs are presented in Table 2. Two different steel fiber types were used—i.e., hooked end and crimped shape steel fibers. The characteristics of the steel fibers are presented in Table 3. The LWAs and the steel fibers used in the LWC and SFLWC are shown in Figure 1.  
\end{table}
Table 1: Properties of the pozzolanic materials.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Aggregate**} & \multirow{2}{*}{**Fineness Modulus**} & \multirow{2}{*}{**Particle Size/(mm)**} & \multirow{2}{*}{**Apparent Density/(Kg/m3)**} & \multirow{2}{*}{**Water Absorption in 24 h/(\%)**} \\  & & & & & \\ \hline Crushed shale ceramicsite & - & 5–20 & 1390 & 7.4 \\ Shale ceramicsite sand & 3.2 & \(\leq\)5 & 1460 & 20.8 \\ Sintered fly ash & - & 4–16 & 1420 & 11.3 \\ ceramsite & 2.7 & \(\leq\)5 & 2670 & 1.2 \\ \hline \hline \end{tabular}  
\end{table}
Table 2: Properties of the aggregates used for lightweight concretes (LWCs).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Shape**} & \multirow{2}{*}{**Length/(mm)**} & \multirow{2}{*}{**Equivalent Diameter/(mm)**} & \multirow{2}{*}{**Aspect Ratio Strength/(MPa)**} & \multirow{2}{*}{**Density/(kg/m3)**} \\  & & & & & \\ \hline Hooked end & 35 & 0.50 & 70 & 1200 & 7800 \\ \hline Crimped & 30 & 0.55 & 56 & 800 & 7800 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Characteristics of the steel fibers.
## 4 Analysis of the Experiment Data
### Vertical Structure Change in Channel
Hence, their energy contributions to the channel were negligible. It is worth mentioning that, due to the fluctuation of the sea surface, S may suffer severe time variance. Sometimes the ratio of S is smaller than the threshold and its energy contribution to the channel is negligible. Figure 9 shows an example where the time variance of S was more obvious than that of other clusters. During the first 32 s, the amplitude of S was very small, making the ratio smaller than the threshold. But at 48 s and 160 s, the amplitude was on the same level as the maximum cluster. Figure 8: (**a**) Detail of B and SB. (**b**) Detail of D and S where the depth is greater than 3405 m.  
Figure 7: (**a**) Experimental and (**b**) simulated CIR pseudo-color maps at different receiving depths at a distance of 14.8 km (D = direct; S = surface; B = bottom; SB = surface-bottom; BS = bottom-surface; SBS = surface–bottom–surface). The above analysis clarifies different structural characteristics in the CIRs of the shadow and direct-arrival zones. In the shadow zone, S was affected by random fluctuations of the sea surface and experienced serious energy loss at large grazing angles. In some instances, the energy of S dropped to negligible levels. At most depths, the path clusters that contributed to the channel were B, SB, BS, SBS, and possibly S. But where the depth was greater than 2605 m, the energy of BS and SBS dropped down to a level that made them negligible. The path clusters in the direct-arrival zone were D, S, B, and SB. Because D was affected only by water absorption during propagation, its energy was higher than that of the other clusters. The amplitudes of BS and SBS, with large transmission losses, violated Equation (2), and their energy contributions to the channel were negligible.
## 6 Sixth-Generation (6G) Testbeds and Platforms
### Experimental Platforms for Sixth-Generation (6G) Communication Channels  
Channel characterization, modeling, and measurements are foundational for system design, performance evaluation, and theoretical analysis in communication systems. New frequency bands, scenarios, and technologies in 6G lead to novel channel characteristics. Small-scale fading (multipath fading) and large-scale fading (path loss and shadowing) play critical roles in wireless networking and transmission. The evolution of communication systems prompts the need for applicable channel testbeds. Current testbeds are categorized into hardware channel sounders and software channel simulators.
# Infrared and Visible Image Fusion: Methods, Datasets, Applications, and Prospects
## 2 Infrared and Visible Image Fusion Methods
### Methods Based on Sparse Representation
(4) Reconstruct fused images The sparse representation coefficients that result can be reconstructed into high-resolution \(MS\) pictures using \(x=D_{h}\alpha\). Figure 6 depicts the flow chart of the sparse representation-based picture fusion algorithm. Although the sparse representation-based image fusion approach can address the issues of low feature information and high registration needs in multi-scale transformation, it is not without drawbacks. Overly comprehensive dictionaries have restricted signal representation capabilities, which results in the loss of texture information in images. The Max-L1 fusion rule lowers the fused image's signal-to-noise ratio and makes it susceptible to random noise. Little blocks overlap in the slip-window technique, which lowers the algorithm's efficiency.
# Hybrid Particle Swarm Optimization Genetic LQR Controller for Active Suspension
## 3 Controller Design
### Full-Order Observer Design  
Constructing the state observer according to the input variables and output variables; therefore, the mathematical model of the full-order state observer [34] can be obtained as (15).  
\[\begin{array}{l}\dot{\hat{X}}(t)=A_{1}\hat{X}(t)+B_{1}W(t)+LC_{1}\big{(}\hat{X} (t)-X(t)\big{)}\\ =(A_{1}+LC_{1})\hat{X}(t)+B_{1}W(t)-LY(t)\end{array} \tag{15}\]  
where: \(\hat{X}\) is the system state estimation vector. \(L\) is the gain matrix of the observer. According to (12) and (15), the state error dynamic equation can be expressed as (16).  
\[\begin{array}{l}e(t)=\hat{X}(t)-\hat{X}(t)=A_{1}X(t)-B_{1}W(t)-(A_{1}-LC_{1} )\hat{X}(t)\\ -B_{1}W(t)-LC_{1}X(t)\end{array} \tag{16}\]  
After simplification, (17) is obtained.  
\[\begin{array}{l}e(t)=\hat{X}(t)-\hat{\hat{X}}(t)=(A-LC)\big{(}X(t)-\hat{X}(t )\big{)}\end{array} \tag{17}\]  
Solving the equation yields:  
\[\begin{array}{l}X(t)-\hat{X}(t)=e^{(A-LC)}\big{(}X(t_{0})-\hat{X}(t_{0}) \big{)}\end{array} \tag{18}\]  
\((A-LC)\) is the system matrix of the observer. The key to the observer design is whether \(\lim\limits_{t\to\infty}X(t)\rightarrow\hat{X}(t)\to 0\) can always be guaranteed under any initial conditions. In other words, if the eigenvalues of \((A-LC)\) are arbitrarily configurable, and the eigenvalues of the matrix \((A-LC)\) could be properly configured, then the state of the observer would quickly approach the state of the system.  
A sufficient necessary condition for all poles of the system or eigenvalues that are arbitrarily configurable is that \((A,C)\) is fully controllable. In other words, the eigenvalues of the matrix \((A-LC)\) have negative real parts, or \(rank\bigg{(}\begin{array}{l}A\\ AC\end{array}\bigg{)}=n\).  
In the control system proposed in this paper, matrix \(\bigg{(}\begin{array}{l}A\\ AC\end{array}\bigg{)}\) can be obtained, and \(rank\bigg{(}\begin{array}{l}A\\ AC\end{array}\bigg{)}=n\). Thus, the control system proposed is observable, and poles of the system are arbitrarily configurable.  
For systems with arbitrary configurations of poles, different poles affect the time for the observer to stabilize, but have no effect on the final state of the observer [35].  
However, when all the real parts of the eigenvalues are strictly negative, then the system is stable.  
Let eigenvalues \(\lambda_{1}=\lambda_{2}=-6\). \(L=\begin{bmatrix}l_{0}\\ l_{1}\end{bmatrix}\). The characteristic equation of the observer can be expressed as follows.  
\[\begin{array}{l}det(\lambda I-A-AC)=0\end{array} \tag{19}\]  
Then. \(l_{0}\) and \(l_{1}\) can be obtained. Therefore, the mathematical model of the full-order state observer can be obtained.  
Figure 5: LQR Control with Particle Swarm Optimization Genetic Hybrid Algorithm Flow Chart.
# Artificial Intelligence in Biomaterials: A Comprehensive Review
## 1 Introduction
### Unsupervised Learning (UL)  
UL is a branch of ML employed when the data under analysis are unlabeled, meaning that the classes or target values are unknown. In unsupervised learning, only the input data are provided during the training phase, and the goal is to identify hidden patterns or structures within these data. This method seeks to uncover intrinsic relationships within the data without the guidance of pre-existing labels shown in Figure 3.  
Figure 2: Supervised learning.  
1. Clustering: Clustering is one of the primary techniques in UL, where the algorithm groups similar data points into clusters. The goal is to ensure that data points within the same cluster are more alike to each other than to those in different clusters. This technique is widely used in market segmentation, social network analysis, and bioinformatics [24]. Common clustering algorithms include: * K-means clustering: Partitions the data into \(k\) clusters by minimizing the variance within each cluster [25]. * Hierarchical clustering: Builds a tree of clusters through either a bottom-up (agglomerative) or top-down (divisive) approach [26].
2. Dimensionality reduction: This method decreases the number of random variables by identifying a set of principal variables. Dimensionality reduction is essential for managing high-dimensional data, enhancing computational efficiency, and boosting model performance. Techniques include: * Principal component analysis (PCA): Identifies the directions (principal components) in which the data varies the most [27]. * t-Distributed stochastic neighbor embedding (t-SNE): A non-linear method for dimensionality reduction that is especially useful for visualizing high-dimensional datasets [28].
3. Association rules: This method is used to find interesting relationships or associations among variables in large databases. Association rules are widely used in market basket analysis to identify items that frequently co-occur in transactions [29].  
One of the primary challenges in unsupervised learning is the lack of ground truth, making it difficult to evaluate the model's performance objectively. Unlike supervised learning, where accuracy can be directly measured against known labels, unsupervised learning often relies on indirect metrics such as cluster cohesion and separation or the interpretability of reduced dimensions [30]. Another consideration is the choice of the algorithm and the number of clusters in clustering algorithms, which often requires domain knowledge and experimentation to determine the most meaningful grouping [31].  
UL is a powerful tool for discovering hidden patterns and structures within unlabeled data. By employing techniques such as clustering, dimensionality reduction, and associa  
Figure 3: Unsupervised learning.  
tion rule learning, unsupervised learning algorithms can reveal valuable insights and drive decision making in various domains. Despite the challenges associated with evaluation and algorithm selection, unsupervised learning remains a crucial component of the machine learning landscape.
###### Abstract  
For this study, we demonstrated three different types of twisted nematic (TN) liquid crystal (LC) terahertz (THz) phase shifters using pristine poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT: PSS) thin films as transparent conducting electrodes (TCEs). The transmittance of spin-coated pristine PEDOT: PSS thin film was as high as 92% in the frequency range of 0.2-1.2 THz. This is among the highest reported. Several TN-LC cells were constructed in a comparative study, which confirmed the reliability of pristine PEDOT: PSS as a TCE layer for THz phase shifter applications. The highest phase shift, required root-mean-square (RMS) driving voltage, and threshold voltage achieved by devices tested were 95.2\({}^{\circ}\) at 1 THz, 7.2 V\({}_{\text{RMS}}\), and 0.5 V\({}_{\text{RMS}}\), respectively. The thickness of the LC layer for the phase shifter was 250 um, approximately half as thick as previous designs. In addition, the pristine PEDOT: PSS-based TN-LC phase shifter exhibited a figure-of-merit (FOM) value of approximately 6.65 degree-dB\({}^{-1}\).V\({}^{-1}\). This compared favorably with previously reported homogeneously aligned phase shifters with an FOM of 2.19 degree-dB\({}^{-1}\).V\({}^{-1}\). Our results indicated that a twisted nematic LC cell with pristine PEDOT: PSS thin films as electrodes is a good combination for a THz phase shifter and wave plates as well as other LC-based THz devices.  
liquid crystals; LC; twisted nematic; TN; phase shifter; PEDOT: PSS; transparent conducting electrode; TCE; terahertz; THz +
Footnote †: journal: Applied Physics Letters
## 3 Methods
### Virtual Image Generation
#### 3.2.2 Two-Stage CGAN Model
As the neighboring pixels in the remote sensing images are more similar than distant pixels, PatchGAN was applied to determine whether they are real or fake with an appropriate patch size that maintains the autocorrelation between the pixels. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Down-Sampling**} & \multicolumn{2}{c}{**Up-Sampling**} \\ \hline \multirow{2}{*}{**Layers**} & **Output Dimension** & \multirow{2}{*}{**Layers**} & **Output Dimension** \\  & **(P,P,F)** & & **(P,P,F)** \\ \hline Conv, L & (256,256,64) & DeConv, BN, D, R & (2,2,1024) \\ Conv, BN, L & (128,128,128) & DeConv, BN, D, R & (4,4,1024) \\ Conv, BN, L & (64,64,256) & DeConv, BN, D, R & (8,8,1024) \\ Conv, BN, L & (32,32,512) & DeConv, BN, D, R & (16,16,1024) \\ Conv, BN, L & (16,16,512) & DeConv, BN, D, R & (32,32,1024) \\ Conv, BN, L & (8,8,512) & DeConv, BN, D, R & (64,64,512) \\ Conv, BN, L & (4,4,512) & DeConv, BN, D, R & (128,128,256) \\ Conv, BN, L & (2,2,512) & DeConv, BN, D, R & (256,256,128) \\ Conv, R & (1,1,512) & DeConv, R & (512,512,9) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: List of the structures and hyperparameters of the U-Net-based generator (P: patch size; F: number of features; Conv: convolution; BN: batch normalization; L: leaky ReLU; Deconv: transposed convolution and cropping; D: dropout; R: ReLU). Figure 9: Pix2 Pix architecture employed in the generation stage. The discriminator includes convolution, batch normalization, and activation function layers to extract local features from the input image. The Sentinel-2-based features extracted through the convolution operation were utilized as inputs for the discriminator. Both the kernel size and strides of the convolution layer were set to 1 to maintain the size of the input patch. A sigmoid activation function was also applied to distinguish between real (1) and fake (0) (Table 4). Similar to the generator, the features were extracted by reducing the dimension with the kernel size and strides set to 4 and 2, respectively. The virtual Sentinel-2 image was finally created using the Sentinel-1 image acquired on the prediction date as the input for the trained CGAN model.
# Smart Inverters for Microgrid Applications: A Review
## 2 Smart Inverters
### Cooperativeness
#### 2.5.3 Ramp Rate Control  
DGs in microgrids are mostly of a renewable energy nature and it is a well-known fact that these type of energies, specifically wind and solar, suffer fluctuations in their power generation due to climate-caused effects. These power ramp rates are required to be controlled in a defined boundary in order to reduce the risk of possible damage occurrences when for example the clouds suddenly clear out over a large solar power station. The concept of ramp rate control is simple and can be represented by a simple equation for a solar panel [122; 123] as in Equation (9):  
\[R(t)=\frac{dP_{PV}}{dt}=\frac{\Delta P}{\Delta T}, \tag{9}\]  
where \(R(t)\) is the instant ramp rate, and \(P_{PV}\) is the instant power generated by the panel. When the ramp rate exceeds a defined value, the output power requires to be regulated. Incorporating energy storage units such as batteries, flywheels and supercapacitors or reliable energy generation units such as fuel cells are the common methods to compensate for these fluctuations and smoothen the generation [124; 125; 126; 127]. Using Equation (10), the energy storage capacity required for ramp rate control according to the power rating/perimeter scale of a solar field and the maximum acceptable ramp rate value can be worked out [128; 129]:  
\[C_{ESS}=\frac{1.8\ P^{*}}{3600}\left[\frac{90}{2\ R_{max}}-\tau\right], \tag{10}\]  
\[\tau=a\cdot l+b, \tag{11}\]  
where \(C_{ESS}\) is the capacity of the energy storage unit, \(R_{max}\) is the maximum ramp rate and \(P^{*}\) is the power of the solar field. Equation (11) represents the time constant \(\tau\), which is an empirical correlation with the shortest measured length of the solar field perimeter, \(l\). (\(a=0.042\ \mathrm{s/m}\) and \(b=-0.5\ \mathrm{s}\)). In [124] implementation of electric double layer capacitor (EDLC) for smoothening the generation of PV panels alongside with the size consideration of the capacitor and the utilization of a fuel cell to compensate for slow generation fluctuations have been discussed. Although most of the ramp rate control methods are based on using energy storage devices, there are other possibilities. For example, In [122], by modifying the MPPT algorithms, which renewable energy systems functions are based on, the ramp rate can be controlled by deviating from the set point of the MPPT when required, to balance the output power.