esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Semi-Active Vibration Control of Pipeline Based on Magneto-Rheological Damper
## 5 Results and Discussions  
The RMS of displacement and acceleration of the controlled and without controlled pipeline are shown in Figure 17. It can be seen that, all three control methods can effectively control pipeline vibration. When the vibration frequency is 1 Hz, 2 Hz, 5 Hz and 8 Hz, the control effect of PID on displacement and acceleration is better than the other two control methods. When the vibration frequency is 3 Hz, 4 Hz, 9 Hz and 10 Hz, the control effect of SMVS on displacement and acceleration is better than the other two control methods.  
The vibration characteristics such as level drop and vibration attenuation rates of the different pipeline vibration control modes are shown in Figure 18. It can be seen that, the three pipeline vibration control methods have better control effects on displacement than acceleration. To comprehensively evaluate the control effect of each control mode on pipeline vibration, the average values of the vibration level difference of the three control modes are shown in Table 5.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Frequency** (Hz)} & \multicolumn{2}{c}{**Displacement**} & \multicolumn{3}{c}{**Acceleration**} \\ \cline{2-7}  & **RMS (mm)** & **Difference (dB)** & **Attenuation** & **RMS (m/s\({}^{2}\))** & **Difference (dB)** & **Attenuation** \\ \hline
1 & 0.2301 & 15.0918 & 82.40\% & 0.1494 & 1.2689 & 13.59\% \\
2 & 0.2566 & 15.1423 & 82.51\% & 0.3082 & 5.4709 & 46.73\% \\
3 & 0.3149 & 13.8117 & 79.61\% & 0.6772 & 4.2198 & 38.48\% \\
4 & 0.2318 & 16.7328 & 85.43\% & 0.7179 & 7.5665 & 58.15\% \\
5 & 0.2719 & 15.7531 & 83.69\% & 0.9828 & 8.2203 & 61.19\% \\
6 & 0.2561 & 16.3829 & 84.83\% & 1.1387 & 9.2626 & 65.58\% \\
7 & 0.2771 & 16.0620 & 84.26\% & 1.3910 & 9.7854 & 67.59\% \\
8 & 0.2688 & 15.6429 & 83.49\% & 0.9102 & 14.4325 & 81.02\% \\
9 & 0.1951 & 17.9017 & 87.27\% & 2.2343 & 7.8643 & 59.56\% \\
10 & 0.4714 & 10.2215 & 69.17\% & 2.6675 & 6.7534 & 54.05\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Vibration response of pipeline under SMVS control.  
Figure 17: RMS of displacement and acceleration: (**a**) Displacement; (**b**) Acceleration.  
The statistical results (shown in Table 5) exhibit that all three semi-active vibration control methods can effectively reduce pipeline vibration, and the simulation results are in basic agreement with the experimental results. SMVS vibration control mode has the best suppression effect on the amplitude and acceleration of pipeline vibration, while PID control is only inferior to SMVS control in the effect of pipeline vibration control.
## 4 Application to Gaussian Graphical Models
### Junction Tree Representation
Since the GW density in Equation (12) has a similar form to the HIW density, the marginal likelihood calculation also factorizes over prime components and separators. The difference from the previous calculation is that for decomposable \(G\), the product in Equation (21) is taken over the cliques \(\mathscr{C}\), whereas in the GW case, the product is taken over prime components \(\mathscr{P}\), which may or may not be complete. Since normalizing constants for the non-complete prime components are generally intractable, they typically require MCMC estimation. Essentially, the factorized representation of the marginal likelihood induced by the junction tree representation of \(G\) splits the original calculation involving the entire graph \(G\) into sub-problems involving the prime components and separators. When \(P\in\mathscr{P}\) is complete, we can rely on the closed form IW normalizing constant. For non-complete \(P\ \in\ \mathscr{P}\), we take the corresponding parameters, \(\Sigma_{p}^{-1}=\Omega_{p},B_{P},\Lambda_{p}\), and write the normalizing constant for the non-complete prime component as  
\[h(P,\delta,\Lambda_{P})=\int|\Omega_{p}|^{\frac{\delta-2}{2}}\exp(-\operatorname {tr}(\Omega_{P}\Lambda_{P})/2)d\Omega_{P}.\]  
Although this quantity remains intractable, it poses a lower dimensional problem for which we can use the procedure outlined in Section 4.2 along with the EPSOM-Hyb algorithm. The posterior normalizing constant \(h(P,\delta+n,\Lambda_{P}+B_{P})\) can be similarly computed. After computing the normalizing constants for each of the prime components and separators, the individual approximations are summed together to form the log normalizing constant approximation corresponding to the original graph \(G\). These steps make up the EPSOM-HybJT algorithm, which is outlined in Algorithm 2.
# Image Structure-Induced Semantic Pyramid Network for Inpainting
## 1 Introduction
To solve these problems, a context-aware image inpainting model based on edge and semantic pyramids is presented here. Specifically, similar to the state-of-the-art two-stage inpainting strategy, the proposed method is classified into two parts: an edge inpainting network and a content-filling network. Moreover, the edge inpainting result is used to constrain the image texture synthesis. In the edge inpainting network, the residual U-Net network combined with convolution and jump connection is used as the edge generator to strengthen the consistency of the contextual structure and alleviate the problem of missing edges in missing areas. In the content-filling network, U-Net is used as the image generator to generate the texture under the constraint of the edge inpainting graph. In addition, based on the complementarity between deep adjacent features, a cross-layer attention transfer model (ATM) is introduced and applied to all coding layers of the image generator to ensure the context consistency of the decoding features at all levels to the maximum extent. The skip connection in the edge inpainting network improves the context structural connectivity, and the content-filling network is guided by the edge inpainting graph to fill smooth and homogeneous texture information in different regions composed of edge information. The application of ATM reduces the loss of structural information and texture details in the inpainting process. The proposed method can generate high-quality recovery results with complete semantics and coherent structures. Specifically, the main work of this article is as described below:  
1. A semantic pyramid inpainting network induced by image structure is proposed in this study. The edge inpainting map generated by the residual U-Net is input into the pyramid content-filling network together with the defective image as a prior condition, and the final inpainted image is obtained through the encoder-decoder process. 2. The ATM module transfers the similarity of feature blocks inside and outside the missing region of the high-level feature map to the lower-level feature map and fills the missing area at the feature level based on this. 3. Experiments were conducted on multiple standard datasets, and qualitative and quantitative comparisons showed that when the restoration task involved large areas of missing or complex structures, the proposed method had higher restoration quality than the existing mainstream methods.
# Real-Time DTM Generation with Sequential Estimation and OptD Method
## 2 Materials and Methods
### Data Reduction with the OptD Method
The percentage of points that should remain in the dataset after reduction is determined. Then, the acquired fragment of the dataset is loaded. This fragment contains points that, in the X0Y or Y0X plane (depending on the measurement direction), define the end of the dataset;  
**FOR loop execution**: steps 2, 3, and 4 are performed within a loop that iterates over each measuring strip until all strips are processed:
2. **Projection and preprocessing**. To maintain the spatial characteristics of the points, the data are projected onto the X0Z or Y0Z plane instead of the X0Y plane;
3. **Generalization using the Douglas-Peucker algorithm**[34]. The algorithm simplifies a line by reducing the number of points while preserving its overall shape. It connects the start and end points of a segment and calculates the perpendicular distance of all intermediate points. Points with a distance (d) below a predefined tolerance (t) are removed, as they do not significantly impact the line's geometry. This process is recursively repeated until no more points can be removed without exceeding the tolerance;
4. **Iterative processing**. A reduced dataset representing the loaded measuring strip is obtained. While the reduction is in progress, the next measuring strip is already being acquired. Once it reaches the required width (determined by the user and measurement type), it enters the OptD algorithm for reduction;
5. **Final data output**. The process repeats until the last acquired measuring strip undergoes reduction, ensuring continuous and efficient data reduction while maintaining essential structural details. This stepwise approach optimizes measurement data storage and processing while preserving key geometric features.
# Efficient Wireless Monitoring and Control of a Grid-Connected Photovoltaic System
## 5 Experimental Verification  
Figure 11 presents the experimental platform of the grid-connected PV system in which the experiments have been conducted. Part number 1 is the WCC, and number 2 consists of the boost converter and the sensors. Part number 3 includes the network nodes from the DC/DC converter and the DC/AC converter. Part number 4 is the inverter. Finally, part number 5 presents the connection of the DC/AC converter to the electrical network. As it can also be seen, a monitoring application is designed to visualize the sensor values and the control parameters to verify if the system is working properly. The sensors used to measure the currents and voltages are LEM sensors. For the voltage, LV25-P sensors are used whereas for the current the sensors are LA25-NP. The temperature is measured by an LM35 sensor and the irradiance is measured with a commercial irradiance meter.
###### Abstract  
The internal vertebral venous plexus (IVVP) is a valeveless venous network running inside the vertebral canal. The objective of this study was to morphometrically describe the IVVP, dural sac, epidural space and vertebral canal of the lumbar segment in dogs with enhanced computerized tomography. Six clinically healthy adult dogs were used for the study. Dorsal reconstructed computed tomography (CT) images showed a continuous rhomboidal morphological pattern for the IVVP. The dural sac was observed as an isodense structure with a rounded shape throughout the vertebral canal. The average percentage area occupied by the IVVP between L1 and L7 vertebrae ranged between 6.3% and 8.9% of the area of the vertebral canal, and the dural sac ranged between 13.8% and 72.2% of the vertebral canal. The epidural space accounted between 27.08% and 86.2% of the lumbar vertebral canal. CT venography is a safe technique that allows adequate visualization and evaluation of the lumbar IVVP and adjacent structures in dogs.  
The internal vertebral venous plexus (IVVP) is a thin-walled, valeveless venous network that is located inside the vertebral canal, communicating with the cerebral venous sinuses. The objective of this study was to perform a morphometric analysis of the IVVP, dural sac, epidural space and vertebral canal between the L1 and L7 vertebrae with contrast-enhanced computed tomography (CT). Six clinically healthy adult dogs weighing between 12 kg to 28 kg were used in the study. The CT venographic protocol consisted of a manual injection of 880 mgl/kg of contrast agent (587 mgl/kg in a bolus and 293 mgl/mL by continuous infusion). In all CT images, the dimensions of the IVVP, dural sac, and vertebral canal were collected. Dorsal reconstruction CT images showed a continuous rhomboidal morphological pattern for the IVVP. The dural sac was observed as a rounded isodense structure throughout the vertebral canal. The average area of the IVVP ranged from 0.61 to 0.74 mm\({}^{2}\) between L1 and L7 vertebrae (6.3-8.9% of the vertebral canal), and the area of the dural sac was between 1.22 and 7.42 mm\({}^{2}\) (13.8-72.2% of the vertebral canal). The area of the epidural space between L1 and L7 ranged from 2.85 to 7.78 mm\({}^{2}\) (27.8-86.2% of the vertebral canal). This CT venography protocol is a safe method that allows adequate visualization and morphometric evaluation of the IVVP and adjacent structures.  
internal vertebral venous plexus; computed tomography; canine
## 3 Results and Discussion
### Bio-Medical Potential of CPL-Rx  
#### 3.5.1 Inhibition of Pro-Inflammatory Enzymes  
Crude polysaccharides from other _R. rugosa_ organs were previously reported to have anti-inflammatory potential [12; 33]. Therefore, we decided to examine CPL-Rx in terms of its influence on the activity of pro-inflammatory enzymes--cyclooxygenenses (COX-1 and COX-2) and hyaluronidase (HYAL).  
Several concentrations (0.25-5 mg/mL) were tested to evaluate the COX-1 and COX-2 inhibitory potential (Table 3). The CPL-Rx activity was compared with acetylsalicylic acid, which is commonly used in pharmacotherapy as a non-selective COX inhibitor. It was observed that the rose root crude polysaccharide influenced both COX isoenzymes in a dose-dependent manner. However, a stronger impact on COX-2 was shown, particularly at the highest concentrations tested, where 45.64 to 71.98% of COX-2 inhibition was observed. COX-1 was much less affected by CPL-Rx. At the highest concentrations tested, the COX-1 activity decreased by 18.91-23.26%. In terms of selectivity and potency, CPL-Rx was found to be similar to the crude polysaccharide from rose leaves [12].  
Hyaluronidase is responsible for degradation of hyaluronic acid; therefore, it is associated with the proper functioning of connective tissue and the course of inflammatory and other physiological and pathological processes (e.g., invasion or metastasis). The HYAL inhibitory assay revealed strong anti-hyaluronidase potential of CPL-Rx. Its IC\({}_{50}\) value (concentration that reduced the activity of the enzyme by 50%) was 335.23 \(\pm\) 4.16 ug/mL, which was even lower than the value previously reported for other highly active rose polysaccharides [12]. Moreover, the CPL-Rx activity was comparable to the potential of EGCG (IC\({}_{50}\) = 280.50 \(\pm\) 3.52 ug/mL). This result suggests the possibility of the use of CPL-Rx as an anti-hyaluronidase agent for pharmaceutical or cosmetic purposes and encourages further research, e.g., on in vivo models.
# A Reconfigurable Radio-Frequency Converter IC in 0.18 \(\upmu\)m CMOS
## 3 System Architecture
### Mixers  
The system in this work includes two double-balanced mixers based on the Gilbert cell in which the input RF signals are fed through the NMOS sources to achieve a better impedance matching and improve their linearity, given that fewer inter-modulation products at high input power are introduced.  
The mixers accept input signals in their RF/IF inputs in a frequency range from 50 MHz to 2700 MHz and they use the internal oscillator for their LO input in a frequency range from 330 MHz to 2700 MHz. Also, their voltage supply is done by non-regulated chokes at 3 V from the PMOS drains through external 200 \(\Omega\) coupling resistors through a 4:1 balun.  
The RF outputs of the mixers are the drains of the MOS transistors, and they must be directly coupled to the load in these terminals by a load resistor and a 4:1 balun, or by two resistors directly connected to \(V_{dd}\). They can feed a load impedance of up to 200 \(\Omega\), but it has to be noticed that an increase in the load results in an increase of the conversion gain and a decrease of the linearity.  
The polarization of each mixer can be programmed internally to operate with a current of 4.8 mA (low-power mode) or 12 mA (high-linearity mode), depending on the requirements of power and  
Figure 5: (**a**) Block diagram of the integer-N PLL with a detail of the divide-by-N based on a \(M/M+1\) program-swallow counter and (**b**) circuit diagram of the LC-VCO with a detail of the elements of the 7-level switched capacitor bank.  
also of input impedance, which is maximum in the low-power mode and minimum when in the high-linearity mode. A circuit diagram of the mixers is shown in Figure 6.
# FPGA Implementation of ECT Digital System for Imaging Conductive Materials
## 5 System Implementation  
The implementation process of the proposed system can be broken down into three steps: 1. Choosing and configuring of the hardware by the Qsys; 2. Writing the hardware description files using Quartus; 3. Functioning the software processor, after deploying the hardware files on the FPGA board, to be united with the program running on the FPGA. Therefore, the hardware and software implementation phases are crucial. In the hardware implementation phase, the following customized modules have to be designed: SDRAM memory, VGA peripheral, processing unit, and an Ethernet interface. All the hardware components are connected by an AX14 bus, whereas the softwarephase is the deployment of the image reconstruction algorithm on the embedded software ARM processor.  
The FPGA platform has two inputs: the sensitivity matrix stored on the SDRAM and the capacitance measurements collected wirelessly and passed to the FPGA through the WBS [29]. The software processor is responsible for taking these two inputs and applies the ILBP algorithm to reconstruct distribution images. The VGA peripheral takes the final images to display it and store it on the SDRAM. The frequency of reading the SDRAM by the VGA controller is 25 MHz. The VGA controller and other hardware components are coded using HDL while the code of the ILBP algorithm is implemented by embedded C language. The proposed system is shown as a block diagram in Figure 4.  
The HDL Workflow Advisor of the HDL Coder is used in our work for simulation and synthesis. The synthesis process is generating a netlist as an HDL file that is used by the software processor. The design has to be tested and verified before the synthesis process. After the verification of the final design, a binary file holding all information for configuring the FPGA board will be generated. Furthermore, the Qsys generates files which are used by the Quartus, while the Quartus writes the responsible top-level module to begin the desired task and calling other modules.
# Interference Mitigation Strategies in Beyond 5G Wireless Systems: A Review
## 5 Critical Analysis and Open Research Challenges
### Future Research Directions
#### 5.8.4 Lightweight AI Models [245; 246]  
Design simplified yet effective AI models (e.g., tiny ML) tailored for deployment on resource-constrained devices such as edge nodes and IoT gateways.
# Application of Machine Learning for Fault Classification and Location in a Radial Distribution Grid
## 4 Case Studies, Results, and Discussion
### Balanced Load (Scenario I)
#### 4.1.1 Fault Detection and Classification  
885 samples for each type of fault and 526 non-fault conditions were collected. These features were fed into ANN for fault detection and classification. The results are shown in Table 2. The detection is represented by the no-fault condition. The diagonal elements of the confusion matrix show the successful classifications while the off-diagonal elements represent those that are unsuccessful. It can be seen that the detection (no fault) and all types of faults are classified with 100% accuracy.
## 3 Preliminaries
### Homomorphic Proxy Re-Encryption (HPRE)  
[PERSON], [PERSON] and [PERSON] first proposed proxy re-encryption (PRE) [31] at EUROCRYPT 1998. The proxy can convert the ciphertext encrypted by the data owner into another ciphertext that can be decrypted by the specified data receiver. Homomorphic proxy re-encryption was introduced in [25], which combines the homomorphic property of HE and the \"key-switching\" property of PRE.  
The following describes our homomorphic proxy re-encryption method, which consists of six algorithms: \(PRE=(Setup,\textit{KeyGen},\textit{Enc},\textit{ReKeyGen},\textit{ReEnc}, \textit{Dec})\). It is based on the additive homomorphic encryption above, and the details are as follows:  
* \(Setup(\lambda)\to pp\): The security parameter \(\lambda\) is taken as input. \(HE.Setup(\lambda)\to pp\) is run to generate \(pp=(N,g)\). Then, the output \(pp=(N,g)\) is obtained.
* \(KeyGen(pp)\rightarrow((pk_{i},sk_{i}),(pk_{j},sk_{j}))\): The public parameter \(pp\) is taken as input. \(HE.KeyGen(pp)\rightarrow(pk,sk)\) is run twice to generate \((pk_{i}=g^{a}\ mod\ N^{2},sk_{i}=a)\), \((pk_{j}=g^{b}\ mod\ N^{2},sk_{j}=b)\), which stands for the public/secret key pair of the data owner and data receiver, respectively. Then, the output \(((pk_{i},sk_{i}),(pk_{j},sk_{j}))\) is obtained.
* \(Enc(m,pk_{i})\to c_{i}\): For a message \(m\in\mathbb{Z}_{N}\), the data owner runs \(HE.Enc(m,pk_{i})\to c_{i}\) to output the ciphertext \(c_{i}=(A_{i},B_{i})\), where \(A_{i}=g^{ar}\ mod\ N^{2},\ B_{i}=g^{r}(1+mN)\ mod\ N^{2}\). Then, the output is \(c_{i}\).
* \(ReKeyGen(sk_{i},sk_{j})\to rk_{i\to j}\): The data owner's secret key \(sk_{i}\) and the data receiver's secret key \(sk_{j}\) are taken as input, and the re-encryption key \(rk_{i\to j}=\frac{sk_{j}}{sk_{i}}=\frac{b}{a}\) is the output.
* \(ReEnc(rk_{i\to j},c_{i})\to c_{j}\): The re-encryption key \(rk_{i\to j}\) and the ciphertext \(c_{i}\) are taken as inputs, and the re-encryption ciphertext \(c_{j}\) is obtained as the output, where \[c_{j} = (A_{j},B_{j})=(A_{i}^{rk_{i\to j}},B_{i})\] (6) \[= (g^{ar}\frac{b}{a},\ g^{r}(1+mN)\ )\ mod\ N^{2}\] \[= (g^{br},\ g^{r}(1+mN))\ mod\ N^{2}\] \[\rightarrow Enc(m,pk_{j}).\]
* \(Dec(c_{j},sk_{j})\to m\): The ciphertext \(c_{j}=(A_{j},B_{j})=(g^{br},g^{r}(1+mN))\) is decrypted with the data receiver's secret key \(sk_{j}=b\), and the output is \(m=\frac{B_{j}/(A_{j}^{\frac{1}{b}})-1\ mod\ N^{2}}{N}\).
# Parameterized Algorithms in Bioinformatics: An Overview
## 2 Genome Comparison and Completion
### Genomic Distances
#### 2.1.1 Double-Cut and Join Distance  
**Problem Description.** The double-cut and join distance needs the most general genome model as intermediary steps, that is, a multi-set of strings and circular strings. A _double-cut and join_ operation splits the genome in two positions and joins the four created endpoints in any way (see Figure 1). For signed genomes, this operation is required to maintain consistent orientation of each gene. We focus on the case where the source and target genomes have a single chromosome (i.e. strings or permutations).  
**Results.** The DCJ distance can be computed in linear time in the signed permutations model [9; 10]. However, it is NP-hard in the unsigned permutation or string models [11]. On the positive side, it admits an \(O(2^{2k}n)\)-time algorithm for unsigned permutations [12].  
An issue with the DCJ distance is that the solution space might be very large, as many different scenarios may yield the same distance. Thus, more precise models have been designed to \"focus\" the solution towards the most realistic scenarios. [PERSON] et al. [13] introduced the _wDCJ_ distance, where intergene distances are added to the genome model and must be accounted for in the DCJ scenario (when breaking between consecutive genes, the number of intergenomic bases must be shared among both sides). This variant makes the problem NP-hard for signed permutations, but fixed-parameter tractable for the parameter \(k\). Another constraint focuses on common intervals, which are segments of the genomes that have exactly the same gene content, but in different orders. A DCJ scenario is _perfect_ if it never breaks a common interval. [PERSON] et al. [14] showed that finding a perfect scenario with a minimum number of operations is FPT for a parameter given by the common interval structure.