esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis and Modeling of Mueller-Muller Clock and Data Recovery Circuits
## 3 Small Signal Model of MM-CDR
### Linearized Analysis of CDR System  
Based on the linear model of the MMPD in Section 2.2 and the analysis of the digital CDR in [8], we can obtain the small-signal model of the MM-CDR. In this section, according to the proposed architecture in Figure 6, we first present the linearized model (see Figure 7) and then analyze its transfer function and jitter tolerance.  
Figure 5: Simulated decimation by voting and boxcar, (**a**) \(\sigma=0.1\) UI, \(\phi_{m}=0.05\) UI, (**b**) \(\sigma=0.06\) UI, \(\phi_{m}=0.01\) UI.  
Figure 6: The architecture of 56 Gb/s quarter-rate MMPD-based digital CDR.  
Generally, the CDR must meet certain jitter performance specifications. Therefore, we first obtain the loop gain:  
\[G(\mathrm{z}^{-1})=\frac{K_{\mathrm{MD}}K_{\mathrm{V}}K_{\mathrm{DPC}}}{1-z^{-1}} (K_{\mathrm{P}}+\frac{K_{i}}{1-z^{-1}}). \tag{15}\]  
where \(K_{\mathrm{MD}}\) is the gain of MMPD; \(K_{\mathrm{V}}\) is the gain to handle the voting; the values of \(K_{\mathrm{i}}\) and \(K_{\mathrm{P}}\) correspond to the proportional and integral paths from the output of the voting to the digital-to-phase converter (DPC); the element \(K_{\mathrm{DPC}}\) is the gain through DPC. These parameters are listed above in Table 3.  
Then, the transfer function of the MM-CDR can be calculated by the following well-known equation:  
\[H_{\mathrm{JTRAN}}(z)=G(z^{-1})/(1+G(z^{-1})) \tag{16}\]  
The jitter tolerance is the maximum amount of input jitter that can be tolerated by the clock and data recovery circuit at a given bit error rate (BER) [15]. Therefore, the jitter tolerance computed by (17) for a target BER is \(10^{-10}\).  
\[H_{\mathrm{JICOL}}(z)=(1-\frac{12\sigma_{\mathrm{ER}}}{T})(1+G(\mathrm{z}^{-1} )). \tag{17}\]  
where \(\sigma_{ER}\) is the error jitter between recover clock and data, and \(T\) is the data cycle.  
Figures 8 and 9 plot the jitter transfer and jitter tolerance separately. It can be seen that as the jitter increases, the bandwidth of the MM-CDR decreases and the jitter tolerance becomes smaller.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Gain of MMPD (\(K_{\mathrm{MD}}\)) & 16.0, 8.7, 5.9 per UI \\ Gain of vote (\(K_{\mathrm{V}}\)) & \(0.58\times 32=19.2\) \\ Proportional path (\(K_{\mathrm{P}}\)) & 1 \\ Integral path (\(K_{\mathrm{i}}\)) & 2\({}^{-13}\) \\ Phase integrator’s sub-resolution (\(N_{\mathrm{SR}}\)) & 6 \\ Number of phases of the PI (\(N_{\mathrm{P}}\)) & 64 \\ \hline \end{tabular}
\end{table}
Table 3: MM-CDR parameter table.  
Figure 7: The linearized model of Figure 6.
## Nomenclature  
\begin{tabular}{l l} \(\mathcal{A}\) & Permeability parameter \\ \(\mathcal{R}\) & Thermal conductivity, W/mK \\ \(C_{p}\) & Specific heat at constant pressure, J/kg-K \\ \(\overline{\rho}\) & Pressure \\ \(\overline{\mathcal{U}}\) & Axial component of velocity, m/s \\ \(\overline{\mathcal{V}}\) & Normal component of velocity, m/s \\ \(\overline{\mathcal{T}}\) & Local fluid temperature, K \\ \(m_{s},n\) & Shape factors for Cu and Al\({}_{2}\)O\({}_{3}\) nanoparticles \\ \(Pr\) & Prandtl number \\ \(\mathcal{R}\) & Permeation Reynold number \\ \(Nu\) & Local Nusselt number \\ MHD & Magnetohydrodynamic \\ CNT & Carbon nanotube \\ Cu & Copper \\ Al\({}_{2}\)O\({}_{3}\) & Aluminium Oxide \\ H\({}_{2}\)O & Water \\
**Greek Symbols** & \\ \(\Phi\) & Solid volume fraction \\ \(\xi\) & Similarity variable \\ \(\mu\) & Dynamic viscosity, N\(\cdot\)s/m\({}^{2}\) \\ \(v\) & Kinematic viscosity, m\({}^{2}\)/s \\ \(\overline{p}\) & Density, kg/m\({}^{3}\) \\ \(\varepsilon\) & Thermal diffusivity, m\({}^{2}\)/s \\ \(\overline{p}C_{p}\) & Heat capacitance \\ \(\alpha\) & deformation parameter \\ \(\vartheta\) & Dimensionless temperature \\ \(\psi\) & Sphericity \\
**Subscripts** \\ \(lnf\) & Hybrid Nanofluid \\ \(bf\) & Nanofluid \\ \(f\) & Base fluid \\ s1 & Solid nanoparticles of Al\({}_{2}\)O\({}_{3}\) \\ s2 & Solid nanoparticles of Cu \\ \(l\) & Lower \\ \(u\) & Upper \\ \end{tabular}
## 3 Validation  
### Analysis Model  
The results of a general-purpose package (Multiphysics FEM software) were compared with the results obtained by the proposed method for a model with surrounding structures to confirm the validity of the proposed method.  
The analysis model is a cylindrical array of 96 piston transducers arranged in 24 staves surrounded by an acoustic window, as shown in Figure 4. Figure 4a shows the geometry, and Figure 4b shows a FE-BE coupling model used in the proposed method to obtain the acoustic radiation impedance, \(Z_{r,ik}\), and the transfer matrix \(\mathbf{H}\). In Figure 4b, only the ellipsoid-shaped acoustic window is the structural finite element, the fluid 1 between the array and the acoustic window and the fluid 2 outside the acoustic window are the boundary elements. The input boundary condition of the FE-BE coupling analysis is shown in Equation (28). The acoustic radiation impedance, \(Z_{r,ik}\), can be obtained by postprocessing the analyzed surface sound pressure result as Equation (29). Although the transfer matrix \(\mathbf{H}\) can be obtained using Equation (35), it is often difficult to access the system matrices (e.g., \(\mathbf{M}\), \(\mathbf{C}\), \(\mathbf{K}\), \(\mathbf{A}_{1}\), \(\mathbf{B}_{1}\), \(\mathbf{A}_{2}\) and \(\mathbf{B}_{2}\)) of the general-purpose package. In that case, it can be obtained by setting the input boundary condition as Equation (28) and postprocessing the sound pressure result in the point of interest field. For example, the \(k\)th column of the transfer matrix \(\mathbf{H}\) can be obtained by setting the input boundary condition as Equation (28). Figure 4c shows the conventional FE model. In the conventional FE model, an open boundary condition, such as perfectly matched layer (PML), is given to the surface of the sphere that is the external fluid domain. Moreover, each transducer is given a boundary condition that allows it to move in only piston mode without rocking mode because the transducer is designed so that the rocking mode does not occur.  
Figure 4: Cylindrical array surrounded by an oblate spheroid acoustic window: (**a**) Geometry. (**b**) FE-BE coupling model. (**c**) Conventional FE model.  
Figure 5a shows the tonpilz transducer used in the array, and Figure 5b shows the transducer FE model used for the conventional analytical method. The material of the piezo-stack is PZT-4, and that of the tail and stress rod is AISI 4340 steel. The density of the head is 2700 \(\left[\text{kg m}^{-3}\right]\). The material properties and dimensions of the analytical model are given in Tables 1 and 2, respectively. The spacing between transducers is set to tenths of \"the length of one side of the square transducer head surface\".  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Component** & **Property** & **Symbol** & **Value** \\ \hline Piston Head (Rigid) & Density & \(\rho\) & 2700 \(\left[\text{kg}/\text{m}^{3}\right]\) \\ \hline \multirow{6}{*}{PZT Ceramic (PZT-4) Piezoelectric coefficient} & \(\begin{array}{c}\varepsilon_{33}^{E}\\ \varepsilon_{11}^{E}\end{
## 5 Security and Privacy Analysis
### Privacy
The parameter \(\varepsilon\) therefore directly controls the maximum information leakage, with the system adaptively adjusting it based on detected attack patterns and arbitration error rates.
## 4 Experimental Results
### Visual Quality of VQ Images  
VQ compression is a widely applied image compression technique. The major difference in our application is that although the length of codebook is a typical size of 256, only 254 codewords are available during the training process. The remaining two codewords, the head and the rear ones, are dummies and reserved for special usage. Based on this formulation, the quality of VQ approximation is evaluated. We adopt the peak-signal-to-noise-ratio (PSNR) as the performance metric, which is defined by  
\[PSNR=10 log_{10}\frac{255^{2}}{MSE} \tag{2}\]  
Figure 18: Nine standard grayscale test images applied in our experiment. Tank (**a**), Bridge (**b**), Elaine (**c**), Lena (**d**), Peppers (**e**), Wine (**f**), Goldhill (**g**), Bird (**h**), Baboon (**i**).  
and the mean-square-error (MSE) is defined by  
\[MSE=\frac{1}{N\times N}\sum_{i=1}^{N}\sum_{j=1}^{N}\bigl{(}I_{ij}-I^{\prime}_{ij} \bigr{)}^{2} \tag{3}\]  
The notations \(I_{ij}\) and \(I^{\prime}_{ij}\) represent the original image and the VQ-decompressed image, respectively. The PSNR values of the VQ images produced with our specialized codebook are listed in Table 1. The image quality is very close to the conventional VQ compression with a codebook of length 256.
## 3 Autonomous Mapping and Navigation with Satellite Imagery and Cadastral Information
### Second Stage: Path Planning  
This module is used to determine the final path that the robotic system or team of robots should follow. The principal work for path planning in autonomous robotic systems is to search for a collision-free path and to cover all the groves. This section is devoted to describing all the steps necessary to generate the final path (see Algorithm 6) using as input the three masks provided by Algorithm 5.  
To perform the main navigation task, the navigation module has been divided into five different sub-modules:  
* Determining the tree lines
* Determining the reference points
* Determining the intermediate points where the robot should navigate
* Path planning through the intermediate points
* Real navigation through the desired path  
First of all, the tree lines are determined. From these lines and the _Path binary map_, the reference points of the grove are determined. Later, the intermediate points are selected from the reference points. With the intermediate points, a fast path free of collisions is generated to cover all the groves. Finally, the robotic systems perform real navigation using the generated path.  
```
\(SortedTreeLines=\text{DetectLines}\{\textit{Trees},\textit{Path}\}\) \([\textit{Vertexes,MiddlePaths}]=\text{ReferencePoints}[Path,SortedTreeLines]\) \(FinalPath=\text{CalculatePath}[Position,Path,MiddlePath,Vertexes Return:FinalPath
```  
**Algorithm 6** Path Planning[CurrentPosition,Trees,Path]
# A Review on the Recovery and Separation of Gallium and Indium from Waste
## 3 Methods of Recovering Gallium and Indium from Secondary Sources
### Sorption and Ion Exchange  
Similar to solvent extraction, the choice of a sorbent for the recovery of gallium and indium from solutions also depends on its affinity for these metals and its selectivity to their forms present in the solution.
## 4 Conclusions  
In this paper, we are presenting our efforts towards the development of an optical packet router with advanced forwarding, buffering and packet rearrangement capabilities, while the packets remain in the optical domain. Our work is focusing on two subsystems with the first one demonstrating forwarding of optical packets encoded with BF-labels at 10 Gb/s, by utilizing a synergy between an FPGA control plane and a Si based 4 \(\times\) 4 electro-optic switching matrix. The FPGA is performing all intensive logical operations i.e., BF-label extraction, membership checking and control signal generation that drive the MZIs of the 4 \(\times\) 4 Si photonics matrix. The second proposed subsystem is relying on a Si chip with long spiral waveguides acting as integrated delay lines for the demonstration of optical buffering and optical TSI, for 10 Gb/s packets.  
The BF-label packet forwarding concept is demonstrated successfully for 1 \(\times\) 4 routing of incoming optical packets, while the look up forwarding table is updated every four packets. This rapid reconfiguration is extremely useful for fast changes in the network topology usually emerging from the virtualization of the hardware resources. The use of BF-encoded forwarding requires a number of look-up table entries that equals the number of switch outgoing ports, suggesting significant savings in router look-up table memory requirements, while the whole concept can easily be expanded to a larger scale. A single FPGA board can easily control more than 100 Si photonic switches, while large scale Si-pho switches have already been demonstrated in the literature [12; 13; 14; 15; 16].  
The variable optical buffering and optical TSI stage is demonstrated for 10 Gb/s data packets with a Si chip featuring low loss spiral waveguides requiring a footprint of only 13.2 mm\({}^{2}\) and a record-high delay versus footprint efficiency of 2.6 ns/mm\({}^{2}\), while the delay path is defined by converting the incoming packets to the desired wavelength. All basic building blocks employed in our setup, including the SOI waveguide delays [51], the SOA-MZI WCs [19] and the AWGs [55] have been already shown as integrated modules on the same um-SOI platform. Additionally, utilizing the differentially biasing scheme, the SOA-MZI WCs have been successfully demonstrated to operate in cascaded stages [56] with up to 40 Gb/s NRZ data [57]. To this end, the proposed variable optical buffer and TSI circuits can be in principle implemented as integrated systems for contention resolution purposes operating with up to 40 Gb/s NRZ data.  
Figure 13: BER measurements and packet eye diagrams.  
**Acknowledgments:** This research has been partially supported by the EC H2020 project L3 MATRIX (contract number 688544) and EC H2020 project ICT-STREAMS (contract number 688172).  
**Author Contributions:** [PERSON], [PERSON], [PERSON] performed the experiments; [PERSON] programmed the FPGA controller; [PERSON], [PERSON] and [PERSON] prepared the manuscript while [PERSON] reviewed both the results and the manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Combating Pathogens Using Carbon-Fiber Ionizers (CFIs) for Air Purification: A Narrative Review
## 4 Discussion
### Effectiveness of Air Ionization in Comparison with Other Pathogen Mitigation Measures
Small air ions have been shown to efficiently decontaminate air. While generating ions through the use of high voltage, both ions and ozone are formed. Exposure to ozone affects pathogens by causing damage, and in combination with ions, it can lead to their elimination [18, 37]. Some research papers have shown that the combination of ozone and negative air ions (NAIs) has a strong effect on bacterial cell death [28]. Air ion disinfection using NAIs in ventilation ductwork has been studied in Ref. [61], showing that the disinfection efficiency is relatively good, but with a high ozone concentration of 68 ppb at 1.5 m/s velocity of air. Some studies aimed to assess the effect of bipolar ionization in a controlled environment, aiming to mitigate the effect of ozone [99]. A significant disadvantage of this approach is that ozone is harmful for humans, so it can only be used in empty spaces. This also raises questions about the accumulation of harmful particles during human presence while not being able to eliminate airborne pathogens from the breathing zone in a timely manner. Recently, there has been increased interest in investigating the effects of small air ions, produced in a way that generates no ozone or negligible ozone concentrations, on airborne pathogens [110]. Carbon fiber ionizers use low enough voltage efficiently, thus producing ions without ozone formation. The effects of unipolar air ions on bacteria in Ref. [10] included increasing antibacterial efficiency with the increasing concentration of ions. Also, NAIs showed lower efficiency than positive ions. Ionizing air has been shown to affect influenza viruses and prevent airborne transmission [63]. In HVAC systems, research shows that they can be decontaminated using air ions against aerosolized bacteria in a ventilation duct flow [62]. Employing air ionization can inactivate pathogens in indoor spaces, either in combination with or separately from air filtration. It has been shown that it can even bring additional health benefits to humans [111].
## 3 AYOLO Model (Attention YOLO)
### Feature Alignment Module--FAM
#### 3.2.2 Feature Alignment and Fusion  
YOLOv6 uses the Rep-PAN [58] pyramid structure to estimate features at different scales. Rep-PAN requires the repetition of pyramid subsampling, which leads to the loss of boundary details [20]. This can cause the misalignment of contextual feature maps. The direct use of the fusion method makes it difficult to estimate the boundary information and causes the misclassification of objects adjacent to the boundary.  
The FAM is meticulously designed to improve feature fusion, reduce network complexity, and preserve important low-level information, especially for small target features. The most important change we see in this module is bilinear pooling. Bilinear pooling has emerged as a powerful technique for small object detection in the field of computer vision [24; 47; 58; 59; 60; 61].  
Addressing downsampling challenges: In traditional YOLO architectures, repeated downsampling in the backbone network leads to the reduction or even loss of small target features with increasing feature levels. The FAM counters this problem by creating a rich receptive field by efficiently combining subsamples, thus strengthening the model's ability to represent and detect small objects with high resolution and low computational cost.  
Optimizing feature map sizes: The FAM ensures the appropriate resizing of feature maps in each layer, adhering to the smallest size in the group to avoid information loss and manage computational costs. This approach facilitates efficient information collection while keeping computational complexity to a minimum, partly due to the integration of transformer modules [11; 62].  
Balancing feature preservation with computational cost: An important aspect of aligning features in the FAM involves maintaining larger feature sizes to preserve essential low-level information that is rich in location and detail. However, as the feature size increases, the computational cost and delay in subsequent blocks also increase. Therefore, it is imperative to control feature size at lower levels to effectively manage delays.  
Strategic selection of the feature alignment phase: Considering these, selecting the most appropriate phase for feature alignment in the FAM is critical in striking a balance between speed and accuracy. After extensive testing and data processing from different layers, Stage 4 was selected for feature alignment in our model. This decision is crucial to ensure efficient computation and reduced latency while maintaining the integrity of low-level information.  
Continuous optimization and testing: Our ongoing research includes experimenting with the number of layers and processing data from various layers to further improve the performance of the FAM. This continuous optimization aims to increase AYOLO's accuracy and efficiency, especially in scenarios requiring high-precision object detection with minimal computational latency. The figure below shows the architecture of our FAM module. (Figure 3).
## 4 Materials and Methods
### Satellite Image Classification  
A pixel-based classification using the hard-clustering algorithm, K-Means Unsupervised Classification, was performed in order to map the main land cover of the study site and segregate vegetated areas from the rest of the surroundings. The classification utilized the aforementioned MNF image, in order to focus on the bands containing the largest quantity of data. All the pixels encompassing the study area of Hemopolis were classified into clusters based on their spectral similarity to one another. The resulting image contained two spectral categories: vegetated and non-vegetated areas. Using a total of 6200 pixels, an accuracy assessment for the WV-3 derived classification resulted in an overall high level of accuracy of 98.8%.
# Enhanced Beacons Dynamic Transmission over TSCH
###### Abstract  
Time slotted channel hopping (TSCH) has become the standard multichannel MAC protocol for low-power lossy networks. The procedure for associating nodes in a TSCH-based network is not included in the standard and has been defined in the minimal 6 TSCH configuration. Faster network formation ensures that data packet transmission can start sooner. This paper proposes a dynamic beacon transmission schedule over the TSCH mechanism that achieves a shorter network formation time than the default minimum 6 TSCH static schedule. A theoretical model is derived for the proposed mechanism to estimate the expected time for a node to get associated with the network. Simulation results obtained with different network topologies and channel conditions show that the proposed mechanism reduces the average association time and average power consumption during network formation compared to the default minimal 6 TSCH configuration.  
Internet of Things (IoT); wireless sensor network (WSN); low-power and lossy network (LLN); time slotted channel hopping (TSCH); network association time; energy consumption Article
# Model Predictive Control of Grid Forming Converters with Enhanced Power Quality
## 3 Frequency Control
### Notch Filter  
The aforementioned strategy focuses on the reduction of the switching while this strategy aims to concentrate the voltage spectrum around the desired frequency by utilizing a digital notch filter. Generally, notch filters are shaping the spectrum at certain frequencies, where they need different weight values, to allow control of harmonics. The notch filter is designed for the IMPC scheme in order to control the switching frequency of the grid-forming converter. The implementation of the notch filter is done using the discrete-time domain. Therefore, the CF can be modified as:  
\[g_{m1}=\lambda_{n}F_{n}(g) \tag{22}\]  
where \(\lambda_{n}\) is the weighting factor and \(F_{n}\) is the second-order band-stop filter. A Tustin discretization of the notch filter is considered in the filter design. Similarly, the derivative term \(g_{I}\) in Equation (19) can be modified as:  
\[g_{m2}=\lambda_{n}F_{n}(g_{I}) \tag{23}\]  
The implemented notch filter in this paper, which has a frequency concentration at 2 kHz is illustrated in Equation (24):  
\[\mathbf{H}_{2 kH}=\frac{0.9849z^{2}-1.875z+0.9849}{z^{2}-1.875z+0.9698} \tag{24}\]  
Notably, changing the filter bandwidth gives a different spreading range around the desired frequency. Similarly to the SP controller, the weighting factor design for both N-CMPC and N-IMPC will be discussed in the results and discussion section.  
Figure 4: Simulation result for the (SP-CMPC): Switching frequency reduction during sweeping the weighting factor \(\lambda_{sw}\) using the CF in Equation (21) with the conventional tracking system.  
Figure 5: Simulation result for the (SP-CMPC): instantaneous voltage error during sweeping the weighting factor \(\lambda_{sw}\) in the same manner as the above figure using the CF in Equation (21) with the conventional tracking system.