esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Link the Marginal PDFs of Wind Speed and Load by D-Vine Copulas
### Pair Copula Theory  
In terms of selecting a copula function, a single multivariate copula may not be a good choice. Hence, it is necessary to find a more accurate and flexible method.  
In this context, pair copula theory is proposed to decompose a multivariate copula into multiple conditional bivariate copulas [37]. The concept is introduced as follows.  
Firstly, the joint PDF \(f(x_{1},\,\ldots,\,x_{n})\) can be decomposed as:  
\[f(x_{1},\ldots,x_{n})=f_{n}(x_{n})\cdot f(x_{n-1}|x_{n})\,\ldots f(x_{1}|x_{2}, \ldots,x_{n}). \tag{8}\]  
we then try to rewrite the conditional PDFs in (8) with conditional bivariate copula functions, which is shown as:  
\[f(x_{i}|x_{v})=c_{io|v-o}(F(x_{i}|x_{v-o}),F(x_{o}|x_{v-o}))f(x_{i}|x_{v-o}), \tag{9}\]  
where \(\mathbf{x_{v-o}}\) is the subset formed by excluding \(x_{o}\) from the set \(\mathbf{x_{v}}\), which is a subset of \(\{x_{1},\,\ldots,\,x_{n}\}\). \(c_{io_{1}|v-o}\) is the conditional bivariate copula of \(x_{i}\) and \(x_{o}\) under the condition of \(\mathbf{x_{v-o}}\).  
The (9) is a recursive expression. Consider a 3-variable example to present the recursion as:  
\[\begin{split}& f(x_{1}|x_{2},x_{3})=c_{1,2|3}(F(x_{1}|x_{3}),F(x_{2}|x_{ 3}))\cdot f(x_{1}|x_{3})\\ &=c_{1,2|3}(F(x_{1}|x_{3}),F(x_{2}|x_{3}))\cdot c_{1,3}(F(x_{1}),F(x_{3}))\cdot f_{1}(x_{1}),\end{split} \tag{10}\]  
where \(c_{1,2|3}\) is the conditional copula function of \(x_{1}\) and \(x_{2}\) under the condition of \(x_{3}\).  
We have already modeled the marginal PDF by SNMM, so the difficulty in (9) lies in how to obtain \(F(x_{i}|\mathbf{x_{v-o}})\). \(F(x_{i}|\mathbf{x_{v-o}})\) can be derived as [38]:  
\[F(x_{i}|x_{v-o})=\frac{\partial C_{ib|v-o-e}(F(x_{i}|x_{v-o-e}),F(x_{e}|x_{v-o -e}))}{\partial F(x_{e}|x_{v-o-e})}, \tag{11}\]  
where \(\mathbf{x_{v-o-e}}\) is the subset of \(\mathbf{x_{v}}\) with both \(x_{o}\) and \(x_{b}\) excluded.  
The proposal of (11) renders (9) practical, so we can use (9) to replace all the conditional PDF in (8). After the substitution, \(f(x_{1},\,\ldots,\,x_{n})\) can be expressed by the product of \(n\) marginal PDFs and \(n(n-1)/2\) conditional copula functions.
## 4 Results and Discussion
### Data Generation Configuration  
To evaluate the quality of the generated images, we establish two baseline models for comparison, both using the YOLOv8-Nano object detection model [48]. We train both models for 20 epochs on different subsets from the train set of the AGAR dataset, using an input size of \(800\times 800\) pixels. Furthermore, we utilized the comprehensive data augmentation pipeline developed by [PERSON] et al. [48] for training our models. This approach was essential for mitigating overfitting, particularly with limited data. The pipeline integrates RandAugment [29], applying a range of geometric transformations (horizontal flips, rotations, and translations) and color adjustments (HSV modifications), alongside Mosaic augmentation [33]. This augmentation strategy was consistently employed in all subsequent YOLOv8 trainings throughout this work.  
The first baseline, trained on all 5241 images from the train set, achieved an AP\({}^{50}\) of 93.7 and an mAP of 64.9, considering only microbial species. For our second baseline, we trained YOLOv8 using a fine-tuning subset of only 1000 real images. This subset was later used to fine-tune models that were previously trained on various synthetic datasets. When trained exclusively on this smaller subset, the overall mAP dropped to 56.4. In the later sections of this paper, we aim to mitigate this performance decrease by introducing synthetic data into the training process. All results of our baseline evaluation can be found in Table 1.
# Graphene-Based Functional Hybrid Membranes for Antimicrobial Applications: A Review
## 3 Fabrication Techniques of Graphene-Based Membranes  
2D graphene and its derivatives are the cornerstone of advanced thin membranes [44]. The molecular and ionic separation mechanism of graphene oxide films is basically selective transport through spacer channels between graphene oxide flakes, as well as defects and wrinkles in the graphene oxide flakes (Figure 2a) [45]. Compared with inorganic membranes or polymer membranes, graphene membranes have a better separation performance and application potential. However, the predominant graphene-based membranes show high permeation fluxes, which are fabricated by reducing their thickness to less than 50 nm. Graphene frameworks with polar functional groups are considered the key properties for obtaining ideal membranes in nanotechnology [44]. The main methods used to prepare graphene membranes include chemical vapor deposition (CVD), the vacuum-assisted method, spin-coating, dropping, and LBL self-assembly. Of the above methods, vacuum filtration and spin-coating are considered the most efficient for the preparation defect-free GO membranes [46].
# FAmesh: Generating Frequency Adaptive Meshes from Single Images under 2D Hole Constraints
## 4 Result and Discussion
### Comparison of Different Patches Size  
In this section, we explore the effect of subgraph size on the performance of the proposed method. Figure 9 shows the CD values of the network for various subgraph sizes. When K = 5, the CD value is the smallest, at 1.763. When K is less than 5, the patch gradually shrinks as K decreases. As a result of the neural network's inability to obtain sufficient local data, the CD value exhibits a sharp upward trend, and the network's performance begins to deteriorate. However, when K is greater than 5, the CD value begins to exhibit a slight upward trend as the patch size increases. This is because the patch is too large and the network is affected by distant, irrelevant points, which degrades network performance.
# An Islanding Signal-Based Smooth Transition Control in AC/DC Hybrid Micro-Grids
## 4 Simulation and Discussion
### Influence of Load Change on Smooth Transition  
The AC/DC hybrid micro-grid is more complex than the traditional micro-grid, and the energy can flow freely through the ILC in the AC and DC sub-networks. Based on the proposed smooth transition control, this section discusses the impact of load variation on smooth transition.  
The simulation process is as follows: when \(t=\) 0 s, the micro-grid starts in grid-connected mode. When \(t=\) 1.5 s, the PCC switches on, and the micro-grid turns to islanding mode. When \(t=\) 3 s, the PCC switches off, and micro-grid turns to grid-connected mode again. During the process, the PV output power is 50 kW, the DC load is 112.5 kW, and AC loads are 30 kW, 75 kW, 120 kW, and 150 kW, respectively. The waveforms of DC voltage, AC voltage, and ILC transmission power are shown in Figures 16-19.  
Figure 14: Regulation state of the traditional smooth transition control.  
Figure 15: Regulation state of the proposed smooth transition control.  
In Table 3, the DC load, AC load, PV output power, ILC transfer power at the instant of mode transition, DC voltage maximum change from grid-connected mode to islanding mode, DC voltage maximum change from islanding mode to grid-connected mode, and maximum AC voltage variation are listed.  
Figure 16: DC voltage, AC voltage, frequency, and ILC transmission power (DC load = 112.5 kW; AC load = 30 kW).  
Figure 17: DC voltage, AC voltage, frequency, and ILC transmission power (DC load = 112.5 kW; AC load = 75 kW).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**DC Load/kW** & **AC Load/kW** & \(P_{\text{pp}}\)/kW & \(\Delta\)_ILC/kW_ & \(\Delta\)_ILdc1_ & \(\Delta\)_ILdc2_ & \(\Delta\)_ILac_ \\ \hline
112.5 & 30 & 50 & 38.33 & 0.24\% & 2.96\% & 1.4\% \\
112.5 & 75 & 50 & 59.04 & 0.35\% & 4.53\% & 2.04\% \\
112.5 & 120 & 50 & 79.85 & 0.42\% & 6.61\% & 2.37\% \\
112.5 & 150 & 50 & 93.48 & 0.47\% & 7.69\% & 2.48\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: AC/DC hybrid micro-grid operating state.  
Since PV output power and DC load do not change during the process, the transmission power of ILC does not change during grid-connected operation, and equals the power absorbed by the DC sub-network. With the increase of the AC load, the transmission power of ILC increases during islanding operation. Thus, the power changes during the transition instant, resulting in an increase of voltage offset during transition process, as shown in Figure 20. In summary, load variation will affect the smooth transition of hybrid micro-grid. The greater the unbalanced power in the AC/DC sub-network, the greater the negative impact on smooth transition takes.  
Figure 19: DC voltage, AC voltage, frequency, and ILC transmission power (DC load = 112.5 kW, AC load = 150 kW).  
Figure 18: DC voltage, AC voltage, frequency, and ILC transmission power (DC load = 112.5 kW; AC load = 120 kW).
# CSNOMA: Carrier Sense Non-Orthogonal Multiple Access
## 1 Introduction
There is a lack of research on the MAC design supporting these advanced PHY layer techniques. There are a few studies that apply the technology used in cellular communication to a distributed MAC, including the works in [20; 21]. The authors of [20] utilize CDMA-like technology, a technology used in the 2 nd generation cellular communication, in MAC design for mobile ad hoc network. The authors of [21] investigate the possibility of OFDMA, a technology used in the 3 rd generation cellular communication, in MAC protocol for cyber-physical systems. We investigate the possibility of cross-layer design of distributed random access scheme with PHY and MAC layers. The main contributions of this paper are summarized as follows. * We propose a new multiple access protocol, carrier sense non-orthogonal multiple access (CSNOMA), which utilizes the interference cancellation technique in the sense of cross-layer design with considering physical (PHY) and multiple access control (MAC) layers. * We show the area spectral efficiency performance of the proposed scheme is better than CSMA/CA, as the node density increases. * We present a practical signaling design which is compatible with IEEE 802.11 DCF mode. The rest of the paper is organized as follows. Section 2 presents previous studies related to this research. We describe the system model in Section 3. We propose and analyze CSNOMA in Section 4. In Section 5, we verify the performance of the proposed scheme with numerical results. Section 6 concludes the paper.
## 2 Two-Loop Quantization Effects  
Quantization effects have been deeply studied in [10; 11; 26], where authors studied limit cycling conditions regarding plant and controller gains besides ADC and PWM resolution in a single-loop voltage control. This section presents dynamic conditions to avoid limit cycles in a two-loop digital current controller converter.  
In this case, both inner and outer small-signal representation of the control to output transfer function can be represented in general form as  
\[G_{p}(s)=\frac{G}{s} \tag{1}\]  
where \(G\) is the gain of the transfer function and \(1/s\) represents the transfer function of an integrator. In the case of the inner current control (see Figure 1a), the transfer Function (1) becomes  
\[G_{pin}(s)=\frac{m_{1}+m_{2}}{s} \tag{2}\]where \(m_{1}\) is the positive slope and \(m_{2}\) is the negative slope of the output current. Finally, for the voltage loop (see Figure 1b) the transfer Function (1) becomes  
\[G_{pou}(s)=\frac{1}{C_{o}s} \tag{3}\]  
where \(C_{o}\) represents the output filter capacitor of the converter. A standard Proportional-Integral (PI) controller is used in both control loops  
\[G_{pi}(s)=K_{p}+\frac{K_{i}}{s} \tag{4}\]  
where \(G_{pi}(s)=G_{pii}(s)\) for the inner loop (Figure 1a) and \(G_{pi}(s)=G_{pii}(s)\) for the external loop (Figure 1b).  
The dynamic model for each loop is shown in Figure 1, where \(q\) is the quantization level for an input or an output signal. Therefore, \(q_{i}\), \(q_{o}\), and \(q_{DPWM}\) represent, respectively, the output current \(i_{L}\), the output voltage \(v_{o}\), and the DPWM quantization level. \(T\) is the switching period (\(1/f_{s}\)). Finally, \(e_{v}\) and \(e_{i}\) are the error signals of the measured voltage and current, respectively. Then, in the inner loop, \(G_{pii}(s)\) generates the control variable \(u\), taking into account the mean value of the output current converter to change the duty cycle. Nonetheless, \(G_{piv}(s)\) for the output voltage gives the current reference for the inner loop based on the error voltage.  
The loop gains of the linear part of the system are defined without quantization [10; 26] as follows,  
\[T_{L}(s)=G_{pi}(s)G_{p}(s). \tag{5}\]  
Therefore, using (1) and (4) we obtain the crossover angular frequency as  
\[\omega_{c}=K_{p}G, \tag{6}\]  
A typical design of PI parameters usually places the zero of the controller at least one decade below of the desired crossing frequency \(\omega_{c}\), thus giving the following condition,  
\[K_{i}/K_{p}<<\omega_{c}, \tag{7}\]  
Then, we have to adjust \(K_{p}\) and \(K_{i}\) in order to obtain the desired phase margin. Phase margin (PM) is usually adjusted to be greater than 50\({}^{\circ}\) by tuning \(K_{p}\) and satisfying (7).  
Figure 1: Proportional-integrator control block diagram of (**a**) inner loop dynamic model and (**b**) outer loop dynamic model.
# Exact Relationship between Black Phosphorus Thickness and Behaviors of Field-Effect Transistors
## 5 Conclusions  
The experimental results do show that the thickness of the FET channel material does have a large effect on the electrical transport characteristics of the device, and the effect is greater when the material is thinner, and this result is basically consistent with previous reports. However, we also have to admit that the results obtained in this experiment are different from those previously reported. It is generally believed that the relationship between BP thickness and BP energy band is such that the significant broadening of the energy band of the material with decreasing thickness occurs only when the thickness is less than \(\sim\)4 nm, and this variation relationship is nonlinear, i.e., when the material thickness is less than \(\sim\)4 nm, the smaller the thickness the faster the increase of the energy band width. However, the experimental results show that the material thickness is near 16 nm, the change of thickness began to have a large impact on the device performance, and the impact was the most significant at this time; after that, as the material becomes thinner, device characteristics also change, but not an obvious non-linear change. This also confirmed that the material band gap, which is significantly affected by thickness in thin BP, is only one factor affecting the performance of the FET device. Stability and contact interface between BP and electrode are also essential consideration in the influence on the properties of thin-layer materials and their devices. Generally, the thinner the BP, the stronger the effect its stability may have on the performance of the device, in particular in the presence of both oxygen and water [30]. Therefore, to prepare 2D BP-FET devices with excellent performance for future nanoelectronic and optoelectronic applications, one needs to consider the combined effects of material thickness, environment, device structure, contact electrodes, and other factors.  
Conceptualization, L.F.; methodology, L.F. and K.L.; data curation, K.L. and M.W.; writing--original draft preparation, K.L. and M.W.; writing--review and editing, L.F.; supervision, L.F.; project administration, L.F.; funding acquisition, L.F. All authors have read and agreed to the published version of the manuscript.  
National Natural Science Foundation of China, grant number 12274319 and 61804010, and by Natural Science Foundation of Tianjin City grant 17 JCYBJC16200.  
Not applicable.  
The authors declare no conflict of interest.
###### Abstract  
Growing global concerns over reducing carbon emissions in the electricity market have accelerated the integration of renewable energy sources and electric vehicles, increasing variabilities and uncertainties across various nodes of power networks. System planners and operators recognize the importance of probabilistic bulk system reliability assessment methods capable of capturing the real-time behavior of components in the emerging systems. In this regard, the paper proposes a methodology for conducting bulk system reliability assessments of power system networks characterized by variable supply and demand profiles at different bulk power points. This paper implements a nodal negative load modeling method to integrate wind power generation in reliability assessment, capturing the cross-correlation between demand-supply variabilities at any node of the network. The multi-state load model employs the load cut-off strategy to reduce the number of demand scenarios, enhancing the computational efficiency. Moreover, the multi-state wind modeling approach considers the penetration levels, ensuring the impact of increasing penetration is appropriately captured. The methodology determines a list of a reduced set of scenarios for which consequence assessment needs to be conducted. The proposed framework and methods can readily be applied by power utilities, as these methods can be incorporated into most commercial software that uses an analytical approach for CSR assessment. The methodology is illustrated using the Roy Billinton Test System (RBTS) and can be effectively applied to other networks.  
competite system reliability (CSR); wind integrated power system (WIPS); load profile variation; cross-correlation; contingency analysis; scenario selection Article
# Generation and Distribution of Quantum Oblivious Keys for Secure Multiparty Computation
## 2 Methods
### Oblivious Key Distribution
The motivation for the choice of this protocol for this task will become more apparent during the following sections as we discuss the security and efficiency characteristics of the composition of \(\pi_{\text{QOT}}\) with \(\pi_{\text{COMH}}\), henceforth referred as the \(\pi_{\text{HOK}}\) (for Hybrid Oblivious Key) protocol for OT. The existence of a reduction from OT to commitments, while proven within quantum cryptography through the \(\pi_{\text{QOT}}\) protocol, is an open problem in classical cryptography. The existence of commitment schemes such as \(\pi_{\text{COMH}}\), which do not rely on asymmetric cryptography, provides a way to obtain OT in the quantum setting while circumventing the disadvantages of asymmetric cryptography. **Protocol \(\pi_{\text{COMH}}\)**  
**Parameters: Message length \(\bar{n}\) and security parameter \(k\). A universal hash family \(\mathbf{F}=\{f:\{0,1\}^{\ell}\rightarrow\{0,1\}^{k}\}\), with \(\ell=4k+2\hbar+4\). A collision resistant hash function \(H\). **  
**Parties: The verifier [PERSON] and the committer [PERSON]. **  
**Inputs: [PERSON] gets a string \(\bar{n}\) of length \(\bar{n}\). **  
_(Commit phase)_  
1. [PERSON] samples \(r\in\{0,1\}^{\ell}\), computes \(y=H(r)\), and chooses \(f\in\mathbf{F}\), such that \(f(r)=\bar{n}\). Then, he sends \((f,y)\) to [PERSON]. _(Open phase)_
2. [PERSON] sends \(r\) to [PERSON]. 3. [PERSON] checks that \(H(r)=y\). If this test fails she aborts the protocol. Otherwise, she outputs \(f(r)\).
## Appendix A
 \tag{16}\]  
and\[-2-2\lambda\omega+3\omega-\omega^{2}>0\] \[\lambda<\frac{\omega^{2}-3\omega+2}{-2\omega}. \tag{101}\]  
Observing Equations (100) and (101), according to convergence based on the SOR scheme, we assume that \(0<\omega<2\) but \(\omega\
eq 1\) and \(\omega\
eq 0\). Below, we discuss the two inequalities separately. (i) For Equation (100):  
We first factorize Equation (100):  
\[\lambda<\frac{\omega^{2}-3\omega+2}{2-2\omega}=\frac{(\omega-2)(\omega-1)}{2( 1-\omega)}=\frac{\omega-2}{-2}. \tag{102}\]  
Substituting \(\omega=0\) and \(\omega=2\) into Equation (102), we can respectively obtain  
\[\frac{\omega-2}{-2}\bigg{|}_{\omega=0}=1 \tag{103}\]  
and  
\[\frac{\omega-2}{-2}\bigg{|}_{\omega=2}=0. \tag{104}\]  
From Equations (103) and (104), we can find that when \(0<\omega<2\) and \(\omega\
eq 1\), the range of \(\lambda\) is between 0 and 1, which is less than 1; therefore, it meets the convergence conditions. (ii) For Equation (101):  
We factorize Equation (101) as follows:  
\[\lambda<\frac{\omega^{2}-3\omega+2}{-2\omega}=\frac{(\omega-1)(\omega-2)}{-2 \omega}. \tag{105}\]  
Using the solution of the inequality in Equation (105), we can find that the extreme value of \(\lambda\) will occur when \(\omega\) is between 1 and 2. Now, to find the extreme value of \(\lambda\) and determine whether it is less than 1, we can differentiate Equation (105); its expression is as follows:  
\[\frac{d}{d\omega}\bigg{(}\frac{\omega^{2}-3\omega+2}{-2\omega}\bigg{)}=\frac {-\omega^{2}+2}{2\omega^{2}}. \tag{106}\]  
When Equation (106) equals zero, it has an extreme value. In other words, when \(\omega=\pm\sqrt{2}\), the extreme value of \(\lambda\) will appear. Because \(\omega=-\sqrt{2}\) is not between 1 and 2, we choose \(\omega=\sqrt{2}\). By substituting \(\omega=\sqrt{2}\) back into Equation (101), we obtain the extreme value of \(\lambda\), denoted as \(\lambda^{{}^{\prime}}\); then,  
\[\lambda^{{}^{\prime}}\!\!=\!\!\frac{\omega^{2}-3\omega+2}{-2\omega}\bigg{|}_{ \omega=\sqrt{2}}=\frac{3-2\sqrt{2}}{2}\approx 0.086. \tag{107}\]  
Due to the extreme value of \(\lambda\), we have \(\lambda^{{}^{\prime}}=0.086\), which is less than 1. Furthermore, we can confirm that \(\lambda^{{}^{\prime}}\) is the maximum extreme value through the second derivative of Equation (105). Therefore, as long as \(\omega\) is between 1 and 2, \(\lambda\) will be less than 1. Thus, it also meets the convergence conditions. In summary, by combining (i) and (ii), we can determine that when \(1<\omega<2\), \(\lambda\) will be less than 1; in other words, the HSOR method can ensure convergence when \(1<\omega<2\). Therefore, we can infer that the CHSOR method will converge for \(1<\omega<2\).