esa-sceva/satcom-chunk-collection huggingface dataset:
# Unity Power Factor Operation in Microgrid Applications Using Fuzzy Type 2 Nested Controllers
## 3 Proposed System
### Layout of the Proposed System  
The proposed system comprises a hybrid MG (HMG) that contains a DC MG, AC MG, and DC-link capacitor, as illustrated in Figure 2. The DC MG has two DG units, PV and fuel cell, battery bank, and DC loads. Each of them is linked to a DC/DC converter. The AC MG is tied to the DC MG via a diode rectifier and a boost DC/DC converter. There are also AC loads such as induction motor drives.  
As the scope of this article was the power factor improvement, the AC MG was modeled as a voltage source while the DC MG was modeled as a current source. This is depicted in Figure 3. The AC MG is mainly used as a backup if the DC MG generation does not satisfy the DC load requirements. The needed measurements are the rectified voltage \(v_{\text{b}}\), rectified current \(i_{\text{b}}\), the DC-link voltage \(v_{\text{dc}}\), and the DC-load current \(i_{\text{dc}}\). The main aims of the proposed controller are: (1) compensate for the DC-link voltage variations due to load changes; (2) realize a near unity power factor for the AC MG; (3) ensure that the injected current satisfies the IEEE standards of harmonics. The control signal modulates the switch of the boost DC/DC converter to achieve the control goals.  
Figure 2: Layout of single-phase grid with DC microgrid and its DC loads.
## 4 Discussion
### Advantages and Drawbacks of PROS  
The advantages of PROS can be summarized as follows: (i) generality--the algorithm can be applied to virtually any function or engineering problem; (ii) no user-defined parameters--unlike other methods that use parameters such as population size, step size and others, PROS does not have even a single parameter to adjust. This makes the algorithm easy to implement as it can be easily used by non-experts or people who have limited knowledge of the nature of the problem at hand; (iii) easy to program--the method is very easy to code in any programming language. This can significantly reduce the labor cost of an optimization process and it is an appealing feature to both practitioners and  
Figure 10: The objective function \(f(x_{1},x_{2})=x_{1}+x_{2}-3x_{1}x_{2}+1\), plotted in \([0,1]^{2}\). PROS is trapped at the local minimum \((0,0)\) since there is no improvement in \(f\) in any of the \(x_{1}\) or \(x_{2}\) directions on their own: (**a**); 3D surface plot and (**b**) 2D contour plot.  
theoreticians; (iv) Good convergence rate--the convergence rate of the algorithm is good, showing superior performance when compared to established algorithms in some of the optimization problems; (v) Excellent computational efficiency--due to its simplicity, the implementation of the algorithm is very fast, outperforming the ones of other established algorithms. This means that using PROS one can achieve a larger number of iterations (obj. fun. evaluations) at the same time, in comparison to the other algorithms, when time is the basis of comparison (instead of the number of obj. fun. evaluations as we have done here); (vi) A tool for measuring performance--since PROS is so simple and it has no parameters to adjust, then, according to Occam's razor, any optimization algorithm which is more complex should exhibit clearly superior performance compared to PROS, in most problems. In other words, PROS can serve as a simple comparison tool for measuring the performance of new optimization algorithms in the future.  
Local search can be considered a drawback of the method. In many cases, the algorithm appears to converge fast in the beginning, compared to the other algorithms, but not so fast later on. It appears that it has good global search capabilities but somehow limited local search capabilities at the vicinity of the optimum. Another drawback is the possibility of the algorithm being trapped in a local optimum under certain conditions, as discussed in detail in Section 3.4.
# Study on Influencing Factors of Helicopter Brownout Evolution Based on CFD-DEM
## 2 Numerical Simulation Theory
### Gas-Solid Two-Phase Flow Model  
For the description of the brownout phenomenon, the most appropriate model is the gas-solid two-phase flow model. In the simulation of the gas-solid two-phase flow, the force between fluid and solid, and the force between solid particles and the wall should be considered. At present, the research of the gas-solid two-phase flow has been more mature, and the main models used are the Euler-Euler method, Euler-Lagrange method, and the dynamic model of particle impingement.  
The Euler-Lagrange method is usually used when considering the motion of a single particle or particles. The fluid phase is still described by the Euler method, and the particle motion is described by ordinary differential equations in Lagrange coordinates.  
In recent years, a discrete element model (DEM) considering particle collision has been developed, and this can be applied to a dense gas-solid two-phase flow. In addition, the model does not directly solve the flow field around the particles and uses a suitable drag model to replace the force between the particles and flow field, which greatly reduces the calculation cost and can track a high number of particles.  
When calculating the force between particles, it is necessary to consider the collision of particles. The common models are the soft ball model and the hard ball model. In this paper, the soft ball model is adopted. The basic assumption is that the particle collision will last for some time, and there will be slight deformation in the collision process (which is shown as overlapping phenomenon in the calculation). On this basis, the spring and damper are used to simulate the particle collision phenomenon.  
The hard sphere model is based on the law of impulse conservation, and the basic assumption is that the collision process of particles is completed in an instant without considering the deformation of particles. In the mathematical model, the collision is completed in a time step, and the recovery coefficient is used to solve the velocity after collision.  
The specific process of simulation is to calculate the kinetic energy loss through the material characteristics and the particle velocity at the time of collision, and to reflect the kinetic energy loss by applying a force in the opposite direction to the particle. The normal deformation and tangential deformation of particles are then calculated according to the material rigidity, particle velocity and resistance. At the same time, the normal resistance and tangential resistance are calculated from the deformation and kinetic energy loss. The normal force will make the particles rebound, and the tangential force will make the particles rotate.  
The gas-solid two-phase flow model's calculation process is shown in Figure 1:
# HLFSRNN-MIL: A Hybrid Multi-Instance Learning Model for 3D CT Image Classification. _Applied_
## 1 Introduction
In the study, to classify CT images into lung cancer patient and normal categories, we propose a multi-instance learning model called the High-Low Frequency Sliding Recurrent Neural Network (HLFSRNN-MIL) to assist doctors in making a diagnosis of the patient's condition. HLFSRNN-MIL leverages the principles of multi-instance learning, and the  
Figure 1: Sample CT images of lung cancer (A: adenocarcinoma, B: small-cell carcinoma, G: squamous-cell carcinoma, Normal: normal. The red box indicates the location of the lesion). model requires only patient-level labels as input and defines a patient's 3D CT image as a bag. The size of each bag is determined based on the patient's specific circumstances, typically ranging from 30 to 300 slices. Each bag undergoes basic feature extraction to obtain feature information. Subsequently, the high- and low-frequency feature information derived from these features is fused, and the prediction probability of each instance within the bag is determined using our modified recurrent neural network. Finally, this information is fed into the MIL classifier to generate bag-level predictions. The paper's main contributions are summarized below:  
1. We use a wide range of preprocessing techniques to mitigate the noise in the data. In this process, resampling, grayscale standardization, noise reduction, and other technologies are used to reduce the impact of noise on training. 2. The HLFFF method is used in the process of feature extraction, which can effectively capture and fuse the high- and low-frequency information from the basic feature information, and improve the sensitivity of the network to various distributions of disease data. 3. The SRNN method is proposed to learn and capture the temporal relationship information in the 2D feature map. This method is based on the Long Short-Term Memory Network (LSTM), which can effectively capture the relationship information between instances and identify the sequential instance fragments that affect the prediction results (that is, the continuous instance fragments of the window size in the package). 4. We propose a HLFSRNN-MIL model based on the ResNet-50 network. The model adheres to the 'end-to-end' architecture concept, and through experiments on TCIA and CC-CCII datasets solves the problem of disease diagnosis using 3D CT images in clinical practice.
# Factorial Numbers and Their Practical Applications
## 3 Theory of Factorial Numbers
### Compression of Factorial Numbers  
Factorial numbers have redundancy, since each of them in binary form with uniform encoding contains \(n\times log_{2}n\) bits of information. At the same time, the factorial number contains \(log_{2}n!\) bit. Therefore, during its transmission and storage, compression may be necessary. The data compression ratio \(S\) depends on the absolute redundancy of the factorial number \(K=n\times log_{2}n-log_{2}n!\) and is defined as \(S=(n\times log_{2}n)/log_{2}n!\). For example, with \(n=4\), the absolute redundancy is \(K=4\times log_{2}4-log_{2}24\approx 3415\) bits, and the compression ratio \(S=8/5\approx 1.745\).  
Table 2 and the graph in Figure 1 show the absolute redundancy and data compression ratio \(S\) as \(n\) changes from 2 to 16.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \(n\) & \(K\) & \(S\) & \(n\) & \(K\) & \(S\) & \(n\) & \(K\) & \(S\) \\ \hline
2 & 1.000 & 2.000 & 7 & 7.352 & 1.598 & 12 & 14.184 & 1.492 \\
3 & 2.170 & 1.839 & 8 & 8.701 & 1.569 & 13 & 15.570 & 1.479 \\
4 & 3.415 & 1.745 & 9 & 10.060 & 1.545 & 14 & 16.960 & 1.467 \\
5 & 4.703 & 1.681 & 10 & 11.428 & 1.524 & 15 & 18.353 & 1.456 \\
6 & 6.018 & 1.634 & 11 & 12.803 & 1.507 & 16 & 19.750 & 1.446 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Redundancy and compression of factorial numbers.  
Figure 1: Redundancy and compression plots of factorial numbers.  
As can be seen from Table 2 and, accordingly, the graph in Figure 1, the absolute redundancy of factorial numbers increases almost linearly, while the compression coefficient slowly decreases and tends to 1. This means that for large \(n\) there is practically no compression.
## 1 Introduction
Besides, some researchers have also revealed the effect of opening size on crack initiation stress by attaching a large number of strain gauges round the circular opening [26; 27; 28]. However, it is difficult to arrange the strain gauges because we do not know where the cracks appear beforehand. By using acoustic emission (AE) technique and PFC software, [PERSON] et al. [33] carried out biaxial compression tests on Berea sandstone samples with a circular opening, reproducing the loading-induced failure round opening underground. Furthermore, the impact of lateral stress coefficient, rock homogeneity index, high-temperature treatment, tensile and dynamic loads on crack growth from a circular opening has also been analyzed experimentally and numerically [34; 35; 36; 37; 38; 39]. In addition to one circular opening, [PERSON] et al. [40], [PERSON] et al. [41] and [PERSON] et al. [42] further studied the cracking responses of samples containing multiple circular openings subjected to uniaxial compression. They stated that the failure is induced by the coalescence of partial openings, and the coalescence behavior is collectively controlled by the rock material, openings and loads. To reflect the actual failure characteristic of openings in rock engineering, more non-circular shapes for openings in specimens were considered in previous studies, such as ellipse, inverted U-shape, trapezoid and rectangle [43; 44; 45; 46; 47; 48; 49; 50], which have shed new light on the failure process of pre-holed rocks. Based on the literature review above, we can make a conclusion that the failure behavior of specimens with circular openings has been basically grasped, but that of the specimens with complex shaped openings has not been thoroughly realized. In mining and tunneling engineering, it is extremely common that two parallel roadways or tunnels are designed adjacent to each other. This makes the failure mechanism more complicated due to the interaction between openings. However, to date, only [PERSON] et al. [51], [PERSON] [52] and [PERSON] et al. [53] reported the crack evolution in rock specimens containingtwo oval or rectangular openings under uniaxial or dynamic loading. Thus, we were inspired to conduct uniaxial compression tests on sandstone samples containing two openings with different configurations to explore the cracking behavior. In addition, the intact specimens and specimens with one single opening, acted as references for fracture analysis, were also tested. The cross-sectional shape of the fabricated openings was designed to be inverted U-shape given that it is widely used for openings in practice. During the tests, we applied the advanced digital speckle and AE techniques to monitor fracture development. An additional numerical study was also performed to simulate the stress distribution round the openings, which is rewarding to explain the fracture mechanism.
## 5 Results and Discussion
High-resolution calculations require large amounts of RAM, since it must simultaneously contain full-size matrices for correct calculation in each process. Also, the requirements for the volumes of hard drives, which must be fast enough, are increasing. The most correct direction for the development of parallel calculations is probably the development of algorithms that in parallel counted several detached buildings with different scenarios of the effect of the heat flux from the forest fire front on the enclosing structures. These algorithms, in conjunction with GIS systems, are able to quickly simulate the impact of a forest fire on entire settlements near the WUI. It is necessary to check the results obtained in this article in comparison with the previously published data from [61]. Three series of comparative analyzes were carried out. It should be noted that in [61], a three-dimensional formulation is considered, implemented using a sequential programming model, which imposed restrictions on the number of nodes in the computational grid in space and time. In the present article, a two-dimensional formulation is considered, implemented in the SPMD-model of computations using the parallelization of computational operations. The first series completely repeated the conditions for carrying out mathematical modeling in [61], except that in this article a two-dimensional formulation was used. The second series of comparative analysis was carried out for conditions when the reflection of radiation from the wall surface was not taken into account, whereas in [61] this was taken into account. The third series of comparative analysis was carried out for conditions when the penetration of radiation into the surface layer of the cladding was taken into account in this work, similar to the Bouguer-Lambert-Beer law. Several scenarios were considered:  
Scenario 1. Impact of a low intensity surface forest fire on a glued plywood wall from a distance of 20 m.  
Scenario 2. Exposure of a high intensity surface forest fire to a glued plywood wall from a distance of 20 m.  
Scenario 3. Impact of a crown forest fire at different heights on a glued plywood wall from a distance of 20 m.  
The results of the comparative analysis are shown in the Figure 10.2  
Taking into account the penetration of radiation into the depth of the cladding layer noticeably changes the values of the calculated temperature, both on the surface and in the depth of the cladding layer. Fundamental differences were noted for the scenario of the impact of a low-intensity surface forest fire. A comparative analysis of the results shows that the danger of a low-intensity surface forest fire cannot be underestimated. It is necessary, as with other types of forest fires, to take measures to preserve the lives and property of homeowners. Compared to other forest fires, a low-intensity surface forest fire is characterized by the lowest rate of propagation over a layer of ground forest fuels. This gives homeowners time to evacuate the forest fire area and to rescue documents and valuables.
# Denial-of-Service Attacks on Permissioned Blockchains: A Practical Study
## 5 Experiments and Results
### Attack Simulation
#### 5.2.6 Consensus Protocol Attack  
In HLF, various consensus protocols can be configured for the network's ordering service. Raft is one of the most widely used consensus protocols in HLF. However, its openness to configuration also makes it vulnerable to attacks by malicious ordering nodes. We demonstrated how improper configuration of Raft's timing parameters could enable DoS attacks. Moreover, a malicious channel manager could alter these settings and send them to other nodes for updates, which exposed the network to further risks. Raft's performance and fault tolerance depend on three timing settings: the tick interval, action tick, and heartbeat tick. These settings play a vital role in determining the frequency of requests, actions, and heartbeat checks, which ultimately impact the network's overall performance and resilience [74]. The tick interval, for instance, controls the frequency of heartbeat messages sent by the leader to the followers. A shorter tick interval results in more frequent heartbeat messages, enabling the network to detect node failures more quickly. In contrast, a longer tick interval increases the time between heartbeat messages, which can lead to the delayed detection of node failures. The action tick, on the other hand, determines the frequency of actions, such as log replication, that are performed by the nodes. Finally, the heartbeat tick influences the frequency of leader election time-outs. Understanding the interplay between these tick settings is essential for striking a balance between the network performance, fault tolerance, and security.  
Figure 10: Comparison of delay and throughput in malicious client attack.  
Figure 9: Malicious client attack scenario where rogue client applications overwhelm the network by sending a huge volume of spam transactions.  
To demonstrate this attack, we assumed an attacker had compromised a channel manager or ordering node with configuration update privileges. We increased the tick interval value in the Raft ordering service, which slowed the execution of the Tick() function. This function initiates a number of events, including making block proposals, heartbeating, and election time-outs. The default value of the tick interval in HLF's Raft consensus was 500 ms. Increasing the tick interval prolonged the time between requests, leader selection, and consensus. The attack scenario, including maliciously configured orderers, is illustrated in Figure 11. The effects of the attack are visualized in Figure 12a,b. These figures illustrate the attack's consequences: increased latency and reduced throughput. The transaction confirmation delays increased by 417%, where they went up from 2.5 s to 13 s due to sluggish block proposals. The throughput was reduced by 81%, where it went down from 23.5 TPM to 4.5 TPM as the nodes struggled with infrequent Tick() cycles.
# Nanometer-Resolution Imaging of Living Cells Using Soft X-ray Contact Microscopy
## 5 Conclusions and Future Directions  
The use of the soft X-ray contact microscopy technique for imaging intact, living cells with nanometer resolution without freezing, chemical fixation, chemical staining, or sectioning s presented. It is a simple method and, as a table-top device, is more accessible for biologists and more suitable for everyday use. Adapting the microscope design to users' requirements makes this technique a helpful instrument in biomedical research. Because the results are difficult to interpret, the correlation with other standard methods to identify specific proteins of interest is required.  
In our opinion, correlative microscopy is an exciting direction of technological development; however, the use of synchrotrons is limited to highly specialized experimental centers. SXCM is a method that allows the observation of intracellular structures in a native state, and the first promising trials to correlate SXCM with fluorescence techniques were performed. Currently developed super-resolution methods, which allow the observation of structures such as FA in living cells, require genetically encoded fluorophores to express the desired proteins. The use of GFP tagging or the modification of the manner in which cell lines are obtained with a stably integrated transgene can lead to protein-translation disorders. Thus, the SXCM technique, as a laboratory system, combined with super-resolution fluorescent techniques, such as STED or PALM, is an interesting imaging option, eliminating the use of expensive, time-consuming X-ray sources, such as synchrotrons, while ensuring the high-resolution imaging of biological specimens. This technique would exploit the potential of SXCM for functional studies of biological samples.  
Conceptualization, A.N.-S., P.N.O., E.A.T., and P.W.; software, W.K., P.W., and H.F.; investigation, A.N.-S., P.N.O., and P.W.; writing--original draft preparation, A.N.-S., P.W., P.N.O., W.K., E.A.T., and H.F.; writing--review and editing, A.N.-S., P.W., E.A.T., and H.F.; visualization, W.K., P.W., and H.F.; supervision, E.A.T. and H.F.; funding acquisition, H.F., A.B., and P.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Science Centre, Poland, under the Beethoven Program, grant number 2016/23/G/ST2/04319, and the European Union's Horizon 2020 research and innovation program, Laserlab-Europe V, grant number 871124.  
Not applicable.  
We would like to thank the referees for their useful comments.  
The authors declare no conflict of interest.