esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The OpenFlow Protocol
### OpenFlow Specifications
#### 3.2.7. OF-CONFIG  
The OF-CONFIG protocol adds configuration and management support to OpenFlow switches. It is also standardized by the ONF. OF-CONFIG provides configuration of various switch parameters that are  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & **Programming language** & **License** & **Comment** \\ \hline NOX[29] & C++ & GPL & Initially developed at Stanford University. NOX can be downloaded from [30]. \\ \hline POX[30] & Python & Apache & Forked from the NOX controller. POX is written in Python and runs under various platforms. \\ \hline Beacon [31] & Java & BSD & Initially developed at Stanford. \\ \hline Floodlight [32] & Java & Apache & Forked from the Beacon controller and sponsored by Big Switch Networks. \\ \hline Maestro [33] & Java & LGPL & Multi-threaded OpenFlow controller developed at Rice University. \\ \hline NodeFLow [34] & JavaScript & MIT & JavaScript OpenFlow controller based on Node.JS. \\ \hline Trema [35] & C and Ruby & GPL & Plugins can be written in C and in Ruby. Trema is developed by NEC. \\ \hline OpenDaylight [36] & Java & EPL & OpenDaylight is hosted by the Linux Foundation, but has no restrictions on the operating system. \\ \hline \hline \end{tabular}
\end{table}
Table 4: List of available open source OpenFlow controllers. LGPL, Lesser General Public License; EPL, Eclipse Public License.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **OF 1.0** & **OF 1.1** & **OF 1.2** & **OF 1.3 \& OF 1.4** \\ \hline Per table statistics & X & X & X & X \\ Per flow statistics & X & X & X & X \\ Per port statistics & X & X & X & X \\ Per queue statistics & X & X & X & X \\ Group statistics & & X & X & X \\ Action bucket statistics & & X & X & X \\ Per-flow meter & & & & X \\ Per-flow meter band & & & & X \\ \hline \hline \end{tabular}
\end{table}
Table 3: Statistics are measured for different parts of the OpenFlow switch.  
not handled by the OpenFlow protocol. This includes features like setting the administrative controllers, configuration of queues and ports, _etc_. The mentioned configuration possibilities are part of OF-CONFIG 1.0 [37], which was released in December, 2011. The initial specification requires OpenFlow 1.2 and later. As of this writing, the current specification is OF-CONFIG 1.1.1 and supports the configuration of OpenFlow 1.3 switches.
## 3 Theoretical Analysis
### The Integration of Polar Codes and Direct Network Coding in a Relay-Assisted VLC System  
The system model for combining polar codes with DNC is shown in Figure 3. The steps of this scheme are as follows:  
* Nodes A and B utilize a polar encoder to encode their respective original information, denoted as \(u_{A}\) and \(u_{B}\). These encoded data are modulated and transmitted to the relay node, where they are received in two distinct time slots, potentially impacted by noise;
* After receiving \(y_{A}\) and \(y_{B}\), the relay node proceeds to estimate the original information through demodulation and decoding processes. Subsequently, it employs the DNC technique to encode the estimated results, \(\widehat{u}_{A}\) and \(\widehat{u}_{B}\), and simultaneously transmits these encoded outcomes to both nodes;
* Node A initially demodulates and decodes the relay's transmission, \(x_{R}\). Subsequently, utilizing its own original data, it performs a decoding operation on \(\widehat{u}_{R}\) to extract node B's message, through the operation \(\widehat{u}_{R}\oplus u_{A}=u_{B}\). Node B follows a similar process to retrieve node A's message. This enables both nodes to successfully exchange information.  
In a scheme that integrates polar codes with direct network coding, the VLC system requires dual equipment setups for demodulation and decoding at the relay node to handle signals from both source nodes simultaneously within a single time slot. This increases both device costs and computational expenses. To address these issues, this paper proposes a hybrid scheme that integrates polar codes with PNC.
# Electrospinning-Based Carbon Nanofibers for Energy and Sensor Applications
## 4 Application of Carbon Nanofibers
### Energy Storage Applications
#### 4.1.4 Dye-Sensitized Solar Cells (DSSC)  
In the DSSC system, in addition to the development of dye materials in order to enhance the performance, it is also possible to consider the development of catalyst materials at the counter electrode [106]. The traditional material for the counter electrode is platinum (Pt), which is an expensive metal. Therefore, an attempt was made to replace it with a more profitable material. Carbon material that has been decorated with metal oxide appears to be a good choice for a replacement for the counter electrode [107, 108].  
[PERSON] et al. introduced a cathode for DSSC constructed from PAN-based CNFs embedded with Co\({}_{3}\)S\({}_{4}\) nanoparticles (Figure 15a) [109]. The synthesized material showed good results concerning the surface area, as well as the current density compared to pure Co\({}_{3}\)S\({}_{4}\), pure CNFs, and pure Pt. Moreover, the surface resistance between the material and the electrolyte was lower in comparison to other materials, suggesting that the material presents outstanding properties even when compared to the Pt electrode. In addition, flower-petal-shaped NiCo\({}_{2}\)O\({}_{4}\) oxides were grown on the surface of CNFs with the hydrothermal calcination of cobalt nitrate and nickel nitrate by [PERSON] et al. (Figure 15b) [110]. The CNFs achieved by the authors had a good specific surface area with an average diameter of around 250 nm. After coating with NiCoO\({}_{4}\), although the diameter of the material increased to 900 nm, the structure of NiCoO\({}_{4}\) made the surface area of materials become three times larger than the bare CNFs. The coating layer also improved the conductivity and the surface resistance of the electrode. [PERSON] et al. also conducted experiments with CNFs coated with MoS\({}_{2}\) that have the shape of a petal but with a much smaller size (Figure 15c) [111]. Though the size of the decorated fibers decreased, the MoS\({}_{2}\) effect was less favorable than that of NiCoO\({}_{4}\). Apart from the differences in the properties of these two metal oxides, the surface area of the CNFs did not improve much with MoS\({}_{2}\) decoration.  
[PERSON] et al. decorated the CNFs with a complex of Pt and Ni\({}_{2}\)P nanoparticles (Figure 15d) [112]. In the beginning, the CNFs were embedded with Ni\({}_{2}\)P nanoparticles, then the product fibers underwent another reaction to attach the Pt nanoparticles.  
Figure 15: (**a**) CNFs decorated with Co\({}_{2}\)S\({}_{4}\) and the performance in DSSC device [109]. (**b**) Flower-petal–like SiC\({}_{2}\)O\({}_{4}\) grown on CNFs used as counter electrode for DSSC [110]. (**c**) CNFs grow MoS\({}_{2}\) mini\(-\)sized petals for solar systems [111]. (**d**) CNFs decorated with Ni2P and Pt nanoparticles for the application of the DSSC counter electrode [112].  
Although Ni\({}_{2}\)P alone cannot be a good catalyst, it is a good co-catalyst to combine with Pt or Pd and contribute to the excellent DSSC performance [113; 114].
## 2 Materials and Methods
### Controller Node Failure-Recovery
#### 2.7.1 Auction Based Task Allocation  
To satisfy design criteria DC1-3, e.g., low computational complexity on the allocating node, good scalability with the number of computing nodes and a deterministic allocation time almost independent of the node number, we decided to choose an auction-based sequential single task allocation approach [50; 51; 52; 53; 54]. In the auction-based sequential single task allocation the initiator or auctioneer, in our case the actuator node, offers a single task to prospective bidders. It does this by sending a task-auction initiation message containing the task context via broadcast to all reachable bidders, i.e., computing nodes. The bidders then locally calculate their suitability for the task expressed as a single scalar value or vector of scalar values. For the computation, they only use the provided task context and local available information. Subsequently, they send the calculated values back as bid to the auctioneer. The auctioneer then selects the winning bid using a simple selection strategy, e.g., selecting the lowest value and announces the corresponding winner thereby ending the auction process. Design criterion DC4 to have low network load is also satisfied, because in the best case, e.g., no network loss, the bidders communicate only once placing their bids, while the auctioneer communicates twice, once to announce the auction and once to announce the winner. Mitigation procedures for communication losses are discussed later, which require a few more communication events, but the overall burden on the network is still very limited. Finally, design criterion DC5, the possibility for multiple objectives driving the allocation choice is also realized by encoding each objective as appropriately scaled scalar value and fusing all of them together, e.g., with a weighted sum, to a single bid value.  
The assignment objectives AO1-3 lead to the two following main selection criteria that we consider important: Proximity to the actuator node and computational load of the controller candidate node.
## 3 Results and Discussion
### Scenario 2 (52)
#### 3.2.5 PO\({}_{4}\)-P  
In scenario 2, the reclamation conditions of scheme B were the same as scenario 1, so the impact on PO\({}_{4}\)-P environmental capacity was consistent with scenario 1, at only 2.58% (Table 15). Scheme C, different from the first three pollutants, had a wide impact on PO\({}_{4}\)-P environmental capacity, reaching 5.19%. Schemes D, E and F, similar to scenario 1, had a significant reduction in EC, reaching more than 15%. However, compared with scenario 1, the impact of scheme E on PO\({}_{4}\)-P environmental capacity slightly reduced, by 2.28%.  
The EC feasibility evaluation results demonstrated that the evaluation index \(R_{4}\) of scheme B was 10, and scheme B was feasible; the evaluation index \(R_{4}\) of scheme C was 8,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Rclamation Scheme** & **NH\({}_{4}\)-N (t/a)** & **EC Change (\%)** & \(R_{2}\) \\ \hline A & 3366 & - & - \\ B & 3217 & 4.45 & 8 \\ C & 3346 & 0.62 & 10 \\ D & 2564 & 23.83 & 0 \\ E & 2673 & 20.59 & 0 \\ F & 2831 & 15.90 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Feasibility evaluation in NH\({}_{4}\)-N environmental capacity.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Rclamation Scheme** & **NO\({}_{\rm X}\)-N (t/a)** & **EC Change (\%)** & \(R_{3}\) \\ \hline A & 5082 & - & - \\ B & 4887 & 3.84 & 10 \\ C & 5045 & 0.07 & 10 \\ D & 3855 & 24.12 & 0 \\ E & 4250 & 16.37 & 0 \\ F & 4021 & 20.88 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 14: Feasibility evaluation in NO\({}_{\rm X}\)-N environmental capacity.  
and scheme C was also basically feasible. Similar to the first three pollutants, schemes D, E and F were not feasible, and working conditions HZ2, HZ4 and HZ5 were prohibited.
# Evaluation of Subway Emergency Evacuation Based on Combined Theoretical and Simulation Methods
## 4 Results and Discussion
### Evaluation of Emergency Evacuation Capacity for Scenario #3
In summary, the emergency evacuation capacity of the subway station at the intersection of Wulukou is up to standard, with a total evacuation time of 323.5 s. During this process, Staircase C and Staircase D have the highest utilization rates, while Exits A, C, and G have the highest cumulative number of evacuees. The total number of evacuees from these three exits reaches 2755, accounting for 68% of the total evacuees.
# Ultrashort Vortex Pulses with Controlled Spectral Gouy Rotation
## 4 Experimental Results and Discussion
### Variation of Grating Parameters
For the smallest elements, the diffraction at the aperture (outer rim of the element) influences the angular spectrum most. By calculating the \"focal length\" of a pinhole (Fresnel number -1), one obtains a maximum angle of diffraction of \(\theta_{max}\sim\arctan(\lambda/2D)\). For an element diameter of 200 \(\upmu\)m and a wavelength of 800 nm, \(\theta_{max}\) should be in the range of 0.115\({}^{\circ}\). In Figure 9, the continuously increasing rotation angle \(\varphi\) is plotted for extended propagation distances. The numbers correspond to the configurations in Table 1. The maximum number of rotations was about 10. The relative coordinate \(z\)-\(z_{j}\) results from different starting points \(z_{j}\) of the zones of detectable rotation. The accumulated phase is found to be linear with small deviations, as is expected for Bessel-like beams. Linearity enables well-defined rotation control. For some curves, the ranges of parameters are not fully covered because some of the measurements had to be terminated for technical reasons like laser stability. Figure 8: Axial rotation periods \(P_{0}\) of the spectral eyes as a function of conical beam angle \(\theta\) and element diameter \(D\) (DOE = fixed spiral grating, SLM = programmable spatial light modulator). It is indicated that both parameters can be used to control the spectral Gouy rotation. Figure 9: Angle of spectral Gouy rotation for extended propagation distances (numbers correspond to configuration numbers in Table 1). The increase of phase appears to be nearly linear. To avoid misinterpretations, we would like to address the fact that the essential information in the curves is not a bandwidth transfer. The curves are plotted in pairs related to the spectral eyes; each upper curve of a pair corresponds to the red-shifted part, and each lower curve to the blue-shifted one. The ordinate values represent the spectral coordinates of the spectral eyes. The distance is not a measure of the output bandwidth, which is nearly constant because of conservation laws.
## 2 Materials and Methods
### Methodology
#### 2.2.6 Land Value Exposure  
The creation of the exposure layer in terms of land value is a vital facet of the fire risk assessment, as it helps understand the potential economic impact in the event of a fire. The exposure layer was developed by combining data on land use, land values in euros per square meter (EUR/m\({}^{2}\)), very-high-resolution satellite images, and orthophoto maps. The land value dataset, obtained from the Greek Ministry of Digital Government, provides valuable information on the spatial distribution of land values within the study area.  
To make a qualitative estimation of the potential financial cost associated with fire incidents, the land value zones were classified into five classes. The classification involved grouping the zones based on their respective land values. The lower classes represent areas that are likely to experience lower economic damages (less total housing price) in case of a fire incident, while the higher classes represent zones where financial damages would be more significant if a fire were to occur.  
By categorizing the land value zones into classes (<1000 EUR/m\({}^{2}\), 1000-1300, 1300-1600, 1600-2000, and >2000 EUR/m\({}^{2}\)), the exposure layer provides an educated understanding of the potential economic risks and consequences associated with fire incidents across different areas within the study site. The final exposure layer was enhanced and updated by the field observations. Field recordings provide valuable ground-truth information about high-risk buildings, in terms of land value, critical infrastructure, hotels, etc., which can significantly impact the potential economic risks and consequences associated with fire incidents.
## 3 Proposed System
### Data Processor Agents
#### 3.4.1 Trip Data Workflow  
First of all, trip information is received as input data. A process of dropping outliers is performed for Trip Data. Trips that lasted less than 2 min are dropped out because they are considered an attempt of using a bike which is malfunctioning, so it is taken and immediately returned to the same station.  
Figure 3: Data processing flow chart of the processes performed by the data processor agent.  
Besides that, the processor data agent requests the weather data agent to provide weather data in the range of dates of the input data. The information provided by the weather agent is cleaned and if there is no weather data for data records, a lineal interpolation is performed in the data. This information is merged with the input information. Then new features are added to the current dataset. These features divide dates into years, months and days of the week, day of the month, hours and other derivative features as seasons, weekends, holidays (obtained for each country with the library Workalendar library [44]). At this point, the received information is cleaned and merged with extra information. The next step involves splitting the information by station in the system and creating departures and arrivals. For each station in the system, that had the same station of origin are converted into departures and all trips with the same station as their destination are converted into arrivals.  
Once this is executed in every arrivals/departures dataset, the information will be grouped by a period T and the total number of arrivals/departures in each period T is summed. An additional step is performed, this is necessary in order to generate records for the periods for which no information on trips is available, periods of the day when the number of trips is zero. This process is called _adding no activity data_. Finally, the information is sent to the persistence data agent that will store it for further utilization by the model prediction agent.
# Antivirus Evasion Methods in Modern Operating Systems
## 4 Antivirus Evasion Mechanisms
### Morphism  
Morphism, specifically polymorphic viruses, is a complex technique that aims to make analysis more difficult by creating an unlimited number of different decryptors. Unlike encryption, no part of the virus is encrypted. Polymorphic viruses use numerous obfuscation techniques to change the appearance of the decryption code from copy to copy, making it harder for antiviruses to recognize the threat. In metamorphism, the entire program code changes instead of just the encryptor's code as in polymorphism. Each copy is built differently, with varying sizes or sequences of code, but its behavior remains unchanged. To detect a virus using metamorphism, advanced behavioral and heuristic detection engines are required [27].  
Tools for detecting code morphism strive to keep up with the development of frameworks for generating encrypted code, such as the tool presented in [30] for changing shellcode with each subsequent run.
## 4 Results
### Pore Pressure Characteristics  
We present the cyclic triaxial test results on cohesive soil samples as a function of the number of cycles. This approach makes it possible to observe how the deformation and excess pore water pressure develop in the soil sample. The excess pore water pressure progress is presented Figure 7. The relationship between the pore pressure and number of cycles is analyzed with pore pressure ratio \(r_{u}\) concept as in Equation (17):  
\[r_{u}(N)=\frac{\Delta u_{N}}{\Delta u_{max}}[-], \tag{17}\]  
where the \(\Delta u_{\text{N}}\) is the excess pore pressure value in the \(N\)-th cycle and the \(\Delta u_{max}\) is the maximal excess pore water pressure, which in the stress-controlled tests on cohesive soil is the maximal pore pressure noted during the test. The well-known pore pressure empirical model, developed by [PERSON] et al. [46; 47], enables us to drive the relationship between the pore pressure ratio and the number of cycles \(N\) as in Equation (18):  
\[r_{u}(N)=\frac{1}{2}+\frac{1}{\pi}arcsin\left[2\left(\frac{N}{N_{L}}\right)^ {\frac{1}{2}}-1\right][-], \tag{18}\]  
where the \(N/N_{L}\) is the cycle ratio, which is the ratio between the actual cycle and the cycle in which maximal pore pressure was observed, \(N_{L}\) (the \(N_{L}\) was the last cycle analyzed in this model). The results of the calculations show that the pore pressure generation has similar characteristics, which can be divided into two phases. The first one is the rapid excess pore pressure increase, which takes place in the first part of the cycle ratio and where the pore pressure ratio achieves between 80% and 90% of the maximal pore pressure. The second phase is the steady pore pressure growth towards \(r_{u}=1\). The number of cycles required to achieve the \(r_{u}=1\) was less than 10,000 in all cases. The minimum number of cycles that were required to build-up of the excess pore water pressure was in the case of sample 1.6, where only six cycles were required.  
The pore pressure generation has the same characteristics, but \(\Delta u_{\text{max}}\) is achieved in different cycles. The samples which generate their pore pressure over the longest time indicate slightly different  
Figure 7: Pore pressure ratio for the test results in confining pressure equal to 18, 90 and 135 kPa (**a**), and for the samples consolidated with confining pressure equal to 45 kPa (**b**).  
behavior to other samples. The pore pressure generated in these samples to the \(r_{u}\) is equal to around 0.9 in the first 1000 cycles and then increases in further cycles to 1.0. We can observe this behavior in tests, giving 1.1, 1.4, 1.5, and 1.7. The impact of \(\Delta\)u\({}_{\text{max}}\) on the number of cycles needed to reach the maximal pore pressure indicates that the higher the pore pressure that builds up, the greater the number of cycles needed to reach \(\Delta\)u\({}_{\text{max}}\).
# Exploring and Visualizing Research Progress and Emerging Trends of Event Prediction: A Survey
## 2 Background Knowledge
### Spontaneous Event Prediction
With the aim of improving the deep neural networks, [PERSON], et al. [46] proposed a dynamic graph convolutional network to provide the context of multi-event prediction results. In addition, some scholars have fused two or more of the above-mentioned methods to predict event time, location, and content. Some researchers developed an EMBERS system integrating five types of prediction models to predict the event time, location, causes, and scale, and the prediction results were sorted by confidence values [13]. A similar system named carbon was proposed by [PERSON], et al. [47] to predict civil unrest events using news and social media data. [PERSON], et al. [48] integrated multiple data mining methods for forecasting the extreme flood events occurring in the next 5 to 15 days. Artificial intelligence, human-machine combination, and hybrid intelligence technologies have also been applied for predicting geopolitical events [49; 50] and epidemic diseases [51] in order to improve the model performance as well as the human-machine interaction experience.