esa-sceva/satcom-chunk-collection huggingface dataset:
# The Cell Membrane of _Sulfolobus_ spp.--Homeoviscous Adaption and Biotechnological Applications
## 2 Cell Membrane and Lipids of _Sulfolobus_ Spp.
### Biosynthesis of Archaeal Membrane Lipids
[56] of an ordered process of cyclization counters the proposition that the rings are already existent in the isoprenoids when added to the glycerol backbone. It confirms a common hypothesis that the cyclization occurs within the GDGT chain. Albeit, it is still not certain if the saturation of the chain, as well as the different head groups, plays a role in the ring formation process. Two genes, Saci_1585 and Saci_0240, were determined to be coding for GDGT ring synthases (Grs). Both respective proteins, GrSA and GrSB, belong to the S-adenosylmethionine (SAM) protein family [56]. Members of the same family are also responsible for the formation of the calditol moiety [39]. Hence, both incorporations require a free radical mechanism. GrSA is responsible for the introduction of rings at the C\({}_{7}\) position of the core GDGT lipid. This has to be done prior to the cyclization of the C\({}_{3}\) position carried out by GrSB [56]. Further, two other genes, Saci_0421 and Saci_1201, in _S. acidocalarius_ were identified for influencing the number of cyclopentane rings. If deleted, the number decreases. However, their exact function in the cyclization process has not yet been determined [57]. Additional to the typical caldarchaeol, the membranes of _Sulfolobus_ also exhibit GDNT. One glycerol is replaced by a cyclopentane containing moiety, called calditol (Figure 2D). For its biosynthesis, D-glucose is converted into calditol via an \"inositol-like\" pathway [58]. C-C bond is formed between C\({}_{1}\) and C\({}_{5}\) of glucose. The possible mechanism of calditol formation includes starting the oxidation at C\({}_{4}\), which resembles the formation of _myo_-inositol. Both moieties are present as polar groups in the cell membrane of Sulfolobales [59]. A connection between the lipid synthesis and homeoviscous adaption is certainly present and of crucial importance. However, on a genetic, regulatory level, this influence on the lipid biosynthesis in response to environmental changes remains an untouched field of investigation and represents a promising area for future research.
## 3 Design and Implementation of the Proposed CBC Method
### Data Organization and Preprocessing  
Classification is performed using two-class and eight-class CBC; therefore, data organization differs for this approach [2]. The types of attacks for two-class classification are listed in Table 1. The labels and subtypes of for eight-class classification are shown in Table 2.  
Figure 6 shows the two-class distribution of the attack and benign classes. Figure 7 illustrates the eight-class distribution with the Mirai, Benign,Web, Brute force, DoS, Recon, DDoS, and Spoofing classes. The data are split with 70% designated for the training set and 30% designated for the test set for both classification problems.  
Figure 6: The data distribution for 2-class classification on the IoTCIC2023 dataset.  
Figure 7: The data distribution for eight-class classification on the IoTCIC2023 dataset.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Labels** & **Subtypes** \\ \hline  & ICMP flood \\ \hline  & RSTFIN flood \\ \hline  & SlowLoris \\ \hline  & ACK fragmentation \\ \hline  & UDP fragmentation \\ \hline  & PSHACK flood \\ \hline  & UDP flood \\ \hline
**DDoS** & HTTP flood \\ \hline  & ICMP fragmentation \\ \hline  & SYN flood \\ \hline  & SynonymousIP flood \\ \hline  & TCP flood \\ \hline
**Brute Force** & Dictionary brute force \\ \hline \hline \end{tabular}
\end{table}
Table 1: Two-class data description of IOTCIC2023.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Labels** & **Types of Attacks** \\ \hline  & HTTP flood \\  & Host discovery \\  & PSHACK flood \\  & ICMP flood \\  & XSS \\  & Backdoor malware \\  & Browser hijacking \\  & Uploading attack \\  & TCP flood \\  & SYN flood \\  & Vulnerability scan \\  & UDP fragmentation \\  & Command injection \\  & ACK fragmentation \\  & RSTFIN flood \\  & DNS spoofing \\  & ICMP fragmentation \\  & SQL injection \\  & Port scan \\  & UDPPlain \\  & Dictionary brute force \\  & Ping sweep \\  & OS scan \\  & GREIP flood \\  & SlowLoris \\  & Greeth flood \\  & UDP flood \\  & ARP spoofing \\  & SynonymousIP flood \\ \hline \hline
**Benign** & Benign traffic \\ \hline \hline \end{tabular}
\end{table}
Table 2: Eight-class data description of IOTCIC2023.
# Distributed Generation Cluster Division Method Considering Frequency Regulation Response Speed
## 3 Comprehensive Cluster Division Index Considering Frequency Regulation Response Speed
### Modularity Index Considering Node Power Reserves  
The concept of modularity, which was first proposed by [PERSON] and [PERSON] and was later extended to weighted network division, is commonly used in community detection of complex networks to measure the strength of the community structure of the network. The modularity metric can be calculated directly from the algorithm to find the optimal number of clusters.  
The main factor affecting the modularity index is the network edge power, and modularity has different meanings depending on the definition of edge power. Considering frequency regulation as the target division, this paper uses the impedance method to weight the electrical distance, which strengthens intra-cluster node connections, weakens the external connections of clusters, and is more in line with engineering practice.  
The cluster frequency control of the distribution network mainly relies on the nodes to flexibly regulate the active output of the PV grid-connected inverters in each cluster. The output is flexibly regulated, which requires each cluster node to have a large regulation capacity, i.e., it should have a large active reserve. Therefore, in this paper, the smaller the power reserve degree, the larger the power reserve between nodes, and the electrical distance described in this way in the whole system space is manifested as the node with less adjustable power approaching the node with more adjustable power.  
The electrical distance between any two nodes \(i\) and \(j\) described by the degree of active reserve at time \(t\) is expressed as follows:  
\[\beta_{ij,t}=\left\{\begin{aligned} & 0,P^{s}_{ij,t}\geq P^{r}_{ij,t} \sigma P^{r}_{ij,t}=0\\ & 1-\frac{P^{s}_{ij,t}}{P^{s}_{ij,t}}\end{aligned}\right.,\text{ Other situations}^{\prime}, \tag{7}\]  
where \(P^{s}_{ij,t}\) is the active power that can be provided by the PV inverters at two nodes at time \(t\), and \(P^{r}_{ij,t}\) is the active demand between two nodes at time \(t\) during node power regulation.  
Combining the electrical distances described in terms of impedance and power reserves, the I-PR integrated electrical distance \(d_{ij,t}\) between any two nodes \(i,j\) at time \(t\) is defined as  
\[d_{ij,t}=\mu_{1}Z^{\prime}_{ij}+\mu_{2}\beta_{ij,t}, \tag{8}\]  
where \(\mu_{1}\) and \(\mu_{2}\) are the weighting coefficients for \(Z^{\prime}_{ij}\) and \(\beta_{ij,t}\), respectively.  
Figure 2: Comprehensive system of indexes.  
The degree of electrical coupling between nodes is described using a modularity definition based on electrical distance weights, as shown in the following equation:  
\[\rho=\frac{1}{2m}\sum\limits_{i}\sum\limits_{j}{(d_{ij}-\frac{k_{i}k_{j}}{2m}) \delta(i,j)}. \tag{9}\]
# Radio Channel Modelling for VHF System Operating in the Offshore Wind Farms Propagation Environment
###### Abstract  
This article discusses several aspects related to the modeling of the radio channel in the wind farm propagation environment. The first part of the article is filled with the analysis of the ITU-R BT.1893-1 model, which--as will be shown--is also applicable to systems operating in the VHF and UHF bands. The conducted measurement campaign makes it possible to model a radio channel for VHF operating systems in the wind farm propagation environment. Thanks to the obtained results, the authors propose the VHF radio channel model for that environment. Using the software implementation of this model, the authors carry out a detailed simulation analysis of the impact of wind turbines on radio systems operating in the VHF band. The main motivation for this work is the growing importance of the offshore wind energy in general, numerous investments in that area in Poland, and the lack of existing mathematical models regarding the wind farms' impact on the VHF systems. The VHF band is of special interest here, as this part of the spectrum is utilized by numerous marine administration and border protection radio systems, which can be adversely affected by the wind turbines (due to interference and/or radio shadowing).  
wind farms; radio wave propagation; radio channel; marine radiocommunication; VHF band; signal interference +
Footnote †: journal: Sensors
# Maintenance Prediction through Sensing Using Hidden Markov Models--A Case Study
## 2 Theoretically Background
### Hidden Markov Models (HMM)
In this way, it is possible to find the forward variable \(\alpha_{t}(i)\), defined as  
\[\alpha_{T}(i)=P(O_{1},O_{2},O_{3},\ldots,O_{T},q_{T}=S_{i}\lor\lambda) \tag{8}\]  
This variable will give the probability of the observation sequence, \(O_{1},O_{2},O_{3},\ldots,O_{t}\) and state \(S_{i}\) at time \(t\) given the model \(\lambda\). Through the forward and backward procedure, we also obtain the backward variable \(\beta_{t}(i)\), defined as  
\[\beta_{t}(i)=P(O_{t+1},O_{t+2},O_{t+3},\ldots,O_{T}\lor S_{t}=q_{i},\lambda) \tag{9}\]  
This variable gives the final probability of the observation sequence from \(t+1\), given the state \(S_{i}\) at time \(t\) and the model \(\lambda\). This variable is not necessary for this first problem, but it will be useful to solve _Problem 3_. The solution for Problem 2 This problem aims to find the best sequence of hidden states that best fits the observed states. This is done using the Viterbi algorithm. The solution for Problem 3  
The third problem is to adjust the HMM parameters to maximize the probability of the observation sequence. This is done using the Baum-Welch algorithm. The Baum-Welch algorithm (or Baum-Welch expectation-maximization algorithm) makes use of both the direct variable \(\alpha_{T}(i)\) and the regressive variable \(\beta_{t}(i)\) when determining updated parameters for the HMM. Because of this, the Baum-Welch algorithm is also known as the Forward-Backward algorithm [11]. As [PERSON] [4] refers, three basic algorithms are used in HMM: the Forward-Backward procedure, the Baum-Welch algorithm, and the Viterbi algorithm, all of which are used for learning and recognizing the parameters of the model \(\lambda=(A,B,\pi)\).
## 1 Introduction
The effective prediction of this situation results in being profitable, specifically in the fishing industry, which is most prone to labour accidents [10]. A fishing vessel is a type of ship that usually has a low weather deck, so when it is sailing or engaged in fishing tasks in adverse sea conditions, especially with a roll amplitude larger than a certain angle, water can submerge part of the deck and then flow back into the sea from the deck. Studies about how the officer on watch can empirically predict these dangerous situations without complex calculations or specific tools installed on board are insufficient and rare. Most of the previous research has focused on high freeboard ships such as container ships, Ro-Ro ships and passenger ships affected by parametric roll [3]. Therefore, the results presented in the present paper can be a valuable guiding tool for officers on watch in order to predict synchronous rolling and avoid undesirable situations. As the rolling angles when the ship is approaching a synchronism condition build up very fast, the present work tries to explain the basic mechanism of ship rolling with simplified theoretical and numerical models, using only one degree of freedom, similar to procedures followed by other scholars [8; 25]. The prediction of the synchronism phenomenon is strictly related to the mathematical modelling of ship motion. Therefore, in this paper, novel and simplified mathematical models were obtained. Furthermore, papers about the estimation of roll damping [26] point out that the estimation of roll damping is a difficult task due to the complexity of quantifying the viscous effect influencing roll motion. However, in this research, an average value of damping factor is used in order to be as simple as possible. Afterwards, in this paper, a new control system making use of feedforward neural network tools is proposed in order to correct the effect of new waves (Tw) encountered in the sea passage and alter the ship's natural roll period (Td) determined by specific loading conditions. With this control system of neural networks, ship operators can be alerted about dangerous situations on board well in advance, so that they can alter Td accordingly. For this mission, it is necessary that weather forecasts are received on board on a regular and updated basis so the ship master and officers can act on the loading condition in advance. The remainder of the present paper is structured as follows: in Section 2, the mathematical models previously used by authors are presented and new ones obtained, together with the presentation of the artificial intelligence used. Section 3, making use of a set of ship rolling simulations, shows the results and application of the present work, obtaining interesting conclusions that are as simple as possible. Section 4 discusses the models and the results, and Section 5 concludes the paper.
## 4 Experimental Verification
### Practical Experiment
#### 4.2.1 Introduction to the Practical Experiment  
The experimental system consists of an information processor, two radars, combined navigation equipment and RTK. Two small boats equipped with angular reflectors are  
\begin{table}
\begin{tabular}{c c c c} \hline  & **X (m)** & **Y (m)** & **R (m)** \\ \hline Uncorrected Platform A & 475.6877 & 26.5548 & 476.4284 \\ Uncorrected Platform B & 508.7279 & 39.6952 & 510.2742 \\ Uncorrected Fusion & 492.2078 & 33.1250 & 493.3212 \\ Modified Platform A & 55.4091 & 0.8984 & 55.4164 \\ Corrected Platform B & 35.6165 & 0.9326 & 35.6287 \\ Corrected Fusion & 10.7591 & 0.0235 & 10.7974 \\ \hline \end{tabular}
\end{table}
Table 4: Position estimation error for scenario one.  
Figure 7: Estimation error of the target position in different observation positions.  
From Figures 8 and 9, it can be seen that in the practical experiment on the lake, corrected fusion tracking is better than the uncorrected single-platform tracking effect. The corrected fusion tracking effect is more than five times better than the uncorrected single-platform tracking effect and can maintain stable tracking of the target. The result illustrates the feasibility of the proposed spatial registration method in the environment lacking geodetic coordinate information of the observation platform. The experiments prove its effectiveness in practical applications, and the proposed method can be used as a quick tracking method in emergency situations.
## 4 Simulation and Experimental Analysis  
### Mesh Stiffness Analysis  
Table 1 shows the parameters of the planetary gearbox. Obviously, the diameter of the root circle of both the sun gear and the planet gear is smaller than the diameter of the base circle. Based on the above-mentioned improved method in Section 2, the TVMS of a pair of the sun-planet gears is calculated and shown in Figure 11 for the different health conditions. It shows the period that was experienced from the crack tooth starting to mesh to the ending of mesh. As the size of the crack grows, the mesh stiffness reduces correspondingly, which is consistent with the change of actual gear mesh stiffness. Correct stiffness calculation is an integral part of gear fault diagnosis. For each crack level, the corresponding vibration signal can be obtained through simulation calculation. By analyzing these vibration signals, the index of fault severity can be obtained, and then the fault severity of the gear system can be detected.  
Figure 11: TVMS of single tooth under different fault degrees.
# Application of Fiber Bragg Grating Sensing Technology for Bol Force Status Monitoring in Roadways
## 4 Application of FBG Sensors for Bolts Force Monitoring in Coal Mine Roadway
### Arrangement of Roadway Bolt Support and Monitoring System  
The roadway bolt support and monitoring system in the coal mine is shown in Figure 8. A \(\Phi\)32 mm hole was drilled in the roadway surrounding rock during the bolt support. The anchor agent and the bolt were successively placed in the hole. The tail end of the bolt was bonded to the surrounding rock with the anchor agent. A bolt plate, a sensor, and a nut were successively installed at the outer end of the bolt. A torque force was applied to the nut so that the bolt generated a pretightening force to form active support effect on the surrounding rock. When the surrounding rock was deformed, the action force between the bolt plate and the surrounding rock and the force status of the bolt were changed accordingly. The force value of the bolt could be monitored online in real time and measured on site with the FBG bolt force sensor.  
The tail fiber of the sensor was flanged to the communication optical cable in the junction box. The other end of the communication optical cable was connected to the channel port of the FBG interrogator. The FBG interrogator was connected to the upper computer using an RJ45 network interface to monitor and display the sensor reflected center wavelength. The acquired data was stored into the server database via Ethernet. A client PC implemented remote visual display and real-time monitoring of bolts force status in roadway by accessing the server.
## 2 Materials and Methods
### Measurement Outcomes
#### 2.3.5 Questionnaires  
The participants filled out the Oxford hip score (OHS) questionnaire at the baseline and the three- and twelve-month follow-ups to assess their self-evaluated function and pain [24]. The OHS is a 12-question patient-reported outcome. Each question is ascribed one to five points, adding up to an overall score from 12 to 60 for each patient that quantifies their hip function, with 12 being the worst and 60 being excellent hip function. The OHS was chosen since it had been validated in Danish and had been shown to be sensitive to changesfollowing a THA [25]. The OHS had, in previous studies, shown that a meaningful clinically important difference (MCID) is demonstrated by a score between five and nine [26,27].  
The participants also filled out questionnaires regarding their training satisfaction, adverse events, and self-reported ADL at the three- and twelve-month follow-ups. Training satisfaction was evaluated using a questionnaire with a 5-point satisfaction score from not satisfied at all (1) to very satisfied (5).
# Comprehensive Review of Traffic Modeling: Towards Autonomous Vehicles
## 3 Traffic Flow and Its Characteristics
### Shape of the Fundamental Relation
[PERSON] et al. [36] analyzed and modified [PERSON] model, introducing a bell-shaped curve to describe the relationship between speed and density, particularly in complex traffic scenarios (Figure 3):  
\[u_{s}=u_{\text{max}}\text{exp}\left[-\frac{1}{2}\left(\frac{k}{k_{m}}\right) ^{2}\right] \tag{16}\]  
Figure 2: Linear density–speed (**a**) and parabolic density–flow (**b**) relations. [PERSON] [37] proposed a hybrid model that combines a parabolic relationship below the critical flow and a linear relationship above it, offering a more nuanced description of traffic flow dynamics (Figure 4). [PERSON]'s model [38], one of the most widely used, presents a bilinear relationship in the density-flow plane, providing a simple yet effective framework for understanding traffic behavior (Figure 5). Figure 4: [PERSON] fundamental diagram. Figure 5: [PERSON]’s fundamental diagram. Figure 3: [PERSON]’s fundamental diagram. While these models laid the groundwork for traffic flow theory, they also have limitations. The LWR model and its modifications are among the simplest traffic flow models but fail to account for more complex phenomena such as capacity drops, hysteresis, relaxation, spontaneous congestion, traffic oscillations, and phase transitions. As traffic systems become more intricate, there is a growing need for models that can capture these advanced dynamics.
# Graph-Based Resource Allocation for Integrated Space and Terrestrial Communications
## 6 Graph-Based RA in CR Networks
### RA Methods in Underlay CR Networks
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & **Application/Assumptions** & **Graph Model Type** & \begin{tabular}{c} **Tasks** \\ **Solved via** \\ **Graphs** \\ \end{tabular} & **Graph Formulation** & \
\begin{tabular}{c} **Optimization** \\ **Method** \\ \end{tabular} &
\begin{tabular}{c} **Performance** \\ **Assessment** \\ \end{tabular} \\ \hline
[85] & \begin{tabular}{c} RA in an interweave CR \\ network/PU protection \\ (realistic) \\ \end{tabular} & Conflict & Channel & \
\begin{tabular}{c} Vertices—SUs pairs; \\ edges—between SUs \\ weights—between SUs \\ weights— \\ interference \\ \end{tabular} &
\begin{tabular}{c} \(R_{SU}\) up to \\ 4.5 b/s/Hz, \\ and \(\beta\) up to 1 \\ for \(N_{SU}=40\) \\ and \(N_{PU}=25\) \\ \end{tabular} \\ \hline
[86] & \begin{tabular}{c} RA in a CR IoT \\ network/channels of \\ equal bandwidth \\ (unrealistic) \\ \end{tabular} & Directed & Channel & \begin{tabular}{c} Vertices—SUs; \\ edges—between SUs; \\ weights—SINR \\ \end{tabular} & \
\begin{tabular}{c} Genetic \\ algorithm \\ \end{tabular} &
\begin{tabular}{c} \(R_{CR,T}\) up to \\ 20 b/s/Hz for \\ \(N_{SU}=60\) \\ \end{tabular} \\ \hline
[87] & \
\begin{tabular}{c} RA in an underlay CR \\ network/Perfect CSI \\ knowledge (unrealistic) \\ \end{tabular} & Interference & Channel &
\begin{tabular}{c} Vertices—SUs; \\ edges—between SUs; \\ weights—received \\ power \\ \end{tabular} & Potential & \(\Omega_{U}=2.1\) for \(N_{SU}>N_{CAP}\) \\ \hline
[88] & \begin{tabular}{c} RA in a CR network/PU \\ and SU synchronization \\ (unrealistic) \\ \end{tabular} & Conflict & Channel & \
\begin{tabular}{c} Vertices—SUs; \\ edges—between SUs; \\ weights—mean \\ throughput \\ \end{tabular} &
\begin{tabular}{c} Up to 46 dB \\ performance \\ loss for \\ loss \\ \(N_{SU}=50\), \\ \(N_{CAP}=5\) and \\ \(N_{c}=8\) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 8: _Cont._CR throughput increases exponentially with the number of channels and the maximum transmission power of the SUs.
## 3 Results and Discussions
### Analysis of the Trunk Detection Results  
In analyzing the model results in detecting tree trunks, we found some cases where a tree trunk is not marked in the ground truth, but the model can still find it in the images. Figure 14 shows an example of this case.  
When the trunks are close together, as is the case in some images, the model cannot detect both of them. This situation is illustrated in Figure 15. Since the overlap occurs while the trunks are far from the camera and the robots are moving through the field, the overlap of the trunks in the close-up image is minimal and therefore does not pose a problem for the robot application. However, more photos with overlapping trunks should be added to the training dataset, and the model should be re-trained to reduce the error in detecting the overlapping trunks.  
When the surroundings are extremely complicated and the trunks are far from the camera, the model also has difficulty accurately identifying all the trunks in the image. Examples of this case are shown in Figure 16. As can be seen, the environment is quite complex because the trunks are far from the camera and there are plants in the middle of the row, making it difficult for the model to identify all the stems in the image.  
Figure 13: Detection results of the trained models with and without enhanced images in the training dataset. Top image: ground truth, bottom left: Model trained with enhanced images, bottom right: Model trained without enhanced images.