esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Controller Deployment in SDN-Enabled 6G Space-Air-Ground Integrated Network
## 3 Results  
### Experimental Scene Description  
The iridium constellation network is used as the data surface of SDN in the simulation scene, and the controller is deployed at the ground gateway station. The iridium constellation consists of 66 working satellites in six polar circular orbits, that is, 11 in-orbit satellites in each orbit plane, separated by an interval of about 4500 km. The satellites are located in low-earth polar orbit 780 km above the Earth and orbit the Earth once every 100 min at a speed of 27,070 km per hour. The constellations are shown in Table 1.  
The data source is from STK (Systems Tool Kit), which collects the latitude and longitude of satellite nodes and the altitude, latitude and longitude of the ground station. The iridium satellite system's satellite cycle is about 100 min, the length of each time slice is set to 1 min, and the satellite constellation's operating cycle will be divided into 100 time slices. The iridium constellation network topology generated by STK software is shown in Figure 5.  
The other parameters are as follows: a single controller can handle up to 60,000 packet-IN request packets, that is, \(Q_{max}=60,000\,(packet-IN)\), and \(\rho\) is 0.85. The switch switchover occurs when the load reaches 85% of the server load to achieve load balancing. The weight ratio of load factor is \((\alpha,\beta,\gamma)=(0.8,0.1,0.1)\). The maximum propagation delay from the satellite node to the controller is \(L_{max}=45\) ms. The weight ratio of propagation delay to load is \((\mu,\gamma)=(0.6,0.4)\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Value** & **Parameters** & **Value** \\ \hline Orbital altitude & 780 km & Satellite planes & 6 \\ Orbital type & Polar orbit & Satellites per plane & 11 \\ Inter-satellite link bandwidth & 10 Mbps & inclination & 86.4\({}^{\circ}\) \\ Angle difference between & 31.6\({}^{\circ}\) & Phase factor of the adjacent plane & 16.36\({}^{\circ}\) \\ adjacent planes & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Iridium system parameters.  
Figure 5: Iridium system network.
[22] investigated the fatigue crack propagation behavior of an Al-Cu-Mg alloy, finding that there is a slight decrease in FCGR as the crack propagates the Goss-orientated ((110) \(<\)001\(>\)) [18] grains, and the fatigue crack is prone to deflect when it goes through the cube-orientated ([001] \(<\)100\(>\)) [19] grains. [PERSON] et al. [23] found that the Goss texture can reduce the FCGR of aluminum alloys, whereas [PERSON] [20] showed that the S ([123] \(<\)634\(>\)) texture has a strong inhibitory effect on the fatigue crack growth of the alloy. Although many studies have been conducted, the relationships between textures and the fatigue properties of aluminum alloy remain unclear. In addition, there is still a lack of effective methods available to control the texture during the fabrication of wrought aluminum alloy. It is known that a high damage-tolerant aluminum alloy is always chosen with a T3 temper to minimize the adverse effect of inclusions, while, during the solution treatment of T3, the texture characteristics, especially deformation textures, would be totally switched due to the inevitable recovery and recrystallization at such high temperatures [24; 25]. The aim of this study is to reveal the effect of texture on the fatigue properties of 2524-T3 aluminum alloy. Here, the texture evolutions of 2524-T3 aluminum alloy during cold rolling and heat treatments were revealed, and a method for the preparation of 2524-T3 aluminum alloy with a strong deformation texture (i.e., a Brass texture) was developed. In addition, the effects of this Brass texture on the fatigue crack propagation behavior of alloy were assessed by the use of an orientation distribution function (ODF), electron back-scattered diffraction (EBSD), and transmission electron microscope (TEM).
# Pico-Sat to Ground Control: Optimizing Download Link via Laser Communication
## 4 Results
### SATLLA-2B's Flight Result
Moreover, we are planning to open the ability to turn on the satellite LEDs to relevant observatories to help us detect SATLLA-2B (equivalent to the concept of TinyGS but with FSO links).
# Process Mining IPTV Customer Eye Gaze Movement Using Discrete-Time Markov Chains +
## 5 Discussion
* **\"Content viewing\"** scenario: when a Content Discovery page first loads, what are customers viewing? Are they drawn to the hero carousel, the navigation, or something else? One outcome of the study was that large images first caught the attention of most people after loading a screen. Therefore, a recommendation could be made to use a large image to enhance a significant part and avoid large images in less important areas. These recommendations have helped to improve the design of the BT Player, as it could be confirmed that certain elements may need to be changed while others could remain the same in order to engage the customer and increase sales. Furthermore, the research findings were shared with the BT product team and other individuals with domain knowledge of BT IPTV and TV customer behaviour. The DTMC-derived gaze movement trajectory, as well as other Markov properties such as steady-statedistribution, expected time to absorption, and so on, were found to be quite reasonable for the use cases. The product team provided some validation of the results by comparing them to the original layout design. Besides, our interactions with the BT team during the course of the eye-tracking study led to the following observations of more general business benefits, with feedback from BT as follows:  
* Eye tracking studies can provide valuable inputs to a human-centred design approach for TV applications;
* Eye tracking results can show the order in which people focus on different parts of a TV application page, which enables designers to review the information architecture and whether some pages are too complex;
* Heat maps derived from eye tracking and information on the order of focus can be used to re-assess \"what should be the key function of this page?\"
# Suitability of NB-IoT for Indoor Industrial Environment: A Survey and Insights
## 3 State-of-the-Art
### Developmental Trends and Technical Features
#### 3.1.2 Internet of Things (IoT)  
A commonly acceptable consideration of IoT is where regular objects (physical and virtual, which can be identified either through their physical or virtual attributes) are connected to a scalable and unifying network infrastructure--the Internet. In other words, an infrastructure that can use some sets of regulatory communication protocols for self-configuration to provide a reliable and easy connection for these objects [27]. While the term \"Internet of Things\" was coined by [PERSON] in the year 1999 [28], the basic concept behind it was the combination and application of various technologies such as sensors, Radio Frequency Identification (RFID), and actuators, etc., to bring about the interaction among objects that applies these techniques in a standardized form [29]. These objects are called smart objects because of their ability to interact with the environment and other objects as well.  
A survey of some factors and technologies that drives IoT was discussed in [30; 31], factors such as communication links (e.g., ZigBee, Bluetooth), identification of objects through electronic product code (EPC) or ubiquitous codes (ucode), smart sensing and computational processes, among other things. The ubiquity of IoT is leading to a paradigm of its kind such as the Internet of People (IoP), Internet of Services (IoS), Internet of Content (IoC), and Internet of Agents (IoA) [32]. Based on the relation of this paradigm with IoT, the authors analyzed these trends and the challenges ahead. The authors in [33; 34] reviewed the architecture, networks, and smart objects that form the building blocks of IoT.
# Design of a Filtering Power Divider with Simple Symmetric Structure Using Stubs
## 5 Experimental Results  
To verify the simulation results, the proposed device is fabricated and measured. The fabricated device is depicted in Figure 12. The proposed WPD experimental results are shown in Figure 13a,b. The E8362B Network Analyzer is considered to perform the experimental measurement for the proposed power divider.  
According to the achieved experimental results, the isolation and insertion losses are approximately 20 dB and 0.3 dB, respectively, at the main frequency. Furthermore, the measured input and output return losses are approximately 21 dB and 26 dB, respectively, at the main frequency. In addition, the second and third harmonics are suppressed with a high attenuation level of 30 dB and 20 dB, respectively. The fractional bandwidth (FBW) of the proposed divider considering a 10 dB input return loss is 17% corresponding to 1.83 GHz up to 2.17 GHz, which is indicated with an FBW1 box in the frequency response in Figure 13a. Moreover, the FBW based on a 3 dB attenuation level is 40% from 1.5 GHz to 2.25 GHz, which is indicated with an FBW2 box in the frequency response in Figure 13a. Figure 13b shows the output return loss (\(\mid\)S\({}_{22}\mid\)) and isolation (\(\mid\)S\({}_{32}\mid\)) of the proposed power divider. As seen in Figure 13b, the desirable output return loss and isolation are obtained near the operating frequency. The performances of the fabricated WPD are compared with related dividers in Table 2. As seen, the designed WPD with a new structure has achieved  
Figure 11: Surface current distribution in the proposed WPD at the frequencies of (**a**) 2 GHz, operating frequency, (**b**) 4 GHz, second harmonic, and (**c**) 6 GHz, third harmonic. The maximum value of magnetic intensity is 2 A/M in all cases.  
high performance compared to the related dividers. The overall size of the proposed power divider is 36.7 mm \(\times\) 27.5 mm, which is equivalent to \(0.32\lambda_{\mathrm{g}}\)\(\times\)\(0.24\lambda_{\mathrm{g}}\), where \(\lambda_{\mathrm{g}}\) is the guided wavelength at the center frequency of 2 GHz.  
Figure 12: Photo of the implemented WPD.  
Figure 13: Experimental and simulated S-parameters of the proposed WPD. (**a**) \(\mathrm{I}\)S\({}_{21}\), and \(\mathrm{I}\)S\({}_{11}\) ; (**b**) \(\mathrm{I}\)S\({}_{22}\) | and \(\mathrm{I}\)S\({}_{32}\) |.
Reliability Analysis of Layered Soil Slopes Considering Different Spatial Autocorrelation Structures  
[PERSON]  
1 Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Ministry of Education, School of Geosciences and Info-Physics, Central South University, 932 South Lushan Road, Changsha 410083, China; [EMAIL_ADDRESS] (S.Z.); 1126060  
model the soil spatial variability in geotechnical probabilistic analysis, e.g., slope reliability analysis. Therefore, it is of great significance to investigate the effects of the autocorrelation structure on slope reliability analysis.  
Many published studies have highlighted the effects of the theoretical ACF on slope reliability analysis. For example, [PERSON] [8] and [PERSON] [16] systematically compared the effects of five commonly used ACFs on the reliability of soil slopes and seepage flow in an embankment, respectively. The same ACFs and SOFs are used for different soil layers in these studies. However, it might be argued that the SOFs for different soil layers can be different, even though the same ACF is adopted. In view of this observation, [PERSON] [17] investigated the effects of SOFs on the reliability analysis of a layered slope, where soil properties were modeled by the same ACF but different SOFs were taken for different soil layers. It was found that when the difference in SOF between different soil layers is large, the slope reliability assessment results might be overestimated or underestimated. However, since different geological formations often vary differently in space, soil properties from different soil layers should be modeled by different ACFs to reflect the soil heterogeneity. As such, the available studies that generally assume the same ACFs for different soil layers in slope reliability analysis tend to be unreasonable. In addition, as stated by [PERSON] [18] and [PERSON] and [PERSON] [19], the variability of soil properties changes with the soil type and location, and no autocorrelation model is univocally preferable over others on the basis of physical motivations. Hence, how to appropriately consider different autocorrelation structures for different soil layers in the reliability analysis of layered soil slopes is still an open question, which poses a challenge to assess the failure risk precisely.  
The aim of this paper was to investigate the effects of ACFs on the reliability analysis of layered soil slopes, where soil properties of different soil layers were considered by different ACFs. Five commonly used ACFs and the non-classical Whittle-Matern model in the literature were investigated. Cholesky decomposition and Monte Carlo simulation were used to simulate the spatial variability of soil properties and estimate the \(P_{f}\) of slopes, respectively. A two-layered cohesive slope and a cohesive-frictional slope with a weak seam were studied to elaborate and compare the effects of multiscale soil spatial variability modeled by different ACFs on slope reliability analysis. Moreover, the influence of various SOFs on reliability analysis was also explored.
# Rail Diagnostics Based on Ultrasonic Guided Waves: An Overview
## 2 An Overview of Rail Defects and of the Techniques for Their Detection
### Defect Detection Techniques Based on Ultrasonic Waves
#### 2.3.3 Guided Waves  
In an effort to overcome some of the technical problems associated with ultrasonic wheel inspections, some research groups are investigating ultrasonic _guided waves_ for rail inspections. Different types of guided waves can propagate in any bounded medium. Inspection tests can be accomplished in _pulse-echo mode_; this means that an ultrasonic transducer is employed to transmit a pulsed guided wave along the structure of interest and that the presence of defects or other structural features is inferred from analyzing the returning echoes sensed by the same transducer [7]. Operation in _pitch-catch mode_ can be also employed; in this case, a transmitter generates a guided wave in the waveguide and a receiver (i.e., a different transducer, placed at the other end of the sample) is employed to detect the incoming wave. If a defect reflects a portion of the transmitted energy, the received signal is weaker than that observed in pristine waveguide conditions; this allows for detecting the presence of defects [40].  
Ultrasonic guided waves offer the following advantages [41]: (a) _guided waves propagate along (rather than across) rails_, and are ideal for detecting critical TDs, even if they are relatively insensitive to parallel fissures [42]; (b) _guided waves allow for improving inspection coverage_, thereby relaxing limits on the maximum achievable speed (if the inspection equipment is installed on-board a moving vehicle); (c) _guided waves can travel underneath shelling and still interact with internal defects_, since they penetrate a finite depth of the surface of rails; iv) _guided waves can penetrate alumino-thermic welds_, hence potentially targeting weld cracks and discontinuities, since they travel in the mid-frequency range (say, in the 20 kHz-1 MHz range).  
Unluckily, the complicated propagation behavior of guided waves cannot be easily managed [43]. In fact, ultrasonic guided waves, especially in complex waveguides such as rails, express a _multi-mode_ character (many modes can propagate simultaneously) and a _dispersive_ character (the propagation velocity depends on frequency) [7]. In practice, tens of different modes can be observed in the employed frequency range; moreover, the dispersion curves characterizing this propagation phenomena are very complicated [43], as it can be easily inferred from Figure 10.  
Figure 10: Dispersion curves (**a**) and mode shapes (**b**) observed in a rail waveguide. Some candidate modes for long range damage detection are highlighted. Modes 1 and 2 are symmetric and anti-symmetric, respectively, and their energy is concentrated in the crown of the considered rail; the energy of mode 3, instead, is more evenly distributed across the rail cross section (a small portion of it is observed in the foot), whereas that of mode 4 is concentrated in the web of the rail (pictures taken from Ref. [44]).
###### Abstract  
To protect the lacquer plates from the Nanhai No. 1 shipwreck from being corroded by microorganisms, a series of studies were conducted on the four water-stored samples. The water samples were collected from the vessel where the lacquerware was stored in June and December 2017. In our study, high-throughput sequencing was conducted to reveal predominant bacterial communities. Then, three different media were used to isolate the dominant bacteria, and the 16S rRNA genes were sequenced. Next, we tested the degradation activity of lignin and cellulose by the isolated bacterial strains. After being cultured on a medium containing carboxymethylcellulose (CMC), almost all the isolated strains (except _Microbacterium_ sp. NK-NH4, _Ochroductrum_ sp. NK-NH9, and _Bacillus megaterium_ NK-NH10) showed the capacity for cellulose degradation. In addition, the lignin peroxidase (Lip) and lacase activity of the strains were shown by culturing the strains on one medium with azure and on another medium with Remazol brilliant blue. The results indicated that the Lip activity of all the strains was low, whereas the lacase activity of _Microbacterium_ sp. NK-NH4, _Bacillus tequilensis_ NK-NH5, _Bacillus subtilis_ NK-NH6, _Bacillus megaterium_ NK-NH10, and _Bacillus velezensis_ NK-NH11 was relatively high. Finally, we tested the bacteriostatic efficacy of four biocides--Preventot(r) D7, BIT 20N, P91, and Euvyl(r) K100. We found that most strains were sensitive to D7 and 20N, while K100 had almost no impact.  
Nanhai No. 1 shipwreck; lacquerware; high-throughput sequencing; ligninolytic enzymatic activity; cellulolytic enzymatic activity; biocide effectiveness; bacterial diversity +
Footnote †: journal: Applied Sciences
## 5 Results and Discussion
### Experimental Studies  
We prepared two phantoms; one mimicking a normal and the other mimicking a pre-cancerous cervical tissue. The fiber optic probe (probe-geometry II) was employed to measure the SRR. We performed the simultaneous recovery of optical parameters of the top layer from the experimental SRR data procured using the probe. Details of the probe, phantom preparation, and the experimental setup have already been described in Section 4. The choice of optical parameters of the cervical tissue-mimicking phantoms was based on [12; 50]. The algorithm proposed to solve Equation (6) was numerically validated in Section 5.1.3 and was implemented on the experimental data to recover the absorption and scattering coefficients of the top layer simultaneously. The results thus obtained are tabulated in Table 5. The algorithm was able to extract the absorption and scattering coefficients of the top layer with a maximum of 6.7% and 3.3% errors \(Error=\frac{TrueValue-RecoveredValue}{TrueValue}\times 100\)), respectively, as compared to their true values.
## 6 Interpretation and Results
### Evaluation of the Borehole Mineral Water Quality
#### 6.4.2 Ion Content and Bacterial Analysis  
The zinc content ranges from 0.003 to 0.05 mg/L, selenium from 0 to 0.005 mg/L, iron from 0.012 to 0.018 mg/L, strontium from 0.1 to 0.211 mg/L, lithium from 0.036 to 0.05 mg/L, fluoride from 0.03 to 0.80 mg/L, cyanide from 0 to 0.002 mg/L, mercury from 0.0002 to less than 0.05 mg/L, hexavalent chromium from 0.0014 to less than 0.004 mg/L, lead from 0.0004 to less than 0.01 mg/L, cadmium from 0.0001 to less than 0.002 mg/L, phenol from 0 to less than 0.002 mg/L, anionic synthetic detergent less than 0.10 mg/L, all of which are within permissible limits. The total bacterial count is less than 3 per mL, and coliform bacteria are less than 3 per L, indicating good sanitary conditions for the water.
# A Survey on Penetration Path Planning in Automated Penetration Testing
## 5 Planning Methods in Penetration Path Planning
### Reinforcement Learning
#### 5.2.1 Classical Reinforcement Learning  
Classical RL relies heavily on tabular data structures to compute action-value functions and direct fitting of policy functions via action trajectories [85]. In the case of limited state and action spaces, classical RL algorithms, such as Q-learning and SARSA, are able to show excellent performance on some simple problems.  
In 2018, [PERSON] et al. [86] analogized the penetration path planning problem to the \"maze treasure hunting problem\". They proposed a method to convert the attack graph generated by the target network into an RL training environment and applied the Q-learning algorithm to plan optimal penetration paths in the attack graph. This approach helps defenders formulate effective network security strategies. Additionally, [PERSON] et al. [87] simplified the PT process to a Capture The Flag (CTF) framework and investigated the influence of various forms of expert prior knowledge on the effectiveness of penetration path planning when integrated with the Q-learning algorithm. [PERSON] et al. [88] utilized information gained from PT as a reward function in RL and employed the Q-learning algorithm to guide intelligent agents in exploring target network information.  
As the problem size and complexity increase, the limitations of classical RL methods become increasingly apparent. Large state spaces or continuous action spaces pose significant challenges for tabular methods, while simple policy function approximation methods may fail to adequately capture the complexity and nonlinearity of the problem.