esa-sceva/satcom-chunk-collection huggingface dataset:
# An Intrusion Detection System for RPL-Based IoT Networks
## 4 IDS Design
### Architecture and Components  
The high-level architecture of the IDS follows the design proposed in [49] and is briefly described in this subsection. In addition to the typical sensor nodes, we consider two new types of devices: (i) _Central IDS_ or _border routers (BR)_, which are centralised detection modules with routing and firewall capabilities, and (ii) _IDS detectors_, which are distributed detection modules that are less powerful, sensor-like devices used to monitor and send suspicious traffic or alerts to the central IDS. In a typical scenario of a small IoT network, there are one BR and several IDS detectors. This means that sensors requiring to communicate with a server will send all the requests through the router. All passing traffic is checked by the BR, which will take the decision as to whether the sending node is malicious or not.  
IDS detectors monitor the sensors' traffic to help in detecting malicious nodes. compromised devices may attempt to internally disrupt the network without having to communicate with the BR or external networks. For such cases, the detectors log network traffic, and if a node's activity resembles a known attack behaviour or triggers a known attack signature, an alert along with any related information is forwarded to the BR for decision-making. The traffic exchanged between the sensors is monitored by the nearest detector within range. Afterwards, a lightweight algorithm is executed to decide whether or not traffic should be forwarded to the BR. The collaboration between the BR and the detectors helps in capturing traffic from both internal and external interfaces. For example, some malicious devices may attempt to establish communication with a remote command & control server in order to obtain further malicious instructions or updates. Other malicious devices may exchange traffic locally. In cases where a wireless channel between the BR and the detectors is unavoidable or preferable, an appropriate and secure wireless communication scheme will be put in place (e.g., [52]).
## 4 Conclusions  
We successfully prepared derivatives of PEDOT:PSS by the reaction with epoxyalkanes and the filtration of the reaction mixture on a conventional nylon filter. FT-IR, TGA, and XPS studies clearly demonstrate that alkyl chains are introduced to PEDOT:PSS by the reaction between the free sulfonic acid of PSS and epoxide group. In the case of derivatives, the greater the length of the reacted alkyl group, the lower the electrical conductivity; on the other hand, it is worth noting that the conductivities of PEDOT:PSS-C4 and -C6 are higher than those of pristine PEDOT:PSS.  
Most of the derivatives were not dispersed in water or alcohol-based solvents; however, PEDOT:PSS-C6 was well dispersed in methanol at a concentration of 0.6 wt.%. We succeeded in preparing a transparent film from this dispersion on a glass or a flexible substrate (PET). An increase in hydrophobicity and subsequence enhanced water resistance of PEDOT:PSS-C6 film were confirmed by measuring contact angle and visually observing the film in water, respectively. The surface roughness of PEDOT:PSS-C6 film was much higher than that of pristine PEDOT:PSS film, and nearly the same roughness as that of PEDOT:PSS film doped by DMSO. Further studies on morphology of PEDOT:PSS-C6 film clearly showed phase separation similar to the doped PEDOT:PSS film. When the flexibility of films was examined by cyclic bending tests, surface resistance of PEDOT:PSS-C6 was found to be more stable compared to that of PEDOT:PSS.  
We tentatively conclude that, in PEDOT:PSS-C6, the introduction of the alkyl group into the sulfuric acid of the free PSS of PEDOT:PSS causes phase separation more distinctively and finally leads to an improvement in conductivity. Furthermore, the alkyl groups introduced into PEDOT:PSS induced the improvements of the flexibility and water resistance of PEDOT:PSS.  
Conceptualization, D.-w.C.; formal analysis, S.-y.J. and N.-Y.P.; methodology, I.-S.H., C.-W.P. and H.-I.K.; writing--original draft, I.-S.H., H.-I.K. and D.-w.C.; writing--review and editing, I.-S.H. and D.-w.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the research grant of the University of Suwon in 2019.  
The authors declare no conflict of interest.
# Risk Prediction for Winter Road Accidents on Expressways
## 2 Data
### Shuttle Radar Topography Mission Data  
Shuttle radar topography mission (SRTM) data are 3D digital elevation model data observed by satellites operated by NASA in 2000 [19]. They provide information on latitude, longitude, and altitude, with a resolution of 30 to 90 m. As the temperature is affected by altitude, this study used SRTM data with 30 m resolution to determine the altitude value of the area surrounding the road accident points and weather event occurrence. In addition, the difference between the altitude of the surrounding area and point of occurrence was used. Furthermore, as the duration of road freezing is prolonged owing to shadows, shadow information was generated using the time information and altitude difference values.
### Advancements in 3D Electromagnetic Tomography
A 3D head MIT simulation model was constructed based on actual CT data, and based on this model, factors affecting the MIT signal were evaluated, as well as factors influencing the safety of MIT devices [19]. Instead of the traditional multi-coil setup, a single coil consisting of several concentric circular wire loops was used for excitation and detection [20]. The feasibility of this method in 3D MIT has also been studied, and it has made breakthrough progress in differentiating tissues in conductivity distribution images of the thoracic spinal column.
## 3 Results and Discussion
### Thermally Expandable Microspheres Designation  
From the previous experimental results, in order to prepare microspheres with excellent expansion properties, both of the properties of shell polymer and blowing agent must be considered. selection of gas-proof shell polymeric composition, simultaneous, balance of \(T_{\rm g}\), and melt viscosity of shell polymer, types and boiling point of blowing agent must be important. Further, when exposed to heat, ductility and intensity of shell polymer also must be an important factor [21; 27]. The properties of shell polymer are mainly displayed by the viscoelasticity, \(T_{\rm g}\), crosslinking degree and compatibility with blowing agent, etc. Shell polymer properties are mainly controlled by shell composition and crosslinking degree. The molecular chain structure, the polymer modulus, and mechanical strength of high elastic states are closely related to the entanglement density and crosslinking degree of the molecular chain of the shell polymer. By adding crosslinking agent, the crosslinked network structure is an effective method to improve the high elastic modulus, elasticity, mechanical strength, and temperature range of high elastic state. However, when the content of crosslinking agent is too high, the crosslinking density and elastic resilience of the molecular chain would increase, which may restrict the expansion process. The adjustment of \(T_{\rm g}\) can be achieved by the copolymerization of hard monomer and soft monomer. For copolymer microspheres containing VDC monomer, it is necessary to add sufficient contents of co-monomer to destroy the crystallinity of PVDC homopolymer. Then the \(T_{\rm g}\) range of copolymer can be controlled by adjusting monomer composition. On the other hand, the expansion of polymer microspheres requires a driving force, which is the internal gas pressure is produced by the vaporization of the blowing agent. Therefore, the blowing agent structure determines the boiling point and the vapor pressure at a certain temperature. The \(T_{\rm st}\) can be adjusted by the combination of the content of the blowing agent with the appropriate boiling point. The boiling point of the blowing agent is lower than the softening temperature of the shell polymer, and it has enough internal gas pressure at the expansion temperature to drive the expansion of the microspheres.
# Frequency Comb-Based WDM Transmission Systems Enabling Joint Signal Processing
###### Abstract  
We review the use of optical frequency combs in wavelength-division multiplexed (WDM) fiber optic communication systems. In particular, we focus on the unique possibilities that are opened up by the stability of the comb-line spacing and the phase coherence between the lines. We give an overview of different techniques for the generation of optical frequency combs and review their use in WDM systems. We discuss the benefits of the stable line spacing of frequency combs for creating densely-packed optical superchannels with high spectral efficiency. Additionally, we discuss practical considerations when implementing frequency-comb-based transmitters. Furthermore, we describe several techniques for comb-based superchannel receivers that enables the phase coherence between the lines to be used to simplify or increase the performance of the digital carrier recovery. The first set of receiver techniques is based on comb-regeneration from optical pilot tones, enabling low-overhead self-homodyne detection. The second set of techniques takes advantage of the phase coherence by sharing phase information between the channels through joint digital signal processing (DSP) schemes. This enables a lower DSP complexity or a higher phase-noise tolerance.  
optical frequency comb; fiber optic communication; coherent detection; digital signal processing (DSP); carrier recovery
## 3 Results
### Effect of Off-Normal Processing Parameters
#### 3.2.1 Ti-6 Al-4V  
The laser pulse energy of 60 mJ was used in the first experiments aimed at characterizing the repeatability of the waveforms for different AM depositions. The three (blue, red, black) waveforms shown in Figure 10 are on nominally the same material (process parameter set A) and roughly the same propagation distances (\(\sim\)23 mm and \(\sim\)39 mm for points 1 and 2 respectively). As in Figure 6, there is variability in the peak-to-peak amplitudes, but the waveforms are all quite similar V-shapes. Probably due to the lower pulse energy, the initial wing in the waveform is less than for the high pulse energy results in Figure 6. The variation in phase is primarily due to the variations in the actual positions of points 1 and 2.  
Off-normal process parameter sets B and C are compared with set A at points 1 and 2 in Figure 11. The waveform for set B (in red) has a much lower amplitude, especially at point 2, suggesting that the reduced deposition laser power of 380 W (see Table 1) results in material having more internal losses that cause more attenuation than the normal laser power (450 W). The waveform for set C (in black) is similar to its counterpart for set A, suggesting that the 0.1 mm difference in hatch spacing does not change the material nonlinearity enough to be detectable. Received signals quite similar to those shown in Figure 11 were obtained for parallel wave paths. The linear frequency spectra for the signals received at point 1 are shown in Figure 12. The spectra were obtained by fast Fourier transformation (FFT) using a rectangular window from 8-11 us and zero padding the time sequence. There are no obvious differences in the spectra resulting from the parameter sets A-C, nor are there any obvious differences from the frequency spectra obtained from the Figure 6 data (these spectra are not shown).  
Figure 11: Ti-6 Al-4V AM depositions for process parameter sets A, B, C in Table 1 received at (a) point 1 and (b) point 2.  
Figure 10: Three different Ti-6 Al-4V AM depositions for process parameter set A in Table 1 received at (a) point 1 and (b) point 2.
## 4 Sensitive Analysis of Rotor Parameters  
### Rotor Optimize Parameters  
Five rotor design parameters are set as optimization variables in the optimization process, including bridge thickness, rib width, stiffener thickness, PM thickness, and PM width. Figure 8 shows the trend of the five optimization parameters for motor efficiency and rotor stress.  
In Figure 8a, when the bridge thickness is greater than 2.5 mm, the motor efficiency drops rapidly. However, the decreasing tendency of rotor stress becomes slow. Therefore, the variation range of the bridge thickness is limited from 1.0 mm to 2.5 mm. As can be seen from Figure 8b, the effect of rib width on motor efficiency is mild. The effect of stiffener thickness on motor efficiency and rotor stress is shown in Figure 8c. With the increase in the stiffener thickness, motor safety is greatly improved, but motor efficiency first increases and then decreases. In order to obtain higher motor efficiency, the variation range of stiffener thickness is set from 2.0 mm to 3.5 mm.  
With the increase in the number of PMs, the efficiency of the motor increases, and the centrifugal force and rotor stress also increase. In addition, the number of PMs used is limited by the installation size. Therefore, the maximum and minimum values of PM thickness are set to 5.5 mm and 7 mm, respectively. The variation range of PM width is limited to 51-54 mm.  
Based on the above analysis, the variation ranges of the five rotor design parameters are listed in Table 4.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Symbol** & **Params** & **Range** \\ \hline \(TH_{B}\) & Bridge thickness (mm) & [1.0, 2.5] \\ \(W_{R}\) & Rib width (mm) & [1.5, 2.5] \\ \(TH_{S}\) & Stiffener thickness (mm) & [2.0, 3.5] \\ \(TH_{PM}\) & PM thickness (mm) & [5.5, 7.0] \\ \(W_{PM}\) & PM width (mm) & [51; 54] \\ \hline \hline \end{tabular}
\end{table}
Table 4: Optimized design parameters and variation range.  
Figure 8: The influence of each parameter on efficiency and rotor stress. (**a**) Bridge thickness; (**b**) rib width; (**c**) stiffener thickness.
# Hot-Pressing of Ti-Al-N Multiphase Composite: Microstructure and Properties
## 3 Results and Discussion  
### Microstructure, Chemical, and Phase Composition Analysis  
Figure 4 shows an SEM (BSE--backscattered electrons) image of the composite's microstructure, produced via the hot pressing method using a powder mixture of AlN:TiN in a 1:1 weight ratio, with the addition of 0.5 wt.% boron nitride (AlN-TiN(BN). The composite exhibits a complex structure characterized by distinct light and dark regions, indicative of variations in chemical composition. No cracks or pores are visible on the sintered material's analyzed surface. The microarea chemical composition analysis indicates that the dark regions predominantly consist of aluminum and nitrogen, with high peak intensities confirming the presence of aluminum nitride (Figure 5a), which remains stable during sintering. The lighter regions display a higher titanium content and significantly lower levels of aluminum and nitrogen, suggesting the presence of a ternary MAX phase (Figure 5b). Quantitative analysis was not conducted due to potential inaccuracies caused by the presence of boron, which is challenging to measure precisely with EDS, and nitrogen, which overlaps with titanium signals. EDS mapping highlights the composite's intricate structure, including undissolved AlN particles that persist after sintering (Figure 6). Phase composition analysis via XRD identifies the presence of Ti\({}_{2}\)AlN, Ti\({}_{3}\)AlN, and TiN phases (Figure 7).  
Figure 4: SEM BSE micrographs and examined microareas (point a and point b) of the chemical composition (EDS).  
Figure 5: EDS spectrograms: examined microareas (**a**,**b**) from Figure 4.
## 3 Theoretical Part
### XR Technologies in Education  
XR (Extended Reality) technologies, which encompass VR (Virtual Reality), AR (Augmented Reality), and MR (Mixed Reality), have emerged as transformative tools in the field of modern education. These technologies provide educators and students with the ability to create and experience immersive environments that significantly enhance learning outcomes. By offering highly interactive and visually stimulating experiences, XR technologies enable students to gain a deeper understanding of complex subjects and engage with educational content in a more hands-on and intuitive way. The integration of the real and digital worlds allows for innovative learning approaches, where virtual simulations, interactive models, and remote experiences can be explored in a manner that was previously impossible or cost prohibitive.  
One of the key advantages of XR technologies is their ability to provide students with access to environments and scenarios that would be otherwise inaccessible, such as exploring historical landmarks, conducting virtual science experiments, or interacting with abstract concepts in a tangible way. These virtual expeditions and simulations are not only beneficial for students who may have limited access to physical resources, but they also foster greater engagement and retention of knowledge. For example, medical students can practice surgeries in a virtual environment, while history students can walk through ancient civilizations, all from the comfort of their classroom or home. Furthermore, XR technologies have been shown to support collaborative learning, where students can work together in shared virtual spaces, overcoming physical limitations and encouraging teamwork and problem solving.  
As XR technologies continue to evolve, they promise even greater possibilities for education, allowing for more personalized learning experiences that cater to the diverse needs of students. The continued integration of VR, AR, and MR into educational contexts will likely lead to more dynamic, effective, and inclusive learning environments that can better equip students for the challenges of the future [14].
# Integrated Model Selection and Scalability in Functional Data Analysis Through Bayesian Learning
## 5 Results
### Results on Public Datasets
#### 5.2.3 Modeling Large-Scale, Dynamic, Geospatial Data
Although global dimension reduction inevitably introduces some residual noise, the kernel-based design is extensible to finer spacing or multiple length scales if higher precision is needed. Furthermore, inference with our model is simply the evaluation of the active 163 active basis functions weighted by the 16 principal components. Interpolation over a 300 \(\times\) 300 grid only takes about two seconds. By contrast, previous methods with Gaussian process regression require a weighted sum of all measured data within a certain window. The parametric representations also facilitate straightforward derivative and integration calculations, which are essential in investigating ocean temperature stratification and heat content [44]. In summary, the ARGO dataset provides an ideal testbed for our method, as it captures the dynamic behavior of high-dimensional geospatial data in a continuous framework. A more comprehensive study of ARGO was beyond this study's scope. Nonetheless, the results here confirm the clear advantages of the proposed method for large-scale, high-dimensional functional data. Figure 9: Geodesic interpolation from BSFDAFast vs. actual ARGO global oceanic measurements at 1, 200, and 300 decibars, at 1\({}^{\circ}\) S and 30\({}^{\circ}\) W, on May 29. Measurements are represented by circles, with the filling color indicating the temperature. Circle sizes show distance in depth and time from the central point.
# Distributed Visual Crowdsensing Framework for Area Coverage in Resource Constrained Environments
## 3 Methodology
### Data Redundancy Elimination
#### 3.3.2 Communication between Two Nodes
As shown in lines 5-7, all branches of common sensor nodes in the local K-CoverPicTree of sensor node \(b\) are merged by sensor node \(a\) to its local K-CoverPicTree, resulting in a new tree called New Combined Tree(_a_). As shown in line 9, sensor node \(a\) identifies  
Figure 5: An example of a communication pattern used in the proposed system. the newly added photos through the identification of the difference between the New Combined Tree(_a_) and its local K-CoverPicTree. Sensor node \(a\) then requests the required photos from sensor node \(b\) and updates its local K-CoverPicTree as shown in line 10. 4. Sensor node \(b\) applies similar steps to Algorithm 6 to merge its local K-CoverPicTree with the local K-CoverPicTree of sensor node \(a\) according to the shared sub-regions (S-common). Sensor node \(b\) merges all branches of common sensor nodes of the local K-CoverPicTree of sensor node \(a\) to its local K-CoverPicTree, resulting in the New Combined Tree(_b_). The newly appended photos are computed by sensor node \(b\) through the difference identification between the New Combined Tree(_b_) and its local K-CoverPicTree. Sensor node \(b\) then requests the required photos from sensor node \(a\) and updates its local K-CoverPicTree. 5. Finally, sensor nodes \(a\) and \(b\) update their local Boolean K-CoverPicTree, after adding the requested photos from each other. The previous steps are repeated when there is a contact between two sensor nodes until each sensor node reaches the maximum utility of the photos or the sensor nodes share the same final set of photos. Given that sensor node \(a\) has the highest probability of delivering photos to the command center, it first starts to request the needed photos from sensor node \(b\) to increase the photo's utility on its storage. Based on that, 15 nodes, which can contact the command center, upload the photos that are received from other sensor nodes inside the target area to the command center. ```
1:Tree(_a_) = Local K-CoverPicTree of sensor \(a\)
2:Tree(_b_) = Local K-CoverPicTree of sensor \(b\)
3:New Combined Tree(_a_) = []
4:New Combined Tree(_a_) = Tree(_a_)
5:for _S'_ = 1:size(S-common) do
6:Merge all child nodes of common Node(_S'_) of Tree(_b_) into the common Node(_S'_) of the New Combined Tree(_a_)
7:endfor
8:Call Algorithm 4 to remove redundant photos in the New Combined Tree(_a_)
9:Requested Photos from sensor \(b\) = Photos IDs that generated from subtracting Tree(_a_) from New Combined Tree(_a_)
10:Local K-CoverPicTree of sensor \(a\) = New Combined Tree(_a_). ```  
**Algorithm 6** Merging local K-CoverPicTrees on sensor \(a\)