esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Laboratory Tests
### Experimental Apparatus  
The experiments were performed in the tidal current tank shown in Figure 15. The tidal current tank was 14.65 m long, 5.9 m wide, and 1.8 m high. At both ends of it, the outer diameter was 5.9 m and the inner diameter was 2.4 m. On the front side of the tank, a glass window was designed for facilitating observation. The speed of the tidal current in the tank could be varied in a large range (0.1 m/s to 2.0 m/s) by controlling the rotating speed of the two propulsion systems installed on either side.  
The sedimentation basin in the tank was built behind the observation window. It was 3.8 m long, 1.35 m wide, and 0.3 m deep. The monopile was made of plexiglass. The outer diameter of the monopile was 0.1 m, its wall thickness was 10 mm, and its height was 2 m. The monopile was placed in the center of the sedimentation basin, as shown in Figure 16. The sand in the sedimentation basin was standard river sand, and the diameter of the sand varied from 0.1 to 0.5 mm. During the experiment, the average flow rate was set to 0.22 m/s to ensure that scour pits could be produced in reasonable testing time.  
Figure 16: Schematic diagrams of the tidal current tank in both sectional and plan views.  
Figure 15: Tidal current tank used for verification test.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Size of Fishnet**} & **Diameter of Fishnet** & **Position of Fishnet above** \\  & **Thread** & **the Seabed** \\ \hline \(l\) & \(d\) & \(H\) \\ \hline Without fishnet & & \\
1 cm & 0.6 mm & 10 cm \\
1.5 cm & 0.8 mm & 20 cm \\
2 cm & 1.2 mm & 30 cm \\
2.5 cm & 1.5 mm & 40 cm \\
3 cm & 1.8 mm & 50 cm \\ \hline \hline \end{tabular}
\end{table}
Table 3: Scenarios considered in the laboratory tests.  
A data acquisition device was specifically designed to record the depth of scour pit. As shown in Figure 17, it was composed of a camera, power supply, motor, ruler, and auxiliaries. The motor had a rated speed of 7 rpm. During data collection, the motor rotated the camera continuously so that the camera could monitor the development of scout pits in all directions.
## 3 Results and Discussions
### Texture Image Analysis
[65] reported that the roughness of the biofilms increased with increasing kefir concentrations, and the AFM images confirmed that the roughness of the biofilm surface is correlated with the concentration of kefir grains. The highest entropy value was reported by the fructans, which is related to the treatment that presented a greater increase in the milk kefir grains (Section 3.2). Conversely, the lower entropy was found in the images of the GAL that could be related to the greater homogeneity of their structures [40,66]. These results could be related to the ASM, where two groups were found, one that includes the CON and FUC and the other one conformed by GAL. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Substrate** & **ASM** & **Contrast** & **IDM** & **Entropy** & **FD** \\ \hline Fractans & \(2.838\times 10^{-4}\pm 9.590\times 10^{-5}\) a & 336.929 \(\pm\) 38.089 c & 1.019 \(\times\) 10\({}^{-1}\)\(\pm\) 0.006 a & 8.541 \(\pm\) 0.185 c & 2.380 \(\pm\) 0.021 b \\ Galactose & \(5.590\times 10^{-4}\pm 1.723\times 10^{-4}\) b & 146.634 \(\pm\) 9.143 b & 1.437 \(\times\) 10\({}^{-1}\)\(\pm\) 0.003 b & 7.900 \(\pm\) 0.264 a & 2.304 \(\pm\) 0.029 a \\ Control & \(3.577\times 10^{-4}\pm 7.606\times 10^{-5}\) a & 81.942 \(\pm\) 13.482 a & 1.520 \(\times\) 10\({}^{-1}\)\(\pm\) 0.008 c & 8.279 \(\pm\) 0.214 b & 2.327 \(\pm\) 0.032 a \\ \hline \hline \end{tabular}  
* [PERSON]’s multiple range test; average \(\pm\) SD; \(n\) = 3. Values with different letters in the same column showed significant difference (\(p<0.01\)). ASM = angular second moment; IDM = inverse difference moment; FD = fractal dimension.  
\end{table} 
Table 2: Effect of the source carbon on the texture parameter of ESEM image. Figure 5: Micrographs obtained by ESEM and 3D surface graphs of the texture of the milk kefir grains, where: (**a**) control (CON; nonfat milk powder) and treatments with nonfat milk powder fortified with (**b**) 2% of fructans (FUC) and (**c**) 2% of galactose (GAL). The scale bar indicates 100 μm. The ASM is inversely correlated to the entropy of the image. [PERSON] et al. [66] reported that higher angular second moment values indicate more uniformity in the image. The GAL presented the higher value for this parameter, which represents a greater homogeneity of the surface, which can be seen in Figure 5. When analyzing the contrast, the statistical analysis of the results showed a significant different (\(p<0.05\)) between all the treatments used (CON < GAL < FUC); a high contrast value indicates a high level of local variation [40,67]. The results showed that fructans presented the high value for the contrast parameter, which indicated that the use of FRU generates a more heterogeneous and therefore more complex surface than the other substrates presented (CON and GAL). Regarding the textural feature of the inverse different moment (IDM), the result showed a statistically significant interaction (\(p<0.05\)) between the carbon source where the FUC showed the lowest value of IDM. The lowest IDM values could be associated with heterogenous images [64].
# A Hybrid Encryption Scheme for Quantum Secure Video Conferencing Combined with Blockchain
## 3 Problem Formulation
### Security Requirements  
This scheme mainly has the following three security objectives:  
First, during the operation of the video conference system, the network monitor cannot analyze the intercepted video encryption information; second, the attacker cannot decrypt the ciphertext of the meeting minutes returned by the alliance chain; third, the video conference system can run stably under the unpredictable external environment.  
In order to achieve the first and second goals, QKD technology is used to replace classical technology, and symmetric quantum keys are generated among users to realize key security sharing and effectively prevent attackers from obtaining keys. The important content and meeting minutes of the video conference are encrypted by the quantum key to prevent attackers from decrypting and obtaining plaintext information.  
To ensure the third security goal, this scheme sets up two key encryption schemes of classical and quantum according to the security level of the video conference content. Considering the unpredictability of quantum channel security and the low rate of quantum key generation, quantum encryption includes two schemes of one-time pad + AES to ensure the operation of the system.
## 3 Related Work
The performance of CCOD is exhibited under 802.11 ax using Deep Q-Network (DQN) and Deep Deterministic Policy Gradient (DDPG). DQN is a showcase algorithm, while DDPG is an advanced method expected to offer higher network performance, particularly in dense areas. In addition, the authors also explained how time series analysis could be applied to recurrent neural networks of both DRL methods. In [22], the authors have designed an intelligent node capable of dynamically adapting a minimum contention window parameter to increase utility at the network level. For this work, authors have adopted Reinforcement Learning framework based on DQN architecture. It learns the optimal minimum contention value from the local observations. This research, however, unrealistically assumes that some nodes in network my deviate from the standard behavior and may choose a reduced Minimum Contention Window unfairly to acquire more channel access. From the literature review, we conclude that the problem of optimization of CW has provided an opportunity to exhibit DRL features and successful applications of DRL in WLAN research. Recent researchers have focused on the used Q-networks and Deep Q-networks (DQN); however, these techniques suffer overestimation problems. To deal with the overestimation problem of DQN and to enhance WLAN performance, we have extended the work presented in [21] and proposed a scheme that uses DDQN to improve the performance of WLAN in dense scenarios. DDQN tackles the problem of DQN by decoupling action selection from the action evaluation. Main Neural Network decides best next action among all the available next actions and target Neural Network evaluates this action to know its Q-value thus providing better final policies.
## 4 Characterization of Real-Life Signals
### Feature Space Design
#### 4.3.1 Features from the RSSI Distribution  
Based on the previous observation of the RSSI distribution, we select several features that have potential for automated technology recognition, as illustrated in Figure 8 and explained as follows:  
* \(\sigma_{\overrightarrow{R}}\): the standard deviation of the RSSI vector \(\overrightarrow{R}\). It indicates the range of variation in the signal strength.
* \(N_{p}\): the number of peaks in the histogram of RSSI is a simple way to describe the shape of the distribution. A point in the histogram is recognized as a peak when it is above its two neighboring points.
* \(\overline{n}\): the average power level of the noise, which corresponds to the location of the leftmost peak in the histogram, and it should be situated to the left of a certain threshold, denoted as \(\theta_{n}\).
* \(p(\overline{n})\): the probability that the measured RSSI is equal to the average noise power level \(\overline{n}\), i.e., \(p(\overline{n})=P(RSSI=\overline{n})\). This is the amplitude of the noise peak in the RSSI histogram, which is proportional to the amount of time the signal is interrupted. It is identified when the peak corresponding to \(\overline{n}\) is above \(\theta_{p}\).  
The relationship between the selected features \(\sigma_{\overrightarrow{R}},N_{p},p(\overline{n})\) and the technologies is summarized in Table 1. The feature space of the RSSI histogram associates each of the selected technologies with at least one stable feature, making the automated signal recognition rather straightforward. The rest of the features (denoted as \(\sigma_{\overrightarrow{z}}\) and \(p_{max}\) in Figure 8) are elaborated in Section 5.3 when mixed signals are also considered as a signal class.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**FeaturesTech** & **Wi-Fi** & **LTE** & **DVB-T** \\ \hline \(\sigma_{\overrightarrow{R}}\) & Variable & High & Low \\ \(N_{p}\) & Variable & High & Low \\ \(p(\overline{n})\) & High & Not visible & Not present \\ \hline \hline \end{tabular}
\end{table}
Table 1: The features vs. technologies.  
Figure 8: An illustration of the features extracted from the distribution of RSSI.
# Autonomous Navigation Framework for Intelligent Robots Based on a Semantic Environment Modeling
###### Abstract  
Humans have an innate ability of environment modeling, perception, and planning while simultaneously performing tasks. However, it is still a challenging problem in the study of robotic cognition. We address this issue by proposing a neuro-inspired cognitive navigation framework, which is composed of three major components: semantic modeling framework (SMF), semantic information processing (SIP) module, and semantic autonomous navigation (SAN) module to enable the robot to perform cognitive tasks. The SMF creates an environment database using Triplet Ontological Semantic Model (TOSM) and builds semantic models of the environment. The environment maps from these semantic models are generated in an on-demand database and downloaded in SIP and SAN modules when required to by the robot. The SIP module contains active environment perception components for recognition and localization. It also feeds relevant perception information to behavior planner for safely performing the task. The SAN module uses a behavior planner that is connected with a knowledge base and behavior database for querying during action planning and execution. The main contributions of our work are the development of the TOSM, integration of SMF, SIP, and SAN modules in one single framework, and interaction between these components based on the findings of cognitive science. We deploy our cognitive navigation framework on a mobile robot platform, considering implicit and explicit constraints for autonomous robot navigation in a real-world environment. The robotic experiments demonstrate the validity of our proposed framework.  
intelligent robot; autonomous navigation framework; triplet ontological semantic model; environment modeling; on-demand database; knowledge-based recognition; hierarchical planning
# Analysis of Blasting Efficiency Using Crack-Inducing Holes and Pre-Splitting Method in Blasting
## 3 Experimental Results and Analysis  
### Ground Vibration Measurement Results and Analysis  
The vibration was measured at the surface directly above the tunnel, and the vibration levels at each location were analyzed. The data measured at NO.1 indicated that, for general blasting, the PPV ranged from 0.468 to 0.805 cm/s, while, for combined blasting, it ranged from 0.495 to 0.730 cm/s. Based on the maximum PPV, the blasting vibration speed was approximately 9.3% lower in the combined blasting. At NO.2, the PPV for general blasting ranged from 0.892 to 1.260 cm/s, while, for combined blasting, it ranged from 0.570 to 0.838 cm/s. Based on the maximum PPV, the blasting vibration speed was approximately 33.5% lower in the combined blasting.  
The result of lower PPV during combined blasting is considered to be due to the formation of cracks between the pre-splitting holes and voids, which caused the blasting vibrations in the cut and expansion sections to be blocked at the periphery, thereby reducing vibrations.  
To analyze the vibration reduction characteristics of similar methods, the relevant literature was reviewed, and correlations were drawn. According to a study that analyzed Smooth and Presplit Blasting through experiments [24], the blast vibration levels at 30 and 50 m showed a reduction of 14% and 40%, respectively, with the Presplit Blasting method. The vibration reduction rate for FEREX excavation, a method similar to Combined Blasting, was between 25% and 39% [25]. FEREX blasting, which uses Pre-Splitting and Empty Holes, follows the same principle of vibration reduction but differs in that it uses Fring units instead of conventional explosives. Nonetheless, the vibration reduction rates of similar methods range from 14% to 40%, which shows a similar correlation to the 33.5% reduction in vibration observed with Combined Blasting.  
The reason for the higher vibration measured at NO.2, which is farther from NO.1, is not entirely clear, but it is speculated that the NO.1 measurement point was installed in the opposite direction of the tunnel excavation, causing the blasting vibrations to be higher at NO.2. This result is consistent with the study by [PERSON] et al. (2001) titled \"A Study on the Vibration Propagation Characteristics of Tunnel Blasting,\" which reports that, during tunnel blasting, the magnitude of vibration decreases in the order of the tunnel's advancing direction, perpendicular direction, and opposite direction.  
Table 3 shows the vibration data measured for both general blasting and combined blasting methods and Figure 7 is a comparative graph.
# Flexural Wave Propagation and Defect States of Periodic Slab Track Structure in High-Speed Railway
## 4 Defect State Analysis of Slab Track
### Influence of Defect Size and Location  
As the size of the contact loss increases, the lower order defect states clearly change since the wave modes are closely related to the support of the isolation layer, as shown in Figure 11a. When the defect size increases from 0.1 m to 0.5 m, the first-order defect state changes significantly with the variation in defect size, with the frequency descending from 108.31 Hz to 77.51 Hz. The second-order defect state does not exhibit significant changes to the variation in defect size. When the defect size increases from 0.1 m to 0.3 m, the frequency of the defective state decreases from 118 Hz to 117.3 Hz, and as the defect size further increases, the frequency of the defective state stabilizes. The frequency of the third-order defect state decreases from 149 Hz to 145.8 Hz and the higher-order defect states are not substantially affected by the defect size.  
Figure 10: Wave propagation in the slab track structure with local contact loss, where A–F represents 6 defect states: (**a**) dispersion curve of supercell, red lines represent the defect states; (**b**) wave modes of defect states (red: rail, black: slab).  
From the perspective of defect identification, a comprehensive evaluation can be facilitated by integrating the first three-order defect states. The location of the defect can be determined according to the first-order defect state, with the second-order and third-order defective states serving as auxiliary indicators. The defect size is closely related to the first-order defective state. As the defect size increases, the frequency of the defect state decreases significantly. Although the frequency variation of the second-order defective state concerning the defect size is relatively minor, due to the influence of elastic wave localization, the second-order defective state exhibits the characteristic of unidirectional propagation of elastic waves, which can be utilized as an auxiliary parameter for defect identification. For the third-order defect state, the wave mode is localized near the defective cell, which will also be beneficial for defect identification.  
Additionally, to investigate the influence of defect location, local defects were placed at the center of the track slab, and the effect of varying defect sizes on the frequency of defect states was analyzed, as depicted in Figure 11b. The findings reveal that when the defect is centrally located within the slab, the types of defect states remain consistent due to the same failure mode compared with the defect at the slab end. However, the frequency associated with these defect states follows a different pattern as the size of the defect changes. The impact of the central defect on wave propagation is relatively minor, with only the first-order defect state exhibiting a decrease in frequency as the defect size enlarges, while other defect states remain unchanged.
# A Throughput Analysis Using a Non-Saturated Markov Chain Model for LTE-LAA and WLAN Coexistence
## 4 Conclusions  
We proposed an analytical model using a Markov chain approach to represent the random backoff and CCA operations of LTE-LAA and WLAN systems in a coexistence scenario, thereby enabling throughput calculations for both systems. Numerical results demonstrated that the CCA threshold and contention window size configured at the LTE-LAA eNB were critical in balancing the throughput between LTE-LAA and WLAN systems. A larger contention window size set at the LTE-LAA eNB led to longer backoff periods, thereby increasing channel access opportunities for WLAN nodes. Similarly, a lower CCA threshold at the LTE-LAA eNB heightened its sensitivity to concurrent WLAN transmissions, resulting in fewer channel access opportunities for the LTE-LAA eNB.  
This work was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) under the metaverse support program to nurture the best talents (IITP-2024-RS-2023-00254529) grant funded by the Korea government (MSIT), and this research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2024-2021-0-01816) supervised by the IITP (Institute for Information & Communications Technology Planning & Evaluation), and this work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2022R1G1A1012117).  
Data are contained within the article.  
The author declares no conflicts of interest.
## 7 System's Architecture
For each cluster \(Cl_{i}\), all English texts belonging to that cluster are concatenated into a single text document \(T_{i}\): \(T_{i}=\bigoplus_{x^{t}_{En}\in Cl_{i}}x^{t}_{En}\). These texts are then passed into the KeyBERT model \(\Gamma_{KeyBERT}\). This model returns either one keyword with a cosine similarity score above 0.5 or the three keywords with the highest similarity scores. Specifically, the output is defined as follows: \[\Gamma_{KeyBERT}(T_{i})=Cl_{i}^{name}= \begin{cases}\{k\}&\text{if }\max(\cos\text{ sim}(k,T_{i}))>0.5,\\ \{k_{1},k_{2},k_{3}\}&\text{top }3\text{ with largest }\cos\text{ sim}.\end{cases} \]In our implementation, KeyBERT utilizes BERT embeddings with the keyphrase n-gram range of one or two words; all other parameters are set to their defaults. After reclustering and renaming, all stored instances, including \(x^{t}\), are relabeled with the new cluster names \(\{Cl_{1}^{name},Cl_{2}^{name},\ldots,Cl_{n+1}^{name}\}\). The cluster name \(Cl_{k}^{name}\), to which \(x^{t}\) is assigned, is then returned. The logical flow of the system is as follows. The incoming text instance is first vectorized (step no. 1) using the LaBSE vectorization model. Then, the binary classifier for _known_ and _new_ topics is applied to the just vectorized text (step no. 2). If the binary classifier detects a _known_ topic, the vectorized text is passed to the multi-class classifier module (in step no. 3) to determine class label(s) from 18 topics, and the determined labels are returned to the user. If the binary classifier (in step no. 2) detects the _new_ class, the vectorized text (together with the input text) is passed to the clustering module (in step no. 4) and memorized (in step no. 4(a)). If the incoming text belongs to one of the known existing clusters (in step no. 4(b)), then this cluster's label is returned. If the text cannot be attached to any of the existing clusters, the stored vectorized instances are reclustered (step no. 4(c)) and relabeled (step no. 4(d)) again. The label of the cluster to which the incoming instance belongs is returned to the user.