esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this paper an efficient superframe structure \(ESS_{ADC}\) of IEEE802.15.4 standard for such IoT based applications, where an adaptive duty cycle is required. The proposed scheme helps PAN coordinator to adjust its duty cycle according to data requests received from its member nodes. The proposed superframe structure is backward compatible with the standard without any inclusion in additional parameters with minute modifications in the existing parameter values. The proposed work is compared with the standard and ESS. The analytical results show that this \(ESS_{ADC}\) improves delay, offer better GTS utilization with better data transmission and accommodates more number of nodes as compared to the original 802.15.4 standard and the \(ESS\) in all respects.  
Figure 12: GTS allocating nodes for random data requesting nodes.  
Figure 13: GTS allocating nodes for varying range of fixed data requesting nodes.  
Author Contributions:This article was prepared through the collective efforts of all the authors. Conceptualization, S.K., A.N.A., M.A.J., B.-h.R. and J.A.; Investigation, S.K., A.N.A. and M.A.J.; Writing--original draft, S.K. and A.N.A.; Writing--review & editing, M.A.J., B.-h.R. and J.A. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2020-2018-0-01431) supervised by the IITP (Institute for Information & Communications Technology Planning & Evaluation.  
The authors declare no conflict of interest.
# Comparison Study on Coarseness Index and Maximum Diameter of Rock Fragments by Linear Cutting Tests
## 4 Comparison of Coarseness Index and Maximum Diameter by Cutting Conditions
### Effect of s/p Ratio  
The \(s/p\) represents the ratio of the spacing to the penetration depth. In mechanical excavation, the cutting conditions are controlled by the \(s/p\). \(SE\) is minimized and \(CI\) reaches its maximum at the optimum \(s/p\)[14]. In this context, the changes in \(CI\) and \(d_{\text{max}}\) with respect to the \(s/p\) were analyzed. The particle size distribution, \(CI\), and \(d_{\text{max}}\) of rock fragments under various cutting conditions are summarized in Tables 3-5 for all rock strengths.  
Figure 9 shows the relationship between \(CI\) and the \(s/p\). Even for the same \(s/p\) value, as the penetration depth increased, the \(CI\) increased. When the \(s/p\) was relatively small (\(s/p<3\)), \(CI\) increased as the \(s/p\) increased. The \(CI\) reached its maximum value near a specific \(s/p\), regardless of the penetration depth. After reaching the maximum value, the \(CI\) value did not change significantly, even when the \(s/p\) increased. Taking a closer look at the cutting of rock of UCS 30 MPa (Figure 9b), \(CI\) reached its maximum near an \(s/p\) of 3, and the values were approximately 520, 600, and 640 at penetration depths of 3 mm, 6 mm, and 9 mm, respectively. After that, even if \(s/p\) exceeded 3, the \(CI\) did not deviate significantly from the maximum.  
Figure 8: Relationship between spacing and (**a**) \(CI\); (**b**) maximum diameter.  
Figure 9: \(Cont.\)As shown in Figure 10, the relationship between \(d_{\rm max}\) and the \(s/p\) was similar to that between \(Cl\) and the \(s/p\). At the same \(s/p\), \(d_{\rm max}\) increased as the penetration depth increased. \(d_{\rm max}\) increased with an increase in \(s/p\), and at a specific \(s/p\), \(d_{\rm max}\) reached a maximum. Subsequently, even if \(s/p\) increased, \(d_{\rm max}\) did not change significantly. The regression curve of \(p\) = 3 mm in Figure 10b, which shows a high correlation (R\({}^{2}\) = 0.970), demonstrates that \(d_{\rm max}\) reached its maximum value at an \(s/p\) of approximately 4, and even at larger \(s/p\) values, \(d_{\rm max}\) was approximately 16 mm.  
Figure 10: Cont.  
Figure 9: Relationship between \(Cl\) and \(s/p\) ratio: (a) rock of UCS 20 MPa; (b) rock of UCS 30 MPa; (c) rock of UCS 40 MPa.  
In summary, \(CI\) and \(d_{\rm max}\) show similar positive relationships with respect to both penetration depth and spacing, but different functions are formed. However, in terms of the relationship with the \(s/p\), \(CI\) and \(d_{\rm max}\) show high similarity.
## 7 Portability and Scalability  
Incorporating portability and scalability into outdoor air conditioning systems is a critical innovation that addresses the rising need for adaptation and flexibility in various cooling conditions. Portable units are gaining popularity because of their portability and setup simplicity, making them ideal for events, temporary workplaces, and other instances when permanent cooling systems are impracticable [25]. These devices are often lightweight and fitted with wheels or handles for easy portability, which increases their adaptability. Beneficial for temporary settings such as outdoor festivals or construction sites, the units' portability enables them to be moved as needed and to provide targeted cooling exactly where required. This versatility is a godsend for moving workstations and a cost-effective alternative to permanent systems, lowering initial and recurring costs. The fast availability and speedy installation of portable units are particularly advantageous for providing immediate respite from extreme heat, whether for emergency cooling requirements or unexpected gatherings. Scalability, another essential characteristic, permits outdoor cooling systems to adapt their cooling capacity to variable demand. This versatility is becoming more critical in contemporary system designs, particularly in locations where cooling needs may change based on time, season, or occupancy levels [15]. Scalable systems perfectly match cooling capacity to actual demand, optimising energy consumption, decreasing operating costs, and minimising environmental effects. Both transportable and scalable technologies contribute to sustainability by conforming to larger energy efficiency targets. By employing just the required cooling capacity for a particular condition, these systems reduce energy consumption and the carbon footprint, contributing to broader environmental and sustainability goals. In conclusion, the introduction of portability and scalability in outdoor air conditioning systems has ushered in a new age of adaptability, cost-efficiency, and environmental awareness. These characteristics allow for the deployment of customisedcooling systems adaptable to a wide variety of applications, representing a breakthrough advance in outdoor cooling [19].
## 4 Numerical Analysis for Verification
### Noise Effects  
The effects of different noise levels on the proposed method were also evaluated. Normally distributed random noise with zero mean and unit standard deviation was added to simulate the effect of measurement noise:  
\[\tilde{\epsilon}=\epsilon_{c}+E_{\pi}N_{noise}std(\epsilon_{c}) \tag{17}\]  
where \(\tilde{\epsilon}\) and \(\epsilon_{c}\) are the simulated noisy and original responses, respectively; \(E_{\pi}\) is the noise level; \(N_{noise}\) is a standard normal distribution vector with zero mean and unit standard deviation; \(std(\epsilon_{c})\) denotes the standard deviation of the original response.  
Noise effects of 2%, 4%, and 6% were added to the simulated monitoring data for case C4 (Section 4.1). The vertical bending strain was first separated from the coupled strain data using the proposed method; then, the RE was calculated by comparing the separated vertical bending strain with its true value. The results are shown in Figure 10.  
Figure 10 shows the RE for the separated vertical bending strain. At noise levels of 2%, 4%, and 6%, the MAVREs were obtained as listed in Table 3. The units wherein MAVREs occurred were 3, 2, and 2, with a notable concentration near the support, where the responses were relatively small. However, for units closer to the middle span, the RE increased slightly with the noise level, with most remaining below 2%. At the 6% noise level, the absolute Res of all units (except for one exceeding 5%) remained at a low level in the estimated vertical bending strain (Figure 10c). This demonstrates that the accuracy of the vertical bending strain estimated by the proposed method only marginally decreases as the noise level increases, while the error remains within an acceptable range.  
Figure 9: REs of identified vertical displacement in different cases: (**a**) C1; (**b**) C2; (**c**) C3; (**d**) C4.  
Figure 11a-c shows the REs of the identified vertical displacement at the three noise levels. Fluctuations in the RE curve gradually intensified as the noise level increased. The MAVRE for the identified displacement across the entire tunnel was highest for the units with small responses at both ends, while the accuracy of identified displacement in the middle span of the tunnel was consistently high. Table 4 lists the MAVREs for the identified vertical displacement at these three noise levels, none of which exceeded 5%. Thus, the proposed method exhibits robustness to various noise levels, effectively enabling identification of the vertical deformation distribution of the tunnel under various noise conditions.  
Figure 11: REs of identified vertical displacement at different noise levels: (**a**) 2%; (**b**) 4%; (**c**) 6%.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline Noise & 2\% & 4\% & 6\% \\ \hline MAVPE & 1.37 & 3.23 & 5.17 \\ \hline \hline \end{tabular}
\end{table}
Table 3: MAVPEs (%) of estimated strain at different noise levels.  
Figure 10: REs of vertical bending strain under different noise levels: (**a**) 2%; (**b**) 4%; (**c**) 6%.
## 4 Eigenvalue Fusion and other Fusion Methods for Detection OFDMA Signals
### Eigenvalue Fusion for OFDMA Signals  
While OFDM assigns an entire block of frequencies to one user, OFDMA is a multiple access technique that allocates to a given user a set or multiple sets of subcarriers, allowing for simultaneous access to the overall band by several users. A set of frequencies is called a subchannel. The formation of a subchannel can be classified in two types: adjacent subcarrier method (ASM), which groups a set of contiguous subcarriers to form a subchannel, and diversity subcarrier method (DSM), in which non-contiguous subcarriers are chosen to form a subchannel [4]. As a consequence, when the proposed eigenvalue fusion scheme is applied to the detection of a primary OFDMA signal, it aims at detecting the signal at the subchannel level, _i.e._, it aims at detecting if a given subchannel is vacant or not.  
Suppose a single OFDMA signal with \(K\) available subcarriers and \(P\) subchannels. Thereby, \(K^{\prime}=K/P\) subcarriers will form a subchannel indexed by \(s\), where \(s=1,2,\ldots,P\). We assume that each CR knows the subcarrier allocation map for each subchannel. This information can be readily available from the primary network standard. A matrix of order \(K^{\prime}\times N\) with sample values at the \(i\)-th CR and \(s\)-th subchannel will be formed according to  
\[\mathbf{A}_{s}^{(i)}=\begin{bmatrix}Y_{s,1}^{(i)}(1)&\ldots&Y_{s,1}^{(i)}(N)\\ \vdots&\ddots&\vdots\\ Y_{s,K^{\prime}}^{(i)}(1)&\cdots&Y_{s,K^{\prime}}^{(i)}(N)\end{bmatrix} \tag{22}\]  
where \(Y_{s,k^{\prime}}^{(i)}(j)\) is the \(j\)-th sample collected by the \(i\)-th CR in the \(k^{\prime}\)-th subcarrier pertaining to the \(s\)-th subchannel. From Equation (22), the next step is to compute the corresponding sample covariance matrices, according to  
\[\mathbf{R}_{s}^{(i)}=\frac{1}{N}\mathbf{A}_{s}^{(i)}\mathbf{A}_{s}^{(i)}{}^{{ \dagger}} \tag{23}\]  
from where \(mK^{\prime}P\) eigenvalues are estimated and sent to the FC. The test statistics for the \(s\)-th OFDMA subchannel are computed at the FC according to the following slightly-modified versions of Equations (14)-(17):  
\[T_{\text{GLRT},s}=\frac{\frac{\frac{1}{m}\sum\limits_{i=1}^{m}\lambda_{1\,,s,i }}{\frac{1}{mPK^{\prime}}\sum\limits_{j=1}^{K^{\prime}}\sum\limits_{z=1}^{P} \sum\limits_{i=1}^{m}\lambda_{j,z,i}}}{} \tag{24}\]\[T_{\text{MMED},\,s}=\frac{\frac{\frac{1}{m}\sum\limits_{i=1}^{m}\lambda_{1,\,s,i}} {\frac{1}{mP}\sum\limits_{j=1}^{P}\sum\limits_{i=1}^{m}\lambda_{K^{\prime},z,i}}}{T _{\text{MED},s}=\frac{1}{m\sigma^{2}}\sum\limits_{i=1}^{m}\lambda_{1,\,s,i}} \tag{26}\]  
\[T_{\text{ED},\,s}=\frac{1}{K^{\prime}m\sigma^{2}}\sum\limits_{j=1}^{K^{\prime} }\sum\limits_{i=1}^{m}\lambda_{j,\,s,i} \tag{27}\]  
where \(\{\lambda_{1,s,i}\geq\lambda_{2,s,i}\geq\cdots\lambda_{K^{\prime},s,i}\}\) are the \(K^{\prime}\) ordered eigenvalues associated with the \(s\)-th subchannel and \(i\)-th CR.
## 3 Potential Security Implications of IoT in 6G Environment
### 6G Usage Scenario-Related Vulnerabilities
#### 3.2.1 Immersive Communication  
Immersive communication in 6G, including XR, holography, and telepresence, opens new possibilities for IoT devices, while simultaneously exposing them to increased security risks. The continuous exchange of personal data, including video and audio streams, raises the potential for privacy breaches, making devices susceptible to attacks like spoofing or man-in-the-middle interception [45]. The real-time nature of these systems, which requires low-latency communication, creates opportunities for attackers to disrupt critical interactions [5]. Additionally, varying network conditions--such as congestion in urban hotspots or slower connections in rural areas--can cause communication delays, signal degradation, or network outages, making devices more vulnerable to manipulation or interference [46]. These inconsistencies across different environments further complicate the security landscape of immersive communications. As immersive communication systems become more integrated into critical applications, the potential for privacy breaches, and AI-generated manipulation such as fake websites, and video or audio streams poses significant future threats leading to a broader range of security vulnerabilities, including fraud, unauthorized access, and reputational damage [13].
## 3 Results
### In Silico Modelling of pri-miR-125a Folding  
Computational modelling of the 2D structure of 51-miR-125a was performed using RNAfold (Figure 2) and RNAstructure (Figure 3) [29; 30].  
Figure 2: The 2D structures of 51-miR-125a with C-variant and U-variant predicted by RNAfold. In grey squares we marked SNPs. (**a**) C-variant MFE model (minimizing free energy), (**b**) U-variant MFE model (minimizing free energy) (**c**) C-variant variant centroid model (minimizing base-pair distance) and (**d**) U-variant centroid model (minimizing base-pair distance).  
Figure 3: The 2D structures of 51-miR-125a with C-variant (**a**,**b**,**d**) and U-variant (**c**,**e**) predicted by RNAstructure. In grey squares we marked SNPs. (**a**) C-variant MFE model (minimizing free energy), (**b**) C-variant MFE model, and (**c**) U-variant MFE model, (**d**) C-variant MaxExpect model (most probable base-pairing) (**e**) U-variant MaxExpect model. (**b**,**c**,**e**) models were cut for better visibility of SNP fragment.  
Predicted models revealed variant-dependent folding of the 51 nucleotides long pri-miR- 125a region comprising rs12976445. The modelling of C-variant 51-miR-125a with RNAfold resulted in two different structures when we applied two modes of analysis: minimizing free energy (MFE) and minimizing base-pair distance (centroid). For the U-variant using MFE and centroid modes, we obtained the same model of RNA structure. In the RNAfold MFE model of the C-variant (Figure 2a), SNP created a base-pairing near the hairpin loop. In the RNAfold centroid model of the C-variant structure, SNP was unpaired within the hairpin loop. In both RNAfold modes MFE and centroid from U-variant, SNP was the first unpaired nucleotide of a long unpaired subsequence located on 3' end of 51-miR-125a.  
Predicting the 2D structure of 51-miR-125a using RNAstructure resulted in two MFE structures for C-variant (Figure 3a,b) and a single model for U-variant. In both, MFE and centroid (Figure 3a,d, respectively), structures predicted for 51-miR-125a C-variant by RNAstructure (Figure 3), we observed the same location of SNP as in the model generated by the RNAfold in the MFE mode (Figure 2a). Moreover, all predicted U-variant foldings (Figure 2b,d and Figure 3c,e) have the same structure, independent of the 2D structure prediction software used or mode. In both structures generated with RNAstructure MFE (Figure 3a) and centroid (Figure 3d) modes, SNP was a part of base-pairing near the hairpin loop. In the remaining three models, RNAstructure MFE C-variant (in Figure 3b) and U-variant RNAstructure MFE and centroid (3c,e), SNP is a part of a long unpaired region starting with SNP (26 th nucleotide) and moving along till the last (51 st) nucleotide. The differences observed between U- and C- variant models in both RNAfold and RNAstructure may suggest SNP-dependent folding of the structure.
# Provision of Energy- and Wavelength-Efficient Traffic
###### Abstract  
Sparse wavelength division multiplex (WDM) enabled distributed satellite cluster networks (DSCNs) have emerged as a promising architecture to accommodate future extensive applications. Networking of the DSCNs will face the challenges of explosively increasing traffic requests, the limited number of wavelengths, and restricted energy provisioning. To address these issues, a novel approach, the two-phase traffic grooming based on the matching algorithm (TPTG_MA), is proposed in this paper. To analyze resource utilization, energy- and wavelength- minimized models are established. After that, we develop the MA to tackle the traffic grooming problem in two phases, including the first phase for traffic aggregation and sub-wavelength assignment (TAASA) and the second phase for sub-wavelength grooming (SG). To evaluate the performance of the proposed TTTG_MA, the direct lightpath grooming (DLG) heuristic and the genetic algorithm (GA) are simulated for comparison. The results demonstrate that the TTTG_MA and DLG_GA outperform TPTG and DLG in the average wavelength utilization ratio (AWUR), the energy consumption saving (ESC), and the blocking probability. Compared with the DLG_GA, the TPTG_MA achieves at most 18% and 23% higher AWUR in the 12-node and 22-node topologies, respectively. In addition, the TPTG_MA can actualize at most 10% ECS improvement over the DLG_GA. At last, the influence of the network size, the number of wavelengths, and the number of hops are discussed.  
distributed satellite cluster networks; sparse WDM; traffic grooming; matching algorithm 2022, 9, 494. [[https://doi.org/10.3390/photonics9070494](https://doi.org/10.3390/photonics9070494)]([https://doi.org/10.3390/photonics9070494](https://doi.org/10.3390/photonics9070494))
## 4 Feature Extraction Techniques  
### Principal Component Analysis (PCA)  
PCA is a technique used for reducing the dimension of a dataset, by converting the variables in the dataset into a smaller set while preserving the information in the original dataset [12; 13]. Consequently, this reduced dataset, called features, can be seamlessly visualised and evaluated using different ML tools. PCA uses basic equations to derive variables, called principal components, from the original dataset. PCA is a very robust feature extraction technique, especially when the sampling points in the dataset to be analysed carry both negative and positive values [12; 13].
# Research on Bandwidth Improvement of Fine Tracking Control System in Space Laser Communication
## 2 Materials and Methods  
### Hysteresis Characteristic  
PZT FSM is the core component of the fine tracking system for space laser communication, and Figure 1 shows its schematic diagram. By changing the control voltage, the PZT can be controlled to both extend and shorten simultaneously, allowing the control of the angle of the steering mirror in one direction.  
Hysteresis exists widely in devices with PZT as an actuator. Due to the inverse piezoelectric effect, a deviation occurs in the displacement curve of the PZT actuator when the voltage rises and falls. This results in a severe nonlinear effect between input and output. For PZT FSM, hysteresis is manifested in the nonlinear, multi-valued, and non-single-path relationship between the control voltage and the mirror deflection angle. The hysteresis characteristic is also related to the frequency of the control signal, which is rate-dependent.  
Figure 2 shows the hysteresis curve measured using a certain type of PZT FSM, and the red arrow indicates the direction of the beam. Figure 2a shows the hysteresis curve with the same input signal amplitude but different frequencies; Figure 2b shows the hysteresis curve with the same input signal frequency but different amplitudes. The test results show that when the input signal is a voltage signal with the same amplitude but varying frequencies, the hysteresis curve appears as follows: When driven by a higher-frequency voltage, the hysteresis curve becomes wider, the output displacement range decreases, and the hysteresis curve rotates clockwise. When the input signal is a voltage signal with the same frequency but varying amplitudes, the hysteresis curve appears as follows: When driven by a larger amplitude voltage, the hysteresis curve becomes wider and the output displacement range increases.
## 4 Case Study--Numerical Experiments
### Out-Bound Logistics Process
#### 4.2.3 Cycle Time Comparison with Analytical and Simulation Method  
Table 10 presents the estimated cycle time (sojourn time) for various scenarios using the analytical method and simulation model. The difference between the results is calculated using Equation (12), ranging between approximately \(\pm\)4% and \(\pm\)8%. The table demonstrates that the analytical method, employing the GEM approximation, offers reliable and robust solutions for analysing finite OQNs. The accuracy and effectiveness of the analytical method make it a valuable tool for studying and evaluating system performance in real-world applications.  
\[Diffence=\frac{(Analytical\;\text{{meth.}}-Simulation\;\text{{meth.}})}{Analytical\; \text{{meth.}}} \tag{12}\]
# Optimal Fractional PID Controller for Buck Converter Using Cohort Intelligent Algorithm
## 5 Conclusions  
This article discusses a novel method for the design of a fractional order PID controller for a buck converter using the cohort intelligence meta-heuristic algorithm. The performance indices of ISE, ITSE, TTAE, and IAE were optimized using the CI algorithm. The ISE index minimization gave the best results. The proposed system showed a fast start-up response and smooth dynamic response. It was observed that the reduction factor of the CI algorithm had an effect on the time domain response, as well as the cost function value. The performance of the system with CI algorithm was compared with the GA, PSO, ABC, and SA optimization algorithms for different cost functions. The CI algorithm gave faster convergence to the optimal solution in comparison to all the other methods. The much lesser function count and computation time required is a clear advantage of the CI compared to the other methods and also reflects the lesser control effort required. The overshoot and the average value of the objective function was the least for the CI method with ISE. The cost function values for ITSE and IAE were found to be lesser for the PSO and ABC methods, but at the cost of a much higher computational time. These show that the CI algorithm can be an effective method for the optimal tuning of the FOPID parameters. Optimization with CI for minimization of weighted combinations of the performance indices can be tried in future. Future research will be performed on extension of CI algorithm for multi-objective optimization and for the control of converters such as the boost and buck-boost converters.  
Conceptualization, P.S.; Methodology, P.W.; Writing--original draft, P.W.; Writing--review & editing, P.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
No data is available in this study.  
The authors declare no conflict of interest.  
Figure 18: Control and error signals.