esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In dentistry, the assessment of periodontal biotype is considered one of the most important parameters with which to plan treatment, and craniofacial morphology might affect it. The aim of this study was to investigate the association between facial typology and gingival biotype in patients by means of two-dimensional and three-dimensional evaluations of facial typology. This study included 121 participants searching for orthodontic treatment (43 M, 78 F; 20.4 \(\pm\) 10.4). Gingival biotype was evaluated based on the transparency of the periodontal probe through the gingival margin of the mid-buocal sulcus for both upper (UGB) and lower (LGB) anterior teeth. SellionNasion'GonionGonation (SN'GoGn) and CondylionGonionMenton (CoGoMe\") angles were measured on two-dimensional cephalosporgrams. Three-dimensional face scans were acquired by means of a three-dimensional facial scanner (3 dMD system) and successively analyzed to assess the facial typology using the ratio between lower facial height (SNMe) and total facial height (NMe). A chi-squared test and regression analysis were used to evaluate the associations between gingival biotype and facial morphology (\(p<0.05\)). The chi-squared test showed that there was no statistically significant association between facial typology and gingival biotype (UGB \(p=0.83\); LGB \(p=0.75\)). The logistic regression showed an association between SNMe/NMe and the UGB (\(p=0.036\)), and SNMe/NMe and LGB (\(p=0.049\)). The decreased ratio of SNMe/NMe might be a protective factor for a thin gingival biotype.  
facial typology; gingival biotype; orthodontic diagnosis; cephalometric analysis; three-dimensional facial scans +
Footnote †: journal: Applied Sciences
# Investigation of Energy Harvesting Capabilities of Metglas 2605 SA1
## 3 Energy Harvesting Application  
Based on the previous measurements on Metglas 2605 SA1 and the general theoretical framework exposed in the previous section, this part proposes to investigate the energy conversion potential of such a material for energy harvesting applications using Ericsson cycles.
# Stochastic Predictive Energy Management of Multi-Microgrid Systems
## 2 Problem Formulation
The LEMS of each MG is responsible to efficiently devise charging/discharging strategies of ESSs in the associated subsystem. Considering the uncertain nature of generation and demand parameters, LEMS tries to keep the possible power mismatch between realized and predicted trajectories of the MG outflow around zero while minimizing the ESS operational cost. Consequently, the control problem of each MG can be considered as a decentralized optimization problem with the following constraints in which (10) is a random constraint. \[s.t.\quad SOC_{i}(t+1)=SOC_{i}(t)+\frac{P_{b,i}(t)}{E_{b,i}}\Delta, \tag{6}\]  
\[P_{b,i}^{min}\leq P_{b,i}(t)\leq P_{b,i}^{max}, \tag{7}\]  
\[SOC_{i}^{min}\leq SOC_{i}(t)\leq SOC_{i}^{max}, \tag{8}\]  
\[\hat{G}F_{i}(t)=\sum_{r=1}^{N_{WT}^{i}}\hat{P}_{WT,r}^{i}(t)+\sum_{r=1}^{N_{PV }^{i}}\hat{P}_{PV,r}^{i}(t)-\hat{L}_{i}(t)-P_{b,i}(t), \tag{9}\]  
\[\delta_{mis,i}(t)=0. \tag{10}\]  
In (6), \(SOC_{i}\) represents state of charge of the ESS in the \(i\)th MG and \(E_{b,i}\) is its nominal capacity. Besides, \(\Delta\) refers to the time length between two consecutive steps, which is assumed to be equal to 1h in this study. In (9), \(\hat{P}_{WT,r}^{i}(t)\), \(\hat{P}_{PV,r}^{i}(t)\), and \(\hat{L}_{i}(t)\) refer to the realized power values of the \(r\)th WT and PV units and the total power consumption in the \(i\)th MG at time slot \(t\), respectively. Taking into account the dynamical equation of the storage devices, successive time steps will be related to each other. Accounting for the system's dynamics and accommodating the stochastic nature of the tracking constraint, a two-stage stochastic MPC approach is adopted to achieve the desired solution strategy. The optimal operating set points designed at this level are followed by the local controllers of each MG. Figure 1 illustrates the proposed hierarchical control structure along with the required input data and associated outputs of each control layer.
# OFDM Emitter Identification Method Based on Data Augmentation and Contrastive Learning
###### Abstract
* We propose a generalized framework, including contrastive learning, data augmentation, and ResNet, which can extract effective features from complex OFDM data under few-shots condition;
* We perform extensive experiments and analyze the results, demonstrating the effectiveness of our method. The results reflect that the preprocessing and training methods in our framework can be migrated to other machine learning models and our works can be effectively migrated to other signals.
## 5 Deformation Control Measures
### Comparative Analysis of Deformation Results  
#### 5.2.1 Comparative Analysis of Support Structure Deformation  
Figure 13 presents a line chart illustrating the maximum deformation points of the diaphragm wall under the three different excavation schemes as a function of excavation depth. As indicated in Figure 13, the maximum horizontal displacements of the diaphragm walls for all three excavation schemes gradually increased with the excavation depth. Plan 3 exhibited the lowest growth rate of the maximum horizontal displacement, with a final maximum displacement of only 31.71 mm, which equated to 0.15% H. Plan 2 provided the second-best deformation control, with a maximum displacement of 36.35 mm at the final excavation sequence, which equated to 0.17% H. The original scheme demonstrated the least effective deformation control. Compared to the excavation method of the original scheme, Plan 3 reduced the maximum horizontal displacement of the diaphragm wall by 15.18 mm, representing a decrease of approximately 32.37%. Meanwhile, Plan 2 achieved a reduction of 10.54 mm, corresponding to a decrease of approximately 22.47%.  
Figure 12: Schematic diagram of deformation control scheme.  
Figure 13: Maximum horizontal displacement of diaphragm wall at different excavation depths.
# A New One-Dimensional Compound Chaotic System and Its Application in High-Speed Image Encryption
## 5 Conclusions  
In this paper, a new one-dimensional compound chaotic map system was proposed. The system is composed of a logistic map and tent map. It has very wide range of chaotic parameters and strong chaotic behavior. It is very suitable for real-time image encryption based on chaos. Using some common tools in chaos theory, we analyzed the performance of the map. The results showed that the proposed map has good chaotic performance and strong chaotic behavior over a large range of parameter values. Furthermore, we designed a fast image encryption scheme with high security based on the new chaotic map. In this encryption scheme, the two processes of permutation and substitution are effectively combined to be realized simultaneously. The combination of these two phases makes the scheme faster and more secure than the traditional encryption structure, which handles permutation and substitution separately. Moreover, each loop of the encryption process takes one row or one column of an image as the processing unit rather than a single pixel as the processing unit, which greatly reduces the loop times of the algorithm, thus significantly improving the encryption speed. Simulation and experimental results show that the proposed image encryption scheme meets all the requirements of real-time applications.  
Conceptualization, S.Z. and C.Z.; methodology, W.Z. and X.D.; software, S.Z.; validation, S.Z., W.Z., X.D. and C.Z.; formal analysis, S.Z.; investigation, C.Z.; resources, W.Z.; data curation, S.Z.; writing--original draft preparation, S.Z.; writing--review and editing, C.Z.; visualization, X.D.; supervision, X.D. and W.Z.; project administration, W.Z.; funding acquisition, W.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of Xinjiang Uygur Autonomous Region, grant number 2020D01C033.  
Not applicable.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Index** & **Ref. [33]** & **Ref. [34]** & **Ref. [35]** & **Ref. [36]** & **Ours** \\ \hline Time complexity & _O(MN)_ & _O(MN)_ & _O(MN)_ & _O(2(M + N))_ & _O(M + N)_ \\ Min encryption & \(1\) & \(1\) & \(2\) & \(2\) & \(1\) \\ rounds & \(1\) & \(1\) & \(2\) & \(2\) & \(1\) \\ Image size (\(256\times 256\)) & \(1735.1\) & \(621.2\) & \(32.0\) & \(27.6\) & \(9.6\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Algorithm encryption speed test results (ms).
## 4 Proposed Protocol
### Multi-Hop Routing Approaches
#### 4.3.3 Route Generation
D. Construction of operator  
Given that the constraint term has already been constructed in Section B, we exclusively construct one operation \(O\) for updating the replica set. We find a random position in the matrix \(L\) with the value of 1, disconnect it from its child node, and then choose a new child node within the same column. Figure 7 shows an example of the operator with arrows indicating the direction of data transmission. E. The Pseudo-Code of the QA algorithm  
Algorithm 1 is the QA algorithm pseudo-code. ```
Initialize : replica set \(M,L_{best}=L^{0},\Gamma=\Gamma^{0},M_{c}=Maxsteps,k=0\)
1. While (\(\Gamma>0\))
2.\(\mathcal{J}_{\Gamma}=-\Gamma/2\ln\tan\bigl{(}\Gamma/\mathcal{T}_{q}\bigr{)}\)
3. While (\(k<n\))
4.\(L^{k}=O\Bigl{\{}L^{k}\Bigr{\}}\)
5.\(\Delta H_{p}=H_{p}(L^{\prime})-H_{p}\bigl{(}L^{k}\bigr{)}\)
6.\(\Delta H_{k}=H_{k}(L^{\prime})-H_{k}\bigl{(}L^{k}\bigr{)}\)
7.\(\Delta H_{\mathrm{C}}=\Delta H_{p}/n-\int_{\Gamma}\Delta H_{k}^{\prime}\)
8.if\(\bigl{(}\Delta H_{p}\bigr{)}<=0\)
9.\(L^{k}=L^{\prime}\)
10.if\(\bigl{(}H_{p}\bigl{(}L^{k}\bigr{)}\bigr{)}<=H_{p}((L_{best}))\)
11.\(L_{best}=L^{\prime}\)
12.elseif\((\Delta H_{k})<=0\)
13.\(L^{k}=L^{\prime}\)
14.elseif\(\bigl{(}\exp(-\Delta H_{\mathrm{C}}/T)\bigr{)}>random(0,1)\)
15.\(L^{k}=L^{\prime}\)
16.\(k=k+1\)
17.endwhile
18.\(T=T-(T_{0}/\mathit{Maxsteps}),\Gamma=\Gamma-(\Gamma_{0}/\mathit{Maxsteps})\)
19.\(\mathcal{M}_{c}=M_{c}-1\)
20.endwhile
21.return\(L_{best}\)
22.end
```  
**Algorithm 1**The implementation of route planning  
Figure 7: An example of the operator \(O\): (**a**) \(L\) matrix before using \(O\); (**b**) \(L\) matrix after using \(O\); (**c**) route before using \(O\); (**d**) route after using \(O\). Here, _Maxsteps_ is the maximum number of steps for Monte Carlo steps. \(L^{\prime}\) is the candidate solution, which is obtained by updating the current solution with the operator \(O\), and the Monte Carlo criterion [38] of the simulated annealing algorithm is adopted in line 18.
# Joint Probabilistic Hypergraph Matching Labeled Multi-Bernoulli Filter for Rigid Target Tracking
###### Abstract  
The likelihood determined by the distance between measurements and predicted states of targets is widely used in many filters for data association. However, if the actual motion model of targets is not coincided with the preset dynamic motion model, this criterion will lead to poor performance when close-space targets are tracked. For rigid target tracking task, the structure of rigid targets can be exploited to improve the data association performance. In this paper, the structure of the rigid target is represented as a hypergraph, and the problem of data association is formulated as a hypergraph matching problem. However, the performance of hypergraph matching degrades if there are missed detections and clutter. To overcome this limitation, we propose a joint probabilistic hypergraph matching labeled multi-Bernoulli (JPHGM-LMB) filter with all undetected cases being considered. In JPHGM-LMB, the likelihood is built based on group structure rather than the distance between predicted states and measurements. Consequently, the probability of each target associated with each measurement (joint association probabilities) can be obtained. Then, the structure information is integrated into LMB filter by revising each single target likelihood with joint association probabilities. However, because all undetected cases is considered, proposed approach is usable in real time only for a limited number of targets. Extensive simulations have demonstrated the significant performance improvement of our proposed method.  
multi-target tracking; labeled multi-Bernoulli filter; hypergraph matching; data association +
Footnote †: journal: Applied Sciences
# Challenges of Using the L-Band and S-Band for Direct-to-Cellular Satellite 5G-6G NTN Systems
## 2 Literature Review  
The current state of research on the topic of this article reveals a significant gap in the understanding of the aspects of co-existence with other satellite systems operating in the n255 and n256 bands. Currently, there is a lack of publicly available articles or reports that adequately address this issue. This scarcity of research on co-existence and regulatory issues in the specific frequency bands is a notable gap in the existing literature. Some insights into co-existence and regulatory matters can be found in the works of some authors, for example, in [8], where the possibility of sharing the spectrum between terrestrial 5G and an NTN segment of 5G is described. The 3 GPP TR 38.863 report titled \"non-Terrestrial networks (NTN) related RF and co-existence aspects\" primarily focuses on frequency allocations based on the ITU-R Radio Regulations (RR) [9]. One of the published articles describes the compatibility between satellite and terrestrial segments of 5G in the adjacent channel [10], while in [11], a methodology simulating compatibility between NGSO (Non-Geostationary Satellite Orbit) and GSO (Geostationary Satellite Orbit) systems, which is quite important when considering co-existence studies between NTN and GSO systems, is described. However, the above-mentioned works do not encompass any simulations involving other satellite systems already operating in the n255 and n256 bands. This omission highlights the lack of comprehensive research in this critical area.  
This issue is anticipated to be thoroughly examined during the ITU-R's coordination process, in which operators will seek to file NTN networks in these bands. The successful filing of NTN networks would necessitate comprehensive studies to demonstrate co-existence compliance with existing satellite networks. Consequently, the lack of studies on the aspects of co-existence with the incumbent satellite systems operating in the n255 and n256 bands shows the need for further research and investigation on that topic, which would aid the satellite operators that seek to implement NTN networks and aim to understand the most suitable spectrum for this purpose.
# Learning Kernel-Based Robust Disturbance Dictionary for Face Recognition
###### Abstract  
In this paper, a kernel-based robust disturbance dictionary (KRDD) is proposed for face recognition that solves the problem in modern dictionary learning in which significant components of signal representation cannot be entirely covered. KRDD can effectively extract the principal components of the kernel by dimensionality reduction. KRDD not only performs well with occluded face data, but is also good at suppressing intraclass variation. KRDD learns the robust disturbance dictionaries by extracting and generating the diversity of comprehensive training samples generated by facial changes. In particular, a basic dictionary, a real disturbance dictionary, and a simulated disturbance dictionary are acquired to represent data from distinct subjects to fully represent commonality and disturbance. Two of the disturbance dictionaries are modeled by learning few kernel principal components of the disturbance changes, and then the corresponding dictionaries are obtained by kernel discriminant analysis (KDA) projection modeling. Finally, extended sparse representation classifier (SRC) is used for classification. In the experimental results, KRDD performance displays great advantages in recognition rate and computation time compared with many of the most advanced dictionary learning methods for face recognition.  
f acce recognition; dictionary learning; kernel discriminant analysis (KDA); sparse representation classifier (SRC) +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Target Organs of MR Imaging
The final classification included 25 papers; of those, four major categories of techniques were investigated. (b) The total number of papers as a function of the publication year. The majority of the studies published the results from the application of the NLM noise reduction algorithm to MR images of the brain (\(n=19\)) [43; 44; 45; 46; 47; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63], followed by studies in the \"other\" category (\(n=3\)), which all acquired MR images of two target organs (brain + knee, brain + breast, and brain + abdomen) [39; 41; 48], followed next by cardiac MR imaging studies (\(n=2\)) [40; 59], and finally by studies of breast MR images (\(n=1\)) [42]. Papers related to the application of the NLM noise reduction algorithm in brain MR imaging were first published in 2008. Additionally, 22 out of the 25 papers were the results of the application of the NLM noise reduction algorithm to MR images of the brain, and only three papers targeted other singular organs in isolation. The brain is one of the most frequently imaged areas in MR imaging, and the reduction of noise is critical for both general and special techniques. In particular, the failure to accurately predict or diagnose brain tumors, cerebral infarctions, and brain hemorrhages due to the noise in the MR images of the brain, can cause serious problems for patients. The NLM noise reduction algorithm has been verified as an excellent method to address the deterioration in the accuracy of the diagnosis of these diseases, until deep learning-based algorithms have been developed and are available. After analyzing the number of publications by year, it was determined that in the early stages, the results of studies on organs other than the brain were published frequently; however, since 2011, the majority of studies published have examined the brain.
# 5G Network Deployment Planning Using Metaheuristic Approaches
## 7 Conclusions  
This study presented a comprehensive approach to 5G network deployment planning in the specific geographical area of Thapathali, Nepal. The initial phase involved meticulous definition of coverage area, user density, and quality of service requirements, followed by the collection of geographical and user data, construction of a detailed network model, and integration of the 3 GPP 38.901 model into the simulation framework. The choice of the 3 GPP 38.901 model was justified based on its compatibility with 5G frequency bands and its proven ability to handle urban propagation environments. The model, integrated into the simulation, underwent rigorous validation against real-world data, ensuring accurate predictions. The utilization of metaheuristic algorithms, such as PSO, GA, GWO, and SA, facilitated effective 5G deployment planning in complex urban landscapes. The comparative analysis of these algorithms revealed PSO and GA as the most effective metaheuristic approaches, striking a commendable balance between the number of base stations, coverage, and network capacity for both 28 GHz and 3.6 GHz carrier frequencies. PSO and GA demonstrated efficiency in refining 5G network deployment by optimizing the balance between reduced infrastructure and improved coverage and capacity. GWO showcased enhancements in coverage and capacity post-optimization, strategically reducing the number of base stations. Simulated Annealing demonstrated improvements despite a reduction in base stations, emphasizing its effectiveness in achieving a refined deployment. In addition to these findings, the study proposed an enhancement by introducing a 2.6 GHz carrier frequency for RMa deployment, aiming to achieve 100% coverage and further improve the performance of the 5G Multi-Tier RAN planning. Overall, the study provided valuable insights into optimizing 5G Multi-Tier RAN planning, offering practical solutions for achieving a balance between infrastructure reduction and network performance improvement in a specific geographical context.  
Conceptualization, B.S., R.G. ([PERSON]), B.R.D. and S.R.J.; methodology, B.S., R.G. ([PERSON]), P.P., S.G. and U.S.; software, B.S., R.G. ([PERSON]), P.P., S.G. and U.S.; experiment analysis, B.S., R.G. ([PERSON]), P.P., S.G. and U.S.; writing--manuscript, B.S., R.G. ([PERSON]), B.R.D., R.G. ([PERSON]), P.P., S.G. and U.S.; validation, B.S., B.R.D. and S.R.J.; supervision, B.S., B.R.D. and S.R.J., writing--review and editing, B.S., R.G. ([PERSON]), B.R.D. and S.R.J. All authors have read and agreed to the published version of the manuscript.  
This research was supported by UGC Nepal Grants ID: CRG-078/79-Engg-01, principally investigated by [PERSON].  
It is a simulation and analysis study. The program code and platform environment details shall be available upon request by the interested researchers.  
The authors declare no conflicts of interest.
# Technology Behavior Model--Beyond Your Sight with Extended Reality in Surgery
## 2 Literature Review
### Determinants of the Adoption of XRSG in Surgery
#### 2.3.3 Image Modeling (IM)  
IM refers to the construction of virtual 3D imaging based on human body systems through XRSG's extended reality technology. It can be integrated with the patient's entity during surgery, facilitating observation and manipulation by medical experts. It reflects the practicality and ease of use of user perception, and changes the operator's behavior through perception, reflecting the change of XRTBM usage behavior. In clinical procedures, minor errors can have serious consequences for the life and health of the patient, as each patient is different. Medical experts primarily use existing imaging equipment to perform procedures, which requires a high level of expertise. XRSG 3D medical mapping addresses existing surgical imaging needs and eliminates the need for medical experts to assess lesions at a technical level. It facilitates operator positioning and measurement of bearings. The use of XRSG is positively influenced by the desire of individuals to use them. Therefore, the hypothesis is as follows:  
**Hypotheses 3 (H3).**_IM has a positive effect on medical experts operating XRU._  
**Hypotheses 4 (H4).**_IM has a positive effect on medical experts operating XREU._