esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Innovations in Oral Research  
The advancements in diagnostics encompass the use of metabolomics in the analysis of human saliva, other oral fluids, and/or tissue biopsies which allow us to differentiate between patients with oral health conditions and those with pathologies (e.g., periodontitis, dental caries, and oral cancers) [1]. One of these is represented by oral candidiasis, which affects a large number of patients wearing dental prosthesis. In fact, the treatment of this condition is based on the use of antifungals, and usually also by the prosthesis replacement. However, a major point of concern is the increasing problem of antibiotics and antifungal resistance, which is developing worldwide. Encouraging results against _Candida albicans_, in vitro, have been shown by the use of a novel device, characterized by the emission of complex magnetic fields [2].  
In the case of dental demineralization, the use of an ozone generator showed the ability to stop the progression of early childhood caries (ECC) with a concomitant decrease of dental hypersensitivity [3]. Another important advancement is the use of bioactive materials in the field of biomedical tissue engineering. One of these is represented by BiodentineTM (BD) (Septodont, Paris, France), a tricalcium silicate cement that showed a stimulatory effect on the odonto/steogenic capacity of cultured human dental pulp stem hPDSCs cells. These properties makes of this material, an encouraging candidate for dentine regeneration [4].  
In the case of the diagnosis of carcinoma, the use of intraarol ultrasonography could be a valid tool for the evaluation of depth of invasion (DOI) and tumor thickness (TT) [5], and the results appear to be highly correlated with those of histologic analysis, which, since 30 years ago, represents a milestone in oral research [6]. Another important tool for the diagnosis and pre-surgical planning is cone-beam computed tomography (CBCT). This allows an optimal visualization of the anatomical structures in order to reduce the risk of nerve injuries [7] during the surgery. Moreover, in the case of large bony defects, the CBCT scans allow us to evaluate the dimensions of the defects and to use the data obtained in the production of 3D bone block substitutes [8, 9]. CBCT also allows us to perform follow-ups to the surgery, including many years later [9].  
In the last few years, the introduction of selective laser melting in implant dentistry has allowed the production of novel titanium surfaces characterized by a tridimensional (3D) framework and able to promote osteogenesis and reduce bacterial interactions [10].  
Moreover, the testing of dental implants on polyurethane blocks allows us to obtain a large amount of information about the insertion and removal torque, as well as the primary stability of fixtures, without the necessity of performing tests on animals and eliminating the risk of bias, due to the high variability of natural bone samples [11].  
The authors declare no conflict of interest.
# Multimodal Framework for Long-Tailed Recognition
## 3 Experiment Details
### Method of Comparison  
* **Rebalancing methods.** This paper compares the proposed approach with several commonly used rebalancing methods, including Focal [1], Class-Balanced [2], LDAM-DRW [3], and Equalization [24]. These are standard approaches commonly used in long-tailed classification to address class imbalance issues. They were chosen to validate the basic effectiveness of the new method on long-tailed data and to demonstrate its improvements over traditional methods.
* **Data augmentation methods.** To demonstrate the effectiveness of the proposed method, this paper compares it with several strong augmentation methods, such as mixup [5], Rebalanced mixup [6], and CAM [22]. These methods alleviate the long-tail problem by generating more samples. Comparison with these methods helps demonstrate the robustness and performance improvement of the proposed multimodal approach under various data augmentation strategies.
* **Two-stage methods.** The proposed approach is also compared with two-stage methods such as LWS [7] and cRT [7] to verify its effectiveness. These methods address the imbalance problem by separating representation learning and classification learning. By using these methods for comparison, we show how our proposed multimodal approach achieves higher classification accuracy within the same two-stage framework.
* **Multi-branch methods.** LFME [9], BBN [4], and RIDE [8] are multi-branch models. This paper compares them with our architecture to demonstrate its effectiveness. These methods improve performance for long-tailed categories by designing different optimization paths for head and tail classes. Choosing these methods highlights the better integration and performance improvement of our multimodal approach in similar structures.
* **State-of-the-art methods.** This paper also compares the proposed method with recent state-of-the-art methods: Meta-weight-net [25], Domain Adaptation [26], LADE [27], MiSLAS [28], and DisAlign [29]. They were selected to directly showcase the competitiveness of the new method against cutting-edge techniques, proving the latest breakthroughs in addressing long-tailed problems.
## 5 Experimental Works and Evaluation  
The evaluation is based on our recently developed VLBHO, comparing it with other meta-heuristic searching algorithms developed for feature selection in high dimensional space, namely, the algorithm developed in the work of [21]. For VLBHO, it has two ways for selecting the exemplar: it might use the position of stars for selecting the exemplar which is named position-based (P) or it might use the fitness of stars for selecting the exemplar which is named fitness based (F). Each of the two ways might be based on S or V shape transfer function from the continuous space to the binary space. Hence, we use the code presented in Table 5 for labelling the methods. We present a sample of the segmentation results before classification in Figure 3.  
For feature selection, we used two types of extractors LBP, and Gabor the parameters of each shown in Table 6. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **LBP** & \multicolumn{2}{c}{**Gabor**} \\ \hline
**Parameter** & **Value** & **Value** & **Value** \\ \hline Radius & 2 & No. of scales & 5 \\ \hline neighbors & 8 & No. of or entaions & 8 \\ \hline Block size & 16 16 & filter bank s ze & size of image \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Datasets configuration of LBP and Gabor features extraction. Figure 3: Samples of segmentation results. We divide the feature space into segments based on the configuration provided in Table 7. The reason for using a segment size of 8 for Gabor compared with a segment size of 1 for LBP is the high number of features generated from Gabor so we used also principle component analysis (PCA) for projecting the features which reduced the features dimension by 95%. For classification, two models were used support vector machine (SVM) and logistic regression. The performance metrics of our developed VLBHO are provided in Table 8 Observing the results, we find that PS and PV of VLBHO have outperformed other models. For Gabor features and SVM classifier, the best-achieved accuracy was 95.982% accomplished with PV. Similarly, PV was superior in terms of all other classification metrics. On the other side, for LBP features PS has accomplished the best classification results with 95.982% accuracy compared with only 80.804% accuracy for VLPSO. For summarization, we present the improvement percentage of VLBHO compared with VLPSO for Gabor and LBP. The results are shown in Table 9. As it is shown, VLBHO has achieved 21% improvement percentage over VLPSO for LBP features when it was called with PS mode which is the maximum improvement percentage. \begin{table}
\begin{tabular}{c c} \hline \hline
**Feature Name** & **Segment Size** \\ \hline Gabor & 8 \\ \hline LBP & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Segment size for feature selection according to the type of feature.
## 3 Experiments and Discussion
### Material Selection Test  
#### 3.2.1 Backing Material  
The silicone rubber backing sensor and ABS backing sensor were tested and compared under the same experimental conditions through the above experimental system. In the experiment, a 0.1-s short pulse excitation signal was applied to both of them, and the output response is shown in Figure 5. The \(x\)-axis and \(y\)-axis show the time and the charge outputs of the sensor, respectively. It can be seen that the output waveform of the ABS backing sensor is of a smaller amplitude and longer tailing compared with the silicone rubber backing sensor.  
When the backing layer is ABS plastic, the deformation of PVDF is only in the direction of thickness. However, the thickness of PVDF is only tens of micrometers, and the deformation in the thickness direction is far less than those in other directions. As a result, the response of the ABS backing sensor is very weak. In addition, because of the bidirectional radiation effect of the piezoelectric element, it not only generates the mechanical waves forward but also backward when the pulse excitation is emitted. The clutter interference from the rear should be discarded, and only the signal from the front should be kept [21]. Therefore, the backing needs a good sound absorption performance to consume the rear clutter interference quickly. In the experiment, due to the better sound absorption performance of silicone rubber backing than that of ABS backing, the clutter can be consumed more quickly, so that the tail of the output response waveform of the silicone rubber backing sensor is smaller than that of the ABS backing sensor.  
The experimental results show that different backings have a significant influence on the sensor performance, and the silicone rubber-backing sensor has a better performance than the ABS backing sensor.
## 3 Results and Discussion
### Implications and Limitations  
This study provides an effective supplement to relevant research in larger enclosed environments. The results about the influence of occupants on the speech intelligibility in automotive cabins also have reference value for aircraft cabins, train cabins, and ship cabins with similar scales. Ensuring good speech intelligibility is important in these spaces because poor speech intelligibility usually represents higher listening effort, which affects the driver's driving attention and thus affects driving safety. This study emphasizes the effect of occupants in speech intelligibility, which can be combined with appropriate cabin acoustic design and certain noise-canceling technology or speech enhancement algorithms to improve the speech intelligibility inside the cabin to the greatest extent possible.  
Some factors that may affect the result were not taken into account in the present study, which are worth further research in the future. First, to simplify the research question, factors such as age, gender, body size, and the clothing of passengers were not taken into account. Second, this paper only selected a typical car cabin, but different types of car designs, interior materials, and sizes may lead to certain differences in results. Also, this paper did not consider the interaction between passengers and the actual background noise in the car even though passengers (or occupants) also have an absorption effect on the background noise, just like speech sound. Moreover, this was only an experimental study, and the mechanism of the influence of occupants on the small-space sound fields in automotive cabins deserves further in-depth analysis. For example, the effects of occupants on the sound propagation and low-frequency resonance modes in the automotive cabin could be examined in detail.
## 5 Implementation and Comparison
### IEEE CEC-2017 Benchmark Test Functions Description  
The first benchmark function used is the CEC2017 competition benchmark function [32], which is a comprehensive suite of benchmark functions that serve to rigorously evaluate the performance of evolutionary computation algorithms, as seen in Table A2[32]. This suite is meticulously curated to present a variety of optimization landscapes, each designed to assess different aspects of algorithmic behavior in seeking optimal solutions. The suite begins with a series of unimodal benchmark functions. These functions are crucial for testing the exploitation abilities of an algorithm due to their singular global optima. Examples include the shifted and rotated Bent Cigar and Zakharov functions, which challenge FVIMDE with narrow ridge solutions and a combination of quadratic and quartic components, respectively. The shifted and rotated Rosenbrock's function, with its notorious narrow, parabolic-shaped valley, and the shifted and rotated Rastrigin's function, a unimodal adaptation of a typically multimodal function, are designed to test precision in locating the global optimum. The suite also includes the shifted and rotated expanded Scaffer's F6 function, which presents a landscape characterized by sharp valleys and ridges [33].  
Following the unimodal functions are the simple multimodal functions. These functions are embedded with numerous local optima, thus evaluating the exploration capabilities of an algorithm and its effectiveness in distinguishing global optima from local ones. The suite features the shifted and rotated Lunacek Bi_Rastrigin function, which combines the Rastrigin function's properties with a dual sphere structure, and the shifted and rotated non-continuous Rastrigin's function, which introduces complexity with its discontinuities. Additionally, the shifted and rotated Levy function and the deceptive shifted and rotated Schwefel function require robust strategies to navigate their complex landscapes [34].  
The hybrid functions in the suite, such as the various iterations of Hybrid function 6, represent a synthesis of unimodal and multimodal characteristics, creating a diverse and dynamic optimization environment. These functions test FVIMDE's adaptability and capacity to manage transitions between different function characteristics effectively. The composition functions are the most intricate, constructed by integrating multiple basic functions with varied weights and biases. These functions, ranging from Composition function 1 to 10, replicate the complex, multimodal landscapes akin to real-world optimization scenarios. They require sophisticated exploration and exploitation strategies to navigate the intricately woven global and local optima.
# Research on Polarization Coding Cooperative Communication Scheme for FSO System
## 4 Outage Probability and Bit-Error Rate Analysis
### Outage Probability Analysis
\begin{array}{c}1;\\ \alpha,\beta;0\end{array} \right.\right] \tag{22}\]  
Combining Equations (20)-(22), the outage probability of the direct transmission link can be obtained as follows:  
\[P_{0}=\frac{1}{\Gamma(\alpha)\Gamma(\beta)}G \begin{array}{c}2,1\\ 1,3\end{array} \left[\alpha\beta\sqrt{\frac{\gamma_{th}}{\overline{\gamma}}} \left| \begin{array}{c}1;\\ \alpha,\beta;0\end{array} \right.\right] \tag{23}\]  
In the cooperative communication system composed of the source node \(S\), relay node \(R\), and destination node \(D\), all branches should participate in information transmission, including the direct transmission link and relay link. Finally, all signals are combined using an equal gain combination at the destination node \(D\). When the relay link is interrupted, the direct link of the cooperative communication system can transmit information; when the direct link is interrupted, the cooperative communication system can transmit information through the relay link. Therefore, the use of cooperative communication reduces the probability of system interruption to a certain extent. In the cooperative communication system, when an interruption event occurs, the direct transmission link must have an interruption event. At the same time, one of the links \(S-R\) and \(R-D\) must be interrupted. Therefore, the outage probability of the cooperative FSO communication system can be expressed as:  
\[ \begin{array}{c}P_{out}=P_{S-R}P_{S-D}+(1-P_{S-R})P_{R-D}P_{S-D}\\ =P_{S-R}P_{S-D}+P_{S-R}P_{R-D}-P_{S-R}P_{R-D}P_{S-D}\end{array}  \tag{24}\]  
where in \(P_{S-R}\), \(P_{R-D}\), and \(P_{S-D}\) are the outage probabilities of links \(S-R\), \(R-D\), and \(S-D\), respectively, which can be obtained from Equation (23).
## 3 Methodology
### Data Collection Methods
#### 3.4.2 Data Sources and Collection Tools
Surveys included Likert-scale questions assessing feedback clarity, perceived usefulness, and self-reported engagement levels as mentioned in Table 6. Each survey consisted of 15 questions--10 Likert-scale items (e.g., rating feedback usefulness from 1 to 5) and 5 open-ended prompts (e.g., describing helpful features)--and took approximately 10-15 min to complete, ensuring minimal disruption while gathering detailed insights into student experiences.
# Optimal Base Station Density of Dense Network:
## 3 The Expected Link Rate of the SmBS Tier
### Interference Distribution in the SmBS Tier  
In the SmBS tier, recall that the user chooses an SmBS offering the strongest received power to connect, thus all other SmBSs interfere the user. The interference can be described as  
\[I=\sum_{i\in\phi_{\mathrm{m}}/b_{0}}P_{\mathrm{m}}r_{i}^{-\alpha}, \tag{6}\]  
where \(b_{0}\) is the serving SmBS of the user and \(r_{i}\) is the distance from \(i\)th SmBS to the user.  
**Lemma 3**: _The PDF of the interference (when \(\alpha=4\)) in the SmBS tier is_  
\[f_{I}\left(I\right)=\frac{\mathrm{e}^{-\frac{\alpha^{2}P_{\mathrm{m}}b_{0}t^{ 2}}{2I^{3/2}}}\pi\lambda_{\mathrm{m}}P_{\mathrm{m}}^{1/2}}{2I^{3/2}}. \tag{7}\]  
Proof of Lemma 3.: See Appendix B.  
With the interference distribution, we can further derive the expected value of the link rate.
## 2 Materials and Methods
### Remote Sensing Data and Analysis
#### 2.5.1 HJ-1A/B CCD Images  
The three small sun-synchronous HJ-1 satellites used for environmental and disaster monitoring and forecasting in China were launched in 2008. Both HJ-1A and HJ-1B employ CCD cameras, while the former includes an infrared camera and the latter a hyperspectral camera (HJ-1C employs an S-band SAR package which was not used in this study). These satellites have a spatial resolution of 30 m and a revisit cycle of four days (the revisit cycle of the constellation is 2 days) [40; 41]. Among the payloads aboard two satellites, the multispectral CCD cameras are important instruments that are widely used in eco-environment monitoring. Each satellite carries two CCD cameras, named CCD1 and CCD2. The HJ-1A/B CCDs have three visible bands (0.43-0.52 \(\upmu\)m, 0.52-0.60 \(\upmu\)m and 0.63-0.69 \(\upmu\)m) and one near-IR and 0.76-0.90 \(\upmu\)m) [61]. This study applied the multispectral CCD camera of visible band 3 and near-IR band 4 for vegetation index analysis (Table 3). For this study, HJ-1A/B data were acquired from July to November 2013 for a total of 28 images (Table 4) with mostly \(\geq\) quality of 7 (relatively to cloud cover \(\leq\)30%). Table 4 also shows the source satellites (1A or 1B), sensors (CCD1 and CCD2), the date of the satellite acquisitions, quality and files observation date. For the Solar elevation and azimuth can be collected from the image header files. The field observation dates, which occurred 17 times during one crop cycle. Even though some full scene of the images obtain lots of clouds more than 50% but as the study area rather small therefore some images were not hidden with cloud and shadow.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Satellite**} & \multirow{2}{*}{**Payload**} & \multirow{2}{*}{**Band No.**} & **Spectral** & **Spatial** & **Swath** & **Repetition** \\  & & & **Range (\(\upmu\)m)** & **Resolution (m)** & **Width (km)** & **Cycle (Day)** \\ \hline \multirow{4}{*}{HJ-1A/B} & \multirow{4}{*}{Multispectral CCD camera} & 1 & 0.43–0.52 & 30 & \multirow{4}{*}{360 (700 for 4 0.76 0.90 0.
## 5 Results and Discussion
### Ablation Study
#### 5.3.2 Ablation 2  
To evaluate our adaptive feature fusion strategy, we fixed the values of \(\alpha\) and \(\beta\) to 0.01 and 1 without adaptive adjustment. We used this strategy to perform feature fusion and train model(X). After that, we set up three adaptive feature fusion strategies. The formulas are as follows:  
\[u\sim\pi(u_{t},\varphi(u_{a};\alpha),u_{v};\theta) \tag{15}\]  
\[u\sim\pi(\psi(u_{t};\gamma),\varphi(u_{a};\alpha),u_{v};\theta) \tag{16}\]  
\[u\sim\pi(\psi(u_{t};\gamma),\varphi(u_{a};\alpha),\phi(u_{v};\beta);\theta) \tag{17}\]  
where \(\psi(.;\gamma)\) represents a sub-network branch, and \(\gamma\) is the parameter of the network. In this ablation experiment, the initial value of \(\gamma\) is set to 1.0.  
The models trained according to the three feature fusion strategies are model(\(A_{l}\)), model(\(TA_{l}\)), and model(\(TAV_{l}\)), and the model performance is shown in Table 7. In Table 7, fixing the values of \(\alpha\) and \(\beta\) will reduce the F1-score, but compared with the features of the three modalities directly concatenated, the F1-score is improved by 3.1% (from 74.2% to 77.3%). This shows that using our proposed feature fusion strategy leads to better model performance. Using the other three feature fusion strategies will cause different degrees of decline. Specifically, using the feature fusion strategy only on the speech modality will cause a 2.2% (from 79.0% to 76.8%) drop, and using the feature fusion strategy on the text and voice modalities will cause a 2.0% (from 79.0% to 77.0%) decrease. Using a feature fusion strategy on three modalities results in a drop of 1.4% (from 79.0% to 77.6%). These results show that our proposed feature fusion strategy can better fuse the features of the three modalities.
# Optomechanical Analysis and Design of Polygon Mirror-Based Laser Scanners
## 6 Optomechanical Design Scheme
* A lens design (achromatic doublet or F-theta lens) may follow or, alternatively, a solution such as the two-mirrors device proposed in Section 4 to linearize the scanning function even without a lens (or with as low cost as possible). * The optical calculus is linked to technological tolerances: It imposes them, but it must also consider their levels to calculate (supplemental) functional errors. * The geometric optical parameters and functions also influence the decisions regarding kinetostatic and dynamic aspects such as torques, calculus of motors, and, related to this, bearings, as well as windage calculus and solutions to decrease it. The type of chosen PM impacts the design with dynamic balance calculus. * The design can be completed after considering (necessary) thresholds implying cost and reliability. One must remark that, as pointed out from the beginning of this work, sensors and control structure (i.e., automation) aspects have not been considered in the present study. However, they are, as in the case of GSs [11; 12; 13] or other scanners, a distinct problem, starting from parameters such as \(f_{\rm s}\), precision, reliability, and resistance to disturbances. They also impact cost and output characteristics. Although the theory in the present study was developed for prismatic normal PMs, it can be applied to pyramidal (normal or inverted) ones, as well. As a guiding tool, the design scheme in Figure 17 can be applied to MEMS PMs, as well, and--in part--other types of optomechanical scanners (including GSs), considering their characteristic parameters concerning the optical design part.