esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Chemical Composition of Yarrow Essential Oils
#### 3.1.2 Comparison of Yarrow Essential Oils of Different Origins  
Due to the lack of specific regulation on quality, it was proposed to compare the obtained composition with commercially available essential oils. For this reason, two samples were purchased, one from the Romanian market and the other one from the Bulgarian market. Considering the blue color conferred by azulene content, the aspect of yarrow essential oil might serve as a first sign of the composition and quality of different origins. Figure 3b intends to illustrate the encountered color differences of yarrow essential oils from different origins. In Figure 3b, it can be observed that in the case of our essential oil and the commercial sample of Bulgarian origin, the blue color is predominant, while in the case of the purchased oil originating from the Romanian market, the color of the oil is more greenish. The observed difference is also reflected in the UV-VIS spectra of the essential oils in alcoholic solution, presented in Figure 3. The highest peaks achieved at the specific absorption wavelength of chamazulen (603 nm) were registered in the case of our essential oil. In the case of the oil of Bulgarian origin, lower peak intensity was obtained, while the yarrow oil of Romanian origin resulted in the lowest peak. The maximum wavelength is consistent with the value provided in the European Pharmacopoesia, which specifies the measurement of chamazulene content in xylene solution at 608 nm [8]. Furthermore, the purchased essential oils were also characterized by GC-MS analysis. The results obtained are presented in Table 2.  
In the yarrow essential oil purchased from the Bulgarian market, 45 components were identified. The major constituents were beta-pinene, sabinene, camphor, carophyllene and linalool. Germacrene D and chamazulene were present in only 6.1% and 3.8%, respectively. Table 3 contains the composition of yarrow essential oil purchased from the Romanian market. Figure 3: Comparison of yarrow essential oils. (**a**) UV-VIS spectra of alcoholic solutions of yarrow essential oils of different origins; (**b**) visual aspect of yarrow essential oils obtained by steam distillation: 1. self-prepared, 2. from Bulgaria, 3. from Romania. The color and order of the curves (**a**) match the color and order of the solutions ((**b**) 1, 2, 3 from top to bottom).
## 3 Research Method
### Modeling Approach
To evaluate the model's generalizability and reduce the risk of overfitting, an internal validation process was conducted using a 70/30 train-test split. Additionally, cross-validation techniques such as k-fold cross-validation were considered to further assess the model's predictive performance across different data partitions. Both cross-validation (CV) and generalized cross-validation (GCV) are well-established methods for estimating the prediction error associated with the \(k\)th fitted model, thereby offering insight into the model's expected performance on unseen data. Although detailed CV and GCV results are not included in this manuscript, their use supports the model's robustness and reliability. Model parameter estimation was carried out using maximum likelihood estimation (MLE), a method that maximizes the conditional likelihood of the observed data given the model parameters. While the log-likelihood function does not constitute a probability distribution itself, it plays a crucial role in model fitting by guiding the selection of coefficients that best reflect the underlying structure of the training data. The resulting log-likelihood value serves as a central metric for evaluating model fit and comparing competing models. To enhance the interpretability and practical applicability of the proposed methodology, particularly for clinicians, a visual flowchart outlining the sequential steps of model development, from data acquisition to risk prediction, has been included in Figure 1. This schematic representation helps clarify the underlying statistical framework and offers an intuitive guide for implementing the model in both clinical and research contexts. Figure 1: Visual workflow of the COPD Severity Prediction Model.
## 4 Resource Management in Heterogeneous Network (RM HetNet)
### Spectrum Allocation (SA)
#### 4.4.2 Millimeter Wave (mmWave)
It was demonstrated that licensed SA with both frequency-domain and time-domain co-channel interference avoidance approaches provide similar performance improvements in average capacity, SE, EE, and cost efficiency. Nonetheless, load balance is not considered. In [136], based on the licensed 28 GHz mmWave, or other spectrum is allocated equally to each BS, they proposed two approaches to enhance spectrum utilization. Principally, the first approach, known as static licensed SA, is on maximizing spectrum utilization by allocating the 28 GHz spectrum equally to all BSs. The second approach, flexible licensed SA, is concerned with increasing spectrum utilization through the spectrum allocated unequally to each BS. Since sharing and reusing the licensed SA to each BS using either static licensed SA or flexible licensed SA can be abused, secondary spectrum trading is essential in the case of static licensed SA to redistribute the spectrum assigned to the BSs. They provide mathematical models for both approaches, allowing the calculation of metrics such as SE, EE, and average capacity. The results demonstrated that the average SE and EE requirements for 6G mobile networks could be achieved using both the first and second approaches. Nevertheless, user mobility needs to be considered. In [137], to improve HetNet backhaul capacity, the author suggested resource allocation algorithms and used geometric analysis to evaluate network efficiency. First, they suggested a centralized SA algorithm for the case that BS is available to collect traffic load information and CSI of each user's equipment. In order to improve the high propagation loss of the mmWave band, densification of BS is necessary. This may induce high computational complexity and require a massive channel state information feedback load for the centralized SA scheme. They discussed the distributed SA scheme that can be individually managed at each BSs to solve this problem. However, they applied geometric analysis to the investigation of the HetNet to gain insights into the system design. They considered stochastic geometry and hybrid beamforming in the mmWave band to arrive at a manageable approximation of co-channel interference. This expression is possible to obtain the insights necessary to avoid the impact of interference co-channels. Numerical results evaluated the strength of the geometric analysis and showed that the proposed algorithm could boost system performance. However, the EE needs more attention and consideration.
## 5 Discussion
### Effectiveness of Improvement Procedures  
The main improvement procedures proposed in the MODMSA are minimum perturbation random initialization, adaptive perturbation control strategy, and profit-oriented local search algorithm. Based on the experiment results, we found that each improvement procedure is vital for the MODMSA. The minimum perturbation random initialization and adaptive perturbation control strategy determine the performance of the MODMSA in diversity. The profit-oriented local search algorithm is able to improve the profits of the non-dominated solutions further. In addition, the framework of the MODMSA and the proposed improvement procedures provide ideas for other dynamic mission-scheduling problems.  
Figure 8: Non-dominated solutions obtained by MODMSA-based variants in different test cases with medium load.
# Efficient Metal Corrosion Area Detection Model Combining Convolution and Transformer
## 3 Analysis and Discussion of Experimental Results
### Experimental Results and Analysis
#### 3.3.4 Comparison and Analysis of Visual Results for Different Models  
To further evaluate the segmentation performance of the proposed model, this paper compares the visual results of different models using the self-collected dataset, Dataset B. The comparison results are shown in Figure 13.  
From the comparison, the characteristics of each model can be observed. Compared to other segmentation methods, the MCD-Net model demonstrates outstanding performance, particularly in the four noise-disturbed examples presented in the figure, where its robustness and resistance to interference are especially prominent. The Hrnet model is able to roughly locate the corrosion areas but struggles with detail capture, particularly around the corrosion edges, resulting in relatively coarse segmentation. The Swin-U-Net model provides a complete corrosion area outline and has good global characteristics, but its edge segmentation is somewhat blurred. The SegFormer model shows good alignment with the actual corrosion areas, but its segmentation of the third instance lacks precision. PSPNet delivers better connectivity in its segmentation results, but for noisy corrosion images, as seen in the second example, it incorrectly segments a large area, indicating its sensitivity to noise.  
In contrast, MCD-Net demonstrates excellent corrosion area detection capabilities across the four examples. In the first instance, it accurately identifies corrosion areas with significant color and texture differences. In the second instance, it shows exceptional noise resistance and robustness. In the fourth instance, it precisely analyzes defects and voids in the target area, avoiding misclassification. The segmentation results are closely aligned with real-world conditions, showcasing excellent accuracy and robustness.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Items** & **Model** & **mIOU/\%** & **FI/\%** & **mPA/\%** & **mPre/\%** \\ \hline
1 & Hrnet [27] & 68.88 & 81.66 & 82.28 & 81.05 \\
2 & Deeplabv3 [28] & 66.60 & 80.68 & 81.60 & 79.79 \\
3 & Pspnet [29] & 70.58 & 82.99 & 83.76 & 82.24 \\
4 & Segformer [30] & 70.93 & 83.00 & 83.52 & 82.49 \\
5 & Swin-U-Net [31] & 67.93 & 80.88 & 81.38 & 80.40 \\
6 & MCD-Net & 75.28 & 86.18 & 87.02 & 85.35 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of performance indexes of different models on Dataset B.
# SPR-Optical Fiber-Molecularly Imprinted Polymer Sensor for the Detection of Furfural in Wine +
## 5 Conclusions  
The proposed platform, based on D-shaped POP and an MIP as the receptor layer, has been demonstrated to be particularly suited for measurements in aqueous matrices since the RI of the layer in contact with the resonant surface (gold) matches the measurable RI range. On the other hand, this strongly depends on the MIP layer's thickness, at least for the MIP here considered.  
It has been found that the \"wine\" matrix has a large effect on the SPR signal since the response of the sensor to 2-FAL is different in water and wine-mimicking solution. The affinity constant is one order of magnitude higher in wine than in water, and consequently, a lower LOD is obtained, reaching interesting values for the determination of 2-FAL in wine.  
The method developed for the determination of 2-FAL in wine could have a high practical interest; on the one hand, for the possible toxic and carcinogenic effects of furanic aldehydes, particularly 2-FAL, on human beings and, on the other hand, for their impact on the aroma of food. Interestingly, the sensor here developed could be easily adapted to the analysis of other furanic compounds.  
Conceptualization, M.P. and N.C.; methodology, M.P., N.C., L.D.M., L.Z., G.A.; validation, M.P., N.C., L.D.M., L.Z., G.A.; formal analysis, M.P., N.C., L.D.M., L.Z., G.A.; investigation, M.P., N.C., L.D.M., L.Z., G.A.; data curation, M.P., N.C., L.D.M.; writing--original draft preparation, M.P., N.C., L.D.M., L.Z., G.A.; writing--review and editing, M.P., N.C., L.D.M., L.Z., G.A. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
This work was supported by the VALERE program of the University of Campania [PERSON] (Italy).  
The authors declare no conflict of interest.
# LDPC-Coded CAP with Spatial Diversity for UVLC Systems over Generalized-Gamma Fading Channel
## 1 Introduction
In this study, the LDPC outlined in IEEE.802.16 standard, whose fundamental codes can accommodate various code rates and packet sizes [20], was adopted. The UOT fading channel was modelled by GG distribution covering from weak to strong turbulence. We derived closed-form expressions of the approximated bit-error rate (BER) for the UVLC spatial-diversity scheme with the equal-gain combining (EGC) at the receiver on the basis of the characteristic function (CHF) of GG random variables. Then, we used Monte Carlo (MC) simulations to investigate the various spatial-diversity orders in combination with LDPC, validating theoretical analysis and the effect to combat UOT. To the best of the authors' knowledge, this is the first study providing comprehensive mathematical derivation validated by MC simulations to evaluate the performance of the LDPC-coded spatial diversity CAP scheme over GG-distributed UVLC fading channels, which is helpful to design a reliable UVLC system in turbulent channels. The remainder of the paper is organised as follows. In Section 2, the UVLC system, fading channel model, and LDPC selection are described. In Section 3, we derived the approximated BER on the basis of the CHF of GG random variables. Then, the Monte Carlo simulation is carried out in various channel scenarios, and the results are presented in Section 4. Lastly, Section 5 concludes the paper.
# Small Dam Drainage with Nonwoven Geotextile after 40 Years of Exploitation
## 1 Introduction
The reduction of the geotextile permeability results from the processes of mechanical, chemical and biological clogging. The authors have highlighted that these processes do not occur in parallel and equally over the entire surface of the material. Changes in the filtration properties of nonwoven geotextile after 18 years are described by [PERSON] et al. (2019). Two types of non-woven fabric (nonwoven needle-punched and woven-strip geotextile) were used as separating and filtering layers in drainage trenches. During the exploitation period, the water permeability of nonwovens decreased about 3.8-fold. The authors explain that clogging can be caused by physical, biological and/or chemical processes. The mechanism of physical clogging is the accumulation of fine particles in the geotextile or before the geotextile filter. Biological clogging requires very specific physicochemical conditions in terms of temperature, alkalinity and the concentration of minerals and organic substances. [PERSON] et al. (2019) described changes in the filtration coefficient of nonwoven geotextile drainage of the small natural barrier in Piask-Szczygliczka. The authors presented the results of the nonwoven geotextile test after the eighth and eleventh years of operation. During this period, the water permeability coefficient decreased almost 4-fold. The article analyzes the impact of fluctuations in the water table in the reservoir on the mechanical clogging on nonwoven geotextile. Access to archival documentation led to the decision to continue the research on geotextile drainage of the Piask-Szczygliczka barrage after 40 years of operation. The use of computer software in predicting seepage under an earth dam has become more popular, but as noted by [PERSON] et al. (2019) and [PERSON] et al. (2018), it is important that these tools should be properly calibrated and validated, especially on the basis of values measured in the field ([PERSON] et al., 2019). Numerical analysis using HYDRUS code can be successfully used to describe seepage under the earth dam in different cases ([PERSON] et al., 2019). The attempt to determine the influence of clogged nonwoven geotextile on water flow in soil using HYDRUS (two-dimensional/three-dimensional (2D/3D)) Standard is another challenge.
# An Object Detection and Localization Method Based on Improved YOLOv5 for the Teleoperated Robot
## 4 Result and Analysis
### Distance Estimation Results and Analysis  
In the actual scene, objects were haphazardly placed on the ground between 0.3 and 3 m from the camera. The effect of the object detection and localization method proposed in this paper is shown in Figure 10.  
The proposed object detection method can accurately and robustly detect the objects. The proposed object localization method can estimate the distance between the object and the camera in real time. It makes it easy for even a novice operator to control the distance between the teleoperated robot and the object, so that the object is in the working space of the robotic arm to achieve fast and accurate localization, which can improve the execution efficiency of the teleoperated robot.  
To further evaluate the accuracy of distance estimation, we calculated the relative error between the estimated distance and the true distance for 378 valid distance estimation data. The relative distance error of all objects was averaged to characterize the accuracy of the distance estimation method in different sub-intervals. The distance estimation accuracy results are shown in Figure 11.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Models** & **Precision (\%)** & **Recall (\%)** & **mAP (\%)** & **F1 (\%)** & **Model Size (MB)** & **Inference Time (ms)** \\ \hline YOLOv5s & 91.7 & 98.6 & 98.6 & 95.0 & 14.4 & 22 \\ YOLOv5m & 91.5 & 98.5 & 98.5 & 94.9 & 42.6 & 35 \\ YOLOv5l & 91.4 & 98.4 & 98.4 & 94.8 & 93.8 & 48 \\ YOLOv5\_Tel & 92.6 & 99.4 & 99.4 & 95.9 & 12.8 & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The simulation results of different YOLOv5 models.  
The proposed distance estimation method can achieve good localization accuracy, with a minimum relative error of 0.76%, a maximum relative error of 2.0%, and an average relative error is 1.12%. The proposed distance estimation method is robust within the experimental range, according to the root mean square error (RMSE) of the relative error, which is 0.4%.
# Classification of Human Motion Data Based on Inertial Measurement Units in Sports: A Scoping Review
## 1 Introduction
### Data Bases, Key Words and Inclusion Criteria  
Three databases that include journals as well as conference proceedings were searched: Scopus and Web of Science to cover interdisciplinary research, as well as IEEE Xplore that covers articles and conference proceedings from, among others, bioengineering, computing and processing or signal processing and analysis. The first database search was performed in July 2020, where journals and conference proceedings were extracted. To update the analysis, another search was performed in March 2022 to further include journal articles from August 2020 to December 2021.  
We adapted the keyword string of [7] by focusing on general (individual) motion classification in sports, but did not concentrate on outcome predictions of matches and games.  
The following keyword string was used for Web of Science and IEEE Xplore:  
((sensor OR accelerometer OR gyroscope OR inertial measurement OR imu) AND (sport* OR prevention) AND (prediction OR classification OR recognition) AND (movement OR motion OR kinematic*) NOT review)  
and the following keyword string was used for Scopus:  
((sensor OR accelerometer OR gyroscope OR inertial measurement OR imu) AND (sport* OR prevention) AND (prediction OR classification OR recognition) AND (movement OR motion OR kinematic*) AND NOT review).  
Articles in which the classification process was not clearly described and in which no active human movements were classified were not considered. In addition, articles that used image or video data were excluded. Further, articles that focused on the general classification of activities into major activities such as lying, sitting, or walking were excluded, as were articles that were not written in English. An overview of the inclusion and exclusion criteria is provided in Table 1. Because some of the papers met more than one exclusion criterion, the sum is greater than the total number of excluded papers.  
The scoping review mainly analysed the classification methods applied in the existing literature and examined the following research questions:  
1. **Data**: Which data sources were used? How large was the sample size? Were resampling techniques/data augmentation techniques applied?
2. **Preparation**: Was dimension reduction applied? How was the data set split into training and validation data?
3. **Methods**: Which methods were used? Do these methods take into account dependencies between the segments/single movements?
4. **Validation**: Which metrics were used for performance evaluation?  
\begin{table}
\begin{tabular}{l l} \hline
**Inclusion** & **Exclusion** \\ \hline
2010–2021 & outside 2010–2021 \\ English & full text not in English \\ classification method is clearly described & no clear description, no classification \\ active human movement & not human movement \\ IMU sensor data & image/video data \\ specific sports activity/motion primitive & general activity classification, fall detection etc. \\ \hline \end{tabular}
\end{table}
Table 1: Inclusion and exclusion criteria.
## 3 Results and Discussion
### Gap Flow in the RDT
However, the velocity profile in the tangential direction is quite different from the one in the axial direction; the flow is viscosity based, as the influence of the pressure difference along this direction is insignificant compared to the shear stress. From Table 5, the contribution of pressure and viscosity to the torque production on the propeller and rim are quite distinct. For the propeller, the pressure is dominant and contributes nearly 90% to the total torque, while the viscous stress is almost fully responsible for the torque generation on the rim, which coincides with the observation in Figure 12b.
# Exact Method for Generating Strategy-Solvable Sudoku Clues
## 2 Preliminaries
### Notations and Terminology  
For convenience, rows and columns are numbered from 0 to 8. A cell is denoted by the pair \((i,j)\) of a row index \(i\) and a column index \(j\). The 9 subgrids of \(3\times 3\) are called _blocks_. Rows, columns, and blocks are collectively called _groups_. A group is identified with the set of all cells in the group. By abuse of notation, we denote by \(G\setminus(i,j)\) the difference of a singleton \(\{(i,j)\}\) from a group \(G\).  
Given a partially completed grid, the goal of a Sudoku puzzle is to fill in all empty cells with digits from 1 to 9 in such a way that:  
* Each cell has a single digit; and
* Each digit appears only once in every group.  
The completed grid is called a _solution_. A _Sudoku_ is a convenient alias for a partially completed grid given as an initial grid. A Sudoku is _proper_ if it has a unique solution. The occurrence of a digit in an initial grid is called a _clue_, and _Sudoku clues_ are the clues in an initial grid. A cell in an initial grid to which some digit is designated to be placed as a clue is called a _clue cell_ or a _clue position_.