esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Characterization of Rheological Properties of Solutions  
Rheological properties are the most important parameters that play a role in controlling the printability and shape fidelity of the 3D-printed scaffolds. Viscosity is a crucial parameter to consider for a successful 3D-printing process. Polymer solutions must be sufficiently viscous to protect their shape during printing and have a crosslinking process. Although the research studies demonstrate that low viscosity increases cell adhesion more, it is also observed that this situation causes the collapse of 3D-printed structures. The optimum viscosity range for high-quality printing was found to be between 400 mm\({}^{2}\) s\({}^{-1}\) and 3000 mm\({}^{2}\) s\({}^{-1}\) in the studies [23; 24]. Surface tension is another critical parameter for producing scaffolds. It prevents the ink from ejecting smoothly in the case of being higher than 40 mN m\({}^{-1}\). Moreover, too-low surface tension could cause the ink to leak from the printhead [25]. The surface tension of the solution closely affects the resolution, spread of printed lines, and quality of print [24]. Density directly associated with the viscosity is another significant parameter that is required to be adjusted for the polymer solutions to be used on 3D printing. Therefore, the viscosity, surface tension, and density of the prepared solutions were evaluated in this study (Table 2). The PLA/CS polymer solutions were prepared by adding CS to PLA solutions at different ratios (Table 1). The PLA and CS polymer concentrations of 17 and 3 wt.% were found to be optimum concentrations for preparing the PLA/CS solution, respectively. It was obtained an increase in the viscosity, surface tension, and density of the PLA/CS solution, compared to pure PLA and CS solutions. This increase did not cause a negative effect on the speed or quality of printing of the 3D-printing process. The rheological properties of levodopa-loaded 17 wt.% PLA/3 wt.% CS (17 PLA/3 CS) solutions were measured. The rheological properties of the levodopa added 17 PLA/3 CS solutions at the concentrations of 0.01, 0.03, and 0.05 mg/mL (17 PLA/3 CS/0.01 LEV, 17 PLA/3 CS/0.03 LEV, and 17 PLA/3 CS/0.05 LEV, respectively) were measured in this study. A slight decrease was obtained in all three values for the 17 PLA/3 CS/0.01 LEV scaffolds. Although the viscosity, surface tension, and density of the 17 PLA/3 CS/0.03 LEV scaffolds increased a little more, it did not cause a negative situation in the printing quality or the structure of scaffolds. In addition, at the 17 PLA/3 CS/0.05 LEV scaffolds, all rheological properties due to the increased because of high amount of levodopa. Clogging occurred at the needle tip because of the increased viscosity and surface tension. This situation caused unsmooth layers. The analyses have proven that changing the rheological properties has a significant effect on printing quality.
## 5 Simulation and Results  
The system used in the experiment was configured with an NVIDIA 3090 GPU, an i9-10980 EX CPU, 256 GB of RAM, CUDA 11.0, and cuDNN 8.0.4. YOLOv4 uses a C-language-based Darknet framework [53]; meanwhile, 3 DPI uses PyTorch 1.9.0 and Python 3.8.10. The size of the input image used for training YOLOv4 was 608 \(\times\) 608 pixels; batch size was 64, learning rate was 0.001, momentum was 0.949, and the decay was 0.005. CSPDarkNet53 was used as the backbone.  
Six performance metrics, i.e., overall accuracy, weighted precision, weighted recall, weighted F1 score, weighted area under curve (AUC), and weighted MCC were used to evaluate the model performance. Each performance metric is expressed as follows:  
\[\text{Overall Accuracy }(\text{OA})=\frac{TP+TN}{TP+FN+FP+TN} \tag{2}\] \[\text{Weighted Precision }(\text{WP})=\frac{\sum_{c\in C}N_{c} \Big{(}\frac{TP_{c}}{TP_{c}+FN_{c}}\Big{)}}{\sum_{c\in C}N_{c}}\] (3) \[\text{Weighted Recall }(\text{WR})=\frac{\sum_{c\in C}N_{c} \Big{(}\frac{TP_{c}}{TP_{c}+FN_{c}}\Big{)}}{\sum_{c\in C}N_{c}}\] (4) \[\text{Weighted F1 Score }(\text{WF1})=\frac{\sum_{c\in C}N_{c} \Big{(}\frac{2\times TP_{c}}{2\times TP_{c}+FN_{c}+FP_{c}}\Big{)}}{\sum_{c\in C }N_{c}}\] (5) \[\text{Weighted AUC }(\text{WAUC})=\frac{\sum_{c\in C}N_{c} \Big{(}\frac{TP}{TP+FN}+\frac{TN}{TN+FP}\Big{)}}{2\sum_{c\in C}N_{c}}\] (6) \[\text{Weighted MCC }(\text{WMCC})=\frac{\sum_{c\in C}N_{c} \Big{(}\frac{(TP+TN)-(FN+FP)}{\sqrt{(TP+FN)\times(TN+FP)\times(TP+FP)\times( TN+FN)}}\Big{)}}{\sum_{c\in C}N_{c}} \tag{7}\]  
In the above, \(TP\), \(TN\), \(FP\), and \(FN\) represent true positive, true negative, false positive, and false negative, respectively; \(C\) is the class of the CXR dataset and \(N_{c}\) is the number of test CXR images in class \(C\).
# Artificial Intelligence in Ophthalmology: Advantages and Limits
## 4 Representative Areas of Application of AI in Ophthalmology
### AI and Glaucoma
#### 4.2.2 Overview of AI in Glaucoma  
Glaucoma AI models are mainly sustained by data issued from Functional Vision trials (perimetry), fundus images, and optical coherence tomography (OCT), because these imaging methods offer very structured data suitable for training AI architectures.  
Glaucoma AI models can be divided into two classes, depending on how these networks analyze the data to achieve _classification_ or _prediction_.  
Classical Machine Learning (ML) Models  
Systems in the first category use traditional ML methods, which commonly consist of three stages, as shown in Figure 5, to extract characteristic vectors from the input data and then provide a classification.  
The initial stage involves preprocessing the input image, together with removing the artifacts in the images, to improve the detection of regions of interest such as the optic cup (OC) and optic disc (OD). Then, a module has to select significant characteristics, as well as clinical parameters such as the cup-to-disc ratio (CDR) and visual features (such as morphological, spectral, and texture features) derived from the image preprocessing stage. The final module is an ML network, e.g., Support Vector Machine (SVM) or Naive Bayes, which can be trained by means of the selected feature vectors for classification or prediction activities.  
Data preprocessing, feature extraction, and classification techniques used in this glaucoma systems have been extensively presented in previous studies [74].  
Deep Learning (DL) Models  
Another class of AI architecture is based on deep learning (DL) methodologies, which are a part of ML methods. DL techniques demonstrated that a multilayer neural network with several levels of abstraction can automatically learn an appropriate depiction of data.  
* A characteristic deep CNN uses only pixel magnitudes as inputs and is able to learn images and categorical labels. For example, Figure 6 shows a multilayer CNN that uses optic disc (OD) images as input and then forecasts glaucoma risk [75].  
Figure 5: Diagram of a traditional ML procedure for glaucoma classification [74].  
* DL networks can detect complicated patterns in input data by automatically tuning internal model parameters that are used to calculate data representations (without feature extraction) and classification in a two-step process.
## 2 Conventional Channel Estimation Methods
### Pilot-Based Channel Estimation Method
#### 2.1.2 LS Channel Estimation  
The criterion of the LS algorithm is to minimize the weighted error between the estimated value and the true one. Considering a simple OFDM system, let \(N_{p}\) denote the number of pilots inserted, \(\mathbf{y}=\left[y(0),y(1),\ldots,y(N_{p}-1)\right]^{\mathrm{T}}\) denote the frequency domain received signal at the pilot position, and \(\mathbf{X}=\mathrm{diag}\{\mathbf{x}\}=\mathrm{diag}\{\left[x(0),x(1),\ldots, x\big{(}N_{p}-1\big{)}\right]\}\) denote the corresponding transmitted signal at the pilot position. Furthermore, assume \(\mathbf{w}=\left[w(0),w(1),\ldots,w\big{(}N_{p}-1\big{)}\right]^{\mathrm{T}}\) represents the noise, and the cost function of the LS estimation result \(\mathbf{\hat{h}}_{\mathrm{LS}}\) can be mathematically represented as:  
\[J\Big{(}\mathbf{\hat{h}}_{\mathrm{LS}}\Big{)}=\left\|\mathbf{y}-\mathbf{X} \mathbf{\hat{h}}_{\mathrm{LS}}\right\|^{2} \tag{3}\]  
The LS algorithm aims to estimate the channel by minimizing the cost function, and this is achieved by setting the partial derivative to zero, as follows:  
\[\frac{\partial\Big{(}J\Big{(}\mathbf{\hat{h}}_{\mathrm{LS}}\Big{)}\Big{)}}{ \partial\mathbf{\hat{h}}_{\mathrm{LS}}}=\frac{\partial\Big{(}\Big{(}\mathbf{ y}-\mathbf{X}\mathbf{\hat{h}}_{\mathrm{LS}}\Big{)}^{\mathrm{H}}\Big{(}\mathbf{y}- \mathbf{X}\mathbf{\hat{h}}_{\mathrm{LS}}\Big{)}\Big{)}}{\partial\mathbf{\hat {h}}_{\mathrm{LS}}}=0 \tag{4}\]  
thus, \(\mathbf{X}^{\mathrm{H}}\mathbf{X}\mathbf{\hat{h}}_{\mathrm{LS}}=\mathbf{X}^{ \mathrm{H}}\mathbf{y}\) can be obtained to derive the LS estimation of pilot subcarriers:  
\[\mathbf{\hat{h}}_{\mathrm{LS}}=\Big{(}\mathbf{X}^{\mathrm{H}}\mathbf{X}\Big{)} ^{-1}\mathbf{X}^{\mathrm{H}}\mathbf{y}=\mathbf{X}^{-1}\mathbf{y}=\left[\frac{ y(0)}{x(0)},\frac{y(1)}{x(1)},\ldots,\frac{y\big{(}N_{p}-1\big{)}}{x\big{(}N_{p}-1 \big{)}}\right]^{\mathrm{T}} \tag{5}\]  
The LS algorithm is widely adopted because of its low computational requirement and simple implementation. Nevertheless, due to the omission of channel and noise factors in the computation process, the estimation performance is vulnerable to inter-carrier interference and noise, thereby limiting the algorithm's precision.  
Figure 6: Lattice-type pilot arrangement.
## 3 Materials and Methods
### Oral Components (Human Saliva and Buccal Cells)  
Stimulated human saliva (HS) samples collected from 12 volunteers (8 females and 4 males, aged 21-43 years old) were pooled and used for this study. Previously, subjects were asked to avoid eating or drinking anything different from water for at least 1 h before saliva collection. Stimulated saliva was obtained by chewing a small piece of parafilm and then spitting it out after 15 min into a sterile tube. Two saliva collections were done on the same day: one in the morning and one in the afternoon. All saliva samples were immediately stored at \(-20\)\({}^{\circ}\)C and then pooled after the last collection. The pool was divided into 50 mL aliquots, which were centrifuged at 2600\(\times\)\(g\) for 15 min at 4 \({}^{\circ}\)C. Supernatants were then stored at \(-80\)\({}^{\circ}\)C until use for a maximal time of three weeks. The pH of human saliva samples was 7.27 and a total protein content of \(980.7\pm 0.57\) mg/L determined by applying the Bicinchonic Acid (BCA) assay (Pierce Thermo Scientific, IL, USA) [45].  
Buccal cells (BC) were obtained from the same 12 volunteers who donated saliva. Cell collection was freshly done for each experiment in the morning at the same time with all the volunteers. The subjects were also asked to avoid eating or drinking anything different from water for at least 1 h before cell collection. Cells were obtained by scraping the internal face of both cheeks for 20 s each with a sterile cotton swab which was then transferred into 10 mL of TBS 0.05M (pH 7.6). Buccal cells from all volunteers were pooled and transferred into the buffer. TBS buffer solution was prepared with 6.05 g of Tris mixed with 8.76 g of NaCl in 1000 mL of deionized milliQ H\({}_{2}\)O (18 m\(\Omega\).cm\({}^{-1}\)). The pH was adjusted to 7.6 with 1 M HCl. TBS solution was stored at 4 \({}^{\circ}\)C. The buccal cell mix was vortexed for 30 s and ultra-sonicated three times for 10 s to avoid aggregates. Cell viability analysis and counting were carried out by using Trypan blue dye (0.4% solution) and a Neubauer chamber. The final cell concentration used was 5.104 cells/mL, and therefore 3.105 cells/vial. This amount was standardized for all the experiments and it was in the range described in previous works [46].  
Both types of sampling procedures for collecting saliva and buccal cells were explained in detail to the subjects who also provided written consent to participate in the study. Previously, the Bioethical Committee of the Spanish National Council of Research (CSIC) approved this work.
## 3 Results and Discussion  
### Influence of Number of Neurons in Sub-Layer on the Performance of ANN Model  
As a common parameter used to quantify the average squared difference between predicted values and actual values in a dataset, the mean squared error (MSE) was applied to evaluate the influence of the number of neurons in a sub-layer on the performance of the ANN model. A lower MSE value indicates that the predictions of the ANN model are closer to the actual values. For example, the reduction in MSE with the epochs (i.e., the iterations of training and validation) of ANN model A with eight neurons is shown in Figure 8. Initially, the weights and biases of the training data were randomly selected, and the MSEs were calculated. Subsequently, the weights and biases were adjusted before the next epoch. The MSE of the ANN model improved with each epoch and eventually converged, indicating that further improvements in the model's performance are unlikely.  
To evaluate the influence of the number of neurons in a sub-layer on the performance of the ANN model, the converged MSE and maximum difference of ANN model A with 6 to 12 neurons were calculated and compared, as listed in Table 1. It is observed that the ANN model with eight neurons provides the lowest MSE and the smallest maximum difference in validation data, i.e., 0.00510 and 2.04%, respectively. Therefore, eight neurons in the sub-layer was the appropriate number for the present ANN model.
## 4 Discussion  
Our study developed and preliminarily validated a functional profiling system for youth athletes that integrates field-based assessments and interpretable machine learning. The system classified athletes into four distinct profiles--Functionally Weak, Strength-Deficient, Stability-Deficient, and No Clear Dysfunction--based on standardized measures of movement quality, strength, and balance. Most athletes showed at least one area of limitation, with Strength-Deficient and Stability-Deficient being the most common profiles. Similar distributions were observed across soccer and handball athletes, suggesting the classification logic may be applicable beyond a single-sport context, although further validation is required.  
Unlike binary screens (e.g., FMS \(\leq\) 14) [14], our system provides specific, actionable targets. If an athlete is classified as Strength-Deficient, the logical response is to prioritize strength development in their program--for example, through age-appropriate resistance training to build muscular power and robustness [35]. In contrast, an athlete identified as Stability-Deficient would benefit more from exercises targeting balance, proprioception, and core stability [36]. By breaking down a general concept of \"functional motor skills\" into specific components, the profiles ensure that training interventions can be individualized and thus more efficient [2,15]. This approach aligns with recent critiques of injury prediction screens, which argue for focusing on modifiable deficits rather than attempting to predict injury with single cut-off scores [37]. SHAP analysis further confirmed the model's logic, showing that classifications were driven by domain-relevant variables--FMS for functional weakness, YBT for stability, and isometric strength for strength deficits--while anthropometric factors had minimal influence. This aligns with the recent literature emphasizing the clinical value of interpretable machine learning models, which enhance trust, transparency, and adoption in decision-support systems [38].
# Effect of Wing Membrane Material on the Aerodynamic Performance of Flexible Flapping Wing
## 2 Materials and Methods  
### Flapping Mechanism  
The flapping mechanism was designed based on the combination of crank-slide and rack-pinion mechanism. Figure 1a shows the schematic of the flapping wing mechanism. Figure 1b presents the assembled computer-aided design (CAD) model of this flapping mechanism. The reduction gear, driven by a DC motor, converts the rotational motion into the linear motion of the slider through the linkage. Racks installed on both sides of the slider convert linear oscillation into pinion gear rotational oscillation.  
From Figure 1a, the relationship of the linkages can be easily derived and expressed as follows:  
\[h=l_{1}\sin\phi+\sqrt{l_{2}^{2}-\ \left(l_{1}\cos\phi\right)^{2}}-\sqrt{l_{2}^{2 }-{l_{1}}^{2}} \tag{1}\]  
where \(h\) is the displacement of the slider, \(\phi\) is the rotation angle of the reduction gear, \(l_{1}\) is the length of the crank, \(l_{2}\) is the length of linkage.  
Thus, the flapping angle \(\psi\) of the leading edges (LEs) is given by:  
\[\psi=h/r \tag{2}\]  
where \(r\) is the radius of the pinion graduation circle.  
The LEs are made of carbon-fiber-reinforced polymer (CFRP) rods of 1.5 mm and were rigidly installed to the pinion gears to output the flapping motion. Therefore, the output flapping angle tends to be a cosine curve, approximately, as referred to in Figure 2, which represents the designed flapping angle in one flapping cycle.  
The main structure of the mechanism is made of organic glass by laser cutting. The flapping frequency is controlled by adjusting the input current of the DC motor. An oscilloscope was used to measure the flapping frequency, which analyzed the output signal of a Hall sensor mounted on the mechanism. The Hall sensor could generate a rising-edge signal once the magnet embedded in the reduction gear passes through the Hall sensor. The maximum achievable flapping frequency of the flapping mechanism is almost 30 Hz. In this paper, the flapping angle is fixed at 120\({}^{\circ}\). Notably, due to the flexibility of the leading edge rod, the actual flapping amplitude might increase with the increase of frequency.  
Figure 1: (**a**) Schematic of the flapping wing mechanism; (**b**) Assembled CAD model of the flapping wing mechanism.  
Figure 2: The designed output flapping angle of the mechanism compared with cosine curve. \(T\) is the flapping period, and \(t\) is the physics time.
# Modeling and Performance of the IEEE 802.11p Broadcasting for Intra-Platoon Communication
## 5 Simulations Verification
### The Effect of Bit Error Rate  
To evaluate the effect of the channel errors, we considered a case that \(\lambda=100\) and \(K=20\). In the comparison, \(p_{b}=1\times 10^{-5}\), \(1\times 10^{-4}\), and \(3\times 10^{-4}\), respectively. The results are shown in Figure 6. In general, our analytical model is quite accurate since the results of analytical results and that of experiments are similar for different bit error rates.  
Figure 6a depicts the impact of bit error rates on delay. We can observe that the delay increased with the rise of \(p_{b}\) for a given \(N\). Moreover, the delay increased with the growth of the platoon size. This is because in larger platoons, more packets need to be sent in a buffer. However, these packets must wait longer in the buffer before being transmitted. In addition, more packets competing for channels leads to higher conflicts. Figure 6b shows that the packet dropping rate against the platoon  
Figure 5: Results for different analytical models: (**a**) delay; and (**b**) packet dropping rate.  
Figure 6: Results for varying \(p_{b}\): (**a**) delay; and (**b**) packet dropping rate.  
size under different bit error rates. Rising bit error rate affects packet dropping rate in the negative direction. For example, the packet dropping rate is 0.05 when \(p_{b}=1\times 10^{-5}\) and \(N=10\), and the packet dropping rate is 0.33 under the condition of \(p_{b}=3\times 10^{-4}\) and \(N=10\). Besides, by increasing the platoon size, the probability of collision grows during the packet transmission, which causes a higher packet loss rate for delivered packets.
# A Bibliometric Analysis and Visualization of In-Vehicle Communication Protocols
## 2 Materials and Methods
### Inclusion and Exclusion Criteria  
The final sample included 2919 publications from January 2018 until March 2025. For this study, we decided to concentrate on this interval to include studies that reflect modern trends and the emerging role of digitization in automotive networks. Documents were eliminated because of a lack of cornerstone peer review, duplication, or focus on communication technologies within vehicles. Setting unambiguous inclusion and exclusion rules provided greater transparency in research and eliminated problems associated with the range and scope of data explored in the previous versions of this study. The detailed explanation of these variables increases the reliability and repeatability of the bibliometric analysis.  
Considering the wide coverage and sound indexing of the Scopus database, a systematic bibliometric investigation has been conducted to make the analysis open and repeatable. Citation metrics utilized the complete counting approach, giving full credit to each co-author and reference in co-authorship and citation measurements. Networks based on co-authorship, keyword co-occurrence, and co-citation were produced using VOSviewer, applying the threshold of at least 10 citations or occurrences, eliminating smaller data, and improving interpretability. This mode, a norm in bibliometric studies, utilizes descriptive methods. However, complete documentation of the search and data handling steps was also conducted to ensure transparency and replicability of findings.
# Optimal Design of a New Rotating Magnetic Beacon Structure Based on Halbach Array
###### Abstract  
At present, the magnetic signals generated by the common artificial magnetic beacons change periodically with sinusoidal law, and there is a multi-value problem in measuring the target orientation by using the phase information of the magnetic signals. According to the characteristics of Halbach array, a kind of artificial magnetic beacon based on Halbach array is studied. This beacon has the characteristics of magnetic signal aggregation, which can avoid the multi-value problem and facilitate the extraction of phase information. Firstly, the array model is established with MATLAB, the magnetization direction of each permanent magnet is adjusted, and particle swarm optimization is used to find the structural parameters with the strongest magnetic field characteristics. Finally, COMSOL physical simulation software is used to verify the usability of the proposed structure.  
artificial magnetic beacons; Halbach array; particle swarm optimization (PSO); COMSOL physical simulation; optimization design +
Footnote †: journal: Applied Sciences