esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Measurement Results
### HTTP Requests  
In this section the speed of usual web browsing between Tor and a direct connection is compared. The speed and duration were measured by executing an HTTP request. Table 3 provides an overview of all PlanetLab nodes, their uptime, and number of successful requests.  
Figure 4 compares the durations of a request run with Tor and without Tor, displaying the 25% and 75% quantile of all requests. In this figure, all nine nodes are listed and it shows that all Tor connections are by far slower than a direct connection irrespective of the request's origin. Only the node in Canada sticks out because it took 4.7 s for the 75%-quantile of direct connection requests, which is by far the highest value for a direct connection. The figure also shows a comparison between the several median durations of a request. It shows that Tor requests from Canada are by far the slowest in our experiment. When comparing direct requests with requests tunneled through Tor, one can observe that the ratio (Tor/direct), which yields 4.8, is similar to the ratios measured at the other PlanetLab nodes. An explanation for this phenomenon could be a high traffic load that slowed down both the Tor connection  
and the direct connection. The average ratio of HTTP request durations (Tor/direct) of all nine PlanetLab nodes is 4.1.  
Another observation is that the four nodes located in Germany, UK, and USA always have the fastest Tor connection compared to the other nodes--only these four nodes have the 75%-quantile below 8.7 s, and all other nodes are at least above 11 s up to 25.9 s. When regarding the median time of a request through the Tor network, the nodes located in Taiwan and Australia are almost as fast as the aforementioned four. Only the PlanetLab nodes in Brazil, Russia and Canada were significantly slower in terms of average, median and quantiles. The fact that connections from Germany, UK and USA are the fastest could probably be related to the fact that more than 50% of all Tor nodes are located in these countries [45]. One reason supporting this supposition might be a smaller geographical distance for the first entry point. Other studies showed that distance influences latency [24, 46].
## 3 Results
### Effect of the PV Array Slope and Azimuth Angle on the Results Considering No PV Surplus Energy  
_Exportation to the Grid and PV Energy Surplus Exportation_  
The results presented above considered the PV array slope and azimuth angle as 0 degrees. In order to verify the influence of these factors on the results, the simulations and optimizations were performed with the aim to reach the optimal slope and azimuth angles and to measure their effect on the LCC obtained with the initial conditions. For the simulations, we considered fixed angles of the PV array during the whole year, and no tracking systems were considered.  
Scenario A (Action 3) was used as the reference. When considering no PV surplus energy exportation, the optimization reached an optimal slope angle of 19.5 degrees and an optimal azimuth angle of \(-\)2.5 degrees, representing a LCC saving of 0.83% (from 936,569 to 925,959 Euros), mainly due to the slope angle optimization.  
When considering the PV surplus energy exportation, the optimization reached an optimal slope angle of 35.5 degrees and an optimal azimuth angle of 3.0 degrees, representing a LCC saving of 9.00% (from 596,146 to 481,449 Euros), 8.99% due to the slope angle optimization, and 0.01% due to the azimuth angle optimization.  
Although the installation of the PV array with angles higher than 0 degrees could represent additional costs with the support structure, these results show that this option is feasible, if considering the PV surplus energy exportation to the grid.
## 2 UAV in Wireless Communication Networks
### Channel Characteristics  
A major difference between aerial UAV stations and ground stations is channel characteristics because a strong LoS link dominates the channel conditions in UAV wireless networks. In addition, the performance of UAV wireless networks depends heavily on channel variation as UAVs move. Therefore, the channel characteristics of UAV wireless networks should be considered to optimally deploy UAVs, thereby guaranteeing the required QoS of applications. Generally, UAV wireless networks consist of two dominant channels, air-to-ground (i.e., UAV-to-ground node) and air-to-air (i.e., UAV-to-UAV) channels.
# DentalArch: AI-Based Arch Shape Detection in Orthodontics
## 2 Materials and Methods
### Arch Form Classification  
In the assessment of dental models, three orthodontic evaluators were enlisted, each with specific qualifications and levels of experience. These evaluators were responsible for measuring 19 parameters related to mesiodistal widths and arch shapes, which included 14 mesiodistal widths, 1 intercanine width, 2 interpremolar widths, 1 interpolar width, and the overall arch shape. Evaluator 1 and Evaluator 2 were residents specializing in orthodontics and maxillofacial orthopedics; Evaluator 3 was a general dentist. To ensure uniformity and dependability in their evaluations, all evaluators underwent calibration by an expert orthodontist before commencing this study. To establish the arch form, a consensus-based approach was implemented. The arch form classification was determined through a majority vote among the three evaluators, effectively mitigating the potential subjectivity inherent in individual assessments. This consensus approach not only ensured a comprehensive evaluation but also provided a robust and reliable reference for arch form classification.  
MeshLab was developed by the ISTI-CNR research center and initiated at the University of Pisa in 2005 [33]. For the purposes of this study, the \"measuring tool\" feature of MeshLab was utilized to conduct manual assessments of 18 parameters on dental models, as illustrated in Figure 2. These measurements were carefully executed twice by the evaluators, with a fifteen-day interval between sessions, to guarantee the precision and reliability of the data collected. Furthermore, to enhance the consistency and validity of their assessments, the evaluators underwent specialized training conducted by an orthodontic expert prior to the commencement of this study.  
The arch forms were categorized into three types: ovoid, square, and tapered. The classification criteria did not prescribe a minimum number of evaluators for consensus, but the aim of using three evaluators was to enhance the reliability of the classification process.  
The chosen arch form classifications--ovoid, square, and tapered--were compatible with common clinical practice in orthodontics [34; 35]. These shapes provide a practical and recognizable framework for describing variations in dental arch forms, facilitating communication among orthodontic professionals.  
Figure 1: Challenges encountered during the model selection process, including label deterioration and model fragility. The red circles and arrows are used to indicate specific issues. In (**C**), the red circle indicates a plaster model where a piece of tooth was detached due to manipulation. In (**D**), the red circle highlights poor digitization.
# Off-Line Camera-Based Calibration for Optical See-Through Head-Mounted Displays
## 3 Methods
### The Off-Axis Pinhole Camera Model of the Eye-Display  
As mentioned in Section 1, the eye-display system is commonly modeled as an off-axis pinhole camera (i.e., the associated virtual rendering camera) where the nodal point of the user's eye corresponds to the center of projection and the imaging plane is the semi-transparent virtual screen. This model describes the projection transformation between the coordinates of a point in the 3D space and the associated 2D point displayed on the imaging plane of the see-through display (Figure 1).  
The intrinsic matrix of the off-axis eye-display model is:  
\[\mathrm{\prescript{off-E}K}=\begin{bmatrix}f_{u}&0&c_{u}\\ 0&f_{v}&c_{v}\\ 0&0&1\end{bmatrix}\in\mathbb{R}^{3\times 3} \tag{2}\]  
where \(f_{u}\) and \(f_{v}\) are the focal lengths in pixels, and they denote the distances between the imaging plane of the display (i.e., the virtual screen) and the pinhole camera projection center (i.e., the nodal point of the user's eye); \(c_{u}\) and \(c_{v}\) are the coordinates in pixels of the principal point that corresponds to the intersection between the principal axis and the virtual screen.  
Using Equation (2), the perspective projection between a 3D point in the eye-display coordinate system \(\mathbf{v}^{\mathrm{E}}\) and its associated 2D projection \(\mathbf{i}^{\mathrm{E}}\), both expressed in homogeneous coordinates, is:  
\[\lambda\mathbf{i}^{\mathrm{E}}=\mathrm{\prescript{off-E}K}\left[\mathbb{I}_{3 \times 3}\ \ \ 0_{3\times 1}\right]\mathbf{v}^{\mathrm{E}}=\mathrm{\prescript{F}\mathbf{p} \mathbf{v}^{\mathrm{E}}} \tag{3}\]  
The above formulation assumes a special choice of the world coordinate system \(\mathrm{W}\), with \(\mathrm{W}\)\(\widehat{=}\) E. In more general terms, by plugging the 6 DoF transformation from \(\mathrm{W}\) to E into Equation (3), we obtain the projection transformation that maps world points onto the imaging plane of the eye-display \(\mathrm{\prescript{F}P}\in\mathbb{R}^{3\times 4}\):  
\[\lambda\mathbf{i}^{\mathrm{E}}=\mathrm{\prescript{off-E}K}\left[\mathrm{ \prescript{F}R}\ \ \ \ \ \mathrm{\prescript{F}{{}_{\mathrm{W}}}\mathbf{i}}\right] \mathbf{v}^{\mathrm{W}}=\mathrm{\prescript{F}\mathbf{p}\mathbf{v}^{\mathrm{W}}} \tag{4}\]  
The goal of all calibration methods is to provide an accurate estimation of the matrix \(\mathrm{\prescript{F}P}\), either by solving it as a whole or by determining each of the four entries of \(\mathrm{\prescript{off-E}K}\) and each of the six DoF of \(\mathrm{\prescript{F}{{}_{\mathrm{W}}}T}\) individually.
# Multiport Energy Management System Design for a 150 kW Range-Extended Towing Vessel
## 4 Simulation
### Simulation of ESS
#### 4.2.1 Case 1: ESS with a Satisfied Initial Battery Condition (75% SoC)  
Figure 18a shows the required output power of the PMSM (\(P_{\text{PMSM}}\)) and the power provided by the battery (\(P_{LB}\)), supercapacitors (\(P_{SC}\)), and diesel generator (\(P_{EG}\)) by using the proposed EMS in this case. In the simulation, the battery's initial SoC was set as 75%. Table 6 lists the power data for nine operating points at different times. The following analysis was conducted.  
Figure 14: PMSM speed demand for the five operation conditions.  
Figure 15: PMSM torque demand for the five operation conditions.  
Figure 16: The designed and simulated response of (**a**) PMSM speed, and (**b**) torque.  
Figure 17: The designed and simulated response of the output power of the PMSM.  
1. During the start up stage (0-4 s), the ESS supplied the PMSM's power demand. Both \(P_{LB}\) and \(P_{SC}\) reached up to 60 kW.
2. Between 4-6 s, the RETV entered the before-cruise stage. The lithium battery powered the RETV with about 120 kW. The \(P_{SC}\) and \(P_{EG}\) remained at 0 W.
3. During cruise (6-10 s), the PMSM power demand initially rose, then stabilized. The supercapacitors assisted the lithium battery in meeting the surge in power demand.  
Figure 18: (a) EMS power curves for case 1; (b) SoC curves of the battery and supercapacitors for case 1.  
The diesel generator system output ramped up to 100 kW between 6-7 s, ultimately supplying 150 kW when cruising at peak speed.
4. In towing (10-14 s), the PMSM required a lower constant power, around 100 kW, provided by the lithium battery. The \(P_{\rm SC}\) and \(P_{\rm EG}\) remained at 0 W.
5. From 14 s to 20 s, the lithium battery was charged due to the reversal of the PMSM. It stabilized at 13.3 kW at 16 s.
6. Table 6 demonstrates the power demand successfully distributed by the EMS throughout one operation cycle. The PMSM-demanded power for one operation cycle was 0.3942 kWh, and the diesel generator system and ESS output power was 0.4415 kWh. The comprehensive energy efficiency of the EMS was approximately 85%.  
Figure 18b shows the SoC curves of the lithium battery and supercapacitors in case 1. As shown, the ESS provided the power demand during start-up, so the SoC of the lithium battery was reduced during 0-6 s. The SoC of the supercapacitors also had a certain decrease during the first operation stage (0-4 s). After that, the lithium battery provided power as the operation stage switched to towing, causing the SoC of the lithium battery to drop to 74.97%. The SoC gradually increased due to reverse charging from the PMSM.
## 3 Biosurfactants
### Classification of Biosurfactants  
#### 3.2.1 Plant-Based Biosurfactants  
A separate group of biosurfactants with a glycosidic structure are saponins composed of a hydrophobic aglycone (sapogenin) and a hydrophilic sugar fragment (glycone). Due to the aglycone structure, they are divided into steroidal saponins and triterpen saponins. Saponins are natural non-ionic surfactants, thus they have wide range of surfactant properties, including emulsification, foaming, micellization, and detergency [71]. Saponins are secondary compounds derived from different parts of plants, such as roots, stems, bark, leaves, seeds, and fruits. Their source of origin may also be marine animals, e.g., star fish or sea cucumber [72; 73]. Saponins are used as emulsifiers in alcohol industry, as solubilizing agents for vitamins and minerals, and as food additives for the reduction of lipids absorption [74]. Saponins are also used as surfactants in cosmetics (i.e., hair cleanser, hair conditioner, hair oil, hair dye, and bathing soap) [75; 76].  
The main plants rich in saponins are: _Glycyrrhiza glabra, Yucca schidigera, Quillaja saponaria, Polygala spp., Hedera helix, Primula spp., Trigonella fonem-gracum, Asculis hipocastanum, Panax ginseng, Saponaria officinalisis_, and _Calendula officinalisis_[75; 76].  
The major commercial sources of saponins added to ointments, creams, deodorizers, and hair-shampoos are _Yucca schidigera_, and Quillaja saponaria. Yucca schidigera contains steroid-like saponins, which have anti-inflammatory, antioxidant effects. _Quillaja saponaria_ contains a high percentage of triterpene saponins, which have anti-inflammatory and analgesic effects [77; 78].
# A Novel Slicing Strategy to Print Overhangs without Support Material
## 2 A Novel Slicing Strategy with Geometrical Pre- and Post-Transformation
### Back-Transformation of G-Code
#### 2.5. Inward Cone: Slicing for Inward Overhangs  
As mentioned in Section 1.2, depending on the direction of the overhang outward or inward cone shaped layers are used. All the above-mentioned concepts of the novel slicing strategy are demonstrated with outward cones (Figure 2a). All these concepts can be adopted for the use with inward cones (Figure 2b). The only difference in the transformation \(T\) and \(T^{-1}\) for the inward cone is in the calculation of the z-value (Equations (5) and (6)), where the z correction term, depending on the x- and y- value, now have a opposite sign.  
\[T\colon\mathbb{R}^{3} \to\mathbb{R}^{3}\] \[\begin{pmatrix}x\\ y\\ z\end{pmatrix} \mapsto T(x,y,z) =\begin{pmatrix}\sqrt{2}\cdot x\\ \sqrt{2}\cdot y\\ z-\sqrt{x^{2}+y^{2}}\end{pmatrix} \tag{5}\] \[T^{-1}\colon\mathbb{R}^{3} \to\mathbb{R}^{3}\] \[\begin{pmatrix}x\\ y\\ z\end{pmatrix} \mapsto T^{-1}(x,y,z) =\begin{pmatrix}x/\sqrt{2}\\ y/\sqrt{2}\\ z+\sqrt{x^{2}/2+y^{2}/2}\end{pmatrix} \tag{6}\]  
The basic concept of the back-transformation and all steps included stay the same. The hyperbolic curves for the movement of the nozzle now are all directed downwards. This can lead to collisions with already extruded material or the printing platform. Therefore, an additional criterion is introduced. Whenever:  
* an inward cone is used for the transformation and
* no material is extruded and
* the nozzle is moved in x- and/or y-direction the nozzle has to move linear from the \"old\" point to the \"new\" point (instead of moving along a hyperbolic arc).
## 3 Results and Discussion
### Mechanical Properties of Concrete
#### 3.1.2 The Flexural Strength  
As shown in Figure 8, the flexural strength of the concrete increased with age within 28 days in the environment with a large temperature difference and saline soil erosion.  
Figure 7: Compressive strength of the four groups of concrete under the erosion of SO4\({}^{2-}\) and Cl\({}^{-}\) and a large temperature difference.  
The flexural strength of the concrete developed faster within 7 days, accounting for 80% to 93.7% of the flexural strength of the concrete at day 28. This was because the mineral admixture was not very active in the initial stages and the strength development of the concrete depended on the hydration of the cement, and the saline soil erosion environment provided the concrete with curing moisture, which made the hydration more adequate, whereas the mineral admixture-doped concrete was relatively dense inside, resulting in poorer moisture absorption and hydration compared to the OC group. At 14-28 days of age, the flexural strength of the OC group developed slowly and the flexural strength of concrete mixed with mineral admixtures developed faster because the SO4\({}^{2-}\) and Cl\({}^{-}\) in the salt solution stimulated the potential activity of the mineral admixtures [40]. Secondary hydration of fly ash and slag with cement hydration products occurred in concrete [41], which filled the internal pores and increased the concrete strength, and fly ash could replace cement to extend the hydration process, allowing for higher strength development over time [42]. Studies have shown that the addition of fly ash and slag to concrete can inhibit the attack of sulfates. Consequently, ordinary concrete without any mineral admixtures would result in more sulfate ions penetrating into the concrete, especially in the surface layer, where they would be enriched. On the one hand, when the external temperature and humidity changed, the sodium sulfate in the concrete repeated the process of crystallization and dissolution; the sodium sulfate crystallization absorbed water and generated mirabilite, resulting in an increase in crystallization pressure and expansion, which caused stress accumulation and cracking of the concrete. On the other hand, the intrusion of sodium sulfate into the concrete occurred chemically to generate ettringle and gypsum and other expansive products; when too much product is generated, large expansion stresses are generated, which crack the concrete and reduce its flexural strength.
# Design of an SVM Classifier Assisted Intelligent Receiver for Reliable Optical Camera Communication
## 5 Experimental Results  
In this section, the performance of our proposed scheme for OCC receivers has been demonstrated explicitly. The overall performance has been measured experimentally, and the experiments were conducted considering different conditions. We have taken a maximum of three LEDs as data transmitting LEDs for our experiment. The distance among transmitters was set to 5 cm when multiple transmitters were used during the experiment. The whole decoding procedure of the receiver side has been performed and analyzed in the Python 3.7 environment. Firstly, we have examined the classifier performance to classify and recognize the accurate data transmitting LEDs by varying kernel functions of the SVM classifier. During the experiment, we have used three LEDs among which the no. of actual data transmitting LEDs have been varied. We have also varied the communication distance (10 cm to 60 cm) to justify the effectiveness of our scheme. However, to examine the efficiency of separating accurate LED regions intelligently, the SVM classifier performance has been investigated demonstrating the receiver operating characteristic (ROC) curve and other key performance parameters, such as _precision_, _recall_, _and_\(F1\)_score_. These parameters are defined as  
\[\begin{split} Precision=\frac{True\,\,positive}{True\,\,positive+ False\,\,positive}=\frac{True\,\,positive}{Total\,\,predicted\,\,positive},\\ Recall=\frac{True\,\,positive}{True\,\,positive+False\,\,negative }=\frac{True\,\,positive}{Total\,\,actual\,\,positive},\\ F1\,\,score=2\times\frac{Recall\times Precision}{Recall+Precision}. \end{split} \tag{12}\]  
We have also evaluated the performances from the communication perspective. It shows satisfactory performance on the BER, data rate, and inter-symbol interference (ISI).
## 4 Discussion
### Performance of BiomedCLIP in Detecting Single-Curve and Double-Curve Scoliosis
This difficulty likely stems from the subtle imaging features of single-curve scoliosis, which are harder for the model to detect. While the model performed better in identifying double-curve scoliosis, this improvement came at the expense of increased false positives. These findings undermine the hypothesis of high sensitivity in recognizing scoliosis types and highlight the need for further optimization, particularly to improve sensitivity for single-curve scoliosis and specificity for double-curve scoliosis. Detecting and classifying scoliosis using artificial intelligence (AI) models remains a significant challenge, especially in differentiating between these curvature types. Studies indicate that despite generally high accuracy, the sensitivity and specificity of AI models vary significantly depending on the curvature type and grade. For example, recent research demonstrated that an AI system achieved high sensitivity (0.97) and specificity (0.88) in detecting scoliosis, particularly in mild and moderate grades, while certain cases showed discrepancies exceeding 5\({}^{\circ}\) in Cobb angle measurement [29]. This suggests that such cases may be more difficult for models to accurately identify. While models demonstrate better sensitivity in double-curve scoliosis, this comes at the expense of an increased number of false alarms, leading to misclassification and reduced specificity [30]. These results underscore the need for further optimization of AI models to improve sensitivity in cases of single-curve scoliosis and increase specificity for double-curve scoliosis cases [31]. Techniques such as deep learning and automated ultrasound imaging are being studied to enhance diagnostic accuracy and reduce human error in measurements [31; 32]. Although AI shows potential in detecting scoliosis, current limitations in sensitivity and specificity require further research and model optimization to achieve reliable clinical results.