esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Study Areas  
_Two Areas in the City of Roma (Central Italy) Were Selected for This Study_  
Volcanic products from Colli Albani volcano are the geological bedrock of site 1 (Figure 1) and represent the local aquifer (Colli Albani hydrogeological unit [26]). The groundwater table, located at a depth of about 18 m, is characterized by high radon levels [23]. Small seasonal fluctuations affect the aquifer and groundwater flow direction is constant throughout the year. The gasoline spillage occurred about 20 years ago; a \"Pump-and-Treat\" system coupled with a set of Soil Vapor Extraction (SVE) wells (Figure 2) is employed here for in situ clean-up of soil and groundwater. Biosurfactants were also added to enhance the separation of the residual NAPLs from soil grains and the biodegradation.  
Site 2, located next to Tevere river, is characterized by an alluvial bedrock (Figure 1). The groundwater table is very shallow (about 2 m below ground level) with significant seasonal fluctuation (up to 0.7 m) and changing flow direction throughout the year. Radon contents are from low to moderate. The NAPL (diesel) spillage is much more recent here (January 2019) compared to site 1, even if previous gasoline leakages were reported in the past. The age of the diesel spill was estimated applying a radiometric method, based on the \({}^{228}\)Th/\({}^{228}\)Ra activity ratio in the NAPL phase extracted from the aquifer [27].  
No mitigation actions have been taken so far, but securing activities such as monitoring, pumping, and local storage of extracted groundwater are operative (Figure 3).  
In both areas, water sampling from purged wells is carried out every three months and the groundwater is analyzed for radon activity concentration and NAPL content, mainly total hydrocarbons, MTBE (Methyl Tertiary-Butyl Ether), and ETBE (Ethyl Tertiary-Butyl Ether). The depth of the groundwater table is measured once a week and occasionally soil gas is extracted for radon and volatile organic compounds (VOCs) detection.  
Figure 1: Hydrogeological conceptual model of Roma (central Italy) with location of the two study areas. Modified from [26].  
Figure 2: Site 1. The mitigation plant consists of a “Pump-and-Treat” system, coupled with a set of Soil Vapor Extraction (SVE) wells. Wells PZ25 and PZ26, located about 200 m E and SE of the petrol station area, are not reported in the map to avoid narrowing the petrol station area too much. Their location is provided in ref [23], Figure 3.
# Automatic Segmentation of Mandible from Conventional Methods to Deep Learning--A Review
## 4 Discussion  
Numerous studies have been published regarding mandible segmentation. Mandible segmentation methods collected in this paper are classified into seven categories. As shown in Figure 2(a), atlas-based (23.4%), CML-based (19.5%) and DL-based (42.9%) methods are frequently used and developed in mandible segmentation. More than three-quarters of the publications (85.8%) utilized these methods. The atlas-based method is the oldest technique in the field of mandible segmentation and has been frequently implemented in commercial software [56, 57, 58, 60, 61]. Low accuracy is reported by using this kind of method due to the image artifacts and low contract. Atlas construction is a time-consuming task when conducting complex nonrigid registration. An extended approach is to apply level set methods that help the initial curve move towards the boundary of the object [9, 63]. The level set function implicitly represents the boundary of a curve or a shape through its zero horizontal tangent plane. The main drawback of using the level set-based methods is the requirement to initialize the contour. Thus, using the contours generated from atlas-based methods as initialization is helpful in this task [9, 63]. The SSM-based method is another approach used by various researchers for mandible segmentation, in which the shape information of the object is taken into consideration in ASM. The method performed well compared to the atlas-based method. In addition to considering the shape information, AAM considers the texture information of the image and learns it in an integrated statistical shape model. The most frequently used image segmentation approach is machine learning, especially deep learning (DL), which produces better results than the other methods. The classical machine learning algorithms demand a considerable human effort in feature extraction. The DL technique has been used frequently since 2018 due to the rapid development of DL techniques, as illustrated in Figure 2(b). Deep learning methods have resolved the issue of automatically extracting the features. The major issue of using deep learning methods is the requirement of a substantial quantity of annotated medical data, which limits the use of supervised learning techniques and avoids overfitting [93]. Furthermore, to increase the size of training data, the deep learning-based methods are trained using data augmentation schemes such as scaling, cropping, affine transformation, elastic deformation, rotations, and noise contamination [81, 82, 93, 98]. Moreover, with the evolution of deep learning, mandible segmentation has achieved remarkable performance compared to classical segmentation approaches.
# Polarization Differential Visible Light Communication: Theory and Experimental Evaluation +
## 3 Measurement Setup
### Receiver Side  
At the receiver side, two cases are considered. The first case, further mentioned as setup A, uses off-the-shelf available hardware to provide an initial evaluation. Two switchable gain Si detectors (Thorlabs PDA36A2, Thorlabs, Newton, NJ, US) are deployed, also equipped with mutually orthogonal polarizers. These polarizers are rotated in 5 degrees increments using a 3D-printed jig to mimic receiver rotation. Figure 5 shows the assembly of setup A for the case where \(\theta=0^{\circ}\). While the mutual distance between transmitters and receivers is ideally negligible, there is still a spacing of 10 cm and 9 cm, respectively between transmitters and receivers in this setup. To minimize the difference in optical path length and irradiance and incidence angle, the receivers are oriented symmetrically relative to the transmitters. The polarizer axes are precisely aligned using a self-leveling crosshair laser (Bosch GLL 3-80, Bosch, Gerlingen, Germany). The setup resembles an indoor environment where \(h=1.35\) m.  
Additionally, a custom receiver was designed for setup B. The receiver is based of a four quadrant (QD) photodiode (Hamamatsu S5981, Hamamatsu, Hamamatsu, Japan) and is shown in Figure 6a. An aperture holding four custom-cut linear polarizing film (Edmund Optics 150 \(\times\) 150  
Figure 4: The two LED transmitters equipped with mutually orthogonal polarizers at \(0^{\circ}\) and \(90^{\circ}\) respectively [17].  
mm Linear Polarizing Film (XP42-18), Edmund Optics, Barrington, NJ, US) is constructed in front of each quadrant, as shown in Figure 5(b). The tolerance of this polarizing film is also specified at \(\pm 2^{\circ}\). The height and size of the aperture are designed as such not to obstruct the field of view of the quadrants. The polarization axes of the polarizing film at each quadrant are orientated at angles of \(0^{\circ}\), \(45^{\circ}\), \(90^{\circ}\) and \(135^{\circ}\), thus creating two pairs of mutually orthogonal polarizers. Pair 1 consists of quadrants QD B and D with polarizers at \(90^{\circ}\) and \(0^{\circ}\) respectively while pair 2 is made up of QD A and C with polarizers at \(135^{\circ}\) and \(45^{\circ}\). The polarization axes of the polarizers at each quadrant are illustrated in Figure 5(c)-f. The receiver is also rotated in 5 degrees increments with a corresponding jig.  
Figure 5: Setup A: two PDA36A2 receivers are equipped with mutually orthogonal polarizers on \(5^{\circ}\) increment measurement jig for \(\theta=0^{\circ}\)[17].  
Figure 6: _Cont._
## 5 Performance Evaluation
### Realistic Scenario Experiments  
In order to evaluate the performance of the proposed algorithm ML-RELM in realistic situations, we employ 20 nodes with the TI CC2530 chip which are deployed outside of buildings (as shown in Figure 13b) and inside of buildings (as shown in Figure 13e) respectively at the Jinling Institute of Technology. Figure 13a,d are satellite images of the test locations, respectively. The size of the outdoor and indoor test-beds is 30 m \(\times\) 5 m and 30 m \(\times\) 16 m, respectively. We set the communication radius of nodes equal to 6 m. Nodes 1#, 2#, 3#, 4#, 5# and 6# are selected as anchor nodes, and the remaining 14 nodes are normal nodes. Other parameters are similar to that in simulation experiments. Three previous methods: (1) The classic DV-hop algorithm [10]; (2) PDM [9]; and (3) LSVR [7;8] are compared.  
As shown in Figure 14, the ML-RELM method proposed in this paper can achieve higher localization accuracy under these two conditions. The average RMS of ML-RELM is about 2.4 outdoor  
Figure 12: Comparison of different algorithms with different number of anchor nodes in the random deployment.  
Figure 13: Nodes distribution under realistic scenarios (**a**) Satellite picture of outdoor location; (**b**) Picture of outdoor test-bed; (**c**) Topological graph of outdoor test-bed; (**d**) Satellite picture of indoor location; (**e**) Picture of indoor test-bed; (**f**) Topological graph of indoor test-bed.  
Compared with DV-hop, PDM and LSVR, the average performance of ML-RELM is improved by 17.93%, 12.78% and 51.55%, respectively. Besides, the average RMS of ML-RELM is about 2.3 indoor. Compared with DV-hop, PDM and LSVR, the average localization accuracy (measured by the RMS) is 61.27%, 39.99% and 45.58% higher than DV-hop, PDM and LSVR respectively.  
In these two group of realistic scenario experiments, we also study the Cumulative Distribution Function (CDF) of the localization errors. CDF shows the percentage of localized nodes whose localization error is smaller than some thresholds, thus tells us whether local damages have proliferated to other areas. Figure 15 shows the CDF of localization errors outdoor and indoor. We can find that when using ML-REML the localization performance is effectively improved. More than 71% nodes are localized with localization error less than 3 m outdoor, and more than 64% nodes are localized with localization error less than 3 m indoor.
## 1 Introduction
exploited the NN using the back-propagation method to fine-tune the PID gains to improve the tracking performance of the 2 degree of freedom (DOF) pneumatic artificial muscle (PAM) [21]. [PERSON] et al. proposed a switching algorithm for tuning PID gains using a learning vector quantization neural network to achieve accurate position control under the change of external payload [29]. [PERSON] et al. considered nonlinear PD computed torque in which the proportional and derivative gains were tuned to minimize the cost function [31]. Different from the FLS, the NN technique uses adaptive laws to minimize the cost function. The convergence of error depends on learning rates. However, no results using NN have been reported to adjust directly the controller gains of the SMC to improve the system performance. Most of the previous articles focused primarily on estimating and compensating uncertainties. Besides, in those previous studies, initial gains of the controller or suitable values of the parameters as well as the slope of the sliding surface were not discussed clearly. Motivated by previous works and in an effort to improve and achieve high tracking performance in the action of external disturbance, a neural network sliding mode control (NNSMC), where the NN is used to directly adjust the controller gains of the sliding mode control (SMC), is proposed to overcome the problem of payload variation for a 3-DOF manipulator. We basically come up with an idea of using back-propagation for updating parameters to adapt to variable working conditions. Not only the robust gains are fine-tuned, but also the slopes of the sliding surfaces are adjusted in order to reduce system tracking errors. Some simulated results and experiments are given to verify an improvement and effectiveness of the proposed control in tracking performance in the presence of an external payload. This paper is organized as follows: Section 2 presents the manipulator dynamics. The motion of the manipulator dynamics is discussed in this section. The proposed algorithm for the system is designed in Section 3. Some simulations are given in Section 4. In Section 5, we discuss the hardware setup for the test bench and some experiments are carried out to verify the proposed algorithm. Conclusions and future work are discussed in Section 6.
## 3 Results and Discussions  
### The Geometry of the Flower of Life  
We aimed to find a way to draw the Flower of Life in Figure 2. After studying the geometry of Figure 2, we found the following solution (Figure 3):  
A circle with the center in A and the radius AB = r = constant is drawn (applicable to all circles which are going to be drawn);  
The B circle is divided into six equal parts, establishing points B, C, and so on;  
Six circles with the centers in these points and with r radius are drawn;  
The circles with the centers in B and C are intersecting in point D; the intersections of the other circles similarly appear;  
Another circle is drawn with the center in D and also with the r radius; this is performed similarly for the other intersection points.  
Based on Figure 3, we wrote the following relationships:  
\[x_{B}=r\cos\varphi;\ y_{B}=r\sin\varphi \tag{1}\]  
\[x_{C}=r\cos(60+\varphi);\ y_{C}=r\sin(60+\varphi) \tag{2}\]  
\[x_{D}=x_{B}+r\cos(60+\varphi);\ y_{D}=y_{B}+r\sin(60+\varphi) \tag{3}\]  
\[AD=2\ r\ \cos 30 \tag{4}\]  
\[AE=AD+r \tag{5}\]  
Figure 3: The geometry of the Flower of Life.  
The relationship (1) gives the position of the point B, with relationships (2) and (3) determining the positions of points C and D, and relationships (4) and (5) calculation the lengths AD and AE.  
The \(\varphi\) angle is measured between the abscissa and AB radius, and so on, for the six chosen points on the initial circle. In Figure 3 for the AB radius, \(\varphi=0\).
## 3 Results and Discussion
### Results of Targeted Numerical Simulations  
The aim of the numerical simulations was to estimate the load-bearing capacity of a thin-walled perforated central beam, with a span ranging from 3 m to 6 m, working in  
Figure 16: Model validation—horizontal displacements of the central cross-section for the 6 m beam.  
Figure 17: Model validation—force–displacement relationship for the RP point.  
the analyzed structural system with a linear load transferred by steel gratings placed over the entire span of the beams. The limit values for deflection in the middle of the span of the central beam, L/250 or L/350, were adopted as the criterion for exhaustion of the load-bearing capacity. Given that the connector was identified as the weakest element of the structural system, both in the laboratory tests and numerical simulations, an additional criterion for exhaustion of the load-bearing capacity was set as reaching 5% LE Max. Principal (Abs) [19] in the connector. LE Max. Principal (Abs) is a logarithmic measure of strain. Nominal values of the material strength parameters were used for calculations at this stage.  
For the beams:  
* Young's modulus E = 200 GPa.
* Poisson's ratio \(\
u\) = 0.3.
* Yield strength \(\mathrm{R_{H}}\) = 350 MPa.
* For the connectors and the steel grating:
* Young's modulus E = 200 GPa.
* Poisson's ratio \(\
u\) = 0.3.
* Yield strength \(\mathrm{R_{H}}\) = 235 MPa.  
Figures 18-23 show the distribution of reduced Mises stresses and the deformation of the system for the last step of the numerical analysis.  
Figure 21: Distribution of reduced Mises stresses and deformation of the system with a span of 6 m.  
Figure 22: Distribution of reduced Mises stresses and deformation of the system with a span of 6 m—cross-section.  
Analyzing the results of the simulations, it can be observed that in all cases the weakest element of the system is the connector, which yields first. Subsequently, the support zones of the beams yield, leading to the loss of stability of the central cross-sections. Figure 24 presents the load-bearing curves for four criteria: L/250, L/350, and 5% LE Max. Principal (Abs) achieved in the connector and the central beam.  
The intersection points marked as **a** and **b** on the load-bearing curves can be termed as turning points, defining the actual progression of the load-bearing curves. For beams with lengths from 3 m to 3.5 m, we can estimate the limit load for the criteria L/250 and L/350 from Figure 24. For beams longer than 3.5 m, the L/250 criterion becomes unattainable because the 5% LE Max. Principal (Abs) in the connector will be reached before such deflection at the middle of the central beam's span is achieved. From point **a** to point **b**, the load-bearing capacity is limited by the L/350 condition and the limit deformation value in the connector. Meanwhile, from point **b**, i.e., for beam spans around 4.1 m, the load-bearing capacity is determined solely by the limit deformation of the connector.
## 3 Hypothesis Development and Research Model
### User-Perceived Factors
#### 3.1.2. Interactivity  
Interactivity, as discussed by [PERSON] et al. [26], has been extensively studied and continues to be a fundamental concept for evaluating the impact of Augmented Reality (AR). Although interactivity is an objective feature, its relevance to consumer behavior is understood through consumers' perceptions of their control over the medium, their ability to engage in bidirectional communication, and their perceptions of the medium's responsiveness [27]. Inherently, AR tools are designed to be interactive, facilitating communication between users and the medium [28]. Nevertheless, current commercial AR applications within the cultural heritage sector focus more on machine interaction--such as providing access to various contents and interacting with interfaces--and less on enabling augmented communication among users.  
Interactivity centers on the user's subjective perception and emphasizes the personal characteristics that evoke a sense of interaction [29]. Enhanced interactivity enables consumers to gather information about a product more effectively by visually inspecting virtual products (e.g., shapes, colors, and functions) that realistically display cultural heritage categories [30]. By projecting the viewer's image into the intended consumption situation, consumers can use the displayed product virtually, increasing their PEU. This consumption experience motivates consumers to engage more actively and efficiently in processing information. This enhancement in information processing elevates the quality of the consumer's search experience, enhancing the perceived usefulness of the shopping experience and the purchasing decision [31]. Regarding the path between interactivity and online mindfulness, [PERSON] and [PERSON] [32] suggest that interactive features in computer-mediated environments may enhance the online mindfulness experience. In environments mediated by AR, the interactivity offered by virtual reality creates an immersive and engaging consumer experience that fosters a flow state [31].  
When AR is used in the consumption of cultural heritage products, interactivity is a key factor that improves the user's acceptance of the technology and optimizes the psychological experience process. Therefore, the following hypothesis is proposed:  
**Hypothesis 2a.** _The interactivity factor positively affects consumers' perceived usefulness in cultural heritage product purchases._  
**Hypothesis 2b.** _The interactivity factor positively affects consumers' perceived ease of use in cultural heritage product purchases._  
**Hypothesis 2c.** _The interactivity factor positively affects consumers' flow experience in cultural heritage product purchases._
# Conscious Inhalation Sedation with Nitrous Oxide and Oxygen in Children: A Retrospective Study
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Total** & **Male** & **Female** & \(p\)**-Value** & **4–6 Years** & **7–10 Years** & \(p\)**-Value** \\ \hline T0 & 1.65 (1.53) & 1.59 (1.51) & 1.72 (1.56) & 0.437 & 1.94 (1.52) & 1.25 (1.46) & \textless{}0.001 \\ T1 & 0.75 (1.35) & 0.70 (1.31) & 0.81 (1.40) & 0.498 & 0.91 (1.50) & 0.52 (1.06) & 0.022 \\ T2 & 0.89 (1.45) & 0.87 (1.42) & 0.92 (1.47) & 0.758 & 1.10 (1.58) & 0.60 (1.18) & \textless{}0.001 \\ \(p\)-value & \textless{}0.001 & \textless{}0.001 & \textless{}0.001 & \textless{}0.001 & \textless{}0.001 & \textless{}0.001 & \\ \hline \end{tabular} Legend: T0, before treatment; T1, at the end of induction; T2, during the treatment. Values are reported as mean (SD).  
\end{table} 
Table 2: Venham score. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Venham Score** & **T0** & **T1** & **T2** \\ \hline
0 (Relaxed), \(n\) (\%) & 113 (30.5\%) & 258 (69.5\%) & 240 (64.7\%) \\
1 (Uneasy), \(n\) (\%) & 82 (22.1\%) & 34 (9.2\%) & 40 (10.8\%) \\
2 (Tense), \(n\) (\%) & 75 (20.2\%) & 30 (8.1\%) & 30 (8.1\%) \\
3 (Reluctant), \(n\) (\%) & 44 (11.9\%) & 23 (6.2\%) & 25 (6.7\%) \\
4 (Interference), \(n\) (\%) & 36 (9.7\%) & 14 (3.8\%) & 24 (6.5\%) \\
5 (Out of contact), \(n\) (\%) & 21 (5.7\%) & 12 (3.2\%) & 12 (3.2\%) \\ \hline \hline \end{tabular} Legend: T0, before treatment; T1, at the end of induction; T2, during the treatment. Values are reported as frequencies (percentages).  
\end{table} 
Table 3: Percentages of children. were found when comparing the Venham scores of male and female patients. All complete results are shown in Table 4.  
During the treatment, none of the patients lost consciousness, and all maintained constant pharyngeal and laryngeal reflexes and visual and verbal contact with clinical staff. More than 97% of children reported no side effects after N2O administration; only 2.7% reported transient and mild side effects (e.g., nausea or vomiting, asthma or cough, headache, hyperexcitability, or sleepiness). No statistically significant differences were observed in relation to gender and age range (Table 5). A total of 335 patients completed the scheduled treatment (90.3%). Patients who did not complete treatment reported a Venham score of 4 or 5.
## 4 Tests and Results  
A detailed analysis is conducted on the impact of the continuity and latency of GNSS real-time products on LEO satellite clocks, focusing on the occurrences of \"end-loss\" and \"mid-gap\" during the determination of LEO satellite clocks when using the \"epoch-estimation\" or \"arc-estimation\" methods.  
Using real data collected onboard Sentinel-3B on DOY 227, 2018, and the CNES real-time satellite orbital and clock products with different latencies or gaps, LEO satellite clocks are determined and analyzed. For comparison purposes, Sentinel-3B clocks estimated using a Kalman filter-based kinematic model with the CNES real-time products without latency and gap are evaluated as well. The Sentinel-3B clocks determined based on a BLS reduced-dynamic model using the final products provided by the Center for Orbit Determination in Europe (CODE) [32] are used as the reference to calculate the clock precision of the above-mentioned scenarios. As \(\Delta T\) in Figure 3 of the manuscript is set to 120 s, each day's results contain 720 sessions.  
Figure 4 displays the real-time clocks of Sentinel-3B estimated in a Kalman filter using the CNES real-time products without latency, denoted as K (see the blue line in the top  
panel of Figure 4), and the corresponding clocks determined based on BLS and CODE final products, denoted as R (see the red line in the top panel of Figure 4). The difference between the two products (after unifying the time reference) is shown in the bottom panel. It can be seen that the precision of the real-time LEO satellite clocks without latency and gaps in GNSS real-time products amounts to about 0.2 ns.