esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
In order to clean the residues, phosphoric acid was applied to the tooth surface and ceramic surface for 1 min, followed by air-water spray for 1 min. The inner surface of the dried porcelain discs was silanized with Monobond Plus, kept for 60 s, and dried. Syntac Primer was applied to the dentin samples with a brush, kept for 15 s, and air-dried. Syntac Adhesive was then applied with the help of a brush and air dried after waiting for 10 s. Heliobond was applied to the tooth surface, and it was thinned with compressed air and kept in a closed container. Variolink Veneer was applied on the porcelain samples, as indicated in group A. All specimens were thermally cycled in a water bath (ST 402; N\(\ddot{\text{u}}\)ve, Izmir, Turkey) 3000 times between 5 \({}^{\circ}\)C and 55 \({}^{\circ}\)C, with a dwell time of 20 s in each bath and a transfer time of 10 s between baths, following the adhesive cementation of the lithium disilicate discs. After thermal cycling, all specimens were subjected to a shear test until debonding with a crosshead speed of 1 mm-s according to the ISO/TS 11405:2003 standards [19] with a universal testing machine (M500-25 KN; Testometric, Lancashire, UK) (Figure 2) [18]. Immediately after the test procedure, the failure type was evaluated with a light microscope (IndentaMet; Buehler, Lake Bluff, IL, USA). Failure types were categorized as follows: Type 1: Adhesive failure (separation of resin cement from dentin tissue or ceramic). Type 2: Cohesive failure (separation of dentin tissue or resin cement from within itself). Type 3: Mixed failure (adhesive and cohesive failure together). Three specimens reserved for SEM and EDS examination were contaminated with try-in paste in the same manner and were subjected to the same surface cleaning procedure as the SBS specimens. Surface area micrographs and elemental analyses were executed with an approximate accuracy of \(<\)1-2% on 6 selected intertubular and intratubular areas on each dentin specimen with a scanning electron microscope (Quanta; FEI, Eindhoven, The Netherlands) assembled with EDS (JXA; JEOL, Nieuw-Vennep, The Netherlands). The data obtained in the study were statistically analyzed using IBM SPSS Statistics vn. 20.0 software. The conformity of numerical variables to normal distribution was assessed with the Shapiro-Wilk test. The homogeneous distribution of the data was shown with the Levene test. Since the data were homogeneously distributed, the one-way ANOVA test was applied. The Dunn-Bonferroni test was applied as a post hoc test for pairwise comparisons between each independent group. A value of \(p<0.05\) was accepted as the level of statistical significance. Weibull analysis was also conducted, and the Weibull modulus was calculated for each group using SPSS 20.0.
# iVRPM: Conceptual Proposal of an Immersive Virtual Reality Pedagogical Model
## 5 Conclusions
The degree of presence depends on \"the fidelity of its sensory components, the nature of interactions and tasks required, the user's focus of attention/concentration, and the ease with which the user adapts to the demands of the environment\" [40] (p. 298). Other authors complement this perspective by emphasizing that presence \"comprises four elements of iVR experience: feelings of physical presence, immersion, agency and embodiment\" [24]. These aspects are interconnected, contributing to reinforce or support each other, but they can also be compromised by inadequate implementations or technical issues [24]. Additionally, the iVRPM highlights that individual factors, such as student involvement and their immersive potential (a term introduced by [PERSON] and [PERSON] [16]), significantly influence activities in virtual environments, enhancing the effectiveness of the educational experience. Involvement represents a psychological state in which individuals focus their energy and attention on a set of stimuli or activities, strengthening the positive perception of presence and control (agency) while being amplified by these sensations. The iVRPM suggests that the essential elements for learning in a virtual environment are a combination of (i) the fidelity (realism) and interactive capabilities of the immersive environment, considered technological features; (ii) individual factors such as involvement and immersive potential [16]; (iii) the connection of the student with their own virtual body or avatar (embodiment) through movements; and (iv) the nature of the planned educational activity, designed to address both cognitive and psychomotor dimensions, ensuring alignment with the desired learning outcomes and the affordances provided by immersive environments. This proposal integrates the affordances provided by virtual environments with educational objectives, linking the activities developed to the proposed levels of interactivity (Absorb, Experiential, and Explore) and the dimensions of knowledge to be acquired. This alignment enables the gradual development of the psychomotor skills required for device usage, as well as progression through the cognitive levels of the taxonomy. In conclusion, the presented iVRPM emphasizes how the perception of presence and control (agency) in iVR environments can enhance student involvement, creating a connection between actions performed and outcomes achieved. This alignment facilitates progress through the levels of the revised [PERSON]'s taxonomy, promoting the development of cognitive and psychomotor dimensions and, consequently, enhancing the effectiveness of immersive educational experiences.
## 3 Results and Discussion
### IPS Parameters Effect
#### 3.2.3 Water Fall-off Position  
Type (4) layout was tested in case 10. Sheets A/B/C/D/E were provided a high heating power density of 10 kW/m\({}^{2}\) to ensure that the water on the protection surface remained in its liquid state. Only sheets D and E were covered with superhydrophobic coating to investigate whether it led to runback water falling off beyond the droplet impingement limit. Aluminum plates covered the sheets A/B/C region to prevent steps between the two regions. Results in Figure 17 showed no ice formation on the heating protected surface, but runback ice formed thereafter. It indicates that the water collected on the sheets A/B/C region did not completely fall off the surface while passing through the sheets D/E region. In case 1, when superhydrophobic coatings covered the sheets A/B/C region, no runback ice formed, indicating that all liquid water fell off within the impingement area. It could be inferred from the comparison of the two test results that it was the droplet impingement area covered with the superhydrophobic coating that effectively impacted droplets falling off.
## 1 Introduction
In previous work [20], we tried short-time Fourier transform (STFT) to convert signal samples into time-frequency images, speed up robust features (SURF) algorithm for feature extraction, K-means algorithm for clustering, and support vector machines (SVM) for signal classification. A simulation experiment was carried in the 2.4 GHz ISM frequency band, with Wi-Fi, Bluetooth and ZigBee as the target signals. The signal generation and perception were carried with Simulink and Matlab. The simulation experiment preliminarily verified the possibility of using time-frequency image for SOP perception. However, there are several problems in the previous work: (1) if there are multiple signals in the same time-frequency image, the result will be classified as the most likely one, and the SVM cannot identify all signal types; (2) the results are only verified by simulation, without considering the hardware implementation feasibility (which has not been actually tested). In response to the above problems, this paper proposed an improved CNN feature extraction and classification method, built a prototype hardware system, and conducted actual experimental tests to verify the effectiveness of the designed perception architecture and algorithm. We still select Bluetooth, Wi-Fi, and ZigBee in the 2.4 GHz ISM frequency band to verify the perception ability. We also improved the time-frequency representation methods. The remaining chapters of this article are as follows: Section 2 describes the model, including the target signal and the design of the signal perception unit. Section 3 analyzes four signal time-frequency joint representation methods, and Section 4 proposes a CNN-based SOP recognition method, this section also illustrate network design, negative learning-positive learning (PL-NL) combined training process and classification result. Section 5 introduces the experimental system and experimental verification. Section 6  
Figure 3: Structure diagram of a new SOP positioning system proposed by this paper. summarizes the work of this paper, significance for SOP positioning system, points out the shortcomings and the direction of future work.
## 5 Formal Specification and Analysis
### Z Notation  
Formal methods are notations based on discrete mathematics such as logic, set theory, graph theory, automata and algebraic systems having sufficient computer tool support used for describing and analyzing properties of software and hardware systems. Formal methods may be classified as property oriented or model descriptive. Property-based methods are used to describe software in terms of properties and invariants. The property-based specification languages are more abstract but are also executable. Model-oriented methods are used to construct models of a system emphasizing both the statics and dynamics of the system. There is a trade-off using model-oriented and property-based specification languages.  
Z notation is a model-centered approach based on sets, sequences, bags, relations, functions and predicate logic [16] which is used at an abstract level of specification of systems. Z can be used for specifying the behavior of distributed as well as sequential programs because of its abstract data types. The Z has standard set operators, for example, union, intersection, comprehensions, Cartesian products and power sets.  
The Z notation is applied in this research because it is a model-oriented approach having rigorous characteristics and is model-based on first order logic with set theory and, consequently, is powerful and very expressive, which is one of the requirements in selecting any specification language for modeling of distributed systems. Moreover, Z is based on the standard mathematical notations used for the specification of abstract properties unlike a detailed description language. Further, the Z notationallows organizing a system into its smaller components using a powerful data structure named schema. The schema defines a way in which state of a system can be described and refined. Refinement is a promising way of Z supporting verifiable transformation from an abstract specification into an executable code. The use of schema structures helped us to reduce the complexity because of its abstract and re-useable characteristics. Formal specification described using Z notation can further be refined and transformed to an implemented system. In this way, we can claim that Z reduces the complexity of systems by abstraction and structuring power, and eliminates unnecessary details. Finally, development from abstraction to detailed analysis made it easy to propose a simple and understandable model ready to be used for simulation.
# The NEWA Ferry Lidar Experiment: Measuring Mesoscale Winds in the Southern Baltic Sea
## 3 Ferry Lidar Campaign within NEWA  
The NEWA Ferry Lidar Experiment started on 7 February 2017, and ended after four months of measurements on 8 June 2017. During this period, the Fraunhofer IWES Ship Lidar System was installed on the vessel Victoria seaways, which belongs to the DFDS Seaways Group and operates on the route from Kiel, Germany, to Klaipeda, Lithuania, in the Southern Baltic Sea. One trip takes about 20 h, and the vessel spends about 4 h in the harbor each time. Figure 3 shows the average route of the ferry; only small deviations from this route were observed during the period of the campaign.  
The Victoria Seaways is a so-called ro-ro (for roll-on/roll-off) ship, designed to carry wheeled cargo, and has a passenger capacity of 600 persons. The vessel has an overall length of 199.14 m and a maximum velocity of 23.5 knots. The ship lidar system was installed on Deck 8, which is about 25 m above sea level (asl). Taking this offset into account, the resulting measurement heights of the lidar have been 65 m, 75 m, 90 m, 100 m, 110 m, 130 m, 150 m, 175 m, 200 m, 225 m, 250 m, and 275 m asl. Figure 4 shows a photograph of the vessel while it is in the harbor and of the installation of the ship lidar system.  
Figure 5 shows exemplary trajectories of the recorded and processed wind data (10-min averages) from the ship lidar system in the NEWA Ferry Lidar Experiment. This example covers four days--25-28 February 2017--of the (in total) four months of data. The plot on the left side shows the path of the system within these four days. The ferry was in the harbor four times (twice in Kiel and twice in Klaipeda) during this period, which is clearly visible when comparing uncorrected and corrected wind data (see the right plots in Figure 5), and the periods where the time series of corrected and uncorrected wind speed overlap. For the uncorrected (raw) wind data, the effects of the ship's motions influencing the lidar measurements essentially appeared as offsets in wind speed (negative or positive, depending on the heading of the ship relative to the prevailing wind direction) and direction (depending on the heading of the ship with respect to true North).  
Figure 4: Ship lidar installation on Victoria Seaways—© Fraunhofer IWES.  
Figure 3: Route of Victoria Seaways, on which the ship lidar system was installed, from Kiel to Klaipeda in the Southern Baltic Sea (reproduced from Reference [15]).
# Human Height Estimation by Color Deep Learning and Depth 3D Conversion
## 4 Experiment Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Person No.**} & \multirow{2}{*}{**Actual Height**} & \multicolumn{2}{c}{**Extracting Human Body**} & \multicolumn{2}{c}{**Extracting Human Body**} \\  & & \multicolumn{2}{c}{**by Mask R-CNN**} & \multicolumn{2}{c}{**by Background Depth Image**} \\ \cline{3-6}  & \multirow{2}{*}{**(cm)**} & **Estimated Height (cm)** & **Estimation Error (\%)** & **Estimated Height (cm)** & **Estimation Error (\%)** \\ \hline
1 & 177 & 179.6 & 1.5 & 172.1 & 2.8 \\
2 & 183 & 182.1 & 0.5 & 176.5 & 3.6 \\
3 & 165 & 164.2 & 0.5 & 161.3 & 2.2 \\
4 & 178 & 176.5 & 0.8 & 173.9 & 2.3 \\
5 & 182 & 180.9 & 0.6 & 177.7 & 2.4 \\
6 & 173 & 174.6 & 0.9 & 175.2 & 1.3 \\
7 & 175 & 174.4 & 0.3 & 171.3 & 2.1 \\
8 & 170 & 169.2 & 0.5 & 167.1 & 1.7 \\
9 & 168 & 167.3 & 0.4 & 165.6 & 1.4 \\
10 & 181 & 178.9 & 0.6 & 177.4 & 1.4 \\ \hline \multicolumn{6}{c}{Average error (\%)} & 0.7 & 2.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of human-height estimation by proposed method while standing. Figure 18: Results of human-height estimation depending on parameters for (8) and (9). (**a**) \(r_{i}\) (**b**) \(T_{o}\).
# Enabling Heterogeneous IoT Networks over 5G Networks with Ultra-Dense Deployment--Using MEC/SDN
## 3 IoT/5G System Structure
### Radio Access Network (RAN)
#### 3.1.3 Cellular Network  
This part represents the heterogeneous 5G cellular cells with the connected MEC servers. Each eNB is connected to an edge server to provide computing resources to the distributed devices in the cell. The proposed system deploys homogeneous MEC servers with equal resources. The MEC server deployed for a cellular call is referred to as C-MEC. I-MEC servers are connected directly to nearby C-MEC servers via high-speed fiber connections. C-MEC servers handle the offloaded tasks from the connected I-MEC servers, cellular devices and corresponding D2D networks. Based on the currently available resources and the QoS latency of the computing task, the C-MEC server handles the computing task or offloads it to the core networks cloud. The proposed offloading algorithm, introduced in Section 4.3, provides the main steps required to perform the energy-aware and latency-aware offloading.
# A Low-Loss Circuit with High-Pass Low-Pass Broadband Flat Negative Group Delay Characteristics
## 2 Circuit Design and Analysis with High-Pass and Low-Pass Negative Group Delay
### Low-Pass Flat Negative Group Delay Circuit
#### 2.2.5 Low-Pass Negative Group Delay Circuits with Different \(L_{2}\) Values  
When the values of \(C_{1}\), \(L_{1}\), \(L_{2}\), \(R_{1}\), and \(L_{3}\) in the circuit remain unchanged, as shown in Table 8, and the \(R_{2}\) values are sequentially changed to 50 \(\Omega\), 100 \(\Omega\), and 200 \(\Omega\), we observe the changes in the circuit transfer function and group delay. The circuit results are identical in all three cases.
## 3 Results
### Cross-Property Relationships
#### 3.5.3 Thermal Properties and Open Porosity  
Conversely to compressive strength, open porosity was inversely related to thermal conductivity and directly to specific heat capacity (Figure 9) with determination coefficients R\({}^{2}\) for the respective linear relationships above 0.823. According to these data, the larger the pore volume, the more effective the thermal insulation (lower conductivity) is and the greater the thermal stability (higher specific heat capacity) when exposed to abrupt outdoor temperature changes. Earlier studies also related lower concrete thermal conductivity to higher pore volumes [41; 44; 45; 47; 48].  
The thermal conductivity of the solid matrix (\(\uplambda\)s) in concrete mixes is given in Table 10. This parameter separates the effect of the volume of open pores from the effect of adding both recycled materials (GRC and MRA) to the mixes. It exhibited higher values than overall thermal conductivity (Table 9). With GRC, \(\uplambda\)s was 1.8% lower in N10/0 and 5.2% lower in N25/0 than in NAC and, in the mixes with MRA, 2.4% lower in R10/50 and 5.4% lower in R25/50 than in R0/50. The combined use of GRC and MRA lowered the mean \(\uplambda\)s value by 1.7% in (R0/50, R10/50 and R25/50) relative to the reference NAC. In other words, the use of 25% GRC had a greater impact than the use of 50% MRA.
# Real-Time Visual Feedback in Singing Pedagogy: Current Trends and Future Directions
## 4 The Resonatory System
### Phonation Types  
Spectrographic displays can also be used to visualize phonation types. However, it should be mentioned that they will often show effects of both glottal and resonance events. For clearer displays of phonation type, EGG, ELG and FLOGG will always be preferable.  
Spectrograms cannot visualize vocal tract resonances per se, but only the resulting spectrum peaks; partials with higher amplitudes appear near or at vocal tract resonances [70; 71]. Figure 18 provides an example of narrow-band spectrograms of the vowel /a/ sung by a male singer, demonstrating different degrees of glottal adduction. The color scale indicates the amplitudes of the individual harmonic partials, with red representing higher and blue lower. Changing from breathy to pressed phonation is associated with a decrease in the intensity of the first partial. Simultaneously, the partials above 2 kHz increase in amplitude.  
A caveat regarding real-time spectrographic displays as visual feedback is that the resulting display heavily depends on the choice and placement of the microphone. For example, dynamic microphones (also called \"stage microphones\") enhance certain frequency ranges and reduce others. Moreover, their proximity effect heavily enhances low frequencies when placed near the lip opening. To circumvent these drawbacks, it is required that, for all lessons, the microphone is the same, preferably omnidirectional and placed always at same distance and position from the student's mouth.
## 4 Ground Segment
### User Terminal Antennas
Furthermore, the optical properties of the lenses can be used to reduce the scan loss, achieving better performance at low elevations. Conversely, lens antennas are usually challenging from a form factor point of view, in terms of low profile and weight, and cost, limiting the usage in some markets. Fully passive solutions using printed circuit board (PCB) technology to produce phase-shifting surfaces have also been described with a centralized feeding point [190; 191] or a printed radial slot array [188] in an attempt to produce low-cost solutions at the expense of a reduced integration. Different satcom markets are normally characterized by distinct requirements and priorities and therefore numerous design approaches and technologies can be successful at the same time, as they may address different needs.
# Go Wider: An Efficient Neural Network for Point Cloud Analysis via Group Convolutions
###### Abstract  
In order to achieve a better performance for point cloud analysis, many researchers apply deep neural networks using stacked Multi-Layer-Perceptron (MLP) convolutions over an irregular point cloud. However, applying these dense MLP convolutions over a large amount of points (e.g., autonomous driving application) leads to limitations due to the computation and memory capabilities. To achieve higher performances but decrease the computational complexity, we propose a deep-wide neural network, named ShufflePointNet, which can exploit fine-grained local features, but also reduce redundancies using group convolution and channel shuffle operation. Unlike conventional operations that directly apply MLPs on the high-dimensional features of a point cloud, our model goes \"wider\" by splitting features into groups with smaller depth in advance, having the respective MLP computations applied only to a single group, which can significantly reduce complexity and computation. At the same time, we allow communication between groups by shuffling the feature channel to capture fine-grained features. We further discuss the multi-branch method for wider neural networks being also beneficial to feature extraction for point clouds. We present extensive experiments for shape classification tasks on a ModelNet40 dataset and semantic segmentation task on large scale datasets ShapeNet part, S3 DIS and KITTI. Finally, we carry out an ablation study and compare our model to other state-of-the-art algorithms to show its efficiency in terms of complexity and accuracy.  
point cloud; semantic segmentation; shape classification; feature shuffle; neural network +
Footnote †: journal: Applied Sciences