esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Routing Scheme for Creating Opportunistic Context-Virtual Networks in IoT Scenarios +
## 1 Introduction
In [13], we set the base of the OCVN concept and proposed a simple routing protocol focused on sharing the interests of nodes that are within the same range. The main issue of such an implementation is that the scope of the OCVN is restricted only to the communication radio of the source node. Instead, in this paper, we propose new routing algorithms (based on [13]) in which the store-carry-and-forward technique is exploited in order to create larger OCVNs. In particular, four versions of the SACAR algorithm have been implemented considering different situations, such as: (i) source and destination nodes are in contact, i.e., there is a direct connection between them; and (ii) source and destination nodes are not in contact and some type of store-carry-and-forward techniques are required [14]. For the latter case, three situations have been analyzed. The first one is based on the fact that a message is only forwarded to those nodes that have some interests in common (in order to create the OCVN). This accomplishes that nodes with no interest in the transmitted contextual information will not compromise their (limited) resources. In the second case, only the nodes that were previously in contact with the destination node can be considered as relaying ones, therefore exploiting the historical contacts between nodes. Finally, a combination of both previous approaches is also considered. Therefore, the main contributions of the present paper can be summarized as follows:  
* the definition of the OCVN concept in an IoT network scenario. * the proposal of an opportunistic routing scheme, namely SACAR, to create OCVNs with the aim of reducing the use of resources in the set of relaying nodes. * a performance evaluation to show the effectiveness of SACAR and to compare it with different solutions retrieved from the literature. * a multivariate analysis to analyze the impact of the number of nodes in a scenario on different network performance metrics. To the best of our knowledge, no prior work has proposed an opportunistic routing scheme in which the selection of relaying nodes is based on the fact that they share interests in common. Results over two representative scenarios show that our proposed SACAR-based solutions outperform other well-known opportunistic routing algorithms in terms of delivery probability and overhead ratio, while the resource usage of relaying nodes is significantly reduced. The rest of the paper is organized as follows. Section 2 overviews previous works. Section 3 introduces the situational context paradigm, while the concept of OCVN is defined in Section 4. The SACAR algorithm is described in Section 5, while in Section 6, an estimation of the resource consumption on relaying nodes is performed. A performance evaluation based on simulations is carried out over two realistic IoT scenarios in Section 7. Finally, in Section 8, some conclusions are drawn, and future works are identified.
# Recognition of Manual Welding Positions from Depth Hole Image Remotely Sensed by RGB-D Camera
## 4 Welding Position Recognition
### Classification of Welding Positions  
When a welder performs an arbitrary welding operation, determining a standard capable of distinguishing which welding position is taken is required. This study proposes criteria for classifying the welding positions using the positional relationship of the extracted coordinates of body joints.  
The welding positions to be recognized are based on the standard postures stipulated by the AWS, the data and advice collected from shipyards, and our simulation. Five positions were defined as flat, horizontal, vertical, overhead, and standing. A representative example of the five positions is shown in Figure 15.  
Since the welding position can be distinguished by the movement of the upper body, arm, and hand holding the welding rod, six joints such as head, neck, shoulder, elbow, hand, and pelvis are chosen as main measurement elements. In this study, a method of classifying five positions using the relative relationship of six joint coordinates is proposed in Figure 16.  
Based on the standing position, a different position is determined whenever the positional relationship of the six joint coordinates changes. For example, as shown in Figure 17, when the coordinates of the neck joint are located farther from the sensor than those of the head joint, and the coordinates of the hand joint are higher than those of the waist joint, it is classified as a flat position. Similarly, when the hand joint is located higher than the neck joint, and the hand joint is positioned to the left of the right shoulder joint for a right-handed person, as shown in Figure 17b, it becomes a horizontal position. Therefore, before proceeding with learning, the data labeling process that specifies the welding position is performed through the criteria set above.  
Figure 15: Five welding positions: (**a**) flat, (**b**) vertical, (**c**) horizontal, (**d**) overhead, and (**e**) standing.  
Figure 16: Flowchart for welding positions classification.
## 4 Reasoner Development
### k-Nearest Neighbour Reasoner  
The k-Nearest Neighbour (k-NN) method employs instance-based learning, wherein the training usually occurs when a new case is seen. This model is based on memorising cases and their corresponding classes, and upon being introduced to a new case during prediction, the model checks the memory to find a similar case with the closest distance to the new case in the feature space. The value of \"k\" is user-defined, and it decides the number of closest samples considered for the classification of a new case.  
For a classifying time series, Dynamic Time Warping (DTW) needs to be set as the distance metric employed in the k-NN model. DTW is used to measure the similarity between the two-time series. In DTW, points of one-time series are mapped to a corresponding point such that the distance between them is shortest. The k-NN algorithm assigns the test case with the label of the majority class among its \"k\" number nearest neighbours.  
The univariate model intakes the time series attribute braking force, while the multivariate model is fed with the features braking force, wheel slip, motor temperature, and motor shaft angular displacement. For the multivariate model, the features are concatenated into a single feature by the model before employing the DTW. The k-NN parameters are shown in Table 6.
# A Comprehensive Review of Machine Learning Models for Optimizing Wind Power Processes
## 6 Discussion  
In this study, all specific scenarios of the wind energy production process were analyzed using ML techniques. Initially, the basic concepts specific to wind energy were presented, highlighting that wind turbines are the central element of the process. Additionally, parameters influencing process optimization were identified, including meteorological forecasting, wind speed and direction, turbine design, turbine height, terrain topology, climatic conditions, and grid connectivity. Subsequently, ML elements were introduced in the context of wind energy, with 17 models identified.
## 4.
### Data Analysis and Statistical Methods
#### 3.5.1 Descriptive Statistics and Data Normalization
* \(L\) = learning time per session. * \(E\) = engagement frequency. * \(\delta,\ \eta,\ \lambda\) = regression coefficients. The model indicated that feedback frequency and engagement positively correlated with knowledge retention \(\left(R^{2}=0.82,\ p<0.001\right)\). An ANOVA test examined whether feedback effectiveness varied between STEM and language students as defined in Table 9.  
The STEM group benefited more from AI feedback than the language group. The interaction effect was statistically significant, suggesting discipline-specific differences in AI feedback efficiency. Note that the \(p\)-values reported for comparisons across outcome variables--conceptual mastery, engagement, cognitive load, and retention--are uncorrected for multiple comparisons. Readers should interpret these significant levels with caution, considering the potential for inflated Type I error rates across multiple tests.
# Cancer Treatment: An Overview of Pulsed Electric Field Utilization and Generation
## 6 Reversible and Irreversible Electroporation Parameters
### Induction of Transmembrane Voltage by PEF  
Transmembrane voltage comprises two components, namely, the resting voltage and the induced transmembrane voltage (ITV). The resting membrane potential of a cell is defined as the electrical potential difference across the plasma membrane when the cell is in a non-excited state. Applying an electric field to a biological cell increases the concentration of ions inside and outside the cell membrane, which results in induced transmembrane voltage with a value that can be in the tens of millivolts [37]. After the removal of the imposed electric field, the ITV ceases and only the resting voltage remains on the cell membrane. The ITV is proportional to the intensity and duration of the electric field, and the effect is imposed on the resting transmembrane voltage for duration of the exposure period. The ITV affects the activation of voltage-gated channels, and if the effect is large enough it can lead to reversible or irreversible membrane electroporation [54; 55; 56]. Studying and understanding ITV is useful for the successful application of electroporation. Unlike the resting voltage, the ITV depends on the type, shape, and orientation of the cell with respect to the imposed electric field. In addition, the induced voltage varies with the position of the point on the membrane. Because the cell is modelled as a capacitive and resistive load, it has a time constant, making the induced voltage lag match the applied electric field, i.e., the appearance of the voltage is delayed by a finite period based on the cell time constant.  
The amplitude of ITV is affected by the duration of the pulse and repetition frequency. As the frequency is increased or the pulse duration is decreased, the induced voltage decreases. For pulses in the nanosecond range with gigahertz frequency, the induced voltage on organelle membranes can reach the same amplitude as the membrane induced voltage ITV, and can even exceed the ITV under certain conditions [37]. If the membrane is electroporated, its membrane conductivity is increased, which reduces the ITV value from the higher value immediately prior to beginning electroporation.  
Traditionally, the transmembrane voltage \(V_{m}\) is the potential difference between the inner surface of the membrane \(V_{in}\) and the outer surface \(V_{ex}\)[37].  
\[\Delta V_{m}=V_{in}-V_{ex} \tag{6}\]
## Nomenclature
\(i_{xx},i_{xy}\)  
MMC instantaneous circulating current  
\(i_{xx},i_{yy}\)  
Load section instantaneous currents  
\(I_{xx},I_{xy}\)  
Load section RMS currents  
\(I_{Lux},I_{1 qu}\)  
RMS load section active currents  
\(I_{Lsh},I_{1 ph}\)  
RMS \(h^{th}\) order harmonic current contents for both load sections  
\(I_{Lux},I_{1 qu}\)  
RMS load section reactive currents  
\(i_{yx},i_{yy}\)  
Instantaemous compensation current references  
\(i_{xx},i_{yy}\)  
Instantaneous compensation currents synthesized by SRPC system  
\(i_{xx,err}\)  
Instantaneous error current  
\(I_{rxr},I_{1 pr}\)  
Reactive RMS compensation currents synthesized by SRPC  
\(i_{xuu},i_{rsl}\)  
Instantaneous upper and lower arm currents of MMC section \(x\) converter  
\(i_{yuu},i_{yl}\)  
Instantaneous upper and lower arm currents of MMC section \(y\) converter  
\(K_{V}\)  
Turns ratio of the V/V power transformer  
\(k\)  
Present sample interval  
\(L_{xu},L_{xl}\)  
Upper and lower arm inductors of the MMC section \(x\) converter  
\(L_{yu},L_{yl}\)  
Upper and lower arm inductors of the MMC section \(y\) converter  
\(N\)  
MMC Voltage Level  
\(N\)-1  
Number of SM in one MMC arm  
\(R_{xu},R_{xl}\)  
Upper and lower arm equivalent resistance of the MMC section \(x\) converter  
\(R_{yu},R_{yl}\)  
Upper and lower arm equivalent resistance of the MMC section \(y\) converter  
\(T_{s}\)  
Sampling period  
\(U_{A},U_{B},U_{C}\)  
Phase-to-line RMS voltages of the three-phase public power grid  
\(U_{AC},U_{BC}\)  
Line-to-line RMS voltages of the three-phase public power grid  
\(u_{x},u_{y}\)  
Phase \(x\) and phase \(y\) instantaneous load section voltages  
\(U_{x},U_{y}\)  
Phase \(x\) and phase \(y\) RMS load section voltages  
\(u_{xu}\)\({}^{*},u_{xl}\)\({}^{*}\)  
Instantaneous reference signals of the upper and the lower MMC arm of section \(x\) converter  
\(u_{xu},u_{xl}\)  
Instantaneous upper and lower MMC arm voltages of section \(x\) converter  
\(u_{yu}\)\({}^{*},u_{yl}\)\({}^{*}\)  
Instantaneous reference signals of the upper and the lower MMC arm of section \(y\)  
\(u_{yu}\)\({}^{*},u_{yl}\)\({}^{*}\)  
\(u_{yu}\)\({}_{uul}\)  
Instantaneous upper and lower MMC arm voltages of section \(y\) converter  
\(u_{dav},u_{dcb}\)  
\(V_{dc}\)  
The reference value of the DC-link voltage  
\(V_{sm}\)\({}^{*}\)  
The reference value of the SM DC-link voltage  
\(V_{smiur},V_{smil}\)  
The individual SM voltage in the upper and the lower MMC arm, respectively  
\(V_{isuur},V_{iol}\)  
Output of MMC SM individual voltage control  
Average DC-voltage in one MMC leg  
\(v_{smx,up},v_{smx,low}\)  
Instantaneous SM voltages of upper and lower arm of section \(x\) converter  
\(v_{smy,up},v_{smy,low}\)  
Instantaneous SM voltages of upper and lower arm of section \(y\) converter  
\(\Delta I\)  
Active RMS compensation currents synthesized by RPC  
\(\phi_{slu},\phi_{slu}\)  
The corresponded phase angles of the \(h^{th}\) order harmonic current contents for both load sections
# An Omnidirectional Near-Field Comprehensive Damage Detection Method for Composite Structures
## 3 Experiments  
### Experimental Setup  
In the experiments, we analyzed the composite materials for an aviation fuel tank design. The tank structure used is presented in Figure 4, where it can be seen that it contained upper and lower fuel tank parts that were both of the T300/QY8911 type with variable thickness in the carbon fiber composite panel on the fuel tank surface. The fuel tank had a carbon fibre composite structure on both ends of the middle thick thin. The thickness of the middle area was 7 mm, the thickness of both its ends was 4.5 mm, and the thickness of the middle area had an obvious transition. On all four sides, there were 7050-T7451 metal aluminum alloy plates, which were connected and fixed to the upper and lower surfaces by two-rows of rivets. Meanwhile, two ribbed structures were arranged on the left and right sides of the plates to enhance the structural strength. The overall size of the fuel tank was 600 mm \(\times\) 300 mm \(\times\) 240 mm (length \(\times\) width \(\times\) height).  
The experimental system is shown in Figure 5, where it can be seen that the experimental system contained the integrated piezoelectric multi-channel scanning system developed by [PERSON] and [PERSON] [15] mounted on the tank under the board surface, which consisted of a uniform two-dimensional cross array and had linear arrays I and II, whose centers were in the coordinate origin and were along the \(x\)-axis and \(y\)-axis, respectively. In the two linear arrays, the diameter and thickness of each of the PZT elements were 8 mm and 0.48 mm, respectively. Each of the linear sub-arrays of the cross array was made up of seven PZT elements with 9 mm spacing between two adjacent elements. The piezoelectric elements in linear array I were labeled from left to right as I-0, I-1,..., I-6, and the piezoelectric elements in linear array II were labeled from bottom to top as II-0, II-1,..., II-6. The discussion about the correct positioning and number of sensors for obtaining the efficient signals can be found in [9].  
In the structural damage detection process, the integrated piezoelectric multi-channel scanning system was in the active mode, and the excitation signal was a five-wave peak narrowband sinusoidal modulation signal with an amplitude of \(\pm\)7 V, and it was applied to the excitation piezoelectric element after being amplified by 10 times by a power amplifier. The central signal frequency was 50 kHz.  
Figure 4: The fuel tank structure.  
The sampling frequency was 4 MHz, including the pre-collection length of 2000 points, and the data sampling point length was 4000 points.
# Femtosecond Pulse Laser Near-Field Ablation of Ag Nanorods
###### Abstract  
Ag nanorods (Ag NRs) with a mean aspect ratio of 3.9 were prepared through a wet-chemical method, and the absorption spectra for various aspect ratios were obtained. The morphology transformation of Ag NRs irradiated with a femtosecond pulse laser was investigated through transmission electron microscopy (TEM). The near-field ablation was dependent on the laser polarization and wavelength. Laser-induced high electric field intensity was observed at the ends, middle, and junctions of the Ag NRs under various ablation conditions. Through simulation, the evolution mechanism was analyzed in detail. The effect of laser polarization angle on plasmonic junction welding was also investigated. By controlling the electronic field distribution, several nanostructures were obtained: bone-shaped NRs, T-shaped NRs, dimers, trimers, curved NRs, and nanodots. This study suggests a potentially useful approach for the reshaping, cutting, and welding of nanostructures.  
Ag nanorods; femtosecond pulse laser; near-field ablation; polarization +
Footnote †: journal: Applied Physics Letters  
Article
## 2 Structure of the Monitoring and Control System
### Sensor Node Design and Implementation
#### 2.2.3 Waterproof Deployment for Anti-Condensation  
The most serious constraint factor for the utilization of NDIR technology is the complicated environmental influence, such as the high humidity in the greenhouse. Even the DAS has a relatively wider application with better fault-tolerance; the dew condensation on the optical part will also bring severe errors. In order to satisfy the application requirement in the greenhouse environment, an intensively-designed waterproof breathable membrane was used as a protection layer for the chamber. Besides a compromise selection between waterproof ability and breathability, the abilities of anti-high-humidity and anti-high-temperature should also be considered. Therefore, the breathable waterproof membrane made of expanded Polytetrafluoroethylene (Eptfe) was selected and covered on the ventilation holes.
# Future Industrial Applications: Exploring LPWAN-Driven IoT Protocols
## 5 Industrial Application Perspective
### Smart Home  
Property supervisors often use alerting measures such as temperature, humidity, safety, water flow, and electric plug sensors to prevent damage and promptly respond to requests without monitoring [102; 103]. This is a normal use of a smart home framework. According to [29; 104], these sensors need reasonably priced, long-lasting batteries. Sigfox and LoRaWAN are better suited for this type of application since they do not require frequent communication or high-quality service, and short-range operation is acceptable. For instance, [PERSON] et al. [105] evaluated the performance of LoRaWAN in a real-world environment in Bangkok, Thailand. Researchers concluded that communication ranges in both outdoor and interior settings are limited. As a result, IoT applications using LoRaWAN technology can be dependable in constrained communication ranges, such as the home or indoor environment.
# Boosting of Denoising Effect with Fusion Strategy
## 4 Experiments
### Comparison of Statistical Significance with Other State-of-the-Art Algorithms  
Although the proposed method demonstrates performance improvements over the performance of the existing methods considered in this study (see Tables 1-7), these improvements may not be statistically relevant. Therefore, we performed a two-way analysis of variance (ANOVA) (and multiple subsequent comparison tests [50]) on the PSNR results shown in Table 2 to determine the statistical significance of the results obtained using the proposed method. The corresponding results are tabulated in Table 8. ANOVA is a statistical analysis method that allows us to interpret and analyze observations made from several populations. It decomposes the observed results into contributions from different sources; then, it determines whether there is a significant difference between the sources of variation or not. Furthermore, it gives a value indicating the amount of variation. In our experiments, a criterion based on the \(p\)-value obtained using the results of the ANOVA is used to evaluate the statistical significance. From Table 8, it can be seen that the \(p\)-values of the paired ANOVA test for evaluating the difference between our method and the comparison methods are all less than 0.05. This demonstrates that the results obtained using the proposed boosting method are statistically significant.
## 2 Parameters Affecting HCP and ER Measurements
### Parameters Affecting the ER  
Many factors can influence ER measurements, and these factors can have a significant effect on the measured values. Specifically, parameters such as the degree of saturation, rebar corrosion, and the depth and moisture condition of delamination [11; 12]. Also, it was found that the electrical resistivity of concrete is sensitive to moisture, with an increase in moisture content leading to a decrease in electrical resistivity [13]. This conclusion is supported by the findings in [14]. However, they recommend that further studies be carried out to better understand the response of partially saturated concrete. [PERSON] et al. [15],as cited in [16], discovered an interesting behavior in concrete wherein a decrease in the degree of saturation from 88% to 77% led to an average twofold increase in resistivity, and a decrease from 88% to 66%, led to a sixfold increase. Temperature also affects ER measurements, and according to [PERSON] and [PERSON] [17], as cited in [18], an increase in temperature leads to a decrease in ER values. This study found that ER values changed by 5% for each 1 \({}^{\circ}\)C in concrete with a degree of saturation under 30% and by 3% for each 1 \({}^{\circ}\)C in concrete with a degree of saturation under 70%. Moreover, cracks and delamination can also impact ER measurement values. [PERSON] and [PERSON] [19] found that the effect of delamination decreases as the delamination depth from the surface increases. The orientation angle of the delamination has a significant effect on ER measurements in the 45\({}^{\circ}\) to 90\({}^{\circ}\) range but only a minor impact in the 0\({}^{\circ}\) to 30\({}^{\circ}\) range. Finally, concrete resistivity may be affected by carbonation [20].