esa-sceva/satcom-chunk-collection huggingface dataset:
# Vibration Control of an Aero Pipeline System with Active Constraint Layer Damping Treatment
## 2 Materials and Methods  
### Theoretical Modeling of Active Constraint Layer Damping  
When establishing the model of driving force between layers of ACLD, the force between the active constraint layer and the pipeline is concentrated at both ends of the active constraining layer according to the assumptions of the point-force model proposed by [PERSON] [28]. The driving force of the active constraint layer is transmitted to the substrate through the points on the edges of the two ends and the viscoelastic layer. When a harmonic voltage is applied to the active constraint layer, the active constraint layer adhered to the surface of the pipeline generates strain due to the inverse piezoelectric effect, thereby generating stress and transmitting it to the pipeline through the viscoelastic layer in the form of shear force. Thus, the influence on the pipeline can be expressed by an equivalent pair of balanced forces and couples at the midpoint of the section of the pipeline. The driving force schematic of the active constraint layer has been shown in Figures 1 and 2. PZT is the piezoelectric transducer and VEM is the viscous-elastic material. \(F_{8}\) is a pair of balance force between two layers. \(T_{1}\) is the tension along the x axis. \(\sigma_{1}\) and \(M_{1}\) are the stress and internal forces of the pipeline, respectively. The relationship of stress and strain on the viscoelastic layer can be expressed as:  
\[\tau=G\gamma=G\frac{u(x,t)}{h}, \tag{1}\]  
Figure 1: Shear force transfer graph. Figure 2: Unit force diagram. where \(\tau\) is the shear stress between the active constraint layer and the viscoelastic layer; \(G\) is the shear modulus of the viscoelastic layer, which satisfies \(G=E/[2(1+\
u)]\); \(E\) is the elastic modulus of viscoelastic layer; \(\
u\) is the Poisson's ratio for viscoelastic layer; \(\gamma\) is the tangent strain of viscoelastic layer; \(h\) is the thickness of the viscoelastic layer. When a harmonic voltage is applied to the active constrained layer, the stress analysis of the active constrained layer is conducted using the infinitesimal (\(dx\)). According to the theory of elastic dynamics, the dynamic equation is established:  
\[\frac{\partial T_{1}}{\partial x}+\frac{\tau}{h}=\rho_{p}\frac{\partial^{2}u (x,t)}{\partial t^{2}}. \tag{2}\]  
The stress which active constraint layer acts on the matrix and \(T_{1}\) are the force and reaction force. \(\rho_{p}\) is the density of the piezoelectric ceramics. Therefore, according to the second type of piezoelectric equation, it can be deduced that:  
\[c_{11}^{e}\frac{\partial^{2}u(x,t)}{\partial x^{2}}+G\frac{u(x,t)}{hh_{p}}= \rho_{p}\frac{\partial^{2}u(x,t)}{\partial t^{2}}, \tag{3}\]  
where \(c_{11}^{e}\) is the elastic stiffness constant, \(h_{p}\) is the thickness of PZT. The rearranged equation can be expressed as:  
\[\frac{\partial^{2}u(x,t)}{\partial t^{2}}-\lambda^{2}\frac{\partial^{2}u(x,t )}{\partial x^{2}}-\xi^{2}u(x,t)=0, \tag{4}\]  
where \(\lambda=\sqrt{c_{11}^{e}/\rho_{p}}\), \(\xi=\sqrt{G/\rho th_{p}}\).
## 5 Proposed Dynamic Cluster Head Election Algorithm  
The philosophy of the proposed model is based on improvement in two different directions, the first is designing an appropriate architecture that helps in the lightness of the network and its ease of expansion, which is discussed, in detail, in Section 4.3. The second is the enhancement in the algorithm that operates on that architecture, which will be discussed in this section.  
The proposed dynamic clustering head election algorithm is structured from two methods. The first is introduced to elect the optimum cluster head, while the second method is introduced to make dynamic self-organization for the clusters when the CH candidate dies or fails. Subsequently, the energy level will be distributed equally among the nodes, the power consumption will be reduced, and, therefore, the lifetime of the network will be improved.
# EEG Emotion Classification Based on Graph Convolutional Network
## 3 Method
### Preprocessing  
In order to retrieve useful signals that correspond to the emotions elicited by stimuli, we adopt several preprocessing techniques: the baseline signal removal method [35], segmentation and normalization.  
Given the trial signal denoted by \(\mathbf{X}\in\mathbb{R}^{C\times T}\), where \(C\) is the number of channels and \(T\) is the time duration, assume the time duration in seconds is \(t\) and the frequency of signal is \(f\), then by the definition of frequency, we have \(T=t\times f\). It suggests that signal \(\mathbf{X}\) can be split up into \(t\) segmentations where we denote each segmentation as \(X_{i}\in\mathbb{R}^{C\times f}\). Similarly, the baseline signal \(\mathbf{X_{b}}\) can be segmented into \(t_{b}\) signals where the \(i^{th}\) segmentation is referred to as \(X_{i}^{b}\in\mathbb{R}^{C\times f}\). The mean of baseline signal \(\mathbf{\overline{X}_{b}}\) can be derived as following:  
\[\mathbf{\overline{X}_{b}}=\frac{\sum_{i=1}^{t_{b}}X_{i}^{b}}{t_{b}}. \tag{1}\]  
Next, \(\mathbf{\overline{X}_{b}}\) will be subtracted from each segmentation of the trial signal:  
\[X_{i}=X_{i}-\mathbf{\overline{X}_{b}}. \tag{2}\]  
The normalization is then applied, i.e., \(X_{i}\) is subtracted by its mean and divided by the standard deviation. Once the preprocessing is completed, we further extract features for each slice. The details of segmentation and feature extraction are described in Section 4.  
Figure 1: The structure of RGNet.
## 2 Numerical Methods and Model Setup
### Model Setup  
A three-dimensional computational domain with 986,487 cells of 150 mm in length and 80 mm by 80 mm in cross section was used. Polyhexcore meshing was adopted to generate hexahedral cells inside the core of the domain and polyhedron cells close to the boundaries. The base-element sizes of the surface mesh and volume mesh were set to 0.5-1 mm and 0.5-2 mm. A 304 stainless steel sheet with a thickness of 0.3 mm was placed 14 mm above the bottom of the flow channel as a spray impingement wall. The spray was injected from the top of the channel at an angle of 50\({}^{\circ}\) toward the gas flow direction. The sectional view of the computational domain is illustrated in Figure 2. The calculations were conducted with the mass transfer, momentum transfer, heat transfer, Lagrangian spray, and Lagrangian wall film models activated.  
Figure 1: Spray impingement regimes based on Stochastic Kuhnke Model.  
To eliminate the influence of grid size on the numerical results, mesh sensitivity analyses were performed before the study began. As shown in Table 1, four cases with different mesh sizes were performed and compared. Figure 3 shows the time-varying maximum heat flux on the thin plate with different mesh sizes. It can be seen from the figure that the time-varying maximum heat flux tends to be the same for case 3 and case 4. Therefore, we adopted the mesh setup of case 3 for further study.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Case** & **Min Cell Length [mm]** & **Max Cell Length [mm]** & **Total Cells** \\ \hline
1 & 1.5 & 3 & 155,731 \\
2 & 1 & 2 & 308,344 \\
3 & 0.5 & 1 & 986,487 \\
4 & 0.4 & 0.8 & 1,449,546 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Different mesh size setups.  
Figure 3: The time-varying maximum heat flux on the thin plate with different mesh sizes.  
Figure 2: Sectional view of computational domain with instruction of boundary conditions.
## 4 Discussion  
This study used fMRI to analyze the difference in the area of activation in the S1 and BA3 regions when the three phalanges of the right four fingers were stimulated with 250 Hz of high-frequency vibration. In the S1 region, stimulation of the little finger and the second node resulted in the largest area of activation. The BA3 region showed similar trends to that of the S1 region, but there were no significant differences.  
Several studies have reported the brain activation associated with low-frequency vibration stimulation of each finger; the highest levels of activation have frequently been found in the S1 and BA3 regions on stimulation of the index finger [2; 6; 9; 12; 16], and a few studies have reported similar results after stimulation of the little finger [1; 7]. Generally, the greatest level of activation has been reported to occur for stimulation of the index finger, which has a large distribution of sensory receptors. However, many studies have reported that a smaller finger size corresponds to a denser arrangement of sensory receptors. This could be one reason for the greater activation seen when stimulating the little finger [24]. In this study, the highest level of activation was observed in the little finger following a high-frequency vibration stimulus. Although there is a need for further research, the results reported in this study may also be elucidated with the reason previously given in cases of low-frequency stimulus [24].  
Several studies have compared the level of activation in the S1 and the BA3 regions after stimulation between each phalange [7; 14; 15; 25]. Many previous studies have demonstrated that the highest degree of activation occurs after low-frequency vibration stimulation to the first phalange; this may be because the first node contains a large number of [PERSON]'s corpuscles that are sensitive to low-frequency stimuli [26]. However, in the present study, the largest area of activation was observed when the second phalange was stimulated with a high-frequency vibration stimulus. Pacinian corpuscles, which are sensitive to high-frequency vibration stimuli, are known to have a much larger receptive field than Meissner's corpuscles [27; 28; 29]. Considering this, even after stimulating the second phalange, the receptors of the adjacent regions (either the first or the third phalanges) may have also been stimulated due to their large receptive field, subsequently leading to a large area of activation. That is, it is thought that more receptors are recruited when stimulating the second phalange compared to the first and third phalanges. However, further research needs to confirm this interpretation.  
Although there was no significant difference, the order of activation area sizes in the BA3 region was similar to that of the S1. This is attributed to the low signal-to-noise ratio of the acquired brain images: BA3 has a smaller absolute area and a smaller activation area than S1. Further experimental or validation studies are necessary.
## 1 Introduction
Given that an important factor in meeting these requirements in WMN-based NAN is the use of routing protocols, the objective of this article is to improve the Optimised Link State Routing (OLSR) Protocol in order to satisfy the required level of reliability in NAN. Existing routing protocols, such as OLSR, select paths to a destination using a single link metric that is calculated from a specific parameter (delay or packet loss) to provide precise link state information about the network. Though the use of multiple link metrics with routing protocols have been considered for reliability improvement in WMN, they have been non-trivial and turned out to be NP-complete (Nondeterministic Polynomial time-complete problems are problems for which it is difficult to estimate the steps or the time required to solve them). This article modifies the OLSR protocol by combining multiple link metrics with the Analytical Hierarchy Process (AHP) algorithm to route SG application traffic in NAN across multiple paths to a data concentrator. The multiple metrics OLSR developed in ns-2 is aimed at selecting optimal links to adaptively provide QoS routing to satisfy targeted SG application traffic. It is worth noting that this work is exploratory and aims to retrofit QoS in order to allow backward compatibility and non-disruptive integration with legacy protocols. The remainder of this paper is organised as follows. Section 2 presents a background and related research work on routing in WMN-based NAN. Section 3 describes the proposed method of retrofitting QoS in NAN. The multiple metrics implementation using AHP is described in Section 4, while the simulation study and results are presented in Section 5. Finally, Section 6 highlights the main findings of this work.
# Integrating Autonomous Vehicles (AVs) into Urban Traffic: Simulating Driving and Signal Control
## 4 Results
### Average Vehicles Delay Results  
Figure 15 presents the vehicle delays across different cycle times (60 to 204 s) for various AV driving behaviors and penetration rates, along with human-driven vehicles. Vehicle delays increase with longer cycle times for all scenarios.  
Cautious AVs consistently show the highest delays across all cycle times and penetration rates due to their conservative driving behavior, which underutilizes available road capacity. Human-driven vehicles also exhibit high delays, though they perform slightly better than Cautious AVs at longer cycle times.  
Aggressive AVs and platooning AVs with 100% penetration demonstrate the best overall performance, achieving the lowest delays. Aggressive AVs reduce delays by exploiting smaller gaps and accelerating quickly, although delays increase slightly at higher cycle times due to traffic instability. Platooning AVs, with their coordinated driving, also achieve low delays, showing a similar performance to aggressive AVs, with only slight differences.  
Normal AVs show a moderate and steady increase in delays with longer cycle times, providing a balanced performance between cautious and aggressive behaviors. In mixed AV environments, vehicle delays are more stable, falling between the extremes of cautious and aggressive behaviors, leading to more-consistent and predictable traffic flow.
## 9 Routing in CRSNs
### CRSN Routing Modules
#### 9.1.2.3.1 Complete Avoidance of PU Zones  
This option is selected to reduce or eliminate spectrum handoff. The resulting implication is that the chosen route might happen to be the longest path to the sink. One study based on this scheme is [70], where the authors computationally analyzed the algorithm to guarantee fair spectrum allocation while minimizing handoff. Because spectrum handoff consumes approximately 96.0% of the average receiving energy or 110.75% of the average transmit energy, the authors attempted to minimize handoffs (_i.e._, limiting unnecessary spectrum handoffs). They used a modified game theory (MGT) strategy to fairly allocate spectrum bands (based on sensor weights, _i.e._, prioritizing the nodes for fairness) while avoiding bands that have a high probability of PU activity to minimize handoffs. The authors formulate the centralized spectrum allocation problem into a multi-objective nonlinear programming problem and then solved it with MGT. However, they did not take congestion into consideration, and their solution can be said to be computationally complex.  
In [61] channels with the least probability of PU activity to nodes in a cluster-based network are assigned. They examine three channel assignment approaches (random-pairing, greedy-pairing and optimization-based channel assignment schemes) while basing the last two on their R-coefficient metric (a dual metric that represents the residual energy of the nodes and the channel conditions). Their priority was to extend both node and network lifetime. The authors also made an assumption about the availability of a control channel that is used by the CH to allocate channels among cluster members. [71] proposes a frequency-hopping algorithm to reduce the interference time _in lieu_ of the QP algorithm. The authors pre-allocate frequency channels for each hopping period at the beginning of the data transmission period using a prediction algorithm that utilizes post-spectrum sensing information. Their channel utilization algorithm is based on a hidden Markov model (HMM). Their main aim was to increase throughput. However, the idea of implementing the presented HMM using resource-constrained nodes can also be said to be computationally demanding and complex. According to [72], a satisfactory real-time constant bit rate (CBR) and best effort (BE) can both be guaranteed in the presence of periodic frequency switching (PS) and triggered switching (TS). TS exhibited better throughput than PS. Ref [73] also proposes a cooperative energy PU detection method. The authors consider the practicability of their proposal by studying the performance when quantization is applied to the energy values before transmission. Their results showed a performance comparable to that of the optimum N-P test at 4-quantization, with the advantage of having less overhead.
## 2 Structure of the Monitoring and Control System
### Sensor Node Design and Implementation
#### 2.2.1 Infrared CO\({}_{2}\) Sensor Design and Implementation
In consideration of the modulation frequency limitation of both the IR heat source and the pyroelectric detector, the IR source was modulated by a 4 Hz square-wave signal. At both filter windows, the possible influential absorption interference from H\({}_{2}\)O and CH\({}_{4}\) is five orders lower than CO\({}_{2}\), so their interferences can be ignored. Since the two received optical beams by the dual-channel detector are highly related, a differential and ratio operation between them was performed to suppress the interferences from the sensor itself and possible outside interference in long-term utilization in the greenhouse. The Beer-Lambert law in Equation (1) shows the relationship between the CO\({}_{2}\) concentration and the received light intensity as:  
\[I\left(\lambda\right)=I_{0}\left(\lambda\right)\exp\left[-K\left(\lambda\right) CL\right] \tag{1}\]  
where \(I\) is the light intensity received by the detector at \(\lambda\), \(I_{0}\) is the initial emitting light intensity of the source at \(\lambda\), \(K\) is the absorption coefficient, \(C\) is the CO\({}_{2}\) concentration, and \(L\) is the optical length. Under modulation, the time-dependent light intensities \(I\left(\lambda,t\right)\) at \(\lambda_{1}\) = 4.26 \(\upmu\)m and \(\lambda_{2}\) = 4.00 \(\upmu\)m will be converted to \(u_{1}\left(\lambda_{1},t\right)\) and \(u_{2}\left(\lambda_{2},t\right)\). Define their amplitude as \(U_{1}\) and \(U_{2}\), respectively. The final CO\({}_{2}\) concentration could be expressed as a function of the differential voltage as:  
\[C=\frac{1}{KL}\ln[\frac{k_{1}}{k_{2}}(1-\frac{\Delta U}{U_{2}})] \tag{2}\]  
In Equation (2), \(k_{1}\) and \(k_{2}\) are the relative optical-to-electrical conversion coefficients at \(\lambda_{1}\) and \(\lambda_{2}\) respectively, \(\Delta U\) is the difference between the amplitude of the detection signal (\(U_{1}\)) and the amplitude of the reference signal (\(U_{2}\)). Such a relation can be obtained through a calibration experiment. The actual relationship between the CO\({}_{2}\) concentration and measured voltage signals can be achieved through calibration.
# Adapted Diffusion for Energy-Efficient Routing in Wireless Sensor Networks
## 3 What We Have Investigated
### Proposed Methods  
We develop a NetLogo model of adapted diffusion providing energy to the nodes for use in data transfer.  
Our system is a CAS as we start with a network of nodes with no knowledge of their surroundings. The nodes have starting energy. We create a fixed sink and a random source. The nodes communicate with their neighbors from the sink and identify their location in relation to the sink. Likewise, the nodes communicate with their neighbors from the source and identify their location in relation to the source. The nodes dynamically gather information on their location. The sink generates an interest and computes the shortest distance to the source using data generated from the source. When the source determines that the data requested by the sink is the data it has, it generates an event and computes the shortest distance to the sink using the distance from the sink. As the search and retrieval occurs energy is used by the nodes receiving and transmitting data. Less energy is required to search for the source as a smaller data package is used. The message from the source is larger and uses more energy as it transmits the data to the sink. When the data are received by the sink a new source is generated with different information. The source now communicates with its neighboring nodes and identifies their location in relation to the source. The system is dynamic in that changes occur such as source location, and information is communicated by transmission of interest and event packets. The locations of the sink and source are computed. The system adapts to the changing source location.
## 3 Results
### Applying BRT Model  
The BRT model was used to reveal the spatial correlation between the existing GE and the GEVs in the study area. The results of the model are shown in Figure 6. They indicate that the factors distance from roads (31.1%), elevation (27.2%), and lithology (11%) had the highest importance on GE, mirroring the outcomes of the RF model, followed by slope degree (7%), drainage density (6.7%), distance from river (5.1%), slope aspect (3.8%), convergence index (2.4%), NDVI (2.2%), plan curvature (1.6%), TWI (1.6%), and LU/LC (0.3%). The gully susceptibility map by the BRT model was also prepared in ArcGIS 10.5 and divided into five classes of very low to very high (Figure 6c). The results of the GE susceptibility class by the BRT model covered 847.87 km\({}^{2}\) of the study area an area distribution in the very low, low, moderate, high, and very high susceptibility classes are 605.37 km\({}^{2}\), 88.38 km\({}^{2}\), 52.01 km\({}^{2}\), 34.13 km\({}^{2}\), and 67.98 km\({}^{2}\), and percentage distribution in the susceptibility classes of are 71.40, 10.42, 6.13, 4.03, and 8.02, respectively. Of the actual GE area of 0.729 km\({}^{2}\), 0.04 (5.55%), 0.03 (4.56%), 0.06 (8.26%), 0.08 (11.34%), and 0.51 km\({}^{2}\) (70.28%) are located in the very low to very high susceptibility classes, respectively.  
Figure 5: Gully erosion susceptibility maps using: (**a**) RF model, (**b**) BRT model, and (**c**) multivariate adaptive regression spline (MARS) model.
_applied sciences_  
_Article_  
Active Correction for Incremental Speaker Diarization of a Collection with Human in the Loop  
[PERSON] \({}^{\star}\), [PERSON], [PERSON], [PERSON] and [PERSON]  
LIUM, Le Mans Universite, [PERSON], CEDEX 9, 72085 Le Mans, France;  
[EMAIL_ADDRESS] (M.S.); [EMAIL_ADDRESS] (L.B.);  
[EMAIL_ADDRESS] (S.M.); [EMAIL_ADDRESS] (A.L.)  
\({}^{\star}\) Correspondence: [PERSON]eceived: 31 December 2021  
Accepted: 4 February 2022  
Published: 9 February 2022  
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**Copyright**: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).  
**Abstract:** State of the art diarization systems now achieve decent performance but those performances are often not good enough to deploy them without any human supervision. Additionally, most approaches focus on single audio files while many use cases involving multiple recordings with recurrent speakers require the incremental processing of a collection. In this paper, we propose a framework that solicits a human in the loop to correct the clustering by answering simple questions. After defining the nature of the questions for both single file and collection of files, we propose two algorithms to list those questions and associated stopping criteria that are necessary to limit the work load on the human in the loop. Experiments performed on the ALLIES dataset show that a limited interaction with a human expert can lead to considerable improvement of up to 36.5% relative diarization error rate (DER) for single files and 33.29% for a collection.  
**Keywords:** speaker diarization; human assisted learning; evaluation