esa-sceva/satcom-chunk-collection huggingface dataset:
# An Electroglottograph Auxiliary Neural Network for Target Speaker Extraction
## 4 Experiments and Results
### Results
#### 4.2.2 Comparison between Genders  
Considering the shortcomings in the same-gender speech extraction performance of the SpeakerBeam network, we compared speech extraction performance when the target and interference were of different genders. Table 5 shows the results which were calculated using the average values for different speakers of the same gender.  
Table 5 shows the SDRi when the target speakers and interference speakers were of different genders. For the SpeakerBeam network, the speaker extraction was better when the speakers were of different genders but the same-gender speech extraction performance was poor. In our experiment, the SpeakerBeam network with EGG signals performed better when dealing with same-gender speech extraction. The SDRi for female-female(FF) speech extraction was 5.12 dB and the SDRi of male-male(MM) speech extraction was 3.46 dB improvement, which meant that the differences between the EGG signals were more significant than the differences between speech signals when the speakers were of the same gender. When the speakers were of different genders, the female speech extraction performance was better. In conclusion, EGG signals could better identify the features of individual speakers in most situations.  
In Figure 10, it can be seen that the speech extraction performance was better when dealing with female speakers and that the speech extraction performance tended to be poor when dealing with male speakers. The different throat construction of males and females could be the main cause of this phenomenon. Additionally, the EGGs of the female speakers were more suitable for speech extraction.  
Figure 11 shows the results of the speech extraction experiments involving different target speakers. The SDRi was better for female speakers than male speakers. The results showed that EGG signals were more useful for female speech extraction.  
Figure 12 shows the SDRi and SISDRi under different SNRs using SpeakerBeam and Pre_EGG_Aux network.
## 2 Background
### ZigBee Network  
IEEE 802.15.4 is a low-power and low-cost standard for wireless communications, destined for personal area networks (PAN). ZigBee networks are basically composed of one or many PAN coordinators, which monitor the entire network by dealing with the ordinary nodes that request association with the PAN managed by the coordinator in order to participate in the network's operations [17]. Two layers are specified in the IEEE 802.15.4 standard: the physical and MAC layers.  
The physical layer supports three frequency bands [17]: a 2450-MHz band (16 channels), a 915-MHz band (10 channels) and an 868-MHz band (1 channel). Concerning the MAC layer, the 802.15.4 standard defines two types of nodes: reduced function devices (RFD) and full function devices (FFD) [17]. The FFD devices are equipped with all MAC layer functions. ZigBee also standardizes the higher layers of the protocol stack [18]. The network layer has the task of organizing routing over a multi-hop network, while the application layer intends to provide a framework for distributed application development and communication.  
ZigBee specifies three principal devices: the coordinator, routers and end devices; where routers and coordinator must be FFD. From these devices, three types of topologies are defined: star, mesh and cluster three. The star topology [19] consists only of end-devices sending packets to the coordinator. The mesh topology [20] is more complex, but robust and resilient to faults. Routers maintain a routing table and employ a route discovery algorithm to construct and update data structures on the path nodes. The routing table is generally consulted by a node before any transmission to decide about the next hop.  
The cluster-tree [21] is a simple hierarchical topology based on a relationship between the father and children, as shown in Figure 1. In this topology, several clusters are present and able to communicate using multi-hop routes. They are also controlled by a PAN coordinator [22; 23]. The cluster topology performs as follows: after the network's construction based on association-request and association-response, the parents of the clusters form a tree structure and act as intermediate routers. For better network control, the maximum number of children and the maximum depth of the tree that each router may have need to be fixed.  
In the next section, we present the architecture of our sensor nodes based on ZigBee specifications over the Arduino platform. The system was designed and programmed in order to follow the characteristics cited above.  
Figure 1: Cluster-tree topology.
# A Review of Fabrication Technologies for Carbon Electrode-Based Micro-Supercapacitors
## 5 Fabrication Technologies of Carbon Electrode-Based Micro-Supercapacitors
### Coating Methods  
Coatings methods applied for the fabrication of thin film electrodes with a high surface area and reproducible high structural homogeneity, include spray coating, layer-by-layer assembly, vacuum filtration, spin coating, and dip coating [150; 151]. With these methods, a coating is achieved by putting a homogeneous dispersion onto a horizontal substrate and letting the solvent evaporate, so a solid film remains, whereby the film thickness can be easily controlled. Thin film electrodes are made from organic, inorganic, or hybrid solutions, and then, the current collector is deposited onto the film by photolithography or spray coating using a shadow mask. Subsequently, quasi-solid-state gel electrolyte is then added onto the film to produce flexible micro-supercapacitors.
## 1 Introduction: Pathologies of the Orbit and the Aim of This Study
### Space-Occupying Endothelial Lesions  
Surgical pathologies of the orbit encompass a variety of conditions requiring complex and often multidisciplinary interventions. The orbit, as a confined anatomical structure surrounded by vital elements, poses significant challenges for surgeons. Numerous lesions can occur within the orbital space, either intraconal (confined within the cone formed by the extrinsic musculature), extraconal (outside of this boundary), or both. Some lesions extend beyond the orbital space to invade adjacent structures, while others originate in neighboring regions and extend into the orbit [1; 2; 3].  
The causes of orbital masses are highly diverse and can be classified based on the pathological process into inflammatory, infectious, vascular, neoplastic, and congenital categories. Based on the cellular origin, orbital masses can be further classified into the following groups: primary lesions, originating directly from the orbit, including the lacimal gland, which account for 82-87% of orbital masses; secondary lesions, extending to the orbit by contiguity from adjacent structures such as the paranasal sinuses, conjunctiva, eye,  
Figure 1: Graphic representation of surgical accesses and major pathologies of the orbit: fractures and bone defects, neoplastic neoformans, and inflammatory pathologies.  
eyelid, lacrimal sac, face, anterior and middle cranial fossae, orbital bone, nasopharynx, palate, and parotid gland, representing 9-11% of cases; and metastatic tumors, which constitute 4-8% of cases (Table 1) [2,4].  
The symptomatology of orbital tumors varies depending on the nature and extent of the pathology. Differential diagnosis is made based on the orbital clinical evaluation, using the mnemonic method of the seven Ps. This includes three Ps from the clinical history: pain, progression, and past medical history, as well as four Ps from the physical examination: protosis, periocular changes, palpation, and pulsation [2,5].  
The imaging techniques commonly used to evaluate the localization and extent of orbital masses are computed tomography (CT) and magnetic resonance imaging (MRI). Thin-slice, multiplanar MRI images with fat suppression and contrast enhancement allow for the delineation of orbital soft tissue lesions [2,6].
## 3 Results and Analysis
### Freeze-Thaw Strain
Inhomogeneities exist in both the pore water distribution and rock properties and the measurement location is fixed, which leads to a fluctuation in frost-heave strain at the measurement point. As the number of freeze-thaw cycles increases, original pore water gains more space for volume expansion during subsequent freeze-thaw cycles, so the destructive squeezing effects of pore water expansion on the rock are weakened in later freeze-thaw cycles. Simultaneously, water loss during pore water migration further reduces frost-heave strain. Comparing the characteristics of two freeze-thaw strain curves reveals that even for the same rock type under identical environmental temperature changes, different freezing directions will significantly influence the frost-heave process, resulting in distinct patterns of rock frost damage, as shown in Figure 5. It can be seen from the figure that under all-directional freeze-thaw conditions, frost damage occurs in the middle of rock samples, as cooling occurs internally from all surfaces, causing unfrozen water to gather at the sample's center, while pore water near each surface freezes, filling rock pores and significantly reducing rock permeability [25]. At this time, the volume expansion generated after the internal water freezes causes significant damage to the rock matrix. In contrast, macroscopic frost damage under unidirectional freeze-thaw occurs in the middle and lower parts of the sample and is considerably less severe than under all-directional freeze-thaw. This reduced severity occurs because unidirectional freeze-thaw lowers the heat transfer efficiency between the sample and the environment, allowing pore water a longer migration time. Frost-heave pressure comprises two components: ice exerting pressure against the rock and unfrozen water pressure [15]. The pore water freezing front advances along the temperature gradient,but pore water remains unfrozen and has not undergone phase change ahead of the freezing front [26]. This unfrozen water has escape channels, enabling it to dissipate some pressure generated by the water-ice phase change during freezing, thereby weakening the frost damage to the rock.
# Novel Resource Allocation Techniques for Downlink Non-Orthogonal Multiple Access Systems
## 5 Results and Discussion
### Complexity Analysis  
The computational complexities of the Hungarian and Dinkelbach algorithms are described in this section. In the Hungarian algorithm, the time complexity is \(O(N^{3})\), where \(N\) refers to the number of users available in the NOMA system that need to be paired to achieve their maximum sum rate or energy efficiency. However, this algorithm shows a higher complexity in large-scale scenarios with a great number of users. In the Dinkelbach algorithm, the computational complexity is mainly based on both the convergence rate of the sequence of the subproblem and the individual complexity of each individual subproblem. It is noted that the Dinkelbach algorithm shows superlinear convergence. Based on this optimization problem expression of Equation (31), it is assumed that the convergence is achieved with \(M_{1}\) and \(M_{2}\) iterations for outer and inner loops, respectively, with the computational complexity of variables (\(Q_{1}\)) and constraints (\(Q_{2}\)) due to polynomial characteristics. Therefore, the inner loop complexity corresponds to \(O(M_{1}M_{2}(Q_{1}+Q_{2}))\). The updates of \(q_{w}\) require \(O(K)\) operations. Hence, the total computational complexity for the proposed Dinkelbach algorithm corresponds to \(O(M_{1}(M_{2}\left(Q_{1}+Q_{2}\right)+O(K)))\).  
For comparison purposes, the complexity of the exhaustive search algorithm is also illustrated, in which \(O(2S!/2^{S})\) is needed to achieve all possible user pairing processes. \(S\) refers to subchannels available in the network. It is concluded that the proposed algorithms have lower complexity than the exhaustive search algorithm. The complexity comparison between the proposed and exhaustive search algorithms is summarized in Table 3.
## 5 Titanium Dioxide as a Photocatalyst for Water Remediation Applications
### Multiple Atom Doped TiO\({}_{2}\) Composites
Photoexperiments were carried out with 1 h UV irradiation produced by 254 nm low-pressure Hg lamp. The pH value of 6.5 was found to be optimal for the reaction and led to the best absorption rates among all samples. Results showed that low concentrations (1-10 mg \(\times\) dm\({}^{-3}\)) of photocatalysts are optimal; for higher concentrations, the degradation rate gradually decreased. The photocatalyst concentration of 5 mg \(\times\) dm\({}^{-3}\) was used as the optimal dose for other tests. P25 showed relatively low photoactivity in the reduction in PCT. The most effective nanocomposites were Pd-TiO  
Figure 9: The applicability of multiple atom-doped titania composites as catalysts in the photodegradation studies of chemical and pharmaceutical pollutants and bacteria; d-TiO\({}_{2}\)—doped titanium(IV) oxide. and Pd/Au-TiO\({}_{2}\), which also presented enhanced parameters when compared to single-doped Au-TiO\({}_{2}\). Additional study with persulfate (S\({}_{2}\)O\({}_{8}\)\({}^{2-}\)) was performed in sonolytic tests. Persulfate generates SO\({}_{4}\)\({}^{-\bullet}\) radicals and after sonic treatment contributes to pyrolysis of water, yielding additional OH\({}^{\bullet}\) and H\({}_{2}\)O\({}_{2}\) ROS. Persulfate addition remarkably improved degradation times in sonolysis. However, sonophotolytic tests still showed higher mineralization rates, with the exception of bare P25 TiO\({}_{2}\). Conditions for mineralization tests were as follows: C\({}_{0}\) equal to 35 uM, pH equal to 6.5, time 1 h, catalyst concentration of 5 mg \(\times\) dm\({}^{-3}\). In all tests, for a given reaction time, PCT was completely degraded. Sonophotocatalysis yielded the highest mineralization rates that were above 50% of initial concentration for all tested samples. Photocatalyst recycling tests were performed with Pd-TiO\({}_{2}\). Although initial reaction speed decreased with each cycle, the photocatalyst retained most of its initial effectiveness (over 75% PCT elimination within 10 min) [127].
###### Abstract  
Wireless multimedia sensor networks (WMSNs) have been improved with the increase of multimedia data. In WMSNs, a centralization problem can occur because of large-size multimedia data. It is necessary to consider device-to-device (D2D) communication. We focus on D2D WMSN based on cellular networks. Sensors in the D2D WMSN can non-orthogonally use a cellular link, which is a wireless communication channel between a sensor and an aggregator, and a D2D link, which is the channel between sensors. As a result, it has more complex interference environments than an ordinary system. Therefore, it is a key factor to manage the varying inter-cell interference effectively for throughput improvement. We propose an interference mitigation scheme that can be applied to D2D WMSN. In the proposed scheme, a cell is separated into six zones and orthogonal frequency is allocated to each zone for cellular links. The frequencies allocated to cellular links are reused by D2D links of neighboring zones. The simulation results show that the throughput of the proposed scheme increases two times compared to a static frequency allocation scheme.  
wireless mobile sensor network; fractional frequency reuse; D2D communication; interference management +
Footnote †: journal: Sensors
## 4 Validation
### Cogging Torque
#### 4.3.1 Stator-Segmented Case  
Figure 22 shows the waveforms, the spectral analysis of the cogging torques calculated by Finite Element and analytical methods, for different \(p_{gap}\) and \(n_{gap}\) values.  
The choice of \(S_{pp}=2/5\) leads to a very low value for the slots contribution to the cogging torque. The numerical estimation of this very small cogging torque contribution is difficult. The high relative difference between cogging torque amplitudes obtained by 2D FEM and analytical method for the reference machine can be explained by these difficulties (as shown on Figure 22(a1)).  
The stator segmentation with magnetic gaps introduces very large cogging torque contribution (Figure 10) compared to the slots contribution. Thus the amplitude of the cogging torque in the case of the stator-segmented structure is important (Figure 22b1,c1,d1).  
Figures 23a and 24a present a comparison between the cogging torque peak values calculated by the two methods in the case of magnetic gaps with different \(p_{gap}\) and \(n_{gap}\) values. Figures 23b and 24b show the correlation values for cogging torque waveforms calculated by numerical and analytical models for the different stator gap width.  
Figure 24a shows that the cogging torque related to the slots is negligible when the number of gaps increase. This is due to the low value of this cogging torque. For the studied numbers of gaps, the analytical models provide accurate estimations of the cogging torque in the case of the stator-segmented structure. The increase of \(n_{gap}\) allows reducing the difference between analytical and numerical results. Indeed, there is 7% of difference for seven gaps versus 4% for the 14 gaps structure.  
It can be noticed that, analytical and numerical models give similar results with a difference of the amplitudes of lower than 7% for all the presented stator-segmented structures, and similar cogging torque spectrum (Figure 22(a2,b2,c2,d2)). The analytical model developed with the mono-gap method allows to well transcribe the increase of the cogging torque peak value with the raise of \(p_{gap}\) and \(n_{gap}\). Then the mono-gap model allows determining the cogging torque of this kind of machine and can be used in a pre-design step.  
Figure 21: Comparison between numerical and analytical calculation of magnetic flux densities due to PM for the RS-machine with \(n_{gap}=6\) gaps and with different \(p_{gap}\). (**a**) Peak values. (**b**) Correlation.  
Figure 22: Waveforms and spectral analysis of cogging torques for the SS-machine with different \(p_{gap}\) and \(n_{gap}\). (**a1–a3**) Reference machine. (**b1–b3**) SS-machine with \(p_{gap}=0.25\) and \(n_{gap}=7\) gaps. (**c1–c3**) SS-machine with \(p_{gap}=0.50\) and \(n_{gap}=7\) gaps. (**d1–d3**) SS-machine with \(p_{gap}=0.50\) and \(n_{gap}=14\) gaps.
# Stable Evaluation of 3D Zernike Moments for Surface Meshes
## 3 Algorithms for Computing the 3D Zernike Moments for a Surface Triangular Mesh
### Basic Idea  
Our task is then to evaluate the integrals of the Zernike polynomials over a tetrahedron \(\sigma_{T}=(0,A,B,C)\). The first step is to perform a change of basis, parameterizing any point inside the tetrahedron with respect to its position with respect to the triangle \((A,B,C)\) (see Figure 3).  
A point \(\mathbf{M}\) with spherical coordinates \((r,\theta,\phi)\) inside the tetrahedron can be characterized by its projection from the origin to the triangle \(T\). Let \(\mathbf{P}\) be this point. The spherical coordinates of this point are \((r_{0},\theta,\phi)\). Note that \(r_{0}\) is a function of \(\theta\) and \(\phi\). The integration over the tetrahedron proceeds in two steps, first a radial integration over \(r\) from \(0\) to \(r_{0}\), and then an integration over the triangle only.  
\[c_{nl}^{m}(T) =\text{sign}(V(\sigma_{T}))\int_{\sigma_{T}}\overline{Z_{nl}^{m} (\mathbf{x})}\text{d}\mathbf{x}\] \[=\frac{3V(\sigma_{T})}{S(T)}\int_{T}\frac{1}{r_{0}^{3}}\bigg{(} \int_{0}^{r_{0}}r^{2}R_{nl}(r)dr\bigg{)}\overline{Y_{l}^{m}(\theta,\phi)} \text{d}\mathbf{P}.\]  
This expression defines a basic 2-step process for evaluating those integrals and therefore the Zernike moments:  
1. For a given \(\mathbf{P}\) inside the triangle \(T\) with distance \(r_{0}\) to the origin \(O\), compute the radial integral \[Q_{nl}(r_{0})=\int_{0}^{r_{0}}r^{2}R_{nl}(r)dr.\]  
Figure 3: _Parameterization of a tetrahedron_. Let \(T=(A,B,C)\) be a triangle in the surface mesh, and \(\sigma_{T}=(0,A,B,C)\) be the associated tetrahedron where \(0\) is the origin. Any point \(\mathbf{M}\) within \(\sigma_{T}\) with spherical coordinates \((r,\theta,\phi)\) is projected onto the triangle \(T\) to a new point \(\mathbf{P}(r_{0},\theta,\phi)\). Note that \(r_{0}\) is a function of \(\theta\) and \(\phi\).  
2. Integrate over all points \(\mathbf{P}\) in the triangle \(T\) to get: \[c_{nl}^{m}(T)=\frac{3V(\sigma_{T})}{S(T)}\int_{T}\frac{Q_{nl}(r_{0})}{r_{0}^{3}} \overline{Y_{l}^{m}(\theta,\phi)}\mathrm{d}\mathbf{P}.\]  
In the following, we provide recurrence relationships to evaluate the \(Q_{nl}\) and describe a numerical way to evaluate exactly the integral in \(c_{nl}^{m}(T)\), using quadrature.
# The Influence of Fatigue Loading on the Durability of the Conveyor Belt
## 2 Fatigue of the Conveyor Belt as a Textile Composite
Regarding the parameters of the fatigue test method, it is necessary that the specimen is large enough to include a large number of weaving segments because the inhomogeneity of the material due to the arrangement of the fibers in it significantly affects the fatigue behavior. The specimen should have a protective layer on the ends so that the jaws of the testing device do not damage the base material and lead to early fracturing [27]. Moreover, stress ratio \(R\) is very important during testing, and it is defined as follows:  
\[R=\frac{\sigma_{MIN}}{\sigma_{MAX}} \tag{1}\]  
It is necessary to control the loading amplitude. When the loading amplitude is below a value of tensile strength of the material, the resulting fatigue is called high-cycle fatigue because it occurs after a large number of cycles. When the loading amplitude is high in relation to the tensile strength of the material, the resulting fatigue is called low-cycle fatigue because it occurs after a small number of cycles. The nature of damage that occurs during the test depends on the loading amplitude. Plain tension damage is present in low-cycle fatigue, while typical fatigue damage occurs in high-cycle fatigue [28]. Textile composites have good resistance to fatigue, which is reflected in the length of life in fatigue testing. However, this does not refer to the number of cycles leading to initial damage. The first phase of material weakening due to fatigue is caused by the creation of damage zones. These zones contain a large number of microscopic damage points and the initial delamination between fiber and matrix. It is important to note that the first phase of material weakening occurs very early, already after several hundred cycles. The second phase is characterized by the gradual degradation of the material, which is reflected in the reduction of the stiffness of the material. More significant damage occurs in the third phase--fiber cracking and uncontrolled delamination between fibers and the matrix, which leads to accelerated deterioration and eventual fracture [29]. The degradation process of the textile composite is shown in Figure 2. First, the delamination of the weft threads and the matrix occurs. During this phase, transverse cracking of the weft threads occurs. In the second phase, the delamination spreads along the weft threads and several close delaminations merge into a large delamination. At the same time, there is a small delamination, the so-called meta delamination between the weft and warp threads. In the third phase, i.e., at the end, the cracks propagate from the weft to the warp threads, and large cracks and fatigue damage occur [30].