esa-sceva/satcom-chunk-collection huggingface dataset:
# Fiber-Based Sensors and Energy Systems for Wearable Electronics
## 3 Fiber-Based Sensors
### Chemical Sensing Fibers
Recently, many studies have utilized three-electrode electrochemical platforms, which have the advantage of miniaturization, high sensitivity, and free-labeling, for detecting pH, cortisol, glucose, and sodium ions in sweat. [PERSON] et al. fabricated the polyester threads coated with carbon ink and glucose oxidation for the working electrode and detected glucose with a highly linear response in the buffer and blood samples [111]. Another sweat sensor for glucose detection was produced by dry spinning gold nanowire (AuNW) and an elastic polymer, as shown in Figure 3e [39]. For the working electrode, the dry spun AuNWs-fiber was functionalized with glucose oxidase via electrochemical deposition. The fiber achieved a linear response up to 200% strain with a sensitivity of 11.7 uA mM\({}^{-1}\) cm\({}^{-2}\) (Figure 3f). The current from the fiber attached to the forearm increased for 1 h after a meal and decreased after 2 h. By the same synthesis method, a gold fiber coated with polyaniline (PANI) detected the hydrogen ion via the deprotonation and reprotonation process [112]. [PERSON] et al. developed the commercial carbon yarn integrated with ellipsoidal iron(III) oxide (Fe\({}_{2}\)O\({}_{3}\)) by the simple hydrothermal method to detect cortisol for recognizing the stress response [56]. The same group improved biocompatibility by incorporating ZnO nanorods into carbon yarns [113]. For monitoring the salinity level of sweat, the PEDOT:PSS-based fiber can be used for detecting the sodium chloride concentration [37]. The PEDOT:PSS fiber was simply fabricated through the wet-spinning process, and its conductivity varied with the concentration of sodium chloride with the high output signal. Since these sweat sensors can detect biomarkers non-invasively, they are easy to access in clinical trials, but it is still difficult to commercialize, due to the low sweat volumes, irregular sweating without stimulation, and the possibility of evaporation.
## 3 Motivation
### Motivation for Balanced Energy Efficiency
After the first node died in LEACH, all the sensors run out of power in a short period of time. Therefore, the last node died later in HEED than that in LEACH. Depends on the definition for network lifetime (from the system starts till the first or the last node dies), LEACH and HEED can have different performances. However, in this paper, we aim to evaluate the network lifetime from another perspective. We argue that the number of nodes left in the network cannot present the QoS of a system. In Figure 2, around 450 round, we can see that LEACH and HEED almost have the same number of nodes in the network. The snapshots of the sensor distribution at 450 round for both protocols are shown in Figure 3. With the same number of sensors left in the network, we can see that LEACH has a better distribution as the sensors still alive can cover a slightly larger area than HEED. Depending on the emphasis of a system, the way to measure the lifetime of a network is not unique. When the first node or the last node dies is not the most important issue in term of the network coverage. The coverage of the network should be considered when measuring the lifetime from a QoS perspective. Since different systems have different requirements, simply evaluating the network lifetime by the number of sensors is not convincing. In a system, we believe that some of the sensors in the network are more important than the others on network coverage level. An extreme case is shown in Figure 4: area A has a higher node density than the rest of the sensing field. The nodes in area A have less impact on the coverage of the network than the nodes outside this area. The sensors that do not have a significant impact on the coverage of the network are allowed to die sooner than the others. To implement multihop communication manner, the sensors can be partitioned into layers to adopt similar strategy in CCS or S-WEB. This has motivated us to provide a distributed clustering algorithm that can not only extend network lifetime, but also  
Figure 2: Number of sensors still alive in the network. guarantee the coverage of the network. The name of the algorithm is Balanced Energy-Efficient (BEE) clustering algorithm and another version supporting multihop inter-cluster communication is called Multihop BEE (BEEM).
## 3 Verification of Theoretical Analysis and Numerical Simulation
### Homogeneous Half-Space Medium
As shown in Figure 8, the relative error of both the numerical solution and the analytical solution is no more than 2.5%, except for the calculation results when the time is greater than 0.1 s. To sum up, the calculation result between the numerical and the analytical solution is consistent under the error tolerance condition. Therefore, the numerical simulation results based on COMSOL are reliable and accurate, and can well reflect the internal physical law of the transient electromagnetic response in the three-dimensional half-space medium to some extent.
# Visual SLAM Based Spatial Recognition and Visualization Method for Mobile AR Systems
## 1 Introduction  
Simultaneous localization and mapping (SLAM) technology is gaining popularity in Drones and Augmented Reality (AR) with advancement in 3D Graphics technology and Machine Learning technology. As the demand for indoor applications increases, especially where there are no existing maps, positioning technologies such as SLAM help technology development in the robotics field. In addition, the use of SLAM in Visual SLAM algorithms and AR has promoted the growth of the SLAM market [1]. Currently, open source solutions for AR are not easily found in SLAM research fields. It burdens SLAM researchers as it requires them to conduct their own research and development with a deep understanding of similar systems when they build a SLAM-based AR system.  
As shown in Figure 1, the SLAM system can be classified into direct SLAM and indirect SLAM. Direct SLAM systems such as LSD-SLAM [2] are mainly used in the AR field while Indirect SLAM systems such as ORB-SLAM [3] and Open-VSLAM [4] are being used in the robot or autonomous driving field. However, as these SLAM libraries are open source projects for general-purpose systems, it is somewhat difficult to implement AR contents on commercial devices such as mobile devices or AR headsets. The SLAM-based AR systems for commercial devices are provided in the form of platforms by companies such as Google, Apple, Masst, and Vuforia, making it difficult to customize.  
In this paper, we design a markerless AR and data pipeline via ORB-SLAM3, which can extract features in real time as it is the fastest among the existing open source SLAM libraries and offers a compromise between the quality and the performance among the modern SLAM systems, to implement a system that supports the development of AR contents on mobile devices or AR headsets through markerless SLAM [5].  
Section 2 of this paper describes the typical SLAM-based AR development tools and the characteristics of ORB-SLAM3 used as the basis of the system proposed in this paper. Section 3 describes the overall configuration of the mobile AR system designed based on Visual SLAM and the functions of each component, and Section 4 shows the results of the proposed SLAM system in a real mobile environment. Finally, Section 5 describes the future development plans and expected effects for this study.
# Rotor Fault Diagnosis Method Based on VMD Symmetrical Polar Image and Fuzzy Neural Network
## 3 Model Establishment
### Rotor Fault Feature Extraction Based on VMD Symmetrical Polar Image
#### 3.1.3 Selection Method of Quadratic Penalty Factor \(\alpha\)  
The choice of the quadratic penalty factor \(\alpha\) determines the bandwidth of each IMF component obtained by the VMD. The smaller the \(\alpha\) value, the larger the bandwidth of the IMF component.  
An important parameter for evaluating the complexity of non-stationary signals is fuzzy entropy. Compared to other methods such as sample entropy and approximate entropy, the value of fuzzy entropy is less affected by related parameters and has stronger stability. The rotating machinery vibration is random vibration, and the signal is highly irregular and complex. When a fault occurs, the impact component in the vibration signal increases, and the regularity and self-similarity of the signal also increase [22], which results in a small fuzzy entropy value of the signal. In order to describe the complexity of the signal more accurately, MFE is selected as the evaluation factor of the penalty factor \(\alpha\). When the optimal value of \(\alpha\) is selected, the MFE value of the reconstructed signal based on the VMD should be the minimum value.  
For signal \(\{x(i),\,i=1,2,\,\ldots,N\}\), given the embedding dimension of the fuzzy entropy and the similarity tolerance r, a new coarse-grained signal \(y_{j}(\tau)\) can be established according to the original signal \(x(i)\):  
\[y_{j}(\tau)=\frac{1}{\tau}\sum_{i=(j-1)\tau+1}^{j\tau}x_{i},\,\,1\leq j\leq \frac{N}{\tau} \tag{5}\]  
where, \(\tau=1,2,\,\ldots,N\), is the scale factor.
## 3 Preliminaries  
An event log \(EL\) contains events. An event \(e\) is a tuple \(e=\langle c,\{(d_{i},v_{i})\}_{i=1,\ldots,D}\rangle\), where \(c\) is the case id and \((d_{i},v_{i})\) are a set of \(D\) attributes and their values. We refer to \(\mathcal{E}\) as the universe of events, and to \(V_{i}\) as the domain of attribute \(d_{i}\), which can be discrete, \(V_{i}=\{v_{i,j}\}_{j=1,\ldots,J}\), or continuous, \(V_{i}=[v_{i,min},v_{1,max}]\subseteq\mathbb{R}\). The case id, activity, and timestamp attributes are normally present in all event log. We refer to them using the letters \(c\), \(a\), and \(t\), respectively, and use a dotted notation to reference them when needed, e.g., \(e.c\) to refer to the case id of event \(e\). For instance, the event \(e=(45,\textit{assess},2020.1.2,\textit{Alice},amount=1000,type=deep)\) captures the fact that, in a process case associated with loan request number \(45\) (_e.c_), the resource Alice has executed a deep _assessment_ (_e.a_) of a loan request of USD 1000 on January 2 nd, 2020 (_e.t_).  
Any continuous domain \(V_{i}\) can be mapped into a discrete domain \(\hat{V}_{i}=\{\hat{v}_{i,j}\}\) if necessary. For instance, the loan amount in the example above can be mapped into three discrete levels (high, medium, low) according to the amount requested. We, therefore, introduce an attribute-specific discretisation function \(disc_{i}:V_{i}\longrightarrow\hat{V}_{i}\), with \(disc(v_{i})=\hat{v}_{i,j}\).  
The sequence of events generated in a given case form a trace \(\sigma=[e_{1},\ldots,e_{n}]\), where \(\forall i\in[1,n]\), \(e_{i}\in\mathcal{E}\), \(\forall i,j\in[1,n]\), \(e_{i}.c=e_{j}.c\), i.e., all events belong to the same case, and \(\forall i\in[1,n-1]\), \(e_{i}.t<e_{i+1}.t\), i.e., events in a trace are ordered in ascending order using the timestamp attribute. The universe of all traces is denoted by \(\mathcal{S}\). Note that attributes of events \(e_{i}\) belonging to a trace \(\sigma\) may be the same \(\forall e_{i}\in\sigma\). We refer to these attributes as case-level attributes. For instance, the amount requested in a loan request process is a case-level attribute. In the remainder, we refer to \(D_{E}\) and \(D_{\mathbb{C}}\) as the set of event- and case-level attributes in an event log \(EL\), respectively, and we assume that (i) all events in \(EL\) may have a different value for the same event-level attributes \(D_{E}\) and (ii) all events in \(EL\) belonging to the same trace have the same values for the case-level attributes \(D_{\mathbb{C}}\). Given a trace \(\sigma\) and an integer \(l<n\), the prefix function \(pr\) returns the first \(l\) events of \(\sigma\), that is, \(pr(\sigma,l)=[e_{1},\ldots,e_{l}]\).
# Ship Detection from X-Band SAR Images Using M2 Det Deep Learning Model
## 3 Methods
### SAR Image Preprocessing
#### 3.1.1. Decibel Conversion  
It is known that a neural network is better optimized when the distribution of the input data follows a normal distribution during neural network training [29]. Therefore, in this study, the distribution of the values of the SAR images was first converted into a distribution similar to the normal distribution. The conversion process of the distribution of the data that are analyzed for actual data analysis is a frequently used technique [30]. In the case of SAR images, it can be converted to the distribution that approximates the normal distribution through log transformation [31; 32]. In Figure 3, the decibel image indicates an image in which the intensity image is expressed in decibels through decibel conversion and can be generated using the following equation.  
\[y=\log_{10}(x) \tag{1}\]  
where \(x\) is a SAR intensity image, and \(y\) is a SAR intensity image converted into decibels.
# Laboratory Validation of Surface-Initiated Transverse Cracking of Asphalt Pavement
## 1 Introduction  
In general, transverse cracking can be categorized into climate-related thermal cracking and load or thermal-related reflective cracking. The sudden dramatic drop of pavement temperature or repeated daily temperature fluctuation could result in transverse cracking initiating from the surface of the pavement and propagating downward [1]. Reflective cracking is caused by the stress concentration at the bottom of asphalt overlay due to the movement of a crack or joint underneath the overlay layer, which will lead to crack propagation upward in asphalt overlays [2]. The movement of the crack or joint in the underneath layer could be caused by temperature or moisture fluctuation, traffic loading, or a combination of the above factors. In addition to temperature or traffic load, material properties such as the degree of aging could also contribute to the initiation of transverse cracking [3].  
Based on field cores, many researchers have pointed out that not all asphalt overlays constructed on top of cracked hot mix asphalt (HMA) pavements or Portland cement concrete (PCC) pavementswill develop reflective cracks starting from the overlay bottom near the cracks of the already existing surface. Sometimes, cracks were observed at both the top and bottom surface of the layer but not in the middle portion. It is possible that transverse cracking initiates from the overlay surface and propagates downward to match the existing transverse cracking in this case. Such cracking type has been widely reported by other researchers [4; 5; 6; 7; 8].  
However, it is still not clear how this type of crack was initiated and what are the associated key factors. Some studies [5; 6] assumed that such crack types may correlate with the pavement structure and material properties, without providing any validation. It is the objective of this paper to provide a laboratory investigation about such crack types, evaluate the causes that would lead to the production of such cracks, and identify the roles of specific factors, such as layer thickness, existing crack or notch, aging, and the binder performance grade. By evaluation, this study identifies the factors that cause surface-initiated transverse cracking and will provide a possible mechanism to explain the initiation of such crack type.
# Performance Optimization of Underwater Communication Links at Different Ranges for AIS Relay to AUV
## 4 Simulations on Block Fading Rayleigh Channels  
The MC-SS-2D waveform was tested and compared to the MC-SS waveform on a multipath Rayleigh block fading channel. Although this channel is not representative of an UAC, it is helpful to compare the modern performances with theoretical BER and assure the proposed solution reaches the better performances.  
A MC-SS-2D is composed of \(M\) OFDM symbols. For each OFDM symbol, an impulse response is generated independently from other impulse responses. One impulse response is composed of \(\ell_{h}\) independent coefficients generated at chip time where each coefficient represents a path and its magnitude in time follows a Rayleigh distribution. Knowing the distribution of the channel, the theoretical error probability can be derived. It corresponds to the probability error on a Rayleigh channel using a diversity of \(L\). Therefore, according to [19]:  
\[Pe=\bigg{[}\frac{1}{2}\bigg{(}1-\sqrt{\frac{\gamma}{1+\gamma}}\bigg{)}\bigg{]} ^{L}\sum_{\ell=0}^{L-1}\binom{L-1+\ell}{\ell}\bigg{[}\frac{1}{2}\bigg{(}1+ \sqrt{\frac{\gamma}{1+\gamma}}\bigg{)}\bigg{]}^{\ell} \tag{3}\]  
Figure 6: MC-SS 2D receiver structure.  
For the simulations on the Rayleigh block fading channel, different SFs have been tested. A 2D OVSF spreading code [17] was used for the MC-SS-2D waveform. MC-SS-2D and MC-SS were both simulated with channel knowledge and channel estimated with a least-square estimation using dedicated subcarriers as pilot.  
With channel knowledge, MC-SS-2D performances are equivalent to Equation (2) as displayed in Figure 7. Results shows that MC-SS-2D and MC-SS have similar performances with a small SF as seen in Figure 7. However, as the SF increases, MC-SS-2D shows better results by distributing its chips in both frequency and time domain. Indeed, with a large SF, distance between two chips of the same data symbol for the MC-SS waveform becomes smaller than the coherence bandwidth impacting the frequency diversity. MC-SS-2D solves this issue thanks to its flexibility. The proposed modem reaches the optimum performances when spreading is well distributed in the time and frequency domains.
## 5 A Practical Example for MADM with IFVs.  
A research facility has arranged to purchase an electronic device. The people in charge, selected the four most appropriate models for further consideration, after the market research. Those are indicated as the four alternatives \(U=\{u_{1},u_{2},u_{3},u_{4}\}\). The evaluation of the alternatives is achieved by the consideration of five attributes by the decision maker (DM). Those attributes are listed as follows: the layout of the product (\(C_{1}\)), the technical assistance (\(C_{2}\)), the brand (\(C_{3}\)), the price (\(C_{4}\)) and the quality of the product (\(C_{5}\)).  
Step 1. The normalized intuitionistic fuzzy decision matrix can be obtained as follows:  
\[R=\left(\alpha_{ij}\right)_{\mathbf{4}\times 5}=\left[\begin{array}{cccccc} \langle 0.4,0.3\rangle&\langle 0.5,0.2\rangle&\langle 0.7,0.2\rangle&\langle 0.4,0.6 \rangle&\langle 0.6,0.2\rangle\\ \langle 0.6,0.1\rangle&\langle 0.4,0.3\rangle&\langle 0.3,0.5\rangle&\langle 0.6,0.2 \rangle&\langle 0.5,0.3\rangle\\ \langle 0.5,0.4\rangle&\langle 0.6,0.1\rangle&\langle 0.6,0.2\rangle&\langle 0.7,0.1 \rangle&\langle 0.3,0.6\rangle\\ \langle 0.6,0.3\rangle&\langle 0.4,0.5\rangle&\langle 0.5,0.3\rangle&\langle 0.8,0.2 \rangle&\langle 0.5,0.2\rangle\end{array}\right].\]  
in which, the element \(\left\langle\mu_{ij},v_{ij}\right\rangle\), with \(i=1,\ldots,4\) and \(j=1,\ldots,5\), represents the intuitionistic membership and non-membership degree of the i-th alternative that satisfies the j-th attribute.
* In the present work, we use [PERSON] et al.'s [PERSON] et al. (2018), modified maximizing deviations method, in order to derive the five weightings of the attributes, and since the numerical example is the same we get:\(w_{1}=0.2162\),\(w_{2}=0.2703\),\(w_{3}=0.1892\),\(w_{4}=0.1081\),\(w_{5}=0.2162\).
* The aggregated results are given by the usage of \(IFACWAA_{w}\) and \(IFACWGA_{w}\) operators and are listed in Tables 6 and 7.
* The score function's results for each alternative and for each type of \(IFACWAA_{w}\) and \(IFACWGA_{w}\) operators are listed in Tables 8 and 9.
* The ranking of the alternatives is achieved and is depicted in Table 10.  
To illustrate the affection of \(\lambda\) parameter in the procedure as \(\theta\) parameter is fixed to 1 (and -1 in Ali-Mikhail-Haq case) we utilized the following graphs that are depicted in Figures 1 and 2 for the \(IFACWAA_{w}\) and in Figures 3 and 4 for the \(IFACWGA_{w}\).
# Progress and Summary of Photodarkening in Rare Earth Doped Fiber
## 2 Mechanism of Photodarkening
When using high-power pumping YDF with a wavelength of 977 nm, clusters composed of three or four ytterbium ions can emit ultraviolet radiation. It was already demonstrated that the clustering of ytterbium emits light around 500 nm through cooperative luminescence from the simultaneous de-excitation of two clustered ions [61]. In 2007, [PERSON] et al. [3] found  
Figure 1: Partial energy level diagram of Tm\({}^{3+}\). Straight arrows correspond to absorption at 1064 nm, whereas wiggly arrows indicate nonradiative decay. that YDF had an absorption peak near 220 nm, which was related to Yb. Yb ion was easy to capture oxygen holes around Al tetrahedron, but when Yb ion was too much, oxygen holes were insufficient, which would lead to the formation of Yb-Al or Yb-Yb dislocation chemical bond. Because the chemical bond of Ge-Ge and Ge-Si in germanosilicate glass was caused by oxygen defect, it was also considered to be oxygen defect here. When 488 nm light was used to irradiate, the absorption peak of ODCs was 220 nm, which can produce two-photon absorption, then release free electrons, and be absorbed by Yb and Al, forming the color center, leading to the phenomenon of PD. In 2012, [PERSON] et al. [62] showed that trace thulium existed in YDF. They thought that the reason for PD was that thulium ion produced ESA at 488 nm and produced ultraviolet light, reaching the charge-transfer absorption band at 230 nm. The results showed that Tm\({}^{3+}\) had a strong influence on the defect generation process in PD (Figure 3). The upconversion of Tm trace impurities in Yb-doped pumped by near-infrared laser was an important reason for the generation of short-wavelength light [63; 64]. Cooperative luminescence, two-photon absorption, and Tm-doping can explain the PD of YDF in the blue and infrared band. In the process of inducing PD, these processes are not independent and need to work together [65]. Figure 3: Schematic of conversion processes in Yb/Tm-doped fiber. Reproduced from [62], with the permission of AIP Publishing. Figure 2: (**a**). possible photon avalanche upconversion process in Tm-doped silica. (**b**). proposed energy transfer upconversion process in Tm-doped silica.