esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Scheme
### Loss Function  
As mentioned in Section 2, layer transfer was adopted to modify a pretrained network for the instance psoriasis image segmentation. The strategy of training the proposed deep learning-based scheme involved reusing the convolutional layers (C1, C2, C3, C4, C5) in a pretrained deep neural network. The others (FPN, prediction head, and Protonet) were retrained on the psoriasis image dataset to generate a modified network. Figure 4 illustrates the strategy of training the proposed scheme using transfer learning. As we know, ImageNetis one of the most widely used image datasets in many applications. It is expected that the convolutional layers of a pretrained YOLACT++ based on ImageNet should contain a lot of rich knowledge extracted from natural images. This means that the convolutional layers in a pretrained YOLACT++ can be reused to analyze psoriasis images. However, the others (FPN, prediction head, and Protonet) should be retrained using psoriasis images to create a modified YOLACT++ for instance psoriasis image segmentation.  
To re-rain YOLACT++ for instance psoriasis image segmentation, a total loss function was determined as follows:  
\[L_{total}=\beta_{1}\cdot L_{cls}+\beta_{2}\cdot L_{box}+\beta_{3}\cdot L_{mask}, \tag{5}\]  
where \(L_{cls}\), \(L_{box}\), and \(L_{mask}\) represent the classification loss, the bounding box regression loss, and mask loss for network training, and \(\beta_{i}\) (\(i\) = 1, 2, 3) is the weight. For object classification, the confidence loss was the softmax loss over multiple classes confidences [22; 26]. For object localization, the smooth \(L_{1}\) loss was applied as the loss function to the box regression subnet [22; 26]. For mask loss, the pixel-wise cross entropy between assembled mask and the ground truth was measured. It is expected that \(\beta_{i}\) can be adjusted according to the requirements in applications. According to \(L_{total}\), the model of YOLACT++ can be modified to achieve instance segmentation for psoriasis images.
# Microbiome-Based Interventions for Food Safety and Environmental Health
## 4 Current Applications for Microbiome-Based Interventions
### Phage Therapy  
Bacteriophages (phages), which are viruses that can specifically target and replicate within bacterial cells, have been utilized as a form of antibacterial monotherapy for over 100 years to treat bacterial infections [6]. Phages replicate via two main life cycles that impact their therapeutic use: virulent phages rapidly kill their bacterial hosts, while temperate phages can integrate into the host genome or switch to a lytic cycle [92]. Temperate phages can also prevent reinfection in their host and alter bacterial traits through lysogenic conversion [93]. Phage therapy involves administering pathogenic phages directly to a patient with the aim of lysing the bacterial pathogen causing a reduction or elimination of the pathogenic bacteria [92]. The primary feature of phages is their high specificity of infection: they usually identify only a small subset of bacterial strains, minimizing the harm to the host's regular microbial community [94]. Furthermore, because phages reproduce exclusively at the site of infection, they are self-limiting and self-dosing, and they vanish when their particular bacterial pathogen is eliminated [95]. Phages have shown promise in effectively killing bacteria resistant to broad-spectrum antibiotics, as their mechanisms of action differ from those of antibiotics, and resistance can often be overcome using different phage isolates, cocktails, or modified phages [96]. This is demonstrated in a study by [97], where a personalized bacteriophage therapy successfully treated a 68-year-old diabetic patient with necrotizing pancreatitis and a multidrug-resistant _A. baumannii_ infection, which had persisted despite multiple-antibiotic therapy. The intravenous and percutaneous administration of nine different lytic bacteriophages into the abscess cavities of the patient led to the resolution of the infection and a return to health, highlighting the potential of tailored phage therapies for managing resistant infections. Phage therapy is also gaining attention for controlling bacterial biofilms, especially as resistance to antibiotics increases. A study by [98] demonstrated that combining bacteriophage T4 with the antibiotic cefotaxime enhanced the eradication of biofilms formed by _E. coli_, showing that phage-antibiotic synergy can significantly improve treatment outcomes. This synergy, where sublethal antibiotic concentrations boost phage activity, resulted in better biofilm control and reduced antibiotic concentrations needed for effective treatment.
# Channel Modeling and Analysis for the Sensor Network Inside Tower Buildings
## 2 Channel Model Inside Tower Buildings  
Considering that the reflection on the metal surface of tower buildings has quasi-optical characteristic and the communication scenario of tower building is normally fixed, ray tracing (RT) method can be used to accurately model the channel inside tower building. RT method is a typical deterministic modeling method, where every possible propagation path is tracked, and channel parameters, i.e., power, delay, and angle of each ray can be calculated according to geometric optics theory and theory of diffraction [34]. The ray-based channel model can be modeled as a superposition of all effective rays as  
\[h(t,\tau,\alpha,\beta) =\sum_{m=0}^{M}a_{m}e^{j\psi_{m}}\delta(t-\tau_{m})\delta(\alpha- \alpha_{m})\delta(\beta-\beta_{m}) \tag{1}\]  
where \(M\) is the number of rays, \(a_{m}\), \(\psi_{m}\), and \(\tau_{m}\) denote the fading factor, random initial phase, and delay of \(m\) th ray, respectively, \(\alpha_{m}\) and \(\beta_{m}\) denote the AAoA and EAoA, respectively [35].  
However, it is complicated to analyze the communication performance of WSN by using (1) directly. In addition, the receiver in real communication system has limited time resolution due to the limited bandwidth. Therefore, ray-based model can be approximated as a cluster-based channel model to simplify the performance analysis. Thus, as shown in Figure 1, we rewritten the ray-based channel model as a superposition of several clusters as  
\[h(t,\tau,\alpha,\beta) =\sum_{n=0}^{N}\bar{a}_{n}e^{j\bar{\tau}_{n}}\delta(t-\bar{\tau}_{ n})\delta(\alpha-\bar{\alpha}_{n})\delta(\beta-\bar{\beta}_{n}) \tag{2}\]  
where \(N\) is the number of clusters, \(\bar{a}_{n}\), \(\bar{\psi}_{n}\), and \(\bar{\tau}_{n}\) denote the fading factor, random initial phase, and delay of the \(n\) th cluster, respectively, \(\bar{a}_{n}\) and \(\bar{\beta}_{n}\) denote the AAoA and EAoA, respectively. It should be mentioned that the number of rays within different cluster is different.
## 5 Analysis of Simulation Results
### Impact of Temperature on Shear Force of Retaining Piles  
Figure 22 demonstrates that the shear force (\(SF\)) on the retaining piles was a positive and negative staggered phenomenon. The distribution of the shear force on retaining pile 1 was different from that of the shear force on retaining piles 2 and 3 due to the corner effect. When the temperature rose by 15 \({}^{\circ}\)C, the shear force on the retaining pile enlarged to resist the deformation of the pile. The maximum change in the shear force on piles 1, 2, and 3 was 17.98, 4.21, and 6.47 KN, respectively, indicating the most significant change in the shear force on pile 1 and the negligible effect of the temperature on piles 2 and 3. The maximum  
Figure 21: The bending moment (\(BM\))–temperature relationship of (**a**) pile 1, (**b**) pile 2, and (**c**) pile 3.  
shear force on the retaining piles was 59.21 kN under the action of the temperature rise. The shear strength of the retaining piles can be calculated by:  
\[F_{s}=A\times F_{\text{\tiny P}}=51.43\times 10^{-4}\times 180\times 10^{6}\approx 9 26\text{ KN} \tag{5}\]  
where \(A\) is the cross-sectional area of the HW175 section, and \(F_{\text{\tiny P}}\) indicates the shear strength of Q345 steel.  
This equation confirms that the shear force on the retaining pile is within an allowable strength range of the materials.
# Interface Engineering Strategies for Fabricating Nanocrystal-Based Organic-Inorganic Nanocomposites
## 3 Inorganic Ligand Exchange
In detail, the extension of the vibrational frequency up to 100 cm\({}^{-1}\) when SCN was bound to various metals, including Au, Ni, Bi, and Fe, strongly indicates that SCN is chemisorbed, and not physisorbed, onto the NC surface. The devices fabricated with NCs-treated CdSe QDs show an average mobility (\(\upmu\)) of 1.5 \(\pm\) 0.7 cm\({}^{2}\) V\({}^{-1}\)s\({}^{-1}\) and an on-off threshold (I\({}_{\text{on}}\)/I\({}_{\text{off}}\)) of \(\sim\)10\({}^{6}\) with a threshold voltage of 7 V, which is comparable to the industry standard for amorphous Si-based thin-film FETs [79]. The surface chemistry related to inorganic ligands was further expanded to halide (NH\({}_{4}\)I, NH\({}_{4}\)Br, NH\({}_{4}\)Cl, and even NaCl or KBr) and even pseudohalide (e.g., N\({}_{3}\)\({}^{-}\) or CN\({}^{-}\)) ligands by [PERSON]'s group [80]. These short halide ligands show reduced toxicity, enhanced air/moisture stability, and higher earth abundance compared to chalcogen-based ligands. The halide anions bind to the electrophilic NC surface in polar solvents with a high dielectric constant, resulting in an electrostatically stabilized dispersion. Intriguingly, group VA ligands (azide ligands) work especially well for III-V NCs (InAs and InP), which show excellent dispersion properties in polar media, as depicted in Figure 13[80]. The devices fabricated with N\({}_{3}\)-terminated InAs QDs show mobility (\(\upmu\)) of 0.16 cm\({}^{2}\) V\({}^{-1}\)s\({}^{-1}\) and an on-off threshold (I\({}_{\text{on}}\)/I\({}_{\text{off}}\)) of \(\sim\)10\({}^{3}\). [PERSON] et al. proposed a synthetic strategy for semiconducting organic-inorganic nanocomposites, with an inorganic ligand treatment (i.e., HBF\({}_{4}\)) [48]. In detail, the process consisted of two consecutive effective ligand exchanges (i.e., inorganic ligand treatment and subsequent bifunctional ligand exchange), followed by click coupling with end-functionalized conjugated polymers (CPs), as depicted in Figure 14[48]. In this study, oleic acid-capped CdSe tetrapods were first subjected to a ligand exchange procedure with HBF\({}_{4}\), producing HBF\({}_{4}\)-treated CdSe tetrapods in NMF solvent. Subsequently, bifunctional ligands (4-azidobenzoic acid and 5-bromovaleric acid) were introduced; this was followed by click coupling with ethynyl-terminated P3 HT, resulting in P3 HT-CdSe tetrapod nanocomposites. In this technique, effective electronic interactions at the NC-CP interface promoted interfacial charge transfer. The photovoltaic devices fabricated using P3 HT-CdSe tetrapod nanocomposites showed a PCE of 1.28% with a J\({}_{\text{sc}}\) of 7.91 mJ cm\({}^{-2}\), V\({}_{\text{oc}}\) of 0.585 V, and FF of 27.8. Figure 13: (**a**) Digital image of InAs NCs before and after NaN\({}_{3}\) treatment; (**b**) TEM image of InAs NCs capped with N\({}_{3}\)\({}^{-}\); (**c**) Plot of I/V measurement of N\({}_{3}\)-capped InAs NCs FET devices. Reprinted with permission from [80]. Copyright 2014, American Chemical Society.
# On Global Quantum Communication Networking
## 1 Introduction
The QKD can also be used to establish QKD-based campus-to-campus virtual private networks employing the IPsec protocol [18] as well as to establish the network setup for using transport-layer security (TLS) based on QKD [19]. However, all of these networks employ the dark fiber infrastructure. Quantum communication over satellite links has already been demonstrated; see for example [20; 21]. In this Special Issue paper, we propose to implement the multipartite QCN by employing the cluster state-based concept [22]. The proposed quantum network can be used to: (i) perform distributed quantum computing, (ii) teleport quantum states between any two nodes in the network, and (iii) enable the next generation of cyber security systems. The cluster states can be described by using the stabilizer formalism and as such they can easily be certified by simple syndrome measurements. In this formalism, the cluster states can be interpreted as codewords of a corresponding quantum error correction code, while corresponding errors can be corrected for by simple syndrome decoding, among others. By performing simple Y and Z measurements on properly selected nodes we can straightforwardly establish the Einstein-Podolsky-Rosen (EPR) pair between any two nodes in the network. Moreover, multiple EPR pairs can be established simultaneously. We further propose a cluster state-based quantum network of satellites that enables global coverage. The quantum satellite network would be composed of quantum subnetworks comprised of low Earth orbit (LEO) satellites. Some of these LEO satellite-based quantum subnetworks can be connected to a subnetwork of medium Earth orbit (MEO)/ geostationary orbit (GEO) satellites. The LEO satellites should be used to interconnect terrestrial cluster state-based quantum networks. This quantum global network can also be used to distribute the entangled states for quantum sensing applications and to enable distributed quantum computing on a global scale. SDN concepts should be used to reconfigure the proposed QCN. The paper is organized as follows. In Section 2, we describe the proposed cluster states-based QCN concept. In Section 3, we describe potential approaches to extend the transmission distance between QCN nodes. In Section 4, we describe the QCN that is currently under development at the University of Arizona. Finally, in Section 5, we provide some relevant concluding remarks.
# Special Feature Vibration-Based Structural Health Monitoring
## 3 Sensors, Parameter Identification, and Signal Processing  
Feature extraction taking into account the influence of surrounding environments and ambient operation conditions is required to increase the accuracy of diagnosis. [PERSON] et al. [11] presented the effects of temperature on the vibration monitoring of concrete slab structures. Experiments were performed to find the influence of temperature on modal properties used for damage diagnosis. [PERSON] and [PERSON] [12] proposed an optimization method for sensor batch design to measure roughness on railhead surfaces. This information allowed the estimation of corrugations and contact between wheel and rail during train operation to analyze rolling noise. The variation of the rolling noise magnitude is an important feature showing the current rail status. [PERSON] and [PERSON] [13] identified the possible defects in gas-insulated switchgear using vibration amplitude for diagnosis of a power grid. The periodicity in the vibration responses was used to extract the vibration feature. [PERSON] et al. [14] investigated the measurement method of the clamping force from the bolt vibration responses. This allowed indirect monitoring of the bolt tightening process without the use of load cells, which is advantageous for application to the manufacturing process.  
[PERSON] et al. [15] proposed a weighted Morlet wavelet-overlapping group sparse algorithm for rolling bearing fault diagnosis. The impulse feature was identified to analyze the possibility of faults. [PERSON] et al. [16] applied variational mode decomposition to diagnose rotor faults. The patterns of time-varying frequency components were identified accurately. [PERSON] et al. [17] investigated an adiabatic fiber taper structure inscribed using femtosecond laser micromachining. The proposed sensor allows health monitoring of arbitrary civil structures with accurate strain measurements. [PERSON] and [PERSON] [18] proposed an adaptive Kalman filter to identify damage using sparse vibration measurements. The filter efficiency was validated by numerical investigations.  
[PERSON] and [PERSON] [19] proposed a data processing system for structural health monitoring by analyzing ambient vibrations. The accuracy was validated by locating damage in the scale-down benchmark structure. [PERSON] et al. [20] estimated parameters to understand the vibration of a testing structure. The algorithm allowed consideration for nonlinear responses and uncertainties. [PERSON] et al. [21] proposed modal frequency sensors to reduce interference in settlement surveillance monitoring of steel transmission towers. The numerical analysis was used for verification of the efficiency and robustness of the Kriging surrogate model. [PERSON] [22] proposed a condition monitoring system for real-time inspection of regional railway lines in Japan. The car body vibration was predicted and used for feature extraction by machine learning algorithm.
# Weak Multiple Fault Detection Based on Weighted
## 6 Conclusions  
This paper presents a weak multiple fault detection algorithm for rolling bearings on the basis of WOGS. The proposed algorithm addresses the problem of weak multiple fault extraction due to the long signal transmission path. The application of the WOGS algorithm to the weak multiple fault diagnosis of rolling bearings demonstrates its validity. The conclusions of this paper are summarized as follows:  
1. This paper proves that the proposed WOGS algorithm can diagnose weak multiple faults without any prior knowledge like OGS does with some prior knowledge.
2. A weighted WOGS is adaptively constructed by analyzing the salient features of the signals--an effective way to enhance impulse characteristics and suppress noise components in the vibration signal.
3. The simulation and the experiment performed and the results obtained show that the proposed WOGS algorithm achieves good results in the area of the weak multiple fault diagnosis of rolling bearings, especially regarding a multiple fault signal due to signal attenuation caused by a long transmission path.  
In future research, we will continue to explore how to select group structure parameters K1 and K2 in the WOGS algorithm to obtain better extraction results. In addition, the complexity of the algorithm needs to be simplified to further improve algorithm's efficiency.  
All the authors contributed to all aspects of the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China under grant number 51675098, and this paper was also supported by Scientific Research Fund of Nanjing University of Information Science and Technology (number 2018r085#).  
The authors declare no conflict of interest.  
Figure 24: Results of soft threshold: (**a**) Time domain waveform. (**b**) Envelope spectrum.
# Magnetic Field-Based Vehicle Positioning System in Long Tunnel Environment
## 4 Experimental Results and Discussion
### Discussion  
In this study, we demonstrated that the position of a vehicle inside a tunnel can be estimated with high accuracy using magnetic sensor readings. Unlike for pedestrians, the magnetic field could be utilized for the navigation of vehicles, because the driver attached the smartphone to the dashboard. In addition, the magnetic sensor could be calibrated as the vehicle position was known before entering the tunnel.  
In future research, we plan to conduct experiments using various models of smartphones and vehicles. Moreover, the performance of the proposed system should be validated for sudden changes in its speed. When driving in a tunnel, there are various traffic situations such as traffic jams or accidents. We plan to conduct research using an accelerometer and gyroscope sensors of smartphones to estimate the accurate position even with various vehicle speed changes. Additionally, since the proposed technology is a fingerprint-based technology, if a large magnetic disturbance not stored in the magnetic fingerprint DB occurs, the performance may be degraded.  
The navigation system proposed in this study is desirable for inclusion in existing navigation systems, and seamless navigation can be achieved with general navigation on the road, where the GNSS is received, and magnetic field-based positioning inside the tunnel. In order to expand the system to more tunnels, building an accurate magnetic fingerprint DB will be a very important key.
# Robust Adaptive Control for Nonlinear Aircraft System with Uncertainties
## 3 Controller Design
### Controller Design
#### 3.3.2 Adaptive Controller  
Next, to accommodate the input uncertainty and matched uncertainty on account of \(\Lambda\) and \(\theta^{T}\), the adaptive controller was given by:  
\[u_{ad}=\hat{\lambda}^{T}(t)x(t)+\hat{\theta}^{T}(t)\Theta(x(t)) \tag{8}\]  
where \(\hat{\lambda}\in\mathbb{R}^{n\times m}\) and \(\hat{\theta}\in\mathbb{R}^{s\times m}\) are free-designed update laws. For the sake of accommodation of the parametric uncertainties, Remark 1 introduces the matching condition as follows.  
**Remark 1**.: _With the selected reference model state matrix [27]\(A_{m}=A+BK_{x}^{T}\), there exist parameters \(\lambda^{*}\) and \(\theta^{*}\) satisfying the matching condition, as follows:_  
\[A_{m}=A+B\theta^{T}\Theta(x(t))+B\Lambda\Big{(}K_{x}^{T}+{\lambda^{*}}^{T}+ \theta^{*T}\Theta(x(t))\Big{)} \tag{9}\]  
_where \(\lambda^{*T}=\Big{(}\Lambda^{-1}-I\Big{)}K_{x}^{T}\) and \(\theta^{*T}=-\Lambda^{-1}\theta^{T}\)._  
Update laws for the estimated parameters were selected as  
\[\begin{split}\dot{\hat{\lambda}}(t)&=-\Gamma_{ \lambda}x(t)e^{T}(t)PB\\ \dot{\hat{\theta}}(t)&=-\Gamma_{\theta}\Theta(x(t) )e^{T}(t)PB\end{split} \tag{10}\]  
where \(\Gamma_{\lambda}=\Gamma_{\lambda}^{T}=\rho_{\lambda}\times\left[\begin{array}[ ]{cc}I_{n}&0\\ 0&0\end{array}\right]>0\) and \(\Gamma_{\theta}=\Gamma_{\theta}^{T}=\rho_{\theta}\times\left[\begin{array}[ ]{cc}I_{s}&0\\ 0&0\end{array}\right]>0\) are free-designed adaptive gains for estimated parameters \(\hat{\lambda}\) and \(\hat{\theta}\), respectively. \(e(t)\) was defined as \(e(t)=x(t)-x_{m}(t)\), which represents the state tracking error. \(P\) is a positive define matrix, satisfying \(P=P^{T}\), which was computed in the next section.
# Miniature Coil for Wireless Power and Data Transfer through Aluminum +
## 5 Conclusions  
This paper presents the first demonstration of power and data transfer through metal using a miniature coil. The system can operate through a 1 mm-thick aluminum plate using a coil of very small size, only 15 mm \(\times\) 13 mm \(\times\) 6 mm. The maximum coil-to-coil power transfer efficiency is 2.4%, and the maximum harvested power is 440 mW operating at 2 kHz. Additionally, the designed coil can harvest power in a variety of materials of different thicknesses. Harvested power is more than 10 mW through a 4.98 mm-thick aluminum plate, and more than 4.5 W through a 0.88 mm-thick stainless steel plate.  
Conceptualization, A.-V.P. and C.S.G.; methodology, J.M.R.-A. and A.-V.P.; software, J.M.R.-A.; validation, J.M.R.-A. and A.-V.P.; formal analysis, J.M.R.-A. and A.-V.P.; investigation, J.M.R.-A.; resources, C.S.G. and B.T.F.; data curation, J.M.R.-A.; writing--original draft preparation, J.M.R.-A.; writing--review and editing, J.M.R.-A., A.-V.P. and B.T.F.; visualization, B.T.F.; supervision, A.-V.P, B.T.F. and C.S.G.; project administration, B.T.F. and C.S.G.; funding acquisition, A.-V.P, B.T.F. and C.S.G. All authors have read and agreed to the published version of the manuscript.  
Figure 19: Transmit (**a**) and receive (**b**) data signal at 100 bps using a 2 kHz carrier.  
Figure 18: Test setup for through metal communication system. Below the metal plate: the coil connected to the outside subsystem. Above the metal plate: the coil and the inside subsystem. Each coil is located at the center of the 1 mm-thick aluminum plate and separated 0.5 mm away from the metal plate.  
**Funding:** This research was funded by Lawrence Livermore National Laboratory grant number B638423.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study is available from the corresponding author upon reasonable request.  
**Conflicts of Interest:** The authors declare no conflict of interest.