esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology  
### Working Principle of FBGs  
An FBG sensor is a typical optical sensor that has the grating with periodic changes, resulting in only light with a specific wavelength \(\lambda_{B}\) being reflected. This wavelength is defined as the Bragg wavelength which can be expressed as:  
\[\lambda_{B}=2n\Lambda \tag{1}\]  
where \(n\) represents the effective refractive index of the optical fiber and \(\Lambda\) refers to the grating period. When the broadband light source enters the grating section the light with the Bragg wavelength cannot pass through, and thus is reflected. Consequently, an FBG has a high selectivity on wavelengths. When the external force or temperature changes, \(n\) and \(\Lambda\) would react to the changes linearly.  
Based on that principle, it is possible to demodulate micro-vibrations induced by the ultrasonic waves from FBG measurements. However, the wavelength shift can be extremely minor, the frequency of which also requires a piece of high-speed demodulation equipment. In this case, it is rational to utilize the edge filter demodulation technique [40], where a narrow-bandwidth light enters the optical fiber and meets the 3-dB points of the reflective wavelength, as demonstrated in Figure 2. The quality of the receiving ultrasound signals using this approach has been proven by researchers [41; 42]. A balanced photodetector is required to receive the light from the transmission light which passes through the grating of the FBG and the incident light. The photodetector not only transforms the change in the optical signals to digitalized electrical signals, but also reduces the noise. The output voltage of the balanced photodetector can be expressed by:  
\[V=2\Delta\lambda_{B}\cdot R_{D}\cdot G\cdot P\cdot g \tag{2}\]  
where \(G\) represents the grating slope; \(g\) is the gain factor; \(P\) and \(R_{D}\) are the laser power and the response of the photodetector, respectively.
###### Abstract
However, the output is reduced by the newly available water flow, and the cost increases owing to paralleling, thereby making the design more complex [13; 16]. Regarding the geometrical modifications of PATs, different researchers have suggested certain design modifications of the impeller [13; 17; 18; 19; 20; 21], which better manage the variable flows. However, these modifications are unable to manage all the flow variations, and the cost is increased. Power electronics-based VFDs can provide any speed profile of variable flow inputs [23; 24]. When the flow decreases, which affects the output of the plant, the VFD changes the speed of the generator, and the output of the plant becomes smooth. However, this method is significantly costly because devices with high ratings are required, causing installation, operational, maintenance, and transportation problems. In this study, a simple technique is proposed to mitigate the adverse effects of flow variations on the output of a plant. This study is novel because a parallel water column (PWC) technique is used to smooth the output of a PAT system while maintaining the water flow at the inlet of the PAT by acting as an auxiliary penstock. With this technique, water columns connected in parallel are used with the existing penstock of a plant to inject additional flow at the inlet of the PAT in the event of a decreasing flow. This additional flow will maintain the smooth rated output of the plant, even at the minimum head. The effectiveness of the PWC technique was confirmed by performing a simulation using ANSYS software for the case study of a variable flow profile. Subsequently, based on the output obtained from the PAT, the electrical output of the generator was analyzed using MATLAB/Simulink software.
## 3 Results
### Protein-Ligands Interactions Analysis  
For in silico study, the post-docking protein-ligand interaction analysis has made use of the BIOVIA Discovery Studio and Maestro post-docking visualization tool, together with the virtual screening approach, to identify the most notable and tested drug-ligand interactions. With the lowest docking value (\(-7.8\) kcal/mol), CID-7311 collaborated with amino acid sites and formed only one hydrogen bond with Leu346, among other interactions, such as hydrophobic interactions with different amino acids. In terms of the six potential medications, the ligand CID-5366020 had the highest binding energy (\(-8.6\) kcal/mol), forming fourteen hydrophobic connections with the remaining protein residues, identified as MET342, MET343, GLY344, LEU345, LEU346, LEU349, ALA350, LEU354, TRP383, LEU384, LEU387, MET388, ILE389, LEU391, VAL392, LEU402, PHE404, VAL418, GLY420, MET421,  
ILE424, PHE425, LEU428, MET517, GLY521, MET522, LEU525, TYR526, MET528, CYS530, VAL533, and LEU536 residues.  
Although CID-91696396 possesses a lower binding energy compared to CID-5366020, proficiency includes hydrogen bonds via the 3 ERT recipient, specifically two hydrogen bonds using the residue Cys530. This makes CID-91696396 interacting via the 3 ERT receptor more powerful and much more effective than CID-5366020. Non-bonded interactions are a fundamental part of drug design techniques, in addition to the fact that non-bonded connections, such as hydrophobic and hydrogen bonds, enhance the durability of both the ligand and the drug and that their number correlates via the interaction of the drug-ligand complex. Additionally, CID-250006068, as well as CID-23725625 (control drug), had an attractive affinity for binding to protein 3 ERT receptors (\(-8.2\) kcal/mol and \(-7.9\) kcal/mol, respectively) whilst engaging no hydrogen bond interactions involving the amino acid sites CID-23725625 (control drug), whereas CID-250006068 also has several hydrophobic connections but no hydrogen bonds. All the interacting bonds that were identified among the selected ligands bioactive compounds and targeted proteins complex estrogen receptors alpha (ER\(\alpha\)) (PDB ID: 3 ERT) are tabulated in Table 4 and Figure 1.  
Figure 1: All the evaluated ligand molecules interacted with the estrogen receptor alpha (ER\(\alpha\)) protein. The left-hand pictures show the three-dimensional structure of the protein–ligand complex, while the right-hand images show the two-dimensional structure. Here, (**A**) shows the 3D and 2D interactions between the ER protein and the control drug olaparib (CID-23725625), whereas comparative interactions with the ER\(\alpha\) protein complex are demonstrated by (**B**) compound 2,4-di-tert-butylphenol (CID-7311); (**C**) compound 2,3-oxidosqualene (CID-5366020); (**D**) compound 5,8,11-eicosatriynoic acid, trimethylsilyl ester (CID-91696396); and (**E**) compound 1-monolinolein (CID-250006068).
## 3 Wireless Communication and Power Transmission  
In implantable sensors, wireless data transmission and wireless power supply are two crucial modules, which exhibit both similarities and differences. Wireless data transmission serves to convey the physiological information collected by the sensor to external devices for real-time monitoring and analysis. Conversely, wireless charging is primarily concerned with providing continuous energy support to the sensor, thereby extending its service life and mitigating the inconvenience and potential risks associated with battery replacement. Both of these technologies rely on wave propagation for implementation. Certain technologies, such as electric field coupling, are capable of simultaneously performing wireless data transmission and wireless power supply, a concept known as simultaneous wireless information and power transfer (SWIPT). The two can operate within the same frequency band or different frequency bands and are regarded as a significant solution for the continuous communication of low-power devices and wireless sensors in the future Internet of Things. Distinct from traditional wireless information transmission and energy transmission systems, the SWIPT system must strike a balance between the communication rate and energy transmission efficiency to achieve optimal energy and information transfer [87]. This section predominantly delineates the common approaches to wireless data transmission and wireless power supply, elucidates their respective advantages and disadvantages, and presents several examples of commercial applications.
# Dynamic Measuring Method of Laser Beam Incident Angle for Laser Doppler Vibrometer
## 2 Basic Measurement Principles
### Measurement Principle of Pavement Deflection Velocity  
The principle of the laser high-speed deflectometer using LDV to measure pavement deflection velocity is briefly described below. The self-weight of the deflectometer will cause the road surface to form a deflection basin in an extensive range. When the deflectometer is in motion, the deflection basin also moves. It brings trouble to traditional contact measurement. As a typical non-contact measurement tool, LDVs non-destructively measure the deformation velocity of different points in the deflection basin and then calculate the deflection value of the road surface [18]. Figure 1a shows the synthesis principle of the LDV measurement \(V_{\text{m}}\). The expressions among velocity \(V_{\text{m}}\), vehicle speed \(V_{\text{k}}\), and pavement deformation velocity \(V_{\text{d}}\) are shown in Equation (2). Based on the measurement principle of LDV, \(V_{\text{m}}\) is the velocity of the road surface relative to the sensor itself along the direction of the laser beam. That is to say, \(V_{\text{m}}\  
overcome the limitation of the narrow space. At present, equipment manufacturers and users have purposefully proposed angle measuring methods suitable for laser high-speed deflectometers, such as the ballast method [21] and the offset method [19]. However, these current methods are cumbersome and time-consuming, such as the need to increase or decrease counterweights, and the search for long straight rigid roads. When the experimental conditions are all available, the angle measurement and calibration process will also take more than 3 h.  
In the following, we will use static and dynamic methods to realize the angle measurement of LDV based on experiments. Considering that the mass of the laser high-speed deflectometer is large (the total weight is more than 10 t), which makes it difficult to move in the vertical direction with high precision, we removed one LDV (Model: Polytec OFV 505/5000) and carried out experiments indoors. The static measuring method draws on the research work of [PERSON] et al. [30]. Furthermore, since this method is traceable, we take its measurement as the true value. For the dynamic measuring method, it is a new method proposed in this paper based on the measurement principle of LDV. The work of the two measuring methods will be described in detail in Sections 3 and 4, respectively. The key processes of static and dynamic measurement methods and their connections are shown in Figure 2.
###### Abstract  
It was recently studied how to achieve the optimal degrees of freedom (DoF) in a multi-antenna full-duplex system with partial channel state information (CSI). In this paper, we revisit the DoF of a multiple-antenna full-duplex system using opportunistic transmission under the partial CSI, in which a full-duplex base station having \(M\) transmit antennas and \(M\) receive antennas supports a set of half-duplex mobile stations (MSs) having a single antenna each. Assuming no self-interference, we present a new hybrid opportunistic scheduling method that achieves the optimal sum DoF under an improved user scaling law. Unlike the state-of-the-art scheduling method, our method is designed in the sense that the scheduling role between downlink MSs and uplink MSs is well-balanced. It is shown that the optimal sum DoF of \(2M\) is asymptotically achievable provided that the number of MSs scales faster than SNR\({}^{M}\), where SNR denotes the signal-to-noise ratio. This result reveals that, in our full-duplex system, better performance on the user scaling law can be obtained without extra CSI, compared to the prior work that showed the required user scaling condition (i.e., the minimum number of MSs for guaranteeing the optimal DoF) of SNR\({}^{2M-1}\). Moreover, the average interference decaying rate is analyzed. Numerical evaluation is performed to not only validate our analysis but also show superiority of the proposed method over the state-of-the-art method.  
defnergy of freedom (DoF); full-duplex systems; hybrid opportunistic scheduling; partial channel state information (CSI); user scaling law +
Footnote †: journal:  
Article
###### Abstract
However, since these fillers tend to be inorganic components, they exhibit poor interfacial compatibilities with the organic components present in the system, ultimately leading to agglomeration of the inorganic components during hydrogel application and resulting in a decreased electrical conductivity and poor mechanical properties [30; 31]. In addition, conductive hydrogels possess various shortcomings in terms of their mechanical strength, antibacterial properties, opacity, and tissue adhesion, which seriously hinder the further development of conductive hydrogel sensors [32; 33]. Inspired by mussels, hydrogels containing catechol groups, such as dopamine and tannins, have recently received increasing research attention [34; 35; 36; 37; 38] since these hydrogels are constructed from various types of interactions, including \(\pi\)-\(\pi\) stacking, cation-\(\pi\) interactions, hydrogen bonds, hydrophobic interactions, and Michael addition reaction. These interactions endow the hydrogel with strong adhesion properties [39], even underwater. However, owing to the facile aging, darker color, and poor biocompatibility of such materials, they require further research and development to enhance their properties and expand their potential application. Thus, we herein report the preparation of hydrogels that exhibit good adhesion, tensile, and fatigue resistance properties. More specifically, we prepare P(BHMP-AM)-Zn\({}^{2+}\) via a one-pot in situ polymerization of BHMP (3-[bis(carboxymethyl)amino]-2-hydroxypropyl-2-methyl-2-propenate), AM (acrylamide), and Zn\({}^{2+}\), using MBA (\(N\),\(N^{\prime}\)-methylenebisacrylamide) as an initiator. Following its preparation, the hydrogel properties are investigated during joint movement, and the hydrogel biocompatibility is evaluated. In addition, we considered that the addition of zinc ions could impart the hydrogel with antibacterial and bactericidal properties, and this assumption was evaluated against Escherichia coli (_E. coli_) and Staphylococcus aureus (_S. aureus_).
## 4 Implementation of MAFS Traffic Scheduling Algorithm
### The Algorithm of MAFS
This makes the behavior of agents more stable and controllable, helping to coordinate the behavior of each agent and enabling better routing strategy optimization and path selection. In the case of deterministic strategies \(\mu_{\theta_{i}}\), the policy gradient is given by:  
\[\
abla_{\theta_{i}}J(\mu_{i})\approx\frac{1}{S}\sum_{j}\
abla_{\theta_{i}}\mu _{i}(a_{i}|o_{i})\
abla_{a_{i}}Q_{i}^{\mu}(s,a_{1},\cdots,a_{N})\big{|}_{a_{i }=\mu_{i}(o_{i})}, \tag{7}\]  
where \(o_{i}\) represents the observation information of the \(i\)th agent, and \(s=[o_{1},o_{2},\cdots,o_{N}]\) denotes the observation vector, which is the network state information. The routing policy of each agent is updated through gradient descent, and the resulting routing policy is improved by interacting with other agents. The above gradient formula adopts the experience replay technique to extend the experience replay buffer, which contains several pieces of information, with its elements represented as a tuple \((s_{t-1},a_{t-1},r_{t-1},s_{t},a_{t},r_{t},s_{t+1})\). The Actor network is updated using the above gradient descent method, and the traffic in the data center network is forwarded through the routing policy selected by the Actor network. The Critic network is updated by the loss function through backpropagation. Its input is the global information about the network state and routing actions, with the loss function calculation formula as follows:  
\[L(\theta_{i})=\frac{1}{S}\sum\Bigl{(}Q_{i}^{\mu}\left(s,s^{\prime},a_{1}, \cdots,a_{N},a_{1}^{\prime},\cdots,a_{N}^{\prime}\right)-y\Bigr{)}^{2}, \tag{8}\]  
where \(y=r_{i}+\gamma Q_{i}^{\mu^{\prime}}\left(s,s^{\prime},a_{1},\cdots,a_{N},a_{1 }^{\prime},\cdots,a_{N}^{\prime}\right)\big{|}_{a_{i}^{\prime}=\mu_{j}^{\prime }(o_{i})}\), \(Q_{i}^{\mu^{\prime}}\) represents the target network, and \(\mu^{\prime}=\{\mu_{1}^{\prime},\cdots,\mu_{N}^{\prime}\}\) is the set of target policies. The policies of other agents can be obtained through fitting approximation without the need for further communication interaction. The process of multi-agent systems training is similar to that of single-agent training. A centralized Critic network can alleviate the difficulty of convergence caused by the dynamic environmental changes in multi-agent scenarios. After the training is completed, each Actor network can adopt the corresponding routing actions based on its own network-observed states.
# Research Progress of Gas Sensor Based on Graphene and Its Derivatives: A Review
## Gas Sensor
### Graphene Gas Sensors with Different Working Principles  
Graphene has excellent electron mobility and large specific surface area, and exhibits good gas sensing properties. Graphene material as a p-type semiconductor contains many holes [13], and has pull electron effect in gas atmosphere. After gas molecules are adsorbed by graphene, the gas molecules will undergo weak hybridization and coupling with the electron on the surface, and the fermi level to move up and down in small increments. The state of electron doping or hole doping will change the fermi level, thus lead to changes in graphene conductivity. Thus, graphene is particularly sensitive to the detection of adsorbed small molecule gases. The donor and acceptor depend on the relative position of the electron energy level orbit of the system. If the valence band of the adsorbed gas is higher than the fermi surface of graphene, the gas molecules act as donor for the electrons; on the contrary, if the valence band is lower than the fermi surface of graphene, the gas molecules act as acceptor.  
The working principles of graphene sensors mainly include the following: resistive, Field Effect Transistor (FET), quality sensitive and MEMS.
## 2 Materials and Methods
### The VCCT for the Proposed Model  
In linear elastic fracture mechanics, the VCCT assumes that the work required to extend a crack by an infinitesimal distance \(\Delta a\) is equal to the work required to close the crack back to its original length. A virtual crack in the VCCT refers to an incremental extension of an existing crack that is not explicitly modeled but numerically considered for energy calculations. Instead of physically propagating the crack, the VCCT estimates crack growth behavior by evaluating the nodal forces and corresponding displacements at the crack tip. This approach enables accurate fracture mechanics analysis without requiring the remeshing of the crack front at each propagation step, thus improving computational efficiency while maintaining accuracy in predicting crack growth. Based on the concept of the two-dimensional VCCT [31], the modified three-dimensional VCCT, developed using the proposed model for cylindrical pipes with an axial crack \(a\), can compute the total strain energy release rate \(G_{total}\) within any layer, as follows  
\[G_{total}=\left(f_{x}^{bot}\ \overline{u}_{x}^{bot}+\ f_{x}^{top}\ \overline{u}_{x}^{top}+f_{\theta}^{bot}\ \overline{u}_{\theta}^{bot}\ +f_{\theta}^{top}\ \overline{u}_{\theta}^{top}+f_{r}^{bot}\ \overline{u}_{r}^{bot}+f_{r}^{top}\ \overline{u}_{r}^{top}\right)/\ \left(2\ t_{i}\ \Delta a\right) \tag{16}\]  
Figure 1: Modeling approach for the proposed laminated cylindrical model. (**a**) Two-layer cylindrical composite. (**b**) The proposed two-layer laminate model.  
where the variables \(f\) are the crack tip forces in cylindrical coordinates. Also, the values, \(\overline{u}\), represent the relative displacements relevant to the nodal variables of the present model. Superscripts denoted by _bot_ and _top_ mean the bottom and top curved surfaces. In addition, \(t_{i}\) is the thickness of layer \(l\). From Figure 2, the relative displacements of layer 1 can be written as follows  
\[u_{i}^{bot}\ =\ u_{i}^{A}\ -\ u_{i}^{B};\ u_{i}^{top}\ =\ u_{i}^{C}\ -\ u_{i}^{D}\qquad \text{for}\quad i=x,\theta,r \tag{17}\]  
The nodal forces are considered at nodal points \(E\) and \(F\). The force at a given point can be calculated by summing the internal forces obtained from two layers with \(\Delta a\). The internal nodal forces in a layer can be calculated from the results of the nodal and non-nodal variables in the proposed elements with the elasticity matrix \([D]_{5\times 5}^{l}\) as follows  
\[f^{*}=\{B^{*}\}_{5\times 1}^{T}\ [D]_{5\times 5}^{l}\ \{\varepsilon\}_{5\times 1} \tag{18}\]  
Here, the strain vector \(\{B^{*}\}\) is a column vector of the strain matrix \(\{\text{B}\}\) in Equation (6). Therefore, the internal force can be expressed as  
\[f^{*}=\{B^{*}\}_{5\times 1}^{T}\ [D]_{5\times 5}^{l}\ [B]_{5\times n}\{u\}_{n \times 1} \tag{19}\]  
where \(n\) is the total number of degrees of freedom in a single layer.
# Boundary-Aware Hashing for Hamming Space Retrieval
## 1 Introduction
Specifically, a convolutional neural network (CNN) is exploited as a backbone network to learn the image representations, followed by a fully connected hash layer (_f(ch)_ to map the image representations into K-dimensional continuous codes. Boundary-aware penalization loss (logarithmic and exponential functions) and quantization error loss are used to learn hash coding during the training process. In the test, the sign function is used directly to encode the output of the hash layer into hash codes, and the Hamming distance is used for measuring the similarity (or dissimilarity) of image pairs. The main contribution of this work is as follows:  
* Based on the idea of contrastive loss with the boundary information, a novel loss function is designed by combining a logarithmic function for similar pairs and exponential  
Figure 1: (**a**) Gradient magnitude of DSH [15] at 16 and 64 bits and (**b**) gradient magnitude of BAH at 16 and 64 bits. Red and blue colors represent the gradient magnitudes of similar and dissimilar pairs, respectively. The solid and dashed lines represent 16 bits and 64 bits, respectively. The black dashed line is the Hamming distance 2.  
functions with boundary information for dissimilar pairs. It can push the dissimilar pairs out of the Hamming ball and pull the similar pairs inside. * An image retrieval framework is proposed based on the designed loss function to resolve extremely imbalanced data. Its gradient of similar pairs outside the Hamming ball is much larger than that of dissimilar pairs. It effectively pulls similar pairs into the ball without up-weighting the similar pairs or other optimization strategies. * Comprehensive experiments on four widely used image retrieval datasets, namely MS-COCO, NUS-WIDE, CIFAR-10, and ImageNet, show that: (i) the proposed method can achieve a stable improvement in recall and accuracy with the encoding length increases; (ii) our method returns a lower ratio of zero images (i.e., ratio of queries that return no images) than the seven state-of-the-art deep hashing methods and in particular can return all retrieval results efficiently on the CIFAR-10 dataset; and (iii) our method is also competitive when handling extremely imbalanced data. The remainder of this paper is organized as follows: Section 2 discusses the related work of this paper. Section 3 describes the BAH method in detail. Section 4 evaluates the proposed method by performing the hash table lookup task on four datasets. Section 5 concludes this paper.