esa-sceva/satcom-chunk-collection huggingface dataset:
# Bidirectional Atmospheric Channel Reciprocity-Based Adaptive Power Transmission
## 2 Theoretical Analysis
### Bidirectional Atmospheric Transmission Channel Model  
The two transceivers a and b are allocated to receive a signal when they goes through a bidirectional optical wireless channel with atmospheric turbulence in the bidirectional atmospheric channel model utilized in this work. At the same end of the link are the two transceivers, a and b. In a short period of time, the send-receive-send transmission process may be completed by the transceivers situated at the two ends of the connection with the same axis symmetry, as schematically depicted in Figure 1.  
We refer to process of transmitting a symbol that go from point a to point b and from point b to point a, respectively, as forward transmission and inverse transmission. The received signal of \(R_{F}\) at the receiving end in forward transmission may be expressed as the following under the assumption of intensity modulation and direct detection [23]:  
\[R_{F}=P_{L}TS_{F}+N, \tag{1}\]  
\(P_{L}\) denotes the optical power of transmitting terminal, \(T\) denotes channel fading due to turbulence effects, \(S_{F}\) is the transmitted symbol, and \(N\) is the independent zero-mean unit-variance Gaussian noise, where the intensity of the turbulent channel fading is represented by the scintillation index of \(\sigma^{2}\), which is defined as follows [24]:  
\[\sigma_{F}^{2}=\frac{E[T^{2}]}{E^{2}[T]}-1, \tag{2}\]  
and the subscript \(F\) in Equation (2) denotes the forward transmitted scintillation intensity, and \(E[\cdot]\) denotes the mathematical variance operation. The outgoing signals are not subject to the same background noise because of the various heights of the transceivers a and b, which might be reflected in the modeling as a high background noise in the inverse-transmission channel. Thus, according to Equation (1), the received signal of \(R_{I}\) that is sent in the other way may be represented as  
\[R_{I}=P_{L}CS_{I}+N, \tag{3}\]  
where \(C\) tabulates the channel fading strength due to turbulent noise and background noise; meanwhile, \(S_{I}\) denotes the transmitted symbol of inverse transmission. In additional, the channel reciprocity of \(\rho\) can be evaluated by the fading strength of the two channels [25], and it is defined as  
\[\rho=\frac{\text{cov}(C,T)}{\sigma_{I}\sigma_{F}}=\frac{E[CT]-1}{\sqrt{(1+ \sigma_{I}^{2})(1+\sigma_{F}^{2})}}, \tag{4}\]  
Figure 1: Schematic of bidirectional atmospheric channel model.  
where \(\sigma_{n}^{2}(n=F,I)\) denotes the channel-fading variance in Equation (4) and cov(\(\cdot\)) represents the covariance function. In contrast, the atmospheric turbulence channel simulation of the channel applied in this paper approximates a lognormal distribution; so, the correlation coefficient and scintillation index can be redefined [26]:  
\[\widetilde{\rho}=\frac{\log(1+\rho\sigma^{2})}{\log(1+\sigma^{2})} \tag{5}\]
## 4 Design and Electrical Performance Characterization
### Input, Inter-Resonator, and Output Coupling Characterization  
This section presents the characterization charts for both input-output couplings and inter-resonator coupling behavior.
## 4 Risk Analysis for Current Protocols
### OneNet  
The NMEA OneNet standard is an evolutionary step from the NMEA 2000 standard. It is an open industry standard that provides a network infrastructure for marine devices and services over IPv6 to allow all OneNet application protocols to co-exist with other protocols and services that operate in parallel on the same network [40].  
A OneNet device uses Multicast Domain Name Server (mDNS) to be discovered on the local network. Multicast DNS is used to resolve host names to IP addresses within small networks that do not include a local name server. Each OneNet application has a unique application ID assigned during production or generated when the application is executed. OneNet Application's meta data (product name, NMEA product code, model, etc.) is stored in JSON format with UTF-8 encoding called Application Information Resource (AIR). A OneNet device can request AIR from other OneNet devices via HTTP GET request; this is used to identify which services are accessible by other devices.  
OneNet has a Secure Mode supported by OneNet's Pairing Service--a software which allows authorized devices to exchange data in an encrypted tunnel. To create a secure tunnel, a Human Interface Device (HID) application performs an HTTPS connection tothe Pairing Service in order to pair the device to the Secure Network. Anonymous Diffie-Hellman is used as the cipher suite for the connection. Once the HTTPS connection is established, pairing then proceeds.  
OneNet devices have a unique verification process to identify which applications are OneNet certified. This is done by using Application Information Service (AIS), which is the same service used to exchange AIR, to provide the Certification Information Resource (CIR) which contains Cryptographic Message Syntax (CMS) messages for certification verification.
## 7 Conclusions  
In this paper, we first analyzed the circumferential stress and strain signals of the pipe wall and the fluid pressure signal on the basis of the FSI and found that the variation of circumferential stress and strain of the pipe wall was highly positively correlated with the pressure fluctuation of internal fluid. We next analyzed the voltage signal of PVDF piezoelectric thin film and the circumferential stress and strain signals of the pipe wall on the basis of the electromechanical coupling and found that the voltage variation of PVDF piezoelectric film sensor was highly positively correlated with the variation of circumferential stress and strain of the pipe wall. In conclusion, the PVDF piezoelectric film sensor can be applied to indirectly measure the pressure fluctuation of internal fluid by measuring the variation of circumferential stress and strain of the pipe wall. Then, the influences of flow velocity pulsation and mechanical vibration caused by the pipeline pump were studied. It was found that the flow velocity pulsation had little influence on the measurements of the variation of circumferential stress and strain of the pipe wall and the internal fluid pressure fluctuation; the mechanical vibration had little influence on the measurements of the variation of circumferential stress and strain of the pipe wall as well as the internal fluid pressure fluctuation by PVDF piezoelectric film sensor when both ends of the pipe were fixed by hoops. Finally, the simulation was verified by experiments. Therefore, the above research is of great significance and can guide the application of the PVDF piezoelectric film sensor in practical engineering measurements of fluid flow parameters in pipe-like structures.  
Conceptualization, S.L. and X.W.; data curation, S.L. and Y.K.; formal analysis, S.L. and X.W.; funding acquisition, X.W.; investigation, S.L. and X.W.; methodology, S.L. and X.W.; project administration, X.W.; resources, X.W.; software, X.P.; supervision, X.W. and X.P.; validation, S.L. and X.W.; visualization, S.L. and X.W.; writing--original draft, S.L., X.W. and Y.K.; writing--review and editing, S.L., X.W., Y.K. and X.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China, grant number 11674351.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 13: Diagrams of (**a**) experimental results; (**c**) simulation results. Frequency analysis diagrams of (**b**) experimental results; (**d**) simulation results.
## 3 The Load Adaptive Auxiliary Current Control
### Limitation of the Duty Ratio  
In the ARSI, to guarantee the ZVS of one set of switches, the auxiliary circuit must be turned on before the opposite set of switches is turned off. Subsequently, the resonant inductor can be charged to the required current. Therefore, the minimum on-time of the opposite set of switches must be longer than the maximum charging time of the resonant inductor. This requirement limits the maximum duty ratio \(D_{\text{max}}\).  
Figure 4 shows the DPWM diagram of the ARSI. The triangle carrier is adopted due to its lower harmonic compared to the sawtooth carrier [22]. Moreover, the current is synchronously sampled at twice the PWM carrier interval, which can eliminate the switching current ripple from the current samples without needing low-pass filtering or complex ripple elimination filters [23]. Then the calculated reference voltage \(v_{c}\) is loaded into a modulator at the point of upper limit and lower limit of the reference voltage \(V_{upperlimit}\) and \(V_{lowerlimit}\), instead of the conventional loading point \(V_{\text{cmax}}\) and \(V_{\text{cmin}}\). The proposed loading method can improve the maximum duty ratio. The reason is given as follows:  
Figure 4: Digital pulse-width-modulation (DPWM) diagram of the ARSI.  
In order to achieve ZVS from zero load to full load, the minimum on-time of the main switches must be longer than the maximum charging time of the resonant inductor:  
\[t_{on\_min}\geq t_{cl\_max}. \tag{8}\]  
The maximum charging time occurs when the output current reaches its maximum value \(I_{o\_max}\). The minimum on-time of the main switches occurs when the duty ratio reaches \(D_{\max}\). Figure 5 shows the DPWM diagram at the condition of \(I_{o\_max}\) and \(D_{\max}\). Figure 5 shows the conventional loading scheme, namely loading at \(V_{\_{\_}{\max}}\) and \(V_{\_}{\min}\). The Kth current is sampled at the point \(V_{\_}{  
The upper limit of the reference voltage can be obtained from Equation (11):  
\[V_{upperlimit}\leq V_{\text{cmax}}-\frac{(V_{\text{cmax}}-V_{\text{cmin}})(t_{ ch,\text{max}}+t_{\text{{dead}}})}{T_{\text{s}}}. \tag{12}\]  
From Equations (10) and (12), the limit of the reference voltage \(v_{c}\) can be improved by adopting the improved loading scheme, resulting in larger \(D_{\text{max}}\).
# Numerical Analysis for Critical Structures Protection against Blast Loading Using Metallic Panels
## 2 Pressures on the Structural Surfaces  
The blast load is characterized by the instantaneous release of an enormous amount of energy, and depends on the explosive materials and the basis of their physical state [42]. Explosions are classified as far-range explosions when the offset distance is equal to or greater than 1.2 m/kg\({}^{1/3}\); and are classified as near-range explosions when the offset distance is less than 1.2 m/kg\({}^{1/3}\). Figure 1 represents a typical pressure versus time plot of a blast wave. The blast wave is characterized by a sudden increase in incident pressure to a certain peak value, and the subsequent decay in the atmospheric pressure (positive phase). After that, the incident pressure decays further underneath the ambient atmospheric pressure producing negative phase pressure. The pressure at any instant of time is defined by the modified Friedlander's equation, as presented in Equation (1)  
\[P(t)=P_{0}+P_{so^{+}}(1-\frac{t}{t_{pos}})e^{-\theta(\frac{t}{t_{pos}})} \tag{1}\]  
where, \(P(t)\) is the incident overpressure at any time instant; \(P_{0}\) is the ambient pressure; \(P_{so^{+}}\) and \(P_{so^{-}}\) are the peak positive incident overpressure and the peak negative incident under-pressure, respectively; \(t_{pos}\) and \(t_{neg}\) are the positive and the negative phase durations for the incident blast wave, respectively; \(t_{a}\) is the arrival time; and \(\theta\) is the decay parameter of the wave. Friedlander equation can be used for the positive phase \((t_{1}\leq t\leq t_{2})\). The peak side-on overpressure is presented in Equation (2) [43]  
\[P_{so^{+}}=\frac{K_{1}}{Z^{3}} \tag{2}\]  
where \(K_{1}\) is a constant depending on the quantity of the explosive charge as in [43]. The scaled distance is represented as \(Z=R/W^{\frac{1}{3}}\), where the variables \(R\) and \(W\) refer to the distance from the center of a spherical charge and the charge mass of equivalent TNT, respectively.
# Image Analysis Using Human Body Geometry and Size Proportion Science for Action Classification
## 3 Proposed Solution
### Human Segmentation  
Bounding Box returned through faster R-CNN is a Quad Tuple i.e., \(BB=\{X_{init},Y_{init},Width_{BB},Height_{BB}\}\). Bounding Box is cropped automatically returning four points \(\{(X_{init},Y_{init})\), \((X_{init}+Height\_{BB},Y_{init})\),\((X_{init},Y_{init}+Width\_{BB}),(X_{init}+Height\_{BB},Y_{init}+Width\_{BB})\}\) termed as BBI and its dimensions are \(Width\_{BB}\times Height\_{BB}\). Pixels of OI (Original Image) within the set of points i.r., \(\{(X_{init},Y_{init}),(X_{init}+Height\_{BB},Y_{init}),(X_{init},Y_{init}+Width\_ {BB}),(X_{init}+Height\_{BB},Y_{init}+Width\_{BB})\}\), are marked as Black. Background of BBI is modelled through BM Algorithm 1.  
```
0: Original Image,Bounding Box Image OI, BBI
0: Background of BBI, BI
1: Create a new Image,BI, of size \(Width\_{BB}\times Height\_{BB}\) and initialize each of its pixel as black.
2:for row \(r_{i}:X_{init}\leq i\leq X_{init}+Height\_{BB}\)do
3: For each column \(cj:Yinit\leq j\leq Y_{init}+\frac{Width\_{BB}}{2}\)
4:\(LN=\{\frac{Width\_{BB}}{2}\) many left neighbours of \(OI(r_{i},c_{j})\}\)
5: Find histogram of neighbouring pixels, \(H=Histogram(LN)\);
6: Find peak of Histogram, \(P=Peak(H)\);
7: Assign color value to pixels in Bounding Box of original image, \(OI(r_{i},c_{j})=P\);
8: Assign color to pixels of background image, \(BI(r_{i}-X_{init}+1,c_{j}-Y_{init}+1)=P\);
9:endfor
10:for row \(r_{i}:X_{init}\leq i\leq X_{init}+Height\_{BB}\)do
11:for column \(c_{j}:Y_{init}+Width\geq j\geq Y_{init}+\frac{Width\_{BB}}{2}\)do
12:\(RN=\{\frac{Width\_{BB}}{2}\) many right neighbours of \(OI(r_{i},c_{j})\}\)
13: Find histogram of neighbouring pixels, \(H=Histogram(RN)\)
14: Find peak of Histogram, \(P=Peak(H)\)
15: Assign color value to pixels in Bounding Box of original image, \(OI(r_{i},c_{j})=P\)
16: Assign color to pixels of background image, \(BI(r_{i}-X_{init}+1,c_{j}-Y_{init}+\frac{Width\_{BB}}{2}-1)=P\)
17:endfor
18:endfor
19:return BI
```  
**Algorithm 1** Background Modeling of BBI Algorithm (BMA)  
Figure 2: Faster-CNN architecture.  
Next, the task is to segment human blob from the enclosing rectangle and is accomplished through presenting a segmentation algorithm (SA). Algorithm 2 takes the output of the BM algorithm, BI, along with BBI as its input and returns a segmented image, SI, having human blob (HB) as output.  
```
0: BBI, BI
0: Segmented Image, SI
1: For Segmented Image(SI), subtract background image(BI) from BBI. i.e., \(SI=BBI-BG\)
2: After applying thresholding over SI by producing binary level image.
3: Fill holes from blobs present in SI.
4: Apply dilation followed by erosion.
5: Apply Gaussian smoothing over SI resulted from above steps.
6: Retain blobs in SI having size greater than threshold.
7: Return SI having Human Blob(HB).
```  
**Algorithm 2** Segmentation Algorithm (SA)
## 3 Examples: Electrical MHD Flow  
Consider steady two-dimensional, incompressible, and laminar electrical MHD Casson nanofluid flow over the sheet. Let the sheet be moved along the positive \(x\)-axis, where \(x\)-axis is placed along the sheet. The \(y\)-axis is placed perpendicular to the sheet. The bottom layer of the fluid coincides with the sheet. Let the sudden movement of the sheet with velocity generate the flow \(U_{w}\). Effects of Darcy-Forchheimer's characteristics, thermal radiation, and viscous dissipation are also considered. Under the boundary lawyer assumptions and by following [42], the governing equations of the flow are expressed as  
\[\frac{\partial u}{\partial x}+\frac{\partial v}{\partial y}=0 \tag{33}\]  
\[u\frac{\partial u}{\partial x}+v\frac{\partial u}{\partial y}=v\bigg{(}1+ \frac{1}{\beta}\bigg{)}\frac{\partial^{2}u}{\partial y^{2}}+\frac{\sigma}{ \rho}\Big{(}E_{\circ}B_{\circ}-B_{\circ}^{2}u\Big{)}-\frac{v}{k_{p}}u-Fu^{2} \tag{34}\]  
\[u\frac{\partial T}{\partial x}+v\frac{\partial T}{\partial y}=\alpha\frac{ \partial^{2}T}{\partial y^{2}}+\frac{\mu}{\rho c_{p}}\bigg{(}\frac{\partial u }{\partial y}\bigg{)}^{2}+\frac{\sigma}{\rho c_{p}}\bigg{(}uB_{\circ}-E_{ \circ}\big{)}^{2}+\tau\Bigg{\{}D_{B}\frac{\partial C}{\partial y}\frac{ \partial T}{\partial y}+\frac{D_{T}}{T_{\infty}}\bigg{(}\frac{\partial T}{ \partial y}\bigg{)}^{2}\Bigg{\}}+\frac{16\sigma^{*}T_{\infty}^{3}}{k^{*}\rho c _{p}}\frac{\partial T^{2}}{\partial y^{2}} \tag{35}\]  
\[u\frac{\partial C}{\partial x}+v\frac{\partial C}{\partial y}=D_{B}\frac{ \partial^{2}C}{\partial y^{2}}+\frac{D_{T}}{T_{\infty}}\frac{\partial^{2}T}{ \partial y^{2}}-k_{1}(C-C_{\infty}) \tag{36}\]  
Subject to the boundary conditions:  
\[u=U_{w}=ax,v=0,-k\frac{\partial T}{\partial y}=h_{f}(T_{w}-T),C=C_{w}\text{ when }y=0\]  
\[u\to 0,T\to T_{\infty}\text{, }C\to C_{\infty}\text{when }y\to\infty\]where \(u\) and \(v\) are, respectively, horizontal and vertical components of velocity. \(T\) and \(C\) are, respectively, temperature and concentration of fluid; \(T_{w}\) and \(C_{w}\) are, respectively, temperature and concentration at the sheet; \(T_{\infty}\) and \(C_{\infty}\) are, respectively, temperature and concentration away from the sheet; \(\gamma\) is kinematic viscosity; \(\beta\) is the Casson parameter; \(\sigma\) represents electrical conductivity; \(\rho\) is the density of the fluid; \(B_{\circ}\) and \(E_{\circ}\) are, respectively, strength of magnetic and electrical fields; \(k_{p}\) represents mean absorption coefficients; \(F\) is a non-uniform inertia coefficient; \(\alpha\) is thermal diffusivity; \(\mu\) is the dynamic viscosity; \(c_{p}\) is the specific heat capacity; \(D_{B}\) is the Brownian diffusion coefficient; \(D_{T}\) represents thermophoresis coefficient; \(k_{1}\) is the reaction rate parameter; \(\sigma^{*}\) is the Stefan-Boltzmann constant; and \(k^{*}\) is the mean absorption coefficient.
# Research on Fault Diagnosis Method for Rotating Machinery Based on Edge Computing
## 4 Fault Diagnosis Method Based on Edge Computing
### Construction of the Dual-Channel CNN Decision-Level Fusion Model
#### 4.3.2 Decision-Level Fusion  
In a typical CNN, the fully connected layer functions solely as a classifier, outputting the probability for each classification label. However, when implementing the fusion of dual input layers, the features from the two input channels can be flattened and concatenated before being fed into the fully connected layer. Therefore, this study incorporates the Dempster-Shafer evidence theory method in the fully connected layer to perform decision-level fusion of the dual-input CNNs, which can significantly enhance the model's robustness, generalization ability, and performance. By effectively fusing the beliefs from the dual-input CNN models and handling information conflicts and uncertainties, this method can provide more accurate and stable prediction results. The Dempster-Shafer evidence theory fuses the output features of multiple feature extraction networks from two CNNs, enhancing the accuracy and robustness of feature representation.
## 3 Methodology
### Construction of Heterogeneous Graph
Our model includes five types of edges: Sentence-sentence edges (S-S), sentence-entity edges (S-E), intra-entity edges within the same sentence (E-E intra), inter-entity edges for the same entity across different sentences (E-E inter), and document-sentence edges (doc-s). Sentence nodes are connected through S-S edges to capture long-range dependencies between individual sentences in the document. The S-E edges connect sentences with all entities within them, modeling the context of entities in the sentences. The E-E intra edges connect different entities within the same sentence, indicating that these entities may be related to the same event. The E-E inter edges connect the same entity across all sentences, allowing the tracking of the entity's occurrences at different positions. Document-Sentence Edge (doc-s): By connecting the document node with the sentence nodes, we have achieved an interaction between the document and sentences. As a result, the document node can attend to information from all other nodes, facilitating the fusion of textual information from different levels and better modeling long-distance dependency relationships between sentences. Our heterogeneous graph enables the simulation of the interaction between sentences and entities from a global perspective and strengthens the connections between documents and sentences while better capturing event information within the document. We apply multi-layer graph convolutional networks to model global interactions. For each node \(i\) with its feature representation, the node representation at the l-th layer can be computed using the following formula:  
\[h_{i}^{l}=Relu\left(\sum_{r\in R}\sum_{j\in N_{i}^{r}}\frac{1}{c_{i,r}}W_{r}^{l} h_{j}^{l-1}+b_{r}^{l}\right), \tag{5}\]  
where \(R\) represents all edge relation types, and \(N_{i}^{r}\) represents the set of all neighboring nodes connected to node \(i\) via relation type \(r\). \(C_{i,r}\) is a normalization constant used for normalization. \(W_{r}^{l}\) denotes the weight matrix corresponding to each edge relation type \(r\), and Relu represents the activation function. We then derive the final hidden state of node \(i\) by combining the output features \(h_{i}^{(l)}\) of node \(i\) in each layer of GCN along the column direction. The combined column vector is linearly transformed using the learnable weight matrix \(W_{a}\) to learn the final hidden state \(h_{i}\) of node \(i\). \[h_{i}=W_{a}\Big{[}h_{i}^{(0)};h_{i}^{(1)};\dots;h_{i}^{(l)}\Big{]}, \tag{6}\]  
where \(h_{i}^{(0)}\) is the initial embedding representation of node \(i\), and \(L\) is the number of GCN layers. Finally, we get sentence embedding vectors and entity embedding vectors. In this way, sentences and entities interact in a context-aware manner for representation.