esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
This work simulates an enhanced cross-seeding-based DWDM-PON system with future channel spacing in the ITU-T G.694.1 standard for DWDM (i.e., 12.5 GHz). Attractive performance characteristics have been targeted through this work. These characteristics include enhancing the downstream (DS) channel capacity, the US bit rate, minimizing the active components, and maintaining the remarkable BER performance. Utilizing this work's design enhancement, the proposed DWDM-PON has successfully achieved a record of 16 channels, a US capacity of 2.5 Gb/s over 25 km of transmission, and a noteworthy average BER of \(10^{-12}\). A comparison between this work and related studies, which are not only based on DWDM-PON systems but also cover other types of high-speed optical networks, was performed to ensure the validity and effectiveness of the proposed system.  
Conceptualization, N.A.M.; Methodology, N.A.M.; Software, N.A.M.; Formal analysis, N.A.M.; Writing--original draft preparation, N.A.M.; Writing--review and editing, A.H.M.; Supervision, A.H.M.; Resources, A.H.M.  
This research received no external funding.  
The authors declare no conflict of interest.
## 5 Comparison of RIN Results  
A correlation between the noise outcomes of the current system with those from the Agilent existing system [65] will now be established. The temperature of the optical amplifier is kept at 25 \({}^{\circ}\)C, and its current is increased from 110 to 125 mA, and the resulting situation is depicted in Figure 10. One of the scenarios is depicted in Figure 11 when the SOA is operated at 125 mA and 25 \({}^{\circ}\)C. Because of this arrangement, we could sustain a wavelength difference \(\Delta\lambda\) of 1.57 nm which was previously not possible. With the help of VC\({}_{\text{o}}\), the value of the intensity ratio between both modes is changed. It is observed that the intensity of the outer mode M\({}_{\text{o}}\) lessens in contrast to the intensity of the inner mode M\({}_{\text{i}}\). This in turn starts reducing the value of RIN accordingly, and the trend continues till the condition of mode equilibrium is reached (both modes with same intensities). When the intensity of the outer mode M\({}_{\text{o}}\) is still decreased, the value of RIN reduces. This value of RIN waivers constantly between 4.7 and 5.3 GHz. A significant variation in RIN mirrors an undamped relaxation oscillation frequency, probably because of the unstable laser condition (sensor threshold due to SOA's limits).  
A view of the experimental outcomes from this investigation is provided in Figure 12 that mirrors worthwhile distinction. The magnitude of average RIN is significantly more for our experimental system. In other words, we can state with confidence that our setup gives precise and accurate data in terms of sensitivity when implemented. This is a very desirable trait for any biomedical application. As a result, our results are in complete agreement with with the ones that we have investigated and presented in this context, on simultaneous grounds [66].  
Figure 10: Illustration of both modes’ existence for SOA at 25 \({}^{\circ}\)C.  
Figure 11: Illustration of (**a**) mode competition in the setup, with SOA @125 mA and 25 \({}^{\circ}\)C, FBG\({}_{\rm i}\) @70 \({}^{\circ}\)C, FBG\({}_{\rm o}\) @30 \({}^{\circ}\)C, and \(\Delta\lambda\) of 1.57 nm, and, (**b**) corresponding RIN spectra under various intensity ratios M\({}_{\rm i}\)/M\({}_{\rm o}\).  
Figure 12: Comparing average RIN from both measurement setups.
# Research on Corrosion Damage Evolution of Aluminum Alloy for Aviation
## 1 Introduction
That is to say, the geometric dimension parameters of corrosion damage are important parameters to describe corrosion damage [13]. However, due to the unclear definition of the dangerous corrosion pit, the parameters characterizing the geometric size of corrosion damage will be different [14]. At present, the corrosion pit width [15; 16], the aspect ratio [17; 18], the projected cross-section area [19; 20], and the depth of corrosion pits [21; 22] are widely used. However, after corrosion, the corrosion pits' morphology on the surface of the specimen is very complex. If the pit surface is simplified to a regular figure to define its length and width, there will be a big gap with the actual situation. Moreover, the corrosion damage parameters are often of micro scale. How to accurately measure the corrosion damage parameters to reduce the error is also very important. Therefore, in engineering practice, it is necessary to find a simple, accurate, and universal corrosion damage evaluation parameter and, on this basis, the law of accelerated corrosion damage evolution is studied. After corrosion, the most intuitive evolution characteristics of corrosion pits are the development along the surface and depth direction and the increase of surface roughness. Therefore, two parameters of the depth of corrosion pits \(d\) (describing the depth of corrosion damage on the surface of the specimen) and the corrosion rate \(C_{R}\) (describing the surface damage degree of the specimen) were introduced as the evaluation indexes of accelerated corrosion damage of 7075 aluminum alloy specimens. According to the real annual average value of atmospheric environmental data in Warning, Hainan, a new corrosion solution was designed and the traditional alternate immersion corrosion method of using the alternate immersion corrosion test box was improved to simulate the environment of the internal structure of the aircraft. On this basis, the depth of corrosion pits obtained by 3D profile of specimen based on the Rh-8800 3D video microscope and binarization images based on MATLAB software were proposed, respectively. The optimal linear regression equation combination of the depth of corrosion pits was obtained, the dynamic evolution model of the depth of corrosion pits, corrosion rate, and fractal dimension was fitted, and the evolution law of accelerated corrosion damage was analyzed systematically.
# Studies of General Precipitation Features with TRMM PR Data: An Extensive Overview
## 3 Lightning Analysis of PR Data
### Lightning Activity and the PR Reflectivity  
Radar reflectivity was an index to the character of rainfall development and convection intensity, and it was noticed that lightning occurrence was accompanied by high radar reflectivity [74; 75; 76; 82]and high rainfall rate [73; 78; 84]. [PERSON] et al. [75] and [PERSON] [76] found that the possibility of lightning was a function of radar reflectivity and the depth of reflectivity for a given magnitude. Similarly, [PERSON] et al. [78] found that the flash rate was a function of the maximum reflectivity and the area and volume of high reflectivity larger than 30 dBZ.  
Lightning is more likely to occur in convective rainfall with strong updraft. Therefore, the accompanying precipitation often showed uniform vertical structure and the absence of the bright band. [PERSON] et al. [74] and [PERSON] et al. [82] noticed that rainfall systems with lightning tended to have a smaller lapse rate of reflectivity with the height above the freezing level than those without lightning. [PERSON] and [PERSON] [85] found that there were weak vertical reflectivity gradients and obscure bright bands at the growth stage of thunderstorms with high lightning density while strong vertical gradients and obvious bright bands at the dissipation stage of thunderstorms with lightning density decreasing rapidly to zero.
# Elastic CRFs for Open-Ontology Slot Filling
## 3 Proposed Model
### Slot Description Encoder  
Let \(X=(x_{1},x_{2},...,x_{n})\) denote the input user utterance and \(D^{i}=(d^{i}_{1},d^{i}_{2},...)\) denote the description of slot \(s^{i}\). In our experiment, slot descriptions are simple complementary phrases, e.g.,' number of people' for the slot #people, 'theatre name' for the slot theatre_name, but other richer expression can be used. The goal of our task is to find all possible text spans in \(X\) as values for each \(s^{i}\). We adapted the IOB tagging scheme as in [3]. Traditionally, the IOB tags are made up three type, '\(B\)', '\(I\)', and '\(O\)', which indicate the beginning position of a value span, the intermediate and ending positions of the value span and the rest position belonging to no values. To be specific, if a word is predicted to have the '\(B\)' tag or multiple words are predicted to have '\(B\), \(I\),.., \(I\)' tags, the word span is the value of a slot. Instead of using a combination of the slot name and one of the IOB tags as in Figure 1, we used the combination of the slot description and one of the IOB tags in order to leverage the semantic meanings of slots. As shown in Figure 2, the slot description encoder takes all slot descriptions as input, and outputs are distributed representations for all possible combinations of the IOB tags and the slot descriptions, such as '\(O\)', '\(B+D^{1}\)', '\(I+D^{1}\)', '\(B+D^{2}\)', '\(I+D^{2}\)',... The set of these new combined slot labels is denoted as \(\mathcal{S}\). We use indexes of these labels to suggest the corresponding positions within the utterance. For example, in Figure 2, '6', 'pm' and 'avatar' are predicted as the positions of '\(B+\) time for movie', '\(I+\) time for movie' and '\(B+\) movie name', which means that '6 pm' is the value of slot movie_time and 'avatar' is the value of slot movie_name. A function \(e(\cdot)\in\mathbb{R}^{d}\) is used to denote the output vector from the slot description encoder as follows:  
\[e(B+D^{i}) =FC\Big{(}f(D^{i})\oplus emb(B)\Big{)} \tag{1}\] \[e(I+D^{i}) =FC\Big{(}f(D^{i})\oplus emb(I)\Big{)}\] (2) \[e(O) =FC\Big{(}\overrightarrow{\mathbf{0}}\oplus emb(O)\Big{)} \tag{3}\]  
where \(FC(\cdot)\) denotes a one-hidden-layer fully connected network and \(f(\cdot)\) denotes an encoder that maps the descriptions into semantic embeddings. In this paper, we use a simple averaging function of all word embeddings in \(D^{i}\) as in [12]. \(emb(\cdot)\) is an embedding lookup function for the IOB tags and \(\oplus\) denotes the concatenation operation. Note that for \(e(O)\), we use a zero vector \(\overrightarrow{\mathbf{0}}\) with the same size as the output vector of \(f(\cdot)\) since the '\(O\)' tag should be independent of any \(D^{i}\). A difference between our slot description encoder and that in [12] is that we leverage the embeddings of the IOB tags so that the dependencies between tags in different slot labels are modeled.
## 2 Mathematical Formulation of Pseudointensity Vector
### PV System Model  
The plane wave decomposition (PWD) of an \(n\)-th wave gives \(a_{lm}^{(n)}(k)=\left[Y_{1}^{m}(\Psi_{n})\right]^{*}s_{n}(k)\)[41]. Describing or transforming the first-order coefficients in the Cartesian direction results in  
\[\begin{split} a_{1(-1)}^{(n)}(k)=s_{n}(k)\sqrt{3/8\pi}(x_{n}+iy_ {n})\\ a_{10}^{(n)}(k)=s_{n}(k)\sqrt{3/4\pi}z_{n}\\ a_{11}^{(n)}(k)=s_{n}(k)\sqrt{3/8\pi}(-x_{n}+iy_{n})\end{split} \tag{5}\]  
with the SH of Equation (5) being computed on the unit sphere. Then, Equation (5) can be rearranged as  
\[\begin{split} s_{n}(k)x_{n}&=\sqrt{8\pi/3}\ \tfrac{1}{2}\Big{(}a_{1(-1)}^{(n)}(k)-a_{11}^{(n)}(k)\Big{)}\\ s_{n}(k)y_{n}&=\sqrt{8\pi/3}\ \tfrac{1}{2}\Big{(}a_{1(- 1)}^{(n)}(k)-a_{11}^{(n)}(k)\Big{)}\\ s_{n}(k)z_{n}&=\sqrt{8\pi/3}\ \tfrac{1}{\sqrt{2}}a_{10}^{(n)}(k),\end{split} \tag{6}\]  
This could represent the weighted sum of the first-order coefficients of the PWD. The weight that corresponds to \(a_{1(m)}^{(n)}(k)\) shares a proportional relationship with the first order, and the SH of degree \(m\) is calculated in the necessary axial direction as\[\begin{array}{l}s_{n}(k)\xi_{n}=\frac{4\pi}{3}\sum\limits_{m=-1}^{1}Y_{m}^{m} \big{(}\varphi_{\xi}\big{)}a_{1(m)}^{(n)}(k)\\ \\ =\frac{4\pi}{3}D\Big{(}k,\ \varphi_{\xi},\mathbf{a}_{lm}^{(n)}(k)\Big{)}\end{array} \tag{7}\]  
Note that Equation (3) was considered when obtaining Equation (7), \(\xi\in\{x,y,z\}\), \(\varphi_{x}=(0.5\pi,0)\), \(\varphi_{y}=(0.5\pi,0.5\pi)\) and \(\varphi_{z}=(0,\ 0)\). Obtaining a vector that points to the _n-th_ DoA, \(a_{00}^{(n)}(k)=\sqrt{\frac{1}{4\pi}}s_{n}(k)\), and computing Equation (2) for \(\xi\in\{x,y,z\}\), gives  
\[\widetilde{\mathbf{I}}(k)=\frac{4\pi\sqrt{4\pi}}{3}\Re\Bigg{\{}a_{00}^{(n)}(k)^{* }\begin{bmatrix}D(k,\varphi_{x},\mathbf{a}_{lm}(k))\\ D\big{(}k,\varphi_{y},\mathbf{a}_{lm}(k)\big{)}\\ D(k,\varphi_{z},\mathbf{a}_{lm}(k)\big{)}\end{bmatrix}\Bigg{\}} \tag{8}\]  
\[=\Re\Big{\{}|s_{n}(k)|^{2}\mathbf{x}_{n}\Big{\}} \tag{9}\]  
for a plane wave in ideal environments, the actual operator is intrinsically real, but the real operator is required for practical scenarios. The PV direction in the spherical domain is computed via Equation (10) and unit vector \(\widetilde{\mathbf{I}}(k)/\|\widetilde{\mathbf{I}}(k)\|\), where \(\|\cdot\|\) is \(l_{2}-norm\).  
\[\theta_{n}=\arccos(z_{n}),\ \phi_{n}=arctan2(y_{n}/x_{n}) \tag{10}\]  
where \(arctan2\) represents the arctangent function that is mapped to an acceptable quadrant based on the nature of \(x_{n}\) and \(y_{n}\).  
The formulation of Equation (8) is similar to that of Equation (2), except that \(\widetilde{\mathbf{I}}(k)\) and \(\mathbf{I}(k)\) face opposite directions because of the dipole steering. \(\widetilde{\mathbf{I}}(k)\) is then considered as the PV with its orientation towards the DoA.
# Adaptive QoS-Aware Multi-Metrics Gateway Selection Scheme for Heterogenous Vehicular Network +
## 3 Adaptive QoS-Aware Multi-Metrics Gateway Selection Scheme
### Gateway Discovery Process
#### 3.3.4 Link Lifetime (\(Llt_{ivnv}\))  
One of the challenges in designing the VANET is the frequent breaking of links due to rapid changes in vehicle position [44]. The link lifetime criterion reflects the stability of the link and the duration in which the link can be sustained without breaking. When the IV sends the GWreq, it inserts its position, location, speed, and moving direction. When the NV receives the GWreq message, it extracts all information needed to compute the \(LLT_{IVNV}\). The current research's LLT computation was inspired by [47]. Referring to Figure 5, the \(LLT_{IVNV}\) is calculated using Equation (8). The NV inserts the \(LLT_{IVNV}\) value into the GWrep message and sends back to the IV.  
Figure 4: Distance between the IV and NV.  
\[LLT_{IVNV}=\frac{\sqrt{\left(k^{2}+l^{2}\right)R^{2}+\left(mk-nl\right)^{2}}- \left(kn+ml\right)}{k^{2}+l^{2}} \tag{8}\]  
where \(R\) is the communication range of the IEEE 802.11p radio, \(k=v_{i}\cos\theta_{i}-v_{j}\cos\theta_{j}\), \(l=v_{i}\sin\theta_{i}-v_{j}\sin\theta_{j}\), \(n=x_{i}-x_{j}\), \(m=y_{i}-y_{j}\). \((x_{i},y_{i})\), and \((x_{i},y_{j})\) are the Cartesian coordinates of the IV and NV, respectively, and the incline of \(\theta_{i}\) and \(\theta_{j}\) (\(0<\theta_{i}\), \(\theta_{j}<2\pi\)) respectively, concerning the \(x\)-axis. \(v_{i}\) and \(v_{j}\) are the speeds of the IV and NV respectively.
## 2 Experimental and Statistical Methods
### Gaussian Process Regression (GPR)  
The GPR method is basically based on Bayesian regression (BR), which can incorporate many variables and can more precisely predict than traditional regression. Assuming that the \(Y=[y_{1},y_{2},y_{3},\ldots,y_{n}]^{\rm T}\) of size \(n\) follows the standard normal distribution, it can be expressed that \(Y\)\(\sim\)\(N(\mu,\)\(C)\) where \(\mu\) is a mean vector and \(C\) is a symmetric covariance matrix. These can be expressed as Equations (1) and (2), and, given these conditions, the BR equation is as Equation (3):  
\[\mu\ =\ [\mu_{1},\ \mu_{2},\cdots,\mu_{n}]^{T} \tag{1}\]  
\[\mathbf{C}\ =\ \left[\begin{array}{cccc}\sigma_{1}^{2}&\sigma_{1} \sigma_{2}&\cdots&\sigma_{1}\sigma_{n}\\ \sigma_{1}\sigma_{2}&\sigma_{2}^{2}&\cdots&\sigma_{2}\sigma_{n}\\ \vdots&\vdots&\ddots&\vdots\\ \sigma_{1}\sigma_{n}&\cdots&\cdots&\sigma_{n}^{2}\end{array}\right] \tag{2}\]  
\[\mathbf{Y}_{i}\ =\ \mathbf{X}_{i}\mathbf{\beta}_{i}\ +\ \mathbf{\varepsilon}_{i},\ \ i=1,\ 2,\ 3, \cdots,n \tag{3}\]  
where \(Y_{i}=[y_{1},y_{2},y_{3},\ldots,y_{n}]^{\rm T}\), \(\mathbf{X}_{i}=[x_{1},x_{2},x_{3},\ldots,x_{n}]^{\rm T}\), and \(\mathbf{\varepsilon}_{i}=[\mathbf{\varepsilon}_{1},\mathbf{\varepsilon}_{2},\mathbf{\varepsilon}_{3},\ldots,\mathbf{\varepsilon}_{n}]^{\rm T}\) have a relationship. Additionally, \(\mathbf{\beta}_{i}\) is a calculated coefficient of each step, the ordinary least squares (OLS) estimation of \(\beta\), which can be calculated with the rule of \(\hat{\beta}=(\mathbf{X}^{\rm T}\mathbf{X})^{-1}\mathbf{X}^{\rm T}\mathbf{Y}\). The GPR method also follows this rule but with a difference, and is presented as Equations (4) and (5):  
\[y\ =\ f(x)\ +\ \varepsilon \tag{4}\]  
\[\mu(x)\ =\ E[f(x)] \tag{5}\]  
where the \(f(x)\) follows the Gaussian process that \(f(x)\sim GP(\mu(x),\)\(k(x,\)\(x^{\prime}))\), \(\mu(x)\) is a mean function, and \(k(x,x^{\prime})\) is a covariance function. The GPR method is usually based on the function process, which is different from BR method. The covariance function has a relationship between the given data and random variables, and this part is used as a kernel [26; 27; 28]. The most representative kernel is the squared-exponential (SQEXP), which this study also followed. The kernel is as follows:  
\[k(x,x^{\prime})\ =\ E[[f(x)-\mu(x)][f(x^{\prime})-\mu(x^{\prime})]] \tag{6}\]
# Task Offloading Based on Lyapunov Optimization for MEC-Assisted Vehicular Platooning Networks
## 3 System Model  
In this section, we first present a system model, including the task offloading scenario, task offloading system model and task offloading queue model. Then, the communication model and computation model are provided. The key notations used in this paper are summarized in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Notation** & **Definition** \\ \hline \(m\) & PM ID \\ \(k\) & Node ID, including MEC server \\ \(t\) & Time \(t\) \\ \(M\) & The number of PM \\ \(Q_{k}(t)\) & Queue k of vehicle or MEC in time \(t\) \\ \(A_{m}(t)\) & Workload of PM \(m\) in time \(t\) \\ \(b_{k}(t)\) & The size of task that arrives at queue \(k\) in time \(t\) \\ \(f_{k}\) & Computation capability of computing node \(k\) \\ \(D_{m}(t)\) & Input data of PM \(m\) at time \(t\) \\ \(B_{m,k}\) & transmission rate between PM \(m\) and node \(k\) \\ \(T_{m}^{k}(t)\) & Execution time of task \(m\) on node \(k\) \\ \(E_{m}^{k}(t)\) & Energy consumption of task \(m\) on node \(k\) \\ \(l_{m,o}\) & Positional distance between vehicle \(m\) and BS \\ \(l_{m,n}\) & Positional distance between vehicles \\ \(w_{1}\) & Transmission bandwidth between vehicles \\ \(w_{2}\) & Transmission bandwidth between vehicle and BS \\ \(V\) & A weight control parameter \\ \(P_{tr}\) & Data transmission power of vehicle \\ \(P_{c}\) & Computation power of vehicle \\ \(P_{i}\) & Idle power of vehicles \\ \(N_{0}\) & Noise power \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of key notations.
## 6 Conclusions  
In this study, the data assimilation methods of PF and POD4 DVar were used to improve the performance of the CERES-Wheat model for yield estimates. Compared with traditional assimilations of crop models, we mainly focus on the uncertainties of wheat yield estimates due to different assimilation strategies and multi-source errors, such as the remotely-sensed LAIs, assimilation strategies, temporal scales (phenological stages and temporal frequencies) and the spatial scale.  
The experimental results showed that the accuracy and temporal resolution of remotely-sensed LAI can be improved and increased by using the PROSAIL model with multi-sensors' optical data (i.e., Landsat OLI, HJ-1 and GF-1). The inversed LAIs could be employed in crop model data assimilations. Compared with the crop model without assimilation, the crop model with the PF-based and POD4 DVar-based strategies provides significantly better regional crop yield estimates, especially for the POD4 DVar-based strategy, which resulted in greater performance of the assimilation model. Although the best yield estimates were obtained when all of the observed LAIs during the crop growing season were assimilated into the crop model, an acceptable estimate of crop yield could also be achieved by assimilating fewer observations between the jointing and antihesis periods of the crop growth season. Good performance was observed when the frequency of the assimilation observations increased from four to 32 days, and the assimilation temporal frequency of eight days also appears to be a reasonable tradeoff between the accuracy and effectiveness in regional wheat estimate applications. In contrast, the accuracy of the regional crop yield estimates generally decreased as the spatial resolution of the LAI decreased from 15 km down to 1 km. Conversely, the computation times were lower for the assimilation of observations with lower spatial resolution. Thus, the use of the POD4 DVar-based strategy to assimilate LAI with moderate spatial resolution (e.g., 1 km), phenological stages between jointing and antihesis stages and assimilation of the temporal frequency of eight days appears to be a reasonable tradeoff between accuracy and effectiveness in regional wheat estimates.  
This work was supported by the Youth Science Funds of State Key Laboratory of Resources and Environment Information System (O8R8A081 YA), Chinese Academy of Sciences, National Natural Science Foundation of China (41371396, 61661136006, 4147335, 41401491), and the Introduction of International Advanced Agricultural Science and Technology, Ministry of Agriculture, P.R. China (948 Program, No. 2016-X38). [PERSON], and [PERSON] conceived and designed the research. [PERSON], [PERSON], and [PERSON] gave comments on the manuscript and checked the writing.  
The authors declare no conflict of interest.