esa-sceva/satcom-chunk-collection huggingface dataset:
# Predicting Wheat Potential Yield in China Based on Eco-Evolutionary Optimality Principles
## 2 Material and Methods
### Predicting Wheat PY Based on the EEO Theory
#### 2.2.1 Simulating Wheat Growth  
The wheat growth is driven by the gross primary production (GPP) estimated by the Pmodel. Based on the EEO theory, the Pmodel considers the plants that can optimize the stomatal behavior to balance the carbon gain and water loss, i.e., a marginal water use efficiency [49]. A least cost of maintaining the carbon assimilation capacity was also taken into the optimizing criterion [29]. The GPP can be estimated by light use efficiency from  
\[GPP=PAR\times FAPAR\times LUE \tag{1}\]  
where PAR represents the photosynthetically active radiation, which takes up a proportion of downwelling shortwave radiation; fAPAR represents the fraction of absorbed photosynthetically active radiation, which can be estimated from LAI by the Beer-Lambert law; LUE represents the light use efficiency calculated from Ta, VPD, press, and Ca. A detailed description of how this LUE is estimated in the Pmodel can be found in the existing literatures [31; 33; 50].
## 2 Materials and Methods
### System Model
#### 2.1.1 Stage of \(US-IRS_{m}\)  
The stage from \(US\) transmitting the signal to \(IRS_{m}\) is referred to as \(US-IRS_{m}\), and a signal propagation schematic of this phase is shown in Figure 2.  
Figure 1: 5G positioning system with intelligent reflective surfaces.  
As shown in Figure 2, the channel from \(US\) to the \(k\)th reflection element of the \(m\)th IRS (\(IRS_{m}\)) is denoted as \(h1_{m,k}\). Its unit is dB, and the specific expression is shown in Equation (1).  
\[h1_{m,k}=\sum_{l_{m,k}=1}^{L_{m,k}}\sqrt{\beta 1_{m}}\cdot\alpha 1_{l_{m,k}} \cdot e^{-j(k-1)2\pi\frac{d}{4}\cos\left(\theta_{l_{m,k}}\right)}, \tag{1}\]  
where \(L_{m,k}\) is the total number of propagation paths (subsequently, the term \"propagation paths\" will be shortened to \"paths\") from \(US\) to the \(k\)th reflecting element of \(IRS_{m}\), \(\beta 1_{m}\) is the large-scale decay factor from \(US\) to \(IRS_{m}\), \(\alpha 1_{l_{m,k}}\) is the path fading coefficient of the \(l_{m,k}\)th path, \(\lambda\) is the signal wavelength, and \(\theta_{l_{m,k}}\) is the arrival angle of the path \(l_{m,k}\) in a range of \([0,\pi]\).  
We assume that the total channel in the stage of \(US-IRS_{m}\) is \(\mathbf{H}\mathbf{I}_{m}\in\mathbb{C}^{K\times 1}\), where \(\mathbb{C}^{K\times 1}\) denotes the complex matrix of \(K\) rows and \(1\) column, as shown in Equation (2).  
\[\mathbf{H}\mathbf{I}_{m}=\begin{bmatrix}h1_{m,1}&\cdots&h1_{m,K} \end{bmatrix}^{T} \tag{2}\]
## 3 Immersed Boundary Method
### Diffuse Interface IBM
#### 3.1.1 Classic IBM  
The original IBM was developed for simulations of heart blood flows with elastic boundaries [8; 9; 10]. The coupled equations can be written as:  
\[\mathbf{f}(\mathbf{x},t)=\int_{\Gamma}\mathbf{F}(s,t)D(\mathbf{x}-\mathbf{X}(s,t))ds, \tag{7}\]  
\[\frac{\partial\mathbf{X}(s,t)}{\partial t}=\mathbf{u}(\mathbf{X}(s,t),t)=\int_{\Omega}\bm {u}(\mathbf{x},t)D(\mathbf{x}-\mathbf{X}(s,t))d\mathbf{x}, \tag{8}\]  
\[\mathbf{F}(s,t)=S(\mathbf{X}(s,t),t), \tag{9}\]  
where \(\mathbf{f}(\mathbf{x},t)\) is the body force in Equation (2) of the fluid domain at the position \(\mathbf{x}\) and time \(t\), and \(\mathbf{u}\) is the fluid velocity. \(\mathbf{F}(s,t)\) is the boundary force at the position \(s\) in the Lagrangian system \(\Gamma\). \(\mathbf{X}(s,t)\) is the corresponding coordinate on the Cartesian mesh \(\Omega\). Equation (7) is the distribution relationship between the boundary force and fluid body force, from Lagrangian points to Eulerian points. Equation (8) is the interpolation of fluid velocity from Eulerian points to Lagrangian points. Equation (9) is applied to compute boundary force, where the function \(S\) can be simplified to [PERSON]'s law for the elastic boundary. This original IBM is naturally suitable for elastic bodies due to the inherent rigid limit.  
An important Dirac delta function \(D(\mathbf{x}-\mathbf{X})\) runs through the development of the diffuse interface IBM. It is a distribution function related to the distance \(\mathbf{r}\) and can be computed using  
\[D(\mathbf{r})=\phi(r_{x})\phi\big{(}r_{y}\big{)}\phi(r_{z}), \tag{10}\]  
where \(\phi\) is the kernel function with many practicable expressions. Figure 3 presents four typical ones. For specific expressions, please refer to [68], where [PERSON] and his co-workers develop more smoothed functions for IBMs. By the way, the study on the distribution function for the IBM attracts many researchers, aiming to improve the calculation stability and accuracy. [PERSON] et al. [69] constructed a new kernel function using Lagrangian velocity interpolation. The moving-least-squares approach has been developed to improve the interpolation for IBMs [24; 70; 71].  
Figure 2: The 2D mesh illustration: Eulerian points representing the fluid domain and Lagrangian points representing the solid surface.
# Joint Radar-Communication Optimization of Distributed Airborne Radar for AOA Localization
## 2 System Model
### Derivation of CRLB for Target Position Estimation under AOA Model  
The ultimate goal of the JRCO algorithm was to achieve a more accurate estimation of the target location by fully utilizing the degrees of freedom in radar-communication systems. Under this condition, it was necessary to theoretically derive the CRLB for target position estimation under the AOA mode. This CRLB would be the objective function for JRCO.  
At this point, the measurement model of the radar system was:  
\[\mathbf{x}=v(\mathbf{u})+\mathbf{n} \tag{16}\]  
Among them, \(\mathbf{x}\), \(v\), \(\mathbf{u}\), and \(\mathbf{n}\) could represent the measurement vector, measurement function, target state vector, and noise vector, respectively. \(\mathbf{x}\) could be either an echo signal or a measurement value of the parameters, such as the bistatic range. \(\mathbf{u}\) is the true value of the target location, which could be represented by \(\mathbf{u}=[x_{u},y_{u},z_{u}]^{T}\) in two-dimensional space.  
The elements in Equation (16) are:  
\[\mathbf{x}=[\theta_{1},\cdots,\theta_{M},\varphi_{1},\cdots,\varphi_{M}]^{T} \tag{17}\]  
\[v(\mathbf{u})=\bigg{[}\arctan\bigg{(}\frac{z_{u}-z_{UAV}}{r_{u,1}}\bigg{)},\cdots,\arctan\bigg{(}\frac{y_{u}-y_{M}}{x_{u}-x_{M}}\bigg{)}\bigg{]}^{T} \tag{18}\]  
\[\mathbf{n}=\bigg{[}\left(\mathbf{n}_{\theta}^{t}\right)^{T},\left(\mathbf{n}_{\varphi}^{t }\right)^{T}\bigg{]}^{T} \tag{19}\]  
where \(r_{u,i}=\sqrt{\left(x_{u}-x_{i}\right)^{2}+\left(y_{u}-y_{i}\right)^{2}}\). The covariance matrix corresponding to \(\mathbf{n}\) is \(\mathbf{C}\).  
The elements in the Fisher information matrix \(\mathbf{J}(\mathbf{u})\) of \(\mathbf{u}\) could be calculated based on the following equation [35]:  
\[\begin{array}{rl}\left|\mathbf{J}(\mathbf{u})\right|_{ij}&=\left[\frac{\partial v( \mathbf{u})}{\partial u_{i}}\right]^{T}\mathbf{C}^{-1}(\mathbf{u})\left[\frac{\partial v (\mathbf{u})}{\partial u_{i}}\right]\\ &+\frac{1}{2}\text{tr}\left(\mathbf{C}^{-1}(\mathbf{u})\frac{\partial\mathbf{C}( \mathbf{u})}{\partial u_{i}}\mathbf{C}^{-1}(\mathbf{u})\frac{\partial\mathbf{C}(\mathbf{u })}{\partial u_{j}}\right)\end{array} \tag{20}\]  
where \(\text{tr}(\cdot)\) represents the trace of the matrix and \(u_{i}\) represents the \(i\)-th variable in \(\mathbf{u}\).  
The CRLB matrix \(\mathbf{C}_{CRLB}\) was:  
\[\mathbf{C}_{CRLB}=\mathbf{J}^{-1}(\mathbf{u}) \tag{21}\]  
This article uses the trace of \(\mathbf{C}_{CRLB}\) as a quantitative standard for the performance of target localization. At this point, \(\mathbf{J}(\mathbf{u})\) is a \(3\times 3\) matrix. Due to the complexity of deriving the elements in \(\mathbf{J}(\mathbf{u})\) and finding the inverse of \(\mathbf{J}(\mathbf{u})\), the numerical value of the trace could be directly calculated as the optimization objective function in subsequent algorithms. The process of optimizing the objective function to obtain a gradient could be achieved using mature convex approximation methods such as successive convex approximation (SCA), which is not repeated here.
# Estimation of BER from Error Vector Magnitude for Optical Coherent Systems
## 1 Introduction  
Spectrally-efficient modulation formats are currently receiving significant attention for next-generation high-capacity optical communication systems [1; 2; 3]. With information encoded on both the amplitude and phase of the optical carrier for \(M\)-ary quadrature amplitude modulation (QAM), accurate measurement analysis needs to be developed to quantify the novel modulation schemes and to reliably predict the performance of optical coherent systems. A number of performance metrics, such as bit error ratio (BER), eye diagram, Q-factor, and, more recently, error vector magnitude (EVM), can be used in optical communications to assess the quality of the transmitted signals [4; 5; 6].  
While BER is the most conclusive figure of merit compared to the other performance metrics for advanced modulation formats, it requires a known pattern, e.g., a training sequence, to be transmitted for continuous performance monitoring in optical networks. On the other hand, EVM is a well known performance metric in wireless digital communication systems to evaluate the quality of vector-modulated signals [7] and is a more appropriate figure of merit at high signal-to-noise ratio (SNR) where a large number of symbols will otherwise be required for accurate error counting [8]. EVM has also been applied to assess the performance of optical channels corrupted by amplified spontaneous emission (ASE) noise and to characterize optical \(I\)-\(Q\) transmitters [5; 6]. Since EVM is better suited than BER counting for unknown symbol sequences, the relationship between EVM and BER is of significant interest for optical coherent systems. In [5], the authors noted that the established relationship between EVM and BER for data-aided reception [9] can also be applied to nondata-aided reception provided that the BER \(<10^{-2}\). However, for the estimated BER from EVM analysis to reflect the expected performance achievable with digital coherent receivers, the relationship between EVM and BER needs to be validated following the compensation of optical impairments using algorithms suitable for real-time implementation.  
In this paper, the estimation of BER from EVM analysis for quadrature phase shift keying (QPSK), 16-QAM, and 64-QAM with carrier phase recovery and differential decoding to compensate for laser phase noise is presented. Differential decoding is commonly employed with carrier phase recovery algorithms to solve the four-fold ambiguity associated with the recovered phase for square-QAM constellations and also to avoid performance degradation due to cycle slips [10; 11]. As such, the BER estimated from EVM in this work is compared to the actual BER expected with differential decoding. The BER derived from EVM analysis for nondata-aided reception is demonstrated to be underestimated following the compensation of laser phase noise for the different modulation formats. A correction factor is thus necessary to reliably estimate the actual BER from EVM and to accurately monitor the performance of optical coherent systems.
# A Fair Cooperative MAC Protocol in IEEE 802.11 WLAN
## 4 Performance Analysis
### Time Fairness  
In most cooperative protocols, the tendency is to use a previous relay node that has provided a successful transmission. This leads to a much faster drain on the resources of successful relay nodes. Moreover, some potential relay nodes are excluded from cooperation to perform cooperation. To evaluate the time fairness of each node, we apply [PERSON]'s fairness index as in [28].  
\[\text{Jain's Index}\ =\ \frac{\left(\sum_{i=1}^{n}x_{i}\right)^{2}}{n\sum_{i=1}^{n }x_{i}^{2}} \tag{12}\]  
where \(xi\) denotes the total data transmission time occupied by node \(i\) among \(n\) nodes.
# Labeling Peptides with Radioiodine: An Overview of Traditional and Emerging Techniques
## 3 Indirect Radioiodination Methods: The Conjugation of the Prosthetic Groups
### Activated Esters and Maleimide Derivatives
Maleimide-functionalized molecules for radioiodination can exhibit varying degrees of stability depending on factors like the specific maleimide derivative, the conjugation strategy, and the surrounding environment. While the maleimide moiety itself is relatively stable, the thioether bond formed with a sulfhydryl group can be susceptible to hydrolysis and thiol exchange reactions, potentially impacting the stability of the radioiodinated conjugate [45; 46; 47]. Figure 5: Typical prosthetic groups (activated esters and maleimide derivatives) used in radioiodination of macromolecules. (* represent the radioactive isotope). In a 2018 paper, [PERSON] et al. [48] reported the labeling of an alkylaldehyde with \({}^{125}\)I using an organotin precursor, with high radiochemical yield (72 \(\pm\) 6 percent) and radiochemical purity (>99 percent) (Figure 6). Then, by exploiting the condensation reaction of aryl-diamine and alkyl-aldehyde in the presence of a Cu\({}^{2+}\) catalyst under mild conditions to create a maleimide prosthetic group, the radioiodinated precursor was bound to the c[RGDfK(C)] aryl-diamine-derived peptide and also to human serum albumin.
# Correlation of Optical Constants and Morphologies with Mueller Matrix for Micro-Rough Surfaces
## 1 Introduction
The simulation method was validated with experimental results measured by Mueller matrix spectroscopic ellipsometry (MMSE). The relationships between the refractive index, \(n\), the extinction coefficient, \(k\), the relative roughness, \(\sigma/\lambda\), the correlation length, \(\sigma/\tau\), and the Mueller matrix elements of randomly micro-rough surfaces were discussed. The aim of this study was to investigate methods of measuring optical constants accurately and inverting the morphologies of randomly rough solid surfaces directly. The remainder of this paper is structured as follows: the model, simulation method, and experimental verification are presented in Section 2; the results and discussion are provided in Section 3; and Section 4 concludes this paper.
## 2 Preliminaries
### Boyen's Signature Algorithm and Its Ring Variants  
The BSS [29] is a hybrid algorithm on the lattice. The parameters of BSS are as follows: given security parameter \(\lambda\) and message length \(\ell\), let \(q=ploy(n)\), \(m\geq 2n\log q\), \(\sigma=\Omega(\sqrt{\ell n\log q})\) and \(\beta=\sigma\omega(\sqrt{\log m})\). The BSS key generation algorithm is as follows:  
1. The algorithm \(\mathit{TrapGen}(n,m,q)\)[30] produces an \(\mathbf{A}\) and a trapdoor base \(\mathbf{T_{A}}\) of \(\Lambda^{\perp}(\mathbf{A})\), where \(\mathbf{A}\) is statistically close to uniform over \(\mathbb{Z}_{q}^{n\times m}\) and \(T_{\mathbf{A}}\in\mathbb{Z}^{m\times m}\) is a short basis for \(\Lambda^{\perp}(\mathbf{A})=\{\mathbf{x}\in\mathbb{Z}^{m}:\mathbf{A}\cdot\mathbf{x}=\mathbf{0} \text{mod}q\}\).
2. Randomly chooses matrices \(\mathbf{A}_{0},\mathbf{A}_{1}\ldots\mathbf{A}_{\ell}\in\mathbb{Z}_{q}^{n\times m}\) and vector \(\mathbf{u}\in\mathbb{Z}_{q}^{n}\).  
\(\quad\)  
\(\quad\)  
Output the public key \(PK=(\mathbf{A},\mathbf{A}_{0},\mathbf{A}_{1}\ldots\mathbf{A}_{\ell},\mathbf{u})\) and the signing key \(sk=\mathbf{T}_{\mathbf{A}}\).  
The BSS signature algorithm is as follows: Upon the input of a fixed-length message \(\mathbf{d}=(d_{1},\ldots d_{\ell})\in\{0,1\}^{\ell}\), the signature algorithm first computes \(\mathbf{A}_{(\mathbf{d})}=[\mathbf{A}|\mathbf{A}_{0}+\sum_{i=1}^{\ell}d_{i}\mathbf{A}_{i}]\in \mathbb{Z}_{q}^{n\times 2m}\); then, it runs the lattice base delegation algorithm \(ExtBasis(\mathbf{A}_{(d)},\mathbf{T}_{\mathbf{A}})\)[31] to generate a short base \(\mathbf{T}_{(d)}\) of \(\Lambda^{\perp}(\mathbf{A}_{(d)})\), and finally runs the preimage sample algorithm \(Sample(\mathbf{T}_{(d)},\mathbf{A}_{(d)},\mathbf{u},\sigma)\)[30] to obtain a signature \(\mathbf{z}\in\mathbb{Z}^{2m}\), satisfying \(||\mathbf{z}||_{\infty}\leq\beta\) and \(\mathbf{A}_{(d)}\mathbf{z}=\mathbf{u}\mathrm{mod}q\).  
By applying BSS to the polynomial ring setting [32] and setting the parameter \(m\) to \(m=\Omega(\log q)\), the signature size can be reduced from \(\ell O(n^{2})\) to \(\ell O(n)\). The signing public key in the ring variant of BSS is \(PK=(\mathbf{a},\mathbf{a}_{0},\ldots\mathbf{a}_{\ell},\mathbf{u})\in(\mathcal{R}_{q}^{m})^{ \ell+2}\times\mathcal{R}_{q}\) and the signing key is \(sk=\mathbf{T}_{\mathbf{a}}\in\mathbb{Z}^{mn\times nm}\). The security of the ring variant of BSS is based on the difficulty of \(R\mathrm{SIS}_{n,m,q,\beta}\), which can be reduced to the hardness assumptions of \(SVP_{\ell\cdot\widetilde{O(n^{2})}}\).
# Market-Oriented Procurement Planning Leading to a Higher Service Level and Cost Optimization
## 3 Design of the Conceptual and Simulation Models
### Conceptual Model Development Based on System Theory and Statistics
The planning horizons are divided in analogy to the enterprise resource planning (ERP) systems structure [40]. Planning horizons of the cybernetics model for inventory management and related decisions are described in Table 1:  
* Long-term planning horizon: in this planning horizon, decisions for increasing storage or production capacities can be taken. New locations can also be opened to increase capacities or shorten replenishment times. In the long-term, the model can decide to increase the service level goals and, therefore, the safety factor for the inventory levels. * Medium-term planning horizon: The model decides on the production level as well as the level and conditions for employees on a monthly basis. In this planning horizon, the forecasting methods and procurement policies can change to be aligned or adjusted to a certain set of goals. * Short-term planning horizon: it considers one week as the smallest period of the model in which changes can take place. In this planning horizon, parameters within the different planning method changes and variability of personnel can be performed to adapt production and inventories to the customer demand. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Area**} & **Short-Term Planning** & **Medium-Term Planning** & **Long-Term Planning** \\  & & **(1 Week–1 Month)** & **(1–3 Months)** & **(3 Months–1 or More Years)** \\ \hline \multirow{3}{*}{1} & Demand & & & & Forecasting methods \\  & planning & Forecasting Parameters & Forecasting method changes & definition and random \\  & planning & & based on forecast accuracy & number generation \\  & & & & methodology \\ \hline \multirow{3}{*}{2} & Inventory & Days between orders for & & \\  & planning & economic order quantity & & \\  & planning & (EOQ) & & \\  & & Reorder point & coefficient & \\ \hline \multirow{3}{*}{3} & Procurement & EOQ quantity calculation & & & \\  & planning & Target level adjustment & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & & \\ \hline \multirow{3}{*}{4} & Organizational & Employees flexibility of 10\% & Production plan based on & Working shifts based on \\  & Decisions & based on weekly forecasts & monthly forecasts & quarter forecasts \\ \hline \multirow{3}{*}{5} & Investment-Risk & & & & Open new warehouse \\  & Strategy & No decision & No decision & (increase of storage capacity) \\ \cline{1-1}  & & & & & Increase production \\ \cline{1-1}  & & & & & capacities \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Planning decisions in the market-oriented procurement model (own elaboration). Figure 2: Conceptual framework for a market-oriented procurement (own elaboration).
# Can We Survive without Labelled Data in NLP? Transfer Learning for Open Information Extraction
## 7 Conclusions and Future Work  
Can we survive without labelled data in NLP? On the basis of our findings: yes! Nevertheless, employing labelled data in NLP tasks still results in better performance. However, the process of collection of labelled data is demanding and, in some cases, inaccessible. In this paper, we utilized training on OIE to diminish the complication of insufficient training data of neural network models in various NLP tasks and encourage model generalization. Since OIE plays a fundamental role in turning massive, unstructured data into factual information that can be used as a foundation to many NLP tasks, we favored OIE as our source task, thereby ensuring our work is useful and beneficial to the NLP community.  
In the domain adaptation experiment, we transferred information learnt from one domain to the other on the same task. The neural model was trained on the OIE news domain and tested on the bio-medical domain. Results obtained from the inductive approach indicated that our ROIE neural model can play a fundamental role in domain adaptation.  
Moreover, our research also covered the transferability to a semantically related task. Results achieved from transferring from the OIE to RE followed the same pattern as transferring from the OIE news domain to the bio-medical domain. Inductive transfer learning achieved promising and comparable results with traditional learning. Thus, our work demonstrates that OIE can act as a reliable source task, not only in domain adaptation but also when transferring to related tasks.  
In the future, we intend to expand our work beyond sequence labelling tasks and experiment with multi-transfer learning thoroughly on several NLP tasks, specifically tasks that are not semantically related to OIE such as sentiment analysis. Additionally, we intend to investigate different transferring mechanisms to study how to leverage knowledge acquired from pre-trained models in varied ways.  
Conception and design of the experiments, I.S. and M.S.; data curation, I.S.; methodology, I.S.; software, I.S.; supervision, M.S.; validation, M.S.; writing--original draft, I.S.; writing--review and editing, M.S. All authors have read and agreed to the published version of the manuscript.  
This work was made possible with funding from the European Union's Horizon 2020 research and innovation program under grant agreement no. 883588 (GEIGER). The opinions expressed and arguments employed herein do not necessarily reflect the official views of the funding body.  
The authors declare no conflict of interest.
## 3 Results and Discussion  
Firstly, simulations were performed. A total of 14 simulations were performed in each city and three climate scenarios were analyzed (current day, 2050, and 20,100), so results are based on 4116 different simulations. Then, such data were independently analyzed according to the climate scenario. Section 3.1 shows and discusses the results obtained in the current scenario, and Section 3.2 discusses the comparative analysis among the different countries. Finally, Section 3.3 deals with the results obtained in 2050 and 2100.