esa-sceva/satcom-chunk-collection huggingface dataset:
# Clock Recovery Challenges in DSP-Based Coherent Single-Mode and Multi-Mode Optical Systems
## 3 Matrix Propagation Model for Optical Fibers  
### Single-Mode Fibers without Coupling between Polarizations  
Neglecting nonlinearities and noise, the transmission through an optical fiber can be described by  
\[\mathbf{E}_{out}(\Omega)=\mathbf{M}(\Omega)\mathbf{E}_{in}(\Omega), \tag{7}\]  
where \(\mathbf{M}(\Omega)\) is a \(2\times 2\) matrix describing the transfer characteristics of the optical fiber supporting 2 polarizations, and \(\mathbf{E}_{in}(\Omega)\) and \(\mathbf{E}_{out}(\Omega)\) are frequency-domain 2-dimensional input and output electrical field vectors.  
Due to fabrication processes, single-mode fibers present birefringence, i.e., orthogonal polarization states will travel through the fiber at different speeds. If there is no coupling between polarization modes, then the fiber transfer characteristic can be described by  
\[\mathbf{M}(\Omega)=\mathbf{V}\mathbf{\Lambda}(\Omega)\mathbf{U}^{\dagger}, \tag{8}\]  
where \(\mathbf{V}\) and \(\mathbf{U}\) are random unitary rotation matrices describing coordinate base changes necessary due to the unknown spatial direction of the birefringence, and \(\dagger\) indicates the conjugate transpose of the matrix. \(\mathbf{V}\) and \(\mathbf{U}\) are defined by \[\mathbf{V},\mathbf{U}\stackrel{{\text{def}}}{{=}}\left[\begin{array}{cc} \cos(\theta)&\sin(\theta)e^{j\phi}\\ -\sin(\theta)&\cos(\theta)e^{j\phi}\end{array}\right], \tag{9}\]  
where \(\theta\) and \(\phi\) are the azimuth and ellipticity angles of the unitary rotation matrix. \(\mathbf{\Lambda}(\Omega)\) is a diagonal matrix describing the linear propagation in each of the polarization modes. Including polarization dependent loss (PDL), differential group delay (DGD), and chromatic dispersion (CD). \(\mathbf{\Lambda}(\Omega)\) can be expressed by  
\[\mathbf{\Lambda}(\Omega)=\text{diag}\left[a_{1}(\Omega)\,,\,a_{2}(\Omega) \right], \tag{10}\]  
\[a_{1}(\Omega)=\exp\left\{\frac{g_{1}}{2}-j\frac{\tau L}{2}\Omega-jD\frac{ \lambda^{2}L}{4\pi c}\Omega^{2}+jS\frac{\lambda^{4}L}{24\pi^{2}c^{2}}\Omega^{ 3}\right\}, \tag{11}\]  
\[a_{2}(\Omega)=\exp\left\{\frac{g_{2}}{2}+j\frac{\tau L}{2}\Omega-jD\frac{ \lambda^{2}L}{4\pi c}\Omega^{2}+jS\frac{\lambda^{4}L}{24\pi^{2}c^{2}}\Omega^{ 3}\right\}, \tag{12}\]  
where \(\mathbf{g}=[g_{1},g_{2}]\) is the PDL vector, \(\tau\) is the DGD, related to polarization mode dispersion (PMD), \(D\) is the CD parameter, \(S\) is the fiber dispersion slope, related to third-order CD, and \(L\) is the length of the fiber.
# Changed Detection Based on Patch Robust Principal Component Analysis
## 3 Methodology
### Low-Rank Decomposition Modeling  
Suppose the longitudinal fundus images have \(N\) frames, \(I_{1},I_{2},\cdots,I_{N}\), and each frame has the size \(m\times n\), \(N\geq 2\). Vectorizing each frame as a vector, these longitudinal fundus images can be regarded as an image matrix with the size \(M\times N\) denoted by \(D\) where \(M=m\times n\) and \(D=[I_{1},I_{2},\cdots,I_{N}]\). Each column of \(D\) indicates one frame of the sequence, and hence \(D\) consists of the following two components in (5):  
\[D=A+E. \tag{5}\]  
where \(A\) is the low-rank background component of \(D\), \(E\) is the sparse change component of \(D\). \(A_{i}\) and \(E_{i}\) correspond to the background image and the change region image of \(I_{i}\).  
The change detection problem can be converted into how to obtain the sparse component of \(D\), which can be solved by low-rank decomposition as the following equation shows:  
\[\arg\min_{A,E}rank(A)+\lambda\|E\|_{0}\quad s.t.\quad D=A+E \tag{6}\]  
where \(rank(A)\) indicates the rank of matrix \(A\), \(\|\cdot\|_{0}\) means the \(l_{0}\) norm which shows the sparsity of the component, and \(\lambda\) is a hyperparameter and the equilibrium factor to trade off the rank of the image matrix and sparsity of the foreground.  
As solving the \(l_{0}\) norm in (6) is a non-deterministic polynomial (NP) problem [26], according to restricted isotropic property (RIP) [27], the \(l_{0}\) norm can be estimated by the \(l_{1}\) norm. The rank of the matrix is measured by the kernel norm of matrix, and (6) can be reformulated to the form of (7), shown as follows [28]:  
\[\arg\min_{A,E}\|A\|_{*}+\lambda\|E\|_{1}\quad s.t.\quad D=A+E, \tag{7}\]  
where \(\|\cdot\|_{*}\) is the kernel norm, and \(\|\cdot\|_{1}\) is \(l_{1}\) norm.  
Many studies [29; 30] have given the solution of (7). Since there are some noises involved in the image for the general cases, (7) can be relaxed from the equality constraint to the inequality constraint to allow some errors, which is shown in (8):  
\[\arg\min_{A,E}\|A\|_{*}+\lambda\|E\|_{1}\quad subj\quad\|D-A-E\|_{F}^{2}<\epsilon \tag{8}\]  
The augmented Lagrange multiplier (ALM) algorithm [31] gives a fast solution method shown in (9):  
\[\arg\min_{A,E}\|A\|_{*}+\lambda\|E\|_{1}+\frac{\mu}{2}\|D-A-E\|_{F}^{2} \tag{9}\]  
where \(\|\cdot\|_{F}\) is the Frobenius norm of the matrix, and \(\mu\) is a parameter used to balance the reconstruction accuracy as well as the low-rank and sparsity of the image matrix.
# Towards Robust Neural Rankers with Large Language Model: A Contrastive Training Approach
## 5 Evaluation Result
### Case Study
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model Type** & **Method** & **MAP** & **MRR** & **nDCG@10** & **P@10** \\ \hline \multirow{4}{*}{DR} & Original & **0.438**(17.0\%/24.1\%) & 0.679(15.3\%/23.6\%) & **0.578**(15.7\%/22.3\%) & **0.316**(18.1\%/24.7\%) \\  & Typo-aware & 0.427(12.4\%/17.5\%) & 0.671(11.0\%/16.5\%) & 0.565(11.4\%/16.3\%) & 0.312(13.3\%/18.0\%) \\  & Dual contrastive & 0.437(17.4\%/24.1\%) & **0.689**(15.7\%/22.7\%) & **0.578**(15.8\%/21.5\%) & 0.314(17.2\%/22.9\%) \\  & Our & **0.438**(**10.2\%/12.7\%**) & 0.682(**8.9\%/12.1\%**) & 0.558(**9.1\%/11.8\%**) & 0.313(**9.3\%/13.1\%**) \\ \hline \multirow{4}{*}{Reranker} & Original & 0.511(13.0\%/16.5\%) & 0.752(10.8\%/14.5\%) & 0.654(13.7\%/15.1\%) & 0.363(12.1\%/16.2\%) \\  & Typo-aware & 0.527(13.1\%/16.6\%) & **0.776**(10.9\%/13.0\%) & 0.652(11.2\%/14.2\%) & 0.360(11.8\%/14.7\%) \\ \cline{1-1}  & Our & **0.529**(**11.9\%/15.2\%**) & 0.771(**10.6\%/12.9\%**) & **0.659**(**10.5\%/13.7\%**) & **0.368**(**10.2\%/13.2\%**) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Results of different robustness training approaches on ANTIQUE. The best accuracy is **bold-faced**, and the second-best accuracy is underlined.
# WISCANet: A Rapid Development Platform for Beyond 5G and 6G Radio System Prototyping
## 3 System Design
### Cross Platform Operation  
WISCANet is compatible with several system architectures, including amd64, aarch32, and aarch64. We have tested WISCANet on numerous platforms, including HEDT and server-class systems, Raspberry Pis, and NVIDIA Jetsons. This enables diverse architectures for development and experimentation, and integrates unique architectural features such as the Jetson CUDA cores or integrated FPGAs on Xilinx MPSoC's.
## 3 Analysis of Queue Length Policy at a Coding Node
### Background  
Without loss of generality, we consider an 802.11 wireless network. To achieve the maximum jitter gain of coding aware routing, a sufficient number of encoded packets must be created at the selected coding node. Whether in the single rate case or multiple rate case, the packet arrival is asynchronous and random due to the DCF random mechanism. This may result in severe delay, jitter and packet loss because packets need to wait to be network-coded with each other and consume the finite buffer. This may greatly reduce the overall jitter benefits. An intuitive idea to improve the long-term jitter in the single rate case and multiple rate case is to allow opportunistic transmission of some packets without network coding. This motivates the study of BLJCAR, which can opportunistically transmit some uncoded packets according to the queue state. There is a trade-off between packet waiting delay and network coding opportunity. The analysis of queue state of the coding node can characterize a unified description of BLJCAR in single rate case and multiple rates case.  
Since we only consider two coding flows, let \(L_{1}\) and \(L_{2}\) be the number of packets type 1 and type 2 from corresponding flow in coding node's queue. The queue state at coding node can be represented by a two-dimensional random variable denoted by \(L=\left(L_{1},L_{2}\right)\). Because the future size of coding node's queue depends only on its current size, the changes over time of \(L\) can be modeled as a Markov process. According to [PERSON] _et al._[16], for that, a stationary policy based on queue length is optimal for the network performance to trade-off coding opportunity and packet waiting delay.  
In BLJCAR, we adopt a queue-length based threshold policy to make efficient decisions for packets at coding node. That is, the selected coding node will wait until either a matching packet arrives or the length of the nonempty queue exceeds its threshold. This is easy to implement. In fact, there is only one output queue in the node. We assume that the optimal thresholds for the both flows are equal. This assumption can highly simplify the description and analysis of queue length policy.
# DLSTM-Based Successive Cancellation Flipping Decoder for Short Polar Codes
## 2 Preliminary
### LSTM Network  
Compared with the traditional Recurrent Neural Network (RNN), the LSTM network is not only capable of handling long-term dependencies but also suitable for sequence-based tasks [20]. It is well known that the SC decoding generates the LLR sequences, and the LSTM networks can effectively extract features and build relationships between sequences. The structure of LSTM is shown in Figure 3, which consists of one state and three gates, namely cell state, forget gate, input gate, and output gate. The cell state \(\mathbf{c}_{t}\) mainly saves the long-term state. The forget gate \(\mathbf{f}_{t}\) determines the proportion of the previous cell state \(\mathbf{c}_{t-1}\) retained to the current state \(\mathbf{c}_{t}\). The input gate \(\mathbf{i}_{t}\) decides the proportion of the input  
Figure 2: The SC decoding tree for a \(P(16,8)\).  
\(\mathbf{x}_{t}\) of the network saved to the cell state \(\mathbf{c}_{t}\). The output gate \(\mathbf{o}_{t}\) controls what message is output. The control of the three gates allows the LSTM network to remember more relevant messages for a long time, which is a great advantage when dealing with the LLR sequences. The gate is a fully connected layer, which can be expressed as:  
\[\text{gate}(\mathbf{x})=\sigma(\mathbf{W}\mathbf{x}+\mathbf{b}), \tag{9}\]  
where \(\mathbf{W}\) is the weight matrix, \(\mathbf{x}\) represents the input vector, \(\mathbf{b}\) is the bias vector, and \(\sigma\) is the sigmoid function:  
\[\sigma(z)=\frac{1}{1+\mathbf{c}^{-z}}\enspace. \tag{10}\]  
The output \(\mathbf{h}_{t}\) of the LSTM is defined by the output gate \(\mathbf{o}_{t}\) and the cell state \(\mathbf{c}_{t}\). The cell state \(\mathbf{c}_{t}\) is determined by the forgot gate \(\mathbf{f}_{t}\), the input gate \(\mathbf{i}_{t}\), the cell state \(\mathbf{c}_{t-1}\) at time \(t-1\), and the input cell state \(\mathbf{c}^{\prime}_{t}\). In addition, the relationship at time \(t\) and \(t-1\) is described as follows:  
\[\left\{\begin{array}{l}\mathbf{f}_{t}=\sigma\Big{(}\mathbf{W}_{f}\cdot[ \mathbf{h}_{t-1},\mathbf{x}_{t}]+\mathbf{b}_{f}\Big{)}\\ \mathbf{i}_{t}=\sigma(\mathbf{W}_{i}\cdot[\mathbf{h}_{t-1},\mathbf{x}_{t}]+ \mathbf{b}_{i})\\ \mathbf{o}_{t}=\sigma(\mathbf{W}_{o}\cdot[\mathbf{h}_{t-1},\mathbf{x}_{t}]+ \mathbf{b}_{o})\\ \mathbf{c}^{\prime}_{t}=\tanh(\mathbf{W}_{c}\cdot[\mathbf{h}_{t-1},\mathbf{x}_ {t}]+\mathbf{b}_{c})\\ \mathbf{c}_{t}=\mathbf{f}_{t}\circ\mathbf{c}_{t-1}+\mathbf{i}_{t}\circ\mathbf{ c}^{\prime}_{t}\\ \mathbf{h}_{t}=\mathbf{o}_{t}\circ\tanh\left(\mathbf{c}_{t}\right)\end{array}\right., \tag{11}\]  
where \(\tanh\) is the activation function defined as:  
\[\tanh(m)=\frac{\mathrm{e}^{m}-\mathrm{e}^{-m}}{\mathrm{e}^{m}+\mathrm{e}^{-m}}\enspace. \tag{12}\]
## 5 Conclusions  
The present study proposes a novel knowledge-graph (KG)-embedding framework called RiQ-KGC, which utilizes geometric rotation information from the quaternion space and integrates multi-hop neighbor information into relation representation using a relation instantiation method. The proposed model builds upon the strengths of quaternion space embedding by enabling the representation of symmetric, anti-symmetric, and inverse relations between entities and enhancing the ability to represent complex relations. Moreover, RiQ-KGC learns intrinsic connections in KGs by applying a large number of parameters and strengthening the association between rotation results and target entities via parameter mapping. Consequently, entities and relations can be more accurately expressed.  
In the future, we aim to explore more accurate and efficient methods of representing neighbor information to support more powerful relation instantiation methods. Entity transformation via relations is a classical notion in graph embedding and integrating entity transformation with advanced deep learning frameworks is a promising direction for future research. We will continue exploring methods to represent entity transformation in deep learning models with the goal of building more advanced and effective embedding models for link prediction.  
Figure 10: Comparison of modeling capabilities for complex relations on four datasets. All subplots share the same legends. To avoid cluttering the layout, we set the legends in the bottom right corner of the last subplot.  
**Author Contributions:** Conceptualization, Y.W. and B.N.; methodology, Y.W.; software, Y.W. and S.J.; validation, S.J.; formal analysis, X.Z.; investigation, Y.W.; resources, X.Z.; data curation, B.N.; writing--original draft preparation, Y.W.; writing--review and editing, B.N.; visualization, S.J.; supervision, G.L.; project administration, B.N.; funding acquisition, G.L. and Q.M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China, grant number 61976032, and the National Natural Science Foundation of China, grant number 62002039.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Recovering Texture with a Denoising-Process-Aware LMMSE Filter
## Appendix A Proof of Texture Estimation Using the LMMSE Filter  
A special case of an LMMSE filter that can estimate \(\mathbf{x}\) from \(\mathbf{y}\) when \(\mathbf{x}\) is independent of the noise \(\mathbf{n}\) is well known as the Wiener filter. However, because the texture \(\mathbf{t}\) is highly dependent on the noise in the problem considered here, a simple Wiener filter is not the solution for Equation (6). In this section, we present the closed-form solution for Equation (6).  
We adopt the following notation:  
\[J(\mathbf{W}) \coloneqq \mathbb{E}[\{\|\mathbf{W}(\mathbf{t}_{i}+\mathbf{n}_{i})-\mathbf{ t}_{i}\|_{2}^{2}] \tag{11}\] \[= \mathbb{E}[(\mathbf{W}(\mathbf{t}_{i}+\mathbf{n}_{i}))^{\top} \mathbf{W}(\mathbf{t}_{i}+\mathbf{n}_{i})]\] \[-2\mathbb{E}[(\mathbf{t}_{i}+\mathbf{n}_{i})^{\top}\mathbf{W}^{ \top}\mathbf{t}_{i}]+\mathbb{E}[\mathbf{t}_{i}^{\top}\mathbf{t}_{i}].\]  
Additionally, we assume that \(\mathbb{E}[\mathbf{t}_{i}]\) and \(\mathbb{E}[\mathbf{n}_{i}]\) are \(\mathbf{0}\). Thus, the first and third terms on the right-hand side in the above equation can be expressed as  
\[\mathbb{E}[(\mathbf{W}(\mathbf{t}_{i}+\mathbf{n}_{i}))^{\top} \mathbf{W}(\mathbf{t}_{i}+\mathbf{n}_{i})] \tag{12}\] \[=\text{tr}(\mathbf{W}\mathbf{R}_{(\mathbf{t}+\mathbf{n}_{i})( \mathbf{t}+\mathbf{n}_{i})}\mathbf{W}^{\top}),\] \[\mathbb{E}[\mathbf{t}_{i}^{\top}\mathbf{t}_{i}]=\text{tr}( \mathbf{R}_{\mathbf{t}_{i}\mathbf{t}}). \tag{13}\]  
The second term on the right-hand side of Equation (11) can be expanded to  
\[-2\mathbb{E}[(\mathbf{t}_{i}+\mathbf{n}_{i})^{\top}\mathbf{W}^{\top}\mathbf{ t}_{i}]=-2\mathbb{E}[\mathbf{t}_{i}^{\top}\mathbf{W}\mathbf{t}_{i}]-2\mathbb{E}[ \mathbf{n}_{i}^{\top}\mathbf{W}^{\top}\mathbf{t}_{i}]. \tag{14}\]  
Because \(\mathbb{E}[\mathbf{a}^{\top}\mathbf{A}\mathbf{b}]=\text{tr}(\mathbf{A}^{\top }\mathbf{R}_{\mathbf{a}\mathbf{b}})\) (where \(\mathbb{E}[\mathbf{a}]\) and \(\mathbb{E}[\mathbf{b}]\) are \(\mathbf{0}\)), the right-hand side of the above equation can be expressed as  
\[-2\mathbb{E}[\mathbf{t}_{i}^{\top}\mathbf{W}\mathbf{t}_{i}]-2\mathbb{E}[ \mathbf{n}_{i}^{\top}\mathbf{W}^{\top}\mathbf{t}_{i}]=-2\text{tr}(\mathbf{W} ^{\top}\mathbf{R}_{\mathbf{t}_{i}\mathbf{t}})-2\text{tr}(\mathbf{W}\mathbf{R} _{\mathbf{t}_{i}\mathbf{n}_{i}}). \tag{15}\]  
Accordingly, the partial derivative of \(J(\mathbf{W})\) with respect to \(\mathbf{W}\) can be expressed as  
\[\frac{\partial J(\mathbf{W})}{\partial\mathbf{W}}=2\mathbf{W}\mathbf{R}_{( \mathbf{t}+\mathbf{n}_{i})(\mathbf{t}+\mathbf{n}_{i})}-2\mathbf{R}_{\mathbf{ t}_{i}\mathbf{t}}-2\mathbf{R}_{\mathbf{t}_{i}\mathbf{n}_{i}}. \tag{16}\]  
With \(\frac{\partial J(\mathbf{W})}{\partial\mathbf{W}}=0\), we can finally obtain the solution for Equation (6) as follows:  
\[\mathbf{W}_{i}^{*}=(\mathbf{R}_{\mathbf{t}_{i}\mathbf{t}}+\mathbf{R}_{\mathbf{ t}_{i}\mathbf{n}_{i}})(\mathbf{R}_{(\mathbf{t}_{i}+\mathbf{n}_{i})( \mathbf{t}_{i}+\mathbf{n}_{i})})^{-1}. \tag{17}\]  
This is identical to Equation (7).
###### Abstract  
We demonstrate bound states in the radiation continuum (BSC) in a linear periodic array of dielectric spheres in air above the light cone. We classify the BSCs by orbital angular momentum \(m=0,\pm 1,\pm 2\) according to the rotational symmetry of the array, Bloch wave vector \(\beta\) directed along the array according to the translational symmetry, and polarization. The most simple symmetry protected BSCs have \(m=0,\beta=0\) and occur in a wide range of the radius of the spheres and dielectric constant. More sophisticated BSCs with \(m\
eq 0,\beta=0\) exist only for a selected radius of spheres at fixed dielectric constant. We also find robust Bloch BSCs with \(\beta\
eq 0,m=0\). All BSCs reside within the first but below the other diffraction continua. We show that the BSCs can be easily detected by bright features in scattering of different plane waves by the array as dependent on type of the BSC. The symmetry protected TE/TM BSCs can be traced by collapsing Fano resonance in cross-sections of normally incident TE/TM plane waves. When plane wave with circular polarization with frequency tuned to the bound states with OAM illuminates the array the spin angular momentum of the incident wave transfers into the orbital angular momentum of the BSC. This, in turn, gives rise to giant vortical power currents rotating around the array. Incident wave with linear polarization with frequency tuned to the Bloch bound state in the continuum induces giant laminar power currents. At last, the plane wave with linear polarization incident under tilt relative to the axis of array excites Poynting currents spiralling around the array. It is demonstrated numerically that quasi-bound leaky modes of the array can propagate both stationary waves and light pulses to a distance of 60 wavelengths at the frequencies close to the bound states in the radiation continuum. A semi-analytical estimate for decay rates of the guided waves is found to match the numerical data to a good accuracy.  
bound state in the continuum; Fano resonance; nanophotonics +
Footnote †: journal: Applied Physics Letters
# Ultrasonic Power and Data Transfer through Multiple Curved Layers Applied to Pipe Instrumentation
## 1 Introduction
[20] adopted a similar setup, but used a wider and thicker steel pipe with a 85 mm outer diameter and 10 mm thickness for power transfer only. The latter achieved 28 dB of insertion loss; considerably lower than [PERSON]'s [17] work, which reached 5.7 dB. A multilayered curved channel can be of interest in applications that concern transmission through concentric pipes. An example of such a scenario is found in the oil and gas industry, and, more specifically, in wellbore monitoring. This case consists of an inner and an outer pipe with liquid between them. The former is known as production tubing and the latter as casing pipe. One is usually interested in monitoring the structural health of regions beyond the casing layers. This can be achieved by placing sensors on the exterior of the casing pipe. Nevertheless, access is only possible in the interior of the tubing. Therefore, the issue is how to power supply the sensors and to get their data. A possible solution is to transfer power and to interrogate the sensor utilizing ultrasonic waves generated in the tubing. This scenario is, however, composed of a curved multilayer structure, being thus more complex than the aforementioned usual applications of ultrasonic through-wall communication. This paper investigates the potential of using a multi-layered structure composed of a water layer and a curved steel barrier aiming for a future wellbore monitoring system. Our experimental setup consisted of an arrangement of a pair of piezoelectric ultrasonic transducers enclosing multiple layers formed by two concentric pipes separated by water. In the inner pipe, a cover made of steel protects the piezoelectric transducer, adding, however, one more acoustic layer to the arrangement. The piezoelectric transducers have a nominal resonance frequency of 1 MHz, and the communication between then uses the impedance variation in order to amplitude modulate the carrier signal [17]. A complementary investigation was carried out by simulating the electro-acoustic channel using a one-dimensional multilayer electro-mechanical model designed in PSpice(r) [18]. The remainder of the paper is organized as follows: Section 2 reviews power and data acoustic channel (PDAC) systems and the power and used data communication method. Section 3 describes the numerical and experimental acoustic channel analysis. Section 4 describes the system implementation and shows the experimental and numerical results. Section 5 concludes the paper.