esa-sceva/satcom-chunk-collection huggingface dataset:
# The Production of Curli Amyloid Fibers Is Deeply Integrated into the Biology of _Escherichia coli_
## 2 Results and Discussion
### CsgD Transcript and Protein Levels Are Altered in Several Strains with CR Phenotypes
In turn, the sRNA _rprA_ can reduce _csgD_ transcription and CsgD protein levels [134]. We observed that an _rcsB_ mutant strain had increased _csgD_ transcripts and CsgD protein levels (Table S9). Taken together, these data suggest that the increased CR binding observed in _rcsB_ mutants is likely the result of lower RprA levels. Two other sRNAs, McaS and GcvB, have also been shown to target _csgD_ transcripts [135]. McaS has been found to interact with Hfq implicating additional levels of sRNA control on _csgD_[136]. The CR screen also revealed possible post-translational control of CsgD (Table S9). For example, _hdfR_, _mltA_, _and fabH_ mutant strains had normal _csgD_ transcripts, but decreased CsgD protein levels, suggesting that these genes are required for the proper translation of _csgD_ or CsgD protein stability (Figure S3 and Table S1). Consistent with this, _hdfR_ and _mltA_ have been shown to interact with the _csgD_ messenger RNA (mRNA) [137]. The FabH protein is an initiator of fatty acid synthesis capable of conjugating acetyl-CoA to acyl-carrier protein (ACP), and lack of _fabH_ or overexpression leads to decreased fatty acid chain length and abundance [138; 139; 140]. The mechanism behind how FabH changes CsgD protein levels warrants further investigation. An intriguing result from the screen was that there were multiple mutants, _ddpD_, _glvG_, and _php_, which had altered CR binding phenotypes, yet normal _csgD_ transcript and CsgD protein levels (Table S9). For example, the _php_ mutant was white on CR plates, had undetectable levels of CsgA and CsgG, but had WT levels of CsgD protein (Tables S1 and S9). Taken together, we suggest that CsgD is inactive in these mutant strains, or that CsgBA cannot be produced or appropriately secreted because CsgG is not present. In any case, it is clear that _php_ is essential for curli biogenesis and it acts downstream of CsgD transcription and translation.
## 2 Theory Background and Methodology
### A Reformative Method Combining with Functional Beamforming  
To further improve the dynamic range of sound source identification and location, the solution of Equation (10) can be reformulated by referring to functional beamforming (FB).  
Firstly, the cross spectral matrix (CSM) of \(\Upsilon\) is calculated with Equation (16) [24]:  
\[\mathbf{C}=\mathbf{\Upsilon}\mathbf{\Upsilon}^{H} \tag{16}\]  
Since \(C\) is nonnegative Hermitian matrix, the spectral decomposition of \(C\) can be written as Equation (17):  
\[\mathbf{C}=\mathbf{V}diag(\gamma_{1},\ldots,\gamma_{\rm N}) \mathbf{V}^{H} \tag{17}\]  
where \(V\) is the unitary matrix whose columns represent the eigenvectors of \(\mathbf{C}.\,\gamma_{1},\ldots,\gamma_{\rm N}\) are the eigenvalues.  
The exponential matrix function of \(C\) is defined by Equation (18) [24]:  
\[\mathbf{C}^{\frac{1}{b}}=\mbox{\boldmath  
It has to be mentioned that the output peaks values of Equation (19) may be smaller than the actual source amplitude. Therefore, to guarantee the true estimation of maximum pressure amplitude, it is suggested that the sound pressure on the reconstruction plane should be modified through combining Equations (10) and (19). Finally, the final result of ERDCESM-\(v\) is as the following Equation (20):  
\[\mathbf{P}_{out}=\begin{cases}\mathbf{P}_{R}&v=0\\ \mathbf{Z}_{i}\frac{\max(|\mathbf{P}_{R}|)}{\max(|\mathbf{Z}_{i}|)}&v=1,2,3,\ldots\end{cases} \tag{20}\]  
To almost completely reconstruct the sound field, ERDCESM with order \(v=0\) is suitable. However, if there is a demand for identifying source location with high accuracy and wide dynamic range, ERDCESM with order \(v=1,2,3,\ldots\) is recommended. So the application of ERDCESM-\(v\) is very flexible and also provides high-precision source localization.
# A Novel Strategy for VNF Placement in Edge Computing Environments
## 2 Related Work
In [19], the authors considered the bandwidth capacity and link delay of the network connection between clouds in which VNFs are allocated to minimize resource usage costs. [PERSON] et al. [24] presented an approach to the VNF placement problem with energy and traffic-aware cost minimization, focusing on minimizing network latency and operational cost. The energy consumption of edge nodes is essential to guarantee QoS due to their power constraints. Energy is spent on tasks such as data generation, transmission, aggregation, storage, and processing. Our proposed energy model, discussed in detail in Section 3, considers the energy consumed for data propagation along with communication links and VNF packet processing in edge nodes. As such, our approach selects edge nodes with low energy consumption according to the SFC priority (latency, capacity, and energy). Methods derived from Greedy approaches are also widely adopted to solve the VNF placement problem [17; 25; 27; 28]. Such approaches quickly converge to a solution but tend to fall into local minimum or maximum. Other approaches consider machine learning-based strategies [16; 19; 23; 24], which typically require datasets in training and testing phases, sometimes difficult to obtain, hindering their practical deployment in dynamic environments. Some papers have recently addressed the VNF instance sharing mechanism, as in [32], where authors restrict the number of SFC that can share the same VNF instances based on the resources each SFC will use in the VNF instance. In their approach, each SFC request needs to define how many resources it will consume in each shared VNF instance, which is not an easy task in a dynamic environment. [PERSON] and [PERSON] [29] computed the number of SFCs that can share the same VNF instances utilizing the number of SDN flows that ingress in the VNF instance. Unlike previous studies, our approach defines the limit to which SFCs can share the same VNF instances based on a value configured by the infrastructure owner during the VNF setup. Unlike previously mentioned VNF placement solutions, we consider several aspects of formulating our solution for the VNF placement problem. We consider four communication delays in our modeling discussed in Section 3, namely transmission delay, propagation delay, processing delay, and queuing delay. Our approach also considers the energy consumption to select edge nodes to allocate VNFs. Additionally, we formulate the problem using the ILP model and present our smart VNF placement algorithm that includes all previously commented upon aspects.
# Around-View-Monitoring-Based Automatic Parking System Using Parking Line Detection
## 4 Experimental Results  
### Experimental Environment  
For the test, the experiment environment was first configured. High-definition cameras (1280 \(\times\) 720) were installed on the test vehicle, which was a Cammsys CEVO-C small electric vehicle [37] that was modified for automatic parking. Electronic power steering was installed in the test vehicle for steering control, and ESC was installed in the test vehicle for braking control. In addition, the drive control used the electric vehicle's motor, and the gear was configured so that it could be changed using electronic control. Figure 17 shows the test vehicle. Figure 18 shows the cameras mounted on the front, rear, left and right sides of the test vehicle. Figure 16: Results of speed and steering control: (**a**) result of speed control and (**b**) result of steering control. Figure 17: Test vehicle. Figure 18: _Cont._  
In the automatic parking experiment, we evaluated the final position after parking. We employed a method to compare the final position and target position after the direct measurement of several points, as illustrated in Figure 22.  
As shown in Figure 22, the distance between each wheel and the parking line as well as the distance between the center of the front of the vehicle and the parking slot were measured. From the positions of these three points, the amount of offset from the center and the heading angle can be obtained. Using the configuration shown in Figure 22, the angle of the vehicle can be obtained as follows:  
\[\theta=\sin^{-1}\biggl{(}\frac{f_{L}-r_{L}}{L}\biggr{)}. \tag{9}\]  
Using angle \(\theta\), the difference in the \(y\)-axis direction away from the center can be expressed as  
\[D_{y}=\biggl{|}\frac{P_{y}}{2}-\biggl{(}\frac{V_{L}}{2}\cos\theta+f_{c}\biggr{)} \biggr{|}. \tag{10}\]  
In addition, the \(x\)-axis offset can be obtained from  
\[D_{x}=\biggl{|}\frac{P_{x}}{2}-\biggl{(}\frac{V_{W}}{2}\cos\theta+c_{L}\biggr{)} \biggr{|}. \tag{11}\]  
The experiment was conducted considering the shape of each parking slot. There were three types of parking division lines in the experiment: perpendicular, parallel, and angle parking lines. The results were obtained by detecting the parking slot according to its type and performing automatic parking. Table 3 presents the experimental results (the lateral error, longitudinal error, and heading angle) for each parking slot type. Figure 22: Measurement points for automatic parking result evaluation. Figure 23 shows the automatic parking results; the upper-left inset image shows the parking slot detection and the upper-right inset image shows the inside of the car. These results confirm that automatic parking using the proposed method was successful.
## 2 Analysis and Optimization of TIF Shape  
### Basic Theory of TIF  
Due to the extreme complexity of the optical polishing process, it is very difficult to establish a mathematical relationship between the material removal rate and all parameters. After simplification, the linear relationship among the material removal rate, polishing pressure, and polishing speed established by [PERSON] is widely adopted [16], which is:  
\[\frac{dz}{dt}=kPV, \tag{1}\]  
where \(\frac{dz}{dt}\) is the material removal rate, \(k\) is the Preston coefficient, which indicates the polishing efficiency, \(P\) is the contact pressure, and \(V\) is the relative velocity between the tool and the workpiece. When the relative velocity and the contact pressure are known, the amount of material \(\Delta\) z(x,y) removed in time \(\Delta\)t can be calculated.  
\[\Delta\mathrm{z}(x,y)\mathrm{=}\int_{0}^{\Delta\mathrm{t}}kP(x,y,t)V(x,y,t)dt, \tag{2}\]  
The polishing TIF is also referred to as the polishing removal characteristic. Theoretically, the TIF is assumed to be a pulse function, which can completely correct the surface shape error. However, in practical situations, since the pulse function cannot be achieved, it is necessary to seek a removal function that is achievable and whose removal characteristic approximates that of the pulse function, that is, the Gaussian-like removal function. If the Gaussian-like TIF with the central removal amount being the peak value is used for polishing, the surface shape error will decrease monotonically as the number of polishing times increases. If a non-Gaussian TIF with a central removal amount of zero or the lowest point is used, as the number of polishing times continues to increase, the surface shape error will first decrease and then increase instead, and it is easy to cause intermediate-frequency errors [17; 18].
###### Abstract  
Exploring the temporal and spatial changes, as well as driving factors, of net primary productivity (NPP) of terrestrial ecosystems is essential for maintaining regional carbon balance. This work focuses on the spatiotemporal variation and future trends of NPP and the response mechanisms of NPP to various driving factors. The Theil-Sen estimator, as well as Mann-Kendall and Hurst exponent methods, were used to analyze the spatiotemporal dynamics and future trends of NPP, and geographical detectors and correlation analysis were used to reveal the response of NPP to various driver changes to environmental factors. The results showed that the NPP was generally on an increasing trend in the Yangtze River Delta region from 2000 to 2019, with the average NPP value of 550.17 g C m\({}^{-2}\) a\({}^{-1}\), of which 85.90% was the increasing regions and 14.10% was the decreasing regions, showing a significant spatiotemporal heterogeneity characteristic. The trend of future changes in NPP is dominated by an anti-persistence trend in the study area, i.e., the opposite of the past trend. Notably, annual precipitation is the most significant positive driver of NPP; while NPP was negatively correlated with population, meanwhile, different land use/land cover (LULC) also significantly affected the spatial distribution of NPP. Besides, there was a two-factor enhanced interaction between the various drivers on NPP, with the highest interaction occurring between temperature and elevation. Overall, this study provides data support for future regional NPP predictions and ecosystem evaluations.  
net primary production (NPP); spatiotemporal variation; climate change; human activity; geographical detectors; Yangtze River Delta
# RCLED Optimization and Nonlinearity Compensation in a Polymer Optical Fiber DMT System
## 4 Nonlinearity Mitigation  
### Operating Point Optimization  
From the analysis of Eq. (9) and Eq. (10), the nonlinearity of RCLED is mainly determined by the bias current \(I_{\text{DC}}\) and the signal variance \(\sigma_{0}^{2}\). To relieve the nonlinear distortions and maximize the power efficiency of RCLED, the parameters \(I_{\text{DC}}\) and \(\sigma_{0}^{2}\) must be optimized in the system design. Note, \(\sigma_{0}^{2}\) can be directly measured by averaging the electrical power \(P_{\text{Eave}}\) of \(x(t)\) in the practical systems.  
To investigate the influence of \(I_{DC}\) on the signal distortions, the error vector magnitude (EVM) metric, defined as the RMS value of the difference between a collection of measured symbols and ideal symbols, is used to measure the distortion degree. The EVM is expressed by  
\[\text{EVM}=\frac{\sqrt{\frac{1}{N_{i}}\sum\limits_{n=1}^{N_{i}}\left|S_{n}-S_ {0,n}\right|^{2}}}{\sqrt{\frac{1}{N_{i}}\sum\limits_{n=1}^{N_{i}}\left|S_{0,n} \right|^{2}}}, \tag{11}\]  
where \(S_{n}\) denotes the normalized received symbol, \(S_{0,n}\) denotes the ideal constellation points, and \(N_{i}\) denotes the number of symbols in the constellation.  
Therefore, an approximate linear region can be roughly obtained through the analysis and comparison of the EVM measurement for different QAM constellations. The modulating signal can be operated in this linear region by optimizing \(I_{\text{DC}}\), but the clipping distortion still occurs with the large peak-to-peak values of the inputs. Hence, the peak-to-peak range of \(x(t)\), in terms of \(P_{\text{Eave}}\), must also be considered in the practical transmitter. To investigate the influence of \(P_{\text{Eave}}\) on the RCLED nonlinearity, the bit error rate (BER) is adopted to measure the communications quality and to obtain an available power range of \(P_{\text{Eave}}\). Additionally, it should be noted that the available input power ranges also depend on \(I_{\text{DC}}\). Therefore, the optimal parameters including \(I_{\text{DC}}\) and \(P_{\text{Eave}}\) are the tradeoffs between the communication quality and the power efficiency, and the details are discussed and verified in Section 5.
# Enhanced Integrated Satellite-Terrestrial NOMA with Cooperative Device-to-Device Communication
## 1 Introduction
The objective function of the pairing scheme aims to maximize the system's sum-rate, by evaluating both the satellite segment where NOMA is applied and the terrestrial D2D link between the users that assists the transmission. Specifically, the whole transmission is divided into two phases. In the first transmission phase, the satellite transmits through NOMA towards a pair of users, sharing the same sub-channel, and assigns them a suitable fraction of the total transmit power in order to guarantee that the achievable users' rates are at least equal to those of the conventional OMA. Then, in the second transmission phase, the strong user, i.e., the one with better satellite channel conditions, decodes the weak user signal, and if the D2D link between them provides a higher rate for the weak user than the achievable rate through satellite NOMA, D2D transmission takes place. The main goal of this study is to identify the pairs of users that can benefit from the terrestrial cooperation combined with NOMA in satellite segment, thereby maximizing the total sum rate of the system compared to standalone NOMA. Towards this end, user pairing is modeled as a maximum weighted perfect matching problem in graph theory, considering the achievable rates in the terrestrial and satellite segments. More specifically, in this work, the following contributions are provided:  
* A novel user pairing scheme is proposed, considering the satellite channels and the terrestrial D2D channels. In order to study this problem, graph theory is invoked--modeling it as a maximum weighted perfect matching problem. * Power allocation for NOMA in each transmission period considers the achieved rates through orthogonal multiple access (OMA). In this way, the proposed power allocation guarantees that user pairing will lead to a higher rate for each pair member and an overall rate increase for the network. * The impact of various system parameters inherent to satellite-based access and D2D networks have been evaluated, i.e., the elevation angle and the bandwidth allocation, respectively. As a result, guidelines are given in order to achieve improved satellite-terrestrial cooperation and spectral efficiency, compared to standalone NOMA and OMA deployments. The structure of the paper is as follows. Section 2 presents the system model, while Section 3 provides the details of SANOCO-D2D scheme. Next, performance evaluation is conducted in Section 4. Finally, conclusions and future directions are given in Section 5.
## 2 Related Work
### Central Bank Communication Analysis  
Official communications from central banks, like the U.S. Federal Reserve's FOMC [23] and the European Central Bank [24], are crucial information sources for understanding monetary policy and predicting market movements. Research in this domain has evolved through several distinct phases, each characterized by increasingly sophisticated analytical approaches.  
Early studies predominantly employed dictionary-based approaches and simple statistical methods to quantify policy sentiment and tone. For instance, [PERSON] and [PERSON] [25] developed lexicon-based measures to capture hawkish and dovish language in FOMC statements, while [PERSON] et al. [26] constructed transparency indices using word counting techniques. However, these methods struggle with linguistic nuances, contextual dependencies, and the evolving nature of central bank language over time [24].  
The second generation of research introduced machine learning techniques to enhance analytical flexibility and capture more complex patterns. Topic modeling approaches such as Latent Dirichlet Allocation (LDA) enabled researchers to identify thematic structures in central bank communications, while Support Vector Machines and ensemble methods improved classification performance for sentiment analysis tasks. Nonetheless, these approaches remained limited by their reliance on hand-crafted features and shallow architectural designs.  
The contemporary phase has witnessed the adoption of deep learning methodologies, particularly pretrained language models based on Transformer architecture. Domain-specific models, like FinBERT [27], have demonstrated excellent performance in financial text analysis [28; 29], successfully capturing subtle semantic relationships and policy implications. However, central bank texts present unique challenges that remain unresolved: their exceptional length often exceeds the context limits of standard Transformer models, and their specialized, diplomatically neutral language requires sophisticated processing capabilities. The need to truncate or oversimplify these documents for analysis continues to limit the effectiveness of current approaches.
# A Connection Between the Kalman Filter and an Optimized LMS Algorithm for Bilinear Forms
## 5 SKF-BF versus OLMS-BF  
The SKF-BF and OLMS-BF algorithms were developed following different theoretical patterns. However, there are strong similarities between these two algorithms, as will be explained in this section.  
The update equations of the SKF-BF are given by (17) and (18), where the Kalman gain vectors have the expressions in (35) and (36). It can be noticed that the updates of the SKF-BF can be expressed as \[\widehat{\mathbf{g}}(n) =\widehat{\mathbf{g}}(n-1)+\mu_{\widehat{\mathbf{g}},\mathbf{K}}(n)x_ {\widehat{\mathbf{h}}}(n)e(n), \tag{69}\] \[\widehat{\mathbf{h}}(n) =\widehat{\mathbf{h}}(n-1)+\mu_{\widehat{\mathbf{h}},\mathbf{K}}( n)x_{\widehat{\mathbf{g}}}(n)e(n), \tag{70}\]  
where the Kalman step-size parameters are  
\[\mu_{\widehat{\mathbf{g}},\mathbf{K}}(n) =\frac{1}{\mathbf{x}_{\widehat{\mathbf{h}}}^{T}(n)\mathbf{x}_{ \widehat{\mathbf{h}}}(n)\left\{1+\frac{M\sigma_{\mathbf{F}}^{2}}{\left[m_{ \mathbf{g}}(n-1)+M\sigma_{\mathbf{F}}^{2}\right]\mathbf{x}_{\widehat{\mathbf{h }}}^{T}(n)\mathbf{x}_{\widehat{\mathbf{h}}}(n)\right\}}}, \tag{71}\] \[\mu_{\widehat{\mathbf{h}},\mathbf{K}}(n) =\frac{1}{\mathbf{x}_{\widehat{\mathbf{g}}}^{T}(n)\mathbf{x}_{ \widehat{\mathbf{g}}}(n)\left\{1+\frac{L\sigma_{\mathbf{F}}^{2}}{\left[m_{ \mathbf{h}}(n-1)+L\sigma_{\mathbf{F}}^{2}\right]\mathbf{x}_{\widehat{\mathbf{ h}}}^{T}(n)\mathbf{x}_{\widehat{\mathbf{g}}}(n)\right\}}}. \tag{72}\]  
Comparing these parameters with the optimal step-sizes from (67) and (68) (also taking (A7) and (A8) into account; see Appendix A), we can notice striking resemblances between SKF-BF and OLMS-BF. In fact, these two algorithms are very similar when  
\[p_{\mathbf{g}}=p_{\mathbf{h}}=0. \tag{73}\]  
On the other hand, as it was indicated in [12], this could represent a reasonable assumption, since  
\[\lim_{n\to\infty}p_{\mathbf{g}}(n)=\lim_{n\to\infty}p_{\mathbf{h}}(n)=0, \tag{74}\]  
suggesting that in the steady-state of the algorithm, the influence of the terms \(p_{\mathbf{h}}\) and \(p_{\mathbf{g}}\) on the step-size parameters diminishes. As will be supported in simulations, (73) can be fairly imposed within the OLMS-BF algorithm, while still leading to a very good compromise between the performance criteria (e.g., convergence rate versus misadjustment). Under these considerations, the OLMS-BF algorithm is summarized in Table 3 (in a practical form that facilitates its implementation).  
\begin{table}
\begin{tabular}{l l l l l l} \hline Initialization: & & & & & \\ \(\widehat{\mathbf{h}}(0)=[1\) & \(0\) & \(\ldots\) & \(0]^{T}\), \(\widehat{\mathbf{g}}(0)=\frac{1}{M}[1\) & \(1\) & \(\ldots\) & \(1]^{T}\) \\ \(m_{\mathbf{h}}(0)=\epsilon_{\mathbf{h}}\), & \(