esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Effect of Biophysical Factors on LUE at Different Time Scales
Seasonal variations in the monthly PAR, T\({}_{a}\), VPD, P, and EF were similar to those in vegetation growth (Figure 8), which is the main reason for the relationship between the monthly PAR, T\({}_{a}\), VPD, P, and EF and LUE, because the vegetation growth determined the monthly GPP [12]. The partial correlation values between the monthly PAR, T\({}_{a}\), VPD, P, and EF and the LUE were negative or around zero (Figure 10b). In addition, the monthly CI was the sole factor influencing the LUE negatively (Figure 9), mainly because the CI influenced the quality of PAR received in the Chinese cork oak plantation, and the general trend in the monthly CI was opposite to that of the monthly GPP (Figure 9). At the annual time scale, the LUE positively and negatively correlated with the GPP and PAR in our study, respectively (Figure 11a). [PERSON] et al. (2010) found that the annual LUE was positively linked with the annual GPP in global forest ecosystems [3], and [PERSON] et al. (2016) pointed out that the annual LUE had a negative effect on the annual LUE in the terrestrial ecosystems of China [26]. The annual LUE was negatively influenced by the annual CI (\(p<0.05\)) in the Chinese cork oak plantation (Figure 11a), implying that the effect of the CI on the quality and quantity of the PAR still played a role in the photosynthetic efficiency at the annual time scale. Compared to other time scales, the relationships between the annual T\({}_{a}\), VPD, P, EF, and NDVI and the LUE were not remarkable at the annual time scale (Figure 11a), indicating that the same biophysical factors had different effects on the LUE at different time scales. According to the results of path analysis and partial correlation analysis, we found that that the quality and quantity of PAR and vegetation growth mainly determined the LUE at different time scales in the Chinese cork oak plantation. In addition, according to the trends in climate change in our study area, the annual LUE of the Chinese cork oak plantation might decrease in the future. Improving management practices, such as fertilization [23] and irrigation [24], might slow or even reverse this trend in the annual LUE in the Chinese cork oak plantation.
# Social and Robust Navigation for Indoor Robots Based on Object Semantic Grid and Topological Map
## 1 Introduction
The robot will extract the target room, object, and spatial relationship from the voice instructions and selects the navigation goal from the corresponding goal space. Therefore, navigation goal selection can be more convenient and social. Next, to improve the success rate and safety of navigation, we use the coordinate of door endpoints and dominant direction to generate the auxiliary navigation points (ANPs) on both sides of the door. The ANPs are used to correct the navigation trajectory, so robot crosses the door vertically. In particular, the generation of ANPs is offline. Finally, based on the ANPs and semantic topological map, global path planning is separated into each topological area and conducted respectively. This provides the correct heuristic direction for global path planning and greatly improves efficiency. Moreover, we verified our system in two real environments and three types of robots. The results show that the system is efficient, robust, and secure. In summary, the contributions of our work are as follows:  
* We propose the indoor robot navigation system based on object semantic grid and topological map. * We use natural language as human-robot interaction form and realize the autonomous and social selection of navigation goal point. * We generate auxiliary navigation points on both sides of the door to improve the success rate of the crossing door and navigation. * We separate the global path planning into each topology area based on the ANPs and topological map, which improves the efficiency of the navigation system. * We verify our proposed system in the real office and home environments. The remainder of this paper is organized as follows. The system overview is introduced in Section 2. The object semantic grid and topological map is introduced in Section 3. The automatic selection of navigation goal is detailed in Section 4. Section 5 presents the generation of ANPs and the algorithm of segmented global path planning. Section 6 presents the experimental results and discussion. Finally, Section 7 concludes this paper.
# Fast Gaussian Filter Approximations Comparison on SIMD Computing Platforms
## 4 Approximations
### FIR Approximations
#### 4.1.1 DFT-Based Convolution  
The discrete Fourier transform (DFT) is a powerful mathematical tool for computing the convolution of a 1D signal. A signal's DFT represents it in the frequency domain, where each frequency component is quantified by its magnitude and phase, making it useful for analyzing and manipulating signals.  
In mathematical language, the Fourier transform is expressed as follows:  
\[F(x)[k]=\sum_{n=0}^{N-1}x[n]\,\mathrm{e}^{-\frac{2\pi i\mathrm{th}}{N}}, \tag{5}\]  
where \(N\) is the size of the input signal. Utilizing the convolution-multiplication property of the DFT, we can compute the convolution via the sequence of discrete Fourier transformations:  
\[y=F^{-1}\big{(}F(g_{\sigma})\cdot F(x)\big{)}. \tag{6}\]  
So, to convolve the image with Gaussian kernel, we can compute the DFT of both signals, perform the element-wise multiplication, and compute the inverse DFT of the result. Note that both signals must be of the same length; if this is not the case, the shorter one must be complemented. Also, DFT (or inverse DFT) should be normalized.  
The computational complexity of the DFT-based convolution (using a fast Fourier transform algorithm [30]) of a 1D signal is typically \(O(N\,\log N)\).
## 3 Methodology
### Performance Metrics
Evaluation metrics such as accuracy, precision, sensitivity, specificity, and F1-Score were calculated using True Positive (TP), True Negative (TN), False Positive (FP), and False Negative (FN) values. This detailed description, including parameters and specific implementations, ensures that readers can reproduce the experimental results accurately. Furthermore, feature extraction for both classes can be performed simultaneously by first applying preprocessing steps to the entire dataset, and then extracting features from the entire set before splitting it into different classes. This approach involves applying techniques such as PCA or ICA to the full dataset, selecting the most relevant features then dividing the data into classes or training/testing sets. This ensures that the features are representative of the entire dataset, leading to more robust and generalizable models.
## 4 Numerical Results
### The Influence of the Corrective Maintenance Periods on the Lead-Time of the Finished Product  
Many uncertainties can disturb the manufacturing process. In the implementation of the maintenance plan realized above, repair periods are negligible. This has no influence on disassembly-assembly plan. In a real industrial case, some failures take important periods, which disturb the manufacturing process and cause a delay in the delivery. Thus, decision makers must consider this repair time in planning before starting the order processing. In our work, we discuss the period that can take corrective maintenance  
Figure 10: Sensitivity diagram of the percentage of remanufactured items.  
actions according to the optimal plan implemented by the manager. We suppose a threshold period noted \(\tau_{\text{s}}\) (Figure 11). This period represents the time that corrective maintenance actions can take it.  
As described above, we propose to study the system in a sequential way. From Figure 11, we first implement the plan of disassembly and assembly which is determined from the optimal ordered date of used/end-of-life product and the vector of release dates for components at level 2. However, this optimal plan does not take into consideration the system repair time that causes the machine breakdowns, therefore the delay in disassembly and assembly processes. In fact, according to the representation of the optimal disassembly-assembly plan (Figure 11), the product can be delivered to the customer at \(T_{PF}-1\).  
Using this plan, we calculated the average number of random failures during the working horizon.  
In the example illustrated above, we found that the average number of failures \(\phi(A,N)=5.84\). Therefore, if we consider the same plan already calculated, we can note that \(\tau_{\text{s}}=\Delta t\).  
Therefore, if we can quantify each repair time, we can say that each failure should not exceed \(1/6\) of a period: \(\tau=\frac{\phi(A,N)}{\tau_{\text{s}}}=\frac{1}{6}\).  
Beyond this period, the decision makers must re-implement the finished product manufacturing plan. Otherwise, a profit-loss risk must be discussed.
## 4 Intravalley Relaxation and Fermi Degeneracy Formation
### Fermi Blocking Effect  
The \(n_{\mathrm{M}}\)-dependence of the relaxation is investigated in this section. As seen in Figure 7, the result does not depend on \(n_{\mathrm{M}}\) strongly around \(n_{\mathrm{M}}=0.003\); both rapid relaxation in the initial stage and very slow one at a later time can be seen. However, we can find a difference in the energy loss rate, which is defined as \(-d\langle E\rangle/dt\), in the initial relaxation process. The present result has shown that the increase of \(n_{\mathrm{M}}\) results in the decrease of the energy loss rate, meaning the slowing down of the initial relaxation speed. This fact is understood by considering the Fermi blocking effect. An electron is able to relax down to a lower energy state only when the lower energy state has holes, due to [PERSON]'s exclusion principle. Thus, the electrons placed in the high energy state cannot move until the hole is created  
Figure 5: The time evolution of the total energy is shown. The dashed line represents the energy relaxation, which is proportional to \(t^{-1}\). The total energy is referenced from that in the completely Fermi degenerated state (CFDS). Adapted with the permission from [11], copyright Journal of the Physical Society of Japan, 2015.  
Figure 6: The time evolution of the electron occupations to 4000 fs is given. In the completely Fermi degenerated state (CFDS), the electron occupation corresponds to the Fermi–Dirac distribution at \(T=0\) K. The energy is referenced from the conduction band minimum (CBM).  
on the low energy side. In the large \(n_{\rm M}\) case, it takes much more time for this trivially due to many carriers. In other words, when the number of carriers is increased without the change of the number of states, the total relaxation flow becomes slow, like a traffic jam.
# A Statistical Approach for Modeling Individual Vertical Walking Forces
## 4 Statistical Characterization of the Walking Parameters
### Simplified Force Model  
The same correlation analysis is performed with reference to the walking parameters characterizing the simplified force model. In this case, the average correlation matrix \(\overline{\mathbf{R}}\) computed from experimental measurements is:  
\[\overline{\mathbf{R}}=\left[\overline{\mathbf{R}}_{ij}\right]=\left[\begin{array}{ c c c c}1.000&-0.312&-0.334&0.133&0.078\\ -0.312&1.000&0.583&-0.027&0.068\\ -0.334&0.583&1.000&-0.237&0.018\\ 0.133&-0.027&-0.237&1.000&0.154\\ 0.078&0.068&0.018&0.154&1.000\end{array}\right] \tag{11}\]  
where the order of the variables \(i,j\) is the following: \(A_{0}\), \(T_{c}\), \(\Delta t\), \(l\) and \(\theta\). A moderate correlation between the following pairs of variables is observed, \(A_{0}-T_{c}\), \(A_{0}-\Delta t\), \(A_{0}-l\), \(T_{c}-\Delta t\), \(\Delta t-l\) and \(\theta-l\), while any other correlation can be deemed negligible. Based on this consideration, it is assumed that the step direction \(\theta\) is statistically independent of the other features, which are, in turn, correlated. Then, the pdf \(p(S)\) of the correlated variables can be simplified as  
\[p(S)=p(A_{0},T_{c},\Delta t,l,\theta)=p(A_{0},T_{c},\Delta t,l)p(\theta) \tag{12}\]  
A good compromise between complexity and accuracy of the model is obtained considering \(N_{c}=3\) components in the definition of the GM fitting the experimental data:  
\[\widehat{p}(A_{0},T_{c},\Delta t,l)=\sum_{i=1}^{N_{c}}w_{i}N\Big{(}\mathbf{\mu}_{i },\sum_{i}\Big{)} \tag{13}\]  
The parameters of the GM of Equation (13) are shown in A (Table A3). As an example, the experimental histogram and the corresponding GM fit of the pair \(T_{c}-A_{0}\) are shown in Figure 8 for the TS 1. The great accuracy of the proposed model based on GM is proved by the results of the Kolmogorov-Smirnov test, which shows that, for all three TSs, random data have passed 100% of tests with a 5% significance level.  
Hence, the transition pdf \(p(S)\) of Equation (12) is approximated as  
\[\widehat{p}(S)=\widehat{p}(A_{0},T_{c},\Delta t,l)\widehat{p}(\theta) \tag{14}\]  
on the basis of Equations (9) and (13). Being the step direction \(\theta\) statistically independent from the other parameters, its pdf (experimental and simulated) is the same described in Section 4.1.  
Figure 8: **(a)** Experimental \(T_{c}-A_{0}\) histogram and **(b)** GM fit for TS 1.
# Chloroplasts at the Crossroad of Photosynthesis, Pathogen Infection and Plant Defense
## 9 Chloroplast Structure and Functions are Manipulated by Phytotoxins
Early steps of ABA [4, 5], ET [6], and JA [7, 8] biosynthesis occur in the chloroplast; SA is primarily synthesized in the chloroplast [11]; ROS could be produced by PSI and PSII [15, 16]; -NO could be made by the nitrate reductase and -NO synthase-like enzyme in the chloroplast [234, 235]; and Ca\({}^{2+}\) spikes could be generated by thylakoid-membrane-localized CAS [276]. The fact that photosynthesis and biosynthesis of these defense-related signaling molecules occur in the same organelle facilitates the interactions between photosynthesis and defense signaling. On the one hand, synthesis of these defense-related signaling molecules requires photosynthetic products such as carbon skeletons, energy, and reducing power, which are conveniently available in the chloroplast. On the other hand, these defense-related signals may influence photosynthesis by regulating the expression of chloroplast-encoded photosynthetic genes and nuclear-encoded chloroplast-targeted photosynthetic protein genes. Chloroplasts are targeted by pathogen effectors [35, 308, 366]. In order to translocate into chloroplasts, some effectors (e.g., ArvRps4 and HopK1 [308, 309]) utilize an N-terminal cleavable chloroplast transit peptide, and some other effectors (e.g., HopI1 and HopN1 [306, 307]) employ a non-cleavable transit peptide. Most chloroplast-targeted effectors also translocate to other subcellular compartments of plant cells. For effectors with a cleavable chloroplast transit peptide, their chloroplast localization and _in planta_ N-terminal processing are essential for their full virulence [308, 309]. After entry into chloroplasts, these effectors may interact with chloroplastic proteins (e.g., PTF1, CBSX2, Hsp70, and PsbQ) and manipulate chloroplast structure and functions (e.g., thylakoid remodeling, expression of photosynthetic genes, photosynthetic water splitting and electron transport, enzyme redox status, and SA biosynthesis). Pathogens also produce phytotoxins to inhibit host defense and enhance pathogenicity; some phytotoxins affect the structure and functions of chloroplasts. Tabtoxin [348, 349] and phaseolotoxin [351, 352, 353, 354] inhibit the activities of two chloroplastic enzymes, Gln synthetase and ornithine carbamoyltransferase, respectively. COR, on the other hand, modulates plant JA signaling, promotes chlorophyll degradation, and inhibits SA biosynthesis in the chloroplast [359, 360, 367]. These three phytotoxins ultimately cause chlorosis of plant tissues [30]. Taken together, these studies suggest that the chloroplast plays a pivotal role in the interplay among photosynthesis, pathogen infection, and plant defense.
### NANC System with Online SPM Using Scaled Auxiliary Noise Injection  
The fixed power auxiliary noise used in Figure 1 contributes to the residual error and causes the low convergence speed of the system. To address this problem, a NANC system equipped with an online SPM subsystem that uses auxiliary noise scaled by one-delayed residual noise signal was proposed in [27], the block diagram of which is depicted in Figure 2. We see that this structure is similar to that of [PERSON]'s method shown in Figure 1, except for the addition of an auxiliary noise power scheduling strategy called noise power scheduling.  
The auxiliary noise power scheduled based on the status of the one-sample-delayed residual noise signal can be expressed as  
\[v_{g}(n)=|e(n-1)|v(n) \tag{18}\]  
The signal \(y(n)\) can be recalculated as  
\[y(n)=y_{p}(n)-v_{g}(n)=y_{p}(n)-|e(n-1)|v(n) \tag{19}\]  
Substituting the above equation into Equation (5), one has  
\[e(n)=p(n)-y_{s}(n)=p(n)-\sum_{j=0}^{M-1}s_{j}y(n-j)=p(n)-\sum_{j=0}^{M-1}s_{j}y _{p}(n-j)+\sum_{j=0}^{M-1}s_{j}|e(n-1-j)|v(n-j) \tag{20}\]  
Similarly, using \(v_{g}(n)\) to replace \(v(n)\) in Equations (15) and (16), one obtains  
\[y_{v}(n)=\mathbf{V_{g}^{T}(n)\hat{S}(n)}=\sum_{j=0}^{\hat{M}-1}\hat{s}_{j}(n) v_{g}(n-j)=\sum_{j=0}^{\hat{M}-1}\hat{s}_{j}(n)|e(n-1-j)|v(n-j) \tag{21}\]  
\[e_{v}(n)=e(n)-y_{v}(n)=e(n)-\sum_{j=0}^{\hat{M}-1}\hat{s}_{j}(n)|e(n-1-j)|v(n-j) \tag{22}\]  
where \(\mathbf{V_{g}(n)}\) is defined as  
\[\mathbf{V_{g}(n)}=[|e(n-1)|v(n)\quad|e(n-2)|v(n-1)\quad\cdots\quad|e(n-\hat{M }-2)|v(n-\hat{M}-1)]^{T} \tag{23}\]  
The FXLMS algorithm for DFC estimation \(\{\hat{a}_{i}(n),\hat{b}_{i}(n)\}_{i=1}^{q}\) and the LMS algorithm for the coefficient vector of \(\hat{S}(z)\) are expressed as  
\[\hat{a}_{i}(n+1)=\hat{a}_{i}(n)+\mu_{i}e(n)\hat{x}_{a_{i}}(n) \tag{24}\]  
\[\hat{b}_{i}(n+1)=\hat{b}_{i}(n)+\mu_{i}e(n)\hat{x}_{b_{i}}(n) \tag{25}\]  
Figure 2: NANC system with online SPM using scaled auxiliary noise injection (\(i\)th channel).  
\[\hat{\mathbf{S}}(\mathbf{n}+\mathbf{1})=\hat{\mathbf{S}}(\mathbf{n})+\mu_{s}e_{v}(n )\mathbf{V_{g}}(\mathbf{n}) \tag{26}\]  
From Equations (18), (20), and (22), the auxiliary noise \(v(n)\) is scaled by an absolute value of the one-sample-delayed residual noise signal. Then, it is added with the output of the NANC subsystem and acts as a secondary source. Meanwhile, it is also entered into the secondary path model. When the system is close to the steady state, the scaled auxiliary noise \(|e(n-1)|\) becomes very small, significantly reducing the residual noise signal of the overall system.
## 2 System Model
### Inter-Cell Interference Considerations in the AG-HetNet  
The small cells, such as the UABS shown in Figure 1, have low transmission power and a finite ability to schedule GUEs compared to MBSs. Therefore, we consider the positive-bias CRE technique defined in 3 GPP Release-8 at UABS to extend the network coverage and increase the capacity by offloading GUEs from congested MBSs or consuming the unscheduled users. However, positive-bias CRE at UABS could potentially increase the interference at the GUEs located in the cell edge or expanded region. To address this interference introduced between the MBS-UABS cells, MBS can use the ICIC techniques defined in 3 GPP Release-10/11 [4, 5]. Using these ICIC techniques, MBSs can transmit radio frames at reduced power levels, of which an example frame is shown in Figure 4.  
As seen in Figure 4, it illustrates an example of radio subframes for AG-HetNet, where frames with full transmit power are the uncoordinated subframes (USF) and subframes with reduced power as the coordinated subframes (CSF). We define \(\alpha_{*}\) as the power reduction factor of radio subframes, and the power reduction factor at MBS is given by \(\alpha_{\text{mbs}}\). The range of the power reduction factor is given by \(\alpha_{\text{mbs}}\in[0,1]\), such that \(\alpha_{\text{mbs}}=0\) corresponds to the 3 GPP Release-10 almost blank subframes (ABS) eICIC technique, \(\alpha_{\text{mbs}}=1\) corresponds to no ICIC, and otherwise, \(0<\alpha_{\text{mbs}}<1\) corresponds to the reduced power FeIC technique defined in 3 GPP Release-11.  
The reduced power FeICIC at MBSs aims at protecting specific UABS subframes from MBS, as seen in Figure 4. To regulate the duty cycle and coordinate the radio frames, we consider factor \(\beta_{\text{mbs}}\) and \((1-\beta_{\text{mbs}})\) at MBS for USF and CSF, respectively. The public safety AG-HetNet's system design assumes the power reduction pattern, radio subframe duty cycle, and spectrum parameters to be shared via the X2 interface, which is a logical interface between the base stations.  
The MBSs in the AG-HetNet greatly benefit from the application of ICIC techniques as it reduces the intercell interference into adjacent cells; however, ICIC also reduces the quality of service (QoS) at the scheduled GUEs. Therefore, to improve the QoS in the AG-HetNet, we consider the 3 DBF technique at each transmitting base station to restrict the beamforming and power transmission at the location of scheduled GUEs [39].  
Figure 3: The CDF describes the combined path loss observed from all the base stations in PSC AG-HetNet. The dashed lines correspond to the scenario where 50% of the MBSs are destroyed, while solid lines correspond to the scenario with 97.5% of the MBSs being destroyed. The CDF is plotted for GUEs distribution using the Matern and [PERSON] clusters processes.