esa-sceva/satcom-chunk-collection huggingface dataset:
# Expansion of Image Space in Enhanced-NA Fresnel Holographic Display
## 5 Conclusions  
We studied the expansion of image space in the enhanced-NA Fresnel holographic display system. The reconstruction behavior largely depends on the angular view range, subjected to the synthesis algorithm of the phase Fresnel hologram. In particular, when the viewing angle is restricted to a diffraction angle, a complete image without suffering from the high-order noises is well observed in numerical and optical experiments. For the implementation of the holographic display, it is important to develop the algorithm for extending the image space, keeping the viewing angle of a holographic image. Since the viewing angle depends on the hologram numerical aperture in principle, the replica noises in both the hologram and image planes could be removed during the iterative optimization algorithm. This strategy makes it possible to recover the limitation of the narrow viewing angle problem due to the smaller pixel pitch of a pixelated modulator, which would realize the holographic display, even using a commercial spatial light modulator.  
This work was partially supported by the Institute for Information and Communications Technology Promotion (IITP) grant, funded by the Korean government (MSIP) (2017-0-00049 and 2021-0-00745).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
Figure 9: Experimental results using the Riemann integral during the repetition processes. (**a**) Synthesized hologram and (**b**) reconstructed image. The inset shows the magnified light intensity propagating from the reconstructed image.
# A Novel Dual Mixing Attention Network for UAV-Based Vehicle Re-Identification
## 4 Analysis and Experiments
### Ablation Experiment and Analysis
#### 4.4.2 The Effectiveness on Which Stage to Plug the Dual Mixing Attention Module  
We designed a set of experiments and demonstrated its effectiveness by adding our proposed DMAM at different stages of the backbone network. Symbol indicates that the DMAM is added after one of the residual blocks of the backbone network.  
The experimental results of introducing DMAM after various backbone residual blocks are shown in Table 4. The findings demonstrate how the various residual blocks introduced to the backbone network impact the network's robustness. As shown in Figure 4, specifically, adding the DMAM behind the 4 th (No. 2) and 5 th (No. 3) residual blocks of the backbone improves the accuracy over the baseline (No. 0). The 5 th (No. 3) residual block improves the accuracy over the baseline (No. 0), achieving 7.22% mAP, 1.17% Rank-1, 1.08% Rank-5, and 0.35% Rank-10 gains. It indicates that the 5 th (No. 3) residual blocks of the module can effectively extract fine-grained vehicle features at these locations.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**No.** & **Conv3\_x** & **Conv4\_x** & **Conv5\_x** & **Rank-1** & **Rank-5** & **Rank-10** & **mAP** \\ \hline
0 & & & & 95.14 & 95.63 & 97.48 & 79.59 \\
1 & ✓ & & & 95.74 & 96.88 & 97.74 & 82.95 \\
2 & & ✓ & & 96.52 & 98.01 & 98.24 & 85.18 \\
3 & & & ✓ & **97.04** & **98.65** & **98.83** & **86.99** \\ \hline \hline \end{tabular}
\end{table}
Table 4: The effectiveness on which stage to plug the dual mixing attention module (DMAM).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Rank-1** & **Rank-5** & **Rank-10** & **mAP** \\ \hline Baseline & 95.14 & 95.63 & 97.48 & 79.59 \\ +CMA & 96.34 & 97.25 & 98.03 & 83.63 \\ +SMA & 96.56 & 97.42 & 98.27 & 84.96 \\ \hline
**Ours** & **97.04** & **98.65** & **98.83** & **86.99** \\ \hline \hline \end{tabular}
\end{table}
Table 3: The role of dual mixing attention module (DMAM).
## 1 Introduction  
Tremendous knowledge from more than fifty years of research has been accumulated regarding the transport behavior of electrons in a variety of two-dimension (2D) materials and systems. It has regained strong interest in the field of physics and material science due to the recent discoveries of topological insulators [2] and new 2D materials [3].  
With different types and strengths of interactions, many fascinating phenomena can emerge at various temperatures and energy scales, such as the Fermi liquid, the Wigner crystal [4], the integer quantum Hall (QH) effect [5], and the fractional QH effect [6]. The scaling theory of localization for noninteracting 2D systems [7] predicts that the electronic states are localized while the temperature \(T\) approaches zero. In a low density and high mobility Si-metal-oxide-semiconductor field-effect transistor (Si-MOSFET), however, a metal-insulator transition (MIT) was observed by [PERSON] [1]. This implies that strong correlation effects play a key role in the MIT [8, 9], as the value of \(r_{s}\) (the ratio between the Coulomb energy and the kinetic energy, \(r_{s}\equiv\ 1/\left[a_{B}^{*}\sqrt{\pi p}\right],\ a_{B}^{*}=\ h^{2}e/m^{*}e^{2}\) is the effective Bohr radius) is much greater than one.  
In this brief review, we will first introduce and focus on the recently discovered low magnetic field reentrant insulating phases (RIPs) in high quality GaAs 2D systems [10, 11, 12, 13] with a new phase diagram, which connects the zero-field MIT with RIPs, integer QH states, fractional QH states, and high magnetic field insulating phases [14]. Resistivity, capacitance and inductance measurements exploring the RIPs and phase diagram are presented and discussed in the context of the transition between a 2D electron liquid and a Wigner crystal (WC), an ordered solid phase of electrons or holes whose formation is driven by the minimization of strong Coulomb interactions in an electronic system with a high enough \(r_{\mathrm{s}}\). Secondly, experimental observations of a possible Wigner crystal melting and new intermediate phases are shown and examined. Finally, relevant theoretical models that try to explain the MIT are briefly discussed.
# RIS-Assisted Multi-Antenna AmBC Signal Detection Using Deep Reinforcement Learning
## 5 Numerical Results and Analysis  
In this section, extensive quantitative and qualitative experiments, analysis, and discussion are performed to demonstrate the effectiveness of the proposed TD3 signal detection method for RIS-based multi-antenna AmBC.
# Isolation and Properties of _Enterobacter_ sp. LX3
## 5 Conclusions  
One strain was obtained from maize rhizosphere in a biochar-amended field. It was a Gram-negative rod with peritrichous flagella and facultative anaerobic growth, which shared key characteristics with the genus _Enterobacter_. This isolate was identified as a new member of genus _Enterobacter_ according to differential phenotypic characteristics combined with phylogenetic analysis, and designed as _Enterobacter_ sp. LX3. Strain LX3 is an IAA-producer producing maximally 200 mg/L of IAA in NB medium. The root system and chlorophyll content were enhanced when isolate LX3 was used to inoculate barley plants.  
Conceptualization, X.L. and W.C.; methodology, Q.C., L.L. and W.T.; formal analysis, J.M. and X.D.; investigation, Q.C., L.L., W.T. and X.D.; resources, J.M.; data curation, L.L.; writing--original draft preparation, W.T.; writing--review and editing, X.L.; visualization, X.L.; supervision, W.C.  
This research was funded by the National Key R&D Program of China (2017 YFD0200800) and the National Natural Science Foundation of China (31771907).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Characteristic** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **12** & **13** \\ \hline Lysine decarboxylase & + & – & – & – & – & – & – & N & – & – & + & – & – \\ \hline \(\beta\)-Galactosidase & – & + & + & + & + & + & + & N & + & + & – & + & + \\ Argin dihydro & + & + & N & + & + & + & + & N & + & + & – & + & – \\ Urea hydrolysis & + & – & + & – & – & – & – & N & – & – & – & – & – \\ H\({}_{2}\)S production & – & – & – & – & – & – & – & + & – & – & – & – & – \\ Voges-Proskauer reaction & + & – & – & + & + & + & + & + & – & + & + & + & – \\ Indole production & + & – & – & – & – & – & – & + & – & – & – & – & + \\ Acid production from: & & & & & & & & & & & & & \\ Inositol & – & – & – & – & – & + & – & – & – & – & + & – & N \\ d-Mannitol & – & + & + & + & + & + & + & N & + & + & + & + & + \\ Sucrose & – & + & + & – & + & + & + & + & + & + & + & + & + \\ d-Sorbitol & + & + & + & – & + & + & + & N & + & + & + & + & – \\ Melibiose & + & + & – & – & + & + & – & – & + & + & + & + & + \\ l-Rhamnose & + & + & – & + & + & + & – & N & + & + & + & + & + \\ \hline \hline \end{tabular}
\end{table}
Table 3: Key characteristics distinguishing strain LX3 from other _Enterobacter_ species. Species: 1, _Enterobacter_ sp. LX3; 2, _Enterobacter bugandensis_; 3, _Enterobacter asburiae_; 4, _Enterobacter cancerous_; 5, _Enterobacter cloace_; 6, _Enterobacter ludwigii_; 7, _Enterobacter orgizpillus_; 8, _Enterobacter tabaci_; 9, _Enterobacter kobei_; 10, _Enterobacter asboris_; 11, _Enterobacter aerogenes_; 12, _Enterobacter xiangfangensis_; 13, _Leclercia aderboxylata_. Symbols: +, 90% or more strains were positive; -, 10% or less strains were positive; N, not be detected.  
**Acknowledgments:** This works was supported by Dalian Polytechnic University.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# AR Presentation of Team Members' Performance and Inner Status to Their Leader: A Comparative Study
## 6 Results
### Questionnaires
#### 6.2.3 Component Feedback  
In the final questionnaire, participants had to choose which components they found most helpful for each condition. The only significant difference is for the Stress component: participants preferred having it displayed with a graphical presentation, which also supports **H2** (\(\chi^{2}(1)=6.0\), \(p=0.014\)).  
An open-ended question asked participants to share their points of view on the textual and graphical presentations of the awareness components (listed in Section 3). Regarding the graphical presentation, 15 participants reported that they liked the Stress component, and 5 said that this presentation improved their understanding of the data. For the textual presentation, 5 participants said they enjoyed the Success component, 6 the Score component, 5 stated that the written numbers were a perk of the textual presentation, and 6 reported that it was easier to understand the data with this presentation.  
Asked if they felt that having stress feedback is essential, all participants answered \"yes\". They were then asked to explain their answer, and 11 participants reported that having stress feedback helped them obtain a better score. One participant explicitly mentioned that having stress feedback was better than determining the stress level from other components.  
In evaluating the possibility of hybrid components (as a mix of textual and graphical presentations), participants were asked which hybrid components they may find helpful. Some participants suggested not doing hybrid presentations of some components and keeping a textual presentation. Conversely, some participants suggested keeping some components with a graphical presentation. No strong tendencies could be identified except for the Task Queue and the Stress components. Among participants with an opinion on the Task Queue component, 6 recommended keeping it with a textual presentation, 2 recommended keeping a graphical presentation, and only 1 suggested changing it to a hybrid component. Opinions concerning the Stress component confirmed the preference for the graphical presentation by 11 participants mentioning it; however, 7 participants still suggested turning it to a hybrid component, with only 1 suggesting keeping it with a textual presentation.
# Comparison of Different Algorithms to Orthorectify WorldView-2 Satellite Imagery
## 3 Results and Discussion
A combination of RPF and PF algorithms gave the best results. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**[PERSON]’s Rigorous Model** & **Min** & **Max** & **Mean** & **St. dev.** & **RMS** \\ \hline \multirow{6}{*}{DEM 1 m} & GCPs: 5 & 0.553 & 1.996 & 1.227 & 0.469 & 1.313 \\  & CPs: 15 & 1.435 & 6.444 & 3.786 & 1.604 & 4.112 \\  & GCPs: 10 & 0.319 & 3.328 & 1.397 & 0.832 & 1.626 \\  & CPs: 15 & 0.505 & 5.097 & 2.250 & 1.355 & 2.627 \\  & GCPs: 15 & 0.203 & 3.864 & 1.680 & 1.005 & 1.958 \\  & CPs: 15 & 0.370 & 3.108 & 1.517 & 0.846 & 1.737 \\ \hline \multirow{6}{*}{DEM 20 m} & GCPs: 5 & 0.508 & 2.102 & 1.317 & 0.538 & 1.423 \\  & CPs: 15 & 1.617 & 9.244 & 4.448 & 2.434 & 5.071 \\ \cline{1-1}  & GCPs: 10 & 0.544 & 8.836 & 3.413 & 2.249 & 4.088 \\ \cline{1-1}  & CPs: 15 & 0.920 & 5.508 & 3.165 & 1.541 & 3.521 \\ \cline{1-1}  & GCPs: 15 & 0.674 & 9.438 & 2.774 & 2.185 & 3.532 \\ \cline{1-1}  & CPs: 15 & 0.927 & 5.190 & 2.854 & 1.360 & 3.162 \\ \hline \multirow{6}{*}{DEM 75 m} & GCPs: 5 & 0.621 & 3.040 & 1.938 & 0.871 & 2.125 \\  & CPs: 15 & 1.298 & 10.945 & 5.203 & 2.836 & 5.926 \\ \cline{1-1}  & GCPs: 10 & 0.689 & 7.822 & 3.282 & 1.934 & 3.809 \\ \cline{1-1}  & CPs: 15 & 0.937 & 8.409 & 3.458 & 2.340 & 4.175 \\ \cline{1-1}  & GCPs: 15 & 0.549 & 8.030 & 2.769 & 1.895 & 3.355 \\ \cline{1-1}  & CPs: 15 & 0.830 & 7.829 & 3.495 & 2.199 & 4.129 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Residuals (in meters) obtained for GCPs (5-10-15) and CPs using [PERSON]’s rigorous model. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**3D RPFs without RPCs** & **Min** & **Max** & **Mean** & **St. dev.** & **RMS** \\ \hline \multirow{6}{*}{DEM 1 m} & GCPs: 45 & 0.050 & 3.695 & 0.886 & 0.728 & 1.147 \\  & CPs: 15 & 0.130 & 7.632 & 2.093 & 1.903 & 2.828 \\  & GCPs: 60 & 0.030 & 3.406 & 0.970 & 0.722 & 1.209 \\  & CPs: 15 & 0.079 & 7.668 & 1.948 & 1.972 & 2.771 \\  & GCPs: 75 & 0.078 & 3.742 & 1.027 & 0.807 & 1.306 \\  & CPs: 15 & 0.266 & 3.527 & 1.354 & 1.078 & 1.731 \\ \hline \multirow{6}{*}{DEM 20 m} & GCPs: 45 & 0.064 & 8.989 & 1.670 & 1.553 & 2.280 \\  & CPs: 15 & 0.840 & 23.205 & 3.778 & 5.362 & 6.559 \\ \cline{1-1}  & GCPs: 60 & 0.117 & 10.034 & 1.870 & 1.596 & 2.458 \\ \cline{1-1}  & CPs: 15 & 1.008 & 6.866 & 2.845 & 1.822 & 3.378 \\ \cline{1-1}  & GCPs: 75 & 0.076 & 9.790 & 1.913 & 1.515 & 2.441 \\ \cline{1-1}  & CPs: 15 & 0.513 & 6.221 & 2.867 & 1.808 & 3.389 \\ \hline \multirow{6}{*}{DEM 75 m} & GCPs: 45 & 0.094 & 6.054 & 1.693 & 1.482 & 2.250 \\ \cline{1-1}  & CPs: 15 & 0.917 & 53.431 & 7.725 & 12.634 & 14.809 \\ \cline{1-1}  & GCPs: 60 & 0.442 & 8.542 & 2.745 & 1.848 & 3.310 \\ \cline{1-1}  & CPs: 15 & 1.578 & 5.397 & 3.813 & 1.268 & 4.018 \\ \cline{1-1}  & GCPs: 75 & 0.090 & 10.415 & 2.482 & 2.107 & 3.255 \\ \cline{1-1}  & CPs: 15 & 0.622 & 13.869 & 4.262 & 3.204 & 5.331 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Residuals (in meters) obtained for GCPs (45-60-75) and CPs using RPFs without RPC file.
# Finite Differences on Sparse Grids for Continuous-Time Heterogeneous Agent Models
## 5 Numerical Results
### Two-Dimensional Model
#### 5.1.1 Adaptive Sparse Grids  
We performed experiments with several types of adaptivity criteria since there is no theoretical rule determining which criterion is optimal. The solution of the HJB equation--the value function--often does not give useful insight. Thus, we are also interested in a good approximation of the policy function describing the controls' consumption \(c_{t}\) and deposit \(d_{t}\). These were computed by the approximated derivatives of the value function as given by (7). Hence, it is not directly clear where the grid should be refined. That is why we study value function adaptivity and policy functions adaptivity. Additionally, we experiment with combinations of both.  
Figure 6: Accuracy of different sparse grid levels: \(e_{2}\) and \(e_{\infty}\)-errors for the value function; deposit policy and consumption policy for states 1 and 2.  
Thus, for the value function adaptivity we used the hierarchical coefficients of the sparse grid value function approximation. Similarly, we used the hierarchical coefficients of the policy function approximation for the policy function adaptivity. Hence, for the two-dimensional model (3) and (4) described in Section 2.2, we can use a value function adaptivity, a consumption function adaptivity, or a deposit function adaptivity.  
Moreover, we used the combination of the above described adaptivity types. One possibility is a _logical combination_. By that we mean the use of a logical operator like OR or AND to combine adaptivity with respect to different functions, i.e., the criteria have to be fulfilled by one of them, i.e., OR, or all of them, i.e., AND, to mark a point for adaptivity. Moreover, one can implement a _weighted combination_ by computing a weighted sum of the hierarchical coefficients of different functions on the same points.  
First, in the two-dimensional case we could visualize the different grids obtained by value function adaptivity versus policy function. Here, we focused on the deposit function since we observed the biggest errors for it, but the observations could be transferred to policy functions in general.  
In Figure 7, the approximation of the value function for state 1 and the sparse grid are shown. Note that we indicate the grid points by their respective function values as bullet points. One can see that, using value function adaptivity, the sparse grid is refined in the area in which the value function is steep. Note that this is not the area where the deposit function is steep. Using deposit function adaptivity, the resulting sparse grid looks completely different, now there are more grid points in the area where the deposit function is steep.
## 5 Conclusions  
In conclusion, this study aimed to explore the potential benefits and challenges of integrating machine workload information into a well-established PHM algorithm for Industry 4.0 and its impact on maintenance strategies in the manufacturing process. A comprehensive literature review revealed that while there is significant interest in this problem and its potential solutions, there is a lack of specific PHM algorithms geared towards practical implementation in the manufacturing domain due to the required computing power.  
To address this gap, a novel approach that integrates machine workload information into a well-established PHM algorithm to estimate the failure probability of machines or equipment was proposed. This approach was tested on a real-world manufacturing system and the results showed that the integration of machine workload information into the PHM algorithm notably improved the accuracy of failure probability forecasting. In particular, for the considered case study, the integration of planned workload information into a classical PHM algorithm resulted in an increased accuracy estimation of the failure probability of 15%.  
The results of this study suggest that this approach has the potential to improve the autonomy of CPSs in accepting or declining scheduled jobs according to their forecasted health state and to lower maintenance costs, while maximising the utilisation of production resources by providing CPSs with a more accurate awareness of their functioning capabilities. However, the proposed approach also presents challenges, such as the integra
Figure 9: Failure prediction at T + 4.  
tion of this approach into a hierarchical manufacturing system and the potential conflicts that may arise from the communication and negotiation among CPSs within the plant. Therefore, future research should investigate these challenges and investigate the potential improvements that can be achieved by integrating machine workload information into PHM algorithms in different industrial contexts. Additionally, future research should focus on how to implement and deploy the proposed approach in the manufacturing domain.  
Conceptualization, G.C. and S.V.; Methodology, S.V.; Software, S.V.; Formal analysis, M.G.; Investigation, S.V.; Writing--original draft, G.C. and S.V.; Writing--review & editing, M.G.; Supervision, T.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 4 Mathematical Programming Problem Formulation
### Network Definition--Constraints  
Constraint (7) describes sensor node buffer evolution, i.e., accumulation when receiving messages (\(l=2\)) and reduction when in sending mode (\(k=3,\ldots,K\)). The inequality (8) models buffer capacity. Let us assume that node 0 (base station) has a buffer sufficient to collect all data sent by other nodes. Equation (9) restricts node operation to single-mode--sleeping, receiving or transmitting--in every moment \(t\). Inequalities (10) and (11) describe media access, namely hidden and exposed node problem [33; 34] respectively. Constraint (10) ensures that every receiving node \(i\) must be able to hear the signal from at most one transmitting node \(j\). To complement this, the constraint (11) ensures that at least one node \(i\) must be able to receive the transmission of node \(j\). Equation (12) guarantees that all data present in the initial time instant  
Figure 2: Available energy states of the module CC2420 [31].  
\(t=0\) must be transmitted to node 0, while the attendant Equation (13) imposes empty buffers in all nodes except base station at last time slot \(t=T\).  
\[\forall_{\begin{subarray}{c}i=0,\ldots,N,\\ t=1,\ldots,T,\\ I=2\end{subarray}}\quad q_{t}^{i}=q_{(t-1)}^{i}+\epsilon_{tt}^{i}-\sum_{k=3}^{ K}\epsilon_{tt}^{i}, \tag{7}\] \[\forall_{\begin{subarray}{c}i=1,\ldots,N,\\ t=1,\ldots,T\end{subarray}}\quad 0\leq q_{t}^{i}\leq Q_{i},\] (8) \[\forall_{\begin{subarray}{c}i=0,\ldots,N,\\ t=1,\ldots,T\end{subarray}}\quad\sum_{k=1}^{K}e_{kt}^{i}=1,\] (9) \[\forall_{\begin{subarray}{c}i=0,\ldots,N,\\ t=1,\ldots,T,\\ I=2\end{subarray}}\quad e_{lt}^{i}+N(1-\epsilon_{lt}^{i})\geq\sum_{j=1}^{N} \sum_{k=3}^{K}(A_{jk}^{i}+B_{jk}^{i})\epsilon_{kt}^{j},\] (10) \[\forall_{\begin{subarray}{c}i=1,\ldots,N,\\ t=1,\ldots,T,\\ k=3,\ldots,K,\\ \end{subarray}}\quad e_{kt}^{i}\leq\sum_{j=0}^{N}A_{ik}^{j}\epsilon_{lt}^{j},\] (11) \[\sum_{i=1}^{N}q_{0}^{i}=q_{T}^{0}-q_{0}^{0},\] (12) \[\sum_{i=1}^{N}q_{T}^{i}=0. \tag{13}\]