esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In this study, a fast-response magnetic enrichment method based on PEI-MNPs was developed to improve the sensitivity of mPCR analysis for the detection of pathogenic _Listeria_ spp. The positively charged PEI-MNPs could separate and enrich pathogenic _Listeria_ spp. within 10 min through electrostatic interaction with the bacteria and had a high CE at a wide pH range and ionic strength. The LOD of _L. monocytogenes_ and _L. Ivanovii_ in lettuce were both \(10^{1}\) CFU/mL, which was significantly increased by 2 orders of magnitude compared with conventional mPCR. In conclusion, the proposed fast-response magnetic enrichment strategy based on PEI-MNPs combined with mPCR analysis can rapidly and sensitively detect pathogenic _Listeria_ spp. in lettuce, which was expected to be a powerful tool for ensuring food safety. In further work, we will focus on exploring the analysis of \"viable but unculturable\" bacterial cells and the potential application of this method in more food samples.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app1236415/s1](https://www.mdpi.com/article/10.3390/app1236415/s1)]([https://www.mdpi.com/article/10.3390/app1236415/s1](https://www.mdpi.com/article/10.3390/app1236415/s1)), Figure S1: SEM images of MNP (a), PEI-MNPs (b), and pathogenic _Listeria_ spp. (c); Figure S2: CE of pathogenic _Listeria_ spp. at the concentrations from \(10^{5}\) CFU/mL to \(10^{6}\) CFU/mL by 100 \(\upmu\)g PEI-MNPs in PBS and lettuce.  
Conceptualization, F.X. and X.B.; Methodology, F.X. and K.W.; Writing--original draft, F.X.; Writing--review & editing, H.X.; Formal analysis, X.B.; Investigation, Y.S.; Project administration, H.X.; Resources, H.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China (2018 YFC1602500), the Research Foundation from Academic and Technical Leaders of Major Disciplines in Jiangxi Province, China (20194 BCJ22004), and the Research Project of State Key Laboratory of Food Science and Technology, Nanchang University, China (SKLF-ZZB-202133).  
Figure 5: Evaluation of the detection limit of pathogenic _Listeria_ spp. in lettuce sample. (**a**) mPCR assay without PEI-MNPs magnetic enrichment. Lane M: DL1000 DNA Marker; lane C: negative control; lanes 1 to 5: \(10^{6}\) CFU/mL to \(10^{2}\) CFU/mL of _Listeria_. (**b**) PEI-MNPs magnetic enrichment combined with mPCR assay, Lane M: DL1000 DNA Marker; lane C: negative control; lanes 1 to 6: \(10^{6}\) CFU/mL to \(10^{1}\) CFU/mL of _Listeria_. The bands at 583 bp, 311 bp, and 475 bp represented _L. monocytogenes_, _L. Ivanovii_, and IAC, respectively.  
Data Availability Statement: The datasets generated during the current study are available from the corresponding author on reasonable request.
## 3 Methodology
Figure 3: General operation of a commercial CSP plant with storage [64]. Several predefined states are established to ensure efficient and adaptive control of power production and surplus management. These states are defined based on the thermal level of the hot tank, assuming--by symmetry and for simplification purposes--that the cold tank exhibits inverse behavior. Figure 5 presents a flow diagram illustrating the control strategy applied to manage the different storage states throughout system operation. Figure 4: Power block operating algorithm. Figure 5: Storage operating algorithm. Minimum Storage: Defined as 5% of the maximum storage capacity and set as a safety threshold. If storage drops below this level, the hot tank's electric resistance activates to maintain the required minimum level. In this state, thermal loss components are excluded from the equations, simulating that this energy is provided by the tank's electric resistances. Storage at this minimum level is expressed as:  
\[STO(k)=STO(k-1)+SUN(k)\ \ \ \ \ \ \text{Scenario I and III} \tag{6}\]  
\[STO(k)=STO(k-1)+SUN(k)+SURP(k)\ \ \ \ \ \text{Scenario II and IV} \tag{7}\]
* Maximum Storage: Defined as the nominal production capacity for 15 h plus the minimum storage level. With this configuration, the plant ranks among the CSP facilities with the highest storage durations, exceeding the 10 h of Crescent Dunes [66,67] while remaining slightly below the 17.5 h offered by Cerro Dominator [68,69]. If storage reaches this maximum due to higher irradiation or electrical surpluses, it is capped at this level. Only the energy necessary to maintain the system's nominal capacity while supplying the power cycle is accepted. Maximum storage is calculated as follows: Figure 4.0 (8)
* Operational Storage: Defined as four times the minimum level, ensuring sufficient storage for continuous operational cycles. When surpluses occur and storage is at maximum capacity, solar energy is prioritized to maintain the system within nominal limits without exceeding capacity.
## 3 The Study on the Evolution of Overlying Strata Fractures  
### Numerical Simulation  
#### 3.1.1 Numerical Model  
To determine the fracture development of the overlying strata, a numerical simulation was conducted using PFC2D v.6.0. Based on the actual geological conditions of the 91-105 working face, a numerical model was established along the strike direction (Figure 2a) to analyze the evolution of overlying strata failure with the progression of mining. The model dimensions are 160 m in width and 70 m in height. To further investigate the differences in overlying strata failure and fracture development characteristics between gob-side entry retaining with roadside filling and the traditional coal pillar method, two additional numerical models were constructed along the dip direction of the 91-105 working face: one representing the roadside filling condition (Figure 2b) and the other with a reserved 15 m coal pillar (Figure 2c). Both models are 220 m in width and 70 m in height, and the simulated roadway measures 5.5 m in width and 3.6 m in height. To simplify the simulation, the model makes the following assumptions:  
1. The strata are horizontal and the thickness of rock layers is stable.
2. In the model, the \"pier column + flexible formwork\" structure is represented by columns with equivalent width and load-bearing capacity, which can accommodate prescribed deformations.
3. Based on the burial depth and the average bulk density of the overlying strata, an additional overlying stratum is added to the top of each model to apply a gravity load of 12.50 MPa.
4. Fixed boundaries are applied to both sides of the models.
# PUF and Chaotic Map-Based Authentication Protocol for Underwater Acoustic Networks
###### Abstract  
A secure and effective authentication and communication scheme between users and underwater sensors plays an important role in improving the detection and utilization of marine resources in underwater acoustic networks (UANs). However, due to the energy limitations and susceptibility to capture of underwater sensors and gateways, it is necessary to design a lightweight authentication protocol that can resist capture of sensors and gateways during attacks. In this paper, a lightweight authentication protocol for UANs based on the Physical Unclonable Function (PUF) and chaotic map is proposed. We used the advantages of PUF to resist sensors and gateways being captured in attacks and the chaotic map to achieve lightweight authentication because the computational cost of the chaotic map is almost one-third that of Elliptic Curve Cryptography (ECC). Additionally, we used the formal security proof in the random oracle model to prove the security of the proposed scheme. Our scheme was more secure and efficient compared with some other related schemes in terms of security and performance requirements, and the proposed scheme is suitable for UANs.  
underwater acoustic networks; authentication; protocol; chaotic maps; PUF +
Footnote †: journal: Applied  
1
Footnote 1: email: _Applied_
## Framework
### Fusion Scheme  
The fusion scheme is shown in Figure 1 and the image fusion algorithm is shown in Algorithm 2. The learned dictionary is used for the estimation of coefficient vectors. For each image patch \(\mathbf{p}_{i}\), a coefficient vector \(\mathbf{z}_{i}\) is estimated by the SOMP algorithm using the learned dictionary. Max-L1 rule [21] is conducted for coefficient fusion shown in Equation (17),  
\[\mathbf{z}^{i}=\sum_{k=1}^{m}\mathbf{z}_{k}*O^{k},\textit{where} \left\{\begin{array}{l}O^{k}=1,\textit{max}(\|\mathbf{z}_{1}\|_{1},\|\mathbf{ z}_{2}\|_{1}...,\|\mathbf{z}_{m}\|_{1})=\|\mathbf{z}_{k}\|_{1}\\ O^{k}=0,\textit{otherwise}\end{array}\right. \tag{9}\]where \(\mathbf{z}^{i}\) is the fused coefficient vector, \(\left\|\bullet\right\|_{1}\) is the \(l_{1}\) norm, and * is an elementary multiplication operation.  
```
0: image patch \(\mathbf{x}_{i}\), sparse code \(\mathbf{z}_{i}^{k}\), learning rate \(\eta_{1}^{1}\), running time \(m\)
0: learned sub-dictionaries \(\mathbf{D}_{1}\), \(\mathbf{D}_{2}\),..., \(\mathbf{D}_{n}\)
1: Initialize \(\mathbf{D}_{1}^{1}\), \(\mathbf{z}_{i}^{0}\)=0 (i=1,2,...,n), \(\eta_{1}^{1}\)= 1
2: Acquire an image patch \(\mathbf{x}_{i}\), when \(k\)=1 and \(i\)=1
3:while\(k\leq m\)do
4: Update sparse code \(z_{i}^{k}=CD(\mathbf{D}_{i}^{k},\mathbf{x}_{i},\mathbf{z}_{1}^{k-1})\)
5: Update \(j\)-th coordinate \(z_{i,j}^{k-1}\) of \(\mathbf{z}_{i}^{k-1}\) according to Equations (4) and (5)
6: Update dictionary \(\mathbf{D}_{i+1}^{k}=P_{B_{m}}(\mathbf{D}_{i}^{k}-\eta_{i}^{k}\
abla_{ \mathbf{D}_{i}^{k}}f_{i}(\mathbf{D}_{i}^{k},\mathbf{z}_{i}^{k}))\)
7: Update \(i=i+1\)
8:if\(i>n\)then
9:\(\mathbf{D}_{1}^{k+1}=\mathbf{D}_{n+1}^{k}\), \(k=k+1\), and \(i=1\)
10:endif
11:endwhile
```  
**Algorithm 1** Online Sub-dictionary Learning Algorithm  
The fused coefficient vectors are restored to an image. The restoring process is based on Equation (10),  
\[\mathbf{I}^{i}=\mathbf{D}\mathbf{Z}^{i} \tag{10}\]  
where the \(\mathbf{Z}^{i}=\{\mathbf{z}_{1}^{i},\mathbf{z}_{2}^{i}...,\mathbf{z}_{m}^{i}\}\) is corresponding to image patches of the fused image and \(\mathbf{D}\) is the learned dictionary.
## 4 Discussion
### Prediction of Undetermined Samples  
Although not trained by undetermined patients, the MLP classifier was used to determine their malignancy, and the predictions were compared with 6-month follow-up results. For the undetermined patients, no clear and definite diagnosis could be provided by the traditional cytopathology and BRAF gene detection. In comparison, the PESI-MS-based machine learning strategy shows the potential for their malignancy determination.  
The 37 undetermined patients can be divided into four groups: the patients marked as suspicious for papillary carcinoma, undetermined significance, insufficient cellularity, and others. For the patients marked with suspicious for papillary carcinoma, the classifier could correctly predict their malignancy with an accuracy of 90.9%. For the patients marked with insufficient cellularity, the accuracy was as low as 61.5%. This is probably because most of the FNAB could provide enough thyroid lesions for either microscopy observation or mass measurement. Resampling or the changing of sampling position should be considered for FNAB. For the prediction of patients marked with undetermined significance, the accuracy is 72.7%, which is not as satisfactory as that of the patients marked with suspicious for papillary carcinoma (90.9%). Probably, a better classifier could be trained when enough undetermined samples were accumulated with reliable follow-up results in the future.
## 5 Results
### Baseline Regression Results  
The results obtained from the two-way fixed effects panel regression are shown in Table 4 (TWFE). Specifically, a 1% increase in port TFP leads to a 0.054% increase in CO\({}_{2}\) emissions from port cities. This means that the productivity improvements in the Yangtze River ports from 2009 to 2019 have increased CO\({}_{2}\) emissions from the corresponding port cities.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Variable** & **Obs.** & **Mean** & **Std.Dev.** & **Min** & **Max** \\ \hline CO\({}_{2}\) & 176 & 3.759 & 0.715 & 1.927 & 5.184 \\ \(TFP\) & 176 & 1.579 & 0.744 & 0.541 & 4.909 \\ \(P\) & 176 & 5.091 & 0.979 & 3.802 & 7.816 \\ \(A\) & 176 & 11.434 & 0.464 & 9.865 & 12.249 \\ \(T\) & 176 & 6.110 & 1.801 & 0.693 & 9.422 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Descriptive statistics of variables for the panel data model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**PORT** & **TFPCH** & **TEC** & **BPC** & **SEC** \\ \hline Chongqing & 1.107 & 0.989 & 1.097 & 1.027 \\ Yichang & 1.054 & 1.011 & 1.033 & 1.172 \\ Wuhan & 1.155 & 1.057 & 1.098 & 1.000 \\ Huangshi & 1.181 & 1.144 & 1.049 & 0.990 \\ Jiujiang & 1.182 & 1.101 & 1.075 & 1.001 \\ Anqing & 1.086 & 1.030 & 1.055 & 0.998 \\ Chizhou & 1.169 & 1.115 & 1.053 & 1.000 \\ Tongling & 1.076 & 1.026 & 1.043 & 1.010 \\ Wuhu & 1.098 & 1.035 & 1.059 & 1.003 \\ Ma’anshan & 1.061 & 1.011 & 1.048 & 1.006 \\ Nanjing & 1.098 & 1.000 & 1.090 & 1.010 \\ Zhenjiang & 1.122 & 1.050 & 1.072 & 0.987 \\ Yangzhou & 1.052 & 1.000 & 1.044 & 1.011 \\ Taizhou & 1.061 & 1.000 & 1.059 & 1.003 \\ Changzhou & 1.048 & 1.000 & 1.032 & 1.040 \\ Nantong & 1.066 & 1.000 & 1.064 & 1.002 \\ Mean & 1.101 & 1.034 & 1.061 & 1.016 \\ \hline \hline \end{tabular}
\end{table}
Table 3: MPI summary of port means (2009–2019).  
We further employed the panel quantile model to ascertain the magnitude and statistical significance of the TFP coefficient at the Q25, Q50, and Q75 quantile levels. The impact coefficient of TFP on CO2 emissions passed the significance test at the Q50 and Q75 quantile levels, with coefficients of 0.048 and 0.058, respectively. This means that a 1% increase in port TFP will lead to a 0.048% increase in CO2 emissions for port cities with medium levels of CO2 emissions, while the effect is more pronounced for port cities with higher levels of CO2 emissions, resulting in a 0.058% increase. However, the impact coefficient of TFP on CO2 emissions did not pass the significance test at the Q25 quantile level, indicating that changes in port TFP do not affect port cities with lower levels of CO2 emissions.
# Reconfigurable Intelligent Surface Assisted Target Three-Dimensional Localization with 2-D Radar
## 3 Three-Dimensional Target Localization
### Crlb
Since it is complicated to directly differentiate \(L_{k}\) with respect to \(x_{1},x_{2},x_{3}\), the Fisher matrix of \(\mathbf{I}_{k}(\mathbf{H}_{k})\) is firstly obtained,  
\[\mathbf{I}_{k}(\mathbf{H}_{k})=-\frac{\partial^{2}L_{k}}{\partial h_{i}(\mathbf{X}_{k}) \partial h_{j}(\mathbf{X}_{k})}=\operatorname{diag}[\sigma_{1}^{2},\sigma_{2}^{2 },\sigma_{3}^{2},\dots,\sigma_{M+2}^{2}],\ i,j=1,\dots,M+2 \tag{33}\]  
According to the chain rule, the Fisher information matrix \(\mathbf{I}_{k}(\mathbf{X}_{k})\) can be derived as  
\[\mathbf{I}_{k}(\mathbf{X}_{k})=\mathbf{T}_{k}\mathbf{I}_{k}(\mathbf{H}_{k})\mathbf{T}_{k}^{\mathsf{T} }= \begin{bmatrix}I_{11}&I_{12}&I_{13}\\ I_{21}&I_{22}&I_{23}\\ I_{31}&I_{32}&I_{33}\end{bmatrix}  \tag{34}\]  
where  
\[\mathbf{T}_{k}=\frac{\partial\mathbf{H}_{k}\mathbf{{}^{\mathsf{T}}}}{\partial\mathbf{X}_{k}} = \begin{bmatrix}\frac{\partial h_{1}(\mathbf{X}_{k})}{\partial x_{k}}&\dots& \frac{\partial h_{M+2}(\mathbf{X}_{k})}{\partial x_{k}}\\ \frac{\partial h_{1}(\mathbf{X}_{k})}{\partial x_{k}}&\dots&\frac{\partial h_{M+2} (\mathbf{X}_{k})}{\partial x_{k}}\\ \frac{\partial h_{1}(\mathbf{X}_{k})}{\partial x_{k}}&\dots&\frac{\partial h_{M+2} (\mathbf{X}_{k})}{\partial x_{k}}\end{bmatrix}  \tag{35}\]  
and  
\[I_{ab}=\sum_{i=1}^{M+2}\frac{1}{\sigma_{i}^{2}}\frac{\partial h_{i}(\mathbf{X})}{ \partial x_{a}}\frac{\partial h_{i}(\mathbf{X})}{\partial x_{b}} \tag{36}\]  
The lower bound \(\mathbf{J}_{k}\) of \(\mathbf{X}_{k}\) can be obtained by inverting \(\mathbf{I}_{k}(\mathbf{X}_{k})\),  
\[\mathbf{J}_{k}=\mathbf{I}_{k}^{-1}(\mathbf{X}_{k})\triangleq[c_{ab}]_{3\times 3} \tag{37}\]  
Among them, the main diagonal element of \(\mathbf{J}_{k}\) is the CRLB on the \(X\)-axis, \(Y\)-axis and \(Z\)-axis, respectively, and the CRLB of the localization accuracy of the whole system can be obtained as  
\[CRLB_{k}=\sqrt{c_{11}+c_{22}+c_{33}} \tag{38}\]  
The CRLB is a theoretical limit to measure the estimation accuracy, which gives the minimum possible value of the estimation error considering all possible observations and target parameter estimation methods. This is a very important performance metric because it reveals the optimal performance that can be achieved by the estimation problem with the given observations. For the RIS-assisted 2-D radar to locate targets, by studying the CRLB of the localization system, a more comprehensive understanding of the system performance in practice can be obtained. In addition, the CRLB helps to deeply understand the advantages of this method to further optimize the system design and improve the overall performance of the RIS-assisted 2-D radar system.
# Static and Seismic Responses of Eco-Friendly Buried Concrete Pipes with Various Dosages of Fly Ash
## 1 Introduction  
The issue of an appropriate collapse probability along with the optimal response of underground concrete pipes has been highlighted in numerous investigations. Indeed, the reduction of vertical and horizontal displacement as the optimal response of buried GPC pipes under static and seismic loading is the central challenge for evaluating the structural performance of underground GPC pipes which need to be improved through reorganizing the effects of material properties of GPC and other main factors on the structural performance of GPC pipes. Based on preliminary studies, the mechanical properties of concrete (compressive strength, flexural strength) and FA inclusion in concrete content are two significant factors in the behavioral performance of concrete elements such as concrete cubes and beams [1, 2, 3, 4, 5, 6, 7].  
Numerous investigations on the performance and design of underground pipes have been carried out using either static or dynamic loads without any precise vindication related to the section of loading type [8, 9, 10]. [PERSON] et al. [11] investigated the behavior of underground flexible pipes under static loading. Moreover, [PERSON] and [PERSON] [12], [PERSON] et al. [13], and [PERSON] and [PERSON] [14] explored the behavior and the design of underground concrete pipes under static surface loads. On the other hand, [PERSON] et al. [15] presented the results of an investigation on the response of a buried flexible pipe for moving truckloads under the maximum load of 107 kN. [PERSON] et al. [16] reported the response of a buried concrete pipe and a rectangular culvert under the effect of a moving aircraft wheelload by FEM. Eventually, [PERSON] et al. [17] conducted a three-dimensional FE study on the behavior of a buried pressurized steel pipe. Other research activities were also conducted using a 3D FE analysis, such as [8; 11; 12; 13; 14; 15; 16; 17; 18].  
In summary, it cannot be conclusively approved by previous investigations whether a static or a dynamic load needs to be applied to assess the behavior of underground pipes. Thus, this study aims to acquire the critical loading conditions on GPC pipes, hence, the major contributions of this study are presented as follows:  
* A robust 3D finite element model is developed for evaluating the optimal displacement of GPC pipes by utilizing fault displacement (static loading) and records of the actual earthquake (seismic loading) as well as traffic loads and self-weight of soil and GPC pipe as live and dead loads in this study;
* The possibility of relative comparison of seismic and static responses of buried pipes under the influence of GPC properties is developed in this field;
* The utilization of element types of solid 185 and pipe 288 compatible with the proposed nonlinear model such as Drucker-Prager has been applied for the first time in this study after validating with similar element types such as solid 65 used in previous investigations.
## 7 Discussion  
The developed framework was trained to detect missed welds and it was validated in the production phase. Although the resulting metrics are promising, there are still limitations that need to be considered. The range of sizes of the joists assembled in the production line is quite broad. In the proposed hardware setup, one camera on either side of the joint was used, but in cases where the joist is very large, the setup cannot provide a complete view of the nodes leading to possible blind spots. Additionally, it is common to stack joists to accelerate the welding process. In such scenarios, one joist may cover part of the other joist node. Currently, this problem is handled by introducing an additional class, i.e., Node-Blocked. In the current production setup, the number of blocked nodes, for which the network cannot predict whether the node is welded or not, is around 3% of the total amount of nodes. To provide a clearer view of the assembly line and nodes, adding cameras with varying angles and FoV to each side of the assembly line could be beneficial. Another issue is that the distance between the camera and the joist is erratic because joists are placed randomly. This can affect the quality of input images as the cameras are manually focused. Employing laser scanners or depth measuring equipment to assess the distance between the camera and the joists, along with cameras that can adjust their focus based on the distance could eliminate this limitation in future. Furthermore, customized joists are assembled in the production line and this may introduce new element types according to the structural design needs. In the conditions of the production phase, the metrics drop compared to the previously applied validation methods. Another reason for the difference between the accuracy of our metric on the test dataset versus the production phase is that balancing classes is considered in the dataset creation while in reality, the input data is severely imbalanced. In the production phase, missed-veld class distribution, compared to its counterpart Node-OK, is 1 to 500. The mentioned fact would put a lot of pressure on the  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **Missed-Weld Class** \\ \hline Predicted number & 116 \\ (Node-based) & 103 \\ \hline True Positive & 239 \\ (Path-based) & 13 \\ \hline Reported number & 88 \\ (Path-based) & 0.888 \\ \hline Detection rate & 2.71 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Framework overall performance during 17 working days.  
minority class metrics. Even if the framework performance is satisfactory for the majority class, a tiny percent of edge cases in the majority can generate significant false positives for the minority class, decreasing its precision metrics.  
Finally, the process developed in this research identifies missing welds on top and bottom chords of joists. It cannot be used to identify missing welds on the other components of the joist, such as wrapping webs, spacers, and bridging, as they are not within the FoV of the camera. For such components, alternatives methods need to be investigated.