esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Geotechnical Guidelines and Practices for the Design of Integral Bridges
### Australian and New Zealand Practices  
The Australian Bridge Design Guideline, AS 5100:2017, does not contain any design information for integral bridges [49; 50]. Therefore, some states have developed their own design guidelines for integral bridges in conjunction with the AS 5100:2017, such as VicRoads BTN2018/010 by Victoria, Bridge Policy Circular BPC 2007/05: Design of integral bridges [51] by NSW, and Design Criteria for Bridges and Other Structures by Queensland Department of Transport and Main Roads (TMR). State design guidelines in Australia on integral bridges regarding both structural and geotechnical (which includes lateral earth pressure) aspects are mainly based on BA 42/96 [45] and/or PD 6694-1 [52].  
According to the design manual published by the Queensland Department of TMR, the increase in lateral earth pressure due to the stress ratcheting effects needs to be considered in the design of the integral bridges, and the design specifications can be based on the geotechnical investigation report, and consideration of established design guidelines, such as:  
* Design Manual for Roads and Bridges, Design of Integral bridges BA 42/96 [45];
* PD 6694-1 [52] Recommendations for the design of structures subjected to traffic loading to BS EN 1997-1:2004. PD 6694-1:2011 is now superseded by PD 6694-1:2011+A1:2020 [25].  
VicRoads BTN2018/010 is primarily based on the UK BA 42/96 [45], recommendations given by the United States Precast/Prestressed Concrete Institute (PCI), and research and case studies conducted in the UK. However, secondary sources for the design and specifications shall be obtained from the AS 5100 and other Victorian and/or Australian design standards [49; 50].  
Figure 3: Earth pressure distribution on full height abutment: (**a**) rotationally rigid abutment; (**b**) rotationally flexible abutment; (**c**) earth pressure distribution.  
Design requirements and limitations specified in the NSW Bridge Policy Circular BPC 2007/05 [51] are based primarily on the UK BA 42/96 [45], design practices in the USA, and VicRoads BTN2007/014.  
Bridge design engineers in New Zealand generally refer to the Transport Agency (NZTA) Bridge Manual for guidance on the design of integral bridges, including specifications on the lateral earth pressure [53]. The geotechnical provisions for calculating the earth pressure distribution behind the abutments of integral bridges are provided in NZTA's Bridge Manual and NZTA research report 577 [54] based on the provisions of BA 42/96 [45] and PD 6694-1 [52]. It has further emphasized the parameters that need to be considered for the SSI analysis for full height integral abutments founded on a single row of vertical piles and integral embedded wall abutments (which will be discussed in Section 3.9.1).
## 4 RF Energy-Harvesting Techniques  
RFEH encompasses various techniques that enable the conversion of ambient RF signals into usable electrical power. These techniques differ in frequency range, circuit complexity, and integration with other energy sources. This section discusses key approaches, including single-band vs. multi-band harvesting, narrowband vs. broadband harvesting,  
Figure 8: Performance metrics of RFEH system for RF-to-DC conversion efficiency [54].  
passive vs. active systems, and hybrid energy-harvesting methods that integrate RF energy with solar, vibration, and thermal sources [57].
# Matching of Energy Provisions in Multihop Wireless Infra-Structures
## 6 Numerical Evaluation
### Numerical Results in Scenario-2: Grid Topology  
The network lifetimes of various routing protocols in the grid-topology scenario are shown in Figure 13a. Matching-EP has improvements of 19, 10 and 23 in HWMP, SP-HWMP, and EC-HWMP, respectively. But E-HWMP has roughly the same network lifetime for Same-EP and Matching-EP. This is because E-HWMP routing will be highly affected by energy provided at each BS. EC-HWMP with Matching-EP has the longest network lifetime up to 1960s. This is because that the EC-HWMP employs both energy and connectivity awareness in HWMP.  
The result of successful packet delivery is shown in Figure 13b. Matching-EP in E-HWMP routing results in the largest number of delivered packets during the network lifetime.  
Figure 13c shows the average matching degree of energy provision to the energy consumption with various routing protocols. In the network with HWMP protocol, the average energy matching degree of BSs increases from 0.74 to 0.80 by utilizing Matching-EP. As for SP-HWMP protocol, the energy matching degree of BSs increases from 0.76 to 0.80 by utilizing Matching-EP. As for E-HWMP protocol, the energy matching degree increases from 0.80 to 0.86 by utilizing Matching-EP. As for EC-HWMP, the matching degree increases from 0.84 to 0.88 by utilizing Matching-EP.  
Figure 12: The network performance in line BS networks. (**a**) Line network lifetimes; (**b**) Successful delivered packets from all STAs to the GW in line BS scenario; (**c**) The average matching degree in Line BS scenario.  
Table 8 shows the simulation result of average \(Diff_{M}(i)\) obtained in the evaluation of Grid WMI with SP-HWMP routing, comparing with the energy differentiation analysis results. The differentiation analysis are according to zone-based _TX-RX-OH_ relations described in previous section, and are based on the simulation setup. It can be seen that the analysis based on _TX-RX-OH_ relations achieves the high analysis accuracy up to 0.92. _TX-RX-OH_ based analysis improves about 8% accuracy compared with the uniform energy provision in the Grid WMI of 16 BSs.
# Simplified Two-Dimensional Generalized Partial Response Target of Holographic Data Storage Channel
## 4 Simulation Results
### Complexity of Proposed Model  
In this section, we compare the complexity of the proposed model, the methods in [13; 16; 22; 23], and the complicated 2D Viterbi detector. We count the operators per the detected bit from the equalizer to the detection in each method and list them in Table 3.  
In Table 3, the number of operations of the DFE model are counted for one iteration. Thus, to achieve the steady state in [23], the number of operations is larger than the numbers in Table 3. In addition, we used the general GPR target in (1) to estimate the operations for the complicated 2D Viterbi. For the GPR target in (1), the trellis of the detector has 64 states and eight input branches in each state. The proposed model takes more calculations compared to the model in [13; 16]. However, our proposed model requires fewer calculations compared to the complicated 2D Viterbi and the interference estimator with parallel structure schemes.
## 3 Methodology
### Multi-Feature Fusion Distance Measure Model of MFF-SLIC  
In the initial segmentation stage of MFF-SLIC algorithm, \(K\) clustering centers are initialized first, and the distance between each pixel in search area and clustering centers is respectively calculated by multi-feature fusion distance measure model based on a local \(k\)-means method, then each pixel would be assigned to the clustering center with minimum distance. Finally, the image \(I\) is segmented into \(K\) superpixels with special sematic characteristics by iteratively.  
During the above process, instead of the traditional \(k\)-means method searching the whole image, the local \(k\)-means defines a local square search area with \(25\times 2S\). The difference of the two methods are shown as Figure 5.  
The main function of the distance measure model is to distinguish dissimilar pixels and assign similar pixels into the same superpixel. The quality of the distance measure model determines the accuracy of superpixel segmentation.  
With the 1-\(N\) mapping relationship of the TLS point cloud and digital images, not only the 2D image features such as color and planar position of pixels but also the 3D TLS point cloud features including spatial geometric as well as other characteristics should be considered for distance measure model. For the geological objects, the local attitude of rock surface is an important component for distance measure model construction. Besides, the 3D spatial location of pixels contributes to distinguishing pixels with similar texture and local attitude but are very far apart from each other in order that they would not be assigned into the same superpixel. Based on the above analysis, this paper defines a multi-feature fusion distance measure model according to 1-\(N\) mapping relationship between the 3D TLS point cloud and digital images, which is used to calculate the distance between unclassified pixels and clustering centers. The flowchart of distance measure model construction is shown as Figure 6.  
Figure 4: The 1-\(N\) mapping relationship between the TLS point cloud and digital images.  
Figure 3: The sketch map of the relation between image coordinate system and pixel coordinate system.
## 5 Coating Materials with Embedded Antibiotics and Other Antibofilm Agents
### Silver Nanoparticles and Silver Ions
#### 5.6.3 Impairment of the Respiratory Chain and Enhancement of ROS Production  
Silver nanoparticles interact with thiol groups from protein complexes of the electron transport chain (ETC) causing the inefficient passage of electrons at the terminal oxidase and, hence, incomplete reduction of molecular oxygen and increased production of ROS (superoxide anion radical, hydrogen peroxide, and hydroxyl radicals) [94; 95; 96; 97; 98; 99; 100]. ROS are known to induce oxidative stress in the cells by attacking all vital biomolecules: lipids, proteins, and DNA, thereby provoking breakdown of the plasma membrane, inactivation of key enzymes, and DNA damage. However, ROS are produced as a natural side-product of the respiratory chain, but their damaging action is controlled by a series of enzymes such as the superoxide radical scavenging enzyme superoxide dismutase, catalase, and peroxidase. Silver ions-induced inactivation of these enzymes corroborated with excessive ROS production due to impaired respiratory chain function produces an imbalance resulting in oxidative stress and cell death. Moreover, silver nanoparticles themselves are able to produce highly reactive hydroxyl radicals in a Fenton-like reaction, as depicted by Equation (5) [100].  
\[\text{Ag}+\text{H}_{2}\text{O}_{2}\rightarrow\text{Ag}^{+}+\text{HO}^{+}+ \text{HO}\bullet \tag{5}\]
# The Development of a Prototype Solution for Detecting Wear and Tear in Pedestrian Crossings
### Adaptive Threshold Classification  
Adaptive Thresholding [6] is a technique employed in image processing to distinguish between the foreground and background of an image. To achieve this, a threshold value is selected, whereby all pixels with an intensity above this threshold are considered to belong to one class, which is represented by the color white. In contrast, pixels with an intensity below the threshold are categorized in another class, which is often represented by the color black.  
In contrast to the application of a single threshold value to the entire image in global thresholding, adaptive thresholding employs the calculation of specific threshold values for different regions of the image. In this method, the threshold value for each pixel is determined based on the characteristics of its neighborhood.  
Several different types of thresholding are employed in image processing, as illustrated in Figure 3.  
* Global Thresholding: This applies a single threshold value to the entire image.
* Otsu's Thresholding: This automatically determines the optimal threshold value based on the image histogram.
* Adaptive Mean Thresholding: This calculates a threshold value for each region of the image based on the average of the intensity values of the pixels in that region.
* Adaptive Gaussian Thresholding: This is like Adaptive Mean Thresholding but calculates the threshold value using a weighted average based on the Gaussian distribution.  
Figure 3: Comparison of threshold methods.  
Figure 2: Illustration of the architecture of a CNN.  
The potential for combining different thresholding methods can assist in the detection of wear and tear on crosswalks. This approach is particularly beneficial in environments where lighting conditions are variable. By leveraging the strengths of each technique, it is possible to obtain more reliable and accurate results in identifying worn areas of crosswalks, which is a crucial objective of this project. Figure 4 illustrates some examples of the application of Adaptive Thresholding in identifying wear and tear on crosswalks. Figure 4 illustrates the outcome of applying the method to a crosswalk exhibiting signs of wear. Figure 4 depicts the result obtained for a crosswalk devoid of such indications, while Figure 4 displays the outcome for a crosswalk exhibiting a considerable degree of wear. As can be observed, this technique yields highly satisfactory results in fulfilling the intended purpose.
## Abstract  
Background: Renal sympathetic denervation (RSD) has provided promising data in its ability to treat therapy resistant arterial hypertension. The effect of RSD on sST-2, a promising biomarker for risk stratification in cardiovascular diseases, has so far not been systematically studied. Methods: We evaluated serum levels of sST-2 and clinical parameter including left ventricular mass (LVM) in 54 patients with resistant hypertension (RH) undergoing bilateral RSD at baseline as well as at one and/or three months. Results: After RSD, mean office blood pressure showed a significant decrease after one month (\(p<0.001\)). On echocardiography a reduction of LVM was observed at three months (\(p<0.01\)). This was accompanied by a significant decrease of sST-2 levels at three months (\(s\)ST-2 baseline: 6310.1 \(\pm\) 3246.0 pg/mL vs. sST-2 three months: 4703.8 \(\pm\) 1585.9 pg/mL, \(p=0.048\)). Furthermore, baseline sST-2 levels were positively correlated with systolic blood pressure at one month (\(r=0.514\), \(p<0.01\)) but not three months, indicating a potential predictive value of sST-2 for early intervention success. Conclusion: In patients with RH, RSD is associated with a significant decrease of sST-2 levels after three months, indicating sST-2 to be involved in remodeling processes after RSD. Furthermore, lower sST-2 levels at baseline might be a potential predictor of early intervention success of RSD.  
Keywords:resistant arterial hypertension; renal denervation; SST2 +
Footnote †: journal: Medical Imaging
## 4 Experiments and Discussions
### Comparison with State-of-the-Art Methods  
The proposed method perturbs 50 pixels and compares with the post and in-the-loop methods based on the BIM attack proposed in [24], on the ImageNet dataset, as shown in Table 5.  
Both the method in [24] and the proposed method are evaluated based on the effectiveness of their \(ASR_{A}\), which is fundamentally rooted in the adversarial attack method, i.e., BIM attack or differential evolution. In the proposed method, allocating additional computational resources can lead to better \(ASR_{A}\) performance. However, this approach may not significantly impact the performance of reversible adversarial attack methods. Because the introduction of reversible algorithms in previous method designs may lead to a significant number of AEs failing, minimizing such occurrences is crucial for enhancing the RAE generation algorithm. What we should focus on is how many AEs can retain their adversarial attribute and become RAEs throughout the complete process, i.e., APR performance.  
On the Inc-Res V2 model, our proposed method APR achieves 94.57%, which is a significant improvement compared to 66.08% of the post method and 83.02% of the in-the-loop method in [24]. This is because, in each step of the algorithm, we strive to ensure that the image quality is not excessively degraded. Additionally, the performance of the proposed method on ASR is also commendable, i.e., our method achieves an ASR of 25.50% and 26.10% on two target models, which is better than 19.92% and 24.60% of the post method in [24]. Compared to the in-the-loop method in [24], the proposed method exhibits only a slight disadvantage in ASR on the IncRes-V2 model. However, this discrepancy can be partly attributed to the higher success rate of generating AEs using the method in [24], which stands at 37.23% for the IncRes-V2 model compared to our 27.60%. Their method adopts a more aggressive strategy to achieve a higher success rate which, however, results in a greater loss of image quality during the conversion from AEs to RAEs.
## 2 The DNFS Algorithm
### The Implementation Steps of the DNFS Algorithm  
This section mainly introduces the implementation steps of the DNFS algorithm. The flowchart of the proposed DNFSm-n is shown in Figure 2. The execution process of DNFSm-n is as follows:  
Figure 1: The general structure diagram of DNFS5-2.  
Step 1. Data preprocessing: Each numerical feature was mapminmax-normalized, and the dataset was divided into a training set and a test set;  
Step 2. Define the structure of the DNFS: Determine m, n, the layers, and the number of submodules of each layer;  
Step 3. Group the training set and the test set: The training set and the test set are divided into several groups of m-dimensionality, and each group corresponds to one submodule;  
Step 4. Training submodule: Traverse each submodule of the current layer, and put the grouped training set into the corresponding submodule for training;  
Step 5. Test submodule: Put the grouped test set into the corresponding submodule, which has been trained to perform the testing:  
Step 6. Determine if the DNFS is completed: If the current layer is the last one of the DNFS structure, the current outputs are directly taken as the final output, and turn to Step 7. Otherwise, the outputs of the current layer are merged into a new dataset, which are adopted as the inputs of the next layer, and return to Step 3;  
Step 7. Obtain the final result.  
There is a special case in Step 3: the group needs to be supplemented if there are not enough features in the grouping to constitute m-dimensional inputs. The main situations are as follows:  
* If this happens when the training set and the test set are grouped in the first layer, the features are selected in turn from the first group that has been divided to supplement them;
* If this happens when the training set and the test set are grouped among the second layer to the last layer, all the outputs of the upper layer are sorted based on the error value in ascending order, and the outputs with the smallest error are selected according to the number of missing ones.
## 3 Results
### Gain Degradation Measurements Results  
The gain degradation measurements were performed through the measurement setup introduced in Section 2.5 and are reported for the two dose rates in Figure 8.  
First, a lower gain degradation was observed at a lower dose rate, in agreement with the lower measured RIA [41, 42].  
Furthermore, for both dose rates, the evolution of the gain degradation with dose has an almost linear trend, in line with the linear trend of the RIA at 980 nm and 1550 nm shown in Figure 7a,b. The error bars associated with the gain degradation in Figure 8 consider the error related to the peak and noise determination of the amplified and injected signals at 1550 nm, which is equal to \(\sim\)3% of the gain.  
Figure 7: Dose dependence of the (**a**) 980 nm RIA (**b**) 1550 nm RIA for various 980 nm pumping conditions.
## 4 Results
### Rice Height Estimation  
The relevance of three methods MLR, SVR and RF applied in Sentinel-1 data with (VH, VV, both VH and VV polarizations) was analyzed using RMSE and \(R^{2}\) values. Tables 3 and 4 report the regression results of rice height estimation with the three methods MLR, SVR, and RF.  
The method used to estimate parameters is supposed to be efficient when its RMSE, AIC and BIC values are as small as possible. The RF method is better than the other methods: \(R^{2}=0.92\), RMSE = 16% (7.9 cm) (with the dual-polarization VV and VH model). For PCA (VV and VH) models, as comparison with the dual-polarization VV and VH cases, they are quite similar. In other words, VV and VH give complementary information in the estimations. Figure 6 shows the results of estimating the rice height by plotting the best configuration for each method.  
As show in Figure 6, the correlations (\(R^{2}\)) obtained by using MLR, SVR and RF are 0.8, 0.89, 0.92 respectively. MLR gives the biggest value of RMSE (12.2 cm--24%), followed by SVR with RMSE = 9.1 cm (18%) and RF with the minimum RMSE = 7.9 cm (16%).  
Next, in Figure 7, the rice height retrieval respect to in-situ data during the cultured period is shown for example on two plots: 2M2 and 4M2. The continuous line presents the estimated height of rice, while the following small circles show the in-situ rice height measurements. Visibly, the correlation between the retrieved and in-situ rice height of the two parcels is good. The rice height retrieval and the in-situ rice height are increasing and close together as the result of rice growth.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**AIC**} & \multicolumn{3}{c}{**BIC**} \\ \hline
**Methods** & **VV** & **VH** & **VV and VH** & **PCA (VH and VV)** & **VV** & **VH** & **VV and VH** & **PCA (VH and VV)** \\ \hline MLR & 295.6 & **293.7** & 294.8 & 296.7 & 295.9 & **294.1** & 295.3 & 297.1 \\ SVR & 273.4 & 272.3 & **261.2** & 262.4 & 273.8 & 272.6 & **261.7** & 262.9 \\ RF & 250.0 & 256.6 & **245.0** & 249.2 & 250.4 & 257.0 & **245.5** & 249.7 \\ \hline \hline \end{tabular}
\end{table}
Table 4: AIC and BIC for rice height estimation. The smallest values are in bold.  
Figure 6: Rice height estimation using MLR, SVR and RF methods. In this figure, the configurations that provide the best results are considered (with dual-polarization VV and VH model).
# A Novel Real-Time Battery State Estimation Using Data-Driven Prognostics and Health Management
## 5 Experimental Setup and Methodology
### Battery Model Estimation  
The Hybrid Pulse Power Characterization (HPPC) test dataset at 25 \({}^{\circ}\)C provided in [41] was utilized to estimate the parameters of a second-order \(RC\) model. A second-order model has two RC networks, \(R_{1}\parallel C_{1}\) and \(R_{2}\parallel C_{2}\), according to Figure 1. The HPPC test consists of pulses separated by a rest period that allows the battery to reach a stable state and is used to obtain the battery OCV versus SOC curve and the circuit components' values \(R_{1}\parallel C_{1}\) and \(R_{2}\parallel C_{2}\).  
Another battery ECM has been presented and compared to the second-order \(RC\) Thevenin model in [45]. For that model, developed by the National Renewable Energy Laboratory (NREL), the equivalent circuit has the components \(R_{b},~{}R_{s},~{}C_{b},~{}C_{s}\), and \(R_{t}\). The terminal resistance \(R_{t}\) models the voltage drop when the battery has a load connected, \(R_{s}\) and \(C_{s}\) model the diffusion effects of the battery, and the bulk resistance \(R_{b}\) and bulk capacitor \(C_{b}\) represents the battery storage capacity.  
The conclusion of the study in [45] is that the \(2 RC\) Thevenin model used herein offers a better performance.