esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Spatial Heterogeneity Analysis  
In this study, the spatial heterogeneity characteristics of NPP were analyzed from global and local scale perspectives. Figure 2 is the scatter diagram of the mean and maximum NPP values in each county-level administrative unit. The [PERSON]'s I index of the mean NPP value and maximum NPP value was 0.71 and 0.59, and the Z values were 40.55 and 35.53, respectively, both of which passed the 1% confidence test. These results show that the global correlation of the mean NPP value was greater than the maximum NPP value. Most county-level administrative units are located in the first and third quadrants of the diagram, and the NPP in the study area had a significant positive correlation on the global scale, with strong spatial clustering and heterogeneity.  
The local autocorrelation results of the mean NPP value show (Figure 3) that the number of county-level administrative units with high-high (HH) and low-low (LL) types accounted for 23.34% of the total number in the study area. There were 10 county-level administrative units with the HL type and 22 with the LH type, accounting for 0.72% and 1.59% of the total, respectively. The number of counties with non-significant results accounted for 51.88% of the total. Combined with Figure 3a, the HH type was mainly distributed in the southeast and eastern marginal areas of the study area. The LL type was mainly distributed in the northwest and west of the study area. The regions that passed the 0.1% significance test were clustered in the northwest of the study area, while regions that passed the 1% significance test were clustered in the southeast of the study area (Figure 3c). The local autocorrelation analysis results of the maximum NPP value show that the number of HH types and LL types accounted for 16.55% of the total number in the study area. There were 18 HL types and 21 LH types with abnormal spatial distribution, accounting for 1.30% and 1.52% of the total, respectively. The number of counties with a non-significant result accounted for 68.79% of the total. Combined with Figure 3b, the HH type was mainly distributed in the southwest, southeast, and northeast of the study area, while the LL type was mainly distributed in the northwest. The regions that passed the 0.1%  
Figure 2: Scatterplot of mean value (**a**) and maximum value (**b**) of NPP in county administrative units.  
significance test were clustered in the northwest and northeast of the study area, while the regions that passed the 1% significance test were clustered in the north-central and northern parts of the study area (Figure 3d).
# Non-Destructive Testing Applications for Steel Bridges
## 6 Summary and Conclusions  
This study investigated non-destructive testing (NDT) methods used for the inspection of steel bridges with a focus on methods applicable to local damage detection. Their applications, advantages, and disadvantages were discussed and tabulated. This study also reviewed ongoing research studies regarding the implementing of the robotic technologies for the structural health monitoring and inspection of steel bridges. The following conclusions can be made from this study:  
* NDT methods are used to detect and characterize any flaws in the steel components with no or minimal damage to the member. * Among all NDT techniques, visual inspection is the least costly and easiest method. However, because visual inspection depends strongly on the level of experience and expertise of the inspector, the results are subjective, and different results might be obtained when different inspectors are involved. Accordingly, when potential for critical damage exists, other techniques should be used in conjunction with this method to assure valuable results. Use of visual aids as well as vision-based techniques with digital processing can increase the accuracy and reliability of inspection results. * NDT methods are normally categorized based on their ability to detect internal defects or external defects, having in mind that some methods can detect defects both on and beneath the surface. Considering the type of steel elements, the type and location of damages, and the capability of NDT methods in detecting the internal and external defects, testing methods applicable to steel bridges were categorized and discussed in this paper. * Visual inspection, dye penetrant, magnetic particle, and eddy current are NDT methods applicable to only surface defects. Visual inspection is the most common and appropriate NDT method for detecting noticeable flaws, which is used for the majority of steel bridges. The dye penetrant method is a practical way for the detection of
# Influence of Deep Foundation Pit Excavation on Adjacent Pipelines: A Case Study in Nanjing, China
###### Abstract  
To investigate the pipeline deformation pattern caused by the excavation of deep foundation pits in composite soil-rock strata, a comprehensive study integrating on-site monitoring and numerical simulation was conducted. This study centered on a deep foundation excavation project in the soft soil in Nanjing's floodplain region. The analyses of pipeline settlement and deformation were performed based on field-measured data. This study investigated the impact of excavation on the mechanical properties of the surrounding soil that resulted in the progressive deformation of adjacent pipelines. Furthermore, numerical simulations were conducted using Plaxis 3D CONNECT Edition v22 finite element analysis software. This study elucidated the influence of factors such as pipeline-pit distance and burial depth on pipeline deformation, conducting a quantitative analysis of their effects. The results indicated that deformation primarily occurs unevenly near pit corners and is less pronounced in soil-rock strata than in single-type soil layers. This study established correlations between pipeline displacements and various factors, offering valuable insights for future excavation projects conducted under similar conditions.  
composite soil-rock strata; numerical simulation; pipeline deformation; sensitivity analysis +
Footnote †: journal: Applied sciences
###### Abstract
Such systems, utilising GNSS channels (GPS (L1 and L5 band) and GLONASS (L1 band)) + external IMUs, have been effectively used in canoe slalom for measures of speed [19], but data processing is out of reach for most consumers at this time, and no information regarding the positional or kinematic accuracy, precision, and integrity, or service continuity has been provided. Considering that athlete positioning is such an important aspect of canoe slalom performance, reflecting strategy and technique [20; 21], it would be highly beneficial to be able to quantify this aspect of performance accurately, and with precision. Recently, a single-frequency (GPS) PNT solution with a sampling rate of 10 Hz reported a discrepancy of -8.9% between the measured total distance and the actual total distance travelled during a simulated dry-land slalom. This resulted in a 3.2% deficit compared to aerial tracking in the same dry-land simulation, and a 5.5% difference when comparing the PNT solution to aerial tracking in a real-world slalom trial [22]. This is not surprising as the accuracy of GNSS degrades significantly when conditions become challenging [23], such as non-linear movement patterns of the athletes and multipath impact [24]. Additionally, sports such as canoe slalom involve frequent direction and acceleration changes [5], where positional sample frequency \(\geq\)20 Hz is essential to obtain useful trajectories to distinguish differences in performance [24]. The latter has been overcome in sports similar in nature to canoe slalom, such as slalom skiing, where real-time kinematic (RTK) positioning was shown to provide accurate positional data (<10 cm) along with sport-specific performance parameters such as distance travelled, speed, and acceleration [24]. DGNSS with real-time base station corrections could be used to determine athlete position to sub-meter accuracy [25]. As such, it is likely that differential methods to GNSS could be used in a real-world scenario for slalom canoeing to enable performers or coaches to quantify trajectory from a performance or developmental perspective. The purpose of this study was to assess the positional accuracy, availability, integrity, and service continuity of a commercially available PNT solution that uses DGNSS-SBAS multiple GNSS channels while sampling at a frequency of 20 Hz during a simulated dry land slalom and real-world on-water slalom. The primary objective was to assess the PNT solution during the dry-land slalom while observing the efficacy to differentiate between trajectories. Furthermore, the simulated results were compared with a non-controlled, on-water, real-world slalom test to validate the findings. Based on a minimum required accuracy for the sport, and pilot work with the PNT solution being used, it was hypothesised that the PNT solution would provide useful (<0.3 m) horizontal accuracy and reliability under both simulated and real-world conditions for slalom canoeing.
## 2 Smoothing Method Analysis
### Cascade Residual Smoothing  
This paper proposes a cascade residual smoothing method to solve the problem wherein traditional smoothing methods ignore short-term fluctuations. The method preserves long-term trends and short-term fluctuations in the raw data and improves the smoothness of the data. As shown in Figure 2, the method uses Gaussian smoothing to extract the main trend of the data and quadratic exponential smoothing to extract short-term volatility features from the residuals. The new data that are ultimately generated contain long-term trends and retain information on short-term fluctuations, allowing the model to capture subtle changes in the data on a smoothed basis.  
The core of this method lies in applying quadratic exponential smoothing to the residual part after the initial extraction of the overall trend by Gaussian smoothing, which is used to extract the short-term trends in the residuals. Quadratic exponential smoothing can take into account both the smoothing component and the trend component of the residuals. Further, it removes noise from the residuals through smoothing and trend extraction while retaining the effective short-term variations. Bridge deflection data often contain fluctuations at multiple frequencies, and the cascade residual smoothing method effectively addresses this complexity through stepwise processing, making it particularly suitable for data preprocessing in bridge health monitoring.
# Synthetic Medical Imaging Generation with Generative Adversarial Networks for Plain Radiographs
## 2 Materials and Methods  
### Generative Adversarial Networks (GANs)  
A generative adversarial network (GAN) is a state-of-the-art deep learning generative model that is composed of two competing neural networks. The first one is a generative network that seeks to create increasingly realistic-looking data based on real data. In the case of the current study, the real data and the data that the generative network is working to emulate are medical images, specifically knee and elbow X-rays. The second network that comprises the GAN is a discriminative network that attempts to discern whether the generated data are real by comparing them with real data [13].  
Training starts when the generator presents random noise to the discriminator, which is also presented with real data separately. The discriminator then tries to determine which data are real and which data are synthetic. The error from the discriminator is then back-propagated to the generator and the discriminator. As a result, both networks ultimately improve. Training continues until convergence is reached and either the generator stopsmaking improvements or the discriminator can no longer discriminate between the images created with the generator and the training data.
## 6 Evaluation
### Performance Evaluation
#### 6.2.3 Memory Load Analysis  
Bloom filters are known for their memory efficiency. This section analyzes the memory overhead of the proposed framework compared to the baseline, TPBF, and standard Bloom-filter-based data integrity solution, \(fSum\)[38]. Figure 9(a) depicts the memory load (i.e., the total memory consumed by the data transfer application while data transfer is in progress), whereas Figure 9(b) depicts the Bloom filter memory overhead of the proposed framework.  
Figure 9(a) compares the memory load of baseline (without integrity), TPBF data integrity method, and CRBF- integrity methods for all workloads described in Section 6.1.2. Figure 9(a) reveals that the CRBF data integrity method verification exhibits an overhead of 5%, 13%, and 5%, for the D1, D2, and D3 workloads, respectively, compared to baseline. However, compared with the TPBF data integrity method, the overhead is minimal to negligible.  
Figure 9(b) depicts the Bloom filter memory overhead comparison of the proposed data integrity framework to that of \(fSum\) and the TPBF method of the data integrity framework. Figure 9(b) indicates that the CRBF method of data integrity exhibits lower memory requirements than \(fSum\) for D1 and D3 workloads. However, we observe slightly higher memory requirements for D2 workloads than \(fSum\). Furthermore, we observe negligible overhead for D1 and D2 workloads compared to the TPBF data integrity method. However, similar to \(fSum\), D2 workloads have slightly higher memory requirements than the TPBF data integrity method, as the file size and stripe size are the same (i.e., 1 MB). Therefore, based on (7), the CRBF data integrity method exhibits a similar or higher memory overhead than that of the TPBF and \(fSum\) solutions. Overall, Figure 10 illustrates that, similar to the TPBF, the proposed CRBF data integrity method is significant regarding memory savings when the dataset contains large or mixed-size files.
## 3 Experimental Evidence of Synchronization between Rossler Systems
### Adaptive Generalized Synchronization between Numerical Models  
An interesting case is a generalized synchronization between two discrete systems, one of which (master) is simulated using the Dormand-Prince eighth-order method and the other using a low-order integration method with a much higher truncation error.  
In our first experiment, slave and auxiliary systems were simulated using [PERSON]'s method; the parameters are listed as follows. For slave system 1, \(x_{s,1}=2\), \(y_{s,1}=-4\), \(z_{s,1}=-2\), initial parameters \(\hat{a}_{1}=\hat{b}_{1}=\hat{c}_{1}=1\). For the auxiliary system: \(x_{s,2}=3\), \(y_{s,2}=-2\), \(z_{s,2}=1\), initial parameters \(\hat{a}_{2}=\hat{b}_{2}=\hat{c}_{2}=2\). The step-size for Heun's and Dormand-Prince methods is \(h=0.01\).The errors in dynamics between the master and two slave systems are given in Figure 4.  
As expected, the minimal achieved error between slave systems is of the order of machine epsilon in double precision, while the error in master and slave systems is similar to the error obtained in the previous experiment.  
To test whether the rate of slave system convergence depends on the integration method, our second experiment was conducted. The master system had the following parameters: \(x_{m}=-1\), \(y_{m}=0\), \(z_{m}=1\), parameters \(a=0.2\), \(b=0.6\), \(c=5.25\). For the slave system 1, \(x_{s,1}=2\), \(y_{s,1}=-4\), \(z_{s,1}=-2\), initial parameters \(\hat{a}_{1}=\hat{b}_{1}=\hat{c}_{1}=1\). For the auxiliary system, \(x_{s,2}=3\), \(y_{s,2}=-2\), \(z_{s,2}=1\), initial parameters \(\hat{a}_{2}=\hat{b}_{2}=\hat{c}_{2}=2\). Figure 5 presents the achieved results.  
As expected, there was no significant difference between integration methods in the resulting convergence rate. The final achieved accuracy had a steady state of machine epsilon order and below. Among all tested first and second-order methods, [PERSON]'s and trapezoidal methods tended to result in slightly more precise synchronization.  
Figure 3: Synchronization error between master and slave systems for different integration methods, smoothed with a median filter; the window size is 600 samples.
# Analysis of Blur Measure Operators for Single Image Blur Segmentation
## 3 Material and Method
### Evaluation Measures  
The relative performance of the blur measure operators can be analyzed quantitatively as they are being tested under the same blur segmentation framework. All of the stated blur measure operators have been applied on the data set for which ground truth depth maps are also available. In this case, ground-truth is our actual observation in which the pixels are clearly labeled as either blurred or sharp. The blur maps are our retrieved results which are obtained by applying blur measure operators, and these can be compared against the ground-truth to provide the relative performance measurements. We have used Precision, recall and F-measure to determine the quality of the retrieved blur maps. Precision is a measure of relevance between the retrieved result and the observation. In our case it refers to the fraction of the detected blurred (sharp) pixels which are actually blurred (sharp) in the ground-truth.  
\[Precision,\ P=\frac{T_{p}}{T_{p}+F_{p}}, \tag{7}\]  
where \(T_{p}\) means that a blurred (sharp) pixel has been correctly detected as blurred (sharp) pixel and \(F_{p}\) expresses that a pixel has been inaccurately detected as blurred (sharp) but it was sharp (blurred) actually.  
Recall, also called as sensitivity in binary classification, is a measure of ability to retrieve the relevant results. In our case, it depicts the fraction of the actual blurred (sharp) pixels which are detected.  
\[Recall,\ R=\frac{T_{p}}{T_{p}+F_{n}}, \tag{8}\]  
where \(F_{n}\) means that a pixel has been inaccurately detected as sharp (blurred) but it was blurred (sharp) actually.  
Being based on different working principles, different blur measure operators may show different capabilities with respect to precision and recall evaluations. A more suitable measure in this situation is F-measure which takes both precision and recall into consideration. The F-measure computes the accuracy of retrieved result by comparing it with the observation. In our case, segmented blur map is compared with the ground truth. The general form of F-measure is \(F_{\beta}\) which is based on [PERSON]'s effectiveness measure [55]. It is the weighted harmonic mean of precision and recall such that recall gets \(\beta\) times more importance as compared to precision. If the performance objective is to be considered, this allows to place more emphasis on recall or precision [56] as might be required or intended.  
\[F-\text{measure},\ F_{\beta}=\frac{(1+\beta^{2})\times P\times R}{\beta^{2} \times P+R}. \tag{9}\]  
These quantitative measures provide an appropriate tool for analysis and evaluation.
# Privacy-Preserving Data Aggregation with Dynamic Billing in Fog-Based Smart Grid
## 4 Our Proposal
### Data Aggregation  
1. Upon receiving \(\mathcal{T}_{ij,m}\) from all covered SMs, \(FN_{i}\) performs the batch verification to verify all reports as \[e(\prod\limits_{j=1}^{n}\sigma_{ij,m},g)=\prod\limits_{j=1}^{n}e(H(ID_{SM_{ij} }||u_{ij,m}||v_{ij,m}||T_{ij,m}),pk_{ij}).\] (1) If Equation (1) holds, it means that all reports are valid.
2. \(FN_{i}\) aggregates \(n\) ciphertexts as \(C_{i,m}=(u_{i,m},v_{i,m})=(\prod\limits_{j=1}^{n}u_{ij,m},\prod\limits_{j=1}^ {n}v_{ij,m})\).
3. \(FN_{i}\) uses its private key \(x_{FN_{i}}\) to generate the signature \[\sigma_{FN_{i,m}}=H(ID_{FN_{i}}\parallel u_{i,m}\parallel v_{i,m}\parallel T_{ FN_{i,m}})^{x_{FN_{i}}}.\]
4. \(FN_{i}\) sends \(\mathcal{T}_{i,m}=(ID_{FN_{i}},u_{i,m},v_{i,m},T_{FN_{i,m}},\sigma_{FN_{i,m}})\) to AC.
## 3 Data and Methods
### Flash-Flood Potential Assessment  
The flash-flood potential index (FFPI) was calculated for the first time by [PERSON] [(85)], and has subsequently been used and adapted by numerous researchers [(53; 5; 85; 86)]. Generally, the FFPI is defined as a dimensionless indicator whose values can be obtained by overlapping many geographical factors that influence the surface runoff process [(87)]. The geographical factors that were widely used in the aforementioned studies for FFPI computation are: slope angle, soil, land use/land cover, lithology, profile curvature, and convergence index. The FFPI values are easily obtained in GIS software by using Map Algebra [(53; 87; 88)]. For the present study, the calculation and spatialization FFPI for the Zabala catchment (for 1989 and 2019) were performed by using the multilayer perceptron model combined with GIS techniques.
## 3 Results and Discussion
Carriers trapped in a deep-level state can create an electric field around themselves. As the trapped charge thermally escapes over time, the electric field strength decreases, and the activation becomes faster with increasing temperature. A deep carrier trap occurred in the path of the leakage current, as shown in Figure 8b, which can form an electric field larger than the piezoelectric field in the p-AlGaN layer. The layer in which the trap existed appears to be the EBL and p-AlGaN interface, and the trapped charges were holes, since, in this case, an electric field generated by the trapped charge was in the opposite direction to the piezoelectric field in p-AlGaN, as shown in Figure 8c. When the system was returned to a zero bias, the trapped holes were activated very slowly and exited the trap, returning to their original state, as shown in Figure 8a. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Temperature** & \(\tau_{1}\) & \(\tau_{2}\) \\ \hline
26 \({}^{\circ}\)C (room temp.) & 0.291 & 1.268 \\
50 \({}^{\circ}\)C & 0.139 & 0.999 \\
100 \({}^{\circ}\)C & - & 0.584 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The fitted time constants at each temperature. Figure 7: Temperature dependence of the photocurrent change with time. The applied biases are the same as in Figure 5.  
The relationship between the activation energy of the trapped carriers and the temperature is expressed by the Arrhenius equation as  
\[\ln\Bigl{(}\frac{e_{n}}{T^{2}}\Bigr{)}=-\frac{\Delta ET}{k}+\ln(J), \tag{2}\]  
where \(e_{n}\) is the recombination rate in the trap level, which is reciprocal to the time constant, and \(\Delta E\) is the energy difference between the trap level and the band-edge state closest to the trap level, namely, the activation energy [25; 26; 27]. We obtained the activation energy by substituting \(\tau_{2}\), measured at three temperature points, into the Arrhenius equation and calculating the slope of the points. As a result, the activation energy of the trap level corresponding to \(\tau_{2}\) was calculated to be 279 meV. The electric field caused by the traps generated around the active layer compromised the operational stability of the device. For example, charging at the hole-trap layer at the interface between the EBL and p-AlGaN can lower the threshold voltage of the device by affecting the electric field of the active layer but can also block hole injection from p-AlGaN into the active layer. We report that the direct reversal of the photocurrent occurs in the DUV LED, caused by the immediate absorption region changes with the excitation wavelength, not by the direction of the applied electric field. We note here that the direction reversal of the photocurrent, depending on the excitation wavelength, can be potentially applicable in remote optical switches.