esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Single-Factor Analysis of Bond Strength
#### 3.3.4 Sensitivity Analysis of Degree of Influence  
Pearson's correlation coefficients, as shown in Figure 12, were obtained to explore the sensitivity analysis of different factors influencing bond strength. The closer the absolute values of Pearson's correlation coefficient were to 1, the stronger the linear correlations between the influencing factors and the bond strength. Based on the values of Pearson's correlation coefficient, the correlations were classified into five classes, as shown in Table 8.  
Figure 11: Microscopic characteristics of the interface between rubber granules, basalt fibers, and concrete. (**a**) The rubber granule–concrete interface. (**b**) The basalt fiber–concrete interface.  
Figure 12: Pearson’s correlation coefficients between influencing factors.  
Pearson's correlation coefficients of bonding length, rebar diameter, and whether the appropriate amounts of rubber granules and basalt fibers were used are shown in Figure 12 as bonding length (\(-0.97\), extremely strong), rebar diameter (\(-0.36\), weak), mixed with \(10\%\) rubber granules (\(-0.205\), weak), and mixed with \(4.56\) kg/m\({}^{3}\) basalt fiber (\(0.127\), extremely weak). Therefore, the order of the sensitivity of these four factors to the bond strength, from the smallest to the largest, is bonding length (\(-0.97\)), rebar diameter (\(-0.36\)), mixed with \(10\%\) rubber granules (\(-0.205\)), and mixed with \(4.56\) kg/m\({}^{3}\) basalt fibers (\(0.127\)). The results also indicate that the first three factors are negatively correlated with bond strength. This conclusion is consistent with the results in Sections 3.3.1-3.3.3.
# High-Precision Tea Plantation Mapping with Multi-Source Remote Sensing and Deep Learning
## 4 Results and Discussion
### Advances, Limitations, and Future Research Directions  
This study highlights the effectiveness of multi-source remote sensing data fusion and advanced deep learning models in achieving high-precision tea plantation mapping. The combination of Sentinel-1 SAR and Sentinel-2 optical imagery enhances classification accuracy, particularly in areas affected by cloud cover. This result aligns with previous research emphasizing the complementary advantages of multi-source imagery in agricultural remote sensing [11; 16]. Compared to earlier approaches based on traditional machine learning methods and convolutional semantic segmentation models [6; 22; 23], the incorporation of SE-UNet, with its attention mechanisms, and Swin-UNet, utilizing a novel transformer-based architecture, demonstrates significant improvements in capturing both local and global spatial features. These advanced models address challenges posed by the complex spatial distribution and varying spectral characteristics of tea plantations more effectively. Additionally, this study conducts a detailed statistical analysis of the effects of terrain factors, such as elevation, slope, and aspect, on classification accuracy. This analysis enhances understanding of terrain-induced variations in mapping precision, providing practical insights for improving classification methodologies in complex landscapes. By quantifying these impacts, the findings contribute to the optimization of tea plantation mapping techniques in regions with diverse topographical conditions.  
However, several limitations must be acknowledged. While this study primarily relies on UNet-based architectures, exploring alternative frameworks could help overcome potential limitations and enhance the flexibility of the approach. Additionally, the process of label creation for polygonal samples in validation may have introduced some degree of human error, which could influence classification accuracy. The reliance on a single-year dataset restricts the ability to capture temporal variations in tea plantation characteristics, and the resolution of remote sensing data limits the precision of tea plantation mapping,especially in regions with fragmented or mixed land covers. Future research should aim to incorporate multi-temporal datasets to account for inter-annual variability and consider the application of more advanced attention-based models for semantic segmentation tasks in tea plantation mapping. Additionally, building on the insights gained from the terrain factor analysis in this study, future work could focus on developing targeted strategies to improve classification accuracy in diverse topographical conditions, thereby enhancing the robustness and scalability of tea plantation mapping methodologies in complex geographical settings.
# A Dynamic Surface Gateway Placement Scheme for Mobile Underwater Networks +
## 3 Background
### Imm  
To confront the additional complexity posed by sensor node mobility PADP implements a tracking algorithm to predict the position of sensor nodes in multiple future time steps. In practice, however, sensor nodes can exhibit complicated movement patterns, indicating that a single model of mobility may be ineffective. Hence, we employ an IMM estimator to accommodate multiple patterns of maneuvering. A brief introduction to the IMM estimator is provided here. A detailed description can be found in [32].  
The IMM is an adaptive estimation approach. Unlike many other methods which assume a particular node movement pattern, the IMM filter incorporates each of the possible node movement patterns, executing a set of filters corresponding to each movement pattern in parallel. The overall state estimate is then determined as a combination of the conditional estimates of the individual models.  
PADP considers two node movement patterns defined as follows:  
* _Uniform motion_: movement in a straight line at constant velocity, modeled by a Kalman filter.
* _Maneuver_: coordinated turn at a constant rate of turn and constant speed, modeled by an extended Kalman filter.  
These two filters run in parallel. \(M^{k}\) stands for the measured position at time k, \(E_{x}^{k}\) is the predicted position using the paralleled filters, and \(N^{k}\) is the final estimated position. The combination of the estimates produced by the two filters determines the final estimate of the node's position as shown in Figure 1.  
Given that nodes' moving pattern, uniform motion, and maneuver, can change from one mode to another, the IMM estimate of the node location is computed as a weighted summation of the estimates from multiple mode-matching filters. The weights of the two parts are calculated by the maximum likelihood estimation method. A brief introduction on the IMM estimator is provided in the sequel, and a detailed description can be found in the book [32]. A Markov chain transition matrix is characterizing the transition probability of the node's moving pattern from one mode to another, is introduced to update the weighting coefficients. Except that the initial condition of each filter is a combination of the model-conditioned estimates of the multiple filters at the preceding state, operation of each filter is identical to that of the Kalman filter or the extended Kalman filter, depending on whether the state equation under the considered mode is linear or not.
## 3 Results  
### Mean and Variance  
The mean NPP and standard deviation over the period (1982-2006) in India are estimated at 0.83 PgC\(\cdot\)y\({}^{-1}\) and 0.03 PgC\(\cdot\)y\({}^{-1}\) which are less than the CASA-model estimate of 1.42 PgC\(\cdot\)y\({}^{-1}\) and 0.06 PgC\(\cdot\)y\({}^{-1}\) for 1981-2006 [18]. The mean and standard deviations are 0.79 and 0.02 PgC\(\cdot\)y\({}^{-1}\)respectively, when the linear trend is removed. Since all available global scale NPP products are model based, it is important to recognize that trends and variability estimated from these products are likely more reliable and consistent than the mean values. As can be seen from Figure 1(a), NPP shows a large spatial variation across the country with high values (\(\sim\)800 gC\(\cdot\)m\({}^{-2}\)-y\({}^{-1}\)) in northeast India, parts of the Western Ghats and the Himalayan forests. Low values are observed in dry regions, such as northwest India, and areas of the western and northernmost parts of India. The spatial pattern of standard deviation (Figure 1(b)) resembles the mean NPP: the absolute magnitude of variation is higher where the mean is higher. It varies from 0 in low-productivity regions to as high as 40 gC\(\cdot\)m\({}^{-2}\)-y\({}^{-1}\) in high productivity regions. However, the coefficient of variation (CV) which is the standard deviation normalized by mean NPP shows high values in regions with low productivity (Figure 1(d)). The spatial pattern of AVHRR-derived NPP, standard deviation and CV are similar to that simulated by the CASA-model [18].
## 3 Lightweight Cryptography for Smart Objects
### Lightweight Cryptographic Hash Functions  
Cryptographic Hash Functions, such as MD5 [45] and SHA-1 [46], are an essential part for any protocol that uses cryptography. Hash functions are used normally for different purposes, such as message integrity check, digital signatures, and fingerprinting. Cryptographic hash functions should ideally be:  
* computationally inexpensive;
* pre-image resistant: given a hash \(h\), it should be difficult to invert the hash function in order to obtain the message \(m\) such that \(h=hash(m)\);
* second pre-image resistant: given a message \(m_{1}\), it should be difficult to find another message \(m_{2}\) such that \(hash(m_{1})=hash(m_{2})\);
* collision resistant: it should be difficult to find two messages \(m_{1}\) and \(m_{2}\), with \(m_{1}\
eq m_{2}\), such that \(hash(m_{1})=hash(m_{2})\) (hash collision).  
In general, for a hash function with an \(n\)-bit output, pre-image and second pre-image resistance require \(2^{n}\) operations, while collision resistance requires \(2^{n/2}\) operations [47]. While the design of standard cryptographic hash functions does not focus on hardware efficiency, lightweight cryptographic hash functions are needed for use in resource-constrained devices in order to minimize the amount of hardware (in terms of GEs) and energy consumption.  
In this subsection, we will overview some proposals for lightweight cryptographic hash functions that go beyond classical MD and SHA families.
###### Abstract  
We evaluated electrocardiogram (ECG) biometrics using pre-configured models of convolutional neural networks (CNNs) with various time-frequency representations. Biometrics technology records a person's physical or behavioral characteristics in a digital signal via a sensor and analyzes it to identify the person. An ECG signal is obtained by detecting and amplifying a minute electrical signal flowing on the skin using a noninvasive electrode when the heart muscle depolarizes at each heartbeat. In biometrics, the ECG is especially advantageous in security applications because the heart is located within the body and moves while the subject is alive. However, a few body states generate noisy biometrics. The analysis of signals in the frequency domain has a robust effect on the noise. As the ECG is noise-sensitive, various studies have applied time-frequency transformations that are robust to noise, with CNNs achieving a good performance in image classification. Studies have applied time-frequency representations of the 1D ECG signals to 2D CNNs using transforms like MFCC (mel frequency cepstrum coefficient), spectrogram, log spectrogram, mel spectrogram, and scalogram. CNNs have various pre-configured models such as VGGNet, GoogLeNet, ResNet, and DenseNet. Combinations of the time-frequency representations and pre-configured CNN models have not been investigated. In this study, we employed the PTB (Physikalisch-Technische Bundesanstalt)-ECG and CU (Chosun University)-ECG databases. The MFCC accuracies were 0.45%, 2.60%, 3.90%, and 0.25% higher than the spectrogram, log spectrogram, mel spectrogram, and scalogram accuracies, respectively. The Xception accuracies were 3.91%, 0.84%, and 1.14% higher than the VGGNet-19, ResNet-101, and DenseNet-201 accuracies, respectively.  
deep learning; pre-configured model; convolutional neural network; time-frequency representation; electrocardiogram; biometrics
## 5 Design Strategy of the Flexible Joint
### Orthogonal Array Test
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Number**} & \multicolumn{3}{c}{**Parameters and the Corresponding Levels**} & \multicolumn{2}{c}{**Results**} \\ \cline{2-7}  & \multirow{2}{*}{\begin{tabular}{c} **Segment** \\ **Length/m** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Width of the** \\ **Flexible** \\ **Joint/m** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Ratio of the Strength of** \\ **Flexible Joint to the** \\ **Strength of Lining** \\ **Segments** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Reduction of** \\ **Maximum** \\ **Shear Stress/\%** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Reduction of** \\ **Maximum** \\ **Axial Stress/\%** \\ \end{tabular} } \\ \hline
1 & 6 & 0.5 & 1/10 & 71.3 & 39.5 \\
2 & 6 & 1.0 & 1/50 & 71.9 & 68.9 \\
3 & 6 & 1.5 & 1/100 & 72.8 & 72.0 \\
4 & 10 & 0.5 & 1/50 & 69.5 & 43.4 \\
5 & 10 & 1.0 & 1/100 & 70.5 & 61.9 \\
6 & 10 & 1.5 & 1/10 & 69.4 & 34.2 \\
7 & 15 & 0.5 & 1/100 & 68.4 & 53.5 \\
8 & 15 & 1.0 & 1/10 & 68.3 & 32.9 \\
9 & 15 & 1.5 & 1/50 & 72.2 & 48.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results of the orthogonal array test. Figure 15: Internal stress distribution along tunnel axis in orthogonal array test. (**a**) Shear stress; (**b**) axial stress. obtained: The optimal combination concerning shear stress reduction is A\({}_{1}\)B\({}_{3}\)C\({}_{3}\), and the optimal combination for axial stress reduction is A\({}_{1}\)B\({}_{2}\)C\({}_{3}\). Since the two optimal combinations are not consistent, the optimal level should be determined according to the primary and secondary relationship of the parameters. In this test, level B\({}_{3}\) is the optimal level according to the influence of the shear reduction. While considering the axial stress reduction, the B\({}_{2}\) level is the best choice. However, referring to the above results, the width of the flexible joint has less influence on the test index. When the width of the flexible joint is taken as B\({}_{2}\), the reduction of the shear stress is 1.82%, which is lower than that of B\({}_{3}\), and the reduction of the axial stress is 5.81%, which is higher than that of B\({}_{3}\). Moreover, gaps between the lining segments that are too wide are not conducive to the safety of the lining structure. Therefore, considering the current lining molding technology, the optimal combination of flexible joint is A\({}_{1}\)B\({}_{2}\)C\({}_{3}\). It is suggested that a reasonable segment length is 6 m, a reasonable width of the flexible joint is 1.0 m, and a reasonable material strength of the flexible joint is 10% of the strength of the lining segments.
# Experimental and Numerical Research on End-Wall Flow Mechanism of High-Loading Tandem Stators
###### Abstract  
Tandem blades have been recognized for their potential to enhance the loading capacity of compressors. However, tandem stators currently do not exhibit advantages due to insufficient understanding of the complex end-wall flow mechanisms. To address this, an extensive study was conducted on tandem stators using experimental and numerical methods. The analysis focused on loss development, three-dimensional flow structures, and interaction mechanisms between front and rear blades. The results indicated the following: (1) The rear blade's influence on the front blade has contrasting effects in mid-span and end-wall regions. The stagnation action of the rear blade increases front blade load, leading to greater corner separation losses. (2) Mid-span flow losses account for over 60% of total losses near stalls, primarily due to increased mixing losses from the migration of front blade corner separations towards the mid-span region in the rear blade channel. (3) At higher mass flow rates, corner separations occur in the rear blade, driven by significant circumferential pressure differences at the front section of the rear blade, causing end-wall fluid migration towards suction surfaces. (4) Corner stalls predominantly occur in the front blade, with associated losses exceeding those in conventional blades.  
high-loading compressor; tandem stators; end-wall regions; flow mechanism; experimental research Article
# A Wear Debris Signal Processing Method Based on Inductive Monitoring for Aero-Engine
## 4 Debris Detection Based on Histogram in the Sliding Window
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Debris No.** & **Detection Time/s** & **Actual Appearance Time/s** & **Error/s** \\ \hline
1 & 62.8080 & 62.8069 & 0.0011 \\
2 & 85.4840 & 85.5042 & \(-\)0.0202 \\
3 & 132.6960 & 132.6960 & 0 \\
4 & 149.4040 & 149.4047 & \(-\)0.0007 \\
5 & 235.3440 & 235.3430 & 0.001 \\
6 & 347.6520 & 347.6510 & 0.001 \\
7 & 375.5560 & 375.5560 & 0 \\
8 & 379.5920 & 379.5930 & \(-\)0.001 \\
9 & 406.4280 & 406.4260 & 0.002 \\
10 & 705.7240 & 705.7210 & 0.003 \\
11 & 947.8720 & 947.8720 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Debris detection results and errors. Figure 9: Typical histograms of debris signature and noise signature: (**a**) signal energy values of debris signature, (**b**) histogram of debris signature, (**c**) signal energy values of noise signature, and (**d**) histogram of noise signature. For the inductive sensors, the type of background noise is mainly the white noise, which cannot affect the detection. Moreover, for the detection results of the aero-engine test, it should be noted that the last debris is also detected even if it appears during the change in the working condition of the aero-engine, which shows its capability in interference resistance.
# Wireless Strain Gauge for Monitoring Bituminous Pavements
## 4 Validation of Measurement Process
### Diametral Compression of a PVC Test Body Instrumented with One Strain Gauge  
We made a test body out of PVC (Figure 6, right) and compressed it (Figure 6, left) to check the linearity of the measurement and validate the electronic section of the measurement channel. A Zwick press with diametrical shims is used to load the test body and obtain a homogeneous strain; also, the diametrical compression is used to calibrate the measurement channel. This method ensures that the measurement channel is providing accurate and consistent readings. Indeed, this procedure is necessary because the uncertainties about the component values and analog offset compensation are too important and the channel measurement can be saturated without mechanical load on the pavement. As can be seen in Figure 7, the response of the gauge is perfectly linear. An offset of approximately 500 N appears between the loading and unloading curves. This shift is due to the stress relaxation and creep deformation of the PVC core.  
Figure 5: Sensor installation in the pavement.  
Figure 6: (**Left**): Diametric compression schematic. (**Right**): View of the PVC core.  
To evaluate the nonlinearity error due to the conditioning circuit based on the asymmetrical Wheatstone bridge, we chose to calculate the worst-case operating condition of the measurement chain. Table 1 resumes the relative error between real and measured strain. The real strain is an imposed displacement range. From an imposed displacement, the corresponding resistance of the gauge is determined thanks to the formulas from [PERSON] [32]. The measured strain is computed from Rg, and takes into account the nonlinear behavior of the conditioning circuit and ADC. The relative deviation is the absolute ratio between the real and measured strain.  
The relative error is infinite at \(0\) um/m, and it is mainly due to the conditioning. In a perfectly balanced Wheatstone bridge, the voltage difference between the two outputs would be zero at zero strains. However, in practice, due to imperfections in the bridge and thermal noise, a small voltage difference will always be present, even at zero strains. As a result, the SNR is \(0\) (\(0\) in linear and \(-\infty\) in dB) when the real strain is \(0\). This error is typically negligible for large strains but may become more significant at lower strains.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Real Strain (\(\mu\)m/m)** & **Rg (Ohm)** & **Measured Strain (\(\mu\)m/m)** & **Rel. Deviation (\%)** \\ \hline \(-200\) & \(119.95\) & \(-200.03\) & \(0.02\) \\ \(-100\) & \(119.97\) & \(-98.81\) & \(1.19\) \\ \(-50\) & \(119.99\)
# Improving Energy Efficiency on SDN Control-Plane Using Multi-Core Controllers
## 3 Materials and Methods
### Proposed Method--Parallel SDN Controller for a Multicore Platform  
An SDN controller is a very complex piece of software used as a platform to develop new applications and to provide APIs for the northbound software. There is an interface for the southbound, usually Openflow [27], and an interface for the northbound, usually some REST API. This dual function (southbound and northbound) by itself is a solid indication to leverage the parallel nature of multicore architectures. The task of reading and processing as many packets as possible can, in turn, be divided into sub-tasks that may run in parallel. One strategy to implement this separation of tasks is the dispatcher/worker, as shown in Figure 3.  
In this paper, we use the [PERSON] [28] controller's default Openflow 1.3 simple-switch implementation with some improvements to have parallel tasks divided by many parallel processes that may run on different cores. The improved version diagram is shown in Figure 4 and its general inner workings described in Algorithm 1.  
```
createParentProcess;  i \(\longleftarrow\) 0 ; whilei < CoreCountdo  createChildProcess(i);  i++;  end whilewhilehasPacketOnQueuedo  findIdIdIdIdProcess;  copyPacketToChildProcess;  waitNextPacket;  end while
```  
**Algorithm 1**Simple multicore switch  
As exposed, the SDN controller should be implemented in a way that takes advantage of the multicore nature of current processors. The implementation presented in Algorithm 1 has this property, as can be seen in Figure 5.  
In Figure 5, we see that as the number of cores grows, the CPU utilization is divided between the available cores. CPU utilization on the graph is shown as reported by the Linux kernel in the /proc/stat file. In our implementation, as the number of available cores increases, the task is better divided by every core leading to a decrease in CPU utilization by core; in addition, this behavior is the same, independent of CPU operation frequency.  
Figure 4: Ryu Simple switch implementation (**A**); and our multicore Ryu Implementation (**B**).  
Figure 3: Multicore tasks implementation on Ryu.