esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Blockchain-Based Multi-Level Location Secure Sharing Scheme
## 4 Our Proposed Scheme
### Our Proposed Scheme
#### 4.2.1 Initialization  
We designed a new method to set the user's location information to multi-precision, specifically, stripping the location data into multi-level virtual location data and offset vectors. The purpose of this is to enable \(LD\)s with different access levels to obtain user location information with different precisions. The location offset vector is recorded into  
Figure 2: Conceptual overview of the proposed scheme.  
the blockchain in the location update phase, which is illustrated in Section 4.2.4. In the initialization phase, \(LP\) generates multi-level virtual location data and the secret key for location updates. The specific steps are as follows.  
First, \(LP\), who is new to the network, executes Algorithm 1, inputting the precision radius set \(\{r_{1},r_{2},\,...,\,r_{N}\}\), to generate virtual location data \(\{Z_{0},Z_{1},Z_{2},\,...,\,Z_{N}\}\), as shown in Figure 3. \(Z_{k}\) denotes a circle area with \(P_{k}\) as the center point and \(r_{k}\) as the radius, \(k\in[1,N]\). The virtual location data meet the following three conditions: (1) The radius length of the circular area \(Z_{k}\) is \(r_{k}\), \(k\in[1,N]\). (2) \(Z_{0}\) is located in area \(Z_{1}\). (3) For \(a,b\in[1,N]\), if \(a>b\), area \(Z_{a}\) completely covers area \(Z_{b}\).  
Second, \(LP\) sends the encrypted virtual location data to \(Fnodes\) and the encrypted symmetric secret key \(k_{ud}^{1}\) for the 1 st location update to \(LD\)s, which requests the location according to the valid licenses introduced in Section 4.1, the details are shown in Algorithm 2. \(Enc()\), \(PK_{Fnode}\) and \(PK_{LD}\) denote asymmetric encryption function, the public-key of \(Fnode\)  
Figure 3: Virtual location data.  
and the public-key of \(LD\). After asymmetric encryption, the data are confidential, even if it is transmitted through an insecure channel.  
**Input:** Virtual location data: \(Z=\{Z_{0},Z_{1},\,...,\,Z_{N}\}\); The secret key for 1-th location update: \(k_{ud}^{1}\);  
```
1:\(Info_{F}\gets Enc(PK_{Fnode},Z)\);
2:\(Info_{D}\gets Enc(PK_{LD},k_{ud}^{1})\);
3:\(LP\) sends \(Info_{F}\) to \(Fnode\);
4:\(LP\) sends \(Info_{D}\) to \(LD\);
```  
**Algorithm 2** Initialization: pseudocode for sharing virtual location data and key \(k_{ud}^{1}\)
# Experimental Study on the Desalination of Waste Leachate Using the Combined Freezing Method
## 2 Materials and Methods
### Methods and Equations  
In this paper, the ice production rate refers to the ratio of ice obtained after desalination to the total mass of wastewater, and the salt rejection is defined as the ratio of the difference between the salinity of the initial wastewater and the salinity of the ice meltwater to the initial wastewater salinity. The conductivity removal rate also responds to the effectiveness of the treatment process in removing salt from the water and is defined as the ratio of the difference between the initial wastewater conductivity and ice meltwater conductivity to the initial wastewater conductivity. The formulas are as follows:  
\[\text{Ice production rate}=\frac{\text{m}_{1}}{\text{m}_{0}}\times 100\% \tag{1}\]  
\[\text{Conductivity removal rate}=\frac{\sigma_{0}-\sigma_{1}}{\sigma_{0}} \times 100\% \tag{2}\]  
\[\text{Salt rejection}=\frac{\text{S}_{0}-\text{S}_{1}}{\text{S}_{0}}\times 100\% \tag{3}\]  
where m\({}_{1}\) is the mass of ice crystals after the desalination treatment, and m\({}_{0}\) is the mass of the initial solution. \(\sigma_{0}\) is the conductivity of the initial solution, and \(\sigma_{1}\) is the conductivity measured after the ice meltwater. S\({}_{0}\) is the salinity of the initial solution, and S\({}_{1}\) is the salinity measured after the ice meltwater.
## 5 Conclusions and Future Work  
Two enhanced LMS methods were introduced for an adaptive digital filtering system combined with IMC, in order to take advantage of having the process model inside the control system structure.  
Major contributions of this research comprise the following. Firstly, the internal model LMS method 1 (IM-LMS 1) is introduced, which is a novel model-based algorithm. Internal mode control is applied in the traditional LMS algorithm, for assuring convergence of the control system without filtered inputs. It also showed acceptable tracking performance with a significantly mistuned plant model. Secondly, the internal model LMS method 2 (IM-LMS 2) is proposed. A new recursive digital filter system is proposed with an internal model controller. This control system enhances robustness, stability, and the ability to manage multi-spectral signals. Moreover, the tracking performance of IM-LMS 2 is improved with increasing complexity and frequency components to be controlled in the output of the disturbance plant, which should be tracked. Thirdly, with numerical analyses, their performances are validated. Proposed algorithms are numerically verified in several cases with single sine waves and multi-spectral signals. Time domain tracking performances and estimation errors in time and frequency domains are compared and their characteristics are discussed.  
This research will be verified with large number of applications for active noise and vibration control, including vibration mitigation, vehicle noise, and vibration attenuation. This research could be used to improve the active control applications in vehicles and mechanical structures with effective management of an expansive range of multi-spectral signals.  
[PERSON] and [PERSON] initiated and developed the ideas related to this research work; [PERSON] and [PERSON] developed novel methods, derived relevant formulations, and carried out performance analyses and numerical analyses; [PERSON] wrote the paper draft under [PERSON]'s guidance; and B.K. finalized the paper.  
This work was supported by the 2016 Yeungnam University Research Grant (216A580005).  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 11: Predicted spectra for a frequency-modulated (FM) signal with least mean squares and internal model least mean squares 2 methods.
# Deep Learning-Based ECG Arrhythmia Classification: A Systematic Review
## 2 Materials and Methods  
### Search Strategy  
This systematic review for DL-based ECG arrhythmia diagnosis is performed based on the literature search with four major scholar databases, i.e., Google Scholar, PubMed, Scopus, and the Digital Bibliography and Library Project, focusing on studies published until December 2022. As many studies do not explicitly mention their classification tasks, we first implement a coarse search to include more candidate studies and avoid overlooking studies for arrhythmia classification. Hence, the searching keywords for the literature search are set as (Deep learning OR deep neural network OR convolutional neural network OR CNN OR recurrent neural network OR RNN OR LSTM) AND (ECG OR electrocardiogram).  
The detailed paper search and refinement process is shown in Figure 1. A total of 3910 studies are obtained in the initial identification step. After removing the duplicates, 2265 unique studies remain. We then perform the refining process to extract studies that are more relevant to arrhythmia classification with DL. After the initial identification step, the obtained studies go through further screening and eligibility evaluation according to the inclusion and exclusion criteria. Specifically, 1694 studies are excluded by screening their titles and abstracts, and 203 papers are also removed based on the full-text assessment. The inclusion criterion enforces that the studies should be published in English and leverage DL to classify arrhythmia with ECG signals. The studies dealing with other tasks, such as emotion detection and drug and alcohol assessment, should be removed. Those studies which do not have full-text available are also excluded. Hence, a total of 368 studies were selected to be included in this review. The whole process is completed by two independent reviewers (QX and QXZ) in order and rechecked by PYL to ensure fair results of paper search and refinement.
# Novel Approach in Vegetation Detection Using Multi-Scale Convolutional Neural Network
## 2 Materials and Methods
### Experimentation Setup  
The experiments were conducted on a robust hardware setup that includes an AMD CPU, Intel processors, and NVIDIA Tesla T4 GPUs, collectively featuring a powerful configuration of 24 cores and 384 GB of memory. This infrastructure enables the efficient processing and analysis of large datasets, facilitating the implementation of complex algorithms required for the research. The experiments were conducted using the Deep Learning ToolboxTM in the MATLAB 2024a platform.  
Bayesian optimization is employed for hyperparameter tuning across all models. This process was applied consistently to ensure that each model, including the proposed MSCNN and the comparison models, had the opportunity to achieve its best performance on the given datasets.  
The training setup for the proposed model applied to the CamVid dataset, for example, includes the following specifications: the Stochastic Gradient Descent with Momentum (SGDM) optimizer is employed, which is known for its efficacy in converging to a local minimum. The learning rate schedule is configured to be piecewise, allowing for adjustments at predetermined intervals; specifically, the learning rate decreases by a factor of 0.3 every 10 epochs, commencing with an initial learning rate of \(1e^{-3}\). A momentum value of 0.9 is utilized to expedite the movement of gradient vectors in the correct directions, enhancing convergence speed. To address overfitting, L2 regularization with a coefficient of 0.005 is integrated. The training process is constrained to a maximum of 100 epochs with a mini-batch size of 3 selected to strike a balance between training speed and model accuracy. Data shuffling is performed at each epoch to ensure diverse mini-batches, fortifying the model's robustness. Furthermore, checkpoints are saved in a temporary directory to facilitate recovery and analysis. Training progress is monitored through verbose output every 2 iterations. These settings collectively aim to establish an effective training routine that boosts model performance while upholding stability and dependability throughout the training phase.
## 4 Grounding Grid Imaging Results and Evaluation Analysis
### Ground Grid Imaging Results
Due to the simple structure of the CNN generator model, the image details of the generated image at the corrosion site are not clear enough, the color space information is not captured and there are artifacts at the corrosion site. The generation network composed of U-Net better retains the spatial information and local features, and the generated image quality is better than the traditional CNN. After adding the CBAM attention mechanism, the network can more adaptively select important channel features, improve the generator's ability to capture the global structure and details of the image and reduce artifacts and distortion. Due to the inevitable deviations in voltage measurements caused by environmental and equipment factors, this paper introduces sufficient Gaussian noise into the boundary voltage measurement results to evaluate the noise resistance and generalization performance of the algorithm. The noise is added as shown in Equation (14). \[X_{noisy}=X+N\Big{(}0,\sigma^{2}\Big{)} \tag{14}\]  
After adding Gaussian noise of different sizes, grounding grid impedance imaging is performed. As shown in Figure 12, the blue part of model 1 represents grounding grid corrosion, and the conductivity of the actual corrosion part is \(2.2\times 10^{6}\) S/m. Model 2 is corroded at the green position, and the conductivity at the actual corrosion position is \(5.8\times 10^{6}\) S/m.As the noise level increases, particularly when the standard deviation of the noise exceeds 5% of the voltage values, the imaging quality is significantly affected. However, when introducing low to moderate noise, the imaging results remain largely  
Figure 11: Comparison of secondary imaging using different algorithms. unaffected. Even with moderate levels of noise, the algorithm is still able to accurately detect the corrosion location and corrosion state, demonstrating the robustness and noise resilience of the proposed method. This indicates that the model can maintain a reliable performance under practical, noisy conditions, making it suitable for real-world applications where measurement noise is inevitable.
# A New Association Scheme for Handling Node Mobility in Cluster-Tree Wireless Sensor Networks
## 3 System and Network Model
### Network Infrastructure
#### 3.1.1 Network Scheduling  
In cluster-tree networks, clustering is just a logical grouping of nodes. When two physically close clusters transmit beacons at similar time instants, data transmissions from adjacent clusters will probably collide, causing retransmissions and packet losses. In order to avoid such inter-cluster collisions, we consider the use of a beacon scheduling algorithm based on a previous work [3], preventing overlaps of beacon frames from adjacent clusters. Thus, the cluster scheduling is organized following a bottom-up scheme, where superframe durations (i.e., the time interval during which the cluster may remain active) are ordered in a bottom-up sequence, i.e., first the deepest clusters, then clusters of the next lowest depth, and so on, until reaching the PAN coordinator (CH0), as illustratedin Figure 3. Considering that active periods of the clusters are composed only of Contention Access Periods, any node within the cluster can try to send its messages during the active period of its cluster.  
To implement this beacon-scheduling scheme, the following protocol constraints must be respected: the length of Beacon Interval (BI); buffer sizing and node speed.
## 2 Results and Discussion
### Fiber Composition  
The SPME fiber composition and selectivity were studied. Three types of fibers (PDMS, CAR/PDMS, and DVB/CAR/PDMS) with different thickness and polarity were studied. These fibers were evaluated for their diverse coverage of volatile metabolites  
Figure 1: Alfalfa glandular trichomes. (**A**). Glandular trichomes on an alfalfa (FSG 420 LH) stem. (**B**). Magnified view of glandular trichomes showing stalks and secretary heads. White bar = 1 mm, red bar = 0.1 mm.  
emitted from alfalfa based on prior SPME studies of plant volatile analyses [24; 25; 26]. The extraction of volatile metabolites emitted from alfalfa leaves was first performed with various fibers at 50 \({}^{\circ}\)C for 20 min. Table 1 shows that the bipolar CAR/PDMS fiber (99 volatile metabolites) and DVB/CAR/PDMS fiber (97 metabolites) extracted twice the number of volatile metabolites as did the non-polar PDMS fiber (48 metabolites).  
These results demonstrate that the bipolar fibers are qualitatively better for isolating a larger diversity of volatile metabolites emitted from alfalfa than the non-polar fiber. Nineteen of the volatile metabolites detected in the PDMS fiber were tentatively identified, while 32 and 38 of the volatiles detected in the CAR/PDMS and DVB/CAR/PDMS fibers, respectively, were putatively identified. These included the quantitative major volatile metabolites emitted from alfalfa leaves and were classified into five functional classes (i.e., alcohols, aldehydes, ketones, terpenoids, and others) (Table 1). The CAR/PDMS and DVB/CAR/PDMS fibers had very similar extraction efficiencies for alcohols, aldehydes, ketones, and others (e.g., 2-methylfuran, 2-ethylfuran, 3-ethylthiophene, 5-ethyl-2(5\(H\))-furanone, (\(E\))-3-hexen-1-ol acetate, ionene, \(\beta\)-Ionone). This included 5 volatile alcohols for each of the CAR/PDMS and DVB/CAR/PDMS fibers respectively; 13 and 14 volatile aldehydes; 6 and 5 volatile ketones; and 6 and 6 volatile others. However, the CAR/PDMS fiber failed to extract 6 of the 8 terpenoid volatile metabolites detected with the DVB/CAR/PDMS fiber [i.e., (+)-(R)-limonene, _cis_-linal oxide, \(\beta\)-linal, \(p\)-menth-1-en-4-ol, \(\alpha\)-terpinol, and \(\beta\)-damascenone], indicating that the CAR/PDMS fiber is more limited in its ability to extract non-polar terpenoid volatile metabolites. Terpenoids serve important physiological and ecological roles as attractants, repellents, anti-feedants, toxins, or antibiotics in most plant species, and have been used by human as pharmaceuticals, flavors, fragrances, food supplements, and natural pesticides [27; 28].  
The number of volatile metabolites extracted in the CAR/PDMS fiber and the DVB/CAR/PDMS fiber were not statistically different; however, the DVB/CAR/PDMS fiber was able to extract more chemically diverse metabolites, including alcohols, aldehydes, ketones, terpenoids, and others. Therefore, the DVB/CAR/PDMS fiber was selected for further optimization due to its greater ability to provide a more comprehensive chemical profile of alfalfa volatiles.
# Accelerated Correction of Reflection Artifacts by Deep Neural Networks in Photo-Acoustic Tomography
## Appendix A Artifacts Correction by Iterative Algorithms
### Landweber Iteration  
The Landweber iteration, also known as the Landweber algorithm, is an algorithm proposed in the 1950s by [PERSON] [56] to solve ill-posed linear systems of the form \(\boldsymbol{\Lambda}\boldsymbol{x}=\boldsymbol{b}\) where \(\boldsymbol{\Lambda}\) is a (not necessarily square) matrix. It is a regularization method that can be viewed as iteratively solving the unconstrained optimization problem  
\[\min_{\boldsymbol{x}}\frac{1}{2}\|\boldsymbol{\Lambda}\boldsymbol{x}- \boldsymbol{b}\|_{2}^{2}, \tag{11}\]  
which leads to the update scheme  
\[\boldsymbol{x}_{k+1}=\boldsymbol{x}_{k}-\gamma\boldsymbol{\Lambda}^{*}( \boldsymbol{\Lambda}\boldsymbol{x}_{k}-\boldsymbol{b}),\hskip 28.452756 ptk=0,1,2,\ldots \tag{12}\]  
where \(\boldsymbol{\Lambda}^{*}\) is the adjoint operator of \(\boldsymbol{\Lambda}\) and \(\gamma\) is a regularization parameter. It is well known the Landweber iteration is convergent if \(0<\gamma<\frac{2}{\sigma_{1}^{2}}\) where \(\sigma_{1}\) is the largest singular value of \(\boldsymbol{\Lambda}\), and it converges to the projection of the true solution on the orthogonal complement of the null space of \(\boldsymbol{\Lambda}\), see the analysis in [14] for instance. In our case, the vector \(\boldsymbol{x}\) is the discretized version of \(p_{0}\), and the vector \(\boldsymbol{b}\) is the discretized version of the measured data \(u|_{[0,T]\times\partial\Omega}\). The iteration exhibits the effect of semi-convergence: it converges before reaching a certain number of iterations but then diverges once beyond. The optimal value of \(\gamma\) and the stopping rule are largely empirical. Some guiding principles exist, but we do not intend to discuss them in this article.
## 3 Results
### Peak Rotation and Translation Scaling Relations
In the case of the IMI station, the correction caused an increase in \(a\)-parameter from 4.54 \(\times\) 10\({}^{-4}\) to 1.00 \(\times\) 10\({}^{-3}\). The same parameters for the PLA station increased from 5.08 \(\times\) 10\({}^{-4}\) to 1.29 \(\times\) 10\({}^{-3}\). Nevertheless, taking into account the model with the intercept for the IMI station, the correction procedure caused an increase in both regression parameters--from 3.73 \(\times\) 10\({}^{-4}\) to 8.74 \(\times\) 10\({}^{-3}\) for the slope and from 1.70 \(\times\) 10\({}^{-6}\) to 5.40 \(\times\) 10\({}^{-6}\) for the intercept. In the case of the PLA station, the regression parameters change their values from 4.20 \(\times\) 10\({}^{-4}\) to 9.47 \(\times\) 10\({}^{-4}\) and from 1.20 \(\times\) 10\({}^{-6}\) to 6.80 \(\times\) 10\({}^{-6}\) for the slope and intercept, respectively. Figure 6: Peak rotation scaling relations for the raw and corrected data: (**a**) Assuming both station records as one model without intercept; (**b**) Assuming both station records as one model with intercept; (**c**) Separation models for the IMI and PLA station without intercept; (**d**) Separation models for the IMI and PLA station with intercept.
## 3 Numerical Results: Performance Evaluation of the Stow Net
### Effects of Wave Excitations  
Figure 7 shows net horizontal length, net vertical deflection, and the net-entrance area with varying wave conditions under the identical current velocity of 1.0 m/s. As can be intuitively expected, larger wave heights cause larger dynamic motions of net assembly. As can be seen in Figure 7a, the dynamic variations of net horizontal length are not significant except for the case of \(Hs\) = 5.0 m since the horizontal current force is dominant under the given submergence depth. In addition, the variations of net horizontal length tend to be larger in the negative direction because mooring and net-thread tensions only act on positive extension. As shown in Figure 7b, the dynamic motions of net vertical deflection significantly increase as wave height becomes more than 2.5 m/s mainly due to the flexible threads at the end of the net assembly. The time histories of net-entrance area show that variations of the area are larger in the downward direction because of the side ropes providing tension in positive stretch, as represented in Figure 7c. The stow net is influenced by wave excitations unless the submergence depth is deep enough, considering fast-decaying wave force with depth. Especially, at \(Hs\) = 5.0 m, the fluctuations induced by waves are large, which may cause a negative influence on fishing efficiency. More dynamic motions are expected at lower current velocities with smaller submergence depth. The large net notions significantly affect the mooring and rope tensions.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Environmental Condition**} & \multicolumn{2}{c}{**Net Horizontal Length**} & \multicolumn{2}{c}{**Net Vertical Deflection**} & \multicolumn{2}{c}{**Net-entrance Area**} \\ \hline \(H_{s}\) **(m)** & \(T_{p}\) **(s)** & \(V_{c}\) **(m/s)** & **Average (m)** & **STD (m)** & **Average (m)** & **STD (m)** & **Average (m\({}^{2}\))** & **STD (m\({}^{2}\))** \\ \hline \multirow{4}{*}{1.0} & & 0.25 & 56.388 & 0.364 & 7.001 & 0.354 & 444.261 & 6.663 \\  & & 0.50 & 59.380 & 0.140 & 3.870 & 0.213 & 425.402 & 7.3
# Drying Stress and Strain of Wood: A Review
## 3 Composition of Drying Strain  
At this stage, scholars are mainly studying wood drying stress and strain combined with rheological theories. It is generally believed that the total strain of wood drying includes the following parts: thermal strain \(\varepsilon_{\text{T}}\), free shrinkage strain \(\varepsilon_{\text{F}}\), elastic strain \(\varepsilon_{\text{E}}\), viscoelastic creep strain \(\varepsilon_{\text{C}}\) and mechano-sorptive creep strain \(\varepsilon_{\text{M}}\). During the drying process, wood will undergo thermal expansion or contraction under the influence of temperature. However, the resulting thermal strain \(\varepsilon_{\text{T}}\) is so small that it can be disregarded.