esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Bio-Phytoremediation by Plants
### Plants' Mechanisms for Desalination  
Salt resistance is a multifaceted characteristic with numerous interacting characteristics, and little is understood about the regulatory networks that are implicated [54]. In particular, plant response to salt stress comprises a number of physiological, metabolic, and molecular pathways. Halophytes use a variety of physiological and molecular strategies, comprising variations in photosynthetic and transpiration rates, the sequestration of Na+ to extracellular or vacuole, the control of stomata aperture and stomatal density, and the concentration and production of phytohormones, as well as the relevant gene expression underpinning these physiological features, such as the transduction of stress signals, transcription factor control, transporter gene stimulation and expression, and synthetase inhibition or activation [55].  
Certain plants, including halophytes and glycopytes, employ the process of salt exclusion [56], which involves preventing sodium from entering the cytoplasm outside the cell by restricting saline element admission and rejecting it in the apoplasmic component [57; 58]. Furthermore, other processes, such as ion compartmentalization, osmoregulation by osmolytes, succinate formation, selective ion absorption and transport, antioxidant reaction, redox control, and energy status, all participate in salt adaptation strategies in halophytes [59]. It has been demonstrated that mucilage synthesis in the halophyte _Kostelekaya virginica_ is positively connected with salt amounts, and polysaccharide content varies among plant parts and treatments (Figure 1) [60].  
Some of these regulatory systems are also functional in non-halophytes, while others are halophyte-specific attributes that have developed [61; 62]. It is evident that various species of halophytes use more than one of these regulatory systems. One set may be active in one category while another set is prevalent in another, resulting in the categorization of halophytes as salt excluders (e.g., _Rhizophora mangle_), salt includes (frequently possessing mechanisms of salt recretion, e.g., species of genus _Tamarix_), and salt accumulators (some species of genus _Atriplex_) [63].
## 2 Materials and Methods
### Encoding of the Temporal and Spatial Grid of the Earth System's Layers
#### 2.3.1. ISEA4H-ESSG Encoding Structure
Among them, for the coordinates of the grid in the first layer, starting from the \(i\)-axis and going counterclockwise, the corresponding binary numbers are {100, 110, 010, 011, 001, 101} in sequence, and the corresponding basic code elements are {4, 6, 2, 3, 1, 5}. The binary numbers in the directions of the \(i\)-axis, \(j\)-axis, and \(k\)-axis for the encoding of the layer are {100, 010, 001} in sequence, and the corresponding basic code elements are {4, 2, 1}, as shown in Figure 12 and Table 2. Finally, the four-aperture hexagonal encoding shown in Figure 13 can be constructed. Figure 11: Hexagonal three-axis coordinate system at different grid levels. Figure 12: The encoding of the 2 nd to nth layers of the four-aperture hexagonal grid. Note: The red hexagon is the initial hexagon; the blue hexagons are the grid and encoding of the first layer; the black hexagons are the grid and encoding of the third layer. 2. Encoding of the Layer Radius  
The encoding of the layer radius is carried out only in a single direction. In the OHQS radial subdivision model, the layer radius direction adopts the rule of equidistant bisection for hierarchical subdivision. The encoding of each segment of the layer radius is described by its subdivision level (\(l\)) and its coordinate (\(k\)) on the radius of the layer body, in the form of \(l\)-\(k\), where the starting point is the inner end point of the radius of the layer body. According to the rule of equal division of the layer radius, in the \(l\)-th level of subdivision, the total number of segments of the layer body in the radial direction is \(K=2^{L}\) for the layer radius; then, the encoding of each segment of the layer radius is \(0\leq k\leq K-1\), as shown in Figure 14. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**(i,j,k)**} & \multicolumn{1}{c}{**(0,0,0)**} & \multicolumn{1}{c}{**(0,0,1)**} & \multicolumn{1}{c}{**(0,1,0)**} & \multicolumn{1}{c}{**(1,0,0)**} \\ \hline Angle with the \(i\)-axis & & \(4\pi/3\) & \(2\pi/3\) & 0 \\ Binary system & 000 & 001 & 010 & 100 \\ \(a\_2\sim a\_n\) & 0 & 1 & 2 & 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Corresponding table of the encoding of the 2 nd to nth layers of the grid and the \(i,j,\) and \(k\) coordinates. Figure 14: Encoding of the layer radius. Figure 13: Four-aperture hexagonal coding. Due to the characteristic of multi-temporal resolution of spatial data, this paper adopts a hierarchical subdivision scheme for temporal subdivision and encoding. Time is divided into different levels such as 100 years, 10 years, 1 year, 1 month, 1 day, 1 h, 1 min, and 1 s for subdivision, and unified encoding is carried out. The subdivision scheme and encoding are shown in Figure 14 and Table 3. Finally, by combining the spatial data type, temporal encoding, and subdivided grid encoding, we can construct the spatiotemporal encoding scheme OHQS-ST for spatial data, integrating Optimized Hexagonal Quadtree Structure with spatiotemporal encoding, as shown in Figure 15.
## 5 Discussion  
The fiber cemented behind casing trial produced high-quality subsurface images, proving that DAS cable data is capable of generating such results, even if the raw field data appear insufficient. The fiber cemented behind casing trial also revealed key insights that were applied to other fiber deployment methods (clamped to tubing, behind inflatable liner, and wireline), such as the need for fine spatial source sampling and the requirement to acquire geophone data to calibrate the DAS fiber cable/interrogator combinations in depth (Table 1).  
The raw data from the clamped to tubing, behind inflatable liner, and wireline methods showed low signal-to-noise ratios and picking first breaks was challenging. However, picking first breaks was easier on the fiber cemented behind casing data, which has the best earth coupling. The first breaks from the fiber behind inflatable liner were easier to pick than from the clamped to tubing and wireline methods. Picking first breaks is critical to the process as it is a foundation for subsequent steps, such as wavefield separation and velocity model building.  
Correct positioning of the cable in relation to true depth is also important, and in this case, geophones near the base of the well were used for calibration. Using the tap tests and nominal cable distance alone provided poor depth estimates, due to factors such as cable twists and bends, signal quality, and other causes. If geophones are not available, other methods such as acoustic logs could be used, provided there is a distinct reflection sequence.  
Figure 9: Raw recording of cable deployment on tubing. (**a**) Shot gathers from far offset; (**b**) shot gathers from near offset.  
The data had significant timing jitter that needed to be smoothed out. This was a result of the fine 20 cm sampling interval, which was also beneficial for the imaging process. The deconvolution of the up-going wavefield using the down-going wavefield as a source signature successfully reduced multiple reflection energies and increased the bandwidth of the up-going wavefield, making it suitable for the final imaging stage. The VSP-CDP transform using the pre-defined velocity model generated high-quality images for the fiber cemented behind casing and the fiber behind inflatable liner data, but the image for the wireline fiber cable in the water-filled well was limited due to only seven short offset shots being used.  
The excellent VSP-CDP images were a pleasant surprise, considering the low quality of the raw input data and initial low expectations. This result highlights the robustness of the processing sequence and the attention to detail given at each step of the process.
# Efficiency Analysis of Manufacturing Line with Industrial Robots and Human Operators
## 3 Human and Robot Factors Influencing the Production Process
Some conclusions from a survey about industrial robots conducted in Canada are as follows [44]:  
* Over 50% of companies keep records of the robot reliability and safety data;
* In robotic systems, major sources of failure are software failure, human error and circuit board troubles from the users' point of view;
* The most common range of the experienced _MTBF_ is 500-1000 h;
* Most of the companies need about 1-8 h for the repair of their robots. Based on the literature analysis [30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44], the main human and robot factors influencing the production process that should be taken into account are presented in Table 1.  
In reference [1], the approximate efficiency of robotic application versus manual application was compared. The efficiency of manual machine tending was about 40%-60% and, for robotic machine, tending was about 90% (without set-up). However, detailed values are dependent on the characteristics of the real workstation. The most decisive factor for industry is the economic cost of production. Implementations of robotic workstations require very high investment cost. It can bring greater work efficiency and savings in labour costs, especially if one robot can replace several human workers.
## 4 Concluding Remarks  
The exergy efficiency analyses that were carried out on pilot scale and bench MD systems illustrate the performance of the separation technologies based on useful energy, i.e., exergy. The AGMD systems tested showed different exergy efficiencies mainly due to the differences in module size and the type of condensation plates used. The exergy efficiencies were also affected to different degrees by the feed-coolant temperature differences in both types of modules. For the Xzero MD module, the exergy efficiency was not significantly affected by the difference in temperature across the module, whereas, for Elixir, a higher temperature difference across the module yielded higher exergy efficiencies and lower exergy destruction. The exergy efficiency results showed that the materials selection of the condensation plate plays a role in optimizing the performance of MD systems. This is clearly an important part of the MD system design and optimization, as the MD module contributes to the majority of the exergy destruction. Materials that could be considered for optimum heat transfer across modules and hence less exergy destruction from MD modules include stainless steel and high density polyethylene, which have better thermal conductivities than polypropylene. The selection of membrane and support material should also be considered when designing MD modules, as membrane and support materials, unlike condensation plates, should have less thermal conductivity for better thermal efficiency of modules.  
This research has been done in collaboration with KTH Royal Institute of Technology, and Politecnico di Torino, PolitTo funded through Erasmus Mundus Joint Doctoral Program SELECT+, the support of which is gratefully acknowledged.  
[PERSON] conceived, designed, and performed the experiments; all three authors analyzed the data; and [PERSON] composed the article under the guidance of the co-authors.  
The authors declare no conflict of interest. The funding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 4 Experiment and Results
### Classification Performance Evaluation Index  
In this paper, not only the experimental results are qualitatively evaluated, but also the quantitative analysis of the experimental results. The confusion matrix of the classification results is used to analyze the misclassification of the samples. The precision, recall and overall accuracy of each category of the experimental results are calculated to analyze the classification performance. Then, the consistency relationship between the predicted results and the true values are analyzed by the Kappa coefficient.  
The class of misclassification can be seen by the confusion matrix. Each column of the matrix represents the number of classes predicted by the classifier, and the total number of columns represents the total number of predictions for that category. Each row of the matrix represents the number of samples that belong to the class, and the total number of rows represents the total number of samples for that category.  
The overall accuracy rate is used to evaluate the classification performance of the model for all categories. The accuracy is obtained by dividing the number of all correctly classified samples by the total number of samples. The larger the overall accuracy rate, the better the classification performance of the model. The smaller the value, the worse the classification performance of the model.  
The Kappa coefficient calculation expression is as follows:  
\[\textit{Kappa}=\frac{N\Sigma_{i=1}^{n}a_{i}-\sum_{i=1}^{n}\left(g_{i}\cdot p_{ i}\right)}{N^{2}-\sum_{i=1}^{n}\left(g_{i}\cdot p_{i}\right)}. \tag{14}\]  
Figure 9: Image preprocessing flowchart.  
In Formula (14), \(a_{ii}\) represents the elements on the diagonal of the confusion matrix, and \(N\) represents the total number of samples. \(g_{i}\) represents the true total number of samples of the class \(i\), which is the sum of the elements of the \(i\) row in the confusion matrix. \(p_{i}\) denotes the total number of samples predicted by the classifier as the class \(i\), which is the sum of the elements of column \(i\) in the confusion matrix.  
The Kappa coefficient can be used to judge the degree of consistency between the prediction results and the true values of the images, so that the performance of different multi-objective classification algorithms can be analyzed. The larger the Kappa coefficient, the higher the consistency of the predicted results with the true values of the data, and the more reliable the classification algorithm.
# A Weighted and Distributed Algorithm for Range-Based Multi-Hop Localization Using a Newton Method
## 3 The Range-Based Localization Problem  
Consider a set of unknown sensor nodes \(\mathbf{S}=\{s_{1},s_{2},\ldots,s_{N}\}\) randomly distributed over a certain large area with 2-D true positions \(\mathbf{u}_{1}=[x_{1},y_{1}]^{T}\), \(\mathbf{u}_{2}=[x_{2},y_{2}]^{T}\), \(\ldots\), \(\mathbf{u}_{N}=[x_{N},y_{N}]^{T}\) and estimated positions \(\mathbf{p}_{1}=[\widetilde{x}_{1},\widetilde{y}_{1}]^{T}\), \(\mathbf{p}_{2}=[\widetilde{x}_{2},\widetilde{y}_{2}]^{T}\), \(\ldots\), \(\mathbf{p}_{N}=[\widetilde{x}_{N},\widetilde{y}_{N}]^{T}\), respectively. It is known that there is a set \(\mathbf{A}=\{a_{1},a_{2},\ldots,a_{M}\}\) of sensor nodes called anchor nodes that are randomly deployed in the same area with a special-hardware to self-localize (i.e., GPS). The anchor nodes have the known positions\([x_{N+1},y_{N+1}]^{T}\), \(\textbf{q}_{N+2}=[x_{N+2},y_{N+2}]^{T},\ldots,\textbf{q}_{N+M}=[x_{N+M},y_{N+M}]^{T}\). It is considered hardware-homogeneity among all \(N+M\) deployed sensor nodes with limited radio range R and \(M\ll N\).  
Under this scenario, a sensor node could require more than one hop to reach neighboring sensors for communications. Then, the set of one-hop neighboring sensors of an unknown sensor \(s_{i}\) can be stated as  
\[\textbf{\Omega}_{i}=\{j\ :\ \left\|\textbf{u}_{i}-\textbf{u}_{j}\right\|<R,\ \ \ i\
eq j,\ and\ j=1,\ldots,N\}, \tag{7}\]  
where \(\left\|\cdot\right\|\) denotes the Euclidean norm. This research assumes that all unknown sensors have \(n\)-hops connectivity to anchor nodes in the network, where the distance estimates, \(r_{ik}\) (for \(k=1,\cdots,M\)), between an unknown sensor \(s_{i}\) and any anchor \(a_{k}\) can be easily calculated by the DV-Hop scheme [13]. Thus, the set of anchor indexes is denoted as \(\textbf{B}=\{N+1,N+2,\ldots,N+M\}\).  
The set of noisy distances between \(s_{i}\) and its neighboring sensors is described as  
\[r_{ij}=d_{ij}+\textbf{e}_{ij}\ \ \ \forall\ j\in\textbf{\Omega}_{i}, \tag{8}\]  
where \(d_{ij}=\left\|\textbf{u}_{i}-\textbf{u}_{j}\right\|\), \(r_{ij}\) represents the noisy distance between sensors \(s_{i}\) and \(s_{j}\), \(d_{ij}\) is the true distance between them, and the term \(e_{ij}\) provides the biasing of the estimated distance introduced by environmental conditions and the measurement technique [26]. Distance estimates among sensor nodes can be carried out by techniques like RSS, TDoA, ToA, AoA, or a combination of them [27; 28; 29]. The localization problem can be formulated as follows:  
\[\min_{\textbf{p}_{1},\textbf{p}_{2},\ldots,\textbf{p}_{N}}\sum_{i=1}^{N}\left( \sum_{j\in\textbf{\Omega}_{i}}f\big{(}\textbf{r}_{ij}-\left\|\textbf{p}_{i}- \textbf{p}_{j}\right\|\big{)}+\sum_{k\in\textbf{B}}f(\textbf{r}_{ik}-\left\| \textbf{p}_{i}-\textbf{q}_{k}\right\|)\right), \tag{9}\]  
which stands for an unconstrained and nonlinear equation where the function \(f(\cdot)\) plays a fundamental role to find position estimates that minimize (9).
## 1 Introduction
Knowing this, we developed a compact system that eliminated the need for a separate pulse forming network (PFN) and used the composite-based NLTL simultaneously as a pulse forming line (PFL) and HPM source [24]. For a 10 \(\Omega\) NLTL comprised of 15% BST/10% NZF, we achieved 160 kW with a 15 kV charging voltage without a biasing magnetic field. Because the NLTL in this combined PFL/HPM format does not require an external PFN, the user may, in principle, select any impedance for the NLTL without needing to match the output of the PFN. Since standard NLTLs are 50 \(\Omega\) to match the remainder of the system, this raises the question of the importance of impedance on NLTL output for this design. This paper shows that increasing the NLTL impedance reduces the RF output because it reduces the current through the NLTL, concomitantly reducing the generated magnetic field and induced shock wave. Section 2 summarizes the experimental setup and NLTL configurations to achieve 10, 25, and 50 \(\Omega\) NLTLs composed of 15% NZF/10% BST. Section 3 provides the results and discussion. We make concluding remarks in Section 4.
# A Novel Hybrid Model for the Prediction and Classification of Rolling Bearing Condition
## 2 Theoretical Foundation
### Prediction Model
#### 2.2.3 Hybrid Prediction Model  
We take the original time series data \(Y_{t}\) as consisting of two components, the linear components \(Y_{lt}\) and the nonlinear components \(Y_{nt}\). The residual data \(r_{t}=Y_{t}-Y_{lt}\) are considered as the distinction between the linear component and original data. Thus, the hybrid model is constructed as:  
\[\left\{\begin{array}{l}\hat{Y}_{lt}=\beta_{1}Y_{t-1}+\beta_{2}Y_{t-2}+\ldots +\beta_{p}Y_{t-p}+\varepsilon_{t}-\theta_{1}\varepsilon_{t-1}-\theta_{2} \varepsilon_{t-2}+\ldots+\theta_{q}\varepsilon_{t-q}\\ \hat{Y}_{t}=\hat{Y}_{lt}+\hat{Y}_{nt}\\ Y_{nt}=Y_{t}-\hat{Y}_{lt}\\ \hat{Y}_{nt}=\omega_{0}+\sum_{j=1}^{H}\omega_{j}f\left(\omega_{0j}+\sum_{i=1}^ {N}\omega_{ij}Y_{n(t-i)}\right)+e_{t}\end{array}\right., \tag{7}\]  
where \(\hat{Y}_{t}\) is the final prediction result, \(\hat{Y}_{lt}\) is the ARMA prediction result, \(Y_{nt}\) is the residual of the ARMA model, and \(\hat{Y}_{nt}\) is the prediction result of the ANN model.
###### Abstract  
Holographic data storage (HDS) utilizes the unique properties of light for writing and reading two-dimensional (2D) data from holographic media, providing significantly higher densities and faster data transfer rates than traditional storage media for short-term dependencies. With its ability to store terabytes of data in a single crystal, HDS has garnered attention as a promising candidate for next-generation storage technologies. However, the 2D interference caused by hologram dispersion during the reading process poses a significant obstacle to achieving reliable and efficient HDS systems. This study proposes a method for enhancing the accuracy of estimating the 2D intersymbol interference (ISI) using a recurrent neural network (RNN) equalizer for HDS systems. The proposed method leverages the ability of RNNs to model complex and temporal dependencies in data and more accurately estimate the interference caused by ISI and interchannel interference (ICI) in HDS systems. In addition, to recreate the relationship between the samples in the training process, RNN is applied to fields such as computer vision, natural language process, speech recognition, and so on. We evaluated the performance of our proposed method on a simulation model of HDS system and compared it with the previous studies. In the simulations, the proposed method outperformed the previous schemes in terms of bit error rate, indicating its potential for improving the reliability and efficiency of HDS systems.  
holographic data storage; interference estimation; machine learning; recurrent neural network; detection; deep learning +
Footnote †: journal: 2023, 13, 11125. [[https://doi.org/10.3390/app132011125](https://doi.org/10.3390/app132011125)]([https://doi.org/10.3390/app132011125](https://doi.org/10.3390/app132011125))
## 4 Experiments
### Parameter Study on Proposed Augmentation Strategy  
To further analyze the impact of augmentation methods on virtual samples in practical experiments, we executed a parameter study employing three distinct augmentation settings as depicted in Table 4. The comparative performance evaluation was conducted on the Cflow model using the TFDS-RP dataset. The three augmentation settings are delineated as follows:  
Setting #1: Utilizing only synthesized false alarm samples generated from the anomaly maps of the anomaly-free training dataset at three binary thresholds [0.4, 0.45, 0.5].  
Setting #2: Utilizing only synthesized false alarm samples generated from the anomaly maps of the anomaly-free training dataset at seven binary thresholds [0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.5].  
Setting #3: Employing synthesized false alarm samples generated from the anomaly maps of the anomaly-free training dataset at three binary thresholds [0.4, 0.45, 0.5] with additional Gaussian noise as augmentation.  
Observations reveal that both Setting #2 and Setting #3 achieved notable improvements in detection results, effectively eliminating image-level false positives compared to Setting #1. This success can be attributed to the utilization of object-level semantic features in our method. Moreover, Setting #2 and Setting #3 achieve identical performance metrics, that is, the increment in threshold numbers or additional augmentation strategies may lead to stable filtering performance when the classifier's training samples are sufficiently abundant.  
Furthermore, all three settings exhibited significant enhancements compared to baseline performances, reaffirming the efficacy of our proposed method.  
This comprehensive parameter analysis elucidates the performance variations across different augmentation settings, substantiating the robustness and effectiveness of our proposed synthesized strategy.