esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The controlled source audio-frequency magnetotelluric method (CSAMT) stands out for its economic efficiency and widespread application in geophysical monitoring. However, the separate inversion of time-lapse monitoring data encounters challenges in comparing and identifying abnormal changes due to variations in data fitting. Furthermore, the utilization of a method akin to Cagniard apparent resistivity for inversion necessitates the simultaneous observation of at least two components of the electromagnetic field, making it unsuitable for extensive three-dimensional observations. This paper proposes a 3D time-lapse electric field inversion algorithm for CSAMT, addressing the complexities in geophysical monitoring. The algorithm introduces two regularization factors and defines an objective function with both temporal and spatial constraints. Synthetic testing reveals the stability of the 3D time-lapse electric field inversion algorithm, demonstrating its effectiveness in delineating underground variations. This solution resolves the challenges posed by the independent inversion of time-lapse monitoring data.  
CSAMT; electric field; time lapse inversion; temporal and spatial constraints; synthetic test 2024 14 1560. [[https://doi.org/10.3390/app14041560](https://doi.org/10.3390/app14041560)]([https://doi.org/10.3390/app14041560](https://doi.org/10.3390/app14041560))
## 2 Materials and Methods
### Experimental Settings  
This study conducted feature optimization experiments with the following control groups: (1) Spectral indices + vegetation indices; (2) spectral indices + vegetation indices + terrain factors; (3) spectral indices + vegetation indices + terrain factors + texture features; and (4) spectral indices + vegetation indices + terrain factors + texture features + morphological features. Details are provided in Table 4. Classification experiments were performed using random forest with a decision tree quantity of 80.  
For training and validating the 3 DCNN model, images were sliced into 256 \(\times\) 256 segments and input into the neural network. In the 3 DCNN network, Conv3d_1 layer utilized convolutional kernels with settings: kernel_size = (5, 5, 5), stride = (1, 1, 1), padding = (2, 2, 2); MaxPooling layer: kernel_size = (2, 2, 2), stride = (2, 2, 2); Conv3d_2 layer: kernel_size = (3, 3, 3), stride = (1, 1, 3); Conv3d_3 layer: kernel_size = (3, 1, 3), stride = (2, 2, 3); Conv3d_4 layer: kernel_size = (1, 2, 2), stride = (1, 1, 2).  
For classification using extracted deep learning features, random forest (RF), naive Bayes (NB), and support vector machine (SVM) were chosen as classifiers for experimentation. Random Forest achieved the best results among these classifiers and was subsequently chosen as the base classifier for AdaBoost. Optimal parameters of 300 iterations and a learning rate of 0.1 were determined through experimental testing conducted in this study.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Feature Situation** & **South** & **East** \\ \hline
**Serial Number** & **Number of Features** & **Total Accuracy** & **Total Accuracy** \\ \hline Spectral Indices + Vegetation & & & \\ Indices = S1 & 17 & 0.9121 & 0.9207 \\ S1 + Terrain Factors = S2 & 20 & 0.9121 & 0.9236 \\ S2 + Texture Features = S3 & 23 & 0.9138 & 0.9266 \\ S3 + Morphological Features = S4 & 47 & 0.9171 & 0.9320 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Feature optimization experiments.  
Figure 5: Structure of ensemble classification model.
# SBNN: A Searched Binary Neural Network for SAR Ship Classification
## 4 Experiments
### Results  
#### 4.3.1 Comparison with CNN-Based SAR Ship Classification Methods  
Table 5 lists the results of SBNN and the four other floating-point CNN-based SAR ship classification methods in the three categories task. Those methods for comparison are: finetuned VGG [13], plain CNN [42], group squeeze excitation sparsely connected CNN (GSESCNNs) [43] and the combination of HOG, PCA and deep learning (HOG-ShipCLSNet) [16]. Notice that SBNN and the above methods except HOG-ShipCLSNet do not require additional computation out of the networks.  
Table 6 lists the results of SBNN with fusion and three other floating-point CNN-based Dual-Polarization SAR ship classification methods in the six-categories task. The counterparts are: VGG with hybrid channel feature loss [15], mini hourglass region extraction and dual-channel efficient fusion network [44] and the squeeze-and-excitation Laplacian pyramid network (SE-LPN-DPFF) [38].  
As shown in Tables 5 and 6, our proposed SBNN outperforms all other methods and reaches the best accuracy: 80.03% and 56.73% for the two tasks, respectively. On the hardware implementation side, SBNN also has great advantages. The number of weightsin SBNN is only 0.37 M. Moreover, the computational complexity of SBNN is very small with a MAdds number of 4.67 M which is almost 19\(\times\) lower than the HOG-ShipCLSNet.  
The confusion matrixes of SBNN in the three-categories task and the six-categories task are shown in Tables 7 and 8, respectively. In the three-categories task, SBNN has a good accuracy for each category. In the six-categories task, most samples of bulk carrier, cargo, container ship and fishing can be classified correctly.
## 2 Related Works
The developed system features embedded processing in wireless smart sensors, including data acquisition, power spectral density calculation, peak picking, post-processing for peak selection, and tension estimation. Contact sensors that are attached to cables to check the condition of cables can malfunction under severe weather conditions, making it impossible to estimate the condition of cables under such unfavorable conditions. Therefore, in [10], a fully contactless video-based stay-cable tension measurement technique for recording videos using a moving handheld camera at a significant distance from the structure itself was proposed. In [11], a framework was proposed that takes wireless sensor data as input, then reconstructs packet loss samples, and provides real-time tension estimation as output. The framework first adopts a compressive sensing algorithm to reconstruct the data due to packet loss. Then, real-time frequency fluctuations were estimated using the Blind Source Separation (BSS) technique by synthesizing the reconstructed responses from several sensors. Finally, real-time cable tension was estimated from frequency variations using the taut-string theory. In [12], the effect of structural and aerodynamic nonlinearity on dynamic wind loads on overhead wires was investigated. A time hysteresis response analysis was performed on the overhead wire to derive an equivalent static wind load when the wind is weak for snowfall conditions. Considering the two main effects of aerodynamic and structural nonlinearity, they proposed a modified method to enable the use of design methods based on equivalent static wind loads. In systems for railway catenary, the tension on the contact wires affects the performance of the line. In [13], a method for monitoring the status of such tension is proposed on the basis of the dependence of the waves propagating through the tensioned wire on the force pulling the wire itself. An analytical formula for propagation of waves in a tensile wire has been proposed, which has been used to obtain vertical acceleration of the wire when the external force acts as a concentrated input. In [14], the dropper span of the catenary system is modeled to investigate the effect of contact wire tension on dropper stress. The response equation of the contact wire and the theoretical equation of the dropper stress are inferred. However, most of the previous studies focus on the cable tension for the purpose of enduring loads such as bridges and the tension of subsea cables, and the research on the overhead wires in the distribution system has been extremely limited. Therefore, we conducted a study on the tension estimation of the overhead wires in the distribution system, which is essential to check and manage the condition of the overhead wires, by using acceleration sensor data.
## 2 Materials and Methods
### Data Descriptions  
In this study, Geo-stationary Ocean Color Imager (GOCI) Level-2A chl-a products were employed to evaluate the performance of the proposed algorithm. GOCI is the first ocean color sensor at geostationary orbit in the world, covering a total area of 2500 km \(\times\) 2500 km, including the Bohai Sea. Given the high spatial resolution of 500 m and temporal resolution of 1 h (8 h per day from UTC 0:16 to 7:16), GOCI images are well suited for large-scale and  
Figure 1: A satellite remote sensing image of the study area in the Bohai Sea region.  
high-frequency ocean monitoring. The chl-a dataset used in the experiments was collected from 1 January to 31 December 2019 within the study area, obtained from the Korea Ocean Satellite Center (KOSC) ([[http://kosc.kiost.ac.kr/](http://kosc.kiost.ac.kr/)]([http://kosc.kiost.ac.kr/](http://kosc.kiost.ac.kr/)), accessed on 17 February 2023). It should be noted that the Level-2A chl-a products have undergone standard processing procedures, including the removal of invalid data due to estimation errors or thick cloud cover [55; 56]. This issue inevitably leads to data loss in the chl-a dataset, which poses challenges for continuous monitoring and analysis. Therefore, this study is motivated to develop an approach that can effectively reconstruct those missing values in the chl-a dataset and improve the utility of chl-a remote sensing products for HAB event monitoring.  
A mask extraction task was first performed on each full-frame Level-2A chl-a image to generate the 2019 chl-a dataset for the Bohai Sea region. The downloaded dataset comprised 2764 images, with a resolution of 808 \(\times\) 842 pixels per image and a total of 300,364 marine pixels (land pixels had already been excluded). The overall missing rate (OMR) of the dataset is 79.09%. Temporally, the spatial missing rate (SMR) of each chl-a image ranges from 6.87% to 100%. Notably, 1531 of these images have an SMR over 95%, accounting for 54.8% of the entire dataset. Spatially, the temporal missing rate (TMR) at each pixel varies from 69.36% to 100%. Among these, 20,417 pixels exhibit a TMR higher than 95%, representing 6.80% of the marine pixels. These pixels are predominantly located in nearshore shallow-water areas, as shown in Figure 2.  
Given that the EOF-based reconstruction algorithm has certain limitations on the original data, images with an SMR over 95% or pixels with a TMR over 95% are unable to provide adequate information in the spatiotemporal matrix, thus affecting the reconstruction performance [37]. Therefore, further analysis and exclusion of these highly missing images and pixels are required. The methods for checking and processing these data will be detailed in Section 2.3.2.
## 4 Parametric Analysis
### Earthquake Records  
Three earthquake records are used as excitation. The selection of these records considers differences in their spectral characteristics. The number of records is limited because the intention is to qualitatively assess the sensitivity of the protection method to the characteristics of the earthquake record, rather than to provide a specific design methodology for the protection system. Figure 3 presents the time histories of the selected records (Figure 3a) and their corresponding pseudo-acceleration elastic spectra (Figure 3b). The following earthquake records are considered in this study:  
1. [label=()]
2. _El Centro_, 1940 California, Array Sta 9, Imperial Valley Irrigation District, 302 Commercial, component 180, position of the station: 32.7953 N, 115.5335 W;
3. _Kobe_, 1995 Japan earthquake, Takarazuka station, 0 deg, ground level, position of the station: 34.8090 N, 135.3440 W;
4. _San Jose_, 1989 Fremont Mission San Jose motion, Loma Prieta earthquake, California USA.  
For clarity, each record is denoted by its corresponding underlined name from this list. The earthquake records were obtained from the 'Center for Engineering Strong Motion Data' [25].  
It is worth observing that, due to the linearity of the model, the fundamental aspect that characterizes the response of the mechanical system is closely related to the frequency content of the seismic excitation.
# Clock Ensemble Algorithm Test in the Establishment of Space-Based Time Reference
## 2 Clock Ensemble Algorithm
### Natural Kalman Clock Ensemble Algorithm
In this paper, it refers to the ideal timescale generated by the algorithm.). Subtract this value from the time deviation of each clock to attain the \"correction clock,\" a total of N correction clocks. Any correction clock can be defined as a timescale generated by the algorithm. In fact, the value of a single timescale cannot be known, and what can be known is the time deviation between the two timescales. Therefore, TA is usually represented by the time deviation between it and the reference clock.
# Discrete Lattice Element Model for Fracture Propagation with Improved Elastic Response
## 2 Novel Discrete Lattice Element Model
### Damage Constitutive Law for Softening  
The description of energy dissipation in the material involves the softening damage law, which relates traction in the discontinuity with the jump in the displacement field. When the stress in the material surpasses the ultimate strength of the specified material, a discontinuity occurs, accompanied by the activation of a jump in the displacement field. As the jump in displacement field increases, indicative of crack opening, the load-bearing capacity decreases, leading to a reduction in stress at the discontinuity and a consequent decrease in material stiffness. Material unloading results in crack unloading, but the accumulated material damage and reduced stiffness persist. Complete unloading causes the crack to close, and the displacement field jump becomes zero. Upon reloading, it depends on the reduced material stiffness, following the inverse value of the softening modulus (Figure 7).  
The damage model is formulated in the thermodynamics framework and free energy \(\psi\):  
\[\psi\Big{(}\overline{D},\,\mathbf{\mathrm{\alpha}},\overline{\overline{\xi}} \Big{)}=\frac{1}{2}\overline{D}^{-1}\mathbf{\mathrm{\alpha}}^{2}+\Xi\big{(} \overline{\overline{\xi}}\big{)}, \tag{22}\]  
where \(\overline{D}\) and \(\mathbf{\mathrm{\alpha}}\) represent the damage compliance modulus and the displacement jump, respectively. The softening variable is defined as \(\Xi(\xi)=-\overline{q}\overline{\overline{\xi}}\). The failure detection function \(\overline{\overline{\phi}}\) can be defined as:\[\overline{\phi}\Big{(}t,\overline{\dot{q}}\Big{)}=|t|-\Big{(}\sigma_{u}-\overline{ \dot{q}}\Big{)}, \tag{23}\]  
where \(t\) represents the traction vector at the discontinuity, \(\sigma_{u}\) is the ultimate stress level, and \(\overline{\dot{q}}\) represents the softening function, defined as:  
\[\overline{\dot{q}}=\sigma_{u}\Bigg{(}1-exp\Bigg{(}-\overline{\dot{\xi}}\frac{ \sigma_{u}}{G_{f}}\Bigg{)}\Bigg{)}. \tag{24}\]  
\(G_{f}\) represents the fracture energy. The expression for the traction vector at the discontinuity can be obtained as  
\[t=\frac{\partial\psi}{\partial\alpha}=\overline{D}^{-1}\alpha,\,\,\overline{D }\in[0,\infty). \tag{25}\]
## 5 Conclusions  
An experimental comparison between aerosol measurements using the CDWL and the RMRL was achieved. To calibrate the aerosol retrieval, a method using boundary values from the Raman lidar was proposed, and the extinction coefficients were compared from the two lidar systems. Good agreements were achieved during the continuous field experiments, with the fitting slopes of 0.97 and 0.96, and the determination coefficients of 0.90 and 0.89. The results prove the accuracy of aerosol retrieval by coherent lidar after correction powerfully, and the aerosol detection by all-fiber wind lidar was extended, which may achieve the miniaturization and stabilization of the lidar for meteorological measurements.  
Conceptualization, M.L. and H.X.; methodology, M.L.; formal analysis, H.X. and M.L.; investigation, M.L.; data curation, L.S. and X.W.; writing--original draft preparation, M.L.; writing--review and editing, M.L., L.S., X.W., J.Y., H.H. and H.X.; visualization, M.L.; supervision, H.X. and H.H. All authors have read and agreed to the published version of the manuscript.  
Strategic Priority Research Program of Chinese Academy of Sciences, grant number XDA22040601.  
The data underlying the results presented in this paper can be obtained from the authors upon reasonable request.  
We would like to thank the Xinjiang Uygur Autonomous Region Meteorological Service for providing support with the observational data.  
The authors declare no conflict of interest.
## 4 Numerical Application
### Robustness Analysis  
To evaluate the robustness of the proposed FAFWI method comprehensively, this section presents analyses from three aspects: method applicability, noise impact, and initial model sensitivity. First, we verified the applicability of the method in complex media by testing it on velocity models of varying complexities, specifically, the 2007 BP TTI model. Second, we assessed the stability and noise resistance of the method by introducing noise interference. Finally, we analyzed the sensitivity of the method to the initial models by testing different initial velocity models. Through these experiments, we aimed to thoroughly evaluate the reliability and adaptability of the FAFWI method for practical applications.
## 4 Confirmation of Conventional Parameters in the DIC for an Optimized Measurement
### Subset Radius  
As for the subset radius, it is normally linked to the noise effect. We know that image noise occurs irregularly during the process of acquisition and signal transformation, which is hard to suppress fully. Based on the published results [25], the impact of noise on the calculation of correlations grows as the subset radius decreases. A subset radius set below a certain threshold is expected to induce errors in the analysis. The upper bound of the subset radius meanwhile does exist that the over-sized subsets will induce the loss of tiny-scaled pattern with interest. Therefore, calibration corresponding to subset radius is essential to be conducted firstly against noise-based and pattern-based phenomena.  
Figure 10 presents a representative example regarding determining an approximate radius range. Obviously, the 2-pixel case fails to measure the strain information due to noise error. It can be predicted that pixels influenced by noise constitute a significant portion of the subsets, which in turn exerts a substantial impact on the computation of the correlation value. As the subset radius expands, its ability to suppress noise becomes more pronounced, followed by the recognition of crack patterns. In this experiment, the subset radius was set as 10 pixels for the identification of a cracking pattern and its moderate magnitude.  
Figure 9: Cracking area selected dimensions of \(230\times 240\) pixels for parameter calibration for DIC processing.
###### Abstract  
The volatile compositions of three closely related _Hypericum_ species growing wild on the island of Crete were studied, all belonging to the section Coridium. Hydro-distillation in a modified Clevenger-type apparatus was performed according to the Hellenic Pharmacopoeia in order to obtain the essential oils, which were analyzed by GC-MS. Identification of the compounds was carried out by comparison of MS spectra and retention indices with literature data, as well as by co-chromatography with authentic samples. In total, 123 different compounds were identified and the main compounds were by order of their abundance as follows: _H. empetrifolium:_\(\alpha\)-pinene, germacrene D, \(\beta\)-pinene, E-caryophyllene; _H. amblycalyx_: \(\beta\)-elemene, \(\beta\)-selinene, \(\alpha\)-pinene, E-caryophyllene, \(\alpha\)-selinene; _H. jovis_: trans-calamenene, \(\alpha\)-selinene, \(\beta\)-elemene. The chemical results revealed the differences and similarities (qualitative and quantitative) between the studied oils, supporting the hypothesis that essential oils from _Hypericum_ spp. do not serve as chemotaxonomic markers. Moreover, the essential oils were subjected to antimicrobial screening. According to the given results, the essential oils possessed better antifungal and anticandidal activities than antibacterial activities.  
_Hypericum_; section Coridium; essential oils; GC-MS; antifungal; anticandidal
## 1 Introduction
In addition, in the context of pavements, the damage assessment is typically interpreted as a pavement condition rating or index. The Pavement Condition Index (PCI) ranges from 0 to 100, which indicates the general condition of a pavement system. This index was originally proposed by the United States Army Corps of Engineers as a rating system for airport pavements [17]. The PCI is widely applied to road pavements and airport pavements and used in asset management. Various distress/severity combinations result in values being deducted from an initial value of 100. The PCI is a complex index covering all forms of general pavement distress related to functional pavement performance as well as structural pavement performance. In airport pavements, certain forms of pavement distress, originating from patching or alligator cracking, can cause loose pieces, which may result in potential damage to aircraft engines and tires. This type of damage to an aircraft engine is defined as foreign object damage (FOD). Thus, road and airport agencies typically use three pavement condition indices: the Pavement Condition Index (PCI), the Structural Condition Index (SCI), and the FOD Index (FOD) [18]. The main differences between the PCI, SCI, and FOD are the pavement distress types factored into their index calculations. The PCI counts around 16 concrete pavement distress types, while the FOD does not count types of pavement distress that have no risk of causing loose foreign objects [18]. On the other hand, a limited group of types of structural pavement distress (e.g., cracking, corner breaks, pumping, spalling) are categorized in the SCI [18]. Recently, a few Special Issues (SIs) related to concrete fatigue, performance, or damage assessment have been published in scientific journals. Table 1 lists those SIs published after 2019. \"Fatigue\" [19] was published as an SI in _Structural Concrete_ by PIP in 2019; it contains 10 papers on fatigue and cyclic loading of high-strength and high-performance concrete. Another SI, \"Fatigue and Fracture of Non-Metallic Materials and Structures\" [20], was published by _Applied Sciences_ in 2019. Ten out of a total of thirty-four papers in this SI address fatigue on bridges, fiber-reinforced concrete (FRC), and other applications, such as asphalt and rocks. The third SI, titled \"Cyclic Deterioration of Concrete\", was published by _Materials_ in 2021 [21]; this SI focused on fatigue, damage evolution, and cyclic loading of high-strength and high-performance concrete. This Special Issue introduces recent achievements in the synthesis, performance, damage characteristics, as well as fatigue of concrete. Unlike articles published in the past, as introduced in Table 1, our Special Issue contains papers that include research findings on concrete pavement.
# Removal of Chromium(VI) by Chitosan Beads Modified with Sodium Dodecyl Sulfate (SDS)
## 3 Results and Discussion
### Adsorption Experiment
#### 3.1.4 Effect of the Adsorbent Dosage on the Adsorption of Cr(VI)  
To determine the effect of the adsorbent dosage on the removal of Cr(VI), the experiments were carried out by varying the dosage (from 0.4 to 1.0 mg/L) and keeping all other parameters constant (temperature: 25 \({}^{\circ}\)C; pH: 4; contact time: 24 h; initial concentration: 1.0 mg/L). The results are shown in Figure 6. The removal of more than 80% Cr(VI) was observed for the 0.8 mg/L dosage, but no notable increase was observed at a dosage of more than 0.8 mg/L. Therefore, 0.8 mg/L was regarded as the optimum dosage for the removal of Cr(VI) in this study. A higher dose provides a larger number of binding sites, which eventually causes the enhanced removal of Cr(VI).