esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Research on Spectrum Sensing Based on Deep Learning
## 6 Challenges and Perspectives
### Study of Dynamic Threshold Settings  
The current threshold settings in perceptual models are set manually or adjusted by algorithms in static situations; e.g., in [28; 29] dual thresholds are set in static environments by combining specific algorithms. In reality, due to frequent changes in environment andsignal conditions, dynamic threshold setting techniques are needed to achieve adaptive and intelligent processing in complex dynamic environments, and researchers need to address related technical and practical challenges in the future.
# Hybrid Local and Global Deep-Learning Architecture for Salient-Object Detection
## 3 Proposed Approach  
### Architecture Overview  
The proposed method consists of two main modules: (a) prediction of saliency maps and (b) refinement of the predicted saliency module. The prediction module consists of fully convolutional network that captures both local and global features. It also contains global-convolution and boundary-refinement blocks that focus on the refinement of features and the better extraction of boundary features. To further emphasise low-contrast images and enhance contours, a contrast module is also embedded in the model. Details are provided in subsequent sections.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Ref** & **Methods** & **Strengths** & **Limitations** \\ \hline
[10] & Frequency-tuned model for large salient objects & Efficiency in computation Fine detection in large objects & Alignment issues and poor performance for low contrast \\ \hline
[11] & FCN-based saliency map & Good saliency refinement & Computational iterative process \\ \hline
[12] & Hybrid saliency detection & improved accuracy & Poor performance for low-contrast images \\ \hline
[16] & Multistage training & Good noise filter & Computationally complex \\ \hline
[17] & Mean shift & Robust against low noise & Low results for highly noisy images \\ \hline
[18] & Intensity- and contrast-based & Fine saliency map & Not efficient for large images \\ \hline
[19] & Image signature & Scalability & Poor performance for small objects \\ \hline
[20] & Background construction for saliency & Better accuracy & High computational cost \\ \hline
[21] & Graph-based manifold ranking & Combination of multiple methods & High dimensionality and computational cost \\ \hline
[22] & Wavelets & effective saliency detection & Loss of spatial information \\ \hline
[23] & Unsupervised technique for saliency & Joint and iterative optimisation & Computational cost is high \\ \hline
[24] & Bootstrap & Time reduction & Limited results shown \\ \hline
[25] & Contextual info & Good results & Perform low for complex backgrounds \\ \hline
[26] & Hierarchical and SVM & Lower computational cost & Perform poor for complex images \\ \hline
[27] & Uses scene knowledge & Good precision & Poor results for low-resolution images \\ \hline
[28] & Blockwise scene scanning & Improved accuracy & Computational cost \\ \hline
[29] & Regions based features & Efficient approach & Perform poor for low contrast images \\ \hline
[30] & Low- and high-level features & Improved accuracy & Bad segmentation for low-contrast images \\ \hline \end{tabular}
\end{table}
Table 1: Summary of related-work review.
# A Proposal for Rapid Assessment of Long-Distance Oil and Gas Pipelines After Earthquakes
## 4 Post-Earthquake Pipeline Safety Assessment Based on
### Coseismic Landslide Hazard Assessment Along Pipelines  
In light of the imperative for rapid post-earthquake pipeline safety evaluation results, it is recommended to employ the composite index method to ascertain the coseismic landslide hazard risk along the pipeline route. The composite index model constitutes a comprehensive measurement method that evaluates the values of each evaluation index (coseismic landslide susceptibility results, ground vibration parameters, rainfall, etc.) using a weighted sum to evaluate the study objectives. The composite index model is as follows:  
\[G=\sum_{i=1}^{\text{m}}w_{i}G_{ij} \tag{1}\]  
where \(G\) is the composite index of the final element; \(w_{i}\) is the weight value of the \(i\)th indicator; \(G_{ij}\) is the quantitative score of the \(j\)th subcategory of the \(i\)th indicator; m is the number of evaluation indicators.  
As demonstrated in Figure 8, the utilisation of the comprehensive index model as an evaluation method necessitates the initial determination of the evaluation element indicators, which are derived from historical coseismic landslide data. Subsequently, the weights of each indicator are calculated through the application of expert scoring, the hierarchical analysis method, machine learning, and other methodologies [10; 25; 26; 27; 28; 29]. The grading and quantitative scoring of the evaluation indicators assignments are then carried out, and the indicator weights and assignments are adjusted in combination with historical data to ensure they meet the accuracy requirements for subsequent evaluation. In the event of an earthquake, the real-time data are processed and combined with the weights and assignments of the indicators of each element as determined in advance. These are then brought into the comprehensive index method model for calculation to obtain the results of coseismic landslide hazard evaluation.  
Figure 7: Schematic diagram of the overall process of landslide susceptibility assessment.
# Quantum Photonic Simulation of Spin-Magnetic Field Coupling and Atom-Optical Field Interaction
## 2 Mechanical Interacting Systems
### Two-Level Atom Interacting with an Electric (Optical) Field
However, the high value of the frequency \(\omega\) means that the electric field is rapidly oscillating, which suggests to make the following change \(|\Psi(t)\rangle=f_{0}(t)\exp(i\omega t/2)|0\rangle+f_{1}(t)\exp(-i\omega t/2)|1 \rangle\equiv\exp\left(i\omega\sigma_{z}t/2\right)|\eta(t)\rangle\). Moreover, in many cases, by parity arguments, it is fulfilled that \(c_{\pm}=0\). Therefore, by substituting this state into Equation (12), we obtain the following Hamiltonian for the state \(\left(f_{0}(t),f_{1}(t)\right)^{T}\equiv|\eta(t)\rangle\),  
\[\hat{H}^{\prime}|\eta(t)\rangle\approx\left(\hbar\bar{\omega}\,{\bf I}-\frac{ \hbar\bar{\delta}}{2}\sigma_{z}+\frac{\hbar\Omega}{2}\sigma_{x}\right)|\eta(t)\rangle, \tag{13}\]  
with \(\delta=(\omega_{0}-\omega)\) the detuning parameter and where we have neglected terms rapidly oscillating of the form \(\exp(\pm i\omega t)\); that is, a temporal Rotating Wave Approximation (RWA) has been made, and a time independent Hamiltonian has thus been obtained. We must stress that under this approximation, the terms \(C_{\pm}\) in Equation (12) can be also neglected independently of the wave-function parity. Finally, note that Hamiltonians given by Equations (6) and (13) have the same algebraic structure.
# A Study of Cislunar-Based Small Satellite Constellations with Sustainable Autonomy
## 6 Discussions
These simulations emphasize the need for such calculations not only on the ground but also in orbit to synergize and solve issues with autonomy as precisely as possible. This can be done only with a proper methodology in implementation. The impact of SRP over the Moon is a huge concern in these times. Hence, the observations and incorporation of the analysis of varying SRP force and Torque are of major importance and an element of prime inclusion in this research. Figure 13 portrays the variations of these SRP parameters on the satellites in the constellation. The reason for its inclusion is mentioned in Section 5.2.3 to contribute towards the growing issues with SRP over the Moon's side of the Cislunar space. Hence, a detailed analysis was carried out to know the scale of this impact on the small satellites and their performance. The result revealed staggering plots of how the SRP can be extremely critical for future missions to be operated as a constellation. Given the method to calculate these parameters remained the same as Earth, the impact observed is multiple times bigger on satellites than for the ones near Earth. The reason for emphasizing the SRP impact over the perturbations due to lunar gravity is due its extreme severity on the small satellites in a constellation in the recent past. Therefore, this paper is dedicated specifically towards analyzing the SRP impact on satellites in the Cislunar space with constellations operating over the Earth and the Moon, respectively. The impact of lunar gravity will be discussed and analyzed in future publications, which is also an important concern to be addressed but less severe than the impact of the SRP. Hence, it was prioritized and studied closely specific to autonomous small satellite constellations operating the limitations of the Cislunar space. Figure 13a,b presents the SRP force and SRP torque's influence on the small satellites in constellation during normal operations. But the moment SRP impact intensifies, the parameters change significantly, as shown in Figure 13c,d. This can be catastrophic for the missions currently operating or planned to fly out to the Moon and operate in that environment for a longer duration. The difference is at least a hundred times showing intensive changes in the satellite's overall placement in a constellation. Hence, this needs to be analyzed and predicted in orbit in synergy with autonomy, and appropriate actions must be taken to avoid loss of or damage to any lunar mission. Though impact can't be reduced with the use of creative methodologies, they can be used to navigate out of danger if required by the given situation. Further research on this particular topic is ongoing and will be published in other articles in due course of time.
## 4 Discussion
Our improved methodology of decellularization can be used for other organs such as the kidney, liver or lung. Our results showed the optimization of the SDS exposure time using an alternating rectangular electric field on the preservation of ECM characteristics in whole-organ decellularization. Our results yielded an improved protocol (1% SDS perfused for 8 h), which uses a lower concentration of SDS than other works on whole-rat-heart decellularization, while being at least as effective in achieving the removal of cellular components. Building a whole rat heart from decellularized scaffolds continues to be a challenge because it requires an enormous number of cells and most of them do not survive or differentiate into cardiomyocytes during the recellularization process. The whole-heart engineering evolution started in 2008, when [PERSON] et al. described the first rodent heart de- and recellularization process [10]. Recellularization of heart scaffolds was performed via direct injections into the ECM and infusion of endothelial cells via the coronary arteries [10; 16; 37]. In previous studies, cardiomyocytes differentiated from hMSCs [20], embryonic stem cells [19] and induced pluripotent stem cells (iPSCs) [21]. In the present study, hMSCs were used for seeding the decellularized ECM with the purpose of generating a bioartificial rodent heart and their pluripotency was confirmed using OM. 5-aza was used as a differentiation agent to induce hMSCs into CMs because it was shown to stimulate cardiomyocytesogenesis [28; 29]. Findings from our study should be widely applicable to decellularized whole-organ biological scaffolds in the development of regenerated organ replacements for transplantation. Although an important fundamental parameter was examined (time exposure of the heart with SDS) within the decellularization process, there are many other important parameters, such as tissue harvesting factors, perfusion factors and sterilization methods, that inevitably affect the quality of the ECM scaffold. Another current limitation and a major area for future development is represented by the functional testing of the bioactivity of the ECM scaffold. A future research direction would be a more comprehensive characterization of cardiomyocyte differentiation and electrical properties, as well as mechanical testing of the bioartificial heart.
## 2 Materials and Methods
### Statistics  
Statistical analysis was performed using OriginPro 2019 software (OriginLab Corporation, Northampton, MA, USA). Student's t-test or one-way ANOVA was used after proving normal distribution. Mann-Whitney U test was used, if normal distribution could not be proven. For results displayed as box plots, the white squares show the mean values. Median values are represented by a black line. The upper/lower whisker represents Q1/Q3 \(\pm\) 1.5 x interquartile range. Results shown by bar chart are expressed as mean value \(\pm\) standard error of the mean (mean \(\pm\) SEM). Levels of significance are labelled as follows: * \(p\) \(<\) 0.05, ** \(p\) \(<\) 0.01. (\(n\)) represents the number of samples used for the measurement per group, (\(N\)) the number of animal experiments from which embryonic samples were recovered.
## 7 Forecasting Results with the Use of an LSTM Network
### Validating the Model  
In order to verify the veracity of the prediction obtained by the model, an initial sequence of 130 values was stored in the database before the prediction was made. Of these 130 values, the last 30 values were stored separately, to compare and test the model's performance (expected value). The model used the first 100 elements of the sequence to obtain the prediction, following the logic present in the flowchart of the previous section. The sequence obtained by this process (the prediction) should correspond to the sequence of 30 elements initially stored (expected value), see Figure 14.  
Figure 14: Logic behind the model’s validation process.  
Figure 13: Forecasted values for the initial sequence.  
The predicted sequence can be seen in Figure 15. The developed model uses the initial sequence (blue color) of 100 values to obtain the prediction (red color). The prediction is then compared with the expected values (green color).  
The sequence of expected values and the values obtained through the mean square error are compared to evaluate the model. These sequences can be seen in Figure 14.  
The _mean squared error_ (MSE), (4) is defined by the mean of the squared difference of the expected (\(y_{i}\)) values and the obtained (\(x_{i}\)) values divided by the number of sequence elements:  
\[MSE=\frac{1}{n}\sum_{i=1}^{n}(y_{i}-x_{i})^{2}. \tag{4}\]  
The result obtained by calculating the MSE is positive, and the closer to 0, the better the model performance. After calculating the MSE, the result obtained for the model evaluation metric was 8.3783. This value normalized between zero and one was 0.0002, a value that reflected a good model prediction.
## 2 Heterojunctions with 1D Metal Oxides Semiconductors
The ZnO NWs were used as the carrier collection channels and UV absorbers with a well-organized structure to efficiently absorb light, whereas CdS nanoparticles were used as the visible photosensizers. Prepared ZnO NWs/CdS structures demonstrate superfast response time and effective sensitivity to visible light and UV in the absence of power. [PERSON] et al. fabricated a SnS/TiO\({}_{2}\) NTs arrays photoelectrode synthesized by anodization combined with electrodeposition technique, which was used to degrade 2,4,6-trichlorophenol under simulated visible light irradiation [54]. [PERSON] et al. prepared photocatalytic binary composite MoS\({}_{2}\)/TiO\({}_{2}\) (NTs) heterojunction (Figure 2). The composite material has demonstrated good photocatalytic disinfection effect and recyclability and as such has a broad field of potential applications in water disinfection [55]. [PERSON] et al. reported on the preparation of novel WO\({}_{3}\) NRs/graphene/BiV\({}_{1-x}\)Mo\({}_{x}\)O\({}_{4}\) heterojunction photoelectrode for photoelectrochemical water splitting. The heterojunction exhibits an enhanced photocurrent density, which makes light conversion efficiency significantly improved [56]. Specifically, these studies provide new strategies and insights for the fabrication of high-efficiency optoelectronic devices. One may see that most of the existing 1D metal oxide heterojunctions are generally based on Si, GaN and Cu\({}_{2}\)O, etc., and such devices show excellent photoelectric performance under normal environmental conditions. However, due to the small size of nanostructures, both light and injection current will cause a significant thermal effect. Since the thermal conductivity of the listed substrates is low, it leads to the rise in the thermal noise phenomenon and fluctuation of the effective barrier height at a high temperature. With the temperature rise, an increase in the leakage current, a drift of the threshold voltage and enhanced thermal noise degree will affect the sensitivity and reliability of the device. In addition, there is a large thermal coefficient difference between the metal oxides and the Si substrate. The interface between these two materials plays an important role in the device performance: the heterogeneity at the interface will cause the effective barrier height to fluctuate with the reduced performance of a device. This phenomenon is more evident in harsh environments such as high temperature and high flux [57; 58; 59]. Figure 1: (**a**) Schematic view of the photodetector and (**b**) normalized photocurrent under illumination of light with different wavelengths [49]. Figure 2: (**a**) Schematic diagram and (**b**) SEM image of the MoS\({}_{2}\)/TiO\({}_{2}\) NTs fabrication process [55].
# Mercury Uptake and Transport by Plants in Aquatic Environments: A Meta-Analysis
## 3 Results and Discussion
### Influencing Factors
#### 3.4.1 Plant Organs  
To express the ability of different plant organs to accumulate Hg from the environment, the corresponding effect sizes of the organs of each plant were combined to calculate mean and 95% CI values. Details in this regard are shown in the forest plot below (Figure 6, plant organs). The Hg concentration in plant roots, stems, and leaves were significantly higher than that in water, but significantly lower than that in sediments. Furthermore, the roots showed a higher Hg accumulation capacity than the stems and leaves; the average effect sizes in water and sediments were 4.716 and \(-\)0.696, respectively. The plant leaves demonstrated the weakest ability to accumulate Hg from water (mean = 3.767, CI: 2.777-4.703), whereas the stems demonstrated the weakest ability to accumulate Hg from sediments (mean = \(-\)1.862, CI: \(-\)2.503--1.275). Therefore, the trends of the Hg absorption capacities of the different plant organs were as follows: root \(>\) stem \(>\) leaf for absorption from water, and root \(>\) leaf \(>\) stem for absorption from sediments.  
Figure 5: Heavy metal concentrations in water, sediments, and plants.  
Figure 6: Mean biological concentration factor (BCF) values of Hg in aquatic plants based on pH, life habit, and plant organs. The pH values were classified as follows: acid (<6.5), neutral (6.5–7.5), and alkaline (>7.5). The error bars represent 95% bootstrapped CIs, and the numerals on the right \(y\)-axis represent the number of observations in each category. “Mean” indicates the overall effect considering all the observed plant families. (**a**) Hg absorption by plants from water. (**b**) Hg absorption by plants from sediments.  
In plants, different organs, such as roots, stems, and leaves, can accumulate different amounts of heavy metals [64; 69]. Moreover, some plant species can resist metal toxicity due to the uneven distribution of heavy metals throughout their organs and tissues [64]. As shown in Figure 6 (plant organs), the Hg concentration in plant organs was significantly higher than that in water, whereas that in the roots was significantly higher than that in the stems and leaves. This result is consistent with the findings of previous studies, which showed that the concentration of heavy metals in the roots is usually higher than that in the stems, leaves, and flowers [60; 70; 71]. This may be because the roots represent the main location where plants absorb heavy metals, and large amounts of Hg accumulates in sediments that are in direct contact with the plant root (Figure 5). At the same time, the regulation of plant hormones and the synergistic effects of rhizosphere microorganisms can positively promote heavy metal accumulation in the roots [64; 72].
# Real UAV-Bird Image Classification Using CNN with a Synthetic Dataset
## 4 Result and Discussion
### Computation Times  
The training times of all networks used in the training and testing phase were tracked, and the durations of all networks were calculated for 10 epochs to make an accurate comparison in terms of time consumption. All training and testing stages' time costs were calculated on a system using the GPU computing system (GeForce GTX 1080 ti, Nvidia Corp., Santa Clara, CA, USA), 128 GB RAM, I7 processors, and the TensorFlow-based and Keras library.  
The processing times of the train, synthetic-image test, and real-image test datasets of the CDNTS layer were measured, and values for different processes are presented in Table 4. The time costs of the CDNTS layer for the train, synthetic-image test, and real-image test datasets were 36.7, 63, and 75.9 milliseconds per image, respectively. The time costs for synthetic- and real-image test datasets with the CDNTS were greater than the train dataset time cost. The train dataset consisted of segmented synthetic images, while the test dataset consisted of non-segmented images with different objects in the background. Due to this difference, the CDNTS layer detected more corners in the test dataset than the train dataset and performed more processing. For the user, the total time spent in the training process is important; in addition, the prediction time of a single image in the testing process is important. The average total time cost in the training process of 59,120 images with the CDNTS layer was 36.16 min, while the average time cost for an image in the test phase was 75.9 milliseconds per image.  
The durations of the most successful networks in experiment 2 using the CDNTS layer and experiment 1 without the layer were tracked and are shown in Figure 10.  
\begin{table}
\begin{tabular}{c c} \hline
**Process Name** & **Process Time (ms)/Each Image** \\ \hline Train dataset & 36.7 milliseconds \\ Synthetic test dataset & 63 milliseconds \\ Real test Dataset & 75.9 milliseconds \\ \hline \end{tabular}
\end{table}
Table 4: The CDNTS layer’s time cost list for the different datasets.  
Figure 9: Experiments 1 and 2: the precision and recall graph of the most successful networks of synthetic image test of squeezeNet with Adam optimizer (blue), real image test of SqueezeNet with Adam optimizer (orange), synthetic image test of CDNTS + AlexNet with SGD optimizer (green), real image test of CDNTS + SqueezeNet with RMSProp optimizer (red).  
In the experiments, the CDNTS layer caused an extra 2202 s of computational time in comparison to the computations without CDNTS. This corresponds to 25% of the total training time. As the number of epoch increases, the ratio of the time cost of the CDNTS layer to the training time decreases.