esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theory and Method  
### Cross-Correlation Seismic Interferometry  
Seismic interferometry generally includes the cross-correlation method [29], the deconvolution method [30], and the cross-coherence method [31]. Although the multi-dimensional deconvolution method and cross-coherence method can alleviate the impact of fewer sources and uneven source distribution on the virtual shot to a certain extent, they still cannot achieve good results for very extreme source distribution. Here, we used the cross-correlation method,which is the most efficient and stable method, to reconstruct the seismic virtual shot. The reconstruction method of cross-correlation can be expressed as:  
\[R(x_{B},x_{A},t)+R(x_{B},x_{A},-t)=\delta(x_{H,B},x_{H,A})\delta(t)-T(x_{A},-t)*T( x_{B},t)\]  
where \(R(x_{B},x_{A},t)\) stands for the seismic response excitation at \(x_{A}\) and received at \(x_{B}\); \(R(x_{B},x_{A},-t)\) represents its noncausal part; \(\delta()\) represents the Dirac function; and \(T(x_{A},t)\) and \(T(x_{B},t)\) represent the transmitted wave responses at \(x_{A}\) and \(x_{B}\), respectively.  
When we use the equation for the mutual correlation reconstruction, we keep the \(x_{A}\) channel unchanged and each of the remaining channels as \(x_{B}\), respectively. Then, we can obtain the reconstruction record with the \(x_{A}\) channel vertex as the excitation point and all channels as the reception points according to the original position of each channel. The reconstruction results here can be divided into two parts: the uncaused part and the causal part. It is a fundamental assumption of seismic interferometry [32] that requires no loss in the medium and a uniform distribution of the sources. Having satisfied these assumptions, we consider the causal part to be symmetrical with the noncausal part. By flipping and summing the causal and non-causal parts, we can obtain a virtual shot record with a higher signal-to-noise ratio. As a result, it is possible to flexibly construct the virtual shot record of a shot point at any detector position when reconstructing passive source seismic data with seismic interferometry without knowing the real source location.  
However, when the number of sources is small under not widely distributed conditions, this will lead to spurious reflections and discontinuities in the waveform of the axes. If the source distribution is not uniform, when the virtual source is located on the side where the source distribution is lower, the reflected wave will be anomalous to a straight line. In contrast, the false-shot recordings where the false seismic source is located on the side with more source distribution will have discontinuous reflected waves and artifacts.
# Improved Safety Analysis Integration in a Systems Engineering Approach
## 6 Case Study
### Step 2: Making Architecture Decision Using FMEA Recommendations  
In this step, the generated FMEA is computed and the most critical failure modes are detected. The most critical failure modes are the failure modes that have class I as risk class. In this example, five different function failure modes are detected, which are 1/fails to perform the translation of pilot instructions because of an hardware defect of the MCU component, 2/fails to perform the translation of pilot instructions because of a synchronization error of the MCU component, 3/fails to perform the adaptation of the mechanical energy because of loss of structural integrity of the geared motor with encoder, 4/fails to perform the measure incidence because of a power loss of the geared motor with encoder, and 5/fails to perform the transformation of the mechanical energy because of a jamming of the geared motor with encoder. To enhance the system architecture, the two critical components should be dubbed and the three concerned diagrams (requirement diagram, activity diagrams, and IBD) will be updated. The new IBD will contain two redundant MCUs with a switch component that permits the switching from the main component to the spare component, and two redundant geared motors with a switch component as shown in Figure 6.  
Then, the new FMEA should be generated to verify the safety level of system 3. It contains components, funtions, and failure modes with new S, O, D, RPN, and risk class values. In addition, it includes the new components such as \"Switch EMCU\" and \"Switch Motor\" with their functions, failure modes, and its criticality. If the class risk of all function failure modes are different to class I, then the system structure is validated and it is time to move to the next step. Otherwise, a new iteration should be done and new structural modifications will be recommended. In our case and as shown in Table 3, all function failure modes have a risk class different to class I. The new system structure is validated and we move to the third step. \begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline
## 2 Related Work
### Autoencoders
The construction of latent space is crucial to the performance of the autoencoder. Typically, each dimension in the latent space represents an important feature. The autoencoder can effectively extract key features, which are then reconstructed as the original data during decoding. This property has led to the successful application of autoencoders in many practical domains, such as image compression and denoising. In image compression and denoising tasks, autoencoders map image data to latent space, remove redundant parts, and then reconstruct clear images through the decoder [42; 43]. In anomaly detection tasks, autoencoders learn the normal patterns of data and can detect abnormal data that deviates from the normal patterns [44; 45]. Additionally, denoising autoencoders (DAEs) introduce noise interference into the autoencoder framework and restore the original data by minimizing reconstruction errors [46]. This method enhances the model's robustness, allowing effective learning and feature extraction even in the presence of noisy data.
## 4 Discussion
Overall, we conclude that transplanting in late November with RCPPS for forage use has distinct advantages in the rice-rape rotation, as it can maintain yield and improve nutritional value of forage rape, so as to alleviate the stubble contradiction between late harvested rice in the Yangtze River delta. All these findings can substantially improve the annual economic benefits for growing rice-rape crops, which will promote the application of the rice-rape rotation system. Considering the conventional use of rapeseed for grains, the rapeseed transplanted on 20 and 30 October had comparable grain yields. However, compared with the 20 October transplant date, the rapeseed transplanted in November had significantly lower grain yields by 17.2% to 22.5% (Figure 3). Grain yield is determined by the number of pods per plant, the number of seeds per pod, and the weight of one thousand seeds. Moreover, the number of pods per plant is considered to be the most important factor in determining yield, which is also easily influenced by agricultural management [31]. Compared with traditional transplanting methods, the increase in yields in the RCPPS is primarily attributed to the number of pods [17]. With the RCPPS in this study, the grain yield was most affected by the number of secondary branches, as indicated by the significant positive correlations between yield and the number of secondary branches and the number of pods and seeds on secondary branches (Table 3). Compared with the earliest transplanting treatment, the number of pods on secondary branches in the November transplanting treatments decreased significantly by 69.7% to 82.4% (Table 2). The late transplanting dates significantly inhibited leaf growing stage and resulted in a reduced number of effective leaves and diameter of pre-winter root (Figures 4 and 5, respectively). [PERSON] et al. (2016) [15] found that substantial dry matter accumulation and the number of effective pods promote high grain yield under the appropriate planting date. In Jiangxi Province, with a simplified transplanting technique, the yield was optimum when sown on 20 September and then nursed for nearly 40 days. Changes in the quantitative processes of yield under different planting dates with RCPPS should be explained in terms root morphology and physiology in future.
# A Supervised Speech Enhancement Approach with Residual Noise Control for Voice Communication
## 6 Conclusions  
This paper derives a generalized loss function which can easily make a balance between noise attenuation and speech distortion with multiple manual parameters. In addition, MSE and other typical loss functions are revealed to be special cases. Both objective and subjective tests are conducted to show that it is important to control the residual noise for supervised speech enhancement approaches, where the residual noise becomes much more natural than before. Moreover, compared with other competitive loss functions, the proposed loss function obtains comparable performance in objective metrics and much better subjective evaluation results when suitable parameter configurations are selected. Further work could concentrate on studying a combination of the residual noise control scheme with objective metrics-based loss functions to improve the naturalness of the residual noise.  
Conceptualization and methodology, A.L. and C.Z.; software and validation, A.L. and R.P.; writing--original draft preparation: A.L.; writing--review and editing, C.Z. and X.L.; supervision: X.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Science Foundation of China under Grant No. 61571435, No. 61801468, and No. 11974086.  
The authors declare no conflict of interest.
## 4 Design of a Self-Adaptive Evolutionary Algorithm
### SAEA Operations
#### 4.6.2 Mutation Operation  
Once the offspring chromosomes have been produced via the crossover operation, the SAEA algorithm applies a custom mutation operator to each offspring chromosome. Unlike crossover, mutation allows exploitation of the identified promising domains of the search space to discover the solutions with higher fitness [20; 21]. Typical mutation operators, which have been widely used in the BSP literature (e.g., swap, insert, scramble, invert), will not be applicable for the adopted hybrid chromosome, as they may generate infeasible offspring chromosomes. A custom mutation operator was developed in this study. The main steps of the proposed custom mutation operator are outlined in Algorithm 3. The data structure for the mutated offspring chromosomes, required by the custom mutation operator, is initialized in step 0. Then, the custom mutation operator starts an iterative procedure (steps 2-21), where the real-valued portion of a given offspring chromosome (i.e., the crossover and mutation probabilities) is mutated using the floating-point mutation (steps 3 and 4). Two random values (\(Val_{1}\) and \(Val_{2}\)), ranging between 0.00 and 1.00, are generated in steps 5 and 6. After that, if the generated value \(Val_{1}\) is less than or equal to 0.50, either the insert mutation operation is applied to the integer portion of the chromosome, representing the berthing positions, or the swap mutation operation is deployed for the integer portion of the chromosome, representing the vessel identifiers (steps 8-12). The latter decision (i.e., alter either berthing positions or vessel identifiers) is made depending on the generated value \(Val_{2}\). If the generated value \(Val_{1}\) is greater than 0.50, either the swap mutation operation is applied to the integer portion of the chromosome, representing the berthing positions, or the insert mutation operation is deployed for the integer portion of the chromosome, representing the vessel identifiers (steps 14-18). The latter decision (i.e., alter either berthing positions or vessel identifiers) is also made depending on the generated value \(Val_{2}\). An iterative procedure is terminated by the custom mutation operator once each offspring individual in the population has been mutated. Note that both insert and swap mutation operators are not applied within the developed custom mutation operator to the berthing positions and vessel identifiers at the same time to avoid significant genetic changes in the chromosomes as a result of mutation (which may further cause disruption of \"building blocks\" [20; 21] and lead to worsening fitness values for the offspring chromosomes).
# Comparisons of Spatially Downscaling TMPA and IMERG over the Tibetan Plateau
## 2 Materials and Methods
### Materials
#### 2.2.4 NDVI Datasets  
The NDVI is typically used to determine the vegetation productivity and distribution [31; 32], and is correlated well with precipitation at annual temporal scale [25; 33; 34]. The MOD13A3 monthly NDVI product from January 2015 to December 2015 with a spatial resolution of 1 km was employed in this study. The MOD13A3 was derived from atmospherically corrected reflectance in the red and near-infrared wavebands of the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Terra satellite, which can be downloaded at [[https://ladsweb.modaps.eosdis.nasa.gov/search/](https://ladsweb.modaps.eosdis.nasa.gov/search/)]([https://ladsweb.modaps.eosdis.nasa.gov/search/](https://ladsweb.modaps.eosdis.nasa.gov/search/)). Annual NDVI value was obtained by averaging monthly NDVI in the year.
## 4 Results of the Optimizations
### Isolated Solution
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & \multicolumn{3}{c}{**Economic Optimization**} & \multicolumn{3}{c}{**Environmental Optimization**} \\ \hline \multirow{2}{*}{\begin{tabular}{c} **Conventional** \\ **Solution** \\ **without TS** \\ \end{tabular} } & \begin{tabular}{c} **Isolated** \\ **Solution** \\ **without TS** \\ \end{tabular} & \
\begin{tabular}{c} **Isolated** \\ **Solution** \\ **without TS** \\ \end{tabular} &
\begin{tabular}{c} **Isolated** \\ **Solution** \\ **without TS** \\ \end{tabular} \\ \hline CHP natural gas cost (k\(\xi\)/y) & 0 & 1458 & 1561 & 624 & 600 \\ BOI natural gas cost (k\(\xi\)/y) & 1096 & 67 & 50 & 13 & 2 \\ Buoght electricity cost (k\(\xi\)/y) & 1340 & 29 & 28 & 1216 & 1257 \\ Sold electricity income (k\(\xi\)/y) & 0 & 365 & 490 & 138 & 140 \\ Photovoltaic incentive (k\(\xi\)/y) & 0 & 68 & 68 & 16 & 0 \\ Operating cost (k\(\xi\)/y) & 2437 & 1121 & 1081 & 1699 & 1720 \\ Maintenance cost (k\(\xi\)/y) & 23 & 120 & 128 & 53 & 52 \\ Total investment cost (k\(\xi\)/y) & 1267 & 4288 & 4021 & 12,138 & 12,518 \\ Annual investment cost (k\(\xi\)/y) & 163 & 421 & 395 & 1175 & 1206 \\
**Total annual cost (k\(\xi\)/y)** & **2622** & **1661** & **1604** & **2927** & **2977** \\
**Reduction wrt conv. solution** & & **36.7\%** & **38.8\%** & **-11.6\%** & **-13.5\%** \\ Electricity emissions (t/y) & 2807 & 61 & 59 & 2545 & 2633 \\ Sold electricity emissions (t/y) & 0 & 1363 & 1806 & 508 & 499 \\ Natural gas emissions (t/y) & 3691 & 6769 & 7173 & 2844 & 2701 \\
**Total annual emissions (t/y)** & **6497** & **5467** & **5427** & **4882** & **4836** \\
**Reduction wrt conv. solution** & & **15.9\%** & **16.5\%** & **24.9\%** & **25.6\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Total economic and environmental results of the optimizations—isolated solutions.
###### Abstract  
A compact dual-band, dual-polarized, implantable antenna was designed using the characteristic mode method for operation in the wireless medical telemetry service (WMTS) band (1.4 GHz) and the industrial, scientific, and medical (ISM) band (2.45 GHz). By utilizing slotting techniques and materials with high dielectric constants, the antenna volume was minimized to 47.4 mm\({}^{3}\), and circular polarization was gained in the WMTS band by using the characteristic mode method. A three-layer physical model of human tissues was constructed in a simulation, and a biogel and minced pork were used in the measurements. The measured peak gains are \(-\)20.85 dBi (1.4 GHz) and \(-\)22.15 dBi (2.45 GHz). The measured effective axis ratio bandwidth in the WMTS band is 170 MHz (1.33-1.50 GHz, 12.0%), and the impedance bandwidth in the ISM band is 390 MHz (2.21-2.60 GHz, 16.2%). At 1.4 GHz and 2.45 GHz, the largest 1 g average SAR values are 376 W/Kg and 318 W/Kg, which comply with IEEE C95.1-1999. Moreover, when the communication chain affinity exceeds 20 dB for 1.4 GHz and 2.4 GHz, the transceiver range reaches 8.2 m and 9.7 m. This antenna can be used for implantable wireless telemetry systems.  
biotelemetry; characteristic mode method; dual-band; dual polarized; implantable antenna +
Footnote †: journal: Sensors
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \begin{tabular}{c} **Characteristics** \\ **of the Particles** \\ \end{tabular} & **Species** & \
\begin{tabular}{c} **Characteristics of** \\ **the Exposure** \\ \end{tabular} &
\begin{tabular}{c} **Assessed** \\ **Endpoints** \\ \end{tabular} & **Main Findings** & **Rf** \\ \hline \multicolumn{6}{c}{**Nanoplastics (NPIs)**} \\ \hline Polystyrene, & \multirow{4}{*}{_E. crypticus_} & Via food & Reproduction & NPIs reduced the weight, & \\ 0.05 to 0.1 \(\upmu\)m & & 0.025, 0.5 and 10\% & Growth & & had a hermetic-like effect & \\  & & 0.025, 0.5 and 10\% & Gut microbiome & on reproduction and & [5] \\  & & 7 days & NPIs detection & changed the gut & \\  & & & & microbiome. & \\ \hline \multicolumn{6}{c}{**Microplastics (MPIs)**} \\ \hline High-density & \multirow{4}{*}{_L. terrestris_} & Via soil spiked & Survival & & \\  & 236, 1261 and & Growth & MPIs had the potential to & \\  & polyethylene, & 4505 mg/kg with 0.35 & MPIs adsorption & act as vector for & \\  & & wt \% zinc & /desorption & increasing uptake of & \\  & & 28 days & capacity and & zinc. & \\  & & & accumulation & & \\ \hline \multicolumn{6}{c}{Reproduction} \\ Polyethylene, & \multirow{4}{*}{_E. Andrei_} & Via soil spiked 62.5, & Survival & Severe histological & \\  & & 125, 250, 500 and & Growth & & damages in the gut. & \\
250 to 1000 \(\upmu\)m & & 1000 mg/kg & Histopathology & & Molecular changes & \\  & & 28 days & and molecular & & related to immune & \\  & & & analysis & system. & \\ \hline Polyethylene, & \multirow{4}{*}{_L. terrestris_} & Via soil spiked & Reproduction & Mortality increased and & \\  & & 7, 28, 45 and 60\% & Survival & growth rate was reduced. & \\  & & 60 days & Growth & MPIs were concentrated & \\  & & & MPIs ingestion & in cast. & \\ \hline Polyvinyl & \multirow{4}{*}{_F. candidia_} & Via soil spiked & Reproduction & & \\ chloride, 80 & 1 g/kg & Growth & inhibited. MPIs changed & \\ and 250 \(\upmu\)m & & 48 h & Gut microbiome & the gut microbiome. & \\  & & & Isotopic turnover & Enhanced 815N and & \\  & & & & 813C values. & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Studies assessing the effects of microplastics and nanoplastics on soil invertebrates.
# Applying Deep Learning to Medical Imaging: A Review
## 4 Deep Learning for Specific Medical Imaging Modalities
### Magnetic Resonance Imaging (MRI)
#### 4.1.2 Challenges and Future Directions  
Despite the promising results, there are still challenges in applying DL techniques to MRI analysis. One of the major challenges is the limited availability of large, annotated datasets. Moreover, the heterogeneity of MRI data, such as differences in image contrast, image resolution, and imaging protocols, makes it difficult to generalize DL models to new datasets. Therefore, developing transferable models that can handle these variations is an important future direction. Additionally, incorporating domain-specific knowledge and incorporating prior information into DL models can further improve their performance.
# Predicting Missing Values in Survey Data Using Prompt Engineering for Addressing Item Non-Response
###### Abstract  
Survey data play a crucial role in various research fields, including economics, education, and healthcare, by providing insights into human behavior and opinions. However, item non-response, where respondents fail to answer specific questions, presents a significant challenge by creating incomplete datasets that undermine data integrity and can hinder or even prevent accurate analysis. Traditional methods for addressing missing data, such as statistical imputation techniques and deep learning models, often fall short when dealing with the rich linguistic content of survey data. These approaches are also hampered by high time complexity for training and the need for extensive preprocessing or feature selection. In this paper, we introduce an approach that leverages Large Language Models (LLMs) through prompt engineering for predicting item non-responses in survey data. Our method combines the strengths of both traditional imputation techniques and deep learning methods with the advanced linguistic understanding of LLMs. By integrating respondent similarities, question relevance, and linguistic semantics, our approach enhances the accuracy and comprehensiveness of survey data analysis. The proposed method bypasses the need for complex preprocessing and additional training, making it adaptable, scalable, and capable of generating explainable predictions in natural language. We evaluated the effectiveness of our LLM-based approach through a series of experiments, demonstrating its competitive performance against established methods such as Multivariate Imputation by Chained Equations (MICE), MissForest, and deep learning models like TabTransformer. The results show that our approach not only matches but, in some cases, exceeds the performance of these methods while significantly reducing the time required for data processing.  
survey data; item non-response; large language models; prompt engineering Article
## 4 Results and Discussion
### AE Basic Parameter
In addition, Figure 11a shows that in the 1500 s and 2000 s, the accumulated energy of RPC beam has a mutation but BFRPC beam has no mutation, which indicates that RPC beam is prone to brittle fracture. BF can absorb part of the external load potential energy through deformation, reducing the potential energy used for the crack expansion. Furthermore, the cracks propagation is controlled and the toughness of RPC is improved. Figures 11b and 12b,c show that the AE event number, cumulative energy, and amplitude of BFRPC-N beam are significantly lower than that of BFRPC beam, and the AE event number, cumulative energy, and amplitude of BFRPC-N-ID beam are significantly lower than that of BFRPC-N beam. Moreover, it cannot be obviously observed that the energy release when the damage of BFRPC-N beam and BFRPC-N-ID beam occurs. The reason is that the external load potential energy will not be transferred to BF, and the damage will be released directly at the place notch and interfacial damage, which is also the essential reason why the notch and interfacial damage reduces the bending resistance of the beam. Therefore, notch and interfacial damage should be monitored in the actual engineering projects. Figure 12: AE parameters amplitude: (**a**) RPC beam; (**b**) BFRPC beam; (**c**) BFRPC-N beam; (**d**) BFRPC-N-ID beam.