esa-sceva/satcom-chunk-collection huggingface dataset:
# A High-Efficiency Super-Resolution Reconstruction Method for Ultrasound Microvascular Imaging
## 3 Experimental Results
### Results of Different Methods  
Figure 4 shows the super-resolution imaging results of three reconstruction algorithms for the same dataset. Because only the echoes of 400 sampling frames are used in the imaging process, the resolution of the image reconstructed by Gaussian PSF deconvolution and scatter plot is limited. The high-order moment calculation and power Doppler integration method performs better in suppressing the background noise but the resolution of the reconstructed image is worse and not satisfactory. As for the vessels in the right bottom corner pointed by the blue arrows, this method fails to figure them out. The proposed method presents the finest microvascular structure and also has a certain inhibitory effect on the artefacts. For the artefacts in the left bottom corner pointed by the yellow arrows, the proposed method has the best suppressing performance. In order to observe the details of the image more clearly, we selected and enlarged a specific area of the image, and the results are shown in Figure 5. The analysis object is the vessels at the top right corner marked by the orange rectangles in Figure 4. As the results show, the proposed method is the only one that can clearly display the microvascular structure, which suggests that our method obtains the best resolution.  
Figure 4: The super-resolution construction imaging results of rabbit kidney using: (**a**) Gaussian PSF deconvolution + scatter plot, (**b**) High-order moment calculation + power Doppler integration, and (**c**) Integral form of Gaussian PSF deconvolution + ASH respectively. All images are shown with a dynamic range of 50 dB. The orange, yellow and blue arrows point to the point target, artefact and the vascular details respectively. The orange box marks the area we choose to enlarge.  
Figure 5: Locally magnified (10 times) microvascular images of rabbit kidney using: (**a**) Gaussian PSF deconvolution + scatter plot, (**b**) High-order moment calculation + power Doppler integration, and (**c**) Integral form of Gaussian PSF deconvolution + ASH respectively. All images are shown with a dynamic range of 50 dB.  
To further compare the resolution performance, Figure 6 shows the lateral variation of the point target on the right top corner in Figure 4, which is pointed by the orange arrows. As seen, our proposed method performs best in the lateral resolution while the moment calculation method performs worst.  
Table 1 gives the statistical results for the resolution and running time of different methods. The result is the same as that of qualitative observation. The best FWHM and PSL indices are obtained by the proposed method. In terms of FWHM, the value of the proposed method is reduced by half in comparison to the original Gaussian PSF method. Meanwhile, the reduction of the PSL exceeds 5 dB. Though our method is not the fastest, its running time is still in the acceptable range. Considering the short sequence that was used, the improvement in the time resolution by our method is obvious.
# Optimal View Estimation Algorithm and Evaluation with Deviation Angle Analysis
## 3 Methods
### Uniform Sampling on a Unit Sphere
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Methods** & **minAveD** & **maxAveD** & **Range** & **stdAveD** & **UniHks** & **UniP** & **NrmHks** & **NrmP** & **Time** \\ \hline CFLS & 1.0441 & 1.1311 & 0.0869 & 0.0261 & 0 & 0.7773 & 0 & 0.8557 & 0.0010 \\ CSDyn & 0.8286 & 1.2514 & 0.4228 & 0.1235 & 0 & 0.0683 & 0 & 0.3685 & 0.0159 \\ InveT & 0.8720 & 1.2764 & 0.4043 & 0.1019 & 0 & 0.2367 & 0 & 0.8364 & 0.0008 \\ Spiral & 0.8517 & 1.2752 & 0.4236 & 0.1319 & 0 & 0.2589 & 0 & 0.3715 & 0.0014 \\ Kspace & 0.9118 & 1.2297 & 0.3178 & 0.0922 & 1 & 0.0334 & 0 & 0.7959 & 0.0152 \\ SCS-AP & 0.7784 & 1.3215 & 0.5431 & 0.1883 & 1 & 0.0004 & 0 & 0.0981 & 0.0009 \\ Ours & 1.0149 & **1.0439** & **0.0290** & **0.0073** & **0** & 0.4686 & **0** & 0.9375 & 0.0693 \\ \hline \hline \end{tabular} UniHks represents the KS test result of uniform distribution: 0 means passing the test, and 1 rejecting the hypothesis. NrmHks represents the KS test result of normal distribution: 0 means passing the test, and 1, rejecting the hypothesis. UniP and NrmP are the \(p\)-values corresponding to the KS test results of a uniform distribution and normal distribution, respectively. The number marked in bold is the best value, and the number marked with an underline in the second best value.  
\end{table} 
Table 3: Calculation results of the sampling points (N = 25). Bold numbers indicate the best results; The underlined number is the second-best result. Figure 5: The visualization of the average distance of k nearest neighbors of each sampling point, generated using seven methods. The color represents the nearest neighbor average distance using the color scheme of Figure 4b. According to these quantitative index calculation results of the sampling points, our method generally demonstrates the best performance according to the four evaluation metrics: maxAveD, Range, stdAveD, and NrmHks. The proposed method is similar to other methods for the remaining five indicators. For example, our minimum distance is relatively large, ranking second among all methods, which is also quite good. Because we use analytical methods to calculate the initial viewpoint position and then use random movement to improve uniformity, the calculation time among these five indicators is more prolonged than using analytical methods alone, but it is more efficient than using the random movement method alone.
## 2 Model Structure and Description
### Soil P Pools, Fluxes and Mineralization  
The soil pools were modeled as containing the three pools of the original model without the P cycle, divided into organic and inorganic pools, as well as three new independent P pools (labile, absorbed and strongly absorbed pools), which all together describe the process of P cycling completely. In contrast to the original TRIPLEX-GHG settings, we combined the non-protected slow pool and the protected slow pool into a single slow pool. In microbial biomass, we allowed P, C, and N to be in organic forms in the slow and passive pools, whereas P was allowed to flow among labile, absorbed and strongly absorbed pools only in their inorganic form. The labile pool is essential since it contains the main P form taken up by plants, while a fraction of P leaches out of the ecosystem due to its solubility. In absorbed and strongly absorbed pools, P becomes inactive and, ultimately, inorganic phosphate ions aggregate in soil, making it difficult for the P to be recycled. The orange arrows in Figure 1 represent the flows of P among different soil compartments, which were modeled using the following equations:  
\[\frac{\mathrm{dP_{mic}}}{\mathrm{dt}}=\mathrm{\sum_{j}C_{micj}}\mathrm{l_{n}t _{j}P_{j}}+\mathrm{\sum_{k}d_{mick}t_{k}P_{k}}-\mathrm{t_{mic}P_{mic}} \tag{5}\]  
\[\frac{\mathrm{dP_{slow}}}{\mathrm{dt}}=\mathrm{\sum_{j}C_{slow}}\mathrm{l_{n}t _{j}P_{j}}+\mathrm{\sum_{k}d_{slow}}\mathrm{k}t_{k}P_{k}-\mathrm{t_{slow}} \mathrm{P_{slow}}-\mathrm{prbiocheminer}\cdot\mathrm{M1} \tag{6}\]  
\[\frac{\mathrm{dP_{pass}}}{\mathrm{dt}}=\mathrm{\sum_{j}C_{pass}}\mathrm{l_{n}t _{j}P_{j}}+\mathrm{\sum_{k}d_{passk}t_{k}P_{k}}-\mathrm{t_{pass}P_{pass}}- \mathrm{prbiocheminer}\cdot\mathrm{M2} \tag{7}\]  
where \(\mathrm{P_{j}}\) is the amount of P in different litter pools (j = microbial, slow or passive); k represents the soil P pools, \(\mathrm{P_{k}}\) is the amount of P in soil pools; \(\mathrm{P_{mic}}\), \(\mathrm{P_{slow}}\) and \(\mathrm{P_{pass}}\) are the respective amounts of P in microbial, slow and passive pools in litter (g P\(\cdot\)m\({}^{-2}\)); \(\mathrm{C_{slow}}\) and \(\mathrm{C_{passj}}\) are the respective amounts of C in litter pools (structural, decomposable and resistant) to different soil pools (slow and passive) (g C\(\cdot\)m\({}^{-2}\)); ln represents the limitation of nitrogen; t\({}_{j}\) is the turnover time constant of litter pool (day\({}^{-1}\)); \(\mathrm{t_{k}}\) is the turnover time constant of soil pools (day\({}^{-1}\)); d\({}_{\text{mick}}\), d\({}_{\text{slow}}\) and d\({}_{\text{passk}}\) are the C allocation from the other two soil pools; prbiocheminer is the biochemical mineralization rate of P (g P\(\cdot\)m\({}^{-2}\)); and M1 is \(\frac{\text{t}_{\text{slow}}\text{P}_{\text{slow}}}{\text{P}_{\text{slow}}+ \text{t}_{\text{pass}}\text{P}_{\text{pass}}}\) and M2 is \(\frac{\text{t}_{\text{pass}}\text{P}_{\text{pass}}}{\text{t}_{\text{slow}} \text{P}_{\text{slow}}+\text{t}_{\text{pass}}\text{P}_{\text{pass}}}\), according to Michaelis-Menten kinetics.
## 7 Raim  
Traditional RAIM techniques provide integrity information, by making use of the available measurements [2]. The basic assumption for RAIM is the presence of only one fault at the same time. This is a strong assumption, but the process may be iterated so including _defacto_ the case of multiple faults. In literature three main RAIM algorithms are proposed: range comparison, least-squares residuals and parity method. All the methods are able to determine the presence of a satellite failure by using the redundancy of the measurements of the over-determined system of linearised equations  
\[\boldsymbol{\Delta\rho}=\mathbf{H}\boldsymbol{\Delta x}+\boldsymbol{e}\]  
where \(\boldsymbol{\Delta x}\) is a vector containing the incremental deviations of the user position from the linearisation point, \(\mathbf{H}\) is the geometry matrix, where the generic row \(j\) is the unit vector pointing from the linearisation point to the considered \(j\)-th satellite, and \(\boldsymbol{e}\) is the vector of PR errors. To detect position errors, RAIM requires at least five satellites in view, while the exclusion of the faulty measurements requires at least six satellites. The snapshot approach means that the system takes decision about the presence of failure by using only current considered observations.  
In this paper, we propose a method of assisted RAIM, based on the idea to detect satellite failures at tracking stage level, since errors like MP are much more visible in the correlation rather than at the PR level.
## 4 Case Study
### Evaluation Design  
We designed three phases to evaluate the effectiveness of the LLM application as follows:  
* Phase 1A (human-only): We followed the PRISMA flow diagram to screen articles for the SR. We applied predefined inclusion and exclusion criteria to select relevant articles. Each article was screened independently by two reviewers, and any conflict was resolved by team discussion. The results were divided into two groups as follows: inclusion (\(n=170\)) and exclusion (\(n=220\)). We used predefined data extraction templates to collect information from each article.
* Phase 1B (hybrid): We used a plugin with Gemini-Pro API (Top-p = 0.1, Temperature = 0.2) to extract data from the same articles. The Gemini-Pro model is an LLM created by DeepMind from Google. This LLM (version 1.0) has an input token limit of 30,720, which corresponds to about 18,432-24,576 English words [32]. Several prompts were engineered iteratively for articles based on the inclusion criteria and relevant keywords from the case study. Each prompt was sent to Gemini-Pro via the plugin and received the responses in JSON format and ran repeat three times because of the uncertainty in the AI output [10]. The data collection period was between 13 January 2024 and 2 February 2024. We developed an LLM application using an API to send the prompts to an LLM and collect the JSON outputs. The API was designed to handle  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline (\(k=45\)) & \multicolumn{4}{c}{_Identifiers_} & \multicolumn{8}{c}{_Data Fields_} \\ \hline \multirow{2}{*}{**Prompts**} & \multicolumn{2}{c}{_“Occupation”_} & \multicolumn{2}{c}{_“Study Type”_} & \multicolumn{2}{c}{_“Work-Related Outcome”_} & \multicolumn{2}{c}{_“Device.Name”_} & \multicolumn{2}{c}{_“Device.Placement”_} \\ \hline  & \(x\) & \(y\) & \(z\) & \(x\) & \(y\) & \(z\) & \(x\) & \(y\) & \(z\) & \(x\) & \(y\) & \(z\) & \(x\) & \(y\) & \(z\) \\ \hline Initial prompt & 21 & 2 & 11 & 27 & 12 & 22 & 24 & 2 & 22 & 25 & 1 & 20 & 25 & 7 & 21 \\ \hline Refined prompt & 18 & 0 & 0 & 4 & 2 & 4 & 18 & 0 & 0 & 34 & 0 & 0 & 23 & 1 & 7 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance of the LLM prompt development strategy, with the few-shot strategy.  
multiple requests simultaneously, which allowed us to collect the data quickly and efficiently. Once we had collected all JSON outputs, we stored them in a file (see Figure 5a). The data were pre-processed before the filter. After converting the JSON file to a spreadsheet using _\"jsoneditoronline\"_, one author filtered the data and removed any records according to the identifiers using Microsoft Excel (see Figure 5b). All records (\(n=390\)) were filtered and screened by one author.
* Phase 2 (evaluation): One author validated the records from the hybrid workflow with the full-text articles of the included articles. Any conflicts between the hybrid and human-only methods were resolved by team discussion against the full-text articles to ensure that humans screened each study twice independently.
# Noise Reduction Based on a CEEMD-WPT Crack Acoustic Emission Dataset
## 2 Basic Theory
### WPT Noise Reduction  
WPT is improved from wavelet transform, which improves the problem of the insufficient high-frequency resolution of wavelet decomposition by decomposing the high-frequency part. Its process is as follows [27]:  
(1) A set of orthogonal wavelet bases is used to decompose the input signal into two parts: high frequency and low frequency, where the scale function \(\alpha(t)\) and the wavelet function \(\beta(t)\) are calculated as follows:  
\[\alpha(t)=\sqrt{2}{\sum_{n}}l(n)\alpha(2t-n) \tag{4}\]  
\[\beta(t)=\sqrt{2}{\sum_{n}}h(n)\beta(2t-n) \tag{5}\]  
In the above equation, \(n\) is the translation coefficient and \(l(n)\) and \(h(n)\) are low-pass and high-pass filters, respectively.  
(2) The signal is decomposed by a wavelet packet.  
\[d_{i+1}^{2k}=\sum_{n}l(n-2t)d_{i}^{k}(n) \tag{6}\]\[d_{i+1}^{2k+1}=\sum_{n}h(n-2t)d_{i}^{k}(n) \tag{7}\]  
In the above equation, \(d_{i}^{k}(n)\) is the NTH coefficient of the \(k\) node of the \(i\) layer of wavelet packet decomposition.  
The threshold method is used to process the wavelet packet coefficient and reconstruct the wavelet coefficient to obtain the signal after noise reduction.  
\[d_{i}^{k}(n)=2\left[\begin{array}{c}\sum l(n-2t)d_{i+1}^{2k+1}(n)+\\ \sum\limits_{t}^{t}h(n-2t)d_{i+1}^{2k}(n)\end{array}\right] \tag{8}\]
## 4 Discussion
### Limitations of the Proposed RR-DTDWT  
One limitation of the RR-DTDWT based seizure detection model is the generalization performance when applied to a new patient. The automated seizure detection models in literature can be divided into two categories; namely, the patient-nonspecific model [64] and the patient-specific model [7]. The former one refers to the model trained by the data of other patients with no data of the test patient involved in model training. This kind of model can save huge costs on data collection and data labeling because for a new patient, no further data collection is required and the model can be used immediately. Data from the other patients can be reserved beforehand, which can be viewed as cost-free. For example, [PERSON] et al. [64] proposed an enhanced, transductive, transfer learning Takagi-Sugeno-Kang fuzzy system construction method (ETTL-TSK-FS) to enhance the generalization performance of automated seizure detection models. The ETTL-TSK-FS method achieved a sensitivity of 91.91%, specificity of 93.16% and accuracy of 94.04% on the CHB-MIT dataset. Although its performance is less effective than most patient-specific models, ETTL-TSK-FS can be viewed as the current state-of-the-art patient-nonspecific model. The patient-specific models take advantage of the useful information of the test patient. However, acquisition of data from the test patient is required to train the seizure detection model or fine-tune the pre-trained model. Most automated seizure detection studies in literature focus on patient-specific classification models [7; 38; 39]. The generalization performance is a common limitation and remains a future work of our study.
## 4 Pathogen Detection in the Future  
Sensors for disease detection and food quality will evolve in the following decade with the aids of nanotechnology and MEMS. Actual NIR, fluorescence and vision sensors can detect more accurately fruit quality and predict diseases better than our eyes. Sensor spectral range is broader than eye's spectral response and sensors are capable of detecting polarized light. Nanotechnology cells will be able to capture gases in bubbles creating internal reactions without affecting fruit or vegetable quality.  
Ethylene, pigment status, diseases and enzymes will be predicted temporarily and spatially throughout the fruit.  
MEMS are nanotechnology chips which can sense and take control actions. MEMS produced extensively have low costs and could help to optimize precise fruit ripening harvest, reducing post harvest costs. The author visualizes the future with a MEM plastic unit added per fruit, memorizing its history and growth characteristics with detail. The MEM will acquire fruit internal nutrient concentration (e.g. calcium and nitrate ions), type of soil, applied fertilization, and pesticide usage. The information will also include commercialization details as transportation, cooling, texture and firmness during controlled atmosphere treatments. Retail daily analysis of ripeness, rots, impact damage, chlorophyll, carotenoids, and water status should be monitored on real time for consumer safety.  
Biosensors will also evolve and will be used together with intelligent biological agents as wasps, spiders or bees providing intelligent crop status sensing. Their attraction or repulsion to fruits can help on detecting fruit ethylene production, respiration rate and fruit ripeness. Even trained parrots could be used as indicators of mango ripeness. Fruit bio-security will be a big issue to assure that consumer health can be guaranteed.  
As sensors improve and provide more information higher data acquisition will be required but algorithms will tend to become simpler. Supervised algorithms will be used if they continue to be trained during their operation; these algorithms would be iterative and will replace multiple regression equations which are used at the present on most processes. Genetic algorithms will be used more often while fuzzy algorithms will disappear as a prediction tool.  
Although remote sensing is a strong tool its view is reserved to the top of the crop-tree canopies. With controlled mirrors crop status within the entire plants could be observed. The mirrors should be controlled to avoid sensor saturation with radiation. By the next decade, entire plant measurements will be placed on the top of the canopy using codes easily interpreted by satellites, avoiding excessive data transmission.
## 5 Performance Measures of PAS
### Availability of PAS  
The availability of the system is a very important maintenance metric. It computes the probability that the system will not experience downtime when workers are required to use it. The upper bound for this metric is 1. In order to calculate the availability of the system, take the Laplace inverse of Equation (30) and set the repair and failure rate for the system components as given in Table 3. One can easily obtain the following availability function given in Equation (32).  
\[\begin{array}{c}Av(t)=0.9998966711-1.2103572\times 10^{-9}e^{-1.500300804\ t}+0.0000333 3558699\\ e^{-1.500050012\ t}-4.0670741\times 10^{-7}e^{-1.260028216\ t}+4.2722323\times 10^{-7} \\ e^{-1.241533050\ t}+0.00004999275835\ e^{-1.200059996\ t}+1.2284304\times 10^{-8} \\ e^{-1.198558070\ t}+0.00001999053893e^{-1.000019992\ t}+4.22255839\times 10^{-9} \\ e^{-0.9999198591\ t}\end{array} \tag{32}\]  
Now, varying the time unit t in Equation (32), one can obtain the following Figure 4 of the availability of the public address system.  
\begin{table}
\begin{tabular}{c c c c} \hline
**S.No.** & **Component** & **Failure Rate (per h)** & **Repair Rate (per h)** \\ \hline
1 & Microphone & 0.00002 & 1.0 \\
2 & Mixer & 0.00005 & 1.5 \\
3 & Amplifier & 0.00006 & 1.2 \\
4 & Speaker & 0.00007 & 1.25 \\ \hline \end{tabular}
\end{table}
Table 3: Failure and repair rates of different components of PAS.
# Simultaneous Coded Plane-Wave Imaging Using an Advanced Ultrasound Forward Model
## 2 Materials and Methods
### Reconstruction Using Delay and Sum  
The results obtained are compared with those provided by the classic reconstruction algorithm DAS [18], using successive PWs. The excitation signal is two periods of sine waves at the central frequency of the probe (8.5  MHz). The received signals \(\mathbf{y}_{\text{DAS}_{i}}^{(k)}\) are apodized in reception using a Tukey window with cosine fraction of 0.75. The f-number is set to 1.75. Note that the DAS acquisitions are performed with only 64 elements and three PWs, which is the same for the proposed method. An estimation of the TRF (\(\hat{\gamma}_{\text{3 PW-DAS}}^{(k)}\)) is obtained after each PW emission/reception. Finally, \(\hat{\gamma}_{\text{3 PW-DAS}}\) is obtained by coherently adding the \(N_{\text{pw}}\) estimations. A post-processing step of envelope extraction and log-compression is applied to \(\hat{\gamma}_{\text{3 PW-DAS}}\) to obtain the final TRF estimation (B-mode).  
For comparison purposes, a second denoised-DAS image is created by applying \(\ell_{1}\) denoising on \(\hat{\gamma}_{\text{3 PW-DAS}}\), such that \(\hat{\gamma}_{\text{3 PW-DAS-L1}}=\underset{x}{\text{argmin}}\{\|\mathbf{x}-\hat{ \gamma}_{\text{3 PW-DAS}}\|_{2}^{2}+\mu\|\mathbf{x}\|_{1}\}\), where \(x\) represents the targeted sparse output image. This enables a comparison with \(\hat{\gamma}_{\text{3 PW-F-L1}}\), which is intrinsically constrained to a sparse solution through the \(\ell_{1}\) regularization term (Equation (27)). The \(\mu\) parameter is set as high as possible, while ensuring that all scatterers remains visible on the reconstructed image.  
Figure 3: Studied mediums. (**a**) Simulated position of the scatterers inside the wire phantom. (**b**) Physical position of the scatterers inside the water tank.