esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Evaluation of the Current Birmingham PEPT Cameras
## 5 Results  
### ADAC Forte  
Figure 5 shows the count rate data measured for a \({}^{11}\)C source mounted at the centre of the FOV of the ADAC Forte operating at a face-to-face separation of 50 cm. As the activity tends to zero, the data approach the straight line corresponding to a sensitivity of 0.014. Combining this value with the angular acceptance of 0.3 implies that each detector has a quantum efficiency of approximately 22% for detecting 511 keV photons, almost identical to the value of 23% reported when this camera was first evaluated.  
As seen in Figure 5, as the activity increases, the data rate quickly drops below that expected for a sensitivity of 0.014, due to the effects of dead-time in the detectors and data acquisition hardware. The data rate saturates at a little over 120 kHz. A source of activity 15 MBq mounted centrally gives a data rate of approximately 110 kHz, and this can be regarded as the maximum desirable activity for use with this system.  
As currently set up, this camera does not report the rate of random coincidences, but based on previous measurements [5], the randoms rate is expected to be relatively low (\(<\)10%) even for an activity of 15 MBq.  
Using the Birmingham PEPT algorithm to analyse data from a low activity \({}^{18}\)F point source mounted at the centre of the FOV, the optimum value of \(f\) was found to be approximately 0.6, and the effective value of \(w\) was found to be approximately 8.5 mm. This value is fortuitously similar to the spatial resolution often quoted for the detector heads of this camera, but corresponds to a completely different quantity. When a gamma-ray interacts in one of the heads, the PMTs localise the centroid of the resulting light with a spatial resolution of around 8 mm (FWHM of the 2D distribution). On the other hand, \(w\) represents the standard deviation in 3D of the LORs around the source and is dominated by the uncertainty in the direction linking the detector heads.  
As the activity increases, the data quality deteriorates slightly: at an activity of 7.5 MBq the optimum value of \(f\) is around 0.45 and the corresponding \(w\) value is 9.5 mm.
## 4 Experiments
### Application and Datasets
#### 4.2.3 Opportunity Dataset  
This dataset consists of data from a rich sensor environment where subjects performed daily activities in the kitchen and living area. It comprises recordings of 12 subjects using 15 networked sensor systems, with 72 sensors of 10 modalities integrated into the environment, in objects, and on the body. Some of the activities included open door, close door, open fridge, close fridge, open drawer, close drawer, clean table, etc. It is an 18-category dataset with null cases also included. The dataset is not well-balanced with the null class problem (see Section 4.2 for null class description).
## 5 The synthesis of SiFAs  
[PERSON] and his group developed the vast majority of the already described SiFA compounds, with the exception of a most recently described SiFA building block bearing a positive charge. Since the Si-F bond is very stable, the synthesis and purification of all fluorosilanes is straightforward and very convenient. The synthesis of most triorganofluorosilanes follows the scheme below (Figure 4).  
First, silyl-protected functionalized _p_-bromobenzenes are reacted with _tert_-butyllithium and subsequently di-_tert_-butyldifluoroosilane at \(-78\,^{\circ}\mathrm{C}\). The product tri-organofluorosilanes (**4-6**) are obtained after final silyl-protecting group removal, releasing the respective functional group that can  
Figure 4: Syntheses of various small SiFA building blocks intended for bioconjugation.  
be further derivatized or used for bioconjugation reactions. In addition to relatively stable -SH, -NH\({}_{2}\), -COOH and -CH\({}_{2}\)OH groups in _para_ position to the Si atom, compounds comprising sensitive and highly reactive functional groups such as -CHO, -NCO, -NCS and even active esters (succinimidyl- as well as pentafluorophenyl esters) could also be synthesized following the outlined synthetic procedures.
## 2 Materials and Methods
### Datasets
#### 2.1.2. Region of Interest (ROI) Segmentation  
The B-mode images stored in database-I, database-II, and database-III are in various sizes where the highest resolution of them is 600 \(\times\) 600 pixels. However, almost 50% of those images contained a large amount of shadowing effect. Therefore, a shadow reduction  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**Database-I**} \\ \hline
**Tumor Type** & **No. of Patients** & **No. of Lesions** & **Method of Confirmation** \\ \hline Fibroadenoma (Benign) & 91 & 100 & Biopsy \\ Malignant & 142 & 150 & Biopsy \\ \hline \multicolumn{4}{c}{**Database-II**} \\ \hline
**Tumor Type** & **No.ofPatients** & **No. of Lesions** & **Method of Confirmation** \\ \hline Cyst (Benign) & 65 & 65 & Biopsy \\ Fibroadenoma (Benign) & 39 & 39 & Biopsy \\ Invasive Ductal Carcinoma (Malignant) & 40 & 40 & Biopsy \\ Ductal Carcinoma in Situ (Malignant) & 4 & 4 & Biopsy \\ Papilloma (Benign) & 3 & 3 & Biopsy \\ Lymph Node (Benign) & 3 & 3 & Biopsy \\ Lymphoma (Malignant) & 1 & 1 & Biopsy \\ Unknown (Malignant) & 8 & 8 & Biopsy \\ \hline \multicolumn{4}{c}{**Database-III**} \\ \hline
**Tumor Type** & **No. of Patients** & **No. of Lesions** & **Method of Confirmation** \\ \hline Benign & & 437 & Revieved by Special \\ Malignant & 600 & 210 & Radiologists \\ Normal & & 133 & Radiologists \\ \hline \multicolumn{4}{c}{Total patients = 996} & Total = 1193 lesions \\ \hline \hline \end{tabular}
\end{table}
Table 1: Patient data summary.  
Figure 1: Example of normalization where the first row is the benign case and the second row is the malignant case. (**A,C**) US B-mode image; (**B,D**) Normalized image.  
operation is performed using adaptive median filtering to minimize the unwanted portion of the image for the smooth detection of the region of interest (ROI), which is depicted in [29]. It should be noted that almost all the background information and lesion size must be preserved to ensure that no features will be suppressed with the shadow reduction operation. Next, the lesion boundary (ROI) is outlined. This process requires a binary input image, specified as a 2-D logical or numeric matrix. For that, the normalized image is subjected to binarization using MATLAB function 'imbinarize'. Images after binarization and ROI segmentation are shown in Figure 2. The lesion boundary region is automatically outlined using MATLAB functions 'bwboundaries' and 'visboundaries', and those functions are developed using the Moore-Neighbor tracing algorithm modified by [PERSON]'s stopping criteria [30]. The nonzero pixels of that binary image belong to an object and zero-valued pixels constitute the background.
## 3 Results
### Characterisation of FA Profiles in BSF Extracts  
Vegetable fats and oils are beneficial for industrial and food purposes, and their quality is closely related to their FA composition [20]. As shown in Table 2, significant (\(p<0.05\)) changes in the FA profiles of the 3 extracts were influenced by the solvents used. Obvious changes can be seen in the extracts' stearic and oleic acid compositions. About 52.32-59.70% of the BSFs' FA composition was dominated by the UFA, primarily oleic (43.90-48.31%), with a significant presence of linoleic (8.04-9.72%) acid. This result explains the high unsaturation value of all extracts, especially the ethanol-extracted BSF. The extracts obtained in this study showed softer properties with high UFA compositions as compared to the BSF reported in the previous study [1], in which 56.19% of the FA composition was saturated. The variation obtained in this study was presumably correlated with the geographical latitude, thus showing variation in their compositions. The composition and quality of the fat may vary depending on the fruit's growth condition. According to [PERSON] [42], the type of a plant, the environment, and the degree to which the seeds ripen determine the FA and the unsaponifiable components of oilseeds. The bambangan fruit used in the study was ripening but over-softening due to transportation and storage, which made the fruit spoil, resulting in variations in the quality parameter. Additionally, the temperature also significantly impacts the content of FA, particularly the UFA [43]. On the other hand, the results obtained in this study are in agreement with the FA profiles of BSF extracted by [PERSON] et al. [12] and [PERSON] et al. [9] and the mango-seed fat extracted by [PERSON] et al. [44] and [PERSON], [PERSON], and [PERSON] [45]. The SFA and UFA for the reported BSF and mango-seed fat ranged from 44.7 to 44.8% and from 29.1 to 58.6%, from 49.2 to 50.2%, and from 41.1 to 70.2%, respectively. Petroleum-ether-extracted BSF has higher quantities of palmitic and stearic acids, followed by the hexane and ethanol extracts. This shows that petroleum-ether-extracted BSF is harder than the other two extracts. In contrast, ethanol extracts have more oleic acid than do hexane and petroleum-ether extracts. The changes in the FA composition for the 3 extracts can be supported by [PERSON] & [PERSON] [17], who reported similar changes in the extraction of mango-seed oil using hexane (palmitic: 8.97%, stearic: 37.37%, oleic: 43.77%, and linoleic: 6.78%), petroleum ether (palmitic: 8.73%, stearic: 37.70%, oleic: 44.75%, and linoleic: 5.67%) and ethanol (palmitic: 8.50%, stearic: 38.50%, oleic: 43.45%, and linoleic: 6.48%). Moreover, BSF produced by the Soxhlet extraction in this study exhibited an FA-type similar to commercial cocoa butter (CB) (palmitic: 24.5-33.7%, stearic: 33.3-40.2%, oleic:
# Intelligent Edge-Cloud Framework for Water Quality Monitoring in Water Distribution System
###### Abstract  
Ensuring consistent high water quality is paramount in water management planning. This paper addresses this objective by proposing an intelligent edge-cloud framework for water quality monitoring within the water distribution system (WDS). Various scenarios--cloud computing, edge computing, and hybrid edge-cloud computing--are applied to identify the most effective platform for the proposed framework. The first scenario brings the analysis closer to the data generation point (at the edge). The second and third scenarios combine both edge and cloud platforms for optimised performance. In the third scenario, sensor data are directly sent to the cloud for analysis. The proposed framework is rigorously tested across these scenarios. The results reveal that edge computing (scenario 1) outperforms cloud computing in terms of latency, throughput, and packet delivery ratio obtaining 20.33 ms, 148 Kb/s, and 97.47%, respectively. Notably, collaboration between the edge and cloud enhances the accuracy of classification models with an accuracy of up to 94.43%, this improvement was achieved while maintaining the energy consumption rate at the lowest value. In conclusion, our study demonstrates the effectiveness of the proposed intelligent edge-cloud framework in optimising water quality monitoring, and the superior performance of edge computing, coupled with collaborative edge-cloud strategies, underscores the practical viability of this approach.  
edge-cloud computing; water quality; data analysis; WDS; classifications Article
# Advancements in Glucose Monitoring: From Traditional Methods to Wearable Sensors
###### Abstract
On the other hand, surface-enhanced Raman spectroscopy (SERS) offers exceptional sensitivity and specificity due to its ability to enhance signals and provide molecular fingerprints of the compounds. This technique has been applied to analyze various biological samples, including glucose, pH, and redox potentials [12; 13; 14]. However, directly detecting biochemicals through SERS has been challenging due to the poor adsorption of molecules. To overcome this issue, SERS sensors based on nanostructures, such as silver or gold nanopillars, have been developed. By employing this technique, significant improvement in the Raman signals has been observed [15; 16; 17]. In addition, several glucose-monitoring methods have been developed for both non-invasive and direct blood glucose monitoring, enabling more convenient and patient-friendly diabetes management [18; 19]. In this review, we focus on discussing various glucose sensors from the perspectives of invasive, minimally invasive, and non-invasive sensing methods. The review also highlights several device designs and explores opportunities for improvement to enhance the sensitivity of established blood glucose-monitoring systems, as well as the latest advances in non-invasive methods. Finally, we offer a forward-looking perspective on the future of painless, non-invasive glucose monitoring for more efficient diabetes care. This is particularly important as diabetes mellitus, one of the most common chronic diseases, is associated with complications such as blindness, cardiovascular disorders, and kidney failure, underscoring the need for accurate and convenient blood glucose monitoring.
## 7 Conclusions  
The ICA algorithm is a newly developed method based on the theory of socio-political developments, which can be used for both linear and nonlinear functions. Recently, the parameters of metaheuristic algorithms have mostly been chosen by trial and error, which is why we decided to carry out a modification to the ICA algorithm to achieve better performance than the original algorithm and improve convergence, in this way avoiding getting stuck in local optima.  
This research presents a modification of the imperialist competitive algorithm to enable dynamic adaptation of parameters utilizing fuzzy logic, this in order that the fuzzy system dynamically moves the ICA algorithm parameters. Firstly, the ICA algorithm was analyzed and each of the parameters used in processing, and then experiments with different ranges and possible values that each parameter could take were analyzed and then the algorithm was applied to Benchmark mathematical functions in order to optimize such functions and after that for comparing with other existing metaheuristic methods and in this way measure how good our method is.  
We designed different fuzzy systems to independently move each of the parameters of the ICA algorithm, since by these parameters the exploitation and exploration of the algorithm are dynamically controlled. We designed and implemented different fuzzy system combinations between parameters and every one of the modifications was applied to the benchmark mathematical functions.  
Finally, after analyzing the results of each of the proposals, we conclude that the fuzzy imperialist competitive algorithm is better than the original algorithm on average; however, we cannot affirm this statistically since our statistical test yield shows that the results are very close. In addition, it was not possible to significantly improve the results of the fuzzy Cuckoo search algorithm and we only managed to improve on some of the reference functions using the dynamic adjustment of the parameters individually, because with the proposed approach it was not possible to get significantly better results.  
After studying the original imperialist competitive algorithm and making the modification using fuzzy logic to dynamically adjust the parameters, we can conclude that the ICA algorithm is good for solving complex optimization problems, in which we can even achieve better results by applying the following actions, which can be considered as future work:  
* Use different numbers of dimensions.
* Optimize the rules on the combined method to see if better results are achieved.
* Use different inputs in the fuzzy system.
* Change the fuzzy systems to be of type-2 to better model uncertainty.
* Apply the different methods to control problems.
* Perform hybridization with other metaheuristic algorithms for improving the ICA algorithm.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(W^{-}\) & \(W^{+}\) & \(W\) & **Level Significance** & \(m=\) **Degrees of Freedom** & \(W0=W\alpha,m\) \\ \hline
5 & 10 & 5 & 0.05 & 5 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 14: Parameters for the statistical test.
# Research on Synthetic Aperture Radar Processing for the Spaceborne Sliding Spotlight Mode
## 3 Ambiguity Elimination in Sliding Spotlight SAR
In this case, the compensation vector for inverse dechirp processing is:  
\[S_{1 de}(\omega,i)=\exp\left(j\frac{2(\omega+\omega_{c})}{c}\sqrt{\left(i \Delta u\right)^{2}+\left(R_{c}\right)^{2}}\right). \tag{17}\]  
After inverse dechirp processing, the echo can be expressed as follows:  
\[S(\omega,i)=\sum_{i=1}^{N}\sigma(x_{n},r_{n})\exp\left(-j\frac{2(\omega+\omega _{c})}{c}\sqrt{\left(x_{n}-i\cdot\Delta u\right)^{2}+\left(R_{c}+r_{n}\right) ^{2}}\right). \tag{18}\]  
The above is the expression of distance-to-frequency signal that does not produce Doppler ambiguity in the signal domain, so the problem of Doppler ambiguity can be completely solved after the above operation. After this, there is no Doppler ambiguity in the echo data. In this case, the data can be imaged by CS algorithm, wavenumber domain algorithm, polar coordinate algorithm and the like. In the following sections, we will discuss two SAR imaging algorithms suitable for sliding spotlight mode.
# DMLU-Net: A Hybrid Neural Network for Water Body Extraction from Remote Sensing Images
## 3 Experiments
### Experimental Results
#### 3.3.1 Experimental Results on GID Dataset
the Intersection over Union (IoU) metric--highly relevant for target detection tasks--is taken as an example: DMLU-Net achieved a 2.91% improvement in the IoU and a 2.30% increase in the F1-score compared to Deeplabv3+. Compared with QTU-NET, DMLU-Net showed a 1.96% improvement in the IoU and a 1.56% increase in the F1-score. These results demonstrate that the proposed method can accurately extract comprehensive surface water information with a high overall accuracy, outperforming five other state-of-the-art water body recognition algorithms. Furthermore, it exhibits strong generalization capabilities, making it suitable for various water body detection tasks. Figure 7 presents a visualization of the prediction results produced by DMLU-Net and other segmentation networks on the LoveDA dataset. As illustrated, DMLU-Net demonstrates superior performance in handling complex backgrounds and extracting multi-scale water bodies. For instance, in the eighth and ninth lines of Figure 8, urban scenes featuring small rivers and ponds are depicted. The other models are more susceptible to interference from buildings and other ground objects, often leading to misclassification or missed detections. DMLU-Net effectively suppresses background noise and enhances the accuracy of water body extraction through the integration of its spectral-spatial attention module (SSAM) and dynamic upsampling module (DySample). The second and fifth lines in Figure 8 mainly demonstrate the model's ability to restore small water bodies. The test results on the two public datasets demonstrate that the method proposed in this study exhibits strong recognition capabilities. It is applicable to various types of water bodies, effectively distinguishes water-land boundaries, and accurately delineates small rivers and ponds. The integration of the DMLKA module enables the model to extract features from the original feature map through multiple channels and to adaptively allocate parameter weights, thereby capturing both local and global information more precisely. The introduction of dice loss enhances the model's sensitivity to small targets and minority classes, mitigating overfitting to dominant categories and reducing the occurrence of missed detections. Furthermore, the inclusion of the spectral-spatial attention module (SSAM) allows the model to effectively utilize water-specific spectral features, thereby improving the accuracy and robustness of water body extraction. Finally, the DySample module facilitates the accurate restoration of feature information to the original resolution.
# The Numerical Study for the Effect of Stiffness Matching on Wheel-Rail Curve Squeal Noise
## 2 Methodology
### Finite Element Complex Eigenvalue Analysis of Wheel-Rail System
The equivalent damping ratio \(\zeta_{\rm i}\) is commonly used to assess the stability of wheel-rail systems and is defined as follows:  
\[\zeta_{\rm i}=-\frac{2\sigma_{\rm i}}{|\omega_{\rm i}|} \tag{20}\]  
When \(\zeta_{\rm i}\) is less than 0, the smaller \(\zeta_{\rm i}\), the wheel-rail system has a greater possibility of self-excited vibration, that is, it is more likely to produce the curve squeal noise.
## 1 Introduction
However, in a scenario where multi-channel data is collected, especially when there is a certain correlation of data between channels, there is inadequate research on the use of neural networks in which the complete data of multiple channels is used for restoring missing data on a neighboring channel. The accuracy of recovering the data of one channel using neighboring channels should be further studied. To be specific, a set of data from a total of eight channels was collected, but the data from Channel 7 were missing. Then, the neural network was used to train the remaining complete data of multiple channels, thus recovering the missing data of Channel 7.