esa-sceva/satcom-chunk-collection huggingface dataset:
# Nanomechanical Characterization of Bacterial Polyhydroxyalkanoates Using Atomic Force Microscopy
## 3 Results
### Elastic Modulus Measurements in PHA  
Nanomechanical characterization was carried out on extracted and fractionated PHA. The elastic modulus of the extracted PHA was calculated with force-distance curves data fitting. Table 1 lists elastic moduli extrapolated for the extracted PHA along with the average composition, molecular weights distribution and thermal properties.  
The elastic properties of the tested materials are very similar, except the PHA produced with a substrate of 100:0 HAc:HPr. P(3 HB-co-2% 3 HV) showed an elastic modulus of 1623 \(\pm\) 172 MPa, while all the other PHA samples showed lower values.  
The PHA produced with a substrate composition ranging from 60:40 to 0:100 HAc:HPr exhibited similar elastic responses to the indentation tests: elastic moduli ranged between 528 \(\pm\) 62 MPa and 885 \(\pm\) 176 MPa, without a specific trend with the HV content, and with an average value of 702 \(\pm\) 145 MPa.
# Efficient Layered Parallel Architecture and Application for Large Matrix LDPC Decoder
## 5 Conclusions  
In this paper, we propose an area efficient LDPC decoder without loss of performance. In the hardware implementation process, this paper completed the overall circuit design of the decoder with the goal of improving area efficiency and ensuring decoding performance and built an FPGA-based decoding performance verification platform. We partially adopted block-parallel processing so that the decoder can update up to three blocks per cycle at the same time, which greatly reduces the iteration time and improves the throughput of the decoder. By adjusting the update order of matrix block, the storage space reuse is realized, and the utilization rate of hardware resources and area efficiency is improved. The performance loss of line-by-line check is solved by using the full parity check early-termination decoding method. Finally, we add an additional hard-decision memory to solve the decoding oscillation caused by overflow errors.  
By parallel processing of three submatrices and storage reuse of node information, 9.864 Gbps was achieved based on TSMC65 nm synthesis with 800 MHz circuit frequency in and area of 2.61 mm\({}^{2}\), with about two to four times TAR improvement. By proposing a mechanism of spare decision storage to avoid errors caused by quantization overflow of the decoder at an input BER of \(2.3\times 10^{-2}\) (SNR about 3.72 dB), the output BER was lower than \(10^{-12}\).  
\begin{table}
\begin{tabular}{l l l} \hline
**BER** & **Noise (\(\sigma\))** & **SNR (dB)** \\ \hline \(2.30\times 10^{-2}\) & \(5.0115\times 10^{-1}\) & 3.7246 \\ \(2.35\times 10^{-2}\) & \(5.0345\times 10^{-1}\) & 3.6849 \\ \(2.40\times 10^{-2}\) & \(5.0572\times 10^{-1}\) & 3.6457 \\ \(2.45\times 10^{-2}\) & \(5.0798\times 10^{-1}\) & 3.6071 \\ \(2.50\times 10^{-2}\) & \(5.1021\times 10^{-1}\) & 3.5690 \\ \(2.55\times 10^{-2}\) & \(5.1243\times 10^{-1}\) & 3.5313 \\ \(2.60\times 10^{-2}\) & \(5.1463\times 10^{-1}\) & 3.4940 \\ \(2.70\times 10^{-2}\) & \(5.1899\times 10^{-1}\) & 3.4209 \\ \hline \end{tabular}
\end{table}
Table 4: BER and corresponding \(\sigma\) and SNR.  
Figure 6: BER/FER performance.  
**Author Contributions:** Methodology and investigation, J.W.; methodology, writing--original draft, J.Y.; validation, G.Z.; software support, X.Z.; project administration and funding acquisition Y.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Key R&D Program of China under No.2019 YFE0120700, the National Natural Science Foundation of China under Grant 62274041.  
**Data Availability Statement:** The authors confirm that the data supporting the findings of this study are available within the article.  
**Conflicts of Interest:** The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# Design and Applications of GLANCE: GLanceable Alarm Notification for a User Centered Experience
## 2 Method
### First Macro-Phase: The GLANCE Project
After the analysis of virtues and flaws of traditional systems, and after the context analysis, Univaq and Technolabs researchers agreed on studying and evaluating a novel fault notification approach based on a peripheral display providing severity-based notifications of incoming alarms including glanceable information on both individual alarms and network overview, small enough to be possibly visualized on (possibly ad-hoc hand-held or wearable) mobile devices. This reflection phase was actually the entrance of the cyclical process conducted under the theoretical premise that a shift of paradigm towards peripheral displays could improve operators' work. The working group conducting the research included three people from Univaq (one senior researcher and two junior researchers with a background in HCI) and two people from Technolabs (one senior electronic engineer with a background in TLC network management and one senior electronic engineer with a background in TLC network management and Information Visualization). Technolabs represented the problem-solving interest, while Univaq represented the research interest. It was agreed that the Univaq researchers had to guide the overall process while the Technolabs people had to be involved as domain experts/stakeholders and to assess design choices. In summary, the elements of the GLANCE project can be summarized as in Table 1.  
Figure 2: State diagram of an alarm. As to project and research objectives, though presented hereinafter all together for the sake of a clear presentation, they were actually the result of continuous reflection and refinements throughout the iterations of the cyclical process. In order to investigate the three identified general research questions, Q0 \"Can the fault notification component of a network monitoring tool be defined according to a peripheral display paradigm?\", Q1 \"To which extent is it possible to offer a glanceable overview along with information on individual faults?\" and Q2 \"Can results achieved for TLC network be generalizable to a more general context of information coming from heterogenous sources?\", the problem objective (PO) and the research objective (RO) for the two parallel cycles, along with their intermediate goals, were defined as in Table 2.  
According to the specification of problem and research objectives, it was agreed that GLANCE had to be developed through an evolutionary design approach starting from the simplest scenario in which we have only one active alarm, successively moving to the scenario with multiple active alarms to be separately addressed in the first two iterations of the cyclical process (on both the research and problem-solving interest cycles), and a third iteration would then be focused on the generalizability of results (mostly on the research interest cycle), as summarized in Table 3.
## 4 Discussion
### Study's Limitations and Future Directions  
Finally, it is worth mentioning that despite the very promising results, the low sample size can be considered a limitation of the study. In fact, the classification outcome, might increase its performance with a larger study sample, relying it on a multivariate analysis approach. Although the sample size of the study could be considered rather small, the classification was performed implementing a leave-one-out cross-validation procedure, thus basically evaluating the out-of-sample performance. Hence, the results obtained are indeed generalizable. Increasing the sample numerosity may produce a further improvement of the classifier's performance by decreasing a possible in-sample overfitting issue. Moreover, a larger sample size will allow analyzing the effect of gender and age on the classification performance. Indeed, such factors affect the skin vasoconstriction /vasodilation capacity. Therefore, a future purpose is to validate the technique on a larger sample of participants. Furthermore, the recent introduction of low-cost IR cameras can increase the availability of thermal IR imaging. Considerations on the use of such low-cost thermal technology for RP patients classification are already addressed in the literature [62; 63]. In addition, the introduction of such an automated procedure based on the recording of a single IR image of the hands at rest, will possibly overcome the implementation difficulties referred to by [PERSON] et al. [64]. Besides, it is likely that thermography may become more widely used given the increased accessibility of the equipment. Both these aspects could pave the way toward the application of the proposed model in everyday clinical practice. However, in this perspective, it is worth noticing that during thermal imaging measurements, the environmental conditions must be controlled and standardized as much as possible to minimize physiological variability.
# Designing Audio Equalization Filters by Deep Neural Networks
## 6 Results
### Sensitivity to Head Movements  
Small head movements may result in a degradation of the equalization performance. For this reason, we assessed the validity of the proposed approach in response to small and large head movements. We analyzed the frequency response at three additional points: PM1 and PM2 (small head movement) and PM3 (large head movement). Their frequency response is shown in Figure 8, while their \(\overline{MSE}\) and \(\overline{\sigma}\) are presented in Table 5, and compared to the one at the M2 microphone, for reference. In line with theory, the error tends to rise for high frequencies, for which the wavelength is shorter or of the same order of magnitude as the distance between microphone M2, however, in the low end the response is almost flat.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Setup**} & \multicolumn{3}{c}{**CNN**} & \multicolumn{3}{c}{**FD \(\beta=0.1\)**} \\ \cline{2-6}  & **Conf** & \(\overline{MSE}\) & \(\overline{\sigma}\) & \(\overline{MSE}\) & \(\overline{\sigma}\) \\ \hline Binaural & \#1 & \(6.19\cdot 10^{-5}\) & 0.035 & 0.05 & 1.21 \\
4 seats & \#1 & \(5.7\cdot 10^{-4}\) & 0.106 & 0.15 & 1.95 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Audio equalization results for the Jeep Renegade with binaural microphones and four microphones (one per seat). The FIR order is 1024.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(\beta\) & \(\overline{MSE}\) & \(\overline{\sigma}\) \\ \hline \(10^{-4}\) & 0.123 & 1.83 \\ \(10^{-3}\) & 0.118 & 1.82 \\ \(10^{-2}\) & 0.108 & 1.81 \\ \(10^{-1}\) & 0.108 & 1.93 \\ \(1\) & 0.281 & 2.71 \\ \(10\) & 0.686 & 4.2 \\ \(100\) & 0.937 & 5.09 \\ V-shaped & **0.101** & **1.829** \\ U-shaped & 0.124 & 1.86 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Effect of the parameter \(\beta\) on the performance. The V-shaped configuration refers to a frequency-dependent \(\beta\) with a minimum of \(10^{-4}\) at 1 kHz and maxima of \(10^{-1}\) at DC and Nyquist, varying linearly on a dB scale. The U-shaped configuration takes a value of \(10^{-4}\) in the range 100 Hz–10 kHz and one elsewhere. Best results for each column are highlighted in bold.  
This issue is common to many widely used offline equalization algorithms, including that in Reference [33]. These algorithms can be complemented with adaptive solutions to tune the filters in real-time. Several solutions have been previously proposed, based, for example, on Kalman filtering and Steepest Descent to adaptively track the frequency response [25] or on the virtual microphone technique [37]. The proposed method could also be expanded to equalize a broader area using multiple microphones concentrated around a volume of space surrounding the listener's head.
# U-SSD: Improved SSD Based on U-Net Architecture for End-to-End Table Detection in Document Images
## 4 Experiment  
### Dataset and Data Pre-Processing  
The training and testing dataset used in this study were provided by a Taiwanese Law Firm, with a total of 1378 tabular data, including diagnostic certificates, accident site maps, and preliminary accident analysis tables. The data are image files, which are mostly composed of large object tables and a few text areas, include scanned images and filmed images. The images were shot from different angles and under different lighting. The cutting ratio of training, validation, and test data was 8:1:1. In addition, open dataset TableBank [53], Github open dataset [54], and ICDAR13 [55] were used for comparison. TableBank, provided by Microsoft Research Asia, is an open dataset for table detection and recognition, which includes a large number of Word documents and LaTex documents consisting of text areas and charts, for a total of 410,000 table data. The Github open dataset, which contains 400 table file images, is publicly provided by Sgrpanchal31 on Github, and is composed of text areas and charts, and most of the tables have no outer frame. The ICDAR13 dataset, which is used for table detection and structure recognition, is provided by the International Conference on Document Analysis and Recognition, and is composed of PDFs, and documents must be converted into images for use in the research model. Data from the EU and US governments consists of text areas and charts, which contain 150 tables and a total of 238 charts.  
Figure 3: The convolution and dilated convolution.  
The quality of data determines the quality of model training, thus, in order to make a model more accurate, data are often transformed and pre-processed before model training, and image size, rotation, and color conversion are often used. As the dataset of this study was symmetrical and neat, the influence of the commonly used methods on this study was limited. According to [PERSON], [PERSON] et al. [15], this paper proposed data conversion for document images, including the text, tables, and blank areas in the document; therefore, the model can better understand the data by thickening the text areas and reducing the blank areas. This paper adopted the image dilation method for image transformation, and the black part in the original image was dilated. Before the dilation, the original image was converted into a gray scale image, binarization was carried out, and the dilation image was generated, as shown in Figure 4.
## 5 Results and Discussion
### Comparison with Machine Learning Methods  
In order to have a fair comparison, the performance of the FSHS-GMDH algorithm is investigated using the train and test data. In doing so, the MLP, GMDH, and FSHS-GMDH algorithms are firstly trained to produce the optimum models. Then, obtained from the training phase, the best setup of each algorithm is employed to estimate the shear wave velocity, according to the test data samples.  
Table 3 represents the statistical results of shear wave velocity estimation. The results are presented in \(\mathrm{R}^{2}\), RMSE, and MSE for the train and test data. It can be seen from Table 3 that the proposed FSHS-GMDH algorithm provides a better performance than the MLP and the original GMDH methods. The FSHS-GMDH algorithm provides the lowest values of RMSE (43.36), MSE (1880.23), and the highest value of \(\mathrm{R}^{2}\) (0.9728) for the train data. Furthermore, according to the results for the test data, the best performance is achieved by the FSHS-GMDH model with the lowest values of MSE (3116.92), RMSE (55.82), and the highest value of \(\mathrm{R}^{2}\) (0.9514). However, the original GMDH model shows better performance for the train data; there is no significant difference between the performance of the original GMDH and MLP models for the test data.
## Interferometry processing
#### 4.2.1 Water Mask
A Speckle-Lee filter with a 5-pixel by 5-pixel window was adopted, resulting in a unique valley-hill pattern in the histogram that represented a better distinction between water and non-water surfaces. Additionally, a normalization between the incident angles were performed. In order to identify a proper threshold, a set of third-order polynomials was employed to fit the histogram in a manner of moving steps. The reason for this was that the third-order polynomial had a shape that best described the histogram of the backscattering coefficient after de-specking and was easier to identify the turning points compared to the higher order polynomials. Each pixel in the SAR image was identified as either land or water after the threshold was established, depending on whether its value was smaller or greater than the threshold. Through an iterative method that maximized between-class variations while simultaneously minimizing within-class variance, the threshold value was established. Finally, using the primary input pre-processed S1 GRD dataset and splitting the training set into water and non-water areas, a supervised classification (random forest) was carried out in the SNAP v.8.0.0 to improve the mapping of water areas.
## 5 Test Results and Analysis
### Positioning Deviation  
After the _Camellia oleifera_ fruit is detected by the YOLOv7 algorithm, we need to further determine the location of the picking point. A classical image processing algorithm and mathematical geometry are used to locate the picking point. In this work, 10 sample images are selected when picking a location point. A centroid calculation method can be used, and then the position deviation between the centroid and the center of the YOLO recognition frame is statistically calculated. A total of 10 sample images are used for the experiment  
Figure 11: Comparison of detection results.  
that determines the point positioning error at the centroid of _Camellia oleifera_ fruit. Table 2 shows that the pixel error value of the image is below 10.  
The analysis in Table 2 demonstrates that the average deviation position of the image extraction centroid point and YOLO identification frame center point is 2.86; the maximum deviation is 6. According to the statistical data, the position deviation of _Camellia oleifera_ fruit detected in this study is relatively small. When the _Camellia oleifera_ fruit outline accuracy is lower, the positioning accuracy of the _Camellia oleifera_ fruit may be relatively large; the pixel difference also affects the actual distance measurement. When the image resolution is lower, the difference between the pixel distance and the actual distance is larger. In general, this method can accurately locate the centroid of _Camellia oleifera_ fruit.
# Optical OFDM for SiPM-Based Underwater Optical Wireless Communication Links
## 3 General Assumptions  
Consider a perfectly aligned link and assume perfect time synchronization between the Tx and the Rx; also, neglect the oceanic turbulence assuming negligible temperature and salinity gradient and sea currents [19; 20]. At the Tx side, an LED or an array of LEDs is used as emitting device. Consequently, intensity modulation (IM) is used at the Tx with direct detection (DD) at the Rx. It is also assumed that the Rx has a perfect knowledge of the aggregate channel impulse response (CIR), i.e., including the impulse responses of the Tx, the aquatic channel, and the Rx. The general models of the Tx and the Rx are specified in the following.
# Efficient and Accurate Hemorrhages Detection in Retinal Fundus Images Using Smart Window Features
###### Abstract  
Diabetic retinopathy (DR) is one of the diseases that cause blindness globally. Untreated accumulation of fat and cholesterol may trigger atherosclerosis in the diabetic patient, which may obstruct blood vessels. Retinal fundus images are used as diagnostic tools to screen abnormalities linked to diseases that affect the eye. Blurriness and low contrast are major problems when segmenting retinal fundus images. This article proposes an algorithm to segment and detect hemorrhages in retinal fundus images. The proposed method first performs preprocessing on retinal fundus images. Then a novel smart windowing-based adaptive threshold is utilized to segment hemorrhages. Finally, conventional and hand-crafted features are extracted from each candidate and classified by a support vector machine. Two datasets are used to evaluate the algorithms. Precision rate (\(P\)), recall rate (\(R\)), and F1 score are used for quantitative evaluation of segmentation methods. Mean square error, peak signal to noise ratio, information entropy, and contrast are also used to evaluate preprocessing method. The proposed method achieves a high F1 score with 83.85% for the DIARETDB1 image dataset and 72.25% for the DIARETDB0 image dataset. The proposed algorithm adequately adapts when compared with conventional algorithms, hence will act as a tool for segmentation.  
adaptive thresholding; classification and feature extraction; smart window features; hemorrhage detection +
Footnote †: journal: Applied Sciences
# Curcumin Innovative Delivery Forms: Paving the Yellow Brick Road' of Antitumoral Phytotherapy
## 4 Innovative Curcumin Formulations and High-Performance Delivery Forms
### Inhalable Curcumin Formulations  
#### 4.3.1 Dry Powder Inhaler for Pulmonary Delivery  
Curcumin delivery by absorption through the pulmonary region aims at achieving high bioavailability with reduced side effects. Curcumin inhalable powders were obtained using carbon dioxide as anti-solvent and polyvinylpyrrolidone and hydroxypropyl-\(\beta\)-cyclodextrinrin (HP\(\beta\)CD) as excipients [199]. The particles presented good aerodynamic properties and cytotoxicity against the H1299 human non-small cell lung carcinoma line, with an IC\({}_{50}\) of 17 uM, but also some toxicity on normal lung cells of the MRC-5 line (IC\({}_{50}\) of 29 uM).
# Vision-Based Damage Detection Method Using Multi-Scale Local Information Entropy and Data Fusion
## 3 Numerical Investigations of the Methodology
### Computational Complexity Analysis  
A computational complexity analysis of the proposed damage detection method is performed. Considering that the computational consumption is mainly focused on the cascading convolution operations during the multi-scale mapping process, the variation of the average computational time with the total number of scales and the total number of measurement points is investigated. Firstly, the total number of scales is varied from 10 to 100, while the total number of measurement points is fixed at 1000. Subsequently, the total number of measurement points is varied from 100 to 1000, while the total number of scales is maintained fixed at 100. The computations are performed on the same terminal (Intel(r) i5-10500 CPU) and the results of the computational complexity analysis are shown in Figure 16.  
Figure 15: Monte Carlo experiments for ablation study on different damage degrees: (**a**) single damage case, (**b**) multiple damage case.  
The average calculation time increases almost linearly with the total number of scales and measurement points, but the linear increase is not significant. For the strict case where the total number of scales is 100 and the total number of measurement points is 1000, the average calculation time is 0.7961 s, which is still within the acceptable range for practical real-time applications.