esa-sceva/satcom-chunk-collection huggingface dataset:
# Impact on the Quality of Life and Physiological Parameters in Patients with Diabetes Mellitus
## 3 Results
The non-parametric Mann-Whitney U test was used to study whether significant differences exist. For the \"emotional role\", \"mental health\" and \"health change\" dimensions, men have better HRQL (Table 3). In general, the male group showed better results in the questionnaire despite having a higher rate of complications derived from DM. To explore this point further, it was decided to study HRQL in the presence of different complications: \"presence of peripheral artery disease\", \"presence of neuropathy\" and \"presence of wounds\". A differentiated analysis was carried out, considering the dichotomy of presence or non-presence as an independent variable. Because most of the dimensions of the SF-36 questionnaire score were reduced in the presence of these complications (\(p<0.05\)), the analysis continued. The summarised results are presented in Table 4. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & **Wounds** & **Neuropathy** & **Fontaine (Between II and IV)** \\ \hline Women (15) & N (positive) * & 3 (15) & 3 (15) & 4 (15) \\ \cline{2-5}  & Percentage & 20.0\% & 20.0\% & 26.7 \\ \hline Men (35) & N (positive) * & 16 (35) & 15 (30) & 4 (33) \\ \cline{2-5}  & Percentage & 45.7\% & 42.9\% & 11.4\% \\ \hline \hline \end{tabular}  
* N (positive): A wound at least, deficient NTS, symptomatic Fontaine (>2).  
\end{table} 
Table 2: Presence of diabetic complications included in the study, segmented according to gender. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Variables**} & **Women** & **Men** \\  & **Mean (SD)** & **Mean (SD)** & **Significance (\(p\))** \\ \hline Total health & 54.2 \(\pm\) (17.5) & 63.77 \(\pm\) (20.21) & 0.073 \\ \hline Physical function & 66.33 \(\pm\) (21.25) & 65.14 \(\pm\) (26.91) & 0.966 \\ \hline Physical role & 60 \(\pm\) (44.12) & 60 \(\pm\) (39.85) & 0.938 \\ \hline Bodily pain & 54.67 \(\pm\) (26.72) & 59.2 \(\pm\) (30.75) & 0.595 \\ \hline General health & 52 \(\pm\) (13.47) & 62 \(\pm\) (20.41) & 0.094 \\ \hline Vitality & 57.33 \(\pm\) (22.67) & 58.71 \(\pm\) (27.02) & 0.164 \\ \hline Social function & 65.47 \(\pm\) (28.57) & 75.91 \(\pm\) (27.28) & 0.200 \\ \hline Emotional role & 37.67 \(\pm\) (4.55) & 69.43 \(\pm\) (42.32) & 0.028 \\ \hline Mental health & 56.27 \(\pm\) (17.60) & 72.83 \(\pm\) (21.32) & 0.003 \\ \hline Health change & 41.67 \(\pm\) (15.43) & 54.46 \(\pm\) (18.66) & 0.028 \\ \hline \hline \end{tabular}  
* Mann–Whitney U test. Statistical study of contrast between HRQL variables according to gender differentiation, for \(p\) value \(\alpha\) = 0.05.  
\end{table} 
Table 3: SF-36 questionnaire for HRQL dimension descriptive analysis and comparisons. The descriptive comparative analysis and subsequent non-parametric tests showed, in \"general health\", \"vitality\" and \"mental health\", more marked low values in the male group for peripheral artery disease. Only the \"physical role\" dimension had worse results in women.
# _In Silico_ Development of SARS-CoV-2 Non-Covalent Mpro Inhibitors: A Review
## 1 Introduction
### The Main Protease (Mpro) Enzyme of SARS-CoV-2
Domain I (residues 8-101) and domain II (residues 102-184) are responsible for the main enzymatic activity, containing the catalytic dyad composed of Cys145 and His41, located in the cleft between these two domains (P1 region) [20] as displayed in Figure 2B. Domain III (residues 201-306) supports stabilising the enzyme structure and aiding in substrate recognition. Figure 1: Schematic representation of the SARS-CoV-2 genome highlighting non-structural proteins, structural proteins, and accessory proteins [11]. Licenced under the CC-BY licence. SARS-CoV-2 and SARS-CoV share 294 amino acids out of 306 residues. The amino acid residues Phe140, Leu141, Asn142, His163, and Glu166 constitute the S1 subsite. Meanwhile, the His41-Cys145 catalytic dyad (Figure 2C), along with Ser144 and Glu143, form the S1\({}^{\prime}\) subsite. The S2 subsite includes His164, Asp187, Arg188, and Met49, Tyr54, while S3 comprises Leu167, Gln189, Met165, Thr190, and Gln192 [21]. To emphasise its critical role in viral replication and pathogenesis, Mpro has become an essential target of many existing vaccines and antiviral medicines [22]. A thorough understanding of the structure-activity relationships (SAR) within its active site subpockets (S1, S1\({}^{\prime}\), S2, and S3) is crucial for designing potent inhibitors (Figure 2D). These subpockets provide specific sites for molecular interactions that can be targeted to enhance inhibitor binding and efficacy. Overall, the structural and functional significance of Mpro is well established, reinforcing its status as a critical target for antiviral drug development against SARS-CoV-2 and its emerging variants.
## 4 Design of Virtual Adaptive Template SLA and Its Implementation
### Implementation  
We used the Ontopy platform to implement our prototype. According to [14] Ontopy is a free software Python module (GNU LGPL v3 license) for ontology-oriented programming and agile  
Figure 11: Virtual SLA template.  
ontology-based application development. The Python 3.4 language was chosen because it is a dynamic object language with multiple inheritance.  
Figure 12 describes the architecture of the Ontopy platform.  
We designed the ontology with Protege, a tool for Ontology Web Language, Decidable Language(_OWL/DL_) projects. We exported classes, instances, and relations into the Ontopy platform. We used Python 3.4 to implement a simple application prototype. Our implementation scenario checks the feasibility of the SLA evaluation virtual model. To this end, we defined three incidents of data and two incidents of service request acceptable per month as the SLA KPL_TARGET value. We simulated the data for the patient context with four files containing the temperature parameter. The temperature values in the files f1, f2, f3, and f4 are erroneous or aberrant values. The structure of the files is f (idpatient, temperature): f1(1,20), f2(2,55), f3(3,60), and f4(4,100). The program reads these files and calculates the number of incidents, which is 4, and checks that 4(IncidentDataNumber) > 3(KPI_TARGET value of the SLA). Then, the prototype triggers the SLA violation alert and the virtual template publishes all the SLA violation information. Figure 13 describes the results of this test with Python 3.4.  
For request incidents, we created two files, A1 and A2. Both files contain, respectively, the number of requests sent by the COPD patient to the emergency physician and the number of requests received by the emergency physician. The structure of the files is A (id, NumberRequest): A1(1,6); A2(2,3). The program reads the files and calculates the difference between the sent and received requests. Then the program compares this result to KPI-TARGET value extracted from the SLA. The system triggers the SLA violation alert and the dynamic model publishes these results.  
The Figure 14 presents a simple example of an incident request test.  
Figure 12: Architecture of Ontopy Platform [14].  
Figure 13: Violation incident data test.
## 4 Experiments  
### Datasets  
Courier orders for information extraction (COFIE) is a private dataset created by us, sourced from a courier company in Zhejiang province. It comprises 496 images of cross-border logistics courier orders, featuring 12 different layouts. Prior to the work presented in this paper, we utilized an object detection algorithm to obtain address area images from the courier order images. These images were then used to construct our dataset for visual text extraction. Figure 4 displays the complete images of courier orders. Figure 5 shows the cropped image of the address area based on the results of object detection.  
We annotated the information in the images using JSON files, covering 11 categories such as name, phone number, province, city, and postal code, amounting to a total of 3506 entries. The detailed statistics for each category are provided in Table 2. In our experiments, we divided the dataset into training and test sets in an 4:1 ratio.  
\begin{table}
\begin{tabular}{l} \hline \hline
**Module and Some Parameters** \\ \hline BART (decoder\_layers = 4) \\ Multi-head attention (num\_heads = 8) \\ GRU (num\_layers = 2, hidden\_size = 512, bidirectional = True) \\ Conv1d (filters = 4, kernel size = 3, padding = 1, dropout = 0.4) \\ Swin Transformer (embed\_dim = 128, layer numbers = [2, 2, 14, 2]) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Special parameters in our model.  
Figure 4: Complete images of courier orders.  
Scanned receipts OCR and key information extraction (SROIE) [17] is an open-source scanned receipt dataset, consisting of 626 training images and 347 testing images. In this dataset, some receipts exhibit poor paper quality, inadequate ink and printing quality, low-resolution scanning with distortion, folded invoices, an abundance of unnecessary interfering texts in complex layouts, and long texts with small font sizes.  
The Consolidated Receipt Dataset (CORD) [16] is a publicly available dataset comprising 800 training images, 100 validation images, and 100 testing images. It is an annotated dataset consisting of receipt images, and the builders have meticulously reviewed the information on the receipts, applying blurring techniques to sensitive information. The dataset exhibits a complex hierarchical structure, consisting of eight superclasses: store, payment, menu, void menu, subtotal, void total, total, and others. These eight superclasses further branch into 54 subclasses. For example, the \"menu\" superclass includes 16 subclasses, encompassing menu name, quantity, unit price, discounted price, sub-menu, and more.
## 1 Introduction
However, the use of phenol and sulfuric acid is potentially dangerous for operators. Another possible alternative is to use trifluoroacetic acid (TFA), which presents lower acidity compared to sulfuric acid, and is also able to hydrolyze inulin and fructan molecules [21]. Chromatographic methods are widely adapted for quantification of chemical or biochemical substances because of its high precision and stability compared to colorimetric methods. After hydrolysis of all fructans to fructose and glucose, inulin quantification can be completed by measuring these sugars by high-performance anion-exchange chromatography with pulsed-anperometric detection (HPAEC-PAD) and high-performance liquid chromatography with refractometer detection (HPLC-RI) [21]. High performance liquid chromatography (HPLC) equipped with evaporative light scattering detector (ELSD) is widely used for quantification of sugar in samples [22; 23]. However, to the best of our knowledge, there are no reports in the available literature evaluating the application of HPLC-ELSD method for inulin determination. Therefore, the aim of this work was to extract inulin assisted by UAE and to establish an inulin determination method based on the combination of TFA-assisted inulin hydrolysis and HPLC-ELSD analysis. The influence of the main extraction parameters such as ultrasound power and extraction temperature, were also studied. The effects of the hydrolysis parameters were evaluated to obtain the optimal hydrolysis conditions. In addition, the validation (linearity, precision, recovery, and stability) of this combined method was carried out.
## 4 Conclusions  
Coupled metabolomics and flavoromics can be used to identify the main metabolites and flavor compounds that change in sacha inchi seeds during germination in order to determine the best nutritional values and guide the improvement of pharmacological products or functional foods made from sacha inchi seeds. In particular, the later stage (6-8 DAG) should be applied as novel alternative plant-based protein, functional foods, and dietary supplements, because they contain significant amounts of amino acids and higher antioxidant activity. FAMEs (palmitic, stearic, linoleic, linolenic, and oleic acids) correlate with ungerminated seeds. Sugars and amino acids (fructose, glycine, isoleucine, proline, and leucine) correlate with 8 DAG seeds. Flavor compounds acetoin and (E,E)-3,5-octaiden-2-one have the highest concentration in ungerminated seeds. Hexanal, (E)-2-hexanal, and 1-octen-3-ol have the highest concentration at 4 DAG and 2-octen-1-ol and hexanoic acid at 8 DAG. The changes in these flavor compounds are biomarkers of flavor indicators in sacha inchi seeds. Amino acids, organic acids, TPCs, and antioxidant activity are higher in  
Figure 7: Antioxidant activity (ABTS and DPPH assays) of sacha inchi seeds at different germination times (\(p\leq 0.05\)). ABTS, 2,2-azobis(3-ethylbenzothazoline-6-sulfonic acid; DPPH, 2,2-diphenyl-1-picrylhydrazyl; DAG, days after germination. Means followed by different letters are significantly different (\(p\leq 0.05\)) according to the [PERSON]’s new Multiple Range Test.  
germinated sacha inchi seeds than in ungerminated seeds. These findings could help in understanding nutritional changes during germination.  
Conceptualization, K.N.J. and K.K.; methodology, K.N.J. and K.K.; software, K.K.; validation, K.K.; formal analysis, K.K.; investigation, K.K.; resources, K.N.J., K.K., Y.L. and K.V.; data curation, K.K.; writing--original draft preparation, K.K.; writing--review and editing, K.N.J., K.K., Y.L. and K.V.; supervision, K.N.J.; funding acquisition, K.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Authors are greatly thankful to the Faculty of Science and Technology of Huachiew Chalermprakiet University and Faculty of Agro-Industry, Kasetsart University for research support. Kasetsart University Research and Development Institute is acknowledged for English proofreading service and support.  
The authors declare that they have no conflict of interest.
## 4 Results and Discussion
### Performance Analysis of Image Communication System
#### 4.2.2 Evaluation of Communication Systems over AWGN Channel
Overall, the 16-QAM system with MMSE channel estimation had the best image quality retention and 1024-QAM with LS had the worst image quality retention throughout the experiments in terms BER, PSNR, and MS-SSIM evaluation metric. For visual analysis, Figure 6 shows three example images from the dataset and the distortions from different communication systems when transmitted over AWGN channel. Figure 5: Performance analysis of various OFDM systems in terms of (**a**) BER, (**b**) PSNR, and (**c**) MS-SSIM with respect to various E\({}_{\text{b}}\)/N\({}_{0}\) under AWGN channel. From the visual inspection, the noise increases across higher order M-QAM; however, with increase in \(\rm E_{b}/N_{0}\), the image quality still improves significantly for lower order M-QAM (16-QAM) compared to higher order M-QAM (128-QAM and 1024-QAM). Although the images may be corrupted at lower \(\rm E_{b}/N_{0}\), the information in the region of interest (ROI) remains visible even with additional noise in the surrounding regions due to the way the traffic sign are designed with different color contrast [76; 77]. In terms of different channel estimation techniques, there is no visible distinction between the LS and MMSE as the images are almost visually identical in comparison to the two channel estimation techniques.
## 1 Introduction  
Silicon carbide (SiC) has outstanding mechanical properties and chemical stability at high temperatures [1; 2; 3; 4] as well as remarkable electronic and optical properties [5; 6]. For this reason, SiC ceramics have found practical applications in very harsh conditions [1; 2]. Recently, porous SiC or SiC composite microspheres have gained increasing interest as new promising materials in many fields. Spherical particles confined in dimensions within the micrometer range exhibit superior properties to their bulk counterparts. They provide good flowability, high packing density and easy access to particle surfaces [7; 8; 9; 10]. In addition, hierarchical porosity gives the materials a high surface area and facile infiltration of liquids to their pores [7]. Porous SiC-based microspheres could be used in many fields, such as: catalysis [11; 12], hot gas filtration [13], thermal insulation [10], sensors [14], absorbers [15; 16], molten metal filtering [17], membranes [18; 19], and tissue engineering [20]. The interest has recently been focused on SiC composites as potential semiconductor materials for absorption of electromagnetic waves [21], anodes of lithium-ion batteries [22] and supercapacitors [23].  
Polymer-derived SiC ceramics and their composites are obtained from various precursors, such as polycarboxosilanes [7; 24; 25], polysilazanes [8; 9; 10; 26], polysiloxanes and silicone resins [27; 28; 29; 30; 31; 32]. Polysiloxanes are commercially accessible and less expensive than other SiC precursors. The method used to produce silicon carbide ceramics and their composites from polysiloxane precursors is based on the carbothermal reduction of silicon oxycarbide (SiC) formed in the first stage of the polysiloxane ceramization [27; 32; 33]. As SiO, CO and CO\({}_{2}\) gas products are generated in this process a porous structure of the ceramic material is formed [25; 34]. A porous structure can also be produced by adding a dispersed sacrificial phase [7; 31; 33]. Carbothermal reduction requires a high temperature at which preservation of the spherical shape of particles may be a problem. A few years ago, we developed the synthesis of polysiloxane microspheres from polyhydromethylsiloxane (PHMS), which proved to be good precursors for the fabrication of SiC ceramic microspheres [35; 36]. Now we found that it is possible to produce spherical SiC particles by heating the modified PHMS derived microspheres for a long time in argon at 1600 \({}^{\circ}\)C until oxygen is removed from this material by carbothermal reduction. Thus, porous ceramic SiC microspheres containing a controlled free carbon content can be obtained from precursor polysiloxane microspheres in reasonable yield in one step. A significant mesoporosity and/or macroporosity, as well as high SiC crystallinity, may be developed in these spherical particles.
## 4 Discussion
### Reduction of Power Line Interference
As for the electric flux induced by the magnetic field of the power line, we tried to minimize the area of the loop enclosed by the electrode wires during the experiment. For example, we kept the wires as short as possible and made sure that the two electrode cables were as close as possible by twisting them together. In this way, the effective area exposed to the power line magnetic field was largely reduced and the effects of the loop flux induced by the magnetic field were also rather limited. The limited power-line induced flux through the loop could be further confirmed by our experimental observations from Figures 6-9 for the case of Active-Shield method. Besides, it should be noted that although the electrodes of the four channels were attached as closely as possible in Experiment 2 and Experiment 3, there were still slight differences in the electrode positions, as shown in Figure 4b,c. Therefore, the amplitude of the EOG and EMG signals of four channels might have shown slight differences, as observed in Figures 7a and 8a. Moreover, although the EOG signal frequency is in the range of 0.1-38 Hz, which has no overlap with the spectrum of power line interference, common software bandpass filter methods cannot obtain satisfactory filtered  
Figure 12: Equivalent circuit of one electrode for the Active-Shield method: (**a**) equivalent circuit of the amplifier and the capacitive coupling introduced by the mains power, (**b**) the equivalent voltage divider circuit of the amplifier. results in routine practices. The reason for this is that the amplitude of the 50 Hz interference is much stronger than EOG signal, and the limited stop-band attenuation at 50 Hz is not enough to bring power line interference below the noise floor level. Hence, our experimental results obviously show that the proposed shielded drive method would potentially be effective for the acquisition of multiple biopotential signals.
# Toward Vulnerability Detection for Ethereum Smart Contracts Using Graph-Matching Network
## 4 Methodology
### Graph Similarity Learning  
Compared to traditional deep neural network models such as the convolutional neural network (CNN) and recurrent neural network (RNN), the graph neural network (GNN) is more applicable to handle graph structured data, whereas the graph matching network (GMN) enhances it through a cross-graph attention-based matching mechanism. In this paper, we use a GNN for graph embedding, and jointly apply a GMN to learn the similarity between each pair of contract graphs.
## 3 Delay Spread, Doppler Shift, and Doppler Spread
### Delay Spread  
Recall (5) and (9) at time \(t\); the propagation time difference between the \(i\)-th RIS and direct links is  
\[T_{0i}(t)=\max_{i_{in},i_{n}}\bigg{\{}\tau_{i_{in},i_{n}}(t)+\frac{\phi_{i_{in },i_{n}}(t)+\gamma_{i_{in},i_{n}}}{2\pi f_{c}}\bigg{\}}-\tau_{0}(t), \tag{15}\]  
where \(\tau_{0}(t)\) and \(\tau_{i_{in},i_{n}}(t)\) are the instantaneous time delays for direct and cascaded links, respectively. \(\phi_{i_{in},i_{n}}(t)\) is the phase shift for the \((m,n)\)-th element of the \(i\)-th RI, and \(\gamma_{i_{in},i_{n}}\) is the RIS HWI. The difference of propagation time across the \(i\)-th RIS itself, \(T_{\text{RIS}i}(t)\), is defined as  
\[T_{\text{RIS}i}(t)=\max_{i_{in},i_{n}}\bigg{\{}\tau_{i_{in},i_{n}}(t)+\frac{ \phi_{i_{in},i_{n}}(t)+\gamma_{i_{in},i_{n}}}{2\pi f_{c}}\bigg{\}}-\min_{i_{in },i_{n}}\bigg{\{}\tau_{i_{in},i_{n}}(t)+\frac{\phi_{i_{in},i_{n}}(t)+\gamma_{i_ {in},i_{n}}}{2\pi f_{c}}\bigg{\}}. \tag{16}\]  
In other words, \(T_{\text{RIS}i}(t)\) is the maximum value of the instantaneous delay difference among the total \(KMN\) cascaded links. Similarly, the delay spread of the \(i\)-th RIS and the direct link is the maximum difference in propagation time overall between the two significant transmission links [14; 42], i.e.,  
\[T_{i}(t)=\max\bigg{\{}T_{0i}(t),T_{\text{RISi}}(t)\bigg{\}}. \tag{17}\]  
Note that \(\tau_{0}\leq\tau_{i_{m},i_{n}}\); thus, \(T_{i}(t)=T_{0i}(t)\). Therefore, at time \(t\), the delay spread for the \(K\) RISs is  
\[T(t)=\max_{i}\bigg{\{}T_{0i}(t)\bigg{\}},i=1,2,\ldots,K. \tag{18}\]  
**Remark 1**.: _From (15) and (18), it can be seen that the RIS HWI \(\gamma_{i_{m},i_{n}}\) increases the delay spread of the whole system. In particular, the upper bound of \(T(t)\) is_  
\[T(t)\leq\max_{i_{m},i_{n}}\bigg{\{}\tau_{i_{m},i_{n}}(t)+\frac{\phi_{i_{m},i_{ n}}(t)}{2\pi f_{c}}\bigg{\}}-\tau_{0}(t)+\frac{a_{\text{HWI}}}{2\pi f_{c}}. \tag{19}\]  
_Thus, the delay spread can be maximally increased by \(a_{\text{HWI}}/(2\pi f_{c})\). In other words, the delay spread becomes large when the RIS HWI is severe. However, the transceiver HWI has no impact on delay spread since it is additional thermal noise._