esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Relapsing Disease  
The clinical manifestations of the 88 patients assessed with US due to possible signs or symptoms of relapsing disease are represented in Table 2. In patients with a confirmed relapse of GCA, the most frequent manifestations were PMR (32%) and headache (28%). Major relapses including ischemic symptoms such as jaw claudication were really rare (2.2%). There were no cases of visual loss during relapsing disease. Relapses were characterized by an isolated rise in inflammatory markers in 10 patients; nevertheless, inflammatory markers were within the normal ranges in 21.7% of relapsing cases.  
US was positive in 31 (67%) of patients with a confirmed relapse. Mean disease duration of patients with a confirmed relapse was 65 \(\pm\) 79 months. US was negative in all patients without a confirmed relapse of GCA.  
US findings were found at the level of the TA in 21 relapsing patients and at the level of the AX in 12 patients. Two had both TA and AX halo signs. Bilateral halos were found in 13 (62%) patients with TA involvement and 3 (25%) with AX involvement.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Relapse Confirmed** & **Relapse Not Confirmed** & **Overall (N = 88)** & \(p\) \\  & **(N = 46)** & **(N = 42)** & **Overall (N = 88)** & \(p\) \\ \hline Headache & 13 (28\%) & 19 (45.2\%) & 32 (36\%) & 0.095 \\ \hline Jaw claudication & 1 (2.2\%) & 0 & 1 (1.1\%) & 0.0336 \\ \hline Tongue claudication & 0 & 0 & 0 & na \\ \hline Blurred vision & 1 (2.2\%) & 2 (4.7\%) & 3 (3.4\%) & 0.520 \\ \hline Visual loss & 0 & 0 & 0 & na \\ \hline Bilateral visual loss & 0 & 0 & 0 & na \\ \hline Scalp tenderness & 0 & 1 (2.3\%) & 1 (1.1\%) & 0.304 \\ \hline Polymylgia rheumatica & 15 (32\%) & 0 & 15 (17\%) & 0.0001 \\ \hline Systemic symptoms & 5 (11\%) & 4 (9.5\%) & 9 (10\%) & 0.818 \\ \hline \(\geq\)2 systemic symptoms & 2 (4.3\%) & 1 (2.3\%) & 3 (3.4\%) & 0.604 \\ \hline Isolated raised inflammatory markers & 10 (21.7\%) & 13 (30.9\%) & 23 (25.8\%) & 0.329 \\ \hline Normal inflammatory markers & 10 (21.7\%) & 7 (16.6\%) & 17 (19.1\%) & 0.547 \\ \hline Fever & 2 (4.3\%) & 2 (4.7\%) & 4 (2.3\%) & 0.928 \\ \hline Weight loss & 4 (8.6\%) & 2 (4.7\%) & 6 (6.7\%) & 0.418 \\ \hline Arthritis/arthalgia & 1 (2.2\%) & 3 (7.1\%) & 4 (2.3\%) & 0.273 \\ \hline Non-productive cough & 0 & 1 (2.3\%) & 1 (1.1\%) & 0.304 \\ \hline Mean ESR value (mm/h) & 36 \(\pm\) 24 & 37 \(\pm\) 30 & 36 \(\pm\) 24 & 0.863 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Clinical presentation of patients with possible relapse assessed at the fast-track clinic.
###### Abstract  
Change detection in biomedical signals is crucial for understanding physiological processes and diagnosing medical conditions. This study evaluates various change detection methods, focusing on synthetic signals that mimic real-world scenarios. We examine the following three methods: classical statistical techniques (thresholding based on mean and standard deviation), Support Vector Machine (SVM) classification, and time-frequency analysis using Continuous Wavelet Transform (CWT). Each method's performance is assessed using synthetic signals, including nonlinear signals and those with simulated anomalies. We calculated the F1-score to quantify performance, providing a balanced measure of precision and recall. Results showed that SVM classification outperformed both classical techniques and CWT analysis, achieving a higher F1-score in detecting changes. While all methods struggled with synthetic nonlinear signals, classical techniques and SVM successfully detected changes in signals with simulated anomalies, whereas CWT had difficulty with both types of signals. These findings underscore the importance of selecting appropriate change detection methods based on signal characteristics. Future research should explore advanced machine learning and signal processing techniques to improve detection accuracy in biomedical applications.  
biomedical signals; change detection; synthetic data; signal processing; time-frequency analysis; machine learning; clinical applications +
Footnote †: journal: Medical Signal Engineering  
1][PERSON] [2]Department of Mechanical Engineering, Florida State University, Tallahassee, FL 32306, USA; [EMAIL_ADDRESS]
# Deep Learning-Based ECG Arrhythmia Classification: A Systematic Review
## 3 Results
### Code Availability  
Sharing the code of DL models online is a way for researchers to reproduce the performance results of existing works. However, only around 6% (20 out of 368) provide the code information directly in their papers, and most of them shared the code through the GitHub platform. In Table 5, to help researchers access the relevant codes conveniently, we list detailed information about the studies whose codes are publicly available. Few studies mention that the codes are available upon request, e.g., [85,112] but are not listed in the table.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Paper** & **Code Platform** & **Database** & **Evaluation Paradigm** & **Class Types** & **Model** \\ \hline \multirow{3}{*}{[23]} & \multirow{3}{*}{Website} & UEA; UCR; & \multirow{3}{*}{Intra-patient} & \multirow{3}{*}{2} & \multirow{3}{*}{SDK-CNN} \\  & & PhysioNet. AF-D1, & & AF-D2, and Two & \\  & & Lead & & & \\ \hline
[29] & GitHub & MITDB & Intra-patient & 5 & CNN (ResNet) \\ \hline
[113] & GitHub & MITDB; CPSC2018 & Intra-patient & 5 & DNN \\ \hline
[114] & GitHub & MNIST and Fashion & \multirow{2}{*}{Intra-patient} & \multirow{2}{*}{2} & \multirow{2}{*}{D-RBFDD} \\  & & MNIST; MITBIH & & & \\ \hline
[115] & GitHub & PTBXL; CPSC2018 & Intra-patient & 2 & SPN-V2 \\ \hline \multirow{3}{*}{[116]} & \multirow{3}{*}{GitHub} & UCI & \multirow{3}{*}{Intra-patient} & \multirow{3}{*}{13} & \multirow{3}{*}{CDNN} \\  & & Machine-learning & & & \\ \cline{1-1}  & & repository-based & & & \\ \cline{1-1}  & & arrhythmia dataset; & & & \\ \hline
[75] & GitHub & 2021 PhysioNet & \multirow{3}{*}{Intra-patient} & \multirow{3}{*}{30} & \multirow{3}{*}{d-RNICA} \\  & & Challenge data & & & \\ \hline
[20] & GitHub & 2020 Physionet & \multirow{3}{*}{Intra-patient} & \multirow{3}{*}{24} & \multirow{3}{*}{CNN (ResNet)} \\  & & Challenge data & & & \\ \hline
[117] & GitHub & Chapman University & \multirow{3}{*}{Intra-patient} & \multirow{3}{*}{4} & \multirow{3}{*}{Hybrid DNN} \\  & & and Shoaving & People’s Hospital & & \\ \cline{1-1}  & & Dataset & & & \\ \hline
[118] & GitHub & Physionet Challenge & \multirow{3}{*}{Intra-patient} & \multirow{3}{*}{2} & \multirow{3}{*}{CNN+ BiLSTM} \\  & & database; AFDB; AF Termination database & & & \\ \hline
[40] & GitHub & CPSC2018 & Intra-patient & 9 & DNN \\ \hline
[98] & GitHub & MITDB & Intra-/Inter-patient & 5 & Faster R-CNN-DNN \\ \hline
[70] & Gitlab & MITDB & Intra-patient & 5 & CNN-BLSTM \\ \hline \hline \end{tabular}
\end{table}
Table 5: The details information of code available studies.
# Plant Design for the Conversion of Plastic Waste into Valuable Chemicals (Alkyl Aromatics)
###### Abstract  
The exponential increase in production and consumption of plastic has led to accumulation of plastic waste in the environment, resulting in detrimental impacts on human health and the natural environment. Plastic pollution not only stems from discarded plastics but also from the chemicals released during plastic production and decomposition. Various waste management strategies exist for plastic waste, including landfilling, recycling, conversion to liquid fuel, and upcycling. Landfilling, which is a prevalent method, contributes to long-term environmental degradation. Recycling is practiced worldwide, but its percentage remains low, particularly in regions like South Asia. Conversion to liquid fuel through pyrolysis has been explored as a viable solution, although commercialization faces challenges. Upcycling, which involves depolymerization and repolymerization, offers an avenue to recycle plastic waste into valuable chemicals, specifically focusing on high-density polyethylene (HDPE) and low-density polyethylene (LDPE). Currently, HDPE and LDPE make up 36% of all plastic trash by mass, but they have the potential to account for far more. When plastic waste is incinerated or buried in the earth, it generates carbon dioxide and heat, which pollute our environment. Depolymerization is a way to chemically recycle plastic waste into monomers, but this process requires a large amount of energy. Controlled partial depolymerization can transform PE into new, high-quality products at a temperature of more than 400 \({}^{\circ}\)C with or without a catalyst. In this study, we provide a novel approach for the conversion of plastic waste, particularly HDPE and LDPE, into valuable alkyl aromatics. By implementing controlled partial depolymerization, we propose a plant design capable of transforming plastic waste into high-quality chemicals. The design aims to optimize energy consumption, process efficiency, and product quality. The research findings contribute to sustainable plastic waste management and the reduction in environmental pollution caused by plastic waste.  
upcycling; pyrolysis; depolymerization; repolymerization; high-density polyethylene (HDPE); low-density polyethylene (LDPE); monomers; catalyst; alkyl aromatics Article
## 4 Results and Discussion
### Power Sharing and Efficiency  
In this section, the complete system is tested again using different sets of R and RL load values. Then, the power shared by the DC sources is analysed along with the efficiency of the MLI. Presented in Tables 7 and 8 are the results obtained from the simulation study and experimental measurement. Based on the switching signals, referring to Equations (6)-(9),  
Figure 16: Experimentally measured blocking voltages across all switches.  
the value for \(\text{I}_{\text{V1}}\), \(\text{I}_{\text{V2}}\), \(\text{I}_{\text{V3}}\), and \(\text{I}_{\text{V4}}\) were 41.24 m, 24.86 m, 48.64 m, and 37.27 m, respectively. With this, the power delivery ratio by the sources could be estimated as follows:  
\[\text{P}_{\text{V1}}:\text{P}_{\text{V2}}:\text{P}_{\text{V3}}:\text{P}_{\text{ V4}}=\text{P}_{\text{V1}}:0.6028\text{P}_{\text{V1}}:5.8701\text{P}_{\text{V2}}:0.7662 \text{P}_{\text{V3}} \tag{21}\]  
From both tables, it can be noted that the source \(\text{V}_{2}\) delivered only \(\sim\)60% of the power provided by source \(\text{V}_{1}\). Source \(\text{V}_{3}\), on the other hand, offered \(\sim\)590% of the total power produced by source \(\text{V}_{2}\). Lastly, source \(\text{V}_{4}\) was found to deliver only \(\sim\)77% of the power distributed by source \(\text{V}_{3}\). These experimental and simulated power-sharing results are very close to the theoretical ratios given in (21). The theoretical input powers under the three tested R loads were calculated to be 112.47 W, 343.17 W, and 540.75 W, respectively. As for the tested RL loads, the theoretical input powers were 39.73 W, 232.6 W, and 480.26 W, respectively. Again, these theoretical values were very comparable to the presented simulation and experimental results. The amount of power required for each source is generally based on the voltage magnitude and frequency at which they are switched ON to produce any voltage level. From the obtained power-sharing results, the amount of power each DC source stage needs to deliver is known. Therefore, the number of PV panels required per DC-DC converter stage can be estimated in the design process if all inputs are to be powered by the PV source. In terms of THD, they are all comparable in the case of R loads. For RL loads, THDv values remain consistent, while THDi values were reduced according to the pf value.
# Influence of Drilling Along Pilot Holes on Drilling Performance and Effective Rock Strength +
## 7 Conclusions  
This study investigated the effects of pre-cored pilot holes on drilling performance and effective rock strength in multiple-diameter hole drilling scenarios. Many carefully thought-out experiments at Memorial University of Newfoundland's Drilling Technology Laboratory have produced valuable insights into bit-rock interactions and drilling parameters. The principal findings of the investigation are as follows:  
Drilling through pre-cored sections significantly improved the Rate of Penetration (ROP). For instance, the ROP in pre-cored sections was approximately 47% higher than in un-pre-cored sections under the same Weight on Bit (WOB) and rotational speed. This enhancement underscores the effectiveness of pilot holes in reducing resistance at the bit-rock interface.  
Despite the increased ROP in pre-cored sections, torque values were consistently higher than in un-pre-cored sections. For example, the torque in pre-cored sections was approximately 10% greater than in un-pre-cored sections, indicating a complex interplay between the bit-rock interaction areas and drilling efficiencies in pre-cored intervals was approximately 28% higher than in un-pre-cored sections at 60 RPM, indicating increased energy consumption despite enhanced penetration rates.  
Drilling with a 1.5-inch diameter pilot hole resulted in an approximately 45% higher ROP and 20% lower torque than the full-face (FF) configuration. A 1.0-inch pilot hole achieved a 25% higher ROP and 12% lower torque than FF drilling.  
Larger pilot hole diameters, such as 1.5 inches, demonstrated a 35% lower Mechanical Specific Energy (MSE) than full-face (FF) drilling, reflecting improved energy efficiency. The 1.0-inch pilot hole configuration reduced MSE by 20% compared to full-face (FF) drilling, emphasizing the energy-saving potential of optimized pilot hole sizes.  
The effective rock strength for a 1.5-inch pilot hole was approximately 30% lower than for FF drilling, indicating that larger pilot holes facilitate easier rock penetration by minimizing the rock's resistance to the applied Weight on Bit.  
The findings of this study highlight the necessity of a thorough approach to drilling performance analysis that considers not only conventional parameters like torque and ROP but also effective rock strength and MSE. Operators can make better decisions about drilling strategies, tool selection, and parameter optimization to increase efficiency and lower costs by considering the effects of pre-cored holes, bit-rock interactions, and the influence of formation strength on drilling parameters.
## 2 Materials and Methods
### Surface Site Concentration  
The adsorption of Zn\({}^{2+}\) on red mud and P-120, P-200, and P-270 was related to the concentration of surface coordination sites of these adsorbents. The surface site concentrations (\(H_{\mathrm{s}}\)) were measured by a potentiometric titrator (ZDJ-4A, Leici, China). In the titration experiments, 0.1 g of the adsorbents was mixed with 50 mL of 0.01 mol/L NaNO\({}_{3}\) in a conical flask, followed by bubbling with pure N\({}_{2}\). After that, the pH value of the solution was adjusted to 4 with 0.6 mol/L nitric acid, and then back-titrated to approximately 10 with 0.3 mol/L NaOH.  
When nitric acid was added to the solution, a cationic exchange reaction between the H\({}^{+}\) in the solution and the coordinated cations (denoted as Me, e.g., Na\({}^{+}\), K\({}^{+}\), Ca\({}^{2+}\), and Mg\({}^{2+}\)) on the surface coordination sites \(\equiv\)SO\({}^{-}\) (S represents Fe, Si, or Al) of the adsorbents occurred as shown in Equation (3). Thus, part of the free H\({}^{+}\) was associated with the coordination sites of the adsorbent. By adding NaOH, the free H\({}^{+}\) in the liquid fraction was first neutralized with OH\({}^{-}\), and then, the coordinated H\({}^{+}\) on the surface sites \(\equiv\)SO\({}^{-}\) of the adsorbents was replaced by cationic Na\({}^{+}\) via cationic exchange (Equation (4)). Finally, the pH of the solution increased due to the addition of overdosed NaOH:  
\[\equiv\mathrm{SO}^{-}\mathrm{Me}^{\mathrm{n}+}+\mathrm{H}^{+}\leftrightarrow \equiv\mathrm{SO}^{-}\mathrm{H}^{+}+\mathrm{Me}^{\mathrm{n}+}, \tag{3}\]  
\[\equiv\mathrm{SO}^{-}\mathrm{H}^{+}+\mathrm{NaOH}\leftrightarrow \equiv\mathrm{SO}^{-}\mathrm{Na}^{+}+\mathrm{H}_{2}\mathrm{O}, \tag{4}\]  
The Gran function value (denoted as \(G\)) was calculated via Equations (5) and (6):  
\[\mathrm{Under\ acidic\ conditions,}\ G=(V_{0}+V_{1}+V_{2})\times 10^{-\mathrm{ pH}}, \tag{5}\]Under alkaline conditions, \(G=(V_{0}+V_{1}+V_{2})\times 10^{-(13.8-\mathrm{pH})}\), (6)  
where \(V_{0}\), \(V_{1}\), and \(V_{2}\) represent the volume (mL) of the initial solution, the titrated HNO\({}_{3}\) and NaOH, separately.  
The \(H_{8}\) (mmol/g) of the adsorbents was calculated with the following equation:  
\[H_{8}=\frac{(V_{\mathrm{eb2}}-V_{\mathrm{eb1}})_{\mathrm{adsorbent}}-(V_{ \mathrm{eb2}}-V_{\mathrm{eb1}})_{\mathrm{blank}}}{m_{2}}\times C_{\mathrm{NaOH}} \tag{7}\]  
where \(V_{\mathrm{eb1}}\) and \(V_{\mathrm{eb2}}\) represent the volume of the consumed NaOH for the neutralization of free H\({}^{+}\) in the liquid solution, and the total H\({}^{+}\) in the liquid solution and the adsorbents surface; \(C_{\mathrm{NaOH}}\) represents the NaOH concentration (mol/L); \(m_{2}\) represents the adsorbent weight (g).
# Two-Sided Matching Decision Method of Electricity Sales Package Based on Disappointment Theory
###### Abstract  
Under the background of the \"dual carbon\" targets and continuously promoted power system reform, the application of a high proportion of renewable energy is becoming increasingly widespread. All sectors of society have greater demands for more appropriate electricity sales packages to guide the behavior of power users, which will in turn help conserve energy, reduce emissions, and finally achieve low-carbon operation of the power market economy. However, the existing methods of recommending electricity sales packages fail to provide appropriate and accurate recommendations for the users lacking preference information. Therefore, this paper proposes a two-sided matching decision-making method of an electricity sales package based on disappointment theory. First of all, according to the incomplete fuzzy preference relationship provided by the power user and the electricity sales package, the respective priority weight vector is calculated, and then the subjective satisfaction matrix of the power user and the electricity sales package is calculated. Next, the adjusted satisfaction matrix is calculated by adding the influence of the theory of elation and disappointment. Then, on the basis of the adjusted satisfaction matrix, an optimization model aiming at maximizing the total satisfaction of electric power customers and electricity sales packages is established, and the optimal stable matching model of electric power customers and electricity sales packages is obtained. Lastly, taking an industrial park in Zhejiang Province as an example, using the bilateral matching method proposed in this article, the optimal matching schemes for five electric power customers and six electricity sales packages is obtained, which shows the effectiveness and rationality of the two-sided matching decision-making method of electricity sales packages based on the disappointment theory.  
renewable energy; disappointment theory; incomplete fuzzy preference relationship; satisfaction matrix; two-sided matching; field survey +
Footnote †: journal: Applied Sciences
## 3 Results
Note that the electricity is delivered to the grid mainly during the night and late afternoon hours. This mainly occurs because the irrigation pumps operate during the central hours, doubling the power consumption. By considering the monthly electric energy ratio between the self-consumption energy and the electric demand (Figure 12), it was determined that in the colder months, this ratio was almost about 90%, while during the summer months, it decreased to 58% although there was higher PV production. This is due to the high summer demand being about 10 times higher than the winter one. From this point of view, the use of energy storage would be a useful solution to reduce the energy delivered to the grid mainly during the winter months. The yearly results of the energy analysis are summarized in Figure 13 and Table 5  
Thermal integration is 37% of the total demand and the electric self-consumption is 65% of the total electric energy demand. The electric production of the solar field covers only 17% of the total electric energy production, a result that confirms the small size of the PV field with respect to the pyrogasifier. The proposed system acquires a reduction of 149 t/y (Table 5). The primary energy saving of 121% is due to the high amount of electric energy delivered to the grid, equal to 55% of the total electricity production. During the winter months, due to the lower electric energy demand when the irrigation pumps operate for only few hours per day, the electric self-consumption with respect to the demand reaches a high value, also 90%. Therefore, the excess of electricity reduces during the summer months although there is higher PV production. Considering the increase in the purchasing costs before and after the energy crisis, EUR 0.70 vs. EUR 1.58/Sm\({}^{3}\) for natural gas and EUR 0.19 vs. EUR 0.66/kWh, the economic indexes, reported in Table 6, clearly improve, with a simple payback period, decreasing from 6.7 to 1.7 years.
## 6 Natural Compounds Inhibiting Protein Expression of Metabolic Enzymes  
### Downregulation of De Novo Lipogenesis by Activating AMPK  
The AMP-activated kinase (AMPK) pathway is one of the most important signaling pathways regulating biological energy metabolism. Activated AMPK represses its downstream target ACC through promoting its phosphorylation and downregulates the genes of multiple lipogenic enzymes [103; 104]. Curcumin was reported as an activator of AMPK in a p38-dependent manner and thus could increase the phosphorylation of both AMPK and ACC in cancer cells [26]. EGCG, quercetin and physcon could also promote AMPK-induced ACC phosphorylation and FASN downregulation, leading to markedly reduced endogenous lipogenesis in cancer cells [30; 42; 43; 46]. Additionally, betulinic acid could also activate AMPK and subsequently reduce ACC activity with concomitant inhibition of glucose-mediated lipogenesis [22; 105].