esa-sceva/satcom-chunk-collection huggingface dataset:
_Applied_  
_sciences_  
_Article_  
**Impact of High Intensity Interval Training Using**  
**Elastic Bands on Glycemic Control in Adults with**  
**Type 1 Diabetes: A Pilot Study**  
**[PERSON] 1, [PERSON] 2,*(), [PERSON] 2,3(), [PERSON] 4,5, [PERSON] 1 and [PERSON] 2,6,7**  
1 Department of Physotherapy, University of Valencia, 46010 Valencia, Spain; [EMAIL_ADDRESS] (R.M.-S.A.); [EMAIL_ADDRESS] (J.C.B.M.)  
2 Centro de Investigacion Biomedica en Red de Diabetes y Enfermedades Metabolocas Acsociadas (CIBERDEM), Instituto de Salul Carlos III, 28029 Madrid, Spain; [EMAIL_ADDRESS] (J.B.); [EMAIL_ADDRESS]a.es (F.J.A.-B.)  
3 Instituto Universitario de Automatica e Informatica Industrial, Universitat Politecnica de Valencia, 46010 Valencia, Spain  
4 Department of Applied Biology-Nutrition, Institute of Bioengineering, University Miguel Hernandez, Alicante Institute for Health and Biomedical Research (ISABIAL), 03010 Alicante, Spain; [EMAIL_ADDRESS]  
5 CIBER Fisiopatologia de la Obsidad y Nutricion (CIBEROBN), Instituto de Salul Carlos III (ISCIII), 28029 Madrid, Spain  
6 Diabetes Reference Unit, Endocrinology and Nutrition Department, Hospital Clinico Universitario de Valencia, 46010 Valencia, Spain  
7 Department of Medicine, Medicine Faculty, University of Valencia, 46010 Valencia, Spain  
8 Correspondence: [EMAIL_ADDRESS]; Tel.: +34-662999050-78268 check for updates  
Received: 9 September 2020; Accepted: 3 October 2020; Published: 7 October 2020  
**Featured Application: Workout routines comprising high intensity interval training (with elastic bands) could be developed that ensure the glycemic safety of type 1 diabetic people while taking advantage of its inherent health benefits.**  
**Abstract: High intensity interval training (HIIT) using elastic bands is easy to do, but no data on its impact on glycemic control in people with type 1 diabetes (T1D) are available. Six males with T1D performed three weekly sessions of HIIT using elastic bands for 12 weeks. Each session consisted of eight exercises. Glycemic control was evaluated by using intermittent scanning continuous glucose monitoring two weeks before study onset (_baseline_) and during the intervention period in the first two (_first stage_) and last two weeks (_last stage_). In the 24 h post-exercise, time-in-range (70-180 mg/dL) was reduced from baseline to the end of the study (67.2% to 63.0%), and time-above-range (>180 mg/dL) seemed to increase from _baseline_ across the study (20.8% \(\rightarrow\) 27.5% \(\rightarrow\) 22.1%, from _baseline_\(\rightarrow\)_first_\(\rightarrow\)_last stage_), but did not show any statistical significance. Time in hypoglycemia (either \(<\) 70 mg/dL or \(<\)54 mg/dL) did not show statistically significant differences. This study shows that a HIIT program with elastic bands is safe and effective to perform in T1D patients, keeping blood glucose levels in a safe range.**  
**Keywords: type 1 diabetes; high intensity interval training; glycemic control; intermittent scanning continuous glucose monitoring; elastic bands; capillary blood glucose**
## 4 Results
### Comparison of Algorithm Performance  
#### 4.3.1 Handover Frequency Comparison  
In power scenarios in remote areas or disaster-stricken areas, the grid user terminals must communicate with satellites, and frequent satellite handovers can cause a series of serious problems. Increased communication interruptions and delays: communication links must be re-established whenever a satellite handover occurs, leading to interruptions and delays that affect real-time performance and stability. Increased energy consumption: satellite handover consumes power from grid terminal equipment, particularly in remote areas with limited energy supplies. Increased system complexity: frequent satellite handover necessitates more complex algorithms and protocols to manage the connection-handover process. Increased data loss and errors: each handover carries a risk of data loss or transmission errors. In power scenarios in disaster areas, transmitted data, crucial for monitoring and control, when lost or erroneous, can lead to misjudgments or operational failures.  
We compared the handover algorithm proposed in this study with the handover frequency of the DRL strategy, the maximum elevation angle strategy, and the maximum remaining service time strategy. As shown in Figure 9, the diamonds represent different experimental values, and the curves indicate normal distribution, which serves to help analyze and interpret the distribution characteristics of the data; the higher the peak and the steeper the curve, the higher the degree of concentration of handover frequency near the mean; the flatter the curve, the greater the degree of dispersion of handover frequency. In addition, the curve shows a long tail or skew, which may indicate the existence of some abnormal data points. Thus, we are able to obtain the average handover frequency of multiple experiments with different handover strategies as well as clearer experimental results. It can be seen that the handover number resulting from the maximum elevation angle based handover strategy is the largest. This occurs because the strategy only considers the impact of the elevation angle on handover, ignoring other factors such as service time, which leads to frequent inter-satellite handover. Our proposed handover strategy exhibits a similar handover frequency as the strategy based on maximum remaining service time, while the frequency in the DRL-based strategy is slightly higher than our proposal. This primarily results from the previously mentioned algorithm selecting only the next-hop satellites for interplanetary handover, which only represents the current optimum, leading to unnecessary handover. Meanwhile, our algorithm focuses on the global number of  
Figure 8: Convergence of mean rewards with episodes.  
handover times in the whole communication duration to realize the optimal selection while satisfying the user's QoS.
## 2 Results and Discussion  
### Isolation and Sequence Analysis of Pc-fps  
A 480 bp putative fragment of the _Pc-fps_ was obtained by degenerate PCR. To obtain the 5'- and 3'-flanking regions of the putative _Pc-fps_, inverse PCR and cassette PCR were conducted with oligonucleotide primers specific for the partial sequences of _Pc-fps_. Sequence analysis showed that the cloned genomic DNA is 3120 bp in length, including the 5'-untranslated region (UTR) of 738 bp, 3'-UTR of 725 bp, five exons, and four introns. The genomic sequence of _Pc-fps_ consisted of 1767 bp and RT-PCR strategies were applied to isolate the putative cDNA of _Pc-fps_. The open reading frame of _fps_ is 1086 bp in length and corresponded to a predicted polypeptide of 361 amino acid residues, with a predicted  
Figure 1: Main steps in the biosynthesis of terpenoids in fungi. The abbreviations used are: HMG-CoA: hydroxymethylglutaryl CoA; MVP: 5-phosphomevalonate; MVPP: 5-diphosphomevalonate; IPP: isopentenyl pyrophosphate; DMAPP: dimethylallyl pyrophosphate; FPP: farnesyl pyrophosphate. Enzymes are indicated in bold; AACT: acetoacetyl-CoA thiolase; HMGS: HMG-CoA synthase; HMGR: HMG-CoA reductase; MVK: mevalonate kinase; PMK: phosphomevalonate kinase; PMD: mevalonate 5-diphosphate decarboxylase; IDI: IPP isomerase; FPS: FPP synthase; SQS: Squalene synthase.  
molecular weight of 41.29 kDa and pI of 5.40. The nucleotide sequences of the _Pc-fps_ and _Pc-fps_ cDNA have been deposited in the GenBank database, with accession numbers JN652835 and JN652836, respectively.
## 4 Results and Discussions
### Effects of Surface Crack Propagation on Meshing Stiffness and Vibration Characteristics
#### 4.2.1 Effects of Surface Crack Propagation Progress on Meshing Stiffness  
The effects of surface crack propagation on the mesh stiffness are shown in Figure 11. It can be found that the meshing stiffness of the gear system reduces gradually with the propagation of the surface crack, and the decrease in mesh stiffness is greater when the crack evolves into a spalling fault. It can be concluded that the spalling fault has a larger affect area and amplitude on the meshing stiffness compared with the surface crack fault.
## 5 Conclusions  
In this study, a rectangular antenna with a rose-shaped slot and two thin rectangular slots is presented for WiMAX and 5G applications. The single antenna operates over a wide frequency range (2-7.62 GHz) with a bandwidth of 5.3 GHz and a peak gain of 3.8 dBi. To achieve higher gain, a \(2\times 1\) antenna array is demonstrated. The array antenna features dual-band operation. It covers frequencies from 2 to 3.71 GHz with a bandwidth of 1.71 GHz and from 5.9 to 7.54 GHz with a bandwidth of 1.63 GHz, along with a peak gain of 6.47 dBi. Moreover, a two-element MIMO antenna is designed using the proposed antenna array. The dual-band characteristics of the MIMO antenna are very similar to those of the antenna array. The proposed MIMO antenna boasts a peak gain of 7.5 dBi, coupled with an  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref.** & **Bandwidth (GHz)** & \
\begin{tabular}{c} **Antenna Dimensions** \\ **(mm2)** \\ \end{tabular} & **ECC** &
\begin{tabular}{c} **Minimum** \\ **Isolation (dB)** \\ \end{tabular} & **Peak Gain (dBi)** \\ \hline
[9] & 3.4–3.6, 4.8–5 & \(150\times 75\) & 0.14 & 17 & 8 \\
[10] & 2.7–3.6 & \(50\times 100\) & 0.009 & 13 & 4.5 \\
[11] & 3.3–5.8 & \(150\times 75\) & 0.03 & 15 & 6 \\
[12] & 3.4–3.7 & \(145\times 70\) & 0.01 & 12 & 5 \\
[13] & 2.38–2.7, 3.19–3.84 & \(150\times 75\) & 0.17 & 10 & 6 \\
[14] & 3.3–6 & \(72\times 72\) & 0.005 & 15 & \textgreater{}2.5 \\
[15] & 3.52–3.6, 5.22–5.38 & \(90\times 90\) & 0.05 & 22 & 4.2 \\ Proposed MIMO & 2.1–3.6, 5.9–7.4 & \(126\times 63\) & 0.0002 & 20 & 7.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the proposed MIMO antenna with related works.  
Figure 18: Simulated MEG results of the proposed MIMO antenna.  
impressive radiation efficiency that reaches up to 99% and an isolation exceeding 20 dB, making it a strong candidate for WiMAX, 5G sub-6 GHz, and 5G sub-7 GHz applications.  
Conceptualization, S.I., S.Y. and M.S.; methodology, S.I.; software, S.I. and S.Y.; validation, S.Y. and M.S.; formal analysis, S.I.; investigation, S.I., S.Y. and M.S.; resources, S.Y. and M.S.; data curation, S.I., S.Y. and M.S.; writing--original draft preparation, S.I.; writing--review and editing, S.I., S.Y. and M.S.; visualization, S.Y. and M.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data are contained within the article.  
The authors declare no conflicts of interest.
## 2 Materials and Methods
### Heterologous Expression in E. coli and In Vitro Characterization of SiTPS  
The coding region of _SiTPS_ (PIIN_06735) was amplified from _S. indica_ cDNA (Supplementary Table S1) and cloned in-frame with a His-tag (C-terminal) into the Ncol digested pET28b+ vector (Novagen), using the In-Fusion(r) HD Cloning Kit (Takara Bio,Kusatsu, Japan), according to the manufacturer's instructions. The resulting plasmid pET_SiTPS was transformed into _E. coli_ C41 OverExpress(tm) cells (Lucigen, Middleton, WI, USA). Heterologous expression and protein purification was performed as described in [42]. Briefly, 500 \(\upmu\)L of an overnight culture (5 mL LB broth containing 50 \(\upmu\)g/mL kanamycin) was used to inoculate 50 mL of production medium (Terrific Both medium (pH 7.0), containing 50 \(\upmu\)g/mL kanamycin). Cultures were grown at 37 \({}^{\circ}\)C in a shaking incubator (180 rpm). When culture OD\({}_{600}\) reached 0.6, 100 \(\upmu\)L of IPTG (Isopropyl \(\upbeta\)-d-1-thogalactopyranoside, 0.2 mM) were added to induce expression. Protein expression proceeded overnight at 16 \({}^{\circ}\)C in a shaking incubator (180 rpm). The following day, cells were harvested by centrifugation (4500 rpm) at 4 \({}^{\circ}\)C for 20 min and lysed using the CellCytic B Cell Lysis Reagent (Sigma-Aldrich, St. Louis, MO, USA), supplemented with 0.1 mg/mL lysozyme, 10 \(\upmu\)L/mL protease inhibitor cocktail (Sigma-Aldrich, St. Louis, MO, USA), 0.2 mg/mL benzoase, 25 mM imidazole, 500 mM NaCl and 5% (_v/v_) glycerol. The cell lysate was further used for protein purification, using the His SpinTrap Kit (GE Healthcare, Chicago, IL, USA). Proteins were desalted with the PD MiniTrap G-25 desalting columns (GE Healthcare, Chicago, IL, USA) according to the manufacturer's instructions and eluted with 600 \(\upmu\)L desalting buffer (20 mM HEPES (pH 7.2), 1 mM MgCl\({}_{2}\), 350 mM NaCl, 5 mM DTT, and 5% (_v/v_) glycerol).  
The in vitro terpene synthase assay was performed in a 50 \(\upmu\)L reaction that contained 5 \(\upmu\)g substrate (GPP, \(E\),_E_-FPP or GGPP (Cayman Chemicals, Ann Arbor, MI, USA)), 100 \(\upmu\)g purified enzyme, 10 mM MgCl\({}_{2}\), 100 mM KCl, 5 mM DTT and 10% _v/v_ glycerol in 50 mM HEPES (pH 7.2). The reaction was overlaid with 500 \(\upmu\)L n-hexane. Reactions were carried out at 30 \({}^{\circ}\)C for 1 h, followed by vortexing to extract the products into the organic phase. Layers were separated by centrifugation and hexane was removed for GC-MS analysis.
## 3 Results
### Arrhenius Constitutive Model  
The stress-strain curves depicted in Figure 7 were utilized in the development of an Arrhenius constitutive model for the purpose of predicting and modeling the flow behavior of the alloy under investigation. The characterization of the deformation response of this particular alloy, taking into account the effects of strain rate and temperature, can be effectively clarified by the utilization of a Zener-Hollomon parameter (Equation (2)) and an exponent-type formula (Equation (3)) [52; 53].  
\[Z=\dot{\epsilon}exp^{\frac{Q}{RT}} \tag{2}\]  
\[\dot{\epsilon}=Af(\sigma)exp^{(-\frac{Q}{RT})} \tag{3}\]  
In the given Equation (2), A represents a material constant, \(\dot{\epsilon}\) denotes the strain rate measured in \(s^{-1}\), \(T\) signifies the temperature measured in Kelvin, \(Q\) represents the effective activation energy of deformation measured in kJ/mol, \(R\) is the gas constant equal to 8.314 J/(molK), and \(f(\sigma)\) is a function related to the flow stress (Equations (4)-(6)):  
\[(\sigma)=\left\{\begin{array}{ll}\sigma^{n_{1}},&for\;\alpha\sigma<0.8\\ exp^{(\beta\sigma)},&for\;\alpha\sigma>1.2\\ [\sinh(\alpha\sigma)]^{n_{2}},&for\;various\;\sigma\end{array}\right. \tag{4}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Strain Rate (s\({}^{-1}\))**} & \multicolumn{3}{c}{**Temperature**} \\ \cline{2-4}  & **460 \({}^{\circ}\)C** & **480 \({}^{\circ}\)C** & **500 \({}^{\circ}\)C** \\ \hline \(1\times 10^{-2}\) & 310 \(\pm\) 20 & 130 \(\pm\) 10 & 110 \(\pm\) 20 \\ \hline \(5\times 10^{-3}\) & 290 \(\pm\) 10 & 210 \(\pm\) 10 & 120 \(\pm\) 20 \\ \hline \(2\times 10^{-3}\) & 310 \(\pm\) 10 & 240 \(\pm\) 20 & 200 \(\pm\) 20 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Elongation to failure (%) after tensile tests at various temperatures and strain rates.  
Figure 7: The true stress vs. true strain curves corresponding to a tensile test in a constant strain rate range of 2 \(\times\) 10\({}^{-3}\)–1 \(\times\) 10\({}^{-2}\) s\({}^{-1}\) at (**a**) 460 \({}^{\circ}\)C, (**b**) 480 \({}^{\circ}\)C, and (**c**) 500 \({}^{\circ}\)C for the alloy after six MDF circles at 350 \({}^{\circ}\)C.  
The constants of the material are denoted as \(\beta\), \(n_{1}\), \(n_{2}\) and \(\alpha\), where \(\alpha\) is defined as the ratio of \(\beta\) to \(n_{1}\). While Equations (4) and (5) are suitable for low stress conditions (\(\alpha\)\(\sigma\) < 0.8) and high stress conditions (\(\alpha\)\(\sigma\) > 1.2), respectively, the hyperbolic sine law (Equation (6)) can be regarded as universally applicable and can be employed across a broad range of stress levels. The Equations (4)-(6) can be expressed in the form provided below (Equations (7) and (8)), taking into account the Zener-Holomon parameter:  
\[\dot{\epsilon}=\left\{\begin{array}{l}A_{1}\sigma^{n_{1}}exp^{(-\frac{Q_{1} }{RT})}-Power\;law\\ A_{2}e^{(\beta\sigma)}exp^{(-\frac{Q_{2}}{RT})}-Exponential\;law\\ A_{3}[\sinh(\alpha\sigma)]^{n_{2}}exp^{(-\frac{Q_{3}}{RT})}-Hyperbolic\;sine\;law \end{array}\right. \tag{7}\]
# Computer-Aided Diagnosis of Alzheimer's Disease via Deep Learning Models and Radiomics Method
## 1 Introduction
### Multimodal Image Fusion Technology  
Multimodal image fusion technology polymerizes the medical images of different modes into a single image set, and the images from different imaging systems are aggregated into a new output image [12]. Different modality medical images focus on different contents, and multimodal fusion technology can achieve the integration of functional image and anatomical image through the designed algorithm. Fusion image contains more useful information and makes the image more suitable for human vision processing, and for this reason it plays an important role in the diagnosis and treatment of diseases.  
Meanwhile, multimodal image fusion is divided into three levels: pixel-level fusion, feature-level fusion and decision-level fusion. Among them, pixel-level fusion is the lowest level fusion, which is the direct fusion between pixels on the pixel level. Figure 3 illustrates how the fusion process works and displays an example of fusion. Pixel-level fusion has many advantages. Compared with others, it can restore the previous information of the image and contain more details of the image. So, the pixel-level fusion has the most application among these three fusion layers.
# Visual Sentiment Analysis Using Deep Learning Models with Social Media Data
## 4 Methodology
### VGG-19 Architecture  
The VGG-19 architecture comes with 19 weight layers, and the number of filters available in each layer doubles as we go deeper into the network. It has a stack of convolutional layers, followed by a max-pooling layer that extracts image features, and in the classification part, it has three fully connected layers and a Softmax layer. We trained our dataset on the VGG-19 architecture by fine-tuning it by freezing the first three convolutional blocks (Block 1, Block 2, and Block 3) and training the remaining layers (Block 4 and Block 5) that could extract features specific to our dataset. We added some additional layers in the classification part, that is, the flattening layer, batch normalization layer, dropout layer, and final dense layers, with 2 classes (positive and negative). The architecture of the VGG-19 model is shown in Figure 2 below.
## 2 Experimental Approach  
In order to clarify the causes of the aforementioned corrosion phenomenon in the AISI 316L stainless steel port piping facility, one 0.6 m long section of the pipe, shown in Figure 1, was extracted from the system. This section contains clear evidence of the commented processes, including a through-wall pitting. Additionally, as it can be observed, for better protection, the pipes were covered with a 0.3 mm thick rubber coating glued to the external surface, which had to be removed for the subsequent analysis trying not to damage the outer face in case it needed to be inspected.  
In order to determine the root cause of the deterioration shown in the tubes, the following analyses were carried out:  
* Visual analysis of inner and outer faces of the sample, in order to find any other evidence of damage that is not detected yet.
* Visual analysis of the video-inspection performed with a robotic submersible camera inside the pipes. There were more than two hours of records, and the most interesting and singular frames were extracted as images and will be shown here.
* Analysis of the chemical composition of pipe material. This was carried out by spark emission spectroscopy, which allows for the determination of the content of alloying elements with high accuracy.
* Analysis of the pipe pitting defects by scanning electron microscopy (SEM). The micromechanisms of the pits were analyzed, as well as the semi-quantitative chemical composition of the material surrounding the pits and other singular points of interest, such as grain boundaries or corrosion products.
* Bacterial culture of the water remaining inside the piping system in an area with an abundance of corrosion products. The sample was analyzed by culturing the mixture of microorganisms present in Kliguer medium and inoculating in a flute beak at 25 \({}^{\circ}\)C for one week.  
In order to complete such analyses, some sub-samples were extracted from the aforementioned tube by means of a refrigerated metallographic precision cutter, assuring that no alterations were generated in the samples.  
Section 3 describes the results obtained and provides some discussion about the different findings.  
Figure 1: Sample of pipe extracted from the port piping system; a through-wall pitting can be observed in the center (black arrow).
## 5 Case Study
### Scalability Analysis of the Proposed Model and Algorithm  
To rigorously evaluate the scalability and computational efficiency of the proposed model under larger problem sizes, this paper conducted extensive simulations using synthetically generated datasets for 30, 60, and 100 orders. These datasets were designed to reflect plausible operational scenarios while focusing on stress-testing the model's performance under increased scale. The evaluation focuses on four indicators: final Pareto set size, average cost per order, average generations to convergence, and average runtime. The results are summarized in Table 9 and visually depicted in Figure 12.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Number of** \\ **Orders** \\ **Pareto Set Size** \\ \end{tabular} } & \multicolumn{3}{c}{**Parameters**} \\ \cline{2-5}  & **Avg. Final** & **Avg. Cost per Order** & **Avg.** & **Avg. Runtime** \\  & **Pareto Set Size** & **(\(\times\)10\({}^{5}\) CNY)** & **Generations to** & **Avg. Runtime** \\ \hline
15 & 12 & 1.695 & 84 & 1178 \\
30 & 19 & 1.689 & 105 & 1744 \\
60 & 28 & 1.680 & 138 & 4432 \\
100 & 37 & 1.671 & 188 & 9775 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Scalability evaluation of the proposed model under increasing order sizes.  
Figure 11: Comparison of HV and IGD variation curves for algorithms with and without CIS.  
As shown in Table 9 and Figure 12, with the number of orders increasing, the size of the final Pareto set grows steadily from 12 to 37, indicating that the algorithm maintains strong diversity and continues to discover a wide range of non-dominated solutions in more complex problem spaces. This suggests good scalability in terms of solution richness. Meanwhile, the average cost per order shows a slight but consistent downward trend from 1.695 to 1.671. This aligns with the expected economies of scale, where larger production volumes allow better resource utilization and cost allocation, thereby reducing unit costs. In addition, the average number of generations required for convergence increases from 84 to 188, which is reasonable as larger-scale problems introduce more decision variables and a broader search space. Similarly, the average runtime increases from 1178 s (15 orders) to 9775 s (100 orders), reflecting the added computational effort required to solve higher-dimensional problems. Nevertheless, the growth remains within acceptable bounds for offline optimization in industrial applications.  
In summary, the proposed model and algorithm demonstrate strong scalability, capable of handling increased order complexity while maintaining solution quality, diversity, and computational feasibility.