esa-sceva/satcom-chunk-collection huggingface dataset:
# NFT Technology for Enhanced Global Digital Registers: A Novel Approach to Tokenization
## 9 Practical Applications and Use Cases  
The application of our decentralized global registry system based on blockchain technology and NFTs is multi-faceted and can be adopted in various sectors due to its unique features and advantages. Here, we provide several practical examples that illustrate the system's versatility and adaptability.  
1. Cultural heritage management. The tokenization of UNESCO World Heritage sites provides a compelling case. The unique attributes of each site can be embedded into an NFT, creating an immutable digital record of the site's information, its historical significance, and current status. This enables a decentralized, globally accessible, and secure database of world heritage sites that can significantly aid in their preservation and management;
2. Intellectual property management. Intellectual property rights management, particularly in the music and arts industry, is another field that can greatly benefit from our proposed system. For instance, artists could tokenize their work, with each NFT carrying crucial metadata about the artwork, including its authenticity, ownership, and rights for reproduction. This not only curbs art forgery and copyright infringement but also opens up new avenues for artists to monetize their work directly;
3. Real estate registry. Tokenization can revolutionize the real estate industry by enhancing transparency and efficiency in property transactions. Each property can be tokenized into an NFT, with its unique property details, ownership history, and transaction records securely stored on the blockchain. This can streamline property transactions, reduce disputes, and increase trust among the parties involved;Academic credential verification. Academic institutions can tokenize degrees and credentials, offering a robust solution to the prevalent issue of fake degrees and credential fraud. Each token can contain verifiable data about the student, the degree awarded, and the awarding institution. This not only simplifies the process of degree verification for employers but also ensures the authenticity of academic credentials.  
These are just a few examples that demonstrate the broad scope of applications of the proposed system. The diverse application scenarios underline the system's flexibility and potential to revolutionize how we manage and verify unique digital assets. Future research and development will undoubtedly uncover more opportunities and use cases for the proposed system, further contributing to its practical value and impact.
# COMA: Propagating Complementary Multi-Level Aggregation Network for Polyp Segmentation
## 4 Discussion  
As demonstrated in Section 2, we proposed three components, including CMM, BPM, and a hybrid loss function, to improve the performance of polyp segmentation. To validate the effectiveness of each component and show the model explainability, we conducted an additional analysis related to the components. The remainder of the analysis considered  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Model** & **mDice** & **mIoU** & \(\mathbf{S_{m}}\) & \(\mathbf{E_{m}}\) & **MAE** \\ \hline \multirow{4}{*}{CVC-ClinicDB} & Nearest & 0.901 & 0.842 & 0.929 & 0.978 & 0.009 \\  & Bilinear & **0.933** & **0.891** & **0.956** & **0.985** & **0.007** \\  & Bicubic & 0.920 & 0.876 & 0.943 & 0.979 & 0.008 \\ \hline \multirow{4}{*}{Kvasir} & Nearest & 0.881 & 0.821 & 0.904 & 0.942 & 0.031 \\  & Bilinear & **0.901** & **0.852** & **0.919** & **0.951** & **0.027** \\  & Bicubic & 0.890 & 0.841 & 0.914 & 0.941 & 0.028 \\ \hline \multirow{4}{*}{ColonDB} & Nearest & 0.750 & 0.672 & 0.842 & 0.883 & **0.037** \\  & Bilinear & **0.754** & **0.689** & **0.849** & **0.897** & **0.037** \\  & Bicubic & 0.753 & 0.687 & 0.846 & 0.885 & 0.038 \\ \hline \multirow{4}{*}{ETIS} & Nearest & 0.681 & 0.604 & 0.821 & 0.846 & 0.014 \\  & Bilinear & **0.711** & **0.648** & **0.844** & **0.887** & 0.015 \\ \cline{1-1}  & Bicubic & 0.699 & 0.637 & 0.837 & 0.846 & **0.013** \\ \hline \multirow{4}{*}{CVC-T} & Nearest & 0.865 & 0.782 & 0.916 & 0.980 & 0.009 \\ \cline{1-1}  & Bilinear & **0.906** & **0.843** & **0.945** & **0.988** & **0.006** \\ \cline{1-1}  & Bicubic & 0.891 & 0.826 & 0.936 & 0.978 & 0.007 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of COMMA performance based on the method of up/down-sampling interpolation method.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Model** & **mDice** & **mIoU** & \(\mathbf{S_{m}}\) & \(\mathbf{E_{m}}\) & **MAE** \\ \hline \multirow{4}{*}{ColonDB} & PraNet & 0.711 & 0.644 & 0.820 & 0.872 & 0.043 \\  & COMMA \({}^{\star}\) & **0.757** & **0.690** & **0.849** & 0.893 & 0.038 \\  & COMMA \({}^{\star}\) & 0.676 & 0.609 & 0.803 & 0.858 & 0.045 \\  & **COMMA** & 0.754 & 0.689 & **0.849** & **0.897** & **0.037** \\ \hline \multirow{4}{*}{ETIS} & PraNet & 0.628 & 0.571 & 0.794 & 0.841 & 0.031 \\  & COMMA \({}^{\star}\) & 0.671 & 0.593 & 0.820 & 0.830 & 0.036 \\  & COMMA \({}^{\star}\) & 0.689 & 0.616 & 0.829 & 0.863 & 0.023 \\  & **COMMA** & **0.711** & **0.648** & **0.844** & **0.887** & **0.015** \\ \hline \multirow{4}{*}{CVC-T} & PraNet & 0.871 & 0.801 & 0.925 & 0.972 & 0.010 \\  & COMMA \({}^{\star}\) & 0.850 & 0.785 & 0.914 & 0.960 & 0.014 \\ \cline{1-1}  & COMMA \({}^{\star}\) & 0.844 & 0.765 & 0.909 & 0.943 & 0.010 \\ \cline{1-1}  & **COMMA** & **0.906** & **0.843** & **0.945** & **0.988** & **0.006** \\ \hline \hline \end{tabular}
\end{table}
Table 5: _Cont._the effectiveness of each module and BPM combinations, visualization of the proposed module operation, and comparison of the proposed loss function and the existing single loss functions.
## 2 Materials and Methods
### Design and Fabrication of Microtool
#### 2.1.2 Fabrication of Microtool  
The microtools were fabricated using a commercial 3D direct laser writing setup (Nanoscribe GmbH, Germany) via 2 PP. The system employs a femtosecond fiber laser with a wavelength of 780 nm and a pulse length of 100 fs. The microtool is fabricated by solidifying the photoresist through a laser directly that scans along a predetermined path. With the advantage of 2 PP, this method can achieve a high resolution of 100 nm, which is beneficial for the fabrication of precision microstructures.  
The material for fabricating the microtool should at least satisfy the following conditions: (1) high-precision fabrication can be achieved; (2) biocompatibility is assured; (3) the refractive index is appropriate, such that the optical trapping efficiency and stiffness of the material in cell culture medium are efficiently high; (4) the density is suitable such that the microtool is basically at the same horizontal plane with the cells and can be manipulated well by optical traps. Therefore, photoresist IP-L 780 (Nanoscribe GmbH) was selected for experimental use in our study. The procedures for the fabrication of the microtool are described as follows. First, the photoresist is prepared over a clean glass substrate by simple drop-casting. Second, the photoresist-coated substrate was mounted on the laser direct writing machine with a 63\(\times\) oil immersion objective (N.A. = 1.4, Zeiss). The microtools were fabricated by laser focus point bottom-up scanning, in which layer by layer solidification is driven by galvanometric mirrors in the x-y directions and by a piezo motor in the z-direction (Figure 2a). The laser power was set to 40 mW with the printing parameters of 0.3 \(\upmu\)m slicing distance and hatching distance. In our study, polymerizing one microtool takes approximately 5 s. Third, the polymerized substrate was finally developed in baths of propylene glycol methyl acetate and isopropyl alcohol for 2 and 5 min, respectively. Figure 2b shows a picture of the fabricated microtool, which was coated with a 5 nm-thick gold layer for enhanced image contrast and imaged under an environmental scanning electron microscope (ESEM, Quanta 250, FEI). The fabricated microtools, without any coating, were removed from the glass substrate and transferred to culture dishes with a micropipette tip before the cell experiments.
## 2 Materials and Methods  
### Participants  
Initially, the sample comprised approximately 90 potential participants, each of whom completed a pre-selection questionnaire. Exclusion criteria included individuals with chronic cardiac or endocrine diseases, habitual smokers consuming more than six cigarettes per day, weekly marijuana users, individuals using other drugs (including daily alcohol intake), athletes training more than 10 hours per week, and individuals who had experienced a \"very stressful\" life event (rated >70 on a scale from 0 to 100) within the past year. All participants completed the Spanish adaptation of the Five Facets of Mindfulness Questionnaire (FFMQ; [48]) to measure stable Mindfulness attitudes. Participants scoring \(\leq\) 50 were excluded from further analysis. The Beck Depression Inventory-II (BDI-II) was also administered, excluding those with scores \(\leq\) 17. Finally, anxiety was assessed using the Spanish adaptation of the State-Trait Anxiety Inventory (STAI-R; [49]), and participants scoring \(\leq\) 30 were excluded.  
After applying these exclusion criteria, the final sample consisted of 60 young, healthy participants (20 men and 40 women), aged between 20 and 29 years. The mean age (M) for male participants was 22.67 years (standard deviation; SD = 2.38), while for female participants, the mean age was 23.27 years (SD = 2.26). Drawing on the literature, this age group is particularly well suited for assessing the effects of mindfulness interventions, as individuals within this range are more likely to experience fluctuations in stress levels and self-efficacy [50]. Body Mass Index (BMI) of 22.877 \(\pm\) 3.211. Due to technical issues, measurement errors in psychological and physiological recordings (n = 4), and instances where participants were aware of the experiment's objectives, potentially compromising objectivity (n = 4), the final sample distribution was as follows: 29 participants in the experimental group (EG) assigned to the brief mindfulness intervention, and 23 participants in the control group (CG).  
Participants were instructed to maintain their usual eating and sleeping habits prior to the experiment day, and to abstain from alcohol, drug use, and strenuous physical activity. Additionally, they were asked to refrain from caffeinated drinks, food, or tobacco for at least two hours before the experiment.
## 2 Methods
### Data Analysis  
Two performance indexes were studied: Navigation time for the state navigator and the success rate for the _Mayo-Pong_ tool. The obtained data was analysed statistically to assess the evolution of the subjects between trials and to compare the patients with the CS. The evolution of the subjects within class was analysed by a \(t\)-test, so normality in the distributions was assumed. The interclass comparison, i.e., between CS and patients, required a previous F-test to check the variances of the distributions. From the obtained result, a standard \(t\)-test was applied when the F-test satisfied the hypothesis of equal variances. Otherwise, a Welch \(t\)-test was applied to compare the populations. For all these tests, it was assumed \(p=0.05\) as the threshold for the null hypothesis [15]. The populations' data distributions were displayed in the corresponding box plots below, where the percentile 75, the median, and the percentile 25 were depicted in a box. These box and whisker plots show the range of each distribution and the atypical cases. The atypical cases, marked with asterisks, are located over the percentile 75 plus the box height or under the percentile 25 extended downwards from the box height. These box plots showed that the distributions were approximately symmetrical, so the \(t\)-test  
Figure 3: Target location on the _state navigator_ tool: 0\({}^{\prime}\) (top left corner) is the rest state, \({}^{\prime}1^{\prime}\) is the desired maximum voluntary contraction (DMVC) for the flexor carpi radialis, \({}^{\prime}2^{\prime}\) is near the DMVC for the extensor carpi radialis, \({}^{\prime}3^{\prime}\) is the desired medium contraction (DMC) for the flexor carpi radialis, and \({}^{\prime}4^{\prime}\) is the DMC for a combination of flexor and extensor. Crosstalk can be noticed in \({}^{\prime}1^{\prime}\), \({}^{\prime}2^{\prime}\), and \({}^{\prime}3^{\prime}\) as a deviation from the axes and simultaneous muscle activation in \({}^{\prime}4^{\prime}\). The residual EMG activity slightly displaces the pointer downwards from the rest state.  
validity could be assumed [15]. Both for the state navigator and for the _Myo-Pong_ tool, the maximum, minimum, and mean values of the performance indexes, i.e., NT and SR, were also depicted for each trial, both for the patients and for the CS. This permitted a comparison of the populations' response to the tools.
## 2 Materials and Methods
### Biocementation Experiments Design  
The biocementation treatments were implemented in the soil using two different methods: injection and mixing. The injection was executed in two stages: in the first stage, the synthesised hybrid enzyme carrier was injected into cylindrical soil specimens of 50-mm diameter and 100-mm length; then, the cementation solution of CaCl\({}_{2}\) and NaHCO\({}_{3}\) (0.1 M) with a pH = 8.5 was injected at the top of the soil specimens at a rate of 3 mL/min and was allowed to percolate by gravity. For the mixing method, the treatments were implemented by hand mixing for 3 min using different mixing ratios of CaCl\({}_{2}\), NaHCO\({}_{3}\) and water, CA and CA-Cu hybrid enzyme carrier, as shown in Table 3. The molarity of the cementation solution used was based on prior optimisation conducted for bioprecipitation. A set of control specimens (untreated soil) was also prepared for comparison purposes, as shown in Table 3. The unconfined compressive strength testing (UCS) of the 50 mm diameter and 100 mm length soil specimens was measured using a Multiplex 50-48-kN loading frame. The axial load was applied at a constant rate of 1%/min.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Treatment** & **Description** & **Sandy** & **CaCl\({}_{2}\)/NaHCO\({}_{3}\)** & **Sterile** & **CA Enzyme** & **CA-Cu** & **Caring Time** \\
**Option** & **Sulf(s)** & **CaCl\({}_{2}\)/NaHCO\({}_{3}\)** & **Distilled** & **(mL)** & **(mL)** & **(Day)** \\ \hline \multirow{3}{*}{Injection} & Control & 300 & 0.0/0.1/0.2/0.5 & 1.5 P.V & 0 & 0 & 7 \\  & CA Only & 300 & 0.0/0.1/0.2/0.5 & 0 & 1.5 P.V & & 7 \\  & CA-Cu & 300 & 0.0/0.1/0.2/0.5 & 0 & 0 & 1.5 P.V & 7 \\ \hline \multirow{3}{*}{Mixing} & Control & 300 & 0.0/0.1/0.2/0.5 & 50 & 0 & 0 & 7 \\ \cline{2-6}  & CA Only & 300 & 0.0/0.1/0.2/0.5 & 0 & 50 & 0 & 7 \\ \cline{1-1} \cline{2-6}  & CA-Cu & 300 & 0.0/0.1/0.2/0.5 & 0 & 0 & 50 & 7 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental conditions for biocementation treatment by injection and mixing.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Water** & **CA** & **CA-Cu** & **CaCl\({}_{2}\)** & **NaHCO\({}_{3}\)** \\
**(mL)** & **(mL)** & **(mL)** & **(mL)** & **NaHCO\({}_{3}\) (mL)** \\ \hline \hline
**Bioprecipitation** & & & & \\ Control & 10 & 0 & 0 & 10 & 10 \\ Free CA enzyme & 0 & 10 & 0 & 10 & 10 \\ Cu-CA hybrid carrier & 0 & 0 & 10 & 10 & 10 \\ \hline
**CO\({}_{2}\) capture ability** & & & & \\ Control & 100 & 0 & 0 & 100 & 0 \\ Free CA enzyme & 0 & 100 & 0 & 100 & 0 \\ Cu-CA hybrid carrier & 0 & 0 & 100 & 100 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental conditions for bioprecipitation and CO\({}_{2}\) capture ability.  
All experiments were performed in triplicate (\(n=3\)) to reduce the experimental errors and confirm the variability and validity of the results.
# Super-Frequency Sampling for Thermal Transient Analysis
## 4 Results and Discussion
### Speed-Up Factor  
From here on, only the first 10 periods are examined to achieve the desired speed-up factor of 10, as this should be sufficient. For an analysis of the temporal resolution of the reconstructed signal, the reconstructed sampling period, \(\Delta t_{\text{r},i}=t^{\prime}_{i+1}-t^{\prime}_{i}\), is calculated for all data points.  
In practice, these \(\Delta t_{\text{r},i}\) follow a distribution with the center of the distribution situated at a value very close to \(1\cdot 10^{-4}\). This is due to the fact that the total time and total number of points are well-defined. The width of the distribution depends on the accuracy of the relative frequencies. In the following text, a typical and worst-case evaluation for the speed-up factor is performed based on the above measurement. In the event of a larger reconstructed sampling period falling into a moment of particular relevance, the aforementioned worst-case scenario manifests itself.  
A histogram of the reconstructed sampling periods with the median, \(\Delta t_{\text{r}}\), and the 95 th-quantile, \(Q_{0.95}\), (i.e., the slowest 5 % of the sampling periods) is shown in Figure 7 (left). The relationship between the reconstructed sampling period, \(\Delta t_{\text{r}}\), and the speed-up factor, \(N\), is given by (2) with \(\Delta t_{\text{r}}=\Delta t_{\text{s}}/N\). The speed-up factor of the sampling frequency is, thus, given by  
\[N_{\Delta t_{\text{r}}}=\frac{\Delta t_{\text{s}}}{\Delta t_{\text{r}}}= \frac{10^{-3}\,\text{s}}{0.98\cdot 10^{-4}\,\text{s}}=10.2\,,\quad N_{Q_{0.95}}= \frac{\Delta t_{\text{s}}}{Q_{0.95}}=\frac{10^{-3}\,\text{s}}{1.2\cdot 10^{-4}\, \text{s}}=8.5\,. \tag{5}\]  
Figure 6: Comparison of the thermal impedance in the first period and the reconstructed values.  
In a subsequent step, the cumulative distribution of the reconstructed sampling period, \(\Delta t_{\rm r}\), is calculated for a different number of periods. Figure 7 (right) shows the result. Curve \"1\" represents the result when the first period is used only (see Figure 6). Here, only the sampling period of the camera with a frequency of \(1/1000\) Hz is present as no reconstruction is performed yet. With each additional period included in the calculation, the relative contribution of the reconstructed sampling periods with a temporal resolution larger than \(10^{-4}\) s decreases. For five periods (time curve), their relative contribution amounts to \(80\,\%\). For ten periods, the mean sampling period of the reconstructed signal is \(1.0004\cdot 10^{-4}\) s. This value is very close to the desired sampling period of \(1\cdot 10^{-4}\) s. An increase to eleven periods does not result in a further improvement in the overall temporal resolution as only periods below \(1\cdot 10^{-4}\) s are added.
## 2 Experimental Setup and Process  
An open loop system was used in the experiment, shown in Figure 1. The spray chamber comprised internal and external parts, as shown in Figure 2, and the inner chamber was a cylindrical stainless steel tub. The pedestal and chamber were connected by flanges and insulated with 50 mm polyurethane foam. The outer chamber consisted of two semicylindrical stainless steel tubs connected by a hinge, and it could provide a low temperature space outside the inner chamber to strengthen the cold insulation; it was insulated with a 50 mm rubber insulation material and a plastic material. The nozzle was a TG 6.5 solid cone pressure atomizer with a nozzle orifice of 2.38 mm diameter. The spray height could be adjusted by varying the length of the spray rod, as shown in Figure 2. The heat source simulation device had a high temperature co-fired ceramic heating device, and the device and a thin film thermocouple were located in the polytetrafluoroethylene (PTFE) base. Moreover, The specifications of the simulated heat sources used in this study are presented in Figure 3. The heat sink surface temperature was directly measured by the thin film thermocouple (seen in Figure 4); compared with a standard thermocouple, the temperature measurement error of the thin film thermocouple was 0.55-1.29%.  
Figure 1: Schematic diagram of experimental system.  
Figure 3: Schematic diagram of Heat source simulation device.  
Figure 2: Schematic diagram of Spray chamber and Spray rods.  
The experiment consisted of the following steps.  
1. After the experimental equipment was connected, the pipeline was purged with nitrogen gas.
2. The valves shown in Figure 1 were opened, and the liquid nitrogen's flow rate was adjusted. The heating power was then adjusted, and the change in the heat sink surface temperature was observed.
3. The experiment was stopped when the CHF was reached.
4. The experimental parameters were changed, and Steps (1)-(3) were repeated.  
In the experiments, there are two different orders of magnitude of flow rates, 30 kg\(\cdot\)h\({}^{-1}\) and 15 kg\(\cdot\)h\({}^{-1}\) respectively (hereinafter referred to as high flow rate (HG) and low flow rate (LG)). By considering different heat sink surface diameters (D), spray heights (H), and flow rates, 50 sets of experiments were conducted. The experimental conditions are presented in Table 1.
# Changes in Body Mass and Movement Strategy Maintain Jump Height Immediately after Soccer Match
## 1 Introduction
Being acutely lighter should, theoretically, facilitate greater jump height attainment in the absence of NMF if the same propulsive net impulse is applied based on the jump height being governed by the take-off velocity, which in turn is governed by the propulsive net impulse relative to body mass [16]. In addition, [PERSON] et al. [12] and [PERSON] et al. [14] reported a reduction in the braking net impulse during the hours immediately following a soccer match. A reduction in the braking net impulse would occur when there is a reduction in the peak velocity (negative direction) during the preceding unweigthing phase. This is the first phase of movement during the CMJ, in which one relaxes the leg extensor musculature to commence hip, knee, and ankle flexion under a constant gravitational acceleration [18]. A reduction in the peak unweigthing velocity would mostly be due, therefore, to a reduction in unweigthing displacement and, thus, countermovement depth, which reflects the peak (negative direction) center mass displacement between the onset of movement and the end of braking. Unfortunately, [PERSON] et al. (2021) and [PERSON] et al. (2021) did not report countermovement depth in their studies. However, countermovement depth showed the largest change (\(\sim\)8%) immediately post-match among the bilateral CMJ variables reported by [PERSON] et al. [11]. A recent study in which bilateral CMJ performances were monitored across several microcyles highlighted the association between the countermovement depth and braking net impulse, which were both reduced by the largest magnitude three days prior to a soccer match fixture [13]. Interestingly, the jump height variables remained relatively unchanged across the weekly microcycle, with pronounced changes observed mainly in the countermovement phase (combined unweighting and braking) of the CMJ [13]. A shallower countermovement depth would lead to reduced propulsive displacement, limiting the take-off velocity by constraining the distance available to propel the body's center of mass and, thus, the mechanical work performed during propulsion. The limited studies mentioned above show that jump height may or may not acutely reduce immediately after a soccer match and that this may be due to concomitant changes in body mass and/or movement strategy (e.g., countermovement depth) during the CMJ. This may mask the presence of NMF if jump height is solely reported. The purpose of this study was, therefore, to monitor soccer-match-induced changes in body mass alongside select CMJ movement strategy and outcome variables among adult male soccer players. Itwas hypothesized that both body mass and countermovement depth would be reduced post-match but jump height would remain unchanged. This study will help to inform soccer researchers and practitioners about alternate CMJ variables pertaining to movement strategy that may be more sensitive for detecting the presence of acute NMF than jump height alone.