esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
The first one started at 209.8 \({}^{\circ}\)C, the second at 233.0 \({}^{\circ}\)C. In the analyzed conditions, i.e., without pressure increase, these reactions were not explosive. It is also worth paying attention to the high value of the enthalpy of the decomposition reaction: For the first step of the decomposition process it was \(-1033\) J/g, and \(-387\) J/g for the second step (Figure 11). Figure 11: DSC analysis of AN and PA mixture composition influence on their thermal properties, heat rate 10 \({}^{\circ}\)C/min. Figure 10: DSC analysis of pure AN, pure PA, and their mixture (20%), heat rate 10 \({}^{\circ}\)C/min. Enthalpy of the mixture decomposition process \(\Delta\)H\({}_{\rm A}\) = \(-1033\) J/g, \(\Delta\)H\({}_{\rm B}\) = \(-387\) J/g (values of **A** and **B** reactions are given in Table 5). The course of the decomposition reaction of the 8% mixture was very mild. As in the case of the 20% mixture, several steps of this reaction were distinguished. The endothermic process was especially noteworthy. It coincided with the melting point of PA66, which was about 252 \({}^{\circ}\)C. The decomposition reaction began at 205.5 \({}^{\circ}\)C, and the stagnation enthalpy was \(-\)654 J/g. From the safety point of view, this was not a potentially dangerous process. The situation changed completely when the amount of polymer was increased to 50%. The course of the reaction was explosive; moreover, the process took place at low temperature (lower than 200 \({}^{\circ}\)C). The decomposition of the mixture was rapid and significant amounts of heat were released in the process: The instantaneous flux of released heat exceeded 60 W/g. The enthalpy of this process was \(-\)1095 J/g. Increasing the amount of polymer to 75% by mass of the mixture resulted in a much milder course of the decomposition reaction. As in the previous cases, the temperature of the decomposition process was very low, around 200 \({}^{\circ}\)C, and the enthalpy of the process was high (\(-\)978 J/g). The decomposition process was also multistep. Contrary to the 50% mixture, the decomposition process was not potentially explosive in the analyzed conditions. Nevertheless, all mixtures of AN with PA decomposed at relatively low temperatures (Table 5). These processes and their products can initiate further reactions taking place in the reaction environment.
## 3 Testing Results
### Damping Ratio Analysis
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Loading Rate** & **Fitting Equation** & **R2** \\ \hline \multirow{4}{*}{X43} & 2 kN/s & y = \(-\)0.063 ln(x) + 0.1167 & 0.772 \\  & 4 kN/s & y = \(-\)0.05 ln(x) + 0.0895 & 0.718 \\  & 6 kN/s & y = \(-\)0.044 ln(x) + 0.0789 & 0.677 \\ \hline \multirow{4}{*}{X48} & 2 kN/s & y = 0.0071 ln(x) + 0.0181 & 0.833 \\  & 4 kN/s & y = 0.0096 ln(x) + 0.0084 & 0.968 \\  & 6 kN/s & y = 0.0104 ln(x) + 0.006 & 0.961 \\ \hline \multirow{4}{*}{X53} & 2 kN/s & y = \(-\)0.018 ln(x) + 0.0549 & 0.520 \\  & 4 kN/s & y = 0.0021 ln(x) + 0.0151 & 0.752 \\  & 6 kN/s & y = 0.003 ln(x) + 0.0095 & 0.983 \\ \hline \multirow{4}{*}{X54} & 2 kN/s & y = \(-\)0.02 ln(x) + 0.0553 & 0.768 \\  & 4 kN/s & y = 0.0071 ln(x) + 0.0181 & 0.992 \\ \cline{1-1}  & 6 kN/s & y = \(-\)0.004 ln(x) + 0.017 & 0.987 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Fitting of the mean damping ratio of a single sample at the same rate.
# Repair Composite Adhesion Strength: A Comparison of Testing Methods
## 4 Conclusions  
During the experimental studies, data on bond strength were obtained under adhesion testing. Cement samples from dry building mixtures for the repair and restoration of concrete and reinforced concrete structures and buildings were tested using measurement methods in accordance with Ukrainian and European standards. The following observations have been concluded from the work.  
According to the Ukrainian standard, 90.5% of samples showed adhesive bond failure (AF-S) caused by weak cohesion between the repair concrete and substrate (CF-S). In contrast, the European method indicated a 76% failure rate.  
Under identical conditions and using the same binders, the Ukrainian standard exhibited moderate variability, with standard deviations of 0.42 and 0.32 for plates 1 and 2, respectively. In contrast, the EU standard displayed greater consistency for these plates, with average deviations of approximately 0.25.  
Figure 7: Relations between the calculated deviation and the experimental concrete plates made according to the EU standard.  
* Mechanical testing between concrete slabs and repair mortars deviated from the average adhesion index (2.411 MPa) by 30% or more in 71 units using Ukrainian methods, and 69 units using European regulations.
* The cohesion between epoxy glue and substrate improves the interface durability due to mechanical interlocking and chemical bonding.
* Reference plates meeting the adhesion criteria for the highest structural class of repair material PM1 as specified in the Ukrainian standard can only achieve an R3 classification according to the European standard.
* Statistical analysis shows that increasing the number of pull-off tests significantly improves the reliability of adhesion strength. However, careful interpretation is crucial; while some cases clearly indicate low adhesion, others require additional testing for definitive conclusions.
* When comparing concrete removal techniques, it is essential to consider the moisture conditions of the substrate concrete and the thickness of the adhesive layer.  
Conceptualization, S.H. and N.A.; methodology, S.H. and K.M; software, M.S.; validation, K.M., N.A. and S.H.; formal analysis, S.H. and K.M.; investigation, S.H.; resources, M.S.; data curation, S.H.; writing--original draft preparation, S.H. and K.M.; writing--review and editing, K.M.; visualization, N.A. and M.S.; supervision, S.H., N.A., K.M. and M.S.; project administration, S.H. and K.M.; funding acquisition, M.S. All authors have read and agreed to the published version of the manuscript.  
This work has been funded by Metal Centre Cakovec under the project KK.01.1.1.02.0023.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# ARCFIRE: Experimentation with the Recursive InterNetwork Architecture
## 4 Simplification of Network Management
These times scale linear up to larger networks, non-linear for the very large metro networks. * Complexity. We measured the complexity of nodes for the operations creation and validation. The minimum complexity for node creation is 5, for node validation is 3. The maximum complexity for node creation is 28, for node validation 48. The largest complexities can be found in the metro networks. The other networks have node creation complexities between 5 and 13, node validation between 3 and 12. From the numbers we can see that most nodes are rather simple in terms of DIFs and PtP DIFs, while being part of comprehensive and complex service provider networks. This simplicity is largely due to the RINA simplification using DIFs instead of complex TCP/IP configurations without scope separation. * Degree of automation. The test runs are fully automated using DMS scenarios. However, we did experience race conditions on slow machines (in this experiment the Raspberry Pi) for all networks,  
Figure 15: Validating the RINA network on Figure 12.  
on medium size machines (in this experiment on Windows/Cygwin) for larger networks, and on all machines for very large networks. So, the degree of automation is 100% on most test runs on most machines, but less in the mentioned conditions. * Touches. The number of manual operations for the management strategy to succeed is 0 on small to large networks on standard or powerful UNIX machines. The number increases by 1 per network for very large networks. * DMS Scale, including time and cost. Taking each individual test run of each of the modelled networks as a network segment, we can see that the DMS is able to handle super large networks. In this context, we created a network with 6950 segments with overall 945,400 nodes in 55:42:05 hours. On all the 4 large UNIX machines combined, and without the very large metro networks, we created a network with 4000 segments and overall 57,600 nodes in 10:07:23 hours. We can state that scaling out the DMS components happens in less than 1 s, while scaling out APEX (the underlying DMS engine) can take 5 or 10 s, depending on the chosen option. This means that we can react to event storms within a few (minimum 5) seconds for the DMS strategy. This time'should' be enough if we get an early indicator of an event storm, and the potential size the storm implies. Since event storms for configuration management are only when either a large network (many nodes) or a large number of smaller networks (with some or many nodes are created. These actions tend to happen in planned operations, not in unusual or accidental operations. One example is the action to add for instance 10,000 new nodes to a mobile network, or to deploy a new metro network, or to initially create an enterprise network.
## 3 Results and Discussion
### Large Impulse Excitation Force
The more red dots, the more differences between the two frames and the difference in time is half a rotation of the shaft. The vibration frequency was the same as the rotational frequency of the shaft. We observed one cycle of the sinusoidal waveform per one complete revolution of the shaft. Such vibrations were not present in each configuration of the tested bearing. [PERSON] and [PERSON] [25] have already investigated this phenomenon using numerical simulations. In addition to numerical research, they carried out an experimental study that showed how to make the foils assembly more rigid (using an additional intermediate foil), thus improving the dynamic parameters of the bearings. Another way of improving the dynamic properties of a foil bearing is to make its top foil more rigid. The phenomenon we observed is not noticeable with the naked eye. Only after the preparation of recordings using an ultra-high-speed camera was it possible to analyse this phenomenon, which we discovered earlier based on the results of numerical research. Research shows that there may be differences between the individual bumps of the bump foil [26]. Even if we assumed that they were roughly equal, the elasticity of individual bumps and the top foil between them have a big influence on the dynamic performance of the foil bearing. A numerical analysis of such a structure is shown by [PERSON] and [PERSON] [27]. In the figures created by the authors, we can see that the top foil located between the bumps is subjected to the pressure found in the lubrication gap. Similar behaviour of a bearing can be seen in a numerical model described by [PERSON] and [PERSON] [28]. In this paper, the authors plotted a hysteresis loop of the individual bumps. This is the same behaviour as that shown in this article, but the results were obtained experimentally. It is very time-consuming to develop a numerical model describing such behaviour, as shown in the two previously referenced articles. The static analysis shown below does not aim to accurately reflect the behaviour of the foils but to show the operating conditions of the analysed bearing.
## 4 Results
### On-Grid Operation
#### 4.1.2 Step Change in Load Power  
The tests were aimed at checking the possibility of regulating the generator power during a step change in the load power in the on-grid mode. The control is based on the signal from the measurement system installed at the connection point to the grid. The current waveforms and voltages during the test are shown in Figures 12 and 13.  
Figure 11: Current harmonics. The on-grid mode, generated power: 20 kW. Source: author’s development.  
Figure 10: Values of phase voltages and currents, and power at the grid connection. The on-grid mode, generated power: 20 kW. Source: author’s development.  
Figure 9: Waveforms of voltage, phase current, and generator current. The on-grid mode, generated power: 20 kW. 1 phase voltage; 2 phase current of the induction generator; 2 phase current on grid connection. Source: author’s development.  
In the on-grid mode, power regulation takes place without the participation of the battery. Tests were carried out with a change in the torque of the drive motor and a constant rotational speed stabilized by a generator converter. The aim was to reduce the power at the point of connection to the grid to zero. After switching on the 12 kW load, the torque of the drive motor was increased (at constant rotational speed) until the active power at the connection point was equal to 0. The time to obtain a new operating point does not exceed 2 s.  
The increase in the current consumption from the power grid is shown in Figure 13a. There is a gradual increase in the torque of the drive motor until the power at the connection is reduced to zero (Figure 13b).
# Decomposition of Multicomponent Micro-Doppler Signals Based on HHT-AMD
## 3 Decomposition Method Based on HHT-AMD
### Analytical Mode Decomposition (AMD)  
[PERSON] [28] proposed a new signal decomposition method called analytical mode decomposition (AMD) in 2012, which can decompose the frequency aliasing signals with closely spaced frequency components into several single-frequency signal components.  
Suppose \(s(t)\) is the temporal signal with \(n\) components. Its Fourier spectrum is \(S(w)\). \(s(t)\) can be written as  
\[s(t)=\sum_{i=1}^{n}s_{i}^{(d)}(t) \tag{11}\]  
In the spectral domain, each signal component has narrow-band character  
\[\left\{\begin{array}{c}s_{i}^{(d)}(t)=x_{i}(t)-x_{i-1}(t)\\ \vdots\vdots\vdots\vdots\\ s_{n}^{(d)}(t)=s(t)-x_{n-1}(t)\end{array}\right. \tag{12}\]  
\[\begin{array}{c}x_{i}(t)=\sin(\omega_{bi}t)H[s(t)\cos(x_{i}(\omega_{bi}t))] \\ -\cos(\omega_{bi}t)H[s(t)\sin(x_{i}(\omega_{bi}t))]\end{array}\right.\quad(i=1,\ldots,n-1) \tag{13}\]where \(x_{0}(t)=0\), and \(H[\square]\) donates the Hilbert transform of the function [22].  
Then the temporal sequence can be regarded as the sum of two signals  
\[s(t)=x_{1}(t)+\overline{x}_{1}(t) \tag{14}\]  
Suppose \(\omega_{b}\) is a positive dimidiale frequency, then  
\[\int_{-\infty}^{+\infty}|x_{1}(t)|^{2}dt=\int_{-\infty}^{+\infty}|\hat{x}_{1}( \omega)|^{2}d\omega/2\pi\leq \int_{-\infty}^{+\infty}|\hat{S}(\omega)|^{2}d\omega/2\pi=\int_{- \infty}^{+\infty}|s(t)|^{2}dt<\infty \tag{15}\]  
\[\int_{-\infty}^{+\infty}|\overline{x}_{1}(t)|^{2}dt=\int_{-\infty}^{+\infty}| \hat{\overline{x}}_{1}(\omega)|^{2}d\omega/2\pi\leq \int_{-\infty}^{+\infty}|\hat{S}(\omega)|^{2}d\omega/2\pi=\int_{- \infty}^{+\infty}|s(t)|^{2}dt<\infty \tag{16}\]  
By denoting \(x_{c}(t)=\cos(\omega_{b}t)\), \(x_{s}(t)=\sin(\omega_{b}t)\), the Hilbert transform of \(x_{k}(t)s(t)\) is  
\[H[x_{k}(t)s(t)]=H[x_{k}(t)s_{1}(t)]+H[x_{k}(t)\overline{x}_{1}(t)] \tag{17}\]  
According to Bedrosian theorem, the relation turns to be  
\[H[x_{k}(t)s(t)]=x_{1}(t)H[x_{k}(t)]+x_{k}(t)H[\overline{x}_{1}(t)] \tag{18}\]  
When \(k\) is evaluated to be \(c\) and \(s\), respectively, we get  
\[x_{1}(t)=\frac{x_{s}(t)H[x_{c}(t)s(t)]-x_{c}(t)H[x_{s}(t)s(t)]}{x_{s}(t)H[x_{c} (t)]-x_{c}(t)H[x_{s}(t)]}  
\(f_{1}\) and \(f_{2}\) as the dimidiate frequency. If we want to extract the signal component with frequency \(f_{i}\), a value between the \(f_{i-1}\) and \(f_{i}\) is used as the dimidiate frequency to decompose the first half of the signal; then a value between the \(f_{i}\) and \(f_{i+1}\) is used as the dimidiate frequency to decompose the first half part of the signal. The signal component with frequency \(f_{i}\) could be obtained by their subtraction. The schematic diagram is shown in Figure 2.
## 3 Materials and Methods
### Segmentation  
Segmentation is a fundamental technique in computer vision and image processing that aims to divide an image into regions with consistent attributes at the pixel level. This pixel-wise segmentation provides precise contour information of recognized objects, leading to more detailed and accurate recognition results.  
Image segmentation is typically categorized into three types: semantic segmentation, instance segmentation, and panoptic segmentation. Semantic segmentation classifies each pixel in an image into predefined categories but does not differentiate between different instances of the same category, resulting in less precise localization of individual objects. Instance segmentation extends semantic segmentation by not only classifying pixels but also distinguishing between different instances within the same category, thus providing more accurate recognition results. Panoptic segmentation combines the strengths of both semantic and instance segmentation, thus offering a comprehensive analysis by categorizing each pixel and providing instance-level segmentation information. This approach integrates semantic labels with instance masks for a more thorough image analysis.  
In scenarios like tuna fishing boats, challenges like crew activity, clutter, exposure issues, lens blurring, or smudging can complicate object recognition. While target detection tasks commonly use rectangular bounding boxes for labeling, these boxes may include significant redundancy and less valid information, particularly in noisy and occluded environments. As a result, the recognition performance of the model can be adversely affected. In contrast, polygonal labeling in segmentation tasks more accurately fits the boundaries of detected objects, thus reducing interference from extraneous information. In contrast, segmentation techniques, particularly instance segmentation, offer a more refined approach. By accurately fitting the boundaries of detected objects and minimizing interference from irrelevant information, instance segmentation enhances the recognition process. Therefore, in complex environments, such as fishing boats, instance segmentation provides a significant advantage over conventional target detection methods by delivering more accurate and reliable results.
## 4 Proposed Method
### Model Formulation  
As in GP-LVMs, we define the generation process from the latent space to the observed space through the Gaussian process. Furthermore, we establish a prior distribution of latent variables by applying the GPLRF scheme in an unsupervised manner. The model of our method is as follows:  
\[\mathbf{y}_{:,d} =\mathbf{f}(\mathbf{X})+\mathbf{\epsilon}, \tag{13}\] \[\mathbf{x}_{:,d} \sim\mathcal{N}(\mathbf{0},\alpha^{-1}\mathbf{L}^{\dagger}),\] (14) \[\mathbf{f} \sim\mathcal{GP}(\mathbf{0},k_{SM}(\cdot,\cdot)),\] (15) \[\mathbf{\epsilon} \sim\mathcal{N}(\mathbf{0},\beta^{-1}\mathbf{I}), \tag{16}\]  
where \(\mathbf{x}_{:,d}\in\mathbb{R}^{N}\) denotes the \(q\)-th column of \(\mathbf{X}\), \(\alpha\) denotes a hyperparameter to control the strength of the regularization, \(\mathbf{L}\in\mathbb{R}^{N\times N}\) is a graph Laplacian of a fuzzy topological representation \(\mathbf{W}\) in Equation (12), and \(\dagger\) denotes the pseudoinverse. From these definitions,we estimate the latent variables and kernel parameters by MAP estimation, and the objective function is given as follows:  
\[\log p(\mathbf{X}|\mathbf{Y}) =\log p(\mathbf{Y}|\mathbf{X})+\log p(\mathbf{X})+C\] \[=\sum_{d=1}^{D}\log p(\mathbf{y}_{,d}|\mathbf{X})+\sum_{q=1}^{Q} \log p(\mathbf{x}_{,q})+C, \tag{17}\]  
where  
\[p(\mathbf{x}_{,q})=\mathcal{N}(\mathbf{x}_{,q}|\mathbf{0},\alpha^{-1}\mathbf{ L}^{\dagger}). \tag{18}\]  
The novelty of our method is the use of the expressive kernel in Equation (15) and the prior distribution utilizing the locally estimated manifold \(\mathbf{W}\) in Equation (14). We further explain the effects of the prior distribution in Equation (18). The log-prior term in Equation (17) can be written as follows:  
\[\log p(\mathbf{X}) =-\frac{\alpha}{2}\sum_{q=1}^{Q}\mathbf{x}_{,q}^{\top}\mathbf{L} \mathbf{x}_{,q}+C\] \[=-\frac{\alpha}{4}\sum_{i=1}^{N}\sum_{j=1}^{N}w_{ij}||\mathbf{x} _{i}-\mathbf{x}_{j}||^{2}+C, \tag{19}\]  
where \(C\) denotes a constant. From Equation (19), the log-prior term is equal to the weighted sum of the distances between connected samples on the neighborhood graph. Latent variables are coordinated to minimize them through optimization; thus, the connected samples become closer, and the local information \(\mathbf{W}\) is reflected in the latent representation. In Equation (17), the first term is equal to Equation (4) and necessitates \(O(N^{3})\) time complexity. In the following section, we introduce the inducing method [47] into Equation (17) and derive a scalable objective function.
## 2 Materials and Methods
### Understanding Factors of Vegetation Cover: Stepwise Backward Regression
#### 2.2.3 Vegetation Directional Persistence  
In addition to the analysis of NDVI absolute values, we investigated the per-pixel time series NDVI values using a measure called vegetation directional persistence. Directional persistence utilizes a novel time series approach based on vegetation indices, such as NDVI, to determine the overall health and persistence of vegetation cover over time, in this study, from 2000 to 2016 [23]. Directional persistence (\(D\)) is a cumulative direction of change over the time period of interest when compared to a benchmark observation of NDVI [16; 23]. Using benchmark NDVI values and comparison NDVI values in years after the benchmark has been shown to be an indicator of vegetation trends over time and space [16; 23]. A random walk statistic is applied to each pixel highlighting statistically significant increase or decrease in vegetation persistence, or health of vegetation at a pixel level [23]. This statistic is based on the accumulation of consecutive outcomes during a Bernoulli process. During this process the probability of a success (step = +1) and a failure (step = -1) are independent of the position of the random walk at that time so the probabilities of a success or failure are not dependent on previous successes or failures [23]. The MODIS NDVI data, described previously, was used for the directional persistence analysis by season (DJF, MAM, JJA, SON). The benchmark seasonal mean NDVI values were obtained for the start of the time series of MODIS data, from 2000 to 2004. This accounts for any anomalous years and corresponds with the timeline from the regression analysis in the first part of this study. The latter part of the NDVI time series was the comparison period from 2005 to 2016 (n = 12). For each season, the mean NDVI pixel value of each of the comparison years was compared with the mean NDVI pixel value from the benchmark [23], for every pixel in the image. If the value in a given comparison year was higher than the benchmark, the pixel was assigned a value of +1, and if the value in a given comparison year was lower than the benchmark, the pixel was assigned a -1. These values for the 12 years were then accumulated to produce a net value of \(D\) for each pixel. Because this method uses a random walk statistic, a statistical significance can be attached to each pixel [23]. Given this is a two-tailed test, for the significance level of 0.05, pixels with values of +/-8 were considered to have a significant trend in either a positive or negative direction. For the purposes of this study, directional persistence values were mapped across the entire region of southern Africa, but overall percentages of positive and negative significant values were extracted inside of parks, as well as inside of buffer zones, so a direct comparison between these two landscapes could be made.
# Ammonia Gas Sensing Characteristic of P3 HT-rGO-MWCNT Composite Films
## 2 Experimental  
### Preparation of the Graphene Solution  
Chemicals such as P3 HT and multi-walled carbon nanotubes (MWCNT), with a purity of 99.99% were purchased from Sigma-Aldrich Ltd., product number: 755125-1G. Reduced graphene oxides (rGO) prepared by the modified Hummers method have been described in detail in previous reports [28; 29; 30]. In this study, 10 mg of rGO was dispersed in 10 mL of \(N\),\(N\)-Dimethylformamide (DMF) solvent using the ultrasonic technique for 1 h to produce a bare rGO-DMF solution.