esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Validation of the Model  
An essential step of the implementation of heat transfer and fluid flow model on the prediction of real-case system performance is the validation of its results against outcomes of an experimental system. It is essential to prove that the proposed model is able to capture the real phenomena experienced by the porous media domain while used as TES. Here, the pilot-test data presented by [PERSON] et al. [28] was used for experimental validation. Dimensions were rescaled, and the boundary conditions were readjusted to conform with the heat storage system proposed there. Hence, the model was run considering both the LTE and LTNE approaches. This was performed seeking the best match for the numerical model and the experimental results. The results are shown in Figure 5a for temperature distribution within the rock bed at different points in time, allowing for the validation of heat transfer, while Figure 5b depicts a comparison of pressure drop development across the bed with time, allowing validation of fluid flow. Both temperature and pressure can be seen to agree well with the experimental results. It is important to note that, although LTE and LTNE results are very similar for pressure drop, they are significantly different for temperature distribution across the bed, with LTNE being closer to the experimental ones. This is particularly true as temperatures develop with time. For that reason, the LTNE method, despite being more computationally intensive, was chosen for all the following simulations presented in this study.
# A Deep Parallel Diagnostic Method for Transformer Dissolved Gas Analysis
## 2 Materials and Methods
### Dataset Acquisition and Processing  
#### 2.1.1 Distribution and Preprocessing of Dataset  
In this paper, 528 cases of transformer DGA fault samples were collected from the State Grid companies as well as relevant papers. Each sample contains five major dissolved gas contents in transformer oil, including H\({}_{2}\), CH\({}_{4}\), C\({}_{2}\)H\({}_{6}\), C\({}_{2}\)H\({}_{4}\), C\({}_{2}\)H\({}_{2}\). The data distribution is shown in Table 1. As can be seen from Table 1, the number of samples of serious faults such as high temperature overheating, low energy discharge, and high energy discharge is much larger than that of minor faults such as medium temperature overheating, low temperature overheating, and partial discharge. This seems to contradict the common assumption that the occurrence frequency of serious faults should theoretically be lower than that of the minor faults. That is because the current sensitivity of sensors and the accuracy of diagnostic methods still need to be strengthened. It is still difficult to effectively diagnose latent faults, which often remain undetected until they become serious faults. From this perspective, it is necessary to study the more accurate diagnostic method of transformers.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Fault Type Code \(i\)** & **Fault Label** & **Number of Fault Samples** \\ \hline
1 & High temperature overheating & 120 \\
2 & Medium temperature overheating & 22 \\
3 & Low temperature overheating & 14 \\
4 & Partial discharge & 20 \\
5 & Low energy discharge & 239 \\
6 & High energy discharge & 113 \\  & Total & 528 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Distribution of the original dataset.  
Figure 1: Deep parallel diagnostic framework.  
Each DGA data was normalized according to Formula (1) into fault characteristic gas index as shown in Formula (2).  
\[p_{i,j}=\frac{X_{i,j}-E(X)}{D(X)} \tag{1}\]  
\[p_{i,j}=[a^{1}{}_{i,j},\ a^{2}{}_{i,j},\ \ldots\,\ a^{n}{}_{i,j}] \tag{2}\]  
where \(X_{i,j}\) represents the fault samples; \(E(X)\) is the mean; \(D(X)\) is the variance; \(p_{i,j}\) represents the normalized value of the \(j\)-th fault sample of the \(i\)-th fault type; \(a^{n}{}_{i,j}\) represents the \(n\)-th normalized fault characteristic gas index of \(p_{i,j}\). In this paper, \(n=5\).
# Harnessing the Potential of Emerging Technologies to Break Down Barriers in Tactical Communications
## 4 Use Cases
### Use Case 4: Fire Control/Support  
Fire control encompasses the coordinated utilization of resources by commanders during the execution of a combat task. It stands as a crucial and fundamental element for enhanced combat control. IoT technology facilitates the transmission of data on a massive scale and in real time, even in complex environments like combat situations. This is achieved at high speeds, ensuring extensive coverage by leveraging the low-band spectrum, particularly frequencies below 1 GHz. Another technology that is of great help in fire support is UAVs. Employing a drone equipped with thermal sensors, it is possible to track the fire and send an alarm with the exact coordinates of the location, in addition to transmitting a multitude of information, such as time speed, temperature, and real-time images of the situation, and all this without putting any human being at risk since everything is automated. Also, due to the autonomy of drones, it is possible that the control center can take control of the drone to manually collect and track the environment. The main advantage of using UAVs is the anticipation and early detection of fires in real time since it analyzes the main factors that can cause the fire, even when there is little visibility on the ground.
###### Abstract  
This study presents an investigation of electrical tree performance as well as the effect of filler concentration of silicone rubber (SiR) filled with atmospheric-pressure plasma-treated silicon dioxide (SiO\({}_{2}\)) nanofiller. Atmospheric-pressure plasma was used to treat the SiO\({}_{2}\) nanofiller surfaces to enhance compatibility with SiR matrices. A fixed AC voltage of 10 kV, 50 Hz was applied to untreated, silane-treated, and plasma-treated nanocomposites with filler concentrations of 1, 3, and 5 wt % to investigate their electrical performance during electrical freeing. The result showed that plasma-treated SiO\({}_{2}\) nanoparticles were uniformly well dispersed and formed strong covalent bonds with the molecules of the SiR polymer matrix. The plasma-treated nanocomposites were able to resist the electrical freeing better than the untreated or silane-treated nanocomposites. The increase in filler concentration enhanced the electrical tree performances of the nanocomposites. The result from this study reveals that the plasma-treated nanocomposites exhibited the best result in inhibiting the growth of electrical treeing compared to the existing surface treatment methods used in this study.  
atmospheric-pressure plasma; electrical treeing; silicon dioxide; silicone rubber +
Footnote †: journal: Applied Sciences  
[PERSON] 1  
[PERSON] 1,2,*  
[PERSON] 1  
[PERSON] 1 and [PERSON] 1  
Footnote 13  
prolonged the treeing lifetime [5], improved the interfacial bond strength between the filler polymer, and decreased the void size as well as the defects at the phase interfaces [6]. However, some researchers have reported that silane coupling did not change the tree inception voltage, which results in the faster propagation of electrical treeing [7,8].  
Atmospheric-pressure plasma treatment was introduced recently to treat nanofillers. Plasma discharges are environmentally friendly and non-toxic. This treatment was able to enhance the compatibility and the characteristics of the interface between the nanofiller and polymer matrices [9,10]. While many studies on electrical tree characteristics in silicone rubber and silicone-rubber nanocomposites have been conducted [11, 12, 13, 14], comprehensive research on electrical tree performance characteristics of silicone rubber mixed with plasma-treated nanoparticles is still lacking. In addition, previous studies have used glow discharges to treat the surface of the nanoparticles; these discharges are difficult to generate at atmospheric pressure conditions.  
In light of the above, this study presents a comprehensive investigation of the electrical tree performance of silicone-rubber-based nanocomposites filled with SiO\({}_{2}\) nanoparticles that have been treated with filamentary plasma discharges under atmospheric pressure. The plasma-treated SiO\({}_{2}\) nanoparticles and the nanocomposites were characterized using X-ray photoelectron spectroscopy (XPS), field emission scanning electron microscopy (FESEM), and energy dispersive X-ray spectroscopy (EDX), respectively.
# NDVI or PPI: A (Quick) Comparison for Vegetation Dynamics Monitoring in Mountainous Area
## 4 Discussion
### Seasonality Parameters Retrieval  
By using distinct thresholds for seasonality parameter retrieval for both the PPI and NDVI, we observed close concordance between the two indices for the SOS, EOS, and length of the season retrieval for deciduous trees, grasslands, and shrublands (Table 2). However, this concordance does not extend to the POS and all parameters for coniferous trees.  
For SOS and EOS retrieval, we adopted a methodology based on amplitude thresholds identified by [39], which demonstrated the best alignment between PPI, NDVI, and ground-observed phenological stages. This approach yielded similar results between the two indices (except for conifers). However, [42] previously illustrated in a study aimed at distinguishing remotely measured plant phenology and snow seasonality that NDVI-derived land surface phenology correlates better with snow seasonality than with actual plant phenology, in contrast to PPI, which aligns well with ground phenology observation and Gross Primary Production (GPP) dynamics. This alignment is attributed to the linearity between PPI and Leaf Area Index (LAI), as proposed by [39]. They argue that the performance of Vegetation Indices (VIs) is not solely determined by vegetation change identification but also by their robustness against background noise. Overall, the use of PPI demonstrates superior performance for SOS and EOS retrieval in areas with seasonal snow cover and should, therefore, be prioritized [39; 42; 56].  
Regarding the discrepancies in POS identification, the tendency of PPI to overestimate seasonal peaks likely exerts an influence. [39] suggest the possibility of applying an outlier filter to address this issue. For coniferous trees, the high level of noise in the signal and the plateau observed in NDVI (Figures 4 and 6) are likely attributable to its known difficulties in retrieving the signal for this vegetation class, consequently influencing all seasonality parameter retrieval [56]. Therefore, PPI appears to be more reliable for dense coniferous forests as well.
# Check for special characters and punctuation
## 4 Experimental Results and Analysis
### Evaluation Methodology
#### 4.1.2 Effectiveness Metrics  
1. Security Metrics: \(\bullet\)Detection Resistance: Measured by detection accuracy of state-of-the-art steganalysis methods \(\bullet\)Statistical Preservation: Evaluated using statistical tests comparing original and stego texts \(\bullet\)Confusion Rate: Proximity to optimal 0.5 detection rate (complete undetectability)
2. Message Integrity: \(\bullet\)Bit Error Rate: Percentage of incorrectly decoded bits \(\bullet\)Message Recovery Accuracy: Percentage of messages perfectly recovered \(\bullet\)Maximum Reliable Capacity: Highest bits-per-word rate with 100% recovery accuracy
3. Text Quality Preservation: \(\bullet\)Readability Scores: Using Flesch-Kincaid and other standard readability metrics \(\bullet\)Natural Language Score: Calculated using GPT-4-omni's perplexity on generated texts \(\bullet\)Semantic Coherence: Measured using embedding-based semantic similarity metrics
# My Smartphone tattles: Considering Popularity of Messages in Opportunistic Data Dissemination
## 7 Performance Evaluation Setup
### The OPS Simulation Framework  
The Opportunistic Protocol Simulator (OPS) ([[https://github.com/ComNets-Bremen/OPS](https://github.com/ComNets-Bremen/OPS)]([https://github.com/ComNets-Bremen/OPS](https://github.com/ComNets-Bremen/OPS))) [26] is a set of simulation models in OMNeT++ ([[https://www.omnetpp.org](https://www.omnetpp.org)]([https://www.omnetpp.org](https://www.omnetpp.org))) to simulate OppNets. It has a modular architecture where different protocols relevant to OppNets are developed and plugged in to evaluate their performance.  
The models of OPS are grouped into protocol layers of a protocol stack. In addition to forwarding protocols, the stack includes a simple link layer, wireless transmission model, application models, mobility models, and user behaviour models [6]. For our simulation studies of Keetchi, we configured those layers as follows.  
**Link layer and wireless transmission** were configured to model delay and throughput of Bluetooth Low Energy and the transmission radiuswas parameterised from real experiments at 30 m [24] (see also Table 2).  
**Application** is the so-called Herald from OPS, which generates random traffic across all nodes.  
**Mobility model** is SWIM [27], which models well location-oriented human mobility over larger periods of time (days and weeks).  
**User behaviour model** is the one which decides whether a particular user \"likes\" a particular message. To model popular and less popular messages, some messages had a higher probability of being \"liked\" than others. Details about this model are provided in [6].  
The exact parameter values are provided in Table 2. The parameters of Keetchi and Epidemic were experimentally acquired, so that each individual protocol performed at its best. The selected parameters for Epidemic also match with usually used parameters in other studies.
# A Comparative Analysis of Feature Detectors and Descriptors for Image Stitching
## 2 Overview of Feature Detectors and Descriptors
### Good Features to Track (GFTT)  
The authors of [13] made a minor change to the Harris corner detection algorithm that yields better results than the Harris corner detector. The authors claimed that detected corners are more uniformly distributed across the image.
## 3 Results
### Dataset Built From Platform  
When all functions (flight control, data acquisition, interactive interface, etc.) and the performance of the high-dynamic platform met the preset requirements, a typical UAV type-550 was selected and flight data acquisition was performed under different conditions. Figure 5 shows the actual scene of the UAV flight, which consists of a UAV, four UWB ground stations, and the host computer. The height of each UWB receiver was set to 1.8 m, and the square field size of four UWB receivers was 15 \(\times\) 15 m\({}^{2}\).  
The UAV flight data were collected in manual remote control mode. The flight measurements were performed in different directions, and the corresponding flight data were collected with different remote control instructions. The acceleration, velocity, and battery voltage were changed in real-time as the UAV control instructions increased. Due to the fluctuation in the measured trajectory by the UWB, the relatively accurate real-time velocity was obtained by trajectory derivation after Gaussian filtering. The test showed that the response time of the UAV after receiving the control instructions is less than 30 ms, so the sampling interval of the UAV flight data was set to 40 ms. After the data pre-processing, the data table between the UAV control instruction, acceleration, velocity, and battery voltage was obtained. Figure 6 shows a set of real-time flight data with a type-550 UAV in a 90\({}^{\circ}\) direction in manual remote control mode. The total experimental dataset includes 12,800 pieces of flight records in eight directions. The x-axis in Figure 6a-d indicate the sampling time in seconds. As shown in Figure 6a,d, the acceleration and control values of the UAV are in the manual remote control mode. The ROLL control value is changed and the other three variables (PIT, YAW, and THR) are all set to 1500 in Figure 6d. Both the speed of the UAV and the battery voltage show a steady rate of change, which is shown in Figure 6b,c. In the subsequent experiments, we found that the maximum acceleration of the UAV reduced as the battery voltage decreased when the UAV was flying with the maximum control value. Thus, it can be determined that different battery voltages will affect the control state of the UAV at the same control command.  
Figure 4: The positioning statistics of the UAV hovering in fixed position: (**a**) x-axis and y-axis position; and (**b**) z-axis position.  
Figure 5: The actual scene of UAV flight with a typical type-550.
## 2 Results and Discussion
### Effect of Pressing and Glycosidase on Volatile Composition in Goji Berry Wine
#### 2.3.2 Effect on Fresh Goji Berry Fermented Wine  
The pressing treatment showed a more obvious effect on the aromatic composition alteration in the fresh goji berry wine fermentation. For instance, 44 volatile compounds exhibited significant changes on their concentration in the wine after the pressing treatment (Table 4). More importantly, the majority of these volatile compounds were the dominant aromatic compounds in the wine. For example, 2-methyl-1-propanol, ethyl acetate, isopentyl alcohol, and 2,3-butanediol showed their highest contents in the fresh goji berry fermented wine after pressing treatment. 1-Hexanol is considered a significant aroma enhancer, with a concentration of 20-100 mg/L in wine [39; 40]. The pressing treatment significantly increased its concentration in the wine, which could contribute its positive odor features in the wine aroma. Glycosidase did not significantly enhance the aromatic levels in the fresh goji berry fermented wine. Six volatiles showed concentration differences in the wines with and without the glycosidase treatment. However, none of them exhibited an OAV value above 1. No significant effect of the interaction between pressing and glycosidase treatments was observed either.  
We speculate that the fresh goji berries, compared to the dried goji berries, possessed more intact cell structures, which limits the extraction of volatile compounds and their precursors in the fresh berries during the vinicification process. However, the pressing treatment might destroy the cell structure to release more aromatic compounds and their precursors, increasing their levels in the wine. Additionally, we also speculate that bound volatiles might exist in low levels in goji berries, since the application of glycosidase did not exert a significant effect on the release of more algycones in the goji berry wines. It should be noted that the glycosidase AR2000 used in this study is a non-specific glycosidase that possesses \(\alpha\)-l-arabinofuranosidase, \(\alpha\)-l-arabinopyranosidase, \(\alpha\)-l-rhamnosidase, and \(\alpha\)-d-glucosidase native activity [41].
## 3 Results
### Results of the Analysis of the Micro-Samples
#### 3.3.2 Result of Microstratigraphic Analysis  
The cross-sections, under visible reflected light and ultraviolet fluorescence, are shown in Figure 9. They were used mainly to investigate the painting technique in different parts of the wall painting. In all cases, the painting layer was well-adhered to the plaster, suggesting the use of a fresco technique. This assessment may be further confirmed by the absence of fluorescence in the painting layer, apart from the pale blue fluorescence that could be associated with calcium carbonate, the main constituent of the plaster and binder of the pigments. The painting layers have different thicknesses, sometimes, as in sample S5 (Figure 9G,H) of only few microns that, in fact, represent the preparatory drawing generally made using so-called sinopia, a red earth pigment based on iron oxides. In the cross-section of sample S5, the protective layer applied in the previous restoration is visible on the surface and exhibits an orange fluorescence. This same response is partially visible in the S1 and S6 samples' cross-sections as well.  
Sample S3, from the blue background in the lower part of the painting, clearly shows the malt glassy particles applied using a fresco technique with a superimposed yellow layer that could be associated with the decorative element under the [PERSON]'s foot. This result suggests that the blue background was painted on the entire surface, at least in this portion of the wall painting, and the yellow decoration was applied after, directly on the blue malt.  
The layers' superimposition is visible in the sample S2 cross-section, where the thin red layer, used to create the false marble decoration of the frame, is covered by a discontinuous black layer that could be sooty material fixed via the protective layer of the 2003-2004 restoration.  
The sample S4 cross-section shows an thick orange layer adhering to the plaster and the two pinky and thick layers used to make the flesh tone.  
The surface of sample S4's cross-section is partially detached and appears discontinuous. This appearance can be explained by the hard cleaning treatment performed during the 2003-2004 restoration, clearly visible in the photo of the painting before our work (see Figure 1A, upper right side of the painting).  
Figure 8: FTIR spectrum of sample S1.  
Figure 9: Microphotographs of the cross-sections of samples S2 (**A**,**B**), S3 (**C**,**D**), S4 (**E**,**F**), S5 (**G**,**H**) and S6 (**I**,**J**) under reflected visible lighting (**A**,**C**,**E**,**G**,**I**) and ultraviolet fluorescence (**B**,**D**,**F**,**H**,**J**).
# A Flexible Localization Method of Multi-Constrained Free-Form Surface Based on the Profile Curves
## 3 The Localization Problem of the Near-Net Shape Blade Surface
### The Profile Curve Calculation of the To-Be-Cut Surface  
In the design coordinate system of the blade, the XOY plane is chosen to be perpendicular to the blade stack axis. The X axis is defined as parallel to the average cord or the axis of the engine, and the positive direction is from the leading edge to the trailing edge. If the X axis is not parallel to the axis of the engine, then the angle between the X axis and the axis of the engine must be rounded to the degree. The Z axis is set to be perpendicular to the engine axis and the Y axis is positively determined based on the right-hand rule. Thus, for the to-be-cut blade surface, a series of section planes would intersect with the blade surface. The intersection profile is the profile curve which is perpendicular to the Z axis.  
The optimization constraints are judged within the blade sections. If the to-be-cut blade surface is covered by the blade billet and inside the profile tolerance band on all blade sections, it would be an available surface. Therefore, the profile curves on the blade sections need to be calculated (Figure 6). The profile curve in this paper is calculated by the Newton iteration method.  
The parametric equation of the blade surface is  
\[s(u,v)=\sum_{i=0}^{m}\sum_{j=0}^{n}N_{i,3}\cdot N_{j,3}\cdot V_{i,j} \tag{11}\]  
where \(V_{i,j}=\left(x_{i,j},y_{i,j},z_{i,j}\right)\) are the control points of the surface.  
Figure 6: Profile curves calculation between the section plane and the to-be-cut blade surface.  
The equation of blade sections is:  
\[z=z_{m} \tag{12}\]  
The intersection of the surface \(s(u,v)\) and the plane \(z_{m}\) satisfies  
\[s_{z}=z_{m} \tag{13}\]  
Which is used to determine the surface parameter value \((u,v)\).  
The transformations of the profile curves on the blade sections are very small. In the optimization, the parameter \(u\) of the nominal blade is inherited. The parameter is discretized into a sequence \([u_{0},u_{1},\cdots u_{n}]\) in the range \([0,1]\). Then the initial value of the Newton iterative method is \(\left[\begin{array}{c}u_{0},u_{1}\cdots u_{n}\\ v_{i},v_{i}\cdots v_{i}\end{array}\right]\). The coordinates of the points \((x_{i},y_{i})\), i = 0,1,\(\cdots n\) on the surface are obtained by substituting \((u,v)\). Finally, the expression of profile curve is obtained according to the calculus of interpolation of the coordinate points on the surface.