esa-sceva/satcom-chunk-collection huggingface dataset:
# Internet Video Delivery Improved by Super-Resolution with GAN
## 7 Experimental Results
### Results of Video Quality Assessment
We assess the quality using the VMAF, a perceptual metric, and the training of CISRDCNN works to optimize the pixel-wise quality. However, pixel-wise metrics, such as PSNR, struggle to express human visual perception [21; 33; 58]. The general conclusion is that the VSRGAN+ model showed the best outcomes among the three metrics: VMAF, PSNR, and LPIPS. The model's loss function (Equation (4)) considers pixel-wise and perceptual-wise features. This shows the versatility of the proposed model by learning how to score high values concerning those metrics. The VSRGAN+ model showed the highest results regarding perceptual quality among the evaluated SR models. We used state-of-the-art SISR [22] building blocks, namely: (i) GAN with a relativistic discriminator; (ii) residual-in-residual dense blocks; (iii) skip \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Methods** & **Resolution** & **SRCNN** & **ESPCN** & **SRResNet** & **SRGAN** & **VSRGAN+** \\ \hline \multirow{3}{*}{PSNR} & 720p & 35.89 (\(\pm\)0.51) & 35.68 (\(\pm\)0.50) & **38.44** (\(\pm\)0.47) & 33.69 (\(\pm\)0.56) & **38.09** (\(\pm\)0.49) \\ \cline{2-7}  & 1080p & 37.19 (\(\pm\)0.43) & 37.01 (\(\pm\)0.42) & **39.65** (\(\pm\)0.37) & 34.14 (\(\pm\)0.56) & **39.34** (\(\pm\)0.39) \\ \hline \multirow{3}{*}{LPIPS} & 720p & 0.064 (\(\pm\)0.004) & 0.064 (\(\pm\)0.004) & 0.057(\(\pm\)0.007) & **0.044** (\(\pm\)0.003) & **0.039** (\(\pm\)0.003) \\ \cline{2-7}  & 1080p & 0.066 (\(\pm\)0.005) & 0.066 (\(\pm\)0.005) & 0.063 (\(\pm\)0.007) & **0.050** (\(\pm\)0.004) & **0.046** (\(\pm\)0.004) \\ \hline \multirow{3}{*}{VMAF} & 720p & 78.52 (\(\pm\)1.80) & 92.53 (\(\pm\)0.66) & 79.37 (\(\pm\)1.27) & 81.41 (\(\pm\)1.12) & **96.62** (\(\pm\)0.55) \\ \cline{2-7}  & 1080p & 82.48 (\(\pm\)1.72) & 93.64 (\(\pm\)0.80) & 82.30 (\(\pm\)1.13) & 83.63 (\(\pm\)1.01) & **97.08** (\(\pm\)0.47) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Video quality assessment values. connections; (iv) enhanced perceptual loss function, and (v) upsampling with the subpixel layer. Adversarial training with a perceptual loss function contributes to a model capable of super-resolving videos with sharp images that are perceptually indistinguishable from the ground truth.
# Towards Tamper-Proof Trust Evaluation of Internet of Things Nodes Leveraging IOTA Ledger
## 4 Proposed Methodology
### Node Reliability
#### 4.1.2 Bit Error Rate (BER)  
BER is a statistical measure that quantifies the percentage of bits that are received incorrectly in a digital communication system. It is computed as follows:  
\[BER_{i}=\frac{\text{{Bit}\ errors}}{\text{{Total}\ bits}} \tag{2}\]where \(BER_{i}\) is the bit error rate of node \(i\). Node trust relies on the ability to detect errors in data transmission. By measuring BER, we assess the effectiveness of error detection in place.
# Communication Architecture for Grid Integration of Cyber Physical Wind Energy Systems
## 1 Introduction
In this study, we propose a framework for cyber physical wind energy systems (CPWESs), which consists of four layers: a WF power system layer, data acquisition and monitoring layer, communication network layer, and application layer. We specified and explained the communication architecture of a wind turbine, met-mast, substation, and control center. We modeled the data transmission of the monitoring and protection systems in wind turbines, a met-mast, and a substation based on IEC 61400-25 and IEC 61850 standards. We considered an actual WF (Southwest offshore project, South Korea) as a case study to evaluate the network topology and configuration. We developed a network simulation platform for the performance evaluation of the proposed architecture. The proposed framework contributes by providing a reference architecture model that can be used for the design and implementation of future CPWESs. The remainder of this paper is organized as follows. Section 2 presents the proposed cyber physical wind energy system. Section 3 explains WF modeling and assumptions. Section 4 discusses and analyzes the simulation results. Finally, Section 5 concludes and gives direction for future work.
# Flying Free: A Research Overview of Deep Learning in Drone Navigation Autonomy
## 4 Discussion
### Areas of Concentrated Research Effort  
The most common project archetype seen throughout the research pool follows DNN-based autonomous movement with a quad rotor drone trained from bespoke data [7] or transfer-learned from a pretrained network [25]. The most frequent focus of research work within the research pool was for basic autonomous movements. Though the quality of various implementations and methods of acquiring results differ, solutions trended towards the same structure of approximately 75-95% navigational accuracy inside the project's use case. Whilst this is a wide range of navigational accuracy achievement and exact tasks will differ across individual research works, the high levels of accuracy for DNN-based navigation policies indicate that they are effective in the environments that they are trained for. Most projects took the approach of reducing complexity either by not relying on subsystems such as GPS or network access, and/or by partially or fully focusing on optimising network efficiency for on-board operation. Most projects also avoided the use of any additional sensors, instead relying on a single camera system. No papers in the research pool considered the use of dual cameras for spatial awareness, which defied author expectations.
# The Effect of Non-Transferred Plasma Torch Electrodes on Plasma Jet: A Computational Study
## 2 Computational Modeling
### Geometry and Mesh
#### 2.2.2 Anode Shapes  
Literature indicates that anode nozzle geometry significantly affects air entrainment in non-transferred plasma torches. [PERSON] et al. and [PERSON] et al. found that stepped nozzles reduce the amount of ambient air mixed into the plasma jet compared to cylindrical nozzles [22; 33]. This reduction leads to higher plasma enthalpy and temperature. Additionally, the stepped design promotes a smoother axial velocity profile by minimizing flow disturbances caused by air intrusion. To investigate these effects, the present study includes case studies comparing cylindrical and stepped anode geometries, as shown in Figure 2 (bottom). These simulations aim to evaluate how anode shape influences plasma flow characteristics and identify which configuration offers greater thermal efficiency.
# Mapping Nanoparticles in Hydrogels: A Comparison of Preparation Methods for Electron Microscopy
## 3 Results and Discussion
### Influence of Fixation, Dehydration, and Drying on the Morphological Structures of the GNR-Hydrogel  
The AAM-BIS hydrogel is sensitive towards ethanol concentrations, but insensitive to pH for the solutions used in this paper [86]. The fixation with glutaraldehyde at pH 7.2 will, therefore, introduce small changes in the hydrogel swelling equilibrium with respect to changes in pore size and shapes. For the dehydration with ethanol, the hydrogel will decrease in size, which can be assumed to result in considerable changes in the size and shape of pores of the polymer network. The pore sizes observed in Figures 1 and 2 are, therefore, likely to be smaller than the pore size of the hydrogel in a hydrated state. The dried hydrogels may also in most cases exhibit an uncertain degree of collapsing characteristics [57,58,62,63,87]. Any degree of collapsing characteristics of the hydrogel may result in a close packing of the GNRs. The amount of close-packed GNRs in the BSE mode SEM images were difficult to characterize in our results due to the low contrast of GNRs. Other microscopy techniques may be used to study its native hydrated state such as by using cryogenic or environmental SEM that omits the fixation, dehydration, and drying [54,55].  
The AAM-AAC-BIS hydrogel is sensitive to both ethanol and pH [86,88]. The fixation with glutaraldehyde at pH 7.2 causes the hydrogel to swell significantly due to the deprotonation of the AAC, making the polymer network highly anionic. This may pose a strain on the polymer network beyond the elastic limit. Thus, the branched polymer network in Figure 3B,D could be a result of the swelling of the hydrogel. The dehydration of the hydrogel with ethanol causes the hydrogel to contract significantly. The pore size observed in Figures 3 and 4 is assumed to be smaller than the pore size of the hydrogel in its native hydrated state.
# Turbulent Drag Reduction Characteristics of Bionic Nonsmooth Surfaces with Jets
## 3 Results and Discussion
### Analysis of the Nonsmooth Surface Inspired by Sand Dunes  
To study the characteristics of drag reduction, the drag force of the smooth surface and nonsmooth surface were calculated. The drag reduction ratios were defined as follows:  
\[R_{D}=\frac{F_{D-smooth}-F_{D-nonsmooth}}{F_{D-smooth}}\times 100\% \tag{15}\]  
\[R_{f}=\frac{F_{f-smooth}-F_{f-nonsmooth}}{F_{f-smooth}}\times 100\% \tag{16}\]  
where \(R_{D}\) is the total drag reduction ratio (including pressure drag and skin friction), \(F_{D-smooth}\) is the total drag of the model with a smooth surface, \(F_{D-nonsmooth}\) is the total drag of the model with a nonsmooth surface, \(R_{f}\) is the skin friction reduction ratio, \(F_{f-smooth}\) is the skin friction of the model with a smooth surface, and \(F_{f-nonsmooth}\) is the skin friction of the model with a nonsmooth surface.  
The results of drag reduction are shown in Table 2, which show that the nonsmooth surface effectively reduced the skin friction with a reduction ratio (\(R_{f}\)) of 39.86%. However, the decrease in skin friction was accompanied by a sharp increase in pressure drag, which lead to a net increase in total drag.  
As shown in Figure 10, the nonsmooth unit cell changed the wall pressure distribution, and a peak pressure point was formed in the front of the nonsmooth unit cell (windward side). The difference in pressure between the windward side and the slip side constitutes the pressure drag, which eventually leads to a net increase in total drag. The rapid increase in pressure drag is a common problem in designing nonsmooth surfaces [41; 51; 52; 53].
## 5 Use Case: Dynamic Radio/MEC Resource Allocation and Slicing Management in High Mobility Scenarios
### System Evaluation  
The proposed system for this use case was implemented in the described realistic scenario and evaluated in two stages. In the initial stage, the FL framework was executed to establish a global model, enabling the Model 1 instances at each node to acquire knowledge about the traffic environments present in the other nodes and to perform slice admission actions efficiently. The second stage involved evaluating the system's operation, which included executing both Model 1 and Model 2 at each node, verifying their proper functionality and ensuring their interaction with the implemented database. This process follows the communication channel between xApps as described in [7], where both models interact with a global database to ensure data consistency.
## 3 Design and Implementation
### Prototyping, Implementation of the First Version of the System and Its Evaluation  
During the prototyping phase, filters were used to assess the appearance of our system, its functionality and its interactivity. We focused on the ergonomic quality of the system and the quality of the input--feedback relationship.  
The biofeedback device (Figure 1a) collects, identifies and utilizes bio-signals which are the result of physiological reactions to stressful situations, such as epidermal ephidrosis (GSR, Figure 1b) and heart rate (HR, Figure 1c). These bio-signals are classified in real-time using a machine learning technique, the Gaussian regression algorithm [24], in relation to human anxiety levels. The system is based on the Arduino open-source electronic prototyping platform. The Arduino board was programmed to collect bio-signal values.  
Next, vectors in the form of <bio-signal value 1, bio-signal value 2> are created, consisting of GSR and heart rate values. The device is connected to a computer through a USB connection (Figure 1d) and collaborates with dedicated software in order to convert bio-signals into an anxiety level on screen and advise the user on how to regulate stress through relaxation techniques.  
The first version was evaluated using the teaching and thinking-aloud protocol methods. Critical usability problems were identified. By being on their fingertips, the sensors hindered the subjects' actions (e.g., typing on a computer) when involved in an activity [25].
## 6 Finite Element Simulations
### Activation Energies Based on the Isocorressional Method  
The activation energies, determined by Equation (4) can be seen in Figure 7.  
Figure 7: **(a)** Activation energies determined with Equation (4) from the dynamic data. **(b)** Activation energies determined by both, the isothermal and dynamic data for \(\alpha\leq 50\%\).  
Figure 7a reveals a more or less constant activation energy for \(\alpha<50\%\) which is diverging above this value. [PERSON] and [PERSON] report in [PERSON] and [PERSON] (2016) the same behaviour for fast curing polyurethane resins. However, no explanation is given why the activation energy diverges. We assume that the above mentioned second, not curing related peak in the heat flow data at high temperatures may be the reason. This peak does not occur at low temperature, thus not at low conversion. Therefore, at high temperatures a compound activation energy is determined--one value for the actual curing process and the process responsible for the second peak. In order to predict the heat flow (cf. Section 7.7) it turned out that it is most practical to use a constant activation energy for \(\alpha>50\%\) of 44 kJ/mol.  
In Figure 7b it can be seen that for \(\alpha>20\%\) both types of datasets (isothermal or dynamic) yield virtually the same activation energy, as mentioned earlier. However, below 20% conversion the determined values for \(E_{\alpha}\) differ slightly from each other, but never more than aobut 4%. Such differences in the activation energy for low conversion have been reported for other types of thermosetting polymers ([PERSON] _et al._, 2016; [PERSON] and [PERSON], 2016) and were attributed to changes in the viscosity of the material. However, investigations of the issues around this matter would require more experiments and are out of the scope of this article, especially considering that the differences are rather small.  
The temperature in a large cast of the material would not be expected to rise as fast as is characteristic for the dynamic experiments, as already mentioned above. It is however expected that stepwise isothermals in a simulation describe the material more accurate. Hence, the development of the activation energy was described with a second order polynomial  
\[E_{\alpha}=\left[42+17\alpha-28\alpha^{2}\right]\,\,\mathrm{kJ/mol}, \tag{12}\]  
which was used to predict the heat flow and in the simulations presented below.  
Lastly, it shall be mentioned that the determined activation energies are not dependent on the initial guess used to minimize Equation (4). Thus, one of the problems in connection with regular fitting is solved by the isoconversional method.
# Routing Protocols for UAV-Aided Wireless Sensor Networks
## 1 Introduction
### Outline of the Paper  
The rest of this paper is organized as follows: in Section 2, the communication technologies of UAWSNs are summarized in brief. In Section 3, the taxonomy of existing routing protocols for UAWSNs is presented with a diagram. In Section 4, the routing protocols are extensively investigated and discussed. In Section 5, they are qualitatively compared in terms of outstanding features and characteristics. In Section 6, open research issues and challenges are summarized and discussed. Finally, the paper is concluded in Section 7.
# Spatiotemporal Mechanical Effects of Framework-Slope Systems Under Frost Heave Conditions
## 3 Results and Discussion  
### Stress-Strain Characteristics of Soil After Freezing  
The stress-strain behavior of frozen soil was examined using CU triaxial tests under confining pressures of 100 kPa, 200 kPa, 300 kPa, and 400 kPa. The shearing rate was set to 0.5 mm/min, and the termination criterion was either 15% axial strain or the appearance of significant plastic deformation. Testing was performed using a TAS-LF fully automated triaxial apparatus manufactured by China Zhejiang Jio Technology Co., Ltd. (Hangzhou, Zhejiang, China). The device features a digital control system capable of automated data acquisition and output. The displacement measurement accuracy reaches \(\pm\)0.1%FS, and the force resolution is better than 0.1 kN.  
The stress-strain curves obtained under different confining pressures are shown in Figure 3. Here, \(\sigma_{1}\) denotes the major principal stress, \(\sigma_{3}\) the minor principal stress, and \(\epsilon\) the axial strain. The results show that natural soil specimens exhibited strain-softening behavior, while frozen specimens displayed clear strain-hardening characteristics. The stress-strain response followed a three-stage pattern. Initially, deviatoric stress increased linearly with axial strain, indicating elastic deformation. After approximately 3% strain, the curve began to flatten, suggesting a transition from elastic to plastic deformation. The failure strength increased with confining pressure, reaching a maximum at 400 kPa. Notably, under frozen conditions, the failure strength was approximately 5.58 to 6.6 times greater than in the natural state.  
Figure 3: _Cont._
## 4 Results and Analysis
### Consistency Assessment with WELD Products
#### 4.2.3 Temporal Profile  
To further check the temporal coherence of the composites, Figure 9a-f shows the NDVI profiles of the six typical land covers, namely the sand land, open water, residential land, peat land, grassland and coniferous forest in Zoige plateau, for comparison. These six typical land covers represented different surface phenology and complexity. The NDVI profiles were retrieved from the corresponding regions cropped from Figure 7. To enhance visual judgment, the corresponding ground photos were shown to reflect the actual ground vegetation types.  
On the whole, the general change patterns of NDVI profiles were apparent from both products. The HJ NDVI and WELD NDVI profiles were in good agreement for most of the land cover types. For example, the profiles for sand land, residential land and open water were very close. This good agreement indicated that the HJ composites had a similar monitoring capability to the WELD products. Of the two products, the WELD obtained the lower NDVI value for most periods of the six land covers. Such low values were more obvious for land cover like peat land, grassland and needle-leaf forest when the growth season began. These low WELD NDVI values can be explained by the fact that the compositing methods depended on the density of observations. In addition, a gap was found in the WELD open water NDVI profile. This was caused by the lack of valid observations in the corresponding month (Figure 6). A sudden fall that does not conform to the trend of vegetation growth that can be seen in the WELD grassland profile. This was most likely caused by cloud contamination. In short, these results suggested that the HJ composites provided a comparably high quality temporal profile and showed a better performance for the time series analysis of different land cover types.  
Figure 8: Monthly composited detailed regions of HJ (row (i)) and WELD (row (ii)) cropped from Figure 6. (**a–d**) are the enlarged details from May, June, July and August, respectively.
## 3 Results  
### Stabilisation Results with Effective Microorganisms  
Figure 4 shows the development of the various chemical parameters over the course of the ensiling period. The VS level (a) remains constant with EM additive. This indicates the successful preservation of the organic matter through the formation of organic acids. In contrast, the DM in the silage without additives shows an increase towards the end, which indicates that the material is drying out. The VS level remains largely the same in both experiments. In the silage with EM, the COD (b) values show an initial increase, which indicates an increment in the solubilised organic load, followed by a decrease. The decrease indicates the stabilisation of the organic decomposition by the EM after an increment at the beginning produced organic acids. After five days, no further carbon compounds are dissolved in solution. The silage without additives shows a gradual increase in COD, after a slight decrease during the same 5 days, indicating an increase in organic load and, possibly, less effective stabilisation. The increase indicates te further degradation of carbon compounds from the biomass matrix. However, this could be beneficial for the biogas yield. Ensiling with EM results in a continuous increase in the concentration of organic acids (c), suggesting increasing acid production (an indicator of successful ensiling). In contrast, the fluctuations in silages without additives indicate less constant acid production, which represents a lower efficiency of ensiling. Both ensilages show a steady decrease in pH value (d), although no significant difference can be seen. This indicates that acid formation is essentially complete after about 12 days. The ammonium concentrations (e) in the silage treated with an EM decrease over the course of the ensiling period. This may indicate that either less protein degradation is taking place or that ammonium is not being produced during this ensiling phase. However, it could also mean that ammonium is absorbed and utilised by microbial activity. In contrast, the ammonium levels in the silage without  
The results indicate that the application of EM seems to have a positive effect on the stabilisation of the food waste silage, especially regarding the preservation of VSs and of organic acid production, which inhibit putrefractive microorganisms and resulting odour nuisance, as no odour nuisance was detected at the end of the experiments with EM. The continued production of acids, the stabilisation of organic matter, and the improvement in visual appearance and odour compared to the control group without additional treatment highlight the effectiveness of EM treatment.  
Figure 4: Stabilisation of pasteurised food waste experiments with; (**a**) VSs (%); (**b**) COD in mg/L; (**c**) organic acids in mg/L; (**d**) pH; (**e**) NH4\({}^{+}\) in mg/L.