esa-sceva/satcom-chunk-collection huggingface dataset:
# Wake-Up Receiver-Based Routing for Clustered Multihop Wireless Sensor Networks
## 5 Experimental Setup and Measurement  
The measurements were carried out indoor in the laboratory at the Leipzig University of Applied Sciences. The cluster size is based on 1 Fog Node and 8 sensor nodes. All nodes and Fog Node are static and distributed over a \(30\,\mathrm{m}\times 30\,\mathrm{m}\) plane. The sink is located outside of the cluster. In order to get more comparative results, we varied the number of relaying nodes from 1 to 3. We compared the different strategies and evaluated the consumed energy and the end-to-end delay.  
Figure 6: Communication using the proposed wake-up approach.
## Abbreviations  
The following abbreviations are used in this manuscript:  
ARIMA Autoregressive Integrated Moving Average  
ADF Augmented Dickey Fuller  
AIC Akaike Information Criterion  
BIC Bayesian Information Criterion  
CNN Convolutional Neural Network  
DNN Deep Neural Network  
GRU Gate Recurrent Unit  
LSTM Long Short Term Memory  
MAE Mean Absolute Error  
MAPE Mean Absolute Percentage Error  
MSE Mean Square Error  
NLP Natural Language Processing  
RMSE Root Mean Square Error  
RNN Recurrent Neural Network  
RBF Radial Basis Function  
SVM Support Vector Machine  
SVR Support Vector Regression  
XGBOost eXtreme Gradient Boost
# Effect of Austenitizing on the Microstructure and Mechanical Properties of Gray Cast Iron
## 3 Results and Discussion
As most of the structure has transformed into ausferrite, the hardness has been significantly increased, and the wear type has changed from adhesive wear to abrasive wear. Although oxygen elements are observed on the surface through energy spectrum analysis, the content is relatively low, indicating that the uniform ausferrite matrix can effectively inhibit the rupture of the oxide film. At this time, the wear type is abrasive wear combined with oxidative wear. Figure 8: Three-dimensional morphology diagrams of different gray cast iron specimens. (**a**) Ht zt; (**b**) At 810; (**c**) At 850; (**d**) At 900, and their two-dimensional profile diagrams; (**e**) Two dimensional contour diagram and partial enlarged view. The surface of the At 900 specimen is similar to that of the At 850 specimen, also showing furrows, abrasive particles, and oxygen elements. However, the structure around  
Figure 9: Wear morphology diagrams of different gray cast iron specimens. (**a**) Ht 2t, (**b**) At 810, (**c**) At 850, (**d**) At 900. Partial energy-dispersive spectroscopy (EDS) diagrams. (**A**) EDS analysis of the area within the square frame in diagram (**a**); (**B**) EDS analysis of the area within the square frame in diagram (**b**); (**C**) EDS analysis of the area within the square frame in diagram (**c**); and (**D**) EDS analysis of the area within the square frame in diagram (**d**). the graphite on the surface has deformed. This is because high-temperature quenching increases the residual stress, causing stress concentration around the graphite, and the brittle matrix structure leads to the deformation of the structure. This is also the reason why the wear coefficient of At 850 is lower than that of At 900. The distribution of oxygen elements is directly related to the wear mechanism. The high oxygen content in Ht zt and At 900 reflects significant oxidative wear, while the low oxygen content in At 850 indicates that it can effectively inhibit the rupture of the oxide film. The uniform distribution of Type A graphite in At 850 disperses stress, whereas the deformation of graphite in At 900 leads to stress concentration and accelerates the peeling of the interface. The relatively high austenite content in At 850 provides a toughness buffer, reducing brittle spalling. In contrast, the excessive austenite content in At 900 leads to carbon supersaturation and an increase in quenching stress, weakening the comprehensive performance. Overall, for the At 850 specimen with an ausferrite content of 81.2%, the matrix has a uniform hardness and the graphite is intact. The wear surface is mainly characterized by shallow furrows and the embedding of trace abrasive particles, with the lowest oxygen content, achieving a balance between a low friction coefficient and high wear resistance. In contrast, for the At 900 specimen, due to the deformation of graphite and the increase in residual stress, interface peeling occurs, the oxygen content rebounds, the wear mechanism becomes complex, and the comprehensive performance deteriorates.
# Role of Restored Underwater Images in Underwater Imaging Applications
## 3 Different Underwater Applications
### Edge Detection  
Since underwater images are used in pattern recognition, image decomposition, visual inspection, and also in important processing tasks in computer vision related to underwater segmentation, the output underwater image needs to be clear, with good texture and details. Edge detection is particularly useful in underwater image processing, in order to localize coral reefs and other related tasks. In this paper, the Sobel edge detector is used to detect the number of edges in an image [27], whereby output from the proposed restoration method is used as input to the Sobel edge detector, in order to ascertain whether the proposed restoration method actually improves features and texture details of objects in an underwater image. A comparison of performances is then made with the input image, in terms of the number of edges detected.
## 3 Apparent Power Difference Due to the Selected Voltage Reference Point: Neutral-Displacement Power  
Let us consider the three-phase four-wire power system shown in Figure 2, where a power analyzer is connected at the PCC between the source and load. It is well known that the values of the source and load apparent powers registered by the analyzer match the actual powers of the two  
Figure 3: Power analyzer with the grounding point (G) used as the voltage reference point.  
subsystems when their neutral points (N and n) have the same voltage, as occurs when either the neutral conductor impedance is zero or the system is sinusoidal and balanced.  
However, when the neutral conductor has impedance, a difference can be observed between the source and load apparent powers in unbalanced and/or distorted systems. This difference is due to the effects of the neutral displacement phenomenon and can be determined based on our proposed neutral-displacement power (\(S_{n}\)). Using [PERSON]'s apparent powers [20], the square of this quantity must be, in our opinion, the quadratic difference between the load and source apparent powers (\(S_{l}\) and \(S_{s}\)),  
\[S_{n}^{2}=S_{l}^{2}-S_{s}^{2}. \tag{18}\]  
The definition of the neutral-displacement power (\(S_{n}\)) in Equation (18) is consistent with (1) the consideration of \(S_{n}=0\) (i.e., \(S_{l}=S_{s}\)) when there is no displacement between the load and source neutral points, and (2) the apparent power is not a conservative quantity; then, the apparent powers of two or more subsystems cannot be arithmetically added or subtracted.  
In this section, the expressions of the load and source neutral-displacement powers and their respective components are established using the apparent power expressions of Buchholz [20]. It should be noted that the expressions of the neutral-displacement powers derived from Buchholz's apparent powers are formally similar to the expressions of the neutral-displacement instantaneous powers established in Section 2.
###### Abstract
Among them is pulsed electric fields (PEF), which is based on the application of high-intensity electric fields in the form of pulses of short duration [12]. These pulses increase the intercellular potential of the cells causing the formation of pores on the cell membrane and/or a permanent disruption of the cell depending on the electric field intensity. Although PEF technology is defined as a non-thermal process, it may cause punctual and transitory increases in temperature that, however, are much lower than those induced by other technologies and do not seem to affect the nutritional properties of the treated foods [13]. The use of PEF technology for the improvement of the quality of different food products and beverages has been linked to its potential to improve organoleptic and functional properties such as color, texture, flavor, or antioxidants and vitamin contents [12]. Thus, PEF pretreatments have been successfully used to enhance the extractability of numerous bioactive compounds from different plant materials [14]. In the last few years, some promising results have been published on the benefits of using PEF to increase the oil yield in the industrial process to extract VOO. Some authors have reported oil yield increases by using PEF technology although the results differ notably depending on the olive cultivar and the conditions selected for the extraction, especially malaxation times and temperatures [15; 16; 17; 18]. Significant increases in the content of bioactive components in the oils have also been reported [15; 16]. However, some important discrepancies may be found among the results obtained by different authors, which in most cases used lab scale extraction systems [16; 17; 19]. In this sense, the aim of this work is to study the effect of PEF technology to confirm the benefits of this technology before making any recommendations on its use in oil mills. To this end, a series of extraction trials were conducted at a pilot and industrial scale. The effects that this innovative technology could have on the yield and quality of the oil was carefully analyzed, as well as the consequences on key aspects of the extraction process, such as malaxation time and temperature.
## 2 Related Work
### Discussion on Unclassified Research Methods  
Beyond the methods discussed above, additional research has explored alternative techniques to address traffic scheduling and resource allocation challenges in satellite networks. Studies on congestion control and bandwidth allocation have leveraged intelligent algorithms to dynamically reallocate resources, reducing congestion and enhancing data transmission efficiency [32; 33; 34]. For example, [PERSON] et al. [32] introduced a congestion-aware bandwidth management system that reallocates resources in real time based on traffic conditions, significantly alleviating peak-load congestion. Dynamic bandwidth allocation methods emphasize load balancing to optimize throughput and reduce latency by adapting resource distribution [35; 36; 37]. For instance, [PERSON] et al. [35] proposed a machine-learning-based approach to predict bandwidth demands and optimize node-level allocation, achieving improved efficiency and latency reduction. Fault tolerance mechanisms address network interruptions and link failures through redundancy and proactive designs to ensure seamless traffic scheduling [38; 39; 40]. [PERSON] et al. [38] developed a proactive redundancy algorithm that pre-computes alternative paths, enabling uninterrupted rerouting and enhancing network resilience. By transforming the non-linear differential equations governing the system's motion into a non-linear programming problem through direct collocation, they minimized the link rates during optimal re-orientations.  
While these methods provide effective solutions, they face limitations for practical applications. Handling network interruptions often requires additional resources, increasing complexity and management overhead, while load-balancing strategies struggle to adapt to sudden demand fluctuations, limiting their real-time responsiveness.  
Existing research on satellite network resource allocation and traffic scheduling has provided valuable insights but remains constrained by challenges such as poor adaptability and high computational complexity in dynamic environments. These limitations highlight the need for a paradigm shift in addressing resource competition, real-time scheduling, and network reliability. To overcome these gaps, this paper rethinks the problem from a collaborative optimization perspective, focusing on balancing fairness and efficiency, while dynamically adapting to fluctuating network conditions. By leveraging cooperative strategies and iterative refinement based on real-time feedback, this study seeks to enhance resource allocation and scheduling decisions, ensuring scalability and robustness in complex satellite network environments.
# The Impact of Solid Fuel Residential Boilers Exchange on Particulate Matter Air Pollution
## 2 Materials and Methods
### Boiler Types  
Overfire boilers--stationary combustion 'hand fired' sources with a natural air supply (no fan ventilation). Fuel is loaded on the grate and the flue gas passes through the layer of fuel. It is the oldest type of boiler.  
Gravity feed boilers--incorporating a large hopper above the grate. The fuel descends on to the fire, fed as required, and an inbuilt thermostatically controlled fan assists combustion, providing a quick response to demand.  
Pyrolysis boilers--involving a thermal decomposition of materials at elevated temperatures in an inert atmosphere. A highly endothermic reaction is carried out in a special heat-resistant combustion chamber.  
Automatic boilers--solid fuel is mechanically fed to a thermostatically controlled combustion. The amount of fuel needed for combustion is automatically managed according to the required boiler performance.  
Number of the solid fuel boilers being exchanged in the \"boiler exchange campaign\" for each of the scenarios is given in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Location**} & \multirow{2}{*}{**Boiler Type**} & \multicolumn{3}{c}{**Scenario A**} & \multicolumn{3}{c}{**Scenario B**} & \multicolumn{3}{c}{**Scenario C**} \\ \cline{3-10}  & & **Coal** & **Wood** & **Total** & **Coal** & **Wood** & **Total** & **Coal** & **Wood** & **Total** \\ \hline \multirow{3}{*}{Dolni Lhota} & Overfire & 6 & 19 & 24 & 1 & 3 & 4 & 0 & 0 & 0 \\  & Gravity feed & 29 & 3 & 33 & 5 & 1 & 6 & 0 & 0 & 0 \\  & Pyrolysis & 0 & 8 & 8 & 0 & 10 & 10 & 0 & 10 & 10 \\  & Automatic & 1 & 1 & 2 & 30 & 17 & 47 & 36 & 21 & 57 \\ \hline \multirow{3}{*}{Kravare} & Overfire & 21 & 90 & 111 & 3 & 3 & 6 & 0 & 0 & 0 \\  & Gravity feed & 111 & 15 & 126 & 5 & 1 & 6 & 0 & 0 & 0 \\  & Pyrolysis & 0 & 7 & 7 & 0 & 7 & 7 & 0 & 7 & 7 \\  & Automatic & 4 & 4 & 8 & 128 & 105 & 233 & 136 & 109 & 245 \\ \hline \multirow{3}{*}{Vratimov} & Overfire & 42 & 142 & 185 & 21 & 101 & 122 & 0 & 0 & 0 \\  & Gravity feed & 225 & 25 & 250 & 150 & 15 & 165 & 0 & 0 & 0 \\ \cline{1-1}  & Pyrolysis & 0 & 59 & 59 & 0 & 59 & 59 & 0 & 59 & 59 \\ \cline{1-1}  & Automatic & 9 & 8 & 17 & 105 & 60 & 165 & 276 & 176 & 452 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Number of solid fuel boilers according to location, boiler type and model scenario.  
Figure 1: Geographical position of the Moravian-Silesian region and selected locations.
# An Image Hashing-Based Authentication and Secure Group Communication Scheme for IoT-Enabled MANETs
## 5 Results and Analysis for Performance Evaluation
### Communication Cost  
We compared our scheme with SRIH and ESIH schemes to calculate the communication cost with the number of nodes. The registration phase of all the nodes is already established when these nodes join the network. Therefore, the proposed IHA remains efficient with the increase in the number of nodes. The authentication phase is further divided into two phases, communication between \(GH_{i}\) and \(Ni\) and communication between \(GH_{i}\) and NA. Figure 7 elaborates the communication cost for phase 1 authentication. In phase 1, for a better understanding, we considered the communication cost for a single node authorization. Thus, \(Ni\) forwards the join request to \(GH_{i}\). In this request, \(Ni\) forwards the ID of 16 bits, 64 bits of timestamp, 64 bits of the nonce value, and 256 bits of the hash message. In this case, 400 bits are forwarded to \(GH_{i}\). The proposed IHA was compared with SRIH and ESIH for the communication cost of authentication phase 1 in terms of the number of nodes. Figure 7b elaborates the communication cost for phase 2 authentication. In phase 2, for a better understanding, we considered the communication cost for a single node. Thus, \(GH_{i}\) forwards the request message of 256 bits to the NA. In reply, the NA forwards the required information message of 400 bits. The proposed IHA was compared with SRIH and ESIH for the communication cost of authentication phase 1 in terms of the number of nodes. The total communication cost was analyzed in both cases based on the total number of nodes. The results show that the communication cost of our proposed scheme is lower than that of the SRIH and ESIH, as these schemes also consider the image processing along with image hashing in the node authentication phases.  
Figure 6: Computational cost is shown for (**a**) group head (GH) and (**b**) network administrator/fog node (NA).
## 4 Discussion
Comparing the obtained results with the accuracy reported in [35], it can be seen that the R\({}^{2}\) are significantly better. These methods include partial least squares regression (PLSR) which is a statistical-based method, and hybrid approach of classical neural networks and the differential evolution algorithm (ANN-DE), and a method similar to the proposed in this paper using convolutional neural networks (CNN). In all of them, a unique model is trained for all the nitrogen categories, producing poor regression results always below 0.8 in R\({}^{2}\). Instead, the approach of creating separate models for each class is able to produce better results above 0.96. In fact, the approach of training different models for the classes was also analyzed in [35], although the R\({}^{2}\) achieved ranged from 0.925 to 0.968. On the other hand, a weak point of the proposed method is that it requires a previous classification of the leaf samples in the corresponding treatment class. Given a new unknown sample, there are three different models that could be applied on it. Thus, a classifier would be required to obtain the class before applying the regression network. This classification problem has been previously studied in our group, finding that it is possible to achieve it with a classification accuracy above 96% [10]. This would complete a system based on two steps: first, classification of the sample; and then, regression of the nitrogen content using the specific model. The results discussed in this section indicate that this approach is able to produce better results than training a single regression model for all the classes.
# Design and Experiments of Autonomous Path Tracking Based on Dead Reckoning
## 2 Autonomous Driving Based on Dead Reckoning  
The autonomous path tracking system used in this paper consists of a differential drive kinematics module, a pure pursuit module, a PID control module, and two actuators that control the steering and throttle pedal, as shown in Figure 1. The differential drive kinematics model calculates the current position in relation to the original position based on rear wheel speed, which can be read from the CAN bus. The pure pursuit model then outputs the target steering angle and target vehicle speed based on the error between the target position in the predefined path and the current position. The target steering angle and vehicle speed are then sent to the PID controller to generate a steering control command and throttle pedal control command as a means of driving the vehicle automatically.
## 3 Preliminaries  
### Dataset  
#### Twitter Data  
We conducted our experiments on the most well-known fake news data repository: FakeNewsNet [31]. The repository contains diverse features that can be categorized into three categories: news content, social context, and spatiotemporal information.  
News content includes the meta-attributes of news (e.g., body text and title), collected from two reliable fact-checking platforms: GossipCop and PolitiFact. Each piece of news is reviewed by domain experts and annotated as real or fake news.  
Social context includes the social engagements of news items. This includes, for instance, the posts that directly spread news pieces and the detailed information of the users, such as their Twitter profile description and the list of Twitter followers of each user.  
As for spatiotemporal information, the spatial information indicates the location explicitly provided in user profiles or posts, and the temporal information indicates the timestamps of user engagements, which can be used to study how news pieces propagate on social media. The detailed statistics of the dataset are illustrated in Table 1.  
In the retweet data crawled from the official Twitter API, the retweet of a retweet points to the original tweet, as Figure 5 has shown. As a result, we cannot establish who discovered a tweet that a user later retweeted when reconstructing interactions. For the above reasons, we could not use the dataset directly to build the network, so we built networks by crawling the corresponding \"follower network\" from FakeNewsNet. The details are described in Section 3.2.  
As shown in Figure 6, the distribution of cascade sizes (popularity) is approximately a power-law distribution, implying that most source tweets do not spread at all, while a small fraction are reposted thousands of times. Source tweets sharing fake news have an average cascade size of 2.92, with a median of 1; tweets sharing real news had an average cascade size of 4.40, with a median of 1.
## 3 Results  
Descriptive statistics showed that the experimental group had three attachments with morphological defects (11.5%), corresponding to premolars (\(n=1\)) and molars (\(n=2\)). Incomparison, the control group showed altered morphologies in six attachments (25%) on incisors (\(n=4\)), amines (\(n=1\)), and molars (\(n=1\)). The Fisher test showed no significant differences (\(p=0.281\)) (Figure 2a). Regarding the detachment of the auxiliary elements from tooth surfaces after their placement, the experimental group showed a detached attachment (3.8%), corresponding to a molar tooth. In the control group, two detached attachments corresponding to incisors (\(n=1\)) and amines (\(n=1\)) were observed (8.3%). The Fisher test showed no significant differences (\(p=0.602\)) (Figure 2b).  
From the analysis of deviations, a colorimetric map was obtained showing the maximum deviations from the reference model. The composite defects (missing areas) around the attachment are represented in blue, while the excesses of composite resin are highlighted in red (Figure 3).  
Figure 3: A colorimetric map of tooth 2.4 of prototype 1, illustrating the deviations from the reference model. Specifically, the map uses a color scale to indicate where the composite resin deviates from the ideal attachment shape.  
Figure 2: Descriptive statistics: (**a**) distribution of attachments with morphological defects and (**b**) those lost in the groups.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Prototypes** & **Mean \(\pm\) SD** \\  & **(mm)** \\ \hline Model 1 & \(-9.91\times 10^{-5}\pm 4.89\times 10^{-4}\) \\ Model 2 & \(-7.27\times 10^{-5}\pm 5.34\times 10^{-4}\) \\ Model 3 & \(-1.12\times 10^{-4}\pm 5.29\times 10^{-4}\) \\ Model 4 & \(3.32\times 10^{-5}\pm 7.01\times 10^{-4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Mean values and standard deviations (SDs) of the dental prototypes’ deviations (mm).  
Figure 5: SEM observations of the resin–enamel interface in three samples from the experimental group. Interface areas were marked at **(a)**\(840\times\), **(b)**\(590\times\), and **(c)**\(770\times\). Surface adhesion between enamel (E) and resin (R) without gaps, cracks, or air bubbles is visible in **(d)**\(1750\times\), **(e)**\(1700\times\), and **(f)**\(1900\times\).