esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 FANET Routing Algorithms  
Since FANETs are characterized by high dynamics, high loss of connectivity, low bandwidth, high latency, low node density, and energy constraints, they pose a serious challenge to designing a reliable SAGIN communication architecture, where the requirements for routing algorithms are even more stringent [66; 67; 68]. Not only that, but different FANET applications have differentiated requirements for QoS. For example, applications such as forest fire rescue require real-time traffic, while applications such as information gathering and mapping can tolerate delays. Different types of applications also have different requirements for resources such as bandwidth. Thus, the differentiated QoS requirements of users can also add to the complexity of routing in a FANET [69; 70]. For this reason, we discuss FANET routing algorithms in this section, divided according to the type of routing algorithm method.
# MOIRA-UNIMORE Bearing Data Set for Independent Cart Systems
## 2 Description of the Experimental Setup
### Cart/Mover  
Each cart in the system is equipped with four primary components that contribute to its functionality and precise motion control:  
1. **Magnetic Plates:** Each cart has a pair of magnetic plates, with five magnets on each plate. These magnets define the cart's length along the track as 50 mm and interact with the linear motor's magnetic field to enable controlled motion.
2. **Encoder Flag:** The cart is equipped with an encoder flag that provides real-time feedback on its actual position along the track. This ensures precise tracking and synchronization of the cart movement with the overall system.
3. **Rolling Element Bearings:** The cart is supported by three rolling element bearings arranged to maintain stability and proper alignment with the guide rail. The configuration includes two smaller, identical bearings positioned on the top and one larger bearing at the bottom. This arrangement secures the cart to the guide rail, ensuring smooth and stable movement. All bearings share the same design characteristics: they feature two rows of deep grooves on the inside and a V-shaped groove on the outer race that matches the profile of the guide rail. In this setup, the bearing's outer race  
Figure 2: The experimental independent cart system, illustrating its modular nature.  
rotates while the inner race remains stationary. The dimensions and parameters of the bearings used in the experimental setup are detailed in Table 1, providing essential information to understand the mechanical characteristics of the system. The bearings used in this study are part of the GFX Hepco guidance systems for Beckhoff XTS. Detailed specifications regarding the bearing materials, including the types of materials in contact (e.g., steel-on-steel), are provided in the manufacturer's documentation [35]. This information is critical for understanding the material interactions and operational conditions of the bearings during the experiments.
4. **Preloading Mechanism:** A spring-like preload mechanism is incorporated to adjust grip and evenly distribute forces across the top and bottom bearings. This mechanism improves stability and provides the necessary radial forces for the bearings, ensuring consistent contact with the guide rail under varying load conditions.  
In addition, the cart can be customized to attach an end effector for transporting payloads, provided the weight remains within the maximum allowable limits. Furthermore, since the cart magnets do not come into direct contact with the motor coils but instead interact through a magnetic field, friction and wear are significantly minimized during operation. Together, these features ensure that the cart delivers reliable, efficient, and precise motion while reducing maintenance requirements, making the system highly suitable for demanding industrial applications. Although some defects, such as chipped magnets, have been reported, the primary focus of condition monitoring in this study is bearing faults, as they represent the most stressed components in this experimental configuration.
# Industrial Application of AI-Based Assistive Magnetic Particle Inspection
## 3 Hardware  
Fluorescent Magnetic Particle Testing uses 365 nm-based UV illumination. The PARADES machine is built around a custom designed lighting system that matches the international aeronautics standard limit of 1500 \(\mu\)W/cm\({}^{2}\). Indeed, international standard [14]ISO 9934-1:2015 requires a minimum of 1000 \(\upmu\)W/cm\({}^{2}\), but the PARADES machine will also be used by aeronautics manufacturers and so needs to reach those specific standards. Airbus Helicopter, Rolls-Royce, Turbomeca and other manufacturers have defined their own standards. For example, a minimum of 1500 \(\upmu\)W/cm\({}^{2}\) is required (Airbus Helicopter EI070, Turbomeca TS-00616, Paris, France). In terms of spectrum, the requirement of the ISO standard [15] (ISO 3059:2012) matches aeronautics manufacturers and the most restrictive requirement is made by Airbus Helicopter. In this case, the UV peak must be within 365 and 370 nm. The FWHM's most restrictive criterion is given by Rolls-Royce RRES-90061 (Derby, UK) as a maximum of 20 nm. The PARADES specification is directly derived from those standards, so the minimal lighting irradiance will be 1500 \(\upmu\)W/cm\({}^{2}\) with a peak between 365 and 370 nm and an FWHM \(<\) 20 nm, in order for PARADES to be able to be used in a variety of domains and by many customers. In the next section, we describe our custom lighting panel based on flood and narrow beam UV spots. As explained in the previous section, the illuminated scene size is 600 \(\times\) 400 mm. First, we will test two versions of UV spots in order to characterize the relative emitted irradiance regarding angle. Then, multi-spot lighting simulation software which has been specifically developed is presented. Next, we propose to optimize UV spot location in order to fulfill the 1500 \(\upmu\)W/cm\({}^{2}\) requirement on the 600 \(\times\) 400 mm scene with a minimum number of spots, based on spot characterization and a simulation algorithm.
# Simulation Analysis of a Three-Degree-of-Freedom Low-Frequency Resonant Mixer
## 3 Dynamic Analysis of the Three-Degree-of-Freedom Low-Frequency Resonant Mixer
### Harmonic Response Analysis of the Three-Degree-of-Freedom Low-Frequency Resonant Mixer
#### 3.2.2 Acceleration Response of the Three-Degree-of-Freedom Low-Frequency Resonant Mixer
The gravity direction can be taken as the positive direction:  
When the equipment operates at a frequency slightly lower than the third-order natural frequency, \(F_{51}=1519\pm 12350.47\) N.  
When the equipment operates at a frequency slightly higher than the third-order natural frequency, \(F_{52}=1519\pm 750.47\) N.  
From Equation (7) and the calculation results, it can be inferred that the direct factors affecting the vibration isolation effect of the three-degree-of-freedom low-frequency resonant mixer are the acceleration amplitude of the assemblies, the amplitude of the sine excitation force, and the phase difference between the three assemblies and the sine excitation force, with the phase difference having a significant impact on the vibration isolation effect. Among them, the working frequency of the equipment is slightly higher or slightly lower than the third natural frequency of the equipment, which will have a great impact on the vibration isolation effect of the equipment by affecting the phase difference between the acceleration and the vibration force of the three components.
# A Novel Self-Adaptive VM Consolidation Strategy Using Dynamic Multi-Thresholds in IaaS Clouds
## 3 Novel Self-Adaptive VM Consolidation Strategy
### Dynamic Multi-Thresholds (DMT)
#### 3.1.1 Predict Future Utilization on Linear Regression  
Since the nonlinear regression has larger time and computational overhead than linear regression, which affects the overall system load to a certain extent, the value obtained by the prediction algorithm is only used as a reference for the threshold adjustment. If too much computational overhead is spent on the prediction while the subsequent adjustment mechanism is ignored, this is not conducive to the dynamic adjustment of the threshold. Based on the above concern and the experiment results, we select linear regression as our prediction algorithm and the next section will give the details of mechanism for the dynamic adjustment of the specific threshold.  
As Linear Regression [12] is a popular approach to statistically estimate the relationship between different inputs and outputs, it can approximate a regression function by modeling the relationship between input and output variables in a straight line, where \(\alpha_{1}\) and \(\alpha_{2}\) are regression coefficients.  
\[y=\alpha_{1}x+\alpha_{2} \tag{3}\]  
To measure the goodness of a regression function is to compare the predicted output variable (\(\tilde{y}\)) with the real one (\(y\)) in data point \(i\), as their difference \(\varepsilon_{i}\) can be considered as the magnitude of the residual at each of the n data points.  
\[\varepsilon_{i}=\ y_{i}-\tilde{y}_{i} \tag{4}\]  
The proposed LR algorithm approximates a prediction function based on the linear regression method. The function shows the linear relationship between the future and current CPUutilization in all hosts as follows: where \(\alpha_{1}\) and \(\alpha_{2}\) are regression coefficient parameters, while \(\widehat{p_{c}}\) and \(p_{c}\) are the expected and current total CPU utilization values of all hosts, respectively.  
\[\widehat{p_{c}}=\alpha_{1}p_{c}+\alpha_{2} \tag{5}\]  
Here \(\alpha_{1}\) and \(\alpha_{2}\) are calculated by estimating the \(k\) last CPU utilization in all hosts. In our experiment, the value of \(k\) is set to 15 in our simulation, because the interval of utilization measurements is eight minutes. The history value of each resource utilization over two hours ago is significant to forecast its short-time future utilization.  
Moreover, the value obtained by the prediction algorithm is only used as a reference for the threshold adjustment, and the predicted value is not completely used as a future threshold value. Therefore, the following adjustment mechanism can be seen as a secondary selection and adjustment of the predicted value. That is to say, the dynamic adjustment mechanism of the thresholds shown in Section 3.1.2 not only considers the value predicted by linear regression obtained in this section, but also compares the current overall workload of the system.
## 5 Discussion and Conclusions  
Experimental and numerical investigations on the lateral-torsional buckling behavior of cantilever beams were presented. First, an experimental device was developed. Such experiments are very demanding, a fact also reported by other authors, see [4; 10]. Overall, the designed testing device worked well, and satisfying results were obtained.  
In most cases, the recalculated load-deflection curves resulted in being in good agreement with the experimentally obtained curves. In some cases, more significant deviations were found, but these deviations could be explained technically (e.g., due to an offset between the single glass panels). The shear-modulus has a strong influence on the ultimate load level. Therefore, the lower bound given in [11] was used and, compared to the tests, conservative results were obtained for nearly all cases. A more advanced calculation under consideration of a creep-law would probably give a better approximation; however, the values of the creep-law are unfortunately still unknown. In conclusion, the lower bound according to [11] is recommended as shear-modulus for numerical simulations as the obtained values from other authors are tendentially higher and therefore less conservative (see the summaries given in [22; 23] or the values given in [27]).  
Based on the measured imperfections, recommendations were given for numerical simulations. The recommended lateral imperfection of \(\
u_{0}=\mathrm{L}/400\) from literature (e.g., [8]) could be confirmed. Additionally, a value of \(\phi_{0}=0.01\) rad as unfavorable rotational imperfection at the free end of the cantilever beam should be taken into account.  
The developed design buckling curves agree with the recommended curve in [8] as a conservative approach. The curves obtained through this investigation are (slightly) more economical and are, therefore, recommended for the case of a cantilever beam under a single load or a distributed load, which is a frequent application of glass beams in bending. In addition, the presented design curves include a serviceability limit.  
Conceptualization, R.T.; Methodology, R.T. and T.N.; Formal analysis, R.T. and T.N.; Investigation, R.T. and T.N.; Resources, R.T.; Data curation, R.T. and T.N.; Writing--Original draft preparation, R.T.; Writing--Review and Editing, R.T.; Visualization, R.T. and T.N.; Supervision, R.T.; Project administration, R.T.; Funding acquisition, R.T.  
This research was funded by the University of Innsbruck (Forschungsforderungsmittel aus der Nachwuchsforderung 2017 und dem Publikationsfond der Universitat Innsbruck).  
The authors thank the University of Innsbruck, for giving us the opportunity to carry out this research and for providing the open access funding.  
The authors declare that there is no conflict of interest. The funders had no role in the design of the study, in the collection, analyses, or interpretation of data, in the writing of the manuscript or in the decision to publish the results.
# Dynamic Scene Segmentation and Sentiment Analysis for Danmaku
## 1 Introduction  
The rapid evolution of online video platforms, particularly the widespread adoption of Danmaku, offers users a distinctive environment for interactive engagement and personal expression in near-real-time. However, the transient nature and large volume of Danmaku data present significant challenges for sentiment analysis. Current methods often struggle to effectively capture the nuanced and dynamic interactions between Danmaku comments and video content. Additionally, current studies fail to fully exploit the interaction between a video's visual information and its associated Danmaku data. [PERSON] et al. [1] underscored the importance of integrating visual information with Danmaku data, concluding that such an approach could substantially improve the accuracy of sentiment analysis by providing a more comprehensive understanding of user sentiments and engagement.  
This study pioneers a deeper understanding of the emotional connection between video content and Danmaku data. We fundamentally re-engineered the shot segmentation technology, inspired by [PERSON] and [PERSON]'s work [2] on shot transition detection, to merge video segments based on emotional coherence and visual similarity.  
The main contributions of this paper are listed as follows:  
1. A scene merging method based on average color histogram similarity is introduced to more accurately identify and merge visually similar consecutive scenes and reduce erroneous scene switch predictions.  
Combining an extended sentiment lexicon with BERT (Bidirectional Encoder Representations from Transformers). A novel fuzzy feature layer is introduced to refine sentiment quantification, mapping multidimensional sentiment scores into clear categories through fuzzy variables and membership functions, enhancing both granularity and interpretability of sentiment classification, addressing the challenges of ambiguity and complexity in emotional expressions.
## 3 Results and Discussion
### Flow Curves of Tested Film-Forming Solutions
#### 3.1.1 _Gelatine_ Solutions with Pumpkin Puree
The flow curves of all tested solutions were described using the Newton model. The viscosity of the solutions decreased with an increasing share of the casein glycomacropeptide solution in the mixture. [PERSON] et al. [17] studied the viscosity of 5% bovine gelatine solutions without and with the addition of 0.25, 0.5, and 0.75% (\(w/w\)) cellulose nanofibers at 60 \({}^{\circ}\)C and shear rates from 0 to 160 s\({}^{-1}\). In the case of a solution without the addition of cellulose nanofibers, they showed that the gelatine solution is a Newtonian solution, while the solution with the addition of cellulose nanofibers is a non-Newtonian solution. In addition, the viscosity of gelatine solutions with the addition of cellulose nanofibers increases with the increase in the addition of cellulose nanofibers, which is explained by the phenomenon of percolation. The non-Newtonian fluid behaviour was attributed to additional interactions between gelatine-derived peptides and cellulose nanofiber peptides. A similar reason may explain the behaviour characteristic of non-Newtonian fluids for composite gelatine solutions with the addition of pumpkin puree tested in this study, regardless of the protein biopolymer concentration.
## 2 Materials and Methods
### Methodology
#### 2.2.3 Model Validation  
The traditional framework of hydrologic modeling includes preparing data sets, dividing a data set into training and test data sets, training the model and assessing the results from the trained model on a test data set. However, the general process described above can cause period-specific output for reservoir inflow forecasts using climate indices such as different combinations of predictors depending on how the data is split. This is because of different periodicity exhibited by predictors. Additionally, the second reason is the difference between the recent and past climate characteristics due to the climate change impact. For example, the average inflows in November and December in the period of 1998-2012 (training data) were 1.4-1.5 million tons, while the average inflows in the recent period of 2013-2016 (test data) were 5.0-6.3 million tons [1]. In this case, there is a lack of learning data for high inflow, which lowers the reliability of the machine learning method because training data did not reflect the recent increasing precipitation trend in winter season. These results indicate the necessity of considering the recent seasonal or monthly streamflow characteristics when we build a statistical model. In order to solve that issue, k-fold cross-validation was proposed instead of dividing the data sets into specific training and testing term. The target area had 19 observed data sets. Each sample data was removed, and the model was trained with the remaining (k - 1) sample data. Finally, the removed sample data was tested using the trained model, and this process is repeated for k times. For each month that is to be predicted in a given year, a separate model is built and trained using the data recorded in the same month for the remaining 18 years.
## 3 Methodology
Figure 7: List of the layers inside the feature extraction (FE) block for the RGB spectrogram channel. The FE block for the HOG and LBP channels will only differ in terms of the input layer, with the input size being 128 \(\times\) 128 \(\times\) 1. Each FE block inside the FEM consists of five layers, namely the convolution layer, with a 3 \(\times\) 3 filter size (which has filter outputs of sizes 16, 32, 64, 128, and 256 in each of the five FE blocks, respectively); the batch normalization layer; the ReLU activation layer; the average pooling layer, with a 2 \(\times\) 2 pool size; and the dropout layer, with a ratio of 0.25, except for the last FE block, where the ratio is 0.5. The three outputs of the FEM obtained from the corresponding channels are fused into a single feature vector at the concatenation layer of the GRU module. The GRU module also consists of a reshape layer of size 16 \(\times\) 768 and a GRU layer of 1024 units. For performance optimization, an average pooling layer and ReLU activation layer were favored over a max pooling layer and Leaky ReLU activation layer. The comparison results shown in Section 4 support this decision. Finally, the classification module receives the output vector of the GRU module to process and classify it into one of the three classes. The classification module consists of a flattening layer, a dense layer of 512 neurons, a ReLU layer, a dropout layer, and a dense layer of 3 neurons (representing the three classes). The final decision is obtained at the dense layer using the SoftMax activation function. A block diagram of the proposed 3-channel fusion-based CNN + GRU architecture is shown in Figure 8.
## 3 Testing Results
### Deformation Characteristics Analysis  
It can be seen that the stress-strain curves are influenced by rock bridge length. Looking beyond the stress-strain curves, the relationship between strain and cycle number at various CLSs is revealed, as plotted in Figure 5. The existence of fissures and the initiation, propagation, and connection of the rock bridge have a great influence on strain development. The evolution curves of axial, radial, and volumetric strain present fluctuating trends. In Figure 5a-d, the graphs depict the maximum axial strain per cycle versus cycle number. It is observed that axial strain increases rapidly during the initial loading stages of each cyclic loading sequence (CLS), after which it stabilizes. However, as the CLS approaches failure, the axial strain increases rapidly once again. This pattern is consistent across all tested rocks with different rock bridge lengths (RBLs), showing a  
Figure 3: Plots of axial stress (\(\sigma\)) and elapsed time (s) for rock containing stepped fissures with rock bridge lengths of 10, 20, 30, and 40 mm.  
similar evolution of axial strain where the incremental rate accelerates continuously until rock failure. The evolution of radial strain with cycle number, as illustrated in Figure 5e-h, shows an initial slow increase followed by a rapid pattern as the cycle number increases. The measured radial strain indicates expansion deformation in the middle part. Radial expansion reflects damage evolution, crack propagation, and coalescence. Influenced by rock bridge length (RBL), radial expansion is significantly affected by the presence of rock bridge segments, with spalling and the collapse of voids enhancing radial deformation development. The progression of radial strain accelerates and reaches its maximum in the final few cyclic loading stages. The evolution of volumetric strain with cycle number is depicted in Figure 5i-l. Volumetric strain serves as a reliable predictor of rock sample fracture and instability. The pattern of the volumetric strain curve closely resembles that of the radial strain curve, suggesting that radial deformation controls volumetric strain and significantly influences overall rock deformation. The curve illustrates a transition in rock behavior from compression-dominated to expansion-dominated deformation. Inflection points on the volumetric strain curve signify unstable crack propagation. Specifically, these inflection points occur at the first, second, third, and fifth CLSs for rock samples with RBLs of 10 mm, 20 mm, 30 mm, and 40 mm, respectively. These points mark the critical stages where crack propagation becomes unstable.