esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Factorial-Analysis-Based Feature Selection
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Features** & **Mean** & **Std. Deviation** & **Communalities** \\  & & & **Extraction** \\ \hline CavityPressure & 139.99 & 0.0076 & 99.4 \\ CavityTemp & 45.02 & 0.1948 & 69.3 \\ DasTemp & 48.27 & 4.5963 & 79.9 \\ EtalonTemp & 44.71 & 0.0429 & 89.9 \\ WarmBoxTemp & 45.01 & 0.0359 & 88.0 \\ OutletValve & 28,652.2 & 402.65 & 86.6 \\ GPS\_ABS\_LAT & 33.5 & 0.0155 & 31.9 \\ WS\_WIND\_LON & 1.01 & 24.393 & 51.7 \\ WS\_WIND\_LAT & \(-\)5.65 & 11.604 & 49.2 \\ WS\_COS\_HEADING & \(-\)0.003 & 0.7081 & 54.7 \\ WS\_SIN\_HEADING & \(-\)0.033 & 0.6717 & 36.9 \\ WIND\_N & 1.46 & 18.535 & 62.9 \\ WIND\_E & \(-\)4.005 & 17.589 & 58.1 \\ WIND\_DIR\_SDEV & 25.22 & 24.215 & 51.9 \\ CAR\_SPEED & 4.17 & 5.5035 & 57.8 \\ CH4 & 2.005 & 0.2032 & 83.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Descriptive statistics and communalities of the factor model (N = 69,831, Initial = 1). ables, such as CavityTemp, WS WIND LON, WS COS HEADING, WIND N, WIND E, WIND_DIR_SDEV, and CAR_SPEED. However, for other variables such as GPS_ABS_LAT, WS_WIND_LAT, and WS_SIN_HEADING, the model does not work very well and only explains about half of the changes. PCA extracted this model. Table 2 shows a rotated component matrix of the suggested factor model. We also extracted the PCA model and rotated Varimax with the Kaiser normalization method, which converged in 15 iterations. In this case, we extracted seven components. The rotated component matrix is called the load, which is the main output for the principal component analysis, which then includes calculating the relationships between each variable and the calculated components. In Table 2, moderate to strong correlations between DasTemp, OutletValve, and component 1; EtalonTemp, WarmBoxTemp and component 2; WS_WIND_LON, WS_SIN_HEADING, and component 3 (here WIND_N shows a positive correlation with component 3) are shown. Furthermore, WIND_E, WIND_DIR_SDEV, and component 4; CavityTemp, CAR_SPEED and component 5; CH4 and component 6; and CavityPressure component 7 were found, with other features and each component showing very low correlations. Accordingly, the first component appears to measure DasTemp, and the second component measures EtalonTemp; the third component measures WS_WIND. The fourth component appears to measure WIND_E, the fifth component measures CavityTemp, the sixth component measures CH4, and the seventh component measures CavityPressure.
## 4 Proposed Intelligent Fault Diagnosis Method  
The intelligent fault diagnosis method of rotating machinery based on adaptive learning dictionary and 1 DCNN proposed in this paper is as follows: firstly, the adaptive learning dictionary is constructed by K-SVD algorithm, the collected vibration signal is sparsely represented by orthogonal matching pursuit algorithm, and the reconstructed signal is obtained; secondly, the reconstructed signal is normalized and divided into a training set, test set, and verification set. Finally, input the training set into the 1 DCNN network model for training; then, use cross-validation to select the optimal model through the verification set, and then evaluate the performance of the model through the test set to complete the fault diagnosis task. The specific method flow is shown in Figure 3, and the details are as follows:  
1. The original vibration signal collected by the sensor is sparsely represented and normalized, and the training set, verification set, and test set are divided according to a certain proportion.
2. Construct one-dimensional convolution neural network and set relevant network parameters: iterative steps, training batches, convolution layers, and convolution kernel size, number, and step size of each layer.
3. Initialize the network parameters, input the training samples into the 1 DCNN network in batches, and begin to perform the forward propagation of the model. The output layer obtains the accuracy and calculates the error.
4. Carry on the back propagation algorithm to the error, update the weight, and offset until the network training is completed.
5. Input the test set into the trained model, output the classification accuracy, and evaluate the classification effect.  
Figure 3: Fault diagnosis process based on adaptive learning dictionary and 1 DCNN.
# Fuel-Water Emulsions as an Alternative Fuel for Gas Turbines: A Project Summary
## 3 Research Results and Discussion
### The Influence of the Presence of an Emulsifier  
#### 3.4.1 Combustion Process  
The research conducted on the effect of adding a surfactant to the base fuel on NOx emissions shows that with increasing amounts of emulsifier, NOx emissions decrease (Figure 16). Due to the nature of the data obtained from individual experimental trials, a statistical analysis of the results was performed. For this purpose, the Student's \(t\)-test was used, and it was checked whether the mean results obtained after adding the emulsifier came from the same population as the means obtained in the reference test. The significance level of \(p=0.05\) was adopted in the analysis. As a result of this analysisit is concluded that statistically significant differences occur only between the case in which the turbine was fed with standard fuel and fuel with the addition of a 5% emulsifier. Details regarding the entire study and the statistical analysis are described in detail in [42].  
Analogously to the NOx emission results, research was carried out on the impact of changes in CO emissions resulting from the addition of a surfactant to the base fuel (Figure 17). In this case, it was shown that statistically significant differences in the results occurred only between the cases in which the gas turbine operated at a rotational speed of 80 krpm and 120 krpm and in which it was fed with base fuel and fuel with a 2% addition of surfactant. The mixture with 2% emulsifier added was characterized by increased CO emissions.  
Figure 16: The influence of emulsifier content on the change in NOx emissions.  
Figure 17: The influence of emulsifier content on the change in CO emissions.  
There was no effect of the addition of 2% surfactant on the thrust generated by the GTM120 gas turbine, on fuel consumption or on the temperature behind the combustion chamber. However, in the case of 5% emulsifier addition, a statistically significant slight decrease in the temperature behind the combustion chamber was noted for a rotational speed of 120 krpm.  
It should be noted that the 2% addition of a surfactant, which was also used in studies on fuel-water emulsion combustion (although when it is directly added to the fuel, it increases CO emissions), does not have a decisive impact on CO emissions during emulsion combustion. During the tests, it was found that the addition of the lowest percentage of water to the fuel showed a dominant tendency to reduce CO emissions (Figure 11), despite the simultaneous addition of 2% emulsifier to the fuel. This proves that when the amount of emulsifier is 2% in the emulsion, the addition of water has a decisive impact on emissions and engine operating parameters.
# Moving Object Tracking and Its Application to an Indoor Dual-Robot Path
## 1 Introduction
Section 2 introduces image processing methods, which include fuzzy color space and mean shift tracking. Section 3 introduces the structure of the control scheme. Fuzzy control is applied to obstacle avoidance and tracking moving objects. RFID is applied to verify the destination. Section 4 shows the results of this study. By image processing and path planning, the omnidirectional wheeled mobile robot can automatically search for and track moving objects. Section 5 describes the advantages and disadvantages of the method based on the results of this study and then conclusions are given. This section also provides future research directions and suggestions.
## 2 Results
### Chronic Exposure
#### 2.2.1 Neuromolecular Experiments  
As shown in Figure 3A, transcription of _casy-1_ was delayed by 0.1 uM TBBP-A and by both mixtures. However, 0.1 uM TBBP-A caused an increased transcription in the L4-stage. Similarly, all testing approaches containing 0.1 uM TBBP-A led to a significant decreased _eat-4_ transcription in L1-stage (Figure 3C). An induction was detected in stage L4 after incubation with 0.1 uM TBBP-A. An increased transcription was also observed for _unc-17_ after incubation with 50 uM DBAA and with the high concentrated mixture at L2/L3-stage (Figure 3E). However, DBAA caused a decrease in stage A1. The selected tolerance level of 2 was exceeded; also no significant changes to the control could be determined. In stage L2/L3, 50 uM DBAA caused a reduction of the transcription of _thp-1_ to factor 0.2 (Figure 3D), but, due to high deviations, there was no statistical difference to the control. As shown in Figure 3B, 0.1 uM TBBP-A and 50 uM TBBP-A caused a more than two-fold induction of _dat-1_ in L1-stage, and there was an induction by 50 uM DBAA and the mixture containing 0.1 uM TBBP-A in stage L2/L3. The stimulating effect of 0.1 uM could also be seen in L4-stage. However, again due to high deviations of the single data, no statistical significance could be derived. The mixture containing the high concentrations of organobromines led to a significantly increased transcription in L4-stage. A1-stage has to be excluded because all repeats for the control-group led to no results. The mixture of 0.1 uM TBBP-A and DBAA caused a significant increased transcription of _unc-47_ in L1-stage and A1, even if the tolerance level of 2 was not exceeded (Figure 3F). In addition 0.1 uM TBBP-A caused a significant increased transcription in stage A1. The mixture of 50 uM TBBP-A and DBAA significantly increased the amount of mRNA in L2/L3- and L4-nematodes, while 50 uM TBBP-A repressed transcription in L4- and A1-stage. For detailed values please refer to Table S8.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Strain**} & \multirow{2}{*}{\(t_{\text{ex}}\)} & \multicolumn{2}{c}{**0.1 T + 50 D Relative to**} & \multicolumn{2}{c}{**50 T + 50 D Relative to**} \\ \cline{3-6}  & & **0.1 T** & **50 D** & **50 T** & **50 D** \\ \hline \multirow{2}{*}{**BZ555**} & 24 h & - & - & - & - \\  & 72 h & - & - & - & - \\ \hline \multirow{2}{*}{**DA1240**} & 24 h & - & - & - & - \\  & 72 h & - & - & - & - \\ \hline \multirow{2}{*}{**EG1285**} & 24 h & **++** & **++** & - & **++** \\  & 72 h & **++** & **-** & **-** & **++** \\ \hline \multirow{2}{*}{**GR1366**} & 24 h & **++** & **++** & - & - \\  & 72 h & **++** & **++** & - & - \\ \hline \multirow{2}{*}{**LX929**} & 24 h & **++** & **++** & - & **++** \\  & 72 h & - & - & - & - \\ \hline \end{tabular}
\end{table}
Table 5: Significances between the mixtures and single compounds.
# Demand Flexibility: A Review of Quantification Methods, Models, and Required Data
## 4 Discussion
### Research Gaps and Future Directions
#### 4.2.5 Equity  
DR is a power grid application that is most closely related to and impacts customers. Yet, how its implementation affects individuals and households is not yet properly explored in the literature.  
Demand flexibility touches on two tenets of energy justice, namely distributional justice and recognition justice. Distributional justice refers to the fair distribution of harms and benefits across a community [73]. This is an important consideration, particularly for DR, because the benefits of DR actions are realized by everyone connected to the grid. However, the burden is only seen by those who choose to participate which could lead to ongoing inequity. Measures to ensure that demand from a specific group or 'class' of customers is not overly utilized during DR events and is a welcome first step to ensure distributional justice. This can, for instance, be achieved by limiting the number of times--either consecutively or in total--that each customer or household can be targeted for demand reduction. Furthermore, metrics to reflect DR benefits can be developed. For instance, improvement in reliability or resilience can be compared across various customer groups to ensure benefits are well distributed across all customers of the power grid. Recognition justice, on the other hand, acknowledges and focuses on the differences in capabilities and limitations of individual customers when it comes to adapting and responding to the undesired side-effects of DR participation [73]. Perhaps the most commonly acknowledged factor is related to the impact of excess indoor temperatures. For example, the effects of increased indoor air temperature due to A/C DR on various age groups. Research shows that those over the age of 65 or under the age of 5 are on average less tolerant of temperature extremes and as such, should be depriitized for DR participation during extreme weather events [74]. Perhaps less understood is how appliance DR and load shifting can affect customer convenience, depending on user demographics and the types of appliances.  
Such equity considerations are a major gap in research across studies seen in the literature. Equity and energy justice, from distributional and recognition angles, need to be properly integrated with and incorporated into demand flexibility models. Equity-focused methods can then be used to target customers with high DR potential and/or to implement targeted and localized DR events.
## 4 Experimental Setup and Results
### Validation of the Admittance Control  
We validated the applied admittance control algorithm by comparing different virtual systems with high and low values of dynamic parameters. The high dynamic parameters used in the equation of the SMD system were set to \(I_{H}\) = 3, \(D_{H}\) = 100\(I_{H}\), and \(C_{H}\) = 557\(I_{H}\). The low dynamic parameters were \(I_{L}\) = 3, \(D_{L}\) = 50\(I_{L}\), and \(C_{L}\) = 183\(I_{L}\).  
In this experiment, the developed device was controlled manually by the human operator. Prior to starting the experiment, the force sensor was calibrated precisely by hanging weights ranging from 0 to 5 kg, in increments of 0.5 kg, on each cable according to its working load.  
A circular trajectory with a radius of 20 mm on the \(X,Y\) axis and an additional linear trajectory of 8 mm on the \(Z\) axis was used. Figure 9 shows the comparison of the end-effector position and applied wrench force between the two systems. The solid blue line and the orange line represent the experimental result with high and low dynamic parameters, respectively. The experimental results indicated a considerable difference in the magnitudes of the respective wrench forces used in the two systems compared with the differences between the respective end-effector positions. Slight oscillations occurred in the presence of the external force because of friction due to the cable and inaccurate geometric parameters. Even where an external force did not exist, slight oscillations also occurred due to the gravitational force on the \(Z\) axis.
## 2 Background
### Blockchain
#### 2.3.4 Fog Computing  
Fog computing (Figure 1) is a decentralized computing model that brings data processing closer to the source, reducing latency and improving real-time capability. It distributes computing and storage tasks to devices at the edge of the network, such as routers or IoT devices, instead of relying on distant cloud servers. This proximity enables faster processing and decision-making, which is crucial for applications such as autonomous vehicles and smart cities.  
* Cloud layer-handles centralized storage and large-scale data processing tasks, such as managing extensive databases or running computationally intensive applications.
* Fog layer-acts as an intermediary between the cloud and edge devices, consisting of local servers, routers, and firewalls. This layer processes data closer to its source, reducing latency and ensuring that time-sensitive applications (e.g., vehicular networks) perform efficiently.
* Edge layer-comprises end devices like vehicles, sensors, and other IoT systems that generate and collect data. These devices communicate directly with the fog layer or through roadside units (RSUs), enabling real-time interactions and local processing.  
By adopting this layered approach, fog computing not only improves system responsiveness but also enhances security and privacy by keeping sensitive data local. At the same time, it reduces the load on cloud infrastructure by completing data-intensive tasks closer to their origin.
# Localized Vector Optical Nondiffracting Subcycle Pulses
## 1 Introduction
The extension of nondiffracting X-waves from the scalar to the vector domain was later developed [57] and expanded to include vortical properties with arbitrary frequency spectra [58]. Recent studies have also explored vector light sheets of nondiffracting light [59; 60] and vector parabolic and elliptical optical bullets [61; 62]. These advances are particularly significant given the applications of radially and azimuthally polarized pulsed beams in material processing [63; 64], along with the utility of nondiffracting beams [65; 66]. In our previous research, we investigated highly non-paraxial vector Bessel-X pulses [67], introducing analytical expressions that reveal novel properties induced by inhomogeneous polarization. We have further explored vector focus wave modes and the generalizations of X-waves and Bessel-X pulses in a dielectric dispersive medium [68]. We chose glass as the material to analyze the dispersion relation of spatiotemporal spectra [28; 68], focusing on the cone angles of forward propagating Bessel beams that allow the pulsed signal to propagate in the Bessel zone with controllable velocity \(V\), which can be superluminal [69; 70], subluminal [71], or even negative (i.e., backward propagation) [32]. We have presented this vector description for optical bullets and studied the electromagnetic structure of the resulting pulsed fields in glass, examining the relationship between the beam's transverse size, central frequency, propagation velocity, and pulse duration. In this work, we turn our attention to ultrafast optics and explore whether nondiffracting optical bullets can combine their subcycle durations with their polarization topologies. For practical applications of nondiffracting and nondispersive light pulses, a precise mathematical model of highly focused vector pulses is essential. Our model enables the investigation of pulsed beam properties with group velocities that can be superluminal, subluminal, or even negative. The results provide insights into the relationships between the transverse size of the beam, the intensities of each component--especially the \(z\) component of the electric field--and the central frequency, polarization, group velocity \(V\), and integration constant \(\gamma\). The significance of longitudinal polarization in Bessel beams lies in their unique properties, which enable an enhanced interaction with matter, precise field control, and novel applications in optics and photonics. As it turns out, localized subcycle nondiffracting vector pulses are physically possible, raising the question of their experimental realizability.
# Fiber Sensing in the 6G Era: Vision Transformers for \(\phi\)-OTDR-Based Road-Traffic Monitoring
## 2 Deep-Learning-Based Traffic Parameter Estimation  
### Vision Transformer Architecture  
Vision transformer [35] represents a paradigm shift in image recognition by leveraging the transformer architecture initially developed for natural language processing. The ViT architecture evaluated in this paper is shown in Figure 3. Firstly, ViT performs input encoding. The input images are divided into non-overlapping patches, which are subsequently embedded into high-dimensional vectors, preserving spatial relationships. The patch positional encoder adds positional encodings to provide information about each patch position. Then, a stack of transformer encoder layers processes patch embeddings, performing self-attention operations to capture global and local image information. The image patches are treated similarly to words in a natural language processing application. Our work uses the transformer encoder's output for vehicle counting and average speed estimation in a classification task. The collected images are presented to the ViT with dimensions of \(224\times 224\) and in RGB color mode. The ViT model includes transformer layers followed by a multilayer perceptron (MLP) classification head, composed of a fully connected (FC) layer with 128 and 140 neurons for the car counting and speed estimation models, respectively, and a dropout rate of 0.2.  
L1 and L2 regularization are applied to the dense layer in the classification head to prevent overfitting, with L1 = 0.001 and L2 = 0.0005. The output layer has six neurons for the vehicle counting model and fourteen neurons for the speed estimation model. The Softmax activation function is used, interpreting each output as a probability value. A learning rate scheduler is set with an initial value of \(lr=0.0001\), decaying by 50% every 10 epochs.  
Figure 3: Vision transformer architecture for traffic parameter estimation. The input image is split into patches, each patch is linearly projected and combined with positional embeddings, and then passed through a transformer encoder for car counting and speed estimation. MLP: multilayer perceptron. The 300 \(\times\) 1300 input images are resized to \(224\times 224\) for further augmentation and patch creation.  
The categorical cross-entropy loss function is used. The model is trained for 100 epochs. The ViT model is pre-trained with ImageNet and has approximately 80 \(\times 10^{6}\) parameters. A checkpoint based on the best validation accuracy is used to save the model weights and applied for inference on unseen data.