esa-sceva/satcom-chunk-collection huggingface dataset:
# The Impact of Power Definitions on the Disaggregation of Home Loads for Smart Meter Measurements
## 3 NILM Approach Based on the Genetic Algorithm
To further enhance robustness, the difference between voltage and current waveforms is used instead of considering them separately, allowing higher immunity. \[\min\left\{abs\bigg{(}P(t)-\sum\limits_{k=1}^{N_{\text{total}}}N_{k}P_{Tx}(t )\bigg{)}+abs\bigg{(}Q(t)-\sum\limits_{k=1}^{N_{\text{total}}}N_{k}Q_{TR}(t) \bigg{)}+abs\bigg{(}D(t)-\sum\limits_{k=1}^{N_{\text{total}}}N_{k}D_{TR}(t) \bigg{)}\right\} \tag{21}\]  
\[\min\left\{abs\bigg{(}P(t)-\sum\limits_{k=1}^{N_{\text{total}}}N_{k}P_{Tx}(t )\bigg{)}+abs\bigg{(}Q(t)-\sum\limits_{k=1}^{N_{\text{total}}}N_{k}Q_{TR}(t) \bigg{)}+abs\bigg{(}D(t)-\sum\limits_{k=1}^{N_{\text{total}}}N_{k}D_{TR}(t) \bigg{)}\right\} \tag{22}\]  
where \(P_{TR}(t)\), \(P_{THk}(t)\), \(Q_{Tk}(t)\), \(Q_{THk}(t)\), \(D_{\text{Ik}}(t)\), \(D_{Vk}(t)\) and \(D_{Hk}(t)\) describe the several power consumptions of the specific type of appliance \(k\) at instant \(t\). The decision variables represent the number of active appliances of each type (\(N_{k}\)). The constraints ensure that the number of active appliances cannot be negative. A value of zero indicates that the appliance is off, while a non-zero value represents the number of identical appliances that are on. To address the load disaggregation optimization problem, a genetic algorithm from the Illinois Genetic Algorithms Laboratory [48] was employed. This algorithm determines integer decision variables, each representing the number of identical appliances in the aggregate load. Algorithm 1 outlines the proposed load disaggregation approach. To evaluate the algorithm's ability to identify individual appliances, real-world data were collected using sensors on various appliances. These data formed the basis of an experimental database containing power consumption patterns of common electrical devices. The algorithm analyzes the total power consumption and attempts to disaggregate it into individual appliance loads. Alternatively, electrical models of different appliances from various manufacturers can be used to create a synthetic dataset. This solution is currently under investigation and requires further re search to provide a comprehensive comparison and identify potential improvements. ```
1: Set \(i\) = 1;
2: Set the maximum number of each equipment;
3: Take 1 st aggregation load;
4: Set _execution_ = 1;
5:while executiondo
6: Apply cost function;
7: Apply the constraint function;
8: Apply GA for load disaggregation
9:if \(i\) = number of aggregation load then
10: Set _execution_ = 0;
11:else
12: \(i\) = \(i\) + 1;
13: Take \(i\)th aggregation load;
14:endif
15:endwhile
```  
**Algorithm 1** Main work flow of the proposed identification of appliance disaggregation.
# Spectroscopic Studies of Laser-Based Far-Ultraviolet Plasma Light Source
## 5 Conclusions  
We discussed in detail the analysis of short-wavelength data obtained from a laser plasma light source, characterized with an aberration-corrected McPherson Seya-Namioka type monochromator sensitive to the vacuum-ultraviolet spectral range. Absolute spectral irradiance calibration of laser plasmas for a wavelength FUV band from \(\approx\)123 to 164 nm was performed using a standard Deuterium lamp. Plasmas were created from fast rotating solid targets (Si, Zn, Mo, Sn and Ta) by two 1.0 \(\upmu\)m lasers, separately; a Q-switched Nd:YAG solid-state laser producing 10 ns FWHM pulses at 100 Hz repetition rates and a fiber laser operating with pulse widths (60-100 ns FWHM) and repetition rates (2-20 kHz). Plasma FUV emission is detected for the laser irradiance as low as \(\approx\)5 \(\times\) 10\({}^{6}\) W cm\({}^{-2}\) using 100 ns FWHM and 20 kHz pulses. It was demonstrated that high-density laser plasmas created by nanosecond laser pulses radiate strongly over the FUV wavelength region. Calculations using a developed collisional-radiative solver and radiation-hydrodynamic code reveal that the FUV spectral radiation mainly consists of a strong continuum emission due to a dense plasma core close to the target, which was superimposed by a vast spectral lines of low-ionized charge states. We measured a maximum conversion efficiency of \(\approx\)(1-3)%/2\(\pi\)sr of the laser light to the FUV spectral band from a few selected targets by both lasers, i.e., 10 and 60 ns FWHM pulses. Particularly, the calculation and experimental results show emission from specific wavelength regions can be enhanced by proper optimization of target and laser parameters. These results are valuable for the lithography and metrology tools in the semiconductor industry as well as to design a high-power incoherent point-like FUV light sources driven by Q-switched mJ infrared laser pulses.  
Project administration, M.R.; supervision and analyzing measurements, M.M., M.R.; software-computational modeling, M.M.; writing--review and editing, M.M., M.R. All authors have read and agreed to the published version of the manuscript. This work was supported in part by the State of Florida. All the data generated and analyzed that support the findings of this study are available from the corresponding author upon request. The authors gratefully acknowledge the University of Chicago for making the FLASH code available. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
# Semantically Controlled Adaptive Equalisation in Reduced Dimensionality Parameter Space +
## 3 Methodology
### Evaluation Criteria
#### 3.2.2 K-Nn  
In order to measure the similarities in inter-class structures within the high and low dimensional space, we apply a K-NN classifier with \(k=1\), as described in [25], and then measure the differences in classification accuracies. The nearest neighbours are found using Euclidean distances with 13 and 2 dimensions, respectively. The accuracies are derived using K-fold cross validation with \(K=20\), where 20% of the data is partitioned for testing. This allows us to measure the extent to which the between-class structures have been preserved in the reduction process, and effectively acts as a supervised structural preservation metric.
# Intelligent Interference Management in UAV-Based HetNets +
## Machine Learning Approach for UAV ICIC and Placement
### Double Deep Q Learning (DDQN)  
In our simulations, we use the double DQN (DDQN) [28] architecture, which leads to better policy evaluation in the presence of many similar-valued actions and reduces over-estimation in action values compared to DQN. The DDQN approach decouples the selection of an action from the evaluation of an action. The \(Q\)-learning equation in (13) is then modified as:  
\[Q_{n}(s,a)\leftarrow(1-\eta)Q_{n}(s,a)+\eta\left[c_{n}+\lambda Q^{\prime}_{n}( s^{\prime},\min_{a}Q_{n}(s^{\prime},a))\right], \tag{17}\]  
where \(Q^{\prime}_{n}\) is the evaluation network that is used to evaluate the policy, while \(Q^{\prime}_{n}\) is the online network that is used to select the optimal actions. All MBSs and UABSs in the UAV HetNet are modeled by a _single_ AI agent, which chooses the values of all relevant network parameters. The neural network parameters are listed in Table 4, while our DDQN approach is presented in Algorithm 3.
## 3 Establishment and Simulation of the Error Model  
The measurement results of the six DOF geometric motion errors of a rotary axis are affected by the installation errors, manufacturing errors, and crosstalk errors of each component. In order to improve measurement accuracy, we analyzed the influence of various errors on the measurement results and established the error compensation model.  
The main processes include the following: (1) Establish the coordinate system of each component: the stationary world coordinate system \"0\" (CS0); the coordinate system \"1\" (CS1), fixed on the target axis C and moving with the axis; the coordinate systems \"2\" (CS2)  
Figure 1: Measurement principle of six degrees of freedom (DOF) motion errors of a rotary axis based on a polyhedral prism. (a) Front view. (b) Top view.  
and \"3\" (CS3), fixed on the polyhedral prism B and moving with the polyhedral prism; the coordinate systems \"4\" (CS4) and \"5\" (CS5), fixed on autocollimators AC1 and AC2; the coordinate systems \"6\" (CS6), \"7\" (CS7), and \"8\" (CS8), fixed on laser displacement sensors LDS1, LDS2, and LDS3, as shown in Figure 2. (2) According to the principle of rigid body kinematics, establish the homogeneous transformation matrix \(T_{n}^{m}\) (\(T_{n}^{m}\) represents the transformation matrix from coordinate m to coordinate n) between each coordinate system to describe the relative motion between adjacent components. (3) Establish the equations of the rays. The ray is tracked by the homogeneous coordinate transformation matrix, and the six DOF geometric motion errors of the target rotary axis are obtained by the spatial coordinate change of the returned ray.
## 3 Biomechanics of Articular Cartilage
### Time-Dependent Behaviour  
The articular cartilage exhibits reversible elastic behaviour between the two equilibrium states of deformation. In order to reach the equilibrium state, a so-called transition phase is necessary, during which the strain or stress values are variable. Such a condition demonstrates that AC's behaviour is time dependent.  
Viscous behaviour consists of the creeping and relaxation behaviour of the articular cartilage. The changes in stress during the application of a constant strain are called relaxation behaviour, during which there is a gradual adaptation of the reaction force at the point of contact with a simultaneous forced constant displacement. Creep behaviour, during which the shape of a specimen subjected to a constant force gradually adapts, is responsible for the changes in deformation during the application of a constant stress. Two sources of viscous behaviour are known: flow-dependent or porous-elastic [157]. They are related to the porous structure of the articular cartilage and the flow of viscous fluid from the pores contained in the solid phase. As this flow is not instantaneous, resistance forces are induced, increasing the overall reaction force of the tissue. Viscoelastic behaviour and flow-independent viscous behaviour are related to ECM viscosity. COL II fibrils and proteoglycans sliding against each other create friction, which increases the strength of the tissue response [158]. Therefore, during the transitional phase of creep or relaxation, some of the energy required for tissue deformation is lost due to the viscous behaviour of the solid and liquid phases [12]. In contrast, the deformation of the specimen increases or the delivered force decreases.  
Time-dependent AC behaviour also shows a velocity dependence. The apparent stiffness of the articular cartilage increases as the load increases, which is related to the viscosity of the solid phase and the ability of the fluid to flow out [159]. Consequently, the AC exhibits different apparent behaviour depending on the loading rate [154; 160]. Small values of load rate influence the appropriate reorganisation of the solid matrix without dissipating energy. In contrast, at high load rates, there is insufficient time for the fluid to drain from the solid phase and reorganise the COL II fibres, resulting in time-independent behaviour [160; 161]. AC shows incompressible behaviour in the solid state, while visco-elastic behaviour is noticeable during loads with values closer to the physiological range. As the loading rate increases, the dynamic response stress also increases in a nonlinear manner [159; 160].  
In conclusion, a comparison of the mechanical properties of articular cartilage samples is only possible if usable equilibrium or dynamic data have been achieved at similar loading rates. This is because the dynamic modulus of elasticity is calculated at higher load rates compared to the equilibrium modulus of elasticity determined at low load rates.
## 2 Operation and Control
### Communications and Automation
#### 2.2.2 Big Data  
As it has been commented, an important aspect is the volume of data (Big Data) and his application generated by the SG. [PERSON] et al. [27] use linear equations and IA to eliminate certain characteristic consumption patterns in customer data. Continuing with this, dynamic energy management (DEM) is dependent on the forecast of the renewable production and the load, and these will be based on the data of the SMs, as shown by [PERSON] et al. [28]. Therefore, it is possible to affirm that the SG must have tools that allow an advanced data analysis, a high-level computing and advanced techniques for cloud computing.  
It is necessary to classify the electric charge, short-term load forecasting (STLF), distributed data mining, dedicated computational grid, cloud data and security. [PERSON] and [PERSON] [29] present an application of cooperative transmission for the meter data collection in SG based on a non-cooperative game model.  
However, big data is important in SG, but it is necessary that the data occupy little space, and for this reason, the compression of the data is fundamental, as shown by [PERSON] et al. [30]. This interconnection between SG and big data must be expanded, and from the interaction, new applications proposals will be developed.
## 2 Materials and Methods
### Overall Methodology
Instead of representing discrete time steps, a new sampling instance, and thus a new planning iteration, is initiated each time a plan finishes or fails. Nevertheless, every single plan aims to have a length equal to the sampling step size \(T\) for the closed loop or the corresponding prediction horizon \(N\) for the open loop. Following the MPC scheme, the proposed approach consists of five steps, which apply to each sampling instance, as depicted in Figure 3. First, the current state of the real-world system is measured. This measurement includes updating the state of vessels, the number of built turbines, and weather forecasts. The second step estimates the duration of offshore operations for a prediction horizon \(N\). Using this information, the third step creates a plan using a Mixed-Integer Linear Program during the open-loop optimization and returns this plan to the closed loop. The closed loop extracts a short-term plan of length \(T\) and calculates when the next sampling instance should occur if everything goes according to plan in step four. Finally, as the last step, the algorithm applies this plan to the real-world system. Figure 3: Steps of the proposed methodology and integration with the MPC scheme. For this article, we simulate the real-world system based on historical weather records. These records cover 50 years of hourly measurements of the wind speed and the wave height in Germany's Northern Sea. As only measurements are available, the following evaluations simulate weather forecasts according to the stated accuracy given in Section 1.1. Consequently, the following subsections also include descriptions of how these forecasts are simulated and how the state is updated based on a simulated real-world system. For a real-world application, these steps can be omitted or simplified.
## 4 Method Verification
### Accuracy of Longitudinal Responses  
According to the 3D finite element calculation model established by [PERSON] et al. [13], the length, width and height are 220 m, 60 m and 80 m, respectively. The length of the tunnel is 200 m. In the middle section of the numerical model, the response of the tunnel is accurate and is not affected by the truncation of both sides. The calculated material parameters are the same as Table 3. The 2.5D substructure method proposed in this paper is verified with a numerical example of the 3D finite element method. The substructure model and the incident angle of seismic wave are the same as those in Section 4.2.  
Figure 15 shows the peak value of radial displacement \(u_{r}\) along the longitudinal direction of 3D tunnel finite element model and 2.5D tunnel finite element model respectively. In fact, when seismic motion is incident on the underlying half space at any incident angle, according to [PERSON]'s law of wave propagation, the seismic peak response along the longitudinal direction of the tunnel is the same. There is only a delay of occurred time of dynamic response, so the peak value should be constant along the longitudinal direction of the tunnel, as shown in the 2.5D finite element method results presented in Figure 15. In the middle section of the tunnel, the seismic response results of the two methods are basically consistent. The two sides of the 3D long tunnel model are truncated, resulting in the error of the numerical calculation results (as shown by the red dashed box in Figure 15). It is found that the 2.5D finite element model can avoid the disturbance caused by the truncation of the tunnel in the longitudinal direction. At the same time, compared with the calculation time of 3D finite element model, the time cost of using 2.5D substructure model is only 1/6, which reflects the efficiency of 2.5D substructure calculation method.  
Figure 14: Normalized axial displacement amplitude \(U_{l}\) (dimensionless frequency \(\eta=0.105\)) when seismic waves are obliquely incident along the longitudinal direction (**a**) P wave incidence and (**b**) SV wave incidence.  
Figure 15: Distribution of peak values of radial displacement \(u_{r}\).
# A Binocular Vision-Based 3D Sampling Moire Method for Complex Shape Measurement
## 4 Experimental Verification and Application
### Experimental Application  
Directed energy deposition (DED)is a representative additive manufacturing technology, and has achieved rapid development in many fields, including aerospace and industrial machinery. In this technology, a laser is used to generate a molten pool in the cladding area, and then moves to realize layer-by-layer printing. During the printing process, the sharp temperature gradient causes residual stress, which will lead to the deformation of the printing specimen or even fracture failure [34]. Consequently, residual stress measurement is very important. Herein, we refer to [PERSON]'s idea [35] to design the cantilever beam, as shown in Figure 10a. The cantilever beam is made of 304 stainless steel, and its size is 169 \(\times\) 20 \(\times\) 5 mm\({}^{3}\). Using the same projector and cameras as in Section 4.1, the deformation of the cantilever beam is measured. A single metal layer is printed in the cladding area on the upper surface of the beam. Owing to the effect of residual stress, the cantilever beam with a cladding layer shows deflection deformation (shown in Figure 10b). In the experiment, the laser scanning speed (\(V\)) was changed to  
Figure 9: The three-point bending experiment and the 3D-SM measurement results. (**a**) Schematic diagram of the experimental device, (**b**) 3D shape of three-point bending specimens under different loads, (**c**) The shape of the specimen’s centerline.  
control the energy density during the printing process, and 3D sampling moire was used to measure the deflection change of the cantilever beam, to study the effects of different printing parameters on the deformation. As shown in Figure 10c, as the scanning speed gradually decreases, the energy density input into the substrate increases, and when the scanning speed decreases to 5 mm/s, the surface of the cladding layer is significantly oxidized. Correspondingly, the deflection measurement results in Figure 10d show that as the scanning speed decreases, the deflection of the cantilever beam tends to decrease. This result indicates that 3D sampling moire is highly beneficial to the analysis of residual deformation and stress arising from the additive manufacturing process.
# Classification of Adulterated Particle Images in Coconut Oil Using Deep Learning Approaches
## 3 Research Methodology
### Experimental Settings and Results
#### 3.2.3 Experiments with Data Augmentation
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**No**} & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Image Input**} & \multicolumn{4}{c}{**Test Model with Confusion Matrix Form**} \\  & & & & **PiCO\_V1 Dataset (\%)** & \\ \cline{5-6}  & & & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline
1 & MobileNetV2 & 224 \(\times\) 224 \(\times\) 3 & 80.20 & 71.61 & 81.15 & 76.08 \\
2 & Standard CNN & 224 \(\times\) 224 \(\times\) 3 & 54.82 & 45.09 & 61.01 & 51.86 \\
3 & Xception & 299 \(\times\) 299 \(\times\) 3 & 72.95 & 64.62 & 76.24 & 69.95 \\
4 & InceptionV3 & 299 \(\times\) 299 \(\times\) 3 & 75.13 & 68.19 & 77.14 & 72.39 \\
5 & ResNet50 & 224 \(\times\) 224 \(\times\) 3 & 68.88 & 62.12 & 72.61 & 66.95 \\
6 & ResNet101 & 224 \(\times\) 224 \(\times\) 3 & 66.44 & 66.56 & 67.29 & 66.92 \\
7 & DenseNet121 & 224 \(\times\) 224 \(\times\) 3 & 68.55 & 62.75 & 69.38 & 65.90 \\
8 & VGG16 & 224 \(\times\) 224 \(\times\) 3 & 68.52 & 60.55 & 73.02 & 66.20 \\
9 & VGG19 & 224 \(\times\) 224 \(\times\) 3 & 70.02 & 69.38 & 71.89 & 68.58 \\
10 & InceptionResNetV2 & 299 \(\times\) 299 \(\times\) 3 & 55.55 & 55.25 & 58.79 & 55.44 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Results of model accuracy from the confusion matrix of the PiCO_V1 dataset. most misclassified class was FiberT2. The model determined that 26 images should be FiberT3. A total of 315 images were misclassified for a 31.45% error rate. In the seventh experiment, we measured the VGG16 model, and the accuracy, precision, recall, and F1 score were 68.52%, 60.54%, 73.02%, and 66.20%, respectively. The most misclassified class was FiberT3. The model determined that 15 images should be FiberT1. A total of 315 images were misclassified for a 31.48% error rate. In the eighth experiment, we measured the VGG19 model, and the accuracy, precision, recall, and F1 score were 70.02%, 69.38%, 71.89%, and 68.58%, respectively. The most misclassified class was FiberT3. The model determined that 20 images should be FiberT1. A total of 299 images were misclassified, representing an error rate of 29.98%. Finally, we examined the InceptionResNetV2 model. The accuracy, precision, recall, and F1 score were 55.55%, 55.25%, 58.79%, and 55.44%, respectively. The most misclassified class was FiberT3. The model determined that 16 and 110 images should be FiberT2 and ParticleT4, respectively. A total of 445 images were misclassified, representing an error rate of 44.5%. The results of the confusion matrix for each model are shown in Table 7.