esa-sceva/satcom-chunk-collection huggingface dataset:
# Power Load Demand Forecasting Model and Method Based on Multi-Energy Coupling
## 4 Case Analysis
### Load Forecasting With AFWA-SVM  
The selected result data set of mRMR was randomly divided into test set and training set. Parameters of adaptive fireworks algorithm are set as shown in Table 6. After optimization by AFWA, the corresponding parameters are shown in Table 7.  
After the training, we respectively used the test set and training set to make forecasting, and the predicted results are shown in Figure 8. In terms of the graph fitting effect, the model fitting degree was good and the forecasting accuracy was high. In the coupling forecasting results, the training set: MSE = 0.840196, RMSE = 0.916622, R2 = 0.998780, MAE = 0.744482, MAPE = 1.770936 and the test set: MSE = 0.661562, RMSE = 0.813365, R2 = 0.997686, MAE = 0.711322, MAPE = 2.013296. In the demand forecasting results, the training set: MSE = 2168.757653, RMSE = 46.569922, R2 = 0.984562, MAE = 37.545733, MAPE = 3.482320 and the test set: MSE = 4455.127204, RMSE = 66.746739, R2 = 0.957014, MAE = 51.794724, MAPE = 4.518415. It is not difficult to find that the forecasting accuracy was good, and the forecasting accuracy of the training set and the forecasting set was similar, thus there was no overfitting phenomenon.  
\begin{table}
\begin{tabular}{c c} \hline
**The Parameter Name** & **Values** \\ \hline The population size & 5 \\ The number of fireworks in gauss explosions & 5 \\ The total number of sparks & 200 \\ Spark upper bound parameter & 100 \\ Spark lower bound parameter & 0.1 \\ Blast amplitude control parameters & 100 \\ Data dimension & 2 \\ Function evaluation number & 1 \\ The number of iterations & 1 \\ \hline \end{tabular}
\end{table}
Table 6: Parameter settings of adaptive fireworks algorithm.  
\begin{table}
\begin{tabular}{c c c} \hline
**Penalty Parameters (c)** & **Width Parameter of Kernel** \\  & **Function (g)** \\ \hline Multi-energy coupled forecasting & 30.9715 & 60.4294 \\ Total load forecasting & 26.4350 & 61.0462 \\ \hline \end{tabular}
\end{table}
Table 7: LSSVM parameter settings after optimization.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Characteristic Factor** & **Contribution Rate of Primary** & **Contribution Rate of Secondary Industry (\%)** & **Quarterly Rate of Tertiary Industry (\%)** & **Total Fixed Asset Contribution Rate of Tertiary Industry (\%)** & **GDP Current Value (100 Million Yuan)** & **Price Index** \\ \hline mRMR & 0 & \(-\)0.066 & 0.73 & 0.96 & 0.94 & 1.30 \\ criterion & 5 & 6 & 4 & 2 & 3 & 1 \\ \hline \end{tabular}
\end{table}
Table 5: mRMR calculation results of total load demand.
# Quantum Key Distribution in the Presence of the Intercept-Resend with Faked States Attack
## Appendix B The \(I\!f\!f\!f\) Attack and Quantum Channel Substitution
### The Photon and the Vacuum Ratios  
In this section, we will introduce a convenient method to detect the presence of the eavesdropper without requiring one to compute deviations from the reference gain, that is \(\Delta Q(+,+)=0\) or \(\Delta Q(\pm,\mp)=0\). For this purpose, let us define the photon ratio \(R\) as the relation between the gains \(\frac{Q_{ER}}{Q_{AB}}\) where the subscript \(EB\) denotes the presence of the eavesdropper and \(AB\) indicates its absence. For double detection events, we represent \(R\) as \(\frac{Q(+,+)_{EB}}{Q(+,+)_{AB}}\), while \(\frac{Q(\pm,\mp)_{EB}}{Q(\pm,\mp)_{AB}}\) for single detection events. In addition, we will define the vacuum ratio \(r\) as \(\frac{e^{-\mu_{ET}}-Y_{0}}{e^{-\mu_{BT}}-Y_{0}}\).  
If the eavesdropper adjusts the channel to achieve \(Q(+,+)_{AB}=Q(+,+)_{EB}\), then Equation (B2) is satisfied. We get that \(R_{(\pm,\mp)}=\frac{r}{\sqrt{2}}\), but \(r=\frac{e^{-\mu_{ET}}-Y_{0}}{e^{-\mu_{BT}}-Y_{0}}\) and \(\eta_{ET}\geq\eta_{BT}\); thus, \(r\leq 1\) and \(R_{(\pm,\mp)}\leq\frac{1}{\sqrt{2}}\). To discard [PERSON]'s presence, we do not need to check that \(\Delta Q(\pm,\mp)=0\), but it must be verified that \(R_{(\pm,\mp)}>\frac{1}{\sqrt{2}}\). Conversely, if [PERSON] modifies the channel to achieve \(Q(\pm,\mp)_{AB}=Q(\pm,\mp)_{EB}\), we get that \(R_{(+,+)}=\frac{2}{r^{2}}\). Since, \(r\leq 1\), we have that the \(IRFS\) attack causes that \(R_{(++)}\geq 2\). To be sure that the system is safe against the \(IRFS\) attack, we do not need to check that \(\Delta Q(+,+)=0\), but it is equivalent to verify that \(R_{(++)}<2\).
# Storage Capacity in Dependency of Supercooling and Cycle Stability of Different PCM Emulsions
## 1 Introduction
The PCM emulsion showed a supercooling of 5.7 K. Compared to water, the emulsion offered, in the best-case scenario, an improvement of the storage capacity by a factor of 2.6, operating it between 20 and 27 \({}^{\circ}\)C, 2.3 in a temperature range between 22 and 29 \({}^{\circ}\)C and 1.7 between 15 and 29 \({}^{\circ}\)C. The stability of the emulsion was proven by particle size measurements done with samples taken after filling the storage tank after three and after six days of operation. The particle size was decreased and more uniform after three days of moving to slightly bigger sizes after six days. [PERSON] et al. [16] investigated a PCM emulsion with a low-cost paraffin and solid content between 59% and 61% in a stirred 46-litre tank with a coiled heat exchanger. A 3.5-5.5 times higher heat transfer coefficient was observed when the emulsion was stirred with 290-600 rpm compared to the non-stirred tank. It was found that the stored energy was 80% higher for an operation temperature between 30 and 50 \({}^{\circ}\)C and 40% higher when operating it between 30 and 60 \({}^{\circ}\)C in comparison to water as the storage fluid. [PERSON] et al. [17] investigated the influence of the degree of supercooling on the storage capacity in n-hexadecane and n-octadecane emulsions. A freezing ratio was defined as the relation of the heat stored when the emulsion was cooled to a certain temperature before it was molten again to the total storage capacity of the full-phase transition. Dependent on the cooling temperature, the freezing ratio was in a range between 30% and 60% for emulsions having a mean particle size of 0.25 \(\upmu\)m.  
Research into PCS development is concentrated on the stabilisation of PCM in emulsions against phase separation (creaming) and on the suppression of supercooling. In many publications, the stability against thermal cycling is tested without applying mechanical stress at the same time. There is little information on how supercooling influences the storage capacity of PCM emulsions in comparison to standard heat transfer fluids. In this study, analyses on the storage capacity for emulsions using different paraffins as PCM in comparison to pure water are presented. All investigated emulsions are prepared using the same formulation, including the nucleation agent. Furthermore, an analysis of the stability is undertaken using repeated thermal cycles and shearing the emulsion with a defined shear rate at the same time (thermal-mechanical stress). The influence of supercooling on the storage capacity is analysed by enthalpy curves obtained via DSC measurements. The degradation due to thermal-mechanical stress is tested by using a rotational rheometer determining the change of viscosity with time, measuring the particle size distribution and the enthalpy via DSC before and after the test.
## 2 Results  
### HS-GC-IMS Analysis  
#### 2.1.1 The HS-GC-IMS Qualitative Analysis of VOCs  
The VOCs of the SS in six different regions were analyzed by HS-GC-IMS. HS-GC-IMS depends on the fast ion-molecular reaction between air clusters and analytes generated by beta ionization and realizes the identification of VOCs [17]. We selected a batch of samples from six regions to make 2D top view, as shown in Figure 1A. The background color of the entire spectrum is blue, the abscissa represents the drift time, the ordinate represents the holding time, and the Ko is 2.032-2.034 cm\({}^{2}\)/Vs [18]. Different hues indicate different concentrations, with white dots indicating a lower concentration and red dots indicating a higher concentration, so the deeper the hue, the higher the concentration [19]. It was observed that the VOCs in SS from different regions were well separated at a retention time of 100-1000 s and drift time of 1.0-2.0 (Figure 1A). Based on the NIST library, VOCs of SS were determined by combining retention indexes (RI), retention times, drift times, and Ko [20]. These analysis results are shown in Table 1, in which forty compounds were tentatively identified. Forty distinct VOCs include five terpen (Figure 1B-a), eight alcohols (Figure 1B-b), ten aldehydes (Figure 1B-c), six esters (Figure 1B-d), five ketones (Figure 1B-e), two phenols, one acid, and three other substances. Figure 1: (**A**) Two-dimensional chromatogram results of VOCs in SS of different regions; (**B**) the VOCs fingerprint of different regions of SS; (**C**) the fingerprints of unknown compounds in SS from different regions.
# A Review of Modular Multilevel Converters for Stationary Applications
###### Abstract  
A modular multilevel converter (MMC) is an advanced voltage source converter applicable to a wide range of medium and high-voltage applications. It has competitive advantages such as quality output performance, high modularity, simple scalability, and low voltage and current rating demand for the power switches. Remarkable studies have been carried out regarding its topology, control, and operation. The main purpose of this review is to present the current state of the art of the MMC technology and to offer a better understanding of its operation and control for stationary applications. In this study, the MMC configuration is presented regarding its conventional and advanced submodule (SM) and overall topologies. The mathematical modeling, output voltage, and current control under different grid conditions, submodule balancing control, circulating current control, and modulation methods are discussed to provide the state of the MMC technology. The challenges linked to the MMC are associated with submodule balancing control, circulating current control, control complexity, and transient performance. Advanced nonlinear and predictable control strategies are expected to improve the MMC control and performance in comparison with conventional control methods. Finally, the power losses associated with the advanced wide bandgap (WBG) power devices (such as SiC, GaN) are explored by using different modulation schemes and switching frequencies. The results indicate that although the phase-shifted carrier-based pulse width modulation (PSC-PWM) has higher power losses, it outputs a better quality voltage with lower total harmonic distortion (THD) in comparison with phase-disposition pulse width modulation (PD-PWM) and sampled average modulation pulse width modulation (SAM-PWM). In addition, WBG switches such as silicon carbide (SiC) and gallium nitride (GaN) devices have lower power losses and higher efficiency, especially at high switching frequency in the MMC applications.  
modular multilevel converter (MMC); submodule topology; output voltage and current control; submodule balancing control; circulating current control; nonlinear and predictive control; power losses; WBG technology (SiC and GaN), MPC for MMC; grid applications; smart grid; battery inverters +
Footnote †: journal: Applied Physics Letters  
1
## 3 Analysis of Monitoring Results  
### Displacement Analysis of Retaining Piles at Different Temperatures  
#### 3.1.1 Horizontal Displacement of Pile Body  
The horizontal displacements of piles 2 and 3 at different temperatures after the excavation of the foundation pit are delineated in Figure 7a,b, respectively; since the monitoring point of pile 1 was damaged, the horizontal displacement of this pile is not presented. The vertical axis represents the length of the pile (\(H\)) in meters, and the horizontal axis indicates the horizontal displacement of the pile (\(D\)) in millimeters. The positive sign implies that the direction of the horizontal displacement is outward along the foundation pit, and the negative sign denotes the reverse direction.  
Figure 6: The variation of the temperature from February to April.  
Figure 7: The displacement-temperature relationship of **(a)** pile 2 and **(b)** pile 3.  
Figure 7a shows that after the excavation of the foundation pit was completed, owing to the role of the earth pressure behind the supporting structure, pile 2 produced a displacement toward the pit, overall. The maximum displacement occurred at a depth of around 2/3 of the excavation depth. Under the action of the thermal stress, the crown beam and the waist beam were elongated to produce jacking force on the retaining pile. Thus, pile 2 was displaced outside the pit, and the horizontal displacement of the pile crown changed most obviously. The horizontal displacement of the pile crown increased by 2.37 mm after the temperature rose by 15 \({}^{\circ}\)C, which played a positive role in the supporting system. However, under the excavation face, the horizontal displacement of the pile was basically stable due to the constraint of the soil.  
Figure 7b indicates that the deformation law of pile 3 was different from that of pile 2, and the deformation amplitude of pile 3 was larger than that of pile 2. Pile 3 rotated around the pile body, and the pile above waist beam 1 moved outward with a temperature rise, while the pile below waist beam 1 moved further into the pit. Combined with the analysis of the horizontal displacement of waist beam 2 by the finite element method given below, this phenomenon can be well explained. The deflection of waist beam 2 increased due to the thermal stress, and its deformation form is shown in Figure 8. The end of waist beam 2 deformed out of the pit, while the middle of the beam deformed into the pit, which drove the bottom of pile 3 to move into the pit; this caused the deformation of pile 3 to consist of rigid rotation and flexural deformation, while pile 2 only experienced flexural deformation. When the temperature rose by 15 \({}^{\circ}\)C, the displacement of the crown of pile 3 increased by 2.90 mm, which was more remarkable than that of pile 2.
# What Makes a UI Simple? Difficulty and Complexity in Tasks Engaging Visual-Spatial Working Memory
## 3 Experiment Description
### Goals and Hypotheses  
The goal of our experiment was twofold: (1) to check if visual complexity and its compression-based measures characterize the difficulty of tasks engaging VSWM, or if proximity-based measures inspired by CA have an advantage, (2) to develop and validate objective performance measure for such tasks, in a fashion similar to [PERSON] throughput. The first goal would result foremost in contributions 1 and 2 as listed in the Conclusions, while the second one would be closely associated with contributions 3 and 4.  
Reasoning about the formulation of the index of difficulty for VSWM-related tasks (_IDM_), we can assume that in visual-spatial perception tasks (visual search and memorization of objects), two components can be identified: informational and spatial. The former relates to information quantity of the \"message\" held in memory, while the latter relates to spatial allocation of its elements. Indeed, the widely accepted model is that the \"what\" and \"where\" information processing is performed in ventral and dorsal streams of the brain, respectively. Our assumption is also supported by [38], where performance in 2D objects selection tasks was best explained with the index of selection difficulty incorporating the number of objects and binary logarithm of their vocabulary size. In that study, the effect of the perceived area size combined with the two other factors was also significant, but the exact nature of their effects is seemingly task-dependent. That is, if the search runs through the memorized information, the performance will be affected by the logarithm of items in memory, such as e.g., images of friends in [47]. Correspondingly, if the search instead runs through some applicable spatial positions, the difficulty of such tasks should be characterized by the number of memorized items (\(N_{I}\)) and the binary logarithm of the size of the considered area (\(S_{0}\)), where the items can be located:  
\[IDM=f(N_{I},\log_{2}\ S_{0}) \tag{11}\]  
The proposed function (11) is based on the above reasoning and our assumptions, which are to be experimentally validated. Thus, we formulated the following hypotheses for the experiment:  
1. Performance in VSWM tasks is best predicted with the same compression-based factors as visual complexity.
2. Performance in VSWM tasks is best predicted with the simpler proximity-based factors.
3. Performance in VSWM tasks is best predicted with our proposed index of difficulty.
4. The values calculated for the corresponding quantitative task setup-independent measure of performance that also encompasses time are consistent with the ones found in the literature.
# Toward Secure Smart Grid Systems: Risks, Threats, Challenges, and Future Directions
## 10 Future Research Directions
### Lightweight Cryptographic Solutions
#### 10.2.3 Adversarial Defense Mechanisms  
These mechanisms investigate methods to enhance ML resilience against adversarial attacks specific to smart grid environments. This includes researching techniques to detect and mitigate adversarial inputs that could compromise the security and reliability of grid operations. Adversarial defense strategies for smart grids involve setting strong cybersecurity measures to guard against data tampering and unauthorized access, such as intrusion detection systems, encryption, and secure communication protocols. Incorporating machine learning algorithms for continuous monitoring and anomaly detection, in addition to regular updates and patches, improves early detection, identification, and mitigation of possible attacks and threats.
# Backscatter Communications: Inception of the Battery-Free Era--A Comprehensive Survey
## 5 BackCom: Wireless Communications
### BackCom Communication Modes  
Like traditional wireless communications, BackCom also has two communication modes: (i) a full-duplex mode used in two-way communications between tag and reader and (ii) a half-duplex mode that allows only one-way communication. Both modes are discussed below:
# Intelligent Air Cutting Monitoring System for Milling Process
## 5 Results and Discussion
### Verification
#### 5.3.3 Experiment Verification 3  
The purpose of this experimental verification is to test the effectiveness of the monitoring system based on cutting vibration signals. The experimental verification method uses the same cutting parameters for the cutting, and the result of using spindle load and vibration signal is compared. The linear cutting path, 200 mm along the X-axis and 100 mm cutting length, is used. Three experiment repetitions, with a constant cutting feed rate of 600 mm/min, are conducted. The detailed cutting parameters are shown in Table 5. The experiment results using spindle load judgment and cutting vibration judgment are shown in Table 6. The uncertainty while using spindle load current judgment for cutting time, air cutting time, and machining time were 0.67%, 1.99%, and 0.44%, respectively. Meanwhile, the uncertainty when using vibration signal judgment for cutting time, air cutting time, and machining time were 1.49%, 1.29%, and 0.04%, respectively. The result shows that the data measured by the two judgment methods are similar, which is about 10 s. The theoretical calculation for cutting length 100 mm and feed rate 600 mm/min is also 10 s. As seen, the cutting time judged by the cutting vibration value is 0.4 s longer than that judged by the spindle load current. The reason is that the monitoring system judged by cutting vibration needs time to process the original vibration signal. On the contrary, the monitoring system judged by spindle load does not need signal processing time, hence, the monitored time is closer to the correct time. The previous two verification experiments confirmed that the monitoring system based on spindle load can accurately calculate the machining time. This experiment confirms that the monitoring system based on cutting vibration judgment is also feasible for machining time monitoring.
## 4 Results and Discussion
### Drought Condition Monitoring with Time-Series Biomass Maps
Biomass in the third week of July in 2007 was even 42.7% lower than the multi-year mean (Figure 8). Figure 9 shows the biomass anomalies in 2007 compared with the mean of 2005-2013 during July. Site 1 was located at the Maodeng Ranch, which is China's largest typical-steppe reserve [48]. Site 2 was occupied by lovely achnatherum, needlegrass, and little-leaf pea shrub [48]. The distribution of the biomass anomalies based on MODIS NDVI-derived biomass and synthetic NDVI-derived biomass data generally showed good agreement. However, the biomass derived from the synthetic NDVI image data showed more details compared with MODIS NDVI-derived biomass data. Terrain textures and roads can be clearly identified in the maps from the synthetic images. In addition, the absolute SAI of biomass maps from the synthetic NDVI images was generally larger than that of the MODIS NDVI-derived biomass images, which implies that the synthetic images can better capture the biomass anomalies than MODIS NDVI-derived biomass images. Figure 8: Time series of precipitation and predicted synthetic NDVI (8-d intervals) for Xilinhot in 2007 (precipitation data at site Xilinhot were obtained from the China meteorological data sharing service system).
###### Abstract  
Multicomponent seismic exploration provides more wavefield information for imaging complex subsurface structures and predicting reservoirs. Ground roll is strongly coherent noise in land multicomponent seismic data and exhibits similar features, which are strong energy, low frequency, low velocity and dispersion, in each component. Ground roll attenuation is an important step in seismic data processing. In this study, we utilized multivariate empirical mode decomposition to multicomponent seismic data for attenuating ground roll. By adding extra components containing independent white noise, noise-assisted multivariate empirical mode decomposition is adopted to overcome the mode-mixing effect in standard empirical mode decomposition. This method provides a more robust analysis than the standard empirical mode decomposition EMD method performed separately for each component. Multicomponent seismic data are decomposed into different intrinsic mode functions in frequency scale. According to different frequency scales of seismic reflection wave and ground roll, intrinsic mode functions with low frequency are eliminated to suppress ground roll, and the remaining are reconstructed for seismic reflection waves. Synthetic and field data tests show that the proposed approach performs better than the traditional attenuation method.  
EMD; MEMD; ground roll; multicomponent seismic data Article