esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### SEM and EDS  
Figure 7a corresponds to the EDS spectrum obtained from the cuvette wall covered with silver nanoparticles. The characteristic X-ray lines around 3 keV confirm the presence of AgNP, while the other peaks that appear, carbon and oxygen, correspond to the chemical composition of the polystyrene cuvette. Figure 7b,c shows the morphology of the AgNP deposited on the polystyrene cell before and after the measurement of an oil sample, respectively. As can be seen, in both micrographs, the size of AgNP is between 50 nm and 90 nm with an irregular shape, homogeneously distributed on the surface of the cuvette, with no apparent variations in the size or shape of the AgNP caused by interaction with the degraded oil. [PERSON] and [PERSON] [12] reported that sensors based on the plasmonic properties of nanoparticles tend to be more sensitive with larger nanoparticles, causing a wider LSPR peak, due to multipolar excitation and radiative damping. Therefore, the size of the deposited AgNP was favorable for the sensing purpose.
## 4 Discussion
### RQ1: Progression Through Acceptance Phases
#### 4.1.2 Life Situations.  
Various life situations, especially related to how much time people have to devote to learning and using technology, can hinder or aid technology acceptance. Such life situations from prior work include age [45; 82], gender (**UTAUT**; [45; 83]), marital status [84], employment status (employed/retired or part/full-time; [85]), and economic condition related to family support [84]. Also, _habit_ (from [86], an extension of the **TRA** and **TPB**)--such as forming habits of using the technology--can influence and predict behavior [87]. In our study, we found that _being busy_, or having many social obligations related to less use of the technology--but that _habits_ or social _routines_ with it related to increased use.  
**What hinders advancement?** Life situations--like work, frequency of social events, and general business--hindered the use of the mobile telepresence robot for social purposes due to having less time to dedicate to the technology and explore its uses. Because [PERSON] was working full-time, it was difficult for her to fit robot use into her routine. Although [PERSON] was retired, her filled social calendar sometimes limited robot use. Limited availability slowed these users' acceptance as they both experienced brief periods of decreased or _Lack of Use7_ due to \"being busy\" ([PERSON] and [PERSON]). In contrast, [PERSON] was retired with a less active social calendar and dedicated more time to the robot, using it several times per week throughout the study. This pattern of business corresponding to reduced robot usage may relate to the robot's use in mobile communication. If it further benefited busy individuals, like by cooking and cleaning, busy users may use it more often.  
**What aids advancement?** When participants' mobile telepresence robot usage decreased, scheduled routines and appointments helped them get back on track with using it, including using the robot for the study session or scheduled routines with family. [PERSON] returned to her robot use routines when her social life slowed down. Similarly, [PERSON] returned to her routine robot use to meet with her grandchildren, who enjoyed playing with the robot. [PERSON] maintained regular routines with his IPs (both relatives) and friends in his neighborhood. Even [PERSON], who disliked the robot, returned to its use for routine study sessions when the researchers could help her set it up (mostly to receive study payments). Established routines with loved ones and the technology gave users designated time with the robots, thus making them robust to disuse. Therefore, even though [PERSON], [PERSON], and [PERSON] each had a short period of _Lack of Use_, it did not deeply hinder their acceptance.
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AWGN & Additive White Gaussian Noise \\ BER & bit error rate \\ BS & Base Station \\ CSI & Channel state information \\ DL & Deep Learning \\ DNN & Deep Neural Network \\ FPA & Fixed Power Allocation \\ OPS & Optimized Power structure \\ KKT & Karush-Kuhn-Tucker \\ LSTM & Long Short-Term Memory \\ LTE & Long Term Evolution \\ ML & Machine Learning \\ MSE & Mean Square Error \\ MMSE & Minimum Mean Square Error \\ MUD & Multiuser detection \\ PD-NOMA & Power Domain Non-Orthogonal Multiple Access \\ QoS & Quality of Service \\ SIC & Successive interference cancellation \\ \end{tabular}
## 3 Results
### Evaluation of LULC and Its Impacts on Streamflow  
The results of monthly runoff calibration and validation for various LULC scenarios at Qianjiang and Tianr are presented in Figures 4 and 5, respectively. Results achieved using LULC 1995 were a bit overestimated, while those using LULC GLC 2000 developed by IFORCE were slightly underestimated. The underestimation with GLC 2000 is shown in the LULC area classification in Table 6. The classified LULC had no urban area and a significant reduction in evergreen forest and increase in the deciduous forest area. Mixed forests dominated the basin, with a share of more than 60%. The urban land area increased from 0.146% in 1995 to 0.587% in 2015. The change in urban land, which is one of the major driving forces for changing runoff, was not significant. LULC (2001-2010) developed by USGS, showed that the map area was influenced mostly by mixed forests (68.425%) and slightly by urban land (0.982%).  
The average observed streamflow at Qianjiang station was 2238.27 m\({}^{3}\)/s. The simulated average streamflow value was 2033.57 m\({}^{3}\)/s (9% decrease) with LULC (2001-2010) USGS; with LULC ESA (1995), it was 2521.11 m\({}^{3}\)/s (12% increase); with LULC ESA (2005), it was 2146.18 m\({}^{3}\)/s (4% decrease); with LULC ESA (2015), it was 2083.9 m\({}^{3}\)/s (6.89% decrease); and, with LULC GLC (2000), it was 1744.037 m\({}^{3}\)/s (22.08% decrease). The average observed streamflow at Tianr hydrological station was 1512.62 m\({}^{3}\)/s. The simulated average streamflow values were 1314.37m\({}^{3}\)/s (13.10% decrease) with LULC (2001-2010) USGS, 1721.55 m\({}^{3}\)/s (13.8% increase) with LULC ESA (1995), 1434.33 m\({}^{3}\)/s (5.17% decrease) with LULC ESA (2005), 1410.56 m\({}^{3}\)/s (6.74% decrease) with LULC ESA (2015), and 1312.33 m\({}^{3}\)/s (13.24% decrease) with LULC GLC (2000). LULC ESA (2005) and LULC USGS (2001-2010) showed the best match with the observed values. LULC ESA (2005) was, therefore, considered as a reference scenario for future streamflow projections based on the best match as well as the most acceptable parameter indices.
## 3 Results and Discussion
The training loss represents the discrepancy between the model's anticipated outputs and the actual values during the training phase. On the other hand, the testing loss measures the difference between the expected outputs and the actual values. We have visualized both the training and testing loss in our scenario in Figure 6. The graph's arrangement is a result of implementing k-fold cross-validation, which displays the loss for each fold. As we performed 10-fold cross-validation, i.e., the dataset was trained and tested ten times, we obtained ten confusion matrices. We calculated a sum of the confusion matrices, presented in Figure 7. The classification report for our model with the Random Forest classifier is given in Table 1. The confusion matrix shows that our model correctly predicted an osteoporosis gait pattern as an osteoporosis case 437 times, while 432 times, the model correctly predicted a healthy case as a healthy case. However, the model showed a slightly higher accuracy in detecting osteoporosis cases (96.63%) compared to its accuracy in  
Figure 5: Force sensor readings at every heel strike during the 10 m walk test. (**a**) People without osteoporosis; (**b**) people with osteoporosis [31]. The overall accuracy of the model using the Random Forest classifier after hyperparameter tuning was found to be 95.40%. Other classifiers performed in the range of 91% to 94% for the same training condition. A similar study on gait classification methods for normal and abnormal gait also reported that the Random Forest technique outperformed Decision Tree and KNN techniques in classifying gait parameters obtained through wearable technology [36]. The Random Forest technique-based model had the benefit of employing ensemble methods. As such, the variance decreased because the results were obtained by averaging multiple decision tree predictions. Overall, the empirical findings supported the proposition that the suggested model is capable of categorizing the force profiles of the participants despite the small size of the training dataset. [PERSON] et al. 24 introduced a gait identification system that utilizes an inertial measurement unit (IMU) to recognize five different walking patterns. Their model was constructed using the Random Forest algorithm, and they demonstrated its superiority compared to other methods such as Support Vector Machines (SVMs) and K-Nearest Neighbors (KNNs). [PERSON] et al. 25 employed the Random Forest algorithm to identify [PERSON]'s disease through the examination of gait patterns. [PERSON] et al. 26 utilized a Random Forest classification technique that demonstrated superior performance compared to the SVM and Bayes approaches. The researchers employed the Random Forest algorithm to assess the significance of several gait variables in the classification of hemiplegia. [PERSON] et al. 13 employed Random Forest, Decision Tree, and KNN algorithms to conduct gait analysis. Their findings demonstrated that the Random Forest algorithm outperformed alternative classifiers.
## 1 Introduction
### Smart Energy Systems  
Societies on a global scale have reached a tipping point from fossil fuel power generation to sustainable alternatives. However, wireless connectivity plays a critical role in this transformation by enabling innovative smart energy systems (SESs) [9]. SES is a novel solution, which integrates energy generating and storage technologies with 'intelligent' applications, regulating and optimizing their usage. Cloud computing can use combined multiple energy sources with storage systems to manage them [10]. Furthermore, significant points to improve SES require real-time performance decisions based on technical features and climatic data, surplus renewable power generation, and building decentralized energy systems with excellent efficiency and lower cost [11]. In addition, to reduce rising environmental hazards such as increasing global mean temperature and greenhouse gas emissions, energy systems are experiencing a rapid transition toward low-carbon intelligent systems [12]. Unlike traditional energy systems, which dispatch various generators to meet changing demand, future energy systems include two-way energy flows between providers and consumers and active engagement of customers as prosumers in various electrical markets [13]. Under the suggested micro-market, not completely controllable loads were rescheduled by changing specific lectures, research timelines and optimization by a self-crossover genetic algorithm (GA) [14]. The numerical findings revealed that the suggested micro-market and algorithm efficiently increased load flexibility and resulted in increased cost savings for intelligent energy systems [15; 16], as shown in Figure 1.
## 5 Results and Discussion
### Experiment Dataset and Settings  
As mentioned earlier, we used the open dataset DEAP to verify the effectiveness of our method, which include EEG signals from 32 channels collected from 32 subjects. Each subject took 40 trials, and each trial lasted 60 s. The sampling frequency of the EEG signal was 512 Hz. With different time windows, we obtained EEG MFI sequences with a different number of EEG MFIs. For example, with a one-second time window, we obtained 2400 MFIs for one EEG MFI sequence. With a two-second time window, we obtained 1200 MFIs for one EEG MFI sequence. However, even with the shortest time window, EEG MFIs are not enough for training a stable emotion recognition model with our method. For this reason, we adoptee data augmentation strategies before training. We added \"salt & pepper\" noise to the MFIs in MATLAB with the command 'imnoise(0'. Image flipping or zooming is not used when augmenting the data. With this method, the original MFI set is expanded 20 times to ensure that we had at least 20,000 MFIs per subject for training. Sufficient training data helps a model with a large number of parameters to converge and generalize well. A five-fold cross-validation method is used to evaluate the performance of our approach, and the average performance of the 5-fold validation processes is taken as the experiment's final results. We trained the model with different time windows to find out if the division of the EEG signal had an impact on the classification's accuracy. The models are trained and tested in the Windows server environment, which included an Intel Xeon(r) V3 CPU (12 \(\times\) 2.4 GHz) and 64 Gb RAM. No GPU acceleration is used in the experiment.
# Efficient Reversible Data Hiding Scheme for AMBTC-Compressed Images
## 3 Proposed Scheme  
In this section, we propose a novel reversible data hiding scheme for AMBTC-compressed images to further improve the performance of previous schemes. The main idea of the proposed scheme is that the AMBTC-compressed data that is generated by the AMBTC algorithm is further encoded losslessly based on the high correlation of neighboring mean values in the two mean tables, \(H\) and \(L\). Once the hidden secret data have been extracted, the original AMBTC-based image, as shown in Figure 1c, will be completely restored. To achieve our idea, first, the AMBTC algorithm is performed on the input image to obtain the high mean table \(H\), low mean table \(L\), and bitmap table \(B\). Then, the embedding process is used to hide a secret message S into the two mean tables while the bitmap table stays unchanged. The difference values of these two tables are calculated and used to embed the secret message. The proposed scheme has two phases, i.e., the embedding phase and the extracting phase, which are described in Sections 3.1 and 3.2, respectively.
## 2 Experimental Conditions and Experimental Program  
The experimental system is shown in Figure 1. The machine tool is a VMC850E vertical machining center manufactured by Shenyang Machine Tool Co. In order to facilitate the measurement of drilling temperature, a vertical-horizontal converter head is installed on the spindle of the vertical machining center to transform vertical machining into horizontal machining. The drilling force measurement system consists of a Kistler 9257B plate force gauge, a Kistler 5070A charge amplifier, a Kistler 5697A data collector, and a computer. The temperature field on the underside of the workpiece during drilling was observed and measured by a Fotric 388 thermal imaging camera. This paper employs the contact thermometry method, measuring the emissivity of the workpiece surface to be 0.30. The drill was a SANDVIK monolithic cemented carbide TiAlN-coated drill with a diameter of \(\Phi\)8 mm, a tip angle of 140\({}^{\circ}\), and a tip height of 1.4 mm. The experimental workpiece was a thin-walled plate of AF1410 high-strength steel, and its physical and mechanical properties are shown in Table 1. To facilitate temperature measurement, we employed dry cutting. Although this method differs from the wet cutting commonly used in industrial environments where coolant is applied, the coolant in wet cutting often fails to reach the cutting zone effectively. Therefore, the cutting temperatures obtained through dry cutting still offer significant reference value for actual machining processes.  
Figure 1: Experimental system for drilling thin-walled AF1410 high-strength steel workpiece. (**a**) Photos of the experimental system. (**b**) Schematic diagram of the experimental system.  
On the basis of the research on the characteristics of axial force change and the mechanism of workpiece deformation and drilling cap formation during the drilling process of a thin-walled plate of AF1410 high-strength steel, a single-factor experimental method was used to study the effects of cutting speed and feed on axial force, deformation, drilling cap formation, and hole edge defects, respectively. Since the drilling temperature has an important influence on the formation of the drill cap and the change in axial force, the experiment was carried out under dry cutting conditions, and a thermal imaging camera was applied to analyze the influence of cutting speed and feed on the temperature field of the workpiece bottom surface when the drill bit was close to drilling out. The center of the drill hole is the geometric center of the workpiece, and the constraint method is a 2-edge (left and right to right) constraint, and the experimental scheme is shown in Table 2.
# Thinfilm Hybrid Nanostructures: A Perspective to Subcycle Opto-Electronics and Coherent Control
## 2 Methods
### Simulation Setup  
The aim of this study is the investigation of the influence of the confinement of bulk plasmons in thin layer thickness in a triple layer nanodisk structures. A bulk plasmon needs a certain volume for its electronic oscillations. If the volume is now confined in one dimension by reducing the thickness, the electronic oscillations are perturbed and the response changes. The nanodisk structures are composed of a gold bulk layer covered by an insulating silica layer and a thin silver top layer as already presented in Figure 1. The plasmon is excited either by a ultrafast laser pulse (\(\lambda=550\) to \(1500\,\mathrm{nm}\), \(\tau\leq 5\,\mathrm{fs}\), see Figure 2) or monochromatically at the resonance wavelength, which will be discussed later.  
In the (10-10-1) gold-silica-silver sandwich simulation the bottom disk has a thickness of \(10\,\mathrm{nm}\) and a diameter of \(88\,\mathrm{nm}\), the top silver disk has a thickness of \(1\,\mathrm{nm}\) and a diameter of \(108\,\mathrm{nm}\). The silver unit cell size has almost the same value as gold and is \(4.079\)[34]. Therefore, the top silver disk is formed by \(2.5\) monolayers. The silica spacer has a conical shape and a thickness of \(10\,\mathrm{nm}\).  
For the simulation of absorption spectra and electric near fields of the disk sandwich the sample is illuminated from above perpendicular to its surface by linearly polarized light as already defined in Figure 1.  
The nanodisk sandwich is excited with Fourier limited light pulses with a spectral range of \(550\,\mathrm{nm}\) to \(1500\,\mathrm{nm}\). The spectrum and the temporal electric field are shown in Figure 2a,b, respectively. The electric field of the laser pulses exhibit a carrier envelope phase of \(\pi/2\), corresponding to a sine-like pulse.  
In all simulations, the bottom and spacing disks are meshed with a \(1\,\mathrm{nm}\) grid in all three spatial directions. The thin disk is meshed in the surface plane with the same \(1\,\mathrm{nm}\) grid, but perpendicular to the surface with a \(0.1\,\mathrm{nm}\) grid. The boundary conditions are defined to be \"open\", which means adding space around the structure, where the electric field of the light pulse and the structure itself are vanishing. By this definition of the simulation environment, the electric field of the pulse is absorbed at the edges of the simulation box, which is even bigger than the simulated structure. Since we observed complex electric field shapes forming whispering gallery modes [35], we did not use any symmetry approaches in simulation to reduce computation time. The CST simulation setup  
Figure 2: Excitation pulses used in the nanodisk sandwich simulations. (**a**) The spectrum; (**b**) the normalized temporal electric field. Reprinted with permission from [24] ©The Optical Society.  
allows for different grid sizes per layer. The volumetric grids are chosen to be 1 nm for the bottom gold and center silica disk of 10 nm thickness and 0.1 nm for the silver top disk.
## 1 Introduction
As it was reported, the immersion in ethanol with ultrasound was more effective in drying time reduction than single ultrasound or ethanol treatments [9; 16; 17; 26]. [PERSON] et al. [11] reported around 50% reduction of carrot drying time subjected to ethanol-ultrasound treatment for 30 min with enhanced rehydration ability of dried material and unchanged carotenoid content. The drying time of scallion stalk treated by means of ethanol (75%) with ultrasound (for 10 min) was reduced from 110 min (for control) to around 60 min [26]. Different studies reported a better rehydration ability, unchanged colour [26], decreased content of polyphenols [9; 17] and worse antioxidant capacity [17] after EtOH+US treatment than the untreated dried material. The combined treatment was studied for different times (5 s-30 min) in different concentrations of ethanol (30-99.8%), but usually a longer treatment time (10-30 min) and higher concentrations (>95%) were investigated [11; 16; 17; 18; 26]. However, from the further utilization of ethanol-ultrasound pre-treatment in industry, it seems reasonable to reduce the treatment time. [PERSON] et al. [9] reported that even 15 s of combined ethanol-ultrasound treatment caused a significant reduction of the drying time of apple. They also noted a higher reduction of drying time when a longer ultrasound-ethanol pre-treatment of apple was performed. The shortening of drying time was in the range of 9.8-18.3% when treatment was carried out for 5-180 s, in comparison to the untreated sample. However, prolongation of the treatment to 3 min caused significant degradation of polyphenols (from 16% for 1 min up to 40% for 3 min EtOH+US treatment in relation to the untreated dried apple) due to extraction of polyphenols by ethanol. Therefore, the time of treatment and the ethanol concentration should be adjusted to each material individually based not only on the energetic aspects but also on the quality of dried material and obtaining the product with designed properties. Furthermore, the impact of ultrasound depended on the applied frequency of ultrasound [24]. Therefore, optimization of the US frequency during immersion in ethanol is required. The aim of the study was to analyse the impact of pre-treatment in ethanol with application of ultrasound on the drying kinetics and selected properties of carrot directly before the treatments and after convective drying. The immersive treatment was carried out in 96% of ethanol for various time (5-180 s) without and with ultrasound assistance of different frequency (21 and 40 kHz).