esa-sceva/satcom-chunk-collection huggingface dataset:
# A Large Capacity Histogram-Based Watermarking Algorithm for Three Consecutive Bins
## 4 Experimental Results and Analysis
### Embedding Capacity and Perceptual Similarity
#### 4.1.1 Embedding Capacity versus Perceptual Similarity  
In order to further test the visual quality of watermarked images, the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) were used for judging the distortion degree between the original and watermarked images. In general, higher values of PSNR and SSIM lead to a better visual quality of the watermarked image.  
For different test images, including Baboon, Barbara, Lena, and Peppers, we have shown the perceptual similarity (under different bin widths, thresholds, and capacities) in Table 2. As shown in Table 2, both PSNR and SSIM in all images were extremely high. For instance, the PSNR values in all images were still more than 50 dB when the embedding capacity was 63 bits. In terms of SSIM, the minimum of SSIM in all images could reach 0.9955. Furthermore, there was only a very small change in all of the SSIM values. Note that given the conditions \(T\) and \(M\), the PSNR values of a given image will decrease with the increase of payload length. This is because there are many more pixelsthat need to be modified to embed more payload bits. If we only consider a concrete embedding capacity (termed as one column in Table 2), PSNR values will decrease when the parameter \(M\) increases. This can be explained by the analyses mentioned above (refer to the Introduction): in short, the bin size used has a direct influence on the visual quality of the resulting images. Based on the above analysis, the visual quality of the proposed scheme was superior.  
Besides, Figure 4 also shows the original images, the watermarked images, and their embedding modifications for Barbara and Lena, in which the bin's width \(M\) and the threshold \(T\) were both set as two, and the embedding capacity \(C\) reached 63 bits. As shown in Figure 4c,f, the modification of the original image caused by the payload embedding was acceptable. Compared with Figure 4a,b together with Figure 4d,e, the visual quality of watermarked images was satisfactory. In summary, the image quality of watermarked images obtained by the proposed algorithm could fully meet the requirements of practical application scenarios.
## 5 Discussion
### Offloading  
Offloading in the context of IoT refers to the process of transferring tasks from one computational entity, such as a fog node, to another entity, often a cloud server. Efficient task offloading is fundamental to enhancing the Quality of Service (QoS) while optimising various parameters including energy efficiency, latency, and data throughput. When offloading is executed ineffectively, it can significantly increase processing times and lead to higher power consumption. For instance, if tasks are disproportionately allocated to certain fog nodes while others remain underutilised, this can result in an imbalance that exacerbates energy waste [34].  
The implications of inefficient task offloading extend beyond mere processing delays; they can increase the load on specific fog nodes, causing them to consume more power than necessary while underutilised nodes languish unused. Therefore, it is imperative to develop and implement effective offloading strategies that can facilitate informed decision making about where tasks are processed--whether on local fog nodes or in the cloud. Research into these offloading strategies has highlighted the importance of optimising resource allocation and ensuring equitable distribution of tasks to minimise energy use and enhance overall system performance [35].  
Figure 3: Causes of energy inefficiency.
# Research on Multi-Mode Braking Energy Recovery Control Strategy for Battery Electric Vehicles
## 4 Co-Simulation and Analysis of Results
### Cruise and Matlab/Simulink Co-Simulation Analysis
#### 4.2.1 Cyclic Condition Simulation
A comparison of the range under the CLTC-P condition is shown in Figure 17. From the figure, it can be seen that the vehicle's range is lowest when the power recovery control strategy is not added, which is 238 km; the range under the single-mode brake energy recovery control strategy is significantly increased, with a range of 324 km and a range contribution rate of 36.1%; after adding the multi-mode brake energy recovery control strategy proposed in this paper, the range is significantly increased, with a range of 337 km, and a range The contribution rate is 41.9%; compared with the single-mode brake energy recovery control strategy, the contribution rate of the range is increased by 5.5%, the range is increased by 13 km, and the range is increased by 99 km compared with the no-brake energy recovery control strategy; so, it can be seen that the effect of the multi-mode brake energy recovery control strategy is significant. Figure 16: NEDC operating condition energy change curve. Figure 18 shows the change in the SOC value curve under CLTC-P condition, from which it can be seen that after adding brake energy recovery control to the battery electric vehicle, the SOC curve is still locally increasing. However, it is generally decreasing, which can effectively improve the range. Before the start of the cycle, the initial value of the HV battery SOC is 80%. After the end of the simulation, the SOC value under the multi-mode brake energy recovery control strategy is 76.98%, which is higher than 76.91% under the single-mode brake energy recovery control strategy and significantly higher than 75.6% under the no-brake energy recovery control strategy. Figure 17: CLTC-P operating range comparison chart. Figure 18: SOC change curve of CLTC-P working condition. Figure 19 show the energy recovery and consumption graphs for the CLTC-P operating conditions. It can be seen that the single-mode brake energy recovery control strategy recovers 3330 kJ of energy and outputs 10,470 kJ of energy, with a brake energy recovery rate of 31.8%. The multi-mode brake energy recovery control strategy recovers 4696 kJ of energy and outputs 11,590 kJ of energy, with a brake energy recovery rate of 40.5%. The brake energy recovery rate is increased by 8.7% compared to the single-mode control strategy. The brake energy recovery rate is improved by 8.7% compared to the single-mode control strategy. Combining the above simulation and verification results of two different working conditions of NEDC and CLTC-P, the multi-mode braking energy recovery control strategy proposed in this paper can significantly improve the efficiency of the braking energy recovery rate and increase the vehicle's range.
# Efficient Removal Of U(VI) Ions from Aqueous Solutions by Tannic Acid/Graphene Oxide Composites
## 3 Results  
### Characterization of TA/GO Composites  
SEM images were obtained to observe the surface morphology and dimensions of the samples. The folding nature of graphene nanosheets was observed throughout the morphology, and a smooth layered structure with an average size of about several micrometers was detected (Figure 1a,c). In comparison to graphene oxide, the surface morphology and size of TA/GO were not significantly different (Figure 1b,d), indicating that tannic acid modification did not destroy the basic structure of graphene oxide.  
The structural defects and electronic characteristics of GO and TA/GO were detected by Raman spectroscopy (Figure 2). The peaks at 1345 cm\({}^{-1}\) (D band) and 1588 cm\({}^{-1}\) (G band) can be assigned to disordered structures (defects and disordered sp\({}^{3}\) carbon atoms and/or other impurities) and the graphite structure (sp\({}^{2}\)-hybridized carbon atoms in graphene sheets), respectively [29]. The peak intensity ratio of D and G bands (I\({}_{\text{D}}\)/I\({}_{\text{G}}\)) reflects the relative defect degree of graphitic materials [27]. In comparison to graphene oxide (I\({}_{\text{D}}\)/I\({}_{\text{G}}\)\(\sim\)0.96), the I\({}_{\text{D}}\)/I\({}_{\text{G}}\) ratio of TA/GO (\(\sim\)0.89) was reduced by about 0.07, indicating that tannic acid/graphene oxide (TA/GO) composites were successfully prepared. Therefore, SEM images and Raman spectroscopy results reveal that the structural integrity of graphene oxide was not destroyed during the tannic acid modification process.  
The characteristics of GO, TA, TA/GO, and TA/GO after uranium adsorption were analyzed by FTIR (Figure 3a). In comparison to graphene oxide, some new peaks were detected on TA/GO at 1717 cm\({}^{-1}\), 1610 cm\({}^{-1}\), 1442 cm\({}^{-1}\), 1318 cm\({}^{-1}\), 759 cm\({}^{-1}\), and 582 cm\({}^{-1}\)[26; 30]. These new peaks were also detected on TA, indicating that tannic acid was successfully grafted on the surface of graphene oxide. After uranium adsorption, a new characteristic absorption peak was detected on TA/GO at -920 cm\({}^{-1}\) due to the presence of UO\({}_{2}\)\({}^{2+}\) ions, indicating that this material successfully adsorbed uranium [15].  
Figure 1: SEM images of GO (**a,c**) and TA/GO (**b,d**).  
GO was composed of four parts with the binding energies of 283.7 eV (C-C), 284 eV (C-O), 286 eV (C=O), and 288 eV (O-C=O) [24]. The C 1s XPS spectrum of TA/GO consisted of four parts with the binding energies of 283.91 eV (C-C), 284 eV (C-O), 285.9 eV (C-O), and 287.8 eV (C=O). It is noticeable from Table 1 that the content of O-C=O increased significantly, indicating that TA was successfully grafted on the GO surface.
## 4 Results and Discussion
### Structural Perspective  
The fatigue properties of welded joints and the stress concentration factor, microstructure, and Vickers hardness have a close relationship. In this study, the fatigue properties of two types of welded joints at low temperature were analyzed from structural and material perspectives, and fractography.  
Stress concentration phenomena are common in various engineering structures, and most of the structural damage is caused by stress concentration. Based on ABAQUS, finite element simulations of T-welded and cruciform welded joints (idealized model) were carried out to investigate the stress concentration factor (SCF) of the two welded joints (Figure 11). Modeling was performed using a three-dimensional solid C3D8R reduced integration element, and mesh refinement was performed at the weld toe of two welded joints. The low temperature material properties were, elastic modulus 214 GPa, yield strength 434 MPa, Poisson's ratio 0.281, and density 7.85 g/cm\({}^{3}\)[39]. One end of the model was rigidly fixed and the other end was applied with a tensile force of 150 kN.  
Figure 11: The distribution of stress: (**a**) T-welded joints and (**b**) cruciform welded joints.  
The SCF can be obtained using Equation (7):  
\[k=\frac{\sigma_{hot}}{\sigma_{n}} \tag{7}\]  
where,  
\(\sigma_{n}\) is the nominal stress (the data of \(\sigma_{hot}\) and \(\sigma_{n}\) were obtained from the tests in the current study).  
Then the SCF of two welded joints were calculated and put in one table, as shown in Table 6. For the FEM, the hot-spot stress of cruciform welded joints was about 28.334 MPa higher than that of T-welded joints under the same load (150 kN). For tests at \(-60\)\({}^{\circ}\)C, the hot-spot stress of cruciform welded joints was about 34.884 MPa higher than that of T-welded joints under the same load (150 kN). Both the FEM and the tests reflected that the SCF of cruciform welded joints is higher than that of T-welded joints. In addition, the error between the hot-spot stress obtained by FEM and the tests was less than 5%, which may have been caused by the measurement error in tests. Besides, the good consistency between the hot-spot stress obtained by FEM and the tests also verifies the reliability of the hot-spot stress obtained from the tests at \(-60\)\({}^{\circ}\)C.
# Joint User Pairing, Channel Assignment and Power Allocation in NOMA based CR Systems
## 1 Introduction
The problem of sum rate maximization in in multi user NOMA systems was considered by [24]. On the other hand, considering CR systems, the authors of [25] optimized power loading and channel allocation for achieving fairness among the SUs. [26] considered optimization of resource allocation for energy efficiency maximization in CR systems. The problem of maximizing the sum rate in a CR system was solved in [27]. Integration of the NOMA into cognitive radio systems can result in further improvement in the network's performance. Some works in the literature have also considered resource allocation for NOMA based CR systems. The authors of [28] optimized power allocation to maximize the number of of admitted SUs in the system. [29] discussed sum rate maximization in a NOMA based CR system. The works reported in the literature have many deficiencies. The NOMA problems considered in [12; 14; 15; 16; 22; 24; 28] ignored the required minimum gap in received power for SIC. Similarly, the frameworks proposed in [16; 18; 19; 24; 27] did not consider the rate requirements of the communicating nodes. Some works [23; 24; 26; 28] did not transform the problem to a convex form so the optimality of the solution cannot be guaranteed. Similar to the systems in [12; 13; 14; 15; 16; 17; 18; 19], the NOMA based CR system considered in [29] consists of a single channel and the solution technique cannot be mapped to a multi channel scenario. In this work, we aim to maximize the sum rate in a multi user NOMA based underlay CR system. The main contribution of this work are listed below:  
* A problem is formulated to maximize sum rate of a multi user NOMA based underlay CR system. * Unlike previous works, the resource allocation is optimized while accounting for the rate requirement of each user and NOMA constraint of sufficient gap in received power for SIC. Further, the constraints of power budget and interference temperature are also taken into consideration. * The original non convex optimization problem is transformed into a convex form and the closed form expressions for optimal power allocation are derived. An efficient technique for the user pairing and channel allocation is proposed. * Simulation results thoroughly discuss the performance of the proposed technique. The remainder of the paper is organized as follows. The system model and problem formulation is presented in Section 2. Section 3 provides the proposed solution technique. The simulation results are discussed in Section 4. Finally, the work is concluded in Section 5.
## 3 Results and Discussion  
### Determination of the Calcination Temperature  
Calcination is a thermal treatment applied to sorbents or catalysts [16]. It is normally performed in the presence of air at temperatures below the melting point of the catalyst or sorbent. It leads to the modification or stabilization of the mechanical properties of the catalyst or sorbent through phase transition or thermal decomposition. The volatile fraction and the chemically bonded CO\({}_{2}\) or water are removed during calcination [12]. It was reported that excessive temperature may lead to catalyst/sorbent sintering. Sintering alters the catalyst/sorbent surface area structure and may affect its activity [17]. Accordingly, an optimum calcination temperature is desired. Thermogravimetric analysis (TGA) was conducted to determine the optimum calcination temperature. Five grams of the sorbent were exposed to air at a 10 \({}^{\circ}\)C/min heating rate, ramping from 25 \({}^{\circ}\)C to 800 \({}^{\circ}\)C. It was observed that the mass loss started at 80 \({}^{\circ}\)C and stabilized above 600 \({}^{\circ}\)C (see Figure 1).  
The mass loss of the sample up to 600 \({}^{\circ}\)C was most likely due to dehydration, as the water of crystallization in the sample started to be released at about 400 \({}^{\circ}\)C and finished at about 630 \({}^{\circ}\)C. This weight loss was due to the thermal decomposition of Ca(OH)\({}_{2}\), which decomposes to CaO at 450-500 \({}^{\circ}\)C [18]. Similar results were observed from the TGA results and XRD data for the used sorbents as discussed in Sections 3.2 and 3.3.1  
Figure 1: TGA profiles for the determination of the calcination temperature for the Ca\({}_{6}\)Mg\({}_{2}\)Ce\({}_{1}\) at heating ramp of 10 \({}^{\circ}\)C/min from 25 \({}^{\circ}\)C to 700 \({}^{\circ}\)C in air and at atmospheric pressure.  
respectively. Accordingly, a calcination temperature of 650 \({}^{\circ}\)C for 3 h was applied to all the prepared samples.
# Scenario Optimisation and Sensitivity Analysis for Safe Automated Driving Using Gaussian Processes
## 3 Gaussian Processes
### Learning the Hyperparameters  
The RBF kernel function \(k(\mathbf{x},\mathbf{x}^{\prime})\) is used in this paper:  
\[k(\mathbf{x},\mathbf{x}^{\prime})=\exp\{-(\mathbf{x}-\mathbf{x}^{\prime})^{T}\mathbf{B}(\mathbf{x}- \mathbf{x}^{\prime})\}, \tag{9}\]  
where \(\mathbf{B}=\{(\sqrt{2}b_{i})^{-2}\}_{i=1}^{d}\), a diagonal matrix of positive smoothness parameters, and \(d\) is the dimension of \(\mathbf{x}\). These parameters are represented in a vector \(\mathbf{\theta}=(b_{1},\ldots,b_{d},\sigma^{2})\), and are called the hyperparameters. The hyperparameters \(b_{i}\)'s have the effect of re-scaling the distance between two inputs \(\mathbf{x}\) and \(\mathbf{x}^{\prime}\). Thus, they determine how close two inputs \(\mathbf{x}\) and \(\mathbf{x}^{\prime}\) need to be such that the correlation between \(f(\mathbf{x})\) and \(f(\mathbf{x}^{\prime})\) takes a particular value.  
A stationary and isotropic RBF was used in this GP, which is invariant to both translation and rotation of the input data set. The hyperparameters can be optimised in order to find the best fit of the GP, given the training data \(\mathbf{\mathcal{D}}\). The optimal hyperparameters are found by maximising the log marginal likelihood  
\[\theta_{max}=\operatorname*{arg\,max}_{\mathbf{\theta}}\log p(\mathbf{f}|\mathbf{X},\mathbf{ \theta})\,, \tag{10}\]  
where the log marginal likelihood is given by  
\[\log p(\mathbf{f}|\mathbf{X},\mathbf{\theta})=-\frac{1}{2}\mathbf{f}^{T}K^{-1}\mathbf{f}-\frac{1} {2}\log|K|-\frac{n}{2}\log 2\pi\,. \tag{11}\]
# Suitable Integral Sampling for Bandpass-Sampling Time-Modulated Fourier Transform Spectroscopy
In this study, a BPS-FTS method based on Michelson moving mirror FTS [14] is used for modeling and simulation. By employing bandpass sampling [16; 17; 18] instead of traditional Nyquist sampling [19; 20; 21; 22; 23; 24] for each narrowband spectrum, we can reduce the number of sampling points across the entire optical path difference of the interferogram. We provide a theoretical derivation of changes in the interference model caused via integral sampling under bandpass sampling theory. Furthermore, using the SMTF criterion [25; 26; 27], we discuss and analyze suitable integral sampling conditions that meet the distortion tolerance for the reconstructed spectrum. Sampled interferograms with different integral sampling times and the corresponding reconstructed spectra are also modeled and analyzed. How the accuracy of the reconstructed spectrum varies with the integral sampling times of the interferogram is investigated, and a new criterion for determining a suitable integral interferogram sampling time for BPS-FTS is acquired. The simulation results demonstrate that the proposed criterion is more universally applicable for integral time sampling in BPS-FTS.
# A Joint Positioning Algorithm in Industrial IoT Environments with mm-Wave Communications
## 5 Simulation Results  
Unless specified otherwise, the simulation setting is given in Table 3. Take an indoor square factory with 500 m \(\times\) 500 m [1], in which devices are uniformly distributed. There are 12 TRPs within the factory, and 3 TRPs perform the measurement for a target device by default. The network is a TDD system operating in 27-28 GHz with a subcarrier spacing as 120 KHz and a 400 MHz bandwidth. The simulation scenario is shown in Figure 6.
## 3 Results and Discussion
### Antioxidant Capacity (FRAP)  
The findings indicate that plant-based clarifying agents may offer a superior means of preserving polyphenols, as evidenced by elevated ferric-reducing antioxidant power (FRAP) values. Statistical analyses demonstrate significant differences (\(p<0.05\)) among the treatments (Figure 3). The results suggest that, while medium doses of clarifiers (MM) achieved a balance between clarity and polyphenol retention, increasing protein doses (MH, HH) resulted in greater losses of antioxidant compounds due to stronger binding and precipitation. The samples that demonstrated the greatest efficacy were SL-MM and KF-HH, which exhibited the highest antioxidant capacities. In comparison with the control sample (47.4 mgAAE/L), the following samples did not demonstrate a statistically significant difference in antioxidant capacity: GL-HH (\(p=0.572\)), GF-MH (\(p=0.136\)), SE-MM (\(p=1.000\)), and SL-HH (\(p=0.97\)), SL-MH (\(p=1.000\)), SL-HM (\(p=1.000\)), SF-HM (\(p=0.136\)). The remaining combinations exhibited a significant increase in antioxidant capacity compared to the control. [PERSON] et al. (2011) [40] examined the impact of enzyme treatment on the antioxidant capacity of apple juice ('Shampion' variety) during storage. In comparison with the control sample (150 mgAAE/L), the treated samples exhibited a decrease in concentration ranging from 136 to 158 mgAAEsl when subjected to various enzymes. Notably, the KE-MM sample yielded the highest value of 111.14 mgAAE/L, which is also lower than the lower limit of 136 mgAAE/L reported in the referenced source. These  
Figure 2: The effect of clarifying agents on apple juice turbidity. MM (medium protein dosage and medium mineral dosage), HM (high protein dosage and medium mineral dosage), MH (medium protein dosage and high mineral dosage). E (gelatin), L (LittoFresh Liquid—plant-based protein), F (FloaClair—plant-based protein). Each value is expressed as mean \(\pm\) standard deviation. The type of mineral-based agent had a significant effect (F(2;101) = 4.194; \(p<0.05\)). The type of protein-based clarification agent had a significant effect (F(2;101) = 62.427; \(p<0.001\)). The types of clarification agents had no significant effect (F(4;101) = 2.261; \(p=0.068\)). The presence of different letters indicates that the groups in question are significantly different. Lower-case letter: comparison of proteins at fixed mineral clarification agents and concentrations.  
findings imply that the diverse enzymatic treatments exert a more pronounced effect on the antioxidant capacity than the clarification procedures employed.
# Reducing the Power Consumption of VR Displays with a Field Sequential Color LCD
## 2 Experiment
### Wavelength-Dependent Birefringence  
Birefringence dispersion is also an important factor for full-color VR displays. To measure birefringence dispersion, a tunable argon ion laser (457 nm, 488 nm, and 514 nm) and a He-Ne laser (633 nm) were employed as probing beams. Results are shown as black triangles in Figure 4. To calculate the birefringence at other wavelengths (especially at 450 nm, 550 nm, and 650 nm), we refer to the single-band birefringence dispersion equation [23]:  
\[\Delta n=G\frac{\lambda^{2}\lambda^{*2}}{\lambda^{2}-\lambda^{*2}}, \tag{3}\]  
where \(\lambda^{*}\) is the mean resonance wavelength and \(G\) is the proportional constant. Using Equation (3) to fit the experimental data, we found \(\lambda^{*}\) = 0.252 \(\upmu\)m, and \(G\) = 2.48 \(\upmu\)m\({}^{-2}\). According to the fitting curve (black solid line) in Figure 4, the birefringence at \(\lambda\) = (450 nm, 550 nm, 650 nm) and \(T\) = 22 \({}^{\circ}\)C is (0.231, 0.200, 0.186), respectively.  
Figure 4: Wavelength-dependent birefringence at 22 \({}^{\circ}\)C. Triangles are measured data and the solid line is the fitting curve with Equation (3).  
Figure 3: Temperature-dependent figure of merit. Squares are measured data and the solid line is the fitting curve with the definition of FoM.