esa-sceva/satcom-chunk-collection huggingface dataset:
# High Imperceptible Data Hiding Method Based on Combination Theory for AMBTC Compressed Images
## 4 Experimental Results
### Results and Discussion  
The experimental findings are presented in Table 2, where the PSNRs, MSEs, and hiding capacity are shown. For the proposed scheme, in terms of hiding capacity, the highest hiding capacity is 201,364 for the image of Lena. While the lowest hiding capacity is 104,284, as experienced by the image of the baboon. On average, the hiding capacity is 171,529. This means 60% of the test images exceed the average hiding capacity. This indicated that the proposed scheme proved to have a high hiding capacity. Figure 9: Image testing: (**a**) airplane; (**b**) baboon; (**c**) boat; (**d**) Lena; and (**e**) pepper. Furthermore, regarding image quality, the experimental results provided excellent findings, whereby all the PSNR values of the test images are above 35.89 dB. The highest PSNR value was shown by the image of the airplane, 38.25 dB. It is widely accepted knowledge in image data-hiding that when the PSNR value of an image exceeds 30 db, image modifications remain undetectable. Therefore, the findings proved that the proposed scheme has high imperceptibility. Further testing was performed by comparing the proposed scheme to those developed by [PERSON] et al. [22], [PERSON] et al. 's scheme, and [PERSON] et al. 's scheme. The main reason for comparing these three schemes is to find the appropriate scheme for embedding a secret message in the complex block. That is why the predefined threshold is equal to 0. The performance comparison of the proposed scheme and others' schemes can be seen in Table 2 and Figure 9.  
As seen in Table 2, [PERSON] et al. 's scheme's PSNR value for all test images is lower than the proposed scheme. The difference is 5 dB overall. This pattern is proportional to the value of the MSE. As the higher the MSE, the lower the PSNR is. Nevertheless, [PERSON] et al. 's approach produces adequate image quality because all their PSNRs are more than 30 dB. The notable difference is in the number of embedded bits. As demonstrated in Figure 10, the proposed method has double the hiding capacity of [PERSON] et al. 's approach. This demonstrates that the proposed approach is highly advantageous for complex images. The second comparison is against [PERSON] et al. 's scheme. As presented in Table 2, the most significant difference in the PSNR value is experienced by the baboon image, 26.39 dB and 37.97 dB for [PERSON] et al. 's scheme and the proposed scheme, respectively. It has been proven that the imperceptibility of the proposed scheme is outstanding. Figure 10 depicts the strange trend of the MSE baboon image value, which corresponds to the PSNR value. This is because the PSNR value is relatively low, precisely 26 dB; therefore, the MSE is very high, 149.30. Moreover, Figure 10 illustrates the most exciting trend regarding hiding capacity in the baboon image. In terms of hiding capacity, as demonstrated in Figure 10, the proposed method has double the hiding capacity of [PERSON] et al. 's approach.
###### Abstract  
Accurate tree species identification through bark characteristics is essential for effective forest management, but traditionally requires extensive expertise. This study leverages artificial intelligence (AI), specifically the EfficientNet-B3 convolutional neural network, to enhance AI-based tree bark identification, focusing on northern red oak (_Quercus rubra_), hackberry (_Celtis occidentalis_), and bitternut hickory (_Carpa cordiformis_) using the CentralBark dataset. We investigated three environmental variables--time of day (lighting conditions), bark moisture content (wet or dry), and cardinal direction of observation--to identify sources of classification inaccuracies. Results revealed that bark moisture significantly reduced accuracy by 8.19% in wet conditions (89.32% dry vs. 81.13% wet). In comparison, the time of day had a significant impact on hackberry (95.56% evening) and northern red oak (80.80% afternoon), with notable chi-squared associations (_p_ < 0.05). Cardinal direction had minimal effect (4.72% variation). Bitternut hickory detection consistently underperformed (26.76%), highlighting morphological challenges. These findings underscore the need for targeted dataset augmentation with wet and afternoon images, alongside preprocessing techniques like illumination normalization, to improve model robustness. Enhanced AI tools will streamline forest inventories, support biodiversity monitoring, and bolster conservation in dynamic forest ecosystems.  
d
## 3 Discussion
### Empirical Case Study and Analysis
#### 3.1.1 Modeling of Wind Power Curves  
To verify the effectiveness of the proposed improved PCF algorithm, Figure 5 shows a schematic diagram of the wind turbine static optimal power curve effects constructed by the PCF algorithm, the B-spline fitting algorithm, and the algorithm of this paper.  
Figure 4: Scatter plot of 10 min wind speed–power data.  
The comparative analysis provided by the figures clearly illustrates that the polynomial fitting algorithm has a poor fitting effect on the stable curve segment after reaching the rated power. The B-spline fitting algorithm has a segment of the curve with a poor fitting effect on the rising segment of the curve from cut-in wind speed to rated wind speed, and the algorithm of this paper combines the advantages of these two algorithms, resulting in a good fitting effect on the corrected wind power data.  
Table 1 provides a numerical comparison of the modeling accuracy and efficiency of the three power curve modeling algorithms.  
Table 1 reveals that the polynomial fitting algorithm achieves a modeling accuracy of 99.1% with a fitting duration of 0.91 s. B-spline fitting offers higher accuracy, reaching up to 99.5%, but requires more time for modeling. In contrast to these two algorithms, the improved algorithm proposed in this paper increases accuracy to 99.8% while also surpassing the B-spline fitting algorithm in terms of modeling speed. Thus, the algorithm introduced in this study outperforms the polynomial and B-spline fitting algorithms in both computational efficiency and precision. Consequently, this algorithm emerges as a preferred choice for power curve modeling within practical engineering contexts, offering simplicity in concept coupled with robustness in execution speed and modeling fidelity.
## 3 Establishment of Ship Emission Calculation Model  
The Ship Traffic Emission Estimation Model (STEAM) based on AIS was proposed by [PERSON] et al. [20]. On the basis of simplifying STEAM to meet the localization requirements, [PERSON] et al. [24] proposed a basic equation suitable for inland ships, as shown in Equation (1).  
The matching of the propeller of the ship affects the energy conversion efficiency of the diesel engine, which in turn affects the fuel consumption, and which will lead to a large deviation in the calculation of the pollutant emission inventory. Therefore, Equation (1) is improved in this paper, and the engine propeller matching coefficient (\(EPM\)) is introduced to further modify the model. The \"bottom-up\" power method was used to calculate the air pollution emissions from ships in the main urban area of Chongqing, and the model was revised by introducing the ship's engine propeller matching condition coefficient. The specific revision process of the model is shown in Section 3.4 below.  
Equation (1) represents the original STEAM model, Equation (2) represents the revised main engine emission model, and Equation (3) represents the auxiliary engine emission model.  
\[E=MCR\times LF\times EF\times FCF\times T \tag{1}\]  
\[E_{Mj}=MCR\times LF_{i}\times EF_{ij}\times FCF\times EPM\times T_{i}\times 10^{ -6} \tag{2}\]  
\[E_{Aj}=ELD\times LF_{i}\times EF_{ij}\times FCF\times T_{i}\times 10^{-6} \tag{3}\]  
where \(E\) is pollutant emission (t), \(M\) is main engine emission model, \(A\) is auxiliary engine emission model, \(MCR\) is the main engine power (kW), and \(ELD\) is auxiliary engine power (kW). Moreover, \(i\) is the driving mode, which is divided into four modes: cruise, low-speed cruise, maneuvering, and mooring. Furthermore, \(j\) is the pollutant type, and it is divided into CO, HC, NOx, PM\({}_{10}\), PM\({}_{2.5}\), SO\({}_{\chi}\) and the greenhouse gas CO\({}_{2}\).  
\(LF\) is the load factor, and the load factor is determined by the engine type and sailing conditions. \(EF\) is the emission factor, and it is determined by the engine type, operating conditions, and fuel type. \(FCF\) is the fuel correction factor. \(EPM\) is the engine propeller matching correction coefficient. \(T\) is time (h).  
The parameters in the model will be studied and determined below.
# Carbonyl Composition and Electrophilicity in Vaping Emissions of Flavored and Unflavored E-Liquids
## 2 Materials and Methods
### Identification of Carbonlys Emitted from Vaping  
One objective of this study was to identify carbonyls in vaping emissions produced from thermal degradation and direct oxidation of alcohol-containing flavoring chemicals during vaping. To collect the vaping emissions, the e-cigarette pen was attached to a cold trap apparatus positioned inside a box filled with dry ice (Scheme 1). The output of the cold trap was connected to a diaphragm pump (Gast Manufacturing Inc., Benton Harbor, MI, USA). A critical orifice was used to control the flow at ~0.46 L per minute. Before the actual collection of vaping emissions, the e-cigarette pen was preconditioned with at least 25 puffs to avoid artifacts from dry puffs. Then, 4 cycles of 25 puffs (i.e., total 100 puffs) of vaping emissions were collected at a 4-s puff and 25-s inter-puff interval using a programmable timer switch (Nearpow, model T319). This vaping topography is within the recommended range by [PERSON] et al. [38]. For every 25 puffs (as a cycle), the vape pen was rested for 5 min.  
The cold trap sample was then treated with 1.5 mL of 1 mM PFBHA solution (dissolved in MiliQ water). The sample was stored at room temperature in the dark for 24 h to allow PFBHA to react with carbonyl compounds in the vaping emission samples. PFBHA can react with carbonyl compounds to form oxime derivatives, which can be analyzed using GC-MS methods [39]. After 24 h, SPME fibers (65 \(\upmu\)m PDMS/DVB fused silica, Supelco) were used to extract the PFBHA-carbonyl oxime products from the sample solutions. The SPME fiber was submerged into each sample for 60 min and used for GC/EI-MS analysis immediately.  
The collected carbonyl-PFBHA analytes on SPME fibers were analyzed using a GC/EI-MS system (Agilent 6890N GC and 5975C MSD). The SPME fibers were manually injected into the GC inlet in splitless mode and desorbed for at least 2 min at 250 \({}^{\circ}\)C. Analytes desorbed from the SPME fibers were separated by an Agilent Technologies DB-5 MS column (30 m \(\times\) 0.25 mm i.d., 0.25-\(\upmu\)m film). The flow rate of the carrier gas (i.e., helium) was 1 mL/min. The GC temperature gradient was set as follows: an initial temperature at 60 \({}^{\circ}\)C held for 4 min, followed by a temperature increase to 90 \({}^{\circ}\)C at a rate of 15 \({}^{\circ}\)C min\({}^{-1}\) and held for 4 min, followed by a temperature increase to 250 \({}^{\circ}\)C at a rate of 10 \({}^{\circ}\)C min\({}^{-1}\), and held for 5 min. The total run time was 31 min. Data acquisition was performed in full-scan mode within \(m/z\) 30-500.
# Accounting for Training Data Error in Machine Learning Applied to Earth Observations
## 1 Introduction
### Characterizing Training Data Error
#### 1.2.2 Current Approaches for Assessing and Accounting for Training Data Error
They also reveal that there is no standard, widely accepted practice for assessing TD error, which, similar to map reference data, is generally not reported and thus implicitly treated as error-free [30].
## 4 Case Studies
### Parameter Settings  
In the calculation of HI, there are two parameters \(m\) and \(k\) in SG filtering. Figure 10 shows the SRMS curve of the 10 # WT when the two parameters take different values. It can be seen that the selection of these two parameters will directly affect the filtering effect of SG. When \(k=2\), the larger the \(m\), the better the smoothing effect. At a fixed \(m\) value, the higher the \(k\) value, the worse the smoothing effect. After comprehensive consideration, this paper selects \(m=30\) and \(k=2\). Figure 11 shows the SRMS curve of the three WTs after SG filtering. It can be seen that SRMS retains the degradation trend of WT bearings based on smooth curves.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Task** & **Source** & **Target** \\ \hline Task1 & 10\# & 19\# \\ Task2 & 10\# & 21\# \\ Task3 & 19\# & 10\# \\ Task4 & 19\# & 21\# \\ Task5 & 21\# & 10\# \\ Task6 & 21\# & 19\# \\ \hline \hline \end{tabular}
\end{table}
Table 2: Transfer learning tasks.  
The MDA-LETCN proposed in this paper needs to cover all time series, with four residual modules set, each with a convolutional kernel size of 3 and channel of 32. \(\alpha\) = 0.005, \(\beta\) = 0.001, and the learning rate is 0.005.  
To verify the effectiveness of MDA-LETCN in predicting RUL, root mean square error (RMSE), mean absolute error (MAE), and score were used as evaluation indicators.  
\[\text{RMSE}=\sqrt{\frac{\sum\limits_{i=1}^{N}(y_{i}-\hat{y}_{i})}{N}} \tag{22}\]  
\[\text{MAE}=\frac{1}{N}{\sum\limits_{i=1}^{N}}|y_{i}-\hat{y}_{i}| \tag{23}\]  
\[Score=\frac{1}{n}{\sum\limits_{i=1}^{n}A_{i}} \tag{24}\]  
where:  
\[A_{i}=\left\{\begin{array}{ll}e^{-\ln\left(0.5\right)\cdot\left(E_{t}/5 \right)}&if\ Er_{i}\leq 0\\ e^{\ln\left(0.5\right)\cdot\left(E_{t}/20\right)}&if\ Er_{i}>0\end{array}\right. \tag{25}\]  
\[Er_{i}=\frac{y_{i}-\hat{y}_{i}}{y_{i}}\cdot 100 \tag{26}\]  
where \(y_{i}\) is the actual RUL, \(\hat{y}_{i}\) is the predicted RUL, and \(N\) is the number of data points participating in the test.
# Design, Challenges and Developments for 5G Massive MIMO Antenna Systems at Sub 6-GHz Band: A Review
## 2 5G Massive MIMO at Sub-6 GHz
### 5G Smartphone mMIMO Array Antenna Techniques
#### 2.2.1 18-Port 5G Massive MIMO  
The mMIMO/diversity (4G/5G) model of the 18-element antenna system was created at sub-6 GHz long-term evolution (LTE-42/43) bands of 3.4-3.6 GHz and 3.6-3.8 GHz, respectively. By using a straightforward slotted antenna with an open slot as the radiator, a small design could be achieved. Additionally, this slot serves as a decoupling component to enhance isolation between other elements. The antenna array elements were constructed on a low-cost FR4 substrate with dimensions of 150 mm \(\times\) 80 mm \(\times\) 1.6 mm, frequently utilized for 6-inch smartphones, as shown in Figure 10. Antenna gain of >5.3 dBi, impedance matching (coefficient of reflection >20 dB), total efficiency (>87%), port isolation (>20 dB), and envelope correlation coefficient (<0.010) over operating frequency were demonstrated by the simulation and measurement results. The performance metric of the MIMO antenna was verified by computing the capacity of the ergodic channel using the Kronecker channel model [58].
## 2 Results and Discussions
### Development of the HPLC-IV-ELSD Quantitative Method
#### 2.1.2 Optimization of the Extraction Method  
Seventy percent MeOH was chosen as the extracting solvent for PV saponins [15; 22; 23]. To optimize the extraction method, PV powder (100 mg) was suspended with 10 mL of 70% MeOH in capped 15 mL conical tubes and extracted with ultrasound. Variables involved in the procedure such as temperature (30, 60, 80 \({}^{\circ}\)C), extraction time (10, 20, 30, 40 min), and extract repetitions (1, 2, 3 times) were optimized. The optimization was based on the peak areas of G-Rg1, G-Rb1, G-Rd, and M-R2, the four most abundant saponins in PV. The content of these four saponins in the extract may closely reflect the extraction efficacy.  
Figure 2: HPLC chromatograms of a standard mixture detected by ELSD (**A**) and UV (**B**); PV extract detected by ELSD (**C**) and UV (**D**). 1, N-R1; 2, M-R1; 3, G-Rg1; 4, G-Re; 5, M-R2; 6, p-RT4; 7, V-R11; 8, V-R2; 9, G-Rb1; 10, G-Rd.  
Figure 3A showed no significant difference in peak areas of saponins in chromatograms of PV extracts at different extraction temperatures. For the extraction duration, as illustrated in Figure 3B, the content of the main saponins in the extract increased rapidly from 10 to 20 min, but from 20-40 min, no significant change was observed. The first 20 min extraction recovered over 96% of saponins compared with the second and third extractions (Figure 3C). Therefore, single extraction for 20 min at 30 \({}^{\circ}\)C was used for the extraction of saponins from PV with the details presented in Figure 3. The optimized extraction method is simple that could significantly save time and solvent. By using this method, many samples could be extracted at the same time, which could increase the analysis efficiency. Specifically, our developed extraction only requires 100 mg of sample powder, which is suitable for high-value samples such as PV.
# Study on Passenger Comfort Based on Human-Bus-Road Coupled Vibration
## 4 Case Study
### Comfort Evaluation  
The weighted acceleration RMS of the human body considering different road surface roughness conditions can be calculated using Equation (14), and the annoyance rate can then be obtained using Equation (15). The steps for calculating the annoyance rate are as follows. According to the data listed in Table 1, the human annoyance rate function based on the coupled human-bus-road vibration can be established. The logarithmic relationship between the fuzzy membership function \(v(u)\) and the vibration acceleration \(u\) is expressed as  
\[v(u)=\begin{cases}0&u<u_{\text{min}}\\ a\ln u+b&u_{\text{min}}\leq u\leq u_{\text{max}}\\ 1&u>u_{\text{max}}\end{cases} \tag{19}\]  
According to Table 1, \(u_{\text{min}}\)= 0.315 m/s\({}^{2}\) and \(u_{\text{max}}\) = 2.5 m/s\({}^{2}\), which are substituted into Equation (19) as  
\[\left\{\begin{array}{l}a\ln(u_{\text{min}})+b=0\\ a\ln(u_{\text{max}})+b=1\end{array}\right. \tag{20}\]  
Solving the above formula, the coefficients are obtained as \(a\) = 0.4827 and \(b\) = 0.5577. Therefore, the annoyance rate of passengers considering the coupled vibration of human-bus-road can be obtained as  
\[A(x)=\int\limits_{u_{\text{min}}}^{\infty}\frac{1}{\sqrt{2\pi}u\sigma}\exp \left\{\frac{-\left(\ln(u/x)+0.5\sigma^{2}\right)^{2}}{2\sigma^{2}}\right\} \times\left(0.4827\ln u+0.5577\right)du \tag{21}\]  
The numerical way to obtain \(A\)(x) in Equation (21) generally requires interpolation, which is inconvenient for application. The logarithmic normal distribution curve can be used to fit the annoyance curve for better application. The fitting curve and the error curve are shown in Figure 15. The maximum error is 2.17%, which satisfies the requirements of evaluation accuracy.  
Figure 14: The pitch and roll acceleration amplitudes with four levels of road roughness.  
as seen in the table, the annoyance rate of the human body is 52.9%. It indicates that 52.9% of passengers feel uncomfortable, which gives out a quantitative evaluation of the uncomfortable degree of the human body. In addition, as listed in Table 1, when the weighted acceleration RMS is 0.5-1.0, the human reaction is \"fairly uncomfortable\", and 0.8-1.6 is evaluated as \"uncomfortable\". Based on that, the weighted acceleration RMS under C-level road surface condition, i.e., 0.9862, could be evaluated as either \"fairly uncomfortable\" or \"uncomfortable\". The overlap between the ranges of two classifications results in the fuzzy judgment based on the standard evaluation, while the evaluation based on the annoyance rate index can avoid this issue and gives out an accurate classification. That is, a more accurate evaluation can be obtained for passenger comfort.
# Adaptive Energy Management in 5G Network Slicing: Requirements, Architecture, and Strategies
## 5 Evaluation
### Optimal Algorithmic Strategy: OptTsNS
#### 5.2.1 Complexity Analysis of OptTsNS  
The complexity of the exact solution is conditioned to the processing of \(AllVarServ\) and \(AllCombServ\). As a function of \(N\), the growth rate can be expressed as:  
\[f(N)=N+Q\times(2\times N\times max\{u_{k,j}^{i}\}+N)+Q\times(2\times max\{u_{ k,j}^{i}\}+1)^{N} \tag{28}\]  
where the third term in Equation (28) is dominant and represents the size of the search space that must be explored to find the optimal combination of services \((VarServ)\) that lead to the minimization of power consumption (optimal power consumption). Therefore, the complexity the algorithmic strategy OptTsNS is exponential with an order of growth \(\mathcal{O}(2^{N})\) that depends on the selected values of \(N\), Ts, \(max\{u_{k,j}^{i}\}\), and \(Q\).
# Design of a Computed Tomography Automation Architecture
## 5 Industrial Applications
### Industrial Robots
#### 5.3.1 Robot Calibration  
[PERSON] et al. [40] worked on improving calibration for robot-based CT systems. They presented a method for calibrating by using mainly only the projection data of a special phantom to minimize reprojection error. The projection data were created by the robot-based system itself. A method was applied to the robot-based CT system to solve for the unknown transformation between the two robots that control the X-ray source and detector, and the transformations for offset and rotation of components relative to the end-effector. The Levenberg-Marquardt algorithm, a damped least-squares method, was then applied to minimize the reprojection error using the manually acquired X-ray projections.  
[PERSON] et al. [41] also focused on the calibration of robotic manipulator systems. The focus of their study was on applications in cone-beam tomography imaging in which complex scan trajectories are achieved with robots. The robotic system in this case held the sample, while the X-ray source and detector remained fixed. Calibration scans were performed that provided geometric information and to link this to the manipulator's positional encoders' data. Optimization algorithms then estimated source and detector location, detector orientation, and the position and orientation of linear and rotational axes of the robotic manipulator system. They also compared the accuracy of the six-axis robot arm manipulator discussed above with a setup comprising of a rotation stage mounted on the top plate of a high precision hexapod mounted on a linear stage. Further research [42] with the same robotic system was used for computed laminography, which is a type of CT normally used for flat samples.  
[PERSON] et al. [43] investigated the positioning accuracy of industrial robots for robotics-based X-ray computed tomography. Only one robotic manipulator was tested, the Kuka KR 15-2 robotic arm. They measured the accuracy of an X-ray CT scan with a robotic manipulator using a laser interferometer. They tracked linear and angular errors in five degrees-of-freedom (DOF). The conclusion of this experiment was that this specific robot had repeatability of \(\pm\)0.04 mm. This concluded that an X-ray CT scanner based on robots can deliver the reasonable image quality and resolution if the system is calibrated well.  
Repeatability experiments were performed with the Motoman SIA5D seven-axis robot [44]. The first experiment involved measuring repeatability with a dial indicator, by having the robot repeat a set of moves before pressing the dial. The second experiment was a test of vibrational impact and trajectory tracking. These were completed to validate the feasibility of this robot for NDT applications at Los Alamos National Laboratory (LANL). The requirements include a maximum of 250 \(\upmu\)m variance and vibration under 25 \(\upmu\)m. Both measures tested below these requirements, with a repeatability of 18 \(\upmu\)m.