esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Question 6: What Is the Combined Effect of Individual IEQ Aspects on Overall IEQ Satisfaction?
#### 3.6.4 Structural Equation Modeling (SEM)  
In addition, structural equation modeling (SEM) was also used to investigate the relationship between overall IEQ satisfaction and the IEQ indicators [72; 87; 88]. SEM is a combination of regression analysis and exploratory factor analysis [87]. Using this method, [PERSON] et al. [87] identified the latent constructs between the IEQ performance, the four IEQ aspects, and their corresponding indicators. Based on their results, they developed a weighting scheme for the four IEQ aspects in the context of hospital environments. By applying SEM, one could determine the ranking importance of different IEQ aspects to the overall IEQ performance. However, an equation that results in an IEQ index must still be developed. To evaluate and compare the IEQ performance of different buildings, [PERSON] [72] developed a second-order model incorporating occupants' satisfaction and established a relationship between the overall IEQ performance and occupants' satisfaction. Then, he proposed a comprehensive occupants' satisfaction index, namely, the percentage of variance in the occupants' overall IEQ satisfaction that can be explained by the IEQ performance, using this as the final output of the model to rate the IEQ performance in healthcare facilities.
# Determining Thresholds for Optimal Adaptive Discrete Cosine Transformation
## 3 Results and Discussion
An example of better compression of an image using the obtained ADCT threshold values over regular 8 \(\times\) 8 DCT is shown in Figure 14. A comparison of image compression metrics is shown in Table 5. The threshold values for ADCT are derived from the obtained first-order equations and are as follows: given the original ITDV equals 11.666, with low target quality, \(ps=0.112\), \(pm=0.441\). One can see that an improvement, big or small, in the compression ratio can be observed in all cases. The biggest improvement in compression is seen at the low-quality level for less detailed images. The explanation is that the considered images tend to have \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Quality** & **Approximation** & **MS-SSIM** & **CR (ADCT/DCT)** \\  & **Equation Order** & **(ADCT/DCT)** & **CR (ADCT/DCT)** \\ \hline High & 1 st & 0.988 & 1.039 \\ \hline High & 3 rd & 0.989 & 1.032 \\ \hline Medium & 1 st & 0.981 & 1.157 \\ \hline Medium & 3 rd & 0.982 & 1.147 \\ \hline Low & 1 st & 0.916 & 1.662 \\ \hline Low & 3 rd & 0.958 & 1.526 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of the ADCT improvements over regular DCT from graphs above. Figure 12: Graphical comparison of proposed ADCT with non-adaptive DCT in terms of approximated bit rate vs. MS-SSIM. larger areas suitable for fitting large fragments of solid color. However, the use of optimal ADCT threshold values can lead to slight drops in the quality measures and overall look of the frame, which would require additional research with a larger dataset and, possibly, additional metrics. Figure 14: Advantage in low target quality compression of ADCT over DCT and preservation of information about vehicles in the scene: (**a**) original image; (**b**) fragmentation quadtree of ADCT and grid of DCT (yellow fragments are solid color); (**c**) resulting images (details about car positions are preserved). Figure 13: Comparison [25] with the existing algorithms.
## 2 Materials and Methods  
ZnO seed layers were first fabricated using the sol-gel method [26]. In detail, the precursor for preparing the seed layers was a mixture of 15 mL sol solution containing 0.75 M zinc acetate dihydrate (Zn(CH3 COO)2 \(\cdot\) 2H2O) mixed with the solvent containing equal quantities of monoethanolamine (HOCH2 CH2 NH2) and 2-methoxyethanol (CH3 OCH2 CH2 OH). The solution was stabilized at room temperature for 24 h. The dip coating of the seed layer was performed at a rate of 3 cm/min after immersing the glass slide in the sol solution for 10 min. The process was repeated three times. After the dip-coating process, the substrate was dried and annealed on a hot plate at 100-250 \({}^{\circ}\)C for 30 min, leading to the gelation of the sol solution on the substrate surface and the formation of the seed layer. The growth of ZnO NRs was prepared by immersing the sample in a chemical bath containing a solution of zinc nitrate hexahydrate (Zn(NO3)2 \(\cdot\) 6H2O) and methenamine (C6H12N4) at the same 0.1-M concentration at 85 \({}^{\circ}\)C for 2 h. The growth of AuNPs on ZnO NRs was facilitated using the PLIPmethod. The ZnO NR substrate was placed inside a glass vial containing 5 mL of 0.33 mM HAuCl4 and 2 mL of the 10 mM H2O2 aqueous solution. The aqueous solution was then irradiated using a Nd:YAG nanosecond pulsed laser operated in 532 nm and 47 mJ laser energy at different irradiation time. Then, the samples were kept placing inside glass vials at different immersion times. The process of fabricating AuNP-decorated ZnO NRs is illustrated in Figure 1. The X-ray diffraction (XRD) patterns of ZnO NRs were obtained in the 2\(\theta\) range of 0 deg-90 deg on a high-resolution X-ray diffractometer SERS measurements were performed using a micro-Raman system (LabRAM HR800; HORIBA Jobin Yvon) with a helium-neon laser as the excitation source operating at a 633 nm wavelength and equipped with a 40x objective lens. Scanning electron microscopy (SEM) was conducted using a JEOL JSM-7800 F field-emission scanning electron microscope (JEOL, Tokyo, Japan).
## 3 Numerical Simulation
### Suspension Bridge System  
To evaluate the effectiveness of modal identification methods, a low-damped suspension bridge model (Figure 4) was constructed and tested both numerically and experimentally. The validation of the numerical model was verified by a vibration test in a laboratory mock-up. The numerical and experimental modal parameters of the bridge mock-up are given in Table 1, as described in previous studies [37; 38; 39].  
Figure 3: Basic flowchart of CP and GED method.  
To investigate the effectiveness of CP and GED in handling random vibration, stationary Gaussian white noise (GWN) was employed to excite the system. The bridge deck was excited using a point force, as illustrated in Figure 4. Vertical acceleration of the deck was measured at 20 locations, situated in close proximity to the hangers. The hangers were numbered from 1 to 10 on one side, commencing from the right, and from 11 to 20 on the opposing side. To implement BSS methods for modal identification, the ambient vibration of the structure is required to be measured for a duration of at least 1000-2000 periods of the lowest natural frequency:  
\[T_{\rm min}=\frac{1000\to 2000}{f_{\rm min}}\sec, \tag{40}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Mode** & **Numerical [Hz]** & **Experimental [Hz]** & **Numerical Mode Shape 3** & **Experimental Mode Shape 4** \\ \hline
1 st B 1 & 4.7 & 5.1 & & \\
2 nd B & 6.5 & 6 & & \\
1 st T 2 & 9.8 & 10.1 & & \\
3 rd B & 12.3 & 11.8 & & \\
2 nd T & 12.1 & 13.2 & & \\
4 th B & 17.6 & 19.1 & & \\ \hline \hline \end{tabular}  
* B, bending mode; 2 T, torsional mode; 3 The blue graphic represents the original form of the bridge deck, the green graphics represent the numerical mode shape the bridge deck; 4 The red graphics represent the experimental mode shape the bridge deck.  
\end{table}
Table 1: Numerical and the experimental natural frequencies and mode shapes of the mock-up.  
Figure 4: The arrangement of sensors and locations of excitation on the bridge deck. The number represent the position number.  
where the lowest natural frequency in Hz, denoted as \(f_{\rm min}\), was determined to be approximately 5 Hz according to the initial FRF analysis. Accordingly, the recording time of 200 s was selected for the ambient vibration measurement. To simulate the actual test environment and investigate the robustness of the methods, zero-mean Gaussian white noise was introduced to the accelerometer outputs, with a standard deviation ranging from 1% to 5% of the signal level.
# Path Planning of a Mobile Robot Based on the Improved RRT Algorithm
## 3 Methods
### APS-RRT Planner
#### 3.1.1 Adjustable Probability Strategy  
The Bias-RRT algorithm uses a fixed probability to expand toward the target state, which reduces the randomness of the expansion process of the RRT algorithm to some extent. However, different goal bias probability values have different levels of adaptability  
Figure 2: The framework of the proposed APSD-RRT algorithm.  
to certain environments. For example, when the environment is relatively sparse, a lower goal bias probability \(p\) may slow down the planner's speed. Clearly, it is more suitable to take a higher goal bias probability value in this case. On the other hand, when there are many obstacles in the environment, a higher goal bias probability \(p\) may lead the planner to a local minimum. In this case, it is better to reduce the value of the goal bias probability as far as possible.  
Based on the above discussion, the adjustable probability strategy is proposed. This strategy adjusts the value of goal bias probability adaptively, according to the growth status of the tree. When the tree reaches the obstacle area, the goal bias probability decreases adaptively. On the other hand, when the tree arrives at the free area, the goal bias probability increases gradually.  
Specifically, at each iteration, the value of \(p\) is calculated using Equation (2), where \(p\in(p_{min},p_{max})\), and \(p_{min}\) and \(p_{max}\) are the lower and upper bounds of the range of \(p\), respectively (line 1 in Algorithm 4). _nfail_ represents the number of failed expansions, and the initial value is set to 0. _nsuccess_ represents the number of successful expansions, and the initial value is set to 1. The expansion success rate is calculated according to \(\frac{nsuccess}{\textit{nfail}+nsuccess}\). The higher the expansion success rate, the lower the expansion failure rate. The value of the goal bias probability \(p\) is adaptively adjusted based on this rate, such that the value of \(p\) decreases as _nfail_ increases and increases as _nsuccess_ increases.  
\[p=p_{min}+(p_{max}-p_{min})\times\left(\frac{nsuccess}{\textit{nfail}+nsuccess }\right) \tag{2}\]  
A random number between 0 and 1 is generated to determine which sampling strategy to choose. If \(\textit{Rand}()>p\), the uniform sampling strategy is utilized. Otherwise, the expansion is carried out in the direction of the target state (line 2-6 in Algorithm 4).  
```
Input: Map Output: \(x_{rand}\)
1: \(p=p_{min}+(p_{max}-p_{min})\times(\textit{nsuccess}/\textit{nfail}+\textit{nsuccess})\)
2: if _Rand_(0,1) \(>p\) then
3:\(x_{rand}\leftarrow\textit{UniformSample}(\textit{Map})\)
4: else
5:\(x_{rand}\gets x_{goal}\)
6: end
```  
**Algorithm 4**_AP_
## 3 Results and Discussion
### Start-Up Test:  
The start-up test, which represents the TEG system's zero point under non-NTD circumstances, is used to evaluate the MPPT approaches. In this instance, all strings had hot-side temperatures of 238 \({}^{\circ}\)C, 171 \({}^{\circ}\)C, 121 \({}^{\circ}\)C, and 81 \({}^{\circ}\)C, respectively, and cold-side temperatures of 52 \({}^{\circ}\)C, 45 \({}^{\circ}\)C, 23 \({}^{\circ}\)C, and 21 \({}^{\circ}\)C. Figure 7a,b compare power and duty cycles, respectively, demonstrating that the MRA adopts to the GM in the fewest iterations attaining the highest tracking efficiency in terms of the power magnitude with negligible oscillations at the GM.  
The large oscillations in the developed PSO may be seen in Figure 7. While these oscillations are also present in GWO because of the random probability selection function, which is used to avoid the LM traps, these periodically increasing oscillations are also present. The monitored powers by the MRA, WOA, GWO, and PSO are shown in Table 1 to be 691.8 W, 691.1 W, 688.9 W, and 686.1 W, respectively, with efficiency values of 99.97%, 99.86%, 99.55%, and 99.14%. Figure 7 confirms that the MRA tracks and settles at the GM in 220.2 ms, which is 80 ms-360 ms quicker than the WOA, GWO, and PSO. Figure 7b shows a comparison of the voltage transients. Energy extraction is another crucial criterion for comparison, and Table 2 and Figure 8 demonstrate that, when compared to other MPPT systems, MRA MPPT outclasses them in energy extraction.  
Figure 7: Comparative analysis of all techniques for Case 1 (a) Duty Cycle Variation (b) Power Tracking (c) Current Variation.
## 2 Overview of Blockchain Technology
### Blockchain Consensus Algorithms
#### 2.2.2 Vote Based Consensus  
In vote-based consensus, messages are exchanged between the nodes, while all nodes verify the blocks together.  
Practical Byzantine Fault Tolerance (PBFT) [103] is a consensus approach based on voting that can prevent crashing nodes and subverting nodes. In PBFT, one node is the leader, while the other nodes are peers. Initially, the nodes send requests for validating transactions to the peers, whereas, after passing a specific number of transactions, the leader node composes them into a block and broadcasts it to the peers in the pre-prepare phase. During the preparation phase, the peers rebroadcast the received block to verify that the received block from the leader is the same. The new block is introduced to the blockchain during the commit phase, provided every node obtains copies from over two-thirds ofall other peers. Thus, PBFT can function in the presence of \(1/3\) of the total nodes being malicious nodes. However, PBFT has poor scalability due to the involvement of all nodes for consensus [104].  
In Delegated Byzantine Fault Tolerance (DBFT), voting determines a set of nodes that verify operations and insert a fresh block on the blockchain in a similar manner to PBFT instead of all nodes [105].  
Raft is a crash-fault tolerance-based consensus approach with the assumption that more than 50% of the nodes function normally. In Raft, a given node can be a leader, follower, or candidate. Candidate nodes select a leader through a voting process. Once a leader is appointed, the leader records all transactions sent by multiple followers. When the leader receives a transaction, it broadcasts the logged transaction and the index of the previous transaction to followers. Followers synchronize with the leader on the transactions according to the transactions received from the leader. Finally, the leader verifies that all nodes have the same transactions and then assembles the transactions together, creating a block, and publishing the block out to all followers to add the block to their blockchain [106].
## 3 Verification of the Model  
### Verification of Tidal Harmonic Constant  
The tidal harmonic constant of the interpolated hydrodynamic model is verified to prove the validity of the model. In this paper, three long-term tidal stations in the study area are selected, namely Yumaowan Tidal Station (\(117^{\circ}06^{\prime}\) E, \(23^{\circ}24^{\prime}\) N), Shantou Tidal Station (\(116^{\circ}44^{\prime}\) E, \(23^{\circ}20^{\prime}\) N), and Haimen Tidal Station (\(116^{\circ}37^{\prime}\) E, \(23^{\circ}11^{\prime}\) N). In the model of the previous section, the tidal level data of three tide points are derived. The measured tidal level data and the derived tidal level data are calculated by the T-tide program, and the tidal harmonic constants of the corresponding positions are obtained. The simulation results are compared with the measured results' tidal harmonic constants of the four main tidal components K1, O1, M2, and S2. See Table 1 for the verification results of the model. It can be seen from the table that the amplitude error of other tidal components is less than 4 cm, and the late angle error is less than 12 degrees, except for M2, which is slightly larger. The mistake of the Shantou tide gauge station is somewhat more prominent, influenced by its geographical location. Generally speaking, the tidal harmonic constant error between the simulated and the measured results is within an acceptable range, which means that the model has sure accuracy and credibility.
###### Abstract  
Background/Objectives: Prolonged sitting is linked to musculoskeletal discomfort, yet optimal sitting posture remains poorly defined, and the consistency with which individuals reproduce specific sitting strategies is unclear. This study examined postural variability across three sitting strategies--comfortable, habitual, and correct--using three common chair types: a stool, computer chair, and ergonomic chair. **Methods:** Thirty healthy young adults (fifteen men, fifteen women) participated. Global sagittal joint angles--head inclination (HI), trunk angle (TA), and knee angle (KA)--were measured using a motion analysis system across five repetitions per condition. **Results:** The chair type significantly influenced HI and TA (\(p<0.001\)), with ergonomic chairs encouraging more upright trunk postures. The sitting strategy significantly affected TA and KA (\(p<0.01\)), with comfortable sitting associated with more extended angles. Women exhibited greater TA (114.8\({}^{\circ}\) vs. 109.0\({}^{\circ}\), \(p<0.001\)) and lower within-subject variability. Substantial postural variability was observed across all conditions, with mean ranges exceeding minimum detectable change thresholds for HI (10.3\({}^{\circ}\)), TA (6.9\({}^{\circ}\)), and KA (11.3\({}^{\circ}\)). Notably, correct sitting reduced KA variability compared to other strategies (\(p<0.01\)). **Conclusions:** These findings highlight the individualized and variable nature of sitting posture, even under controlled instructions. The results question the reliability of memory-based seat adjustments and emphasize the need for dynamic, user-centered ergonomic design and personalized clinical guidance to support musculoskeletal health.  
2025
# Measurement of Contrast and Spatial Resolution for the Photothermal Imaging Method
## 3 Numerical and Experimental Analysis
### Setting the Study Parameters  
The phase delay signals derived by the photothermal imaging method are significantly affected by the thermal diffusion length (\(L_{d}\)), which is expressed as Equation (3) and presented in units of length (m).  
\[L_{d}=\sqrt{\frac{\alpha}{\pi f}}. \tag{3}\]  
Here, \(\alpha\) is thermal diffusivity (m\({}^{2}\)/s) of the material to be measured, and \(f\) is modulation frequency (Hz) of the pump beam.  
As shown in Figure 5, the length variables of the photothermal imaging method that affect the photothermal effect and phase delay include the pump-beam radius (\(R_{b}\)), the thickness of the first layer (here, the copper layer, \(L_{f}\)) of the copper-resin double layer, the probe beam's relative position (\(X_{0}\)), and the z-position of the probe beam (\(L_{h}\)). Theoretically, if the ratio of each factor to the thermal diffusion length is the same, the same phase delay profile should appear when they are nondimensionalized on the basis of the thermal diffusion length (\(L_{d}\)) [1,2,24]. As the experimental results in Figure 6 show, the values of \(R_{b}/L_{d}\), \(L_{f}/L_{d}\), \(X_{0}/L_{d}\), and \(L_{h}/L_{d}\) are the same in three conditions of different thermal diffusion lengths depending on the pump-beam frequency, as listed in Table 2. Thus, they formed the same scanning profile.  
Among these factors, the relative position and z-position of the probe-beam (\(L_{f}\)) are determined when a photothermal phenomenon is detected or phase delay data are acquired. These do not directly affect the phenomena of the photothermal effect. Hence, the present study derives image quality change related to two parameters: the first (copper) layer thickness (\(L_{f}\)) of the copper-resin double-layer and the pump-beam radius (\(R_{b}\)).
# A Survey of CNN-Based Network Intrusion Detection
## 3 Background of Study
### Convolutional Neural Network
#### 3.2.2 Pooling Layers  
The downsampling operation implemented by the pooling layer usually decreases the feature maps' in-plane dimensionality, which in turn introduces translation invariance to small movements and distortions. It also reduces the number of subsequent learnable parameters. Interestingly, there are no learnable parameters in any of the pooling layers. Nonetheless, just like the convolutional layer, filter size, stride, and padding serve as hyperparameters [90]. Many features still remain after the convolutional layer, which makes the network very complex. The pooling layer must thus be able to undersample the features to make the network less complex and to prevent overfitting. Max-Pooling and Average Pooling are the most widely applied pooling methods in CNN systems [99]. In Max-Pooling (Equation (3)), the maximum value of all neurons in a specific region (R) is taken to represent the output of the entire region [15]:  
\[Y_{i}=\frac{max}{i\in\Re}x_{i} \tag{3}\]  
Typically, the pooling layer follows the convolutional layer. Its key purpose is to decrease the size of the feature matrix, enhance training speed, and minimize parameters to avoid overfitting issues. The input of the pooling layer is divided into different regions based on its size. In Max-Pooling, the maximum value in each region is chosen to represent the region, while in Average Pooling, the mean value of the region is taken to represent the entire region [17].
## 5 Conclusions  
This paper demonstrated a high-speed VLC system employing a 450-nm GaN LD. Due to the characteristic of the system devices, the modulation bandwidth (-10 dB) of the system is about 1 GHz. PS bit loading DMT modulation was utilized to raise the spectral efficiency at such limited bandwidth. As far as we know, this is the first time that a PS bit loading scheme is applied to an LD-based VLC system. Compared to bit and power loading, higher entropy can be achieved in the same SNR and modulation bandwidth limitation. Experimental results validate the potential of a PS bit loading scheme for AIR improvement. With PS bit loading, we successfully achieved an AIR of 10.23 Gbps at the signal bandwidth of 1.5 GHz. The free space transmission distance was set at 1.2 m and longer distance may be anticipated without about 9.6 dB insertion loss of optical power induced by the absorptive ND filter. The NGMI among all subcarriers were strictly above 0.92, revealing the error-free transmission with 8.7% FEC overhead. The gap between the tested NGMI and the NGMI threshold shows there exist margins in the system. So, a more suitable entropy-loading strategy can be studied in the future to further improve system performance.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Signal Bandwidth (GHz)** & **1.3** & **1.4** & **1.5** & **1.6** & **1.7** \\ \hline AIR (Gbps) & 9.508 & 9.935 & 10.230 & 10.067 & 9.898 \\ Min NGMI & 0.931 & 0.933 & 0.932 & 0.926 & 0.921 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The AIR at different signal bandwidths.  
Figure 9: The corresponding constellation diagrams with different entropy.  
**Author Contributions**: Conceptualization, G.L. and F.H.; methodology, G.L.; software, G.L. and P.Z.; validation, G.L., F.H. and P.Z.; formal analysis, G.L and P.Z.; investigation, P.Z. and C.W.; resources, F.H. and G.-R.L.; data curation, F.H. and C.W.; writing--original draft preparation, G.L.; writing--review and editing, N.C.; visualization, F.H. and C.W.; supervision, C.W. and N.C.; project administration, N.C.; funding acquisition, N.C. All authors have read and agreed to the published version of the manuscript.  
**Funding**: This work was partially supported by the National Key Research and Development Program of China (2017 YFB0403603) and the NSFC project (No.61925104).  
**Conflicts of Interest**: The authors declare no conflict of interest.