esa-sceva/satcom-chunk-collection huggingface dataset:
# The Application of an Impedance-Passivity Controller in Haptic Stability Analysis
## 3 Planning and Design of Impedance-Passivity Controller (IPC)
### Experimental Platform  
The SensAble PHANToM Omni was treated as a haptic device in this study. The system was implemented on an PC running Windows 10 Enterprise Edition, with Intel Xeon E5-2678 v3, 64 GB DDR4 RAM, and a 1124 MB NVIDIA GeForce GTX 1080 Ti as the graphics card. The haptic feedback is provided by the SensAble PHANToM Omni, which has a maximum force output of 3.3 N (Newton) and has six degrees of freedom for position and orientation sensing. As shown in Figure 5, the experimental platform was successfully constructed by using Microsoft C++, GLUT, and MFC. In addition, graphic visualization and haptic rendering were also implemented through the OpenGL and PHANToM Omni Library, respectively. Through the above introduction, the proposed impedance-passivity method is added to this study to provide the implementation of stability haptic feedback.
## 3 System Model
### Peak-To-Average Power Ratio  
When the frequency domain OFDM signal of the \(t\)th antenna is denoted as \(\mathbf{X}_{t}=[X_{t}[0],X_{t}[1],\ldots,X_{t}[N-1]]^{T}\), the time domain signal \(\mathbf{x}_{t}\) can be represented as  
\[\mathbf{x}_{t}=\mathbf{F}_{N}^{H}\mathbf{X}_{t},t=1,\ldots,N_{t}, \tag{3}\]  
where \(\mathbf{F}_{N}^{H}\) is the \(N\)-point Inverse fast Fourier transform (IFFT) matrix, which can be represented as \(F_{n,k}=\frac{1}{\sqrt{N}}e^{-\
icefrac{{2\pi nk}}{{N}}/N}\), \(0\leq n\), \(k\leq N-1\), and \(x_{t}(n)=\frac{1}{\sqrt{N}}\sum\limits_{k=0}^{N-1}X_{t}[k]e^{\
icefrac{{2\pi nk }}{{N}}}\).  
The PAPR of the OFDM signal for the \(t\) th antenna can be expressed as  
\[PAPR=\frac{LN\cdot\left\|\mathbf{x}_{t}^{o}\right\|_{\infty}^{2}}{E[\left\| \mathbf{x}_{t}^{o}\right\|_{2}^{2}]}, \tag{4}\]  
where \(\mathbf{x}_{t}^{o}\) is the oversampled signal of \(\mathbf{x}_{t}\) and \(L\) is the oversampling factor to catch the PAPR of analog domain. \(E[\cdot]\), \(\left\|\mathbf{x}_{t}^{o}\right\|_{\infty}\), and \(\left\|\mathbf{x}_{t}^{o}\right\|_{2}\) denote the expectation of \([\cdot]\), the \(l_{\infty}\)-norm, and \(l_{2}\)-norm of vector \(\mathbf{x}_{t}^{o}\), respectively. Most of the nonlinear distortion due to high PAPR are caused in the analog domain, but most of the signal processing for PAPR reduction are performed in the digital domain. The PAPR of digital domain is not necessarily the same as the PAPR of the analog domain. However, it is well-known that four-times of oversampling (\(L=4\)) is sufficient to satisfactorily approximate the PAPR of the analog domain [42]. For simplicity, from now on, we omit the oversampling superscription.  
Since the clipping process is performed in each antenna, the output of the clipping process can be expressed as follows:  
\[\widehat{x}_{t}(n)=\begin{cases}x_{t}(n),&\left|x_{t}(n)\right|\leq A_{max}, \\ A_{max}e^{\
icefrac{{\theta_{n}}}{{n}}},&\left|x_{t}(n)\right|>A_{max},\end{cases} \tag{5}\]  
where \(x_{t}(n)\) is the OFDM signal before the clipping process, \(A_{max}\) is the maximum allowable signal amplitude and \(\theta_{n}\) is the phase of the OFDM signal. The clipping level can be set using the clipping ratio (CR), \(\
u\) which is expressed as follows [17]:  
\[\
u=\frac{A_{max}}{\sqrt{P_{in}}}, \tag{6}\]  
where \(P_{in}\) is the average input power of the OFDM signal. The maximum allowable signal amplitude should be determined by PA input linear dynamic range. Reducing \(P_{in}\) reduces nonlinear distortion, however, also reduces power efficiency. Reducing \(P_{in}\) is called as input back-off (IBO) that we mentioned previously.  
The spectrum leakage after clipping can be a serious problem to the adjacent channel. To prevent this problem, filtering after clipping is quite useful. However, the filtering procedure causes a peak-regrowth of the time domain OFDM signal. Thus, iterative clipping and filtering is widely used to suppress it [22]. In this paper, five times of iteration, which is reasonable for a real system, are performed to ensure satisfactory performance in both out-of-band radiation and in-band PAPR reduction.
# An Efficient Scalable Scheduling MAC Protocol for Underwater Sensor Networks +
## 6 Performance Evaluation
### Implementation  
We implement ED-MAC in Aqua-Sim, an NS-2 based simulator for underwater sensor networks. Unless specified otherwise, we use the following values are used for various parameters in our simulation, which is widely used in the literature [6; 50]. The power consumption on transmission mode is two watts, the power consumption on receive mode is 0.75 Watts, and the power consumption on sleep mode is 8 mW. The data packet size is set to 2000 bits and the control packet size is set to 100 bits. The bit rate is 10 Kbps and the maximum transmission range is 100 meters. We set the simulation time to be 3600 s. Our simulation results are presented by varying two parameters: offered traffic and number of nodes to study the load effects and network scalability in each protocol. In the first parameter, all nodes are distributed in a 10,000 m\({}^{2}\)\(\times\) 200 m area. We deploy 10 nodes into the network with varying offered traffic in this area. In the second parameter, however, all nodes are distributed within an area of 62,500 m\({}^{2}\)\(\times\) 500 m, while keeping the offered traffic fixed to 0.25 packets per second and increasing the number of nodes until 100 nodes in this narrow area. In either way, all sensor nodes, located between a sink and anchored nodes, can move horizontally within the speed of 2 m/s by following a Random Walk 2D mobility model. This movement can only be in an \(X\)-\(Y\) plane, which is mostly used in an underwater environment. In our simulation setup, we consider \(T_{b}\) and \(T_{Delay}\) as 30 s each. The simulation parameters are summarised in Table 2.
## 3 Safety Evaluation of the DSM
### Fuel Type and Source Term  
Korea has various types of nuclear power plants, and the types of fuel used are also diverse. Thus, it is necessary to select a representative fuel, and the most widely used PLUS7 fuel assembly in Korea was selected for the calculation of the source term. The source terms to be considered in this safety evaluation were gamma rays and neutron rays, and were calculated using the ORIGEN-S module of SCALE 6.1 [12]. Among the source terms, the gamma rays caused by the nuclear fuel assembly's structural material radiation were also taken into account for the evaluation and calculated using the relative flux fraction of each structural material's area to the neutron flux in the effective fuel area. The characteristic SNF value used in the source term evaluation was 5 wt.% of initial uranium-235 enrichment; the burnup was 45 GWd/MTU, and the cooling period was evaluated for 5 years, 10 years and 15 years. Source term values and decay heat output values of the effective fuel area for each cooling period were used as input data for shielding and thermal analyses. The axial profile was not considered in the decay heat calculation, and the decay heat was calculated by summing the calorific values of the light element, actinide, and fission products. Figure 2 shows the decay heat and radioactivity according to the cooling period.  
Figure 2: Decay heat and activity of the PLUS7 fuel assembly.
## 6 Transformer Disassembly Results
### Experimental Results of Internal Component Disassembly  
To validate the effectiveness of the automated disassembly system for transformer internal components, 50 standardized experiments were conducted to evaluate system performance in disassembly time, success rate, and safety. The experiments took place in a controlled laboratory environment, with the transformer designated for disassembly shown in Figure 15.  
A multi-view RGB-D camera was employed to capture 2D images and 3D point cloud data of the transformer interior, ensuring comprehensive coverage of all disassembled parts. The cameras were strategically mounted on the top and four sides of the transformer to effectively acquire data on screws, copper plates, winding stud  
Figure 15: Transformer interior before disassembly.  
High-performance computing equipment facilitated data processing, while an automated cutting robot arm performed the disassembly operations. A stable experimental environment was maintained to ensure the accuracy of the cutting process.  
To objectively evaluate the system's performance, three key metrics were established: disassembly time, disassembly success rate, and safety assessment. Disassembly time refers to the total duration from system activation to the completion of internal component removal. Disassembly success rate is the proportion of successful disassembly operations relative to the total number of experiments conducted. The safety assessment focuses on detecting any damage to critical transformer components, such as windings and key electronic parts, during the disassembly process.  
In the 50 standardized transformer cutting experiments, the system achieved an average disassembly time of 876.6 s, with the fastest recorded at 848.2 s and the slowest at 896.7 s. Out of these operations, 49 were successful, resulting in a disassembly success rate of 98%. The sole failure occurred during silicon steel sheet processing due to a path planning error. Notably, no successful disassembly operation damaged core components, demonstrating effective cutting path planning that mitigated risks to critical parts. The results are visually represented in Figure 16 and summarized in Table 2.
## 5 Conclusions  
Our study investigated interannual relationships between ecosystem-scale WUE during the different seasons and the photosynthetic phenological factors in summer-active ecosystems of the Northern Hemisphere, and identified patterns of WUE response to phenology as well as dominant control on WUE. We find that the WUE during spring and autumn closely relates to the start and end dates of growing season, respectively, over most of the study area. Meanwhile, the main pattern of WUE response to phenology suggests that during spring (or autumn), an increasing WUE with an advancing SOS (or a delaying EOS) is mainly attributed to a steeper increase in GPP relative to increase in ET, because of the higher phenological sensitivity of GPP relative to ET. However, summer WUE was weakly related to SOS during the study period. In summary, we argue that phenology plays an important role in the regulation of terrestrial ecosystem water-use efficiency, and it is necessary to take into account phenological factors when explaining and predicting patterns of the coupled carbon-water cycle of territorial ecosystems.  
**Supplementary Materials:** The following are available online at www.mdpi.com/2072-4292/9/7/664/s1, Table S1: Info of the valid FLUXNET sites in this study, Table S2: Variations in seasonal WUE with the phenological factors for each PFT through the panel analysis.  
**Acknowledgments:** This work was supported by the National Natural Science Foundation of China (No. 41601442); the Jiangsu Provincial Natural Science Foundation of China (No. BK20150579); and the China Postdoctoral Science Foundation (No. 2016T90442 & 2015M57043). We thank the Numerical Terradynamic Simulation Group at the University of Montana, the Centre for Environmental Data Analysis, The National Centers for Environmental Prediction and the Earth System Research Laboratory to provide satellite-derived product and climate data. This work used eddy covariance data acquired by the FLUXNET community and in particular by the following networks: AmeriFlux, CarboEurope-IP and Fluxnet-Canada.  
**Author Contributions:** [PERSON] and [PERSON] had the original idea for the study and designed the research. [PERSON], [PERSON] and [PERSON] analyzed the data. [PERSON] drafted the manuscript, which was revised by all authors. All authors read and approved the final manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Study on Components Determination and Performance Evaluation of LS Pre-Maintenance Agent
## 3 Optimum Composition Ratios
### Dispersant and LS Agent
#### 3.4.2 LS Agent  
The LS agent selected was Nano-grade black powder. The main component is carbon black. We mixed it into SBRII-C modified asphalt (commonly used in high altitude strong UV regions), according to the dosage range determined by the existing studies of asphalt anti-UV aging additives [3]. The dosage was determined as 3, 5, and 7% of the asphalt mass and stirred for 10 min to ensure it dispersed evenly. The 25 \({}^{\circ}\)C penetration, softening point, and 5 \({}^{\circ}\)C ductility were measured before and after adding the LS agent. UV aging was performed for 12 h with a self-developed artificial UV aging device to measure the asphalt indexes again. The results are shown in Table 6.  
Figure 5: Dispersant appearance contrast. (**a**) Dispersant A; (**b**) Dispersant B; (**c**) Dispersant C.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Consolidation Time/day**} & \multicolumn{6}{c}{**Dispersant Amount/\%**} \\ \cline{2-7}  & **0** & **0.2** & **0.4** & **0.6** & **0.8** & **1** \\ \hline A & 45 & 40 & 30 & 30 & 20 & 20 \\ B & 45 & 45 & 40 & 40 & 30 & 30 \\ C & 45 & 60 & \textgreater{}85 & 70 & 55 & 50 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The Consolidation results of three dispersants.  
From Table 6, the 25 \({}^{\circ}\)C penetration and softening point change were relatively small before UV aging. After aging, the penetration increased and the softening point decreased with the increase of the LS agent, of which the effect of 3% was limited, the effect of 5% and 7% were better and close to each other, proving that the LS agent can improve asphalt low temperature anti-cracking performance after UV aging. 5 \({}^{\circ}\)C ductility before and after aging were both reduced. The addition of the LS agent had a negative effect on asphalt ductility, the reason being the LS agent is a powdered solid, which will impact the plastic deformability of asphalt, thus its dosage should be controlled in order to reduce the negative effect to the asphalt while ensuring the anti-UV aging ability. Considering the significant degree of LS agent to asphalt, the effect of the LS agent in 5% and 7% were close, showing that a good anti-aging effect can be achieved in the 5-7% dosage. In order to reduce the adverse effects of the additive on asphalt rheology while controlling material costs, the dosage was determined as 5%.
# Activation-Based Pruning of Neural Networks
## 3 Methodology
### Global Activation-Based Pruning  
Algorithms 1-3 demonstrate the steps for pruning the network during training. Figure 1 illustrates pruning a fully connected feedforward network. The figure depicts that connection of neurons with activation counters 34 and 41 removed. This is a visual representation of activation-based pruning. We implemented the pruning by setting the incoming weights of the pruned neurons to 0.  
```
1:Input:\(X\): image data, \(W\): weight matrix.
2:Require:\(F_{ta}\): final training accuracy, \(C_{ta}\): current training accuracy, \(T_{p}\): target prune percentage, \(C_{p}\): current prune percentage, \(P_{p}\): total pruned percentage, \(N_{p}\): number of pruning cycles, \(P_{l}\): pruning list in \(N_{p}\) cycles.
3:Procedure 1: Main
4:Calculate \(P_{l}\) for \(N_{p}\) pruning cycles.
5:\(P_{p}\gets 0\)
6:for\(C_{p}\in P_{l}\)do
7: Call update neuron.
8:if\(P_{p}<T_{p}\)then
9: Retrieve \(C_{ta}\).
10:if\(C_{ta}\geq F_{ta}\)then
11: Stop training.
12:return[Exit the algorithm]
13:elseif accuracy reached for next pruning cycle then
14:\(W_{new}\leftarrow\) call prune.
15:\(W\gets W_{new}\)
16:\(P_{p}\gets P_{p}+C_{p}\)
17:endif
18:endif
19:endfor
```  
**Algorithm 1** Global activation-based pruning.  
```
1:Input:\(M\): random batch of data from input \(X\), \(L\): hidden layers of the network.
2:Require:\(A_{l}\): activation counter of neurons for layer \(l\).
3:for each \(m\in M\)do
4:for\(l\in L\)do
5: Calculate output: \(F_{l}\leftarrow\sigma(W_{l}^{T}F_{l-1})\).
6: {the for loop signifies a matrix operation}
7:for\(F_{l}(r,c)\in F_{l}\)do
8:if\(F_{l}(r,c)\
eq 0\)then
9:\(A_{l}(r,c)\gets A_{l}(r,c)+1\)
10:endif
11:endfor
12:endfor
13:endfor
```  
**Algorithm 2** Update neuron.  
```
1:Input:\(L\): hidden layers of the network.
2:Require:\(R\): list for activation counter and associated weights, \(C_{p}\): current prune percentage, \(P_{index}\): prune index.
3:for\(l\in L\)do
4:\(R\gets R+(A_{l},W_{l})\). {add activation counters and associated weights to \(R\)}.
5:endfor
6:if pruning type \(=\) neuron then
7: Sort \(R\) on the activation counter in ascending order.
8:else
9: Sort \(R\) on the product of the activation counter and weights in ascending order.
10:endif
11:\(P_{index}\gets C_{p}\times len(R)\). {\(len(R)\): No. of elements in \(R\)}.
12:\(R_{W}(0,P_{index})\gets 0\). {Assign 0 value to weights}.
13:\(W_{new}\gets R_{W}\). {Set new weights for all hidden layers}.
14:return\(W_{new}\)
```  
**Algorithm 3** Prune.
## 4 Results and Discussions
### Proposed Regression Model of the Response  
In order to achieve equations of the response \(u_{2}\) and \(u_{3}\), a regression process with two interaction factors is carried out using Minitab@19. The significance of this regression is \(\alpha=0.05\). The estimated effects and the coefficients for \(u_{2}\) response are exhibited in Table 4 where the factors with no influence on them are eliminated. It is noticed that if the effect of each input parameter or interaction has \(p\)-value higher than the significance of \(\alpha\), it does not strongly impact the response. For example, the factor of \(X_{ba1}\) has \(p\)-value of 0.111 superior to \(\alpha=0.05\), which means that \(X_{ba1}\) is not significant to the response \(u_{2}\). The regression equation of the \(u_{2}\) response is described by following model (Regression Equation in Uncoded Units):  
Figure 6: The evolution of response as a function of input parameters and their interactions (**a**) \(u_{2}\) and (**b**) \(u_{3}\)\[u_{2} = 4.241+0.01830\;u_{t}-1.106\;X_{\text{h1}}+0.190\;X_{\text{h2}}+1.925 \;X_{\text{h3}}-0.00601\;AS_{1}+0.004379\;AS_{2} \tag{36}\] \[-0.00686\;AS_{3}-0.000058\;T_{\text{e}nt}+0.00397\;C_{\text{g}it}+0. 1013\;C_{\text{g}}+0.1261\;C_{\text{s}}-0.000036\;u_{t}{}^{*}\!AS_{1}\] \[+0.000030\;u_{t}{}^{*}\!AS_{2}-0.000014\;u_{t}{}^{*}\!AS_{3}+0.000405 \;u_{t}{}^{*}\!C_{\text{g}it}+0.000587\;u_{t}{}^{*}\!C_{\text{g}}-0.001144\;u_{t }{}^{*}\!C_{\text{g}}\] \[+0.000131\;X_{\text{h1}}{}^{*}\!T_{\text{e}nt}-0.327\;X_{\text{h2 }}{}^{*}\!C_{\text{s}}-0.2243\;X_{\text{h3}}{}^{*}\!C_{\text{g}}+0.000012\;AS_{ 1}{}^{*}\!AS_{3}\] \[-0.000404\;AS_{1}{}^{*}\!C_{\text{g}}+0.000670\;AS_{1}{}^{*}\!C_{ \text{s}}+0.000101\;AS_{2}{}^{*}\!C_{\text{g}}-0.000218\;AS_{2}{}^{*}\!C_{ \text{s}}\] \[+0.000105\;AS_{3}{}^{*}\!C_{\text{g}}+0.000001\;T_{\text{e}nt}{}^{* }\!C_{\text{g}}-0.00676\;C_{\text{g}it}{}^{*}\!C_{\text{g}it}{}^{*}\!C_{\text{ s}}-0.007003\;C_{\text{g}}{}^{*}\!C_{\text{g}}\]  
It can be said that the experimental data are greatly consistent with the proposed model when the minimum value of R-square is approximately 98% (all of them are more than 98%). In the case of \(u_{3}\) response, the results obtained from regression process show the difference from those of \(u_{2}\) response. Indeed, \(u_{t}\) and \(X_{\text{h2}}\) have no influence on the response, moreover, only eight interactions between input parameters have impact on it (cf. Table 5). It is observed that the factors of B, D, E, and H have \(p\)-value of 0.078, 0.184, 0.146, and 0.052 respectively, larger than significance \(\alpha\) (0.05). Hence, these parameters have little influence on the \(u_{3}\) response. However, the interactions of BH, BL, DG, EG, and EK have \(p\)-value inferior to \(\alpha\). For this reason, they strongly influence the response of \(u_{3}\)
## 5 Conclusions  
In this paper, a new hardware algorithm for an integer based discrete cosine transform (IntDCT) was proposed that provides a good approximation of the discrete cosine transform and at the same time offers an efficient VLSI implementation using the systolic array architectural paradigm. Using a multi-objective optimization, the proposed integer transforms searches for an efficient IntDCT matrix which has the coding performance as close as possible to the DCT transform but implemented with reduced hardware/power and high-speed performances obtaining using pipelining and parallelism. The proposed integer DCT algorithm has been efficiently restructured into five regular and modular computational structures of lengths of four and one of length two called pseudo-cycle convolutions. These regular and modular computational structures allow for an efficient VLSI implementation using systolic arrays. Moreover, besides an efficient VLSI implementation with high speed, the proposed integer DCT algorithm and its VLSI architecture allow for efficient incorporation of the obfuscation technique that significantly improves the hardware security with a low overhead which maintains all the implementation performances.  
Conceptualization, D.F.C.; methodology, D.F.C. and A.C.; software, A.C.; validation, D.F.C. and A.C.; formal analysis, D.F.C. and A.C.; investigation, D.F.C. and A.C.; resources, D.F.C. and A.C.; writing--original draft preparation, D.F.C.; writing, review, and editing, D.F.C. and A.C.; visualization, D.F.C.; project administration, D.F.C.; funding acquisition, D.F.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by a grant of the Romanian Ministry of Education and Research, CNCS--UEFISCDI, project number PCE 172/2021 (PN-III-P4-ID-PCE2020-0713), within PNCDI III.  
Not applicable.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **TSDCT**[33] & **RICT**[34] & **ACCESS**[29] & **TCAD 2022**[30] & **Proposed** \\ \hline Area [\(\mathrm{\SIUnitSymbolMicro m}^{2}\)] & 55,486 & 27,414 & 9835 & 10,813 & 4693 \\ Improvement [\%] & 91.5 & 82.9 & 52.3 & 56.6 & - \\ Critical Path [ns] & 6.04 & 6.16 & 6.69 & 6.73 & 0.419 \\ Improvement [\%] & 93.1 & 93.2 & 93.7 & 93.8 & - \\ Power [mW] & 4.56 & 5.06 & 2.20 & 3.25 & 1.132 \\ Improvement [\%] & 75.2 & 77.6 & 48.6 & 65.2 & - \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of hardware performances.
## 3 Results and Discussion
### Visual Confirmation
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Sample 1–Sample 2** & **Test Statistic** & **Std. Test Statistic** & **Adj. Sig. a** \\ \hline \multirow{3}{*}{Ingredient List Area on OUI} & Color–Layout & \(-\)4.386 & \(-\)0.768 & 1.000 \\ \cline{2-5}  & Color–Content & 23.091 & 4.041 & 0.000 \\ \cline{2-5}  & Layout–Content & 18.705 & 3.274 & 0.003 \\ \hline \multirow{3}{*}{Ingredient List Area on MUI} & Color–Layout & \(-\)4.705 & \(-\)0.828 & 1.000 \\ \cline{2-5}  & Color–Content & 12.818 & 2.255 & 0.072 \\ \cline{2-5}  & Layout–Content & 8.114 & 1.427 & 0.461 \\ \hline \multirow{3}{*}{Cooking Step Area on OUI} & Color–Layout & \(-\)4.523 & \(-\)0.791 & 1.000 \\ \cline{2-5}  & Color–Content & 24.591 & 4.299 & 0.000 \\ \cline{2-5}  & Layout–Content & 20.068 & 3.508 & 0.001 \\ \hline \multirow{3}{*}{Cooking Step Area on MUI} & Color–Layout & \(-\)1.636 & \(-\)0.291 & 1.000 \\ \cline{2-5}  & Color–Content & 17.318 & 3.081 & 0.006 \\ \cline{1-1} \cline{2-5}  & Layout–Content & 15.682 & 2.790 & 0.016 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Pairwise comparisons of three elements in visual confirmation. Figure 7: Mean values of three elements in different interface areas. \"color\" remained consistent, even though the \"color\" and \"layout\" provided slightly better support to the users compared to the OUI. As observed, the test users were accustomed to performing visual confirmation primarily through the \"content\" throughout the entire interaction, while the \"color\" and \"layout\" served as secondary supporting elements.