esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Algorithm Verification
### Data Analysis
#### 4.2.2 Comparative Analysis of Temperature Field Calculation Results  
The temperature field simulation value from 0 h on 13 August 2013 to 24 h on 14 August 2013 was compared with the measured value of the temperature field in the corresponding period in the reference to verify the accuracy of the three-dimensional sunshine temperature field algorithm proposed in this paper. At the same time, the temperature field calculation results of the first three days were imported into the calculation model as the initial temperature field to eliminate the influence of the initial temperature of the model on the simulation results. The atmospheric temperature data for the selected date are shown in Table 2. We selected the measuring points \(A\), \(B\), \(C\), and \(D\) of the midspan section of the experimental model to analyze the temperature field changes of the shading side web, bottom plate, sunward side web, and top plate, respectively. The measuring point arrangement is shown in Figure 12.  
Figures 16 and 17 show a comparison of temperature field simulation results from the three-dimensional sunshine temperature field algorithm proposed in this paper and measured results from reference [37]. In the figure, the simulation results of the temperature field are the same as the measured results. In addition to the period from 10 a.m. to 5 p.m. on 13 August, the simulation results of the roof were quite different from the measured results. At the other locations, the two were more consistent. The maximum errors between the simulated and measured values of the roof, floor, sunny side web, and shade side web temperatures were 3.2 \({}^{\circ}\)C, 3.3 \({}^{\circ}\)C, 3.2 \({}^{\circ}\)C, and 3.1 \({}^{\circ}\)C, respectively. During the period from 10 a.m. to 17 p.m. on 13 August, the weather turned from sunny to cloudy, and the direct solar radiation was weakened, resulting in a large difference between the simulation results and the measured results of the roof.  
To sum up, the calculation results of the sunshine shadow and temperature field obtained by the calculation algorithm of the three-dimensional temperature field of the bridge structure considering the influence of the sunshine shadow proposed in this paper are consistent with the actual situation and can be used for the simulation of the three-dimensional sunshine temperature field of the actual bridge structure and the analysis of its temperature effect.
## 2 Motivating Applications and Use Cases
### Training of Machine Learning Model Use Case  
The training and inference of machine learning models for computer vision and language modeling are extremely processing-intensive tasks that require an enormous amount of computing power.
## 4 Results and Discussions
### Interaction Energy Analysis  
The interaction energy between C-S-H and asphalt is shown in Figure 16. The results indicate that SCA modification can increase the interaction energy of the interface under both dry and wet conditions. Under dry conditions, the interface interaction energy of the SCA modification increased by 32.1%. This increase is attributed to the ability of SCA modification to alleviate the harmful effects of Ca ions and water molecules on the C-S-H surface, thereby improving the interaction energy of the interface. Under wet conditions, the interaction energy of the interface decreased by 47.15%. This is primarily due to the fact that the interaction energy of the interface is mainly governed by van der Waals forces, and the presence of water at the interface increases the distance between the asphalt and  
Figure 15: The number of hydrogen bonds for C-S-H/water interface.  
C-S-H interface, resulting in a 48.2% reduction in van der Waals forces. Consequently, the interaction energy of the interface decreases. In comparison, the SCA modification under wet conditions showed a 63.9% increase in interaction energy compared to the unmodified interface. This indicates that SCA modification can effectively enhance the water resistance of the interface. This is because SCA modification reduces the hydrophilicity of the C-S-H interface, diminishing the impact of water molecules on the interface performance and consequently increasing the interaction energy at the asphalt and C-S-H interface.
# Blockwise Frequency Domain Active Noise Controller Over Distributed Networks
## 3 Prototype Description
When these requirements are fulfilled and assuming a network of synchronized nodes, the proposed distributed algorithm can achieve the same performance as the centralized version. The sampling rate of the audio card was fixed at 44.1 kHz, the lowest possible rate, because of audio signals are typically sampled at that value (CD quality). The audio card offers different values of B between 16 and 2048 samples. Due to the real-time condition explained previously, we have selected \(B=2048\) as a tradeoff between the processing time of the network and the buffering time. Figure 5: Scheme of the ANC prototype. The incremental communication strategy is represented by dashed lines. It should be noted that the algorithms execute their processing in real time but, for simplicity, the communication network and the network distributed processing are emulated. All the nodes share the same central processing unit (CPU) but the implemented code allows a distributed and independent processing, simulating the processing carried out in a real distributed ASNs. Therefore, a basic networking hardware dedicated to the information exchange (a physical layer of the network) is not considered. The communication among the nodes is virtual, thanks to the code designed in MATLAB(r) software.
# Comparison of L1 and L5 Bands GNSS Signals Acquisition
## 2 GNSS Signals and Acquisition
### GNSS Signals
Therefore, to be as general as possible, two cases will be considered for the signal: The first with coherent integration only, without  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**System** & **L1 C/A** & **LS** & **E1** & **ESa** & **ESb** \\ \hline Center frequency (MHz) & 1575.42 & 1176.45 & 1575.42 & 1176.45 & 1176.45 & 1176.45 \\ \hline Channel combination & - & Quadrature & Code multiplexing & Quadrature & Quadrature \\ \hline Channel & Data & Data & Pilot & Data & Pilot & Data & Pilot & Data & Pilot \\ \hline Bandwidth & 2.046 & 20.46 & 20.46 & 14.322 & 14.322 & 20.46 & 20.46 & 20.46 \\ \hline Minimum bandwidth & 2.046 & 20.46 & 20.46 & 4.092 & 4.092 & 20.46 & 20.46 & 20.46 \\ \hline Modulation & BV5K & BP5K & BP5K & \begin{tabular}{c} CBOC (6,1) \\ (10) \\ \end{tabular}  & BP5K & BP5K & \ \begin{tabular}{c} BP5K \\ (10) \\ \end{tabular}  & BP5K & BP5K &
\begin{tabular}{c} BPany limit; and the second with a coherent integration of one secondary code period, i.e., 20 ms for the L5 signal and 100 ms for Galileo signals, followed by further non-coherent integration.
# Patellar Tendon Force Differs Depending on Jump-Landing Tasks and Estimation Methods
## 3 Results
### Effect of Jump-Landing Task on Additional Dependent Variables  
The statistical analysis revealed significantly higher peak vertical GRF (\(p\) < 0.006), higher peak anteroposterior GRF (\(p\) < 0.001), and higher loading rate of peak vertical GRF (\(p\) < 0.001) during FJL compared to VJL (Table 2).  
Participants also generated a statistically significant greater peak knee extension moment (\(p\) < 0.001) and greater peak quadriceps force (\(p\) < 0.001) during FJL. Finally, the statistical analysis showed that participants landed with a greater hip flexion angle at initial contact (\(p\) < 0.001), a greater knee flexion angle at initial contact (\(p\) = 0.033) and a greater knee flexion range of motion (\(p\) < 0.001) during FJL. No statistically significant differences were found on other kinematic variables. Figure 3 illustrates the means and standard deviations of the PTF estimated with inverse dynamic and static optimization methods, the net knee extension moment, and the quadriceps force for all participants during VJL and FJL tasks.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Variable** & \multicolumn{2}{c}{**Peak PTF (BW)**} & \multicolumn{2}{c}{**PTF Loading Rate (BW/s)**} \\ \hline \hline  & **Task** & **VJL** & **FJL** & **VJL** & **FJL** \\ \hline \multirow{2}{*}{Estimation method} & Inverse dynamic & 4.04 \(\pm\) 0.64 & 5.03 \(\pm\) 0.77 & 33.24 \(\pm\) 7.32 & 57.43 \(\pm\) 13.53 \\ \cline{2-6}  & Static optimization & 2.43 \(\pm\) 0.41 & 2.85 \(\pm\) 0.45 & 29.19 \(\pm\) 14.27 & 50.43 \(\pm\) 13.7 \\ \hline \multirow{2}{*}{Interaction} & \(p\)_-value_ & 0.064 & & 0.650 \\ \cline{2-6}  & _Partial omega squared_ & / & & / \\ \hline \multirow{2}{*}{Factor 1. Jump-landing task} & \(p\)_-value_ & \textless{0.001} * & \textless{0.001} * \\ \cline{2-6}  & _Partial omega squared_ & 0.258 & 0.454 \\ \cline{2-6}  & _Partial omega squared_ & (Large) & (Large) \\ \hline \multirow{2}{*}{Factor 2. Estimation method} & \(p\)_-value_ & \textless{0.001} * & 0.093 \\ \cline{2-6}  & _Partial omega squared_ & 0.728 & & / \\ \hline \hline \end{tabular}  
Notes: \(p\)-values as revealed by two-way ANOVA, * \(p\)-value < 0.05.  
\end{table}
Table 1: Mean \(\pm\) standard deviation of patellar tendon force (PTF) parameters estimated through inverse dynamic and static optimization methods for volleyball players during vertical (VJL) and forward (FJL) jump-landings.  
Figure 2: Patellar tendon force (**A**) and loading rate (**B**) of patellar tendon force experienced during vertical and forward jump-landing tasks estimated through inverse dynamic and static optimization methods. * Statistically significant difference (\(p\)-value < 0.05). Diamond symbols represent outliers.
# AI-Based Modeling and Monitoring Techniques for Future Intelligent Elastic Optical Networks
## 4 Use Cases  
### Use Case 1: AI-Based Nonlinear Noise Modeling  
A use case for modeling the nonlinear SNR with ML is discussed as below. This use case is based on the methods proposed in [5]. The structure of the ML-based estimator is shown in Figure 2a. For this model, an analytical model provides a relatively low-accuracy result in a short time. Afterwards, the pre-calculated result is input to a ML engine together with the processed system features related to nonlinear interference. The system features are shown in Table 5. These features can be easily obtained by a central controller and the processing time is short. For this modeling scheme, the GN model can provide an approximate value with lower precision compared with the SSFM, and the ANN only needs to learn the residuals between the real value and the approximate one. In this way, only with a simple-structure ANN, the estimation result can be accurate. The simulation setup is  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Targets** & **Algorithms** & **Input Features** \\ \hline Detection, Identification & Finite State Machine [70] & BER, received power in receiver \\ \hline Detection, Identification & Random Forests, Support Vector Machine [71] & The trend of BER \\ \hline Identification & Convolutional Neural Networks [12] & Optical spectrum \\ \hline Detection & Support Vector Machine, Decision Trees [15] & Optical spectrum \\ \hline Detection & One-class Support Vector Machine [72] & Tap value of the adaptive filter \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary of the AI-based failure management techniques discussed in Section 3.2.  
shown in Figure 2c and the detailed description can be found in [5]. In Figure 2b, results show that when combining the ANN with the coherent GN model (CGN) or the IGN model, the estimation accuracy can be significantly improved.
## 4 Experiments and Results
### Experiments
#### 4.4.1 Ablation Experiment
Compared to RL-Fusion+FineTune, RL-Fusion's performance is slightly lower by 0.3%. On the C-Eval dataset, RL-Fusion shows a significant improvement, outperforming the baseline by 1.9%, rising from 76.7% to 78.6%, and surpassing RL by 0.7%, from 77.9% to 78.6%. However, RL-Fusion performs 1.6% worse than RL-Fusion+FineTune. On the Yayi (NER) dataset, RL-Fusion shows a marginal improvement of 0.1 over RL, increasing from 2.4 to 2.5, and outperforms the baseline by 0.4, rising from 2.1 to 2.5. However, compared to RL-Fusion+FineTune, RL-Fusion exhibits a 1.3-point decrease, with RL-Fusion+FineTune achieving 3.8. Through this series of ablation experiments, we not only validate the effectiveness of the RL-Fusion approach but also demonstrate that the combination of RL and SOTA LLM real-time evaluation feedback for parameter selection significantly enhances model performance on Chinese NER tasks. Specifically, on the MMLU dataset, RL-Fusion shows a 0.7% improvement over RL, and on the C-Eval dataset, it improves by 0.7%; although the improvement on the Yayi (NER) dataset is smaller (only 0.1), RL-Fusion still outperforms RL-Fusion+FineTune in comparison. These findings provide compelling evidence for the future application of model fusion strategies in complex NLP tasks.
# Optimizing Roller Design to Improve Web Strain Uniformity in Roll-to-Roll Process
## 2 Strain of Web in Roll-to-Roll Process
### Strain Analysis Model of Web  
ANSYS Workbench R18.0 was used to conduct an FE analysis on the web strain caused by tension for a conventional roller in the roll-to-roll process. To analyze the strain of the web when tension is applied to a single span, the FE model consisted of a roller and a web. A schematic of the FE model is shown in Figure 1. The diameter and length of the roller were 60 and 350 mm, respectively, and the roller was composed of aluminum. The web was composed of a polyethylene terephthalalate (PET) film with a width of 160 mm, a span length of 200 mm, and a thickness of 0.2 mm. The mechanical properties used in the analysis are shown in Table 1. PET films are viscoelastic materials; however, we considered them to be linear elastic materials because of the low strain in this case study. The element size of the web is 5 mm and the web was modeled as a uniform hexahedron mesh; the number of elements was 3324 and the number of nodes was 19,289. The element size and shape were predetermined after checking the FE model's convergency. In addition, boundary conditions such as the load, degrees of freedom, and friction conditions were set in the FE model. The tension (10 MPa) was applied at the load end along the transport direction of the web. At the fixed end of the web, which touched the roller, all degrees of freedom except the width direction of the web were constrained. The degree of freedom in the width direction of the web allowed for deformation due to Poisson's ratio. Moreover, the contact between the web and roller resulted in friction with a friction coefficient of 0.2.  
Figure 1: Schematic of finite element (FE) model: Red lines are fixed in the machine direction (MD); the blue line represents the target printing line.  
In the actual process, the transfer of the web is a dynamic process. In this study, however, we modeled the process with a constant transfer speed and under static conditions for simplicity. If the strain deviation is compensated under static conditions, it will also be compensated in the dynamic state.
## 6 Security and Efficiency Analysis
### Efficiency Analysis
#### 6.2.4 Message Size  
In other studies, it has  
The evaluations of the graphics of Figures 8 and 9 are as follows:  
* In the registration process, while the message sizes in other studies are 190 bytes of SN, 380 bytes of CN, and 170 bytes of the BS, in the proposed protocol they are 90 bytes of SN, 270 bytes of CN, and 140 bytes of the BS.
* In the authentication process, while the message sizes in other studies are 110 bytes of SN, 290 bytes of CN, and 180 bytes of the BS, in the proposed protocol they are 60 bytes of SN, 200 bytes of CN, and 150 bytes of the BS.
* In the proposed protocol, IDcard is kept only in CNs. Only small-size IDs and PVKs are kept in SNs. In other studies, IDcards are kept both in SNs and CNs. This is the reason why the message size is low in the SN.
* The proposed protocol uses the ECC algorithm for transactions between SN-CN. The reason for this situation is the insufficient capacity of the nodes, as explained earlier.
* The size of the message increases because it is used in the digital signature in smart contract transactions.
* Other studies' use of blockchain at the SN-CN level is unsuitable for the WSN's structure. This situation may be the reason for the higher results obtained by other studies.
* During the registration and authentication stages, the message size of CNs is significant because they communicate with both SNs and the BS.
* In other studies, blocks are held on CNs, while in ours they are held on the BS.
* In other studies, transactions are slower because blocks are kept at the CN level. As the number of blocks increases, problems arise in terms of time and efficiency.
* The most important difference is that they use the public blockchain (permissionless) structure and we use the private blockchain (permissioned) structure.
## 3 Results and Discussion
### Experimental Flow Test to Determine \(d_{h}\) and \(A_{\text{eff}}\)
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \(\Delta P\) (Pa) & \(H\) (m) & \(\dot{m}\) (kg/s) & \begin{tabular}{c} \(A_{\text{eff}}\) \\ (mm\({}^{2}\)) \\ \end{tabular} & \begin{tabular}{c} \(d_{\text{h}}\) (mm) \\ (Flow Test) \\ \end{tabular} & \begin{tabular}{c} \(d_{\alpha\eta}\) (mm) \\ (CT) \\ \end{tabular} & \begin{tabular}{c} \(msth\) \\ (mm) \\ \end{tabular} & \begin{tabular}{c} \(f_{d}\) \\ (No Unit) \\ \end{tabular} & \begin{tabular}{c} \(V\) (m/s) \\ (No Unit) \\ \end{tabular} & \
\begin{tabular}{c} \(R_{\text{sd}}\) \\ (No Unit) \\ \end{tabular} &
\begin{tabular}{c} **Flow** \\ **Number** \\ **(No Unit)** \\ \end{tabular} \\ \hline
197,500 & 20.14 & 0.0041 & 0.467 & 0.77 & 0.79 & 0.023 & 0.064 & 8.75 & 6157 & 0.0029 \\
185,500 & 18.91 & 0.0039 & 0.467 & 0.77 & 0.79 & 0.023 & 0.064 & 8.47 & 5962 & 0.0029 \\
166,500 & 16.97 & 0.0037 & 0.467 & 0.77 & 0.79 & 0.023 & 0.0643 & 8.02 & 5644 & 0.0029 \\
153,000 & 15.60 & 0.0036 & 0.467 & 0.77 & 0.79 & 0.023 & 0.0645 & 7.69 & 5422 & 0.0029 \\
141,500 & 14.42 & 0.0035 & 0.467 & 0.77 & 0.79 & 0.023 & 0.0646 & 7.34 & 5211 & 0.0029 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Experimental flow test comparison with CT analysis for the functional hydraulic diameter for specimen N\({}_{2}\). \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \(\Delta P\) (Pa) & \(H\) (m) & \(\dot{m}\) (kg/s) & \begin{tabular}{c} \(A_{\text{eff}}\) \\ (mm\({}^{2}\)) \\ \end{tabular} & \begin{tabular}{c} \(d_{\text{h}}\) (mm) \\ (Flow Test) \\ \end{tabular} & \begin{tabular}{c} \(d_{\alpha\eta}\) (mm) \\ (CT) \\ \end{tabular} & \begin{tabular}{c} \(msth\) \\ (mm) \\ \end{tabular} & \begin{tabular}{c} \(f_{d}\) \\ (No Unit) \\ \end{tabular} & \begin{tabular}{c} \(V\) (m/s) \\ (No Unit) \\ \end{tabular} & \
\begin{tabular}{c} \(R_{\text{sd}}\) \\ (No Unit) \\ \end{tabular} &
\begin{tabular}{c} **Flow** \\ **Number** \\ **(No Unit)** \\ \end{tabular} \\ \hline
90,500 & 9.23 & 0.0084 & 1.15 & 1.22 & 1.27 & 0.024 & 0.0547 & 7.36 & 8081 & 0.0088 \\
82,500 & 8.41 & 0.0080 & 1.15 & 1.22 & 1.27 & 0.024 & 0.0548 & 7.02 & 7723 & 0.0088 \\
69,500 & 7.09 & 0.0074 & 1.15 & 1.22 & 1.27 & 0.024 & 0.0551 & 6.45 & 7129 & 0.0089 \\
62,000 & 6.32 & 0.0071 & 1.16 & 1.22 & 1.27 & 0.024 & 0.0553 & 6.09 & 6760 & 0.009 \\
46,500 & 4.74 & 0.0062 & 1.16 & 1.22 & 1.27 & 0.024 & 0.00559 & 5.26 & 5860 & 0.009 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Experimental flow test comparison with CT analysis for the functional hydraulic diameter for specimen N\({}_{1}\). evaluate the functional characteristics of the component in different fluid flow regimes without the necessity of additional functional models at different \(\dot{m}\).
###### Abstract  
This study investigates the operational efficiency of the lab-scale oxidation ditch (OD) functioning in simultaneous nitrification and denitrification modes, focusing on forecasting biochemical oxygen demand (BOD\({}_{5}\)) concentrations over a five-day horizon. This forecasting capability aims to optimize the operational regime of aeration tanks by adjusting the specific load on organic pollutants through active sludge dosage modulation. A comprehensive statistical analysis was conducted to identify trends and seasonality alongside significant correlations between the forecasted values and various time lags. A total of 20 time lags and the \"month\" feature were selected as significant predictors. These models employed include Multi-head Attention Gated Recurrent Unit (MAGRU), long short-term memory (LSTM), Autoregressive Integrated Moving Average-Long Short-Term Memory (ARIMA-LSTM), and Prophet and gradient boosting models: CatBoost and XGBoost. Evaluation metrics (Mean Squared Error (MSE), Mean Absolute Error (MAE), Symmetric Mean Absolute Percentage Error (SMAPE), and Coefficient of Determination (R\({}^{2}\))) indicated similar performance across models, with ARIMA-LSTM yielding the best results. This architecture effectively captures short-term trends associated with the variability of incoming wastewater. The SMAPE score of 1.052% on test data demonstrates the model's accuracy and highlights the potential of integrating artificial neural networks (ANN) and machine learning (ML) with mechanistic models for optimizing wastewater treatment processes. However, residual analysis revealed systematic overestimation, necessitating further exploration of significant predictors across various datasets to enhance forecasting quality.  
machine learning algorithms; wastewater treatment; effluent quality; soft sensors Article