esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### GP for System Setting Recognition
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method for Feature Extraction** & **Model** & **Training Accuracy (\%)** & **Training Time (s)** & **Testing Accuracy (\%)** \\ \hline Classification-based GP & DT & 90.4 & 2.6 & 67.03 \\  & k-NN & 90.8 & 1.25 & 67.03 \\ Clustering-based GP & DT & 96.0 & 0.71 & 67.30 \\  & k-NN & 94.5 & 0.72 & 66.85 \\ No feature selection & DT & 99.99 & 3.53 & 31.52 \\  & k-NN & 100 & 56.94 & 61.14 \\ PCA & DT & 81.9 & 1.78 & 61.30 \\  & k-NN & 74.9 & 1.63 & 57.44 \\ ReliefF & DT & 74.8 & 1.93 & 61.79 \\  & k-NN & 64.1 & 1.57 & 52.86 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Classification performance. Finally, the performance of the extracted features for classification and clustering are also evaluated based on the ability to distinguish unknown conditions. The time-domain features extracted from raw signals collected during test 6, which considers the system under a \"novel\" operating condition (F6), are added to the training set in the feature evaluation step of the methodology. In Figure 6, the constructed features with the clustering GP-based (left) and the classification GP-based (right) are shown, where distinct colors represent distinct settings. The results of classification models trained on these datasets are summarized in Table 9, including the training accuracy obtained using the features obtained through the PCA and the Relieff. From Table 9, it can be seen that when the system-level feature is constructed with the classification-based GP, classification models trained with the additional operating condition provide a class prediction accuracy higher than 90%. On the contrary, the classification accuracy is lower than 90% when using the clustering-based GP. In addition, ReliefF provides lower training accuracy than GP-based features, while the PCA produces similar results as the classification-based GP. The direct conclusion of this result is that feature construction provides more discriminant features than feature selection.
# CFD Assessment of Car Park Ventilation System in Case of Fire Event
## 2 Materials and Methods
### Simulation Setup in OpenFOAM
#### 2.3.4 Definition of Smoke as a Scalar Quantity  
In this study, the smoke concentration produced by a fire is defined as a scalar quantity. In OpenFOAM, a scalar quantity refers to a physical quantity that has magnitude but does not have a specific direction associated with it. It represents a single value at each point in a computational domain. Examples of scalar quantities in OpenFOAM include pressure, temperature, concentration, and turbulence kinetic energy. The smoke concentration production rate \(\dot{m}_{s}\) [kg/m\({}^{3}\)s] can be calculated as follows:  
\[\dot{m}_{s}=\dot{M}_{s}/V_{s} \tag{7}\]  
where \(\dot{M}_{s}\) represents the rate at which smoke mass is produced and \(V_{s}\) indicates the volume of the smoke source. According to [34], fire events and incidents involving vehicles estimate a burning rate of 260 kw/m\({}^{2}\) for a fire area of 6 m\({}^{2}\), which is equivalent to burning 145 kg of wood. The rate of fuel mass consumption, denoted as \(\dot{m}_{f}\), is estimated to be 0.61 kg/s [35]. To calculate the production rate of smoke concentration, the fuel mass rate can be converted using the yield factor \(y_{p}\)[34], as follows:  
\[\dot{M}_{s}=y_{p}\ \dot{m}_{f}=0.61\times 0.018=0.01098\ [\text{kg/s}] \tag{8}\]  
According to the recommendations in [36], for an indoor car park without a sprinkler system, the suggested dimensions for the fire source area are 5 m \(\times\) 5 m. Based on experimental data in [35], the height of the resulting smoke source from a car fire is measured to be 0.8 m. Therefore, the volume of the smoke source \(V_{s}\) can be calculated as 5 m \(\times\) 5 m \(\times\) 0.8 m. By utilizing Formula (3), the rate of mass concentration production is determined to be \(5.49\times 10^{-4}\) kg/m\({}^{3}\)s. This value is then assigned as the smoke source intensity within the fvOptions dictionary, specifically for a cellZone with a boxed shape in the topoSet dictionary.
## 2 Materials and Methods  
### Sample Preparation  
The Si waste powders from a wire saw slicing process for Si wafers in the manufacturing of solar cells were used in this study. The as-received powders from Kyocera Corporation (Higashiomi, Shiga, Japan) were used for experiments without any further processing. The Si powder was mixed with a binder polymer of polyimide and carbon black at a mass ratio of 75:15:10 and added to an organic solvent of N-methyl-2-pyrrolidone (NMP). The binder was dissolved in NMP solvent, where Si powder and carbon black particles were dispersed in it. The mixture was then agitated by a ball mill (ANZ-10S, Nitto Kagaku Co., Ltd., Nagoya, Japan) for 2 h to obtain a slurry. A layer of the slurry with thicknesses of 8, 13, and 20 \(\upmu\)m was deposited onto a 50-\(\upmu\)m Cu foil using a film applicator (Hohsen Corp., Osaka, Japan) and dried at 210 \({}^{\circ}\)C for 30 min to remove the NMP. After this step, the slurry layer is considered a solvent-free electrode tape. Then the Cu foil with slurry layer was transferred to the sample stage for laser irradiation.
## 3 Application to Reflectarray for Compact Antenna Test Range
### Comparison with Other Techniques in the Literature
the upper and lower templates instead of the relevant figures of merit. By following this strategy, the results shown in Table 4 were obtained. Although these certainly indicate a net improvement over the starting point of Table 1 (all compliances increase, all maximum ripples decrease), the results do not fully comply with the imposed specifications. However, as shown earlier, when directly optimizing the relevant FoM, a 100% compliance is achieved in dual-linear polarization at three different frequencies. Moreover, to achieve the results shown in Table 4 a total of 700 iterations of the algorithm were necessary across all six stages, but only 187 iterations were needed to obtain the results shown in Table 2. This is consistent with the results shown for far-field synthesis [48], where directly optimizing the FoM provided better results in fewer iterations than using upper and lower templates. Regarding computational efficiency, directly optimizing the FoM also offers advantages over using upper and lower templates. The present study, as well as others [28; 32; 34], employed the LM algorithm to minimize a cost function at some point in the optimization procedure. This algorithm requires the computation of a Jacobian matrix (see Figure 2), which is a data structure that takes more memory to store. In the present investigation, the maximum size of the Jacobian matrix is \(4N_{f}\times LN\), where \(N_{f}\) is the number of frequencies, \(L\) is the number of geometrical features of the unit cell that are being optimized, and \(N\) is the total number of reflectarray elements. For the example considered here, \(N_{f}=3\), \(L=2\) or \(6\), and \(N=1936\). Thus, the maximum size would be 2.13 MB, assuming double-precision complex numbers. If upper and lower mask templates are employed, the size of the Jacobian considerably increases, since, now, all points into which region \(\Omega\) is discretized are stored per frequency in the Jacobian matrix. In the present case, region \(\Omega\) was discretized in a grid of \(150\times 150\) points. Thus, the size of the Jacobian in that case is 11.68 GB. Thus, by optimizing the FoM the size of the Jacobian matrix is reduced by more than three orders of magnitude. If more than one NF plane was optimized, the size of the Jacobian would further increase. In this case, the memory footprint could be alleviated to some extent by reducing the number of points where the NF is calculated. For instance, in [43] an NF grid of \(81\times 81\) points is employed. In this case, the size of the Jacobian would be 3.4 GB, which is still more than three orders of magnitude larger than when directly optimizing FoM.
## 1 Introduction
[PERSON] _et al._[16], like [PERSON] _et al._[13], also stated that a future operational system could continuously receive stem data sent from the operating harvester and these data could be used to continuously update an ALS-based reference database, and thereby enable improved prediction of stem attributes for stands not yet harvested. [PERSON] _et al._[17] studied forest resource data updating using a harvester's species-specific timber volume data. In that study, the pre-thinning ALS-derived accuracies (RMSE-%) were 21.6% and 21.7% for an area-based ALS inventory (see [18]) and ITC-based inventory, respectively (validated pre-felling by means of field-measured plots). Following the thinning operation, the timber volume data were updated based on the harvester data, and the corresponding RMSE-% values were 29.4% and 31.6%. However, the absolute RMSE values remained at the pre-felling level. [PERSON] _et al._[17] concluded that harvester data have potential for updating stand-level data. If sophisticated databases showing the locations and species of individual trees are available, tree-level harvest scheduling can be optimized; e.g., by using a species-mingling index as suggested by [PERSON] and [PERSON] [19]. The aim in this study was to test the accuracy of various instruments utilizing GNSS mounted on machinery in motion under forest canopies of varying densities to enable us to understand the current state-of-the-art in GNSS-based positioning under forest canopies. The tested GNSS devices include a low-cost, consumer handheld navigator, two single-frequency receivers with external mini-antennas, and a geodetic dual-frequency receiver with an external antenna. Both real-time positioning and post-processed positioning with and without inertial measurement unit (IMU) data were analyzed to find out what kind of equipment could be added to the harvesters to achieve the required positioning performance for reliable tree matching. In the future, the stem data collected by means of harvesters could be used for enhancing pre-harvest inventory data to predict logging yields from forest stands. There is a high operational requirement for these kind of high-resolution forest data. Modern wood flow optimization tools could be used more efficiently and provide savings through precision forestry.
## 2 Performance Indicators of System Response
### Penalties  
Most nature-inspired algorithms do not natively allow us to solve multiple objective-constrained optimization problems. However, the multiple objective constrained optimization problems can be simply redefined into single objective problems using weighting factors to properly balance the objectives and constraints. Let us consider the following multiple objective functions:  
\[\min\mathcal{F}_{1}(x) \tag{17a}\] \[\min\mathcal{F}_{2}(x)\] (17b) \[\max\mathcal{F}_{3}(x) \tag{17c}\]In the case of application, when the optimization algorithm is used to minimize the objective function \(\mathcal{F}_{3}(x)\geq 0\), Equation (17c) can be rewritten as follows:  
\[\max\mathcal{F}_{3}(x)=\min\frac{1}{1+\mathcal{F}_{3}(x)} \tag{18}\]  
Next, to reduce the multiple optimization problem to a single optimization problem, the following method can be used [22; 27]:  
\[\min\biggl{(}\mathcal{F}_{1}(x)+\alpha_{1}\min\mathcal{F}_{2}(x)+\alpha_{2} \frac{1}{1+\mathcal{F}_{3}(x)}\biggr{)} \tag{19}\]  
where \(\alpha_{1}\) and \(\alpha_{2}\) are weighting factors that must be properly selected to balance the considered objectives. In the literature, the weighting factors were also defined as follows [24]:  
\[\min\biggl{(}(1-\beta_{1}-\beta_{2})\mathcal{F}_{1}(x)+\beta_{1}\min\mathcal{ F}_{2}(x)+\beta_{2}\frac{1}{1+\mathcal{F}_{3}(x)}\biggr{)} \tag{20}\]  
and [25; 28]:  
\[\min\biggl{(}(1-e^{\tau_{1}}-e^{\tau_{2}})\mathcal{F}_{1}(x)+e^{\tau_{1}}\min \mathcal{F}_{2}(x)+e^{\tau_{2}}\frac{1}{1+\mathcal{F}_{3}(x)}\biggr{)} \tag{21}\]  
where \(\beta_{1}\), \(\beta_{2}\), \(\gamma_{1}\) and \(\gamma_{2}\) are weighting factors. It should be noted that all of the above-mentioned options allow us to achieve the same balance between the objective functions. The selection of the method is an individual preference of the user. Specifically, Equation (21) can be considered in the case of an unknown value of the particular objectives. Due to the usage of exponential functions, the balance between functions with significantly different orders of magnitude may be easier.  
A similar approach can be used to include the constrained optimization problem in a single objective function:  
\[\min\mathcal{F}(x) \tag{22a}\] \[\mathcal{G}(x)\geq 0 \tag{22b}\]  
In such a case, the function \(\max\) can be used in the following way:  
\[\min\biggl{(}\mathcal{F}(x)+\delta\max(0,\ -\mathcal{G}(x))\biggr{)} \tag{23}\]  
where \(\delta\) is a weighting factor. If \(\mathcal{G}(x)\) is less than zero, then the constraint-related part will affect the objective function. For this reason, the \(\delta\) weighting factor should have an enormous value compared to the value of \(\mathcal{F}(x)\) to reject the solution if the constraint is violated.
## 5 Results and Discussion
### Number of Drifters  
Determining the optimal number of drifters to be deployed is an important aspect of our Lagrangian DA assessment. To this end, four experiments in addition to the Base-Test were conducted with 2, 4, 12 and 14 drifters while keeping other parameters the same as those in the Base-Test (Table 3). Error assessments in terms of RMSE for both velocity components are shown in Figure 5. A major improvement in the RMSE occurs when 14 drifters are used. However, the optimum value is attained with eight drifters because this results in the maximum improvement in RMSE with the fewest number of drifters. When the number of drifters exceeds eight, the error reduces marginally by \(\sim\)2%. The optimum value of eight drifters determined here supports the selection in the Base-Test (Table 3). In all cases, DA is effective even when a limited number of drifters were used (i.e., two drifters), which affirms the value of assimilating minimal Lagrangian drifter data to improve model estimates, possibly for locations poorly covered by observation systems. This indicates that a small number of drifters can result in a significant improvement to model predictions. With more observations, our analysis shows better improvement and is, therefore, less likely to have unrealistic features in our hydrodynamic model outputs that can appear when a sparse network of observations is analysed. Although it would be more beneficial to record more data, this optimum number of drifters is dependent on many factors including the flow domain being modelled (e.g., eddy dominant or quiescent (slack) sections of the domain) [21].  
Figure 4: Sensitivity of DA performance to frequency of assimilation. Drifter data were assimilated at 1, 2, 5 and 10 min intervals. The Base-Test configuration is applied.
# An Efficient Parallel Algorithm for Detecting Packet Filter Conflicts
## 2 Related Work
### Existing Conflict-Detection Algorithms
As with the data structures used in [4; 29; 30], using the trie-based data structure incurs high memory usage. [PERSON] and [PERSON] [6] discovered that when 2D filters are translated to geometric area representation, if two filters are conflicted, their represented area will cause overlap in the plane, and the line segment belonging to the two areas may have a perfect crossing. Therefore, they designed a magnifying mechanism to ensure that each filter conflict exhibited a perfect crossing. They used a plane sweep method [32] to determine all perfect crossings in the plane, and through the position, filter conflicts could be detected. The algorithm had a time complexity \(O(n\text{log}n+S)\) and space complexity \(O(n)\). The experimental results showed that for five filter sizes with various prefix lengths of filter, their proposed algorithm was 4 to 17 times faster than [3], with 4 to 8 times less memory requirement, and was 4 to 27 times faster than [4], with 6 to 205 times less memory requirement. [PERSON] et al. [33] studied the tuple-space search algorithm [34], observed the relationship between tuples, derived the relationship for searching among tuples by generating markers, and proposed a tuple-based conflict-detection algorithm (TCDA). The execution of conflict detection in each filter could be accelerated by adding a marker pointer and a filter pointer. Finally, the TCDA can detect all conflicts with time complexity \(O(nW+S)\) and space complexity \(O(nW)\). Compared with [6], the experimental results showed that the proposed algorithm can reduce the detection time by 19.6% to 97.7%. More importantly, it reduced the storage requirements of most filter databases. The performance improvement over time is particularly significant for filter databases with many conflicts. Neither [6] nor [33] proposed a method for 5 Dconflict detection. [PERSON] et al. [35] used a formal method to analyze the meaning of IPv6 firewall filters and developed a method to find the filter conflicts. However, that research did not illustrate how to support 5D conflict detection.
## 6 Conclusions  
In order to better assist modeling and improve modeling efficiency, this paper, inspired by the approach of [PERSON] et al. [34], proposes a recommendation method that fuses domain knowledge and SysML models. The method not only considers the influence of general and domain knowledge on the modeling process, but also utilizes the SysML model as training data for recommendation of subsequent modeling. As an example, in the aerospace domain, the manually constructed SysML models are used as a historical model set and their validity is verified to better support the experiments. Using domain-specific text and systems engineering domain model data, the original pre-trained model based on generic text is fine-tuned with the aim of recommending appropriate classes and attributes for a specific development domain. Based on the list generated from the recommendations, the rules for constructing the new SysML model are specified, and from the results, it can be seen that the recommendation list takes both data sources into account in a comprehensive manner, and based on the formulated criteria for judging the validity of the recommendations, the approach proposed in this paper is practical and efficient for SysML modeling.  
Looking ahead, there are plans to explore other recommendation methods, such as knowledge graphs or alternative models, for even more precise recommendations. The graphical data structure of knowledge graphs bears similarities to the information structure of SysML models. Leveraging the entities, attributes and relationships from knowledge graphs can enrich descriptions, store more SysML information and ultimately improve recommendation accuracy and user satisfaction.  
As engineering projects continue to evolve, there will be an influx of new domain knowledge. A complete recommender system can be designed by adding a front and back end. In the future, the system engineering domain model can be reconstructed based on single-shot recommendation lists. New domain texts can be incorporated for word embedding, and the deep learning model can be further fine-tuned. As the model iterates, texts, recommendations and constructed models from each iteration can be recorded in a database, forming a multi-version evolution lifecycle for models/records. This approach effectively enhances the quality and cost efficiency of model-based systems engineering development.  
Conceptualization, J.Z. and S.Y.; methodology, J.Z. and S.Y.; modeling, J.Z.; software, J.Z.; validation, J.Z.; investigation, J.Z.; writing, J.Z.; tuition, S.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 4 Application
### Non-Clinical Application
#### 4.2.4 Polygraph  
Telling a lie activates the autonomic nervous system (ANS). Therefore, there is a significant difference of mental stress which results in variations in physiological signs depending on whether someone is telling a lie or the truth. When someone is interrogated, the iPPG has been proposed as a polygraph for lie detection [2].
# Auxiliary Information-Enhanced Recommendations
## 4 Experiments
### Ablation Analysis  
To verify the effectiveness of each module in our proposed MFN4 Rec algorithm, we conduct ablation analysis to measure the contributions of each module to the performance improvement. To be specific, three simplified versions of MFN4 Rec are designed to: (1) only keep the multi-GRU layer and combine the final hidden states of all GRU-based RNNs as the input for next-item prediction, denoted as MFN4 Rec-g; (2) only keep the gated multi-modal memory network and take its output as the input for prediction, denoted as MFN4 Rec-m; (3) remove the gated multi-modal memory network and add the final hidden states of all modalities as the multi-modal memory representation \(\mathbf{u}^{t}\), while others remain the same as MFN4 Rec, denoted as MFN4 Rec-add. We compare the performance of these three simplified versions with that of MFN4 Rec under the same experimental setting. The results are shown in Figure 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **Recall@20** & **MAP@20** & **Recall@30** & **MAP@30** \\ \hline BPR & 4.6398 & 1.5384 & 7.6127 & 1.7624 \\ VBPR & 4.6410 & 1.5438 & 7.6136 & 1.7723 \\ LSTM & 6.7616 & 1.7227 & 8.9707 & 1.8208 \\ p-RNN & 6.8645 & 1.9688 & 8.8463 & 1.8347 \\ MV-RNN(Con) & 6.5580 & 1.7509 & 8.0625 & 1.8739 \\ MV-RNN(Fus) & 6.8783 & 1.8133 & 8.2881 & 1.8764 \\ MV-RNN(3 mDAE) & 6.1330 & 1.4085 & 8.0270 & 1.8469 \\ VCSRS & 6.8742 & 1.9904 & 9.0214 & 1.8726 \\ MFN4 Rec(ours) & **7.6629** & **2.2697** & **9.7815** & **2.3709** \\ \hline \hline \end{tabular}
\end{table}
Table 4: The performance comparison with baseline algorithms on the Amazon Phone dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **Recall@20** & **MAP@20** & **Recall@30** & **MAP@30** \\ \hline BPR & 0.641 & 0.168 & 0.812 & 0.176 \\ VBPR & 0.700 & 0.181 & 0.922 & 0.190 \\ LSTM & 1.443 & 0.283 & 1.982 & 0.301 \\ p-RNN & 1.484 & 0.301 & 1.939 & 0.320 \\ MV-RNN(Con) & 2.113 & 0.522 & 2.827 & 0.554 \\ MV-RNN(Fus) & 2.157 & 0.508 & 2.867 & 0.538 \\ MV-RNN(3 mDAE) & 2.243 & 0.541 & 2.995 & 0.570 \\ VCSRS & 2.238 & 0.537 & 2.992 & 0.574 \\ MFN4 Rec(ours) & **2.384** & **0.572** & **3.111** & **0.609** \\ \hline \hline \end{tabular}
\end{table}
Table 3: The performance comparison with baseline algorithms on the Amazon Clothing dataset.  
The experimental results show that MFN4 Rec-m performs the worst; it only considers the interaction relations across modalities while ignoring the sequential dependencies within modalities. Particularly, the lack of item ID information significantly reduces the recommendation performance. MFN4 Rec-g and MFN4 Rec-add consider the sequential dependencies with modalities, while not fully capturing the complex interaction relations across modalities, and thus they cannot perform very well. In summary, such observations demonstrate the significance of effectively capturing both sequential dependencies within modalities and interaction relations across modalities.