esa-sceva/satcom-chunk-collection huggingface dataset:
# Text Normalization Using Encoder-Decoder Networks Based on the Causal Feature Extractor
## 4 Discussion
Regarding the undetectable errors reported in Sproat and Jaitly [6], it can be firmly confirmed that they are not an issue in these models as they appear by chance due to solvable errors. Specifically, these errors are highly related with the attention mechanism, as Table 11 shows, since the most common error is getting stuck in an infinite loop. These problems cause the model to lose focus and jump around when confounding similar parts of the input. Therefore, this could be greatly improved by using more sophisticated attention models that, for example, focus on local neighborhoods, take into account the index, or force the model to put more focus in the next character of the input. Finally, we discuss the results obtained on the third experiment. Figure 11 shows that, during training, the model quickly converged. There is a gap between training and generalization error that the model has not been able to solve. However, the results obtained on the test set are very promising: It achieved 92.74% accuracy and 5.44% CER, against the 99.8% accuracy and 13.43% CER obtained by the models of [PERSON] and [PERSON] [6] and [PERSON] et al. [9], respectively. However, this comparison with previous works has to be carefully taken, since there are differences that do not allow a direct comparison. For example, in the case of [6], the dataset contains about 40 million sentences. This is translated into training times between 5 and 10 days using a system with eight GPUs. Compared to that, our method used 1 million sentences from the same dataset, and the training time was 22 h with 1 GPU. The difference is also in the underlying model of the encoder, which is a 4-layer bidirectional LSTM in [6] and the convolutional CFE encoder in our case, both working at character level. Lastly, in order to achieve the best accuracy of 99.8%, an additional finite state filter is applied to guide the decoding, while our method is exclusively based on neural networks. Considering the second work, by [PERSON] et al. [9], it must be noted that it is specific for Japanese text normalization. The system is also based on an encoder-decoder architecture, using bidirectional recurrent neural networks in the encoder, working at character-level. Nevertheless, the corpus is quite different, containing a set of over 200,000 synthesized sentences using 3500 Kanji characters; the training time was not reported. Consequently, we consider that the results obtained by the proposed CFE are promising, and point out a viable direction to solve the problem of text normalization in a data-driven fashion. It is computationally less expensive than LSTMs, and the analysis of the errors has shown that it not prone to produce undetectable or unrecoverable errors, thus answering question 1.
## 8 Experiments
### Error Analysis  
We analyzed the types of errors for normalization models with characters as basic units. The results are presented in Table 14. Because character-based statistical models were taken as the baseline in our research, they were also taken as the reference in the statistical tests. Results are shown for correct conversion (Corr), missing conversions (Miss), wrong conversions (Wrng), unwarranted conversions (Unwr), inserted words (W-ins), deleted words (W-del), and \(p\)-values for both statistical significance tests. Looking at the results for Gos and GosVL, we see that transformer models outperform the LSTM models, while in all three Artur datasets, LSTM models perform slightly better. Looking at the types of errors for GosVL, we see that most of the improvement is due to the reduction of missing conversions. The number of unwarranted conversions is slightly higher than with LSTM models. In the context of corpus preparation, all errors should be manually corrected. However, removing unwarranted conversions might be a quicker task since we only need to copy the literal form to the standard form, especially with custom software that would have this function implemented with a single keystroke. Therefore, we do not want to consider all error types with an equal cost in post-editing. In general, by comparing results on the remaining datasets, we can see similar trends between models. We argue that, in practice, the difference in performance on GosVL between the LSTM model and the transformer model is higher than suggested by the overall result. Using the same viewpoint on the Artur datasets even suggests that transformer models might be more beneficial in post-editing, despite the overall results showing better performance with the LSTM model. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Vocabulary Size = 5000**} & \multicolumn{2}{c}{**Vocabulary Size = 1000**} & \multicolumn{2}{c}{**Vocabulary Size = 500**} \\
**Dataset** & **WER [\%]** & **CER [\%]** & **WER [\%]** & **WER [\%]** & **WER [\%]** & **CER [\%]** \\ \hline GosVL & 24.28 & 20.30 & 14.21 & 11.66 & 4.88 & 2.21 \\ [PERSON] & 8.86 & 5.34 & 8.26 & 4.69 & 10.83 & 6.96 \\ [PERSON] & 6.71 & 4.23 & 4.53 & 2.42 & 4.54 & 2.27 \\ [PERSON] & 15.95 & 11.35 & 7.65 & 4.08 & 7.60 & 3.97 \\ [PERSON] & 6.19 & 3.45 & 5.37 & 2.67 & 4.88 & 2.21 \\ [PERSON] & 5.57 & 2.78 & 5.41 & 2.75 & 5.02 & 2.34 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Evaluation of subword-based transformer models. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Dataset** & **WER [\%]** & **CER [\%]** & **BLEU** & **chrF** \\ \hline [PERSON] & **2.50** & **0.69** & 94.10 & 98.06 \\ [PERSON] & **4.87** & **1.61** & 89.33 & 95.73 \\ [PERSON] & **2.00** & **0.54** & 95.33 & 98.52 \\ [PERSON] & **3.65** & **1.24** & 91.62 & 96.76 \\ [PERSON] & **2.30** & **0.59** & 94.52 & 98.41 \\ [PERSON] & **3.00** & **0.90** & 93.10 & 97.53 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Evaluation of character-based transformer models. Results that surpass the baseline are highlighted in bold.
## 3 System Models  
Figure 1 shows the physical nodes in our aquaculture network, detecting the multitarget location and other tracking information. Sensors (including normal sensing nodes and anchors in charge of information processing and fusion) are randomly located in a \((-500,500)\times(-500,500)\) m\({}^{2}\) area, according to a two-dimensional Poisson distribution with a density of \(\lambda_{0}\), in which the ratio of anchors is \(\gamma_{0}\), i.e., \(N_{A}(1-\textit{hop})=\pi\gamma_{0}\lambda_{0}\). Multiple targets, Chinese crabs in this study, move along with maneuvering trajectories in the sensor deployment area and are detected by the sensor nodes (normal sensors or anchors) implementing the IGGM-PMBM tracking strategy.  
Multiple targets, equipped with recognizable sensors, can also communicate with other sensors in this sensor network. Each node can establish its one-hop and two-hop neighbor lists through an HTC scheme. In each timestep, nodes including targets can perform the HTC scheme to obtain its one-hop and two-hop neighbors, before initializing the filter. Each HTC request packet contains the node ID, location, and current moment. Receiving this request packet, nodes (including targets) can obtain distance information of their one-hop and two-hop neighbors. Consecutive position information detected from the request packet of specific targets is used to obtain the targets' velocity. Location and velocity information can be used for the iterative IGGM-PMBM filter to obtain target tracking.
## 4 Results
### Ablation Results  
To evaluate the effectiveness of various modules, we designed eight ablation experiments using the LiTS dataset. We chose U-Net with CII as the baseline to conduct the experiments. The first set was the baseline experiment. Sets 2 through 4 involved adding DLE, ResBlock, and CAE, respectively, on the basis of Experiment 1, which we used to verify the effect of each module on the baseline. Sets 5 to 7 added different combinations of modules onto the baseline to explore the dependencies among them. To verify the performance of the proposed method (MDAU-Net), set 8 added all modules to the first set to conduct training.  
The results of the ablation experiments are displayed in Table 3 and Figure 7(b). As depicted in Table 3, in the third set of experiments, the RVD attained a value of 0.0293, demonstrating that the inclusion of the residual encoder effectively preserved shallow  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Dice** & **Precision** & **Recall** & **VOE** & **RVD** \\ \hline Baseline & 0.9067 & 0.9392 & 0.9019 & 0.1694 & 0.0759 \\ Baseline + reverse residual & 0.9080 & 0.9399 & 0.9054 & 0.1674 & 0.0872 \\ Baseline + forward residual & 0.9145 & 0.9403 & 0.9366 & 0.1398 & 0.0478 \\ Baseline + bidirectional residual & **0.9204** & **0.9424** & **0.9408** & **0.1262** & **0.0446** \\ \hline \hline \end{tabular} Bold text in the table represents the optimal results.  
\end{table}
Table 2: The performance of validity experiments on the LiTS dataset.  
Figure 8: This image shows the radar chart results from the validity experiment in Section 4.3 and the ablation experiment in Section 4.4: (**a**) radar chart of validity experiments, (**b**) radar chart of ablation experiments.  
features, thereby enhancing accuracy in organ boundary contour segmentation. In the fourth set of experiments, the Dice coefficient reached a value of 0.9447, indicating that valuable information was efficiently multiplexed within the cascaded adaptive feature extraction unit (CAE), resulting in increased similarity between the segmentation results and the ground truth. The results from other experimental sets showed that the addition of the ResBlock, CAE module, and DLE module each had a distinct positive impact on performance. Additionally, the radar plot in Figure 8b illustrates that MDAU-Net (red contour) achieved comparable Dice and RVD values while exhibiting superior accuracy, recall, and reduced error between predictions and ground truth.
# Computational Fluid Dynamics of Compartent Fires: A Review of Methods and Applications
## 3 CFD Simulation in Compartment Fires
### Physical Models
#### 3.3.1 Turbulence Modeling
Besides that, Constant Smagorinsky and Deardorff are not capable of handling the buoyant flows, near-wall turbulence, and anisotropy, which lead to inaccuracies in predicting fire behavior, smoke spread, and heat transfer [55]. Similarly, WALE and Vreman both use the fixed coefficient (i.e., 0.6 and 0.07 in FDS default setting, respectively [57; 58; 62]) but the WALE model inherently adjusts the SGS eddy viscosity to vanish near solid walls without needing additional wall-damping functions [62]. This means that by approaching a wall, the eddy viscosity is automatically reduced to zero in response to the no-slip condition (where velocity must be zero at the wall), ensuring that small-scale eddies near the wall are properly resolved. The same situation is applicable for Vreman, where the model effectively limits the eddy viscosity to prevent over-damping of turbulence in near-wall flows [63]. K equation sub-models provide a more refined and accurate approximation of the SGS turbulence effects in regions with strong turbulence gradients, such as near walls [64], which make them appropriate to be used in the case of a compartment fire. Comparing the different LES sub models reveals the more appropriate choices to use in the compartment fire simulation. Dynamic Smagorinsky, WALE, and Vreman are more accurate in the case of a compartment fire as they can adapt well to the near-wall flows that are dominant in confined spaces. While the Constant Smagorinsky and Deardorff methods can be used in compartment fires, they may not be able to capture the near wall effects or transient behavior accurately. In practice, choosing between models depends on the specific flow characteristics and the available computational resources. If near-wall accuracy is needed, WALE or Vreman might be preferred. Although, if dynamic adaptation to local flow conditions is important, the Dynamic Smagorinsky model is a better option, despite its higher computational cost compared to WALE and Vreman. From a grid sensitivity point of view, Dynamic Smagorinsky is highly sensitive to grid resolution, whereas the WALE and Vreman models are moderately sensitive but more robust on coarser grids. The Constant Smagorinsky model, while also grid-sensitive, is the most basic and less sensitive overall. Table 2 ([12; 41; 57; 58; 59; 62]) categorizes the different SGS models which are available in different CFD tools and summarizes the comparison between them, presenting their weak points with regard to compartment fire features.
# Interpolation-Filtering Method for Image Improvement in Digital Holography
## 2 Image Quality Enhancements in Digital Holography
### Negative Factors in Digital Holography  
The factors limiting the quality of information obtained during hologram registration are speckle noise [5; 75; 76], unwanted diffraction orders [23; 77], temporal [22; 78; 79], and spatial [22] noise of the digital camera.  
Speckle noise arises due to the presence of a random interference pattern of diffusely scattered light beams recorded by a digital camera along with useful information [80]. In most cases, speckle noise makes the highest contribution to the degradation of the quality of the reconstructed image. To analyze speckle noise, its standard deviation is used [35; 81]:  
\[\begin{split}\sigma_{\rm Sp}=\rm CI\\ C=\sigma_{\rm Sp}/\rm I=1/\sqrt{N}\end{split} \tag{3}\]  
where C is the speckle coefficient, N is a number of uncorrelated components that are used in the calculation, and I is the average intensity of the reconstructed image.  
Digital camera noise arises due to differences in pixel characteristics and random processes during registration [26]. At low brightness, their contribution to the total signal in the reconstructed image is significant [22]. At higher brightness, their contribution is less than that of the speckle noise one.  
If the object and reference beams are coaxial, the diffraction orders overlap in the reconstruction. This complicates the use of an on-axis scheme because additional processing is required to extract the information [23]. In the case of off-axis holography [3], the diffraction orders will be spaced apart, but even in this case, there may be partial overlap, which degrades the image quality of the useful diffraction order. This type of noise usually does not give numerical estimates of its manifestation due to the localization of noise and the development of approaches to its suppression based on frequency filtering [77], iterative procedures [41], etc. [82; 83; 84].  
Figure 1: Principal scheme of digital hologram recording.
## 3 Methods
### Training Loss  
#### 3.3.1 Registration Loss  
(1) Mixed similarity loss  
The existing multimodal similarity measures utilized as loss functions in the network remain inefficient, thereby impacting the registration performance of the training process. For instance, the NMI is computationally difficult and unsuitable for gradient-based methods. Similarly, the NCC is dependent on the domain and cannot be generalized to all modalities. The translation-based registration framework can alleviate the problem of hand-crafted multimodal similarity measures. However, during the image-to-image translation process, there may be a distribution mismatch, especially when dealing with images that have a complex appearance. Therefore, it is unreliable to rely solely on translation algorithms to transform multimodal registration into unimodal registration. Hence, to reduce the impact of mismatched images generated during the translation process,we propose a mixed similarity loss that focuses on the structural information of the images, aiming to maximize the similarity between the registered image and the target image.  
To penalize appearance discrepancies between target and warped images, we propose a similarity loss \(L_{reg}(R)\). \(L_{reg}(R)\) is defined as  
\[L_{reg}(R)=\left\|H_{YP}(G_{enc}^{y\to x}(Y))-H_{XP}(G_{enc}^{x\to y}(X_{w}^{ \prime}))\right\|_{1}^{sum} \tag{6}\]  
where \(sum\) represents summing them up together. We utilize separate embeddings \(H_{XP}\) and \(H_{YP}\) to extract the 256-dimensional feature stacks from the input image pairs and then project them onto 64-dimensional vectors. \(H_{XP}\) and \(H_{YP}\) consist of 2-layer MLP, alongside convolutional, ReLU, average pooling, linear transformation, ReLU, and final linear transformation layers. To explore the potential mapping relationships in the feature space, we extract features shared between the warped image and the target image to guide the accurate registration.  
To minimize the appearance dissimilarity between the warped image of the translated image \(Y_{w}^{\prime}\) and the target image \(Y\), \(L(TP,R)\) is defined as  
\[L(TP,R)=\left\|Y_{w}^{\prime}-Y\right\|_{1} \tag{7}\]  
So, the mixed similarity loss \(L_{ms}\) is computed by  
\[L_{ms}=L_{reg}(R)+L(TP,R) \tag{8}\]  
(2) Smooth loss  
To prevent discontinuity in the deformation field \(\phi\) when making the warped image closer to the target image, the smooth loss \(L_{smooth}\), which is simiar to the TV loss [34], is employed to constrain \(\phi\) to avoid excessive distortion of the warped image.  
\[L_{smooth}(\phi)=\sum_{p\in\Omega}\left\|\
abla\phi(p)\right\|^{2} \tag{9}\]  
\[\
abla\phi(p)=\left(\frac{\partial u(p)}{\partial x},\frac{\partial u(p)}{ \partial y}\right) \tag{10}\]  
\[\frac{\partial u(p)}{\partial x}\approx\phi((p_{x}+1),p_{y})-\phi(p_{x},p_{y}) \tag{11}\]  
\[\frac{\partial u(p)}{\partial y}\approx\phi(p_{x},(p_{y}+1))-\phi(p_{x},p_{y}) \tag{12}\]  
where \(\Omega\) represents the positions of the neighboring pixels of \(p\).
## 2 Method
As for the complexity of this method, note that the complexity of K-means is O(t\(\cdot\)K\(\cdot\)n\(\cdot\)d), where  
* t = number of iterations until convergence
* K = number of clusters
* n = number of data points
* d = number of features  
The Elbow Method for Optimal K requires running K-means multiple times with different K values, resulting in O(K_max\({}^{2}\)_t_avg\(\cdot\)n\(\cdot\)d), where K_max is the maximum number of clusters tested, and t_avg is the average number of iterations across all K values. As for the Feature Importance Calculation, computing the importance metric (ratio of feature distance to total distance) for each feature is O(n\(\cdot\)d). Furthermore, KNN has a prediction complexity of O(n\(\cdot\)d) per test point, while Random Forest has a training complexity of approximately O(m\(\cdot\)n\(\cdot\)log(n)\(\cdot\)\(\surd\)d), where m is the number of trees (25 in this paper). The iterative feature removal process repeats these steps up to d times (once per feature). In the worst case, this process removes one feature at a time, requiring O(d) iterations of the above steps. Therefore, the overall time complexity is dominated by the repetitive application of K-means and the classifiers in the iterative feature removal process, resulting in approximately O(d\({}^{3}\)\(\cdot\)t\(\cdot\)K\(\cdot\)n) in the worst case. While the worst-case complexity is O(d\({}^{3}\)\(\cdot\)t\(\cdot\)K\(\cdot\)n), the average case is closer to O(d\({}^{2}\)\(\cdot\)t\(\cdot\)K\(\cdot\)n), since most real-world datasets do not require removing features one by one until they reach the minimum. The early stopping criteria typically halt the process after removing a fraction of features.
## 2 Methodology
### Modeling Method of the Evaporation Duct Characteristics  
The modified refractive index profile at a specific position can be predicted by theoretical methods based on the Monin-Obukhov similarity theory [22; 23; 24; 25], utilizing observed meteorological parameters at the sea surface and a specific height. Therefore, the evaporation duct height (EDH) was determined as the altitude of the minimum modified refractive index.  
The Naval Postgraduate School (NPS) model was introduced in 2000 [25]. The feasibility of the NPS model was analyzed in previous studies [19; 25] and it also performed well in prediction accuracy compared with measured data [25; 26]. One of its specific features is that it first obtains the vertical profile of temperature \(T\) and specific humidity \(q\). During the calculation, the scale parameters and thermodynamic roughness height of the sea surface are defined by the COARE 3.0 algorithm [27], and the wind speed and temperature under stable conditions are calculated by the profile stability functions [27]:  
\[\begin{array}{ll}\psi_{h}&=-\frac{b_{h}}{2}\ln(1+c_{h}\xi+\xi^{2})\left(- \frac{a_{h}}{B_{h}}+\frac{b_{h}c_{h}}{2B_{h}}\right)\times\\ &\left(\ln\frac{2\xi+c_{h}B_{h}}{2B_{h}^{2}+c_{h}+B_{h}}-\ln\frac{c_{h}-B_{h}} {c_{h}+B_{h}}\right)\end{array} \tag{3}\]  
where \(a_{h}=b_{h}=5\), \(c_{h}=3\), \(B_{h}=\sqrt{5}\), \(\xi=z/L\) is the Monin-Obukhov parameter, which is used to express the atmospheric stability; \(z\) is the altitude; and \(L\) is the similarity length.  
According to the temperature profile \(T(z)\), the atmospheric pressure profile \(p(z)\) can be obtained by integrating the hydrostatic equation and the ideal gas law [28]:  
\[p(z_{2})=p(z_{1})\exp\left(\frac{g(z_{1}-z_{2})}{R\overline{T_{v}}}\right) \tag{4}\]  
\[\overline{T_{v}}=[T_{v}(z_{1})+T_{v}(z_{2})]/2 \tag{5}\]  
where \(z_{1}\) and \(z_{2}\) are altitudes; \(R\) is the gas constant for dry air with 287.04 J/kg/K; \(g\) is the gravitational acceleration; and \(T_{v}\) is the virtual temperature.  
Hence, the water vapor pressure \(e\) can be obtained [24]:  
\[e=\frac{qp}{\varepsilon+(1-\varepsilon)q} \tag{6}\]  
Figure 2: Comparison between MAGIC and NCAR datasets.  
where \(\varepsilon\) is a constant with 0.62197 [24].  
Therefore, the refractivity profile is determined according to Equation (2), and the modified refractive index can be analyzed sequentially.
# Comparison of Analytical Methods for Determining Methylesterification and Acetylation of Pectin
## 2 Materials and Methods
### Determination of DM by Titration  
The DM of pectin was determined using the titration method [28]. Pectin (500 mg) was refined with HCl-ethanol solution to remove organic acid impurities. It was then titrated with 0.1 M NaOH using phenolphthalein as the indicator, and the volume consumed was recorded as V1. Afterwards, saponification of pectin was initiated by adding 20 mL of 0.5 M NaOH. When the reaction was completed, 20 mL of 0.5 M HCl was added, and the excess HCl was neutralized with 0.1 M NaOH; the volume consumed was recorded as V2. The volume of V1 was consistent with the content of GalA without esterification, and V2 was consistent with the content of esterified GalA in the pectin sample. The esterification degree of each pectin sample was determined by three groups of independent experiments.  
The calculation of DM content is:  
\[\mathbf{DM}=\frac{\mathbf{V2}}{\mathbf{V1}+\mathbf{V2}}\times\mathbf{100}\% \tag{1}\]
# Travel Time Prediction on Long-Distance Road Segments in Thailand
## 4. Results and Discussions  
The results of the three experiments are shown and discussed in this section. Since the distances between road segments were different, for example, the length of the selected road segment of highway number 1 was 332 km, and that of highway number 331 was 80 km, as mentioned in Section 4.4, the error of each model was normalized by the length, so the results were reported as MAE per 100 km instead of the original MAE. Thus, an MAE value such as 31.25 for the ARIMA in Section 4.1 can be interpreted as follows: the evaluation of the ARIMA technique with the test set provided an error of \(\pm\)31.25 min within 100 km.