esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Network** & **\(n\)** & **Method** & **\(\mathbf{It.}\)** & \(\mathbf{A.}\) & \(\mathbf{D.}\) & \(\mathbf{I.}\) \\ \hline \multirow{8}{*}{3000} & \multirow{3}{*}{1000} & coACO & 6.4 ± 4.76 (1) & 0 \(\pm\) 0 (0) & 1 \(\pm\) 0 (1) & 1 \(\pm\) 0 (1) \\  & & ACO & 43.8 ± 26.17 (4) & 0 \(\pm\) 0 (0) & 1 \(\pm\) 0 (1) & 1.9 \(\pm\) 2.33 (0) \\  & & K2 & - & 0 \(\pm\) 0 (0) & 2.5 \(\pm\) 0.85 (1) & 4.40 \(\pm\) 1.90 (2) \\  & & B & - & 0 & 1 & 3 \\ \cline{2-6}  & \multirow{3}{*}{3000} & coACO & 2.7 ± 1.83 (1) & 0 \(\pm\) 0 (0) & 0 \(\pm\) 0 (0) & 1 \(\pm\) 0 (1) \\  & & ACO & 47.9 ± 39.02 (2) & 0 \(\pm\) 0 (0) & 0 \(\pm\) 0 (0) & 3.3 \(\pm\) 2.95 (1) \\  & & K2 & - & 0 \(\pm\) 0 (0) & 1.3 \(\pm\) 0.68 (0) & 4.6 \(\pm\) 0.84 (3) \\  & & B & - & 0 & 0 & 7 \\ \hline \multirow{8}{*}{ASIA} & \multirow{3}{*}{5000} & coACO & 4.9 ± 3.87 (1) & 0 \(\pm\) 0 (0) & 1 \(\pm\) 0 (1) & 0 \(\pm\) 0 (0) \\  & ACO & 42.2 ± 22.58 (11) & 0 \(\pm\) 0 (0) & 1 \(\pm\) 0 (1) & 2.9 \(\pm\) 2.88 (0) \\  & & K2 & - & 0 \(\pm\) 0 (0) & 2.2 \(\pm\) 0.79 (1) & 4.8 \(\pm\) 1.48 (3) \\  & & B & - & 0 & 1 & 3 \\ \hline \multirow{8}{*}{8000} & coACO & 6.2 ± 4.00 (2) & 0 \(\pm\) 0 (0) & 0 \(\pm\) 0 (0) & 1 \(\pm\) 0 (1) \\  & ACO & 52.1 ± 31.88 (5) & 0 \(\pm\) 0 (0) & 0 \(\pm\) 0 (0) & 3.5 \(\pm\) 3.27 (1) \\  & & K2 & - & 0 \(\pm\) 0 (0) & 0.9 \(\pm\) 0.57 (0) & 5.3 \(\pm\) 2.11 (2) \\  & & B & - & 0 & 0 & 4 \\ \hline \multirow{8}{*}{10,000} & coACO & 4.6 ± 3.60 (1) & 0 \(\pm\) 0 (0) & 0 \(\pm\) 0 (0) & 0 \(\pm\) 0 (0) \\  & ACO & 20.5 ± 21.74 (1) & 0 \(\pm\) 0 (0) & 0 \(\pm\) 0 (0) & 3.2 \(\pm\) 2.15 (0) \\  & & K2 & - & 0 \(\pm\) 0 (0) & 0.9 \(\pm\) 0.57 (0) & 5.5 \(\pm\) 2.64 (2) \\  & & B & - & 0 & 0 & 8 \\ \hline \multirow{8}{*}{ALARM} & coACO & 64.7 ± 13.12 (17) & 0 \(\pm\) 0 (0) & 5.2 ±1.62 (4) & 12.7 \(\pm\) 3.53 (9) \\  & ACO & 81.8 ± 25.02 (57) & 0 \(\pm\) 0 (0) & 9.2 ±1.69 (6) & 20.8 \(\pm\) 5.03 (14) \\  & & K2 & - & 0 \(\pm\) 0 (0) & 15.6 \(\pm\) 2.55 (13) & 22.6 \(\pm\) 4.25 (17) \\ \cline{1-1}  & & B & - & 0 & 6 & 42 \\ \cline{1-1} \cline{2-6}  & \multirow{3}{*}{5000} & coACO & 81.1 ± 15.82 (46) & 0 \(\pm\) 0 (0) & 1.2 \(\pm\) 0.42 (1) & 14.6 \(\pm\) 2.99 (10) \\  & & ACO & 58.5 ± 22.27 (20) & 0 \(\pm\) 0 (0) & 5.4 ± 1.429 (3) & 58.5 \(\pm\) 22.27 (20) \\ \cline{1-1}  & & K2 & - & 0 \(\pm\) 0 (0) & 10.1 \(\pm\) 1.969 (7) & 33.4 \(\pm\) 3.84 (26) \\ \cline{1-1}  & & B & - & 0 & 1 & 97 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Statistical results for various methods (\(\mu\pm\sigma\) (best)). **Acknowledgments:** The authors also gratefully acknowledge the helpful comments and suggestions of the reviewers, which help to improve the article. **Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Case Study  
### Project Overview  
A reservoir is located at the edge of the mountain range, with a controlled drainage area of 362 km\({}^{2}\). The reservoir area is a canyon topography, showing a Y shape. The satellite map, contour map, and topographic map of the reservoir are shown in Figure 5a-c. The reservoir is responsible for the water supply to nearby cities, with an average annual water supply of 72.3 million m\({}^{3}\). The water retaining structure is a roller compacted concrete arch dam with double-curvature, of which the dam height is 98.5 m. The 12 m-wide crest outlet is located in the middle of the dam, and the bottom outlet is arranged on the left and below of the crest outlet. To analyze the structural safety state of the concrete arch dam, a 3D FEM model considering the transverse joints, inducing joints, monitoring points, and weak strength surface on the foundation is established, as shown in Figure 5d.  
The FEM model includes the concrete arch dam body and the left and right abutment mountains. Its foundation extends for 150 m to the upstream, downstream, both banks, and the bottom of the dam foundation, respectively. The model adopts eight-node hexahedron elements, with 14,590 elements for dam body, 8365 corresponding nodes, 102,938 elements for foundation and 1,191,543 corresponding nodes. A fixed constraint is imposed on the bottom of the model, and a normal constraint is imposed on the periphery of the model. The main loading conditions include self-weight, water load, uplift pressure, temperature load, and sediment pressure in the calculation process. The water load is applied to the upstream face of the dam and the reservoir area by face force. In addition, contact elements  
Figure 5: Location of the concrete arch dam and its FEM model.  
are set between the blocks in the dam abutment and the mountains. ABAQUS software is used for this numerical simulation. Material properties of the dam and foundation are shown in Table 2.  
To master the change of uplift pressure in the dam foundation, a vertical section and three cross-sections are laid out at the dam foundation using vibrating wire osmometers. The vertical section of the uplift pressure monitoring points is arranged between the curtain grouting and drainage pipe in the dam foundation. There are 13 osmometers (P1-P13) arranged in the vertical section, which can directly observe the reduction of uplift pressure behind the curtain. The 2# cross section, in the left quarter arch of the dam, has 3 osmometers (P1-1-P1-3), P2-1-P2-3 in 3# cross section in the arch crown, and P3-1-P3-3 in 4# cross section in the right quarter arch. The data collection method is automatic acquisition and monitoring. The layout of uplift pressure monitoring points in this concrete arch dam is shown in Figure 6.
## 5 Discussion
### Major Areas of Discussion in UR Publications
#### 5.1.3 Geographic Information System (GIS)  
The growing availability of 'big data' has prompted hopes that the world can be more predictable and controllable. Real-time management has the potential to overcome instabilities induced by delayed input or a lack of knowledge. However, there are significant limitations to this: having too much data might make it impossible to distinguish between accurate and ambiguous or wrong information, resulting in poor decision-making. Having too much information may result in a more obscure rather than a more truthful image [56].  
GIS is a digital ability to collect, store, verify, and display data about locations on the land surface [114]. GIS can offer more accurate and meaningful information about the UR indicators of cities to urban policy makers and high-level decision maker [115]. It is possible to transform raw data into a more tangible and understandable tool that researchers and practitioners can use more frequently while spending less time digesting and generating new insights in this broad field of study by analyzing and visualizing UR dimensions, indicators, and parameters.  
Multi-hazard spatial and geographical scales analysis is essential for improving resilience and disaster response in rural towns and cities vulnerable to severe seasonal weather [116].  
Based on a cooperative geographical resilience assessment technique that includes three resilience evaluation methods and the use of geo-visualization techniques, including the use of GIS for data processing, assessment, visualization, mapping, and model processing, spatial decision-support tools can be developed. This approach integrates the territory's technical, urban, and social components while emphasizing the multiple alternatives available to promote regional resilience through collaboration and the use of a visual tool [117]. There are various services such as Google Maps, Google Earth, and free and/or open-source tools such as QGIS (Quantum GIS), GRASS, SAGA, Monteverdi, Sextante GIS, and Orfeo Toolbox, which can help to develop multiple GIS-based models [118].
# Optimal Design and Fish-Passing Performance Analysis of a Fish-Friendly Axial Flow Pump
## 3 Numerical Simulation Method and Main Parameters of the Model
### Model's Geometric Structure and Parameters  
The model used in the calculation was a three-dimensional model of the full flow channel of the axial flow pump, including the inlet channel, the runner section, the diversion mechanism section, and the outlet channel. The main parameters for the design and operation of the axial flow pump are shown in Table 3.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Rated head Hr (m) & 6.0 \\ Rated speed nr (r-min\({}^{-1}\)) & 300 \\ Rated discharge Qr (m\({}^{3}\cdot\)s\({}^{-1}\)) & 8.0 \\ Runner diameter D1 (mm) & 1475 \\ Number of runner blades Nb & 3 \\ Number of guide vanes Ngv & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Main parameters of the wheel model.  
The geometric structure and components of the axial flow pump are shown in Figure 3.
## 4 Model Implementation and Evaluation
### Ablation Study  
In the first experiment, we utilized data augmentation techniques on the balanced Kvasir v2 dataset using the static LR and the ILRC processes with four DL models: EfficientNet-B0, ResNet101v2, InceptionV3, and InceptionResNetV2.  
Results with Static LR process:  
Table 10 presents the results of the first experiment using a static LR with the four DL models. The average accuracies were as follows: EfficientNet-B0 achieved 97.604%, ResNet101v2 reached 96.812%, InceptionV3 obtained 97.562%, and InceptionResNetV2 scored 97.625%.  
Results with ILRC Process:  
Table 11 displays the results of the first experiment using the ILRC process with the same four DL models. The average accuracies were as follows: EfficientNet-B0 at 97.770%, ResNet101v2 at 97.375%, InceptionV3 at 98%, and InceptionResNetV2 at 98.062%.
## 3 Results and Discussion
### Measurements of Main Physical Properties of Products
#### 3.2.3 Bulk Density Parameter  
Since the target of refractory products is to achieve the maximum bulk density, the lower porosity is desirable [16]. Therefore, following the slight decreasing trend as shown in the aforementioned results of WA(%) and AP(%) parameters, the respective bulk density (BD) values are presented in Figure 5 regarding the thermal treatment at 1300 \({}^{\circ}\)C for 30 min and for all the examined products, obtained either from the raw mineral samples (i.e., as collected directly from the mine area), as well as from the thermally pre-treated ones, showing an increasing trend in the final product, when increasing the hematite percentage for most of the examined samples, except for the sample W7. The BD values considering the hematite addition are similar to the BD levels of common magnesium refractories (i.e., 2.8-2.95 g/cm\({}^{3}\)), whereas slightly higher values found for the thermally pre-treated samples (at 850 \({}^{\circ}\)C for 30 min), prior to pelletization, due to the decrease of LOI values during the pre-heating procedure [17; 18]. Finally, the optimum results, regarding the BD data, are observed for the sample W6 PT, due to its higher content of (useful) olivine mineral formation, during the pre-treatment process due to the higher presence of MgO in the raw material.  
As already mentioned by [PERSON] et al. (2015), the increase of bulk density can be either attributed to the higher true density of Fe\({}_{2}\)O\({}_{3}\) (5.24 g/cm\({}^{3}\)) in comparison to MgO (3.58 g/cm\({}^{3}\)), or to the formation of respective MgFe\({}_{2}\)O\({}_{4}\) spinel (4.51 g/cm\({}^{3}\)) [15], along with the increase of relevant shrinkage values. Furthermore, the study of [PERSON] et al. (2009) showed that the formation of magnesioferrite spinel is encouraged at lower temperatures and in the presence of nano-iron oxide particles, since it decreases the eutectic temperature, hence improving the sintering process. Furthermore, it was also reported that the oxidation-reduction reactions, e.g., Fe\({}^{2+}\) oxidizes to Fe\({}^{3+}\) at 300 \({}^{\circ}\)C, whereas Fe\({}^{3+}\) reduces to Fe\({}^{2+}\) at temperatures lower than 1400 \({}^{\circ}\)C, are the main reasons for the differences in the volume of products [19]. Although existing in rather small percentages (around 5 wt.%) in the product, the presence of hematite seems to facilitate the interconnection between the formed granules, thus increasing the bulk density as well as the mechanical strength of the produced fired materials.  
Figure 4: The effect of hematite addition on the values of (**a**) WA%, and (**b**) AP% parameters for the obtained products considering after the thermal treatment at 1300 \({}^{\circ}\)C for 30 min.
# Untargeted Profiling and Differentiation of Volatiles in Varieties of Meat Using GC Orbitrap MS
## 3 Results and Discussions
### Identification of Volatile Compounds in Different Meats  
The identification was processed through the MS pattern in NIST and homeflavor libraries, the retention index, and the HRF. When using the traditional NIST spectral matching at unit resolution, the HRF assigned tentative identification was based on the use of high-resolution mass spectra. The HRF scores obtained for the candidates should be higher than 95. The match factor based on the MS pattern should be higher than 750. Additionally, the difference in the retention index should be less than 20 for the homeflavor library while within 50 for the NIST library. It should be emphasized that the home-built library is necessary since the MS pattern could be changed according to the different MS analyzer. The fragments in the low-mass range would be lost for orbitrap MS. This would lead to the penalized score for the identification if the NIST library was used. With the optimum analysis conditions, five different kinds of boiled meats were analyzed, and 148 volatile compounds in total were identified as shown in Table 1 based on the above identification rules. Based on this simple cooking method at a low temperature for reflecting the original flavor of meats, the number of the detected volatiles was certainly large. Additionally, the final list of volatile compounds was used to run the statistical analysis for the assessment of the meat grouping and to determine differential volatile compounds. It could be found that hydrocarbons, aldehydes, ketones, esters, acids, furrans, sulfur-containing compounds, and lactones composed the main components of volatile compounds in meat from Table 1. Aromatic hydrocarbons including toluene, xylenes, and ethylbenzene were the dominant component, mainly coming from the feedstuffs [27]. The alkalanals and alkenals such as hexanal, octanal, 2-decenal, and (2E,4E)-heptadieneal were  
Figure 3: The influence of desorbing time on some typical volatiles. mainly formed through the autoxidation of the lipids [28]. The acetaldehyde and the aromatic aldehydes were produced through the Strecker degradation of amino acids and their derivatives, such as glycine, phenyl alanine, and so on [29]. Furans, alcohols, acids, and ketones were also from the autoxidation of the lipids with unsaturated fatty acids [30]. As for the sulfur-containing compounds, thiamine degradation, Maillard reaction, Strecker degradation, and its further oxidation could be their main formation routes [5; 31; 32]. Furaldehyde and their derivatives would be formed through the Maillard reaction [29]. Lactones are a group of cyclic esters and are formed by the intramolecular condensation of hydroxy fatty acids [33; 34]. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Component Name** & **CAS Number** & \ \begin{tabular}{c} **Retention** \\ **Time (min)** \\ \end{tabular}  & **Formula** & **RI** & **\(\Delta\)RI** & \begin{tabular}{c} **Identification** \\ **Method** \\ \end{tabular}
###### Abstract  
To increase underwater acoustic signal detectability and conserve energy, nodes leverage directional transmissions. In addition, nodes operate in a three-dimensional (3D) environment that is categorized as inhomogeneous where a propagating signal changes its direction based on the observed sound speed profile (SSP). Coupling 3D directional transmission with frequent node drifts and the varying underwater SSP complicates the process of selecting suitable transmission angles to maintain underwater communication links. Fundamentally, utilizing directional transmission while nodes are drifting causes breaks in established communication links and thus nodes need to find new angles to reestablish these links. Moreover, selecting arbitrary transmission angles may lead to overlapping beams or result in leaving an underwater region uncovered. To tackle the abovementioned challenges, this paper proposes an autonomous beam selection approach that optimizes underwater communication by selecting non-overlapping beams while mitigating the possibility of missing a region, i.e., maximize coverage. Such optimization is achieved by utilizing a structured angle selection mechanism that accounts for the capability of the used transducer. Moreover, we introduce an algorithm suited for resource constrained nodes to classify rays into different types. Then we divide the underwater medium into regions where each region is identified by the limits of the coverage area of each ray type. Finally, we utilize the limits of these regions to aid nodes in selecting the best ray to reestablish communication with drifted nodes. We validate our contribution through simulation where actual SSPs are leveraged to validate the beam classification process.  
underwater beam selection; underwater acoustic; underwater inhomogeneity +
Footnote †: journal: Applied Signal Processing
## 4 Evaluation
### Experimental Results  
The first experiment was to evaluate the effectiveness of the proposed embedding methods by comparing the performance changes, as shown in Table 2.  
In Table 2, the baseline model (WordEmbed) uses fixed GloVe embeddings as input vectors. CharEmbed, TunedEmbed, and ClassEmbed refer to the character-level word embeddings, the fine-tuned word embeddings, and the class embeddings that are proposed in this paper, respectively. As shown in Table 2, the proposed embedding methods contributed to increasing the performance of FAQ classification.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Accuracy** \\ \hline WordEmbed (baseline) & 0.705 \\ WordEmbed + CharEmbed & 0.756 \\ WordEmbed + Char \& TunedEmbed & 0.784 \\ WordEmbed + Char \& TunedEmbed + ClassEmbed & 0.810 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance changes according to the use of embedding methods.  
Figure 5: Distribution of the number of queries over the full FAQ categories.  
Figure 6: Distribution of FAQ categories according to the number of queries.  
The second experiment was to compare the proposed model with the previous models, as shown in Table 3.  
In Table 3, CNN is the sentence classification model based on a CNN [2] in which pretrained word vectors are converted into feature maps by convolution operations based on multiple filters. OKAPI is the Okapi BM25 retrieval model [21] which is a state-of-the-art ranking function used in document retrieval. BERT-Multilingual is a multilingual version of BERT [15] that is pretrained using a large multilingual text corpus, including Korean. In our experiments, BERT-Multilingual was fine-tuned for 15 epochs by using the FAQ dataset. As shown in Table 3, the proposed model outperformed both the well-known sentence classification model and the keyword search model.  
The last experiment was to compare the performance changes of the proposed model according to the size of training data, as shown in Figure 7.  
In Figure 7, FAQ-\(n\) indicates FAQ categories in which \(n\) queries (i.e., \(n\) training data) are contained. The parenthesized values indicate the number of FAQ categories associated with each FAQ-\(n\) in the test data. It can be seen from the figure that the proposed model needed at least five training data per FAQ category in order to obtain an accuracy of more than 0.8.
# FDCNet: Frontend-Backend Fusion Dilated Network Through Channel-Attention Mechanism
## 3 Our Proposed Method--FDCNet  
Many previous methods introduced the multi-column fusion network architecture to reduce the errors caused from head scale variation due to the perspective effect. They can fuse the feature maps of various receptive fields from layers of various filter sizes or different columns. But various kernels may bring about more parameters and computations while multi-column architecture may make the network more complex. Hence, inspired by Reference [12], we propose to fuse the frontend and the backend feature maps of the one-column and one-size kernel network through channel-attention mechanism. The network will be more robust for the head scale variation and background noise, and meanwhile keep the conciseness. Furthermore, the dilated layer is exploited in the last part of our network, and SSIM-based loss is added into the loss function.  
Our proposed architecture is shown in Figure 1. We call it FDCNet (Frontend-backend fusion dilated network through channel-attention mechanism). Next, we will elaborate on our model from four aspects.
# A Game Model and Fault Recovery Algorithm for SDN Multi-Domain
## 1 Introduction
The main contributions of this paper are as follows: (1) In order to solve the switch migration problem caused by unrecoverable failure of SDN controller, a solution based on game-enhanced model is proposed, and a game model for SDN multi-domain controller is constructed to provide a theoretical basis for the remapping between switches and controllers; (2) A method for constructing a controller game domain is proposed: the target controller and out-of-control switch are selected based on the remaining capacity of controller and the transition relationship between devices to establish a controller game domain; (3) The issue of mapping between the out-of-control switch and the target controller in the controller game domain is transformed into a linear programming problem for solution, and the migration cost of the out-of-control switch and the load pressure on the controller are comprehensively considered. A multi-population particle swarm optimization algorithm with repulsive interaction [7] is employed to determine the optimal mapping between controllers and switches; (4) Aiming at the potential unrecoverable faults that may occur in SDN controllers, an SDN controller fault Multi-Autonomous Recovery Algorithm (SDN-MARA) based on multi-domain controller game model is proposed, which boosts high fault detection accuracy and rapid recovery. Finally, comparative experiments demonstrate that the proposed model and algorithm in this paper can balance the migration cost of switches and the load pressure on controllers while reducing propagation delay in SDN controllers, significantly improving fault recovery efficiency.