esa-sceva/satcom-chunk-collection huggingface dataset:
# Improving the Performance of Vietnamese-Korean Neural Machine Translation with Contextual Embedding
## 2 Transformer Model
### The Decoder of the Transformer  
The decoder of the transformer receives the embedding representation of the encoder as an input and returns the target sentence as an output. Like the encoder, the decoder contains a stack of N sub-decoders. A decoder has two inputs: the output of the previous decoder and the representation of the encoder, as shown in the following Figure 3.  
The <sos> and <eos> tokens, respectively, indicate the beginning and the ending of the sentence. Suppose, for example, that the output of the decoder is a sentence in the form \(y=(y_{1},y_{2},\ldots,y_{m})\), where \(m\) is the length of the sentence. At the first time step, where \(t=0\), the <sos> _output_ is <sos>, and the _output <eos>_ is \(y_{1}\); at the next time step, \(t=1\), the <sos> _output_ is <sos>\(y_{1}\), and the _output <eos>_ is \(y_{1}\)\(y_{2}\); at the last time step, when full output is generated, the token <eos> is created and the output of the decoder is \(y_{1}\)\(y_{2}\ldots\)\(y_{m}\)<eos>.  
There are three sub-layers on each decoder block, including masked multi-head attention, multi-head attention, and the feed-forward network. In addition, the output of each sub-layer is connected to an add-and-norm component. As with the encoder component, the positional encoder layer is added to the decoder component, as shown in Figure 4.  
The masked multi-head attention is the first layer in the decoder. It operates in a manner similar to the multi-head attention mechanism in the encoder. For head \(i\), the attention matrix \(Z_{i}\) is computed as follows:  
\[Z_{i}=softmax\ \Bigg{(}\frac{Q_{i}K_{i}^{T}}{\sqrt{d_{k}}}\Bigg{)}V_{i} \tag{3}\]  
where \(Q_{i}\), \(K_{i}\), \(V_{i}\) are the query, key, and value of the input matrix and \(d_{k}\) is the dimension of the key vector. The final attention matrix, \(M\), is generated by concatenation of all attention matrices and a new weight matrix \(W_{0}\), as in the following equation:  
\[M=Concatenate\ (Z_{1},Z_{2},\ldots,Z_{h})W_{0} \tag{4}\]  
Figure 3: A simple demonstration of the decoder.  
The result of this final attention in the masked multi-head attention layer is fed into the multi-head attention layer. As shown in Figure 4, each multi-head attention layer has two inputs: the representation of the encoder (R) and the output of the masked multi-head attention (\(M\)). The key and the value of this layer are calculated in a manner similar to the method used for the encoder layer, but with a small difference. The weight matrix of the encoder representation is multiplied by the weight matrices of the query and the key, respectively.
# Allogeneicic Demineralized Dentin Matrix Graft for Guided Bone Regeneration in Dental Implants
## 2 Materials and Methods
### Measurement of Buccal Marginal Bone (BMB) Resorption Using Cone-Beam Computed Tomography (CBCT)  
BMB resorption around the implant was measured using linear cone-beam computed tomography (CBCT, Vatech, Seoul, Korea) measurement tools (EasyDent Viewer, Vatech, Seoul, Korea) on the center-aligned dental implant in a cross-sectional slice from the base of the implant to the crest of the buccal marginal bone height. The time points were immediately after implantation with GBR (T1: Figure 2a,d), prosthetic loading (T2: Figure 2b,e), and after 12 months of functional loading (T3: Figure 2c,f). On the cross-sectional view of the CBCT, a vertical reference line was drawn from the radiolucent center of the cover screw to the mid-cylinder of the fixture. The horizontal reference line at the marginal crest of the bone, grafted, or formed, was drawn perpendicular to the vertical reference line (Figure 2a). The BMB was measured based on these reference lines relative to the marginal crest and dental implant apex at T1, T2, and T3. BMB resorption was classified as initial resorption (T1 to T2) and functional resorption (T2 to T3). If there was an increase in BMB height at 12 months compared with the baseline, the amount of resorption was set at zero.  
Figure 1: Surgical procedure. (**a**) Immediately after extraction, an implant was placed in the mandibular left second molar. (**b**) Allogeneic demineralized dentin matrix (Allo-DDM) was applied for guided bone regeneration. (**c**) Blood absorption and coagulation can be seen on Allo-DDM particles without covering the membrane. (**d**) At 4 months, surgical exposure to the prosthetic procedure showed hard tissue formation around the implant. Tissues over the cover screw of the implant (black circle) were harvested for histological examination. Allo-DDM: allogeneic demineralized dentin matrix.
## 3 System Validation
### Initial Testing And Analysis  
When the first tests of the algorithm were performed, it was observed that a constant drift was present, causing the estimated trajectory to consistently shift to the right. Specifically, the alteration produced all movements on the horizontal axis to the right to be magnified by a constant ratio. This issue is illustrated in the left side of Figure 7, where a test shows the estimated route followed by the camera after four loops were completed in a 70-mm square path. The initial pose is marked in red, and the final pose in orange. This drift is caused by the optic effects from the lenses, the image capturing and processing, and system set-up errors.
## 3 Fault Diagnosis Experiment and Analysis
### Experimental Analysis of Variable Motor Load  
As can be seen from Table 2, each model has achieved high diagnosis accuracy under constant load. However, in the actual work process, the load of the motor often changes; this means that the speed of the bearing changes, so it is very important that the model can still be effectively diagnosed under the condition of variable motor load. In order to test the performance of the model proposed in this paper under varying loads, three datasets A, B, and C (corresponding to three loads of 1 HP, 2 HP, and 3 HP, respectively), were used for training and testing. As shown in Figure 8, A\(\rightarrow\)B means that A is used as the training set and B is used as the test set, and the same is true for other cases. AVG represents the average accuracy of the model under these variable conditions.  
As can be seen from Figure 8, the average diagnosis accuracy of ViT + IFI model under variable load conditions is only 88.08%, lower than the average diagnosis accuracy of Resnet50 + IFI of 94.88%. The advantages of the ViT model have not been reflected, but the performance of the TLViT + IFI model, which uses a large amount of data for pre-training, has been significantly improved, and the average diagnosis accuracy under variable load conditions has reached 98.96%, which is more than 10% higher than that of ViT + IFI. The average diagnosis accuracy of TLResnet50 + IFI is 96.22%, indicating that TLViT model also has better performance under variable load conditions than TLResnet50. The average diagnosis accuracy of TLViT + GASF, TLViT + GADF, and TLViT + CWT models by using only one image as model input was 88.57%, 88.63%, and 96.30%, respectively. Moreover, the difference between the highest accuracy and the lowest accuracy is 7.99%, 7.81%, and 5%, respectively, while the average diagnosis accuracy of the TLViT + IFI method used in our study is 98.96%, and the difference between the highest accuracy and the lowest accuracy is 2.35%, indicating that the proposed method had higher diagnosis accuracy and better generalization.
# Automatic Segmentation of Mandible from Conventional Methods to Deep Learning--A Review
## 3 Results
### Methodology
#### 3.4.3 Level Set-Based Methods  
The level set-based image segmentation algorithm is modified from the snake algorithm [113]. It requires an initial contour curve, and then, the curve evolution is performed via minimizing the functional energy. To obtain a better prediction, the studies [9; 63] make use of the atlas method to generate an initial contour in their segmentation tasks. Patch-based sparse representation has been applied to estimate the patient-specific atlas and then combined with a level set framework using the rule of maximizing a posteriori probability. With these methods, the mandible segmentation problem can be converted into a convex optimization problem [9]. Table 5 shows a summary of the level set-based method used. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Study** & **Year** & **Datasets** & \begin{tabular}{c} **No. of** \\ **Patients** \\ \end{tabular} & \begin{tabular}{c} **Performance** \\ **Dice (\%)** \\ \end{tabular} & \begin{tabular}{c} **ADD (mm)** \\ \end{tabular} & \begin{tabular}{c} **HD (mm)** \\ \end{tabular} & \begin{tabular}{c} **9 SHD (mm)** \\ \end{tabular} & \begin{tabular}{c} **VOE (\%)** \\ \end{tabular} & \begin{tabular}{c} **Image** \\ **Modalities** \\ \end{tabular} & \
\begin{tabular}{c} **Time/** \\ **Equipment** \\ \end{tabular} & \multirow{2}{*}{**Category**} \\ \hline Abdolali [18] & 2017 & In-house & 120 & 91.38 \(\pm\) 2.06 & 0.71 \(\pm\) 0.09 & — & — & — & CBCT & 5 min/CPU & SSM-based \\ \[PERSON] [37] & 2012 & In-house & 30 & — & 0.50 \(\pm\) 0.00 & 11.30 \(\pm\) 3.50 & — & 14.0 \(\pm\) 1.9 & CT & — & SSM-based \\ \[PERSON] [38] & 2013 & In-house & 30 + 6 & — & 0.40 \(\pm\) 0.08 & 10.26 \(\pm\) 3.16 & — & 11.0 \(\pm\) 20 & CTtrain/ & — & SSM-based \\ \[PERSON] [39] & 2009 & MICCA 2009 & 18 & 88.40 & — & 8.40 & — & — & CT & 15 min/CPU & SSM-based \\ \[PERSON] [40] & 2006 & In-house & 15 & — & — & — & — & — & CT & — & ASM-based \\ \hline [PERSON] [41] & 2015 & PDCCA & 40 & 88.13 \(\pm\) 5.55 & — & — & 2.83 \(\pm\) 1.18 & — & CT & 5 min/CPU & ASM-based \\ \hline [PERSON] [42] & 2009 & In-house & 106 & — & 0.50 \(\pm\) 0.10 & 6.20 \(\pm\) 2.30 & — & — & CBCT & — & ASM-based \\ \hline [PERSON] [43] & 2015 & PDCCA & 48 & 92.67 \(\pm\) 1.00 & — & — & 1.98 \(\pm\) 0.59 & — & CT & 30 min/CPU & AAM-based \\ \hline [PERSON] [44] & 2009 & MICCA 2009 & 18 &
\begin{tabular}{c} 76.10 \(\pm\) 5.10 \\ (reduce the 13 th case) \\ \end{tabular} & — & — & — & — & CT & 17 min/CPU & AAM-based \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of SSM-based, ASM-based and AAM-based methods.
## 5 Conclusions
### Limitations and Further Study Suggestions  
First, the current study collected data from only South Korean respondents. One suggestion is for future studies to focus on cross-cultural differences in order to overcome the limitation of generalization. Second, the respondents were potential consumers and this study used a structured questionnaire. Future research can consider an exploratory study by conducting qualitative methods in order to discover barriers to consumer behavior in addition to psychological and quality risks. It can also consider a study on the predictors of customer satisfaction and loyalty by collecting data from actual visitors. Third, this study only focused on PRT as a negative predictor of AT. Consumers tend to fear and mistrust new technology-based food products, and this notion is called food technophobia [58; 93]. Consumers with high food technophobia may perceive more risks and have more negative AT than customers with low food technophobia. It can also be hypothesized that this phobia strengthens the effect of risk perception. This study suggests that future research should consider this phobia in the new technology-based food industry context. Lastly, cultured meat technology is also regarded as innovative food tech in addition to smart farming. The Guardian [94] in fact reported that the world's first cultured meat restaurant emerged in Israel. Future research can plan a comparative study between ISFR and restaurants by using cultured meat.  
Conceptualization, [PERSON] and [PERSON]; methodology, [PERSON] and [PERSON]; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON] and [PERSON]; supervision, [PERSON] All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 4 Use Case Evaluation
### Semantic Reasoning with CYPHER Queries
#### 4.3.3 Co-Semantics Social Relationships  
To identify _co-semantics_ relationships between sensors, we use the _hasSemanticSimilarity_ property of the _Sensor_ nodes. If two sensors have a high enough _hasSemanticSimilarity_ value, they are considered to be _co-semantics_. Herein, we present an example of the Cypher query used to identify _co-semantics_ relationships:  
**MATCH** (s1: Sensor), (s2: Sensor)  
**WHERE** s1 <- s2 AND s1.hasOntology <-> s2.hasOntology AND s1.hasSemanticSimilarity > 0.8 AND s2.hasSemanticSimilarity > 0.8  
**CREATE** (s1)-[-CO_SEMANTICS]->(s2)  
This query matches pairs of _Sensor_ nodes (s1, s2) that are not identical and have different _hasOntology_ values, but have a _hasSemanticSimilarity_ value greater than 0.8. The query then creates a CO_SEMANTICS relationship between the two sensors. However, in the case that the sensor nodes have different ontologies, an ontology alignment process is required, as explained in the next section.
# Robustness of Double-Layer Group-Dependent Combat Network with Cascading Failure
## 4 Simulation Experiments on Robustness
### Experiment Results and Analysis
#### 4.2.6 Robustness to Different Parameter \(\delta\)  
Apart from the attack mode, tolerance limit of dependent failure, and parameters of load and capacity, the robustness of the DGCN of CSOS is also affected by the endurance parameter \(\delta\). Let the endurance parameter \(\delta_{\text{G}}\) of the functional net change in [0.1, 0.9] at an interval of 0.1, and the parameter \(\delta_{\text{W}}\) of the communication net also change according to this rule. Simulation experiments are carried out with different parameters for two different hierarchical networks, and the results are shown in Figure 13. As can be seen from the figure, for the functional net, the smaller the \(\delta_{\text{G}}\) is, the less robust the combat network is. With the increase of \(\delta_{\text{G}}\), that is, the endurance proportion of the overload state increases, the probability of failure in the overload state will decrease. Thus, the robust performance of the system will correspondingly improve. For the communication net, the robustness curves of the system almost completely coincide with the increase of \(\delta_{\text{W}}\), and the reason is also the same as that in Section 4.2.3.
## 3 QoS Correlation Analysis  
The initial step of the experiment was to determine the nature of actual internet traffic. In order to accomplish this, we collaborated with a UK network analytics firm Teragence UK [28] that was able to offer us a massive volume of real internet traffic data collected from the most popular UK internet service providers. This was made possible by partnership with network operators and through crowd-sourcing. This stage was intended to determine if open source network emulators, such as NetEm, could emulate real internet traffic.  
The data were gathered through active measurements involving the injection of probes into the network, which were then captured by the Teragence servers. These probes were extremely lightweight and did not interfere with the network's operation. The data were anonymised to protect identity and comply with the General Data Protection Regulation (GDPR) of the European Union. Four UK-based network operators' data were used for the correlation analysis of QoS metrics and to determine the shape of packet delay distributions in this study. For each operator, five million rows of data from January 2019, to May 2019, were evaluated.  
The empirical data contained a number of QoS indicators, of which the four most significant were utilised for the study. They were:  
* Signal Strength
* Average delay of Packets
* Packet Delay variation or Jitter
* Average packet loss ratio (PLR)
## 1 Introduction  
The degradation of rangeland in semi-arid and dry sub-humid regions due to climate change and/or human activity has long been an issue attracting public concern, especially in northwest China. By the end of 2011, the area of rangeland in China was 39.30 million km\({}^{2}\), accounting for 41.70% of its territory. However, more than 90% of China's rangeland is subject to various degrees of degradation [1], and rangeland degradation has become a serious issue that results in ecological and socio-economic ramifications. Accurate mapping and monitoring of rangeland degradation are essential for further assessing and combating the increasing problem [2]. Since the 1980s, remotely sensed data has gradually become the main data source of research in rangeland degradation, which can provide multi-temporal and various spatial scale information of rangeland degradation [3; 4; 5; 6; 7; 8; 9].  
The Linxia rangeland is located in the ecotone of farming-grazing, in northwest China. As the first renowned rangelands in the headstream of Yellow river, Linxia rangeland was once known for being a flourishing, prosperous, and productive ecosystem in the 1800s. However, since the 1980s, overgrazing and excessive cultivation gradually deteriorated its ecological sustainability, which led to adverse impacts to the middle and lower reaches of the Yellow River. To combat the worsening issue, the Chinese government implemented two protection policies, the \"Grain for Green\" program and a grazing ban, which has been in place since 2000, to convert cultivated farmland back to rangeland and promote the rehabilitation of rangeland vegetation in the ecotone of farming-grazing. Until recently, no study has been done to accurately detect and precisely monitor the dynamics of the Linxia rangeland. In the present study, a rangeland gradation system for the years of 1996, 2001, 2006, and 2011 was established based on multi-temporal Landsat images and field survey, and the Linear Spectral Mixture Analysis (LSMA) methods were selected to interpret the data from the four periods (1996, 2001, 2006, and 2011).
# Decentralized Robust Control of a Network of Inverter-Based Distributed Generation Systems
## 4 Simulation Results
### Robustness Analysis  
As mentioned in Section 3.1, the LC filter has uncertain parameters which are considered in the process of the robust control design. The variations of these parameters are in the range of \(\pm\)10%. Figure 10 demonstrates the time response of the sequentially-designed control system. It can be clearly seen that the voltage closely tracks the reference voltage in spite of the 10% parameter variations in the closed-loop system.  
Figure 8: The load voltage of inverter #4 (\(Inv_{4}\)) of the network of IBDGs depicted in Figure 5 when a 21 (kW) and 10 (kVAR) load is applied at 0.054 (s): (**a**) full scale and (**b**) zoomed scale.  
Figure 9: Harmonic contents of the load voltage of inverter #4 (\(Inv_{4}\)) in Figure 5 for the case of the distorted nonlinear load current.
# Topology Optimization Based Parametric Design of Balloon Borne Telescope's Primary Mirror
## 1 Introduction
The flexible structure was designed and tested to verify that the primary mirror assembly satisfies the surface shape requirements of \(\lambda\)/50 nm under various working conditions [13]. In addition, the topology and parametric-optimization-based lightweight design of a space reflective mirror were studied by [PERSON] et al. [14]. [PERSON] and [PERSON] used the topology optimization method to solve the axial support position sensitivity problem of the primary mirror. After topology optimization, not only was the sensitivity of the axial support position reduced, but the lightweight ratio and the fundamental frequency were also improved [15]. In recent years, a multi-objective topology optimization method based on the compromise programming method has been widely used by researchers. [PERSON] et. al optimized the steel wheel using the compromise programming method, which is employed to define the objectives of multi-objective and multi-stiffness topology optimizations; the optimized wheel decreased mass was reduced by 4.7% [16]. For [PERSON] et. al, on the basis of obtaining the weight coefficients, the compromise planning method was used to optimize the topology, and the stiffness and fundamental frequency of the optimized frame structure were significantly improved [17]. In this paper, the parametric design of the primary mirror using the compromise programming method based on topology optimization is presented. The multi-objective topology optimization design of the primary mirror of the shell element was made using Optistruct. Considering the continuous change of gravity in the observation, the weighted compliance of the primary mirror under different observation angles was taken as the objective to constrain its mass and fundamental frequency. The base structure approach and the manufacturing constraints were used to improve the manufacturability of the primary mirror. The results of the topology optimization provide a different material distribution for each stiffener (rib). Therefore, the ribs can be easily grouped based on the topology optimization results. To solve the conflicting goals for the RMS, the mass, and the fundamental frequency, the compromise programming method was used for parametric design to find a compromise solution and realize automatic parameterization update iteration with Isight to improve the parameterization efficiency and save in optimization time. The mass and RMSz of the primary mirror after parametric design were improved compared with those of the initial structure. Therefore, this optimization method can solve the contradictory multi-objective problem. The method can be applied to a balloon-borne telescope's primary mirror design in the future. The balloon-borne telescope mission is in the manufacturing phase and is expected to be launched in 2022.
## 3 Results and Discussion
### Artificial Neural Network  
Table 3 summarizes the results obtained with the pressure residuals of the water distribution network. The model's performance was awarded by the model's confusion matrix, accuracy report, receiver operating curve, precision, recall, and F1-score. The table shows that the ANN classifier performed well, with a leakage localization and detection accuracy of 0.61, recall = 0.61, F1-score = 0.61, and precision = 0.61%.  
Additionally, Figure 4 shows the model's performance in leak detection and localization across each zone. The figure indicates the model's performance in zone 1 and zone 2. For zone 1, the recall, precision, and F1-score results were 1.0, 0.61, and 0.76, respectively, while the zone 2 recall, precision, and F1-score results were 0.0, 0.0, and 0.0. When the artificial neural model is applied in the analysis of the data simulated from EPANET, the effect of uncertainty conditions corresponds to [\(-\)4,5] of the network, meaning that the demand uncertainty and noise level uncertainty also contributed in the overall performance of the classifier. The use of this classifier in leak localization across the zones is to provide possible leak locations within the water distribution network, which can be in several possible locations, and the magnitudes of such leaks are 30, 35, 40, 45 L/s for each node. Furthermore, the receiver operating curve (ROC) indicates the model's performance regarding leak detection and localization. Figure 5 shows the model's training history for accuracy, validation loss, model loss, validation accuracy, and training loss.  
Figure 4: Confusion table showing the detection and localization of leak events across the two leak zones using an ANN model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline Artificial neural network & 0.61 & 0.61 & 0.61 & 0.61 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Classification report for the artificial neural network model.