esa-sceva/satcom-chunk-collection huggingface dataset:
# Bi-LSTM-Based Neural Source Code Summarization
## 3 Related Works and Motivations
### Motivation  
Java code summarization techniques showed various performances depending on the size of the dataset, the type of encoder input, the number of encoders, and the encoder-decoder implementation. A comparison between the comment generation techniques for Java code is provided in Table 1.  
It was noted that the LSTM with a single AST input in [15] outperformed an RNN with both code and AST inputs on the same dataset in [27]. Additionally, the GRU neural networks [13] with both code and AST inputs outperformed the RNN with two inputs in a small dataset [27]. On the other side [29], AST increased the BLEU score in a transformer model with a single encoder and slightly decreased the BLEU score in a double encoder architecture [30]. The main disadvantage of the transformer models is their inefficiency in processing long sequences [32].  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Related Work** & **Year** & **Input Information** & **Neural Network** &
\begin{tabular}{l} **Encoders** \\ **Number** \\ \end{tabular} & **Dataset Size** & **Result (BLEU)** \\ \hline
[23] & 2018 & AST & LSTM & 1 & 69,708 & 38.17\% \\ \hline
[26] & 2019 & AST & LSTM & 1 & 588,108 & 20.4\% \\ \hline
[13] & \multirow{2}{*}{2019} & Code + AST & GRU & 2 & \multirow{2}{*}{2.1 millions} & 19.6\% \\ ast-attendgru & & Code & GRU & 1 & & 19.4\% \\ \hline
[27] & 2020 & Code + AST & RNN and tree-RNN & 2 & Java 588,108 & 17.04\% \\ \hline
[29] & \multirow{2}{*}{2021} & Code & Transformer-based & 1 & \multirow{2}{*}{Java 588,108} & 43.801\% \\ ComFormer & & Code + AST & Encoder–Decoder & 1 & & 48.437\% \\ \hline
[30] & \multirow{2}{*}{2021} & Code & \multirow{2}{*}{2021} & 1 & \multirow{2}{*}{Java 137,007} & 5.27\% \\ API2 Com & & Code + AST & Transformer-based & 2 & & 5.26\% \\  & & Code + API & Encoder–Decoder & 2 & & 5.28\% \\  & & Code + AST + API & & 3 & & 5.26\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of Java code comment generation methods.  
In addition, in [16], Python code comment generation with a Bi-LSTM encoder-decoder showed an improvement over an LSTM-only encoder-decoder proposed in [15]. Bi-LSTM may extend the LSTM capabilities by training the input in forward and backward directions.  
In this work, we study the effect of the addition of the AST information to the code information on the performance of a Bi-LSTM double encoder structure for Java code comment generation, and we compare it to the GRU model in [13].
## 1 Introduction
While the OGC rightly focuses on issues of abstract interoperability and interface specifications [30], actual implementations of metadata-based specifications are invariably predicated on the assumption that metadata instances will be forthcoming (e.g. United Nations Food and Agriculture Organisation's GeoNetwork). For initiatives that explicitly address metadata generation using browser and desktop-based metadata editors (e.g., Gigateway, Go-Geo! ), assistance rarely exceeds guiding manual metadatacompletion and consequently does little to surmount perceptions of a task as being tedious and of low priority [31, 32]. Some proprietary packages (e.g., ESRI's ArcGIS, Cadcorp's SIS) do incorporate metadata management applications capable of generating rudimentary element sets for given geospatial resources, although these invariably demand significant human input to meet compliance with even the most concise of standards. It is this need for human input that contributes towards a'metadata bottleneck' [33] that hinders documentation, whether for resource discovery, management or exploitation. Attempts to address this impedance have to date largely fallen within two camps. Those of the first strive to motivate and mobilise metadata authors (education drives and workshop events) [4, 12, 34]; those of the second assume mobilisation, and focus on steering metadata completion (metadata profiles, guidelines and editors). While the role both strategies play in encouraging data documenting practices can be significant, their impact in diminishing the burden of metadata authorship remains open to question. This paper proposes a method to mitigate this burden through the automation of metadata authoring process steps. Whether from the perspective of cataloguing internal digital holdings, exposing marketable datasets online via metadata services or fulfilling legal requirements by contributing to national spatial data infrastructures (NSDI), the implications of metadata automation are several. By providing computational support to metadata authors, costs of generation can be lessened whilst rendering the prospect of creation less daunting for those yet to undertake it. Such support also reduces the opportunity for human error during metadata record completion; it enables more records to be produced with equivalent effort; and it facilitates easier update of existing metadata following changes to their underlying data. Further, authoring resources can as a result be released for application elsewhere, whether for more intellectually challenging documentation tasks (e.g. quality control, descriptive metadata) or otherwise. It is thus seen to play an important role in efforts to diminish the stigma associated with metadata and its creation, a critical consideration if documentation practices are to be more widely promoted.
## 4 Model Data
### Model Summary and Statistical Analysis  
The variables selected in each step analyzed in the different layers are displayed in Table 2. This first approach presents an incremental DEA multistage model whose main purpose is the measurement of efficiency at different layers from a strategic perspective. The model is created from a resources and capabilities view. The starting point of this model is that the network development and its impact on organizations modify the way airlines manage their services. Several factors linked to the analysis of the types of network defined in the previous sections have been selected as efficiency components in order to corroborate this starting point. On the one hand, operational networks as physical resources are studied; on the other, the incorporation of social media networks and their relationships with the customer have also been evaluated. Social media estimates must be understood as intangible components that are measured in different intermediate homogeneous steps, thereby analyzing their influence on the airline sector efficiency.  
Table 3 shows all the quantitative variables used and its descriptive statistics for the 43 European airlines analyzed.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Business Management**} \\ \hline  & **Average** & **Total** & **Std. Deven.** & **Range** \\ \hline Number Employees & 11,825.95 & 508,516.00 & 25,382.25 & 117,889.00 \\ Total assets & 13,702,935.81 & 589,226,239.77 & 44,264,015.18 & 208,781,412.00 \\ Sales & 114,455.45 & 4,921,584.31 & 296,460.59 & 1,388,115.46 \\ Destinations & 101.49 & 4364.00 & 74.15 & 299.00 \\ Millons passengers’ 14 & 21.19 & 911.06 & 27.55 & 104.80 \\ \hline \multicolumn{4}{c}{**Network Management**} \\ \hline  & **Average** & **Total** & **Std. Deven.** & **Range** \\ \hline Degree & 964.86 & 41,489.00 & 509.18 & 2075.00 \\ Eigencentrality & 0.17 & 7.19 & 0.23 & 1.00 \\ \hline \multicolumn{4}{c}{**Online Social Network Management**} \\ \hline
**Twitter** & **Average** & **Total** & **Std. Deven.** & **Range** \\ \hline Likes & 1176.12 & 50.573.30 & 1719.78 & 7302.00 \\ Tweets/Days & 35.32 & 1518.95 & 59.08 & 248.71 \\
**Facebook** & **Average** & **Total** & **Std. Deven.** & **Range** \\ \hline Likes & 22,385.59 & 962,580.30 & 41,101.47 & 226,596.00 \\ Publication/days & 1.68 & 72.13 & 2.37 & 14.86 \\
**YouTube** & **Average** & **Total** & **Std. Deven.** & **Range** \\ \hline Number of Videos & 162.68 & 6995.30 & 336.93 & 2149.00 \\ Views & 18,914,748.87 & 813,334,201.30 & 80.961,888.12 & 527,191,826.00 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Statistical summary of the variables in different DEA layer models.
# Comparison of Machine Learning Methods to Up-Scale Gross Primary Production
## Materials and Methods
### Methods  
The flowchart of up-scaling GPP using machine learning method is shown in Figure 2. Firstly, the GPP from eddy covariance systems were up-scaled based on the MODIS NDVI, land-cover types, FVC and the meteorological datasets using five machine learning methods (Cubist regression tree, RF, SVM, ANN, DBN). A footprint source area model (FSAM) [64; 65; 66] was adopted to obtain the footprint of GPP at flux tower sites used in this study. Then the up-scaled GPP were validated at in situ scale, at footprint scale and at regional scale. At last, up-scaling GPP using different machine learning methods were compared, sensitiveness of the input data to the up-scaled GPP was analyzed, and the uncertainty of the methods was discussed.
# Feature Interaction in Terms of Prediction Performance
## 5 Conclusions  
This paper focused on two-way feature interaction. Although N-way feature interaction can be measured, it is relatively complex to interpret. The term \"interaction\" involves various aspects, which necessitates further research in feature interaction to identify new techniques for the interpretation  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Proposed** & **[PERSON] [8]** & **[PERSON] [17]** \\ \hline R package & (self-implement) & vip [23] & iml [19] \\ Measure of interaction & prediction performance & partial dependency & H-statistics \\ Negative interaction & yes & no & no \\ Meaning of interaction & increases or decreases & influences variable & influences partial \\  & prediction performance & importance & dependency \\ Time complexity & \(O(N)\) & \(O(N^{2})\) & \(O(2N^{2})\) \\
[0,1] standardization & no & no & yes \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of proposed method and previous works.  
of black box learning models. The feature interaction measure proposed in this study is simple to understand and can be quickly calculated. The proposed method can measure influences between features in a prediction model as a point of model performance (accuracy or error). It can capture negative interaction, whereas previous works captured only positive interaction. The proposed method is useful to understand the role of a feature and interaction between features in a prediction model. Furthermore, it may be used to improve model performance. For example, if a feature has low importance and a high negative interaction, we may consider removing it from the prediction model. This is a topic for further research.  
This work was supported by the ICT & RND program of MIST/IITP [2018-0-00242, Development of AI ophthalmologic diagnosis and smart treatment platform based on big data].  
The author declares no conflict of interest.
# Comparative Study of Energy Efficient Routing Techniques in Wireless Sensor Networks
## 3 The Proposed Taxonomy
### Latency Aware and Energy-Efficient Routing
#### 3.9.1 Cluster-Based  
The hierarchical scheme of cluster-based protocols aims to balance the energy efficiency and delay metrics in order to enhance the network lifespan. As explained earlier in this survey, several routing protocols have been proposed and have attempted, in different ways, to achieve energy efficiency in WSNs.
## 2 Encryption Principle
### Performance Analysis  
In order to verify the performance of elliptic curve encryption, this paper uses MATLAB for simulation verification, mainly considering the influence of the plaintext length and key \(k\) value on the cost of encryption time, as shown in Figures 4 and 5.  
Considering the influence of the plaintext length on encryption performance, a plaintext with a fixed length of L = 10, 100, 1000 is selected for elliptic curve encryption and decryption. L indicates the number of characters from 0 to 9. A total of 10 characters are in a group. For example, if L = 100, it is 10 groups of digits from 0 to 9. As shown in Figure 4, the time spent for elliptic curve encryption and decryption increases with the increase in equal intervals of key values. Therefore, for a plaintext with a large amount of data, a smaller \(k\) value should be selected to save time.  
On the basis of Figure 4, considering the influence of the plaintext length and key \(k\) value on encryption performance, 10 plaintexts of different lengths and 4 different \(k\) values  
Figure 4: Relationship between time cost and key \(k\) value change.  
Figure 5: Relationship between plaintext and time cost.  
are selected for encryption and decryption, as shown in Figure 5. With the same \(k\) value, the time cost of elliptic curve encryption and decryption is positively correlated with the plaintext length as the plaintext length increases. Similarly, when the plaintext length is the same, the larger the key \(k\) value, the more time it takes. The difference between the four curves with different \(k\) values is not large, which further indicates that the \(k\) value has little influence on the time cost of encryption and decryption, while the plaintext length has great influence on it.  
The results show that when the elliptic curve is used for encryption, it is necessary to consider the influence of the plaintext length and \(k\) value, and select the appropriate value to obtain better encryption performance and save time.
# Model for Wall Shear Stress from Obliquely Imping Planar Underexpanded Jets
## 2 Methodology
The analysis is performed using non-dimensional parameters, such as the normalized maximum wall shear stress is defined as, \(\tau_{max}^{*}=\frac{\tau_{max}}{\frac{1}{2\rho_{o}U_{o}^{2}}}\), where \(\rho_{o}\) and \(U_{o}\) are the density and velocity at the nozzle exit, respectively. The objective of this work is to develop a set of equations for the prediction of the maximum normalized wall shear stress as a function of the following non-dimensional parameters: jet height to width ratio--\(\frac{H}{W}\), the nozzle pressure ratio--\(NPR\), the jet Reynolds number--\(Re=\frac{U_{o}W}{\
u_{o}}\), and the jet impingement angle--\(\theta\), a naught subscript describes the properties at the nozzle exit.
## 4 Case Studies
### Case Study 2: Insufficient Source Domain Label Transfer Experiment
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Method**}} & \multicolumn{3}{c}{**DAN**} & \multicolumn{3}{c}{**Deep CORAL**} & \multicolumn{3}{c}{**DANN**} & \multicolumn{3}{c}{**Proposed**} \\
**proportion** & **1/2** & **1/4** & **1/8** & **1/16** & **1/2** & **1/4** & **1/8** & **1/16** & **1/2** & **1/4** & **1/8** & **1/16** & **1/2** & **1/4** & **1/8** & **1/16** \\ \hline
0\(\rightarrow\)1 & 98.92 & 97.58 & 94.68 & 91.12 & 98.77 & 97.63 & 93.95 & 92.87 & 99.54 & 98.87 & 97.16 & 97.02 & **99.80** & **99.12** & **98.06** & **97.23** \\
1\(\rightarrow\)2 & 97.20 & 96.47 & 94.89 & 94.78 & 97.88 & 96.83 & 95.85 & 93.40 & 98.72 & 97.96 & 96.11 & 95.86 & **99.28** & **99.07** & **97.73** & **97.19** \\
2\(\rightarrow\)3 & 98.46 & 95.89 & 93.94 & 93.17 & 97.07 & 97.14 & 94.23 & 93.00 & 99.21 & 98.59 & 96.72 & 94.93 & 99.64 & 93.91 & 97.66 & 96.54 \\
2\(\rightarrow\)1 & 95.90 & 94.36 & 93.08 & 92.84 & 95.30 & 94.36 & 92.17 & 92.05 & 98.58 & 97.78 & 95.62 & 94.22 & **99.70** & **98.69** & **96.44** & **96.10** \\
3\(\rightarrow\)2 & 98.12 & 97.37 & 96.63 & 95.90 & 98.31 & 96.87 & 96.05 & 94.71 & 98.95 & 98.22 & 97.62 & 96.41 & **99.72** & **98.98** & **98.79** & **97.38** \\ Average & 97.72 & 96.33 & 94.64 & 93.56 & 97.59 & 96.57 & 94.45 & 93.21 & 99.00 & 98.28 & 96.65 & 95.57 & **99.63** & **99.03** & **97.74** & **96.89** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Average testing accuracies in the fault detection of case study 2.  
hand, the approach proposed in this study projects all types of labels perfectly to the same area, demonstrating significant clustering and separability. This is sufficient to show that the proposed networks can perform cross-domain operation states detection reliably in the case of less available label data.
# Calculation Method for Settlement Deformation of Existing Tunnel Induced by Underpass Construction
## 3 Calculation of Additional Stresses in Tunnel Undercutting
### Additional Stress Caused by Shield Shell Friction  
As seen in Figure 4, set the shield machine's length to \(L\) and the excavation surface's coordinates to \((0,0,h)\). The integral transformation equations are \(ds=Rd\theta dx_{0}\), \(y=R\cos\theta\) and \(z=h-R\sin\theta\), which are obtained by integrating along the cylindrical plane for the microelement coordinates \((-x_{0},R\cos\theta,h-R\sin\theta)\) of the shield surface:  
\[\begin{split}&\sigma_{f}=-\int\limits_{-L}^{0}\int\limits_{0}^{2 \pi}\frac{f(x-x_{0})}{8\pi(1-\
u)}\left[-\frac{1-2\
u}{R_{6}^{3}}+\frac{1-2\
u }{R_{5}^{3}}-\frac{3(3-4\
u)(z+h-R\sin\theta)^{2}}{R_{6}^{5}}-\frac{3(z-h+R\sin \theta)^{2}}{R_{5}^{5}}+\right.\\ &\left.\frac{6(h-R\sin\theta)}{R_{6}^{5}}(h-R\sin\theta+(1-2\
u) (z+h-R\sin\theta)+\left.\frac{5z(z+h-R\sin\theta)^{2}}{R_{6}^{2}}\right)\right] Rd\theta dx_{0}\end{split} \tag{18}\]  
Among them:  
\[R_{5}=\sqrt{\left(x-x_{0}\right)^{2}+\left(y-R\cos\theta\right)^{2}+\left(z-h+ R\sin\theta\right)^{2}}\]  
\[R_{6}=\sqrt{\left(x-x_{0}\right)^{2}+\left(y-R\cos\theta\right)^{2}+\left(z+h- R\sin\theta\right)^{2}}\]  
where: \(\sigma_{f}\) is the additional stress caused by shield shell friction; \(f\) is the friction; \(L\) is the shield's length.  
The following simple equation is used to compute the shield shell friction [25]:  
\[f=K\left(\frac{W}{\pi D}+\frac{f_{\
u}+f_{h}}{2}\right) \tag{19}\]  
\[f_{\
u}=\gamma h \tag{20}\]  
Figure 4: Shield friction calculation schematic diagram.  
Figure 3: Schematic diagram for the cutter plate’s increased thrust calculation.  
\[f_{h}=\gamma(h+D/2)\tan^{2}(\frac{\pi}{4}-\frac{\varphi}{2}) \tag{21}\]  
where: \(r\) is the soil's weight; \(\varphi\) is the friction angle within the soil; \(W\) is the shield machine's self-weight per unit length; and \(K\) is the friction factor between the shield shell and the soil.
## 1 Introduction
Due to these favorable properties, PEEK-based materials have been used in the prosthodonic field in dental implants, provisional abutments, and implant-supported restorations, fixed dental prostheses and removable denture frameworks [27]. The clinical success of an RPD depends on a correct fit accuracy of the prosthetic framework to the oral structures, ensuring the necessary support, stability, and retention [3; 7; 21; 28]. A correct fitting to the oral structures prevents not only deformation and fracture of the RPD but also patient discomfort, oral mucosa injuries, wear and teeth mobility or loss [3; 4; 6]. The space between the internal surface of the prosthesis and the supporting tissues represents the misfitting of the denture from a clinical point of view [29]. The misfitting of a prosthetic framework and the supporting tissues has been classified by some authors based on the following range values: intimate contact (absence of space or presence of a space between 0 and 50 \(\upmu\)m); clinically acceptable space (space between 50 and 311 \(\upmu\)m) [7; 30]. There are qualitative and quantitative methods to evaluate the fit accuracy of RPD frameworks [15; 17; 21]. While the former is faster and more accessible, the latter requires more complex procedures [31]. The assessment methods can also be distinguished based on the type of tools used and the data collection technique. Thus, analog methods include (i) direct measurement using calibrated instruments (e.g., orthodontic arch wires or endodontic pluggers) [7; 32; 33] and (ii) indirect methods that involve the use of elastomeric materials (e.g., silicone) to fill in the putative gap [3; 29; 31]. Several authors have described the fit accuracy assessment of RPD frameworks [3; 7; 11; 15; 29; 34; 35]. Some studies compare the fit accuracy of PEEK frameworks manufactured using different CAD-CAM techniques [21; 36], while others compare PEEK with Co-Cr frameworks obtained via conventional methods [37]. However, studies comparing the fit accuracy of PEEK and Co-Cr frameworks manufactured with digital techniques are limited and scarce [11]. In line with these findings, this in vitro pilot study aimed to evaluate Co-Cr and PEEK frameworks' fit accuracy, produced using two digital techniques (additive and milling), in [PERSON]'s Class I and III edentulous arches. Our research question was as follows: Are there fit accuracy differences between SLM-printed Co-Cr vs. milled PEEK frameworks in both [PERSON]'s Classes I and III?
## 2 Combined Seal Structure and Material Parameters
### Material Parameters  
Rubber is a typical nonlinear material and exhibits complex properties that require suitable constitutive models for accurate description. Numerous scholars have conducted experimental research to establish constitutive models to capture the nonlinear behavior of rubber materials. Some commonly used rubber material constitutive models [21; 22; 23] include the Neo-Hookean model, [PERSON] model, Mooney-Rivlin model, Yeoh model, and Ogden model. Among these models, the Mooney-Rivlin (M-R) model stands out for its ability to accurately describe the real properties of rubber materials, which has been extensively validated through numerous experiments [24]. The M-R constitutive model demonstrates high accuracy under small and medium strain, making it particularly suitable for stress levels below 30% (compression). Considering that this study focuses on an O-ring with a maximum compressive strain of 25%, the three-parameter M-R model fits well with the compressive stress-strain curve. Therefore, the three-parameter M-R model is adopted, with its strain energy density function represented as follows [25]:  
\[\text{W}=\text{C}_{10}\left(\text{I}_{1}-3\right)+\text{C}_{01}\left(\text{I}_ {2}-3\right)+\text{C}_{11}\left(\text{I}_{1}-3\right)^{2}\]  
where W is the strain energy density, I\({}_{1}\) and I\({}_{2}\) are the first and second strain tensor invariants, and C\({}_{10}\), C\({}_{20}\), and C\({}_{11}\) are M-R coefficients.  
In this study, EPDM rubber was chosen as the material for the O-ring. Uniaxial compression tests were conducted on the EPDM material using an electronic universal testing machine, as shown in Figure 3. The tests were carried out at three different temperatures: 15 \({}^{\circ}\)C, \(-60\)\({}^{\circ}\)C, and 130 \({}^{\circ}\)C. During the experiments, the EPDM sample was placed in the electronic universal testing machine, and it was compressed to 25% of the maximum strain at a speed of 10 mm/min. Subsequently, the specimen was rebounded at the same speed. This compression and rebound process was repeated four times for each sample at the specified temperatures. The stress-strain experimental data at the low temperature, normal temperature, and high temperature were obtained through repeated compression and rebound tests, as shown in Figure 4. The fitting curves of the M-R constitutive models at the three temperatures are shown in Figure 5, and the material parameters of the EPDM materials at the three temperatures are shown in Table 2. In addition to the O-ring, the material parameters of the combined sealing structure are shown in Table 3[26; 27].  
Figure 3: Electronic universal testing machine.