esa-sceva/satcom-chunk-collection huggingface dataset:
# An Improved CNN Model for Within-Project Software Defect Prediction
## 3 Approach
### Handling Class Imbalance  
Perfectly balanced datasets rarely exist in real life, so we had to face class imbalance problems when we trained the models for software defect prediction. Imbalance means that the number of buggy samples was not proportional to the number of clean samples. The class imbalance problem generally decreases the performance of classification models.  
Figure 7: Example of Java code for add calculation and its parsed abstract syntax tree (AST) tokens.  
We considered three strategies to solve the problem: (1) under-sampling, which means reducing the number of samples from the majority class; (2) over-sampling, which means increasing the number of samples from the minority class; and (3) a combination of over- and under-sampling, which aims to get more robust results. Because our inputs were categorical data, some powerful methods like SMOTE and its variations could not be used. Thus, we used random over-sampling for simplicity. During random over-sampling, existing buggy and clean files were duplicated until their ratio reached 50:50. Handling the class imbalance applied to the training set only.
## 5 Requirements of UWSNs
### Accessibility  
Each sensor node communicates to each other within a communication range located in the region. The communication range is another important requirement for UWSN which affect the density of nodes, deployment feasibility and the network cost of the targeted monitoring area. There are two communication modes for underwater sensor networks; Acoustic and Optical communication. Underwater acoustic wireless communications have been one of the most used technology as it is accessible and requires communication over great distances. However, acoustic waves still have many shortcomings including scattering, excessive delay because of the low propagation velocities, high attenuation, low bandwidth, and adverse effects on the underwater creatures. Recently, orbital angular momentum has developed as an alternative multiplexing freedom to encrypt data onto vortex beams for enhancing the capacity of acoustic communication [18]. Due to the limitations of acoustic communication, another approach is to use optical waves. According to [19], the current research on underwater optical communications focus on expanding the data rate and transmission range. Optical waves have the advantage of higher data rate, low latency, and energy efficiency at the expense of limited communication ranges.
# Analysis and Impact Evaluation of Missing Data Imputation in Day-ahead PV Generation Forecasting
## 2 Forecasting Model and Missing Data Imputation Methodology
### Data and Prediction Model
Our prediction model was trained by 9 input features and 1 output dataset corresponding to year 2016, and it was validated by 9 input features and 1 output dataset corresponding to 2017. As shown in Figure 2, the 9 inputs are: month, hour, temperature (\({}^{\circ}\)C), relative humidity (%), wind speed (m/s), wind direction (\({}^{\circ}\)), solar irradiance (W/m\({}^{2}\)), precipitation (mm), and cloud cover. In the study, the missing data imputation was performed for all lossy data, and PV generation was predicted by means of SVR. The detailed theory and SVR optimization process are presented in [10]. In addition, we used the regression learner app in MATLAB R2018a software to implement and test the model. In the regression learner app, the median Gaussian SVM was selected as it affords the highest accuracy among SVR models. The median Gaussian SVM algorithm was executed on a computer with an Intel(r) Core(tm) i7-7700 CPU clocked at 3.60-GHz with 16.0 GB of RAM. Figure 3 depicts our power prediction model. In Figure 3, LI, MI, KNN, and MICE denote the linear interpolation, mode imputation, k-nearest neighbors, and multivariate imputation by chain equation algorithms, respectively. A more detailed description of the four methodologies is provided in Section 2.2. Further, Figure 4 depicts the simulation process of the proposed model. Figure 4: Process of the simulation for PV forecasting model. Figure 3: Support vector regression (SVR)-based photovoltaic (PV) power prediction model utilizing missing data imputation. LI, linear interpolation; MI, mode imputation; MICE, multivariate imputation by chain equation. Figure 2: Time step of data and time horizon of the forecasting model.
# Behaviour Investigation of SMA-Equipped Bar Hysteretic Dampers Using Machine Learning Techniques
###### Abstract  
Most isolators have numerous displacements due to their low stiffness and damping properties. Accordingly, the supplementary damping systems have vital roles in damping enhancement and lower the isolation system displacement. Nevertheless, in many cases, even by utilising additional dampers in isolation systems, the occurrence of residual displacement is inevitable. To address this issue, in this study, a new smart type of bar hysteretic dampers equipped with shape memory alloy (SMA) bars with recentting features, as the supplementary damper, is introduced and investigated. In this regard, 630 numerical models of SMA-equipped bar hysteretic dampers (SMA-BHDs) were constructed based on experimental samples with different lengths, numbers, and cross sections of SMA bars. Furthermore, by utilising hysteresis curves and the corresponding ideal bilinear curves, the role of geometrical and mechanical parameters in the cyclic behaviour of SMA-BHDs was examined. Due to the deficiency of existing analytical models, proposed previously for steel bar hysteretic dampers (SBHDs), to estimate the first yield point displacement and post-yield stiffness ratio in SMA-BHDs accurately, new models were developed by the artificial neural network (ANN) and group method of data handling (GMDH) approaches. The results showed that, although the ANN models outperform GMDH ones, both ANN- and GMDH-based models can accurately estimate the linear and nonlinear behaviour of SMA-BHDs in pre- and post-yield parts with low errors and high accuracy and consistency.  
Keywords:shape memory alloy (SMA); SMA-equipped bar hysteretic dampers (SMA-BHDs); hysteresis curves; artificial neural network (ANN); group method of data handling (GMDH) +
Footnote †: journal: Applied Sciences
## 4 System modeling
### Partial least square regression (PLSR)
#### 4.1.5 \(k\)-nearest neighbors (\(k\)-NN)  
\(k\)-NN is a relatively straightforward pattern recognition method for sensor design [60, 61]. Assuming there are \(N\) samples belonging to \(C\) classes, in order to classify a new sample, \(k\)-NN needs to find \(k\) nearest neighbors of this new sample from the \(N\) samples based on distance or similarity. Let the \(k\) nearest samples be composed of \(k_{1}\) from class 1, \(k_{2}\) from class 2,..., and \(k_{\mathrm{C}}\) from class \(C\). Thus, the decision rule is:  
\[\mathbf{x}=\underset{j}{\arg\max}:k_{j}\ \ (j=1,2,...,C) \tag{7}\]  
The new sample belongs to the class that has the most neighbors of this sample. The key parameter for this method is \(k\). A larger value of \(k\) could reduce the influence of outlier or noise but could generate clouded class boundaries. Usually small \(k\) values (3 or 5) are preferred [6].  
The advantages of \(k\)-NN are (1) mathematical simplicity and (2) no need for statistical assumptions for sample distribution. However, \(k\)-NN may not work well when the sample numbers of each class are not comparable. In such cases, using weighted distance for clustering is a feasible choice. Meanwhile, considering the determination of \(k\) could be empirical and arbitrary, an adaptive \(k\)-NN was recently proposed to provide a robust categorization result [1], where the categorization of a new sample is not solely based on the number of its neighbors in each class. This adaptive k-NN approach presented a good application in detecting air pollutants with a sensor array [1].
# A Preprocessing Strategy for Denoising of Speech Data Based on Speech Segment Detection
## 4 Performance Evaluation
### Results and Discussion
#### 4.2.2 The Execution Time of Denoising
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \begin{tabular}{c} **Wavenet-Based** \\ **Denoising** \\ \end{tabular} & \begin{tabular}{c} **Energy-Based** \\ **Filtering** \\ \end{tabular} & \begin{tabular}{c} **Entropy-Based** \\ **Filtering** \\ \end{tabular} & \begin{tabular}{c} **Fuzzy-Based** \\ **Filtering with** \\ **Fuzzy Clustering** \\ \end{tabular} & \begin{tabular}{c} **Energy-Based** \\ **Filtering with** \\ **Fuzzy Clustering** \\ \end{tabular} & \begin{tabular}{c} **Entropy-Based** \\ **Filtering with** \\ **Fuzzy Clustering** \\ \end{tabular} \\ \hline \multicolumn{1}{c}{\begin{tabular}{c} Denoising Time \\ **(seconds)** \\ \end{tabular} } & 3867 & 2318 & 1904 & 1996 & 1938 & 2000 \\ \hline \multicolumn{1}{c}{\
\begin{tabular}{c} Signal Length \\ **(seconds)** \\ \end{tabular} } & 2072.04 & 1234.37 & 945.50 & 1092.16 & 1044.86 & 1094.29 \\ \hline \multicolumn{1}{c}{
\begin{tabular}{c} Denoising Time/ \\ Original Signal Length \\ \end{tabular} } & 1.866 & 1.12 & 0.919 & 0.963 & 0.935 & 0.965 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The execution time and length comparison between the proposed method and Wavenet-based denoising.
## 2 IIDG Model for FFC Injection
### Current- and Power-Based Concept  
Applying the standard, i.e., current-based concept, is the reference line current phasor \(\underline{I}_{\text{L1}}\) calculated as  
\[\underline{I}_{\text{L1}}=I_{\text{Re}}+jI_{\text{Im}}=I_{\text{n}}\Big{[}f_{ \text{Re}}^{+}(U^{+}/U_{\text{n}})\,I_{\text{ref,pu}}(t)+j\,f_{\text{Im}}^{+}(U ^{+}/U_{\text{n}})\,+j\,f_{\text{Im}}^{-}(U^{-}/U_{n})\Big{]}^{*} \tag{1}\]  
where \(I_{\text{ref,pu}}(t)\) refers to the time-dependent normalized reference current, \(J_{\text{n}}\) is the IIDG's nominal line current, and \((\cdot)^{*}\) stands for the complex conjugate. Note that this concept complies with the requirements given in [2]. Figure 3 shows the functional scheme for the current-based concept, where \(\underline{U}_{L1,2,3}\) denote line voltage phasors at the IIDG's connection point, \(\underline{U}^{+}/\underline{U}^{-}\) denote positive and negative sequence voltage phasors, while \(I_{\text{Re}}^{+}/I_{\text{Im}}^{+}\), and \(I_{\text{Im}}^{-}\) are real and imaginary parts of the positive and negative sequence current phasors. Furthermore, the magnitude of the line current phasors is limited as \(I_{L1,2,3}\leq 1.2I_{\text{n}}\). Moreover, the voltage at the connection point is a nonlinear function of operating conditions and reference currents, which are also a function of voltages at the same connection point. A delay is introduced in the calculation by a first-order system with a time constant of 1 ms to break the algebraic loop.  
The limitation of the current-based concept is that the generated power will depend on actual line voltages at the connection point. Therefore, active and reactive power are introduced using the same voltage characteristics instead of currents. Thus, applying the proposed, i.e., power-based concept is the reference line current phasor \(\underline{I}_{\text{L1}}\) calculated as  
\[\underline{S}=P+jQ=P_{\text{n}}\Big{[}f_{\text{Re}}^{+}(U^{+}/U_{\text{n}})P_{ \text{ref,pu}}(t)+j\,f_{\text{Im}}^{+}(U^{+}/U_{\text{n}})\,+j\,f_{\text{Im}}^ {-}(U^{-}/U_{\text{n}})\Big{]} \tag{2}\]  
Figure 3: Functional scheme for calculating IIDG’s reference line current phasors—current-based concept.  
Figure 2: Voltage characteristics for positive and negative sequence components.  
\[\underline{L}_{L1}=\frac{2}{3}\left(\frac{\underline{S}}{\underline{U}^{+}}\right)^ {*} \tag{3}\]  
where \(P_{\text{ref,pu}}(t)\) refers to the time-dependent normalized reference power, \(P_{\text{n}}\) is the IIDG's nominal capacity, and \((\cdot)^{*}\) stands for the complex conjugate. Furthermore, the functional scheme for the reference phasor currents calculation (Figure 3) should be modified according to the power-based concept (2) and (3). Note that this concept also complies with the requirements given in [2]; however, its behavior is more definite than the current-based.
###### Abstract  
The selection of grouting methods and parameters significantly affects the improvement in the pile-bearing capacity of cast-in-place bored piles. This study proposes a comprehensive set of test methods for constructing model piles, performing grouting at the pile tip and pile side. A series of single-pile grouting and static load tests were conducted using these test methods. The results reveal that pile-side grouting is more effective in controlling pile settlement compared to tip grouting. Furthermore, tip-side-combined grouting exhibits superior reinforcement effects compared to the other two grouting methods. After grouting, a grout bubble is formed at the outlet, consisting of a compact diffusion zone internally and a split diffusion zone externally. Additionally, a vertical diffusion of grout occurs along the pile body, establishing a lateral friction resistance enhancement region. Within this region, the lateral friction resistance of the pile shows a negative correlation with the distance from the grouting outlet. The test results emphasize the significance of grouting volume and its impact on the bearing capacity, settlement control, lateral friction resistance, and grout bubble size in grouted piles, while the influence of variation in grouting pressure in a small range on bearing characteristics is not significantly apparent.  
cast-in-place pile; post-grouting; grouting volume; grouting pressure; grout diffusion range +
Footnote †: journal: Applied  
Article  
Experimental Study on Post-Grouting Pile Vertical Bearing Performance Considering Different Grouting Methods and Parameters in Cohesive Soil
## 2 Study Area and Data Preparation
### Gauge-Based Weather Data  
Even though flood and drought hazards prevail in the basin, the local flood/drought management system remains unsatisfactory, particularly for precipitation observations [(43)]. As shown in Figure 1 and Table 1, merely five weather stations are sparsely distributed in such an extensive basin with a controlled area of 110,350 km\({}^{2}\). The daily precipitation data at these five stations from 1 April 2014 to 31 December 2015 were collected from DMH. The daily maximum and minimum air temperature data at these stations for the same period were also obtained. These weather stations collect daily data from 03:00 UTC (Coordinated Universal Time) of the current day to 03:00 UTC of the next day, and the daily precipitation and air temperature data at all the stations are complete from 1 April 2014 to 31 December 2015, with no data missing. These five weather stations are not included in the Global Precipitation Climatology Centre (GPCC) gridded gauge-analysis precipitation data set.  
To perform streamflow simulations using a distributed hydrological model, nearest neighbor method was used to interpolate the gauge-based precipitation and air temperature data to the 0.25\({}^{\circ}\) spatial scale. The topographical effects were disregarded in the precipitation interpolation, whereas the near-surface air temperature was assumed to decrease by 0.65 \({}^{\circ}\)C per altitude rise of 100 m.
# A Cost-Optimized Model for Dynamic Port Hinterland
## 3 Methodology  
### Initial Hypotheses for Hinterland Calculation  
The proposed methodology for calculating port hinterland addresses the growing need for economic optimization of the supply chain [1]. This approach reflects the evolution from static geographical perspectives to dynamic, economically-oriented hinterland concepts [73].  
This methodology adopts the perspective of cargo owners and logistics route decision-makers rather than traditional transport company-centered approaches. This alignment with current supply chain management trends emphasizes cost optimization as a critical factor for business competitiveness [74].  
The calculations focus on variables directly affecting cargo owners and logistics managers, such as fuel costs, Emissions Trading Systems, and stevedoring costs, enabling more accurate cost evaluation [75]. This framework provides a comprehensive representation of factors influencing logistics decisions.  
The methodology must adapt to evolving market conditions and technological innovations. The growing importance of sustainability requires consideration of additional factors, including carbon footprint costs and environmental regulations compliance [76]. Recent advances in data analytics and artificial intelligence offer opportunities for refining cost optimization through machine learning algorithms [66].  
Recent global disruptions, such as the Suez Canal events in 2021 and 2024, highlight the need for hinterland models that optimize both cost and resilience. Incorporating insights from viable supply chain network research could enhance future iterations of this methodology [64].
## 4 Discussion and Conclusions  
Given the importance of user-centered design, it is imperative for students to learn how to identify user needs and turn these needs into the catalyst for creative solutions [1]. Participatory design can produce products that meet user needs by allowing students and users to work together [2; 3; 4; 5; 7]. However, it is difficult for users to participate in design education at a teaching site [5]. The present study  
Figure 6: One of the solutions generated from the student group for a female self-defense product.  
developed a userbot system based on real user information and AI technology. In this study, students talked to AI userbots through oral real-time dialogs to explore real female users' product preferences, use experience, and needs for self-defense products, and eventually the students collaborated with the userbots to generate design solutions. Based on the development of the system and the verification process, the conclusions of the present study can be summarized as follows:  
1. Using AI userbots to represent real-world users.  
The userbots' knowledge is based on real users' experiences. The information, collected through persona design and interviews, is all included in the database, and Dialogflow's algorithm allows the system to automatically accumulate new information during the conversation and correct the content of the responses, making the responses of the userbots more complete [26].
2. Combining intent and flow-based modes to increase the fluency of dialog [14; 15; 16].  
For a chatbot, the fluency of dialog is an important indicator for user experience satisfaction. Dialogflow's intent-based algorithm can accurately match students' speech to a high-confidence-level intent to generate a correct response from the userbot [26]. When there was no agreement intent, the flow-based dialog process could continue dialog without being interrupted.
3. Using persona design and interviews to construct the initial database of the userbots.  
Interviewing real users and making personas ensures the userbots' responses about products are consistent with those of real users. When different real users have different views on the same product, multiple userbots can be included in the system to provide students multiple and complete real information.
4. This system has expandability of the design project.  
The present study used the project of female self-defense product design to develop AI userbots in the system. Userbots can also be built for various design projects through the same process described in this study. When userbots with knowledge of the various uses of products are built into the system, the common characteristics, dialogs, and responses of the userbots can support other userbots through AI technology to increase the accuracy and completeness of dialogs between userbots and students. We expect to further establish AI userbots and develop product design projects for users whom students are not familiar with, need to pay more attention to, and have to collaborate with, such as pregnant women, elderly or disabled people.
## 5 Summary and Outlook
### Advantages and Disadvantages of the Sensor in Terms of Performance Indicators
For instance, the microring cascading MZI structure and the double microring cascading structure discussed in this study are potential designs to consider. To enhance the sensing sensitivity of the sensor and widen the detectable range, a larger \(FSR\), lower loss, high extinction ratio, and broader bandwidth are essential. Upon analyzing the loss of the optical waveguide, it becomes evident that a smaller bending radius is necessary to achieve a larger \(FSR\), while a larger bending radius is required for a lower loss. Therefore, to minimize bending loss, it is necessary to optimize the confinement of the optical field with a narrower bending radius. Additionally, the performance of the sensor can be improvedby incorporating multi-ring cascading. As a result, the waveguide can be structurally engineered to achieve this goal. It is evident that different types of optical waveguide sensors have varying performance indicators when detecting and measuring small concentrations of specific chemicals, viruses, or bacteria. These performance indicators are influenced by factors such as sensor preparation, detection substance properties, and sample processing, so it is necessary to carry out targeted design and optimization according to specific ap-plication requirements.
# Compositional Analysis and Aroma Evaluation of Feijoa Essential Oils from New Zealand
## 3 Materials and Methods
### Compositional Analysis of Feijoa Essential Oil  
The isolated essential oils were further dissolved in hexane and subjected to compositional analysis by gas chromatograph-mass spectrometry (GC-MS) (Shimadzu GCMS-QP2010 Plus, Kyoto, Japan). Briefly, 10 \(\upmu\)L of pure essential oil was dissolved in 1 mL of hexane and a further 10 \(\upmu\)L of 2-methyl-3-heptanone (312 ug/mL) was added as the internal standard. A DB-5 MS column (30 m \(\times\) 0.25 mm i.d., 0.25 \(\upmu\)m film thickness (Agilent technologies, Inc., Santa Clara, CA, USA)) was employed. Helium (>99.99%) was used as the carrier gas at a flow rate of 1.5 mL/min. The injector temperature was 280 \({}^{\circ}\)C using a splitless injection mode with a sampling time of 1.00 min. The oven program was set as follows: Temperatures and times of 35 \({}^{\circ}\)C (0 min), 3 \({}^{\circ}\)C/min to 150 \({}^{\circ}\)C (5 min), 5 \({}^{\circ}\)C/min to 230 \({}^{\circ}\)C (5 min), and 20 \({}^{\circ}\)C/min to 280 \({}^{\circ}\)C (5 min). The electron-impact mass spectra were generated at 70 eV, with a scan range from 41 to 500 m/z, the ion source temperature was 200 \({}^{\circ}\)C, and the MS interface temperature was 250 \({}^{\circ}\)C.  
The identification of the volatile compounds in feijoa essential oil was firstly conducted by the comparison of MS with the NIST Mass Spectral Library, through the Unknown Analysis software (Agilent Technologies, Inc, Santa Clara, CA, USA). These compounds were further confirmed with available reference analytical standards and literature records, as well as by comparing the linear retention index (LRI), calculated according to [PERSON] and [PERSON] [45], with that which was recorded in the NIST webbook ([[http://webbook.nist.gov/chemistry/](http://webbook.nist.gov/chemistry/)]([http://webbook.nist.gov/chemistry/](http://webbook.nist.gov/chemistry/))) [46]. A semi-quantification of essential oil constituents was conducted according to [PERSON] et al. [30], relative to the internal standard 2-methyl-3-heptanone.