esa-sceva/satcom-chunk-collection huggingface dataset:
[14] showed that the thickness of the composite more significantly affects the hole quality compared to the speed or feed parameters. [PERSON] et al. [15] investigated the effect of the tool material in the drilling process. [PERSON] et al. [16] showed that the composite type had more impact on the thrust force during the drilling process in comparison to the feed rate and rotational speed parameters. They reported that the cutting speed did not significantly affect the thrust force. [PERSON] et al. [17] used an ultrasonic vibration device during the drilling process to reduce the thrust force and delamination factor. [PERSON] et al. [18] showed that a lower thrust force and better hole quality were obtained with a combination of higher rotational speeds and lower feed rates. [PERSON] [19] showed that adding the nano-SiC to the composite can increase its tensile strength, and consequently, the thrust force and the delamination factor increase at a higher weight fraction of nano-SiC in the composite material. According to their results, the delamination factor increases with the weight fraction of nano-SiC and feed rate, and the spindle speed decreases. [PERSON] et al. [20] reported that the feed and point angle dramatically affected the thrust force and entry and exit delamination. In contrast, speed just influenced the exit delamination and the effect of the tool diameter was not significant on delamination in entry and exit and thrust force. [PERSON] et al. [21] reported that the delamination factor increases by increasing the drill diameter, using a low spindle speed and a high feed rate. [PERSON] et al. [22] showed that the thrust force and the exit burr height in the drilling of composites increased by increasing the feed rate and rotational speed. However, the lowest roughness was obtained at a low feed rate and high speed. [PERSON] et al. [23] showed that internal cooling of the tool results in a lower drill temperature which subsequently decreases the damage factor. From the mentioned studies, it can be concluded that delamination and thrust force are two critical parameters that can affect the quality of drilled holes in composite materials. These parameters can be reduced by selecting the optimum value of drilling parameters, including the rotational speed and feed rate. Although many studies tried to reduce the delamination by considering the various drill bits and process parameters, there are no papers investigating the effects of ply angle on the thrust force and delamination factor when drilling CFRP composite materials. Hence, in this article, the impact of rotational speed, feed rate, and angle between layers on the thrust force and delamination factor were investigated via experiments. The optimum values of parameters were obtained by Central Composite Design (CCD), which led to a reduction in the thrust force and delamination factor which was verified by the final experiment. The results revealed that the obtained model could effectively predict the delamination factor and thrust force in the drilling of CFRP composites.
# Identifying Key Characteristics of Business Rules That Affect Software Project Success
## 5 Discussion  
As can be seen from the obtained results (see Tables 2 and 3), research questions RQ2 (i.e., a set of rules specifically prepared for a particular project positively affects the software implementation project success) and RQ4 (i.e., ease of understanding of BR positively affects the software implementation project success) have a statistically significant effect.  
The necessity to define a BR set specifically for a particular project (RQ2) originates from the fact that projects vary in size, price, number of developers, complexity, number of development teams [47], and other unique characteristics [48]. The definition of BR and especially business terms must be understandable to business users (e.g., using SBVR [9; 25]) and developers (e.g., platform-specific BR, like Ilog, Bonita, etc.). BR should be specifically prepared for a particular project [4; 12].  
One of the main causes of software implementation project failure [49] are poorly defined requirements, which also include poorly defined BR for the project. Consequently, we should improve understanding of BR (RQ4) by standardizing their content and form e.g., a shared rule set facilitates successful work [12]. Based on this finding, the more general BR in the form of good practices can be developed [50; 51]. Moreover, standardization helps users and software developers to build a shared understanding of business terms and BR, which facilitates successful and efficient SW implementation [52; 53].  
Other research questions (RQ1 (i.e., defining BR in written form positively affect the software implementation project success), RQ3 (i.e., consistency of BR positively affect the software implementation project success), RQ5 (i.e., proper definition of BR positively affect the software implementation project success) and RQ6 (i.e., traceability of BR from business level to implementation level positively affect the software implementation project success)), according to the obtained results, are not statistically significant. Although the literature review presented in the related works shows that those questions are important [9; 12; 29], our study did not find statistically significant influences of these BR characteristics on software implementation project success.  
Figure 2: Graphical representation of mean differences of studied groups for each research question.
# Dialog CIN: Contextual Inference Networks for Emotional Dialogue Generation
## 4 Proposed Dataset
### Data Features  
In this section, we conducted a thorough analysis of the LifeDialog dataset and summarized several notable characteristics of the dataset, including:  
* We enriched the corpus with a diverse set of features, thereby enhancing the semantic representation of the dialogues. These features encompass emotional attributes of utterances, sentence type characteristics of utterances, and topic-related attributes of dialogues.
* We manually annotated seven different and common emotions, such as happiness, sadness, and anger, in the LifeDialog dataset to ensure the quality of the annotations, and provided sufficient data support for other related tasks, such as emotion recognition in dialogues.
* The LifeDialog dataset encompassed dialogues from a diverse range of topics, including, but not limited to, daily life, workplace, travel, and more. This broad coverage of topics distinguished it from domain-specific dialogue datasets that focused on particular domains like medical or legal conversations. As a result, the LifeDialog dataset exhibited a high level of versatility and applicability across a wide range of domains and applications in the field of natural language processing.
* In contrast to datasets like the LCCC dataset and other Chinese dialogue datasets that are primarily derived from posts and replies on social media platforms, the LifeDialog dataset is exclusively composed of manually written and carefully selected data. This meticulous data curation process guarantees that our dataset maintains higher grammatical standards and more accurately captures the characteristics of real-life conversations.
# Design of 1-Bit Digital Reconfigurable Reflective Metasurface for Beam-Scanning
## 2 Materials and Methods
### The Theory of Reflective Metasurface  
An RRM, which consists of \(m\times n\) elements, is illuminated by a feed source locating at the position vector \(\overrightarrow{r}_{f}\). The reradiated electric field from the RRM in an arbitrary direction can be calculated by [2]  
\[E(\hat{u})=\sum_{m=1}^{M}\sum_{n=1}^{N}F(\overrightarrow{r}_{mm}\cdot \overrightarrow{r}_{f})A(\overrightarrow{r}_{mm}\cdot\hat{u}_{0})A(\hat{u}_{0} \cdot\hat{u})\cdot\exp(-jk_{0}(|\overrightarrow{r}_{mm}-\overrightarrow{r}_ {f}|-\overrightarrow{r}_{mm}\cdot\hat{u})+j\alpha_{mm}) \tag{1}\]  
where \(\hat{u}_{0}\) is the vector of the main beam direction. \(F\) is the function of the radiation pattern of the feed, and \(A\) is the function of the radiation pattern of the array element. \(\overrightarrow{r}_{mm}\) is the position vector of each element, and \(\alpha_{mm}\) is the phase compensation of each cell. The phase compensation of each RRM element for a desired direction can be computed by  
\[-jk_{0}(|\overrightarrow{r}_{mm}-\overrightarrow{r}_{f}|-\overrightarrow{r}_ {mm}\cdot\hat{u})+j\alpha_{mm}=2n\pi(n=1,2,3\ldots) \tag{2}\]  
When the phase compensations of all units can meet (1) and (2), the RRM can radiate in the required direction. The direction of the incidence wave \((\theta_{i},\varphi_{i})\) is \((30^{\circ},0^{\circ})\) and the direction of the reflection wave \((\theta_{r},\varphi_{r})\) is \((\theta_{r},90^{\circ})\), where \(\theta_{r}\) can vary from \(-60^{\circ}\) to \(+60^{\circ}\).
# Aircraft Track Anomaly Detection Based on MOD-Bi-LSTM
## 2 Algorithm Description
### Bi-LSTM for Track Outliers Detection
#### 2.3.1 Introduction of Bi-LSTM Neural Network Model  
Bi-LSTM consists of a forward Long Short Term Memory Network (LSTM) and a backward LSTM. Because track outliers do not occur in isolation, the forward and backward structure of the Bi-LSTM model is more suitable for track outliers detection. The LSTM neural network unit is the basic unit that constitutes the Bi-LSTM neural network, and its network cell structure is shown in Figure 2.  
The LSTM model can be regarded as an optimized recurrent neural network model, which is mainly aimed at the gradient problem in the process of long sequence training. The model is composed of input signal \(x_{t}\) at time \(t\), cell state \(C_{t}\), temporary cell state \(\hat{C}_{t}\), hidden layer state \(h_{t}\), forget gate \(f_{t}\), memory gate \(i_{t}\), and output gate \(\sigma_{t}\)[32]. The working principle is as follows:  
1. The forget gate screens weak correlation information and deletes it: \[f_{t}=\sigma\Big{(}W_{f}[h_{t-1},\ x_{t}]+b_{f}\Big{)}\] (10) where \(h_{t-1}\) represents the hidden layer information at the previous moment; \(x_{t}\) represents the current input; \(W_{f}\), \(b_{f}\) is the training parameter; \(\sigma\) is the sigmoid function (neural network activation function); \(f_{t}\) represents the weight of retained information.
2. The input gate screens the information of the strong correlation degree, and the sigmoid layer and the hidden layer jointly update the information in the cell state: \[\begin{array}{c}C_{t}=f_{t}\cdot C_{t-1}+\sigma(W_{c}[h_{t-1},x_{t}]+b_{c}) \cdot\\ \tanh(W_{c}[h_{t-1},x_{t}]+b_{c})\end{array}\] (11) where \(W_{c}\) and \(b_{c}\) represent training parameters; \(C_{t-1}\) represents the information of a previous cell state; \(C_{t}\) represents information about the current state of the cell.  
Figure 2: LSTM structure.  
The output gate determines the final output information: \[h_{t}=\sigma(w_{0}[h_{t-1},x_{t}]+b_{0})\cdot\tanh(C_{t})\] (12) where \(w_{0}\) is the weight matrix, and the weight matrix used in this paper is a random matrix, that is, random initialization is carried out at the beginning of model training, and the weight matrix is iteratively updated through the back propagation of training loss. \(b_{0}\) is the offset and \(\sigma\) is the activation function. Combining forward LSTM and backward LSTM, a Bi-LSTM model can be obtained. The Bi-LSTM structure is shown in Figure 3.  
Input signals \(\{x_{0},x_{1},\ldots,x_{n}\}\) are fed into the forward LSTM model and the backward LSTM model, respectively. Then output the forward implicit vectors \(\{h_{F0},h_{F1},\ldots,h_{Fn}\}\) and the backward implicit vectors \(\{h_{B0},h_{B1},\ldots,h_{Bn}\}\). The forward and backward implicit vectors are spliced to get: \(\left\{[h_{F0},h_{B1}],[h_{F1},h_{B(n-1)}],\ldots,[h_{Fn},h_{B0}]\right\}\), namely \(\{h_{0},h_{1},\ldots,h_{n}\}\).
## 4 Analysis of the Existing Datasets
### Comparison of Surveillance Datasets
#### 4.8.1 Based on Recording Environment  
Various applications, such as public monitoring, are used indoors and outdoors. However, applications such as traffic monitoring are primarily used in outdoor environments. Examples of outdoor environments are train stations, parks, crosswalks, streets, etc.; examples of indoor environments are shopping malls, schools, factories, subways, etc.  
Datasets produced in indoor environments are less affected by light changes and lens distortions than datasets produced in outdoor environments. Outdoor environments are heavily affected by weather events such as sun, clouds, rain, and snow.  
We can separate the datasets produced in indoor and outdoor environments. Some datasets consist of videos recorded in both environments. Table 11 shows the distribution of the datasets by recording the environment. In selecting datasets, it is useful to consider the effects of external factors on the datasets.
## 2 Materials and Methods
### Domain-Specific BERT Fine Tuning  
The Masked Language Model (MLM) is crucial in the pre-training phase of BERT [23]. In this research, we developed a Chinese whole-word masking task to enhance the model's adaptation to domain-specific data, particularly for emergencies in gas pipeline networks. This fine tuning method utilizes BERT's ability to learn contextual information by masking certain words in the input and predicting these obscured words. During the whole-word masking phase, we first use the Jieba tokenizer, a Chinese word segmentation tool, to segment the input text and identify complete words. After establishing word boundaries, we apply a whole-word masking strategy, masking all characters within a selected word simultaneously. This approach better aligns with the characteristics of the Chinese language, especially in processing multi-character words, as it more effectively preserves semantic information and enhances the model's adaptability to complex linguistic structures.  
During the vocabulary selection phase, we used a random sampling strategy to decide which words to mask in each input sequence. Specifically, a certain proportion of words in each sequence is randomly selected for masking, ensuring variation in masked words across different sequences. This approach improves the model's generalization ability. Approximately 15% of the words in each input sequence are randomly selected for masking. These pre-processed text data are then used as inputs for the model. The model outputs a probability distribution for each word in the vocabulary, tailored to each masked position. During this phase, all model parameters, including those established during pre-training and any additional parameters introduced, are trainable [22]. BERT fine tuning was implemented in Python using Hugging Face's Transformers library, with custom domain-specific data processing steps.
# Assessment of the Length and Depth of Delamination-Type Defects Using Ultrasonic Guided Waves
## 1 Introduction
Such magnitude variation is related to the excitation frequency as the phase velocity of A\({}_{0}\) mode is frequency and thickness dependent. As a result, different magnitude variation patterns at different excitation frequencies can be observed. Having such patterns at different frequencies allow to increase the accuracy of damage depth estimation. This is very important in the presence of strong dispersion, as the rapid changes in velocity of guided wave modes can introduce large errors. Furthermore, the information on the damage depth can be exploited for the assessment of the defect length as the actual depth of the defect determines the phase velocities of A\({}_{0}\) and S\({}_{0}\) modes in the upper and lower sub-laminates. Then, the delamination size can be evaluated by measuring the ToF between the wave packets of the converted and direct A\({}_{0}\) modes. Hence, the approach proposed in this research can provide information about the absolute depth and the length of the damage employing a few time traces measured at a series of different excitation frequencies. The technique proposed in this study is essentially different from the currently existing ones as it uses multiple excitation frequencies, which leads to different patterns of A\({}_{0}\) mode magnitude variation and, subsequently, to better resolution. Most of the available research studies use constant a-priori wave velocities for damage size estimation which is not valid in the presence of dispersion. The technique proposed in this paper can be used even in the presence of the dispersion of A\({}_{0}\) mode as it exploits the dependency of the phase velocity on the thickness of the layers above and below the defect. This paper focuses on the proof of the concept of the proposed delamination depth and length estimation method by analyzing a single delamination-type defect. However, in further studies, it is expected to extend the presented method to extract features of multiple parallel delaminations which are a common outcome of low velocity impact damage.
## 4 DRCE-IS Combined with D-Vine Copula and SNMM to Assess the Power System Reliability
### Pre-Simulation Stage of DRCE-IS
#### 4.2.4 Procedures of Pre-Simulation Stage  
The procedures of the pre-simulation stage are detailed as below.  
**Step 1**: Input the original PMFs and joint PDF of RVs in the reliability assessment. Set \(t=0\). Specify the sample size \(n_{s}\) in each iteration.  
**Step 2**: In the \(t\)-th iteration, draw \(n_{s}\) new samples by using \(\{\omega_{l,t}\,|\,l=1,\,\ldots,\,n_{L}\}\), \(\{\omega_{f,c,t}\,|\,c=1,\,\ldots,\,n_{c}\}\), \(\{\omega_{f,c,t},\omega_{d,w,t}\,|\,w=1,\,\ldots,\,n_{w}\}\), \(\mu_{t}^{LW}\) and \(\sigma_{t}^{LW}\). Denote the samples by \(\{x_{ss,s}\,|\,s=1,\,\ldots,\,n_{s}\}\).  
**Step 3**: Quantify the performance of each sample in the term of reliability. The frequently used quantifying method is the minimum load shedding (MLS) model, whose objective function is the quantifying function \(S(x_{ss,s})\).  
The MLS model is expressed as:  
\[\begin{split}\min\limits_{P_{gh},P_{gh}}S(x_{ss,s})=1^{\mathbf{T }}P_{gh}\\ s.t.\left\{\begin{array}{l}1^{\mathbf{T}}P_{g}-1^{\mathbf{T}}P_{d}+1^ {\mathbf{T}}P_{gh}=0\\ P_{gmin}\leq P_{g}\leq P_{g\max}\\ 0\leq P_{gh}\leq P_{d}\\ -P_{linear}\leq b\mathbf{A}\delta\leq P_{linear}\\ A^{\mathbf{T}}b\mathbf{A}\delta=P_{g}+P_{gh}-P_{d}\end{array}\right.\,,\end{split} \tag{42}\]  
where \(P_{g}\), \(P_{gh}\) and \(\delta\) are the control variable of the model. \(P_{g}\) is the vector of generation power at all buses including wind power, whose upper and lower limits are \(P_{gmax}\) and \(P_{gmin}\). \(P_{gh}\) is the vector of load shedding amount at all buses. \(\delta\) is the vector of the voltage phase angle of all buses. \(P_{d}\) is the vector of the load demand at all buses. \(b\) is a diagonal matrix, whose diagonal elements are the reciprocal of the reactance of all transmission lines. \(A\) is the branch-node association matrix. \(P_{linear}\) is the vector of maximum transmission capacity of all lines.  
**Step 4**: Sort the performance of \(\{x_{ss,s}\,|\,s=1,\,\ldots,\,n_{s}\}\) and obtain a series \(\{S_{1},S_{2},\,\ldots,\,S_{nS}\}\), where \(S_{1}\geq S_{2}\geq\ldots\geq S_{nS}\). Locate the \(\rho\)-quantile of the series and denote it as \(S_{\rho\text{-}ns}\).  
**Step 5**: In the \(t\)-th iteration, set a threshold \(\alpha_{t}\) for \(\{S_{1},S_{2},\,\ldots,\,S_{nS}\}\) by:  
\[\alpha_{t}=\left\{\begin{array}{ll}0&S_{\rho\text{-}ns}>0\\ S_{\rho\text{-}ns}&S_{\rho\text{-}ns}\leq 0\end{array}\right.. \tag{43}\]According to the value of \(\alpha_{t}\), we can define the system state \(I(x_{ss,s})\) as:  
\[I(x_{ss,s})=\left\{\begin{array}{ll}1&\text{if }S(x_{ss,s})>\alpha_{t}\\ 0&\text{if }S(x_{ss,s})\leq\alpha_{t}\end{array}\right.. \tag{44}\]  
**Step 6**: By (26), (30), (35), (36), (39) and (40), we can update the parameters of the IS PMFs and IS PDF with the samples generated in Step 2 and the system states obtained in Step 5.  
**Step 7**: If \(\alpha_{t}=0\), end the pre-simulation stage and switch to the main simulation stage. Otherwise, set \(t=t+1\) and return to Step 2.  
The pre-simulation procedures are shown in Figure 2.
## 3 Results and Discussion
### MLP Neural Networks  
The MLP is a commonly used ANN. In this section, we discuss its use to predict the compressive strength of LWAC. Although these neural networks can be designed using several hidden layers, the use of only one hidden layer improved the performance of these configurations. Hence, we focus here on the analysis of MLP neural networks with a single hidden layer. Several different activation functions were used: the identity function, the logistic sigmoid function, the hyperbolic tangent function and the ReLU function. The solvers used to minimise the squared error were SGD, L-BFGS and Adam. To simplify the models and prevent overfitting, Ridge or L2 regularisation was applied. Based on the L2 norm, this type of regularisation introduces an additional term to the loss function to penalise large weights and thus forces the weights of the network to take small values [51]. The values considered for the strength of the regularisation term were \(\alpha=0,0.0001,0.001,0.01,0.05,0.08,0.09,0.1,0.11,0.12,0.2,0.5,1,2\). Note that \(\alpha=0\) means that no regularisation was applied. To determine the number of neurons for the hidden layer, architectures were designed with between one and \(2^{7}\) neurons in this layer. Figure 10 displays the change in the relative error as the number of neurons increases, where the L-BFGS optimiser and the ReLU activation function are used. This configuration gave the best performance for our problem. Table 7 summarises several goodness-of-fit measures for some of the models. A suitable value for \(\alpha\) was found to be 0.1, as this value prevented overfitting and achieved more stable results, regardless of the number of neurons used (see Figure 10c). In this way, we avoid the problem in which a few neurons dominate the behaviour of the network, and we force non-informative features to have weights close to or equal to zero. We should point out that the ANN models considered here outperformed the best ANN architecture proposed in [6]. For example, with \(\alpha=0.1\), we obtained a mean RMSE value of 3.705 with 30 neurons in the hidden layer and a value of 3.6765 with 40 neurons (see Table 7). These values are lower than 3.745, which was the RMSE for the best model in [6]. Similar results were obtained with values of \(\alpha\) very close to 0.1. Note that without Ridge regularisation, the models may be overfitted to the training data (see Figure 10a). This issue also occurs when very small values of \(\alpha\) are applied (\(\alpha\to 0\)). In contrast, when \(\alpha>1\), the models are clearly underfitted (see Figure 10d). In fact, we obtained mean values of RMSE greater than four for \(\alpha=1\) and greater than 4.4 for \(\alpha=2\).
## 4 Results
### Karst Features Delineation  
#### 4.3.1 Karst Depression  
This study identified 79 potential dolines or karst depressions by visualising the SRTM-DEM in the Niah-Subis build-up (Figure 8a). The largest doline (0.147 km\({}^{2}\)) was found in the middle of the paleo-karst build-up, while the smallest doline (9.8 m\({}^{2}\)) was found closer to the surface river network (Figure 8b). The doline size distribution demonstrates the inter-relationship between the surface and underground drainage systems in the development of karstic processes. The subsurface drainage dissolved the subaerial limestone build-up, causing the ceiling of the structure to collapse. On the topography map, it appears as a closed depression (Figure 8a). The Niah regional lineaments and doline distribution were compared (Figure 8b), demonstrating that the larger dolines were bounded or intersected by lineaments of greater intensity.  
Figure 8: (**a**) Potential karst depression identified from the DEM (yellow shapes) and topography map with surrounding surface drainage system (blue lines). (**b**) Regional lineaments (red lines) and the potential karst depression (yellow shapes).