esa-sceva/satcom-chunk-collection huggingface dataset:
# False Alarm Reduction Method for Weakness Static Analysis Using BERT Model
## 2 Related Work and Background
### Bidirectional Encoder Representation with Transformers (BERT)  
Transformer was first proposed in 2017 to solve sequence-to-sequence tasks while easily processing long-range dependencies [33]. It outperformed alternative neural models in natural language understanding and generation task performance, becoming a representative architecture of natural language processing (NLP). The construction of this architecture has an encoder-decoder architecture and similar internal structures, including attention, feedforward, and normalization layers. The tokenizer parses the input sequence into a token. Each token is then transformed into a vector using word embedding, and the location information is added to the embedding. It also parallelizes many calculations by allowing the tokens to flow independently through the stack.  
BERT, which stands for Bidirectional Encoder Representation with Transformers, is a bidirectional transformer pretrained using a combination of masked language modeling objective and next sentence prediction on a large corpus of English text [34]. That is, the learning information is from left to right and right to left. It used only the encoder blocks from the original Transformer architecture. In the BERT model, the self-attention mechanism [33] is a key component of the Transformer, and is a process that allows better encoding of words at the current target location by looking at and taking hints at words at different locations in the sentence while processing each word in the entered sentence. This is accomplished by extracting the contextual relationship between the three components, _queries, keys_, and _values_. Typical areas to which BERT is applied include single-sentence classification, two-sentence relationship classification, or two-sentence similarity, relationship, sentence token classification, and machine reading answer classification. A representative study applying the BERT model in the field of programming languages is a multi-programming language model pretrained for programming language and natural language pairs, such as CodeBERT [35], GraphCodeBERT [36], and CodeT5 [37].  
In this study, the BERT model analyzes weaknesses within C/C++ source codes, which have complex syntax and semantics, based on the characteristics of excellent processing of semantics of natural language text. Using this model, it is not only possible to analyze a specific line with weaknesses but also to obtain a score on the relationship between the weakness lines analyzed for each line based on the line by using the self-attention mechanism where weaknesses occur.
# Time-Evolving Graph Convolutional Recurrent Network for Traffic Prediction
## 4 Experiments and Discussion  
### Datasets  
We evaluate the proposed model TEGRCN on two public real-world traffic datasets, METR-LA and PEMS-BAY, which were released by [PERSON] et al. in [12]. METR-LA records the traffic speed data collected from 207 sensors on the highways of Los Angeles County, ranging from 1 March 2012 to 30 June 2012. PEMS-BAY records the speed data collected from 325 sensors in the bay area by California Transportation Agencies, ranging from 1 January 2017 to 30 June 2017. The units of the speed data are both miles per hour. Some basic statistics of these two datasets are listed in Table 2. Each sensor in the road network is viewed as a traffic node. The numbers of the edges are counted according to the distance-based static adjacency graphs. We can see that there is a relatively high proportion of missing values in METR-LA while the counterpart in PEMS-BAY is almost negligible. Following the data pre-processing procedures in [12], we set the interval between two time steps to 5 min. Then, each dataset is split in chronological order, with 70% for training, 10% for validation and 20% for testing. Z-score normalization is applied to the inputs.  
In these two datasets, only the traffic speed in the road network is provided, so the number of traffic features is \(F=1\). Take METR-LA as an example, the graph of traffic network contains \(N=205\) nodes, so the traffic features (also called as graph signals) at a specific time slot \(t\) can be denoted as \(\mathbf{X}_{t}\in\mathbb{R}^{205\times 1}\). To make multiple-steps-ahead predictions with the historical observations, the series of these traffic features are input to the encoder-decoder of the proposed model step-by-step.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Nodes** & **Edges** & **Size** & **Sample Rate** &
\begin{tabular}{c} **Missing** \\ **Proportion** \\ \end{tabular} \\ \hline METR-LA & 207 & 1515 & 34272 & 5 min & 8.109\% \\ PEMS-BAY & 325 & 2369 & 52116 & 5 min & 0.003\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: The statistics of of datasets.
# The Response of Vegetation Dynamics to Climate in Xinjiang from 1991 to 2018
## 3 Results
### Temporal and Spatial Variations of Vegetation Characteristics
#### 3.1.3 GPP Change Trend  
As an effective indicator of ecosystem productivity, GPP also showed significant spatial and temporal distribution characteristics in Xinjiang. Figure 5 indicates the distribution of GPP during the growing season, non-growing season, and annual average. It can be seen that the spatial distribution of GPP in Xinjiang vegetation varied greatly, with higher values in northern Xinjiang and lower values in southern Xinjiang. The annual average GPP ranges from 0 to 1285 g C m\({}^{-2}\) yr\({}^{-1}\), with the highest GPP values during the growing and non-growing seasons reaching 392 g C m\({}^{-2}\) yr\({}^{-1}\) and 179 g C m\({}^{-2}\) yr\({}^{-1}\).  
The high-value areas were mainly distributed in northern Xinjiang, specifically in the western and northern Tianshan Mountains, Altai Mountains, and the northwestern Junggar Basin. The distribution of annual average GPP was primarily influenced by growing-season GPP values, particularly in the higher-latitude Altai Mountain region. In contrast to northern Xinjiang, the annual average GPP in southern Xinjiang did not exceed 500 g C m\({}^{-2}\) yr\({}^{-1}\). Agricultural areas surrounding the Tarm Basin were the main regions with relatively high GPP values in southern Xinjiang.  
Overall, the multi-year average GPP of Xinjiang vegetation was 274.0 g C m\({}^{-2}\) yr\({}^{-1}\), with values ranging between 236.9 g C m\({}^{-2}\) yr\({}^{-1}\) and 316.1 g C m\({}^{-2}\) yr\({}^{-1}\) from 1990 to 2018. There was an overall increased trend (_slope_ = 1.19, R\({}^{2}\) = 0.52), indicating a continuous improvement in ecosystem status. However, areas where the GPP trend decreased by more than 15 were mainly found in the Tianshan Mountains, Altai Mountains, and the northwestern part of the Junggar Basin in northern Xinjiang. In contrast, agricultural areas around the basins and low-altitude areas in the mountains were the main regions of GPP increase. Compared to the trend distribution of NDVI and LAI, GPP trend values suggested a decline in the Altai Mountains, the area around the Junggar Basin, the southern Tianshan Mountains, and high-altitude regions in the mountains. Regions showing an increasing GPP trend were similar to the distribution of the other two indicators.  
Figure 4: The spatial distribution characteristics for (**a**) non-growing season, (**b**) growing season, and (**c**) annual value and changing trends (**d**) of LAI.
## 3 Methodology
### Assessment of the ISS Images
#### 3.1.2 Retrieving the Low-Light Suburban Areas  
Three different ISS image parts in the study areas were first selected to obtain the optimal thresholding values of brightness for separating bright urban areas and low-light suburban areas (Figure 3). We focused on only low-light suburban areas to avoid duplicating efforts, given that numerous studies have shown that ISS imagery is very useful to map lighting types and land surface within bright urban areas [45; 47; 48].  
In the image of Calgary, the optimal thresholding values were found to be 35 for the red band, 30 for the yellow band, and 25 for the blue band, respectively. Areas with brightness values above these numbers are bright urban areas and the others are low-light suburban areas. Similarly, the optimal thresholding values in the Komsomsk image were found to be 50 for the red band, 50 for the yellow band, and 45 for the blue band, respectively (Figure 4).
# Psychoeducation on Stress and Anxiety Using Virtual Reality: A Mixed-Methods Study
## 4 Discussion
### Implications for Clinical Practice  
The findings of this study have some practical implications for clinical practice. Firstly, the VR psychoeducational experience tested in this study could be included for psychoeducation within CBT-based stress and anxiety management programs among healthcare workers. Currently, we are running a Randomized Controlled Trial (RCT) study on a sample of doctors and nurses to investigate the use of MIND-VR within home-based training programs for stress and anxiety management [132]. Secondly, the methodology used for MIND-VR can also be adopted for creating other VR psychoeducational experiences, offering guiding elements in designing and evaluating such virtual contents.
## 3 Automated Operational Modal Analysis Based on Modal Uncertainty
### Pre-Cleaning: Removal of Spurious Modes  
The pool of candidate modes provided by SSI contains spurious modes that need to be removed. This study cleans these spurious modes by using only basic conditions that real modes must exhibit. Accordingly, the mode cluster resulting from this process will still partially include spurious modes. These remaining spurious modes will be removed in a later process. The basic conditions that should be exhibited by real modes are set as follows: (1) a frequency higher than \(\frac{f_{k}}{\sigma_{I}^{2}}\) (\(f>\frac{f_{k}}{2}\)), (2) a coefficient of variation related to the frequency smaller than \(0.1\) (\(\text{CV}_{f}=\frac{\sigma_{I}^{2}}{\mu_{I}}\leq 0.1\)), (3) a damping ratio below \(0.1\) (\(\xi\leq 0.1\)), (4) a MAC index exceeding \(0.8\) (\(\mu_{MAC}\geq 0.8\)), and (5) a standard deviation of MAC greater than  
Figure 1: Comparison of cumulative probability distributions of sample MAC index distribution and normal distribution. (**a**) Case with high average and low standard deviation; (**b**) case with low average and high standard deviation.  
0.2 (\(\sigma_{MAC}\leq 0.2\)). Here, \(\sigma_{f}\), \(\mu_{f}\), \(\mu_{mac}\) and \(\sigma_{mac}\) are obtained by the method presented in Section 2.2.  
The first condition is driven by Equation (3) from which a computed frequency smaller than \(\frac{f_{i}}{2\lambda}\) indicates a high possibility of having an inaccurate mode. The second condition (\(\mathrm{CV}_{f}\leq 0.1\)), which is significantly looser than \(\mathrm{CV}_{f}<0.015\), which was adopted by [PERSON] and [PERSON] (2014), reduces the risk of removing a real mode. The third condition, that the damping ratio be between 0.1 and 0.2, reduces the risk of removing real modes with low frequencies that are characteristics of civil structures such as bridges. The eventual similarity of the mode shape between two modes is usually decided by looking to see if the MAC value exceeds 80\(\sim\)90% ([PERSON] and [PERSON], 2014). The fourth condition (\(\mu_{MAC}\geq 0.8\)) involves the altered mode shape and the average of the MAC values. The fifth condition (\(\sigma_{MAC}\leq 0.2\)) relates to the standard deviation of the MAC values and has a low risk of omitting real modes.  
A review of these five conditions reveals that each of them is loose and reduces the risk of removing a real mode. Moreover, there is no need to adjust these conditions for particular measured data since these conditions are lax. However, the application of all these conditions together has been verified to remove practically all the spurious modes (see Section 4).
## 6 Prevalent Criteria
### Security Issues, Challenges, IoT, and Blockchain Usage  
The arrangement of ML models in ISs for multi-objective healthcare management brings up important security issues. The sensitivity and reliability of healthcare data make ML systems a key target for cyber threats [107]. Security challenges contain the risk of unauthorized access to patient records, potential manipulation of medical data, and the endangerment of ML systems across adversarial attacks. Similarly, the black-box facet of many ML algorithms complicates perceiving and confirming their decision-making procedures, boosting questions about the interpretability and accountability of healthcare anticipations. Ensuring robust encryption, access drawbacks, and secure system training and arrangement techniques is significant for preserving patient privacy, conserving data incorporation, and instilling reliability in the security of HISs in the healthcare area [108].  
The incorporation of ML applications with the IoT in ML-powered ISs in healthcare management represents a transformative synergy with useful benefits. IoT technologies, namely wearable sensors and remote tracking tools, provide massive volumes of real-time patient data. ML applications may use this data for predictive analytics, individualized treatment suggestions, and optimization of healthcare procedures. Within ML algorithms, IoT-enabled models may analyze patient symptoms, recognize abnormalities, and build a timely perspective for healthcare practitioners. This incorporation boosts the productivity of healthcare services, enables proactive and individualized care techniques, and allows the complete development of patient findings. The incorporation of ML and the IoT in healthcare management frameworks holds great promise for increasing decision-making, resource allocation, and the entire quality of healthcare services in a multi-objective domain [109].  
The utilization of blockchain technology in integration with ML applications inside ISs leveraged for multi-objective healthcare management builds a secure and transparent model for controlling health data. Blockchain, which is detected for its decentralization and tamper-resistance, may develop the consistency and privacy needed for patient data and transactions. When incorporated with ML, it leads to raised analytics and visions from securely saved and shared data, enabling an appropriate healthcare structure, predictive modeling, and better decision-making [110]. The integration of blockchain and ML into healthcare management models has the possibility to solve data interoperability challenges, develop patient-oriented care, and establish reliable and auditable settings that prioritize data security and privacy in the multi-objective vision of healthcare management.
# Preamble-Based Adaptive Channel Estimation for IEEE 802.11p +
## 2 IEEE 802.11p Standard and Channel Model
### Channel Model in Vehicular Environments  
Generally, a mobile radio channel in vehicular environments is a time-varying channel including multi-path propagation and a large Doppler shift [12; 13; 14]. Such a channel can be implemented as a tapped delay line model (TDL) which is commonly used for implementing the multi-path channel. The fading of the paths constituting each tap is summed up to obtain Rician or Rayleigh fading, \(h(t,\tau)\), which varies with the duration of each tap. The impulse response of small fading with time-frequency correlation through Doppler and delay spread is represented as follows.  
\[h(t,\tau)=\sum_{l=1}^{L}\beta_{l}(t)\delta(\tau-\tau_{l}(t))e^{j2\pi f_{d,l}t}, \tag{1}\]  
where \(\beta_{l}(t)\), \(\tau_{l}(t)\), and \(f_{d,l}\) represent the amplitude, delay, and Doppler shift of the \(l\)-th path, respectively. As the performance of an actual wireless system depends on the state of the channel, it is very important to analyze the statistical characteristics of the wireless channel; thus, various studies on vehicle to everything (V2X) channels have been conducted [32; 33; 34; 35; 36; 37; 38; 39].  
In this study, we use the TDL model proposed in [32; 33] to consider the Doppler and delay spread in realistic vehicular environments. These channel models are classified into six models according to different scenarios. In the TDL model, each tap has been schematized, including tap power, delay value, and Doppler spectrum. A concise summary of the proposed six-channel model is provided in [32]. Of the six models, we consider only two channel models (as shown in Table 2), i.e., V2V Expressway Oncoming and V2I Urban Canyon, owing to the importance of the real situation in the six models.  
Figure 2: Pilot arrangement of IEEE 802.11p.
## 6 Future Outlook  
In the last decade, the advancement in source and detector technologies brought in the benefits of considerable cost reduction and miniaturization of broadband time domain instruments as portable devices [41]. Recent studies on small footprint sources and detectors could further promote the miniaturization of TD-DOS towards wearable devices [187; 188]. Some initiatives were taken in this direction by EU funded projects. Another key challenge was found in the analysis of heterogeneous media, where recent advances in multi-layer tissue analysis provide hope to further increase the accuracy of analyzed data, thus enabling reliable quantification of tissue constituents in heterogeneous tissue structures [48; 152; 189]. There is increasing interest in TD-DOS to explore new human tissue types and locations [52; 53; 190], a further continuation in this direction could add to the exploration and invention of new methodologies to address pressing clinical needs using broadband TD-DOS.  
Figure 27 summarizes the current status and future trends in broadband TD-DOS. One trend of growth is foreseen in the extension of broadband TD-DOS to the SWIR region (1000-1900 nm) [121; 132]. Though few attempts were made till date, a dedicated approach in this direction would open up many more opportunities as the depth sensitivity could be higher due to relatively low scattering in that wavelength range. The measurement of tissue chromophore performed by broadband TD-DOS could  
Figure 26: Absorption spectra of different Triton-X-based water/lipid phantoms at different lipid concentrations (A: 70%, B: 50%, C: 30%). For comparison, pure lipid and pure water spectra [186].  
complement other technologies like an ultrasound for morphological information, elastography to estimate tissue stiffness, diffuse correlation spectroscopy to monitor blood flow. Some exploration is already under progress in this direction [36; 187; 188; 191]. A further push is foreseen in the near future to bring in a multimodal broadband TD-DOS, where the specificity of TD-DOS will be combined with complementary information from other modalities. A preliminary attempt to standardize diffuse optics has been performed in the past [49; 50; 51]. Recently, renewed attempts to further strengthen it could bring in the much-needed standards that would harmonize TD-DOS measurements across laboratories [192]. Also, an open data of broadband, well-characterized, stable, reproducibly recipe to make phantom kits, and anatomically correct anthropomorphic tissue phantoms for clinical exploration could further support the cause of standardization [193].
# An Active Indoor Noise Control System Based on CS Algorithm
## 2 The Indoor ANC System Based on CS Algorithm
### The Preknowledge of ANC Algorithm  
Broadband ANC may be described using mathematical model notion. For linear ANC, the filtered-X LMS (FXLMS) method [13] has a minimal computing cost, and it serves as the foundation for many additional algorithms. It is compatible with feedforward, feedback, and hybrid ANC systems. Figure 2 depicts the block diagram of the FxLMS algorithm, where the output \(y(n)\) is computed as follows:  
\[y(n)=w^{T}(n)\cdot x(n)=\sum_{i=0}^{N-1}w_{i}(n)\cdot x(n-i), \tag{1}\]  
Figure 1: The proposed indoor ANC system.  
where \(w^{T}(n)=[w_{0}(n),w_{1}(n),\cdots,w_{N-1}(n)]^{\rm T}\) is the coefficient vector of \(W(z)=-{\rm P}(z)/{\rm H}(z)\) at time \(n\); \({\rm P}(z)\) and \({\rm H}(z)\) denote the function of the primary-path and the secondary-path, respectively. \(x(n)=[x(n),x(n-1),\cdots,x(n-N+1)]^{\rm T}\) is the reference signal vector at time \(n\). (The meaning of unmentioned symbols is explained in the Appendix A). The residual noise (error signal) can be expressed as  
\[e(n)=d(n)-y^{\prime}(n), \tag{2}\]  
where the acoustic signal transmitted to the error microphone through the primary channel is represented by  
\[d(n)=x^{\rm T}(n){\rm P}. \tag{3}\]  
The basic FxLMS algorithm's update equation can be written as  
\[w_{i}(n+1)=w_{i}(n)-\mu\cdot xr(n-i)\cdot e(n),\ i=0,1,...,N-1, \tag{4}\]  
where \(\mu\) is the step size that should satisfy the following equation  
\[0<\mu<\frac{1}{NP_{y}}, \tag{5}\]  
where \(P_{y}\) is the power of \(y(n)\). \(xr(n)=[xr(n),xr(n-1),\cdots,xr(n-N+1)]^{\rm T}\) represents the filtered version of the reference input, and it can be denoted as  
\[xr(n)=\mathfrak{c}^{\rm T}x(n)=\sum_{i=0}^{M-1}c_{i}x(n-i), \tag{6}\]  
where \(c\) is the coefficient vector of the secondary-path estimate \(C(z)\). Normalized LMS (NLMS) [22] improves the step size of the LMS to stabilize the filter convergence. The NLMS algorithm's weight vector update formula is as follows:  
\[W(n+1)=W(n)+\mu(n)\cdot X(n)\cdot e(n), \tag{7}\]  
where  
\[\mu(n)=\frac{\alpha}{X^{\rm T}(n)X(n)+\gamma}. \tag{8}\]  
where \(\alpha\in(0,2)\) is a constant value that controls the convergence, and \(\gamma\in[0,1]\) is used to control the step size. The FxLMS/FxNLMS methodology has been widely applied to ANC systems in current typical scenarios; however, both approaches must be updated to allow for a larger range of processing in the indoor environment proposed in this work.
# Link Connectivity and Coverage of Underwater Cognitive Acoustic Networks under Spectrum Constraint
## Appendix B Ambient Noise  
Usually, the ambient noise in underwater environments is modeled by four kinds of sources: turbulence, shipping activities, waves, and thermal noise [22]. Specifically, the noise caused by shipping activities depends on activity factor \(s\) (ranging from 0 to 1). The noise of waves, brought by wind, can be expressed as a function of wind speed \(w\) (m/s). Then, the formula between power spectral density of the four noise sources and frequency in kHz in dB re \(\mu\) Pa per Hz are given by [29] as follows:  
\[\begin{split} 10\log N_{t}(f)&=17-30\log(f),\\ 10\log N_{s}(f)&=40+20(s-0.5)+26\log(f)-60\log(f+0.0 3),\\ 10\log N_{w}(f)&=50+7.5w^{1/2}+20\log(f)-40\log(f+0. 4),\\ 10\log N_{th}(f)&=-15+20\log(f),\end{split} \tag{10}\]  
where \(N_{t}(f)\), \(N_{s}(f)\), \(N_{w}(f)\), and \(N_{th}(f)\) express the noise produced by turbulence, shipping activity, wind, and thermal, respectively.  
Then the total noise can be expressed as:  
\[10\log N(f)=10\log(N_{t}(f)+N_{s}(f)+N_{w}(f)+N_{th}(f)). \tag{11}\]