esa-sceva/satcom-chunk-collection huggingface dataset:
# STA-GCN: Spatial-Temporal Self-Attention Graph Convolutional Networks for Traffic-Flow Prediction
## 3 Dataset and Methodology
### Spatial-Temporal Self-Attention Graph Convolution Networks (STA-GCN)
#### 3.3.1 Temporal Self-Attention  
The self-attention mechanism is the core algorithm of the Transformer model, which can capture the dependency relationship between sequences through the query matrix \(Q\), key matrix K, and value matrix V. Similarly, the dependency relationship between traffic-flow features at different time steps can be captured by the self-attention mechanism. The formula for calculating the temporal self-attention is shown in Equation (3).  
\[A^{\mathcal{T}}=\textit{softmax}\Bigg{(}\frac{Q^{\mathcal{T}}\left(K^{ \mathcal{T}}\right)^{T}}{\sqrt{d^{\mathcal{T}}}}\Bigg{)}V^{\mathcal{T}} \tag{3}\]  
The \(Q^{\mathcal{T}}\in\mathbb{R}^{L_{Q}\times d^{\mathcal{T}}}\), \(K^{\mathcal{T}}\in\mathbb{R}^{L_{K}\times d^{\mathcal{T}}}\), and \(V^{\mathcal{T}}\in\mathbb{R}^{L_{V}\times d^{\mathcal{T}}}\) refer to the time-series query matrix, key matrix, and value matrix, respectively. These matrices are used in the self-attention mechanism to capture the dependency relationship between traffic-flow features at different time steps. The query matrix A contains the queries that are compared with the keys in the key matrix B, and the value matrix C contains the values corresponding to the keys. The softmax function performs a normalization operation on the temporal dependencies at each time step. The \(\sqrt{d^{\mathcal{T}}}\) scaling factor \(\sqrt{d^{\mathcal{T}}}\) is applied to prevent significant differences in the probability distribution of the matrix resulting from the dot product of points \(Q^{T}\) and \(\left(K^{\mathcal{T}}\right)^{T}\) after passing through the softmax function. After obtaining the attention-probability matrix, a linear layer is applied to obtain \(f^{\mathcal{T}}\in\mathbb{R}^{S\times d}\), which is then  
Figure 2: Structure diagram of STA-GCN model.  
residually connected with \(f\in\mathbb{R}^{5\times d}\) and normalized along the \(S\times d\) dimension. Finally, the output is passed through a ReLu activation function.  
Due to the different geographical locations of transportation nodes, different transportation nodes exhibit different traffic conditions during a certain time period. As shown in Figure 3, there is a significant difference in the traffic-speed changes of the five transportation nodes during the time period from 8:00 to 9:00, indicating the existence of heterogeneity in spatial traffic characteristics. Therefore, it is necessary to capture the time-varying dependence relationship for each transportation node. As shown in Figure 4, the spatial-temporal information from multiple transportation nodes is summed up after passing through a time-attention mechanism, forming an input \(F^{\mathcal{T}}\in\mathbb{R}^{N\times S\times d}\) to the temporal gated convolution.  
Figure 4: Temporal module structure.  
Figure 3: Traffic-speed variation at different transportation nodes during the same period.
## 2 Materials and Methods
### Hybrid Control System Architecture
#### 2.1.3 Network Architecture  
The LSTM network comprises three main layers structured to process temporal data effectively:  
1. [label=(A)]
2. Input Layer: - Receives process input/output data sequences; - Normalizes input data to the range [\(-\)1, 1]; - Implements sliding window technique for temporal context.
3. LSTM Hidden Layers: - First hidden layer: 32 LSTM units with tanh activation; - Second hidden layer: 16 LSTM units with tanh activation; - Dropout rate of 0.2 between layers for regularization.
4. Dense Output Layer: - Linear activation for parameter prediction; - Outputs system parameters in real-time.  
Mathematical Formulation  
The LSTM-based model identification process can be mathematically expressed as follows:  
\[\text{i}(t)=\sigma(\text{Wi}\cdot\text{x}(t)+\text{Ui}\cdot\text{h}(t-1)+\text {bi}) \tag{1}\]  
Figure 2: Implementation flowchart of the hybrid control system.  
\[\text{f}(t)=\sigma(\text{Wf}\cdot\text{x}(t)+\text{Uf}\cdot\text{h}(t-1)+\text{bf}) \tag{2}\]  
\[\text{o}(t)=\sigma(\text{Wo}\cdot\text{x}(t)+\text{Uo}\cdot\text{h}(t-1)+\text{ bo}) \tag{3}\]  
\[\text{c}(t)=\text{f}(t)\odot c(t-1)+\text{i}(t)\odot\tanh(\text{Wc}\cdot\text{ x}(t)+\text{Uc}\cdot\text{h}(t-1)+\text{bc}) \tag{4}\]  
\[\text{h}(t)=\text{o}(t)\odot\tanh(\text{c}(t)) \tag{5}\]  
\[\text{\hat{y}}(t)=\text{Wh}(t)+\text{b} \tag{6}\]  
where  
- i(t) is the input gate activation at time t  
- f(t) is the forget gate activation at time t  
- o(t) is the output gate activation at time t  
- c(t) is the cell state at time t  
- h(t) is the hidden state at time t  
- x(t) is the input vector at time t  
- W and U are weight matrices  
- b represents bias vectors  
- \(\sigma\) denotes the sigmoid function  
- \(\odot\) represents element-wise multiplication  
Training and Adaptation  
The network is trained using a two-phase approach:  
(A) Offline Training:  
- Utilizes historical process data.  
- Minimizes mean squared error loss function.  
- Implements Adam optimizer with a learning rate of 0.001.  
- Batch size: 32.  
- Training epochs: 100.  
(B) Online Adaptation:  
- Continuous update using a sliding window of recent data.  
- Window size: 500 samples.  
- Update frequency: every 100 ms.  
- Adaptive learning rate based on prediction error.  
The loss function for training is defined as follows:  
\[L=\alpha_{1}\left|\hat{y}(t)-y(t)\right|^{2}+\alpha_{1}\left|\hat{\theta}- \theta\right|^{2} \tag{7}\]  
where \(\alpha_{1}\) and \(\alpha_{2}\) are weighting factors, \(y(t)\) is the actual output, and \(\theta\) represents system parameters.
# Full-Abstract Biomedical Relation Extraction with Keyword-Attentive Domain Knowledge Infusion
###### Abstract  
Relation extraction (RE) is an essential task in natural language processing. Given a context, RE aims to classify an entity-mention pair into a set of pre-defined relations. In the biomedical field, building an efficient and accurate RE system is critical for the construction of a domain knowledge base to support upper-level applications. Recent advances have witnessed a focus shift from sentence-to document-level RE problems, which are more challenging due to the need for inter- and intra-sentence semantic reasoning. This type of distant dependency is difficult to understand and capture for a learning algorithm. To address the challenge, prior efforts either attempted to improve the cross-sentence text representation or infuse domain or local knowledge into the model. Both strategies demonstrated efficacy on various datasets. In this paper, a keyword-attentive knowledge infusion strategy is proposed and integrated into BioBERT. A domain keyword collection mechanism is developed to discover the most relation-suggestive word tokens for bio-entities in a given context. By manipulating the attention masks, the model can be guided to focus on the semantic interaction between bio-entities linked by the keywords. We validated the proposed method on the Biocreative V Chemical Disease Relation dataset with an Fl of 75.6%, outperforming the state-of-the-art by 5.6%.  
relation extraction; chemical-induced disease; pretrained language models; keyword attention; BERT; transformer
## 3 Results and Discussion
The median was around 80-95 \(\mathrm{\SIUnitSymbolMicro g}\)/m\({}^{3}\) before emergency state from 18-19 h, and an evident reduction to the median of about 15-25 \(\mathrm{\SIUnitSymbolMicro g}\)/m\({}^{3}\) is visible during the emergency state. Additionally, during the most active hours of the day (period from 9 h to 17 h), a noticeable drop in the level of air pollution was observed, indicated by lower median and smaller interquartile range (median around 50 \(\mathrm{\SIUnitSymbolMicro g}\)/m\({}^{3}\) before emergency state during this time of day, and about 25 \(\mathrm{\SIUnitSymbolMicro g}\)/m\({}^{3}\) during emergency state in this time of day). * Observing CO daily profiles following conclusions can be reached. During the active hours of the day, in the period from 9 h to 17 h, the level of air pollution stayed similar in, going around 0.8 mg/m\({}^{3}\) with a relatively narrow interquartile range (Figure 6c). During morning hours, the situation is similar, with a slight increase during the emergency state, with median still being in the range from 0.8 to 1 mg/m\({}^{3}\), but with larger interquartile ranges during the emergency state. On the other hand, from 18 h to 23 h, there is a slight reduction in the CO level during the emergency state. Since there are no noticeable changes in the daily profiles before and during the emergency state for CO levels, it can be argued that pollutants with domestic heating source profiles mostly exhibited constant levels. * An interesting increase in PM25 levels in the early morning and in the nighttime at different monitoring sites over the area of the city of Novi Sad, even during lockdown, has also been noticed (Figure 6d). However, during the most active hours of the day (period from 9 h to 17 h) a noticeable drop in the level of air pollution was evident, as indicated by a lower median and smaller interquartile range. Figure 6: _Cont_. Equivalent analysis was also done for Liman SEPA NN2 station and is shown in Appendix D. Similar and comparable results were observed all over the EU in various studies focusing on COVID-19 influences ([PERSON] et al., 2019; [PERSON] et al., 2020). Results for levels of NO\({}_{2}\), PM\({}_{10}\) and PM\({}_{2.5}\) over the EU member countries have been presented by EEA (EEA, 2019). Data show, as in the city of Novi Sad, that NO\({}_{2}\), a pollutant mainly emitted by road transport, has decreased in many European cities where lockdown measures were applied. Changes in levels of PM\({}_{2.5}\) may also be expected due to lockdown measures, however, a consistent reduction cannot yet be seen across European cities, as is the case in Novi Sad. This is likely due to the fact that the main sources of this pollutant are more varied, including, at the European  
Figure 6: Rumenacka SEPA, NN1 station, hourly boxplots before (**left**) and during emergency state (**right**) (**a**) SO\({}_{2}\) (**b**) NO\({}_{\rm x}\) (**c**) CO (**d**) PM\({}_{2.5}\). level, the combustion of fuel for the heating of residential, commercial and institutional buildings, industrial activities and road traffic.
# Emerging Non-Canonical Functions and Regulation by p53: p53 and Stemess
## 1 Introduction
### Stemness in Normal and Cancer Cells
The frequencies of CSCs in tumors are higher than non-malignant stem cells in normal tissues. The ability of stem cells to undergo asymmetrical and symmetrical division enable deregulated signaling pathways associated with self-renewal and stem cell development to control tumor progression, metastasis and recurrence. Hence, these normal stem cell development pathways (i.e., Notch and Wnt/\(\beta\)-catenin) may be manipulated to prevent CSC genesis [93; 94; 95; 96; 97; 98; 99; 100]. Stem cells and cancer cells have a highly proliferative potential giving rise to normal and malignant heterogeneous tissues with cells exhibiting unique clonogenic properties [101; 102; 103]. According to the CSC theory, only a low percentage of unique cells are capable of initiating and maintaining a tumor [103]. Non-malignant tissues possess normal homeostatic mechanisms, unlike tumors that experience a deregulation of self-renewal [98; 99]. CSCs identified from human brain and colon cancer express CSC cell surface marker CD133 and have the capacity to self-renew and differentiate, while CD133-tumor cells lack tumorigenicity [75; 76; 77]. Through transcriptional repression of CD133, \(wt\)p53 can inhibit CSCs. p53 recruits histone deacetylase 1 (HDAC1) to the CD133 promoter and reduces histone H3 acetylation. Reduction in CD133 expression inhibits tumor cell proliferation, colony formation and the expression of Nanog, Oct4, Sox2 and c-Myc stemness transcription factors [104]. Transcription factors serve critical functions in ESC self-renewal, and many are overexpressed in tumors. Somatic cell reprogramming into iPSCs can be initiated by epigenetic changes and the expression of transcription factors Sox-2, c-Myc, Oct-4, Klf-4 and Lin-28 [105]. Also associated with this programming is the ESC and inner cell mass (ICM) pluripotency factor Nanog [106]. After DNA damage, p53 binds to the Nanog promoter and suppresses Nanog expression through Ser215 p53 phosphorylation and p53 transcriptional activity [107]. [PERSON] et al. (2013) [108] described the cross-talk signaling between focal adhesion kinase (FAK), p53, Mdm2 and Nanog in cancer stem cells. FAK binds and phosphorylates Mdm2, activates p53 degradation and has been implicated in CSC proliferation, motility, invasion and differentiation [109]. Since CSCs behave similarly to non-malignant stem cells, it is not surprising that \(mut\)p53 plays a role in this population as \(wt\)p53 is important to non-malignant stem cells. CSCs are less susceptible to chemotherapy compared to the vast majority of the tumors due to a slow proliferation rate, efficient DNA repair, high expression of anti-apoptotic proteins and ABC multi-drug pumps [110; 111; 112; 113]. Interestingly, p53 splice variant isoform \(\Delta\)133p53\(\beta\) promotes CSC potential in MCF-7 breast cancer cells, stimulating the expression of key pluripotency factors SOX2, OCT3/4 and Nanog [24; 114].
###### Abstract  
In this study, we report on the effectiveness of electrochemical and biological wastewater treatment for artificially prepared industrial wastewater, comprising small amounts of technologically important dyes, namely Acid Mixture composed of Acid Violet 90 (AV90) and Acid Red 357 (AR357) in Na\({}_{2}\)SO\({}_{4}\) (ESS--electrolyte supporting solution), as well as their impact on the environment, using _Lemna minor_ as a bioindicator. Our study revealed that among the tested dyes, the raw ones (AM in ESS+OM) and those subjected to electrooxidation with the use of an iron anode and a copper cathode [AMFe/Cuox in ESS+OM (OECD medium is a medium recommended by the Organization for Economic Co-operation and Development for _Lemna_ sp. Growth Inhibition Test)] were the most phytotoxic for _L. minor_. No phytotoxicity was detected for the tested plants in solution after electrooxidation with graphite anode and cathode (AM\({}_{\text{Co}}\) in ESS+OM). Quantitative identification of acid mixture removal was carried out by supplementary UPLC/MS-MS (Ultra-Performance Liquid Chromatography/tandem Mass Spectrometry) and UV-VIS (UltraViolet-Visible spectroscopy) instrumental analysis. The final removal after electrochemical and biological treatment of AV90 and AR357 dye components was 98 and over 99%, respectively. The results suggest that it may be a suitable replacement/addition for the generally used wastewater treatment methods.  
electrooxidation; galvanic cell; _Lemna minor_; wastewater treatment; acid mixture
## 3 Results and Discussion
### Composite Multiscale Entropy (CMSE) Analysis  
The signals registered during the experimental studies were also subjected to Composite Multiscale Entropy (CMSE) analysis. Using this method, dynamic experimental signals can be verified. The entropy level determines the degree of disorder in the measured signals. The findings of the analysis by this method are given in Figures 9-12.  
Figure 8: (a) Peak-to-peak and (b) RMS values for vibration acceleration with different feed per tooth.  
Figure 7: (a) Peak-to-peak and (b) RMS values for vibration acceleration with different cutting speeds.  
Figure 11: CMSE for acceleration signals registered with different cutting speeds.  
Figure 10: CMSE for displacement signals registered with different feed per tooth.  
Figure 9: CMSE for displacement signals registered with different cutting speeds.  
The CMSE was determined for \(\tau=50\); however, due to the lack of apparent variation in the further scale factor range, the plot area was restricted to \(\tau=0\)-20. The concept of CMSE is based on the coarse-graining procedure that uses a coarse-grained time series, as an average of the original data points within non-overlapping windows by increasing the scale factor \(\tau\). The length of each coarse-grained time series is \(N\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\! \!\!\!\!\!  
disorder compared to the shrinkfit toolholder. The presented results lead to the conclusion that the ER and hydraulic toolholder types can be used over a wider range of feed per tooth \(f_{2}\) changes with stability maintained during the milling process.  
Comparing vibration displacement and acceleration signals, a significantly lower level of Composite Multiscale Entropy was observed for the latter. The higher level of CMSE in the case of the displacement signal is a consequence of the milling strategy (trochoidal milling). The acceleration signal reflects the rapidity of the milling process dynamics. A lower level of CMSE for the acceleration signal indicates a more stable milling process. Thus, in the context of the signal analysis carried out using Composite Multiscale Entropy, it can be concluded that trochoidal milling shows a lower abruptness of dynamic changes. This has a more advantageous effect on the stability of the milling process.
# Slip Estimation Model for Planetary Rover Using Gaussian Process Regression
## 3 Data Collection
### Output Slip Observations  
The slip of the rover is measured based on the rover body frame, as shown in Figure 5. While the rover was driving on natural terrain, the slip could be broken down into longitudinal slip (slip_x), lateral slip (slip_y), and rotational slip (slip yaw) [31]. This paper assumed that the slip yaw was trivial and could be canceled out by appropriate orientation control [32]; we mainly focused on slip_x and slip_y of the rover, defined as follows:  
\[\text{slip\_}x=(v_{c}-v_{x})/v_{c} \tag{1}\]  
\[\text{slip\_}y=v_{y}/v_{c} \tag{2}\]  
where \(v_{c}\) is the expected velocity of rover, \(v_{x}\) is the actual longitudinal velocity, and \(v_{y}\) is the lateral slip velocity of the rover (Figure 6).  
Slip cannot be directly observed. It needs to be calculated using position and attitude information from the rover in combination with wheel speed information. As shown in Figure 6, time \(n\) and time \(n+1\) were the adjacent sampling moments. The slip observations were calculated in the manner that follows.  
1. First, we obtained the rover's position, \((x_{C\_n},y_{C\_n},z_{C\_n})\) and \((x_{C\_n+1},y_{C\_n+1},z_{C\_n+1})\) at time of \(n\) and \(n+1\), respectively. Then, we calculated the moving distance of the rover in the site coordinate system (Figure 6). \[(\Delta x_{C},\Delta y_{C},\Delta z_{C})=(x_{C\_n+1}-x_{C\_n},y_{C\_n+1}-y_{C\_ n},z_{C\_n+1}-z_{C\_n})\] (3) It should be noted that \((\Delta x_{C},\Delta y_{C},\Delta z_{C})\) was measured in the site coordinate system, but the slip was measured based on the rover body frame, so \((\Delta x_{C},\Delta y_{C},\Delta z_{C})\) must be converted to the rover body coordinate system \((\Delta x_{B},\Delta y_{B},\Delta z_{B})\).
2. Next, we calculated the actual longitudinal velocity \(v_{x}=\Delta x_{B}/\Delta t\) and lateral slip velocity \(v_{y}=\Delta y_{B}/\Delta t\) of the rover. It was known that the sampling frequency of the rover's position and attitude was about 20 Hz, so the time interval \(\Delta t=0.05\ s\).  
Figure 6: The velocity decomposition.  
3. We calculated the expected velocity of the rover. Due to the small time interval, we considered that the rover moved in a straight or near-straight path during the interval time \(\Delta t\), and the expected velocity of the rover was defined as follows: \[v_{c}=\left(\sum_{i=1}^{6}(w_{i}r)\right)/(6\Delta t)\] (4)
4. We calculated the slip_x and slip_y according to Equations (1) and (2), respectively.
## 1 Introduction  
Wireless Sensor Networks (WSNs) are a highly significant technological development, enabling the efficient gathering and sharing of data across multiple domains [1]. These networks are composed of small sensor nodes that possess capabilities for sensing, data processing, and wireless communication. WSNs are applied in a variety of areas, including environmental monitoring, healthcare, industrial automation, and smart cities [2, 3, 4, 5, 6], etc.  
Despite their numerous benefits, WSNs face significant challenges, particularly in terms of energy efficiency and network longevity. Traditional clustering protocols, such as the Low-Energy Adaptive Clustering Hierarchy (LEACH), have been widely used to address these issues. However, LEACH and similar protocols often fail to optimize energy consumption [7, 8, 9, 10] effectively in large-scale networks due to their static and non-adaptive nature resulting in the uneven energy distribution among sensor nodes, leading effective for a variety of applications, supporting their continued expansion and utility in numerous industries.
## 2 Numerical Models
### Model Verification  
The independence of load steps and meshes, as well as the reasonableness of fluid-solid coupling contact parameters, has been confirmed with an error of only 2.13%. To verify the rationality of the model, the impact strength test was carried out according to the Chinese standard _TB/T1335-1996_[26]. The impact vehicle collided with a stationary, non-braking transport vehicle equipped with the test tank container at specific velocities of 4 km/h, 5 km/h, 6 km/h, 7 km/h, and 8 km/h. After the collision between the impact vehicle and the transport vehicle, the twist lock attached to the transport vehicle collided with the corner fittings of the tank container. The two vehicles were then connected by buffers and traveled together in the direction of the rail. In order to measure stress, several strain gauges were installed on the tank containers, especially on the frame. The results indicate that the simulation results are in good agreement with the experimental results, suggesting that the finite element model is reasonable [15].  
\begin{table}
\begin{tabular}{c c c c} \hline
**Item** & **Density (t/mm\({}^{3}\))** & **Elastic Modulus (MPa)** & **Poisson’s Ratio** \\ \hline Impact vehicle & \(0.115\times 10^{-4}\) & \(0.201\times 10^{6}\) & 0.3 \\ Transport vehicle & \(0.295\times 10^{-8}\) & \(0.201\times 10^{6}\) & 0.3 \\ Twist lock & \(0.785\times 10^{-8}\) & \(0.201\times 10^{6}\) & 0.3 \\ Corner fitting & \(0.785\times 10^{-8}\) & \(0.201\times 10^{6}\) & 0.3 \\ GFRP & \(0.185\times 10^{-8}\) & \(0.720\times 10^{5}\) & 0.26 \\ \hline \end{tabular}
\end{table}
Table 2: Other structural material properties.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Item** & **Value** & **Item** & **Value** \\ \hline Temperature (\({}^{\circ}\)C) & \(-161.87\) & \(c_{V}\) (J/kg\(\cdot\)k) & 2056.3 \\ Pressure (MPa) & 0.1 & \(k_{T}\) (1/Pa) & \(2.22\times 10^{-9}\) \\ Density (kg/m\({}^{3}\)) & 460 & \(\alpha\) (1/K) & 0.00346 \\ Sound velocity (m/s) & 1341.3 & 70 & 1.648 \\ Viscosity (MPa\(\cdot\)s) & \(0.118\times 10^{-9}\) & & \\ \hline \end{tabular}
\end{table}
Table 3: Material properties of LNG.