esa-sceva/satcom-chunk-collection huggingface dataset:
# Heterologous Production of Flavour and Aroma Compounds in _Saccharomyces cerevisiae_
## 2 Heterologous Compounds Derived from Intermediates of the Shikimate Pathway
Although levels of this highly valuable flavouring compound produced by the above recombinant strain are not yet adequate for its industrial application, this study paves the way for future development of _S. cerevisiae_ cell factories for commercial raspberry ketone production. **Cinnamaldehyde** is the main impact flavonoid found in the bark, lives and fruit of the cinnamon tree _Cinnamomum zeylanicum_[22; 23]. It is the main organic compound that contributes the flavour and aroma of cinnamon and is commonly used as a flavouring compound in the food and beverage industries [24; 25]. Many chemical methods exist to produce this flavonoid synthetically, however steam distillation of the oil derived from the cinnamon bark remains the most economical and commonly used method [26; 27]. Cinnamaldehyde can be heterologously produced in _S. cerevisiae_ through an extension of the shikimate pathway (via phenylalanine), through the activity of two heterologous enzymes; phenylalanine ammonia lyase 2 (PAL2) which converts phenylalanine into _trans_-cinnamic acid, and aryl carboxylic acid reductase (ACAR) which converts _trans_-cinnamic acid into cinnamaldehyde (Figure 1). As also seen for the production of vanillin, activation of ACAR was achieved by overexpression of a phosphopantetheinyl transferase [28]. Although successfully achieving a proof of concept for de novo production of cinnamaldehyde, only negligible amounts of this compound were produced, primarily due to a combination of cinnamaldehyde toxicity (\(\sim\)0.68 mM in _S. cerevisiae_) and off-target metabolism towards cinnamyl alcohol and hydrocinnamyl alcohol [28; 29]. Thus, while heterologous production of cinnamaldehyde has been achieved, the transition to an industrial scale will require further metabolic optimization.
# Short-Term Wind Speed Forecasting Using Statistical and Machine Learning Methods
## 2 Models
### Benchmark Models
#### 2.2.2 Generalised Additive Models (GAMs)  
GAMs are models which allow for an additive relationship between its dependent and independent variables [34]. GAMs are more flexible compared to the Generalised linear models (GLMs) which allow for a functional relationship between response and predictor variables. GAMs make use of non-linear forms and smooth functions of predictor variables in modelling that is applicable in different forms. A GAM can be formulated in its simplest form as given in [34]:  
\[g(\mu_{t})=\mathbb{A}\theta+\sum_{j=1}^{p}f_{j}(x_{jt})+\varepsilon_{t},\quad Y _{t}\sim EF(u_{t},\phi)\quad t=1,\cdots,n, \tag{5}\]  
where \(Y_{t}\) represents the univariate response variable from an exponential family distribution having mean \(u_{t}\), \(\phi\) is the scale parameter, \(g\) represents the smooth monotonic link function, \(\mathbb{A}\) is a design matrix, \(\theta\) represents an unknown parameter vector, \(f_{j}\) is an unknown smooth function of the predictor variable \(x_{j}\) that may have a vector value and \(\varepsilon_{t}\) denotes independent identical distribution random errors [34; 35].
# EEG Emotion Classification Based on Graph Convolutional Network
###### Abstract
Studies in neuroscience reveal that the functionality of brain regions and their interactions are important factors in the process of emotion production [9; 10; 11; 12]. Extracting interactions and features firm regions rather than channels allows us to follow the nature of brain structures and has the potential to create the ability to interpret the association between brain activity and emotion states. Based on such a point, there are several works proposed to tackle emotion recognition from the perspective of brain regions. Ref. [13] uses Bi-LSTM to capture the regional feature and global feature; Refs. [14; 15] focus on extracting features for hemisphere. Although these approaches develop effective ways to present regional features, they do not thoroughly investigate the topological structure and interactions among brain regions. Ref. [16] addresses this issue by constructing local and global graphs. However, it takes the assumption that the connection only exists between nodes or regions that have similar characteristics, which limits the scope on investigating the complex structure of regions. In this study, we propose a model that investigates both the internal and the global activity of brain regions to identify emotions. Our model firstly adopts a preprocessing technique and feature extractions on the input EEG signal. Next, the channels within the same region are grouped together to form regional data. The region encoder is applied to extract the representative features of each region. For the purpose of learning topological structure among different regions, the graph convolutional network (GCN) is employed to learn more discriminative features. The main contributions of our study include:  
1. We propose a model that solves emotion recognition based on region-level representation to learn the activity inside and across various brain regions. Such interactions have been proven to be highly relevant to human emotion state from a neurological point of view. 2. To capture the correlation between brain regions, we construct a graph on EEG signals and employ graph spectral filtering with dynamical adjacency matrix. This approach is more applicable to study the interplay of brain areas since it does not limit itself to the notion of geographic closeness and provides flexibility in detecting function-level interactions. 3. To thoroughly investigate how to formulate regional-level characteristics, we conduct a comprehensive experimental study in terms of different region encoders, region division strategies and input features. The results show that our approach outperforms many existing methods on DEAP and Dreamer datasets.
## 7 Transcription-Coupled Repair (TCR): RNA Polymerase as the DNA Damage Sensor  
The first evidence of transcription coupled repair (TCR) was reported decades ago [126, 127, 128, 129]. Studies in both mammalian cells and _E. coli_ showed that actively transcribed genes were repaired much more rapidly than the rest of the genome and that this could be attributed to the repair of the 'template' strand [127, 128, 129]. Subsequently, it was revealed that the strand-specific repair is due to a specialized form of NER and that the protein responsible for the coupling of excision repair with transcription, the transcription-repair coupling factor (TRCF), in bacteria is Mfd (mutation frequency decline) [130, 131]. The Mfd protein increases the rate and the efficiency of the repair, compared to GGR [132, 133, 134]. In addition, the efficiency of repair is significantly increased for substrates not effectively recognized by UvrA, such as cyclobutane pyrimidine dimer (CDD) [135].  
The transcription coupled repair pathway operates when a bulky DNA lesion on the template strand causes stalling of the transcription elongation complex (EC); the lesion inhibits transcription as well as hinders movement of DNA replication forks. During TCR, the stalled RNAP and the nascent transcript are removed from the DNA template and the UvrA or UvrA\({}_{\text{2}}\)B\({}_{\text{2}}\) complex is loaded onto the DNA to initiate excision repair of the DNA lesion. According to the 'pervasive transcription' model [136], the entire genome is being transcribed, albeit at low levels. In this view, RNAP is a particularly useful DNA damage sensor in that it is persistently surveilling the genome for lesions using efficient one-dimensional diffusion.  
Since its discovery, TCR was believed to be mediated by the Mfd protein [137, 130, 131, 77, 133], which recognizes the stalled RNAP and displaces it by forward translocation [25]. However, recent studies suggest that there could be an alternative TCR pathway that is independent of Mfd and involves RNAP backtracking elicited by UvrD [28, 29, 138]. Both of these pathways are discussed below.
# A Testbed to Evaluate the FIWARE-Based IoT Platform in the Domain of Precision Agriculture
## 3 Fiware
### FIWARE NGSI Context Management
#### 3.2.3 FIWARE NGSI API  
The OMA NGSI-9 and NGSI-10 interfaces are HTTP RESTful APIs [45]. The FIWARE versions of these APIs allow mediation between a large number of services. The NGSI-9 interface helps makes the contextual information produced by an entity accessible to other entities. In so doing it provides two operations (among others): _registerContext_ to register the entities that publish their data and _discoverContextAvailability_ to discover them. The NGSI-10 API enables the contextual information exchange itself. To do that it offers, among others, _updateContext_ operations to update contextual information and _subscribeContext_ to create subscriptions to contextual information.
## 3 Preliminaries
### Variational Formulation
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Symbol** & **Description** & **Definition** \\ \hline
**Variational Data Assimilation (VarDA)** & _(notations used in Section 3.1)_ \\ \(\mathbf{x}\) & State vector & Model variables (e.g., temperature, winds, pressure) \\ \(\mathbf{x}^{b}\) & Background state & Prior estimate of the state \\ \(\mathbf{x}^{a}\) & Analysis state & Optimized estimate after data assimilation \\ \(\mathbf{x}^{t}\) & True state & Theoretical true state of the atmosphere, unknown in practice \\ \(\mathbf{y}^{o}\) & Observations & Atmospheric and multi-modal observational data \\ \(\mathcal{H}\) & Observation operator & Map from model space to observation space \\ \(\mathbf{H}\) & Tangent linear of the observation operator & \(\mathbf{H}=\frac{\partial\mathcal{H}}{\partial\mathbf{x}}\) \\ \(\mathbf{B}\) & Background error covariance matrix & Represents uncertainty in background state \\ \(\mathbf{R}\) & Observation error covariance matrix & Represents uncertainty in observations \\ \(J_{cost}(\mathbf{x})\) & Cost function & Minimization objective in variational data assimilation \\ \hline \hline \multicolumn{3}{c}{**Background Error Covariance Matrix** _(notations used in Section 3.2)_ \\ \(\varepsilon\) & Background error vector & Difference between background and true state \\ \(\sigma_{t}^{2}\) & Variance & Diagonal element of \(\mathbf{B}\), representing the background error variance of \\  & Variance & the i-th state variable, \(i=1,2,\ldots,n\) \\ \(\rho_{mn}\) & Correlation coefficient & \(\rho_{mn}=\frac{\sigma_{mn}^{2}}{\sigma_{n}^{2}}\), \(m\
eq n\); measures linear dependence between \\  & & different variables \\ \(\mathbf{C}\) & Correlation matrix & Matrix composed of \(\rho_{mn}\) \\ \(\mathbf{D}\) & Diagonal matrix & Contains the background error standard deviations \\ \hline \multicolumn{3}{c}{**Deep Reinforcement Learning** _(notations used in Section 3.3)_ \\ \(\mathcal{S}\) & State space & Set of all possible states in the RL environment \\ \(\mathcal{A}\) & Action space & Set of all possible actions an agent can take \\ \(P(s_{t+1}|s_{t},a_{t})\) & State transition function & Conditional probability density function of transitioning to \(s_{t+1}\) \\ \(R(s_{t},a_{t})\) & Reward function & \(\mathrm{\hat{A}}\) scalar reward received after taking action \(a_{t}\) in state \(s_{t}\), quantifying the immediate impact of the action \\ \(\gamma\) & Discount factor & A scalar ranging between 0 and 1 that discounts long-term rewards \\ \(\pi_{\theta}\) & Policy & A stochastic policy parameterized by \(\theta\) that maps each state to a \\ \(G_{t}\) & Return at \(t\) & Distribution over actions \\ \(\tau\) & Trajectory & A sequence of states and actions: \(\tau=(s_{0},a_{0},s_{1},a_{1},\ldots,s_{T},a_{T})\), where \(T\) denotes the total time \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of mathematical notations used in Section 3.
## 3 Methodology
### Preliminary  
Reinforcement learning algorithms involve a learning agent interacting with its environment to find and select actions based on the current state and feedback signals received to optimize a predefined objective. These algorithms rely on reward functions, which provide feedback signals to guide the learning process by evaluating the effects of taken actions. However, designing an appropriate reward function is not trivial, as it can be challenging to guide the learning agent without getting trapped in local optima [34]. Consequently, even seemingly solvable reinforcement learning scenarios may produce unexpected results. The goal of reinforcement learning is either to maximize the expected return of a reward signal or to minimize costs for optimization problems, indicating how well the learning agent performs [35]. The RL reward signals can be classified into two categories: sparse and dense rewards. Sparse rewards provide feedback only at the end of a learning episode, indicating whether the agent succeeded or failed in its task. On the other hand, dense rewards give feedback at every stage of the decision-making process, making them more effective for learning compared to their sparse counterparts. However, learning based on sparse rewards can be time-consuming as the agent needs to explore more to collect rewards and learn the optimal policy. Traditional multi-objective RL optimization approaches frequently combine dense rewards linearly and reduce them to a single scalar objective using methods like the weighted sum [36]. While this approach aims to guide the learning agent in progressing toward its goal, it often necessitates intensive weight tuning to set a priori preferences among objectives.  
In this study, we utilize'reward shaping', incorporating heuristic knowledge from the system designer to provide learning agents with additional reward signals. These extra rewards enable the agent to differentiate between good and bad actions early in the learning process, encouraging exploration in areas of the state space that hold promising solutions. Such prior knowledge can be integrated into the reward function or initial agent values. Despite often deploying reinforcement learning agents without prior information, previous research [37] has demonstrated the benefits of providing heuristic knowledge in guiding exploration. Our study shows that designing meaningful reward shapings leads to faster and improved solutions.
# A Review: Factors Affecting Internal Two-Phase Flow-Induced Vibrations
## 2 Factors Affecting FIV in Internal Two-Phase Flow
### Effect of Flow Regimes
[PERSON] and [PERSON] [63] also reported the behavior of force transducer signals at the elbow due to different flow regimes. Force signals are composed of regular impulses in slug flow regime, which can be attributed to the passage of liquid slugs. The force spectrum in bubbly flow regime is rather broadband due to the presence of bubbles, whereas a mixture of narrow-band and periodic components was detected in churn flow. The force signal observed in annular flow was composed of sharp impulses, which can be due to droplet entrainment. Although the majority of the literature primarily focuses on how FIV is affected by different flow regimes, it is also reported that vibration can cause a change in flow regime. [PERSON] et al. [87, 88] observed the effect of oscillation on horizontal two-phase flow patterns in rectangular mini-channel. The flow behavior became increasingly disturbed under the influence of mechanical oscillations when compared to a stationary condition. The oscillation effect of the two-phase flow behavior was herein confirmed [88]. Moreover, it was reported than when the test section oscillated above a certain level, stratified flow would change into annular flow [88]. In conclusion, FIV behavior changes with a change in flow regime and it is a flow regime specific phenomenon. Flow-induced vibrations in slug and churn flow regimes are more intense and a further investigation is necessary, especially in annular flow regime, which appears to have received the least attention by researchers. In addition, FIV research relies on flow regime maps developed in 1970s and 1980s [82, 89], and new approaches, such as flow regime map developed using machine learning algorithms or flow regimes maps for specific experimental conditions, are encouraged to be used.
# Real-Time Phase Bias Estimation for BeiDou Satellites Based on Consideration of Orbit Errors
## 2 Methods  
The well-known ionosphere-free (IF) combination is commonly used in satellite clock estimation and PPP processing to eliminate the first-order ionosphere delay. The IF carrier-phase and pseudorange observations between the satellite \(k\) and the receiver \(i\) can be written as:  
\[\begin{array}{l}P_{IF,i}^{k}=\alpha P_{1,i}^{k}-\beta P_{2,i}^{k}=\rho_{i}^{ k}+cdt_{i}-cdt^{k}+orb_{i}^{k}+\varepsilon_{IF,i}^{k}\\ L_{IF,i}^{k}=\alpha L_{1,i}^{k}-\beta L_{2,i}^{k}=\rho_{i}^{k}+cdt_{i}-cdt^{k}+ N_{IF,i}^{k}+orb_{i}^{k}+\varepsilon_{IF,i}^{k}\end{array} \tag{1}\]  
where \(\alpha=f_{1}^{k2}/(f_{1}^{k2}-f_{2}^{k2})\) and \(\beta=f_{2}^{k2}/(f_{1}^{k2}-f_{2}^{k2})\). \(P_{i}^{k}\) and \(L_{i}^{k}\) are the code and carrier phase observations, respectively, at frequency \(f^{k}\). \(\rho_{i}^{k}\) is a nondispersive delay that includes the geometric delay, the tropospheric delay, and any other delay that has an identical effect on all observations. \(dt_{i}\) and \(dt^{k}\) are the receiver and satellite clock biases, respectively. \(c\) is the speed of light in a vacuum. \(N_{IF,i}^{k}\) denotes the ambiguity of the IF carrier-phase observation. \(orb_{i}^{k}\) is the satellite orbit error mapped into the receiver-satellite direction, which is typically assessed using signal-in-space ranging error (SISRE) values [28]. The satellite-induced code bias (SICB) of the BDS must be corrected using e.g., the model reported by [PERSON] and [PERSON] (2015) [29]. Note that the GEO satellites can be corrected using the same model that is used for the IGSO satellites [30]. Finally, \(e_{IF,i}^{k}\) and \(e_{IF,i}^{k}\) denote the unmodeled code and carrier phase errors, respectively, which include multipath effects and noise. Both the phase center correction and the phase windup effect [1; 2; 31] must be considered during the modeling process.  
All real-time processes in this study are based on Equation (1). First, we fix the ultra-rapid orbit to enable estimation of the satellite clocks using a forward Kalman filter. To simulate a real-time scenario, we used the latest predicted orbits with 3 h of release latency. These clocks and the predicted orbits were then fixed as shown in the subsequent FCB estimation. [PERSON] et al. [32] and [PERSON] and [PERSON] [33] have previously proved that the current PPP ambiguity resolution methods are equivalent in theoretical terms. Therefore, in this study, we focus solely on the method that was proposed by [PERSON] et al. [5], in which the FCB determination is critical. Finally, the satellite's orbit, clock and FCB products are broadcast to all PPP users with a simulated latency of 3 s to assess the PPP-IAR performance.
# Dynamic Low-Emission Zones for Urban Mobility: A Systematic Review
## 4 Results
### Access-Control Systems
#### 4.6.1 Secure and Privacy-Preserving Lightweight Access-Control System for Low-Emission Zones [32]  
This study introduces a lightweight and privacy-preserving access-control system for LEZs that prioritizes deployability in real-world scenarios while addressing the limitations of existing methods. The proposed system requires only low-cost infrastructure and leverages common smartphones as the primary interface, eliminating the need for expensive roadside units or vehicle-installed devices. The key contributions are as follows:  
* Privacy Preservation: The system ensures that honest drivers maintain their anonymity. Cryptographic protocols guarantee that only non-compliant drivers are identified, addressing privacy concerns raised by current intrusive LEZ systems.
* Low-Cost Infrastructure: Using lightweight cryptographic protocols, the system operates effectively on single-board computers, reducing implementation and maintenance costs.
* Smartphone Integration: Drivers interact with the system using their smartphones, which serve as both the identification tool and access validator, removing the need for dedicated vehicle hardware.
* Real-Time Data Collection: The system collects metadata on vehicle entries and exits, which can be analyzed to optimize LEZ management and policies without compromising user privacy.
* Feasibility Validation: Laboratory and field tests confirmed the system's effectiveness under real-world conditions, demonstrating its ability to handle varying traffic speeds and environmental constraints.  
Widely available technologies such as Bluetooth and Wi-Fi may leverage dynamic LEZ implementation, providing scalability, cost efficiency, and public acceptance. They ensure seamless integration and adaptability across diverse urban environments, enhancing deployment feasibility and operational flexibility.
## 5 Finite Element Method (FEM) based Numerical Simulations
### Parametric Analysis Results
#### 5.3.1 Effect of Friction Coefficient  
Figures 17 and 18 provide the correlations of friction coefficient with the initial friction load and ultimate strength, respectively. For MC-45 and MC-35 specimens, the initial friction load increased nonlinearly with the growing friction coefficient; for MC-55 and MC-60 specimens, the initial friction load increased linearly with the growing friction coefficient. The smaller the shear-compression ratio, the greater the friction coefficient affects the initial friction load. Moreover, the ultimate strength of all specimens increased linearly with the growing friction coefficient. The smaller the shear-compression ratio, the greater the friction coefficient affects the ultimate strength. For the specimens with the shear-compression ratio of 0.70 and the friction coefficient of 0.6, the ultimate strength was regarded as the initial friction load in this paper. Thus, the corresponding curve in Figure 18 is a polyline.  
Figure 16: Simulated and test results on the relationship between load and relative slippage of specimens.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Initial Friction Load \(\mathbf{F_{1}}\)**} & \multicolumn{3}{c}{**Ultimate Strength \(\mathbf{F_{U}}\)**} \\ \cline{2-7}
**Specimen** & **Mean Test** & **Simulated** & **Relative** & **Mean Test** & **Simulated** & **Relative** \\  & **Value/kN** & **Value/kN** & **Error/\%** & **Value/kN** & **Value/kN** & **Error/\%** \\ \hline MC-35 & 2207.95 & 2417.11 & 8.65 & 2207.95 & 3134.60 & 29.56 \\ MC-45 & 1384.45 & 1261.34 & \(-\)9.76 & 1570.78 & 1370.20 & \(-\)14.64 \\ MC-60 & 435.98 & 460.57 & 5.34 & 788.60 & 743.70 & \(-\)6.04 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison between simulated results and test results.