esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Features  
The employed feature set is divided into two categories: one consisting of hand-crafted string-based features from the MT outputs, _SSE_ and _Sr_, and the other consisting of commonly used NLP Metrics. The first category contains (i) simple features (e.g., distances like Levenshtein [34], longest word for _S1_, _S2_, _Sr_, _SSE_, features using the Length Factor (LF) [35]), (ii) features identifying the noise in the corpus (e.g., repeated words/characters, unusually long words in number of characters), and (iii) features providing linguistic information from the _SSE_ in EN (e.g., the length of the _SSE_ in number of words and number of characters). The feature set was inspired by the work of [PERSON] [36,37]. The second category contains the NLP metrics, that is, the BLEU score, METEOR, TER and WER for _(S1_, _S2_), _(S1_, _Sr_), _(S2_, _Sr_). To calculate the BLEU score, an implementation of the BLEU score from the Python Natural Language Toolkit library [38] is adopted. For the calculation of the other three metrics, the code from GitHub [39] is used. The total number of features is 82. A detailed description of the feature set can be found in Reference [21].  
In the present work, the employed feature set is extended and two additional novel linguistic feature pairs, which belong to the first category, have been used (increasing thereby the feature dimensions from 82 to 86). These features are similarity-based. The first feature _cnt_ shows the percentage of identical words between the MT outputs and _Sr_, without taking into account the word order. The second feature _cnt_ shows the percentage of identical parts of MT output included in the _Sr_. More specifically, this feature shows whether the MT output is a contiguous subsequence of _Sr_. The features are defined in Equations (1) and (2) respectively:  
\[c_{mt}=\frac{|S_{mt}\cap S_{r}|}{|S_{mt}\cup S_{r}|} \tag{1}\]  
\[r_{mt}=\frac{|S_{mt}\cap S_{r}|}{|(S_{mt}\cap S_{r})^{\prime}|}\,with|(S_{mt }\cap S_{r})^{\prime}|\
eq 0. \tag{2}\]  
where _Smt_ is one of the _S1_, _S2_.  
As an example, if  
\(\textit{Sr}=\{\tau_{1}(\textit{the}),\textit{unnegica}\) (_department_), \(\tau_{\textit{good}}\textit{top}(\textit{specify})\), \(\\(S2=\{\eta_{1}\) (_the_), _unregoix_ (_department_), _unregoix_(_department_), _un_goodiopi_, _un_typ_ (_the_), _negio_oo_ (_period_), then _cnt_ = 0.57, _rmt_ = 1.3 for _S1_ MT output and _cmt_ = 0.43, _rmt_ = 0.75 for _S2_ MT output.  
All feature values were calculated using MATLAB, and their values have been normalized and vary between 0 and 1.
## 3 Results and Discussion
### Aldehydes  
Fifteen aliphatic and aromatic aldehydes were identified in fresh _Pleurotus_ samples, 12 of them in _P. ostreatus_ and 14 in _P. eryngii_ (Tables S1 and S2). Aliphatic aldehydes are biochemically derived from fatty acids, whereas the aromatic ones derive from amino acids [41]. Aldehydes confer a fresh, floral grassy and fatty aroma [17; 29], and are characterised by low odor thresholds, i.e., they can be perceived even in small concentrations [40]. As a general trend, three out of four _P. eryngii_ strains contained more volatile aldehydes than _P. ostreatus_ strains, while this was also the case for mushrooms cultivated on WS and/or GM (Figure 1). Hexanal was the predominant aldehyde in both species (in accordance to the findings of a recent study [2]), followed by 2-octenal, n-octanal and 2-heptenal. _P. eryngii_ contained three times more benzaldehyde than _P. ostreatus_ (\(17.72\pm 11.17\) vs. 5.58 \(\pm\) 2.96 \(\mathrm{\SIUnitSymbolMicro g}\)/g f.w). According to [PERSON] et al. (1998) [41], benzaldehyde was the major volatile compound in fruitbodies of _P. eryngii_. This aromatic aldehyde derives from the catabolism or oxidative degradation of phenylalanine [29; 42; 43] which is present in the _Pleurotus_ mushrooms studied [44]. Benzaldehyde together with phenylacetaldehyde and short-chain aldehydes (pentanal, hexanal, octanal, nonanal, _trans_-2-heptenal) detected in the mushrooms studied (Table 1), are oxidative degradation products of oleic and linoleic acids; according to a recent report, they are among the major contributors of edible oil flavors during ambient storage [45]. Heptanal, 2-hexenal, phenylacetaldehyde, 2,4-nonadienal, and 2,4 decadienals were also present in the volatiles of both mushroom species, at slightly higher concentrations in _P. eryngii_; finally, nonanal, undecenal and 3-dodecenal were detected only in _P. eryngii_, while 2-phenyl-2-butenal was recorded onlyin _P. ostreatus_. The aldehydes identified in this study have been previously reported in several mushroom species [2; 17; 29; 40].
# Game Theory-Based Interactive Control for Human-Machine Cooperative Driving
## 4 Vehicle Interactive Control Strategy
### Shared Control Strategy
#### 4.1.1 Driving Safety Field  
The driving safety field \(E\) is an effective method to dynamically assess the degree of driving risk in the ego vehicle through the surrounding environment. It mainly consists of the road potential field, the obstacle potential field, and the driver behavior field [28]. In this paper, an obstacle potential field \(E_{o}\) is adopted to construct the driving safety field.  
The obstacle potential field is used to assess the impact of obstacles on the road that pose a risk of collision with objective vehicles. It mainly includes surrounding vehicles moving on the road, stationary obstacles, or stopped vehicles. Our study only considers the obstacle potential field between the objective vehicle and surrounding moving vehicles. The objective vehicle can be either an HSCV or CAV on the road, and its potential field value under the coordinate \((X,Y)\) is defined as follows [31]:  
\[\left\{\begin{aligned} & E^{V_{i}}(X,Y)=k^{obs}\cdot e^{-\left\{ \frac{\left(X-X^{V_{i}}\right)^{2}}{2\sigma_{x}^{2}}+\frac{\left(Y-Y^{V_{i}} \right)^{2}}{2\sigma_{y}^{2}}\right\}^{\alpha_{1}}+2\sigma_{x}^{V_{i}}\xi}\\ &\xi=sgn(X-X^{V_{i}})\frac{\left(X-X^{V_{i}}\right)^{2}}{2\sigma _{x}^{2}}/\sqrt{\frac{\left(X-X^{V_{i}}\right)^{2}}{2\sigma_{x}^{2}}+\frac{ \left(Y-Y^{V_{i}}\right)^{2}}{2\sigma_{y}^{2}}}\end{aligned}\right. \tag{11}\]  
\((X^{V_{i}},Y^{V_{i}})\) is the position of the center of mass of \(V_{i}\) with respect to the objective vehicle, where the surrounding vehicle consists of the LV and NV around the objective vehicle. The convergence coefficients \(\sigma_{x}\) and \(\sigma_{y}\) are used to adjust the influence range of the longitudinal and lateral obstacle potential fields, respectively. \(v_{x}^{V_{i}}\) is the longitudinal velocity of the moving obstacle vehicle. \(k^{obs}\), \(c_{1}\), and \(c_{2}\) represent the shape coefficients.
# Toxins of Prokaryotic Toxin-Antitoxin Systems with Sequence-Specific Endoribonuclease Activity
## 3 Physiological Roles
### Persister  
The importance of TA systems on the production of dormant cells has been implicated [134]. Due to the stochastic nature of the growth state variation, partially due to TA induction, patterns among the members of a community, a fraction of the population becomes transiently metabolically inactive [135; 136]. These dormant cells are insusceptible to stresses, such as antibiotic treatment, and are called persisters [137]. A higher level of TA toxins including _dinJ_, _yoeB_, _yefM_, _relE_, _chpA_ and _mqsR_ is also observed in non-dividing persister cells [124].  
A TA module, _hipAB_, was originally proposed to be linked to persister cell formation [138; 139]. In this particular model, the HipA concentration above a threshold level determines the number of persister cells and the duration of dormancy in a ppGpp-dependent manner [140]. HipA phosphorylates and inactivates glutamyl tRNA synthetase [141]. The resulting accumulation of the stalled ribosome increases ppGpp through the activation of ppGpp synthetase I (RelA). Additionally, HipAB can bind to the promoter region of _relA_, possibly modulating the amount of RelA, thereby ppGpp level [142]. Lon-dependent degradation of TA antitoxins is crucial for HipA-induced persister formation, as it depends on the activation of at least ten other TA systems [136; 143].  
Recent studies have shown that TA toxins can increase the fraction of persisters even in the cells lacking ppGpp, albeit at lower levels [144]. Many TA toxins (MqsR, MazF, GhoT, YafQ, RelE) increase persisters when overexpressed in _xrelA\(\Delta\)spoT_ cells, which cannot produce ppGpp [144].  
It is interesting to note that the deletion of _rpoS_, as well as stress resistance genes also has been shown to increase persisters [145]. As mentioned above, MqsA modulates _rpoS_ expression at the transcriptional level [96]. A number of persisters within a population can also be influenced by TsB, a TA toxin that lacks ribonuclease activity, but affects the proton motive force, [125]. Many different treatments that are associated with reduced growth rate appear to increase in persisters [145]. More study is required to decipher the genetic and biochemical basis for the formation of persisters, TA systems and the general stress response.
## 3 Material and Methods
Calculation of private desirabilities for each value of the used criteria: \[ \begin{split} d_{max\_expr}=exp(-exp(-Y_{max\_expr}))\\ d_{var}=exp(-exp(-Y_{var}))\\ d_{entr}=exp(-exp(-Y_{entr}))\end{split} \] (5)
4. Calculation of the general index of the gene expression profiles significance as a geometric mean of the private desirabilities: \[GI=(d_{max\_expr}\cdot d_{var}\cdot d_{entr})^{1/3}\] (6)  
A higher value of the general index (6) corresponds to a higher significance level of the appropriate GEP. Figure 3 shows the structural block chart of the algorithm that forms subsets of informative gene expression profiles based on Harrington's desirability function and evaluating the model's adequacy by calculating the classification accuracy of the investigated samples. Figure 2: Harrington’s desirability function and standard marks on the desirability scale. Implementation of the model assumes the following stages:  
1. Formation of gene expression matrices and vectors of statistical criteria and Shannon entropy values. 1.1. Formation of a gene expression matrix, the values of which are determined by using both DNA microarray experiments or an mRNA molecule sequencing method. 1.2. For each gene expression profile, the maximum expression value, variance, and Shannon entropy are calculated and the vectors of the received values are formed. 2. Formation of subsets of various informativity level gene expression profiles using the Harrington desirability method. 2.1. Calculation of the coefficients of the linear equation to transform the scales of quality criteria of GEPs into the scale of the dimensionless parameter Y, taking into account the boundary values of the corresponding criteria by Equation (3). 2.2. For each value of the statistical criteria and Shannon's entropy, calculate the value of the parameter Y by Equation (4). 2.3. Calculation of private desirabilities for each of the criteria by Equation (5) and the generalized desirability index by Equation (6). 2.4. Formation of subsets of GEPs considering the level of their significance using the results obtained during the implementation of steps two and three of this procedure. 3. Application of the sample classification model using subsets of gene expressions with various significance levels. 3.1. Choice and adjustment of the classifier and formation of data classification quality criteria. 3.2. Classification of objects, the attributes of which are gene expression values with various significance levels. 3.3. Calculation of data classification quality criteria and formation of vectors of these criteria values. Figure 3: Structural block chart of the algorithm that forms the subsets of informative GEPs based on Harrington’s desirability function and assessment of the model adequacy. Analysis of the obtained results. Analysis of the classification results and the formation of a conclusion regarding the level of adequacy of the proposed model.
## 2 Materials and Methods
### Outlier Detection and Replacement
#### 2.3.3 OLS Outlier Identification Method
For each observation point, the [PERSON]'s distance is defined as \[D_{i}(M,C_{o})=\frac{\big{(}\hat{\beta}_{(i)}-\hat{\beta}\big{)}^{T}M(\hat{ \beta}_{(i)}-\hat{\beta})}{C_{o}}\,i=1,2,\cdots,n,\] (8) where \(M=X^{T}X\) is the deviation matrix of the observed data, \(C_{o}=p\hat{\sigma}^{2}\) is the mean square error, and \(n\) is the sample size. When \(|D_{i}|>4/n\), it is judged as the strong influence point.
## 2 The Method of Wavefront Shaping  
As shown in Figure 1, the initial wavefront distribution of the input laser beam can be expressed by \(\varphi_{\text{in}}(x,y)\). The input beam is reflected by the DM and then passes through the laser system and arrives at the wavefront measurement system at the end. The output wavefront distribution expressed by \(\varphi_{\text{out}}(x,y)\) can be accurately measured by a Shack-Hartmann wavefront sensor. The formula for the entire process is depicted as follows:  
\[\varphi_{\text{in}}(x,y)+2S_{\text{DM}}(x,y)+F(x,y)=\varphi_{\text{out}}(x,y) \tag{1}\]  
where \(S_{\text{DM}}(x,y)\) is the surface distribution of the DM, and \(F(x,y)\) is the wavefront distortion caused by the laser system. There are several factors affecting the low-frequency wavefront distortion in the laser system [39], including (1) the low-frequency surface distortion of the reflecting mirror; (2) wavefront distortion caused by the transmission through optical elements such as polarizer and window; (3) pumped dynamic thermal distortion and residual heat; (4) the spherical aberration and eccentric coma; and (5) air turbulence. The latter factor has the least influence on the system because of the random deformation. Under the wavefront distortion caused by the first four factors, the relationship can be seen as linear between the input wavefront and the output wavefront through the laser system. In order to study the closed-loop control of the output wavefront, the input wavefront distribution and the distortion caused by the laser system are merged into a transmission function expressed as  
\[H(x,y)=\varphi_{\text{in}}(x,y)+F(x,y). \tag{2}\]  
Under the initial case, the transmission function \(H(x,y)\) can be calculated from the initial output wavefront \(\varphi_{\text{out}}^{(0)}(x,y)\) and the DM surface \(S_{\text{DM}}^{(0)}(x,y)\). The first compensation DM surface distribution \(S_{\text{DM}}^{(1)}(x,y)\) used for wavefront shaping in the laser system can be calculated from the aimed wavefront distribution \(\varphi_{\text{aim}}(x\),\(y)\) and the transmission function \(H(x\),\(y)\). The formulas are as follows:  
\[\left\{\begin{array}{l}H(x,y)=\varphi_{\text{out}}^{(0)}(x,y)-2S_{\text{DM}}^{ (0)}(x,y)\\ S_{\text{DM}}^{(1)}(x,y)=\frac{1}{2}[\varphi_{\text{aim}}(x,y)-H(x,y)]\end{array} \right.. \tag{3}\]  
The laser system itself (i.e., the aberrations) is actually linear, but the mirror actuators may not be. Thus, the output flattening wavefront distribution cannot be achieved after the first wavefront compensation. Under the circumstances, the iterative method is used for wavefront shaping [40]. The iterative format is as follows:  
\[S_{\text{DM}}^{(n+1)}(x,y)=\frac{1}{2}[\varphi_{\text{aim}}(x,y)-\varphi_{ \text{out}}^{(n)}(x,y)]+S_{\text{DM}}^{(n)}(x,y) \tag{4}\]  
where \(S_{\text{DM}}^{(n)}(x,y)\) represents the DM surface distribution at the \(n\)-th iteration, and \(n\) = 0, the initial DM surface. \(\varphi_{\text{out}}^{(n)}(x,y)\) represents the \(n\)-th output wavefront distribution, and \(n\) = 0, the initial wavefront.
## 4 Case Studies
### Port of Singapore  
The Port of Singapore (www.mpa.gov.sg, accessed on 4 February 2024) is among the top two busiest ports in the worlds according to cargo tonnage handled. It is the world's largest transhipment port, and the world's fifth largest container port. The port terminals aim to reduce absolute emissions by at least 60% by 2030 from 2005 levels, and achieve net zero emissions by 2050. The committed and planned decarbonisation efforts include the following [30]:  
* **Operational measures**: From 2019, developed digitalPORT, which provides a one-stop platform for port call transactions and regulatory clearance for operational efficiency; the JIT Planning and Coordination Platform can facilitate direct berthing on arrivals and on-time departures to enhance ship turnaround and reduce ship emissions. Moreover, the port plans to seek energy optimisation from 2025.
* **Electrify cargo handling equipment**: From 2015, adopted new electric automated RMG cranes; during 2019-2027, replace diesel RTG cranes with electric RTG cranes; in 2021, adopted battery AGVs; during 2023-2025, adopt electric prime movers; during 2025-2030, electrify port equipment (AGVs, quay cranes);
* **Low-carbon fuel for prime movers**: In 2021, adopted LNG-fuelled prime movers to replace diesel-fuelled prime movers; during 2025-2030, will adopt energy-efficient grab dredger; biofuels for domestic harbour crafts.
* **Renewable energy**: 2025-2030, solar energy; 2025-2050, hydrogen prime movers and green electricity; by 2050, harbour craft will be powered by alternative energy solutions such as electrification and net-zero fuels.
* **Infrastructure**: 2023-2030, support pilots for electric harbour craft and charging infrastructure; for international shipping, support the supply of biofuels (e.g., bio-methanol, bio-LNG) as well as hydrogen derivatives (e.g., ammonia, e-methanol, liquefied hydrogen).
* **Digitalisation**: From 2019, developed Singapore Maritime Data Hub, which acts as a one-stop data repository and centralised data exchange platform in order to enable the integration of Maritime Singapore with the global trade ecosystem; will improve cyber security intelligence through early detection and quick response to cyber security threats; from 2025, will adopt smart grids.  
* **Incentives and collaboration**: The digitalPORT tool promotes cross-sector collaboration among value-chain partners; decarbonise supply chain through efficiency gain; support technical development and incentivise the adoption and uptake of electric harbour craft and low-carbon fuels for seagoing vessels.
## 5 Network and Data Stream
### Representation of Path and Data Stream  
The definition of the data stream model of network is used to obtain a feasible path under a known network topological graph. Firstly, a network topology is represented by a correlation matrix \(A\in R^{V\times L}\) of nodes and links, where \(V\) denotes the total number of nodes and \(L\) denotes the total number of links. In the matrix, the element \(a_{ij}\) represents the relationship between the node \(i\) and the link \(j\):  
\[a_{ij}=\left\{\begin{array}{rcl}1,&if&e_{j}\in E^{+}(i)\\ -1,&if&e_{j}\in E^{-}(i)\\ 0,&others\end{array}\right. \tag{1}\]where \(E^{-}(i)\) and \(E^{+}(i)\) respectively represent the input and output links of the node \(i\).  
If a path successively includes the following links \(e_{1},e_{2},...,e_{n}\), then the path would be defined as \(P=e_{1}e_{2}...e_{n}\). \(X(P)\) is a vector whose length is \(L\) (\(X(P)\in R^{L}\)). For the \(i\)th element in \(X(P)\):  
\[x_{i}(P)=\left\{\begin{array}{ll}1,&\mbox{if}\quad e_{i}\in P\\ 0,&\mbox{others}\end{array}\right.,\quad 1\leq i\leq L \tag{2}\]  
For a data stream \(k\), it can be expressed as a vector \(U_{k}\in R^{V}\). For the \(i\)th element in \(U_{k}\):  
\[u_{i}=\left\{\begin{array}{ll}1,&i=s(k)\\ -1,&i=d(k),\quad 1\leq i\leq V\\ 0,&\mbox{others}\end{array}\right. \tag{3}\]  
where \(s(k)\) and \(d(k)\) respectively represent the source and destination nodes of the data stream \(k\). Because the path \(P_{k}\) of the data stream \(k\) must satisfy the limitation: \(AX(P_{k})=U_{k}\), a set of potential paths of the data stream \(k\) can be obtained. All the possible solutions of \(X(P_{k})\) are just the potential paths.
## 5 Conclusions  
1. This study employs and correlates ground-based and drone-based ground-penetrating radar (GPR) technologies. Integrating these two distinct GPR methods enhances the accuracy and effectiveness of archaeological surveys, ushering in a new approach for comprehensive subsurface mapping.
2. The effective utilization of drone-based GPR technology in clayey soil at the Hulata solar panel construction site showcases the versatility of drone-based technology in tackling demanding soil conditions and, hence, opens up new avenues for archaeological inquiries in regions where clayey soil was previously deemed a hindrance.
3. This study enhances the reliability and accuracy of subsurface mapping by independently deploying ground-based and drone-based GPR systems technologies and cross-referencing their findings.  
Conceptualization, M.F. and V.F.; methodology, M.F. and V.F.; software, M.F.; validation, M.F. and V.F.; formal analysis, M.F.; investigation, M.F. and V.F.; resources, V.F.; data curation, V.F.; writing--original draft preparation, M.F. and V.F.; writing--review and editing, M.F. and V.F.; visualization, M.F.; supervision, V.F.; project administration, M.F. All authors have read and agreed to the published version of the manuscript.  
V.F. acknowledges the support from the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie RISE project EffectFact, grant agreement no. 101008140.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
Conflicts of Interest: Author [PERSON] was employed by the company Geo-Scope Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.