esa-sceva/satcom-chunk-collection huggingface dataset:
# Icon Generation Based on Generative Adversarial Networks
## 2 Materials and Methods
### Generative Adversarial Network, GAN  
GAN is a deep learning model which consists of two modules, the generator and the discriminator (Figure 2a), and the samples generated by the generator obey the real data distribution by adversarial training [6]. Among them, the discriminator aims to determine whether the input samples are real samples or fake samples generated by the generator; while the generator tries to generate samples that the discriminator cannot distinguish, and the two modules have opposite objectives and are trained alternately in an attempt to reach Nash equilibrium in this \"MiniMax\" game [29].  
The mutual game process between generator and discriminator can be expressed by a value function, and the generation problem of the model can also be transformed  
Figure 2: (**a**) The basic framework of GAN. Where G is the generator; D is the discriminator; z is generally random noise sampled from a Gaussian distribution, z generates a false sample G(z) after passing through the G; x is the true sample, and the discriminator makes a true or false judgment on the input sample. (**b**) The structure of CGAN. The biggest difference to traditional GAN is that the label of the sample will be used as input to both the generator and the discriminator.  
into a minimax problem solving \(V(G,\ D)\), and the objective function of the GAN can be expressed as:  
\[\min_{G}\underset{D}{max}V(D,G)=E_{x\sim p_{data}(x)}[logD(x)]+E_{z\sim p_{z}(z)} [log(1-D(G(z)))], \tag{1}\]  
where \(x\) is a real sample and \(z\) is random noise. The goal of the generator is to minimize the upper expression, and the goal of the discriminator is to maximize the upper expression.
_Int. J. Mol. Sci._**2013**, _14_, 13727-13747; doi:10.3390/ijms140713727  
International Journal of  
Molecular Sciences  
ISSN 1422-0067  
www.mdpi.com/journal/ijms  
_Article_  
**The Organization of the Quorum Sensing _luxI/R_ Family Genes in _Burkholderia_**  
**[PERSON] 1, [PERSON] 1, [PERSON] 2, [PERSON] 1,3, [PERSON] 1-4 and [PERSON] 1,2,*  
International Centre for Genetic Engineering and Biotechnology (ICGEB), Padriciano 99,  
Trieste 32149, Italy; E-Mails: [EMAIL_ADDRESS] (K.S.C.);  
[EMAIL_ADDRESS] (S.H.); [PERSON]] (B.G.C.)  
Faculty of Information Technology, PazmanyPeter Catholic University, Prater u. 50/a,  
Budapest 1083, Hungary; E-Mail: [EMAIL_ADDRESS]  
The Capes Foundation, Ministry of Education of Brazil, Cx postal 250, Brasilia,  
DF 70.040-020, Brazil  
Authors to whom correspondence should be addressed; E-Mails: [EMAIL_ADDRESS] (V.V.);  
[EMAIL_ADDRESS] (S.P.); Tel.: +39-40-375-7300 (S.P.); Fax: +39-40-226-555 (S.P.).  
_Received: 30 May 2013; in revised form: 20 June 2013 / Accepted: 24 June 2013 / Published: 2 July 2013_  
**Abstract:** Members of the _Burkholderia_ genus of Proteobacteria are capable of living freely in the environment and can also colonize human, animal and plant hosts. Certain members are considered to be clinically important from both medical and veterinary perspectives and furthermore may be important modulators of the rhizosphere. Quorum sensing via _N_-acyl homoserine lactone signals (AHL QS) is present in almost all _Burkholderia_ species and is thought to play important roles in lifestyle changes such as colonization and niche invasion. Here we present a census of AHL QS genes retrieved from public databases and indicate that the local arrangement (topology) of QS genes, their location within chromosomes and their gene neighborhoods show characteristic patterns that differ between the known _Burkholderia_ clades. In sequence phylogenies, AHL QS genes seem to cluster according to the local gene topology rather than according to the species, which suggests that the basic topology types were present prior to the appearance of current _Burkholderia_ species. The data are available at [[http://net.icgeb.org/burkholderia/](http://net.icgeb.org/burkholderia/)]([http://net.icgeb.org/burkholderia/](http://net.icgeb.org/burkholderia/)).  
**Keywords:** quorum sensing; _N_-AHL; local topologies; chromosomal arrangements; _Burkholderia_
## 4 Analysis of Security and Performance
### Security Analysis
#### 4.1.3. Replay Attacks  
The \"hello\" message is used to claim that the vehicle is present, and neighboring vehicles use the \"hello\" message to generate pairing value. Hence, replaying the hello message affects one more neighbor, but this neighbor cannot perform a mutual pairing to perform any attack without the private keys. The time stamp in the \(HMAC\) of a broadcast message and a communicated message can resist the replay attack.
# Application of Canola Oil Biodiesel/Diesel Blends in a Common Rail Diesel Engine
## 3 Results and Discussion
### Combustion Characteristics
#### 3.1.2 Brake Specific Fuel Consumption and Brake Specific Energy Consumption  
Brake specific fuel consumption is a measure of the fuel efficiency of an internal combustion engine that burns fuel and produces output power. Figure 6a shows the effects of the use of ULSD, BD20, and PCO fuels on BSFC at various brake mean effective pressures. As shown in Figure 6, the BSFC decreased as the BMEP increased, because at low engine loads, the fuel injection pressure and turbulence intensity in the combustion chamber are lower than at high engine loads. The effects of fuel atomization and the air/fuel mixture are not better at higher engine loads, so the fuel consumption is its largest at the lowest BMEP. These results are similar to [45]. In contrast, for BD20 and PCO, the BSFC increased as the BMEP increased because the lower calorific value of canola oil is lower than in ULSD, which leads to BD20 and PCO fuels' consuming more fuel to produce the same power, compared with ULSD. At 0.13 MPa, from ULSD to PCO, the increase in BSFC is the largest. In particular, the BSFC of PCO increases because the density and viscosity of PCO is high, making it difficult to atomize. In addition, the BSEC is another important engine parameter to evaluate its capability when using fuels with different calorific values. As shown in Figure 6b, under different combustion conditions, the average BSEC for ULSD, BD20, and PCO fuels are 15.34 MJ/kWh, 16.08 MJ/kWh, and 18.01 MJ/kWh, respectively. For BD20 and PCO fuels, the BSECs were 4.8% and 17.42% higher, respectively, compared with ULSD. The lower calorific value and higher viscosity of canola oil biodiesel leads to increases in the BSEC. However, with increasing BMEP, the BSEC had a decreasing trend due to higher power output.  
Figure 5: Effect of BMEP on ignition delay for test fuels.  
Figure 6: Effect of test fuels on (**a**) BSFC (brake specific fuel consumption); and (**b**) BSEC (brake specific energy consumption) according to BMEP.
# A Network-Based Model for Optimization of Container Location Assignment at Inland Terminals
## 8 Conclusions  
This paper defines and analyzes criterion categories for container storage optimization at inland terminals that, when integrated into an appropriate model, can decrease the number of unproductive manipulations and thereby reduce the time and cost of container management. The criteria and model were developed using inland container data for 11 European inland terminals, one of which further served as the basis for a simulation to validate the proposed model. The model estimates the retention time based on ranking criteria developed through interviews with terminal operators (decision makers), and in the validation simulation, it reduced unproductive manipulations by 37%. The model reduced inbound container unloading time as well as container storage time at the terminal. This should translate to lower handling costs and higher inland terminal productivity.  
The presented model requires analysis of the container retention time statistics for each criterion category. This enables containers to be stored in tiers ranked according to average container retention time. Such an approach reduces the number of unproductive manipulations and speeds up container shipping from the terminal. Although the model may lead to longer train retention than under the conventional storage principle of \"first-in, first-out\", the simulation suggests that this prolongation is negligible compared to the increase in inland terminal productivity.  
Conceptualization, M.S.; Formal analysis, I.B.; Investigation, T.R.; Supervision, I.B.; Validation, B.I.; Writing--original draft, T.R.; Writing--review & editing, B.I. and M.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 2 System Model and Complexity Analysis  
For readers' convenience, Table 1 provides a list of symbols used in this paper with their definitions.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Symbol** & **Definition** \\ \hline \(T_{k}\) & \(k\)-th user terminal \\ \hline \(R_{l}\) & \(l\)-th relay \\ \hline \(\mathcal{S}\) & Set of terminals and relays \\ \hline \(m_{i}\) & Information symbol with values in \(\{0,1\}\) \\ \hline \(a_{i}[k]\) & BPSK symbol with values in \(\{1,-1\}\) \\ \hline \(b_{i}[k]\) & Differentially encoded symbol with values in \(\{1,-1\}\) \\ \hline \(f_{j}j\) & Channel coefficient from node \(i\) to node \(j\) \\ \hline \(y_{ij}[k]\) & Received signal at node \(j\) from node \(i\) \\ \hline \(n_{ij}\) & Noise samples in the channel from node \(i\) to node \(j\) \\ \hline \(N_{0}\) & Variance in additive channel noise \\ \hline \(E_{b}\) & Bit energy \\ \hline \(P_{l}\) & Transmission power of node \(i\) \\ \hline \(\varphi_{ij}[k]\) & Phase of \(y_{ij}[k]y_{ij}^{*}[k-1]\) \\ \hline \(\theta_{i}[k]\) & Phase of \(a_{i}[k]\) \\ \hline \(\theta_{n}[k]\) & Phase noise term affecting the decision variable \\ \hline \(\mathbf{G}_{sys}\) & Systematic generator matrix of a linear block code \\ \hline \(\mathbf{G}_{nonsys}\) & Nonsystematic generator matrix of a linear block code \\ \hline \(\mathbf{P}_{K\times L}\) & Parity submatrix of a systematic generator matrix \\ \hline \(l(W)\) & Symmetric capacity of channel \(W\) \\ \hline \(W(y|x)\) & Transition probabilities of a channel \\ \hline \(Z(W)\) & Bhattacharyya parameter of channel \(W\) \\ \hline \(W_{N}^{(i)}\) & \(i\)-th synthesized channel from a collection of \(N\) channels \\ \hline \hline \end{tabular}
\end{table}
Table 1: Table of main symbols.
# Effect of NS-DBD Actuator Parameters on the Aerodynamic Performance of a Flap Lifting Device
## 4 Simulation Results and Analysis
### Comparisons of Discharge Pulse Width  
Pulse width is the maximum duration of a single-nanosecond pulse during discharge. When the frequency is determined, and the period T of a single pulse is certain, a different pulse width W corresponds to a different duty cycle P, as shown in Formula (4).  
\[\text{W}=\text{T}\times\text{P} \tag{4}\]  
In this part, the actuator at the lower trailing edge is selected as the research object. Under the condition that the inlet wind speed is 10 m/s, the flap deflection angle is 10\({}^{\circ}\), the discharge frequency is 100 Hz, and the discharge energy density is 10\({}^{13}\) W/m\({}^{2}\), four discharge pulse widths are selected, that is, 50, 100, 200, and 500 ns.
# A Deep Learning Approach for Short-Term Electricity Demand Forecasting: Analysis of Thailand Data
## 5 System Model
### Performance Analysis
#### 5.4.1 Mean Absolute Error (MAE)  
One useful key performance indicator (KPI) for evaluating forecast accuracy is the mean absolute error (MAE). To ensure that positive and negative residuals never cancel out, we will compute the residual for each data point using only its absolute value. After that, we compute the mean of each of these residuals. The MAE, which merely calculates the absolute difference between the model's predictions and the actual demand data, is the most appropriate metric. The residual's absolute value is utilized, so the under- or overperformance of the model is not displayed. The formal equation of MAE is shown below:  
\[\text{MAE}=\frac{1}{n}\sum_{t=1}^{n}|y_{t}-F_{t}|, \tag{16}\]  
where  
n = total number of data points;  
y \(t\) = actual electricity demand at a time \"t\";  
F \(t\) = predicted electricity demand at a time \"t\";
## 3 Results
### IRMS Analysis
The changes in isotopic composition over time (immersion in seawater) of the shopping bags (ULB and LSB) showed that over the 73-day exposure period and under variable conditions of temperature and salinity (i.e., 24.24-28.76 \({}^{\circ}\)C and 31.19-36.28 PSU, respectively), the \(\mathrm{\delta^{13}C}\) values of both Mater-Bi\({}^{\oplus}\) bags showed a gradual decrease towards more negative values (Figure 1). The recorded \(\mathrm{\delta^{13}C}\) variation (\(\mathrm{\Delta\delta^{13}C}\)) was 2.16 and 2.84% for ULB and LSB, respectively. During the experiment, all ULB and LSB samples showed structural changes with the formation of numerous heterogeneous pinholes, cracks and grooves. The evidence that ULB fragments were not found at T6 is presumably linked to the mechanical action of waves, rather than to chemical degradation [13; 29]. The PE control bags did not show a marked decrease of \(\mathrm{\delta^{13}C}\) and their observed \(\mathrm{\delta^{13}C}\) variation (\(\mathrm{\Delta\delta^{13}C}\) = 0.41%) was close to the uncertainty of the analytical method, which is different from what was observed for compostable bags. By applying the mixing approach to the isotopic values of the samples (T0-T6), where T0 indicated values relative to the starting composition of the shopping bags, we observed that the percentage of starch fraction decreased at a similar rate in both types of shopping bags, independently of its initial proportion. Indeed, between T0 and T5 (T6 was analyzed only for LSB) 14% and 13% of starch were lost, respectively for ULB and LSB (Figure 2). This implied a progressive enrichment in polyesters in both types of bags, even if ULB showed an initial difficulty in degrading. Figure 1: Changes in \(\mathrm{\delta^{13}C}\) values in polyethylene bag (PE), lightweight shopping bag (LSB) and ultra lightweight bag (ULB) during exposure time points. The degradation process of starch follows zero-order kinetics (Figure 3). The uncertainty associated with different immersion times was found to range between 2% and 5% and the degradation process curve can be considered statically robust. Figure 3: Estimation of starch component degradation time in ultra lightweight bag (ULB) and lightweight shopping bag (LSB). Figure 2: Changes in composition of ultra-lightweight (ULB) and lightweight (LSB) shopping bag samples during exposure to coastal marine environments. Considering the intercept in the \(X\)-axis, we could predict the time necessary for the biodegradation of starch components considering the environmental conditions present during the experiment, estimating it in about 180 and 124 days for ULB and LSB, respectively.
## 3 Results
However, after applying the new inversion method, the overall resolution significantly improves, and the signals from the reservoir interval (T2-T3) are effectively restored. Additionally, the restored stratigraphic information aligns well with the lithology (limestone), gamma (low values), reflectivity (high values), and interpreted geological horizons (T2-T3), demonstrating that the results obtained by the new inversion method exhibit a high degree of credibility. Building upon high-resolution processed seismic data, structural characterization of the target reservoir beneath the coal seam was conducted to further validate the accuracy of the inversion results. Figures 13(a) and 13(b) display the coherence attribute horizon slices of the target layer before and after inversion, respectively. Comparative analysis demonstrates that the original coherence attributes (Figure 13(a)), affected by strong reflection shielding effects from overlying strata, exhibit poorly resolved geological features including faults and karst cavities. In contrast, the high-resolution coherence slice (Figure 13(b)) reveals enhanced geological details, clearly delineating multiple NE-SW-trending faults with associated secondary fracture systems. The spatial relationships between karstic voids and fault networks are markedly improved, with substantially elevated identification accuracy. Notably, high-productivity gas wells (marked by red circles) in this interval are preferentially distributed in areas with well-developed fracture-cave reservoirs. The gas productivity of these wells shows a strong correlation with interconnected dissolution channels and fracture networks, which constitute effective reservoir spaces. Validation confirms superior spatial correspondence between high-yield well locations and favorable fractured-vuggy reservoirs in the high-resolution coherence slice compared to the original data. Figure 13: Cont.
# Stable Evaluation of 3D Zernike Moments for Surface Meshes
## 4 Reconstructing a Shape from Its 3D Zernike Moments  
Recall that once a shape \(S\) has been characterized with its Zernike moments \(c_{nl}^{m}\), its density \(\rho(\mathbf{x})\) at any point \(\mathbf{x}\) in \(\mathbb{R}^{3}\) can be reconstructed using Equation (14)  
\[\begin{split}\rho(\mathbf{x})&=\sum_{n}^{N}\sum_{l}\sum_ {m=-l}^{l}c_{nl}^{m}Z_{nl}^{m}(\mathbf{x})\\ &=\sum_{n}^{N}\sum_{l}\sum_{m=-l}^{l}c_{nl}^{m}\sqrt{2n+3}R_{nl}( r)Y_{l}^{m}(\theta,\phi),\end{split} \tag{33}\]  
where \((r,\theta,\phi)\) are the spherical coordinates of \(\mathbf{x}\). The reconstruction is exact when \(N\to+\infty\), while the Zernike moments \(c_{nl}^{m}\) and the Zernike polynomials are complex, the reconstruction \(\rho(\mathbf{x})\) is real.  
The equation above defines a simple algorithm for reconstructing the shape density in \(\mathbb{R}^{3}\). If the points \(\mathbf{x}\) are chosen to be the nodes of a 3D grid, a surface mesh can then be reconstructed using the marching tetrahedron algorithm [49]. We note, however, that special care is needed when evaluating the radial polynomials \(R_{nl}(r)\) and the spherical harmonics \(Y_{l}^{m}(\theta,\phi)\) to avoid numerical instabilities when \(n\) is large. This is described below in the Implementation section.
## 4 Results
### Carotenoids Content Estimation  
Carotenoids play a crucial role in lettuce and basil, which contributes to plant physiology but also human nutrition [48; 49]. In plants, they are integral to photosynthesis, assisting in the process of light absorption and energy transfer while additionally protecting chlorophyll from oxidative damage caused by extensive light [49]. The protection of plants from stress conditions, such as high light intensity or environmental fluctuations, is provided by carotenoids such as lutein and beta caroten [49; 50]. They are connected to the overall plant health and vigor, which makes them a reliable marker of stress and metabolic activities. Carotenoids, from a nutritional perspective, are valuable as precursors to vitamin A, and they possess antioxidant properties, which can contribute to human health [49]. Leafy greens such as lettuce, spinach, and basil are a significant dietary contributor to carotenoids consumption [51; 52]. In the articles collected, there is a wide range of spectral technologies, including both hyperspectral imaging systems and spectrometers. Hyperspectral imaging systems cover ranges from 350 nm to 2500 nm. These systems provided detailed spectral signatures associated with carotenoid content. Spectrometers operated between 340 nm and 900 nm. The most common range used in all studies is 400 nm to 1000 nm. These setups enabled the researchers to capture the detailed spectral signatures associated with carotenoid content. For instance, in baby leaf lettuce, a hyperspectral camera operating within 350 to 2500 nm (Tables 7 and 8) demonstrated significant utility for capturing carotenoids related reflectance. Additionally, high-throughput hyperspectral analysis techniques for lettuce utilized advanced spectral configurations, offering comprehensive data for carotenoids quantification and enabling precise characterization of carotenoid variations. Hyperspectral and spectroscopy combined with machine learning have proven to be a powerful tool for the detection of carotenoids. Across presented studies, accuracies were reported with values R\({}^{2}\) exceeding 0.85 and RMSE values of 0.03. Used methods include Partial Least Squares Regression (PLSR), Principal Component Regression (PCR), and Artificial Neural Networks (ANNs), with multitask learning models like MTCNN used as well. In some cases, advanced multitask learning models showed strong performance, particularly for spinach, achieving R\({}^{2}\) values above 0.8644 (Table 7) in VNIR and 0.8167 in NIR ranges.
# A Brief Review on Challenges in Design and Development of Nanorobots for Medical Applications
## 4 Biocompatibility and Toxicity of Nanorobots  
Researchers have not completely understood the interaction mechanisms between nanorobots and living systems, as the complexity with the interaction of materials used to build nanorobots, with biological matter vary that may lead to change the nanorobots' surface characteristics based on the environment that they are placed [134]. Thus, the associated potential risks and hazards are not fully identified. Experimental studies have shown that the particles of the same material may exhibit completely different behaviour for different reasons, such as differences in surface coating [135]. Besides, there are many more parameters that should be taken into account while working with nanorobots, including material, size, shape, surface coating, sensory and actuation mechanism used, working environment in which the nanorobot is functioning [136]. Hazard identification of the use of nanorobots in the human body remains at an early stage; however, some efforts have been made to identify major entry routes such as lung, gut, and possibly skin as well as putative targets such as lung, liver, heart, and brain [137].  
Depending on the design of the mechanism, the material used, driving and sensing systems implemented, and the site of deposition in the human body, the level of toxicity may vary in severity [138]. Consequently, to maintain clinical relevance, information on toxicity is presented using a system-based approach focusing on lung, dermal, liver, and nervous system targets that are well discussed in [135].
## 3 Proposed Approach
### Functional Analysis and Requirements Definition
#### 3.1.2 Process Performance  
As well as the product performance indicators, the process indicators also need to be studied. As they are time dependent, their values change as the assembly sequence changes. The process performance indicators are used to determine which assembly sequence offers the best operation quality. The indicators studied in this paper are: total assembly time (T\({}_{\text{AT}}\)), insertion ratio (I\({}_{\text{R}}\)), handling ratio (H\({}_{\text{R}}\)), assemblability ratio (A\({}_{\text{R}}\)) and complexity factor (CF). These process performance indicators are formulated by Equations (4)-(8). The process performance requirements defined for the speed reducer example are modeled in Figure 5. It is noticed that the working group gives the limit values of each indicator. For example, the design efficiency (D\({}_{\text{e}}\)) value must exceed 60% for the case study.  
\[\text{Total assembly time }(\text{s}):\text{ T}_{\text{AT}}=\text{ T}_{\text{H}}+\text{T}_{\text{i}} \tag{4}\] \[\text{Insertion ratio }:\text{ I}_{\text{R}}=\frac{\text{T}_{\text{i}}}{\text{NUP}}\] (5) \[\text{Handeling ratio }:\text{ H}_{\text{R}}=\frac{\text{T}_{\text{H}}}{\text{NUP}}\] (6) \[\text{Assemblability ratio }:\text{ A}_{\text{R}}=\frac{\text{A}\cdot\text{T}_{\text{min}}}{\text{T}_{ \text{AT}}}\cdot 100\] (7) \[\text{Complexity factor }:\text{ C}_{\text{F}}=\text{T}_{\text{AT}}-2.35(\text{A}+\text{B}) \tag{8}\]  
where  
* \(\text{T}_{\text{i}}\): insertion time.
* \(\text{T}_{\text{H}}\): handeling time.
* \(\text{NUP}\): the amount of unrepeated essential parts (A).
* \(\text{T}_{\text{min}}\): Minimum assembly time in the industry's assembly line.