esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Design and Results
### Experimental Results and Analysis
Overall, CNN-LSTM, by combining the spatial feature extraction of convolutional neural networks and the temporal dependency modeling of LSTM, comprehensively understands the dynamic variations in multi-modal physiological signals, enhancing the accuracy and robustness of driver-state monitoring, particularly in complex driving scenarios. In summary, multi-modal fusion significantly enhances the accuracy and robustness of models in monitoring driver physiological states, especially in complex driving environments. The CNN-LSTM model utilizing three modalities outperforms single-modal and traditional MLP models. Despite the effective improvement in predictive performance achieved through multi-modal fusion, the generalization capability of the model still requires enhancement in cases of significant individual physiological differences. For instance, certain drivers' skin conductance signals may be insensitive to stress, potentially leading to misjudgment. Future research could consider incorporating personalized models or adaptive learning mechanisms. This model can be applied to actual driver-state-monitoring systems to help detect fatigue, stress, and other conditions, thus preventing traffic accidents. Future work could further enhance driving safety through integration with in-vehicle systems.
# Experimental Comparison of Diesel and Crude Rapeseed Oil Combustion in a Swirl Burner
###### Abstract  
In pursuing maximum energy efficiency, local utilization of various crude fuels came into view. The present paper compares the combustion characteristics of standard diesel oil and crude rapeseed oil; the latter is an excellent model for high-viscosity liquid fuels. The combustion tests were performed in a 15 kW atmospheric turbulent swirl burner; the liquid fuels were atomized by a plain-jet airblast atomizer. Firstly, the acoustic signal is evaluated, since the instabilities of swirl combustion are accompanied by characteristic pressure fluctuations. The spectral analysis was performed by Wavelet transform, which fits excellently to the acoustic spectrum of combustion noise. This multi-scale technique features increased spectral resolution at lower frequencies at the expense of lower temporal resolution, providing excellent performance at both low-frequency, well-localized components and high-frequency, broadband phenomena. The joint probability density function of two characteristic frequencies was plotted with the result that flame acoustics match for the two fuels. Secondly, their pollutant emissions were compared and evaluated under similar conditions with the conclusion that crude rapeseed oil can substitute diesel oil in a limited operating range. Note that the distinct material properties already mean differences in all atomization, evaporation, and mixing characteristics, hence, the latter result is not intuitive.  
rapeseed oil; liquid fuel; pollutant emission; combustion; probability density function; wavelet transform; CO; NOx +
Footnote †: journal: Applied Sciences
## 2 Results and Discussion
### Potential Medium Chain Fatty Acid (MCFA)-Derived Precursors of Ethyl Esters
#### 2.1.4 Acyl-Amino Acid Conjugates Potentially Exploiting Permeases  
Addition of nonanoyl alanine to the MGJM at a concentration of 100 mM resulted in a significant increase in the production of ethyl nonanoate in the resulting wine (Figure 2A). Similar to when the free fatty acid was added to the musts, 1 mM nonanoyl alanine appeared to be toxic to the yeast as there was negligible weight loss in these samples; however, ethyl nonanoate concentrations were significantly higher than the control. Nonanoyl isoleucine addition also caused an increase in ethyl nonanoate production when added in concentrations at 100 mM and above (Figure 2B). The toxicity seen when 1 mM nonanoyl alanine was added to the MGJM was not observed after the addition of nonanoyl isoleucine to the MGJM (Figure 2A,B).  
Ethyl dodecanoate concentrations were significantly higher only in the wine produced after the addition of 1 mM dodecanoyl alanine to the must, which led to a mean wine concentration of almost 10,000 nmol\(\cdot\)L\({}^{-1}\), or 2.24 mg\(\cdot\)L\({}^{-1}\) (Figure 2A). On the contrary, no significant difference was seen in ethyl dodecanoate production at any level of dodecanoyl isoleucine addition (Figure 2B).  
The transport mechanism for extracellular alanine and isoleucine is via the general yeast amino acid permeases or other permeases with a less broad specificity [36]. However, the conjugated MCFAs are perhaps more likely to enter the cell in a manner similar to MCFAs. It would appear that the alanine conjugates are transported more easily into the cell as the addition of both the nonanoyll and dodecanoyl variants to the musts resulted in wines with greater concentrations of their respective ethyl esters than did the equivalent addition of the isoleucine conjugates (Figure 2). Alternatively, the alanine conjugates could be more easily esterified or hydrolys into the free fatty acid, which would increase the available substrate for esterification. Increased transport or hydrolysis of the conjugate could also explain the increased toxicity of the nonanoyl alanine conjugate at 1 mM compared to the isoleucine conjugate. The identification and quantification of acyl-amino acid conjugates in plants has not been reported although it is only recently that such compounds have been described in mammals and insects [24,25]. Future targeted studies may reveal their presence in the plant kingdom as well.
## 2 Materials and Methods
### Test Equipment and Methods  
The HS-YS4A Rock Acoustic Parameter Test System (Xiangtan Tianhong Electronic Research Institute, Xiangtan, China) is used to measure the P-wave velocity of the specimens. The MTS 815 rock test system (Mechanical Testing & Simulation, Eden Prairie, MN, USA) is used for the loading test, which has the maximum loading capacity of 4600 kN and the frame stiffness of 10,500 kN/mm. The Micro-II Digital AE System is employed to monitor the acoustic emission signals generated within the specimens during the testing process, and the amplitude threshold was set at 45 dB. After the rupture, the fracture was analyzed using the Scanning Electron Microscopy (SEM) technique. Figure 3 shows the test procedure and the test equipment.  
All specimens are divided into two groups, on which the monotonic compression test and the cyclic-loading and unloading test are conducted. Table 1 shows the test scheme for slate specimens considering the loading path and the loading angle. The specimen is named as the loading path-foliation angle--cyclic stress step. For example, U-0 represents the specimen with a foliation angle of 0\({}^{\circ}\) under monotonic loading, and C-30-20 represents the specimen with a foliation angle of 30\({}^{\circ}\) under cyclic loading and unloading with the cyclic stress step of 20 MPa. The monotonic compression test is performed by displacement loading at a rate of 0.06 mm/min, until the specimen failure. For the cyclic-loading and unloading test, as illustrated in Figure 4, the load is first applied at a rate of 0.5 MPa/s, with increments of 20 or 40 MPa per stage. Once the upper stress of the cyclic loading reaches about 75% of the uniaxial compressive strength (UCS), the loading mode is changed to the displacement loading with a rate of 0.06 mm/min, and the increment of each stage is 0.1 mm.  
Figure 4: The cyclic-loading and unloading path.  
Figure 3: The test procedure and the test equipment.
# Quorum Sensing Regulation of Competence and Bacteriocins in _Streptococcus pneumoniae_ and _mutans_
###### Abstract  
The human pathogens _Streptococcus pneumoniae_ and _Streptococcus mutans_ have both evolved complex quorum sensing (QS) systems that regulate the production of bacteriocins and the entry into the competent state, a requirement for natural transformation. Natural transformation provides bacteria with a mechanism to repair damaged genes or as a source of new advantageous traits. In _S. pneumoniae_, the competence pathway is controlled by the two-component signal transduction pathway ComCDE, which directly regulates SigX, the alternative sigma factor required for the initiation into competence. Over the past two decades, effectors of cellular killing (i.e., fratricides) have been recognized as important targets of the pneumococcal competence QS pathway. Recently, direct interactions between the ComCDE and the paralogous BlpRH pathway, regulating bacteriocin production, were identified, further strengthening the interconnections between these two QS systems. Interestingly, a similar theme is being revealed in _S. mutans_, the primary etiological agent of dental caries. This review compares the relationship between the bacteriocin and the competence QS pathways in both _S. pneumoniae_ and _S. mutans_, and hopes to provide clues to regulatory pathways across the genus _Streptococcus_ as a potential tool to efficiently investigate putative competence pathways in nontransformable streptococci.  
quorum sensing; competence; transformation; bacteriocin; fratricide; gene regulation +
Footnote †: journal: _S. pneumoniae_ and _Streptococcus mutans_
## 6 Application Example  
In order to exemplify the usefulness of this proposal, we will analyze some aspects of the response process conducted by first response task forces on 22 February 2012, after a train crash at a central station in Buenos Aires, Argentina. After 8:30 am a train crashed with the end of the line at the _Once Station_. The accident left fifty-two dead people and over six hundred injured.  
The Once Station is the third most important in Buenos Aires. It is the head of line for a very large rail network that goes to the west of the city. Only 3 km away from the city center, this station is a hub for buses, subway and other train lines. Every day one million people go through this hub for transportation connections (train-bus-subway) or as their final station. Clearly this is a key and crucial place in which emergency response processes must be planned, because the potential impact of a hazardous event is usually high. During the emergency preparedness process (_i.e._, before an emergency happens) the first response organizations should determine the best way (in terms of time and effectiveness) to diagnose the situation, conduct a search and rescue, and assist injured people [36]. Counting on a model that allows designers to analyze possible limitations of the response plan can help increase the effectiveness of the activities involved on it.
## 4 Results and Discussion
### Flow Regime Identification  
The difference in pressure properties of each flow regime makes it possible to identify flow regimes using their pressure manifestation. Pressure fluctuation and average pressure data are discussed in the following sub-sections to develop important criteria to identify different flow regimes.  
Figure 6: Comparison of flow morphology of simulation results against experimental results from the literature for (**a**) stratified (\(U_{SL}\) = 0.02 m/s, \(U_{SG}\) = 0.1 m/s) [38], (**b**) slug (\(U_{SL}\) = 1 m/s, \(U_{SG}\) = 2.44 m/s) [38], and (**c**) annular flow (\(U_{SL}\) = 0.5 m/s, \(U_{SG}\) = 20 m/s) [39] regimes.
# Precision Farming in Hilly Areas: The Use of Network RTK in GNSS Technology
## 1 Introduction
Through the latter, a geo-referenced data survey, allows observation of the characteristics of the field to be cultivated (e.g., size and perimeter) and defines subsequent planning phases. An information flux between rural machines and the farm's management can be performed. In this way, through these devices, it is possible to optimize automation with vehicles, which can repeat (or exclude) a path already done. Lastly, an appropriate technical analysis points out (i) the enhancement points and agronomic practices to be implemented or, simply, (ii) a unique business database reporting the surfaces of the cultivated areas and the activities carried out on them. GNSS technologies can rapidly get involved when critical events emerge due to a specific incident or agent. This smart advice could a save of products and reduce environmental impact, without (i) having to maximize the use of technical vehicles (e.g., seeds and fertilizers) and (ii) taking account of the tangible need for crops or the (qualitative and quantitative) presence of weeds. Even if these techniques are mainly spreading in flat areas, increasing attention to site-specific management in precision farming is emerging in hilly contexts [30]. In the latter areas, precision agriculture continues to be very limited due to the effort required during the assessment. Hills can be a relevant cause for missing observations [31]. One of their main limits is the highest error probability, even where hills are small, compared to the flatter regions [23; 32]. The positioning system (as GPS) can continuously record both the in-field and the correct position of a vehicle in use [33]. All the collected data can then be processed through a Geographic Information System program (GIS). The latter can produce valid agro-technological assessments and multifaceted examinations of rural fields [34]. Measuring the effective distribution of such systems in less-favored areas, a project was carried out under the rural development program (RDP) in the Lazio Region 2007/2013 measure 1.2.4. The latter aims to assess financial farming conditions. European support might be granting merely the supplementary financing needed to implement the project in more attractive farms (in terms of local sources or banking system) [35]. The experimental activities envisaged in the project concerned:  
* the employment of a permanent station network (Network RTK), exportable as a connection model to other users, (i) improving the efficiency of applications in precision agriculture and (ii) exporting processing data in the areas (to be tested) in a corporate GIS program;* the execution of experimental assessments on defined areas, evaluating how to reduce the use of technical vehicles in three different ways of driving (manual, assisted, automatic);
* the identification of areas with special orographic constraints, using the GIS program, sending warnings to operators both at entry and exit from the area, and even in cases of excessive inclination of the vehicle during its use (avoiding potential risk of overturning).
# Susceptibility and Remanent Magnetization Estimates from Orientation Tools in Borehole Imaging Logs
## 2 Materials and Methods
Therefore, we can define the remanent magnetization components as:  
\[M_{R_{x}}=F_{x}-H_{x}-\chi H_{x}=M_{R}\cos I_{R}\cos D_{R}, \tag{8}\] \[M_{R_{y}}=F_{y}-H_{y}-\chi H_{y}=M_{R}\cos I_{R}\sin D_{R},\] \[M_{R_{z}}=F_{z}-H_{z}-\chi H_{z}=M_{R}\sin I_{R}.\]  
The vertical components of remanent magnetization in Equation (8) allows to estimate the inducing remanent field inclination \(I_{R}\) with the help of previously estimated remanent magnetization magnitude \(M_{R}\),  
\[I_{R}=\arcsin\left(\frac{F_{z}-H_{z}-\chi H_{z}}{M_{R}}\right). \tag{9}\]  
Similarly, remanent magnetization declination \(D_{R}\) can be estimated from the horizontal components of remanent field (\(M_{R_{x}}\) and \(M_{R_{y}}\)) as:  
\[D_{R}=\arccos\left(\frac{F_{x}-H_{x}-\chi H_{x}}{M_{R}\cos I_{R}}\right)\quad \text{or}\quad D_{R}=\arcsin\left(\frac{F_{y}-H_{y}-\chi H_{y}}{M_{R}\cos I_ {R}}\right). \tag{10}\]  
The estimates of magnitude \(M_{R}\), inclination \(I_{R}\) and declination \(D_{R}\) completely define the remanent magnetization. We have shown that estimates of magnetic susceptibility and remanence are possible through simple calculations when magnetic field data from orientation tools in borehole imaging logs are available. The proposed methodology was applied to real data example from Campos Basin, Brazil.
# A Framework for Assessing the Interference from NGSO Satellite Systems to a Radio Astronomy System
## 3 Framework of the Interference Analysis
### Beam Scheduling  
It is important to consider that the beam is not active at all the time, as the activation probability of the beams has a significant impact on the analysis of interference. For example, when satellites are located at the poles or in remote oceanic areas, only a small number of beam resources are needed to satisfy the communication requirements due to the low density of users in these regions. In this study, we considered three different beam activation probabilities for various beam scheduling strategies. To facilitate the analysis, we defined the average number of visible satellites and population density at latitude \(\gamma\) as \(n(\gamma)\) and \(d(\gamma)\), respectively. These quantities play a key role in determining the activation probability of the beams and, therefore, the level of interference experienced by the ground-based RAS.  
In the first beam scheduling method, the beam activation probability was assumed to be 1 for all latitudes, which did not accurately reflect real-world beam scheduling policies. In particular, for polar orbits, the population density is very low while the number of satellites is relatively high. As a result, the activation probability of the beams in the polar region should be reduced to account for this imbalance. In the following beam scheduling policies, we considered both the number of visible satellites and human population density in determining the activation probability.  
The second method assumed that the activation probability of the beam was 1 at the latitude \(\gamma_{n,min}\) where the average number of visible satellites was minimal, which was denoted as \(n(\gamma_{n,min})\). For another latitude \(\gamma\), the beam activation probability should be adjusted according to the ratio of the average number of visible satellites at the latitude to the average minimum number of visible satellites, which is \(\frac{n(\gamma_{min})}{n(\gamma)}\).  
For the third beam scheduling method, the beam activation probability was determined based on the population density at different latitudes. We assumed that the demand for communication services was proportional to the population density and set the beam  
Figure 5: Time slots allocation of an NGSO satellite.  
activation probability at the latitude with the highest population density to 1. The beam activation probability at latitude \(\gamma\) was denoted as \(p(\gamma)\), and could be expressed as follows:  
\[p(\gamma)=min(1,\frac{n(\gamma_{d,max})d(\gamma)}{n(\gamma)d(\gamma_{d,max})}) \tag{33}\]  
where \(\gamma_{d,max}\) is the latitude with the largest population density, and \(\gamma_{d,max}=argmax_{\gamma}d(\gamma)\).
## 7 Case Study: Position Control of a Rotor by PII\({}^{1/2}\)DD\({}^{1/2}\) Control
### Discussion of the Results  
Starting from the results presented in Sections 8.2 and 8.3 we can draw the following conclusions:  
* The DTS and ET experimental results are in good agreement; therefore, DTS can be considered a valuable tool for the tuning of mechatronic systems with FO controllers.
* Both the PII\({}^{1/2}\)DD\({}^{1/2}\) controllers decrease the tracking error remarkably (Table 2, ET, maximum tracking error: \(-71\%\) for PII\({}^{1/2}\)DD\({}^{1/2}\)\({}_{H}\) and \(-34\%\)for PII\({}^{1/2}\)DD\({}^{1/2}\)\({}_{L}\) with respect to PID; mean tracking error: \(-77\%\) for PII\({}^{1/2}\)DD\({}^{1/2}\)\({}_{H}\) and \(-49\%\) for PII\({}^{1/2}\)DD\({}^{1/2}\)\({}_{L}\) with respect to PID), even if the increase in maximum torque and control effort is limited (ET, maximum torque: \(+5\%\) for PII\({}^{1/2}\)DD\({}^{1/2}\)\({}_{H}\) and \(+10\%\) for PII\({}^{1/2}\)DD\({}^{1/2}\)\({}_{L}\) with respect to PID; control effort: \(+4\%\) for PII\({}^{1/2}\)DD\({}^{1/2}\)\({}_{H}\) and \(+14\%\) for PII\({}^{1/2}\)DD\({}^{1/2}\)\({}_{L}\)).
* The error reduction is higher with the tuning \(C_{H}\), which is not surprising, since the gains are higher, but surprisingly the maximum torque and control effort are lower with the tuning \(C_{H}\). As a matter of fact, observing the torque time histories (Figures 10 and 13) it is possible to note that, with the addition of the half-order terms, the torque is delivered with lower delay even with the discrete-time calculation, consequently  
Figure 13: Trapezoidal speed law response, comparison of ET (continuous line) and DTS (dashed line): angle \(\theta\) (**a**), error \(e_{\theta}\) (**b**) and motor torque \(M\) (**c**); PID: blue; PII\({}^{1/2}\)DD\({}^{1/2}\)\({}_{H}\): red; PII\({}^{1/2}\)DD\({}^{1/2}\)\({}_{L}\): green; set-point: black.  
reducing the tracking error. This positive effect of the half-order terms is higher with the \(\text{PII}^{1/2}\text{DD}^{1/2}{}_{H}\) tuning: observing the detail zooms of Figure 13c, it is possible to notice that the torque peaks are more anticipated with the \(\text{PII}^{1/2}\text{DD}^{1/2}{}_{H}\) tuning with respect to the \(\text{PII}^{1/2}\text{DD}^{1/2}{}_{L}\) tuning.
* This confirms the better control readiness of the \(\text{PII}^{1/2}\text{DD}^{1/2}\) controller, already shown by the continuous-time simulations of Section 7.
## 3 Results
### Influence of Variables on Coal Quality Indices  
In order to understand the contribution of spectra and texture features to coal quality indices prediction, SHAP values of different variables were calculated by applying Python's SHAP library. Figure 8 shows the contribution values of the top 10 variables in coal quality index prediction sorted by importance, where red dots represent higher values and blue dots represent lower values. For instance, Ash can be found to increase with decreasing spectra reflectance at 2206 nm, 2212 nm, 2201 nm, 2195 nm, 2217 nm, and 2386 nm. The typical absorption valley of inorganic minerals such as kaolinite is located near 2200 nm, where lower reflectance indicates a higher inorganic mineral content, which corresponds to increased ash in coal. Figure 9 shows the relative contribution of spectra and texture features in the prediction of coal quality indices calculated from the average absolute SHAP  
Figure 7: Scatter plots of actual and predicted coal quality indices values obtained using the optimal LSSVR model based on the combined spectra-texture features. (**a**) Ash; (**b**) VM; (**c**) MC; (**d**) FC; and (**e**) CV.  
values. As shown in Figure 9, the contribution of spectral features is significant in predicting Ash, FC, and CV, while both spectral and texture features contribute equally to VM. In contrast, the contribution of texture features is much greater than that of spectral features in MC. Section 3.4 reveals that the difference in \(R_{p}^{2}\) between the model with combined features and the model with spectra features is much smaller for Ash (0.009), VM (0.018), and CV (0.003) than for MC (0.207) and FC (0.039). Therefore, when combining the spectral modeling accuracy (Table 4), variable contributions (Figure 9), and the above analysis, it can be concluded that Ash, VM, and CV can generally achieve high-precision predictions using only spectral features, whereas MC and FC require modeling with combined spectral-texture features. This may be attributed to the two metrics being more sensitive to the textural changes of the coals. These findings could serve as a reference for tasks involving different coal detection requirements.  
Figure 8: Contributions of the top ten significant variables by SHAP values in the coal quality indices optimal prediction models.