esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 SRB Activities in Acidic Environments
### Response of SRB to Acidic Environments
#### 3.1.2 Regulation of Protein Synthesis  
The regulation of protein synthesis is essential for bacteria to survive under acidic stress as it is a common mechanism that supports the bacterial acid stress response [50]. The protection or repair of macromolecules such as DNA and proteins are promoted by specific proteins which are usually generated in acidic environments. Various chaperone proteins which play an important role in the synthesis, transport, folding, and degradation of proteins have been considered as significant acid tolerance factors [51]. This was found in periplasm of different Gram-negative bacteria which possess HdeA and HdeB, which are two periplasmic chaperones [52]. HdeB has been recognized as the protection of enteric bacteria from gastric acid damage and HdeA is the protection of bacteria against acid stress due to the accumulation of organic acids [52]. Even though this mechanism is common in various type of bacteria, especially Gram negative bacteria [53], there is no report about the mechanism on SRB. Alternatively, it was found in _D. vulgaris_ under acidic stress, six genes in GO term \"GO:0005840-ribosome\", including _rplC, rpsQ, rpsO, rpsI_ and _yfIA_ was enriched [35]. The bacteria have developed different mechanisms in order to relieve the stress caused by antibiotic inhibition such as fusidic acid [54]. Previous study has shown that protein factor-related mechanisms have also been found to reduce antibiotic stress by binding to the ribosome, such as fusidic acid resistance in staphylococci through the action of the FusB-like proteins [55].
# Replay Speech Detection Based on Dual-Input Hierarchical Fusion Network
## 3 Research Process
### Feature Extraction
Compared to traditional Fourier transform, it is more flexible in balancing temporal and frequency resolution, allowing for more accurate spectral analysis of complex speech signals while reducing computational complexity, and is therefore widely used in music and speech signal processing. The details of the feature extraction parameters are as follows: (1) For Spec: a Hanning window with a size of 25 ms, a step of 10 ms, and 512 Fast Fourier Transform (FFT) points; (2) For LFCC: a 60-dimensional feature vector, a window length of 20 ms, 512 FFT points, 20 filters, plus delta and double delta coefficients; (3) For CQT: a Hanning window, nine octaves, 48 bins per octave, and a step of 16 ms. All features were trimmed along the temporal axis to maintain an accurate 400 frames [23].
## 4 Experimental Evaluation
### Experimental Results Compared to Baseline Models
The 2-conv CNN + BiLSTM model, combined with the Mean Teacher framework, achieves better traffic classification results under different labeled traffic data compared to the baseline models. This not only significantly enhances the accuracy of traffic recognition but also substantially reduces the required running time and model parameter size. In short, compared to the other five approaches, our model framework has much higher overall accuracy, precision, recall, and F1 score.
## 5 Conclusions  
In this paper, we discussed the technical challenges in vehicular OCC systems. Owing to these challenges, many studies has been carried out to enhance the performance of the OCC to be feasible with the vehicular communication scenario. The blurring process on an image is also a critical issue that needs to be considered in this context. This paper focuses on analyzing the blurring phenomenon on an image and its effects on vehicular OCC systems. In this regard, we provided a mathematical model and a simulation method of a blurred image.  
We also introduced a vehicular OCC system using a hybrid waveform of RoI signaling and a high-rate waveform for data streaming. HS-PSK, which is a combination of the low-rate scheme S2-PSK and the high-rate scheme DS8-PSK, was introduced in IEEE 802.15.7-2019 standard [30] as a solution  
Figure 13: The comparison of SER performance between an NN-based decoder and a matched filter on different dimming cases with a blur kernel size of \(60\times 60\).  
for vehicular OCC systems, and S2-PSK was already presented in [8]. As an update, we contribute a novel decoding method using AI concept for the existing DS8-PSK single-carrier modulation scheme. A simulation was performed on grayscale images of the LED array which were captured and rescaled to be all 100 \(\times\) 200 in size. Our SER versus SNR simulation has not only considered the blurring level but also concerned the AWGN effect on all images in both training and testing dataset. Also, the performance of our proposed decoder for DS8-PSK has been compared to a previous DS8-PSK decoder using a traditional matched filter. Finally, we provided experimental results to validate the robustness of the proposed NN-based decoder over simulating channel conditions.  
All authors contributed to this paper: [PERSON] and [PERSON] proposed the idea and simulation methodology; [PERSON] collect data and create the first version of data augmentation software; [PERSON] optimized software, proposed and designed the NN-based decoder, performed all experiments and wrote paper; [PERSON] verified the experiment process and results, reviewed and edited paper; and [PERSON] supervised the work and provided funding support. This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2018-0-01396) supervised by the IITP (Institute for Information & communications Technology Promotion). The authors declare no conflict of interest.
## 2 Materials and Methods
### The Tree2C Implementation
#### 2.1.2 Tree2C: How It Works  
The program code is organized in three logical modules: (1) input modules; (2) processing module; (3) output modules. In detail, there are two input modules: the ARFF loader and the tree decoder. The former loads the data from the ARFF file [22] used to develop the model and which Tree2C uses to generate the code to check the applicability domain (see below). The latter reads the tree output file and decodes the decision trees, the number of which depends on the method used to build the model. Moreover, this module finds the attributes, which are directly involved in the model and are a subset of those included in the ARFF file. Each decoded tree is stored as a list of nodes hierarchically ordered according to the distance from the root of the tree. Each node includes the logical condition, defined by (1) an attribute, (2) a logical operator, and (3) a threshold value. The condition must be fulfilled to split the branch and to move to the next node or to yield the final prediction if no branch is further available. Next, the processing module checks if all the tree attributes are comprised within the ARFF file, renames them, removing the characters which are not compatible with some programming languages (e.g., spaces, dashes, punctuation marks, etc.), and calculates the corresponding applicability domain. Moreover, the processing module sets the correct output module according to the selected programming language which can be object-oriented (C++, Fortran 90, Java, JavaScript, JScript, Lua, PHP, Python, REBOL and VBScript), non-object-oriented (C) and C-Script. Finally, all the output modules translate the node lists of the decision trees into a set of IF THAN ELSE conditions and (if requested) include the source code required to determine the applicability domain according to the syntax of the selected programming language. Moreover, the output module for the C-Script, which is the scripting language implemented by the VEGA environment, can add the code required to calculate the attributes by the host program and eventually by MOPAC7 or MOPAC2016 [23]. Since C-Scripts are ready-to-run and can be directly used within the VEGA environment to perform predictions, the output module can also install the generated scripts in the VEGA environment to be easily and promptly used for new predictions.
# A Comparative Study of the Application of Different Commercial Software for Topology Optimization
## 5 Results
### Optimization of a Pillow Bracket  
As is shown in Figure 8, the optimization of the pillow bracket resulted again in almost identical designs in SolidWorks and ABAQUS, while the designs in ANSYS Mechanical differentiated a little bit from them. The main observed difference in the first optimization case, with the 50% weight reduction, was that ANSYS Mechanical resulted in one optimized part, while the other two software platforms created two optimized parts based on the initial design. That was not the case in the optimization with maximum weight reduction, where all software resulted in design solutions with two parts. The initial weight of the model was 0.785 Kg. ABAQUS again resulted in the lightest design equal to 0.111 Kg, which corresponds to a weight reduction of 85.9%. The second best design in terms of weight was the optimized design by ANSYS Mechanical, with an 85.6% weight reduction, and finally, SolidWorks could reduce the weight of the pillow bracket by 84.7%. The optimization time in this case study was almost the same in SolidWorks and ANSYS Mechanical, while in ABAQUS, it was approximately nine times more.  
Figure 6: The optimized designs of bell crank lever for 50% and maximum weight reduction in (**a**) SolidWorks, (**b**) ANSYS Mechanical, and (**c**) ABAQUS.  
Figure 7: The results of the validation studies of bell crank lever for 50% and maximum weight reduction in (**a**) SolidWorks, (**b**) ANSYS Mechanical, and (**c**) ABAQUS.  
The FEA in this case study was also conducted with the same finite elements, 3 mm tetrahedral, resulting in 27,649 elements. The plots of the Von Mises stress in both optimization cases are shown in Figure 9. It was observed that the optimized designs taken from SolidWorks and ABAQUS for 50% weight reduction had smaller maximum stresses (29 MPa and 29.3 MPa) compared to the original design (31.3 MPa). The lowest maximum stress for the second optimization case was 307.8 MPa and was found in the validation study of the optimized design created by ANSYS Mechanical. The maximum stresses of the optimized designs in SolidWorks and ABAQUS were 348.4 MPa and 314 MPa, respectively. It seems that ABAQUS again offered the best design solution in terms of weight and acceptable yield strength.
# Numerical Simulation Study on the Performance of Buried Pipelines under the Action of Faults
## 4 Analysis of Parameters Affecting Pipeline Response under Fault Action
### Influence of Pipe Wall Thickness
In summary, the wall thickness has a significant impact on the performance of buried pipelines. Generally speaking, the thicker the pipe wall, the safer the pipeline, but the amount of steel used will also increase accordingly. Therefore, it is recommended to choose an appropriate diameter to thickness ratio to balance economy and safety.
## 3 Multi-UGV Path Planning Modeling
### Objective Function  
In the process of multi-UGV path planning, different factors affecting the effect of multi-UGV path planning are considered, including the shortest path steps in the training process of UGV, the length of the planned path, the time used for planning, the reward of UGV, and the convergence of the algorithm. The objective function also evaluates these factors at design time, so in this study, the objective function of multi-UGV path programming is summarized as Equation (6):  
\[f=f_{S}+f_{d}+f_{r}+f_{c} \tag{6}\]  
where \(f_{S}\) is the smallest average shortest path step of UGVs, \(f_{d}\) is the shortest average path length of UGVs, \(f_{r}\) is the shortest average calculation time of OWS Q-learning algorithm under the same experimental environment setting, the average reward function value of UGVs is the largest, \(f_{c}\) is UGVs to achieve the target point in a round of iterative learning, \(f\) is a combination of the above four indexes.
## 2 Validation of the Numerical Model  
The numerical simulations were performed with the SAFIR software [22]. The numerical model was validated against two previous experimental tests existing in the literature [23; 24].
# Path Tracking Control Strategy Based on Adaptive MPC for Intelligent Vehicles
## 3 MPC Controller Design Based on Stability Boundary
### Vehicle Stability Boundary
\right],\xi(k)\text{ represents the status of the vehicle at the $k$-th time}.\]  
In the phase plane of the yaw rate/lateral velocity, a closed stability boundary can be constructed by employing Equations (11) and (12), as illustrated in Figure 2. The extent of the boundary is influenced by the longitudinal speed of the vehicle and the side deflection angle of the rear wheel. Specifically, the stability boundaries L\({}_{1}\) and L\({}_{3}\) are constrained by Equation (11), whereas the stability boundaries L\({}_{2}\) and L\({}_{4}\) are subject to the constraints imposed by Equation (12). When the vehicle's state variables lie within the stability boundary, driving stability can be ensured. Furthermore, it is worth noting that when the vehicle tire operates within the nonlinear region for a brief duration, it does not inevitably result in vehicle instability. In order to avoid overly restricting the solution space of the controller, a relaxation factor can be introduced into Equation (13), leading to its revised formulation as follows:  
\[-G_{\text{sh}}(k)-S_{\text{sh}}(k)\leqslant H_{\text{sh}}(k)\zeta(k)\leqslant G_ {\text{sh}}(k)+S_{\text{sh}}(k) \tag{14}\]  
In this context, \(S_{\text{sh}}(k)=\left[\alpha_{r,\text{sh}}\quad\dot{\varphi}_{\text{sh}} \right]^{T}\) represents the relaxation factor vector pertaining to the stability boundary constraint; \(\alpha_{r,\text{sh}}\) denotes the relaxation measure for the rear wheel side deflection constraint; and \(\dot{\varphi}_{\text{sh}}\) signifies the relaxation parameter for the yaw rate constraint.
## 2 Materials and Methods
### Evaluation of TSNP Application in Biosensing and Diagnostics  
#### 2.4.1 TSNP Gold Coating  
Nanoplate protection against etching by chloride ions present in physiological environments was achieved by covering the edges of the nanoplates with gold to keep their plasmonic resonance properties. Gold edge coating of the TSNP was carried out following the method reported by [44], consisting of the use of 20 \(\mu\)L of 0.5 mM HAuCl4 salt and 18.9 \(\mu\)L of 10 mM ascorbic acid per 1 mL of TSNP solution. In this method, there is a reduction in AuCl4 and inhibition of Ag oxidation at the surface of the TSNP, resulting in the deposition of a thin layer of gold in the edges of the TSNP due to galvanic replacement.
# Automatic Decentralized Vibration Control System Based on Collaborative Stand-Alone Smart Dampers
## 2 Modal Parameters Identification
### Non-Dimensional Damping Ratio Evaluation  
Among the several techniques used to evaluate the non-dimensional damping ratio, a time domain decrement method has been chosen, to limit the computational effort. A time domain method allows a faster identification since all the measures needed for the computation of the modal parameters (the damping ratio \(\xi_{i}\) and the structural gain \(R_{i,j}\)) can be recorded through just one harmonic excitation of the structure for each mode. The time domain decrement method relies on the assumption that the primary structure can be modeled as a single DOF system. During modal parameter estimation, just one mode at the time is excited by one actuator and no disturbance forces are supposed to excite the structure. Moreover, such hypothesis is verified only if a given vibration mode is not affected by the others.  
As for the sweep, the CCU sends the command to the stand-alone devices to start the modal parameter identification procedure. One by one, each actuator excites one by one all the modes. The actuator applies a constant amplitude harmonic force, whose frequency matches the desired mode natural frequency. As soon as the excitation gets a steady-state condition, the acceleration of the primary structure and the acceleration of the proof-mass are recorded (the proof-mass acceleration signal is available through another digital accelerometer, which is placed on the proof-mass and directly connected to the PCB). Then, the harmonic excitation stops and the device acquires the vibration decay of the excited mode. An algorithm will chose one peak at the beginning (\(u_{1}\)) and one at the end (\(u_{2}\)) of the vibration decay. The parameter \(u_{1}\) is always chosen as the second peak of the vibration decay (the reason of this choice will be give afterwards in Section 5.3.1). The parameter \(u_{2}\), instead, is the first peak whose amplitude is less then the 20% of \(u_{1}\). Once this harmonic excitation has been carried out for any \(i\)-th observable vibration mode, the non-dimensional damping ratio \(\xi_{i,j}\) is evaluated for any vibration mode as:  
\[\xi_{i,j}=\frac{\Delta u}{2\pi\,n\,u_{m}} \tag{1}\]  
where \(\Delta u=u_{1}-u_{2}\), \(u_{m}=\frac{u_{1}+u_{2}}{2}\) and \(n\) is the number of periods considered between the two peaks.
## 3 Results and Discussion
### Comparison of Particle Combustion in the Channel and Furnace Space  
Figure 14 shows the change in readings of the thermocouple installed at the center of the particles. Most temperature profiles are characterized by the maximum value due to the intense heating of the thermocouple junction when char or wood residue burns near it. The temperature maximum corresponds to the moment of detachment from the holder or afterburning of the particle residue on it and can be used to estimate the complete combustion time of the samples.  
Figure 13: Degree of overlapping of the devolatilization and char residue combustion stages in a single-stage conversion process. The time intervals used to evaluate the degree of overlapping are indicated.  
When testing the particles in the free space of the furnace, increasing the air velocity leads to a decrease in their combustion time (Figure 14a).  
The experiments with the inert channel are characterized by a slightly different pattern. An initial increase in air velocity from 10 to 40 m s\({}^{-1}\) leads to an intensification of the process and a decrease in the combustion time of the particles (Figure 14b). A further increase in air velocity causes the opposite effect, which is an increase in combustion time. Differences between the dynamics of combustion of particles in the furnace and the channel can be explained by the different temperatures of the air supplied to the surface of the samples. In the first case, the tugree flow was mixed with the heated air in the muffle. In the second case, channel design excluded hot air injection from the furnace space, and cold tugree air was supplied to the sample. Thermocuple measurements showed that the difference in air temperature between the first and the second case was 150-200 \({}^{\circ}\)C.  
It is interesting to compare the cases of combustion of particles in the furnace and the channel as performed at 20 m s\({}^{-1}\) and 800 \({}^{\circ}\)C. Measurements indicate the different temperatures of the air supplied to the particles under these conditions. In spite of this, the experiments produce the dynamics of the particle mass change that are similar to each other (Figure 15). It is likely that this pattern is due to the presence of a flame swirl between the particle and the channel, which contributes heat to the combustion process and compensates for the lower air temperature. The pattern confirms the hypothesis, as put forward by the study, of the intensification of the process of combustion of particles in the inert channel.  
Figure 14: Change of particle center temperature in time at different tuyere air velocity. Combustion in the free space of the furnace (**a**) and the inert channel (**b**), T furnace—800 \({}^{\circ}\)C, the captions indicate air velocities.  
Figure 15: The change in particle mass over time at a furnace temperature of 800 \({}^{\circ}\)C and air flow velocity of 20 m s\({}^{-1}\).
###### Abstract  
In this paper, a better power optimizer architecture has been presented for PV strings, using a buck converter for each PV module to address partial shading conditions. The buck converter, though rarely used, is a natural converter for partial shading effects, as it converts the lower current of the shaded module to a higher output current. Usually, the advanced architecture activates the isolated converters (complex) of only shaded modules to draw extra current from the inverter's DC-link node to maintain the string current (\(l_{string}\)). On the other hand, the conventional architecture activates converters (basic) of all modules regardless of their shading status. The proposed architecture contains a unique design with a new schematic layout, where it activates the buck converters of only shaded modules without drawing extra current from the DC-link. Thus, it combines the benefits of both architectures--selective converter operation, basic topology, high efficiency, low voltage stress, and low control complexity--while eliminating their drawbacks. The designing philosophy, control mechanism, and fundamental operation of the proposed architecture have been comprehensively explained and validated through simulation experiments. Three levels of shading are used to test the proposed architecture for string containing three PV modules: (1) a single module moderate (15%) shading level, (2) a single module strong (50%) shading level, and (3) a double module extreme (75%) and moderate (25%) shading levels. The results show a successful operation of the proposed architecture as it maintains a common \(l_{string}\) for an inverter, where all the shaded modules remain active. The architecture exhibits an average efficiency over 97% under normal conditions. A comparative analysis of architectures has been presented to indicate the enhanced features of the proposed architecture.  
photovoltaic system; photovoltaic efficiency; power conversion efficiencies; distribution systems; electric power systems; renewable energy +
Footnote †: journal: Applied  
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