esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Case Study: Progressive Damage of a Masonry Arch Bridge Model
The localization results obtained with these two sets of localization index are displayed in Figure 9.  
Examining the damage localization index along the span (Figure 10a), at the DS-1 loading level (30 kN), the damage index produced similar values for both regions A\({}_{1}\)-A\({}_{2}\) and A\({}_{2}\)-A\({}_{3}\). Although plastic hinge formation had initiated at the loaded side (A\({}_{2}\)-A\({}_{3}\)), there was also noticeable separation between the spandrel walls and the arch ring on either side of the bridge. This widespread separation introduced global changes in the system's dynamic properties, affecting both sensor regions similarly. As a result, the damage index could not distinctly localize the damage at this load level. However, as the load increased and hinge formation was completed at DS-2 (60 kN), accompanied by the propagation of cracks, the index successfully identified the correct damaged region (A\({}_{2}\)-A\({}_{3}\)), where localized stiffness loss dominated the dynamic response. In the lateral direction (Figure 10b), at DS-1, cracks primarily formed on the front face of the arch. By comparing transmissibility-based index values between the front face and midsection (A\({}_{2}\)-A\({}_{4}\)) and between the midsection and back face (A\({}_{4}\)-A\({}_{6}\)), the damage index clearly pinpointed the affected side. As the load increased further to DS-2 and shear and tension cracks propagated through the arch's depth, the back face was also damaged. Consequently, the damage index values across these lateral regions converged, reflecting a more uniformly distributed damage state through the arch's thickness. This behavior is consistent with typical masonry arch bridge failure modes, where damage initiates on the loaded face and progresses inward with increasing severity. Figure 9: Principal components of the DSFs for the training and testing data with the OCSVM decision boundary.
# Broadband Millimeter-Wave 5G Power Amplifier Design in 22 nm CMOS FD-SOI and 40 nm GaN HEMT
## 3 Results  
For all of the measurements shown in this work, the RF input is probed and the DC voltages are provided by gold wire-bonded pads to a custom PCB (printed circuit board), where more bypass capacitors are added for better stability on all DC traces. Later in this paper, modulated 5G NR data will be used as the RF input signal, and the PAs are measured with the state-of-the-art mm-Wave PXIE (PCI express extensions for instrumentation) system by National Instruments (NI), which can produce modulated 5G NR signals of up to 1 GHz BW and up to 44 GHz [11]. In the following discussions, besides the 3-dB BW conventionally used, the BW of these PAs is often also compared by the absolute BW, defined as \(2(\frac{f_{H}-f_{L}}{f_{H}+f_{L}})\)[12] where \(f_{H}\) is the upper frequency and \(f_{L}\) is the lower frequency of the 3-dB BW.
# Internet of Things Geosensor Network for Cost-Effective Landslide Early Warning Systems
## 3 Inform@Risk Monitoring System
### LoRa Sensor Nodes
#### 3.4.3 Low-Cost Chain Inclinometer (LCI)  
The LCI is similar to the SN but includes inclination sensors every m, resulting in up to 6 inclination measurements in the subsurface. They are all connected in serial to the I2C bus. Each of them is wired to a different digital port or addressed differently on the I2C bus, respectively. In contrast to the SN, the casing should be made of PVC in order to allow for deformation of the rod to determine the shear depth. The inclination sensor is the same as for the SN. Currently due to limitations of the used digital bus (I2C) the max. achievable depth for the LCI is about 6 m. The groundwater measurement is achieved with a float switch each m, alternating with the inclination sensor. They act as a series of resistors in parallel, which increase the resistance with increasing groundwater level. They are connected to the analog input of the microprocessor or the ADC. Therefore, the groundwater measurement of the LCI is not as good as the one the SN provides, since it is not continuous. Still, the most important information--whether there is water in the top meters--can be gained this way.
## 1 Introduction
The method has great potential in improving identification accuracy and robustness in the presence of both murmurs and environmental noises. Continuing from the above studies, a collapsible finger-ring-shaped hybrid smart stethoscope, as shown in Figure 1, is presented in this paper. The device can be worn on the finger and then placed on the right side of the chest where the second intercostal space and parasternal line intersect for simultaneous PPG signal measurement and heart-sound acquisition [17]. The detected PCG signal and the peak of the PPG is detected in preprocessing by an advanced Shannon entropy envelope. According to the relationship between heart sound and PPG signal, an automatic PCG analysis algorithm based on computing the time interval between S2 and the peak of PPG was developed, and implemented through a high-performance Bluetooth Low Energy (BLE) SoC ARM processor for system control, data processing, and wireless communication. The computed data was transmitted to an Android-OS-based smartphone application for data display and storage. The prototype device was manufactured and subjected to accuracy and usability testing by: comparing its readings with those of a professional physiological signal acquisition device; enlisting 20 young adults to test the performance of PPG measurements and heart-rate acquisition; and verifying the accuracy of the developed automatic heart-sound-analysis algorithm in S1 and S2 heart-sound extraction.
## 5 Experimental Verification and Analysis  
The optimized parameters were used in the new experiment to verify that the method is effective. The result is shown in Table 7. No.18 of the above CCD experiment achieved the minimum value of 0.390 \(\upmu\)m, so it was suitably chosen as the comparison group.  
From Table 7, compared with reference group, when \(\lambda_{a}\) is 0.6 and 0.7, the mean value of surface roughness went down by 2.05% and 4.62% respectively, and the mean value of MRR of both went up 11.11% to 0.020 g/s; when \(\lambda_{a}\) is 0.8 and 0.9, the mean value of surface roughness went down by  
Figure 8: Response surfaces for the optimal value of each grinding parameter. (**a**) \(V_{S}\) = 7.13 m/s, \(V_{W}\) = 2.00 mm/s; (**b**) \(F\) = 21.35 N, \(V_{W}\) = 2.00 mm/s; (**c**) \(F\) = 21.35 N, \(V_{S}\) = 7.13 m/s; (**d**) \(P\) = 398.16#, \(V_{W}\) = 2.00 mm/s; (**e**) \(P\) = 398.16#, \(V_{S}\) = 7.13 m/s; (**f**) \(P\) = 398.16#, \(F\) = 21.35 N.  
Figure 9: Respond surfaces in two different abrasive sizes. (**a**) \(P\) = 320#, \(V_{W}\) = 2.00 mm/s; (**b**) \(P\) = 400#, \(V_{W}\) = 2.00 mm/s.  
10.51% and 14.87% respectively, and the mean value of MRR of both went up 5.55% to 0.019 g/s. In addition, the error ranges in the values of surface roughness and MRR of both groups are estimated to be \(-0.05\) to 0.05, and \(-0.001\) to 0.001 respectively.These results reveal a trend that the optimized value of both surface roughness and MRR decrease along with the increase of surface roughness weighting coefficient.  
Furthermore, 2D profiles of the workpiece surfaces from optimized and unoptimized group are extracted by using laser scanning confocal microscope (OLYMPUS, OSL4100), it shows that the surface quality of 45 steel from optimization group shown in Figure 10a,b, has significantly improved compared with that obtained from the unoptimized group, shown in Figure 10c-e. The experiment results show the prediction model is reliable, that is to say, this method is effective for optimizing grinding parameters.
# Anomaly Detection in Automotive Industry Using Clustering Methods--A Case Study
## 2 Clustering Algorithms
### Particle Swarm Optimization (PSO)  
The Particle Swarm Optimization (PSO) algorithm is the most used, popular, and famous swarm-inspired method for optimization [10]. It was inspired by the social behavior of animals that behave with flock characteristics, simulating their collective intelligence [13; 53]. Beyond the optimization applications with real data, PSO has been widely used in binary, combinatorial, and clustering optimization problems [9]. In optimization problems, positions represent a candidate solution to the current task. However, in clustering, this depends on the coding and parameters [25].  
The learning process of the particles comes from two sources: their own experience, called cognitive learning, and the combined learning of the whole cluster (or a topological neighborhood) called social learning. The first case is represented by the best position of the particle (**pBest**) reached until the current iteration, and the best position represents social learning achieved considering, for example, the entire population (**gBest**). Together, cognitive and social learning are used to calculate the velocity of particles and their next position [13].  
The most used encoding scheme for partial clustering considers a particle a complete candidate solution [10]. In this case, the agent contains the spatial coordinates of all \(n_{k}\) centroids concatenated in a vector. The PSO can be applied directly, using the position and velocity, according to Equations (11) and (12), respectively:  
\[\mathbf{x}_{i}(\mathrm{t}+1)=\mathbf{x}_{i}(\mathrm{t})+\mathbf{v}_{i}( \mathrm{t}+1) \tag{11}\]  
\[\mathbf{v}_{i}(\mathrm{t}+1)=\omega\times\mathbf{v}_{i}(\mathrm{t})+q_{1} \times r_{1}\times\left(\mathbf{pBest}_{i}(\mathrm{t})-\mathbf{x}_{i}(\mathrm{ t})\right)+q_{2}\times r_{2}\times\left(\mathbf{gBest}(\mathrm{t})-\mathbf{x}_{i}( \mathrm{t})\right) \tag{12}\]  
where \(\omega\) is the inertia weight, \(\mathbf{x}_{i}\) is the position of the particle, \(\mathbf{v}_{i}\) is the speed, \(\mathbf{pBest}_{i}\) is the best position ever found by each particle, \(\mathbf{gBest}\) is the best position found by the group, \(q_{1}\) and \(q_{2}\) are two previously defined constants, and \(r_{1}\) and \(r_{2}\) are two numbers randomly generated in the interval [0, 1].  
The algorithm is often initialized by randomly scattering particles over the search space. The same process generates the initial speeds, but they are initialized equal to zero in some other cases. The inertia weight \(\omega\) is often less than one, and it is used to avoid divergence in the response. It is also common to limit the speed of the particles to an interval [\(-v_{max}\); \(+v_{max}\)] [54].
# Gaze-Guided Control of an Autonomous Mobile Robot Using Type-2 Fuzzy Logic
###### Abstract  
Motion control of mobile robots in a cluttered environment with obstacles is an important problem. It is unsatisfactory to control a robot's motion using traditional control algorithms in a complex environment in real time. Gaze tracking technology has brought an important perspective to this issue. Gaze guided driving a vehicle based on eye movements supply significant features of nature task to realization. This paper presents an intelligent vision-based gaze guided robot control (GGC) platform that uses a user-computer interface based on gaze tracking enables a user to control the motion of a mobile robot using eyes gaze coordinate as inputs to the system. In this paper, an overhead camera, eyes tracking device, a differential drive mobile robot, vision and interval type-2 fuzzy inference (IT2 FIS) tools are utilized. The methodology incorporates two basic behaviors; map generation and go-to-goal behavior. Go-to-goal behavior based on an IT2 FIS is more soft and steady progress in data processing with uncertainties to generate better performance. The algorithms are implemented in the indoor environment with the presence of obstacles. Experiments and simulation results indicated that intelligent vision-based gaze guided robot control (GGC) system can be successfully applied and the IT2 FIS can successfully make operator intention, modulate speed and direction accordingly.  
Keywords:eye gaze tracking; interval type-2 fuzzy logic; vision system; mobile robots; intelligent control +
Footnote †: journal:
## 3 Results and Discussion
### Assessment of Building Base Shear and Displacement (Drift)
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Load Case**} & \multirow{2}{*}{**Base Frame**} & \multicolumn{6}{c}{**Traditional Masonry**} & \multirow{2}{*}{**Type 1 SIM Panel**} & \multicolumn{3}{c}{**Type 2 SIM Panel**} \\ \cline{2-2} \cline{4-11}  & & & & **Panel** & & & **Base** & & & **Base** & & **Base** & & **Drift** \\
**Sear** & **(kN)** & **Drift** & **Ratio** & **Shear** & **(mm)** & **Ratio** & **(kN)** & **(mm)** & **Ratio** & **(kN)** & **(mm)** & **Ratio** \\ \hline
1 & 66.7 & 6.12 & 0.06\% & 79.1 & 0.54 & 0.01\% & 19 & 3.45 & 0.03\% & 18 & 1.7 & 0.02\% \\
2 & 105.1 & 9.65 & 0.
###### Abstract  
This study evaluated the effects of triclosan-containing polyester surfaces under various conditions at concentrations of between 400 ppm and 850 ppm. _Staphylococcus aureus_ was chosen for the tests because it rapidly develops resistance to many antimicrobial agents. The results show that dry and humid conditions have bacteriostatic activity that inhibits the growth of _S. aureus_, with a greater effect under dryness (\(p<0.05\)). Further, concentrations as low as 400 ppm showed activities of 0.99 log\({}_{10}\) and 0.19 log\({}_{10}\) for dry and humid conditions, respectively. The study of the association between triclosan concentrations and bacterial inhibition showed a high correlation for dry (R\({}^{2}\) = 0.968) and humid conditions (R\({}^{2}\) = 0.986). Under wear conditions, triclosan showed a gradual reduction in its bacteriostatic activity due to successive washing/drying treatments (\(p<0.05\)). Thus, the use of triclosan in low concentrations is suggested as achieving bacteriostatic activity. Moreover, its use can be considered as complementary to the cleaning and disinfection procedures carried out in the food industry. However, it must not replace them. Manufacturing processes must be improved to preserve the triclosan properties in the antimicrobial materials to control microorganisms involved in cross-contamination between surfaces and food.  
Staphylococcus aureus; triclosan; surfaces; antimicrobial activity; bacterial inhibition; epifluorescence microscopy; humidity; dryness; wear condition
## 5 Simulations
### Simulation Results and Discussion: Exploring Findings and Analysis
Figure 6: Analyzing temporal variation of the tracking error in the second joint of a robot manipulator. As a result of tracking in the joint space, it is observed that the proposed controller achieves the smallest tracking errors, indicating the highest tracking accuracy along the XYZ axes in three dimensions, as depicted in Figure 8. This signifies that the proposed controller successfully minimizes the deviations between the desired and actual trajectories, resulting in superior tracking performance characterized by enhanced accuracy and precision. It is worth noting that the other controllers, namely SMC, TSMC, and FTSMC, exhibit similar trends, showcasing their respective effectiveness in achieving accurate tracking as well. However, it should be mentioned that the PID controller shows poorer tracking performance compared to the other methods, as evident from the larger tracking errors in the joint space. In Figure 9, the control inputs for each controller are displayed. The proposed controller exhibits smooth and comparable control efforts to the PID controller while delivering superior tracking performance. On the other hand, controllers like SMC, TSMC, and FTSMC demonstrate good tracking performance, but their control inputs exhibit chattering behavior. Based on this simulation study, it can be concluded that the proposed controller is more effective in achieving tracking control for a robot in the presence of uncertain terms and unknown dynamics compared to the other controllers. The proposed controller strikes a balance between smooth control and accurate tracking, making it a favorable choice for practical applications. Based on the simulation results, it is evident that the proposed controller surpasses other controllers in terms of trajectory tracking performance, demonstrating reduced settling time and overshoot. Additionally, it exhibits improved robustness against unknown dynamics, external disturbances, and uncertainties. Moreover, the chattering effect, commonly observed in sliding mode-based controllers such as SMC, TSMC, and FTSMC, is significantly mitigated by incorporating an NN for the real-time approximation of unknown dynamics, resulting in a smoother control signal. Overall, the simulation results validate the effectiveness and superiority of the proposed controller, specifically the TSMC design framework based on an RBFNN and PPCA, in achieving precise trajectory tracking performance with enhanced robustness and minimized chattering effect. Figure 7: Analyzing temporal variation of the tracking error in the third joint of a robot manipulator. Figure 8: Examining the time evolution of the X, Y, and Z axis errors in the desired and actual trajectories.
###### Abstract  
The increasing emission of greenhouse gases that comes with the rise in industrialization is harmful to the environment. Thus, finding new renewable energy sources is becoming increasingly important in the energy field. One such renewable energy source is biomass, which provides valuable energy carriers--for example, biofuels. The objective of this work was to evaluate the release of total reducing sugars (TRSs) from mesquite pod seed hulls by chemical and enzymatic hydrolysis. The husks were crushed and separated by screens (#16, #30 and #50). The effect of hydrolysis time (10, 20, and 30 min) and sulfuric acid concentration (0, 0.25, and 0.5 N) was analyzed. The #50 mesh showed the highest TRS release, increasing from 3.19 to 17.49 g/L as the reaction time was extended. Additionally, enzymatic hydrolysis with endo-1, 4-g-xylanase and \(\beta\)-glucanase enzymes was evaluated on the solid and liquid fractions obtained. Statistical analysis with Design Expert showed that, for the solid fractions, after 31 h there were no significant differences, reaching 79.46 g/L TRS. In the liquid fractions, the TRS released reached 113.37 g/L after 54 h of enzymatic treatment. The release of TRS by chemical hydrolysis was also modeled with artificial neural networks, considering the particle size, the hydrolysis time, and the sulfuric acid concentration. The coefficient of determination (\(r^{2}\)) indicates that the ANNs present a better data fit (\(r^{2}\) > 0.99) to predict the experimental conditions that maximize the study variables.  
chemical hydrolysis; enzymatic hydrolysis; biofuels; biomass; mesquite (_Prosopis Iaevigata_); response surface methodology; artificial neural networks +
Footnote †: journal: Applied Sciences  
Article
# Conversion between Logic and Algebraic Expressions of Boolean Control Networks
## 4 Conversion of Expressions of Boolean Control Networks
### From Algebraic Expression to Logic Expression  
The logical networks \(N_{\text{in}}\) and \(N_{\text{out}}\) can be reconstructed from the algebraic expression (7) through conversely applying Algorithm 1.  
Let us illustrate the algorithm above with the following example.  
**Example 4**: _Reconstruct the logic network of BCN (18)._  
1. _Calculate_ \(a_{i}\) _for each_ \(i\in\{0,1,\ldots,31\}\) _by (_19_), which is presented in the last column of Table_ 1_._
2. _Through the decimal-binary conversion, one can calculate_ \(b_{ji}\) _for each_ \(i\in\{0,1,\ldots,31\}\) _and_ \(j\in\{1,2,3\}\)_, which is presented in Table_ 1_._
3. _Let_ \[\mathcal{S}_{1} =\{i\in\{0,1,\ldots,31\}\,|\,b_{1i}=1\}=\{10,11,14,15,26,27,30,31\},\] \[\mathcal{S}_{2} =\{i\in\{0,1,\ldots,31\}\,|\,b_{2i}=1\}=\{0,2,4,\ldots,30\},\] \[\mathcal{S}_{3} =\{i\in\{0,1,\ldots,31\}\,|\,b_{3i}=1\}=\{8,9,10,\ldots,31\}.\]_Then, the logic expression of \(N_{in}\) is_  
\[\left\{\begin{array}{l}x_{1}(t+1)=\sum_{i\in\mathcal{S}_{1}}m_{i}=x_{2}(t)u_{1} (t),\\ x_{2}(t+1)=\sum_{i\in\mathcal{S}_{2}}m_{i}=\bar{u}_{2}(t),\\ x_{3}(t+1)=\sum_{i\in\mathcal{S}_{3}}m_{i}=x_{1}(t)+x_{2}(t).\end{array}\right.\]  
_\({}_{\blacksquare}\)_  
**Step 4**: _Calculate \(\bar{u}_{i}\) for each \(i\in\{0,1,\ldots,7\}\) by (20), which is presented in the last column of Table 2._  
**Step 5**: _Through the decimal-binary conversion, one can calculate \(\bar{b}_{1i}\) for each \(i\in\{0,1,\ldots,7\}\), which is presented in Table 2._  
**Step 6**: _Let_  
\[\bar{\mathcal{S}}_{1}=\{i\in\{0,1,\ldots,7\}\mid\bar{b}_{1i}=1\}=\{0,2,4,6\}.\]  
_Then, the logic expression of \(N_{out}\) is_  
\[y_{1}(t)=\sum_{i\in\mathcal{S}_{1}}m_{i}=\bar{x}_{3}(t).\]  
_Hence, the logic expression of BCN (18) is (15), whose logical network is shown in Figure 1. \({}_{\blacksquare}\)_
## 2 Materials and Methods
### Study Data
#### 2.2.1 NTL Data  
The NPP-VIIRS (Suomi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite) data, jointly provided by NASA and NOAA, has a spatial resolution of 500 m, enabling more precise capture of light intensity distributions within urban agglomerations, particularly in areas with concentrated economic activity and population density. Available since 2011, these data have a daily revisit cycle. The dynamic range of NPP-VIIRS NTL data is extensive, effectively detecting weak light sources and providing insights into economic activities in smaller and emerging cities, thus enhancing the accuracy of urban agglomeration spatial structure identification [64]. In this study, we access 2023 NPP-VIIRS NTL data for the PRD urban agglomeration through NASA's Earth Observing System Data and Information System (EOSDIS) website. The data undergo preprocessing, including cloud removal, radiance correction, and contrast adjustment, resulting in the preprocessed NTL data (Figure 2).