esa-sceva/satcom-chunk-collection huggingface dataset:
# Topology-Aware Anchor Node Selection Optimization for Enhanced DV-Hop Localization in IoT
## 6 Simulation and Results Analysis
### Comparison of Localization Stability Under Non-Uniform Network Topology  
Figure 8 presents the cumulative distribution functions of normalized localization errors for the original DV-Hop, GWODV-Hop, and proposed algorithms across three network topologies. A steeper curve indicates stronger stability of the algorithm. The curves for the proposed algorithm and GWODV-Hop rise more rapidly than those of the original DV-Hop. In the O-type network, however, their increase rates are relatively similar. The results indicate that localization stability is highest in the O-type network and lowest in the W-type network for all three algorithms. Furthermore, Figure 8 clearly shows that the curve for the proposed algorithm is always steeper than those for the original DV-Hop and GWODV-Hop across all three networks, indicating superior localization stability.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{3}{c}{**Original DV-Hop**} & \multicolumn{3}{c}{**GWODV-Hop**} & \multicolumn{3}{c}{**Proposed Algorithm**} \\ \cline{2-10}  & **C-Type** & **O-Type** & **W-Type** & **C-Type** & **O-Type** & **W-Type** & **C-Type** & **O-Type** & **W-Type** \\ \hline Average value & 1.1699 & 0.8545 & 1.3217 & 0.5577 & 0.4512 & 0.7635 & 0.4571 & 0.3920 & 0.6210 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Average normalized error of each algorithm within communication radius 15–27 m.  
Figure 7: Localization accuracy under different communication radii. (**a**) C-type network; (**b**) O-type network; (**c**) W-type network.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{3}{c}{**Original DV-Hop**} & \multicolumn{3}{c}{**GWODV-Hop**} & \multicolumn{3}{c}{**Proposed Scheme**} \\ \cline{2-10}  & **C-Type** & **O-Type** & **W-Type** & **C-Type** & **O-Type** & **W-Type** & **C-Type** & **O-Type** & **W-Type** \\ \hline Average value & 1.1665 & 0.7373 & 1.4176 & 0.5561 & 0.3950 & 0.8214 & 0.4554 & 0.3603 & 0.6742 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average normalized error of each algorithm with 15–45 anchor nodes.
# Data-Driven Fault Detection and Diagnosis: Challenges and Opportunities in Real-World Scenarios
## 4 Experimental Design
### Dataset 3: Suction Cups  
Like the second dataset, this dataset refers to a critical part of a packaging machine whose malfunction strongly affects the products' quality. The component under analysis consists of four suction cups that push the cartoon forward by rotating. A full cycle lasts 0.134 s. In a cycle, the pressure of the suction cups on the cartoon may decrease, causing the detachment of one or more suction cups. If two suction cups detach, the component cannot perform its function correctly and an intervention is required. The detachment of the suction cup is almost instantaneous. A pressure sensor is, therefore, placed on the component to detect the pressure drops corresponding to the detachment of the suction cups. Signals are collected at a sampling frequency of 10 kHz, and three experiments are conducted. First, the data are collected under a nominal condition; then, two fault conditions are generated, having one (Fault s1) and two (Fault s2) suction cups detached, respectively. The pressure values in the different conditions are shown in Figure 3.
# Imbalanced Diagnosis Scheme for Incipient Rotor Faults in Inverter-Fed Induction Motors
## 2 Background
### Time-Domain Fault Signatures  
Statistics computed from time domain signals have been employed as features for several fault diagnosis approaches in literature [6; 7; 11]. High-order statistics are sensitive to non-Gaussian distribution measurements. On the other hand, low-order statistics (e.g., first and second moments) are more robust. In Table 1, the time-domain statistical features used in this study are defined.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**Variables in Time-Domain**} \\ \hline
**Variable** & **Notation** & **Formula** \\ \hline
1 st. Moment & \(m_{1}\) & \(\frac{1}{N}\sum x[n]\) \\
2 nd. Moment & \(m_{2}\) & \(\frac{1}{N}\sum(x[n]-\xi[n])^{2}\) \\
3 rd. Moment & \(m_{3}\) & \(\frac{1}{N}\sum(x[n]-\xi[n])^{3}\) \\
4 th. Moment & \(m_{4}\) & \(\frac{1}{N}\sum(x[n]-\xi[n])^{4}\) \\
2 nd. Cumulant & \(c_{2}\) & \(m_{2}-m_{1}^{2}\) \\
3 rd. Cumulant & \(c_{3}\) & \(m_{3}-3\cdot m_{1}m_{2}+2\cdot m_{1}^{3}\) \\
4 th. Cumulant & \(c_{4}\) & \(m_{4}+3m_{3}m_{1}-3\cdot m_{2}^{2}+12\cdot m_{2}m_{1}^{2}\) \\  & & \(-6\cdot m_{1}^{4}\) \\ Skewness & _Skew_ & \(\frac{m_{3}}{(\sqrt{m_{2}})^{3}}\) \\ Kurtosis & _Kurt_ & \(\frac{m_{4}}{(\sqrt{m_{2}})^{4}}\) \\ Absolute mean (ABM) & \(|\xi|\) & \(\frac{1}{N}\sum x^{\xi}\) \\ Peak value & PV & \(\frac{1}{2}(\max(x[n])-\min(x[n]))\) \\ Squared root value & SRV & \(\left(\frac{1}{N}\sum\sqrt{|x|}\right)^{2}\) \\ RMS value & RMS & \(\sqrt{\frac{1}{N}\sum_{n=0}^{N-1}[x[n]-x]^{2}}\) \\ Crest factor & CF & \(\frac{\
u}{\mathrm{RMS}}\) \\ Shape factor & SF & \(\frac{\mathrm{RMS}}{|\xi|}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics computed from the time-domain signal.
# Electromagnetic Field-Aware Radio Resource Management for 5G and Beyond: A Survey
## 5 Challenges and Open Research Topics
### Open Research Topics
#### 5.2.8 EMF-Aware IoT Applications  
The proliferation of IoT devices, autonomous vehicles, and smart city applications in 5G networks increases EMF exposure complexity. Research is needed to develop strategies that manage EMF exposure while ensuring seamless connectivity, efficient device management, and robust data transmission. Optimization frameworks that dynamically adjust power levels and network parameters in real-time environments could improve both safety and performance.
## 1 Introduction  
Stick-slip, as a form of unstable slip on faults, is a source of crustal earthquakes [1]. According to the stick-slip theory, the occurrence of most crustal earthquakes is due to the sudden slip of pre-existing faults blocks, while the rock fracturing is only reflected in the extension of fault ends and the wear on the fault surface [2; 3]. Therefore, the occurrence of crustal earthquakes depends on the stability rather than the strength of seismic faults. A large number of seismic activities have occurred in sedimentary rocks due to the fluid injections for shale gas development and the disposal of wastewater from oil and gas fields [4; 5; 6; 7]. Sandstone was chosen as the study object for this test.  
In recent years, many scholars have studied the unstable slip of faults blocks [8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20]. They found that the high normal stress and the rough surface increase the shear stress drop during unstable slip. Fault gouge also has an important influence on the slip both in natural faults and experimental-laboratory faults [21; 22; 23; 24; 25; 26; 27]. Most previous studies focused on the effects of normal stress, roughness, and slip displacement on the fault strength or friction coefficient but have not fully researched the transition of faults from stable to unstable slip. Additionally, the impact of fault gouge on fault block slip stability also needs to be further explored.  
In order to study the effect of fault gouge on fault block slip stability, some researchers conducted shear tests using fault gouge of different materials, shapes, thicknesses, and humidity conditions [28; 29; 30; 31; 32; 33] and observed the changes in the thickness, composition, porosity, and microstructure of the gouge [34; 35; 36; 37; 38]. It is found that the faults could be weakened by fault gouge when the seismic slip rate is 0.1-10 m/s, independent of the rock type [34], and the weakening mechanisms that they assume to be dominant include flash heating, nanopowder lubrication, a dehydration reaction, decarbonation reaction and thermal pressure [39; 40; 41; 42]. These studies reveal as the fault gouge influences fault mechanical properties. As a matter of fact, the fault gouge layer generated by fault block slip, due to the wear of the fault surface, participates in the slip and affects the fault stability, but it was not fully examined in previous research.  
This study aims to explore the transition of fault from stable to unstable slip and evaluates the impact of the fault gouge layer on slip stability which has not been fully researched. We conducted direct shear tests with three kinds of normal stress and two kinds of roughness surfaces under constant normal load, measured the shear stiffness of the fault stable slip, and then studied the factors affecting the stability of fault block slip.
## 4 Precoding Schemes  
Since several LED lamps are typically deployed in an indoor scenario in order to provide constant and uniform illumination, each user may receive a useful signal from multiple optical APs. Thus, downlink VLC lends itself naturally to MIMO systems. In this sense, a vast amount of works focused on exploiting the benefits of MIMO channels have been derived for RF systems. At this point, the upcoming question may be formulated; Is the well-known MIMO signal processing for RF systems, e.g., [135; 136; 33], applicable to VLC systems?. The general answer is; yes, but it is not straightforward. In the following, we provide an introduction to precoding schemes, including a review of the issues to be solved for implementing precoding signal processing in VLC, as well as concepts about the capacity of VLC MIMO systems. After that, a review of precoding schemes for VLC derived from the state of the art is detailed.
# Evaluation of Algae-Based Biodiesel Production Topologies via Inherent Safety Index (ISI)
## 2 Materials and Methods
### Process Description
#### 2.1.2 Topology 2  
In-Situ Transesterification  
The production of biodiesel from microalgae by in situ transesterification method is shown in Figure 3. Microalgae (1000 kg/h) and nutrients (9000 kg/h) are fed into the system in the first stage where the biomass is cultivated. Then, microalgae are harvested by centrifugation and sent to a drying process to remove excess moisture. The dry biomass goes to the next step, where the lipid extraction and transesterification are carried out using methanol and sulfuric acid as a catalyst in a molar ratio of methanol: lipids and catalyzer: lipids equal to 12:1 [48] and 1:1 [49], respectively. From this unit, the carbohydrate and protein-rich phase is discarded, while the biodiesel and the glycerol-rich stream is sent to the neutralization stage. In the neutralization step, calcium oxide is added to neutralize the catalyst; calcium sulfate and water leave this unite as waste.  
The neutralized stream is subjected to a distillation process to recover the methanol and sent it back to the process. In the washing stage, water is added to remove the glycerol, allowing the biodiesel separation. The biodiesel-rich stream is sent to a flash separation unit where all impurities are removed; while the stream containing the glycerol is subjected to a distillation process to purify the glycerol. Biodiesel is obtained as the main product at a rate of 223 kg/h, while glycerol is produced as a by-product at a rate of 127.25 kg/h. The transesterification reactions are shown in Equations (1)-(12), the neutralization reaction is presented in Equation (13).  
Applications of \"in situ\" technology allow the elimination of certain stages, thus reducing installation and maintenance costs of equipment and energy consumption [51].
###### Abstract
Thus, it further becomes important to integrate the human body model to get more accurate results, instead of just analysing the active or semi-active seat suspension. From the literature review, it was observed that rare work is reported on the study of the integrated model of the semi-active seat suspension, consisting of both the vehicle suspension and the human models. Further, no work is available that studies the semi-active suspension system with such an integrated model. Further, the implementation of ISO road profiles while investigating such systems is missing. Thus, in this work, an integrated model of seat suspension comprising the semi-active quarter car suspension and human model is proposed. The MR damper, governed by the fuzzy logic-based self-tuning PID controller (FLST-PID), is implemented in the seat suspension. The FLST-PID determines the desired force and then it is compared with the force generated by the MR damper, and on the basis of the comparison when the MR damper, is switched either on or off. The FLST-PID controller is designed under the constraints of the suspension deflection limitation. Numerical simulations are performed to evaluate the performance of the integrated model on the various types of road profiles including the bump and ISO road profiles of different grades such as C, D and E-grades. Finally, the effectiveness of the semi-active suspension with the MR dampers is compared with the passive suspension on the bump and ISO road profile with different speeds that vary from 30 km/h to 120 km/h.
# The Heat Shock Protein 60 and Pap1 Participate in the _Sporothrix schenckii_-Host Interaction
## 3 Results
### Hsp60 and Pap1 Participate in the Sportthrix schenckii Adhesion to Extracellular Matrix Proteins
Yeast-like cells were added to the plates, nonadherent cells were washed, and cells bound to extracellular matrix proteins were detected with anti-rGp70 antibodies and peroxidase-conjugated anti-rabbit IgG. No antibody, cells were pre-incubated with PBS; Control (rHsp60) and Control (rPaP1), cells were pre-incubated with pre-immune sera; anti-rHsp60, cells were pre-incubated with polyclonal anti-rHsp60 antibodies; and anti-rPaP1, cells were pre-incubated with polyclonal anti-rPaP1. Results are means \(\pm\) SD of three independent experiments performed by duplicate. * \(p\) < 0.05 when compared to cells pre-incubated with PBS. * \(p\) < 0.05 when compared to cells pre-incubated with anti-rHsp60 or rPaP1. * \(p\) < 0.05 when compared to cells pre-incubated with anti-rHsp60. Next, to confirm that both recombinant Hsp60 and Pap1 possess adhesive properties, we performed similar experiments with immobilized ECM components but, in this case, with homogenates from cells expressing the recombinant proteins. Since we detected the adhesion of these proteins to the immobilized ECM proteins using the specific polyclonal antibodies, results were unlikely affected by the endogenous adhesins present in the bacterial system. The homogenates containing rHsp60 showed a strong ability to adhere to fibrinogen, but also adhesion to laminin, elastin, and fibronectin (Figure 4). However, adhesion to thrombospondin-1 and type-I and type-II collagens was minimal, closer to the detection threshold (Figure 4). For the case of cell homogenates containing rPap1, these showed adhesion to all the ECM components, except for thrombospondin-1 (Figure 4). The homogenates showed the highest adhesion to fibrinogen and fibronectin (Figure 4). Control experiments using cell homogenates from nontransformed cells or cells transformed with the empty expression vector gave no significant readings (Figure 4). Moreover, the ability of homogenates containing rPap1 to adhere to laminin and elastin was similar to that observed for homogenates containing rHsp60, but for both fibrinogen and fibronectin was significantly higher (Figure 4). Similarly, experiments without secondary antibodies or with pre-immune sera instead of polyclonal anti-rHsp60 or anti-rPap1 gave no detectable signals (data not shown). Finally, experiments where cell homogenates were incubated at 100 \({}^{\circ}\)C for 15 min, to denature proteins, gave no detectable signals (not shown). Collectively, these results suggest that rHsp60 and rPap1 are adhesins that bind differently to ECM components.
## 2 Materials and Methods
### Analysis  
Turbidity was measured using a Hach 2100 N Turbidimeter (Hach Company, Loveland, CO, USA). A total organic carbon (TOC) analyzer (Dong-il SHIMADZU Corp., Seoul, Korea) was used to measure the DOC of samples after filtration through a cellulose acetate membrane (pore size: 0.45 \(\upmu\)m). A spectrophotometer (Cary 50 Probe, Varian Australia Pty, Ltd., Melbourne, Australia) was used to measure the UV\({}_{254}\) of samples after filtration through a membrane (pore size: 0.45 \(\upmu\)m). A pH meter (Thermo Fisher Scientific Inc., Singapore, Singapore), which was calibrated daily using standards of pH 4.0, 7.0, and 10.0, was used to measure the pH of the water samples. A microscope was used to count the algal cells. A zeta-potential & particle size analyzer (Otsuka Electronics Co., Ltd., Osaka, Japan) was used to determine the zeta potential of the coagulant.  
The concentrations of geosmin and 2-MIB were determined using a gas chromatograph equipped with a Polaris Q ion-trap mass spectrometer (Thermo Fisher Scientific, Waltham, MA, USA). Separation of different compounds was achieved using a J&W CP-Sil 5 CB MS column (length: 30 m; internal diameter: 0.25 mm; film thickness: 0.25 \(\upmu\)m; Agilent Technologies, Santa Clara, CA, USA). The GC oven temperature was held at 40 \({}^{\circ}\)C for 2 min, increased by 7 \({}^{\circ}\)C min\({}^{-1}\) to 200 \({}^{\circ}\)C, which was held for 2 min. Helium at a constant flow rate of 1 mL min\({}^{-1}\) was used as the carrier gas. The split vent was opened for 3 min after the injection. The electron impact ionization modes were as follows: ion source temperature of 230 \({}^{\circ}\)C; the transfer line temperature of 280 \({}^{\circ}\)C; solvent delay time of 5 min; electron energy of 70 eV. The full scan mass spectra were obtained at a mass-to-charge ratio scan ranging from 50 to 350 amu to determine appropriate masses for selected ion monitoring [18].  
A colorimetric method for the determination of peroxidase with \(N\),_N_-diethyl-_p_-phenylenediamine (DPD) was used to determine the concentrations of H\({}_{2}\)O\({}_{2}\) and chlorine using a DR/250 spectrophotometer  
Figure 1: Schematic design of the combined treatment, plant-mineral composite (PMC) coagulation treatment followed by UV-based advanced oxidation processes (UV-AOP) treatment.  
(Hach, Loveland, CO, USA) at 530 nm. The spectrophotometer was calibrated using 10 mL of water sample as a blank. A 10 mL water sample pipetted into a sample cell. Then, one DPD free chlorine Powder Pillow was added and mixed well. After adding 25 \(\mu\)L (1 g/L peroxidase stock solution) of peroxidase reagent, the chlorine concentration measured in mg/L. For the H\({}_{2}\)O\({}_{2}\), the chlorine result was divided by two [19; 20; 21].
# Space Robot Sensor Noise Amelioration Using Trajectory Shaping
## 4 Discussion  
The mean and standard deviation values are compared to the baseline step response error values and tabulated as percentage differences in Table 6. The sinusoid trajectory method shows the most improvement, with an error value 97.39% closer to the desired trajectory than the step response. The whiplash compensation method, on the other hand, proves to be less accurate than the step response, showing a 42.16% increase in mean error.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Rotation Angle** & **Rotation Angle \(\theta\)** & **Rotation Angle \(\theta\) Error** \\ \hline Step & 0.0102 & 0.2051 \\ Sinusoid & \(-\)2.66e\(-\)4 & 0.0141 \\ Whiplash & \(-\)0.0145 & 0.2097 \\ \hline \end{tabular}
\end{table}
Table 4: Reaction wheel error values. “e\(-\)4” notation indicates “\(\times\)10\({}^{-4\cdot\cdot\cdot}\).  
Figure 6: Monte Carlo analysis of random perturbations for the step function input trajectory, final rotation angle value is on the abscissa and final rotation angle rate value is on the ordinant, each blue dot represents one Monte Carlo trial (**a**) space robot rotation angle in the presence of angle sensor noise; (**b**) space robot rotation rate in the presence of angle rate sensor noise; (**c**) space robot rotation rate in the presence of both angle sensor noise and angle rate sensor noise.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Rotation Angle** & **Rotation Angle \(\theta\)** & **Rotation Angle \(\theta\)** \\ \cline{2-5}  & **Rotation Angle** & \(\theta\)**Error** & **Rotation Angle \(\theta\)** & **Error** \\ \cline{2-5}  & **R Error Mean** & **Standard** & **Standard** & **Error Mean** & **Standard** \\  & **Deviation** & & **Deviation** & **Deviation** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Monte Carlo analysis of random perturbations. “e\(-\)4” notation indicates “\(\times\)10\({}^{-4\cdot\cdot\cdot}\).  
Driving the flexible spacecraft system with a sinusoidal trajectory is the solution with minimal error when sensor noise is not included. The sinusoidal trajectory generation scheme creates a near step response trajectory, without the discontinuities associated with a step response. The whiplash response is similarly continuous however, it shows more errors than the baseline step response. These results confirm the whiplash solution as a suboptimal result, as was first proposed in [2].  
When normally distributed random sensor noise is added to the state and rate sensors, the results align more closely to real world applications. The Monte Carlo simulations performed are summarized in Table 7.
# Identification of Land Use Mix Using Point-Based Geospatial Data in Urban Areas
## 4 Discussion  
Dynamically identifying LUM using POI data is a non-trivial and challenging task. This study demonstrates that point-based data, combined with Voronoi triangulation, can effectively identify the LUM index for urban areas. As presented in Table 2, our methodology achieved 51% classification accuracy, 48% precision, 69% recall, and an F1-score of 57%.  
Considering local spatial autocorrelation, we observed an increase in accuracy when analyzing fishnets with LUM values similar to their neighboring areas. For fishnets displaying high-high or low-low LUM value clusters, the accuracy reached 71%, accompanied by 63% precision, 92% recall, and an F1-score of 75%, according to Local Moran's I with a significance level of \(p\)-value less than or equal to 0.01.  
Additionally, an analysis of POI intensity ranges revealed that the number of POIs within a fishnet is critical for accurate LUM value identification. Our results indicated that a higher number of POIs within a fishnet corresponded to increased accuracy. Specifically, when the number of POIs ranged between 29 and 60, the accuracy improved to 78%, with 77% precision, 91% recall, and an F1-score of 83%. This demonstrates the importance of having a sufficient and representative number of POIs to classify LUM accurately.  
By comparing the results from Ankara to those from Kadkoy, we observe that the latter demonstrates a slightly better accuracy and F1-score due to its higher density of mixed-use developments. This highlights the capability and adaptability of our POI-based methodology across divergent urban contexts, offering valuable insights for urban planners and policymakers.  
These findings emphasize the robustness and applicability of our POI-based approach for LUM identification in diverse urban settings. The results from Kadkoy, in particular, underscored the effectiveness of our methodology in a densely populated urban environment with a high density of mixed-use developments. This further validates that a minimum POI density of approximately 109 POIs per km\({}^{2}\) is optimal for predicting an area's LUM classification accurately.  
Moreover, the method's adaptability allows for real-time updates and dynamic tracking of urban land use changes, essential for responsive urban planning. The high accuracy observed in densely populated areas like Kadkoy underscores the model's potential for application in similar urban settings.  
Future research could expand on this by integrating additional data sources, such as social media check-ins or transportation data, to enhance the model's robustness and applicability. Additionally, exploring the method's applicability in different urban contexts can provide insights into its scalability and generalizability.