esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Simulations  
In this report, we performed two simulations. Firstly, to verify the proposed method, we performed simulation I satellite model of which has model error and model uncertainty as shown in Equations (13) and (14), and compared with a LQR controller, SDRE controller, and SDRE controller SDW matrix of which is fixed. Secondly, to verify the SDRE servo cooperative controller the SDW matrix of which is proposed gain, we performed simulation II the RW has an initial wheel speed which is a disadvantageous condition for satellite attitude maneuver, and we assume that measuring error of RW wheel speed occurs.  
We assumed that a small rigid satellite is 400 kg with reference to ASNARO-2 and DRC antenna is 50 kg with reference to DRC antenna of ALOS-2 which has been proven to perform communication between the DRT satellite. In addition we assume that the satellite was flying over Mongolia at 49\({}^{\circ}\) north of the latitude, and 90.75\({}^{\circ}\) east of the longitude. We calculated the initial and target antenna joint angles with the assumption that the DRC antenna points to KODAMA, which is also known as Japanese data relay test satellite [6]. We considered the motor delay characteristics of primary delay to be useful in modeling the actuator and the antenna joint motor. In this study, the primary delay time constant is 0.2. Table 9 shows each parameter.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**SDW Matrix as Shown in Equations (45) and (46)** & **SDRE** & **SDRE Servo** \\ \hline settling time & 51.86 s & 52.82 s & 57.04 s \\ \hline \hline \end{tabular}
\end{table}
Table 6: Settling time of simulation SDW matrix of which is shown in Equation (45) and Equation (46).  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{\(|\Omega-\Omega^{lim}|>20\) rpm} & \(|\Omega-\Omega^{lim}|\leq 20\) rpm \\ \hline \(\mathbf{Q}_{\phi_{e}}\) & \(10^{6}\) & \(10^{4}\) \\ \(\mathbf{R}_{\hat{\Omega}}\) & \(10^{1}\) & \(10^{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Design guidelines of proposed method.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Symbols** & **Proposed** & **Symbols** & **Proposed** & **Symbols** & **Proposed** & **Symbols** & **Proposed** \\ \hline \(\mathbf{Q}_{q_{e}}\) & Equation (45) & \(\mathbf{Q}_{\omega_{e}}\) & \((10^{3},10^{3},10^{3})\) & \(\mathbf{Q}_{\phi_{e}}\) & Equation (47) & \(\mathbf{Q}_{\phi_{e}}\) & \((10^{2},10^{2})\) \\ \(\mathbf{Q}_{q_{e}}^{int}\) & Equation (46) & \(\mathbf{Q}_{\omega_{e}}^{int}\) & \((10^{3},10^{3},10^{3})\) & \(\mathbf{Q}_{\phi_{e}}^{int}\) & \((0,0)\) & \(\mathbf{Q}_{\phi_{e}}^{int}\) & \((0,0)\) \\ \(\mathbf{Q}_{\Omega_{e}}\) & \((0,0,0)\) & \(\mathbf{R}_{\Omega}\) & Equation (48) & \(\mathbf{R}_{\tau_{\phi}}\) & \((10^{3},10^{3},10^{3})\) & & \\ \hline \hline \end{tabular}
\end{table}
Table 8: SDW matrix parameters of proposed SDRE integral type servo method.
# Hybrid Particle Swarm Optimization-Jaya Algorithm for Team Formation
## 7 Discussion  
**Advantages:** The I-PSO-Jaya algorithm demonstrates several key advantages over the existing works, namely, I-PSONSO, I-GWO, and I-JMSO, based on the experimental results.  
Experimental results for communication cost analysis: The I-PSO-Jaya algorithm consistently achieves lower communication costs, particularly as the number of skills increases. For example, in experiment 9, 10 skills were used in the Academia Stack Exchange dataset, where I-PSO-Jaya achieved a communication cost of 0.37, significantly outperforming I-PSONSO (0.48), I-GWO (5.07), and I-JMSO (4.97). Similarly, in the ACM dataset, I-PSO-Jaya recorded the lowest communication cost of 0.04 for scenarios with 10 skills, outperforming I-PSONSO (0.05), I-GWO (1.14), and I-JMSO (1.14). These results highlight the I-PSO-Jaya algorithm's effectiveness in complex, multi-skilled team formations, offering substantial improvements in communication cost reduction compared to other proposed algorithms.  
Experimental results for sample mean communication cost analysis: I-PSO-Jaya consistently achieves lower mean communication costs as the number of skills increases. In experiment 9 with 10 skills using the Academia Stack Exchange dataset, the mean communication cost is 1.6313, outperforming I-PSONSO (1.7036), I-GWO (7.0825), and I-JMSO (7.1301). Similarly, in the ACM dataset, I-PSO-Jaya achieved the lowest mean communication cost of 0.06, outperforming I-PSONSO (0.0619), I-GWO (3.5659), and I-JMSO (3.5789). These results demonstrate the I-PSO-Jaya algorithm's superior ability to reduce average communication costs, particularly in complex scenarios, compared to other existing algorithms.  
Experimental results for the standard deviation of communication cost analysis: The I-PSO-Jaya algorithm significantly reduces the standard deviation of communication costs, indicating greater consistency and reliability. For example, in experiment 9, 10 skills were used in the Academia Stack Exchange dataset, where I-PSO-Jaya achieved a standard deviation of 0.5014, outperforming I-PSONSO (0.5199), I-GWO (0.60655), and I-JMSO (0.7213). Similarly, in the ACM dataset, I-PSO-Jaya recorded the lowest standard deviation of 0.0605, outperforming I-PSONSO (0.0691), I-GWO (0.8774), and I-JMSO (0.9014). This indicates that the I-PSO-Jaya algorithm excels at maintaining low variability in communication costs, ensuring more predictable and stable team formations, which is crucial for complex, multi-skilled tasks.  
**Limitations:** Our proposed approach, I-PSO-Jaya, is efficient in scenarios requiring more skills, yielding more satisfactory solutions in such complex, multi-skilled team formations. However, this also means that our algorithm has higher computational time than others. This is because I-PSO-Jaya is tuned explicitly for handling more complex team formation problems, making it less efficient for cases requiring fewer skills.
## 4 SLA Specification for the Fog Storage Services
### SLA Parameters
#### 4.4.2 Throughput  
Throughput refers to the volume of application data that are transmitted over a period of time, excluding the bits from protocol overhead and retransmission, such as TCP/IP and HTTP headers, and acknowledgements. In the context of storage, throughput differs for read and write operations (i.e., download and upload operations).  
Within this work, read throughput refers to the expected number of bytes received per unit of time at the client from a storage server, whereas write throughput refers to the expected number of bytes sent per unit of time from the client to the file server. In both cases, only the bytes that comprise the file are counted. In some published works, such as [51], this metric is also known as the goodput (or application throughput).
###### Abstract  
The permanent magnet (PM) brushless DC (BLDC) machine has been widely used in various applications due to its high reliability and simplicity. In aerospace applications, the requirements of its dynamic acceleration characteristic and fault protection capability have further increased. This paper proposes an optimized inverter topology composed of a boost converter, two protective power switches, and a three-phase full bridge for the dynamic response improvement and fault protection capability enhancement of PM BLDC machines. The corresponding drive logic, control method, and protection strategy are designed to develop a drive system for an aeronautical fuel pump. The response performance is significantly improved in handling under-voltage and over-rated conditions by utilizing the boost converter during different processes, and the protection capability is enhanced to deal with over-voltage and over-current fault isolation. A boost converter-fed BLDC pump system prototype is built and tested. It is concluded that the simulation and experiment results verify the rationality and validity of the proposed method.  
Keywords:boost converter; BLDC machine; dynamic response improvement; fault protection enhancement +
Footnote †: journal: Applied Sciences
# A Review of Indoor Air Quality in Social Housing Across Low- and Middle-Income Countries
###### Abstract  
This review focuses on the indoor air quality (IAQ) in social housing across developing nations to identify the key factors that influence IAQ, research gaps, and opportunities for future interventions. A total of 43 studies from 2000 to 2024 were identified and categorized into the following five themes: Health and Comfort, Direct Measurements, Retrofitting and Maintenance, Comparative Studies, and Housing Conditions and Socio-Economic Factors. Residents of social housing in low- and middle-income countries (LMICs) face disproportionately higher exposure to harmful pollutants, including PM2.5 (up to 1360 \(\mathrm{\SIUnitSymbolMicro m^{3}}\) during cooking), CO (with peaks at 150 ppm), and formaldehyde (up to 4.802 ppm), often exceeding World Health Organization (WHO) guidelines. Approximately 76% of the studies were published post-2019, reflecting increased global awareness of IAQ's health implications, improved measurement and simulation technologies, and heightened attention to indoor environments following the COVID-19 pandemic. Despite the growing body of research in this direction, most of the found studies focus on warm and humid climates, leaving gaps in the understanding of IAQ issues in other, colder climate regions. Cost-effective solutions, such as natural ventilation and adaptive architectural design, show promise, but empirical testing is lacking, especially in diverse geographical contexts. In addition, culturally driven occupant behaviors significantly influence IAQ, but remain understudied in the context of low-income residents in developing countries. There is a need for expanded research that considers regional climate differences, socio-economic factors, and occupant behavior to develop effective interventions to improve IAQ and health outcomes in these settings.  
indoor air quality; IAQ; social housing; developing countries; health disparities; particulate matter; low- and middle-income; public health; LMIC; indoor air pollutants +
Footnote †: journal: Applied sciences
## 3 Characterization Results
### Resolution of the Instrument  
The output bandwidth \(\Delta E_{FWHM}\) of a monochromator depends on both the widths of the entrance and exit slit. As the exit slit width is decreased, the effective bandwidth will generally decrease. If the exit slit is narrower than the projection of the entrance slit, the exit-slit-width will not reduce the bandwidth appreciably. This situation is undesirable in that diffracted energy is lost (the peak relative intensity is low) since the exit slit is too narrow to collect all of the diffracted light at once. The opposite situation is also undesirable, since the FWHM is excessively large (or, similarly, an excessively wide band of wavelengths is accepted by the wide slit). Therefore, the situation is optimal when the exit slit width matches the width of the spectral image, the relative intensity is maximized while the FWHM is minimized.  
The measured spectral widths are reported in Table 2 and Figure 8 for the discrete spectral lines emitted by the source. The resulting spectral bandwidth is below 10 meV, with a \(\sim\)60 \(\upmu\)m wide projection of the entrance slit. The measured values are best fitted assuming a 37 \(\upmu\)m wide input slit, which is well within the uncertainty of the slit mounting.  
Figure 8: Full-width-at-half-maximum (FWHM) bandwidth measured on the exit slit plane (dot-points) and theoretical best-fit assuming a 37 \(\upmu\)m entrance slit (solid lines).  
Figure 7: Acquired CCD images at the image plane of two emission lines selected by the G\({}_{1}\) grating. (**a**) 13.48 eV; and (**b**) 21.22 eV.
## 3 Results
### Influence of Phenology and Climate on Carbon Cycle Changes  
Based on the results of phenology inversion and carbon cycle simulation, combined with meteorological data and the SEM model, the relative importance of meteorological factors such as Rad, Pre, Tmax, and Tmin and phenological factors such as SOS, EOS, and LOS on GPP were calculated.  
Figure 12 illustrates the relative significance of phenological and meteorological factors on changes in the carbon cycle in the forest ecosystem of Hangzhou. As shown in Figure 12, the flux coefficients (in absolute value) of phenological and meteorological factors on GPP were ranked as follows: EOS (1.34) > Tmax (1.15) > LOS (0.64) > Pre (0.56) > Tmin (0.38) > SOS (0.18) > Rad (0.11). For NPP, the flux coefficients (in absolute value) were ordered as follows: Tmax (1.31) > EOS (1.30) > LOS (0.50) =Tmin (0.50) > Pre (0.46) > SOS (0.11) = Rad (0.11). For NEP, the pass-through coefficients (in absolute value) were ranked as follows: LOS (2.55) > SOS (1.51) > EOS (1.09) > Tmax (0.50) > EOS (0.18) > Rad (0.11) Although phenological factors are important in determining the temporal and geographical variability of carbon cycling in Hangzhou forest ecosystems, meteorological factors also have a significant impact on the carbon cycle.  
Figure 13 illustrates both the direct and indirect effects of phenology and climate change on carbon cycle changes. From Figure 13, it can be seen that the direct influence of phenological changes on carbon cycle changes was 0.85, while the direct influence of climate change on carbon cycle changes (the absolute value of the throughput coefficient) was 1.03. The direct impact of climate change on carbon cycle changes was slightly larger than that of phenological changes. Additionally, climate change can also indirectly have an influence on the carbon cycle through phenological changes, with the indirect influence of climate on the carbon cycle calculated as 0.51 (0.6 \(\times\) 0.85 = 0.51).  
Figure 12: The impact of phenological characteristics and climate variables on GPP (**a**), NPP (**b**), and NEP (**c**).
## 3 Results
### Irrigated Landcover Classification  
Figure 7 shows the results of the irrigated landcover classification for the optical approach. Figure 7A shows the four classes as identified in Figure 6A. Figure 7B is the map of the irrigated landcover class only. Figure 8 shows the results of the irrigated landcover classification based on the thermal approach, where Figure 8A shows the four classes as identified in Figure 6B. Figure 8B is the map of the irrigated landcover class only.  
A visual comparison of the two maps of irrigated landcover (Figures 7B and 8B) shows minor differences. The map of the optical approach (Figure 7B) shows slightly more pixels (13.86% of the total area) as irrigated in comparison to the map of the thermal approach (12.43% area) (Figure 8B, Table 3).
## 4 Corrosion Protection Measures
### Cathodic Protection
#### 4.2.2. Impressed Current Method of Cathodic Protection
[PERSON] [24] used COMSOL Multiphysics to study the influence of AC current on coated X70 steel and the delamination of 3-layer polyethylene coating with defects. They found that the pit current density of small defects was greater than that of large defects. [PERSON] [17] used COMSOL Multiphysics to simulate the stray current corrosion dynamics. They also calculated the distribution and intensity change in electrolyte potential in the cathodic protection system by solving [PERSON]'s three-dimensional equation, which shows that the increased applied voltage causes the pipeline potential to move further forward, resulting in accelerated corrosion by stray currents and the corrosion effect with sacrificial anode cathodic protection is much smaller than that without cathodic protection when the two pipelines are in the crossing state. [PERSON] [117] simulated and analyzed the corrosion model with COMSOL Multiphysics. The simulation mainly includes the effects of DC stray current on electrode potential, pH value, iron dissolution, and corrosion rate. His team found that the higher the DC stray current was, the higher the electrode potential, and the electrode potential moved forward. In addition, the pH value of the soil around the grounding electrode was also affected by direct current, and additional direct current injection led to an increase in hydrogen ion concentration. Finally, DC stray current can accelerate the dissolution rate of anode iron and change the rate of electrode thickness. The higher the stray current density, the faster the anode dissolution rate and the faster the grounding electrode corrosion rate. The research results are of great guiding significance for the establishment of corrosion prediction models. Figure 5: Schematic diagram of impressed current cathodic protection.
## 6 Challenges and Future Research Scopes
### Workload Distribution and Flow Control  
Machine learning (ML) algorithms can predict outcomes or classify objects using training data and improve their performance with repeated experience on the task. ML has achieved great success in several areas, including speech recognition, web search, purchase recommendation, encrypted network traffic classification, etc. Today, IoT or end devices generate unprecedented volumes of data that need to gather, process, and analyze, which consumes very high network resources. Therefore, machine learning has the potential to help resource management, workload distribution, and flow control in an SDN-enabled edge cloud network [18; 57; 93; 94]. Researchers in [18] proposed a hybrid deep learning model for effective distribution of workload for edge cloud IoT networks. [PERSON] et al. [95] proposed a pre-emptive algorithm for scheduling distributed ML jobs in edge cloud networks to minimize the average job completion time. In the study presented by [PERSON] et al. [96], they proposed a framework for flow control in SDN edge cloud networks that enables real-time predictions based on IoT sensor data. In this framework, they used a Random Forest machine learning method. Researchers from industry and academia started working in this area but still lots of work needs to be done.
## 3 Design of Passive Torsional Compliant Suspension
### Design of Compliant Spiral Torsional Suspension
First, even with 100% infill, small voids or imperfections can appear within the PLA structure, reducing its effective strength and stiffness and causing the actual stiffness to fall short of the theoretical value, which assumes a perfectly solid, isotropic beam. Second, a classic spiral spring formula presumes a continuous rectangular cross-section throughout, whereas our C-STS diverges from this ideal shape--for instance, the central region includes a \(\phi\)30 mm circular slot for the inner shaft holder, deviating significantly from a flat rectangular beam. Moving forward, we plan to develop a large-deformation analysis using a pseudo-rigid-body model to better capture the compliant aspects of the C-STS's geometry, incorporating the actual cross-sectional profile and 3D-printed structure to derive a more robust theoretical stiffness value that closely matches real-world measurements. Although these factors largely concern the spiral portion of the suspension, the wheel's spokes could also introduce additional compliance. To verify the effect of spoke compliance, we performed a SolidWorks 2024 simulation (Figure 9) in which the wheel's center of rotation (CoR) was fixed and a 20 N normal load was applied at three radial points (P1, P2, and P3) on the spoke. Using polylactic acid (PLA) properties, by measuring the resulting deformation, we derived the spoke's torsional stiffness (\(k_{t,spoke}\)) at each point and combined it with that of the spiral spring to obtain the total stiffness (\(k_{t,total}\)). Table 3 compares \(k_{t,spoke}\) to the spiral spring stiffness \(k_{t,total}\). Our results indicate that, at most, the spoke contributed only 1.7% to the overall torsional stiffness--becoming even less significant under typical loading conditions. In particular, P3 (corresponding to the contact location at transition) exhibited a slightly higher stiffness than P1 or P2, yet its effect remained negligible compared to the spiral spring. Consequently, assuming a rigid spoke in our primary model does not compromise accuracy. Figure 9: Stiffness simulation setup and results: (**a**) simulation setup showing the wheel’s CoR fixed and a 20 N normal load applied, (**b**) P1, (**c**) P2, and (**d**) P3 represent the three radial points on the spoke where the load is applied.
## 5 Conclusions  
In this paper, a concise processing method, ISCV, is proposed for a current signal based on the relevant theory of effective values. In addition, the successful ViT from the field of NLP is applied to bearing fault diagnosis. Based on this model, an ISCV-ViT method for bearing fault diagnosis based on motor current signals is proposed.  
The ISCV-ViT has the following characteristics: (1) The current signal is easy to obtain and does not require additional sensor costs. This is very meaningful for fault diagnosis of complex and precise equipment, especially in industrial applications. (2) Compared with the current popular time-frequency characteristic analysis methods for motor current signals, the proposed ISCV method is more concise and effectively avoids some complex noise removal processes. (3) Aiming to address the problem of timing characteristic recognition of the current signal, the ViT model that is applied can more accurately capture global features that require attention due to the multihead self-attention (MSA) mechanism. Meanwhile, the parallel computing architecture greatly reduces the network size.  
Furthermore, the feasibility and generalization of the proposed method are verified through the public dataset PU and the laboratory dataset SZTU. In subsequent research, we will further optimize the model to improve convergence speed and accuracy. In addition, we believe that fusing frequency features can further improve model accuracy based on ISCV.  
Conceptualization, F.C. and X.Z.; Methodology, X.Z.; Validation, Z.Q.; Formal analysis, Z.Y.; Investigation, Z.Y.; Resources, Z.Q.; Writing--original draft, F.C. and X.Z.; Writing--review & editing, F.C., X.Z. and B.X.; Supervision, F.C.; Project administration, B.X.; Funding acquisition, F.C. and B.X. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Key Research and Development Program of China, grant number 2022 YFP610400; the Guangdong Province Key Construction Discipline Scientific Research Capacity Improvement Project, grant number 2022 ZDJS114; the School-enterprise Cooperation Research Foundation of Shenzhen Technology University for Graduate Student, grant number XQHZ202302; the Shenzhen UAV Test Public Service Platform and Low-altitude Economic Integration and Innovation Research Center, grant number 29853 MKCJ202300205.  
The study did not require ethical approval.  
The study did not involve humans.  
Figure 13: Accuracy and standard deviation of different methods under different conditions on the laboratory dataset SZTU.