esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on the Dynamics of the Space Tubular Expandable Structure Driving Deployment Unit
## 2 Methods
### The Spatial Nodal Dynamic Description of the Thin-Walled Flexible Beam Structure  
To achieve both driving and pre-tensioning functions, the inner-drive unit must withstand significant axial forces and bear strong bending moments. Therefore, we have opted to replace each rod element with beam elements, utilizing a triangular element structure. By maintaining the coupled rigid-flexible state of the inner-drive unit and considering the thin-walled characteristics of internal components, we conduct analyses on each flexible beam structure using a thin-walled configuration.  
To achieve smooth inter-stage transitions within the inner-drive unit of the deployable cylindrical structure of a space telescope and effectively suppress nonlinear vibrations among internal components, conducting motion-elastic dynamic analysis of the triangular configuration of the thin-walled flexible beam structure is of utmost importance. As discussed in Section 1, the absolute nodal coordinate formulation (ANCF) is based on finite element and continuum mechanics theories, providing a more accurate description of structural self-rotation and deformation under external forces. To provide a clearer explanation of the dynamic analysis of the thin-walled flexible beam structure using the absolute nodal coordinate formulation (ANCF), we establish a unit nodal matrix based on the flexible beam structure and then discretize the matrix. Furthermore, we extract the position vectors of the mid-surface, forming a collection of position vectors for any point on the mid-surface of the thin-walled flexible beam structure. By utilizing the strain and curvature of the unit, we transform the unit mass matrix to obtain the unit dynamic equations. Moreover, we obtain the generalized external forces using the principle of virtual work and introduce them into structures with different configurations and morphologies. During this process, we transform the unit mass matrix of the thin-walled flexible beam element to derive the corresponding unit mass matrix. Finally, by constructing the dynamic equations, we can obtain node velocities and node displacements. The specific process is illustrated in Figure 5.
## 2 Related Work
### Summary  
Based on previous studies, MI communication technology has significant advantages for underwater communications, including low power consumption, low attenuation, and stable transmission across boundaries. The development of methods such as coil design, metamaterials, and repeaters has greatly promoted the development of underwater MIcommunication technology. Combining MIMO technology with underwater MI communication can help mitigate the effects of insufficient MI channel capacity. However, traditional signal detection algorithms for MIMO technology face the challenge of balancing complexity and BER. Acoustic and optical MIMO signal detection are two leading communication technologies that have been studied experimentally. Nevertheless, the signal detection problem of the MI MIMO system is unavoidable in the actual deployment.  
Although few studies have combined the WOA with MIMO signal detection, the WOA is a promising candidate for signal detection in underwater MI-MIMO systems due to its simple implementation, fast convergence, and high computational accuracy. Improving the WOA can reduce the probability of falling into a local saddle point and increase computational accuracy. In addition, the channel characteristics of MI differ from acoustic and optical media, and power and performance are limited in underwater networks. Therefore, for underwater signal detection in MI-MIMO systems, complexity should be considered in addition to BER.  
This paper aims to develop a low-complexity signal detection method for underwater MI-MIMO systems by combining the WOA with traditional MIMO signal detection. We hope this study can inspire further exploration of this promising method by relevant researchers.
## 3 Experimental Section  
### MRSI Scans  
The CS, TV, MaxEnt, and GS reconstruction methods were quantitatively compared using phantom data from six fully-sampled 4D EP-COSI scans of a 500 mL gray matter brain phantom, which contained _in vivo_ concentrations of the metabolites: aspartate (Asp), choline (Ch), creatine (Cr), gamma amino butyric acid (GABA), glucose (Glc), glutamate (Glu), glutamine (Gln), glutathione (Gsh), lactate (Lac), myo-Inositol (mI), \(N\)-acetyl-aspartate(NAA), \(N\)-acetyl-aspartyl-glutamate (NAAG), phosphocholine (PCh), phosphoryl ethanolimine (Pe), taurine (Tau), and threonine (Thr). The scans were acquired with the volume-based 4D EP-COSI sequence on a Siemens 3T Trio scanner running VB17 using a 12-channel head coil with the following parameters: \(32\times 32\) cm\({}^{2}\) FOV, \(2\times 2\times 2\) cm\({}^{3}\) voxel size, 100 \(t_{1}\) increments, TR/TE/averages = 5 s/23 ms/1 avg, 256 time points along the \(t_{2}\) readout, and spectral bandwidths of 1250 Hz and 1190 Hz along \(F_{1}\) and \(F_{2}\), respectively. The volume of interest (VOI) was positioned within the phantom to allow for a \(4\times 4\) grid of voxels to be acquired per phantom scan. Standard phase corrections and channel combination were applied to each scan, with no baseline corrections used. The phantom data was then apodized using a sine-squared filter along \(t_{1}\), and a skewed sine-squared filter with skew parameter 0.5 along \(t_{2}\). Following phase correction and apodization, the fully sampled scans were retrospectively under-sampled 2\(\times\), 4\(\times\), 6\(\times\), 8\(\times\), and 10\(\times\) along the \(k_{y}-t_{1}\) plane using the 2D Poisson-gap sample masks shown in Figure 3[17].  
In addition to the phantom data comparisons, each reconstruction method was also evaluated on retrospectively under-sampled _in vivo_ 4D EP-COSI brain data acquired with the same sequence parameters, except for TR/TE = 1.7 s/23 ms and outer volume suppression (OVS) prior to each TR to suppress the scull marrow fat resonances. The brains of two healthy human volunteers aged 23 and 36 years were scanned using the Siemens 3T Trio scanner with a 12-channel head coil, using the same post-processing and retrospective NUS sample masks as the phantom scans prior to reconstructing with CS, TV, MaxEnt, and GS.
## 2 Methods
### Demodulation Algorithm for Spatial Frequency Domain Multiplexing SDOCT  
As shown in Figure 2, after taking the Fourier transform of the 2D spectral interferogram \(I(x,k)\) along the \(x\)-direction (step (i) in Figure 2), we obtain the spatial spectrum of the 2D spectral interferogram, which can be expressed as  
\[\widetilde{I}\left(\
u,k\right)=FT_{x\to\
u}\left\{I(x,k)\right\} =C_{A}(\
u+\
u_{c1},k)+\hat{C}_{A}(\
u-\
u_{c1},k)\] \[+C_{B}(\
u+\
u_{c2},k)+\hat{C}_{B}(\
u-\
u_{c2},k) \tag{2}\]  
where \(\
u\) is the Fourier transform pair of \(x\). \(C(\
u,k)\) and \(\hat{C}(\
u,k)\) are the pair of Hermit conjugate term due to the Fourier transform of the real valued interference signal and contain the same information on the OCT signal in the spatial frequency domain. The subscripts \(A\) and \(B\) in Equation (2) indicate the spatial frequency contents corresponding to depths \(Z_{A}\) and \(Z_{B}\), respectively. \(\
u_{c1}\) and \(\
u_{c2}\) are the center spatial carrier frequencies, which are proportional to the pivot-offset distances. For reconstruction of the complex valued spectral interferogram, two band-pass filters with center frequencies of \(\
u_{c1}\) and \(\
u_{c2}\) are applied to the spatial frequency spectrum (step (ii) in Figure 2). We then obtain the separated spatial frequency content \(C_{A}(\
u+\
u_{c1},k)\) and \(C_{B}(\
u+\
u_{c2},k)\).  
Two complex-valued spectral interferogram \(\widetilde{I}_{A}\left(x,k\right)\) and \(\widetilde{I}_{B}\left(x,k\right)\) can now be reconstructed by performing the inverse Fourier transform from the spatial frequency domain back to the spatial domain for the terms \(C_{A}(\
u+\
u_{c1},k)\) and \(C_{B}(\
u+\
u_{c2},k)\) (step (iii) in Figure 2). For reasons of energy conservation, the data have to be scaled by a factor of 2. Then, after the \(k\) space re-sampling the two full-range OCT  
Figure 2: Data processing flowchart of the spatial frequency domain multiplexing SDOCT system. FT: Fourier Transform.  
images corresponding to the depths of \(Z_{A}\) and \(Z_{B}\) can be calculated by inversely Fourier transforming the complex-valued linear-in-\(k\) spectral interferogram \(\widetilde{I}_{A}\left(x,k\right)\) and \(\widetilde{I}_{B}\left(x,k\right)\) along the wavenumber direction. Finally, an extended range OCT image is formed by concatenating the image regions that correspond to the effective ranging depths of the two full-range OCT images.
# Application and Assessment of Cooperative Localization in Three-Dimensional Vehicle Networks
## 8 Simulation Results
### Relative Position Only Measurement Assessment  
In this section, we assume each vehicle can reliably obtain its own orientation using an IMU and/or a gyroscope and does not measure the relative orientation of other vehicles. Therefore, the only means of CL is through relative position measurements. We use Scenario 2 for the simulations, but only relative position measurements are considered. Table 8 lists the error statistics for only \(\Delta r\) across the 100 simulations. Our first observation is that the inclusion of absolute orientation measurements drastically reduces the overall error across vehicles, since IMUs can provide relatively accurate orientation information. Our second observation is that CL does not achieve statistically significant improvement in any of the cases. Therefore, the additional computational cost of CL does not justify its use in conjunction with the kinematic model presented in this and other works. There were no advantages in applying CL to this set because the equations of motion (4) and (5) and consequently the state equations (7) and (30) do not depend on position variables. We can further conclude that the improvements observed in Section 8.2 are only due to relative orientation measurements. These conclusions have not been previously reported and are valid even when applying CL to 2D vehicle models employed by other researchers [13; 14].  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **Simultaneous Measurements (\(b\to t\))** \\ \hline Case 1 & \(1\to 2\) \\ Case 2 & \(1\to 2,2\to 3\) \\ Case 3 & \(1\to 2,2\to 3,3\to 1\) \\ Case 4 & \(1\to 2,2\to 3,3\to 1,2\to 1\) \\ Case 5 & \(1\to 2,2\to 3,3\to 1,2\to 1,3\to 2\) \\ Case 6 & \(1\to 2,2\to 3,3\to 1,2\to 1,3\to 2,1\to 3\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Simultaneous measurement descriptions for Scenario 2.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Position**} & \multicolumn{4}{c}{**Orientation**} \\ \cline{2-9}  & \(\mathbf{e_{r_{med}}}\) & \(\mathbf{e_{r_{med}}}\) & \(\mathbf{e_{r_{med}}}\) & \(\mathbf{e_{r_{med}}}\) & \(\mathbf{e_{q_{med}}}\) & \(\mathbf{e_{q_{med}}}\) & \(\mathbf{e_{q_{med}}}\) & \(\mathbf{e_{q_{med}}}\) \\ \hline Propagate & 18.08 & 0.04 & 17.98 & 18.16 & 0.36 & 0.00 & 0.36 & 0.36 \\ Case 1 & 14.20 & 2.93 & 9.95 & 23.07 & 0.33 & 0.05 & 0.24 & 0.47 \\ Case 2 & 10.28 & 4.76 & 3.69 & 24.40 & 0.23 & 0.08 & 0.09 & 0.50 \\ Case 3 & 8.44 & 3.66 & 2.61 & 20.94 & 0.18 & 0.07 & 0.06 & 0.37 \\ Case 4 & 8.23 & 4.60 & 2.56 & 23.84 & 0.17 & 0.07 & 0.06 & 0.33 \\ Case 5 & 10.73 & 4.57 & 3.47 & 23.46 & 0.20 & 0.07 & 0.05 & 0.39 \\ Case 6 & 11.33 & 4.13 & 4.66 & 24.18 & 0.21 & 0.06 & 0.10 & 0.40 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Position (m) and quaternion error statistics across 100 simulations of _all vehicles_ for Scenario 2.
# Bandwidth-Based Wake-Up Radio Solution through IEEE 802.11 Technology
## 5 Wake-Up Radio System Evaluation
This could be improved with the use of more selective filters, but that increases the complexity of the circuitry. A key component in the WuR system proposed and evaluated in this paper consists in the ability of the transmitter to change the signal bandwidth on a per-packet basis, which is a common feature in modern Wi-Fi radios. In this way, we have verified that already existing devices, such as TI CC3200 [29], allow this functionality. Specifically, the TI CC3200 device, which supports IEEE 802.11n specification, is able of switching between signal bandwidths of 20 MHz and 40 MHz for consecutive transmissions. However, in this case, we observed that an additional delay is introduced due to this switching function, which increases the inter-frame time above the SIFS value. In our experiments, this inter-frame time has been observed to be between 100 and 200 \(\upmu\)s. Thus, further research needs to be conducted in the real implementation of the proposed WuR system, which is out of the scope of the present work and is considered as part of our future work. We have to note that the operational ranges provided by the proposed IEEE 802.11-based WuR solution are smaller than the maximum achievable distances employing the IEEE 802.11 primary interfaces for data exchange. Ideally, having the same maximum operational ranges for both primary and WuR interfaces is desirable. Having a shorter reach in the secondary radio limits the applicability of the solution to shorter distances, but the problem of having different ranges in both radios becomes more significant when the secondary radio exceeds the maximum distance achieved by the primary (the system is able to wake up the primary radio, but then the primary radio cannot operate effectively). However, it is worth mentioning that using the fastest MCSs available for the different IEEE 802.11 amendments, the distances corresponding to the communication through IEEE 802.11 primary interfaces will be reduced to ranges around, or less than, the values provided by the WuR solution exposed. Under those conditions, the range of both WuR and primary interface data communications will be aligned. Certainly, the maximization of the WuR operational range is a relevant issue in this topic, and further work is planned to be undertaken in this direction. Figure 11: BER at WuRx output of Figure 6 vs. distance, for propagation model B (**a**) and F (**b**).
# A High Full Quality CMOS Image Sensor for Space Applications
## 4 Conclusions  
A high FWC CIS for space applications was presented in this paper. The proposed pixel achieves high FWC by utilizing an in-pixel MOS capacitor and multiple charge transfer. In addition to the FWC, two important performance measures for space applications (MTF and radiation tolerance) were explained and a method to improve MTF was suggested. The radiation damage effect on an in-pixel MOS capacitor, which has not been reported in the other literatures, was studied by fabricating and measuring a CIS with two different size of the MOS capacitors. It was found that the size of the capacitor should be determined with consideration of the radiation damage effect because a small increment in the MOS capacitance can severely increase dark current under a radiation environment. The fabricated 3000 \(\times\) 3000 CIS achieved an FWC of 103,448 electrons and metal shielding in the horizontal direction improved the MTF by 300%.  
W.-T.K. and B.-G.L. conceived and designed the CIS; W.-T.K., C.P., and H.L. conducted measurements of the CIS; L.L. consulted radiation test methodology.  
This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program(IITP-2019-0-01433) supervised by the IITP (Institute for Information & communications Technology Promotion) and the GIST Research Institute Project through a Grant provided by GIST in 2019.  
The authors express their sincere thanks to the staff of the MC-50 Cyclotron Laboratory (KIRAMS), for the excellent operation and their support during the experiment and DB HiTek for the CIS fabrication.  
Figure 11: Radiation damage effects with two different in-pixel MOS capacitors: (a) 16.3 fF MOS capacitor (b) 19.6 fF MOS capacitor.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **This work** & [45] & [46] & [47] & [48] \\ \hline Pre-Rad & 133 & 6600 & 190 & 3135 & 20 \\ \hline TID * & 491 & 932 & 180,000 & 1554 & 6110 \\ (Percentage increase) & (368\%) & (700\%) & (2727\%) & (818\%) & (195\%) & N/A \\ \hline Proton ** & 623 & 2585 & N/A & N/A & N/A & 500 \\ (Percentage increase) & (468\%) & (1941\%) & & & & (2500\%) \\ \hline \end{tabular}
* TID: total dose of 7–8 krad of cobalt-60 gamma ray. ** DDD: total proton fluence of \(1\times 10^{10}\sim 1.5\times 10^{10}\)/cm\({}^{2}\) under 50 MeV.  
\end{table}
Table 3: Comparison of the dark current with other image sensors (unit: e\({}^{-}\)/s).
## 4 Discussion and Final Remarks
### General Discussion
The true similarity score is obtained by combining multiple similarity metrics as follows: (i) calculation of the prediction performance for all pairs of methods using the root mean square error (RMSE), (ii) selection of the best-performing pair (lowest RMSE), (iii) training a linear model on that pair to stack their predictions, and (iv) normalization of the results as percentages. Table 2 summarizes the RMSE for each pair of methods for cycle 1. The best pair for cycle 1 corresponds to TAM and RF-DTW, likely because they provide diverse yet interpretable signals about the true similarity, combining strengths such as stability (TAM) and capturing variations (RF-DTW). The estimated stacked percentages are 29.1, 40.5, 13.4, 17.0%, compared to 33% from trajectory 1 (C/3), 40% from trajectory 2 (1C), 13% from trajectory 3 (2C), and 13% from trajectory 4 (3C). Additionally, overall user patterns can be inferred from our diagnosis approach. Thus, at the end of the second life, for instance, RF-DTW estimated that cycle 1 operated at an average C-rate of 1.3C compared to an actual value of 1.2C, based on the contribution of the four aging trajectories. A diverse definition of aging trajectories, based on the combination of stress factors such as temperature, state-of-charge, depth-of-discharge, and C-rate, can potentially provide users with insights into aging that contribute to a better understanding of the battery's state-of-health (SOH) [20]. Although our current validation relies on synthetic data, there are strong reasons to support the generalization of our similarity-based diagnostic approach to real-world second life battery data. Our approach does not depend on where or how the data has been generated but, instead, on the trends and shape of degradation, which are patterns that can be analyzed across different chemistries when appropriately normalized. Unlike existing ML approaches that require training on specific datasets, our approach does not require retraining on real-world data, as it compares cycles against existing trajectories using interpretable similarity metrics, such as DTW and RF-based proximity. These methods are unsupervised; hence, they are robust to domain shifts since our approach is a diagnostic lookup rather than a black-box predictor. Moreover, aging contributions are mapped to stressors, which is relevant when applied to real-world data. As more trajectories become available, our approach becomes more extensible and specific without requiring structural redesign. Finally, the low complexity and interpretability of our approach make it a suitable candidate for real-time diagnostics, which is a key requirement in second life applications where data availability may be limited.
# Low-Cost IoT Air Quality Monitoring Station Using Cloud Platform and Blockchain Technology
## 4 Results and Discussion
Connectivity can be established using integrated Wi-Fi and Bluetooth modules. An NDIR CO\({}_{2}\) sensor, with exceptional accuracy and reliability, can be used for measuring the CO\({}_{2}\) levels, an electrochemical CO sensor, with enhanced accuracy, can be used for CO measurements, a particulate matter sensor (PM2.5/PM10) can be used to detect fine particulate matter, and high-precision sensor can be used for temperature and humidity. An advanced cloud platform, such as the ThingSpeak paid plan or Azure IoT, can be used. An advanced paid blockchain solution with optimized mechanisms for scalability, such as the Ethereum paid plan or IBM blockchain solutions, can be used. This scenario offers more sensitivity and accuracy and can be established if there is a need for better accuracy. Equipped with enhanced capabilities but at a higher cost, blockchain technology is able to identify a wider range of harmful substances with better accuracy in zones with severe weather. By showing these two scenarios, we aim to show the ability to adjust our air quality monitoring station solution across different regions and applications. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**CO\({}_{2}\) PPM (MQ135)**} & \multicolumn{1}{c}{**CO PPM (MQ7)**} & \multicolumn{1}{c}{**Temperature**} & \multicolumn{1}{c}{**Humidity**} \\  & & (\({}^{\circ}\)C) & **(\%)** \\ \hline  
\hline \end{tabular}
\end{table} 
Table 3: Sample of the results gathered by the station before sunset in autumn. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**CO\({}_{2}\) PPM (MQ135)** & **CO PPM (MQ7)** & \
\begin{tabular}{c} **Temperature** \\ **(°C)** \\ \end{tabular} &
\begin{tabular}{c} **Humidity** \\ **(\%)** \\ \end{tabular} \\ \hline  
\hline \end{tabular}
\end{table} 
Table 4: Sample of the results gathered by the station before sunrise in spring. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**CO\({}_{2}\) PPM (MQ135)** & **CO PPM (MQ7)** & \ \begin{tabular}{c} **Temperature** \\ **(°C)** \\ \end{tabular}  & \begin{tabular}{c} **Humidity** \\ **(\%)** \\ \end{tabular}  \\ \hline
347 & 284 & 31.2 & 51 \\ \hline
348 & 284 & 31.1 & 52 \\ \hline
344 & 281 & 31.1 & 52 \\ \hline
340 & 283 & 31.2 & 53 \\ \hline
345 & 295 & 31.2 & 52 \\ \hline
347 & 290
## 1 Introduction
Moreover, most open source projects that are being developed today are also based on earlier wireless technologies that do not meet the low-power requirements of today's sensing applications. Taking that into account, in this article, we explore the implementation of state-of-the-art low-power wireless communication technologies for sensing applications and outline the key challenges and difficulties that need to be addressed with the aim of boosting and facilitating the development of these wireless technologies. Specifically, we contribute to this objective in three different ways: first, by introducing the operation of TSCH and D7 AM in a way that is easy to understand and that targets the application requirements; second, by presenting the hardware and software requirements to implement both TSCH and D7 AM; and third, by identifying the scope and suitability of TSCH and D7 AM according to the sensing requirements. The remainder of the article is organized as follows. Section 2 summarizes the operation of both TSCH and D7 AM. Section 3 discusses the requirements to implement TSCH using commercial hardware. Section 4 discusses the requirements to implement D7 AM using commercial hardware. Section 5 discusses the suitability of TSCH and D7 AM according to different sensing application profiles. Finally, Section 6 concludes the article.