esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Classification of Optimization Objectives
### Multi-Objective Optimization Focussed on Operation Related Problems in WSNs
#### 3.2.3 Target Tracking  
Target tracking in the field of observation is one of the critical tasks performed by the wireless sensor network. Minimization of number of selected sensors for efficient target tracking has been modeled as multi-objective optimization problem in [89] which achieved a pareto optimal trade-off between the number of selected sensors and the accuracy of estimation. A generalized unscented Kalman filter tracking algorithm has been proposed in [90]. The proposed algorithm considered energy efficiency and target tracking performance simultaneously by using a multi-objective optimization formulation.
## 2 Materials and Methods
### Determination of Organic Acids  
Extraction of organic acids was performed according to the method by [PERSON] et al. (2013) with some modifications. For this, 0.5 g of lyophilized sample was milled in 20 mL of deionized water and shaken at room temperature for 1 h. Then, the sample was centrifuged at 12.857\(\times\)\(g\) for 20 min at 22.5 \({}^{\circ}\)C, and the supernatant was collected and passed through a 0.22 \(\upmu\)m membrane filter before analysis by ion chromatography. An IC system (ICS-1100, Thermo Fisher Scientific Inc., Waltham, MA, USA) was equipped with a conductivity detector, a protection column (IonPac AG23, 4 mm \(\times\) 50 mm), an anion-exchange analytical column (IonPac AS23, 4 mm \(\times\) 250 mm), electrolytically regenerated suppressor (AERS 500, 4 mm), and a reagent-free RFC-30 controller with an ECG-MSA cartridge. The identification and quantification of organic acid components were based on the method by [PERSON] (Kent) [PERSON] et al. (2014), comparing the relative retention times and peak areas of the samples to standard substances. The limit of detection (LOD) and limit of quantitation (LOQ) were determined based on a signal-to-noise ratio (S/N) of 10:1 and 3:1, respectively. The specific values are as follows: lactate LOD was 0.65 \(\upmu\)g/L, LOQ was 2.17 \(\upmu\)g/L, acetate LOD was 1.07 \(\upmu\)g/L, LOQ was 3.55 \(\upmu\)g/L, propionate LOD was 1.74 \(\upmu\)g/L, and LOQ was 5.81 \(\upmu\)g/L.  
Furthermore, we have determined the recovery rates for each organic acid through the standard addition method. Two sets of recovery experiments were conducted, each with three replicates. In the first set, LB medium was supplemented with the following concentrations of organic acids: lactate (2.0 mg/L), acetate (0.73 mg/L), propionate (0.08 mg/L), formate (0.34 mg/L), pyruvate (0.10 mg/L), succinate (0.05 mg/L), malate (0.02 mg/L), oxalate (0.13 mg/L), and citrate (0.40 mg/L). In the second set of experiments, the concentration for each compound was reduced to 50% of that in the first set. The experimental recovery for each compound was calculated by dividing the determined amount by the supplemented amount and multiplying by 100%.
## 2 Materials and Methods
### Coherence Analysis  
Coherence analysis is generally recognized as a standard metric for EEG data analysis. The coherence of EEG activity in different brain regions is used to measure the synchronicity of oscillations in two distinctive areas of the neocortex (namely, functional connectivity) for different functional states and processes [25], and emotions [26]. In the present study, coherence is used to indicate statistically significant similarity of neural source activity in a particular frequency range in two distinct cortical regions.  
In this work, the coherence was calculated according to the following pipeline. The pairs of different EEG channels were chosen for every frequency sub-band, and the coherence was calculated [27] using the entire recordings. The validity of the coherence coefficient between pairs of electrodes was addressed by conducting surrogate data analysis [28]. The phase randomization technique was applied to obtain the surrogate of the EEG signal. For one of the EEG signals, the Fourier Transform was acquired, and then the random number from range \(-\pi\ \dots\ +\pi\) was added to the phase of each harmonic component. Afterwards, the inverse Fourier transform was performed, and the coherence was calculated between one initial EEG signal and the surrogate EEG. Each pair of electrodes underwent 100 iterations of the procedure. The \(t\)-test (\(p\) < 0.05) was performed to define coherence coefficients, which differed significantly from the surrogate data. If the coefficient was significantly different, it was chosen for further analysis; otherwise, it was neglected. As a result, we obtained the valid EEG coherence values between the electrode pairs for every frequency range and pair of experiments.
## 2 Models and Methods
### Friction Laws  
The classical slip-weakening friction law, modified slip-weakening laws and the rate-state friction law was used to simulate the effect of the fault barrier on the rupture process.  
(1) Slip-weakening friction law  
Firstly, the classical slip-weakening friction law is presented, and its mathematical expression is as follows [10; 41; 42]:  
\[\mu = \left\{\begin{array}{c}\mu_{s}-(\mu_{s}-\mu_{d})\frac{|s|}{D_{c}} \,\ \ \ \ \ |s|<D_{c}\\ \mu_{d}  
where \(\mu_{s}\) is the static friction coefficient, \(V\) is the slip rate, \(V_{c}\) is the characteristic velocity, \(\alpha_{f}\) and \(\beta_{f}\) are the constants of the evolution effect, \(\theta\) is the state parameter, \(t_{c}\) is the characteristic time scale, and the characteristic slip distance \(D_{c}\) is approximately equal to the product of the characteristic velocity \(V_{c}\) and the characteristic time scale \(t_{c}\).
## 5 Conclusions  
The acquired EEG data were confirmed to be qualitative and contain movement-related information when participants performed imagined movements. Decoding MI from these EEG data was shown to be feasible with a relatively low number of sensors. Decoding accuracy does not significantly decrease when using fewer sensors. The worst loss in performance for an individual participant was observed for the smallest subset of eight sensors with a difference of 0.1 in median accuracy, while the difference in maximum accuracy was lower at 0.05. On average, including all participants, no statistically significant difference was observed (\(p\) = [0.18-0.91]).  
Therefore, using commercial EEG devices with a small number of sensors for BCI applications is feasible. Additionally, this shows robustness in decoding performance if fewer sensors become available due to one or more faulty sensors. However, more work is necessary to establish a standard optimal set of sensor locations by using more advanced decoding methods and investigating inter-individual differences in optimal sensor locations. Using consumer-grade EEG systems for MI decoding applications is, therefore, feasible and would enable the creation of affordable and reliable BCI control systems.  
Conceptualization, A.D., F.G., O.R. and K.D.P.; methodology, A.D., U.M., S.G. and K.D.P.; software, A.D.; validation, all authors; formal analysis, A.D. and K.D.P.; investigation, A.D., F.G., O.R. and K.D.P.; resources, K.D.P.; data curation, A.D.; writing--original draft preparation, A.D.; writing--review and editing, K.D.P. and B.V.; visualization, A.D.; supervision, F.G., O.R., A.N. and R.M.; project administration, K.D.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of Human Sciences of Vrije Universiteit Brussel (ECHW_364.02, 7 June 2022).  
Informed Consent Statement: Informed consent was obtained from each participant prior to the start of the first session. Participants were orally given an overview of the experimental procedure and asked to read and sign the informed consent form that contains a detailed explanation of the experiment.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the highly personal nature of these data.  
The authors would like to thank the people who participated in the data-gathering experiments and the students who assisted in the execution of these experiments. This research was made possible thanks to the EUTOPIA Ph.D. co-tutelle program and the Strategic Research Program Exercise and the Brain in Health and Disease: The Added Value of Human-Centered Robotics. UM gratefully acknowledges funding from the European Union's Horizon 2020 Research and Innovation Program under grant agreement no. 952401 (TwinBrain--TWINning the BRAIN with machine learning for neuro-muscular efficiency).  
The authors declare no conflict of interest.
# Experimental Study on Chromaticity Control in Visible Light Communication Systems
## 1 Introduction
In an indoor environment, visible light communication not only realizes the communication function but also meets the lighting requirements. It is also suitable for some special occasions, such as bars and studios, where the light source is not only white light but also requires adjustable color and chroma. As a result, the multi-color transmission system should also consider the light mixing and chroma control of the light source. This article conducts experiments and analysis on the chromaticity control of light sources in RGB LED visible light communication systems. By controlling the duty cycle of PWM in each channel and changing the proportions of red, green, and blue LEDs, respectively, different color temperatures can be obtained to achieve the purpose of controlling the chromaticity of mixed-color LEDs.
# Predicting User's Measurements without Manual Measuring: A Case on Sports Garment Applications
### Anthropometrics for Customer-Tailored Design
#### 1.1.2 Shape Model  
To process the massive set of 3D scanned models available in the Wear database, a mathematical analysis should be made. One option for this is to describe the 3D shapes in a statistical shape model (SSM) [14]. In an SSM, all shapes are aligned, such that any two shapes can be compared point-to-point. Representing shapes as a mesh, then the collection of all possible shapes is described as a high dimensional linear space, endowed with a statistical distribution that indicates the probability density for each shape. In a statistical shape model, computational techniques can be applied for, e.g., retrieving the average model, principal component decomposition, etc. Intuitively and practically, an average shape of the population is at the core, and per point in the model, its spatial variation within the population is stored. Despite a shape model's ability to accurately describe the dataset's shapes, it is difficult for non-specialists to interpret in- and outputs and link models to practical use. The model houses a ton of information, but extracting specific needed measurements is not that straightforward. Algorithms are required to match principal components in the shape model with measurements and parameters known to users [14].  
In general, using a shape model to predict a user's body shape is possible by combining multiple anthropometric features as inputs and return the shape that best match these inputs. The shape model does this for all points in the 3D mesh model, and thus, compiles a new mesh representation. Various methods are available, depending on the needs [18].
# Investigation of the Dynamic Buckling of Spherical Shell Structures Due to Subsea Collisions
## 2 Static Buckling Criteria
### Verification of FEM Model  
The evaluation result of the spherical shell under concentrated load is shown in Figure 2, which can be verified by the FEM model. The model is based on a perfect hemispherical shell with a radius of 5 m and a thickness of 0.1 m. The displacement has been applied at the apex of the shell, with the bottom of the shell fixed in all of the X-Y-Z directions, as shown in Figure 2a. The ideal elastic-plastic model has been adopted as the constitutive model in which the elastic modulus is 210 GPa, the yield stress is 750 MPa, and the Poisson ratio is 0.23, as shown in Figure 2b. The Shell181 element in ANSYS 18.0 is used with the large displacement option on. Figure 2c shows a comparison plot of the reaction force of the apex versus the deflection ratio, where the deflection ratio was defined as \(\omega/t\), and \(p^{*}\) was defined as shown in Equation (2).  
Figure 2: _Cont._Figure 2c shows the comparison of the reaction force at the apex point versus the corresponding displacement between the numerical data and experimental results from [PERSON] [49]. In order to demonstrate the influence of the discretization, the numerical result with different meshing size is displayed. Since the results of size = 0.3 m and size = 0.1 m are the same, the size 0.3 m has thus been adopted in this paper. It can be concluded that the results agree very well, and the result trend is not linear or bilinear, but rather is irregular. At the beginning, the slope is steep, and after \(p^{\prime}\) at around 2.2, the slope becomes very mild. Thus, the value of \(p_{c}\) can be defined by \(p^{*}\) = 2.2.
# Mechanism to Estimate Effective Spectrum Availability Inside Smart Buildings
## 2 Related Work  
Obtaining effective TVWS, both outside and inside buildings, is a growing concern, so new studies are trying to improve the information on TVWS geolocated databases or to complement the process of indoor spectrum measurements.  
A study tried to find an effective TVWS to enable a low-cost, last-middle-mile access infrastructure for rural regions [17]. It found that the effective available TVWS quantity in rural regions can be lower than the TVWS quantity reported by geolocated databases. This may be due to the interference toward the primary users. Another study [19] tried to deploy an LTE stand-alone system working in the 5 GHz unlicensed spectrum that shares the spectrum with Wi-Fi devices. The simulations showed that the stand-alone LTE system can provide more capacity and coverage than the Wi-Fi network.  
In Verulam, South Africa, a comparative study [20] was carried out of the TVWSs measured and those predicted by a geolocated spectrum database. The results show that, in sites with a physical blockage, the difference between compared values can be very large. For this reason, this study suggests that, for greater precision, the TVWS geolocated-database information should be compensated for with real-time measurements.  
For the case of spectrum availability inside a building, a study [21] proposed a model to estimate a TVWS link budget. That model uses internal measurements in a few sites inside a building and compares them with other models and obtains good results in terms of the root-mean-square error. Another indoor study proposed a hybrid system that combines Broadband Power Line Communication (BPLC) and a TVWS CR system to improve the system capacity; interference was mitigated with an iterative precoding algorithm [22].  
Additionally, an experimental study determined both the behavior and a statistical model of the spectrum availability to find the joint availability of the channels of several TV incumbent operators inside a building in Guayaquil city, Ecuador, to quantify the nominal capacity that could be available for use by the SSs based on the CR [15]. Another two studies used the ESA concept, the first one [23] using it to characterize the available spectrum opportunities inside buildings and showing that the SR-CRWS could operate like the SS there, and the other one [24] focusing on discovering the effects of shadowing phenomena on the spectrum availability in building interiors.  
Unlike the studies presented, this study aimed to estimate and quantify the ESA inside an SB using three known propagation models to validate the outdoor path loss component. The propagation models used in this proposed mechanism were validated using two measurement campaigns carried out inside two buildings located in the urban area of the city of Guayaquil, Ecuador. This study used TV band frequencies for their propagation properties inside buildings.
# An Overview of CubeSat Missions and Applications
## 6 CubeSats as Platforms for Chemical and Biological Experimentation in Space
The satellite integrated a microfluidic and optical monitoring system and successfully miniaturized lab equipment into its small form factor. It completed a two-day mission in low Earth orbit, demonstrating successful student-led space research and bio-experimentation.
# An Energy-Efficient Strategy for Microcontrollers
## 4 Experiments and Results
### DVFS, Power Modes, and Their Overheads
This step will introduce an overhead of approximately 70 ms and 4.218 mJ, which is similar to that of processor clock frequency transitions. As a low power MCU, the ESP32/Xtensa processor provides two low-power modes, namely light-sleep mode, and deep-sleep mode. In light-sleep mode, the RF module, the CPU and part of the system clock are suspended. In deep-sleep mode, all modules are closed except for RTC modules, and the wake-up procedure of deep-sleep mode is similar to the system reboot. Compared with light-sleep mode, it takes much longer to wait for the system clock to stabilize after waking up from deep-sleep mode. The working current of the ESP32 module and the wake-up time from these low-power modes to active mode are measured, and the results are listed in Table 4. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Mode** & **Regulator** & **Working Current (mA)** & **Power Consumption (mW)** \\ \hline Normal & N/A & 12.81379 & 64.06895 \\  & Main & 6.25984 & 31.29220 \\  & Low power & 6.22557 & 31.12785 \\  & Main & 4.82017 & 24.10085 \\ Stop & Low power & 4.56274 & 22.81370 \\ Standby & N/A & 0.92958 & 4.64790 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Working current of the core board in different power modes. \begin{table}
\begin{tabular}{c c} \hline \hline
**Power Mode** & **Return Time (μs)** \\ \hline Sleep & 0.6745 \\ Stop & 5.517 (clock reconfigure excluded) \\ Standby & 23.61 (clock reconfigure excluded) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Time required to return from different power modes. The waking-up period can be quite notable; thus, the working current during these periods is also measured. The results are 34.57 mA for light-sleep mode and 47.64 mA for deep-sleep mode. Consequently, the one-time energy overhead for applying light-sleep mode and deep-sleep mode is 0.078 mJ and 27.393 mJ, respectively.
# A Co-Design-Based Reliable Low-Latency and Energy-Efficient Transmission Protocol for UWSNs
## 3 Design Challenges
### High Latency and Limited Channel Capacity  
Data transmission in UWSNs will confront high-latency and low-bandwidth acoustic communication. The acoustic propagation speed (about 1500 m/s in seawater) is much slower than light speed in RF wireless networks. Limited channel capacity and high dynamics of channel quality would also affect the quality of acoustic propagation. Even the transmission of small control packets will also have a certain impact on the end-to-end latency. A quantity of routing protocols lets sensor nodes broadcast control packets to select the next-hop before forwarding data packets. Similarly, control packages are also used in MAC protocols to reserve channels for avoiding the collisions. No matter which layer of protocols, the transmission of control packets will affect the end-to-end latency. More seriously, collision at receivers is more prone to occur because of too many control packets. In addition, long propagation times and flying packets would lead to high bit error rates (BER) then further reduce the reliability of transmission. Once a collision or BER has occurred, senders will typically utilize routing protocols to enable the retransmission mechanism for solving this problem (senders will send the previous packet to its next-hop again), which would further increase end-to-end delay. For these reasons, the number of control packets must be considered to improve the QoS of UWSNs.
## 4 Experiments
### Deployment Experiment  
To verify the actual deployment performance of the workflow, the workflow is tested on an NVIDIA Jetson TX2. Inference is performed using only the CPU of the TX2, not the GPU. The proposed workflow is observed for real-time performance using only the CPU with ARM architecture.  
Figure 7 visualizes 200 frames of each module's time cost in the workflow during runtime. The average data of statistical information are shown in Table 4. They manifest the low time cost. Most of the data fluctuations in Figure 7 come from the time it takes to read a single-frame image from the camera. The deployment performance can reach 26.67 FPS or above, which provides real-time performance for the dangerous driving action recognition task.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Dataset** & **Classifier** & **Acc (\%)** & **IT (s)** & **Param (KB)** \\ \hline \multirow{7}{*}{State Farm} & Random forest & 91.372 & 0.202 & 44,439 \\  & Gradient boosting & 93.564 & 0.064 & 1612 \\  & SVM & 91.112 & 3.023 & 6786 \\  & DNN & 92.034 & 0.179 & 1128 \\  & 1D-CNN & 83.834 & 2.813 & 2253 \\  & GCN & 70.673 & 1.779 & 81 \\  & BLS & 92.164 & 0.041 & 364 \\  & SC-GCN [39] & 91.45 & / & / \\  & DDDS [34] & 93.12 & / & / \\  & Proposed method & 93.639 & 0.071 & 507 \\ \hline \multirow{7}{*}{Driver Skeleton} & Random forest & 91.738 & 0.526 & _77,431_ \\  & Gradient boosting & 90.830 & 0.113 & 1641 \\  & SVM & 85.535 & 16.314 & 14,098 \\  & DNN & 90.741 & 0.432 & 1128 \\  & 1D-CNN & 83.395 & 1.481 & 809 \\  & GCN & 71.429 & 4.356 & 81 \\  & BLS & 91.671 & 0.151 & 364 \\  & 2S-GCN-TAtt [38] & 90.5 & / & / \\  & Beh-MSFNet [37] & 95.4 & / & 3614.72 \\  & Proposed method & 93.512 & 0.182 & 507 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparative experiment with the baseline and the state-of-the-art methods.  
Figure 6: The classification performance comparison on State Farm dataset and Driver Skeleton dataset. (**a**) Performance comparison on State Farm dataset. (**b**) Performance comparison on Driver Skeleton dataset.  
In addition, the smoothing effect of the smoothing algorithm on the results is visualized to verify its effectiveness in noise reduction. The window size of the smoothing algorithm is set to 20, which means that the current detection result will also consider the detection results of the first 20 frames. The detection results are shown in Figure 8, and it can be seen that the smoothing algorithm significantly reduces the noise in the detection results. Combined with historical data, it provides more stable and reasonable detection results for coherent human movements.