esa-sceva/satcom-chunk-collection huggingface dataset:
# Epileptic Seizure Detection in Neonatal EEG Using a Multi-Band Graph Neural Network Model
## 2 Database  
In this study, a public neonatal EEG database [21], which was recorded at the University Hospital of Helsinki, is used for validation purposes. The database contains scalp EEG recordings from 79 term neonates. The recordings were manually annotated by three highly experienced clinical experts. A total of 343 seizures from 39 neonates WERE annotated consistently by all the three experts. EEG was recorded at a sampling rate of 256 Hz using the NicOne EEG amplifier with 19 electrodes positioned according to the international 10-20 system. A bipolar montage was generated for annotation according to the standard longitudinal bipolar layout: Fp2-F4, F4-C4, C4-P4, P4-O2, Fp1-F3, F3-C3, C3-P3, P3-O1, Fp2-F8, F8-T4, T4-T6, T6-O2, Fp1-F7, F7-T3, T3-T5, T5-O1, Fz-Cz, and Cz-Pz, as shown in Figure 1. More details about the above mentioned 39 neonates are given in Table 1.
## 4 Discussion on Mechanisms
### Comparison with Other Literatures  
By comparing results from this study with those of previous similar studies, it becomes evident that LD exhibits the highest potential as a highly effective biosorbent for MB removal. LD has a higher removal capacity than most of the reported biomasses in Table 5 and was higher than many treated and activated biomasses [121]. Additionally, both CH and HH provide relatively high MB adsorption capacities: 128 mg/g and 110 mg/g, respectively, and thus are also potential biosorbents. As these two husks are waste biosorbents from nature, these two biosorbents were naturally dried and have no direct application in any value-added industry. RS could also be a potential adsorbent compared to many other biosorbents.  
Figure 11: Potential MB interaction with the surface chemistry of biosorbents. Images adapted from [PERSON] et al. [95] and [PERSON] et al. [99].  
Industrial wastewaters can have dye loadings up to 1500 mg/L (1.5 mg/g) [25]. Thus, all biosorbents presented in this study would be suitable for dye removal from real effluents. However, real effluents also include other compounds, and this has been shown to reduce the capacity of adsorbents compared to laboratory-created simulated effluents [66]. This study demonstrates that natural biomasses have the potential to be used as biosorbents with respectable removal capacities without pre-treatment. This allows them to be a low-cost option for MB dye removal. However, scaling up the use of natural biosorbents for an industrial scale will require investigations. Fixed-bed columns have been used as a continuous flow process in biosorption for dye removal, but issues such as pH control of the bio-sorbate solution, lack of regeneration potential, and concerns over spent biosorbent disposal remain [131].
## Materials and Methods
### Optimization of HMT Regime and GPF Addition  
The Design Expert software (Stat-Ease, Inc., Minneapolis, MN, USA) trial version was employed to create the experimental matrix in order to evaluate the effects of factors (temperature, time, moisture, GPF level) on the considered responses (dough G\({}^{\prime}\), G\({}^{\prime\prime}\), firmness, pasta color, TPC, DF, firmness, gumminess and RS content). The study was carried out on 30 resulting experiments (Table 1). Response surface methodology (RSM) with a central composite design (CCD) was used for experiment planning and data processing. The option of face centered at \(\alpha=1\) with six points at the center and three repetitions for each experiment was selected. Mathematical model fittings were evaluated trough \(F\) sequential test, coefficient of determination (\(R^{2}\)), and adjusted coefficients of determination (\(Adj\)-\(R^{2}\)). Analysis of Variance (ANOVA) performed by Design Expert software was used to establish the significant effects at \(p<0.05\) of the factors and their interactions on the responses. The optimization of HMT regime and GPF level was done by means of desirability function. The constraints applied consisted of maximization of G\({}^{\prime}\), G\({}^{\prime\prime}\), TPC, RS, DF, and pasta firmness, while \(L^{\star}\), dough hardness, and pasta gumminess were kept within the range.  
RSM is a well-known tool successfully employed in different industries, with various applications in food formulation, quality, and processing technologies optimization. RSM is based on simple algebraic equations although they present an advantage regarding ease of use [31].
# A System-Level Methodology for the Design of Reliable Low-Power Wireless Sensor Networks
## 6 Resulting Design Methodology and Case Studies
### Case Studies
#### 6.2.2 99.999% Reliability Target  
A requirement of 0.99999 in terms of packet success probability is now assumed, which corresponds to a 99.999% reliability figure of merit. This level of reliability is required by applications such as industrial IoT [67; 68; 69] and wide area situation awareness [7]. Additionally, a forward and feedback channel bit error probability \(P_{e}=10^{-3}\) is assumed.  
Figures 25 and 26 show that the requirement is no longer met by the studied convolutional FEC code. Therefore, a code presenting a longer free distance, \(d_{m}\), is required. Furthermore, it can be concluded from Figure 25 that, when using BR, seven retransmissions are needed (\(R=8\)) and, when using 4-Rep-ACK, seven retransmissions are required (\(R=8\)). However, for the latter, that can be achieved only when \(L\geq 4\). Figure 26 shows that with \(L=3\), a large number of 3-Rep-ACK transmissions (i.e., \(R>20\)) are required, making this L value unviable.  
Figure 27 shows that, in this case, using 4-Rep-ACK retransmissions is the least expensive choice in terms of energy consumption and latency, provided that the feedback channel is significantly more reliable (i.e., \(L=4\)).  
Figure 24: (**a**) Energy consumption per measurement and (**b**) system latency for 99% reliability target.  
Figure 25: Meeting 99.999% reliability target by using BR and 4-Rep-ACK retransmissions.  
It can be concluded from these case studies that, for SAW-ARQ, a noisy feedback channel can severely degrade system performance and make it worse than an open-loop system (i.e., BR approach) [70]. However, when the feedback channel is much less error-prone, the SAW-ARQ protocol can be as reliable as when using the time and energy inefficient BR. However, in WSN applications, this is not the case and the feedback channel is also error-prone with similar probabilities [70; 71]. For this reason, ACK-NACK responses have to be sent with a stricter reliability requirement in mind, leading to a larger value of the L requirement for the ACK approach [72; 73].
## 2 Sector-Specific Applications
### Electronics  
In electronics manufacturing, low-code accelerates firmware testing and IoT device management. A study by [PERSON] et al. [55] highlights how platforms like App Builder enable automated test generation for embedded systems, reducing validation cycles compared to manual scripting. For instance, TSMC employs a low-code framework to simulate semiconductor fabrication workflows, integrating AI models for defect prediction directly into visual dashboards [56].  
Low-code also addresses the complexity of cross-platform IoT integration. Schneider Electric's EcoStruxure platform uses drag-and-drop tools to unify PLCs, sensors, and ERP systems, enabling non-technical staff to design energy-efficient production lines. This aligns with [PERSON] et al. [28], who note that low-code's modular architecture reduces interoperability challenges in heterogeneous IoT ecosystems.
# Research on Mechanical Properties and Damage Constitutive Model of Water-Bearing Coal
## 2 Experimental Methods
### Sample Preparation  
The coal blocks used to prepare the samples were extracted from 11,215 working faces of the main coal seam in a mine in Yulin City, China. The ZS-100 rock coring machine (Jinan Mining and Rock Testing Instrument Co., Ltd., Jinan, China) was used to drill the core along the vertical bedding direction of the coal block, and then the obtained coal core was processed into the standard raw coal sample (\(\phi\)50 mm \(\times\) 100 mm) shown in Figure 3 through cutting, grinding, and other processes. The processing error of the coal sample meets the tolerance requirements of the standard GB/T 23561.7-2009. The mass and wave velocity of the coal samples were measured using an electronic balance and an acoustic wave instrument to ensure sample uniformity, and coal samples with minor dispersion were chosen for experiments.  
The preparation steps for coal samples with varying water content are as follows:  
1. The coal sample selected by mass and wave velocity was put in a steady temperature drying oven, and the drying temperature was set at 105 \({}^{\circ}\)C. After 12 h, the heated coal sample was moved to the dryer and cooled to room temperature to measure the mass \(m_{dry}\) of the dried coal sample;  
Figure 3: The standard raw coal samples.  
Figure 2: The ASMB2-8 static strain meter.  
2. The dried coal sample obtained by the first step was heated in a boiling vessel for eight hours and then cooled to room temperature. Finally, the coal sample was wiped off the surface water to measure its mass \(m_{sr}\). The following formula can be used to calculate the saturated moisture content of a coal sample: \[\omega_{sr}=\frac{m_{sr}-m_{dry}}{m_{dry}}\times 100\%\] (1)
3. The water-saturated coal sample obtained in the second step was heated in a constant temperature and humidity drying oven, and the drying temperature was set at 40 \({}^{\circ}\)C. The mass of coal sample \(m_{w}\) was measured every 30 min and the moisture content of the coal sample was calculated. When the water content of the coal sample was close to the set water content, the measurement time interval was shortened to five minutes until the coal sample reached the planned moisture content. The following is the formula for calculating the moisture content of a coal sample: \[\omega=\frac{m_{w}-m_{dry}}{m_{dry}}\times 100\%\] (2)
## 3 Results and Discussions
### Time Effect and Rate Kinetics  
The results of ibuprofen adsorption by Raw-RGL and AC-RGL are indicated in Figure 7 and show that the uptake of ibuprofen is fast at the beginning of the adsorption process. The rapid uptake of ibuprofen is due to various active sites. The uptake of ibuprofen was particularly fast within the initial 1-35 min interval by the Raw RGL. From 45 to 105 min, the sorption slowly decreased due to sites being filled and therefore restricted [55]. The reaction rate onto AC-RGL was quicker and occurred between 1 and 10 min compared with the Raw-RGL. The maximum sorption capacities were 68.97 and 101.96 mg/g for the Raw-RGL and AC-RGL.  
The kinetic data of ibuprofen adsorption by Raw-RGL and AC-RGL were fitted into the pseudo-first-order (PPO), pseudo-second-order (PSO) models and intraparticle diffusion (IPD) and the data are shown in Table 5. This was done to estimate whether the adsorption process fit the PFO, PSO, or IPD models. The best-fit model must have a correlation coefficient close to 1. Table 4 shows that PFO was the best fit with \(t^{2}\) of 0.995 and 0.993 for Raw-RGL and AC-RGL, respectively. The best fit for PFO shows that the uptake of ibuprofen by Raw-RGL and AC-RGL involves Van der Waal forces of attraction and that process was physisorption [56]. IPD kinetic estimation shows whether adsorption occurred in the pores or the surface. Table 4 shows that the estimated surface adsorption (ESA) was the main process compared with the estimated pore adsorption (EPA). ESA is between 83.12 and 87.12%, and EPA ranges from 12.88 to 16.88%.
## 3 Results and Discussion
### TEM Analyses  
Figure 5 displays TEM images of TiO\({}_{2}\) nanoparticles and POA@TiO\({}_{2}\) and PPA@TiO\({}_{2}\) nanocomposites. TiO\({}_{2}\) presents an almost spherical shape and uniform nanoparticle size with a diameter of about 100 nm, similar to those reported in the literature [44; 45; 46]. Moreover, all products had a spherical morphology, which was likely induced by absorbing monomers on the surface of TiO\({}_{2}\) through H-bonding and electrostatic attraction after HCl acidified the TiO\({}_{2}\) spheres. The TiO\({}_{2}\) surface was modified during the acidification stage, and no other surface treatments were necessary [43]. Moreover, TEM images of the nanocomposites indicate that TiO\({}_{2}\) particles were successfully dispersed into the polymer matrix [47].  
Figure 4: CV recorded on glassy carbon electrode modified by synthesis samples in HClO\({}_{4}\) (1 M) at a 50 mV.s\({}^{-1}\) scan rate.  
Figure 5: TEM images of (**a**) TiO\({}_{2}\) nanoparticle; (**b**) PPA@TiO\({}_{2}\), and (**c**) POA@TiO\({}_{2}\) nanocomposites.
# A Survey on Secure WiFi Sensing Technology: Attacks and Defenses
## 4 Defenses for WiFi Sensing
### Active Defense
Thus, they constructed an advanced secure WiFi sensing system to exactly modify the private semantic information of CSI while still reserving the other required information. Moreover, to deal with various classifiers, they transformed original signals into adversary signals by training a deep learning architecture with the output of the classifiers. Similarly, [PERSON] et al. [80] attempted to develop an adversarial deep network architecture for human behavior preservation, which aims to make the targetable private behaviors of a human being not recognizable by general classifiers, while the recognition of other information remains unaffected. To achieve this, the proposed model is capable of intentionally modifying CSI data extracted from received WiFi signals, constraining the new classification results to match the adversarial requirements. To prevent eavesdroppers from obtaining estimations of wireless channels and stealing sensitive information, IRShield [56] was proposed as a novel countermeasure leveraging the advanced capabilities of metasurfaces. As shown in Figure 6, a dedicated algorithm is integrated into the system to generate randomized metasurface configurations, dynamically altering the wireless channel environment to achieve channel obfuscation. This randomness effectively confounds the attacker by providing misleading or confusing sensing results, making it difficult for them to extract accurate information from the wireless signals. As the first practical attempt to exploit metasurfaces as a countermeasure for unauthorized wireless sensing, IRShield represents a significant step forward in privacy-preserving technology. Designed for plug-and-play integration with existing wireless infrastructures, IRShield can be easily deployed as a standalone extension without requiring major overhauls of the current system architecture. This makes it particularly appealing for environments where privacy is paramount.
# A New Hybrid Positioning Method by Fusion of BDS and 5G Signal Using the Particle Swarm Method
## 3 BDS-5G Signal Fusion Location Algorithm
### BDS-5G Signals Joint Solution and MOPSO-TOA/TDOA Model Algorithm  
The objective function of the BDS-5G signal joint solution is constructed from Equation (5) as follows:  
\[\left\{\begin{aligned} f_{n1}(x,y,z)&=\left|\left| \rho_{n}-\sqrt{\left(x_{sn}-x\right)^{2}+\left(y_{sn}-y\right)^{2}+\left(z_{sn }-z\right)^{2}}-\delta\right|\right|\\ f_{n2}(x,y,z)&=\left|\left|c\Delta t_{n}-\sqrt{\left(x _{bn}-x\right)^{2}+\left(y_{bn}-y\right)^{2}+\left(z_{bn}-z\right)^{2}}-d_{1} \right|\right|\end{aligned}\right| \tag{9}\]  
The algorithm flow is as follows:  
1. Initialize particle swarm: given the population size N, randomly generate the position \(X_{i}\) and speed \(V_{i}\) of each particle;
2. Calculate the fitness value of each particle with the objective function \(f_{n1}(x,y,z)\), \(f_{n2}(x,y,z)\);
3. Calculate the individual extreme value of each particle and the global extreme value of the objective function;
4. Calculate the mean value \(g_{best}\) and distance \(d_{g_{best}}\) of the global vector;
5. Calculate the distance \(d_{p_{best}}[i]\) between each particle \(g_{best}[n,i]\);
6. Calculate the individual extreme value \(p_{best}[i]\) to be used when updating the position and speed \(V_{i}\) for each particle \(X_{i}\);
7. Update the position \(X_{i}\) and velocity \(V_{i}\) of each particle with the results \(g_{best}\) and \(p_{best}[i]\) of (3) and (6).
## 2 Materials and Methods
### Laboratory Data Analysis
#### 2.3.2 Ps Estimation Method 1: Empirical Relationship with SPL and \(f_{\rm o}\)  
The first method of Ps estimation relies on an empirical relationship found with SPL and \(f_{\rm o}\). For laboratory data analysis, the SPL was computed directly from a given vowel segment in the acoustic microphone signal as  
\[\rm SPL\ [dB\ SPL\ @\ 15\ cm]=20\log_{10}\biggl{(}\frac{\rm MICrms}{20\ \mu Pa} \biggr{)}, \tag{1}\]  
Figure 2: Illustration of how reference estimates of subglottal pressure were defined in a male study participant with a typical voice (M01) in modal phonation. (**A**) Time-aligned signals and associated spectrograms are plotted for the acoustic microphone (MIC), oral airflow, neck-surface accelerometer (ACC), and intraoral pressure (IOP) sensors (S = silence; V = vowel). A zoomed-in version of the boxed region is displayed in (**B**) to illustrate the definition of each vowel segment, silent interval, and IOP pulse.  
where MICrms is the RMS value of the middle 50 ms of the microphone vowel segment. The \(f_{\rm o}\) of this 50 ms segment was computed from the accelerometer signal as the reciprocal of the first peak location in the normalized autocorrelation function; if a subharmonic exists that is at least 0.25 of the first peak, the \(f_{\rm o}\) is recomputed according to the location of the subharmonic. This recomputation is necessary due to the effect of the subglottal resonance that can boost the second harmonic magnitude above that of the first harmonic. These measures of SPL and \(f_{\rm o}\) were then input into the following formula to estimate Ps [58; 60; 61]:  
\[\rm Ps\ [kPa]=0.14+0.06(f_{\rm o}/f_{\rm ON})^{2}+10^{(SPL-88.5)/27.3}, \tag{2}\]  
where \(f_{\rm ON}\) is the nominal speaking \(f_{\rm o}\) value for males (120 Hz) and females (190 Hz).
# Analysis of Precise Orbit Determination for the HY2D Satellite Using Onboard GPS/BDS Observations
## 4 Results and Discussion
### SLR Validation for the POD of HY2D  
SLR is an independent measurement technique compared to GNSS, and thus SLR validation can validate objectively the accuracy and reliability of GNSS-derived orbits [11; 38]. Here, we select the SLR normal point data from the corresponding periods of the HY2D satellite (download: [[ftp://edc.dgfi.tum.de/pub/slr/data/npt_crd/](ftp://edc.dgfi.tum.de/pub/slr/data/npt_crd/)]([ftp://edc.dgfi.tum.de/pub/slr/data/npt_crd/](ftp://edc.dgfi.tum.de/pub/slr/data/npt_crd/)), accessed on 10 February 2022). The SLR residual results cannot verify the accuracies of the components in each direction or orbital position directly, but they denote that the distance precision between the given SLR station and the validated satellite. When the higher cut-off angle is set, the SLR residuals can better reflect the radial accuracy. Because HY2D is an altimetry satellite, its radial precision is our primary concern. In this study, we set the cut-off angles as 20, and give the time series of SLR residuals in Figure 10.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Doy** & **R** & **T** & **N** & **3 DRMS** \\ \hline
187 & 1.5 & 4.1 & 3.1 & 5.3 \\
188 & 1.5 & 4.1 & 3.0 & 5.3 \\
189 & 1.5 & 4.1 & 3.0 & 5.3 \\
190 & 1.5 & 4.1 & 3.1 & 5.3 \\
191 & 1.5 & 4.1 & 3.0 & 5.3 \\
192 & 1.5 & 4.1 & 3.0 & 5.3 \\
193 & 1.5 & 4.1 & 3.1 & 5.4 \\
194 & 1.5 & 4.1 & 3.1 & 5.4 \\
195 & 1.6 & 4.1 & 3.1 & 5.4 \\
196 & 1.5 & 4.1 & 3.1 & 5.3 \\
197 & 1.5 & 4.1 & 3.1 & 5.3 \\
198 & 1.6 & 4.1 & 3.1 & 5.3 \\
199 & 1.5 & 4.2 & 3.1 & 5.4 \\
200 & 1.5 & 4.1 & 3.1 & 5.4 \\ Mean & 1.5 & 4.1 & 3.1 & 5.4 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The precision statistics of HY2D satellite using spaceborne BDS data (unit: cm).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Doy** & **R** & **T** & **N** & **3 DRMS** \\ \hline
201 & 1.6 & 3.6 & 2.3 & 4.5 \\
202 & 1.5 & 3.5 & 2.0 & 4.3 \\
203 & 1.5 & 3.5 & 2.2 & 4.4 \\
204 & 1.5 & 3.5 & 2.1 & 4.3 \\
205 & 1.5 & 3.5 & 2.1 & 4.3 \\
206 & 1.5 & 3.5 & 2.1 & 4.3 \\
207 & 1.4 & 3.5 & 2.1 & 4.  
As can be seen from Figure 10, most of the residual values are located within \(\pm\)8.0 cm, and have no obvious abnormal value. The orbits of the first 14 days are calculated using spaceborne BDS data, and the next 22 days orbits are calculated using spaceborne GPS data. The range of SLR residuals for BDS-derived orbits is slightly bigger than that of GPS-derived orbits, which is related to that the precision based on spaceborne GPS data are slightly better than that of spaceborne BDS data. After the statistical calculation of these residuals, their average value and standard deviation are, respectively, \(-\)0.7 cm and 3.3 cm, which illustrate that these residuals have no significant deviations. The results also indicate that the orbital accuracy of HY2D satellite can satisfy the current high-precision altimetry requirements.
# W-Band 4 th Order Waveguide Filter Based on Double Layer SU8 Microfabrication
## 3 Experimental Results
### Difficulties and Solutions in the Fabricating
#### 3.2.4. Chip-to-Chip Alignment  
The alignment between the SU-8 chips also has a certain impact on the entire filter's performance. Misalignment will cause a detuning response or excite higher-order harmonics. The sensitivity analysis of the designed filter is shown in Figure 9. It shows how the misalignment of only the coupling irises layer of about 10 \(\upmu\)m in the horizontal and vertical directions with respect to the SU-8 chip1 or 3 affects the entire filter's response. To improve the alignment accuracy between SU-8 chips, the alignment pin holes in the waveguide flange we designed to adopt the combination of circular and elliptical alignment holes in [16], and the alignment rate of this method can reach 99.5%. The designed flange contains 4 waveguide flange alignment holes, which is described in the IEEE1785.2a standard [17].  
Figure 8: Multi-layer overlay marking (**a**) Design of the engraved marks and measured marks for masks (**b**) Precision measurement of overlays, and the maximum tolerance is 13.34 \(\upmu\)m.
## 2 Materials and Methods
### Animal Experiments  
BALB/c mice (male, 18-20 g, 6 weeks) were obtained from Koatech (Seoul, Korea). The experiments were carried out under the instructions of the Institutional Animal Care and Use Committee of Dongguk University (IACUC-2017-015). Concentrations of 0.1% and 1% indirubin were as used for the topical treatment in this study because the common concentrations of corticosteroids that are used in topical applications range from 0.05% to 1% [18]. The mice were allocated randomly to one of four groups (n = 5 per group): the normal control group (NC group), the IL-22-treated group (IL-22 group), the IL-22 plus 0.1% indirubin treatment group (0.1% IR group), or the IL-22 plus 1% indirubin treatment group (1% IR group). Briefly, the mice were anesthetized with isoflurane and intra-dermally injected with vehicle (1X PBS, 20 uL) or mouse IL-22 recombinant protein (0.05 ug/uL, 20 uL) (BioLegend, San Diego, CA, USA) into the right ear using a 30-gauge needle daily for six days. In the 0.1% and 1% IR groups, the right ears were topically treated with 20 uL of 0.1% or 1% of indirubin 1 h before IL-22 administration on each of the six days. All of the mice were sacrificed one day after the final treatments, and the right ears were collected. The ear thicknesses were assessed by a thickness gauge (Mitutoyo, Tokyo, Japan).