esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this paper, we have presented V-band measurements in an indoor residential environment. Angular PL measurements are performed for both LOS and NLOS scenarios, as well as PER and throughput estimation measurements at 60 GHz using IEEE 802.11 ad nodes. The channel model is implemented in a ray-launching algorithm used for simulating network performance, including coverage and throughput, based on a pre-defined access point location.  
The angular PL measurements using a spectrum analyzer-based channel sounder with an omnidirectional TX antenna show that multiple reflections are received, via wall and window reflections. Double-directional angular PL, measured via the TG platform, differs from the angular PL measurements, as the TG platform only scans the azimuth plane over a sector of 90\({}^{\circ}\). In the network performance simulation tool, rays are launched in both azimuth and elevation planes, and multipath components are present that were not captured via the channel sounder or TG platform, e.g., the floor and ceiling reflections, due to the antenna radiation patterns and polarizations. When taking the transmitter's antenna radiation pattern into account, simulation results correspond to the measurement results using the TG platform, whereas a generic indoor model underestimates PL. Simulated throughput corresponds well to the throughput obtained via the TG platform for LOS scenarios, whereas for NLOS scenarios, the simulated throughput is lower. Furthermore, PER measurements show that the maximum simulated throughput cannot be obtained.  
Future work includes further optimization of the ray-launching algorithm by taking into account spatial correlation of multipath components so that it is not necessary to not perform the full algorithm for every single grid point. When the simulation time is lowered, a heuristic will be deployed to perform network planning, i.e., define the optimal location of the access point. Furthermore, the current implementation is only applicable for vertically or horizontally polarized antennas and does not consider de-polarization effects of reflection and transmission. This could be extended to allow generic polarizations, i.e., allow the antennas to be rotated in the three-dimensional space.  
Conceptualization, B.D.B. and A.A.; methodology, B.D.B. and D.P.; software, B.D.B., A.A. and D.P.; validation, B.D.B. and A.A.; formal analysis, B.D.B.; investigation, B.D.B.; resources, W.J.; data curation, B.D.B.; writing--original draft preparation, B.D.B.; writing--review and editing, D.P. and W.J.; visualization, B.D.B.; supervision, D.P.; project administration, W.J.; funding acquisition, W.J. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study, as well as the processing scripts, are made available in the NIST NextG Channel Model Alliance Database.  
This work was executed within the EOS project multi-service wireless network (MUSE-WINET).  
The authors declare no conflict of interest.
## 4 Results and Discussion
### MT-InSAR Data Precision, Calibration and Validation
As the scatterer originally exhibited a \(V_{\text{LOS}}\) of \(+0.26\) mm/year, the adjustment meant a \(V_{\text{LOS}}\) shift of \(-3.87\) mm/year, which was applied to all PS and DS targets. At MENO station, the calibrated \(V_{\text{LOS}}\) from the MT-InSAR analysis equals \(-0.19\) mm/year, thus matching extremely well with the \(-0.16\) mm/year \(V_{LOS}\) measured at the GPS station and providing validating evidence for the MT-InSAR dataset. To account for the velocity shift due to calibration of the PS and DS targets to MESO station, their time series were also adjusted as follows [61]:  
\[d_{i}(t_{n})=d_{iid}(t_{n})-V_{sh}*[t_{n}-t_{0}] \tag{9}\]  
where \(d_{i}(t_{n})\) is the adjusted position of target \(i\) at time \(t_{n}\), \(d_{iid}(t_{n})\) is its original position at time \(t_{n}\), \(V_{sh}\) is the \(V_{\text{LOS}}\) shift and \(t_{0}\) is the first date of the time series.
# Stiffness-Oriented Structure Topology Optimization for Hinge-Free Compliant Mechanisms Design
## 2 Complaint Mechanism Optimization Problem and Formulation
### Complaint Mechanism Optimization Problem
The general eigenvalue problem can be formulated as follows,  
\[\mathbf{K}\Phi_{\mathrm{j}}=\omega_{\mathrm{j}}^{2}\mathbf{M}\Phi_{\mathrm{j} }=\lambda_{\mathrm{j}}\mathbf{M}\Phi_{\mathrm{j}} \tag{2}\]  
where \(\lambda_{\mathrm{j}}=\omega_{\mathrm{j}}^{2}\) is the jth eigenvalue, \(\omega_{\mathrm{j}}\) is the jth eigen-frequency, \(\mathbf{K}\) and \(\mathbf{M}\) are the global stiffness and mass matrix respectively, \(\Phi_{\mathrm{j}}\) is the corresponding eigenmode normalized with respect to the kinetic energy. Assuming that damping can be neglected, the problem of maximizing a single eigen-frequency \(\omega\) is equivalent to minimizing \(1/\lambda\), which can be formulated as follows [30]:  
\[\mathrm{Min}\,\frac{1}{\lambda_{\mathrm{j}}} \tag{3}\]  
Subject to:  
\[\mathbf{K}\Phi_{\mathrm{j}}=\lambda_{\mathrm{j}  
\[0<\rho_{\min}<\rho_{\rm e}<1,\;{\rm e}=1,\ldots,{\rm N_{e}}\]  
where \(\mathbf{N}\) is the total number of eigenmodes related to the topology optimization and \(\mathbf{N_{e}}\) is the number of elements. The symbol \(\alpha\) is the volume fraction \(\rm V*/V_{0}\), where \(\rm V_{0}\)is the admissible volume of the design domain and \(\rm V*\) is the given available volume of design material. j and k are the eigenmode number. The density \(\rho_{\rm e}\) is the design variable, to avoid singularity happening, the minimal value of the design variable \(\rho_{\min}=10^{-3}\). \(\rm k_{e}\) and \(\rm m_{e}\) are the stiffness and the mass matrices with penalization factors \(\rm p\) and \(\rm q\), respectively. The globe stiffness matrix \(\mathbf{K}\) which is assembled by the element stiffness matrix with design variables in the finite element structural analysis can be calculated as:  
\[\mathbf{K}=\sum_{\rm e=1}^{\rm N_{e}}{\rm k_{e}}=\sum_{\rm e=1}^{\rm N_{e}}\rho _{\rm e}^{\rm p}\mathbf{K}_{\rm e}^{0} \tag{4}\]  
where \(\mathbf{K}_{\rm e}^{0}\) is the element stiffness matrix with fully solid material and penalization factor \(\rm p\)\(\geq\mathbf{1}\). In this paper, all the examples \(\rm p=3\). Similarly, the globe mass matrix \(\mathbf{M}\) can be presented as follows:  
\[\mathbf{M}=\sum_{\rm e=1}^{\rm N_{e}}{\rm m_{e}}=\sum_{\rm e=1}^{\rm N_{e}} \rho_{\rm e}^{\rm q}\mathbf{M}_{\rm e}^{0} \tag{5}\]  
where \(\mathbf{M}_{\rm e}^{0}\) is the element mass matrix with fully solid material and penalization factor \(\rm q\geq\mathbf{1}\). In this paper, all the examples \(\rm q=1\).
# Dust Emission Thresholds in Loess Soil Under Different Saltation Fluxes
## 2 Materials and Methods  
### Soil Sample Setup  
Two different materials, loess soil and dune sand, were used to form the experimental soil samples with specific dust (<63 \(\upmu\)m) and sand contents (see Section 2.2). Those materials were collected from two sites in the northwestern Negev, Israel, where sand transport and dust emission have been extensively studied. The loess material (Figure 1a) was sampled from the topsoil (0\(\rightarrow\)2 cm) of a natural area with no human disturbance [11]. The soil contained a mix of clay, silt, and sand particles, which is an ideal case for dust emission by saltation. The sand material was collected from an active sand dune in the northwestern Negev. The Negev dunefield is located in the eastern part of the Sinai- Negev erg [14]. The sand has a typical size (mode at \(\sim\)250 \(\upmu\)m), and it contains less than 2% of clay- and silt-sized particles [15]. The bulk materials of the loess and sand samples were analyzed to determine the particle size distribution (PSD) by the ANALYSETTE 22 MicroTec Plus (Fritsch, Idar-Oberstein, Germany) laser diffraction, which measures particles in the size range of 0.08-2000 \(\upmu\)m. The replicates (100 mg) of each sample were dispersed in Na-hexametaphosphate solution (0.5%) by sonication (38 kHz). PSD data was calculated using the Fraunhofer diffraction model with a size resolution of 1 \(\upmu\)m using MasControl software (Version 1.8, Idar-Oberstein, Germany) (Figure 1b).  
The loess material was first crushed for aggregate disintegration, and then was sieved by an electronic sieving apparatus with horizontal and vertical motions (RETSCH AS 300 Control, Haan city, Germany) to receive the dust material (<63 \(\upmu\)m). The sieved material was analyzed by the laser diffractometer to ensure that the particle size is less than 63 \(\upmu\)m. The sand dune was added to the loess-sieved material (<63 \(\upmu\)m) in a known weighted ratio to form samples with specific weight percentage of silt-clay and sand fractions. The samples were kept at air-dry state (<1.5% gravimetric water content) to eliminate effect of soil-water mass [16]. Four reference samples of 15 kg each with various sand/dust percentage were prepared: \"sand 100%\" (0% dust); \"sand 90%\" (10% dust); \"sand 75%\" (25% dust); \"sand 50%\" (50% dust). The samples were analyzed by the laser diffractometer to test the sand and dust fractions (Figure 1b).
###### Abstract  
The advent of fifth-generation (5G) systems and their mechanics have introduced an unconventional frequency spectrum of high bandwidth with most falling under the millimeter wave (mmWave) spectrum. The benefit of adopting these bands of the frequency spectrum is two-fold. First, most of these bands appear to be unutilized and they are free, thus suggesting the absence of interference from other technologies. Second, the availability of a larger bandwidth offers higher data rates for all users, as there are higher numbers of users who are connected in a small geographical area, which is also stated as the Internet of Things (IoT). Nevertheless, high-frequency band poses several challenges in terms of coverage area limitations, signal attenuation, path and penetration losses, as well as scattering. Additionally, mmWave signal bands are susceptible to blockage from buildings and other structures, particularly in higher-density urban areas. Identifying the channel performance at a given frequency is indeed necessary to optimize communication efficiency between the transmitter and receiver. Therefore, this paper investigated the potential ability of mmWave path loss models, such as floating intercept (FI) and close-in (CI), based on real measurements gathered from urban microcell outdoor environments at 38 GHz conducted at the Universiti Teknologi Malaysia (UTM), Kuala Lumpur campus. The measurement data were obtained by using a narrow band mmWave channel sounder equipped with a steerable direction horn antenna. It investigated the potential of the network for outdoor scenarios of line-of-sight (LOS) and non-line-of-sight (NLOS) with both schemes of co- (vertical-vertical) and cross (vertical-horizontal) polarization. The parameters were selected to reflect the performance and the variances with other schemes, such as average users cell throughput, throughput of users that are at cell-edges, fairness index, and spectral efficiency. The outcomes were examined for various antenna configurations as well as at different channel bandwidths to prove the enhancement of overall network performance. This work showed that the CI path loss model predicted greater network performance for the LOS condition, and also estimated significant outcomes for the NLOS environment. The outputs proved that the FI path loss model, particularly for V-V antenna polarization, gave system simulation results that were unsuitable for the NLOS scenario.  
Keywords:5G; 38 GHz; millimeter wave; channel propagation; path loss; MIMO +
Footnote †: journal:  
_Acticle_
# Effect of Surface Mass Loading on Geodetic GPS Observations
## 2 Materials and Methods  
### GPS Data  
We used daily GPS data provided by the International Global Navigation Satellite System Service (IGS), which combines the GAMIT (SPOCA) and GIPSY (JPL) solutions. The GAMIT and GIPSY solutions contain the unconstrained geodetic station coordinates, which have been corrected for some geophysical effects (Table 1).  
The combined solutions are used so that random processing noise introduced by any individual analysis will be compressed to enhance the signal-to-noise ratio. [PERSON] et al. [2] confirmed that the scatter in the vertical component for the combined solutions is smaller by 5-15 mm compared to the scatter in any other analyses. We removed offsets, outliers and the linear trend as described in Nikolaidis in GPS observations [17]. Due to the removal of offsets and outliers as well as other reasons (e.g., the equipment changes or failures), missing data is an inevitable problem in GPS observations. At present, it is difficult to use interpolation methods to calculate the true value due to the complexity of GPS, although the filling of missing data with interpolation will introduce an artificial error. Therefore, GPS site selection is performed using the following criteria. First, the time span of GPS time series should be longer than 2.5 years [18]. Additionally, to minimize the interpolation effect, the number of continuous data gaps should not exceed 15 and the ratio of missing data to entire observations must not be more than 1% for each site [19]. Finally, the selected stations should
# Perceptual Characteristics of Voice Identification in Noisy Environments
## 4 Discussion
### Impact of Noise Type  
In all three types of noise environments, as the SNRs increase, the perceptional accuracy increases first and then becomes stable. The perception results can be estimated by Equations (1)-(3).  
In the high SNR (\(\geq\)0 dB) environment, although the spectral and time characteristics of the three noises are different, the impact of the noise type on voice identity perception is not significant. When the SNR reaches 16 dB, the perception results under the three types of noise interference are almost the same, with an accuracy of approximately 0.96. The reason may be that when the SNR reaches a certain threshold, auditory attention is focused on specific stimuli, while actively suppressing noise and other environmental interference. [PERSON] et al. determined that the speech intelligibility curves of monosyllables under three types of noises overlap when the SNR exceeds 16 dB [25]. This is similar to the results of our experiment.  
In a low SNR (<0 dB) environment, the noise type will affect voice identity perception. First, the changing trend of white noise is similar to that of pink noise, but the perceived accuracy of pink noise is less than that of white noise. White noise and pink noise are all full-band noises, but their energy distributions are not the same. The energy of white noise is evenly distributed. The energy of pink noise constantly decays from low frequency to high frequency, which is more consistent with the perceptual (logarithmic) uniformity. Maybe that is why the influence of pink noise is more apparent than white noise. Moreover, compared with white noise and pink noise, speech noise has a more significant impact on voice identity perception, and its changing trend is also obviously different. The study has shown that the subjective feelings of pink noise and white noise are roughly the same, which is different from speech noise [26]. This is similar to our perception of these three types of noise. The perceptual difference between speech noise and the other two may be due to its concentration energy in the low-frequency and medium-frequency bands. This frequency band is close to the human voice's frequency areas, so speech noise has a greater impact on voice identity perception.
## 4 Conclusions  
In summary, we have novelly constructed a \"MOF on MOF\" heterojunction assembled by a conductive Ni-MOF core and a hydrophobic ZIF shell, which exhibits enhanced electrocatalytic CO\({}_{2}\)RR performance and suppressed HER activity. The XPS and EXAFS data demonstrate that the heterojunction induces strong electronic coupling between Ni-MOF and ZIF-L, facilitating fast kinetics in CO\({}_{2}\)RR. Furthermore, a volcano-like correlation between the coverage of ZIF-L on Ni-MOF and electrocatalytic performance was observed, attributed to the balance between conductivity, hydrophobicity, and CO\({}_{2}\) adsorption. Based on the above results, we have constructed, for the first time, a \"MOF on MOF\" model structure by investigating the interface structure, adhesion energy, and chemical interactions at the Ni-MOF@ZIF-L interface. Interestingly, the heterojunction shows higher adhesion energy for CO\({}_{2}\) and lower adhesion energy for H\({}_{2}\)O compared with that of pure Ni-MOF. It was found that the electron transfer at the interface is significantly strengthened, which is consistent with the experimental results. Our results thus provide new insights into the rational design of heterojunctions for efficient CO\({}_{2}\)RR over HER.  
Figure 7: (**a**) SEM images; (**b**) contact angle images; (**c**) polarization curves in CO\({}_{2}\)-saturated or (**d**) Ar-saturated electrolytes; and (**e**) Faradaic efficiency as a function of growth time (0 h, 0.5 h, 1 h, 2 h, and 4 h) for the Ni-MOF@ZIF-L heterojunction. (**f**) Schematic illustration of the catalytic process of the Ni-MOF@ZIF-L heterojunction.  
**Author Contributions:** Conceptualization, J.Z.; writing--original draft preparation, L.W.; supervision, X.H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was partly supported by the Outstanding Scientific Research and Innovation Team Program of Higher Education Institutions of Anhui Province (No. 2023 AH010015).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available upon request from the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# DDPG-MPCC: An Experience Driven Multipath Performance Oriented Congestion Control
## 3 Performance Evaluation
In Figure 7, we present our new observations of the evolutionary graphs of the utilities experienced by DDPG-MPCC and MPTC in relation to the number of MIs. We clearly observe that the utility of the DDPG-MPCC, which is initially rather low, improves significantly more than the utility of the MPTC. Observe that the utility of DDPG-MPCC increases as MIs progress and follows the same trend as that of the sending rates in Figure 6. To further explore and understand such an important capability of maximizing  
Figure 6: Comparison of the evolution of the average sending rates of MPTC [5] and DDPG-MPCC on the number of MI (network scenario c, Figure 2). utility with DDPG-MPCC, we quantify the average rewards over MIs, and we can see in Figure 8 that the performance of DDPG-MPCC in terms of utility maximization is due to the intrinsic ability to gradually move towards improving the rewards. It is noted that the average episodic reward for MI in Figure 8 increases sharply, thus maximizing the utility of the network.
## 2 Materials and Methods
### Color Parameters  
Parameters L* (lightness/darkness), a* (redness/greenness), and b* (yellowness/blueness) of the CIE L*a*b* color space were determined using a CR-400 Chroma meter Konica Minolta (Osaka, Japan) with a port (\(\mathcal{O}\) 8 mm) and the D65 illuminant, and standard observer 2\({}^{\circ}\). The device was calibrated with a standard white ceramic tile. The measurements were performed on the surface of slices of 10 mm. Altogether, eight measurements were taken. To better highlight differences in color, the indexes of color saturation (parameter C* Equation (2)) and the hue (h\({}^{\circ}\)--hue angle Equation (3)) were calculated according to the formula [33]:  
\[\mathrm{C}^{*} =\sqrt{\mathrm{a}^{*2}+\mathrm{b}^{*2}} \tag{2}\] \[\mathrm{h}^{\circ} =\arctan\left(\frac{\mathrm{b}^{*}}{\mathrm{a}^{*}}\right) \tag{3}\]
# Influence of Cracking on the Durability of Reinforced Concrete with Carbon Nanotubes
## 2 Materials and Methods
### Testing Procedures
#### 2.5.3 Chloride Penetration Resistance  
The chloride penetration resistance was determined by the rapid chloride migration (RCM) test, according to LNEC E463 [39] and NTbuild492 [40], which provides a chloride ion migration diffusion coefficient (_Dcl_) under non-steady conditions. Three \(\mathcal{O}100\times 50\) mm cylindrical specimens were tested per each concrete composition. An external electrical potential was applied to force migration of chloride ions into the specimen, which was then split in two halves and sprayed with a silver nitrate solution. The chloride penetration depth was determined as the limit of the white silver chloride precipitation. Seven measures were taken from each exposed face. The diffusion coefficient was calculated according to Equation (2), where T represents the average value of the initial and final temperature of the anodic solution (\({}^{\circ}\)C), L is the specimen's thickness (mm), \(U\) is the absolute value of the applied voltage (V), t is the test duration (hours), and \(x_{d}\) is the average value of the measured chloride penetration depth (mm).  
\[D_{cl}=\frac{0.0239(273+T)^{L}}{(U-2)^{t}}\left(x_{d}-0.0238\sqrt{\frac{(273+T) Lx_{d}}{U-2}}\right)\ \left[\times 10^{-12}m^{2}/2\right] \tag{2}\]
## 5 Application to MIMO Secure Communications
Once synchronization is achieved, we end up with  
\[\mathbf{F}\left(Y\left(t\right)\right)-\mathbf{M}\left(t\right)X(t)\approx \mathbf{0},\]  
which allows us to reconstruct the original messages as  
\[\widehat{S}\left(t\right) = \widetilde{R}\left(t\right)-\mathbf{F}\left(Y\left(t\right)\right)\] \[\approx S\left(t\right).\]  
In order to test the theory, computer simulations were carried out on Matlab, where the source messages were assumed to be sinusoidal of the form  
\[R\left(t\right)=\textbf{H}\widetilde{S}\left(t\right)+V\left(t\right),\]0  
with  
\[R\left(t\right)=\textbf{H}\widetilde{S}\left(t\right)+V\left(t\right),\]1 \[R\left(t\right)=\textbf{H}\widetilde{S}\left(t\right)+V\left(t\right),\]2 \[R\left(t\right)=\textbf{H}\widetilde{S}\left(t\right)+V\left(t\right),\]3  
Note that \(\omega_{i}\) denotes the normalized angular frequency. The resulting sine waves are depicted in \[R\left(t\right)=\textbf{H}\widetilde{S}\left(t\right)+V\left(t\right),\]4 Figure 7 shows the transmitted and equalized signals, respectively. After synchronization is achieved, the two waveforms overlap. The random-like chaotic nature of the transmitted signals is apparent. Figure 8 shows the recovered messages. Clearly, the recovered messages contain additive noise, which is a natural consequence of the amplifications employed at the receiver as well as imperfections in the equalization process. A low-pass filter can be used to combat the noise as can be seen in \[R\left(t\right)=\textbf{H}\widetilde{S}\left(t\right)+V\left(t\right),\]5  
\[R\left(t\right)=\textbf{H}\widetilde{S}\left(t\right)+V\left(t\right),\]6 The original messages \(s_{i}\left(t\right)\) to be transmitted by \(n\) transmit antennas in a MIMO secure communication system. \[R\left(t\right)=\textbf{H}\widetilde{S}\left(t\right)+V\left(t\right),\]7 The recovered messages \(\widehat{s}_{i}\left(t\right)\) from the \(n\) receive antennas. \[R\left(t\right)=\textbf{H}\widetilde{S}\left(t\right)+V\left(t\right),\]8 The transmitted signals \(\widetilde{s}_{i}\left(t\right)\) as well as the equalized signals \(\widetilde{r}_{i}\left(t\right)\) for an SNR of 40 dB.