esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In [10; 11], the both communication systems were investigated in the V2V context from perspective of the end-to-end delay, packet delivery ratio, and throughput and from the perspective of the average packet reception ratio, respectively. In more detail, the authors of [10] evaluated the performance of the IEEE 802.11p standard and LTE technology for vehicular networks under various network conditions and parameter values in terms of reliability, delay, and scalability. For [11], the evaluation indicated that LTE-based alternatives, including the LTE multicast and LTE sidelink, provide better performance than the IEEE 802.11p in terms of the reliable communication range reached in all of the studied scenarios. Finally, the corresponding technologies were compared from the point of view of periodic and aperiodic messages of constant and variable sizes in [12]. In summary, as can be clearly seen from the papers listed above, most studies of the performance have focused on the V2V communication context when dealing with the technologies' performance or benchmarking them in terms of their performance. To the best of our knowledge, there is no work dealing specifically with the benchmarking of C-V2X (neither 4G-based nor 5G-based) and DSRC in the context of V2I communication. It is worth noting here that V2I communication is particularly important when it comes to traffic management. Therefore, we decided to focus on this issue in this paper. So, we benchmarked the DSRC and LTE-based C-V2X in the infrastructural mode, as well as in the device-to-device mode 3, in terms of the Packet Delivery Ratio (PDR) and end-to-end delay in the context of V2I communication and urban scenarios from the perspective of traffic intensity, message generation frequency, and communication perimeter. The obtained results are put into the context of the networking and connectivity requirements of popular V2I C-ITS services in terms of the PDR and end-to-end delay. The remainder of this paper is organized as follows. Section 2 describes the test setup and simulation environment. In Section 3, the experimental results are presented and discussed. Section 4 provides the final conclusions.
## 3 Results and Discussion
### Estimation of Bearing Capacity Using DNN

Table 5: Training conditions for the deep neural network. The performance on the train set was improved dramatically and continuously until approximately 1800 epochs, whereas the performance of the validation set improved to certain epochs and then degraded due to the overfitting. The results of the analysis indicated that a trained model with two hidden layers with a node of 10, batch size of 20, drop rate of 0, and epoch of 2000 provides the most accurate result. Therefore, the final deep learning model was established using all data under the above-mentioned learning conditions. The comparisons of the observed and the predicted ultimate bearing capacity for the cross-validation and all data in the DNN considering the optimal structure are shown in Figure 11.  
The MAE, bias, and COV in bias for the cross-validation results of the DNN were 74.9 kPa, 0.999, and 16.0%, respectively, and the performance of the deep learning model was slightly improved because all data were used for deep learning: the MAE and bias were 62.1 kPa and 0.999, respectively. Although the MAE of the final deep learning model increased by considering the cross-validation, the cross-validation error decreased by 46.6 kPa, indicating that a robust prediction is feasible.
## 2 Coal Samples and Experiments
### CH\({}_{4}\) Diffusivity Measurement  
The CH\({}_{4}\) diffusivity measurement is based on the volumetric method [32], which was carried out as shown in Figure 1. The experimental device consists of the seal diffusion chamber, the thermostat, the gas source (CH\({}_{4}\) and He) and the data collecting system. The seal diffusion chamber includes sample cell (SC) and reference cell (RC) with high-precision pressure transducer. The thermostat, with an accuracy of \(\pm\)0.1 \({}^{\circ}\)C, is used to keep the seal diffusion chamber at the stated temperature of 30 \({}^{\circ}\)C.  
Before the adsorption and diffusion experiments, all coal samples were ground to 60-80 mesh (0.18-0.25 mm) and were dried at 105 \({}^{\circ}\)C for 24 h. Then 40 g coal samples are weighed and put into sample cells immediately. The adsorption and diffusion measurements were kept at a temperature of 30 \({}^{\circ}\)C. The procedures are as follows: first, the empty volume of the sample cell and reference cell were measured by a volume expansion method using helium (He) gas. Second, the volume of the coal matrix should be determined by the volume expansion method using helium. Both above steps should be repeated three times. Third, measurements were performed by injecting CH\({}_{4}\) with increasing pressures, and the pressure of the seal diffusion chamber at each time interval was recorded. Finally, the CH\({}_{4}\) effective diffusion coefficient can be evaluated with the unipore diffusion model [33]:  
\[\frac{M_{t}}{M_{\infty}}=1-\frac{6}{\pi^{2}}\sum_{n=1}^{\infty}\frac{1}{n^{2}} exp\left(-\frac{Dn^{2}\pi^{2}t}{r_{p}^{2}}\right) \tag{2}\]  
where \(M_{t}\) corresponds to the total mass of gas adsorbed (kg/m\({}^{3}\)) at time \(t\) (s), \(M_{\infty}\) is the total amount of gas adsorbed (kg/m\({}^{3}\)) at indefinite time, \(D\) is the effective diffusivity (m\({}^{2}\)/s), \(r_{p}\) is the diffusion path length (m). After a step change in surface concentration, this relationship may be written for the case of gas desorbing from coal particles as:  
\[\frac{V_{t}}{V_{\infty}}=1-\frac{6}{\pi^{2}}\sum_{n=1}^{\infty}\frac{1}{n^{2}} exp\left(-\frac{Dn^{2}\pi^{2}t}{r_{p}^{2}}\right) \tag{3}\]  
where \(V_{t}\) corresponds to the total volume of gas desorbed (cm\({}^{3}\)) at time \(t\) (s); and \(V_{\infty}\) is the total adsorbed or desorbed volume (cm\({}^{3}\)).  
For the very small values of time (\(t<600\) s), we can obtain the relationship among adsorption rate data, the effective diffusivity \(D_{e}\) (m\({}^{2}\)/s) and time \(t\) (s) [16].  
\[\frac{V_{t}}{V_{\infty}}=6\left(\frac{D_{e}t}{\pi}\right) \tag{4}\]  
The effective gas diffusion coefficient (\(D_{e}\)) is defined as the ratio of the gas diffusion coefficient (\(D\)) to the square of the diffusion path length \(r_{p}\): \(D_{e}=D/r_{p}^{2}\).
###### Abstract  
Recently, to simulate sound propagation inside architectural spaces at high frequencies, the application of computationally expensive wave-based numerical methods to room acoustics simulation is increasing gradually. Generally, standard room acoustics simulations in the frequency domain are performed based on the lossless Helmholtz equation. However, for acoustics simulation at high frequencies, consideration of the sound attenuation effect caused by air absorption is an aspect to increase the reliability of predictions. Although a simple approach based on the lossy Helmholtz equation is available to include the air absorption effect in the frequency domain, its accuracy and efficiency are still not discussed well. This paper presents an accuracy and efficiency estimation of FEM based on the lossy Helmholtz equation via two numerical problems in two dimensions: a plane wave propagation problem up to 20 kHz in a long duct with 1 km length and a sound propagation problem in a real-scale office up to 6 kHz. Results revealed that the lossy Helmholtz equation-based FEM can include the air absorption effect accurately up to 20 kHz. Moreover, a possibility of providing a higher computational efficiency at higher frequencies is suggested when the magnitude of the pure-tone sound attenuation coefficient is large.  
air absorption; complex wave number; finite element method; lossy Helmholtz equation; frequency domain; room acoustics simulation +
Footnote †: journal: Applied  
Acoustics Simulation Using a Frequency Domain FEM with Air Absorption: 2D Study.
## 2 Electrical Modeling of BTS in Free Space  
The BTS comprises the uplink FSK signal channel and downlink telepowering signal channel. It facilitates the bidirectional wireless transmission of energy and information via an alternating electromagnetic field generated between the OAU and the balise. In free space conditions, the medium is considered non-conductive. The center frequency of the uplink signal is 4.234 MHz. The present study focuses on the uplink signal transmission process of the balise as a representative case for subsequent analysis.
## 2 Simulation
### Simulation Workflow  
Figure 1 illustrates the workflow employed to simulate the dissolution of carbonate rocks during the CO\({}_{2}\) injection and sequestration process. This simulation begins with the assignment of initial property values to the geological model, encompassing parameters such as initial reservoir pressure (\(P_{\text{ini}}\), MPa), initial reservoir porosity (\(\Phi_{\text{ini}}\), %), initial pore volume (\(V_{\text{por}}\), in m\({}^{3}\)), time interval (\(\Delta t\), s), residual brine saturation (S\({}_{\text{w}}\), m\({}^{3}\)/m\({}^{3}\)), pore or fracture radius (\(r\), m), and CO\({}_{2}\) injection rate (\(q_{\text{ini}}\), mol/min). Specifically, \(P_{\text{ini}}\), \(\Phi_{\text{ini}}\), and \(V_{\text{por}}\) are standardized across all simulation scenarios at 1 MPa, 10%, and 10 m\({}^{3}\), respectively, with the assumption that the residual gas in the carbonate reservoir is methane. Distinct values for pore radius (\(r_{\text{Por}}\), m), fracture radius (\(r_{\text{f}}\), m), and CO\({}_{2}\) injection rates for each simulation scenario are provided in Table 1. Residual brine saturation is determined through equations [2; 3; 4] for pore-dominant and fracture-dominant reservoirs.  
Figure 1: The flowchart to describe CO\({}_{2}\) injection and carbonate rock matrix dissolution.  
Upon each CO\({}_{2}\) injection into the reservoir, the simulation updates the quantity of CO\({}_{2}\) in the pore space (\(N_{\text{CO}_{2}\text{,gas}}\)). Subsequently, the injected CO\({}_{2}\) starts to dissolve into the residual brine, and the dissolved CO\({}_{2}\) in the brine accounts for the brine volume (\(V_{\text{w}}\)) change in response to CO\({}_{2}\) concentration change, which is characterized by using the swelling correlation. The pressures of CO\({}_{2}\) in both gas and liquid phases are updated employing the Peng-Robinson equation of state (P-R EOS) and Henry's law, respectively. The equilibrium between these phases is assessed by comparing their pressure differences. If the difference falls below a threshold of \(\sigma\) = 0.001 bar, the system is deemed at equilibrium, and then the system is prompted to calculate the CO\({}_{2}\)-brine-rock matrix interaction rates. If not, 0.1% of CO\({}_{2}\) from the gas phase is dissolved into the liquid phase for another comparison, continuing until equilibrium criteria are met.  
The acidic brine, resulting from dissolved CO\({}_{2}\), reacts with the carbonate rock matrix, where the reaction rate is calculated using Toughreact's model as referenced in the previous section. This reaction updates the concentrations of dissolved Ca\({}^{2+}\) (\(N_{\text{Ca}^{2+}}\), mol) and CO\({}_{2}\) (\(N_{\text{CO}_{2}\text{,water}}\)) and calculates the enlargement of pore space due to CO\({}_{2}\)-brine-rock matrix interaction. This procedure is repeated at an incrementing time \(\Delta t\) of 10 min at each iteration until a continuous injection period of 72 h is concluded. Upon reaching this end condition, the simulation compiles outcomes, including changes in reservoir pressure, space volume, CO\({}_{2}\) concentration, and porosity.
# Robust U-Nets for Fetal R-Peak Identification in Electrocardiography
## 2 Methods
### Data Pre-Processing  
This first stage processes and prepares both training and testing data for subsequent stages. First, the fetal ECG signal is downsampled to 250 Hz to mitigate heightened computational costs without substantial benefits. Lower sampling rates are considered sufficient for most clinical applications and are more practical in terms of data storage and processing requirements. Furthermore, [PERSON] et al. found that the effective field of view of units in CNN-based networks can be significantly increased by reducing the sampling rate [22].  
The second step is to generate pulse train maps based on true peak annotations, where each pulse shares a uniform width and is centered on the R-peak position. Figure 2 provides  
Figure 1: The proposed three-step UNet-based fetal R-peak detection method.  
an illustrative example of a raw fetal scalp ECG signal (blue) and the corresponding generated pulse train plot (black). The pulse train uses a label of one for each R-peak region, and zeros for other sample points. [PERSON] et al. reported that the average range of fetal QRS complex duration is between 54.72 ms (manual measurement) and 58.34 ms (algorithmic measurement). At a sampling frequency of 250 Hz, this range corresponds to 13.68 to 14.50 samples [23]. Therefore, a pulse width of 15 samples was chosen. This approach not only highlights the peak region but also helps to achieve data balance, thereby mitigating potential challenges related to label imbalance.  
The final step is segmentation, employing a fixed sliding window of 1000 samples (equivalent to four seconds at a frequency of 250 Hz), with a shift of 200 samples. Baseline fetal heart rate typically ranges from 110 to 160 beats per minute (bpm), with an observed decrease with advancing gestational age, resulting in higher heart rate in early pregnancy compared to later stages [24]. Therefore, the selected segment length of 1000 samples used in this study allows for encapsulating approximately 7-10 heartbeats. Furthermore, the chosen shifting length of 200 samples is selected to be short enough to enhance the ergodicity of model training while long enough to reduce training time and ensure ample new information is available in each segment to yield an effective model. The processed segments of fetal ECG data and corresponding pulse train maps were then used in subsequent training and testing stages.
# Circular Regression Applied to GNSS-R Phase Altimetry
## 6 Conclusions  
In this article, we present an innovative signal processing approach to ground-based GNSS-R phase altimetry. This approach is based on an original interferometry instrument providing phase delay observations that have a linear evolution in a certain time reference frame. The slope of this linear evolution provides a direct measurement of the height to estimate. In our approach, the phase observations are processed as angles, defined in the circular domain, and the measurement noise is assumed to follow a von Mises distribution. We accordingly derive a linear-circular regression estimator of the phase slope in the maximum likelihood sense. In an experimentation on synthetic data, we evaluate the proposed technique in terms of accuracy as a function of the satellite elevation span and of the signal carrier to noise ratio. We show that centimeter accuracy can be reached in common cases for a period of observation of 100 s. In an experimentation on real data, an original hardware receiver architecture is proposed. This architecture, based on the use of an optic fiber delay line, allows the separated recording of direct and reflected signals on a classic, inexpensive mono-channel receiver. The experiments on real data show that although GNSS-R signals are affected by external interferences that were not modeled, the centimeter accuracy can be obtained with the proposed estimator through various methods. When only one satellite footprint is available, a continuous observation of 10 min allows obtaining the decimeter accuracy. In that same context, a discontinuous observation of 1 min of data points over 20 min allows obtaining centimeter accuracy. This implies that the proposed altimetry technique can be used efficiently in the case of data losses. When several satellite footprints are available, two in the presented experimentations, the proposed estimator can fuse the available data and reach 1-cm accuracy for a 30-s continuous observation.  
This work has been funded by the Pole metropolitan Cote d'Opale de la region Hauts-de-France.  
[PERSON] and [PERSON] proposed and derived the circular-linear regression and its application to GNSS-R phase altimetry. [PERSON] and [PERSON] carried out the experiments on synthetic data. [PERSON], [PERSON] and [PERSON] designed and performed the experiments on real data. [PERSON] analyzed the data. All authors were involved in the writing of the paper.  
The authors declare no conflict of interest.
# Polarization Radiometric Calibration in Laboratory for a Channeled Spectropolarimeter
## 1 Introduction
### Principle of the Fieldable Channeled Dispersive Imaging Spectropolarimetry
\right], \tag{4}\]  
where \(A_{fore}(\sigma,\theta)\) and \(A_{imag}(\sigma,\theta)\) are the transmittance of the fore-optics and imaging optics, respectively. \(D_{fore}(\sigma,\theta)\) and \(D_{imag}(\sigma,\theta)\) are the diattenuation of the fore-optics and imaging optics, respectively. \(\delta_{fore}(\sigma,\theta)\) and \(\delta_{imag}(\sigma,\theta)\) are the retardance of the fore-optics and imaging optics, respectively. Similarly, the dispersive imaging spectrometer can be regarded as a reflector, which is composed of many reflecting interfaces. The equivalent Mueller matrices at the field of view \(\theta\) is given by  
\[M_{spec}(\sigma,\theta)=A_{spec}(\sigma,\theta)\cdot\left[ \begin{array}{cccc} 1&D_{spec}(\sigma,\theta)&0&0\\ D_{spec}(\sigma,\theta)&1&0&0\\ 0&0&-1&-\delta_{spec}(\sigma,\theta)\\ 0&0&\delta_{spec}(\sigma,\theta)&-1\end{array} \right], \tag{5}\]  
where \(A_{spec}(\sigma,\theta)\), \(D_{spec}(\sigma,\theta)\) and \(\delta_{spec}(\sigma,\theta)\) are the transmittance, diattenuation and retardance of the dispersive imaging spectrometer, respectively. For the fieldable CDISP, these polarization properties increase with the increase of field angle, so they will affect the measurement results and can not be ignored. In this system, the slit is placed along the X-axis, and only the field of view in X-direction exists. The azimuth of incident plane of each viewing chief ray is a fixed value of \(0^{\circ}\), not a variable. Therefore, the Mueller matrices of sub-module would not be affected by azimuth angle.
## 3 Results and Discussion
### Electrochemical Characterization
A clear relaxation process that corresponds to a semicircle in the Nyquist plot for frequencies higher than 4 kHz (fmax: 1-2 MHz) can be observed in Figure 7c for the three solution/sample systems (s/s), which corresponds to the parallel association of a resistance (R, associated to charge transport) and a capacitor (C, associated to charge adsorption), and it is in agreement with Nyquist plots reported in the literature for porous systems, where the contribution of the electrolyte solution between the electrodes and the solid surface is also included [43]. The fit of these values allows us to estimate the electrical resistance and capacitance associated to each solution/sample system [43; 60; 61], and these values are indicated in Table 5. The higher electrical resistance and capacitance values exhibited by the NPAS-Sf sample are associated to its more compact structure, with lower content of NaCl solution ions (mobile charge), while the increase of solution percentage in the sample increased its conductivity (electrical resistance reduction). It should be indicated that similar impedance curves, with a unique  
Figure 7: Impedance curves for the electrode/0.002 M NaCl solution/NPAS/0.002 M NaCl solution/electrode system. Nyquist plot (a); Bode plots: (**b,c**). NPAS-Sf (\(\bullet\)), NPAS-Ph (\(\blacktriangledown\)), and NPAS-And (\(\circ\)). \(\omega=2\pi\)f.  
semicircle for the sample/solution system, were previously reported for two commercial inorganic membranes (a fibrous stainless steel structure as support covered by alumina nanoparticles plus an external layer of ZrO2 nanoparticles [62], which have 80-100 \(\upmu\)m of nominal thickness, 25 nm or 100 nm pore size, and 25% or 45% porosity [63]) and even for a regenerated cellulose nanoporous membrane (2.5-3.0 nm pore size and (56 \(\pm\) 2) \(\upmu\)m thickness [39]) mainly due to the high hydrophilic character of the polymer; however, three separated contributions were obtained by analyzing the Nyquist plots obtained for a solution/nanofiltration membrane, which corresponded to dense and porous sublayers of the composite polyamide/polysulfone nanofiltration membrane, respectively, and there was the contribution of the electrolyte solution placed between the electrode and the membrane surface [64], due to the very thin (100-150 nm) and compact structure of the dense layer needed for water desalination (-98% NaCl rejection). All these measurements were performed with the same equipment as those used in this work.
## 3 Network Scenario
### Packet-Switched Xhaul Access Network  
The considered implementation of the packet-switched Xhaul access network is based on the NGFI architecture presented in the IEEE 1914.1 specification [7]. The NGFI allows for separating the DU and CU processing entities from the RUs associated with the antenna  
Figure 1: Network slicing in a packet-switched Xhaul network: example of DU/CU placement and routing of FH/MH flows for eMBB and URLLC slices.  
sites and their placement at processing nodes spread over the network. These processing nodes can be either small DCs or processing pool (PP) nodes dedicated to DU/CU processing; in this paper, the term PP represents any of the two options. We assume that the capacities of PPs are limited, and the DU/CU processing at PPs involves some load reflecting the CPU and memory usage [49]. Additionally, joint DU processing at the same PP for a group of RUs (cluster) is considered to effectively implement multi-cell coordination mechanisms [50].  
The NGFI architecture specifies that the RAN elements are connected using a packet-switched Xhaul transport network responsible for routing Xhaul (FH/MH) flows between the RUs, radio processing nodes, and a central node (hub) of the RAN. The packet-switched network uses TSN Ethernet switches, according to the IEEE 802.1 CM specification [8].
# Research on Vibration Comfort of Non-Motorized Lane Riding Based on Three-Axis Acceleration
## 2 Analysis of Factors Affecting Riding Vibration Comfort
### Road Characteristics
#### 2.1.2 Influence of Roadway Cross Section on Cycling Vibration Comfort  
The non-motorized roadway cross section usually includes lanes, sidewalks, and medians. The presence or absence of dedicated lanes significantly impacts riding smoothness; dedicated lanes facilitate stable travel, reducing acceleration fluctuations. On-street parking also affects riding conditions, as non-motorized vehicles often navigate alongside motorized traffic or sidewalks. The interaction of these elements creates four cross-section types [27]: (A) shared lanes without sidewalks or dedicated non-motorized lanes; (B) non-motorized lanes without sidewalks, separating motorized and non-motorized traffic; (C) sidewalks without non-motorized lanes, using elevation differences for separation; and (D) both sidewalks and non-motorized lanes, using physical barriers for segregation. With increasing urban road service levels, types C and D are more prevalent. Hence, this study identifies the presence of non-motorized lanes, segregation, and on-street parking as potential factors affecting riding vibration comfort.