esa-sceva/satcom-chunk-collection huggingface dataset:
# Probabilistically-Shaped DMT for IM-DD Systems with Low-Complexity Fast WHT-Based PDSP
## 4 Results and Discussion
### Spectrum Analysis  
The electrical spectra of the transmitted signals for original, digital Preq, WHT-based FDSP and fast WHT-based PDSP schemes are shown in Figure 5a-d, respectively. For the digital Preq scheme, to resist high-frequency attenuation, the mapped QAM symbols are multiplied by a pre-equalization factor via round-trip feedback. Therefore, as shown in Figure 5b, its high-frequency power is relatively high.  
After 50 km SMF transmission, the spectra of the received original, Preq, WHT-based FDSP and fast WHT-based PDSP precoded signals are shown in Figure 6a-d, respectively. Except for the Preq scheme, the high-frequency components of the spectra show obvious attenuation (see Figure 6a,c,d) owing to the low-pass filtering induced by the optical/electrical devices and fiber dispersions. The flat signal  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multicolumn{2}{c}{**Value**} & \multirow{2}{*}{**Unit**} \\ \cline{2-3} \cline{1-1}  & **FDSP** & & **PDSP** \\ \hline Modulation format & & 64 QAM & - \\ IFFT/FFT size & & 256 & points \\ Data SCs & & 96 & - \\ Data SCs for Precoding & 96 & 64 & - \\ CP length & & 8 & - \\ TS per frame & & 1 & - \\ DMT symbols per frame & & 900 & - \\ Clipping ratio (CR) & & 13 & dB \\ Bandwidth & & 4.5 & GHz \\ \hline \hline \end{tabular}
\end{table}
Table 2: Some key parameters of the precoded PS-DMT frame.  
Figure 5: Spectral of the transmitted signals with (**a**) original scheme, (**b**) Preq scheme, (**c**) WHT-based FDSP scheme and (**d**) Fast WHT-based PDSP scheme.  
also indicates that the Preq scheme can compensate for the high-frequency attenuation of the PS-DMT signal in the IM-DD transmission system.
## 2 Related Work and State-Of-The-Art Technologies
### State-Of-The-Art Technologies
#### 2.2.2 Container Virtualization--Docker  
According to [5; 29; 30; 31], compared to virtual machines, virtual containers sharing the host operating system and also reducing management overhead are lightweight and more portable. Docker is the most well-known open source application container platform. Docker utilizes resource isolation features, such as cgroups and Linux kernels to build isolated containers [5; 6; 32; 32]. Using Docker files that describe a complete, static and executable version of an application or service including all of their other runtime dependencies, such as libraries or interpreter environments, Docker images can be created to run applications or services within Docker containers, since Docker images include all of the dependencies needed to execute applications or services within a container [6; 33; 32].  
By using Docker, the presented framework can abstract the runtime environment of workflow tasks to Docker images that run as corresponding stand-alone executables in Docker containers on the underlying computing cluster. This contributes to a modular and loosely coupled approach. Moreover, thanks to the many benefits of Docker containers and microservices technology, the framework has high scalability and extended flexibility.
## 4 Methods and Devices for Assessing Erythrocyte (RBC) Aggregation  
Aggregation of red blood cells (RBCs) is a physiological, reversible phenomenon in which the cells stack into denser structures by clumping their wide surfaces together, forming so-called \"rouleaux\". It usually occurs at low shear rates and can influence blood viscosity and flow.  
RBC aggregation is mostly associated with plasma proteins, especially fibrinogen, which promotes aggregation when present in high concentrations. In vitro, aggregation is often induced using high molecular weight dextrans [39; 40; 41; 42; 43]. Two main models explain the aggregation mechanism:  
1. Bridging model--Macromolecules bind to RBC surfaces, linking them together.
2. Depletion model--Low macromolecule concentrations near RBCs create an osmotic pressure gradient, pulling cells together.  
Though the idea was conceptually different, [PERSON] et al. (1997) [25] proposed that both mechanisms may operate together, specifically, that depletion brings cells close enough for bridging to occur. A synergistic interaction among fibrinogen, albumin, and immunoglobulin (Ig) may also contribute to aggregation.  
Normally, RBCs form linear or branched rouleaux, which can develop into 3D networks, significantly increasing blood viscosity, especially at low flow rates. RBC aggregation is crucial in microcirculation and is elevated in several pathological conditions (e.g., diabetes, thrombosis, myocardial infarction, and cerebrovascular diseases). Therefore, RBC aggregation is a key factor in determining the hemorheological properties of blood.  
Various techniques for measuring RBC aggregation have been developed and are described elsewhere [17; 26; 27].
## 3 The Proposed Approach
### Adjustment of the Reliability Model  
The reliability models addressed in this work consider the study of repair systems in which the failure rate is described by a non-homogeneous Poisson process (NHPP). This means that the time intervals between the failures are not independent, and the failure rate is described by a fault intensity function that varies with time, in general, in an increasing way. Therefore, the first step of the reliability model adjustment is the definition of an intensity function parameterization that best fits systems failure data.  
Here, we apply the traditional Power Law proposed by [PERSON] [19], which is one of the main ways to characterize the failure intensity function in Equation (1),  
\[u(t)=\frac{1}{\lambda^{\beta}}\cdot\beta\cdot t^{(\beta-1)} \tag{1}\]  
in which \(\lambda\), and \(\beta\) are the model parameters that must be estimated, with \((\lambda>0,\beta>0)\), where \(\lambda\) is the parameter of scale and can be interpreted as the time during which exactly a failure is expected to occur, and \(\beta\) is the parameter of shape and represents the variability of the expected number of faults compared to time.  
The choice of the power law to describe the failure intensity function is due to its great adjustment power to the most diverse formats by the variation of its parameters, as can be seen in Figure 1.  
Figure 1: Failure intensity function of different values in the power law parameters.  
The estimation of the parameters \(\lambda\) and \(\beta\) also results in the function of an average failure number \(\mu(t)\), which describes the accumulated number of expected failures to time \(t\), according to Equation (2).  
\[\mu(t)=\int_{0}^{t}u(t)dt=\frac{1}{\lambda^{\beta}}\cdot t^{\beta} \tag{2}\]
###### Abstract
Therefore, an approximate mathematical model is necessary to effectively represent these variations. To refine the proposed model, the equation parameters of the double S-curves and the modified Heidler function have been determined using the polynomial equations obtained through curve fitting as a reference. These parameters were optimized using the grey wolf optimization (GWO) algorithm to achieve the best fit for the polynomial curves. The structure of this study consists of five sections. The second section presents the electrical circuit of the 60 MVA EAF, along with its single-phase equivalent model and time-domain methods for the V-I characteristics commonly employed in the literature, such as the exponential, hyperbolic, exponential-hyperbolic, and nonlinear resistance models. The third section explains the stochastic mathematical approach of the proposedV-I characteristic model, and the implementation of the GWO algorithm. The fourth section evaluates the static and dynamic properties of various arc models, assessing their harmonic performance and comparing them with real-time measurements. The fifth section summarizes the key findings and emphasizes the practical applications of the proposed methodology.
## 3 MIMO UWB Antenna
### Antenna Array with MEBG  
To enhance the MC effect between the antenna array, a MEBG was used as a decoupling structure. Figure 6 presents the structural layout of the UWB MIMO antenna with the MEBG. The initial patch antenna structure's dimensions were selected to achieve a resonance frequency of 6.85 GHz, corresponding to the UWB's center frequency. A decoupling structure of a modified EBG was used between the two antennas, with each antenna having its MEBG behind the radiation element connected to the ground plane. In Figure 7, the variation of the S-parameter (S\({}_{11}\) and S\({}_{12}\)) of the decoupling structure with various  
The performance of the antenna was examined using the S-parameter, surface current distribution, gain and voltage standing wave ratio (VSWR), radiation pattern, and ECC. The ECC can be estimated from the S-parameter using the expression given in Equation (13) [38].  
\[\text{ECC}=\frac{\left|\text{S}_{11}^{*}\text{S}_{12}^{*}+\text{S}_{21}^{*} \text{S}_{22}^{*}\right|^{2}}{\left(1-\left|\text{S}_{11}\right|^{2}-\left| \text{S}_{12}\right|^{2}\right)\left(1-\left|\text{S}_{22}\right|^{2}-\left| \text{S}_{21}\right|^{2}\right)} \tag{13}\]  
where S\({}_{11}\) denotes the power reflected from the input port of the antenna, S\({}_{12}\) is the power transmitted from the output port, S\({}_{21}\) is the power transmitted from the input port, and S\({}_{22}\) denotes the power reflected from the output port. To assess the impact of the decoupling method on the performance of the antenna, an analysis was also carried out for the antenna before the MEBG decoupling structure was introduced. The antenna design was carried out using HFSS modeling tools on a dual-core computing system.
# Method of Suppressing Rayleigh Waves Based on the Technology of Time-Domain Differential Detection
## 1 Introduction
Existing methods for suppressing Rayleigh waves rely on the practical effectiveness of establishing mathematical models and systematically revealing the time-frequency distribution characteristics and suppression mechanisms of multi-channel Rayleigh waves. However, significant elastic differences often exist between the positions of detectors in seismic exploration areas in practice. Therefore, fully considering the individual characteristics of Rayleigh waves in local and global distributions during the suppression process is challenging. This leads to related processing methods or algorithms lacking universal practical operability and being susceptible to human factors. Generally, the frequency dispersion of Rayleigh waves over adjacent small channel distances within 1 m is relatively minor. Two detectors are preset at adjacent small channel distances at each detection point. In the recordings, the travel times of the Rayleighwave and the underlying interface reflection wave differ, with the former being longer and the latter shorter. By applying time-shift differential processing when the temporal offset matches the Rayleigh wave arrival times between adjacent detectors, subtracting the two recorded signals can effectively suppress the Rayleigh wave. However, due to excessive phase shifts, the reflected wave signals are damaged but remain, which can be directly utilized later or repaired through deconvolution algorithms. This surface wave suppression strategy is flexible, does not rely on signals from other points, and efficiently suppresses surface waves at independent detection points. This study discusses the theoretical and experimental aspects of the differential detection method, verifying its effectiveness and universal applicability.
# Efficient Route Management Method for Mobile Nodes in 6 TiSCH Network
## 4 Performance Analysis
### Scheduling Performance Analysis
#### 4.2.1 Communication Stability Analysis  
To compare and analyze the communication stability of the network in which moving nodes exist, the distributed scheduling MSF and Orchestra scheduling techniques proposed by the 6 TiSCH working group were compared by implementing the 6 TiSCH simulator.  
As the first experiment to analyze the communication stability of scheduling, we compare and analyze the latency of each scheduling technique. Figure 8 shows the result of the latency of packets generated by a mobile node to a parent node, and Orchestra, a competitive-based scheduling technique between nodes, has significant latency. Mobile-real-world implementation algorithms with root node fixed paths have the least latency.
## 2 Materials and Methods
### Determination of Molecular Weight of PWPs  
The mass average molar mass (Mw) and number average molar mass (Mn) were evaluated by high pressure size exclusion chromatography (HPSEC) coupled with three detectors: Multi-angle laser light scattering detector (MALLS, Mini-DAWN, Wyatt Technology Corp., Santa Barbara, CA, USA), Differential refractive index (DRI) detector (RID-10 A, Shimadzu, Duisburg, Germany) and UV-vis detector (SPD-20A, Shimadzu, Duisburg, Germany). The HPSEC line consisted of an SB-G guard column and three columns in series (SB-806 HQ, SB-804 HQ and SB-803 HQ). The system was eluted with NaNO\({}_{3}\) 0.1 M and NaN\({}_{3}\) 0.5 mM, filtered through a 0.2 \(\mu\)m, 47 mm membrane filter (Anotop 47, Whatman, Maidstone, England), and carefully degassed. PWPs (10 mg) was previously solubilized in 10 mL of the elution phase under stirring for 24 h and then filtered through a 0.2 \(\mu\)m syringe filter (Anotop 10, Whatman, Maidstone, UK). The solution was injected through a 100 mL full loop and the elution was performed with a flow rate of 0.5 mL/min. Data were evaluated using ASTRA software.
## 5 Experimental Results and Analysis
### Data Source and Preprocessing  
The data for this experiment are mainly divided into two categories. Firstly, they were collected by an expressway information technology company in a certain province through the ETC gantry system, with a total of 1581 and 2289 pieces of ETC transaction data on 1 May and 1 June 2021, respectively. The original transaction data table contains 103 fields, recording information about various parts of the vehicle and gantry, including multiple information fields such as license plate number, gantry ID, transaction data, time, and gantry longitude and latitude. The parts are shown in Table 6. The * in the table was desensitized due to the privacy concerns of the data used in our experiment.  
Secondly, the Gaode Map API was used to crawl the distance of sections and generate topology relationship data of expressway ETC gantries, which included ETC gantries of different sections and the actual section distance. Some attributes are shown in Table 7.
## 2 Materials and Methods
### Experimental Design
#### 2.1.1 Soil Amendments  
Soil amendments applied in the experiment included nine exogenous organic or mineral materials (Zabrze compost-ZC, GWDA compost-GC, Belchatow biosolids-BB, Grabow biosolids-GB, calcium phosphate-CP, iron oxide-IO, bentonite-BE, rock waste-RW, limestone-CC), characterized by different properties, origins, and potential for limiting the availability of TE in soils.  
Detailed description of the applied soil amendments, as well as of the dose of their application, is provided below, while the physicochemical properties of the multicomponent amendments are summarized in Table 1:  
1. Zabrze compost (ZC): Produced in Zabrze in pile-composting process, based on a range of biodegradable-waste types (green waste, kitchen waste, and municipal biosolids) (dry compost dose equal to 10% of soil mass);
2. GWDA compost (GC): Produced by GWDA Pita in a pile-composting process using mixture of biosolids, food waste, municipal green waste, and agricultural biodegradable waste as a substrate (dry compost dose equal to 10% of soil weight);
3. Belchatow biosolids (BB): Produced in a municipal sewage treatment plant in Belchatow (dry sludge dose equal to 10% of the soil mass);
4. Grabow biosolids (GB): Produced in the small municipal sewage treatment in Grabow on Pilica river (dry sludge dose equal to 10% of soil mass);
5. Calcium phosphate (CP): Added to soil as reagent-grade calcium phosphate CaHPO\({}_{4}\) (dose of phosphorus equal to 2% of soil weight);
6. Iron oxide (IO): Added to soil as amorphous iron oxide in the form of reagent grade Fe(OH)\({}_{3}\) (a dose of iron equal to 2% of soil weight);
7. Bentonite (BE): A fossil aluminium phyllosilicate clay, consisting mostly of montmorillonite (dose of dry matter equal to 5% of soil weight);
8. Rock waste (RW): Rock waste from mining process with a high content of clay minerals (dry matter dose of material equal to 10% of soil mass);
9. Limestone (CC): Added to soil as reagent-grade calcium carbonate (a dose of calcium carbonate equal to 10% of the weight of the soil).  
Rates of amendments were selected based on those applied for remediation studies of heavily metal-contaminated soils in the past [2; 10; 19]. We also adopted the principle that amendment doses cannot create physically difficult conditions in the soil. Rates of all organic amendments were unified to 10% of the soil weight. CP and IO were added as doses equivalent to approx. 4 t ha\({}^{-1}\) of P and Fe, respectively. Bentonite and RW rates (5 and 10% of soil weight, respectively) were set arbitrarily, taking into account the clay content in these materials, which was much greater in bentonite.
## 3 Methodology
### DESTformer Architecture  
In this section, a detailed description of the overall architecture of DESTformer is provided as shown in Figure 1. Combining the idea of time series decomposition, improvements are made to transformer, which includes a frequency domain transform module, a multi-view attention mechanism, a multi-scale attention mechanism, and corresponding encoders and decoders.
## 5 Numerical Simulation  
### Methodology  
Numerical simulations were performed using the nonlinear package of ABAQUS software (Abaqus, Version 6.14) for comparison with the experimental results. The impulsive pressure loadings obtained from the actual free-drop tests were used as input data for the simulations. The elastic cylindrical test model was used for the experiments as well as numerical simulations.  
Figure 16 shows that the cylindrical test model was modeled using the four-node shell element (S4R) from the ABAQUS element library. Five integration points were used through the thickness, and the default values of hourglass controls were used for this element. Convergence simulation was performed for several mesh sizes. The mesh sizes of each test model were determined to be approximately five times its thickness, as shown in Figure 16.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **In Air [Equation** & **Experimental** & **Experimental** \\  & **(2)]** & **[Drop Test]** & **[Hammer Test]** \\ \hline Deadrise Angle 10\({}^{\circ}\) & 34.8 [Hz] & 33.3 [Hz] & 34.1 [Hz] \\ Cylindrical & 34.1 [Hz] & 33.3 [Hz] & 35.5 [Hz] \\ \hline \hline \end{tabular}
\end{table}
Table 6: Natural frequency from theoretical calculation, free-drop test, and hammer test.  
Figure 15: Hammering test result for cylindrical model (**left**) with deadrise angle of 10\({}^{\circ}\) (**right**).  
The contact between the test model and support frame was expressed using the \"surface-to-surface contact\" option from the in-contact library employed in ABAQUS. The penalty and \"hard\" contact methods were used to define the tangential and normal interaction behaviors of any possible self-contact among the model parts during impact. The friction coefficient between the model and support frame was set at 0.2, as proposed by [24]. The acting pressure value was obtained from the experiment, and the time of pressure acting differed from P1 to P4, as shown in Figures 5 and 16.  
The test model and the frame to which it is connected are modeled to be fully fixed, as is the case with the actual experiment. As shown in Figure 16, the top of the model was modeled to be in a fully fixed state, and a tie contrast technique was used to achieve this.  
Numerical Prediction Results for Model with Deadrise Angle of 10\({}^{\circ}\) and Cylindrical Model  
Nonlinear dynamic analysis was performed using ABAQUS tool. Figure 17 shows the result of the comparison between the experiment and numerical simulation in terms of strain. It was confirmed that the results obtained from the experiment are consistent with those obtained from the numerical simulation. The measurement point of strain was the point of S1, as shown in Figure 5, which is located 100 mm, in the both horizontal and vertical directions, from the center. The result of the simulation is also the strain result at the same location.  
Figure 16: Finite element model of the cylindrical model (**left**) with deadrise angle of 10\({}^{\circ}\) (**right**).  
Figure 17: Comparison of strain values obtained from experimental results and numerical simulation.
# Quasi-Passive Resistive Exosuit for Space Activities: Proof of Concept
## 3 Experimental Evaluation
### Overall Assessment  
#### 3.2.1 Experimental Protocol  
One healthy subject (male, 30 years old, 1.7 m tall, 70 kg) was recruited in the study. These tests were conducted in a controlled laboratory environment compliant with the experimental protocol approved by the Ethics Committee of Liguria, Italy (protocol number: 001/2019). The subject performed five couples of walking tasks wearing the Xosoft Gamma exosuit (described in Section 2.2) and without wearing it (reference condition). Before starting to walk on a treadmill, the subject performed isometric maximal voluntary contraction (MVC) three times for each investigated muscle, according to SENIAM recommendations ([PERSON] et al. 2000). The order of each walking tasks conditions (with and without the exosuit) was chosen randomly for each of the five testing days, a short period to recover from fatigue was taken between each consecutive run. The walking tests were conducted on a treadmill at a constant speed of 3 km/h (0.8 m/s) for a total duration of three minutes in each instance.  
During each test, the participant's fully-body kinematics, lower limb surface electromyography (EMG) and the exosuit's on-board sensors were recorded. The data from the three sensors (kinematic data, EMG data and exosuit's data) were synchronized. An Xsens wearable motion tracking system was used (MTw Awinda 3D Wireless Motion Tracker,  
Figure 7: Knee rotation angle by means of the LSQ algorithm (**A**) and Extended Kalman filter algorithm (**B**) using the IMUs located on the leg with parallel orientations.  
Xsens Technologies B.V. Enschede, The Netherlands) at a sampling rate of 100 Hz to acquire full-body kinematics. An 8-channel Wi-Fi transmission surface electromyograph (FreeEMG300 System, BTS, Milan, Italy) was used to acquire the surface myoelectric signals at a sampling rate of 1000 Hz. After skin preparation, bipolar Ag/AgCl surface electrodes (diameter 2 cm), prepared with electroconductive gel, were placed over the muscle belly in the direction of the muscle fibers (distance of 2 cm between the centres of the electrodes) according to the European recommendations for surface electromyography [97] and the atlas of muscle innervation zones [98]. Four bipolar electrodes were placed bilaterally on the vastus lateralis (VL) and the biceps femoris (BF) because of their role as knee extensors and flexors, respectively. More in detail, both on left and right lower limbs, the probes on VL, deputy for the knee extension, were placed at 2/3 on the line from the anterior spina iliac superior to the lateral side of the patella, while the electrodes on BF, which is responsible for flexion and lateral rotation of the knee joint, were placed at 50% on the line between the ischial tuberosity and the lateral epicondyle of the tibia as specified in the SENIAM's Recommended sensor placement procedure [99].