esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This work proposes that laser pulses can generate finite amplitude Rayleigh waves for process monitoring during additive manufacturing. The noncontact process monitoring uses a pulsed laser to generate Rayleigh waves, and an adaptive laser interferometer to receive them. Experiments and models in the literature show that finite amplitude waveforms evolve with propagation distance and that shocks can even form in the in-plane particle velocity waveform. The nonlinear waveform evolution is indicative of the material nonlinearity, which is sensitive to the material microstructure, which in turn affects strength and fracture properties. The measurements are made inside a directed energy deposition additive manufacturing chamber on planar Ti-6 Al-4V and IN-718 depositions. By detecting the out-of-plane particle displacement waveform, the in-plane displacement and velocity waveforms are also available. The waveform evolution can be characterized (i) for one source amplitude by reception at different points or (ii) by reception at one point by applying different source amplitudes. Sample results are provided for intentionally adjusted key process parameters: laser power, scan speed, and hatch spacing.  
laser generation; laser interferometer; nonlinear ultrasonics; Rayleigh waves; additive manufacturing 2022212208
## 3 System Model
Within this context, messages are modelled by a set \(\mathbb{S}\) of \(M\) message streams:  
\[\mathbb{S}=S_{1},S_{2},...,S_{M} \tag{2}\]  
Messages generated at each message stream \(S_{i}\) may be the consequence of periodic measurements of environment variables, being a message stream \(S_{i}\) characterized as follows:  
\[S_{i}=(C_{i},P_{i}) \tag{3}\]  
* where \(P_{i}\) is the period of message stream \(S_{i}\), which is not synchronised with the beacon arrivals at the sensor node;
* \(C_{i}\) is the maximum length of each message in a message stream \(S_{i}\). This parameter corresponds to the amount of time required to access the wireless channel, to transmit entirely the message, and to receive the acknowledgement, when required;
* the \(k\)-th message of a message streams \(S_{i}\) is represented by \(M_{i}^{k}\). Figure 4: Bottom-up cluster active period scheduling. Figure 3: Top-down cluster active period scheduling. As messages are forwarded through the cluster-tree, the utilisation factor imposed by a specific message stream depends on the depth of its generator node. We define \(U\) as the total effective utilisation factor imposed by set \(\mathbb{S}\):  
\[U=\sum_{i=1}^{M}\frac{C_{i}}{P_{i}}\times depth_{i}, \tag{4}\]  
where \(depth_{i}\) corresponds to the depth of the node that generates \(S_{i}\) (considering the root node to be of depth 0, according to Figure 2).
# Digital Systems in Smart City and Infrastructure: Digital as a Service
## 7 Digital Server
### Time Syncnorization  
Network Time Synchronization establishes a reference time for the entire Smart Infrastructure Digital Systems, Transmission, Servers and Management via the use or Network Time Protocol (NTP) or Precision Time Protocol (PTP) that achieves milliseconds or microseconds of Coordinated Universal Time respectively. Time synchronization is based on a hierarchical stratum level where Stratum 0 is the reference high precision timekeeping clock synchronized to atomic clocks, GPS, Global Navigation Satellite System (GNSS) or Low Frequency radio sources and lower Stratums corresponds to the Digital Transmission equipment topology.
## 6 Discussion
### Innovative Aspects of the Proposed Method  
The proposed method introduces several innovative aspects in the prioritization of failure modes within FMEA. First, this study presents for the first time a combination of FMEA, ROC, and the CoCoSo method to improve the prioritization of failure modes. The hypothesis that this integration would lead to improved prioritization was confirmed by the promising results of the study. As far as the authors are aware, this particular combination has never been explored before, which is an important contribution to this topic.  
Furthermore, the integration of the ROC method with the CoCoSo model represents a novel approach that has proven to be excellent in differentiating the weighting of FMEA variables. The ability to assign weights based on the order of importance of the FMEA variables proved to be an extremely practical method. This practicality is particularly valuable because it remains effective regardless of the number of FMEA variables involved, ensuring scalability and adaptability for different assessment contexts.  
Furthermore, the integration of FMEA variables as decision criteria in the CoCoSo model represents a novel methodological approach that creates a seamless link between risk assessment and multi-criteria decision making.  
Another important contribution of this study is the adaptation of the FMEA scores determined for each variable within the CoCoSo method to create the decision matrix. Thisprocess cannot be executed directly as it requires an in-depth analysis of the scope of each FMEA variable and an adjustment of their scores according to the benefit-cost paradigm used in the CoCoSo method. The identification of this need and the solution developed for it represent a valuable addition to the methodological contributions of this work.  
By introducing these methodological innovations, the proposed approach successfully addresses the key challenges associated with traditional FMEA methods while providing a structured, adaptable, and effective solution for prioritizing failure modes. These contributions emphasize the originality and practical relevance of the study.
# Reflection Propagation Law of Electromagnetic Waves in U-Shaped Roadway
## 4 Reflected Field Strength Calculation
### Reflected Field Strength at Receiving Point
#### 5.2.2 Antenna Frequency  
Figure 15 shows the reflected field strength variation in the antenna transmitting frequency of 2.5, 4.5, and 5.2 GHz with the increase in the distance between the relay node and the receiving point. Let the distance from the relay node to the reflection plane \(m_{5}\) be \(w_{3}\) = 2.5 and the distance from the receiving point to the reflection plane \(m_{6}\) be \(w_{2}\) = 2.5 m; the heights of both the relay node and the receiving point are 2 m, and the receiving point is moved in the same way as in Figure 14.  
Figure 15 shows that the reflected field strength at the receiving point shows an overall attenuation trend as the distance between the relay node and the receiving point increases. When the distance between the relay node and receiving points exceeds 10 m, due to the presence of a corner, the attenuation of the reflected field strength at the receiving point at the frequency of 5.2 GHz is higher than that at the frequency of 2.5 and 4.5 GHz, which is consistent with the conclusion from previous studies [29].
## 7 Performance Evaluation
### Communication Cost

Table 3: Comparison of functionalities and security properties. block of a symmetric encryption or a symmetric decryption has a length of 256 bits. The size of an identity, a password and a biometrics value is 128 bits. The size of a random number or a hash value is 160 bits. A single elliptic curve point multiplication operation has a length of 320 bits. The size of each timestamp is 32 bits. The total communication rounds and the length of all transcripts conveyed in each authentication session are considered as the communication cost of the protocols. In the login and authentication phase of my proposed protocol, the transcripts include \((C_{i,m1},c_{i,m21},\ c_{i,m22},\ c_{i,m23},T_{i,m})\) and \((\theta_{m,i})\), which consume a length of (320 bits + 3*160 bits + 32 bits) and 256 bits, respectively. The total length is 1088 bits. In addition, the protocol is executed in two rounds of communication. The costs of the remaining protocols are calculated in a similar way. For a fair comparison, I do not include the communication between \(S_{m}\) and \(S_{n}\) of the proposed protocol in the evaluation. Table 4 and Figure 5 tabulate the detailed comparison of the communication cost of different models. It is observed that my proposed work is one of the most efficient protocols. Only the work of [PERSON] et al. [22] is more efficient than mine in this evaluation. However, according to Table 3, my work provides many more functionalities and security properties compared to that of [PERSON] et al. [22].
# Opportunities and Challenges for Error Control Schemes for Wireless Sensor Networks: A Review
## 5 Review on ECS Methods on WSN
### Adaptive Technique  
We introduce channel adaptive techniques in Section 2.4 to combat a time varying channel. In this section, we review adaptive ECSs introduced by researchers for WSN and broadly classified into two areas: Single hop and Multi-hop networks.
# Laser Absorption Sensing Systems: Challenges, Modeling, and Design Optimization
## 3 Fundamentals of Different LAS Measurement Schemes
### Direct Absorption Spectroscopy  
The direct absorption method infers gas properties directly from the fractional transmission of light \(I_{l}/I_{0}\) using the Beer-Lambert relation. One of the key problems to solve in DAS is thus to determine the baseline, i.e., the light intensity \(I_{0}\) before absorption by the medium. Certain sensor systems incorporate an additional reference signal with a non-resonant laser or a broadly tunable light source to correct for the baseline [12]. However, this is at the expense of added components and may introduce potential errors from additional optical etalons. The more commonly adopted approach is to mathematically fit the baseline using non-absorbing portions within a scan.  
Two background correction strategies are often adopted, namely, the Levenberg-Marquardt (LM) fitting algorithm [49; 50; 51], and the advanced integrative (AI) fitting algorithm [52]. Both fitting algorithms involve an iterative process, and the major difference lies in the fitting speed and multi-line fitting capability. More details are summarized in Table 1 below, and here we illustrate a typical fitting procedure with an example of AI fitting algorithm.  
AI fitting process:  
Step 1: Background correction: a polynomial fit is applied to infer the background from the non-absorbing portions;  
Step 2: Retrieval of line center position \(v_{0}\), which is estimated from the two symmetrical intervals from the maximum of background-corrected absorbance curve, thus no explicit initialization is needed;  
Step 3: Retrieval of absorbance area: absorption linewidth is predetermined, and then the Voigt function is calculated;  
Step 4: Voigt fit: several numerical approaches can be used to approximate the Voigt function [53; 54] as no analytical form is available.  
Most commonly, by using a low-order (order two to four) polynomial to fit the baseline and subsequently fitting the absorbance curve with a Voigt profile through an iterative process, the gas properties can be inferred. This works well for isolated or slightly overlapped features and if the baseline is reasonably smooth and the raw spectrum has a good SNR. However, when noises from harsh environments causing severe beam steering and baseline fluctuation carry frequency components comparable to the scanning/detection bandwidth, uncertainties in the DAS detection will significantly grow. Efforts to deal with such background drift will be detailed in Section 4.
###### Abstract  
Due to the high detection efficiency of the airborne time-domain electromagnetic method, it can quickly collect electromagnetic response data for large area-wide regions, but it also brings great challenges to the inversion interpretation of the data because there are numerous survey data that need to be inverted. Conventional optimal inversion and fast imaging methods still take a long time to obtain conductivity and depth information, which affect the efficiency of real-time data interpretation. In this paper, we present a deep learning inversion method that can be used to solve the fast inversion problem of airborne time-domain electromagnetic data; the method uses a one-dimensional convolutional neural network. The network structure consists of two parts containing different numbers of convolutional and pooling layers. The training sample dataset was generated via two ways of constructing geoelectric models through forward modelling. To check the effectiveness of our deep learning inversion strategy, we tested it on synthetic data and two types of survey data. The experimental results show that this inversion method is effective and that it can be applied to airborne time-domain electromagnetic data collected using different observation systems. The proposed inversion method can obtain better inversion results for both simple and complex stratigraphic structures and requires significantly less computation time compared to conventional optimal inversion methods.  
deep learning; fast inversion; airborne time-domain electromagnetic; convolutional neural network +
Footnote †: journal: Applied sciences
# Characterization of HVSR and VRSR in the Loess Plateau of China Based on Strong-Motion Data
###### Abstract  
In recent decades, China has collected extensive strong-motion data from the Loess Plateau, which is valuable for understanding the dynamic characteristics of loess sites and the effects of site conditions on seismic motions. The horizontal-to-vertical Fourier spectrum ratio (HVSR) and the horizontal-to-vertical velocity response spectrum ratio (VRSR) are widely used to study site dynamics. This study analyzes strong-motion data from the Loess Plateau to identify key features of the HVSR and VRSR curves. The results show that these spectral ratio curves effectively capture the dynamic behaviors of loess sites, minimizing the influences of earthquake magnitude and propagation path. While the spectral ratio peaks are less affected by magnitude, epicentral distance, and focal depth, they are significantly influenced by site conditions. Conversely, the dominant periods estimated from these curves are strongly influenced by magnitude, epicentral distance, and focal depth. For sites located on the Loess Plateau, the average amplification factor is approximately 3, with a mean predominant period of 0.4 s. These results provide valuable insights into the dynamic characteristics of loess sites and have practical implications for seismic design in the region.  
loess plateau; strong ground motion data; HVSR; VRSR; amplification factor; superior period
# CFD-DEM Simulation of Dust Deposition on Solar Panels for Desert Railways
## 4 Conclusions  
In this paper, the CFD-EDM method was used to simulate the deposition process of dust (10 \(\upmu\)m) and fine sand (100 \(\upmu\)m) on a solar panel under the conditions of airflow. Different from the research of [22; 33], the soft sphere model and the CFD-EDM method were applied in this work, which was used to simulate in detail the interactions between dust and a solar panel so that the deposition and rebound behavior of the dust particles on the solar panel would be more believable than the rebound or deposition before simulation. For the deposition of dust particles, the simulation results showed that most of them bypassed the solar panel with the air flow, and few dust particles could settle on the surface of the solar panel (the deposition rate was only 4.6%), while the deposition rate of fine sand was relatively large (up to 32%). In the case of the fine sand deposition, the deposition number per second decreased with the increase in the wind speed in the case of weak and medium cohesion forces due to more rebounding particles, but for the case of strong cohesion forces, the deposition rate was basically unchanged, which was mainly because the rebound rate of fine sand reached a minimum. These results are of great scientific significance for understanding the law of dust deposition on photovoltaic panels.  
Conceptualization, [PERSON] and X.L.; software, [PERSON] and L.Q.; investigation, X.L.; writing--original draft preparation, [PERSON] and J.W.; writing--review and editing, [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
Figure 14: Comparison of the settlement amount and deposition rate of fine sand particles with different cohesion parameters and wind speeds. (**left**) Settlement amount. (**right**) Dust deposition rate.  
Figure 15: Change in light transmittance with time in dusty environment.  
**Funding:** This research was financially supported by the National Natural Science Foundation of China (12064034), the Leading Talents Project of Science and Technology Innovation in Ningxia Province in China (2020 GKLRLX08), the Natural Science Foundation of Ningxia Province (2022 AAC03117), the Department of Education Jiangsu Province | Natural Science Research of Jiangsu Higher Education Institutions of China (19 KJB560005), the Major Science and Technology Project of Ningxia Autonomous Region (2022 BDE03006), and the Fundamental Research Funds for the Central Universities, Lanzhou University (lzujbky-2022-kb08).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** All the correlated data are available on request from the author [PERSON].  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Regression Estimation Results
### Developing Countries (48)  
Estimation results of the impacts of the influencing factors and control variables on ecological consumption for the developing countries are presented in Table 10. As can be seen from Models (1) and (2), the estimation results based on the country random effects model and the country fixed effects model showed some differences. The Hausman test was conducted to select an appropriate model. The result of the Hausman Test shows that the country fixed effects model was more appropriate and should be selected.  
In Models (3) and (4), the estimated coefficients and significant extents of URB, REN, SER, INT, MYS, Ln(GNIPC), and Ln(GNIPC)\({}^{2}\) showed small differences. REN, SER, and INT had statistically significant and negative impacts on EF. Ln(GNIPC) and Ln(GNIPC)\({}^{2}\) had significantly negative and positive impacts on EF, respectively, which validated a U-shaped relationship between ecological consumption and income. According to the estimated coefficients of Ln(GNIPC) and Ln(GNIPC)\({}^{2}\) in Model (3), the turning point value of GNIPC of the U-shaped relationship was 706. The scatter plot of EF and GNIPC (Figure 5) further certifies the U-shaped relationship and the turning point. For the sample, there were about 95% of the observations with GNIPC values higher than the turning point.  
Figure 4: Scatter plot of EF and GNIPC of the developed countries.  
To sum up, for the developing countries, renewable energy consumption, service industries, and internet usage were the significant influencing factors of reducing ecological consumption; urbanization and education had no significant impacts on ecological consumption; the relationship between ecological consumption and income was U-shaped, and the EKC hypothesis was not supported.
# Combustion and Emission Characteristics of a Biodiesel-Hydrogen Dual-Fuel Engine
## 1 Introduction
The comparison study revealed that biodiesel-hydrogen combustion allowed operation with higher EGR rates without worsening of soot specific emissions (g/kWh). The operation at higher EGR rates led to significant NOx specific emission benefits of up to 64% while carbon monoxide and total hydrocarbons were also reduced. In [20] it was found that the addition of hydrogen for medium and high loads has a positive effect on engine performance, while for small loads the share of hydrogen makes unfavorable ignition conditions and it has a negative impact on engine performance. It was also observed that the hydrogen addition has a positive effect on soot and CO emission for all operating ranges. It was also found that the share of biodiesel in the combustion process with diesel fuel and hydrogen contributes to reducing the amplitude of engine block vibrations [21]. In the paper [22] focusses on the operation and specific emissions output of the dual-fuel internal combustion engine running on fully renewable fuels (biodiesel and hydrogen) and the results are compared with the conventional petroleum-derived diesel engine. Biodiesel-hydrogen operation shows significant benefits in the reduction of carbon and soot emissions but deteriorates the NOx formation compared to the conventional diesel-powered engines. Hydrogen is attracted an internal combustion engine fuel positively affects the global carbon emissions problem. In a compression ignition engine, hydrogen should be combined with a more reactive fuel to avoid misfiring and ensure smooth combustion. In most of the studies, the diesel fuel is often used as the secondary fuel. In preliminary tests, it turned out that the biodiesel powered engine was characterized by higher soot emissions than the engine powered by diesel fuel. It is known from the literature that the share of hydrogen in the co-combustion of fuels contributes to reducing soot emissions. This was one of the reasons that encouraged to check the combustion and emission process of the engine powered by biodiesel and hydrogen. The paper presents the results of co-combustion of a hydrogen with biodiesel in a compression-ignition engine. An industrial engine operated at constant load during the research. The authors have attempted to replace biodiesel with hydrogen as much as possible. The evaluation of the combustion process was analyzed, taking into account the combustion stability, the impact of hydrogen on the thermal efficiency of the engine as well as for the emission of toxic components of exhaust gas and soot. The aim of the work is to investigate the possibilities of using alternative fuels as renewable energy sources to power thermal machines, including internal combustion engines.
# Star Type Wireless Sensor Network for Future Distributed Structural Health Monitoring Applications
## 2 Materials and Methods
### Verification of Data Integrity  
Recording the swinging of a pendulum made of a 30-cm-long, 0.1-cm-thick, and 6-cm-wide plastic beam, an object of known parameter and predictable motion, the accuracy of the angle measurements and the reliability of data transmission were assessed.