esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Analysis
### Processes and Data Inputs in the Planning of Road Schedules
#### 3.1.1 Road Schedule Management Planning  
This stage involves defining the processes and data inputs that will be used to manage the schedule throughout the lifecycle of the road infrastructure project. Specifically, Stage I includes seven key processes: (1) identifying factors affecting the construction process, (2) defining procedures for incorporating additions, (3) defining work milestones, (4) defining schedule planning models, (5) defining work zones or phases, (6) defining time units, and (7) establishing schedule parameters and the overall schedule management plan. These processes are supported by various inputs, such as the planning method, project scope and terms of reference, historical databases (KPIs), technical specifications, drawings, and control criteria. The outputs of this stage include a structured planning model, milestone controls, time units, classification of work zones, and a schedule management plan, which together establish the foundation for subsequent scheduling activities. It addresses aspects related to the structure and control of the schedule, establishing the required levels of precision based on the deliverables' characteristics and the project's construction processes. Additionally, the necessary time units for planning and controlling activities are determined, which, in road projects, may include hours, days, weeks, or other units depending on the project's scale. Furthermore, the procedures for incorporating necessary additions and corrective activities for deficiencies detected in the schedule during construction are described. In terms of monitoring, the metrics for measuring schedule performance as construction progresses are established, and the criteria for updating these metrics are based on recorded progress. Depending on the organization and its protocols, the characteristics of reports detailing the schedule status at specific points in the construction process are defined. With this information, the procedures required to manage and control the schedule during the construction phase are documented, providing both the scheduling and control professionals with a clear roadmap for handling events and updates during the construction process. It is recommended that the various project stakeholders be involved in gathering their insights and observations, reducing the likelihood of schedule deficiencies and avoiding schedule and budget deviations.
# Design and Analysis of Novel Low-Cost Linear Vibration Motor for an Electronic Cigarette
###### Abstract  
The spring structure design and magnetic circuit optimization were considered to design a novel linear vibration motor with advantages of lower cost and similar motor performance compared to a coin-type vibration motor. A vibration motor is inserted in an electronic cigarette to provide haptic feedback to remind the user that the electronic cigarette is ready to operate. For the new generation of electronic cigarettes, new vibration motors require smaller size, lower cost, and acceptable motor performance. A three-dimensional finite element method has been used to find the coil's magnetic flux density and von Mises stress distribution in the spring. Moreover, the electromagnetic-mechanical coupling method has been employed to find the motor acceleration on a dummy jig. The acceleration of the vibration motor was obtained to verify the analysis results by experiment. The experiment results verified the analysis results of the linear vibration motor. Compared to a coin-type vibration motor, the novel linear vibration motor has 9.09% and 80.77% reductions in total thickness and manufacturing cost, and a 40.0% improvement in acceleration.  
linear vibration motor; electromagnetic-mechanical coupling; finite element method; electronic cigarette +
Footnote †: journal: Applied Sciences
# Development of a Digital System for Monitoring Emergency Conditions in 20 kV Distribution Networks
## 1 Introduction
### Research Aim and Objectives  
The research aim is to identify practical opportunities for automating the monitoring of ground faults in different parts of the lines based on the digitalization of the functions of zero-sequence voltage measurements, transformations, and the transmission of information to the centralized dispatcher server. The option of placing measurement nodes along the lines is considered, and this option for automating the accident monitoring process will allow obtaining the necessary information on the presence or absence of induced voltage and its parameters without personnel visiting the measurement sites. In this regard, in the course of research it is necessary to determine the methods, technologies and equipment required to generate the original electrical analog signals, perform the necessary transformations of the original analog signals into digital form, transmit digital signals via wireless communication channels, as well as receive signals and automated centralized processing of these signals in the dispatch service for their further use as intended. Obviously, when choosing wireless signal transmission technologies, it is necessary to take into account the resistance of the technology to electromagnetic interference, which in the immediate vicinity of the wires of power transmission lines has a high intensity of impact on the wireless receiving and transmitting equipment. In this regard, it is necessary to carry out modeling of the practical environment and determine the technology that meets the formulated requirements for the degree of protection from interfering influence.  
Based on the definition of the required signal transmission technology made as a result of modeling, it is necessary to select equipment available for practical use, preferably manufactured in the CIS countries. Taking into account the choice of equipment and technologies, it is then necessary to form the structure of the information network and develop a diagram of such a network.
## 4 Performance Evaluation
### Complexity Analysis  
Computational complexity is concerned with the power consumption of an algorithm performs. We calculate computational complexity as an arithmetic complexity by using the numerical number of the multiplications. Polyphase decomposition is similar to parallel computing consist of splitting computation on each polyphase component which works simultaneously. Thus, the numerical number of the multiplications in each polyphase is reduced by \(1/K\) ratio.  
We perform IDFT/DFT transformations with the using the polyphase decomposition of the prototype filter to simplify the transceiver structure. According to proposed method, the summary of the transmitter implementation is given in the following steps:  
1. \(M\) times of \(K\)-point IDFT transformations for
2. \(K\times M\) times multiplication with \(L/K\) length of the filter  
It is assumed that the \(K\) point IDFT and DFT transformation is implemented by the IFFT or FFT algorithms requiring \(K\text{log}_{2}K\) multiplications. Accordingly, the operation on (7) requires \(K\text{log}_{2}K\) number of multiplications for the each subsymbol. Additionally, \(KM\) times \(L/K\) number of the  
Figure 3: The receiver part of the hexagonal multi carrier lattice structures.  
polyphase filter coefficients (i.e., \(L\): filter length). The analysis of the computational complexity is introduced in Table 1. A comparison of the proposed hexagonal GFDM and traditional GFDM, according to the computational complexity, is given in Figure 4. Accordingly, the complexity is reduced with the using polyphase decomposition. Compared to the traditional GFDM, the difference in the number of multiplications is derived from parallel processing. The computational complexity load is shared among the subcarriers. Thus, the practical application of polyphase filters is to use \(K\) filters with \(L/K\) taps.
# Hot Topic Community Discovery on Cross Social Networks
## 3 Hot Topic Community Discovery Model
### LB-LDA Model
#### 3.2.2 LB-LDA Model Description  
Suppose given a corpus with \(M\) documents denoted by \(C=\{d_{1},d_{2},\ldots,d_{M}\}\), containing \(V\) terms denoted by \(W=\{w_{1},w_{2},\ldots,w_{V}\}\). These corpora constitute \(K\) topic labels, expressed as \(T=\{l_{1},l_{2},\ldots,l_{k}\}\). For document \(d_{m}=\{w_{1},w_{2},\ldots,w_{r}\}\), the topic labels are denoted as \(T_{m}=\{t_{1},t_{2},\ldots,t_{K}\}\), and \(t_{k}\in\{0,1\}\), which indicates the existence of the topic labels contained in the current text in the topic labels set \(T\). For example, the 1 st, 3 rd, and 7 th topic labels exist in \(d_{m}\), in the \(T_{m}\) vector, the number of digits \(t_{1},t_{3}\) and \(t_{7}\) are set to be 1, and the rest are set to be 0. Based on Section 3.2.1, the \(d_{m}\) can be enlarged to \(d_{m}\prime=\{b_{1},b_{2},\ldots,b_{C_{n}^{2}}\}=\{(w_{1},w_{2}),(w_{1},w_{3}), \ldots,(w_{r-1},w_{r})\}\). Let \(\stackrel{{\rightarrow}}{{\alpha}}\) and \(\stackrel{{\rightarrow}}{{\beta}}\) be hyper-parameters. Similar to the LDA model, LB-LDA model is a three-layer topic model including document layer, latent topic layer and word layer. In contrast to the traditional LDA model, two words in one biterm \((w_{p},w_{q})(p\
eq q)\) share one latent topic label, and the topics in latent topic layer have definite meanings. A graphical generation representation is show in Figure 4 and described as follows.  
For each topic label \(k\), \(k\in\{1,2,\ldots,K\}\)  
Generate a topic-word distribution \(\varphi_{k}\sim Dir(\stackrel{{\rightarrow}}{{\beta}})\)  
For each document \(d\prime\)  
For each topic \(k\in\{1,2,\ldots,K\}\)  
Generate \(\Lambda_{k}^{d}\in\{0,1\}\sim Bernoulli(\gamma)\)  
Generate \(\stackrel{{\rightarrow}}{{\alpha^{d}}}=L^{d}\times\stackrel{{ \rightarrow}}{{\alpha^{d}}}\)  
Generate \(\theta^{d}=(\theta_{1},\theta_{2},\ldots,\theta_{l_{Md}})\sim Dir(\alpha^{d})\)  
For each biterm \(b_{i}\) in document \(d\prime\)  
Generate \(z_{i}\in\{\lambda_{1}^{d},\lambda_{2}^{d},\ldots,\lambda_{Md}^{d}\}\sim Mult( \theta^{d})\)  
Generate word \(w_{i,1},w_{i,2}\in W\sim Mult(\varphi)\) and form \(b_{i}=(w_{i_{1}},w_{i_{2}})\)  
In this procedure, we need to explain the calculation method of \(\Lambda^{d}\) and \(L^{d}\), and this part mainly refers to L-LDA. For each document \(d^{\prime}\), we firstly get Bernoulli distribution \(\Lambda^{d}\). Then we define the vector of document's labels to be \(\lambda^{d}=\{k\Big{|}\Lambda_{k}^{d}=1\}\). Next, we define a document-label matrix \(L^{d}\) and the size is \(M_{d}\times K\), in which \(M_{d}=\Big{|}\lambda^{d}\Big{|}\). The element in \(L^{d}\) is set as Equation (1), where \(i\) means each row and \(i\in\{1,\ldots,M_{d}\}\), \(j\) means column and \(j\in\{1,\ldots,K\}\).  
\[L^{d}_{i,j}=\left\{\begin{array}{cc}1&(\lambda_{i}^{d}=j)\\ 0&\textit{other}\end{array}\right. \tag{1}\]
## 3 Results and Discussion  
The data selected for the analyses were taken from the AERONET database for AODs for clear-sky conditions (level 2.0), i.e., fully quality-assured data. In our studies, we concentrated on temporal trends of AOD variations over time at the same wavelength, \(\lambda=500\) nm (AOD_500). However, in some cases, we encountered the absence of specific data for this wavelength, and then we used values for the closest wavelengths, i.e., AOD_551 or AOD_555. The multiannual variations of AODs at particular stations are divided into two time spans, until 2011 and from 2011 on.  
Typically, an optical depth of less than 0.1 indicates clear sky conditions with maximum visibility, whereas values over 0.4 and especially those reaching 1 indicate very haz conditions. We are aware that the AOD itself is just one element of \"an atmospheric pollution puzzle\" and that many other features and parameters play an important role in precise determination of the aerosol composition, which can also enhance the study on the origins of the particles. However, in this study, we use the AODs as a measure of certain trends and particular cases of high aerosol episodes. Due to the fact that our data are scattered among different stations, which have/had operated in different time periods, accurate analyses of chemical composition are not a subject for this study.  
We have looked into additional parameters, such as single scattering albedo or asymmetry factor; however, brief analyses of the data showed significant deficiency in accurate data. Thus, the conclusions from these analyses did not provide any additional or convincing arguments. Therefore, we support our observations with information about the times and regions of wildfires in 2019, as well as the particle pathways during that period.  
However, realizing that the AODs provide us with incomplete information on the nature of aerosol particles and their impact on climate, we have also analyzed the size distribution of particles, indicating that fine mode particles were most abundant in the analyzed cases, which, together with other supporting information, indicates the highly possible presence of biomass burning aerosols in the analyzed periods.
## 3 Basic Theory of Slope Characterization Models
### Basic Theory of Micrometric Weibull Distribution Modeling of Nonhomogeneous Slopes  
Heterogeneity is one of the fundamental characteristics of rocks, attributed to their composition of various mineral particles bound together. Consequently, rocks exhibit significant differences in the physical and mechanical properties of their microscopic structures (mineral particles). This heterogeneity manifests macroscopically as non-uniformity and nonlinearity under external loading. In numerical simulations, the heterogeneity of rock microscale media can be achieved by assigning different physical and mechanical parameters to microscopic structural units (grids or cells) of rocks. The Weibull distribution can effectively reflect the macroscopic statistical regularities of rock mechanics parameters [36]. Therefore, modeling weathered rock masses by assuming that the parameters of microscopic structural units of rocks follow the Weibull distribution enables the realization of heterogeneous modeling.
## 5 Attacks on Edge-Based IoT
### Network-Level Attacks  
Distributed denial of service (DDoS) attacks and man-in-the-middle (MitM) attacks are significant network-level threats in EC-IoT environments. DDoS attacks overwhelm a network or system with traffic, rendering it inaccessible to legitimate users. In the context of EC-IoT, the decentralized nature of edge nodes can make them susceptible to DDoS attacks due to the distributed architecture [90]. These attacks can disrupt the availability of IoT services and compromise the network's reliability [91].  
On the other hand, MitM attacks occur when a malicious actor intercepts and potentially alters the communication between two parties without their knowledge. In EC-IoT networks, where devices are wirelessly connected to the edge of the network, attackers can exploit the wireless nature of the communication to carry out MitM attacks [92]. These attacks can lead to unauthorized access to sensitive data transmitted between IoT devices and the edge network, posing a severe security risk [21].  
Both DDoS and MitM attacks exploit vulnerabilities in the network infrastructure of EC-IoT systems. DDoS attacks target service availability by flooding the network, while MitM attacks focus on intercepting and manipulating data in transit. Understanding these attack vectors is crucial for developing robust security measures to safeguard EC-IoT environments against malicious activities.  
Figure 3: Edge-based IoT attack model with examples of each sub-model.
# Research on Semi-Active Vibration Control of Pipeline Based on Magneto-Rheological Damper
## 4 Experimental Study on Semi-Active Vibration Control
### PID Vibration Control Experiment  
When the sinusoidal excitation frequencies are 1 Hz, 5 Hz, and 10 Hz, the vibration response test results of the pipeline under the PID vibration control are shown in Figure 15. When the vibration control of the pipeline is controlled by the PID vibration control, the statistical results of the vibration response of the pipeline under different excitation frequencies are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Frequency** \\ **(Hz)** \\ \end{tabular} } & \multicolumn{2}{c}{**Displacement (mm)**} & \multicolumn{3}{c}{**Acceleration (m/s\({}^{2}\))**} \\ \cline{2-7}  & **RMS (mm)** & **Difference (dB)** & **Attenuation** & **RMS (m/s\({}^{2}\))** & **Difference (dB)** & **Attenuation** \\ \hline
1 & 0.1047 & 21.9312 & 91.99\% & 0.0895 & 5.7194 & 48.24\% \\
2 & 0.1657 & 18.9410 & 88.70\% & 0.1928 & 9.5454 & 66.68\% \\
3 & 0.2879 & 14.5904 & 81.36\% & 0.6580 & 4.4697 & 40.23\% \\
4 & 0.2229 & 17.0728 & 85.99\% & 0.5958 & 9.1858 & 65.27\% \\
5 & 0.1705 & 19.8068 & 89.78\% & 0.6861 & 11.3419 & 72.90\% \\
6 & 0.2003 & 18.5174 & 88.14\% & 0.7711 & 12.6486 & 76.69\% \\
7 & 0.2805 & 15.9560 & 84.07\% & 1.3702 & 9.9163 & 68.07\% \\
8 & 0.1491 & 20.7619 & 90.84\% & 0.8985 & 14.5449 & 81.26\% \\
9 & 0.2730 & 14.9836 & 82.18\% & 1.9606 & 8.9993 & 64.52\% \\
10 & 0.1973 & 17.7867 & 87.10\% & 1.4246 & 12.2016 & 75.46\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Vibration response of pipeline under two-state pipeline vibration control.  
Figure 15: _Cont._  
As shown in Table 3, when the vibration of the pipeline is controlled by the PID control, the displacement and acceleration of the pipeline are significantly reduced. As shown in Table 3, the displacement drop of the pipeline vibration at different excitation frequencies ranges from 12.9361 to 22.3107 dB, the acceleration drop range is from 4.6154 to 16.3444 dB, the displacement attenuation range is from 77.45% to 92.34%, and the acceleration attenuation range is from 41.22% to 84.77%, and the vibration isolation rate ranges from 86.74% to 98.83%.
## 4 Discussion
### Research Implications and Recommendations  
Crop models providing a detailed representation of the N cycle, including those evaluated in [PERSON] et al. [31], are typically too complex to be applied within a MDF approach that requires a large ensemble of model runs [28]. Our use of the intermediate complexity DALEC-Crop model and using CARDAMOM to calibrate just two N dilution function parameters represents a considerably less complex and, thus, faster means of optimising parameters based on observations. Furthermore, CARDAMOM efficiently generates a large ensemble of model estimates that allows for a robust uncertainty characterisation of the N and yield estimates.  
Our DALEC-Crop calibration could potentially be applied on an operational basis to generate leaf N estimates and yield predictions at the field and sub-field scale. There are studies that have estimated wheat N content from satellite EO data [52; 53]. Inferring leaf N content directly from EO data is complicated by a typically high correlation between LAI and canopy chlorophyll content. Furthermore, solely relying on EO data can lead to gaps in the estimation time-series due to cloud cover [54]. The use of DALEC-Crop, however, can generate estimates of daily N content based on meteorological inputs, which could complement existing precision agricultural technologies. Modern technology linked to precision fertiliser spreaders have the capacity to enable farmers to vary applications to fine resolutions of up to 10 \(\times\) 10 m\({}^{2}\)[55]. Combining our modelling framework with data available from current EO sensors with spatial resolutions finer than 20 m data, including Sentinel-2, could therefore support such precision agricultural management operations.  
The empirical N dilution function implemented in DALEC-Crop involved estimating leaf N that, through feedback with the model's photosynthesis sub-model (ACM), was effective at characterising the variability in LAI, yield and leaf N across the treatments. Generally, as leaf N increases so too does the assimilation rate of CO2 [4]. This enhancement in photosynthesis in response to leaf N, however, can be reduced by water stress due to a decrease in stomatal conductance [56]. Our implementation of DALEC-Crop did not account for water stress nor were there any moisture stress occurrences reported during the two growing seasons. Future climate projections suggest droughts will be more frequent in western Europe [57]. We would, therefore, recommend the use of testing ACM and DALEC predictions of soil moisture-growth interactions [58] when spatially and temporally upscaling our modelling approach.
# Independent System Calibration of Sentinel-1B
## 7 Radiometric Calibration
### Relative Radiometric Accuracy  
To verify the relative radiometric accuracy, several point targets are deployed within one scene. Then, the absolute calibration factor is derived from each point target. The standard deviation of the calibration factor across all point targets within one scene is then a measure for the relative radiometric accuracy. While the RCS of a corner reflector is defined by its geometry, i.e., by its leg length, the transponder RCS is derived from measurements involving electronic devices. At least four targets (transponders and corner reflectors) per overpass were deployed within the swath (SM) or sub-swath (IW, EW) for each beam which was measured in flight, compared to Figure 2.  
As depicted in Figure 23, the standard deviation varies between 0.05 and 0.42 dB during the commissioning phase of Sentinel-1B with an average of 0.25 dB. Neither a dependency of the acquisition mode nor of the number of covered targets is observed within the results. Both are depicted for each acquisition: the mode by color (blue: SM, red: IW, green: EW) and the number of covered targets inside the circle center.