esa-sceva/satcom-chunk-collection huggingface dataset:
# A Goal-Oriented Evaluation Methodology for Privacy-Preserving Process Mining
## 2 Background
### Process Mining  
Process mining [6] extracts the fundamental significance of actual processes by analyzing collections of recorded events. It facilitates the efficient auditing and monitoring of business processes in a timely manner. Various factors play a crucial role in process mining, including the correlation of events, their associated time stamps, and other relevant attributes. An event log constitutes the foundational input for the application of process mining techniques. During execution, an event log systematically records multiple instances of a process, commonly referred to as cases. An event log is composed of a collection of systematically recorded event records. Each record contains a minimum of one timestamp, an activity reference, and a case identification reference, ensuring precise tracking and contextual association within the event log. Within a case, events are systematically arranged in sequential order based on their respective timestamps.  
Process mining comprises three fundamental objectives: _process discovery_, _conformance checking_, and _model enhancement_. Process discovery utilizes event logs as input to construct a model that accurately represents real-world process workflows. Conformance checking facilitates the identification of deviations between the designed process model and the actual observed behavior in a real-world setting. Based on these principles, model enhancement refines the existing process model through the utilization of event logs.  
Several process mining techniques have been previously developed, including Heuristic, Genetic, Inductive, and Fuzzy [7]. All of these methodologies aim to refine the representation of control flow within processes, ensuring well-informed and structured decision-making within an organization. An increasing number of commercial process mining tools are also available, including _ProM_[8], _Disco_[9], and _PM4 Py_[10]. _ProM_ is a highly extensible framework that accommodates a diverse range of plugins, facilitating the implementation of various process mining methodologies. As Java serves as its implementation language, the framework exhibits platform independence, ensuring compatibility across diverse operating systems. Leveraging process data, researchers can efficiently generate visual representations through _Disco_'s innovative and commercially available process mining technology. _PM4 Py_ is designed for seamless integration with process mining science workflows, facilitating the manipulation and analysis of event logs. It provides support for standard process mining formats, such as XES and CSV, as well as various algorithms, including Inductive Miner [11]. Additionally, it enables conformance checking through alignments and token-based replay [12]. It is a Python (>=3.9)-based open-source library designed to support both academic research and industrial applications, facilitating advanced process mining analyses and implementations.
## 4 Result
### Pore Type and Morphology
#### 4.2.1 Organic Matter Pores  
The organic matter pores in the Funing Formation shale of the Gaoyou Sag are predominantly identified by their occurrence as widespread or stratified organic matter dispersed among inorganic minerals. Irregularly shaped polygonal, circular, or oval pores are formed within the organic matter, commonly displaying average dimensions spanning from tens to hundreds of nanometers, with a smaller subset falling within the micrometer range. (Figure 3). The continental shale discovered in the study area has undergone comparatively lesser degrees of thermal maturation when compared to marine shale. As a result, it exhibits a deficiency in the well-developed honeycomb-like dense organic-matter pores that are commonly observed in marine shale formations. Nevertheless, it is worth noting that within this particular continental shale, the organic matter has the ability to establish connections with the pores present in the inorganic mineral matrix by means of contraction fractures found within the organic matter. This phenomenon plays a significant role in facilitating the availability of reservoir space and percolation channels for shale oil within the geological formation.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Sample** & **Depth** & **Quartz** & **Feldspar** & **Calcite** & **Dolomite** & **Siderite** & **Pyrite** & **Clay** & **TOC** & **RO/\%** \\
**ID** & **(m)** & **(wt.\%)** & **(wt.\%)** & **(wt.\%)** & **(wt.\%)** & **(wt.\%)** & **(wt.\%)** & **(wt.\%)** & **(wt.\%)** & **RO/\%** \\ \hline L-1 & 3460.2 & 30.1 & 2.5 & 8.8 & 2.8 & 30.6 & 2.8 & 22.4 & 1.813 & 0.9 \\ L-2 & 3656.15 & 20.8 & 22.3 & 15.2 & 16.7 & 0 & 3.7 & 21.3 & 0.554 & 0.89 \\ L-3 & 3694.92 & 22.7 & 6.9 & 3.7 & 48.1 & 0 & 1.4 & 17.2 & 1.426 & 0.86 \\ L-4 & 3717.6 & 20.2 & 2.8 & 10.6 & 51.8 & 0 & 0 & 14.6 & 1.031 & 0.91 \\ L-5 & 3704.06 & 34.7 & 17.9 & 4.1 & 7.9 & 0 & 2.7 & 32.7 & 1.106 & 0.88 \\ L-6 & 3680.01 & 24.7 & 30.4 & 12.7 & 18.1 & 0 & 1.7 & 16.4 & 0.734 & 0.81 \\ \hline \hline \end{tabular}
\end{table}
Table 1: TOC content and mineral composition of all samples.
## 1 Introduction
### Half Duplex (HD) and Full Duplex (FD) Radios  
Half duplex (HD) [16; 17; 18], a traditional SD-CRN operation mode, is based on a time-slotted frame structure where the sensing phase is scheduled sequentially with the transmission phase. In HD-CRNs, the node is either in the sensing or transmission mode; thus unable to receive and transmit on the same frequency simultaneously. An increase in the duration of the sensing phase reduces the overall system capacity; an increase in the duration of the transmission phase impairs the sensing accuracy. The other drawbacks of HD-based operations include: (i) sacrifice of duration for the sensing phase in each time slot, (ii) spectrum collision or waste when PUs arrive or leave during the transmission phase, and (iii) requirement for transmission to be divided into small discontinuous slots even if the spectral hole is long and continuous. Such limitations in HD-CRNs result in the inefficient use of system resources. To consider the above limitations, in-band full duplex (FD) radios have been proposed [18; 19; 20]. The FD-CRNs can simultaneously perform sensing and transmission in each time slot. The FD-enabled nodes are able to use the spectral holes uninterruptedly and continuously. Interference to the primary network is minimized with the continuous sensing in FD-CRNs. Thus, FD-CRNs have considerable potential to enhance the spectrum utilization and overall system capacity. As a downside, FD-CRNs are achieved at a cost of increased energy consumption and hardware complexity. The major limitation of FD radios is the self-interference (SI) of transceivers because of small spacing and power difference between the transmitting and receiving antennas. Recent developments in potential FD techniques, i.e., SI suppression (SIS) and SI cancellation (SIC) have facilitated the implementation of radical FD-CRN systems. However, imperfect SIS and SIC are practical achievable, resulting in non-zero residual SI power. The performance of FD radios highly depends on these SI mitigation capabilities. Excessive SI can even result in the reduced capacity for FD systems, even falling below that of HD systems [20; 21; 22].  
For ease of reference, we summarize our commonly used notations in Table 1.
## 4 Experiments and Results  
### Datasets  
The datasets used in this paper are a set of publicly available datasets provided by the Performance Measurement System (PEMS), a California transportation management system. The PEMS04 used in this paper is data generated by 307 detectors collecting data at 5-min intervals for a total of 59 days. The PEMS08 is data generated by 170 detectors collecting data at 5-min intervals for a total of 62 days. The data collected by each detector at each acquisition contains three dimensions of features: flow, average velocity, and average occupancy. In this paper, we will retain the flow rate component for prediction experiments. The raw traffic of node 10 in both datasets is shown in Figure 2a,b, where (a) is the traffic data of node 10 of PEMS04, 16,992 is the quantity collected in 59 days at this node, and (b) is the traffic data of node 10 of PEMS08, 17,856 is the quantity collected in 62 days at this node. In the process of frequency domain feature extraction, we subjected the original traffic flow sequence to FFT, and the spectrograms of node 10 in both datasets after FFT transformation are shown in Figure 3a,b.  
Specifically, the number of samples collected in a day is 288, and we take the traffic flow data of any week to analyze, as shown in Figure 3(a),b. Analyzing from the time domain, it can be seen that a clear periodicity is presented. However, other characteristics presented in the data cannot be obtained from the time domain.  
Figure 4: Weekly traffic flow and spectrogram at node 10.  
Figure 3: Spectrogram of node 10.  
Figure 2: Visualization of traffic flow at node 10.  
Amplitude represents the strength or energy of a signal at a specific frequency. The greater the amplitude, the more substantial the contribution of that frequency component to the signal. Amplitude is typically used to identify the dominant frequency components of the signal. Phase, on the other hand, indicates the relationship between a specific frequency component and time. Phase information is crucial for understanding the signal's waveform, periodicity, and time delays. When multiple signals are superimposed, phase differences can cause interference, which can, in turn, affect the final shape of the signal.
# Correspondence between Multilevel Graph Partitions and Tree Decompositions
## 4 Correspondence between Multilevel Graph Partitioning and Tree Decompositions
### Example Transformation  
The multilevel node-based partition of Figure 4 corresponds to the rooted tree decomposition of Figure 1b. Consider the left blue cell \(C=\{m,o,p\}\), boundary \(B_{C}=\{f,p\}\) and separator \(S_{C}=\{m\}\). The corresponding bag \(X\) is \(C\setminus S_{C}=\{f,m,p\}\), which is a bag in Figure 1b.  
Alternatively, consider the bag \(X=\{e,p,i,c\}\) and denote by \(C\) the corresponding cell. The parent \(X_{p}\) is the bag \(\{p,i,c\}\). The descendants are \(\mathcal{X}_{d}=\{\{h,e,p,c\},\{d,e,c\}\}\). Using the formulas, we obtain \(B_{C}=\{p,i,c\}\). The separator \(S_{C}\) is \(\{e\}\). We therefore have \(\bigcup\mathcal{X}_{d}=\{e,p,i,c,h,d\}\) and thus the cell is \(C=\{e,h,d\}\).  
Finally, let us consider a different example graph. It have three nodes \(a\), \(b\), and \(c\). There are no edges, i.e., the graph is not connected. Further consider a multilevel partition with five cells, namely \(\{a,b,c\}\), \(\{a,b\}\), \(\{a\}\), \(\{b\}\), and \(\{c\}\). This translates into a tree decomposition, with two empty bags. \(\{a,b,c\}\) corresponds to \(\emptyset\), and \(\{a,b\}\) corresponds to \(\emptyset\), and the cells \(\{a\}\), \(\{b\}\), and \(\{c\}\) are equal to their bag. Fortunately, our algorithms also work on this multilevel partition. The two \(\emptyset\) bags are discerned by their position in the backbone. The children bags of the bag corresponding to the cell \(\{a,b\}\) are \(\{a\}\) and \(\{b\}\). The union over all descendants is \(\emptyset\cup\{a\}\cup\{b\}\), which is equal to the original cell \(\{a,b\}\). The construction for \(\{a,b,c\}\) is analogous. This example shows, that in corner cases, it is important that the sets of bags and cells are multisets.
## 3 Tvdi-Based Drought Index Monitoring
### Tvdi Drought Monitoring Index  
#### 3.1.1 Calculation of the Temperature Vegetation Drought Index (TVDI)  
TVDI is a kind of drought monitoring index constructed based on the NDVI index and LST index. It utilizes vegetation conditions to indirectly reflect the soil's water content. The feature space shows a specific distribution pattern, as depicted in Figure 4. As surface temperature rises, soil water evaporates faster due to the increased temperature. Conversely, when surface temperature decreases, the water evaporation rate slows down. The edge composed of the maximum surface temperature is termed the dry edge, while the opposite edge is the wet edge. In a defined state, the dry edge area represents arid regions with soil water content at a relative value of \(0\), whereas the wet edge area represents wet regions with soil water content at a relative value of \(1\). The closer the dry edge, the more severe the aridity; conversely, the closer the wet edge, the more humid the area. In practice, the  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Class** & **SVM** & **KNN** & **RF** & **1D-CNN** & **ViT** & **SF** & **RNN** & **HCRNN** \\ \hline BuildUp & 98.35 & 98.01 & 99.30 & 96.81 & 99.11 & 99.13 & 98.99 & 98.93 \\ Forest & 97.87 & 95.65 & 97.31 & 93.87 & 97.19 & 97.88 & 97.01 & 99.03 \\ Water & 97.47 & 97.89 & 97.32 & 96.25 & 97.64 & 98.64 & 97.04 & 98.07 \\ Bareland & 93.06 & 97.00 & 92.50 & 93.25 & 93.80 & 95.86 & 95.31 & 97.56 \\ Sugarcane & 87.7 & 88.89 & 86.81 & 84.44 & 89.77 & 85.42 & 89.54 & 91.50 \\ Rice & 70.99 & 75.93 & 67.28 & 65.22 & 80.86 & 76.54 & 72.84 & 74.07 \\ Otherland & 74.08 & 80.34 & 77.05 & 71.84 & 81.20 & 88.49 & 81.39 & 89.09 \\ \hline OA (\%) & 94.50 & 94.35 & 94.61 & 91.76 & 95.34 & 95.98 & 95.14 & 97.03 \\ AA (\%) & 88.50 & 90.53 & 88.23 & 85.95 & 91.37 & 91.71 & 90.93 & 92.61 \\ Kappa & 0.9247 & 0.9232 & 0.9262 & 0.8877 & 0.9366 & 0.9452 & 0.9337 & 0.9596 \\ \hline \hline \end{tabular} This table is from our team’s paper.  
\end{table}
Table 2: Classification results of different classification methods on the Laibin 2019 dataset.  
NDVI-LST feature space is typically triangular or trapezoidal. The formula for calculating the TVDI index is as follows:  
\[TVDI=\frac{LST-LST_{min}}{LST_{max}-LST_{min}} \tag{2}\]  
In the above equation, LST represents the maximum value of surface temperature in an image element with the same NDVI value. LST\({}_{\text{min}}\) is then the minimum value of surface temperature in image elements with the same NDVI value. A linear fit of the sum of the same NDVI values yields an expression for the wet and dry sides:  
\[LST_{max}=a_{1}+b_{1}NDVI \tag{3}\]  
\[LST_{min}=a_{2}+b_{2}NDVI \tag{4}\]  
In the above equation, \(a_{1}\), \(b_{1}\), \(a_{2}\) and \(b_{2}\) are the fitting coefficients for the dry-side and wet-side equations, respectively.
# A Fast Wavelet-Based Bridge Condition Assessment Approach Using Only Moving Vehicle Measurements
## 4 Numerically Verification  
To verify the effectiveness of the proposed approach, a quarter-car VBI model is adopted as illustrated in Figure 4. The properties of the VBI system are listed in Table 1. An approach with 30 m is simulated in this model to ensure the balance of the dynamic behavior of the vehicle before entering the bridge. Rayleigh-type damping is considered for the bridge in this model, with a damping ratio of 2%. In this case, the 1 st natural frequency of the bridge, \(f_{\rm{b1}}\), is 2.17 Hz. The vehicle's frequencies, \(f_{\rm{v1}}\) and \(f_{\rm{v2}}\), are 0.57 Hz and 9.89 Hz, respectively. The vehicle travels across the bridge at a constant velocity of 2.5 m/s. The scanning frequency of the VBI system is 1000 Hz. The damage model of [PERSON], et al. [44] is used to simulate the bridge damage, assuming that the longitudinal length of the damage covers an area three times the beam depth. In this region, bridge element stiffness varies according to the degree of damage, which is defined as the ratio of damage/crack depth to intact beam depth. For example, a damage level of 0.4 or 40% means that for a beam with a depth of 0.8 m, the crack depth is 0.32 m.  
\begin{table}
\begin{tabular}{c c c c} \hline \multicolumn{2}{c}{**Bridge Properties**} & \multicolumn{2}{c}{**Vehicle Properties**} \\ \hline Span & 20 m & m\({}_{\rm{s}}\) & 14,300 kg \\ \hline Density & 4800 kg/m\({}^{3}\) & k\({}_{\rm{s}}\) & 200 kN/m \\ \hline Modulus & \(2.75\times 10^{10}\) N/m\({}^{2}\) & k\({}_{\rm{a}}\) & 2500 kN/m \\ \hline Depth & 0.8 m & m\({}_{\rm{a}}\) & 700 kg \\ \hline Width & 4.0 m & c\({}_{\rm{s}}\) & 10 kN s/m \\ \hline \end{tabular}
\end{table}
Table 1: The properties of the VBI model.  
Figure 4: The VBI model with a quarter car.  
Figure 3: A flowchart for bridge damage detection.
## 2 Research on Anti-Skid Mechanisms of Asphalt Pavement
### Factors Affecting the Skid Resistance of Asphalt Pavements
#### 2.2.3 Influence of Asphalt Binder on the Skid Resistance of Pavements  
Asphalt binder plays a role as a binder material with properties like adhesion, thermal insulation, and waterproofing. The asphalt binder and fine aggregates, in conjunction with the mineral powder, combine to form asphalt slurry, which serves to enhance the interfacial adhesion between the aggregates. Asphalt mixtures can be classified into two distinct categories based on the manufacturing process and the temperature at which it is utilised. These categories are hot mix asphalt (HMA) and warm mix asphalt (WMA), respectively. The Salient details and frictional properties of asphalt mix classifications are shown in Table 1.  
The skid resistance of asphalt pavements is primarily influenced by the characteristics of the materials used, including the aggregate, mix gradation, and asphalt binder. Additionally, the skid resistance can be affected by vehicular factors, such as the type tread design, the contact area between the tyre and road, tyre inflation pressure, and the speed at which the vehicle travels, as well as environmental conditions.
###### Abstract  
As global warming progresses, forest fires have become more frequent, leading to the destruction of forest biodiversity and consequently affecting Earth's ecosystems and human living conditions. The ability to predict the long-term trend of forest fires holds significant value for fire prevention and management. In Yunnan Province, China, a region rich in forest resources, this study utilized temperature, average annual rainfall, relative humidity, and wind speed data from 1991 to 2021. We forecasted forest fires using the stepwise regression and autoregressive integrated moving average (ARIMA) model, incorporating the collected forest fire data. The findings reveal a negative correlation between rainfall and forest fire incidence, whereas wind speed exhibited a positive correlation. The ARIMA model forecasts a cyclical trend in fires from 2022 to 2033, with considerable fluctuations in the number of fires, notably in 2027 and 2033. The projected affected area is anticipated to show a marked increase from 2028 onwards. This research not only provides a novel methodology for forecasting forest fires but also lays a scientific foundation for the development of future fire prevention and mitigation strategies.  
ARIMA model; fire prediction; stepwise regression model; time series analysis +
Footnote †: journal: Applied sciences  
Article
# Optimized Analytical-Numerical Procedure for Ultrasonic Sludge Treatment for Agricultural Use
## 10 Experimental Activity: Some Survey Results
### On the Reduction of Water Content  
Finally, the final water content of dry sludge was studied and analyzed, revealing that sonication alone is inadequate for effective sludge conditioning, making it necessary to combine with heat treatment by reducing the water content by 9%.  
**Remark 2**: _It is important to note that, in addition to evaluating the \(\zeta\) potential and conductivity, further sludge characteristics were analyzed to assess the treatment effects. Before sonification, the sludge exhibited a compact morphology with a non-uniform particle distribution and cohesive agglomerates, as shown in Figures 10 and 11. During ultrasound treatment, the cavitational effect progressively disintegrated these structures, facilitating the release of intracellular and extracellular materials (Figure 12). The increase in chemical oxygen demand (COD) observed during sonification indicates enhanced bioavailability of organic components and improved biodegradability, beneficial for subsequent biological processes. Additionally, the combination of sonification and thermal treatment led to a moisture content reduction, resulting in an 11-12% decrease in sludge weight, as highlighted in Figure 12. These findings confirm the process's effectiveness in improving sludge manageability and its suitability for agricultural applications._
# Oceania's 5G Multi-Tier Fixed Wireless Access Link's Long-Term Resilience and Feasibility Analysis
## 3 5G Base Coverage Network Layout
### Potential Health Hazards Linked with 5G Technology  
Mobile phone technology's usage of electromagnetic radiation has sparked worries about potential health dangers, such as a higher risk of developing some malignancies. The World Health Organization (WHO) claimed in 2014 that there was no evidence connecting mobile phone use to harmful health impacts [68]. However, all radio frequency radiation, including mobile signals, was categorized as potentially carcinogenic by the WHO and the International Agency for Research on Cancer (IARC) [69].  
Concerns about the safety of radiofrequency radiation were raised after it was found that male rats exposed to high amounts of it developed a malignant tumor in the heart, according to a toxicology study released by the US Department of Health in 2018 [70]. In the study, rodents were subjected to radiation from mobile phones for two years, nine hours a day, beginning before birth. However, no correlations to cancer were discovered in the tested female rats or mice. The research also revealed that radiation-exposed rodents outlived their control counterparts.  
The WHO and the Food and Drug Administration (FDA) both regard 5G as safe. [PERSON] further claims that \"all wireless communications use nonionizing electromagnetic radiation to transmit energy and information through space without the need for wires\". According to him, 5G's electromagnetic waves \"have a greater frequency, allowing it to transmit more information\". It also enters the body less deeply and with a shorter wavelength than energy with a lower frequency [71].  
The UK government states that, while a very small increase in overall exposure to radio waves is possible when 5G is added to the existing network, the overall exposure is expected to remain low. The frequency range of the current 5G signals is substantially lower than what the International Commission on Non-Ionizing Radiation Protection deems to be harmful and falls within the non-ionizing band of the electromagnetic spectrum [72].