esa-sceva/satcom-chunk-collection huggingface dataset:
# Acoustic Backscatter Communication and Power Transfer for Batterless Wireless Sensors
## 6 Conclusions  
In this work, we have reasoned that backscatter communication can be of significant interest in reducing power consumption during communication between sensor nodes embedded in metals. We reviewed current studies for wireless acoustic power transfer and communication using guided lamb waves in metal structures, showing no demonstration of combined power transfer and backscatter communication exists to date.  
We present a prototype, give detailed information about the prototype hardware, and investigate the potential of wireless power transfer over larger distances than the existing studies. We showed that a maximum of more than 10 mW can be transferred over 1 m with an efficiency of 8%. Over 3 m distance, efficiency is reduced drastically to only 2.8%, but still, 4 mW could be transferred. The presented harvester requires the source to provide at least 200 uW for successful harvesting, but we discuss potential optimizations to reduce this requirement. For acoustic backscatter communication, we tackle two challenges for real-world deployments of such devices, i.e., (a) self-interference of the backscatter reader limits the distances over which backscatter communication can be used, and (b) low-power devices use inaccurate and unstable oscillators that significantly impede the decoding of messages. The presented prototype showed that by using digital phase-locked loops, we can successfully track the errors induced by the unstable oscillators and decode messages at a data rate of 2 kbit s\({}^{-1}\) over 3 m, which was not feasible without tracking mechanisms in place. This enables low-cost and low-complexity implementations of battery-free embedded sensor nodes in closed metal containers for structural and industrial monitoring, which can be constructed from off-the-shelf discrete components.  
The following supporting information is publicly available: [[https://www.mdpi.com/article/10.3390/s23073617/s1](https://www.mdpi.com/article/10.3390/s23073617/s1)]([https://www.mdpi.com/article/10.3390/s23073617/s1](https://www.mdpi.com/article/10.3390/s23073617/s1)). Schematics and PCB-Layout for the presented hardware, source code for the firmware on the reader and tag node, as well as software-defined receiver code and channel simulation [25]. Data S1: Recorded samples during message transmission, Data S2: the recorded channel impulse responses.  
Methodology, P.O.; Software, P.O.; Validation, B.-C.R.; Writing--original draft, P.O.; Writing--review & editing, B.-C.R. All authors have read and agreed to the published version of the manuscript.  
This research was partially funded by the German Research Foundation (DFG) under grant number 492151098.  
Not applicable.  
* Research data is partly uploaded as Supplementary Material.  
The authors declare no conflicts of interest.
## 1 Introduction
(IDW), Ordinary Kriging (OK), Ordinary CoKriging (OCK), and Regression Kriging (RK) methods, which represent four categories, respectively, were selected to interpolate a continuous surface for soil salinity prediction. Our specific research objectives were (1) to predict soil salinity using spatial interpolation methods and (2) to identify a suitable spatial interpolation method for soil salinity severity mapping. The proposed research framework can reduce the cost and time required for soil salinity surveying and mapping compared with the traditional methods of government agencies in Thailand. For instance, the LDD conducts a traditional survey by collecting soil profile information to map saline soil and its severity [14]. This traditional method requires a lot of time and resources. In addition, the distribution of soil salinity and its severity can be applied as primary information to solve the existing saline soil problem for agricultural uses.
# Causality Analysis with Different Probabilistic Distributions Using Transfer Entropy
## 4 Analysis of Simulation Results
### Causality for the Dataset with Gaussian Noise and Cauchy Disturbance  
In this subsection, the simulation data impacted by both Gaussian noise and Cauchy disturbance is analyzed (see Figure 4). Again, the Transfer Entropy approach based on the Gaussian probability distribution and \(\alpha\)-stable distribution is used on the simulation data.  
The results for the first case are presented in Table 5. The causality graph relating to Table 5 is shown in Figure 13. The presented graph has little resemblance to the assumed one in Figure 5. The reason for such poor behavior may be a characteristic of the data. Continuous changes in the values of the regulation errors can be noticed, as well as a clear trend in the data. Previous research using real data [43] has shown that TE is not resistant to sudden changes in variables values; hence, the Transfer Entropy method proves to be ineffective, and the values determined through it for the coefficients should be disregarded entirely.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \(T_{row-column}\) & \(e_{1}\) & \(e_{2}\) & \(e_{3}\) & \(e_{4}\) & \(e_{5}\) \\ \hline \(e_{1}\) & NA & 0.1284 & 0.0729 & 0.0725 & 0.1016 \\ \(e_{2}\) & 0.1499 & NA & 0.0508 & 0.0387 & 0.0511 \\ \(e_{3}\) & 0.0450 & 0.0531 & NA & 0.0322 & 0.0401 \\ \(e_{4}\) & 0.0435 & 0.0371 & 0.0349 & NA & 0.0399 \\ \(e_{5}\) & 0.0430 & 0.0518 & 0.0409 & 0.0433 & NA \\ \hline \end{tabular}
\end{table}
Table 4: Calculated TE coefficients based on the \(\alpha\)-stable distribution for the dataset with Gaussian noise.  
Figure 12: Causality diagram for the dataset with Gaussian noise using the TE approach based on the \(\alpha\)-stable distribution.  
The results for the second case are presented in Table 6. The causality graph relating to Table 6 is shown in Figure 14.  
The resulting causality relationships are only partially correct. Precise indications refer to regulation errors \(\epsilon_{4}\rightarrow\epsilon_{1}\) and \(\epsilon_{2}\rightarrow\epsilon_{5}\). The other relationships resulting from the Transfer Entropy calculations are misleading, and do not reflect those presented in Figure 5 for the assumed causality graph. Many questions arise from these results; in particular, it seems that changing the probability density function from a Gaussian distribution to an \(\alpha\)-stable distribution is effective for a dataset containing Gaussian noise. Of course, we are dealing here with data of a different nature, for which the solution can only be a deeper data analysis [24].
# CONDA-PM--A Systematic Review and Framework for Concept Drift Analysis in Process Mining
## 3 Concept Drift in Process Mining
### Distinguishing Concept Drift from Other Concepts
#### 3.1.1 Concept Drift and Noise  
The imprecision in the mentioned definition disperses concept drift into different meanings with different purposes and associated techniques. An example of this dispersion is _noise_ which refers to _rare and infrequent behaviour_[2]. Noise is considered as a challenge to any process mining approach [1] and is filtered out in several process mining techniques, like heuristic miner [14] and fuzzy miner [15]. A problem in this context is to find a balance between producing underfitting and overfitting models. Although noise is allowed according to the process model, it is not preferred to be taken into account when discovering a descriptive model, as its inclusion would result in unstructured spaghetti-like models.
## 2 Materials and Methods
### Vegetation Transpiration Model (VTM)  
In a crop field, evapotranspiration comprises evaporation (E) from the soil and intercepted canopy water and transpiration (T) from plants [66; 99]. During the crop growing season, transpiration usually exceeds evaporation [100; 101; 102]. At the foliar level, there is a strong linkage between photosynthesis (gross primary production, GPP) and transpiration. This often results in the calculation of the leaf-level water use efficiency as the ratio between these two (i.e., \(\mathrm{WUE_{Leaf}=GPP/T}\), expressed in mol CO\({}_{2}\)/mol H\({}_{2}\)O). The Vegetation Transpiration Model (VTM) predicts daily transpiration as an outcome of GPP and \(\mathrm{WUE_{Leaf}}\), and it was first tested in grassland ecosystems [103]. Literature data indicate that the \(\mathrm{WUE_{Leaf\_C3}}\) for C3 plants is 500 \(\mathrm{\SIUnitSymbolMicro mol}\) CO\({}_{2}\)/\(\mathrm{\SIUnitSymbolMicro mol}\) H\({}_{2}\)O, while for C4 plants, the \(\mathrm{WUE_{Leaf\_C4}}\) is 250 \(\mathrm{\SIUnitSymbolMicro mol}\) CO\({}_{2}\)/\(\mathrm{\SIUnitSymbolMicro mol}\) H\({}_{2}\)O [104].  
\[\mathrm{T=(C3F\times 1/\mathit{WUE_{Leaf\_C3}}+C4F\times 1/\mathit{WUE_{Leaf\_C4}}) \times GPP,} \tag{10}\]  
If C3F = 1.0, T (mm H\({}_{2}\)O/day) = 0.33 (mm H\({}_{2}\)O/g C/m\({}^{2}\)/day) \(\times GPP\) (g C/m\({}^{2}\)/day) (11)  
If C4F = 1.0, T (mm H\({}_{2}\)O/day) = 0.165 (mm H\({}_{2}\)O/g C/m\({}^{2}\)/day) \(\times GPP\) (g C/m\({}^{2}\)/day) (12)
## 4 Speech Emotion Recognition Datasets
### English Language Datasets
#### 4.1.2. Tess  
TESS (the Toronto Emotional Speech Set) [26] dataset was developed to support research on emotion recognition in speech. It consists of recordings in which two actresses utter the same phrases, expressing different emotions. Each actress recorded 200 different sentences, which were selected to evoke a variety of emotional states. The TESS dataset contains recordings reflecting six basic emotions: anger, disgust, fear, happiness, pleasant surprise, and sadness, as well as a neutral state. The audio files are of high quality, which allows them to be used in research on the acoustic aspects of emotional speech. Thanks to carefully selected recordings and the participation of actresses of different ages, TESS is a valuable tool in emotion recognition research, especially in the context of age differences in the perception and expression of emotions.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Language** & **Emotions** & **Audio Track/** & **Num. of Samples** & **Technical Info** \\ \hline RAVDESS & English & angry, calm, disgust, fearful, happy, sad, surprise, and neutral & ✓/✗ & 1440 & 4 s, 48 kHz, 16 bit/sample \\ TESS & English & anger, disgust, fear, happiness, pleasant surprise, sadness, and neutral & ✓/✗ & 2800 & lack of informations \\ CREMA-D & English & anger, disgust, fear, happy, sad, and neutral & ✓/✗ & 7442 & lack of informations \\ IEMOCAP & English & fear, frustrated, happy, sad, surprise, and neutral & ✓/✗ & 1150 & lack of informations \\ SAVEE & English & anger, disgust, fear, happiness, sadness, surprise, and neutral & ✓/✗ & 480 & 44.1 kHz \\ Chatbot & Polish & anger, disgust, fear, happiness, sadness, surprise, and neutral & ✗/✗ & 8458 & - \\ nEMO & Polish & anger, fear, happiness, sadness, surprise, and neutral & ✓/✗ & 4481 & 2.5 s, 24 kHz, 16 bit/sample \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of technical information of datasets for the problem of emotion recognition in Polish speech.
## 4 Results
### Ablation Study  
As part of the design process, we tested each component separately on five data sets. This helps us evaluate each component's specific effectiveness in the hierarchy. Table 6 lists the performance of ARIMA and ANN models, along with the proposed hybrid one. For all five data sets, the performance of the hybrid model is better than that of individual ARIMA and ANN models in terms of MSE and MAD metrics. However, the performance gap varies significantly from one data set to another. In two data sets, namely, hourly electricity rates and wheat yield, hybrid forecasting has superior performance over ARIMA and ANN. In other data sets, the gap between the hybrid model and ANN is relatively small.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & **MSE** & **MAD** \\ \hline SunSpot & Only ARIMA & 307.055 & 12.7472 \\  & Only ANN & 241.415 & 12.070 \\  & Proposed hybrid model, ARIMA-MLP & 240.896 & 11.416 \\ Canadian lynx trapping & Only ARIMA & 0.0235 & 0.1181 \\  & Only ANN & 0.0134 & 0.1119 \\  & Proposed hybrid model, ARIMA-MLP & 0.0129 & 0.0986 \\ Hourly electricity rates & Only ARIMA & 30.6729 & 4.4025 \\  & Only ANN & 19.8867 & 3.1804 \\  & Proposed hybrid model, ARIMA-MLP & 14.7566 & 2.5634 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Ablation study of test data sets.  
Figure 10: Hybrid forecast vs. actual data over the test data (wheat yield).  
Figure 9: Historical wheat yield in Turkey.
## 2 Mechanism of Microbial-Induced Calcite Precipitation (MICP)  
### Reaction Mechanism of MICP  
The MICP is a widely existing bio-mineralization reaction in nature, accompanied by different microbial activities and chemical processes [31]. Different from the mineralization on the geological surface of earth, bio-mineralization refers to the process in which inorganic elements selectively precipitate from the environment to form minerals on a specific organic matrix with the participation of biological cells. This bio driven mineralization reaction mainly occurs in four ways: urea hydrolysis, denitrification, sulfate reduction, and ferric iron reduction [32]. The method of using microorganism-induced denitrification to precipitate calcium carbonate not only has high cultivation cost, but also has low efficiency of generating calcium carbonate [33]. However, it is undeniable that denitrifying bacteria can grow in situ and play a role under anoxic conditions [34]. In the process of microbial-induced sulfate, the sulfate reduction will produce the hydrogen sulfide gas, which is harmful to the environment and human body [35]. In addition, hydrogen sulfide gas also results in accelerating the corrosion of steel bars in structures. For the ferric iron reduction method, the requirements for the oxidation substrate are very high, meaning that it only works when the solubility of the oxidation substrate is low [36]. The urea hydrolysis method is simple and efficient, given that there is no additional reaction condition and no environmental pollution [37]. Because matrixes (urea and CaCl\({}_{2}\)) have a high solubility in water solution, the MICP process can generate a lot of CaCO\({}_{3}\) in a short time [38]. Scholars generally believe that MICP using urea hydrolysis has an enormous potential in soil treatment [39]. In addition, among all biochemical reactions, the reaction of urea hydrolysis is a main technological path to produce ammonium ions and carbonate ions, and its reaction process is relatively simple. Therefore, the reaction mechanism of urea hydrolysis MICP was taken as an example in the following section.
## 2 Related Work
### Quality of a Derived Process Tree  
Several configurations can be applied to a configurable process tree. In order to assess the quality of a derived process tree to represent the observed behavior in an event log, a quality metric must be defined. Quality is usually measured considering a trade-off among the following four quality criteria: fitness, precision, generalization and simplicity [8,22,26]. Conformance checking is a sub-discipline of process mining that allows comparing the behavior allowed by a process model with the behavior recorded in an event log to find commonalities and discrepancies, and also to compute metrics for each of the four quality criteria [26].  
**Definition 7** (Conformance).: _Let \(Q\) be a process tree, and let \(L\) be an event log. Let \(f\), \(p\), \(g\), \(s\) be the fitness, precision, generalization and simplicity metrics as defined in [20] with a range [0, 1], one being the target value, and let \(W=(w_{f},w_{p},w_{g},w_{s})\) be the weights given to each metric, respectively. Conformance is defined as:_  
\[\text{conformance}(Q,L,W)=\frac{f(Q,L)\cdot w_{f}+p(Q,L)\cdot w_{p}+g(Q,L) \cdot w_{g}+s(Q,L)\cdot w_{s}}{w_{f}+w_{p}+w_{g}+w_{s}} \tag{1}\]  
In this article, we have defined the conformance metric based on [20] and used the corresponding implementation to evaluate the quality of a given process tree. However, the proposed framework is generic and independent of the conformance metric used.  
An optimal configuration is the one that allows obtaining a derived process tree that better represents a given event log, e.g., has the best conformance value.  
**Definition 8** (Optimal configuration).: _Let \(Q^{\alpha}\) be a configurable process tree; let \(L\) be an event log; and let \(W\) be the weights of the quality metrics. A configuration \(c\in C(Q^{\alpha})\) is an optimal configuration if and only if there is not another configuration \(c^{\prime}\in C(Q^{\alpha})\) such that \(\text{conformance}(\text{derive}(Q^{\alpha},c^{\prime}),L,W)>\text{ conformance}(\text{derive}(Q^{\alpha},c),L,W)\)._  
Our goal is to automatically obtain a configuration to derive a process tree from a configurable process tree that maximizes the conformance function for a given event log. To accomplish this goal, we have implemented three strategies, which are detailed in the next section.  
Figure 5: Running example that illustrates the derivation framework. A configurable process tree and and event log, shown in (**a**) and (**b**), are the inputs. The internal representation of a configuration used by the three strategies is represented in (**c**). The obtained configuration is shown in (**d**). Finally, the output, a derived process tree, is shown in (**e**).
## 4 Performance Evaluation Metrics  
Performance assessment is necessary to assess ResVGG-SwinNet's multi-label DDoS attack detection performance. Traditional performance measures like accuracy, precision, recall, and F1-Score are valuable, but they do not fully address multi-label classification and DDoS detection concerns [39]. Three new measures are included to further assess the model's capacity to identify threats, minimise false positives, optimise resource utilisation, and preserve computational efficiency.
# Comparison of SNAP-Derived Sentinel-2A L2A Product to ESA Product over Europe
## 5 Discussion
As expected, these differences were typically related to slope (Figure 9), although not as clearly as for reflectance. This finding is explained by the fact that the SL2P algorithm used for land surface parameters ingests almost all spectral bands and applies a nonlinear regression to estimate each parameter [26]. As such, the propagation of differences in BOA reflectance to differences in geophysical quantities is non-trivial. Indeed, it is exactly for this reason that the observed differences in reflectance due to slope are of concern. Even if a single operational DEM was used for all processors, the sensitivity to slope implies that there is an additional underlying uncertainty in input reflectance that must be included when developing and evaluating land surface parameter algorithms and their products.
## 3 Results and Discussion
### Perturbation Step  
The first eight measurements in the piston-liner subroutine are _piston lateral displacement_, _piston tilt angle_, _piston lateral velocity_, _piston tilt angular velocity_, _piston axial position_, _piston reciprocating velocity_, _crank angle_ and _cylinder pressure_ (Figure 3). The perturbation step sizes for these eight measurements (not related to the deformation of bodies), which were estimated using the default serial finite forward differencing method, were recorded during one operational cycle of the crankshaft mechanism. The evolution of the relative perturbation steps (the ratio between the actual and initial perturbation step) is depicted in Figure 4.  
Figure 4: Relative perturbation steps of subroutine’s measurements not related to deformation. The values are plotted depending on the crankshaft angular position.  
Figure 3: Subroutine’s measurements not related to the deformation for the multibody dynamics model of the piston–liner interaction.  
The relative perturbation steps of the measurements _piston lateral displacement_, _piston axial position_ and _piston tilt angle_ do not significantly change their sizes during the simulation. This does not hold for other measurements, such as the _piston reciprocating velocity_ and _the cylinder pressure_, which change their perturbation steps, sometimes quite rapidly--_the piston lateral velocity_, _piston tilt angular velocity_ and _crank angle_ are depicted in Figure 4 by the scale logarithmic scale. Figure 4 illustrates the perturbation step size adjusted as per the current simulation conditions so that the optimal value may be estimated. For instance, the trend of relative perturbation steps of the measurements of _the piston axial position_ and _piston reciprocating velocity_ represents the typical piston primary motion of the crankshaft mechanism. This also applies to the measurement of _cylinder pressure_ when the related perturbation step adapts according to the pressure in the combustion chamber of a four-stroke internal combustion engine.  
As previously mentioned, the MBD solver is able to adapt the perturbation step size to the actual simulation conditions (change in the measurement). This way, the Jacobian is estimated effectively, and small number of iterations is necessary to find the converged solution. Unfortunately, some MBD solvers are unable to pass these effectively estimated perturbation steps to the subroutine (the case of the MBD solver used in this paper). However, most of the measurements in the subroutine of EHL are related to the deformation (the modal coordinates and their time derivatives), and their perturbation steps tend to remain nearly constant throughout the simulation.
## 3 Methodology
### Metrics  
Two types of parameters were evaluated from the recorded data: those associated with the algorithm's quality and those associated with the communication quality. To assess the former, the true positive rate, false positive rate, positive omission rate, and processing times were used. To obtain these parameters, various graphs were presented after each execution of the algorithm, such as their location in a video image, their optical signal, or the execution times of each phase. Additionally, scripts were also developed to obtain these data by clicking on the pixel of interest to determine the reasons for positive omissions.  
In contrast, to assess the quality of the communications, the Signal-to-Noise Ratio (SNR) and Bit Error Rate (BER) were used. These parameters were obtained using the Gaussian Mixture Model, whose expressions can be seen in (5) and (6). In these equations, \(\mu_{0}\) and \(\sigma_{0}\) are the mean and standard deviation of the first Gaussian, \(\mu_{1}\) and \(\sigma_{1}\) are the mean and standard deviation of the second Gaussian, and \(\alpha\) is the weighting or relative contribution of the first Gaussian to the SNR calculation. The first Gaussian represents the signal, and the second Gaussian represents the background noise.  
\[SNR=\frac{1}{2}\frac{|\mu_{1}-\mu_{0}|^{2}}{\alpha\sigma_{0}^{2}+(1-\alpha) \sigma_{1}^{2}} \tag{5}\]  
\[BER=\frac{1}{2}\text{erfc}\left(\sqrt{\frac{SNR}{2}}\right) \tag{6}\]