esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Analysis of the Impact of the Firing Conditions and Pr Content
For the effects on density:  
\[\rho\ =\ \rho_{0}\exp(-m/\mathrm{T_{f}})\mathrm{t_{f}^{\prime\prime}(Pr)^{p}}. \tag{7}\]  
This may be linearized by reverting to logarithmic scales:  
\[\ln(\rho)\ =\ \ln(\rho_{0})-m/\mathrm{T_{f}}+n\ln(\mathrm{t_{f}})+p\ln(\mathrm{ Pr}), \tag{8}\]  
where \(\mathrm{T_{f}}\) is in K, \(\mathrm{t_{f}}\) is in h, Pr content is in nominal % (see Table 1), and the multivariate fitting (Figure 9D) yields the parameters shown in Table 6. Table 6 also shows the overall changes (\(\Delta\rho\)) expected on varying each factor independently while keeping the other factors at their intermediate level; this is consistent with the corresponding correlations in Table 5.  
Similar transformations of independent variables were assumed to perform multivariate analysis for changes in conductivity and its activation energy, and also for the lattice parameter. This dependence for a generic property Y is given by:  
\[\ln(\mathrm{Y})\ =\ \ln(\mathrm{Y}_{0})-m/\mathrm{T_{f}}+n\ln(\mathrm{t_{f}})+p \ln(\mathrm{Pr}), \tag{9}\]  
and the relevant coefficients and impact on these properties are shown in Table 6. These values confirm the guidelines provided by the correlation matrix. The improved quality of multivariate fitting (Figure 9D-F) can be assessed by comparing the values of the correlation coefficient \(r\) shown in Table 6 with the correlation matrix in Table 5.  
Multivariate analysis of the effects on the onset of the pyrochlore phase cannot be based on log-log scales since this phase is absent in some samples or its residual content is below the detection limit of X-ray diffraction. In this case, multivariate analysis was performed without the transformation of independent variables, yielding:  
Figure 9:0  
where \(\mathrm{T_{f}}\) is in \({}^{\circ}\)C, \(\mathrm{t_{f}}\) is in h, and Pr content is in nominal %. The revealed effects of firing conditions and praseodymia additions on the properties of 5 YSZ ceramics are in agreement with the proposed pathway of the phase evolution during the direct firing of pelletized precursors and underline the key role of kinetic limitations. Increasing praseodymia additions results mainly in a larger fraction of the low-conducting pyrochlore phase and a lower density (a higher porosity). This, in turn, has a negative effect on the ionic conductivity, which is lower compared to dense 8 YSZ ceramics  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multicolumn{6}{c}{**Coefficients**} \\ \cline{2-7}  & **In(Y\({}_{0}\))** & \(m\) & \(n\) & \(p\) & \(r\) \\ \hline \(\rho_{\mathrm{exp}}\) & \(\mathrm{g/cm^{3}}\) & \(1.63\) & \(359.831\) & \(0.0020\) & \(-0.071\) & \(0.97\) \\ \(\Delta\rho_{\mathrm{exp}}\) & & & \(-0.06\)  
(Figure 8A). Increasing the time and temperature of firing promotes the homogenization of the samples with corresponding effects on the properties, but the statistical relevance of these variables within the selected ranges is lower compared to PrO\({}_{y}\) additions.
# Event-Based Path-Planning and Path-Following
## 2 Problem Formulation
### Problem Formulation  
The problem that is addressed in the paper consists in designing a control scheme that allows for the AUV governed by Equations (1) and (2) to follow a given reference path \(P_{ref}\), bypassing encountered obstacles detected by FLS.  
The designed system must meet the following requirements.  
1. The path-following controller (control laws for \(\mathcal{F}\) and \(\mathcal{G}\)) should be designed in such a way that the virtual target tracking errors satisfy \[\lim_{t\to\infty}|s_{e}(t)|\leq s_{e}^{\infty},\quad\lim_{t\to\infty}|y_{e}(t )|\leq y_{e}^{\infty},\quad\lim_{t\to\infty}|\psi(t)|\leq\psi^{\infty},\quad \lim_{t\to\infty}|v_{t}(t)-u_{0}(t)|\leq v_{t}^{\infty},\]where \(s_{c}^{\infty}\), \(y_{c}^{\infty}\), \(\psi^{\infty}\), and \(v_{t}^{\infty}\) are positive constants that characterize the tracking accuracy.
2. The paths generated by path-planning algorithms for detouring obstacles and returning to the reference path should be at least first-order differentiable (\(C^{1}\) continuous), lie no closer than safe distance \(D_{s}\) from obstacles, and satisfy kinematic constraints that are given by minimum turning radius \(R_{\min}\).
3. Whenever possible, the AUV must follow the reference path \(P_{ref}\), which means that after leaving \(P_{ref}\) to detour an obstacle or a group of obstacles the AUV must return, if possible, to \(P_{ref}\) at the point closest to the leaving point.
4. The system must be efficient for various environments, regardless of the number and location of obstacles.  
Note that most path planning studies focus on generating a safe path that leads the AUV to a static target point. In contrast, we consider a setting where the AUV should, as far as possible, follow a given reference path that passes through a sequence of waypoints. This requirement entails the need to change the active segment of the reference path carefully. We assume that the reference path, which is constructed by a global planner [6; 7] or received from the operator, does not change during the mission. Focusing on the path planning problem, we believe, for simplicity, that the desired speed \(u_{0}\) of the AUV while travelling along the path is constant and chosen in such a way that the AUV can follow it accurately.
## 4 Experiments and Results
### Performance Metrics  
We measure the following core performance indicators:  
* Coverage ratio (CR): The percentage of nodes that receive at least one copy of the broadcast packet.
* Packet delivery ratio (PDR): Measures the percentage of successfully received packets out of the total sent, indicating network reliability.
* Redundancy ratio (RR): The average number of duplicate broadcasts each node receives. This was one of our main factors to reduce in Model A.
* Average Delay (AD): The mean end-to-end delay until the last node receives the broadcast from the source.
# Comparison of Field and Laboratory Tests for Soil Suitability Assessment in Raw Earth Construction
## 2 Experimental Program
### Testing Methodology  
An initial literature review made it possible to determine the important characteristics of raw earth construction and to identify the various relevant tests. The state-of-the-art review by [18] has listed the physical, chemical, and mineralogical characteristics studied in the scientific literature according to the construction techniques. [PERSON] [28] carried out a regulatory assessment for the selection of soils for the construction of unstabilized earth to provide a means of comparison between the different approaches and recommendations proposed. They showed that the particle size distribution is the most frequently mentioned property, followed by the OM and salt content. The recommendations also mention plasticity, cohesion, shrinkage, and compactability, but often with imprecisions and variations. The nature of earth and water are key points to understanding the behavior of earth materials and thus, unsaturated soil mechanics can be applied [33]. Raw earth is considered a non-standard material in the building industry in most countries, but some nations have already developed or are in the process of developing regulations for earth construction like in New Zealand [41; 42; 43].  
The correlation between field tests and laboratory tests is crucial in understanding the overall soil characteristics. Field tests are often more time-efficient and cost-effective, but they provide a qualitative assessment [44]. Laboratory tests, on the other hand, offer a more detailed and precise evaluation of soil properties [45; 46].
# Research on Interference Mechanism of 25 Hz Phase Sensitive Track Equipment from Unbalanced Current
## 2 Cause Analysis of Abnormal Red Ribbon
### Generation Mechanism of Unbalanced Traction Current
#### 2.1.2 Adjacent Rail Traction Current Interference  
In order to reduce the interference caused by the adjacent track as much as possible, the rail spacing is generally recommended to be greater than 4400 mm. However, in the actual situation, the electromagnetic effect caused by the larger traction current will inevitably affect the weak electrical signal of the adjacent circuit. The schematic diagram is shown in Figure 2.  
In Figure 2, the traction current of the right rail is \(I_{1}\) and \(I_{2}\). Electromagnetic induction will induce an induced current of \(I_{0}\) in the left rail circuit, which will interfere with the left rail circuit [17]. The direction of the induced magnetic field is outward, and the size is represented by the sparsity of the dots.  
Figure 1: Traction current wheel rail circuit.  
It is assumed that the traction current of the two tracks on the right is in an ideal state, i.e., \(I=I_{1}=I_{2}\). The synthetic magnetic induction intensity can be expressed by Equation (1) [5,18].  
\[B=B_{1}+B_{2}=\frac{\mu I}{2\pi}(\frac{1}{a}+\frac{1}{b})=\frac{\mu I}{2\pi d} \tag{1}\]  
In Equation (1), \(\mu\) is the surrounding permeability; \(B\) is the synthetic magnetic field strength; the magnetic induction intensity generated by the two rails is \(B_{1}\), \(B_{2}\), direction from inside to outside; \(a\) and \(b\), respectively, represent the distance between the disturbed track and the two tracks on the right; \(d\) is the equivalent distance between the equivalent current and the disturbed track \(d=ab/(a+b)\).  
The magnetic flux in the closed loop area of the left rail \(\phi\) can be expressed as Equation (2).  
\[\phi=\int_{S}d\varphi=\int_{d}^{d+c}\frac{\mu I}{2\pi x}Idx=\frac{\mu I_{m}}{ 2\pi}\ln(\frac{d+c}{d})\sin(\omega t) \tag{2}\]  
In Equation (2), \(l\) is the length of the disturbed rail; \(I_{m}\) is the current amplitude; \(d\) represents the distance between the equivalent wire and the closed loop; \(c\) is the rail spacing; \(\omega\) is the angular frequency of the current.  
Correspondingly, the induced electromotive force can be expressed as Equation (3).  
\[E=-\frac{d\phi}{dt}=-\frac{\mu I_{m}\omega}{2\pi}(\ln\frac{d+c}{d})\cos(\omega t) \tag{3}\]  
From the analysis of Equation (3), it can be seen that when the traction current is transmitted in the rail, the induced current will be generated in the adjacent return circuit due to the induced magnetic field, which will invade the adjacent track and cause an unbalanced current, thus causing interference to the adjacent track circuit. The induced current is affected by track impedance, traction current, equivalent disturbance distance, rail spacing and surrounding permeability.
# Breaking Alert Fatigue: AI-Assisted SIEM Framework for Effective Incident Response
## 2 Background and Related Work
### Feature Engineering
#### 2.3.1 Feature-Based Approach  
Several studies have aimed to standardize the format of security alert logs to facilitate analysis and improve threat detection. [PERSON] et al. [42] highlighted the challenges associated with handling logs in various formats generated by different security appliances. These formats include the log event extended format (LEEF) [43], common event format (CEF) [44], intrusion detection message exchange format (IDMEF) [45], and the common event expression format (CEE) [46]. The lack of a standard format makes it challenging to analyze and compare threat alerts across different systems.  
To address this issue, [PERSON] et al. [47] proposed a model that can read and normalize different log formats using named-group regular expressions and a knowledge base. Similarly, [PERSON] et al. [48] introduced a new common log format that consolidates all necessary information from different formats into a single format. These efforts towards standardization can improve the efficiency and effectiveness of security operations and enable more accurate threat detection.
## 3 Results and Discussion
### Experimental Validation of the Proposed Formulas
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Total TAC (mg CaCO\({}_{3}\cdot\)L\({}^{-1}\))**} \\ \cline{3-6}  & & **X \({}^{a}\)** & **X + 596** & **X + 2979** & **X + 5958** & **X + 11,916** & **X + 17,874** \\ \hline \multirow{10}{*}{**MODEL**} & \multirow{3}{*}{**Y \({}^{b}\)**} & NA \({}^{c}\) & & & 3\%/153\% & 3\%/215\% \\  & & NA & & & 1\%/14\% & 3\%/13\% \\ \cline{2-6}  & & \multirow{3}{*}{**Y + 1000**} & 22\%/18\% \({}^{d}\) & & & \\  & & 8\%/13\% \({}^{e}\) & & & \\ \cline{2-6}  & & \multirow{3}{*}{**Y + 5000**} & & 28\%/7\% & & \\  & & & 5\%/7\% & & \\ \cline{2-6}  & & \multirow{3}{*}{**Y + 10,000**} & & 30\%/1\% & & \\  & & & & 7\%/2\% & \\ \cline{2-6}  & & 211\%/8\% & & 33\%/1\% & \\  & & 40\%/1\% & & 9\%/1\% & \\ \cline{2-6}  & & \multirow{3}{*}{**Y + 30,000**} & 302\%/9\% & & 35\%/1\% & \\  & & 61\%/1\% & & 8\%/2\% & \\ \hline \hline \end{tabular}  
* Initial TAC value of degassed digestate using Equation (11), X = 2598 mg CaCO\({}_{3}\cdot\)L\({}^{-1}\). \({}^{b}\) Initial FOS value of degassed digestate using Equation (12), Y = 1016 mg HAc\(\cdot\)L\({}^{-1}\). \({}^{c}\) MAPE is not available since X and Y calculated with new formulas were considered as real values in degassed digestate. \({}^{d}\) MAPE for TAC\({}_{\text{Nordmann}}\)/MAPE for FOS\({}_{\text{Nordmann}}\). \({}^{e}\) MAPE for TAC\({}_{\text{new}}\)/MAPE for FOS\({}_{\text{new}}\).  
\end{table} 
Table 3: MAPE (mean absolute percentage errors) for TAC/FOS with [PERSON]’s method (first line) and with new formulas (second line) for each sodium bicarbonate and sodium acetate condition in the digestate. effect on the output results (i.e., A and B values). However, one should be fully conscious of the fact that there exist different calculation methods to report the total VFA content; VFA could be calculated at first in the molar concentration and converted to an equivalent acetic acid mass concentration; the total VFA content could also be a simple sum of all mass concentrations of the concerned acids. The first method did not modify the FOS calculation, for the original [PERSON]'s method as well as the proposed model, while the effect of the second one could be significant.
## 3 Results
### Correlation between Three Asymmetry Indices  
The three asymmetric indices are designed to reveal the detailed diversity of vegetation growth during the increasing and decreasing productivity stages based on phenological signals and direct vegetation greenness status. To examine whether these indices are consistent with each other and can jointly explain the asymmetric features in vegetation growth throughout the growing season, the Spearman correlation analysis is conducted at pixel scale (Figure 7). The spatial pattern of correlation coefficients shows that the three indices are not significantly related to each other in the central portion of NEC that is mainly covered by cropland. This phenomenon may be a consequence of the variable growth process of crops influenced by human management activities. For instance, irrigation offsets water deficits during the arid spring thus enhancing vegetation growth. Additionally, fertilization added to the soil at various times may provide sufficient nutrients to improve growth of cultivated vegetation. Many more pixels show a negative relationship between AsylL and AsyR, which is particularly pronounced in the southeastern broad-leaved forest (Figure 7a). In other regions with natural vegetation, AsyL and AsyR have low correlations (around \(-0.4\)-\(0.2\)). AsyL is negatively correlated with AsyV in most areas of natural vegetation, with the exclusion of the eastern broad-leaved forest. Strong, negative relationships between AsylL and AsyV were found in the northern coniferous forest and steppe in the western part (Figure 7b). Compared to AsyL, AsyV has a more evident correlation with AsyR reflecting from the spatial distribution. For AsyR and AsyV, a dominant number of the significant pixels show a strong negative relationship, especially in the northern coniferous forest and western broad-leaved forest (Figure 7c).
# State-of-the-Art Internet of Things in Protected Agriculture
## 4 IoT Applications in Protected Agriculture
### Agri-food Supply Chain Traceability
It is worth noting that the current agricultural product safety traceability system only focuses on a certain level of supply chain or a certain type of goods. With the participation of multiple projects or multiple sessions, we believe that the focus of future research should be on more complex and systematic supply chains.
## 2 System Topology  
### Topology of Conventional PET  
Subject to the withstand voltage and power rating of existing power semiconductor devices, conventional PETs mainly employ the structure of CHB-MLI for medium- and high-voltage applications, as shown in Figure 1. Due to the single-phase structure of CHB-MLI, three sets of the same single-phase CHB-MLI PET are needed to meet the application scenario of a three-phase distribution network, which results in the extensive use of high-frequency transformers and controlled switching devices. As passive components constitute a large proportion of the volume and weight in PETs, the extensive use of high-frequency transformers is unfavorable for improving the power density of PET. In addition, the output DC voltage of a cascaded structure contains the double-frequency component which requires an additional double-frequency filter to ensure the output voltage quality.  
At the isolation stage of conventional PETs, the isolated DC/DC converters employ the connection of ISOP. Every port converter of the output stage connects to the low-voltage DC-BUS of the isolation stage in parallel, thereby causing the similarity of the voltage levels of the output ports. In this case, additional DC/DC converters need to be installed to obtain access to AC/DC grids with significantly different voltage levels, which will complicate the system and reduce economic benefits.
## 2 Methods  
### Design and Sensor Fabrication  
The sensor consists of a sensing whisker shaft, a coin vibrator, printed circuit board, an L-shaped clamp, and an elastomer skin. The system level block diagram is shown in Figure 2. The whisker dimensions and material are as important as changing the dimensions of the whisker changes the fundamental frequency. Since the fundamental frequency range of natural whiskers is between 35 Hz and 110 Hz [9], it is important that the artificial whisker's fundamental frequency lies in the same range. Using the analytical and FEM (Finite Element Model) 3D methods described in the later sections gave us the ability to determine the dimensions, material and bonding of the whisker to achieve this goal.  
Any material that has a high frequency response, and is miniature in physical size, is an ideal candidate for sensing applications. For these reasons, besides many other advantages [31; 32; 33], the PVDF film was chosen as the sensing material. One of the major advantages of using PVDF is that the whisker shaft structure is formed using PVDF film, and since the shaft itself is a sensing element, it eliminates the need for additional sensing elements at the base of the whisker, as reported in most previous designs, reflected in Table 1. This also leads to an overall, more compact sensing unit.  
A 0.2 mm thick PVDF film (Measurement Specialties, Hampton, VI, USA) was cut to make a 17 mm long and 3.75 mm wide shaft. Since PVDF can be damaged if subjected to high temperatures, two metallic electrodes (with height equivalent to the elastomer skin) were crimped to the base of the shaft. These electrodes were then soldered onto a printed circuit board (PCB). A coin vibrator that can vibrate up to 200 Hz was then clamped down on the PCB (1.6 mm FR-4) with the help of a plastic (Acrylonitrile-Butadiene-Styrene) L-shaped clamp, as shown in Figure 3. Since natural whiskers whisk in a frequency range from 5 to 25 Hz [10; 18; 19; 20], the 200 Hz vibrator was deemed sufficient for the task. A secondary small piece of PVDF was pasted on to the vibrator. Signals from the secondary PVDF was used to give feedback to the controller to cater for variations in the frequency of the vibrator. A shielded cable, with a Molex (Lisle, IL, USA) connector on the other end, was soldered onto the PCB. This integrated the sensor PCB with the main system PCB as shown in Figure 3. Finally, a 1.5 mm thick polydimethylsiloxane (PDMS)-based silicone elastomer with a Shore A hardness of 20 (Pinkysil, Barnes, Australia) was used to encapsulate the coin vibrator and the base of the PVDF based whisker shaft.  
Figure 2: The block diagram of the overall system. The microcontroller adds the ability of controlling the oscillation frequency of the coin vibrator, therefore giving the ability to mimic the natural whiskers.
# A New Method for Constructing the Static-Voltage-Stability Boundary with a Conversion Strategy
## 3 Analysis of Boundary Tracking Methods
### Typical Failure-Scenario Analysis  
Many simulations have found that inefficient or non-convergent boundary-point tracking occurs during the calculation process, mainly in the following two scenarios:  
(1) Tangent estimation stage  
In the process of tangent estimation, the calculation of the tangent involves the inverse operation of the gradient matrix. The dependent variable \(\Delta\mathcal{S}\) of the systems (10) exhibits high-dimensional characteristics, and the cost of inverse operation is relatively high. Furthermore, when the boundary presents a harsh form, it often consumes machine time and resources, as shown in Figure 4.  
Figure 4: The schematic diagram in the prediction stage.  
As is shown in Figure 4, due to the significant boundary shape changes at the boundary point \(\mathsf{S}_{3}\), the estimated value \(\mathsf{S}_{4}^{pre-\text{tan}}\) via the tangent method, i.e., Equation (11), is far from the true value of \(\mathsf{S}_{4}\), which can easily cause the phenomenon of difficult convergence in the correction stage. If the information regarding \(\mathsf{S}_{1}\), \(\mathsf{S}_{2}\) and \(\mathsf{S}_{3}\) is fully utilized in the prediction process and \(\mathsf{S}_{4}\)\(\mathsf{S}_{4}^{pre-fit}\) is fitted outside the boundary, the distance between the predicted point and the exact value is close and the divergent situation can be avoided.  
(2) Hyperplane correction stage  
The correction stage of the hyperplane is the process of correcting the \(\mathsf{S}^{\text{pre}}\) given in the tangent estimation stage to the boundary point \(\mathsf{S}^{\text{cor}}\). The solution of this stage mainly depends on the initial value and the correction method. The initial value is mainly determined in the predictor stage. In certain scenarios, the phenomenon of tracking boundary points becomes ineffective due to the influence of correction methods. Suppose there is a convergence domain as follows:  
\[\Theta_{k}=\{\,\mathsf{S}|\,\mathsf{S}-\mathsf{S}_{k}|<\varepsilon,\boldsymbol {\mathcal{V}}(\mathsf{S}_{k})=0\}, \tag{15}\]  
where \(\varepsilon\) is a small parameter constant characterized the shape of convergence domain. As is shown in Figure 5, the SNB saddle points along the P-V curve in the \(\boldsymbol{b}_{1}\) and \(\boldsymbol{b}_{2}\) directions are too far away, i.e., \(\Theta_{1}\cap\Theta_{2}=\varnothing\), resulting in a divergence simulation from \(\mathsf{S}_{1}\) to \(\mathsf{S}_{2}\).  
It is obvious that the corrected methods can affect the performance of computation. When the hyperplane in Equation (13) is applied, it is geometrically impossible to intersect with the boundary curve. Hence, Equation (13) cannot converge mathematically. Thus, more reliable correction methods need to be explored.