esa-sceva/satcom-chunk-collection huggingface dataset:
# Fully Open-Source Meeting Minutes Generation Tool
## 2 Related Work in Literature
### Video-Splitting Methods  
The system segments the recorded presentation video using a _scene-change detection_ algorithm to analyze differences between scenes. This can be classified into two approaches: image pixel data, which refers to the color values of each pixel, and image structure data, which considers the spatial arrangement of edges, shapes, and textures [5].  
Common algorithms, such as _mean square error (MSE)_ and histogram matching, offer faster scene detection but fail when pixel-level or geometric alterations occur [6; 7]. Incontrast, structure-based approaches (e.g., texture, contrast, and luminance) better preserve image information during compression [8]. The _structural similarity index measure (SSIM)_ focuses on structural changes rather than pixel differences, assessing image quality based on human visual perception [9].  
Given these insights, the _SSIM_ algorithm is ideal for our use case, which involves analyzing compressed presentation videos without geometric alterations. This choice will be tested in subsequent experiments to validate its effectiveness and justify its selection over other methods to ensure that it meets the specific needs of our system.
## 4 MPPT Control Design
### Current Regulation  
In order to track the estimated current \(I_{mpp}\), the PI controller and backstepping algorithm are designed to force the stack power \(P_{stack}\) to track the optimal point \(P_{mpp}\). By acting on the duty cycle of the boost converter, the operating current \(x_{1}\) will be forced to track, as much as possible, the current \(I_{mpp}\).
## 3 Test Results
### Effect of the Loaded Edge Distance  
Figure 11 shows the ratio of the test results of the fastener series with a loaded sheathing edge of 15 or 20 mm to those of the series with a loaded sheathing edge of 10 mm at the same temperature. Figure 11a reveals that the connection shear strength increases with increasing loaded edge distance at ambient and elevated temperatures. For the single-layer sheathing connection, the shear strength became approximately 1.4 to 2.1 times of that of GPB10S by increasing the edge distance from 10 to 20 mm; when the loaded edge distance increased from 10 to 15 mm, the shear strength increased by approximately 1.1 to 1.6 times, except at 400 \({}^{\circ}\)C. For the double-layer sheathing connection, increasing the loaded edge distance from 10 to 20 mm increased the shear strength by approximately 1.3 to 2.3 times. The shear strength of GPB15D was very close to that of GPB10D from 250 \({}^{\circ}\)C to 450 \({}^{\circ}\)C. At other temperatures, the shear strength was approximately 1.1 to 1.3 times greater by increasing the loaded edge distance from 10 to 15 mm. Moreover, it should be noticed that the effect of the loaded edge distance on the connection shear strength would become insignificant when it was long enough. In this case, the connection failure mode would likely become screw pull-through.  
Although significant scatter existed in the other test results, some basic findings are clear: (a) the absorbed energy of the connection increases with increasing loaded edge distance at ambient and elevated temperatures, as shown in Figure 11b; and (b) the effect of the loaded edge distance on the initial connection stiffness is insignificant at room temperature; the initial stiffness of GPB15D is close to that of GPB10D at elevated temperatures; and for other series, the initial stiffness also increased with increasing loaded edge distance beyond 100 \({}^{\circ}\)C, as shown in Figure 11c.
## 2 Methodology
### Model Overview
#### 2.2.2 Internal Variables  
Internal variables are intermediate values generated through arithmetic operations on design variables. They are still variables, whose values are affected by design inputs, but cannot be directly manipulated by a user, as the term \"internal\" implies. For example, the user cannot directly control the dimensions of remote sensing optics, but the combination of satellite altitude, field of regard, and GSD requirement determines the necessary dimensions of optical instruments. The list of internal variables is provided in Table 3.
# Satellite Measured Ionospheric Magnetic Field Variations over Natural Hazards Sites
## 5 Results
### Earthquakes
#### 5.2.4 Earthquake M7.1 Ridgecrest, USA  
The Ridgecrest, USA, EQ (USGS ID ci38457511) had a magnitude M7.1 with a focal depth of 8 km and is the largest one from the corresponding seismic sequence. The distribution of the magnetic field fluctuations are shown in Figure 6. CDSM shows a wavelike behaviour, FGM1 a sinusoidal modulation along the bands.  
Figure 6: CSES investigation of the earthquake USGS event ci38457511 with magnitude M7.1, Ridgecrest, USA, focal depth 8 km, with a spatial dimension (box size) of \(\pm\)5.5 degrees, only crossings during night times are considered. **Top**: Distribution of CSES CDSM magnetic field fluctuations grouped in individual frequency bands. CDSM is found to display a wavelike behaviour. The time span around the EQ is \(\pm\)2 segments, i.e., in this case \(\Delta\)t = \(\{-4.73\), \(-3.75\), \(-2.76\), \(0.27\), \(1.25\)] days shift to the EQ at 2019-07-06T03:19:53. **Bottom**: Distribution of the CSES FGM1 magnetic field fluctuations after wavelet analysis. The colour coding is the same as in Figure 2, geomagnetic activity is \(|\)Dst\(|\)\(\leq\) 17 nT for the selected time span.
# Improving Real-Time Position Estimation Using Correlated Noise Models +
## 3 Position and Uncertainty Estimation Methods
### Kalman Filter with Higher Order Noise  
We formulate sophisticated methods that use Bayesian inference of the position and its uncertainties. Kalman filters [4] are a type of sequential Bayesian inference algorithms, often useful for real-time state estimation. An improved version, the Unscented Kalman Filter (UKF), allows state estimations on nonlinear systems by approximating the prior distribution by well-chosen \"sigma\" points [33; 34]. The UKF, like other Kalman filters, assumes that a state, \(\mathbf{x}_{n}\), changes between two consecutive measurements, and this change can be described \(f:\mathbf{x}_{n}\mapsto\mathbf{x}_{n+1}\), called the forward map. Furthermore, the measured quantities fully determine the state \(\mathbf{x}\), i.e., \(\mathbf{g}:\mathbf{x}\mapsto\mathbf{\hat{g}}\). Since one's knowledge of the system of interest is incomplete, one often models it by a stochastic process, in which a random variation, known as process noise, is also added to the forward map. Likewise, \(\mathbf{g}\) may also be contaminated by noise, representing random measurement errors. The Augmented-state UKF (AUKF) is a variant of the UKF, which describes the measurement and process noise distributions by \"sigma\" points [33; 34; 35; 36; 37]. The AUKF is described in Algorithm A1 located in Appendix A. Most Kalman filter schemes require the process and measurement noise to be additive; however, within the AUKF, the forward map and observation map are allowed in any form. The AUKF is therefore sufficiently flexible to handle quite general processes. Such processes include simultaneous estimation of OU parameter \(\theta\), which we can constrain to be positive by estimating its logarithm, and possibly moving-average processes [8; 33; 38; 39; 40], although moving-average processes would require a different choice of sigma points from those described in Algorithm A1. The AUKF will also be able to handle nonlinear forward maps due to some complicated sensor dynamics.
# Preventing Overturning of Mobile Cranes Using an Electrical Resistivity Measurement System
## 3 Methodology
### Underground Safety Evaluation Algorithm  
To analyze the causes of ground subsidence through electrical resistivity exploration, it is essential to understand the electrical characteristics of the ground, which are influenced by various materials. Factors such as soil water content, porosity, and layered structure affect these characteristics, necessitating a thorough analysis of the measured data under different environmental conditions [56; 57; 58; 59]. Typically, electrical resistivity exploration data are analyzed through an inversion process using mathematical modeling and numerical solutions to interpret the subsurface structure [60].  
This study focuses on analyzing the causes of ground subsidence related to soft ground influenced by groundwater and small cavities, such as sinkholes. In general, from an electrical/electronic engineering perspective, areas with high water content, such as those saturated with groundwater, exhibit low electrical resistivity, whereas regions with high porosity, such as cavities, exhibit high resistivity [30]. Based on these principles, this study proposes a method for determining ground safety by identifying abnormal resistivity zones. The pseudo-depth level of the measured apparent resistivity data can be expressed as a time series, as illustrated in Figure 7. The \(x\)-axis represents the measured depth of the ground, and the \(y\)-axis indicates the electrical resistivity. An average electrical resistivity range for the measured data is established, and any abnormal resistivity zones are identified based on significant deviations of either high or low resistivity values from this average. The average resistivity range can be reliably obtained by exploring the measurement area. High loads during mobile crane operations can lead to ground subsidence, posing safety risks. Therefore, the underground safety evaluation algorithm can serve as a qualitative criterion to assess the likelihood of groundwater presence or cavities beneath the ground by observing abnormal resistivity zones using only pseudo-depth levels.  
Figure 6: Foldable electrode device and water supply device.
## 3 Results
### Spatiotemporal Distribution of Water Quality Indicators
#### 3.2.2 Total Phosphorus (TP)  
The spatiotemporal distribution of TP was similar to that of NH\({}_{3}\)-N. The monthly mean concentration ranged from 0.38 to 0.49 mg/L, again confirming serious eutrophication (Figure 9). The TP concentration in the dry period remained at around 0.38 mg/L, began to increase during the onset of the peak rain season in April, reached its maximum value in May, and then slowly decreased. The TP concentration remained stable during the summer flood period and decreased during the transition into the dry season. Figure 10 shows the result of the spatial distribution of the TP concentration. Each figure uses the same color band, with one color representing one TP concentration and one figure representing one period. Similar to NH\({}_{3}\)-N, TP reached its widest spatial distribution during the peak rain season (Figure 10b), followed by the flood period (Figure 10c) and dry period (Figure 10a). During the peak rain season, the concentration of TP in water body was high, which was higher than 0.4 mg/L. During the wet season, the concentration of TP was high, mainly in the range of 0.32-0.36 mg/L. During the dry season, the concentration of TP is relatively low, mainly in the range of 0.24-0.30 mg/L. The TP concentration in Sanyang wetland was high in the peak rain season and wet season, and the TP concentration in the severely polluted area and the wetland outlet area was at a higher level in each section.  
Figure 10: _Cont._  
Figure 9: Monthly variations in total phosphorus (TP) in the Sanyang wetland in 2016.
## 4 Experiment and Results
### Interest Topics Experiment  
During the first experiment, LDA models were built based on BOW to extract interest topics for each user. The inputs for the LDA model are a dictionary and corpus. Dictionaries refer to unique words in the document and possess a unique id for each word. Corpus refers to the number of times a specific word appears in each document. The training phase of LDA is considered to adjust some parameters to get optimal performance--the number of topics extracted was 8.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Twitter dataset users & 100 \\ Passive users & 24 \\ Active users & 73 \\ Male users & 50 \\ Female users & 50 \\ Number of tweets & 86,256 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Dataset Description.  
The results of the LDA model are a group of words with which each topic of interest is correlated, each with its own probability in relation to the topic. Of interest is that, in some situations, topic keywords might not be sufficient to understand what a topic is about. Consequently, it must find the dominant topic, which is the topic number in the user's tweets with the highest percentage contribution. This research depends on five top dominant topics to infer interest topics for each user.  
The extracted LDA topics for all users are illustrated in Figure 8. A range of interesting topics were identified, including those such as racism, games, and videos.  
This research also analysed the lengths of interest topics for each individual user. It has been observed that the lengths of users' interests differ from one person to another, due to the fact that each person hold different interests from others, as shown in Figure 9. To illustrate this, consider that, while a given individual may have a sole focus, another may be concerned with six.  
During the second experiment, k-means models were implemented based on the elbow method of choosing the optimal value for k. The main purpose of the elbow method is to ensure good results for the cluster. The optimal value of k is known when the inertia does not decrease significantly, as shown in Figure 10, where the optimal value of k is 12.  
Figure 8: The Extracted LDA topics.  
Figure 9: Lengths of Interest Topics.
# Imaging Electron Dynamics with Ultrashort Light Pulses: A Theory Perspective
## 1 Introduction
However, an analysis of a time-resolved photoelectron probability within the QED framework has shown that this expectation is also incorrect [32]. Thus, an intuitive approach to interpret an outcome of a time-resolved measurement can lead to erroneous results. In this article, we review the theoretical framework based on the QED, which has been developed to describe the interaction of an ultrashort light pulse with a coherently evolving electronic system. In particular, we will concentrate on three techniques, which have been suggested to image electron dynamics in real space and real time, and analyzed within the QED framework, namely, time-resolved NRXS, time-resolved RXS and TRARPES. We will show that results of the QED analyses of the corresponding time-resolved signals can be represented in a common way although these techniques relay on different processes. Time-resolved signals obtained by means of these techniques have a similar temporal dependence on the evolution of the electronic state of a system being probed, which does not coincide with the temporal dependence of its electron density. We will discuss that accounting for certain features characteristic to the corresponding processes, analogous time- and space-dependent quantities of a nonstationary electronic system are encoded in corresponding time- and momentum-resolved signals. A common procedure based on a Fourier analysis of these signals can be applied to disentangle these quantities. This review article is organized as follows. We describe a coherently evolving electronic system from a quantum-mechanical prospective in Section 2. In Section 3, we introduce the basics of the QED treatment of the interaction of an ultrashort light pulse with such an electronic system. We review the QED analysis of time-resolved NRXS from a nonstationary electronic system and its results in Section 4. The theory and suggested applications of time-resolved RXS employed for imaging of nonstationary electronic systems are reviewed in Section 5. The TRARPES technique is considered in Section 6. We discuss a limitation on a probe-pulse duration for an appropriate time-resolved measurement in Section 7. We summarize the review in Section 8.
## 3 Background and Theory
### High-Pass Filtering  
The next preprocessing step for filtering our EEG data involves using high-pass Butterworth filters (HPFs) [24], which allow higher-frequency components of EEG signals to pass while diminishing lower-frequency components. These filters are widely used to reduce low-frequency noise, effectively preserving target higher-frequency brain activity. HPFs are particularly useful for mitigating artifacts, correcting electrode drifts during recordings from scalp-based BCI systems and improving the quality of EEG data analyzedin this study. The mathematical expression for a Butterworth high-pass filter of order \(N\) with a cutoff frequency \(\omega c\) is generally given in Equation (2):  
\[C(z)=\frac{B(z)}{A(z)} \tag{2}\]  
Let \(B(z)\) and \(A(z)\) denote the \(Z\)-transforms of the filter coefficients \(b\) and \(a\), respectively. A fourth-order high-pass filter is implemented, with its transfer function incorporating \(b_{i}\) and \(a_{i}\), as given by (3):  
\[C(z)=\frac{b_{0}+b_{1}z^{-1}+b_{2}z^{-2}+b_{3}z^{-3}+b_{4}z^{-4}}{1+a_{1}z^{-1 }+a_{2}z^{-2}+a_{3}z^{-3}+a_{4}z^{-4}} \tag{3}\]  
To configure the filter, we compute the coefficients \(b\) and \(a\) for a fourth-order high-pass Butterworth filter with a normalized cutoff frequency \(\omega c\). The application of this filter to EEG data requires executing multiplication in the \(Z\)-domain, as indicated in Equation (4):  
\[\mathit{Filtered}\ \mathit{Signal}(z)=C(z)\cdot EEG\ \mathit{Signal}(z) \tag{4}\]
## 3 Numerical Simulation
### One-Dimensional Numerical Simulation
#### 3.1.2 History Matching  
Taking the cumulative oil production and H\({}_{2}\)S production of the one-dimensional CSS experiment as the fitting targets, the matching was carried out by mainly adjusting the relative permeability curve and kinetic parameters, such as activation energy and frequency factor in the developed numerical simulation model. As shown in Figure 8, the simulated cumulative oil production and H\({}_{2}\)S production are consistent with those  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Reservoir temperature, \({}^{\circ}\)C & 46 \\ Reservoir pressure, kPa & 8000 \\ Rock compressibility, 1/MPa & \(4\times 10^{-5}\) \\ Rock heat capacity, J/(m\({}^{3}\cdot\)C) & \(2.58\times 10^{6}\) \\ Thermal conductivity of rock, J/(m\({}^{3}\cdot\)d\(\cdot\)C) & \(1.634\times 10^{5}\) \\ Thermal conductivity of water phase, J/(m\({}^{3}\cdot\)d\(\cdot\)C) & \(5.99\times 10^{4}\) \\ Thermal conductivity of oil phase, J/(m\({}^{3}\cdot\)d\(\cdot\)C) & \(9.77\times 10^{3}\) \\ Thermal conductivity of gas phase, J/(m\({}^{3}\cdot\)d\(\cdot\)C) & \(1.9\times 10^{3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: The reservoir fluid and rock properties used in the model.  
Figure 6: Viscosity–temperature curve of the heavy oil.  
Figure 7: Relative permeability curve: (**a**) the oil–water relative permeability curve; (**b**) the gas–liquid relative permeability curve.  
obtained by experiments. The average absolute errors between the measured and simulated cumulative oil production and H\({}_{2}\)S production were 4.31% and 12.46%, respectively. All the results indicate that the measured and simulated H\({}_{2}\)S production had a good consistency, and the developed model can accurately predict H\({}_{2}\)S production during CSS processes. Therefore, the calibrated parameters were used to predict H\({}_{2}\)S production in the following reservoir numerical simulation.
# Hard Fusion Based Spectrum Sensing over Mobile Fading Channels in Cognitive Vehicular Networks
## 2 System Model and Problem Formulation  
In this section, we briefly introduce the network model, the channel model, the sensing model, and the assumptions made in the CVNs under consideration.
# Quantifying Total Phosphorus and Heavy Metals in Residential Septage
## 3 Results and Discussion
### Land Application of Septage and Potential Environmental Impacts
Similar concerns exist regarding the accumulation of heavy metals in land application sites and the potential environmental and public health impacts. Of the heavy metals analyzed in the current study, copper, nickel, and zinc are essential plant micronutrients, whereas cadmium and lead are not essential elements [94]. The long-term use of septage at application sites may cause an accumulation of highly persistent toxic metals in soils that may inhibit crop growth and eventually translocate into plant tissues [15,94,100]. Consuming crops grown in metal-contaminated soils can endanger human health [15], potentially resulting in the development of acute or chronic diseases [100]. To mitigate environmental and public health damages from land application, the US EPA [69] mandates (40 CFR 503.13) that any biosolid material applied must not exceed the ceiling concentrations for heavy metals that pose a risk. The ceiling concentrations and mass loads for the 5 heavy metals focused on by the current study are summarized in Table 3.  
In the current study, heavy metal concentrations were substantially lower than the ceiling concentrations mandated by the US EPA (Table 3), indicating that it may take long-term application of septage to exceed these concentrations. Septage is monitored before it can be applied to land to ensure compliance. Compliance samples should be a homogenized sample of septage (scum, liquid, and sludge). If the compliance sample only contains the liquid portion of wastewater, past studies and data from the current study suggest that the concentrations of TP and heavy metals may be underestimated. Continued application of septage with underestimated pollutant concentrations and masses may likely result in environmental health risks (e.g., eutrophication from TP leaching, metal accumulation in the soil profile, or leaching to water resources). Toxic metal accumulation in the tilled horizon of agricultural soils could result in lower crop yields or bioaccumulation in plant matter, resulting in human health risks. Currently, the US EPA's [13] guide for land appliers contains a monitoring section with general recommendations, but it does not emphasize the importance of collecting a representative composite sample of all septage layers in the context of environmental and public health. By excluding the scum and sludge layers in compliance samples, the mean concentrations and masses of TP could be underestimated by up to 15 times. Similarly, the mean concentrations and masses of heavy metals may be underestimated by between 1.4 and 21 times depending on the metal and the source of septage.