esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Fly Ash Obtained from the Incineration of Municipal Solid Waste in Agriculture
## 7 Discussion  
Analyzing the studies in the specialized literature, we found that by applying certain amounts of ash to the degraded soils, the following characteristics of the soil were improved: electrical conductivity, physical, chemical, and biological properties; texture; bulk density; porosity; the compactness of the soil was reduced; increased organic carbon and organic matter values; water holding capacity [59] of sandy soils increased when 40% fly ash was added to the soil; improved plant growth; and increased soil pH value. The texture of sandy and clayey soils can be changed to a clayey texture, and soil encrustation on the surface was reduced [107, 108, 109].Adding certain amounts of ash to the sandy soil improved the growth of certain crops and increased the amount of water available for the plants. The analysis of the soil to which different concentrations of ash were added revealed an increase in the potassium and phosphorus content of the soil, and the following microelements:manganese, calcium, nickel, copper, and zinc had progressive increases with the increase in the concentration of added ash.  
Water holding capacity and plant production were improved, and the acidity of clay-sand soil was reduced by adding 200-400 t/ha fly ash [28].  
The price of fly ash (AUS 2926) is lower than that of fertilizers (AUS 8800). Fly ash can be used to improve soil acidity by replacing agricultural lime, providing nutrients to plants, and improving the properties of sandy soil [107].  
Since the application of chemical fertilizers on the soil led to its degradation, it is necessary to find some solutions to be able to use the degraded soils.  
The use of fly ash in agriculture is profitable, and economically, 10 million hectares of land out of 150 million hectares of cultivated land can be safely occupied for fly ash application per year. At a fly ash dose of 20 mt per hectare, 200 million tons of fly ash would be consumed per year. Fields treated with fly ash would provide an additional yield of 5 million tons of grain per year [20]. Plant growth was twice as high in ash-applied soils compared to ash-free soils [51; 52].  
In soils where an amount of ash of 3000-5000 kg ha\({}^{-1}\) was applied, tree production increased and the acidity of forest soils was reduced [96].  
By applying a 5% amount of fly ash to the soil, peanut production increased from 0.19 to 2.3 t/ha [107].  
The benefits brought by ash in agriculture depend on the type of incinerated waste, the type of soil on which it is applied, the type of crops, the concentration of ash that is applied, and the treatment applied so as not to have a negative impact on the environment and agricultural crops [73; 82; 83].
## 3 Results
### Sensor Placement Combinations
\begin{table}
\begin{tabular}{c c} \hline \hline
**Combinations** & **F1-Score** \\ \hline \hline \multicolumn{2}{c}{1 anatomical location} \\ \hline \hline \begin{tabular}{c} Cervix \\ Chest \\ Lumbar region \\ Lumbar region \\ Right wrist \\ Left wrist \\ \end{tabular} & \begin{tabular}{c} 0.933 \\ 0.932 \\ 0.919 \\ 0.888 \\ \end{tabular} \\ \hline \hline \multicolumn{2}{c}{2 anatomical locations} \\ \hline \hline \begin{tabular}{c} Cervix, Chest \\ Cervix, Lumbar region \\ Cervix, Right wrist \\ Cervix, Left wrist \\ Chest, Right wrist \\ Chest, Left wrist \\ Chest, Left wrist \\ \end{tabular} & \begin{tabular}{c} 0.931 \\ 0.927 \\ 0.915 \\ 0.906 \\ 0.932 \\ 0.919 \\ 0.908 \\ 0.922 \\ 0.904 \\ 0.898 \\ \end{tabular} \\ \hline \hline \multicolumn{2}{c}{3 anatomical locations} \\ \hline \hline \begin{tabular}{c} Cervix, Chest, Lumbar region \\ Cervix, Chest, Right wrist \\ Cervix, Lumbar region, Right wrist \\ Cervix, Light wrist \\ Cervix, Light wrist \\ Chest, Light wrist \\ Chest, Light wrist \\ Chest, Light wrist \\ Chest, Light wrist \\ Light wrist \\ \end{tabular} & \begin{tabular}{c} **0.931** \\ 0.918 \\ 0.913 \\ 0.920 \\ 0.901 \\ 0.924 \\ 0.909 \\ 0.909 \\ 0.909 \\ 0.904 \\ 0.904 \\ \end{tabular} \\ \hline \hline \multicolumn{2}{c}{4 anatomical locations} \\ \hline \hline \
\begin{tabular}{c} Cervix, Chest, Lumbar region, Right wrist \\ Cervix, Chest, Lumbar region, Left wrist \\ Cervix, Light wrist, Light wrist \\ Cervix, Light wrist \\ Cervix, Light wrist, Light wrist \\ Cervix, Light wrist, Light wrist \\ Chest, Light wrist \\ \end{tabular} &
\begin{tabular}{c} **0.922** \\ 0.914 \\ 0.906 \\ 0.905 \\ 0.909 \\ 0.909 \\ 0.909 \\ 0.908 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Sensor placement combinations and corresponding F1-scores by considering data fusion of accelerometer, gyroscope, and magnetometer signals. In summary, the single chest IMU consistently outperforms other placements, demonstrating its superiority in capturing the necessary data for the investigated activities. Indicatively, Tables 2 and 3 provide the confusion matrices of the proposed LSTM model by considering all the anatomical locations and only the IMU at the chest, respectively. This comparison highlights the practicality of achieving high accuracy with fewer sensors, thereby reducing system complexity and cost. These confusion matrices offer a thorough breakdown of the model's predictions towards identifying activities where the model is performing well or poorly. The LSTM network with both all anatomical locations and using only the chest IMU demonstrate considerable overall performance, as shown by the diagonal dominance in the confusion matrices. Nevertheless, some classes, such as \"Bending\" and \"Lifting crate\", appear to exhibit slightly higher rates of misclassification. This could be attributed to the inherent similarity between these activities [24]. The more zeros appearing in Table 3 indicate that the LSTM model based on the data provided by the IMU on the chest is performing better in accurately classifying the data and minimizing errors.
# Neutralization Dynamics of Slow Highly Charged Ions in 2D Materials
## 1 Introduction
As soon as the ion enters the surface (or the electron jellium), the picture of single electron transfer and isolated ionic states breaks down. However, applying a similar model description inside the solid by assuming a peel-off of the outer electrons due to their large radii and subsequent screening from surface atoms inside the binding radius was attempted [29; 30]. The now recharged ion would capture electrons with significantly higher binding energies resonantly from the target atoms, stripping them from their core electrons. A hollow atom of second generation may be formed [31]. Subsequent non-radiative and radiative deexcitation in the hollow atom (or hollow ion if not entirely neutralized yet) and re-fill of emitted Auger electrons from the target leads finally to a neutral projectile in its ground state. This model for inside-the-solid neutralization appears reasonable if medium charged ions are applied and a solid of mostly heavy atoms as well as large thickness is used. Going to heavy ions with high charge states, a very light target material and single atomic thickness, we show that the model of peel-off and second generation hollow atom formation needs to be refined. Figure 1: (Color online) Potential energy for Ar and Xe ions as a function of the charge state, e.g., Xe\({}^{40+}\) has a potential energy of 38.5 keV. The potential energy release (see Figure 1) close to the surface may lead to significant material modification. Considering the amount of deposited energy, it may not be surprising that surface nanostructures from individual ion impacts are formed [33; 34; 35]. Depending on the material and the charge state, pits with desorption yields in the order of a few 1000 atoms/ion (Figure 3a), caldera-like structures (Figure 3b) and nm-sized hillocks (Figure 3c) are observed [33; 35; 36]. However, due to the nature of energy deposition, i.e., by electron transport and electronic deexcitation, only insulators and semi-conductors are prone to nanostructure formation by HCI impact [34; 37]. The potential energy is initially converted into electronic excitations at the surface (and kinetic energy of emitted electrons). Electron-phonon coupling leads, on a longer time scale, to heating of the lattice resulting in sputtering or desorption of atoms, nano-melting or sublimation. Because the potential energy deposition is the main driving force for material removal by HCIs, it is termed _potential sputtering_[38]. On metals, electronic excitations get screened and dissipated before electron-phonon coupling can set in. The lattice remains cold and therefore no nanostructures are observed.
## 5 Performance Testing
### Crowd Analysis Test  
As our usage scenario involves deployment in university lectures, labs and classrooms, it is important to determine the effects of crowds of people on the availability, stability and quality of the iBeacon signal. Since a human body is composed of approximately 70% water, it interferes with radio signals in the 2.4 GHz spectrum in which iBeacon operates ([PERSON] et al., 2017). Crowd analysis test was conducted in Room Z414 by comparing positioning results for the case with and without students in the room. The tests followed the same procedures for fingerprint collection, but two sets of random test points were collected. One set was collected in a classroom with 22 students, and another set was tested in an empty room. Two sets of data were then analysed for accuracy.  
The negative impact of the pedestrian was evaluated in the previous outdoor environmental conditions test, and the results suggest the human body attenuates wireless signals. The influence of a crowd in a room was tested in this experiment by mounting the iBeacons on the ceiling to enhance the line of sight for better signal transmission. Figure 14 compares the CDF of RMSE results from the classroom with 22 students and the classroom without students. The positioning performance of the empty room was better than that of the occupied classroom. The overall RMSE increased from 1.10 m to 1.23 m, with an increment error rate of 1.17% when the room was occupied with students.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Room** &
\begin{tabular}{c} **Longest Diagonal of** \\ **Room (m)** \\ \end{tabular} & **RMSE (m)** & **Percentage Error (\%)** \\ \hline Typical classroom & & & \\ (Room ZB214) & 18.6 & 1.54 & 8.26 \\ Computer laboratory & & & \\ (Room ZS1010) & 21.6 & 2.23 & 10.35 \\ Laboratory (Room V721) & 17.5 & 2.17 & 12.41 \\ Typical lecture theatre & & & \\ (Room Z205) & & & \\ Small lecture theatre & & & \\ (Room Z414) & 11.1 & 1.10 & 9.93 \\ \hline \hline \end{tabular}
\end{table}
Table 3: RMSE and percentage error results of the venue test.  
We anticipated that the density of students in classrooms would attenuate the signal to a certain extent. However, the iBeacon placement tests confirmed the superiority of ceiling placement scheme over wall-based scheme, and demonstrated that the negative effect of the human body on positioning accuracy performance could be mostly negated by increasing the line of sight propagation, gained by ceiling placement of the iBeacons. We recommend that any concerns regarding the attenuation of the signal by a crowd be allayed by adopting a ceiling placement scheme.
# Three Rapid Methods for Averaging GPS Segments
## 3 Three Rapid Methods
### Curvy Model B: Medoid  
Although the previous model works better than random guess, it is somewhat naive. We therefore consider Medoid as a more solid alternative. Medoid is defined as the sample whose total distance to all other samples is minimal, shown in Equation (2). This can also be applied to the segment averaging problem if we define suitable distance function between the GPS trajectories, as shown in Algorithm  
Figure 8: Examples of curved sets. The trajectories of minimum length and the median number of points are considered as representatives.  
Figure 7: Examples of applying the linear model.  
4. We use Hausdorff [12] as it is invariant to the travel distance and produces competitive results. Potentially better choices could be found in [11], but as Medoid is not the main strategy, we settle with the Hausdorff distance.  
```
1:Input: GPS trajectories: X
2:Output: representative: Y
3:\(\text{j}\leftarrow\text{find the medoid from X via Eq. (2)}\)
4:\(\text{Y}=\text{X}_{\text{j}}\);
```  
**Algorithm 4** (RapSeg-B): CurveSegment (X):  
In the context of GPS trajectories, we define Medoid as the input sequence \(x_{\text{j}}\) that minimizes the following function.  
\[argmin\sum_{i=1}^{k}Hausdorff\left(x_{i},\ x_{j}\right)^{2} \tag{2}\]  
The downside of the Medoid is its higher time complexity. Assuming that there are \(k\) trajectories to consider, each having at most \(n\) points, the time complexity of single Hausdorff computation takes O(\(n^{2}\)), and therefore the total time complexity of finding the Medoid is O(\(k^{2}n^{2}\)). It is possible to use faster distance measures like Euclidean and Fast-DTW [13], but issues like having trajectories of different lengths and different point orders can have unwanted effects. In addition, linking components from multiple libraries can easily cancel the effect of an otherwise good idea--as also happened in [8]. We, therefore, settle here with the standard Hausdorff function:  
\[Hausdorff(A,\ B)=\ max\left\{\begin{array}{ll}d(A,\ B)\\ d(B,A)\end{array}\ d\ (A,\ B)=\ sup_{a\in A,\ b\ \in B}\text{inf}dist(a,b)\right. \tag{3}\]  
where \(A\) and \(B\) are the two trajectories and \(dist\) is the Euclidean distance between two points.
## 4 Implementation issues
### Encoding and decoding  
The basic algorithms for encoding/decoding are as follows:  
Figure 1: Basic algorithms for **(a)** encoding, and **(b)** decoding.
# Inter-Session Repeatability of Marker-Less Motion Capture of Treadmill Running Gait
## 3 Results
### Intra-Trial Variability  
Average intra-trial variability for all lower extremity joint angles was less than 3\({}^{\circ}\) (Table 2, Figure 5). When averaged across all joints and planes, intra-trial variability was 1.7\({}^{\circ}\). Maximum variability (4.7\({}^{\circ}\)) occurred for knee flexion during the swing phase.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Rearfoot Strike**} & \multicolumn{2}{c}{**Non-Rearfoot Strike**} & \multicolumn{2}{c}{**Full Population**} \\ \cline{2-7}  & **n** & **\%** & **n** & **\%** & **n** & **\%** \\ \hline
**Sex** & & & & & & \\ Female & 10 & 47.6\% & 4 & 21.1\% & 14 & 66.7\% \\ Male & 3 & 14.3\% & 4 & 21.1\% & 7 & 33.3\% \\
**Footwear** & & & & & & \\ Adidas & 1 & 4.8\% & 0 & 0.0\% & 1 & 4.8\% \\ Asies & 2 & 9.5\% & 0 & 0.0\% & 2 & 9.5\% \\ Brooks & 0 & 0.0\% & 2 & 10.5\% & 2 & 9.5\% \\ Hoka & 3 & 14.3\% & 1 & 5.3\% & 4 & 19.0\% \\ Mizuno & 1 & 4.8\% & 0 & 0.0\% & 1 & 4.8\% \\ New Balance & 2 & 9.5\% & 1 & 5.3\% & 3 & 14.3\% \\ Nike & 1 & 4.8\% & 2 & 10.5\% & 3 & 14.3\% \\ On Cloud & 2 & 9.5\% & 0 & 0.0\% & 2 & 9.5\% \\ Saucony & 1 & 4.8\% & 2 & 10.5\% & 3 & 14.3\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Twenty-one participants completed the study procedures with sex, footwear and foot strike breakdown below. Foot strike was determined from visual inspection and center of pressure data.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Intra-Trial**} & \multicolumn{2}{c}{**Inter-Session**} & \multicolumn{1}{c}{**Variability**} \\ \cline{2-5}  & **Average (\({}^{\circ}\))** & **Maximum (\({}^{\circ}\))** & **Average (\({}^{\circ}\))** & **Maximum (\({}^{\circ}\))** & **Ratio** \\ \hline
**Ankle** & & & & & \\ Plantar/Dorsi & 1.8 & 3.4 & 1.2 & 2.0 & 0.67 \\ Ad/Abduction & 1.5 & 2.4 & 1.0 & 1.6 & 0.67 \\ Int/Ext. & 2.2 & 3.4 & 1.5 & 2.0 & 0.68 \\ Rotation & & & & & \\ Knee & & & & & \\ Flex/Ext & 2.6 & 4.7 & 1.5 & 2.3 & 0.58 \\ Ad/Abduction & 1.1 & 1.8 & 0.7 & 1.2 & 0.62 \\ Int/Ext. & 2.1 & 3.0 & 1.4 & 1.8 & 0.67 \\
**Hip** & & & & & \\ Flex/Ext & 1.6 & 2.5 & 1.4 & 1.9 & 0.88 \\ Ad/Abduction & 1.1 & 1.4 & 0.6 & 0.8 & 0.55 \\ Int/Ext. & 1.3 & 1.6 & 1.0 & 1.3 & 0.77 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Average and maximum variation metrics across entire running cycle within individual trial and between testing sessions. Variability ratio computed as quotient between average inter-session variability divided by average intra-trial variability.
## 4 Advancements in Remote Sensing Technologies for Coastal Monitoring
### Applications of Machine Learning and Artificial Intelligence in Coastal Monitoring
Finally, for AI models like WaterNet, scalability needs to consider how well the model fits and whether this can be achieved through partnerships with local stakeholders and open datasets, such as archival results from Landsat. Simplification of the AI models by reducing the required computation and merging them with low-cost data collection may enable greater technology access. Addressing these limitations means that WaterNet and similar models will enable wider global applicability. This ought to yield more robust means for monitoring shorelines and managing environments for underdeveloped countries. Although there has been high accuracy exhibited by AI models in shoreline segmentation, the challenge lies in their generalizability in diverse coastal morphologies. For instance, WaterNet [26] recorded a segmentation accuracy of \(>\)99% using high-quality images, which are unlikely to be obtained in urban or sediment-rich coastal environments without the need for model retraining. [PERSON] et al. [55] also noted that their model (J-Net Dynamic) worked better on natural beaches compared to artificial constructions. Furthermore, [PERSON] et al. [21] reported that regional datasets were used to boost machine learning classifications. These observations point to the fact that although AI is a powerful tool, it tends to need regional calibration and is unlikely to be directly transferable across multiplex environmental conditions without fine-tuning or model retraining. While the focus of this review remains shoreline-specific AI applications, recent progress in deep learning architecture such as MR-DCAE [93] and Cross-Triplet Context Learning [94] demonstrate transferable techniques that could be applied to coastal monitoring tasks. These models emphasize robust feature learning from limited labeled data and context-aware recognition, which are especially useful in scenarios with sparse ground truth or complex spatial features. Their methodological frameworks provide a foundation for future shoreline detection models to operate under unsupervised or semi-supervised conditions.
# Power Flow Calculation Methods for Power Systems with Novel Structure UPFC
## 2 The General Structure UPFC and Its Equivalent Model  
### The General Structure of UPFC  
As shown in Figure 1, the differences between the novel UPFC structures and the conventional structure include: (1) the shunt side of the UPFC may be connected to a different bus with the series side; (2) the series side of the UPFC may contains multiple converters. Considering the adaptability to the conventional structure and the convenience of explanation, this paper takes the structure shown in Figure 2a as the general structure of UPFC for analysis. Other structures will be discussed on the basis of this general structure.  
When the decoupled model is adopted, the UPFC and its connected line can be equivalent to the power injections of corresponding buses (which are \(P_{lm}+jQ_{lm}\), \(P_{ml}+jQ_{ml}\) and \(P_{ne}+jQ_{ne}\)), as shown in Figure 2b. The equivalent circuit of Figure 2a is shown in Figure 2c. In Figure 2c, \(\underline{u}_{l}\), \(\underline{u}_{k}\), \(\underline{u}_{m}\) and \(\underline{u}_{n}\) are the voltages of bus \(l\), bus \(k\), bus \(m\) and bus \(n\), \(\underline{i}_{1}\), \(\underline{i}_{2}\), and \(\underline{i}_{n}\) are the currents of corresponding lines. \(r_{lm}\), \(x_{lm}\) and \(b_{lm}\) are the resistance, reactance, and susceptance of the transmission line. \(\underline{u}_{B}\) and \(\underline{u}_{E}\) are the equivalent voltage sources of the series converter and the shunt converter, \(x_{B}\) and \(x_{E}\) are the reactance of the series transformer and the shunt transformer, respectively. \(P_{\text{dc}}\) is the active power exchanged between the series side and the shunt side of the UPFC.  
According to Figure 2c, the equivalent power injections of the UPFC under different control strategies can be deduced as follows.
## Materials and Methods  
### Mineral and Reagents  
The fluorite mineral used in the experiments was obtained from the company Minera de Orgiva S.L. (Spain). This mineral contains fluorite, calcite, and quartz as major mineral components. After a series of physical upgrading processes, such as crushing and screening, the granulometric curve of the mineral was determined using a motorized sieve shaker from 36 to 180 \(\upmu\)m (Table 1). The 80 percent passing sieve size (P80) was 86 \(\upmu\)m.  
Chemical composition was measured using X-Ray Fluorescence (XRF) with the equipment \"ARL Optim\"X WDRXF\" from Thermo Fisher Scientific, with 50 kV, Rhodium anode and LiF200, InSb and AX06 crystals, together with the Oxsas 2.2 software from Thermo Fisher Sc (Table 2).  
Oleic acid (OA) with 98% purity was used as a collector agent. Analytical grade of hydrochloric acid (99%) and sodium hydroxide (99%) were applied as pH regulator. Quebracho tree (97%) (QT), white dextrin (97%) (WD), potato starch (97%) (PT), carboxymethyl cellulose (97%) (CMC), and sodium hexametaphosphate (98%) (SHMP) were used as depressant agents. All the chemical reagents were supplied by Sigma Aldrich Co., except quebracho tree and carboxymethyl cellulose, which were supplied by Silvateam Co.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Particle Size (\(\upmu\)m)** & **Mass Weight (\%)** & **Cumulative Weight (\%)** \\ \hline \(>\)180 & 1.16 & 1.16 \\
150–180 & 3.45 & 4.61 \\
120–150 & 6.18 & 10.79 \\
90–120 & 7.67 & 18.46 \\
75–90 & 12.71 & 31.17 \\
52–75 & 19.04 & 50.21 \\
36–52 & 23.81 & 74.02 \\
0–36 & 25.98 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Particle size distribution.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**CaF\({}_{2}\)** & **CaCO\({}_{3}\)** & **MgCO\({}_{3}\)** & **SiO\({}_{2}\)** & **Fe\({}_{2}\)O\({}_{3}\)** & **Pb** & **Others** \\ \hline
44.12 & 37.22 & 2.80 & 14.04 & 0.68 & 0.28 & 0.85 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Chemical analysis of the mineral (mass fraction, %).