esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results  
### Evaluation of VI-Based SOS against in Situ GPP-Based SOS  
SOS values of the VIs for the pixel location corresponding to FLUX towers were plotted against SOS estimates from in situ based GPP flux measurements to evaluate which of the three vegetation index SOS estimates that represent ground conditions most accurately (Figure 2). From the data that was available in this study, it can be seen that the relationship between satellite and ground observations is rather low. PPI SOS is better correlated with the GPP (\(r=0.50\); \(p<0.01\)) phenology as compared to EVI (\(r=0.11\); \(p=0.16\)) and NDVI (\(r=0.08\); \(p=0.24\)), indicating a more accurate capturing of SOS by PPI. NDVI shows SOS dates that start much earlier than SOS from GPP, PPI and EVI Both EVI and PPI SOS dates are on average relatively close to GPP-based SOS (average \(\pm\) std. dev. GPP SOS = 128 \(\pm\) 21; PPI SOS = 126 \(\pm\) 18; EVI 116 \(\pm\) 25; and NDVI = 99 \(\pm\) 21). Separating the data set into different land cover classes did not improve the relationships (Figure S1 and Table S1 in Supplementary Materials).  
Seasonality of the VIs (estimated from the average of pixels used in VI phenology evaluation against GPP phenology) shows that NDVI seasonality differs significantly from the PPI seasonality by the earlier increase in spring and later decrease in autumn (Figure 2D). The average EVI curve follows the PPI curve closer than the NDVI curve with regards to spring increase and autumn decrease, but with less pronounced changes during the transition periods between growing and non growing seasons. Especially for NDVI, an abrupt shift in values can be observed (e.g., year 2000, 2005, 2007-2008, 2010-2011 and 2013), coinciding with changes in snow cover (checked by using the MODIS/Terra Snow Cover 8-Day product, MOD10A2).  
between PPI SOS and NDVI/EVI SOS is also observed (7-16 days difference). For areas of grassland and open shrulands, PPI SOS, however, precedes NDVI and EVI SOS with similar difference values to NDVI and EVI ranging from 5-11 days.  
Figure 3: _Cont._
# Boiling Flow Pattern Identification Using a Self-Organizing Map
###### Abstract  
In the paper, a self-organizing map combined with the recurrence quantification analysis was used to identify flow boiling patterns in a circular horizontal minichannel with an inner diameter of 1 mm. The dynamics of the pressure drop during density-wave oscillations in a single pressure drop oscillations cycle were considered. It has been shown that the proposed algorithm allows us to distinguish five types of non-stationary two-phase flow patterns, such as bubble flow, confined bubble flow, wavy annular flow, liquid flow, and slug flow. The flow pattern identification was confirmed by images obtained using a high-speed camera. Taking into consideration the oscillations between identified two-phase flow patterns, the four boiling regimes during a single cycle of the long-period pressure drop oscillations are classified. The obtained results show that the proposed combination of recurrence quantification analysis (_RQA_) and a self-organizing map (_SOM_) in the paper can be used to analyze changes in flow patterns in non-stationary boiling. It seems that the use of more complex algorithms of neural networks and their learning process can lead to the automation of the process of identifying boiling regimes in minichannel heat exchangers.  
boiling flow patterns; two-phase flow instability; flow boiling; self-organizing map; recurrence quantification analysis +
Footnote †: journal: Applied Physics Letters B
# Fire-Induced Changes in Soil and Implications on Soil Sorption Capacity and Remediation Methods
## 4 Fire-Induced Changes in Soil and Choice of Soil Remediation Technologies
### Chemical Remediation Methods
#### Encapsulation  
Encapsulation as a remediation method involving mixing contaminated soil with concrete, lime or asphalt to immobilize the soil, preventing spread of the contamination [136]. Recent studies have also highlighted the potential of metallic nanoparticles of Fe [145], Co [146], and Ni [147] for use as encapsulators because of their unusual magnetic, optical, and electrochemical properties and chemical catalytic activities. pH-dependent precipitation, redox-controlled precipitation of insoluble compounds, sorption potential, and incorporation into crystalline components are some mechanisms involved in the interaction between soil, contaminants contained in the soil and binder used for encapsulation. This method is similar to soil isolation except for the fact that in encapsulation, the whole contaminated soil volume is immobilized. The choice of material used for encapsulation varies with the type of contaminant. In heavy metal contaminated soil, cement is commonly used because of its high pH which is able to precipitate the metals. Polyvinyl alcohol, chitosan, alginate, agar, polyacrylamide and polyurethane have been successfully used to prevent leaching of organic materials from contaminated soil by encapsulation [148]. Successful use of encapsulation to remediate fire-affected soils may depend on the material used for encapsulation because of the prevailing pH condition of the soil after the fire event and the expected use of the encapsulated material.
###### Abstract  
This study involved performing tests to detect Parkinson's disease (PD) based on voice changes, including speech phonation, articulation, and prosody, in patients with PD using different types of speech signal. For this purpose, during the first stage of the investigation, three separately modeled PD diagnosis systems using different types of speech signal characteristics were defined. The classification results were obtained when the SVM method was applied compared to the k-nearest neighbors method applying 1-nn in general. The tests were carried out within the database of patient voice recordings collected in the Department of Neurology at the Medical University of Warsaw. The second stage of the research was the selection of descriptors. The SFFS (sequential floating forward) method was applied together with the k-nn and SVM classifier. These subsets were used to create a new system based on a descriptor loose integration. Within the experiments conducted, general diagnosis results lead to improved classifier performance only in certain cases. This prompted the authors to conduct the last experimental research stage--selection at the feature fusion stage. Feature evaluation ranking methods (Relief, Fisher Score, F-tests, Chi-square) were applied for this purpose. With 10-fold validation, the k-nn method achieved an recognition rate of 92.2% with 91.1% sensitivity and 93.3% specificity.  
voice processing: Parkinson's disease; speech signal; modality integration 1
## 2 Materials and Methods
### Measurements of CO\({}_{2}\) Fluxes  
The Avignon site is part of the CarboEurope-IP Regional Experiment [42] and the CarboEurope-IP Ecosystem Component. CO\({}_{2}\) fluxes were measured continuously using the eddy covariance (EC) technique at 30 min intervals by means of a Young 81000 3D sonic anemometer (Young, Traverse City, MI, USA) and a LI-7500 open path CO\({}_{2}\)/H\({}_{2}\)O analyzer (Li-Cor, Lincoln, NE, USA). We used Level 4 (i.e., quality-checked and gap-filled) data products from the CarboEurope database [33]. The footprint analysis suggests that--on average--85-90% of the fluxes originated from the studied field. Because EC measures Net Ecosystem Exchange (NEE), which is the algebraic sum of the CO\({}_{2}\) fixed by plants (GPP) and ecosystem respiration (\(Re\)), NEE was partitioned to derive GPP. For this purpose, day-time measurements were fitted to a hyperbolic dependence with PAR according to [43]:  
\[\text{NEE}=\frac{a_{1}\times\text{PAR}}{a_{2}+\text{PAR}}+R_{e} \tag{6}\]  
with fitting parameters \(a_{1}\), \(a_{2}\), and \(R_{e}\). With this formulation, the estimate of NEE at zero PAR gives the mean daytime respiration \(R_{e}\), which was subsequently fit to an exponential function of temperature and extrapolated to daytime temperature. The mean value for \(R_{e}\) during the measurement period was \(4.6\pm 1.0\)\(\upmu\text{molC}\cdot\text{m}^{-2}\cdot\text{s}^{-1}\) (1 sd). This is a rather moderate value compared to the total carbon flux. By subtracting \(R_{e}\) from NEE, we computed the half-hourly average of GPP as:  
\[\text{GPP}=-\text{NEE}+R_{e} \tag{7}\]
# Filtering Organized 3D Point Clouds for Bin Picking Applications
## 3 New Filtering Method
are considered outliers since they are floating in space unconnected to the remaining data points acquired from surfaces of real, physical objects (which are in mechanical equilibrium, supported by the bottom of the bin). Finding two identical clusters on two z-cuts \(\mathbf{Z}_{d}\) and \(\mathbf{Z}_{d+\Delta d}\) is expected to be rare for datasets of reasonable quality as the majority of clusters are in the configuration 2, i.e., one is a subset of another, see clusters \(\mathbf{C}_{d}(i_{2})\) and \(\mathbf{C}_{d+\Delta d}(j_{2})\) in Figure 6. Note that z-cuts can be constructed only from the organized point cloud and the proposed method is applicable only to datasets in this format. In Figure 7, a schematic flowchart of the new procedure is shown. In a nutshell, the new procedure tags the outliers by running a loop indexed by depth \(d\) from \(d_{min}=\Delta d\) to \(d_{max}\) with step \(\Delta d\). For each depth \(d\), it checks two binary matrices, \(\mathbf{Z}_{d}\) and \(\mathbf{Z}_{d+\Delta d}\) defined in (2) and searches for pairs of identical clusters \(\mathbf{C}_{d}(m)\ \equiv\mathbf{C}_{d+\Delta d}(m^{\prime})\) which have areas smaller than a predefined threshold, i.e., \(\mu_{d}(m)=\mu_{d+\Delta d}(m^{\prime})<\mu_{thr}\). If any such \(M\) pairs are found, then the indices corresponding to the pixels in the identified clusters define a subset of 3D points, which are separated from all other data points by at least \(\Delta d\) distance. As explained earlier, such floating points cannot be acquired from real, physical objects and are therefore labeled as outliers in the organized 3D point cloud and are appended to the list of outliers \(LO\). As mentioned earlier in Section 2, generic SOR algorithms filter out outliers by thresholding a statistical parameter calculated for each datapoint, e.g., the mean or standard deviation of distances to the nearest points. The distances calculated for points in the clusters identified by the proposed method can be quite small, and then, SOR-type procedures would misclassify them as inliers. Filtering adopted by the proposed method, as explained in Figure 6, is based on a different principle and is not vulnerable to the pitfalls of thresholding. This difference causes efficient filtering by the proposed procedure and SOR missing a majority of the outliers relevant for bin-picking, as demonstrated on datasets acquired in the experiment described in the next section. Figure 6: Schematic drawing illustrating three possible relations between clusters on two z-cuts: \(\mathbf{Z}_{d}\) and \(\mathbf{Z}_{d+\Delta d}\).
# Service for Deploying Digital Twins of QKD Networks
## 5 Validation and Results
### Functional Validation  
The first experiment presented is a functional validation for the main functionalities of the developed solution, such as key formation and synchronization between nodes. The scenario used in this experiment consists of three pre-deployed machines over the OpenStack infrastructure in 5 TONIC. Each of the virtual machines operates as a site in a QKD network. A scheme of this scenario can be found in Figure 8.  
In this experiment, the QKD network client defines a single application using the QKD Network client package, thus communicating with the QKD network digital twin making use of a standardized API (the ETSI QKD 004 [27]), in the same way he or she would communicate with a real QKD network.  
In each site of the scenario, the application creates key streams with the neighboring sites, retrieves the exchanged keys, and checks that they match for the connected nodes. The exact sequence of actions executed in the test is shown in Figure 9.  
Following the scenario topology, which consists of three sites connected in sequence, two key streams are created: one between Module A and Module B, and one between Module B and Module C. Once 20 keys have been exchanged in each stream, the test application retrieves them at all the nodes and compares them by pairs, verifying that the keys of the neighboring nodes match.  
The result of this validation was positive, proving the correct functioning of key formation processes and synchronization tasks.
## 3 Results and Discussion
### Properties of CO2 Content and Lifetime in MNB Water  
The CO2 content in the MNB water changed with the generator cycle running time and the storage time at room temperature, as shown in Figure 10. This shows that the CO2 content in the water increased with the cycle time and then increased in a stable linear fashion. The CO2 content in the water reached the maximum value of approximately 6.5 mg/L in the linear increase stage when the generator running time was 30-40 min. After that, the CO2 content in the water was basically in a stable state. This result was consistent with the variation of the dissolved oxygen content in the MNB water in [29], which verified that MNB technology can increase the solubility of gas in water.  
After the 60 min experiment, the MNB water sample was stored at room temperature to investigate the storage property of CO2 MNPs. The change in the CO2 content in the MNB water with the storage time is shown in Figure 11. It shows that the CO2 content had no obvious decrease within 48 h, which indicates that the lifespan of the MNPs prepared by the generator was longer. The CO2 content fluctuated in the range of 6.3-6.6 mg/L. This proved that the MNPs increased the CO2 content in the water.  
Figure 9: Particle size distribution of the CO2 MNPs under different circulation times for the generator.
## 2 Background
### Levels of Decision Making  
There are the following two levels of DM: Project- and network- level. The project-level DM only analyzes one infrastructure management project or considers an infrastructure network as a whole and only one management plan or strategy is generated for the project or the entire network. The network-level DM focuses on the segments of an infrastructure network and generates a management strategy for each segment of the network. In general, project-level DM only has a single object, while network-level DM has multiple objects. Figure 2 presents the number of articles published, in decades, focusing on the levels of DMs.  
Figure 2: Levels of decision making.  
As shown in Figure 2, among the reviewed articles, the majority of them study network-level decision making, while considerable amount of them are at the project level. Some articles consider both levels in their DM. The numbers of both project- and network- level DM are increasing with the growth of research interests in this field, though the network-level DM has a faster speed. It is important to notice that the discussion on M&R management is a dominating subject among the reviewed articles and most of the recent articles focus on the network-level DM, while the research on other management, e.g., update, often use the project-level DM. In terms of M&R management, project-level DM is often used to analyze a special project when the infrastructure, e.g., a bridge, is very important, when a complex model is established, or when complicated outcomes are analyzed [21; 22; 23]. Network-level DM considers not only the entire network but also analyzes the individual segments. It produces a management plan/strategy for each segment through the analyzed period so that the whole network can satisfy its management goal and requirements. It is more helpful when managing a network of infrastructure [24; 25].
## 3 Experimental Results
### Training Data Collection  
For training, public datasets [36] including General 100, Urban 100, BSDS100, and BSDS200 were used to prepare continuous-tone color images. The total number of continuous-tone color images was 500. General 100, urban 100, and BSDS200 were used for training, whereas BSDS100 was used for validation. The same training and validation sets were used to train all the deep-learning-based methods: the proposed SALDL, PRL, U-net, DDN, and DCNN. The three subnetworks of the GCM-based residual subnetwork, IRS, and SARDS used the same training and validation datasets. For digital halftoning, the continuous-tone color images were converted into grayscale images; subsequently, error diffusion [42] was used to transform the grayscale images into halftoned images. The Floyd-Steinburg filter [1; 42] was used for error diffusion. The Laplacian operator was applied to the grayscale images to obtain Laplacian maps. To obtain the training patches, three types of patches were extracted randomly from the grayscale original images, Laplacian maps, and halftoned images. The extracted patch was of size 32 \(\times\) 32. In this study, grayscale patches were used for training because error diffusion can be easily applied to them. To apply the proposed trained network to color images in the test phase, the color image was first separated into R, G, and B planes. Subsequently, the proposed network was applied to each plane independently.
## 3 Methodology
### Support Vector Machine (SVM)  
The SVM method employs non-linear decision boundaries to carry out regression and classification tasks, offering significant flexibility within the feature space where these boundaries reside. It relies on principles of statistical learning and structural risk reduction. However, SVM parameters are not predetermined, and there is no prior knowledge about the distribution of inputs and outputs. During training, input and output values are aligned in training sets, enabling the derivation of decision functions to classify new datasets based on these alignments. The equation representing the SVM-derived plane is expressed as Equation (4) [39].  
\[f(x)=(w,x)+b\]  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Algorithms** & **Advantages** & **Disadvantages** \\ \hline LightGBM [33] & - & Efficient and faster training compared to traditional gradient boosting algorithms & - & Limited support for certain data types, such as text or images (requires feature engineering) \\  & - & Handles large-scale data and high-dimensional features effectively & - & Can be sensitive to noise or outliers in the data \\  & - & Recsistant to overfitting and provides good accuracy and generalization performance & - & Model complexity can increase with the number of trees \\ \hline SVM [34] & - & Effective for high-dimensional data and can handle large feature spaces & - & Training time can be slow for large datasets \\  & - & Provides good generalization performance, even with limited training data & - & Not well-suited for large datasets with noise or outliers \\  & - & Robust overfitting and works well with non-linear data by using kernel tricks & - & Limited interpretability compared to some other models \\ \hline CFNN [35; 36] & - & Highly versatile and can model complex non-linear relationships & - & Require large amounts of training data to achieve good performance \\  & - & Capable of automatic feature extraction and learning hierarchical representations & - & Can be computationally expensive, especially for deep architectures \\  & - & Scalable to large datasets with the availability of computational resources & - & Limited interpretability and often considered as “black boxes” \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the three models.  
In this context, the weight vector \(w\) and the scalar constant \(b\) play crucial roles. The training data is denoted by \(n\)-dimensional vector \(x\). By computing the dot product of \(w\) and \(x\), and adding the scalar \(b\), the function's result is obtained. Each training datum is represented by an \(n\)-dimensional vector. The dataset, comprising \(m\) data points, assigns each data point to one of the elements in the set \(y\in\{+1,-1\}\). The SVM model illustrates class samples as points in space, aiming to separate instances of different classes with an open vector, maximizing the space between them [39].  
In this work, SVM hyperparameters were fine-tuned, leading to optimal parameters: C = 133, epsilon = 0.02, and gamma = 0.7.
## 2 Methods
### Median Root Prior  
The median filter is used as the energy function in the MRP algorithm so that the MRP algorithm can suppress isolated noise. The neutron projection images often contain isolated noise, which affects the quality of reconstructed images. Hence, the MRP algorithm is introduced into the reconstruction process to realize the local monotone constraint of the image and to suppress the influence of isolated noise. The MRP algorithm formula is shown in Equation (8), and the noise suppression formula of the reconstructed image is shown in Equation (9):  
\[\eta_{\text{MRP}}^{(n)}(i,j)=\frac{1}{1+\gamma\frac{f^{(\omega)}(i,j)-\text{ Med}(f^{(\omega)}(i,j))}{\text{Med}(f^{(\omega)}(i,j))+\epsilon}} \tag{8}\]  
\[f_{\text{MRP}}^{(n)}(i,j)=\eta_{\text{MRP}}^{(n)}(i,j)\times f^{(n)}(i,j) \tag{9}\]  
where \(n\) is the iteration number of the reconstruction algorithm. \(\text{Med}(f^{(n)}(i,j))\) is the median filtering algorithm by \(3\times 3\) or \(5\times 5\) pixel neighborhood. \(f^{(n)}(i,j)\) is the reconstructed image after \(n\) iterations. \(f_{\text{MRP}}^{(n)}(i,j)\) is the image of suppressed noise. \(\epsilon\) is a perturbation parameter that prevents the denominator from being infinite, which is \(10^{-6}\) in this paper. \(\gamma\) is the noise suppression intensity parameter of the MRP algorithm with the value ranging from 0 to 1. If a reconstructed image contains locally nonmonotonic structures such as isolated noise spikes, the MRP factor is not equal to 1 because of the inequality between the pixel value and its median value. Then, the factor will modify the pixel value, and these structures will be suppressed. According to our research, if \(\gamma=0\), the MRP is not used to suppress noise. A large \(\gamma\) corresponds to a strong weight to suppress isolated noise.