esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Validation of Water-Based Microwave-Assisted Digestion Method  
Firstly, the validation of the proposed method was conducted by comparing the fluoride concentration levels obtained through F-ISE after WB-MAD and classical microwave-assisted acid digestion (MAAD) of sample A. The latter made use of diluted HNO\({}_{3}\). The description of the literature-reported method is as follows: weigh 0.3 g of toothpaste sample and dissolve it in 10 mL of HNO\({}_{3}\) (0.05% HNO\({}_{3}\)) for digestion at 190 \({}^{\circ}\)C, over 20 min [4]. The results are illustrated in Figure 6, where agreement of the literature-reported MAAD  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Analytical Features** & **Specification** \\ \hline Correlation coefficient (R\({}^{2}\)) & 0.995 \\ Standard deviation (SD) in mV & 0.01039. \\ Limit of detection (LOD) in ug/kg & 0.10070 \\ Limit of quantification (LOD) in ug/kg & 0.33820 \\ Method detection limit (MDL) in ug/kg & 0.00302 \\ Method quantification limit (MDQ) in ug/kg & 0.01007 \\ Precision based on intraday (\%) & \(\leq\)0.7 \\ Accuracy (\%) & \(\leq\)96.56 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Determined analytical figures of merit for the proposed WB-MAD method using sample A. Digestion conditions: sample amount, 0.05 g; digestion time, 60 min; digestion temperature, 180 \({}^{\circ}\)C; water volume, 10 mL and \(n\) = 3.  
Figure 5: A two-point calibration plot of the mV reading for the diluted fluoride standards versus the log of the actual fluoride ion concentration.  
method was low (71.33 to 92.10%) compared to that of the WB-MAD method (96.56 to 99.88%) The low digestion efficiency of MAAD is attributed to the reaction of fluoride ions in the presence of acid, which forms HF, as illustrated in Figure 7 [8]. The HF gas is highly volatile, and it is easily released into the atmosphere [41].
## 3 Results and Discussion
### Dissolution Parameters  
The corroded volume, surface area, mean radius, tip radius, and immersion length, were calculated from the MCPs of all individual steps [15]. Figure 4 shows the changes in the sample length, mean corroded radius, volume, and mass as a function of the dissolution time for the magnesia dissolution at 200 rpm and 1450, 1500, and 1550 \({}^{\circ}\)C. All the parameters herein were determined by excluding the grooves. However, the concentration of magnesia in the bulk slag and the corresponding slag viscosity depend on the total amount of dissolved magnesia; hence, the mass flux from the groove was included in calculating these parameters. The absolute value of all the parameters increased with an increase in the experimental temperature due to the lower slag viscosity and larger solubility limit at higher temperatures. The absolute values of the average rate of volume loss from the grooves alone were 4.4, 10.8, and 23.0 mm\({}^{3}\)/min at 1450, 1500, and 1550 \({}^{\circ}\)C, respectively, thereby contributing 16, 19, and 23% to the total volume loss rate. With an increase in the temperature from 1450 \({}^{\circ}\)C to 1500 \({}^{\circ}\)C, the mean rates of change in the mean corroded radius, volume, and mass rise 2.0-2.2 times; moreover, with a temperature increase from 1500 \({}^{\circ}\)C to 1550 \({}^{\circ}\)C, the mean rates of change in the quantities rise 1.7-2.0 times. The mean rate of change in the sample length amplified 2.27 times when the temperature increased from 1450 \({}^{\circ}\)C to 1500 \({}^{\circ}\)C, and it increased 1.16 times when the temperature increased from 1500 \({}^{\circ}\)C to 1550 \({}^{\circ}\)C. A marginally declining slope or an nearly linear trend in the dissolution parameters designates quasi-steady dissolution [15].  
Figure 3: Virgin and corroded samples with MCPs of the last steps of magnesia dissolution superimposed.  
Figure 2: MCPs for magnesia dissolution at 200 rpm at (**a**) 1450 \({}^{\circ}\)C, (**b**) 1500 \({}^{\circ}\)C, and (**c**) 1550 \({}^{\circ}\)C.
# Assessing Ambisonics Sound Source Localization by Means of Virtual Reality and Gamification Tools
## 4 Results
### Sound Localization Second Round: Horizontal and Vertical Planes
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Real Position** & **Perception Mean** & **V** & **u** & **Significant** \\ (**deg**) & **Angle (deg)** & **V** & **u** & **(\(\alpha=0.05\))** \\ \hline
0 & 354.05 & 37.38 & 6.71 & TRUE \\
22.5 & 44.60 & 33.85 & 6.08 & TRUE \\
45 & 61.77 & 52.60 & 9.44 & TRUE \\
67.5 & 78.11 & 54.16 & 9.72 & TRUE \\
90 & 88.98 & 56.48 & 10.14 & TRUE \\
112.5 & 91.83 & 53.61 & 9.62 & TRUE \\
135 & 99.00 & 41.99 & 7.54 & TRUE \\
157.5 & 116.71 & 36.73 & 3.00 & TRUE \\
180 & 253.92 & 4.585 & 0.82 & FALSE \\
202.5 & 255.34 & 16.60 & 2.98 & TRUE \\
225 & 269.93 & 31.58 & 5.67 & TRUE \\
247.5 & 250.21 & 50.21 & 9.01 & TRUE \\
270 & 275.56 & 57.13 & 10.2 & TRUE \\
292.5 & 280.41 & 54.63 & 9.81 & TRUE \\
315 & 297.85 & 51.93 & 9.32 & TRUE \\
337.5 & 321.94 & 44.76 & 8.04 & TRUE \\ \hline \hline \end{tabular}
\end{table} 
Table 1: V-test results for the first round (horizontal plane). Post hoc comparisons were conducted to further investigate the differences between the elevations for each specific angle. The results are summarized in Table 2.  
Significant differences were found between the elevations at 90\({}^{\circ}\) (\(\chi^{2}\) = 13.7125, \(p\) < 0.001) and 270\({}^{\circ}\) (\(\chi^{2}\) = 8.5941, \(p\) < 0.01), indicating that the responses were significantly different between elevations 0\({}^{\circ}\) and 35\({}^{\circ}\) at these angles. However, no significant differences were observed for the angles 0\({}^{\circ}\) (\(\chi^{2}\) = 1.9283, \(p\) = 0.1649) and 180\({}^{\circ}\) (\(\chi^{2}\) = 0.8570, \(p\) = 0.3546), suggesting that the responses were not significantly different between the elevations for these angles.
# Digital Twins, Virtual Devices, and Augmentations for Self-Organising Cyber-Physical Collectives +
## 4 Digital Twins and Virtual Nodes in Self-Organising Cyber-Physical Systems
### The Pulverisation Meta-Model of Self-Organising Cyber-Physical Systems
In this setup, the cost and performance of an actual system vary importantly with different deployments of the logical entities over the available edge-fog-cloud infrastructure, as shown by the cost-performance profiles presented in [11]. Importantly, the model is simulation-friendly, and allows informed decisions on the cyber-physical mapping to be performed before the actual deployment of the system. In this work, we extend [11] to its extreme consequence, by considering the integration of purely virtual devices into self-organising CPSs. Pulverised Aggregate Model of the Reference Example  
Here, we briefly explain how the reference example presented in Section 3.1 can be modelled using the aggregate instantiation of the pulverisation framework--whose details can be found in [11]. * Each explorer logically maps to a logical device \(\delta\), which is partitioned into sub-components as per Figure 3;
* The behaviour \(\beta\) consists of a round-based execution of an aggregate program expressing the logic of coordination and exploration (the technical details are not important here but the program is publicly available at [44]);
* The sensors \(\sigma\) include those logical sensors needed by program \(\beta\); in this application, we require a identity sensor (providing the UID of the device), a position sensor (e.g., providing the GPS coordinate of the device), and a sensor for estimating the distance from neighbours;
* The actuators \(\alpha\) might include a clue for the direction of movement (e.g., for a human with a smartphone), or a high-level control for moving in that direction (e.g., for an autonomous robot);
* The communication component \(\chi\) handles round-based interaction with neighbours--identified according to a logical neighbouring relationship based on the estimated spatial distance--cf. Figures 2 and 4. The specific content of the message broadcast to neighbours depends on the executed aggregate program;
* A deployment delivers such components, properly integrated into a working system/middleware, onto physical devices for execution--cf. Figure 4. For instance, a straightforward deployment would deploy logical devices to the computer of each corresponding explorer; however, more complex deployments are possible in general [11] (e.g., only \(\sigma\)s and \(\alpha\)s on the explorers' physical devices and the other sub-components on the cloud). Additional details about this configuration are provided when discussing the simulation environment in Section 5.
## 4 Discussion
### Limitations of This Study  
A limitation of this study is the small number of volunteers. To compare SWE machines a cross-centre study had to be performed, which limited the number of volunteers available. Despite the small number of volunteers, differences in \(c_{s}\) estimates between machines were significant and consistent for all breasts and all tissue types. This study compared \(c_{s}\) estimates between machines in the skin. In skin, and offered layer tissue, such as the cornea, shear waves may be guided modifying the \(c_{s}\) and introducing dispersion [44] and the relationship between \(c_{s}\) and the elastic modulus can no longer be simply approximated by E = 3\(\rho c_{s}\)[2]. Variation in the skin boundary conditions may also affect \(c_{s}\)[40]. Previous studies of 2D-SWE in skin have controlled for this effect. To investigate the relative stiffness of sclerotic and normal skin the relative \(c_{s}\) of sclerotic and normal skin was normalised by the ratio of skin thicknesses [45; 46; 47]. Similarly, in a study of the corneal collagen cross-linking using the Axplorer, direct comparison of the stiffness of corneas with and without collagen cross-linking was justified by monitoring the central cornea thickness and ensuring there was no difference in cornea thickness between paired measurements [48]. In this work, cross-machine comparisons of \(c_{s}\) with the transducer in the same position on the breast were made and therefore skin thickness will be the same for both measurements. For future studies of radiation-induced breast toxicity using 2D-SWE, relative measures of radiation-induced skin toxicity that rely on comparison with the contralateral breast should consider variations in skin thickness and boundary conditions [45]. Another limitation is the use of a different operator to acquire _in vivo_ data from each machine, which may introduce bias in \(c_{s}\) estimates. We also expect that interoperator variation may contribute to the poor correlation overserved between the elastograms acquired with different machines but of the same tissue sample. A further source of variation may be the placement of the probe on the breast, including changes in orientation. We used a surgical marker pen to mitigate against this variation, however, we expect that the heterogeneous nature of the breasts means that a small variation in probe placement could result in a small variation in \(c_{s}\) between scans. However, our use of two operators was by design, because it mimics the way that data would be acquired in a future multicentre study, it was expected that interoperator variation, which includes variations in probe placement, to be a small effect. Studies report good agreement between operators both _in vitro_ and _in vivo_[14; 19; 20; 37] and care was taken to give the two radiologists the same instructions and to allow them to practice data acquisition using a phantom.
# Autonomous Active Tag Using Energy Harvesting Strategies
## 2 Materials and Methods
### Power Module
#### 2.2.4 Storage and Regulation Submodule  
The storage section supports both a supercapacitor and an external battery. As commented before, the use of a battery is conceived in this first prototype for actual application scenarios with testing purposes.  
The supercapacitor has to support a voltage of at least 4.2 V, which is the output voltage of the submodules connected: solar, mechanical and debugger. We have reached a compromise between lifetime, size and cost. Considering these three factors, we have chosen one that has an estimated operating life of 10 years with 1 Farad of capacity, measurements of 17.00 mm \(\times\) 8.50 mm \(\times\) 12.00 mm, and an affordable price. The maximum stored energy is 4.2 mWh, 15.12 J. However, it can easily be replaced by another supercapacitor with a different capacity and similar measurements if a specific application requires it. The space reserved for a battery connector is for the auxiliary battery mentioned before.  
In the regulation section, the input comes from the supercapacitor (and battery if connected), where the necessary minimum voltage for the regulator to work is 2.5 V. The output voltage of the regulator powers the processing module with a fixed value of 1.8 V and an efficiency of around 87.5%.
## 3 Results and Discussion
### AE Method Application for Bridge Monitoring
#### 3.2.2 Analysis of Noise Parameters  
The first experiment consisted in the recording and analysis of noise at various sections of the bridge. As a data collection system, a 4-channel external USB I/O module \"E20-10\", manufactured by \"L-Card\" company (Moscow, Russia), was used. This system provides the consistently recording data with the sample rate 2.5 MHz for every channel. Two sensors of GT200 type (130-200 kHz) were connected to the module, which were installed on the bridge. The signal from the sensor went to the preamplifier with a gain of 26 dB and a frequency filter of 30-500 kHz.  
The AE data were taken in 3 different zones of the bridge structure (Figure 5).  
Zone I: In the area of the expansion joint near the shore support (Figure 5a), sensors were installed inside the beam structure near the bolted joints. The distance between the sensor and the expansion joint is 5 m. Zone II: In the area of attachment of the beam and the support (Figure 5b), AE sensors were installed on the lower girder and on the support. Zone III: In area of the exit hatch section of the box beam (Figure 5c), AE sensors were installed inside the beam structure at a distance of 3 m from each other symmetrically with respect to the hatch.  
For all signals from passing vehicles, the energy is concentrated in the frequency region up to 60 kHz.  
Figure 4: Breach of the cladding layer.  
Vibration of the bridge from passing vehicles was the most common type of noise (Figure 6). Some characteristic features of these signals are a long duration (1-3 s) and relatively high amplitudes of about 56-90 dB\({}_{\text{AE}}\). The primary reason for the appearance of these signals is the fact that the passage of the cars leads to vibration and friction of poorly fixed structural elements.  
Figure 5: Experiment scheme: (**a**) Zone I; (**b**) Zone II; (**c**) Zone III. AE sensors are marked as yellow-green circles.  
Figure 6: Acoustic emission (AE) signals from passing vehicles: (**a**) Zone I; (**b**) Zone II; (**c**) Zone III.  
Signals caused by impacts of structural elements have amplitudes similar to signals from passing vehicles, of the order of 56-90 dB\({}_{\text{AE}}\) but they have 1-2 times shorter duration, not exceeding 50,000 \(\upmu\)s (Figure 7). The energy of the signals, as a rule, lies in the frequency range up to 200 kHz. The primary cause of these signals is the vibration of weakly fixed structural components of the bridge, as a result of which their collision occurs. An example may be the impact of a ladder or hatch on the bridge girder or the impact of wheel sets on the expansion joint [14].
# Towards the Real-World Deployment of a Smart Home EMS: A DP Implementation on the Raspberry Pi
## 2 Fundamentals of the Chosen EMS
### Main Features  
The flowchart of the chosen EMS is shown in Figure 1. As for many EMSs, the processing starts with the _Forecasting_ stage, in which the 24-h ahead profiles of load demand and environmental variables tied to renewable generation are forecast on the basis of past data. To achieve good results, the _Forecasting_ stage of the chosen EMS is based on the Nonlinear AutoRegressive network with eXogenous inputs (NARX). This Artificial Neural Network (ANN) has been successfully used in time-series modeling thanks to its simple implementation and its adaptive learning process, even with small-scale data [39; 40; 41]. In the considered case, the exogenous input is the environmental temperature.  
The subsequent optimization stage considers forecast load demand and plans to satisfy it while pursuing the chosen goals. This stage is split into two tasks, i.e., _Planning_ and _Online Replanning_, aiming to provide advantages both to the end user and to the grid manager at the same time.  
The _Planning_ task optimizes the user's cash flow (CF) over the planned period (24 h) and produces a set of power reference values, one for each system component (i.e., electrical loads, storage systems, and renewable generators). In other EMSs, this set is directly sent to lower level devices that perform the instantaneous control of the hardware. This approach, therefore, neglects forecasting errors. Instead, in the chosen EMS, the output of the _Planning_ task is only used to compute the optimal Grid-Exchanged Power Profile (GEPP) for the whole next day. The GEPP is a vector of power values across the time steps of a day. In particular, these values are positive when the grid supplies the smart house, or negative when the power from renewable generators is injected into the grid. The planned GEPP is transmitted over the Internet to the grid manager as an obligation, to which the user commits himself.  
Then, the _Online Replanning_ task is repeated during each 24 h cycle. It aims at minimizing the deviation between the actual and the transmitted GEPPs and computes the actual set of power references to obtain this goal. These values are delivered to the different system components by the  
Figure 1: Flowchart of the proposed EMS.  
Local Command_ stage via an internal HAN. Besides minimizing the user's cash flow, the proposed EMS reduces the uncertainty on the GEPP thanks to its _Online Replanning_ stage. The public utility can exploit this last feature to optimize power generation/distribution and to improve its economic policy planning.
# Parameterized Algorithms in Bioinformatics: An Overview
## 5 Phylogenetics
### Combining and Comparing Phylogenies  
**Problem Description.** An approach to reconstruct a phylogeny from the genomes of a set of species is to first reconstruct the phylogenies of the genes (using multiple alignments and after clustering them together into families) into so-called \"gene trees\" and then to combine these trees into a tree representing the evolutionary history of the set of species called the \"species tree\" (see also Section 5.3 for more on the divergence between gene trees and species trees). In general, given trees \(T_{i}\) each on the taxon-set \(X_{i}\), we want to \"amalgamate\" the trees into a single tree \(T\), which, since it agrees with and contains all the \(T_{i}\), is called an _agreement supertree_. This problem is known as Tree Consistency (and, sometimes Tree Compatibility).  
Tree Consistency (TCY)  
**Input:** trees \(T_{1},T_{2},\ldots,T_{t}\) on \(\mathcal{X}_{1},\mathcal{X}_{2},\ldots\), respectively  
**Question:** Is there a tree \(T^{*}\) on \(\mathcal{X}:=\bigcup_{i}\mathcal{X}_{i}\) with \(\forall_{i}\;T^{*}|_{\mathcal{X}_{i}}=T_{i}\)?  
For surveys about the combination of phylogenies (\"consensus methods\") we refer to [PERSON] [143] and [PERSON] [144].  
**Results.** For unrooted trees, TCY can be solved in polynomial time if all \(T_{i}\) share a common taxon [145] but is NP-complete in general, even if all input trees contain four taxa [145] (such a \"quartet\" is the smallest meaningful unrooted tree since unrooted trees with at most three taxa do not carry any phylogenetic information). This restricted problem is also known as Quartet Inconsistency. TCY can be solved in polynomial-time for two unrooted (non-binary) trees [146]. More generally, using powerful meta-theorems [147; 148] (problems formulatable in \"Monadic Second Order Logic\" (MSOL) are FPT for the treewidth of the input structure), TCY is fixed-parameter tractable for the treewidth of the display graph [149; 150] (that is, the result of identifying all leaves of the same label in the disjoint union of the input trees), which is smaller than the number \(t\) of trees. [PERSON] et al. [151]improved the impractical running time resulting from the application of the meta-theorems, showing an \(O^{*}(2^{O(t^{2})})\)-time algorithm.  
For rooted trees, Tree Consistency can be solved in polynomial time [152; 153] (even for non-binary trees) but, due to noisy data and more complicated evolutionary processes, practically relevant instances are not expected to have an agreement supertree [154; 155]. Thus, derivations of the problem arose, asking for a smallest amount of modification to the input such that an agreement supertree exists. The most prominent modification types are removing trees (Rooted Triplet Inconsistency), removing taxa (Maximum Agreement Supertree), and removing edges (Maximum Agreement Forest), which we discuss in the following.
# Non-Invasive Painting Pigment Classification Through Supervised Machine Learning
###### Abstract
Despite significant progress in hyperspectral pigment classification, there remains a need for simpler and more accessible solutions that maintain diagnostic accuracy. This study introduces an efficient and non-invasive methodology for pigment classification based on imaging performed across three well-defined spectral bands: the ultraviolet (320-400 nm), visible (400-700 nm), and near-infrared (700-1000 nm) bands. By capturing the characteristic spectral responses of pigments under these illumination conditions, the proposed approach enables accurate classification while maintaining low computational complexity. Unlike hyperspectral imaging, which typically requires the processing of hundreds of contiguous spectral bands, this method relies on a reduced set of three strategically selected bands. This design choice significantly minimizes the burden of data acquisition and post-processing while still ensuring reliable performance [3]. The principal advantage of this approach lies in its operational efficiency and ease of implementation. The use of only three spectral bands allows for the rapid extraction of discriminative pigment features without the need for specialized hyperspectral instrumentation. As such, the method is particularly well-suited for conservators, heritage professionals, and researchers who require a fast and dependable tool for pigment identification in situ. The results indicate that spectral information acquired via visible-light reflectography (VIS), ultraviolet reflectography (RUV), and near-infrared reflectography (NIR) provides sufficient discriminatory power to support accurate pigment classification, thereby offering a pragmatic alternative to more complex and resource-intensive imaging systems [8].