esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 EMG Signal  
EMG signals capture the electric potential movements created by the muscles, which always have a potential difference when the muscles are electrically or neurologically contracted. At least one pair of electrodes is required to acquire the signal. To simultaneously capture the activities of multiple muscles, an array of multiple electrodes is used [32; 33]. Two kinds of EMG signals exist, surface EMG (sEMG) and intramuscular EMG (imEMG) signals. sEMG signals acquired using Myo bracelet are used in this research. sEMG signals are captured by recording muscle activities from the skin surface. imEMG signals are captured from the muscle tissue that is acquired by percutaneous wire needle electrodes inserted into muscle with a single surface reference electrode on the skin. When compared with imEMG, sEMG is much easier to capture and it is non-invasive. In this research, sEMG signals are captured from the forearm muscle on the hand as a biometric information for user authentication using multi-channel Myo bracelet. Figure 2 shows the raw sEMG signals captured by Myo armband while the user is doing three hand gestures without any signal processing technique applied to the signals. The sEMG sensors of the armband are numbered from one to eight to be able to match the signals with the forearm muscles. The signals are represented in time domain with the amplitude of the signal on y-axis and the samples on the x-axis.
## 2 Location and Description of the Experimental Site  
The experimental plot was established at the historic mine site of Tharsis, in southwestern Spain (Figure 1), a world-class massive sulfide deposit hosted primarily by black shale sequences from the latest Devonian [25]. The biggest deposit is exposed in the Filon Norte open pit (Figure 1a), with total original reserves of about 90 million tons containing average grades of 46.5% S, 0.7% Cu, 2.7% Zn plus Pb, 35 g/t Ag, and 0.9 g/t Au [26]. Besides, a wide variety of PHE (As, Cd, Hg, Sb, and Tl, among others) are usually present in the ore as trace elements.  
Tharsis has a long history of metal extraction dating back from prehistoric (third millennium B.C.) and Roman to modern times [27]. Past mining and smelting activities have left an extensive legacy of heavily contaminated soils. The surface soil has been subjected to chemical loading for decades in the absence of legal and regulatory frameworks for mine closure and environmental remediation. In the vicinity of the mining area, the soil is adversely affected by acid mine drainage (AMD) emanating from the waste dumps, thus being unable to support plant life [22; 28; 29]. However, small agricultural holdings have persisted by adopting traditional farming practices that enable cultivation of food crops [30], all at risk of contamination [31]. Elevated levels of PHE in soil and vegetation can be detected up to 2-3 km downwind of the Tharsis mines [32; 33].  
The experimental plot (1 \(\times\) 1 m) is regarded as representative of an area impacted by AMD discharges arising from the mine wastes of Filon Norte pit (Figure 1b), with an extension of about 1.5 ha. The site (UTM coordinates: X = 666,533 and Y = 4,163,175) is located 250 m above sea level and subject to a Mediterranean climate (_Csa_ in the Koppen climate classification system) characterized by hot, dry summers and mild winters. During the experimental period (2007 to 2019), the average minimum and maximum temperatures were 5 \({}^{\circ}\)C and 34 \({}^{\circ}\)C, respectively, and the average annual rainfall was 389 mm, according to data recorded in the nearest weather station.
## 2 Materials and Methods
Another measurement was carried out after 24 h. The test subjects were allowed to move freely in the time between the two measurements but were not allowed to take a shower or bath or expose themselves to any physical stress, to avoid measurement errors, for example those due to sweating or wet skin. Here too, the test subjects had to acclimatize to the room conditions before the measurement. The wound dressings were removed in the respective order and all skin parameters were measured again. The Courage & Khazaka Multi Probe Adapter (Courage & Khazaka, Cologne, Germany) with Corneometer CM825 (Courage & Khazaka, Cologne, Germany) and Tewameter TM Hex (Courage & Khazaka, Cologne, Germany) were used for all measurements. Three measurements were taken using the Corneometer probe, and the average value was used in the analysis to account for fluctuations in the individual measurements. The Tewameter probe measured data over a period of 30 s until the device obtained a stable measurement value. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Wound Dressing Category** & **Type of Wound Dressing** & **Area of Application** & **Product Composition** \\ \hline Simple dressings & Gazin & Simple wound coverage & 100\% cotton \\  & Solvaline & Bodily fluid absorption & 80\% viscose, 20\% polyester \\  & Metallic & Superficial wounds & Viscose coated with aluminum \\ \hline Foam dressings & Allevyn non-adhesive/adhesive foam dressing & & Polyurethane foam \\  & Allevyn thin foam dressing & & \\  & Allevyn thin foam dressing & & \\  & Ulcers, postoperative wounds & & \\  & Meiplex & & \\  & Meiplex border flex & & \\  & Biatain non-adhesive/adhesive & & \\  & & & \\ \hline Brand film dressings & Suprasorb P & Ulcers, decubiti, diabetic foot & \\  & non-adhesive/adhesive & syndrome, postoperative wounds & \\  & Suprasorb sensitive border & Used for more fragile and & \\  & lite/non border & damaged skin & \\ \hline Transparent dressing & Tegaderm & Covering of venous catheter sites, & \\  & & wound observation & \\ \hline Synthetic skin substitutes & Suprathed moist/dry & & \\  & Epigard & Skin grafting, burn wounds, & \\  & Xenoderm & difficult to heal wounds & \\  & & Xenoderm & \\  & & & \\  & Biobrane & & \\ \hline Scar plasters & Proiform & Hypertrophic scars, colloids, & \\  & Cica care & traumatic and post-operative & \\  & Scar FX & scars and scar prevention & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Wound dressings. The collected data were compiled in an Excel 2016 spreadsheet. Statistical analysis was performed using Python ([[https://www.python.org/](https://www.python.org/)]([https://www.python.org/](https://www.python.org/))). After testing for normal distribution using the Shapiro-Wilk test, two types of statistical tests were applied. _t_-tests for dependent samples were performed to test for day-dependent measurement differences. In order to test for significant differences between the individual wound dressings, _t_-tests for independent samples were carried out.
## 3 Results  
This section describes the results of the needle pose estimation accuracy test, described in Section 3.1, and three types of tests performed on phantoms using the developed solution: the final location error of the injected needle, described in Section 3.2; the material collection efficiency for an imitation lesion, described in Section 3.3; and the time needed to conduct the injection, described in Section 3.4.  
The obtained needle tracking refresh rate was 20 Hz. The algorithm was implemented in C++, and the system ran on a workstation with an Nvidia RTX3080 graphics card and AMD Ryzen 9 5950X processor. The overall performance proved feasible for real-time operations.
# A System-Level Methodology for the Design of Reliable Low-Power Wireless Sensor Networks
## 6 Resulting Design Methodology and Case Studies
### Case Studies  
Considering the measurements reported in Section 3, the example illustrated in Table 3 shows how to estimate the maximum achievable data rate when the input parameters are known.  
First, the received power, \(P_{r}\), is estimated using the PL model considered at the targeted range. Second, the SINR is determined. Finally, the achievable data rate is calculated. In this case, the latter is estimated to be around 44.668 kbps. However, when compared to the measurements reported in Figure 12b, an error of 10.66% is observed as the actual data rate is 50 kbps. This is acceptable as noise and signal attenuation do vary over time. Therefore, in WSN applications, \(D_{R}\) and \(P_{t}\) are dynamically changed during operation [10; 46]. Theoretically, increasing \(P_{t}\) by 10 dBm (i.e., \(P_{t}=0\) dBm) would result in achieving the same range at 500 kbps and, therefore, lowering the energy per measurement from 213 \(\upmu\)J to 62 \(\upmu\)J, as shown in Figure 6. However, practically, the measurements in Figure 12b show that the range becomes around 80 m which is 15 m shorter (i.e., a 15.8% range reduction) than the theoretically estimated range of 95 m. This can be tackled by further increasing \(P_{t}\), which will always be a better compromise for the CC1310 radio chip in terms of latency and energy consumption as shown in Figure 6. Moreover, it is safe to say that this is also true for a wide range of current generation and similar wireless transceivers in the market.
## 3 Results
### Active Strategies: Energy Demand Reduction  
The reduction of energy demand is shown in the following figures (Figures 4 and 5), where the results are grouped with reference to the climatic zones, showing the changes of the control parameter as a change of PCM thickness. The contribution of PCMs in the reduction of heating and cooling energy consumption was split so as to differentiate the strategies. The percentage of energy reduction (\(ER_{\%}\)) refers to the difference between the total energy demand in the baseline case (\(E_{T\_baseline}\))--without PCM--and the total energy demand in each PCM-implemented case (\(E_{T\_PCM}\)). This difference was then divided by the total energy demand of the baseline case, as described in the following equation (Equation (10)):  
\[ER_{\%}=100*\frac{E_{T\_baseline}-E_{T\_PCM}}{E_{T\_baseline}} \tag{10}\]  
Figure 3: Enthalpy curve for the PCM-enhanced plasterboard.  
Overall, it can be easily argued that the type and magnitude of energy reduction are strictly dependant on building typology, and outcomes show opposite trends when comparing the two models. On the one hand, in the small office model, the benefits of PCMs are clearer and increase in colder climates, with an average that rises from 2.57% for Zone B to 5.13% for Zone G. On the other hand, benefits for the midrise apartment model are lower and countertrend, considering that the main contribution is related to heating demand reduction and that the zone average decreases (with a less evident trend) in colder zones. However, both models confirm that an increase in PCM thickness corresponds to an increase in benefits, with a strong logarithmic correlation between thickness and energy reduction (Figure 6).  
Figure 4: Small office, total energy demand reduction for the analyzed cities: (**a**) Climatic Zone B, (**b**) Climatic Zone C, (**c**) Climatic Zone D, (**d**) Climatic Zone E, (**e**) Climatic Zone F, and (**f**) Climatic Zone G.  
Figure 5: Midrise apartment, total energy demand reduction for the analyzed cities: (**a**) Climatic Zone B, (**b**) Climatic Zone C, (**c**) Climatic Zone D, (**d**) Climatic Zone E, (**e**) Climatic Zone F, and (**f**) Climatic Zone G.  
Figure 6: Logarithmic correlation between zone average and PCM thickness for (**a**) small office and (**b**) midrise apartment in the active strategies case.
## 2 Materials and Methods
### Data Analysis  
The full dataset consists of 40 rows, 1 for each sampling point, and 42 columns (Appendix A). For each of them, fresh biomass (\"BIO\") information is collected and called the target variable. Vegetation indices are integrated with agronomic parameters and represent the predictive variables.  
The pipeline described in the following paragraphs was processed, coding the algorithm in the Python (3.12.7 version) programming language. The main libraries used include Pandas (2.2.2 version), Matplotlib (3.9.2 version), NumPy (1.26.4 version), and Scikit-learn (1.5.1 version).  
Three independent feature selection techniques were applied to identify the most informative variables. The features retained were those consistently ranked higher in at least two methods. To further reduce redundancy and mitigate multicollinearity, Principal  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Index** & **Formula** & **Reference** \\ \hline NDVI & \(\frac{NIR-R}{NIR+R}\) & [16] \\ GNDVI & \(\frac{NIR-G}{NIR+G}\) & [17] \\ NDRE & \(\frac{NIR-RF}{NIR+R}\) & [18] \\ CVI & \(\frac{NIR-Red}{G^{2}}\) & [19] \\ EVI 1  & \(G\times\frac{NIR-R}{(NIR+1<N-R-C2\times B+L)}\) & [20] \\ SAVI 2  & \(\frac{NIR-R}{NIR+R+L}\times(1+L)\) & [21] \\ TI & \(\frac{NIR}{NIR}-273.15\) & Pix4D Documentation \\ \hline \hline \end{tabular}
\end{table}
Table 4: Vegetation indices formulas and references.  
Component Analysis (PCA) was performed on the selected features. The first three principal components were used as predictors in the regression models. This transformation ensured that the input variables were uncorrelated. Then, the pipeline involved robust scaling using _Robust Scaler_ and an exhaustive hyperparameter search using leave-one-out cross-validation to ensure a reliable and unbiased evaluation of various regression models. For each regressor, the search space was defined (Table 5). By visualizing both training and test predictions along with performance metrics, this approach not only identifies the best-performing model but also provides insights into its generalization capacity, which is crucial for non-destructive biomass estimation. For each model, the best combination of hyperparameters was selected based on the minimum Mean Absolute Error (MAE) value obtained during the leave-one-out cross-validation.
# A Study on the Stoichiometry of Casein/Chitosan Gel Complexes as a Delivery System for Quercetin
## 2 Materials and Methods
### Methods
#### 2.2.8 Release Simulation of the Active Compound and Mathematical Modelling of the Process  
Different quantities of the five types of quercetin-loaded complexes, all equivalent to a 10 mg mass of the active compound, were used for in vitro simulation of the release process. The required amounts were dispersed in 1 mL of phosphate buffer (pH = 5.5), mimicking skin pH, and placed into dialysis membranes (12,000 Da MWCO, Sigma, St. Louis, MO, USA). Each particle-containing membrane was placed into 20 mL of the same buffer into a beaker containing 150 \(\mu\)L of TWEEN 20. These solutions were kept at 32 \(\pm\) 0.5 \({}^{\circ}\)C and continuously stirred by a magnetic stirrer for 24 h. At chosen time frames, 3 mL of the dissolution media was taken out for spectrophotometric analysis with Metertech SP-8001 (Metertech Inc., Nangang, Taipei, Taiwan) and replenished through the addition of fresh buffer. The concentration of the collected sample was measured at 256 nm, and the amount of released quercetin was calculated using a previously determined calibration curve.  
A zero-order model of release kinetics was used for fitting the experimental data.
## 2 Results and Discussion
### Commercial EO Adulterated with Vegetable Oils
These results showed that the commercial sample was adulterated about 40-50% with vegetable oil. For a further confirmation, the absolute quantification was also carried out on the enantiomers of the markers of the CL-2 EO. The quantitative analysis was carried out with a Multiple Headspace Solid-Phase Microextraction (MHS-SPME) combined with an enantioselective GC-FID-MS to avoid the degradation of the cyclodextrin column performance due to non-eluted residues of heavy vegetable oil. Table 5 reports the absolute concentrations of the enantiomers of linalool and linalyl acetate in the genuine, spiked with vegetable oil and CL-2 EO. The absolute concentrations of the single enantiomers clearly decreased with increasing the degree of adulteration, indicating the reliability of this approach to measure the EO adulteration with vegetable oils. The results on the enantiomeric recognition of the CL-2 EO showed that it was adulterated between 40% and 50% with a heavy vegetable oil. The degree of adulteration of the sample analysed should be considered as indicative, since it was not possible to analyse the sample before the adulteration. This confirms the need for a representative genuine GC pattern. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**Essential Oil**} & \multirow{2}{*}{\begin{tabular}{c} **(Linalool)** \\ **(g/100 g)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **(Linalool)** \\ **(g/100 g)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **\(\sigma\)** \\ **(g/100 g)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **(Linalyl Acetate)** \\ **(g/100 g)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **(4-Terpineol)** \\ **(g/100 g)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **\(\sigma\)** \\ **(g/100 g)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **(\(\alpha\)-Terpineol)** \\ **(g/100 g)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **\(\sigma\)** \\ **(g/100 g)** \\ \end{tabular} } \\ \hline _C. limon_ & genuine & 10.5 & 0.2 & 25.9 & 0.4 & & & \\  & 5\% spiked & 9.6 & 0.1 & 23.8 & 0.6 & & \\  & 20\% spiked & 7.1 & 0.3 & 21.8 & 0.2 & & \\  & 50\% spiked & 5.0 & 0.1 & 14.4 & 0.3 & & \\ \hline _L. angusifolia_ & genuine & 23.4 & 1.2 & 27.0 & 0.9 & & \\  & 5\% spiked & 20.1 & 0.5 & 23.4 & 0.2 & & \\  & 20\% spiked & 18.4 & 0.3 & 21.2 & 0.6 & & \\  & 50\% spiked & 13.4 & 0.2 & 15.3 & 0.4 & & \\  & CL-2 EO & 14.5 & 0.3 & 16.8 & 0.2 & & \\ \hline _M. alternifolia_ & genuine & & & & 44.2 & 2.7 & 8.5 & 1.5 \\  & 5\% spiked & & & & 42.0 & 2.2 & 6.9 & 0.9 \\  & 20\% spiked & & & & 33.5 & 1.8 & 3.5 & 0.2 \\  & 50\% spiked & & & & 26.7 & 2.1 & 2.4 & 0.1 \\ \hline \end{tabular}
\end{table} 
Table 4: Absolute concentrations of the selected marker compounds in the investigated essential oils.
# Recent Advances in Efficient Dynamic Graph Processing
## 7 Conclusions  
As real-world networks continue to grow in size and complexity, dynamic graph algorithms will play an increasingly vital role in real-time analytics such as anomaly detection and risk assessment and recommendation, where responsiveness is critical. In this paper, we conduct an extensive survey on recent advances in efficient dynamic graph processing. We systematically review over 170 research articles, focusing on the latest developments in handling dynamic graphs. We first analyze and compare different computational complexity models designed to evaluate algorithm efficiency in dynamic graphs. Then, we classify studies on dynamic graph processing according to different problems, including centrality, coloring, cohesive subgraph, path traversal, and graph separation. For each class of works, we review the representative studies addressing various types of graph problems under different update types. These approaches commonly leverage localized re-computation, parallel, and distributed processing, and adaptive data structures to improve overall performance.  
However, limitations of existing works still persist, particularly in scenarios involving large-scale, high-frequency updates or wholesale graph restructuring, where some algorithms still suffer from high computational complexity. Efficiency is highly dependent on factors such as update patterns, graph size, and structural properties. Future research directions should focus on (1) optimizations for high efficiency, tightening worst-case guarantees for dynamic graph problems; (2) dynamic algorithms for various types of graphs, such as attributed, uncertain, and temporal graphs; (3) parallel and distributed approaches for large-scale graphs, leveraging GPU acceleration and distributed frameworks; (4) efficient approaches for streaming graphs, developing algorithms that efficiently handle continuous, high-velocity updates. Bridging the gap between theoretical advances and practical implementations will be crucial for unlocking their full potential in more realistic applications.  
Conceptualization, Z.C. and L.Y.; methodology, Z.C., W.Z. and L.Y.; software, K.L.; validation, K.L., L.Y. and Z.Y.; formal analysis, Z.C.; investigation, Z.C. and K.L.; data curation, Z.C. and K.L.; writing--original draft preparation, Z.C. and L.Y.; writing--review and editing, Z.C., L.Y., W.Z. and Z.Y.; visualization, L.Y.; supervision, Z.C.; project administration, Z.C. All authors have read and agreed to the published version of the manuscript. This work was supported by National Key R&D Program of China 2022 YFF0712100, National Natural Science Foundation of China NSFC62472225, NSFC62402216, Natural Science Foundation of Jiangsu Province BK20241381, JSTJ-2023-KH055. The authors declare no conflicts of interest.
## 3 Duckweed as an Alternative Source of Nutrients and Bioactive Compounds
### Minerals  
Duckweed (_Lemmaccue_ family) is recognised for its rapid growth and potential as a sustainable food/feed source. Its mineral content includes essential nutrients (\(\sim\)0.2-7%) that are vital for maintaining good health, such as potassium (K), iron (Fe), calcium (Ca), and phosphorus (P), along with trace elements such as manganese (Mn), zinc (Zn), and copper (Cu), which are important for various metabolic processes [78; 85].  
Duckweed also has the ability to absorb potentially harmful heavy metals from its environment, which makes it useful for bioremediation but raises concerns when used as food or feed. Duckweed can accumulate heavy metals like cadmium (Cd), lead (Pb), arsenic (As), chromium (Cr), cobalt (Co), nickel (Ni), selenium (Se), and zinc (Zn) [36]. The concentrations of both nutrient-rich elements and heavy metals in duckweed are highly dependent on the surrounding water quality and the specific cultivation environment. For instance, exposure to contaminated water can lead to significant accumulation of heavy metals, rendering the biomass unsuitable for consumption [27], while duckweed grown in wastewater-fed environments has been found to have higher concentrations of nitrogen and phosphorus compared to duckweed collected from natural water bodies.  
The mineral content of duckweed is a crucial factor that contributes to its diverse applications, ranging from phytoremediation, food, and animal feed to biofuel production. Further research is needed to fully understand the mechanisms underlying the mineral accumulation in duckweed and to optimise its cultivation for specific purposes [24; 71; 86; 87].