esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods  
### Experimental Sites and Instrumentation  
The 325-meteorological tower and the Doppler wind lidar (Windcube200, Leosphere, Orsay, France) are co-located at the Institute of Atmospheric Physics (IAP), Chinese Academy Sciences in Beijing city center (39.97\({}^{\circ}\)N, 116.37\({}^{\circ}\)E) (See Figure 1a).  
The measurements are taken at 15 levels (i.e., 8 m, 15 m, 32 m, 47 m, 65 m, 80 m, 100 m, 120 m, 140 m, 160 m, 180 m, 200 m, 240 m, 280 m, and 320 m above ground level) by a suite of meteorological instruments (010C cup anemometers, 020C wind vanes, Metone, Grants Pass, OR, USA, HC2-S3, Rotornic, Switzerland) deployed on the 325-m meteorological tower, which provides observations of the WS, wind direction, relative humidity (RH), and air temperature (T) measurements. In addition, turbulence measurements are taken using sonic anemometers (Model Windmaster Pro, Gill, UK) and four-component radiation using downward-pointing and upward-pointing pyrgeometers, and pyranometers (CNR1, Kipp & Zonen, Delft, The Netherlands) are maintained at the same heights (i.e., 47 m, 140 m, and 280 m) on this tower. The detectable range of the lidar spans from 100 m to  
Figure 1: The map of urban Beijing area and location of the 325-meteorological tower and the Doppler wind lidar at the Institute of Atmospheric Physics (IAP) station (**a**), and the aerial photograph of the urban Beijing area around met tower (4 km \(\times\) 4 km) (**b**). The wind rose shows the results observed in these 31 days.  
several kilometers above the ground (depending on the aerosol concentration), with a vertical range resolution of 50 m.  
In addition, the PM\({}_{2.5}\) mass concentrations measured at the Beijing Olympic Sports Center (Aoti surface station), located approximately 2 km north of IAP station, were obtained from the China National Environmental Monitoring Center website ([[http://106.37.208.233:20035/](http://106.37.208.233:20035/)]([http://106.37.208.233:20035/](http://106.37.208.233:20035/))). Cloud fractions observations were from the Nanjingo weather station, about 20 km from the IAP station.  
As shown in Figure 1b, the IAP station is surrounded by a highly dense area of tall buildings. Around this station, there are about 92.1% impervious built-up surfaces and commercial and residential tall buildings (10-60 m), within a radius of 5 km of the tower [33; 34]. By using the 47 m level wind measurements, we can speculate that strong northerly winds prevailed in winter, while southerly winds are lighter with a speed lower than 3 m s\({}^{-1}\).
## 3 Results
### Serotyping
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & & & & & & & \\ \cline{5-8}
**Isolate** & **Serotype** & **Source** & \(\begin{array}{c}\textbf{\emph{stx}}_{1}\\ \textbf{\emph{Variants}}\end{array}\) & \(\begin{array}{c}\textbf{\emph{stx}}_{2}\\ \textbf{\emph{Variants}}\end{array}\) & \(\begin{array}{c}\textbf{\emph{ea}}\end{array}\) & \(\begin{array}{c}\textbf{\emph{stp}}\end{array}\) & \(\begin{array}{c}\textbf{\emph{Ipah}}\end{array}\) & **PG** \\ \hline E44 & O53:H51 & rectal & 1c & & & & A \\ * Z15 & -H10 & rectal & & & + & & B1 \\ C28B & -H14 & rectal & 1c & & & & B1 \\ * D3 & -H34 & carcass & & & + & & B1 \\ Z2 & -H34 & rectal & 1c & & + & & B1 \\ Z29 & -H16 & rectal & 1a1c & & & & B1 \\ V25 & O100:H21 & rectal & 1c & & & & B1 \\ V22 & O100:H28 & rectal & 1c & 2g & & & B1 \\ D28 & O104:H2 & carcass & 1c & & & & B1 \\ \# D15 & O105 AB:H16 & carcass & & & & + & B1 \\ \# D15B & O120:H16 & carcass & & & & + & B1 \\ V13 & O146:H10 & rectal & 1c & & & & B1 \\ C23 & O146:H21 & rectal & 1c & 2b-2g & & & B1 \\ Z3 & O146:H21 & rectal & 1c & & & & B1 \\ Z19 & O146:H21 & rectal & 1c & & & & B1 \\ B7 & O146:H8 & rectal & 1a-1c & 2g & & & B1 \\ B13C & O150:NM & carcass & 1c & & & & B1 \\ Z5 & O174:H16 & rectal & 1c & & & & B1 \\ Z25 & O174:H16 & rectal & 1a-1c & & & & B1 \\ V15 & O176:NM & rectal & 1c & & & & B1 \\ C24 & O185:NM & rectal & 1c & 2b & & & B1 \\ * D53 & O28 AC:H21 & carcass & & & + & & B1 \\ \({}^{\circ}\) E3 & O28 AC:H21 & rectal & & & & + & B1 \\ E15B & O32:H27 & rectal & 1c & & & & B1 \\ E20 & O32:H7 & rectal & 1c & & & & B1 \\ E30 & O32:H7 & rectal & 1c & & & & B1 \\ D26 & O34:H14 & carcass & 1c & & & & B1 \\ Z9 & O34:O145:H45 & rectal & 1c & & & & B1 \\ Z16 & O37:H10 & rectal & 1a-1c & & & & B1 \\ B1 & O6:H16 & rectal & 1c & & & & B1 \\ Z20 & O70:H10 & rectal & 1a–1c & & & & B1 \\ Z17 & O76:H19 & rectal & 1a–1c & & & & B1 \\ Z21 & O76:H19 & rectal & 1c & & & & B1 \\ Z26 & O76:H19 & rectal & 1c & & & & B1 \\ Z32 & O76:H19 & rectal & 1a–1c & & & & B1 \\ Z34 & O76:H19 & rectal & 1a–1c & & & & B1 \\ E16 & O8:NM & rectal & 1c & & & & B1 \\ E18 & O8:H2 & rectal & 1c–1d & & & & B1 \\ C28 & O84:H14 & rectal & 1c & & & & B1 \\ C27 & O91:H10 & rectal & 1c & & & & B1 \\ Z12 & O91:H28 & rectal & 1a–1c & & & & B1 \\ Z13 & O91:H42 & rectal & 1a–1c & & & & B1 \\ Z14 & O91:H47 & rectal & 1a–1c & & & & B1 \\ Z18 & O91:H47 & rectal & 1c & & & & B1 \\ C3 & O96:H20 & carcass & 1c & & & & B1 \\ V5 & O176:H21 & rectal & 1c & & & & B1 \\ B3 & O176:NM & rectal & 1c & & & & B2 \\ B15 & O6:NM & rectal & 1c & & & & C \\ V11 & O153:NM & rectal & 1c & & & & F \\ \hline \hline \end{tabular}  
* Isolates: unmarked STEC;
* EICC. PG: Phylogenetic group; rectal: rectal swab; carcass: carcass: swab; NM: no mobile; \(\div\): gene presence.  
\end{table} 
Table 4: Association between serotype, phylogenetic group and virulence genes of diarrhea. _E. coli_ (DEC) isolated from sheep slaughtered in an abattoir.
## 3 TR Rule and Its Approximate Combination Rules
### TR1 Combination Rule and Simplified Combination Coefficients
It is straightforward to expand the two-effect component extreme value combination to the multi-effect component extreme value combination. The simplified combination Equation of the TR1 rule for the extreme value combination of [PERSON]n\)-effect component can be written as:  
\[f_{R_{\max}}(R_{i})=\
u_{0,i}^{+}T\cdot\frac{\left(R_{i}-\mu_{i}\right)}{\sigma_{i }^{2}}\cdot\exp\left[-\frac{\left(R_{i}-\mu_{i}\right)^{2}}{2\sigma_{i}^{2}}- \
u_{0,i}^{+}T\cdot\exp\left(-\frac{\left(R_{i}-\mu_{i}\right)^{2}}{2\sigma_{i }^{2}}\right)\right] \tag{46}\]  
In Equation (44), \(C\) is the covariance matrix of \(R_{1}\), \(R_{2}\), \(R_{3}\), \(\ldots\), \(R_{n}\); \(C^{-1}\) is the inverse matrix; and \(|C|\) is the absolute value of the determinant of \(C\). The expression of \(C\) is as follows:  
\[C=\left[ \begin{array}{cccc}\sigma_{1}^{2}&\rho_{12}\sigma_{1}\sigma_{2}& \ldots&\rho_{1n}\sigma_{1}\sigma_{n}\\ \rho_{21}\sigma_{2}\sigma_{1}&\sigma_{2}^{2}&\ldots&\rho_{2n}\sigma_{2}\sigma _{n}\\ \vdots&\vdots&\ddots&\vdots\\ \rho_{n1}\sigma_{n}\sigma_{1}&\rho_{n2}\sigma_{n}\sigma_{2}&\ldots&\sigma_{n }^{2}\end{array} \right] \tag{47}\]  
\(\mathbb{E}[R_{\max}]\) obtained by numerical integration of Equation (42) is the extreme value of the combination effect of TR2 rule. Because Equation (42) is a multiple integral, the TR2 rule-based combination extreme can only be obtained by numerical integration.
# Calibration of Magnetometers with GNSS Receivers and Magnetometer-Aided GNSS Ambiguity Fixing
## 2 Measurement Models
### Measurement Model for Magnetic Field Sensor  
We model the magnetic flux measurement in the sensor-fixed \(s\)-frame at epoch \(t_{n}\) as a sum of true magnetic flux, measurement bias \(b_{m^{\mathrm{s}}}\) and measurement noise \(\varepsilon_{m^{\mathrm{s}}}\):  
\[m^{\mathrm{s}}(t_{n})=R_{\mathrm{g}}^{\mathrm{s}}(t_{n})\cdot m^{\mathrm{g}}( t_{n})+b_{m^{\mathrm{s}}}(t_{n})+\varepsilon_{m^{\mathrm{s}}}(t_{n}), \tag{1}\]  
where the magnetic flux is expressed in the local geomagnetic (g-) frame (right-hand coordinate system with the first axis pointing towards geomagnetic north pole, the second axis parallel to plane of magnetic flux, and the third axis perpendicular to magnetic flux and pointing downwards) and transformed into the sensor-fixed frame. The transformation \(R_{\mathrm{g}}^{\mathrm{s}}\) is performed in three steps:  
* transformation from local _geomagnetic_ frame into local _geographic_ navigation-frame, depending on the magnetic declination \(\delta\
u\),
* transformation from local geographic navigation-frame into body-fixed frame, depending on the attitude of sensor's platform (roll \(\varphi\), pitch \(\theta\) and heading \(\psi\)),
* transformation from body-fixed frame into sensor-fixed frame, depending on misalignment errors of sensors (roll offset \(\Delta\varphi\), pitch offset \(\Delta\theta\) and heading offset \(\Delta\psi\)).  
The transformation \(R_{\mathrm{g}}^{\mathrm{s}}\) follows as:  
\[R_{\mathrm{g}}^{\mathrm{s}}=R_{\mathrm{b}}^{\mathrm{s}}(\Delta\varphi(t_{n}), \Delta\theta(t_{n}),\Delta\psi(t_{n}))\cdot R_{\mathrm{n}}^{\mathrm{b}}( \varphi(t_{n}),\theta(t_{n}),\psi(t_{n}))\cdot R_{\mathrm{g}}^{\mathrm{n}}( \Delta\
u), \tag{2}\]  
with  
\[R_{\mathrm{b}}^{\mathrm{s}}=R_{1}(\Delta\varphi)R_{2}(\Delta\theta)R_{3}( \Delta\psi), \tag{3}\]  
and  
\[R_{\mathrm{n}}^{\mathrm{b}}=R_{1}(\varphi)R_{2}(\theta)R_{3}(\psi) \tag{4}\] \[= \left(\begin{array}{ccc}\cos(\theta)\cos(\psi)&\cos(\theta) \sin(\psi)&-\sin(\theta)\\ \sin(\varphi)\sin(\theta)\cos(\psi)-\cos(\varphi)\sin(\psi)&\sin(\varphi)\sin (\theta)\sin(\psi)+\cos(\varphi)\cos(\psi)&\sin(\varphi)\cos(\theta)\\ \cos(\varphi)\sin(\theta)\cos(\psi)+\sin(\varphi)\sin(\psi)&\cos(\varphi)\sin (\theta)\sin(\psi)-\sin(\varphi)\cos(\varphi)&\cos(\varphi)\cos(\theta)\\ \end{array}\right)\]  
and  
\[R_{\mathrm{g}}^{\mathrm{n}}=R_{3}(\Delta\
u). \tag{5}\]
# Classification and Mapping of Paddy Rice by Combining Landsat and SAR Time Series Data
## 4 Results
### Paddy Rice Mapping Index (PMI)  
PMI was developed when considering the phenological characteristics of paddy rice in terms of NIR and SAR backscattering coefficients (Equations (5) and (6)). PMI means the slope of NIR or SAR backscattering coefficients between the transplanting and harvest seasons because the difference of NIR and SAR backscattering coefficients between the two seasons in paddy rice is much higher than other classes. Since optical sensor data has higher data availability at a lower cost than SAR data, NIR based PMI might have a higher practicality than SAR based PMI.  
\[\text{PMI }(\text{NIR})=\frac{\text{NIR1 }(\text{Harvest})-\text{NIR1 }(\text{transplanting})}{\text{NIR1 }(\text{Harvest})+\text{NIR1 }(\text{transplanting})} \tag{5}\]  
\[\text{PMI }(\text{SAR})=\frac{\text{SAR }(\text{Harvest})-\text{SAR }(\text{transplanting})}{\text{ SAR }(\text{Harvest})+\text{SAR }(\text{transplanting})} \tag{6}\]  
Since PMI is related to the probability of a pixel being paddy rice, it is necessary to determine an optimum threshold to identify paddy rice areas. The optimization of the threshold of NIR based PMI was conducted using reference data for the entire study areas (3,354,085 pixels at site A and 2,694,510 pixels at site B) based on the overall accuracy of binary classification (paddy rice or non-paddy rice). The optimization of SAR based PMI was not conducted due to the limited number of time series data. Figure 6 shows the change of accuracy with increasing thresholds of NIR-based PMI from \(-0.4\)to 0.4 at interval of 0.02 for 13 years for both sites A and B (from 2004 to 2016). There are missing years due to clouds. PMI was calculated using NIR images from Landsat 5, 7, and 8. The highest accuracies were achieved when the threshold was near 0.2 for 10 years at both sites. Site A (\(\sim\)85%) shows higher accuracy than site B (\(\sim\)80%) because site A consisted of relatively homogeneous land cover, as discussed above. Figure 6c shows the box plot of optimal thresholds for 13 years at both of the sites. We determined 0.2 to be the optimal threshold of PMI, and a pixel is determined to be paddy rice when the PMI value of the pixel is greater than 0.2. In addition, it should be noted that the accuracy did not change much with the thresholds between 0.18-0.22 for both sites.
## 3 Bio-Functional Compounds and Health Benefits of Kiwi Fruit and Its Juice
### Kiwi Nutritional Value and Bioactive Composition
#### 3.2.1 Kiwi Vitamins
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Bioactives** & **Molecular Formula** & **Molecular Weight** & \(\log K_{\text{ow}}\) \\ \hline Gallic acid & C\({}_{7}\)H\({}_{6}\)O\({}_{5}\) & 170.12 & 0.855 \\ Caffeic acid & C\({}_{9}\)H\({}_{8}\)O\({}_{4}\) & 180.16 & 1.110 \\ P-coumaric acid & C\({}_{9}\)H\({}_{8}\)O\({}_{3}\) & 164.16 & 1.590 \\ Chlorogenic acid & C\({}_{16}\)H\({}_{18}\)O\({}_{9}\) & 354.32 & \(-\)1.014 \\ Protocatechuric acid & C\({}_{7}\)H\({}_{6}\)O\({}_{4}\) & 154.12 & 0.914 \\ P-hydroxybenzoic acid & C\({}_{7}\)H\({}_{6}\)O\({}_{3}\) & 138.12 & 1.394 \\ Cryptochlorogenic acid & C\({}_{16}\)H\({}_{18}\)O\({}_{9}\) & 354.32 & \(-\)1.014 \\ _Flavonol glycosides_ & & & \\ Quercetin 3-rutinoside & C\({}_{27}\)H\({}_{30}\)O\({}_{16}\) & 610.53 & \(-\)1.109 \\ Kaempfer 3-rutinoside & C\({}_{27}\)H\({}_{30}\)O\({}_{15}\) & 594.53 & \(-\)0.629 \\ _Fatty acids_ & & & \\ Alpha-linolenic acid & C\({}_{18}\)H\({}_{30}\)O\({}_{2}\) & 278.44 & 7.299 \\ Linoleic acid & C\({}_{18}\)H\({}_{32}\)O\({}_{2}\) & 280.45 & 7.514 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._Last, but not least, another important vitamin in kiwi is folate and its derivatives. Foldates are an indispensable constituent of the human diet, serving as a crucial form of water-soluble vitamin B (Vitamin B6). They play a vital role in numerous metabolic pathways, particularly in carbon transfer reactions like the synthesis of purines and pyrimidines, as well as the interconversion of amino acids. In 2002, the WHO and FAO stated that the daily recommended intake was 400 \(\upmu\)m/day for adults. The same quantities were given by the IOM in 2004 and so, the EU also approved this amount. The term folate encompasses the whole pack of folic acid derivatives, which includes naturally occurring polyglutamates found in food, as well as the synthetic form of folic acid, commonly used for fortifying food and as a nutritional supplement. It is important to note that mammalian cells are incapable of synthesizing folate, and its deficiency has been linked to a wide range of disorders [22]. Women of childbearing age and who are planning for pregnancy, are advised to consume 600 \(\upmu\)g/day of folate, since it has been shown to potentially safeguard against developmental complications, including neural tube defects, which may arise within the fetus [15; 23]. Kiwi is often acknowledged as a valuable source of dietary folate, as it is contained in high proportions in many kiwi varieties compared to other fruits. The contribution of folate in kiwi can also substitute for the loss of this vitamin due to heating, namely through the cooking of the green foliage of other vegetables [15].
# Dynamic Voltage Optimization Based on In-Band Sensors and Machine Learning
###### Abstract  
A feedback-based architecture is presented for the distribution grid which enables the use of Machine Learning (ML) techniques for various applications, including Dynamic Voltage Optimization (DVO) and Demand Response (DR). In this architecture, sensor devices are resident on the distribution grid and therefore have a unique awareness of multiple system parameters. This enables the use of ongoing ML techniques for implementation of critical applications in the Smart Grid. Monitoring devices are placed at the endpoints and monitoring/control devices are placed along the power line on various types of grid-resident systems. Because the devices are grid-resident and interact directly with other devices on the same physical link, applications such as ML-assisted DVO can be targeted with very high confidence.  
volt/var optimization; dynamic voltage optimization; demand response; conservation voltage reduction; conservation voltage regulation; peak shaving; smart grid; machine learning Article
## 5 Applications of PCMs in Buildings
### PCM Panels and Wallboards  
There are numerous applications of panels and wallboards of incorporated PCMs with conventional construction materials. In addition, there has also been research for the manufacturing of panels and wallboards of different materials, such as polymers [94; 95] and metals [96]. Hang Yu et al. [97] investigated the fabrication of diatomic-based form-stable composite PCM wallboard for external building walls. Sayilacksha Gnanachelvam et al. [98] studied the utilization of tree wallboards (gypsum plasterboard, fiber cement board, and magnesium sulphate board) and a bio-PCM mat as fire resistant wallboards for their application to light-gauge steel-framed (LSF) wall systems made of cold-formed steel studs. Gypsum plasterboards provide better fire resistance that the other wallboards, and fiber cement boards provided the lowest. Behzad Maleki et al. [99] investigated the fabrication of plaster wallboards enhanced with nanocapsules with CuO nanoparticles. The PCM plaster wallboard reduced the thermal load of building by 13.83%.
# Laser R-Test for Angular Positioning Calibration and Compensation of the Five-Axis Machine Tools
## 3 System Measurement Method
\right] \tag{13}\]  
Applying Cosine theorem on \(\rm\Delta B^{\prime}\)OB yields the following  
\[\rm a^{2}=\rm b^{2}+c^{2}-2 bc.\cos(\theta) \tag{14}\]  
Figure 11: Ball lens coordinate frame \(\rm BX_{b}Y_{b}\). Figure 12: Applying the Cosine theorem. Laser R-test has the following advantages: contactless measurement provides no wearing issue; it has high resolution, simple configuration, and easy installation; it is capable for A-, B-, and C-type machine tools and the turning-milling compound machine tool with the five-axis synchronous measurement path and multiple measurement items. The setup of the Laser R-test is that the sensor module and the ball lens module are respectively installed on the spindle and the worktable of the machine tool to be tested, and the ball lens should be placed inside the working range of the sensor module as shown in Figure 15. The measurement principle does not require calibrating the concentricity between the measurement device and the rotary table. After the instrument hardware setup, the users input the ISO specification measuring path [1; 2; 3] into the machine tool. The quadrant detector is used to measure the path of the ball lens trajectory; the difference of comparison with the machine coordinate of the machine tool can be used to analyze the linear axis backlash, rotation center, positioning, and five-axis assembly error of the machine tool. In this research, the commercial instrument XR-20 (Renishaw, UK) is used to verify and have a comparison with our Laser R-test. That process is deployed later. In this section, Figure 16 shows the Renishaw XR-20 installed in different configurations to measure the A-axis and the C-axis, while the Laser R-test could measure different rotary axes without re-installing components. This advantage helps to save a lot of time while XR-20 needs to spend hours to re-configure the system. At present, most industries use a high-precision rotary table installed on the rotary axis as the basis for measurement, which takes a lot of time to calibrate the concentricity between the instrument and the rotary table. Therefore, the system developed in this study uses the Laser R-test and uses the XYZ Cartesian coordinate system positioning signal with a Cosine theorem to measure each angular positioning error. This system can automatically compensate for each type of CNC controller (e.g., FANUC, HEIDENHAIN) to enhance the rotary axis positioning accuracy and efficiency of the machine tools, also significantly reducing the instrument installation time and measurement time. The angular positioning measurement process on the rotary axes is shown in Figure 17.
## 4 Discussion
### DSi, DIN, and SRP Fluxes across the Water Sediment Interface  
Over \(\sim\)4 months, from June to September, in the 0-2 cm sediment horizon in _V. spiralis_ meadows, we estimated an average BSi decrease of 9.6 g Si m\({}^{-2}\), representing the difference between the initial and final areal BSi concentrations (Table S1). In the same site, the average DSi release from sediment to the water column obtained from net daily fluxes integrated over the same time span (Figure 6) was 12.8 g Si m\({}^{-2}\). At the BS site in the bare sediment, the BSi loss from the 0-2 cm horizon calculated in the same way was 5.7 g Si m\({}^{-2}\), while the net DSi efflux for the same period was 3.9 g Si m\({}^{-2}\). The concordance between the decrease of sedimentary BSi bulk and fluxes demonstrates how both vegetated and bare sediments were a net source of DSi, probably resulting from BSi dissolution, with the DSi efflux from the SAV meadow being almost three times greater than in bare sediment. These findings corroborate the hypothesis that SAV can mediate the DSi transport from pore water to the water column. Otherwise, silica fluxes were important when compared to overlying water: the DSi released at BS and VS sites represented 4.2% and 9.3% of DSi in the water column.  
At both the VS and BS site, BSi decreased mainly during the early part of the season, with a negligible amount of efflux from sediment in late summer. Furthermore, the sedimentary DSi flux under both light and dark conditions was related to the sedimentary areal BSi (Figure 7). This pattern was probably due to the fast decay of detrital BSi from different sources, primarily SAV and its epiphytes, which are known to have a short half-life of just a few weeks [29]. It is likely that part of the lost BSi was taken up and retained by SAV, but then exported from the meadow as detached leaves [79]. The Si assimilation by MPB accounted for a slight BSi increase at the BS site (Figure 2) but, at \(<\)1 g m\({}^{-2}\), it was not sufficient to compensate for the total BSi loss. In contrast, the rocky site accumulated BSi and the DSi flux was always directed from the water column to the epilithic algal community. DSi uptake at this site can be explained by the higher Si demand of the EA community, as evidenced by the higher BSi content in macroalgal tissues. However, despite the higher BSi tissue concentration, the total BSi bulk at this site was one order of magnitude lower than in SAV meadows and bare sediments as a consequence of lower plant biomass and sediment accumulation on rock surfaces.  
To sum up, from late spring to late summer, the soft bottom in the littoral zone of Lake Iseo appeared to perform as a DSi source and, comparatively, as DIN and SRP sink, whereas the steep, rocky shore behaved as a DSi sink and DIN and SRP source. Such DIN and SRP patterns are not unexpected because in shallow and soft organic sediments, nitrate can be denitrified [14; 35], while SRP can be either assimilated or co-precipitated [78; 84; 85]. Moreover, the high Si:N and Si:P ratios in macroalgal tissues can explain the Si consumption in RS, which exceeds N and especially P uptake.