esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Impacts of Vegetation Dynamics on GPP Dynamics  
Figure 7 illustrates the changes in forest coverage within the ecological restoration areas over the past 20 years. The trend in vegetation growth closely paralleled that of the GPP. From 2001 (2.5%) to 2020 (4.8%), the average forest coverage in Northern China increased by 0.08% per year, leading to an overall increase of 92% relative to the 2001 value. This growth is supported by a coefficient of determination (R\({}^{2}\)) of 0.62, highlighting the significant impact of ecological engineering on vegetation greening in Northern China, in conjunction with other contributing factors.  
Figure 6: Interannual trends of climate factors (solar radiation (**a**), precipitation (**c**), and temperature (**e**)) and partial correlation coefficients between these climate factors (solar radiation (**b**), precipitation (**d**), and temperature (**f**)) and annual GPP, with only statistically significant values (\(p<0.05\)) shown.  
We also conducted a partial correlation analysis to evaluate the relationship between forest coverage and GPP, complemented by pixel-wise significance statistics (Figure 8). A significant positive correlation between forest cover and GPP was observed in 85.0% of the study area, particularly in the northern part of Northeast China and most regions of Northwestern China. This finding suggests that increases in forest coverage have contributed to GPP growth. Notably, strong positive correlations were identified in northern Xinjiang, the Loess Plateau, and Northeastern China, which together account for 32.0% of the study area.
## 4 Simulation Analysis of PER and EE for Three-Stage Incremental Cooperative Communication  
In this section, we present the performance evaluation of our proposed work, which is compared to the existing schemes in [6], in terms of PER and EE. All results are obtained from the expressions presented in Section 6. Simulation parameters are given in Tables 3 and 4. We only consider the case of on-body sensor nodes, as we further implement these models in on-body WBAN protocols.
# Multi-Year Monitoring of Ecosystem Metabolism in Two Branches of a Cold-Water Stream
###### Abstract  
Climate change is likely to have large impacts on freshwater biodiversity and ecosystem function, especially in cold-water streams. Ecosystem metabolism is affected by water temperature and discharge, both of which are expected to be affected by climate change and, thus, require long-term monitoring to assess alterations in stream function. This study examined ecosystem metabolism in two branches of a trout stream in Minnesota, USA over 3 years. One branch was warmer, allowing the examination of elevated temperature on metabolism. Dissolved oxygen levels were assessed every 10 min from spring through fall in 2017-2019. Gross primary production (GPP) was higher in the colder branch in all years. GPP in both branches was highest before leaf-out in the spring. Ecosystem respiration (ER) was greater in the warmer stream in two of three years. Both streams were heterotrophic in all years (net ecosystem production--NEP < 0). There were significant effects of temperature and light on GPP, ER, and NEP. Stream discharge had a significant impact on all GPP, ER, and NEP in the colder stream, but only on ER and NEP in the warmer stream. This study indicated that the impacts of temperature, light, and discharge differ among years, and, at least at the local scale, may not follow expected patterns.  
climate change; stream metabolism; cold-water streams; long-term monitoring
## 3 Case Studies of Pilot and Full-Scale STRBs  
### Industrial Sludge with High Fat Content (Denmark and Sweden)  
Table 2 shows different STRB systems that treat sludge from WWTPs that receive large wastewater inflow from various industries: the Tinglev STRB (abattoir), Kolding STRB (abattoir), Skagen pilot STRB (fish-industry), and Skive STRB (abattoir) in Denmark and a pilot STRB in Kristianstad, Sweden, that receives wastewater from the food processing industry, i.e., dairies, abattoirs (20-25%), chicken farms, and others. The sludge in these systems is characterized by a high LOI (65-76% ds), and high fat (15,000-30,000 mg/kg ds) and oil (2300-7000 mg/kg ds) contents. Table 2 also presents the respective dewatering results [53]. The high fat and oil content affected the dewatering process; the observed maximum infiltration rate was almost ten times lower (0.001-0.004 L/s/m\({}^{2}\)) than the one in the Helsinge STRB in Denmark treating domestic sewage sludge (0.015-0.020 L/s/m\({}^{2}\)) (Figure 4). It is reported that the treatment of sludge with LOI between 50 and 65% results in a maximum infiltration rate between 0.008 and 0.020 L/s/m\({}^{2}\)[38]. A high content of fats and oil in sludge often results in low dewatering that is not completed between two consecutive loads. Thus, the duration of the resting period is not long enough in between loads, which creates an anaerobic wet residual sludge.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**STRB**} & \multirow{2}{*}{**Main Wastewater Origin**} & \multirow{2}{*}{\
\begin{tabular}{c} **Infiltration Rate** \\ **(L/s/m\({}^{2}\))** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Fat Content in** \\ **Feed Sludge** \\ \end{tabular} } & \multicolumn{3}{c}{**Dry Solids (ds)**} & \multicolumn{3}{c}{**Loss on Ignition (LOI)**} \\  & & & & & (\%) & & (\%) \\ \hline \multirow{2}{*}{**Tinglev (F)**} & Abattoirs & 0.002–0.005 & 21 & 0.4–0.6 & 10–15 & 75 & 70 \\ \hline \multirow{2}{*}{**Kolding (F)**} & Abattoirs & 0.002–0.008 & 30 & 0.5–1.0 & 15–25 & 65 & 60 \\ \hline \multirow{2}{*}{**Skive (F)**} & Abattoirs & 0.001–0.004 & 15 & 0.8–1.2 & 2.9–7.1 & 76 & - \\ \hline \multirow{2}{*}{**Skagen (P)**} & Fish industry & 0.001–0.003 & 16 & 0.5–3.0 & 5–14 & 75 & 80 \\ \hline \multirow{2}{*}{**Kristianstad (P)**} & Dairies, abattoirs, chicken farm and others & 0.003–0.020 & 29 & 0.5–2.5 & 8–14 & 70 & 75 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Dewatering efficiency of various STRBs in Denmark and Sweden treating sludge with high fat contents (P = pilot, F = full-scale).
## 3 Methods
### Prediction
#### 3.2.1 Consumption Prediction  
According to the literature [13; 16], household appliances are divided into three categories:  
* devices for occasional use: all devices used when needed and not for continuous use (e.g., TV, PC, or similar).
* non-interruptible devices: all devices that cannot be interrupted and require continuous use of current, or are independent of the user's use (e.g., refrigerators, lighting, alarms).
* interruptible devices (shiftable): all devices that can be interrupted and used later, or programmed (e.g., dishwashers, washing machines, or other smart appliances).  
The time of use of many devices, both occasional and interruptible types, depends on the habits and needs of the users, while for non-interruptible devices this does not apply. Instead, the time of use is influenced by the season (winter or summer seasons). In this work, each device can be used simultaneously or deferred with other devices over the course of the day, 24 h. Moreover, the kWh consumption of household appliances has been computed according to the literature [14; 15; 17] in order to achieve comparable values.  
Different profiles of households (i.e., a person or a group of people who use electricity to power their home) can be distinguished, which will therefore have different consumption. In this paper, the term \"user\" (or \"users\") refers to the concept of \"household\" (respectively \"households\"). Consumption depends on the number of people in the household, such as the number of devices and the frequency of use of those devices.  
In the proposed system, three standard types of households have been used from the UK-DALE dataset:  
1. household composed of two young people (aged between 21 and 30);
2. household composed of two elderly persons (with ages over 60 years);
3. four-person household (with two household members included with ages between 30 and 50 years and the other two household members ages between 0 and 20 years of age).  
Figure 1: The system architecture.  
For the consumption prediction, the SARMAX algorithm has been used on the UK-DALE dataset. The experiments were done by conducting training over the previous 7 days and a test over the next day. The data have granularity per minute; thus, the training is performed on 10,080 values per minute (7 days) and the test is performed on 1440 values per minute (1 day).
## 7 Analysis of Group 3 Performance
### Data Overview: Group 3
#### 7.2.3 C4f6  
For C4F6, the Pearson correlation coefficient was \(-0.608\) with a \(p\)-value of \(0.00\), indicating a strong and statistically significant negative correlation between plasma levels and the unused rate 1-Ui. These results suggest that the unused rate for C4F6 decreases as plasma levels increase. The Spearman correlation coefficient of \(-0.528\), with a similarly low \(p\)-value, reflects a moderate negative rank correlation, pointing to a significant but somewhat weaker inverse relationship between plasma intensity rank and 1-Ui rank. These findings confirm that increased plasma intensity reduces the unused rate for C4F6, and the correlation strength varies.  
To summarize, the negative correlations across all gases indicate that the unused rate 1-Ui consistently decreases as plasma intensity increases. The strength of this inverse relationship varies among the gases, with some exhibiting stronger correlations than others. The uniformly low \(p\)-values across all gases affirm that these correlations are statistically significant. These results align with the general tendency that increased plasma intensities enhance the availability of reactive radicals by promoting the dissociation of input gases, which are the primary agents in etching or cleaning processes. As plasma intensity rises, the abundance of these radicals increases, leading to more effective interactions with the  
Figure 12: Scatter plot with trend line between plasma level and 1-Ui for all gases.  
target surfaces or substrates. This, in turn, reduces the proportion of unused gas, reflected as a decrease in 1-Ui. In addition, higher plasma intensities accelerate reaction rates by providing the activation energy necessary for chemical reactions to proceed, ensuring that a larger fraction of the gas is consumed in desired reactions, thereby lowering the unused gas rate. Together, these mechanisms explain why increased plasma intensity correlates with a lower unused gas rate across all gases, emphasizing the importance of optimizing plasma conditions to maximize process efficiency.
## 4 Results
### PPP Time Transfer in Static Model with HAS Service
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Time-Links**} & \multicolumn{3}{c}{**960 s**} & \multicolumn{3}{c}{**15,360 s**} \\ \cline{2-7}  & **G** & **E** & **E/G** & **G** & **E** & **E/G** \\ \hline BRUX-PTBB & 2.37 \(\times\) 10\({}^{-14}\) & 2.38 \(\times\) 10\({}^{-14}\) & 2.35 \(\times\) 10\({}^{-14}\) & 4.82 \(\times\) 10\({}^{-15}\) & 4.20 \(\times\) 10\({}^{-15}\) & 4.04 \(\times\) 10\({}^{-15}\) \\ NIST-PTBB & 7.44 \(\times\) 10\({}^{-14}\) & 4.92 \(\times\) 10\({}^{-14}\) & 4.64 \(\times\) 10\({}^{-14}\) & 1.61 \(\times\) 10\({}^{-14}\) & 1.56 \(\times\) 10\({}^{-14}\) & 1.06 \(\times\) 10\({}^{-14}\) \\ OP71-PTBB & 2.59 \(\times\) 10\({}^{-14}\) & 2.58 \(\times\) 10\({}^{-14}\) & 2.50 \(\times\) 10\({}^{-14}\) & 5.40 \(\times\) 10\({}^{-15}\) & 5.39 \(\times\) 10\({}^{-15}\) & 4.53 \(\times\) 10\({}^{-15}\) \\ TWTF. & 9.49 \(\times\) 10\({}^{-14}\) & 7.80 \(\times\) 10\({}^{-14}\) & 6.24 \(\times\) 10\({}^{-14}\) & 2.18 \(\times\) 10\({}^{-14}\) & 2.08 \(\times\) 10\({}^{-14}\) & 1.43 \(\times\) 10\({}^{-14}\) \\ USN7-PTBB & 6.26 \(\times\) 10\({}^{-14}\) & 4.75 \(\times\) 10\({}^{-14}\) & 4.38 \(\times\) 10\({}^{-14}\) & 1.11 \(\times\) 10\({}^{-14}\) & 1.10 \(\times\) 10\({}^{-14}\) & 9.31 \(\times\) 10\({}^{-15}\) \\ USN8-PTBB & 6.67 \(\times\) 10\({}^{-14}\) & 6.51 \(\times\) 10\({}^{-14}\) & 4.34 \(\times\) 10\({}^{-14}\) & 1.24 \(\times\) 10\({}^{-14}\) & 1.10 \(\times\) 10\({}^{-14}\) & 9.36 \(\times\) 10\({}^{-15}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The MDEV of GPS (G), Galileo (E), and Galileo/GPS (E/G) PPP in the static model at 960 s and 15,360 s for six time-links (unit: ns). Figure 9: The MDEV of GPS, Galileo, or Galileo/GPS PPP time transfer in the static model.
# Converging Telco-Grade Solutions 5G and beyond to Support Production in Industry 4.0
## 3 Modeling Approaches and Frameworks in the Field of Industrial IoT
### MDE-to-IoT Challenges
#### 3.2.3 Real-Time Models to Supervision and Monitoring of Running IoT Systems  
Real-time models can help with monitoring and modifying the behavior of an IoT system at runtime in response to changes in the system. Modeling techniques can now be used during runtime, taking them beyond the design and implementation phases.  
Supporting models at runtime is still uncommon in MDE-to-IoT approaches, with only Papyrus for IoT, Eclipse 4 diac, and the previously mentioned MagicDraw-based approach [41] supporting real-time models and monitoring. Papyrus for IoT enables the use of development-time models to monitor running system states. Papyrus' ecosystem enables monitoring of micro-service states as well as starting and stopping them from Papyrus. The ARTMon [56] monitoring tool can also check real-time properties at runtime.  
At runtime, Eclipse 4 diac provides features for monitoring the data and event variables of function blocks. The MagicDraw-based approach [41], includes a validation module based on VIATRA [57]. The validation module can check well-formedness criteria during both design and runtime.
# Microbiological Safety and Health Properties of Markleted Fermented Root Vegetables
###### Abstract
To assess total antioxidant capacity (TAC), the cupric ion reducing antioxidant capacity (CUPRAC) and 2,2-diphenyl-l-picrylhydrazyl (DPPH) assays are frequently applied [25; 27; 28]. These two methods are complementary: the DPPH assay is primarily used to measure antioxidants with hydrophobic properties, while the CUPRAC assay can also detect compounds with hydrophilic structures [23; 29]. Notably, the CUPRAC assay offers an advantage over the DPPH method due to its use of reagents with a pH closer to physiological conditions (pH 7) compared to the DPPH assay (pH 5). Consequently, the CUPRAC assay is considered to provide a more accurate representation of the behavior of antioxidants within the human body [23; 30]. Furthermore, the DPPH assay has certain limitations, as the instability of the DPPH radical can lead to variability in methodologies, results, and their interpretation [31]. The primary objective of this study was to evaluate the potential of fermented root vegetables to contribute to a balanced diet and support human health. Specifically, the research focused on assessing the antioxidant potential and microbiological composition of commercially available fermented root vegetables on the Polish market, i.e., carrot (_Daucus carola_ L. var. _sativus_ [Hoffm.] [PERSON]), radish (_Raphanus sativus_ L. var. _sativus_), white radish (_Raphanus sativus_ L. var. _lingipinntatus_ [PERSON]), celery root (_Apium gravelens_ L. var. _rapaceum_ [Mill.] [PERSON]), and betroot (_Beta vulgaris_ L). The selection of products was determined by their availability on the Polish market and the limited range of products offered by domestic producers. These root vegetables represent some of the most commonly fermented plant materials in Poland, reflecting both traditional culinary practices and consumer preferences. Their selection also aligns with the current market trends, where these vegetables are readily accessible in fermented form, either pasteurized or unpasteurized. The antioxidant potential of the fermented vegetables was compared with values reported in the literature for their fresh counterparts. In addition, the microbiological quality of the fermented vegetables was examined, with particular emphasis on potential contamination. The hypothesis that pasteurized fermented vegetables have lower microbial counts and reduced antioxidant potential compared to non-pasteurized products was tested. Furthermore, the microbiological composition of the products was assessed against FAO/WHO criteria for probiotic food.
# Experimental Design of a Mobile Landing Platform to Assist Aerial Surveys in Fluvial Environments
## 5 Results
### Simulation Results  
Concerning the performed simulations, we will showcase three test runs. The tests will use the implemented waypoint tracking algorithm and P and PD-based controllers for the surge speed and heading of the vessel respectively from Section 4.1.1. The three simulated runs correspond to a U-shaped and a straight-line trajectory and a GPS waypoint trajectory tracking. The presence of heavy disturbances was neglected because this vehicle was not developed to navigate in the sea, but in calm inland water environments where the oscillations are approximately null. Nevertheless, an effort was made to introduce noise in the regulators.  
The implemented controllers for surge speed and heading where tuned using an initial estimate from a Ziegler-Nichols' tuning and then fine-tuned manually to a rise time below 2 s and no oscillations. The final gains for both controllers where \(K_{p}=82.65\) for the surge speed controller and \(K_{p}~{}=~{}0.5\), \(K_{d}~{}=~{}0.6\), \(N=21.4\) for the heading controller, where \(K_{p}\), \(K_{d}\) and \(N\) are the proportional and derivative gains and the filter coefficient respectively. The radius of acceptance used was \(\rho_{0}=1\) m.  
Figure 16 shows the results for a \"U\" shaped trajectory simulation with added noise (Gaussian) at the controller input, to evaluate their performance. Figure 17 also illustrates velocity and heading tracking in a straight-line trajectory under noise. The heading angle convention used measures clockwise from north in degrees \(0^{\circ}\) to \(359^{\circ}\), where \(0^{\circ}\) is north, \(90^{\circ}\) east, \(180^{\circ}\) south and \(270^{\circ}\) west Sensor noise has been modeled as white noise with zero mean and \(\sigma_{Speed}=0.2~{}[m/s]\) and \(\sigma_{Heading}=1~{}[^{\circ}]\) standard deviation. Sample time for the noise was 0.2 \(s\). As it can be seen from the results, the implemented controllers are sufficiently robust to deal with the noise while maintaining control of the vessel.  
Figure 16: Position of the Strider V1.0 during the U-shaped trajectory test (**left**) and current and commanded speed and heading for the U-shaped trajectory test (**Right**).  
Figure 17: Current and commanded speed and heading for the straight-line trajectory test.  
Figure 18 presents the trajectory followed by the vessel during a waypoint tracking test using coordinates under the WGS84 system, which provides a framework for calculating distances and transposing to Cartesian coordinates. The list of ordered waypoints used in this test is shown in Table 2.
## 3 Results
### Cell Attachment and Proliferation  
The results of fluorescence observation and cell counting are shown in Figures 8-13. The initial cell attachment can be evaluated by the results of fluorescence observation and cell counting of 2 h cultured cells. As mentioned in Section 2.7, a 10 \(\mu\)L concentrate of osteoblast cells was injected onto the surface of the gel so as not to have it flow down the gel, excluding external factors such as precipitation or uneven dispersion of cells. As shown in Figure 8, many more cells adhered to PVA-GO-H than to PVA-H after 2 h of inoculation. The gels were washed twice by phosphate buffer solution PBS before counting so the numbers of cells shown in Figure 9 are believed to reflect the cell adhesion state. The numbers of adhered cells increased with increasing GO concentration, revealing that GO can improve cell attachment and that the GO surface is a good environment for cell proliferation.  
Similar results appeared after three days' culturing, as shown in Figures 10 and 11. The variation in the number of attached cells between samples prepared under the same conditions was very large, which may be due to the difference in surface texture and incomplete detachment of cells when counting the number of adherent cells. It was confirmed that GO can improve cell attachment in the initial culture, whereas the oxidation time of GO appeared to have no significant influence on cell compatibility, based on the results of cell count (Figures 9 and 11).  
Figure 7: Comparison of [PERSON]’s modulus of different samples at high WC (water content).  
Figure 8: Fluorescence of cells cultured for 2 h: (**a**) PVA-H, (**b**) 0.05% GO (0.5 h), (**c**) 0.40% GO (0.5 h), (**d**) 0.05% GO (30 h), and (**e**) 0.40% GO (30 h).  
Figure 9: Number of cells attached to gels after 2 h of culturing.  
Figures 12 and 13 show fluorescence observation and cell counting of a prolonged culture (12 days). It should be noted that these two groups of experiments (3 days and 12 days) did not use the same samples because the gels had to be abandoned after Calcein staining. The difference between the numbers of cells for 3 days and 12 days of culturing may be due to the variations of samples and experimental operations. Although the PVA-GO-H surfaces showed better cell adherence, the cells did not show an exponential growth in prolonged culturing except for the 0.4% 0.5 h sample. However, the numbers of cells adhered on PVA-GO-H were still more than that of PVA-H,  
Figure 11: Number of cells attached to gels after three days of culturing.  
Figure 10: Fluorescence cells cultured for three days: (**a**) PVA-H, (**b**) 0.05% GO (0.5 h), (**c**) 0.40% GO (0.5 h), (**d**) 0.05% GO (30 h), and (**e**) 0.40% GO (30 h).  
indicating that the GO surface is a good environment for cell proliferation. The 0.4% 0.5 h PVA-GO-H may be an excellent material, but further research is required to elucidate the mechanism.