esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Bottom Kerf Width  
Table 5 displays the results of the analysis of variance (ANOVA) conducted on the bottom kerf width, revealing that the cutting speed (CS) is the primary significant factor. The quadratic term of the laser cutting speed (CS\({}^{2}\)) was found to have a significant effect among the quadratic terms, and the interaction effect of the laser power and cutting speed (LP \(\times\) CS) was also significant. The lack-of-fit test indicated that the analysis performed was appropriate. The regression equation for the bottom kerf width, based on the significant parameters using coded values, can be represented using Equation (3).  
\[\left(\text{Bottom kerf width}\right)^{0.32}=+13.31-0.27\times\text{LP}-0.89 \times\text{CS}+0.02\times\text{LP}\times\text{CS}-0.02\times\text{CS}^{2} \tag{3}\]  
Figure 8 illustrates the response surface analysis for the bottom kerf width as a function of the input parameters. Figure 8a reveals that the effect of the cutting speed changes direction based on the focal plane position. Specifically, the impact of the cutting speed on the bottom kerf width is more pronounced at the minimum level of the FPP than at the maximum level of the FPP, and vice versa. This phenomenon is attributed to the interaction effect of these two parameters. Additionally, Figure 8b demonstrates that decreasing the laser power and increasing the cutting speed leads to a reduction in the bottom kerf width. This observation can be explained with Equation (3), where a decrease in heat input results in less material being melted and heated, leading to a narrower kerf width.  
Figure 8: Response surfaces of the bottom kerf width in terms of (**a**) the focal plane position and cutting speed, and the (**b**) laser cutting speed and laser power (Red dots in the figures correspond to bottom kerf width in that point, red color: more bottom kerf width and blue color: less bottom kerf width).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Source of Variation** & **Degree of Freedom** & **Sum of Squares** & **Mean Squares** & **F Value** & \(p\) **Value** \\ \hline Model & 4.89 & 4 & 1.22 & 2.98 & 0.0637 \\ A-LP & \(4.093\times 10^{-3}\) & 1 & \(4.093\times 10^{-3}\) & \(9.966\times 10^{-3}\) & 0.9221 \\ B-CS & 1.97 & 1 & 1.97 & 4.79 & 0.0491 \\ AB & 1.30 & 1 & 1.30 & 3.16 & 0.1007 \\ B\({}^{2}\) & 1.62 & 1 & 1.62 & 3.95 & 0.0701 \\ Residual & 4.93 & 12 & 0.41 & & \\ Lack of Fit & 4.92 & 10 & 0.49 & 88.35 & 0.0112 \\ Pure Error & 0.011 & 2 & \(5.565\times 10^{-3}\) & & \\ Total & 9.82 & 16 & & & \\ \hline R-Sq = \% 49.82 & & & R-Sq (adj) = \% 33.10 & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Revised analysis of variance of the bottom kerf width.
# Power Control Techniques for Interference Management-
## 3 Analysis and Findings
### Sources  
#### 3.2.1 Most Relevant Sources  
Table 7 shows the journals that published the most articles regarding power control from 2018 to 2023. The journal with the most papers published was \"_IEEE access_\" in all databases. The highest record publications from Scopus, WoS, and IEEE Xplore were 33, 147, and 330, respectively. The second most popular sources were \"_IEEE transactions on communications_\", \"_IEEE transactions on wireless communications_\", and \"_IEEE open journal of the communications society_\" with records of 8, 44, and 17, respectively. Clearly, \"_IEEE access_\" is ahead of each of them by a large margin, which indicates its high impact on power control techniques.
## 3 Materials and Methods
### Energy Model  
Aerial survey is the quickest and most efficient way of collecting building-related data. However, the usability of such information is limited, as explained above. Primarily, aerial thermographic images give information about the distribution of the roof temperatures and, secondarily, about the heat losses through the facades, as can be noticed in the narrow band around the building outline, but this is only indicative. Generally, the major areas of the applications of the aerial thermographic roof images are:  
* Determination of the roof temperature of buildings, which provides accurate results for buildings with heated top-floors only;
* Discovering hot spots; some weak points of a single building, particularly on the roof;
* An indicative classification of buildings based on the relative roof temperature.  
In the spatial decision support system of the EnergyCity project, an indicative energy model was applied. It is not the aim of this paper to present the energy model in detail, so only a short overview will be given. The model was based on the so-called relative roof temperature. The roofs were classified into five classes based on their roof temperature compared to the city average value. The upper and lower limits of the ranges were different for the seven cities, because of the different climates and construction types.  
From the aerial survey, it is possible to determine the building outline, the perimeter and the covered ground area. The average building height can be obtained from other data sources, such as the comparison between the digital surface model and the digital elevation model. These three types of numerical information are essential to calculate the most important geometric data for energy calculations: the heated volume (V) and the total exposed surface (\(\Sigma\)A\({}_{\text{surface}}\)). The specific heating energy consumption is calculated for the five temperature ranges, practically defining five heating energy consumption ranges. The consumption ranges were determined on the basis of typical energy consumption values obtained from other building stock databases from the city.  
Not only the final heating energy consumption, but also the primary energy consumption values can be estimated by the SDSS, taking into account the primary energy factors laid down in the national building regulations. Similarly, the CO\({}_{2}\) emission is determined from the final energy value and the CO\({}_{2}\) emission factor of the used energy source. The used energy source was extracted again from other data sources.  
As a conclusion, the energy model is partly based on the roof temperatures, some geometric data extracted from the digital cartography and the aerial measurements, but it is inevitable to apply other types of additional data.
# One-Dimensional Consolidation Properties of Soft Clay under Multi-Stage Loading
## 3 Test Results and Discussion  
### Time Variation of One-Dimensional Compression Deformation  
Figure 2 illustrates the relationship between time and deformation under multi-stage loading and single-stage loading conditions. The deformation of the specimen during single-stage loading can be categorized into three stages: rapid increase, slow increase, and stabilization. In the initial stage of loading (approximately 0-5 min), the specimen exhibits significant compression deformation. The magnitude of deformation is directly proportional to the applied load, with the initial stage accounting for more than 85% of the total deformation. This behavior can be attributed to the soft marine sedimentary soil composition of the soil sample, which possesses relatively large pores and high water content, rendering it in a soft plastic state. Additionally, the soil has a high permeability coefficient, leading to the rapid discharge of pore water and the consequent compression of the samples.  
The pore ratio and permeability coefficient decrease as the effective stress increases further (second stage). Consequently, the pore water pressure dissipates until the permeability coefficient rapidly stabilizes and the pore ratio enters the third stage. In this stage, the deformation of the sample enters a stable growth phase (5 min-60 min), which contributes to approximately 13.5% of the total deformation. Overall, the deformation in this stage exhibits minimal changes over time, gradually reaching a stable state.  
Under multi-stage loading, the time-deformation law of each stage of the load is similar to that of single-stage loading. Multi-stage loading can be divided into three stages: rapid increase, slow increase, and stabilization. The total time-deformation law is the cyclic extension of the law of each loading step, and the total deformation is the superposition of each step loading deformation. In the test, the loads at all levels were  
Figure 2: Variation in deformation with \(t\) for multi-stage loading and single-stage loading.  
equal (\(\Delta\)P = 50 kPa), yet each load did not result in equal degrees of deformation. Overall, the deformation gradually decreased with each step, indicating the nonlinearity of the compression deformation of saturated soft soil. Figure 2 shows that the final amount of deformation during multi-stage loading and single-stage loading is approximately the same, but the deformation rate of single-stage loading is significantly greater than that of multi-stage loading. Therefore, when treating the foundation with graded stacking, it is recommended to increase the load of each stage as much as possible to accelerate the consolidation rate of the foundation and ensure its strength.
## 3 Results and Discussion  
The numbers of mesophilic fungi in the parsley samples ranged from 1.10 \(\times\) 10\({}^{4}\) to 2.53 \(\times\) 10\({}^{5}\) CFU/g. The numbers of psychotrophic fungi were higher: 2.36 \(\times\) 10\({}^{4}\)-1.24 \(\times\) 10\({}^{6}\) CFU/g. The highest number of psychotrophic fungi was found in the sample of parsley No. 3 (autumn). The lowest numbers of both psychotrophic and mesophilic fungi were found in the sample of parsley No. 2 (summer). In the other samples, the numbers of fungi were at the level of 10\({}^{5}\) CFU/g. The numbers of mesophilic and psychotrophic bacteria were 1.23 \(\times\) 10\({}^{6}\)-1.61 \(\times\) 10\({}^{8}\) CFU/g and 1.10 \(\times\) 10\({}^{7}\)-5.45 \(\times\) 10\({}^{7}\) CFU/g, respectively. The highest number of mesophilic bacteria was found in the sample of parsley No. 2 (summer). The sample the most contaminated with psychotrophic bacteria was parsley No. 1 (spring) (Table 1). The level of microbial contamination varied both quantitatively and qualitatively, depending not only on the season but also on the types of microorganisms. The highest levels of psychotrophic and mesophilic fungi were determined for samples collected in autumn, statistically different from other seasons, and the smallest number of psychotrophic fungi was observed for summer samples (Table 1). The highest results for mesophilic bacteria were the in the summer samples, which were statistically different from the samples collected in other seasons. Comparing the statistical differences between the number of examined groups of microorganisms, it was found that diversity was observed mainly between the numbers of bacteria (statistically higher) and the numbers of fungi (Table 1). It is widely known that microbial processes are associated with seasonal environmental variations, which affect microbial activities. The levels of microbial contamination should be considered high and are comparable to other studies in which microbial contamination of parsley leaves reached levels of \(10^{4}\)-\(10^{5}\) CFU/g (fungi) and \(10^{6}\)-\(10^{8}\) CFU/g (bacteria) [38; 50]. Studies of green plants indicate that 100% of parsley leaf samples are most contaminated with mesophilic bacteria and fungi [38]. High numbers of both fungi and bacteria may be found in both organic and conventionally grown vegetables [13]. It is worth noting that rinsing, trimming, and packaging can be ineffective at reducing microbial contamination [51; 52] and may even contribute to secondary contamination. Among the isolated and identified bacterial groups (Table 2), MALDI TOF analysis confirmed that the dominant bacterial species belonged to _Pseudomonas fluorescens_ (Gram-negative rods, oxidase-positive), _Klebsiella oxytoca_ and _Acinetobacter jolnsoniti_ (Gram-negative rods, oxidase-negative), _Staphylococcus warneri_ (Gram-positive cocci, oxidase-negative), and _Bacillus megaterium_ (Gram-positive, spore-forming rods, oxidase-negative).
## 2 Materials and Methods
### Static In Vitro Digestion Method  
For digestibility of sauerkraut juice, DSJ, and experimental bread and meat samples, a standardized static in vitro digestion method by [PERSON] [20] has been used. It was performed in a model environment of the gastrointestinal tract (GIT)--a bioreactor Multifors 2 (Infors-HT, Bottmingen, Basel, Switzerland) in which digestibility and transit of nutrients are simulated. The process is controlled by the computer program Iris 6 Pallalel bioprocess Control Software (Infors-HT, Bottmingen, Basel, Switzerland). First, 30 g of the sauerkraut juice, bread and meat samples, and 1 g of DSJ were placed in the bioreactor with a pH and temperature control, and simulated saliva fluid (SSF) is then added and kept for 2 min at 37 \({}^{\circ}\)C. The transition to the stomach is simulated by introducing the simulated gastric fluid (SGF), which consists of a concentrated electrolyte solution, the enzyme pepsin, CaCl\({}_{2}\), and deionized H\({}_{2}\)O. Gastric acid secretion was simulated by adding 1 M HCl and adjusting pH to 3.0 \(\pm\) 0.2. Digestibility in the stomach is simulated for 2 h. Next, the stomach content is neutralized to pH 7.0 \(\pm\) 0.2 by adding 1 M NaHCO\({}_{3}\) and simulated intestinal fluid (SIF), which consists of a concentrated electrolyte solution, enzymes (trypsin, chymotrypsin, \(\alpha\)-amylase, lipase), bile salts, CaCl\({}_{2}\), and deionized H\({}_{2}\)O, thus simulating the transit to the duodenum. Digestibility in the small intestine was simulated for 2 h. After the digestion process, the content is frozen to stop the enzymatic activity.  
The bioavailability index (BAC) was calculated using the following equation [26]:  
\[\text{BAC }=\frac{\text{CGE}}{\text{CBE}}\]  
where CGE--the TPC and antiradical activity after gastrointestinal digestion; and CBE--the TPC and antiradical activity in the samples before digestion.
# Inter-Annual Variability of Peatland Vegetation Captured Using Phenocam- and UAV Imagery
## 4 Discussion
### Links Between Vegetation Greenness and Ecosystem Functioning  
Using four years of phenocam imagery and UAV data at the site, peatland vegetation phenology was found to be a strong predictor of GPP. Both phenocam-derived \(G_{cc}\) and UAV-derived VIs (particularly those employing the red-edge band, e.g., the reNDVI1) displayed strong correlations with GPP estimates, despite notable differences in the quality of the two phenological datasets. This strong relationship between GPP and phenocam-derived greenness is in agreement with observations from a number of studies [53; 94; 106; 107; 108].  
In the present study, phenocam-derived greenness displayed the strongest correlation with GPP during green-up and green-down. In contrast, during the peak growing season when the vegetation canopy was fully developed, the relationship between \(G_{cc}\) and GPP was not statistically significant (\(p>0.05\)). This finding indicates that meteorological conditions, rather than vegetation greenness likely control GPP during the peak growing season. Such strong seasonal variability in the drivers of CO\({}_{2}\) flux has been observed at a number of peatland sites [53; 94; 109]. Although phenology acts as a dominant control over peatland carbon dynamics during the transition seasons (i.e., when the vegetation canopy is developing), abiotic conditions such as temperature and WTD play a key role in regulating CO\({}_{2}\) exchange during the peak growing season [53; 94; 109]. Interactions between peatland phenology, weather and GPP are, however, complex. In a regional study of European peatlands, for example, [PERSON] et al. [94] identified phenology as a key predictor of variability in photosynthetic CO\({}_{2}\) uptake at the seasonal level, and showed that temperature and radiation affected GPP indirectly, by influencing plant phenology.  
Such regional studies provide valuable insights that can be used to better characterise the role of vegetation phenology in terrestrial ecosystem models (e.g., ref. [110]).
## 2 Case Description
### Economic Aspects  
Excluding the Belimo actuators and Siemens LOGO! relay (which were already installed in the building), the retail price of the wireless solution was 68000 (see left half of Figure 3). For comparison, the price of the wired solution would be 616,000 (see right half of Figure 3).  
It was expected that the installation would reduce the heat demand of the actual ventilation unit by 50%. This would have yielded an annual energy saving of approximately 15 MWh/yr or 61600/yr at the current heat price (6107/MWh). The return on investment for the wireless and wired solutions is shown in Figure 3.  
Figure 3: Return on investment for the wireless solution (**left**) and the wired solution (**right**).  
The price of the wired solution is higher, partly due to the use of different CO\({}_{2}\) sensors. The Vaisala sensors use more accurate technology and do not require such frequent calibration (the manufacturer guarantees 5% accuracy over the course of five years). On the other hand, the wireless solution allows remote calibration on a frequent basis, as required by the sensor manufacturer, which means that such accurate sensors are not needed.  
The price for the installation of the wireless sensors has not been included in the calculation because it would have been carried out by the researchers. However, installing the wireless solution only requires attaching the sensor nodes to a wall in each flat and connecting the central node to the internet (the rest is done remotely). This is clearly less labour-intensive than wiring each sensor through the finished building while it is in use. Moreover, it does not require highly skilled professionals to do this work.  
The actual payback period will strongly depend on the real energy savings and will also be affected by the energy price. The price is expected to rise every year, which will further shorten the payback period.  
In this setup, all the preparations were made in Denmark (but it could be done anywhere in the world) to be shipped as a do-it-yourself kit that can be added to the existing building automation.
## 5 Challenges and Future Research Directions
### Traffic Patterns and Energy Harvesting  
WPT is a promising approach for improving the energy efficiency of UDNs. However, adapting the power distribution based on traffic variations remains a challenge. Research should focus on developing intelligent energy-harvesting techniques that dynamically  
Figure 4: Key challenges and future research directions.  
adjust to network load and traffic demands. Additionally, collaborative energy-sharing mechanisms could be explored to optimize power use across small cells [61].
# Pulse-Shape Discrimination of SiPM Array-Coupled CLYC Detector Using Convolutional Neural Network
## 2 Data Acquisition  
In our experiment, we used a CLYC scintillator with an \({}^{6}\)Li enrichment degree of 95% manufactured by Beijing Glass Research Institute, a cylinder with a size of \(\varphi\)3.81 cm \(\times\) 3.81 cm. The silicon photomultiplier uses the 8 \(\times\) 8 array from SensL where each element is composed of 3 \(\times\) 3 mm\({}^{2}\) J-series SiPM elements. A \(\varphi\)62 mm readout board was designed to sum all the outputs of each sensor to a single-channel output and amplify this output by a Trans-Impedance Amplifier (TIA) circuit. The SiPM's bias voltage was converted by a 5.5 V input on the readout board and was set to 28.5 V. A 2 mm-thick aluminum shell was machined to assemble this detector. An oscilloscope (6104 A, Lecroy) operating at a frequency of 500 Ms/s in 12-bit mode was used to acquire detector pulses with a length of 20 \(\upmu\)s. The properties of CLYC and SiPM are shown in Tables 1 and 2. The experiment uses a \({}^{137}\)Cs source with an activity of 10 kBq to collect gamma signals. The \({}^{252}\)Cf source with an activity of 9 kBq is moderated by polyethylene with a size of 30 cm \(\times\) 30 cm \(\times\) 24 cm to obtain thermal neutrons. The \({}^{252}\)Cf source and the CLYC detector were placed in a cylindrical hole of \(\varphi\)10 cm \(\times\) 14 cm inside the polyethylene, as shown in Figure 2, to obtain a higher thermal neutron flux. The energy spectrum of \({}^{252}\)Cf neutron source is shown in Figure 3. After calculation, the ratio of thermal neutrons to nonthermal neutrons was 1:2.61, so the \(\upgamma\) signal in the article was mainly collected by \({}^{137}\)Cs. We set the signal threshold to 420 mV (equivalent electron luminescence energy is about 325 keV), and then randomly divided the signals collected by the CLYC detector into two groups: a training set and a test set, where the training set is the signal at the \(\upgamma\) peak and the thermal neutron peak of the collected signal; the test set signal has no special requirements. The training set was used to select the charge comparison method's parameters and train CNN, while the test set was utilized to compare the charge comparison method's and CNN's _PSD_ performance.  
\begin{table}
\begin{tabular}{c c} \hline
**Physical Quantity** & **Parameter** \\ \hline melting point/\({}^{\circ}\)C & 640 \\ \hline density/g-cm & 3.31 \\ \hline refractive index & 1.81 @405 nm, @ means the measurement condition \\ \hline emission spectrum/nm & 275–450 \\ \hline emission peak/nm & 370 \\ \hline Decay times/ns & 1(CVL), 50 (Ce\({}^{3+}\)), 1000 (Ce-STE) \\ \hline Light yield & \(\sim\)20,000 ph/MeV (gammas), \(\sim\)70,000 ph/thermal neutron \\ \end{tabular}
\end{table}
Table 1: CLYC’s main properties.
## 2 Methods  
The MVSS method was proposed in this paper to improve the signal-to-noise ratio (SNR), to enhance the focusing effect, and to improve the resolution. The core concept of MVSS beamforming is to assign a weight to each receiver. When higher weights are assigned to receivers with higher SNRs, the imaging quality is improved. The receiver array is divided into several subarrays to improve the robustness when obtaining the weights. In each subarray, to improve the resolution of the image and increase the SNR, we designed a weight for each receiver according to the covariance matrix of signals. After the imaging results of each subarray are superimposed, the obtained image is multiplied by the signal coherence matrix to reduce the influences of sidelobe interference and focusing errors. The proposed method is based on a homogeneous subsurface assumption, and it is a time domain raytracing method, which means the propagation path of waves is static. Notably, MVSS beamforming reduces to basic DAS beamforming without spatial smoothing, neglecting to calculate the weight of each receiver, and excluding the coherence factor (CF) matrix. Though DAS beamforming and Kirchhoff migration are coded in different ideas, they share similar principles, such as they are both prestack time migration and raytracing methods. Background velocity plays an important role in Kirchhoff migration and other migration methods, but it does not in DAS beamforming. DAS beamforming can be understood as an extremely simplified Kirchhoff migration, in which we assume that the wave propagates along a straight line and the subsurface is homogeneous.