esa-sceva/satcom-chunk-collection huggingface dataset:
# Slow Pyrolysis as a Method of Treating Household Biowaste for Biochar Production
## 3 Results and Discussion
### Properties of the Biowaste
#### 3.1.2 Ultimate Analysis Results of Tested Biowaste  
Kitchen biowaste had a carbon content of 43.36%, closely matching the literature values of 41.70% [49] and 46.10% [31]. Spring and autumn garden biowaste had lower carbon contents of 31.94% and 29.99%, respectively, which are below the 50.12% reported for garden waste [50] and the 42.85-45.57% range for grass [52; 53].  
The hydrogen content of the kitchen biowaste was within the upper range of 5.66-7.47% reported in the literature [31; 49; 51; 52]. The spring and autumn garden samples had lower hydrogen contents, similar to that of the municipal solid waste organic fraction (4.70%) [30].  
On a dry basis, kitchen biowaste contained 3.12% nitrogen, similar to the 3.49% reported for canteen food waste [49]. Spring and autumn garden waste samples contained 2.84% and 1.19% nitrogen, respectively--much higher than the literature values for garden waste, which range from 0.14% to 0.20% [50; 51]. The spring garden waste nitrogen content is comparable to that of lawn grass (2.68%) [52].  
The oxygen content in kitchen biowaste (38.87%) is close to the values reported by [PERSON] et al. (37.09%) [49] and [PERSON] et al. (36.55%) [31]. Spring garden biowaste had an oxygen content of 42.82%, which falls within the typical range of 39.40% to 45.91% for garden waste [29; 51]. However, autumn garden waste had a significantly lower oxygen content of 20.92%, even below the 26.39% reported for municipal yard waste [31].  
The sulfur contents were 0.10% for kitchen, 0.07% for spring garden, and 0.03% for autumn garden biowaste--substantially lower than the 0.40% found in the organic fraction of MSW [30]. The values for the garden waste samples align with the 0.03-0.08% range noted in the literature [50; 51].  
Kitchen biowaste had the highest chlorine content (0.71%), likely due to the presence of table salt (NaCl), which consists of about 60% chloride by weight [54]. Spring and autumn garden biowaste had significantly lower chlorine contents (0.05% and 0.01%, respectively), well below the 0.74% found in grasses [53].
# Impact of 50 Hz Electromagnetic Field on the Growth of _Chlorella vulgaris_
The comparative experimentation of the pilot plant highlighted the fact that, by stimulating the microbial activity with ELF-EMF, the specific electricity consumption of the wastewater biological treatment stage is reduced by approximately 50%; this represents an electricity saving of approximately 33% from the wastewater treatment plant's total energy consumption [29]. Similar results of ELF-EMF stimulation of microbial cultures have been reported with applications to hydrocarbon pollution remediation [30], as well as in various biotechnologies for the production of biofuel and bioenergy [19]. It was experimentally found that the exposure to 100 Hz EMF of up to 250 V/m stimulates the ATP synthesis in _Escherichia coli_[31]. On the other hand, excessive EMF of 3000 V/m inhibits lactose uptake in the case of _Kluyveromyces marxianus_[32]. Regarding microalgae, it was shown that under the influence of a periodic direct current, the algal biomass growth of Scenedesmus obliquus and its lipid yield are significantly improved [33]. Given these considerations, a fully automated device for increasing phototrophic microalgae production by exposing the culture medium to ELF-EMF was recently designed and made [34]. In this context, the aim of the work is to study the behavior of the microalgae _Chorella vulgaris_ in the presence of ELF-EMF of 50 Hz. The selection of the microalgae strain for the experimental study was based on several advantageous characteristics, such as metabolic robustness, simple life cycle, rapid reproductive capacity, and industrial relevance within its genus [35; 36].
## 3 Measurement Results of System Evaluation
### Measurement Results
The SR-Rx in the iTDS-2 detects the Ack packet and changes the communication band of the iTDS-2 from 432 MHz to 27 MHz accordingly. Figure 10: Measurement result of the digitally assisted SR-Rx: (**a**) an overall waveform with the transmitted/recovered data and the corresponding quench signal; (**b**) change of the quench period with the oscillation output according to the carrier; and (**c**) a magnified view of the oscillation output with the quench signal. Figure 12 compares power consumptions between the iTDS-1 and the iTDS-2. Compared to the iTDS-1, the average power consumption of the iTDS-2 is reduced by 24% and 28% to 2.8 mW and 3.3 mW in 27 MHz and 432 MHz bands, respectively. This is because of the built-in SAR-ADC and the low power FPGA in the iTDS-2, which have substituted an MCU (MSP430) in the iTDS-1, while consuming only 20% of its power. The Tx power consumption, however, is increased in the iTDS-2 to improve the wireless link robustness. Even though the Tx is turned on with only 9% duty cycle, its average power consumption is 1.12 mW and 1.62 mW, at 27 MHz and 432 MHz, respectively. The AFE and four magnetometers in the iTDS-2 consume 0.36 mW and 0.47 mW, with 50% and 2.3% duty cycling ratio, respectively. Figure 11: Operation of the dual-band radio when there is interference larger than the predefined threshold level at either 27 MHz or 432 MHz band. Figure 12: Comparison between the iTDS-1 and the iTDS-2 power consumptions.
# Intelligent Traffic Engineering for 6G Heterogeneous Transport Networks
## 4 Proposed ML Techniques, MILP Framework, and Methodology
### Matching Process  
The MILP was employed to determine the optimal resource assignment based on the cluster transport network assignment. Each value from the UE cluster and the transport network was checked similarly during the resource assignment process. Step 9 in Figure 4 implies the iteration process of the similarity check between each cluster and the transport network properties or attributes. \(x_{ij}\) is the decision variable where the value of '\(i\)' represents the clusters assigned to the available '\(j\)' transport network as resources. Hence, \(x_{ij}\in_{1}\) to determine the output of the cluster transport assignment. \(x_{ij}\in\{0,1\}\), \(i\) = the transport networks, \(j\) = the clusters  
This study evaluated the assignment output by matching clusters with transport networks with qualities compatible with the cluster. Similarly, the same processes were applied to examine the corresponding matching assignment on the clusters formed by various UE types (see Figure 5). Consequently, three significant clusters were created from the eMBB, URLLC, and mMTC UE types, and the same matching assignment output using MILP was evaluated. The process also considered clustering each UE type in an instance.  
Figure 4: The end-to-end flow of the clustering and resource assignment.  
Furthermore, the total DL or UL size, maximum packet delay budget values, and minimum packet loss rate of the cluster were recorded and represented as cluster attributes. Lastly, similarity checks were performed between the attributes, clusters, and transport networks.
# GF-1 Satellite Imagery Data Service and Application Based on Open Data Cube
## 3 Experiment and Results
### Experimental Results
#### 3.3.3 Spatiotemporal Analysis of NDVI and NDWI  
In order to verify the availability of GF-1 satellite image data managed by the ODC grid, this paper selected two sample points in the study area for analysis, and the annual change curves of NDVI and NDWI time series of the selected sample points are displayed in Figure 7. According to the NDVI and NDWI change curves, the change trend of vegetation and water body at the sampling point and its surroundings can be inferred. As can be seen in Figure 7, the vegetation and water bodies in the study area showed an overall decreasing trend, indicating a deteriorating overall ecological environment. In Figure 7a, the highest vegetation amount was presented at the point on 12 September 2017, which was influenced by the establishment of the Xiongan New Area and the development of relevant protection policies. In Figure 7b, the lowest value of water volume is shown at the point on 4 September 2018, due to the reduction in water in the reservoir caused by the unusual weather.  
On the basis of the loaded GF-1 imagery data, we calculated the spatial distribution of NDVI and NDWI in and around the Miyun Reservoir, as shown in Figure 8. The area and color shades of vegetation and water bodies in Figure 8 reflect the changes in vegetation and water bodies in the Miyun Reservoir and the surrounding area. For example, the second image in Figure 8a from the top left and the second image in Figure 8b from the top left clearly show a significant decrease in the area of the Miyun Reservoir in 2015, indicating a relatively dry climate in that year. The eighth image in Figure 8a from the top left and the eighth image in Figure 8b from the top left show the largest area of the Miyun Reservoir in 2021, indicating relatively abundant precipitation in that year. In short, due to improvements in speed and processing gained by ODC and the good user interaction of Jupyter Notebook, the change monitoring and application analyses became simple and feasible. At the same time, users can also feel the necessity of developing this ODC framework.  
Figure 7: NDVI and NDWI time series of sampling sites on the Miyun Reservoir. (**a**) Annual change curves of NDVI time series for the selected sample points (116.987\({}^{\circ}\) E, 40.534\({}^{\circ}\) N) from 2014 to 2021. (**b**) Annual change curves of NDWI time series for the selected sample points (116.945\({}^{\circ}\) E, 40.534\({}^{\circ}\) N) from 2014 to 2021.
## 3 Results and Discussions
### Correlation of LST and Indices of Land Surface Characteristics in Harare  
At city scale, LST strongly and positively correlated with NDBI (0.659) and UI (0.769), while there was some positive correlation with NDBal (0.050) (Figure 6). On the other hand,  
Figure 4: LCZ map for Harare (**a**) and derived LST spatial structure (**b**).  
Figure 5: Variations of LST between and within LCZs.  
there was a strong negative correlation with NDVI (\(-0.786\)) and MNDWI (\(-0.741\)), while it was weak with Aspect (\(-0.019\)) and Elevation (\(-0.130\)).
## 4 Experimental Setup and Results
### Svm
The 200 most important features ranked according to importance are retrieved. Figure 2: SVM performance based on the F-measure for the Actual 1024 attributes and the F-measure based on the attributes selected by the corresponding algorithm. Principal Components is 0.718, Relief Evaluation is 0.79, and the Symmetrical Uncertain Evaluation is 0.767. These results are interesting and shed valuable light on the attributes selection. Despite the great reduction in the actual data attributes, the Correlation Evaluation and the Relief Evaluation F-measure is slightly higher than the F-measure of the actual data attributes, which can lead to an interesting result that is the selected attributes by these two algorithms can successfully represent the actual dataset. The lowest F-measure of 0.718 is obtained for principal components. The Subset Evaluation has slightly less F-measure (0.762) than the actual attributes (0.784). From these F-measure values, it is noted that the loss in F-measure is not that large in comparison to the advantage of reducing the amount of processed dataset. Furthermore, it is quite worth mentioning that even the F-measure is increased in two cases. As the point of this article is to analyze the impact of reducing data on performance, the results show an interesting trend. As 100% data is represented with the 1024 attributes, the attribute selection methods provide a reduced set of data. For the Subset Evaluation, 84 attributes are selected with an F-measure of 0.762. This means that while just using the 8% of data from the actual data, we get only a 2% decrease in performance. From the physical data perspectives, we can deduce that only 8 GB data, from the 100 GB will approximately give the same performance of the 100 GB data. This is even more interesting with the Correlation Evaluation and the Relief Evaluation where F-measure is slightly higher than the actual F-measure of the actual data attributes. That means the smaller set can represent the larger dataset.
## 3 Sensing Per Crop
### Sensing Grape Diseases  
Downy mildew _(Plasmopara vitcola)_ is one of the most important fungal diseases for wine grapes and can lead to considerable losses in grape yield and quality. Downy mildew attacks all green parts of the grapevine (Table 1). On young leaves, the disease will appear on the upper surface as small yellow spots referred to as oil-spots [225]. As these spots enlarge they may appear to cover most of the leaf, especially if there is more than one spot on the leaf. Total crop loss may occur if severe infection is not managed, especially near flowering. Under warm, humid conditions (\(>\)98% humidity and \(>55^{\circ}\)F) at night, white, fluffy sporulation develops on the lower surface of the leaf. White spore masses also develop on infected flower and fruit clusters eventually wither and die [226]. Spores are spread to new leaves and clusters by wind and rain and the fungus require a film of water for infection. The disease can spread rapidly under warm conditions with frequent rain or dew.  
_Botrytis cinerea_ can infect all green parts of the vine being bunch rot the biggest problem. Early in the season, buds and young shoots may be infected and turn brown. Inflorescences may also be blighted and winter away. The fungus can infect grape berries directly through the epidermis or through wounds, and may continue to invade the entire cluster. Infected white grapes turn brown and purple grapes become reddish. During dry weather, infected berries dry out; in wet weather, they tend to burst and become covered with a grayish mold, which contains millions of spores. These spores are spread during moist periods by wind to new infection sites. The disease is favored by temperatures of 59-68\({}^{\circ}\)F and free water or at least 90% humidity [227].
# Learnable Nonlocal Contrastive Network for Single Image Super-Resolution
## 3 Method
### Adaptive Target Generator (ATG)  
Most SR models are trained by seeking a mapping between LR samples and HR samples in the training dataset. When the image predicted by the model does not fit the ground truth (GT) image, even if the output is a potential valid solution, the model is also penalized according to the training loss function. Inspired by the ATG network [23], we alleviated the ill-posedness of this SR task by introducing an adaptive target generator model to the SR model. By introducing the ATG network, the model was given some flexibility, thereby assisting the super-resolution model to generate more accurate reconstructed images. Different from the exploration of generative adversarial networks in [23], the proposed LNLCN model has extremely deep convolutional network layers, and we will explore the performance of ATG on deep convolutional architecture models. The adaptive object generation network (ATG) is presented in detail in Figure 3, which contains a localization network consisting of four fully connected layers. All layers, except the last one, contain the same number of BN layers and the ReLU function.  
During model training, the ATG network works in a patch-like fashion to adaptively generate new targets based on the predicted image for the current iteration of the SR model; therefore, there is no requirement for additional data preparation nor to conduct preprocessing. Specifically, the ATG network first slices the model prediction output \(f(x_{i})\) into nonoverlapping image slices with stride p and size p \(\times\) p, and then slices the original ground truth (GT) image into overlapping image slices with stride p and size s \(\times\) s, where p \(<\) s. The image patch size of GT is set slightly larger than the image patch size of \(f(x_{i})\) because this strategy needs to use the real target as the search space. Then, the affine transformation matrix \(\theta_{i,j}\) is obtained through the positioning network, and the \(y_{i}\) image slices are further sent to the bilinear sampling network for feature change. The \(j\)-th \(y_{i}\) image slices are converted into corresponding \(f(x_{i})\) image slices, and, finally, our adaptive target is created by combining the transformed image patches of size p \(\times\) p. Through the ATG network, we create a new target \(\widetilde{y}_{i}\) that is most relevant to \(f(x_{i})\) predicted by  
Figure 3: Structure of ATG. \(f(x_{i})\) and \(y_{i}\) represent the current prediction of the network and original real images. By converting each nonoverlapping part of \(y_{i}\) into a corresponding region of the network output \(f(x_{i})\), the adaptive target y is further rearranged to generate a new target.  
the SR network while keeping the original content unchanged, which is further used for network training.
## 2 Physical and Mathematical Model
### Mathematical Model
#### 2.2.1 Model Simplification  
To optimize the calculation process, the following assumptions are made, taking into account the existing circumstances of the project while also guaranteeing calculation precision:  
1. The gas leakage process at the leakage hole is isentropic, which means that the mass flow rate of HBNG at the leakage hole remains constant.
2. The influence of air viscosity is disregarded.
3. It is assumed that the HBNG is composed exclusively of methane and hydrogen. Following a leakage, these components become mixed with the surrounding air. These components are also believed to leak, behave as ideal gases, and not undergo any chemical reactions with each other.
4. The walls of the natural gas compartment are adiabatic, which signifies the absence of heat transfer between the system and its surroundings.  
\begin{table}
\begin{tabular}{c c c} \hline
**Symbol** & **Terminology** & **Unit** \\ \hline \(\rho\) & Gas density & kg/m\({}^{3}\) \\ \(h\) & Natural gas enthalpy value & J/mol \\ \(pdivU\) & Surface forces perform work on a & J \\ \(\lambda\) & Thermal conductivity & W \(\cdot\) m\({}^{-1}\cdot\) K\({}^{-1}\) \\ T & Thermodynamic temperatures of & K \\ \(\phi\) & Energy dissipation function & \\ \(E_{k}\) & Endothermic terms for micro-unit & \\ \(\mu\) & Dynamic viscosity & N \(\cdot\) s/m\({}^{2}\) \\ \(F_{x}\),\(F_{y}\) & The force acting on the \(x\)\(y\) coordinate axes & N \\  & of the micro-unit & \\ \(Y_{i}\) & Pulsating expansion term for component i & \\  & in compressible turbulence & \\ \(l_{i}\) & Diffusive flux of component i & \\ \(R_{i}\) & Chemical net source term for a substance i & \\ \(X_{i}\) & Mole fraction of component i & \\ \(D_{ij}\) & Diffusion coefficient & m\({}^{2}\)/s \\ \(k\) & Turbulent kinetic energy & \\ \(\varepsilon\) & Turbulent dissipation rate & \\ \(\mu_{t}\) & Turbulent viscosity & \\ \(G_{k}\) & Turbulent kinetic energy from mean & \\  & velocity gradient & \\ \(Y_{M}\) & Dissipation rate from compressed & \\  & turbulent dynamic expansion & \\ \(S_{K}\),\(S_{\varepsilon}\) & Custom source items & \\ \(C_{\alpha 1}\),\(C_{\alpha 2}\),\(C_{\alpha 3}\),\(\sigma_{k}\),\(\sigma_{\varepsilon}\) & Model coefficients for turbulence equations & \\ \(t\) & Gas flow time & s \\ \(P\) & Absolute pressure & Pa \\ \(\xi\) & Mass fraction of component & \% \\ \(M_{a}\) & The relative molecular mass of air & \\ \(M_{m}\) & The relative molecular mass of methane & \\ \(L_{m}\) & Explosive limits of gas mixtures & \% \\ \(V_{H_{2}}\) & Volume fraction of hydrogen in the gas & \% \\ mixture & & \\  & Volume fraction of methane in the gas & \% \\ \(V_{CH_{4}}\) & mixture & \\ \(L_{H_{2}}\) & Explosive limit of hydrogen & \% \\ \(L_{CH_{4}}\) & Explosive limit of methane & \% \\ \(v\) & Air velocity & m/s \\ \(V\) & Gas compartment volume & m\({}^{3}\) \\ \(A\) & Ventilation opening area & m\({}^{2}\) \\ \hline \end{tabular}
\end{table}
Table 2: The list of symbols.
## 2 Experiment Setup  
Figure 1 shows the layout of the laser system, which comprises one master oscillator, one four-pass high gain pre-amplifier, and three stages of main amplifier.  
The master oscillator is a commercial industrial-grade sub-nanosecond Nd:YVO\({}_{4}\) Q-switched laser (Picolo AOT, InnoLas Laser, Munich, Germany), providing 1064 nm light, with 55 uJ, 450 ps at 1 kHz. Pulses from the Nd:YVO\({}_{4}\) Q-switched laser pass through an optical isolator composed of a PBS, an FR, and a HWP to avoid unwanted reflected light or even optical damage to the master oscillator. Before they are injected into the pre-amplifier, laser pulses are aligned and expanded to 2.0 mm with a beam expander (BE1), which suits the pump mode of the pre-amplifier. The pre-amplifier is an off-axis four-pass diode-end-pumped Nd:YVO\({}_{4}\) amplifier, where the Nd:YVO\({}_{4}\) crystal is 4 \(\times\) 4 \(\times\) 10 mm\({}^{3}\), a-cut, 0.3%-doped, and has double-end-wedged cut at 2\({}^{\circ}\). Both sides of the Nd:YVO\({}_{4}\) have been coated to transmission of 1064 nm and 808 nm. The crystal is end-pumped by a 500 W quasi-CW (QCW) fiber-coupled diode (E15F8S22-808.3-500Q-IS39, DILAS, Mainz, Germany), with pump cycle 100 us at 808 nm. Light from the pump diode is coupled into an 800 um core fiber, the output of which is focused into Nd:YVO\({}_{4}\) crystal with a spot diameter of 2.4 mm. A plane-concave lens (Lens1) is used for compensating thermal lens of Nd:YVO\({}_{4}\). To achieve compact architecture, a prism is used to reflect light and decrease off-axis angle to keep uniformity of amplified beam. A TFP, an FR, and a HWP are used as an isolator to output amplified pulses.  
The pulses are further amplified in a main amplifier which composes of three stages, as illustrated in Figure 1. The main amplifier with single-pass form has high optical efficiency. In consideration of suppression of SRS effect and thermal fracture in gain medium, each amplifier stage has two identical sub-amplifiers. The gain medium in the main amplifier is 0.2 at.% doped Nd:YVO\({}_{4}\) crystals with the same length of 15 mm and double-end-wedged cut at 2\({}^{\circ}\), but have different apertures: crystals at stage 1 are 5 \(\times\) 5 mm\({}^{2}\), crystals at stage 2 are 6 \(\times\) 6 mm\({}^{2}\), and crystals at stage 3 are 7 \(\times\) 7 mm\({}^{2}\). Both sides of the Nd:YVO\({}_{4}\) have been coated to transmission of 1064 nm and 808 nm. Each sub-amplifier was pumped by 50 mJ, 100 \(\upmu\)s (10% duty cycle) pulses provided by a 500W QCW fiber coupled diode (E15F8S22-808.3-500Q-IS39, DILAS, Mainz, Germany) operating at 808 nm. Coupling ratios of the pump beam at each amplifier stage are 1:3.7/1:5/1:5. Beam expanders (BE2-BE4) are used to mode matching of different stages for good beam quality. Focal length of plane-concave lens (Lens2-Lens4) used for compensating thermal lens of every stage's Nd:YVO\({}_{4}\) crystals are 500 mm, 800 mm, and 1000 mm respectively. A 9 \(\times\) 9 \(\times\) 20 mm\({}^{3}\) type I LBO (\(\theta\) = 90\({}^{\circ}\), \(\phi\) = 10.8\({}^{\circ}\)) crystal is used for the extra-cavity frequency doubling.