esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Soil Water Repellency  
WR testing provided results ranging from easily wettable to extremely water repellent. Detected WDPT\({}_{\rm A}\) values were classified according to the scale proposed by [PERSON] et al. (2015) as follows: wettable (<5 s, 23), slightly (5-60 s, 2), strongly (60-600 s, 5), severely (600-3600 s, 2), and extremely water repellent (>3600 s, 13 samples). Coefficients of variation calculated for Log WDPT\({}_{\rm A}\), Log WDPT\({}_{\rm P}\), and MED values were 1.13, 0.61, and 0.71, respectively. Actual and potential WR data (WDPT\({}_{\rm A}\), WDPT\({}_{\rm P}\), MED) showed significant correlation (r \(>\) 0.9, \(p\) \(<\) 0.001). In general, drying at room temperature led to a certain increase in WR in the majority of initially wettable soils. On the other hand, a decrease in WDPT refers mainly to samples that exhibited considerable repellency in the field-moist state. For the purpose of statistical analysis, we used a zero value for wettable samples that showed WDPT \(\leq\) 1 s (Log 1 = 0).  
Although some samples did not exhibit WR either in the moist state or after drying at room temperature, it was found that all soils have the potential to become water repellent when heated. WDPT of all samples increased significantly after heating the soils at 150 \({}^{\circ}\)C for 30 min. The observed increase in repellency may be explained by a change in the spatial arrangement of organic molecules, which resulted in coverage of hydrophilic mineral surfaces by organic moieties ([PERSON] et al., 2015).
## 4 Examined Distributed Consensus Gossip-Based Algorithms
### Broadcast Gossip Algorithm  
The next examined algorithm is \(BG\), whose principle can be described as follows: at each time instance \(k\), one of the agents (let us refer to it as \(v_{i}\)) is selected uniformly at random [22; 26]. Subsequently, this agent broadcasts its current inner states to all its neighbors, which updates their inner states as follows [26]:  
\[x_{j}(k+1)=\gamma x_{j}(k)+(1-\gamma)x_{i}(k)\text{ for }\forall j\in\mathcal{N} _{i} \tag{4}\]  
Here, \(\gamma\) is the mixing parameter taking a value from the following interval [26]:  
\[\gamma\in(0,1) \tag{5}\]  
In our analyses, its value is set to \(0.9\). The inner state of all the other agents (including the broadcasting one) for the next time instance is determined as follows [26]:  
\[x_{j}(k+1)=x_{j}(k)\text{ for }\forall j\
otin\mathcal{N}_{i} \tag{6}\]  
Thus, this algorithm does not require a pairwise communication whereby it is significantly simplified in contrast to the two previous approaches. However, as shown in [26], the precision of \(BG\) can be much lower than the precision of concurrent approaches as the inner states may not approach the value of the estimated aggregate function.
# Microalgal Biomass Generation via Electroflotation: A Cost-Effective Dewaring Technology
## 2 Overview of Microalgal Biosystems  
The growth characteristics and biochemical composition of microalgae are highly dependent on the cultivation conditions, which can be divided into four main categories, namely photoautotrophic, heterotrophic, mixotrophic, and photoheterotrophic [18; 19; 20; 21]. Table 1 shows a summary of the characteristics of different microalgal cultivation conditions. Photoautotrophic cultivation is widely used to grow microalgae. The algae utilize light and CO\({}_{2}\) (inorganic carbon) as energy and carbon sources, respectively, for growth through photosynthesis [19]. Heterotrophic cultivation offers ways to address low cell density challenges resulting from insufficient access to photons. Heterotrophic cultivation has the potential to produce higher lipid content and yield high valuable biochemical compounds from certain microalgal species. [PERSON] et al. (2020) reported that both biomass and lipid content obtained for the unicellular alga _Scenedesmus acumintatus_ via heterotrophic cultivation could achieve an ultra-high cell density compared to phototrophic cultivation [22]. However, the heterotrophic system is not economically feasible for commercialization as it accounts for about 80% of the total cultivation medium cost due to the requirement of additional carbon sources for growth [23; 24]. There are limited reports on mixotrophic and photoheterotrophic cultivation of microalgae as these two methods are also limited by light requirements, contamination risks, and high operational costs.  
Growth medium consists of essential nutrients (e.g., nitrogen and phosphorus), which are required to cultivate microalgae beside light and CO\({}_{2}\). Certain microalgal species are capable of converting those freely available nutrients, such as nitrogen, phosphorus, and heavy metals from wastewater into biomass [25]. Microalgae, such as _Chlorella, Scenedesmus_, and _Chlamydomonas_ can contribute to phyco-remediation and biomass production, simultaneously. Thus, these microalgal species, not only reduce the cost of microalgal cultivation, but also serve as a natural alternative for wastewater treatment [26]. Various microalgae cultivation systems have been developed to offer optimal biomass productivity for a specific microalgal species [27; 28; 29]. Table 2 shows different cultivation systems that have been tested and used in laboratories for large-scale production of microalgae. There are two main established technologies for microalgae cultivation namely open ponds and a controlled closed cultivation system that employs different types of photobioreactors [30]. Open ponds generally offer minimal capital and operating costs, with low energy input for culture mixing, but the risk of contamination is high.
# High-Fidelity Drone Simulation with Depth Camera Noise and Improved Air Drag Force Models
## 4 Conclusions and Discussion
### Comparison with Other Drone Simulators  
In Table 3, we compared our proposed simulator with popular drone simulators such as Gazebo [47], Hector [20,48], RotorS [49] and FlightGoogles [50] across several criteria: visual quality (which is related to the rendering Engine), provided sensors and sensor noise models, interface to configure multiple vehicle models (the number of vehicles), support for virtual reality (VR) headset, and embedded aerodynamic drag force models. It is seen from the table that, compared to other simulators, Airsim stands out as a notable drone simulation tool in all listed criteria. It excels in providing high visual quality, supporting VR headset interfaces, and offering a wide range of sensor models commonly used in drones, including IMU, GPS, RGBD, and Lidar. Airsim's application program interface (API) facilitates the easy launch and control of multiple vehicles, providing an advantage over other simulators like Gazebo, Hector, RotorS, and FlightGoogles. Although all simulators offer embedded aerodynamic drag force models, the specific inputs required for each model vary.  
In comparison to the current Airsim simulator, we introduced two significant contributions. Firstly, we presented a novel depth camera noise model, which is not available in Airsim, offering an advantage over other simulators, as indicated in Table 3. Additionally, we introduced an enhanced aerodynamic drag force model, which we proposed and verified in Section 3. Our focus was on improving the existing aerodynamic drag force model  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Speed** & **Airsim Drag Force Model** & **Rotor Drag Force Model** \\ \hline
5 m/s & 0.57 m/s2 & 0.64 m/s2 \\
10 m/s & 1.48 m/s2 & 0.98 m/s2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: RMSE errors of Airsim drag force model and rotor drag force model in outdoor experiments.  
Figure 20: (1) Distance, (2) Z-axis measured acceleration, and (3) Z-axis calculated acceleration with rotor drag force model and (4) Airsim drag force model at (**a**) 5 m/s flight and (**b**) 10 m/s flight.  
in Airsim, so we did not assess our model against other aerodynamic drag force models in different simulators. Nevertheless, this will be a direction for our future work.
# Seismic Response of Aeolian Sand High Embankment Slopes in Shaking Table Tests
## 2 Shaking Table Tests  
### Mechanical Properties and Microcosmic Shear Failure Mechanism of Prototype Sand  
The prototype sand was taken from the Mu Us Desert in China. It has been blown by wind for a long time; therefore, sand particles are fine, contain little silt and clay, and most of the particle sizes are in the range of 0.074-0.25 mm. It shows good water permeability, i.e., the sand surface shows almost no physical adsorption of water, and the maximum water absorption is less than 1%. These characteristics result in the non-plastic feature of Aeolian sand, which means that the molding of sand is difficult and the shear strength is poor even after forming. The physical characteristics of prototype sand are as follows according to the laboratory tests: initial density \(\rho=1.59\) g/cm\({}^{3}\), uniformity coefficient \(C_{u}=1.68\), coefficient of curvature \(C_{c}=0.96\), internal friction angle \(\varphi=37.5^{\circ}\), maximum dry density \(\rho_{d}=1.75\) g/cm\({}^{3}\), and optimum water content \(\omega=12.36\%\). According to the Test Methods of Soils for Highway Engineering (JTG E40-2007) [38], the prototype sand belongs to fine and poorly graded sand.  
The seismic capability of aeolian sand high embankments mainly depends on the occlusion force between sand particles. The micro-failure of the Aeolian sand under horizontal accelerations can be described as a shear-trituration between sand particles, which leads to the generation of cracks. The failure mechanism of prototype sand is shown in Figure 2. Therefore, the shear strength of Aeolian sand plays an important role for the study of seismic performance of Aeolian sand embankment slopes, and shear modulus is a significant parameter for estimating the shear strain resistance of materials. Furthermore, according to the principle of soil dynamics, a functional relationship exists between earthquake wave velocity and shear modulus, which is shown in Equation (1). The shear modulus also determines the propagation velocity of seismic waves in sandy soil. Therefore, when conducting shaking table tests on aeolian sand high embankments, to make the test results more reliable, dynamic shear modulus is a significant parameter for the model design.  
\[V_{s}=\sqrt{\frac{G}{\rho}} \tag{1}\]  
where \(G\) represents the shear modulus and \(\rho\) represents the initial density of the sand.
# A Variable Step Size Normalized Least-Mean-Square Algorithm Based on Data Reuse
## 5 Conclusions  
In this short communication, we developed a simple VSS-NLMS algorithm from a data reuse perspective. The analysis of the data reuse process in conjunction with the NLMS algorithm revealed the convergence modes of this algorithm. As a consequence, a sequence of normalized step sizes can be a priori scheduled to further design a VSS mechanism. Therefore, the resulting VSS-NLMS algorithm based on this approach had almost the same computational complexity as the conventional NLMS algorithm. Simulations were performed in the context of acoustic echo cancellation, and the results indicated the good performance of the proposed algorithm, in terms of its convergence rate, tracking capability, and accuracy of the estimate. In future works, we plan to implement and assess the performance of the proposed VSS-NLMS algorithm in a real-world experimental framework of an AEC application. In addition, we aim to extend the solution toward the affine projection algorithm, targeting further performance improvements, especially in terms of the convergence rate and tracking.  
Conceptualization, A.-G.R.; software, C.P.; methodology, J.B.; formal analysis, S.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by a grant of the Romanian Ministry of Education and Research, CNCS--UEFISCDI, project number: PN-III-P1-1.1-TE-2019-0420, within PNCDI III.  
Figure 10: Misalignment of the NLMS algorithm with \(\alpha=1\) and \(\alpha=0.03\), misalignment of the NPVSS-NLMS algorithm, and misalignment of the VSS-NLMS algorithm using \(l=0.5\) and the lower bound \(\alpha=0.03\). The input signal is speech, \(L=512\), and the ENR decreases from 20 dB to 10 dB after 120 s.
## 3 Experimental Section
### Sequence Analysis and Assembly  
Raw Illumina sequence data were preprocessed before assembly. FastQC (version 0.7.2) [42] was run to obtain sequence statistics and to determine cleaning parameters. Removal of adapter contamination was first performed using Cutadapt [43]. In-house Perl5 scripts were subsequently used for windowed quality clipping at a quality score cutoff of 25, a 12 bp 5' trimming to reduce bias associated with random priming during library preparation and finally a 35 bp minimum length filter (removal was carried out on read pairs).  
The Trinity _de novo_ RNA-Seq assembler (Release 19 May 2011) was used to generate the full transcriptome assembly. The following parameters were set for Trinity module \"Butterfly\"; graph compaction option: edge-thr = 0.26, path extension mode = compatible_path_extention, min_contig_length = 300, paired_fragment_length = 270 (50 bp + estimated median fragment size of readset). Standard settings were used otherwise.  
CLC Genomics 4.8 (48014) provided by CLC bio (www.clcbio.com) was used as sequence handling program in general, and the assembler provided with the program, though designed for mapping use, was used with the following settings: Similarity = 0.8, Length fraction = 0.5, Insertion cost = 3, Deletion cost = 3, Mismatch cost = 2, Min distance = 180, Max distance = 300, and with a contig cutoff at 200.
# Recent Advances in Hydrothermal Oxidation Technology for Sludge Treatment
###### Abstract  
With the rapid development of urbanization and the widespread adoption of wastewater treatment facilities, the volume of sludge produced has steadily increased. Hydrothermal oxidation (HTO) technology offers an effective solution for sludge reduction, harmless disposal, and resource recovery, making it a highly promising method for sludge treatment. In recent years, HTO has attracted significant attention due to its efficiency and environmental benefits. This paper provides a detailed explanation of the fundamental principles of HTO in sludge treatment, with a focus on the removal of organic pollutants, nitrogen transformation, and phosphorus recovery. The influence of key operational parameters, such as reaction temperature, time, initial oxygen pressure, and pH, on the performance of HTO treatment is also explored. In addition, the research status of HTO sludge treatment and an example of product recovery after treatment are also discussed. It examines the challenges associated with scaling up HTO for large-scale sludge treatment, along with potential research directions for future work. Special attention is given to the innovation of catalysts, with the goal of achieving self-catalysis in sludge treatment. Moreover, considering that ammonia nitrogen (NH\({}_{3}\)-N) is a major intermediate product in HTO, its removal, as well as the prediction and planning of other unintended products, remains a key issue. Further areas of interest include improving sludge dewatering performance and enhancing the production of valuable single carboxylic acids, which can boost resource recovery efficiency. This paper also highlights the diversification of sludge applications after HTO treatment. By providing insights into future development trends, this review offers valuable references for further research and practical applications. The ultimate goal is to support the development of HTO as a sustainable and efficient solution for sludge treatment, addressing environmental concerns while maximizing resource recovery opportunities.  
sludge; hydrothermal oxidation technology; principle; influencing factors; optimization scheme; resource utilization +
Footnote †: journal: applied sciences
# Solar Sail Transfer Trajectory Design for Comet 29P/Schwassmann-Wachmann 1 Rendezvous
## 3 Numerical Simulations
#### Case Study  
This subsection gives a detailed calculation of the characteristics of the optimal transfer trajectory for specific value of \(a_{c}\). In this context, the (canonical) value of \(a_{c}=1\,\mathrm{mm}/\mathrm{s}^{2}\) has been selected to describe the time variation of both the spacecraft osculating orbital parameters and the sail attitude during the orbit-to-orbit transfer. In this case, the optimal  
Figure 8: Initial and final true anomaly in a rapid transfer using an ideal or optical force model.  
Figure 7: Comparison between the minimum flight time obtained with an ideal (\(\Delta t|_{\mathrm{id}}\)) and an optical (\(\Delta t|_{\mathrm{op}}\)) force model as a function of the characteristic acceleration \(a_{c}\).  
flight time is \(\Delta t=5306\) days, that is, about 14.5 years; see also Figure 3b. The time variation of the classical orbital parameters of the spacecraft osculating orbit is shown in Figure 9, where one can observe that the change in orbital inclination is substantially performed in the first part of the transfer, that is, when the Sun-spacecraft distance is still in the order of one astronomical unit. The sketch of the optimal transfer is reported in Figure 10.  
Finally, the optimal control law, in terms of the time variation of the component of the normal unit vector \(\hat{\mathbf{n}}\) in the RTN reference frame, is reported in Figure 11. Note that the three components of \(\hat{\mathbf{n}}\) are roughly constant for \(t\geq 5\) years, that is, the sail attitude (with respect to the RTN reference frame) is nearly fixed for about 60% of the transfer. In particular, such an attitude maximizes the transverse component (i.e., the component perpendicular to the radial direction) of the propulsive acceleration \(\mathbf{a}\). This is an interesting result, which is consistent with the model described in the next section, where a two-dimensional approach is used to obtain an accurate approximation of the optimal transfer performance with a reduced computation time.  
Figure 9: Time variation of the spacecraft osculating orbit elements during the optimal transfer with \(a_{c}=1\,\mathrm{mm}/\mathrm{s}^{2}\) and an optical force model. Circle \(\rightarrow\) start; square \(\rightarrow\) arrival.
The employment of a laser with a wavelength between 630 and 980 nm, as in low-level laser therapy (LLLT), is one such strategy. Due to the lack of heat and tissue damage caused by these lasers, LLLT is often called a \"cold laser\" [35]. These red and near-infrared electromagnetic waves have been shown to promote the proliferative capacity of MSCs by stimulating the action of growth factors and enzymes and the synthesis of ATP within the cells [36; 37]. The therapeutic effect of LLLT is related to many parameters. The wavelength, power and energy densities, type of laser used, pulse structure, duration, and repetition of irradiation are all factors that determine the effectiveness of LLLT for bio-stimulation [36]. Because of its distinct properties, LLLT has proven to be a useful technique for regenerating damaged tissues. Tissue repair, accelerated wound healing, and inflammatory modulation are all areas where LLLT has been put to use [38]. By stimulating many signaling pathways involved in osteogenic differentiation, LLLT also expedites bone healing [39; 40]. Oral diseases such as mucositis, TMJ inflammation, aphthous stomatitis, and dental hypersensitivity have also been treated with LLLT [41; 42; 43]. Because LLLT has been shown to be a successful treatment method for a wide variety of disorders, it was used to biologically stimulate MSCs. LLLT has been shown to have a stimulatory effect on DPSCs, SHED [44], SCAP [45], and PDLSCs [46], promoting their proliferation, viability, and differentiation. The bio-stimulatory effect of LLLT is related to the cellular absorption of LLLT, which takes place in the mitochondrial respiratory chain, and results in an increase in ATP levels, the upregulation of growth factors, and the inhibition of apoptosis. In addition, the laser's anti-inflammatory and anti-edematous actions are caused by an increase in the body's microcirculation, which speeds up blood flow to the affected area. On the other hand, not a lot is known about how LLLT affects GMSCs. In light of this, the purpose of this research was to investigate the effect of LLLT on the osteogenic and proliferative capabilities of GMSCs, with the end goal of enhancing the regenerative potential of GMSCs as a conveniently available and promising form of MSCs. We hypothesized that LLLT with a low energy density (1.5 J/cm\({}^{2}\)) would promote viability and induce osteo/odontogenic differentiation more effectively than high-level laser therapy (HLT) with a high energy density (3 J/cm\({}^{2}\)).
# A Design of a Parcel Delivery System for Point to Point Delivery with IoT Technology
## 2 Background: Underlying IoT Technology
### ThingPlug Usage  
There are three ways to leverage a ThingPlug Network Server. The first case is to use one terminal device. In this case, we can communicate with a ThingPlug Network Server using API provided or with ThingPlug via a LoRa module. The second case is to use the terminal devices near the fixed gateway. In this case, the wireless start topology method allows you to connect the terminal device with the gateway. The gateway then collects the sensors from the terminal device and transmits them to the ThingPlug server. At this time, wireless communication between the terminal and gateway can then use LoRa RF, and communication between the gateway and ThingPlug can use Ethernet. The third case is the terminal device that moves a wide area. In this case, it is not possible to cover all terminal devices in which one gateway is moving extensively. Therefore, we use a Low Power Wide Area Network (LPWAN) that can communicate with a ThingPlug server anywhere. LPWANs include LoRaWAN and Sigfox [18; 19; 20].
## 7 SensorNC Applications
### TDMA Optimization
#### 7.3.1 General TDMA Design Problem
The TDMA design problem can then be formulated as:  
max. \[Z=f-s\] s.t. \[s\leq\frac{f}{n}\] \[d(f,s|r,b,C)=\max_{1\leq i\leq n}d_{i}(f,s|r,b,C)\leq D\] \[\frac{s}{f}\cdot C\geq F_{max}r\] \[f\geq 0\]  
We obtain an optimization problem with a linear objective function, and only four constraints, a great simplification. Most of the reduction in complexity is due to the lower number of decision variables as, e.g., for the rate constraints that collapse into a single one. For the delay constraints, this is not as easily seen, but the reader may ascertain her/himself that there is always one flow that is the worst-case flow and whose delay constraint is dominating all of the other flows' delay constraints as they are all facing the same situation when considering the parameters. For example, in a fully-occupied \(n\)-ary tree, one can choose any leaf node and its corresponding flow as the flow whose delay constraint is the dominating one.
## 3 Results  
### Relationships Between Crop Yields and Different Variables  
The relationships of crop yields with monthly SIF, EVI, and climate variables were quantified using the Pearson product-moment correlation coefficients (r) (Figure 3). Monthly VPD generally had negative correlations with both corn and soybean yields, especially in the peak growing season (June, July, and August) (\(p<0.05\)) (Table S5). Precipitation in all months was positively correlated with both corn and soybean yields. Correlations of monthly means and maxima of SIF and EVI with corn and soybean yields sharply increased from May to June and then decreased from August to October. In September and October, SIF showed stronger positive correlations with the yields than EVI. In most of the months, both monthly maxima of SIF and EVI performed better in indicating the yields than the corresponding monthly means. Therefore, monthly maxima of SIF and EVI were used in further analysis.