esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methods
#### 2.3.2 Biome-BGCMuSo Model  
The Biome-BGCMuSo model was developed from the Biome-BGC model series [38], and we use the latest version of the model, Biome-BGCMuSo 6.2. Compared with Biome-BGC, Biome BGCMuSo improves the model's ability to simulate carbon and water cycles in managed ecosystems and provides options for managing cropland, grassland, and forest, among which the most important modules of the model are the carbon flux module, phenology module and soil flux module. The GPP of vegetation is calculated by [PERSON]'s [39] photosynthetic reaction mechanism model in the carbon flux module, and maintenance respiration is a function of reactive nitrogen content. Leaf development affects the accumulation of carbon and nitrogen in leaves, stems, roots, and litter, and is calculated by the phenology module. The soil flux module describes the decomposition of dead plant material and soil carbon pools [40].  
The time scale of the Biome-BGCMuSo model is daily, and at least four input files are used for each execution: they are initialization file (INI file), meteorological data file (MET file), soil property file (SOIL file), and physiological ecological parameter file (EPC file). The simulation process of the model is divided into two stages: the first phase is the spinup simulation, where the model runs from a lower soil carbon and nitrogen level and it stops until a steady state is reached; the second phase is the normal simulation stage, which uses the initial values obtained in the initialization stage to simulate NPP of GPP [38]. To bring the model to an equilibrium state, daily meteorological data from 1992 to 2018 were selected and used in this study; in the procedure, the first 20 years were used for the spinup simulation phase and the last 7 years for the normal simulation phase.
## 5 Conclusions  
This study aimed to estimate the external disturbance torque to detect obstacle collisions in an unstructured environment without the use of additional expensive sensors. The technique used the nonlinear observer SPO and gravity model to estimate the external disturbance torque. Collision occurred if the sum of all external disturbance torques exceeded the set threshold. This technique was evaluated at different speeds. With increasing speed, the error in the estimation increased because the nonlinear inertia and Coriolis force terms were ignored. The end-effector contact force was also estimated based on the estimated external torque and verified using the contact force in the Simulink toolbox. An APF integrated with a desired Cartesian trajectory was employed to avoid the obstacle. A repulsive force was applied to the Cartesian trajectory space when the robot approached the recorded obstacle position. During obstacle avoidance, the contact force and estimated external torque became zero, validating the avoidance. The relationship between the threshold radius and gradient descent was formulated by performing several simulations. The proposed technique can also be applied to existing systems. In the APF, parameter selection (such as the threshold radius and gradient descent rate) plays a key role in smooth collision avoidance. In the future, an optimized parameter selection using APF will be investigated to improve collision avoidance. The implementation of hardware in the proposed technique is also suggested for future work.  
Conceptualization, M.S.; methodology, M.S. and H.K.; writing--original-draft preparation, M.S.; writing--review and editing, M.S., H.K. and M.C.L.; supervision, M.C.L. and H.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Nuclear Research and Development Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (MSIT, Korea) (NRF-2019M2C9A1057807) and was funded by Korea Institute of Machinery and Materials, (Project number: NK238A, 2022, Korea).  
Not applicable.  
All relevant data are within the text.  
The authors declare no conflict of interest.  
Figure 22: Continuous obstacle avoidance trajectory versus time. WTC = without collision avoidance. WTCA = with collision avoidance.
# Nitrogen Oxide Removal by Coal-Based Activated Carbon for a Marine Diesel Engine
## 2 Experimental System and Methods  
### Experimental System  
In order to study the effects of different gas compositions, concentrations, and temperatures on the denitration ability of the coal-based AC, a simulating gas supply system is used to simulate real diesel exhaust gas in this paper. The experimental system is shown in Figure 1. It mainly includes a gas supply unit, quartz reactor, temperature control unit, flue gas analyzer (Testo 350, Freiburg, Germany), and exhaust gas absorption device. The gas supply unit mainly includes gas cylinders, pressure reducing valves, mass flow controllers (Seven Star CS200 AD, Beijing, China), mixing chamber, etc. The temperature control unit includes a tubular heating furnace (Ruipu Electric Equipment Factory, 220V/3 kW, Yangzhou, China) and temperature controller. N\({}_{2}\) and O\({}_{2}\) are high-purity standard gases. NO is a 10% standard gas with N\({}_{2}\) as the carrier gas, and so are NO\({}_{2}\) and NH\({}_{3}\). The concentrations of each gas component in the mixed gas are controlled by the mass flow controllers. The quartz reactor, a hollow cylinder of 26 mm inner diameter, 2 mm wall thickness, and 300 mm length, is placed in the tubular heating furnace. The experimental temperature is controlled by a temperature controller with \(\pm\)1 \({}^{\circ}\)C accuracy. The flue gas analyzer can continuously monitor the temperature and the concentrations of NO, NO\({}_{2}\), and CO of the mixed gas with a sampling period of 1 s by an electrochemical method. In order to prevent the exhaust gas from polluting the air, the gas passing through the flue gas analyzer is sent to an exhaust gas absorption device for treatment.
# Influence of Soil Parameters on Detecting Voids behind a Tunnel Lining Using an Impact Echo Method
###### Abstract  
Tunnel lining cavities are a common problem that may affect the bearing capacity of the tunnel-supporting structure, as well as the tunnel service life. The impact echo (IE) method can be used to detect voids behind tunnel linings. For a long tunnel, the surrounding rocks/soils are inhomogeneous and anisotropic, with parameters that vary with tunnel mileages. It is interesting to analyse whether alterations of the soil parameters affect the non-destructive test results. A laboratory experiment was performed in this study, in which voids behind a concrete plate, representing the tunnel lining, were designed to model the ineffective contact between the soil and the tunnel. The IE method was employed to inspect the existence of the void using different signal analysis approaches in the time, frequency and time-frequency domains. Furthermore, the fractal box-counting dimension was calculated for the purpose of quantitative evaluation. Different soil parameters and void sizes were considered, and finally, a finite element model was built and parameter analysis was accomplished using the software ABAQUS. The results demonstrated that: (1) A comprehensive analysis of vibration signals in the time, frequency and time-frequency domains was useful for identifying voids, while the box-counting dimension was useful for evaluating voids quantitatively. (2) Soils with large density and Young's modulus differences had a certain influence on void detection, while those with large water content and Poisson's ratio differences had little influence. (3) The box-counting dimension value was stable within the area where the void existed behind the tunnel; when the detection point was beyond twice that of the void dimension, it was difficult to locate the void.  
Keywords:non-destructive test; impact echo method; tunnel void detection; laboratory test; fractal analysis +
Footnote †: journal: Applied Sciences
# SatGuard: Satellite Networks Penetration Testing and Vulnerability Risk Assessment Methods
## 4 Implementation and Results  
In accordance with the proposed penetration testing method for satellite networks, we selected corresponding segments and automation technologies within the x and y dimensions to conduct the penetration testing. Given that the ground segment is a crucial and vulnerable part of the satellite system, our focus was set on the ground station. In the context of semi-automatic penetration testing, we opted for the approach based on LLMs.
# Anaerobic Digestate from Biogas Plants--Nuisance Waste or Valuable Product?
## 3 Results and Discussion
### Crop Yields  
The results from this study showed that the use of digestate increased maize yields, and the form of digestate was a factor determining their size. Such fertilization effects have been observed in previous research studies with maize and other plants [39; 40] (Table 5). The literature provides considerable data on the fertilizer value of digestates. Significant yield potential of digestate had also been demonstrated by [PERSON] et al. [41]. According to [PERSON] et al. [28], digestate application resulted in a maize yield as high as that obtained by using urea. Meanwhile, [PERSON] et al. [42] reported that the use of digestate from AD resulted in lower aboveground crop biomass production than the application of mineral fertilizer. [PERSON] et al. [43] reported that the yield potential of digestate is higher when it is used in combination with mineral phosphate fertilizers. The average yield of maize in our study ranged from 252.75 g FM pot\({}^{-1}\) on the control object to 447.50 g FM pot\({}^{-1}\) on the object treated with DG2. For maize plants treated with DGBPs, considerably higher yields were obtained than for the control. However, considerably lower maize yields were obtained when fertilized with the pelletized form of DGs and SFs. This suggests that this form of digestate is rather suitable as a slow-nutrient-release (mid- and long-term) organic fertilizer. According to [PERSON] et al. [44], pellets from digestate should find application in the private garden sector. A considerably greater (short-term) yield-generating effect was observed after fertilization with PDG1 and PDG2 than with digestate solid fraction pellets (PSF1 and PSF2). It appears unjustified to dry digestate for the purpose of retaining nutrients that easily flow to the liquid fraction during DG separation. The difference in the yields of maize between PSF1 and PSF2, and between PDG1 and PDG2, averaged approximately 38 g FM pot\({}^{-1}\) (11%). Relatively high crop yields were obtained on soils treated with unprocessed digestate DG2 and liquid fraction obtained from that DG (LF2). On average, the maize yields obtained under these treatments exceeded the control yield by 75%.  
In summary, study results highlight that irrespective of substrates used for the production of biogas, an evidently better yield-generating effect is provided by unprocessed digestate and the liquid and solid fraction of digestate than SFs and DGs pellets. Regression analysis (Table 6) showed that among the applied nutrients, only the dose of NH\({}_{4}\)+-N has a statistically significant relationship with maize yield, indicating that yields mainly benefitted from these nutrients. This confirms that different forms of DGs containing an active form of nitrogen are a suitable alternative to mineral nitrogen fertilizers. Results suggest that digestate processing techniques should especially consider the retention of mineral N in the fertilizer mass. Heavy metal (HM) contents in the tested products had no significant effect on yields.
## 3 Damage Characteristics
### Expansion and Shrinkage Properties
During the drying phase, the specimen undergoes contraction, leading to a volume of the specimen after drying (\(V_{d}\)) that is consistently smaller than \(V_{0}\). Consequently, despite the gradual decrease in \(\eta_{a}\), it implies a gradual increase in \(V_{d}\). Which aligns with the observations made by [PERSON] [5] and [PERSON] [6]. On the one hand, the \(\delta_{a}\) and \(\eta_{a}\) solely characterize the overall wet-dry cycling process in terms of the test's expansion and contraction. It fails to specifically delineate the expansion and contraction degree, as well as its pattern of change, within a single cycle of the specimen. On the other hand, the amounts of expansion and contraction vary across  
Figure 3: (**a**) Sample volume change; (**b**) sample image; (**c**) absolute expansion rate and absolute shrinkage rate; (**d**) relative expansion rate and relative shrinkage rate. each wet-dry cycle, suggesting that the deformation of the specimen after each cycle is not fully reversible. Consequently, assessing the deformation due to expansion and contraction during wet and dry cycling cannot solely rely on the specimen's initial volume. Therefore, the introduction of \(\delta_{r}\) and \(\eta_{r}\) is necessary to provide a more nuanced understanding of the specimen's deformation behavior. \(\delta_{r}\) represents the rate of change in volume after expansion during the ith wet-dry cycle, relative to the volume before expansion. \(\eta_{r}\) represents the rate of change in volume after contraction during the ith wet-dry cycle, relative to the volume before contraction. The calculation formula is as follows:  
\[\delta_{r}=\frac{V_{\text{w(i)}}-V_{\text{d(i-1)}}}{V_{\text{d(i-1)}}}\times 1 00\% \tag{3}\]  
\[\eta_{r}=\frac{V_{\text{w(i)}}-V_{\text{d(i)}}}{V_{\text{w(i)}}}\times 100\% \tag{4}\]  
In the aforementioned formulae, \(\delta_{r}\) represents the relative expansion rate (%); \(\eta_{r}\) the relative shrinkage rate (%). \(\delta_{r}\) and \(\eta_{r}\) of the first wet-dry cycle cannot be calculated. \(\delta_{r}\) and \(\eta_{r}\) quantify the degree of expansion and contraction, as well as their changing patterns, of the specimen during a specific wet-dry cycle. These indices reflect the differences in the expansion and contraction properties of the specimens after different numbers of wet-dry cycles. As illustrated in Figure 3d, as \(N\) increases, the \(\delta_{r}\) and \(\eta_{r}\) values of the specimens exhibit an overall decreasing trend. This indicates that the expansion and contraction properties of the specimens degrade over successive wet-dry cycles. However, the responses of \(\delta_{r}\) and \(\eta_{r}\) to wet-dry cycling differ. Specifically, \(\delta_{r}\) is more sensitive to wet-dry cycling, whereas the impact on \(\eta_{r}\) is less pronounced. This suggests that dry and wet cycling has a greater influence on expansion deformation than on shrinkage deformation. This finding aligns somewhat with the results reported in [PERSON]'s study [9].
###### Abstract  
In order to improve the adsorption efficiency of biochar in water treatment, biochar-graphene (BG) composites were prepared by the one-step dip coating method and applied to remove phthalates from water. Firstly, the materials and equipment needed for the experiment are introduced. The steps of preparing graphene oxide (GO) by the improved Hummers method and BG composites by one-step dip coating are discussed. Then, the morphology characterization, adsorption performance measurement, and isothermal model of BG composites are introduced. Finally, the structure characterization, adsorption kinetics, and adsorption isotherms of BG composites are analyzed. The results show that the properties of biochar could be changed by one-step dip coating, and the biochar could form composites with graphene. Compared with biochar, biochar-graphene composites have greater surface area and porosity. When the pyrolysis temperature was 600 \({}^{\circ}\)C, the specific surface area of biochar was 8.4 m\({}^{2}\)g\({}^{-1}\), and the specific surface area of the biochar-graphene composite was 221.3 m\({}^{2}\)g\({}^{-1}\). When the temperature was 300 \({}^{\circ}\)C, the specific surface area of biochar was 11.01 m\({}^{2}\)g\({}^{-1}\), and the specific surface area of biochar-graphene composite was 251.82 m\({}^{2}\)g\({}^{-1}\). The formation of graphene on the surface of biochar can increase the stability of the composite and acts as a very high potential active site. The porous structure and surface properties of biochar-graphene composites regulate the adsorption rate of pollutant molecules, thereby improving the adsorption performance. When the adsorption equilibrium was reached, the adsorption effect of phthalate esters on the biochar/graphene composite at the pyrolysis temperature of 600 \({}^{\circ}\)C was the best, and the adsorption capacity of Dimethyl phthalate (DMP)was 35.2 mg/g, that of Diethyl phthalate (DEP) was 26.4 mg/g, and that of Dibutyl phthalate (DBP) was 25.1 mg/g. The adsorption effect of DMP was the best. The results of the isotherm study indicate that the adsorption of phthalates by BG composites has great potential, which provides a good theoretical basis for the application of BG composites in environmental protection in China.  
water treatment; one step dip coating method; carbon-based materials-graphene composites; adsorption capacity; the isothermal model +
Footnote †: journal: Applied Physics Letters  
Article
# Closing Connectivity Gap: An Overview of Mobile Coverage Solutions for Not-Spots in Rural Zones
## 3 Potential Solutions
### Other Engaging Solutions
#### 3.2.2 Satellites  
Satellite-based architecture has furnished an outstanding architecture to hook up the highly dispersed and remote rural nodes due to their scalability and flexibility in reaching vast geographical areas. In function of the developed network scope, the satellites' orbit relies on defined classification [111]: LEO (altitude between 500 km and 2000 km), MEO (altitude into the range 5000 and 20,000 km), and GEO (altitude of 35,800 km).  
Underneath the condition of service-as-primary-resource, LEO architecture, on the one hand, solves latency issues [112]; on the other hand, it has added remarkable bit rate capacities by multi-beam technology [113]. In contrast, e.g., GEO holds limited these parameters. Heading to the best alternative for rural not-spots, LEO has become the best complementary structure of terrestrial networks in the countryside, figuring out several shouldered challenges, such as routing problems and raining attenuation [114].  
In order to provide seamless and continuous service by LEO satellite networks, these approaches have adopted constellation shapes whereas QoS is guaranteed, fueled by novel routing protocols regarding UE location and exploiting deterministic LEO topology. Therefore, the route bottlenecks should be foreseen in any pair of end-users, as the authors said in [115]. By avoiding design planning deficiencies, the system's user capacity becomes greater, and the covered geographical zone becomes larger [114].
# Hamiltonian Monte Carlo with Random Effect for Analyzing Cyclist Crash Severity
## 4 Results
### Environmental and Roadway Characteristics  
#### 4.3.1 Light Condition  
The results highlighted that biking at night, compared with the daytime, increases the crash severity of bikers, \(\hat{\beta}=0.60\). However, the uncertainty associated with this parameter should be taken into consideration since the confidence interval (CI) is wide, including 0.
## 3 Experimental Testbed & Evaluation Methodology
### Implementation of Experimental Testbed  
For our experimentation, we consider a long-term evolution system based on the Open Air Interface [17]. Our choice of the long-term evolution module is based on the availability of a reliable open source platform. Hence, user equipment, CU, DU, and RU are based on Open Air Interface implementation comprising full long-term evolution functionalities whereby the CU is collocated with the DU.  
To exploit the concept of multi-path diversity with channel coding in the Cloud-RAN platform, we implemented further amendments in the Open Air Interface to:  
* Decouple RAN functions between (CU + DU) entity and RU entity;
* Support channel coding in order to encode/decode data streams;
* Packetize/depacketize packets to transmit/receive them over the Ethernet-based fronthaul interface;
* Support the multiple Ethernet-based fronthaul by transmitting over multiple Ethernet ports, each connected to a separate Ethernet link.  
The experimental setup and data flow are shown in Figure 2. The (CU + DU) and the RU run on two separate servers each with 8 gigabytes of random access memory (RAM) with a Xeon 1220, and 4 cores. While the user equipment runs on a personal computer with 4 gigabytes of RAM with an Intel core i5. All hosts have a network interface card with a speed of 1 Gigabit and operate on Ubuntu 14 with a low latency kernel. The system operates on frequency division duplex on band 7 with 5 MHz bandwidth. The main configuration parameters are listed in Table 1; other Open Air Interface parameters are set to their default values.  
The (CU + DU) entity accommodates MAC, Radio Link Control (RLC), Packet Data Convergence Protocol (PDCP) and Radio Resource Control functionalities, while PHY and Radio Frequency functionalities are located in the RU. The (CU + DU) entity is connected to 4 Ethernet fronthaul links each 3 meters long and with a capacity of 1 Gigabit. The four links are connected to a 1 Gigabit layer 3 switch that switches packets to the right port based on internet protocol addresses. The output port of the switch is connected via a 3 meter long Ethernet fronthaul, with a capacity of 1 Gigabit, to the RU. This latter is connected to the user equipment via Universal Serial Bus 3 cables and two Universal Software Radio Peripherals, which are responsible for transmitting and receiving radio frequency signals. The Universal Software Radio Peripherals are connected through SubMiniature version A cables of a 1 meter long. In this experimentation, the analysis is applied only to the downlink communication.