esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation and Experimental Results
### Experiments  
The experiments were performed to evaluate the performance of the proposed method. The proposed method was compared with the PI controller and the backstepping method. The PI control method shown in Figure 1 was used to compare the position tracking performance. For a fair performance comparison, a feedforward term to compensate for the load torque was added to the mechanical dynamics controller as follows:  
\[i_{q_{r}}=K_{\rho\omega}e_{\omega}+K_{i\omega}\int_{0}^{t}e_{\omega}d\tau+\frac{ 2}{3p\Phi}\hat{t}_{L} \tag{25}\]  
where \(K_{\rho\omega}\) and \(K_{i\omega}\) are the control gains. The control gains of the PI controller were chosen by using the method [24]. The backstepping method proposed in [25] was used in the experiments. Experimental setup is shown in Figure 7. The proposed method and the PI controller were coded in C Language using TMS320F2808. A surface-mounted PMSM (APM-SB03 ADK-9) produced by Kwapil & Co was used. An encoder (2500 pulses per  
Figure 5: Position and velocity tracking errors of the proposed method.  
Figure 6: Load torque estimation performance of the nonlinear observer.  
evolution) coupled to the PMSM was used to obtain the position. The sampling rate was set to be 5 kHz. The currents were measured by the current sensors embedded in the motor drive. Twelve-bit analog to digital (A/D) converters and 12-bit digital to analog (D/A) converters were used. The power supply (\(\pm\)24) was used. The motor coupled to the PMSM was used to generate the load torque.  
The position tracking performances of three methods are shown in Figure 8. Due to the friction, the relatively large position tracking error appeared near zero velocity in three methods. After the zero velocity period, the position tracking errors were reduced by the backstepping method and the proposed method. Furthermore, the ripples were reduced by the backstepping method and the proposed method. Because the desired state values generated by the system modeling and the optimal tuned gains were used in the proposed method, The position tracking error of the proposed method was relatively smaller than that of the backstepping method. The unavoidable position ripples appeared due to the encoder coupling effect, PWM driver noise, modeling uncertainty, nonideal sinusoidal flux distribution, and the friction. Figure 9 shows the load torque estimation performance of the nonlinear observer. In practice, it is impossible to generate the square load torque. Furthermore, the estimated load torque may include the nonlinearity of the PWM driver, parameter uncertainty, and the friction as well as the load torque. Thus, the estimated load torque reflected these values in the form of fluctuations as shown in Figure 9.  
Figure 7: Experimental setup.
# A New Methodological Approach for the Assessment of the \({}^{238}\)U Content in Drinking Water
## 2 Materials and Methods
### Diagnostics Techniques and Samples Measurements  
#### 2.2.1 High Purity Germanium (HPGe) Gamma-Ray Spectrometry  
For the gamma spectrometry analysis, the first aliquot, as it was, and the second one, preconcentrated, were inserted in a Marinelli container with a volume of 250 mL, while the third one, preconcentrated, was stored in a 20 mL vial. After that, they were counted for 70,000 s and spectra were analyzed in order to obtain the activity concentration of \({}^{238}\)U by means of that of its daughter \({}^{234m}\)Pa, with which uranium is in secular equilibrium. The 1001.03 keV gamma-ray line was used to determine \({}^{234m}\)Pa specific activity.  
The experimental set-up was composed of a positive biased Ortec HPGe detector (GMX), whose operating parameters are reported in Table 1.  
It was placed inside lead wells to shield the background radiation environment. It is worth noting that, for the sample holder geometry of 250 mL, efficiency and energy calibrations were performed using a multipeak Marinelli geometry gamma source (BC-4464) of 250 mL capacity, covering the energy range 60-1836 keV, customized to reproduce the exact geometries of samples in a water-equivalent epoxy resin matrix. Moreover, the ANGLE 4 code was employed for the efficiency transfer factors calculations to the 20 mL vial sample holder geometry [30]. The Gamma Vision (Ortec) software was used for data acquisition and analysis [31].  
The activity concentration of the investigated radioisotope was calculated using the following formula [32; 33]  
\[C=\frac{N_{E}}{\varepsilon_{E}t\gamma_{d}V} \tag{1}\]  
where \(N_{E}\) indicates the net area of the radioisotope photopeak, \(\varepsilon_{E}\) and \(\gamma_{d}\) are the efficiency and yield of the photopeak, respectively, \(V\) is the volume of the sample (L) and \(t\) is the live time (s).  
The measurement result uncertainty, coverage factor k = 2, was calculated taking into account the following components: uncertainty of the counting estimation, of the calibration source, of the efficiency calibration, of the background subtraction and of the \(\gamma\)-branching ratio [34].  
The quality of the gamma spectrometry experimental results was certified by the Italian Accreditation Body (ACCREDIA) [35].
## 2 Sensor Platform LSI Network System
### Sensor Platform LSI
#### 2.2.2 Adaptation Function  
For relieving data congestion, the sensor platform LSI has a simple adaptation function to reduce the number of data packets sent by a specific LSI and avoid a specific LSI from being dominant in the serial bus line. As shown in Figure 5, this function includes two processes: \"Data Wait\" and \"Bus Wait\". In the \"Data Wait\" process, when the digital sensing data exceeds the threshold value, a data sending start signal waits for a certain number of clock cycles (\(n\)) in 1 MHz. With the increase in the number of  
Figure 4: Schematic of collision detection circuit.  
data packets (_i_) that enter this process, \(n\) increases linearly with an increment of \"Data Wait Increment\" that is configurable. When \(i\) reaches the set value of the configurable \"Data Wait Times\", \(n\) and \(i\) are reset to 0.  
After \(n\) clock cycles in 1 MHz, the data sending start signal waits for another certain number of cycles (_m_) in a data sending clock, and the number of attempts (_j_) to send the data packet is reset to 0. After \(m\) clock cycles, the LSI attempts to send the data packet by checking the bus status for four cycles in its data sending clock (e.g., 4 us in 1 MHz transmission speed). If the bus logic level judged by the LSI's I/O cell is logic 0 during these four checking cycles, the LSI regards the bus status as free, or busy otherwise. When the bus is free, the sensor platform LSI starts sending the data packet. When the bus is busy, the sensor platform LSI will try to send the same data packet again after _mnext_ cycles in its data sending clock. With the increase in the number of attempts (_j_) to send the data packet, \(m\) increases linearly with an increment of \"Bus Wait Increment\" which is configurable. When \(j\) reaches the set value of the configurable \"Bus Wait Times\", \(m\) is reset to 0, the data packet is discarded and a new data packet enters the \"Data Wait\" process. When a data collision happens during a packet transmission, the sensor platform LSI stops sending data immediately. The suspended data packet is discarded and a new data packet comes to the \"Data Wait\" process.
# Numerical Simulation of Hot Jet Detonation with Different Ignition Positions
## 4 Conclusions  
This paper uses a two-dimensional numerical simulation method to study the effect of different ignition positions inside the hot jet tube on the distance and time of detonation initiation. The hot jet formation, the vortex-flame interaction and character of the initiation time and distance to detonation initiation are analyzed in detailed. The primary conclusions of this study include as following:  
(1) The mechanism of the hot jet detonation initiation is fast jet flame and the vortex-flame interaction. The vortexes increase turbulence intensity to accelerate blending between the unburned mixture and high-temperature products and increase superposition of the waves. Therefore, the increasing flame wrinkling surface area results in faster mass and energy release, thus increasing the flame propagation speed and accelerating DDT.  
(2) Wall ignition is significantly better than top ignition on DDT. When the ignition position changes from the top to the wall inside the hot jet tube, the DDT distance and time are both showing an obvious reduction. Two differences should be paid special attention: one is the vortexes entrain flame early. Another is the scale of vortex is big and two large-scale vortexes simultaneously entrain flame. The reason mainly lies in the formation of hot jet with stronger intensity in cause of wall ignition.  
(3) The different wall ignition positions also affect the detonation wave formation. The \(t_{DDT}\) and \(x_{DDT}\) all reduce firstly and then increase with \(\zeta_{Y}\) increasing. The shortest \(x_{DDT}\) is 810 mm and the fastest \(t_{DDT}\) is 2.055 ms at \(\zeta_{Y}=30\) mm. This is because not only the jet velocity is ensured to be about 550 m/s, but also the jet pressure is the largest due to the pressure wave is reflected on the wall and top of the hot jet tube.  
Conceptualization, H.Z.; formal analysis, S.L.; methodology, N.Z.; investigation, X.C.; writing--original draft preparation, X.J.; writing--review and editing, Z.L.  
The authors would like to acknowledge the Fundamental Research Funds for the Central Universities (Grant No. HEUCFP201719) for supporting this work.  
The authors declared that there was no conflict of interest.
###### Abstract  
As an important component to quantify the carbon budget, accurate evaluation of terrestrial gross primary production (GPP) is crucial for large-scale applications, especially in dryland ecosystems. Based on the in situ data from six flux sites in northwestern China from 2014 to 2016, this study compares seasonal and interannual dynamics of carbon fluxes between these arid and semi-arid ecosystems and the atmosphere. Meanwhile, the reliability of multiple remotely-derived GPP products in representative drylands was examined, including the Breathing Earth System Simulator (BESS), the Moderate Resolution Imaging Spectroradiometer (MODIS) and data derived from the OCO-2 solar-induced chlorophyll fluorescence (GOSIF). The results indicated that the carbon fluxes had clear seasonal patterns, with all ecosystems functioning as carbon sinks. The maize cropland had the highest GPP with 1183 g C m\({}^{-2}\) y\({}^{-1}\). Although the net ecosystem carbon exchange (NEE) in the Tamarix spp. ecosystem was the smallest among these flux sites, it reached 208 g C m\({}^{-2}\) y\({}^{-1}\). Furthermore, distinct advantages of GOSIF GPP (with R\({}^{2}\) = 0.85-0.98, and RMSE = 0.87-2.66 g C m\({}^{-2}\) d\({}^{-1}\)) were found with good performance. However, large underestimations in three GPP products existed during the growing seasons, except in grassland ecosystems. The main reasons can be ascribed to the uncertainties in the key model parameters, including the underestimated light use efficiency of the MODIS GPP, the same coarse land cover product for the BESS and MODIS GPP, the coarse gridded meteorological data, and distribution of C\({}_{3}\) and C\({}_{4}\) plants. Therefore, it still requires more work to accurately quantify the GPP across these dryland ecosystems.  
carbon management; eddy covariance; BESS GPP; MODIS GPP; GOSIF GPP
## 2 Relevant Existing Contributions
### Software Platforms  
Containers and microservices are regarded as one of the most natural approaches for deploying IoT applications in various cloud-based environments, for example, in the agricultural sector [46]. Nevertheless, a significant security problem determined by this model relates to the software containers that are not securely patched and which consequently produce vulnerable microservices. Although some existing contributions aim to reduce the implied security risks using vulnerability detection tools, outdated databases prevent a proper detection process to occur on newly published vulnerabilities. The research work reported in [47] describes a system intended to enhance container-side security models, which targets unknown attack patterns using a \"mimic defence network\". More precisely, a resource pool that contains attack pattern images is built. Following this, the variability of the execution outputs is analysed to detect potential unknown vulnerabilities. The possible continuous attacks are essentially mitigated through the usage of a graph-based scheduling strategy. This strategy optimizes the randomness and heterogeneity of the attack pattern images that are considered to replace the existing images. Additionally, a system prototype is implemented and described. The authors claim that the results of the experiments show that it is necessary to send 54.9% more random requests to complete the attack successfully. The authors also claim that the reported approach further enhances the defence success percentage by approximately 8.16%.  
The integrated collection of personal health data represents a relevant research topic, which is enhanced further by the development of next-generation mobile networks that can be used to transport the acquired medical data. The gathering of personal health data has recently become feasible using relevant wearable personal devices. Nevertheless, these devices do not possess sufficient computational power and do not offer proper local data storage capabilities. The scientific contribution that is reported in [48] presents an integrated personal health metrics data management system, which considers a virtualized symmetric 5G data transportation system. The personal health data are acquired using IoT wearable devices [49] through a client application component, which is typically deployed on the user's mobile device, regardless it is a smartphone, smartwatch, or another kind of personal mobile device. The collected data are secured and transported to the cloud data processing components using a virtualized 5G infrastructure and homomorphically encrypted data packages. The system has been comprehensively assessed through the consideration of a real-world use case, which is presented. The system is assessed through a field trial, which considers residents of Brasov City, Romania. The paper demonstrates that it is possible to design, implement and deploy a complex distributed system, which uses IoT devices for data collection, and implements complete end-to-end data privacy mechanisms through the consideration of homomorphic encryption [50] routines.
## 3 Information Leakage Analysis of Merkle Tree-Based Authentication Schemes
### Previous Schemes and Their Vulnerabilities
#### 3.2.4 Merkle Tree-Based Authentication with Transmission in Encrypted Form
A comparison of the computation time required for the proposed approach by data size is illustrated in Figure 8.

The detailed experiment results are summarized in Table 5. The computation time increases as the volume of the data (consequently, the size of Merkle tree) increases, and most of the time is used to construct the tree. For 1 MB of data, Merkle tree generation requires 92.1% of the time, while the time used by the prover to obfuscate the sibling path and to add a random bitstring is just 1.6% and 6.4%, respectively. On the verifier side, mask removal is additionally performed, taking a similar amount time as the verification. However, it is logarithmically proportional to the number of data blocks and accounts for negligible amount of time. Figure 8: Computation time of the proposed authentication scheme. #### 6.2.5 Analysis of Computation Overhead

Based on analyses of individual algorithms, the computation overhead for the prover and the verifier is summarized in Figures 9 and 10, respectively. Although _Merkle tree-based authentication_ (the first bar in the figures) does not consider information leakage, its computation overhead is used as a reference for ideal computation efficiency. On the prover side, the operation of _authentication based on a hardcore function_ (the second bar) is performed on the entire data while repeating the log of the data bit-length, leading to a computation overhead that is linearly proportional to data size. All of the other algorithms only require all of the data when constructing a Merkle tree, and the proof generation process has a relatively low overhead because it deals only with the logarithm of the data in bit-length. For'data smaller than 10 MB in size, the proposed scheme demonstrates the most efficient computation (next to the one adopting only a Merkle tree). For data over 10 MB in size, the computation overhead is very similar for the three algorithms exploiting Merkle trees. This indicates that the overhead generated by encryption/decryption and random masking in the proposed scheme is negligible. On the verifier side, there is a relatively clear difference between the algorithms because the computation required is lower than that of the prover. Other than _authentication based on Merkle tree_, the proposed scheme exhibits the greatest efficiency, followed by authentication based on Merkle tree with encrypted transmission, with hardcore function-based authentication demonstrating the lowest efficiency. The majority of the overhead is due to key agreement stage in the algorithm requiring encrypted communication and sibling path obfuscation in the proposed scheme. However, this difference does not exceed 1 ms regardless of the data size in the experimental results. Furthermore, the proposed scheme might be able to further narrow the gap by optimizing the bitwise exclusive-or (XOR) operation, which is not natively supported in Python.
## 2 Materials and Methods
### RT-qPCR  
After successful cultivation under either FSS or hypoxic conditions for up to 7 days, the cells were lysed, and the total RNA was extracted using the GenElute Mammalian RNA Miniprep Kit (Sigma-Aldrich, Taufkirchen, Germany) following the manufacturer's guidelines. The RNA was quantified by means of optical density measurement usingnanodrop (Thermo Fisher Scientific, Dreieich, Germany). Approximately 1 ug of RNA for each experimental group was digested with 1.2 units of recombinant DNase I (Sigma-Aldrich, Taufkirchen, Germany) at 37 \({}^{\circ}\)C for 30 min. The RNA was then reverse transcribed using a mixture of MultiScribe(tm) Reverse Transcriptase (Thermo Fisher Scientific, Dreieich, Germany) and Random Hexamer Primer N6 (Genaxxon, Ulm, Germany) following the manufacturer's instructions. The resulting cDNA was used for either qualitative or quantitative gene expression analysis. The primers used in the present study were listed in Table 1. For qualitative PCR, a volume of 2.5 uL of cDNA was added to a PCR mix in a thermal cycler according to the following protocol: 95 \({}^{\circ}\)C for 5 min, 94 \({}^{\circ}\)C for 30 s, 60 \({}^{\circ}\)C for 30 s, 72 \({}^{\circ}\)C for 30 s, and 72 \({}^{\circ}\)C for 1 min. Real-time PCR was performed by adding cDNA in triplicate with GoTaq(r) qPCR Master Mix (Promega, Walldorf, Germany) and the primers listed into a CFV96 Touch Real-Time PCR Detection System (Biorad, Feldkirchen, Germany). The gene expression levels of MyoD, myogenin, myosin heavy chain 1 (MHC1), myosin heavy chain 7 (MHC7), mammalian target of rapamycin (mTOR), vascular endothelial growth factor (VEGF), hypoxia-inducible factor 1 alpha (HIF1a) and nitric oxide synthase 1 (NOS1) were quantified using RT-qPCR and analyzed using the 2\({}^{-\Delta\Delta\text{CT}}\) method as described in [54]. The data present gene expression relative to an endogenous reference 185 gene in the target sample compared to a control.
## 2 Materials and Methods
### Quality Characters
#### 2.2.3 Main Active Compound Content  
Chromatographic Condition  
The content of spinosin, jujuboside A, jujuboside B, and betulinic acid was determined with an analytical Shimadzu LC-20 AT apparatus (Shimadzu Corporation; Tokyo, Japan), which was equipped with an Agilent ZORBAX SB C\({}_{18}\) column (4.6 mm \(\times\) 250 mm, 5 \(\upmu\)m). The mobile phase consisted of 0.1% formic acid/water (\(v/v\), A) and acetonitrile (B). The gradient elution was performed as the following protocol: 0-6 min, 0-14% B; 6-13 min, 14-33% B; and 13-22 min, 33-80% B. The flow rate was 1.0 mL/min. The injection volume was 10 \(\upmu\)L and the column oven temperature was set at 35 \({}^{\circ}\)C. The detection wavelength was selected at 335 nm for spinosin, 210 nm for betulinic acid, and 203 nm for jujuboside A and jujuboside B.  
Preparation of Sample Solution  
A 1.0 g powder sample was precisely weighed, combined with 25 mL of a 70% ethanol solution, and weighed again. It was then subjected to an ultrasonic extraction for 30 min (100 W, 40 kHz). After cooling, the sample was re-weighed, and any weight loss was compensated with a 70% ethanol solution. The mixture was filtered through filter paper. A 15 mL portion of the filtrate was concentrated under reduced pressure at 50 \({}^{\circ}\)C, and the resulting dried extract was dissolved in 5 mL of methanol to form the sample solution. Finally, the solution was filtered through a 0.22 \(\upmu\)m membrane filter prior for HPLC analysis.  
Preparation of Standard Solution  
A mixed standard solution of spinosin, jujuboside A, jujuboside B, and betulinic acid was prepared with methanol to a final concentration of 10.96, 25.20, 25.08, and 13.23 mg/mL, respectively. The standard solution was filtered through a 0.22 \(\upmu\)m membrane filter before HPLC analysis. Samples were analyzed three times repetitively.  
Method Validation  
The precision was estimated by six consecutive injections of a standard solution. The stability was evaluated using a sample solution stored at 25 \({}^{\circ}\)C for different time intervals: 0, 2, 4, 8, 12, 24, and 48 h. Meanwhile, the repeatability was assessed by conducting six replicate analyses of the same sample. A known amount of mixed standard solution was added to the quantified sample (0.50 g), and the recovery rate was analyzed using thespiked sample. The linearity was established via a calibration curve of peak area versus concentration [22].