esa-sceva/satcom-chunk-collection huggingface dataset:
## Data
### Gps Tec  
The GPS consists of 24 satellites, evenly distributed in six orbital planes around the globe, at an altitude of about 20,200 km. The ionosphere is a dispersive medium for GPS signals (\(f1=1575.42\) MHz and \(f2=1227.60\) MHz) in the microwave band. Therefore, signals traveling through this medium are affected proportionally to the inverse of the square of their frequencies. This effect can reveal information about the parameters of the ionosphere in terms of the TEC of a slant path, which is the integral of the electron density between a ground-based receiver and an associated GPS satellite. From the broadcast ephemeris (e.g., satellite time, elevation, and location) and a given ionospheric (shell) height at about 350 km altitude [55], the slant TEC along the ray path can be converted, usually using a simple cosine function of the satellite zenith into the vertical TEC (VTEC, for simplicity hereafter, TEC) at its associated longitude and latitude [56; 57]. Since each receiver could simultaneously trace up to 10 or more GPS satellites, a regional image over the local network area can be constructed by about 10 times the receiving points every 30 s.
# A LoRaWAN Network Architecture with MQTT2 MULTICAST
## 4 SDN-Based IoT Network Architecture Design  
Figure 3 shows the proposed architecture, which consists of an SDN network connecting edge nodes. These nodes will implement MQTT proxy functionality and/or AI processing. These edge nodes will connect with a node or cluster that will implement the different entities of a LoRaWAN network, that is, the network server, the application server and a bridge that allows the connection between the network server and the gateway, as well as the necessary databases. The gateway allows the radio connection with the LoRaWAN nodes, which will be the sensors and actuators that send environmental information. A typical LoRaWAN network would also include an MQTT broker, which in this case is replaced by the MQTT proxy.  
Figure 3: Proposal for an IoT network architecture for the collection, processing and use of environmental variables.  
These proxies forward MQTT messages using UDP between them, which reduces the required Round-Trip Times (RTTs)--and thus latency--and allows the multicasting of these messages, thus reducing the amount of traffic within the SDN network. These enhancements are explained in Section 5.  
In order to reduce the number of MQTT topics between proxies, which will be mapped to multicast IP addresses (see Section 5.3), we propose to follow the TTN approach with \"application/[ApplicationID]/device/[DevEUI]/[EventType] \" as topic. By aggregating them based on their ApplicationID, all the MQTT messages corresponding to the same application will be mapped to the same multicast IP address.  
Finally, we propose that the SDN network follows a tree topology, which will (1) allows to have hierarchical levels (e.g., local, regional and national), and (2) will simplify the routing for multicast (which are based on trees).
## 2 Methodology
### The Experimental Debye-Waller Factors or B-Factors  
Numerical results from dynamic calculations are commonly compared with experimental data used in organic chemistry and correlated fields. The B-factor, also called the temperature factor or Debye-Waller parameter, is one of the main research lines when dealing with proteins and their flexibility. It is influenced by the dynamic disorder caused by the temperature and the static disorder [46]. Equation (4) shows the most widespread form for this factor, where \(a\) is the mean displacement caused by thermal motion extracted by crystallography attenuation of X-ray or neutron scattering. It basically represents the displacement variation from the equilibrium position of a given atom [47].  
\[B=\frac{8\ \pi^{2}}{3}\langle a^{2}\rangle \tag{4}\]  
The researchers use the Debye-Waller factor for a wide range of different purposes. Some of them use the Debye-Waller factor to infer the flexibility of atoms, chains, or regions. Many studies initially considered only the B-factor of the C\({}^{\alpha}\) atoms, as these are usually associated with the most representative atom of the amino acid within the protein backbone [48; 49; 50]. However, nowadays, the temperature factor is found in the PDB file for all atoms for every single static configuration of the protein. It must be pointed out that the provided X-ray data accuracy is not better than 10% [47] and the collected results must be used with care [51; 52; 53]. The resolution is one of the parameters that has a significant effect on the experimental results. Scholars have also indicated that highly flexible regions, having B-factors generally higher than the other areas of the protein, can behave like hinges that drive the motion associated with the protein conformational change [54].  
The B-factor can be theoretically computed using the results derived from the dynamic analysis by applying a direct summation of the eigenvectors. Equation (4) is adapted into Equation (5) and used in this work to calculate the B-factors arising from the normal modes, as reported in [15; 55]. The computed B-factor for every node \(i\) is the result of the summation of the \(3N-6\) non-rigid mass-weighted modes \(n\). The sum can be truncated for values lower than \(3N-6\), usually decreasing the necessity to add higher frequencies, as \(B_{i}\) grows according to the inverse square of the angular frequency \(\omega_{n}\). The term \(k_{b}\) in Equation (5) indicates the Boltzmann constant (-1.38 \(\times\) 10\({}^{-23}\) J/K) and \(T\) is the absolute temperature in Kelvin.  
\[B_{i}=\frac{8\ \pi^{2}}{3}k_{b}T\sum_{n=7}^{3N}\frac{\left(a_{ix}^{2}+a_{iy}^{2}+ a_{iz}^{2}\right)_{n}}{\omega_{n}^{2}} \tag{5}\]  
Alongside the fact that the goal is to compare segments of the same protein, a better form to analyze the local flexibility is through the normalized B-factor (\(B^{\prime}\)). This is defined via Equation (6), where \(\sigma\) is the standard deviation and \(\mu\) is the average value of the B-factors profile.  
\[{B^{\prime}}_{i}=\frac{B_{i}-\mu(B)}{\sigma(B)} \tag{6}\]
## 4 Experiments and Result
### System Experiment Environment
#### 4.3.2 Method of Evaluation of the Experiment  
We compared the two educational groups by four criteria.  
1. Knowledge acquisition: Post-test
2. Knowledge retention: Retention-test
3. System usability: SUS
4. Presence in a virtual environment: IPQ  
Figure 7: Experimental procedure.  
Criteria 1 and 2 use tests to assess educational efficiency. The degree of knowledge acquisition of each participant was measured using a post-test that covered the aircraft maintenance procedures and the related equipment and parts. Ten days after the experiment, participants responded to a retention-test. To accurately measure knowledge retention, the material covered in this test differed from that covered in the post-test. In both tests, either five or ten points were given according to the difficulty of each question. The post-test and retention-test were scored by simulation educators to ensure a consistent assessment. For reliability, the results were reviewed once more by expert engineers.  
Criteria 3 and 4 evaluate whether the proposed system is suitable as an educational method. The SUS was used to evaluate each user's perceived usability of the system through a ten-item questionnaire. The responses to each question were recorded using a five-point Likert scale. This questionnaire contains a balance of positive and negative questions. Scores were calculated by subtracting one point per positive item and five points per negative item. The sum of all the scores was multiplied by 2.5 to get the SUS value. This value measures usability with a variable value between 0 and 100. We also used the IPQ to evaluate the participants' degree of presence in the virtual environment. By evaluating the 14 items of the IPQ questionnaire on a seven-point Likert scale, we can determine four measures of presence. The first is general presence (G), which evaluates total presence in general. The second is spatial presence (SP), which refers to the sense of being physically present in the virtual environment. The third is participation (INV), which measures interest and participation. The fourth is experiential realism (REAL), which measures the subjective experience of realism in the virtual environment.
# SPH Simulation of Hydraulic Jump on Corrugated Riverbeds
## 4 Influence of Corrugation Height and Length on Hydraulic Jump  
The height and length of corrugation are important design parameters for corrugated riverbeds. In this section, a parametric study is conducted to assess how these two factors affect the wave energy dissipation efficiency. Four corrugation shapes (two corrugation heights and two lengths) are studied. For each corrugation shape, two gate heights and three Fr numbers are tested. There are 24 cases in total, as shown in Table 7. It can be seen that, for the same riverbed, the energy dissipation rate increases with the Fr number. The practical implication is that a corrugation riverbed can dissipate more energy when the upstream dam releases water in which circumstance the inflow to the open channel has a large velocity. For better illustration, how the water depth (after hydraulic jump), jump length, and energy dissipation rate change with the corrugation wave height and length are shown in Figure 10. When the corrugation height and reservoir gate height remain constant, both the water depth and jump length increase with the corrugation height for all the Fr numbers; yet the corrugation height has a negligible influence on the energy dissipation rate. In addition, it is found that, for the same corrugation shape (\(S\) and \(t\) fixed), as the reservoir gate height increases, both the water depth after hydraulic jump and the jump length increase significantly, while the energy dissipation rate decreases. \begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Case**} & \multirow{2}{*}{**Fr Number**} & \multicolumn{4}{c}{**Corrugated Bed \(\eta\) (\%)**} & \multirow{2}{*}{**Smooth Bed \(\eta\) (\%)**} \\ \cline{3-3} \cline{5-6}  & & **Experiment** & & **Simulation** & **Error (\%)** \\ \hline A & 4 & 49.32 & 46.57 & \(-\)5.58 & 39.22 \\ B & 5 & 59.22 & 57.76 & \(-\)2.47 & 48.55 \\ C & 6 & 67.18 & 64.42 & \(-\)4.11 & 56.05 \\ D & 7 & 68.48 & 67.36 & \(-\)1.64 & 63.43 \\ \hline \end{tabular}
\end{table} 
Table 6: Energy dissipation rates of Cases A–D. Figure 9: Mean flow velocity of Cases A–F along the channel. When the corrugation height of the riverbed and the gate height are fixed, the corrugation wave length displays a negative relationship with the water depth after hydraulic jump, i.e., the larger the corrugation length, the smaller the water depth. In practical applications, water depth determines the apron height of a stilling basin. A larger water depth will lead to a higher apron, thereby increasing the construction cost. Therefore, the project cost can be reduced by using riverbed with a reasonably longer corrugation wave length. On the other hand, the jump length increases with the corrugation wave length within a certain range. Increasing the jump length increases the length of the water channel and
# Single-Parameter-Tuned Attitude Control for Quadrotor with Unknown Disturbance
###### Abstract  
In this paper, methods are presented for designing a quadrotor attitude control system with disturbance rejection ability, wherein only one parameter needs to be tuned for each axis. The core difference between quadrotor platforms are extracted as critical gain parameters (CGPs). Reinforcement learning (RL) technology is introduced in order to automatically optimize the controlling law for quadrotors with different CGPs, and the CGPs are used to extend the RL state list. A deterministic policy gradient (DPG) algorithm that is based on an actor-critic structure in a model-free style is used as the learning algorithm. Mirror sampling and reward shaping methods are designed in order to eliminate the steady-state errors of the RL controller and accelerate the training process. Active disturbance rejection control (ADRC) is applied to reject unknown external disturbances. A set of extended state observers (ESOs) is designed to estimate the total disturbance to the roll and pitch axes. The covariance matrix adaptation evolution strategy (CMA-ES) algorithm is used to automatically tune the ESO parameters and improve the final performance. The complete controller is tested on an F550 quadrotor in both simulation and real flight environments. The quadrotor can hover and move around stably and accurately in the air, even with a severe disturbance.  
Keywords:quadrotor attitude control; reinforcement learning; active disturbance rejection controller; covariance matrix adaptation evolution strategy +
Footnote †: journal: Applied sciences
## 2 Experimental Setup  
Our home-made TPI system is based on a pump-probe configuration in transmission mode that produces broadband THz radiation in the range 0.2-3 THz. The experimental layout is illustrated in Figure 1.  
Two twin PCAs (G10620-11, Hamamatsu) are used for both THz generation and detection. The system is powered by a mode-locked femtosecond laser (FemtoFiberNIRpro, Toptica) at 780 nm, with 100 fs pulse duration and 80 MHz repetition rate. A 50:50 beam-splitter splits the laser beam into two parts: the pump and the probe beams (each 15 mW in power). Dielectric mirrors (M, BB1-E03, Thorlabs) convey the two laser beams towards emitter and receiver PCAs. The pump beam is modulated by the THz emitter bias voltage (\(-7/7\) V, 1 kHz) and then focused on the THz PCA emitter. The generated THz radiation is then collimated and focused onto a target-sample by using two polymethylpentene plano-convex lenses (TPX, MenloSystems, f = 50 mm). After the sample, the transmitted THz beam is recollimated and refocused onto the THz PCA detector using two TPX lenses (f = 50 mm). Simultaneously, the probe beam is used to gate the THz PCA detector. A motorized delay line (DL, DDSM100/M Thorlabs) is used to temporally and coherently sample the THz electric field, and thus both the amplitude and phase are measured (See Section S1 in Supplementary Materials). The signal from PCA detector is filtered and detected by a lock-in amplifier (LIA, SR530, Stanford). The analog output of LIA is collected and digitalized by a dedicated National Instrument acquisition card at 16-bit (DAQ, NI, 6361-BNC connector) and then transferred to a computer for data collection,visualization and off-line analysis. In order to collect THz images, the sample is aligned perpendicularly to the propagating THz radiation and can be moved in the THz beam, by a 3D imaging module. It is formed of a motorized 3D linear translation stage (PT1/M, Thorlabs with 25 mm travel range and 10 \(\upmu\)m accuracy and servo actuators Z825B, Thorlabs with \(<\)5 \(\upmu\)m repeatability) that moves the specimen along the THz beam propagation axis (z-axis, 1D motion) and perpendicularly to it (x,y-axes, 2D motion). The object surface is sampled on a discrete grid composed of square pixels and an acquisition for each pixel is made. Therefore, the measurement is performed with a step-by-step sampling and it can be acquired in _fixed DL_ or _spectral_ modes. For _fixed DL_ images, the DL is set to a fixed position, for example at the location that corresponds to the maximum of the THz electric field E\({}_{\text{MAX}}\). Thus, only E\({}_{\text{MAX}}\) is acquired in each pixel. For _spectral_ images, instead, the information of a complete DL temporal scan is recorded, in each pixel. It allows the reconstruction of the whole THz pulse. By means of Fast Fourier Transform (FFT), images at given frequencies can be extracted from _spectral_ images.
## 1 Introduction
In particular, phytoremediation is emerging as a low cost, highly efficient remediation technology [11; 12; 13; 14; 15; 16; 17; 18]. Phytoremediation removes heavy metals from the soil using plants that absorb, enrich, and stabilize the heavy metal pollutants. In recent years, significant progress has been made in the remediation of Cd-contaminated soil by plants. Phytoremediation techniques are divided into different categories based on their function, such as volatilization, extraction, and stabilization [19; 20]. However, Cadmium can affect root elongation in plants but may also promote radial growth and increase root diameter in some plants [21; 22; 23; 24]. There are differences in the abilities of different plants to remove Cd [25; 26; 27; 28]. Economic development has meant that environmental protection is becoming increasingly important and strengthening the control of heavy metal pollution is particularly crucial [29; 30]. Researchers have proposed prevention and control methods, such as the construction of an appropriate legal system, agronomic restoration, and adjustments to planting structure [31]. Soil remediation research over the past 5 years has focused on the potential use of biochar to treat soil pollution [32; 33; 34], soil pollution remediation [35; 36; 37], plant remediation technologies [38; 39; 40], soil characteristics and microbial communities [41; 42; 43], and environmental risk assessment [44; 45; 46; 47; 48]. Plant remediation, as an effective non-secondary pollution technology, has attracted widespread attention for the environmental management of metal mines [49]. The average Cd pollution levels in Chinese soil is 7.0% and current research is investigating a variety of plant and microbial remediation technologies [50; 51; 52; 53]. The key to plant restoration is to select plants with good enrichment characteristics [54]. Plant remediation, as a green and sustainable pollution control method, has important applications in mitigating soil heavy metal toxicity [55; 56; 57; 58; 59; 60; 61; 62], especially with the current demand for \"green mines\". Furthermore, the efficiency and reliability of plant remediation technology can be further improved, which means that it should provide stronger support for heavy metal pollution control in the future.
## 4 Steam Electrolysis and Thermo(-Electro-)Chemical Cycles
### Water Splitting Thermochemical Cycles
Overall, there are clear advantages regarding the thermodynamic yield of each phase, the use of acids only at low temperatures (improving corrosion issues), the absence of catalysts, and the possibility of storing intermediate products also for long times. On the other hand, the number of steps is increased, and nickel compounds are quite toxic. A totally different approach is exemplified by the mixed-ferrite cycle. In the example reported in Table 3, both steps can be carried out below 800 \({}^{\circ}\)C [94; 101]. While in routes like sulfur-iodine or similar commonly available chemicals, in these processes, nanostructured mixed oxides are oppositely synthesized. As a result, only gas solid reactions are present,  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Process**}} & \multicolumn{1}{c}{\multirow{2}{*}{**No. of**}} & \multicolumn{1}{c}{**Reactions**} & \multicolumn{1}{c}{**Pros (+) \& Cons (–)**} & \multicolumn{1}{c}{**Ref. **} \\  & \multicolumn{1}{c}{**Steps**} & & & & \\ \hline \multirow{9}{*}{Modified sulfur–iodine (NIS)} & \multirow{3}{*}{5} & 2H\({}_{2}\)O + I\({}_{2}\) + SO\({}_{2}\) \(\rightarrow\) H\({}_{2}\)SO\({}_{4}\) + 2 HI & & \\  & & **(Bunsen reaction, 20–120 \({}^{\circ}\)C)** & + & Less corrosive intermediates \\  & & Ni + H\({}_{2}\)SO\({}_{4}\) \(\rightarrow\) NiSO\({}_{4}\) + H\({}_{2}\) & + & All reactions with high rates, near to \\  & & **(20–100 \({}^{\circ}\)C)** & & 100\%, without the necessity of catalysts \\  & & NiSO\({}_{4}\) \(\rightarrow\) NiO + SO\({}_{2}\) + \(\dagger\)\(\circ\) O\({}_{2}\) & + & Possibility to easily store, if necessary, \\  & & **(s+900 \({}^{\circ}\)C)** & & intermediate compounds \\  & & NiO + 2 HI \(\rightarrow\) NiI\({}_{2}\) + H\({}_{2}\)O & \(-\) & Numerous separation steps \\  & & **(s+100 \({}^{\circ}\)C)** & & \(-\) & Toxic compounds, if Nickel is used \\  & & NiI\({}_{2}\) \(\rightarrow\) Ni + I\({}_{2}\) & & \\  & & **(s+600 \({}^{\circ}\)C)** & & \\ \hline \hline \multicolumn{1}{c}{\multirow{6}{*}{Mixed ferrites}} & \multirow{6}{*}{2} & \multirow{6}{*}{+} & \multicolumn{1}{c}{In principle, only gas–solid reactions,} \\  & & & with easy separation processes \\  & & & + & Reaction temperature below 800 \({}^{\circ}\)C, if \\  & & & & ferrite nanoparticles (2–20 nm) are used \\  & & & (Mn\({}_{1/3}\)Fe\({}_{2/3}\)) O\({}_{2}\) (s) +3 CO\({}_{2}\) (g) + H\({}_{2}\) (g) & \(-\) \\  & &  
thus simplifying the necessary equipment and the separation processes. The disadvantages are the high preparation costs of ferrites and the necessity, in the first step, to operate with a large excess of steam with respect to the stoichiometry; this leads to the problem of separating the produced hydrogen from a substantial amount of water and, unless a proper separation membrane will be developed, to a drastic decrease in the global cycle efficiency.
## 4 Performance Evaluation
### Network Configuration Selection  
Network configuration selection consists in conducting repeated experiments with varying network hyperparameters with the aim of identifying the optimal set of settings for the network's architecture and training configuration. At the beginning of the experiment, the hyperparameter selection was performed with the aim of improving the performance of the models so that they obtain the best possible results. The three models were trained with varying hyperparameter values and tested on the validation set. Then, the best model architecture was identified by selecting the one that achieved the lowest validation loss. Table 2 shows the optimal hyperparameters for the three tested network architectures.
# Blockchain Based Secure Routing and Trust Management in Wireless Sensor Networks +
###### Abstract  
In this paper, an encryption and trust evaluation model is proposed on the basis of a blockchain in which the identities of the Aggregator Nodes (ANs) and Sensor Nodes (SNs) are stored. The authentication of ANs and SNs is performed in public and private blockchains, respectively. However, inauthentic nodes utilize the network's resources and perform malicious activities. Moreover, the SNs have limited energy, transmission range and computational capabilities, and are attacked by malicious nodes. Afterwards, the malicious nodes transmit wrong information of the route and increase the number of retransmissions due to which the SNs' energy is rapidly consumed. The lifespan of the wireless sensor network is reduced due to the rapid energy dissipation of the SNs. Furthermore, the throughput increases and packet loss increase with the presence of malicious nodes in the network. The trust values of SNs are computed to eradicate the malicious nodes from the network. Secure routing in the network is performed considering residual energy and trust values of the SNs. Moreover, the Rivest-Shamir-Adleman (RSA), a cryptosystem that provides asymmetric keys, is used for securing data transmission. The simulation results show the effectiveness of the proposed model in terms of high packet delivery ratio.  
authentication; blockchain; Rivest-Shamir-Adleman; secure routing; smart contract; trust evaluation; wireless sensor network +
Footnote †: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).