esa-sceva/satcom-chunk-collection huggingface dataset:
# Combined Effect of Ferrous Ion and Biochar on Cadmium and Arsenic Accumulation in Rice
## 3 Results
### Available Cd and As  
To analyze the bioavailability of Cd in soil, DTPA extraction method was used to obtain available Cd content under different treatments (Figure 3). Compared with control, the available Cd were reduced in all treated soil, which were significantly decreased by 23% in 5 BC-1 Fe(II) (\(p<0.05\)). It is worth noting that, although BC treatment reduced the available Cd, it had no significant difference with control (\(p<0.05\)), as well as 1 BC-1 Fe(II) and 3 BC-1 Fe(II) treatments. Combination of BC and Fe(II) could effectively retain the available Cd in soil, and this effect was mainly caused by application of BC.  
We further analyzed the changes of available As concentrations after application of amendments (Figure 3). The available As concentration is lowest in 1 BC-1 Fe(II) treatment, which reduced 33% compared to control. In addition, the BC, 3 BC-1 Fe(II), and 5 BC-1 Fe(II) also reduced the content of available As, but there was no significant difference compared to control.
## 3 Proposed PUF Design
### PUF Challenge and Response  
In general, a challenge \(C\) is composed of a set of sub-challenges, whereas one sub-challenge \(c_{k}\) consists of two inverter addresses \((a,b)\mid a,b\in\{0,1,2,...M-1\}\) and the inverter input biasing voltage \(V_{in}\). Equation (1) defines the resulting sub-challenge tuples, where \(c_{k}\) denotes the \(k\)-th sub-challenge of \(C\). Furthermore, \(r_{k}=f(c_{k})\) denotes the sub-response of \(c_{k}\) and the \(k\)-th sub-response of \(R\):  
\[c_{k}=(a,b,V_{in}). \tag{1}\]  
Basically, two arbitrary inverter addresses \(a\) and \(b\) can either be arranged in the combination \(\{a,b\}\) or \(\{b,a\}\). Due to the definition of the sub-challenge \(c_{k}\) (see Equation (1)), for each pair of inverter addresses, one response bit \(r_{k}\) is produced. When reversing \(\{a,b\}\) to \(\{b,a\}\), the binary value of response bit \(r_{k}\) should change to \(r_{k}\). This coherence can be used in a system test in order to detect defective inverters. When determining the maximum response bit width \(L_{max}\in\mathbb{N}\), such reversed addresses are omitted in PUF, since they would degrade the self-information in proportion to the absolute bit width. Thus, the condition \(\{a,b\}=\{b,a\}\) must be valid for all sub-challenges. For \(M\) inverters, the maximum response bit width \(L_{max}\) is determined by the binomial coefficient, as shown in Equation (2):  
\[L_{max}=\frac{M\cdot(M-1)}{2}. \tag{2}\]
## 6 Protocol Comparison and Security Analysis
### Key Safety  
When the user needs to establish a new account, the user uses the key generator provided by the authentication center to directly generate the public key, private key and address locally. In this process, the private key is not transmitted through any channel, and neither the authentication center nor the satellite knows the user's private key. In the cryptocurrency technology, the user address is generated by the user's public key through a predetermined hash algorithm. The user address and the public key form a one-to-one correspondence and are stored in the blockchain. In the authentication process, the attacker attempts to intercept the information and tamper with the information using the attacker's public key and private key, and send it to the satellite. When the attacker is not registered, it does not have a legal public key. Since the attacker's address is not stored in the blockchain, the satellite will not believe such information. When the attacker has registered, since the transaction which is submitted in the registration stage is saved on the blockchain and the attacker's legal public key corresponds to the transaction information, the attacker cannot interfere with other users. Therefore, the public key identity of legitimate users is guaranteed.
# Coupled Mode Sound Propagation in Inhomogeneous Stratified Waveguides
## Appendix C Radiation Condition  
The initial value of the admittance matrix (Equation (23)) corresponds to the radiation condition, which is represented by the admittance matrix in the range-independent region \(x>x_{R}\). The transverse normal modes \(\phi_{i}(z)\) in a range-independent multilayered waveguide are solutions of the eigenproblem  
\[\frac{d}{dz}\left(\frac{1}{\rho(z)}\frac{d\phi_{i}(z)}{dz}\right)+\frac{ \omega^{2}}{\rho(z)c^{2}(z)}\phi_{i}(z)=\frac{1}{\rho(z)}k_{xi}^{2}\phi_{i}(z), \tag{16}\]  
with boundary conditions \(\phi_{i}(0)=0\), \(\phi_{i}^{\prime}(h_{l})=0\), and continuous conditions \(\phi_{i}(h_{j}^{-})=\phi_{i}(h_{j}^{+})\) and \(\phi_{i}^{\prime}(h_{j}^{-})/\rho_{j}(h_{j}^{-})=\phi_{i}^{\prime}(h_{j}^{+}) /\rho_{j+1}(h_{j}^{+})\), \(j=1,2,\ldots,J-1\), where \(k_{xi}\) are the axial wavenumbers. To solve \(\phi_{i}\), we first employ a mode expansion,  
\[\phi_{i}(z)=\sum_{n=0}^{N-1}w_{i,n}\psi_{n}(z), \tag{17}\]where \(w_{i,n}\) are the modal amplitudes, and \(\psi_{n}(z)\) are transverse normal modes in a homogeneous waveguide with a constant depth equal to \(h_{I}\), i.e., \(\psi_{n}(z)=\sqrt{2/h_{J}}\sin\big{(}(n+1/2)\pi z/h_{J}\big{)}\). Applying the operator  
\[\int_{0}^{h_{J}(x)}\psi_{m}^{*}(\cdot)dz,\]  
one can obtain  
\[\mathsf{A}^{-1}(\mathsf{K}-\mathsf{L})\mathsf{W}=\mathsf{W}\mathsf{K}_{x}^{2}, \tag{101}\]  
where the elements in \(\mathsf{W}\) are \(w_{i,n}\), and \(\mathsf{K}_{x}\) is a diagonal matrix with diagonal elements being \(k_{xi}\). Therefore, the eigenvalue problem governed by Equation (100) is transformed into a matrix eigenvalue problem of Equation (101). The axial wavenumbers \(k_{xi}\) and the modal amplitudes \(w_{i,n}\) can be obtained by eigen-decomposing the matrix \(\mathsf{A}^{-1}(\mathsf{K}-\mathsf{L})\).  
Alternatively, for environments of which the properties are invariant in the horizontal, the coupling among modes is lost. The second-order coupled mode equation is then reduced to  
\[\mathbf{P}^{\prime\prime}=\mathsf{A}^{-1}(\mathsf{L}-\mathsf{K})\mathbf{P}. \tag{102}\]  
Since \(\mathbf{S}=\mathsf{Y}\mathbf{P}\) and \(\mathbf{P}^{\prime}=\mathsf{A}^{-1}\mathbf{S}\), the admittance matrix in the transmission region takes the following form  
\[\mathsf{Y}=\sqrt{\mathsf{A}(\mathsf{L}-\mathsf{K})}. \tag{103}\]
## 2 Experimental Setup and Methods
### Spray Disturbance  
The measurement point, 13 cm downstream from the injection, was chosen so that a film forms at this location at each operating point. This causes the laser beam to pass through the spray when AdBlue is injected, resulting in a deflection of the laser beam and a disturbance of the measurement signal. In order to be able to reliably trace this disturbance to the spray droplets, the opening time of the injector was also recorded with an optocoupler. Each time the injector opens, the voltage signal drops. With this it was possible to superimpose both signals. Figure 5 (left) shows the raw signal of the film thickness in black, and the start and the end of the injection of AdBlue. In the area where spray is injected, the signal has significant interference. Figure 5 (right) shows a closeup of the same film signal and additionally the signal of the optocoupler. Each time the injector opens, the film signal is disturbed. The disturbance starts when the injector opens and continues until shortly after it closes, which can be explained by the spatial offset between the measurement point and the nozzle. The droplets flying through the measuring area reflect the laser beam and the remaining signal arriving at the I\({}_{\text{x}}\) detector does not contain accurate information about the film. To compensate for this effect, this time frame was excluded from further processing. The result is shown in Figure 5 (left, red line).  
\begin{table}
\begin{tabular}{c c} \hline Film thickness & 100 \(\upmu\)m \\ \hline Reflection on film surface \(\Delta\delta_{\text{r,rel}}\) & 4.9\% \\ \hline Reflection on window surface I\({}_{w}\) & 1.6\% \\ \hline Fluctuations in signal intensity & 1.5\% \\ \hline Urea concentration & 1.2\% \\ \hline Maximum total errors & 9.2\% \\ \hline \end{tabular}
\end{table}
Table 2: Overview of all quantified errors.  
Figure 5: (a) Raw signal of the film thickness in black, the start and the end of AdBlue injection, and the film signal without spray disturbance in blue. (b) Section of film signal and the signal of the optocoupler for two arbitrary injection events.
# LoRaWAN Networking in Mobile Scenarios Using a WiFi Mesh of UAV Gateways
## 5 Simulation Results
### Study of the Impact of the CRM Algorithm  
The CRM algorithm has been developed to increment the coverage provided by the UAV swarm, without adding more UAVs. In fact, the AE-PRR obtained with just the VSF algorithm is affected by a high percentage of lost packets. Depending on the application-specific QoS requirements, this might be a major drawback. It is shown that the CRM algorithm effectively improves the AE-PRR performance of the system in comparison with the basic LoRaUAV VSF algorithm operating alone.  
In the first simulations, the size of the monitored area (2500 m of area side and 3000 s of simulation time) has been chosen specifically to favor CRM and hence to show its intent. A visual comparison of a sample scenario with three teams of GNs is plotted in Figure 5. In all the performed simulations, the CRM algorithm significantly improves the AE-PRR, with most of the increments lying between 14% and 26% (2-5 teams), but with peaks of more than 50% (one team). In absolute terms, the achieved AE-PRR is low. The ATD seems to be the QoS metric that is most negatively affected by the CRM algorithm, as shown in Figure 5B. However, a deeper analysis shows that the ATD metric is greatly influenced by a small number of packets with large delays. In fact, the CRM algorithm allows for more and longer disconnections in order to achieve a higher PRR. Some packets may therefore stay  
Figure 4: Average End-to-End Packet Reception Ratio (AE-PRR) comparison when the LoRaUAV Virtual Spring Force (VSF) algorithm and the DF VSF algorithm are used in a scenario with three (**A**) and five (**B**) teams of 20 GNs.  
in the buffer for a long time before being sent. To prove this hypothesis, 10 individual simulations have been run for a scenario having 12 UAVs, three teams of GNs and \(K_{p}=25\). The total individual delays of all received packets have been recorded. The percentage of buffered packets more than doubles when the CRM algorithm is active (from \(\approx\)2-3% to \(\approx\)4-8%) and their permanence in the buffer increases drastically (from \(\approx\)15-34 s to \(\approx\)34-181 s). If buffered packets are not considered, the average experienced delay is \(\approx\)55 ms, a value that is far lower than the reported ATD values.  
The second set of simulations has considered the more reasonable default settings in Table 2. The results are very encouraging, as concluded from the comparison between Figure 6A,B, where CRM significantly improves the performance of VSF, usually by more than 30%.  
Figure 5: AE-PRR (**A**) and Average Total Delay (ATD) (**B**) comparison when the CRM algorithm is activated in a scenario with three teams of 20 GNs.
## 3 Collision-Free Hybrid MAC Protocol Based on Pipeline Parallel Transmission  
### Motivation  
The random handshaking method proposed in [18] supported arbitrary multiple successful handshakes in a transmission cycle, which can increase the parallel transmission opportunity. However, control packet collisions will occur in this method and lead to data packet collisions. Implementing the handshaking in a collision-free manner can solve the problem of control packet collision while supported arbitrary multiple successful handshakes. TDMA is a good choice for the implementation of collision-free handshaking, where the RTS and CTS packets can be transmitted in separated time slots. The disadvantage of the TDMA based handshaking is that it will bring a long delay to the handshaking process, leading to low transmission efficiency if the data packets are still transmitted in the same channel, especially when the scale of the network is large. Transmitting control and data packets through multiple channels is a possible solution to this problem, because data transmission can still be performed during handshaking. Therefore, a hybrid multi-channel MAC structure is adopted in this paper, which has the following features:  
First, a hybrid multi-channel MAC framework with pipeline transmission is employed. It is a single rendezvous protocol, where handshaking is performed in a dedicated control channel while the data packets are transmitted in one or multiple data channels. The TDMA based handshaking and data transmission are implemented as a pipeline to provide high throughput for the network. The pipeline cannot reduce the transmission time of a single data packet, but can improve the transmission efficiency effectively for multiple data packets.  
Second, time domain parallel transmission is employed in the handshaking time slot allocation and data packet scheduling to further improve the performance of the proposed protocol. By making use of the propagation delay differences, the time slots of the handshaking and the transmission schedules of data packets are more compact.  
Third, the replication computation technique is employed in data packet scheduling to reduce the control overhead in distributed environments. By using replication computation, each source node can obtain the transmission channels and time of all source nodes via independent calculation, which can implement the data packet scheduling via two-way handshakes and reduce the requirement of control information exchange significantly.
# Short-Term Wind Speed Forecasting Using Statistical and Machine Learning Methods
## 3 Analysis of the Forecasting Results
### Results of Variable Selection  
The needed predictor variables from the 48 variables in the dataset were selected using the least absolute shrinkage and selector operator (Lasso). Lasso in the Python 3 programming tool was implemented using the standard Lasso (Lasso) and the cross validation Lasso (Lassocv). Lassocv proved more robust than Lasso because it selected more variables for its variable selection. Lasso using  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Min** & **1 st Qu** & **Median** & **Mean** & **3 rd Qu** & **Max** & **Std.** & **Skewness** & **Kurtosis** \\ \hline
0.2134 & 4.3674 & 6.8670 & 7.1725 & 9.6473 & 20.8555 & 3.475177 & 0.3963677 & \(-\)0.4654862 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary statistics of the response variable WS_62_mean (m/s).  
Figure 3: Distribution of Wind Speed (m/s) across the week, month, day and year in the dataset.  
the R programming tool on the other hand was implemented using the \"glmnet\" library for the benchmark models' variable selection. A comparison of variable selection for the models is summarised in Table 3.  
Table 3 gives the selected variables along with their coefficients from these two programming tools. For Lassocv from the Python 3 kernel, the selected 14 variables were added with the other lagged variables and used for the ANN model. In addition, the benchmark (BMLasso) model from the R kernel selected 10 variables; the lagged variables were added to the selected variables and used for both the GAM and SGB models, respectively. The lagging was done for 10-min lag, 1-h lag, one-day lag and two-day lag. In all variable selection processes, only 10-min lag was selected by the various Lasso variable selection processes.  
SGB, being one of the benchmark models, ensures a form of variable selection in its working principle. Out of the 10 variables selected by [PERSON], this model selected variables based on their relative importance for its computation. Table 4 shows the selected variables from SGB.
# MIMO Signal Detection Based on IM-LSTMNet Model
## 2 Methods
### MIMO-OFDM Signal Detection Model Based on FrFT
#### 2.2.4 MIMO-OFDM Signal Detection Based on the IM-LSTMNet Model  
The signal detection in the MIMO-OFDM system using IM-LSTMNet is different from existing MIMO-OFDM receivers that first explicitly estimate channel state information (CSI) and then use the estimated CSI to detect or recover transmitted symbols. The method based on IM-LSTMNet implicitly estimates the CSI and immediately recovers the transmitted symbols. Figure 8 shows the signal detection framework of the MIMO-OFDM system by a neural network [21].  
In Figure 8, in order to obtain a valid IM-LSTMNet for signal detection, there are two stages. In the offline training stage, MIMO signals are preprocessed by ZF detection using MIMO-OFDM signal samples. The preprocessed signal is trained to IM-LSTMNet so that it can learn channel state features better. In the online signal detection, the offline trained IM-LSTMNet is put into the MIMO-OFDM system model for signal detection. In each simulation, the data at the receiver are used as a neural network input to generate an output that recovers the transmitted data without explicitly estimating the wireless channel. The process is as follows: the MIMO signal is first modulated by QPSK and then transformed by IrfFT. CP is inserted into the signal, and the newly generated signal is sent through a Gaussian channel with noise after the parallel-to-serial transformation. After the conversion of serial to parallel and CP removal, the received signal can be obtained. The received signal in the frequency domain can be obtained after passing through the FrFT. The received signal is preprocessed by ZF detection, which is used as input to pass through the offline trained neural network model, thus recovering the transmitted signal end-to-end and obtaining the signal.
## 2 Resolution of the Kinematic Problem by Traditional Methods  
The two robotic systems that were studied in this project are a Unimate's PUMA 560 robotic arm (Danbury, CT, USA) and the walking hexapod robot BH3-R, built and distributed by Lynxmotion Inc. (Swanton, Vermont, USA). Both robots, shown in Figure 2, are non-redundant open-chain robotic systems, and they will be used as test benches for the procedure developed in this work.  
The first step for the resolution of the Kinematic Problem by any of the traditional methods is to calculate the Forward Kinematics of the analyzed robot.  
Figure 2: Robotic systems used in this project: Unimate’s PUMA 560 robotic arm (**left**) and BH3-R hexapod walking robot by Lynxmotion Inc. (**right**).  
For simplicity reasons, we will focus only on the first three links of the robotic arm, just before the robot's wrist. This simplification is valid because the last three links of the arm comply with the conditions of Pieper's Theorem [3,8], effectively conforming an in-line wrist. Therefore, the end effector of this robot will be considered to be at the anchor point of this in-line wrist, just at coordinate system number 4, shown in Figure 3.  
Applying the [PERSON]'s method, with the parameters presented in Table 1, the solution to the Forward Kinematics problem of the PUMA 560 is the homogeneous transformation shown in Equation (1), which establishes the transformation between the base of the robot and the anchor point of its in-line wrist. The term \"\(q_{(2i3)}\)\" in Equation (1) is equal to \"\(q_{2}+q_{3}\)\".  
\[{}^{0}A_{4}=\begin{bmatrix}\sin(q_{1})\sin(q_{(2i3)})&\sin(q_{1})\cos(q_{(2i3) })&-\cos(q_{1})&-\sin(q_{1})|q_{2}\cos(q_{2})+d_{4}\cos(q_{(2i3)})+a\sin(q_{(2 i3)})|-d_{2}\cos(q_{1})\\ -\cos(q_{1})\sin(q_{(2i3)})&-\cos(q_{1})\cos(q_{(2i3)})&-\sin(q_{1})&\cos(q_{1 })|q_{2}\cos(q_{2})+d_{4}\cos(q_{(2i3)})+a\sin(q_{(2i3)})|-d_{2}\sin(q_{1})\\ -\cos(q_{(2i3)})&\sin(q_{(2i3)})&0&d_{1}-a_{2}\sin(q_{(2i)})-d_{4}\sin(q_{(2 i3)})+a_{3}\cos(q_{(2i3)})\\ 0&0&0&1\end{bmatrix} \tag{1}\]
# Research on a Low Melting Point Al-Si-Cu (Ni) Filler Metal for 6063 Aluminum Alloy Brazing
## 3 Results and Discussion
### Effect of Cu and Ni on the Microstructure of Al-Si-Cu (Ni) Filler Metals  
The microstructures of the filler metals were analyzed by SEM, XRD, and EDS. Figure 4 shows the XRD patterns of Al-Si-Cu alloys and Al-Si-Cu-Ni alloys. The XRD results show that the Al-6.5 Si-(10, 15, 20) Cu were mainly composed of the \(\alpha\)-Al phase, a Si phase, and a \(\theta\)(Al\({}_{2}\)Cu) phase. The Al-6.5 Si-20 Cu-(1.0, 1.5, 2.0) Ni were mainly composed of the \(\alpha\)-Al phase, a Si phase, a \(\theta\)(Al\({}_{2}\)Cu) phase, and \(\delta\)-Al\({}_{3}\)CuNi (Al\({}_{3}\)Ni\({}_{2}\)).  
Figure 5 shows the SEM images of the filler metals. Figure 5a-c indicates that the Al-6.5 Si-(10, 15, 20) Cu filler metals are composed of the \(\alpha\)-Al solid solution phase, bulk primary Si, and a \(\theta\)(Al\({}_{2}\)Cu) intermetallic compound. With increasing Cu content, the number of \(\theta\)(Al\({}_{2}\)Cu) phases gradually increases, and the size gradually increases. According to Figure 5d-f, the Al-6.5 Si-20 Cu-(1.0, 1.5, 2.0) Ni filler metals had reticular structure, and the reticular solid solution phase was the \(\delta\)(Al\({}_{3}\)Ni\({}_{2}\)) phase according to the EDS analysis and XRD patterns [21,22]. With increasing Ni content, the number of bulk primary Si phases decreased, part of the Si phase was refined, showing a fibrous structure, and the brittle \(\theta\)(Al\({}_{2}\)Cu) phase gradually transformed from a bulk to a lamellar-type structure, as shown in Figure 5d-e.  
Figure 4: XRD patterns of the (**a**) Al-Si-Cu and (**b**) Al-Si-Cu-Ni filler metals.  
To further analyze the elemental distribution of the filler metals and the influence of Ni on the microstructure of the Al-Si-Cu filler metals, an EDS surface scanning analysis was carried out on the microstructures of the Al-6.5 Si-20 Cu and Al-6.5 Si-20 Cu-1.5 Ni filler metals. The EDS element mapping are shown in Figure 6, and the EDS results are shown in Table 3. The EDS analysis showed that A and G were mainly Si phases, C and E were mainly \(\alpha\)-Al phases, B and D were mainly Al\({}_{2}\)Cu phases, and F was mainly a \(\delta\)(Al\({}_{3}\)Ni\({}_{2}\)) phase. The element distribution diagram indicates that part of the Si phase was embedded in the \(\alpha\)-Al phase, and part of the Cu was replaced by Ni in the \(\theta\)(Al\({}_{2}\)Cu) phase region, causing the local intermetallic compound \(\theta\)(Al\({}_{2}\)Cu) to change from elongated to discontinuous. The blade shape can effectively reduce the adverse effect of the intermetallic compound \(\theta\)(Al\({}_{2}\)Cu) on the solder alloy.
# IoT-Based Smart Surveillance System for High-Security Areas
## 3 Results
### Data and Information
#### 3.1.1 Frequency  
The GMS produces an analog frequency, which remains in an analog form even after bouncing off a potential target. The GMS converts the analog signals into digital format and makes a decision regarding whether an object has been detected; afterward, the information is forwarded to Arduino UNO for further processing.
## 5 Transmission Spectrum and Radial Displacement Field Study Analysis  
In vibration theory, transmittance and level drop are used to reflect the structural vibration transmission characteristics. For finite-period radial metamaterial structures, the transmission spectrum is usually used to characterize their transmission properties. As shown in Figure 11, the transmission spectrum is calculated by loading the acceleration excitation on the excitation side of this finite-period structure when the free surface in the Z-direction maintains a stress-free boundary condition, and the acceleration response after passing through the finite period is recorded on the response side. The transmission spectrum is generally defined by Equation (6):  
\[T=20\log(a_{2}/a_{1}) \tag{6}\]  
where \(a_{2}\) and \(a_{1}\) are the transmitted and incident accelerations, respectively.