esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Absorption and Emission Cross Sections  
Figure 5a shows the absorption spectrum, obtained through the cutback technique [38,39], of two EDF samples, one pristine and the other pre-irradiated at 3 kGy (dose rate of 0.28 Gy/s, without injecting any signal into the fiber, therefore without considering the PB effect. It must be noted that there is only a slight increase in fiber losses, compared to the pristine losses, with an absorbed dose of 3 kGy.  
Figure 5b shows the absorption and emission cross sections, both in pristine and irradiated samples, obtained using the mathematical formula presented in Section 2.3. The increase in fiber losses leads to an increase in the absorption and emission cross sections, of 0.5 \(\times\) 10\({}^{-25}\) m\({}^{2}\) at 1530 nm, at a TID of 3 kGy.  
Through the simulation code, it has been obtained that the gain variation in an EDFA associated with these small changes in the absorption and emission cross sections is less than 1%. As a consequence, the radiation slightly affects the spectroscopic parameters, up to a TID of 3 kGy. The radiation-induced gain degradation must then be due mainly to the RIA phenomenon.  
Figure 4: \({}^{4}\)I\({}_{13/2}\) energy level lifetime as a function of the dose.  
Figure 5: (**a**) Spectral attenuation and (**b**) Er\({}^{3+}\) absorption and emission cross sections of two samples: one pristine (red curves) and one irradiated at 3 kGy (blue curves).  
Therefore, in the code simulations, whose results will be reported in the next section, the cross sections were considered constant. The absorption and emission cross sections at 980 nm and 1550 nm values used in the simulations have been reported in Table 2.
## 4 Simulations
### Stability  
In this subsection, we compute the gain margin of the force control system in Figure 3. Figure 11 presents the Bode plot of the system with controller \(G_{c}=K_{p}+K_{d}s\) and \(G_{c}=K_{p}+K_{d}s+\frac{K_{i}}{s}\). The gain margin is computed in the crossing of the magnitude Bode plot with zero decibels (see [18]); this intersection is indicated with an arrow in Figure 11. For the PD controller, the gain margin is equal to 11.1 dB, which is equivalent to 3.6 (\(10^{(11.1/20)}\)) in magnitude. For the PID controller, the gain margin is equal to 16 dB, which is equivalent to 6.3 (\(10^{(16/20)}\)) in magnitude. This gain margin represents the maximum value of the control gain (magnitude) that one can use without compromising the stability of the system. From the analysis presented in Section 3.3, the gain for the system with the PD controller is \(K_{CM_{max}}=3.6\), and for PID, the magnitude is \(K_{CM_{max}}=6.3\). From the design curves in Figure 5, the control gain \(K_{p}\) can be increased until 3.6 times without losing stability when the PD controller is used.  
Now, the simulation is executed with \(G_{c}=K_{p}+K_{d}s\), and two values of \(K_{p}\), i.e., \(K_{p_{1}}=3K_{p}\) and \(K_{p_{2}}=3.45K_{p}\). The resulting force error \(e=f_{d}-f\) is presented in Figure 12a,b. One can observe that the higher the gain, the more oscillations in the force error \(e\). Furthermore, a gain of \(K_{p_{3}}=3.6K_{p}\) was tested, but these results are not presented in Figure 12, since this gain produces instability. Note that the simulation results correspond with the gain margin presented in Figure 11, since oscillations appear when the control gains are closer to the gain margin.  
Figure 11: Bode plot and stability margins.  
Figure 12: Force error \(e=f_{d}-f\) with PD controller. **(a,b)** The control gain \(K_{p}\) increases. **(c,d)** The stiffness \(K\) increases.  
In accordance with Section 3.3, one can relate the gain margin shown in Figure 11 with the maximum value of stiffness that preserves stability. This value is \(K_{max}\approx K_{GM_{max}}K\approx 3.6~{}K\). Figure 12c,d present the simulation of the force control system with two different values of stiffness, \(K_{a}=3.2~{}K\) and \(K_{b}=3.45~{}K\), where \(K=3000\). The resulting force error \(e\) presents oscillations when \(K\) increases. For stiffness values higher than \(3.5~{}K\), the system lost stability. This unstable case is not presented in Figure 12 for visibility purposes. The simulation matches with the estimated gain margin, since oscillations/instability appear when the value of stiffness \(K\) approaches/reaches the gain margin.
## 2 Experimental Database and Methods
### Input Correlations  
The 1476 points dataset is divided into two parts, a training set (80%) to feed the model and a testing set (20%) for comparison with ML predicted values. Input data are pre-processed before being fed to the ML pipeline. This normalization stage aims to restrict the input parameters in the range given by  
\[x=\frac{x-x_{min}}{x_{max}-x_{min}} \tag{1}\]  
and, next, a correlation test is applied for all input features to check for independency. Figure 3 is the correlation matrix, which depicts the correlation coefficients between input variables. Values range from \(-1\) to \(1\), where \(-1\) indicates a strong negative correlation, 0 indicates no correlation, and 1 indicates a strong positive correlation. If two variables are highly correlated (negatively or positively), it may be necessary to remove one of them from the model to avoid overfitting or to improve the model's performance. It is observed that some kind of correlation exists between \(D\) and \(H\), \(E\!f\) and \(f\!f\), and \(t\) and \(f\!f\), though further investigation is needed to clear out if these input pairs may be omitted from the model.  
The variation inflation factor (\(V\!IF\)) is a popular measure and is employed to provide an estimate of high-multicollinearity between variables  
\[V\!IF=\frac{1}{1-R_{i}^{2}} \tag{2}\]  
where \(R_{i}^{2}\) the coefficient of determination. As a general rule, if \(V\!IF>10\) for a specific input, then this input can be omitted from the model. Our calculations have given the values presented in Table 3, where it is shown the ML model is to run with all eight available input parameters.  
Figure 3: Correlation matrix for the eight inputs.
## 2 Materials and Methods
### Bioinformatic Analysis and Molecular Modeling of EctABC\({}_{S.\ salarius}\) Structure  
Sequence alignments of EctA\({}_{S.\ salarius}\), EctB\({}_{S.\ salarius}\) and EctC\({}_{S.\ salarius}\) with other different ectoine production proteins were performed using ClustalX software and ESPript 3.0 program ([[http://espript.ibcp.fr/ESPript/ESPript/](http://espript.ibcp.fr/ESPript/ESPript/)]([http://espript.ibcp.fr/ESPript/ESPript/](http://espript.ibcp.fr/ESPript/ESPript/)) (accessed on 30 June 2021)), respectively. The phylogenetic tree was inferred using the Neighbor-Joining method and conducted in MEGA7 [26]. In order to reveal the interaction between the substrate and all three ectoine biosynthetic enzymes, the three-dimensional structural modelling of EctA\({}_{S.\ salarius}\), EctB\({}_{S.\ salarius}\) and EctC\({}_{S.\ salarius}\) were modelled by \"SWISS-MODEL Workspace\" ([[https://swissmodel.expasy.org/](https://swissmodel.expasy.org/)]([https://swissmodel.expasy.org/](https://swissmodel.expasy.org/)) (accessed on 30 June 2021)) using the reported crystal data of (PI)EctA:CoA:DAB tertiary crystal structure from _Penaibacillus lautus_ (PDB code 6 SLL) [27], (Cs)EctB:PLP tertiary crystal structure from _Chromolabacter salicgens_ (PDB code 6 RL5) [28] and (PI)EctC:ADABA crystal structure from _Penaibacillus lautus_ (PDB code 5 ONO) [29] as the template, respectively. The hypothetical surface graphics, cartoon diagrams and stick models were illustrated by PyMOL (The PyMOL Molecular Graphics System, Version 2.0, Schrodinger, LLC, New York, NY, USA).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Strain/Plasmid** & **Relevant Characteristics** & **Source** \\ \hline Gene & & \\ _ectABC\({}_{S.\ salarius}\)_ & Ectoine biosynthesis cluster from & \\ _Salinicola salarius_ 1A01339 & This study \\ Plasmid & & \\ pDK-6 & Km\({}^{r}\), _tac_ promoter, cloning vector & Sangon Biotech \\ pDK-_ectABC\({}_{S.\ salarius}\)_ & Km\({}^{r}\), _tac_ promoter, EctABC\({}_{S.\ salarius}\) & This study \\ Strain & & \\ _E. coli_ DH5\({}_{\alpha}\) & Cloning strain & Sangon Biotech \\ _E. coli_ BL21(DE3) & Expression strain & Sangon Biotech \\ _E. coli_ BL21(DE3)-pDK6 & Recombinant bacteria for control & This study \\ _E. coli_ BL21(DE3)-pDK6- & & \\ _ectABC\({}_{S.\ salarius}\)_ (BL-SsEtCt) & Engineered strain for producing ectoine & This study \\ \hline \hline \end{tabular}
\end{table}
Table 1: Bacterial strains and plasmids used in this study.
## 5 Implementation: Deep-learning in IoMT Edge Devices  
### Deep Learning  
Deep Neural Networks (DNNs), as a branch of Artificial Intelligence (AI), are rapidly growing in both academia and industry, showing superior advantages in different domains such as pattern recognition, speech recognition, image classification, and computer vision [83; 84]. They are also opening the path to new critical domains as E-health, self-driving cars, and surgery robots, where high precision and safety are the highest priority. As shown in Figure 8, a DNN is composed of a number of neurons, arranged in layers as input, output, and hidden layers, where each neuron performs a simple Multiply-And-Accumulate (MAC) operation.  
DNN computation is composed of two main steps of _training_ and _inference_. In the _training_ phase, weights are learned and, in the _inference_ phase, the learned weights are fixed and deployed on the underlying hardware. _Training_ is an offline process while _inference_ is an online process. So, the inference time and power consumption should be kept as small as possible, especially if the DNN is going to be deployed on edge devices in the IoMT.  
There are several popular deep learning architectures such as Convolutional Neural Networks (CNN), Deep Belief Networks (DBN), and Recurrent Neural Networks (RNN). Deep Learning architectures can be executed on different hardware platforms which offer different levels of performance and power efficiency, ranging from powerful devices in data centers down to resource-limited edge devices. A large body of research focuses on designing new optimized platforms or algorithms which could facilitate DNN deployment. Among them, the deployment of DNNs on embedded systems is more challenging as these platforms have limited computation resources and storage, and are usually battery-enabled. In the following, we investigate different platforms to execute DNNs. Then, we review different approaches to reduce power in resource-constraint embedded devices.
# Radar-Assisted Multiple Base Station Cooperative mmWave Beam Tracking
## 3 Radar-Assisted Multi-Rsu Cooperative Beam Tracking
### Frame Structure Design and Beam Tracking Procedure  
As shown in Figure 3, the DFRC data stream is divided into multiple time slots which correspond to different optimal transmit-receive beam pairs. In each time slot, only when the beam of the RSU and the vehicle are aligned to each other, can the downlink quality-of-service (QoS) be guaranteed. In each time slot, the DFRC signal is partially reflected by the vehicle and partially received at the vehicle, therefore sensing and communication tasks are executed in parallel. In time slot 1, some initial access processes need to be performed first, i.e., the RSU completes the channel measurement by transmitting the downlink pilot and receiving the uplink feedback and thus obtains the initial AOD and AOA. Then the RSU transmits the DFRC signal and the vehicle receives the signal by using the optimal transmit-receive beam pair, followed by the vehicle for uplink feedback transmission. In parallel, the RSU receives radar echoes with a time delay from the vehicle and uploads them to the ES, where AOD prediction based on multi-RSU cooperation is performed. The prediction will then assist in the calculation of beamforming vectors for future moments.  
More specifically, each RSU uses the radar echoes to calibrate the predicted kinematic parameters according to the EKF algorithm, then all the RSUs will report their calibration results to ES, where the more accurate vehicle position is obtained based on the reported kinematic parameters and the weights of each RSU. Starting from the second time slot, the transmit beamforming design no longer requires downlink pilot and uplink feedback and is based on cooperative AOD prediction. However, for the receive beam design in a linear mobility scenario, a two-steps prediction will be performed in time slot 1, i.e., the AOD of time slot 2 and time slot 3 will be predicted, where the AOD prediction of time slot 2 is used for the transmit beam design of time slot 2 and the AOD prediction of time slot 3 will be included in the DFRC signal sent in time slot 2 for the formulation of the vehicle's receive beam in time slot 3. For curvilinear mobility scenarios, AOD and AOA are different, and thus MUSIC algorithm is adopted to estimate AOA and formulate the receive beam. The process of the subsequent time slot is the same as that of the previous time slot.  
As shown in Figure 4, beam tracking procedure is divided into two steps. In step I, we employ the EKF algorithm to track vehicle kinematic parameters and report them to the ES, while we have designed two scenarios which are vehicle linear and curvilinear mobility, respectively. In step II, ES calculates the optimal predicted position of the vehicle through the results reported by each RSU and their respective weights and sends the prediction results to each RSU for the input of EKF at the next slot. In the following, we will give a more detailed description of these two steps.
## 3 Results and Analysis
### Failure Mode Analysis of Frame Test
#### 3.1.2 Specim KJ-2  
1. Load control phase  
When the positive load was 20 kN, the crack began to appear from above the left end of L1, with a crack width of about 0.01 mm and a crack length of 8 cm. and the crack was closed after unloading. As shown in Figure 10a, when the load was continued, some new cracks were generated at both ends of L1 and the right end of L2, and the cracks continued to develop with the increase of load. When the load was +30 KN, cracks began to appear at the bottom of the right side of column Z1, with a crack length of 15 cm. When the load reached +60 kN, the cracks at L3 and L4 beam ends began to extend to the beam side, and the width reached 0.2 mm.  
Figure 9: Experimental phenomenon diagram of KJ-1. (**a**) Cracks in the beams of KJ-1. (**b**) Cracks in the bottom of columns of KJ-1. (**c**) Failure modes of KJ-1.  
With the increase of load, some new cracks continued to appear. The generation position moved to the mid-span direction, and the original crack continued to develop through the crack. As shown in Figure 10b, when the load was \(-105\) kN, the crack at the column bottom began to penetrate and develop to the side. When it is loaded to \(+125\) kN, new cracks at the column bottom were generated upward. When loaded to \(+145\) kN, cracks began to appear at L3 and Z2 nodes and L1 and Z2 nodes. When the load was \(-150\) kN, the crack width at the upper end of Z1 and Z2 reached 0.2 mm, and the crack at the left end of L3 reached 1 mm. At this time, the plastic hinges at both ends of L1 and L2 were basically formed.  
2. Displacement control phase  
When the positive load was 32 mm, no new cracks were generated, and the existing cracks were widened. When the reverse load was \(-32\) mm, the Z3 column base concrete was crushed. It can be seen from the measured value of reinforcement strain that the plastic hinge at the bottom of the column were basically formed. When the positive load was 48 mm, the crack width of beam and column exceeded 2 mm, and a large number of vertical cracks appeared in the column. The concrete at the bottom of Z1, Z2, and Z3 columns was crushed, of which the bottom of Z3 was the most seriously damaged, and the concrete was crushed, bulging, and spalling.  
When the reverse load was \(-64\) mm, the concrete in the plastic hinge area of the beam was crushed (Figure 10c), in which the ends of L1 and L2 were damaged most seriously, the concrete was crushed and peeled off, and the longitudinal reinforcement of the stirrup was exposed. When the positive load was 80 mm, the concrete at the column bottom was crushed and separated seriously, and the cracks at the beam-column joints continued to develop. When the reverse load was \(-80\) mm, Z1, Z2, and Z3 stirrups were exposed, the longitudinal bars on both sides were also exposed, and the specimen was damaged.  
Figure 10: Experimental phenomenon diagram of KJ-2. (**a**) Cracks in the beams of KJ-2. (**b**) Cracks in the bottom of columns of KJ-2. (**c**) Failure modes of KJ-2.
# A Double Rate Localization Algorithm with One Anchor for Multi-Hop Underwater Acoustic Networks
## 3 The DRL Algorithm
### The Implementation of DRL Algorithm  
We also assume that the underwater network contains one anchor \(\mathbf{N_{0}}\), 2 reference nodes \(\mathbf{N_{1}}\) and \(\mathbf{N_{2}}\) selected by the anchor, M ordinary nodes. Set \(\mathbf{N_{0}}\) be the origin of coordinate, \(\mathbf{N_{0}}(0,0)\).  
Based on the scope of underwater network and nodes' power, we choose an appropriate code-width \(T_{s}\) for the Double Rate Scheme.  
The network uses TDMA (Time Division Multiple Address) protocol and TOA (Time of Arrival) ranging method [17; 18] to reduce the transmission interference in UANs.  
Step 1: The anchor \(\mathbf{N_{0}}\) broadcasts \"hello\" to the other nodes with the sending time stamp by LM mode.  
Step 2: Same as Step 1, other nodes broadcast time stamp information by LM mode to the whole network.  
Step 3: Every node can make some distance between itself and the other nodes, then send the ranging information to the anchor by HM mode.  
Step 4: The anchor \(\mathbf{N_{0}}\) selects the optimal reference nodes \(\mathbf{N_{1}}\) and \(\mathbf{N_{2}}\) according to Equation (10) with the ranging information in Step 3. Then by setting \(\mathbf{N_{0}}\mathbf{N_{1}}\) be the \(x\)-axis, \(\mathbf{N_{2}}\) in the first quadrant, we can get the relative coordinate system as shown in Figure 2.  
Figure 2: The relative coordinate system.  
Step 5: \(\mathbf{N_{0}}\) gets the location information of each node and the topology of whole network according to Equation (8). Then the localization procedure ends.  
The localization process is shown in Figure 3.  
If the scope of UANs is out of the range of the LM transmission mode, the localization procedure should be accomplished by different clusters which are divided by the transmission range of LM mode. The boundary nodes which have obtained their positions will be the anchor in new cluster and the localization procedure will end when all the unknown nodes have been localized.
## 3 Error Model and Cross-Identification Algorithm  
There are several factors that contribute to the errors of industrial robot end effectors, including assembly and installation errors during the robot manufacturing process, wear and tear errors over extended use, deformations due to loads, and more. These factors collectively result in deviations in the pose of the robot end effector. In fact, the above errors can be categorized into three types: geometric errors, non-geometric errors, and random errors. Among these, geometric errors primarily refer to deviations in link parameters caused by inaccurate manufacturing and installation. Non-geometric errors mainly denote elastic deformation deviations due to temperature and loads, while random errors are induced by electromagnetic interference and collisions. [PERSON] et al. have indicated that approximately 95% of errors are caused by geometric parameter deviations [28]. Therefore, this paper focused on compensating for industrial robot end effector errors caused by geometric parameter deviations. This section first establishes an industrial robot geometric parameter error model (TM-MDH) based on modified Denavit-Hartenberg parameters and the transformation errors of the tool coordinate frame and measurement coordinate frame. Secondly, to address the disadvantage of easily falling into local optimization when identifying geometric parameters simultaneously, a geometric parameter cross-identification method (C-LM) based on the Levenberg-Marquardt algorithm is proposed.
## 3 Model Calibration and Optimization
### Genetic Algorithm Optimization Framework
#### 3.2.5 Model Integration  
The DSSAT-GA coupling employs asynchronous evolution: (1) GA generates 10 fertilization scenarios; (2) parallel DSSAT simulations compute yield responses; (3) fitness evaluation triggers next-generation evolution.
## 1 Introduction
### Fast Fourier Transform; Origin of High Peak to Average Power Ratio or Crest Factor  
A block diagram of OFDM is presented in Figure 1, which presents the process of generating orthogonal frequency division multiplexing (OFDM) at the transmitter and the receiver. The orthogonality characteristic is provided by Fourier transform which is applied to signal in form of inverse and discrete as IDFT in transmitter part, and as discrete form as DFT in the receiver part [8]. As result of Fourier transform, there are construction and destruction effects and some signal peaks cancel each other, and some add which creates high peaks in the time domain. The peak to average power ratio (PAPR) parameter for the signal \(S(t)\) can be calculated by (1).  
\[PAPR_{dB}=10\log_{10}\frac{P(t)_{max}}{P(t)_{mean}} \tag{1}\]  
where maximum power, or \(P_{max}\), and average power, or \(P_{mean}\) can be measured as:  
\[\begin{split} P_{mean}=mean(P(t))=mean(\text{abs}\,(S(t))^{2} )\\ P_{max}=max(P(t))=max(\text{abs}\,(S(t))^{2}),\end{split} \tag{2}\]  
where \(S(t)\) is time domain signal and can be represented as:  
\[S(t)=\sum_{n=0}^{N-1}s(n)e^{\frac{-2\pi nt}{N}},t=0,1,...,N-1 \tag{3}\]  
where \(N\) is the transform size or the number of sample points in the data frame, and \(j=-1\), \(s(n)\) is the signal in frequency domain.  
It should be noted that the block diagram in Figure 1 represents the signal generation for one user only, and it should not be confused with multiple access techniques such as orthogonal frequency multiple access (OFDMA) for 4G techniques, or recently proposed non-orthogonal multiple access (NOMA) techniques for 5G techniques [9]. In practise, as the value measured from Equation (1), is completely random due to its nature, and it varies from 5 dB to 14 dB, for example, in a LTE signal with bandwidth of 512 Mhz; therefore, the probability of accordance of PAPR is considered to compare two signals together. This probability is defined and explained in Section 3.2.