esa-sceva/satcom-chunk-collection huggingface dataset:
# Combined Use of LoRaWAN Medium Access Control Protocols for IoT Applications
## 5 Network Configuration and Performance Metrics
### Performance Metrics  
The performance metrics used in this work to assess LoRaWAN are the packet loss ratio (PLR), the packet discard ratio (PDR), the end-to-end delay (e2 eDelay), and the energy consumption ratio per transmission (\(ECR^{tx}\)).  
The PLR represents the ratio between lost and transmitted messages, measured at the application layer and expressed as a percentage, according to Equation (2):  
\[PLR=\left(\frac{N_{LOSTmsg}}{\mathcal{N}_{TXmsg}}\right)*100=\left(1-\frac{N_{ RXmsg}}{\mathcal{N}_{TXmsg}}\right)*100 \tag{2}\]  
where \(\mathcal{N}_{TXmsg}\), \(\mathcal{N}_{RXmsg}\), and \(\mathcal{N}_{LOSTmsg}\) are the number of messages transmitted, correctly received, and lost, respectively, measured by each end-device. This parameter is assessed for both protocols under consideration.  
The PDR represents the ratio of the number of messages discarded by the MAC layer after performing the maximum number of backoffs to the number of messages received by the MAC layer from the application layer, measured by each end-device and expressed as a percentage, according to Equation (3),  
\[PDR=\left(\frac{N_{D1 SCmsg}}{\mathcal{N}_{GENmsg}}\right)*100 \tag{3}\]  
where \(\mathcal{N}_{GENmsg}\) and \(\mathcal{N}_{D1 SCmsg}\) are the number of messages received from the application layer and discarded by the MAC layer after performing the maximum number of backoffs, respectively, measured by each end-device. Note that the PDR is meaningful only for the end-devices that use LBT AFA. In fact, for the end-devices that adopt Pure ALOHA, the PDR is always equal to zero, as all the messages are sent without performing any channel assessment or backoff algorithm.  
The e2 eDelay is the time interval between message generation at the source node (\(T_{Gen}\)) and reception at the destination node (\(T_{RX}\)), measured at the application level, calculated as in Equation (4):  
\[e2 eDelay=T_{RX}-T_{Gen} \tag{4}\]  
In the simulated scenarios, the source node is an end-device, while the destination node is the NS. To evaluate the delay on the LoRa link only, we assume that the delay introduced by the communication between the GW and the NS is equal to 0.  
Finally, \(ECR^{tx}\) is defined as the ratio between the average energy consumed per transmission by an end-device that adopts LBT AFA (\(ECR^{tx}_{LBT,AFA}\)) and the one consumed by an end-device that uses Pure ALOHA (\(ECR^{tx}_{PureALOHA}\)), expressed as a percentage, according to Equation (5),  
\[ECR^{tx}=\left(\frac{ECR^{tx}_{LBT,AFA}}{ECR^{tx}_{PureALOHA}}\right)*100 \tag{5}\]  
The following section describes how the simulation was performed and discusses the results obtained in the aforementioned scenarios, using different configurations and under different workload conditions.
## 5 Experiments and Validation
### Performance Validation of HDPP Compared with Conventional Methods  
Experiments with the PDM, LSTM, and PDM + LSTM methods predict dynamic positions of two test paths. LSTM predicts joint positions using ETE data. DI data aid PDM + LSTM in predicting the PDM's residual error. The results are presented in Figures 16 and 17, and detailed comparisons are presented in Table 3.  
Due to the high order of prediction errors, the results of the LSTM network are not shown in Figures 16 and 17. As can be seen from the comparison between the PDM and LSTM network in Table 4, the PDM has higher accuracy and stability, which indicates that the prior knowledge of physics has better generalization ability than the pure data-driven model, especially with a limited dataset volume. Based on the PDM, the residual error compensation by neural networks effectively increases the accuracy of prediction in the PDM + LSTM method and HDPP. Compared with the PDM + LSTM method, the proposed HDPP has smaller prediction errors in terms
## 3 Results
### Results of the Biofilm Production
#### 3.2.6. Detected Genes Responsible for the Biofilm Production in Analyzed Aeromonas salmonicida  
Molecular sequencing was conducted on selected strains (F352, F187, F410, F710, F262, F1413, and F458), and the following genes important for biofilms were found: _pfs_, _luxR_, _lysR_ (Type 2 autoinducer (AI-2 system)), _fgB_, _fgC_, _fgE_, _fgE_, _fgC_, _fgM_, _fgM_, _fR_, _ffhA_, _ffiE_, _fjl_, _mf2_ (Lateral flagellar gene cluster), _filM_ (polar flagella), _flpD_, _flpD_, _fplD_, _plB_, _mshD_ (Type IV pili), _mshl_, _mshl_, _mshL_, _mshC_, _mshP_, _mshQ_ (adhesins), _vgrG_ (The type VI secretion system (T6 SS), _cysB_, _asnC_, _mhaR_, _hfq_ and LysR family (transcriptional regulators), vapA which is the part of the A450 LPS O-antigen and A-layer gene cluster, helicase genes family (hrpA, recQ), _rpoN_, _arsR_ (alternative sigma factor \(\sigma\)54), and family of phosphodiesteraseterase associated genes (FOB40_RS00700). Tables including all relevant citations regarding the aforementioned genes' roles in the creation of the biofilm in the case of Aeromonas genus have been added in the Supplemental Materials. Differences between analyzed strains were found in case of _lysR_ gene and presented in a form of genetic distance between analyzed strains (Table 2). Differences were relatively small and show that studied strains are more closely related to each other than to the reference genome strain, which is also backed up by phylogeny tree (Figure 7) and which suggests that in the case of the studied genes, their roles have not been changed due to the changes in their sequences. In case of the other genes, no significant differences were detected. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Investigated Isolates/Strains**}} & \multicolumn{2}{c}{**Reference Strain**} & \multirow{2}{*}{**F352**} & \multirow{2}{*}{**F187**} & \multirow{2}{*}{**F410**} & \multirow{2}{*}{**F710**} & \multirow{2}{*}{**F262**} & \multirow{2}{*}{**F1413**} & \multirow{2}{*}{**F458**} \\  & & & & & & & & & & \\ \hline Reference strain & & & & & & & & & \\ GCA\_000196395.1 & & 0.46 & 0.48 & 0.12 & 0.48 & 0.46 & 0.48 & 0.46 \\ F352 & 0.46 & & 0.13 & 0.04 & 0.13 & 0.00 & 0.13 & 0.00 \\ F187 & 0.48 & 0.13 & & 0.04 & 0.00 & 0.13 & 0.00 & 0.13 \\ F410 & 0.12 & 0.04 & 0.04 & & 0.04 & 0.04 & 0.04 & 0.04 \\ F710 & 0.48 & 0.13 & 0.00 & 0.04 & 0.13 & 0.00 & 0.13 \\ F262 & 0.46 & 0.00 & 0.13 & 0.04 & 0.13 & 0.13 & 0.00 \\ F1413 & 0.48 & 0.13 & 0.00 & 0.04 & 0.00 & 0.13 & & 0.13 \\ F458 & 0.46 & 0.00 & 0.13 & 0.04 & 0.13 & 0.00 & 0.13 \\ \hline \hline \end{tabular}  
* The strains in regard to _xupA_ are totally identical, even with the whole reference genome (GCA\_000196395.1) but differed in one amino acid from the standalone reference gene for vap/A (KR704893.1). The overall whole-genome similarity between analyzed strains was found to be in the range of 95–98%, and the GC% content was in the range 58–60%, which corresponds with the standard values for this subspecies.  
\end{table} 
Table 2: Genetic distance between analyzed strains on basis of _lysR_ gene.
# Low-Computational Extended Orthogonal Matched Filter Structure for Multiuser Detection
## 2 Principle of the EOMF
### Computational Complexity of an EOMF  
Table 1 summarizes the computation in the EOMF and its computational complexity. In addition, the computational complexity of the NLMS algorithm is summarized in Table 2 from the calculation of the weight \(\mathbf{U}\) for beamforming. Here, \(\mathbf{X}\) is an input vector of the circular array antenna, and \(H\) is the complex conjugate transposition. By multiplying and adding weights, the computational complexity of the number of weights is required from Table 1. Additionally, the computational complexity of the spreading sequence length is required to calculate the correlation values of MF. Since the required number of MFs is the spreading sequence length \(N\), the computational complexity of \(N^{2}\) is required from (c) and (d) in Table 1. Furthermore, the computational complexity of the NLMS algorithm is \(2K\) when obtaining the beamforming weights for the number of antenna elements \(K\) from Table 2. The computational complexity is the same in the case of null steering. The computational complexity of the NLMS algorithm is \(2(N-1)\) since the length of the LC weight vector in the OMF is \(N-1\). Thus, the computational complexity of the EOMF, \(C_{\text{EOMF}}\) is as follows in Equation (16):  
\[C_{\text{EOMF}}=K+K+N^{2}+(N-1)+2K+2K+2(N-1)=N^{2}+3N-3+6K \tag{16}\]
## 6 Results and Discussion: Back-Emf
### SE: Evaluation on a 24-Slot, 22-Pole PMSM and MPMSM  
In Figure 10, the back-emf outcomes of the FE evaluation of a 24-slot, 22-pole PMSM and a 24-slot, 22-pole MPMSM are presented for SE. The results were extracted considering the rotor structure completes one full turn to correctly obtain the harmonic content of the back-emf signal, which is depicted in the figures. In Figure 10a,c, the three-phase back-emf waveforms are presented for a 24-slot, 22-pole MPMSM and PMSM, respectively. In turn, Figure 10b,d show the harmonic spectrum of the back-emf for both machines, modular and conventional, when SE is present.  
From Figure 10, and similar to what was observed in Figure 8, eccentricity does not have a significant impact on the back-emf magnitude, and no significant unbalance between phases is observed either.  
\begin{table}
\begin{tabular}{c c c c} \hline
**ECC** & **Modular [V]** & **Monolithic [V]** & \(N_{pg}\) \\ \hline
18S 12P & 0.526 & 0.657 & 6 \\
18S 20P & 10.386 & 9.091 & 2 \\
24S 20P & 1.331 & 1.312 & 4 \\
24S 22P & 6.660 & 6.286 & 2 \\
24S 28P & 1.065 & 1.301 & 4 \\ \hline \end{tabular}
\end{table}
Table 15: Maximum back-emf unbalance for different PMSM and MPMSM slot/pole combinations in the presence of DE.  
Figure 10: FE evaluation of the back-emf in the presence of SE for a 24-slot, 22-pole PMSM and MPMSM: (**a**) back-emf waveform for the modular machine, (**b**) back-emf spectrum for the modular machine, (**c**) back-emf waveform for the monolithic machine, (**d**) back-emf spectrum for the monolithic machine.
# Development and Assist-As-Needed Control of an End-Effector Upper Limb Rehabilitation Robot
## 3 Assist-As-Needed Control of the EULRR
### Assist-As-Needed Control Based on Cartesian Impedance Control
The stiffness value \(K_{tun}\) in Equation (17) can be constructed according to the position of the patient's hand:  
\[\psi=\frac{K_{1}-K_{0}}{R_{W}} \tag{19}\]  
\[K_{tun}=\left\{ \begin{array}{ll}K_{0}&d\leq R_{W}\\ K_{0}+|d-R_{W}|\cdot\psi&R_{W}<&d<2R_{W}\\ K_{1}&d\geq 2R_{W}\end{array} \right. \tag{20}\]  
where \(\psi\) is the gradient of the stiffness gradient field, \(K_{0}\) and \(K_{1}\) are the upper and lower limits of the stiffness gradient field, which can be adjusted according to the needs of patients' rehabilitation training tasks. Therefore, different stiffness gradient fields can be constructed by adjusting \(K_{0}\) and \(K_{1}\). According to Equations (14) and (20), the robot assistance force during the rehabilitation training of the patient can be calculated as follows:  
\[F_{assist}=\left\{ \begin{array}{ll}0&d\leq R_{W}\\ \left[K_{0}+|d-R_{W}|\cdot\psi\right]\cdot 2|d-R_{W}|&R_{W}<&d<2R_{W}\\ K_{1}\cdot d&d\geq 2R_{W}\end{array} \right. \tag{21}\]According to the conversion relation between the wrench at the end of the robot and the joint torques and the dynamic equation of robot, see Equation (7), the torques applied in robot joint can be obtained as follows:  
\[\tau_{assist}=J^{T}(q)\cdot F_{assist} \tag{22}\]  
Therefore, given the rehabilitation training trajectory \(R\), there is a controllable assistance force field in the space around the trajectory, and its distribution diagram is as shown in Figure 6.  
The simplified version of the control chart of the AAN controller based on the impedance control proposed in this paper can be found in Figure 7. On the right side is the EULRR in interaction with the patient's arm \(\tau_{int}\); the position sensors measure the actual joint angles \(q_{act}\) that is used to calculate the actual position of the robot end-effector \(P_{act}\), which is fed into the visual interface implemented in the Unity game engine (Unity Technologies). The interface gives visual feedback about the end-effector position as well as the start position \(P_{S}\) and target position \(P_{E}\), which define the training trajectory. This information is used to calculate the nearest neighbor point \(P_{NN}\), the reference point \(P_{ref}\), and the channel radius \(R_{W}\), and the channel stiffness \(K_{tun}\), the stiffness field gradient \(\psi\), and the reference point \(P_{ref}\) of the current position are used to calculate the assistance forces \(F_{assist}\) according to Equation (21). The transposed Jacobian \(J^{T}(q)\) is used to calculate the corresponding joint torques \(\tau_{assist}\) that are used together with the torques from the compensation model \(\tau_{comp}\) (consisting of friction, gravity, and Coriolis compensation) as a reference for the torque-controlled motor drives.
# Analysis of Synchronous Generators' Local Mode Eigenvalues in Modern Power Systems
## 3 Local Mode Analysis of Synchronous Generators without AVR  
### Eigenvalue Analysis  
In the analysis, the turbo- and hydro-type synchronous generators with and without AVR system were considered separately. Due to the non-linearity and complexity of the analyzed plant, the fundamental mechanism analysis did not lead to useful conclusions, and especially not to quantitative results. Analytical treatment based on a linearized model proved to be too extensive and non-transparent. Experimental analysis (measurements) was not possible (allowed) for the majority of the power plants. Therefore, we used numerical eigenvalue analysis.  
The assessment of local oscillations was performed on the basis of the analysis of the eigenvalues of the linearized H-P model. The linearized model of a synchronous generator has three eigenvalues, two complex conjugates and one real. The dominant complex conjugate eigenvalues are essential because they describe local oscillations. The real part and imaginary parts of the complex eigenvalues are related to the frequency and damping of the local oscillation.  
\begin{table}
\begin{tabular}{c c} \hline Auxiliary operating point variables & \(\delta_{\text{op}}\), \(V_{\text{d,op}}\), \(V_{\text{q,op}}\), \(I_{\text{d,op}}\), \(I_{\text{q,op}}\), \(E_{\text{op}}\), \(E^{\prime}_{\text{q,op}}\), \(E_{\text{qa,op}}\) \\ \hline \end{tabular}
\end{table}
Table 2: Auxiliary operating point variables necessary for calculation of the parameters of the H-P model.  
To analyze the oscillations of synchronous generators, the eigenvalues \(\lambda_{1}\), \(\lambda_{2}\), and \(\lambda_{3}\) were calculated by the characteristic Equation (3):  
\[\begin{split}\det(\lambda\mathrm{I}-\mathbf{A})=0\to\\ 2K_{3}H\mathrm{T^{\prime}}_{40}\,\lambda^{3}+\left(K_{3}D\mathrm{ T^{\prime}}_{40}+2H\right)\lambda^{2}+\left(K_{1}K_{3}\mathrm{T^{\prime}}_{40} \omega_{\mathrm{s}}+D\right)\lambda+\left(K_{1}\omega_{\mathrm{s}}-K_{2}K_{3} K_{4}\omega_{\mathrm{s}}\right)=0\end{split} \tag{3}\]  
where \(\mathbf{A}\) denotes the system matrix of the H-P state-space model in (1).  
Dynamic mode \(\mathrm{e}^{\sigma\mathrm{t}}\sin(\omega t+\theta)\) corresponds to the complex eigenvalues \(\lambda_{1,2}=\sigma\pm\mathrm{j}\ \omega\). The real part of eigenvalue \(\sigma\) determines the time constant of decay of the amplitude of the oscillations; in-1/\(\sigma\) seconds, the amplitude decays to 36.79% (i.e., \(\mathrm{e}^{-1}\) pu) of the initial amplitude (for a negative value of \(\sigma\)). The imaginary part of eigenvalues \(\omega\) represents damped (or actual) frequency in \(\mathrm{rad}/\mathrm{s}\). The eigenfrequency (or natural frequency) \(\omega_{0}\) and damping ratio \(\zeta\) are given by:  
\[\omega_{0}=\sqrt{\sigma^{2}+\omega^{2}},\ \ \zeta=\frac{-\sigma}{\sqrt{ \sigma^{2}+\omega^{2}}} \tag{4}\]
# Childhood Uveitic Glaucoma: Complex Management in a Fragile Population
## 4 Medical Management
Although Prostaglandin Analogs (PgAs) are considered the first-line treatment for non-uveitic glaucoma, their use is not recommended in uveitis eyes because of their proinflammatory effect. However, they can be safely used in quiescent uveitis undergoing immunomodulatory therapy. PgAs increase the uveoscleral outflow and are the most effective pharmaceutical option in monotherapy, with IOP reduction of up to 35% in adults and up to 27% in children [59]. They have the best systemic safety profile and show mainly local side effects such as ocular irritation, itchiness, iris and eyelid pigmentation, periocular fat atrophy and eyelash elongation [58]. In addition, reactivation of herpes keratitis, exacerbation of uveitis and macular edema have been reported [66,67]. Latanoprost and Travoprost administered once a day shows an excellent efficacy and safety profile in pediatric clinical trials compared to Timolol or Dorzolamide. However, the IOP-lowering effect seems to be poorer in children than in adults [59]. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Study** & **Topical IOP Lowering Drugs** & \begin{tabular}{l} **Efficacy in IOP** \\ **Reduction** \\ \end{tabular} \\ \hline [PERSON] et al. (2005) [60] & \begin{tabular}{l} Dorzolamide 2\% vs. timolol \\ gel 0.25–0.50\% \\ \end{tabular} & 20–23\% vs. 25\% \\ \hline [PERSON] et al. (2008) [61] & \begin{tabular}{l} Brinzolamide 1\% vs. \\ levobetaxolol 0.5\% \\ \end{tabular} & 20\% vs. 16\% \\ \hline [PERSON] et al. (2009) [62] & \begin{tabular}{l} Betaxolol 0.25\% vs. timolol gel \\ 0.25–0.50\% \\ \end{tabular} & 9\% vs. 12.15\% \\ \hline [PERSON] et al. (2011) [63] & \
\begin{tabular}{l} Latanoprost 0.005\% vs. \\ timolol 0.25–0.50\% \\ \end{tabular} & 26\% vs. 21\% \\ \hline [PERSON] et al. (2017) [64] &
\begin{tabular}{l} Travoprost with 0.5\% vs. \\ timolol 0.25–0.50\% \\ \end{tabular} & 27\% vs. 25\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Main RCTs for medical treatment in pediatric glaucoma. A phase III trial designed to assess the safety and efficacy of Bimatoprost compared to Timolol in children was prematurely discontinued in 2015 due to insufficient enrollment. (Clinicaltrial.gov identifier: NCT01068964)  
Alpha Adrenergic Agonists (AAAs), such as Brimonidine tartrate, are scarcely studied in children. These drugs can activate alpha-2 receptors in the bulbar vasomotor center resulting in life-threatening side effects in patients under 23 months, such as apnoea, lethargy, hypotension, bradycardia and hypothermia. These severe adverse events may be related to an immature and permeable blood-brain barrier, so AAAs are not safely prescribed for children under 2 years old and should be used with caution in older children [65].
# Model Predictive Control for Six-Phase Induction Machines with Insight into Past Current Errors
## 5 Experimental Results  
### Experimental Rig  
The test bench employed to obtain the experimental results of the proposed RAE-MPC is presented in Figure 8. The electric drive is formed by a six-phase IM connected to dual two-level three-phase VSCs (Semikron SKS22F), fed by a single dc-link. Regarding the control system, it is based on a digital signal processor from Texas Instrument (TMS320F28335), programmed using a JTAG and the software Code Composer Studio v5.3.0. The sensor setup comprises four hall effect probes (LEM LAH 25 NP) and an encoder (GHN510296R) in order to measure stator currents and mechanical speed, respectively. To provide a load torque to the six-phase IM, its shaft is coupled to a dc-machine working as a generator. The armature of the dc-machine is connected to a variable passive load with the objective of dissipating power, making the load speed-dependent. Table 1 presents a summary of the test rig parameters, which have been obtained using ac time domain and standstill with inverter supply tests [40; 41].  
Figure 8: Diagram of the experimental test bench used to assess the control performance.  
Figure 7: Example of the effects of the RAE-MPC in the desired current errors.
# A Measure Based on Beamforming Power for Evaluation of Sound Field Reproduction Performance
## 3 Proposed Method
### Properties of the Beam-Power Error  
In order to investigate the properties of the beam-power error, two sound fields are evaluated with the spatial error and the beam-power error via numerical simulations. A reference sound field has a monopole in a free field, corresponding to the desired field. To compare with the reference condition, two modifications are made: inclusion of room reflections and change in the monopole position, i.e., change in wave fronts. The position of the monopole \(\overset{\rightarrow}{r}_{v}\) is (0, 0.9 m), and the order of HOA, \(N\), is 7.  
In the first modification, the reproduced field has room reverberation on top of the desired field. A small room (4.5 m \(\times\) 4.4 m \(\times\) 2.5 m) is assumed, and reflections are simulated by the image source method [44,45] with the pressure reflection coefficient, \(\rho\), varying from 0.1 to 0.9. Late reflections that arrive after the mixing time (\(t_{mix}=\sqrt{V}=7.0\) ms) are excluded with a time window. The mixing time has been proposed as a criterion that separates early reflections from late reflections [46, 47, 48]. Figure 4 shows the beam-power distributions of the desired and the reproduced fields when the reflection coefficient is 0.9. The side lobes of the reproduced field have greater values than the desired field due to the strong reflections, but the main lobe has a small difference in these two curves, decreasing the beam-power error. Figure 5 compares the beam-power error with the spatial error with respect to the reflection coefficient. The beam-power error has at least 20 dB lower values than the spatial error, having lower values than \(-20\) dB even for high reflection coefficients.  
Figure 3: Setup for the beamforming method of a simple example.  
In the second modification, a wrong monopole location is used for the reproduced field in an anechoic condition. This error should be effectively detected because it is induced by the error in the direction of sound waves, which can be easily perceived by human listeners. Figure 6 shows the spatial error and the beam-power error with respect to the difference of the monopole direction, \(\Delta\phi\), where both errors differ by 1.7 dB on average and 2.0 dB at the most. This means that the beam-power error can detect the error due to the change in the direction of the monopole as effectively as the spatial error does.  
Figure 4: Normalized beam-power distributions of the desired and the reproduced fields with \(\rho=0.9\).  
Figure 5: Spatial error and beam-power error with respect to the reflection coefficient.  
Figure 6: Spatial error and beam-power error with respect to \(\Delta\phi\).  
In summary, the beam-power error is much less sensitive to room reflections than the spatial error, but detects faults in the direction of arrival as effectively as the spatial error does.
# Earthquake Detection Using Stacked Normalized Recurrent Neural Network (SNRNN)
## 3 Stacked Normalized Recurrent Neural Network (SNRNN) Architecture  
This section discusses, in detail, layer by layer, the proposed stacked normalized recurrent neural network model. The architecture of the SNRNN is shown in Figure 2. The first layer is the data layer, which receives the dataset and input to the proposed model. The second layer performs data preprocessing operations like removing irrelevant event information and splitting the dataset into training and testing subsets. The following three layers ensemble the SimpleRNN, GRU, and LSTM in a stack: The SimpleRNN model receives the preprocessed data and is trained on it to extract valuable features in the third layer. The fourth layer uses the GRU model to refine the features set output using the SimpleRNN. In the fifth layer, LSTM performs the final feature selection using its memory cells. The last layer is the output layer, which finally detects the earthquake event's magnitude and depth.