esa-sceva/satcom-chunk-collection huggingface dataset:
# Disjoint Tree Mergers for Large-Scale Maximum Likelihood Tree Estimation
## 3 Results
### Summary of Observed Trends  
The DTM pipeline that had the best accuracy used IQ-TREE as the starting (and guide) tree as well as for the constraint trees, and then combined the constraint trees using GTM. However, IQ-TREE could not be used to compute the starting tree on the RNASim50k datasets, and it was computationally intensive on the RNASim10k datasets. Therefore, we use FastTree for the starting and guide tree on the datasets with 10,000 or more sequences.  
The comparison between this GTM pipeline and the ML heuristics (RAxML, FastTree, and IQ-TREE), shown in Figure 2 (error rates) and Figure 3 (running time), show that the GTM pipeline is competitive with the best of the ML heuristics for accuracy. GTM always matches or improves on IQ-TREE and FastTree for accuracy and, although RAxML is more accurate than the GTM pipeline on the 1000M1-HF dataset, GTM either matches RAxML's accuracy or improves on it on the other datasets.  
Thus, overall, the GTM pipeline provides accuracy that is competitive with the best competing methods, and it is (with FastTree) the only method in this collection that was able to run within the time limit provided on the largest dataset. Furthermore, GTM is  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **Method** & **Running Time (h)** & **Tree Error** \\ \hline FastTree & 8.3 & 8.0 \\ IQ-TREE & N.A. & N.A. \\ RAxML & 168 * & 100.0 \\ \hline DTMs, FastTree starting tree & & \\ GTM & 14.1 & **7.5** \\ Constrained-INC & N.A. & N.A. \\ \hline \hline \end{tabular}
\end{table}
Table 7: Results on the RNASim50k datasets. The average running time (in hours) and tree error (FN) rates over 10 replicates. Running time assumes that each method was run with 16 cores. Each method was given 64 GB. “N.A.” indicates a failure to produce a tree due to a memory allocation problem. The asterisk (*) for RAxML running time indicates that it had not been completed by that time, and we used the best scoring tree it found in that time period. The most accurate result is boldfaced.  
just slightly slower than its starting tree, making it feasible to use GTM even on very large datasets, since FastTree is generally able to run on even ultra-large datasets.  
Figure 2: Comparing GTM pipeline to ML methods for error rates. Error rates for trees computed using standard ML methods and a GTM pipeline, combining IQ-TREE constraint trees. The starting tree is IQ-TREE for the smaller datasets (i.e., 1000M1-HF, RNASim1000, and Cox1-HET) and FastTree on the larger datasets (i.e., RNASim10k and RNASim50k). All of the ML methods were given true alignments and allowed to run to completion, except for RAxML, which was limited to 24 h on the 1000M1-HF, RNASim1000, and Cox1-HET datasets and limited to 168 h on the RNASim10k and RNASim50k datasets. On the RNASim50k datasets, IQ-TREE failed to return a tree and RAxML returned a tree with an error rate of 100%, and so the results for RAxML and IQ-TREE are not shown for that condition.
# Experimental Investigation of Controlled and Uncontrolled Rectifiers for Low-Power Wind Turbines
## 3 Rectification Types
### Classification on the Basis of Controllability  
All the uncontrollable rectifiers provide constant DC output. However, the wind speed is intermittent; it often increases randomly. Wind energy systems can withstand higher winds, but the output power must be controlled according to the load. The output power of the rectifier can be controlled using a thyristor instead of simple diodes [21]. In addition to anode and cathode terminals, thyristors have an additional control terminal called a gate terminal. Unlike diodes, the thyristor does not conduct by applying a positive voltage across it unless a gate terminal is charged. Thus, the conduction time during the positive AC cycle can be controlled by controlling the firing angle of the thyristor. Once the gate is fired, the thyristor keeps conducting even if the gate signal is removed until the forward current reaches zero [24]. However, in reverse-biased configurations, the thyristor behaves similarly to diodes blocking the voltage until there is a peak inverse voltage. Rectifier circuits are further classified as half-controlled and fully controlled by thyristors. The half-controlled rectifiers consist of both uncontrollable and controllable power devices, that is, diodes and thyristors (SCRs), while the fully controlled rectifier consists of only controllable SCRs to do the rectification. The fully controlled rectifier offers a broader range of control during higher winds compared to half-controlled or uncontrolled rectifiers [25]. However, controlled rectifiers need additional gate control circuitry and sensors to rectify, increasing cost and complexity.
# Resource Allocation to Massive Internet of Things in LoRaWANs
###### Abstract  
A long-range wide area network (LoRaWAN) adapts the ALOHA network concept for channel access, resulting in packet collisions caused by intra- and inter-spreading factor (SF) interference. This leads to a high packet loss ratio. In LoRaWAN, each end device (ED) increments the SF after every two consecutive failed retransmissions, thus forcing the EDs to use a high SF. When numerous EDs switch to the highest SF, the network loses its advantage of orthogonality. Thus, the collision probability of the ED packets increases drastically. In this study, we propose two SF allocation schemes to enhance the packet success ratio by lowering the impact of interference. The first scheme, called the channel-adaptive SF recovery algorithm, increments or decrements the SF based on the retransmission of the ED packets, indicating the channel status in the network. The second approach allocates SF to EDs based on ED sensitivity during the initial deployment. These schemes are validated through extensive simulations by considering the channel interference in both confirmed and unconfirmed modes of LoRaWAN. Through simulation results, we show that the SFs have been adaptively applied to each ED, and the proposed schemes enhance the packet success delivery ratio as compared to the typical SF allocation schemes.  
LoRaWAN; spreading factor assignment; inter-SF and intra-SF interference; urban environment +
Footnote †: journal: Sensors
## 2 Materials and Methods  
### Chemicals, Reagents, and Materials  
Cultivation medium: Glucose, fructose, malic acid, citric acid, potassium dihydrogen phosphate, potassium sulfate, magnesium sulfate heptab hydrate, sodium chloride, ammonium chloride, tyrosine, tryptophan, isoleucine, aspartic acid, glutamic acid, arginine, leucine, threonine, glycine, glutamine, alanine, valine, methionine, phenylalanine, serine, histidine, lysine, cysteine, proline, manganese (II) sulfate monohydrate, copper (II) sulfate cobalt (II) chloride hexahydrate, boric acid, ammonium molybdate, sodium hydroxide, myo-inositol, pantothenic acid hemi-calcium salt, thiamine hydrochloride, nicotinic acid, pyridoxine, biotin, ergosterol, oleic acid, and Tween 80 were purchased from Sigma (Merck, Darmstadt, Germany). Calcium chloride dihydrate and sodium bicarbonate were purchased from Honeywell (Charlotte, NC, USA). Zinc sulfate heptab hydrate was purchased from Prolabo (VWR, Avantor, Radnor, PA, USA). Potassium iodide was purchased from Merck (Darmstadt, Germany).  
Flow cytometry: Phosphate buffer saline (PBS) was purchased from Sigma (Merck, Darmstadt, Germany). The 5-6-carboxyfluorescein diacetate (cFDA) was purchased from Molecular Probes (Thermo Fisher Scientific, Waltham, MA, USA).  
Headspace solid-phase microextraction coupled to gas chromatography (HS-SPME-GC/MS): Ethanol 96%, 1-propanol, 3-methylbutyl acetate, 3-methylbutanol, ethyl octanoate, ethyl decanoate, 1-hexanol, 2-phenylethyll alcohol, and octanoic acid were purchased from Sigma-Aldrich (St Louis, MO, USA). SPME fiber divinylbenzene/carbouxan/polydimethylsiloxane 50/30 \(\upmu\)m, 1 cm-long (DVB/CAR/PDMS), were from Supelco (Bellefonte, PA, USA).
## 4 Conclusions  
The high-efficiency, broadband and polarization-independent characteristics of the designed grating are obtained by the use of proper materials for the dielectric layers and optimization of the parameters of the grating. The grating parameters were optimized by RCWA and SA algorithms and the diffraction process of the reflective grating can be explained by the modal method. The diffraction efficiencies and the PDL of the designed grating with optimized parameters were calculated, and the results showed that the diffraction efficiencies of \(-1\)st order for TE and TM polarizations exceeded 98% ranging, from 1015 nm to 1085 nm, in Littrow mounting (46.35\({}^{\circ}\)), and the average efficiency from 1000 nm to 1100 nm reached 98.92% with a low average value of PDL (0.051 dB). Therefore, the designed grating with optimized parameters exhibited better performance than previously reported gratings. The electric field distribution of the designed grating was simulated by FEM and the distribution demonstrated that the designed grating can diffract most of the incident light to the \(-1\)st diffraction order on the operating wavelength and has polarization-independent characteristics. In addition, most of the electric field is distributed outside the grating, which helps to improve the LIDT of the designed grating. Furthermore, the performance of large fabrication tolerance and incident angle tolerance of the designed grating demonstrate proof of the reliability of the grating's fabrication and application. The fabrication of the double-layer dielectric rectangular grating has been reported [36] and the metal mirror can be manufactured by the method of physical vapor deposition [17; 37]. In conclusion, the diffraction efficiencies and tolerance analysis of the designed metal-dielectric grating have been expounded in detail. The results indicate the potential of the designed grating in the practical application of interferometers, spectrometers and CPA systems.  
Writing--original draft preparation, G.J.; visualization and supervision, W.J., [PERSON], [PERSON] and C.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (Grant No. U21A20509), Science and Technology Planning of Guangzhou (Grant No. 202007010001), and Introduced Innovative R&D Team Project under \"The Pearl River Talent Recruitment Program\" (Grant No. 2019 ZT08 ZZ79).  
No applicable.  
The data used to support the findings of this study are available from the authors upon request.  
The authors declare no conflict of interest.
# Deep Image Compression with Residual Learning
## 2 Proposed Framework
### Improved Importance Map Method for Rate Control  
Importance map method for rate control was firstly proposed by [PERSON] et al. [7]. Instead of rounding and entropy, they defined rate loss on importance map and adopted a simple binarization for quantization. This method for rate control is content-weighted and benefit to improve visual quality. Here, the part of the architecture in [7] and some novel technologies (e.g., binarization with probability depicted in Section 2.4 and smooth bit allocation with sigmoid function) are adopted. The process is shown in Figure 4.  
Intermediate feature maps are utilized to yield content-weighted importance map \(p(x)\). The importance map has only one channel, with the same size as the encoder and the range of (0,1). The value of the map determines the amount of bit allocation and the compression level. A mask \(b(x)\) from \(p(x)\) with the same size as \(\hat{q}\) is generated. The final quantized bit \(\hat{q^{\prime}}=b(x)\odot\
## Nomenclature  
\begin{tabular}{l l}
**Symbol** & **Description** \\ \(V_{0}\) & rotor peripheral speed [m/s] \\ \(W\) & wind speed [m/s] \\ \(\alpha\) & rotor rotation angle and the blade position relative to the rotor [\({}^{\circ}\)] \\ \(R\) & rotor radius [m] \\ \(D\) & rotor diameter [m] \\ \(F_{xD}\), \(F_{yD}\) & load components resulting from the air drag [N] \\ \(F_{xW}\), \(F_{yW}\) & wind load components generating on the blade [N] \\ \(\lambda\) & tip speed ratio \\ \(P_{x}\) & aerodynamic drag [N] \\ \(P_{y}\) & lift force [N] \\ \(C_{x}\), \(C_{y}\) & aerodynamic coefficients of the drag force and the lift force \\ \(C_{m}\) & torque coefficient \\ \end{tabular}  
\begin{tabular}{l l} \(C_{p}\) & power coefficient \\ \(\rho\) & the air density [kg/m\({}^{3}\)] \\ \(A_{ref}\) & the effective area of the blade [m\({}^{2}\)] \\ \(A\) & swept area of the rotor [m\({}^{2}\)] \\ \(H\) & blade height [m] \\ \(M_{1}(\alpha)\) & the torque generated by one blade as a function of the angle of the rotor rotation \(\alpha\) [Nm] \\ \(M_{2}(\alpha)\) & the torque generated by the second blade as a function of the angle of the rotor rotation \(\alpha\) [Nm] \\ \(M_{3}(\alpha)\) & the torque generated by the third blade as a function of the angle of the rotor rotation \(\alpha\) [Nm] \\ \(M_{III}(\alpha)\) & total torque for three rotor blades relative to the x, y coordinate system is the sum of the torques for the three rotor \\  & blades \(M_{1}(\alpha)\), \(M_{2}(\alpha)\), and \(M_{3}(\alpha)\) [Nm] \\ \(P(\alpha)\) & turbine power [W] \\ \(t\) & time [s] \\ \(\
abla\) & Nabla operator \\ \(\overrightarrow{v}\) & the velocity vector [m/s] \\ \(p\) & pressure [Pa] \\ \(\mu\) & viscosity [kg/ms] \\ \(\overrightarrow{F_{b}}\) & body force per unit volume [N] \\ \(e_{t}\) & the total internal energy [J] \\ \(\overrightarrow{\tau}\) & the viscous stress tensor \\ \(\hat{S}_{g}\) & the energy of other processes that is not represented in the total energy [J] \\ \(k\) & turbulent kinetic energy [J/kg] \\ \(\omega\) & the specific dissipation rate [J/kg\(\cdot\)s] \\ \(G_{k}\) & the production of \(k\) \\ \(G_{\omega}\) & the production of \(\omega\) \\ \(\Gamma_{k}\), \(\Gamma_{\omega}\) & the effective diffusivity of \(k\) and \(\omega\) [m\({}^{2}\)/s] \\ \(D_{\omega}\) & cross diffusion term \\ \(S_{k}\), \(S_{\omega}\) & user-defined source terms [W] \\ \(G_{b}\), \(G_{\omega b}\) & buoyancy terms \\ \(\omega_{R}\) & rotor angular velocity [rad/s] \\ \(\omega_{B}\) & blade angular velocity [rad/s] \\ \(TSS\) & time step size for transient simulation [s] \\ \(da\) & rotor angle angular increment [\({}^{\circ}\)] \\ \(dt\) & time increment [s] \\ \(di\) & distance to inlet [m] \\ \(do\) & distance to outlet [m] \\ \(dc\) & blade domain diameter \\ \(dx\) & cell size \\ \(L\times W\) & domain size \\ \(Co\) & Courant number \\ \(CFL\) & Courant-Friedrichs-Lewy criterion \\ \(y+\) & dimensionless wall distance \\ \end{tabular}
## 3 Developed Solution Approach
### Updating and Diversifying Solutions in the Reference Set  
During the improvement phase of the developed MOSS, the set \(\mathcal{T}\) is used to update the reference set \(\mathcal{R}\) such that \(|\mathcal{R}|\) does not exceed the specified fixed value of the parameter _RefSetSize_. This is done by first removing all solutions from set \(\mathcal{R}\) and adding them to set \(\mathcal{T}\). Then, an ordered list, \(\hat{\mathcal{T}}\), is generated based on solutions in \(\mathcal{T}\) in which solutions are ordered based on their dominance relations. That is, if \(\mathbb{S}^{1}\) dominates \(\mathbb{S}^{2}\), \(\mathbb{S}^{1}\) will be ordered first, and vice versa. If the two solutions do not dominate each other, their order will be arbitrary.  
In the given solutions' order in \(\hat{\mathcal{T}}\), solutions are iteratively added to set \(\mathcal{R}\) until its size reaches _RefSetSize_. To maintain the diversity of the solutions in set \(\mathcal{R}\), before a solution is added, its distance from every already added solution is evaluated as described in Section 3.3. This distance must exceed the predefined distance threshold value (\(\bar{\Lambda}\)). Otherwise, the solution will not be added. After all solutions in \(\hat{\mathcal{T}}\) are examined, if the size of the resultant set \(\mathcal{R}\) is found to be less than _RefSetSize_, additional solutions will be generated and added to it using the same procedure described in the construction process, as in Section 3.2.  
The developed reference set-updating procedure aims to maintain both solution quality and diversity throughout iterations. Furthermore, to avoid long-term entrapment in local optimal solutions, a diversification strategy is annexed to the reference set-updating process. In this strategy, the percentage changes in the summations of the values of the makespan and the MWFT for all solutions in set \(\mathcal{R}\) are evaluated before and after every improvement cycle. If these percentage changes are found to be less than 2%, and the set of non-dominated solutions (set \(\mathcal{D}\)) has not been changed by adding new solutions during the improvement cycle, the solutions in the reference set \(\mathcal{R}\) will be diversified. The diversification is achieved by replacing one-quarter of the solutions with newly constructed ones using the construction procedure described in Section 3.2. New randomly constructed solutions are improved using tabu search, as usual. In addition, at most one-quarter of the solutions in the reference set \(\mathcal{R}\) are replaced with solutions selected arbitrarily from the non-dominated set \(\mathcal{D}\).
## 3 System Dynamics Analysis
### Analysis of the Impact Response of the Flexible Space Robot and Satellite  
When capturing the satellite, we ignore the effects of other external forces and only consider the impact of the collision. The forces at point \(P\) of the space robot end and point \(P^{\prime}\) of the satellite are opposite to each other, that is, \(\mathbf{F}_{\mathrm{P}}=-\mathbf{F}_{\mathrm{P^{\prime}}}\). When solving the system dynamics model in a collision process from Equation (6), one obtains:  
\[\mathbf{D}_{N}\ddot{\mathbf{q}}_{z}+\mathbf{H}_{N}\dot{\mathbf{q}}_{z}+\left[\begin{array}{c} \mathbf{0}_{2\times 1}\\ k_{\mathrm{b}}x_{\mathrm{b}}\\ 0\\ -\mathbf{K}_{\mathrm{m}}\mathbf{\sigma}\\ \mathbf{K}_{\mathrm{s}}\mathbf{\delta}\\ \mathbf{K}_{\mathrm{m}}\mathbf{\sigma}\\ \end{array}\right]=\left[\begin{array}{c}\mathbf{0}_{3\times 1}\\ \tau_{0}\\ \mathbf{0}_{6\times 1}\\ \tau_{\mathrm{m}}\\ \end{array}\right]-\mathbf{J}^{\mathrm{T}}\left(\mathbf{J}_{3}^{\mathrm{T}}\right)^{- 1}\mathbf{D}_{3}\ddot{\mathbf{q}}_{3} \tag{7}\]  
During the capture process, the space robot collides with the satellite at time \(t_{0}\), and the capture is completed in a very short time \(\Delta t\). Then, the end effector of the space robot tightly connects to the satellite capture point, and the two constitute a system. Because the contact and collision occur instantaneously, the generalized coordinates of the space robot and the satellite do not change significantly in time \([t_{0},t_{0}+\Delta t]\), but the collision impact causes a sudden change in the generalized velocity and acceleration. According to the analysis above, Equation (7) is integrated in the period \([t_{0},t_{0}+\Delta t]\) that leads to:  
\[\mathbf{D}_{N}\left[\dot{\mathbf{q}}_{z}(t_{0}+\Delta t)-\dot{\mathbf{q}}_{z}(t_{0})\right] +\mathbf{J}^{\mathrm{T}}\left(\mathbf{J}_{3}^{\mathrm{T}}\right)^{-1}\mathbf{D}_{3}\left[ \dot{\mathbf{q}}_{3}(t_{0}+\Delta t)-\dot{\mathbf{q}}_{3}(t_{0})\right]=\mathbf{0}_{12\times 1} \tag{8}\]  
After capturing, the end effector of the robot will have the same speed as the target capture point. Based on Equation (3), we have:  
\[\dot{\mathbf{q}}_{3}(t_{0}+\Delta t)=\mathbf{J}_{3}^{-1}\dot{\mathbf{q}}_{z}(t_{0}+\Delta t) \tag{9}\]  
Substituting Equation (9) into Equation (8) yields:  
\[\dot{\mathbf{q}}_{z}(t_{0}+\Delta t)=\left[\mathbf{D}_{N}+\mathbf{J}^{\mathrm{T}}\left( \mathbf{J}_{3}^{\mathrm{T}}\right)^{-1}\mathbf{D}_{3}\mathbf{J}_{3}^{-1}\mathbf{J}\right]^{-1} \left[\dot{\mathbf{q}}_{z}(t_{0})+\mathbf{J}^{\mathrm{T}}\left(\mathbf{J}_{3}^{\mathrm{T} }\right)^{-1}\mathbf{D}_{3}\dot{\mathbf{q}}_{3}(t_{0})\right] \tag{10}\]  
The above equations represent the impact effect of the space robot in the process of capturing the satellite. The analysis shows that the generalized speed of the space robot at time \(t_{0}+\Delta t\) can be obtained only by contacting the motion state of the space robot and the satellite before the collision.
# Unmanned Surface Vehicle Using a Leader-Follower Swarm Control Algorithm
## 2 USV Modeling and Leader-Follower Swarm Formation  
### Modeling of the USV  
Figure 1 and Table 1 show the USV coordinate system and the notation used by the Society of Naval Architects and Marine Engineers.  
The rigid body dynamics modeling of the USV was designed based on the Newton-Euler equation and vectorial matrix dynamics. Roll, pitch, and heave motions were excluded from modeling because the USV is a three-DOF platform that performs translational motion along the X and Y axes and rotational motion around the Z axis. According to [16], the rigid body dynamic equation of the USV can be expressed in the form of Equation (1).  
\[\dot{M}_{RB}\
u+C_{RB}(\
u)\
u=\tau_{RB} \tag{1}\]  
Here, \(M_{RB}\) is the mass and inertia matrix of the USV, \(C_{RB}(\
u)\) is the Coriolis force and centripetal force matrix, and \(\tau_{RB}\) is the external force caused by the USV thruster. However, a method such as Equation (1) is an expression of an ideal situation. To represent it in a realistic expression, the definition of fluid force should be included in the rigid body dynamic equation. The rigid body dynamic equations including fluid force are expressed as Equation (2).  
\[(\mathfrak{m}-X_{\dot{u}})\dot{u}-u_{r}+(Y_{\dot{v}}v_{r}r+Y_{\dot{r}}r- \mathfrak{m}X_{G}r)r-\Big{(}X_{u}+X_{u|u|}|u|\Big{)}u=\tau_{X} \tag{2}\] \[(\mathfrak{m}-y_{\dot{v}})\dot{v}+(\mathfrak{m}X_{G}-Y_{\dot{r}}) \dot{r}+u_{r}-X_{\dot{u}}u_{r}r-\Big{(}Y_{\dot{v}}+Y_{v|v|}|v|\Big{)}-\Big{(}Y_ {r}+Y_{r|r|}|r|\Big{)}r=\tau_{Y}\] \[(\mathfrak{m}X_{G}-N_{\dot{v}})\dot{v}+(I_{Z}-N_{\dot{r}})\dot{r} -(Y_{\dot{v}}v_{r}r-Y_{\dot{r}}r+\mathfrak{m}X_{G}r)u+X_{\dot{u}}u_{r}v-\Big{(} N_{v}+N_{v|v|}|v|\Big{)}v\] \[-\Big{(}N_{r}+N_{r|r|}|r|\Big{)}r=\tau_{N}\]  
Here, \(\tau\) is the thrust of the USV thruster. Detailed definitions of the variables and conditions for Equation (2) can be found in [17; 18; 19].  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Force and Moments** & \
\begin{tabular}{c} **Linear and Angular** \\ **Velocity** \\ \end{tabular} &
\begin{tabular}{c} **Position and Euler** \\ **Angle** \\ \end{tabular} \\ \hline Motion in the X & \(X\) & \(u\) & \(x\) \\ direction (surge) & & & \\ \hline Motion in the Y & \(Y\) & \(v\) & \(y\) \\ direction (sway) & & \(Z\) & \(w\) & \(z\) \\ direction (leave) & & & \\ \hline Rotation about the & \(K\) & \(p\) & \(\phi\) \\ X axis (roll) & & & \\ \hline Rotation about the & \(M\) & \(q\) & \(\theta\) \\ Y axis (pitch) & & & \\ \hline Rotation about the & \(N\) & \(r\) & \(\psi\) \\ Z axis (yaw) & & & \\ \hline \end{tabular}
\end{table}
Table 1: Notation of the Society of Naval Architects and Marine Engineers for the unmanned surface vehicle.  
Figure 1: Coordinates of the unmanned surface vehicle.