esa-sceva/satcom-chunk-collection huggingface dataset:
# Model Reference Adaptive Control of Vehicle Slip Ratio Based on Speed Tracking
## 2 Problem Formulation
### Problem Formulations  
The control of braking force is mainly determined by the road adhesion coefficient, while the road adhesion coefficient is also a function of the slip ratio. Therefore, the target of the control system is to maintain the slip ratio around the desired \(\lambda^{*}(t)\). From the definition of slip ratio, the optimal braking force can be obtained when \(v_{w}=(1+\lambda^{*})v\). Accordingly, the control problem can be solved from the point view of vehicle speed tracking and the wheel speed can be controlled through the braking torque in (3). The wheel speed dynamic (3) is re-described as:  
\[\begin{cases}\frac{dv_{w}}{dt}=b_{1}v_{w}+b_{2}T_{b}+b_{3}\mu(\lambda)\\ v_{w}=\omega_{w}r_{w}\end{cases} \tag{5}\]  
where, \(b_{1}=-\frac{c_{f}r_{w}}{l_{w}}\), \(b_{2}=\frac{r_{w}}{l_{w}}\), \(b_{3}=-\frac{r_{w}^{2}mg}{l_{w}}\), \(\omega_{w}\) is the system state, \(T_{b}\) is the control input, \(v_{w}\) is the controlled output which can be measured by wheel speed sensors, and \(\mu(\lambda)\) is an unknown and uncertainty term.  
The vehicle speed dynamic (2) is rewritten as  
\[\frac{d_{v}}{dt}=a_{1}v^{2}+a_{2}\mu(\lambda) \tag{6}\]  
where, \(a_{1}=-\frac{c_{g}}{u_{w}m}\), \(a_{2}=g\).  
To sum up, the control problem is to find the optimal braking torque \(T_{b}\), for the system (5), to realize the vehicle speed tracking control so that the desired slip ratio can be well tracked.
## Materials and Methods
### Tower-Based Measurements
#### Meteorological and Flux Observations  
The meteorological and flux variables were observed using an automatic weather station (AWS) and EC system [34], which were fixed on the tower approximately 3, 5, and 20 m above the canopy at the XTS and DM sites. The collected meteorological variables involved in this study included PAR, broadband incoming shortwave radiation (R\({}_{\rm in}\)), air temperature (Ta), air pressure (p), wind speed (u), vapor pressure deficit (VPD), and air humidity (Rh). These data were measured and recorded every 10 min with a CR1000 unit (Campbell Scientific Inc., Logan, UT, USA). Similar to the calculation of SIF\({}_{\rm T30}\) in Equation (1), PAR and all other meteorological variables were averaged into 30 min intervals to correspond to the flux and SIF observations.  
Near the AWS, an EC system was deployed to measure the exchange of energy, water, and CO\({}_{2}\) across the canopy-atmosphere interface. The AWS included a 3D sonic anemometer (CSAT3, Campbell Scientific Inc., Logan, UT, USA) for measuring three-dimensional velocity and temperature and an open-path infrared gas analyzer (Li-7500, Li-Cor, Lincoln, NE, USA) for measuring CO\({}_{2}\) and H\({}_{2}\)O density. The main output parameters involved in this study include the net ecosystem exchange of CO\({}_{2}\) flux (NEE), latent heat flux (LE), sensible heat flux (H), friction velocity (u*), and atmospheric CO\({}_{2}\) concentration (Ca). The data were stored in a CR3000 data logger (Campbell Scientific Inc., Logan, UT, USA) and processed with an average time of 30 min at a sampling frequency of 10 Hz.  
The GPP data at 30 min intervals (GPP\({}_{\rm T30}\)) were calculated using the online tool available at the Max Planck Institute for Biogeochemistry (MPI-BGC) website ([[http://www.bgc-jena.mpg.de/~MDIwork/eddyproc/](http://www.bgc-jena.mpg.de/~MDIwork/eddyproc/)]([http://www.bgc-jena.mpg.de/~MDIwork/eddyproc/](http://www.bgc-jena.mpg.de/~MDIwork/eddyproc/))), with input variables including R\({}_{\rm in}\), Ta, u*, rH, LE, and H. Gap filling of the EC and meteorological data was performed with methods similar to those in Reference [40]. Daytime NEE was partitioned into GPP and ecosystem respiration based on the night-time partitioning algorithm [41]. GPP\({}_{\rm daily}\) was calculated by integrating the GPP from sunrise to sunset for one day, which was similar to the calculation of SIF\({}_{\rm daily}\) in Equation (2), and has a time-integrated unit of gC/m\({}^{2}\)/d.
## 4 Radar-Based Wind Retrieval  
The radar-based wind fields have been analyzed at the first and the third episodes of the current event. The retrieval of radar-based three-dimensional wind fields was based on a pythonic library, PyDDA, using Doppler velocity data from the weather radar at Tate's Cairn, another S-band radar at Qiu Yu Tan in Shenzhen and their locations are shown in Figure 1b and the variational dual-Doppler wind retrieval method as suggested by [PERSON] et al. (2020) [14], [PERSON] et al. (1999, 2004) [15,16] and [PERSON] et al. (2012) [17]. For the first episode as seen in Figure 13a,b, near the location where the approach was aborted, there was a convergence of winds as well as a sharp reflectivity gradient with significant downward motion of the air, particularly for heights below 2 km above the sea level. The maximum downward velocity near the bottom of the measurement domain (the height of the radar) was around 6 to 7 m/s, which is generally consistent with the aircraft observation.  
During the third episode as seen in Figure 13c, the aircraft was about 2.3 km above sea level (i.e., around 1.7 km above the radar height), and at that location, there was a strong reflectivity core with a maximum intensity in excess of 60 dBZ (in purple). In association with this, there were significant upward and downward motions as revealed from the radar-retrieved wind field. The maximum upward and downward velocities had a magnitude in the order of 15-20 m/s as indicated in the ellipses in Figure 13c, which is slightly larger but still generally consistent with the aircraft observations. This is the first time in Hong Kong that we have obtained first-hand aircraft data during a microburst for which a comparison with the radar-based wind field could be directly conducted.  
Figure 13: _Cont._  
the wind retrieval method provided good estimation of the change in environmental wind fields.
###### Abstract  
A novel metal-free quinoxaline-based molecular framework with a dual donor and dual acceptor (DD-\(\pi\)-AA) motif has been introduced. Four sensitizers (**AP6**, **AP8**, **AP9**, and **AP12**) have been synthesized and fully characterized via UV-Vis absorption, cyclic voltammetry, density functional theory (DFT) calculations, time-correlated single photon counting (TCSPC), and in dye-sensitized solar cell (DSC) devices. Structural modifications to both the donor and acceptor/anchor regions were evaluated via structure-property relationships without altering the quinoxaline \(\pi\)-bridge. Through careful dye design, a broadly absorbing near-infrared (NIR) sensitizer extending electricity production to 800 nm is realized in DSC devices. Ground- and excited-state oxidation potentials were measured to show energetically favorable charge transfer events. Importantly, the dye structure was found to have a strong influence on dye energetics in different environments with structural elements allowing for either similar or dramatically different solution versus film measurements. The DSC device electrolyte was also found to have a significant influence on dye energetics as well. Electron transfer events were probed for each dye with DSC device measurements and with TCSPC studies. The results are correlated to the dye structures.  
Keywords: photovoltaics; dye-sensitized solar cells; organic dyes; chromophore; molecular engineering; dye energetics; fluorescence spectroscopy; density functional theory +
Footnote †: journal: Applied Physics Letters
## 5 Conclusions  
This paper proposed a dynamic coordinated optimization model for joint dispatch of active and reactive resources in ADN with ESSs. Taking the power losses, the operation cost, and the voltage deviation of the distribution network into consideration, an MINLP optimization model was formulated, which was further transformed into an MISOCP model by SOCP relaxation to reduce the difficulty of problem solving. The comparison with the original MINLP model (solved by PSO) on 33- and 69-bus distribution network shows the effectiveness and efficiency of the proposed method.  
The main contributions and the conclusions are summarized as follows:  
(1) The power losses, the operation cost, and the voltage deviation can be improved significantly after the coordinated active-reactive optimization.  
(2) The SOCP relaxation is used to transform the original MINLP model into a MISOCP model, thus reducing the difficulty of problem solving and verifying the exactness of SOCP relaxation.  
(3) ESSs help to reduce the total operation cost of an ADN effectively.  
(4) The reactive power regulation of DGs and ESSs play an important role in improving the system security and economy.  
Writing--original draft, L.W.; writing--review and editing, X.W.; conceptualization, C.J.; resources, S.Y.; data curation, M.Y.  
This research was funded by National Key R&D Program of China, grant number 2018 YFB0905200 and Project Supported by State Grid Henan Economic Research Institute, grant number 52170018000S.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Analysis of LC/MS-MS Conditions  
An injection volume of 10 \(\mu\)L was used for determining the levels of chloramphenicol classes, MG, LMG, and quinolone classes and 20 \(\mu\)L for nitrofuran metabolites. Chloramphenicol, thiamphenicol, and florfenicol levels were analyzed through gradient elution by using the A1 eluent (0.1% MeOH) and B1 eluent (100% MeOH). A mobile phase gradient was started at 40% B1 for 1 min at a flow rate of 0.5 mL/min, linearly increased to 90% B1 at 4 min, and subsequently maintained constant until 6 min. Thereafter, it was changed to 40% B1 after 6.1 min and maintained constant until 9 min. Danofloxacin, difloxacin, enrofloxacin, flumequine, and sarafloxacin levels were analyzed through gradient elution by using the A2 eluent (0.1% FA) and B2 eluent (100% MeOH). A mobile phase gradient was started at 10% B1 for 1 min at a flow rate of 0.5 mL/min, linearly increased to 95% B1 at 4 min, and subsequently maintained constant until 8 min. Thereafter, it was changed to 10% B1 after 8.1 min and maintained constant until 9 min. The A3 eluent (0.005 M ammonium acetate in 0.1% FA) and B2 eluent (0.005 M ammonium acetate in MeOH) were used as the mobile phase for nitrofuran metabolite analysis. The mobile phase gradient was started with 30% B2 at a flow rate of 0.3 mL/min, increased linearly to 95% B2 in 4 min, further maintained until 6 min, subsequently changed to 30% B2 after 7 min, and maintained constant until 10 min. The A4 eluent (0.1% FA) and B4 eluent (MeOH) were used as the mobile phase for MG and LMG analyses, respectively. These dyes were separated following the gradient program. The mobile phase gradient was started with 10% B4 for 1 min at a flow rateof 0.5 mL/min, increased from 10% to 95% B4 in 4 min, and maintained constant until 8 min. Finally, B4 was changed to 10% after 8.1 min and maintained constant until 11 min. The MS source conditions in ABI 4000 QTRAP were as follows: ion spray voltage of 4.5-5.5 kV, curtain gas of 15 psi, nebulizer gas of 50 psi, auxiliary gas of 60 psi, and source temperature of 50 \({}^{\circ}\)C. MS/MS experiments were conducted in multiple reaction monitoring modes (MRMs) for simultaneous detection of all targets, with two precursor-to-product ion transitions monitored for each analyte. The mass spectrometer was set to detect negative and positive ESI interface modes for chloramphenicol and other veterinary drugs, respectively. Supplementary Table S1 lists the retention times and the precursor and corresponding product ions obtained through MRM detection in LC-amenable veterinary analytes. The dwell time for each MRM transition was set at 5 ms. Analyst software (version 1.4, Applied Biosystems, Foster City, CA, USA) was used for instrument control and data acquisition.
# AxP: A HW-SW Co-Design Pipeline for Energy-Efficient Approximated ConvNets via Associative Matching
## 3 Co-Design Pipeline: Concept and Methodology
### Approximation Pipeline (AxP)
#### 3.4.1 Clustering Engine  
During the clustering stage, the ConvNet's weights are merged into the lowest possible number of clusters using a similarity distance metric. Once the engine returns the cluster centroids, all the weights in a certain range are mapped on the corresponding centroid values. This procedure affects the ConvNet model complexity, that is, the lower the number of clusters, the lower the cardinality of the weight-set. Indeed, only the values of the cluster centroids are the weight patterns to be stored.  
The clustering procedure was built upon the jenks natural breaks (JNB) algorithm [47]. It is an iterative method whose objective is to maximize the inter classes variance while minimizing the intra-class variance. The weight tensors are unrolled and treated as a 1D vector, then sorted by magnitude during a pre-processing step to achieve speed-up. Weopted for a layer-wise clustering granularity with a differentiated strategy for convolutional and fully connected layers. Each filter in a CONV layer is clustered independently, while the entire weight matrix is considered for an FC layer. The resulting number of clusters are \(N_{\textit{cinv}}\) and \(N_{\textit{fc}}\) for CONV and FC layers respectively. The differentiated clustering strategy adopted for the two types of layers is motivated by empirical evidence as it decreases both the number of centroids and the accuracy drop.  
As a practical example, the histograms reported in Figure 5 show the effect of the clustering step on the weights-set of an FC layer using \(N_{\textit{fc}}=16\) clusters. The original peak frequency for near-zero values (\(\approx\)5k) is doubled after the clustering step (\(\approx\)10k), which suggests that the occurrence of zero patterns gets doubled at inference-time, hence opening to a higher matching probability exploitable by an associative-memory based processing element.
# An Effective Wide-Bandwidth Channel Access in Next-Generation WLAN-Based V2X Communications
## 6 Discussion  
As described in the previous section, the conventional AIFS scheme, which is a similar approach to the wideband channel access scheme adopted in IEEE 802.11n and IEEE 802.11 ac, does not perform fair contention with 10-MHz transmissions in the secondary channel. This is because the channel access procedure of a 20-MHz frame in the conventional AIFS scheme performs a back-off procedure only in the primary channel, while channel access for 10-MHz frame transmissions in the secondary channel senses the channel for the entire back-off period.  
The proposed 20-MHz channel access scheme, called the all back-off AIFS scheme, enables fair contention of both channels despite channel congestion on each channel because of the back-off procedure that considers the channel states of the primary and secondary channels. In addition, as all back-off AIFS includes two receivers to enable PD and ED sensing in both channels, overprotection leading to the performance degradation of 20-MHz frame transmission can be effectively prevented.  
On the other hand, if there is one receiver in the station, only ED sensing can be applied to the secondary channel when PD sensing (decoder) is applied to the primary channel. In such an ED sensing, the channel access scheme is not a single channel, but also the channel access scheme is not a single channel.  
Figure 11: Performance comparison between 10 MHz transmission and 20 MHz transmission under the same channel load.  
Figure 10: Comparison between channel access schemes under high load in a primary channel.  
sensing case, since the decoding of frames is usually infeasible, a back-off operation in the secondary channel should be performed based on the EIFS period to provide enough time for the transmitter of frames in the secondary channel to receive an ACK frame. This EIFS sensing operation is a basic procedure in the IEEE 802.11 specification whenever frame decoding to acquire the frame transmission duration fails.  
Although the EIFS sensing ensures fairness and ACK frame protection, the EIFS period leads to the overprotection of 10-MHz frame transmissions in the secondary channel because the channel access scheme in a 10-MHz frame waits for the AIFS period before resuming the back-off procedure when it decodes frames and acquires the frame transmission duration. In other words, during the EIFS and AIFS time difference, the back-off counter is decreased and the transmission of the 10-MHz frame may be performed in case of expiration of the back-off procedure. Meanwhile, the channel access procedure for the 20-MHz frame is still waiting for the EIFS period. As a result, the medium access delay of the 10-MHz frame in the start AIFS + end AIFS scheme and all back-off EIFS period is further decreased, while the delay of the 20-MHz frame transmission is further increased.
## 4 Experimental Results
### Computational Complexity Comparison  
Comparisons of the computational complexity between the proposed method and some other popular fault feature extraction methods are shown in Table 1.  
Usually, the computational complexity of the spectral kurtosis method and empirical mode decomposition method are \(O(N\log N)\). The frequently used time-frequency analysis methods include the short-time Fourier transform, continuous wavelet transform and Hilbert-Huang transform. The computational complexity of these methods is also \(O(N\log N)\). The sparse representation method requires iterative optimization to solve many sub-problems of the whole model. At present, many mature iterative optimization algorithms have been applied to solve sparse representation models, such as the alternating direction method of multipliers (ADMM) [33], the split augmented Lagrangian shrinkage algorithm (SALSA) [34], majorization-minimization (MM) [35] and the forward-backward splitting (FBS) algorithm [36]. However, these iterative optimizations of sparse representation always involve a matrix-vector. This operation has \(O(rN\log N)\) cost and \(r\) is the redundancy factor [10]. Furthermore, calculating the inverse matrix may be involved sometimes. So, the computational complexity of the sparse representation method, if the construction of the sparse dictionary is not taken into account, is at least \(O(rN\log N)\), which obviously limits the execution efficiency of the algorithm.  
The EM algorithm used in the proposed method has computational complexity \(O(N)\) when estimating model parameters iteratively. Besides, the computational complexity of the discrete wavelet transform is also \(O(N)\). So, the computational complexity of the whole proposed method is \(O(N)\) and the fault feature extraction results can be obtained quickly.
# An Interactive Transient Model Correction Active Sonar Target Tracking Method
## 3 Simulation Verification and Offshore Testing
### Offshore Testing
#### 3.2.1 Offshore Testing Description  
The tracking duration is approximately 4114 s, with a sonar scan period of approximately 17.1 s, resulting in a total of approximately 240 scan cycles. Due to changes in the relative motion of the target and the sonar, resulting in fluctuations in the amplitude of the echo signal, as well as target shift, steering maneuver, etc the measurement data are not continuously sampled. In practice, a total of 155 cycles of underwater target radial distance and azimuth measurements were collected. Figure 6 shows the true target track derived from the underwater target navigation equipment data, as well as the position measurements obtained by converting the target's radial distance and azimuth detected by active sonar into Cartesian coordinates.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Measured Values** & **KF Results** & **TMC Results** \\ \hline MSE of Radial Distance & 1918.5 & 56,408.0 & 3286.9 \\ \hline MSE of Azimuth & 0.22 & 26.73 & 0.24 \\ \hline \end{tabular}
\end{table}
Table 2: MSE of simulation test.  
Figure 5: Comparisons of radial distances and azimuths. (**a**) Comparison of radial distances, (**b**) Comparison of azimuths.
## 4 Results and Discussion
### Far Field Noise Radiation  
In the Section 4.2, the wind speed effect on the flow characteristics of cavities in the landing gear (brake system and torque link) are analyzed. In order to further study the wind speed effect on the aerodynamic noise generated by the complex flow of various components, the tire (A), brake system (B), torque link (C), and shock strut (D) were selected as the research objects (as shown in Figure 22).  
The surface sound pressure of components A to D were extracted as sound sources, and calculate their aerodynamic noise characteristics at different wind speeds. A point 3 m away from the landing gear was selected to compare the SPL evolution with frequency of different parts under the same wind speed (as shown in Figure 23) and the SPL evolution with frequency of the same part under different wind speeds (as shown in Figure 24). Table 4 shows the OASPL of different components under different wind speeds.  
From Figures 23 and 24 and Table 4, it can be concluded that: (I) Under different wind speeds, the contribution of A\(\sim\)D components' sound pressure level to the total noise is as follows: shock strut \(>\) tire \(>\) torque link \(>\) brake disc. (II) At the speed of 34 m\(\cdot\)s\({}^{-1}\) to 55 m\(\cdot\)s\({}^{-1}\), the contribution of each component to the total noise increases with the increase of the speed. In addition, the amplitude of the sound pressure level of the torque link and brake disc also increases significantly, which has a great impact on the total noise. (III) At the speed of 55 m\(\cdot\)s\({}^{-1}\) to 75 m\(\cdot\)s\({}^{-1}\), the amplitude of sound pressure level of torque link and brake disc increases slightly, and the contribution to the total noise decreases obviously. The increase of the total noise mainly comes from the main parts such as shock strut and tire.  
Figure 22: Parts of the landing gear.  
Figure 24: _Cont._  
Figure 23: SPL evolution with frequency at different components. (**a**) 34 m\(\cdot\)s\({}^{-1}\) (**b**) 55 m\(\cdot\)s\({}^{-1}\) (**c**) 75 m\(\cdot\)s\({}^{-1}\).