esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Conclusions  
To sum up, we characterized three popular high-permittivity substrates (RO3010, RO/duroid 6010.2 LM, and TMM10i), commercially available from Rogers Corporation, in the sub-THz range based on THz-TDS measurements in reflection mode. It was shown that by fitting the complex relative permittivity of the substrates to a single Lorentzian term, the theoretically calculated reflection spectra accurately matched the measurements. The substrates RO3010 and RT/duroid 6010.2 LM showed a significant dispersion in the investigated frequency range, which has to be taken into account in THz component design. In comparison, the substrate TMM10i showed both a lower dispersion and significantly lower loss tangent, both highly desirable properties. Thanks to the standard Lorentzian dispersion model used for their fitting, the presented reference data can be directly incorporated in design and optimization workflows of devices for emerging THz applications, including time-domain simulation methods or semianalytical algorithms.  
Conceptualization, W.F. and D.C.Z.; methodology, W.F. and D.C.Z.; measurements, W.F., F.M., L.M. and D.C.Z.; software, W.F.; data processing, W.F. and D.C.Z.; writing--original draft preparation, D.C.Z.; writing--review and editing, all authors; supervision, R.B. and D.C.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data presented in this study are available from the corresponding author upon reasonable request.  
We would like to thank [PERSON] and Rogers Corporation for providing us with the substrates investigated in this work.  
The authors declare no conflict of interest.
## Setup and Methods
These dimensions balanced the effects on thermal inertia (small volume to minimize the turbocharger lag) and conversion efficiency (maximum residence time under the diameter constraint). The turbocharger was relocated due to the pre-DOC presence being necessary to adapt the oil supply. However, the small pre-DOC size avoided any interference of the turbocharger and close-coupled ATS with the engine block nor other components. Therefore, the original exhaust line was only modified by the installation of the pre-DOC. \begin{table}
\begin{tabular}{l c} \hline Engine type & HSDI diesel \\ Emission standards & Euro 6 \\ Displacement & 1606 cm\({}^{3}\) \\ Bore & 80.1 mm \\ Stroke & 79.7 mm \\ Number of cylinders & 4 in-line \\ Compression ratio & 16:1 \\ Rated power @ speed & 100 kW @ 3500–4000 rpm \\ Rated torque @ speed & 320 Nm @ 2000–2500 rpm \\ Fuel injection & Common-rail direct fuel injection \\ Turbocharger & VGT \\ EGR & Cooled high and low pressure \\ \hline \end{tabular}
\end{table} 
Table 1: Main characteristics of the engine. \begin{table}
\begin{tabular}{l c c c} \hline
**Magnitude** & **Instrument** & **Range** & **Accuracy** \\ \hline Speed control & SIEMENS dynamometer & 6000 rpm & \(\pm\)2 rpm \\ Torque control & SIEMENS dynamometer & \(\pm\)450 Nm & \(\pm\)0.5 Nm \\ Air mass flow & Sensiflow DN80 & 20 to 720 kg/h & \(\pm\)2\% \\ Fuel mass flow & AVL 733S Fuel meter & 0 to 150 kg/h & \(\pm\)0.2\% \\ Temperature & K-type thermocouples & \(-\)200 to 1250 \({}^{\circ}\)C & \(\pm\)1.5 \({}^{\circ}\)C \\ Mean pressure & Kistler piezo-resis sensor & 0–10 bar & linearity 0.2\% \\ CO\({}_{2}\) & NDIR & 0–20 (\% Vol) & \(\pm\)1\% (full scale) \\ CO\({}_{1}\) & NDIR & 0–5000 (ppm) & \(\pm\)1\% (full scale) \\ CO\({}_{\text{H}}\) & NDIR & 0–12 (\% Vol) & \(\pm\)1\% (full scale) \\ THC & HFID & 0–5000 (ppm) & \(\pm\)1\% (full scale) \\ NO\&NOx & CLD & 0–10,000 (ppm) & \(\pm\)1\% (full scale) \\ \hline \end{tabular}
\end{table} 
Table 2: Main characteristics of the instrumentation. The performance of the ATS was analyzed with the baseline ATS and with pre-turbine pre-DOC by means of driving cycles defined by the WLTC. For each configuration, both cold (starting with engine coolant at room temperature) and warm (starting with coolant at 90 \({}^{\circ}\)C) were performed consecutively. Before each WLTC, the DPF was regenerated to avoid the effects of different initial DPF soot loading on the engine backpressure. Finally, the procedure was done twice to ensure the repeatability of the results.
## 4 Metro Track Geometry Defect Identification Model and Its Optimization Method
### MTGDI-CNN Based on DARTS  
#### 4.2.1 Model Architecture  
The CVD is natural multi-channel one-dimensional (1D) data, and the signal amplitude of each channel fluctuates with the track mileage as a dimension. However, the specific type of TGD generally responds to the CVD of multiple channels [13; 15; 18; 19]. Moreover, the vibration caused by the vehicle condition will be reflected in all CVD channels, and the intensity of these vibrations also varies with the operating speed [13; 24]. Therefore, the metro TGD-identification model requires not only the ability to analyze various vibration data channels but also the ability to analyze the relationship between different channels. The most commonly used convolution operations in CNN are 1D convolution or two-dimensional (2D) convolution operations. One-dimensional convolution operation offers a more direct approach for processing the original vibration data for TGD identification without introducing other feature-extraction methods [43; 44]. Several related studies have been conducted in the field of defect identification [32; 45]. However, 1D convolution assumes that different channels of vibration data are relatively independent, and the ability to handle the relationship between different channels of vibration data is relatively weak. Considering the characteristics of CVD, the use of 1D convolution will reduce the TGD-identification performance. In this study, a 2D convolution was employed, and an MTGDI-CNN based on DARTS was proposed by collocating the vibration signals into 2D data, which realized the identification of metro TGD using a portable detector. The overall architecture of the model is illustrated in Figure 6.  
The main body of the MTGDI-CNN is composed of stacked cells, the input is sample \(\mathbf{X}_{s}\) generated from the CVD and heterogeneous factors, which are the speed and vehicle conditions, whereas the output is the predicted label \(\hat{Y}_{s}\) of the se  
Figure 6: Structure of MTGDI-CNN based on DARTS optimization method.  
within the mileage range, \(s\). The input \(\mathbf{X}_{s}\) is processed by the initial convolution (Init Conv) and subsequently processed by the stacking structure comprising the normal and reduction cells to output the predicted label, \(\hat{Y}_{s}\). Each cell in the stacked structure is connected to the previous two cells. The model contains two reduction cells located at 1/3 and 2/3 of the total number of cells. Each reduction cell is preceded by several normal cells.
###### Abstract  
There are few studies on optimizing the dynamic and static characteristics of direct-drive turntables. In terms of dynamic and static characteristic analysis, most studies only analyze the dynamic and static characteristics of direct-drive turntables in a single machining position and working condition. The optimization is mainly for individual parts without considering the overall structure of the turntable. A multi-objective optimization method based on the back-propagation neural network (BP) and the non-dominated sorting genetic algorithm is proposed to ensure the machining accuracy of the direct-drive turntable, reduce the total mass, and improve its dynamic and static characteristics. In this paper, the workpiece and direct-drive turntable are studied as a whole. Static and modal analyses determine the maximum deformation locations and vulnerable parts of the turntable. Topology optimization analysis was used to find the redundant mass parts. We determined the optimization objectives and dimensional parameters based on the direct-drive turntable's structural and topology optimization results. Using a central composite experimental design, we obtained test points and fitted them to a response surface model using a BP neural network. A multi-objective genetic algorithm then obtained the optimal solution. After multi-objective optimization, we reduced the mass of the direct-drive turntable by 9.02% and 21.394% compared with the topologically optimized and original models, respectively. The dynamic and static characteristics of the direct-drive turntable increased, and a lightweight design was achieved.  
direct-drive turntable; dynamic and static characteristics; optimization design; multi-objective optimization +
Footnote †: journal: Applied Sciences
## 3 The Proposed DSEC Method
### Data Segmentation Strategy  
The training set in the TSSP problem can be divided into the stable set and the unstable set, and the stable samples usually outnumber the unstable samples. To obtain a relatively balanced training set, the data segmentation strategy is proposed. The basic idea of the data segmentation strategy is to divide the stable set into multiple non-overlapping stable subsets, ensuring that the samples in each stable subset are not more than the unstable samples.  
The specific processes of data segmentation strategy are shown below.  
Step 1: Given the stable set \(S\) with \(N_{S}\) samples and the IR value, determine the number of stable subset \(T\) by  
\[T=ceil(IR), \tag{7}\]  
where \(ceil\) represents the ceiling function.  
Calculate the remainder \(P\) by  
\[P=\text{mod}(\frac{N_{S}}{T}). \tag{8}\]  
Set \(k\) and \(P_{0}\) both equal to 1.  
Step 2: If \(P_{0}\leq P\), go to step 3; otherwise go to step 4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**IR**} & \multicolumn{3}{c}{**Classification Performance**} \\ \cline{2-4}  & **TSR (\%)** & **TUR (\%)** & **GM (\%)** \\ \hline
1 & 74.00 & 75.00 & 74.50 \\
2 & 91.00 & 68.00 & 78.66 \\
4 & 96.25 & 51.00 & 70.06 \\
6 & 98.50 & 40.00 & 62.77 \\
8 & 98.38 & 37.00 & 60.33 \\
10 & 98.40 & 33.00 & 56.98 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Confusion matrix of TSSP.  
Step 3: Determine the number of samples in stable subset \(S_{k}\) by  
\[|S_{k}|=\frac{N_{S}-P}{T}+1. \tag{9}\]  
Create the stable subset \(S_{k}\) by randomly selecting \(|S_{k}|\) samples from stable set \(S\) without replacement. Set \(P_{0}=P_{0}+1\), \(k=k+1\), then go to step 5.  
Step 4: Determine the number of samples in stable subset \(S_{k}\) by  
\[|S_{k}|=\frac{N_{S}-P}{T}. \tag{10}\]  
Create the stable subset \(S_{k}\) by randomly selecting \(|S_{k}|\) samples from stable set \(S\) without replacement. Set \(k=k+1\).  
Step 5: If \(k\leq T\), return to step 2; otherwise go to step 6.  
Step 6: Output \(T\) stable subsets \(S=\{S_{1}\), \(S_{2}\),..., \(S_{T}\}\).  
From the strategy hereinbefore, if IR is an integer, the number of samples in each stable subset is the same as that of unstable samples. If not, the number of samples in each stable subset is less than that of unstable samples.
## 1 Introduction
### Active Disturbance Rejection Control  
Active disturbance rejection control is an innovative strategy; its fundamental principle lies in augmenting the state-space model of the system with a supplementary fictitious first-order differential equation. This fictitious equation defines all the undesirable system uncertainties, dynamical properties, and external disturbances, denoted as a \"generalized disturbance\" [3]. This fictitious state, as well as the states of the dynamical plant, is estimated in a real-time manner by the extended state observer (ESO). which is the central part of the active disturbance rejection control (ADRC) [4]. It achieves an immediate and dynamic estimation and elimination of the generalized disturbance by returning its prediction to the control port with basic calculations using control law with output feedback. With ADRC, controlling a sophisticated uncertain nonlinear plant is transformed approximately into a simple linear time-invariant system [5; 6]. The supremacy that makes it such a powerful control instrument is that it is a model-free method, instead of a model-based technique. Mainly, ADRC comprises of an ESO, a nonlinear state error feedback (NLSEF), and a tracking differentiator (TD), as shown in Figure 1, where \(r\in\mathbb{R}\) is the desired signal, \(\left(\begin{array}{cccc}r_{1}&r_{2}&\ldots&r_{\rho}\\ \end{array}\right)^{T}\in\mathbb{R}^{n}\) is the transient profile, \(\rho\) is the relative degree, \(v\in\mathbb{R}\) is the control input, and \(\left(\begin{array}{cccc}\xi_{1}&\xi_{2}&\ldots&\xi_{n+1}\\ \end{array}\right)^{T}\in\mathbb{R}^{n+1}\) is the expanded estimated vector, which includes the predicted states \(\xi_{1},\ldots,\xi_{n}\) of the system and the predicted generalized disturbance \(\xi_{n+1}\).  
Numerous engineering control challenges have been adequately resolved over the past 20 years through the successful application of ADRC. These include a high purity distillation column [7], the tension and velocity regulations in web processing lines [8], high-performance motion control [9], DC-DC power converters [10], chemical processes [11], vibrational MEMS gyroscopes [12], hysteresis compensation [13], fault diagnosis [14], non-circular turning processes [15], high pointing accuracy and rotation speed [16], omnidirectional mobile robot control [17], the control of model-scale helicopters [18], flexible-joint manipulator control [19], and differential drive mobile robots [5]. More recently, manufacturers that have successfully adopted ADRC technology include Texas Instruments Inc., which utilized ADRC in its motion control chips (InstaSPIN(tm)-MOTION) [20]. Parker-Hannifin Corporation recently adopted ADRC over its production lines at an extrusion plant and halved  
Figure 1: Structure of the conventional SISO active disturbance rejection control (ADRC) configuration, \(\rho\) is the nonlinear system’s relative degree.  
its energy usage [21]. Finally, the National Superconducting Cyclotron Laboratory in the United States deployed ADRC in some high-energy particle accelerators for the regulation of electromagnetic fields [22].
# Programmable Deterministic Zero-Copy DMA Mechanism for FPGA Accelerator
## 3 Programmable Deterministic Multi-Queue Accelerator Model
### Application Affinity for Serverless  
In a multi-core endpoint that runs multiple cloud functions simultaneously, the cache of each core stores the information the function service uses. The service may be scheduled to other cores by the operating system when running so that the CPU cache hit rate decreases, leading to the program running less efficiently. The most intuitive way to solve this problem is to bind a serverless node to a CPU core. The CPU binding will always run in RTC (run-to-completion) mode on the specified core, reducing context switching.  
The Accelerator supports the affinity feature of the cloud-function services via the RSS multi-queue, the ZC driver, and the deterministic DMA module acting together. The driver is responsible for initializing the accelerator, registering the kernel interface, and allocating DMA-accessible buffers for descriptors. The driver reads the parameters exposed by the  
Figure 4: Zero-copy workflow.  
accelerator using registers, such as the number of queues and the timestamp, and combines them with the number of CPU cores to derive the number of queues (Num) from being activated by Num = Min (number of queues, number of CPU cores). At this point, the driver allocates the respective memory pool for the queues and requests Num interrupt numbers, using irqbalance [30] to assign a unique IRQ number to each queue.  
The application affinity applies in two scenarios, the first one being load balancing. The RSS module sends packets to different queues in a balanced manner according to the flow rules when it receives a packet, after which the queue issues an interrupt request to the bound core according to the IRQ number. The DMA controller copies the packets to the memory pool corresponding to that queue. At this point, the service can obtain packet by polling the memory pool corresponding to that irp number. The second scenario is based on the priority of the application to isolate essential business processes. Real-time processes with high scheduling priority can be bound to a designated core, ensuring real-time operations scheduling and preventing processes on other CPUs from interfering with that real-time process. Binding the control-plane threads and each data-plane thread to different CPUs eliminates the performance consumption of repeated scheduling back and forth, and the threads finish their work without interfering.
## 5 Performance Evaluation and Discussion
### Performance Evaluation  
Figure 8 shows that the energy consumption increases when it gets further away from the sink node, which shortens the network lifetime. The MSE algorithm consumes less energy with the mobile sink inside the network. However, we cannot explain why the decreasing rate of live nodes get slower in round 150-200 while the fixed sink lies in location (100, 300). To our best knowledge, this phenomenon may be caused by the uniform distribution.  
Figure 9 shows that the energy consumption rate of LEACH is much larger than our MSE. Consequently, the energy in a LEACH network get drained much earlier than in a MSE one at the location about 400 rounds away from the sink node. The MSE energy consumption for both methods are almost linear before 500 rounds, while the linear part for the energy consumption curve of LEACH is before round 150. For the better illustration of these two energy consumption curves, we only choose the data within 1,500 rounds in order to magnify the bi-linearity of the energy consumption curve of LEACH.  
Figure 8: Influence on sink node locations.  
Figure 9: Energy consumption comparison.  
Figure 10 shows the number of live nodes during the simulation lifetime. We choose 1,500 rounds here to have clearer view. We can find that the time when the first node dies in MSE is much longer than that in LEACH and SEP, nearly 2.5 times longer than SEP and 5 times longer than in LEACH. Figure 10 also indicates that the number of live nodes decreases more and much faster as time goes by.  
Table 2 shows the round when the first node dies in the three protocols respectively. The longer the time for the first node to die, the more balanced the network will be. In a network which requires a more stable working time, the proposed MSE method will be more suitable.  
Figure 11 shows the number of packets received by the sink. As illustrated, the result shows that MSE has a higher number of data received than LEACH and SEP. In the first 200 rounds, the three proposed algorithms have nearly the same packet delivery numbers. However, after 500 rounds, the sensors in the network with the LEACH protocol drain out totally and transmit no packets, while SEP and MSE continue delivering and forwarding data. The network using SEP drains out at about 2,500 rounds, while MSE lasts for almost 5,000 rounds. The amount of packets received by the sink node in the network using the MSE protocol is thus about 4 times larger than that of LEACH and 2 times larger than SEP.  
\begin{table}
\begin{tabular}{c c} \hline
**Algorithm** & **First Death Round** \\ \hline LEACH & 100 \\ SEP & 211 \\ MSE & 525 \\ \hline \end{tabular}
\end{table}
Table 2: Round when first node dies.  
Figure 10: Network lifetime comparison.
## 2 Materials and Methods
### Docking-Based Screening  
Surflex Docking: The Mbb FAS-I structure in complex with FMN was retrieved from the PDB database (PDBID: 6 GJC). FAS-I is a large \(\alpha\)6 subtype complex consisting of six long polypeptide chains. Each chain is 3069 amino acids long and contains seven catalytic domains. For primary screening and docking, the Surflex-Dock program of sybyl 2.1 was used in screen mode [33]. For the receptor preparation step, only chain A was retained, while the other chains and water molecules were removed. The FMN molecule was also retained as it is found tightly bound to FAS-I. In addition, hydrogen atoms were added, and atom types and AMBER charges were assigned to protein atoms. Ligand NADPH was extracted from the structure of FAS-I of thermomyces lanuginosus (PDB:4V59), which is homologous to mycobacterial FAS-I. Ligand-based protomol was generated using NADPH, keeping the threshold 0.5 and bloat set to default. The molecular docking was done with default settings. In the next step, re-ranking of high scoring hits was done using the Geom-X mode. The spin density for search is higher in Geom-X mode and set to the value of nine, while in screen mode it is three. Therefore, accuracy in ranking the hits on the basis of docking score is enhanced as the search becomes denser. Surflex is based on the Hammerhead procedure for docking the flexible ligands into the binding site of the receptor. It is based upon the generation of ligand fragments and their alignment onto the identified probes; the remaining fragments of ligands were then docked. The scoring function is empirical and derived through a weighted sum of non-linear functions of protein-ligand atomic Van der Waals surface distances. Hydrophobic, entropic, polar, solvation, repulsive, and crash terms are included in the scoring function. The score predicts binding affinity in -log 10(Kd) units.
## Appendix A Proof of Theorem 1  
By substituting (1), (4) into (5), (8), respectively, we obtain the received signals at IoT relays \(R_{1}\) and \(R_{2}\) in odd and even transmission blocks, respectively. By applying SIC, we obtain an instantaneous bit-rate threshold at coupled IoT relays as shown in (16) and (17). By substituting expression (16) or (17) into expression (40) and after the algebraic transformation, we obtain OP at coupled IoT relays in closed-form by applying PDF over Rayleigh distribution as follows:  
\[OP_{R_{r}}(t) =1-\Pr\biggl{\{}\left|h_{S,R_{r}}\right|^{2}\geq\frac{\gamma}{(1- \lambda_{R_{r}})\beta\rho}\biggr{\}}\] \[=1-\int\limits_{\frac{\gamma}{\left(1-\lambda_{R_{r}}\right)\beta \rho}}^{\infty}\frac{1}{\sigma_{S,R_{r}}}\exp\biggl{(}-\frac{x}{\sigma_{S,R_{ r}}}\biggr{)}dx\] \[=1-\exp\biggl{(}-\frac{\gamma}{(1-\lambda_{R_{r}})\beta\rho \sigma_{S,R_{r}}}\biggr{)}. \tag{18}\]  
In Lemma 1, this study assumed that IoT hub \(S\) has been equipped with the number of antennas \(A_{S}\) and coupled IoT relays have been equipped with the number of antennas \(A_{R}\). By employing TAS protocol at IoT hub \(S\), SC protocol at coupled IoT relay as in [15] and applying CDF as given by [21] (Equation (57)), we obtain OP at coupled IoT relays for MIMO scenario as follows:  
\[OP_{R_{r}}(t) =1-\Pr\biggl{\{}\max\limits_{\left\{|A_{S}\times A_{R}\right\}} \biggl{\{}\left|\mathbf{H}_{S,R_{r}}\right|^{2}\biggr{\}}\geq\frac{\gamma}{(1 -\lambda_{R_{r}})\beta\rho}\biggr{\}}\] \[=\sum\limits_{\psi=0}^{A}\frac{(-1)^{\psi}A!}{\psi!(A-\psi)!}\int \limits_{\frac{\gamma}{(1-\lambda_{R_{r}})\beta\rho}}^{\infty}\frac{1}{\sigma _{S,R_{r}}}\exp\biggl{(}-\frac{x}{\sigma_{S,R_{r}}}\biggr{)}dx\] \[=\sum\limits_{\psi=0}^{A}\frac{(-1)^{\psi}A!}{\psi!(A-\psi)!}\exp \biggl{(}-\frac{\psi\gamma}{(1-\lambda_{R_{r}})\beta\rho\sigma_{S,R_{r}}} \biggr{)}, \tag{19}\]  
where \(A=A_{S}A_{R}\).