esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
A research team from the Beijing Institute of Technology conducted a study on the influence of the law of machining surface integrity on the flexural fatigue life of 34 CrNiMo6 high-strength steel and the hard turning machinability of 45 CrNiMoVA hardened ultra-high-strength steel [12; 13]. Comprehensive research literature has shown that the machining of high-strength steels is mainly carried out on thick-walled parts. In thin-walled part cutting, a lot of research has been carried out mainly on milling of aluminum alloys [14; 15], titanium alloys [16; 17], high-temperature alloys [18], and metal matrix particle-reinforced composites [19], and in thin-walled part drilling, the research team of Dalian University of Technology has carried out a lot of fruitful studies on fiber-reinforced composites [20; 21; 22]. [PERSON] [23] investigated the effect of drilling parameters such as feed rate, cutting speed and drill diameter on the drilling quality of glass fiber reinforced plastic composites. [PERSON] [24] carried out experimental studies on pre-cooling of carbon fiber reinforced plastic plates prior to drilling and obtained better drilling results. Some scholars have also studied the drilling process and drilling mechanism for thin-walled parts of aluminum alloys [25], titanium alloys [26], and metal matrix particle-reinforced composites [27]. However, the SiCp/Al composite material is relatively brittle, and the edges of drilled holes are prone to chipping defects. Titanium alloy materials have high specific strength, low thermal conductivity, and work hardening properties, but their strength is much lower than that of AF1410 high-strength steel. Therefore, the variation characteristics and effects of axial forces during drilling of SiCp/Alcomposite materials and titanium alloys are different from those of AF1410 high-strength steel. However, there are very few research papers currently available on the drilling of thin-walled, high-strength steel components. High-strength steel possesses unique mechanical properties such as high mechanical strength and toughness, and its thin-walled structures are increasingly widely applied. Drilling is an important machining process for most of the thin-walled parts of high-strength steel, and the axial force and deformation during the drilling process have an important influence on the drilling accuracy and edge quality. In this paper, the axial force and deformation characteristics of the drilling process of AF1410 thin-walled parts of high-strength steel, the formation mechanism of the drilling cap, edge crimping defects, and the influence of drilling speed and feed rate on it are investigated through experiments. It provides a foundation for the research on the drilling mechanism and deformation control of thin-walled high-strength steel components and also offers theoretical support for the development of precision machining processes for thin-walled high-strength steel components.
# Trajectory Tracking Control Method Based on Adaptive Higher Order Sliding Mode
## 2 Design of Adaptive High-Order Sliding Mode Trajectory Tracking Controller
### Establishing the Optimal Nominal Trajectory  
Given the initial state and terminal state constraints, the establishment of the optimal nominal trajectory is a typical two-point boundary value problem. To reduce the probability of interception, the flight time of the missile must be as short as possible [19]. Moreover, considering the stability issues affecting the control quality, the optimized performance indicators designed for this issue are  
\[J=Kt_{f}+\int\dot{u}_{y}^{2}dt \tag{9}\]  
Here, \(K\) is a constant and \(t_{f}\) is the terminal time, that is, the total flight time. The selection of performance indicators not only ensures the shortest terminal time but also ensures that the nominal trajectory control is relatively smooth. It also ensures the feasibility and rationality of using steering gear for control during engineering implementation.  
In this paper, a nominal trajectory satisfying the initial and terminal constraints is established by a nonlinear programming function based on the discrete state equation of the Gaussian pseudospectral method. To improve the somewhat inadequate numerical calculations that arise when using the numerical iteration method, this paper introduces a concept of normalization [20; 21] where the nominal trajectory in Equation (1) is established by normalizing the state variables \(x\), \(y\), and \(v\) into the state equation.  
Nominal initial conditions: initial velocity: \(v=1360\) m/s, initial trajectory inclination: \(\theta=0^{0}\), initial range: \(x=0\) km, initial altitude: \(y=10\) km.  
Desired terminal state: desired terminal trajectory inclination: \(\theta_{n}=-20^{0}\), desired terminal height: \(y_{n}=8\) km.  
The nominal trajectory established in this paper is shown in Figures 1 and 2. The entire nominal trajectory is relatively smooth, and there is no significant control chattering. It should be noted that the established nominal trajectory is the optimal open-loop nominal trajectory. As a result, a tracking control law must be designed to track the nominal trajectory to ensure that the terminal state meets the requirements.
## 3 Numerical Simulations
### Deformation Analysis Based on Multi-Field Coupling  
The mounting ring is the direct load-bearing component of molding forces, while the guide ring, bound to the mounting ring, serves as an indirect load-bearing component. Under the combined effects of temperature loading and pressure loading, both components undergo certain deformations [19]. When subjected solely to temperature loading, the mounting ring and guide ring exhibit expansion deformation [20; 21]. Figure 10 shows the axial deformation cloud maps of the mounting ring under identical temperature loading but varying molding forces. As illustrated, the expansion deformation of the mounting ring decreases upon applying molding forces, with the deformation magnitude further decreasing as the force increases. It can be inferred that molding forces induce compressive deformation in the axial direction of the mounting ring. Figure 11 presents the radial deformation cloud maps of the guide ring under identical temperature loading but different molding forces under the same working conditions (with identical molding forces); the deformation magnitude of the guide ring gradually decreases from the outer circumference inward. When the molding force increases from 70 T to 80 T, the radial deformation of the guide ring slightly increases, with the outer circumference expanding by only 0.001 mm. The simulation process effectively replicates the actual working conditions of the mold.  
Figure 9: Stress cloud maps of the upper and lower surfaces of the tread segment: (**a**) 70 T (**b**) 80 T (**c**) 90 T (**d**) 100 T.  
To facilitate comparison with subsequent experiments and further validate the feasibility and accuracy of the simulation model and method, four uniformly distributed measurement points were selected on the upper surface of the mounting ring, as shown in Figure 12, and three uniformly distributed measurement points were taken at a position 20 mm below the outer surface of the guide ring, as illustrated in Figure 13.  
Figure 11: Radial deformation cloud maps of the guide ring: (**a**) 70 T (**b**) 80 T.  
Figure 10: Axial deformation cloud maps of the mounting ring: (**a**) 70 T (**b**) 80 T.
## 3 Experimental Section
### Mapping, Functional Annotation and Pathway Assignments  
Based on the assembly statistics, only the Trinity assembly was taken forward. To obtain a wide base of evidence for determining function, the assembly set was annotated with MAGPIE through protein level similarity searches against NCBI's non-redundant (nr) database and the viridiplantae subset of NCBI RefSeq using BLASTX (_E_-value \(<10^{-3}\)) and against the NCBI Conserved Doman Database (CDD) using RPS-BLAST (_E_-value \(<10^{-2}\)). The InterPro suite of protein family and domaindatabases was also queried by Hidden Markov model (HMM) searches performed with HMMER [44] (\(E\)-value \(<10^{-10}\)). GO terms were associated to individual transcripts based GIDs extracted from their respective search hits. High level annotations were determined as BLASTX hits with \(E\)-value \(<10^{-35}\), RPS-BLAST hits with \(E\)-value \(<10^{-25}\), and finally HMM results with \(E\)-value \(<10^{-20}\) as well as a percentage similarity of at least 65%. These high level annotations were subsequently used in mapping and assigning EC values to their respective contigs. The weighed sum of annotations for each contig was summarized to give a putative functional description [45; 46; 47].  
All hits annotated to with text P450 or Cytochrome were blasted against a set Minimal set of Plant cytochromes P450 and the cytochromes P450 were then annotated to their specific family; full length cytochromes P450 obtained a CYP number from [PERSON] (The University of Tennessee Health Science Center, Memphis, TN, USA) [36].  
To determine metabolic pathways, the Kyoto Encyclopedia of Genes and Genomes (KEGG) mapping was used [47]. To obtain full pathway annotation and to identify the functional hierarchies, all contigs from the Trinity assembly were submitted to the KEGG Automatic Annotation Server (KAAS) [45], and the single-directional best hit information method was selected and listed in Table S1. KAAS annotates every submitted sequence with KEGG orthology (KO) identifiers, which represents an orthologous group of genes directly linked to an object in the KEGG pathways and BRITE functional hierarchy [45; 46] and thus incorporates different types of relationships that exist in biological systems (_i.e._, genetic and environmental information processing, cellular processes, and organismal systems).
## 3 Results and Discussion
### Residual Compressive Strength
This is because SF with a high melting temperature of 1370 \({}^{\circ}\)C is incorporated in the cocktail fiber specimens, which does not melt and remains inside the concrete after high-temperature exposure. Therefore, adding SF in the cocktail fiber specimens plays a significant role in preserving the high residual strength of the concrete after heating to high temperatures. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Temperature\({}^{\circ}\)C** & **NC** & **PF1** & **PF2** & **SF20** & **SF40** & **SF50** & **SPF201** \\ \hline
20 & 65.1 & 57.1 & 54.8 & 68.4 & 67.6 & 68.5 & 62.4 \\
200 & 62.0 & 52.9 & 58.4 & 67.2 & 61.9 & 74.9 & 55.3 \\
400 & 73.2 & 53.8 & 55.4 & 76.9 & 71.4 & 75.4 & 57.8 \\
600 & 47.7 & 38.6 & 38.0 & 47.8 & 48.7 & 52.7 & 41.1 \\
800 & spalling \({}^{1}\) & 13.7 & 14.8 & spalling & spalling & 20.1 & 18.5 \\
1000 & spalling & 5.5 & 7.4 & spalling & spalling & 7.1 & 5.2 \\ \hline \hline \end{tabular} \({}^{1}\) Severe spalling of cubic compressive specimens occurs and no residual strength was recorded.  
\end{table} 
Table 4: Residual compressive strength of the fiber-reinforced SCC after heating to various target temperatures. Figure 11: Residual compressive strength of the fiber-reinforced SCC subjected to different high temperatures: (**a**) SF and (**b**) PF + SPF. In order to satisfy the workability requirements of the fresh SCC, the steel fiber content is restricted and it is usually less than 60 kg/m\({}^{3}\)[53]. In this circumstance, the influence of the steel fiber content on the compressive strength of concrete is marginal. Based on the regression analysis, the reduction factor of the compressive strength of the fiber-reinforced SCC at elevated temperatures can be mathematically expressed as follows:  
For SF-SCC:  
\[k_{c}(\theta)= \begin{cases}1.0&20^{\circ}\text{C}\leq T\leq 100^{\circ}\text{C}\\ 3.0\times 10^{-4}T+0.952&100^{\circ}\text{C}<T\leq 400^{\circ}\text{C}\\ 7.0\times 10^{-7}T^{2}-2.7\times 10^{-3}T+2.060&400^{\circ}\text{C}<T\leq 1000^{ \circ}\text{C}\end{cases} \] (1a) For PF-SCC: \[k_{c}(\theta)= \begin{cases}1.0&20^{\circ}\text{C}\leq T\leq 100^{\circ}\text{C}\\ -1.0\times 10^{-4}T+1.000&100^{\circ}\text{C}<T\leq 400^{\circ}\text{C}\\ 7.0\times 10^{-7}T^{2}-2.5\times 10^{-3}T+1.872&400^{\circ}\text{C}<T\leq 1000^{ \circ}\text{C}\end{cases} \] (1b) where \[k_{c}(\theta)\] and \[T\] denote the reduction coefficient of the compressive strength, \[k_{c}(\theta)=f_{\text{cUT}}/f_{\text{cU20}}\] and the test temperature, \[{}^{\circ}\text{C}\], respectively. Figure 12 shows the comparison of the reduction coefficient with test data in the present study and predicted equations in EC2 [26] and the study of [PERSON] [54] and the abovementioned model. Figure 12 illustrates that the proposed model is more suitable for estimating fiber-reinforced SCC's residual compressive strength under various high temperatures.
# Predictive Modeling of Signal Degradation in Urban VANETs Using Artificial Neural Networks
## 3 Artificial Neural Networks  
Artificial Neural Networks (ANNs) are powerful models for statistical learning, drawing inspiration from the intricate workings of biological neural networks. ANNs have gained significant popularity within machine learning [23]. Several research papers [24; 25] in the realm of medicine harness artificial neural networks to address a diverse range of challenges. These networks are composed of interconnected \"neurons\" that communicate with each other via synapses, mimicking the neural connections found in living organisms. The strength of these connections is adaptively modified based on input and output signals, making ANNs highly suitable for supervised learning tasks.  
A typical neural network comprises three essential components: the input layer, the hidden layer(s), and the output layer. This structure can be envisioned as a black box, as illustrated in Figure 1 below, encapsulating the intricate computations performed by the network. The input layer receives the initial data, which is then processed through the interconnected hidden layer(s), ultimately producing an output through the output layer.
# Markov Chain Model-Based Optimal Cluster Heads Selection for Wireless Sensor Networks
## 4 The Proposed Clustering Protocol
### Settling Phase
#### 4.4.4 Dealing with Non-Associated Cluster Members (NACM)  
Some sensor nodes in the network are not receiving a CH advertisement message from any CH. These nodes wait for a specific time slot for the CH advertisement message and after that predefined time slot, these nodes start sending their data directly to the BS. In the literature of WSN [7; 8; 9; 10; 11], these self-generated CHs are known as _force cluster heads_. These self-generated force CHs are a waste of system resources, because all the time in a round these force CHs repeatedly send their information to the BS which affects the network stability. We also define a strategy to deal with these force CHs and we named them Non-Associated Cluster Members (NACM). In MOCHs, if a node does not receive any CH advertisement message, it will send a data forwarding request to the CH of its closest neighbor node. The requested CH will assign a data slot for this \(NACM\) node at the request. Then the neighbor node will forward the data of this \(NACM\) node during the assigned slot as shown Figure 8. The energy consumption of a \(NACM\) node to forward a data packet of \(l\) bits is:  
\[E_{NACM}=2 lE_{elec}+l\varepsilon_{fs}d_{(N,NBR)}^{2} \tag{22}\]  
Here, \(d_{(N,NBR)}^{2}\) is the distance between a field node and its neighbor (NBR). The energy consumption of a neighbor node to receive a data packet of \(l\) bits is:  
\[E_{NBR}=lE_{elec} \tag{23}\]
# Tropical Cyclone Winds from WindSat, AMSR2, and SMAP: Comparison with the HWRF Model
## Methods
### Time Interpolation
For example, Figure 11 shows side-by-side scatterplots comparing matchups between WindSat winds and both the unshifted (Figure 11a) and shifted (Figure 11b) interpolated HWRF fields for the WindSat pass over Hurricane Teddy shown in Figure 10. It can be seen that, in this case, shifting the HWRF winds prior to performing time interpolation led to a slight improvement in standard deviation and very little change in the value of the overall bias when compared with WindSat for winds between 10 and 60 m/s. We found that this held for most of the storms that we used in our study. Ultimately, one needs to look at each individual storm case and judge if a simple time interpolation is sufficient, or if the more cumbersome shifting methodology needs to be applied. In general, if one is performing statistical comparisons involving many storms/passes, the use of this shifting methodology can be expected to have little to no impact on the overall results. Figure 10: Three wind fields for the WindSat pass over Hurricane Teddy on 19 September 2020 at 10:08 UTC. **(a)** The resampled HWRF wind field that has been temporally interpolated to the time of the WindSat overpass, **(b)** the resampled HWRF winds that have been shifted to the WindSat storm center and then temporally interpolated to the time of the WindSat overpass; i.e., the shifting methodology described in the text, and **(c)** the wind field from the WindSat pass itself.
# Performance Evaluation of IEEE 802.11 ah Networks With High-Throughput Bidirectional Traffic
## 4 Performance Evaluation and Discussion
### Reliable Monitoring Scenario
#### 4.3.1 Reliable Monitoring: Simulation Setup  
In this scenario, we analyze how many stations can be reliably supported by a single AP over a large area (up to 1 km) when using MCS0 and a 2 MHz channel. Each sensor station sends its measurements periodically to the TCP server at the AP. Each measurement consists of a payload of 100 bytes, corresponding to 190 bytes on the wire (26 B for WLAN quality of service data, 8 B for LLC, 20 B for IPv4 header, 32 B for TCP header, 100 B of data and four remaining bytes of the frame itself). Each measurement is acknowledged at the application layer (90 bytes). The payload size is also increased up to 400 bytes to evaluate the grouping multiple measurements together and sending them at a slower rate. The sampling interval \(T_{s}\) is the time between two subsequent sensor measurement transmissions. Each station starts transmitting at a random moment within the sampling interval to prevent bursts of traffic and transmissions randomly deviate from the interval to simulate clock drift.  
To determine the upper limit of attainable density, we gradually increased the contention per RAW slot by increasing the amount of channel traffic. The contention per RAW slot is defined as the number of stations with which station \(x\) shares its RAW slot (if there are \(s\) stations in a RAW slot, the contention is \(s-1\)). The attainable density is determined as the number of stations for which all stations can deliver their data reliably, without packet loss. If the amount of traffic is too large, i.e., in overly dense network, the EDCA queues will fill up and frames will be dropped. The contention per RAW slot is increased with increments of 5, so the maximum number of stations in the obtained results might be somewhat smaller than the real maximum, depending on the TIM and RAW configuration. The minimal contention that has been chosen is the value where all TCP connections remain established and the EDCA queues do not have more than one frame at the end of the simulation. The total number of stations can be calculated according to Equation (5). We assigned \(N_{STAs}/N_{TIM}\) stations to the first TIM group only to reduce the simulation time. This action is motivated by the fact that each  
Figure 7: Increasing the number of TIM groups can increase the data rate 1–15% for MCS8.  
TIM interval contains the same RAW, so all TIM groups would behave equivalently. A 536-byte TCP segment size is used in this scenario because of the low data rate of MCS0.  
We ran over 20,000 simulations with different configuration parameters listed in Table 4. Thirty servers with 2\(\times\) Hexacore Intel E5645 (2.4 GHz) CPU (Virtual Wall testbed in Ghent, Belgium), 24 GB RAM, 1 \(\times\) 250 GB hard disk and 1-5 gigabit nics per node were used for this endeavor, as each ns-3 simulation with very large number of stations can take several hours to complete.
## 2 Materials and Methods
### AoR Estimation
#### 2.2.1 Instantaneous and Mean Helical Axis  
The algorithm for IHA estimation used is based on the original work of [PERSON] et al. [23], and it has been already outlined in previous works from the authors [10,19]. It considers a screw-like helical motion assumption. Firstly, this procedure requires the transformation of shank and foot markers' global trajectories in the local coordinate system (LCS) of one of the segments considered, that is, the shank. A representation of the shank LCS used to evaluate IHAs is presented in Figure 3a, whose elements are as follows:  
* \(\mathrm{O_{S}}\), the origin located at the mid-point of the SDistMed-SDistLat segment;
* \(v\), medio-lateral axis oriented from SDistLat to SDistMed;
* \(u\), axis orthogonal to the plane including SDistMed, SDistLat, and SProxLat, oriented forward;
* \(w\), axis orthogonal to the plane (\(u\),\(v\)).  
Then, a rigid body fitting algorithm based on singular value decomposition (SVD) is run to calculate an equivalent rigid body model of the foot. The latter is needed to estimate the six DoFs (three translational, three rotational) of the foot relative to the shank LCS that are then filtered through a self-tuning, 0-phase-shift, 2 nd-order Butterworth digital filter [37]. The filtered DoFs and their time derivatives are finally used to calculate the axis parameters \(\mathbf{n}_{\mathrm{i}}\) and \(\mathbf{s}_{\mathrm{i}}\) that are the direction vector and the position vector representative of the IHA at the i-th time frame. It is well known that the calculation of the axis parameters is affected by the magnitude of the angular velocity \(\omega_{\mathrm{i}}\). It can be evaluated starting from an estimate of the attitude vector \(\mathbf{\theta}_{\mathrm{i}}\), used to define the rotational DoFs, and its time derivative. Since low \(\omega_{\mathrm{i}}\) values compromise the calculation of the IHA, a threshold of 0.25 rad/s is often considered to filter out the time frames that may lead to ill-posed estimation of the helical axis.  
Given the full representation of the joint kinematics provided by a set of IHAs, a mean helical axis (MHA) can be calculated by a weighted least squares algorithm as detailed in the literature [10,38,39]. According to [PERSON] et al. [34], the most appropriate choice of the weight is:  
\[w_{i}=\sin^{2}(\theta_{i}/2),\]  
Figure 3: **(a)** Shank local coordinate system (IHA/SARA); **(b)** foot local coordinate system (SARA).  
with \(\theta_{\rm i}\) as the angle of rotation corresponding to the rotational DoFs for the i-th time frame. Other formulations consider different weighting factors [23; 32; 34], as follows:  
\[w_{i}=1 \tag{2}\]  
\[w_{i}=\sin(\theta_{i}/2) \tag{3}\]  
To evaluate the effect of the different weights, the MHA estimation was performed considering all the three solutions presented in Equations (1)-(3).