esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Trajectory Planning and Tracking Methods for Coal Mine Mobile Robots
## 4 Experiments on Trajectory Planning and Tracking of CMRRs
### Experiments on Trajectory Tracking of CMRRs
#### 4.2.1 Trajectory Tracking Test on a Flat Surface in the Simulated Laneway
And the maximum error is close to 0.2 m, and the difference between the standard deviation of the two algorithms is also very small, within 0.002 m.  
The probable reasons for the maximum error approaching 0.2 m are as follows: (1) In the process of tracking the sinusoidal curve-type trajectory, the robot went through several large-scale steerings, which led to a larger cumulative error in the laser odometer, causing the lidar localization algorithm to generate cumulative error, making the tracking error larger. (2) The robot is a tracked differential model, which is prone to side-slip during continuous turns, resulting in larger tracking errors. Analyze the reasons for the difference in tracking effectiveness between MPC and PID algorithms in tracking sinusoidal curves. PID uses the lateral tracking error as an error feedback term, which is a single-input, single-output controller with the goal of keeping the lateral tracking error as close to zero as possible. On the other hand, MPC predicts the state in the next period of time and solves the most reasonable control quantity at the current moment, so that the robot's position error when reaching the target position is 0, i.e., the robot's X- and Y-axis coordinates and the heading angle are as consistent as possible with the desired position of the target point. Therefore, when MPC is tracking the whole sinusoidal curve, it first adjusts the attitude so that the position is basically the same as the given one when it reaches the initial point of the target trajectory. PID, on the other hand, is unable to do so. Anyway, both of the algorithms complete the tracking of the target trajectory.
# Effects of the Ventilation System by Using Jet Fans during a Fire in Road Tunnels
## 1 Introduction
Key: heat release rate (\(HRR\)): energy generated in the unity of time by the fire (emitted power); \(t_{0}\): the time when finishes the growth phase and starts the full development one (after [9]). direction of vehicular traffic (the typical case), the back-layering can reach people who are trying to escape on foot after leaving their cars. Furthermore, when a powerful ventilation system is activated after the fire has been started, the consequences can be dramatic; the flames can suddenly increase in length and the fire can easily spread to other vehicles. Depending on the geometries and the flow of traffic, modern tunnels typically use a variety of ventilation methods [13]. Systems for horizontal and transverse ventilation are regarded as standards. Using two different circuits, the transverse ventilation enables autonomous control of the fresh and exhausted air along the tunnel's length. It is used for long tunnels with two-way traffic, but it has expensive startup and maintenance expenses. In one-way traffic tunnels, jet fans are used as part of the longitudinal ventilation system to bring fresh air in that direction [14; 15; 16]. Usually, the fans are mounted on the tunnel ceiling to regulate smoke longitudinally. Since ventilation is crucial during a tunnel fire, it is fundamental to proceed with the proper fan sizing in order to give exposed people time to flee before the environment becomes unsuitable for survival. In this work, the effects of jet fans on the evolution of fires and on the control of toxic fumes in one-way road tunnels will be analyzed, using analytical and numerical methods. More specifically, a newly developed calculation procedure is presented; it is able to permit a fast analysis in order to evaluate the risk of the back-layering phenomenon during a fire in a one-way road tunnel. The calculation method will be then applied to a studied case of a road tunnel in Northern Italy with a length of 800 m. On the basis of the obtained results, specific considerations will be developed in order to define the characteristics of the fans to be used and their location along the tunnel.
## 5 PCB Life Prediction Analysis  
The overall life prediction for the board is shown in Figure 18. Several concerns were highlighted that would need to be addressed from a design point of view. The first of these is the thermal mechanical strain, which caused a small number of the components to fail.  
Figure 17: The wear-out of PCB semiconductor.  
Figure 18: Overall assessment of the operational lifetime of the power PCB.  
From the results, this occurred predominately for the larger components, particularly those in the vicinity of the heatsink and also manufactured from plastics that have a high expansion rate, e.g., test sockets and the large smoothing capacitors. The transformer and large inductive load also failed due to thermal expansion.  
The actual board has some mitigation, such as the through-bolting of the inductor and retaining clips for the IGBTs mounted to the heatsink, which were not incorporated into the FEA model. The other test where failure was evident was due to random vibration. In this case the same larger components failed, because they were subject to more energy from movement. This would have been compounded by the fact the board was simulated using the lengthy stand-offs provided. Again, by providing mitigation for the vibration, in the form of damping and reduction in the stand-offs, this problem could be solved. The last point of interest in the results was from the solder fatigue analysis. All of the solders except PB90 SN10 provided adequate resistance to fatigue for the given operational lifetime. This solder is a leaded solder that is still used in aerospace, military, and other safety-critical applications due to concerns over \"tin whiskers\". The fact that some failure occurred highlights the importance of the careful choice of solder and that, despite having high confidence in the components, board, and design layout, the \"glue\" that holds the assembly is just as important.  
The overall life assessment of the board yields a probability of failure rate of 5% in the 30-year life of the board. Addressing the concerns shown by the software and re-testing would see a further reduction in this failure rate, thus showing how design for reliability (DFR) can play a significant part in the design verification stage, thus saving time and money.
## 1 Introduction
aeruginosa_, and results showed improved healing of the wound accompanied by the eradication of the bacteria, as well as enhanced wound closure [35]. Pexiganan (MSI-78), an analog of imagaini, is currently in Phase 3 clinical trial for treating diabetic foot infection [36]. The in vitro antibacterial testing of pexiganan against a panel of pathogens that cause diabetic foot infections showed the effectiveness of the peptide [36]. Previous work has been based on either discovered peptides or rational design (WLBU2). Our work seeks to expand this prior work by implementing computational peptide design. There are a large number of natural AMPs that have been discovered in living organisms as well as synthetic AMPs that have been developed by rational design [37]. However, the implementation of computational approaches to generate antimicrobial peptides based on databases could result in the invention of many more novel compounds with improved antimicrobial activity [38]. Designing and testing computationally designed AMPs will expand the number of experimentally validated AMPs that could serve as templates for peptide design and be a part of high-quality datasets to aid further computational peptide design pipelines [39]. In previous work, we designed novel peptides (PHNX) using a combined computational strategy of database filtering technology (DFT) plus a positional analysis on a dataset derived from APD3 [37], and we were able to design a novel AMP with specific activity against gram-negative bacteria, called PHNX-8 [40]. In this work, we sought to computationally design new AMPs that would have activity against MDR _A. baumantii_ using a similar computational approach. This series of peptides was named \"HR2N\" as in \"horizon\", as they represent a new series of peptides that could potentially treat severe wound infections. Our hypothesis was that computationally designing a new peptide from a large set of peptides with reported activity against any strain of _A. baumantii_ should increase the likelihood of obtaining peptides with activity against MDR_A. baumantii_, which is demonstrably 82% more challenging for an organism with all the efflux pumps and multiple drug-resistance mechanisms [41; 42]. We assembled a curated set of peptides from the Giant Repository of AMP Activities (GRAMPA) database that have reported activity against any strain of _A. baumantii_[43]. The GRAMPA database contains data collected from various other databases, including APD [37], DADP [44], DBAASP [45], DRAMP [46], and YADAMP [47]. Our set of collected peptides (374) was analyzed using DFT plus positional analysis (\"DFT plus PA\") to generate novel peptides that we predicted would be active AMPs. We then selected and tested the newly designed AMPs against multidrug-resistant (MDR) strains of _A. baumantii_ for antibacterial, antibiofilm, and resistance acquisition, as well as evaluated them for hemolysis and toxicity.
###### Abstract  
Recently, a significant interest in space exploration has emerged, driven by the lack of resources and the quest for answers to issues like climate change. New technologies give us the possibility of exploring our solar system and its surroundings in greater detail. But current space communications operate with a lack of efficiency due to the vast distances between celestial bodies within our solar system. Also, factors such as bandwidth asymmetry contribute to disruptions in the satellite communication network. This paper proposes the definition of the infrastructure of an interplanetary communication network, built upon a communications protocol featuring dynamic routing. This infrastructure aims to optimize information transmission by adapting communications to surrounding conditions. The envisioned infrastructure involves strategically placing network nodes at key Lagrange points around each planet within the asteroid belt. The nodes will be aware of their position, integrating sensing capabilities and intelligent algorithms. Next to each planet, a node with more capabilities will collect information from nanosatellites orbiting a planet and relay the information back to Earth. This structure will allow decision-making processes based on exploration data of the most significant celestial bodies within the asteroid belt, providing valuable insights such as constant monitoring of the dark side of the moon and difficult-to-reach zones in the solar system.  
interplanetary internet; communication network; celestial bodies exploration +
Footnote †: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# An Overview of Material Extrusion Troubleshooting
## 3 Troubleshooting of ME Parts
### Printer-Associated Problems
#### 3.1.3 Stops Extruding during Mid Print  
If the printer stops extruding during the middle of a print, this could be a result of running out of filament, a snapped or stripped filament against the drive gear, mid-print clogged nozzle, an overheated extruder motor driver, and/or a file or CAD file or G-code error. Solutions include the following:  
1. Replacing a new spool of filament. Newer and more expensive printers have a position sensor that stops the printing job when no filament is detected.  
Figure 6: The optimum nozzle height and first layer width.  
Replace the snapped filament and/or reduce the idler tension by loosening the filament if it is too tight or if there is a lot of plastic shavings or debris caused by the drive gears (refer to 3.1.6: Grinding filament).
3. Remove the filament and run through a diagnostics test to confirm that fans are functioning properly. Ensure that the hot-end fan is attached properly. It should direct the air inside while spinning. Check that all wires are still connected to the correct place.
4. Ensure that the printing temperature is correct for the loaded filament. Make minor temperature adjustments of \(\pm 5\)\({}^{\circ}\)C. Alternatively, use a different filament to check if the clog is caused by the current filament that could be out of acceptable tolerance.
5. Turn off the printer to allow the electronics to cool down and check the cooling fan. Overheated motors can cause the printer to shut down.
6. Check the generated G-code, preview the slices using the slicing software. Repair any errors on the \(STL\) File, or upload a new file if necessary.
# A Traffic Information Detection Method at Single Intersection Based on Wi-Fi Data
## 2 Theoretical and Technical Foundations
### Key Technology
#### 2.2.3 LSTM Neural Network Model  
The special gate structure of the LSTM neural network model enables it to remember or forget long-term information selectively. It has significant advantages in time series prediction. The traffic information data of intersections are time series data with a spatial-temporal correlation. Hence, the LSTM neural network model is suitable for predicting the traffic information data.  
The typical LSTM neural network model structure is shown in Figure 2. Among them, the function of the forgetting gate is to use the sigmoid function to retain the cell state information of the previous moment selectively, and its input is the input information \(x_{t}\) of the current moment and the hidden layer information \(h_{t-1}\) of the previous moment, as in (3). The function of the input gate is to obtain the candidate cell state information \(\overset{\sim}{\mathrm{C}}_{t}\) by using the \(\tanh\) function and update the cell state information \(C_{t}\) at this moment by combining the cell state information \(C_{t-1}\) at the previous moment, as in (4). The output gate calculates the final output information \(h_{t}\) and determines \(C_{t}\), \(x_{t}\), and \(h_{t-1}\) when the information can be passed to the next time. The final output information \(h_{t}\) is calculated in (5).  
\[f_{t}=\sigma\left(W_{f}\cdot[h_{t-1},x_{t}]+b_{f}\right) \tag{3}\]  
\[\overset{i}{\mathrm{C}}_{t}=\tanh(W_{c}\cdot[h_{t-1},x_{t}]+b_{c}) \tag{4}\]  
\[C_{t}=f_{t}\cdot C_{t-1}+i_{t}\cdot\overset{\sim}{\mathrm{C}}_{t}\]  
\[o_{t}=\sigma(W_{o}[h_{t-1},x_{t}]+b_{o})\]  
\[h_{t}=o_{t}\cdot\tanh(C_{t})\]
## 4 Illustrative Example
### Model Formulation  
The model is based on the kinematic equations of motion introduced in Equation (1) with constants defined in Table 1. To improve the model's fidelity for real-world applications, a lower order equivalent system transfer function \(G_{\text{LOES}}\) is used for course and climb as well as the angle of attack dynamics to mimic a rigid-body behavior, and thus the rotational dynamics. This is defined as follows (in the Laplace domain [1]).  
\[\begin{split} G_{\text{LOES}}\left(s\right)&=\frac{ \omega_{0}^{2}}{s^{2}+2\zeta\omega_{0}s+\omega_{0}^{2}}\,,\\ \dot{\gamma}_{K,\text{cmd}}&\xrightarrow{G_{\text{ LOES}}(s)}\dot{\gamma}_{K}\,,\\ \dot{\chi}_{K,\text{cmd}}&\xrightarrow{G_{\text{ LOES}}(s)}\dot{\chi}_{K}\,,\\ \frac{m\Delta\,\text{f}_{z}}{Z_{\alpha}}& \xrightarrow{G_{\text{LOES}}(s)}\dot{\alpha}_{K}\end{split} \tag{12}\]  
Beside the dynamic relations in Equation (12), the following kinematic relations are used to calculate quantities required to, e.g., evaluate the error dynamics in Equation (9) or transformation matrices:  
\[\begin{split}\phi&\approx\mu_{K}=\arctan\left( \frac{V_{K}}{g}\dot{\chi}_{K}\right)\,,\\ \theta&\approx\alpha_{K}+\gamma_{K}\,,\\ \dot{\psi}&\approx\dot{\chi}_{K}\end{split} \tag{13}\]  
Thus, Equations (12) and (13) together with the basic kinematic equations of motion in Equation (1) and a position propagation in navigational frame specify the used generic aircraft model for the simulation assessment. The control inputs, i.e., the specific forces, are calculated by integrating the results obtained when applying Equations (9) and (10) (with zero specific force in \(x\) and \(y\) direction as well as negative gravitational acceleration \(g\) in \(z\) direction, in order to account for the gravity and ensure a level flight, in the beginning of the simulation). It should be noted that this time-integration step reduces the efficiency of the proposed scheme in the context of this study as the specific force derivatives are not directly used as control variables. However, this formulation will interface with the inner-loop controller described in [11; 12] that utilizes specific force derivatives as input. Nonetheless, the effectiveness of the approach is still shown in the following examples by this simplified setup.  
Furthermore, the gain matrices specified in Table 1 used for the pseudocontrol calculation in Equation (11) are not specifically tuned and only designed to provide stability. A specific tuning can further improve the results, while, again, the simplified setup already proves the applicability of the proposed method.  
Take into account that the desired trajectory, i.e., the path the simulation aircraft should follow, is specified using command signals for the desired velocity as well as climb and course rate. Finally, the desired jerk in Equation (9) is set to zero as minimum jerk trajectories are generally most energy-efficient and best perceived by passengers [13; 14].
## 2 Study Area and Data  
### Study Area  
The Xinjiang Uygur Autonomous Region (XUAR) is the largest autonomous region in China, covering 1,660,000 km\({}^{2}\), which is more than one-sixth of China's territory. Due to the variations in topography and climate, the land cover types vary greatly in the XUAR as shown in Figure 1. The Junggar Basin and the Gurbantunggut Desert lie between the Altay and Tianshan Mountains. The Tarim Basin and Taklamakan Desert are situated between the Tianshan and Kunlun Mountains. The entire area can be divided to northern and southern parts by Tianshan Mountains. The land is mainly covered by grassland and sandy desert [37, 38, 39]. Forest scatters within high mountains and along rivers. Oasis landscapes ranging from small to moderate in size (0.01-15,000 km\({}^{2}\)) have developed in river deltas, alluvial-diluvial plains and the edges of diluvial-alluvial fans [40]. Agricultural land and human settlements are found in the oases as these provide stable water supply in this arid region.  
Located in the hinterland of Eurasia continent, it has a variable arid to semi-arid continental climate with annual precipitation of 100-200 mm and mean annual temperature of 10-15 \({}^{\circ}\)C [41]. The annual precipitation is unevenly distributed with a general decreasing pattern from northwest to southeast across XUAR (Figure 2). The annual precipitation of the northern XUAR is 210 mm while the southern XUAR has an annual precipitation of 100 mm.The mean July temperature is 27.1 \({}^{\circ}\)C and the mean January temperature is \(-17.1\)\({}^{\circ}\)C [41]. The areas within high mountain ranges have a typical mountain climate, which is characterized by long, cold winters and short, hot summers. The growing season for vegetation extends from March to November in XUAR. Typical crops planted in these areas include wheat, cotton, corn, and rice, as well as vegetables and fruits such as sugar-beet and melon [42].
# Time Domain Spherical Harmonic Processing with Open Spherical Microphones Recording
###### Abstract  
Spherical harmonic analysis has been a widely used approach for spatial audio processing in recent years. Among all applications that benefit from spatial processing, spatial Active Noise Control (ANC) remains unique with its requirement for open spherical microphone arrays to record the residual sound field throughout the continuous region. Ideally, a low delay spherical harmonic recording algorithm for open spherical microphone arrays is desired for real-time spatial ANC systems. Currently, frequency domain algorithms for spherical harmonic decomposition of microphone array recordings are applied in a spatial ANC system. However, a Short Time Fourier Transform is required, which introduces undesirable system delay for ANC systems. In this paper, we develop a time domain spherical harmonic decomposition algorithm for the application of spatial audio recording mainly with benefit to ANC with an open spherical microphone array. Microphone signals are processed by a series of pre-designed finite impulse response (FIR) filters to obtain a set of time domain spherical harmonic coefficients. The time domain coefficients contain the continuous spatial information of the residual sound field. We corroborate the time domain algorithm with a numerical simulation of a fourth order system, and show the proposed method to have lower delay than existing approaches.  
spatial audio recording; spherical harmonic; time domain signal processing 2021  
[PERSON], [PERSON], T.D.; [PERSON] Time Domain Spherical Harmonic Processing with Open Spherical Microphones Recording _Appl. Sci._**2021**, _11_, 1074. [[https://doi.org/10.3390/app1103](https://doi.org/10.3390/app1103)]([https://doi.org/10.3390/app1103](https://doi.org/10.3390/app1103))  
1074
# GIS-Analysis for Active Tectonics Assessment of Wadi Al-Arish, Egypt
## 3 Data and Methods
### Mountain Front-Sinusosity Index (Smf)  
The mountain front sinuosity index (Smf) is used to clarify the interaction between erosion processes and tectonic actions. Erosive processes tend to produce more sinuous mountain fronts through rivers and streams, whereas active tectonics result in straight fronts, often associated with active geological structures [63; 64]. This index is derived by:  
\[\text{Smf}=\text{Lmf}/\text{Ls}\]where Lmf defines the total length of the mountain front along the mountain foot, while Ls measures the straight line of the mountain front. Values of Smf are calculated from aerial photography or topographic maps. However, the value extracted depends on the scale [63]. For more accuracy, large scale topographic maps are used to extract the Smf index values. The value of this index is used to differentiate between tectonically active fronts and fronts that are produced by erosional processes.