esa-sceva/satcom-chunk-collection huggingface dataset:
# Stability and Heat Input Controllability of Two Different Modulations for Double-Pulse MIG Welding
## 5 Conclusions  
In this work, a comparative analysis was performed on the process stability and heat input controllability of TP-MIG welding and DP-MIG welding. The following conclusions are drawn from the experimental results:  
1. Owing to abrupt changes in current induced by pulse train transitions in DP-MIG welding, the welding arc makes a sharp sound. In addition, the probabilities of welder burn-back and the formation of screw-like wires during long welding operations are higher in DP-MIG welding than in TP-MIG welding. In TP-MIG welding, the presence of transitional pulses in the pulse train transitions helps stabilize the welding process and soften the sound of the welding arc. The weld formed by TP-MIG welding has a high level of aesthetic quality.
2. The input energy can be reduced by adjusting the number of pulses in the low-energy pulse train or the base time of the pulses. It was found that varying the number of pulses can reduce the current by 8 A and the input energy by approximately 14%. The welds formed by TP-MIG welding have finer grain sizes than those formed by DP-MIG welding. Under the same conditions, the input energy can be reduced by 12% by adjusting the base time of the pulses. In addition, the density of the fish scales increases as the frequency decreases.
3. The energies produced by TP-MIG welding and DP-MIG welding decrease as the welding speed increases. An increase in the welding speed from 0.6 m/min to 1.0 m/min decreases the area of the softened zone by 21% and 27% in DP-MIG welding and TP-MIG welding, respectively. In addition, the joint formed by TP-MIG welding is harder than the joint formed by DP-MIG welding. Additionally, the joint formed by TP-MIG welding has better tensile properties than the joint formed by DP-MIG welding. Therefore, TP-MIG welding is a superior technique in terms of its efficacy in reducing welding energy input.  
[PERSON] and [PERSON] jointly conceived and designed the experiment. [PERSON], [PERSON] and [PERSON] jointly performed the experiment and conducted data analysis. [PERSON] and [PERSON] analyzed the data and plotted the figures. [PERSON] and [PERSON] wrote this paper. [PERSON] and [PERSON] proofread and translated the paper. [PERSON] revised the paper, supervised research, and provided financial support.  
This paper was supported by the National Natural Science Foundation project of China (51875213) and the High-Level Leading Talent Introduction Program of Guangdong academy of sciences [grant numbers 2016-GDASRC-0106]; Natural Science Foundation of Fujian [grant numbers 2018J01503].  
**Acknowledgments**: In this section, you can acknowledge any support given which is not covered by the author contribution or funding sections. This may include administrative and technical support, or donations in kind (e.g., materials used for experiments).  
**Conflicts of Interest**: The authors declare no conflict of interest.
## 1 Introduction
Subsequently, the surrounding water-containing soft soil layer is artificially frozen by installing a freezing tube in the inner wall of the jacking pipe. A closed freezing curtain is created within the scope of the tunnel excavation section, which finally constitutes a large-scale composite support structure of \"frozen soil curtain and jacking pipe\" [28]. Figure 1: Schematic presentation of FSPR applied in the Gongbei Tunnel. In Figure 1, the underground excavation section of the Gongbei Tunnel, the key project of the Zhuhai link of the Hong Kong-Zhuhai-Macao Bridge in China, achieved successful application of FSPR for the first time in the world, with good engineering results. In the construction scheme of this project, artificial ground freezing technology was used to freeze the ground to form a frozen soil curtain, thereby sealing the water between the pipes and improving the bearing capacity of the pipe roof. The freezing construction period takes 180 days, and considering the damage caused by excessive frost heaving, the thickness of the frozen soil curtain is designed in advance, and the cooling capacity is strictly controlled [29]. According to the field measured data, after 90 days of freezing, the thickness of the frozen soil curtain remains unchanged, because the cooling supply and heat absorption of the freezing tube reach a state of balance, so the temperature field after that can be regarded as a quasi-steady state. Model analysis and calculation of the freezing temperature field is the basis of theoretical research on FSPR, which can provide strong support for similar methods in freezing construction parameter designs, process monitoring, and target prediction [30]. During the whole freezing construction process, it is necessary to grasp the frozen soil curtain thickness and temperature distribution law based on the calculation and analysis of the temperature field, and to evaluate the reliability of water sealing between pipes [31]. In this article, inspired by the existing analytical results and to better adapt to the complex ground freezing conditions in the practical project, we have established a simplified model by considering different soil freezing points and tube placement forms of FSPR. Through the conformal mapping function and separation of variables solution, the analytical solution of the freezing temperature field of two kinds of freezing tubes with equal spacing in a straight line in the image plane is derived, after which the analytical solution of steady-state temperature field in the object plane is obtained. The accuracy of the analytical solution is verified by comparing the numerical solution of this project in the range of soil freezing point 0\(--\)1.5 \({}^{\circ}\)C, and the influence of the parameter differences on the freezing temperature field is also discussed to provide a theoretical reference for popularization and applications of similar construction methods.
# PUE Attack Detection by Using DNN and Entropy in Cooperative Mobile Cognitive Radio Networks
## 4 Experiments  
A testbed based on SDR is used for the experiments; the Ettus N210 device is the SDR selected. It has been implemented for similar purposes and can work in the MCRN frequencies; it can be configured to work with Linux or Windows, and it has the libraries or  
Figure 3: Proposed CSS using entropy and DNN for multiple PUE detection.  
drivers to achieve several communication scenarios [30]. Each SU uses an RTL-SDR as a spectrum analyzer [12]. The N210 device generates the _PUE attack_; another SDR is used for the FC and each SU. The MCRN consists of some SU trying to make a phone call; this is achieved by using the OpenBTS software and GNURadio [31; 32], as we work in [10]. The testbed for the experiments is shown in Figure 4.  
The devices were positioned in a grid, and energy, entropy, and the DNN were calculated at that time and positioned with one PUE attacker to be able to measure the practical threshold. Then, the PUE attacker was moved to every place in that grid. That information is recorded and is the input for DNN algorithms. Once the measurements are taken and the algorithm learns how the attack works, the attacker takes a random position, and the SUs measure the results. With the energy and entropy results measured in each SU, the DNN predicts the _PUE attack_ presence; this value is binary and transmitted to the FC. Notice that if any SU detects a signal, it changes its frequency immediately to prevent interference with a genuine PU while it detects the PUE's presence. The FC takes a global decision and transmits a broadcast message to any SU in the network; the whole system learns and records the attackers' information, creating a database with a blacklist of the attackers' data. For example, the grid of the device's position is shown in Figure 5.  
If the PUE is out of the grid, it can detect it or not, depending on the SU position. The system needs at least one SU to detect it; if not, the PUE attacker is out of the system's coverage and will not be detected.  
Figure 4: Testbed for experiments based on SDR, GNURadio, and OpenBTS.  
Figure 5: Example of the grid of the position of the device in the experiment.  
There are two parts to the process. The first part is where the algorithms learn how the attack is achieved and the threshold is measured. In this part, some samples are taken at each position, and with one PUE attacker, the frequency changes to detect some frequencies in the range. This learning process can be seen in Figure 6.  
The second part is the detection once the system has learned how to detect the _PUE attack_. This flowchart shows the steps that each SU takes. The _PUE attack_ starts with a random position; this process can be seen in Figure 7.  
Figure 6: The learning system of the _PUE attack_ detection flowchart.  
Figure 7: Detection system flowchart.
# An In-Vehicle Smartphone RTK/DR Positioning Method Combined with OSM Road Network
## 4 Discussion  
This paper studies in-vehicle smartphone positioning in urban environments and proposes an RTK/DR positioning method that integrates OSM road network information. The test results show that the accuracy of heading angle and position achieved by using this method is higher than conventional RTK positioning results: the heading angle accuracy reaches 2 degrees, and the horizontal positioning error is lower than 1.5 m.  
In DR, the raw IMU data of the smartphone contain a relatively high noise, and attitude error calculated by angular velocity integral accumulates faster. It can be deduced from Figure 7 that, without any processing, the heading angle error of the smartphone reaches 30 degrees in 600 s. At the same time, it was possible to find that, although the heading angle of the smartphone is inaccurate, its variation per second can accurately reflect the turning situation of the vehicle, as shown in Figure 3. The direction of the OSM road network is more reliable in the straight part, even though the direction of the turning part does not match the actual value. Therefore, this work uses OSM road direction along the straight line to correct the heading angle. The statistical results in Table 1 show that this method highly improves the heading angle accuracy and makes the trajectory of the DR more parallel and consistent with the benchmark.  
In the RTK/DR positioning method, the measurement update switch effectively detects multiple jump points of RTK positioning. According to Table 4, the time when the measurement update is stopped accounts for 28.6% of the total time, during which DR performs state updates to maintain vehicle navigation. Finally, according to the statistical results shown in Figure 9 and Table 3, it can be deduced that the heading angle correction method and the measurement update switch can effectively improve the lateral positioning accuracy. However, as the velocity includes RTK velocity and heading angle error, there is a decrease of the forward accuracy of approximately 1%. RTK/DR has a better horizontal positioning accuracy than RTK and is more reliable in urban environments.  
Experimental results show that the method described in this paper still has some limitations: The velocity update of DR is seriously dependent on the velocity used by Doppler measurement, so there is no improvement of the forward positioning accuracy. Moreover, when the vehicle turns, there is a lack of suitable maps to correct the heading angle, so the DR quickly accumulates direction errors in the roads having long turns. Thus, in the near future, it is necessary to continue to study the multi-source data fusion positioning of in-vehicle smartphones in order to improve its direction, velocity, and positioning accuracy.
## 3 Spatial Correlation Network Analysis of Air Quality in China
### Spatial Correlation Network Characteristics of Air Quality in China
Air quality in Henan had affected 14 provinces, and the air quality of the other provinces had affected fewer than 10 provinces. The air quality index of Hebei, the center of serious air pollution, had decreased by 72.73% in 2019 compared with 2014.
# Neutral Copper Gas Resistivity Measurements by Means of an Exploding Wire in Air
## 3 Resistivity Limit Values
The absence of radiation coming from the expanding gas is also noticeable, indicating a very low or null degree of ionization. These values show that the metallic gas can be considered as a classic gas. In order to check the hypotheses and the values of the limits of the resistivity presented here, the experiment was simulated using a simplified 1D version of a full 3D multi-component (neutrals, ions, and electrons), two temperature, arbitrary, Lagrangian-Eulerian, finite volume code [22]. The coupling with the exploding wire experiment is described in [23]. Using a mean value between the upper and lower resistivity limits, many features of the electrical signals are well reproduced. As Figure 4 shows, simulated and experimental values of the initial peak of the voltage are in very good agreement. Time length of the dark pause--marked both by the voltage and current plateau and the slope of rise up (down) of the current (voltage) at the end of the dark pause--is also very well reproduced by the calculations. Similar observations can be made about the values of the radial border of the plasma expansion, as shown in \[\langle\rho\rangle=\frac{RS}{l}, \tag{11}\]7 It is worth mentioning that changes in the mean resistivity value used in the code as small as 10% lead to durations of the dark pause which are noticeably different from those experimentally registered. Additionally, changes of similar magnitude in the slope of the resistivity meant different peak shapes as compared to those observed in the electrical signals. \[\langle\rho\rangle=\frac{RS}{l}, \tag{11}\]8 Mean copper gas resistivity experimental limits (**—**upper, **—**lower) as a function of the absorbed energy for all measured voltages. Errors bars represent the statistical error among all cases. The lower scale indicates a mean gas temperature of a copper gas with a heat content equal to the measured electrical energy absorbed by the wire.
# Longer Ubiquinone Side Chains Contribute to Enhanced Farnesol Resistance in Yeasts
## 2 Materials and Method  
### Yeast Strains and Media  
The yeast strains used in this study are listed in Table 1. _C. albicans_ SC5314 and WO-1 were obtained from [PERSON], University of California at San Francisco, and [PERSON], University of Iowa, respectively. _S. cerevisiae_ BY4741 and its isogenic mutants, \(\Delta\)_coq1_::pYES, \(\Delta\)_coq1_::pYES + _At2g34630_, and \(\Delta\)_coq1_::pYES + _COQ1_ were obtained from [PERSON], University of Nebraska-Lincoln [21]. The \(\Delta\)_coq1_::pYES was effectively a petite strain in that it was unable to grow on glycerol + ethanol agar plates [21]. The expression capacity of the pYES system was ca. 60% of the BY4741 parent, with pYES + COQ1 producing UQ \(\leq\)6 and pYES + At2g34630 producing UQ 7-9 [21].  
Yeast extract-peptone-dextrose (YPD) medium containing 1% yeast extract, 1% peptone, and 2% glucose was used for routine growth of yeast stock cultures and in farnesol sensitivity assays. YPD was supplemented with 0.05% (_w/v_) D-galactose as an inducer for all _S. cerevisiae_ strains, and G418 (Geneticin purchased from Gold Biotechnolgy, St. Louis, MO, USA, 200 \(\mu\)g/mL) as the selective antibiotic for strains constituted with plasmid vector unless specifically mentioned. The glucose-salts-biotin (GSB) medium contained 1 g/L of (NH4)2 SO4, 2 g/L of KH2 PO4, 50 mg/L of MgSO4.7H2O, 50 mg/L of CaCl2.2H2O supplemented with sterile 30 mL of a 50% (wt/vol) glucose stock and 0.4 mL of the vitamin stock (containing 0.002% biotin, 0.02% pyridoxine and 0.02% thiamine in 20% ethanol) [2]. GSB was used as a minimal medium for preparing _C. albicans_ cell stocks for inoculum preparation and ubiquinone analysis. Modified glucose-proline-phosphate (GPP) medium [27] was used for preparation of anaerobic cells from _C. albicans_ SC5314. Synthetic complete (SC) medium used to grow opaque cells from _C. albicans_ SC5314 was composed with 0.67% yeast nitrogen base, 2% glucose and supplemented with appropriate amino acids [28].  
Figure 1: Structures of ubiquinones and famesol. Ubiquinone consists of a benzoquinone ring (aromatic moiety) and a side chain of variable isoprenoid units. _S. cerevisiae_ has six isoprenoid units in its side chain (UQ6), while most _Candida_ sp. have seven (UQ7), and _C. albicans_ has nine (UQ9). Farnesol, is a C15 sesquiterpene alcohol with only three isoprenoid units.
## 3 Results
### Daily Rainfall Probability Distribution Function  
PDFs computed from the six GPPs and the rain gauge data over the Ankavia catchment are shown in Figure 5. Overall, all GPPs' PDFs follow the same general trend as the rain gauge PDF, except for ERA5 and CHIRPS for specific ranges. ARC2, CHIRPS, IMERG, PERSIANN, and RFEv2 tend to overestimate the precipitation class between 0-0.2 mm/day. Most GPPs tend to underestimate the precipitation class between 0.5 to 10 mm/day, while ERA5 overestimates frequency in that precipitation range. Furthermore, most GPPs tend to underestimate the frequencies in precipitation classes > 150 mm/day. More specifically, ARC2 cannot retrieve precipitation events > 150 mm/day. IMERG cannot retrieve precipitation events > 200 mm/day, while ERA5 and RFEv2 can retrieve it but strongly underestimate this class. Only CHIRPS and PERSIANN perform well for rainfall > 200 mm/day, although they underestimate this rainfall range.  
Figure 4: Spatial distribution of the statistical evaluation metrics across the Ankavia watershed based on the point-gridded approach at a daily time scale from September 2018 to August 2020.
# GraphDBSCAN: Optimized DBSCAN for Noise-Resistant Community Detection in Graph Clustering
## 3 Proposed Method
### Compress Feature Representation  
Building on the spatial proximity features derived in the previous section, we employ an autoencoder to compress these features into a compact 5-dimensional representation, addressing the high dimensionality and potential noise in the proximity matrix to enhance clustering accuracy. The autoencoder architecture consists of an input layer and output layer, each matching the number of features in the proximity matrix, and a hidden layer with 5 neurons, chosen empirically to balance model complexity and information retention, mitigating overfitting while preserving critical nonlinear network structures. During training, the autoencoder minimizes reconstruction error using mean squared error (MSE) and the Adam optimizer, iteratively learning to encode the essential structural properties of the graph. This dimensionality reduction not only accelerates computation but also refines node representations by filtering noise, enabling more robust community detection in subsequent steps. The resulting 5-dimensional features form a graph spatial proximity matrix, serving as input for the optimized DBSCAN parameter tuning in Section 3.3, ensuring efficient and accurate outlier detection and clustering.
## 5 Discussion
### Traffic Pattern and Node Mobility  
Two key parameters in sensing applications are the traffic pattern and node mobility. Some sensing applications have deterministic traffic requirements, whereas others have an event-based nature or are subject to mobility. For example, control loops require a certain amount of bandwidth and a bounded latency, _i.e._, 1 pps (packet per second) with a maximum latency of 10 ms, to ensure the correct operation of the device under control. Furthermore, periodic sampling applications, such as utility metering, have similar traffic requirements. Contrarily, event detection applications require a certain group of nodes to react to certain events, e.g., a node triggers an alarm when its temperature sensor goes above a certain threshold, or require to collect data only from a given subset of nodes present in the network, e.g., those nodes that have a temperature over a certain value. In both cases, the traffic pattern is not predictable,because the event that triggers communications has an _ad hoc_ nature. Furthermore, when nodes are subject to mobility, the access to the network cannot be deterministic either. Therefore, the use of a network resource cannot be anticipated, and hence, the network cannot be scheduled efficiently.  
Due to the characteristics of TSCH, where actions occur at known times and the network is fully scheduled, sensing applications with deterministic traffic requirements can benefit from such an approach. However, TSCH is not well suited for applications where the traffic pattern is non-deterministic, because of the overhead required to re-schedule the network every time a node has new requirements. Furthermore, because the traffic pattern is deterministic, a certain overhead is incurred by nodes when the network resources are assigned, but not utilized. The latter can be leveraged by very efficient vendor-specific hardware devices, but cannot be assumed as a general rule. Contrarily, D7 AM is suitable for sensing applications where traffic is highly asynchronous, because the network is only triggered under certain circumstances. However, such an approach leads to an increased unreliability, due to contention effects that also increase energy consumption. This is especially critical in highly dense networks, because contention effects are more severe.
# Cognitively Driven Arabic Text Readability Assessment Using Eye-Tracking
## 5 Results and Discussion
### Readability Prediction at the Word Level  
To answer our research questions, in both rounds, we compared the performance of the regression models with different performance measures: correlation coefficient (CC) between the predicted and the actual readability score. The actual readability score is the score obtained from OSMAN, and thus it is based on the linguistic features used by OSMAN only without considering the eye-tracking data. The predicted score is the score obtained from the regression model. Other performance measures are mean absolute error (MAE), root mean squared error (RMSE), relative absolute error (RAE), and root relative squared error (RRSE). The results are shown in Table 4.  
As shown in Table 4, using eye-tracking data in the prediction task improved the correlation coefficient between the predicted and the actual readability scores and decreased all error types. This observation is evident for non-tree-based models, LR, SMO-reg, and MLP, compared to the M5P Tree model. The improvement in the correlation coefficient and the decrease of all error types in the readability prediction task implied that there was a relationship between using eye movement measures and the ability of the used algorithms to model words' difficulty level more accurately. However, in the case of the REPTree, we can observe that it did not perform similarly to the other models, as its performance decreased when using gaze data.