esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Effect of a Single Factor on the AS and Suspension of EFLPs
#### 3.2.3 Hydrochemical Component  
Figure 3 shows that the effects of the three hydrochemical components (NaF, NaNO\({}_{3}\) and Cu(NO\({}_{3}\))\({}_{2}\)) on the AS and suspension of EFLPs at 1.1 \(\upmu\)m D\({}_{50}\) were different. Among them, Cu(NO\({}_{3}\))\({}_{2}\) had the most obvious influence, mainly because the positive charge of Cu\({}^{2+}\) was more than that of Na\({}^{+}\)[46]. Meanwhile, although there was the same concentration (5 mmol/L) of Na\({}^{+}\) from NaF and NaNO\({}_{3}\), the former was significantly greater than the latter in terms of the effect, which is difficult to explain in terms of the amount of ionic charge and even inconsistent with conventional understanding [54]. A similar phenomenon has been reported in other references. For example, [PERSON] et al. (2020) [55] found that Li\({}^{+}\), Na\({}^{+}\), K\({}^{+}\), Rb\({}^{+}\) and Cs\({}^{+}\) exhibit substantially different effects on the colloidal aggregation of inorganic clay minerals; the effect of KCl was bigger than that of NaCl. This proved that anions affect the aggregation, deposition and suspension of particles as cations do [46; 49; 56]. Therefore, since both anions and cations have an effect on EFLP existence, at the same time, there may also be interactions between their effects. This also indicates that the effects of anions and cations are not simply additive or subtractive, but have complex interaction relationships [46; 57; 58]. This means that a multi-factor test is better to determine the characteristics of these complex interaction relationships.
# Null Subcarrier Index Modulation in OFDM Systems for 6G and Beyond
## 9 Conclusions  
In this study, a novel index modulation method called NSC-OFDM-IM was introduced. The modulation was carried out for a \(\widetilde{M}\)-size subgroup, which had a number of \(\widetilde{M}-1\) active subcarriers. Unlike in the existing OFDM-IM schemes, where the index selection is performed for the purpose of determining the active subcarriers in the subgroup, in the proposed NSC-OFDM-IM scheme, the null subcarrier location is selected to convey the transmitted \(p_{0}\) bits to the null subcarrier index. It was shown that the combinadics algorithms for the index selection and index detection were no longer required since the proposed method utilizes the null subcarriers rather than active subcarriers. Thus, the NSC-OFDM-IM scheme has a significantly reduced complexity as compared to the existing OFDM-IM system. Simulations and analyses were carried out in terms of spectral efficiency and bit error rate performances, and the results were compared with the OFDM and OFDM-IM systems. The proposed scheme had better spectral efficiency and bit error rate performances than the conventional OFDM system, without causing any performance degradation to the existing OFDM-IM scheme.  
The structure of the complex subgroups, whose sizes were chosen as a power of two, allows an easy and flexible design of the scheme at the transmitter and receiver. The proposed method and presented results motivate future works on designing a low-complexity and flexible index selection and index detection algorithms for the transmitter and receiver systems. This approach is a novel way of representing data transmission using the null subcarrier indices, and it outperformed the existing OFDM-IM system in terms of reducing the computational complexity without the need for a look-up table or combinatorial algorithms for index mapping/demapping operations. In our future studies, we will combine the proposed method with a reduced-complexity LLR detection mechanism so that the complexity can be further decreased to improve the performance of the detection at the receiver.  
Not applicable  
The authors declare no conflict of interest.
## 2 Problem Formulations  
### Unmanned Aerial Vehicles Kinematic Model  
In the event that the UAV is endowed with a tri-loop autopilot for velocity, altitude control, and heading angle. The kinematic model of the UAV can be simplified as follows [38]:  
\[\left\{\begin{array}{l}\dot{x}_{i}^{\alpha}=r_{i}^{\varphi}cost_{i}^{ \varphi}\\ \dot{y}_{i}^{\varphi}=p_{i}^{\varphi}sin\varphi_{i}^{\varphi}\\ \dot{h}_{i}^{\alpha}=\lambda_{i}^{\alpha}\\ \dot{v}_{i}^{\varphi}=\frac{1}{\alpha_{i}}\Big{(}v_{i}^{\alpha_{inp}}-v_{i}^{ \varphi}\Big{)}\\ \dot{y}_{i}^{\varphi}=\frac{1}{\alpha_{i}}\Big{(}\dot{y}_{i}^{\varphi_{inp}}- y_{i}^{\varphi}\Big{)}\\ \dot{\lambda}_{i}^{\alpha}=+\frac{1}{\alpha_{i}}\Big{(}h_{i}^{\varphi_{inp}}- h_{i}^{\alpha}\Big{)}-\frac{1}{\alpha_{i}}\lambda_{i}^{\alpha}\end{array}\right. \tag{1}\]  
Figure 1: Illustration of the interaction manners. (**a**) Comparison of the fixed-distance (**left**) and topological (**right**) interaction structures; (**b**) local convergence issue of the topological interaction structure.  
where \((x_{i}^{\varpi},y_{i}^{\varpi},h_{i}^{\varpi})\) denote the position of UAV \(i\) in \(\varpi\in[sub-swarm,\ parent-swarm]\); \(v_{i}^{\varpi}\), \(v_{i}^{\varpi_{inp}}\) are the rate and control input commands of horizontal velocity; \(v_{i}^{\varpi}\), \(\Psi_{i}^{\varpi_{inp}}\) are the rate and control input commands of the heading angle; \(\lambda_{i}^{\varpi}\) is altitude change rate and control input commands of agent \(i\) in \(\varpi\); \(\alpha_{\psi}\), \(\alpha_{\psi}\), \(\alpha_{h}\), and \(\alpha_{\lambda}\) are the self-driving instrument control parameters. The following are the UAV flight condition constraints that are taken into account.  
\[\left\{\begin{array}{l}v_{min}<v_{i}^{\varpi}<v_{max}\\ \left|\dot{\lambda}\right|\leq\phi_{max}\mathrm{g}/v_{i}^{\varpi}\\ \lambda_{min}<\lambda_{i}^{\varpi}<\lambda_{max}\end{array}\right. \tag{2}\]  
where \(v_{min}\) and \(v_{max}\) are the minimal and ceiling horizontal speeds, respectively; \(\varphi_{max}\) indicates the maximum lateral overload; \(\mathrm{g}\) denotes the acceleration of gravity; \(\lambda_{min}\) and \(\lambda_{max}\) present the minimum and maximum height change rates, respectively, which are all greater than zero.
# Multi-Pedestrian Tracking Based on KC-YOLO Detection and Identity Validity Discrimination Module
## 2 Related Work
### Attention Mechanisms  
The attention mechanism is a mechanism that mimics human attentional processes, and it is widely used in deep learning [24]. This mechanism enables the rapid extraction of key information from the environment and allows the observer to scrutinize the details of the object. After the attention mechanism, different regions will have their own weights so that the system can focus on the important information. This mechanism was initially introduced in the sphere of computer vision and is now utilized across various domains, such as natural language processing, speech recognition, and recommendation systems. The versatility of attention mechanisms lies in their ability to enhance model performance by focusing on relevant information while reducing the computational burden associated with processing unnecessary or redundant data. Therefore, they constitute a crucial component of advanced machine-learning models in diverse domains [25]. Based on their different scopes of action, attention mechanisms have been classified into three categories.  
The spatial attention mechanism originates from the rationale that certain regions within input images are extraneous to recognition or segmentation tasks. The mechanism processes only regions pertinent to the task, preserving task-relevant regions while suppressing extraneous ones. An exemplary embodiment, the Spatial Transformation Network (STN) by Google DeepMind, learns preprocessing operations from input data that align with the specific task [13].  
In the detection task, the input images pass through both the spatial and channel dimensions, one after the other. The network provides a significantly more comprehensive understanding of the underlying information based on the inter-channel dependencies [19]. The prominent channel attention model, SENet, compresses the input feature map spatially while preserving its channel dimension. SENet devises channel weights, adapts them during training, and then utilizes them to amplify crucial channel information while dampening insignificant channel data. Consequently, the network's feature extraction efficiency is notably enhanced [6].  
Hybrid attention mechanisms amalgamate spatial and channel methods. However, certain models inadequately address the inherent interplay between features, rendering them unable to concurrently process both spatial and channel features. In this domain, representative models include the CBAM and dual-attention networks.
###### Abstract  
The article presents the modeling of temporary temperature and phase share calculations during SAW (submerged arc welding) overlaying of steel elements. The input heat of a melted electrode and the heat of direct electric arc impact have been taken into consideration in the temperature field solution. The characteristic areas (fusion, full and incomplete transformation), have been determined by solidus, \(A_{3}\) and \(A_{1}\) temperatures, respectively. The limit temperatures of the phase transformations during cooling, based on the cooling rate in the temperature range 800-500 \({}^{\circ}\)C according to S355 steel time-temperature-transformation welding diagram, have been determined. The JMAK (Johnson-Mehl-Avrami-Kolmogorov) law and KM (Koistinen-Marburger) formula were used in the phase change kinetic description. Theoretical considerations were illustrated with examples of temperature and phase share computations for welding overlaid S355 steel plate. The analysis of the history of changes in temperature and structural components (phases) was carried out based on the results of numerical simulations as well as metallographic examination after SAW overlaying. The dimensions of the HAZ (heat-affected zone), obtained experimentally, and the structure types confirmed the results of the computation.  
weld overlaying; temperature calculation; phase transition; modeling; Submerged Arc Welding 1
## 3 Results  
### Al\({}_{2}\)O\({}_{3}\) Film Thickness  
Since a growth per cycle (GPC) is the amount of film thickness deposited in one cycle, the final thickness is determined by the cycle number rather than the response time [29]. The accurate prediction of the ALD response of the substrate is spatially dependent on the film thickness profile [30]. The precursor coverage \(\theta\) is given by Equation (4) [14] as:  
\[\Theta_{\text{Al}_{2}\text{O}_{3}}(x)=\frac{d_{\text{ALD}}(x)}{N_{\text{cycles }}\cdot\text{GPC}_{\text{max}}} \tag{4}\]  
where \(d_{\text{ALD}}\) is the ALD layer thickness, \(N_{\text{cycles}}\) is the number of ALD cycles, and GPCmax at an assumed full saturation level. The GPC found in this MD study is 1.2 A that yields to a film thickness of 1.7 A (First cycle), where a GPC rate of 0.11 nm (1.1 A) per ALD processing cycle has been measured experimentally [30]. The precursor coverage \(\theta\) was equivalent to 1. The surface was assumed to be fully covered. Figure 5a shows that to reduce the total energy of H-terminated Al\({}_{2}\)O\({}_{3}\) surfaces, the atoms in the surface region rearrange to form a different structure than bulk Al\({}_{2}\)O\({}_{3}\) to reduce repulsive interactions (the short-ranged Buckingham term in Equation (3)) and increase attractive electrostatic interactions between Al and O [7; 8; 31; 32]. In Figure 5b, the temperature of the system is relaxed at 573 K. It shows that as the system's temperature rises, the kinetic energy of the system rises, and the frequency of the atoms' vibrations rises with it. The thermal motion becomes more vigorous as the temperature rises from a thermodynamic standpoint [33].  
Figure 6a,b visually demonstrate the layering of hydroxyls that are found in an ALD process. Most ALD procedures, for example, produce a sub-monolayer of growth after each cycle, which is usually attributed to steric effects from the precursor's ligands obstructing active sites, competing chemisorption pathways, or other factors [34]. It is observed that rather than being integrated into the majority of the film, the H atoms prefer to rise to the surface. Due to steric hindrance effects, the -OH distribution on the surface has a significant  
Figure 4: Model validation using a total density of the system at 300 \({}^{\circ}\)C at the first monolayer (1 st ALD cycle).  
impact on the ALD growth mode. This suggests that avoiding populated -OH during the early stages of ALD is crucial for minimizing nucleation times.
## 1 Introduction
For instance, the Fano X130, which excels in high-precision detail capture, features a scan angular resolution of 0.036\({}^{\circ}\) and a beam divergence of 0.19 mrad, a measuring range of 0.6 m to 130 m, and a single measurement error of \(\pm\)2 mm; a single scan can generate a high-density point cloud containing approximately 1371 million points. Additionally, the Robosense Helios 1615 laser scanner, known for its fast measurement speed and suitability for mobile mapping and large-scale three-dimensional laser scanning, employs a 32-line scanning mechanism, offers a 360\({}^{\circ}\) horizontal field of view and a 70\({}^{\circ}\) vertical field of view (covering \(-\)55\({}^{\circ}\) to +15\({}^{\circ}\)), and can achieve a point-cloud acquisition rate of 576,000 pts/s in single-return mode. In addition, the Optech LYNX Mobile Mapper, which is suitable for engineering surveys and large-scale infrastructure measurements (such as tunnels), features a maximum measuring range of 200 m, a range precision of 8 mm, and a range accuracy of \(\pm\)10 mm (1\(\sigma\)). This device has a laser measurement rate of 75-500 kHz (with multiple measurements possible per laser pulse), a scanning frequency of 80-200 Hz, uses a 1550 nm (near-infrared) laser, and offers a full 360\({}^{\circ}\) scanning field of view. Besides laser scanning devices, other data acquisition devices, such as the Intel RealSense D435i, are also used for data collection tasks. The Intel RealSense D435i is a measurement device that generates depth images based on infrared stereo vision and related technologies. It features an RGB frame resolution of 1920 \(\times\) 1080 and a field of view of approximately 69\({}^{\circ}\)\(\times\) 42\({}^{\circ}\). This device is capable of capturing both RGB and depth information simultaneously. Together, these devices provide a reliable data foundation for the aforementioned denoising methods. However, in point-cloud denoising methods for tunnel data, filter-based denoising techniques often focus on the distribution of individual point clouds and tend to have poor robustness when dealing with complex point-cloud distributions. Machine learning-based point-cloud denoising methods, represented by clustering and neural networks, often involve complex parameter tuning or require large amounts of data collection and labeling. As a result, these methods typically have long preparation periods and substantial workloads. Optimization-based denoising methods, such as point-cloud fitting, often require manual data preprocessing. The quality of the preprocessing directly affects the ellipse-fitting results, which in turn impacts the denoising performance. However, for large-scale point-cloud data in practical inspection tasks, performing manual preprocessing on all the data would consume substantial resources. Therefore, this study proposes an ellipse-fitting denoising method based on the Huber loss function. This method achieves good fitting results and effectively denoises tunneling-structure point-cloud data without the need for preprocessing. Additionally, it offers good computational efficiency.
## 3 Setup
### Current Controller
The voltage \(V_{S}\) is used to measure the current and shown in \[v_{S}(t)=(v_{A}(t)-v_{L})\cdot G_{S}=R_{S}i_{L}(t)\cdot G_{S} \tag{6}\]7 The step response of the current controller asymptotically follows the input signal without overshoot and thus demonstrates the behavior of a first order low pass filter. The test signal transitions to 500 mV, so that the time constant can be read when 315 mV is reached. This occurs after 43 us and thus corresponds to a cutoff frequency of 23 kHz. The current regulator is thus sufficiently fast for frequencies usually measured during EIS. Figures 12 and 13 show the results when combining the signal generation unit and the current controller. Once again, the test impedance was used as \(R_{L}\), which was designed on the basis of [PERSON]'s dissertation [33]. First of all, it is noticeable that there is no visible difference between these two signals. However, the signals correspond rather to a first order delay element than a step function. This is due to the fact that the input signal is generated by the signal generation. The charging of the capacitor in the sample and hold part is mainly responsible for the slow voltage rise. Therefore, the current controller is dimensioned sufficiently fast to output the generated signals. \[v_{S}(t)=(v_{A}(t)-v_{L})\cdot G_{S}=R_{S}i_{L}(t)\cdot G_{S} \tag{6}\]8 Step response of the PID current controller. \[v_{S}(t)=(v_{A}(t)-v_{L})\cdot G_{S}=R_{S}i_{L}(t)\cdot G_{S} \tag{6}\]9 Schematic of the PID current controller. Figure 13 takes a closer look at the control deviation \(V_{E}\). At the beginning of the step, when the slope is at its maximum, there is a small control deviation. In relation to the step height of 400 mV, the maximum deviation of 6.3 mV is still relatively small.
# Clutter Cancellation and Long Time Integration for GNSS-Based Passive Bistatic Radar
## 4 Simulation Results and Discussion
### Simulation Results
#### 4.1.2 Detection Results of Target with Constant Acceleration  
Usually, the motion of the air target is not smooth, and the acceleration must be considered. In this section, the proposed technique is tested by the target with constant acceleration. We set the acceleration of the target as \((-3,\,-5,\,1)\) m/s\({}^{2}\), while the velocity and initial position of the target remain unchanged. The SNR of the target echo is set as \(-46\) dB. ECA cancels the clutter from the first 50 range cells.  
Figure 10: Coherent processing results based on the proposed method.  
Figure 9: Energy integration ratio (EIR) versus the RA.  
Figure 12a is the detection result that is based on FT without clutter cancellation. As can be seen, the target cannot be identified due to the strong direct and multipath signals. Figure 12b provides the RD map obtained by FT with clutter cancellation, from which we can see that the target echo has not been accumulated effectively. Although the target can be identified in the RD map, the Doppler spans several resolution cells. Figure 13 shows an RD map obtained by the MFRT. The target echoes have been gathered and a bright spot can be identified in the map. However, the Doppler spread has not been eliminated effectively.  
Figure 14a is the Doppler variation during the CPI. When comparing with Figure 7a, the Doppler changes more severely due to the maneuvering of the target. The maximum curve fitting error is 1.8 Doppler cells and it can be negligible in the subsequent processing.  
Next, the proposed processing technique is applied to obtain a high coherent integration gain. The coarse RA that is estimated by FrFT is \(-0.0079\). The accurate RA is obtained by maximizing the EIR and the search interval is set to \([-0.089,-0.069]\), with a step length of 0.001. The running results indicate that, when the RA equals \(-0.0083\), the EIR achieves maximum.  
The signal to background ratio has been improved due to the enhanced energy concentration by the proposed method. The RD map that is given in Figure 15 also indicates  
Figure 11: Doppler cross-section of the target.  
Figure 12: Coherent processing results based on FT. (**a**) Detection result without clutter cancellation. (**b**) Detection result with clutter cancellation.  
that the target echoes have been accumulated in both range and Doppler dimensions. A bright spot can be identified in the RD map.  
Figure 14: Doppler variation during the CPI. (**a**) Doppler variation and curve fitting result. (**b**) Curve fitting error.  
Figure 13: RD map based on MFRT.  
Figure 15: Coherent processing results based on the proposed method.  
When compared with the traditional target detection method, the proposed method can obtain a higher coherent integration gain, and a longer detection range is guaranteed. Both of the the simulation results presented above indicate the effectiveness of the proposed processing technique.
## 4 Furrowing Depth Stability Control--Furrowing Depth Detection Technology
### Noncontact Detection Method  
The more typical furrowing depth detection at present is the use of noncontact measurement methods. Noncontact furrowing depth detection mainly uses ultrasonic or optical ranging sensors to detect the height of the frame relative to the ground, which is calculated by mathematical modeling to obtain the furrowing depth of the furrower [75; 76]. [PERSON] et al. developed a soil height change acquisition system based on an infrared ranging sensor and a microcontroller, which has a measurement accuracy of 3 mm and a stability of 91.8% and can support furrowing profiling control [65]. [PERSON] et al. designed a laser sensor-based mechanical weeding device for depth control. The results showed that the sensor depth measurement was not affected by soil moisture content, soil texture, and light, and the depth measurement accuracy could reach 1 mm [66]. To reduce the surface stubble and residual straw on the noncontact sensor detection errors, scholars proposed using noncontact sensors to detect changes in the height of the planter frame and, using mathematical model calculations, to get the depth of the open furrow. In order to detect soil firmness at the same depth, wheel-LVDT sensors were utilized to obtain frame height variations, and by adjusting the tractor's three-point suspension angle, soil firmness was maintained, which in turn kept the operating depth constant. The standard error during the operation of the machine can reach 0.033 m, which can meet the operational requirements [68]. [PERSON] et al. developed a seeding depth measurement system using a linear displacement sensor and an ultrasonic sensor to measure the distance of the trencher relative to the rack and the rack relative to the ground, respectively; by fusing the signals transmitted by the two sensors, the actual furrowing depth of the furrower can be obtained, and the seeder can still achieve accurate seeding at speeds up to 12 km/h [64]. In order to adapt to the detection accuracy of furrowing depth under different terrain conditions, [PERSON] et al. developed a tillage depth detection system, which uses optical sensors to detect its distance from the ground, inclination sensors to detect the pitch angle of tractors, and lift arm sensors to measure the position of lifting arms, then fuses the data detected by the three sensors, and calculates the tillage depth based on mathematical models [67].
# Generalized Spoof Detection and Incremental Algorithm Recognition for Voice Spoofing
## 4 Experimental Methods
### Training Details
#### 4.2.1 Training Parameters for Voice Spoof Detection  
Four sets of voice spoof detection experiments were conducted. The experiments were all based on the layered convolution neural network (LCNN) architecture, which takes the extracted LFCC features as input and outputs a confidence score to characterize the classification results. We set \(\alpha\) = 20 and m = 0.9 for AM-Softmax for the hyperparameters in the loss function and \(\alpha\) = 20, \(m_{0}\) = 0.9, and \(m_{1}\) = 0.2 for OC-Softmax. We used the adam optimizer [28] with the \(\beta_{1}\) parameter set to 0.9, and the \(\beta_{2}\) parameter was set to 0.99 to update the weights in the model. We used a stochastic gradient descent (SGD) optimizer [29] for the parameters in the loss function. The batch size was set as 16. The learning rate was set at 0.0001. The number of epochs was set to 50. The early stop time was set at 5.