esa-sceva/satcom-chunk-collection huggingface dataset:
# A Method Improves Speech Recognition with Contrastive Learning in Low-Resource Languages
## 5 Experimental Results
### Discussion on the Number of Negative Samples  
We compared the effects of using different numbers of negative samples on our model and present the results in Table 4. From the table, it is evident that simply increasing the number of negative samples does not necessarily improve the model's performance. In fact, it may worsen the model's ability to learn representation and reduce the accuracy of speech recognition. Therefore, we suggest that improving the quality of the set of negative samples is the right approach to enhance the model's representation learning ability and improve the speech recognition effect. Increasing the number of negative samples alone does not necessarily improve the model's representation learning ability. This also shows that it is possible to improve low-resource language speech recognition effects using our method.
# Enhancing Meteor Observations with Photodiode Detectors
## 6 Conclusions  
Meteor studies play a key role in astronomy and planetary science, offering valuable insights into the chemical composition and physical properties of interplanetary matter. Meteors, as ephemeral phenomena in the night sky, present observational challenges that require precise and sensitive detection methods. Developments in observational and detection technologies have contributed to a significant expansion of our knowledge of these fascinating phenomena.  
In this article, we presented a novel photodiode detector for automatic monitoring and recording of meteors in the night sky. Our electronic circuit, consisting of photodiodes, low-noise operational amplifiers and a microcontroller, is characterized by high sensitivity and the ability to detect even faintly visible objects. The experimental data present a variety of observational cases, showing the detector's effectiveness in situations such as pixel saturation, the presence of other similar objects in the sky, meteor fragmentation, its presence near the horizon, or observations with partial cloud cover.  
A comparison of the results obtained with our detector with those obtained with a traditional camera with a wide-angle lens underscores the advantages of our approach, which allows for novel approach to identification and analysis of meteors. Particularly valuable is the fact that our detector can successfully identify meteors even in the presence of interference, such as clouds or other objects in the sky.  
In the future, we intend to further refine and optimize our detector. Our goals include (a) increasing the observational range by trying various positioning of modules, (b) preparing signal-processing algorithms for meteor detection, and (c) eventually installing the system in our remote observing station in Otivar (Spain). We believe that further development of our detector will create new opportunities in astronomy and enable more efficient meteor monitoring and research.  
[PERSON], [PERSON], [PERSON] and [PERSON] conceived the experiment(s), [PERSON], [PERSON], [PERSON] and [PERSON] conducted the experiment(s), [PERSON], [PERSON], [PERSON] and [PERSON] analyzed the results. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Silesian University of Technology, grant numbers BK-246/RAu-11/2025, 02/140/RGJ24/0031, 02/140/RGJ24/0030.  
**Institutional Review Board Statement**: Not applicable.  
**Informed Consent Statement**: Not applicable.  
**Data Availability Statement:** The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Face Swapping Consistency Transfer with Neural Identity Carrier
## 3 Methods
### Initial Face Swapping  
As shown in the left of Figure 2a, current face-swapping methods can be regarded as a facial attributes disentanglement and re-combination process of identity and target portraits, in which \(X_{id}\) provides identity information of the identity and \(X_{t}\) provide attribute information of the target. We use existing face-swapping methods to generate face-swapping proxies \(X_{ref}\). By fusing the identity and attribute embeddings, the swapped results \(X_{ref}\) will inherit \(X_{id}\)'s identity traits and have \(X_{t}\)'s other information. Due to the limitation of existing methods, the \(X_{ref}\) can suffer from the problems of inconsistency and visual artifacts.
# Mid-State Kalman Filter for Nonlinear Problems
## 4 Nonlinear Filter Performance Evaluation
### Cramer-Rao Low Bound for Nonlinear Systems  
For nonlinear systems, the optimal Bayesian nonlinear filter is impossible [19], so there are many approximate suboptimal algorithms. They have a theoretical optimal performance lower bound called Cramer-Rao Low Bound (CRLB). It is not only a benchmark for performance analysis, but also can be used to design the parameters of a suboptimal algorithm. It has important theoretical and application value.  
Literature [20; 21] provides the CRLB iterative formula for discrete nonlinear systems, but it only provides the definition containing the expected operation without the final equations. Therefore, this paper deduces the CRLB for nonlinearity systems with Gaussian white noise.  
For the nonlinear system in Equation (1), the unbiased estimate of the state \(x_{k}\) is \(\hat{x}_{k}\)  
Defining \(\mathbf{\widetilde{F}}_{k}=\frac{\partial f_{k}(x_{k})}{\partial x_{k}}\) and \(\mathbf{\widetilde{H}}_{k+1}=\frac{\partial h_{k+1}(x_{k+1})}{\partial x_{k+1}}\). The initial information matrix \(\mathbf{J}_{0}\) is the inverse of the initial state covariance matrix \(\mathbf{P}_{0|0}\):  
\[\mathbf{J}_{0}=\mathbf{P}_{0|0}^{-1} \tag{33}\]  
Through the above derivation process, the CRLB provides the lower bound of the mean square error of unbiased estimation and gives the mean square error of an ideal nonlinear filter. In this paper, the RMSE curves of all filters are compared with the CRLB curves of the nonlinear model. The degree of approaching the CRLB curve reflects the accuracy and performance of the corresponding algorithm.
# Mapping Atmospheric Exposure of the Intertidal Zone with Sentinel-1 CSAR in Northern Norway
## 2 Materials and Methods
### Methods
#### 2.2.3. In Situ Data Collection
* _Finnvika and Grottfjord_ At two locations, Finnvika (mudflat) and Grottfjord (shallow sandy beach), only single low-water GPS tracks were collected, on 8 May 2020 around 9:10 p.m. \(\pm\)30 min, well below MLWS at an estimated lower than 1% atmospheric exposure, and around 6:15 p.m. \(\pm\)10 min, around MLW, respectively.
# Survey on Ranging Sensors and Cooperative Techniques for Relative Positioning of Vehicles
## 4 Cooperative Relative Positioning
### GNSS-Based Relative Localization
#### 4.2.2 Relative Positioning  
Approaching the problem of relative positioning, in which the position towards other vehicles is the aim, from a cooperative perspective has experienced less attention. Probably due to the ever-growing availability of precise and less expensive ranging sensors, the usage of GNSS for relative positioning has been relegated to second place. The aim in GNSS-based relative positioning is to extend the limited perception range of on-board ranging sensors, while at the same time providing accurate relative position and velocity in an ego vehicle frame meeting the stringent requirements of safety-critical driver assistance applications regarding availability and reliability. Using the information contained inside CAMs or BSMs, it is possible to locate another vehicle in relation to the ego vehicle in the way a ranging sensor would do. This vehicle can thus be modeled with a rectangular cuboid, with the correct vehicle dimensions and oriented on the 2D plane with the relative heading in relation to the ego vehicle.  
[PERSON] et al. have analyzed the use of GNSS absolute positions and speed contained in CAMs to estimate the relative position to other vehicles using a particle filter in [26; 89]. In [90], [PERSON] identified the effectiveness of sharing absolute positions using low-end, mid-range and high-end GNSS receivers and comparing the output to a radar sensor. He concluded that low-cost GPS receivers are not capable of meeting the requirements of safety-critical applications as they showed longitudinal errors of around 2 m and lateral errors above 3 m while driving in a highway environment.
## 1 Introduction
An edge reversal error separation method based on an AFM was proposed for measuring cutting edge radius of diamond tool without the effect of the AFM tip radius by the authors of [22]. Firstly, a replicated cutting edge was obtained by indenting a diamond tool into a soft metal material. Then, an AFM was applied to scan the actual and the replicated cutting edges. The cutting edge radius of the diamond tool was obtained by taking the difference between the AFM images of the actual and the replicated cutting edges when the elastic recovery of the replicated cutting edge was small as compared with the cutting edge radius. Molecular dynamics (MD) simulations were carried out to investigate the effect of elastic recovery on cutting edge radius in our previous researches [23; 24]. It was verified that when the indentation depth of the replicated cutting edge was set to be larger than 200 nm, the elastic recovery of the replicated cutting edge could be ignored. However, a large indentation depth would cause more measurement uncertainties in the AFM measurement of the replicated tool cutting edge. Meanwhile, a nanoindentation instrument has been designed for replicating the diamond tool cutting edge onto a soft workpiece surface [25]. The displacement of the diamond tool was monitored by a capacitive sensor of a fast tool servo (FTS) unit, which was employed to drive the diamond tool. The workpiece was pasted on a cantilever whose deflection was detected by another capacitive sensor. The indentation depth of the diamond tool was obtained from the outputs of the two capacitive sensors. However, it was a time-consuming process to replicate the diamond tool cutting edge using this nanoindentation instrument. In addition, a contact damage would be generated on the workpiece surface when the tool-workpiece contact was established before the replication. The contact damage would influence the indentation depth and the profile of the replicated cutting edge. In this paper, we present high-precision cutting edge radius measurements of single point diamond tools using an AFM and a reverse cutting edge artifact based on the proposed edge reversal method and the designed nanoindentation system. After introducing the measurement principles, the operation of the nanoindentation system is optimized for achieving high-precision control of the indentation depth. The effects of the elastic recovery and the AFM cantilever tip on the measurement accuracy and the measurement uncertainty are investigated. A series of experiments are preformed to verify the reliability and the capability of the proposed method and the designed system.
## 6 Engineering Application
### An Engineering Example  
As shown in Figure 9, the finite element model of a plate arch bridge is established in MIDAS 2021. Through finite element analysis, it is known that the internal force of the midspan section of the main arch ring is consistently the largest. Therefore, the midspan section of the main arch ring is selected as the control section to evaluate the reliability assessment.  
Figure 8: Absolute error vs. failure probability.  
Figure 9: The finite element model of a plate arch bridge.  
For parameters that are easy to measure, such as geometric size, elastic modulus, thickness of protective layer, etc., statistical characteristics can be obtained according to existing research and the measured data. On the other hand, those parameters that cannot be easily obtained are taken as intervals according to actual experience, such as the bulk density of materials, and the coefficient of variation is considered to be 5%. The input variables are described in Table 8.  
By fitting the simulation results with a response surface, it is obtained that  
\[\begin{array}{ll}M&=R-S\\ &=x_{5}y_{5}(x_{2}-x_{6})\\ &-(-329.89-85.4071x_{1}+1303.25x_{2}+5.08929x_{3}+1.73643\times 10^{-6}x_{4}\\ &+2.11363y_{1}+8.32423y_{2}+2.56098y_{3}+1.74568y_{4}\\ &+4.6875x_{1}^{2}+358.073x_{2}^{2}+200x_{3}^{2}+1.8574\times 10^{-14}x_{4}^{2} \\ &+0.00643491y_{1}^{2}+0.000388889y_{2}^{2}+0.00108507y_{3}^{2}+0.000104097y_{4} ^{2})\end{array} \tag{26}\]  
where \(x_{1}\) represents the width of the main arch ring, \(x_{2}\) represents the thickness of the main arch ring, \(x_{3}\) represents the pavement thickness, \(x_{4}\) represents the elastic modulus of the concrete, \(x_{5}\) represents the reinforcement area, and \(x_{6}\) represents the thickness of the protective layer. \(y_{1}\) is the unit weight of the concrete, \(y_{2}\) is the unit weight of the packing, \(y_{3}\) is the unit weight of the pavement, \(y_{4}\) is the vehicle load, and \(y_{5}\) is the tensile strength of reinforcement.  
Finally, through the proposed method, it is obtained that the estimated reliability index is 4.2 and the reliability estimation result is 7.91 \(\times\) 10\({}^{-7}\), indicating that the structure is safe. On the other hand, the reliability estimation result obtained by the MCSs is 9.00 \(\times\) 10\({}^{-7}\), and the corresponding error is 1.09 \(\times\) 10\({}^{-7}\). Such a low value of error demonstrates the sufficient accuracy and good practicality of the proposed method.
## 3 Method
### LH Algorithm
#### 3.2.1 Feasibility Analysis  
The feasibility of the proposed LH algorithm depends on obstacles. We analyze the algorithm's feasibility via two cases: obstacles on sub-tracks and obstacles on the main track. The two cases are illustrated in Figure 5a and Figure 5b, respectively.  
Case One: obstacles on sub-tracks  
In Figure 5a, the searching path starts along the main track from node \(O\) to position \(i_{d}\) and then turns from the main track to sub-track \(i_{d}\). We search for neighboring nodes in the four orthogonal directions (front, right, back, and left) along the main track and the node ahead the route in sub-tracks. The traversed nodes are highlighted in yellow. If there are no obstacles, the path goes to (\(i_{d}j_{d}\)) the blue box in Figure 5a, and returns to 'Arrival'.  
If an obstacle, a black box, is detected, the search is blocked and then retreats to the main track. The algorithm pushes the search one step back to position \(i_{d}-1\) along the main track and checks the possibility to jump across the new sub-track \(i_{d}-1\) to position \((i_{d},j_{d})\) from the opposite direction via another main track. If it is not possible to jump, the algorithm checks blockage along the new sub-track and continues the'retreat-one step back-check-jump-check' loop to another sub-track until Arrival. If no steps can be pushed back, then no feasible path can be found, and the algorithm returns 'No Solution'.  
The searching process in Case One is displayed via the red curve in Figure 5a.  
Figure 5: The topological representation for the Lattice-Hopping Algorithm. (**a**) The feasible planned path when the obstacle is on the sub-track; (**b**) the feasible planned path when the obstacle is on the main track.  
When the start node is on the right side of the destination \((i_{d},j_{d})\), the algorithm pushes the search back to \(i_{d}+1\) after retreat. In other words, our algorithm always pushes the search one step back toward the start node. This process is shown via the purple curve.  
Case Two: obstacles on the main track  
Once obstacles are on the main track, e.g., on position \(i_{d}\), the searching route turns into the sub-track at node \(i_{d}-1\) or \(i_{d}+1\) and jumps to another main track, which is parallel to the current one. This is shown in Figure 5b in red and purple curves, respectively.  
Therefore, the proposed algorithm can always find a feasible solution if it is possible.
## 4 Simulation and Discussion
### Composite Scattering of Canyon/Valley Environment with Different Incident Azimuth Angles  
This subsection investigates the monostatic composite scattering characteristics of canyon and valley environments with different incident azimuth angles. The RMS height \(h_{\text{rms}}=0.2\lambda\), the wind speed \(v_{19.5}=0.2\) m/s, and the slope angles \(\alpha_{1}=30^{\circ}\), \(\alpha_{3}=45^{\circ}\). The incident pitch angles are \(30^{\circ}\), \(45^{\circ}\), \(60^{\circ}\) respectively, the incident azimuth angles are \(0^{\circ}\sim 360^{\circ}\). The illustration of simulation geometry is shown in Figure 17.  
Figure 16: Bistatic RCS simulation results: (**a**) canyon, HH-polarization; (**b**) canyon, VV-polarization; (**c**) valley, HH-polarization; (**d**) valley, VV-polarization.  
As shown in Figure 18a, each curve has two significant peaks located at \(\varphi_{s}=90^{\circ}\) and \(\varphi_{s}=270^{\circ}\), respectively. When the incident pitch angle is \(30^{\circ}\), the highest peak value occurs at \(\varphi_{s}=90^{\circ}\), where the incident wave hits the left slopes vertically and reflects vertically. Observing the red curve, the incident pitch angle is \(45^{\circ}\), and the highest peak value appears at \(\varphi_{s}=270^{\circ}\), where the incident wave and reflection field are vertical to the right slope. As for the blue curve, when the incident pitch angle is \(60^{\circ}\), though the peak values are not the highest in the three curves, the values are still much higher than the values at other azimuth angles. This phenomenon can be attributed to the multiple bouncing when the incident planes are vertical to the slopes, which may raise the power distribution in the opposite direction of the incident. When the incident azimuth angles are any angles rather than \(90^{\circ}\) and \(270^{\circ}\), we can barely find an angle whose value dominates over those of other angles. The semi-enclosed cavity structure makes the power redistribute, and the target is harder to be detected. What's more, when we detect a target flying above the canyon or valley environment, it is better to avoid the side view directions. Similar analyses can be concluded from Figure 18b to Figure 18d.
# Optimal Routing for Time-Driven EH-WSN under Regular Energy Sources
## 7 Conclusions  
In this paper, first a generic model has been developed to relate the duty cycle and traffic load of any node in a time-driven duty-cycled EH-WSN. This model results from a relatively simple extension of a previous result on the energy consumed by TinyOS nodes executing time-driven applications. Then, the focus has been put on the routing strategy. Specifically, it has been demonstrated that the MHC criterion minimizes the average traffic load across the network and maximizes the average duty cycle of nodes. Note that this is a primary goal in energy-harvesting WSN, since larger duty cycles are expected to optimize network performance.  
The main result obtained in this paper has been validated via simulation by comparing MHC with other relevant routing protocols, such as CTP and a generic GRP (though any protocol that generated intra-layer connections would have served the same purposes). Thus, this comparison encompasses three widespread and at the same time quite different routing criteria for sensor networks: Number of hops, number of transmission tries and geographical distance. Simulations results reveal that MHC substantially outperforms the other protocols, especially beyond moderately large network sizes. This also represents a better performance in terms of scalability. Accordingly, this paper suggests assigning top priority to the MHC criterion in the development of routing protocols for time-driven duty-cycled EH-WSN.  
An important issue for further research in EH-WSN is to determine the optimal routing strategy when other conditions are taken into account: Power control enabled, transmission impairments, locally poor energy-harvesting situations and even non-regular energy sources. Also, a predictive algorithm, like any of those referred to in Section 3, could be included into node operation in combination with the routing protocol. In this way, the node would be able to obtain estimates of future energy intakes and dynamically adjust its duty cycle and routing topology if necessary.  
This research received no external funding.  
The solar irradiance data used in this paper were obtained from the NASA Langley Research Center (LaRC) POWER Project funded through the NASA Earth Science/Applied Science Program.  
The authors declare no conflict of interest.
###### Abstract  
In recent years, the importance of computer security has increased due to the rapid advancement of digital technology, widespread Internet use, and increased sophistication of cyberattacks. Machine learning has gained great interest in securing data systems because it offers the capability of automatically detecting and responding to security threats in real time, which is crucial for maintaining the security of computer systems and protecting data from malicious attacks. This study concentrates on phishing attack detection systems, a prevalent cyber-threat. These systems assess the features of the incoming requests to identify whether they are malicious or not. Although the number of features is increasing in these systems, feature selection has become an essential pre-processing phase that identifies the most important features of a set of available features to prevent overfitting problems, improve model performance, reduce computational cost, and decrease training and execution time. Leveraging genetic algorithms, known for simulating natural selection to identify optimal solutions, we propose a novel feature selection method, based on genetic algorithms and locally optimized, that is applied to a URL-based phishing detection system with machine learning models. Our research demonstrates that the proposed technique offers a promising strategy for improving the performance of machine learning models.  
feature selection; genetic algorithm; phishing detection +
Footnote †: journal: Applied sciences
## 5 Methodology
### Deep Symbolic Regression  
[PERSON] et al. [46] introduced a deep symbolic regression framework using reinforcement learning (RL) to recover symbolic functions in noisy environments. Building on this, [PERSON] et al. [47] developed the Physical Symbolic Optimization (PhySO) framework, which combines deep RL and recurrent neural networks (RNNs) to derive analytical symbolic expressions from physical data while maintaining unit consistency. PhySO can use dimensional analysis to narrow the search space, ensuring that generated expressions align with physical principles.  
Unlike conventional SR methods [27], which disregard physical units, PhySO ensures dimensional homogeneity by imposing constraints during equation generation. The PhySO process begins with data acquisition, followed by candidate symbolic expression generation using RL and RNNs. These expressions are structured as binary trees, with each node representing predefined elements, tokens, such as variables, operators, and constants. PhySO encodes these elements as categorical tokens and formulates expressions in prefix notation.  
To optimize the search, PhySO evaluates symbolic expressions through a reinforcement learning approach, assigning rewards based on equation accuracy and unit consistency. The reward function balances accuracy (measured by Normalized Root Mean Square Error (NRMSE)) and simplicity, promoting models that are both precise and simple, avoiding overfitting. PhySO's strategy uses a risk-seeking policy and entropy regularization, reinforced by the Adam optimizer. The network architecture relies on Long Short-Term Memory (LSTM) networks, which process input data sequentially and adjust probability distributions over actions. A masking mechanism filters invalid operations to ensure unit consistency.