esa-sceva/satcom-chunk-collection huggingface dataset:
# Learning Accurate Pseudo-Labels via Feature Similarity in the Presence of Label Noise
## 3 Materials and Methods
### Problems with Generating Pseudo-Labels  
The research indicates that even when the training set is heavily corrupted, DNNs are capable of capturing and amplifying valuable information from noisy training data. Therefore, integrating the network's predictions of training samples during the training phase for label correction can prove advantageous. The core pseudo-labeling approach entails utilizing a weighted average of the original label and the predicted label as the training objective, employing an exponential moving average mechanism denoted by \(z_{i}=\alpha\widetilde{y}_{ij}+(1-\alpha)p_{ij}\). This exponential moving average mechanism helps alleviate the instability issue associated with model predictions and enables our algorithm to fully adapt the training labels when necessary, where \(z_{i}\in[0,1]^{c}\), and \(z_{i}^{T}=1\). The corrected label, \(z_{i}\), is employed alongside the predicted label, \(p_{i}\), for computing the cross-entropy loss, and it can be expressed as follows:  
\[L_{CE}=-\frac{1}{n}\sum_{i=1}^{n}\sum_{j=1}^{\mathcal{C}}z_{ij}\log p_{ij} \tag{2}\]  
However, during the initial phases of training, the model's predictive accuracy might not reach a satisfactory level, leading to instability in the corrected labels. Additionally, in cases where the original label, \(\widetilde{y}_{ij}\), contains noise, the algorithm assigns a weight of \(1-\alpha\) to the actual predicted class. However, our ideal situation involves assigning full weight to the correct class label. Consequently, this approach could potentially impact the model's classification accuracy.
## 4 Experiments and Analysis
### Comparative Experiments of Existing Methods
Enhanced Accuracy: By aligning model selection with the maneuver's characteristics, we anticipate improvements in prediction accuracy, particularly for maneuvers where STA-NET's performance is currently lacking. 2. Reduced False Positives/Negatives: A more tailored approach allows for finer discrimination between maneuvers, potentially reducing misclassifications and enhancing the system's reliability. 3. Adaptability: This strategy introduces a layer of adaptability, enabling the system to evolve and incorporate new methods or findings from ongoing research.
# Optimization Algorithm for Surface Defect Detection of Aircraft Engine Components Based on YOLOv5
## 2 Materials and Methods
### YOLOv5 Algorithm  
YOLO is a widely used one-stage object detection algorithm that is applied to the tasks of object classification and localization in images or videos. Its main feature is fast inference, allowing for efficient object detection in real-time or high-speed scenarios.  
To continuously improve the performance of the algorithm, the authors of YOLO have released multiple versions of the model. The YOLOv5 used in this paper is an improvement based on YOLOv4. During the model training phase, YOLOv5 incorporates various new techniques and functionalities to enhance its performance. It introduces mosaic data augmentation, which combines multiple images to increase the diversity of training data. It utilizes adaptive image scaling and anchor box calculations to adapt to objects of different scales. The Spatial Pyramid Pooling [21] (SPP) layer is introduced to enhance the detection receptive field. YOLOv5 also incorporates the Focus and Cross-Stage Partial (CSP) structures to improve and optimize the base network. Furthermore, the FPN [22] (Feature Pyramid Network) and PAN [23] (Path Aggregation Network) structures are added to further enhance the model's performance. Improved methods such as DIoU-NMS and GIOU Loss are used for prediction filtering and training, resulting in significant improvements in both accuracy and speed for YOLOv5.  
Currently, YOLOv5 has been updated to version 7.0 and provides five different models: n/s/m/l/x, catering to different usage scenarios and needs. These models gradually increase in size and computational complexity, corresponding to improved accuracy. Considering the target application scenario in this paper, we have selected the smaller and faster's' version model, which ensures detection accuracy while maximizing processing speed to handle various complex scenes.
# Fault Diagnosis in Solar Array I-V Curves Using Characteristic Simulation and Multi-Input Models
## 2 Current-Voltage Curves  
Among the mathematical models for solar modules, the single-diode model is the most widely used solar cell model [12]. From the equivalent circuit, the current-voltage characteristic equation of the solar cell can be obtained. The Bishop Equation (1) [13] with reverse bias characteristics is as follows:  
\[I=I_{ph}-I_{D}\bigg{\{}\exp\bigg{[}\frac{q(V+IR_{s})}{n_{1}kT}\bigg{]}-1\bigg{\}} -\frac{V+IR_{s}}{R_{sh}}\bigg{[}1+a\bigg{(}1-\frac{V+IR_{s}}{U_{b}}\bigg{)}^{- n}\bigg{]} \tag{1}\]  
where \(I\) is the output current of the solar cell, \(V\) is the terminal voltage of the solar cell, \(q\) is the charge of an electron, \(n_{1}\) is a curve-fitting constant, \(k\) is the Boltzmann constant, \(T\) is the cell temperature, \(R_{s}\) is the series resistance of the cell, \(R_{sh}\) is the parallel resistance of the cell, \(a\) and \(n\) are curve fitting coefficients, \(U_{b}\) is the reverse bias voltage of the cell, \(I_{D}\) is the diode reverse saturation current, and \(I_{ph}\) is the photocurrent of the solar cell.  
The formulas for \(I_{D}\) (2) and \(I_{ph}\) (3) are as follows:  
\[I_{D}=I_{or}(\frac{T}{T_{r}})^{3}\exp\bigg{[}\frac{qE_{go}}{n_{2}k}\bigg{(} \frac{1}{T_{r}}-\frac{1}{T}\bigg{)}\bigg{]} \tag{2}\]  
\[I_{ph}=[I_{SCR}+K_{i}(T-273.15-25)]G/1000 \tag{3}\]  
where \(I_{or}\) is the saturation reverse current of the reference diode, \(T_{r}\) is the reference temperature, \(E_{go}\) is the bandgap energy, \(n_{2}\) is a curve-fitting constant, \(I_{SCR}\) is the reference photocurrent, \(K_{i}\) is the temperature coefficient, and \(G\) is the irradiance.  
Although a two-diode model is used for simulating in MATLAB Simulink, adjusting the solar cell model to a \"5-parameter mode\" corresponds to the single-diode model shown in Figure 1, where the second diode's saturation current is zero, and the parallel resistance is infinite. Only the short-circuit current \(I_{sc}\), open-circuit voltage \(V_{\alpha}\), irradiance \(I_{r0}\) used for measurement, quality factor \(N\), and series resistance \(R_{s}\) need to be adjusted to simulate different I-V curve scenarios. Since the parallel connection of the modules in this model under the \"5-parameter model\" cannot be adjusted, subsequent curve simulations will be conducted by modeling the series-parallel connection between modules and strings.  
Through MATLAB Simulink, seven states of the string current-voltage curves of the solar system (normal, aging fault, shading fault, PID fault, short-circuit fault, hot spot fault, and crack fault) are simulated to serve as training data for the solar system fault classification model, with 5000 data points for each state of the solar system string current-voltage curve, totaling 35,000 training data points.
# Data-Driven Analysis of Student Engagement in Time-Limited Computer Laboratories
## 6 Engagement Analysis
### Students' Profiling
* Students with profile _P7_ show high commitment (_High Persistence_ and _High Concentration_), good self-confidence (_High Confidence_ and _High Autonomy_), but worse results (_Low Understanding_). Figure 3: Distribution of the students according to engagement dimensions and corresponding levels. H = High, L = Low, A = Autonomy, U = Understanding, R = Reflection, CF = Confidence, C = Concentration, P=Persistence. * Students with profiles _P1_ and _P4_ need some help (_Medium/Low Confidence_ and _Low Autonomy_) but still demonstrate the capability to focus on the task (_High Persistence_ and _High Reflection_) and to obtain good results (_Medium Understanding_). * Students with profiles _P3_ and _P6_ show some indecision, going back and forth among exercises (_Medium Persistence_), or simply they want to get an overall idea of what they are requested to do in the whole lab session. This behavior does not compromise their performance: they focus on the task (_High Persistence_ and _High Reflection_) and obtain good results (_Medium Understanding_), with more (profile _P3_) or less (profile _P6_) self-confidence and autonomy. * Students with profile _P5_ show serious difficulties in performing the requested tasks (_Low Understanding_) despite their commitment (_High Concentration_ and _High Reflection_) and the help they request and obtain (_Medium Confidence_ and _Low Autonomy_).
# Double Trapezoidal Wave Transmitting System with Controllable Turn-Off Edge
## 2 Theoretical Model of Turn-Off Edge Based on Passive Clamping  
The turn-off time of emission current depends on the voltage at both ends during the turn-off process. The turn-off time decreases rapidly as voltage increases [14]. Turn-off time is commonly increased by high clamping voltage. A TVS diode is a typical passive clamping device. When the voltage at both ends of the TVS diode is higher than the clamp voltage, the diode breaks down instantaneously and keeps the voltage at the clamp voltage. The clamping process based on a TVS diode is shown  
Figure 1: The circuit of the filament model.  
in Figure 2. The transmitting coil is equivalent to a circuit composed of inductance (Lc), resistance (Rc), and stray capacitance (Cs) [15]. \(i(t)\) is the current flowing through the transmitting coil and \(U_{c}\) is the voltage at both ends of the coil.  
The battery supply voltage of the transmitting bridge circuit is \(U\). Set the turn-off time of the transmitting bridge circuit as zero time, and the current through the coil when \(t<0\).  
\[i(t)=I_{0}=\frac{U}{R_{c}}(t<0) \tag{1}\]  
Stray capacitance inevitably exists in the transmitting coil. When the emission current is turned off, the current flowing through the coil drops sharply, and the circuit composed of resistance (\(Rc\)), inductance (\(Lc\)) and stray capacitance (\(Cs\)) vibrates violently, resulting in high voltage overshoot:  
\[\left\{\begin{array}{c}R_{c}i(t)+L_{c}\frac{di(t)}{dt}=-U_{c}(t)\\ i(t)=C_{s}\frac{dU_{c}(t)}{dt}\end{array}\right.\quad 0\leq t<T_{1} \tag{2}\]  
where \(U_{c}\) is the voltage across capacitor \(Cs\).  
At \(T_{1}\), the overshoot voltage is greater than the breakdown voltage of the TVS diode, and the voltage at both ends of the diode remains at the clamp voltage \(V_{c}\). The current turns off approximately linearly.  
\[R_{c}i(t)+L_{c}\frac{di(t)}{dt}=-U_{c}(t)=-V_{c}\quad T_{1}\leq t<T_{2} \tag{3}\]  
When the current flowing through the transmitting coil drops to zero, the TVS diode returns to the open-circuit state. The transmitting coil forms a closed loop until the current disappears.  
The overshoot voltage rises rapidly, and \(T_{1}\) is too short to be ignored. The current \(i(t)\) flowing through the coil at any time in the process of passive clamping:  
\[i(t)=\frac{U}{R_{c}}e^{-\frac{R_{c}}{t_{c}}(t-t_{1})}+\frac{V_{C}}{R_{c}} \Big{(}e^{-\frac{R_{c}}{t_{c}}(t-t_{1})}-1\Big{)} \tag{4}\]  
Turn-off time of emission current is:  
\[t_{d}=-\frac{L_{c}}{R_{c}}Int\frac{V_{C}}{V_{C}+U} \tag{5}\]  
Figure 2: The clamping process based on a TVS diode.  
The above shows that changing the clamp voltage during the current drop can change the current turn-off time.
## 4 Result and Discussion  
In order to analyze the impact of transmitter positions on the DL illuminance and \(SNR\) uniformities, ray-tracing simulations were carried out for twelve iterative arrangements (see Figure 3) in MATLAB(r) environment using [PERSON]'s benchmark ray tracing algorithm [35] and performance metrics related to illumination and communication were computed. Subsequently, the optimum case that was acquired with high uniformity of illuminance, low deviation in \(SNR\), and lower average \(D_{rms}\) was compared with coordinates obtained from Algorithm 1. These optimized locations were then compared with the conventional CLQ scheme. Even though the results for CLQ presented in [13] ignored the multipath dispersion effects which are critical from the communication perspective, to maintain a fair comparison in this study, we used the same simulation parameters (see Table 1) for both of the scenarios under consideration, except the LED locations. The outcomes of these simulations are discussed below, which have been sub-divided into two main sections to be analyzed from illumination and communication perspective. The illumination part is dedicated to DL only, whereas the communication part covers in-depth analysis of various parameters that affect the DL and UL communication performances in an OBAN system.
# A Noise-Tolerant Carrier Phase Recovery Method for Inter-Satellite Coherent Optical Communications
## 2 System Model
The two analog electrical signals described in Equations (3) and (4) are subsequently digitized by ADCs. The resulting digital signals undergo processing by FPGA for matched filtering, clock synchronization, channel equalization, carrier recovery, and data demodulation. The equivalent complex signal prior to carrier recovery can be expressed as follows:  
\[r_{B}[\![k]\!]=\sqrt{E_{s}}a_{k}e^{i\theta[\![k]\!]}+n[\![k]\!], \tag{5}\]  
where \([\![\cdot]\! ]\) indicates discrete processing and \(\theta\) is the carrier phase, which can be described as  
\[\theta[\![k]\!]=2\pi\Delta fkT+\theta_{c}(kT)-\theta_{\text{LO}}(kT), \tag{6}\]  
where \(2\pi\Delta fkT\) represents the phase introduced by the residual FO, and \(\theta_{c}(kT)-\theta_{\text{LO}}(kT)\) signifies the PN associated with \(\delta_{v}\). The objective of the carrier recovery method is to estimate and compensate for \(\theta\). The \(\theta\) in Equation (6) that required estimation is influenced by the FO \(\Delta f\) and the variance \(\sigma_{g}^{2}\) of \(\theta_{c}(t)\). The ideal constellation, characterized by \(\Delta f=0\) and \(\sigma_{g}=0\), is illustrated in Figure 4a. Figure 4b depicts the constellation with a normalized FO of \(\Delta fT=4\times 10^{-4}\), where the data exhibit rotation and appear ring-shaped. In Figure 4c, the constellation with PN \(\sigma_{g}=0.005\) is shown, where the data experience slight rotation and predominantly appear strip-shaped. It is noted that the constellation cannot be demodulated effectively in the presence of FO, and the demodulation performance is adversely affected by PN. Figure 3: The receiver topology of inter-satellite coherent optical communication system.
## 4 Case Study
### Deployment of the CVROSS
#### 4.2.4 Calculation of Available Parking Spaces  
The calculation of available parking spaces was then tested by applying the CVROSS, after obtaining the data on parking gaps. Based on the proposed algorithm, the length of each parking gap (in pixels) was divided by the length of each type of vehicle and the constant reserved for parking that particular vehicle. The number of vehicles available for parking in the individual traffic lane was computed by the algorithm. If the number of private cars (which are the shortest vehicles in the scenario) was equal to zero, this meant that a particular parking gap was was wasted. Furthermore, the CVROSS was able to add up the lengths of all vehicles and available parking spaces, and show these as occupancy and vacancy respectively, as well as showing the wasted spaces in the individual traffic lane, to provide users with information on the general traffic conditions. Therefore, by dividing the length of parking gaps by the length of each type of vehicle, the CVROSS--using the function for calculation of available parking spaces--proved its ability to compute, and provide users with, information about the number of spaces available for parking in each area. Thus, it is useful and  
Figure 14: Fuzzy logic in MATLAB runtime compiler.  
Figure 15: Parking gaps in the three individual traffic lanes.  
helpful to users in making immediate decisions and for self-regulation. For example, if there are no longer any available parking spaces, drivers can decide to park their vehicles on other roads, to prevent traffic congestion occurring. Furthermore, the CVROSS is also applicable to object recognition and matching. Figure 16 shows the aggregation of available parking spaces in specific lanes. The data on parking gaps collected from several cameras are summarized to form the set of aggregated results of parking gaps and used to assign various types of vehicles to the empty gaps. Thus, the CVROSS is still able to compute the parking gaps and available parking spaces to show occupancy and vacancy, as well as the wasted spaces, for the situation where there is occupation by an object in the traffic lane.
# Code Obfuscation: A Comprehensive Approach to Detection, Classification, and Ethical Challenges
## 8 Identifying Obfuscation Techniques: The Starting Point
### LLMs and Obfuscation Detection  
When evaluating the capability of large language models (LLMs) to detect obfuscation in code, we tested three models: ChatGPT; Perplexity AI; and Copilot. The results varied in their ability to identify and describe the obfuscation present in a Python script, showcasing the different approaches and limitations of each model.  
For the obfuscated code that was analyzed by the models, see the following repository URL (accessed on 18 December 2024): [[https://github.com/[PERSON]/ML-Based-Detection-of-Code-Obfuscation/blob/main/Datasets/Obf_data_PyArmor/file_2.txt](https://github.com/[PERSON]/ML-Based-Detection-of-Code-Obfuscation/blob/main/Datasets/Obf_data_PyArmor/file_2.txt)]([https://github.com/[PERSON]/ML-Based-Detection-of-Code-Obfuscation/blob/main/Datasets/Obf_data_PyArmor/file_2.txt](https://github.com/[PERSON]/ML-Based-Detection-of-Code-Obfuscation/blob/main/Datasets/Obf_data_PyArmor/file_2.txt)).  
**ChatGPT**: ChatGPT successfully identified the provided code as obfuscated. It pointed out specific characteristics, such as the binary data format and the presence of non-readable programming syntax, as key indicators of obfuscation. This model recognized the use of byte encoding and unusual characters, which made the code difficult to interpret directly, demonstrating its ability to detect obfuscation effectively.  
**Perplexity AI:** Perplexity AI also detected obfuscation in the code. It highlighted the unusual encoding, lack of readable structure, and presence of binary data as signs of obfuscation. This model noted the use of mixed character types and long, unbroken strings, further supporting its conclusion that the code was intentionally obfuscated.  
**Copilot:** Copilot, in contrast to the other models, did not provide an analysis or recognition of the obfuscated code. It simply stated that it could not assist with the request, indicating a limitation in its ability to detect or interpret obfuscated code.
## 1 Introduction
Due to the growing interest in the use of circular polarization, we investigate, in this paper, the feasibility of the FCP and DCP modes of CP-SAR and its performance on rice phenology monitoring. For this purpose, a ground-based CP-radar system was adopted, and the time-series C-band backscattering data of a Japanese rice paddy (_Oryza sativa_ L.) were analyzed. The rice samples were repeatedly observed within an anechoic chamber from germination to ripening stages. To analyze the radar backscatter as a function of growth stages, different polarimetric signatures and target decomposition techniques are exploited for both FCP and DCP data. In addition, \(H/\bar{\alpha}\) decomposition for the DLP mode is also examined for comparison purposes. The paper is organized as follows: the polarimetric decomposition and analysis methodologies for the FCP, DCP, and DLP modes are demonstrated in the next section. Section 3 provides an explanation of the employed ground-based CP-radar system and its polarimetric calibration, the phenological description of the measured rice samples, and the methodology used in data analysis. The experimental results and discussion are given in Section 4. The last section concludes the paper.
## 3 The 16 \(\times\) 24 Antenna Array
In addition, the simulated and measured normalized radiation patterns at the Tx and Rx bands of the antenna array are shown in Figure 13 and  
Figure 11: The measurement setup of the 16 \(\times\) 24 antenna array in the near-field anechoic chamber. Figure 10: Photographs of the fabricated 16 \(\times\) 24 antenna array: (**a**) Top view. (**b**) Bottom view. (**c**) Bottom view of Substrate 1. (**d**) Top view of metal reflection cavity. (**e**) Top view of Substrate 2. (**f**) Bottom view of Substrate 4. Figure 14, respectively. It can be seen that the co-pol and cross-pol radiation patterns are highly satisfactory on both the \(E\)-plane and the \(H\)-plane. The measured cross-polarization level is below \(-38.3\) dB at the boresight. The half-power beamwidths (HPWs) at the Rx band of the \(H\)-plane (\(\varphi=0^{\circ}\)) and \(E\)-plane (\(\varphi=90^{\circ}\)) are basically \(5.9^{\circ}\) and \(3.2^{\circ}\), and the ones at the Tx band are \(2.8^{\circ}\) and \(5.1^{\circ}\). Due to the non-uniform subarray scheme of the proposed 1D phased array, the sidelobes are suppressed. The first sidelobe level is less than \(-18\) dB and \(-19\) dB at the Rx and Tx bands, respectively. Furthermore, the simulated and measured results of the gains are illustrated in Figure 12b. It can be seen that the measured and simulated gains at the Tx-band are  
Figure 14: Simulated and measured radiation patterns of the \(16\times 24\) antenna array at the Tx-band: (**a**) \(14\) GHz. (**b**) \(14.25\) GHz. (**c**) \(14.5\) GHz. Figure 9.0 Measured and simulated results of the \(16\times 24\) antenna array: (**a**) S-parameters. (**b**) Gains in the normal direction. Figure 9.1 Simulated and measured radiation patterns of the \(16\times 24\) antenna array at the Rx-band: (**a**) \(12.25\) GHz. (**b**) \(12.5\) GHz. (**c**) \(12.75\) GHz. highly satisfactory. However, the simulated gain is 2.4 dB higher than the measured ones at 12.5 GHz. In fact, the difference is caused by the influence of surface metal coating during the fabrication. The comparison of the simulated and measured MS-line insertion loss with and without electroless nickel immersion gold (ENIG) technology is illustrated in Figure 9.2  
As shown in Figure 15b, the attenuation factor with the ENIG technology varies from 0.7 to 1.6 dB/cm throughout the Ku-band. Therefore, the ENIG technology for the dielectric substrates increases the attenuation of the microstrip feed-line and then decreases the measured gain performances.
# Dynamic Data Filtering of Long-Range Doppler LiDAR Wind Speed Measurements
## 4 Results
### Evaluation of Filtering Based on Staring Measurements
#### 4.1.3 Error Analysis
E.g., the behaviour of the histogram based dynamic data filter standard deviation curve in Figure 14b shows a root functional increase from 0 m/s to 10 m/s and again from 10 m/s to the maximum error. This hypothesis is supported by the second peak of the same graph in Figure 14b around about 10 m/s. Similar behaviour can be seen for the remaining filters in Figures 14b and 15b.