esa-sceva/satcom-chunk-collection huggingface dataset:
# Impact of Weather Factors on Unmanned Aerial Vehicles' Wireless Communications
## 2 Methodology
### Mathematical Models
#### 2.1.6 Effect of Mobility and Multipath Propagation on BER
Doppler shift\(f_{d}\) to an applicable path is given by [28] as follows:  
\[\Delta F=f_{d}=\frac{v_{r}.f_{c}}{c}\text{cos}(\alpha) \tag{14}\]  
where \(v_{r}\) is the relative speed between the transmitter and the receiver, \(c\) is the speed of light, and \(\alpha\) is the incident angle of the incoming signal. Oscillator phase noise, associated with the transmitter and receiver, results in spectrum broadening of the clock. This broadened spectrum of the clock becomes a major cause for the loss of orthogonality in OFDM transmission. Phase noise \(\theta(t)\) is governed by the  
Figure 6: Multipath propagation channel and related effects. Wiener process. Accordingly, the statistical mean of \(\theta(t)=0\). The variance of the phase error is given by [26]:  
\[\sigma_{\theta}^{2}=4\pi\beta \tag{15}\]  
where \(\beta\) denotes the 3 dB linewidth of the Lorentzian power spectrum density of the oscillator. SNR degradation due to frequency offset is approximated by [31]:  
\[SNR_{deg_{\Delta F}}=\frac{10}{\ln 10}\frac{1}{3}\bigg{(}\pi N\frac{\Delta F}{R _{S}}\bigg{)}^{2}\overline{\gamma_{b}} \tag{16}\]  
where \(\Delta F\) is the carrier offset caused by the Doppler shift, \(N\) is the number of sub-carriers, \(R_{s}\) is the subcarrier spacing, and \(\gamma_{b}\) is the theoretical SNR for a given channel without any frequency offset. SNR degradation due to phase noise is approximated by [31]:  
\[SNR_{deg_{\beta}}=\frac{10}{\ln 10}\frac{11}{60}\bigg{(}4\pi N\frac{\beta}{R _{S}}\bigg{)}\overline{\gamma_{b}} \tag{17}\]  
where \(\beta\) is the spectral linewidth caused by phase noise. Substituting \(\Delta F\) in Equation (16) by the corresponding Doppler shift expression from Equation (14) and taking the maximum value of \(\cos(\alpha)\), \(SNR_{deg_{\Delta F}}\) could be expressed as  
\[SNR_{deg_{\Delta F}}=\frac{10}{\ln 10}\frac{1}{3}\bigg{(}\pi N\frac{\gamma_{r}f_ {c}}{R_{S}c}\bigg{)}^{2}\overline{\gamma_{b}} \tag{18}\]  
Expressing the total SNR degradations as the sum of \(SNR_{deg_{\Delta F}}\) and \(SNR_{deg_{\beta}}\), the effective SNR \(\psi\) can then be expressed as  
\[\psi=\overline{\gamma_{b}}\bigg{(}1-\frac{10}{ln10}\frac{1}{3}\bigg{(}\pi N \frac{\gamma_{r}f_{c}}{R_{S}c}\bigg{)}^{2}-\frac{10}{ln10}\frac{11}{60}\bigg{(} 4\pi N\frac{\beta}{R_{S}}\bigg{)}\bigg{)} \tag{19}\]  
After simplifying the constant terms, \(\psi\) can be expressed as  
Figure 5:0  
Replacing \(\overline{\gamma_{b}}\) in Equation (8) by \(\psi\), we obtain the degraded BER as  
Figure 5:1
# Application of Thin-Film Microextraction to Analyze Volatile Metabolites in A549 Cancer Cells
## 3 Materials and Methods
### Sampling Optimization
#### 3.4.2 Selection of Sampling Protocol  
TFME extractions were carried out according to two different protocols using the same pooled RPMI 1640 culture medium. For each sampling mode, three independent replicates were used (\(n=3\)).  
Protocol A, referred as external headspace (EX-HS). Once the 5 mL of pooled medium was placed in 20 mL gas tight vial, the initial headspace was purged with nitrogen 6.0 (additionally purified on Agilent Carrier Gas Purifiers) for a short period to remove exogenous contaminants and left for 30 min at room temperature (20 \({}^{\circ}\)C) to reach a new equilibrium in the headspace. A 1/4\" Teflon tube sharpened to a level tip was then introduced to the vial through a silicon septum in a screw-cap and connected by means of a Swagelok 1/4\" straight unit to the sorption glass tube containing TFME sheets, fixed by glass wool. Five cycles of bidirectional transfer of 2 mL of headspace gas through a glass tube with TFME sheets were done with a glass syringe (Socorex Isba SA, Ecublens, Switzerland) to equally distribute VOCs in the system. Afterwards, a shut-off valve placed behind tube with TFME sheets was closed for a 30 min long extraction (Figure 6A).  
Protocol B, referred to as internal headspace (IN-HS). TFME sheets were suspended on a steel hook under the silicon septum in a screw-cap inside the 20 mL glass vial containing 5 mL of pooled medium (Figure 6B). Extraction took place at ambient temperature (20 \({}^{\circ}\)C) over 30 min. Afterwards, TFME sheets were detached from the hook and tightly closed in a sorption glass tube.
###### Abstract  
Understanding the reaction path and mechanism of chlorinated volatile organic compound (CVOC) destruction is important for designing efficient catalysts, especially for the application of noble metal-based materials. Herein, several typical noble metals, Ru, Rh, Pt, Pd, Au, and Ir, supported on \(\gamma\)-Al\({}_{2}\)O\({}_{3}\) catalysts were synthesized by the hydrazine hydrate reduction method for 1,2-dichloroethane (1,2-DCE) elimination. Various character measurements were conducted, and the results suggest that the high-valence state of noble metals is beneficial for the 1,2-DCE reaction as it enables the enhancement of the mobility of the surficial active oxygen species of catalysts. Among the noble metals, Ru/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\) expresses superior catalytic reactivity, with a 90% pollutant conversion rate at 337 \({}^{\circ}\)C, and competitive CO\({}_{2}\) selectivity, 99.15% at the temperature of total oxidation. The distribution of by-products and the degradation routes were analyzed online by GC-ECD and _in situ_ diffuse reflectance infrared spectroscopy, which may provide helpful insight for the future application of noble metal-based catalysts for CVOC elimination in industrial fields.  
1,2-DCE; noble metal; catalytic oxidation; \(\gamma\)-Al\({}_{2}\)O\({}_{3}\); by-products _Applied Sciences_  
A Systematical Comparison of Catalytic Behavior of NM/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\) (NM = Ru, Rh, Pt, Pd, Au, Ir) on 1,2-Dichloroethane Oxidation: Distributions of By-Products and Reaction Mechanism. _Appl. Sci._**2023**, 13, 36.
## 3 Transformative Impact and Ethical Implications of AI in Healthcare
### Patient Rights and Informed Consent in AI Healthcare  
The integration of Artificial Intelligence (AI) in healthcare has brought about a paradigm shift in the dynamics of patient consent, necessitating a reevaluation of traditional informed consent models to accommodate the unique challenges posed by AI technologies. The conventional approach, where patients are informed about the risks and benefits of medical procedures or treatments, is no longer sufficient in AI-driven healthcare. Instead, a more nuanced and ongoing consent process is required to address the complexities introduced by AI systems. AI and privacy concerns are at the forefront of this shift. The increasing control of patient health information by private corporations due to public-private partnerships in AI implementation raises significant challenges in ensuring privacy and patient agency. These corporations may have competing goals that do not prioritize data protection, thus complicating the consent process. Additionally, the opaque nature of AI systems, often described as 'black boxes', makes it difficult for medical professionals to supervise or understand the decision-making processes of algorithms, further necessitating more robust safeguards for patient data [1,2].
# A Road Defect Detection System Using Smartphones
## 3 A Proposed Road Defect Detection System
### An Automatic Data Collection Mechanism for Road Defect Classification
#### 3.1.1 Raw Data Collection  
To facilitate the classification of road defects, our methodology employs the accelerometer of smartphones and dashcam video footage as sources of raw data. This approach is notably practical, allowing for widespread application without the necessity for specialized equipment. We have developed an Android application specifically for the purpose of raw data acquisition from smartphones. This application captures data from the three-axis acceleration sensor, along with time stamps, at a sampling rate of 100 Hz. Given the variability in smartphone placement and orientation within the vehicle, a challenge arises in maintaining data consistency.  
To address this issue, we compute the root mean square (RMS) value of the acceleration sensor's x, y, and z axes to standardize the data collection process, as delineated in Equation (1). This methodology ensures that acceleration values are consistent and comparable, irrespective of the smartphone's position or orientation within the vehicle, as illustrated in Figure 2. The equation for calculating the RMS value of the accelerometer data is as follows:  
\[ACC_{RMS}=\sqrt{\left(ACC_{X}\right)^{2}+\left(ACC_{Y}\right)^{2}+\left(ACC_{ Z}\right)^{2}} \tag{1}\]  
Speed bumps, which rise above the road surface, create a distinct signature in the accelerometer data, characterized by an initial increase and subsequent decrease in the accelerometer's RMS values. This dual-pattern occurrence is due to both the front and rear wheels of the vehicle passing over the bump, as depicted in Figure 2a. Conversely, manholes and potholes, which are depressions in the road surface, generate an inverse pattern in the accelerometer data. This pattern, showcased in Figure 2b,c, is marked by a decrease followed by an increase in the accelerometer readings, corresponding to the  
Figure 1: The architecture of automatic data collection mechanism for road defect classification.  
vehicle's wheels dipping into and then emerging from these indentations. Similar to speed bumps, this pattern repeats twice for each vehicle wheel engaging with the defect.  
Despite the clear patterns each type of road feature produces, distinguishing between manholes and potholes using accelerometer data alone is challenging due to their similar patterns. Nevertheless, these differences, albeit subtle, can be effectively discerned using deep learning models that have been trained on comprehensive datasets. Such models analyze the nuanced variations in acceleration and deceleration patterns caused by the vehicle's interaction with these road anomalies, enabling the classification and differentiation of each road feature type based on their unique impact on the vehicle's accelerometer data.
## 6 Performance Evaluation
### Simulation-Based Performance Analysis
#### 6.4.1 Simulation Environment and Setup  
The simulation environment was configured to mimic a typical IoD deployment scenario. We used Contiki OS (version 2.7) as the operating system for the simulated nodes and the Cooja simulator (version 3.0) for network emulation. The network topology consisted of 80 client nodes (representing operator devices and drones acting as relays) and 1 sink node (representing the ground control station). This multipoint-to-point traffic scenario is common in IoD applications where multiple drones transmit data to a central control station [17]. The wireless medium was simulated using Cooja's Unit Disk Graph Model (UDGM), allowing for configurable transmission and interference ranges, as well as packet reception ratios. We set the transmission range to 50 m and the interference range to 55 m. To evaluate performance under varying network conditions, the packet reception ratio (RX ratio) was set to 70%, representing a moderately lossy wireless channel common in IoD environments [17]. The simulation ran for a total duration of 1 h (3600 s). Each client node was configured to generate application messages at a random interval between 2 to 6 s (average 4 s), simulating the periodic transmission of authentication requests and other data.
# Bearing Capacity of a Concrete Grouting Pad on the Working Surface of a Highway Tunnel Shaft
## 4 Parameter Sensitivity Analysis
### Effect of Section \(M_{p}\) Parameters on Bearing Capacity  
The impact of the cross-section \(M_{\rm P}\) parameter on \(q_{\rm u}\) is examined under various \(b\) value conditions. The influential parameters of cross-section \(M_{\rm P}\) are grouting pad diameter \(2r_{2}\), thickness \(B_{0}\), and concrete material tensile strength\(f_{\rm t}\), respectively, discussing the relationship between each parameter and \(q_{\rm u}\), as shown in Figures 11-13.  
Figure 10: The \(r_{1}\)–\(r_{2}/c\) relationship when \(b\) = 1.  
Figure 9: Effect of the value of \(r_{2}/r_{1}\) on \(q_{\rm u}\) when \(b\) = 1.  
Figure 11 shows that the bearing capacity \(q_{\rm u}\) of the grouting pad decreases as the diameter \(2r_{2}\) increases when the thickness \(B_{0}\) of the grouting pad is constant, which is consistent with the theory of thin plate failure. Moreover, increasing the value of \(b\) for any diameter condition results in a greater bearing capacity \(q_{\rm u}\) of the grouting pad. For a  
Figure 11: The \(q_{\rm u}\)–\(2r_{2}\) relationship curve.  
Figure 12: The \(q_{\rm u}\)–\(B_{0}\) relationship curve.  
Figure 13: The \(q_{\rm u}\)–\(f_{\rm t}\) relationship curve.  
vertical or 7 m diameter shaft, when \(b\) = 1, \(q_{\rm u}\) is approximately 2.4-3.3 times greater than when \(b\) = 0.5 or \(b\) = 0.75.  
Figure 12 shows that the bearing capacity \(q_{\rm u}\) of the grouting pad \(2r_{2}\) increases with the thickness \(B_{0}\). This indicates that a thicker grouting pad can bear a greater capacity \(q_{\rm u}\) under the same load conditions. Furthermore, the bearing capacity \(q_{\rm u}\) of the grouting pad increases with the value of \(b\) for any given thickness condition. For a 2.0 m thick grouting pad, when \(b\) = 1, \(q_{\rm u}\) increases by 59.8% compared to \(b\) = 0.5 and 31.5% compared to \(b\) = 0.75.  
Figure 13 shows that the bearing capacity \(q_{\rm u}\) increases with the design value of the tensile strength \(f_{\rm t}\) of the grouting pad concrete material when the diameter of the grouting pad \(2r_{2}\) and the thickness \(B_{0}\) remain constant. This indicates that under the same load conditions, the bearing capacity \(q_{\rm u}\) is greater with higher tensile strength of the grouting pad concrete material. For example, when \(b\) = 1, \(q_{\rm u}\) increases by 59.8% compared to \(b\) = 0.5 and by 31.5% compared to \(b\) = 0.75. To achieve better bearing performance when designing the grouting pad poured with C20 concrete, following the principles of \"large thickness, high-strength materials, and unified strength yield criteria\" is recommended.
# A Flexible FPGA-Based Stochastic Decoder for 5G LDPC Codes
## 4 Proposed Architecture
### Routing Network
#### 4.3.4 Pipeline Registers  
Stochastic layered decoding involves binary operations, causing each layer's message updates to either alter or leave prior layer updates unchanged. The decoding schedule needs to store updates from each previous layer before moving on to the next. Adding a single pipeline stage to the suggested architecture can sometimes improve BER performance without negatively impacting the decoding process. In some BGMs, a column-wise permutation occurs, allowing for column-orthogonality in block columns. Adding a pipeline stage during the data path does not negatively impact decoding performance when processed in this order [27]. If a permutation is not found, introducing a few \(\tau\) vacant stall layers among non-orthogonal columns can be developed. Finding the necessary minimum values of \(\tau\) for each BGM and adding this supplementary pipeline stage is advantageous. Decreasing the number of block rows while keeping the number of block columns constant can enhance the coding rate. Simulations show that high-rate codes require more \(\tau\) vacant stall layers among non-orthogonal columns compared to low-rate codes.
# Image Segmentation Approaches for Weld Pool Monitoring during Robotic Arc Welding
## 3 State of the Art Methods for Laser Line Segmentation
### Difference Operation Based Method  
In Reference [21], the difference method was proposed to reduce the unevenly distributed background. The differenced image is obtained by the following operation.  
\[f_{d}(x,y)=f(x+\Delta d,y)-f(x,y), \tag{2}\]  
where \(\Delta d\) denotes the step size of the operation and it is determined by off line analysis of the average width of the laser lines. The step size should be greater than or equal to the width of the laser line in the captured images and it is selected as 10 in this research work. The laser lines are then segmented from the differenced image by the threshold selection method.
# Feature Learning via Correlation Analysis for Effective Duplicate Detection
## 5 Experimental Results  
In this study, bug reports were preprocessed by classifying them into duplicate and nonduplicate categories based on their status and features (e.g., components and products) retrieved from the bug repository. The preprocessing process involved applying the top-K feature selection algorithm to extract distinctive feature words for each category. These extracted features were subsequently used to train a BERT-based model for predicting duplicate bug reports.  
The BERT model was trained with specific hyperparameters that were selected based on a systematic evaluation process to optimize its performance for the task of duplicate bug detection. The key hyperparameters used were max sequence length (max_seq_length), learning rate (learning_rate), and batch size (batch_size).  
The value for max_seq_length was set to 256 to balance capturing sufficient contextual information in the bug reports while maintaining computational efficiency. This value was chosen based on an empirical analysis of the average length of bug reports in the datasets, which indicated that most bug reports could be adequately represented within this limit. A shorter sequence length resulted in the truncation of critical information, whereas a longer sequence length increased computational costs without significant performance gains.  
The learning rate was set to 1 \(\times\) 10\({}^{-8}\), which was determined through a grid search optimization process. Lower learning rates (e.g., 1 \(\times\) 10\({}^{-9}\)) resulted in slower convergence during training, while higher rates (e.g., 1 \(\times\) 10\({}^{-7}\)) caused instability and overfitting, as observed in validation metrics. The chosen learning rate provided a stable convergence pattern and effectively minimized the loss function.  
The batch size was set to 16 after evaluating its impact on model performance and training efficiency. Smaller batch sizes (e.g., 8) led to noisier gradient updates and longer training times, while larger batch sizes (e.g., 32 or 64) required higher memory resources, limiting the scalability of the model. A batch size of 16 offered a good trade-off between computational efficiency and model accuracy.  
The architecture of the BERT model and its components, summarized in Table 2, reflects the use of layers critical for feature embedding, contextual understanding, and classification. These architectural decisions, combined with the optimized hyperparameters, contributed to the robustness and scalability of the model in detecting duplicate bug reports across diverse datasets.  
\begin{table}
\begin{tabular}{l l l} \hline \hline  & **APACHE** & **JDT/PLATFORM** \\ \hline \# Of Reports & 44,049 & 42,555/79,353 \\ \hline \multirow{3}{*}{Status} & FIXED/INVALID/WONTTIX/LATER/ & \multirow{3}{*}{FIXED/INVALID/WONTTIX/DUPLICATE/ } \\  & REMIND/DUPLICATE/WORKSFORME/ & \\  & MOVED/CLOSED/INFORMATIONPROVIDED & \\ \hline \multirow{3}{*}{} & KDE & \multirow{3}{*}{CORE/FIREFOX/THUNDERBIRD} \\  & & \\ \cline{1-1} \cline{3-3} \cline{3
## 3 Automated Data-Generation and Labeling
### Radiative Transfer  
Radiative transfer is the propagation of light (both visible and outside the visible spectrum) through a scene or environment. In computer graphics, radiative transfer simulations refer the class of techniques used to solve the rendering equation [31]. While many techniques of varying fidelity have been proposed for radiative transfer, path-tracing [32] is among the most popular because path-tracing calculations can theoretically converge upon an exact solution to the rendering equation. MAVS uses path-tracing to simulate the transport of light and interaction with different surfaces in a scene.  
For outdoor scenes, MAVS treats the entire sky as a light source, using the Hosek-Wilkie sky model to account for the sky color based on the time of day, position of the sun, and aerosol content in the air [33]. This results in shadows with realistic soft-edges and appropriate color-fitting based on sky conditions. Path-tracing also gives reduced aliasing effects when compared to primary raytracing. The primary drawback of path-tracing is the computational cost. Mitigation techniques for slow image generation will be discussed in the Section 4 \"Methods for Generating Randomized UAV Images\".
# On the Relationship between Self-Admitted Technical Debt Removals and Technical Debt Measures
## 2 Related Work
### Detection of SATD  
[PERSON] and [PERSON] [10] conducted a qualitative analysis on the technical debt (TD) in the source code of open-source projects and observed that developers often \"self-admit\" the technical debt by inserting comments indicating that the code is temporary and will need to be reviewed in the future. Furthermore, after a manual analysis of the code, they identify 62 different comment schemes indicating SATD. They showed that in software projects, SATD is very common, and that it is introduced mainly by experienced developers. [PERSON] et al. [11] propose a SATD Detector, which is a tool that can automatically detect SATD comments using text mining and highlight, list, and manage detected comments in an integrated development environment (IDE). This tool consists of a Java library and an Eclipse plug-in.  
[PERSON] and [PERSON] [12] have developed an approach that allows identifying SATD instances in the code through comments posted by the developers. The proposed approach is based on model matching and classifies the SATD into five types: design, defect, documentation, requirements, and tests.  
[PERSON] et al. [13] have also surveyed developers involved in the introduction and/or removal of technical debt. They have found that SATD is predominantly removed when bugs are fixed or new features are added, and that it is removed by the same person that introduced it.  
[PERSON] et al. [8] conducted a depth quantitative and qualitative study to understand how SATD is removed in the source code. On the one hand, they assessed whether the SATD is \"accidentally\" removed, and on the other hand to what extent the removal of the SATD is documented. Therefore, they have deepened the study of the relationship between the removal of comments that document SATD and the related changes to the source code, highlighting through their results that a large percentage of removal of SATD comments occurs \"accidentally\" when the whole method is removed. Furthermore, the removal of SATD is documented in commit messages in only 8% of cases.  
In addition, [PERSON] et al. [14], analyzed whether the refactoring operations coincide with the removal of SATD to understand if it is a cause-effect relationship or a randomness. They found that there is a greater chance that refactoring will take place where there has been a removal of the SATD, but that the two operations are unrelated because sometimes, the changes affect different parts of code.  
The proposed study differently from the aforementioned research studies focuses on the measures of technical debt to understand if and how they are related to each other. In particular, this study starts from the results of [PERSON] et al. [8], because we used their dataset consisting of a set of methods labeled with SATD at the design level and the commit ID in which the SATD was removed.