esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 VRDeepSafety Simulation Environment
### VRDeepSafety vs. Existing Datasets  
AVs have driven the creation of numerous datasets aimed at improving safety and efficiency. However, many of these datasets have limitations that VRDeepSafety address-es. This section compares VRDeepSafety to existing datasets, emphasizing how VRDeepSafety overcomes their challenges.  
VRDeepSafety simulates real-world driving circumstances through a realistic virtual reality (VR) environment, including unpredictable interactions and changing environments. In contrast, many existing datasets frequently rely on simplified simulations or real-world data that do not capture all the complexities of driving. VRDeepSafety's realistic simulations ensure that models trained on it are better prepared for real-world issues.  
Additionally, VRDeepSafety can generate a wide variety of driving scenarios. These include urban traffic, rural obstacles, and different weather conditions. Existing datasets tend to focus on specific situations, such as intersection accidents, limiting their useful-ness. By offering more diverse scenarios, VRDeepSafety assures models are more adaptable and robust.  
Furthermore, VRDeepSafety includes V2X communication data, providing crucial context about surrounding vehicles, traffic signals, and infrastructure. This feature is lacking in existing datasets, which mainly rely on onboard sensors like LiDAR, radar, and cameras. These richer data in VRDeepSafety improves the models' accuracy trained on VRDeepSafety. Further, VRDeepSafety is designed for efficient large-scale data generation, which is critical for AD research. Existing datasets often face scalability issues due to high data collection costs or simulation constraints.  
The annotation quality of VRDeepSafety is exceptional. Each scenario is carefully labeled with details about object identities, trajectories, and V2X signals. This ensures the development of precise algorithms for tasks like object detection and accident prediction. Existing datasets, however, often suffer from inconsistent or incomplete annotations, limiting their effectiveness. VRDeepSafety's high-quality annotations lead to better model training.  
Table 5 provides a comparative overview of these features across different datasets, highlighting the advantages of VRDeepSafety over nuScenes and V2X-Sim. In summary, VRDeepSafety addresses limitations in current datasets by providing more realism, diverse scenarios, richer data, high annotation quality, and large-scale data generation. These advantages establish a new benchmark for future AD research.
## 5 Proposed Model Quality Control in the GALDAM
### Model Quality Checking for Continuously Improving Inspection Capability  
The model quality checking mechanism is designed to determine whether the defect inspection network model \(M_{k}^{DS}\) can be further improve or not. If \(M_{k}^{DS}\) performs stable in accuracy, then the \(M_{k}^{DS}\) is the optimal model, used for defective bean inspection in the robotic arm system. The criterion for implementing this idea is modeled as that difference values of the loss function of the defect inspection network with model \(M_{k}^{DS}\) by feeding \((IMG^{AIQC},L^{AIQC})\) are smaller than a threshold \(\theta^{DMQC}\) after consecutive \(\pi\)-times checks. More precisely, the criterion of evaluating whether the model training is stopped or not can be expressed as the following equation.  
\[\sum_{i=k-(\pi-1)}^{k}\llbracket\phi_{i}<\theta^{DMQC}\rrbracket-\pi \geq 0,\quad\text{Current model is qualified. Quit the training process.} \tag{8}\] \[\text{where }\phi_{k}=\frac{loss(IMG_{k}^{AIQC}|M_{k}^{DS})- loss(IMG_{k-1}^{AIQC}|M_{k}^{DS})}{loss(IMG_{k-1}^{AIQC}|M_{k}^{DS})}.\]  
Note that \([x]\) is a Boolean operator which equals 1 if condition \(x\) holds, otherwise 0, and \(loss(D|M)\), stands for the loss function of the defect-inspection network with the model \(M\) by feeding the data set \(D\).  
The detailed steps of the model quality checking procedure are described as follows.  
* Calculate \(\phi_{k}\) in Equation (8): \(\phi_{k}=\frac{loss(IMG_{k}^{AIQC}|M_{k}^{DS})-loss(IMG_{k-1}^{AIQC}|M_{k}^{ DS})}{loss(IMG_{k-1}^{AIQC}|M_{k}^{DS})}\).
* Check model quality by computing the criterion: \(\sum_{i=k-(\pi-1)}^{k}\llbracket\phi_{i}<\theta^{DMQC}\rrbracket\overset{?}{=}\pi\).
* If the condition \(\sum_{i=k-(\pi-1)}^{k}\llbracket\phi_{i}<\theta^{DMQC}\rrbracket=\pi\) holds, then the current model is the best model, which is the output of this algorithm. That is, two following steps is performed in this subcase: \(M^{DS*}=M_{k}^{DS}\); Return \(M^{DS*}\);
* Otherwise, the whole model training process shall continue with preparing new data set for the following iteration: \(k=k+1\); \(IMG_{k}^{+}=IMG^{AIQC}\); \(L_{k}^{+}=L^{AIQC}\); go back to the DSMC module; (see Figure 4).  
Notice that the parameter \(\pi\) controls the degree of model quality, and the administrator can determine \(\pi\) according to application requirements. The large \(\pi\) would find out if a model is performing more stably, but it spends more training time, and vice versa.  
Figure 10: Some instances of heavily bean-overlapping images that are discarded. The overlapping area of beans is marked in red rectangles.
## 3 Performed Full-Scale/Model Test Comparisons
### Low-Rise Buildings
#### 3.1.2 Texas Tech University's Full-Scale Measurement Program  
In the 1980s, a set of full-scale experimental facilities were created at Texas Tech University's Wind Engineering Research Field Laboratory in Lubbock, TX, USA, including a 9.1 m \(\times\) 13.7 m \(\times\) 4.0 m experimental building, a 49 m meteorological tower, and a pressure measurement system. The site location was carefully selected as the surrounding terrain was flat, with changes in elevation at less than a rate of 0.5%. The experimental building was fully instrumented with pressure taps. As the building was able to rotate on a concrete foundation, the instrumented portion of the building that was of interest could be oriented according to the ambient wind for data collection without losing any valid strong-wind scenario.  
[PERSON] [9] reported the measurements for both the full-scale building and wind tunnel model of that building. The wind tunnel measurements were shown to be acceptable for the wall pressures but quite inadequate for the roof corner pressures. [PERSON] [10] believed that the inaccuracy could be associated with the incorrect simulation of the ABL turbulence near the model. It was found that a considerable improvement of the wind tunnel modeling of roof corner pressures can be achieved by placing the small spires directly upstream of the model to correctly simulate the turbulence intensities and the spectral densities.
# Design and Validation of a Cascading Vector Tracking Loop in High Dynamic Environments
## 2 Structure and Methods
### Cascading Tracking Methods
\right]+V_{k} \tag{16}\]  
In (8), the feedback of the pre-filter only consists of the tracking results of a single channel, but in the main filter (17)-(19), the feedback is provided by the navigation results. Meanwhile, the position of the receiver is determined by the pseudorange, which causes the carrier phase information to be covered by the pseudo-distance information with large perturbations. Therefore, the difference (\(\Delta\theta\)) between the actual signal and replication carrier phases must be obtained through the pre-filter rather than the main filter. Compared with the feedback of pre-filter (8), in Equations (17)-(19), the effect of the second derivative of the Doppler frequency on the feedback can be ignored. \(\Delta\theta\) is still the difference between the true and receiver NCO's carrier phases. \(f_{v}\) represents the carrier Doppler frequency converted from the carrier and corresponding satellite velocities. \(f_{a}\) represents the carrier Doppler frequency first derivative converted from the carrier and corresponding satellite accelerations:  
\[\left(\omega_{nco}\right)_{k}=\frac{\Delta\theta_{k}}{T}+2\pi f_{v}+\pi f_{a} \mathbb{T}, \tag{17}\]  
\[f_{v}=\left[\overset{\rightarrow}{i_{i}}\left(\overset{\rightarrow}{v_{i}} -\overset{\rightarrow}{v}\right)+\Delta t_{k}\right]\frac{f_{L1}}{c}, \tag{18}\]  
\[f_{a}=\left[\overset{\rightarrow}{i_{i}}\left(\overset{\rightarrow}{a_{i}} -\overset{\rightarrow}{a}\right)\right]\frac{f_{L1}}{c} \tag{19}\]
# LSTM Attention-Driven Similarity Learning for Effective Bug Localization
## 5 Experimental Result
### Baseline  
To evaluate the performance of the proposed bug localization method, we compared it against established baseline models. The primary baseline used in this study was DNNLoc [2], a model that integrates deep neural networks (DNN) with a revised Vector Space Model (rVSM) technique. The rVSM component calculates text similarity between the bug report and source code files, while the DNN component enhances the model's ability to learn complex patterns and relationships from these data. DNNLoc is widely recognized in the field and is publicly available, enabling consistent and reproducible comparisons across various research studies.  
In addition to DNNLoc, the proposed method was also evaluated against other well-known bug localization techniques, including Naive Bayes (NB) [2], learn-to-rank (LR) [2], and BugLocator (BL) [2]. The Naive Bayes approach employs probabilistic text classification to predict buggy files, while the learn-to-rank method applies machine learning algorithms specifically designed to optimize ranking performance. BugLocator, another widely used method, leverages information retrieval techniques to calculate the relevance of source files based on bug reports.  
These baseline models were selected for comparison because they represent diverse and complementary approaches to bug localization, including statistical methods, machine learning, and information retrieval techniques. By comparing the proposed method with these baselines, we aimed to assess its relative strengths and weaknesses in terms of accuracy, ranking effectiveness, and overall performance.  
The inclusion of DNNLoc as the primary baseline is particularly significant because it combines rVSM for text similarity with deep learning for feature extraction, making it a strong and representative benchmark in the field. Furthermore, comparing the proposed method with NB, LR, and BL provides a broader context, showcasing its ability to outperform techniques rooted in different methodologies. This comprehensive evaluation ensures a robust validation of the proposed model's effectiveness in improving bug localization performance.
# Does Below-Above Canopy Air Mass Decoupling Impact Temperate Floodplain Forest CO2 Exchange?
## 3 Results
### Carbon Exchange Dynamics during the Study Years  
Substantial increase in NEE, in comparison with previous years, was observed during the year 2018. The studied forest showed the smallest NEE in 2019 and second, after 2020, highest annual \(\mathrm{R_{eco}}\). During the measurement period, the year 2020 exhibited the highest GPP. In this year also, the highest annual \(\mathrm{R_{eco}}\) was observed (Figure 3).  
Figure 3: Annual totals of gross primary production (GPP), net ecosystem exchange (NEE) and ecosystem respiration (\(\mathrm{R_{eco}}\)) for the measurement period 2015–2020 at the floodplain forest in Lanzhot, Czech Republic.  
Figure 2: Time series of daily mean air temperature (\(\mathrm{T_{air}}\)), soil temperature (\(\mathrm{T_{soil}}\)) and daily sums of precipitation (P) over the whole study period at the study site Lanzhot, Czech Republic. The lines in the uppermost two panels indicate local polynomial regression fittings (loess; \(\mathrm{span}=0.1\)).
## 1 Introduction
### Objectives of the Study  
Given the influence of mechanical stresses on the fracture healing process and outcome, discussed extensively in the _Introduction_ section, the study presented in this paper attempts to differentiate between commonly used implant devices in terms of mechanical behavior, more precisely, the implant deformation as a function of axial force applied to a bio-mechanical assembly bone--implant device.  
The main objectives can be concisely described as follows:  
* Investigate experimentally the correlation between the compressive force applied axially to a bio-mechanical system bone--implant device and the deformation of the implant.
* Develop an MLA model that is capable of predicting the implant deformation as a function of applied force and implant type.
* Establish if in case of similar mechanical stress, different types of implant devices perform significantly different (in a statistical sense) in terms of micromovement at the fracture site.  
An experimental model bone--implant device was built based on an artificial bone model in order to reproduce as closely as possible the mechanical behavior of the assembly. Three implant devices were studied by applying axial force to the bone--implant device assembly and measuring the deformation.
# The Impact of Automated Vehicles on Road and Intersection Capacity
## 2 Methods
### Traffic Flow Equilibrium State Analysis  
The equilibrium state of traffic flow refers to the ideal state, where the traffic density of a road remains constant, and all vehicles maintain the same speed. Through the analysis of the balanced state of the artificially driven vehicle, the flow-density relationship diagram can be obtained. Although the self-driving vehicle has not been driven on roads on a large scale at the present stage, we may assume that, under ideal conditions, the self-driving vehicle must eventually form a balanced state, that is, all vehicles have the same speed, zero speed difference, and zero acceleration. In the equilibrium state, since the road density is constant and the vehicle speed is the same, the time headway between vehicles must also be the same. With the help of the equilibrium \"speed-headway\" function relationship, the basic road capacity under different traffic environment can be deduced.  
For traffic flow with the same vehicle composition in a single environment, all vehicles have the same speed and time headway when they reach the equilibrium state. For traffic flow with different vehicle compositions in mixed environments, when all vehicles reach the equilibrium state, the speed is the same, but the distance between the heads is different. The difference of the following model will result in different relationships between the balance speed and the distance between the heads of different vehicles.  
Let \(h_{dI}\), \(h_{dA}\), and \(h_{dC}\) represent the equilibrium space headway of an artificial vehicle and an autonomous vehicle, respectively, where Formula (8) represents the relationship between the equilibrium speed and space headway.  
\[\left\{\begin{array}{l}h_{dI}=f_{I}(v_{b})\\ h_{dA}=f_{A}(v_{b})\\ h_{dC}=f_{C}(v_{b})\end{array},\right. \tag{8}\]  
where \(v_{b}\) represents equilibrium speed, and \(f_{I}\), \(f_{A}\), and \(f_{C}\) represent the \"speed-front distance\" function relationships of the balanced states of manual, ACC autonomous driving, and CACC autonomous driving vehicles, respectively.
## 5 Iron, ROS and Iron Chelators That Can Cause DNA Damage in Cancer
### Iron Chelators and ROS
#### 5.1.2 Di-2-Pyridylketone Isonicotinoyl Hydrazone (PKIH)  
Di-2-Pyridylketone Isonicotinoyl Hydrazone (PKIH) analogues were designed after the 2-Pyridylcarboxaldehyde Isonicotinoyl Hydrazone Series. They are tridentate chelators that can form octahedral complexes with Fe (II) and use the nitrogen in the imine group and the carbonyl oxygen as donor atoms [180]. PKIH series were much more selective iron chelators than their predecessors. Compared to other previous iron chelators PKBBH, PKBH and PKTH, it had the greatest effect inhibiting the proliferation of SK-N-MC neuroepithelioma cells with IC50 values of 1-3 \(\upmu\)m while having the weakest effect on the non-cancerous fibroblasts [181]. PKIH was shown not to increase ascorbate oxidation; it did result in DNA degradation in the presence of hydrogen peroxide by stimulating benzoate hydroxylation and increased intracellular ROS [182; 183]. Interestingly, these studies showed that its cytotoxic effect could be reversed by adding catalase, a potent antioxidant.
# Polar Codes and a M-ary Modulation-Based OFDM-PLC System
## 6 Conclusions  
This paper proposes an OFDM-PLC impulse noise control system based on polar code encoding, using a multi-damping sinusoidal function superimposed to improve the modeling of impulse noise. By combining coding and modulation techniques, a polar code module is introduced into the OFDM-PLC system and the BER performance of the polar code-based system under high-order QAM and APSK modulation is investigated. Simulation results demonstrate that the proposed polar coding scheme has lower complexity compared to the LDPC coding scheme, and can reduce the error rate during PLC transmission of impulse noise interference. This is of significant practical importance for actual PLC systems.  
Figure 9: BER Performance of PC and LDPC at Different Code Rates.  
**Author Contributions:** Software, Y.W.; Writing--original draft, Z.W.; Writing--review & editing, R.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was financially supported by the Research Innovation Program for College Graduates of Jiangsu Province (No. SJCX23_1204), the Science and Technology Special Fund (Social Development R&D Plan) of Jiangsu Province (SBE2022740885).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not Applicable, the study does not report any data.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# AEA-RDCP: An Optimized Real-Time Algorithm for Sea Fog Intensity and Visibility Estimation
## 2 Literature Review
### Image-Based Sea Fog Strength Measurement Algorithm Using DC (RDCP)  
The Image-based Sea Fog Intensity Measurement Algorithm, known as RDCP, leverages the initial processes of the DCP method to extract DC values from images. This algorithm estimates the intensity of sea fog by calculating the percentage of DC values that fall below a predefined threshold, derived from empirical data. The formula for calculating the RDCP percentage is as follows in Equation (2):  
\[\text{DCP percentage}(\%)=\frac{\text{number of pixels whose DC value is less than the treshold}}{\text{total number of pixels in an image}} \tag{2}\]  
According to RDCP research, it has been revealed that there is a significant difference in the distribution of pixel values between foggy and clear images. As shown in Figure 1, extracting the DC values from images taken on clear days and those with dense fog and plotting them on a graph shows a noticeable rise in pixel values at a specific threshold. This threshold is then used to measure fog intensity by calculating the proportion of pixels below this value. For this comparison, 10 images each from clear and foggy conditions were used, and, as illustrated in Figure 2, foggy images contain almost no pixels with DC values below 100, whereas clear images have relatively more pixels below this threshold. Based on this finding, a threshold of 100 (on a scale of 0 to 255) was established. However, this value is not fixed and may be adjusted depending on the local environmental conditions. A higher percentage of pixels with DC values below 100 indicates clearer weather, while a lower percentage signifies denser fog [11; 24].  
Figure 1: An example of a comparative image set for calculating the DCP percentage: (**a**) No-fog image; and (**b**) Dense-fog image.  
Figure 2: Image comparison from Figure 1: Threshold estimation graph for fog classification.  
In the RDCP methodology, as depicted in Figure 3, the sky region is excluded from the analysis based on the horizon line. This exclusion ensures that the percentage of DC values below the threshold is calculated only from the sea region of the image. This step is crucial in order to prevent the DCP algorithm from mistakenly identifying bright objects or light sources, which are common on clear, sunny days, as fog. By focusing on the sea region, this approach reduces the influence of atmospheric light, allowing for a more accurate estimation of fog density and visibility [25].
## 3 Patulin Control Methods
### During juice production: preparation for crushing, filtering, clarification and pasteurization
Repeated studies have shown that, while unstable at high pH [46], patulin is relatively stable to thermal degradation in the pH range of 3.5 to 5.5, with lower pH leading to greater stability [225, 226]. The half-life of patulin held at 25 \({}^{\circ}\)C at pH 6.0 and 8.0 has been shown to be 55 and 2.6 days respectively [220]. In another study, no reduction of patulin occurred during concentration and pasteurization [228], while another study did show a 50% reduction of patulin in apple juice treated for 20 min at 80 \({}^{\circ}\)C [229] a much longer treatment than used in standard pasteurization procedures. In addition, studies by [PERSON] _et al_. [230] showed pasteurization treatments between 60 and 90 \({}^{\circ}\)C for 10 seconds were only able to reduce patulin levels by 18.8%. Evidence also shows that patulin is non-volatile and upon distillation, in order to produce apple aroma, patulin remains within the juice concentrate [231]. Finally, not only is the pasteurization process unable to significantly reduce patulin levels, toften fails to fully remove heat resistant patulin-producing fungi, such as _B. nivea_ and _B. fulva_[232], allowing for potential continued production of patulin within the finished juice.
# Wi-Senser: Contactless Head Movement Detection during Sleep Utilizing WiFi Signals
## 3 System Design
### Data Processing
#### 3.3.3 Mean Filter  
Although the wavelet filter can effectively remove high-frequency environmental noise from the CSI amplitude sequence, it cannot filter out low-frequency interference noise with similar frequencies to human motion signals. Therefore, Wi-Senser further utilizes a mean filter to filter out low-frequency interference noise and extract low-frequency CSI amplitude fluctuations that record head motion changes. Specifically, the mean filter treats consecutive sample data points as a data queue with a length of \(L\). For each new measurement data point, it removes the first sample data point in the previous queue and adds a new sample data point to the end of the queue to form a new data queue with a length of \(L\). Then, it performs arithmetic operations on the new data queue and uses the operation result as the final result of the new measurement task. That is, assuming the input is \(X\) and the output is \(Y\), its calculation formula is as follows:  
\[Y_{n}=\frac{X_{n}+X_{n-1}+X_{n-2}+\cdots+X_{n-L+1}}{L} \tag{11}\]  
In the experiments, Wi-Senser empirically set \(L\) to 20 to perform filtering tasks. Figure 6 shows the CSI subcarriers further filtered by the mean filter, where it can be observed that compared with Figure 5, the CSI amplitude signals after mean filtering are smoother and more regular, the hierarchy between subcarriers is clearer, and all head motion-induced CSI amplitude fluctuations can be fully recorded. The experimental results demonstrate the effectiveness of the mean filter used by Wi-Senser to filter out low-frequency interference noise. In addition, Figure 7 shows the comparison of CSI amplitude changes before and after data processing. It can be clearly seen that compared with the original CSI amplitude signal (as shown in Figure 7a), the CSI amplitude signal obtained by Wi-Senser's filtering channel is smoother, and the CSI amplitude fluctuations caused by head motion are more obvious, while various noises are effectively filtered out (as shown in Figure 7b). The experimental results demonstrate the effectiveness of the filtering channel used by Wi-Senser to filter out various noises.  
Figure 5: CSI subcarriers filtered by the wavelet filter.  
Figure 6: CSI subcarriers filtered by the mean filter.