esa-sceva/satcom-chunk-collection huggingface dataset:
# Cybersecurity and Medical Imaging: A Simulation-Based Approach to DICOM Communication
## 3 Methodology
### Pydicom: Modify and Write DICOM Files with Python  
In this research, _Pydicom_[46] was used as a tool to read DICOM files and send them to the Application Entity (AE). The AE supports Presentation Contexts, which consist of SOP Classes and Transfer Syntaxes [39; 42; 46]. The integration as presented in Figure 1 focuses on the integration between the AE and the PACS server.  
_Pydicom_ is a widely-used Python library that offers a range of functionalities for working with DICOM files. It is specifically designed to handle DICOM data, allowing users to read, manipulate, and write DICOM files effectively. In the context of our presented DICOM simulator, _Pydicom_ is utilized to read the DICOM files from the local folder and prepare them for transmission to the PACS server. The library provides the necessary tools and functions to parse the DICOM files, extracting relevant information and organizing it into a structured format.  
One of the key functions provided by the _Pydicom_ library is _dcnread()_. This function is used to read and parse DICOM files in Python. It accepts the path or a file-like object of the DICOM file as input and returns a Dataset object. The Dataset object represents the parsed data and contains various attributes and methods to access and manipulate the DICOM information. By utilizing the _dcnread()_ function, the DICOM simulator can read the DICOM files and extract the required information, such as patient data, imaging parameters, and study details. This information can then be utilized to simulate the transmission of DICOM data to the PACS server.
## 5 Experimental Data Analysis
### Experimental Result  
Three kinds of acoustic signals of different parts and different operating states of the column were extracted and divided into two angles. First, from the perspective of the time domain, the energy ratio of the acoustic signal was used to judge. Then, from the perspective of the frequency domain, the acoustic signal was processed according to the sixth-order LPC. The characteristic vector parameters of the distillation column's acoustic signal under different operating conditions are shown in Table 2. The waveform of the original acoustic signal and the spectrum of linear prediction are shown in Figure 4. Figure 4a shows the waveform of the original acoustic signal and the linear prediction spectrum of the upper layer's normal operation; Figure 4b shows the waveform of the original acoustic signal and the linear prediction spectrum of the flooding of the upper layer; Figure 4c shows the waveform of the original acoustic signal and the linear prediction spectrum of the flooding of the lower layer.  
Table 2 shows that the sectional energy ratio of the acoustic of 'Flooding of the upper layer' increased first and then decreased, and its attenuation degree was convex. There was no large change in the section energy ratio of 'normal operation of the upper layer', as the section energy ratio represents the attenuation speed of the acoustic signal. The attenuation speed of the acoustic signal of the 'normal operation of the upper layer' was very slow, while the section energy ratio of the 'Flooding of the lower part' accounted for 55.26% in the first section. Therefore, the two types of acoustic can be distinguished according to the segmented energy ratio.  
Figure 3: Block diagram of the acoustic signal processing system.  
The double-domain feature extraction method was analyzed taking the distillation column as an example. The nine feature vector parameters were applied to the feature extraction of the column's acoustic signal.
## 2 Materials and Methods
### Equipment  
The agricultural drone used in the experiment was a multicopter (SG-10p, Hankook-samgong, Seoul, Republic of Korea) with eight rotors (Figure 2). An agricultural drone was equipped with a flight controller (K++, JIYI, Shanghai, China). Maximum thrust of rotor is 5.1 kg. The detailed specifications of the agricultural spraying drone are listed in Table 1. The pump installed on the drone had a rated pressure of 0.8 MPa and a rated flow rate of 5.5 L/min. During the experiment, the chemical solution tank was filled with 10 L of the plant protection product solution before the flight. Only the two front nozzles were used to eliminate the overlapping effect of spraying the front and back nozzles. The nozzles were installed in the pipe located just below the rotor. The horizontal distance between the nozzles was 158 cm and the spray width of the drone was 4 m. The drone was operated by a professional using a remote control (T12 Data link Remote Controller, Skydroid, Quanzhou, China).  
Figure 1: View of experimental site. On the left, the field with rice cultivar ‘Sindongjin’, and right, Soyabean cultivar ‘Daechan’.  
Conventional flat fan nozzles (XR110015 VS, Teejet Technologies, Glendale Heights, IL, USA) and AI nozzles were used with drones. The XR110015 VS has a flat fan-shaped spray form, and the droplet size is classified as fine on a general spraying pressure. In the AI nozzle, the sprayed droplet diameter is enlarged via air induction. The AI nozzle is less affected by wind compared with other nozzles. To develop a new AI nozzle for aerial spraying, a research on nozzle design factors was conducted and a new AI nozzle was developed based on it [40]. The developed AI nozzle has a spray rate of 0.6 LPM, an air to liquid mass ratio (ALR) of 0.0005, and a spray angle of 110 degrees. The volume median diameter (VMD) was measured based on the standardized method to identify the characteristics of the droplets sprayed from the two nozzles [44]. The VMD is an important factor in determining the amount of drift because it determines how far the droplets travel horizontally in the wind. If the VMD of the spray droplets is smaller, spray droplets can move farther away depending upon the wind speed and air inversion characteristics at the time of spraying [45]. The measurement results showed that the VMDs of the flat fan and AI nozzles were 189 and 450 \(\upmu\)m, respectively. The specifications of the nozzles are listed in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Wazzle** & **Pressure (MPa)** & **Spray Angle** & **VMD (\(\upmu\)m)** \\ \hline XR110015 & 0.28 & 110 & 189 \\ AI & 0.28 & 110 & 450 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Characteristics of flat fan and AI nozzles.  
Figure 2: Agricultural spraying drone with quad rotor (SG-10P).
## 4 Experiments
### Hardware
During the mapping process, the robot systematically explored the environment. Initially, it focused on smaller loops to achieve quick loop closure, before gradually expanding its exploration to cover the entire environment. This strategy minimized the accumulation of localization errors and ensured the robot could effectively close larger loops without encountering significant drift. After completing the loop closure, the robot revisited certain areas to refine the map's details, ensuring that both the global structure and local features were captured accurately. By following this approach, the robot was able to fully explore the environment and generate high-quality maps that facilitated its navigation and task performance, including the critical sub-task of automatic recharging.LiDAR mapping, visual mapping, and LiDAR-vision fusion mapping are shown in Figure 10.  
The figure showcases the navigation performance of the robot using two different sensor configurations: LiDAR-only mapping (top row) and LiDAR-visual fusion mapping (bottom row). The six images highlight the robot's path planning and navigation from three different starting positions, with the goal of reaching the charging station. In the LiDAR-only mapping, the robot successfully detects random obstacles and adjusts its path accordingly using the improved A* algorithm combined with the dynamic window approach, ensuring smooth navigation to the target. This method is effective for obstacle avoidance and path optimization, as LiDAR provides precise environmental mapping.LiDAR mapping navigation and LiDAR-vision fusion mapping navigation results are shown in Figure 11.  
On the other hand, the visual-only mapping system struggled to complete the navigation task due to its sensitivity to lighting variations and difficulty in recognizing environmental features in low-light or cluttered areas. Pure visual data often led to incomplete maps, causing the robot to fail in planning a reliable path. The fusion of LiDAR and visual sensors significantly improved the mapping quality and navigation accuracy. The bottom row demonstrates how combining LiDAR's accurate distance measurement with the visual system's environmental details provides the robot with a more robust and comprehensive map, allowing it to navigate complex environments more effectively and reliably than using either sensor independently. Figure 10: Comparison of different mapping methods. (**a**) LiDAR mapping. (**b**) Visual mapping. (**c**) LiDAR and visual mapping.
# Time-Varying Communication Channel High Altitude Platform Station Link Budget and Channel Modeling
## 5 Conclusions  
The characteristics of the near space HAPS are analyzed under the fast time-varying channel. The HAPS link budget mathematical model and wireless communication channel model are proposed respectively. The bit error rate under different HAPS elevation angles, signal to noise ratios are considered in this paper. The simulation results show that the greater the signal-to-noise ratio, the greater the gain and transmission power of the HAPS link. The bit error rate of the link channel in the coding case is better than that of the un-coded one. The key technical issue to be studied in the next step is to describe the time-varying network in terms of duration. Multi-source multi-suspension capacity model of HAPS link and super-radius overlapping coverage scene of near space interference domain capacity model.  
**Author Contributions:** The authors performed the experiments and analyzed the results together. Introduction, methodology, cosmetic detection algorithms and proposed model were written by [PERSON] and [PERSON]; while data collection, experimental results and conclusion sections were written by [PERSON] and [PERSON].  
**Funding:** This research was funded by National Planning Office of Philosophy and Social Science of China (No. 17 XXW004), Young Fund Project of Humanities and Social Sciences Research of Ministry of Education of China (No. 16Y860010), Social Science of Humanity of Chongqing Municipal Education Commission (No. 17 SKG144), Science and Technology Research Program of Chongqing Municipal Education Commission (No. KJ1600923, No. KJ17092060, No. KJ1600928), 2018 Chongqing Science and Technology Commission Technology Innovation and Application Demonstration Project (No. cstc2018 jscx-msybX0049). Natural Science Foundation of China (No. 61503052, No. 61571069, No. 61501065, No. 61502064). Open Fund Project of Chongqing Technology and Business University, Research Center of Chongqing University Network Public Opinion and Ideological Dynamic (No. KFJ102107024), China Postdoctoral Science Foundation (No. 2017M612911), Research Foundation of the Natural Foundation of Chongqing City (No. cstc2016 jcyjA0076). The author [PERSON] also thanks for the financial support from CSC (China Scholarship Council, No. 201608505142).  
**Acknowledgments:** The authors are grateful to editors and anonymous referees for their very valuable comments and suggestions, which have significantly helped improve the quality of this paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 10: The BER (Bit Error Rate) under different PSK, Golay, Hamming.
# Constructing More Complete Control Flow Graphs Utilizing Directed Gray-Box Fuzzing
## 4 Methodology
### Illustrating with Examples  
In this section, let us discuss the motivating example in Figure 1 again. As we noted above, the edges \(d\to e\) and \(d\to f\) are indirect jumps, and \(d\to e\) determine whether the vulnerability in function _add_ can be discovered successfully. If we use LLVM's APIs to construct the CFG for this example, the constructed CFG will not contain the two indirect jump edges, which leads to the failure to discover the vulnerability.  
On the contrary, we can find the vulnerability by using our proposed approach. First, we construct the static CFG which is the same as the CFG constructed by existing tools, and still does not contain the indirect jumps. Then, with the indirect jump location (line 16) as the target, a DGF is conducted to attempt to generate test cases that exercise the indirect jump instruction. Through using different test cases to execute and monitor the target program, the indirect jump relations can be resolved. If a test case that satisfies the constraint \(a>b\) in line 10 is generated, such as \((a=10,b=5)\), the edge \(d\to f\) will be discovered. On the other hand, the edge \(d\to e\) can be discovered when a test case that violates the constraint is generated, such as \((a=5,b=10)\). Thus, we can construct a CFG that contains indirect jumps and discover the vulnerability based on the CFG.  
For the simple example, it is easy to generate a test case that satisfies the constraint to exercise the indirect jump. In such case, DGF-based and CGF-based test case generations have similar performance. However, in real-world programs, constraints may be so complex that it is difficult to generate test cases that satisfy the constraints through undirected approach. Our DGF-based test case generation can show advantages in handling complex programs, which will be presented in Section 6.
## 2 Materials and Methods
### Determination of Antioxidant Capacity
#### 2.5.1 DPPH Clearance Rate  
The DPPH clearance rate was determined, as described previously [29]. Microcapsules (200 mg) were ultrasonically (400 W) mixed with deionized water (10 mL) at room temperature for 1 min, then centrifuged at 1611 g for 10 min. For each sample, supernatant (2 mL) was mixed with DPPH (2 mL, 1 mmol/L, in anhydrous ethanol), with the same volume of water as the control, and incubated at room temperature for 30 min, protected from light. Finally, the absorbance at 517 nm was measured with a UV-VIS spectrophotometer, Genesys 10S (Thermo Scientific, Waltham, MA, USA), and ethanol as the blank. The above experiments were repeated, with VC serving as a positive control, and the DPPH free radical scavenging activity (SADPPH) was calculated as follows:  
\[\text{SA}_{\text{DPPH}}(\%)=1-(\text{A}_{\text{sample}}-\text{A}_{\text{blank }})/\text{A}_{\text{control}}\times 100\% \tag{3}\]  
where A\({}_{\text{sample}}\) is the absorbance of the sample solution, A\({}_{\text{control}}\) is the absorbance of the deionized water control, and A\({}_{\text{blank}}\) is the absorbance of the absolute ethanol blank.
# Structural-Missing Tensor Completion for Robust DOA Estimation with Sensor Failure +
## 6 Conclusions  
In this paper, a structural-missing tensor completion-based robust DOA estimation algorithm is proposed to address the array sensor failure problem. The structural-missing pattern of the incomplete tensor signal is investigated, based on which the spatio-temporal dimension augmentation method is designed to construct a six-dimensional augmented Hankel tensor with sufficiently dispersed missing elements. As such, the augmented Hankel tensor can be effectively completed by solving the Hankel tensor nuclear norm minimization problem with ADMM. Implementing inverse Hankelization on the completed Hankel tensor yields equivalent tensor signal of an unimpaired sensor array, such that the completed covariance tensor can be derived. Finally, the CPD of the covariance tensor leads to closed-form DOA estimation. According to both analytical and numerical results, the proposed algorithm achieves superior estimation performance compared to the competing methods, including enhanced estimation accuracy, angular resolution, robustness to sensor failure, source identifiability, and moderate computational complexity.  
Conceptualization, B.L. and F.C.; methodology, B.L., F.C. and H.Z.; validation, F.C., H.Z., Z.S. and C.Z.; writing: B.L., F.C. and H.Z.; supervision, Z.S. and C.Z.; project administration, Z.S. and C.Z.; funding acquisition, Z.S. and C.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Open Research Project of the State Key Laboratory of Industrial Control Technology under Grant ICT2023B25.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflict of interest.  
Figure 10: Comparison of computational time.
# Vegetation Growth Response and Trends after Water Deficit Exposure in the Loess Plateau, China
## 2 Materials and Methods
### Data Sources
#### 2.2.3 Vegetation Indices  
Two different vegetation indices, namely NDVI and EVI, were employed in this study. NDVI was selected, as it is widely used to assess vegetation status at regional and global levels. It is related to the vegetation canopy structure and coverage, as well as the photosynthesis of the vegetation canopy [43; 44]. On the other hand, EVI was calculated by incorporating the light blue band of the spectrum, which mitigated the influence of the atmospheric and canopy background and enhanced the sensitivity in areas with a dense biomass [45]. Monthly NDVI and EVI data were obtained from moderate resolution imaging spectroradiometer (MODIS) product data (MOD13C2, V6).
# Self-Organized Fuzzy Neural Network Nonlinear System Modeling Method Based on Clustering Algorithm
## 5 Conclusions  
The object of this paper is to improve the real-time ability and generalization of non-linear system modeling. SOFNN-CA is initialized by the k-means algorithm in the offline learning phase, which provides the training center parameters for the self-organization phase. In addition, we propose the MD index, neuron-based DPI, and AS index, which have low computational complexity and can achieve a fast and efficient self-organization mechanism. The parameter update adopts the improved adaptive LM algorithm, which has the advantages of fast convergence and a strong search capability, improving the learning speed. SOFNN-CA is tested by two benchmark problems and two real-world problems. The experimental results show that its prediction performance is greatly improved over the prediction performances of its competitors, where the network structure is more transparent, the training time is shorter, and the test performance is superior. SOFNN-CA can be applied to the online modeling of dataflow-based nonlinear systems, where the offline learning stage can be used to extract information from incomplete data.  
The shortcoming of this study is that the adaptive setting of the parameter thresholds cannot be implemented, so the next steps of the study are to implement SOFNN-CA for the adaptive adjustment of thresholds, apply it to the prediction of ammonia nitrogen content in sewage treatment plants, and realize practical industrial tests.  
[PERSON] suggested the idea of the work and designed the experimental method. [PERSON] wrote the paper and performed the experiments. Both authors critically revised the paper. All authors have read and agreed to the published version of the manuscript.  
This research is supported by the scientific research project of the Education Department of Liaoning Province, and the approval number is JDL2020013.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Rule Neurons** & **Training RMSE** & **Testing RMSE** & **CPU Times (s)** \\ \hline SOFNN-CA & 3 & 1.2709 & 1.0280 & 16.8202 \\ SOFNN-ALA [20] & 5 & 1.1822 & 1.2481 & 15.7461 \\ ATO-RBF [44] & 18 & 1.7643 & 1.4900 & - \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of different methods in benzene-content prediction.  
Figure 11: (a) Training neuron change plot; (b) training RMSE plot; (c) test result plot; (d) test error plot.
# Lab-to-Field Generalization Gap: Assessment of Transfer Learning for Bearing Fault Detection
## 2 Materials and Methods
### Part 1: Raw Data Preprocessing
#### 2.3.1 Data Exploration and Cleaning  
Proper data cleaning is essential to ensure that model performance variations stem from actual model behavior rather than inconsistencies in data quality. Because this study evaluates performance under varying conditions, eliminating outliers prevents biases that could distort the results and ensures a fair comparison between ML and TL models.  
To achieve this, raw time-series data, including vibration signals and the corresponding voltage and speed values, were analyzed to detect distortions. Five criteria were established to identify unreliable data segments that were excluded from further analysis. Figure 4 presents an example recording of the development dataset with no added unbalance (0D) along with examples for each criterion, which are described below:  
* Criterion 1: Beginning of recording. In [22], the authors recommended discarding the first 50,000 data points (approximately 10 s). However, data exploration revealed a longer unstable period, lasting up to 63 s in the development datasets and up to 43 s in the evaluation datasets. Because of the lack of detailed information on the recording procedure, the entire unstable segment at the beginning was considered distorted.
* Criterion 2: Voltage change. The data points recorded one second before and one second after each voltage change were affected by sudden speed fluctuations, leading to instability.
* Criterion 3: Cycle change. The data points recorded during the voltage drop between the two recording cycles were identified as being distorted.
* Criterion 4: Unexpected speed jumps. Sudden and unexplained speed jumps were observed in some of the datasets. Because these anomalies were not addressed in the data description, they were removed.
* Criterion 5: End of recording. Some recordings extended beyond the stop of rotation and speed drop, and these segments were excluded from analysis.  
Figure 3: The detailed workflow of Part 1, raw data preprocessing, and Part 2, domain data preparation and splitting.
## 6 Simulation Verification and Analysis
### The Analysis of Transient Damping Characteristics of APFBC-VSG Control  
To validate the transient oscillation suppression capabilities of the proposed control strategy, simulation experiments were performed on the APFBC-VSG control with varying compensation coefficients \(K_{FB}\), as illustrated in Figure 14. The figure demonstrates that  
Figure 12: Simulation results of fixed damping control under different \(D_{\omega}\).  
Figure 13: Simulation results of TDP-VSG control under different transient damping coefficients.  
at \(K_{\text{FB}}=0\), the APFBC-VSG control effectively resembles the TVSG control, exhibiting obvious transient oscillation and power overshoot during given power and grid frequency disturbances. With the implementation of transient feedback compensation and as \(K_{\text{FB}}\) progressively increases, both transient oscillations and power overshoots in the APFBC-VSG output are substantially mitigated. When the coefficient \(K_{\text{FB}}=15\), the system's transient oscillations are eliminated, and the output power under both given power and grid frequency disturbances exhibits minimal disturbance overshoot. The simulation results are consistent with the output characteristics and transient performance theoretical analysis of the APFBC-VSG control as shown in Equation (17), validating the effectiveness of the APFBC-VSG control strategy, particularly in enhancing system stability performance.