esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Computer Vision and Deep Learning Applications in Crop Growth Management
## 2 Computer Vision  
Computer vision is a crucial branch of artificial intelligence. It involves receiving, understanding, and interpreting visual information by inputting digital images or video data into a computer, and then reasoning appropriate decisions or executing tasks based on this information. Like many technologies, computer vision is an interdisciplinary field that achieves its functions by imitating the human visual system. However, it relies on algorithms and mathematical models to process data rather than human visual mechanisms. Computer vision is widely applied in fields such as agricultural automation, autonomous driving, and medical imaging.  
The human visual mechanism works by the eyes receiving light signals, transmitting them to the brain for analysis, and then reacting based on the analysis results. In contrast, computer vision captures image data through a camera, analyzes the data via algorithms, and finally infers and outputs results. The first step in processing tasks for computer vision is to extract meaningful information from images or videos, such as RGB information, point cloud information, depth information, etc. The obtained information is then processed through cropping, scaling, feature extraction, and other operations. Finally, the processed information is used for training and inference. Therefore, acquiring high-quality original visual data is of vital importance for computer vision. Just as the human visual system relies on the eyes to obtain information, computer vision depends on various visual sensors, namely cameras, to acquire visual information [22]. Common cameras used in computer vision include monocular cameras, stereo vision cameras, structured light cameras, etc. Meanwhile, data generated by multispectral and hyperspectral cameras, spectral sensors, and other devices can also be processed and analyzed through computer vision technologies.
## 4 Security and Performance Analyses
### Security Analyses
#### 4.2.8. Resisting Denial-of-Service Attacks (Threat Model 5)  
In the proposed scheme, if \(Pat\)'s smartcard \(SC:\{NID_{P},Y_{p}\}\) is stolen or lost, a malicious attacker \(A\) who has \(SC:\{NID_{P},Y_{p}\}\) cannot successfully perform an undetectable online password-guessing attack to make new passwords, so the proposed scheme is resistant to denial-of-service attacks.
# Hybrid Deep Learning-Based Intrusion Detection System for RPL IoT Networks
## 4 Proposed Hybrid Deep Learning-Based IDS (DL-IDS)
### Data Preprocessing  
The log files created by the root, which contain data collected from all network nodes, are considered raw data and cannot be used directly as inputs to the DL-IDS model. Therefore, the first step is to preprocess those files. Hence, Python with supported Pandas [25] and Numpy [26] libraries are used to process the raw datasets. Some of the features, such as the DIO packet count, DIS packet count, rank changes count, and others, cannot be used because their values are accumulated each time. Therefore, the first stage is to calculate their values at the sending time by finding the difference between the previous value and the current value at the receiving time. The average packet delivery ratio (PDR) and delay features are also calculated at this stage, as shown in Equations (1) and (2), respectively.  
\[PDR=\frac{number\ of\ packets\ received}{number\ of\ packets\ sent} \tag{1}\]  
\[Delay=(time\ packet\ received_{i}-time\ packet\ sent_{i}) \tag{2}\]  
where \(i\) is node's packet sequence number.  
Subsequently, since the individual sample traffic might not give enough information to the DL model to process, it is good to group some samples to compute the feature averages. Therefore, the traffic instances are grouped according to the receiving time window (in seconds), and the average feature value is taken at this window time. This will also reduce the total number of instances within the dataset when fed to the DL model, which will reduce the learning time. In our implementation, we found that taking the average of the samples every 2 s gives the best result.  
After that, we apply normalization techniques to the data features to be on a similar scale to fit the DL-IDS model; Equation (3) is applied to each column feature \(X\) to produce a normalized value between 0 and 1. Now, the dataset is ready to feed the DL model.  
\[Xnorm=\frac{X-X_{min}}{X_{max}-X_{min}}, \tag{3}\]
# Efficient Implementation of Polymer Microwave Fiber Links Employing Non-Galvanic Coupling Mechanism
## 2 PMF Overview
### PMF Materials and Processes  
The polymer microwave fiber or PMF can provide a directional transmission channel for mm-wave transmission and decrease the path loss significantly. It can be regarded as a unique type of dielectric waveguide that is flexible, cheap and usually produced from polymers such as polystyrene, polyethylene, polypropylene and Teflon (PTFE) [24]. These polymer materials have, as a common attribute, a low dielectric loss tangent in the order of \(10^{-4}\) and a relatively low relative dielectric constant between 2 and 3, which makes them a perfect candidate for sub-mm-wave applications. Lower or higher relative dielectric constants lead to a larger or smaller PMF cross-section, respectively. Moreover, polymer materials exhibit very low material dispersion, which enables the use of PMFs for broadband links.  
In conclusion, due to the aforementioned design process, the PMF allows a data link with more relaxed mechanical tolerances, leading to a more robust, lower-cost product.
# Deep Learning-Based Hip X-ray Image Analysis for Predicting Osteoporosis
## 1 Introduction
We used image segmentation, image matting, data augmentation, and DXA reports to classify the BMD and T-score values of the hip X-ray images and designed the experiments using a deep learning algorithm model to predict the BMD and T-score of the hip. A deep learning algorithm model was used to design an experiment to predict whether the femoral neck, greater trochanter, Ward's triangle, and overall hip findings on hip X-ray images constitute osteoporosis and to establish a risk classification model of osteoporosis to assist orthopedic surgeons in diagnosis, hoping to reduce the surgeon's time spent in diagnosis. The research question of this study is divided into two parts. The first part is that in image segmentation, the segmentation results of four areas (femoral neck, greater trochanter, Ward's triangle, and total hip) on X-ray images may affect the subsequent image classification results and whether the segmentation model can correctly and efficiently segment the contours of the four areas for subsequent image classification experiments to be conducted. The second part is in the depth learning method: what are the prediction results of different models, and which model builds the best prediction of osteoporosis and can more accurately predict the BMD of patients? In addition to exploring the correlation between the interpretation of hip X-ray images and BMD and the experimental accuracy of the depth model, we also analyzed two more areas of the hip, namely the greater trochanter and the Ward's triangle, to provide physicians with more aspects to diagnose and analyze the patients. The study will be carried out using CNN and supervised learning methods. This study will also use CNNs and supervise learning to mark the femoral neck, greater trochanter, Ward's triangle, and whole hip as the data input of the whole experiment, and then the results of the experiment will be further explored, hoping to provide some help for the medical diagnosis of osteoporosis.
## 2 Materials and Methods
### Cell Viability Study  
For the cell viability study on the implant surface, a study of the mitochondrial energy balance of a human MG-63 osteoblastic cell line (Sigma-Aldrich, St. Louis, MI, USA) was performed. This type of cell is a good in vitro model, since it maintains its differentiated phenotype throughout progressive subcultures. Cells were cultured in T75 vials until reaching 80% confluence. Implants were placed horizontally inside each T75 culture flask, and cells were cultured on its surface at a density of \(6\times 10^{5}\) cells, submerging the implant in the culture medium. Likewise, a control culture was established in Petri dishes at the same cell density. Cell death control was also performed to establish the separation between viable and non-viable cells. Twenty-four hours after culture, each dental implant surface was washed profusely with phosphate-buffered saline (PBS) to recover all attached cells. Pellet was obtained by centrifugation and 500 uL of resuspended medium was analyzed.  
Flow cytometry was performed with the MitoProbeTM JC1 kit (Invitrogen, Carlsbad, CA, USA). The JC-1 reagent allowed to evaluate the red/green ratio of mitochondrial activity based on live cells/dead cells. The analysis was performed in the Gallios flow cytometer (Beckman Coulter, Carlsbad, CA, USA). Fluorescence measurements were made at 529 nm (green, diminished membrane potential due to cell damage) and 590 nm (red, intact membrane potential). This test was performed to determine changes in mitochondrial membrane potential during apoptosis processes as the membrane potential is a key indicator of cell health or injury. The results were expressed in mitochondrial activity ratio.
## 3 Simulation Results
### Jamming Detection for mmWave Conditions  
Millimeter waves encompass frequencies of 24 GHz and above. Millimeter-wave (mmWave) bands offer increased bandwidth and data transfer rates, although they have a limited coverage range. Consequently, mmWave signals rely significantly on line-of-sight (LOS) propagation to ensure effective coverage.  
Figure 10: Chirp Jammer Case, SJR = \(-\)10 dB, SCS = 60 kHz. (**a**) the Rx signal spectrum and (**b**) EVM vs. RB.  
Figure 9: Chirp Jammer Case, SJR = \(-\)10 dB, SCS = 15 kHz. (**a**) the Rx signal spectrum and (**b**) EVM vs. RB.  
In the previous sections, experiments are conducted on the utilization of 5G in the sub-6 GHz frequency range. In this section, on the other hand, the channel conditions are changed, taking into consideration the deployment of 5G in the mmWave frequency band, along with the corresponding channel conditions. In this context, the carrier frequency is adjusted to 28 GHz, the transmission channel type is set to CDL-D (LOS), and OFDM SCS is configured at 60 kHz. Figures 11 and 12 show that the EVM vs. RB metric can be successfully used to detect jamming attacks under mmWave data transmission conditions.
## 4 Conclusions  
In this study, the influence of the various process conditions on MoS\({}_{2}\) preparation via wet chemical synthesis was investigated. Based on numerical simulations, it appears that the S-reactor is better for the preparation of MoS\({}_{2}\) particles obtained by wet chemical synthesis. The S-reactor allows for controlling the process through the reactor flow rate. Moreover, the size of the zone, in which agitation is most intense, is much more significant in the case of the S-reactor. The difference in these values is up to 128%. Various process parameters of MoS\({}_{2}\) synthesis (initial concentration of reagents, substrate flow velocity, process temperature, and reducing agent) were investigated. The best operating conditions were: \(-0.2\) M initial concentration, \(20\) mL/min reagent flow, and \(20\)\({}^{\circ}\)C temperature. For these conditions, the L\({}_{10}\) specific size of the MoS\({}_{2}\) particles after synthesis is in the range of  
Figure 12: Raman spectra of MoS\({}_{2}\) prepared with CA and FA at 1% laser power.  
Figure 13: SEM images in (**a**) SE mode and (**b**) BF-STEM mode of MoS\({}_{2}\) particles obtained with the use of CA; (**c**) SE mode and (**d**) BF-STEM mode of MoS\({}_{2}\) particles obtained with the use of FA.  
0.190-0.226 \(\upmu\)m. Particles obtained with the use of formic acid are much smaller than in the case of citric acid. The specific sizes of the final products synthesized using CA and FA are 0.769 and 0.413 \(\upmu\)m, respectively. However, the obtained particles show lower stability, which was confirmed by Raman spectroscopy.  
Conceptualization, Z.B. and L.M.; methodology, Z.B., M.M.-P. and L.M.; validation, Z.B. and L.M.; formal analysis, Z.B.; investigation, Z.B., M.M., M.M.-P. and L.M.; writing--original draft preparation, Z.B., M.M., L.M.; writing--review and editing, L.M.; visualization, M.M., Z.B. and L.M.; supervision, L.M.; project administration, Z.B.; funding acquisition, Z.B. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Science Centre through the project [No 2021/41 /N/ST8/03764].  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to time and technical limitations.  
The authors declare no conflict of interest.
## 4 Parameter Setting and Analysis
### Supervoxel Resolution  
Supervoxel resolution refers to the spatial resolution used in the process of supervoxel segmentation to divide a point cloud into supervoxels. Generally, a higher supervoxel resolution can provide more accurate detailed information, but it also increases the computational burden and storage requirements. In the context of processing highway point cloud data acquired using a mobile laser scanning system, the common range for supervoxel resolution is typically between 0.1 and 0.5 m. In this chapter, we describe and discuss the series of experiments conducted to select the appropriate supervoxel resolution size.  
Based on the results shown in Figure 9, we observed that on flat roads, there was no significant difference in the segmentation accuracy when the supervoxel resolution size increased from 0.1 m to 0.2 m. However, when the supervoxel size exceeded 0.2 m, the segmentation accuracy significantly decreased. For instance, with a voxel size of 0.3 m, the recall rate and F1 score decreased significantly to 94.3% and 94.9%, respectively (as shown in Figure 9a). This decrease in accuracy was attributed to the inclusion of a large number of non-road point clouds because of the choice of a larger supervoxel resolution size. Additionally, for sloped road surfaces, a larger supervoxel resolution size resulted in lower accuracy.  
Furthermore, Figure 10 illustrates the computational time for different supervoxel resolution sizes in the two experimental scenarios. As shown in figure, compared to using a 0.1 m supervoxel resolution model, the processing time was significantly reduced when using a 0.2 m or 0.3 m supervoxel resolution model. For both scenes, the computational time with a 0.2 m supervoxel resolution was 3-4 times faster than that with a 0.1 m supervoxel resolution, while the accuracy did not significantly change.  
Taking both accuracy and processing efficiency into consideration, we selected 0.2 m as the supervoxel resolution size for our research.  
Figure 10: Execution time of five different supervoxel sizes: (**a**) Site 1; (**b**) Site 2.  
Figure 9: Accuracy analysis of five different supervoxel resolution sizes: (**a**) Site 1; (**b**) Site 2.
## 4 Three-Dimensional World Point Reconstruction and Structural Displacement Extraction  
A flowchart for the proposed UAV vision-based earthquake-induced structural displacement monitoring framework is shown in Figure 8. Given the sub-pixel coordinates of the reference targets in each video frame from the target detection and tracking algorithms described in Section 3, the 3-D world points are reconstructed based on the following camera projection equation [41]:  
\[s\begin{bmatrix}u\\ v\\ 1\end{bmatrix}=K[R|t]\begin{bmatrix}X\\ Y\\ Z\\ 1\end{bmatrix}=\begin{bmatrix}f_{x}&\gamma&u_{0}\\ 0&f_{y}&v_{0}\\ 0&0&1\end{bmatrix}\begin{bmatrix}r_{11}&r_{12}&r_{13}&t_{1}\\ r_{21}&r_{22}&r_{23}&t_{2}\\ r_{31}&r_{32}&r_{33}&t_{3}\end{bmatrix}\begin{bmatrix}X\\ Y\\ Z\\ 1\end{bmatrix} \tag{7}\]  
where \(s\) is an arbitrary scale factor; \([u~{}v~{}1]^{T}\) is the homogeneous representation of the image point coordinates; \([X~{}Y~{}Z~{}1]^{T}\) is the homogeneous representation of the world point coordinates; \(K\) is the camera intrinsic matrix \(\langle f_{x}\rangle\) and \(f_{y}\) are the focal lengths of the camera, \(u_{0}\) and \(v_{0}\) are the principal point coordinates in the image plane, and \(\gamma\) is the image skew parameter); and \([R\,|\,t]\) is the camera extrinsic matrix (\(R\) is a \(3\times 3\) orthogonal matrix representing the camera orientation, and \(t\) is a \(3\times 1\) vector representing the camera position in world coordinates).  
To solve for the world coordinates of the reference targets, camera calibration is performed first to obtain the camera intrinsics \(K\) (Section 4.1). The camera intrinsic parameters are also used to remove the lens distortion effects from the raw video frames. Then, camera pose recovery is applied to estimate the camera extrinsic matrix \([R\,|\,t]\) (Section 4.2). The 3-D world points are finally calculated by solving Equation (7) (Section 4.3).
## 1 Introduction
### Objectives  
The aim of this paper is to present the development of a UAV-borne LiDAR system using lightweight and low-cost sensors, and demonstrate its capability of collecting spatially dense, accurate, and repeatable measurements for forestry inventory applications. This paper outlines and assesses the accuracy of a modified workflow to produce a UAV-borne LiDAR point cloud. Within this workflow, a technique for accurately georeferencing LiDAR points is presented, which includes a novel inclusion of orientation estimates from HD-video using a modified version of the structure from motion (SfM) algorithm outlined in [PERSON] _et al._[29]. The fusion of these orientation observations with observations from the GPS receiver and the MEMS-IMU within a Sigma Point Kalman Smoother is proposed in order to overcome the presence of large orientation errors which occur in GPS/MEMS-IMU based fusion systems. We evaluate the system in terms of absolute spatial accuracy as well as the accuracy of derived for forest metrics at the individual tree level.
# Impact of Iterative Bilateral Filtering on the Noise Power Spectrum of Computed Tomography Images
###### Abstract  
A bilateral filter is a non-linear denoising algorithm that can reduce noise while preserving the edges. This study explores the characteristics of a bilateral filter in changing the noise and texture within computed tomography (CT) images in an iterative implementation. We collected images of a homogeneous Neusoft phantom scanned with tube currents of 77, 154, and 231 mAs. The images for each tube current were filtered five times with a configuration of sigma space (\(\sigma_{d}\)) = 2 pixels, sigma intensity (\(\sigma_{r}\)) = noise level, and a kernel of \(5\times 5\) pixels. To observe the noise texture in each filter iteration, the noise power spectrum (NPS) was obtained for the five slices of each dataset and averaged to generate a stable curve. The modulation-transfer function (MTF) was also measured from the original and the filtered images. Tests on an anthropomorphic phantom image were carried out to observe their impact on clinical scenarios. Noise measurements and visual observations of edge sharpness were performed on this image. Our results showed that the bilateral filter was effective in suppressing noise at high frequencies, which is confirmed by the sloping NPS curve for different tube currents. The peak frequency was shifted from about 0.2 to about 0.1 mm\({}^{-1}\) for all tube currents, and the noise magnitude was reduced by more than 50% compared to the original images. The spatial resolution does not change with the number of iterations of the filter, which is confirmed by the constant values of MTF50 and MTF10. The test results on the anthropomorphic phantom image show a similar pattern, with noise reduced by up to 60% and object edges remaining sharp.  
bilateral filter; computed tomography; noise power spectrum +
Footnote †: journal: _Algorithms_
###### Abstract  
Using a doubly-fed induction generator (DFIG), with an additional active or reactive damping controller, is a new method of suppressing the inter-area oscillation of a power system. However, using active power modulation (APM) may decrease the damping of the shaft oscillation mode of a DFIG and the system damping target cannot be achieved through reactive power modulation (RPM) in some cases. Either single APM or RPM does not consider system damping and torsional damping simultaneously. In this paper, an active-reactive coordinated dual-channel power modulation (DCPM) damping controller is proposed for DFIGs. First, considering the electromechanical parts and control structure of the wind turbine, an electromechanical transient model and an additional damping controller model of DFIGs are established. Then, the dynamic objective function for coordinating the parameters of the additional damping controller is proposed. The ratio between the active power channel and reactive power channel modulation is derived from the parameters optimized by the particle swarm optimization algorithm. Finally, the effectiveness and practicability of the designed strategy is verified by comparing it with a traditional, simple damping controller design strategy. Standard simulation system examples are used in the comparison. Results show that the DCPM is better at maximizing the damping control capability of the rotor-side controller of a DFIG and simultaneously minimizing adverse effects on torsional damping than the traditional strategy.  
low-frequency oscillation; damping control; torsional oscillation; doubly-fed induction generator; particle swarm optimization +
Footnote †: journal: Applied Sciences