esa-sceva/satcom-chunk-collection huggingface dataset:
# BUU-LSPINE: A Thai Open Lumbar Spine Dataset for Spondylolisthesis Detection
## 2 Materials and Methods
### Ground Truth Data  
After we obtained permission from the Burapha University Hospital Institutional Review Board (IRB), we followed an annotation pipeline to make our dataset, as shown in Figure 2. Starting with collecting patient DICOM images from the hospital database, we filtered data using the following exclusion criteria to ensure that data consisted of all lumbar vertebrae and had good-quality pictures. Data with these criteria were excluded from our dataset.  
Exclusion criteria:  
Low quality and high noise (challenging to distinguish the vertebrae);  
Embedded medical devices (cover the lumbar vertebrae area);  
Missing some of the lumbar vertebrae;  
Highly damaged vertebrae (challenging to determine the vertebral body).  
\begin{table}
\begin{tabular}{c c c} \hline
**No** & **Field Name** & **Description** \\ \hline
1. & Image Type & X-ray (PF) \\
2. & Image View & AP and LA \\
3. & Body Part & Lumbar spine (LSPINE) \\
4. & Total number of patients & 3600 patients \\
5. & Total number of images & 7200 images \\
6. & Total number of & 621 patients \\
7. & Total number of & 788 instances (Some patients include multiple instances) \\
8. & Datasets storage size & 18.5 GB \\ \hline
9. & Ground truth & Vertebral position, \\  & & Spondylisthesis diagnosis, \\  & & LSTV \\ \hline
10. & Lumbar vertebral position & \\  & targets & The coordinate corner points of L1 to L5 \\ \hline
11. & Spondylisthesis diagnosis & Left lateralofisthesis, \\  & targets & Rapid lateralofisthesis \\  & targets & Anteriorolisthesis \\  & & Retrolisthesis \\ \hline
12. & LSTV targets & Sacralization, \\  & & Lumbarization \\ \hline
13. & File types & Images: jpg \\  & & Ground truth: csv \\ \hline
14. & Locations & Burapha University Hospital, Thailand \\
15. & Year period & 2000–2021 \\
16. & Age range & 6–97 years old \\ \hline
17. & Image dimensions & Height: 1434–3408 pixels, \\  & & Width: 860–3040 pixels, \\  & & Pixel Spacing: 0.125–0.175 mm \\ \hline
18. & X-ray imaging parameters & AP & kV: 70–75 kV \\  & & mAs: 32–40 mAs \\ \hline  & & LA & kV: 80–90 kV \\  & & mAs: 50–60 mAs \\ \hline \end{tabular}
\end{table}
Table 2: BUU-LSPINE description.  
We then submitted the filtered dataset to experts and physicians for review. Experts were computer vision experts, and physicians were radiologists and orthopedics doctors of KIOM and BUU. Next, we horizontally flipped all right-facing LA views to appear as left-facing views in the images. Subsequently, physicians annotated the ground truth of vertebral positions, LSTV, and made a diagnosis of spondylolisthesis. Ground truths are annotated on the CSV files, as shown in Table 3 for the AP view and Table 4 for the LA view. Finally, we conducted a dataset review meeting, inviting experts from various fields, such as orthopedics, radiology, and image processing. The complete version of the verified dataset, including X-ray images of the lumbar spine and the corresponding ground truth annotations of lumbar vertebrae position and spondylolisthesis diagnosis, is included.
## 3 Tax Fraud Modeling with Neural Networks
### Dimension Adjustment: Reduction of the Dimension According to the Main Components
#### 3.3.2 Generalization: Calculation of Taxpayer Fraud Probabilities  
One of the advantages provided by predictive models for tax fraud detection purposes consists of their utilization to calculate tax avoidance probabilities at the individual level. The neural network output classifies each taxpayer as fraudulent or not fraudulent, in addition to unveiling an individual taxpayer's tendency towards fraudulent practices. In other words, it does not only classify the individual according to their likelihood to commit fraud, but also computes tax fraud probability per taxpayer. Figure 7 illustrates the probability density of the propensity to commit fraud by means of the Multilayer Perceptron. It can be seen that fraud probability is denser for small values but also has high values of around 0.8 probability.  
On the other hand, not only do neural networks serve to classify persons with a tendency to indulge in fraud or not, but they are also of use for computing taxpayer fraud probability on an individual basis, and this is especially important for tax inspection purposes. Tax Inspections could be planned to include all the taxpayers whose fraud probabilities exceed a specified value or, at least, include a sample of such persons in case the resources available for inspection are insufficient.  
The representation of the probability density concerning the likelihood to commit fraud obtained with the Multilayer Perceptron shows that the probability is logically denser for small values since there are considerably more taxpayers who comply with their tax obligations rather than those who evade tax. However, for fraud probability values greater than 0.5, we observe that the density increases up to values close to a fraud probability of 0.8. This fact indicates the existence of an insignificant pocket of fraud with high fraud probability values. It is still interesting to note that the density of fraud is higher for very small fraud values, as well as for high fraud values of close to 0.8 fraud probability. Therefore, we could refer to a polarization aspect of the likelihood to commit fraud.
## 5 Challenges and Future Research Directions
### Vertical Densification  
Deploying small cells at different elevation levels (vertical densification) can enhance network coverage, but it also introduces deployment challenges, including signal interference, complexity in infrastructure management, and spectrum reuse issues. Further research is required to develop optimized deployment models, interference mitigation strategies, and performance evaluation techniques to improve the effectiveness of vertically layered networks [61].
# A Review on Methods and Systems for Remote Collaboration
## 4 Visual Display Technologies
### Mixed Reality (MR)  
Unlike VR, which implies a complete immersion experience that shuts out the physical world, mixed reality (MR) is a method that mixes the real world with the artificial world. Digital objects are superimposed, and images are overlaid onto the real world. MR systems enable distant collaborators to feel as though they are in the same space. The user needs a headset capable of MR (e.g., Microsoft HoloLens) in order to experience the involvement of virtual objects with the real world or physical view.  
[PERSON] and [PERSON] in [81] presented a remote collaboration solution based on mixed reality. The real objects were virtualized using computing virtualization and displayed at the remote user's site. The local user could see how the remote user interacted with the virtual objects and could take the same actions on the real objects.  
To increase the collaborators' shared view and reduce the ambiguity of detictic expressions, [PERSON] et al. [82] proposed the use of shared virtual landmarks in a mixed reality environment.  
[PERSON] et al. [83] proposed a design for an MR system where both the novice and the expert wore head-mounted devices with video capturing capabilities. Based on the object of interest from the novice's environment, a proxy object is created in a virtual environment that can be seen by the expert in a split display together with the remote live video feed. The novice can also see the virtual proxy object and the expert's gestures.  
[PERSON] et al. [84, 85] proposed an MR collaboration system that used a live 360-degree panorama in a 3D reconstructed scene. A remote user could choose the way in which he interacted with the local user's environment, either via a live 360-degree panorama video, using past static images captured from the 360-degree panorama, or using the 3D reconstructed scene. In [86], the authors compared the results obtained when allowing the user to switch between the 3D view and the 360-degree panoramic view of the local environments. The results showed that the users reported that their concentration was better when solving their task using the 3D mode and had a good understanding of the collaborator's focus in the 360-degree mode.  
In addition to the capabilities of the system proposed by [PERSON] et al. in [84], [PERSON] et al. [87] allowed the local user to share a 2D first-person view of the local environment. The local surroundings were captured as a 3D scene and displayed on the remote VR HMD. The expert could analyze the virtual space from three different perspectives: the 2D first-person view, a 3D static view, and a 360-degree panoramic view. The results showed that the users preferred the 360-degree view, as it offered more control and independence compared to the 2D view.
# The Application of Structural Reliability and Sensitivity Analysis in Engineering Practice
## 3 Case Study
Figure 10: Graphical illustration of the elasticity of the reliability index based on mean value for ULS. probability that the actual probability of failure does not exceed the indicated value 50%: p_f_8.7 \(\times\) 10\({}^{-6}\) 75%: p_f_1.1 \(\times\) 10\({}^{-5}\) 90%: p_f_1.3 \(\times\) 10\({}^{-5}\) 95%: p_f_1.4 \(\times\) 10\({}^{-5}\) 99%: p_f_1.7 \(\times\) 10\({}^{-5}\)  
equal tail  
probability that the actual probability of failure is within the specified interval 50%: [6.8 \(\times\) 10\({}^{-6}\) p_f_1.1 \(\times\) 10\({}^{-5}\)] 75%: [5.7 \(\times\) 10\({}^{-6}\) p_f_1.2 \(\times\) 10\({}^{-5}\)] 90%: [4.7 \(\times\) 10\({}^{-6}\) p_f_1.4 \(\times\) 10\({}^{-5}\)] 95%: [4.1 \(\times\) 10\({}^{-6}\) p_f_1.6 \(\times\) 10\({}^{-5}\)] 99%: [3.1 \(\times\) 10\({}^{-6}\) p_f_1.9 \(\times\) 10\({}^{-5}\)] ================================================================= Variance based sensitivity analysis  
first-order sensitivity index  
Mean of LSF values: 7.0 \(\times\) 10\({}^{2}\) Variance of LSF values: 3.3 \(\times\) 10\({}^{4}\) Standard deviation of LSF values: 1.8 \(\times\) 10\({}^{2}\) C.o.V. of LSF values: 2.6 \(\times\) 10\({}^{-1}\) Variable lS_i  
D 15.3 \(\times\) 10\({}^{-1}\) d 17.8 \(\times\) 10\({}^{-2}\) q 15.4 \(\times\) 10\({}^{-3}\) l12.2 \(\times\) 10\({}^{-2}\) f 13.6 \(\times\) 10\({}^{-1}\)  
Sum of S_i 1.0 \(\times\) 10\({}^{0}\) Values of the Sobol indices and the coordinate vector \(\alpha\)\(\mid\)\(\mid\) for SLS and ULS are listed in Table 4.  
Comparing the values of vector \(\alpha\) obtained using FORM with the values of the Sobol indices from the Monte Carlo method, a consistent trend can be observed. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Variable**} & \multicolumn{2}{c}{**SLS**} & \multicolumn{2}{c}{**ULS**} \\ \cline{2-5}  & **Sobol Index S\({}_{i}\)** & **Coordinate \(\alpha\) Vector \(\mid\)\(\mid\)\(\mid\)** & **Sobol Index S\({}_{i}\)** & **Coordinate \(\alpha\) Vector \(\mid\)\(\mid\)\(\mid\)** \\ \hline D & 6.1 \(\times\) 10\({}^{-1}\) & 8.0 \(\times\) 10\({}^{-1}\) & 5.3 \(\times\) 10\({}^{-1}\) & 7.9 \(\times\) 10\({}^{-1}\) \\ \hline l & 2.7 \(\times\) 10\({}^{-1}\) & 4.9 \(\times\) 10\({}^{-1}\) & 2.2 \(\times\) 10\({}^{-2}\) & 2.1 \(\times\) 10\({}^{-1}\) \\ \hline d & 6.3 \(\times\) 10\({}^{-2}\) & 2.8 \(\times\) 10\({}^{-1}\) & 7.8 \(\times\) 10\({}^{-2}\) & 3.6 \(\times\) 10\({}^{-1}\) \\ \hline E & 3.8 \(\times\) 10\({}^{-2}\) & 1.9 \(\times\) 10\({}^{-1}\) & - & - \\ \hline fy & - & - & 3.6 \(\times\) 10\({}^{-1}\) & 5.2 \(\times\) 10\({}^{-1}\) \\ \hline q & 1.7 \(\times\) 10\({}^{-2}\) & 1.2 \(\times\) 10\({}^{-1}\) & 5.4 \(\times\) 10\({}^{-3}\) & 1.0 \(\times\) 10\({}^{-1}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The values of Sobol indices and coordinate \(\alpha\) vector \(\mid\)\(\mid\) for SLS and ULS.
Linear and nonlinear codes depend on the relationship between parity bits and information bits [10]. Aside from the need for video content protection in the interests of multimedia providers, with the growing enforcement of privacy laws, privacy protection of transmitted data is of primary importance [11]. Encryption is a well-known technique to provide confidentiality to data transmitted over insecure channels. Encryption can be applied as naive, for the hiding of full video content, or selective encryption, which limits encryption to key elements in the compressed bitstream [12]. Consequently, the following key questions arise:  
**Q1:**: What will happen to the selectively encrypted video data after transmitting over wireless channels, which are prone to errors? **Q2:**: Will it be possible to recover the multimedia content with an equivalent visual quality? **Q3:**: Will the decryption of error-corrupted video still succeed at the receiver end, or will it fail? Considering these research questions, the contributions of this paper are as follows:  
1. This paper provides a novel and pioneer prototype (to the best of the authors' knowledge) for protecting the video quality of selectively encrypted H.264/AVC compressed videos while transferring over erroneous wireless networks. 2 Selective Encryption (SE) using two-round secure process is applied to the selected syntax elements of an H.264/AVC CABAC encoder to achieve video privacy, and it maintains the video's format compliancy and compression efficiency for effective channel bandwidth utilization. * The Gilbert-Elliot model is implemented for the simulation of an error-prone channel. * A Random Linear Block coding-based FEC mechanism is deployed on the encrypted H.264/AVC bitstreams for the recovery of bit-errors. The results are verified using various Video Quality Metrics and evaluation criteria. The remainder of this paper is organized as follows. Section 2 describes related studies to this subject. Section 3 describes the details of the proposed scheme. Section 4 illustrates the promising visual results based on video-quality metrics for the tested videos. The limitations of the work to date, together with future work extensions, are also given in this section. Finally, Section 5 concludes the paper by summarizing how its research fits into the current technological environment.
## 4 Results
### Dataset X3  
#### 4.3.1. _C_ree Plot Analysis for Dataset X3  
In Figure 12, the scree plot generated from the PCA results provides insights into the variance explained by each principal component.  
Figure 12 illustrates the scree plot for Dataset X3, offering insights into variance explained by each principal component:  
* High Initial Variance: Approximately 30% of the variance is explained by the first principal component, signifying substantial capture of dataset information.
* Noticeable Decline: A discernible decline post the first component suggests diminishing contributions from subsequent components.
* PCA Appropriateness: Given the high variance explained by the first component, PCA appears appropriate for dimensionality reduction while retaining crucial information.
## 3 Results
### Phytochemical Content of Cold and Hot Water Pericarp Extracts
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Accessions**} & \multirow{2}{*}{**T \({}^{\circ}\)C**} & **DPPH** & **ABTS** & **FRAP** & **CV** & **EC\({}_{\textbf{50}}\) (DPPH)** & **EC\({}_{\textbf{50}}\) (ABTS)** \\ \cline{3-6}  & & **mmol TEAC 100 g\({}^{-1}\) DM** & **mg AAeq g\({}^{-1}\) DM** & **AUC\({}_{\textbf{+0.5}}\) (\%C)** & **mg mL\({}^{-1}\)** & **mg mL\({}^{-1}\)** \\ \hline \multirow{3}{*}{**MURG**} & 20 & 24.29 \(\pm\) 1.16 * & 33.33 \(\pm\) 1.24 & 3.28 \(\pm\) 0.27 & 3.02 \(\pm\) 0.12 * & 0.042 * & 0.028 \\  & 95 & 31.77 \(\pm\) 2.40 * & 34.02 \(\pm\) 0.67 & 3.20 \(\pm\) 0.16 & 2.04 \(\pm\) 0.07 * & 0.034 * & 0.029 \\  & 20 & 23.91 \(\pm\) 1.34 & 31.17 \(\pm\) 0.31 & 3.10 \(\pm\) 0.25 & 2.16 \(\pm\) 0.13 * & 0.032 & 0.031 \\  & 95 & 27.52 \(\pm\) 2.84 & 29.57 \(\pm\) 0.92 & 3.33 \(\pm\) 0.34 & 2.97 \(\pm\) 0.19 * & 0.039 & 0.034 \\  & 20 & 27.20 \(\pm\) 2.89 & 35.71 \(\pm\) 1.50 & 3.55 \(\pm\) 0.93 & 3.15 \(\pm\) 0.15 & 0.035 & 0.031 \\
**ILDP** & 95 & 28.17 \(\pm\) 2.39 & 34.51 \(\pm\) 0.91 & 3.35 \(\pm\) 0.45 & 3.17 \(\pm\) 0.31 & 0.029 & 0.032 \\
**COEV** & 20 & 15.31 \(\pm\) 0.36 * & 21.03 \(\pm\) 0.37 & 2.18 \(\pm\)0.09 & 1.52 \(\pm\) 0.37 & 0.058 & 0.046 \\  & 95 & 20.07 \(\pm\) 2.94 * & 25.11 \(\pm\) 2.49 & 2.54 \(\pm\) 0.29 & 1.87 \(\pm\) 0.28 & 0.051 & 0.037 \\  & 20 & 9.12 \(\pm\) 1.74 * & 11.60 \(\pm\) 0.20 * & 2.61 \(\pm\) 0.21 * & 2.61 \(\pm\) 0.20 * & 0.057 * & 0.040 * \\  & 95 & 14.22 \(\pm\) 0.24 * & 18.59 \(\pm\) 0.05 * & 3.53 \(\pm\) 0.11 * & 3.78 \(\pm\) 0.25 * & 0.038 * & 0.026 * \\
**A \(\times\) T** & & \# & \# & \# & \# & n.s. & n.s. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Antioxidant capacity determined by DPPH, ABTS and FRAP tests, and by cyclic voltammetry, in the extracts of the pericarp chestnut samples.
## 5 Experimental Investigations  
### Introduction  
To determine a time to failure curve and a possible reduction factor for a certain service life, e.g., 100 years, for the material combination described in Section 4, long-term tests are carried out. By means of twenty short-term tests with a concrete age of 28 days, the reference load levels for the long-term tests are defined.  
The test specimens of the long-term tests were initially preloaded with a final crack pattern for 24 h in tempered water on half of the reference load to ensure a uniform internal load distribution before the constant load is applied. In further tests the influence of the preloading is investigated.  
Due to the environmental factors explained in Section 2, the preloading and long-term durability tests are performed under the combined impact of stress, temperature, moisture and alkalinity. The aim of the testing concept developed [19], and originally planned for these tests, was to carry out long-term tests over 200, 1000 and 5000 h. To derive a reduction factor at the end of the service life, a semi-logarithmic relationship between stress and time from variation of the constant stress levels is assumed. To determine a reduction factor, an extrapolation of the time to failure curve is necessary. However, the concept, which is successfully applicable to glass reinforcement, cannot be applied to the long-term tests with the carbon reinforcement presented in this paper, as no failure due to the exposure occurred during the test periods. In residual capacity tests, no reduction could be determined after the end of exposure. Therefore, a different approach will be necessary.
## 3 Rolling Optimization Algorithm
### Objective Function  
For the \(i\)-\(th\) UAV, the position at time \(t+1\) is shown as in Equation (1). In the equation, \(p_{i}^{t}\) denotes the position of the \(i\)-\(th\) UAV at time \(t\), while \(v_{i}^{t}\) describes its velocity. The sampling interval is \(\triangle t\).  
\[p_{i}^{t+1}=p_{i}^{t}+v_{i}^{t}*\triangle t \tag{1}\]  
Because we used a grid model for discretization, the position \(p\) and velocity \(v\) are represented by column vectors, as shown in Equation (2). Furthermore, items in the \(v_{i}^{t}\) is limited to be \(-1\), \(0\), or \(1\) to guarantee that UAVs stay in the crossing points of the grid model.  
\[p_{i}^{t}=[x_{i}^{t},y_{i}^{t},z_{i}^{t}]^{T}\] \[v_{i}^{t}=[vx_{i}^{t},vy_{i}^{t},vz_{i}^{t}]^{T} \tag{2}\]  
The best trajectories of multiple UAVs have the shortest flying distance, satisfying the collision-free constraint. In this paper, the total flying distance of all the UAVs is considered as the objective function.  
Figure 3: Conceptual model of the rolling optimization algorithm (ROA).  
Figure 2: The top view of the cubic cells and representation of 2D state space. Therein, red points are not allowed for UAVs to reach, while only green points are possible waypoints in the next interval for UAVs.  
The mathematical expression is shown in Equation (3). Therein, \(T_{i}\) denotes the number of intervals that the \(i\)-\(th\) UAV spends from the base to the destination, while \(N\) is the number of UAVs.  
\[D=\sum_{i=1}^{N}\sum_{t=1}^{T_{i}-1}dist(p_{i}^{t+1}-p_{i}^{t}) \tag{3}\]
## 5 Future Work
### Rainfall Prediction  
After rainfall mapping, rainfall paths and intensities can be forecasted in the short term based on the variation of the spatial distribution of rainfall over time. Commonly, such short-term forecasts are supported by radar data. However, in economically backward developing countries or in areas unavailable to radar, rainfall maps based on CMLs may be an alternate option [196]. [PERSON] et al. [185] constructed nowcasts for the first time in the Netherlands using country-wide rainfall maps based on CMLs and found that CMLs gave better results than radar in general. Recently, [PERSON] et al. [184] also carried out rainfall field nowcasting experiments based on CMLs in Jiangyin City, China, and found that the LSTM-based nowcasting algorithm has better performance for stratiform and mixed precipitation compared to convective precipitation. In addition, based on the assumption that atmospheric variables related to rainfall change prior to a rainfall event affect CMLs, CMLs can also directly predict whether and how much rain will fall in the future without the need for rainfall mapping [197]. Further research on techniques for predicting rainfall using CMLs could be useful for disaster prevention and control, especially in poor tropical regions where extreme events are common.
# Research on New Drilling Downlink Communication Based on Anisotropic Magnetoresistive Technology
## 3 Experimental Work
### Construction of Experimental Platform  
Considering that the communication system needs to be installed near the bit in practice and the complexity and high cost of the lifting and drilling process, it is a good choice to build an experimental simulation platform for pre-testing in the ground environment to verify the feasibility of the communication system based on anisotropic magnetoresistance detection. The model of the ground simulation experiment platform is shown in Figure 8.  
Figure 6: Magnetic induction intensity at any point of downhole drill string \(H=1000\) m.  
Figure 7: Overall hardware block diagram of the system.  
In the process of the experiment, the working process of the test platform is as follows: Firstly, the information to be transmitted is input into the transmitter. After coding, modulation, and other operations, the high-frequency carrier signal is taken as the carrier, and the signal is sent by the excitation module of the transmitter. Secondly, in the magnetoresistance sensor part, the magnetic field signal generated by the weak current signal is received by the magnetoresistance sensor, which is converted into voltage and transmitted to the receiver. Finally, after the receiver obtains the signal and a series of operations such as amplification, filtering, and decoding, the useful information carried by the signal is restored to realize wireless communication. In order to calculate the BER reasonably, the computer is used as the upper computer to receive and count the BER of the communication system. As a result, the experimental platform is built, as shown in Figures 9 and 10.