esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Case Study
### Parametric Causality Inference and Anomaly Detection on a Real Satellite Telemetry Dataset  
To verify the performance of our method in spacecraft telemetry anomaly detection, we select a real satellite for the experiment. The name of this satellite is \"Satellite A\". Satellite A is a military communication satellite, it enables any ground, sea and air communication stations in the coverage area to communicate with each other at the same time, and has the function of transmitting information such as telephone, telegram, fax, data, and television. The telemetry dataset of satellite A contains more than 20 telemetry parameters, and the labels of anomalies are given by experts and the operation system of the satellite. For the purpose of confidentiality, we do not give the actual meaning of each telemetry parameter in this satellite. Instead, we use A, B, C,... T to indicate them. Our satellite telemetry data has been declassified through normalization and uploaded together with the paper (named \"experiment data.csv\"). Among these parameters, B, D, Q, and T are are the important parameters summed up by most experts and engineers in the long-term satellite anomaly detection task based on the basic structure and design principles of the satellite. These parameters have a crucial impact on the normal operation of the satellite. Parameter B represents the output power of the southern solar array, parameter D represents the output  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Causal Inference Algorithm**} & \multicolumn{4}{c}{**Dataset1**} & \multicolumn{4}{c}{**Dataset2**} \\ \cline{2-7}  & **Pre (\%)** & **Rec (\%)** & **F1** & **Pre (\%)** & **Rec (\%)** & **F1** \\ \hline PCMCI & 80 & 60 & 0.7273 & 50 & 50 & 0.5 \\ NCE & 60 & 60 & 0.6 & 50 & 66.67 & 0.5714 \\ MESS & 80 & 60 & 0.7273 & 62.5 & 55.56 & 0.5882 \\ IMESS & **100** & **83.33** & **0.9192** & **87.5** & **77.78** & **0.8235** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance comparison between IMESS and three baseline algorithms.  
Figure 10: Causal inference results of IMESS and three baseline algorithms on Dataset1 and Dataset2.  
power of the northern solar array, parameter Q represents the shell temperature of the transponder, and parameter T represents the bearing shell temperature of the momentum wheel. The dataset has 67,968 items of data sampled in 236 days, and the sampling interval is 5 min. Table 3 gives the basic information of this satellite telemetry dataset.
# Spatialization and Analysis of China's GDP Based on NPP/VIIRS Data from 2013 to 2023
## 2 Study Area and Data
### Data and Preprocessing  
#### 2.2.1 Data  
The data for this study mainly include NPP/VIIRS NTL data, geographic information data, and socio-economic statistics. NPP/VIIRS NTL data come from the National Oceanic and Atmospheric Administration (NOAA) of the United States. Its full name is the Visible Near-Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-orbiting Partnership (Suomi NPP) satellite. The data resolution is about 500 m, and the product types include annual products, monthly products, and daily products. This paper uses the annual average product data from 2013 to 2023 for related research. Geographic information data include China's administrative boundaries, and provincial, prefectural, and county administrative boundaries; socio-economic data include GDP data, population data, etc., which come from the statistical yearbook published by the Chinese government every year.
# Disturbance Observer-Based Anti-Shock Controller for Laser Beam Steering Systems
## 5 Conclusions  
In this study, we investigated how to improve the disturbance-rejection performance of an anti-shock controller for FSM systems by using a DOB to improve its tip-tilt control performance under external shocks. First of all, we analyzed the robustness of the DOB system quantitatively through the simulated performance of the DOB system. Also, the design parameters of the DOB system were experimentally derived.  
Eventually, when the 15 Hz bandwidth \(Q_{20}\) filter was used in the DOB-based anti-shock controller for FSM systems, the CES value decreased by 25.3% compared to that in the case without the DOB. Furthermore, when the 15 Hz bandwidth \(Q_{31}\) filter was used in the DOB-based anti-shock controller for FSM systems, the CES value decreased by 31.9% compared to that in the case without the DOB. Furthermore, to improve the disturbance-rejection performance of the DOB-based anti-shock controller for FSM systems, the bandwidth of the \(Q_{20}\) and \(Q_{31}\) filters used therein was set to 30 Hz. The CES value decreased by approximately 48.4% and 59.1% when using the \(Q_{20}\) and \(Q_{31}\) filters, respectively, compared to that in the absence of the DOB.  
These results indicate that the disturbance-rejection performance of the DOB-based control system improves as the order of the denominator and numerator of the \(Q\) filter increases, and as the bandwidth of the \(Q\) filter increases. Consequently, the proposed anti-shock controller performed efficiently in suppressing external disturbances in the FSM system.  
Figure 11: Performance results obtained using DOB-based anti-shock controller for FSM control systems: (**a**) PI controller, (**b**) \(Q_{20}\) filter (15 Hz), (**c**) \(Q_{31}\) filter (15 Hz), (**d**) \(Q_{20}\) filter (30 Hz), and (**e**) \(Q_{31}\) filter (30 Hz).  
**Funding:** This work was supported by an Agency for Defense Development (ADD) grant funded by the Government of the Republic of Korea in 2024.  
**Data Availability Statement:** Data is contained within the article.  
**Conflicts of Interest:** The author declares no conflicts of interest.
## 3 Results and Discussion
### Test Results by ANN Structure
Figures 11-13 show the dynamic displacement over time for the midpoint, where the largest displacement is expected to occur. Figure 14 shows the overall behavior of the bridge at the time of maximum displacement for each speed. The following observations are made from the graphs. 1. The ANN trained using three different velocities predicts the displacement for different speed-load scenarios with remarkably high accuracy. This means that it is possible to predict the displacement of a bridge due to a vehicle moving at various velocities in the field. 2. The ANN predicts the overall behavior of all the girders at a given point in time, as well as the change in dynamic displacement of a given point over time. 3. The results provide that the proposed method to predict vertical dynamic displacements of bridge from longitudinal strains is feasible. \[\text{Difference}=\left|\text{Disp\_FEM}-\text{Disp\_ANN}\right|\] (8)  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{3}{*}{**Case**} & \multirow{3}{*}{\ \begin{tabular}{c} **No. of** \\ **Hidden** \\ **Layers** \\ \end{tabular}  } & \multirow{3}{*}{ \begin{tabular}{c} **ANN** \\ **Structure** \\ \end{tabular}  } & \multicolumn{3}{c}{**Velocity of Vehicles Crossing Bridge (Test)**} \\ \cline{4-9}  & & & \multicolumn{2}{c}{**40 km/h**} & \multicolumn{2}{c}{**60 km/h**} & \multicolumn{2}{c}{**80 km/h**} \\ \cline{4-9}  & & & \multicolumn{2}{c}{**MSE**} & \multicolumn{2}{c}{**(\(\times\)10\({}^{-3}\))**} & \multicolumn{2}{c}{**\(R\)**} & \multicolumn{2}{c}{**MSE**} & \multicolumn{2}{c}{**(\(\times\)10\({}^{-3}\))**} & \multicolumn{2}{c}{**\(R\)**} \\ \hline
1 & & & 55-28-55 & 0.70 & 0.995533 & 0.37 & 0.996351 & 0.44 & 0.993415 \\
2 & & & 55-55-55 & 0.68 & 0.995636 & 0.35 & 0.996528 & 0.43 & 0.993614 \\
3 & & & 55-110-55 & 0.67 & 0.995699 & 0.34 & 0. Figure 11: Test results at midpoint of girder for 40 km/h. Figure 12: Test results at midpoint of girder for 60 km/h. Figure 13: Test results at midpoint of girder for 80 km/h. Figure 14: Overall behavior of girder at maximum displacement.
# In-Plane Material Handling: A Systematic Review
## 3 Results
### Answer to RQ3  
This last subsection of Section 3 addresses the third research question (RQ3), concerning the latest in-plane solutions for package handling along transport lines.  
Packages and parcels used in warehouses and factories for intralogistic purposes typically fall into the category of macroscopic objects, with weights ranging from light to heavy [95]. According to Figure 6, the main technologies for handling packages within the identified classes are pneumatic, variable-morphology, and rotor systems. However, among these, according to the reviewed literature, only rotor systems are truly effective for feeding, sorting, orienting, and generally manipulating parcels. This is due to the working principles employed by these technologies. Pneumatic surfaces, which use air jets or pressure fields, are complex to control and are preferable only when a contactless solution is required. Variable-morphology surfaces, which use vertical motion to manipulate objects in plane, operate at a slower pace and have reduced handling capabilities compared to rotors. Furthermore, they are limited to handling light and compact (yet still macroscopic) objects, similar to pneumatic surfaces.  
Therefore, the answer to the third research question is rotor systems, with a focus on the latest solutions presented in [3, 4, 7, 73]. These devices were introduced in the previous subsection (Section 3.3), where they were also categorized based on their rotor-axis mobility. While their detailed presentation is not repeated here, this section analyzes their strengths and weaknesses.  
Figure 7: Number of articles according to the employed technology (**a**) and distribution over the years (**b**).  
In reviewing the literature, it is noted that, with one exception [73], all proposals involve fully or even over-actuated rotors. This means that the rotors used in these systems are motorized, at least for spinning, and in the case of the second type of rotor class, the swiveling is also motor-driven. As noted in [73], this often results in at least two motors per module, for example, one motor for each perpendicular omnirwheel, as seen in [5; 9; 31; 88], or one motor for swiveling and one for spinning, as observed in [52]. In some of the latest commercially available solutions, such as the _Cellveyor_[3], there are three driving rotors, and in the devices proposed in [4; 7], there are five and nine motorized wheels or rollers, respectively.  
As a result, these systems excel in manipulation due to their distributed actuation [9]. However, they are complex to control, consume significant amounts of energy, and are expensive to build and maintain [73].  
To address these issues, we proposed a solution in [73]. However, this does not preclude future research into designs that reduce complexity without compromising performance. This article aims to describe the latest advancements in in-plane manipulation as well as their applications and characteristics.
## 3 ME-SSA Transmitter Design and Waveform Definition
### Quasi Constant Envelope Waveform  
One of the objectives of the MASSIVE project was to reduce the complexity (hence the cost) of the satellite IoT terminals. The E-SSA based air interface solution such as S-MIM [5] uses conventional linear spreading where the BPSK modulated spreading spectrum signals are filtered by a square raised cosine filter (SRRC).  
In the MASSIVE project, an alternative modulation/spreading approach was implemented based on the generation of a quasi-constant envelope signal, as proposed in [11] and investigated in [9]. The proposed structure is shown in Figure 3.  
According to [11], there are up to four CE spreading sequences which are evaluated off-line and stored, hence there is one sequence per each possible phase difference among the QPSK modulated symbols. The phase differences between consecutive QPSK symbols are used to select one of the four CE spreading sequences for the chips at the edge of each spread symbol so that the phase transitions from one symbol to the next remain continuous.  
Figure 3: Constant envelope spreading modulator structure.  
This will avoid any abrupt phase transition, hence resulting in a quasi-constant signal envelope. From the transmitter perspective the quasi-constant envelope would allow the power amplifier to operate close to the saturation point which would result in a higher power efficiency without introducing any spectral side-lobes or distortion. At the receiver, there is no modification required since the alternation of the spreading sequence at the edge of each symbol can be neglected especially for spreading factors larger than 16.
## 4 Data Sources and Evaluation Indicators
### Evaluation Indicators  
Several evaluation metrics are employed in this paper, which include the mean Average Precision (\(mAP\)) with an Intersection over Union (IoU) threshold of 0.5, model weight measured in megabytes (MB), detection speed quantified in frames per second (\(FPS\)), and model parameters. The calculation of map is related to _precision_ and _recall_.  
\[precision:p=\frac{TP}{TP+FP} \tag{17}\]  
\[recall:r=\frac{TP}{TP+FN} \tag{18}\]  
\[mAP\text{ \text@ }0.5=\int_{0}^{1}p(r)\mathrm{d}r \tag{19}\]  
where \(TP\) is the number of positive cases that are correctly classified; \(FP\) is the number of negative cases that are misclassified; \(FN\) is the number of positive cases that are misclassified; \(p(r)\) is a two-dimensional curve function of the precision rate on the recall rate; and the \(mAP\) value is the area of the two-dimensional graph formed by calculating the accuracy rate and the recall rate.  
The model weight is the size of the weight file generated during the training process; the model parameter quantity indicates the parameter quantity required for model training. The detection speed is expressed as the maximum number of frames that can be detected within 1 s; _FPS_ is the number of frames transmitted per second in the screen. The more frames per second, the smoother the action displayed on the screen. Equation (19) is the calculation process of _FPS_.  
\[FPS=\frac{FrameNum}{ElapsedTime} \tag{20}\]  
where _FrameNum_ is the total number of frames of a video, and _ElapsedTime_ is the time consumed to run the video.
# RACF: A Multimodal Deep Learning Framework for Parkinson's Disease Diagnosis Using SNP and MRI Data
## 2 Materials and Methods
### Dataset and Preprocessing
#### 2.1.2 MRI Preprocessing  
The T1-weighted sMRI data used in this study underwent a comprehensive preprocessing pipeline to ensure both image quality and spatial consistency, in line with the requirements of deep learning models. Initially, the raw images in DICOM format were converted to NIFTI format using the dcm2 niix tool, which preserved the original scanning parameters and geometric information. Following this, the converted images underwent  
\begin{table}
\begin{tabular}{l c c c c} \hline
**Label** & **Number** & **Age (Mean \(\pm\) SD)** & **Gender (M/F)** & **UPDRS (Mean \(\pm\) SD)** \\ \hline PD & 285 & 61.7 \(\pm\) 9.6 & 184/101 & 69.86 \(\pm\) 24.56 \\ HC & 139 & 60.5 \(\pm\) 11.7 & 95/44 & 2.16 \(\pm\) 2.84 \\ \hline \end{tabular}
\end{table}
Table 1: Demographic characteristics of participants.  
N4 bias field correction using the SimpleITK library. This iterative correction process effectively removed low-frequency intensity biases caused by magnetic field inhomogeneities, thereby enhancing the contrast between gray matter and white matter.  
Next, skull stripping was performed using the Brain Extraction Tool (BET) from the ANTs toolkit. This procedure removed the skull and non-brain tissues, leaving only the brain parenchyma for further analysis. A hybrid segmentation approach was employed, combining template-based atlas matching with adaptive thresholding, ensuring that the brain parenchyma was accurately preserved while generating a binary mask for subsequent steps.  
The skull-stripped images were then spatially normalized by registering them to the Colin27 standard template space using FSL's FLIRT tool. This step involved applying rigid-body transformation and utilizing mutual information as the similarity metric. The images were resampled to an isotropic resolution of \(1\times 1\times 1\) mm\({}^{3}\) to achieve precise anatomical correspondence across all subjects. To ensure intensity uniformity, which is critical for computational analysis, we performed whole-brain signal scaling followed by cross-channel normalization, thereby standardizing input characteristics for optimal model convergence during network training.  
Finally, to ensure consistency in the dimensions of the input data for the deep learning model, the images were resampled from their original size of \(181\times 217\times 181\) to \(96\times 96\times 96\) using bilinear interpolation. This resampling step preserved important anatomical details while generating standardized three-dimensional brain images suitable for model input.
## 3 Results
### Synthesis of Studies Included  
There is unanimity across the studies regarding the fact that type I is the most frequent configuration. The rest of [PERSON]'s configurations were considered incisors with two canals, regardless of their path in the canal system. When there were two canals, the most frequent classification was type III, with the exception of four studies [8; 22; 23; 35]. There was no heterogeneity as to the frequency of the rest of configurations, with studies where the next most frequent configurations were type II [14; 23; 26; 27; 30; 33; 34], type V [8; 19; 20; 21; 24; 25; 28; 29; 32], or type IV [31]. The remaining types [VI, VII and others] were generally present in very low or non-existent percentages. Five studies reported symmetry between the incisors on the right and left side of the patient [8; 19; 20; 23; 26; 33]. Lateral incisors showed a higher prevalence in the existence of a second canal compared to central incisors in fourteen studies [7; 14; 20; 21; 22; 25; 27; 28; 29; 31; 32; 33; 34; 35]. The central incisor had a higher prevalence in only three [19; 23; 26]. Likewise, we calculated the weighted average of all the percentages in the existence of a second canal, obtaining 17.2% in the central and 23.7% in the lateral incisors. Only two studies reported a relationship between age and the presence of a second canal [24; 29]. The remaining studies did not assess patient age or, if so, did not find any significant differences [8; 30]. Regarding gender influence, there was no unanimity as to a higher prevalence of second canals in men [19; 20; 24; 31] than in women [27; 29], and there were even certain studies where no significant differences were found [8; 22; 25; 30; 34; 35]. Geographical distribution indicated a lower incidence of a second canal in the Chinese population [14; 29; 32; 33; 34]. There was no direct relationship between voxel size and the increased prevalence of a second canal.  
\begin{table}
\begin{tabular}{l l l} \hline
**Section and Item** & \(n\) & **(\%)** \\ \hline
**1. Title** & 19 & 100 \\
**Introduction** & & \\ \hline
**2. Keywords** & 19 & 100 \\
**3. Aim** & 19 & 100 \\
**Methods** & & \\ \hline
**4. Participants (in vivo assessment)** & 19 & 100 \\
**5. CBCT** & 19 & 100 \\
**6. Morphology concept and assessed teeth** & 19 & 100 \\
**(variables)** & 19 & 100 \\
**7. Assessment** & 19 & 100 \\
**8. Observers** & 13 & 68.4 \\
**9. Potential sources of bias** & 15 & 78.9 \\
**10. Final simple size** & 15 & 78.9 \\
**11. Reliability** & 9 & 47.4 \\
**12. Statistical analysis** & 13 & 68.4 \\
**13. Ethics Committee** & 13 & 78.9 \\ Results** & & \\ \hline
**14. Primary outcomes** & 19 & 100 \\
**15. Other analysis** & 15 & 78.9 \\
**16. Visual documentation support** & 16 & 84.2 \\ Discussion** & & \\ \hline
**17. Outcomes interpretation** & 19 & 100 \\
**18. Strength and limitations** & 6 & 31.5 \\
**19. Generalizability** & 19 & 100 \\
**20. Future research** & 4 & 21.0 \\ \hline \end{tabular}
\end{table}
Table 3: Specific Preferred Reporting Items for Cross-sectional Studies on Root and Root Canal Anatomy Using Cone-beam Computed Tomographic (CBCT).
## 5 Results and Discussion
The parameters used in this simulation are similar to those in Figure 4:1 User 1 of the blind-RIS-AP-NOMA has a lower outage probability than user 2 due to the higher channel gain and greater power allocation. The presence of RIS elements in the environment boosts array gain, lowering the probability of an outage. Blind-RIS-AP-NOMA achieves nearly identical results as blind-RIS-SR-NOMA when compared to the traditional NOMA system. For various choices of \(N\), the outage probability of user 1 of the blind-RIS-AP-NOMA uplink is compared in Figure 4:2 Figure 9's explanation is identical to that of Figure 4:3 In Figure 10, the outage probability of user 2 of the blind-RIS-AP-NOMA uplink is shown for various values of \(N\). The proposed blind-RIS-AP-NOMA uplink's sum capacity (b/s/Hz) is shown in Figure 11, for various values of \(N\). It has been observed that raising \(N\) increases the sum capacity regardless of whether power allocations are optimal or sub-optimal. When optimal powers are allocated, as per (36) and (37), against sub-optimal power allocations, as per (40) and (41), the sum capacity is increased. For \(N=32\) and \(20\) dB SNR, the sum capacities of the optimal and sub-optimal power allocations are \(13.11\) b/s/Hz and \(12.64\) b/s/Hz, respectively. Other \(N\) values yield essentially equal results. For optimal and sub-optimal power allocations, the sum capacities (b/s/Hz) of NOMA, blind-RIS-SR-NOMA, and blind-RIS-AP-NOMA are compared in Figure 4:4 For all systems, the sum capacity of the optimal power allocation is greater than that of the sub-optimal power allocation. The blind-RIS-AP-NOMA system's capacity is nearly identical to that of the blind-RIS-SR-NOMA system. For 32 RIS elements and \(20\) dB SNR, the sum capacity of the optimal power assigned blind-RIS-SR-NOMA, blind-RIS-AP-NOMA, and traditional NOMA is \(13.21\) b/s/Hz, \(13.11\) b/s/Hz, and \(8.13\) b/s/Hz, respectively. In comparison to the traditional NOMA system, this represents a \(\approx\)38% increase in sum capacity. The sum capacity of the optimal and sub-optimal power assigned blind-RIS-SR-NOMA is \(14.12\) b/s/Hz and \(13.64\) b/s/Hz, respectively, for \(64\) RIS elements and \(20\) dB SNR. The sum capacity for the optimal power allocation is \(\approx\)3.4% higher than for the sub-optimal power allocation. The sum capacity of optimal power allotted blind-RIS-SR-NOMA is improved by \(\approx\)6.44% by increasing \(N\) from 32 to 64. The number of reflecting elements can be increased to further improve the results. Figure 4:5 Outage probability comparison of blind-RIS-AP-NOMA user 2 for various choices of \(N\). Figure 4:6 Outage probability comparison of blind-RIS-AP-NOMA user \(1\) for various choices of \(N\).