esa-sceva/satcom-chunk-collection huggingface dataset:
# A Global Overview of SVA--Spatial-Visual Ability
## 5 Results
### Analysis by the Age Categories and Assessment Tools
#### 5.1.7 Mixed Age Groups (A7)  
Table 9 shows studies that took into account individuals from all age groups. Author [PERSON] established through the findings in his study that parental education correlates with age trends in a variety of cognitive and success areas through both domain-general and domain-specific routes, which implied that while socioeconomic disparities are mostly evident in worldwide aspects of cognitive development, they have progressive connections to certain kinds of academic accomplishments [11]. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & **Country** & **Year** & **Age** & **Sector** & **Objective** \\ \hline \hline \multirow{4}{*}{[11]} & \multirow{4}{*}{USA} & \multirow{4}{*}{2013} & \multirow{4}{*}{5 to 18 years} & \multirow{4}{*}{B} & Evaluate the degree to which \\  & & & & developmental pathways particular to a \\  & & & & certain domain or generic to a domain may \\  & & & & account for socioeconomic variations in \\  & & & & age patterns in various kinds of cognition \\  & & & & and success.—MRT (OB) \\  & & & & Evaluate spatial ability tests with varying \\  & & & & degrees of domain specificity to \\  & & & architecture while looking into variations \\  & & & & in test performance between genders with \\  & & & & consideration of general reasoning \\  & & & & ability.—MRT, SVA 1 (2D to 3D), SVA 2 (3D to 2D) \\  & & & & & 2D) \\  & & & & Investigate the association between spatial \\  & & & & ability details and interior-design \\  & & & & performance with two sets of spatial \\  & & & & ability.-measuring instruments, AISAT [13] \\  & & & & being one of them.—MRT, SVA 1 (2D to 3D), SVA 1A (2D to 3D), SVA 1A (2D to 3D), SVA 2 (3D to 2D) \\  & & & & Explore the correlation between \\  & & & & visual\(\rightarrow\)spatial aptitude and the acquisition \\  & & & & of computer-graphics knowledge.—MRT, SVA 1 (2D to 3D (OB) \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Mixed age groups—A7. A South Korea- and USA-based study by [PERSON] and [PERSON] revealed that 2D-to-3D visualisation skill, as measured via the Architecture and Interior Design Domain-Specific Spatial Ability Test (AISAT) [13] corresponded with the resultant object's distinct characteristics and multi-dimensional constitution. While the performance of men exceeded that of women in general spatial ability, women outperformed men in the area of design, which emphasised the significance of developing 2D-to-3D visualisation ability in interior-design curricula to enhance creative outcomes [13]. Two separate German studies conducted 5 years apart both found that males outperformed women in mental rotation. [PERSON], [PERSON], and [PERSON]
# Effective Seed Scheduling for Directed Fuzzing with Function Call Sequence Complexity Estimation
## 3 System Overview
### Complexity Estimation of Function Call Sequence
#### 3.1.1 Two-Stage Function Sequence-Based Seed Scheduling  
The seed generated by the distance-guided directed fuzzer is scheduled based on the seed's distance from the current position to the target site, and a seed with a shorter distance is assigned more energy to fuzz. In this scheduling scheme, only the stage of the seed reaching the target point is considered. However, during the real-world fuzzing POC reproduction campaign, the fuzzer still needs time to trigger the bug after the seed can reach the target site. The seed-scheduling strategy of this stage is ignored by most DGF methods, e.g., AFLGo. Therefore, we designed a two-stage scheduling strategy for seeds before reaching the target point and after seeds have reached the target site, with the help of the estimation of function sequence complexity.
# Multi-Phase Stator Current Tracking with Gradual Penalization of Commutations
## 4 Experimental Results  
A five-phase IM is considered for the presentation of the method and its assessment (see Figure 4). The five-phase induction motor has the parameters shown in Table 1. The experimental setup included two three-phase inverters SKS-22F (SEMIKRON DANFOSS, Nurnberg, Germany), of which just five power legs were connected to the machine (phases a, b, c, d, and e). The DC-link voltage was provided by an appropriate DC power supply. A MSK28335 control board (Technosoft, Blue Ash, OH, USA) and a TI TMS320F28335 digital signal processor (Texas Instruments, Dallas, TX, USA) were used to implement the control algorithms.  
For the assessment of the proposal, the root mean squared (RMS) control error in the \(\alpha-\beta\) and \(x-y\) planes and the average switching frequency (ASF) are used. These are defined mathematically in what follows:  
\[E_{\alpha-\beta} = \sqrt{\frac{1}{(k_{2}-k_{1}+1)}\sum_{k=k_{1}}^{k_{2}}e_{\alpha \beta}^{2}(k)} \tag{22}\] \[E_{x-y} = \sqrt{\frac{1}{(k_{2}-k_{1}+1)}\sum_{k=k_{1}}^{k_{2}}e_{xy}^{2}(k)}\] (23) \[ASF = \frac{1/(2\cdot 5)}{T_{s}(k_{2}-k_{1}+1)}\sum_{k=k_{1}}^{k_{2}}SC(k) \tag{24}\]  
where \(e_{\alpha\beta}\) is the actual (not predicted) tracking error for \(\alpha-\beta\) plane, \(e_{xy}\) is the actual tracking error for \(x-y\) plane, and \(l_{i}\) is the amplitude of the \(i\)-th harmonic component of stator currents. These quantities are defined over a temporal horizon defined by the discrete-time indices \(k_{1}\), \(k_{2}\), which should be large enough to provide robust estimations (some electrical cycles).
# The Effectiveness of Dental Protection and the Material Arrangement in Custom-Made Mouthguards
Moreover, the attributes of mouthguards are not easy to identify because they are influenced not only by the impact absorption ability of the material but also by the occlusal relationship and the conformability of the mouthguards' construction [7]. Nevertheless, the most popular material used for mouthguard production is a low-stiffress 28% ethylene vinyl acetate (EVA) [10], mainly due to its shock-absorbing capabilities and high flexibility. Both characteristics allow relatively high deformation under loads, increasing contact area and time and decreasing the magnitude of peak efforts [4]. The mouthguard thickness is also a parameter design that has been well studied [11; 12; 13], and experimental and numerical studies seem to agree in a thickness between 3 and 4 mm. Still, in terms of energy absorption and transmission of forces, 4 mm seems to be the ideal thickness. The material arrangement within the mouthguard design has gained attention from some researchers [14; 15; 16]. Regarding this design variable, the experimental works of [PERSON] et al. [14] and [PERSON] et al. [15] indicate that the inclusion of one air layer in the mouthguard material improves the characteristic impact of mouthguards. At the same time, the numerical study of [PERSON] et al. [16] reported some behavior differences. The authors reported that the material's elastic modulus inside the MG influenced the stress distribution on the buccal surface of enamel. However, it did not affect the bone tissue stress, periodontal ligament strain, or root dentin tissue stress. Nevertheless, the numerical study was performed using bidimensional simplifications, namely the plane stress condition, contributing to some differences between the numerical and experimental behaviors [17]. Hence, the numerical models developed in this study use tridimensional constitutive relations, i.e., constitutive equations without any simplification, to verify if this assumption might be a factor contributing to those differences. The main purpose of this study was to evaluate, through the finite element method, the influence of the material arrangement in a custom-made mouthguard on the stress level of intraoral structures during an impact. The material arrangement included one mouthguard with a layer of soft material, in this case, EVA foam [1], another one considered a space layer between two layers of EVA, and a third model assumed that the mouthguard was produced with a compact EVA layer.
## 2 AI for the Study of Orbital Fractures
### Applications of AI in Orbital Fractures
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \begin{tabular}{c} **Objective of** \\ **the Study** \\ \end{tabular} & **AI Model Used** & **Data Input** & **Results** & **Validation** & **Advantages** \\ \hline \multirow{4}{*}{\begin{tabular}{c} [PERSON] et al. \\ (2025) [14] \\ \end{tabular} } & \multirow{4}{*}{\begin{tabular}{c} BOF diagnosis \\ and \\ classification \\ (site and \\ timing) \\ \end{tabular} } & \multirow{4}{*}{Neuro-T} & A total of 233 patients, with 233 images. CT & Accuracy & Comparison & \multirow{4}{*}{\begin{tabular}{c} Complete and \\ fast diagnosis \\ \end{tabular} } \\  & & & orbit in axial format, & 99.5\%, F1 & diagnosis and & \\  & & & 2D images, with ROI & score 99.4\%, & expert & fast diagnosis \\  & & & cropped from 512 \(\times\) & AUC 0.99 & radiological & \\  & & & 512 to 224 \(\times\) 224 px & & reports & \\ \hline \multirow{4}{*}{\begin{tabular}{c} [PERSON] et al. \\ (2020) [22] \\ \end{tabular} } & \multirow{4}{*}{\begin{tabular}{c} Automatic \\ recognition of \\ BOF \\ \end{tabular} } & \multirow{4}{*}{\
\begin{tabular}{c} InceptionV3 + \\ XGBoost \\ \end{tabular} } & A total of 94 BOF + 94 & & \multirow{4}{*}{
\begin{tabular}{c} Manual \\ annotations \\ and \\ comparison \\ with XGBoost \\ \end{tabular} } \\  & & & controls, with about & & \\  & & 5640 slices. Axial & & \\  & & & orbital CT scans, & \\  & & 512 \(\times\) 512 px slice, 3D & & \\  & & & Slicer annotations on & & \\  & & 30 slices/patient & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Application of AI models for orbital fractures.
# Orbit Decomposition Method for Rotordynamic Coefficients Identification of Annular Seals
## 1 Introduction
To study the effects of elliptical orbit parameters on the results of the present method, two major parameters are investigated, whirl amplitude and ellipticity.
# An Improved Multi-Chaotic Public Key Algorithm Based on Chebyshev Polynomials
## 2 Preliminary
### Improved Algorithm (Cmpkc-ki)
#### 3.3.2 Combination with the AES Algorithm to Obtain the Improved Algorithm  
Based on steps 1 to 5 of the improved public key encryption algorithm in Section 3.3, \(x_{nA+nB}\) and \(x_{nB+nA}\) can be used as the keys for encryption and decryption in the AES algorithm, thus achieving the combination of chaotic public key encryption and AES private key encryption. The specific process is shown in Figure 9.  
It is worth noting that after calculating \(x_{nA+nB}\) and \(x_{nB+nA}\), they cannot be directly applied to the AES algorithm in most cases. This is because, in practical applications, AES keys need to be represented as hexadecimal strings, Base64 encoded strings, or raw byte sequences. Thus, we need to continue to convert the values of \(x_{nA+nB}\) and \(x_{nB+nA}\) into the corresponding format.  
Step 6: [PERSON] and [PERSON] each transcode the exchanged keys \(x_{nA+nB}\) and \(x_{nB+nA}\) into 32-byte random binary keys using Algorithm 2.  
```
1:Input: decimal_number, random_key
2:Output: 32-byte random binary key, Hexadecimal representation
3:
4:Function decimal_to_random_key(decimal_number):
5: decimal_bytes:= Convert decimal_number to byte array(decimal_number)
6: hash_object:= Create SHA-256 hash object (decimal_bytes)
7: hash_result:= Get digest from hash_object
8: random_key:= First 32 bytes of hash_result
9: return random_key
```  
**Algorithm 2** Convert a decimal number to a 32-byte random binary key  
Step 7: [PERSON] inputs the plaintext into the AES algorithm for encryption.  
Step 8: [PERSON], upon receiving the ciphertext, uses the key \(x_{nB+nA}\) to recover the plaintext using Algorithm 3.  
```
0: plaintext, key Output ciphertext, decrypted_data
1: Function encrypt (plaintext, key):
2: cipher: = Create AES cipher object with key in CBC mode
3: iv: = Generate random initialization vector (IV)
4: padded_plaintext: = Pad plaintext to AES block size
5: ciphertext: = Encrypup padded_plaintext with cipher using the IV
6: Return iv; ciphertext
7: Function decrypted(ciphertext, key, iv):
8: cipher: = Create AES cipher object with key in CBC mode and IV
9: decrypted_bytes: = Decrypt ciphertext with cipher
10: decrypted_data: = Unpad decrypted_bytes to AES block size
11: Return decrypted_data
```  
**Algorithm 3** Encrypypt and decrypt using the AES algorithm.  
Figure 9: Combined application of the improved multiple chaotic system key exchange process and AES algorithm.
## 2 Methodology
### Link Budget Analysis  
Link budget analysis is a critical aspect of wireless communication system design that allows for the estimation of the signal quality and strength between transmitters  
Figure 6: In-house IoT terminal designed by DEWA research and development center.  
Figure 7: Horizontal and vertical omnidirectional antennas [32].  
and receivers. In a wireless communication system, the transmitted signal experiences attenuation, which reduces its strength and quality as it propagates through the air. Link budget analysis is the process of determining the total attenuation of the transmitted signal and comparing it to the minimum required signal strength at the receiver to achieve reliable communication.
## 2 System Model and Channel Description
### System Model
Similarly, the source node \(N_{2}\) can also extract the binary sequence transmitted by node \(N_{1}\). Compared to the complex processing of the UA phase at the relay node, involving techniques such as mapping and modulo-2 summation, the issue during the DB phase resembles common point-to-point transmissions and thus requires no special treatment [30]. Therefore, we focus solely on the BER performance of node \(N_{R}\) during the UA phase, and the details regarding the terminal receivers are omitted.
# A Hybrid Genetic Algorithm for Ground Station Scheduling Problems
## 3 Ground Resource Scheduling Model
### Optimization Objective
#### 3.2.2 Maximizing Task Revenue  
For a given task list \(T\), where the revenue-influencing factors for each task are defined as the effective execution time and task priority, the formula for calculating the total task revenue (TTR) of task list \(T\) is as follows:  
\[\mathrm{TTR}=\sum\limits_{1\leq k\leq n}x_{k}\cdot p_{k}\cdot(time_{e}^{k}-time _{s}^{k}) \tag{3}\]  
\(\{k|1\leq k\leq n,n\in Z\}\) represents the range of task id, \(n\) denotes the total number of tasks, and \(time_{e}^{k}-time_{s}^{k}\) indicates the effective execution time duration of task \(k\).
# Applications of Deep Learning to Neurodevelopment in Pediatric Imaging: Achievements and Challenges
## 2 Methods
### Public Datasets and Repositories  
Sample size is one of the most critical issues for training a deep learning algorithm as the number of trainable parameters grows exponentially with deep architectures. However, data collection is expensive and time-consuming for medical images. Fortunately, more and more data repositories and data-sharing platforms are available recently, making it possible to conduct medical imaging studies on a large scale. Table 1 lists the available public datasets and repositories involved in the studies reviewed in this manuscript. Some repositories collect data from multiple independent sites and provide a large number of subjects. The Autism Brain Imaging Data Exchange (ABIDE) dataset and IMaging-PsychiAtry Challenge (IMPAC) dataset focus on autism spectrum disorder (ASD) recognition and provide data of subjects with ASD and healthy controls. The ADHD-200 consortium collects data for attention deficit hyperactivity disorder (ADHD) patients and healthy controls. The Healthy Brain Network (HBN) dataset and Human Connectome Project Development (dHCP) project are data collections for typically developed individuals. The UNC/UMN Baby Connectome Project (BCP) collects data of infants and pre-school age children. Other datasets including a large number of participants such as UK Biobank and International Consortium for Brain Mapping (ICBM) involve healthy controls as well as patients with various neurodevelopmental disorders at all ages.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & \begin{tabular}{c} **No. of** \\ **Sites/Projects** \\ \end{tabular} & **Population** & **Technique** & **Citation** \\ \hline \multirow{3}{*}{\begin{tabular}{c} Autism Brain Imaging Data \\ Exchange I (ABIDE I) \\ \end{tabular} } & 17 independent & \begin{tabular}{c} 539 subjects with ASD and \\ 573 healthy controls (age \\ 7–64 years) \\ \end{tabular} & sMRI, rs-fMRI & [31] \\  & & & 7–64 years) & \\ \hline \multirow{3}{*}{\begin{tabular}{c} Autism Brain Imaging Data \\ Exchange II (ABIDE II) \\ \end{tabular} } & 19 independent & \begin{tabular}{c} 521 subjects with ASD and \\ 593 healthy controls (age \\ 5–64 years) \\ \end{tabular} & sMRI, rs-fMRI, & [32] \\  & & & 549 subjects with ASD 601 & \\  & & & (age 0–80 years) & \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} ADHD-200 Consortium \\ \end{tabular} } & 8 independent &
\begin{tabular}{c} 285 subjects with ADHD \\ 491 healthy controls (age \\ 7–21 years) \\ \end{tabular} & sMRI, rs-fMRI & [34] \\  & & & 500,000 subjects & sMRI, rs-fMRI, & \\  & & & (age 40–69 years) & DTI & [35] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Public datasets.  
Figure 5: Architecture of generative adversarial networks (GAN) [29].
## 4 Survey Findings and Results from and Through Discussion
### Statistical Criteria: Correlations
(2-tailed) & & 0.000 & 0.000 & 0.006 & 0.014 & 0.207 & 0.001 & 0.003 & 0.022 & 0.004 & 0.011 \\ \hline \multirow{2}{*}{**Organization**} & \multicolumn{2}{c}{Pearson} & \multicolumn{2}{c}{**Pearson**} & \multicolumn{2}{c}{**} & \multicolumn{2}{c}{**} & \multicolumn{2}{c}{**} & \multicolumn{2}{c}{**} & \multicolumn{2}{c}{**} & \multicolumn{2}{c}{**} & \multicolumn{2}{c}{**} & \multicolumn{2}{c}{**} \\  & Correlation & 0.327 \({}^{**}\) & 1 & **0.365 \({}^{**}\)** & 0.066 & 0.293 \({}^{**}\) & 0.185 \({}^{*}\) & 0.195 \({}^{*}\) & 0.126 & 0.046 & 0.097 & 0.201 \({}^{*}\) \\  & Sig. (2-tailed) & 0.000 & & 0.000 & 0.450 & 0.001 & 0.031 & 0.023 & 0.144 & 0.593 & 0.265 & 0.019 \\ \hline \multirow{2}{*}{**Interesting**} & \multicolumn{2}{c}{Pearson} & \multicolumn{2}{c}{**Pearson**} & \multicolumn{2}{c}{**} & \multicolumn{2}{c}{**} & \multicolumn{2}{c}{**} & \multicolumn{2}{c}{**} & \multicolumn{2}{c}{**} & \multicolumn{2}{c}{**} & \multicolumn{2}{c}{**} & \multicolumn{2}{c}{**} \\  & Correlation & 0.361 \({}^{**}\) & **0.365 \({}^{**}\)** & 1 & 0.031 & **0.362 \({}^{**}\)** & 0.235 \({}^{**}\) & **0.509 \({}^{**}\)** & 0.228 \({}^{**}\) & 0.098 & 0.273 \({}^{**}\) & 0.256 \({}^{**}\) \\  & Sig. (2-tailed) & 0.000 Additionally, it should be noted that the strongest correlation from the performed correlation analysis is between the parameter of the \"interesting suggestions\" and the parameter of the \"knowledge\" (r = 0.550, \(p\) < 0.01) (Table 17). Undeniably, this survey finding is indeed very interesting, considering that the parameter of the \"knowledge\" had the lowest average score in the fully grouped subtotal responses from the evaluation form (i.e., MEAN: 4.27 and SD: 0.714) (Table 6). Also, this correlation confirms the literature that they are inextricably linked, especially in relation to technological pedagogical content knowledge [192; 193; 194; 23; 25; 40], as the interactive radio-themed lesson was designed and implemented utilizing advanced technologies. Furthermore, two more statistically significant positive correlations are also observed between (a) the parameter of the \"expectations\" and the parameter of the \"interesting suggestions\" (r = 0.361, \(p\) < 0.01) (Table 17); and (b) the parameter of the \"expectations\" and the parameter of the \"organization\" (r = 0.327, \(p\) < 0.01) (Table 17). Moreover, this statistical analysis also showed that the most non-correlated factors among the parameters are the parameter of the \"venue\", highlighting again the crucial importance of communication classroom, which also contributes to the overall learner experience [134]. In general, all these survey findings and results are characterized as particularly very interesting and should be investigated further. Finally, more details about these correlations are provided in Table 17.
# A Study on the Stall Detection of an Axial Compressor through Pressure Analysis
## 4 Compressor Stall Detection Algorithm  
As is analyzed in the experimental pressure data, the compressor rotor blade is the best region for detecting compressor working conditions. In order to predict the compressor working conditions such as surge margin, a real-time stall detection algorithm is proposed. The core idea of the algorithm is to utilize the pressure periodicity, as is shown in Figure 22. Figure 22a shows the compressor blade tip region pressure in a stable condition and a good periodicity. Compressor working conditions in Figure 22b are in a stall state. The pressure signal shows an obvious difference when conditions are stable.  
The calculation algorithm can be expressed as,  
\[C_{t}=\frac{\sum\limits_{i=n-wnd}^{n}|(P_{i}-P_{ave})\cdot(P_{i-N}-P_{ave})|}{ \sqrt{(\sum\limits_{i=n-wnd}^{n}(P_{i}-P_{ave})^{2})\cdot(\sum\limits_{i=n-wnd }^{n}(P_{i-N}-P_{ave})^{2})}} \tag{2}\]  
where \(C_{t}\) is the pressure correlation coefficient, \(wnd\) is the size of the correlation measure window, \(n\) is the index of the current pressure sample, \(P_{i}\) is the present pressure signal, \(P_{ave}\) is the window pressure signal, and \(N\) is the sample number of the rotor rotational time delay between A and B.  
In order to verify the effectiveness of the stall detection algorithm, a dataset of compressor surge margin of 5.8% is added to the verification. Figure 23 shows the calculated results of compressor pressure correlation coefficients. Whether with compressor inlet distortion or not, the correlation coefficient calculated at surge margin of 14.2%, 5.8%, and 1.5% all showed consistence in the value region. Although the compressor inlet distortion generated significant influence on the compressor inlet and outlet pressure characteristics of amplitude frequency and periodicity, the blade tip region pressure still kept a good characteristic of the amplitude frequency and periodicity. Therefore, a relationship is  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Working State** & \multicolumn{2}{c}{**Surge Margin of 14.2\%**} & \multicolumn{2}{c}{**Surge Margin of 1.5\%**} \\ \hline
**Position** & **No Distortion** & **Sole Distortion** & **No Distortion** & **Sole Distortion** \\ \hline inlet & 0 & 2 & 221 & 730 \\ outlet & 0 & 2 & 6 & 5 \\ blade tip & 0 & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The analyzed pressure amplitude–frequency results of _Nam_.  
Figure 22: Analysis of correlation at steady (**a**), and stall working conditions (**b**).  
established between the pressure signal and the compressor surge margin. The results also showed that the stall detection algorithm kept a good monotonicity regarding surge margin versus the compressor pressure correlation coefficient.