esa-sceva/satcom-chunk-collection huggingface dataset:
# Algorithms for Calculating Generalized Trigonometric Functions
## 5 Algorithms for Calculation Space Transform Based on _Ateb_-Functions
### Orthogonal Ateb-Transform with One Parameter  
Let us consider that \(m=1\); in that condition, _Ateb_-sine and _Ateb_-cosine are presented as \(sa(n,1,t)\) and \(ca(1,n,t)\). Let \(x(t)\) be a real function; then, _Ateb_-transform can be shown in the following form  
\[X(n,\omega)=A(n,\omega)-iB(n,\omega), \tag{32}\]  
where  
\[A(n,\omega)=\int_{-\infty}^{\infty}x(t)\cdot ca(1,n,\omega t)dt, \tag{33}\]  
\[B(n,\omega)=\int_{-\infty}^{\infty}x(t)\cdot sa^{n}(n,1,\omega t)dt. \tag{34}\]  
If we note that _Ateb_-cosine is even and _Ateb_-sine is odd, we obtain inverse _Ateb_-transform with the following formula  
\[x(t)=\frac{1}{\Pi(n,1)}\int_{-\infty}^{\infty}(A(n,\omega)ca(1,n,\omega t)-B( n,\omega)sa(n,1,\omega t))d\omega, \tag{35}\]  
where \(\Pi\) is a half-period of _Ateb_-function. The right part of Formula (35) depends on the parameter \(n\).  
The properties, i.e., the rate of increase or decrease, of the period of the _Ateb_-functions \(ca(1,n,\omega t)\) and \(sa(n,1,\omega t)\) will vary depending on \(n\). The dependence of the _Ateb_-function on the parameter \(n\) allows us to choose the form of \(ca(1,n,\omega t)\) and \(sa(n,1,\omega t)\) corresponding to \(x(t)\).  
For the existence of the _Ateb_-transform for the function \(x(t)\), it is sufficient to fulfill the same conditions that are sufficient for the existence of the orthogonal Fourier transform.
## 5 Application  
In order to verify the rapid response capability of vehicle terminal for time-sharing rental electric vehicle based on acoustic communication technology, the vehicle terminal (recorded as A car) without the acoustic communication technology and the vehicle terminal (recorded as B car) with the acoustic communication technology are placed in the same weak signal area, such as the underground garage, to compare their response time and effectiveness.  
Two electric vehicles are parked in two underground parking garages for testing respectively, one of the underground parking garage is an F1 building in a business district, the other one is an F1 building in a community. The GPRS intensity of two places is tested by using R&S's network signal special test instrument. After testing, the GPRS signal strength of two parking garages was \(-90\) dBm and \(-104\) dBm. According to the device description of the signal number and the level of the received level of the contrast relationship (show as in Table 5), the GPRS signal strength of F1 building in a business district was relatively good, but not sufficient. The GPRS signal strength of F1 building in a community was weak, but the GPRS signal was basically covered.  
In this environment, mobile phones were used in two parking garages on the electric car electronic lock to carry out five times the unlocking and locking operation and record the response time by the second chronograph. The specific statistical results are shown in Tables 6 and 7.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**A Vehicle Operation** & **Response Time** & **B Vehicle Operation** & **Response Time** \\ \hline
1 st shutting & 1.92 s & 1 st shutting & 0.87 s \\
1 st unlocking & 2.68 s & 1 st unlocking & 0.74 s \\
2 nd shutting & failed & 2 nd shutting & 0.69 s \\
2 nd unlocking & 1.58 s & 2 nd unlocking & 0.78 s \\
3 rd shutting & 1.98 s & 3 rd shutting & 0.81 s \\
3 rd unlocking & 3.73 s & 3 rd unlocking & 0.75 s \\
4 th shutting & failed & 4 th shutting & 0.82 s \\
4 th unlocking & 2.12 s & 4 th unlocking & 0.88 s \\
5 th shutting & 2.87 s & 5 th shutting & 0.79 s \\
5 th unlocking & 2.39 s & 5 th unlocking & 0.86 s \\ \hline \hline \end{tabular}
\end{table}
Table 6: Test results of F1 building in a business district.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Signal Lattice** & **Received Level Intensity** \\ \hline
5 lattice & \(>-91\) dBm \\
4 lattice & \(-91\) to \(-101\) dBm \\
3 lattice & \(-101\) to \(-103\) dBm \\
2 lattice & \(-103\) to \(-107\) dBm \\
1 lattice & \(-107\) to \(-113\) dBm \\ No signal & \(<-113\) dBm \\ \hline \hline \end{tabular}
\end{table}
Table 5: The correlation between signal lattice and received level intensity.  
It can be seen from Tables 6 and 7 that the acoustic communication technology could realize the rapid response of the vehicle terminal, which effectively solved the problem of prolonged and slow response in the vehicle terminal of the electric vehicle in weak signal area.
## 2 Related Work
### Educational Content Recommendation  
Educational content recommendation has gained significant attention in recent years, with researchers exploring various methodologies to enhance learning experiences. Recommendation systems in education aim to address challenges such as learner engagement, content relevance, and adaptability. Recommendation algorithms are generally based on (i) heuristic rules, (ii) content similarity, or (iii) behavior similarity. Rule-based recommendation systems involve intricate human logic and consideration of content attributes such as difficulty level, completion time, and types, providing a pre-configured content subscription. Content similarity-based recommenders focus on the content itself, projecting the content into numeric representations within a feature space and evaluating the semantic distance between texts. The semantics of learning materials can be captured through ontology-based approaches, representing educational concepts [1; 3; 18], or through contextual semantic meanings of entire sentences [19; 20] in an approach based on contextual semantics. Learning materials can then be linked using methods like exact concept phrase matching or using word embeddings for STS. Recommenders based on user behavior tailor recommendations to individual students' needs, analyzing profiles based on knowledge level, preferences, and learning style. Collaborative filtering is a user behavior-based approach that groups students with similar behavior patterns and recommends resources based on collective engagement. Content recommendation systems often leverage information retrieval and semantic search technologies for content processing, integrating rule-based and behavior-based perspectives for more relevant recommendations. [PERSON] et al. [PERSON] et al. (2018) combined an ontology-based content similarity approach with students' behaviors, using extracted concepts from the learning contents to construct a knowledge graph and aggregating concept similarities and students' behavior (student-concept interactions) into the knowledge graph for collaborative content recommendation. [PERSON] et al. [PERSON] et al. (2019) used a graph database, Neo4j, to store the constructed knowledge graph that contains Wikipedia articles, textbook content, and the student model, and used Neo4j's internal full-text search engine, Lucene, to calculate the relevance scores for the recommendations. Hybrid approaches, combining multiple methods, are also prevalent ([PERSON] et al., 2016; [PERSON] et al., 2017; [PERSON] et al., 2017; [PERSON] et al., 2017).
# Assessment of Pohang Earthquake-Induced Liquefaction at Youngil-Man Port Using the UBCSAND2 Model
## 2 Methodology
### UBCSAND2 Calibration for Undrained CDSS Tests on Pohang Sand
Figure 9 compares the \(CSR\)-\(N_{\text{liq}}\) curves from the CDSS tests and UBCSAND2-calibrated results for Pohang sand in loose and dense states with \(\sigma^{\prime}v_{0}=100\) and \(200\) kPa, respectively. There is a good agreement between the trend line from the UBCSAND2-clibrated results and CDSS test results. The effect of confining stress on CSR is also shown, in which the increase of the vertical effective stress from \(100\) to \(200\) kPa results in the decrease of CSR for both density states of Pohang sand. However, this effect is more significant in loose sand compared to that in dense sand. From the calibration results for the undrained CDSS tests on Pohang sand, the generic input parameters of the UBCSAND2 model depicted in Table 1 are used to numerically assess the liquefaction from the Pohang earthquake in the next section. Figure 6: Comparison of the UBCSAND2 model for dense Pohang sand and laboratory CDSS test at \(CSR=0.14\), \(\sigma^{\prime}v_{0}=100\) kPa, and \(\alpha=0.0\) in the variations of (**a**) stress path, (**b**) shear stress–strain curve, (**c**) excess pore water pressure generation, (**d**) shear strain and number of cycles to liquefaction. Figure 8: Comparison of the UBCSAND2 model for dense Pohang sand and laboratory CDSS test at \(CSR=0.14\), \(\sigma^{{}^{\prime}}v_{0}=100\) kPa, and \(\alpha=0.1\) in the variations of (**a**) stress path, (**b**) shear stress–strain curve, (**c**) excess pore water pressure generation, (**d**) shear strain and number of cycles to liquefaction. Figure 7: Comparison of the UBCSAND2 model for loose Pohang sand and laboratory CDSS test at \(CSR=0.14\), \(\sigma^{{}^{\prime}}v_{0}=100\) kPa, and \(\alpha=0.1\) in the variations of (**a**) stress path, (**b**) shear stress–strain curve, (**c**) excess pore water pressure generation, (**d**) shear strain and number of cycles to liquefaction.
# Toward Sustainable Mars Exploration: A Perspective on Collaborative Intelligent Systems
## 6 The Way Forward
The rapid acceleration of near-Earth satellite and terrestrial autonomous vehicle deployments sets the stage for these technologies to be leveraged for the Moon and Mars. Increasing the onboard ML and sensing capabilities of both satellites and vehicles for terrestrial use directly translates to more effective, efficient, and intelligent systems for Mars exploration, observation, and habitation. While this concept paper primarily focuses on the technical and engineering aspects of sustainable Mars exploration, it is essential to situate these developments within the broader context of cosmic and Earth-space sustainability. Recent initiatives by organizations such as NASA and the Secure World Foundation have highlighted the importance of ethical, legal, and environmental considerations in space activities. For instance, NASA's Space Sustainability Strategy outlines a framework for responsible space operations [79], and the Secure World Foundation has published resources addressing challenges in ensuring the security, safety, and sustainability of outer space activities [80]. These discussions encompass topics like planetary protection, space debris mitigation, and the long-term stewardship of extraterrestrial environments. Although a comprehensive analysis of these ethical dimensions is beyond the scope of this paper, we acknowledge their significance and emphasize that future work should integrate these considerations into international regulatory frameworks to ensure responsible and sustainable exploration of Mars.
# Sub-Band Backdoor Attack in Remote Sensing Imagery
## 5 Sota Defense Mechanisms
### Strip  
The STRong Intentional Perturbation (STRIP) [17], a runtime backdoor detection system approach, was proposed in 2019. This method aims to identify backdoored models by purposefully changing incoming inputs, and then observing the randomness or similarities of the target class's predictions to these perturbed inputs. If a particular class consistently exhibits low entropy with various types of perturbations, this indicates the model under suspicion may be malicious. In contrast, a benign model will display varying entropy levels in response to different perturbations. STRIP uses two detection metrics to evaluate the capability of backdoor detection: false rejection rate (FRR) and false acceptance rate (FAR). FAR indicates the percentage of samples regarded as poisoned samples by the STRIP method, although they are benign. Whereas FRR indicates the percentage of benign samples indicated as the poisoned samples by STRIP [17].
# Recent Advances in Ultrafast Structural Techniques
## 2 Structural Techniques Involving Ultrashort X-ray Pulses
### Introduction to Fourth-Generation Fs-Hard X-ray Sources: X-ray Free Electron Lasers  
Since the first ultrabright shot of fs-hard X-rays at LCLS in 2009, four additional XFEL user facilities [66; 67; 68; 69; 70; 71] became operational: SACLA (Japan, 2011), PAL-XFEL (Korea, 2016), SwissFEL (Switzerland, 2016), and EuXFEL (Germany, 2017). Figure 5 shows the schematic of SACLA. In an XFEL, ultrabright electron bunches are created in an RF-photoinjector or a DC electron gun and accelerated by a linac to kinetic energies that normally range from 5 GeV to 15 GeV (up to 17.5 GeV at EuXFEL). As the case of synchrotrons, the duration of the X-ray pulses corresponds to the length of the electron bunches in the undulator. Magnetic chicanes are commonly used to compress the electron bursts before they arrive to the undulator. Depending on the electron bunch charge, X-ray pulses with typical lengths ranging from 5 fs to 400 fs can be readily obtained via self-amplified spontaneous emission (SASE), and even Fourier limited X-ray pulses through the implementation of self-seeding [72]. In addition, sub-fs X-ray pulses are also attainable by selectively spoiling the transverse emittance of the electron beam [72; 73]. With up to \(\sim\)10\({}^{13}\) X-ray photons/pulse XFELs produce peak fluxes which are about 10\({}^{4}\)-10\({}^{5}\) times brighter than synchrotrons. Perhaps the only disadvantage of XFELs over synchrotrons is the fact that each electron bunch in an XFEL experiences a single pass before hitting a block at the end of the beam line. Such high X-ray flux, relatively small bandwidth \(\sim\)10\({}^{-3}\)-10\({}^{-2}\) (SASE), and short pulse duration \(\sim\)100 fs provide XFELs outstanding single-shot diffraction quality for ultrafast structural dynamics. In the following sections, the main structural techniques currently implemented at XFEL facilities will be introduced.  
Figure 5: **Schematic of SACLA: a compact XFEL.** EG, 500 keV electron gun; DF, deflector with collimator; SHB, 238 MHz subharmonic buncher; BS, 476 MHz booster; L-CC, L-band correction cavity; L-APS, L-band alternating periodic structure (APS) typed standing-wave cavity; C-CAT, C-band correction acceleration tube; S(C)-TWA, S(C)-band travelling-wave acceleration tube; BC\(n\) (\(n\approx\) 1–3), \(n\)th bunch compressor; EDS, electron beam diagnostic section; SM, switching magnet; UND, undulator line; BL\(n\) (\(n\) = 1, 3), \(n\)th beam line; XDS, X-ray diagnostic system; EHA, experimental hall; OH, optical hutch; EH, experimental hutch. Only two beam lines beamlines (BL1 and BL3) were available in 2012. SACLA has the capability to install five independent FEL beamlines, and there are three currently in operation. Please refer to Reference [66] for details about each insertion device. Image adapted with permission from Springer Nature.  
Main Applications at X-ray Free Electron Lasers (XFELs) for the Study of Structure and Dynamics of Matter
## 2 Materials and Methods
### Laboratory Setting--Dataset
#### 2.1.4 Experimental Design  
The experimental protocol in controlled settings (Figure 3) comprised six recording sessions, with two sessions held per day--one in the morning and one in the afternoon. The initial four sessions took place on two consecutive days, referred to as _Day 1_ and _Day 2_. The remaining two sessions occurred one week later, on _Day 9_. Each session included four conditions, during which participants randomly performed the two MATB conditions (Easy and Hard) twice. To prevent habituation or expectation effects, certain task parameters were randomly altered across the experimental sessions, including the order of stimulus presentation, radio frequencies, and the sequence of emergency lights. Each condition had a duration of 2.5 min. In summary, the complete dataset consisted of twelve pairs of conditions (four pairs of Easy and Hard conditions for each of the three experimental days, with two pairs conducted in the morning and two in the afternoon). In conclusion, before each Easy and Hard repetition, a 1 min baseline was recorded by asking the participants to fix the MATB task interface without reacting.  
Due to recording issues, participant 10 was excluded from the analyses in this study. Furthermore, during data cleaning, similar recording issues were detected in two sessions of participant 9, which were consequently excluded from the analysis.  
Since we had only 9 participants available, in order to perform more accurate statistical analyses, we considered the sessions as independent variables for statistics rather than individual participants. This approach is justified by the fact that the sessions were recorded at different times, such as different parts of the day or on different days, and can thus be considered events independent from each other.
# A Python-Based Indoor Channel Model with Multi-Wavelength Propagation for Color Shift Keying
## 4 Implementation of the Indoor Channel
### Recursive Model
#### 4.4.2 Cross-Talk Interference  
The cross-talk block implements the discrete version of the Equation (16) to create the cross-talk interference matrix. The spectral resolution in the discrete version is equal to 1 nm in an interval of 380 nm and 780 nm.
## 4 Prediction Based on GA-PSO-BP Neural Network
### Comparison of BP, PSO-BP, and GA-PSO-BP Calculation Accuracy
In order to balance the model's ability to improve prediction accuracy while remaining as close as possible to the fitness obtained by fitting using the original test set, the hybridisation aspect of the genetic algorithm is introduced into the particle swarm iterative optimisation search process. Avoiding the phenomenon of the model falling into local search, it solves the problem that the model can easily fall into premature convergence and is unable to continue to iterate to seek the optimal solution when dealing with multi-dimensional problems, and it also achieves a balance between prediction accuracy and computational efficiency. The process parameter combination for the hydroforming of aluminium alloy variable diameter tubes, which yielded the highest prediction accuracy, was determined using the GA-PSO-BP hybrid algorithm: The internal pressure is 40 MPa, the axial feed is 4 mm, and the friction coefficient is 0.15. After forming, the minimum wall thickness of the part is  
Figure 11: Comparison of error between prediction and simulation results: (**a**) Min. wall thickness. (**b**) Max. wall thickness. (**c**) Max. forming height. 1.27 mm, the maximum wall thickness is 1.53 mm, and the maximum height of forming is 5.11 mm. The finite element simulation results are shown in Figure 13.  
The specific forming process was divided into four stages of 5 s, 7 s, 10 s, and 20 s. The three neural network algorithms mentioned above were used to predict the three forming parameters, respectively, and, finally, the predicted values at different stages were compared with the simulated real values in terms of error. From the combined error comparison results, it was observed that the maximum expansion height parameter predictions exhibited larger errors across all three models. It was verified that the factors influencing expansion height were more complex during the initial hydroforming phase, when the tube first reached the state of plastic deformation, and before the tube reached the better-filled rate, as mentioned in the previous section. The influence of the aluminium large-section rate-of-change tube was included, which resulted in increased difficulty in achieving a linear relationship between the charge and internal pressure. Furthermore, the error comparison across different forming stages demonstrated that the forming parameter prediction errors of the GA-PSO-BP neural network model were maintained within \(\pm\)4.5% for all stages. Figure 12: Comparison of the prediction errors of different prediction models for the forming parameters: (**a**) MAE, (**b**) RMSE, (**c**) \(R^{2}\). Figure 13: Error plots of finite element results of the forming process versus model predictions: (a) 5 s, (b) 7 s, (c) 10 s, (d) 20 s.
## 2 Proposed Numerical Scheme
### Stability Analysis  
A linearized or linear equation is considered for finding the stability region of the proposed scheme. To do so, let the equation be expressed as  
\[y^{\prime}=\overline{\eta}y \tag{14}\]  
Applying the first stage of the proposed scheme on Equation (14) yields  
\[\overline{y}_{i}=y_{i-1}+h\overline{\eta}y_{i}+h^{2}\overline{\eta}^{2}y_{i-1 }=\Big{(}1+h^{2}\overline{\eta}^{2}\Big{)}y_{i-1}+h\overline{\eta}y_{i} \tag{15}\]  
The second stage for discretizing Equation (14) can be expressed as follows:  
\[y_{i}=y_{i-1}+h\bigg{\{}\frac{5}{9}\overline{\eta}y_{i}+\frac{1}{2}\overline{ \eta}y_{i-1}-\frac{1}{18}\overline{y}_{i}\bigg{\}} \tag{16}\]  
Substituting Equation (15) into Equation (16) gives  
\[y_{i}=y_{i-1}+h\bigg{\{}\frac{5}{9}\overline{\eta}y_{i}+\frac{1}{2}\overline{ \eta}y_{i-1}-\frac{1}{18}\Big{(}1+h^{2}\overline{\eta}^{2}\Big{)}y_{i-1}-\frac {1}{18}h\overline{\eta}^{2}y_{i}\bigg{\}}=\Big{[}1+\frac{z}{2}-\frac{z}{18} \Big{(}1+z^{2}\Big{)}\Big{]}y_{i-1}+\bigg{(}\frac{5}{9}z-\frac{z^{2}}{18} \bigg{)}y_{i} \tag{17}\]where \(\overline{u}=zh\).  
Equation (17) can be re-arranged as  
\[y_{i}=\frac{\left[1+\frac{\tilde{z}}{2}-\frac{z}{18}\left(1+z^{2}\right)\right]}{ \frac{5}{9}z-\frac{z^{2}}{18}}y_{i-1} \tag{18}\]  
The stability region for the proposed scheme, when applied to Equation (14), is expressed as  
\[\left|\frac{1+\frac{\tilde{z}}{2}-\frac{z}{18}\left(1+z^{2}\right)}{\frac{5}{9} z-\frac{z^{2}}{18}}\right|<1 \tag{19}\]  
So, the scheme will remain stable if it satisfies inequality (19). The appropriate values of \(h\) and \(\mu\) can be chosen to obtain a stable solution.
## 2 Materials and Methods
### Dosimetry Equipment  
The chambers used in this study were as follows: a Scanditronix Wellhofer FC65-G cylindrical chamber (SN 1630) with a cavity volume of 0.6 cc, featuring a graphite wall and an Al central electrode; a Scanditronix Wellhofer CC15 cylindrical chamber (SN 3560) with a cavity volume of 0.3 cc, featuring a PMMA wall with graphite coating and an Al central electrode; and a Scanditronix Wellhofer NACP-02 plane-parallel-type chamber with 0.7 mm entrance window made of Mylar foil (SN 13505). Ionization chambers used in this study were calibrated at the IBA standard laboratory, with their calibrations traceable to the German reference laboratory (PTB). As recommended, we cross-calibrated the plane-parallel chambers against the FC65-G reference cylindrical chamber in each of the photon beams under study [1; 2; 5]. All doses were measured using a Wellhofer computerized water phantom (IBA Dosimetry, Schwarzenbruck, Germany).  
The percentage depth dose (\(PDD\)) is the absorbed dose measured at depth d, and on the central axis to that, measured at reference point \(d_{max}\) for a certain field size at the phantom surface at 100 cm SSD [10; 11; 12]. The \(PDD\) measurements were performed using FC65-G reference ionization chamber mounted on a Wellhofer computerized water phantom (IBA Dosimetry).