esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Genetic Approaches
### Inhibition of DUX4 at mRNA Level  
The transcription of DUX4 mRNA can be directly reduced by targeting RNA level, including antisense oligonucleotide (ASOs) or RNA interference (RNAi) therapy (Figure 3). Oligonucleotides can inhibit DUX4 expression in many ways; the most widely tested is an antisense oligonucleotide (AOs). ASO has been proven to successfully target DUX4 pre-mRNA in immortalized FSHD myocytes and restrain its polyadennylation, resulting in the degradation of DUX4 mRNA [71]. [PERSON] et al. [59] and [PERSON] et al. [60] (Table 1) tested the efficiency of different phosphorodiimide morpholino oligomers (PMOs) targeting  
DUX4 transcripts. Both studies identified two PMOs that effectively suppressed DUX4 and target gene expression in FSHD myotubes cultures. [PERSON] et al. (Table 1) [61; 62] showed the use of locked nucleic acid (LNA) and 2\({}^{\prime}\)-O-methoxyethyl (2\({}^{\prime}\)-MOE) gapmer ASOs that support the breakdown of DUX4 mRNA by RNase H in immortalized myotubes, and in vitro experiment, LNA and 2\({}^{\prime}\)-MOE ASOs was injected into tibialis anterior of FLExDUX4 mice. The results showed that both ASOs could reduce the DUX4 level of injected muscle. Additionally, a study [63] tested a cEtASO targeting the open reading frame of exon 1 of DUX4 transcripts in ACTA1-MCM mice (expressing low levels of DUX4). In this study, the DUX4 ASO was administered via subcutaneous injection for systemic delivery. The results showed significant reductions in DUX4 mRNA; protein levels in the quadriceps, triceps brachii, gastrocnemius, and tibialis anterior muscles; and improvements in skeletal muscle pathology. Unfortunately, there are currently no clinical trials for ASO-based treatments for FSHD, but these findings suggest that systemic delivery of ASOs targeting DUX4 is a promising strategy.  
Another method is RNA interference, which specifically realizes mRNA degradation or destruction through complementary binding with the target RNA sequence. (RNAi) therapies utilizing small interfering RNA (siRNA) were used to target the 3\({}^{\prime}\) untranslated region transcribed from pLAM, the coding region, and the region upstream of the DUX4 transcription start [64; 65; 72] to degrade the level of DUX4 mRNA.To date, FRG1 is the only candidate gene identified to induce FSHD in animal models. A study has developed FRG1-targeted microRNA vectors (miRRG1) [66] (Table 1); in vitro experiments demonstrated that the expression of FRG1 was significantly reduced in mice with FRG1 overexpression. Additionally, the in vivo delivery of miRRG1 via AAV6 further decreased FRG1 expression and improved the muscle quality and histopathology of the mice, thereby validating the feasibility of developing RNAi-based gene therapy for the treatment of FSHD.
## 2 Mathematical Model of the Induction Motor  
To analyze the operation of an IM during transient processes, the choice of the electric motor's mathematical model is an important and often decisive factor. An efficient solution for such tasks involves using a mathematical model in orthogonal coordinates, where the primary variables are the voltages and currents of the electric machine.  
In the first stage of modeling the dynamic operation regimes of an IM, conventional assumptions are traditionally applied. These assumptions enable the analysis of an ideal ized machine, rather than a real electric machine. The idealized machine is characterized by the following [49]:  
1. No magnetic circuit saturation, hysteresis, or iron loss: this means that the magnetic materials in the motor are assumed to behave perfectly, without any energy losses or limits on how much magnetism they can handle;
2. No current displacement in winding copper: the flow of electric current in the motor's windings is assumed to be evenly distributed, without any irregularities caused by the changing frequency of the current;
3. Sinusoidal distribution of magnetic flux density and magnetizing forces in space: the magnetic field inside the motor is assumed to be smooth and evenly spread out, creating consistent forces that drive the motor;
4. Independence of winding leakage inductance from rotor position: the magnetic leakage, which is the small amount of magnetic energy that escapes from the windings, is assumed to stay the same regardless of the rotor's position;
5. Complete symmetry of windings: the windings (the coils of wire in the motor) are assumed to be perfectly balanced and identical, which simplifies the analysis.  
The idealizations made in this study, such as assuming negligible current displacement in the copper windings, are commonly used in simplified models for induction motors (IMs) to reduce computational complexity while maintaining accuracy in most industrial applications. These assumptions are typically valid when the motor operates under standard conditions where current displacement effects are minimal. However, in cases where the motor design or operational environment leads to significant current displacement effects (such as high-frequency operation or specific motor designs), these idealizations may limit the accuracy of the model. For motors where current displacement in the windings plays a more prominent role, more advanced models, which account for these effects, are necessary [50; 51].
## Results
### Evaluation of Leishmania Activity  
All studied _Leishmania_ strains were susceptible to _L. luisieri_ EO, and we observed a more prominent effect on _L. infantum_ (IC\({}_{50}\)/24 h = 63 \(\mathrm{\SIUnitSymbolMicro\text{g}}\)/mL), followed by _L. tropica_ (IC\({}_{50}\)/24 h = 38 \(\mathrm{\SIUnitSymbolMicro\text{g}}\)/mL) and _L. major_ (IC\({}_{50}\)/48 h = 31 \(\mathrm{\SIUnitSymbolMicro\text{g}}\)/mL) viability. _L. viridis_ essential oil displayed less activity against _L. infantum_ (IC\({}_{50}\)/24 h = 263 \(\mathrm{\SIUnitSymbolMicro\text{g}}\)/mL) (Table 2).  
The main compounds, 1,8-cineole, borneol, and linalool did not reveal any effect on _L. infantum_ promastigotes at tested concentrations, while \(\alpha\)-pinene revealed some activity against _L. infantum_ (IC\({}_{50}\)/24 h = 161 \(\mathrm{\SIUnitSymbolMicro\text{g}}\)/mL).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Compounds** & _Lavandula luisieri_ (\%) & _Lavandula viridis_ (\%) \\ \hline Monoterpene hydrocarbons & 4.7 & 17.3 \\ Oxygen containing monoterepenes & 75.7 & 58.3 \\ Sesquiiterpene hydrocarbons & 2.4 & 18.6 \\ Oxygen containing sesquiterpenes & 3.0 & - \\ Others & 1.2 & 1.9 \\ Total identified & (49) 86.9 & (38) 96.1 \\ \hline \hline \end{tabular}
\begin{tabular}{c c} \hline \hline \multicolumn{3}{c}{\(\alpha\)-Pinene (9.2)} \\  & \(\alpha\)-Pinene (2.3) & Camphene (2.7) \\  & 1,8-cineole (18.9) & 1,8-cineole (29.7) \\ Major compounds (\textgreater{}2.0\%) & Linalool (3.1) & Linalool (9.0) \\  & Necrodane derivatives (36.0) & Campher (10.0) \\  & Lavandulyl acetate (7.2) & Borneol (2.7) \\  & & \(Z\)-\(\alpha\)-bisabolene (6.3) \\  & & Selina-3,7(11)-diene (6.6) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Major composition of _Lavandula luisieri_ and _Lavandula viridis_ essential oils.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **L. infantum** & _L. tropica_ & **L. major** \\  & **IC50 \(\mathrm{\SIUnitSymbolMicro\text{g}}\)/mL (Cl) *** & **IC50 \(\mathrm{\SIUnitSymbolMicro\text{g}}\)/mL (Cl) * & **IC50 \(\mathrm{\SIUnitSymbolMicro\text{g}}\)/mL (Cl) * \\ \hline _L. luisieri_ & 63 (52–77) & 38 (33–45) & 31 (25–38) \\ _L. viridis_ & 263 (248–279) & \textgreater{}400 & \textgreater{}400 \\ \(\alpha\)-pinene & 161 (149–175) & \textgreater{}400 & \textgreater{}400 \\ 1,8-cineole & \textgreater{}400 & \textgreater{}400 & \textgreater{}400 \\ Borneol & \textgreater{}400 & \textgreater{}400 & \textgreater{}400 \\ Linalool & \textgreater{}400 & \textgreater{}400 & \textgreater{}400 \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Lavandula luisieri_ and _Lavandula viridis_ essential oils activity and major compounds on _Leishmania_ promastigotes viability.
# Effect of the Planetesimal Belt on the Dynamics of the Restricted Problem of 2 + 2 Bodies
## 3 Equilibrium Points  
The equilibrium points are the positions of an infinitesimal body where the motion of the minor vanishes. Thus, the velocity and acceleration of \(P_{i},i=1,2\) are zero, i.e., \(\dot{x_{i}}=\dot{y_{i}}=\dot{z_{i}}=\ddot{x_{i}}=\ddot{y_{i}}=\ddot{z_{i}}=0\). Applying these conditions in Equation (2), we obtain,  
\[\begin{array}{rcl}U_{x_{1}}&=&\mu_{1}\left(n^{2}x_{1}-\frac{(1-\mu)(x_{1}-\mu )}{r_{11}^{2}}-\frac{\mu(x_{1}-\mu+1)}{r_{21}^{2}}-\frac{\mu_{2}(x_{1}-x_{2}) }{r^{3}}\right)\\ &&-\frac{M_{b}x_{1}}{\left(R_{1}^{2}+T^{2}\right)^{\frac{1}{2}}}=0,\end{array} \tag{3}\]  
Figure 1: Restricted \(2+2\) body problem model.  
\[U_{y_{1}} = \mu_{1}\Bigg{(}n^{2}y_{1}-\frac{(1-\mu)y_{1}}{r_{11}^{3}}-\frac{\mu y _{1}}{r_{21}^{3}}-\frac{\mu_{2}(y_{1}-y_{2})}{r^{3}}\Bigg{)}-\frac{M_{b}y_{1}}{ \big{(}R_{1}^{2}+T^{2}\big{)}^{\frac{3}{2}}}=0, \tag{4}\] \[U_{z_{1}} = -\frac{(1-\mu)z_{1}}{r_{11}^{3}}-\frac{\mu z_{1}}{r_{21}^{3}}- \frac{\mu_{2}(z_{1}-z_{2})}{r^{3}}=0, \tag{5}\]  
\[U_{x_{2}} = \mu_{2}\bigg{(}n^{2}x_{2}-\frac{(1-\mu)(x_{2}-\mu)}{r_{12}^{3}}- \frac{\mu(x_{2}-\mu+1)}{r_{22}^{3}}-\frac{\mu_{1}(x_{2}-x_{1})}{r^{3}}\bigg{)}\] \[-\frac{M_{b}x_{2}}{(R_{2}^{2}+T^{2})^{\frac{3}{2}}}=0,\]  
\[U_{y_{2}} = \mu_{2}\Bigg{(}n^{2}y_{2}-\frac{(1-\mu)y_{2}}{r_{12}^{3}}-\frac{ \mu y_{2}}{r_{22}^{3}}-\frac{\mu_{2}(y_{2}-y_{1})}{r^{3}}\Bigg{)}-\frac{M_{b}y _{2}}{(R_{2}^{2}+T^{2})^{\frac{3}{2}}}=0, \tag{7}\] \[U_{z_{2}} = -\frac{(1-\mu)z_{2}}{r_{21}^{3}}-\frac{\mu z_{2}}{r_{22}^{3}}- \frac{\mu_{1}(z_{2}-z_{1})}{r^{3}}=0. \tag{8}\]  
Simplifying Equation (5), we obtain,  
\[z_{1}=z_{2}\frac{\mu_{2}}{r^{3}\bigg{[}\frac{(1-\mu)}{r_{11}^{3}}+\frac{\mu}{ r_{21}^{3}}+\frac{\mu_{2}}{r_{2}^{3}}\bigg{]}}. \tag{9}\]  
Using the value of \(z_{1}\) from (9) in (8), we obtain either \(z_{2}=0\) or \(c=0\), where  
\[c=\Bigg{[}\frac{(1-\mu)}{r_{21}^{3}}+\frac{\mu}{r_{22}^{3}}+\frac{\mu_{1}}{r^ {3}}-\frac{\mu_{1}\mu_{2}}{r^{6}\bigg{\{}\frac{(1-\mu)}{r_{11}^{3}}+\frac{\mu }{r_{21}^{3}}+\frac{\mu_{2}}{r^{3}}\bigg{\}}\bigg{]}}.\]  
However, we observe that \(c\) is nonzero for all values of \(\mu\), \(\mu_{1}\), and \(\mu_{2}\). Therefore, \(z_{2}\) must be zero and hence \(z_{1}=0\). Consequently, all equilibrium points of the restricted \(2+2\) body problem lie on the \(XY\) plane. Hence, the solutions of the equilibrium points can be obtained from Equations (3), (4), (6) and (7).
## 2 QPSK-Oriented All-Optical Matching System and Noise Analysis
### QPSK-Oriented All-Optical Matching System
Such schemes would allow the QPSK-oriented all-optical matching system to output stable and high-quality optical signals under the influence of noise at a rate of 100 Gbaud.
# Digital Twin-Based Automated Guided Vehicle Scheduling: A Solution for Its Charging Problems
## 2 Problem Formulations  
This paper considers the AGV scheduling in in a discrete manufacturing workshop producing parts for internal combustion engines, as shown in Figure 1. The workshop includes six machines for processing parts and three AGVs for transporting parts.  
The general layout of the workshop is shown in Figure 2. Here, only the AGV scheduling is considered, and the allocation of the machine has been completed by default. Therefore, each workpiece process in the processing corresponds to a unique machine. In the material storage area \(S\), the blanks of the workpieces are initially placed. The blanks are carried by the AGV to the corresponding machine \(Mx\) after an order is placed. After several processes, the finished workpieces are transported by AGV to the parts storage area \(F\). A simple example is shown in Figure 1. Order 1 contains two machining operations, which will be performed on \(M2\) and \(M6\), respectively. Consequently, three transport tasks with time-constrained are generated, Task 1 (\(S\)-\(M2\)), Task 2 (\(M2\)-\(M6\)) and Task 1 (\(M6\)-\(F\)). These three transportation tasks will be performed by AGV1, AGV2 and AGV3, respectively. The AGV will charge in the case of insufficient power before the performance of transportation tasks.  
The studied dynamic scheduling problem is subjected to the following assumptions:  
* AGV's collision and deadlock are not considered;
* The AGV drives with the principle of the shortest path;
* An AGV can only carry one workpiece each time;
* AGV loading and unloading time can be ignored;
* Charging devices are capable for all AGVs at the same time;
* AGV cannot perform charging tasks during transportation;
* There is no capacity limit for material storage areas and finished product storage areas;
* The speed of the AGV remains constant during driving.  
Figure 1: Discrete manufacturing workshop.
## 3 Results and Discussions
### Artificial Neural Network (ANN)
#### 3.4.2 Performance Evaluation of the ANN Model  
The performance of the developed neural network model for predicting TL glow curves was evaluated using three key metrics: Mean Absolute Error (MAE), Mean Squared Error (MSE), and the coefficient of determination (\(R^{2}\)).  
The MAE, given in Equation (2), measures the average absolute derivation between the predicted (\(y_{i}\)) and actual (\(y_{i}\)) values across \(n\) observations. By representing the typical deviation in the same unit as the output variable, the MAE offers an intuitive interpretation of the model's prediction errors.  
\[MAE\ =\ \frac{1}{n}{\sum}{\left|{y_{i}-\hat{y}_{i}}\right|} \tag{2}\]  
The MSE, given in Equation (3), calculates the average of the squared differences between predicted and actual values. By squaring the errors, the MSE places a stronger emphasis on larger deviations, making it particularly useful for identifying models that may suffer from occasional large errors.  
\[MSE\ =\ \frac{1}{n}{\sum}{\left({y_{i}-\hat{y}_{i}}\right)^{2}} \tag{3}\]  
To assess the proportion of variance in the luminescence response explained by the model, the coefficient of determination (\(R^{2}\)), shown in Equation (4), was computed, where \(\overline{y}_{i}\) represents the mean of the actual observed values. This metric compares the sum of squared residuals to the total sum of squares, where a value closer to 1 indicates a strong correlation between predictions and actual values.  
\[R^{2}\ =\ 1-\frac{{\sum}{\left({y_{i}-\hat{y}_{i}}\right)^{2}}}{{\sum}{\left({y_{ i}-\overline{y}_{i}}\right)^{2}}} \tag{4}\]  
The combination of these metrics offers a well-rounded evaluation of the model's performance, providing information on both the magnitude and distribution of errors. The obtained results demonstrate the model's effectiveness in accurately predicting TL glow curves based on dose values and demonstrate its potential applicability in thermoluminescence analysis and related fields.
# Research on Personalized Course Resource Recommendation Method Based on GEMRec
## 3 Graph-Enhanced Multimodal Recommendation Method: GEMRec
### Multimodal Feature Fusion and Entity Extraction Strategy
#### 3.2.1 Cross-Modal Preprocessing  
For video data processing, this study employs deep learning-based computer vision techniques to extract entity information. The process begins with uniform sampling to obtain video keyframe sequences. Each keyframe is processed using the YOLOv5 object detection model to identify visual entities such as charts and formulas in teaching scenarios. Additionally, Optical Character Recognition (OCR) technology extracts textual information displayed on screens, while scene segmentation networks understand the overall semantic structure of teaching scenarios. These visual features not only supplement concept relationships not explicitly stated in text but also capture the progression of knowledge points through temporal analysis, as defined by  
\[\mathrm{V_{t}=f_{visual}(YOLOv5(F_{t}),OCR(F_{t}),Scene(F_{t}))} \tag{6}\]  
where \(V_{t}\) represents the comprehensive visual features at time \(t\), and \(F_{t}\) denotes the keyframe at time t.  
For audio data, we designed a multi-level processing pipeline. First, Automatic Speech Recognition (ASR) technology converts speech to text, preserving verbal expressions from the lectures. Second, acoustic feature analysis examines volume, speech rate, and pauses to identify emphasized concepts. The acoustic features are encoded as  
\[\mathrm{A_{t}=f_{acoustic}([volume_{v},pitch_{v},pause_{t}])} \tag{7}\]  
Furthermore, dialogue behavior analysis technology captures concept associations from teacher-student interactions, particularly evident in question-answer sessions and discussions.  
To ensure temporal consistency across different modal features, we established a timestamp-based alignment mechanism. By mapping video frames, audio segments, and  
Figure 2: Multimodal knowledge graph entity relationship construction process.  
textual content onto a unified timeline, we achieve synchronized multimodal data analysis. This alignment mechanism provides the foundation for subsequent feature fusion, enabling the system to comprehensively utilize advantageous information from each modality, formulated as  
\[\text{M}_{t}=[\text{V}_{t};\text{T}_{t};\text{A}_{t}] \tag{8}\]  
where \(M_{t}\) represents the aligned multimodal features at time t, and \(T_{t}\) denotes the textual features.
## 4 Discussion  
Based on the results obtained, it can be concluded that the FEM is suitable for calculating stresses and deformations in a stiffener, such as the one proposed. Additionally, the authors aimed to test the hypothesis that a multiple regression system could be established as a substitute for FEM calculations.  
As observed in the comparative result tables, the values obtained through multiple regression are very close to those generated by the Finite Element Method (FEM), yielding excellent generalisation results (using test examples). The results provided show the good generalisation of the different multiple regression models tested, since they are results calculated in a cross-validation process for patterns considered in each case as test patterns. Therefore, the best multiple regression model (quadratic) could serve as a suitable alternative to FEM calculations in this design problem. Once the models are established, the results can be computed instantly, as the process reduces to multiplying the identified parameters by the values of the design variables.  
From the analysis of relevant variables, it can be highlighted that the most relevant variable, as expected, is the parameter P3; its interactions with P4 and P5, as well as its quadratic term adds a small degree of nonlinearity that produces the best results. This result was expected since it is the parameter that produces the highest stiffness of the set. The joint model with quadratic and interaction terms has been found to produce for test examples an R greater than 0.98 in any replication of the experiment.  
Finally, the study of stiffener plate performance modelling can help prevent stiffener plates from breaking and thus extend the crane's service life.
## 3 Results
### Microhardness  
Hardness profiles from the top surface into the material along the build direction are given in Figure 9 for the investigated specimens; a typical error bar is given. The profiles are shown for the first 1 mm only, because the bottom part is very rough and porous (Figures 2-4).  
The hardness increases slightly with increasing depth for all three specimens, which may be due to the refinement of the martensite morphology with the increasing number of heating cycles, as described earlier by [PERSON] et al. [13]. The repeated heating cycles could also cause increased hardness by precipitation of Ti\({}_{3}\)Al at the right temperatures (approx. below 600 \({}^{\circ}\)C [27]), so called self-tempering. For the line scan specimen, the hardness decreased again from about a depth of 0.6 mm, indicating that a coarsening of the microstructure or a phase change occurred. This depth is consistent with the observation of the decomposition martensite into lamellar \(\alpha/\beta\) in the bottom part of the line scan specimen. Decomposition of martensite is normally associated with a hardening increase for the Ti-6 Al-4V alloys, and this observation will be discussed later.  
Figure 8: Backscatter electron micrographs of polished cross sections of selective laser melting (SLM) specimens fabricated with (**a**) line scan, (**b**) island scan stripes, and (**c**) island scan random strategies, showing the martensitic microstructures and decomposition of \(\alpha^{\prime}\) to \(\alpha/\beta\) in the bottom of (**a**). Top, middle, and bottom refers to the distance from the top surface in the cross sections (last melted layer).
## 3 Test Results and Analysis
### Test Analysis
#### 3.2.2 Deformability Analysis  
The deflection of each test beam is shown in Figure 7. Before cracking, the deflection of each specimen was basically the same under the same load, and each test beam was in the elastic deformation stage. The bamboo fiber and steel wire mesh had little effect on the deformation of the beam. After the concrete cracked, the test beam entered the elastoplastic stage. Under the same load, the deflection of the concrete test beam mixed with bamboo fiber and steel wire mesh was small, and, as the load continued to increase, the deformation gap became more obvious. This is because the addition of bamboo fiber and steel wire mesh can share the tensile stress with the concrete therefore preventing the cracks from spreading further. Then, the stress and deformation of the BFRP bars at the cracks are reduced, so that the global stiffness of the beam is improved, and the deflection is significantly reduced. The L-1-0 rigidity improvement effect of the concrete beam with only layout steel wire mesh in the whole beam section was the most obvious.  
Figure 6: Comparison of the cracking load of each specimen.  
Figure 7: Comparison of the deformation of each test beam.  
As shown in Figure 8, comparing specimens L-1-0, L-1-30, and L-1-45, when the layout of the steel wire mesh remained unchanged, along with the increase in the length of the bamboo fiber, the rigidity of the beam was also significantly improved. The 45 mm bamboo fiber had a better improvement effect. This is because the concrete specimens with a bamboo fiber length of 45 mm had a higher tensile strength than 30 mm (as can be seen from \(f_{t}\) in Table 5) and were able to bear more tensile stress in the concrete specimens to reduce the deformation of the specimens. Comparing specimens L-0-30, L-1/2-30, and L-1-30, when the length of bamboo fiber remained the same, different steel wire mesh layouts could also significantly affect the deformation of BFRP bar concrete beams. The BFRP bar concrete beam with steel wire mesh in the full beam section showed less deformation, indicating that the steel wire mesh in the full beam section can better connect the cracked concrete specimens into a whole, enhance its integrity, and improve the deformability.