esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
In this paper, we have studied college students' career choices based on their professional skills, behavior regularity, and other related behaviors. Additionally, the study has offered several important insights into improving the model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Acc** & **Micro-F1** & **Precision** & **Recall** \\ \hline Decision Tree & 0.532 & 0.562 & 0.541 & 0.584 \\ SVM & 0.606 & 0.621 & 0.610 & 0.632 \\ Random Forest & 0.623 & 0.633 & 0.620 & 0.647 \\ Logistic Regression & 0.605 & 0.607 & 0.601 & 0.615 \\ XGBOOST & 0.604 & 0.622 & 0.617 & 0.627 \\ ACCBOLR & 0.623 & 0.624 & 0.626 & 0.623 \\ ACCBOX & 0.638 & 0.647 & 0.629 & 0.666 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of ACCBOLR and ACCBOX with baselines.  
Figure 3: (**a**) Effect of reducing the model capacity. (**b**) Effect of a novel regularization term.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Acc** & **Micro-F1** & **Precision** & **Recall** \\ \hline XGBOOST & 0.604 & 0.622 & 0.617 & 0.627 \\ XGBOOST+RC & 0.613 & 0.633 & 0.621 & 0.646 \\ XGBOOST+NR & 0.622 & 0.634 & 0.619 & 0.650 \\ ACCBOX & 0.638 & 0.647 & 0.629 & 0.666 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ablation study of contributions of different modules.  
We have proposed a prototypical cluster center generation approach to use the priori information from each college. Motivated by the cluster assumption that examples in the same cluster should have the same label, we have introduced a novel regularization item to bridge the gap between the real-world examples and prototypical cluster centers. The results of multiple experiments demonstrate that our approach is superior to other approaches to career choice prediction.  
In future studies, three directions can be followed with interest. First Cluster Centers can be discovered in a more precise method. In addition, our model can be extended from using only behavioral data to using multimodal data, such as adding school achievement and questionnaire data. Furthermore, it is meaningful to improve our model to not only predict career choices but also advise on career planning, such as advising on the courses required.  
All authors have contributed equally to the work. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Key Research and Development Program of China under grant 2016 YFB1000905, and the National Natural Science Foundation of China under Grant No. 61572109.  
The authors declare that there is no conflict of interest.
## 1 Introduction
### Constraints
#### 3.2.2 System Operational Constraints  
(1) Power Balance Constraint  
To meet the demand for power balance, the generation power of various types of power sources, the charging and discharging power of new energy storage, and the charging and discharging power of hydrogen energy storage should meet the power demand of the electricity load at all times, as shown in the following formula:  
\[\sum_{i\in\Phi}P_{i,t,k}+\sum_{l=1}^{N1}P_{i,l,k}^{stor}+\sum_{i=1}^{N3}P_{i, l,k}^{P2H}=F_{t,k}^{load} \tag{10}\]  
In the formula, \(F_{t,j}^{load}\) represents the total electricity load power of all of society at moment \(j\) in the \(\mathbf{t}-\mathbf{th}\) planning year.  
(2) Energy Balance Constraint  
The electricity generation of each type of power source in each planning year should meet the total electricity demand of the society, and the electricity consumption for hydrogen production should equal the green electricity required for hydrogen production. The specific formula is as follows:  
\[\left\{\begin{array}{l}\sum\limits_{i\in\Phi}^{8760}P_{i,t,k}\Delta T=Q_{t} ^{load}\\ \sum\limits_{k=1}^{8760}P_{i,1,t,k}^{N3}\Delta T=Q_{t}^{P2H}\end{array}\right. \tag{11}\]In the formula, \(Q_{t}^{load}\), \(Q_{t}^{P2H}\) represent the total electricity demand and the electricity demand for green hydrogen production in the \(\mathbf{t}-\mathbf{t}\mathbf{h}\) planning year, respectively.
3. Carbon Emission Constraint The carbon emissions of the power industry in each planning year are equal to its carbon emission quota, with the specific constraint shown in the following formula: \[\sum_{k=1}^{8760}\left(\sum_{i\in\Phi}\zeta_{i,t}P_{i,t,k}-\sum_{i=1}^{N2}\sum_ {j=1}^{3}\lambda_{i,j,t}P_{i,j,t,k}^{CCUS}\right)\Delta T=Q_{Car,t}^{power}\] (12)  
In the formula, \(\zeta_{i,t}\) represents the carbon emission intensity coefficient of power source \(\mathbf{i}\) in the \(\mathbf{t}-\mathbf{t}\mathbf{h}\) planning year, where the carbon emission intensity coefficients for hydropower, nuclear power, biomass power generation, new energy generation, and pumped storage are 0. \(\lambda_{i,j,t}\) represents the carbon dioxide capture rate of the \(j-\mathbf{t}\mathbf{h}\) type of CCUS upgrading station \(\mathbf{i}\) in the \(\mathbf{t}-\mathbf{t}\mathbf{h}\) planning year. \(Q_{Car,t}^{power}\) represents the carbon emission quota of the power industry in the \(\mathbf{t}-\mathbf{t}\mathbf{h}\) planning year.
4. Power Source Technical Parameter Constraint The technical parameter constraints for each type of power source are detailed in reference [21], and the characteristic constraints of new energy storage, CCUS, and hydrogen energy technologies are shown in Equations (1)-(3).
# Kinematics and Performance Analysis of the 2T1R/1T2R Reconfigurable Decoupled Parallel Mechanism
## 4 Performance Analysis
### Performance Index in 1T2R Mode  
In 1T2R mode, the input motion screw and transmission force screw of serial chain 1 and serial chain 2 are the same as in 2T1R mode, so the calculation is not repeated.  
In 1T2R mode, serial chain 3 is in R\({}^{\prime}_{31}\)R\({}^{\prime}_{32}\)P\({}^{\prime}_{33}\)P\({}^{\prime}_{34}\) mode. If R\({}^{\prime}_{31}\) in serial chain 3 is selected as the driving joint, then the input motion screw \(\mathbf{\xi}^{\prime}_{31}\) of serial chain 3 is  
\[\mathbf{\xi}^{\prime}_{13}=\mathbf{\xi}^{\prime}_{31}=\begin{pmatrix}0&1&0&;&-Z_{A^{ \prime}_{3}}&0&X_{A^{\prime}_{3}}\end{pmatrix} \tag{49}\]  
Assuming that R\({}^{\prime}_{31}\) is locked, \(\mathbf{\xi}^{\prime}_{31}\) is removed from the kinematic screw system of serial chain 3. Solving the reciprocal product for the remaining kinematic pairs, the transmission force screw of serial chain 3 is  
\[\mathbf{\xi}^{\prime}_{\text{T3}}=\begin{pmatrix}0&0&0&;&0&1&0\end{pmatrix} \tag{50}\]  
If the driving unit of serial chain 1 is retained, then the five-dimensional constraint screw system, composed of the constraint screw system provided by the serial chain, and the transmission force screw of the remaining serial chain act on the moving platform. At this point, the mechanism is a single-degree-of-freedom mechanism. Solving for its reciprocal product, the output motion screw \(\mathbf{\xi}^{\prime}_{\text{O1}}\) of serial chain 1 can be obtained as follows:  
\[\mathbf{\xi}^{\prime}_{\text{O1}}=\begin{pmatrix}0&0&0&;&0&0&1\end{pmatrix} \tag{51}\]  
Similarly, the output motion screws \(\mathbf{\xi}^{\prime}_{\text{O2}}\) and \(\mathbf{\xi}^{\prime}_{\text{O3}}\) for the remaining serial chains can be obtained as follows:  
\[\begin{array}{l}\mathbf{\xi}^{\prime}_{\text{O2}}=\begin{pmatrix}1&0&0&;&-\frac {Y_{\text{H}^{\prime}_{3}}\sin\theta_{31}}{\cos\theta_{31}}&Z_{\text{C1}}-\frac {X_{\text{C1}}\sin\theta_{12}}{\cos\theta_{12}}&0\end{pmatrix}\\ \mathbf{\xi}^{\prime}_{\text{O3}}=\begin{pmatrix}0&1&0&;&\left(-Z_{A^{\prime}_{3} }\right)&0&X_{\text{B1}}\end{pmatrix}\end{array} \tag{52}\]  
By substituting \(\mathbf{\xi}^{\prime}_{i1}\), \(\mathbf{\xi}^{\prime}_{\text{T}i}\), \(\mathbf{\xi}^{\prime}_{\text{Oi}}\) (\(i\) = 1, 2, 3) into Equations (40)-(42), the motion/force transmission performance indexes of the mechanism under 1T2R motion mode can be obtained, as shown in Figure 8.  
Figure 7: The motion/force transmission performance index of the mechanism: (**a**) LTI in the workspace and (**b**) LTI of the mechanism (\(\alpha\) = 0).  
From Figure 8, it can be seen that in 1T2R mode, the motion/force transmission performance of the reconfigurable mechanism increases with the increase of the \(Z\) value, and is evenly distributed in the \(\alpha\)-axis direction, showing an increasing trend with the increase of the \(\beta\) absolute value.
# Stabilization of Argan Oil Nanoemulsions Using Chitosan Extracted from Pink Shrimp Shells
## 4 Conclusions  
This study demonstrated the effectiveness of chitosan, extracted from pink shrimp shells, as a natural and sustainable stabilizer for organ oil nanoemulsions. Through a rigorous extraction and characterization process, the chitosan exhibited exceptional physicochemical properties, including high purity, a significant degree of deacetylation, and a notable molecular weight. These attributes played a critical role in enhancing the stability, droplet size uniformity, and shelf life of the nanoemulsions, even under challenging storage conditions. The findings underscored the ability of chitosan to stabilize nanoemulsion droplets effectively through polymer adsorption, with stabilization performance directly correlating with increased chitosan concentration. The use of the phase inversion composition (PIC) method, combined with chitosan, proved to be a highly efficient approach for formulating robust nanoemulsions with significant potential for delivering bioactive compounds in medical and cosmetic applications.  
Figure 6: Effect of chitosan concentration on zeta potential distribution.  
Beyond advancing the field of sustainable bioactive delivery systems, this study offers a promising avenue for valorizing shrimp shell waste, aligning with environmental sustainability principles. The results underscore the dual benefit of utilizing a renewable byproduct while addressing industrial needs for effective and eco-friendly stabilizing agents. While our study demonstrates the strong stabilizing effects of chitosan in nanoemulsions, certain limitations should be considered, particularly regarding scalability and potential variations in chitosan quality and properties, which may impact its consistency as a stabilizer in large-scale applications. Future research should focus on scaling this method for industrial production and exploring its applicability to other bioactive-loaded nanoemulsions. Expanding these findings could open new opportunities for sustainable and versatile formulations across a wide range of industries. Additionally, future studies should focus on assessing the bioavailability, oxidative stability, and efficacy of these nanoemulsions in protecting cells against oxidative stress, ensuring their potential for biomedical and therapeutic applications.  
Conceptualization, Y.M., F.T., M.C. and A.T.; Methodology, Y.M., I.B. and I.K.; Investigation, Y.M., I.B., I.K., N.M. and L.R.; Resources, Y.M.; Data curation, I.K., N.M. and L.R.; Writing--original draft, Y.M.; Writing--review & editing, Y.M., I.B., N.M., L.R., F.T., M.C. and A.T.; Supervision, F.T., M.C. and A.T.; Project administration, M.C.; Funding acquisition, Y.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Moroccan National Center for Scientific and Technical Research (CNRST) Research Excellence Scholarships program grant number 23 UAE2020. The APC was funded by the University of Texas Rio Grande Valley, USA.
# Initial Experimentation of a Real-Time 5G mmWave Downlink Positioning Testbed +
## 4 HOP-5G mmWave Positioning Testbed
The AoA algorithm also collects the most recent position solution published by the GNSS receiver manager over MQTT. Finally, the AoA algorithm outputs the AoA estimation together with the RSRP measurements and GNSS position solution. Figure 3: Architecture of the HOP-5G mmWave positioning testbed.
## 4 Results and Discussion
### Hys_En Optimization According to Different Constraints
In consideration of the results pertaining to story accelerations, it was observed that R_En_45_40 and R_En_40_50 exhibited a relatively similar trend, whereas R_En_50_30 demonstrated a comparatively lower one (Figure 11a). The relatively high \(D\) value associated with R_En_50_30 resulted in a notable reduction in the calculated story acceleration values. For all cases, higher story accelerations were found at 1 and 4 compared to 2 and 3. It can be stated that the cases with the highest and lowest \(ISDR\) are R_En_40_50 and R_En_50_30, respectively. In all cases, the highest \(ISDR\) is calculated at the first floor, and all cases are within the constraint value of 0.01. Figure 11: (**a**) Story accelerations and (**b**) inter-story drift ratio (\(ISDR\)) graphs for optimum isolation parameters of R_En cases. Figure 10: The convergence curves for the best run of R_En: R_En_50_30, R_En_45_40, and R_En_40_50.
## 3 Results and Discussion
### Effect of Allicin on Auxin Distribution PIN Localization  
The cytoskeleton fulfils a wide variety of tasks in the cell and is involved, among other things, in transport processes in the cell. In order to investigate the influence of cytoskeletal integrity--here primarily concerning actin--on important cellular transport processes, we chose the localization of PIN proteins, whose localization is crucial for regulated auxin transport in tissues. Again, GFP-tagged versions of the proteins, in this case PIN1 and PIN3, were used to track their localization in vivo. In addition, the expression of the constructs was under the respective native promoter, so that the localization of the expression could also be followed at the tissue level.  
In the untreated control, the PIN1 showed expression mainly in the region of the central cylinder (Figure 5A); at the cellular level, the strongest fluorescence was observed on the basipetal side of the cell. The exposure to allicin with a concentration of 2 mM led to a complete loss of this clear localization pattern, and GFP fluorescence was weakly distributed throughout the cell (Figure 5B). This corresponded exactly to the observations made by [PERSON] during treatment with BSO, which suggests that allicin was also active here via the glutathione pool [31].  
Figure 4: Effect of 2 mM allicin on the actin and tubulin cytoskeleton after exposure for 24 h. While controls exposed to water only showed a typical filamentous structure of both the actin and tubulin, they were completely dissolved after 24 h exposure to allicin. (**A**) shows the influence of allicin on the actin cytoskeleton, (**B**) shows the influence of allicin from the plexus of tubulin in the cell.  
In contrast to the PIN1, the localization of the PIN3 was also found in the area of the cortex, but it was more prominent in the area of the root apical meristem. Again, fluorescence was strongest at the basipetal end of the cell (Figure 5C).  
As observed with the PIN1, complete disruption of the PIN localization had also occurred with PIN3 by treatment with 2 mM of alicin. The GFP signal, which indicated the localization of the PIN1 protein, was diffusely present at both the cellular and tissue levels (Figure 5D). These results exactly mirrored the effects of the GSH1 inhibitor bathione sulfoximine (BSO) on PIN distribution [31]. BSO led to a reduction in cellular GSH levels, and this mirrored the result of alicin titrating out and oxidizing the GSH pool [13].
## 3 Results  
### Runoff Analysis  
#### 3.1.1 Observed Runoff Estimation  
Seven sets of rainfall and drainage data with 1-day rainfall and 3-day rainfall were selected separately in the study area, as shown in Table 1.  
As the 3-day rainfall of the number seven event was 72.6 mm in Table 1, which was close to the maximum 3-day rainfall of 70.96 mm, its return period was regarded as 3-year. Apart from the number seven event, the rainfall of the other six events was less than the  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**1-Day Rainfall**} & \multicolumn{3}{c}{**3-Day Rainfall**} \\ \cline{2-9}
**FID** & **Date** & **Rainfall** & **Drainage** & **Runoff** & **Date** & **Rainfall** & **Drainage** & **Runoff** \\  & **(mm)** & **(10\({}^{4}\) m\({}^{3}\))** & **(mm)** & & **(mm)** & **(10\({}^{4}\) m\({}^{3}\))** & **(mm)** \\ \hline
1 & 11 September 1991 & 14.7 & 44.05 & 0.35 & 28 August–1 September 1988 & 14.2 & 197.27 & 1.60 \\
2 & 26 August 2009 & 18.2 & 52.67 & 0.43 & 16 August–18 August 1991 & 48.3 & 344.02 & 2.80 \\
3 & 28 August 2009 & 21.1 & 90.97 & 0.74 & 3 October–5 October 1994 & 23.3 & 201.1 & 1.60 \\
4 & 21 July 2014 & 8.9 & 38.30 & 0.31 & 19 May–21 May 2010 & 33.3 & 212.62 & 0.99 \\
5 & 23 July 2014 & 31.8 & 21.07 & 0.17 & 14 August–16 August 2010 & 28.5 & 118.26 & 0.96 \\
6 & 5 August 2016 & 7.5 & 7.66 & 0.06 & 11 August–13 August 2014 & 9.5 & 50.75 & 0.41 \\
7 & 22 September 2018 & 17.8 & 15.03 & 0.12 & 24 July–26 July 2018 & 72.6 & 1004.34 & 8.17 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Observed runoff under different rainfall durations.  
3-year return period. In this paper, they were all defaulted to be less than the 3-year return period, and no specific division was made.
## 3 Results and Discussion
### Wavelets Analysis  
In this section, the results for the wavelet analysis applied on the time-domain signals of group 1 are presented in Figure 10. The figures are represented in two columns; see Figure 10a for the P1 measurements and Figure 10b for the P2 measurements.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Ripening Stage**} & \multicolumn{2}{c}{**Interest Peaks from 10 to 100 [Hz]**} & \multicolumn{2}{c}{**Interest Peaks from 100 to 1000 [Hz]**} \\ \cline{2-5}  & **P1** & **P2** & **P1** & **P2** \\ \hline UR1 & 23.34 & 25.86 & 774 & 766.06 \\ UR2 & 24.02 & 25.43 & 767.9 & 769.03 \\ SMR1 & 28.63 & 31.32 & 802.98 & 826.74 \\ SMR2 & 39.  
Each row denotes the ripening stage. In the frequency range from 10 to 100 Hz, we can observe that with the ripening of the fruit, the scalogram magnitudes in frequencies close to 40 Hz increased. These differences are more straightforward in the measurement point P1, as the maximum magnitude is around 10 for the UR stage, about 14 for the SMR stage, and approximately 18 for the RP stage. In addition, around 40 Hz, the maximum magnitude occurs in a slightly higher frequency for the RP fruit.  
At measurement point P2, the maximum magnitudes in the scalogram go from 8 (UR) to 18 (RP), showing a significant difference between the unripe and ripe stages. In P2, the semi-ripe and ripe stages have the same maximum magnitudes. However, it is still clear that around 40 Hz, the frequency with a higher scalogram magnitude is slightly higher for the ripe fruit.  
For RP1, it is noticeable that the magnitude of the scalogram has more intensity at the end of the time domain, keeping a similar trend for P1 and P2. For the UR1 case, the intensity is distributed in the scalogram and presented minor concentrations in some areas of the graph. Nevertheless, the SMR1 also exhibited an accumulation of intensity that for P1 was more intense at the beginning of time but for P2 was more intense at the end. In these graphs, the different concentration patterns evidenced in the scalograms were reflected due to the differences in the velocity amplitudes for the fruits that reflected the shifts in the vibration energy levels.  
Figure 9: Scaled mechanical impedance. (**a**) 10–100 Hz for P1, (**b**) 100–1000 Hz for P1, (**c**) 10–100 Hz for P2, (**d**) 10–1000 Hz for P2.
# Bridge Health Monitoring via Displacement Reconstruction-Based NB-IoT Technology
## 2 Methodology
### Reconstructed Dynamic Displacement Algorithm
It is seen that the proposed method requires less \(\mathbf{N}_{\mathbf{w}}\) that lessens the computational cost, and reduces the effect of time lag. In addition to the cost, Table 4 shows that the proposed method also delivers displacements with higher accuracy. It is seen that the performance of the current algorithm is able to reduce the required window size  
Figure 4: Optimization results of \(\mathbf{N}_{\mathbf{w}}\) value with respect to various frequencies and interval times. Figure 3: Ground acceleration of El Centro. and enhance the accuracy of constructed displacement compared to literature. The current algorithm incorporates the Newmark-Beta as the main component to construct the displacement and utilizes the Moore-Penrose pseudo inverse to tackle the non-symmetrical inverse problem. While the window size is optimized using symbiotic optimization search (SOS). However, it is difficult to distinguish the contribution of each component to the increase in performance.
# A Study on the Maximum Reliability of Multi-UAV Cooperation Relay Systems
## 2 System Model and Problem Formulation
### Modeling UAV Communication System
Assuming that the AtG communication links are Nakagami-\(k\) fading channels, \(k\in[0.5,1)\), the PDF of \(\|g_{n}\|\) is as follows:  
\[f_{\|g_{n}\|}(x)=\frac{2k_{n}{}^{k_{n}}x^{2k_{n}-1}}{\Gamma(k_{n})\Omega_{n} {}^{k_{n}}}e^{-\frac{k_{n}{}^{2}x^{2}}{\Omega_{n}}}, \tag{9}\]where \(x\geq 0\), \(\Gamma(\cdot)\) is the Gamma function, \(k_{n}\) is the shape parameter of Nakagami fading and indicates the severity of fading, and \(\Omega_{n}=E\Big{\{}\big{|}g_{n}\big{|}^{2}\Big{\}}\) is the average fading powers. Using (8) and (9), the PDFs of \(\gamma_{n}\) obeying the gamma distribution are as follows:  
\[f(x|y_{n},z_{n})=\frac{x^{y_{n}-1}e^{-\frac{x}{z_{n}}}}{z_{n}y_{n}\Gamma(y_{n})}, \tag{10}\]  
where \(y_{n}\) is shape parameter and \(z_{n}\) is scale parameter. The cumulative distribution function (CDF) of \(\gamma_{n}\) can be derived as follows:  
\[F_{\gamma_{n}}(x)=\Gamma\bigg{(}\frac{x}{z_{n}},y_{n}\bigg{)}, \tag{11}\]  
where \(\Gamma(\cdot,\cdot)\) is the incomplete Gamma function. By averaging the instantaneous BER of the joint PDF of link SNR \(\gamma_{1}\), \(\gamma_{2}\), and \(\gamma_{3}\), the total BER can be obtained as follows:  
\[P_{e}=H(z_{1},\,y_{1})+H(z_{2},\,y_{2})+H(z_{3},\,y_{3})-2H(z_{1},\,y_{1})H(z_ {2},\,y_{2})-2H(z_{1},\,y_{1})H(z_{3},\,y_{3})-2H(z_{2},\,y_{2})H(z_{3},\,y_{ 3}) \tag{12}\]  
where \(H(z_{n},\,y_{n})=\frac{1}{2}-\frac{1}{2}\sqrt{\frac{z_{n}}{1+z_{n}}}\sum_{N=0 }^{y_{n}-1}\frac{(N)}{\big{|}(\left\{\left(1+z_{1}\right)\right\}\big{|}^{N}}\), while \(y_{n}=k_{n}\) and \(z_{n}=\frac{\Omega_{n}}{2e^{2}k_{n}\Omega_{n}}\).