esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Findings and Results
### Features Selection
In conclusion, feature selection analysis across multiple models underscores the dominant role of demographic and educational factors, particularly age and grade, in predicting student dropout rates in Somaliland. While socioeconomic factors such as household income are significant, their impact varies across models. Logistic and probit regressions highlighted age and grade as critical predictors, and sex and household size also played notable roles. However, in models such as naive Bayes, random forest, KNN, and SVM, grade overwhelmingly influences predictions, indicating a need for careful model validation to avoid overfitting and ensure a balanced consideration of all relevant features. This comprehensive evaluation provides a clear direction for future feature engineering efforts to enhance predictive accuracy and model robustness, ultimately aiding the development of targeted interventions to reduce dropout rates.
## 2 Methodology  
### Denoising Autoencoder (DAE)  
The Denoising Autoencoder (DAE) is a representative unsupervised learning-based neural network model that is widely used to remove noise from input data and extract core features of the original signal [12]. As a variant of the Autoencoder (AE), the DAE is trained by intentionally adding noise to the input data and learning to reconstruct the original signal. This process enables the model to learn robust feature representations [13].  
As illustrated in Figure 1, a typical DAE consists of two components: an encoder and a decoder [12]. The encoder transforms the noisy input signal into a compressed, low-dimensional latent representation. The decoder then attempts to accurately reconstruct the original signal from this latent representation. Through this process, the DAE learns to extract features that are less affected by noise, thereby preserving only the meaningful information contained in the signal [14].  
The objective function of the Denoising Autoencoder (DAE) is typically designed to minimize the Mean Squared Error (MSE) between the original input and the reconstructed output [12], as expressed by the following equation:  
\[\mathcal{L}(x,\hat{x})=\left\|x-x^{\prime}\right\|^{2} \tag{1}\]  
where \(x\) denotes the original input signal and \(x^{\prime}\) represents the signal reconstructed by the DAE.  
In the field of fault diagnosis using vibration data, the DAE has attracted considerable attention as a preprocessing technique that significantly improves diagnostic accuracy by removing noise and extracting key features from the data [15].
## Materials and Methods
### Maxwell Model
2:  
\[\alpha_{1/2}=\frac{P_{1}}{P_{2}}=\frac{D_{1}S_{1}}{D_{2}S_{2}}. \tag{2}\]  
The Maxwell equation is as follows [32]:  
\[P_{r}=\frac{P}{P_{m}}=\ \bigg{[}\frac{2(1-\Phi)+(1+2\Phi)\lambda_{dm}}{(2+\Phi)+(1 -\Phi)\lambda_{dm}}\bigg{]}, \tag{3}\]  
where:  
\(P_{r}\)--relative permeability of gas components,  
\(P\)--effective permeability of gas components in MMM (Barrer), \(1 Barrer=\frac{cm_{STP}^{3}\cdot cm}{cm^{2}\cdot s\cdot cmHg}\cdot 10^{-10}\),  
\(P_{m}\)--permeability of gas components in a polymer matrix (continuous phase) (Barrer),  
\(\Phi\)--volume fraction of filler particles,  
\(\lambda_{dm}\)--\(P_{d}/P_{m}\) permeability ratio, and  
\(P_{d}\)--permeability of components in the dispersed phase. This Maxwell model is one of the most often-used models by scientists during the pre-selection process of membranes. It is suitable for a diluted suspension of spherical particles with low filler concentration values, where the value of a volume fraction of the filler particles is less than 0.2 (0 < \(\Phi\) < 0.2) and does not take into account the particle size limit (\(\Phi_{m}\)). This is because of the assumption that the streamlines around particles are not affected by the presence of nearby particles. For higher values of volume fraction \(\Phi\), the Maxwell model cannot predict the gas permeability through the MMMs. Furthermore, the effect of particle size distribution, and the shape and aggregation of particles, are not taken into account. Nevertheless, this model is easy to solve for \(P_{r}\). Essentially, the Maxwell model is suitable for describing of the gas permeation process through mixed-matrix membranes, characterized by perfect morphology (no defects). However, this expression could be modified to take into account the nonideal morphology of these membranes exhibiting various types of interfacial defects, such as pore blocking, interfacial voids and polymer chain stiffening [33; 34; 35; 36; 37; 38; 39; 40; 41]. Another model used to describe the transport of gases through mixed-matrix membranes is the Bruggeman model, given by the following formula:  
\[(P_{r})^{\frac{1}{3}}\bigg{[}\frac{\lambda_{dm}-1}{\lambda_{dm}-P_{r}}\bigg{]} =\ (1-\Phi)^{-1}. \tag{4}\]  
This model does not take into account the effects of particles shape and their size distribution, aggregation of particles and the particle size limit (\(\Phi_{m}\)). However, it covers a broader range of volume fraction \(\Phi\) compared to the Maxwell model. As it could be seen from Equation (4), it should be solved numerically for permeability.
# Weighted \(z\)-Distance-Based Clustering and Its Application to Time-Series Data
## 3 Proposed Methods
### Weighted SCC-I (SCC-Iw)
Therefore, we want to minimize:  
\[\mu_{j}(\mathbf{x}_{i})=\exp\{-Z_{w}(i,j)\}. \tag{8}\]5  
Combining Equation (11) and Equation (12), together with the constraints on weights, we'd like the weights to minimize  
\[\mu_{j}(\mathbf{x}_{i})=\exp\{-Z_{w}(i,j)\}. \tag{8}\]6  
subject to \(\sum_{k=1}^{n}w_{j,k}=1\), \(w_{j,k}\geq 0\), \(k=1\), \(\ldots,n\), \(1\leq j\leq J\)  
which, by Equation (7), is equivalent to minimizing  
\[\mu_{j}(\mathbf{x}_{i})=\exp\{-Z_{w}(i,j)\}. \tag{8}\]7  
subject to \(\sum_{k=1}^{n}w_{j,k}=1\), \(w_{j,k}\geq 0\), \(k=1\), \(\ldots,n\), \(1\leq j\leq J\)  
where \(\alpha\) is a positive real constant. Through quadratic programming, optimal values for the weights can be derived from Equation (14). Now, we are ready to present the SCC-IW algorithm. We adopt \(Z_{w}(i,\ j)\), instead of \(Z(i,\ j)\), in SCC-IW. Also, whenever a new cluster is created, its weights are each initialized to be \(\frac{1}{n}\). At the end of the current iteration, we minimize Equation (14) to find optimal weights which will be used in the next iteration. The SCC-IW Algorithm 3 can be summarized below. ```
Perform SCC on X with weighted \(z\)-distance, and initialize the weights for each newly created cluster; repeat for each instance, instance \(i\), \(1\leq i\leq N\)  Remove instance \(i\) from its cluster and update the existing clusters;  Compute \(Z_{w}(i,j)\), \(1\leq j\leq J\) ;  Create a new cluster or assign instance \(i\) to the cluster with largest MD, and initialize the  weights for each newly created cluster; endfor  Derive optimal weights by solving Equation (14) through quadratic programming; until assignments are stable; end SCC-IW
```  
**Algorithm 3** SCC-IW  
Note that SCC-IW takes the training set X as input and outputs J clusters \(C_{1}\),..., \(C_{J}\). It is not surprising that SCC-IW can work well. In each iteration, optimal weights are derived. For a dimension which is more useful for clustering, it is more important and is, therefore, given a larger weight. To illustrate how SCC-IW works, a simple example is given in Appendix B.
# A Study on the Vehicle Routing Planning Method for Fresh Food Distribution
## 4 Example Analysis
### Path Planning of Front Warehouse  
The path planning of the front warehouse includes mainline distribution and branch distribution. Main line delivery is the allocation of refrigerated trucks from the large warehouse in the city center to supply fresh products to the front warehouse, and each front warehouse is served by a refrigerated truck, as shown in Figure 5.  
Two electric vehicles can be used in each front warehouse area to complete delivery services. The electric vehicle starts from the front warehouse, selects the appropriate number of vehicles to load goods based on the corresponding customer demand and electric vehicle capacity in the area and delivers goods to the customer points along the optimal path in the sequence. After completing the delivery, it returns to the front warehouse, forming a closed-loop route. We design a delivery path to minimize the delivery cost at a lower level and solve it using the APSOGA fusion algorithm. The algorithm parameters are \(c_{1}=0.5\) and \(c_{2}=0.5\), the maximum number of iterations is \(\lambda=100\), and other parameters are fresh product unit mass loss \(\omega_{t}=0.02\), fresh product corruption rate \(\delta=5\%\) and mainline unit time cooling cost C\({}_{rk1}=1.2\). After calculation, the mileage traveled by all electric vehicles for distribution is 4895.211 km, and the distribution cost is CNY 158,536.6. The distribution mileage and cost of electric vehicles in each region are shown in Table 5.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Latitude** & **Longitude** & **Choose or Not** \\ \hline
1 & 22.5938 & 114.2725 & Yes \\
2 & 22.5556 & 114.2401 & Yes \\
3 & 22.6057 & 114.3118 & Yes \\
4 & 22.5640 & 114.2508 & No \\
5 & 22.5658 & 114.2464 & Yes \\
6 & 22.5618 & 114.2339 & No \\
7 & 22.5949 & 114.2536 & Yes \\
8 & 22.5605 & 114.2320 & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 4: Position of front warehouses.  
Figure 5: Trunk delivery route.
## 1 Introduction
In fact, for the past three years or so, as part of \"alternanza scuola lavoro\" projects (\"school-work internship\", now identified in Italy by the acronym PCTO), numerous schools have been successfully involved in the utilization of MLTs. Taking a cue from this collaborative approach, we would also like to involve the community of so-called \"makers\" (Non-specialized people, i.e., not necessarily specialized technicians/engineers, who use popular hardware/software, such as Arduino to make a wide variety of tool/gadgets themselves. This community is becoming very large, as evidenced by the growing number, and the success, of dedicated fairs and websites/forums). From this CNR-ISMAR experience in developing and testing MLTs came the idea of using low-cost IoT technologies for the more ambitious goal of introducing the cluster concept (the Smart Drifter Cluster) to the mesoscale monitoring of marine currents, with further possible applications for the recovery of people/materials lost at sea and the monitoring of off-shore pollutant spills/marine litter. In fact, the MARTA project (\"SVILUPPO DI UNA INNOVATIVA MULTIPIATATAFORMA SMART DRIFTER-UMV-SAPAR PER INDAGINI MARINE\"-(M.A.R.T.A. ), POR CreO FESR Toscana 2014-2020, Call R&S 2020, Activity 1.1.5.a1. Partners: SIGMA INEGNERIA S.R.L. (coordinator), MDM TEAM S.R.L., DMG) brings together companies and research institutions with the aim of making the Smart Drifter Cluster idea, developed within CNR-ISMAR, a reality. The final purpose is to create an integrated system for remote, \"real-time\" data acquisition on marine currents and pollutants in a much cheaper and logistically simpler way than the current ones. For massive production (some thousands of pieces), the target production cost of each drifter is about 200%; the target cost is less than twice this amount. These figures are significantly less than those for standard drifters used in oceanography. In addition, the Smart Drifter Cluster is also designed to minimize the cost of remote data communication. This is achieved by minimizing the use of satellite communication thanks to the introduction of the GSM/G4/LoRa network. If these objectives are successfully realized, similar to the previous MLTs (Mobile Litter Traps) experience, it will create opportunities to engage citizens in remote marine monitoring/sensing and marine-litter tracking.
# Trajectory Prediction of Marine Moving Target Using Deep Neural Networks with Trajectory Data
## 3 Materials and Methods
### Multi-Dimensional Trajectory Features
Moreover, the change ratio of the position displacement to the initial time is calculated as Equation (9). The extending properties extracted from the trajectory sequence samples \(\mathrm{T}_{i}\) are as follows:  
1. The spatial-temporal information of the current state: \[T_{i}=\left(t_{ij},\lambda_{ij},\varphi_{ij}\right)_{s\times 3}\] (6)
2. The spatial-temporal change information of the current state relative to the initial state: \[\Delta T_{i}=\left(\Delta t_{ij},\Delta\lambda_{ij},\Delta\varphi_{ij} \right)_{s\times 3}=\left(\left(t_{ij},\lambda_{ij},\varphi_{ij}\right)-\left(t_{i1 },\lambda_{i1},\varphi_{i1}\right)\right)\] (7)
3. The spatial-temporal change information of the current state relative to the previous state: \[dT_{i}=\left(dt_{ij},d\lambda_{ij},d\varphi_{ij}\right)_{s\times 3}=\left\{ \begin{array}{cc}\left(\left(t_{ij},\lambda_  
Taking the multi-dimensional trajectory feature matrix as the input of the CNN model, it is necessary to satisfy the input format requirements of the CNN [29] and conform to the design idea of local connection and weight sharing of the CNN [28]. Therefore, resize \(F_{i}\) from \(s\times 15\) to \(1\times s\times 15\). In other words, \(F_{i}\) can be regarded as 15 feature maps with a size of \(1\times s\). It can be seen that each feature map with a size of \(1\times s\) is the same type of feature information along the time domain, and the features at adjacent times are of local correlation, which is consistent with the local connection feature of the CNN.
## 2 Materials and Methods
### RP-HPLC-DAD Analysis of the Selected Polyphenol Compounds  
The plant material (fruits and leaves from _Hippocampae rhamn_s L. and _Cornus mas_ L.) was collected from the Botanical Garden of the Slovak University of Agriculture in Nitra, Slovak Republic. Samples were prepared by extracting 2 g of dried plant material in 20 mL 80% ethanol (\(v/v\)) for 4 h using a horizontal shaker Unimax 2010 (Heidolph Instruments, GmbH, Schwabach, Germany). The extracts were filtered through a syringe with PVDF filters (0.45 \(\upmu\)m, 13 mm) (Agilent Technologies, Waldbronn, Germany). All studied polyphenol compounds were determined by HPLC-DAD Agilent 1260 (Agilent Technologies, Santa Clara, CA, USA) according to [PERSON] et al. (2019) [31]. All analyses were conducted on a Cortecs C18 column (4.6 mm \(\times\) 150 mm \(\times\) 2.7 \(\upmu\)m; Waters, Milford, MA, USA). The mobile phases were 0.1% H\({}_{3}\)PO\({}_{4}\) in ddH\({}_{2}\)O (\(v/v\)) (A) and acetonitrile (B). The gradient elution program was as follows: 0-1 min, isocratic elution (90% A, 10% B); 1-5 min, linear gradient (85% A, 15% B); 5-10 min, linear gradient (80% A, 20% B), held until 12 min; 12-15 min, linear gradient (30% A, 70% B). A 3 min post-run was included. The mobile phase flow rate was 0.6 mL/min. The column was maintained at 30 \({}^{\circ}\)C, and samples were kept at 4 \({}^{\circ}\)C in the autosampler. Detection was performed at 265 nm, 320 nm and 372 nm, with spectra scanned from 210 to 600 nm. Spectral data were collected and processed using the Agilent OpenLab ChemStation software for LC 3D Systems C.01.06 (Agilent Technologies, Santa Clara, CA, USA). Compounds were identified by comparing their retention times and absorbance spectrum profiles with those of known standards.
## 1 Introduction
* Improve vehicle utilization rates by adjusting routes and schedules based on demand fluctuations. * Enhance environmental sustainability by decreasing fuel consumption and lowering emissions. * Provide a flexible, scalable transport model applicable to other industrial and corporate mobility networks. This study differs from others by using dynamic linear programming (DLP) explicitly tailored to manage an inhomogeneous fleet of shuttle vehicles in an industrial context at the Yalova Shipyard. Unlike previous studies, this research integrates a dynamic, user-friendly software interface designed for real-time operational planning, allowing for continuous adjustments to meet the fluctuating transport demands of the workforce. In addition, the study quantifies significant reductions in operational costs, specifically fuel consumption, maintenance and taxes, providing practical and directly measurable benefits beyond the theoretical efficiency and sustainability improvements typically emphasized in previous literature. The study also serves as a practical framework for optimizing employee transport networks, with potential applications in urban planning, corporate mobility management and smart transportation systems. Future research could explore the integration of electric and autonomous shuttle fleets into optimization models, further aligning workforce mobility with global sustainability initiatives.
# Reconfigurable Multi-Beam Receiver Based on Microwave Photonics Technology
## 2 System Architecture
### Key Parameters Analysis
#### 2.1.1 Number of Switch Elements  
The multi-port switch in the reconfigurable structure is constructed by cascading the switch element, which is a \(2\times 2\) switch, but only one input port is used. The function of the multi-port switch is to reconfigure the optical path and send the optical carrier to the suitable splitter. The number of switch elements in the structure partly represents the hardware and control complexity. The only distinction between the structure DP and EP is the combiner-switch parts before the true-time delay units. But they have the same number of switch elements, which contain \(N\cdot\text{log}_{2}(M)\) mode-switches and an \(N\cdot(M-1)\) combiner-switch.  
Figure 3: Illustration of structure equal-precedence.
# Modelling of a Non-Transferred Plasma Torch Used for Nano-Silica Powders Production
## 5 Conclusions  
In this work, a pilot plasma system, which contained a non-transferred arc system with a power of 30 KW, was used to synthesis nano-sized silica. The plasma operated on nitrogen, and the nitrogen tail flame could be heated to the range of 4273.15 K to 6773.15 K. A 2D numerical model was established to predict the plasma flow and heat transfer inside and outside of the torch. Simulation results showed that the plasma temperature and velocity became higher with increasing current, while the electrothermal efficiency was reduced. Additionally, the thermal efficiency decreased with increasing enthalpy because more energy was removed by cooling water. With increasing current, the plasma jet core with high velocity and temperature was expanded in axial and radial directions. Numerical simulation results were in good accordance with the experimental measurements. In addition, the initial nanosilica particles elaborated in the experimental device were characterized by X-ray diffraction (XRD) and transmission electron microscopy (TEM) to identify the purity, size, and crystalline nature of the materials and to study their mean size. The arc plasma method enabled one to produce spherical silicon ultra-fine powder of about 20 nm in diameter.  
Conceptualization, S.E., I.G. and I.A.A.; methodology, S.E., I.A.A. and I.G.; formal analysis, I.A.A., S.E., S.S.A. and I.G.; investigation, I.A.A., S.E., S.S.A., A.A.A. and I.G.; resources, I.A.A., S.S.A. and A.A.A.; writing--original draft, S.E., I.A.A. and I.G.; writing--review and editing, S.E., I.A.A. and I.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
Figure 15: TEM images of nanosilica.
## 2 Theories and Methods
### 3D Simulation Algorithm for GPR  
The perfectly matched layers (PML) absorbing boundary condition is considered to solve the wave propagation problem of an open area [43]. The synthetic data are calculated with the 3D vector finite element method in the frequency domain, which involves solving vector Maxwell's equations for GPR-type wave propagation [44]. The vector electric wave equation can be expressed as [44; 45]  
\[\
abla\times\left[\overline{\mathbf{u}}^{-1}\cdot(\
abla\times\mathbf{E}) \right]-\omega^{2}\overline{\epsilon}\cdot\mathbf{E}=-j\omega\;\mu\;\mathbf{J}, \tag{2}\]  
where \(\mathbf{E}\) (V/m) is the electric field; \(\mathbf{J}\) (A/m\({}^{2}\)) is the excitation current density; \(\overline{\mathbf{u}}=\mu\overline{\Lambda}\) and \(\overline{\epsilon}=\varepsilon_{ef}\overline{\Lambda}\), respectively, represent the diagonal magnetic permeability and dielectric permittivity tensors of size 3 \(\times\) 3, in which \(\mu\) (H/m) is the magnetic permeability, with \(\mu_{r}=\) assuming 1, and \(\mu=\mu_{0}=4\pi\times 10^{-7}\) (H/m) is the vacuum permeability; \(\varepsilon_{ef}=\varepsilon-j\sigma/\omega\)(F/m) is the equivalent dielectric permittivity; \(\omega=2\pi f\) is the angular frequency (rad/s); \(f\) (Hz) is the temporal frequency; \(\varepsilon\) (F/m) and \(\sigma\) (S/m) represent the magnetic permeability and dielectric permittivity; and \(\overline{\mathbf{\Lambda}}=\hat{\mathbf{\kappa}}\hat{\mathbf{\kappa}}x_{x}+\hat{\mathbf{y}}\hat{ \mathbf{\gamma}}\alpha_{y}+\hat{\mathbf{z}}\hat{\mathbf{z}}\alpha_{z}\) represents the tensor matrix of size \(3\times 3\) corresponding to the PML (\(\alpha_{x}\
eq 1\), \(\alpha_{y}\
eq 1\), and \(\alpha_{z}\
eq 1\) in the PML region).  
After performing the mesh division of the computing area and placing the wavefields at the edges, the area integration of (2) is carried out using [PERSON]'s method, which can result in a linear system as follows [11; 28]:  
\[\mathbf{\Lambda}(\omega,\mathbf{\mathrm{m}})\mathbf{\mathrm{u}}=\mathbf{\mathrm{q}}(\omega), \tag{3}\]  
where \(\mathbf{\mathrm{A}}\) is the Helmholtz operator, which is a highly sparse coefficient matrix shared by all sources related to the parameters of the subsurface media to be determined and frequencies; \(\mathbf{\mathrm{q}}\) is the discretized excitation source term related to the location of the excitation source and the antenna frequency; and \(\mathbf{\mathrm{u}}\) is the solution to this linear system. The relationship between \(\mathbf{\mathrm{u}}\) and the predicted data \(\mathbf{\mathrm{d}}^{cal}\) in (1) can be expressed as follows [11; 28]:  
\[\mathbf{\mathrm{d}}^{cal}=\mathbf{\mathrm{P}}\mathbf{\mathrm{u}}, \tag{4}\]  
where \(\mathbf{\mathrm{P}}\) is the multi-offset observation matrix, which represents the projection operator for the observation of the simulated wave field \(\mathbf{\mathrm{u}}\). We adopt the direct solver MUMPS [46] to solve the linear system in (3). For given subsurface parameters and frequencies, only one inverse calculation on matrix \(\mathbf{\mathrm{A}}\) in (3) is required, which can easily solve right-end term problems.