esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Result and Discussion  
### Manufacture of PAN Nanofiber  
In order to produce the uniform formation of nanofibers by electrospinning, the main factors, such as solution viscosity, flow rate, voltage, and distance between the syringe tip and the collector plate, should be controlled [28]. The concentration and applied voltage are important, directly affecting nanofiber quality. The polymer solution is radiated when the concentration is low and the applied voltage is high because resistance of the electric force overcomes the surface tension in the syringe tip, but beads are created on the fiber surface. On the other hand, the polymer solution is not radiated when the applied voltage is low since resistance to the electric force does not overcome the surface tension [29]. In this study, to mix 1 g PAN with 10 mL DMF, they were stirred for 24 hours and radiated under the same conditions listed in Table 3. Each radiated for 15 minutes to control the flow rate, voltage, syringe tip, and collector plate in the electrospinning procedure and the collective fiber phenomenon was imaged using a scanning electron microscope (JEOL, Akishima, Japan).  
The Figure 3 shows the SEM images of PAN nanofiber samples corresponding to the various controlled conditions. The fiber has a stable form without beads. In addition, the diameter of the nanofiber is formed corresponding to a dimensional range of 200-300 nm. The optimum condition is a mix of 1 g PAN polymer, 10 mL DMF solution, voltage of 10 kV, 28 cm of distance between syringe tip and collector plate, and flow rate of 0.3 mL/h.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample** & **Flow Rate (mL/h)** & **Voltage (kV)** & **Distance (cm)** \\ \hline (a) & 0.1 & 10 & 28 \\ (b) & 0.2 & 10 & 28 \\ (c) & 0.3 & 12 & 28 \\ (d) & 0.3 & 14 & 28 \\ (e) & 0.3 & 10 & 23 \\ (f) & 0.3 & 10 & 18 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental conditions to manufacture PAN nanofiber samples by the electrospinning method.  
Figure 2: Process setup of the lamination of heat roller: (**a**) PAN (polyacrylamideonitrile) nanofiber, non-woven fabric (2-layers structure); and (**b**) non-woven fabric, PAN nanofiber, non-woven fabric (three layer structure).  
In the cases of samples (a) and (b), the flow rate was controlled; the Taylor cone was unstable, however, the collection of the fibers was realized. This was not complete nanofiber and did not create beads. In the cases of samples (c) and (d), they were applied a voltage, and the diameters of the fibers were in the range of 100-200 nm, but the Taylor cone was also unstable, and then the PAN + DMF solution resulted in the falling phenomenon from the end of the syringe tip to the collector plate. In addition, in the cases of samples (e) and (f), the syringe tip distance to the collector plate was controlled; as a result, the diameters of the fibers were small. When the distance of the collector plate and the syringe tip was too close, the fiber shape did not form. In addition, the diameters of the fibers were also not uniform.
# Formal Verification and Analysis of 5G AKA Protocol Using Mixed Strand Space Model
## 4 Our Proposed 5G-AKA' Protocol and Its Security Analysis
### The Server's Guarantee of the 5G-AKA' Protocol  
**Theorem 12**.: _Suppose: (1) \(\sum\) is a space for the 5G-AKA' protocol, and \(\mathcal{C}\) is a bundle containing a server strand \(t\in\mathit{Serv}_{\mathrm{I}}[\mathit{UE},\mathit{SN},\mathit{HN},\mathit{ SUCI},\mathit{SNN},\mathit{RAND}_{\mathit{SN}},\mathit{RAND},\mathit{AUTN},\mathit{HXRES}^{*}, \mathit{Result},\mathit{K_{\mathit{SEAF}}},\mathit{SUPI}]\); (2) \(K\
otin\mathcal{K}_{P}\) and \(K_{\mathit{SN},\mathit{HN}}\
otin\mathcal{K}_{P}\); (3) \(x\), \(\mathit{RAND},\mathit{RAND}_{\mathit{SN}}\) is uniquely originating in \(\sum\). Then, \(\mathcal{C}\) contains a unique initiator strand \(s\in\mathrm{Init}_{\mathit{I}}[\mathit{UE},\mathit{SN},\mathit{HN},\mathit{ SUCI},\mathit{RAND},\mathit{AUTN},\mathit{RES}^{*}]\) and a unique respondar strand \(r\in\mathrm{Resp}_{\mathrm{I}}[\mathit{UE},\mathit{SN},\mathit{HN},\mathit{ SUCI},\mathit{SNN},\mathit{RAND},\mathit{AUTN},\mathit{HXRES}^{*},\mathit{Result},\mathit{K_{\mathit{SEAF}}}, \mathit{SUPI}]\)._  
Proof of Theorem 12.: Since \(BK=\mathit{KDF}(K,x\cdot y\cdot G||\mathit{SNN})\), \(BK\
otin\mathcal{K}_{P}\) according to assumption (2). Because \(CK=f_{3}(BK,\mathit{RAND})\) and \(IK=f_{4}(BK,\mathit{RAND})\), \(CK\
otin\mathcal{K}_{P}\) and \(IK\
otin\mathcal{K}_{P}\), so \(\mathit{CK}||\mathit{IK}\
otin\mathcal{K}_{P}\). By assumption (3), \(x\) is uniquely originating in \(\sum\) so \(\mathit{RES}^{*}=\mathit{KDF}(CK||\mathit{IK},\mathit{SNN}||\mathit{RAND}|| \mathit{RES})\subset term(<t,3>)\) must originate on a unique initiator strand \(s\in\mathrm{Init}_{\mathrm{I}}[\mathit{UE},\mathit{SN},\mathit{HN},\mathit{ SUCI},\mathit{RAND},\mathit{AUTN}^{\prime},\mathit{RES}^{*}]\), where \(\mathit{SQN}^{\prime}\subset\mathit{AUTN}^{\prime}\). Since \(BK\
otin\mathcal{K}_{P}\), \(\mathit{MAC}^{\prime}=f_{1}(BK,\mathit{SQN}^{\prime}||\mathit{RAND}|| \mathit{AMF})\subset\mathit{AUTN}^{\prime}\subset term(<s,2>)\) must originate on a server strand \(t^{\prime}\). Since \(RAND\) is uniquely originating in \(\sum\), \(t^{\prime}=t\), so \(\mathit{SQN}^{\prime}=\mathit{SQN}\) and \(\mathit{AUTN}^{\prime}=\mathit{AUTN}\). Hence, \(s\in\mathrm{Init}_{\mathrm{I}}[\mathit{UE},\mathit{SN},\mathit{HN},\mathit{SUCI, RAND, AUTN, RES\({}^{*}\)]. By assumptions (2) and (3), \(K_{SN,HN}\
otin\mathcal{K}_{P}\) and \(RAND_{SN}\) are uniquely originating in \(\sum\), so \(\{RAND_{SN}||RES^{*}\}_{K_{SN,HN}}=term(<t,3>)\) must originate on a unique responder strand \(r\in\text{Resp}_{\text{I}}[UE^{\prime},\,SN,\,HN
## 4 Proposed System
### Convolutional Layer  
The convolutional layer produces the feature map by performing the convolution between the \(n\times n\times d\) dimension input image and \(k\times k\) sizes M kernel filters where n defined height, width and d defined the depth of the image. This multiplication is performed pixel by pixel by travelling the filter from left to right of the input images, and zero padding is added around the image pixel to protect the shrinkage of the original input size. The feature map calculation procedure of the convolutional layer is performed according to Equation (2)  
\[G_{x}^{(l)}=\sum_{y=1}^{m_{1}(l-1)}F_{x,y}^{(l)}\times\mathcal{C}_{y}^{(l-1)}+ Bias^{(l)} \tag{2}\]  
where \(G_{x}^{(l)}\) defined output feature with \(x_{th}\) feature map of layer \(l\), and filter \(F(l)\) are matrices connecting with the \(y_{th}\) feature map, \(m_{1}\) defined the kernel, and \(G_{y}^{(l-1)}\) defined the input feature.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Augmentation Technique** & **Range** \\ \hline Zoom & 0.5–1.0 \\ \hline Brightness range & 0.2–1.0 \\ \hline Rotation & 0–30 degree \\ \hline Shear & 0–10 degree \\ \hline Width shift range & 0.2 \\ \hline Height shift range & 0.5 \\ \hline flip & True \\ \hline \hline \end{tabular}
\end{table}
Table 1: Augmentation Techniques and the Possible Ranges.
# A Comparative Study for Control of Quadrotor UAVs
## 2 Materials and Methods
### System Control
#### 2.2.5 Inner Loop--Model Predictive Control  
The state space form of the nonlinear attitude dynamical model in equations from (32) to (35) is exploited to design the model predictive attitude stabilizer. The MPC method is based on two phases: prediction and optimization. A prediction of the model's states, \(\mathbf{x_{p}}\), is made over a certain prediction horizon, \(\mathrm{T_{P}}\), by integration of the model itself. An optimal control input \(\mathbf{u}\) is chosen to bring the predicted state to the reference \(\mathbf{r}\). The optimization problem formalized in (52) is solved to compute \(\mathbf{u}^{*}(t\to t+\mathrm{T_{P}})=(\mathrm{U_{2}}(t\to t+\mathrm{T_{P}}), \mathrm{U_{3}}(t\to t+\mathrm{T_{P}}),\)\(\mathrm{U_{4}}(t\to t+\mathrm{T_{P}}))\)  
\[\mathbf{u}^{*}\big{(}\mathbf{M}\big{)}=\mathrm{arg\,min}\big{(}\mathbf{u} \big{(}\mathbf{M}\big{)}\big{)}\,\ \mathrm{subject\ to:} \tag{52}\]  
\[\mathbf{x_{err}}\big{(}\mathbf{M}\big{)}=\mathrm{r}\big{(}\mathbf{M}\big{)}- \mathbf{x_{p}}\big{(}\mathbf{M}\big{)}\,\]  
\[\mathbf{u}\big{(}\mathbf{M}\big{)}\in\left[\mathrm{U_{LB}},\ \mathrm{U_{UP}}\right]\,\ \mathrm{M}=[t\to t+\mathrm{T_{P}}]\]  
where \(\mathbf{U_{LB}}\) and \(\mathbf{U_{UP}}\) denote the lower and upper bounds of the optimal control input \(\mathbf{u}^{*}\), which would be subject to saturation anyway, \(\mathbf{x_{err}}\) is the predicted state's error vector, Q, R and P denote diagonal matrices that contain the cost function \(\mathrm{J}(\mathbf{u}(\mathbf{M}))\) weight factors. The receding horizon strategy is applied to the aforementioned formulation of the problem at each time \(\mathrm{T_{z}}\). \(\mathrm{T_{s}}\) denotes the sampling time. A constant input parametrization is chosen such that \(\mathbf{u}^{*}(t\to t+\mathrm{T_{z}})=\mathbf{u}^{*}(\mathrm{T_{z}})\). \(\mathrm{q_{i}}\), \(\mathrm{r_{i}}\), \(\mathrm{p_{i}}\), \(\mathrm{T_{s}}\), \(\mathrm{T_{P}}\) and \(\mathrm{T_{z}}\) represent the control parameters. Refer to [30] for the theoretical foundations of MPC and further details.
# MLKD-Net: Lightweight Single Image Dehazing via Multi-Head Large Kernel Attention
## 4 Experimental Results
### Quantitative and Qualitative Evaluation  
The proposed MLKD-Net was quantitatively evaluated against state-of-the-art single-image dehazing methods on the SOTS dataset using the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) as performance metrics. The evaluation encompasses prior-based models (DCP [2], DehazeNet [13]), CNN-based models (FFA-Net [16], LKD-Net [11], DEA-Net [18], OKNet [10], ConvIR [31]), and Transformer-based models (Dehamer [6], DehazeFormer [7], MB-TaylorFormer [8]). As shown in Table 2, both MLKD-Net-Tiny and MLKD-Net-Small delivered competitive performance on the SOTS-Indoor and SOTS-Outdoor datasets.  
Specifically, MLKD-Net-Small achieved a PSNR of 37.58 dB on SOTS-Indoor and 37.26 dB on SOTS-Outdoor. According to Table 2, when compared to the state-of-the-artMB-TaylorFormer [8], MLKD-Net-Small achieved highly competitive results, with 90.9% fewer parameters and only a 1.9% reduction in the PSNR on the SOTS-Outdoor dataset. Moreover, it surpassed LKD-Net [11], a similarly scaled CNN-based model, by 0.54 dB on SOTS-Indoor and 0.38 dB on SOTS-Outdoor. These findings demonstrate that the proposed Multi-Head Large Kernel Attention (MLKA) module effectively captures spatial features while keeping the model lightweight and efficient.  
We also conducted a qualitative evaluation, with the results presented in Figures 2 and 3, which compares dehazed outputs from MLKD-Net and the baseline methods on the SOTS dataset. The proposed model restores clearer structures and stronger contrast, particularly under dense haze conditions. Especially, as shown in Figure 3, MLKD-Net demonstrated noticeably sharper restoration in several scenes than other models, particularly in fine details such as the outlines of buildings.
## 3 Materials and Methods
### Chemical Analysis
#### 3.4.4 Analysis of Volatile Organic Compounds via HS-SPME-GC-MS  
The analysis of 72 volatile organic compounds (VOCs) was performed using headspace-solid phase microextraction coupled with gas chromatography-mass spectrometry (HS-SPME-GC-MS) [56]. Quantification of VOCs was carried out using the MISN approach. Briefly, 4.3 g of olive oil sample and 0.1 g of an internal standard (ISTD) solution (prepared in refined olive oil free of interfering VOCs) were added into a 20 mL screw-cap vial. The concentration of ISTDs was as follows: 4-methyl-2-pentanol, 11.8 mg/kg; 6-chloro-2-hexanone, 15.9 mg/kg; 3-octanone, 11.5 mg/kg; ethyl acetate d\({}_{8}\), 10.1 mg/kg; butanol d\({}_{10}\), 11.4 mg/kg; 3,4-dimethylphenol, 38.0 mg/kg; trimethylacetaldehyde, 29.1 mg/kg; acetic acid d\({}_{3}\), 10.2 mg/kg). VOCs were pre-concentrated by exposing a 1-cm DVB/CAR/PDMS (50/30 \(\upmu\)m) SPME fiber (Supelco, Belfonte, PA, USA) to the vial headspace for 20 min at 45 \({}^{\circ}\)C. Desorption was carried out in the injection port of a 7890a GCsystem (Agilent Technologies, Santa Clara, CA, USA) at 260 \({}^{\circ}\)C for 2 min in splitless mode. Separation was achieved using a DB InnoMAX capillary column (50 m \(\times\) 0.2 mm i.d., 0.4 \(\upmu\)m), and detection was performed with a quadrupole 5975c-series Mass Spectrometer detector (Agilent Technologies, Palo Alto, CA, USA). The oven temperature was initially set at 40 \({}^{\circ}\)C, then ramped to 156 \({}^{\circ}\)C at 4 \({}^{\circ}\)C/min, and finally to 260 \({}^{\circ}\)C at 10 \({}^{\circ}\)C/min. Helium (supplied by Nippon Gases (Sesto Fiorentino, Italy), purity \(>\) 99.999%) was used as the carrier gas at a flow rate of 1.2 mL/min. The transfer line and ion source were maintained at 250 \({}^{\circ}\)C and 230 \({}^{\circ}\)C, respectively. The MS operated in scan mode (\(m/z\) 30-350 Th, 1500 Th/s) with an ionization energy of 70 eV. Each VOC was identified by comparing mass spectra and retention times with those of authentic standards. For each compound, the most appropriate internal standard was selected, and quantification was carried out using a six-point linear calibration curve (area ratio vs. amount ratio) built from the corresponding pure standard. Further methodological details can be found in [56].
# Compilation of a City-Level & Four-Digit Industry Code MRIO Table Based on Firm-Level Data
###### Abstract  
Scholars have attempted to compile various multi-region input-output (MRIO) tables for different countries. However, due to city-level data scarcity and methodology constraints, almost no MRIO table covers a large number of cities with more disaggregated sectors in countries with large economies, such as China. Based on two large-scale firm-level datasets, the China Annual Survey of Industrial Firms (CASIF) survey and the China Customs Data (CCD) database, from 2000 to 2013, this paper uses China as a case study and presents a new compilation method to construct an MRIO table covering 284 prefecture-level administrative cities and 334 four-digit sectors, which is by far the most comprehensive MRIO table with the largest number of cities and the most segmented industries in China. Unlike existing MRIO tables constructed based on provincial single-region IO (SRIO) tables, we use information along with various linear constraints implied by sector-level and firm-level statistics. This paper expands on the direct decomposition method by developing auxiliary econometric models necessary for estimations and consistency adjustment. In addition, a comparative analysis shows the reliability of our method, which guarantees better coherence and comparability with the MRIO officially published by the National Bureau of Statistics of China (NBS). Therefore, our proposed methodology provides the possibility of producing more disaggregated MRIO tables in other similar contexts.
## 3 Results and Discussion
### Postharvest Storage Assessment
Figure 3: Prediction of potato storage period at 5 °C based on the wavelength regions that were extracted with the genetic algorithm as a feature extraction technique of the spectra that were captured externally on the skin of the tubers from 3 commercial potato cultivars (Spunta, Banba, and Sylvana). Partial least square regression analyses were performed on the pooled data of all three cultivars (**A**), as well as on the data within each cultivar (**B–D**). Figure 2: Spectra reflectance data upon pre-processing (normalization, Savitzky–Golay first derivative and mean centering) that were externally captured on the skin of the tuber during storage for 0, 2.5, or 5 months at 5 °C (**A**) from 3 commercial potato cultivars (Spunta, Banba, and Sylvana), along with the most important regions within each cultivar for the freshness assessment (**B–D**). Indeed, within each cultivar the most significant regions in predicting the postharvest storage life of potatoes were slightly different both in the externally (Figure 2B-D) and internally (Figure 5B-D) captured spectra, which however do not improve the accuracy of the prediction in comparison to the pooled data (Rcv = 0.944-0.952 and Rcv = 0.803-0.901, for the external and internal measurements, respectively). Figure 4: Root mean square error (RMSE) of calibration and cross validation regression analyses in the prediction of potato storage period at 5 \({}^{\circ}\)C according to the latent variables that are included in the model based on the wavelength regions that were previously identified with the genetic algorithm as a feature extraction technique of the spectra that were captured externally on the skin (**A**) and internally in the flesh (**B**) of the tubers. Figure 5: Spectra reflectance data upon pre-processing (normalization, Savitzky–Golay first derivative and mean centering) that were internally captured in the flesh of the tuber during storage for 0, 2.5, or 5 months at 5 \({}^{\circ}\)C (**A**) from 3 commercial potato cultivars (Spunta, Banba, and Sylvana), along with the most important regions within each cultivar for the freshness assessment (**B–D**).
## 3 Results and Discussion
### Machine Learning-Based Prediction of Flexural Strength  
Figure 8 delivers the relationship between the actual measured flexural strength values and the machine learning (ML) predicted values. According to the findings of the training set, the NNR model demonstrates the best overall performance, achieving an R\({}^{2}\) of 0.9319. The RF model ranks second, with an R\({}^{2}\) of 0.9122. Both the DTR and KNNR models exhibit similar performance on the training set [106], while RR attains the worst with the lowest R\({}^{2}\). During the testing phase, the NNR model continues to maintain optimal prediction performance, showcasing good generalization capability [107,108]. The DTR model significantly improves in the testing set, becoming the algorithm with the least absolute error. In contrast, the KNNR and RR models demonstrate a noticeable decline in performance on the testing set. Figure 9 conducts a comparative analysis between the actual measured compressive strength values of foam concrete and the machine learning predicted values. Figure 10 displays the absolute error values between the actual measurements and the ML predictions. Error analysis reveals that the RF model exhibits significant overfitting, while the GPR model maintains relative stability. The SVM model demonstrates excellent generalization capabilities. By comparing the performance differences between the training and testing sets [108, 109, 110], it is observed that the RF model shows clear overfitting, with an R\({}^{2}\) of 0.9122 in the training set dropping to 0.8377 in the testing set. The R\({}^{2}\) of the GPR model's testing set is slightly lower than the training set, yet it remains relatively stable [110]. The SVM model exhibits good generalization capability [111], with a training and testing R\({}^{2}\) difference of only 0.0642. Based on the combined evaluation of absolute error and correlation coefficients, it is recommended to prioritize the NNR and DTR models for practical engineering applications [112]. Meanwhile, optimizing the parameters of KNNR and RR models was required to improve their predictive performance. The NNR model consistently delivers accurate predictions across both low and high strength ranges, reflecting strong generalization. In contrast, the RF model performs well on the training set but shows signs of overfitting. The DTR model demonstrates a noticeable improvement during testing, especially in mid-to-high strength predictions, suggesting better adaptability. Conversely, the KNNR and RR models exhibit increased prediction error in both extremes of the strength range, indicating limited robustness. The GPR model provides relatively stable performance but slightly underestimates at the low-strength end. Meanwhile, the SVM model shows balanced behavior with minimal over- or underestimation, especially across moderate strength values, supporting its generalization capability. Overall, NNR and DTR offer the most reliable performance across the full range of values.
# A Review of the Synthesis and Applications of Polymer-Nanoclay Composites
## 2 Synthesis
### In-Situ Polymerization Method
#### 2.3.4 Miniemulsion Polymerization  
Miniemulsion polymerization allows composites to generate from polymerization within monomer droplets (50-500 nm) [119]. Ultrasonication is commonly applied in polymerization processes, but it is energy consuming. Very recently, various low-energy methods have been introduced, including in-situ surfactant generation and the use of CO\({}_{2}\)[145]. The miniemulsion methods show advantages over other polymerization methods, such as being free of harmful solvents, high monomer conversion, and high molecular weight. Moreover, the nucleation step in miniemulsion is not as complex as in emulsion polymerization [146]. Thus, miniemulsion has been incorporated into the CLRP method to encapsulate both nonexfoliable and exfoliable nanoclays and provide excellent dispersion [147]. Depending on droplet/particle sizes and reactant concentrations, miniemulsion CLRP, such as NMP miniemulsion, ATRP miniemulsion, and RAFT miniemulsion, may be accompanied by the effects of compartmentalization [119].  
[PERSON] et al. [148] applied the miniemulsion polymerization method to fabricate a hybrid latex composite film using a combination of poly(methyl methacrylate-co-butyl acrylate) (PMMA) with MMT nanoclays. Miniemulsion using larger amounts (>8.0 wt%) of nanoclays delivered an effective way to disperse the MMT, which led to high latex viscosity, particle size, and a relatively high amount of coagulum. The synthesized film has the potential to benefit the paper and board industries that produce high quality barrier paper for food packaging. [PERSON] et al. [149] utilized ultrasound-assisted miniemulsion polymerization in order to fabricate PMMA/halloysite nanoclay composites. The incorporation of ultrasonication in the miniemulsion process improved the dispersion of nanoclays in the polymer matrix, and led to enhanced mechanical and thermal properties. [PERSON] et al. [43] reported an alternative for the preparation of fluorinated copolymer/nanoclay composites using a cationic reversible RAFT-assisted Pickering miniemulsion method (inverse miniemulsion RAFT). This novel synthesis approach increased the polymerization rate drastically. The miniemulsion (emulsion size around 200 and 300 nm) step resulted in spherical copolymer/laponite particles, which had significantly low surface energies. Thus, the fabricated composite was expected to be a candidate for hydrophobic coating and fire retardance applications. The inverse miniemulsion CLRP (ATRP and RAFT) method has been a prominent focus of recent research efforts [119].
## 3 Resonant Converter Families
### Classification of Resonant Converter  
Resonant converters are classified into three main categories, which are conventional, quasi, and multi-resonant converters, as shown in Figure 23. Conventional resonant converters can be classified into two categories: phase-shift modulated and load resonant converters (_LRCs_). The other type, quasi-resonant converters (_QRCs_), is considered as a combination of resonant and _PWM_ converters, where the underlying principle is to replace the power switch with a resonant switch [79]. Studies by [16; 80; 99; 100] presented brief and useful explanations about quasi-resonant converters (_QRCs_), as well as different types of multi-resonant converters that have been projected to overcome the weaknesses of _QRCs_. It should be noted that two resonant capacitors with a resonant inductance (called a multi-resonant network) can be used to achieve zero voltage switching [86]. The following subsections elaborate different conventional resonant converters, including series, parallel, and series-parallel load resonant converters.