esa-sceva/satcom-chunk-collection huggingface dataset:
# MIPART: A Partial Decision Tree-Based Method for Multiple-Instance Classification
## 5 Discussion  
As mentioned in the \"Experimental Results\" section, the 15% improvement refers to the average accuracy achieved by the MIPART method over counterpart techniques, including Citation-KNN, MIBoost, MIEMDD, MILR, MISVM, and MITI. This improvement was observed across 12 benchmark multi-instance datasets: Musk1, Musk2, Elephant, Fox, Tiger, mutagenesis3_atoms, mutagenesis3_bonds, mutagenesis3_chains, component, eastwest, function, and westeast. The results were obtained by calculating the average performance of all methods on these datasets and determining the difference relative to MIPART. In addition, the MIPART method demonstrated a 4.7% average improvement over existing state-of-the-art methods in the field [48], presented in Table 5. Our analysis includes accuracy measurements on multiple instance datasets, including Musk1, Musk2, Elephant, Fox, and Tiger. As highlighted in the comparison results, MIPART outperformed a range of techniques, including SimpleMi, MIiWrapper, TLC, NoEncode, MiAcci, MiAcci, and MiENC, across the Musk1, Musk2, Elephant, Fox, and Tiger datasets. These findings suggest that the MIPART method represents a substantial advancement in multi-instance classification, offering a powerful tool for analyzing complex multi-instance datasets.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **Method** & **Accuracy** \\ \hline \hline \multirow{8}{*}{Musk1} & SimpleMi & 72.90 \\  & MiWrapper & 79.60 \\  & TLC & 85.20 \\  & MiAcci & 85.10 \\  & MiAcc\({}_{\text{e}}\) & 86.70 \\  & MiENC & 83.80 \\  & NoEncode & 83.10 \\ \cline{2-3}  & **Proposed** (MIPART) & **86.96** \\ \hline \hline \multirow{8}{*}{Musk2} & SimpleMi & 72.30 \\  & MiWrapper & 81.70 \\ \cline{1-1}  & TLC & 78.80 \\ \cline{1-1}  & MiAcci & 84.80 \\ \cline{1-1}  & MiAcc\({}_{\text{e}}\) & 82.20 \\ \cline{1-1}  & MiENC & 85.50 \\ \cline{1-1}  & NoEncode & 83.30 \\ \cline{1-1} \cline{2-3}  & **Proposed** (MIPART) & **86.27** \\ \hline \hline \end{tabular}
\end{table}
Table 5: The comparison of MIPART with state-of-the-art methods over various datasets in the accuracy metric (%).
# Resource Management in Cloud Radio Access Network: Conventional and New Approaches
## 2 Evolution of C-RAN Architecture, and Its Types, Advantages, and Challenges  
C-RAN is a mobile network architecture where baseband resources are pooled in a centralized and virtualized manner so that the resources can be shared among different base stations. This section provides an overview of the evolution of this architecture from the traditional base station through RAN with distributed RRH. Different types of C-RAN architecture described in the literature, the advantages of this architecture, and a short introduction of the H-CRAN architecture is also presented in this section.
## 3 Results and Discussion
### Rheological Properties
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Sample No.**} & \multirow{2}{*}{**Firmness (N)**} & \multicolumn{2}{c}{**Surf.**} & \multicolumn{2}{c}{**Ultra.**} & \multicolumn{2}{c}{**Storage Mod.**} & \multirow{2}{*}{**Tan Delta**} & \multirow{2}{*}{**Water Activity**} \\  & & **Roughness R\({}_{\mathbf{q}}\)** & & **Viscosity** & & **at 43.6 \(\mu\)Nm** & & **at 43.6 \(\mu\)Nm** \\  & & **(nm)** & & **(mPas-gcm\({}^{-3}\))** & & **(Pa)** & \\ \hline \multirow{2}{*}{1} & 0.209 \({}^{\text{a}}\) & 412 \({}^{\text{a}}\) & 369 \({}^{\text{a}}\) & 22.8 \({}^{\text{a}}\) & 0.261 \({}^{\text{a}}\) & 0.944 \({}^{\text{d}}\) \\  & (0.02) & (14) & (18) & (0.04) & (0.004) & (0.003) \\ \multirow{2}{*}{2} & 0.199 \({}^{\text{b}}\) & 452 \({}^{\text{b}}\) & 326 \({}^{\text{b}}\) & 21.9 \({}^{\text{b}}\) & 0.262 \({}^{\text{a}}\) & 0.951 \({}^{\text{c}}\) \\  & (0.03) & (16) & (9) & (0.03) & (0.008) & (0.002) \\ \multirow{2}{*}{3} & 0.184 \({}^{\text{c}}\) & 589 \({}^{\text{c}}\) & 294 \({}^{\text{c}}\) & 22.7 \({}^{\text{a}}\) & 0.255 \({}^{\text{a}}\) & 0.956 \({}^{\text{b}}\) \\  & (0.03) & (12) & (11) & (0.03) & (0.004) & (0.002) \\ \multirow{2}{*}{4} & 0.170 \({}^{\text{d}}\) & 764 \({}^{\text{d}}\) & 234 \({}^{\text{b}}\) & 18.4 \({}^{\text{c}}\) & 0.270 \({}^{\text{b}}\) & 0.967 \({}^{\text{a}}\) \\  & (0.06) & (31) & (18) & (0.05) & (0.006) & (0.004) \\ \hline \hline \end{tabular}  
* \({}^{\text{a}}\) staS, standard deviation is shown in brackets; means within a column followed by various letter (a–d) are significantly different (\(p\) < 0.05).  
\end{table} 
Table 5: Influence of yogurt dry ingredient storage time and temperature on physical properties of produced yogurt. structure and improving the rheological properties resulted in a decrease in water activity. The stronger texture bonds more water molecules, resulting in a smaller vapour pressure in the space above the yogurt. The yogurt dry ingredients stored at a lower temperature for a shorter period of time produced yogurt with a stronger texture and better binding of water.
# Broadening Bandgap Width of Piezoelectric Metamaterial by Introducing Cavity
## 4 Conclusions  
In this research, acoustic metamaterial consisting of piezoelectric transducers integrated to host substrate with inductive circuits was studied. A lumped-parameter, semi-analytical model of piezoelectric metamaterial was formulated based on continuum mechanics characterization. The equivalent unit-cell mass, stiffness, and electro-mechanical coupling parameter were derived, which were wavenumber-dependent. The role of the system-level electro-mechanical coupling was identified and analyzed in detail. While the frequency range of the bandgap is determined by the LC circuit resonant frequency, the bandwidth of the bandgap is primarily determined by the electro-mechanical coupling. Then, a method for bandwidth broadening through incorporating cavity in the substrate of the unit-cell was presented. Fundamentally, the cavity introduced could increase the system-level electro-mechanical coupling, thereby benefiting the bandgap behavior. It was demonstrated that the bandgap width of the piezoelectric metamaterial could be expanded from 45 Hz to 126.7 Hz. The analytical formulation and cavity synthesis can be used to guide the design optimization of the piezoelectric metamaterial.  
[PERSON] and [PERSON] conceived the idea and carried out the theoretical analysis. [PERSON] performed the FEM simulations. All authors discussed the results and contributed to the writing and editing of the manuscript.  
This research was founded by NSF under grant CPS--1544707 and start-up funding of Southeast University, China, under grant 1122007115.  
The authors declare no conflicts of interest.  
Figure 9: Transmission diagrams for piezoelectric metamaterial beam.
# Performance and Effectiveness of the Passive-Compliant Citrus-Picking Manipulator
## 3 Parameter Optimization of Picking Manipulator  
### Citrus Mathematical Model and Grasping Component Optimization  
There are many varieties of citrus, including varieties such as thick-skinned citrus, oranges, graprefruits, and lemons. This study primarily focuses on Navel oranges from the Hubei province, with fruits that are round or oval in shape. The fruits are usually fairly uniform in shape, with moderate thickness and fragile stems, but they are tightly wrapped and not easily peeled.  
A mathematical expression for the size and shape of the citrus is established. Since each gripper component of the harvesting robotic arm has an independent compliance characteristic, it is assumed that the citrus can be modeled as a sphere for analysis. The coordinates \((x,y,z)\) of any point on the surface of the citrus satisfy Equation (1).  
\[\frac{x^{2}}{r^{2}}+\frac{y^{2}}{r^{2}}+\frac{z^{2}}{r^{2}}=1 \tag{1}\]  
In the equation, \(r\) represents the horizontal diameter of the citrus, in millimeters.  
Using the range of horizontal diameters of the citrus as the criterion, the citrus is modeled for gripping in SolidWorks 2021. To ensure that the inner surface of the gripping components maintains contact points with citrus fruits of any size, CAD was employed to simulate and optimize the relevant finger parameters, resulting in the determination of the gripping component dimensions and two gripping postures, as shown in Figure 3. \(l_{1}\) is the length of the distal phalanx, in mm; \(l_{2}\) is the length of the middle phalanx, in mm; \(l_{3}\) is the length of the proximal phalanx, in mm; \(l_{4}\) is the length of the base, in mm; \(O\) is the pivot point; \(\delta\) is the angle of rotation of the gripper component relative to the pivot point \(O\), in \(\circ\); \(\theta\) is the angle between the distal and middle phalanges, in \(\circ\); \(D_{\rm max}\) is the maximum gripping diameter, in mm; and \(D_{\rm min}\) is the minimum gripping diameter, in mm. The optimal parameters were determined as follows: the length of the distal phalanx of the gripper component, \(l_{1}\), is 31 mm, the length of the middle phalanx, \(l_{2}\), is 35 mm, and the angle between them, \(\theta\), is 140\(\circ\). Considering the overall structure of the end-effector, the proximal phalanx length, \(l_{3}\), is set to 93 mm, and the length of the gripper base, \(l_{4}\), is set to 30 mm, as shown in Figure 3. When the gripper base is in a horizontal position, the gripping range is maximized, with the gripping diameter, \(D_{\rm max}\), approximately 100 mm. When the gripper component rotates relative to the pivot point \(O\) by an angle \(\delta\) of 16.83\(\circ\), the gripping diameter, \(D_{\rm min}\), is 65 mm.  
Figure 3: Size of the grasping component and two grasping postures.
## 3 Results and Discussion
### Regression Modeling and Statistical Analysis
#### 3.2.1 Biomass Model  
The Model F-value of 5.2 indicated that the model was statistically significant. There was only a 0.10% probability that such a large F-value could arise due to noise. Model terms are considered significant when their \(p\)-values are below 0.0500. Table 1 shows the possible combinations of experiments based on the CCD paradigm, with biomass being the primary response. After performing the ISM combinations and recording the experimental data, the biomass ranged from 7.8 to 75.4 g.L\({}^{-1}\)d\({}^{-1}\), depending on the nutrients in the medium and the illuminance conditions. A high-order polynomial regression equation was used to fit the linear model, as shown in Figure S1. The model was well-synchronized with the data and showed no significant lack of fit, accurately representing the interaction between factors and response. The relevance of the linear model for biomass was evaluated using ANOVA, and the results are reported in Tables 2 and 3. Biomass was significantly affected by N (5000 mg-L\({}^{-1}\)), P + K (250 mg-L\({}^{-1}\)), S (70 mg-L\({}^{-1}\)), LI (137.5 umol m-\({}^{-2}\)s\({}^{-1}\)), and PP (24 h). The correlation between the predicted and actual values was remarkable, with a coefficient of 29.06. Furthermore, a 3D response surface plot illustrates the effects of N, P + K, S, LI, and PP individually or in combination on biomass concentration, and adequately optimizes the verified variables. This study is one of the few to explore CCD using multiple variables, including N, P + K, S, PP, and LI, to increase biomass productivity.
## 3 A-ECMS Based on Driving Pattern Recognition
### Implementation of the A-ECMS  
The schematic diagram of the adaptive-ECMS strategy based on driving pattern recognition is shown in Figure 8. Vehicle speed is fed back to the controller, and the DPR algorithm regularly recognizes the driving pattern according to the feature vectors. Then, the adapter in the A-ECMS controller updates the nominal equivalent factor according to the driving pattern and the current battery SOC. Finally, the ECMS algorithm computes the optimal controls based on the real-time equivalent factor and the torque request.
## 3 Results and Discussion
### Molecular and Structural Modifications Identified by FTIR Spectroscopy
#### 3.3.2 Dehydration and Hydrogen Bond Disruption (3400-3200 cm\({}^{-1}\) Region)  
The broad absorption band around 3400-3200 cm\({}^{-1}\), attributed to O-H and N-H stretching in keratin and bound water molecules, showed a notable reduction in intensity after both PAW treatments. This reduction indicates dehydration and disruption of hydrogen bonding within the protein matrix. The effect was more pronounced following PAW-N exposure, likely due to its higher ionic strength and acidity, which enhances moisture loss.  
This observation is supported by EDS data showing a marked decrease in oxygen content in Sample 1--from 21.7% (Control) to 9.4% (PAW-N)--suggesting desorption of water and oxidation products. In contrast, PAW-P-treated hair retained higher oxygen content (17.1% in Sample 1), indicating less dehydration. Similar trends were observed in Sample 4, where oxygen dropped from 22.0% to 21.4% after PAW-N but remained at 18.7% after PAW-P, consistent with FTIR and SEM results indicating more pronounced structural loss in PAW-N conditions.
## 3 Equivalent Circuit Parameters Calculation (Rotary Part)
### Equivalent Circuit Parameter Calculation Results  
According to the above expressions, the excitation reactance, equivalent rotor resistance and reactance in Figure 5 can be computed. The stator resistance and reactance can be obtained by the following expressions [15].  
\[R_{1}=\rho\frac{l}{\lambda_{e}}\]  
\[X_{1}=4\pi f\mu_{0}\frac{N_{1}^{2}}{\mu_{I}}l_{eF}\sum\lambda\]  
\[\sum\lambda=\lambda_{s}+\lambda_{\delta}+\lambda_{i}+\lambda_{E}\]  
where \(l\) is the length of a winding conductor and \(A_{o}\) is the conductor section. \(\lambda_{s}\), \(\lambda_{\delta}\), \(\lambda_{i}\), and \(\lambda_{E}\) are stator slot leakage reactance, harmonic leakage reactance, teeth leakage reactance, and end-winding leakage reactance, respectively. The computed results of the equivalent circuit parameters are shown in Figure 9.  
Figure 9 shows that the stator resistance, stator reactance and equivalent rotor reactance are constants. The equivalent rotor resistance and excitation reactance decrease with the increasing slip, and the excitation reactance is less than the equivalent rotor resistance.
## 4 Optimal Design Problem Formulation
### Overview of the Optimization Tool: Multi-Objective Particle Swarm Optimization  
PSO is a population-based approach for solving discrete and continuous optimizations problem that stemmed from and mimic the navigation behavior of swarms of bees, flocks of birds, and schools of fish. To obtain the optimal value of the objective function at each search, two different solution points are obtained which are called the local best, \(Pbest_{i}=(p_{i1},p_{i2},...,p_{id})\) and the global best, is \(Pbest_{g}=gbest=(p_{g1},p_{g2},...,p_{gd})\); and the positions of the particles for the next objective function evaluation is estimated as given below:\[V_{id}^{t+1} =w\times v_{id}^{k}+c_{1}\times rand_{1}\times(\mathit{Pbest}_{id}-X_ {id}) \tag{18}\] \[+c_{2}\times rand_{2}\times(\mathit{gbest}_{d}-X_{id})\]  
\[X_{id}^{k+1}=X_{id}^{k}+V_{id}^{k+1} \tag{19}\]  
\[w=w_{damp}\times\frac{iter_{max}-iter}{iter_{max}}+w_{i} \tag{20}\]  
_iter_ is the iteration count, \(iter_{max}\) is the total iterations. \(w_{i}\), \(w_{f}\) are the minimum and maximum range of the inertia weight. The multi-objective PSO approach adopted in this work is described [54]. The repository particles guides the search within the _efficient_, _non-inferior_ and _admissible_ pareto front by sorting out the non-dominated solutions. The exploratory capacity of the algorithm is strengthened by a special mutation operator just like in NSGA II algorithm as explained below. If \(\vec{f}(\vec{x})\) consists of \(n\) objective functions each with \(m\) decision variables, then the multi-objective problem can be defined as finding the vector \(\vec{x}^{*}=[x_{1}^{*},x_{2}^{*},...,x_{m}^{*}]^{T}\) which minimizes \(\vec{f}(\vec{x})\) as shown:  
\[\text{minimize}\ \ \vec{f}(\vec{x}) =\left[f_{1}(\vec{x}),f_{\vec{2}}(x),...f_{n}(\vec{x})\right]\ \text{for}\ \ \vec{x}^{*}\in\varepsilon \tag{21}\] \[\vec{g}(\vec{x}) \leq 0\] (22) \[\vec{h}(\vec{x}) =0 \tag{23}\]  
\(\vec{g}\) and \(\vec{h}\) are sets of inequality and equality constraints, respectively. A point \(\vec{x}^{*}\in\chi\) is pareto optimal if for every \(\vec{x}\in\chi\) and \(I=1,2,...,k\) either:  
\[\forall i\in I(f_{i}(\vec{x})=f_{i}(\vec{x}^{*})) \tag{24}\]  
or at least there is one \(i\in I\) such that  
\[f_{i}(\vec{x})>f_{i}(\vec{x}^{*})) \tag{25}\]
## 2 Materials and Methods
### Odonto/Osteogenic Differentiation of hDPSCs with BiodentineTM  
To study the impact of BiodentineTM on the odonto/osteogenic signaling pathway, hDPSCs were treated with BiodentineTM for 3 weeks. At first, hDPSCs (P3) were cultured in 6-well plates at a seeding density of 1 \(\times\) 10\({}^{5}\) cells/well in alpha MEM supplemented with 5% platelet lysate (PL) until the cells reached 60-70% confluence. After that, the derived cells were treated with either 2 mg/mL BiodentineTM as the test group and osteogenic media (osteogenesis differentiation kit, Gibco, Waltham, MA, USA) as the positive control, whereas un-induced cells cultured in their growth media (alpha MEM +5%PL) were used as a negative control group. Media were exchanged thrice per week.
## 4 Experimental Results Analysis and Discussion
### Comparison Methods
Overall, these results indicate that the proposed approach offers high precision and robustness in scenarios with limited labeled data. The experimental results validate the effectiveness of the introduced hybrid encoder architecture, the self-supervised contrastive learning mechanism, and the VAT-based fine-tuning strategy for the SEI task. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Methods** & **\{10\}** & **\{15\}** & **\{20\}** & **\{25\}** & **\{30\}** & **\{35\}** & **\{40\}** \\ \hline Baseline & 40.93\% & 48.67\% & 60.27\% & 60.60\% & 68.20\% & 79.27\% & 80.93\% \\ MAML & 36.67\% & 55.52\% & 68.52\% & 72.06\% & 76.56\% & 81.06\% & 82.45\% \\ SimCLR & 49.93\% & 57.53\% & 70.93\% & 75.27\% & 80.53\% & 83.93\% & 86.00\% \\ SA2 SEI & 43.13\% & 50.53\% & 68.43\% & 79.12\% & 83.06\% & 88.54\% & 91.08\% \\ Proposed & 54.73\% & 64.93\% & 89.27\% & 94.27\% & 95.93\% & 97.07\% & 97.20\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Accuracy comparison of various methods.
## 3 Method
### Parameter Estimation  
To investigate the factors affecting the severity of injuries in YAM motorcycle crashes, this study utilized the random parameter logit model with heterogeneity in means and variances. This model allows for multiple levels of unobserved heterogeneity by enabling the coefficients of the parameters to vary across each crash and interactions between the crash-level factors and the unobserved characteristics (i.e., the distribution of random parameters). Unlike the standard logit model, this approach takes into account the varying effects of unobserved factors on injury severity in different crashes. The modeling process began by defining a function that determines the severity of injury in YAM motorcycle crashes, as follows [27]:  
\[S_{jm}=\beta_{j}X_{jm}+\varepsilon_{jm}, \tag{1}\]  
where \(S_{jm}\) denotes severity function of YAM sustaining injury severity \(j\)-level in motorcycle crash \(m\), \(\beta_{j}\) denotes vectors of estimable parameters, \(X_{jm}\) represents vectors of various crash-level factors that affect the injury severity of the motorcyclist (e.g., wearing a helmet, exceeding speed limit, crashing on an unlit road, etc. ), and \(\varepsilon_{jm}\) represents an error term. The probability function of the YAM crash \(m\) sustaining injury severity \(j\) in the random parameter estimation, can be written as [28]:  
\[P_{m}(j)=\int\frac{EXP\big{(}\beta_{j}\ X_{jm}\big{)}}{\sum_{\forall j}EXP \big{(}\beta_{j}\ X_{jm}\big{)}}f(\beta|\rho)d\beta, \tag{2}\]  
where \(P_{m}(j)\) is the probability of a YAM crash \(m\) sustaining the injury severity \(j\). \(f(\beta|\rho)\) represents the density function of \(\beta\), and \(\rho\) denotes a vector of parameters describing the density function (mean and variance), and all other terms are previously defined. To allow for heterogeneity in means and variances, let \(\beta_{jm}\) be a vector of estimable parameters that vary across crash, which can be written as [28]:  
\[\beta_{jm}=\beta_{j}+\Theta_{jm}Z_{jm}+\sigma_{jm}EXP\big{(}\omega_{jm}W_{jm }\big{)}\
u_{jm}, \tag{3}\]  
where \(\beta_{j}\) denotes the mean of the random parameter vector, \(Z_{jm}\) is a vector of explanatory variables capturing heterogeneity in the mean \(\beta_{j}\), \(\Theta_{jm}\) represents a vector of corresponding estimable parameters, \(W_{jm}\) represents vector of explanatory variables capturing heterogeneity in the standard deviation \(\sigma_{jm}\), and \(\
u_{jm}\) is the disturbance term [29]. The models were run using Nlogit 6 and a random draw of Halton = 1000, which is considered reasonable
## 4 Findings and Discussion
### Explainable Frameworks in XAI
#### 4.3.5 What Is CEM and What Is Its Applicability to IDSs for ICVs?  
The contrastive explanation method (CEM) is a method for generating explanations for AI models to understand the decision-making process of a model by comparing the decision made for a specific input to the decision made for a similar but different input [111]. Its classification model explanations are provided in [111]. Furthermore, it retrieves the characteristics that must be sufficiently present to predict the input instance will belong to the same class. The minimal attributes that must be altered in order to associate the input instance with a different class are also identified. This is local, post hoc, and model-neutral [128]. According to [128], CEM, when used for neural networks, highlights not only the pertinent positives but also the pertinent negatives in its explanation, making it a preferred choice over LRP. The ability of CEM to extract both positive and negative pertinent features can help in reducing error during the diagnosis of a target. It is, however, yet to be seen how CEM could be applied effectively to the IDS for ICVs where non-image data is dominant. This is important if the CEM is to be used for intrusion detection in ICVs in real-time by monitoring and analyzing network traffic, system logs, and other data sources to identify unusual or suspicious activity. CEM is vital to interpreting the AI model predictions.