esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Given the limited buffer resources at the gateway and the dynamic nature of traffic, inefficient resource allocation can lead to a few large flows monopolizing buffer space, thereby reducing reordering efficiency and degrading user experience. Managing these limited resources to provide fair and efficient reordering services for flows of varying sizes and out-of-order patterns remains a critical challenge. This paper introduces a gateway-based IP-ICN hybrid multipath transmission system, which leverages the data buffering capabilities of gateway nodes to handle disordered packets. Additionally, it proposes a Reordering Buffer Management Method (RBMM) for limited resources to enhance resource utilization and allocation fairness. The main contributions of this paper are as follows:  
* In the IP-ICN multipath transmission system, we introduce a gateway framework that encompasses protocol conversion methods, functional module design, and the data processing workflow, with particular emphasis on the operation of the packet reordering module. * We design RBMM that operates through two mechanisms: First, RBMM employs an improved dynamic threshold (DT) scheme to allocate an adjustable reserved buffer space, which adapts to traffic fluctuations to manage congestion and improve buffer resource utilization. Second, RBMM uses a flow-priority-based buffer replacement strategy to evict packets from lower-priority flows during congestion, ensuring a more balanced allocation of resources. * We conduct a series of experiments to evaluate the performance of the proposed method. The results demonstrate that RBMM can effectively regulate buffer partitioning, ensuring high transmission performance while significantly conserving buffer resources. Across experiments with various packet and flow sizes, RBMM outperforms the comparison methods in terms of queuing delay and flow completion time, showcasing its ability to improve fairness in buffer resource allocation. The rest of this paper is organized as follows. In Section 2, we review research on the deployment of ICN in IP networks and on addressing packet disorder issues caused by multipath transmission. In Section 3, we describe the gateway design for the hybrid IP-ICN multipath transmission system, with a focus on implementing the packet reordering functionality. In Section 4, we provide a detailed explanation of the RBMM. In Section 5, we present simulation experiments and discuss the results. Finally, in Section 6, we conclude our study and outline future research directions.
## 2 Proposed Cycloconverter
### Operation  
Here, the operating principle of the proposed single phase to single phase step down cycloconverter has been discussed. For reducing harmonics, the output of the proposed cycloconverter for one-fourth frequency conversion has been selected, as seen in Figure 4a. From this figure, it is observed that the total output consists of eight modes. The positive cycle of the proposed topology has consisted of the first four modes and the remaining modes are responsible for generating a negative half cycle. In that case, the input 50 Hz frequency is converted into 8 \(\times\) 50 or 400 Hz. In the case of generating mode-1 of the output voltage, the firing pulse is given to the Tiac T1 and T4. For this, the output voltage will be \(V_{m}/2\). In mode 2, the firing pulse is given to T2 and T3 Triacs, then the output voltage will be \(V_{m}\). In mode 3, the output voltage is \(V_{m}\), this voltage can be generated by applying the pulse to the Tiac devices T1 and T5. For generating mode 4, the current is flowing in Tiac devices T3 and T4, the generated voltage of this mode is \(V_{m}/2\). These modes 1, 2, 3, and 4 are for the positive level of output voltage.  
Figure 3: (a) Proposed cycloconverter topology (1/3) and (1/4) frequency conversion of 50 Hz; (b) voltage stress of individual switches of proposed cycloconverter.  
In mode 5, two switching devices, T\({}_{3}\) and T\({}_{4}\), are used for generating the required voltage level \(-V_{m}/2\). In mode 6, the current passes through the switching devices T\({}_{1}\) and T\({}_{5}\) and its voltage level is \(-V_{m}\). For generating the voltage of mode 7, the firing pulses are given to T\({}_{2}\) and T\({}_{3}\). The final mode is mode 8, where the switching devices T\({}_{1}\) and T\({}_{4}\) are active and generating the voltage level of \(-V_{m}/2\). The modes of operation diagrams are presented in Figure 5 such that the red line indicates the current paths. Again, the same circuit can be used as one third frequency conversion of input frequency of 50 Hz. In this conversion, only six modes are used, which are presented in Figure 4b. The switching pattern for the one third and one fourth conversion is presented in Table 1.  
Figure 4: Expected output response for (**a**) one\(-\)fourth frequency conversion and (**b**) one\(-\)third frequency conversion of proposed cycloconverter.  
Figure 5: _Cont._
# Research on Hybrid Relay Protocol Design and Cross-Layer Performance Based on NOMA
## 3 Cross-Layer Performance Analysis of Two-Hop Relay System Based on NOMA
### Cross-Layer Performance Analysis
\begin{array}{l}T_{o1}^{OMA}=T_{o1}^{NOMA}=T_{\text{H}}+T_{\text{D}} +T_{\text{AT}_{w}}+T_{\text{SIFS}}+T_{\text{DIFS}}+2\delta,\\ T_{o2}^{OMA}=2T_{\text{PRS}}+T_{\text{P}}+T_{\text{D}}+2T_{\text{RIFS}}+T_{ \text{CIFS}}+T_{\text{AT}_{\text{pl}}},\\ T_{o2}^{NOMA}=2T_{\text{PRS}}+T_{\text{P}}+T_{\text{D}}+2T_{\text{RIFS}}+T_{ \text{CIFS}}+2T_{\text{AT}_{\text{pl}}}.\end{array} \right. \tag{36}\]  
The system throughput expression is as follows:  
\[S^{NOMA}=\frac{P_{s}^{OMA\prime}.T_{D}+P_{s}^{\text{D}_{1}}.T_{D}+P_{s}^{\text {D}_{1},\text{D}_{2}}.2T_{D}}{\overline{T_{e}}+\overline{T_{e}}+\overline{T_{ e}}+\overline{T_{o}}}. \tag{37}\]The packet loss probability \(p_{sys}\) of the system consists of two parts: transmission failure due to collision during packet competition and transmission failure due to channel interruption. The expression is as follows:  
\[p_{sys}=1-(1-p)\Big{(}1-P_{\text{out}}^{NOMA}\Big{)}. \tag{38}\]  
The average delay \(\overline{T^{NOMA}}\) required for successful two-hop transmission of packets in NOMA scheme is shown as follows, including the average backoff delay experienced by stations before sending packets, the time when packets collide during two-hop transmission, and the cost of other time intervals:  
\[\overline{T^{NOMA}}=\text{E}[X]\text{E}[slot]+\frac{1-P_{s}^{NOMA}}{P_{s}^{ NOMA}}T_{c}^{NOMA}+T_{s}^{NOMA}, \tag{39}\]  
where \(\text{E}[X]\) is the average time slot number of the retreat process taken by the node to successfully transmit the data packet, and \(\text{E}[slot_{1}]\) represents the average time length of the BC retreat time slot of the first hop wireless and the second hop power line. The expression is as follows:  
\[\text{E}[X]=\frac{(1-2p)(CW\text{min}+1)+p\cdot CW\text{min}\big{[}1-(2p)^{m} \big{]}}{2(1-2p)(1-p)}, \tag{40}\]  
\[ \begin{array}{l}\text{E}[slot]=(1-P_{tr})\sigma+P_{tr}(1-P_{s})T_{c}+P_{ \text{o}MA}^{OMA}\cdot T_{\text{o}1}^{OMA}+P_{o2}^{OMA}\big{(}T_{s1}^{OMA}+T_{ o2}^{OAM}\big{)}\\ +P_{o1}^{NOMA}\cdot T_{\text{o}1}^{NOMA}+P_{o2}^{NOMA}\big{(}T_{\text{s1}}^{NOMA }+T_{\text{o}2}^{NOMA}\big{)}\\ +P_{s}^{OMA}\cdot T_{s}^{OMA}+P_{s}^{\text{D}_{1}}\cdot T_{s}^{\text{D}_{1}}+P_ {s}^{\text{D}_{1}}\cdot T_{s}^{\text{D}_{2}}\cdot T_{s}^{\text{D}_{1}}\cdot T_{ s}^{\text{D}_{2}}.\end{array}  \tag{41}\]
###### Abstract  
Advances in material science and intelligent systems have led to an increasing use of large-deflection flexible structures in the aerospace industry, including flexible-wall wind tunnel nozzles, deformable wings, and variable nozzles for aircraft engines. These structures have attracted significant research interest due to their variable aerodynamic performance, functional diversity, and dynamic response characteristics that distinguish them from rigid structures. Large-deflection flexible aerodynamic structures typically consist of flexible structural surfaces and actuators. Precise deformation control and optimized structural design are crucial for achieving their full performance potential. However, few existing technological tools can effectively guide the implementation of such deformation control and optimized design. In this paper, we first established a mechanical model of a multi-pivot flexible nozzle based on a typical wind tunnel flexible nozzle. We then derived a theoretical model of beam deformation with multi-point dynamic constraints using the principle of variability. Next, we created a deformation solution method based on radial basis point interpolation to evaluate nozzle profile accuracy. Finally, we established a complete surrogate-based optimization process for a large-deflection flexible nozzle and experimentally verified it using a wind tunnel nozzle prototype equipped with laser tracking and flexible sensors. The results show that the nozzle's profile accuracy remains within \(\pm 0.2\) mm under specified operational conditions.  
flexible structures; nozzle plates; deformation control; surrogate models; structural optimization; flexible sensors Article
## 3 Results and Discussion  
### Modeling of Pectin Yield in MW-HPCO\({}_{2}\) Extraction  
The results of the experiments carried out at conditions defined in the Box-Behnken design were analyzed with multiple regression analysis to obtain a statistical model. The results best fitted to a second-order polynomial model. The backward elimination was applied to remove insignificant factors and interactions from the model. The model equation for pectin yield (Y1) is given below:  
\[\mathrm{Y1}=21.95+9.97*\mathrm{A}-1.40*\mathrm{B}-0.8*\mathrm{C}-2.71*\mathrm{ A}*\mathrm{B}-1.6*\mathrm{A}*\mathrm{C}-1.7*\mathrm{B}*\mathrm{C}-7.55*\mathrm{A}^{2}-1.65* \mathrm{B}^{2} \tag{6}\]  
The model predicted was significant at the 95% confidence level. High values of adjusted and predicted R\({}^{2}\) and nonsignificant lack of fit indicated that the statistical model fit well with the data (Table 2). Adequate precision measures the signal to noise ratio and it is desirable that the ratio be greater than 4. This value was 22.6 and indicated that the model was convenient for navigating the design space. Using analyses of variance (ANOVA), the accuracy of the model and the linear and interactive effects of the factors on the pectin yield (Y1) were examined. ANOVA results showed that the linear effects of temperature and time were statistically significant (\(p<0.05\)) on pectin yield, while the effect of liquid/solid ratio was insignificant (\(p>0.05\)). Similarly, [PERSON] et al. [30] reported that microwave power (temperature) and irradiation time were significantly effective on yield in pectin extraction from grapefruit albedo. As in MW-HPCO\({}_{2}\) extraction, [PERSON] et al. [31] also stated that the linear effect of solid: liquid ratio is insignificant for microwave-assisted pectin extraction from apple pomace. The effects of extraction temperature \(*\) liquid/solid ratio (A \(\times\) C) and time \(*\) liquid/solid ratio (B \(\times\) C) interactions were statistically nonsignificant (\(p>0.05\)) on pectin yield, while temperature \(*\) time (A \(\times\) B) interaction was significant (\(p<0.05\)).
###### Abstract  
A method to calibrate the overlap factor of Lidar is proposed, named unmanned aerial vehicle correction (UAVC), which uses unmanned aerial vehicles (UAVs) to detect the vertical distribution of particle concentrations. The conversion relationship between the particulate matter concentration and the aerosol extinction coefficient is inverted by the high-altitude coincidence of the vertical detection profiles of the UAV and Lidar. Using this conversion relationship, the Lidar signal without the influence of the overlap factor can be inverted. Then, the overlap factor profile is obtained by comparing the signal with the original Lidar signal. A 355 nm Raman-Mie Lidar and UAV were used to measure overlap factors under different weather conditions. After comparison with the Raman method, it is found that the overlap factors calculated by the two methods are in good agreement. The changing trend of the extinction coefficient at each height is relatively consistent, after comparing the inversion result of the corrected Lidar signal with the ground data. The results show that after the continuously measured Lidar signal is corrected by the overlap factor measured by this method, low-altitude aerosol information can be effectively obtained.  
UAVC; Raman-Mie Lidar; overlap factor; low-altitude aerosol +
Footnote †: journal: Applied Sciences
## 1 Introduction
To achieve this, an improved BWO-BOA (Black Widow Optimization-Butterfly Optimization Algorithm) was utilized, which employs a dynamic adaptive search strategy for improving the actual BOA and solving the issues of low precision, easy entrapment in the local optima, and slow convergence. Satisfactory results have been attained. Additionally, [PERSON]. [7] applied a hybrid model to perform feature selection and thus accomplish ID. The execution model utilized PSO (Particle Swarm Optimization) and GWO (Grey Wolf Optimization). Further, two models were used, namely, PSO-GWO-ANN (PSO-GWO-Artificial Neural Network) and PSO-GWO-NB (PSO-GWO-Naive Bayes). Frequently repeating features from the suggested model were assessed. PSO-GWO was run for a specific iteration count. An individual feature selection framework was run independently, and the chosen feature set was saved. The features of PSO-GWO were tested in the subsequent phase. The UNSW-NB15 dataset was utilized for evaluation. Further, experimentations were executed with two classifiers, ANN and NB. The outcomes reveal that PSO-GWO shows acceptable performance in selecting the features of ID. Moreover, the combination of the features of PSO-GWO afforded better results with fewer features. Initially, common features of attack and normal behavior were eliminated. The RF algorithm was utilized to assess the significance of predictors for ID. Subsequently, RF was employed to work with minimal selected predictors for classifying attack behavior and normal users. Lastly, classifications were considered for intruder detection. Experimentations were undertaken, and it was confirmed that the classifier's performance could be enhanced with regard to accuracy with the use of RF [8]. In spite of several attempts having been made to use existing models, there is scope for a further improvement in accordance with accuracy. Moreover, only a few studies have considered three datasets when performing ID. Hence,to alleviate such pitfalls, the current study proposes appropriate ML-based methods for effective IDS detection. The main objectives of this study are:  
* To pre-process the data via the identification of missing values, and the scaling and filling of the missing values so as to accomplish effective prediction;
* To select suitable and relevant features using the proposed OWSA (Optimal Whale Sine Algorithm) for enhancing classifier performance;
* To predict the intrusions by deploying the proposed AWRF (ANN Weighted Random Forest) to attain a better prediction rate;
* To assess the performance rate of the proposed work with regard to evaluation metrics for proving the effectiveness of this system.
# Wavelet Packet Decomposition to Characterize Injection Molding Tool Damage +
###### Abstract  
This paper presents measurements of acoustic emission (AE) signals during the injection molding of polypropylene with new and damaged mold. The damaged injection mold has cracks induced by laser surface heat treatment. Standard test specimens were injection molded, commonly used for examining the shrinkage behavior of various thermoplastic materials. The measured AE burst signals during injection molding cycle are presented. For injection molding tool integrity prediction, different AE burst signals' descriptors are defined. To lower computational complexity and increase performance, the feature selection method was implemented to define a feature subset in an appropriate multidimensional space to characterize the integrity of the injection molding tool and the injection molding process steps. The feature subset was used for neural network pattern recognition of AE signals during the full time of the injection molding cycle. The results confirm that acoustic emission measurement during injection molding of polymer materials is a promising technique for characterizing the integrity of molds with respect to damage, even with resonant sensors.  
acoustic emission; injection molding; cracks; feature vector; pattern recognition Article
## 5 Motion Learned Using Gaussian Process Dynamical Model  
We obtained the humanoid robot joint angles using the particle filter estimation detailed in Section 3. The humanoid robot was supposed to wave its hands, as shown in Figure 6. The learning data set \(X\) comprised 500 frames of the humanoid robot joint angles selected from 1000 frames of continuous motion data.  
The learning hyperparameters were initially set to \(\alpha=[1.0,1.0,1.0,\exp(1.0)]\), \(\beta=[1.0,1.0,\exp(1.0)]\), and \(\omega_{j}=1.0\) (for more details, please see [14]). The GPDM learned 3D motion models are illustrated in Figure 7(a). According to [11], if learned trajectories are smooth, prediction based on the learned motion models tends to be accurate. We used the GPDM [21] to make predictions, as Gaussian process prediction [11] retrieves a dynamical mapping for state transition and GPDM decoder mapping to reconstruct the humanoid robot joint angles.  
For dynamical mapping \(f_{x}\) in the 3D state space, state \(\mathbf{x}_{t+1}\) is predicted from \(\mathbf{x}_{t}\) as follows:  
\[\mathbf{x}_{t+1}\sim\mathcal{N}(f_{x}(\mathbf{x}_{t});\sigma_{X}^{2}(\mathbf{x}_{t})\mathbf{I}), \tag{5}\]  
\[\mathbf{x}_{t+1}=f_{x}(\mathbf{x}_{t})=X_{2:T}^{\mathrm{T}}K_{X}^{-1}k_{X}(\mathbf{x}_{t}, X). \tag{6}\]  
Similarly, for GPDM decoder mapping \(f_{\theta}\), given latent state \(\mathbf{x}_{t}\), humanoid robot joint angle \(\mathbf{\theta}_{t}\) can be reconstructed as  
\[\mathbf{\theta}_{t}\sim\mathcal{N}(f_{\theta}(\mathbf{x}_{t});\sigma_{\Theta}^{2}(\bm {x}_{t})\mathbf{I}), \tag{7}\]  
\[\mathbf{\theta}_{t}=f_{\theta}(\mathbf{x}_{t})=\Theta^{\mathrm{T}}K_{\Theta}^{-1}k_{ \Theta}(\mathbf{x}_{t}). \tag{8}\]  
In dynamical prediction mapping \(f_{x}\) and decoder mapping \(f_{\theta}\), \(\sigma_{X}^{2}(\mathbf{x}_{t})\) and \(\sigma_{\Theta}^{2}(\mathbf{x}_{t})\) represent the corresponding variances. \(k_{X}(\cdot)\) and \(k_{\Theta}(\cdot)\) are kernel functions. \(K_{X}\) and \(K_{\Theta}\) are kernel matrix.  
Figure 7(b) depicts the modeled and predicted 3D latent variables. The predicted latent variables are consistent and mostly overlap with the learned GPDM for motion. Hence, dynamical mapping \(f_{x}\) and the learned GPDM motion model are accurate. Figure 9 shows the angles from two joints of the humanoid robot reconstructed using GPDM decoder \(f_{\theta}\), demonstrating the accuracy of decoder \(f_{\theta}\).  
Figure 8: GPDM learned 3D dynamical motion model and model-based prediction.
# Improved Cell Allocation Strategies Using K-Means Clustering in Congested 6 TiSCH Environments
## 2 Related Work
Unlike earlier studies, we investigate how group node positioning influences cell operation in TSCH, focusing on scalability, throughput delay, and reliable communication. Furthermore, we propose a dynamic allocation method for enhancing network efficiency that makes adjustments based on node mobility and network responsiveness during data transmission.
## 3 Results and Discussion
### Statistical Analysis
#### 3.3.2 Correlation Analysis of Aggregate Characteristics and Water Damage Resistance  
To conduct the correlation study of aggregate characteristics and water damage resistance, the output layer of the random forest analysis model was the complete evaluation index \(l_{i}\) of the asphalt mixture against water damage. The random forest analysis model was first created using the RStudio 4.2.1 program. The output layer's resistance to water damage (\(l_{i}\)) of the asphalt mixture was then made the dependent variable, while other aggregate characteristic parameters were employed as covariates. Figure 11 displays the independent variable importance analysis. Among the indicators of aggregate characteristics, the degree of correlation with water damage resistance is in the order of SiO\({}_{2}\), roughness, CaO, shape factor, fractal dimension, and quartz. The CaO content has an effect on the ability to resist water damage, but is not the dominant effect among chemical elements, a conclusion shared in previous scholarly studies. Roughness, shape factor, and fractal  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Comprehensive**} & \multicolumn{5}{c}{**Aggregate**} \\ \cline{2-6}  & **Type B Limestone** & **Granite** & **Basalt** & **Type A Limestone** & **Diabase** \\ \hline \(l_{i}\) & 0.940377 & 0.568443 & 0.886701 & 0.856893 & 0.748623 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of fuzzy comprehensive evaluation of water damage resistance.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Water Damage Resistance**} & \multicolumn{5}{c}{**Aggregate**} \\ \cline{2-6}  & **Type B Limestone** & **Granite** & **Basalt** & **Type A Limestone** & **Diabase** & \\ \hline \(W_{b}\) & 0.5101 & 0.3348 & 0.9638 & 1.0000 & 1.0000 & 0.1809 \\ \(MS\) & 1.0000 & 1.0000 & 1.0000 & 0.9869 & 0.9232 & 0.1962 \\ \(MS_{0}\) & 0.0000 & 0.3240 & 0.6503 & 0.7240 & 0.8598 & 0.1756 \\ \(R_{Tl}\) & 0.9008 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.2623 \\ \(TSR\) & 0.8320 & 0.7056 & 0.8150 & 0.9753 & 0.9185 & 0.185 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Normalized results of water damage resistance index data.  
dimension are relatively well correlated with water damage resistance, with roughness taking the lead as a reference.
## 4 Simulation and Results Analysis  
### Experiments  
Commercial lithium iron phosphate batteries (LIPB) were tested in this study with a nominal voltage of 3.7 V and a nominal capacity of 2200 mAh. The experiment was conducted on a test bench manufactured by Wuhan LANBTS (BT2016C), and the software (Control software client, LANBTS, Wuhan, China) installed on the PC was used to control the charging and discharging of the battery according to the given operating conditions. The terminal voltage and the current of the battery were recorded at a certain frequency. The acquired data is used for model parameter identification and SOC estimation.  
Firstly, the SOC and OCV data were obtained using the test bench in conjunction with the experimental method mentioned in Section 2. These data were fitted using MATLAB 2014b (TheMathWorks, Natick, Massachusetts, USA) to obtain OCV-SOC plots and relationships. Secondly, a hybrid pulse power characterization (HPPC) test was performed to identify the parameters of the Thevenin equivalent circuit. The test procedure can be found in the [31; 32; 33; 34] and will not be described in detail. Thirdly, a capacity test for finding the standard capacity of the test battery was designed, and its procedures are shown below. Several batteries were placed in a temperature chamber at 25 \({}^{\circ}\)C for two hours. Then, the batteries were discharged to 2.2 V at a constant discharge current (1 C). After waiting for two hours, the batteries were fully charged with a constant current. This process would be repeated three times and the average of the test capacity was taken as the standard capacity of the test batteries. Finally, the batteries were respectively tested under the constant current working condition and the Beijing Bus Dynamic Stress Test (BBDST) working condition. Online battery SOC estimation was conducted using the collected data.