esa-sceva/satcom-chunk-collection huggingface dataset:
# Discrete Data Rate Adaptation for Wireless Body Area Networks
## 5 Evaluation
### Running Times  
We also examined the running time of the proposed algorithms. The algorithms were implemented in C++. The tests were run on a 2.4 GHz processor with 8 GB memory. Table 3 shows the average running times in microseconds for different test series. As shown in the table, the greedy algorithm can produce results much faster than the dynamic programming approach. The dynamic programming algorithm required 50-150 times more time than the greedy method. The ILP-based method was clearly the slowest.  
Our dynamic programming-based approach has a rather high complexity because it traverses the routing tree and runs dynamic programming for each node. The greedy algorithm has a faster running time than the dynamic programming algorithms and can provide similar results.
## 3 Data and Methods  
### Data Sources  
The study period is from 1993 to 2024, covering a total of 32 years. The data were sourced from Landsat series satellite imagery. Landsat 5-TM, Landsat 7-ETM+, Landsat 8-OLI, and Landsat 9-OLI satellite imagery for the period from 1993 to 2024, with cloud cover less than 10%, was downloaded from the Geospatial Data Cloud Platform of the Computer Network Information Center, Chinese Academy of Sciences ([[http://www.gscloud.cn](http://www.gscloud.cn)]([http://www.gscloud.cn](http://www.gscloud.cn)) accessed on 10 January 2025), as shown in Table 1. The Landsat 5 TM, Landsat 7 ETM+, Landsat 8 OLI, and Landsat 9 OLI satellites have a 30-m spatial resolution for multispectral bands. The Landsat 7 ETM+, Landsat 8 OLI, and Landsat 9 OLI satellites have a 15-m  
Figure 2: Annual runoff and sediment load changes for the Xinqiman–Kelenlik river reach: (**a**) variations in runoff and sediment from 1993 to 2024 at the Xinqiman Station; (**b**) variations in runoff and sediment from 1993 to 2024 at the Yingbaza Station.
# Oil Sludge as a Rejuvenator for Aged Bitumen: Structural and Thermal Effect
## 5 Conclusions  
This study has demonstrated that oil sludge can be effectively used as a rejuvenator for aged bitumen. The incorporation of 1-5% oil sludge led to increased penetration values (from 60 to 71 \(\times\) 0.1 mm) and softening points (from 55 \({}^{\circ}\)C to 72 \({}^{\circ}\)C), indicating enhanced pliability and thermal performance. FTIR spectroscopy revealed a decrease in oxidation-related groups (notably sulfoxide), SEM showed improved surface uniformity, and TGA confirmed reduced thermal residue. Additionally, the asphaltene-to-maltene ratio dropped from 0.355 in aged bitumen to 0.320, 0.280, and 0.257, suggesting improved colloidal balance and partial structural recovery.  
Figure 6: Schematic representation of the rejuvenating mechanism of oil sludge in aged bitumen.  
Beyond technical performance, this approach offers environmental benefits by repurposing hazardous oil refinery waste into a valuable material for road construction. The findings support the integration of oil sludge into sustainable pavement technologies. Future research will focus on the long-term durability of treated binders under field conditions and further elucidation of molecular interactions using advanced analytical tools such as GC-MS and NMR.  
Conceptualization, G.A. and C.O.R.; methodology, S.A.; validation, G.A., C.O.R. and I.B.; formal analysis, I.B. and Y.M.; investigation, G.A., S.A. and I.B.; resources, Y.M., I.B. and M.Y.; data curation, G.T.; writing--original draft preparation, C.O.R.; writing--review and editing, G.A.; visualization, G.T.; supervision, G.A. and C.O.R.; project administration, G.A. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan (Grant No. AP22786142 Utilization of oily waste as a potential rejuvenator in reclaimed asphalt pavement; BR27199103 Development of Eco-Friendly Packaging Materials from Recyclable Paper and Biomass Waste with Adaptive and Enhanced Protective Properties).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflicts of interest.
# Sentiment Analysis of Students' Feedback on E-Learning Using a Hybrid Fuzzy Model
## 3 Methodology
### Proposed Model
#### 3.5.2 The Defuzzify Layer  
The output obtained from the BiLSTM layer is still considered a fuzzy value that should be converted to a crisp value. Therefore, the defuzzification operation is performed in this layer. The defuzzify layer is trained with a ruleset and by a defuzzification function to produce a crisp output. This function is defined in Equation (3):  
\[d(x,r)=\sum_{i=0}^{n}x_{i}r_{i} \tag{3}\]  
where x is the fuzzy value (vector) obtained from the BiLSTM layer, and r is the ruleset obtained by training and adjusting the connection weights.
# Substitution Effects on the Optoelectronic Properties of Coumarin Derivatives
## 3 Results and Discussion
### Electronic Properties of the Compounds Using PBE XC Functional  
To further affirm our results, we repeated the electronic structure calculations with PBE XC functional and 6-31G\({}^{\star}\) basis set, since due to the computational efficiency it is considered as a reference XC functional. In Table 3, the calculated observables obtained using PBE XC functional and the associated difference with respect to B3 LYP XC functional is reported. The \(\Delta\)E gap and IE\({}_{\text{V}}\) values are underestimated, while EA\({}_{\text{V}}\) value is overestimated for the compounds using PBE XC functional. However, the general trend of the calculated observables among the compounds remains consistent (Table 3).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Compound** & **Couramin** & **A** & **B** & **C** & **D** & **D** \\ \hline E\({}_{\text{HOMO}}\) (eV) & \(-\)6.50 & \(-\)6.87 & \(-\)6.07 & \(-\)6.45 & \(-\)6.26 & \(-\)6.59 & \(-\)5.61 & \(-\)6.04 & \(-\)5.58 & \(-\)5.97 \\ E\({}_{\text{LUMO}}\) (eV) & \(-\)1.88 & \(-\)2.28 & \(-\)2.04 & \(-\)2.32 & \(-\)2.23 & \(-\)2.58 & \(-\)1.71 & \(-\)2.14 & \(-\)2.04 & \(-\)2.48 \\ \(\eta\) = (E\({}_{\text{LUMO}}\) - E\({}_{\text{HOMO}}\))/2 (eV) & 2.31 & 2.30 & 2.01 & 2.07 & 2.01 & 2.01 & 1.95 & 1.95 & 1.77 & 1.75 \\ CP = (E\({}_{\text{LUMO}}\) + E\({}_{\text{HOMO}}\))/2 (eV) & -4.19 & \(-\)4.58 & \(-\)4.05 & \(-\)4.39 & \(-\)4.25 & \(-\)4.59 & \(-\)3.66 & \(-\)4.09 & \(-\)3.81 & \(-\)4.23 \\ Ionization energy IE\({}_{\text{V}}\) (eV) & 8.41 & 8.71 \({}^{\star}\) & 7.64 & 7.96 & 7.72 & 7.98 & 7.11 & 7.48 & 7.14 & 7.46 \\ Electron affinity EA\({}_{\text{V}}\) (eV) & \(-\)0.04 & 0.49 & 0.35 & 0.80 & 0.67 & 1.09 & 0.15 & 0.65 & 0.48 & 1.00 \\ Quasi particle gap E\({}_{\text{G}}\) (eV) & 8.45 & 8.22 & 7.29 & 7.16 & 7.05 & 6.89 & 6.96 & 6.83 & 6.66 & 6.46 \\ \hline \hline \multicolumn{10}{c}{\({}^{\star}\) Experimental value = 8.72 eV from reference [66].} \\ \end{tabular}
\end{table}
Table 2: Electronic properties of the Coumarin and derived compounds **A**, **B**, **C** and **D**, employing B3 LYP XC in combination with basis set 6-31G\({}^{\star}\), and 6-311+G\({}^{\star}\) (red).
## 3 Parameter Sensitivity Analysis Based on Orthogonal Design
### Sensitivity Analysis Results of the Sobol Method  
Figure 7 illustrates the first-order sensitivity indices and the global sensitivity indices for the input parameters across six output metrics, using Equations (9) and (10) introduced in Section 2.2, as well as the sum of sensitivities of individual sensitive parameters affecting the parameters in different output responses. It is evident that when using crown displacement, convergence around the tunnel, maximum/minimum principal stress, and the proportion of the plastic zone as output variables for sensitivity analysis, the specific rankings of the seven input factors vary across different output indicators from the perspective of significance analysis.  
For the crown displacement, the four most sensitive factors in descending order are burial depth, saturation density, elastic modulus, and Poisson ratio. In terms of the B-C/D-E convergence, the four factors with the highest sensitivity indices are Poisson ratio, cohesion, internal friction angle, and burial depth. Regarding the maximum/minimum principal stresses, the four most sensitive factors are burial depth, saturation density, elastic modulus, and Poisson ratio. As for the proportion of the plastic zone, the four factors with the highest sensitivity indices are cohesion, internal friction angle, burial depth, and Poisson ratio. From the perspective of factors with minimal influence, various output models consistently exhibit factors of relative insignificance. For instance, external hydrostatic pressure and Poisson ratio have a negligible effect on the evolution of plastic zones, and the internal friction angle has a minimal impact on the settlement of arch crowns.  
Comparative analysis of \(S_{1}\) and \(S_{T}\) reveals that the interaction among various input parameters exerts differential impacts on distinct factors. In general, burial depth, Poisson ratio, and saturation density significantly influence the response of cavern displacement, which is particularly evident in their impact on tensile and compressive stress within the cavern. In terms of displacement effects, burial depth, elastic modulus, and saturation density primarily affect the cavern's vertical displacement characteristics, while the Poisson ratio predominantly influences its horizontal displacement traits. Cohesive force is identified as the principal factor affecting the proportion of the plastic zone. The influence patterns of \(S_{1}\) and \(S_{T}\) are consistent. These conclusions are consistent with those of previous  
Figure 6: Regression prediction performance of PSO-XGBoost and XGBoost. (**a**) Crown displacement; (**b**) B–C convergence; (**c**) D–E convergence; (**d**) maximum principal stress; (**e**) minimum principal stress; (**f**) proportion of plastic zone.  
studies [43]. Specifically, the interplay between factors has the most pronounced effect on the elasticity modulus, Poisson ratio, and internal friction angle. Therefore, selecting the mechanical parameters of rocks for parameter inversion simplifies the inversion analysis process and ensures the accuracy of the results.
# Cross-Layer Protocol Based on Directional Reception in Underwater Acoustic Wireless Sensor Networks
## 5 Simulation Results  
In this section, we evaluate the performance of the CLIC for UASNs using the NS3 platform. First, simulation settings are given to analyze how relevant parameters affect network performance. The performance of different routing protocols is then compared in terms of PDR, end-to-end delay, energy consumption, and energy efficiency. The PDR is the ratio of the number of packets successfully received by the sink to the number of packets sent by source nodes. End-to-end delay is the routing time of data packets transmitted from source nodes to the sink. Energy efficiency is defined as the ratio of the total size of successfully received data packets to the total energy consumed by the network. The common parameters used in the simulation are presented in Table 1.  
A UASN consisting of multiple sensor nodes and a sink node is considered. Sensor nodes are evenly and randomly distributed in a three-dimensional space measuring 3 km \(\times\) 3 km \(\times\) 1 km and are allowed to move randomly with a maximum speed of 1 m/s. They continuously send data packets to the sink through multi-hop routing. The sink node is fixed on the surface of the network and has infinite energy.
## 2 Existing Sensor Network Platforms  
Recently, a number of sensor network platforms have been proposed. Table 1 compares the prices and features of our EMMNet and some typical systems. The MICA2 more is a third-generation mode module used for enabling low-power wireless sensor networks. It is supported by the MoteWorks wireless sensor network platform for reliable _ad hoc_ mesh networking [14]. The TelosB mote is an open source platform designed to enable cutting-edge experimentation for the research community. TelosB bundles all the essentials for lab studies into a single platform, including USB programming capability, an IEEE 802.15.4 radio with integrated antenna, a low-power microprogrammed control unit (MCU) with extended memory, and an optional sensor suite (TPR2420) [15]. The MICAZ is also a 2.4-GHz mode module based on the Atmel ATmega128L [16]. Unlike most existing platforms, the Imote2 contains the Intel PXA271 CPU, which can operate at 13-416 MHz with dynamic voltage scaling [17]. Building on the highly successful Tmote Sky, Moteiv introduced Tmote Mini, the latest generation of wireless sensor network hardware. Moteiv's Tmote Sky and Tmote Mini platforms both combine Texas Instruments MSP430 microcontrollers with TI/Chipcon low-power radios [18, 19]. In addition, BTnode is a versatile, autonomous wireless communication and computing platform based on a Bluetooth radio, low-power radio, and microcontroller. The low-power radio is the same as that used on the Berkeley MICA2 more, making BTnode a twin of MICA2 [20].  
As shown in Table 1, among all these platforms the smart sensor in EMMNet provides the longest communication distance. It is thus suitable for applications in complex urban environments, where electricity meters in tall buildings are often installed inside metal chests and separated by concrete walls. The external memory space of the data collector in EMMNet is 4 MB and that of data concentrators is 8 MB. The external memory space of EMMNet is large, because collectors and concentrators need to record the data and parameters from hundreds of electricity meters. The cost of EMMNet is also lower than that of any other platform listed in Table 1. A dynamic power-management technique is an additonal feature of our system. This technique dynamically reconfigures the system to provide the requested services. The current draw of the processor is 8 mA in active mode, and it drops to about 30 uA while in sleep mode.
## 3 Experimental Results
### Visual Quality Evaluation
In this figure, texture lines are detected well, which means that the predicted Laplacian map provides the SARDS with spatial information regarding areas that are flat, lined, or textured. This information enables the proposed SALDL to be adaptive to local image structures. Consequently, the texture representation of the detail layer can be improved, and noisy dot patterns on flat areas can be effectively suppressed. The ISMP can be regarded as a type of attention network, and the predicted Laplacian map is a spatial attention feature map. Based on Equations (3) and (11), the DDN and PRL methods use the base layers generated using the proposed GCM-based residual subnetwork. The DDN is one of the deep learning architectures proposed for inverse halftoning because it was derived from the GCM proposed to predict Gaussian-blurred images. In the existing PRL methods, no specific models exist for the residual learning of the base layer. In addition, the existing PRL cannot produce satisfactory results from low-quality base layers. To our best knowledge, this study is the first to perform the abovementioned comparison, and the experimental results confirmed that the proposed SALDL can be used as a new deep learning model for inverse halftoning that enables residual learning for both the base and detail layers by incorporating image decomposition into the deep learning framework. Tables 2 and 3 show the results of the PSNR and SSIM evaluations for the small texture and BSDS100 datasets, respectively. As expected, the proposed SALDL method demonstrated the best performance among all the methods, and it surpassed the state-of-the-art inverse halftoning methods based on deep learning. This indicates that the proposed image decomposition model is effective in obtaining high-quality continuous-tone images from halftone images. The proposed base layer design, based on the GCM, enables residual learning by narrowing the output brightness range. The structure-aware residual deblurring strategy can remove the blurring of the predicted base layer and restore the image structures effectively. The proposed SALDL is a new PRL for inverse halftoning. By contrast, the PSNR and SSIM of the DDN and PRL were lower than those of the proposed method. This confirmed that the DDN and PRL were restricted in terms of restoring the original images from low-quality base layers. Tables 2 and 3 show that the average PSNR of the U-net was slightly better than that of the PRL. This implies that the U-net is an extremely effective model for inverse halftoning. In other words, decomposing input halftoned images into multiple resolutions is an extremely effective approach. If the SRDAS and GCM-based residual subnetworks are built similarly to U-net, then the performance of the proposed method may be improved. Figure 9: Laplacian maps predicted using ISMP.
# Dynamic Modeling and Output Characteristics Analysis of the Hub-Drive Reduction System
## 3 Dynamic Modeling of the Wheel Hub Transmission Reduction System
There are usually working on a mine road, and the hill resistances are comparable to rolling resistance. In addition, considering the huge load, with a maximal value up to 400 t, the mass of the vehicle in translational motion will affect the drive system through the tires and their contact with the ground. Thus, in this study, three key input factors were considered: the motor power input, the vehicle's equivalent moment of inertia, and the rolling friction load acting on the wheels. Motor Power Input: The motor serves as the driving force for this dynamic model. To ensure a smooth system startup, a step function (time, t0, x0, t1, x1) was used to model the motor input. This function defines the motor speed increasing linearly from x0 to x1 over the time interval from t0 to t1. Specifically, the input speed was as follows: step(time, 0, 0, 1, 6000 d). This represents the motor accelerating from 0 to 6000 degrees per second within 1 s, after which the speed remains constant. The input speed curve is shown in Figure 3.  
Figure 3: Input angular velocity curve of the hub motor. Vehicle Equivalent Moment of Inertia: The vehicle's equivalent moment of inertia was 162 kg\(\cdot\)m\({}^{2}\), representing the total load transmitted through the hub system. Initial Load from Road Conditions: Hill resistances, rolling resistance, loads and some other aspects will have great effects on the initial load of such a system. Here, we mainly considered the horizontal part of mining off-road, which means the effects of road slope and air resistance could be simplified. Thus, the primary load came from the rolling friction generated at the contact between the tires and the road surface, which created a frictional torque that hinders the vehicle's movement. And the rolling friction torque exerted by the road on the vehicle could be considered the initial load. The vehicle's mass was 300 tons, its wheel radius was 1.905 m, and its rolling friction coefficient was between 0.035 and 0.05. The resulting friction torque was defined as 50,000 Nm, acting axially on the hub frame in the opposite direction to the motor input torque. To fully ensure computational accuracy and efficiently utilize computational resources, the integration parameters of the wheel hub transmission reduction system were defined as follows: the Gstiff integrator was employed for dynamic solving, with the integration format set to SI2, and the integration error tolerance was defined as \(1\times 10^{-4}\). The simulation time was set to 20 s, which was divided into 20,000 steps.
## 3 Method
### Multi-Scale Feature Fusion  
In convolutional networks, deeper layers capture more semantic features, while shallow layers contain more geometric information. Therefore, to better fuse the feature information from both deep and shallow layers, RPS-YOLO, based on YOLOv8, integrates RFP to perform two rounds of \"processing\" on the image. As shown in Figure 3(b), both \"processing\" passes utilize the PAN-FPN structure. To place more emphasis on shallow features, the first \"processing\" pass reduces one downsampling step, while the second \"processing\" pass incorporates additional feedback connections from the first \"processing\" pass into the bottom-up backbone layer to generate richer feature information.  
Figure 3: RPS-YOLO architecture: (**a**) first “processing”; (**b**) second “processing”; (**c**) Head layer; (**d**) SCC2f module; (**e**) SC module; (**f**) detect module.  
The multi-scale feature fusion structure of YOLOv8 is shown in Figure 4a. Let \(b_{i}\) represent the feature map extracted during downsampling, \(n_{i}\) represent the feature map re-extracted after upsampling, and \(h_{i}\) denotes the feature map produced by the PAN backbone. The subscript \(i\) represents the feature map that has been downsampled by a factor of \(2^{i}\), and \(Conv\) denotes the convolution operation in each module. When the maximum downsampling number is N and the maximum upsampling number is M, the YOLOv8 structure can be defined by Equations (1)-(3):  
\[b_{i}=Conv(b_{i-1})(0<i\leq N) \tag{1}\]  
\[n_{i}=\begin{cases}Conv(b_{i+1}+b_{i})(i=N-1)\\ Conv(n_{i+1}+b_{i})(N-M\leq i<N-1)\end{cases} \tag{2}\]  
\[h_{i}=\begin{cases}Conv(n_{i-1}+n_{i})(i=N-M+1)\\ Conv(n_{i}+h_{i-1})(N-M+1<i<N)\\ Conv(b_{i}+h_{i-1})(i=N)\end{cases} \tag{3}\]  
where \(b_{0}\) represents the original image. In the RPS-YOLO structure, the feedback connections \(h_{i}\) are added to the FPN, and the corresponding \(h_{i}\) from the two stages are fused, as shown in Figure 4b. The superscript \(t\) denotes the number of \"processing\" passes. Let the maximum downsampling number for each \"processing\" pass be \(N^{t}\) and the maximum upsampling number be \(M^{t}\). Then, the definition of \(b_{i}^{t}\) in RPS-YOLO is given by Equation (4):  
\[b_{i}^{t}=\begin{cases}Conv\big{(}b_{i-1}^{t}\big{)}\ (t=1,0<i\leq N)\\ Conv\big{(}b_{i-1}^{t}\big{)}\big{(}t>1,0<i<N^{t-1}-M^{t-1}\ or\ N^{t-1}<i\leq N^{t} \big{)}\\ Conv\Big{(}b_{i-1}^{t}+n_{i}^{t-1}\big{)}\big{(}t>1,i=N^{t-1}-M^{t-1}\big{)}\\ Conv\Big{(}b_{i-1}^{t}+h_{i}^{t-1}\big{)}\big{(}t>1,N^{t-1}-M^{t-1}<i\leq N^{t-1 }\big{)}\end{cases} \tag{4}\]  
where \(b_{0}^{t}\) represents the original image. The expressions for \(n_{i}^{t}\) and \(h_{i}^{t}\) are similar to those in Equation (1). Finally, \(h_{3}^{1}\) and \(h_{4}^{1}\) are fused with \(n_{3}^{2}\) and \(h_{4}^{2}\), respectively, achieving two rounds of \"processing\" on the image. This results in a broader recognition range, encompassing tiny, small, medium, and large objects.