esa-sceva/satcom-chunk-collection huggingface dataset:
# Salp Swarm Algorithm Optimized A* Algorithm and Improved B-Spline Interpolation in Path Planning
###### Abstract  
The efficiency and smoothness of path planning algorithms are critical factors influencing their practical applications. A traditional A* algorithm suffers from limitations in search efficiency, path smoothness, and obstacle avoidance. To address these challenges, this paper introduces an improved A* algorithm that integrates the Salp Swarm Algorithm (SSA) for heuristic function optimization and proposes a refined B-spline interpolation method for path smoothing. The first major improvement involves enhancing the A* algorithm by optimizing its heuristic function through the SSA. The heuristic function combines Chebyshev distance, Euclidean distance, and obstacle density, with the SSA adjusting the weight parameters to maximize efficiency. The simulation experimental results demonstrate that this modification reduces the number of searched nodes by more than 78.2% and decreases planning time by over 48.1% compared to traditional A* algorithms. The second key contribution is an improved B-spline interpolation method incorporating a two-stage optimization strategy for smoother and safer paths. A corner avoidance strategy first adjusts control points near sharp turns to prevent collisions, followed by a path obstacle avoidance strategy that fine-tunes control point positions to ensure safe distances from obstacles. The simulation experimental results show that the optimized path increases the minimum obstacle distance by 0.2-0.5 units, improves the average distance by over 43.0%, and reduces path curvature by approximately 61.8%. Comparative evaluations across diverse environments confirm the superiority of the proposed method in computational efficiency, path smoothness, and safety. This study presents an effective and robust solution for path planning in complex scenarios.  
s +
Footnote †: journal: Applied sciences
# Toward a Comprehensive Understanding and Evaluation of the Sustainability of E-Health Solutions
## 7 Case Study
### Case Study Protocol
#### 7.1.2 Units under Test  
There are two units under test: (1) the sustainability model, and (2) the assessment method that is proposed to evaluate the sustainability of e-solutions in healthcare. The model is assessed in terms of its comprehensiveness in covering all essential dimensions and aspects that technology experts consider in practice. The assessment method is evaluated in terms of its applicability and effectiveness in anticipating the sustainability levels of e-solutions in healthcare.
## 4 Results
MAE: Mean Absolute Error, MAPE: Mean Absolute Percent Error, RMSE: Root Mean Squared Error, GRU, LSTM, Bi-LSTM, SD-Bi-LSTM, CEEMDAN-Bi-LSTM, SD-CEEMDAN-Bi-LSTM. Tables 3 and 4 compare the MAPE values for all the models by month for the one-day-ahead and the three-day ahead prediction. The training period is from 20 August 2017 to 31 December 2018, and the prediction period is from 1 January 2019 to 30 May 2019. The results indicate that the proposed model is significantly superior to the single models and other hybrid models. MAPE of the SD-CEEMDAN-BiLSTM model is the lowest among all the models. The average forecasting accuracy of the proposed model reaches 97.93% and 97.29% in the one-day-ahead and the three-day-ahead prediction, respectively. Figure 7: Predictions and actual aggregated charge power during the three-day-ahead prediction period in spring for (**a**) all prediction models, (**b**) single prediction model, (**c**) hybrid prediction model. Figure 8: Predictions and actual aggregated charge power during the three-day-ahead prediction period in winter for (**a**) all prediction models, (**b**) single prediction model, (**c**) hybrid prediction model.
# A Novel Process Recommendation Method That Integrates Disjoint Paths and Sequential Patterns
## 4 Experimental Results and Analysis
### Accuracy Test  
In order to demonstrate the accuracy of the proposed method more intuitively, this study compared it with the maximum common sub-graph and minimum common graph method (MCS) proposed in [4], the matrix distance similarity method (MDS) proposed in [12], the target profile similarity method (TPS) proposed in [25], and the vertex-disjoint path similarity method (VPS) proposed in [26].  
When the number of fixed recommendation nodes was five, ten reference models with \(n\) nodes were randomly extracted from the process library separately (where \(n\) ranged from 1 to 6 in the real dataset and from 7 to 13 in the simulation dataset), and the correct recommendation rate accuracy was calculated using each of the five methods. To more  
Figure 8: Effectiveness test of the proposed algorithm for the process recommendation.  
accurately examine the recommendation accuracy, accuracy was achieved by applying different weight signatures to different bit orders of the correct recommendation node in the recommendation results.  
The accuracy was defined as follows:  
1. When the correct recommendation node appeared in the first position of the recommendation node set (i.e., the top 1 recommendation node), the accuracy was 100%.
2. When the correct recommendation node was in the second position, the accuracy was 80%, and so on.
3. If the correct recommendation node was in the fifth position of the recommendation node set, the accuracy was 20%.
4. If the recommended node set did not include the correct recommended node, the accuracy rate was 0.  
Based on the real dataset, this study evaluated the recommended accuracy of the short-process models, divided into two dimensions: the reference model and the process library model. The process library model of the real dataset was short. As shown in Figure 9a, the proposed method generally had a higher accuracy than the other four methods. Meanwhile, when the number of reference model nodes was only 1 or 2, the accuracy of this method remained stable at above 70%. This finding demonstrates that the modeling of a shorter reference model during process recommendation could provide a useful reference for pre-modeling.  
As shown in Figure 9b, the method proposed in this study also performed better in terms of _Accuracy_ when the input reference model was longer, or the process library model was longer.  
The proposed method's average _Accuracy_ was 89.67% for 6 reference models with 6 different numbers of nodes in the real dataset, which was higher than the VPS (76%), MCS (74.67%), TPS (78%), and MDS (75.33%). Similarly, for 70 reference models with 7 different numbers of nodes in the simulated dataset, the average accuracy was 90.57%, which was higher than the VPS (72.29%), MCS (71.43%), TPS (85.14%), and MDS (81.14%). These results demonstrate the effectiveness of the proposed method in enhancing the accuracy of short-process models and its superiority over the compared methods in terms of accuracy.
# Adaptive Cruise Control for Intelligent City Bus Based on Vehicle Mass and Road Slope Estimation
## 3 Problem Formulation
### State Space Equation for ACC  
The displacement, velocity, and SoC of the bus are defined as the state variables, so the state vector is \(\mathbf{x}=\left[s_{veh},v_{veh},SoC\right]^{T}\). EM torque, braking torque, and gear position are selected as the control vector \(\mathbf{u}=\left[T_{EM},T_{Brk},i_{g}\right]^{T}\). The road slope \(\theta\) can be obtained by EFK, the distance between the two vehicles \(s_{ref}-s_{veh}\), and the speed of the preceding vehicle \(v_{veh}\) can be obtained by the radar. These external inputs are defined as \(\mathbf{d}=\left[\theta,s_{ref}-s_{veh},v_{ref}\right]^{T}\), where \(s_{veh}\), \(s_{ref}\),\(v_{veh}\), and \(v_{ref}\) are the traveling distance, speed of current vehicle, and preceding vehicle. The system states space equation can be written as follows:  
\[\dot{\mathbf{x}}=f(\mathbf{x},\mathbf{u},\mathbf{d}) \tag{7}\]
# Comparison of Dual-Combustion Ramjet and Scramjet Performances Considering Combustion Efficiency
## 4 Conclusions  
To provide theoretical guidance for the engine selection of a hypersonic vehicle, the overall performance of DCR and scramjet engine was compared via computational fluid dynamics numerical simulation technology. The scramjet had a single-stage injection and double cavity structure. The length of the DCR was consistent with the scramjet combustor but without the double cavity structure. Kerosene, C\({}_{12}\)H\({}_{23}\), with an equivalence ratio of 0.8, was employed as the fuel, and the reactive flow was modeled using six-species and four-step chemistry. Flight Mach numbers Ma 6 and Ma 7 and an altitude of 30 km were selected as the operating conditions for comparison. The conclusions of the study are as follows. 1. The two types of engines had different combustion modes. The scramjet was a near wall combustion mode, while the DCR was a central combustion mode, which had small pressure fluctuation, small temperature gradient, good heat release uniformity, and high combustion stability. 2. When the Mach number increased, the total pressure recovery capability of scramjet was superior to that of DCR. Specifically, the total pressure recovery coefficient of scramjet was 0.9% lower than that of DCR under the Ma6 condition, but 5.6% higher than that of DCR under the Ma7 condition. 3. The combustion efficiency of DCR was 35.6% and 25.4% higher than that of scramjet under Ma6 and Ma7 conditions, respectively. When the Mach number increased from 6 to 7, the combustion efficiency of the scramjet remained almost unchanged, and that of DCR decreased by 10%. This was because the dissociation rate of CO\({}_{2}\) into CO increased with increasing temperature. 4. The performance of DCR was better than that of scramjet under both conditions. However, the performance advantage of DCR decreased as the Mach number increased. Specifically, under the conditions of Ma6 and Ma7, the specific impulse or specific thrust of DCR was 2.67 times and 1.51 times of scramjet, respectively. Conceptualization, Z.W. ; methodology, Z.W. ; software, X.W. ; validation, X.W. ; formal analysis, X.W. ; investigation, X.W. ; resources, Z.W. ; data curation, X.W. ; writing--original draft preparation, X.W. ; writing--review and editing, Z.W. ; visualization, X.W. ; supervision, Z.W. ; project administration, X.W. ; funding acquisition, Z.W. All authors have read and agreed to the published version of the manuscript. This study was supported by the National Natural Science Foundation of China (No. U21B2086) and the Taizhou Science and Technology Bureau of China (No. 2002 gy07). **Acknowledgments:** Thanks to my father and mother, who are ordinary Chinese farmers and worked hard to pay for my education. I finally became a university teacher after going through many difficulties. This paper is dedicated to my deceased mother, may her spirit rest in heaven. In addition, my wife has given me great support during my study. **Conflicts of Interest:** The authors declare no conflict of interest. **Nomenclature**
## 2 Methodology
### Revision of Rating Scores and Weights
#### 2.2.4 Genetic Algorithm  
The GA is a metaheuristic method, which was inspired by the process of natural selection [39]. It is commonly used to generate high-quality solutions to optimization and search problems by bio-inspired operators such as mutation, crossover, and selection [40]. In the GA, a population of candidate solutions, called individuals, to an optimization problem is evolved toward better solutions [41]. The evolution is an iterative process, which starts from a population of randomly generated individuals and terminates when a maximum number of iterations has been produced or when the specified fitness function converges to a stable value [42].  
To obtain the optimum solution, the GA was implemented as follows: (1) One thousand individuals were randomly generated, whereby each individual had eight genomes encoded as array of bits and was in the range of 0-5; (2) The parameters of this algorithm were set, that is, 1000 as maximum number of generations, 0.8 as crossover operator probability, 0.001 as mutation operator probability, and 0.0001 as the tolerance for the convergence difference; the selection scheme for mating was the roulette wheel selector; and (3) The objective function for Pearson's \(\rho\) correlation coefficient was set. The best individual of the last generation was the solution, that is, the best weight vector.
###### Abstract  
This work describes the design, implementation, and validation of a wireless sensor network for predictive maintenance and remote monitoring in metal-rich, electromagnetically harsh environments. Energy is provided wirelessly at 2.45 GHz employing a system of three co-located active antennas designed with a conformal shape such that it can power, on-demand, sensor nodes located in non-line-of-sight (NLOS) and difficult-to-reach positions. This allows for eliminating the periodic battery replacement of the customized sensor nodes, which are designed to be compact, low-power, and robust. A measurement campaign has been conducted in a real scenario, i.e., the engine compartment of a car, assuming the exploitation of the system in the automotive field. Our work demonstrates that a one radio-frequency (RF) source (illuminator) with a maximum effective isotropic radiated power (EIRF) of 27 dBm is capable of transferring the energy of 4.8 mJ required to fully charge the sensor node in less than 170 s, in the worst case of 112-cm distance between illuminator and node (NLOS). We also show how, in the worst case, the transferred power allows the node to operate every 60 s, where operation includes sampling accelerometer data for 1 s, extracting statistical information, transmitting a 20-byte payload, and receiving a 3-byte acknowledgment using the extremely robust Long Range (LoRa) communication technology. The energy requirement for an active cycle is between 1.45 and 1.65 mJ, while sleep mode current consumption is less than 150 nA, allowing for achieving the targeted battery-free operation with duty cycles as high as 1.7%.  
electromagnetically harsh environments; far-field wireless power transfer; industrial internet of things; rectenna; wireless sensor networks +
Footnote †: journal: Sensors
# Data Exchange in Cluster Structure for Longevity of IoT
## 5 Conclusion and Future Works  
In the IoT environment, various things can communicate with other things for information-sharing. In this mechanism, many service applications have been developed and launched to support smarter human life. In WSNs, a specific sink node is used to transfer the data collected from leaf nodes. In order to avoid fast energy consumption at the sink node, many clustering methods have been proposed to achieve efficient energy consumption in WSNs. However, in contrast to WSNs, all the things deployed in IoT networks can perform the role of a sink node to collect data. Therefore, the existing clustering methods for WSNs have limitations for IoT service applications. If these exiting methods are applied to IoT applications, they generate severe network traffic in the process of data collection. This results in inefficient energy consumption by the things and shortens the lifetime of IoT networks.  
In this paper, to enhance energy efficiency in IoT networks, after assigning properties to each thing, a cluster-property-based data transfer method was proposed for the data exchange processes. The proposed method achieves effective data exchange to handle the issued queries because it forms energy efficient clusters from the property of things. In the experiments, to measure the performance of the data transfer methods, the dead round of the last node, dead node rates, and average energy consumption in IoT networks were chosen as the metrics. The proposed method, named CPDT showed vastly superior performances in terms of all the metrics as compared to the existing method. In particular, in terms of the average energy consumption rate per round, the CPDT method achieved the energy-saving effectiveness of more than 10 times as compared to the existing method because the CPDT only executes the things relevant to the property in the queries, instead of utilizing the participation of all the things. From the experiments, we demonstrated that the CPDT worked on the IoT networks as the more reliable state. Moreover, it contributes to the longevity of IoT networks.  
For more reliable data exchanges, the data exchange protocol will be operated in the course of the advertisement phase and setup cluster phase proposed in the CPDT. Especially, when urgent information transmission and speedy operation are required in IoT networks, the time delay will induce a serious impact on the IoT service applications. In our future work, we will study an advanced data exchanging protocol to support these issues, which will operate on the CoAP (constrained application protocol) in the IoT environment.  
Figure 9: Average energy consumption per round.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The author declares no conflict of interest.
## 3 System Model
### Deployment Model  
We consider a cellular deployment of mmWave NR BS as shown in Figure 1. The coverage area of each BS is approximated by a circularly-shaped area of radius \(R\) estimated using the mmWave propagation model and the set of MCS [14]. Each site is associated with three antennas each covering the sector with the angle of \(120^{\circ}\). The height of mmWave NR BS is fixed and set to \(h_{A}\). The operational frequency is \(f_{c}\). Each mmWave NR BS antenna is assumed to operate over \(B\) resource blocks. Recall, that the bandwidth of the resource block is affected by the utilized NR numerology. In our study, we use numerology 3.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Notation** & **Description** \\ \hline \(r_{B}\) & blocker radius \\ \(h_{B}\) & blocker height \\ \(h_{U}\) & UE height \\ \(h_{A}\) & NR BS height \\ \(v\) & UE speed \\ \(P_{A}\) & transmit power \\ \(G_{A}\) & BS antenna array gain \\ \(\phi_{U}\) & UE antenna array gain \\ \(\xi_{T}\) & path loss exponent in LoS non-blocked state \\ \(M_{T}\) & interference and shadow fading margin \\ \(\kappa\) & array HPBW \\ \(\theta_{m}\) & array maximum \\ \(\theta_{Mb}^{\pm}\) & upper and lower 3-dB points \\ \(J\) & number of antenna arrays \\ \(N_{U}\) & number of UE antenna array configurations \\ \(N_{T}\) & number of NR BS antenna array configurations \\ \(N_{0}\) & density of noise \\ \(\Lambda\) & intensity of user requests \\ \(\delta\) & density of UEs \\ \(R\) & NR BS cell radius \\ \(p_{B}\) & LoS blockage probability \\ \(\xi\) & beam’s arc circumference \\ \(\Delta\) & number of void beams \\ \(Q\) & minimum UE to NR BS distance \\ \(b_{M}\) & base layer demand \\ \(b_{U_{t}}\) & \(l\)-s enhancement layer demand \\ \(C_{l}\) & mean number of sessions with \(l\)-s enhancement layer \\ \(\vec{p}_{U}\) & QoS profile \\ \hline \hline \end{tabular}
\end{table}
Table 1: \(Cont.\)  
Figure 1: System model.  
We consider a field of humans forming a Poisson point process (PPP) with density \(\lambda_{B}\) blocking the propagation paths. Blockers are modeled by cylinders with the fixed height and width, \(h_{B}\) and \(r_{B}\), respectively. The height of UE is \(h_{U}\), \(h_{U}<h_{B}\).
## 3 Results
### Effects of Different Treatments on the Content of Soil Active Organic Carbon
#### 3.3.3 Effects of Different Treatments on Soil Microbial Biomass Carbon Content  
The results presented in Figure 6 indicate that the application of organic manure and maturing agent led to a significant increase in soil microbial biomass carbon (MBC) content across all time periods. Over time, there was an overall trend of initially increasing and then decreasing soil MBC content. Specifically, at 90 days, soil MBC content was at its highest, with the organic manure treatment showing significantly higher levels compared to the other treatments. At 60 days, the soil treated with T2 and T3 had significantly higher MBC content than the other treatments, while there was no significant difference between T4 and T1 treatments. By 90 days, the soil treated with T2 and T3 continued to exhibit significantly higher MBC content compared to the other treatments, with no significant difference between T1 and T4 treatments. This trend was consistent at 120 days, with T2 and T3 treatments showing significantly higher MBC content compared to the others, although the difference was not significant compared to the T4 treatment. By 150 days, T2, T3, and T4 treatments had increased soil MBC content by 21.60%, 19.33%, and 10.97%, respectively, compared to T1 treatments. In comparison to T4, T2 and T3 treatments increased by 9.59% and 7.54%, respectively.
## 3 Results
### Detection of THz Radiation in GaSe:S Crystals
#### 3.3.3 Electro-Optic Coefficient of GaSeS Samples
Using Equations (6)-(8), we can write:  
\[J_{2}=s_{2}I_{2},\]1  
\[J_{2}=s_{2}I_{2},\]2  
Considering \(E_{THz}\) is the same in both cases:  
\[J_{2}=s_{2}I_{2},\]3  
From here, the electro-optical coefficient for GaSe:S crystals can be expressed as:  
\[J_{2}=s_{2}I_{2},\]4  
where \(L\) is the corresponding crystal thickness; \(n_{GaSe}\), \(n_{GaAs}\) are the refractive indices in the IR range, and \(R\) and \(\rho_{THz}\) are the optical and terahertz reflection coefficients, respectively. The values for GaAs are known to be: \(r_{GaAs}\) = \(r_{41}\) = 1.5 pm/V [61], \(n_{GaAs}\) = 3.4 [62], and \(n_{GaAs}\) = 3.6 [55]. The values of optical and terahertz refractive indices for GaSe:S crystals are determined by the previously described data (Figures 1 and 2). For simplicity, \(n_{\text{THz}}\) for GaSe:S crystals are taken at a frequency of 0.3 THz. The resulting values of \(r_{GaSe}\) = \(r_{22}\) are shown in \[J_{2}=s_{2}I_{2},\]5  
The absolute measurement error of the electro-optical coefficient is estimated to be about \(10^{-3}\) pm/V.
## 3 Results
### Soil Physicochemical Properties Under Different Tillage Practices  
The soil physicochemical properties were significantly affected by the combined influence of straw incorporation and tillage practices under the three treatments. The results showed notable differences in organic matter content, nitrogen, phosphorus, potassium levels, and pH across the treatments. These variations may directly or indirectly influence herbicide degradation kinetics in the soil (Table 2).  
Soil physicochemical properties exhibited notable variations across the different tillage treatments, reflecting the influence of management practices on nutrient dynamics. The organic matter content ranged from 17.6 to 20.89 g/kg, available nitrogen from 64 to 84.02 mg/kg, available phosphorus from 20.7 to 26.27 mg/kg, and available potassium from 172 to 208 mg/kg. Soil pH values varied between 6.34 and 7.1. Soil property variations across treatments are presented below.  
Among all treatments, the highest organic matter content was observed under conventional CK, reaching 20.89 g/kg. This value was followed by SS at 18.9 g/kg and ST at 17.6 g/kg. The organic matter content was lowest in the ST treatment, while the available nitrogen content was 82.6 mg/kg, slightly lower than that of CK (84.02 mg/kg).  
The available phosphorus concentrations varied across treatments, with the highest value observed in SS at 26.27 mg/kg, followed by CK at 23.46 mg/kg and ST at 20.7 mg/kg. For available potassium, the highest value was observed in CK at 208 mg/kg, followed by SS at 203 mg/kg and ST at 172 mg/kg. The highest soil pH was recorded under CK at 7.1, while the lowest was found under ST at 6.34.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Tillage** & **pH** & **SOM (g/kg)** & **Available** & **Available** & **Available** \\
**Method** & & & **Nitrogen** & **Phosphorus** & **Potassium** \\  & & & **(mg/kg)** & **(mg/kg)** & **(mg/kg)** \\ \hline CK & 7.1 & 20.89 & 84.02 & 23.46 & 208 \\ SS & 6.59 & 18.9 & 64 & 26.27 & 203 \\ ST & 6.34 & 17.6 & 82.6 & 20.7 & 172 \\ \hline \end{tabular}
\end{table}
Table 2: Soil physical and chemical properties under different tillage methods.