esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimized Wireless Sensor Network Architecture for AI-Based Wildfire Detection in Remote Areas
## 3 WSN Architecture
### Sensor Node Arbitration  
ZigBee, based on IEEE 802.15.4, is optimized for low-power, low-data-rate wireless communication [44]. Efficient packet structure is essential for performance. In the proposeddesign (Table 1), the payload allocates two bytes for device ID, two for a unique header (UH) to ensure reliable delivery, one byte for priority and status flags (e.g., battery level, sensor errors), and two bytes for temperature and humidity readings.  
Unlike static arbiters, dynamic arbiters can adjust priority orders in real time [45], enabling more effective bandwidth allocation. Their primary advantage lies in their ability to prevent starvation, making them well-suited for shared-access systems such as Multiprocessor Systems on Chip (MPSoCs) and Networks on Chip (NoCs), especially in asynchronous systems [46; 47; 48; 49; 50]. In WSNs, arbitration schemes address issues of fairness and starvation while optimizing energy consumption during communication [51; 52; 53; 54; 55]. These schemes are typically implemented on the Medium Access Control (MAC) layer, where conventional protocols often lack prioritized channel access and may suffer from inconsistent fairness.  
To handle access conflicts in the proposed forest fire detection system, where multiple sensor nodes may simultaneously attempt to reach the same intermediate node, a dynamic arbitration mechanism is implemented within each router. This mechanism assigns priorities based on fire urgency, reducing the average response time to the control center. Each data frame includes two priority bits (high: 10, medium: 01, and low: 00) embedded as the first two bits of the priority-level byte. Priority is determined by fire severity and the estimated likelihood of occurrence, as evaluated by an AI model analyzing temperature and humidity data.  
Under the proposed strategy, the sensor node with the highest priority is granted access during each request. If multiple nodes share the same priority, access is rotated in a round-robin manner after each successful transmission. Once access is granted, the requesting node is moved to the end of the priority queue, preventing resource domination and ensuring fair access. Figure 6 illustrates the round-robin priority shift: when requester \(\mathrm{X_{i}}\) seeks access and \(\mathrm{X_{j}}\) was the most recently served, \(\mathrm{X_{j}}\) holds the lowest priority. \(\mathrm{X_{i}}\) gains the highest priority only if none of the requesters between \(\mathrm{X_{j+1}}\) and \(\mathrm{X_{i-1}}\) have submitted access requests. Based on the above specifications, the round-robin algorithm implemented in the sensor node arbiter is illustrated in Algorithm 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**UH** & **ID** & **Priority Level** & **Temperature** & **Humidity** \\ \hline
2 bytes & 2 bytes & 1 byte & 1 byte & 1 byte \\ \hline \hline \end{tabular}
\end{table}
Table 1: Payload frame.  
Figure 6: Round-robin priority shift mechanism.
## 7 Results and Discussion
### Influence of DCR Accuracy  
In fact, the model exists with errors because of the inaccuracy DDCR estimation which is caused by the errors of the measured position and velocity of LEO and GNSS satellite. In this section, the impact of the DDCR error on the retracking performance is discussed. Figure 13 shows the change of the MSE between the retracked waveform and the aligned one over the DDCR. From the figure, it is  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{3}{c}{**1 s**} & \multicolumn{3}{c}{**3 s**} \\ \cline{2-6}  & **UK-DMC** & **UK-TDS-1** & **UK-DMC** & **UK-TDS-1** \\ \hline \multirow{2}{*}{CLS} & proposed & 0.026 & 70.10 & 0.044 & 61.80 \\ \cline{2-6}  & \(\gamma=0.050\) & 0.041 & 85.82 & 0.078 & 89.94 \\ \cline{2-6}  & \(\gamma=0.005\) & 0.103 & 111.90 & 0.115 & 202.57 \\ \hline \multirow{2}{*}{TSVD} & \(p=400\) & 0.044 & 88.75 & 0.130 & 95.43 \\ \cline{2-6}  & \(p=1000\) & 0.116 & 86.79 & 0.329 & 202.74 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of MSE between retracked waveform and the one with the TRP of 1 ms using proposed and CSL and TSVD for UK-DMC and UK-TDS-1 data.  
Figure 12: Fitting error of PW, LES and TES between Z-V waveform and analytical Model (18).  
clear that the accuracy of the DDCR has important influence on the retracking result. Compared to the retracking for the waveform with the TPR of 3 s, retracking the waveform of 1 s requires lower DDCR accuracy. In addition, from Figure 13b, it could be also seen that for the UK-TDS-1 data the waveform of 1 s has better retracking result than the one of 3 s besides DDCR being 0.75 chips/s. According to the analysis above, retracking approach proposed above could recover the distorted waveform, however, it is needed to improve the TPR on-board to obtain higher retracking performance. In other words, the TPR on-board should be reasonably selected considering the retracking and final retrieval accuracy.
# Study on Anchorage Performance of New High-Strength Fast Anchorage Agent
## 5 Conclusions and Prospects  
We studied various aspects of the anchoring performance of a new, high-strength, fast anchorage agent by combining a laboratory test and field test and provided corresponding construction suggestions according to the performance. The conclusions are as follows:  
1. The active SiO2 content in the new, high-strength, fast anchorage agent was high and could react with Ca(OH)2 to produce calcium silicate hydrate (2 CaO-SiO2-nH2O), which was the main reason for its early strength;
2. According to the compressive strength test, the compressive strength of the new anchorage agent reached 30 MPa after 1 day, and its mechanical properties were highly sensitive to the water-to-material ratio. Therefore, the water-to-material ratio should be strictly controlled to ensure the agent's effectiveness;
3. According to the in-hole strain tests and the findings from free conditions, hydration reaction nodes with different water-to-material ratios acted for 5-6 h before the expansion performance reached its peak value;
4. The bonding of the anchoring agent to the rock layer was greater than the reinforcement, and the feedback of this property to the water-material ratio was not strong. Therefore, in actual construction, the contact area between the steel strand and the anchor solid should be increased to effectively increase the anchoring anchor cables;
5. Through an on-site anchor cable construction and drawing test, it was proved that the new anchoring agent could reach the designed drawing strength after 30 h, and its position value differed little from the calculation, indicating that the new, high-strength, fast anchoring agent could be effectively applied in field construction.  
Writing--original draft, H.L.; writing--review and editing, K.W.; validation, Z.D.; data curation and conceptualization, T.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (U2006213), the National Natural Science Foundation of China (Science and Technology Activities Project) (42142041), and the Open Laboratory Fund (BHKF2021Z03).  
Not applicable.  
The data used to support the findings of this study are included within the article.  
The authors declare no conflict of interest.
# Solar Power System Assessments Using ANN and Hybrid Boost Converter Based MPPT Algorithm
## 1 Introduction  
The electrical energy plays a key role in the economic development of a country. From last decade, rapid increase in consumption of electricity and demand is recorded [1, 2]. In order to fulfill power demands, installation of new power plants are time consumption and high expensive procedures [3]. The renewable energies, such as wind and solar, appear to be appropriate solutions to cover energy demand while reducing environmental pollution and toxic materials [4]. Furthermore, the renewable energy resources are followed by many countries, which comes from geothermal heat, tides, rain, wind, and sunlight. Major remote sites in each country of the globe utilizing renewable energy [5].  
The renewable energy generated power depends on site selection and climate condition. Furthermore, there is a need of storage system power induced by renewable energy to insure the continuity of available power [6]. Various types of resources are existed in the entire globe, however, among of all these resources solar energy is considered economical, inexhaustible, and sustainable energy. Photovoltaic (PV) cells mostly use for bulk and small power and gradually increased day by day [7]. The output of PV depends on different factor such as temperature, weather conditions, module materials, and is used in different applications, i.e., light sources, battery charging, water pumping, space, satellite power system, remote islanded power system, etc. This work explores the applications of artificial neural network (ANN) and hybrid boost converter (HBC) for enhancing the efficiency for MPPT to increase the smoothness of solar power system. The deep learning based ANN mechanism has key features to optimize the input data and produce the purified desire outcomes. Furthermore, currently the ANN approach is widely used in several fields for the purpose of optimizing structure complexity and predict the expected outputs. For example, in [8] the ANN technique is utilized for predicting droughs, in [9] the ANN method is studied for accurately measure the wind speed and predict its results, in [10] the particular matter is discussed for the Ankara city through ANN and similarly, in the field of solar system the ANN is applied in [11] for forecasting the generation of solar system. Thus, in this proposed work the ANN mechanism is estimated for optimize the output of MPPT and HBC.
## 3 Hybrid Control Design and Analysis
### Hybrid Control Strategy  
As indicated above, both different position control methods have advantages and characteristics. IHSMC controller has fast response speed and good dynamic performance, but the problem is that it is difficult to eliminate chattering. The PID-IDA-PBC controller has high accuracy and strong disturbance effects, but it is difficult to take into account both dynamic and steady performance. In this section, we propose a smooth switching strategy based on velocity error to complement the advantages of the two control strategies. In order to avoid the instability of the system in the switching point neighborhood, a Gaussian function is introduced as a smooth switching factor. Compared with the problem that the switching point is difficult to be determined in previous studies [13; 14; 28], the rate of change of velocity can be used as the switching index to solve this problem. Then the smooth switching function  
\[f(\alpha)=e^{-\frac{(\alpha-l)^{2}}{2\alpha^{2}}} \tag{28}\]  
where \(\alpha=|\dot{e}|=|\dot{q}-\dot{q}_{d}|\) is rate of position error, \(c\) is a constant, \(l\) is upper limit of speed error switching.  
Then hybrid control strategy can be expressed as  
\[\left\{\begin{array}{ll}i_{qi}^{*}=i_{qsm_{i}}^{*}&h_{i}\leq\alpha_{i}\\ i_{qi}^{*}=(1-f(\alpha_{i}))\;i_{qsm_{i}}^{*}+f(\alpha_{i})i_{qspc_{i}}^{*}&l_ {i}\leq\alpha_{i}\leq h_{i}\\ i_{qi}^{*}=i_{qspc_{i}}^{*}&\alpha_{i}\leq l_{i}\end{array}\right. \tag{29}\]  
where \(h\) is upper limit of speed error switching, \(i=1,\ldots,\;n\).  
The use of the position error change ratio as a basis for smooth switching is considered to be that the faster the error convergence speed for a stable system, the higher its response rate. For the trajectory tracking system, a higher the position error change ratio means a faster the response rate and when the system enters the steady-state, we expect the position error to change slowly and gradually approach zero. Hence, the switching interval is selected and the size of the switching function parameter is adjusted according to the rate of change of the position error. In the steady state, due to the high-frequency small chattering caused by the disturbance, the hybrid control process is newly entered until the chattering is eliminated. Hence, when \(h_{i}\geq\alpha_{i}\) the IHSMC control strategy is adopted to improve the dynamic performance of the system; when \(\alpha_{i}\leq l_{i}\), the PID-IDA-PBC control strategy is used to improve the control accuracy and anti-interference ability of the system; when \(l_{i}<\alpha_{i}<h_{i}\), the smoothing switch is realized by smoothing function.
# Delamination Detection in Bimetallic Composite Using Laser Ultrasonic Bulk Waves
## 4 Results and Discussion  
### Characteristics of Laser Ultrasonic Waves in a Cu/Al Bimetallic Composite  
The acoustic impedance of aluminum was 1.7 \(\times\) 10\({}^{7}\) kg/(m\({}^{2}\cdot\)s), and the acoustic impedance of copper was 4.16 \(\times\) 10\({}^{7}\) kg/(m\({}^{2}\cdot\)s). When the ultrasonic wave propagated vertically from the aluminum layer to the composite interface, the sound pressure reflectance was (4.16 \(-\) 1.7)/(4.16 \(+\) 1.7) = 0.4198, and the sound pressure transmittance was 2 \(\times\) 4.16/(4.16 \(+\) 1.7) = 1.4198. Figure 4 shows the propagation of ultrasonic waves in the Cu/Al bimetallic composite. When ultrasonic waves were incident obliquely from the aluminum layer to the composite interface, reflection, refraction, and mode conversion occurred at the interface. The propagation characteristics of laser ultrasonic in Cu/Al bimetallic composite were analyzed by the finite element method. A pulsed laser was used  
Figure 3: Cu/Al bimetallic composites sample.  
Figure 2: Schematic diagram of experimental device.  
to excite ultrasound in the aluminum layer. The out-of-plane displacements extracted at nodes with 1.5 mm distances to the center of the pulsed laser spot are shown in Figure 5. The laser ultrasonic longitudinal waves (L) were fast and reached the composite interface first, generating reflected longitudinal waves (2L) and mode converted waves (LS). The longitudinal waves entered the copper layer through the composite interface and generated transmitted longitudinal waves, which were reflected by the bottom surface of the model and generated reflected longitudinal waves (2L\({}_{\text{b}}\)). The laser ultrasonic transverse waves (S) propagated to the composite interface, generating reflected transverse waves (2S) and mode conversion waves (SL). The transverse waves entered the copper layer through the composite interface and generated transmitted transverse waves, which were reflected by the bottom surface of the model and generated reflected transverse waves (2S\({}_{\text{b}}\)). The LS and SL had different propagation paths, but they reached the detection point at the same time. The five types of waveforms, including the 2L, 2S, 2L\({}_{\text{b}}\), 2S\({}_{\text{b}}\) and LS-SL, were identified by the calculated wave velocity.
## 3 Results and Discussion
### Kink Effect in the J-V Characteristic
The total series resistance (\(R_{s}\)) was determined by linearly fitting the \(J\)-\(V\) characteristics, at different temperatures, around \(V_{oc}\). The inverse of the fitting slope represents the value of \(R_{s}\). The barrier height was estimated to be 306 \(\pm\) 9 meV from the slope of \(ln(R_{s}T)\) vs. \(1/T\) (Figure 8a) [35; 36; 37]. As reported in Figure 6b, the barrier height varies with the wavelength of the incident light. This happens because the penetration length depends on the wavelength and so does the thickness of the material where the photogenerated holes pile up [38]. This experimental fact may be appreciated from Figure 8b, where the barrier height is plotted as a function of the light penetration depth (remembering that approximately 82% of light is absorbed within a thickness of two penetration lengths, according to the Beer-Lambert-Bouguer law). This result has two consequences: first, it completely rules out the dependence of the kink on potential barriers at the contacts, and, second, it shows that the barrier, due to trapped holes, is at its maximum at the junction region near the interface and then decreases into the p-type material. Figure 8: The data points on both graphs were obtained by illuminating the sample with a LOX 1000 lamp. (**a**) Arrhenius plot of ln(T-R\({}_{S}\)) (in K-\(\Omega\)cm\({}^{2}\)) as a function of 1/T. Each experimental value was derived from the J-V measurements taken at temperatures ranging from 196 K to 353 K without optical filters. (**b**) The height of the additional barrier as a function of distance from the junction: each data point was obtained using bandpass filters centered at 650 nm, 750 nm, 850 nm, and 950 nm, and all were normalized to the same number of absorbed photons.
## 3 Methodology
### Model Assumptions  
Before constructing the transportation organization optimization model considering the inventory cost of multilevel supply chain nodes, the following assumptions were made about the situation related to the transfer:  
1. It was assumed that the rate of coal consumption per unit time of power plants and coal chemical plants is fixed and can be known in advance and that there also exists a class of coal consumption places, which are sold directly without considering their inventory changes, and that coal can only be provided by coal source and coal storage and distribution bases and cannot be trans-shipped between coal consumption places.
2. Assuming that the transportation process of the coal industry chain includes the production, transportation, and sale of coal resources, coal can be transported directly from the coal source to the coal consumption place, or it can be stored for a period of time by the storage and distribution base and then transported to the coal consumption place.
3. It was assumed that coal is mined and processed internally, that there is no inventory cost at the coal source, that the total coal production can meet the demand of the coal consumption place, and that there is no outsourcing of coal.
4. The same batch of coal can only be transited at the transportation nodes for a given transportation mode, and each node is transited at most once.
5. During the decision-making cycle, the structure of the integrated coal transportation network and the train schedule are known and do not change.
6. Since this paper considers the coal demand refined to the hour, to simplify the problem, OOD pairs of alternative sets are used, i.e., according to the actual supply and demand situation input, possible transportation of \(k\) batches of coal is determined after cost comparison to choose whether the batch of coal is issued or not, and if the batch of coal is issued, the corresponding coal source for transportation to the location of coal consumption is determined.
# An Efficient Meshless Numerical Method for Heat Conduction Studies in Particle Aggregates
## 1 Introduction
Meshless methods use regularly or irregularly distributed spatial points, usually called nodes, with a weight function and local support defined in each of these nodes. This reduces considerably the difficulties associated with mesh generation. In recent years, many authors used meshless methods to solve heat conduction problems. The smooth particle hydrodynamics method was reported for solving unsteady-state heat conduction problems [14]. The element-free Galerkin method was used for solving heat transport problems [15; 16]. The meshless local Petrov-Galerkin (MLPG) method created by [PERSON] and [PERSON] [17] was also applied in heat conduction systems [18; 19; 20]. In the present work, three dimensional (3D) thermal conduction problems within particulate systems were solved. The MLPG method, which is based on the expression of differential equations in the local weak form, was implemented. Two distinctive methods for the approximation of differential operators and unknown functions, moving least squares (MLS) [21] and discretization-corrected particle strength sxchange (DC PSE) [22], were examined, and a new sophisticated interpolation procedure was proposed, allowing easier grid refinement. The predictions of the method were compared with analytical ones, with the predictions of other numerical techniques and commercial software packages, as well as with experimental data from the literature. Initially, the method was validated for a well-known literature problem. The convergence of the method was compared on the basis of two field descriptors, locally with the temperature distribution resulting from the finite difference (FD) method, and globally with the derived effective conductivity. Particle aggregates were constructed with the diffusion limited aggregation (DLA) method and with a ballistic aggregation (BA) technique. The effective thermal conductivity of the aggregates was studied, addressing successfully the problem that is associated with the contact between neighboring particles. It was shown that the present work offers a further utilization of the MLPG approach, providing robust, fast, and very accurate solutions.
## 3 Results
### Expression Analysis of miRNAs Identified in G. sinensis Pods  
To identify whether these miRNAs are involved in the regulation of saponin synthesis in _G. sinensis_ pods, the expression of 74 known miRNAs was analyzed in three developmental stages of the pods (Table S5). The results demonstrated that many differentially expressed miRNAs (DEM) in _G. sinensis_ were expressed in special periods during the pods' development. For instance, the expression of _miR89767_ was the highest in July and September, while the expression of _miR8396b-3p_ was the highest in May. In total, the number of upregulated miRNAs was almost consistent with that of downregulated miRNAs from May to July. Intriguingly, with the development of the pods, the number of upregulated miRNAs was much higher than that of downregulated miRNAs from July to September (Figure 4). For example, highly expressed _miR2095-3p_, _miR8396b_, and _miR6194_ were significantly upregulated in September; meanwhile, _miR2093-5p_ was dramatically induced in July (the most abundant DEM with 89,975 reads) and September (the third most abundant DEM with 80,110.5 reads). Furthermore, the expression of some miRNAs showed different trends in different periods. For example, _miR838-3p_ and _miR84415a-3p_ were significantly downregulated from May to July but were upregulated from July to September (Table S5).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**miRNA** & **Sequence (5’-3’)** &
\begin{tabular}{c} **Precursor** \\ **Length (nt)** \\ \end{tabular} & **MFE** & \multicolumn{3}{c}{**Read Count**} \\ \cline{1-2} \cline{5-6}  & & **GSM** & **GSJ** & **GSS** \\ \hline _gsi-smR1_ & CCUUCCUUCCAUUCUCUUAG & 226 & \(-\)59.7 & 72 & 0 & 133.5 \\ \hline _gsi-smR5_ & UUGGACUCUCUUUUCCUCAUG & 148 & \(-\)85.3 & 41.5 & 2.5 & 206 \\ \hline _gsi-smR6_ & UCUUACCCACACCUCUAGCCC & 300 & \(-\)97.2 & 1139 & 60 & 182.5 \\ \hline _gsi-smR7_ & UGCAGAACAAGUCCCCAGCUUU & 211 & \(-\)68.6 & 0 & 20 & 2.5 \\ \hline _gsi-smR9_ & AAGAACUCUUUAUCCAAUUCG & 103 & \(-\)51.1 & 0 & 0 & 11 \\ \hline _gsi-smR10_ & UGGACUCUCUUUUCCUCAUG & 143 & \(-\)82 & 0 & 0 & 63 \\ \hline _gsi-smR25_ & AAGUUGAGAACAUUGAGGC & 120 & \(-\)49.4 & 0 & 2.5 & 3 \\ \hline _gsi-smR34_ & UGGUGACACACGGGAUGAAGCU & 226 & \(-\)79.1 & 571.5 & 8.5 & 0 \\ \hline _gsi-smR35_ & UGGUGACACCCGGAUGAAAGCU & 349 & \(-\)118.4 & 0 & 11.5 & 0 \\ \hline _gsi-smR54_ & UUUUCGUCUCUCGAGUUUCUUA & 254 & \(-\)108.6 & 1134 & 18 & 392.5 \\ \hline _gsi-smR55_ & UUUUCGUCUCGGUUUCUUA & 235 & \(-\)99.4 & 1886 & 0 & 28.5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Novel miRNAs (part) identified in _G. sinensis_ pods.