esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
The method achieves an overall recognition accuracy of 96.6%, demonstrates good practicality and considerable potential for broader application. However, this method is mainly suitable for scenarios where crop rows have clear and distinct structures. In the later stages of crop growth, as the overlap and intersection between rows become more pronounced, the complexity of the field layout increases, and traditional clustering methods based on single-cluster assignments face certain limitations when handling overlapping and transitional regions. Therefore, future research should consider introducing clustering frameworks that can characterize the uncertainty and imprecision between clusters, such as the Belief-Peak Evidential Clustering method [38], to more accurately represent the structure of crop rows under complex field conditions and provide more robust and reliable support for subsequent autonomous navigation and precision agricultural operations. Overall, although the proposed SugarRow-YOLO model and crop row extraction method demonstrate potential for practical application, they have not yet been deployed or validated in real field environments or actual agricultural equipment. Future research will focus on the deployment and performance validation of the method in practical scenarios to further promote its engineering implementation and real-world application. Additionally, we will explore techniques such as model pruning and quantization to improve inference speed and meet real-time processing requirements.
## 5 Parameter Analysis
### Effect of Capsule Depth  
The buried depth of the capsule head \(H_{\mathrm{t}}\) is set as 0 m, 4 m, 8 m, 12 m, and 16 m, respectively, to investigate its effect on the lateral displacement and bending moment of the pile. Figures 17 and 18 show that the lateral displacement of the pile reaches the maximum value when the depth of the capsule head is 0 m, where the lateral displacement of the pile head is about 9.7 mm and the maximum bending moment is around \(-\)281 kN\(\cdot\)m. The depth corresponding to the maximum lateral displacement also increases with the increase in the capsule depth. The lateral displacement of the pile head remains positive when the capsule depth is 4 m, and the maximum lateral displacement and bending moment are almost constant when the depth of the capsule is under 8 m. The value of the lateral displacement and bending moment are around 4.4 mm and 460 kN\(\cdot\)m, respectively, when the depth of the capsule is under 8 m. Hence, it can be considered that when the depth of the capsule is under 8 m, the change in the capsule depth will only affect the locations of the maximum lateral displacement and bending moment rather than their values.
Based on this, the present study introduces accumulated temperature parameters to regulate nutrient quality during the drying process. It utilizes regression models to create reference process diagrams that visualize the relationship between drying parameters and nutrient indices, thereby systematically enhancing the accuracy of the drying procedure. The effects of various technological parameters on the nutritional quality of rice during its drying were examined, leading to a proposed control strategy based on effective accumulated temperature. The key control variables selected include hot-air temperature, humidity, initial moisture content, wind speed and tempering ratio. A central composite design scheme was employed for experimentation, allowing for an investigation into the interactions between process parameters and nutrient quality through response surface analysis. Following data analysis, a regression model was established to optimize comprehensive process parameters; additionally, a reference chart featuring both process retrieval and prediction functions was developed. The results indicated that the optimized drying process not only effectively preserves the nutritional quality of rice but also significantly enhances drying efficiency. This study enriches our understanding of the mechanisms underlying changes in rice quality while providing a scientific basis for practical rice-drying operations. Furthermore, the constructed process reference map facilitates both query and prediction capabilities regarding processes; it offers theoretical support and practical guidance for designing intelligent control systems as well as visualizing regulatory adjustments in future applications--thus laying a solid foundation for optimizing and intelligently implementing rice-drying processes.
## 2 Design and Implementation
### Antenna Design  
For 432 MHz band, data is delivered by electromagnetic propagation at a nominal range of 0.3-2 m for environmental access and wheelchair navigation [12, 42]. The antenna geometry should be carefully  
Figure 5: Dual-band data link operation at 27/432 MHz and the Ack link at 27 MHz: (**a**) block diagram and (**b**) timing diagram.  
designed to optimize the wireless link propagating from the intraoral environment [43]. We selected a planar inverted-F antenna (PIFA) by considering three important parameters: radiation efficiency, antenna gain (directivity), and polarization [44,45]. Radiation efficiency can be increased by the PIFA because it has a wide radiation surface while it can be folded into a thin structure inside the arch-shaped dental retainer. The PIFA has limited backward radiation, which minimizes the electromagnetic power absorption towards the back of the head, while enhancing forward radiation towards the thinnest part of the intraoral surrounding to deliver more power to the TDS-UI receiver. Finally, the PIFA exhibits moderate to high gain in both vertical and horizontal states of polarization, which is also proper for the iTDS where the angle between Tx and Rx antennas changes according to the users' body posture [46, 47, 48].  
The PIFA was simulated using HFSS software (ANSYS, Canonsburg, PA, USA). Figure 6a shows the PIFA model mounted onto a simplified human mouth model used for the initial tuning. Figure 6b shows the PIFA model mounted onto the Ansoft-Aarkid human head model used for the simulation results, for which we applied the actual frequency characteristics of tissues, bones, and materials of the iTDS-2. Figure 6b,c shows the horizontal and vertical radiation patterns (over \(x\)-\(z\) and \(y\)-\(z\) planes), respectively, which confirm the strong forward radiation characteristic of the PIFA. An average radiation efficiency of 9.26% was obtained from these simulations.  
Figure 6: Simulation results of the PIFA: (**a**) a PIFA model inside the simplified mouth model; (**b**) a PIFA model inside the Ansoft-Aarkid human head model; (**c**) a radiation pattern at the horizontal plane at \(\Phi=270^{\circ}\); and (**d**) a radiation pattern at the vertical plane at \(\Phi=270^{\circ}\).  
Impedance matching is another important factor in antenna performance to minimize the power loss caused by reflection from the antenna input back to the transmitter output [49, 50]. In this design, the CLC network connected at the antenna feed-point, adjusts the antenna input impedance to be matched with the output impedance of the on-chip transmitter [51]. It is, however, difficult to avoid impedance mismatch because the mouth environment is continuously changing with the tongue and jaw motion, and the impedance of the antenna is changing accordingly. We plan to address this issue by incorporating an adaptive impedance matching scheme, which we have already tested in a prototype using a commercial-off-the-shelf (COTS) version of the iTDS [39].
# New Protocol and Architecture for a Wastewater Treatment System Intended for Irrigation
## 3 Results
### Consumed Energy  
Figures 12 and 13 show the node energy. The nodes have an energy harvesting system that has been dimensioned to ensure that it can cover the node's energy requirements. In comparison, Figure 12 shows the remaining energy along with the simulation in each location in the worst scenario (winter with three consecutive days with a reduced energy harvesting). We can see the different locations in Figure 12a-f. In all four locations, we identify a similar trend. Node 2 is the node that consumes more energy since it receives the information of Nodes 1 and 3 and sends all the information to the gateway. Nodes 1 and 3 of each location have the same tendency in their energy levels in general terms. No significant differences can be seen among the energy levels of Nodes 1 and 3, along with the locations. Nonetheless, the energy of CHs differs from one location to another.  
The CHs of locations with greater changes in water quality (Figure 12a-c) send more packets which are translated into more energy consumption. The CH with higher energy consumption is the CH of UA2 (Figure 12b). This node reaches a minimum energy of 12,320 mW/h just before the sunrise of the fifth day. The rest of the CHs, located in areas with fewer or no changes in water quality (Figure 12d-f) have higher energy levels. Another interesting trend can be identified in PO3 (Figure 12f). We can see a sharp decrease in energy levels at midday due to the large amounts of data sent at that moment. The absence of water quality conditions causes this during the rest of the day. Since the other CHs send data when the nodes detect a change, this decrement of energy level is distributed throughout the day.  
Finally, Figure 13 shows the energy level of CHs in three different simulations for which we have modified the energy harvesting conditions. Figure 13a represents the worst conditions, the winter period with just 9 h of energy harvesting and three consecutive days with a reduction of 50% in the energy harvesting. These results summarize the data shown in Figure 12. Figure 13b shows a period of winter but without attenuated energy harvesting. We can identify that the CH of UA2 still has lower levels of energy than the others. Nevertheless, the difference is lower than in the previous scenario. On the other hand, Figure 13c shows a period of summer without attenuated energy harvesting. In this case, we have 14 h of energy production. We can see that the energy levels of CHs are similar to those in previous cases. During summer, nodes have their batteries full for most of the day.
# High-Precision Tracking of Free-Space Optical Communication System on Mobile Platforms
## 5 Conclusions  
We developed a new free-space optical communication system between moving platforms with a unique tubular folding optical path design and tracking mechanism. Our prototype achieved high-precision targeting and tracking on the mobile platform. On a moving platform, the tracking error within 100 m distance is less than 1 mrad. In the current prototype design stage, the weight of the precise targeting load module is 4 KG, and the total weight of the system is 10 KG. We believe that this system is expected to provide stable communication links between vehicle platforms and for two-way link connections between high-altitude mobile platforms and ground.  
Conceptualization, N.S.; hardware, Y.W. ([PERSON]); software, N.S.; validation, N.S. and J.L.; writing--original draft preparation, N.S.; writing--review and editing, N.S. and Y.W. ([PERSON]); project administration, J.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Figure 12: Data collected by photoelectric detector in dynamic tracking.  
Figure 13: Eye diagram of tracking experiments between mobile platforms.
# Immobilization of (Aqueous) Cations in Low pH M-S-H Cement
## 3 Results
### Ex Situ Characterization  
#### 3.2.1 FTIR  
From FTIR spectra (Figure 2a and full spectra presented in Figure S1) the formation of M-S-H in all samples could be demonstrated. The spectra exhibited a broad band around 3600 cm\({}^{-1}\) and a sharper but lower in intensity band at 1650 cm\({}^{-1}\) associated with H\({}_{2}\)0 vibrations. A detailed view of the characteristic area for silicate vibrations from 1200-800 cm\({}^{-1}\) is presented in Figure 2a. These different bands are a result of varying interconnection degree of the silica tetrahedra (from \(Q^{0}\) where there are no bridging oxygen atoms per tetrahedron to \(Q^{4}\) where the four oxygens are shared). The main band from 1100 to 950 cm\({}^{-1}\) is related to the Si-O stretching vibrations of \(Q^{3}\)[27; 28]. The fact that mainly \(Q^{3}\) silicate sites have been observed in M-S-H confirms the layered silica structure like that found in clay minerals. The band located around 950-870 cm\({}^{-1}\) corresponds to the \(Q^{2}\) Si-O antisymmetric stretching vibration [25; 43; 44]. It is worth noting that the formation of brucite (Mg(OH)\({}_{2}\)) with a characteristic band at 3698 cm\({}^{-1}\) was neither detected by FT-IR or XRD although the pH values were measured above 10.5.  
The FTIR spectra of M-S-H obtained in presence of secondary metal cations were comparable to the FTIR of the M-S-H obtained in the control experiment (Figure 2a) indicating the formation of M-S-H in all the cases. Only the Fe, Cr, and Zn containing precipitates show a stronger overlap between the \(Q^{2}\) and \(Q^{3}\) bands than other samples because of a shift of the \(Q^{2}\) band to higher wavenumber. In the meantime, a shoulder in the  
Figure 2: (**a**) Fourier transformed infrared spectroscopy (FTIR) spectra of M-S-H with additive ions. \({}^{*}\) mark shoulders belonging to \(Q^{4}\) silica networks and arrows bands at 960 and 690 cm\({}^{-1}\) from carbonate groups in the Ba sample; (**b**) X-ray diffraction (XRD) diffraction pattern for M-X-S-H normalized to the reflex at 59.9\({}^{\circ}\) 2 theta.  
Q\({}^{4}\) region (>1190 cm\({}^{-1}\)) appeared and the broad band at 615 cm\({}^{-1}\) was less pronounced in spectra obtained for the precipitates containing Cr and Al. In the case of product obtained in the presence of Ba, weak bands in the range 1400-1500 cm\({}^{-1}\) and 960 and 690 cm\({}^{-1}\) pointed to impurities originating from carbonate groups.
# Beam Propagation Factor of a Cosh-Airy Beam
## Appendix A
[CrossRef]
* [21] [PERSON]; [PERSON]; [PERSON] Creating Airy beams employing a transmissive spatial light modulator. _Appl. Opt._**2016**, _55_, 6095-6101. [CrossRef]
* [22] [PERSON]; [PERSON]; [PERSON] Novel finite airy array beams generated from Gaussian array beams illuminating an optical Airy transform system. _Prog. Electromagn. Res. M_**2016**, _49_, 41-50. [CrossRef]
* [23] [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Generation of one-dimensional terahertz Airy beam by three-dimensional printed cubic-phase plate. _IEEE Photonics J._**2017**, \(9\), 1-7. [CrossRef]
* [24] [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON] Wideband mm-wave non-diffracting Airy beam forming. In Proceedings of the 12 th European Conference on Antennas and Propagation (EuCAP), London, UK, 9-13 April 2018; pp. 1-5. * [25] [PERSON]; [PERSON]; [PERSON]; [PERSON] Propagation properties of cosh-Airy beams. _J. Mod. Opt._**2018**, _65_, 314-320. [CrossRef]
* [26] [PERSON]; [PERSON]; [PERSON] Propagation of cosh-Airy beams in uniaxial crystals orthogonal to the optical axis. _Opt. Laser Technol._**2019**, _116_, 72-82. [CrossRef]
* [27] [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Phase transition of cosh-Airy beams in inhomogeneous media. _Opt. Commun._**2018**, _427_, 147-151. [CrossRef]
* [28] [PERSON]; [PERSON]; [PERSON]; [PERSON] Self-healing properties of cosh-Airy beams. _Laser Phys._**2019**, _29_, 025001. [CrossRef]
* [29] [PERSON]; [PERSON] Second-order spatial characterization of hard-edge diffracted beams. _Opt. Lett._**1993**, _18_, 1669-1671. [CrossRef]
* [30] [PERSON]; [PERSON]; [PERSON]; [PERSON] Beam propagation factor of partially coherent Laguerre- Gaussian beams in non-Kolmogorov turbulence. _Prog. Electromagn. Res. M_**2012**, _22_, 205-218. [CrossRef]
* [31] [PERSON]; [PERSON] Beam propagation of a higher-order cosh-Gaussian beam. _Opt. Laser Technol._**2009**, _41_, 202-208. [CrossRef]
* [32] [PERSON] The beam propagation factors and the kurtosis parameters of a Lorentz beam. _Opt. Laser Technol._**2009**, _41_, 953-955. [CrossRef]
* [33] [PERSON] Generalized beam propagation factors of truncated partially coherent cosine-Gaussian and cosh-Gaussian beams. _Opt. Laser Technol._**2010**, _42_, 489-496. [CrossRef]
* [34] [PERSON]; [PERSON] _Table of Integrals, Series, and Products_; Academic Press: New York, NY, USA, 1980. * [35] [PERSON]; [PERSON] Laser beam characterization with use of second order moments: An overview. _Opt. Soc. Am._**1998**, _17_, 200-207.
# Cluster Head Selection Method for Edge Computing WSN Based on Improved Sparrow Search Algorithm
## 3 System Model  
The two-layer WSN system based on edge computing mainly consists of an edge layer and a sensor node layer. In the edge layer are edge computing nodes, responsible for collecting relevant data information of sensor nodes, assisting cluster head selection, and transmitting sensor data. Generally, the cluster head node is not fixed and must be re-selected every round. When selecting the cluster head in each round, the nodes should be evenly distributed, and the load of the cluster head nodes should be balanced. After the cluster head selection is completed, ordinary nodes will join the nearest cluster according to the distance, and member nodes are assigned time slots in the cluster to complete data transmission. Nodes only transmit data in their assigned time slots and sleep in other time slots to reduce power consumption. At the end of each rotation period, the cluster head will be re-selected, and the new cluster head will re-select the cluster head according to the energy consumption.  
We use \(N=\{n_{1},n_{2},n_{3},\ldots,n_{k}\}\) to denote all sensor nodes and all edge nodes are given by the set \(S=\{s_{1},s_{2},s_{3},\ldots,s_{k}\}\). The set \(M=\{m_{1},m_{2},m_{3},\ldots,m_{j}\}\) represents all clusters, we use \(C_{q}=\{c_{1},c_{2},c_{3},\ldots,c_{m}\},C_{q}\in M\) to represent the \(q\)-th cluster, and the cluster head node in the cluster is defined as \(C_{q}^{h}\). When a node runs out of energy, we put it into the set \(G\).  
Generally, in a WSN, once the corresponding sensors are arranged, they will not move anymore. In set \(C_{i}\), all sensor nodes can communicate with each other and can communicate with edge nodes at the same time. Edge nodes can detect the remaining energy of different sensor nodes in the set. After the cluster head selection is completed, data transmission is mainly performed by the cluster head node and the edge node, as shown in Figure 1, which is the network structure diagram after the cluster head selection is completed. In the figure, all sensors at the sensor node layer may become cluster heads. The sensor node layer can represent a cluster formed in different areas, and the node pointed at by the arrow is defined as the cluster head. When cluster heads are re-selected, their regions may change. In the edge layer, some servers may be far apart geographically.  
Figure 1: Edge network architecture.
# Evaluation of Novel Preformed Particle Gel System for Conformance Control in Mature Oil Reservoirs
## 2 Results and Discussion
### Core Flooding Test
#### 2.4.1 Effect of Particle Size  
Three gel samples were prepared to evaluate the effect of particle size on the gel injection and plugging efficiency: the base case of dry particle sizes of 600-800 microns, the small particle size of 250-600 microns, and a mix of equal ratios between the two sizes.  
In Figure 12, the gel particles were initially dry with a size of 600-800 microns and fully swelled in 2% KCl brine. They were injected at a flow rate of 1 mL/min into a fractured sandstone core with a fracture width of 1 mm. The average stabilized pressure during gel injection was approximately 247 psi. The post-water flood initiated at a flow rate of 0.1 mL/min, with the water breakthrough pressure at about 65 psi, stabilizing at approximately 25 psi. The flow rate was incrementally increased, and at a flow rate of  
Figure 11: Before gel placement, injected water flows through the fracture, causing excessive water production and low oil recovery due to conductivity differences. Gel filling the fracture redistributes water, reducing water production and increasing oil recovery.  
Figure 10: Temperature effect on gel strength.  
0.5 mL/min, the gel began to be produced in the effluent for a short period, causing the pressure to decrease and stabilize at about 7 psi. The same procedure was repeated for other particle sizes.  
The stable gel injection pressure was recorded for three experiments (Figure 13), indicating that a smaller particle size corresponded to a lower recorded injection pressure. However, in the experiment involving a mix of particle sizes used in equal amounts, the stable injection pressure fell between that of the larger and smaller particles.  
To evaluate the plugging efficiency of the CMC/PAMBA PPG in fractured sandstone cores, the permeability of the fractured core was calculated along with the residual resistance factor. In Figure 14, it is evident that the smaller particle size exhibited better packing within the fracture, as the permeability values showed no significant changes with increasing post-water injection flow rates. In the other two experiments involving larger particle sizes, although the permeability was initially lower during the post-water flooding, it increased with higher flow rates. This observation suggests that larger particles  
Figure 12: Core flooding data for PPG (dry size 600–800 microns) fully swollen in 2% KCl in a fractured sandstone core (Experiment #2).  
Figure 13: Gel particle size effect on gel injection pressure.  
were more prone to deterioration, potentially creating channels. In Figure 15, the residual resistance factor was calculated to represent the plugging efficiency of the gel, and the high values of Frr suggested a plugging efficiency as high as 99%.
## 3 Research Methodology
### The Adopted Ensemble Model (RF, XGB, and HGB) with Hyperparameter Tuning  
The model was trained with dataset 1, and we employed nine distinct state-of-the-art models in this investigation. In order to facilitate the model training process, we ensured that the data were scaled between 0 and 1 utilizing a min-max scaler. The min-max scaler equation is as follows: \(X\) represents an individual column of the dataset, and \(X_{\text{min}}\) and \(X_{\text{max}}\) represent the minimum and maximum values of their respective columns.  
\[X_{\text{scaled}}=\frac{X-X_{\text{min}}}{X_{\text{max}}-X_{\text{min}}} \tag{1}\]  
Subsequently, we implemented the grid search cross-validation methodology to identify the optimal parameters for each model. Achieving the optimal performance necessitates tuning the models. We utilized 80% of the data for training and 20% for testing. Three of the nine models demonstrated a superior performance in comparison to the others. Consequently, we selected these three models (RF, XGB, and HGB) for the ensemble model development. In order to enhance the accuracy, we implemented the voting ensemble methodology. In order to achieve the highest level of accuracy among all the models, we implemented the hard-voting mode. Dataset 2 was employed to evaluate the model's robustness. We utilized the Weighted Arithmetic Index (WAI) method to calculate the water quality index for 316 data points in dataset 2, as it was not specified. Figure 3 represents the entire process of developing the ensemble ML model.