esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Approach for Data Processing
These results have to be tempered (i) by the maps of tie point density (Figure 6) showing a higher spatial coverage for Flight 2, and (ii) by the average density of tie points (computed in a radius of 1 m--Table 1) showing a higher density for Flight 3, both with PhotoScan(r) and MicMac(r). The fact that the numbers of tie points detected by PhotoScan(r) and MicMac(r) are not of the same order of magnitude is due to the parametrization of image alignment: with a number of tie points limited to 4000 for every image in PhotoScan(r) and image size reduced by 3 in image matching in MicMac(r). That can imply differences in matching robustness or PhotoScan(r) may have a smaller reprojection error tolerance. Figure 5: Example of tie points identification in photos from different flight lines for Flight 2 (**a**) and Flight 3 (**b**). (**a**) In this example, among 719 tie points detected, 525 matchings have been detected as valid (blue lines) and 194 as invalid (red lines). (**b**) In this example, among 1457 tie points detected, 1161 matchings have been detected as valid and 296 as invalid. Over a first phase of processing, the 19 GCPs (Figure 3a--red targets) were all used as control points within the bundle adjustment to reconstruct a DEM and an orthophotograph (Figure 7). For each flight, the datasets are processed using both PhotoScan(r) workflow and MicMac(r) workflow with \"F15P7\" optical camera model. In a second phase, the number of used GCPs was reduced to only the 5 GCPs (selected among the 19 GCPs) in the South-Eastern part, so-called \"control region\" (Figure 8) to simulate a very poor GCPs distribution. To assess which flight plan strategy provides the best DEM quality for an ineffective GCPs distribution, each dataset was processed for the \"5 GCPs\" configuration, using both the default PhotoScan(r) workflow and, concurrently, MicMac(r) \"F15P7\" workflows and MicMac(r) workflow with a standard Fraser's distortion model. Figure 6: Tie point density calculated in a radius of 1 m for the different scenarios. Figure 7: Reconstructed orthophotograph (**a**) and DEM (**b**) generated with PhotoScan from the dataset acquired during Flight 1 and processed using the 19 GCPs.
# A Lightweight Crop Pest Detection Method Based on Convolutional Neural Networks
## 3 Results
### Comparison Experiment of the Prior Boxes Generated by Different Clustering Methods  
For comparing the performance of the two clustering methods used in the generation process of the prior boxes, this experiment compared the relevant metrics of six groups of prior boxes, and the results are shown in Table 3. The intersection over union (IoU) is the ratio of the intersection and union of two boxes, and Avg_IoU is the average IoU of all labels in the CP15 dataset with the prior boxes, which objectively shows the degree of matching of the prior boxes generated by different clustering methods with the CP15 dataset. This experiment also shows the size distribution range of the prior boxes with the width range and height range. It can be seen from the experimental results in Table 3 that the prior boxes generated by k-means++ in all six groups of experiments have a wider size distribution, which can help crop pest detection methods capture pest objects of multiple sizes. The visualization of the size distribution of the prior boxes in Figure 11 also shows the superiority of the size distribution range of the prior boxes generated by k-means++. Meanwhile, the prior boxes generated by the k-means++ method in all six groups of experiments also have higher Avg_IoU, indicating that the prior boxes are better matched with the CP15 dataset. Such experimental results demonstrate that the k-means++ method has the advantage of a consistent and stable performance in the generation of the prior boxes, which is more beneficial for detecting crop pests.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Group** & **Method** & **Avg\_IoU** & **Width\_Range** & **Height\_Range** \\ \hline \multirow{2}{*}{1} & k-means & 71.01 & [19; 318] & [20; 317] \\  & k-means++ & 71.67 & [23; 377] & [22; 381] \\ \hline \multirow{2}{*}{2} & k-means & 71.36 & [22; 367] & [20; 366] \\  & k-means++ & 71.48 & [23; 375] & [24; 378] \\ \hline \multirow{2}{*}{3} & k-means & 70.57 & [21; 317] & [12; 313] \\  & k-means++ & 71.57 & [24; 377] & [23; 381] \\ \hline \multirow{2}{*}{4} & k-means & 71.29 & [20; 347] & [20; 348] \\  & k-means++ & 71.40 & [24; 375] & [23; 380] \\ \hline \multirow{2}{*}{5} & k-means & 71.27 & [20; 346] & [20; 348] \\  & k-means++ & 71.46 & [20; 367] & [22; 366] \\ \hline \multirow{2}{*}{6} & k-means & 71.17 & [21; 333] & [22; 334] \\  & k-means++ & 71.38 & [19; 375] & [22; 380] \\ \hline \end{tabular}
\end{table}
Table 3: The degree of matching and the distribution range of the prior boxes.  
Figure 10: The (**a**) loss curve and (**b**) mAP curve during the training process.
## 4 Results and Discussion  
### Activity Concentrations of the Radioisotopes  
The activity concentrations (Table 1) reported herein and used to assess the health hazards of the studied granites, are those obtained from the HPGe detector technique, while those from the ICP-MS analysis are used to confirm the results. Correlations between the concentrations of \({}^{238}\)U, \({}^{232}\)Th, and \({}^{40}\)K obtained from the above-mentioned two techniques are shown in Figure 2. The radionuclides concentration values are highly consistent in both techniques with Pearson correlation coefficients of 0.929, 0.968, and 0.857 for \({}^{238}\)U, \({}^{232}\)Th, and \({}^{40}\)K, respectively. Table 1 and Figures 3 and 4 clearly show that the \({}^{238}\)U activity concentrations of ElSibai-Abu ElTiyur granite varies varied from 16.89 \(\pm\) 1.58 to 72 \(\pm\) 6.48 Bq/kg with an average of 38.72 \(\pm\) 3.38 Bq/kg, whereas the concentrations of \({}^{232}\)Th in the samples, changed from 21.97 \(\pm\) 2.31 to 70 \(\pm\) 6.33 Bq/kg with a mean value of 38.23 \(\pm\) 2.99 Bq/kg. On the other hand, the values of \({}^{40}\)K activity concentrations were comparatively large (from 689.23 \(\pm\) 49.77 to 1037.73 \(\pm\) 55.91 Bq/kg with an average of 860.71 \(\pm\) 18.59 Bq/kg). Figure 4: Activity concentration of \({}^{40}\)K compared with its GAV in regular soil and building materials. Figure 3: Activity concentration of \({}^{238}\)U and \({}^{232}\)Th compared with their global average values (GAV) in regular soil and building materials. Figure 2: Correlation between \({}^{238}\)U, \({}^{232}\)Th, and \({}^{40}\)K activity concentrations estimated using ICP-MS and HPGe. Furthermore, the mean values obtained for both \({}^{238}\)U and \({}^{232}\)Th concentrations are slightly more than GAVs of these radionuclides in regular soil, namely 35 and 30 Bq/kg for \({}^{238}\)U and \({}^{232}\)Th, respectively [21], but they do not surpass their values in building materials, namely 50 and 50 Bq/kg for \({}^{238}\)U and \({}^{232}\)Th [22]. Table 1 and Figure 3. While the mean concentration of \({}^{40}\)K increases by a factor of 2.15 and 1.72 when compared to their GAVs (400 and 500 B/kg as documented by UNSCEAR [21] and UNSCEAR [22]) in regular soil and building materials, respectively (Table 1 and Figure 4). This result shows the suitability of the EISibai-Abu EITiyur granites for use as building materials in dwellings. Table 2 and Figure 5 show the results of the natural radioactivity levels of the investigated granites versus the previous studies on Egyptian granites as well as on those from other countries. The normalization for radionuclides concentrations values of \({}^{226}\)Ra (\({}^{238}\)U), \({}^{232}\)Th, and \({}^{40}\)K from previous studies was carried out by the values of the current work (Figure 5). It is found that the radioisotopes concentrations for \({}^{238}\)U and \({}^{232}\)Th for EISibai-Abu EITiyur granitic samples are smaller than those reported from most of the previous literature (Table 2 and Figure 5), reflecting their safe use as tiling materials in dwellings.
# Upscaling Gross Primary Production from Leaf to Canopy for Potato Crop (_Solanum tuberosum_ L.)
## 2 Materials and Methods
### Leaf-Level Measurements
#### 2.4.1 Upscaling Approaches  
The upscaled canopy GPP was based on light-use-efficiency models. The photosynthetic light-response curves (An-I curves) were fitted to three different and well described mathematical models like rectangular hyperbola-based models: (1) Michaelis-Menten as in [PERSON] and [PERSON] [46; 47]; (2) hyperbolic tangent-based models [48]; and (3) exponential-based models [49]. The mathematical models that better fit the An-I curve data obtained in the field measurements were determined via sum of squared residuals (SSR) comparison [50]. Models with the lowest SSR were found in [PERSON] [46], [PERSON] [47] and [PERSON] [49], and they have basically the following structure:  
\[A_{n}=\frac{\Phi*Is*A_{gmax}}{\Phi*I+A_{gmax}}-R_{d} \tag{4}\]  
Michaelis-Menten function ([PERSON]) [46]  
\[A_{n}=\frac{\Phi*Is*A_{gmax}}{\sqrt{\Phi_{(\text{lo})}^{2}*I^{2}+A_{gmax}^{2}}} -R_{d} \tag{5}\]  
Michaelis-Menten type function ([PERSON]) [47]  
\[A_{n}=\left\{A_{gmax}\left[1-\exp\,\left(\frac{\Phi*I}{A_{gmax}}\right)\right] \right\}-R_{d} \tag{6}\]  
Exponential type function ([PERSON]) [49], where \(\text{A}_{\text{n}}\) is the net photosynthetic rate [\(\upmu_{\text{mol}}\) (\(\text{CO}_{2}\)) m\({}^{-2}\) s\({}^{-1}\)]; \(A_{gmax}\) is the light-saturated gross photosynthetic rate [\(\upmu_{\text{mol}}\) (\(\text{CO}_{2}\)) m\({}^{-2}\) s\({}^{-1}\)]; \(R_{d}\) is the dark respiration rate [\(\upmu_{\text{mol}}\)(\(\text{CO}_{2}\)) m\({}^{-2}\) s\({}^{-1}\)]; \(I\) is the photosynthetic photon flux density PPFD [\(\upmu_{\text{mol}}\) (photon) m\({}^{-2}\) s\({}^{-1}\)] and \(\Phi\) is the quantum yield of assimilation [\(\upmu_{\text{mol}}\) (\(\text{CO}_{2}\)) \(\upmu_{\text{mol}}\) (photon) \({}^{-1}\)]. The light-saturated gross photosynthetic rate (\(A_{gmax}\)) and quantum yield of assimilation (\(\Phi\)) data obtained from the light-use-efficiency models and for each canopy stratum (upper, middle, and low) were fitted to third-degree and fourth-degree polynomial regressions to describe their behavior during canopy growth over time.
# FL-MD3 QN-Based IoT Intelligent Access Algorithm for Smart Construction Sites
## 5 Conclusions and Future Work
### Future Work  
Future research could further explore the application effectiveness of the FL-MD3 QN algorithm in smart construction site scenarios with varying complexities and scales, as well as its robustness and scalability under diverse network environments. Additionally, integrating FL-MD3 QN with advanced technologies like blockchain may enhance the overall performance of IoT systems in smart construction sites. Addressing potential challenges such as communication latency, model synchronization, and security risks, further optimization and refinement of the multi-objective functions in FL-MD3 QN could improve adaptability to increasingly complex transmission environments. Emphasis should be placed on enhancing the algorithm's applicability in real-world scenarios through dynamic interference modeling, anti-interference design, and real-world edge computing validation. Integrating digital twin and blockchain technologies could strengthen anti-interference capabilities and privacy protection, ultimately facilitating the transition from simulation to real-world deployment.  
Figure 6: BER analysis of different models.  
**Author Contributions:** Conceptualization, Q.Z. and Y.L. ([PERSON]); methodology, J.X. and Y.L. ([PERSON]); software, W.L. and F.P.; validation, Y.L. ([PERSON]); formal analysis, Y.L. ([PERSON]); investigation, W.W. and F.P.; data curation, F.P.; writing--original draft preparation, Y.L. ([PERSON]); writing--review and editing, Y.L. ([PERSON]); visualization, Y.L. ([PERSON]); supervision, W.L., Y.L. ([PERSON]), and Y.L. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the China Three Gorges Corporation--Research, Development, and Application of Data Acquisition and Secure Transmitter for Environmental Monitoring IoT Devices (NBZZ202400302).  
**Data Availability Statement:** The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** Authors [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] were employed by the company Three Gorges High-tech Information Technology Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Effect of Water Content and Cementation on the Shear Characteristics of Remolded Fault Gouge
## 4 Discussion
### Shear Strength Model for High-Clay-Content Fault Gouge
In this study, the model achieved \(R^{2}=0.8309\) on the training set and \(R^{2}=0.8172\) on the test set, demonstrating both strong fitting performance and generalization capability. Most data points lie near the \(y=x\) line, further confirming the model's ability to accurately predict shear strength under diverse conditions. These results validate the applicability and robustness of the proposed model and provide a solid foundation for fast shear strength prediction under complex hydro-mechanical-cementation conditions, supporting future engineering applications and numerical simulation studies. It should be noted that this model is developed based on experimental results and supported by literature data (as show in Figures 9 and 10). It is applicable to fault gouge with high clay content (typically \(>\)30%) under unsaturated conditions and with silicate-based cementation. While it extends the Mohr-Coulomb criterion, the model also incorporates the effect of weak cementation through the evolution of cohesion. Its applicability may be limited for materials with highly localized structural failure or complex degradation mechanisms beyond the scope of cohesion-friction description. Figure 11: Relationship between predicted and measured values.
# A Study on the Properties of Recycled Aggregate Concrete and Its Production Facilities
## Mechanical Properties of the Recycled Aggregates Using Concrete and Durability Characteristics
### Durability Characteristics  
#### 5.3.1 Freezing and Thawing Resistance  
Freezing and thawing experiments of the recycled aggregate concrete were conducted according to KS F 2456 \"standard test method for resistance of concrete to rapid freezing and thawing\" and the experimental results are shown as Figure 12.  
The durability index of ordinary concrete was 86, in the general strength territory and high strength territory. The durability indices of the recycled aggregate concrete that used the recycled coarse aggregate were 85, 87, and 89, in the general strength territory and 87, 89, and 91, in the high strength  
Figure 12: Relative dynamic elastic modulus and number of cycles: (**a**) Recycled coarse aggregate replacement, (**b**) recycled fine aggregate before modification, and (**c**) recycled fine aggregate after modification.  
territory at replacement rates of 30, 60, and 100%, respectively. The durability indices of the recycled aggregate concrete that used recycled fine aggregate after modification, exhibited slightly higher durability indices than that of ordinary concrete, with values of 84, 90, and 91, in the general strength territory and 88, 90, and 92, in the high strength territory for replacement rates of 30, 60, and 100%, respectively. Furthermore, in the recycled aggregate concrete that used the recycled fine aggregate after modification, the durability indices were 82, 84, and 87, in the general strength territory and 85, 83, and 87 in the high strength territory, for replacement rates of 30%, 60%, and 100%, respectively which were slightly lower than those of the recycled coarse aggregate and recycled fine aggregate, before modification. The durability index was thought to have increased due to a buffer effect, caused by the expansion and relaxation of moisture due to freezing and thawing. This is occurs due to a lowering of the air-spacing in the concrete as a result of the entrained air bubbles attached to the recycled aggregate. Furthermore, the amount of air entrained in the concrete containing recycled fine aggregate, after modification, is relatively small. In Figure 13, the relationship between the durability index and air volume of the recycled aggregate concrete is shown. As a result of the analysis, the significance probability was 0.856, so it was considered that there was a slightly high significance probability between the durability index and air volume of the recycled aggregate concrete. Accordingly, to improve the frost resistance, it was necessary to secure an appropriate air volume through combination management, through the use of an AE agent and other factors.
# Enhancing Smart Agriculture by Implementing Digital Twins: A Comprehensive Review
## 3 Reviewing Relevant Technologies and DT Components  
Although the implementation of DTs in agriculture can vary in complexity and scope based on the specific architecture of the DT scheme, in Figure 5, a representative architectural framework for implementing DTs in agriculture is illustrated. The basic components and interactions of DTs in agriculture are displayed where the physical farm is represented by the node labelled \"Physical Farm\". Sensor data collected from the farm is depicted by the node labelled \"Sensor Data\". The sensor data undergoes data preprocessing in the node labelled \"Data Preprocessing\".  
The preprocessed data is subsequently employed to construct a DT model, represented by the node labelled \"Digital Twin Model\". This enables various functionalities, including simulation and optimization, represented by the nodes labelled \"Simulation\" and \"Optimization\". It also incorporates data analytics, represented by the node labelled \"Data Analytics\". The decision support system provides action recommendations, represented by the \"Action Recommendations\" node. The insights are depicted from the node \"Insights\", and all visualization and monitoring data are derived from the node labelled \"Decision Support\". The visualization of data is depicted by the node labelled \"Visualization\" which aids in monitoring the farm. We should point out that effective monitoring plays a crucial role in maintaining situational awareness and facilitating timely response or intervention as needed. That is represented in the figure by the node \"Monitoring\". The significant active role of the stakeholders that manage the making and implementing of the decisionsinvolved in organizing and operating an agriculture establishment is displayed by the node \"Farming Practice and Experience\", besides the optimization provided by the DT concept.  
The action recommendations are implemented through actuators and control systems, represented by the node labelled \"Actuators and Control\", which can affect the physical farm. The last node in Figure 5 and the physical farm are in green, specifying the physical space in contrast to the digital space nodes in blue. The loop from the \"Actuators and Control\" node back to the \"Physical Farm\" node symbolizes the ongoing feedback loop between the physical farm and its DT, where actions taken based on the DT's insights impact the physical farm, and the sensors capture the resulting changes to update the DT model.  
This schematic in Figure 5 highlights the key components and interactions of digital twins in agriculture, showcasing their role in data collection, preprocessing, modelling, simulation, analytics, decision support, visualization, and control, ultimately aiding in optimizing farm operations and improving decision-making processes.  
Furthermore, the interconnections among these entities play a vital role as they are essential for the functioning of the DT system in an integrated manner, as emphasized by [PERSON] et al. in their research [PERSON] et al. (2017).
## 1 Introduction
On the other hand, other models, like the  
Figure 2: Waveforms of Class-E PA. (**a**) Switch-driver voltage. (**b**) Switch current. (**c**) Switch voltage. (**d**) Load resistance current. (**e**) \(L_{SH}\) current. (**f**) \(C_{SH}\) current. ones proposed in [17; 18], provide accurate analytic equations, which avoid iterative solutions with lower accuracy predictions under high values of parasitic elements of the amplifier components. The model that is presented in [17] considers two design parameters, which are \(D\) and the resonance frequency mismatch (\(q\)). The model proposed in [18] considers three design parameters: \(D\), \(q\), and the normalized slope of the switch voltage (\(S\)). The \(S\) parameter allows for defining three operation modes: non-ZVS, suboptimal operation (i.e., ZVS and non-ZVDS), and optimal operation (i.e., ZVS and ZVDS). Commonly, the design equations synthesized from these analytical models are summarized into a set of math expressions that establish a link between circuit components and input parameters, these expressions are called the PA design set [17; 18; 19]. Under the amplifier optimal operation, the design set proposed by [17; 18; 19] is the same, and it allows the calculation of a large number of possible combinations of the circuit parameters, which guarantee the ZVS and ZVDS operation of the ideal Class-E with FDI under the specifications and restrictions of the particular application. However, this set is not widely used perhaps, because the model implementation demands extensive mathematical development, and to the best knowledge of the authors, a systematic methodology to explore the freedom of this analytical model is not reported, in particular with the possibility to analyze the design space when considering the involved trade-offs between design specifications and restrictions. This paper proposes a novel design methodology of the Class-E with FDI based on the analytical model that was proposed by [17; 19]. Furthermore, to simplify its adoption by the academic and scientific community, we share the PA model implementation, and the code used to apply the proposed method. In addition, this paper has been written following a tutorial approach to motivate related engineers and students with basic knowledge in circuit theory in order to integrate these amplifiers in their projects by following this paper (and its additional material) as a practical handbook. We devote particular attention to the selected examples to present the impact of the proposed methodology to optimize the PA. The outline of this paper is as follows. Section 2 presents the Class-E PA modeling and design equations. Section 3 describes the design methodology and explains two design approaches. Section 4 shows four design study cases. Finally, in Sections 5 and 6, the conclusions and the future work are presented.