esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Verification
### Heat Dissipation System Experiment  
In the experimental design of this paper, a thermocouple (K-type) was connected to the junction of the cold plate and the heat source. Each cold plate was arranged with one measuring point at the upper, middle, and lower ends, respectively, and the four groups of cold plates had a total of 12 measuring points, as shown in Figure 14. They were also connected with the temperature data acquisition component to measure the temperature data in real time.  
The experimental time of the fluid circulation heat dissipation system was 60 min, the coolant fluid was fluorinated liquid, the total heat source of the overall system was 840 W, the initial temperature of the coolant was 25.6 \({}^{\circ}\)C, and the ambient temperature was 26.6 \({}^{\circ}\)C. The temperature change of each measuring point during the experiment is shown in Figure 15. The experimental results show that it takes about 35 min to enter a steady state where the system state maintains a certain fixed range and no longer changes with time. According to the observations, the maximum overall temperature was 35.8 \({}^{\circ}\)C at the position of the C2 temperature sensor, while the minimum value was 32.4 \({}^{\circ}\)C at the position of the A3 temperature sensor. The temperature difference between the maximum and minimum values of the overall temperature was 3.4 \({}^{\circ}\)C, which is not much different from the simulation results of Exp. 3 (optimal design parameter) in Table 4 with a heat source surface temperature equalization of 3.0 \({}^{\circ}\)C, and meets the specification requirements (the temperature difference is 5.0 \({}^{\circ}\)C). This also proves the correctness of the results obtained in the simulation stage, as well as the reliability of the optimal design method adopted in this paper.  
Figure 14: Location of the temperature measuring points.  
Figure 15: _Cont._
## 2 Materials and Methods
### Data Preparation  
#### 2.2.1 Landsat Time Series and Annual Composite Data  
Landsat data are suitable for long time series investigations of forests in the study area because they provide a data record initiated in 1972 with a medium spatial resolution of 30 m [15; 41] and six channels ranging from the visual band to the short wave infrared band [42]. Therefore, the Landsat 5 TM, 7 ETM+, and 8 OLI sensors were used as data sources. We used 34 years (1987-2020) of Landsat data (Tier 1, Collection 1), where the effects of atmospheric, topographic, reflectance, and satellite sensors have previously been corrected [43]. All Landsat data were obtained from the Google Earth Engine (GEE) Python API, a cloud computing platform for geospatial analysis [44]. The _simpleComposite_ algorithm on the GEE platform was used to generate annual cloud-free composite scenes of the study area by accessing the median of the least cloudy pixels after converting the images to top-of-atmosphere (TOA) reflectance from all images that fell within each date range [45]. This composite achieved consistency and coherence for forest dynamics monitoring [46; 47].
## 3 Experiments
### Evaluation with Lightweight CNNs Method  
We evaluated the proposed lightweight CNNs methods using the FlickrLogos-32 dataset. Detection accuracy in mAP, number of parameters in millions and image detection time in seconds are given in Table 6. For the Lightweight architectures, training has been conducted with a batch size of 4 for 140 epochs. The rest of the parameter setting is used as before. Due to the limited data, we initialize the network weights on PASCAL-VOC non-logo object detection images [36]. We observed that when we apply depthwise and pointwise convolution operation there is a drastic reduction in network parameters compared to the standard convolution operation. The reduction in parameters leads to faster computation speed but slightly declines detection accuracy. For comparison, we implemented CPD and Lightweight CNNs modules with CenterNet, LogoNet and Dual-Attention LogoNet.  
CenterNet architecture based on CPD method (CenterNet-CPD) achieves 77.9% detection accuracy while the number of parameters is 72.42 million and detection time is 0.1145 s. LogoNet-CPD network achieves a greater accuracy 78.8% with detection time of 0.1073 s. However, Dual-Attention LogoNet network achieves 78.9% accuracy with a 0.1145 s detection time. LogoNet-CPD and Dual-Attention LogoNet-CPD use around 73.19 million computation parameters. With our lightweight modules, CenterNet (CenterNet-Lightweight) achieves 79.0% accuracy with 0.0833 s detection time. This architecture uses 27.94 million parameters. Dual-Attention LogoNet-Lightweight achieves 79.5% accuracy with a detection time of 0.0979 s. The proposed LogoNet-Lightweight network achieves a significantly higher accuracy rate of 79.7%, which is slightly less than the baseline (CenterNet) and LogoNet methods (80.7% and 82.2%). Whereas, LogoNet-Lightweight takes a detection time of 0.0885 s per image, which is about 20% faster than the baseline method. The LogoNet-Lightweight and Dual-Attention LogoNet architectures use only 28.73 million parameters. The parameters used are only about 15% of the parameters used in the normal baseline method (CenterNet). We found that LogoNet-Lightweight, which incorporates only spatial attention module, achieves a better perfromance in terms of detection time and accuracy. This approach leads to faster training and convergence of the network. Since depthwise convolution operations are used in the lightweight modules, channel-wise attention is not very effective. A model with low parameters and considerable accuracy rate is preferable for edge computing devices. We believe our lightweight algorithm is more suitable to run on low-spec machines or for edge computing than conventional algorithms.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **mAP** & **Detection Time** \\ \hline CenterNet & 88.0 & 0.1093 s \\ LogoNet & 88.3 & 0.1156 s \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance Evaluation of State-of-the-Art Detection Methods on Logos-32 Plus Dataset.
# Design and Test of a Single-Row Harvesting and Cutting Integrated Handheld Garlic Harvester
## 2 Materials and Methods
### Overall Structure and Working Principle of the Machine
#### 2.1.2 Working Principle and Main Technical Parameters  
The single-row harvesting and cutting integrated handheld garlic harvester adopts the process scheme of single-row harvesting, gathering and clamping, and cutting and harvesting. During field operation, the degree of soil loosening varies in different garlic planting environments, and the operator adjusts the height-adjustable soil-breaking mechanism according to the degree of soil loosening so that the digging shovel can be adjusted to a suitable height for digging garlic. The supporting gathering device can hold up the fallen garlic seedlings in the field so that the digging work can proceed normally. The cutting knife in the cutting and collecting system can cut the garlic on the chain to achieve the effect of separating the garlic seedlings and the garlic head. The garlic seedlings are transported to the rear end of the machine with the clamping chain, and the garlic head falls into the bottom collection box to complete the garlic harvesting process. The power of the gasoline engine is transmitted through the belt drive, and the clamping chain, cutting knife, and front-end power wheel are powered by one axis and multiple motions simultaneously. The speed of the cutting knife and front power wheel can be controlled by a speed reducer, which is equipped with four gears to meet the requirements of machine speed control during garlic harvesting.  
The main technical parameters of the whole machine are shown in Table 1.  
A workflow diagram is shown in Figure 2.
It is important to note that this research complies with relevant legislation, including the Soil Framework Directive (SFD) and the Water Framework Directive (WFD), as well as European standards such as ISO 10381-1:2002 (Guidelines for the design of sampling programs) [14], ISO 17402:2008 (Soil Quality) [15], and Legislative Decree 152/2006 [16]. Key elements such as the selection of appropriate sampling techniques, sample management, and the calculation of field chemical parameters are addressed in ISO 10381-1:2002, ISO 17402:2008, and Legislative Decree 152/2006. Additionally, this study is aligned with ISO 23611-6:2013 [17], which provides comprehensive guidelines for designing soil invertebrate sampling programs. Although this section of ISO 23611 is intended for global application in all soils populated by invertebrates, most available data pertain to temperate regions. However, studies from tropical and boreal regions, supported by theoretical considerations, suggest that the principles outlined in ISO 23611 are broadly applicable. ISO 17402:2008 establishes requirements and guidelines for selecting and applying methods to assess the bioavailability of contaminants in soil and related materials. While it does not prescribe specific methods, it defines boundary conditions and principles for method application and development, ensuring minimum standards for methodological reliability. Results obtained using these methods can serve as estimates of bioavailability within a risk assessment framework. Contaminants addressed by ISO 17402:2008 include metals (and metalloids), organic pollutants, and organometallic compounds. The standard also applies to natural metals derived from geo logical and pedological processes (natural pedogeochemical content) and can be extended to sediment analysis. The remainder of this manuscript is organized as follows: Section 2 reviews the state of the art, focusing on the traditional use of Portland cement. Section 3 outlines the characterization of contaminated soils and preparation of alternative materials and presents the results of geotechnical and chemical-physical analyses. Section 4 presents the results of the mixture used to stabilize contaminated soils, including the composition, advantages of selected materials, and the pelletization process, detailing factors influencing agglomeration, process conditions, and granule characteristics. Section 5 presents preliminary analyses of contaminated soils, focusing on soil characteristics, leaching tests, and the potential of industrial by-products for stabilization, before treatment processes. Finally, Section 6 concludes this study, offering reflections on the potential future use of green materials and perspectives for further research.
# Concrete Dam Deformation Prediction Model Research Based on SSA-LSTM
## 5 Conclusions  
In this study, three models, namely SSA-LSTM, BP, and LSTM, were used to construct a deformation prediction model for four measurement points of a concrete gravity dam,  
Figure 8: Residual plots of different model. (**a**) EX3-4a. (**b**) EX3-3a. (**c**) EX2-7a. (**d**) EX2-6a.  
and the following conclusions were drawn by comparing and analyzing the prediction and residual effects:  
1. For the random outliers in the dam deformation monitoring data, multiple denoising algorithm weights are used to complement each other in terms of data characteristics and noise types, which can obtain a better denoising effect on the measured data and obtain real data that can reflect the working state of the dam, effectively eliminating the influence of coarse differences on the prediction accuracy of the model.
2. Due to the different characteristics and intrinsic components of each measurement point, the SSA optimization algorithm proposed the optimal range of parameters and averaged parameter values, which can better obtain the characteristics of the dam deformation sequence in time sequence and take into account the backward and forward correlation of the input information, thus improving the prediction accuracy of the LSTM model.
3. The SSA-LSTM model established in this paper has higher prediction accuracy and stability than the BP and LSTM models, and the modeling results are consistent with the actual engineering situation, providing a new technique for predicting dam deformation with high accuracy. The method is simple and efficient and can be applied to the prediction analysis of other monitoring effects of dams with modification.  
In the later stage, further improvements can be made to the optimization strategies and solving methods of the SSA optimization algorithm itself to enhance the extraction of features and temporal correlations in dam deformation sequences. Additionally, the LSTM model can be combined with other neural network models to improve overall predictive performance. Depending on the data structure and specific problem, leveraging the strengths of different models can effectively address challenges in practical applications.  
Conceptualization, J.M. and T.L.; methodology, J.M.; software, J.M. and Y.C.; validation, J.M. and Y.C.; formal analysis, J.M.; investigation, J.M. and F.W.; resources, T.L. and H.Q.; data curation, J.M.; writing--original draft preparation, H.Q.; writing--review and editing, J.M.; visualization, J.M.; supervision, T.L.; project administration, T.L.; and funding acquisition, T.L. All authors have read and agreed to the published version of the manuscript.  
Not applicable.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 3 Results and Discussion
### Backbone Curve  
The backbone curve of each specimen can reflect the structural characteristics at different stages, including strength, stiffness, ductility, energy dissipation capacity, and collapse resistance. The backbone curves of the specimens in this test are shown in Figure 10.  
Figure 10: Backbone curves of the specimens.  
Figure 10 shows that the three backbone curves are divided into ascending and descending stages, without abrupt changes in bearing capacity and stiffness, and the curves are smooth, showing the characteristics of typical backbone curves. At the same time, the stiffness and bearing capacity of the specimen change with the state of the specimen. From the initial stage of loading to the cracking point, the backbone curve is approximately straight, and the inclination angle is large, indicating that the stiffness of each specimen is maximum at the beginning, and the bearing capacity also increases, which is in the elastic state. The backbone curve of each specimen flattens gradually after cracking, indicating that the stiffness is gradually degraded, and the bearing capacity continues to increase until the yield point. The bearing capacity of the specimen increases slowly after yielding. After the specimen reaches the peak point, the backbone curve is in the descending stage, and the bearing capacity decreases sharply until the specimen is destroyed.  
By comparing the backbone curves of the specimens, the slope of the backbone curve gradually decreases with the increasing coal gangue replacement rate, indicating that the initial stiffness of the specimens gradually decreases. In addition, the larger the replacement rate of coal gangue is, the smaller the bearing capacity and ultimate displacement of the specimen are.
## 4 Performance Evaluation
### Outage Probability
#### 4.1.1 Outage Analysis of Max-Select Relaying  
In case 1, the instantaneous SNR of the \(i\) th end-to-end link is as follows:  
\[\gamma_{SD_{i}}=\underset{i=1,2\cdots,N}{\text{min}}\big{\{}\gamma_{SR_{i}}, \gamma_{R_{i}D}\big{\}}, \tag{33}\]  
where \(\gamma_{SR_{i}}=\max\!\left\{\gamma_{SR_{i}}^{FSO},\gamma_{SR_{i}}^{RF}\right\}\) and \(\gamma_{R_{i}D}=\max\!\left\{\gamma_{R_{i}D}^{FSO},\gamma_{R_{i}D}^{RF}\right\}\). The UAV node \(i=I_{1}\) is selected according to the best channel quality, and the maximum end-to-end instantaneous SNR of case 1 can be expressed as  
\[\gamma_{SD1}=\underset{i=1,2\cdots,N}{\text{max}}\big{\{}\gamma_{SD_{i}}\big{\}}, \tag{34}\]  
Therefore, the outage probability of case 1 can be given as  
\[P_{out1}=\Pr[\gamma_{SD1}<\gamma_{th}]=\prod_{i=1}^{N}\!\left[1-(1-F_{\gamma_{ SR_{i}}^{FSO}}(\gamma)F_{\gamma_{SR_{i}}^{RF}}(\gamma))(1-F_{\gamma_{R_{i}D}^{FSO}}( \gamma)F_{\gamma_{R_{i}D}^{RF}}(\gamma))\right]\!, \tag{35}\]  
After substituting (28) and (31) in (35), the outage probability can be obtained.
## A Dynamic Reputation Management System for Mobile _Ad Hoc_ Networks \({}^{\dagger}\)  
**[PERSON] *, [PERSON] and [PERSON]**  
School of Computer Science, University of Hertfordshire, College Lane, Hatfield, Hertfordshire AL10 9 AB, UK; E-Mails: [EMAIL_ADDRESS] (H.X.); [EMAIL_ADDRESS] (B.C.)  
\({}^{\dagger}\) This paper is an extended version of our paper published in the 6 th Computer Science and Electronic Engineering Conference (CEEC), 2014, pp. 133-138. IEEE, 2014.  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +44-1707-281093 ; Fax: +44-1707-284303.  
Academic Editors: [PERSON] and [PERSON]  
_Received: 30 January 2015 / Accepted: 14 April 2015 / Published: 17 April 2015_  
**Abstract:** Nodes in mobile _ad hoc_ networks (MANETs) are mandated to utilize their limited energy resources in forwarding routing control and data packets for other nodes. Since a MANET lacks a centralized administration and control, a node may decide to act selfishly, either by refusing to respond to route requests from other nodes or deceitfully by responding to some route requests, but dropping the corresponding data packets that are presented for forwarding. A significant increase in the presence of these misbehaving nodes in a MANET can subsequently degrade network performance. In this paper, we propose a dynamic reputation management system for detecting and isolating misbehaving nodes in MANETs. Our model employs a novel direct monitoring technique to evaluate the reputation of a node in the network, which ensures that nodes that expend their energy in transmitting data and routing control packets for others are allowed to carry out their network activities while the misbehaving nodes are detected and isolated from the network. Simulation results show that our model is effective at curbing and mitigating the effects of misbehaving nodes in the network.  
**Keywords:** selfish nodes; deceitful nodes; direct monitoring; total reputation; reputation management; MANETs
# A Review of Research on Spectrum Sensing Based on Deep Learning
## 2 Spectrum-Sensing Methods
### Summary of Conventional Spectrum-Sensing Methods  
Since some spectrum-sensing methods perform well only under specific conditions, we need to select appropriate methods for different task conditions or different environments.  
Single-node spectrum sensing is mainly suitable for resource constraints, low interference, a relatively stable spectrum environment, and simple spectrum detection tasks. When there is a lot of interference or the spectrum conditions change quickly, or when the network is very big, choosing cooperative spectrum-sensing methods can make it possible to sense a wider range of frequencies and perform more complicated tasks like spectrum sharing and interference suppression.  
To further summarize the various algorithms in traditional spectrum sensing and to show more clearly the main differences and application scenarios between single-node spectrum-sensing methods and cooperative spectrum-sensing methods, we summarize the above-mentioned methods in Table 3.  
Figure 8: Relay-assisted cooperative spectrum detection modeling.
## 4 Design Optimization
### Results of Optimal Design and Verification

Table 6: Optimal values of the design variables. Figure 10: Sensitivity plot of temperature. Figure 9: Sensitivity plot of stress. form of continuous variables with lower and upper limit values. The optimizations were conducted with an orthogonal arrangement identically to the earlier experiments, and each design variable was defined to have three levels. \[ \begin{split}\text{Find Vane-Shape, Number, Diameter, Length}\\ \text{To minimize Stress, Temperature}\\ \text{Shape = \{Radial, Curved, Pin-Fin\}}\\ \text{5 \leq Number of Ventilation Blade \leq 30}\\ \text{2 mm \leq Diameter of Ventilation Blade \leq 6 mm}\\ \text{18 mm \leq Length of Ventilation Blade \leq 58 mm}\\ \end{split}  \tag{10}\]  
Eight test points were created, and the model was constructed with the design variables of the test points of the ventilation blade in consideration of the optimal value of the holes derived from the previous design variables. Individual structural and thermal analyses were then carried out. The design variables and objective functions were defined to minimize stress and temperature through the optimal plan to formulate the optimal design problem. When the values of the objective functions according to the weights were compared after the optimization work, it was judged to be important to consider both objective functions that had large changes compared to the initial values. Therefore, the second optimal design was carried out, and it was identified that the stress decreased by 14.8 MPa, from 64.04 MPa to 49.20 MPa, and the temperature decreased by 10.77 \({}^{\circ}\)C, from 45.89 \({}^{\circ}\)C to 35.12 \({}^{\circ}\)C compared to the plain solid disc. The optimum values of the ventilation blades were found as follows: diameter of 4 mm, length of 58 mm, curved shape, and 30 blades. The optimum values are shown in Table 8.
## 2 Materials and Methods
### Spatiotemporal Characteristics Analysis  
#### 2.3.1 Classification of NDVI and LULC  
The study area is located in south China, where the vegetation coverage is generally good. The NDVI data were divided into 6 categories: low coverage (0-0.1, LC, code 1), low coverage (0.1-0.3, LC, code 2), medium coverage (0.3-0.5, MC, code 3), high coverage (0.5-0.7, HrC, code 4), high coverage (0.7-0.85, HC, code 5), and very high coverage (0.85-1, VHC, code 6).  
The LULC data were divided into 6 categories: cultivated land (CL, code 1), forest land (FL, code 2), grassland (GL, code 3), water body (WB, code 4), built-up land (BL, code 5), and unused land (UL, code 6). These are the first level categories in the classification  
Figure 2: Research framework.  
system of the China Multi-Period Land Use and Land Cover Remote Sensing Monitoring Dataset (CNLUCC).