esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The accurate estimation of aboveground net herbaceous production (ANHP) is crucial in rangeland management and monitoring. Remote and rural rangelands typically lack direct observation infrastructure, making satellite-derived methods essential. When ground data are available, a simple and effective way to estimate ANHP from satellites is to derive the empirical relationship between ANHP and plant-absorbed photosynthetically active radiation (APAR), which can be estimated from the normalized difference vegetation index (NDVI). While there is some evidence that this relationship will differ across rangeland vegetation types, it is unclear whether this relationship will change across grazing management regimes. This study aimed to assess the impact of grazing management on the relationship between ground-observed ANHP and satellite-derived APAR, considering variations in plant communities across ecological sites in the shortgrass steppe of northeastern Colorado. Additionally, we compared satellite-predicted biomass production from the process-based Rangeland Analysis Platform (RAP) model to our empirical APAR-based model. We found that APAR could be used to predict ANHP in the shortgrass steppe, with the relationship being influenced by ecosite characteristics rather than grazing management practices. For each unit of added APAR (MJ m\({}^{-2}\) day\({}^{-1}\)), ANHP increased by 9.39 kg ha\({}^{-1}\), and ecosites with taller structured herbaceous vegetation produced, on average, 3.92-5.71 kg ha\({}^{-1}\) more ANHP per unit APAR than an ecosite dominated by shorter vegetation. This was likely due to the increased allocation of plant resources aboveground for C\({}_{3}\) mid-grasses in taller structured ecosites compared to the C\({}_{4}\) shortgrasses that dominate the shorter structured ecosites. Moreover, we found that our locally calibrated empirical model generally performed better than the continentally calibrated process-based RAP model, though RAP performed reasonably well for the dominant ecosite. For our empirical models, R\({}^{2}\) values varied by ecosite ranging from 0.49 to 0.67, while RAP R\({}^{2}\) values ranged from 0.07 to 0.4. Managers in the shortgrass steppe can use satellites to estimate herbaceous production even without detailed information on short-term grazing management practices. The results from our study underscore the importance of understanding plant community composition for enhancing the accuracy of remotely sensed predictions of ANHP.  
NDVI; herbaceous biomass; remote sensing; grazing management; ecological site; APAR; ANHP 2024
# Federated Reinforcement Learning Based AANs with LEO Satellites and UAVs
## 5 Conclusions  
In this paper, we proposed AAN with LEO satellites and high-altitude UAVs equipped with FRL techniques. By utilizing FRL-based UAVs, which could determine their next destination based on the collected various information, our proposed system automatically detects the area where communication resources are scarce based on the network traffic map. Moreover, the UAVs improve itself continuously without the director's guidance, and the system could respond to changing environment without further configuration change. We evaluated our proposed system with traffic map simulation and network simulator. Based on the evaluation result, we showed that the proposed system could provide network service in various area, including the area where network resource demand distribution rapidly changes. In the evaluations, the UAVs could communicate with the satellites and the terrestrial devices, so the UAVs could process the network traffic load in densely populated area, which could alleviate the load of the satellites. With this feature, the UAV-aided AAN enhanced the throughput of communications between the ground devices and the satellites and shorten the path length between them. As a result, our proposed AAN provided 3.25 times more communication resources and had 5.1% lower latency than satellite-only AAN. Moreover, the UAV-aided AAN had fewer network path changes than the satellite-only AAN, which provided more stable Internet services to the users.  
As a future work, we will utilize more various simulators to consider more environmental factors including the network characteristics, population shift model, and the network traffic demand model. Furthermore, we will analyze more various indicators representing the performance of the AAN and satellite Internet service. Moreover, we plan to integrate our proposed system with terrestrial network, which has abundant but difficult network resources to be shared with areas far from the terrestrial network area.  
Conceptualization, S.Y. and W.L.; methodology, S.Y. and W.L.; software, S.Y. and W.L.; validation, S.Y. and W.L.; formal analysis, S.Y. and W.L.; investigation, S.Y. and W.L.; resources, S.Y. and W.L.; data curation, S.Y. and W.L.; writing--original draft preparation, S.Y. and W.L.; writing--review and editing, S.Y. and W.L.; visualization, S.Y. and W.L.; supervision, W.L.; project administration, W.L.; funding acquisition, W.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. 2021R1G1A1092939).  
Not applicable.  
Figure 13: Round trip time and it’s improvement rate of UAV-aided AAN w.r.t satellite-only AAN.
# Cross-Sectional Shape Optimization of Cylindrical Elastomer Spring for Sensitive Cargo Container
## 1 Introduction
[PERSON] [15] performed a rail vehicle dynamics analysis of a nonlinear rubber spring model to represent the mechanical behavior of rubber suspension components in rail vehicle dynamics. As such, there are many studies on rubber springs for suspension devices for railway vehicles, but studies on rubber springs for anti-vibration for containers are insufficient. In particular, it is difficult to evaluate the hyperelastic behavior of natural rubber, and due to the convergence of nonlinear analysis, it is necessary to investigate natural rubber springs exclusively for containers. In this study, to develop an elastomer spring with sufficient characteristics even at high loads instead of natural rubber springs that are difficult to meet the target stiffness and strength characteristics:  
Figure 1: Cylindrical rubber spring: (**a**) wagon container; (**b**) internal module of cylindrical natural rubber spring installed with pallet. First, the NFEA of the existing rubber spring was used to obtain the structurally weak point. Second, a study on the optimal shape design of a polyurethane spring having the same hardness as rubber was conducted. To this end, the three-dimensional shape was modeled with CATIA, and then NFEA was performed using ABAQUS [16; 17]. In addition, material constant prediction and spring cross-sectional shape optimization were performed using I-SIGHT [18]. An initial prototype was manufactured for verification, and based on the structural analysis results of this model, the optimal upgrade product was designed, the improvement effect was evaluated, and the analysis results were verified under displacement control conditions. As a result of the verification, the correlation coefficient between the predicted value and the measured value was 0.928 as a result of the compression load-displacement test of the developed product.
## 3 Results and Discussion
### Optical Band Gap Study of the PLC Nanocomposite  
The shift in the electrons from the highly occupied molecular orbital (HOMO) \(\pi\)-band to the lower unoccupied molecular orbital (LUMO) \(\pi^{\star}\)-band of electronic states is linked to optical band gaps. The absorption of photons in the medium is explained by the \(\alpha(\
u)\) absorption coefficient, which is expressed as \(\alpha(\
u)=2.303\ (A/d)\), where \(d\) is the distance into the medium (cm), and \(A\) is the absorbance, defined by \(A=\log\ (I_{0}/I)\), where \(I_{0}\) and \(I\) are the intensities of the incident and transmitted beams. For \(\alpha>10^{4}\) cm\({}^{-1}\), the PANI-based composites exhibited band gap energies within the HOMO and LUMO levels, which were calculated by the absorption coefficient using the equation [16].  
\[\alpha=\frac{A\ \left(\mathrm{h}\
u-E_{\mathrm{g}}\right)^{n}}{\mathrm{h}\
u} \tag{5}\]  
where \(\mathrm{h}\
u\) is the incident photon energy; \(h\) is the Planck constant; \(E_{\mathrm{g}}\) is the optical band gap energy; \(A\) is a constant known as the disorder parameter, which is dependent on the composition and independent of the photon energy; and \(n\) is the power coefficient, with the value determined by the type of possible electronic transitions, i.e., 1/2, 3/2, 2, or 1/3 for direct allowed, indirect allowed, direct forbidden, and indirect forbidden, respectively [39]. The direct allowed band gaps of the PLC nanocomposite before and after EB irradiation were evaluated by extrapolating the linear portion of the curves to the energy axis for \((\mathrm{a}\mathrm{h}\
u)^{2}=0\) from the plot of \((\mathrm{a}\mathrm{h}\
u)^{2}\) versus photon energy (\(\mathrm{h}\
u\)), as shown in Figure 8. The \(E_{\mathrm{g}}\) values decreased with an increase in the EB dose from 2.15 eV of unirradiated to 1.84, 1.81, and 1.77 eV after 40, 80, and 120 kGy EB doses, respectively.  
The decreasing trend in the \(E_{\mathrm{g}}\) values with the EB dose is primarily due to the fragmentation of the long polymer chain into smaller segments and the formation of free radicals. This occurs because of the creation of traps in the polymer matrix during the chain scission  
Figure 8: The \((\mathrm{a}\mathrm{h}\
u)^{2}\) vs. photon energy (\(\mathrm{h}\
u\)) plots for the PLC nanocomposites before and after EB irradiation with various doses.  
process, resulting in an increase in the number of polarons at higher doses [40]. This helps form more free electrons in the conduction band, allowing them move more easily, hence increasing the conductivity. This was well correlated with the XRD analysis, which showed an increase in the amorphousness with the EB dose.
## Materials and Methods
### Image Analysis
#### 2.3.1 Visual Image Analysis  
The radar backscatter recorded by the SAR sensor can be considered as a measure of the surface roughness, with smoother surfaces rendered dark in the resulting image and rougher surfaces appearing brighter. Characteristic surface properties of the various structures and habitats in the tidal area therefore lead to corresponding patterns and textures in the radar image. A major difference is seen between water-covered areas and exposed areas such as sediment surfaces and biogenic structures. Although the water surface appears highly variable due to currents, wind, and waves--sometimes in interaction with surface active agents such as biofilms--it can be clearly distinguished from the emerged tidal flats, especially if the edges are markedly distinct. Even from gradual transitions, which are also common in tidal areas, visual references to the surface morphology can be obtained. On the flats, residual water trapped in hollow surface structures helps to detect or identify geomorphic surface characteristics from TerraSAR-X images, such as depressed areas, bedforms, or draining systems. Residual water also contributes to the identification of typical large-scale structures and habitats with specific roughness properties such as mussel beds, fields of shell detritus or mud fields. Associated puddles caught in the humpy sediment surface of a mud field or pools within mussel beds are characteristic features reflected by specific patterns of backscatter in the SAR image.
## 3 Qualitative Analysis of the Application of AR in Industrial Assistance and Training
### AR for Manufacturing Process Analysis
AR, with its capacity to provide real-time visualisations, is distinguished by its adaptability and flexibility, enabling the constant optimisation of quality protocols and the agile adaptation of quality standards to the changing dynamics of the industrial sector. Furthermore, AR's intrinsic capacity to integrate virtual information into real-world contexts provides a unique perspective for the identification and correction of deficiencies in production processes. Resource allocation strategies in industry are a crucial aspect that determines the efficiency and effectiveness of production processes. A concrete example of this integration can be found in the work of [PERSON] et al. [57], where AR was used for real-time motion capture. This enabled a detailed understanding of equipment and operators, which in turn enabled a more accurate allocation of tasks and resources. As a result, the right people were assigned to specific functions, optimising the efficiency of manufacturing processes. The real-time visualisation of production processes through AR enables the detailed analysis of resource requirements and consumption at each stage, facilitating more accurate planning and optimal resource allocation. This approach minimises waste and maximises utilisation, as it allows for the identification of areas where resources can be more efficiently allocated. Finally, _energy efficiency_ analysis AR offers a unique platform to visualise, monitor and analyse energy consumption in real time, resulting in better energy management and control. The study by [PERSON] et al. [55] implemented AR to analyse energy efficiency by leveraging 3D spatial mapping and RGB-D data. This approach enabled a detailed visual representation of energy flows and consumption patterns in an industrial context. The ability to visualise this data in a three-dimensional format provided a clear view of how and where energy was consumed, identifying areas for potential improvement. The application of AR in energy efficiency analysis is not limited to visualisation; the integration of deep learning-based algorithms and data analytics techniques into AR allows for a more accurate and contextual interpretation of energy metrics. This can lead to the identification of patterns and trends that might otherwise go unnoticed, which in turn can lead to more effective optimisation strategies. In addition, the ability to overlay real-time data and analytics on a visual representation of the industrial environment can help engineers, technicians and other professionals better understand how their actions and decisions impact energy consumption. This holistic view can foster a greater responsibility and awareness in energy management, driving changes in practices and behaviours towards more sustainable and efficient energy use.
## 3 Results  
### Optimization of Floral Fragrance Pattern Analysis in Flowering Stages of Aster yomena Using E-nose  
Flowers emit different fragrances at various stages of flowering which is species specific [34]. To initially identify the flowering stage suitable for the analysis of the flower scent of the _Aster_ species, _'Aster yomena'_ was selected among nine _Aster_ species to perform E-nose. Multivariate statistical analysis (PCA, DFA) of the E-nose sensor data set and the relative fragrance intensity (RAI) were analyzed. E-nose data for each flowering stage of _Aster yomena_ were depicted as a two-dimensional PCA score plot using two significant principal components (PC1 and PC2) that accounted for 94.349% and 4.887% of the total variance of 99.236% (Figure 2a). In the PCA plot, the flower scent of _Aster yomena_ exhibited a distinct pattern according to the flowering stage. It was found that the scent pattern S3 and S4 were similar compared to other stages because of their overlapping parts. From these results, it could be confirmed that the characteristics of fragrance varied depending on the stage of flower development.  
DFA score plots of the _Aster yomena_ E-nose data were plotted as two-dimensional DFA score plots with two principal components (DF1 and DF2) accounting for 71.406% and 24.804% of the total variance of 96.21% (Figure 2b). Relative aroma intensity (RAI) of the E-nose is expressed as the distance between blank (air) and control (sample). A higher RAI value indicates that the aroma intensity of the sample is stronger [34]. The RAI value for each flowering stage was different for each stage (Figure 2c). As the flowering stage progressed, the RAI value increased, and the highest RAI value was 0.48 during the full flowering period. Therefore, based on these results, the standard for flower fragrance analysis of _Aster_ species was set as the full flowering stage with the highest relative aroma intensity.
# Exploring the Utility of 3-D-printed Laboratory Equipment
## 2 Material and methods
### Motion and Displacement Analysis  
For accurate performance analysis of both linear stages and laboratory jack, a laser displacement sensor (Keyence LT-9010M, Itasca, IL, USA) and a digital caliper (Mitutoyo 500-196, Aurora, IL, USA) were used for accurate measurement. The laser displacement sensor provided 0.1-\(\upmu\)m measurement accuracy within a total range of 600 \(\upmu\)m for the stage movement analysis; the digital caliper provided 10-\(\upmu\)m measurement accuracy with 15 cm of total stroke, so all deflection measurements except for the miniature laboratory jacks were conducted with the digital caliper. Each measurement was repeated three times for the statistical report. For linear stages, displacement was measured for rotational angle of the knobs versus linear translation. In addition, linear translation per revolution was continuously measured through the whole stroke. Repeatability tests for stages in both scales were also conducted  
Figure 1: A variety of 3-D printed linear stages. **(A)** A 2 \(\times\) 2-inch 1-D stage that provides a stroke of 1.08 mm with one rotation of the knob. **(B)** Modification of the two combined stages that was uniquely designed for a multispectral imaging system, using a parallel positioning concept. The aim of this stage is to provide 2-D motion while maintaining a low stage profile. **(C)** XYZ stage assembled by combining three 2 \(\times\) 2-inch 1-D stages.  
by turning the stage three revolutions in one direction and then back in the reverse direction while continuously measuring the linear translation per revolution. For the laboratory jack, combinations of calibration masses (Neewer stainless steel set, Amazon, Seattle, WA, USA) were used to generate 100, 200, 500, 700, and 900 g of mass to replicate a vertical load on the jack.
# CNN-Based Defect Inspection for Injection Molding Using Edge Computing and Industrial IoT Systems
## 4 Experiment and Result Analysis
### Evaluation Metrics  
We calculated the receiver operating characteristic (ROC) curve, Matthews correlation coefficient (MCC), accuracy, and F1-score to evaluate the performance of the classifier for bearing defects in noisy situations. The MCC is used in machine learning as a measure of the quality of binary and multiclass classifications. It takes into account true and false positives and negatives and it is generally regarded as a balanced measure, which can be used even if the classes are of very different sizes. The MCC equation is:  
\[\text{MCC}=\frac{|\text{TP}|*|\text{TN}|-|\text{FP}|*|\text{FN}|}{\sqrt{(|\text {TP}|+|\text{FP}|)(|\text{TP}|+|\text{FN}|)(|\text{TN}|+|\text{FP}|)(|\text{ TN}|+|\text{FN}|)}} \tag{1}\]  
The ROC curve is a widely used method of evaluating the effectiveness of a diagnostic method. It represents the relationship between sensitivity and specificity on a two-dimensional plane. The larger the area under the ROC curve, the better the model. Sensitivity and specificity can be expressed by the following equation.  
* Confusion Matrix: A matrix that shows the predicted class result compared to the actual class at once;
* Positive (=Normal Status): Normal situation that the quality manager wants to maintain (OK);  
Figure 4: Vision Sensors used in Proposed System.  
Figure 5: Product on rail.  
Negative (=Anomaly): Unusual situation in which the quality manager needs to be involved (NG);
* False Positive (=Type I Error = Missing Error): A situation where AI misses when a failure occurs (FPR);
* False Negative (=Type II Error = False Alarm): A situation where AI reports a failure even though it is not a failure (FNR).  
Specificity is the rate at which the model recognizes false as false. The equation is as follows:  
\[\text{Specificity}=\frac{\text{TN}}{\text{TN}+\text{FP}} \tag{2}\]  
Recall is the proportion of the true class to what the model predicts as true. The parameters recall and precision have a trade-off. Recall, also called sensitivity, can be expressed as follows:  
\[\text{Recall (Sensitivity)}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{3}\]  
Precision is the ratio of the true class to what the model classifies as true. The equation is as follows:  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{4}\]  
Accuracy is the most intuitive indicator. However, the problem is that unbalanced data labels can skew the performance. The equation for this parameter is the following:  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{FP}+\text{FN}+ \text{TN}} \tag{5}\]  
The F1-score is called the harmonic mean, and if data labels are unbalanced, it can accurately assess the performance of the model. The equation is given as follows:  
\[\text{F1-score}=2\frac{\text{Precision}*\text{Recall}}{\text{Precision}+ \text{Recall}} \tag{6}\]
## 4 Experiments and Analysis
### Experiment2: Validation of Joint Classification Network  
Using the network proposed in Figures 4 and 5, experiments are conducted on simulated images and real data. The parameters of the network are the same as previous experiments and the experimental process is as follows: (i) Using the joint classification method proposed in this paper, the binary images of highlight and shadow are input in pairs into the feature extraction network shown in Figure 4 for feature extraction; the network shown in Figure 5 is used to complete feature fusion and classification; (ii) the feature extraction network shown in Figure 4 is used to input the segmented highlight, shadow, and original images respectively, and realize classification process through this single network.  
The average classification accuracy obtained after ten experiments is shown in Table 5. Results show that the classification accuracy obtained by feature extraction using the original image of the object is the lowest with 67.1% of real data, followed by the shadow image of the object with 81.3%. When using highlight images for object classification, the accuracy of simulated data is increased by 11.4% and 6.6%, respectively. Compared with original and shadow images, the accuracy of real data is increased by 18.6% and 4.4%, respectively. The joint classification method proposed in this paper has the highest classification accuracy, reaching 93.1% and 90.8% respectively. The reason may be that the segmented images are binary, and the extracted features are shape features. In some angles, different objects will have the same shape, which is easy to cause the possibility of misclassification. The method proposed in this paper uses the feature information of both two parts at the same time to get higher classification accuracy.