esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this paper we have shown how to program the network at run time for bulk data transfer jobs using AmoebaNet and BigData Express application. To achieve the QoS-guaranteed network services for scientific campus networks, we applied SDN technology for AmoebaNet. In this paper we tested the AmoebaNet solution with three different test cases to achieve the goal of a dynamic programmable network for large-scale scientific data transfer. The evaluation of test cases clearly showed that the AmoebaNet allowed applications to dynamically program the networks at run-time for bulk data transfer jobs, and it could provide \"Application-aware\" networking services. It was also able to support differentiated service to ensure the required QoS services for priority traffics. It provisioned the QoS-guaranteed network paths between two ends with the fastest possible time and it also provided the enterprise level network services along with the integration of ESNet OSCARS WAN connection services. Ideally, it is very import to compare the AmoebaNet solution with other available solutions with respect to the performance and capabilities. However, these comparisons are very difficult for us because of the following reasons. First, there are a limited number of solutions available to provide the dynamic network programmability feature for bulk data transfers in large-scale distributed scientific environments. Second, although there are many SDN research projects and efforts, very few projects have developed software packages or systems that can be practically deployed. In future work, we will work on the enhancements of current architecture of AmoebaNet. These features includes: (a) new primitives for advance reservation systems; (b) generalized scheduler that coordinates with multiple AmoebaNet's services located in different sites; (c) enablement of multi-domain functionality; (d) improvement of existing algorithms; and (e) per protocol and per flow based QoS path computation.  
Conceptualization, S.A.R.S.; methodology, S.A.R.S. and S.-Y.N.; software, S.A.R.S.; validation, S.A.R.S.; formal analysis, S.A.R.S.; resources, S.-Y.N.; writing--original draft preparation, S.A.R.S.; writing--review and editing, S.-Y.N.; supervision, S.-Y.N.; funding acquisition, S.-Y.N.  
Figure 9: Evaluation results of the parallel submitted tasks.  
**Funding:** This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2008-00458).  
**Acknowledgments:** This work has been performed while the authors were participating in the joint-project between Fermi National Accelerator Laboratory and Korea Institute of Science and Technology Information.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Quantum Coding via Quasi-Cyclic Block Matrix
## 2 Preliminaries  
Some relevant notation and basic construction methods for quantum error correction codes are first briefly reviewed below.
## 4 Experimentation and Results
### Statistical Analysis
#### 4.5.2 Statistical Tests  
We used a two-sample \(t\)-test for the accuracy values across the training, validation, and test sets to determine whether the observed variations in performance measures (such as accuracy, precision, and recall) between these sets were statistically significant. Statistical significance was determined at a significance level of \(\alpha=0.05\). The results show minimum variation in generalization, which reveals that while the differences between the validation and test sets were significant (\(p=0.04\)), the differences between the training and validation sets were not statistically significant (\(p=0.13\)).  
Figure 14: Overview of latency and computing cost.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Metric** & **Value** \\ \hline Average Detection Time & 0.050 s per frame \\ Average Memory Consumption & 200 MB per inference \\ CPU/GPU Load & Manageable for deployment \\ \hline \hline \end{tabular}
\end{table}
Table 9: Overview of latency and computing cost.  
When paired with statistical studies, the assessment findings from the YOLOv5 model show how efficient it is at monitoring and detecting products in retail settings. The performance measurements are sufficient to support the validity of the proposed method despite their apparent modesty. The model's promise for real-world applications is further supported by its accuracy and dependability across many datasets, which show its adaptability to various retail settings. Furthermore, by confirming the importance of our technique in developing retail analytics and offering insightful information about customer behavior, these results provide the foundation for future improvements and refinements.
## 1 Introduction
### Overview  
In this study, we seek to show that the difficulties in using remote sensing to determine the \(G\times E\times M\) factors affecting crop growth are strongly connected to variability in the relationship of satellite measurments and crop state variables and that the variability in the relationship is in a large part caused by site and growth stage specific factors. In order to achieve these objectives, this study uses field-scale crop growth model simulations powered by accurate agromanagement information and collocated with satellite data at the Mead, Nebraska Ameriflux sites, supplemented by ground-truth data from additional sites for validation. Crop growth model simulations are used from only the Mead, Nebraska Ameriflux sites because geolocated agromanagement information, vital [66] to strong simulation performance, is difficult to collect, partially due to farmer concerns about data privacy [68], limiting available information about commercial-sized plots. The availability of collocated crop growth model simulations allows us to (a) analyze the sensitivity of the genotype x management (\(G\times M\)) factors retrieval by the satellite to variability in the relationship of satellite measurments and crop state variables and (b) use time-series analysis to analyze the uncertainty caused by this variability. Furthermore, the collocated crop growth model simulations are used to demonstrate the possibility of training and testing agricultural remote sensing algorithms with farmer-collected agromanagement data across a wide range of spatiotemporal variability, following the concept we introduced in [67] at the regional scale. Specifically, as in [67], the crop growth model simulations based on the provided data can be used to train and test remote sensing retrieval algorithms and, with sufficient farmer participation, a large swath of the spatiotemporal variability of the secondary factors affecting the retrievals can be covered. This dataset would allow further research to find methods to optimally use available weather, soil, and remote sensing data to create algorithms to map the regional-scale variability in \(G\times E\times M\). As a result, by using crop growth model simulations at a fixed number of sites where the \(G\times M\) parameters are known, a remote sensing retrieval algorithm could be trained to map \(G\times M\) parameters where they are unknown and where no high quality collocated crop growth model simulations are available.
# Comparing PlanetScope and Sentinel-2 Imagery for Mapping Mountain Pines in the Sarntal Alps, Italy
## 3 Results  
### GLCM Window Size Optimization  
The GLCM window size showed a noteable effect on the overall accuracy of the PS and S2 feature spaces (Figure 4). In general, overall accuracy increased at small GLCM window sizes for both satellite feature spaces. The effect of larger GLCM window sizes showed a stronger impact on overall accuracies of PS (0.82-0.89) than S2 (0.86-0.90) feature spaces. Thereby, S2 approaches the maximum OA at a GLCM window size of 9 pixels (90 m) and saturates afterwards. In comparison, the OA of PS rises until a GLCM window size of 29 pixels (87 m) and decreases then with increasing window sizes. Within this range, the GLCM secondary statistics can best describe the mountain pine stands in the study area and determine the optimal GLCM window sizes (GLCMMAX) for PS (29 \(\times\) 29 pixels) and S2 (9 \(\times\) 9 pixels).
## 3 Experiments and Result Analysis
### Evaluation Criteria  
For evaluating the performances of different classification methods, the recall rate, overall accuracy (\(OA\)), validation accuracy (VA) and kappa coefficient (Kappa) are considered to perform comparison.  
The overall accuracy can be defined as follows  
\[OA=\frac{M}{N}, \tag{29}\]  
where \(M\) is the total number of pixels that correctly classified, and \(N\) is the total number of all pixels. Similarly, VA is the proportion of validation samples that are correctly classified to all validation samples. The recall rate can be written as follows:  
\[Recall=\frac{X}{Y}, \tag{30}\]  
Figure 4: Location maps and Ground truth map of the experimental site.  
where \(X\) is the number of samples that are correctly classified for a certain class, \(Y\) is the number of samples of this class.  
The kappa coefficient arises from the consistency test and is commonly used to evaluate the classification performance, it measures the consistency of the predicted output and the ground-truth. Here, we use kappa coefficients to evaluate the entire classification accuracy of the model. Unlike \(OA\) and recall rate that only involve correctly predicted samples, the kappa coefficient considered various missing and misclassified samples that located at the off-diagonal of confusion matrix. The kappa coefficient can be calculated as follows:  
\[Kappa=\frac{OA-P}{1-P},P=\frac{1}{N^{2}}\frac{M}{i}s_{i}s_{i} \tag{31}\]  
where \(N\) is the total number of samples, \(M\) is the number of crop categories, \(s_{i}\) and \(s_{i}\) are, respectively, the sum of the \(i\)-th row and \(i\)-th column elements of confusion matrix.
## 4 Methodologies and Implementation  
### Research on the Application of DDPM in Biomedical Image Segmentation  
The application of DDPM in biomedical image segmentation has emerged as a promising avenue, addressing the challenges associated with noisy and complex medical images. Research in this domain focuses on leveraging DDPM not only as a standalone segmentation tool but also as a comprehensive framework for image preprocessing and segmentation algorithms. This comprehensive approach aims to enhance the accuracy and reliability of biomedical image segmentation, critical for applications such as disease diagnosis and treatment planning. In this detailed exploration, we will delve into the key aspects of research applying DDPM to biomedical image segmentation, incorporating both image preprocessing and segmentation algorithms.  
Figure 2: Schematic diagram of brain MRI glioma segmentation.
## 2 Materials and Methods
### Lidar Data from the GEDI Mission  
NASA's Global Ecosystem Dynamics Investigation (GEDI) is a large footprint lidar system that has been mounted on the International Space Station (ISS) since 2018 and generates waveforms. GEDI provides measurements of forest vertical structure in temperate and tropical forests between 51.6\({}^{\circ}\) north and south latitude. The overall objective of the mission is to determine the effects of changing climate and land use on ecosystem structure and dynamics [15].  
The GEDI laser system consists of three lasers: one that is split into two lasers (coverage laser) and two full-power lasers that remain unchanged. The tracks are located within a \(\sim\)4.2 km wide strip spaced \(\sim\)600 m apart. The footprint spacing per track is 60 m. The GEDI data consist of footprint and gridded datasets that contain information about the 3D properties of the vegetation. These data are associated with different levels of data processing. For the analyses in this paper, we used the Level2A data, which contain ground elevation, canopy top height, and relative height percentiles (here, we used a relative height at 95% (RH95)) [15].  
The lidar data used in this study were collected worldwide between 2019 and 2020. Only the data located inside the Amazon rainforest ecoregion [22] were selected for this analysis. Each GEDI footprint has a diameter of \(\sim\)25 m. The quality of each GEDI shot is checked by a \"quality flag\", which allows one to easily remove erroneous and/or lower quality waveforms [23]. After filtering, about 110 million individual GEDI measurements were available to study the structure of the Amazon rainforest. It should be noted that the temporal information in the GEDI data was not used in this study. The GEDI waveforms provide a structure-based snapshot of forest aboveground biomass and productivity that can be disentangled using the FORMIND forest model.
## 6 Conclusions  
To address the mission scheduling problem of multi-subsystem coupling in the process of AEOS Earth observations, RS for AEOS with multi-subsystem coupling was proposed in this paper. This method solves the scheduling problem through state prediction and scheduling strategy reasoning. From the analysis of a single condition, it can be deduced that the RS we proposed can realize the selection of a suitable scheduling strategy according to different coupling states. The selected scheduling strategy also obtained a better objective function value \(J\), which embodies the role of knowledge reasoning in the scheduling method. Compared with the heuristic methods, RS gained a better scheduling result while having the same calculation time. Through a comparison with the meta-heuristic method (TS), RS could obtain a solution close to that of the meta-heuristic method with a significant reduced calculation time. Overall, RS had good calculation results and high computation efficiency in scheduling Earth observation missions of AEOS with multi-subsystem coupling.  
Conceptualization, Y.D. and C.H.; Methodology, C.H.; Software, C.H.; Validation, C.H., H.L. and Y.L.; Formal analysis, H.L. and Y.L.; Investigation, C.H.; Resources, C.H.; Data curation, C.H.; Writing--original draft preparation, C.H.; Writing--review and editing, H.L. and Y.L.; Visualization, C.H. and H.L.; Supervision, Y.D. and H.L.; Project administration, C.H.; Funding acquisition, Y.D. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data used to support the findings of this study are available from the corresponding author upon request.  
This work was partially supported by the Key Laboratory of Spacecraft Design Optimization and Dynamic Simulation Technologies, Ministry of Education.
# An Effectively Finite-Tailed Updating for Multiple Object Tracking in Crowd Scenes
## 3 Problem Formulation and the Approach
### The Proposed Updating Scheme  
The appearance feature plays an important role in accurately tracking the object. When the object is occluded by the environment or other objects, the update strategy of FairMOT and some other current methods greatly limits us from obtaining a good appearance template. The appearance update mechanism of the FairMOT and some other current methods is to linearly combine the Re-ID feature of the current frame and the accumulated appearance template of the previous frame. This update mechanism is not suitable for MOT in dense scenes, which will cause a serious misidentification of the current object if the appearance template of the previous frame is incredible, owing to some severe occlusions. Compared to the FairMOT algorithm, we propose a novel appearance template update mechanism, which uses the Re-ID feature of the current frame and multiple previous frames' appearance templates to obtain the actual appearance template of the current frame. We use a dynamic method to assign weights to the appearance templates of these frames. In this way, we can get a robust and distinguishable appearance template. Even if the previous frame is incredible, owing to some severe occlusions, our proposed method can still identify the object in the current frame successfully. The comparison between our proposed method and the current method is shown in Figure 2. The \(T_{i}\) here represents the i-th frame to be input into the feature-extraction network.  
Herein, we propose a dynamic weight-distribution method. In this method, we first set the weight of the \(T_{n}\) frame \(\gamma\) to 0.1, so that the appearance template we obtained can adapt to the changes in the appearance of the object. Then, we set the weight of the \(T_{0}\) frame \(\mu_{0}\) as high as possible to ensure that its appearance information will not decay too quickly over time. \(\mu_{0}\) is set to 0.6 in our experiment. For the rest of the frames, we dynamically distribute their weights according to the values of \(\gamma\) and \(\mu_{0}\). Through this weight-distribution strategy, we can get a more robust appearance template to improve the identification performance of the object in the current frame. The specific formula for our update mechanism is shown below.  
\[f_{n}^{{}^{\prime}}=\gamma f_{n}+\sum_{i=0}^{N-1}\mu_{i}f_{i} \tag{7}\]  
\[\gamma+\sum_{i=0}^{N-1}\mu_{i}=1 \tag{8}\]  
We take \(f_{i}\) as the appearance template obtained in the \(T_{i}\) frame, \(f_{n}\) as the Re-ID feature obtained in the current frame, and \(f_{n}\) as the updated actual appearance template of the current frame. \(\gamma\) and \(\mu_{i}\) are hyperparameters.  
We explore how many frames the finite-tailed window has in our update mechanism in the following experiments. The specific experimental results can be seen in the Section 4.3 in the experimental part.
###### Abstract  
Numerous hydrogels made of poly-\(\gamma\)-glutamic acid (\(\gamma\)-PGA) and various cross-linkers have been explored, but only limited data on hydrogels made of \(\gamma\)-PGA and poly(ethylene glycol) (PEG) are available. In this study, \(\gamma\)-PGA, a biodegradable and edible biopolyamide, was successfully cross-linked with selected PEGs to obtain a series of hydrogels. The swelling behaviour of these hydrogels was investigated under various pH conditions. It was also found that the structure of the cross-linker (linear or branched) affected the hydrogels' swelling behaviour. In addition, in disc diffusion assay, hydrogel discs loaded with antibiotic were tested against _Staphylococcus aureus_ and _Escherichia coli_. Prolonged activity of hydrogels loaded with antibiotics in comparison to paper discs containing antibiotics was observed. Moreover, the protective effect of hydrogels on entrapped probiotic cells subjected to low pH was investigated. The hydrogel swelling ratio and amount influenced the survival rate of the protected bacteria. Considering potential biomedical applications of hydrogels, cytotoxicity was evaluated towards two cell lines, MSTO and PANC 1.  
hydrogels; poly-\(\gamma\)-glutamic acid; poly(ethylene glycol); probiotics Article Synthesis of Hydrogels Made of Poly-\(\gamma\)-Glutamic Acid (\(\gamma\)-PGA) for Potential Applications as Probiotic-Delivery Vehicles
# Research and Analysis of MEMS Switches in Different Frequency Bands
## 3 The Research Status of MEMS Switches in Different Frequencies
### Research Regarding Insertion Loss and Isolation of MEMS Switches in Different Bands
#### 3.1.7 Some Materials Used in MEMS Switches in Different Frequency Bands  
Table 7 summarizes some materials used in different bands. Au was generally used as the conductive material of switch due to its low resistivity and low Young's modulus, leading to low-actuation voltage and low insertion loss in different bands. However, the low elastic modulus of Au tends to cause the reliability problem of stiction of the switch, resulting in a decrease in reliability; therefore, Cu or alloy were also used as the bridge material of the switch to increase the hardness. Si\({}_{3}\)N\({}_{4}\) or SiO\({}_{2}\) were generally used as the dielectric layer of the switch material, but in order to further improve the isolation of the switch in high frequency, materials such as Ta\({}_{2}\)O\({}_{5}\) and HfO\({}_{2}\) with high dielectric constants, were adopted. High-resistance silicon and GaAs are used as the materials of the substrate to achieve low insertion loss.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Number** & **Band** & **Insertion Loss** & **Isolation** & **Reference** \\ \hline
2 & 500–750 GHZ & 0.2 dB & \textgreater{}25 dB & [58] \\ \hline
3 & 500–750 GHZ & 0.7–2.7 dB & 17–25 dB & [59] \\ \hline
5 & 500–550 GHZ & 0.5–1.5 dB & - & [60] \\ \hline
6 & 500–750 GHZ & 2.5–3 dB & 19–20 dB & [61] \\ \hline \end{tabular}
\end{table}
Table 6: The performance parameters of MEMS switches for submillimeter applications.
## 3 Result and Discussion  
### Visualization Characteristics of FEGLD Plasma  
Since the plasma and liquid surface interaction area could be varied by changing the discharge gap, Figure 2 shows the discharge images of FEGLD at the discharge gap of 10 mm, 13 mm, and 15 mm, captured by a Canon 550D digital camera with 500 ms exposure time, under the measurement conditions of 3.5 L/min gas flow rate and 30 kV pulse peak voltage. As Figure 2 shows, when the discharge gap is set at 10 mm, there is a discharge area over the liquid surface with the diameter larger than 20 mm, and no flames or unstable discharge model transition can be observed. In the middle of this, a mean discharge area has the most intense emission intensity. While enlarging the discharge gap distance from 10 mm to 15 mm, as shown in the figure, there is an obvious contraction in the discharge area over the liquid surface.
# Dynamic Multi-Compartment Vehicle Routing Problem for Smart Waste Collection +
## 5 Methods and Results
### Genetic Algorithm for Solving the DM-CVRP  
DM-CVRP is an NP-hard problem; exact-solving methods have become very time-consuming. So we have selected the genetic algorithm as an optimization tool in this article. The choice is mainly due to its simplicity, use ease, and efficiency in high-complexity problems. In this section, we adapt the genetic algorithm for each static M-CVRP at each time slice to solve the DM-CVRP.  
Our system designed to solve DM-CVRP is composed of two central systems:  
* The first preprocessing phase is responsible for receiving and adding new stations and divides the working day into a predefined number of time slices. Each slice represents a static VRP. This system manages all the entrances and exits and organizes the GA.
* The second solving phase, which contains the route optimization process and solutions, executes the GA to solve the static VRP in each time slice generated by the preprocessing step. To this end, a hybrid GA was proposed to find the best solution for each given time slice until the stopping condition.  
[PERSON] developed a Genetic Algorithm in 1975. The algorithm's idea is to translate the problem into a genetic representation or a chromosome, which is used to define a solution. Each solution is associated with a numerical value called fitness according to the function of this solution. At each iteration, a certain number of solutions are selected according to their fitness to create new ones by applying genetic operators (crossover and mutation). At the end of the iteration, a replacement step is applied to select the solutions for the next generation. We use GA in this paper, and we hybrid it by heuristic methods.  
The general structure of the Genetic Algorithm is as follows:  
1. Encoding the solutions
2. Generate the initial population
3. Repeat  
* Selection
* Crossover
* Mutation
* Replacement  
Until the condition stops.  
Our hybrid genetic algorithm is shown in Figure 8.
## 3 Approach and Analysis: Architecture and Methodology
### Methodology: Machine Learning-Based Approach
#### 3.3.2 Environment  
Regarding environment, dog sounds were acquired, and their features were analyzed in order to learn the features of different types of dog sounds. Environment comprises input sound, learning layers, label, and rewards.