esa-sceva/satcom-chunk-collection huggingface dataset:
# A Very High-Resolution Urban Green Space from the Fusion of Microsatellite, SAR, and MSI Images
## 3 Results
### Central Jakarta  
The Central Jakarta region is the smallest among the five administrative regions. It covers only 47.56 km\({}^{2}\), and approximately 13.25 km\({}^{2}\) of this area, which is roughly 27.85%, is classified as urban green space (Figure 8). This urban green space consists mainly of mixed areas (Figure 9), which cover 4.93 km\({}^{2}\), followed by grassland spanning 3.86 km\({}^{2}\); shrub-covered areas of 2.98 km\({}^{2}\); and a small, forested area measuring 1.48 km\({}^{2}\).  
Figure 8: The portion of urban green space in Central Jakarta.  
Figure 7: Urban green space of West Jakarta. The base map is CartoDB Dark Matter. QGIS version 3.22.8-Bialowieza was used to create the map.  
Despite the limited availability of urban green spaces in Central Jakarta, it boasts the highest number of city parks compared to the other five administrative regions. Central Jakarta is home to five city parks, namely Taman Medan Merdeka (Monas), Taman Menteng, Taman Suropati, Taman Situlembang, and Taman Lapangan Banteng. All of these city parks are open to the public. The most famous urban green spaces that are open to public in the Central Jakarta are the Gelora Bung Karno Complex and the National Monument (Monas). Gelora Bung Karno Complex consists of multiple sports centers and city forests, while the Monas is an open green space consisting of grass, a small forest, and mixed areas.  
There are 295 small interactive parks, environmental parks, and other urban green spaces, such as green lane roads and water banks. Additionally, there are four open spaces specifically designated for cemeteries.
# A Review on the Recovery and Separation of Gallium and Indium from Waste
## 1 Introduction
In very highly developed countries, probably 8% of it ends up in landfills or is incinerated. Most of it is mixed with other waste and subjected to incomplete recycling, during which appropriate conditions are not provided for the recovery of valuable materials. The rest, on the other hand, is either refurbished and found a new life in less developed countries, or illegally smuggled under the guise of becoming scrap (it is estimated that around 7-20% of WEEE is smuggled). In middle- and low-income countries, the situation is much worse. Those countries either do not yet have a fully developed system for managing this waste, or there is no such system at all, and e-waste is managed by the informal sector. Therefore, it is very often processed in inappropriate conditions, which causes serious damage to the health of not only the employees themselves but also people (mainly children) living in the vicinity of such processing plants. Of course, incorrect handling of WEEE also threatens the environment due to the numerous hazardous substances it contains [3]. It is also worth noting that the Basel Convention recognizes most electronics as hazardous waste [2]. In addition to the benefits that recycling will bring to the planet and the health of its inhabitants, economics also come into play. It is estimated that the value of all materials recovered from WEEE in 2019 was approximately USD 10 trillion, and the natural resources available to us are depleting [3]. The amount of gallium in all known bauxite deposits is estimated to be at 1 megaton; however, less than 10% of gallium in the bauxite and zinc resources is considered to be potentially recoverable [16]. The above-mentioned facts encourage the improvement of gallium and indium recovery methods from waste and the implementation of appropriate procedures of recycling. This work aims to present a comprehensive overview of the state-of-the-art references in the field of gallium and indium recovery from secondary resources. It reviews the scientific knowledge about methods of waste pretreatment (dismantling, sorting, crushing, and roasting), followed by hydrometallurgical techniques (leaching, extraction, ion exchange, sorption, and precipitation), and finally focusing on the production of pure metals or their oxides. Additionally, the work includes a discussion on assisted methods for leaching and separating gallium and indium from each other and other metals which distinguishes this review paper from others. An advantage and novelty of this study is a suggestion of research areas that require intensified scientific activities at the present time.
## 4 Results and Discussions
### Evaluation Metric  
This work used the mean squared error (MSE) shown in Equation (4) to evaluate the performance of the model, as the prediction of the MRR is a regression problem. Notably, the MSE was also the metric used in the competition. A smaller MSE means that an estimate is more accurate.  
\[MSE=\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y_{i}})^{2}, \tag{4}\]  
where \(n\) is the size of the estimates, \(y_{i}\) is the ground truth, and \(\hat{y_{i}}\) is the predicted result.  
Besides the MSE, the mean absolute error (MAE) that measures the difference between \(y_{i}\) and \(\hat{y_{i}}\) is another metric for the evaluation of our proposed method. Equation (5) shows the definition of the MAE. Like the MSE, a smaller MAE means that an estimate is more accurate.  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}|y_{i}-\hat{y_{i}}|. \tag{5}\]
# Product Demand Prediction with Spatial Graph Neural Networks
## 4 Experiments
### Data Analysis
By examining the mean deal probability across various groups within these features, we gain insights into how different factors influence transaction outcomes. The findings from this analysis are visualized in Figure 3, facilitating a more intuitive understanding of these relationships. * deal probability and region: Upon examining the relationship between deal probability and region, it is observed that the mean deal probability across all regions hovers around 15%. This uniformity suggests that while regional factors may influence the volume of ads, they do not significantly differentiate the likelihood of a deal being closed. This finding could indicate that other factors beyond geographical location play a more pivotal role in influencing deal probability. * deal probability and city: Similar to the observation with regions, the analysis of deal probability and city reveals that all cities also exhibit a mean deal probability of approximately 15%. This consistency across cities further supports the notion that the likelihood of closing a deal is not heavily dependent on specific urban centers, highlighting the importance of looking beyond geographic specific  
Figure 3: The histograms for mean deal probability in each group of corresponding variables, including region, user type, city, and parent category. * deal probability and top-level category: A more nuanced insight emerges from the relationship between deal probability and top-level category. The \"Services\" category stands out with the highest mean deal probability at 40%, followed by \"Transport\" and \"Animals\" at 25%. This distinction suggests that ads within the \"Services\" category are significantly more likely to result in a deal, possibly due to the inherent nature of services being in higher demand or more immediately consumable compared to physical goods. This disparity underscores the potential for tailoring strategies based on category-specific demand dynamics. * deal probability and fine-grain category: The analysis of deal probability and user type reveals a notable difference in mean deal probabilities between Private Users (15%) and Shop Users (5%). This discrepancy suggests that ads posted by private individuals are three times more likely to close a deal than those posted by shops. This could be attributed to a variety of factors, including perceived trustworthiness, pricing differences, or the nature of the goods and services offered by these user types.
# Elderly Fall Detection in Complex Environment Based on Improved YOLOv5s and LSTM
## 6 Experimental Results and Analysis
### Performance of CSI Fall Detection Algorithm Based on Improved LSTM Model  
In this study, as the target's posture changed from standing up to falling down, the changes in the CSI signals' amplitude were set as class 1 for data annotation, and as the target's posture changed from standing up to sitting down, the changes in the CSI signals'  
Figure 19: The fall detection effect of the improved YOLOv5s.  
Figure 18: The fall detection effect of the original YOLOv5s.  
amplitude were set as class 2, applying deep learning methods to the data and judging the current posture of the target.  
The dataset was divided into an 8:2 format, with 80% of the data extracted for iterative learning and 20% of the data used for detection. Then, the dataset formed by CSI amplitude data and corresponding labels was brought into the model for training. After training, the deep learning model performed algorithm verification on the previously intercepted test data, indicating the estimated accuracy of the model.  
In grid training, the initial learning rate was 0.012 and the forgetting factor was set to 0.1. The forgetting layer was pushed forward to reduce the influence of volatility and improve the learning rate to make the model learning more effective.
# A Mapping Model of Propeller RANS and LES Flow Fields Based on Deep Learning Methods
## 4 Experiments
### Experimental Results of Feature Extraction Model for Flow Fields
#### 4.2.3 Experiments of Model Performance and Feature Extraction  
After fixing the flow field size at 0.26 \(\times\) 0.26, we conducted comparative experiments with varying input velocity channel quantities and different feature extraction networks,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & _V\_axial_ Error Rate & _V\_tangential_ Error Rate & _V\_radial_ Error Rate &
\begin{tabular}{c} **Average Prediction** \\ **Time (Seconds)** \\ \end{tabular} \\ \hline All flow fields & 3.56\% & 8.89\% & 11.4\% & 6.78 \\
0.25 \(\times\) 0.25 & 2.73\% & 7.98\% & 9.59\% & 6.67 \\
0.255 \(\times\) 0.255 & 2.69\% & 7.70\% & 8.94\% & 6.61 \\
0.26 \(\times\) 0.26 & 2.72\% & 7.65\% & 8.89\% & 6.58 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of experimental results for different input flow field sizes.  
using a test dataset of flow fields with a size of 200. For both RANS and LES data, we trained two separate models using the same neural network architecture and compared their error rates individually.  
Tables 2 and 3 summarize the results of model performance on the testing set, which contains approximately 200 samples. From the tables, it can be observed that the single-velocity prediction model achieves approximately 0.3% higher accuracy compared to the three-velocity prediction model, but at the cost of a significantly increased processing time. Moreover, it is evident that the two-stream network outperforms the ResNet-18 feature extraction network in terms of accuracy of feature extraction, which highlights the advantages of incorporating temporal information for feature regression of propeller RANS and LES.  
It can also be observed that almost all the results exhibits a gradual increase in the error rates for the three velocity components. Upon analyzing the RANS and LES data, we found that the average values of the three velocities decrease sequentially, while the variances increase accordingly. Since neural networks require data normalization as a preprocessing step, involving subtracting the mean and dividing by the standard deviation, the normalization of the three velocity components amplifies prediction deviations disproportionately. Consequently, the current situation arises when the comprehensive error rate is calculated using Equation (4).
## 3 Results and Discussion
### Effect of Surface Material and Nutrient Composition on Biofilm Formation and eGFP Expression
#### 3.2.3 Plasmid Copy Number  
The stability of plasmids within _E. coli_ cells was assessed by high-performance liquid chromatography (HPLC; Figure 4j-l). The highest number of plasmid copies per cell was obtained in biofilms formed in TB medium on SST and PVC on day 1 (647 and 661 PCN-cell\({}^{-1}\), respectively; \(p<0.05\)), and on SIL on days 1 and 3 (590 and 615 PCN-cell\({}^{-1}\), respectively; \(p<0.05\)). This result is consistent with eGFP production. LB was the medium that contributed the least to plasmid maintenance within cells, achieving on average 63, 20, and 109 plasmid copies for SST, PVC, and SIL materials, respectively. In turn, biofilms formed in M9 ZB medium exhibited higher plasmid copy numbers than those developed in LB (on average 207, 182, and 307 plasmid copies for SST, PVC, and SIL, respectively). These results are not consistent with the eGFP production observed in biofilms developed in LB and M9 ZB media. Although plasmid stability is an important factor in heterologous protein production [73], our previous studies found no association between gene dosage and eGFP expression [24; 25].
# A Crypto Accelerator of Binary Edward Curves for Securing Low-Resource Embedded Devices
## 3 Proposed Hardware Architecture
### Memory Unit  
In order to efficiently handle intermediate and final results, the proposed architecture integrates a memory unit with a capacity of \(10\times m\) as illustrated in Figure 1. This memory unit plays a crucial role in storing and managing the data within the system. Each of the 10 memory locations holds data elements with a width denoted by m. The storage elements \(W_{1}\), \(Z_{1}\), \(W_{2}\), and \(Z_{2}\) are dedicated to storing the initial projective points, while \(T_{4}\), \(T_{1}\), \(W_{0}\), and \(T_{3}\) are utilized for storing the updated projective points. The intermediate results are stored in \(Z_{0}\) and \(T_{2}\). To ensure efficient data access and manipulation, the architecture incorporates a \(1\times 10\) demultiplexer (DEMUX) controlled by the WRITE_ADDR signal. This DEMUX facilitates the storing of different elements in their designated memory locations. Furthermore, two multiplexers, RF_1 and RF_2, controlled by the RF_1_ADDR and RF_2_ADDR signals, respectively, are employed to retrieve the required storage elements from the memory unit. These multiplexers enhance the flexibility and accessibility of the architecture's data-retrieval process. Both RF_1 and RF_2 have a size of \(10\times m\). The outputs of RF_1 and RF_2 are referred to as MUX_RF_1 and MUX_RF_2, respectively.
## 3 Results and Discussion
### Accuracy Rate for Inference Model  
The trained Caffe and Keras models were separately tested with 53,594 test images that included No DR, Mild DR, Moderate DR, Severe DR, and Proliferate DR. Then, the predictions were aggregated to separate as Healthy and Unhealthy. A No DR result is Healthy, whereas a Mild DR to Proliferate DR is Unhealthy. For example, for a Mild DR test image, a No DR result is counted as Healthy classification and Moderate DR is counted as an Unhealthy classification. We chose these two classifications because of our end intention for the envisioned device, which is to give the healthcare professional conducting the test and the patient one of two specific types of feedback: \"The eye appears to be healthy. Please consult an eye specialist if there are any concerns,\" or \"A visit to an eye specialist is recommended. The system detects diabetic retinopathy symptoms.\" In addition, because of the higher risk of a false negative result, specificity is very important for this application.
# Joint Location-Dependent Pricing and Request Mapping in ICN-Based Telco CDNs For 5G
## 5 Evaluation
### Experiment Results  
#### 5.2.1 Results of LDP  
Using the sites' congestion level under the BASE case as historical data, we first evaluate the performance of LDP. The relationship of the sites' congestion level and bandwidth price of four traffic patterns is shown in Figures 4-7, respectively. In general, variation of the sites' congestion level of Traffic Pattern 1 and 2 is obviously bigger than that of Traffic Pattern 3 and 4. Prices in the former two traffic patterns are set differently across all sites. In Figure 4, sites 1-6 is not as congested as sites 7-10, thus have a lower price. The prices of sites 7-10 are higher than those of sites 1-6. Note that among all the sites, the site with a higher congestion level has a higher price, conforming with the pricing function shown in Figure 3. In Figure 5, the prices are also location-dependent. Remember that in Algorithm 2, when prices are set differently in different locations, a site's congestion level is compared with the weighted sum of congestion level as reference value. Site 3 and site 7, the most congested sites in Traffic Pattern 2, contribute much to the weighted sum. Therefore, they are priced the same as the most congested sites in Figure 4. In Traffic Pattern 3 as shown in Figure 6, the traffic is distributed smoothly across different sites, the congestion level of every site is around 0.4. Thus, the prices are set as the same value based on the mean value of the congestion level. In Traffic Pattern 4, as shown in Figure 7, the sites' congestion level fluctuates around 0.6 within a very small range. Thus, the prices are set as the same value that is higher than that in Figure 6. The reason is that Traffic Pattern 4 has a higher mean value of the sites' congestion level.  
Figure 5: LDP prices in Traffic Pattern 2.  
Figure 6: LDP prices in Traffic Pattern 3.  
Figure 7: LDP prices in Traffic Pattern 4.
###### Abstract  
Commercially available autonomous photochemical reflectance index (PRI) sensors are a new development in the remote sensing field that offer novel opportunities for a deeper exploration of vegetation physiology dynamics. In this study, we evaluated the reliability of autonomous PRI sensors (SRS-PRI) developed by METER Group Inc. as proxies of light use efficiency (LUE) in an aspen (_Populus tremuloides_) forest stand. Before comparisons between PRI and LUE measurements were made, the optical SRS-PRI sensor pairs required calibrations to resolve diurnal and seasonal patterns properly. An offline diurnal calibration procedure was shown to account for variable sky conditions and diurnal illumination changes affecting sensor response. Eddy covariance measurements provided seasonal gross primary productivity (GPP) measures as well as apparent canopy quantum yield dynamics (\(\alpha\)). LUE was derived from the ratio of GPP to absorbed photosynthetically active radiation (APAR). Corrected PRI values were derived after diurnal and midday cross-calibration of the sensor's 532 nm and 570 nm fore-optics, and closely related to both LUE (\(R^{2}\) = 0.62, \(p\) < 0.05) and \(\alpha\) (\(R^{2}\) = 0.72, \(p\) < 0.05). A LUE model derived from corrected PRI values showed good correlation to measured GPP (\(R^{2}\) = 0.77, \(p\) < 0.05), with an accuracy comparable to results obtained from an \(\alpha\) driven LUE model (\(R^{2}\) = 0.79, \(p\) < 0.05). The automated PRI sensors proved to be suitable proxies of light use efficiency. The onset of continuous PRI sensors signifies new opportunities for explicitly examining the cause of changing PRI, LUE, and productivity over time and space. As such, this technology represents great value for the flux, remote sensing and modeling community.  
remote sensing; PRI; LUE model; eddy covariance +
Footnote †: journal: Sensors
## 3 Results
### Kaempfer Induces G2/M Arrest in HeLa Cells  
To determine whether Kaempfer affects HeLa cells' proliferation by arresting the cell cycle, DNA content of different phases of the cell cycle was evaluated and compared with the untreated control. Kaempfer treatment halts the cell cycle at the G2/M phase; the cells' population was raised in a dose-dependent manner from 19.4%, 32.8% to 46.7% at 30, 40 and 50 uM, respectively at 48 h, compared to 10.7% in the DMSO control. However, at 50 uM there was a concomitant increase in Sub-G1 population from 1.23% to 7.05% (Figure 2A,B).  
Figure 2: **(A)** Flow cytometry analysis: Analysis of DNA content of PI stained HeLa cells, treated with 30, 40 and 50 uM of kaempfer for 24 and 48 h, was compared with DMSO, which induced G2/M (Figure 2A,B).  
arrest cell cycle arrest with an increase in the sub-G0 apoptotic population. (**B**) Graphical representation of the cells' distribution in different phases of the cell cycle at 48 h is expressed as a percentage. (**C**) Annexin V/PI double staining kaempfer-induced apoptosis in HeLa cells after treatment (30 uM, 40 uM and 50 uM) for 24 and 48 h in comparison with DMSO control. Representative picture of dot plots showing different stages of apoptosis. Left lower quadrant (FITC-/PI-) = Viable cells, right lower quadrant (FITC+/PI-) = early apoptotic cells and right upper quadrant (FITC+/PI+) = late apoptotic cells. (**D**) Graph demonstrating % distribution of apoptotic cells in their respective quadrants. Early apoptotic cells proportions were increased at both time- and concentration-dependent manner compared to control. * represents \(p<0.05\); ** = \(p<0.01\), *** \(p<0.001\).
# More on Pipelined Dynamic Scheduling of Big Data Streams
## 6 Simulation Results and Discussion
### Load Balancing  
We conducted two sets of experiments regarding load balancing. In the first set, we try to find how the migrations affect the load balancing, so we computed the latency during the migration periods. The overall migration process required from 10 to 100 s, depending on the number of tasks migrated and the corresponding data (tuples) that they processed. We repeated the migration experiment for 20 times and for different cluster sizes and we regularly computed (every 5 s) the average standard deviation of the load being delivered to each node (see Figures 4-7). An increase of the standard deviation value indicated less load balancing among the system's nodes.  
The approach of [PERSON] and [PERSON] is based on using the link information through dataflow checkpoints. A timeout period is employed, where no data but only in-flight messages are transmitted. The timeout regulation helps [PERSON]'s and [PERSON]'s to have somehow better balancing results compared to the MT scheduler, as Figures 4-7 show. In the MT-Scheduler, the only regulation policy used is that the users are allowed to configure and regulate the data locality, in order to maintain execution of the tasks as close to the data. This cannot guarantee load balance. As Figures 4-7 show, this strategy suffers from higher imbalances. Our work was found to have smaller standard deviation values compared to the other schemes, thus better balancing. For the communications implemented within each sub-cluster, there is a one-to-one communication among the system nodes. Some imbalances incur (notice that some points have higher values as seen in Figures 4-7) in cases when some nodes are somehow more overloaded and the migrated tasks are not evenly redistributed to maintain fairness. However, one can notice that, because our communication strategy generally keeps load balancing among the nodes, there are only a few of these points.  
In the second set of experiments, we compute the average load balancing for a period of one hour (3600 s). Specifically, Figure 8a shows the average load balancing for clusters of at most 50 nodes, while Figure 8b presents the load balancing results for a cluster of 50-100 nodes. Apparently, as more and more data streams are generated and more migrations are involved, all the proposed schemes will suffer larger imbalances. Still, our scheme manages to have smaller increases in the standard deviation value. Notice in Figure 8a,b that the curve corresponding to our scheme appears to be growing slow and rather smoothly, as a result of the stepwise communication strategy being employed.  
Figure 4: Load balancing comparisons. \(Q=5\), 1 sub-cluster.  
Figure 5: Load balancing comparisons. \(Q=25\), 5 sub-clusters.  
Figure 6: Load balancing comparisons. \(Q=50\), 10 sub-clusters.  
Figure 7: Load balancing comparisons. \(Q=100\), 20 sub-clusters.