esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Test
### Experimental Procedure  
Measurement data from the combustion experiment were used to test the proposed method. The experimental procedure is summarized below.  
1. A simulation model with the same physical dimensions as those of the combustion system was set up to obtain POD basis vectors. Boundary conditions and the calculated model, which were close to the actual constraints, were set up. Calculation model parameter settings were the same as described in Section 3. In the mesh independency test, a mesh with 2,641,471 grid nodes was in the asymptotic range of convergence, and 10 measurement data on the plane at 0.65 m height were used to compare with the simulation result. The relative error between the simulation result and measurements was 1.3%. As a result, 2,641,471 grid nodes were used in this experiment test. Fuel velocities were set to 0.44, 0.54, 0.64, and 0.74 m/s. The excess air coefficient \(\alpha\) was set to 0.8, 0.9, 1, 1.1, and 1.2. A \(20\times 85\) sample dataset was established with the simulation results, and POD basis vectors were extracted from it. In this experiment, when the basis vector number and sensor number increased to six, reconstruction errors tended to be stable. Finally, optimal sensor placement was calculated by the proposed optimization algorithm.
2. As shown in Figure 9, 10 thermocouples can measure the temperature at the same time. The sensor placement optimization algorithm was tested by comparing the reconstruction results from optimal placement with those from three random placements. A combustion experiment was set up with a fuel mass flow of 1.5 m/s and an excess air coefficient \(\alpha\) of 1.25, and when combustion was stable, six temperature values were measured to reconstruct the temperature field. Another nine temperature values were used to compare with the reconstruction data. These temperature values were collected by two measurements. Sensor locations in different cases are shown in Figure 10. Furthermore, all temperature values in the possible measurement sites were collected to validate the overall reconstruction result. First, 10 temperature values in different locations were collected in a measurement; then, after changing sensor locations and ensuring that the measurement was stable, another 10 temperature values were collected. The measurements were repeated until  
Figure 9: Measurement sites.  
all values were collected from 85 sites. Each measurement took 10 s, and the averages of the measurement data were used to test the proposed method.
3. The measurement data and POD basis vectors were used to reconstruction the temperature field with the proposed method.
# Measuring Gas Flaring in Russia with Multispectral VIIRS Nightfire
## 4 Discussion
### Seasonality in Observation and Effect of COVID-19 Pandemic on Gas Flaring
* As Active Fire Products are more sensitive to lower-temperature hotspots, wildfire presence in the data cannot be excluded. Eastern Siberia is known for the frequent occurrence of wildfires which can coincide with places of human activity, such as remote oil fields. Most of the fields in the region are characterized by large gas flares at central processing units with lesser flares at well clusters and exploration sites, and it is the latter two which can have their FRP significantly overestimated if a wildfire occurs nearby. While a gas flare is a stable heat source which results in a circular pattern of AFP or Nightfire detections around the source, a wildfire can be characterized as sporadic, non-persistent, moving phenomenon, which can be used to filter it out. * Another source of noise in the data can be other hotspots present near gas flares, especially at downstream facilities (refineries, gas processing plants). They tend to have lower temperatures and thus are more likely to be detected with AFP than with Nightfire. Although complicated with known issues, initial analysis has shown that Active Fire Product data exhibit a strong correlation with Nightfire estimates, despite the presence of non-simultaneous observations and lack of detection frequency data. Indirectly, it also confirms an assumption that gas flares tend to have stable daily (or day-to-day) flaring pattern. With the application of cloud cover data and a wider dataset, it might be possible to use summer daytime VIIRS Active Fire Product observations to more accurately account for the existing gap in Nightfire observations.
# High-Performance Garbage Collection Scheme with Low Data Transfer Overhead for NoC-Based SSDC
## 2 Background
### Garbage Collection in SSDs  
The erase operation in flash memory differs significantly from that in other data storage types, such as hard disks. In flash memory, each memory cell must be initialized before future writes can occur, and erasure must be performed at the block level due to the physical structure of the memory. A key aspect of the erase operation is that it is executed in units of blocks; if a page within a block is marked for erasure, it is considered invalid until the entire block has been erased. Furthermore, overwriting data in flash memory requires a preceding erase operation because of the device's physical characteristics. When a page in a block needs to be updated, the existing page is invalidated, and the new data are written to another valid page. This operational mechanism implies that SSDs can contain many blocks with numerous invalid pages, leading to significant storage inefficiencies. To reclaim this storage space, blocks with a high number of invalid pages undergo a process known as GC. During this process, valid data from the target block is copied to another block--a procedure referred to as \"copy-back\"--after which the entire original block is erased.  
Figure 2 illustrates a snapshot of SSD where GC is in progress. Block\({}_{1}\) in channel\({}_{0}\) is full of invalid pages except one valid page which is page\({}_{1}\). This means a significant waste of storage, and it is desirable to initialize block\({}_{1}\) after moving page\({}_{1}\) in another place such as page\({}_{1}\) in channel\({}_{1}\) as indicated by the red arrow. This process is called copy-back. Once the copy-back operations are complete for the GC target block, an erase operation is applied to block\({}_{1}\) in channel\({}_{0}\) to reclaim the storage.  
Figure 1: Conventional SSD organization (FMC: flash memory controller).  
While the GC increases the available storage space, it does so at the cost of SSD execution time. That is, frequent copy-back operations lead to a significant increase in the write and read operations to the flash memory requested by the host. Also, the frequent GC leads to a reduction in the device's overall lifespan because NAND flash memory cells have a relatively short lifetime compared to other types of storage.
# Optimizing the Parameters of Long Short-Term Memory Networks Using the Bees Algorithm
###### Abstract  
Improving the performance of Deep Learning (DL) algorithms is a challenging problem. However, DL is applied to different types of Deep Neural Networks, and Long Short-Term Memory (LSTM) is one of them that deals with time series or sequential data. This paper attempts to overcome this problem by optimizing LSTM parameters using the Bees Algorithm (BA), which is a nature-inspired algorithm that mimics the foraging behavior of honey bees. In particular, it was used to optimize the adjustment factors of the learning rate in the forget, input, and output gates, in addition to cell candidate, in both forward and backward sides. Furthermore, the BA was used to optimize the learning rate factor in the fully connected layer. In this study, artificial porosity images were used for testing the algorithms; since the input data were images, a Convolutional Neural Network (CNN) was added in order to extract the features in the images to feed into the LSTM for predicting the percentage of porosity in the sequential layers of artificial porosity images that mimic real CT scan images of products manufactured by the Selective Laser Melting (SLM) process. Applying a Convolutional Neural Network Long Short-Term Memory (CNN-LSTM) yielded a porosity prediction accuracy of 93.17%. Although using Bayesian Optimization (BO) to optimize the LSTM parameters mentioned previously did not improve the performance of the LSTM, as the prediction accuracy was 93%, adding the BA to optimize the same LSTM parameters did improve its performance in predicting the porosity, with an accuracy of 95.17% where a hybrid Bees Algorithm Convolutional Neural Network Long Short-Term Memory (BA-CNN-LSTM) was used. Furthermore, the hybrid BA-CNN-LSTM algorithm was capable of dealing with classification problems as well. This was shown by applying it to Electrocardiogram (ECG) benchmark images, which improved the test set classification accuracy, which was 92.50% for the CNN-LSTM algorithm and 95% for both the BO-CNN-LSTM and BA-CNN-LSTM algorithms. In addition, the turbofan engine degradation simulation numerical dataset was used to predict the Remaining Useful Life (RUL) of the engines using the LSTM network. A CNN was not needed in this case, as there was no feature extraction for the images. However, adding the BA to optimize the LSTM parameters improved the prediction accuracy in the testing set for the LSTM and BO-LSTM, which increased from 74% to 77% for the hybrid BA-LSTM algorithm.  
Deep Learning (DL); Long Short-Term Memory (LSTM); Convolutional Neural Network (CNN); Bees Algorithm (BA); Bees Algorithm Convolutional Neural Network Long Short-Term Memory (BA-CNN-LSTM)
# Review of Photodetectors for Space Lidars
## 3 Space Lidar Detectors from Short-Wave to Mid-Wave Infrared
### InGaAs APDs  
InGaAs APDs in linear mode have been used in space lidars for laser wavelengths up to 2 \(\upmu\)m. They are used in the Methane Remote sensing Lidar Mission (MERLIN) at the 1.645 \(\upmu\)m methane absorption line wavelength, which is to be launched in 2028 [36]. They were also used in the Lunar Flashlight, which is a multiwavelength reflectometer at 1.064, 1.495, 1.850, and 1.990 \(\upmu\)m laser wavelengths for lunar volatile measurements in permanent shadow regions from low orbital altitudes of from 10 to 20 km [37; 38]. Lunar flashlight was launched in December 2022. Although the spacecraft did not enter lunar orbit due to a malfunction of the propulsion system, the instrument with the InGaAs APD was functional after launch.  
InGaAs APDs have a high QE (>60%) up to a 2 \(\upmu\)m wavelength range. They are also relatively low-cost. However, the useful gain of InGaAs APDs is typically from 20 to 30 before the dark noise and APD gain excess noise become overwhelming. As a result, the dominant noise source is still the preamplifier. The combined NEP from the APD and preamplifier is much higher than that of the APD given in the datasheet. The dark currents of InGaAs APDs are also much higher compared to silicon APDs. Cooling the APD only reduces the dark currents but not the preamplifier noise. The NEP of the best InGaAs APD lidar receiver is in the range of 100-200 \(\textrm{fW}/\textrm{Hz}^{1/2}\), which is about 10 times that of linear mode silicon APDs with the same preamplifier. InGaAs APDs are also more susceptible to space radiation damage than silicon APDs [39; 40].  
InGaAs APDs in Geiger mode can detect single photons, but they have not been used in space lidars at present. The afterpulsing rate of InGaAs APD is much higher than that of silicon APDs. They must be gated in order to maintain an acceptable afterpulsing rate [41], which makes them more difficult to use in space lidars.
## 2 Methodology
### MEMD-Based Identification Methods
#### 2.2.1 MEMD-Based SSI (MSSI) Method
\(j\omega_{i}\sqrt{1-\xi_{i}^{2}}\)) [27], and the mode shapes are the multiplication of eigenvectors \(\Psi\) and output matrix \(C\). The number of the singular values in Equation (5) denotes the system order \(n\). Note that in practical structures, it is quite difficult to determine the system order directly from the singular value of the Toeplitz matrix since the singular values associated with \(\mathbf{U}_{2}\) tend to be small numbers rather than zero under the influence of noise. If the selected system order deviates from the required order, the identified results will miss some physical modes or yield some spurious modes. Generally, the stabilization diagram of the identified modal parameters with the increasing order is employed to decide the order of the system to search the stable poles. However, the quality of the stabilization diagram is still highly related to the noise and for the structures with quite close modes, it is still not easy to pick up the exact system order [29; 30; 31; 32]. Thus, the MEMD is deployed as a preprocessing tool, aiming to reduce the effect of noise and obtain a credible system order. For the MSSI method, the multivariate signals are firstly decomposed into several aligned IMFs. Basically, the divided signals contain only a dominant component in the frequency domain, and the majority of the noise is filtered out.
# NMR in Metabolomics: From Conventional Statistics to Machine Learning and Neural Network Approaches
## 2 Conventional Approaches
### Unsupervised Methods
#### 2.1.3 Self-Organizing Maps (SOMs)  
Self-organizing maps (SOMs) were introduced by [PERSON] [50] and are widely employed to cluster a database, reduce its dimension and detect its properties by projecting the original data in a new discrete organization of smaller dimensions. This is performed by weighting the data throughout proper vectors in order to achieve the best representation of the sample. Starting from a randomly selected vector, the algorithm constructs the map of weight vectors for defining the optimal weights, providing the best similarity to the chosen random vector. Vectors with weights close to the optimum are linked with each unit of the map allowing to categorize objects in map units. Then, the relative weight and the total amount of neighbors reduce over time. Therefore, SOMs have the great power of reducing the dimensionality of the system while preserving its topology. For that reason, they are commonly adopted for data clustering and as a visualization tool. Another great asset of SOMs concerns the shapes of the clusters that do not require being chosen before applying the algorithm, whereas other clustering techniques usually work well on specific cluster shapes [51]. However, some limitations are evidenced using SOMs. In fact, they are normally of low quality, and the algorithm must be run many times before a satisfactory outcome is reached. Further, it is not easy to furnish information about the whole data distribution by only observing the raw map. Figure 4 reports the cluster of subjects involved in the study of renal cell carcinoma (RCC) by (NMR)-based serum metabolomics that was generated by using SOM (including the weighted maps for the considered 16 metabolites) [52].  
The achieved results clearly separate healthy subjects (left region) and RCC patients (right region) within the SOM. Moreover, the weighted maps of the individual metabolites allow to identify a biomarker cluster including the following seven metabolites: alanine, creatine, choline, isoleucine, lactate, leucine, and valine. These may be considered for an early diagnosis of renal cell carcinoma [52].  
Figure 3: An example of a dendrogram obtained by means of hierarchical cluster analysis performed on \({}^{1}\)H NMR data on the plasma metabolome of 50 patients with early breast cancer. From the analysis, 3 different groups are classified: LR-1 (red), LR-2 (blue) and LR-3 (green). In this case, the Ward algorithm is adopted for measuring the distance. Figure reprinted from Ref. [48] under the terms of the CC-BY license.
## 2 Study Area and Data Sets
### Data Set Description
#### 2.2.3 Data for Hydrologic Model Construction and Implementation  
The sources of the streamflow data and remote sensing images for conducting the hydrologic model in the XRRB are listed as follows:  
1. Daily streamflow data (inflow runoff data) from 2000 to 2019 were obtained from the Xinfengjiang Reservoir Administration, and the location of the hydrological station is shown in Figure 1.
2. Other meteorological data forcing required in the hydrological simulation, including daily maximum and minimum temperatures, solar radiation, wind speed, and relative humidity, over the period of 2000 to 2019 is obtained from the China Meteorological Administration ([[http://data.cma.cn](http://data.cma.cn)]([http://data.cma.cn](http://data.cma.cn)); accessed on 10 May 2020). These data are used to calculate evapotranspiration and related processes.
3. The soil types in the XRRB are derived from the soil map in the Harmonized World Soil Database (HWSD) (2009), with a resolution of 1 km ([[http://www.fao.org](http://www.fao.org)]([http://www.fao.org](http://www.fao.org)); accessed on 10 May 2020).
4. The land use/cover is collected from the Resource and Environment Science and Data Center ([[http://www.resdc.cn](http://www.resdc.cn)]([http://www.resdc.cn](http://www.resdc.cn)); accessed on 10 May 2020) and reclassified into four classes for the SWAT model, including forest, grassland, cultivated land, and water body.
###### Abstract  
This paper explores data caching as a key factor of edge computing. State-of-the-art research of data caching on edge nodes mainly considers reactive and proactive caching, and machine learning based caching, which could be a heavy task for edge nodes. However, edge nodes usually have relatively lower computing resources than cloud datacenters as those are geo-distributed from the administrator. Therefore, a caching algorithm should be lightweight for saving computing resources on edge nodes. In addition, the data caching should be agile because it has to support high-quality services on edge nodes. Accordingly, this paper proposes a lightweight, agile caching algorithm, EDCrammer (Efficient Data Crammer), which performs agile operations to control caching rate for streaming data by using the enhanced PID (Proportional-Integral-Differential) controller. Experimental results using this lightweight, agile caching algorithm show its significant value in each scenario. In four common scenarios, the desired cache utilization was reached in 1.1 s on average and then maintained within a 4-7% deviation. The cache hit ratio is about 96%, and the optimal cache capacity is around 1.5 MB. Thus, EDCrammer can help distribute the streaming data traffic to the edge nodes, mitigate the uplink load on the central cloud, and ultimately provide users with high-quality video services. We also hope that EDCrammer can improve overall service quality in 5G environment, Augmented Reality/Virtual Reality (AR/VR), Intelligent Transportation System (ITS), Internet of Things (IoT), etc.  
Keywords:resource-limited edge node; lightweight and agile caching; advanced PID controller; qualified caching-as-a-service +
Footnote †: journal: Applied
# Strategies for Deploying a Sensor Network to Explore Planetary Lava Tubes
## 3 Mapping and Localization  
For Mapping and Localization, we use an Iterative Closest Point (ICP)-based Pose-Graph Simultaneous Localization and Mapping (SLAM) algorithm on scans generated by the onboard LIDAR sensor [35]. The algorithm exploits the scan registration algorithm and graphical model optimization to build an ICP-based localization system in an unknown environment for SphereX equipped with LIDAR sensors, as shown in Figure 6. It consists of two main layers: an ICP layer and a Graph layer. The keyframes \(K_{i}\) and their associated scans form the ICP layer. In the graph layer, nodes \(n_{i}\) are associated with each keyframe. The first keyframe \(K_{0}\) is associated with the first acquired scan and the associated node \(n_{0}\) is created in the graph. Moreover, the initial robot pose with respect to the world origin is added to the pose graph. While the robot moves to acquire local maps, the robot frame \(R\) is always expressed with respect to the closest keyframe. At every instant a new scan is acquired, the ICP layer is initiated, which runs the ICP algorithm to find a transformation to align the new scan with the current local map. A threshold value is defined, which decides if a new keyframe associated with the new scan is to be created or the scan to be discarded. If the overlap is lower than the threshold, the scan is accepted; otherwise, it is rejected. If the scan is accepted and a new keyframe is created, the robot pose is corrected, and the new frame and the transformation between the new and the former keyframes are added to the graph as a node. Finally, the local map is rebuilt by merging the newest keyframe with the current local map. The system always searches for potential loop closure, and when it detects one, a local map is built between the loop closing keyframes using the ICP algorithm. If the overlap between them is below the threshold, the new transformation between the loop closing keyframes is added to the graph. This triggers an optimization sequence that repositions the keyframes using the optimization results. Finally, the local maps are reconstructed using the new repositioned keyframes, and the robot pose in the current keyframe is also updated.  
Figure 5: (**a**) 3D model of a cave with vertical entrance for analysis modeled in Blender software; (**b**) Placement of multiple sensors along the cave to maintain a direct line-of-sight connection link.
# A Hybrid MCDM Model for Live-Streamer Selection via the Fuzzy Delphi Method, AHP, and TOPSIS
## 2 Conceptual Framework  
This study assists the managers of shopping websites in selecting live-streamers by combining the fuzzy Delphi method, AHP, and TOPSIS. In this conceptual framework:  
1. The first level is the objective. The objective is to select the best live-streamers.
2. The second level is the selection dimensions.  
Important criteria are classified into dimensions based on past literature and interviews with the managers of shopping websites.
3. The third level is the selection criteria.  
A set of selection criteria are compiled by interviewing the managers of shopping websites and reviewing related studies. Next, a questionnaire with a nine-point Likert scale is designed based on the fuzzy Delphi method and filled out by the managers of shopping websites. The higher the score of a criterion, the higher its level of importance. Important criteria are retained according to the geometric mean provided by the respondents.  
According to the standard for screening criteria in the fuzzy Delphi method, a criterion with an over 80% importance level is generally regarded as important [41]. In other words, a criterion with a geometric mean of over 7.2 is regarded as a vital criterion based on conversion using the questionnaire on the nine-point Likert scale. This study establishes the selection criteria for live-streamers by referencing the discussed standard, where a criterion with a geometric mean of over 7.2 is regarded as an important criterion and should be retained. After confirming the selection criteria, they are classified into dimensionsbased on the literature review and interviews with the managers of shopping websites. Then, the second round of questionnaires is designed to assist a case study shopping website in selecting the most suitable live-streamer using AHP and TOPSIS.  
The fourth level is the solution.  
The managers of a shopping website in the case study are assisted in selecting the best live-streamer based on the hierarchical framework combined with AHP and TOPSIS.