esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods
### Land Use and Land Cover Classification  
In this study, the Landsat 5/7/8 surface reflectance data provided by the GEE platform were processed by cloud removal, terrain correction [34], BRDF correction [35], etc., and the LULC of the Kokyar Project Area was classified based on the random forest model. First, we used the QA quality assessment band generated by the Cfmask model to mask the cloud pixels in the data and to eliminate the impact of cloud on the LULC classification. Second, we applied the well-performing Sun Canopy Sensor + C correction model to each scene to avoid the problematic under- or overcorrection observed in the steep slopes. Third, we normalized the reflectance values using the nadir bidirectional reflectance distribution function (BRDF) correction to obtain seamless mosaics. In order to improve the classification accuracy, this study selected the NDVI, NDWI, NDBI, BAEI, NDBai, DBI, DBSI, and TPI as the characteristic variables of the random forest model (Table 2) [36]. Based on the surface characteristics of the study area, this study finally selected five land types, namely cropland, grassland, water, bare land, and built-up area. The spatial resolution of the LULC in this study was 30 m, and the temporal unit was annual.
# Studies of General Precipitation Features with TRMM PR Data: An Extensive Overview
### Existing Studies on Precipitation Features Using the PR Data  
In addition to the two primary goals of determining surface rainfall patterns and latent heating profiles, the actual much longer life cycle of the TRMM was helpful for studies on characteristics of precipitation with enough samples in the tropics and subtropics, compared to the expected duration in orbit of about three years. In fact, most of the existing studies using the PR data were precipitation observations for a period of years, and some were studies on the physical phenomena during precipitation processes including latent heat and lightning. Although the TRMM and the PR had ended in 2015, related studies still continued using PR data storage. The University of Utah Precipitation Measuring Missions (PMM) science group has accomplished a great deal studying precipitation features from the PR data. The group has established an event-based reanalysis database of precipitation feature from temporally and spatially collocated TRMM data and, afterwards, climatological descriptions of the identified features are generated [14]. Other research groups include the Houze group and the International Precipitation Working Group (IPWG) [15]. The Houze group interpolated and geolocated the PR data into a three-dimensional Cartesian grid, and from this stored dataset it is possible to identify structures of echoes that satisfy certain criteria [16]. The authors of this article have been paying attention to this field since 1997 [4; 17] and especially in the \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Products**} & \multicolumn{2}{c}{**Name**} & \multicolumn{2}{c}{**Brief Description**} \\ \hline \multirow{2}{*}{Level 1} & 1B21 & PR Power & total received power and the noise level clutter contamination. \\  & 1C21 & PR Reflectivity & radar reflectivity factors without attenuation correction. \\ \hline \multirow{2}{*}{Level 2} & 2A21 & Surface Cross Section & the surface cross section and the path attenuation. \\  & 2A23 & PR Qualitative & the detection of rain and the bright band, and the classification of rain types. \\  & 2A25 & PR Profile & vertical profiles of rainfall rates and the attenuation corrected radar \\  & & & reflectivity factors. \\ \hline \multirow{2}{*}{Level 3} & 3A25 & PR Rainfall & monthly accumulations of 1C21, 2A21, 2A23 and 2A25 products at 5\({}^{\circ}\times\)5\({}^{\circ}\) grid & \\  & 3A26 & Surface Rain & monthly probability distribution of path-averaged rain rates and the rain \\  & & & rates on fixed heights at 5\({}^{\circ}\times\)5\({}^{\circ}\) grid. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: TRMM (Tropical Rainfall Measuring Mission) standard PR (Precipitation Radar) products.
# A Novel Excitation Approach for Power Transformer Simulation Based on Finite Element Analysis
## 2 2D Finite Element Analysis Model  
### ANSYS Maxwell Solver Equation  
The software of ANSYS Maxwell uses four Maxwell equations, which can be written as:  
\[\
abla\times H=J+\frac{\partial D}{\partial t} \tag{1}\]  
\[\
abla\times E=-\frac{\partial B}{\partial t} \tag{2}\]  
\[\
abla\cdot D=\rho \tag{3}\]  
\[\
abla\cdot B=0 \tag{4}\]  
In Equations (1)-(4):  
\(B\): magnetic flux density (T);  
\(H\): magnetic field intensity (A/m);  
\(E\): electric field (V/m);  
\(D\): electric displacement (C/m\({}^{2}\));  
\(J\): conduction-current density (A/m\({}^{2}\));  
\(\rho\): charge density (C/m\({}^{2}\)).
# A Lyapunov-Optimized Dynamic Task Offloading Strategy for Satellite Edge Computing
## 6 Conclusions  
We propose an offloading strategy for the dynamic task allocation problem in satellite constellations, utilizing a combination of Lyapunov and Hungarian algorithms. Firstly, we construct a dynamic model for task offloading among satellites and formulate an optimization problem that minimizes the average delay and energy consumption with a long-term constraint on the task queue length. To solve this problem, we employ Lyapunov optimization to convert the long-term optimization objective into an assignment problem within a single time slot. The Hungarian algorithm is then utilized to solve the resulting assignment problem. We validate the effectiveness of our algorithm through experimental simulations, investigating the impact of various parameters. Our results demonstrate the efficacy of our proposed approach.
## 2 Methodology
### Mathematical Model of the Dehumidifier Used as AWG  
Moist air is a non-reactive mixture of dry air and water vapour. The mass ratio of water vapour and dry air is better known as specific humidity, varying depending on the area's features. In a given thermodynamic state, each of the components has certain properties that help determine the amount of energy required to condense water vapour. For a given system, during any continuous process in which there are interactions between dry air and water vapour, whether a humidifier, a dehumidifier or a dryer, humid air is supplied, and water can be extracted or added to that air and transfer heat, Q, from or to the system, for various purposes. The masses of water vapour (\(m_{v}\)) and dry air (\(m_{a}\)) are extensive properties that contributed to the mixture's total mass (\(m_{t}\)), which are related as follows:  
\[m_{v}+m_{a}=m_{t} \tag{1}\]  
For the atmospheric water generator system, this mixture is the entry point, since energy calculations are made from it. For the process, the AWG system was considered an interaction process between dry air and water vapour represented by the physical model of Figure 7.  
Figure 6: Psychometric zone of opportunity for the use of atmospheric water generator (AWG) in Matehuala, SLP, Mexico.  
During the dehumidification process, mass and energy exchanges took place. Energy exchanges were estimated by considering the properties of the mixture under the system's inlet and outlet conditions. Inlet properties were determined by the system surroundings, while the outlet parameters were determined by the properties required for condensation and were proposed according to the percentage of water vapour reduction by condensation; in this case 25%, 50% and 75% reduction of water vapour. These percentages were selected so as not to affect the area's environmental surroundings or the ecosystems present in the region.  
The following assumptions were made for the development of the model:  
* The dry air entering the system changes only temperature in the process.
* Inlet specific humidity is necessarily greater than outlet specific humidity.
* Condensed water is in the sub-cooled state.
* Inlet and outlet enthalpy of water vapour depends on the amount of moisture contained in the water vapour.
* The proposed water vapour reduction is 25%, 50% and 75%.
## 2 Materials and Methods  
### Materials  
Table 1 shows the chemical composition of the materials used in this work. Conventional Portland cement (Cementos Portland Valderrivas, Madrid, Spain) (CEM I) was used as a reference and for the preparation of the different hybrid cement systems. F-type fly ash (Puentenuevo steam plant, Cordoba, Spain) (ASTM C618-03) and blast furnace slag (Aviles factory, Asturias, Spain) were used as raw materials. In addition, two wastes from the ceramic industry were used for the preparation of hybrid cements. They are residues from the preparation of ceramic wall tiles (labeled as \"porous waste\") and from the sintering of porcelain tiles (labeled as \"porcelain waste\"). Both wastes were supplied by Keraben (Keraben, Nules, Spain). The average particle size was determined in all materials (measured using a Mastersizer 200 Hydro2000 SM equipment) (Malvern Instruments, Worcestershire, UK, Table 2).  
Regarding the ceramic wastes, both types present a similar chemical composition similar to the chemical composition of fly ash, with high contents of SiO\({}_{2}\), Al\({}_{2}\)O\({}_{3}\) and Fe\({}_{2}\)O\({}_{3}\) and lower CaO percentages than slag. Therefore, during their activation, they will possibly form a gel similar to the N-A-S-H type. It should be noted that when comparing both ceramic wastes, the porous material has a higher percentage of CaO than the porcelain material. Furthermore, porcelain waste shows a very high Na\({}_{2}\)O content compared to the other proposed wastes.
## 3 Materials and Methods
### Rainfall Datasets
#### 3.1.2 TRMM Data  
The Tropical Rainfall Measuring Mission (TRMM), as the predecessor of GPM, is a joint mission launched by NASA and JAXA in 1997 to study rainfall in tropical regions. It is a research satellite that aims to help us understand the precipitation distribution and changes in tropical regions. TRMM has accumulated a large amount of precipitation data with high temporal and spatial resolution, and is widely used in various precipitation studies [33; 34; 35].  
TMPA data are one type of TRMM data; they have a spatial resolution of \(0.25^{\circ}\times 0.25^{\circ}\) and a coverage range of \(50^{\circ}\) N-\(50^{\circ}\) N. The daily dataset selected in this study was accumulated from the research-quality 3-hourly TRMM (TMPA) rainfall estimate. (Source: [[https://disc.gsfc.nasa.gov/datasets](https://disc.gsfc.nasa.gov/datasets)]([https://disc.gsfc.nasa.gov/datasets](https://disc.gsfc.nasa.gov/datasets))).
# Internet of Bikes: A DTN Protocol with Data Aggregation for Urban Data Collection
## 6 Performance Evaluation of IoB-DTN
### Impact of Buffer Management Policies
This means that there are older packets that are transmitted and the fact that they have similar delay distribution shows that they are sent very quickly. From our results, we can classify two categories of policies: KONP and NP on the one side; and GPP and LC on the other side. It is clear from our evaluations that KONP is the worst policy as it has poor performance on loss rate and delivery delay. The performances of GPP outperforms slightly LC in all cases despite their different behaviors: GPP prioritizes own production against redundancy, whereas LC is based on redundancy to give place to new packets. For the purpose of confirmation of our results, we simulated ten scenarios for GPP and NP policies by varying the paths of bikes in each simulated scenario. The average findings obtained on loss rate are depicted in Figure 7 and results on delivery delays are shown in Figure 8. Clearly, we obtained the same results as those presented here. In the rest of this paper, we consider GPP as the buffer management policy for all our simulations. Figure 6: Delivery delay. Figure 8: _Cont_. Figure 7: Average loss rate.
###### Abstract  
Due to various typical unstructured factors in the environment of photovoltaic power stations, such as high feature similarity, weak textures, and simple structures, the motion model of the ORB-SLAM2 algorithm performs poorly, leading to a decline in tracking accuracy. To address this issue, we propose PE-SLAM, which improves the ORB-SLAM2 algorithm's motion model by incorporating the particle swarm optimization algorithm combined with epipolar constraint to eliminate mismatches. First, a new mutation strategy is proposed to introduce perturbations to the pbest (personal best value) during the late convergence stage of the PSO algorithm, thereby preventing the PSO algorithm from falling into local optima. Then, the improved PSO algorithm is used to solve the fundamental matrix between two images based on the feature matching relationships obtained from the motion model. Finally, the epipolar constraint is applied using the computed fundamental matrix to eliminate incorrect matches produced by the motion model, thereby enhancing the tracking accuracy and robustness of the ORB-SLAM2 algorithm in unstructured photovoltaic power station scenarios. In feature matching experiments, compared to the ORB algorithm and the ORB+HAMMING algorithm, the ORB+PE-match algorithm achieved an average accuracy improvement of 19.5%, 14.0%, and 6.0% in unstructured environments, respectively, with better recall rates. In the trajectory experiments of the TUM dataset, PE-SLAM reduced the average absolute trajectory error compared to ORB-SLAM2 by 29.1% and the average relative pose error by 27.0%. In the photovoltaic power station scene mapping experiment, the dense point cloud map constructed has less overlap and is complete, reflecting that PE-SLAM has basically overcome the unstructured factors of the photovoltaic power station scene and is suitable for applications in this scene.  
ORB feature; image matching; multi-objective optimization; particle swarms; epipolar constraints; simultaneous localization and mapping (SLAM) +
Footnote †: journal: Applied  
applied sciences
# Optimizing Energy Efficiency in Cloud Data Centers:
## 5 Proposed Reinforcement Learning-Based VM Placement in Cloud Environment Using RF_SOM Classifier
### Baseline Algorithms Form VM Placement
#### 5.3.1 Power Aware Best Fit Decreasing (PABFD) for VM Placement  
The PABFD (power aware best fit decreasing) algorithm is a heuristic approach designed for the efficient placement of virtual machines in cloud computing environments, with the emphasis on minimizing energy consumption. The PABFD algorithm starts by sorting virtual machines in descending order according to their demand for resources such as CPU, memory and bandwidth. This ensures that the most demanding virtual machines are considered first, optimizing the use of available resources.  
Once selected, each virtual machine is placed in the host with the lowest energy consumption, according to a \"best fit\" strategy. This approach not only improves energy efficiency but also maintains the performance and reliability of the cloud infrastructure.  
The time complexity of the power aware best fit decreasing (PABFD) algorithm for VM placement can be analyzed by breaking down its primary steps:  
Sorting VMs: sort the VMs in decreasing order based on their resource demands. If there are N VMs, the sorting step has a time complexity of \(O(N\log(N)\).  
Placement of VMs: place each VM into a physical host. Let M be the number of physical hosts. For each VM, the algorithm must check each host to determine the best fit, which involves evaluating the power consumption for each potential placement. This step has a time complexity of \(O(N\cdot M)\), as it involves iterating through all hosts for each VM.  
Combining these two steps, the overall time complexity of the PABFD algorithm is \(O(N\log(N)+O(N\cdot M)\). In summary, the time complexity is \(O(N\log(N)+N\cdot M)\).  
The sorting step typically dominates when the number of hosts M is significantly smaller than the number of VMs n, but in scenarios where M is comparable to N, both terms contribute significantly to the complexity. The detailed steps of the PABFD algorithm are presented in the following Algorithm 3.  
```
1:Input: vmLists, hostLists, nparticles, maxIter
2:Initialize particles randomly
3:for i = 1 to maxIter do
4: Evaluate energy consumption-based function for each particle based on vms and hosts
5: Update personal best positions for each particle
6: Update global best position
7: for each particle do
8: Update velocity and position based on personal and global best positions
9: end for
10:Return Best VM placement configuration found
11:endfor
``` ```
**Algorithm 3**PABFD for VM placement
# AI-Driven Safety and Security for UAVs: From Machine Learning to Large Language Models
## 2 Background
### UAV Safety and Security Threats
#### 2.1.2 UAV Security Threats
In addition, UAV networks usually adopt a distributed or SON structure, which has some vulnerabilities despite offering flexibility and fault tolerance [62]. The mobility of UAV nodes and frequent topology changes also increase the complexity of network management and easily lead to security vulnerabilities.
# Experimental Evaluation of Unicast and Multicast CoAP Group Communication
## 5 Evaluation
### Performance Evaluation on a Wireless Sensor Testbed
#### 5.2.4 Group Size  
As shown in the previous subsections, using large groups can have a negative impact on the reliability of the group. In our tests, unicast groups started to become unreliable after a group size of 30 members. Multicast groups are generally unreliable, but the reliability also becomes worse with an increasing group size. The reason for this is that with the increase in group size, the density of the nodes typically also increases, and as a result, more collisions occur in the network. Furthermore, for the unicast-based solution, the group size directly affects the response time since the EM adds a delay between the requests it sends to the members. One simple solution is to split the groups. However, splitting the groups does not bring much benefit, when both groups are still using the same RF channel. When using our group communication solution, one can use more than one GW and create different WSNs that use different IEEE 802.15.4 RF channels. The groups are split accordingly.  
In order to test this approach and to demonstrate the use of more than one GW to create two WSNs that are overlapping in the physical space, but are using different RF channels, we have created a new experiment. In this experiment, each GW is communicating with a network of 10 sensor nodes using its own RF channel (IEEE 802.15.4 Channels 25 and 26). The two GWs are connected via an Ethernet cable, and routing is enabled between them. We have repeated the same test as in Section 5.2.3, but now using a group that consists of two smaller groups. Figure 13 shows the response time vs. the speed of interfering Wi-Fi traffic for the new experiment along with the results for groups of 10 and 20 members from the previous section for comparison. As expected, the response time for the group of two smaller groups is better than that of the one big group, although the total number of nodes was the same in both cases (20 nodes). Further, the response time is larger than the case of a single group with 10 members. The reason here is that we use nested groups, i.e., a group that contains two groups. This results in some additional processing overhead and also inserts a delay of 100 ms between the requests being issued to the different subgroups. Additionally, since we used two neighboring IEEE 802.15.4 channels, a small amount of interference between the channels is present. The reason for selecting two neighboring channels was to have both channels equally interfered from the Wi-Fi Channel 13, which overlaps both of the used IEEE 802.15.4 Channels 25 and 26. In a production setting, one should not use a neighboring channel to also avoid this limited amount of interference. The selection of channels should also take into consideration which Wi-Fi channels are used.  
Figure 13: Entity response time in the presence of Wi-Fi interference for an entity of 20 members versus a nested entity consisting of two smaller entities having each 10 members.
## 2 Construction of a Water Resource Optimization Model
### Constraints  
When addressing the multi-objective optimization of water resource allocation, it is essential to consider various constraints, including water demand and water supply. Solutions that satisfy these constraints are the only ones that hold practical significance. The constraints selected in this study include water availability constraints, water demand constraints, pollutant emission constraints, and non-negativity constraints for variables. (1) Water Availability Constraint:  
\[max\sum_{k=1}^{K}\sum_{i=1}^{I}\sum_{j=1}^{J}x_{ij}^{k}\leq W_{i}^{k} \tag{4}\]  
In the formula: \(W_{i}^{k}\) represents the maximum available water supply from water source \(i\) in supply sub-region \(k\). (2) Water Demand Constraint  
\[D_{jmin}^{k}\leq\sum_{i=1}^{I}\sum_{j=1}^{J}x_{ij}^{k}\leq D_{jmax}^{k} \tag{5}\]  
\[D_{jmin}^{k}=\varepsilon_{j}^{k}\times D_{jmax}^{k} \tag{6}\]  
In the formula: \(D_{jmax}^{k}\) represents the upper limit of water demand for water user \(j\). \(D_{jmin}^{k}\) represents the lower limit of water demand for water user \(j\). \(\varepsilon_{j}^{k}\) is the minimum water supply guarantee rate for water user \(j\) in supply sub-region \(k\). (3) Pollutant discharge limit constraint:  
\[\alpha_{j}^{k}\beta_{j}^{k}\times\sum_{i=1}^{I}x_{ij}^{k}\leq Q_{j}^{k} \tag{7}\]  
In the formula: \(Q_{j}^{k}\) represents the maximum allowable discharge concentration of local water pollutants in supply sub-region \(k\). \(\alpha_{j}^{k}\) is the sewage discharge coefficient in the effluent of water user \(j\) in supply sub-region \(k\); \(\beta_{j}^{k}\) represents the pollutant discharge concentration in the effluent of water user \(j\) in supply sub-region \(k\).  
(4) Non-negativity constraint on variables:  
When addressing real-world problems, all referenced variable values must be guaranteed to be non-negative.
# miRNA-Gene Association Prediction Method Based on Deep Learning Model
###### Abstract  
MicroRNAs (miRNAs) are small RNA molecules consisting of approximately 22 nucleotides; they regulate gene expression and are employed in the development of therapeutics for intractable diseases. Predicting the association between miRNAs and genes is crucial for understanding their roles in molecular processes. miRNA-gene associations have been studied using deep learning methods, but these methods present various constraints. Through addressing the limitations of previous methods, this study aimed to achieve better performance than the state-of-the-art (SOTA) methods for studying miRNA-gene associations. We constructed the most extensive embedded dataset to date, comprising 717,728 miRNA-gene pairs, specifically designed for our deep learning model. Further, we applied an embedding method used for protein embedding for transforming our gene sequence data. Moreover, we constructed a sophisticated negative dataset based on three distance criteria, unlike most studies that randomly designate negative data. Leveraging the data and insights from these approaches, we built a deep learning model with the best performance among SOTA miRNA-gene studies (area under the receiver operating characteristic curve = 0.9834). In addition, we conducted a case study using the learned model to predict potential positive data. We also aimed to identify miRNAs closely associated with a gene linked to various cancers.  
bioinformatics; miRNA; gene; association prediction; deep learning Article