esa-sceva/satcom-chunk-collection huggingface dataset:
# Near Real-Time Irrigation Detection at Plot Scale Using Sentinel-1 Data
## 4 Results
### Results over Catalonia
Figure 9 represents the distribution of the number of irrigation points detected over irrigated plots in Catalonia, Spain as a function of months. For each month (including approximately five SAR acquisitions at each acquisition mode), the number of irrigation peaks detected over all the irrigated plots is calculated. While Figure 9a represents the distribution of irrigation points for the morning acquisitions, Figure 9b shows this distribution for the evening acquisition. For the morning acquisition mode (Figure 9a), the number of irrigation points reaches a low value in February 2018 (1040 points \(\sim\)0.7%) and then starts to increase gradually between March 2018 and May 2018. A sharp increase of the irrigation points exists between May 2018 (3827 \(\sim\)3%) and June 2018 (21,787 \(\sim\)16%) and then continues to increase to reach a maximum value in July 2018 (25,380 \(\sim\)19%). In August 2018, the number of irrigation points slightly decreases to attain approximately 15%. Then the number of detected irrigation points starts to decrease until reaching a minimum value in November 2018 (103 \(\sim\)0.01%). Similar behavior is registered in the evening acquisition (Figure 9b) where the number of irrigation points reaches low value in February 2018 (2735 \(\sim\)1%) and then increases until reaching its maximum value in July 2018 (29,286 \(\sim\)18%). A slight decrease is recorded in August 2018 (27,252 points \(\sim\)16%). The number of detected irrigation points then starts to decrease to reach a minimum value of 257 points \(\sim\)0.01% in November 2018. Figure 8: Distribution of the irrigation points detected over irrigated (blue) and non-irrigated (orange) plots in Catalonia Spain for (**a**) morning SAR acquisition, (**b**) evening SAR acquisition, (**c**) intersection between morning and evening acquisitions and (**d**) combination between morning and evening acquisitions. As a result, Figure 9 shows that most of the detected irrigation points exist within the period between March 2018 and September 2018. Indeed, 80.0% of the total detected points using the evening acquisition mode (Figure 9) exist within the period between 01 March 2018 and 30 September 2018. Similarly, 74.5% of the total detected points exist within the same period for the morning acquisition mode (Figure 9). Only 12% of the irrigation points (in both morning and evening) correspond to the period between November 2017 and February 2018. This low percentage mostly corresponds to the false detection of irrigation events since irrigation rarely occurs over this period and rainfall events are abundant. Therefore, the results show that most of the detected points using our proposed irrigation detection method correspond to the dry season where irrigation mostly occurs in the region.
# Spectral Information Divergence-Driven Diffusion Networks for Hyperspectral Target Detection
## 4 Results
### Description of Experimental Datasets  
In this experiment, three real HTD datasets are selected to validate the proposed algorithm: the Gulfport dataset (part of the ABU dataset), the Los Angeles dataset, and the San Diego dataset. Their basic information is summarized in Table 1. The Gulfport and Los Angeles datasets are derived from the ABU dataset collected by [PERSON] et al. (2019), with aircraft as the detection objects. Low signal-to-noise spectra and spectra impacted by water vapor interference, including bands 1-6, 33-35, 97, 107-113, 153-166 and 221-224, are eliminated for the San Diego dataset. The remaining 189-dimensional spectra are used for experiments, with aircraft as the detection objects. The image sizes of the three datasets shown in Table 1 represent cropped dimensions.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Los Angeles** & **Gulport** & **San Diego** \\ \hline Sensor & AVRIS & AVRIS & AVRIS \\ Spatial resolution (m) & 7.1 & 3.4 & 7.5 \\ SpatialSize & 100\(\times\) 100 & 100\(\times\) 100 & 100\(\times\) 100 \\ Number of bands & 205 & 191 & 189 \\ Spectral range (mm) & 0.4–2.5 & 0.4–2.5 & 0.37–2.51 \\ Spectral resolution (mm) & 10 & 10 & 10 \\ Number of Target (Pixels) & 87 & 60 & 57 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic information on Los Angeles, Gulfport and San Diego.
# Effect of AQM-Based RLC Buffer Management on the eNB Scheduling Algorithm in LTE Network +
## 5 Network Model
### Scheduling Algorithms  
To meet the QoS requirements, one of the important features in the LTE system is multi-user scheduling. Multi-user scheduling is responsible for distributing available resources among active users. Both downlink and uplink packet schedulers are deployed at the eNB, and, since there is no inter-channel interference provided by OFDMA, they work with a granularity of one TTI and one RB in the time and frequency domain, respectively. Resource allocation for each UE is usually done based on the comparison of per-RB metrics. These metrics can provide the status of transmission queues, channel quality, resource allocation history, buffer state and QoS requirements. The main differences among resource allocation strategies exist due to the trade-off between computational complexity and decision optimality. To define a resource allocation policy for the LTE system, the main design factors that always should be taken into account are spectral efficiency, scalability and complexity, fairness and QoS provisioning. A good overview of different allocation strategies such as Maximum Throughput (MT), Blind Equal Throughput (BET), Proportional Fair (PF), Throughput to Average (TTA), Round Robin (RR), Priority Set Scheduler (PSS), and Token Bank Fair Queue (TBFQ) etc., introduced for LTE systems, highlighting pros and cons related to each solution, can be found in [37].
## 3 Simulation Methodology
### Validation of the Model

Table 4: The EnergyPlus simulated data (primary energy consumption values) for the base model. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline \multirow{3}{*}{**Building Name**} & \multirow{3}{*}{**Area**} & \multirow{3}{*}{**Energy** (**kWh**)} & \multicolumn{8}{c}{**Monthly Site Energy Consumption**} \\ \cline{3-14}  & & & **Jan.** & **Feb.** & **Mar. ** & **Apr. ** & **May. ** & **Jun. ** & **Jul. ** & **Aug.** & **Sep.** & **Oct.** & **Nov.** & **Dec.** & **Total** \\ \hline \multirow{3}{*}{\(1\)  
Figure 5: Comparison of electrical measurement data and base model simulation data. Figure 6: Comparison of gas measurement data and base model simulation data. The MBE was \(-4.88\%\) and the Cv(RMSE) was 13.62% when comparing actual and EnergyPlus simulation gas energy consumption. Simulation gas energy values were similar. Both the measured and simulated data exhibited high gas energy consumption during the summer season (Jun-August) and winter season (December-February), with particularly high peaks in August and December. Intermediate season use was relatively low, with the lowest consumption occurring in May and October.
# Microcontroller Unit-Based Wireless Sensor Network Nodes: A Review
## 2 Wireless Sensor Node: An Overview  
Although several wireless sensor node platforms based on the MCU have been proposed, they generally have the same basic architecture. They differ with regard to processing and memory specifications, communication capabilities, power supply and consumption, sensor support, applications and so on. A basic wireless sensor node consists of the sensing (sensors interfacing and sensors), computation (a microcontroller), communication and power supply sub-systems as depicted in Figure 1.
## 2 System Architecture
### SynchHubs  
SynchHubs are low in size, weight, and power devices, such as smartphones or tablets, which handle data acquisition from wearables in either one-to-one or many-to-one pairing configuration. To date, we have deployed the system using Android smartphones and Android Samsung Galaxy Tab Active3 and S6 Lite tablets. The data transfer and synchronization is facilitated through the custom application, called SIGMA+ Health (S+) for the Defense Advanced Research Projects Agency (DARPA) program that supported the development (Figure 3).  
The S+ app builds on the Garmin Companion SDKs for Android and iOS to provide direct integration with supported devices and enable data logging of IBIs, step counts, respiratory rate, and SpO\({}_{2}\). Data acquisition between the S+ app and the wearable is executed via a Bluetooth Low Energy connection that supports persistent pairing for automated background synchronization (sync). The data are stored in an encrypted SQLite database until the app initiates data transmission. During data transmission over HTTPS (Hypertext Transmission Protocol Secure), encrypted Java Script Object Notation (JSON) messages are conveyed to one or more network-addressable SANs using standard web services. These endpoints are modifiable and specific to the cohort.  
The initial setup associates the user with a unique cohort through a study PIN and a unique participant ID that does not contain personally identifiable information. The participant ID allows the application to track the user's data from one or more devices throughout the study. Within the app, users can view the connectivity status of their wearables (as to whether they are paired) and manually trigger a data sync if required. They can also determine the status of an in-progress sync or time since the last sync and  
Figure 3: Architecture of the custom SIGMA+ (S+) mobile application for the synchronization and transmission of high-resolution data from Garmin smartwatches. IBI = interbeat interval (ms); JSON = Java Script Object Notation; Resp = respiration rate (per minute); SAN = storage and analytics node; SpO\({}_{2}\) = blood oxygen saturation (%); UTC = Coordinated Universal Time.  
successful upload. The SynCHub can also provide the GPS coordinates of the mobile device when needed.
# Social Media Analytics for Disaster Response: Classification and Geospatial Visualization Framework
## 2 Related Works  
Recent studies have increasingly explored the use of social media posts to improve the effectiveness of emergency response. By analyzing real-time posts, researchers and emergency management teams can improve situational awareness, optimize resource allocation, and facilitate faster decision-making during crises. Advanced machine learning and deep learning techniques have been employed to classify disaster-related information, detect urgent needs, and filter out misinformation, thereby improving the effectiveness of disaster response efforts. These studies highlight the challenges of using social media posts during a disaster, including difficulties processing large volumes of data in real time, the presence of misinformation and irrelevant content, and the challenges in collecting comprehensive data. The vast and unstructured nature of social media posts necessitates efficient computational techniques to promptly extract meaningful insights. Additionally, the spread of false or misleading information can hinder effective decision-making, requiring robust filtering mechanisms. Moreover, social media coverage of disasters is often inconsistent, leading to gaps in situational awareness that can impact emergency response efforts. Overcoming these challenges is crucial for improving the reliability and effectiveness of social media-based disaster response systems.
# An Improvement on Estimated Drifter Tracking through Machine Learning and Evolutionary Search
## 2 Literature Review  
ADIOS [12], developed by the United States in the early 1990s, is one of the widely used decision-making support systems for spilled oil. These support systems share the characteristics of simplicity, high performance, open-source programming, and an extensive oil database. The performance of ADIOS has continued to improve [13] and provides a basis for newly developed oil weathering models. Unfortunately, it cannot use data on the current state [14] and simulate the trajectory of the oil spill. The ADIOS model requires information on spilled oil situations, environmental conditions and prevention strategies, and calculates optimal prediction results by inputting minimum information obtained or expected in the field.  
The prediction models for oil spill movements were developed for accurate and detailed analysis. Oil companies, consulting sectors, national agencies, and research centers use certain models worldwide, which enable them to input various marine weather and environmental data to consider oil weathering, and are thus suitable for planning stages and research scenarios for different types of oil and marine weather conditions. Related models are GNOME [7], OILMAP [15] OSCAR [16], OSIS, GULFSPILL [17], MOHID [6], etc. Among them, MOHID [6], which is used as a benchmark measure of performance in this study, was first developed in 1985 at the Marine and Environmental Technology Research Center (IST) of the Instituto Superior Tecnico (IST), affiliated with the University of Lisbon, Portugal. MOHID is a multifunctional three-dimensional numerical analysis model that can be applied to coastal and estuary areas, which basically calculates physical actions in coastal and estuary areas such as tide and tsunami. It consists of more than 60 modules that can calculate fluid properties (water temperature, salinity, etc.), Lagrangian movement, turbulence, sediment movement, erosion and sedimentation, meteorological and wave conditions, water quality and ecology, and oil diffusion.  
Recent advancements in operational maritime, weather forecasting, and computing technologies have allowed for automated prediction in desktop and web environments. Such prediction models include MOTHY [18], POSEIDON OSM [19], MEDSLICK [20], MEDSLICK II [5], OD3D [21], LEWAY [22], OILTRANS [14], BSHmod.L [23], and SEATRACK Web [24]. Some models cannot process 3D ocean data or consider stokes drift and the vertical movement of droplets. Research is underway to provide user requirements, convenient and comprehensive user-friendly environments, and geographic information system (GIS) results to improve the model landscape.  
Typical models that predict Lagrangian drifter trajectory are known to be less accurate for large structures [25]. Therefore, [PERSON] et al. [25] used long short-term memory (LSTM) to accurately predict the velocity of drifters. They used the drifter data obtained from the Gulf of Mexico. Their model was much more accurate than the model using the Maxey-Riley equation [26] in terms of root mean square error (RMSE).
## 5 Conclusions  
In this paper, we have developed a stationary 3D Hemispherical Array Photoacoustic Imaging System using 50 single element transducers. The performance of the system on several phantoms, using two light illumination configurations, i.e., overhead and full-field illumination, with different image reconstruction algorithms, was evaluated. CNR of the images were used for quantitative analysis of the results. The DMAS reconstruction algorithm with full-field illumination produced the images with the best quality and quantitative results. In the future, utilizing techniques to increase the number of view angles for ultrasonic detection, a more sophisticated image reconstruction algorithm considering the speed of sound map of the biological tissue, and a fluence compensation algorithm will be explored.  
Conceptualization, K.A.; methodology, K.K. and K.A.; software, K.K., R.M., M.M. and K.A.; validation, K.K., R.M., S.M.R., K.J., M.M. and K.A.; formal analysis, K.K., R.M., M.M., M.D.V., N.d.J. and K.A.; investigation, K.K., R.M., K.J., M.M. and K.A.; resources, K.A.; data curation, K.K., M.M. and K.A.; writing--original draft preparation, K.K., R.M. and M.M.; writing--review and editing, K.K., R.M., M.Z., M.M., M.D.V, N.D.J and K.A.; visualization, K.K., R.M. and K.A.; supervision, K.A.  
MI-Kickstart Early-Stage Funding--Fast Forward Medical Innovation, Michigan Translational Research and Commercialization (MTRAC), National Institutes of Health (NIH) (R01 EB027769, R01 EB028661).  
**Acknowledgments:** The authors would like to thank the Wayne State University Manufacturing Engineering department for use of their Makerbot 3D printer.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Specifications** & **Cost (S)** \\ \hline Transducers & 50 single elements/512 elements & \(\sim\)15K/\(\sim\)65K \\ Amplifiers & Low-noise 24 dB/customized & \(\sim\)7.5K/\(\sim\)25K \\ DAQ & 56 channel/512 channel (or 64 channel with MUX) & \(\sim\)20K/\(\sim\)50K \\ \hline \hline \end{tabular}
\end{table}
Table 3: Estimated cost of the proposed system.
## 5 Optimization Example
### Optimization Program  
MATLAB language was used for the optimization analysis of an SRHPC frame structure, as shown in Figure 5. The optimal iteration process based on fuzzy reliability included two layers, the outside layer and the inner layer. The outer layer was used to optimize the design variables, and the inner layer conducts the fuzzy reliability analysis of the structure. The optimization process is shown in Figure 5 and the main programs are presented in a paper by [PERSON] [31].  
The life-cycle cost of the structure is related to the selection of the weight coefficient in the optimization objective function. In this paper, the optimization of the SRHPC frame structure shown in Figure 4 with different weight coefficient combinations was carried out and the relationship between the reliability of the structure and the weight coefficient \(\alpha_{1}\) was obtained, as shown in Figure 6. By analyzing the results in Figure 6, it was found that structural reliability decreased with the increase of the weight coefficient \(\alpha_{1}\), which means that the reliability of the structure is reduced and failure probability increases. Meanwhile, it was also found that the structural reliability was stable when the weight coefficient \(\alpha_{1}\) was [0.4, 0.6]. Therefore, the weighting factors \(\alpha_{1}\) = 0.4, \(\alpha_{2}\) = 0.6 are recommended for the SRHPC frame structures.  
Using the procedures mentioned above to optimize the SRHPC frame structure shown in Figure 4, we obtained the results shown in Figure 7 and Table 5. By analyzing the results in Figure 7 and Table 5, it was found that the initial cost of the structure declined by 31.0%, while the structure failure loss expectation only increased from 31.6% of initial cost to 38.2%. The life-cycle total cost of the structure dropped by 27.31% after optimization when the optimization method described in this paper was adopted. Therefore, the purpose of this optimization method was achieved.  
Figure 5: The flow chart of the optimization design of an SRHPC frame structure.  
Figure 6: The relationship between reliability and weight coefficient \(\alpha_{1}\).
# Numerical Evaluation of Hot Air Recirculation in Server Rack
###### Abstract  
In air-cooled data centers, hot aisle containment is used to separate hot and cold air from mixing to improve cooling effectiveness. However, this creates a significant pressure imbalance between the cold and hot aisles, with the latter being high. The hot aisle high pressure creates bac pressure that pushes against the server-unit fan system, which subsequently results in hot recirculation and insufficient server-unit cooling. This study examines the application of series-configured server fans with the intention of increasing the system pressure head to overcome the hot aisle containment back pressure and eliminate server hot air recirculation. A detailed computational fluid dynamics model for the Dell 2950 2U server is calibrated and validated using existing experimental test results. Furthermore, the impact of changing the server fan system to a series configuration by adding four or more server fans is investigated under different hot-aisle pressure conditions. It was found that changing the server fan system configuration from parallel to series arrangement positively improved the available system static pressure, but it did not result in the elimination of hot air recirculation. The server with the series-configured fan system experienced an average inlet air temperature increase of 9% when compared to the original server under similar conditions. This study serves as a base for integrating liquid and air-cooling systems to form hybrid cooling systems for high-density racks in legacy data centers.  
computational fluid dynamics; data center; server; thermal design power; hot air recirculation; back pressure; temperature distribution +
Footnote †: journal: Applied Sciences
###### Abstract  
This research investigates the performance of small concrete beams that are reinforced with glass fiber-reinforced polymer (GFRP) bars, basalt fiber-reinforced polymer (BFRP) bars, and traditional steel bars. It addresses the limitations of traditional steel reinforcement, emphasizing the need for alternative strategies. Fiber-reinforced polymer (FRP) materials, including GFRP and BFRP, are examined for their mechanical characteristics compared to steel. The experimental program focuses on ultimate load-bearing capacity, deflection, deformation at different load levels, and failure modes. The concrete specimens, prepared according to Eurocode, consist of six small concrete beams measuring 80 \(\times\) 120 \(\times\) 1100 mm with varied reinforcements. The study reveals that GFRP-reinforced beams outperform BFRP and steel reinforcements in ultimate load-bearing capacity, showcasing enhanced structural performance. The GFRP-reinforced beams exhibit capacity and resilience characteristics surpassing those of both BFRP and steel, whereas the deflection observed was higher on both fiber-reinforced beams than on the steel-reinforced beams. The examination of failure modes reveals that the concrete beams that were reinforced with FRP bars showed a bending property before failure, while those reinforced with steel broke easily without bending much. This comprehensive research contributes to advancing our understanding of FRP materials' application in concrete structures, paving the way for further optimization and overcoming limitations in reinforcement materials.  
BFRP bars; GFRP bars; concrete beam; steel bars +
Footnote †: journal: Applied Sciences
## 1 Introduction
The temperature dependence of magnetization can also be influenced by the atomic nature of the core/shell interface where interface magnetic anisotropy is introduced and may vary the total magnetic anisotropy of the particle significantly [6]. For core/shell nanoparticles, magnetic properties are very interesting. The magnetic properties of such a core/shell system can be tuned by changing the shell material and dimensions. Recently, tremendous efforts have paved the way to control magnetic properties by varying the core/shell phase compositions, shape, and dimensions [29; 30; 31; 32; 33; 34; 35]. Though, there are reports regarding the magnetic effect in different core/shell systems, the effect of shell thickness and field cooling on \(M_{\text{S}}\) and its temperature dependence, \(M_{\text{S}}(T)\) in ferrimagnetic/ferrimagnetic core/shell system was not reported (in our understanding). In this work, we have conducted detailed magnetic measurements on Fe\({}_{3}\)O\({}_{4}\)/\(\gamma\)-Fe\({}_{2}\)O\({}_{3}\) core/shell nanoparticles of fixed core diameter and variable shell thickness. The core diameter of all samples is 8 nm, while the shell thickness was varied from 1 to 5 nm. We investigated the effect of shell thicknesses and field cooling on \(M_{\text{S}}\) and \(M_{\text{S}}(T)\).