esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The key ecosystem features such as vegetation cover (e.g., Leaf Area Index (LAI)) [6; 21], water and heat fluxes (e.g., evapotranspiration (ET)) [21], and photosynthesis (e.g., Gross Primary Productivity (GPP)) [27] et al. reflect the close linkage between water and plant physiology. The quantitative analysis of the time-lag and cumulative effects of the interactions between changes in water conditions and vegetation ecosystem functions at the pixel scale will deepen our understanding of the interactions between water and the biosphere [6; 9; 10] and help to implement more effective vegetation management strategies. The Songnen Plain in northeastern China is located in the semi-arid and semi-humid climate transition zone with poor ecosystem stability. Coupled with human activities such as long-term overgrazing and unreasonable reclamation, vegetation is more sensitive to drought [28; 29]. To the best of our knowledge, there are few reports using remote sensing data to analyze the interaction between drought and vegetation ecosystem function in the Songnen Plain over time and the cumulative effects. The objectives of this study were to (1) investigate the characteristics of drought and ecosystem function changes in the Songnen Plain from 2000 to 2020 based on trend analysis of SPEI and time-series Moderate Resolution Imaging Spectroradiometer (MODIS) LAI, ET, and GPP data, (2) determine the bidirectional causal relationship between drought and ecosystem function by Granger causality test analysis, and (3) evaluate the changes in drought and ecosystem function over time (time-lag effect) and cumulative effects after mutual impact using impulse response function analysis.
# Design of a Noise Mitigation System Using Lightweight Graded Micro-Porous Material
## 2 Establish Predictive Methods to Calculate Flow Properties of Polydisperse Particles
### Polydisperse Smooth-Surface Glass Bead Composite Material  
#### 2.1.1 Polydisperse Beads Specimen for Normal Incidence Impedance Test  
Polydisperse bead specimens of 100 mm diameter and 25 mm thickness were made by mixing equal-weight 1, 2, and 3 mm diameter glass beads. The specimen fabrication was the same as previously explained and is described in detail in references [33; 35]. We measured absorption coefficient versus frequency using a normal incidence impedance tube with a two-microphone setup (see Figure 3) according to the ASTM E1050-12 standard [39] for the frequency range of 250 to 1600 Hz. The incident sound pressure level (SPL) was 100 dB. The plane sound waves pass through a sample, placed against a rigid backing, and get reflected from the rigid backing, resulting in a pressure and velocity difference between the incidence and the reflected waves. The difference is then measured by two microphones on the tube to calculate the absorption coefficient. The test is limited to normal incidence and requires proper calibration and flat surface specimens; at high frequencies, the tube diameter is small which can affect the measurement resolution, etc. The measured absorption coefficient that is in the form of signals was smoothened (smoothening factor, RL = 0.15) using a robust locally weighted regression (rLowess) method [40] in MATLAB R2024a. A rLowess method that was applied to a fatigue model for data reduction [41] is adapted here. The experimental absorption coefficient versus frequency response of the 25 mm thick specimen is shown as a solid line (red color) in Figure 4. This experimental result is reported in our previous works [35] and re-reported in this work, as it was used to calculate the flow properties by the multivariable-fit inverse method based on the G-JCAPL method.
## 3 MIMO Tapped Delay Line Channel Model  
A MIMO channel can be described using a channel matrix, each element of which corresponds to one of the propagation paths between the transmitting and receiving antennas, in which case the MIMO channel with memory can be described by a three-dimensional matrix:  
\[\mathbf{H}(l)=\begin{bmatrix}h_{11}(l)&\ldots&h_{N_{T}1}(l)\\ \vdots&\ddots&\vdots\\ h_{1N_{R}}(l)&\ldots&h_{N_{R}N_{T}}(l)\end{bmatrix}\quad l=0,1,\ldots,L, \tag{5}\]  
where \(N_{T}\) and \(N_{R}\) are the number of transmitting and receiving antennas, respectively, and \(L\) is the channel impulse response length in the samples.  
In this case, the propagation of the signal through the channel is expressed mathematically as the multiplication of the transmitted signal vector by the channel matrix for  
Figure 3: FBMC-OQAM data transmission model.  
each beam. For tapped delay line channels, the expression for the received FBMC-OQAM MIMO signal, provided that it passes through the channel with memory, is defined as:  
\[\mathbf{Y}(k)=\sum\limits_{l=0}^{L-1}\mathbf{H}(l)\mathbf{S}(k-l)+\mathbf{n}(k), \tag{6}\]  
where \(\mathbf{Y}(k)=\left[y_{1}(k),y_{2}(k),\dots,y_{N_{R}}(k)\right]^{T}\) is the column vector of received signals, \(\mathbf{S}(k)=\left[s_{1}(k),s_{2}(k),\dots,s_{N_{T}}(k)\right]\) is the row vector of transmitted signals, \(\mathbf{n}(k)=\left[n_{1}(k),n_{2}(k),\dots,\right.\)\(\left.n_{R}(k)\right]^{T}\) is the noise vector, where \(n_{i}(k)\) is the zero-mean white Gaussian noise, and \(k\) is the discrete-time index.
## 4 Classification of MAC Protocols Based on Channel
### Cluster-Based Multi-Channel MAC Protocols
#### 4.9.3 Acb Mac  
It is a novel MAC protocol based on a clustering MAC with a blockchain framework providing reliable and secure communication. It uses the decentralized blockchain concept for vehicle authentication and registration before they communicate. The control packet of IEEE 802.11 was modified for handling hidden nodes, packet drop, and packet overloading on channel access. Cluster-based concepts provide a faster and efficient method of communication. The messages are divided into safety and non-safety messages and groups the vehicles as general vehicles and emergency vehicles. Priorities are set for general and emergency vehicles so that there is less delay on transmission of safety messages.  
The protocol minimizes computational as well as storage overheads by efficient blockchain implementation. The protocol can be enhanced for different attacks and threats with reputation management, including more security features. A clustering based on the SDN architecture can be implemented in future enhancement for better network performance.
## 5 Experiments
### Experimental Effect  
Detection on the DOTA dataset is shown in Figure 13. It can be seen that the method proposed in this paper can detect densely distributed targets and objects with large aspect ratios, demonstrating good robustness.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**BELOPS/s**} & **DOTA1.0** & **DOTA1.5** & **HRSC2016** & **FPS/Frame** \\  & & **mAP/\%** & **mAP/\%** & **mAP/\%** & **Change** \\ \hline ResNet-50 & 197.1 & 65.5 & 63.6 & 52.1 & 23.6 \\ ResNet-152 & 243.0 & 67.8 & 68.9 & 58.2 & 17.1 \\ ConvNext-XL [34] & 249.0 & 83.9 & 72.9 & 89.3 & 17.5 \\ Inception-ResNet101 & 204.0 & 82.6 & 79.5 & 89.1 & 24.4 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance of different backbone networks using the method proposed in this paper.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Backbone**} & **DOTA1.0** & **DOTA1.5** & **HRSC2016** & **FPS/Frames** \\  & & **mAP/\%** & **mAP/\%** & **mAP/\%** & **Change** \\ \hline R2 CNN [35] & ResNet101 & 72.3 & 67.5 & 79.5 & 5.5 \\ RetinaNet & ResNet101 & 77.4 & 68.7 & 86.3 & 22.4 \\ ReDet [36] & Darknet53 & 78.6 & 72.7 & 82.1 & 24.5 \\ R3 Det [37] & ResNet101 & 79.8 & 73.4 & 83.5 & 12.0 \\ FCOS & ResNet101 & 80.3 & 74.1 & 90.3 & 17.5 \\ S2A-Net [38] & ResNet101 & 81.4 & 76.3 & 81.2 & 2.0 \\ EfficientNet-B7 [39] & ResNet101 & 62.1 & 53.2 & 80.7 & 12.8 \\ Oriented R-CNN & ResNet101 & 81.8 & 78.4 & 86.2 & 21.1 \\ Ours & FAS-Inception-ResNet101 & 82.6 & 79.5 & 89.1 & 24.4 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of experimental results between the proposed method and mainstream dense rotated-image object detection models.
# MDFULog: Multi-Feature Deep Fusion of Unstable Log Anomaly Detection Model
## 4 Experiment and Analysis
### Result Analysis
#### 4.3.3 Robustness and Efficiency  
This article also evaluated the robustness and efficiency of different log parsing methods on various log volumes. In this experiment, the Spell [7,8], AEL [40], Drain [9], IPLoM [6], and MDFULog models were selected for comparison, and three datasets were selected, namely HDFS, BGL, and Android. Figure 7 shows the Accuracy of the log parsing methods on different log volumes. The horizontal axis denotes the number of logs, and the vertical axis denotes the resolution accuracy for different numbers of log statements. IPLoM uses an iterative partition strategy to divide log messages into groups according to message length, token location, and mapping relationship. The effect of IPLoM on HDFS data was poor. With the increase of the log data, the Accuracy was less than 0.8. On the BGL data, Spell also showed a significant fluctuation. Spell uses the longest common subsequence algorithm to parse logs in a stream. When the log data were complex and changeable, the effect of Spell decreased significantly. On the Android data, MDFULog also dropped slightly when the number of logs increased. Android contains many log templates, and the parsing is much more complicated. Drain applies a fixed-depth tree structure to represent log messages and effectively extracts common templates. Compared with the other algorithms on the Android data, it performed better. The experimental results showed that MDFULog has good robustness to different log volumes and types of log entries when changing the log volume.  
Figure 8 shows the efficiency of the log resolution method on different log volumes. The horizontal axis represents the number of logs, and the vertical axis represents the time taken to parse a different number of log statements. The parsing time increases with the increase of the logs. With the linear growth of the log size, Drain, IPLoM and MDFULog had high efficiency on different datasets. AEL divides logs into multiple groups. When the BGL data volume is large, the reason for the low efficiency is that it needs to compare multiple logs. Other algorithms cannot scale effectively with the increase or decrease of the log volume. The MoLFI algorithm cannot parse 1 GB of complex log data in 5 h. The type of log also affects the efficiency of the log parsing algorithm. Log parsing is typically successful when the log data are straightforward and there are not many different event templates. Since HDFS logs, for instance, only include 30 event templates, all log resolution  
Figure 7: Accuracy of log parsing methods on various log volumes. (**a**) HDFS. (**b**) BGL. (**c**) Android.  
methods can resolve 1 GB of data each hour. However, the parsing procedure will be slow for logs containing many event templates (such as Android).
# Biosynthesis of Zinc Oxide Nanoparticles Using Seaweed: Exploring Their Therapeutic Potentials
## 2 Materials and Methods
### Applications of Biosynthesisized Zinc Oxide Nanoparticles  
#### 2.5.1 Antimicrobial Activity of Biosynthesisized Zinc Oxide Nanoparticles  
Two Gram-negative bacterial pathogenic strains (_Escherichia coli_ and _Klebsiella pneumoniae_) were obtained from the Bacteriology Laboratory, Faculty of Science, Tanta University. Microbial suspensions were prepared in a sterile saline solution (0.9% NaCl) at a turbidity of 106 to 108 CFU/mL. The bacterial strains were cultivated in MacConkey solid medium. In addition, two fungal strain, _Candida albicans_ and _Aspergillus niger_, were cultivated on Sabouraud Dextrose agar medium. The antimicrobial activities of the biosynthesized ZnO-NPs were evaluated using the disk diffusion method [22]. After being placed in Petri dishes that had already been pre-inoculated with 100 mL of each microbe suspension, sterilized paper disks loaded with 15, 30, and 60 g/disk of ZnO-NPs were incubated at 37 \({}^{\circ}\)C. Standard antibiotics were used as a positive control, and seaweed extracts and zinc acetate solution were used as negative controls. The inhibition zone widths were measured after the incubation period. The positive antibacterial controls were ciprofloxacin (CIP), amoxicillin (Ax), and ampicillin-sublactam (SAM). The positive antifungal controls were fluconazole (FLU), itraconazole (ITC), and metronidazoleacole (MT) [23].
## 4 Results and Discussion
In colors are reported the expected extension of land flooding in 2050 and 2100 for **(a)** RCP 2.6 and **(b)** RCP 8.5 climatic scenarios for a mean land subsidence at 4 \(\pm\) 2.5 mm/year. Maximum rate of land subsidence for this area is 10 \(\pm\) 2.5 mm/year. The expected maximum loss of land is 1.528 km\({}^{2}\) (high-resolution maps are inserted in Supplementary Materials Maps S12 and S13). The potential extension of the flooded area depends on the topographic features and the expected RSRL. The largest flooding is expected in the coastal plain of Catania and the Lentini area, where the complex topography and the San Leonardo channel can drive a marine ingression up to 5 km inland, both for the RCP 2.6 and 8.5 scenarios (Figure 10). In the Augusta bay, the expected extension of the potential flooded area is relevant in the northern sector, reaching up to 500 m and 1 km inland for the RCP 2.6 and RCP 8.5 scenario, respectively (Figure 11). The most important flooding is expected for the coast of Siracusa, where the flooded area will possibly extend for more than 2 km inland in both RCP scenarios (Figure 12), involving the local salt-marsh, part of the city and the railways. The RSRL scenario for the pocket beach of Piccio, located in the proximity of Asinaro river mouth (Figures 13-15), is less critical both for the RCP 2.6 and RCP 8.5 scenarios, but still with a partial submersion of the backshore. The natural reserve of Vendicari is exposed to a potential flooding extending up to 1 km inland, involving the lagoons of Pantano Roveto and Pantano Piccolo (Figure 12). The expected RSRL in Marzamemi bay, may cause a flooding extension of about 300 m inland (Figure 13), determining the potential loss of the foreshore and backshore and leading to the abandonment of the historical fisherman village. Concerning the role of the dune system in counteracting the SLR, field evidence and optical satellite images show that over the last two decades, the beach-dune systems have been subjected to a continuous retreat in consequence of multiple factors (Table 3), mainly, the sea-level rise, the increasing intensity of extreme marine events, VLM and deficit on the sediment supply in a coastal system. The high rates of land subsidence highlighted by InSAR, suggest that future scenarios could be driven by overtopping processes, rollover or dune breaching [112; 113], probably similar in timing and architecture of resulting deposits, to retrogradation processes occurred during the marine transgression after the last glacial maximum (LGM) in different low-gradient settings [114; 115; 116]. In particular, the time series of satellite images of the sandy coasts at Siracusa between 2009 and 2020 (Figure 8, Table 3), show the occurrence of dune breaching near the salt-marshes over the last decades. Considering the expected accelerated SLR trend in the RCP 8.5 emission scenario, the beach-dune system will be more vulnerable in the next years with respect to the past decades due the combined effects of SLR, VLM and negative mass balance on the coasts.
# Detection of Forest Tree Losses in Cote d'Ivoire Using Drone Aerial Images
## 2 Materials and Methods
### Overall Methodology
#### 2.2.4 Orthomosaic and Digital Surface Model Production  
Our methodology for processing and analyzing the aerial images (Figure 2) was based on the principles of photogrammetry and stereoscopy. The first step was performed in the Agisoft Photoscan Professional software (version 1.4.0) and includes orthorectification and mosaicking steps. The process is subdivided into the alignment of aerial images, the production of a scatter plot, gridding, and texturing, allowing us to generate a digital surface model (DSM) and an orthomosaic [24; 28; 32]. As the images are located by GPS, alignment aims to reconstruct the acquisition geometry of the aerial images from the identification of link points; point cloud aims to produce a 3D model, and mesh aims to reconstruct an analysis grid to facilitate orthomosaic production; finally, texturization aims to create the texture for the 3D model [33]. The orthomosaics and DSMs were cleaned to remove poor (blurred) quality due to edge effects. The quality of photogrammetric processing was evaluated following several parameters, including the percentage of aerial image alignment and projection errors associated with the aerial image stitching process.
## 2 Materials and Methods
### Methods
#### 2.2.2 Model Calibration and Validation Methods
\begin{table}
\begin{tabular}{c c c c} \hline
**PFTs** & **\(\mathrm{g_{1}}\)** & **Sites for Calibration (Latitude\({}^{\circ}\), Longitude\({}^{\circ}\))** & **Sites for Validation (Latitude\({}^{\circ}\), Longitude\({}^{\circ}\))** \\ \hline \multirow{2}{*}{CSH 1} & \multirow{2}{*}{1.14} & US-KS2 (28.61, \(-\)80.67) [60; 67] & RU-Vrk (67.05, 62.94) \\ \hline \multirow{4}{*}{CRO} & \multirow{4}{*}{10} & IT-BCi (40.52, 14.96) [68] & \multirow{4}{*}{FR-Gri (48.84, 1.95) [71]} \\  & & IT-Cas (45.07, 8.72) & & BE-Lon (50.55, 4.75) [72] \\  & & DE-Seh (50.87, 6.45) [69] & & DE-Kli (50.89, 13.52) [73] \\ \hline \multirow{4}{*}{DBF} & \multirow{4}{*}{1.66} & IT-Col (41.85, 13.59) [74] & IT-RoI (42.41, 11.93) [76] \\  & & FR-Fon (48.48, 2.78) [75] & IT-RoI (42.39, 11.92) [77] \\  & & DE-Inf (51.33, 10.37) [70] & DE-Hai (51.08, 10.45) [78] \\ \hline \multirow{4}{*}{ENF} & \multirow{4}{*}{0.62} & IT-Ren (46.59, 11.43) [79] & IT-Lav (45.96, 11.28) [83] \\  & & CZ-BK1 (49.50, 18.54) [80] & CH-Dav (46.82, 9.86) [84] \\  & & NL-Loo (52.17, 5.74) [81] & DE-Obe (50.79, 13.72) \\  & & RU-Fyo (56.46, 32.92) [82] & DE-Tha (50.96, 13.57) [85] \\ \hline \multirow{2}{*}{EBF} & \multirow{2}{*}{0.62} & FR-Pue (43.74, 3.60) [86] & IT-Cpz (41.71, 12.38) [87] \\ \hline \multirow{4}{*}{GRA} & \multirow{4}{*}{1.14} & IT-Tor (45.84, 7.58) [88] & IT-MBo (46.01, 11.05) [91] \\  & & CH-Cha (47.21, 8.41) [89] & CH-Fru (47.12, 8.54) [92] \\  & & CH-Oe1 (47.29, 7.73) [90] & DE-Gri (50.95, 13.51) [73] \\  & & CZ-BK2 (49.49, 18.54) & \\ \hline \multirow{2}{*}{MF} & \multirow{2}{*}{0.62} & CH-Lae (47.48, 8.36) [93] & BE-Vie (50.30, 6.0) [95] \\  & & BE-Bra (51.03, 6.0) [94] & \\ \hline \multirow{2}{*}{OSH} & \multirow{2}{*}{10} & ES-Lgs in 2008 (36.93, \(-\)2.75) [96] & ES-Lgs in 2007 (36.93, \(-\)2.75) [96] \\ \hline \multirow{2}{*}{WET} & \multirow{2}{*}{0.62} & CZ-wet (49.02, 14.77) [97] & DE-SfN (47.81, 11.33) [99] \\  & & DE-Spw (51.89, 14.03) & DE-Akm (53.87, 13.68) \\ \hline \multirow{4}{*}{WET} & \multirow{4}{*}{0.62} & \multirow{4}{*}{\({}^{1}\) Abbreviations: closed shrublands (CSH), croplands (CRO), deciduous broadleaf forest (DBF), evergreen needleleaf forest (ENF), evergreen broadleaf forest (EBF), grasslands (GRA), mixed forest (MF), open shrublands (OSH), and wetland (WET). Site locations are shown in Figure 1. Please refer to the official website ([[https://fluxnet.org/sites/site-list-and-pages/](https://fluxnet.org/sites/site-list-and-pages/)]([https://fluxnet.org/sites/site-list-and-pages/](https://fluxnet.org/sites/site-list-and-pages/)), accessed on 19 February 2023) for more detailed descriptions.} \\ \hline \end{tabular}
\end{table} 
Table 1: Sites for model calibration and validation and the calibrated \(\mathrm{g_{1}}\) for nine plant functional types (PFTs).
# Internet of Drones: Routing Algorithms, Techniques and Challenges
## 5 Future Research Directions
### Security  
The last but major concern and challenge for WSNs and UAV-based WSNs is network security. Failures of data transfers or data breaches occur as a result of compromised security. Malicious activities, extraction of information, device duplication, and phishing can prove to be major threats to network security. It has been observed that the attacks on IoT devices can be mitigated at the cost of performance. The heterogeneity of devices poses as a problem to IoT. It was noted that some networks based on IoT require constrained security measures. Security is therefore a main parameter to be ensured for any device, whether WSN, UAWSN, or IoT.  
Cluster based WSN are also not free from security attacks. The network structure and routing protocols should account for the security concerns. The techniques for network security management are hashing and key-based encryption. For added security, WSNs can be integrated with wireless or wire-line security systems. In smart cities, the security is seldom compromised. This is due to their robust infrastructure. Mobile systems, however, must have an integrated security system within the WSN environment.
# Optimizing Routing Protocol Design for Long-Range Distributed Multi-Hop Networks
## 6 Simulation Verification
### Simulation Parameter Settings  
A simulation scenario with 200 end nodes and 1 gateway distributed in a 100 m \(\times\) 50 m \(\times\) 18 m space was set up in the simulation software, with specific simulation parameters, as shown in Table 3.  
The business types of terminal nodes in large buildings are simplified to simulate business characteristics in a stratified large-building scenario. The specific parameters for business types are set as shown in Table 4.  
In the simulation experiments of fixed-layout scenarios, where the number and locations of all nodes are fixed, business nodes are randomly generated for each simulation group. The experiments involve 20 groups, and after the end of each simulation, the load balance of the nodes is calculated. In the simulation experiments for fixed-node-number scenarios, 20 groups of simulations are conducted, with the locations of nodes randomly changed within each simulation group. Each simulation group involves 20 repetitions based on the fixed-layout scenario, and the average result of these 20 repetitions is considered the outcome. In mixed-scenario simulations, where the number of terminal nodes ranges from 60 to 200, simulations with fixed node numbers are conducted for every 20 additional terminal nodes. This approach ensures changes in both the number and positions of nodes in the network.