esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Getting accurate and up-to-date information on the cultivated land area and spatial arrangement of maize, an important staple crop in the Ningxia Hui Autonomous Region, is very important for planning agricultural development in the region and judging crop yields. This work proposes a machine-learning methodology to extract corn from medium-resolution photos obtained from the Sentinel-2 satellite. The Google Earth Engine (GEE) cloud platform is utilized to facilitate the process. The identification of maize cultivation regions in Huinong District in the year 2021 was performed through the utilization of support vector machine (SVM) and random forest (RF) classification techniques. After obtaining the results, they were compared to see if using the random forest classification method to find planting areas for maize was possible and useful. Subsequently, the regions where maize was cultivated were combined with image data from the Moderate Resolution Imaging Spectroradiometer (MODIS), which has a high temporal resolution. The Normalized Difference Vegetation Index (NDVI) contemporaneous difference method, which gives regular updates, was then used to track the growth of maize during its whole growth phase. The study's results show that using the GEE cloud platform made it easier to quickly map out data about where to plant maize in Huinong District. Furthermore, the implementation of the random forest method resulted in enhanced accuracy in extracting maize planting areas. The confusion matrix's evaluation of the classification performance produced an average overall accuracy of 98.9% and an average Kappa coefficient of 0.966. In comparison to the statistics yearbook of the Ningxia Hui Autonomous Region, the method employed in this study consistently yielded maize-planted area estimates in Huinong District with relative errors below 4% throughout the period spanning 2017 to 2021. The average relative error was found to be 2.04%. By combining MODIS image data with the NDVI difference model in the year 2021, the high-frequency monitoring of maize growth in Huinong District was successful. The growth of maize in Huinong District in 2021 exhibited comparable or improved performance in the seedling stage, nodulation stage, and the early stage of stannite pulling and spitting, possibly attributed to the impact of climate and other relevant elements. After that, the growth slowed down in August, and the percentage of regions with slower growth rates than in previous years gradually increased. However, overall, the growth of maize in Huinong District during the year 2021 showed improvement relative to the preceding years. The present study introduces a novel approach that demonstrates the capability to accurately extract corn crops in the Huinong District while simultaneously monitoring their growth at a high frequency.  
**Keywords: growth monitoring; Google Earth Engine; machine learning; Sentinel-2; MODIS**
# A Graph Convolutional Network-Based Method for Congested Link Identification
## 2 Proposed Model
### Congestion Inter Model  
Equation (7) formulates the relationship between the route status \(y_{i}\) and the link statuses \(x_{i}\) as \(y_{i}=\sum\limits_{j=1}^{n}(d_{ij}\cap x_{j})\). The goal of congested link identification is to find the link state \(x_{i}\) by solving Equation (7), which gives **Y** and **D**. If the state \(x_{i}\) of the link \(l_{i}\) can be uniquely determined by Equation (7), then the link \(l_{i}\) is identifiable. However, for end-to-end measurements in real networks, there are usually more links than the measured  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Links**} & \multicolumn{8}{c}{**Routes**} \\ \cline{2-9}  & \(\mathbf{r_{1}^{1}}\) & \(\mathbf{r_{1}^{2}}\) & \(\mathbf{r_{2}^{1}}\) & \(\mathbf{r_{2}^{2}}\) & \(\mathbf{r_{3}^{1}}\) & \(\mathbf{r_{3}^{2}}\) & \(\mathbf{r_{4}^{1}}\) & \(\mathbf{r_{4}^{2}}\) \\ \hline \(l_{1}\) & 1 & 0 & 1 & 0 & 1 & 0 & 1 & 0 \\ \(l_{2}\) & 0 & 1 & 0 & 1 & 0 & 1 & 0 & 1 \\ \(l_{3}\) & 1 & 0 & 1 & 0 & 0 & 0 & 0 & 0 \\ \(l_{4}\) & 0 routes involved, indicating that there are more unknown variables than known variables in tomography, making it a typical ill-posed problem [33].  
From a mathematical point of view, there are infinitely many solutions for the ill-posed problem of congested link identification. As a result, most state-of-the-art methods either make some assumptions about the nature of the various solutions or try to retrieve additional constraints to find a particular solution.
# A Secured Intrusion Detection System for Mobile Edge Computing
## 1 Introduction
* Additionally, a dedicated secure communication layer is integrated into the framework to mitigate potential attacks on the intrusion detection system itself, ensuring the overall robustness of the proposed solution. Through this research, we strive to contribute to the ongoing discourse on securing mobile computing environments, emphasizing the critical role that intrusion detection plays in fortifying the integrity and confidentiality of data processed at the edge. The subsequent sections will delve into the intricacies of our secured edge-based intrusion detection framework, its design principles, implementation details, and comprehensive evaluation, shedding light on its efficacy in addressing the evolving security challenges within the realm of mobile computing. The rest of the paper is organized in such a way that Section 2 describes the comprehensive literature background and related state-of-the-art methods. Section 3 explains the types of attacks on mobile edge computing discussed in this study. Further, Section 4 presents the SEC-IDS proposed framework in detail. Section 5 discusses the implementation, results, and comparative analysis with the existing methods. Lastly, we conclude our study in Section 6.
# A MAC Protocol to Support Monitoring of Underwater Spaces +
## 3 Description of the UWSO-TDMA Protocol
### Enhancements  
The basic allocation policy contemplates only one slot per node. However, some nodes have more information to transmit, as they become the gateway for the previous sub-frame or upstream nodes. The synchronizing nodes aggregate the information from all of the nodes in the following sub-frame with their own information. In the case of a large tree structure, synchronizing nodes close to the SB will have a large amount of information to transmit and still use only one slot. In order to solve this situation, once the network topology and structure is learned by the clock synchronization procedure, some of the synchronizing nodes may be allocated more than one slot for transmitting the aggregated messages.  
TDMA MAC protocols usually introduce an important delay, since messages require as many frames as hops to reach the sink. In cases like the one proposed here, it is useful to reverse the transmission order. As every node is synchronized and aware of the exact moment in which they should transmit, the frame may be turned around. In this way, a message starting in the farthest node to the SB in the network will be transmitted first, and as being received by the successive synchronization nodes, they are aggregated to the different sub-frame messages until reaching the SB in the period of just one frame. With this strategy, the delay introduced by the MAC is reduced to just one frame.  
The previous enhancement facilitates another one, which is the possibility of implementing a streaming solution for this kind of network. In the actual state, the TDMA assumes there is a short transmission window in which all nodes are ready to receive and transmit followed by a long silent window in which all of the nodes sleep, providing good energy management. However, in certain cases or in particular scenarios, the reverse transmission order of the frame allows an almost continuous transmission of information. In fact, as the slot allocation guarantees that there are no collisions, a new frame may be started at two slots' time distance. This may be used in the cases in which the slot time is not enough for sending the data collected at the nodes, for example audio or video streaming.  
Figure 9: Data transmission flowchart.  
Even more, the network may operate in different modes allowing low and high energy consumption according to some particular situation.
# Technology Identification, Evaluation, Selection, and Optimization of a HALE Solar Aircraft
## 3 Energy Balance Analysis
### Weight Estimation  
Existing solar aircraft structure weight prediction equations are based on data of actually designed aircraft and empirical equations. [PERSON] [22] suggested that the sum of all components that make up the airframe structure, such as spar, leading edge, trailing edge, and ribs, is the total weight, but this methodology is very detailed, and there are many variables that must be set initially, which is limited. [PERSON] [23] proposed a prediction equation using the aspect ratio (_AR_), main wing area (_S_), and the number of boom tails (_n_) as variables, as shown in Equation (18), based on the data of sailplanes with twin boom tails.  
\[W_{S}=8.763n^{0.311}S^{0.778}AR^{0.467} \tag{18}\]  
However, this is optimized for a large scale sailplane, so [PERSON] proposed a new equation as shown in Equation (19) using the least square fitting method based on the sailplane data [7].  
\[W_{S}=0.44S^{1.55}AR^{1.3} \tag{19}\]  
Usually the solar aircraft weight prediction equation produces excessive prediction than the actual weight since it is applied to the configuration of a large scale sailplane and twin boom tail. Therefore, in this study, a newly derived weight prediction equation was used based on the data of a successful human-powered aircraft. Human-powered aircraft, like solar aircraft, operate in the similar low Reynolds number and have a similar weight range using light-weight materials such as carbon composites. In other words, based on the data of human-powered aircraft, using the aspect ratio and main wing area as variables, a prediction equation can be expressed through regression analysis [24].  
\[W_{S}=-0.0008 AR^{2}-0.005S^{2}+0.53 AR+12.88S+0.027 AR\cdot S-10.46 \tag{20}\]  
The payload \(W_{P}\) is fixed according to the design requirements, and the solar cell weight \(W_{SC}\) can be calculated using Equation (21). \(m_{solar}\) is the weight per unit area (kg/m\({}^{2}\)), \(S\) is the wing area (m\({}^{2}\)), and \(S_{ff}\) is the solar cell attachment ratio (%) to the wing area [17].  
\[W_{SC}=m_{solar}S_{ff}S \tag{21}\]  
Battery weight \(W_{B}\) can be obtained using Equation (22) [17].  
\[W_{B}=\frac{E_{available,B}}{\dot{E}_{B}} \tag{22}\]  
where \(E_{available,\ B}\) is the available battery energy (Wh), \(\dot{E}_{B}\) is the battery energy density (Wh/kg).  
When the configuration of the referenced Zephyr S is applied, estimated weights of structure, payload, solar cell, motor and propeller, and battery are shown in Table 4. Payload was set to 5 kg, which is the payload value of Zephyr S, and motor and propeller is assumed to be 3 kg. Figure 5 shows the weight breakdown of a reference HALE solar aircraft.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Variables** & **Value** & **Unit** \\ \hline Structure & 48.9 & kg \\ Payload & 5.0 & kg \\ Solar cell & 12.1 & kg \\ Motor \& propeller & 3.0 & kg \\ Battery & 33.3 & kg \\ Total & 102.3 & kg \\ \hline \hline \end{tabular}
\end{table}
Table 4: Initial weight distribution of HALE solar aircraft.
## 1 Introduction
One approach to mitigate the motion effects is the utilization of mechanical compensation systems, where the lidar is operated on actively or passively stabilized instrument platforms [15; 23; 28; 29]. Size, weight, and energy constraints make this form of motion compensation, however, infeasible for many buoy-based applications. Therefore, motion correction algorithms were developed and applied to FLS data during the post-processing stage [18; 20; 25; 30]. The testing and validation of the efficiency of the corresponding compensation algorithms requires systematic experimental efforts. As highlighted in [PERSON] et al. [31] and [PERSON] and [PERSON] [27], systematic and controlled motion experiments are to date very scarce. Only a controlled setup of co-located moving and stationary lidar wind profilers allows distinguishing between the motion-induced errors in lidar measurements and potential systematic and random errors arising from the lidar measurement principle, setup, or the atmospheric conditions during the measurements. This manuscript addresses this research gap by detailing and presenting a controlled-motion lidar experiment conducted in August 2011 at the campus of the University of Agder in Grimstad, Norway. In this experiment, two different types of profiling wind lidar systems, the pulsed WindCube V1 (manufactured by Leosphere, France) and the continuous-wave (CW) ZephIR 300 (manufactured by Natural Power, UK) were installed on a 6 degree-of-freedom (DOF) hexapod motion platform and exposed to 55 different controlled motion test cases. Two fixed-reference lidars of corresponding types were positioned 5 m from the platform. Most of the test cases were characterized by sinusoidal motion (in one or two degrees of freedom) with constant amplitude and frequency. The collected data from this experiment are analyzed statistically, highlighting the impact of various motion patterns and characteristics on measurements of mean wind speed and estimation of turbulence intensity. In addition, the performance of novel motion correction approaches to reduce the motion-induced error in various motion scenarios is evaluated.
# FL-MD3 QN-Based IoT Intelligent Access Algorithm for Smart Construction Sites
###### Abstract  
With the deployment of fifth-generation (5G) mobile communication technology and rapid advancements in artificial intelligence and edge computing, smart construction sites have emerged as a critical direction for the construction industry's transformation and upgrading. However, existing intelligent Internet of Things (IoT) access algorithms often struggle to simultaneously meet practical requirements for high-efficiency data transmission rates, low latency, and secure privacy-aware access in the dynamic and complex environments of smart construction sites. To address this, this paper proposes a federated learning-based Multi-Objective Dueling Double Deep Q-Network (FL-MD3 QN)-based IoT access algorithm for multi-site, multi-modal, multi-user IoT systems under the same Base Station (BS). First, a three-objective optimization mathematical model was established. The optimization goals include maximizing data transmission rates, minimizing transmission delays, and maximizing reliability. Constraints such as bandwidth, rate, bit error rate (BER), and security/privacy are defined. Second, the FL-MD3 QN algorithm is developed to solve this optimization problem. This algorithm can adaptively adjust the access strategy to cope with the complex and ever-changing communication needs of smart construction sites and, by introducing a federated learning mechanism, it achieves collaborative optimization of multiple construction site IoT systems while ensuring user privacy. Simulation results demonstrated significant advantages of the FL-MD3 QN algorithm. For latency, it achieved markedly lower delays across multi-modal services compared to benchmark algorithms, with the shortest training time. In transmission rates, FL-MD3 QN delivered the highest average rates, particularly excelling in video services. Under high signal-to-noise ratio conditions, FL-MD3 QN achieved exceptionally low BER values. Additionally, it attained high levels in average access success rate and average reward value, confirming its robust adaptability and optimization performance in complex smart construction environments.  
s +
Footnote †: journal: Electronics Letters
# Comparative Study of Energy Efficient Routing Techniques in Wireless Sensor Networks
## 3 The Proposed Taxonomy
### Network Topology (Structure)
#### 3.6.5 Geo-Routing (Direct Communication)  
In position-based routing, sensors are aware of their geographic location, and they are addressed based on their GPS location and via node coordination. In fact, the position of the source, the intermediate nodes and the destination should be known to help the routing process, which limits the role of routing tables and can help in energy consumption.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Protocol**} & \multicolumn{2}{c}{**Mobility-Based**} & \multirow{2}{*}{**Fault**} & \multirow{2}{*}{**Energy**} & \multirow{2}{*}{**Load**} \\ \cline{2-2} \cline{4-4}  & **Mobile Sink** & & & **Mobile Sensor Nodes** \\ \hline
**MBC** & ✓ & N A & ✓ & N A \\ \hline
**FTCP-MWSN** & ✓ & ✓ & ✓ & N A \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of mobile-node-based routing protocols for WSNs.  
In general, this type of routing strategy contains two phases: a location service provides a source node with the position of the desired destination then it defines the routing path from source to destination [88].  
Geocast  
In some cases, the detection of an event or of a particular phenomenon requires the collaboration of several sensor nodes. Indeed, only one sensor node can provide limited or inaccurate information. Instead of a single node, geo-casting addresses all sensors in a particular static geographic region determined by the source node or constructed dynamically by the relaying of some sensors. Geocast routing uses different routing strategies to forward packets to the desired geographic destination, such as unicast transmission and flooding [89]. Among the geocast routing protocols used in WSNs, we find:  
* **GeoGRID**  
GeoGRID is designed as a geocasting protocol for WSNs [90]. It divides the destination region into grids. It uses a preselected routing path through the nodes to forward packets to the desired area. This increases the reliability and robustness of the protocol, but they cause a network overload. Research, such as [91], show that GeoGRID requires some optimizations that enables the workload distribution to be dynamically adjacent to extend the network lifespan.
# Shock Vibration Control of SDOF Systems with Tubular Linear Eddy Current Dampers
## 2 Tubular Linear Eddy Current Damper with Halbach Array (TLECD-HA)
### Force-Velocity Relationship of the TLECD  
The change of damping force of the ECD with the variation of relative velocity is strongly nonlinear in the condition of high velocity, whereas this relationship can be nearly linearized at low velocity. A semi-empirical and semi-analytic model proposed by [PERSON] [27] can well represent the trait of damping force in the ECD, and its applicability has been proved in ball screws with high energy density type axial eddy current damper (BS-ECD) [38]. The eddy current damping force represented by the parametric [PERSON]'s model has the following form:  
\[F_{ECD}=\frac{2F_{max}V_{\sigma}v}{V_{\sigma}^{2}+v^{2}} \tag{1}\]  
where \(F_{max}\) is the peak damping force for the ECD, and \(V_{\sigma}\) is the critical relative velocity when the peak damping force is reached. It is noticeable that these two mechanical model parameters are determined by the physical design parameters of the TLECD-HA. Figure 2 depicts the force-velocity relationship obtained from simulations using the finite element method (FEM) and the fitting curve acquired using [PERSON]'s semi-empirical model. It can be noted that [PERSON]'s model is still applicable to the TLECD-HA. In the following modeling and response analysis, the focus is on the mechanical characteristic of the TLECD instead of its physical configuration, and therefore, the TLECD is simply referred to as the ECD.  
Figure 1: Longitudinal cross-section of the TLECD-HA.  
Figure 2: Fitting curve versus FEM results.
# Music Similarity Detection Through Comparative Imagery Data
## 3 Experimental Results: Validating the Framework
### Findings from the Experimental Results  
In addition to \"Happy Birthday\" and \"Jingle Bells\", we also experimented with other songs. The overall experimental results of the feature-based analysis and visualization have shed light on which features may have strong association with similarity detection in the context of copyright infringement and plagiarism. Among the thirty-three features studied, most do not have such strong association, e.g., the seven rhythm and beat features in Table 4 and four texture features in Table 5. For these two groups of features, the findings are consistent with the general understanding as to what may contribute to the judgment of music plagiarism.  
We have identified four features that have stronger indicative power than others. They are (i) _melodic interval histogram_, (ii) _folded fifths pitch class histogram_, (iii) _wrapped vertical interval histogram_, and (iv) _distance between two most common vertical intervals_. These features are all transpose-invariant and can detect similarity to an adequate extent where different chords and embellishments are added to create variations of a music piece. Moreover, when a combination of these alterations were applied to the original version of a music piece, the performance of these features did not deteriorate.  
Figure 8: Heatmaps of rhythmic value histogram for comparing “Happy Birthday” with itself (positive reference), its swing-genre variation, and “Jingle Bells” (negative reference). The lack of distinguishing power can be observed.  
Figure 9: Heatmaps of _similar motion_ for comparing “Happy Birthday” with itself (positive reference), its swing-genre variation, and “Jingle Bells” (negative reference). The lack of distinguishing power can be observed.  
The type of swing-genre variations has been found to be a challenge for identifying similarity visually. Nevertheless, this provided us with stronger motivation to train ML models and to investigate whether the patterns resulting from such variations could indeed be detected by ML models even though they are not too obvious to the human eye. These experiments also inspired us to use comparative imagery as input data to train ML models for similarity detection. Since humans cannot visually assess thousands of heatmaps in order to compare one piece of music with many songs in a data repository, a good-quality ML model would be significantly more cost-effective. Meanwhile, as the judgment of plagiarism is a highly sensitive matter, it is necessary for humans to be able to scrutinize and interpret the judgment of any ML model for similarity detection. Hence, being able to visualize the feature-based comparative imagery allows explainable AI to support such scrutiny and interpretation.