esa-sceva/satcom-chunk-collection huggingface dataset:
# A Real-Time Detection of Pilot Workload Using Low-Interference Devices
## 1 Introduction
Applying the same workload level label to the whole task overlooks the continuous changes in workload during task performance, making the models developed in these studies less suitable for actual flight scenarios. Moreover, these studies often overlook the different relationship between workload and human physiological characteristics among individuals. Physiological responses to workload specifically denote the body's automatic, measurable reactions to the demands placed on an individual during tasks [25]. However, these responses vary not only across different tasks but also across individuals due to factors such as genetics, physical condition, and experience [26]. For instance, when different pilots face the same high-workload task, some may not show noticeable differences in heart rate, while others may experience a significant increase. Individual differences in how indicators like EEG, eye movement, and heart rate variability respond to workload can be observed in existing studies [27; 28; 29]. These individual differences make many current models less effective and reliable when applied to a broad number of pilots in practice. To address these challenges, this study developed a novel real-time pilot workload detection method using low-interference devices, called the Adaptive KNN-Ensemble Pilot Workload Detection, abbreviated as AKE-PWD. The main contributions of this study are as follows:  
1. The proposed method utilizes low-interference detection metrics that hardly interfere with flight operations, including the eye movement indicators from telemetry eye trackers and the shifting rate center of gravity in the pressure-sensing seat cushion. 2. The proposed method analyzes the relationship between multi-source data and workload by utilizing a 10 s time window, allowing it to detect continuous workload levels within a single task in real time. 3. The proposed model integrates the strengths of three base learners and employs a soft voting mechanism, achieving high prediction accuracy through refined structural design. It incorporates a KNN module before the ensemble classifier to identify the clusters of individual's physiological feature-workload mapping pattern. By differentiating the processing of each cluster of physiological features, it enhances the model's generalizability across different pilots. The remainder of this paper is organized as follows: Section 2 reviews the relevant studies; Section 3 introduces the proposed AKE-PWD methodology; Section 4 details the experimental setup and evaluates the proposed methodology; Section 5 presents the results and discussion; and Section 6 concludes the study.
# AutoElbow: An Automatic Elbow Detection Method for Estimating the Number of Clusters in a Dataset
They are both similarly comparable to the L-method of [PERSON] et al. [16]. The ADM technique, on the other hand, is considered to be comparable to the angle-based approach of [PERSON] et al. [17]. While the aforementioned methods have yielded satisfactory results in a variety of application areas; nevertheless, they can be limited in situations where an elbow-based graph returns as a smooth curve [15]. Furthermore, a few of their algorithmic procedures, such as in the KPA and its variants, can be computationally costly. In addition, several of these methods, such as the KPA, ADM, and ESA methods, require a minimum of three graph points in order to estimate the elbow point, which limits their use in the case of sparse graphs. Consequently, in this article, we present a simple yet effective method for automatically estimating the elbow point and, as a result, the approximate number of clusters in the dataset. The proposed approach yields a convex function based on the ratio of the squared Euclidean distance between each point on the graph and three reference points, namely the origin (minimum), the furthest (maximum) point, and the \"heel\" of the normalized graph. The maximum value of this function inherently returns the optimum elbow point, and consequently the approximate number of clusters in the dataset. We note that the current method does not inform users whether or not the K-means algorithm is the best tool to use for clustering. Instead, it only helps to automate the elbow method towards determining the appropriate number of clusters in the dataset to be clustered. As a result, we present clustering researchers with a description of the suggested approach as well as the pseudocode that implements the solution. Therefore, the contributions of the present article are highlighted as follows:  
1. Based on the elbow graph, we propose a new method for automatically determining the potential number of clusters in a data set. The AutoElbow method provides an accurate estimation of the elbow for different \(K\) values and is stable compared to other approaches across different datasets having small and large clusters, different cluster shapes, high dimensionality, and unbalanced distributions. 2. The proposed method can be used to easily automate the K-means algorithm or any other unsupervised clustering algorithm that requires the selection of \(K\) beforehand. The rest of the article is structured as follows: Section 2 presents a summary of the related work, Section 3 details the AutoElbow method and its corresponding pseudocodes, Section 4 presents and discusses the results obtained, and our conclusions are drawn in Section 5.
## 1 Introduction
NASA's Harmonization of Landsat and Sentinel-2 (HLS) dataset provides moderately high-resolution measurements of surface reflectance with a revisit time of several days [16], which allows for the monitoring of the seasonal patterns of vegetation using derived NDVI timeseries from the available surface reflectance [17]. [PERSON] and [PERSON] [10] demonstrated that the seasonal timeseries of HLS-derived NDVI provide a distinct seasonal temporal \"fingerprint\", which could be used to classify HLS pixels to specific patch-types. Thus, using HLS, it is possible to track the spatial heterogeneity of wetlands at a sufficient spatial (~30 m) and temporal resolution (seasonal-annual) and provide the necessary input for improving the global estimation of methane emissions from wetlands. The goal of this study was to implement and validate an HLS-derived NDVI-based classification of within-wetland eco-hydroological patch-types at a coastal brackish marsh in Louisiana and use the resulting yearly vegetation cover as a driver of the wetland land-unit in an ELM, which in turn simulates the resulting methane and carbon fluxes. We used an updated version of the ELM that simulates the wetland land unit and corresponding dynamics of the methane flux. We used ground observations of patch-level and whole site-level fluxes from the simulated study site to evaluate the improvement in predicted methane flux and validate the effectivity of using the HLS-driven classification approach for the estimation of whole-wetland methane emissions. Thus, through this short communication, we would like to shed light on the efficiency of using remote sensing products for improving the predictions of important greenhouse gas emissions in earth system models. In the upcoming sections, we describe the HLS-based classification method used to derive the NDVI timeseries and obtain the resulting within-wetland eco-hydrological patch distribution for years 2016-2023 at our study-site. Then, we present and discuss all the classification results and corresponding ELM simulations with which we validate our classification using the high-resolution WorldView data and validate our ELM-modeled flux using Eddy Covariance measurements performed at the site.
# Dynamic Obstacle Avoidance and Path Planning through Reinforcement Learning
## 4 Discussion
### Performance Metric and Evaluation Method of RL Propagation Model in Autonomous Mobile Robot  
Different ways of evaluating the performance metrics of a reinforcement learning algorithm in a mobile robot path planning and obstacle avoidance were synthesized by this review. However, the Gazebo simulation remains the most explored simulation environment. It represents a virtual environment that permits the training of the mobile robot using the RL techniques. However, the dimension and the nature of the training environment vary across studies, some with static obstacles and others with dynamic or complex obstacles. A mapping grip and turtle bot are often equipped in the simulated environment created with Gazebo, and the propagation model's efficiency is evaluated [61]. The turtle bot is the experimental agent in the simulated environment, whose success rate is evaluated against the target goal in the simulated simple/complex environment. Some authors prefer to use a test map [49] that evaluates the success rate of the proposed model in a 3D simulated virtual environment [50]. Different types of sensors are applied in each model in the test environment. Other notable means are 2D range sensors [53], kinetic sensors, and simple test scenarios. However, the performance of the RL algorithm, a reward function value, a punishment value, a state space, or a Q value are evaluated, and finally, the accumulated reward value is considered [54; 59]. The reward function is essential in reinforcement algorithms, especially in training RL results [55]. The RL model is considered good if its reward value is evaluated as a positive reward value [56]. And for this to occur, the distance between the goal position and the mobile robot agent is less than a set value, making it effectively complete the target navigation [57]. However, when the minimum distance between the mobile robot and dynamic obstacles is less than a safe distance, like a point where collision is possible, the mobile robot agents oftentimes are punished by allocating a penalty value like a negative reward value [58]. To this end, an accumulated reward is calculated. In a negative reward function scenario, the training episode is ended. In this way, the RL model success rate is evaluated.
# Battery Life Prediction for Ensuring Robust Operation of IoT Devices in Remote Metering
## Appendix A Supplementary Experimental Details  
### Additional Analysis and Metrics  
This appendix provides supplementary information that supports the main text by including extended performance comparisons and additional analyses that further validate our results. The table below summarizes the performance of SOC estimation (State of Charge) by comparing the Mean Absolute Percentage Error (MAPE) and Mean Squared Error (MSE) across various methods, with the values for #1, #2, and #3 corresponding to three independent experiments and the 'Average' column reporting their mean. MAPE quantifies errors as percentages, making it useful for interpreting performance relative to SOC values, while MSE penalizes larger errors more heavily due to the squaring effect, offering a sensitive measure for detecting significant deviations. The analysis shows that the proposed C-SDFormer outperforms the compared methods by achieving a notably lower MAPE of 12.82 and an MSE of only 0.275, demonstrating its superior accuracy and robustness in SOC estimation across diverse experimental conditions.
## 6 Discussion
Our G-causality analysis suggested that _GPP_ did not directly control \(R_{s}\) in either treatment or control site, and that \(RAD_{n}\) was a primary environmental constraint on \(R_{s}\) through its effects on \(T_{s}\) at short time scales. At longer scale, both \(SWC\) and \(RAD_{n}\) controlled \(R_{s}\). This finding is similar to other studies suggesting no diurnal relationship between _GPP_ and \(R_{s}\)[67] and in contrast to others showing that _GPP_ or photosynthesis constrains \(R_{s}\) over diurnal timescales [15,58,59,68]. Respiration from our sites is principally limited by soil moisture and radiation (heat to the soil), suggesting that \(RAD_{n}\), which is remotely sensed with high accuracy, may serve as a useful predictor of \(R_{s}\). In another site, the Duke Forest, where a significant link between _GPP_ and \(R_{s}\) was established [15], moisture and temperature were less limiting factors. Furthermore, under natural conditions, there is a substantial attenuation of the diurnal amplitude of photosynthesis along the phloem path length, primarily influenced by vegetation height and phloem conductivity [69]. At this site, trees were quite tall (around 25 m). Unfortunately, information about phloem conductivity was not available. Several studies have shown that recently produced photosynthate is a key driver of root respiration and thus, girdling will typically decrease soil respiration [54,66]. Species-specific responses to girdling can be highly variable [70] and in some cases, no effect of girdling on soil respiration was reported [71]. Studies of respiration in pest-infected forests, where the trees were girdled by insects, found mixed results from no effect whatsoever, to a large decline [72,73]. Other methods for testing _GPP_ and \(R_{s}\) interactions were reviewed [60]. Among non-manipulative approaches, methods based on time series analyses, such as cross-correlation, pulse-response and Fourier decomposition, were suggested to detect lagged response. However, we note that only G-causality is able to provide a directionality of the interactions, which is the real signature of a causal mechanism. Furthermore, the multivariate approach investigated in this study showed that including more biophysical factors may reveal indirect or apparent interactions that, in lower dimension systems, may appear as direct and significant. However, the proposed method has also some limitations. Long, continuous and multiple time series are required to construct robust spectral matrices and reduce noise. Although G-causality is less sensitive to random Gaussian noise, other non-Gaussian source of error (e.g., spikes) may cause unpredictable effects. Any additional variable that contains such errors may reduce the statistical power to the extent that weak interactions may not be detectable.
## 5 Numerical Results
### Performance Comparison  
We compare the performance of the PER-DDPG-based scheme, TD3-based scheme, and random IRS phase scheme under different transmit power budgets and different numbers of IRS elements. We set the number of IRS elements \(N\times N\) as 100, 256, and 400, and the number of users \(K\) as 10, 16, and 20, respectively. Figure 6 illustrates that the achievable weighted communication data rate is directly proportional to the maximum transmit power budget and the number of IRS elements. It can be seen from the figure that the TD3-based algorithm achieves the best communication performance, the PER-DDPG-based algorithm is slightly inferior to the TD3 scheme, and the random phase shift-based one has the worst performance.  
Figure 7 plots the sensing SINR versus the transmit power budget, where the number of users \(K\) and IRS's elements \(N\times N\) are set to 10 and 100, respectively. The radar sensing SINR consistently increases with the expansion of transmit power budget, but the growth speed gradually slows down. Our proposed DRL-based algorithms show a better performance than the baseline.  
Figure 5: Convergence performance under different discount factors.  
Figure 6: The weighted achievable data rate versus the transmit power budget.  
Figure 8 depicts the correlation between the number of users and the system energy consumption. We set the number of IRS elements \(N\times N\) as 100. As depicted in the plot, it is evident that the system energy consumption increases with the growing number of users. With the rising number of users, the amount of tasks offloaded to the base station rises, resulting in a growth in energy consumption. The proposed two schemes dramatically reduce the overall execution energy consumption compared with local execution methods, and the TD3-based scheme is slightly better than the PER-DDPG-based scheme.  
Figure 9 describes the relationship between the number of users and the offloading delay, and the number of IRS elements \(N\times N\) is set to 100. The figure demonstrates that an increase in the number of users leads to a rise in data processing time due to resource competition among the users. Compared with the local execution method, the proposed DRL methods greatly reduce the overall average execution delay of the task, and the TD3 algorithm has the lowest total delay.  
Figure 8: The total energy consumption versus the number of users.  
Figure 7: Sensing SINR versus the transmit power budget.
###### Abstract  
This study proposes a new numerical method for the free vibration analysis of elastically restrained tapered Rayleigh beams with concentrated mass and axial force. The beam model had elastic support, concentrated mass at both ends, and axial force at the right end. The elastic supports were modeled as translational and rotational springs. The shear force and bending moment were determined under the assumption that the sum of the forces at arbitrary positions and the joint between the beam and elastic supports always becomes zero. Therefore, a frequency determinant is established considering the free-free end condition at both ends, but various boundary conditions were constructed by adjusting the values of the elastic springs in the frequency equation. This assumption simplified the deduction procedure, and the method's efficiency was demonstrated through various comparisons. In particular, the value of compressive loading at which the first natural frequency vanished was investigated by considering the taper ratio based on the relationship between the elastic support and compressive loading. The analyzed results can be adopted as benchmark solutions for other approaches. The frequency determinant employs the transfer matrix method; however, numerical methods can easily be utilized in other approaches.  
concentrated mass; axial force; tapered beam; elastic support; buckling load Article
## 4 Results and Discussion
### Assessment of Monthly Marine Gravity Field from Dual Satellite Constellation
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Grid Resolution** & **Scenarios** & **Mean (mGal)** & **RMS (mGal)** & **Coverage Rate** & **Revisits** \\ \hline \multirow{3}{*}{\(10^{\prime}\)} & IOD & \(-0.04\) & \(20.35\) & \multirow{3}{*}{\(87.83\%\)} & \multirow{3}{*}{\(4.81\)} \\ \cline{2-2} \cline{5-6}  & Operational & \(0.04\) & \(9.73\) & & \\ \hline \multirow{3}{*}{\(15^{\prime}\)} & IOD & \(-0.07\) & \(9.87\) & & \\ \cline{2-2} \cline{5-6}  & Operational & \(-0.06\) & \(5.93\) & & \\ \hline \multirow{3}{*}{\(20^{\prime}\)} & IOD & \(-0.08\) & \(6.52\) & \multirow{3}{*}{\(92.24\%\)} & \multirow{3}{*}{\(19.28\)} \\ \cline{2-2} \cline{5-6}  & Operational & \(-0.08\) & \(4.82\) & & \\ \hline \multirow{3}{*}{\(25^{\prime}\)} & IOD & \(-0.09\) & \(5.22\) & \multirow{3}{*}{\(92.46\%\)} & \multirow{3}{*}{\(30.16\)} \\ \cline{2-2} \cline{5-6}  & Operational & \(-0.09\) & \(4.44\) & & \\ \hline \multirow{3}{*}{\(30^{\prime}\)} & IOD & \(-0.10\) & \(4.68\) & \multirow{3}{*}{\(92.50\%\)} & \multirow{3}{*}{\(43.43\)} \\ \cline{2-2} \cline{5-6}  & Operational & \(-0.10\) & \(4.27\) & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Evaluation of monthly gravity anomaly simulation for PARS IOD and Operational dual-satellite constellation with different grid resolutions. Figure 8: Monthly marine gravity anomaly from Operational GNSS-R dual-satellite constellation (\(30^{\prime}\) resolution). average revisits and improve inversion accuracy by increasing the number of networked satellites or reflection channels is limited. At this stage, the influence of altimetry accuracy becomes more pronounced.
## 4 Monitoring System Framework
### Cloud Service System Construction
#### 4.3.5 Information Publishing System  
The monitoring system has an information publishing system formed of three main parts: a central control system, a network cloud platform and a terminal display system. The central control system is installed on the management and control server. Its main function modules are data management, analysis management, terminal management and user management. By receiving,  
Figure 13: System substation monitoring page effect map.  
analyzing, publishing and monitoring the monitoring data and it can manage and control all the function modules in a unified way. The network cloud platform is the information transfer bridge between the central control system and the terminal display system. Accessing the database through a cloud platform is more secure and reliable than traditional way. The terminal display system includes a media player, video-audio transmitter, video-audio repeater and display terminal. The system issues the field data transmitted by the cloud server, analyzes and generates pressure-time curves automatically, judges the safety and stability state of the monitoring site roadway in real time and automatically sends out early warning signals.
# Optical Boundaries for LED-Based Indoor Positioning System
## 4 Investigations Showing the Effect of Defined Optical Boundaries  
Three investigations which are carried out to show the effects of receiver-based optical boundaries are explained in this section. First is the effect of positioning protocol design for a single LED transmitter, next is the effect of overlap for multiple LED transmitters in an overlap region, and then, the effect of tilt in the overlap region. Finally, the effect of all these on positioning accuracy is quantified.
# Hydrothermal Manufacture of Zeolitic Lightweight Aggregates from Clay and Marine Plastic Litter
## 3 Results and Discussion
### Zeolitic Lightweight Aggregates Obtained from Kaolin and Different Proportions of Marine Plastic
#### 3.1.2 Technological Properties of the ZLAs
Regarding the density of the materials obtained, the decrease in porosity of the material has led to the \(\rho_{\mathrm{rd}}\) values increasing between 10% and 24% compared to the fired material from which they come. Thus, in the sample without added plastic, the starting density was 1.49 g/cm\({}^{3}\), which after the hydrothermal treatment rose to 1.79 g/cm\({}^{3}\). In the case of samples with MPF, the density before zeolitization was \(\sim\)1.35 g/cm\({}^{3}\), increasing after it to 1.66 g/cm\({}^{3}\) in materials with 5 wt.% of plastic added and somewhat less (1.49 g/cm\({}^{3}\)) in those with 10 wt.% MPF (Figure 6c). It is important to highlight that all values are below the limit of 2.00 g/cm\({}^{3}\) established by the EN 13055-1 [41] standard, which means that the materials obtained could be used as lightweight aggregates. Finally, an essential parameter is the mechanical strength, in this case measured as the crushing strength of single specimens. According to Figure 6d, several interpretations can be made. Firstly, the only-fired materials have a very low strength, between 0.04 and 0.45 MPa, and the addition of plastic tends to reduce it. However, this is corrected with a significant increase in the crushing strength after zeolitization, presenting values of 5.5, 4.4, and 2.3 MPa in the samples with 0, 5, and 10 wt.% MPF, respectively. This represents an increase ratio of 12, 34, and almost 58 times the strength of the starting fired material (Table 3), which allows lightweight materials with improved mechanical strength to be obtained, in this case zeolitic lightweight aggregates. Beyond these technological improvements, the environmental impact generated by the burning of plastic waste is an aspect that will be evaluated in a future study. In the fight against plastic landfilling, the most widely used way to dispose of plastic waste in Europe is energy recovery (over recycling). This means that most of the plastic waste is already incinerated to obtain heat, electricity, or fuel, so there is a suitable and safe technology for this [42], which could be adapted to the manufacturing method presented in this work. Effect of the Addition of the Rejected Mg-Clay (Rich in Smectite and Sepiolite) on the Zeolitization of Lightweight Aggregates