esa-sceva/satcom-chunk-collection huggingface dataset:
# Offloading of Atomic Tasks in Satellite Networks:
## 5 Numerical Experiments
### Results Analysis
As shown in Figure 6, the horizontal coordinates indicate the six scale-up datasets and the vertical coordinates indicate the time consumption calculated by different methods. Subplot (a) depicts the time comparison between OCMF, ISSR, and FCFS, where the time consumption trace of OCMF and ISSR are similar. However, ISSR is a desirable hypothetical approach. The time cost tends of FCFS increase with the data volume increases and the growth rate reaches the highest at dataset D6. Therefore, it is not suitable for scheduling scenarios with high volume tasks which causes the time consumption growth rate of FCFS to be faster with the growing datasets. Subfigure (b) depicts the OCMF, PSO, GA, and DE methods performance comparison result, where the OCMF method has the lowest time consumption and the growth rate does not change significantly with the dataset scale increases. During the dataset D1 to D5, the time cost growth rate of the group wisdom algorithm shows an increasing trend. The growth rates of GA and DE show a decreasing trend on dataset D6. Therefore, GA and DE have advantages in dealing with large-scale task scheduling problems. According to subfigures (a) and (b), it can be seen that the proposed algorithm OCMF can better adapt to the growth of the task scale and has less cost consumption. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{6}{c}{**Time Consumption (s)**} \\ \cline{2-7}  & **D1** & **D2** & **D3** & **D4** & **D5** & **D6** \\ \hline ISSR & 18.94 & 31.82 & 157.46 & 294.19 & 471.91 & 1530.55 \\ OCMF & 14.23 & 30.55 & 158.28 & 292.76 & 452.11 & 1523.86 \\ FCFS & 48.53 & 92.22 & 543.37 & 976.78 & 1422.89 & 5019.90 \\ PSO & 65.07 & 116.07 & 3635.15 & 17,667.49 & 52,237.70 & 113,016.89 \\ GA & 63.16 & 107.67 & 920.51 & 17,702.55 & 50,301.48 & 44,665.74 \\ DE & 66.04 & 110.88 & 7904.71 & 34,649.01 & 80,317.24 & 62,888.36 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Time cost calculation results for multiple algorithms on different datasets.
# Man-in-the-Middle Attacks in Mobile Ad Hoc Networks (MANETs): Analysis and Evaluation
## 5 Results and Discussion
### Discussion  
It is evident from the subsections above that the messages delayed and messages dropped due to MITM assaults have a high influence on the MANET. In this paper, the network in this scenario presented high E2 ED, the propagation of a high number of delayed messages and dropped messages, and higher PLR. Thus, in order to achieve a secure approach in MANET, these MITM metrics must be considered prior to deployment in a real scenario. For example, singing and verifying messages can decrease the probability of broadcasting forage data among nodes in the MANET. Moreover, approaches based on trust can also be applied within the system to resist MITM assaults with delaying and dropping abilities. An environment based on trust can enable the legal nodes to determine and estimate the validity and authenticity of the received messages, therefore decreasing the probability of assailants executing MITM assaults. Similar to this, another plausibility verification inside the network may also prove to be useful in limiting the spread of harmful messages. These verification steps may be established according to the message threshold and detection ranges, the data approval area, and different mobility-controlled factors such as the direction and speed of the nodes. Therefore, symmetry or asymmetry cryptographies should be executed in order to encrypt the messages exchanged among the nodes. As a result, our proposal will prevent MITM attacks from intercepting communications between legal nodes.
## 3 Results
### Water Use Efficiency
Figure 2: (**a**) Tomato fruit yield and (**b**) water use efficiency (WUE) under different salinity (S) stress conditions and nutrient recipes (N). The columns with different lowercase letters are significantly different (\(p<0.05\)). \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Treatments**} & \multicolumn{2}{c}{**Fruit Yield (\(\text{kg m}^{-2}\))**} & \multicolumn{3}{c}{**WUE (\(\text{kg m}^{-3}\))**} \\ \cline{2-7}  & **Season 1** & **Season 2** & **Mean** & **Season 1** & **Season 2** & **Mean** \\ \hline (a) Salinity level & & & & & & \\ SI (2.5 dS m\({}^{-1}\)) & 26.4 \({}^{\text{a}}\) & 28.8 \({}^{\text{a}}\) & 27.6 & 30.2 \({}^{\text{a}}\) & 32.3 \({}^{\text{a}}\) & 31.3 \\ S2 (6.0 dS m\({}^{-1}\)) & 26.3 \({}^{\text{a}}\) & 27.2 \({}^{\text{a}}\) & 26.8 & 29.4 \({}^{\text{b}}\) & 31.4 \({}^{\text{b}}\) & 30.4 \\ S3 (9.0 dS m\({}^{-1}\)) & 15.2 \({}^{\text{b}}\) & 15.9 \({}^{\text{b}}\) & 15.6 & 16.9 \({}^{\text{c}}\) & 18.4 \({}^{\text{c}}\) & 17.7 \\ (b) Nutrient recipe & & & & & \\ N1 (control) & 23.6 \({}^{\text{a}}\) & 24.8 \({}^{\text{a}}\) & 24.2 & 26.3 \({}^{\text{a}}\) & 28.4 \({}^{\text{a}}\) & 27.4 \\ N2 (N1 + 15\% K) & 22.7 \({}^{\text{b}}\) & 24.9 \({}^{\text{a}}\) & 23.8 & 25.9 \({}^{\text{b}}\) & 28.0 \({}^{\text{b}}\) & 27.0 \\ N3 (N1 + 15\% Ca) & 22.6 \({}^{\text{c}}\) & 23.5 \({}^{\text{b}}\) & 23.1 & 25.9 \({}^{\text{b}}\) & 26.5 \({}^{\text{c}}\) & 26.2 \\ N4 (N1 + 15\% K + Ca) & 21.8 \({}^{\text{d}}\) & 22.7 \({}^{\text{c}}\) & 22.3 & 24.9 \({}^{\text{c}}\) & 25.6 \({}^{\text{c}}\) & 25.3 \\ (c) Grafting & & & & & & \\ GR (Grafted) & 25.2 \({}^{\text{a}}\) & 25.9 \({}^{\text{a}}\) & 25.6 & 28.6 \({}^{\text{a}}\) & 29.4 \({}^{\text{a}}\) & 29.0 \\ NG (Non-grafted) & 21.7 \({}^{\text{b}}\) & 20.5 \({}^{\text{b}}\) & 21.1 & 24.6 \({}^{\text{b}}\) & 23.3 \({}^{\text{b}}\) & 24.0 \\ (d) Tomato cultivars & & & & & \\ Valouro-RZ & 23.2 \({}^{\text{a}}\) & 23.9 \({}^{\text{a}}\) & 23.6 & 29.2 \({}^{\text{a}}\) & 30.3 \({}^{\text{a}}\) & 29.8 \\ Ghandowra-F1 & 23.5 \({}^{\text{a}}\) & 23.5 \({}^{\text{a}}\) & 23.5 \({}^{\text{b}}\) & 26.5 \({}^{\text{b}}\) & 27.0 \({}^{\text{b}}\) & 26.8 \\ Feisty-Red & 23.7 \({}^{\text{a}}\) & 22.3 \({}^{\text{b}}\) & 23.0 & 24.7 \({}^{\text{c}}\) & 24.8 \({}^{\text{c}}\) & 24.8 \\ \hline \hline \end{tabular} Note: The values with different superscript letters in a column are significantly different (\(p<0.05\)).  
\end{table} 
Table 4: Response in terms of tomato yield and water use efficiency (WUE) to different salinity and nutrient treatments.
## 3 Methodology
### Spatio-Temporal Visual Similarity Modelling
#### 3.2.1 Semantic Similarity Scoring Scheme  
We use the cosine similarity principle to measure inter-frame similarity; that is, we measure the cosine angle between the two frame vectors of interest. The cosine similarity between the \(i\)th and the \(j\)th frame is calculated by taking the normalised dot product as follows:  
\[sim(x_{i},x_{j})=||\psi(x_{i})||\cdot||\psi(x_{j})|| \tag{2}\]  
where, \(\psi(x_{i})\) is the normalised vector based on the list of visual tags for frame \(x_{i}\). This results in a spatial similarity matrix. The similarity measure is converted into a distance measure based on the following Equation:  
\[d_{1}^{2}(\psi(x_{i}),\psi(x_{j}))=1-sim(x_{i},x_{j}) \tag{3}\]  
An example of utilising the spatial similarity matrix to retrieve the top four similar frames from a video is shown in Figure 5.  
Figure 4: Effect of \(\alpha\) (from left to right 0, 5, and 10) on similarity matrix \(S_{ij}\). Higher values of \(\alpha\) enforce temporal connections between nearby frames and increase the quality of the detected shots.  
Figure 5: An example of utilising the spatial similarity matrix to retrieve top four similar frames from a video. The video used is Season 5 Episode 21 of _FRIENDS_ show.
# Delivering Extended Cellular Coverage and Capacity Using High-Altitude Platforms
## 2 System Model and Performance Metrics  
The HAP extended coverage system can be used to provide wireless coverage and capacity over a significantly wide area, and it can also be used to provide umbrella cells for other wireless systems. This section introduces the HAP extended coverage system architecture and highlights the important parameters and models for efficient beam pointing usingthe proposed algorithm. The metrics for performance analysis are also presented. Subsequently, all discussions and analysis throughout this paper are based on the HAP downlink.
# Optimization of the Multi-Facility Location Problem Using Widely Available Office Software
#### 1.1.3 In the Area of the Euclidean Multi-Facility Location Problem  
In [8], the authors proposed a pseudo-gradient technique that classifies the new facilities into distinct categories based on their coincidence with other facilities in order to derive a descent method for solving Euclidean MFLP.  
Author in [9] also approached Euclidean MFLP by using a general e-subgradient method in which search directions are generated based on the subdifferential of the objective function over a neighborhood of the current iterate.  
In [10] developed an algorithm which, from any initial point, generates a sequence of points that converges to the closed convex set of optimal solutions to the problem Euclidean MFLP.  
For the multi-facility location problem with no constraints on the location of the new facilities, authors in [11] derived some sufficient conditions for the coincidence of facilities that are valid in a general symmetric metric.  
[PERSON] et al. [12] suggested the use of polynomial-time interior point algorithm to solve the MFLP problem. They presented a procedure in which an approximate optimum to Euclidean MFLP can be recovered by solving a sequence of linear equations, each associated with an iterate of the interior point algorithm used to solve the dual problem.  
1.1.4. In the Area of the Rectangular Distance Minimax Location Problem  
[PERSON] [13] introduced this problem with linear constraints which (a) limit the new facilities location and (b) enforce upper bounds on the distances between new and existing facilities and between new facilities. He used dual variables that provide information about the complete range of new facility locations which satisfies the MiniMax criterion.  
Authors in [14] presented a method involving the numerical integration of ordinary differential equations and was computationally superior to methods using nonlinear programming.
## 4 Results and Discussion
### Soil Moisture Content  
At the end of the evaporation experiment, the soil moisture content was obtained and presented in Figure 5. For the nano-biochar particle application, the highest increase in soil moisture content, distinguished from the control soil, was at a depth of (15-20 cm, depth) for D2 treatments, followed by D1 and D3 treatments. Distinguished from the non-treated soils, for nanoparticle biochar, the percentage of increases in soil moisture content ranged  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Treatment** & **Water Added** & \multicolumn{4}{c}{**Evaporation (mm)**} & \multicolumn{2}{c}{**Cumulative**} & \multicolumn{2}{c}{**Water**} \\
**(macro)** & **(mm)** & **Week 1** & **Week 2** & **Week 3** & **Week 4** & **Evaporation (mm)** & **Retained (mm)** & **Recovery * \%** \\ \hline Control & 50.94 & 8.76b & 6.13c & 11.51 & 6.88a & 33.28b & 10.27c & 85.50 \\
0–5 cm & 50.94 & 7.79a & 5.59b & 11.51 & 7.79a & 32.69a & 9.20b & 82.22 \\
5–10 cm & 50.94 & 7.74a & 5.37a & 11.24 & 9.78b & 34.13b & 9.78b & 86.20 \\
10–15 cm & 50.94 & 8.90b & 5.62b & 11.16 & 6.88a & 32.55a & 7.79a & 79.20 \\ LSD (0.05) &......... & 0.143 & 0.027 & 0.72 & 0.196 & 0.13 & 0.16 & \\ Treatment & & & & & & & & \\ (Nano) & 50.94 & 9.19c & 5.65c & 12.38d & 7.18c & 34.40c & 6.96a & 81.20 \\ control & & & & & & & & \\
0–5 cm & 50.94 & 9.61d & 6.74d & 12.02c & 9.48d & 37.85d & 11.65b & 97.17 \\
5–10 cm & 50.94 & 5.21a & 4.96a & 10.07b & 4.43b & 24.67a & 14.30c & 76.51 \\
10–15 cm & 50.94 & 7.93b & 5.18b & 9.49a & 3.21a & 25.81b & 11.66b & 73.56 \\ LSD (0.05) &......... & 0.019 & 0.191 & 0.002 & 0.002 & 0.02 & 0.15 & \\ \hline \hline \end{tabular}  
* Recovery% = (water retained in soils by equation #2/ (added water—cumulative evaporation)) \(\times\) 100. Same letters are not significant.  
\end{table}
Table 2: Evaporation and amount of water retained (in mm) after 4 weeks of wetting/evaporation cycles.  
from 5% to 8% for D1, from 10% to 22% for D2, and from 8 % to 15% for D3. Additionally, for macro-biochar, these increases are categorized from 4% to 8% for D1, from 7% to 12 % for D2, and 10% for D3. These findings demonstrated that applying biochar at depths of 0-5 and 5-10 cm, using particle sizes of less than 1 mm, leads to larger increases in soil moisture content in the soil columns. The soil surface (0-5 cm) frequently contains the lowest values of soil moisture content on account of the raised rate of evaporation. The layer treated with biochar has the most amount of soil moisture ever measured as compared to untreated soil, particularly in the top 5-10 cm of the soil column. Accordingly, the maximal soil moisture content was noticed in both the nano and macro-biochar 10 cm and 20 cm layers, as presented in the 10-cm amended layer (Figure 5A,B). These obtained results disclosed that adding macro and nano-biochar to sandy soil restricted the flow of soil water into the deep layer and improved soil water conservation, like the results of additional studies conducted before and that agreed with [11; 38; 39].
# Multi-Layered Interactive Target Guidance with Visual Safety in Convex-Shaped Obstacle Environments
## 1 Introduction
The trajectory planning layer employs optimal control to generate task-achieving trajectories through target interaction, while the safety layer utilizes control barrier functions to modify the planned motion for visual safety. The main contributions of this paper are threefold: (i) A new control barrier function for ensuring visual safety with convex-shaped obstacles. (ii) Derivation of conditions for the existence of control inputs that ensure visual safety. (iii) A unified formulation of the interactive guidance task as an optimal control problem. For (i), a prior work [18] proposed two control barrier functions to guarantee visual safety for spherical obstacles. Here, we extend these results to general convex-shaped obstacles and propose a new control barrier function. For (ii), using control barrier functions ensures system safety by formulating the control law as a solution to a convex optimization problem. While multiple safety constraints can be introduced, guaranteeing feasibility is generally challenging. We demonstrate that feasibility can be ensured with two constraints when limited to a single obstacle. For (iii), the previous research treated the tasks of driving a target into a region and preventing it from entering separately. We present a unified formulation of these tasks as a single optimal control problem, allowing switching based on the sign of a parameter. The remainder of this article is organized as follows. In Section 2, the system model of pursuit-evasion, sensor model, and environment model considered in this article are presented. Then, the concept of visual safety is introduced. In Section 3, we provide control barrier functions for visual safety. By combining the control barrier functions and optimal control problems, a multi-layered control architecture is introduced in Section 4. The results of simulations are presented in Section 5. Finally, Section 6 concludes this article.
# Geolocation, Calibration and Surface Resolution of CYGNSS GNSS-R Land Observations
###### Abstract  
This paper presents the processing algorithms for geolocating and calibration of the Cyclone Global Navigation Satellite System (CYGNSS) level 1 land data products, as well as analysis of the spatial resolution of Global Navigation Satellite System Reflectometry (GNSS-R) coherent reflections. Accurate and robust geolocation and calibration of GNSS-R land observations are necessary first steps that enable subsequent geophysical parameter retrievals. The geolocation algorithm starts with an initial specular point location on the Earth's surface, predicted by modeling the Earth as a smooth ellipsoid (the WGS84 representation) and using the known transmitting and receiving satellite locations. Information on terrain topography is then compiled from the Shuttle Radar Topography Mission (SRTM) generated Digital Elevation Map (DEM) to generate a grid of local surface points surrounding the initial specular point location. The delay and Doppler values for each point in the local grid are computed with respect to the empirically observed location of the Delay Doppler Map (DDM) signal peak. This is combined with local incident and reflection angles across the surface using SRTM estimated terrain heights. The final geolocation confidence is estimated by assessing the agreement of the three geolocation criteria at the estimated surface specular point on the local grid, including: the delay and Doppler values are in agreement with the CYGNSS observed signal peak and the incident and reflection angles are suitable for specular reflection. The resulting geolocation algorithm is first demonstrated using an example GNSS-R reflection track that passes over a variety of terrain conditions. It is then analyzed using a larger set of CYGNSS data to obtain an assessment of geolocation confidence over a wide range of land surface conditions. Following, an algorithm for calibrating land reflected signals is presented that considers the possibility of both coherent and incoherent scattering from land surfaces. Methods for computing both the bistatic radar cross section (BRCS, for incoherent returns) and the surface reflectivity (for coherent returns) are presented. a flag for classifying returns as coherent or incoherent developed in a related paper is recommended for use in selecting whether the BRCS or reflectivity should be used in further analyses for a specific DDM. Finally, a study of the achievable surface feature detection resolution when coherent reflections occur is performed by examining a series of CYGNSS coherent reflections across an example river. Ancillary information on river widths is compared to the observed CYGNSS coherent observations to evaluate the achievable surface feature detection resolution as a function of the DDM non-coherent integration interval.  
land processes; calibration; GNSS; GPS; reflectometry; bistatic radar; CYGNSS 20202121317; doi:10.3390/rs12081317
## 4 Results
### Data Overview and Preparation  
After completing the preprocessing steps, the resulting data frame consists of multiple meteorological variables from different weather stations across Greece. The data frame includes columns representing various meteorological attributes such as wind direction, wind speed, temperature, and relative humidity for each station. Additionally, the data frame incorporates a column for the target variable, which is the daily energy production measured in megawatt hours (MWh). A description of the dataset that was utilized for the implementation of the approach is presented as follows:  
* The date represents the timestamp for each observation, initially stored in nanoseconds since epoch format.
* Wind direction, maximum wind speed, wind speed, temperature, and relative humidity data were collected from 10 meteorological stations across Greece. These stations were selected based on their geographical diversity, ensuring coverage of regions with high renewable energy potential, as well as data availability and reliability. The selected stations are strategically located in areas with significant solar and wind energy prospects, providing the necessary meteorological input for accurate energy production forecasting.
* Energy defined as the target variable representing the amount of energy produced on each respective day.  
After preprocessing, the dataset consisted of 1009 rows, each representing daily observations of weather conditions and the corresponding energy production. The data spanned from the year 2015 to 2018, with daily observations, and included meteorological features from different locations, all of which were converted to numeric format and processed to  
\begin{table}
\begin{tabular}{l} \hline \hline GREENIA PSEUDOCODE \\ \hline
**Explainable Artificial Intelligence (XAI)** \\ _# Define function to get model predictions for SHAP_ \\ _def model_predict_3d(flattened_data)_: \\ reshaped_data = reshape_to_3d(flattened_data) \\ return model(reshaped_data).detach().numpy() \\ _# Perform SHAP analysis_ \\ explainer = shap.KernelExplainer(model_predict_3d, X_test_flat[30]) \\ shap_values = explainershap_values(X_test_flat) \\ shap_table = create_shap_table(shap_values, feature_names) \\ _# Save SHAP results_ \\ save_shap_explairer_and_values(explairer, shap_values, save_dir) \\ _\# Strategic Planning (Natural Language Explanations)_ \\ _# Generate strategic insights using GPT-3.5-turbo-instruct_ \\ for day, best_individual in enumerate(best_individuals): \\ prompt_text = create_prompt(shap_summary, best_individual) \\ explanation = gpt_3_5 turbo_instruct(prompt_text) \\ append_to_strategic_recommendations(explanation) \\ _# Compile and save the strategic plan_ \\ strategic_plan = compile_strategic_recommendations() \\ save_strategic_plan_to_file(strategic_plan, save_dir) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Cont.  
remove missing values or filled using the mean of the nearest neighbors. Finally, the daily energy production in MWh was used as the primary variable for prediction.
## 3 System Model and Problem Formulation  
This section starts by introducing the multi-UAV-aided MEC system model. Following this, optimization problems pertaining to task offloading, resource allocation, and load-balancing models are formulated with the aim of minimizing the total system cost.
## 5 Results and Discussion
### Validation of Numerical Simulation with Experimental Data  
To validate the numerical solutions, the pressure and efficiency of the microbubble pump with a novel 'S-shape' impeller was compared to the experimental results of the flow rates, as shown in Figure 9. The figure shows that the local pressure and efficiency obtained by numerical simulations match well with the experimental results. The computed pump efficiency and pressure had a maximum error of 4 per cent compared to the experimental data, except for the lowest flow coefficient of 0.07. It is noted that the pump efficiency obtained by experimental measurements and numerical simulations at the design flow coefficient of 0.39 are 34 and 36 percent, respectively. Throughout the comparisons of numerical analysis results and experimental data for the pump performance, it can be said that the pump efficiency was simulated correctly in the present numerical simulation.