esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Vegetation Productivity as a Function of Environmental Drivers
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Explanatory Variables** & **Estimated Coefficient** & **Standard Errors** & \(p\)**-Value** \\ \hline Fall precipitation & \(-0.22\) & \(0.018\) & \(5.82\times 10^{-35}\) \\ Fall temperature & \(1.08\) & \(0.17\) & \(7.93\times 10^{-12}\) \\ Spring precipitation & \(0.13\) & \(0.02\) & \(1.003\times 10^{-11}\) \\ Summer precipitation & \(-0.164\) & \(0.015\) & \(2.23\times 10^{-26}\) \\ Summer temperature & \(-0.68\) & \(0.18\) & \(0.005\) \\ Winter precipitation & \(0.05\) & \(0.014\) & \(1.69\times 10^{-4}\) \\ Winter temperature & \(-0.69\) & \(0.087\) & \(4.72\times 10^{-13}\) \\ Aspect & \(-0.0001\) & \(9.07\times 10^{-6}\) & \(1.36\times 10^{-36}\) \\ Elevation & \(-8.58\times 10^{-5}\) & \(5.47\times 10^{-6}\) & \(3.1\times 10^{-52}\) \\ Vegetation types & \(0.001\) & \(4.82\times 10^{-4}\) & \(4.56\times 10^{-5}\) \\  & & _Intercept = 0.48_ & \\  & & _RMSE = 0.002_ & \\ _Whole model_ & & _Adjusted R-Square = 0.37_ & \\  & & _p-value = 1.51 \(\times\) 10\({}^{-171}\)_ & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Statistical results of the stepwise regression model for the response variable (vegetation productivity) showing the estimated error, standard error, and \(p\)-value for the statistical significance explanatory variables (\(p\)-value \(<\) 0.05).
## 3 Design of Physical Layer Security Communication System Using Proposed LPD Signal  
The analyses in Section 2 show that the designed LPD signal can be exactly detected if and only if the sampling rate is synchronized perfectly. Otherwise, a different sampling rate may lead to a destructive combination after sampling. Then, the intrinsic sampling clock offset between the transmitter and the receiver can be used for secure transmission. In this section, the application of the designed LPD signal for physical layer security is discussed.
# Production Optimization in a Grain Facility through Mixed-Integer Linear Programming +
## 2 Related Work
Data analysis incorporates within its functionalities tasks, such as statistical analysis of data sets extracted according to required indicators, diagnostic optimization based on Data Envelopment analysis, prediction analysis based on curve fitting, classification analysis based on clustering algorithm, etc. The implementation in industry of the designed system was shown to outperform other previous approaches. [PERSON] et al. [29] presented a complete review of optimization models focused on harvest and production planning for food crops. Optimization models have been used extensively to provide decision makers with insights on issues related to harvest and production planning in agri-food supply chains. [PERSON] et al. studied in depth several neglected topics and provided promising research directions to stimulate research interest on agri-food supply chains, specifically the planning of harvest and production and other tasks intrinsically related, such as optimization of the production chain, minimization of downtime in the plant, maximizing the efficiency of operations within the processing plant, suitable use of available machinery, etc. Several other articles related to food supply chain and logistics have applied LP or MILP models for optimization. [PERSON] et al. [30] proposed a MILP model for the beef industry, extended to minimize cost and greenhouse gas emissions in transportation. [PERSON] et al. [31] applied a nonlinear programming model to minimize refrigeration cost in the storage process of leafy greens. This article contributes towards this line of research, by modeling and studying, both theoretically and empirically, a relevant grain production problem. In turn, the case study presented in this article is one of the first reported studies applying ad hoc systematic optimization models for a real grain production company in Uruguay.
## 3 Results and Discussion  
### Correlation Analysis  
In order to understand the relationships between the variables, we performed a correlation analysis, visualized through a heatmap. The heatmap highlights both the strength and direction of the linear relationships between features, with color intensity indicating the magnitude of correlation. Figure 2(a) presents a heat map that illustrates the correlation coefficients between the features in the dataset and the target variable, which is boride thickness. The correlation coefficients range from \(-\)1 to +1. Features with correlation values near zero indicate minimal influence on boride thickness, while those with values closer to \(-\)1 or +1 indicate a stronger linear relationship with the target variable. A positive correlation implies that as the feature value increases, boride thickness also tends to increase. Conversely, a negative correlation implies that an increase in the feature value is associated with a decrease in boride thickness. A positive correlation indicates that as one variable increases, the other also rises, whereas a negative correlation implies that as one variable increases, the other decreases. When the feature selection approach based on the correlation coefficient is used for this problem, the resulting feature importance ranking is as shown in the bar graph in Figure 2(b). The bar graph illustrates the degree of influence each feature has on boride thickness. According to the bar graph in Figure 2(b), the five features, which are T (\({}^{\circ}\)C), t (h), C, Fe, and Al, exhibit a positive correlation with the boride thickness, indicating that as these features increase, the output value also rises. On the other hand, the remaining features show a negative correlation, meaning an increase in these features leads to a decrease in the output value. This pattern is also in good agreement in the heat map in Figure 2(a).  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Steel** & **C** & **Mn** & **Si** & **Cr** & **Ni** & **Mo** & **S** & **P** & **N** & **Fe** \\ \hline
316L & 0.019 & 1.6 & 0.05 & 17.4 & 12.3 & 2.2 & 0.001 & 0.002 & 0.06 & 66.368 \\ \hline \end{tabular}
\end{table}
Table 2: Chemical composition (wt.%) of 316L stainless steel (SS).  
Figure 3: (**a**) Heat map correlation coefficients of process parameters and (**b**) importance of features with correlation coefficient on boride thickness.
## 2 Materials and Methods
### Magnetron Sputtering of Cu Layer and Obtained Cu Seed Mesh  
The deposition of the Cu layer on the templates was carried out by magnetron sputtering of a copper (99.9% purity) rectangular target with a size of 700 \(\times\) 100 mm\({}^{2}\). Argon with a purity of 99.99% was used as a working gas. Argon was supplied to the working chamber through a mass flow controller. The working chamber was evacuated using a turbomolecular pump. The residual and working gas pressures were 3.0 \(\times\) 10\({}^{-3}\) Pa and 4.0 \(\times\) 10\({}^{-1}\) Pa, respectively. The templates were placed at a distance of 10 cm from the target. Power to the magnetron sputtering system was supplied from a unit generating DC pulses with a frequency of 133 kHz. Spraying took place in the mode of stabilization of the discharge power, which was maintained at a level of 1.5 kW. In this case, the discharge current was 2.7 A.  
A quartz sensor was used to control the Cu film thickness by measuring the resonant frequency change. The LCM was sputtered with 300 nm Cu, and the SCM was sputtered with 100 nm Cu. The reason for the different thicknesses of the seed mesh structure is due to the fact that for the LCM template, due to the small fill factor, the surface resistance of the seed mesh with a thickness of 100 nm was more than 150 \(\Omega\)/sq. It has been observed that the high resistance of the seed mesh leads to non-uniform growth of electroplated copper, which is unacceptable for the proposed process. Therefore, we deliberately increased the thickness of the seed Cu mesh for the LCM template to 300 nm.
## 3 Plastics Used for Food Packaging
### Selected Plant-derived Polymers
#### 3.3.3 Polysaccharides
The tensile strength and elongation at break values are comparable to appropriate values of HDPE and LDPE (Table 4). However, the use of chitosan to produce flexible packaging is still limited due to high sensitivity to humidity and moisture and high oxygen permeability. As chitosan films are highly permeable to water vapor, their use in food packaging is restricted [234; 235; 236]. To overcome these limitations of this otherwise excellent polysaccharide biomaterial, several attempts have been made such as blending the films with other natural or synthetic polymers and the addition of several active and functional substances like fillers, plasticizers, crosslinkers, and natural oils [234]. Several studies are published that focus on improving the barrier and mechanical properties of chitosan-based films. [PERSON] et al. developed a composite flexible film using chitosan as base polymer matrix, xylan to improve mechanical properties, and carvacrol (a monoterpenoid phenol) to control microbial protection. The results show that adding xylan significantly increases the elongation at break of the composite films and exhibits higher tensile strength and Young's modulus. The incorporation of carvacrol and xylan in the composite films was not effective in terms of antimicrobial activity [101]. Chitosan is another potential packaging material that is investigated with lignin as additive. The addition of lignin to chitosan improves the tensile strength, storage modulus, glass transition temperature, and degradation temperature, compared to pure chitosan films [237]. Moreover, lignin confers the scavenging properties to the chitosan films: this antioxidant activity is highly dependent on the film structure, functional properties, and surface activity, which is, in turn, dependent on moisture [238] and the homogenization process of the film-forming suspension [239]. Despite favorable properties of chitosan like its antioxidant and antimicrobial activity as well as a good biocompatibility and biodegradability, there are some drawbacks such as its dissolution in acidic media and poor thermal properties. Thus, the combination of chitosan with other polymers in binary or ternary films is intensively studied for various systems. [PERSON] et al. investigated films based on PVA, chitosan, and LNPs for food packaging applications and found that lignin improved tensile strength and Young's modulus as well as the thermal stability of the systems [240]. In conclusion, Table 5 summarizes advantages and disadvantages of the polymers discussed in Section 3.
###### Abstract  
Underwater wireless sensor networks (UWSNs) are significantly different from terrestrial sensor networks in the following aspects: low bandwidth, high latency, variable topology, limited battery, low processing power and so on. These new features pose many challenges to the design of self-organizing routing protocol for UWSNs. This paper focuses on the application of Ad Hoc On-demand Distance Vector (AODV) routing protocol in UWSNs. In order to solve the problems of packet collision and excessive energy consumption associated with the flooding-based routing discovery method and the periodic hello packet routing maintenance mechanism of AODV, a routing discovery and maintenance method based on location and energy information is proposed, and it is referred to as the route-focusing AODV (RFAODV) routing protocol. In the RFAODV protocol, the routing discovery process is focused on a few nodes through forwarding area control and dynamic delay adjustment. In addition, feedback from a media access control layer and residual energy control are used for routing maintenance. We implement the RFAODV and evaluate its performance according to the sea trial data as parameters in the NS-2. The simulation results show that compared with the other protocols, RFAODV improves the routing discovery success ratio by at least 18%, increases the packet transmission ratio by at least 4%, reduces the protocol overhead by at least 15% and reduces the energy consumption by at least 5% under various simulation scenarios. RFAODV is suitable for large-scale, high-load and dynamic networks underwater wireless sensor networks.  
AODV; route-focusing; route discovery; forwarding control; route maintenance Article
# Validating an Operational Flood Forecast Model Using Citizen Science in Hampton Roads, VA, USA
## 2 Methods
### Tidewatch Storm Tide Modeling
* Post-processing to combine multi-core parallel outputs into binary model outputs, and then into ASCII text for geospatial translation via Python scripts using the raster calculator in GRASS GIS. * Extraction of results at Tidewatch charts stations for surge guidance and key points for Tidewatch Maps at 220 Hydrologic Unit Cells (HUCs) throughout tidewater Virginia. * Retrieval of SCHISM inundation model outputs for GIS post-processing
* Extraction of source data from the station output from the 2D hydrodynamic forecast simulation. * Clear the previous run's data to archive, and import new simulation data to differentiate each day's morning and evening simulation updates,
* Construct a web-enabled time-aware street-level GIS map from SCHISM grid outputs. * Publish the output map of 37 time-aware rasters overlaid with Tidewatch Station time series data. Upon completing these steps, model and visualization performance metrics indicated, on average, that step one requires 1 h to complete pre-processing, step two requires 1.5 to 2.5 h to run the simulation  
Figure 5: Automation flowchart for SCHISM model initiation and simulation for every 36-h forecast. The Tidewatch model updates twice daily, outputting mapping outputs at AdaptVA.org at noon and midnight local time (05:00 and 17:00 UTC). (variable based upon volume of inputs), using 72 nodes on VIMS' high performance computing cluster, and step three requires 2 to 3 h in post-processing to combine the model's binary outputs, and translate/index them to hourly geospatial outputs to a convenient interactive web-map presentation format. The SCHISM model consumes approximately 2.40 GB of input data to model the US East and Gulf Coasts for every simulation, twice daily (1.75 TB annually). Comparatively, the SCHISM model outputs the mosaicked results of over 200 combined sub-grid sub-basin rasters to form the Tidewatch Map's composite 36 h. time aware layer cache at 16 zoom-level pyramids for a 21.25 GB product after each simulation (15.48 TB annually). These steps allowed users to interact with flood data from the global scale to street-level in a single web map (Figure 6).
# Intelligent Fault Diagnosis of Rolling Element Bearings Based on Modified AlexNet +
## 3 Experimental Verification Based on Vibration Signals
### Experimental Outcome
#### 3.2.2 Evaluation Measurements of the Proposed System  
In this study, the precision ratio, recall ratio, and F1 measure are calculated to further investigate and analyze the proposed method's performance in terms of classification. The probability of truly positive values out of all projected positive values is referred to as precision. Recall measures how often truly positive values are expected to be positive values. F1 represents the harmonic mean of recall and precision [31] as follows:  
\[\text{Precision }(\text{P})=\frac{\text{TP}}{(\text{TP}+\text{FP})} \tag{10}\]  
Figure 11: Performance of hybrid models.  
Figure 12: Impact on DWT.  
\[\text{Recall }(\text{R})=\frac{\text{TP}}{(\text{TP}+\text{FN})} \tag{11}\]  
\[\text{F1}=\frac{2\times\text{ TP}}{2\times\text{ TP}+\text{FP}+\text{FN}} \tag{12}\]  
where TP is the number of actual positive events, FP is the number of false positives, and FN is the number of false negatives. Based on Equations (10)-(12), precision, recall, and F1 can be calculated on the experimental findings of the proposed system in Table 7. As shown in Figure 13, the precision rate is 98.93%, the recall rate is 100%, and the F1 measure is 99.46%.
## 4 Design and Methodology
### Data Preprocessing and Feature Extraction  
Data preprocessing is the initial step in training our models. The dataset comprises many transactions, totaling 284,807, prompting us to randomly select 40% of the data for preprocessing. Subsequently, the data are divided into a 70% portion for the training dataset and a 30% portion for the testing dataset. Furthermore 70% is further partitioned within the training data, with 60% allocated to the training dataset and 10% to the validation dataset. The dataset undergoes cleaning and PCA transformation, after which we scale/normalize the data to standardize features and mitigate outlier instances. Following this, we execute the subsequent steps: converting NumPy arrays to PyTorch tensors, establishing data loaders, and segregating features (input) from labels (output). Notably, the features consist of 30 principal components derived from PCA (V1 through V28), with \"Time\" and \"Amount\" remaining unaltered by PCA transformation. Additionally, the \"Class\" feature functions as the output variable, taking 1 for fraudulent transactions and 0 for legitimate ones. All features except the \"Class\" feature serve as inputs.
# Background and Recent Advances in the Locata Terrestrial Positioning and Timing Technology
## 3 Locata Development History and Mathematical Models
### Locata Mathematical Model
#### 3.2.1 Locata Measurements
\tag{2}\]  
where \"\(\Delta\)\" denoting the single-differencing operation between two LocataLite measurements at the receiver. For precise positioning, the SD tropospheric delay should be corrected for using an appropriate tropospheric model, or estimated as an additional state in the Kalman filter [21; 34].
## 5 Simulation Analysis  
The simulation of this paper is based on the satellite self-organizing network platform introduced before, mainly focusing on the performance of packet delay and packet loss rate when making routing decisions through SQL-CBRP, and comparing it with the Dijkstra algorithm based on the minimum number of hops and the GPSR algorithm based on the shortest distance.
# MIMO Signal Detection Based on IM-LSTMNet Model
## 2 Methods
### MIMO-OFDM Signal Detection Model Based on FrFT
#### 2.2.1 Convolutional Neural Network Module  
Convolutional neural networks (CNNs) are advantageous for dealing with high-dimensional unstructured data such as images, where many classical CNNs were originally used for image classification. The CNN's advantage is the sparse interaction feature, which provides storage requirements and statistical efficiency by setting the size of the convolution kernel to be smaller than the size of the input. The second advantage stems from the concept of parameter sharing in CNN, where the convolution kernel is applied to the entire input to create a feature map. In addition, convolution and pooling operations allow the CNN to process the input data to the desired output size. Finally, CNNs can catch high-level representational features by themselves for mining intrinsic information.  
A CNN can capture temporal and spatial dependencies from raw data, automatically learning and extracting features closely related to MIMO-OFDM signal detection. The convolutional neural network module in the IM-LSTMNet model is shown in Figure 5. We select two convolutional layers with convolutional kernel [1\(\times\)1], step size [1\(\times\)1], channel number 32, and channel number 64. Since the ReLU activation function has no complicated mathematical operations, the calculation cost is small and the convergence is faster. The ReLU activation function is selected for connection in the convolutional neural network module.
# Web-Based Toolkit for Performance Simulation and Analysis of Power Line Communication Networks
## 2 PLC Technologies
### Overview
BPL includes a variety of systems that target high data rates and operate at frequencies from 1 MHz to 250 MHz. BPL can be used in the low and medium voltage ranges [14] and enables multimedia communication in home networks in the first case and distribution automation, remote control, and even AMI in the second case. Besides the industrial solutions based on the OPERA specification, the main GLP-oriented standards using medium voltage and low voltage distribution infrastructures are IEEE 1901 (in particular the access system specification) and ITU-T G. 9960 (ITU-T G.hn) [23].
# Measuring Student Engagement through Behavioral and Emotional Features Using Deep-Learning Models
## 4 Results
### Computation of Student Engagement Level  
After classifying the behavior and emotion, the Student Engagement Level (SEL) in the Online Classroom Environment (OCE) is computed. To achieve this, the confidence scores from the classification models, along with the survey scores for each engagement state, are integrated. Figure 10 illustrates the process of computing the SEL from the given input image frame.  
First, the CNN model classifies the student as engaged or non-engaged. In the case of engagement, the model's confidence score for the corresponding image frame is recorded. The same image is then sent to the ResNet50 model for emotional state classification, and the model's confidence score for the detected emotional state is also captured.  
Next, the average survey score for the detected emotional state is obtained. Finally, the Student Engagement Level (SEL) is computed using the formulation provided in Equation (1):  
\[SEL_{i}=\frac{\left(MCS_{i}\right)_{B}+\left(MCS_{i}\right)_{E}+\left(SAS \right)_{ES}}{3} \tag{1}\]  
where \(\left(MCS_{i}\right)_{B}\) refers to the model's confidence score for the classification of the behavior of the student in image \(i\), \(\left(MCS_{i}\right)_{E}\) presents the model's confidence score for the classification of the emotion of the student in image \(i\), and \(\left(SAS\right)_{ES}\) denotes the student's average survey score for that particular emotional state. Table 8 depicts some sample image frames and their corresponding SEL.  
Figure 10: Student engagement level computation.
# Underwater Acoustic Target Tracking: A Review
## 5 Classification Underwater Acoustic Target Tracking Algorithms
### Classification Based on Instrument-Assisted Method  
Instruments used to track underwater targets can be classified as acoustic imaging sensors, TASAs, and UWSNs. Based on the former, the kinematic state (position, velocity, etc.) and other features (size, color, etc.) of the target can be determined by images or video sequences, while the latter can only achieve motion tracking based on the acoustic echo signals. In this section, the algorithms based on different instruments are introduced. These algorithms are compared in the aspect of accuracy, complexity, and cost of tracking based on different instruments, as well as the strength and weakness of the corresponding tracking instrument.